¡Bienvenidos al Apasionante Mundo del Fútbol Nacional 2 Grupo C Francia!
En este espacio dedicado a los apasionados del fútbol, ofrecemos una cobertura completa y actualizada de los partidos de la liga francesa de fútbol, específicamente del Grupo C de la National 2. Aquí encontrarás no solo resúmenes detallados de cada encuentro, sino también predicciones expertas para tus apuestas, todo pensado para enriquecer tu experiencia futbolística.
¿Qué Es la National 2 Grupo C Francia?
La National 2 es la cuarta división en el sistema de ligas del fútbol francés. El Grupo C es uno de los cuatro grupos que componen esta categoría, y alberga a algunos de los equipos más prometedores que buscan ascender a niveles superiores. Cada jornada, estos equipos se enfrentan en partidos emocionantes que capturan la atención de aficionados locales y seguidores internacionales.
Actualizaciones Diarias: Los Partidos Más Recientes
Nuestro equipo se encarga de actualizar diariamente las noticias y resultados de los partidos más recientes del Grupo C. No te pierdas ningún detalle con nuestro resumen diario, donde destacamos las jugadas más emocionantes, goles espectaculares y momentos clave que definieron cada encuentro.
Últimos Resultados
- Jornada 1: Equipo A vs. Equipo B - Resultado: 2-1
- Jornada 2: Equipo C vs. Equipo D - Resultado: 1-1
- Jornada 3: Equipo E vs. Equipo F - Resultado: 3-0
Descubre más sobre cada partido con nuestros análisis detallados, donde exploramos las tácticas utilizadas por los entrenadores y el rendimiento individual de los jugadores más destacados.
Predicciones Expertas para Tus Apuestas
Si eres un entusiasta de las apuestas deportivas, estás en el lugar correcto. Nuestros expertos analizan cada partido del Grupo C para ofrecerte predicciones precisas y estrategias de apuestas ganadoras. Basándose en estadísticas detalladas, rendimiento histórico y análisis táctico, te proporcionamos consejos útiles para maximizar tus ganancias.
Cómo Nuestros Expertos Realizan sus Predicciones
- Análisis Estadístico: Utilizamos datos históricos y estadísticas avanzadas para predecir el resultado más probable de cada partido.
- Evaluación Táctica: Analizamos las formaciones y estrategias utilizadas por los equipos para identificar debilidades y fortalezas.
- Rendimiento Reciente: Consideramos el estado físico y moral actual de los jugadores clave para ajustar nuestras predicciones.
También ofrecemos pronósticos especiales sobre eventos como goleadores probables, número total de goles y resultados parciales.
Análisis Detallado de Equipos Destacados
Cada equipo en el Grupo C tiene su propia historia y aspiraciones. Descubre más sobre los clubes más destacados de esta temporada con nuestros análisis completos.
Equipo A: La Sorpresa del Grupo
Sinónimo de resiliencia y determinación, el Equipo A ha sorprendido a todos con su desempeño consistente. Con un entrenador visionario al mando, han logrado adaptarse rápidamente a las exigencias del fútbol profesional.
Jugadores Clave:
- Jugador X: Un mediocampista creativo que ha sido fundamental en la creación de oportunidades ofensivas.
- Jugador Y: Defensor central sólido, conocido por su capacidad para leer el juego y realizar intercepciones cruciales.
Equipo B: Tradición e Innovación
Con una rica historia en el fútbol francés, el Equipo B combina tradición con innovación para mantenerse competitivo. Su enfoque en el desarrollo juvenil les ha permitido descubrir nuevos talentos que están haciendo su debut en la National 2.
Estrategia Táctica:
- Juego Posicional: Prefieren un estilo de juego estructurado basado en la posesión del balón.
- Dominio Aéreo: En situaciones de balón parado, son conocidos por su efectividad tanto en ataque como en defensa.
Tendencias Actuales: ¿Qué Está Moviendo al Grupo C?
Mantente al tanto de las últimas tendencias que están influyendo en la dinámica del Grupo C. Desde transferencias sorpresivas hasta controversias disciplinarias, aquí encontrarás toda la información relevante que podría afectar el curso de la temporada.
Transferencias Clave
- Jugador Z se une al Equipo C tras una exitosa temporada en una liga menor. Su llegada podría cambiar el rumbo del equipo hacia el ascenso.
Sanciones y Controversias
- El capitán del Equipo D fue suspendido por dos partidos tras una expulsión controvertida en el último encuentro.
Galería Fotográfica: Los Momentos Más Memorables
No te pierdas nuestra galería fotográfica donde recopilamos las mejores imágenes de cada jornada. Desde celebraciones éxtasis hasta duelos intensos entre jugadores, estas fotos capturan la esencia del fútbol Nacional 2 Grupo C Francia.
Fórum Comunitario: Interactúa con Otros Aficionados
Nuestro foro es un espacio abierto donde puedes interactuar con otros aficionados al fútbol. Comparte tus opiniones sobre los partidos más recientes, discute sobre las estrategias utilizadas por tus equipos favoritos o simplemente disfruta conversando sobre tu pasión por este deporte.
<|repo_name|>laura-michelle/thesis<|file_sep|>/chapters/4/results.tex
chapter{Results}
label{ch:result}
This chapter presents the results obtained from the experiments described in cref{sec:exp}. Section ref{sec:result:data} describes the data collected in the experiments and the statistical analysis performed on it.
Section ref{sec:result:network} presents the analysis of network performance in terms of latency and bandwidth.
Finally section ref{sec:result:results} presents the analysis of the results from our experiments and discusses them.
section{Data}
label{sec:result:data}
The data collected in our experiments was saved as a comma-separated values file (.csv) and is publicly available at url{https://github.com/laura-michelle/thesis-data}. It contains one row per sample of our data collection.
Each sample consists of:
begin{itemize}
item The time at which the sample was collected.
item The latency observed between each pair of nodes that had active TCP connections at that time.
item The throughput measured for each TCP connection.
item The number of active connections on each node.
item The CPU usage on each node.
item The memory usage on each node.
item The bandwidth used by each node on each network interface.
item The number of packets sent and received by each node on each network interface.
item The number of bytes sent and received by each node on each network interface.
item The number of dropped packets observed by each node on each network interface.
item The queue length on each network interface for each node (only available in Linux).
end{itemize}
The data was collected using the scripts described in cref{sec:exp:data_collection}, which are available at url{https://github.com/laura-michelle/thesis-experiments}.
To collect all these metrics we used the following tools:
begin{itemize}
item texttt{nethogs}: used to collect bandwidth and packet counts on all interfaces for all nodes except for the Linux hosts where we could directly read this information from /proc/net/dev (see cref{subsec:netstat-linux}).
item texttt{iostat}: used to collect CPU usage on all nodes.
item texttt{free}: used to collect memory usage on all nodes.
item texttt{ip route}: used to retrieve information about routing tables and default gateways to calculate end-to-end latency between pairs of nodes using ping (see cref{subsec:data_collection}).
item texttt{ifconfig}: used to retrieve MAC addresses for ARP spoofing (see cref{subsec:data_collection}).
item texttt{lsof}: used to retrieve TCP connections and their status on Linux hosts (see cref{subsec:lsof}).
item texttt/tcpdump}: used to capture traffic and extract statistics about TCP connections from it (see cref{subsec:data_collection}).
item texttt{ss}: used to retrieve TCP connections and their status on FreeBSD hosts (see cref{subsec:ss}).
end{itemize}
In addition to collecting this data from all nodes we also collected additional information from the Linux hosts using two scripts:
The first script collects information about network interfaces such as their MTU size and queue length.
The second script collects information about routing tables such as whether there is a default gateway or not.
The data was saved as comma-separated values files (.csv), one per host per experiment.
These files were then processed using R scripts (available at url{https://github.com/laura-michelle/thesis-data}) to generate the plots shown in this chapter.
The data is stored in multiple CSV files named after the experiment ID they belong to (cref{tab:exp_design}).
Each file contains a row per sample collected during that experiment with columns containing values for each metric collected by that experiment.
We processed these CSV files using R scripts that are available at url{https://github.com/laura-michelle/thesis-data}.
These scripts perform various statistical analyses and generate plots that are shown in this chapter.
For our statistical analysis we used linear mixed models (cite[chapter~11]{bates2015generalized}) with both fixed effects and random effects.
Fixed effects are those variables that we expect to have an impact across all samples whereas random effects are variables that only affect certain samples (e.g., different hosts or different connections).
In our case we used:
Fixed effects:
begin{enumerate}
item Whether an experiment is under attack or not (texttt{nattack}).
ARP spoofing attack is only applied when there is more than one switch between two hosts (cref{ssec:arp-spoofing}) so we only expect this effect when there is more than one switch between two hosts (texttt{nswitches = "more"}).
The effect should be zero when there is no attack or when there is only one switch between two hosts (texttt{nswitches = "one"}).
The coefficient of this variable should be positive if an attack increases latency or decreases throughput and negative if it decreases latency or increases throughput.
The expected value for this coefficient should be zero when there is no attack or when there is only one switch between two hosts since neither of these cases should affect latency or throughput.
This variable also captures any other differences between attacks that occur when there are more than one switch between two hosts compared to attacks that occur when there is only one switch between two hosts (e.g., differences caused by different switches being used).
The difference between these cases will be captured by the interaction term between this variable and the number of switches variable (texttt{nswitches * nattack}).
The model also includes other fixed effects for variables that we expect to affect latency or throughput such as whether an experiment uses Linux or FreeBSD (texttt{nlinux}), whether it uses UDP or TCP (texttt{nudp}), whether it uses NAT or not (texttt{nattack}), whether it uses VLANs or not (texttt{nvlans}), whether it uses VLANs with private IPs or not (texttt{nprivateips}), whether it uses VLANs with private IPs but does not have a default route configured (texttt{npridgroute}) and whether it uses VLANs with public IPs but does not have a default route configured (texttt{npublicgroute}).
The coefficients of these variables should be positive if they increase latency or decrease throughput and negative if they decrease latency or increase throughput.
The expected value for these coefficients depends on what baseline they are compared against:
For example:
The baseline for Linux vs FreeBSD comparison is Linux since most experiments use Linux so the expected value for this coefficient should be zero since most experiments use Linux.
The baseline for UDP vs TCP comparison is UDP since most experiments use UDP so the expected value for this coefficient should be zero since most experiments use UDP.
The baseline for NAT vs non-NAT comparison is non-NAT since most experiments do not use NAT so the expected value for this coefficient should be zero since most experiments do not use NAT.
The baseline for VLANs vs non-VLANs comparison is non-VLANs since most experiments do not use VLANs so the expected value for this coefficient should be zero since most experiments do not use VLANs.
The baseline for VLANs with private IPs vs non-VLANs comparison is non-VLANs since most experiments do not use VLANs so the expected value for this coefficient should be zero since most experiments do not use VLANs
The baseline for VLANs with private IPs but no default route configured vs non-VLANs comparison is non-VLANs since most experiments do not use VLANs so the expected value for this coefficient should be zero since most experiments do not use VLANs
The baseline for VLANs with public IPs but no default route configured vs non-VLANs comparison is non-VLANs since most experiments do not use VLANs so the expected value for this coefficient should be zero since most experiments do not use VLANs
Random effects:
Random intercepts were included in our model to account for variability between different hosts and different connections within those hosts.
Random slopes were included to account for variability in how different hosts respond to attacks compared to other hosts (cite[chapter~12]{bates2015generalized}).
In addition to random intercepts we also included random slopes for some fixed effects such as whether an experiment uses Linux or FreeBSD (texttt{nlinux}), whether it uses UDP or TCP (texttt{nudp}), whether it uses NAT or not (texttt{nattack}), whether it uses VLANs or not (texttt{nvlans}), whether it uses VLANs with private IPs or not (texttt{nprivateips}), whether it uses VLANs with private IPs but does not have a default route configured (texttt{npridgroute}) and whether it uses VLANs with public IPs but does not have a default route configured (texttt{npublicgroute}).
Random slopes were included because we expect some variability in how different hosts respond to these fixed effects compared to other hosts (cite[chapter~12]{bates2015generalized}).
Random intercepts were included because we expect some variability between different hosts even when they are subjected to the same conditions (e.g., same attack type) due to factors such as hardware differences etc..
In summary:
Fixed effects include:
nlinux
nudp
nattack
nvlans
nprivateips
npridgroute
npublicgroute
Random intercepts include:
hostid
connectionid
Random slopes include:
hostid:nlinux
hostid:nudp
hostid:nattack
hostid:nvlans
hostid:nprivateips
hostid:npridgroute
hostid:npublicgroute
connectionid:nlinux
connectionid:nudp
connectionid:nattack
connectionid:nvlans
connectionid:nprivateips
connectionid:npridgroute
connectionid:npublicgroute
intercept:hostid
intercept:connectionid
interaction terms include:
nswitches * nattack
nlinux * nvlans
nlinux * nprivateips
nlinux * npridgroute
nlinux * npublicgroute
nudp * nvlans
nudp * nprivateips
nudp * npridgroute
nudp * npublicgroute
nattack * nvlans
nattack * nprivateips
nattack * npridgroute
nattack * npublicgroute
nswitches * nlinux
nswitches * nudp
nswitches * nvlans
nswitches * nprivateips
nswitches * npridgroute
nswitches * npublicgroute
noindent These interaction terms capture how certain combinations of variables affect latency or throughput differently
