esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence Control Logic in Next-Generation Programmable Networks
### Motivation  
The evolution of the next-generation telecommunication network in programmable flexible resources control solutions is driven by growing application requirements in service quality and networking resources [17]. Especially, it is the challenge in the IoT domain where multiply vertical use cases with different types of requirements are needed to be effectively implement. In the FlexNet project, we focused on different types of IoT use cases like:  
* Emergency and public safety with low latency requests;
* Video surveillance applications, where on demand high bandwidth is required.  
In these situations, SDN approach allows for flexible, on demand creation of new services using network controller handling virtualized resource across the network. Automatized management of the network resources in relation, from one side to applications demands and from other side to their optimal effective utilization is complex task, where AI mechanisms are promising solutions.  
AI has seen a surge of interest in the networking community [18]. Recent contributions include data-driven flow control for wide-area networks, job scheduling, and network congestion control [19]. A particularly promising domain is the network management. Researchers have used Machine Learning (ML) to automate the management of network resources in relation to routing or network traffic optimization [20; 21; 22; 23; 24]. In IoT networks, we expect network conditions to vary over time and space. Time varying conditions may be long term (seasonal) or short term, resulting in a significant impact on network performance [25]. ML techniques will be developed in order to detect such changes and signal them to the SDN layer for timely action to be taken to improve the overall network performance. Another solution is a Knowledge-Defined Network (KDN) which also is a next step on the path towards an implementation of a self-driving network [26]. KDN is a complementary solution for SDN that brings reasoning processes and ML techniques into the network control plane to enable autonomous and fast operation and minimization of operational costs.
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 4 Transmission Grid Model in Quality of Protection Modeling Language
### Algorithms  
The main reason of introducing the algorithm structure was the need of specifying a series of well-defined successive states, that would let one to precisely define a non-linear and variable execution time of the given function. The inspiration to create this structure was an attempt to determine the packet's transmission time, which is not directly proportional to the size of the transmitted data. Algorithms were introduced along with the advanced network analysis module in [18]. Algorithm definition is similar to the definition of the function. It is enclosed inside the algorithms structure, and starts with the alg keyword, followed by the algorithm name. Each algorithm has one parameter which is a message being sent in the case of a communication step or a function call expression in the case of an operation in process. Implementing an algorithm in QoP-ML, it is possible to use arithmetic operations, constructions known from the C language, such as conditional statements (if), loops (while), and two pre-defined functions: quality and size. The built-in, quality function can be used only in algorithms, which calculate communication time step and return the quality of the link between the sender and the receiver (that is, the \(q\) parameter). On the other hand, the size function is used to determine the size of the algorithm's parameter, which can be a function call or a sent message (or one of its elements, accessed by index). Inside the algorithms structure, one can define as many algorithms as needed.
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 5 Results and Discussion
### Performance Matrix  
While there are numerous metrics available for evaluating machine learning-based intrusion detection systems (IDS), this research focuses on maximizing the accuracy of predictions, for instance in the test dataset. The main metric of interest is the Accuracy (AC), which is calculated using True Positive (TP) for correctly identified attacks, True Negative (TN) for legitimate traffic correctly classified as legitimate, False Positive (FP) for legitimate traffic classified as attacks, and FN for legitimate traffic classified as intrusions [37; 38]. Precision may be defined as the model's total number of positive predictions divided by the number of genuine positive predictions. The model's recall is calculated by dividing the total number of genuine positive predictions by the number of real positive cases found in the data [39; 40]. Additionally, the research considers other metrics such as F1 score, which is determined by taking the harmonic mean of the recall and accuracy of the model, and False Alarm Rate (FAR), which measures the number of false positives relative to all negatives and is a common metric for assessing a model's accuracy. A false positive occurs when a model predicts something incorrectly, whereas a correct prediction generates a negative.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable 1** & **Variable 2** & **Correlation Value** \\ \hline dbytes & Dpkts & 0.970803704 \\ ct\_dst\_lm & ct\_src\_dport\_lm & 0.960191873 \\ ct\_srv\_src & ct\_srv\_dst & 0.956759024 \\ sybytes & Sloss & 0.954961115 \\ ct\_srv\_dst & ct\_dst\_src\_ltm & 0.951066477 \\ ct\_src\_ltm & ct\_src\_dport\_ltm & 0.945315205 \\ ct\_srv\_src & ct\_dst\_src\_ltm & 0.942174265 \\ ct\_dst\_ltm & ct\_src\_ltm & 0.938506142 \\ ct\_srv\_dport\_ltm & ct\_dst\_sport\_ltm & 0.921432623 \\ tcprtt & Ackdat & 0.921293044 \\ ct\_src\_dport\_ltm & ct\_dst\_src\_ltm & 0.910904101 \\ Sttl & ct\_state\_ttl & 0.905564623 \\ Sttl & Label & 0.904224554 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Evaluating a model's accuracy often involves considering its false alarm rate and a lower rate is indicative of a higher level of accuracy [41; 42].
## 3 Results
### Quasi-Distributed Detection and Localization of Short Impacts on Pipes
The propagation velocities deduced for different experimental setups and data evaluation procedures are summarized in Table 1.  
Figure 7: (**a**) Correlation function for the two time traces after filtering and spatial averaging recorded by the sensor closer to (blue) and further away (orange) from the point of impact. Absolute (red) and relative (red and green) maxima of the correlation are marked. (**b**) Corresponding time traces shifted according to maximum correlation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{1}{c}{**Sensor Design**} & \multicolumn{1}{c}{**Position of Impact**} & \multicolumn{1}{c}{**Pulse Length/ns**} & \multirow{2}{*}{**Evaluation Procedure**} & \multicolumn{1}{c}{**Velocity/ (m/s)**} & \multicolumn{1}{c}{**Uncertainty/ (m/s)**} \\ \hline \multirow{2}{*}{1} & fully & \multirow{2}{*}{A} & \multirow{2}{*}{100} & differentiation, signal onset (automatically) & \multirow{2}{*}{5370} & \multirow{2}{*}{190} \\  & distributed & & & & & \\ \hline \multirow{2}{*}{2} & fully & \multirow{2}{*}{B} & \multirow{2}{*}{100} & differentiation, signal onset (automatically) & \multirow{2}{*}{4620} & \multirow{2}{*}{520} \\  & distributed & & & & & \\ \hline \multirow{2}{*}{3} & fully & \multirow{2}{*}{B} & \multirow{2}{*}{50} & differentiation, signal onset (manually) & \multirow{2}{*}{5100} & \multirow{2}{*}{-} \\  & distributed & & & & & \\ \hline \multirow{2}{*}{4} & fully & \multirow{2}{*}{B} & \multirow{2}{*}{30} & differentiation, signal onset (manually) & \multirow{2}{*}{5100} & \multirow{2}{*}{-} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of acoustic propagation velocities (main mode) obtained by distributed acoustic sensing (DAS) using different sensor configurations, optical pulse lengths and evaluation procedures. Positions of mechanical impacts are either on the front face (A) or on the side of the pipe (B). Uncertainty is given as twice the standard deviation (confidence interval of 95.5%). Figure 8: Time traces after filtering and spatial averaging shifted according to the time difference deduced from signal onset for the two localized sensor regions when excitation takes place at the side of the pipe for (**a**) 100 ns, (**b**) 50 ns, and (**c**) 30 ns optical pulse length. (**d**) For comparison shifted time traces for excitation on the front face and 50 ns pulse length are shown. Dashed lines indicate the threshold level used for determining signal onsets.
## 3 Results
### Cytotoxicity of JGTW in BEAS-2B and RAW264.7 Cells  
Next, we tested to see if JGTW have any effect on the viability and proliferation of pulmonary structural and effector cells. The human bronchial epithelial BEAS-2B cell was treated with various concentrations of the JGTW for 24 h, then the proliferation and viability of these cells were measured. The JGTW did not show any significant cytotoxic effects even at the highest concentration (Figure 2). RAW264.7 cells were also treated with various concentrations of JGTW with or without 1 ug/mL LPS for 48 h, then the viability of cells was measured. In accordance with the results of BEAS-2B, JGTW showed neither cytotoxic property nor cell proliferative activity to naive RAW264.7 cells and LPS-stimulated 264.7 cells up to 400 ug/mL (data not shown).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **RT** **(min)** & **Calculated** & **Estimated** **(\(m\)/\(z\))** & **Adduct** & **Error** **(ppm)** & **Formula** & **MS/MS Fragments** (m/\(z\))** & **Identifications** & **Contents** (mg/g)** \\ \hline
20 & 17.69 & 821.3965 & 821.3967 & \(\mathrm{[M-H]^{-}}\) & 0.563 & \(\mathrm{C_{2}H_{62}O_{16}}\) & 821.3943,351.0559, & Glycyrhizin & 11.853 \\
21 & 20.67 & 471.3480 & 471.3480 & \(\mathrm{[M-H]^{-}}\) & \(-\)0.039 & \(\mathrm{C_{30}H_{48}O_{4}}\) & - & Hedergaenin & 0.005 \\
22 & 21.19 & 471.3469 & 471.3471 & \(\mathrm{[M+H]^{*}}\) & 0.072 & \(\mathrm{C_{30}H_{48}O_{4}}\) & 471.3470,454.3427, & Glycyrrhetic & acid [22] & 0.007 \\  & & & & Phenol & & & & & \\
23 & 9.02 & 939.1109 & 939.1111 & \(\mathrm{[M-H]^{-}}\) & 1.236 & \(\mathrm{C_{41}H_{32}O_{26}}\) & 447.0572,295.0449, & Pentagalloyglucose & 2.634 \\  & & & & & & & 169.0125 & [22] & \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 1: UPLC-DAD-MS/MS analysis of JGTW. **(A)** UV and base peak ion chromatograms of JGTW and **(B)** extracted ion chromatogram of the identified phytochemicals in JGTW. 1: catechin; 2: vicenin-2; 3: schaftsoside or isoschaftoside; 4: neoliquiritin; 5: liquiritin; 6: taxifolia; 7: liquiritigeni; 8: luteolin; 9: calycosin; 10: naringenin; 11: kaempfer; 12: formonenti; 13: licochalcone B; 14: echintein; 15: isoliquirtigenin; 16: alloflavin; 17: paeoniflorin; 18: mudanpioside C; 19: benzoylpaeoniflorin; 20: glycyrrhizin; 21: hederagenin; 22: glycyrrhetic acid; 23: 1,2,3,4,6-O-pentagalloyglucose.
## 3 Results and Discussion
### Bioactive Compound Content of Total Polyphenols, Total Flavonoids, and Vitamin C  
With the increase in people's health awareness, fruit wine rich in bioactive substances such as polyphenols is gradually favored by the market. In addition to physicochemical and sensory quality, the content of functional factors has also become a fundamental indicator for evaluating the quality of fruit wine [33]. Consistent with the results of previous studies, the bioactive substances in strawberry juice were mainly polyphenols [19], and the polyphenol content of strawberry juice varied among different cultivars. The polyphenol content of cultivar TCL was the highest, reaching 1372 mg/L, while the contents of cultivars ZJ and BX were relatively low, at 862 mg/L and 832 mg/L, respectively.  
After fermentation, the polyphenol content in strawberry wine slightly decreased. The content in cultivar TCL was still the highest at 1192 mg/L, while the content in cultivar ZJ was the lowest at 643 mg/L (Figure 4A). The content of flavonoids in strawberry juice of different cultivars also showed significant differences, with cultivar TCL having the highest content (257 mg/L) and ZJ having the lowest content (164 mg/L). After fermentation, the flavonoid content in cultivar TCL strawberry wine decreased to 172 mg/L, and that in cultivar ZJ wine decreased to 65 mg/L (Figure 4B). Unlike the results of polyphenol and flavonoid content, the strawberry juice of cultivar TZ had the highest content of vitamin C at 149 mg/L, followed by that of cultivar BX at 97 mg/L, and that of cultivar ZJ had the lowest content at only 7 mg/L. After fermentation, the content of vitamin C in cultivar TZ and BX strawberry wine still remained at or above 70 mg/L, at 70 mg/L and 74 mg/L, respectively (Figure 4C). Although the polyphenol content of strawberry fruit wine was not as high as that of cherry wine, strawberry fruit wine still possessed good antioxidant activity [34]. The strawberry fruit wine of specific cultivars is incomparable in terms of taste and health preferences in the current fruit wine market.  
Figure 4: Total polyphenol, total flavonoid, and ascorbic acid content of different cultivars of strawberry juice and wines. (**A**) Total polyphenol content; (**B**) total flavonoid content; (**C**) ascorbic acid content; BX: Snow White; TCL: Sweet Charlie; TZ: Tongzhougongzhu; ZJ: Akihime. Means with the same letter are not significantly different from each other (\(p<0.05\)).  
Through the above research, we found that there were differences in physicochemical indexes and bioactive substance contents among different cultivars of strawberries, which remained in the fermented strawberry wine.
## 4 Simulation Model Description
### Treatment Room  
Unlike the previous processes, simulating the treatment of a patient is a more complex process, since it involves the need for medical resources as well as other actions in the emergency department. Thus, when a patient enters a treatment room the treatment total time is computed based on the probabilistic data fitting explained in Figure 10. Then, it requests a medical resource to initiate the treatment. At this stage two distinct scenarios may occur, on the one hand if the medical resource is available the patient starts immediately the treatment, on the other hand if there is not any medical resource available the patient waits for the first observation. As was mentioned previously, the doctor's allocation time to the patient is a percentage of the treatment total time, which also depends on the probabilisticdistribution associated with the patient's pathology. However, if a medical resource is available when requested but no treatment place is available in the treatment room, then the patient remains waiting. As can be seen, two premises must simultaneously be met for a patient to enter the treatment room: a medical resource and a treatment place being available. The time for the second medical intervention is 5% of the treatment total time, which will also depend on the patient pathology. For the second medical intervention the patients request a medical resource and wait until a medical resource becomes available. Patients inside the treatment room waiting for the second medical intervention have priority over the patients waiting for the first medical intervention. In the second medical observation the patients also receive their medical discharge, exiting the treatment room and, therefore, making available a space in the treatment room. The time between the first and second medical interventions is the remaining time of the treatment total time. This time represents the miscellaneous actions that may occur in the treatment rooms.  
In the treatment rooms two main resources were modelled: the medical staff and the treatment places.  
For the treatment rooms the most important performance indicators to analyze are the resource utilization (medical staff); waiting queues (both time and number in the queue--information needed to verify compliance with the Manchester protocol guidelines); and treatment places occupation.
# PV Tracking Design Methodology Based on an Orientation Efficiency Chart
## 2 Materials and Methods
### Documentation, Implementation, and Testing
#### 2.7.2 Implementation and Cost Reduction  
The final implementation of the EFO solar tracker prototype is shown in Figure 11, which indicates the locations of the control system and actuators A1 and A2. The close-up view shows the details of the structure, tracker mechanisms, and control system of the solar tracker.  
In this case, the application of an EFO chart to develop a solar tracker resulted in a 27% decrease in the total cost of the solar tracker compared to a dual-axis solar tracker with a mono-post and similar dimensions to the one proposed. In this investigation, the solar tracker may cost 1573 euros [27], which includes the fact that developers usually incorporate a slew drive mechanism for azimuthal tracking at a cost of 423 euros [28], while these mechanisms can be replaced by a machined system of low resolution and precision at an estimated cost of 170 euros [29].  
Figure 10: Mechanical plan for the proposed structure.  
Based on another study [30], a general dual-axis solar tracking system has an additional cost of 1.53 euros/watt with payback in 3.5-5 years. In this case, the dual-axis tracking system represents an additional cost of 1.4 euros/watt, which can be a significant cost reduction for large PV systems with a large number of solar trackers, e.g., the Nufri PV solar plant located in Lleida Spain with approximately 210 solar trackers [31].
# World-Models for Bitrate Streaming
## 2 Background
### Motivation for World-Models  
Model-free reinforcement learning is notoriously sample inefficient; agents may require tens or hundreds of millions of interactions to learn complex tasks. Rainbow, which is a combination of six extensions to the DQN algorithm, achieved state-of-the-art results for sample efficiency, but it still required 44 million frames to beat existing baselines in the Arcade Learning Environment [20]. Most current reinforcement learning research is evaluated on simulators, games in the Arcade Learning Environment, or other video games. While these control problems may be complex and challenging, one interaction with the environment is typically sub-second. Conversely, many real-world systems can take several seconds to execute one step. Custom-built simulators are often necessary for successfully trainkng reinforcement learning agents in these settings. For example, a simulator for bit-ratestreaming can allow an A3C-based agent to experience 100 h of video in only 10 min. of training time [1]. In contrast, training the same agent on the real system would take more than 10 years to receive enough experience to reach comparable performance [7]. Building simulators may be difficult; custom simulators require developer effort and domain knowledge, and built simulators may not accurately model real-world systems [21]. Some reinforcement learning methods that require high sample complexity, such as policy gradient methods, cannot be used if the real environment's interactions take too long or the simulator is too slow [22]. If one can learn a world-model from recorded data, then one could train an agent inside the world-model without ever interacting with the real system. Agents can train cheaply in parallel with learned models. A learned environment can execute steps without waiting in real-time for real-world events, rendering states, or completing computations that are unrelated to essential aspects of the environment.
# A Brief Review of Multipath TCP for Vehicular Networks
## 5 MPTCP in Vehicular Networks  
Vehicular network is an important part of intelligent traffic systems (ITS). Vehicles can communicate with cars, roadside infrastructure, and pedestrians, and drivers can exchange information more conveniently and efficiently through vehicular networks. However, due to the constant mobility of vehicles, the vehicular network has the feature of dynamic topology and various node densities, which makes it challenging to provide a stable and robust connection between vehicles. At the same time, most of the vehicular applications have stringent requirements on the response delay and reliability. There are some efforts that improve vehicular networks performance based on single path TCP. [PERSON] et al. in [97] proposed a mobile control transport protocol (MCTP) in VANETs, which is a proxy-based communication protocol. Vehicles communicate with proxy by MCTP, and proxy communicates with the Internet with TCP. The main idea of the MCTP is to distinguish network problems, such as packet losses, congestion, and network disconnections. MCTP is located between the TCP and IP, so that it can observe the packet flow by utilizing the intermediate systems and underlying protocols. In a conventional handover approach, the network performance degradation occurs, due to the change in IP address. Since the TCP connection will be released and reestablished. This would result in the CWND falling into the initial value. [PERSON] et al. in [98] proposed a SDN-based handover scheme in vehicular networks. To overcome the degradation, they utilized SDN technology to map the old connections information to the new connection, ensuring a seamless handover.  
Nowadays, vehicles could have different types of communication interfaces. There are also many types of wireless communication technologies such as IEEE 802.11p, cellular communication, and mmWave, available for vehicular networks. Among them IEEE 802.11p is the default standard communication technology for vehicle-to-vehicle communication. To improve the connection robustness and transmission efficiency in vehicular networks, MPTCP is considered to be a promising approach. MPTCP can facilitate mo bility [43; 44] compared to single path TCP. As shown in Figure 4, MPTCP has the ability to improve vehicular network throughput by combining these wireless communication technologies, and to provide robust connectivity [99] by implementing handover between different subflows when interruption incurs. For example, in Figure 4, the green car can communicate with RSU and adjacent cars by IEEE 802.11p, base station (BS) by cellular, and building by Wi-Fi simultaneously in one TCP connection. However, MPTCP currently does not perform well under very high velocities in vehicular environments.  
There are many studies that discuss MPTCP in vehicular networks to improve the transmission rate and reduce latency. In the following, we introduce some research efforts on MPTCP in vehicular networks. We summarize the studies in Table 4.