esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 2 Flight Conflict Network
### Determine the Edge Weight  
The contiguous edges of the flight-conflict network represent potential flight conflicts between aircraft in the airspace, and the edge weights should reflect the urgency of the conflict. Definition \(t_{\text{c}}\) is the time when an aircraft maintains its current flight status and a conflict is expected to occur. When designing the weights of the edges, it is necessary to satisfy: (1) the smaller the distance between aircraft, the larger the relative speed, the more urgent the conflict, the larger the weights of the edges, i.e., the edge weights increase with the decrease in \(t_{\text{c}}\); (2) as \(t_{\text{c}}\) decreases, the urgency of the flight conflict is changing more and more drastically, and the weights are increasing more and more; (3) considering the principle of the speed obstacle law and the practical significance of the flight conflict, the weights of the edges should be positive value.  
In summary, define the network edge rights:  
\[\omega_{ij}=\exp(-t_{c}) \tag{4}\]  
\[t_{c}=\frac{\left|AC\right|}{v_{r}} \tag{5}\]  
\[\left|AC\right|=\min\{\left|AC_{1}\right|,\left|AC_{2}\right|\} \tag{6}\]  
Selecting the negative exponential function of the predicted conflict time \(t_{c}\) as the weight has the following advantages: (1) The negative exponential function is a decreasing function on its domain. The longer the \(t_{c}\) is, the smaller the weight is, and the weaker the conflict intensity is. (2) When forming the edge, the value of \(t_{c}\) is greater than 0, so the value range of the negative exponential function is [0,1], which can play a role in uniting the weights. (3) The absolute value of the slope of the negative exponential function increases gradually with the decrease in the independent variable, which can reflect that the urgency of the potential flight conflict changes fiercely in the process of \(t_{c}\) decreasing, which is consistent with the actual conflict situation.  
Figure 2: Three-dimensional speed barrier conflict-detection model.
# Independent Double-Boost Interleaved Converter with Three-Level Output
## 2 Converter Topology Analysis  
### Converter Topology, Switching States, and Presumptive Waveforms  
The proposed converter topology, named an independent double-boost interleaved converter and henceforth referred to as IDBIC, is based on a patent application [33] developed especially for PV/wind energy harvesting and battery energy conversion systems. The basic converter topology is represented in Figure 1, while the main operating stages of the converter are presented in Figure 2, in which nine independent switching states (S1-S9) are underlined.  
Figure 1: The electronic schematic of the proposed converter—IDBIC.  
Figure 2: Switching stages for the proposed IDBIC converter.  
The characteristic waveforms during operation are introduced in Figure 3, in which the continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operations are exemplified in conjunction with the S1-S9 switching states. These four presumptive switching patterns are mainly triggered by the duty cycle values of the T1 and T2 transistor command signals. In view of this, for a duty cycle smaller than 0.5, the inductor currents have steeper falling slopes during the S2 and S3 switching stages. For a duty cycle larger than 0.5, inductor current waveforms and behavior similar to the regular boost interleaved converter can be observed.  
As documented in these switching stages and presumptive functioning patterns, the two integrated boost converters work independently from each other, with the inputs connected in parallel and the outputs in series. From this observation, one of the key characteristics of the proposed double-boost converter is defined.
## 3 System Model  
The system architecture used in this work is adopted from LEACH [2], that has the basic system model for a cluster-based WSNs scenario. It has proved to have energy savings above a factor of 7 compared to flat architecture. The network is composed of sensor nodes randomly distributed in a geographical area, and grouped into clusters. There are Normal Sensor Nodes (SN) and Cluster Heads (CH). Each SN in every cluster communicates directly with the CH and every communication within the cluster is handled by the CH. A Base Station (BS) with unlimited energy is located far away from the sensor network as illustrated in Figure 1.  
A CH in each cluster is responsible for communicating the aggregated data of the cluster to the BS. The model is further enhanced to meet our communication scenario, communication between the CH and BS can either be direct or cooperative. Direct communication is used when the channel condition is good. If the channel conditions are fair, communication can be either direct or cooperative, based on the CQI and distance ratio between the CH and BS. If the channel is bad, cooperative communication is considered. For cooperative communication, a relay cluster head (RCH) is selected to aid the CH communication to BS. RCH selection is based on its distance away from the BS, its remaining energy level and the CQI along its link. The CH communicates with RCH, which then relays the information to the BS.
# Constrained Connectivity in Bounded X-Width Multi-Interface Networks
## 1 Introduction
### Related Work
Hence, this problem is a generalization of the Maximum Flow problem in standard networks. The Minimum Cost Flow problem aims at ensuring the following two goals: (i) a communication sub-network between two given nodes which has minimum energy consumption; (ii) a minimum amount \(B\) of communication bandwidth. In practice, the problem aims at finding the minimum cost set of interfaces to activate among the input network so that two specified nodes \(s\) and \(t\) are guaranteed to exchange data with at least \(B\) bandwidth. Clearly, the solution might result in a complex graph with source \(s\) and tail \(t\) composed of nodes with active interfaces. This problem is a generalization of the Minimum Cost Flow problem in standard networks. The theoretical results of multi-interface wireless networks can be applied in various applications such military applications and the tactical networks that are provided with new software technologies [17; 18] which employ resource-constrained mobile devices. The main resource that must be preserved is the battery power of each device thus a suitable usage of the interfaces could be crucial for improving the network lifetime.
## 3 Field Data Acquisition and Processing
### Exploratory Data Analysis  
Prior to the modeling process, exploratory data analysis was carried out to derive insights about the dataset. Since the size of sample was greater than 30, the assumption for normality was relaxed [50]. The strength of the linear relationship between the output and the input variables was measured using [PERSON]'s correlation coefficient 'r' and a correlogram was plotted, as shown in Figure 11. A rule-of-thumb given in Equation (28) was utilized to determine the strength of correlation, where 'n' represents the sample size, and thus an absolute value greater than or equal to 0.23 was considered a good linear correlation.  
\[|r|\geq\frac{2}{\sqrt{n}} \tag{28}\]  
It may be observed from the correlogram that \(\rho_{r}\) had a very strong relationship with \(v_{f}\), followed by \(S\), \(B\)/\(d\), \(W\), \(B\), \(l_{s}\)/\(B\), and \(W_{lc}\). Although \(S\) and \(B\) were strongly correlated with the output variable and have been used by many researchers as influencing factors for predicting flyrock distance, there is hardly any information in the literature signifying the direct effect of \(S\) on \(v_{f}\). Hence, for the development of predictive models \(B\), \(B\)/\(d\), \(l_{s}\)/\(l_{hh}\), \(l_{s}\)/\(B\), \(\rho_{r}\), and \(W\) were finalized as the input variables. Descriptive statistics of the dataset are summarized in Table 3, and the standardized distribution of the variables can be observed from the box-and-whisker plot shown in Figure 12.  
Figure 11: Correlogram showing [PERSON]’s correlation between the input variables and the output.
# Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning
## 3 Servo System Control Strategy Based on Reinforcement Learning
### PID Servo System Control Scheme Based on Reinforcement Learning  
#### 3.2.1 PID Parameter Tuning Method Based on Reinforcement Learning  
This study uses actor-critic to construct the framework of reinforcement learning agent 1, with a PID servo system as its environment object. The tracking error curve of incentive function is acquired. The deterministic policy gradient algorithm is used to design an action network, and the DQN algorithm is used to design an evaluation network. PID parameter self-tuning is realized. The structure diagram of the adaptive PID control algorithm based on reinforcement learning is shown in Figure 3.  
Figure 2: Diagram of the actor–critic algorithm structure.  
In Figure 3, the upper dotted line frame is an adaptive PID parameter regulator based on reinforcement learning, which is composed of a reinforcement learning agent; the lower dashed box has a PID servo controller and a speed servo system as agent environment interaction objects. \([X(t-1),e(t-1),X(t),e(t),H(t+1),H(t+2)]\) is a set of reinforcement learning agent states \(s_{t}\). \([X(t),e(t),X(t+1),e(t+1),H(t+2),H(t+3)]\) is a set of states \(s_{t+1}\) at the next moment, which is acquired by the interaction of agent 1 and servo environments. \(X(t-1)\), \(X(t)\), and \(X(t+1)\) denote the angular velocity at the last moment, the current moment, and the next moment of the servo system, respectively. \(H(t+1)\), \(H(t+2)\), and \(H(t+3)\) refers to the need to track the signal of the next moment, after two times, and after three times, respectively. \(e(t-1)\), \(e(t)\), and \(e(t+1)\) are the output error values of the previous moment, at the current time, and at the next moment, respectively. \(\mu(s_{t}|\theta^{\mu})\) indicates the action selected by the actor evaluation network according to the state \(s_{t}\), and \(\mu\Big{(}s_{i+1}|\theta^{\mu-}\Big{)}\) indicates the action selected by the actor target network according to the state \(s_{i+1}\). The PID parameters \(\big{[}K_{p},K_{i}\big{]}\) are a set of action variables \(\mu\) to the reinforcement learning agent. The reward function is constructed by the absolute value of the tracking error of the output angle of the servo system. The data are obtained through the interaction of the agent and the servo environment and stored in the memory \(M\). The network update data are obtained by memory playback, and the neural networks are trained using Equation (14). The reinforcement learning agent is obtained on the basis of the current state \(s_{t}\) to select the appropriate \(\big{[}K_{p},K_{i}\big{]}\), and automatic tuning of the parameters of the PID servo system is realized.
## 3 Methodology
### Prediction Using GP  
In the case of a regression modelling problem, an output variable \(q\) can be approximated, given function \(h(r)\) with an additive noise \(\varepsilon_{i}\) component inherent in the dataset as shown in Equation (3).  
\[q_{i}=h(r_{i})+\varepsilon_{i} \tag{3}\]  
Assuming this noise component \(\varepsilon_{i}\) has a zero mean and variance \(\sigma_{n}^{2}\), the prior on the noisy data is expressed in Equation (4) as:  
\[\text{cov}(q)=g\big{(}r,r^{\prime}\big{)}+\sigma_{n}^{2}I_{n} \tag{4}\]  
where \(I_{n}\) is a matrix of the n-dimensional unit.  
The GP \(h(r)\) (see Equation (1)) is then precisely considered in Equation (5) as:  
\[h(r)\sim\text{GP}\Big{(}b(r),g\big{(}r,r^{\prime}\big{)}+\sigma_{n}^{2}I\Big{)} \tag{5}\]  
It should be emphasized that the GP model training, seeks to ascertain the best possible hyperparameter set \(\Theta=\big{[}\beta,\chi,\upsilon_{s}^{2},\sigma_{n}^{2}\big{]}\) that best fits the data sets. This can be done by the use of a maximum possible method [69] in which the log-likelihood function is maximized (Equation (6)).  
\[\log(p(q|r,\Theta))=\frac{1}{2}\text{log}\Big{(}\text{det}\Big{(}g\big{(}r,r^ {\prime}\big{)}+\sigma_{n}^{2}I\Big{)}\Big{)}-\frac{1}{2}q^{T}\Big{(}g\big{(} r,r^{\prime}\big{)}+\sigma_{n}^{2}I\Big{)}^{-1}q-\frac{n}{2}\text{log}2\pi \tag{6}\]  
Of all the maximum likelihood functions available, the conjugate gradient method is the most widely used [79] and hence was used in this study. It finds the optimal hyperparameter sets by using the partial differential of the log-likelihood function (Equation (6)) in relation to the hyperparameter set, \(\Theta\) as shown in Equation (7).  
\[\begin{array}{rl}\frac{\partial}{\partial\Theta_{i}}\text{log}(p(q|r,\Theta ))&=\frac{1}{2}q^{T}\text{G}^{-1}\frac{\partial\text{G}}{\partial\Theta_{i}} G^{-1}q-\frac{1}{2}tr\Big{(}\text{G}^{-1}\frac{\partial\text{G}}{\partial \Theta_{i}}\Big{)}\\ &=\frac{1}{2}tr\Big{(}\big{(}\beta\beta^{T}-\text{G}^{-1}\big{)}\frac{\partial \text{G}}{\partial\Theta_{i}}\Big{)}\end{array} \tag{7}\]  
where \(\beta=G^{-1}q\) and \(G=g(r,r^{\prime})\).  
Given the joint prior distribution of the training output variable, \(q\) at point a and the value \(q_{*}\) to be predicted at the test point \(r_{*}\) expressed in Equation (8), the GPR model is able to predict \(q_{*}\) by calculating the posterior distribution \(p(q_{*}|r,q,r_{*})\) (Equation (9)).  
\[\begin{bmatrix}q\\ q_{*}\end{bmatrix}\sim GP\bigg{(}\begin{bmatrix}b(r)\\ b(r_{*})\end{bmatrix},\begin{bmatrix}g(r,r)+\sigma_{n}^{2}I&g(r,r_{*})\\ g(r_{*},r)&g(r_{*},r_{*})\end{bmatrix}\bigg{)}, \tag{8}\]  
\[p(q_{*}|r,q,r_{*})\sim GP(\overline{q}_{*},cov(q_{*})), \tag{9}\]  
Here \(\overline{q}_{*}\) (Equation (10)) is the mean value which is the estimation of \(q_{*}\) and \(cov(q_{*})\) (Equation (11)) is the predictive variance matrix of the test data, which reveals the credibility of the prediction values [79].  
\[\overline{q}_{*}=b(r_{*})+g(r_{*},r)\Big{[}g(r,r)+\sigma_{n}^{2}I\Big{]}^{-1} (q-b(r)) \tag{10}\]  
\[cov(q_{*})=g(r_{*},r_{*})\Big{[}g(r,r)+\sigma_{n}^{2}I\Big{]}^{-1}g(r,r_{*}) \tag{11}\]
## 3 IVR-Based SDO Streaming Distance Education System
### IVR-SDO Streaming Education System Architecture
#### 3.1.3 Relay Server  
The SDO client and image server have considerable networking loads because large SDO images are transmitted. Furthermore, if the SDO image server also takes charge of user management, the complexity increases with the number of users. The relay server for each channel reduces this problem. For example, six-channel video relay servers, and a one-channel relay server are allocated for transmitting SDO images and data, respectively. Thus, independent relay servers are allocated to different types of media. Although the relay server suffers from increased transmission time compared to the direct data transmission method, it reduces the data transmission load of the SDO image server and achieves scalability by increasing the number of relay servers. Problem channels are restored without affecting the other channels using a relay server for each channel. This study used relay servers for six-channel video, one-channel voice, and one-channel avatar/hand motion data. Here, the difference between the SDO and other data channels is that the SDO video channel is only used for downloading as a simplex channel that only transmits data in one direction, whereas the voice, head, and hand channels are full duplex channels that transmit data in both directions.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### LTE-A Delay
#### 4.1.3 User Plane (PC5 Interface)  
As mentioned earlier, some of the late LTE's versions supported direct communication between UEs, through the so-called PC5 interface. PC5 used for vehicular communications is categorized as two transmission modes, namely Modes 3 and 4 [22]. Mode 3 refers to network scheduled transmission, whereas Mode 4 refers to transmission on autonomously selected resources. In Mode 3, the eNB is responsible for providing the UEs with control information, including the type of scheduling [22].  
Mode 3 uses dynamic scheduling with BSR or semi-persistent scheduling. Dynamic scheduling with BSR follows the same principle as in the UL transmission case, omitting the last two steps (including UE transmission and eNB reception). In SPS, the UE may provide feedback information to the eNB, in order to allocate the SPS resources effectively and reduce the transmission latency. The latter can be achieved if the scheduling configuration  
Figure 2: End-to-end (E2E) user plane (UP) latency in LTE-A (Uu).  
is synchronized with the packet arrival time. Consequently, the data transmission occurs immediately [22].  
At this point, it should be mentioned that when a UE is in the RRC IDLE state, it can only transmit a packet in Mode 4. A UE will have to switch to the RRC CONECTED state to transmit using Mode 3. This implies that when the UE has delay-critical data to transmit, this additional burden has to be considered for Mode 3 (50 ms for RRC connection). On the contrary, this delay is optional for the UE in Mode 4, with the limitations of the local view of sensing, which may phase collisions on the receiver's side (e.g., the hidden terminal problem). Another control plane component that should be considered for both modes is the transmission of UE sidelink information for a resource configuration request which occurs when the UE receives the RRC reconfiguration message from the eNB. The assumption for this delay component is 29.1 ms [27].  
The sensing period and the SPS delay relate to sensing windows and scheduling windows, respectively, which may vary [22]. The analysis of these steps is presented in Table 7 considering the min., mean, and max. values of the potential delays; the RRC connection delay is not considered, since it is assumed that the UE is already in a condition to transmit.
## 2 Problem Description and Overall Program
### Overall Program  
The method presented in this research comprises three main stages: assessment modeling, online assessment, and validity verification. The overall program of the method is shown in Figure 3.  
(1) In the assessment modeling phase, the component historical telemetry data are first preprocessed. The benchmark range is determined using metrics such as variance and standard deviation. From this, the healthiness assessment indicator for the component's whole-life data, i.e., HDDD, is calculated. Simultaneously, the whole-life data are employed to assign appropriate weights to individual parameters. Subsequently, the health status classes are classified based on the spectral clustering method and reasonable thresholds are determined for the subsequent online assessment.  
(2) In the online assessment phase, health benchmark state data are first collected and used to build a benchmark model of the component. Once the benchmark data collection is complete, the real-time component health assessment commences. During the real-time assessment process, the health status indicator of the component is calculated using the improved Wasserstein distance (WD). The results of the HDDD calculation are then compared with predefined thresholds to determine the corresponding health status level of the component.  
Figure 2: Effectiveness of MMD calculations for data with different change types.  
(3) In the validity verification phase, trends in the long-term data are extracted using the seasonal decomposition (SD) algorithm. These trends extracted through SD are analyzed and compared to the results obtained from the HDDD based on the WD. Comparative analyses are used to verify the effectiveness of the method in capturing trends in component health status changes.
# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 3 Background
### Deep Learning  
DL is a subfield of ML that aims to mimic how the human brain processes information by utilizing neural networks composed of multiple layers, referred to as DNNs. These networks are designed to automatically learn and extract hierarchical features from raw data, making them exceptionally well suited for tasks that involve large and complex datasets. DL models have demonstrated remarkable success in various applications, including image recognition, natural language processing, and speech recognition, owing to their capacity to uncover intricate patterns within data that may not be evident to traditional ML algorithms [4].  
In the context of detecting DDoS attacks, DL offers several advantages. DDoS attacks are prevalent cyber threats that inundate a network or system with an overwhelming traffic volume, disrupting regular operations. They are particularly challenging to detect due to their ability to mimic legitimate traffic patterns, rendering traditional rule-based methods less effective. DL models excel in discerning nuanced and evolving patterns within network traffic data, which can aid in the early identification of DDoS attacks. DL techniques can be applied to DDoS-attack detection in various ways. One common approach is to utilize DNNs, such as CNNs, to process network traffic data. CNNs are adept at extracting spatial features from data, making them suitable for tasks involving structured inputs, like network packet headers. One of the critical advantages of DL in DDoS detection is its adaptability. DNNs can be trained to automatically learn and adapt to new attack strategies, making them highly effective against evolving threats. Additionally, feature engineering, a labor-intensive step in traditional ML, is often unnecessary with DL. These models can autonomously extract relevant features from the raw data, reducing the burden on security analysts. Nonetheless, DL models are not without their challenges. They typically require large volumes of labeled data for training, which can be scarce in the case of DDoS attacks. Ensuring the interpretability of deep models is another concern, as black-box models may not provide insights into why an attack was flagged. Despite these challenges, adopting DL in DDoS detection showcases the potential for more accurate and adaptive security systems to identify previously unseen attack patterns, strengthening network resilience and security.