esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining
## 3 Optimization Problem Transformation  
In this part, the goal is to maximize the secrecy rate of the communication system. Then according to the system model, we can formulate the optimization problem of the secrecy rate with the transmit power constraint as follows. \[\max_{\mathbf{L}\mathbf{J}_{1},\mathbf{J}_{2},\mathbf{Z}_{\mathbf{G}}} \min_{\mathbf{H}_{ij,k}\in\mathcal{H}_{ij,k}}R_{S} \tag{16a}\] \[\text{s.t.}\ \|\mathbf{L}\|^{2}\leqslant P_{S},\ \|\mathbf{J}_{1}\|^{2} \leqslant P_{S},\ \|\mathbf{J}_{2}\|^{2}\leqslant P_{R},\] (16b) \[tr((\mathbf{G}\mathbf{H}_{SR,1}\mathbf{L})^{K}+(\mathbf{G} \mathbf{H}_{I}\mathbf{J}_{1})^{K}+\sigma_{R}^{2}\mathbf{G}^{K})\leqslant P_{R} \quad\forall\mathbf{H}_{ij,k}\in\mathcal{H}_{ij,k} \tag{16c}\]  
However, due to the non-convexity of the optimization problem, it is difficult to solve. To deal with the high non-convexity of the function \(-\log\lvert\cdot\rvert\), the objective function in (16a) is transformed into an equivalent counterpart through the WMMSE algorithm, which can be solved by the BCD method. The following introduces the WMMSE algorithm. **Lemma 1**. : _Define the mean-square error (MSE) matrix_  
\[\mathbf{\hat{N}}\triangleq(\mathbf{T}\mathbf{H}\text{-}\mathbf{E})^{K}+ \mathbf{T}\mathbf{R}\mathbf{T}^{H} \tag{17}\]  
_where \(\mathbf{R}\succ\mathbf{0}\). Then we have_  
\[-\log\lvert\mathbf{N}\rvert=\max_{\mathbf{K}\succ 0}\ \log\lvert\mathbf{K}\rvert -\text{tr}(\mathbf{K}\mathbf{N})+\text{tr}(\mathbf{E}) \tag{18}\]  
\[\log\Bigl{\lvert}\mathbf{I}+\mathbf{R}^{-1}\mathbf{H}^{K}\Bigr{\rvert}=\max_ {\mathbf{K}\succ 0,\mathbf{T}}\ \log\lvert\mathbf{K}\rvert-\text{tr}(\mathbf{K}\mathbf{\hat{N}})+\text{tr}( \mathbf{E}) \tag{19}\]  
To reformulate the parts of \(-\log\lvert\cdot\rvert\) in the objective function, we apply Lemma 1 on (13) and introduce the MSE matrix \(\mathbf{N}_{i}\) and auxiliary matrices \(\mathbf{K}_{i}\), \(\mathbf{T}_{i}\),which have been defined in (17) and (19). So, the achievable secrecy rate of the legitimate can be reorganized as  
\[R_{D} =\log\bigl{\lvert}\mathbf{E}+\mathbf{\Gamma}_{D}\bigr{\rvert}= \log\Bigl{\lvert}\mathbf{E}+(\mathbf{H}_{RD,2}\mathbf{G}\mathbf{H}_{SR,1} \mathbf{L})^{K}\mathbf{Q}_{D}^{-1}\Bigr{\rvert}\] \[=\max_{\mathbf{K}_{D}\succ 0,\mathbf{D}_{D}}\ \log\lvert\mathbf{K}_{D} \rvert-\text{tr}(\mathbf{K}_{D}\mathbf{N}_{D})+\text{tr}(\mathbf{E}) \tag{20}\]  
where  
\[\mathbf{N}_{D}=(\mathbf{T}_{D}\mathbf{H}_{RD,2}\mathbf{G}\mathbf{H}_{SR,1} \mathbf{L}-\mathbf{E})^{K}+\mathbf{T}_{D}\mathbf{Q}_{D}\mathbf{T}_{D}^{H} \tag{21}\]  
Applying Lemma 1 on (14), the achievable rates of \(E_{1}\) and \(E_{2}\) can be transformed as (22) and (23).
###### Abstract  
Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings).  
C-V2X; eco-routing; ITS; CAV; VANET; smart cities; environmental applications; vehicular networks;V2V; V2I +
Footnote †: journal: _Sensors_**2023**, 23, 2314. [[https://doi.org/10.3390/s23042314](https://doi.org/10.3390/s23042314)]([https://doi.org/10.3390/s23042314](https://doi.org/10.3390/s23042314))
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 3 System Design and Architecture  
Collaborative DDoS mitigation requires multiple SDN domains networked together as depicted in Figure 1. Each domain is a complete AS with egress and ingress routers. Any single autonomous system may be comprised of multiple SDN controllers which communicate with each other via our proposed C-to-C protocol. At the border of any AS sits SDN controllers that are capable of communicating with the neighboring AS's controllers to transfer attack definitions (Attack definition basically consists of the malicious IP addresses that are exchanged in the payload of C-to-C protocol). These AS can roughly be divided into a _Source Domain_, an _Intermediate Network Domains_ and a _Destination Domain_.  
The source network is what the attack traffic is initiating from. The intermediate network(s) is comprised of multiple SDN domains connected with each other. The destination network(s) is the one that the victim is residing in. Attack traffic initializes from the source domain(s). It passes through intermediate networks to reach the destination network.  
Figure 1: High-level Architecture of Collaborative DDoS framework.  
In this paper, we have leveraged SDN to effectively mitigate the DDoS attack closest to the source. Our primary assumption in this work is that a detection engine will inform our SDN controller about possible attack information based on which we will mitigate the DDoS attack. This detection engine may consist of very effective and sophisticated detection mechanisms, like the one proposed in [30], which can be both internal as well as external to the AS. In the following subsections we discuss the internal component architecture of the controller. Furthermore, we have elaborated the payload structure of the C-to-C protocol and summarized the overall collaborative DDoS mitigation work flow.
## 2 Materials and Methods
### Simulated Datasets  
The population PK estimates used in the current analysis were obtained from a recent publication [23]. In that study, non-linear mixed effect modeling was applied to the actual C-t data of the donepezil BE study (see above). Using the population estimates of parameters and error (between-subject and residual), simulations were performed in order to generate 2 \(\times\) 2 crossover BE datasets of 1000 subjects each (SimukTM, MonolixTM v2021R2). In addition, three different absorption kinetics were simulated by adjusting the absorption rate constant appropriately: (a) slower absorption by setting the absorption rate constant to half of the original (0.5\(\times\)), (b) equal to the original (1\(\times\)), and (c) faster absorption by setting the absorption rate constant to twice the original (2\(\times\)). All other PK parameters were kept to the actual population pharmacokinetic modeling estimates [23]. The isolation and exploration of absorption kinetics were enabled by changing only the absorption rate constant while leaving the other parameters unchanged.  
In addition, three types of sampling schemes were investigated for each case of absorption kinetics: sparse, typical, and dense (Table 2). The use of sampling schemes with varying sampling frequencies allowed investigation of how sensitive the PK parameter calculations were to errors caused by the sampling schedule. The \"typical\" sampling scheme for each absorption kinetic type (Table 2) was designed to characterize the C-t profile appropriately using an optimum number of samples. Sparse designs were developed by omitting sampling points, usually one by one. Dense designs were created by adding sampling points. In all cases of simulated designs, the last measurement point was set at 72 h, according to the current regulatory guidelines [3].  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Sampling Schedule (in Hours)** \\ \hline \multicolumn{3}{c}{Actual dataset} \\ \hline Sampling according to the study protocol & 0, 0.5, 1, 2, 3, 4, 6, 8, 12, 24, 48, 96, 144, 192 \\ \hline \multicolumn{3}{c}{Simulated datasets} \\ \hline Slow absorption (0.5\(\times\)) & \\ \hline Sparse & 0, 2, 5, 7, 8, 10, 16, 24, 48, 72 \\ \hline Typical & 0, 1, 2, 4, 5, 6, 6.5, 7, 7.5, 8, 9, 10, 12, 16, 24, 36, 48, 72 \\ \hline Dense & 0, 1, 2, 3, 4, 4.5, 5, 5.5, 6, 6.33, 6.67, 7, 7.33, 7.67, 8, \\ 8.5, 9, 10, 12, 14, 16, 24, 36, 48, 72 \\ \hline \end{tabular}
\end{table}
Table 2: Sampling schedules used in the analysis. The sampling schedule for the actual data was the one reported in the study protocol. For the simulated datasets, the “typical” sampling scheme for each absorption kinetic type was designed to characterize the C-t profile appropriately using an optimum number of samples. Sparse designs were developed by omitting sampling points, usually one by one. Dense designs were created by adding sampling points.
# Technology Trends for Massive MIMO towards 6G
## 2 Metasurface-Enabled Massive MIMO
* Energy efficiency: Compared to traditional massive MIMO systems that rely on power-hungry phase shifters, the reconfiguration of intelligent metasurfaces involves minimal hardware and power costs, making them more energy-efficient. However, the emergence of the metasurface techniques for massive MIMO has introduced novel challenges to the field:  
* As we have introduced before, the metasurface could be categorized into several types, each having its own use case [11]. It requires further investigations on what specific use cases are suitable to deploy the IRS, RRS, and IOS, respectively. * The refracted and reflected signals of IOS are coupled with each other, determined simultaneously by the states of PIN diodes. Such a coupling effect makes it unknown whether IOS has the same impact on EM waves when the signal impinges on different sides of the surface. * To fully exploit the refract-and-reflect characteristic of IOS, it is also necessary to explore the optimal position and orientation of the IOS given the BS and user distribution to extend the coverage on both sides of the IOS [12]. * A beamforming scheme should be reconsidered and tailored for the IOS-aided transmission since the reflected and refracted beams towards different users are dependent of each other [13]. While the aforementioned advantages and challenges focus mainly on communication aspects of the metasurface-enabled massive MIMO, it is important to note that their benefits are not restricted to communication alone. The ability to control and customize wireless environments also holds substantial promise in the area of wireless localization and sensing. As we transition towards the 6G era, these functions are becoming increasingly crucial. The following section discusses how the metasurface-enabled massive MIMO enhances wireless localization and sensing.
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 4 Hand Posture Feature Extraction  
A dynamic hand gesture often consists of two components: motion and shape. Motion components contain the changing information with respect to time of the overall hand for global motions and the fingertip positions for local motions. The shape components represent the hand shape at the specific time by the hand joint positions and hand components (palm, wrist, fingers at the base, middle or tip regions) in the corresponding domains (skeleton or depth).  
In this study, we only calculate the global motions of the overall hand. The local motions can be exploited from the hand shape changes with respect to time. Therefore, the hand shape feature models sometimes archive better performance than the motion models due to capturing the local motions from the shape changes.  
Furthermore, there are three ways to divide the types of hand posture features. The first group is based on skeleton and depth data. The second group is based on the means of feature extraction, such as the handcrafted features and deep learning features. The last group is the components of the gesture, such as motion and shape.  
In this section, we mention three main groups: handcrafted skeleton features (motion, skeleton shape, and normalized points), joint point-cloud feature (input from normalized points to exploit 3D geometric characteristic), and depth shape feature (input from the depth data to determine the hand components).
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 3 Materials and Methods  
This study aims to build a state-of-the-art framework to assess the efficacy of technological advancement in the context of text-based content analysis. In this study we collected data from Twitter using _Twitter Streaming API_ and apply standard preprocessing techniques of NLP that ranges from tokenization, stemming, lemmatization, computing TF-IDF features, etc., to generate a dataset. The main objective of this study is to develop a model that can predict a class of a given tweet either as neutral or extreme.  
We proposed a framework that involves a two-step process of using machine learning methods in order to get better understanding of the data and predictive ability. The first step is to perform an Exploratory Data Analysis (EDA) [37; 38], where the objective is to have a better understanding of underlying hidden patterns in the data. As part of an EDA, we use to apply Principal Component Analysis (PCA) to reduce dimensions of our dataset in order to visually observe hidden patterns in the data. As the second step of our analysis, we apply various machine learning classification models such as SVM, naive Bayes', decision tree, KNN and ensemble classification methods with boosting and bagging approaches. The evaluation of classification models is a key step. We compute evaluation measures such as accuracy, precision, recall and F-score in order to evaluate predictive performance of the algorithms applied. In our experiments, we also demonstrate the impact of data size on the performance of classification models.  
In subsequent sub-sections, the key stages of our analysis are explained. These stages are data collection, data pruning, preprocessing, feature extraction, and exploratory data analysis, predictive model building and its evaluation. Figure 2 represents the schematic representation of the proposed framework of classifying tweets either as extreme or as neutral.
# An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
## 4 Performance Assessment
### Impact of Road Segments in the Reliability of Data Dissemination
EEAPD versus (Delay-Based and AddP): EEAPD performs better than other protocol approaches by 70% when the road segments are 450. This is for two reasons. First, the AddP protocol takes consideration of beacon exchange such as neighbors' speed, angle, position etc. before rebroadcast this can lead delay of data rebroadcasting while EEAPD considers only the surrounding vehicle density as one of the parameters on making rebroadcasting decision no need of beacon exchange. Second, EEAPD has a strategy to maintain the usage of network bandwidth by reducing the relaying vehicles while increasing the road segments in communication range. Therefore, the network resource i.e., bandwidth consumption will significantly decrease. This helps to minimize the broad storm problem. Figure 12 presents end-to-end delay comparison performance between protocols. As known the transmission delay is a very important metric for the reliability of safety data dissemination in the vehicular environment, EEAPD outperforms than other protocols approaches on time is taken by vehicle to rebroadcast the message and received at the destination in an urban environment. Due to vehicles being close together in the road segment the delay present is constant to all protocols by increasing road segments. Specifically, EEAPD has reduced the transmission delay to around 50 ms compare to S1 PD where its transmission is approximate to 70 ms. This observation shows that the road segment will consider the best choice of relaying node for message rebroadcast at the cost of dissemination delay. Moreover, under various road segments (slots) as depicted in Figures 9-12 it illustrates an important feature of road segments. The more road segments increase the more best relaying node for transmitting a message is selected. EEAPD is better than SEAD, S1 PD, delay-based and AddP in term of reducing the number of relaying vehicles involving in the data dissemination process when the number of road segments increases. Therefore, this clarifies the impact of the road segments of EEAPD in a real-time environment. To examine the performance evaluation of the proposed protocol, the simulation is conducted under a high number of road segments (450) in order to analyze the adaptability in a realistic environment feature. The brief summary of various performance metrics according to the impact of road segments in a realistic environment is presented in Table 3.
## Discussion  
### HMGR is Largely Conserved among Diatoms and Lacks a Conventional Sterol Sensing Domain  
To investigate sequence characteristics of the rate-limiting HMGR enzyme in diatoms, we identified the genes putatively encoding the enzyme HMGR from 28 different diatom species. While a putative HMGR homologue was detected in 28 of the diatom species, the failure to detect HMGR transcripts in transcriptomic sequences of some diatoms belonging to _Chaotocros_ genus (Table S2) may support the hypothesis that that those diatoms may solely rely on the MEP pathway to produce isoprenoids [15]. The lack of obvious HMGR transcripts may also have occurred due to low expression or down-regulation of these and other genes related to the MVA pathway under the conditions in which RNA sequencing was performed. Nevertheless, conserved HMGR genes were detected in many diatoms for which genomic or transcriptomic data are available. These HMGR genes diverge between pennate and centric diatoms (Figure 2), which are separated by 90 million years of divergent evolution [41]. Presence of putative HMGR in most of the diatom species analyzed is an indicator of presence of MVA pathway operating in diatoms, as it was previously reported by transcriptomics analysis in the diatoms _P. tricrontum_ and _T. pseudonana_[15]. The MEP pathway appears functional in the two model diatoms, which indicates that both cytosolic MVA and plastitial MEP are simultaneously operating in _P. tricornutum_ and _T. pseudonana_, as is the case in plants [15; 22]. Presence of both pathways could represent an advantage for terpenoid's production, due to a potentially higher metabolic flux and greater pool of available precursors [17].  
The organization of the _N_-terminal transmembrane domain of diatom HMGR differs significantly to their mammals and yeast counterparts. While most of the diatoms analyzed in this study presented three transmembrane domains (Figure 2, Table S2), mammals possess five and yeast, seven. The presence of transmembrane domains is likely related to anchoring the protein within the ER membrane, but the consequences of this structural difference for diatom HMGR in terms of regulation of enzyme expression and activity are unknown. In mammals and yeast, HMGR possess a SSD involved in sensing oxysterol molecules that activate feedback regulation leading to degradation of the protein [28; 42]. Despite the lack of a conventional sterol-sensing domain in the HMGR enzymes of diatoms (Figure 2), several studies have shown a transcriptional response of MVA enzymes to perturbations in sterol metabolism [11; 15]. The fact that diatom sequences do not possess a canonical SSD opens several possibilities, including that HMGR may not play the same regulatory role in diatoms as it does in other organisms; i.e., the MVA pathway may be regulated through a different mechanism that does not involve _HMGR_ feedback regulation. Another possible explanation is that HMGRs of diatoms and plants possess a non-conventional SSD sequence, a motif with different characteristics than the already described in mammals and yeast.
# Engineering Biomimetic Gelatin Based Nanostructures as Synthetic Substrates for Cell Culture
## 3 Results
Chemical Characteristics of the Scaffolds to Determine the Presence of P188 and Gelatin in the Composites  
Figure 7A shows the ATR-FTIR analysis of PCL and PCL/gelatin fibrous scaffolds in comparison with the PCL and gelatin raw materials. The spectrum obtained for PCL nanofibrous scaffolds was almost identical to the one obtained for pure PCL raw material. Both have the characteristic asymmetric CH2 stretching at \(\sim\)2944 cm\({}^{-1}\), symmetric CH2 stretching at \(\sim\)2867 cm\({}^{-1}\), carbonyl (C = O) stretching at \(\sim\)1293 cm\({}^{-1}\), and C-H and C-O stretching at \(\sim\)1237 cm\({}^{-1}\). All of these bands are also observed in PCL/gelatin composite fibers along with the amide I (at \(\sim\)1629 cm\({}^{-1}\)) and amide II (at \(\sim\)1522 cm\({}^{-1}\)) bands corresponding to the C = O stretching vibration and the N-H bending coupled with C-N stretching vibration of the gelatin, respectively. This confirms the presence of gelatin in the PCL/gelatin composite fibers. Figure 7B, on the other hand, shows the ATR-FTIR spectrum of all the three different samples of PCL/gelatin/P188 (85:10:5, 80:10:10, 70:10:20) compared to that of the pure P188 raw material. Three characteristic peaks exhibited by pure P188 are, the O-H stretch at \(\sim\)3010 to 2800 cm\({}^{-1}\), C-H stretching  
Figure 7: (**A**) Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) spectra of PCL and PCL/gelatin composite electrospun nanofibers compared with the PCL and gelatin pure raw materials. (**B**) ATR-FTIR spectra of three different concentrations of PCL/Gelatin/P188 composite electrospun nanofibers compared with the pure raw material of P188 (were %T is the transmission percentage). at \(\sim\)2881 cm\({}^{-1}\), and C-O group stretching at \(\sim\)1100 cm\({}^{-1}\). Presence of these peaks, along with the peaks corresponding to the PCL and gelatin in all the three PCL/gelatin/P188 composite samples confirms the successful blending of P188 with the PCL/gelatin to produce the substrates. Moreover, as shown in Figure 7, the intensity of the peak at \(\sim\)1100 cm\({}^{-1}\) (C-O stretching of P188) increases with the increase in the concentration of P188, corroborating its presence in the scaffolds.