esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Activity Recognition Using CSI Information with Nexmon
## 6 CSI Data Collection
### Challenges of Router Frequency Hopping and AGC While CSI Data Collection  
As described in Section 4.2 in the data collection process challenges occurred--two of which are explained below.  
1. The first challenge is frequency hopping/automatic frequency band steering on 802.11 ac standard. In fact, this is not a problem for the 802.11 ac standard; instead, it is the feature for the better utilization of the frequency spectrum with better data traffic and power management. The method of signal bandwidth and power steering based on the throughput is enabled in most routers to achieve this, a clear-channel assessment approach is used.  
When Raspberry Pi is configured to 40 Mhz in monitoring mode to capture the CSI data with the Nexmon tool and when the traffic on the network is high enough for the bandwidth on 40 Mhz, then the router automatically hops to use 80 MHz bandwidth, and so the CSI data capturing gets stopped on the configured channel.  
Similarly, when the receiver is configured in monitoring mode at 80 MHz, if the network traffic exceeds the maximum throughput of 80 MHz, the next hopping for 160 MHz is performed.  
The frequency hopping can be avoided by carefully controlling the network traffic and packet transmission on the selected band to avoid exceeding the throughput limit.  
The second challenge is automatic gain control (AGC) compensating for the signal amplitude attenuation and adaptive loading applied by the radio modules automatically. Thus, the router modulates the PSD of each subcarrier. The variation in the amplitude occurs for individual subcarriers instantaneously irrespective of the change in the surroundings. It creates sharp spikes in CSI data and a false impression of activities in the experimental setup's surroundings. In this research work, no counter techniques were implemented to handle this challenge. However, removing the sharp spike outliers and limiting the amplitude maximum range axis by taking the optimal average of the activity countermeasure helped to tame the undesired artificial variations. Alternatively, one could apply CSI calibration techniques described in [35] or [36] to compensate for undesired effects of the AGC.
# A Multi-Scale Video Longformer Network for Action Recognition
## 3 Method
### 3D Longformer Attemblock and Mlphlock  
The 3D Longformer Attemblock consists of a Layer Normalization Layer, 3D Longformer Attention, and a Dropout Layer. Input video features undergo Layer Normalizationand then pass through a multi-head self-attention layer (MSA) comprising the 3D Longformer Attention. The output of MSA is combined with the input features through residual connections and Dropout, as shown in Equation (5).  
\[Y=MSA(LN(X))+X. \tag{5}\]  
The 3D Longformer Attention, characterized by convolution-like sparse computations, effectively reduces local redundancy and decreases computational complexity while maintaining global modeling capabilities. To elaborate, an equivalent number of global tokens is introduced, corresponding to the temporal dimension \(T\). These global tokens enable attention calculations involving all local tokens within the input video features, facilitating global modeling and communication among distant local tokens via the global tokens. Local tokens are allowed to perform attention calculations with both global tokens and local tokens within a spatial window. Since actions demonstrate temporal coherence across video frames, we enable attention calculations for local and global tokens at different frames, as illustrated in Figure 3.  
In 3D Longformer Attention, Q, K, and V are divided into global attention and local attention branches, corresponding to the attention calculation for global tokens and local tokens, respectively. In the global attention branch, standard multi-head attention calculations are performed. In the local attention branch, tokens outside a specified window size are masked, allowing only tokens within the window size to participate in attention calculations. The results from both the global and local branches are concatenated, as illustrated in Figure 4. By setting the window size to 15 \(\times\) 15, the 3D Longformer Attention selectively employs a convolution-like sparse computation mechanism for local window attention calculations in stage1 (\(H\times W\): 56 \(\times\) 56) and stage2 (\(H\times W\): 28 \(\times\) 28), reducing local redundancy and extracting features. In stage3 (\(H\times W\): 14 \(\times\) 14) and stage4 (\(H\times W\): 7 \(\times\) 7), the local window attention transforms into a dense computation mechanism akin to transformers to enhance performance.  
The MlpBlock consists of a Layer Normalization Layer, an Mlp Layer, and a Dropout Layer. Video features outputted from the 3D Longformer AttenBlock undergo Layer Normalization, Mlp processing, and are then combined with the input through residual connections. Finally, a Dropout layer is applied before output, as shown in Equation (6).  
\[Z=MLP(LN(Y))+Y. \tag{6}\]  
Figure 3: Diagram of the 3D Longformer Attention mechanism. We add the same number of global tokens as the temporal dimension \(T\) in attention calculations; local tokens only perform attention calculations within the window size in spatial dimension.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 5 AI-Powered VANET Solutions
### Clustering  
The clustering process is a crucial application of V2X technology. It provides several advantages, including the mitigation of air drag, enhancement of fuel economy, alleviation of traffic jams, and reduction in traveling duration. The implementation of surveillance applications that utilize live video over V2X networks has the potential to enhance vehicle security and convenience by providing image information through the utilization of front and rear-view cameras. Nevertheless, the implementation of video streaming in vehicle platoons introduces novel obstacles, including the potential constraints on the spectrum of the primary vehicle transmitting data over the MAC layer in IEEE 802.11p. A cluster of moving vehicles is established through the collaboration of vehicles, wherein the cluster head (CH) functions as an internet connection that serves the collaborative group. Clustering algorithms must meet the IoV communication performance requirements while identifying and documenting abrupt alterations in mobility, network, and cluster behavior [101; 168; 182]. The clustering process utilizes the k-means algorithm; included in this analysis are the distance, direction, signaling magnitude, trustworthiness, and category of communications [52]. An agglomerative hierarchical clustering method incorporates factors such as velocity, direction, and specific QoS parameters to organize data into clusters of vehicles [68]. Standard procedures for initiating the clustering process involve peer detection, cluster head selection, affiliation, announcement, and maintenance. A learning algorithm [46; 183] in which agents determine traffic behavior and velocity at intersections selects cluster heads (CHs) based on vehicle relative velocities. GMMs and k-means algorithms enhance the cluster's stability inside an extremely volatile setting [7; 70; 183]. In the fuzzy logic system (FLS), a level of certainty is considered rather than just using the values true or false. A learning factor is introduced for rewards with positive results and penalized or decreased for negative results. This mechanism progressively finds the best CH selection strategy. Fuzzy logic uses relative velocities between vehicles as CH selection criteria and driver intentions to assess eligibility [184]. AI is a promising approach for planning, scheduling, and optimizing these issues. Some of the AI and ML techniques used with VANETs are described in Table 9, which can solve specific problems. Fuzzy-logic-based algorithms can provide better stability than ML algorithms that predict the future movement of the vehicle. As a result, hybrid algorithms that combine ML algorithms with fuzzy logic algorithms can provide better stability than ML algorithms [184].
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Applications of Opportunistic Routing Protocols  
#### 5.3.1 Odcr  
In a recent publication, [PERSON] et al. proposed Opportunistic Density Cluster-based Routing (ODCR) [55], an application of the opportunistic routing protocol for enhancing the energy efficiency and reliability of WSNs, using a density-based clustering mechanism which is specifically recommended for emergency and disastrous situations. Unlike the previously-mentioned protocols, in ODCR, the authors consider the nodes' mobility features, and integrates edge computing with opportunistic routing. For data forwarding, ODCR uses a group of nodes (clusters) and also isolated nodes, where nodes in a cluster share the information among the cluster-members and an isolated node passes the data opportunistically to a group of potential neighbor nodes (potential nodes are selected based on their data delivery probability (DDP) value). Concerning the underlying MAC mechanism, this protocol used a self-assigned sender-initiated CSMA/CA mechanism. Like ORD, ODCR also aggregates data in order to reduce the number of transmissions. The experimental result provided in ODCR showed that this protocol outperforms LORA and the conventional WSN clustering protocols with regard to energy consumption, throughput and packet loss ratio.  
However, in ODCR, since they have used DDP and MPC (Minimum Period Connectivity) as routing matrices and did not mention the duty-cycling of nodes, this indicates that the network nodes are always active and do not use the duty cycle mechanism. If duty-cycling is applied to ODCR, it is possible to extend the network lifetime remarkably. Moreover, it did not address the localization error calculation, which may occur because of node mobility. There is also the factor of data aggregation leading to increased latency, which can be a huge issue in emergency applications.
# Software Portfolio Optimization: Access Rejection versus Underutilization
## 5 Case Study
### Model Calibration  
The main parameters of the calibration system are summarized in Table 2. Based on Figure 6, the average rate of NNU license requests by \(S_{1}=580\) heavy users (of \(K_{1}=700\) heavy users) is 471 per hour, that is, \(\lambda_{1}\frac{S_{1}}{K_{1}}=471\) and, therefore, the overall average rate of use of NNU licenses by heavy users is \(\lambda_{1}=568.4\). The average number of requests for a floating license (Figure 5) by all light users (946) and 120 heavy users (\(K_{1}-S_{1}=700-580\) = 120) during peak hours is \(\overline{\lambda}=156\). Consequently, using Equation (9), we can calculate the average rate of requests by all light users: \(\lambda_{2}=\overline{\lambda}-\frac{(K_{1}-S_{1})\lambda_{1}}{K_{1}}=58.56\).  
Figure 4: Estimated blocking probabilities for November 2020.  
Figure 3: Number of user requests denied. Each color represents a different day in November 2020.  
The duration of license use by peak hour in November 2020 is depicted in Figure 7. Using these data, we calculate the average user's working time accessing the software as \(\frac{1}{\mu}=0.847\) h (\(\mu=1.18\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(c_{1}\) & 1924 \\ \(c_{2}\) & 10,000 \\ \(\lambda_{1}\) & 568.4 \\ \(\lambda_{2}\) & 58.56 \\ \(K_{1}\) & 700 \\ \(K\) & 1646 \\ \(\mu\) & 1.18 \\ \(b\) & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of the calibration system.  
Figure 5: Hourly requests for floating licenses with each color representing a different day in November 2020.  
Figure 6: Hourly requests for NNU licenses with each color representing a different day in November 2020.  
Consequently, formulating (9)-(12), based on the data in Table 2, gives:  
\[\underset{S_{1},S_{2}}{\text{min}}C=\underset{S_{1},S_{2}}{\text{min}}\{1924\ S_{1}+10,000\ S_{2}\}\]  
subject to  
\[B\bigg{(}S_{2},531.32-\frac{S_{1}}{826}\bigg{)}\leq 0.011; \tag{14}\]  
\[U\bigg{(}S_{2},531.32-\frac{S_{1}}{826}\bigg{)}\geq 0.869; \tag{15}\]  
\[0\leq S_{1}\leq 700,\ S_{2}\geq 1, \tag{16}\]  
where, in accordance with (8), \(a_{2}=\frac{568.4(700-S_{1})}{1.18.700}+\frac{58.56}{1.18}=531.32-\frac{S_{1} }{826}\).
## 2 Related Work  
### Atrial Fibrillation Detection  
Machine learning algorithms have the potential to improve heart abnormality detection and classification by automatically learning and extracting atrial fibrillation patterns from large volumes of ECG data.  
Many atrial fibrillation detection methods strongly rely on strategies that convert ECG recordings into a representation suitable for distinguishing between normal and abnormal heartbeats. Consequently, methods based on time, frequency, and morphological features derived from ECG recordings have been extensively implemented to automatically detect and identify episodes of atrial fibrillation [5]. Classifiers used to detect atrial fibrillation from hand-crafted features range from Support Vector Machine (SVM), decision trees, Multi-Layer Perceptron (MLP) to more advanced models constructed as a cascade or combination of different models. However, the efficiency of detection depends on the consistency and utility of the hand-crafted features.  
Deep learning-based classifiers have achieved remarkable state-of-the-art results in a wide range of domains, including healthcare. Consequently, solutions for detecting cardiac arrhythmia in electrocardiograms based solely on the raw signal, with no feature engineering, are becoming more prevalent [6]. Unlike traditional machine learning algorithms, deep learning algorithms have feature engineering modules embedded into the model. When the model is exposed to large volumes of data during the training phase, the relevant features are extracted in line with the problem domain. Essentially, deep learning models learn to derive an abstract representation from the ECG signals and, at the same time, categorize the recording based on the extracted features, whereas traditional machine models learn how to categorize ECG signals based on hand-crafted features.  
Despite clear challenges that arise in designing and selecting appropriate features from ECG signals, feature-based machine learning models trained on hand-crafted features are still widely applied for atrial fibrillation detection. This tendency is observed in several works that explored the ability of machine learning and deep learning techniques to enhance early detection of atrial fibrillation on short recordings acquired using AliveCor's single-channel ECG device [7]. Overall, approaches vary from hand-crafted features fed to a Random Forest (RF) or MLP to direct processing of the raw signal by Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNNs). However, the results revealed that models based on features extracted from ECG recordings performed slightly better in differentiating atrial fibrillation from noise, normal or other rhythms in short-term ECG recordings.
## 3 Results and Discussion
Notwithstanding, this represents the first study in which a direct chromatographic method has revealed a statistically significant difference of the content of Ile and Leu between the two established groups, with \(p=0.016\) and \(0.005\), respectively (Figure 2A,B), whereas, the content of Tyr was only slightly different in the two groups (\(p=0.094\), Figure 2C). On the other hand, the difference of the Val levels between the two groups was not statistically significant. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample \#** & **SCC (\(\times\)10\({}^{3}\))** & **Ile (\%/mL)** & **Leu (ug/mL)** & **Val (ug/mL)** & **Tyr (ug/mL)** \\ \hline  
\hline \hline \end{tabular} Data are expressed as mean \(\pm\) standard deviation of at least three experiments.  
\end{table} 
Table 2: Content values of selected amino acids in Group II, along with SCC value. Furthermore, the bivariate Pearson correlation analysis between amino acid content and SCC value was also performed. A positive Pearson's correlation coefficient, \(r\), was found for Ile (0.397), Leu (0.495) and Tyr (0.348), standing for a positive correlation between the two variables. On the contrary, correlation was missed in the case of Val (0.055). On the basis of this result, only a moderate correlation was found for Ile, Leu and Tyr content versus SCC value. Taking into account these results, we can argue that the content level of the BCAAs Ile and Leu is significantly altered with the occurrence of mastitis. The observed alteration of BCAAs levels is anything but surprising. Indeed, an increasing number of metabolomic investigations clearly demonstrate that BCAAs can be potential biomarkers of disease [22, 23]. Accordingly, several recent studies reveal that BCAAs are biomarkers associated with insulin resistance [24, 25], risk of cardiovascular disease [26], stage I and II chronic kidney disease [27] and ischemic stroke [28]. The level of BCAAs discriminates lean from obese individuals and has the potential to predict populations at risk for cardiometabolic disease, type 2 diabetes as well as mortality from ischemic heart disease. Additionally, it has been found that BCAAs levels for patients with hepatitis B virus infection vary depending on the degree of liver damage. Furthermore, serum levels of BCAAs have been observed to decrease in patients with liver cirrhosis. Also interestingly, serum BCAAs to His ratios have been found to be significantly associated with knee osteoarthritis.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 2 Methods
### Binaural Rendering of Ambsonic Signals
The spherical coordinates of all loudspeaker configurations used in this paper, unless stated otherwise, are Lebedev configurations [29]. Lebedev grids are particularly suited to practical reproduction of Ambsonic signals due to their near-exact orthonormal properties with relatively low number of loudspeakers [37]. Additionally, with exception of the \(L=38\) grid, the \(L=50\) grid nests the lower-order Lebedev grids, making between-order comparisons over loudspeakers practically viable [38]. For each order of Ambisonics used in this paper \(\{M=1,M=2,...,M=5\}\) (the exact vertices of which were obtained from [39]), the loudspeaker positions are illustrated in Figure 1. All HRIRs used in this paper, unless stated otherwise, are from the Bernschutz Neumann KU 100 dummy head HRIR database [40]. Figure 1: Loudspeaker layouts of the Lebedev configurations used in this paper with corresponding order of Ambisonics.
# Student Dropout Prediction for University with High Precision and Recall
## 5 Experiment and Analysis
### Prediction Performance of the SDP System  
We compare the prediction performance of the SDP system with logistic regression, artificial neural network, gradient boosting, and ensemble (gradient boosting, random forest, and support vector machine) methods. Note that the SDP system combines the results from the two models and prioritizes the results over precision. Table 14 summarizes the results. The proposed method's accuracy, dropout recall, dropout precision, and F1 metrics show the best results compared to the other methods. The algorithm ranked in second place for accuracy and dropout recall rate is artificial neural networks. In this experiment, we organized data by combining students of all grades. In the case of SDP, we inferred the results from each model and combined them to make one result, and the metric was measured by comparing it with the validation result set. As for the SDP system, its accuracy and dropout F1 were 0.989 and 0.786, respectively, which were higher than the other models. As for the dropout precision, artificial neural networks showed the highest value at 0.870, but their dropout recall was 0.442, which was lower than average compared to the other models. The gradient boosting method had the same dropout recall value as the SDP system at 0.755, but the dropout precision and dropout F1 were 0.181 and 0.095 lower than the SDP, respectively. In this experiment, we can see that the SDP made with the hybrid model came out better than the other models.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 6 Conclusions  
In this paper, we discuss the use of a battery-powered wireless sensor network to perform device-free wireless sensing of a crowd's size in a subway station by detecting the presence of rail vehicles. Simultaneous rail vehicle detection and crowd size estimation allows for switching between multiple pre-trained regression models, reducing the impact caused by the changing environment.  
We demonstrated the capability of using attenuation within a wireless sensor network for reliably detecting the presence of rail vehicles. We achieved perfect detection when looking at the event-based metric. On a sample basis, we achieved an overall accuracy of 93.5 % on the test set.  
We improved the level of accuracy for crowd size estimation by using two separately trained polynomial regression models depending on whether a rail vehicle is present or not in comparison to a single regression model. Switching between the two models improved the MAE from 6.192 people to 3.567 people for the entire test set. When no rail vehicle was present, we achieved an MAE of 3.342 people on the test set. When a rail vehicle was present, we achieved an MAE of 4.478 people on the test set.  
To obtain our full model, we combined both regression models based on the output of the vehicle detection model. This full model is able to perform real-time predictions on a per-sample basis for both environment states. The final results reflect trends and magnitudes that are very similar to those observed in the ground-truth data.