esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO
## 3 Efficient Antenna Selection via Enhanced Branch-and-Bound Optimization
### Upper Bound of Channel Capacity of MIMO System  
Given a channel matrix \(\mathbf{H}\) with independent and identically distributed (i.i.d.) complex Gaussian entries, we initially assume each transmit antenna is received by a dedicated set of \(N_{r}\) receive antennas without interference from other signal components. As established in references [35; 36; 37], the upper bound on the capacity of fully digital MIMO systems can be formulated as:  
\[C_{ub}=\sum\
olimits_{l=1}^{N_{t}}\ \log_{2}\left(1+\frac{\rho}{N_{t}}\gamma_{l }\right), \tag{7}\]  
where the random variables \(\{\gamma_{l}\}_{l}=1,\,2,\,\cdots,\,N_{t}\), represent the chi-square distributed with \(2N_{r}\) degrees of freedom, corresponding to the transmit antennas \(l=1,\,2,\,\cdots,\,N_{t}\). Conversely, if we assume that each receiving antenna has its own \(N_{t}\) transmitting antennas, the upper bound on the capacity of a full-digital MIMO system can be given by Equation (8):  
\[\widetilde{C}_{ub}=\sum\
olimits_{d=1}^{N_{t}}\ \log_{2}\left(1+\frac{\rho}{N _{t}}\varphi_{l}\right), \tag{8}\]  
where the random variables \(\{\varphi_{l}\}_{l}=1,\,2,\,\cdots,\,N_{r}\), denote the chi-square distributed with \(2N_{t}\) degrees of freedom [35], corresponding to the receive antennas \(l=1,\,2,\,\cdots,\,N_{r}\). Based on the aforementioned equations, the upper bound of the system capacity after implementing antenna selection at the receiver end can be expressed as:  
\[C_{S}=\sum\
olimits_{l=1}^{d}\ \log_{2}\left(1+\frac{\rho}{N_{t}}\varphi_{(l)} \right), \tag{9}\]  
where the random variables \(\{\varphi_{(l)}\}_{l}=1,\,2,\,\cdots,\,N_{r}\), denote the ordered chi-square distributed with \(2N_{t}\) degrees of freedom, i.e., \(\varphi_{(1)}>\varphi_{(2)}>\ldots>\varphi_{(N_{r})}>0\). Therefore, it can be concluded that the antenna selection method exclusively selects the top-\(d\) largest antennas. Furthermore, as demonstrated in [35], \(d<N_{t}\) constitutes a necessary condition for Equation (9) to establish an upper bound.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 4 The Proposed MAC Address Randomization Covert Channel
### Covert Channel Metrics
#### 4.3.2 Covert Channel Efficiency  
As demonstrated in Equation (2), we decided to define the covert channel efficiency as the received-to-transmitted PR frames ratio expressed in percentage (%). Covert channel frame efficiency is a vital metric, as it provides insights into the actual delivery of data, enabling us to assess the extent of data loss within the covert channel. By evaluating this metric, we can gain valuable information about the overall health of the transmission  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value and Unit** \\ \hline Packet Size & 2000 [Bytes] \\ Offered Load & 20 [Mbps] \\ Transport protocol & UDP \\ IEEE standard & 802.11 ac \\ Frequency band & 5 GHz \\ Channel width & 20 MHz \\ Number of Tx and Rx antennas & 1 \\ Mobility Model & Constant mobility \\ Regular STA Probing & Passive \\ Probe Request Interval & Constant Interval \\ MCS Index & VHT9 \\ Guard Interval & 800 [ns] \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
channel and identify any parameters that may be causing interference with the covert channel, and pinpoint areas that require improvement or mitigation strategies:  
\[Efficiency=\frac{Rx\ PR\ Frames}{Tx\ PR\ Frames}\times 100\ [\%] \tag{2}\]
## 4 Results and Discussion
### Correlation Between Bedrock and Frequency  
In this section, the bedrock depth values derived from the obtained Vs models were analyzed as a function of their fundamental frequency \(f_{0}\) (e.g., [45; 68]). The resulting  
Figure 11: Vs30 and soil classification in the city of Ain Témouchent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vs (m/s)** & **Soil Type** & **Classification of Soil** \\ \hline Vs \textgreater{} 1500 & Hard rock & A \\ \hline
760 \textless{} Vs \textless{} 1500 & Rock & B \\ \hline
360 \textless{} Vs \textless{} 760 & Very dense soil and soft rock & C \\ \hline
180 \textless{} Vs \textless{} 360 & Stiff soil & D \\ \hline \hline \end{tabular}
\end{table}
Table 3: NEHRP codes for soil type and classification [52].  
Figure 10: Variation in bedrock depth within the study area.  
relationship, illustrated in Figure 12, is nonlinear and can be described mathematically by Equation (3):  
\[D(f_{0})=a*f_{0}^{b} \tag{3}\]  
where \(a\) and \(b\) are the regression coefficients, \(f_{0}\) is the fundamental frequency, and D is the bedrock depth.  
The best fit is given by Equation (4), with a correlation coefficient of 0.91:  
\[D(f_{0})=271.63*f_{0}^{-1.158} \tag{4}\]
# Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
## 4 Results
### Brief Discussion  
Sections 4.2-4.4 present an optimization of the model hyperparameters for the \(k\)NN, quadratic SVM and Gaussian SVM, respectively. In each subsection, the confusion matrix for the best (optimized) model is presented. In this subsection, the best models are compared among them. That is, a comparison among the \(k\)NN, quadratic SVM and Gaussian SVM methodologies is given. In particular,  
Figure 8: Performance measures (per-unit) corresponding to the Gaussian SVM strategy for the multiclass classification problem for a box constraint \(C=50\) and with respect to the kernel scale \(\gamma\) (horizontal axis).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(C\) & \(\gamma\) & \(\overline{\mathbf{acc}}\) & \(\overline{\mathbf{ppv}}\) & \(\overline{\mathbf{tpr}}\) & \(\overline{\mathbf{F}}_{1}\) & \(\overline{\mathbf{tnr}}\) \\ \hline  & 0.1 & 0.756 & 0.730 & 0.395 & 0.396 & 0.800 \\  & 0.2 & 0.830 & 0.818 & 0.579 & 0.625 & 0.861 \\  & 0.5 & 0.940 & 0.922 & 0.852 & 0.878 & 0.952 \\  & 1 & **0.987** & **0.973** & **0.970** & **0.971** & **0.991** \\  & 2 & 0.985 & 0.969 & 0.965 & 0.967 & 0.990 \\
50 & 5 & 0.979 & 0.959 & 0.95 & 0.954 & 0.985 \\  & 10 & 0.946 & 0.933 & 0.865 & 0.890 & 0.956 \\  & 15 & 0.925 & 0.928 & 0.812 & 0.852 & 0.938 \\  & 20 & 0.924 & 0.927 & 0.809 & 0.849 & 0.937 \\  & 30 & 0.923 & 0.926 & 0.808 & 0.848 & 0.936 \\  & 50 & 0.920 & 0.920 & 0.802 & 0.842 & 0.934 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **0** & **1** & **2** & **3** & **tpr** & **fnr** \\ \hline
0 & 25.16 & 2 & 1 & 15 & 320.8 & 1\% \\
1 & 7 & 1231 & 40 & 2 & 96.9 & 4\% \\
2 & 1 & 45 & 1230 & 4 & 96.9 & 4\% \\
3 & 36 & 6 & 3 & 1235 & 96.9 & 4\% \\ ppv & 25.84 & 96.76 & 97.76 & 95.84 & & \\ fdr & 2\% & 4\% & 3\% & 2\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confusion matrix for the Gaussian SVM algorithm for the case \(C=50\) (box constraint) and \(\gamma=1\) (kernel scale).  
Figure 9 shows the accuracy, precision, recall, F1 score and specificity measures for the best \(k\)NN, quadratic SVM and Gaussian SVM models. It is noteworthy that the Gaussian SVM accomplishes the highest performance for all the indicators. Thus, it is the recommended approach to be employed with the proposed SHM strategy. Finally, it is also important to note that the quadratic SVM has a close performance to the Gaussian SVM but the \(k\)NN falls far behind in all the indicators in general, and more markedly for the recall and F1 score measures. Therefore, its use is inadvisable. As a final remark, the performance of the Gaussian SVM over the quadratic SVM may depend on the nature of the data or even on how this data is preprocessed and what features are extracted. In this sense, the exceeding performance of the Gaussian SVM has been reported in the literature as a machine learning model for the prediction of the viscosity of nanofluids [43] or, in the field of fault diagnosis, to get the operation status of a wind turbine [44].
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 2 Orbit Dynamics  
In recent years, low-thrust propulsion has gained increased relevance in space mission design. In fact, for specified mission profiles and payloads, low-thrust propulsion allows substantial propellant savings at the price of increasing (even considerably) the time of flight. Recently, nonlinear control was proven to be an effective option for real-time feedback guidance in orbit transfers, as well as for orbit maintenance Tundra and Molniya (2016); Tundra and Molniya (2018); Tundra and Molniya (2019).
## 4 Problem Solution
### Joint Device Association and Power Control Online Optimization Algorithm  
Combining Algorithms 1 and 2, we propose a Joint Device Association and Power Control Online Optimization (JDAPCOO) algorithm of 5G and satellite converged networks for transmission and distribution scenarios, as shown in Algorithm 3. Within each time slot, the device power is first initialized, the device association strategy is solved according to Algorithm 1, and then the device power is solved using Algorithm 2 according to the obtained device association strategy. The algorithm terminates until both the device association strategy and the power control scheme are solved for each time slot during the network operation time. For simplicity, we add the Algorithm 3 flowchart in Figure 3.  
```
1:Input: Device set \(\mathcal{I}\), UAV set \(\mathcal{U}\), ground 5G base station set \(\mathcal{B}\), channel state information, PD power maximum \(P_{\max}\) and the minimum transmission rate requirement of the PDs \(R_{th}\). Output: Optimal device association strategy \(\{\mathbf{x}_{\mathrm{u}},\mathbf{x}_{\mathrm{b}}\}\) and optimal power control scheme \(\{\mathbf{P}\}\).
2:repeat
3:for each time slot do
4: Initialize PDs' power and decide the device association strategy \(\mathbf{x}_{\mathrm{u}}(t)\) and \(\mathbf{x}_{\mathrm{b}}(t)\) according to Algorithm 1.
5: Based on \(\mathbf{x}_{\mathrm{u}}(t)\) and \(\mathbf{x}_{\mathrm{b}}(t)\) obtained from Algorithm 1, the power control scheme \(\mathbf{P}(t)\) is decided using Algorithm 2.
6: Update channel state information.
7:\(t=t+1\).
8:endfor
9:until\(t>T\).
10:Return\(\{\mathbf{x}_{\mathrm{u}},\mathbf{x}_{\mathrm{b}}\) and \(\mathbf{P}\}\).
```  
**Algorithm 3** Joint Device Association and Power Control Online Optimization (JDAPCOO) algorithm
## 2 Materials and Methods
### Data Reprocessing  
For the road DEM construction proposed in this study, we first obtained the road area and then extracted the sideline and centerline of the road. Therefore, the data preprocessing applied in this paper was based on feature extraction of UAV image data. Mask R-CNN is an instance segmentation algorithm proposed by [PERSON] et al. [24] in 2017. Instance segmentation involves recognizing target contours at the pixel level. It can not only recognize different classes, but also mark multiple different objects in the same class of objects separately. Additionally, Mask R-CNN can simultaneously perform target detection and instance segmentation in a network framework [25]. Mask R-CNN uses a residual network (ResNet) to extract features, and combines it with feature pyramid network (FPN) to improve the ability to detect small targets [26]. In this paper, ResNet-50 combined with FPN is used as the backbone network to extract road boundary features.  
Given the strong dependence of the training of the deep learning model on the number of training samples, and taking into account factors such as hardware conditions and personal capabilities, this experiment used a GPU of RTX-2060 SUPER under a Windows environment. We selected a total number of 270 samples, of which 30% of the data was designated as verification data, and the default step size was set to export the image capture of training samples. The learning rate when training all layers was 0.0001, with each maximum epoch representing the number of times that the data set would pass forwards and backwards through the neural network. It was set to 40 in the training network, and the batch size was the number of training samples to be processed at one time for training. For this experiment, we finally set the number to 8 for the training. The backbone network is set to specify the preconfigured neural network to be used as the framework for training the new model. For this experiment, ResNet-50 was selected. ResNet-50, a preconfigured model, was the residual network trained on the ImageNet dataset, which contains more than one million images and has a depth of fifty layers. The local segmentation results are shown in Figure 2.  
Due to the influence of well covers, trees, vehicles, and other ground objects, boundary information derived from the terrain classification results had certain disadvantages: the shape of the segmentation edge was not accurate enough, and there were many line segment nodes. Therefore, for the terrain boundary, the point removal algorithm was used to eliminate redundant node information in the boundary line, and then the incorrect boundary information was manually modified.
## 3 Materials and Methods
### Hand Landmarks Identification  
The choice of adopting a landmark-based instead of an image-based approach has been made for several reasons. Landmarks allows to focus on key points, such as finger joints and tips, which are crucial for accurately capturing the specific gestures used in sign language. In contrast, whole images include unnecessary background data and surrounding objects, introducing noise that can distract the AI model from focusing on the hand movements that matter most. Moreover, hand landmarks also reduce data complexity, allowing the model to process a small set of key points instead of every pixel in an image. This makes the AI system more efficient and faster, especially in real-time applications. By concentrating on the hand's structure and movement, landmark-based approaches can more precisely capture subtle differences between similar gestures. Full-image approaches, on the other hand, may obscure these details due to distractions or occlusions. Furthermore, focusing on hand landmarks helps the model generalize better across different users by identifying patterns based on the essential hand structure rather than relying on the entire visual scene. This approach improves recognition accuracy and consistency allowing also for the employment of a smaller dataset.  
The Hand Landmarker of the Google package Mediapipe [37] has been used to automatically obtain the x and y coordinates of 21 landmarks in each hand picture in our datasets using the RGB image. Mediapipe Hands uses a two-stage tracking pipeline, including (1) a palm detector, which provides a bounding box of a hand inside the frame, and (2) a hand landmark model, which predicts the hand landmarks (Figure 3a). Specifically, the model can predict 21 hand landmarks triplets comprising x, y, and relative depth. However, since Mediapipe estimated the depth from the RGB image, a different approach was chosen in the current investigation to obtain a more precise value for the third coordinate. Indeed, the z coordinate was computed considering the depth map provided by the Kinect camera. Specifically, the z coordinate for each landmark was obtained using the normalized x and y coordinates multiplied by the Kinect depth map shape.  
Figure 2: “A” hand sign captured by Azure Kinect.  
All of the coordinates obtained for each landmark from each frame were then normalized to make the classifier less dependent on the position of the hand or the distance from the camera. Figure 2(b) shows an example of hand landmark acquisition through Mediapipe during execution in real-time of the application.  
This framework was chosen as it demonstrated real-time inference speed on mobile GPUs with high prediction quality. In particular, the Mediapipe Hand model was tested on a custom database composed of in-the-wild, in-house, and synthetic images and reached an average precision of 94% in the best configuration of the palm detector and an MSE of about 10 for the hand landmark model, with a latency on the full pipeline of about 17 ms on a CPU and 12 ms on a GPU.
## 1 Introduction
### Frequency Containment Reserves (FCR)
#### 3.3.2 Measurement and Quantification
The reason for the stricter suggestion is that the measurements will be used for the remuneration of a DRES, while the standards refer to measurements used for control purposes. Validation  
The actual participation of a DRES as a FCR entity can be validated by comparing the measured \(P(t)\) with the one calculated by equations (10), (11) or (12) as a function of the measured frequency \(f(t)\) considering also the various parameters set by DSO (see Figure 10). In this way a mutual trust between DSO/aggregator and DRES owner can be established. The same trust between DSO and TSO can be established provided that the DRES parameters and its locational information within a distribution grid are transparent to the TSO via a mutually accessible DRES registry [4].
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
## 4 Case Study  
### Base Case Information  
The main objective is to examine the impacts of variability and uncertainty on flexibility. The test system was a modified IEEE-RTS-96. The generation system consists of 26 generating units, and its installed capacity is 3105 MW. Basic information on the failure and repair rates of generating units is provided in Table 1 (Appendix A). Wind power plants of 1250 MW were added to the system;  
Figure 3: Process of scenario generation and sensitivity analysis.  
for reference, their capacity value (based on the effective load carrying capability) was computed as 79.88 MW [32]. Figure 4 shows the profile of the forecast net load, with the 2531 MW peak load occurring at hour 19. The hourly UC was solved using dynamic programming [33]. The hourly reserve capacity was calculated on the assumption that all generating units are in a spinning state; this assumption was used to evaluate the maximum flexibility of the system. The value of \(RSP_{t}\) was computed through programming in MATLAB (R2012b version) [34]. This simulation was performed on a PC with 16 GB RAM and 3.7 GHz CPU. As shown in Figure 5, \(VGFE_{t}\) and \(NLFE_{t}\) were assumed to follow a normal distribution, with a standard deviation of 5% of the forecast value.  
The hourly \(RSP_{t}\) values are shown in Figure 6. The maximum value of \(2.0982\times 10^{-4}\) occurs at hour 19, because the high net load lowers the available reserve capacity at that time, even though larger values of net load variation occur in other time intervals. The selected time interval was therefore 18-19 h. The values of the net load at 18 and 19 h of the base case were 2507 MW and 2531 MW, respectively. As shown in Table 2, the high (low) net loads were set at 110% and 120% (80% and 90%) of the net loads at hours 18 and 19, respectively. The range of increased variability and uncertainty was varied from 0% to 20%, with a step size of 1%. For reference, 20% of the increased variability is calculated as 506 MW, i.e., multiplying 2531 MW by 30%. As discussed previously, the uncertainty of the base case was set at 5% of the net load; this value indicates the standard deviation of the net load forecast error, and was computed as 127 MW, i.e., multiplying 2531 MW by 5%.  
Figure 4: Forecast net load profile.  
Figure 5: \(NLFE_{t}\) and \(VGFE_{t}\) distribution. A normal distribution was described using a seven-step approximation.
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 4 Discussion
[40] evaluated the sensitivity to be 74%. However, in mentioned paper, the authors studied acute knee injuries with hemorrhosis, which could have influenced the results. Our results also correspond with the literature regarding the sensitivity and specificity of MRI for lateral meniscus lesions. [PERSON] et al. [38], in their meta-analysis, estimated a sensitivity ranging from 66% to 87%. In our study, we found 70% sensitivity with a specificity of 85%. It is well demonstrated in literature that MRI has a lower sensitivity in detecting lateral meniscus lesions, which some authors attribute to its anatomical structure [41]. Moreover, the location of a lesion impacts the diagnostic accuracy of MRI. Both in the published papers [42] and in our study, the posterior parts of the meniscus have been more accurately diagnosed than the anterior regions. In our study we found MRI to have a 75% sensitivity in detecting ACL rupture. In the available literature, the evaluated sensitivity was not reported lower than 77%, even though some studies have been conducted on 0.2T coils [43; 44]. [PERSON] et al. [38] brought attention to a possible bias of the studies due to their founding by coil manufacturers. Nevertheless, it does not alter the fact that in our study the sensitivity was lower than has been previously reported in other papers. In 2012, [PERSON] et al. [45] evaluated the phenomenon of partial ACL tears found in MRI and their implication on surgery planning. The authors concluded that the evaluation of partial ACL tears is unreliable in that partial tears, and even the usage of 3T coils, do not improve results [46]. Moreover, the MRI examinations in our study were made in different departments and we were not able to retrieve the referrals of all examinations to verify if the referral included all important information for the leading radiologist. However, this situation resembled a typical clinical setting and the results might be more helpful in every day practice. This data suggest that clinical examinations are grossly superior to the MRI in detecting ACL deficiency, and that MRI has limited application in detecting isolated ACL tears.