esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### DAIDA Framework
#### 2.2.2 The CNN Model  
This section describes in detail the domain adaptive technique used to solve the problem of spatial inconsistency of features at different arm positions. Then, the architecture and training process of the proposed method are described.  
In the practical application of myoelectric prosthetic hands, the effectiveness of pattern recognition may be greatly reduced due to changes in the distribution of signal features caused by changes in arm position. Different arm positions result in different muscle activations, leading to different data distributions [21]. This is one of the reasons why traditional models are unstable in action prediction under different arm positions. To address this problem, the DAIDA algorithmic framework proposed in this paper recognizes the sEMG signals of the upper arm under different arm positions. The idea of domain adaptation is utilized to make the feature learning distribution of the source domain close to the target domain.  
The DAIDA structure was constructed from two networks with almost identical CNN structure. The CNN model used serves as the baseline model in the study. The CNN is composed of two modules, a feature extraction module and a classification module, as shown in Figure 3. The feature extraction module consists of two blocks, each of which encapsulates a convolutional layer, followed by a batch normalization layer (BN), a Parametric Rectified Linear Unit (PReLU) layer, a dropout layer (\(p\) = 0.5) and a max-pooling layer. Inspired by the success of excellent models like Xception, which perform well in classification tasks and demonstrate good generalization ability on new datasets through transfer learning with approaches such as feature extraction and fine-tuning, we adopt the inception structure to replace regular convolution in order to efficiently capture the features of various scales of the input data [27, 29]. This structure employs multiple parallel branches with different convolution and pooling operations to extract multi-scale information from the input feature map. Among them, branch one uses 1 \(\times\) 1 convolution for channel transformation of the input, branch two performs spatial dimensionality reduction through 3 \(\times\) 3 max-pooling and 1 \(\times\) 1 convolution and branches three and four use 5 \(\times\) 1 convolution and 7 \(\times\) 1 convolution with different dimensions, respectively, to extract features at different scales. The outputs of these branches are spliced in the channel dimension to generate the final feature map. The classification module also comprises two blocks, each consisting of a fully connected layer, a batch normalization layer, a PReLU layer and a dropout layer (\(p\) = 0.5). The number of neurons in the fully connected layer is 1000 and 500, respectively. Adaptive moment estimation (ADAM) is employed to optimize the CNN with an initial learning rate of 0.001.  
In order to compare the effectiveness of feature extraction with score-parallel convolution, we compared various structures, as shown in Figure 3.
## 2 Overview of the System and Its Modeling
### Model of the Wind Power Station  
The single wind generator in the wind power station consists of a permanent magnet synchronous generator (PMSG), AC-DC-AC power converter and a wind turbine, of which aerodynamical characteristics are expressed as follows in Equations (2)-(5) [19]. The speed of the wind turbine is controlled by a pitch angle controller, as shown in Figure 6, which is operated when the speed exceeds the rated speed. The output of the PMSG is regulated according to the maximum power point tracking (MPPT) characteristics. Table 5 shows the machine parameters of the PMSG.  
\[P_{turb}=\frac{1}{2}\rho C_{p}(\lambda,\beta)\pi R^{2}{V_{w}}^{3}\left(\text{W}\right) \tag{2}\]  
\[C_{p}(\lambda,\beta)=0.5\Big{(}\Gamma-0.022\beta^{2}-5.6\Big{)}e^{-0.17\Gamma} \tag{3}\]  
\[\lambda=\frac{\omega_{turb}R}{V_{w}} \tag{4}\]  
\[\Gamma=\frac{R}{\lambda}\cdot\frac{3600}{1609} \tag{5}\]  
where \(P_{turb}\) is the output of the wind turbine (W), \(\lambda\) is the tip speed ratio, \(R\) is the length of the turbine blade (m), \(\omega_{\text{wtb}}\) is the speed of the wind turbine (rad/s), \(\rho\) is the air density (kg/m\({}^{3}\)), \(\beta\) is the pitch angle of the turbine blade (deg) and \(V_{\text{w}}\) is the wind speed (m/s).
# Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
## 2 Preliminaries  
### Coupled-Constraint Consensus-Based Bundle Algorithm (CCBBA)  
CCBBA [8] is an extension to a market-based task allocation model called Consensus-Based Bundle Algorithm (CBBA) [3]. CCBBA was designed to address the inadequacies of CBBA, namely the inability to feasibly resolve problems with complex spatial and temporal relationships. As far as we know, CCBBA is the first algorithm to propose such relationships which mimic the requirements of emergency operations in the real world. Coupled constraints, which affect both space and time, can be succinctly described as follows:  
**Definition 1**.: _(Spatial coupled constraints)_  
1. _Unilateral dependency: Task_ \(A\) _can only be assigned if task_ \(B\) _is assigned;_
2. _Mutual dependency: Tasks_ \(A\) _and_ \(B\) _must either both be assigned or not at all;_
3. _Mutual exclusivity: Only either task_ \(A\) _or_ \(B\) _can be assigned at each time._  
**Definition 2**.: _(Temporal coupled constraints)_  
1. _Simultaneous: Tasks_ \(A\) _and_ \(B\) _must begin at the same time;_
2. _Before: Task_ \(A\) _must end before task_ \(B\) _begins;_
3. _After: Task_ \(A\) _must begin after task_ \(B\) _ends;_
4. _During: Task_ \(A\) _must begin when task_ \(B\) _is in progress;_
5. _Not during: Task_ \(A\) _must either end before task_ \(B\) _begins, or begin after task_ \(B\) _ends;_
6. _Between: Task_ \(A\) _must begin after task_ \(B\) _ends and end before task_ \(C\) _begins._  
These constraints may be non-exhaustive but are more than sufficient to meet the requirements of real-world applications. Mathematically, the constraint relationships between the tasks can be denoted as two matrices--the dependency matrix, \(\mathcal{D}\), and temporal matrix, \(\mathcal{T}\). The entry \((q,p)\) in \(\mathcal{D}_{q,p}\) describes the spatial constraints between the \(q^{\text{th}}\) and \(p^{\text{th}}\) elements in the form of a coded variable shown in Table 1 while in \(\mathcal{T}_{q,p}\), the value \((q,p)\) in the temporal matrix specifies the maximum amount of time \(q\) can begin after \(p\) begins.  
Although CCBBA can handle most coupled-constraint task allocation, it faces convergence issues in certain problem scenarios due to latency. As the problematic scenarios cannot be accurately predicted beforehand, it is infeasible to depend solely on CCBBA in emergency operations.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Code** & **Relationship** \\ \hline
0 & \(p\) is independent of \(q\) \\
1 & \(p\) is unilaterally dependent on \(q\) \\ \(-1\) & \(p\) and \(q\) are mutually exclusive \\ \hline \hline \end{tabular}
\end{table}
Table 1: Code for Dependency matrix entry \(\mathcal{D}_{q,p}\).
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 3 Proposed Method
### Noise Parameter Determination  
Bias affecting inertial sensors output are compensated by means of Kalman filters. A key issue is the determination of the entries of the so-called covariance matrix of the process noise, usually indicated as \(Q\). Without loss of generality, \(Q\) can be determined as a diagonal matrix whose entries are the variance associated with bias instability and random walk of either accelerometers or gyros. Suitable values of the matrix \(Q\) promote a proper convergence of the Kalman filter, thus allowing to accurately estimate navigation parameters. Allan deviation approach has been exploited to set the desired values.  
To this aim, measures of accelerations and angular rates provided by the redundant configuration have been acquired upon an observation interval of 72 h with a sampling period \(t_{c}\) equal to 8 ms; this way, a whole number of samples \(M\) equal to 32.4 MSamples for each sensing axis has been acquired. Allan variance is evaluated for a specific cluster time \(\tau=mt_{c}\) from raw digitized data according to  
\[\sigma^{2}(mt_{c})=\frac{1}{2(mt_{c})^{2}(M-2m)}\sum_{k=1}^{M-2m}(S_{k+2m}-2S_ {k+m}+S_{k})^{2} \tag{20}\]  
where \(S_{k}\) stands for the \(k\)-th sample of angle or speed sequence obtained as cumulative sum of angular rate and acceleration data, respectively.  
According to Equations (15) and (16), the needed values of bias instability and random walk can be evaluated by singling out the curve portions characterized by slope equal to 0 and \(-0.5\).
## 2 Materials and Methods  
### Participants and Experimental Procedure  
The kinematic marker data of 19 healthy recreational runners (9 females and 10 males) during treadmill running at different gradients were retrieved from the C3D files of a peer-reviewed open access dataset [24]. The study was approved by the local ethics committee (nr. 2019-1138) and was conducted in accordance with the Declaration of Helsinki [24]. All participants provided informed consent prior to the measurements.  
The experimental procedure was fully described by [PERSON] et al. [24; 25]. In brief, all participants completed a single test session and were instructed to avoid strenuous activity for 36 h, alcohol for 24 h, caffeine for 6 h, and a heavy meal 1 h before the session. Prior to data collection, participants were instructed to run for 8 min at a fixed-paced speed of 2.78 m/s to familiarize themselves with treadmill running. Each participant randomly performed a single session of treadmill running on a CAREN system (Motek, The Netherlands) at various gradients (\(-6\), \(-3\), \(0\), \(+3\), \(+6\) degrees), wearing their own shoes. Rest periods were allowed between trials. For each running condition, the treadmill speed gradually increased from zero to a constant speed of 2.78 m/s (approximately equivalent to 10.008 km/h) within 30 s, remained constant for 30 s, and then gradually decreased to a stop. However, there was no explicit justification provided for the choice of these particular speeds [24]. Twenty-six retroreflective markers were attached to anatomical landmarks on the trunk (C7, T10, xiphoid process, sternum), pelvis (ASIS and PSIS), and lower extremities (thigh, knee, lateral malleolus, heel, 2 nd and 5 th metatarsal bones). Running movements were recorded using a 12-camera three-dimensional motion capture system (Vicon Nexus v2.1, Oxford Metrics Group, Oxford, UK) with a sampling frequency of 100 Hz. Participant characteristics are detailed in Table 1.
## 3 Proposed Scheme
### Sub-Nets  
To achieve instance segmentation, as shown in Figure 3, YOLACT++ contains two branches: The prediction head and the prototype mask generation. The prediction head branch, composed of three parts, is exploited to predict the class confidences, the bounding box information, and mask coefficients. To predict the class confidences, the bounding box information, and mask coefficients for multiscale psoriasis regions, the prediction head analyzes the multilevel feature maps, P3-P7. The first two outputs of the prediction head are the class confidences and the bounding boxes of each detected object for the three classes (\(C_{n}\), \(C_{p}\), and \(C_{b}\)). This means that the prediction head of YOLACT++ can identify and locate each individual psoriasis region and skin area in an image. Non-maximum suppression (NMS) is also adopted to eliminate the bounding boxes that cover the same psoriasis regions or normal skin areas. The third part of the prediction head is utilized to predict mask coefficients where one coefficient corresponds to a prototype.  
The prototype generation branch, Protonet, is used to predict a set of prototype masks for the entire image. Similar to FCN, the Protonet analyzes the feature map P3 to obtain some prototypes. Then, the prototypes from Protonet are linearly combined using the corresponding predicted mask coefficients shown as follows:  
\[\overline{\Psi}=\sum_{j}\alpha_{j}\cdot\Psi_{j}, \tag{4}\]where \(\alpha_{j}\) and \(\Psi_{j}\) denote mask coefficient and the corresponding prototype, respectively, and \(\overline{\Psi}\) means the weighted prototype. Then, \(\overline{\Psi}\) is cropped with a predicting bounding box. After thresholding the cropped \(\overline{\Psi}\), the binary mask information of each object can be obtained. This means that YOLACT++ can output an arbitrary shape for each psoriasis region and skin area. Therefore, the proposed scheme based on YOLACT++ can not only identify each individual region but also assign the pixel-level semantic information for a psoriasis image.
## 2 Tracking Algorithms Overview  
A single angles-only sensor is not sufficient for accurate RSO tracking. In contrast, using two angles-only sensors allows one to determine the range, and thus the 3D location, of an object via simple triangulation [38; 39; 40; 41]. Typically, an error in the sensor measurement always prevails because the sensors do not exactly point towards the RSO, making it necessary to find the most probable RSO position. Without the error in measurement, the triangulation becomes trivial. In [42; 43] a ground-based optical system was proposed, consisting of two ground-based optical sensors located at two different sites to track the debris using simultaneous optical measurements.  
Several algorithms are proposed in the literature to solve this problem. For instance, in [44], the proposed algorithm estimates the most probable position of the RSO as the midpoint of the shortest line joining the two lines of sight. In [45], the most probable RSO position is estimated as a point on the shortest line between the two lines of sight that subtends an equal angle with respect to the observation sites. Although numerical solutions are proposed to calculate position errors in the triangulation solution, relating the measurement errors with the experimental parameters is a huge challenge. In [46], the analytical expressions for the position error for tracking an RSO in space for several observation-station configurations is presented. The current work deals with AN and tracking through simultaneous optical measurements performed by multiple heterogenous platforms that constitute a DSS architecture. In order to justify the aim of the current work, a suitable tracking algorithm with high position accuracy is tested in different operating scenarios (ground-based tracking, space-based tracking).
## 2 Research Area and Data
### Evaluating Indicator  
When evaluating the efficacy of remote sensing classification models, the following metrics are commonly acknowledged for their relevance. These metrics serve to effectively gauge the precision and dependability of the models:  
1. Overall Accuracy (OA): OA serves as a universal performance gauge by determining the ratio of correctly classified pixels to the total pixel count. Its value stems from its all-encompassing view of the model's classification effectiveness across every category. As OA evaluates all classes, it is an ideal benchmark for assessing a model's overall efficacy.  
2. Average Accuracy (AA): The AA metric calculates the mean accuracy for each class's classification. It is particularly useful when uniform performance across categories is critical. AA helps to discern if the model maintains consistent accuracy across various classes, ensuring equitable consideration for each.  
3. Kappa Coefficient: This coefficient accounts for the possibility of correct guesses by chance and measures the agreement between actual and predicted classifications. It is a critical metric for discerning whether the model has genuinely learned from the data or is merely guessing. A higher Kappa value suggests a more dependable model.  
4. Inference: The inference phase involves using the trained model on unseen data for classification. This step is crucial for testing the model's ability to generalize to new scenarios. A model that excels in inference is more likely to be valuable in real-world remote sensing applications.  
Collectively, these metrics offer a thorough evaluation of a model's performance, ensuring that it not only achieves high accuracy but also maintains consistent and reliable performance across different classes and in practical use cases, making them essential in the realm of remote sensing classification.
## 3 Experimental Verification
### Experimental Results and Analysis
#### 3.2.3 Attention Fusion Unit Visualization  
Based on the gradient at the output of the attention fusion unit, which is fed back to the original signal, the attention weights corresponding to the signal can be obtained. Figure 10 shows SRMANet's attention weights for partial single-fault conditions. Figure 11 visualizes the attention weights for compound fault conditions.  
The color bars at the bottom of the figure show the colors corresponding to the different attention weight sizes after normalization. A similar approach was used in [37] to visualize the bearings in question. This study is somewhat different, however, as the planetary gearbox signal is more complex and has more internal disturbances. To better illustrate the representative features learned by SRMANet, the output features of the attention fusion unit are further extended and refined in this study.  
According to the results of this study, SRMANet's ability to learn the features and characteristics of what we think of as a rule of thumb is not consistent. As in the case of planetary gear wear (Figure 10--C4) and sun wheel broken tooth failure (Figure 10--C7), SRMANet does not assign a high learning weight to some of the large impulses. Most of the segments of gear wear faults were assigned high weights. Moreover, by comparing different degrees of sun wheel broken tooth faults (Figure 10--C7,C8), it can be found that SRMANet is relatively weak at capturing weak fault features. Finally, for compound faults, SRMANet is able to effectively localize faults to the impulse characteristics in the time domain.  
Figure 9: Visualization of the SE-Res block channel attention distribution.  
Figure 11: Visualization of attention weights for compound faults. C10, C11 correspond to the Pattern Label in Table 2.  
Figure 10: Visualization of partial single-fault attention weights. C0–C8 corresponds to the Pattern Label in Table 2.
# Practical Performance Analysis of Interference in DSS System
## 5 Field Test and Performance Analysis
### Neighbor Cell Interference Test  
During the downlink test, all base stations turn on, and the neighbor cell base stations are Xinbin Life Cell, Xinbin Procuratorate, and Xinbin Urban Development Bureau in Table 1, configured as NR Only or LTE Only. We perform downlink ring tests at six test points, respectively, to observe the downlink throughput.
# Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
## 3 Case Study  
### Intelligent Surrounding Rock Classification System Software  
The tunnel surrounding rock intelligent classification system by the drill and blast method was established based on the intelligent surrounding rock classification models based on SVM, RT, and ET. It can automatically record and transmit the drilling parameters and intelligently classify the surrounding rock with carriers of the intelligent drill jumbo.  
Specifically, the surrounding rock grade of the tunnel face is determined by the votes of three models (SVM, RT, and ET). When all types of the surrounding rock levels are not superior, the higher grade is taken as the surrounding rock grade of the tunnel face in consideration of safety.  
In addition, these samples for the models do not require consideration of the modification conditions (i.e., low crustal stress, without groundwater or drip outflow of groundwater, and without a main weak structure surface). Therefore, in consideration of safety and popularization, the grade determined from these models is the basic surrounding rock grade; the grade amended by the groundwater state, major weak structural surface, and crustal stress state is the final surrounding rock grade.  
The specific process is shown in Figure 10.  
The system interface is shown in Figure 11.  
Figure 10: Classification process of the intelligent surrounding rock classification system.
## 2 Materials and Methods
### Characterization Methods  
XRD powder patterns were recorded on a Panalytical X\({}^{\prime}\)Pert \(\theta\)/2\(\theta\)-diffractometer equipped with an Xcelerator detector using automatic divergence slits and Cu k\(\alpha\)1/\(\alpha\)2 radiation (40 kV, 40 mA; \(\lambda\) = 0.15406 nm, 0.154443 nm). Cu beta-radiation was excluded using a nickel filter foil. Finely ground samples were mounted on silicon zero background holders and data collection was performed with 0.086\({}^{\circ}\)\(\cdot\)s\({}^{-1}\). The obtained intensities were converted from automatic to fixed divergence slits (0.25\({}^{\circ}\)) for further analysis. Peak positions and profiles were fitted with the pseudo-Voigt function using the HighScore Plus software package 4.9 (Panalytical). Phase identification was performed by using the PDF-2 database of the International Center of Diffraction Data (ICDD). The primary crystallite size was calculated by using the Scherrer Equation:  
\[D=\frac{\text{K}\lambda}{\beta\text{COS}(\theta)} \tag{1}\]where \(D\) is the average primary crystallite size, K is the Scherrer constant (0.94), \(\lambda\) is the X-ray wavelength (Cu K\(\alpha\) = 1.5406 A), \(\beta\) is the line width at FWHM in radians, and \(\theta\) is the Bragg angle in degrees.  
The BET surface area and the pore size distribution were calculated by using the Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods, respectively. Prior to the analysis, the samples were degassed at 150 \({}^{\circ}\)C for 5 h.  
UV-Vis diffuse reflectance spectra (DRS) were recorded on a AvaSpec 2048 fiber optical spectrometer (Avantes BV, Apeldoorn, The Netherlands) equipped with an Ava Light-DHS light source and an FCR-19 UV200-2-ME reflection probe by using BaSO\({}_{4}\) as the reference standard material.  
The SEM micrographs were recorded using a Merlin VP compact device (Zeiss, Oberkochen, Germany).  
The photoelectrochemical experiment was performed on a typical three-electrode setup on a photoelectrochemical CIMPS system (Zahner Zennium, PP211, Kronach, Germany). A Pt wire and an Ag/AgCl (3 M NaCl) electrode served as the counter electrode and reference electrode, respectively, and the electrolyte was a 0.5 M Na\({}_{2}\)SO\({}_{4}\) solution. The working electrode was prepared by means of a coating method: typically, 0.02 g of photocatalyst was added into 0.9 mL of isopropanol in the presence of 0.1 mL of Nafion solution (5 wt%). After ultrasonic dispersion for 10 min, a homogeneous ink was obtained, which was dripped onto FTO glass with an active area of 1.5 \(\times\) 1.5 cm\({}^{2}\) and subsequently dried naturally in the air. A UV LED lamp (\(\lambda\) = 365 nm, Zahner, Kronach, Germany) was used as the light source. The photocurrent response curves were measured with the light on and off every 20 s during the measurements. Electrochemical impedance spectroscopy (EIS) was carried out through a potential static method in the frequency range of 0.01-100 KHz.