esa-sceva/satcom-chunk-collection huggingface dataset:
# Bone Chemical Composition Assessment with Multi-Wavelength Photoacoustic Analysis
## 2 Materials and Methods
### Bone Specimens  
To assess MWPA measurements in a clinically relevant animal model, fresh porcine rib bones were obtained from a local grocery store, as shown in Figure 1c,d. Three groups of bones were studied, each with five specimens (n = 5), including Group 1 for untreated bones, Group 2 for bones treated with EDTA for 13 h, and Group 3 for bones treated with EDTA for 26 h. According to the literature [26; 27; 28], bone can be decalcified by EDTA solution (0.5 M), while the organic materials in the bone will stay. Based on our observation, however, the blood in the bone was also partly lost after the bone was immersed in the EDTA solution for a certain period of time. To confirm the decalcification of the bones after being treated with EDTA, each bone was imaged by a Faxitron microradiograph (see Figure 2a-c). The images were further processed using Matlab software to evaluate the mean grayscale value for each sample as an indicator of the BMD in the bone. The mean and standard division of the gray values are \(125.20\pm(9.143),77.41\pm(15.99),74.17\pm(15.48)\), as shown in Figure 2d, for Groups 1, 2, and 3, respectively. Unpaired \(t\)-tests were conducted to compare the grayscale values among the three groups, with a null hypothesis that there was no statistically significant difference between any two of the three groups. \(p\)-values of 0.03 and 0.02 were archived between Group 1 and Group 2, and also between Group 1 and Group 3, respectively, demonstrating that the average BMD for untreated bones in Group 1 was significantly larger than those in the demineralized bones in Group 2 and Group 3. No statistically significant difference was observed between Group 2 and Group 3, although the average BMD of Group 3 was slightly lower than that of Group 2.  
Figure 1: Experimental setup. (**a,b**) The schematic diagram of the experiment setup. (**c,d**) The photographs of the bone samples. ROI stands for the regions of interest. (**e**) An example of the photoacoustic (PA) signal generated by the bone specimen. (**f**) An example of the power spectrum density (PSD) of the PA signal generated by the bone.
# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
## 3 Position-Based Distributed Beamforming: System Model and Methods  
This study investigates distributed beamforming in WSNs, leveraging the knowledge of sensor positions to determine the appropriate phase adjustment for each node to perform beamforming. A simulation testbed is employed to evaluate the impact of inaccurate position information on beamforming performance. We assume the presence of an anchor node responsible for position estimation. This anchor node transmits its estimated positions, along with the receiver's position, to all other nodes in the network. Upon receiving this information, each node calculates its required phase adjustment.  
A two-dimensional schematic of the simulated environment is depicted in Figure 3. The system comprises multiple sensor nodes, represented as temperature sensors for illustrative purposes. These nodes are assumed to be coplanar, residing within a single plane. A Central Processing Unit (CPU) or an equivalent processing unit with a fixed and known position, is responsible for determining the absolute positions of the sensors within the simulation. However, the communication link between the sensors and the CPU is not explicitly modelled in this study. Instead, we focus on the cooperative communication between the sensors and a designated receiver--such as a drone, whose position is known--that captures the transmitted signals. Accurate time synchronization among sensors is essential for enabling effective cooperative beamforming. In this work, we assume that such synchronization has already been achieved using state-of-the-art techniques [15, 16], as our primary objective is to investigate the impact of sensor position accuracy on the performance of distributed beamforming.  
The system parameters used in the simulations are summarised in Table 1.  
With the system characteristics defined, Gaussian Minimum Shift Keying (GMSK) signals are transmitted from each node. GMSK modulation is widely used in Internet of Things (IoT) systems because of its low power requirements, high accuracy, and noise robustness as it maintains a constant envelope, which also leads to reliable performances for low-powered sensors [17]. Then, random position errors are introduced to the sensor node locations to simulate realistic deployment scenarios, as illustrated in Figure 4. Additive White Gaussian Noise (AWGN) is added to the composite received signal to achieve a specified SNR.  
Figure 3: Schematic of simulation testbed.  
Position errors are generated using a Gaussian random number generator with a predefined standard deviation. These error values are then added to the absolute positions of the sensor nodes, simulating inaccuracies in position estimation. These position errors lead to incorrect phase adjustments in the beamforming process, increasing the number of errors in the receiver, ultimately resulting in a degraded Bit Error Rate (BER), as is possible to conclude analysing Equation (8) and as will be shown later in Section 5.  
\[BER=\frac{N_{errors}}{N_{bits}} \tag{8}\]
## 3 Algorithm Principles  
### Basic Principles of XGBoost  
XGBoost is an efficient and scalable gradient boosting framework. Its core idea is to iteratively build a series of decision tree models and combine them into a powerful ensemble model [37]. Each new decision tree corrects the errors of the combined prediction model formed by all previous trees. Assuming that the training set has \(n\) samples, with each sample feature as \(x_{i}\in R^{m}\) and label (target) as \(y_{i}\in R\), the model output is expressed as follows:  
(1) Model Formulation: Combines predictions from \(K\) decision trees; balances prediction loss \(l\) (Mean Square Error, MSE) and regularization \(\Omega\); penalizes tree complexity (\(T\): leaf nodes, \(w\): leaf weights).  
\[\begin{split}\text{Ensemble output}:&\hat{y}_{i} =\sum\limits_{k=1}^{K}f_{k}(x_{i}),\;f_{k}\in F\\ \text{Objective function}:&\text{Obj}=\sum \limits_{i=1}^{n}l(y_{i},\hat{y}_{i})+\sum\limits_{k=1}^{K}\Omega(f_{k})\\ \text{Regularization term}:&\Omega(f)=\gamma T+ \frac{1}{2}\lambda\|w\|^{2}\end{split} \tag{1}\]  
(2) Additive Training: Sequentially adds trees to correct residuals; \(g_{i}\) and \(h_{i}\) are the first and second derivatives of the loss, respectively.  
\[\begin{split}\text{Iterative updates}:&\hat{y}_{i} ^{(t)}=\hat{y}_{i}^{(t-1)}+f_{t}(x_{i})\\ \text{Second}-\text{order Taylor approximation}:& \text{Obj}^{(t)}\approx\sum\limits_{i=1}^{n}\left[g_{i}f_{t}(x_{i})+ \frac{1}{2}h_{i}f_{t}^{2}(x_{i})\right]+\Omega(f_{t})\end{split} \tag{2}\]  
(3) Tree Parameterization: Minimizes loss for leaf \(j\) (samples in \(l\)); computes optimal objective value.  
\[\begin{split}\text{Leaf weight optimization}:& w_{j}^{*}=- \frac{\sum\limits_{i\in l_{j}}g_{i}}{\sum\limits_{i\in l_{j}}h_{i}+\lambda}\\ \text{Optimal objective value}:&\text{Obj}^{*}=- \frac{1}{2}\sum\limits_{j=1}^{T}\frac{(\sum\limits_{i\in l_{j}}g_{i})^{2}}{ \sum\limits_{i\in l_{j}}h_{i}+\lambda}+\gamma T\end{split} \tag{3}\]  
(4) Tree Splitting: Maximizes reduction in loss (\(G\), \(H\): gradient statistics).  
\[\text{Gain criterion for splits}:\text{Gain}=\frac{1}{2}\Bigg{[}\frac{G_{L}^{2}} {H_{L}+\lambda}+\frac{G_{R}^{2}}{H_{R}+\lambda}-\frac{(G_{L}+G_{R})^{2}}{H_{L }+H_{R}+\lambda}\Bigg{]}-\gamma \tag{4}\]  
(5) Regularization Strategies: \(\eta\) (learning rate) scales tree contributions.  
\[\text{Weight shrinkage}:\hat{y}_{i}^{(t)}=\hat{y}_{i}^{(t-1)}+\eta f_{t}(x_{i}) \tag{5}\]  
XGBoost mitigates overfitting while enhancing ensemble diversity. However, three hyperparameters, number of trees (\(K\)), tree depth (\(d\_max\)), and learning rate (\(\eta\)), critically influence performance. \(K\) governs model capacity, \(d\_max\) controls feature interaction complexity (with higher values increasing regularization costs), and \(\eta\) balances convergence speed and generalization (higher \(\eta\) accelerates training but reduces precision).  
Optimal configurations vary across datasets, and empirical settings often fail. This study uses RIME to synchronize hyperparameter optimization and improves XGBoost's learning and generalization capabilities.
## 2 Concepts and Architecture of Multi-Sensor Signal Processing
### Implementation of ADIMSS  
The concept of ADIMSS is implemented in the Matlab platform. The design process is done in one way of using a _bottom-up_ approach, where the optimization process runs from one block after another block starting from the sensor block to the classification block. An _offline_ mode is set in the process of generating intelligent sensor systems, whereas an _online_ mode is done for testing the generated intelligent sensor systems. The first process is to collect the signals of sensors using DAQ toolbox. Then, the raw signals of sensors are divided into training and validation sets. The training set is used to design the systems in the learning process (model with different structures and meta-parameters). The trained system that performs best on the validation set is then selected as the final system. In the ADIMSS implementation, the validation techniques are available for the user or designer to select one of four options, which are holdout method, k-fold cross-validation, leave-one-out (LOO) cross-validation, and bootstrap method [18].  
Figure 8 shows the list of toolboxes used in our ADIMSS implementation. All the functions or subroutines set by designers are invoked in the main program. When a new function is developed, this new function can be straightforwardly added in the list of the toolbox and directly called in the main program. The procedures of individual local optimization are shown in Figure 9. The dataset from one step or block is processed and recorded along with the label or class information. The number of examples or patterns is same from the dataset of sensor raw signals, but the dimensional data feeded into each block is different. The raw data are captured by means of signal acquisition device (multi-sensor) and extracted by feature computation block. The extracted feature data are saved into a matrix form, where the row index represents features and the column index represents patterns or examples. The label or class affiliation of patterns is saved in a row vector of separated file.  
Figure 8: The list of toolboxes used in ADIMSS framework.  
Figure 9: The local optimization procedures in ADIMSS.
# Numerical Analysis of Cyclic Impact Damage Evolution of Rock Materials under Confining Pressure
## 3 Results and Analysis
### Defining Damage Variable  
Damage variables can be defined in various ways, such as elastic modulus, wave velocity, energy dissipation, and crack density. In the historical progression of damage definitions, crack density is one of the earliest methods to characterize damage variables. However, the practical measurement of crack density poses significant challenges. In numerical simulation, the crack generation and propagation result in the failure of elements. As failed elements are deleted in the finite element model, the volume reduction induced by element failure provides insight into the propagation of cracks. The volume of rock specimen after each impact can be recorded in numerical simulations with relatively low computational cost; then, the crack density method is employed to characterize the damage variable. As the crack density can be characterized by volume reduction after each impact in numerical simulation, the damage variable can be expressed as:  
\[D=1-\frac{V_{i}}{V} \tag{4}\]  
where \(D\) is the damage variable, and \(V_{i}\) and \(V\) are the volume of rock specimens after \(i\)th impact and before impact, respectively.  
The volume of rock specimens can be derived from the volume-time history curve obtained by the post-processing software LS-PREPOST. The volume of the cylindrical rock specimen before impact is 49.035 \(\times\) 10\({}^{-6}\) m\({}^{3}\). The smaller the volume of the rock specimen after impact, the greater the damage to the rock specimen.
###### Abstract  
Pustular skin diseases, with pustular psoriasis (PP) being the prototype, are immune-mediated diseases characterized by the presence of multiple pustules, resulting from neutrophil accumulation in the layer of epidermis. Sterile skin pustular eruption, like PP, is also observed in 20-30% of patients with adult-onset immunodeficiency syndrome (AOID) and anti-interferion \(\gamma\) autoantibodies (IFN-\(\gamma\)), leading to challenges in classification and diagnosis. While the mechanism underlying this similar phenotype remains unknown, genetic factors in relation to the immune system are suspected of playing an important role. Here, the association between human leukocyte antigen (HLA) genes, which play essential roles in antigen presentation, contributing to immune response, and the presence of skin pustules in AOID and PP was revealed. HLA genotyping of 41 patients from multiple centers in Thailand who presented with multiple sterile skin pustules (17 AOID patients and 24 PP patients) was conducted using a next-generation-sequencing-based approach. In comparison to healthy controls, _HLA-B*13.01_ (OR = 3.825, 95%CI: 2.08-7.035), _C*03.04_ (OR = 3.665, 95%CI: 2.102-6.39), and _DQBI*05.02_ (OR = 2.134, 95%CI: 1.326-3.434) were significantly associated with the group of aforementioned conditions having sterile cutaneous pustules, suggesting a common genetic-related mechanism. We found that _DPSI*05.01_ (OR = 3.851, \(p\) = 0.008) and _DRB1*15.02_ (OR = 3.195, \(p\) = 0.033) have a significant association with pustular reaction in AOID patients, with PP patients used as a control. A variant in the _DRB1 gene_, rs17885482 (OR = 9.073, \(p\) = 0.005), was observed to be a risk factor for PP when using AOID patients who had pustular reactions as a control group. _DPBI*05.01_ and _DRB1*15.02_ alleles, as well as the rs17885482 variant in the _DRB1 gene, were proposed as novel biomarkers to differentiate PP and AOID patients who first present with multiple sterile skin pustules without known documented underlying conditions.  
**Keywords: adult-onset immunodeficiency; anti-interferon \(\gamma\) autoantibodies; human leukocyte antigen; pustular psoriasis; pustular reaction; pustular skin diseases**
## 6 Sequence-to-Sequence Models
### Statistical Model  
The statistical model was first used in statistical machine translation (SMT). It is based on a noisy channel model to translate source segment \(x\) into target segment \(y^{*}\) using the equation [34]:  
\[y^{*}=\operatorname*{arg\,max}_{y}p(y|x)=\operatorname*{arg\,max}_{y}p(x|y) \cdot p(y). \tag{1}\]  
The probability \(p(y)\) is computed by the language model and \(p(x|y)\) by the translation model. The language model predicts the probability of the segment in the target language. It is defined as the product of the conditional probabilities of each word given the previous ones:  
\[p(y)=p(y_{1},y_{2},\ldots,y_{n})=\prod_{i=1}^{n}p(y_{i}|y_{i-N+1},\ldots,y_{i- 2},y_{i-1}). \tag{2}\]  
The probability of the word is only conditioned by \(N-1\) previous words, and the language model is called the \(N\)-gram model. The main problem is that long-range dependencies are lost. The translation model for each word in the source segment predicts the translation into the target segment. Additionally, it predicts the number of words in the target language and the number of words that need to be generated from scratch. It has been shown that the decomposition of sequences into data-driven phrases is beneficial. The source sequence\(x\) is broken down into \(I\) phrases \(\bar{x}_{i}\), and each source phrase \(\bar{x}_{i}\) is translated into a target phrase \(\bar{y}_{i}\). A phrase translation model is used:  
\[p(\bar{x}_{1}^{l}|\bar{y}_{1}^{l})=\prod_{i=1}^{I}\phi(\bar{x}_{i}|\bar{y}_{i}). \tag{3}\]  
In addition to the translation model, the log-linear structure of statistical architecture enables the use of more than one model:  
\[y^{*}=\operatorname*{arg\,max}_{y}\sum_{m}\lambda_{m}h_{m}(x,y), \tag{4}\]  
where \(\lambda_{m}\) represents the weight of the \(m\)th model \(h_{m}(x,y)\). The weights could be optimized following the minimum error rate procedure on a held-out development set [35]. Readers interested in more details about SMT are advised to read the paper [36], which also includes a schematic diagram of SMT model training.
# Neural Graph Similarity Computation with Contrastive Learning
## 3 Methodology
### Graph Similarity Prediction Network
#### 3.2.1 Graph Convolutional Layer  
The input to each graph convolutional layer \(l\) is the embedding matrix \(\mathbf{X}^{l}\) for all nodes. The message passing process can be expressed as:  
\[\mathbf{X}^{l+1}=\sigma(\mathbf{\hat{D}}^{-\frac{1}{2}}\mathbf{\hat{A}}\mathbf{\hat{D}}^{-\frac {1}{2}}\mathbf{X}^{l}\mathbf{W}^{l}), \tag{1}\]  
where \(\mathbf{W}^{l}\) is the learnable weight matrix in the \(l\)-th layer, \(\sigma\) is the activation function, \(\mathbf{A}\) is the adjacency matrix of the graph and \(\mathbf{\hat{A}}\) is the adjacency matrix with self-loops. \(\mathbf{\hat{D}}\) is the diagonal matrix that contains node degree information, i.e., \(\mathbf{\hat{D}}_{ii}=\sum_{j}\mathbf{\hat{A}}_{ij}\). As thus, \(\mathbf{D}^{-\frac{1}{2}}\mathbf{\hat{A}}\mathbf{D}^{-\frac{1}{2}}\) refers to the normalized matrix of \(\mathbf{\hat{A}}\) based on the degree information. The output node embedding matrix is denoted as \(\mathbf{X}^{l+1}\), which also serves as the input matrix to layer \(l+1\). The node embedding matrix sent to the first layer is usually randomly initialized or set to the original features of the nodes.
## 4 Discussion
### Implementation and Performance Research of the Cloud Fault Diagnosis Algorithm  
In this study, the machine learning fault feature representation was established through the simulation of motor vibration signals under different fault types, and the acquisition and processing of vibration signals [48]. The experimental analysis object mainly used a differing load output voltage signal after the rectifier circuit failed. To effectively test the experimental results of the subsequent fault diagnosis method, it was necessary to code the motor fault types, as shown in Table 1. According to fault location, the traction motor fault types were divided into a--stator fault, b--rotor fault, c--air gap eccentric fault and d--bearing fault. The fault types were then further divided into 15 types: a, b, c, d, a-b, a-c, a-d, b-c, b-d, c-d, a-b-c, a-b-d, a-c-d, b-c-d, and a-b-c-d. Among them, the last 11 types were combined fault models, such as a-b, which indicated that both stator and rotor faults occurred at the same time.  
SIMPACK software was used to simulate different fault states, run the motor, obtain the operation data, and synthesize the data set with 15 fault types and one normal state type. The structure of the SAES-DNN model was determined by repeated optimization, and a six-layer network was designed. The input layer was 256 neurons, the two hidden layers were 65 neurons, and the output layer was 16 neurons. The learning rate was 0.1, and the number of iterations was 60 times. At the same time, we compared the accuracy and running time of fault diagnosis under CNN, WDCNN, MLNN, SAE-DBN, BP-MLNN and SAES-DNN models based on different numbers of training samples. The initial parameters of the six models are shown in Table 1. It can be seen from Figure 5a,b that the SAES-DNN method achieved the minimum calculation time and the highest accuracy under the same computing resources and data, which shows that the SAES-DNN method has better feature learning and diagnostic ability than traditional deep learning methods.  
The above results also demonstrate that our method has slight advantages in classification accuracy and time loss over 10 iterations. Compared with SAE-DBN and WDCNN, the accuracy of the proposed method improved by 0.8% and 0.9%, respectively. Through the cloud training samples, our method shows advantages in accuracy and training time for the fault classification model. Higher accuracy and time efficiency can improve fault diagnosis and maintenance, and more efficient model construction time can allow the method to be applied to other mechanical parts in a shorter time [49]. To study the effects of increased sample data or expanded fault types on the proposed method (which would be useful for practical applications), the text training model must first be updated. Furthermore, further research would be needed to determine the degree of influence.
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 3 Materials and Methods  
This section proposes a prompt-assisted-offset correlator (PAOC) technique to mitigate carrier and subcarrier multipath errors for BOC signals. Firstly, the principle of the PAOC technique is introduced. Then, the PAOC discriminators and complementary filters are described in detail. Finally, an implementation architecture for applying the PAOC technique in the DBT loop is provided.
## 3 Evidential Reasoning Algorithm
### Analysis Results and Uncertainty Review
The lower limit of the system condition, at which intervention is not yet required but preparation for system maintenance should be started, is evaluated as \"sufficient\", i.e., when the overall system rating decreases or approaches the predefined lower value, preparations for maintenance of the components with the worst condition must be started and maintenance carried out before the system rating decreases to the unsatisfactory value, i.e., before the overall system rating falls below the permissible critically low value. Critical parts of the system can be assigned high-weight coefficients so that their possible unsatisfactory state is reflected in the overall state of the system. An example of evaluating the state of a power transformer illustrates the complexity of the computation and aggregation process. The influence of incomplete assessments of baseline attributes on general attributes and the final evaluation of the condition is presented. The influence of the weighting of individual attributes on the aggregation process becomes visible. The example illustrates the importance of a system for remote monitoring and data collection as well as supplementing the data collected in this way with data collected through measurements and regular inspections of the system. With the help of such a tool or a toolbox, it is possible to see the current condition of the object, the deterioration of the condition before and after a certain period of use and repairs or the improvement of the condition after the maintenance process. It is possible to observe how and to what extent individual maintenance actions affect the condition of the observed system and it is possible to maintain the condition of the system at a certain qualitative level by influencing the condition of various baseline attributes. Further development of the proposed model could be in the direction of improving the organization of data acquisition, placing a greater emphasis on continuous measurements and data collection, and incorporating algorithms for input data analysis, state assessment and condition prediction of the observed objects. Since most modern equipment in power plants has some degree of embedded measurement, i.e., embedded systems and diagnostic capabilities, adding such algorithms and interfacing with remote data acquisition equipment should be a feasible task.