esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 2 Methodology
### Estimation and Optimization of the EKF Rotor Angular Velocity and Rotor Position
#### 2.2.2 Optimizing the EKF Noise Covariance Matrix Using Immune-Based PSO
The data show that SMC-w-IPSO has a stronger searching ability and can improve the accuracy of the search optimization. The SMC-w-PSO can maintain a higher speed of searching optimization.
## 4 Results
### Spatio-Temporal Distribution
The spatial orientation of the eighth class was relatively consistent with that of the seventh class, which was mainly occupied by alpine shrubs (Circles ash1. and ash2. in Figure 4a). Figure 4: Distribution of the net primary productivity (NPP) and its changing trend in the Three-River Headwaters region, including (**a**) the average NPP from 2001 to 2020, av: alpine vegetation, as: alpine steppe, am: alpine meadow, ash: alpine shrub land; (**a1**) the area percentage corresponding to the eight classes in the (**a**); (**a2**) the average NPP and its one standard deviation (SD) along the longitude; (**a3**) the average NPP and its SD along the latitude; (**b**) the changing trend of NPP from 2001 to 2020; (**c**) the changing characteristics of the NPP from 2001 to 2020. The spatial distribution of the NPP had a certain trend, which was lower in the north, higher in the south, lower in the west, and higher in the east. From west to east, the average NPP increased from 18.3 gCm\({}^{-2}\)yr\({}^{-1}\) to 177.0 gCm\({}^{-2}\)yr\({}^{-1}\). From north to south, the average NPP increased from 17.7 gCm\({}^{-2}\)yr\({}^{-1}\) to 181.9 gCm\({}^{-2}\)yr\({}^{-1}\). The estimated NPP had greater spatial variability in the east to west direction than in the north to south direction (Figure 4(a1,a2)). From west to east, the CV was 48.15%, while from north to south, the CV was 32.7% (Figure 4(a1,a2)). From 2001 to 2020, the estimated NPP had improved or remained stable in most areas of the TRH region. Among them, the significantly increased area was 1.2 \(\times\) 10\({}^{5}\) km\({}^{2}\), accounting for 32.1% of the total area. The slightly increased area was 1.9 \(\times\) 10\({}^{5}\) km\({}^{2}\), accounting for 50.14% of the total area. The stable area was 5.7 \(\times\) 10\({}^{4}\) km\({}^{2}\), accounting for 14.84% of the total area (Figure 4b). The distribution of the increasing trend had a certain degree of aggregation, while the decreasing trend was relatively scattered. The significant increase was mainly concentrated on the eastern and northern sides. The slight increase was mainly concentrated on the midwestern side, and the stable one was mainly concentrated on the western side. The decrease was relatively scattered on the western side. Although there was an increasing trend in most of the TRH region, it was uncertain whether most of the increasing trends would continue. Statistically, a total of 3.4 \(\times\) 10\({}^{5}\) km\({}^{2}\) of the changing trend was uncertain, accounting for 89.8%; 9.3 \(\times\) 10\({}^{3}\) km\({}^{2}\) of the vegetation will keep the significantly increasing trend, accounting for 2.4%; only 8.4 \(\times\) 10\({}^{3}\) km\({}^{2}\) of the vegetation would continue to increase slightly, accounting for 2.2% of the total area (Figure 4c). The distribution of the NPP's changing characteristics was scattered, and only the \"persistently remain stable\" was mainly concentrated on the western side.
## 3 Low-Cost Multi-Sensor
### Hardware Description

Table 1: Parameters of sensors included in the developed multi-sensor. Figure 3: Block scheme of the PROMET&O multi-sensor, which comprises the set of sensors (on the **left**), the connectivity and power board (in the **center**), and the microcontroller (on the **right**). The set of sensors are located in a dedicated volume inside the multi-sensor, while the power and connectivity board is responsible for supplying all subblocks and for routing the interconnections. The operations of the multi-sensor are scheduled by the firmware running in the microcontroller. cloud server through the WiFi module. It is based on an ARM Cortex M7 architecture, and it is provided with 1.1 MB RAM and 2 MB flash on-board memories. In addition to the main three blocks, the multi-sensor device includes a microSD slot, allowing to change on-the-fly the functional parameters of the multi-sensor, e.g., the report and sampling time, and two arrays of Light Emitting Diodes (LEDs) to provide users with a visual feedback of the measured IEQ. The blocks identified in Figure 3 have been disposed of, as shown in Figure 4a, where the interior of the multi-sensor is shown. The structure of the device has been conceived to have the potential sources of heat, i.e., the power and connectivity board and the microcontroller, separated from the sensors. This was achieved by means of a vertical diving wall to provide thermal insulation between the sensors array and the remaining blocks of the multi-sensor. Furthermore, air quality sensors were placed on a vertical V-shaped chassis, which allows their sensitive elements to be as close as possible to the hem of the case. This should improve the response times of the sensors and, hence, the sensor outputs should be significant of the indoor environment to monitor. Finally, the overall assembled device is shown in Figure 4b. After analyzing the main components of the PROMET&O device, a comprehensive cost analysis was carried out, including all the necessary parts for device assembly. The results are reported in Table 2, with the cost of the sensor kit calculated as the sum of the individual prices of each employed commercial sensor, as listed in Table 1. It is worth noting that sensors account for 25% of the overall cost, with the most significant expense related to the 3D printing of the housing. Figure 4: Photograph of **(a)** the internal parts and **(b)** the assembled PROMET&O multi-sensor. The vertical dividing wall in **(a)** allows for separating the IEQ sensors from the microcontroller and power and connectivity board, which are situated on the rear side (not visible in the image). The outer cylindrical case, which is shown in **(b)**, features multiple apertures for microphone and illuminance sensors as well as for air ventilation purposes.
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.3.1 Trust Score Computation
To maintain numerical stability and avoid division by zero, a small correction term \(\varepsilon=10^{-9}\) is added to the denominator. Pairwise deletion is employed to handle missing data effectively, ensuring the robustness and accuracy of correlation estimates. The absolute value of the computed correlation is taken prior to normalisation in Equation (2), thus guaranteeing non-negative and stable weights in subsequent federated learning rounds. The sum of all weights remains mathematically normalised to 1, ensuring a balanced contribution from each factor:  
\[w_{N}+w_{VS}+w_{E}+w_{C}+w_{P}+w_{U}=1. \tag{4}\]  
This ensures that no single factor inordinately influences the overall trust score. The dynamic adjustment mechanism recalculates weights based on statistical insights from recent training rounds. This process ensures the weighting system remains responsive to the IoV's real-time dynamics, enabling robust and adaptive trust evaluations across changing vehicular states, network conditions, and adversarial threats.
## 2 Proposed Bearing Fault Diagnosis Method using Acoustic Emission Signals
### EfficientNet CNN Model for Bearing Fault Diagnosis  
Scaling up the CNN from a baseline network is broadly implemented to achieve superior precision [12]. EfficientNet is based on this principle to create a CNN network to achieve better accuracy and efficiency [12]. A pre-existing CNN structure is defined by the function \(N=\underset{i=1..s}{\bigcirc}F_{i}^{L_{i}}(X_{(H_{i},W_{i},C_{i})})\), where \(F_{i}^{L_{i}}\) denotes layer \(F_{i}\), which is repeated \(L_{i}\) times in stage \(i\), and \(\langle H_{i},W_{i},C_{i}\rangle\) represents the shape of input tensor \(X\) in layer \(i\)[12]. Functional changes are made in the scaling dimension parameters, including depth \(d\) (i.e., the number of CNN layers in the network), width \(w\) (size of the CNN layers), and resolution (resolution of the input image). EfficientNet provides a constraint in selecting these three parameters, called the compound scaling method, which uses compound coefficient \(\theta\) to uniformly scale the network width, depth, and resolution in a reasonable manner. Equation (2) illustrates those parameters and their constraints.  
\[\begin{split} depth:d=\alpha^{\theta}\\ width:w=\beta^{\theta}\\ resolution:r=\gamma^{\theta}\\ \alpha\cdot\beta^{2}\cdot\gamma^{2}\approx 2\\ \alpha\geq 1,\beta\geq 1,\gamma\geq 1\end{split} \tag{2}\]  
In Equation (2), \(\alpha\), \(\beta\), and \(\gamma\) are constants that can be found using a small grid search. Intuitively, \(\theta\) is a user-specified coefficient that controls how many resources are available for model scaling, whereas \(\alpha\), \(\beta\), and \(\gamma\) specify how to assign these extra resources to the network width, depth, and resolution, respectively.  
Figure 4 shows the architecture of EfficientNet-B0 developed based on mobile inverted bottleneck MB-Conv [17], where it is also added the squeeze-and-excitation optimization [18]. MB-Conv is designed to run deep networks on personal mobile devices. Therefore, MB-Conv is not only effective but also requires low energy consumption. The EfficientNet architecture is designed using depth-wise separable convolution as efficient building blocks, linear bottlenecks between layers, and shortcut connections between the bottlenecks. EfficientNet is a group consisting of 8 models, B0-B7.  
The EfficientNet-B0 model was used by selecting the parameters based on the following process. First, we let \(\theta=1\) and used a grid search for \(\alpha\), \(\beta\) to rescale the model from B0 to B1. After that, we had the set of parameters \(\alpha=1.2\), \(\beta=1.1\), and \(\gamma=1.15\). Then, we fixed \(\alpha\), \(\beta\), and \(\gamma\) as constants and scaled up the baseline network under different \(\theta\) values using Equation (2) to obtain EfficientNet-B1 to B7. In this study, EfficientNet-B0 was used because it can sufficiently extract useful bearing fault information and does not use redundant computational resources.
## 3 Results
### Hydrophobicity Characteristic--Dimers  
The status of monomer units of FdI and FdII treated as parts of dimers is described by \(RD\) values of 0.56 and 0.58, respectively (Table 3). Note that \"parts of dimers\" above means that \(T\) and \(O\) distributions were calculated for each dimer as a whole (3D Gauss capsule fit to its two monomers together), after which segments of \(T\), \(O\) and \(H\) matching the range of chain A (\(\sim\)50% of dimer drop's data) were extracted and normalized, yielding the status of \"monomer as part of dimer\". _RD_ above 0.5 means that the individual chains do not fit, as a whole, into expected hydrophobicity distribution when the 3D Gauss capsule encompasses them both. Values for the complete complex (both chains together) are similar: 0.55 for Fdl and 0.58 for FdII. One explanation has already been given in the previous section: when monomers with a globular shape bind using mostly polar residues, center of the drop ellipsoid (where high concentration of hydrophobicity is expected) is taken by a double amount of mostly polar residues. The second reason can be seen in Table 3 and Figure 5: local discordance between \(T\) and \(O\) happens in many locations. Beta fragment 31-33 becomes very accordant in dimeric form of FdII. Being hydrophobic and close to the center of the complex allows its \(T\) value to raise and reach \(RD\) of 0.19. Because its corresponding fragment in FdI (34-36) is located on the outside of the dimer, it remains discordant with an almost-maximal \(RD\) value of 0.97. The \(RD\) status of P-P interface increases from 0.18 to 0.28 in FdII but drops to 0.29 from 0.46 in FdI. This is an effect of tightly packed residues in the center of FdI complex, which allows them to gather more hydrophobicity from the neighbor chain and provide with \(O\) what \(T\) is expecting at that location. This accordance is mostly caused by G23, G41 and A42, which seem to \"work\" towards the local and global complex accordance. On the other hand, accordance in FdII interface seems to be held by V32, the residue with highest observed hydrophobicity closest to the center of the drop (but also to the ligand--before it in the sequence is a P-L interacting A31).
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 5 Performance Comparison
### Rate Loss Analysis
: _Example 8 shows that when the entropy of the target distribution is taken to be the target rate \(k/n\) (\(1.75\) bit/I-D in Example 8), CCDM and MPDM are not able to obtain \(2^{k}\) sequences. This is due to the inevitable nonzero rate loss of the DM schemes for finite blocklengths. For such \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architecture** & \(k\) & \(k/n\) & \(E\) & \(\mathbb{H}(\tilde{A})\) **or**\(\mathbb{H}(\tilde{A})\) & \(R_{\text{loss}}\) \\ \hline CCDM & 367 & 1.6991 & 11.00 & 1.7504 & 0.0513 \\ MPDM & 374 & 1.7315 & 11.00 & 1.7504 & 0.0189 \\ SpSh & 374 & 1.7315 & 10.90 & 1.7448 & 0.0133 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters Computed in Example 8. Figure 7: Flowchart for the computation of rate loss for CCDM, MPDM and SpSh. cases, we increase the SNR that the target distribution is optimized for, until we obtain \(2^{k}\) output sequences for the DM schemes._  
Figure 8 shows rate loss vs. blocklength for CCDM, MPDM, and SpSh. The target distribution is the same as Example 8. The target \(k\) for ESS is the number of bits achieved by MPDM at each \(n\). We observe that MPDM and SpSh clearly outperform CCDM. Furthermore, the more efficient signal space usage of SpSh becomes particularly apparent at very short blocklengths. Here we note that in Figure 8, we used the distribution averaged over all sequences inside an \(n\)-sphere, i.e., sphere distribution, to compute the rate loss for SpSh. This relies on the fact that the sphere distribution is an accurate approximation for the distribution averaged over the \(2^{k}\) sequences that are actually transmitted, i.e., operational distribution. The operational distribution and the actual rate loss depend on the SpSh algorithm that is employed. However, the difference in rate loss for different SpSh algorithms is only significant for ultra short blocklengths. As an example, at \(n=8\) and \(k/n=1.75\) bit/1-D with 8-ASK, the rate loss computed using the sphere distribution is 0.0924 bit/1-D, while the actual rate losses are 0.0912, 0.0908 and 0.0907 for ESS, (Algorithm 1 in [23]) and SM, respectively. The operational distribution of SM can be computed using the ideas presented in [100].
## 2 Method
### Drift Elimination
#### 2.2.1 Transformer Encoder  
As illustrated in Figure 2, the encoder part of the Transformer model is formed by stacking multiple encoder layers of the same structure. Each encoder layer consists of two sub-layers: a multi-head attention module and a feed-forward neural network module. Residual connections are used around the sublayers [32], and each sublayer is followed by a layer normalization module [33].
## 2 Materials and Methods
### Unloaded Sample Preparation
Firstly, the initial confining pressure value here refers to the stress state of the rock before the slope excavation, and the confining pressure value after unloaded is used in the subsequent creep experiments. Secondly, the higher confining value of the confining pressure is chosen to obtain a significant unloading effect. Triaxial loading and triaxial unloading tests were conducted on saturated sandstone. At an initial confining pressure of 15 MPa, the triaxial peak strength of the sandstone was 160.8 MPa, the unloading damage confining pressure value was 4 MPa, and the residual confining pressure value of the unloaded specimen was 6.75 MPa. At an initial confining pressure of 21 MPa, the triaxial peak strength was 201 MPa, the unloading damage confining pressure value was 9 MPa, and the residual confining pressure value of the unloaded specimen was 12 MPa. Figure 3 shows the stress-strain curve of the unloading test. As seen in the figure, the unloading stress-strain curve can be divided into the axial pressure loading phase with approximate linear elastic growth, the unloading confining pressure phase with significant lateral expansion, and the unloading sampling phase with deformation recovery. Axial pressure loading stage (A\(\rightarrow\)B): with the increase in axial stress, the stress-strain curve 27 February 2023 exhibits an approximately linear growth trend; this stage is mainly dominated by the elastic deformation of the pore skeleton contraction, and axial compression deformation dominates. Unloading confining pressure stage (B\(\rightarrow\)C): the confining pressure decreases, the deviating stress continues to increase, the lateral expansion caused by the weakening of lateral restraint continues to increase, the primary fracture expansion and the new fracture derivation are the main reasons for the unloading damage of the specimen, the axial strain is small at this stage, and the lateral expansion deformation dominates. Unloading sampling stage (C\(\rightarrow\)D): This stage unloads according to the original loading path. The deformation of the specimen is recovered during the unloading process, but not entirely, and there is some unrecoverable plastic damage, mainly lateral damage deformation. Axial damage deformation is small. Comparing the unloading stress-strain curves under the two confining pressure conditions, it can be seen that there is a greater deviating stress under the high confining pressure conditions, and the unloading response is more obvious, specifically in terms of greater strain variation and more unrecoverable plastic damage deformation. Figure 2: Unloading test stress path (\(\sigma_{c}\) indicates peak triaxial strength).
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 3 Preliminaries
### Parameter-Server Architecture  
For distributed SGD, there are various strategies and infrastructures that support the communication and synchronization between the workers. In this research, we focus on the parameter-server (PS) architecture [39; 40; 41; 42; 43], which is one of the most popular strategies to enable distributed training.  
The system is composed of the server nodes and the worker nodes, as illustrated in Figure 1. Typically, the training data and the major workload of computation are distributed onto the worker nodes. For cloud computing, the worker nodes are placed on the cloud, where more worker nodes accelerate the training. For edge computing, the worker nodes are placed on the edge devices, where more worker nodes bring more training data. The server nodes, located on the cloud, are used for synchronization among the worker nodes. In summary, the workers conduct computation on their local data, and the resulting updates are then merged by the server. Such merge/synchronization operations cause the communication overhead. Different algorithms send different types of updates to the server, e.g., gradients or updated model parameters. Thus, the same PS architecture could be used in different algorithms and scenarios.
## 5 Resolution Methods
### Iterated Local Search Algorithm
The different steps of the iterative local search method in one iteration are illustrated through an example in Figure 9. The algorithm begins by generating an initial solution using one of the assignments and sequencing heuristics. Then, one of the SO sequence operators is applied. If no improvement of the solution is obtained after the application of the SO operator, the next step consists of applying an AO assignment operator on the operation chosen beforehand. If the application of the AO operator does not improve the initially generated solution, an SO operator with the new affectation obtained after the application of the AO operator is applied. If the solution is still not improved, a perturbation operator is applied on this solution, and the algorithm moves on to the next loop iteration. This operator consists of applying one of the randomly chosen sequencing or assignment operators. This process is repeated until the maximum number of perturbations is not reached. Figure 9: Steps of the iterative local search algorithm for one iteration. Table 3 gives the common and different elements between the two developed algorithms. In this table, the heuristics for the generation of initial population, affectation, and sequence operators of the two iterated local search algorithms are given. The difference between the two algorithms lies in the heuristics used for the generation of initial solutions. In Table 3, the heuristics allowing one to find the best solutions are given. The affectation and sequence operators of the first iterated local search algorithm are based on random choices of operations, while in the second algorithm, the choice of these operations is based on specific rules more suited to the considered problem. The pseudocode of the generic iterated local search algorithm is given in Algorithm 3. ```
1. Initialization: The initial solution is generated by using the previously described assignment and sequencing heuristics ;  current solution \(\leftarrow\) initial solution ;
2. Evaluation: Evaluation of the initial solution ;  SimilarDegree \(\gets 0\) ;
3. Application of the SO sequence operator;
4. If the solution is improved in 3, then ;  current solution \(\leftarrow\) solution obtained in 3, else go to 5;
5. Application of the AO affectation operator ;
6. If the solution is improved in 5, then;  current solution \(\leftarrow\) solution obtained in 5, else go to 7;
7. Application of the PO operator on the current solution ;
8. Update SimilarDegree by calculating the distance between the current solution and the improved one;
9. If the degree of similarity between the solutions obtained in 8 is less than a predefined threshold,;

then go to 3, else go to 10 ;
10. End of algorithm
```  
**Algorithm 3**Iterative local search algorithm
# TCCCD: Triplet-Based Cross-Language Code Clone Detection
## 6 Experimental Results
### RQ2: Effectiveness on Specific Language Pairs  
The results presented in Table 3 demonstrate the effectiveness of various methods on specific cross-language pairs, including CLCMINER, CLCDSA, C4, and TCCCD. Among these methods, TCCCD exhibited the best effectiveness.  
In comparison to CLCMiner, TCCCD exhibited significant effectiveness in all specific cross-language code pairs, with an average increase of 0.59 in precision, 0.33 in recall, and 0.48 in the F1-score.  
Compared to CLCDSA, our approach showed similar recall on Java and Python clone pairs, but CLCDSA had a higher average recall, indicating that it tended to classify more input code pairs as clones. However, our approach outperformed CLCDSA in the precision and F1-score metrics, with an average increase of 0.44 in the precision and 0.27 in the F1-score.  
Based on the experimental results, we observed that our approach consistently outperformed C4 in terms of the precision, recall, and F1-score on each specific cross-language pair, with a notable 0.06 increase in precision for the C++ and C# cross-language pairs. This improvement can be attributed to the fact that UniXcoder was pre-training on data that include C++ and C# code, while CodeBERT lacked such pre-training, making UniXcoder more effective in representing C++ and C# code. Additionally, the adoption of UniXcoder and triplet learning in TCCCD contributed to its superior effectiveness over the baseline methods across all specific cross-language pairs.