esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Position Error and PDOP of the Satellite Permutations  
For the sake of analysis simplicity, this paper disregards the influence of time on positioning accuracy, necessitating only three satellites to position the observation points. Consequently, adhering to the satellite selection criteria outlined in Section 4, when the number of observable satellites exceeds three, satellites with higher elevation angles and combinations with lower PDOP values should be chosen to achieve positioning. To minimize the computational burden, the four satellites with the highest elevation angles: S2, S16, S8, and S10 were selected from Figure 11, and the PDOP values for the permutations S2-S8-S10, S2-S8-S16, S2-S10-S16, and S8-S10-S16 were computed. Figure 12 displays PDOP1, PDOP2, PDOP3, and PDOP4, respectively. In Figure 12, it is apparent that the  
Figure 10: The PDOP of s single satellite.  
Figure 9: Single satellite position error.  
PDOP values of these satellites converge over the observable time period. Figure 13 illustrates the three-dimensional positioning errors for the aforementioned four satellite permutations. The positioning errors for the permutations S2-S8-S10, S2-S8-S16, S2-S10-S16, and S8-S10-S16 are denoted as Deltar1, Deltar2, Deltar3, and Deltar4, respectively. Figure 13 reveals that, over time, the positioning error Deltar2 for the permutation S2-S8-S16 stabilizes within 20 m, indicating superior positioning accuracy. Additionally, the elevation angles of the three satellites S2, S8, and S16 are the highest among the 18 satellites.  
Figure 11: Satellite elevation angle.  
Figure 12: PDOP of the satellite permutations.  
Figure 13: Position error of the satellite permutations.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 3 CFP Firmware Design Scheme
### MCU Design
#### 3.3.4 IC Communication Interface Function  
The IC communication interface function drives communication between the MCU and external chip. The MCU controls the ADCs, DACs, FPGA and optical component ICs. Table 2 describes the communication interface between the peripheral ICs and MCU. Between the FPGA and MCU, an 8051 EMIF is used for high-speed data processing. EMIF is a bus protocol for communication with external memory devices on circuit boards in integrated circuits. In this paper, the MCU considers the FPGA to be the external memory because CFP standard memory is implemented in the FPGA, as shown Figure 5. Using the address bus, data bus, RD (read), WR (write), and address latch enable (ALE), the 8051 MCU accesses the memory of the FPGA. The ADCs and DACs communicate with MCU through the SPI embedded in the chip. Moreover, optical driver ICs communicate with the MCU via the SMbus interface. UART communication between the host and MCU is implemented using the vendor IO pin of the CFP 148 pin connector, so that debugging and CFP value settings can be done easily. Figure 8 shows the MCU program flow chart. When power is supplied by default or an external reset signal is input, the MCU starts program execution. First, the MCU initializes the MCU system clock setting and MCU peripheral devices such as the EMIF, SMbus, SPI, UART, and timer. At this time, the system clock starts the FPGA.  
The ADC/DAC initialization process initializes the external ADC/DAC chip. When initializing the CFP control variables, all the global variables required for the firmware are initialized and the firmware enters the polling loop. Within the polling loop, the CFP handles external requests coming into the UART. In the MDIO instruction process, the MCU accesses the internal status register of the FPGA and checks whether there is an MDIO request from the current host. If there is a request, the MCU reads the corresponding address and value and processes it accordingly. In the CFP state process, the state transition is managed as in the MSA standard. The CFP DDM process monitors the value from the ADC and updates the DDM value in the CFP standard register. The FAWS process is used to indicate faults, alarms, and warning conditions according to the CFP status.
# A Novel Method of Seismic Signal Detection Using Waveform Features
## 4 Implementation
### Training  
In order to get the optimal hyperparameter \(\theta=(\sigma_{f}^{2},\sigma_{n}^{2},l)\), we find the maximum value of the marginal likelihood function by the gradient ascent using training data set \(D=(X,y)\). The gradient ascent is similar to the gradient descent excepting the subtraction replaced by the addition during the gradient update process. There are various methods of gradient descent, such as random gradient descent, mini-batch gradient descent, momentum method gradient descent, and so on. We use the Adam gradient descent method in the momentum method. The advantage of this method is that the learning rate is automatically adjusted during the training process, and parameter updating is relatively stable. It is easy to implement and computationally efficient. Moreover, the hyperparameters gained in this way are usually reliable.  
The training process is shown in Figure 10. In the beginning, the hyperparameters are randomly initialized. Each time the gradient rises, the partial derivative matrix needs to be calculated according  
Figure 8: The probability distribution of the average value of the signal. From (**a**) to (**c**), the subgraphs are the train set data distribution, the seismic test set distribution, and the noise test set distribution of the energy mean feature. The red dotted line represents the statistical distribution, while the blue dotted line is distribution obtained by the Gaussian process using prior information.  
Figure 7: The probability distribution of the ratio of the STA/LTA curve. From (**a**) to (**c**), the subgraphs are the train set data distribution, the seismic test set distribution, and the noise test set distribution of the ratio feature. The red dotted line represents the statistical distribution, while the blue dotted line is the distribution obtained by the Gaussian process using prior information.  
to Equation (15), and the three hyperparameters are simultaneously updated according to Equation (14). In the training process, a threshold is necessary to terminate the procedure. When the loss function is less than the set threshold, the marginal likelihood value is considered to be the maximum, and the three hyperparameters are output while the training is terminated. Otherwise, the training is continued until the marginal likelihood value is maximized.When the kernel hyperparameters \(\sigma_{f}^{2}\), \(\sigma_{n}^{2}\), \(l\) are optimized by training, the posterior distribution at the test point \(x_{*}\) is  
\[p(x_{*}|X;y)=\text{N}(x_{*};\mu_{y},\Sigma_{y}), \tag{24}\]  
The matrix \(X\) of shape \(n\times n\) describes the location inputs corresponding to history events \(E\). Then, we calculated the posterior mean \(\mu_{y}\) and covariance \(\Sigma_{y}\). By the posterior distribution, we can get the probability of the test point \(x_{*}\).
## 2 Materials and Methods
### Requirements towards Optimization Algorithms  
The missing availability of criteria for choosing a proper optimization algorithm is one of the fundamental problems for engineers in optimization. Most publications use different test functions, and the authors validate the algorithms for computationally inexpensive test functions. Another problem is the number of validated objectives. Furthermore, the behavior of the black box (multimodality, non-linearity) and requirements from the framework itself (particular environment, e.g., Python 3) need to be considered. Papers and articles present different measures to choose a suitable optimization algorithm. Hence, this paragraph briefly reviews the requirements for the well-founded selection of an optimization algorithm. [PERSON] and [PERSON] (2018) surveyed a wide variety of algorithms; the authors found that for 45 published algorithms from 2008 to 2016, a maximum of three objective functions, mainly two, were evaluated. The amount of DVs commonly lies below 30. Mostly, no time for the training of the surrogate models is mentioned, which is founded on the argument that the expensive function evaluations are predominant. Still,  
Figure 1: The flowchart of the IKOS framework describes the single process steps of the optimization process.  
Figure 2: (**a**) shows the b-pillar and hull surface, while (**b**) shows the b-pillar enveloped by the hull surface.  
training can be a significant factor for large data sets or quick assessment. In addition, the factors are mainly relative to the other algorithms analyzed in a specific paper: the following criteria can be formulated considering the efficiency of the algorithms:  
* The dimension of the objective and decision space;
* Maximum number of expensive function evaluations;
* Applicable problem characteristics (e.g., multimodality, divided Pareto-frontier).  
Hence, the authors summarize that a good algorithm for optimizing black-box functions generates legitimate solutions with few evaluations even for unknown characteristics. The main questions towards the choice of algorithms are stated in [36], which are:  
* Which algorithm deals the best with the problem?
* For which kind of problem can the algorithm be used?  
Commonly, both questions can only be answered after testing the algorithm for the problem. The user's experience influences the algorithm's choice, as do the software, licensing, calculation (evaluation) cost, and the maximum time for the optimization. Because of their discontinuity, the numerical noise, and the multimodal character of crash simulations' local search strategies are not applicable for multi-objective optimization. Global search algorithms such as SAs, GAs, and EAs can usually be applied [7], while newer (adaptive) approaches focus on a balanced behavior of exploration and exploitation [12].  
The following section reviews the choice, implementation, and validation of one of the mentioned algorithms into the IKOS framework.
## 3 Thickness Determination  
### Peak Area Calculation  
A precise determination of the net area of peaks of interest is fundamental for an accurate thickness estimation through the differential attenuation method [8]. To achieve such precision, XRF spectra are usually manually inspected. This approach is feasible when analysing few or dozens of spectra, but impracticable when dealing with MA-XRF datasets composed of at least thousands of spectra. To overcome this problem, an algorithm was proposed to automatically determine the net peak areas of interest.  
The spectrum evaluation is performed in three steps: (a) calculate the full-width at half maximum (FWHM) for a gaussian centred at the theoretical peak position, (b) search for a peak maximum in a 2*FWHM window, verify if it matches with the theoretical centre, and (c) check the signal-to-noise ratio (SNR) criteria. If the peak maximum is slightly offset due to calibration issues, the FWHM window iscentred. The theoretical values used for centring and locating the peaks are obtained from the xraylib package [20]. Background contribution is calculated following the Statistical Non-Linear Iterative Peak Clipping (SNIPBG) method [24] and the parameters are set by evaluating randomly sampled spectrum instead of a derived spectrum.  
Since MA-XRF spectra usually present low statistics, the net area is calculated over the raw data. No fitting is performed to save on computational time. Therefore, the proposed method requires that the peaks of interest be well separated and defined, as no deconvolution method is yet implemented.
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
## 4 Experiments
### Hyperparameter Sensitivity Analysis
#### 4.4.1 Effect of Group Number on Detection Performance  
In complex IoT traffic scenarios with large-scale labeled datasets, our method aggregates classes into groups to facilitate more effective feature learning and simplify decision boundaries. To analyze the effect of group number \(G\), we vary this hyperparameter and examine its influence on AUROC and FPR95.  
As shown in Figure 6, increasing \(G\) leads to a degradation in OOD detection performance, reflected by a decrease in AUROC and an increase in FPR95. This supports our hypothesis that an overly large number of groups may assign semantically similar classes into different clusters, leading to blurred decision boundaries and reduced separability. Conversely, too small a group number results in highly heterogeneous groups, dispersing the feature distribution and limiting the effectiveness of the distance-aware loss.  
To support the choice of group number, we computed the silhouette coefficient as an unsupervised clustering quality metric (see Algorithm 3). For instance, on the IoT Sentinel dataset, the silhouette score peaked at \(k=3\) (\(S=0.3199\)), while the best OOD performance was achieved at \(k=4\), with a 19.5% reduction in FPR95. This discrepancy suggests that the  
Figure 6: Impact of different group numbers on OOD detection performance measured by AUROC and FPR95, using IoT Sentinel as the in-distribution dataset and Shenzhen power grid traffic as the OOD dataset. (**a**) shows the AUROC variation with increasing group numbers. (**b**) shows the corresponding FPR95 changes.  
silhouette alone may not be sufficient to guide OOD-aware clustering. Future work will consider performance-aware automatic group selection strategies.  
```
1:
2:Class mean vectors \(\mathbf{M}\in\mathbb{R}^{n\times d}\) where \(n\)=number of classes Minimum group number \(k_{\min}=2\) Maximum group number \(k_{\max}=10\)
3:Optimal group number \(k^{*}\)
4:Initialize \(k^{*}\gets k_{\min}\), \(s^{*}\leftarrow-1\)
5:for\(k=k_{\min}\) to \(k_{\max}\)do
6: Cluster \(\mathbf{M}\) into \(k\) groups:
7:\(\mathcal{C}_{k}\leftarrow\mathrm{KMeans}(n\_clusters=k).fit(\mathbf{M})\)
8:\(labels\gets\mathcal{C}_{k}\_labels_{-}\)
9: Calculate silhouette Score:
10:\(a_{i}\leftarrow\) mean intra-cluster distance for group \(i\)
11:\(b_{i}\leftarrow\) nearest-cluster distance for group \(i\)
12:\(s_{k}\leftarrow\frac{1}{k}\sum_{i=1}^{k}\frac{b_{i}-a_{i}}{\max(a_{i}b_{i})}\)
13: Updated optimal group number:
14:if\(s_{k}>s^{*}\)then
15:\(k^{*}\gets k\)
16:\(s^{*}\gets s_{k}\)
17:endif
18:endfor
```  
**Algorithm 3** Automatic group number selection via silhouette score
## 2 Overview of Supplementary Damping Systems
### Brief History and Development of Damping Technologies
#### 2.3.6 Evolving Design Philosophies  
The development of damping technologies has been accompanied by evolving design philosophies in earthquake engineering:  
* 1970s-1980s: Focus on life safety and collapse prevention.
* 1990s-2000s: Emergence of performance-based seismic design.
* 2000s-Present: Emphasis on resilience and rapid post-earthquake recovery.  
This comprehensive overview of supplementary damping systems provides a solid foundation for understanding their classification, operating principles, and historical development. It sets the stage for more detailed discussions of specific damping technologies, their analysis, and implementation in subsequent sections of the review paper. The evolution of these systems reflects the ongoing efforts to enhance structural performance and resilience in the face of seismic hazards.
## 3 Standard Cyber Security Protocols
### Gb/t 22239  
This is setup for information security technology in China. For the information system, it contains five security protection abilities to protect from hackers and reinstate to the previous state.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline \multicolumn{2}{c}{**Type**} & \multicolumn{1}{c}{**Speed**} & \multicolumn{1}{c}{**Distance**} & \multicolumn{1}{c}{**Demerits**} \\ \hline \multirow{4}{*}{Cellular Network} & GSM & 14.4 kbps & 1–10 km & Poor speed \\ \cline{2-5}  & GPRS & 170 kbps & 1–10 km & Poor speed \\ \cline{2-5}  & 3G & 2 Mbps & 1–10 km & Expensive \\ \cline{2-5}  & WIMAX & 75 Mbps & 50 km & Limited stock \\ \hline \multirow{4}{*}{Short Range} & ZigBee & 250 kbps & 50 m & Small distance and poor speed \\ \cline{2-5}  & 6 LoWPAN & 250 kbps & 10–100 m & Small distance and poor speed \\ \cline{2-5}  & Bluetooth & 1–2 Mbps & 15–30 m & Small distance \\ \cline{2-5}  & Wi-Fi & 54 Mb/s & 100 m & Small distance \\ \cline{2-5}  & UWB & 110 Mb/s & 10 km & Small distance \\ \hline \multirow{4}{*}{LPWAN} & LoRa & 0.3–37.5 kbps & 3–5 km & Low data range \\ \cline{2-5}  & SIGFOX & 0.1 kbps & 3–10 km & Low data range \\ \cline{1-1} \cline{2-5}  & eMTC & \textless{}1 Mbps & 5 km & Licensed network \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between different wireless technologies.
## 3 Results
### Genetic Programming (GP) Predictions  
Figure 5 highlights how well the GP model predicts \(C_{c}\) values and shows a strong match between the predicted and actual results. In the scatter plot, most data points from both the training and testing datasets align closely with the 1:1 reference line. This approach reflects excellent accuracy. Many points also fall within the \(\pm\)10% deviation bands and show that the model can generalize well with new data. However, the testing database shows slightly more scatter than the training data. This point shows that the model could be improved for more complex or extreme cases.  
The histogram, boxplot, and violin plot in Figure 5 provide more evidence of the GP model's reliability. The histogram shows that the predicted values closely match the actual data distribution, especially for lower \(C_{c}\) values. The boxplots reveal very little difference between the median and interquartile ranges of the actual and predicted values, with only a few outliers. The violin plots show that the predicted data have a similar shape and spread to the actual values, further confirming the model's consistency. These results show that the GP model captures nonlinear relationships well, and its symbolic regression makes it easy to interpret. This combination of accuracy and interpretability makes the GP model a powerful tool for predicting soil compressibility.  
The following equation shows the GP proposed equation. Also, Table 6 represents parameters and constants:  
Figure 5: Evaluation of actual vs. predicted \(C_{c}\) using GP through various visualizations.  
\[\begin{split}\text{Y}=(\text{r}_{1}{}^{2}+(\text{x}_{1}-\text{x}_{2})^{2} \times 4\times\text{r}_{1})^{2}\times(\text{x}_{3}-\text{r}_{3}{}^{2}\times \text{x}_{1}+(\text{r}_{2}+\text{x}_{3})\times(\text{x}_{3}+\text{x}_{1})- \text{x}_{4}{}^{4}+(\text{x}_{2}{}^{2}-\text{x}_{3}{}^{2})\times(2\times\text{ x}_{1}-\text{r}_{3}-\text{x}_{4})\times((\text{x}_{1}-\text{r}_{1})\times\\ (2\times\text{x}_{2})-(3\times\text{x}_{3}+\text{r}_{1})))\end{split} \tag{23}\]  
Table 7 presents the performance metrics of the GP method in predicting the \(C_{c}\) for the training and testing datasets. The GP method demonstrates strong predictive performance, with a high \(R^{2}\) value of 0.934 for the training database, indicating an excellent fit to the data. For the testing database, the \(R^{2}\) value is 0.827, reflecting good generalization capabilities. Additionally, the low mean absolute error (MAE) values of 0.039 and 0.040, along with mean squared error (MSE) values of 0.003 for both datasets, highlight the GP method's ability to capture complex relationships and provide accurate predictions for both training and testing datasets.  
The GP model was optimized using an evolutionary strategy that balances accuracy and interpretability. The best configuration was obtained by tournament selection and elitism, ensuring diversity while avoiding premature convergence. Table 8 shows optimized hyperparameters for GP.
# Exploring Structural Changes in Ge-Te Amorphous Films Through Small-Angle Neutron Scattering
## 2 Materials and Methods
### Sample Preparation  
Five thick amorphous films with measured compositions of Ge\({}_{0.17}\)Te\({}_{0.83}\), Ge\({}_{0.21}\)Te\({}_{0.79}\), Ge\({}_{0.28}\)Te\({}_{0.72}\), Ge\({}_{0.30}\)Te\({}_{0.70}\), and Ge\({}_{0.45}\)Te\({}_{0.55}\) were prepared via thermal co-evaporation using a MEB 500 system from Plassys. Microscope slides were employed as substrates. Prior to deposition, the chamber was evacuated to a pressure of 10\({}^{-7}\) mbar. Germanium pieces (from Goodfellow, Huntingdon, UK, 99.999%) were evaporated by electron beam bombardment, while tellurium pieces (from Sigma-Aldrich, St. Louis, MI, USA, 99.999%) were evaporated from two current-induced heated sources. During deposition, the substrate holder was rotated and heated to approximately 70 \({}^{\circ}\)C. The deposition rate for each element was automatically controlled using a thin film deposition controller.  
The samples (approximately 150-200 mg) required for the SANS measurements were prepared by scraping the 5-7 um thick co-evaporated films. This process produces fragments that are primarily two-dimensional, with a thickness equivalent to that of the film and dimensions of 1-2 mm in the other directions. The film compositions were verified using Electron-Probe Micro-Analysis (EPMA) with a CAMECA SX-100 (Paris, France) instrument. The amorphous nature of all as-deposited films was confirmed through X-ray diffraction, performed using a PANalytical XPERT (Almelo, The Netherlands) diffractometer with a Cu (K\({}_{\alpha}\)) source for an incident wavelength of \(\lambda=1.5406\) A.