esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology  
The data used in this paper are the last published horizontal GPS velocities for Turkey in [15] shown in Figure 1. The velocity field is obtained from 10 years of data between 2008 and 2018 of Continuously Operating Reference Stations Turkey (CORS-TR) and some other networks to improve the velocity field [15]. The data consist of CORS-TR data which are homogeneously distributed in Turkey, Turkish National Permanent GNSS Network (TNPGN), and Marmara Region Continuous Network (MAGNET). 19 TNPGN stations operated by the General Directorate of Mapping, and 11 MAGNET stations from the Marmara Region Continuous Network (MAGNET) which is operated by the Scientific and Technical Research Council of Turkey (TUBITAK), Marmara Research Centre, Earth and Marine Sciences Research Institute (EMSRI) are included. The velocities derived from TNPGN and MAGNET GPS data cover the period between 2008 and 2015.  
A total of 188 GPS stations' data were used for clustering analysis. Firstly, we determined the optimum number \((k)\) of clusters using the Gap statistics method which is designed to be applied to any distance measure and clustering technique. After determining the \(k\) value, we generated clustering models from different clustering techniques (members) for building the ensemble clustering approach. Last but not least, we combined the outputs of the members to obtain the final clustering results using different ensemble clustering methods and determined the quality of the final solutions (Figure 2).
## 4 Experiments
### Experiments on Prediction  
In the simulated environments, three sets of comparative experiments were designed within a 10 m \(\times\) 10 m area: five low-speed moving obstacles and one high-speed obstacle; nine low-speed obstacles and one high-speed obstacle; nine low-speed obstacles and one ultra-high-speed obstacle. During data acquisition, low-speed obstacles were simplified as circular mobile robots with a radius of 0.15 m and speed threshold below 0.2 m/s. In contrast, high-speed obstacles were simplified similarly with a radius of 0.15 m and speed threshold above 1.1 m/s. In the third comparative experiment, the speed threshold of the ultra-high-speed obstacle was above 3 m/s. We obtained position and velocity information in the X and Y directions of obstacle Brownian motion for 10 s in the simulated 10 m \(\times\) 10 m environment of the three comparative experiments, forming our dataset.  
To quantitatively evaluate the performance of our system, we compared the proposed method with traditional and state-of-the-art dynamic obstacle trajectory predictors on mobile robot platforms [27; 28]. Table 2 presents the comparison of Average Displacement Error (ADE) [28] and Final Displacement Error (FDE) [28] for both position and velocity estimates. As shown in Table 2, the proposed method achieves superior accuracy in positional estimation. Experimental results indicate that the inferior performance of other methods stems from their inability to differentiate between low-speed and high-speed obstacles, causing positional jumps in fast-moving obstacles that introduce noise and reduce trajectory estimation accuracy for slow-moving objects.  
The results also show that, compared to Method II, our trajectory prediction system delivers improved velocity predictions while maintaining comparable positional accuracy. Although Method II employs Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for trajectory prediction, it struggles to produce accurate results when there are significant variations in displacement magnitudes.  
In contrast, our proposed method uses a speed-based classification approach that stores only limited data on high-velocity dynamic obstacles. By focusing first on accurately predicting a large number of slower-moving objects, the method reduces noise introduced by faster entities, thereby yielding more precise trajectory estimates.
## 3 Results
### Feature Importance Extraction  
To assess the non-linear contribution of soil hydrothermal conditions to GPP, feature importance extraction experiments are conducted using the Random Forest algorithm. The experiments focus on two independent variables, soil moisture and soil temperature, among four dimensions, namely the overall vegetation zone and specific vegetation types (grassland, shrub, and alpine vegetation). The results are presented in Figure 7. Considering the impact of soil moisture on GPP, the feature importance values across three layers exceeded 30%, with a ranking of SM-L3 > SM-L1 > SM-L2. Notably, deep soil moisture consistently emerges as the most influential variable, contributing 35.54%, 37.35%, 35.56%, and 35.05% to GPP in the vegetation zone and the three dominant vegetation types, respectively. Furthermore, the feature importance values of the deep soil moisture gradually decrease with the vegetation coverage area. This suggests that for vegetation types with larger distribution areas, the promoting effect of deep soil moisture is more significant. In addition, the feature importance extraction reveals that the surface soil temperature plays the most crucial role in GPP, which is more pronounced than linear correlation results. It contributes over 45% to GPP in the vegetation zone and 44.96%, 43.28%, and 33.47% to GPP in the three dominant vegetation types. Moreover, there is no significant differentiation in the contribution degrees of SM-L2 and SM-L3 to GPP. These findings indicate that, in the Qilian Mountains, compared to other layers, the surface soil temperature is essential for GPP, and this relationship often exhibits a non-linear effect.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Soil Hydrothermal Conditions** & **Soil Layer** & **Grassland** & **Shrub** & **Alpine** \\ \hline \multirow{4}{*}{Soil moisture} & SM-L1 & 0.1298 & 0.0101 & \(-\)0.2480 \\  & SM-L2 & 0.3420 & 0.2754 & 0.0074 \\  & SM-L3 & 0.5169 & 0.4784 & 0.3678 \\  & ST-L1 & 0.5577 & 0.5539 & 0.5529 \\ Soil temperature & ST-L2 & 0.5546 & 0.5531 & 0.5558 \\  & ST-L3 & 0.5493 & 0.5427 & 0.5487 \\ \hline \end{tabular}
\end{table}
Table 3: Correlation coefficients between GPP and soil hydrothermal conditions (validated by the double-tailed \(t\)-test).  
Figure 6: Spatial distribution and GPP multi-year average values of different dominant vegetation types in the Qilian Mountains area.
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 3 Methodology
### Embedding Procedure
#### 3.1.1. Secret Sharing Stage  
Unlike the EMD method, which converts the secret message into 5-based secret digital, the method proposed in this paper firstly groups the random binary secret message with every 4 bits to obtain a secret number sequence of \(S=\{S_{1},S_{2},S_{3},S_{4},\cdots,S_{n}|0\leq S\leq 15\}\), and then let \(S=S+1\), and \(S\) becomes a secret number sequence from 1 to 16.  
Let the prime number \(p=17\), take three secret numbers \(s=\{s1,s2,s3\}\) from the secret number sequence \(S\) in turn, and use \(s\) as the coefficient of the polynomial to generate a second-degree polynomial:  
\[f(x)=s_{1}x^{2}+s_{2}x+s_{1}\ mod\ p\]  
where \(s\geq 1\) and \(0\leq f(x)\leq 16\).  
Take any 4 positive integers \(x=[x_{1},x_{2},x_{3},x_{4}],\ (1\leq x\leq 16)\) that are not equal to each other and substitute them into the polynomial to get \(y=[f(x_{1}),f(x_{2}),f(x_{3}),f(x_{4})]\). If \(y\) contains an element of 0, recalculate 4 different integers until \(y\) does not contain a 0 element.  
Let \(x=x-1\), \(y=y-1\), \(0\leq x\leq 15\), \(0\leq y\leq 15\). Let \([x,\ y]\) be converted to a 4-bit binary secret. \((x_{1},f(x_{1})),(x_{2},f(x_{2})),(x_{3},f(x_{3})),(x_{4},f(x_{4}))\) will be distributed to 4 participants as a share.  
Repeat the above steps until all the secret numbers are processed.  
As shown in Figure 3, in the secret sharing stage, \(S=\{1,2,3,4,5,6,7,\cdots,S_{l}\}\), and the two groups of three secret numbers are \(S_{1}=\)\(\{1,2,3\}\), \(S_{2}=\)\(\{5,6,7\}\), respectively. Take them as the binomial coefficient, the two generated polynomials are \(f_{1}(x)=x^{2}+2x+3\) and \(f_{2}(x)=5x^{2}+6x+7\). \(X_{1}=\)\([1,3,5,7]\) and \(X_{2}=\)\([2,4,6,8]\) are any positive integers obtained randomly, obtain \(Y_{1}=\)\([6,\,1,\,4,\,15]\) and \(Y_{2}=\)\([5,\,9,\,2,\,1]\) by calculating the polynomials \(f_{1}(x)\) and \(f_{2}(x)\). After subtracting 1 from \(X_{1}\), \(X_{2}\), \(Y_{1}\), \(Y_{2}\), obtain \(X_{1}=\)\([X_{1}^{1},\,X_{1}^{2},\,X_{1}^{3},\,X_{1}^{4}]\)\(=\)\([0,\,2,\,4,\,6]\), \(X_{2}=\)\([X_{2}^{1},\,X_{2}^{2},\,X_{2}^{3},\,X_{2}^{4}]\)\(=\)\([1,\,3,\,5,\,7]\), \(Y_{1}=\)\([Y_{1}^{1},\,Y_{1}^{2},\,Y_{1}^{3},\,Y_{1}^{4}]\)\(=\)\([5,\,0,\,3,\,14]\) and \(Y_{2}=\)\([Y_{2}^{1},\,Y_{2}^{2},\,Y_{2}^{3},\,Y_{2}^{4}]\)\(=\)\([4,\,8,\,1,\,0]\). \(X_{1}\), \(X_{2}\), \(Y_{1}\), \(Y_{2}\) are converted to binary and embedded into the bitmap of the smooth blocks of the compressed image.
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 4 Results and Discussion
### Experiment Details  
For the experiment, we selected several attention modules for comparison, including encoder, CA (coordinate attention) [26], SCCA (split coordinate channel attention), CBAM (convolutional block attention module) [27], SENet (squeeze-and-excitation network) [28], triplet [29], GCB (global context block) [30], SKNet (selective kernel network) [31], NLB (non-local block) [32], and APNB (asymmetric pyramid non-local block) [33]. The objective is to confirm the role of attention modules in salient object detection and to compare the highlighting effects of different attention modules on salient object detection. The prediction time for a single image, the mean absolute error (MAE), F\({}_{1}\)-score, and model size are compared and analyzed.  
The split ratio of the squeeze block and the kernel size of spatial attention in the CBAM is set as 16 and 7 \(\times\) 7, respectively. The average transformation and the ratio used in SE is adaptive average pooling and 2, respectively. The reduction ratio in the SCCA, CA is 4, and the kernel size of the convolution of line 1 in the SCCA is 3 \(\times\) 3. The overall architecture of attention modules is depicted in Figure 6.  
Figure 5: The architecture of the SCCA-encoder.
## 4 Experimental Results and Discussions
### Comparison with Existing Schemes  
To further describe the performance of the proposed color image watermarking scheme, we compare the proposed with exiting schemes [2; 6; 8; 18; 19]. The results are shown in Tables 2-4. Considering that the QDFT and TD hybrid transform allow the watermark energy to propagate synchronously in the three color image channels, when a channel is replaced by another channel of a color image, the watermark can still be extracted. Hence, we test the effect of re-composition for RGB channels, which is regarded as a special attack in this paper. The specific experimental results are shown in Table 5. Beyond that, this part also conducts an attack experiment, attack types including noise,  
Figure 14: PSNR of watermarked images.  
Figure 13: Distribution of PSNR and NC.
###### Abstract  
Ionizing radiation induces the degradation of electronic systems. For memory devices, this phenomenon is often observed as the corruption of the stored data and, in some cases, the occurrence of sudden increases in current consumption during the operation. In this work, we propose enhanced experimental instrumentation to perform in-depth Single-Event Effects (SEE) monitoring and analysis of electronic systems. In particular, we focus on the Single-Event Latch-up (SEL) phenomena in memory devices, in which current monitoring and control are required for testing. To expose the features and function of the proposed instrumentation, we present results for a case study of an SRAM memory that has been used on-board PROBA-V ESA satellite. For this study, we performed experimental campaigns in two different irradiation facilities with protons and heavy ions, demonstrating the instrumentation capabilities, such as synchronization, high sampling rate, fast response time, and flexibility. Using this instrumentation, we could report the cross section for the observed SEEs and further investigate their correlation with the observed current behavior. Notably, it allowed us to identify that 95% of Single-Event Functional Interrupts (SEFIs) were triggered during SEL events.  
radiation testing; radiation effects; instrumentation; reliability; harsh environments; SRAM; PROBA-V; single-event latch-up; single-event upsets; single-event functional interrupt +
Footnote †: journal: Electronics Electronics
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 3 Methodology
### FPN, Detector and Loss Function
By down-weighting easily classified examples, the approach prioritizes difficult ones, improving model performance, especially in class-imbalanced scenarios. The classification loss \(\mathcal{L}_{\text{cls}}\) quantifies the discrepancy between the predicted class probability distribution \(p_{\text{pred}}(c)\) and the ground-truth distribution \(p_{\text{true}}(c)\), implemented through categorical cross-entropy:  
\[\mathcal{L}_{\text{cls}}=-\sum_{c=1}^{K}\left[p_{\text{true}}(c)\log p_{ \text{pred}}(c)\right] \tag{18}\]  
where \(p_{\text{true}}(c)\in\{0,1\}\) represents the one-hot encoded ground-truth label for class \(c\), and \(p_{\text{pred}}(c)\in(0,1)\) denotes the predicted probability after softmax normalization over \(K\) object categories. This formulation explicitly measures the information-theoretic distance between the predicted class posterior and the true data distribution. By combining these loss components, the model learns to optimize the object detection task while maintaining a balance between bounding box accuracy, object confidence, and classification accuracy.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 3 Experiment 1: Preliminary Word Error Rate and Listening Effort Measurements
### Analysis and Results
#### 3.2.1 Word Error Rates from HSR  
Figure 1 shows all the WER results. The blue bars represent 'All words WER' from 'not familiar' listeners; and the orange bars represent the same from 'familiar' listeners. Patterned blue and orange bars represent the 'content words only WERs' for the 'familiar' and 'not familiar' categories, respectively. OM, OF, HM, and HF are acronyms for Oesophageal Male, Oesophageal Female, Healthy Male, and Healthy Female, respectively.  
As can be seen in Figure 1, the mean 'content words only WERs' and 'All words WERs' differ in value, but only by a small number (mean value of 2.73 percentage points). The 'content words only WERs' were highly correlated with the 'All words WERs' (Pearson's r = 0.889, \(p<0.001\) ). Therefore, we can see that the trend of the nonpatterned bars and the patterned bars is similar.  
Now, focusing on the speakerwise WERs for familiar and unfamiliar listeners, we can see that mean WER is always higher for OS compared to HS, as expected. There is no major difference in the WER for familiar and unfamiliar listeners in the case of OS. It is confirmed by a two-sample Kolmogorov-Smirnov (KS) test that the data came from the same continuous distributions (Alpha = 0.05). This result corroborates the conclusions in [8], where intelligibility was scored similarly by expert and novice listeners. For HS, there is a slight difference of around 3 points in the mean WER, but, as can be seen in Figure 1, the difference is not meaningful. From a two-sample KS test, we find that the data (HS-familiar and HS-nonfamiliar) came from the same continuous distributions (Alpha = 0.05). Categorical mean WERs can be found in Table 1.  
The ANOVA results show that familiarity with OS had no effect on WER (F(1,55) = 0.007, \(p=0.934\)). On the other hand, speaker-type had a strong effect on WER (F(1,55) = 223.593, \(p<0.001\), \(\eta^{2}=0.788\)). These results can also be observed in Figure 1.  
The audio device used by the listener had no effect on HSR WER (F(3,1256) = 0.707, \(p=0.548\)).  
Figure 1: Mean speakerwise ‘All words’ and ‘content words only’ Word Error Rates (WER) for ‘familiar’ and ‘not familiar’ listeners. OM1, OM2, OM3, OF1 are oesophageal speakers; HM1 and HF1 are healthy speakers. Higher WER corresponds to lower intelligibility. Error bars show 95% confidence intervals.
## 3 Results and Discussion
### Identification and Quantification of Organic Acids Compounds in Brine  
The evolution of organic acids was in general similar to that of TTA (and a parabolic pattern was expected) (Figure 3). During sweetening, succinic is the dominant organic acid, followed by lactic, and then by acetic and citric (to similar concentrations).  
Citric acid is inherent in olive flesh, while lactic and acetic acids arise from microbial activity [42]. The presence of succinic acid is related to microbial conversion of citric to succinic acid via a potential shift from hetero- to homo-fermentative metabolism of LAB [43]; this appears to be the case in our study, as discussed below in further detail. The peaks of concentrations of succinic and lactic acids were attained by 3 months, whereas citric and acetic acids peaked at 4 months. By the end of the process, acetic acid became dominant, seconded by lactic acid. However, succinic acid decreased faster than the others by 7 months, followed by lactic and citric--and acetic acid, at a much lower rate.  
Based on previous studies, a higher salt content and mild room temperature favor the enzymatic activity of LAB over yeasts, thus causing lactic acid concentration to increase as the numbers of LAB increase and to decrease more slowly than others do thereafter; yeasts resistant to salt could, however, also be responsible for the slower decrease in acetic acid at the final stage of fermentation [6,36].  
It is noteworthy that the presence of acetic acid can be attributed to the activity of yeasts or heteroferrentative LAB, able to generate acetic acid from fermentable material under particular conditions of environmental stress, as well as to metabolize citric acid [37]. Nevertheless, acetic acid did not vanish by 11 months and even became the most concen  
Figure 2: Changes in viable numbers of (**a**) yeasts and (**b**) lactic acid bacteria in olive samples during spontaneous fermentation of _Cobranos_ table olives.  
trated organic acid in brine; this is so because some producers deliberately add vinegar during the process in attempts to empirically control pH--information not provided in the questionnaire (as requested) but confirmed _a posteriori_ after having visited the producers.  
Inspection of Figure 3 unfolds a large probability that citric and succinic acids were depleted in full; this agrees with the findings by [PERSON], [PERSON], and [PERSON] [32], who reported similar observations at the final stage for other varieties of table olives.
## 4 Experiment and Analysis
### Matching Electricity Consumption Patterns for New Customers
The algorithm in this paper has been applied in a local power grid to help the electric power company to quickly classify users' electricity consumption patterns and achieve electricity consumption pattern matching for new users. Utilizing BERT, the power company can promptly identify the power usage behavior of new users and provide personalized power usage suggestions, which significantly improves user satisfaction and grid operation efficiency. To verify that the improvements in the proposed DistilBERT model are statistically significant compared to other baselines, we conducted paired t-tests based on 10 independent runs for each model (using different random seeds). The means and standard deviations of each metric are shown in Table 5. We further calculated the \(p\)-values for the performance comparisons between DistilBERT and the best-performing baseline (Transformer).
# The Impact of Hollow Wear on the Stability of High-Speed Railway Vehicles
## 3 Impact of Hollow Worn Wheel on Vehicle Stability
### Evaluation of Vehicle Stability Using Bifurcation Configuration Method
#### 3.2.2 Analysis of Hopf Bifurcation Diagram for Wheelsets  
For vehicle dynamics systems, engineers focus on bifurcation speed points and bifurcation configurations, as the former relates to nonlinear critical speeds and the latter relates to the operational state of the vehicle after instability. Figure 10 shows the lateral vibration bifurcation configurations and corresponding nonlinear critical speeds for the five types of worn wheels with varying speeds, respectively. The speed of the vehicle is depicted on the horizontal axis, while the vertical axis illustrates the lateral vibration amplitude of the wheelset at various speeds. The threshold point of the limit cycle indicates the vehicle's nonlinear critical speed.  
According to the lateral vibration limit cycle amplitude of standard profile wheel W1 and worn wheel W2, their bifurcation configurations are subcritical, as shown in Figure 10a,b, with corresponding nonlinear critical speeds of 640 km/h and 480 km/h, respectively. As the wheel wear intensifies, the bifurcation characteristics change from  
Figure 10: Bifurcation configuration diagram of wheel’s lateral amplitude.  
subcritical configuration to supercritical configuration, while the nonlinear critical speed decreases. As shown in Figure 10c, the operating mileage of the corresponding wheel is 95,000 km, and the nonlinear critical speed is 400 km/h, which already has the characteristics of a supercritical configuration. For the more severely worn wheels W4 and W5, the simulated nonlinear critical speed of the vehicle is reduced to 220 km/h and 180 km/h. Once the vehicle speed exceeds the nonlinear critical speed, the lateral displacement limit cycle amplitude of the wheelset cannot converge to zero, and the vehicle's operational stability is significantly reduced. In this situation, it is unable to meet the requirement of a maximum operating speed of 250 km/h for a high-speed vehicle.  
In addition, Figure 7 shows the equivalent conicity curves of each wheel tread. The equivalent conicity slopes of the tread surfaces of wheels W1 and W2 are positive at this position, which both have subcritical bifurcation configurations. The equivalent conicity slopes of the tread surfaces of wheels W3, W4, and W5 are negative at the corresponding positions, and the bifurcation configurations of these wheels are supercritical configurations. It can be inferred that there is a certain correlation between the slope of the equivalent conicity curve and the type of bifurcation configuration.