esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation of the Active Support System for the 4m SiC Primary Mirror
### Correction of the Primary Mirror Surface Shape Errors Caused by Thermal and Wind Loads
#### 4.4.1 Thermal Load
The first 30 orders of bending modes were again used, and the results are shown in Figure 22. Setting the rms to \(<\)\(\lambda/30\) (\(\lambda\) = 632.8 nm) as the standard, the results indicated that the designed active support system could effectively correct the 4m SiC primary mirror surface shape error caused by an axial temperature difference of 0.25 \({}^{\circ}\)C, radial temperature difference of 0.25 \({}^{\circ}\)C, and transverse temperature difference of 0.2 \({}^{\circ}\)C.
## 3 Results
\({}^{*}\) Significant differences for \(p\)-value \(\Table 4 illustrates the between-interval comparisons for V\({}_{\rm MAX}\), V\({}_{\rm MEAN}\), A\({}_{\rm MAX}\) and A\({}_{\rm MEAN}\) during the concentric phase (P3) of the BP exercise. The results of repeated measures ANOVA revealed significant differences among intensity intervals in V\({}_{\rm MAX}\) (\(p<0.001\), np2 = 0.908), V\({}_{\rm MEAN}\) (\(p<0.001\), np2 = 0.889), A\({}_{\rm MAX}\) (\(p<0.001\), np2 = 0.808) and A\({}_{\rm MEAN}\) (\(p<0.001\), np2 = 0.789). Pairwise comparisons showed that V\({}_{\rm MAX}\) and V\({}_{\rm MEAN}\) were significantly greater during interval 1 compared to intervals 2 (V\({}_{\rm MAX}\): \(p<0.001\), g = 1.51; V\({}_{\rm MEAN}\): \(p<0.001\), g = 1.16) and 3 (V\({}_{\rm MAX}\): \(p<0.001\), g = 3.24; V\({}_{\rm MEAN}\): \(p<0.001\), g = 2.70). Likewise, V\({}_{\rm MAX}\) and V\({}_{\rm MEAN}\) were significantly higher during interval 2 in comparison with interval 3 (V\({}_{\rm MAX}\): \(p<0.001\), g = 1.97; V\({}_{\rm MEAN}\): \(p<0.001\), g = 1.7). Similarly, A\({}_{\rm MAX}\) and A\({}_{\rm MEAN}\) were significantly greater during interval 1 compared to interval 2 (A\({}_{\rm MAX}\): \(p<0.001\), g = 1.32; A\({}_{\rm MEAN}\): \(p<0.001\), g = 1.22) and interval 3 (A\({}_{\rm MAX}\): \(p<0.001\), g = 2.06; A\({}_{\rm MEAN}\): \(p<0.001\), g = 2.32). A\({}_{\rm MAX}\) and A\({}_{\rm MEAN}\) were also significantly higher during interval 2 compared to interval 3 (A\({}_{\rm MAX}\): \(p<0.001\), g = 1.08; A\({}_{\rm MEAN}\): \(p<0.001\), g = 1.30). Table 5 shows the comparison between the different intensity intervals for P\({}_{\rm MAX}\), P\({}_{\rm MEAN}\) and W during the concentric phase (P3) of the BP exercise. The results of repeated measures ANOVA revealed significant differences among intensity intervals in P\({}_{\rm MAX}\) (\(p<0.001\), np2 = 0.542), P\({}_{\rm MEAN}\) (\(p<0.001\), np2 = 0.534) and W (\(p<0.001\), np2 = 0.563). In detail, pairwise comparisons showed that P\({}_{\rm MAX}\), P\({}_{\rm MEAN}\) and W were significantly greater during interval 1 compared to intervals 2 (P\({}_{\rm MAX}\): \(p<0.001\), g = \(-0.7\); P\({}_{\rm MEAN}\): \(p=0.002\), g = 0.66; W: \(p=0.006\), g = 0.54) and 3 (P\({}_{\rm MAX}\): \(p<0.001\), g = 1.23; P\({}_{\rm MEAN}\): \(p<0.001\), g = 1.24; W: \(p<0.001\), g = 1.33). P\({}_{\rm MAX}\), P\({}_{\rm MEAN}\) and W were also significantly higher during interval 2 compared to interval 3 (P\({}_{\rm MAX}\): \(p<0.001\), g = 0.77; P\({}_{\rm MEAN}\): \(p<0.001\), g = 0.84: W: \(p<0.001\), g = 1.04).
### Tissue Thermometry  
Nowadays, technologies that allow us to acquire data about internal and external thermal distributions of tissues provide suitable characteristics for performing a precise analysis. Microwave thermography is one of the techniques used to measure temperaturesat a depth of several centimeters (up to 6 cm) under breast surface [39]. Passive microwave thermography has been applied to the detection of electromagnetic tissue emissions, radiated in the range between 1 and 10 GHz. This method is based on using thermal abnormalities to detect breast cancer in early stages. In fact, this technique is particularly associated with malignant tumor detection due to its relationship with the fast growing rates and temperature elevations produced [37]. Infrared (IR) thermography is another technique applied to external temperature distribution measurements of breasts, reaching thermal sensitivities up to 20 mK. Combined with microwave thermometry, IR has been applied to breast cancer diagnosis; such is the case of RTM-01-RES (MMWR LTD 2019), a medical device that allows us to visualize infrared and microwave emissions from the body.  
Magnetic resonance imaging (MRI) has been used to produce thermal maps inside tissues too. MRI based on proton resonance frequency (PRF) provides real-time internal thermal information with a sensitivity of 1-2 \({}^{\circ}\)C, for a spatial resolution in the order of millimeters [40]. When applied in a high-strength field, this can be used on tissues partially composed of fat, such as the breast [41; 42]. According to studies performed in previous years by means of using specific contrast agents based on paramagnetic nanoparticles, thermal precision tended to improve to by approximately 0.1-0.3 \({}^{\circ}\)C [43]. However, due to the high cost of this technique and its capability to provide information in real time, MRI has been mainly focused on thermotherapy, providing guidance related to heat deposition induced by electromagnetic or ultrasonic radiation.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 3 The Proposed DSAH Framework
### Definitions  
Suppose that the multimedia training data contains \(M\) modalities, represented by \(\mathbf{X}=\{\mathbf{X}^{(1)},\mathbf{X}^{(2)},...,\mathbf{X}^{(M)}\}\), where \(\mathbf{X}^{(n)}=\{\mathbf{x}_{1}^{(m)},\mathbf{x}_{2}^{(m)},...,\mathbf{x}_{ n}^{(m)}\}\in\mathbb{R}^{d_{m}\times n}\) is the \(m\)-th modality  
Figure 1: The framework of Discrete Semantics-Guided Asymmetric Hashing (DSAH). In our proposed DSAH, we first handle the nonlinear relations in different modalities with the kernelization, then an asymmetric learning scheme is proposed to effectively perform the hash learning process; meanwhile, our proposed method fully considers the label information to enhance the semantic information. In addition, a discrete optimization algorithm is proposed to solve the discrete problems.  
data features and \(d_{m}\) is the dimension of modality \(\mathbf{X}^{(m)}\). In this paper, we focus on the supervised hashing paradigm; therefore, label information \(\mathbf{L}\in\{0,1\}^{c\times n}\) is available, \(c\) denotes the number of categories, and \(l_{i}^{j}=1\) indicates the \(j\)-th instance belongs to category \(i\), and \(l_{ij}=0\) otherwise. \(\mathbf{B}\in\mathbb{R}^{k\times n}\) denotes the hash codes, where \(k\) is the length of hash codes. \(f(\cdot)\) denotes the hash function. The main notations used in this paper are listed in Table 1.
## 2 Materials and Methods
### Statistical Analysis  
The Kolmogorov-Smirnov test was used to verify the normality of the data distribution. For normally distributed data, the means \(\pm\) standard deviations (SDs) were calculated. For bivariate analyses, the intraclass correlation coefficient (ICC) was used to assess the consistency of the BF% measurements obtained using the CUN-BAE equation and the BIA method.  
It should be noted that once there is a need to compare two methods, the correct statistical approach is not evident. Many studies provide only the Pearson's correlation coefficient between the outcomes of two measurement methods as an indicator of agreement [45]. However, bivariate correlation is not recommended as a statistical analysis approach to assessing the comparability between methods. Within this case, our study proposed replacing one method (BIA) for another (the CUN-BAE); therefore, Bland-Altman graph plots were used to graphically assess the agreement between the BF% calculated using the CUN-BAE equation and the BF% estimated using the BIA method [46; 47]. In the Bland-Altman graph plots, the \(y\)-axis represented the difference between the two approaches (\(\Delta\) BIA \(-\) CUN-BAE equation (BF%)), and the \(x\)-axis displayed the average of the two measurements ([BIA + CUN-BAE]/2 (BF%)). Whilst it was recommended that 95% of the data points should fall within \(\pm\)1.96 SDs of the mean differences, the upper and lower limits of agreement (95% CIs) were estimated.  
As the agreement between the two methods (CUN-BAE and BIA) depended on the total bias, the differential bias (\(\alpha\)), which represented the constant difference between the true trait and the measurement method, was calculated for all cases. Additionally, the proportional biases (\(\beta\)) were calculated using a linear regression analysis to confirm or deny the trends in the Bland-Altman graph plots which displayed the athlete subsamples stratified by sex, age, and sport discipline. In cases where \(\alpha\) = 0 and \(\beta\
eq\) 1, it was proposed that the measurement method was unbiased.  
The statistical analysis was performed using the Statistical Package for the Social Sciences (IBM SPSS Statistics) version 25.0 for Windows (IBM Corp, Armonk, NY, USA). In all of the tests used for the data analysis, a 2-tailed \(p\)-value \(\leq\) 0.05 was considered statistically significant. Statistical visualization and graphical representation of the data were performed using SPSS software version 25.0.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 4 Hybrid Positioning Systems
### Map Hybrid Systems
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Technologies**} & \multirow{2}{*}{**RSS**} & \multicolumn{2}{c}{**IMU**} & \multirow{2}{*}{**Anchors**} & \multirow{2}{*}{**Area (m\({}^{2}\))**} & \multirow{2}{*}{**Error (m)**} & \multirow{2}{*}{**Cost**} & \multirow{2}{*}{**Scalability**} \\ \cline{5-5} \cline{7-10}  & & & & **Position** & & & & & & \\ \hline [PERSON] et al. [47] & WiFi, MEMS & Fingerprinting & Foot & SHS & 11 & Floor & 1.65 & Medium & Limited by calibration \\ [PERSON] et al. [48] & WSN, MEMS & Propagation model & Hip & SHS & 62 & 1125 & 4 & Low & Yes \\ [PERSON] et al. [49] & WSN, MEMS & Propagation model & Waist & SHS & 9 & 100 & 2.3 & Low & Yes \\ [PERSON] et al. [64] & WSN, MEMS & Propagation model & Waist & SHS & 8 & 155 & 0.9 & Low & Yes \\ [PERSON] et al. [50] & RFID, MEMS & Propagation model & Foot & Stradown & 71 & 2200 & 1.35 & Low & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RSS-IMU hybrid positioning systems.
## 5 Simulation Analysis
### Simulation Results and Analysis of Fusion Algorithm
#### 5.2.2 Simulation under Temporary Static Obstacle Environments  
To test the ability of the hybrid algorithm in this study to deal with emergencies, the following experiments were carried out: the experiment uses 20 \(\times\) 20 raster maps and adds 0, 1, 2, and 3 static obstacles on the global static path planned by the improved A* algorithm. The experimental results are shown in Figure 9. In Figure 9, the red solid line is the actual running track for the AGV, the black dotted line is the global path planned by the improved A* algorithm, \"** indicates the key nodes, and the gray square is the temporary static obstacle added.  
Figure 8: Simulation results for concave obstacles.  
Figure 9: Fusion algorithm path planning in a temporary static obstacle environment.  
As shown in Figure 9, the blue triangle represents the starting point, the blue circle represents the destination point, the red solid line represents the actual travel path, the black dashed line represents the global path, and the asterisk represents local target points. In the absence of temporary static obstacles, the AGV travels along the global static path under the guidance of key nodes through the fusion algorithm and successfully reaches the target point. In the case of temporary static obstacles, the AGV travels along the global static path under the guidance of key nodes through a fusion algorithm. When encountering temporary static obstacles, the improved A* algorithm provides key nodes as local target points for the DWA algorithm, completes obstacle avoidance using the DWA algorithm, and finally reaches the target point smoothly. Therefore, the fusion algorithm not only ensures that the path is globally optimal but also realizes obstacle avoidance efficiently and safely, enabling the AGV to reach the target point smoothly.
## 3 Motivation
### Analysis of Two Classical Schemes
As a result, the reactive scheme may not be suitable for carrier-grade networks that require the failure recovery time to be less than 50 ms.  
No matter whether the proactive scheme or the reactive scheme is adopted, backup paths need to be calculated before or after failures. Generally, we always find out the shortest path and switch all the flows affected by the failure over to the same backup path. However, the available resources of the backup path are limited, such as bandwidth. If the required bandwidth of flows exceeds the threshold of the backup path, it may cause flow congestion or interruption, which finally affects the whole network performance. As a result, it is necessary to propose a failure recovery scheme that can make full use of both proactive and reactive schemes, and avoid the flow overload simultaneously. Figure 1: An example of a proactive scheme.
## 2 Distributed Optical Fibre Sensors
### Raman-Based Distributed Sensing
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Type/Fibre Type** & **Operating Wavelength** & **Spatial Resolution** & **Sensing Range** & **Temperature Resolution** & **Acquisition Time \({}^{\ddagger}\)** \\ \hline \multirow{4}{*}{Short-range/MMF} & 904 nm & 7.5 m & 2 km & 0.4 \({}^{\circ}\)C & 12 s \\  & 975 nm & n/a & 3 km & 2 \({}^{\circ}\)C & 70 s \\  & 860 nm & n/a & 2 km & 1 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 60 s \\ \hline \multirow{4}{*}{Middle-range/MMF} & \multirow{4}{*}{1064 nm} & n/a & 5 km & 2 \({}^{\circ}\)C & 120 s \\  & & n/a & 12 km & 2 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 600 s \\  & & 1.4 m & 5 km & 0.28 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 10 s \\  & & 1.4 m & 8 km & 1 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 10 s \\  & & 1.4 m & 10 km & 2.25 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 10 s \\  & & 0.35 m & 5 km & 0.2 \({}^{\circ}\)C & 180 s \\ \hline \multirow{4}{*}{Long-range/SMF} & \multirow{4}{*}{1550 nm} & 5 m & 15 km & 1 \({}^{\circ}\)C & 600 s \\  & & 10 m & 30 km & 1.5 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 600 s \\  & & 2 m & 30 km & 2.75 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 10 s \\  & & n/a & 10 km & 1.2 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 60 s \\  & & n/a & 32 km & 6.0 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 300 s \\  & & n/a & 16 km & 1.8 \({}^{\circ}\)C (\({}^{\ddagger}\)) & 180 s \\ \hline \multirow{4}{*}{Raman OFDR/MMF} & \multirow{4}{*}{980 nm} & 1 m & 0.80 \({}^{\circ}\)C & 200 s \\  & & 1.5 m & 4 km & 0.29 \({}^{\circ}\)C & 158 s \\  & & 3 m & & 0.88 \({}^{\circ}\)C & 27 s \\  & & 1480 nm & 3 m & 10 km & 3.0 \({}^{\circ}\)C & 60 s \\ \hline Raman OFDR/SMF & \multirow{4}{*}{1550 nm} & 2 m & 30 km & 1.9 \({}^{\circ}\)C & 300 s \\ \cline{1-1}  & & & 4 km & 0.15 \({}^{\circ}\)C & 600 s \\ \cline{1-1}  & & & 8 km & 0.65 \({}^{\circ}\)C & 600 s \\ \hline \hline \end{tabular} \({}^{\ddagger}\) Time to reach the indicated temperature resolution; \({}^{\ddagger}\) Resolution at the maximum specified range.  
\end{table} 
Table 2: Performances of some commercial DTS (sources: producers’ datasheets, technical notes, and scientific papers).
# Copernicus Global Land Cover Layers--Collection 2
## 2 Methodological Overview
### EO Data Pre-Processing  
One main improvement in Collection 2 of the GCLS-LC100 product was the reprocessing of the PROBA-V archive to improve the quality of the EO input data. The complete PROBA-V L1C archive (unprojected, Top-of-Atmosphere data) was translated into the PROBA-V UTM Analysis Ready Data (ARD) archive following the recommendations for ARD datasets by [PERSON] et al. [28]. The geometric processing included the projection into the UTM coordinate system, fully aligned with the Sentinel-2 tiling grid in naming as well as tile dimensions [29]. This reduced distortions in the High North, and will allow continuity of the service beyond the lifetime of the PROBA-V sensor. Through an improved atmospheric correction, the reflectance values were converted to surface reflectance (Top-of-Canopy).  
From the PROBA-V UTM ARD, the 5-daily PROBA-V UTM multi-spectral image data with a Ground Sampling Distance (GSD) of \(\sim\)0.001 degrees (\(\sim\)100 m), is used as primary EO data, while PROBA-V UTM daily multi-spectral image data with a GSD of \(\sim\)0.003 degrees (\(\sim\)300 m) as a secondary source. In addition to the Status Mask (SM) cleaning for each image, a temporal cloud and outlier filter was applied to further clean the data, built on a Fourier transformation [30; 31; 32]. As a final step, median compositing was carried out in order to archive regular 5-daily time steps in the 100 m and 300 m PROBA-V EO time series, upgrading the PROBA-V UTM ARD archive into the PROBA-V UTM ARD+ archive. From this cleaned and outlier screened data, a Data Density Indicator (DDI) was calculated, which was used as an input quality indicator in the supervised learning process.  
As a final step in the input EO data preparation, to improve the data density in the 5-daily 100 m time series, the 100 m and 300 m EO datasets were fused using a Kalman filtering approach,  
Figure 1: Workflow overview for generation of Copernicus Global Land Cover Layers. Adapted from the Algorithm Theoretical Basis Document (ATBD) [25].  
based on [PERSON] et al. [33] and tested by [PERSON] et al. [34], but further adapted to heterogonous surfaces. This upgraded the PROBA-V UTM ARD+ archive into the PROBA-V UTM ARD++ archive, containing temporally cleaned, consistent and dense 5-daily image stacks for all global land masses at 100 m resolution.
## 6 Conclusions  
This paper has presented TSCH and D7 AM, the latest generation of low-power wireless technologies suited for distributed sensing applications. Taking into account its characteristics, TSCH is more suitable for periodic sensing applications, where nodes are static and a dense deployment can be ensured. Contrarily, D7 AM is more suitable for _ad hoc_ sensing applications, where nodes are either mobile or static and need to upload small bits of information. The paper has focused on providing a deep overview of the protocols and deriving the implementation details to facilitate its adoption for industrial implementers. The article initially claimed that industries are not still shifting to these new standards because they are not well understood and the costs of their adoption are still high, due to the lack of open-source initiatives and knowledge that can be transferred. However, this situation is in the process of changing, thanks to projects, such as OpenWSN and OSS-7, which provide a reference implementation of such standards, as well as extensive documentation.
## 4 Results and Discussion
### Ablation Study on Different Loss Functions  
The ablation study on different loss functions aimed to verify the advantages of the proposed sliding loss function in time-series and spatial data tasks. A comprehensive evaluation was conducted by comparing the sliding loss function with cross-entropy loss and focal loss using four metrics: precision, recall, accuracy, and F1-score. The experimental results demonstrated that the sliding loss function outperformed the other two loss functions across all the metrics. In the time-series task, the proposed method achieved precision, recall, accuracy, and F1-score values of 0.95, 0.91, 0.93, and 0.93, respectively. For the spatial data task, these metrics were 0.93, 0.90, 0.92, and 0.91, respectively. While focal loss showed improvements over cross-entropy loss in certain scenarios, it fell short in recall and F1-score compared to the sliding loss function. Cross-entropy loss exhibited the weakest performance in both tasks, indicating its limited suitability for multimodal tasks.  
As shown in Table 6, from a theoretical perspective, the differences in performance among the loss functions can be attributed to their design logic and capabilities in modeling error distributions. However, cross-entropy loss is insensitive to long-tail distributions, resulting in subpar performance in recall and F1-score, particularly in tasks involving significant dynamic characteristics, such as time-series data. Focal loss enhances the model's learning capability for minority classes, thereby outperforming cross-entropy loss in multimodal tasks. However, the limitation of focal loss lies in its insufficient sensitivity to local dynamic characteristics, making it less adaptable to fine-grained feature changes in multimodal data. This localized computation enhances the model's sensitivity to data's dynamic variations while avoiding the neglect of local patterns by global optimization objectives. Furthermore, the integration of the sliding loss function with the sliding-window computation network reinforces joint modeling of local features and global patterns, resulting in superior performance in multimodal tasks. The mathematical advantage of the sliding loss function lies in its ability to balance global optimization with localized sensitivity, thereby improving robustness when handling complex dynamic data. This capability enables higher prediction accuracy and generalization in multimodal scenarios. The experimental results confirm the theoretical strengths and practical effectiveness of the sliding loss function, offering a more efficient and precise solution for multimodal data processing.
## 4 Performance Evaluation
### Contact Time  
The contact time distribution between nodes indicates how much data can be transmitted and received. In the simulation, the percent of contact time shorter than 10 s is 12.2% and the percent of contact time shorter than 5 s is 5.5%, hence most of contact time is longer than 5 s as Figure 5 shows. Assuming the WiFi bandwidth to be 10 MB/s, if nodes transmit a video no larger than 55 M, the probability of transferring the file successfully among all contacts is more than 94.5%. Let P (contact time \(\geq\) X) be the probability of contact time longer than X s.