esa-sceva/satcom-chunk-collection huggingface dataset:
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method
### Feature Extraction  
In the feature extraction sub-network, we embed a spatial scale attention module based on the Swin Transformer. While extracting the global features of the target object, this module helps capture detailed local features, satisfying the tracker's requirement to learn global contextual features and enhancing the perception of local features.  
Patch Merging plays a role in pooling and downsampling by dividing and laying the feature map. However, it only utilizes low-level information of the image, limiting the further expression of target object features. As shown in Figure 3, the spatial scale attention module is constructed based on the original Patch Merging.  
Figure 2: Architecture of SSTrack.  
We introduce a layer of covariance pooling before Patch Merging. Compared to regular pooling, covariance pooling considers the covariance relationships between features, selects a value that represents the distribution of a feature map by calculating the covariance matrix of the feature map, which is the second-order information, enhancing the expressive capability of spatial information [31]. This approach better captures the correlations within input features. Dilated convolution [32] increases the receptive field by introducing dilation in the convolutional kernel, aiding the network in more effectively capturing a broader range of contextual information. Inspired by the dilated pyramid in image segmentation, [33] three dilated convolution branches with 1, 2, and 4 dilation rates are incorporated, forming a hierarchical Transformer. This design achieves the utilization of multiscale information within a single hierarchy. Finally, to fuse the dilated features with global spatial features, the outputs of each branch are concatenated and dimensionally reduced. This module outputs a feature sequence with the exact dimensions as the input, providing a feature sequence containing more spatial scale information for subsequent structures.  
The input image (H \(\times\) W \(\times\) 3) is divided into a sequence of patch tokens through the Patch Partition, undergoes a linear embedding, and enters three consecutive stages. In each stage, the Swin Transformer Block first extracts global features from the feature sequence of the current hierarchy. Subsequently, the spatial scale attention module is applied to extract and fuse local detailed information, and the result is fed into the next stage for further processing. The output of the third stage serves as the final output of the feature extraction sub-network.
## 3 Results
### Magnetic Properties  
One of the key parameters describing the properties of the magnetic core are its no-load losses; hence, this parameter was investigated for the tested cores and the measurement results are shown in Figure 4 for the excitation power supply with a frequency of 50 Hz and 60 Hz.  
The alternating magnetic flux flowing through the core induces voltage and current not only in the secondary winding but also in the core itself, resulting in an eddy current. The eddy current generates a magnetic flux that interrupts the magnetic flux in the core and it can be generated not only within individual lamination (ribbon), but also in the core stack as a so-called interlaminar eddy current [8]. Therefore, the use of the developed bonding material with good dielectric properties can lead to an effective reduction in eddy current losses.  
Figure 4: Core losses as a function of operating flux density for toroidal cores: reference and bonded with silane-based sols. Measurement performed at a frequency of (**a**) 50 Hz and (**b**) 60 Hz.  
The measurement results indicate that, in the case of a magnetic core bonded with silane sols, the core loss values are significantly lower than for the reference core. At the saturation level (around 1.5 T), the recorded losses are 23% lower at 50 Hz, while they are 19% lower at 60 Hz. It can, therefore, be concluded that the application of a sol-based binder on the surface of an amorphous steel ribbon enables the formation of a well-defined dielectric layer that not only acts as a bonding material but also efficiently reduces core losses.
# Secure ECDSA SRAM-PUF Based on Universal Single/Double Scalar Multiplication Architecture
### Related Works  
As interest in PUFs grows, more and more potential attacks against PUFs have been proposed. Protocol attacks on PUFs are outlined in [6]. Additionally, refs. [7; 8] detail various protocol attack strategies, including accessing the PUF temporarily, reusing previous PUF sessions, establishing stealth channels for malicious activities, and exploiting error correction schemes for information leakage. Furthermore, refs. [9; 10] suggest a silicon attack method involving invasive techniques to manipulate or explore all possible PUF values or alter chip PUF values. Despite SRAM-PUF's ability to resist some attacks, ensuring absolute security solely through initial values remains a challenge. For instance, in practical settings, the CPU's connection to SRAM enables access to SRAM data through software programs, potentially exposing its initial value, as mentioned in [11]. Even if SRAM is not directly linked to the CPU, invasive methods like scanning electron microscopes or thermal laser stimulation can also be deployed to measure initial values [12].  
Due to the risks associated with initial SRAM value leakage, this paper proposes a modified SRAM-PUF algorithm to address security concerns and enhance SRAM-PUF technology with the help of elliptic curve cryptography (ECC). The elliptic curve digital signature algorithm (ECDSA) can ensure confidentiality and is applicable for maintaining data integrity and authenticity [13]. Therefore, the initial SRAM value leakage problem can be solved. In ECDSA, curves are defined over prime fields and binary fields [14]. Binary fields offer advantages in modular operations due to their carry-free property, making ECC over binary fields more suitable for hardware implementations, as shown in Appendix B. Elliptic curve scalar multiplication (ECSM) and elliptic curve double scalar multiplication (ECDSM) significantly influence ECDSA performance. [PERSON] [15] introduced a speed-oriented ECSM architecture over \(GF(2^{m})\) with dual multipliers operating in parallel. Ref. [16] presents a throughput/area-efficient ECSM architecture utilizing a novel segmented digital serial multiplier for acceleration. [PERSON] [17] proposed a high-speed ECSM architecture employing a single multiplier and a low-latency ECSM architecture using three multipliers. These architectures modify the Lopez-Dahab Montgomery ECSM algorithm to manage data dependencies effectively. The authors of [18] introduce a flexible asymmetric crypto ECSM processor capable of handling ECSM over standard binary curves and binary huff curves. Additionally, [PERSON] [19] presents a compact ECSM architecture tailored for small embedded applications that utilizes a ROM-based state machine to maximize hardware resource utilization.
## 5 Result and Discussions
### EEMS Calibrations  
The input data received from the sensors is forwarded to the system and results are shown at an Android application. This system is fully automatic. The four types of sensor data are received from three sensors and the calibrated output is processed using Neural Network Model with the help of classification tables shown below. Table 5 shows three classes (low, normal, high) for various levels of air temperature detected by the DHT22 temperature and Humidity sensor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature/\({}^{\circ}\)C** & **Class** & **Weather** \\ \hline
16 \({}^{\circ}\)C–18 \({}^{\circ}\)C & Normal & Winter \\ \(<\)16 \({}^{\circ}\)C & Low & Winter \\
20 to 23.5 \({}^{\circ}\)C & Normal & Summer \\ \(>\)23.5 \({}^{\circ}\)C & High & Summer \\ \hline \hline \end{tabular}
\end{table}
Table 5: Temperature Levels.  
Figure 17: Stimulation Results, (**a**) case 1 and 2, (**b**) case 3.  
Table 6 shows three classes (dry, normal, wet) for various levels of air humidity level detected by the DHT22 temperature and Humidity sensor.  
Table 7 shows three classes (normal, alcohol, lighter) for various levels of Smoke detected by the MQ2 gas sensor.  
Table 8 shows three classes (very good, moderate, fair, poor) for various levels of air pollution detected by the PMS5003 dust sensor.  
Table 9 shows proposed NN output result in two major classes i.e., continuous or impaired, based on calibrations range of three attached sensors in proposed EEMS, if obtained sensors reading are in normal range, NN predictions will be positive, and showed output continuous as favorable exterior environment may lead continuous wound healing but if obtained calibrated data ranges are not in normal ranges then output will negative which suggest that wound healing may impaired due to unfavorable exterior environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Humidity (M\({}^{3}\)-M\({}^{-3}\))**} & **Class** \\ \hline
0–20\% & Dry \\
20–60\% & Normal \\
60–100\% & Wet \\ \hline \hline \end{tabular}
\end{table}
Table 6: Humidity Levels.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Air Quality ppm**} & **Class** \\ \hline
100–150 & Normal \\
700 & Alcohol \\
750 & Lighter \\ \hline \hline \end{tabular}
\end{table}
Table 7: Smoke Levels.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**PM2.5 Particles MG/M\({}^{3}\)**} & **Class** \\ \hline
0–8.9 & Very Good \\
9.0–25.9 & Moderate \\
26.0–39.9 & Fair \\
40.0–106.9 & Poor \\ \hline \hline \end{tabular}
\end{table}
Table 8: Air Pollutant Levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Sr. No**} & \multicolumn{1}{c}{**Temp/\({}^{\circ}\)C**} & \multicolumn{1}{c}{**Humidity/\%**} & \multicolumn{1}{c}{**Air**} & \multicolumn{1}{c}{**Dust**} & \multirow{2}{*}{**NN/Decision**} \\ \cline{1-1} \cl
## 3 Methods
### The NeuroPredict Platform
#### 3.1.4 Medical Monitoring and Evaluation Functionalities in the NeuroPredict Platform  
The NeuroPredict platform aims to monitor and manage patients suffering from one of the following three neurodegenerative conditions: AD, PD, and MS. Each of the 3 targeted conditions has its own specific symptomatology, necessitating customized monitoring for effective management of the disease, including symptoms tracking, evaluation of disease progression or effectiveness of treatment, and evaluation of general health status in order to provide the best treatment plan for the monitored subject. The monitoring and evaluation functionalities described in Table 2 have been identified as necessary and useful for each of the targeted conditions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Disease** & **Functionality** \\ \hline \multirow{7}{*}{Alzheimer’s disease (AD)} & Daily activities tracking \\ \cline{2-3}  & Location tracking and safety \\ \cline{2-3}  & Cognitive function evaluation \\ \cline{2-3}  & Sleep quality assessment \\ \cline{2-3}  & Behavior and mood monitoring \\ \cline{2-3}  & Electrical brain activity monitoring \\ \cline{2-3}  & Voice processing for speech and language changes \\ \hline \multirow{7}{*}{Parkinson’s disease (PD)} & Abnormal movement monitoring \\ \cline{2-3}  & Posture and balance assessment \\ \cline{2-3}  & Cognitive function evaluation \\ \cline{2-3}  & Electrical brain activity monitoring \\ \cline{2-3}  & Daily activities tracking \\ \cline{2-3}  & Physical activities tracking \\ \cline{2-3}  & Polysomnography \\ \cline{2-3}  & Voice processing for speech changes \\ \cline{2-3}  & Reaction time analysis \\ \hline \multirow{7}{*}{Multiple sclerosis (MS)} & Mobility changes monitoring \\ \cline{2-3}  & Physical activity tracking \\ \cline{2-3}  & Respiratory function evaluation \\ \cline{2-3}  & Muscle function assessment \\ \cline{2-3}  & Bladder function assessment \\ \cline{2-3}  & Cognitive function evaluation \\ \cline{2-3}  & Voice processing for speech clarity \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Medical monitoring and evaluation functionalities in the NeuroPredict platform for each of the three monitored diseases: AD, PD, and MS. These have been further analyzed and grouped into categories depending on the targeted monitored ability or parameter. The obtained groups are presented in Table 3 and described in the following paragraphs.
## 4 Mapping the LBCS into an Ontology
### OWL Class and Individual Names and Automatic Ontology Construction  
Since the original names of LBCS concepts include spaces and other characters that are not compatible with the entity name rules in OWL, it was necessary to convert these names into more suitable ones. Some fixed rules were devised for the conversion of names to ensure consistency across the ontology:  
1. The CamelBack notation, recommended for OWL class names, was applied.
2. Commas were replaced by underscores.
3. Parentheses and everything inside them were removed.
4. A letter and a dot were added before each name, identifying the dimension to which the concept belonged (A. for activity, F. for function, S. for structure, SI. for site and O. for ownership).  
Two examples are given below to illustrate the name conversion process:  
_Regional center (enclosed mall with two or more anchors)_ was converted into _S.RegionalCenter_, as it is a subclass of the Structure (S) dimension.  
_Agriculture, forestry, fishing and hunting_, was converted into _F. Agriculture_Forestry_Fishing And Hunting_.  
Since the LBCS standard has around 1000 concepts and 170 pages of descriptions, it was necessary to develop a mechanism to build the ontology automatically, according to the structure described in the previous sections. This was achieved by parsing the official LBCS standard document [28] with a Java application built solely for this purpose, based on OWL-API 3 [29]. The document pages with the description of the concepts have a very regular structure and the hierarchy level can be inferred from the code numbers, which made this task easier.
## 4 Conclusions  
In order to talk about the mechanical modeling between crystalline and non-crystalline ceramics, an experiment was conducted to alumina ceramics and fused silica ceramics. Through this experiment and analysis, some conclusions have been derived as below.  
(1) The relation between the maximum undeformed chip thickness and grain size of alumina significantly affects the single normal grinding force. If \(H_{\text{max}}\) is less than 3.32 \(\upmu\)m, a larger \(H_{\text{max}}\) leads to a larger normal grinding force per grit. In this condition, the result is well reflected by the predictive grinding force model. When \(H_{\text{max}}\) passes 3.32 \(\upmu\)m, the normal grinding force as a function of \(H_{\text{max}}\) will not follow the predictive grinding model. A parameter \(k=3.32\) as a critical depth is proposed to improve the grinding force model which is close to the grain size. We can also get the conclusion that for a given maximum undeformed chip thickness, the smaller the grain of a wheel, the bigger the single-grit force.  
(2) For un-crystalline ceramics fused silica, the normal grinding force cannot be predicted by previous mechanical models under brittle removal conditions. The grinding force appears to follow the trend of grinding force modeling in ductile conditions.  
(3) The specific energy of alumina increases with decreasing \(H_{\text{max}}\), which is a result of all grinding parameters. The removal mechanism of polycrystalline ceramics is changing from brittle fracture and interganular fracture to ductile removal and transgranular fracture with decreasing max undeformed chip thickness. It is well reflected by the specific energy. The specific grinding energy tends to be flat when \(H_{\text{max}}\) is larger than approximately 4 \(\upmu\)m.  
(4) Compared with alumina ceramics, the specific grinding energy of fused silica fluctuates with increasing \(H_{\text{max}}\) when \(H_{\text{max}}\) ranges from 0.66 \(\upmu\)m to 6 \(\upmu\)m, owing to the porous and non-crystalline microstructure. When \(H_{\text{max}}\) is larger than 6 \(\upmu\)m, brittle fracture becomes the mainly removal method.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 5 Conclusions  
Based on the characteristics of SVD and DCT, a self-recovery fragile watermarking with variable watermark capacity is presented in this paper. The trace of singular value matrix of each block is served as the authentication watermark, and its left and right singular vectors are used to classify the image blocks into smooth blocks and texture blocks. For different blocks, different length recovery watermark is generated. In other words, the watermark capacity is variable. To ensure the security of the algorithm, the watermark is encrypted by binary pseudo-random sequences before embedding. Several experiments are conducted to test the performance of the presented method. The experimental results prove that the presented fragile watermarking scheme has good watermark imperceptibility. Besides, the use of three-level detection strategy improves the tamper detection accuracy greatly. Compared with other two self-recovery watermarking methods, the reconstructed images obtained by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **FPR** & **FNR** & **PSNR (dB) of Recovered Image** \\ \hline [PERSON] et al. [13] & 0 & 1 & 19.48 \\ [PERSON] et al. [25] & 0.44\% & 3.51\% & 36.36 \\ The proposed method & 0.46\% & 3.32\% & 39.53 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparisons for content-only attack using Clock image.  
Figure 13: Large area tampering test: **(a)** Left 50%; **(b)** tamper detection result; **(c)** recovered image (PSNR = 32.05 dB); **(d)** middle 80%; **(e)** tamper detection result; **(f)** recovered image (PSNR = 19.69 dB).  
the presented method have a better image quality. For the future task, we will analyze the selections of different parameters and its specific effects on the performance of the proposed method. Based on the analysis, we will further improve the tamper detection accuracy and recovery ability of the proposed method.  
This work was supported by the National Natural Science Foundation of China (No. 61702303, No. 61201371); the Natural Science Foundation of Shandong Province, China (No. ZR2017 MF020, No. ZR2015 PF004); and the Research Award Fund for Outstanding Young and Middle-Aged Scientists of Shandong Province, China (No. BS2013 DX022).  
[PERSON] and [PERSON] conceived the algorithm and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON] drafted the manuscript; [PERSON], [PERSON], and [PERSON] revised the manuscript. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
## 4 Discussion
In contrast, the auditory distraction process is passive and lacks such interaction between the treatment provider and the patient, which may have contributed to inferior findings when compared to brief relaxation therapy. In an experimental study, musical distraction was not effective in reducing the anxiety level in younger populations [43]. Although inferior to brief relaxation therapy, this study demonstrated that auditory distraction does effectively reduce the anxiety level of patients undergoing tooth extraction. The mechanism of action could be through the diversion of attention from a self-perceived unpleasant dental treatment. It is suggested that music influences the brain, leading to relaxation by distraction, operating across the neuroendocrine and sympathetic nervous systems [44]. The results of the current study should be interpreted in accordance with the weaknesses. Only female participants were included and therefore the findings may not be reproducible in male patients. It has been shown in earlier studies that female patients are more apprehensive of dental extractions than males [45; 46], so the level of change in anxiety before and after the intervention in male patients may not be similar to female patients. However, because the interventions have worked on the more anxious female participants, it is highly plausible that they may also work with the comparatively less anxious male patients. Additionally, having only female patients contributed to the homogeneity of the study sample, thus adding to the robustness of the results. The findings on the anxiety level of the participants also rely on the validity of the Hierarchical Anxiety Questionnaire, and future studies may use other assessment scales to evaluate the results.
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 4 Results
### Precipitation Product Intercomparisons at Daily Scale
#### 4.2.2 Quantitative Intercomparisons at Daily Scale
TAMSAT3 tends to underestimate daily precipitation, both quantitatively and in the identification of the precipitating days, in Rwanda and Burundi and on the Kenyan coasts when compared to the other two products; MSWEP shows a tendency to underestimate precipitation, both quantitatively and in the detection of precipitation days, in almost all of EA with the exception of some limited areas, mostly in the mountainous regions of Ethiopia. In Figure 14, the CC, ME, and MAE spatial patterns for SA are displayed and the corresponding spatially-averaged values are summarized in Table 6.  
On average, CC values are better in SA than in EA, with a slight decay in the case of MSWEP-TAMSAT3 comparison (Table 6). The best CC results are obtained from the CHIRPS-MSWEP intercomparison, as is apparent from Figure 14a and the low standard deviation associated with the mean CC value in Table 6, which indicates a substantially homogeneous CC spatial pattern. A common aspect of the CHIRPS vs. TAMSAT3 and MSWEP vs. TAMSAT3 intercomparisons is the presence of an area characterized by CC values lower than 0.2 covering the Namibian coast and the area of Cape Town, already highlighted as problematic in the dichotomous statistics for TAMSAT3. Unlike EA, the correlation is much more substantial between the CHIRPS and MSWEP datasets in SA. Mean ME values near 0 are accompanied by high standard deviations (Table 6), demonstrating a more marked ME variability over the region, particularly evident for TAMSAT3 (Figure 14b). TAMSAT3 underestimates the daily precipitation on the east coast of Madagascar compared to the other two products, with ME < -2 mm day\({}^{-1}\) and a corresponding MAE \(\geq\) 5 mm day\({}^{-1}\) (Figure 14c) with respect to an average daily precipitation \(\geq\) 6 mm day\({}^{-1}\), as well as on the coasts of Mozambique and in the Cape Town region, where ME \(\cong\) -1 mm day\({}^{-1}\) and MAE > 2 mm day\({}^{-1}\) are found associated with a daily precipitation \(\cong\) 3 mm day\({}^{-1}\). For the remaining parts of SA, the ME values from the CHIRPS vs. TAMSAT3 and MSWEP vs. TAMSAT3 intercomparisons are much lower, in the range of \(\pm\) 0.2 mm day\({}^{-1}\), with MAE up to 3 mm day\({}^{-1}\). As for the CHIRPS vs. MSWEP comparison, the greater differences are limited to Madagascar, where a more pronounced overestimation by MSWEP with respect to CHIRPS is evident, and Mozambique, where CHIRPS overestimates daily precipitation. In any case, the percent MAE values (i.e., the ratio between MAE and mean daily precipitation of the compared product) are always > 50%, due to very low daily totals, especially in Namibia, Angola and RSA (< 2 mm day\({}^{-1}\)). Finally, note the agreement of the maps shown in Figure 14b with the findings of the BIAS index maps in Figure 12c for the dichotomous statistics.