esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experiment Setup  
To detect stress in individuals, the PPG and GSR signals were recorded. PPG is a non-invasive technique that uses a light source and a photodetector placed on the skin's surface to measure changes in blood volume. A typical PPG device consists of a light source that emits light into the tissue and a photodetector that captures the reflected light [9]. The intensity of the reflected light correlates with fluctuations in blood volume. PPG is commonly employed to estimate beat-to-beat intervals and is widely used for monitoring cardiac arrhythmias, stress, or sleep quality through HRV analysis [26; 27].  
The GSR, or EDA, refers to changes in the electrical conductance and resistance of the skin resulting from sweat gland activity and has been widely utilized for stress and emotional state recognition [28; 29]. This concept is based on the premise that skin resistance fluctuates in response to the activity of the sweat glands, which, in turn, is modulated by the sympathetic branch of the autonomic nervous system. Consequently, changes in skin resistance (or skin conductance) are considered a reliable biomarker for stress conditions.  
Measurements were performed using Shimmer 3 GSR+ sensors (Shimmer Sensing, Dublin, Ireland) [30; 31]. The Shimmer 3 GSR+ is a compact, lightweight sensor that can be affixed to various body locations to measure the GSR. The primary function of the GSR+ unit is to assess the galvanic skin response between two reusable electrodes attached to the fingers of one hand. Additionally, the unit features an extra port for connecting an optical pulse sensor, enabling the measurement of PPG signals from a finger or earlobe.
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 4 Performance Evaluation
### Results  
#### 4.2.1 Impact of the Cache Size  
Figure 7 shows the performance metrics when varying the cache size of the edge domain from 0.25% to 2% of the content catalogue size. As expected, it can be observed that, as the cache size increases, performance improves for all the considered schemes. Indeed, the higher the storage space at the edge, the higher is the number of contents that can be stored, with consequent advantages in terms of reduced retrieval delay, number of hops, and exchanged traffic.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Content catalog size & 15,000 contents \\ \hline Content size & 1000 Data packets \\ \hline Data packet size & 1000 bytes \\ \hline Content Popularity & Zipf-distributed with \(\alpha=1\) \\ \hline Scenario & GT-ITM [17] \\ \hline Edge link latency & Uniformly distributed in [2; 5] ms \\ \hline Number of consumers & 20–70 \\ \hline Number of edge nodes & 28 \\ \hline Caching capacity & From 0.25\% to 2\% of the catalogue size \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main simulation settings.  
Being oblivious of content popularity and topology information, the simplest CEE solution shows the worst performance in terms of content retrieval delay, due to higher number of hops traversed to reach the requested content. It generates the highest load of exchanged NDN packets. The Prob scheme slightly outperforms CEE, because it does not cache indiscriminately contents but it tries to better distribute them in the CS of edge nodes.  
As the cache size of the edge domain increases, differences among the two decrease, because there is higher chance to find storage space for caching contents within nodes.  
Figure 7: Metrics when varying the cache size of the edge domain from 0.25% to 2% of the content catalogue size (number of consumers equal to 60).  
PaCC outperforms all the other schemes in terms of all the considered metrics. However, the gap with BEP reduces as the edge domain is more capable to cache contents.  
To conclude the analysis, Figure 8 shows the percentage of Interest packets that reach the origin server because it has not been found a CS matching at the edge. Reasonably, the traffic to the cloud reduces when the storage space at the edge increases, with PaCC outperforming all the benchmark schemes.
## 2 The Algorithm
### Sparse Seed Point Extraction
This is because higher-level images have lower resolutions, so the threshold values become gradually stricter. Once a feature point is successfully matched, it is marked in the reference and auxiliary images to avoid repeated matching. Finally, the normal of sparse seed points is estimated by a so-called conditional-double-surface-fitting method. This relies on the fact that the local object surface around a 3D seed point can be represented by a quadratic surface fitted over its neighboring seed points. Therefore, the normal of a 3D seed point can be estimated from the normal of the fitted surface. This works well on the condition that the surrounding seed points are sufficiently dense, and the 3D seed point is not far from the center of the surrounding points. Details of the conditional-double-surface-fitting method will be discussed a little later in Section 2.3. If the above conditions are not satisfied for a 3D seed point, its normal will simply be set as the line from itself through the optical center of the reference image.
# Incrementally Mining Column Constant Biclusters with FVSP Tree
## 3 Experiments
### Experimental Results
#### 3.2.5 Testing on the Semeion Dataset  
Experiment results for the incremental method and batch method on the semeion dataset are shown in Table 9. The incremental method costs much less in terms of runtime and memory than the batch method on all minimal support rates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Minimal Support Rate**} & \multirow{2}{*}{**Methods**} & **Runtime (s)** & **Memory (MB)** \\  & & **(Mean \(\pm\) std)** & **(Mean \(\pm\) std)** \\ \hline \multirow{2}{*}{0.01} & Incremental & **117.56 \(\pm\) 4.99** & **89.22 \(\pm\) 0.08** \\ \cline{2-4}  & Batch & 150.6 \(\pm\) 1.51 & 144.06 \(\pm\) 1.36 \\ \hline \multirow{2}{*}{0.008} & Incremental & **114.26 \(\pm\) 5.16** & **97.6 \(\pm\) 6.17** \\ \cline{2-4}  & Batch & 159 \(\pm\) 2.34 & 145.44 \(\pm\) 2.80 \\ \hline \multirow{2}{*}{0.006} & Incremental & **119.43 \(\pm\) 8.52** & **79.4 \(\pm\) 0.73** \\ \cline{2-4}  & Batch & 176.2 \(\pm\) 10.80 & 147.76 \(\pm\) 3.15 \\ \hline \multirow{2}{*}{0.004} & Incremental & **122.09 \(\pm\) 9.43** & **80.18 \(\pm\) 0.38** \\ \cline{2-4}  & Batch & 171.8 \(\pm\) 9.49 & 147.28 \(\pm\) 2.43 \\ \hline \multirow{2}{*}{0.002} & Incremental & **115.65 \(\pm\) 6.90** & **88.68 \(\pm\) 4.74** \\ \cline{2-4}  & Batch & 171.4 \(\pm\) 5.77 & 149.44 \(\pm\) 4.80 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results on the mushroom dataset. (bold means better result)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Minimal Support Rate**} & \multirow{2}{*}{**Methods**} & **Runtime (s)** & **Memory (MB)** \\  & & **(Mean \(\pm\) std)** & **(Mean \(\pm\) std)** \\ \hline \multirow{2}{*}{0.01} & Incremental & **26.16 \(\pm\) 0.98** & **83.12 \(\pm\) 0.78** \\ \cline{2-4}  & Batch & 48.22 \(\pm\) 109 & 177.17 \(\pm\) 0.52 \\ \hline \multirow{2}{*}{0.008} & Incremental & **26.99 \(\pm\) 0.42** & **83.67 \(\pm\) 0.23** \\ \cline{2-4}  & Batch & 48.90 \(\pm\) 0.89 & 177.86 \(\pm\) 0.41 \\ \hline \multirow{2}{*}{0.006} & Incremental & **27.39 \(\pm\) 1.60** & **84.14 \(\pm\) 0.11** \\ \cline{2-4}  & Batch & 49.24 \(\pm\) 1.45 & 178.66 \(\pm\) 0.41 \\ \hline \multirow{2}{*}{0.004} & Incremental & **27.84 \(\pm\) 0.12** & **84.64 \(\pm\) 0.34** \\ \cline{2-4}  & Batch & 49.91 \(\pm\) 0.25 & 178.95 \(\pm\) 0.21 \\ \hline \multirow{2}{*}{0.002} & Incremental & **38.05 \(\pm\) 1.02** & **87.12 \(\pm\) 0.26** \\ \cline{2-4}  & Batch & 60.86 \(\pm\) 0.32 & 180.48 \(\pm\) 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experiment results on the Spambase dataset. (bold means better result)
## 7 Conclusions  
A platform for dealing with the DOA estimation of weak nonstationary polarization signals via utilizing T-F cross terms when both strong and weak signals coexist was proposed. This platform, termed SPTFDs based on cross terms, uses the spatial, polarization and T-F characteristics of the signals incident on the dual-polarized antenna array. The signal source separation is based on their DOA, their polarization and T-F peculiarities. The application of TFDs indicates the signal source time-varying frequency characteristics. It considers cross terms' T-F points with high energy concentrations when the weak non-stationary signal is submerged in noise. The eigendecomposition of the SPTFDs matrix from the T-F characteristics of the incident signals is utilized for defining the algorithm, which is polarization DOA estimation using dual algorithms based on T-F cross terms. The simulation results show that this algorithm is superior to the other existing DOA estimation algorithms for weak nonstationary signals when strong and weak nonstationary signals exist simultaneously.  
Conceptualization, S.S. and A.L.; methodology, S.S.; software, S.S.; validation, S.S., A.L. and C.Y.; formal analysis, S.S.; investigation, S.S., A.L. and X.W.; resources, X.W. and C.Y.; data curation, S.S. and X.W.; writing--original draft preparation, S.S.; writing--review and editing, S.S. and X.W.; visualization, S.S.; supervision, X.W. and C.Y.; project administration, A.L. and C.Y.; funding acquisition, A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province (No. ZR2020 MF013) and the National Natural Science Foundation of China (No. 62031015, No. 61971159).  
Not applicable.  
Data Availability Statement: Data are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Ssdd  
The SSDD made the first ship-specific dataset in SAR images available to the public. Additionally, labels and bounding boxes are designed following the Pattern Analysis, Statistical Modeling, and Computational Learning Visual Object Classes (POACAL VOL)standard. The information that can be seen in Table 2 reveals that SSDD has a total of 1160 images. Out of these images, a total of 947 were offshore and 213 were inshore images. The majority of the images were taken through sensors. The study used the COCO dataset for the definition of target size and counted the number of images. The number of images whose target size area is >64 \(\times\) 64 is the least inshore or offshore in Table 2. If the pre-attention stage is divided into A and B subdatasets, the threshold is set to be >64 \(\times\) 64, and the help for the improvement of model accuracy will be small. SSDD dataset details are in Table 3. Our conjecture has also been verified in Section 4.3.1. In the experimental section of Section 4, the ratio of the training set is 70%. The ratio of the validation set is 20%, and the test set is 10% [31].
## 4 Experiments and Analysis
### Quantitative Comparisons
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**T90 [5]**} & \multicolumn{4}{c}{**C60 [5]**} & \multicolumn{4}{c}{**U45 [48]**} \\ \cline{2-10}
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MSE\(\downarrow\)** & **UCIQE\(\uparrow\)** & **UIQM\(\uparrow\)** & **UCIQE\(\uparrow\)** & **UIQM\(\uparrow\)** & **UCIQE\(\uparrow\)** & **UIQM\(\uparrow\)** \\ \hline UDCP(ICCVW’13) [9] & 13.415 & 0.749 & 0.228 & 0.572 & 2.755 & 0.560 & 1.859 & 0.574 & 2.275 \\ IBLA(TIP’17) [49] & 18.054 & 0.808 & 0.142 & 0.582 & 2.557 & **0.584** & 1.662 & 0.565 & 2.387 \\ WaterNet(TIP’19) [5] & 16.305 & 0.797 & 0.161 & 0.564 & 2.916 & 0.550 & 2.113 & 0.576 & 2.957 \\ UWCNN(PR’20) [22] & 17.949 & 0.847 & 0.221 & 0.517 & 3.011 & 0.492 & 2.222 & 0.527 & 3.063 \\ FUNIE-GAN(RAL’20) [43] & 17.114 & 0.701 & 0.216 & 0.564 & **3.092** & 0.556 & 2.867 & 0.545 & 2.495 \\ PRW-Net(ICCVW’21) [15] & 20.787 & 0.823 & 0.099 & **0.603** & 3.062 & 0.572 & **2.717** & **0.621** & 3.026 \\ Shallownet(AAAI’21) [38] & 18.278 & 0.855 & 0.131 & 0.544 & 2.942 & 0.521 & 2.212 & 0.545 & 3.109 \\ Ucolor(TP’21) [14] & 21.093 & 0.872 & 0.096 & 0.555 & 3.049 & 0.530 & 2.167 & 0.554 & 3.148 \\ UIEC’2-Net(SPIC’21) [50] & 22.958 & 0.907 & 0.078 & 0.599 & 2.999 & 0.580 & 2.228 & 0.604 & 3.125 \\ MLLE(TP’22) [23] & 19.561 & 0.845 & 0.115 & 0.592 & 2.624 & 0.581 & 1.977 & 0.597 & 2.454 \\ PUIE-Net(ECCV’22) [16] & 21.382 & 0.882 & 0.093 & 0.566 & 3.021 & 0.543 & 2.155 & 0.563 & 3.192 \\ UHD-SFNet(ACCV’22) [18] & 18.877 & 0.810 & 0.144 & 0.559 & 2.551 & 0.528 & 1.741 & 0.585 & 2.826 \\ NU2 Net(AAAI’23,Oral) [17] & 22.419 & 0.923 & 0.086 & 0.587 & 2.936 & 0.555 & 2.222 & 0.593 & **3.185** \\ MFEF(EAAI’23) [19] & **23.352** & 0.910 & **0.076** & 0.602 & 1.333 & - & - & - & - \\ HAAM-GAN(EAAI’23) [40] & 22.947 & 0.889 & 0.081 & 0.602 & 3.164 & 0.569 & 2.867 & 0.606 & 3.161 \\ \hline Ours & 23.905 & **0.915** & 0.071 & 0.620 & 2.833 & 0.585 & 2.242 & 0.625 & 3.142 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results on the T90 [5], C60 [5], and U45 [48] datasets. The training set of UIEB was used for training. The best results are underlined. The second-best results are in **bold**. \(\uparrow\) represents that higher is better, and \(\downarrow\) represents that lower is better. Additionally, for the EUVP dataset, we employed the PSNR, SSIM, UCIQE, and UIQM as quantitative metrics to assess the recovery outcomes for more-extensive research. Notably, the results in Table 3 produced by LPS-Net demonstrate excellent performance in terms of the PSNR, SSIM, and UICIQE, yielding the highest scores. This observation highlights the robustness and generalizability of LPS-Net for underwater image enhancement.
## 3 Results of the Design Verification of the Proposed Solutions
### Labyrinth Seal Check Results  
The results of checking the tooling labyrinth seal at spindle speeds of 0 to 2000 rpm and coolant supply volumes of 1.0 to 10.0 L/min (with the coolant pressure in kPa corresponding to each volume value) are shown in Figure 15.  
During the check, three conditions of the tooling were detected when the spindle speed and coolant volume were changed. In condition (a), the labyrinth seal did not perform its function, and at the same time, no coolant flowed through the center hole of the shank. In condition (b), the seal still did not perform its function, but coolant flowed through the center hole. In condition (c), the seal was functioning correctly and there was coolant flow through the shank. Conditions (a), (b), and (c) did not overlap and appeared sequentially as the spindle speed increased. Increasing the volume of supplied coolant shifted the transition boundaries from one state to another. At a low coolant supply volume (1.0 L/min/7 kPa), the tooling stayed in condition (c) (correct operation) until the spindle speed reached 1400 rpm. With a further increase in speed and constant pressure, the tooling moved to condition (a). The reason for the transition was the increasing resistance for the coolant to flow from the static tooling piece into the 6.0 mm diameter channels of the rotating shank. If the coolant volume and pressure were insufficient, this led to the coolant being squeezed outward through the labyrinth seal. As the volume of supplied coolant increased, condition (b) appeared between conditions (a) and (c), at which point the created pressure was sufficient to deliver the coolant into the rotating shank, but it exceeded the ability of the labyrinth  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} \(s_{z}\), \\ **mm/Tooth** \\ \end{tabular}
## 4 Discussion and Results  
### Regional Distribution of Radioactive Elements  
During the formation of metamorphic rocks, variations in environmental conditions, such as pressure, temperature, and fluid activity, can lead to changes in the concentrations of naturally occurring radioactive elements. These variations are closely linked to the composition of the original source rock that formed the mgmatite, as well as the specific metamorphic processes and stages it underwent. As a result, the diversity and abundance of radioelements can vary considerably, providing valuable geochemical indicators of a region's geothermal energy potential. Migmatites, in particular, typically form near the base of the crust through partial melting, a process that facilitates the mobilization and redistribution of minerals, including radioactive elements, within the rock.  
In the study region, spatial variations in the concentrations of K (wt.%), Th (ppm), and U (ppm) indicate that areas with elevated K and Th levels in mgmatitic rocks are closely associated with zones of known geothermal activity. K concentrations range from 0.002 to 8.164 wt.%, Th values from 1 to 65 ppm, and U concentrations from 0.50 to 15 ppm (Figure 3). These variations were observed in mgmatite samples collected in the vicinity of geothermal wells drilled by the General Directorate of Mineral Research and Exploration in the Simav region. The observed spatial association between high radioelement concentrations in mgmatitic praisses and geothermal activity suggests a strong geothermal energy potential in the area. These findings support the use of radioelement geochemistry as a valuable tool in geothermal exploration, particularly in regions where migmatitic and other high-grade metamorphic rocks are present.
## 2 Materials and Methods
### Outcomes  
Preoperative pulpal and periapical diagnoses were established through clinical examination, including cold sensitivity testing, palpation, percussion, and periapical radiographs. Patients completed a preoperative questionnaire assessing pain characteristics, including a visual analog scale (VAS) score (0-10). Cavity size was categorized as deep caries, extremely deep caries, or no caries based on the periapical radiograph. \"No caries\" referred to restored teeth without detectable secondary caries. \"Deep caries\" involved lesions reaching the inner quarter of dentin, approximately 0.5-1.0 mm from the pulp, with a residual layer of hard dentin. \"Extremely deep caries\" extended through the full dentin thickness, with inevitable pulp exposure [20].  
At the 6-month recall appointment, a clinical examination was conducted, which included periodontal probing, percussion, palpation, and sensibility testing with cold spray. Additionally, a radiograph was taken to evaluate the status of the apical tissues and the continuity of the periodontal ligament space.  
Failure was indicated by the presence of a sinus tract, periapical radiolucency, pain following percussion tests, clinical or radiographic signs and symptoms of root resorption or apical periodontitis, radiological widening of periodontal ligament, or radiographic appearance of bone disturbance or loss.  
A favorable outcome was indicated by no pain or other subjective symptoms, radiological evidence of dentine bridge formation, the absence of clinical and radiographic signs of internal root resorption, and a lack of periapical radiolucency or widening of the periodontal ligament apically, as well as no pain following vertical or horizontal percussion.  
The primary endpoint of this study was to evaluate the success rate of preoperative pulp diagnosis (i.e., severity of clinical symptoms). Two types of success rates were defined based on the cold pulp test, clinical findings, and radiographic evaluation. The multinomial outcome categorized cases into three groups: (1) _success_, defined as a positive response to the cold test and the absence of any clinical or radiographic signs of pulp or periapical pathology; (2) _uncertain_, defined as no response to the cold test, without clinical or radiographic signs of necrosis or infection; and (3) _failure_, defined as no response to the cold test in combination with clinical or radiographic evidence of infection or pulp necrosis.  
The bivariate outcome used a stricter definition of success by classifying uncertain cases as failures.  
The secondary endpoint assessed the association between material, age, sex, and VPT modalities. All of the data were collected in the REDCap system (Research Electronic Data Capture-Vanderbilt, [[https://redcap.onkobank.com/](https://redcap.onkobank.com/)]([https://redcap.onkobank.com/](https://redcap.onkobank.com/)), accessed on 16 March 2025).
## 4 Results
### Cloud Mask
As earlier defined (Section 3.2) for POD, FAR, and HR, the categories of \"probably cloudy\" and \"probably clear\" scenes were not included in the statistics, while for the fractional agreement both in \"cloudy\" condition and in \"clear\" condition, the four scenes were all considered in the statistics. Therefore, the POD value was 0.973 for cloudy-sky conditions and 0.868 for clear-sky conditions, which was higher than the fractional agreement values (0.932 and 0.734). It should be noted that although the POD scores for cloudy conditions were higher than those for clear conditions, the FAR score was 0.093 for the cloudy condition, which was higher than that for clear-sky scene (0.04). In other words, there were more collocated samples that were misjudged as cloudy than those misjudged as clear. This finding is contrary to the result of the full-disk comparison [31]. In the full-disk comparison, FAR scores were higher for clear-sky scenes than for cloudy-sky scenes. The results of the three months were similar. The fractional agreement and POD values were both above 0.9 under cloudy-sky conditions, while they were above 0.7 and 0.8, respectively, under clear-sky conditions. The HR scores of each month were higher than 0.9, indicating a high consistency of cloud mask between AGRI and MODIS. In general, the AGRI cloud mask compared more favorably with MODIS cloudy scenes than for clear scenes. This result is consistent with the result of the full-disk comparison [31]. It can be inferred that AGRI cloud mask may be designed to be clear-sky conservative; that is, if there is uncertainty in the spectral tests, the AGRI cloud mask may tend to label the  
Figure 2: The fraction of AGRI cloud mask results (i.e., cloudy, probably cloudy, probably clear, or clear) for each category of MODIS cloud mask results of matched samples. The color bar represents the different classifications of cloud mask: red, yellow, green and blue represent “cloud”, “probably cloud”, “probably clear”, and “clear”, respectively. scene as cloudy. In addition, the AGRI cloud mask retrieval requires clear contrast between clear-sky and cloudy-sky conditions, which is dependent on both surface and atmospheric properties. The TP is associated with cold surfaces, causing low contrast between clear-sky and cloudy-sky scenes. Therefore, it is difficult to assign the pixels \"clear\" or \"cloudy\" in the algorithm confidence.