esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Analysis of Skin Temperature Asymmetries in Elite Young Tennis Players
## 3 Results
### Correlation Analysis  
Players completed a visual analogue scale indicating their pre- and post-training fatigue and pain status (Table 1). Significant post-training fatigue increase was found in all regions (\(p\) < 0.05). In contrast, no variation in pain perception was observed. Figure 4: Results of skin temperature comparisons. Differences found between the dominant and non-dominant sides, at the same time, are shown by an asterisk (\({}^{\circ}\)\(p\) < 0.05; \({}^{\star\star}\)\(p\) < 0.01; \({}^{\star\star\star}\)\(p\) < 0.001). On the other hand, # indicates the differences found between the pre and post of each of the regions (# \(p\) < 0.05; # \(p\) < 0.01). Furthermore, Table 2 shows the results of the correlations between thermal symmetry variation and age, racket weight, string tension, years of experience, BMI, general fatigue and pain changes. The most significant results are shown in bold (Table 2), and in Table 3, the mean variations of the thermal symmetries found in each ROI are expressed. The correlations found were:  
* Racket weight and variation in thermal symmetry in the anterior arm (\(p\) = 0.03, r = 0.71) and lower back (\(p\) = 0.04, r = 0.70), both positively and moderately correlated. * BMI correlates negatively with symmetry variation in anterior shoulder temperature (\(p\) = 0.04, r = \(-\)0.68) but positively with posterior knee temperature (\(p\) < 0.01, r = 0.81). The former was a moderate correlation, while the latter was strong. * The variation in overall fatigue correlates positively with the variation in temperature symmetry in the arm (\(p\) < 0.01; r = 0.81) and posterior shoulder (\(p\) < 0.01; r = 0.81) regions. Both correlations were strong. * Overall pain variation and thermal symmetry variation in the posterior knee (\(p\) < 0.01; r = \(-\)0.81) and anterior leg (\(p\) = 0.02; r = 0.75) show a correlation. The first was negative and strong, while the second was positive and moderate. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Pre-Training (SD)** & **Post-Fatigue (SD)** & **Pre vs. Post (\(p\) Value)** \\ \hline \multicolumn{3}{c}{**Fatigue perception**} \\ \hline General & 4.6 (2.1) & 6.7 (3.1) & **0.01** \\ Trunk & 3.3 (2.3) & 4.3 (2.9) & **0.05** \\ Shoulder & 2.7 (2.0) & 4.6 (2.8) & **0.01** \\ Arms & 2.9 (2.1) & 5.4 (3.8) & **0.04** \\ Gluteus & 2.9 (2.6) & 4.0 (3.4) & **0.03** \\ Thigh & 3.3 (2.5) & 5.1 (3.1) & **0.02** \\ Knee & 1.2 (2.1) & 2.0 (2.6) & **0.01** \\ Leg and Feet & 3.2 (2.3) & 6.0 (3.9) & **0.01** \\ \hline \multicolumn{3}{c}{**Pain perception**} \\ \hline General & 2.5 (1.4) & 2.1 (1.3) & 0.39 \\ Trunk & 2.2 (2.0) & 2.1 (1.9) & 0.66 \\ Shoulder & 1.4 (1.8) & 2.6 (3.3) & 0.06 \\ Arms & 1.5 (2.2) & 1.3 (1.7) & 0.44 \\ Gluteus & 1.9 (1.4) & 2.2 (1.2) & 0.19 \\ Thigh & 1.8 (1.7) & 1.4 (1.0) & 0.27 \\ Knee & 1.3 (1.8) & 1.5 (2.2) & 0.25 \\ Leg and Feet & 1.1 (1.0) & 1.5 (1.4) & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pre- and post-training fatigue and pain data. Mean (SD). 0 (no pain/fatigue); 15 (very painful/fatigue).
## 2 Literature Review
### Optical Character Recognition Methods  
Second, we reviewed works on _OCR_ to extract text from images. This involves training deep learning models on a collection of text images with the corresponding reference text labels. Commonly, the performance of _OCR_ models is related to the characteristics of the trained text image [29]. Thus, it is important to select the appropriate _OCR_ model for each use case.  
In [30], [PERSON] et al. proposed an invoices document processing method to digitize physical documents. The methods involve the usage of _computer vision_ and _OCR_ techniques. The data extracted from the physical documents are structured in JSON and CSV formats. From the experimentation, _Tesseract OCR_ demonstrated superior performance compared to other _OCR_ models. However, this approach is limited to only the invoice images, which leads to poor text recognition from images that are different from the trained image.  
In [31], [PERSON] et al. evaluated _EasyOCR_'s performance for extracting textual information within Latin characters under image degradation. This study aimed to highlight the capabilities and limitations of _EasyOCR_. Based on the results, _EasyOCR_ excels at recognizing unique lowercase and uppercase characters including C, S, U, and Z. However, _EasyOCR_'s character detection accuracy decreases, ranging from 30 to 40% when recognizing characters with fonts under size 18.  
In [32], [PERSON] proposed a real-time system for identifying spray mark characters on moving steel slabs in the manufacturing process. The system utilizes high-sensitivity cameras, optical filters, temperature control systems, and the _PaddleOCR_ model. The research addresses challenges related to complex lighting conditions, high temperatures, and fast-moving steel slabs. The implemented system has been operational for a year and has achieved positive detection rates every week. The minimum weekly positive rate is reported to be 91.2%, while the minimum weekly detection rate is 98.7%. However, due to the limitations of the cameras, the conveyor needs to be slowed down in the manufacturing process so as not to blur the images and create recognition errors.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 4 Experimental Results and Analysis
### Ablation Experiment  
To assess the effectiveness of the proposed edge detection model, ablation experiments, comprising four sets of control experiments, were designed for comparison. \"DoU Loss\" denotes training using a joint loss function; \"Attention\" involves the addition of an attention mechanism at crosslayer connections; and \"CGM\" indicates the utilization of the cross guide module. Edge detection was executed on the ice cover dataset, and the experimental results are presented in Table 2.  
Table 2 illustrates that UNet, serving as the baseline, yielded the lowest experimental results. Training with the DoU loss function and the weighted cross-entropy loss function concurrently led to improvements in ODS and OIS by 1.7% and 1.2%, respectively, indicating the network's ability to discern pixels near real edges. Incorporating the attention mechanism based on the UNet network model resulted in 1.5% and 1.1% enhancements in the two evaluation indicators. In the final set of control experiments, the cross-guide module enhanced the ODS performance index by 1.2% and the OIS performance index by 0.8%, affirming the CGM's efficacy in guiding sampling image restoration. The experimental results in Table 2 clearly demonstrate the improvement effect of each component on CG UNet, which strongly proves that our improvement is effective for final edge detection.
## 2 The Challenges of Image Classification  
Classification of remote sensing imagery is usually applied to a large multidimensional dataset that often consists of many cross-correlated measurements in multiple spectral bands acquired at different times. Furthermore, the neighboring pixels within an image time series feature spatio-temporal relationships that represent the evolution (in time and space) of the phenomenon to be classified (e.g., land cover, cloud types). This allows for the exploitation of additional features, apart from spectral information, such as image filters, topology of objects, edge filters, principal components and other numerical combinations of spectral bands [32]. However, the images within a time series are often distorted by a number of factors, such as variable illumination/acquisition geometry, anisotropy of reflectance, atmospheric disturbances, topographic effects, geolocation shifts, temporal degradation of imaging instrument, data gaps (e.g., missing scan lines), residual cloud/snow cover, variable surface moisture, radio-frequency interference, thermal noise and many more. This turns image classification into a complex problem, which may be difficult to solve even for an intuitively simple task such as cloud detection on satellite imagery.  
The classification of physical phenomena on remote sensing imagery is a complex process that can be divided into the following steps: (1) selection of suitable remote sensing sensor(s) featuring appropriate spatial, temporal, radiometric and spectral resolutions; (2) selection of training samples; (3) image pre-processing; (4) feature extraction; (5) feature selection; (6) selection of suitable classification algorithm(s); (7) image classification; (8) image post-classification (optional filtering of classification results, e.g., clustering small groups of pixels to fulfil minimum mapping unit criteria); (9) accuracy assessment. Steps 1-4 require expert knowledge for a particular classification problem and can be regarded as experimental design. Steps 5-9 can be generalized and automated to some extent in order to facilitate the image classification process and to reduce the human workload. There are several common challenges to supervised image classification related to sparse, mislabeled and imbalanced datasets featuring classes that may be inseparable, and which are discussed in the following subsections.
## 4 Proposed Protocol Design
### Significance of the Proposed Methodology  
Features of the proposed node deployment and data collection in land mine detection are given as follows:  
1. Minimum number of sensor nodes is used to cover a large network region that decreases the complexity and cost of installation.
2. It offers an effective technique for implementing a fully automated land mine detection system that reduces human participation and prevents human life risk.
3. Horizontal search efficiently monitors the affected region of the land mine allowing easy identification of the secure region, while random node deployment does not ensure the entire sensing area.
4. The suggested PRM-ACK based routing avoids collision and congestion. So, it is highly appropriate for the collection of event-driven data.
5. The suggested data routing technique offers a precise location of the buried place of explosive material enhancing the safety of land mine disposal.
## 2 Related Work
### On-Body Communication  
An on-body communication system is by definition a communication system that enables body-attached devices to communicate with each other. A previous study categorized on-body communication methods on the basis of their signal transfer methods: simple circuit type, electrostatic coupling type, and waveguide type [6].
## 3 Results  
In Table 1, the general characteristics of participants are shown as well as the pre- and post-intervention differences in the CDSS intervention group versus dietary advice group. It is noted that the CDSS intervention group consisted mainly of males (-70%). The mean age and body mass index (BMI) of the CDSS intervention group was 48.9 years and 35.2 kg/m\({}^{2}\), respectively. The percentage of overweight subjects was similar between the two groups (30.8% in the CDSS intervention group vs. 32.3% in the dietary advice group, \(p\) = 0.153), while the intervention group consisted of more obese subjects (17.9% in the CDSS intervention group vs. 9.5% in the dietary advice group). The levels of several biochemical markers of glucose and lipid metabolism were not different between the CDSS intervention group and the control group. Moreover, it is noted that the pre- and post-effects in the CDSS intervention and dietary advice groups are seen in Table 1. In the CDSS intervention arm, body weight, BMI, total body fat, glycated hemoglobin and triglycerides were significantly decreased at follow-up. Lean tissue was borderline decreased, and lean mass index was decreased, while glucose and cholesterol (total, LDL- and HDL-cholesterol) did not change. No difference was observed in the dietary advice group. It is noted that grouped results for both genders may be misleading, since anthropometric and body composition measurements are different in men and women. For this reason, sex-specific analysis was also conducted. In sex-specific analysis, body weight, BMI and total body fat were reduced in both sexes (Table 2). More particularly, body weight was reduced from 87.9 \(\pm\) 14.9 kg to 83.3 \(\pm\) 13.9 kg in men and from 114.6 \(\pm\) 25.4 kg to 109.2 \(\pm\) 21.4 kg in women. BMI was reduced from 35.2 (28.4-37.5) kg/m\({}^{2}\) to 33.2 (27.4-35.4) kg/m\({}^{2}\) in men and from 26.4 (22.4-28.5) kg/m\({}^{2}\) to 24.9 (21.7-27.4) kg/m\({}^{2}\) in women. Total body fat was reduced from 46.9 \(\pm\) 9.8% to 43.7 \(\pm\) 9.7% in men and from 39.9 \(\pm\) 13.8% to 37.3 \(\pm\) 11.5% in women. Lean tissue and lean mass index remained unchanged in both men and women. In addition, beneficial changes were documented in glucose, total cholesterol and triglycerides in male participants (Table 2). No sex-specific analysis was conducted in the dietary advice group, since a low number of men were recruited. Body weight patterns identified using PCA analysis are shown in Table 3. More particularly, two distinct patterns were identified: a high BMI-high % body fat pattern explaining 60.4% of the total variance and a high BMI-high lean tissue pattern explaining 38.4% of the total variance. The total variance explained was as high as 98.9%. In Table 4, Spearman correlation coefficients are shown between the absolute % weight loss and several variables of body composition (raw data or body weight patterns), as well as several biochemical variables. In detail, lean mass, lean mass index and a high BMI-high
## 2 Methodology
### A Protocol for the Assessment of Gait of Parkinson's Disease Patients Using Wearable Sensors
#### 2.2.4 The Retropulsion Test  
The Retropulsion test, also known as pull test, is a commonly used test in clinical practice for assessing postural stability in PD patients. The test is configured in line with the description of the MDS-UPDDS item 3.12-Postural Stability. Participants place themselves in the position of the Static Balance test, while the instructor, after informing the participant what is about to happen, quickly and forcefully pulls participants from their shoulders backward, to displace their center of gravity, so that they are forced to undertake steps backward to retain their position (Figure 3b).
## 3 Results
### Field Experiment  
A reduction in the NDVI value is observed after each vine removal until it reaches the base soil values (NDVI values between 0.17 to 0.19), except in pixel 1 (Figure 5). The starting NDVI values, which correspond to the maximum vine cover, were 0.251, 0.321 and 0.306, for pixels 1, 2 and 3, respectively. In Figure 6a all values from all of the pixels from the dataset were used and a linear relationship was found between the reduction in NDVI and TLA (R\({}^{2}\) = 0.72) with a slope of 0.32 (y = 0.3104x), showing that for each 20% of reduction in the TLA, the NDVI is reduced by around 7%, similar to the greenhouse experiment result. Moreover, if the dataset is disaggregated by pixel (Figure 6b), the determination coefficients are 0.92, 0.68 and 0.99 for pixels 1, 2 and 3, respectively.  
Figure 4: (**a**) Example of the mask used to isolate the vines; (**b**) The relationship between them. Normalized Difference Vegetation Index (NDVI) reduction (%) and Total Leaf Area (TLA) reduction (%) as pixel-based.  
Figure 3: (**a**) Removal of the vines; (**b**) Detail of the removed vines; (**c**) Marked pixels in the vineyard and (**d**) Example of the leaf area measurements using the Easy Leaf Area application.  
The \(p\)-values for the comparison of each pair of removal steps are shown in Table 1. As expected, all removal steps had significant differences in TLA. However, concerning NDVI, only a few pairs of treatments had significant differences and in no case between two consecutive steps, which indicate a low sensitivity of the index to small changes in the vegetation amount.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**TLA**} \\ \hline  & & **Step0** & **Step1** & **Step2** & **Step3** & **Step4** \\ \hline  & Step0 & & 0.018 & 0.003 & 0.001 & 0.001 \\ \cline{2-6}  & Step1 & 0.396 & & 0.002 & 0.001 & 0.001 \\ \cline{2-6} NDVI & Step2 & 0.120 & 0.148 & & 0.001 & 0.001 \\ \cline{2-6}  & Step3 & 0.039 & 0.045 & 0.113 & & 0.002 \\ \cline{2-6}  & Step4 & 0.013 & 0.011 & 0.005 & 0.079 & \\ \hline \hline \end{tabular} Grey colour denotes \(p\)-values 0.05. Diagonal values were excluded and marked in black.  
\end{table}
Table 1: \(p\)-values from the \(t\)-test comparison of Normalized Difference Vegetation Index (NDVI) and total leaf area (TLA) in the field experiment.  
Figure 5: (a) Detail of the vine removal by phase. (b) TLA (m\({}^{2}\)) and NDVI values in each pixel.  
Figure 6: Relationship between NDVI (%) and TLA reduction (%) (a) in all pixels (b) per pixel.
# Remanufacturing of Industrial Electronics: A Case Study from the GCC Region
## 3 Industrial Product Remanufacturing
### Challenges and Constraints in Industrial Remanufacturing  
The reasons and obstacles to achieving successful industrial remanufacturing for EoL products have technical and economic aspects [6].  
Firstly, remanufacturing can be challenging when dealing with end-of-life electronic equipment that includes motherboards because the equipment may be obsolete. Finding replacement modules in the market can be difficult due to OEM policies. In such cases, technicians may attempt to replace worn-out components with current equivalent parts, which can lead to the loss of critical product features. The quality and condition of the used products can also vary, making it hard to achieve consistent results.  
One of the significant technical challenges in the remanufacturing process is the lack of PCB schematics, which can help troubleshoot faulty equipment. Consequently, without PCB schematics professional technicians often rely on their knowledge and expertise to troubleshoot the equipment. This process requires significant effort and time as the technicians must reverse-engineer the equipment to fully understand its design and operation.  
Furthermore, some industrial motherboards or onboard microprocessor PCBs, such as PLCs, use custom-made and complex integrated circuits without datasheets. This issue can make diagnosing and repairing faulty equipment challenging as limited information about the components is available. In addition, some ICs may have an erased or missing part number, further complicating the diagnosis process. Therefore, proper documentation and knowledge sharing between the OEMs and the remanufacturing industry is essential to facilitate the repair and maintenance of such equipment.  
On the other hand, remanufacturer groups can require significant investment in specialized equipment, technologies, and software. These tools must be capable of handling various products and materials and the costs of acquiring and maintaining them can be high.  
Hence, ensuring consistent quality across different products and batches is a significant challenge. In addition, there is always a risk that remanufactured products may not meet the required standards and may have to be scrapped or reworked. However, industrial remanufacturing must comply with strict health, safety, and environmental standards and regulations.  
Lastly, consumers may perceive remanufactured devices negatively, considering them inferior to new products. This perception can make market and selling remanufactured products difficult, reducing demand and profitability. For that reason, making remanufacturing economically viable is challenging for businesses, especially when competing with cheaper imported products.  
Overcoming these challenges and constraints requires significant time, resources, and expertise. Nevertheless, remanufacturing can offer considerable benefits, including reduced environmental impact, improved resource efficiency, and increased profitability.
## 4 Discussion
The analysis of different absorbed dose profiles reveals that in the tumoral spherical regions absorbed dose values derived from the MC simulation are lower (up to 14% in absolute value) than absorbed dose values calculated with the convolution method, due to a density increase in lesions of about 20% with respect to the surrounding tissue. Therefore, it is necessary to include morphological patient-specific information in the treatment planning system, including careful calibration of the CT and possibly high quality CT systems (to allow HU-based density estimation for each voxel) to avoid inaccuracies based on the assumption of homogeneous tissues. The use of the highest possible quality CT could be of special value to improve the information especially in inhomogeneous tumors or e.g., in bone metastases. The concept of voxel dosimetry is bright and suitable, but it is a highly complex association between image reconstruction, segmentation, density, PVE, activity recovery, and absorbed dose calculation. All these issues can concur to limitations which still need to be well understood and solved. The scientific community is investing appreciable efforts to improve and assess the reliability and accuracy of dosimetry at the voxel level in volumes of interest of various scenarios. Concerning image quantification, relevant studies are ongoing to highlight and compensate imaging and reconstruction deficiencies that may lead to unrealistic absorbed dose distributions for different organ substructures, lesions or voxel dimensions [43]. As regards the potential impact of density and inhomogeneities, the results of this study, although theoretical, based on a MC approach represent a proof of concept and challenge analysis of more complex situations with real patient data of different clinical situations. This is in fact the topic of a current study of some of the authors, and preliminary results will be soon presented.