esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations of the Evidence  
General eye metrics and tracking face several challenges, including variability in how fixations, saccades, and blinks are measured, as well as reliance on small, context-specific datasets that limit generalisability to real-world applications. Environmental factors such as lighting and camera positioning, along with individual differences like age and fatigue, also compromise reliability. In machine learning methods, biases in participant demographics and dataset composition, along with risks of overfitting, hinder generalisation. Many studies lack robust cross-validation, further weakening the reliability of reported accuracy metrics.  
Reading comprehension studies often rely on predefined reading tasks and behaviours, which fail to capture natural variability in reading strategies. Subjective labelling, such as self-reported comprehension, introduces bias, while differences in reading ability and language proficiency among participants add variability to outcomes. Typography research frequently focuses on non-Latin scripts or artificial layouts that lack everyday applicability, with limited participant diversity and a narrow exploration of text formatting or highlighting techniques.  
Synthetic eye-tracking data, while useful in some contexts, often struggle to replicate real-world variability, especially for biometric applications like ADHD detection. The validation of synthetic data models against large-scale real-world datasets is also insufficient, limiting their broader applicability. Mobile eye-tracking systems face environmental sensitivities, hardware limitations, and a narrow focus on basic metrics, with a limited exploration of complex dynamics like pupil dilation or saccade velocity. These combined challenges highlight the need for more standardisation, diverse datasets, and robust methodologies across eye-tracking research.
###### Abstract  
Due to the increasing ease of use and the superiority of the results, distributed strain measurements, utilizing Optical Backscatter Reflectometry (OBR), have become more important and widespread over the last few years. Strains are calculated from the difference between an actual optical Raleigh backscattering measurement and an initial reference value. However, under certain physical conditions, e.g., pinching or microbending of the optical fiber, no meaningful strain values are yielded by the commonly-used method to analyze OBR data. Such conditions were experienced in this study where the optical fiber was embedded into hardening epoxy for measuring shrinkage due to curing. In this work, it is shown that a new data analysis method called the \"running reference analysis method\" can overcome such obstacles and deliver meaningful strain values in circumstances in which the traditional method fails. In the new approach, each measurement is compared to the previous measurement, and the strain differences are added up to the absolute strain value. This method does not require a new experimental technique and will also work on old measurement files. It is also useful for other types of (OBR) strain measurements that contain many outliers and is not restricted to the investigation of cured epoxy.  
Optical Backscatter Reflectometry; OBR; strain measurements; running reference; epoxy; cure; in-situ
# Method to Predict Performances of PCB Silicone Conformal Coating under Thermal Aging
###### Abstract
Though stress build-up in polymeric coatings has been extensively studied, there is no systematic study of crack appearance in silicone conformal coatings used for PCB protection that combines simplified model approach evaluation with FEA and experimental validation. In this work, two approaches are studied, and their capability to predict the suitability of a silicone coating to be used in conformal coating application is assessed. Approach 1 is the BMSB method for material assessment. Approach 2 is simplified equations of stress build-up in a bi-layer plate resulting from CTE mismatch obtained assuming plane stress approximation. Stress values calculated this way are validated by comparing FEA solutions of continuous mechanics equations and experimental observations. A _rigid_ and a _soft_ silicone conformal coating were used to test the above methods, aiming to determine the best performing product for each conformal coating application that differ regarding their service conditions and the range of temperature exposure.
## 3 Results
### Dataset Preparation  
In this study, a total of 6079 single solar panel images were utilized, categorized into three distinct classes: broken, clean, and dirty. Specifically, the dataset comprised 2245 images of broken panels, 2130 images of clean panels, and 1704 images of dirty panels. Each class was assigned a numerical label for model training purposes, where broken panels were labeled as 0, clean panels as 1, and dirty panels as 2.  
Figure 3: (**a**) Original image. (**b**) Data augmentation outcome-1. (**c**) Data augmentation outcome-2. (**d**) Data augmentation outcome-3. (**e**) Data augmentation outcome-4. (**f**) Data augmentation outcome-5.  
To ensure consistency in model training and evaluation, all images were resized to 320 \(\times\) 320 pixels, preserving their aspect ratios and essential structural features. The dataset was carefully partitioned to prevent data leakage, ensuring that test images were not included in the training phase of any model.  
For the YOLO-based models (YOLOv8-m and YOLOv9-e), the dataset was divided into the following three subsets:  
* 70% of the images were allocated for training;
* 20% were designated for validation;
* 10% were reserved for testing.  
The YOLO algorithm is predicated on the principle of data segmentation, with the primary rationale for this being the enhancement of the precision of the model's performance evaluation during the training process. The dataset is thus divided into three segments: 70% for training, 20% for validation, and 10% for testing. The prevailing convention is to allocate 80% of the resources to training, 10% to validation, and 10% to testing to facilitate the model's capacity to learn from a more extensive array of samples, as characterized by a greater proportion of training data. However, in this study, the proportion of the validation set is increased to 20% to detect the overfitting tendencies of the model earlier and more effectively during training. Consequently, it is feasible to calibrate the model parameters and hyperparameters through the evaluation process on the validation set. The fundamental rationale behind the 70-20-10% data separation is to enhance the efficacy of monitoring training performance and augment the model's generalization capability.  
This split ensured that the models were trained on a diverse set of images while maintaining an independent test set for unbiased performance evaluation. The exact number of images assigned to each subset is provided in Table 1.  
In contrast, for the CNN model, the dataset was first classified according to the three predefined categories: broken, clean, and dirty. The CNN algorithm itself handled the automated partitioning of images into training, validation, and testing subsets, ensuring an optimal distribution for model training.  
This structured approach to dataset preparation was essential for maintaining model generalizability and ensuring fair comparisons between different deep learning architectures.
# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
## 3 Results  
Since many wireless sensor networks are used to monitor the physiological parameters of people, the analysis presented here of the influence of the human body shadowing effect on wireless sensor networks operating in the 2.4 GHz band was performed for a typical indoor environment. Analysis was first performed in the XGtd(tm) program using the human body model presented above. Experimental measurements were then performed in a similar indoor environment.
## 3 Performance Analysis and Simulations  
### Irradiance Uniformity vs. Optical Loss  
Adding the SOE to the CPV unit improves the irradiance uniformity but causes optical loss in the unit. This section analyzes, evaluates, and compares the irradiance uniformity and optical efficiency of the proposed CPV unit to the conventional CPV unit. In this case, the additional Si cell is not included in the analysis and evaluation. In other words, the considered CPV unit contains the MJ solar cell, but not the Si solar cell.  
The geometrical structure of the proposed CPV unit was designed and simulated by using the commercial optical modeling software, LightTools(tm) [33]. In our design and simulations, the material used for the Fresnel lens was PMMA with a refractive index of 1.49. The material used for the plano-concave lens was borosilicate glass N-BK7 with a refractive index of 1.52. The simulation parameters were the same as those of the design shown in Table 1. Since the LightTools software does not supports circle solar cells, square solar cells with size of 20 mm \(\times\) 20 mm were used in the simulation models.  
Ray-tracing layouts of the conventional CPV unit and the proposed CPV unit with the SOE are shown in Figure 6a,b, respectively. In the proposed CPV unit, the POE concentrated and focused direct sunrays on the SOE's surface, and then the SOE distributed the sun rays over the solar cell uniformly, as shown in Figure 6b.  
Figure 5: Ray-tracing of the designed CPV unit with the SOE and Si solar cell.
## 2 WSN-Based ITS Applications
### Traffic Safety Applications  
Traffic safety applications deal with the prevention of accidents. In order to fulfill this purpose they make sensor devices work proactively to warn drivers about potentially dangerous situations, such as the presence of obstacles, animals, adverse road conditions (ice or water) and vehicles either stopped (queue-end warning) or driving in the opposite direction (overtaking assistance, wrong-way driving warning). The collaboration among these devices enables to notify drivers of events beyond line-of-sight, thus increasing the available time of response.  
There are two ways of approaching these applications, although it could also be possible to develop applications that use a combination of both. In the first approach, upon the detection of the arrival of a vehicle by a static sensor node, the latter activates the subsequent static nodes in order to obtain the condition of the following stretches of the road. This approach has been employed by different applications to support overtaking assistance [11] or animal detection [12], by checking that, correspondingly, there are no vehicles or animals present within a safety zone defined by the application.  
The second approach consists of making road information available to nodes before vehicles reach them. This implies that, whenever some data of interest is acquired, it is disseminated within a certain area so that, later, they are gathered by passing vehicles (store & forward scheme). This approach is well suited for the detection of non ephemeral events, such as the occurrence of dangerous road conditions. In this category of applications it is common to find collaboration between WSNs and VANETs, which helps spreading information and prolongs the lifetime of static nodes. As an example, [13] suggests emplacing static nodes at the beginning of each road, which allows all vehicles accessing it to learn in advance about the conditions of the road (previously gathered by other vehicles). A denser deployment is used in [14], in which WSNs monitor the road and VANET disseminates the information either to other vehicles traveling in road segments without WSN infrastructure or to distant static nodes which will warn drivers in the absence of other vehicles.
## 3 Results and Discussion
### Linear Dynamic Range and Limit of Detection  
The standard curves were obtained by preparing multiple hydroxylamine solutions with various concentrations in 0.1 M PBS and measuring them on the PPy NTs/GSPE. Figure 9 illustrates a voltammogram of the DPVs at variable hydroxylamine contents (0.005, 0.07, 1.0, 5.0, 15.0, 30.0, 75.0, 100.0, 150.0, 200.0, 250.0, and 290.0 uM of hydroxylamine). According to our findings, there was an elevation in the peak currents with increasing hydroxylamine content. The standard curve of the DPV peak current for the hydroxylamine oxidation versus hydroxylamine concentration (Inset, Figure 9) demonstrates admirable linearity over a broad range of concentrations (0.005-290.0 uM) at pH = 7, with a correlation coefficient of 0.9998. In addition, the detection limit at 3\(\sigma\) equaled 0.001 uM. Table 1 lists the limit of detection and the linear range of the PPy NTs/GSPE compared with some modified electrodes used for hydroxylamine determination [56; 57; 58; 59; 60; 61].
# Profiling of Physical Qualities of Highly Trained Portuguese Youth Soccer Players
## 3 Results
### Age Categories  
The results revealed that there were statistically significant differences in the IMTP (\(p<0.001\); \(\eta^{2}_{\rm P}=0.33\)), CMJ (\(p<0.001\); \(\eta^{2}_{\rm P}=0.495\)), SJ (\(p<0.001\); \(\eta^{2}_{\rm P}=0.29\)), and sprint (\(p<0.001\); \(\eta^{2}_{\rm P}=0.23\)) test results among all age categories.  
Regarding IMTP performance (Figure 2A), significant differences were found in the following comparisons: U23 and U17 (U23 = 1706 \(\pm\) 269 N; U17 = 1386 \(\pm\) 199 N; \(p=0.001\); \(d=1.68\)); U23 and U16 (U23 = 1706 \(\pm\) 269 N; U16 = 1330 \(\pm\) 228 N; \(p<0.001\); \(d=1.51\)); and U19 and U16 (U19 = 1565 \(\pm\) 176 N; U16 = 1330 \(\pm\) 228 N; \(p=0.007\); \(d=1.15\)).  
Figure 2: Comparison of test performances between age groups. (**A**) Isometric Mid-Thigh Pull performance in Newtons with significant differences (*) between U23 and U16 (\(p=0.001\)), U23 and U17 (\(p<0.001\)), and U19 and 16 (\(p=0.007\)); (**B**) Countermovement Jump height with significant differences (#) between U23 and U16 (\(p<0.001\)), U19 and U16 (\(p<0.001\)), and U17 and U16 (\(p=0.003\)); (**C**) Squat Jump height with significant (#) differences between U23 and U16 (\(p<0.001\)), U19 and U16 (\(p<0.001\)), and U17 and U16 (\(p<0.001\)), and U17 and U16 (\(p<0.001\)). (**D**) Broad Jump distance with significant differences (*) between U23 and U16 (\(p=0.002\)), and U19 and U16 (\(p=0.008\)); (**E**) sprint speed with significant differences (#) between U23 and U16 (\(p=0.002\)), U19 and U16 (\(p<0.001\)), and U17 and U16 (\(p<0.001\)).  
For the SJ (Figure 2B), significant differences were detected between U23 and U16 (U23 = 36.26 \(\pm\) 4.98 cm; U16 = 30.26 \(\pm\) 3.87 cm; \(p<\) 0.001; \(d\) = 2.17); U19 and U16 (U19 = 36.11 \(\pm\) 3.57 cm; U16 = 30.26 \(\pm\) 3.87 cm; \(p<\) 0.001; \(d\) = 1.57); and U17 and U16 (U17 = 35.21 \(\pm\) 4.14; U16 = 30.26 \(\pm\) 3.87 cm; \(p=\) 0.003; \(d\) = 1.24).  
In relation to the CMJ (Figure 2C), there were also significant differences in the following comparisons: U23-U16 (U23 = 38.60 \(\pm\) 4.88 cm; U16 = 29.84 \(\pm\) 3.83 cm; \(p<\) 0.001; \(d\) = 2.00); U19-U16 (U19 = 39.53 \(\pm\) 3.68 cm; U16 = 29.84 \(\pm\) 3.83 cm; \(p<\) 0.001; \(d\) = 3.56); and U17-U16 (U17 = 36.03 \(\pm\) 3.81 cm; U16 = 29.84 \(\pm\) 3.83 cm; \(p<\) 0.001; \(d\) = 1.62)  
For the BJ (Figure 2D), the only differences that were found were when comparing the following: U23-U16 (U23 = 2.02 \(\pm\) 0.11 cm; U16 = 1.86 \(\pm\) 0.13 cm; \(p=\) 0.002; \(d\) = 2.01); and U19-U16 (U19 = 2.00 \(\pm\) 0.15 cm; U16 = 1.86 \(\pm\) 0.13 cm; \(p=\) 0.008; \(d\) = 12.76).  
Regarding the sprint (Figure 2E), there were significant differences in the following comparisons: U23-U16 (U23 = 7.61 \(\pm\) 0.24 m/s; U16 = 7.28 \(\pm\) 0.35 m/s; \(p=\) 0.002; \(d\) = 1.10); U19-U16 (U19 = 7.62 \(\pm\) 0.16 m/s; U16 = 7.28 \(\pm\) 0.35 m/s; \(p<\) 0.001; \(d\) = 1.30); and U17-U16 (U17 = 7.73 \(\pm\) 0.31; U16 = 7.28 \(\pm\) 0.35 m/s; \(p<\) 0.001; \(d\) = 1.37).
## 3 Model Parameter Estimation
### Propulsion Noise  
To obtain the emission synthesizer parameters of the propulsion noise, controlled measurements on a chassis dynamometer (see Figure 8b) and at idling engine under free field conditions were performed. Calibrated audio recordings at a sampling frequency of \(f_{\mathrm{s}}\) = 44.1 kHz at different microphone positions around the vehicle and at different engine conditions were taken. During the measurements on the chassis dynamometer, four microphones were placed on the ground at emission angles \(\varphi\approx\{0,60,120,180\}^{\circ}\) at distances \(r=1\) to 2 m from the vehicle. During the free field measurements, four additional microphones were placed on the ground at the identical emission angles, but at larger distances of \(r^{\prime}\) = 4.5 to 7 m. The free field measurements were used to correct  
Figure 8: Photographs showing the measurement set-ups for tire noise (**a**) and propulsion noise (**b**). In (**a**), the coast-by situation is depicted with two measurement microphones placed at different distances and a camera connected to a laptop; (**b**) shows the lab with a passenger car on the chassis dynamometer, the airstream fan in front of the car and two microphones on the floor at the left-hand room edge (emission angles \(\varphi=60^{\circ}\) and \(120^{\circ}\)).  
Figure 9: Measured tire noise regression parameters \(A_{i}\) (**a**) and \(B_{i}\) (**b**) of 13 tires and the values according to the Harmonoise model [21] (dotted lines).  
for the room influences of the lab, as explained in Section 3.2.5. On the chassis dynamometer, measurements were typically taken at engine speeds of \(n\approx\{1000,2000,3000,4000\}\) rpm and engine loads of \(\Gamma\approx\{0,40,70,100\}\%\). The measuring point pairs \(\langle n,M\rangle\) of a measurement performed on a Ford Focus 1.8i are depicted as circles in Figure 3. The topmost points at 1000 to 4000 rpm are at full load, _i.e._, \(\Gamma=100\%\). To confine tire noise, low vehicle speeds were aimed for by choosing low driving gears. Mostly, it was the second gear, which resulted in vehicle speeds <50 km/h.  
To these recordings, a series of signal analysis steps were applied, which are outlined in a signal flowchart in Figure 10. These steps are further explained in the following sections. For the signal processing, a signal length of 4 s is used.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 4 Results
### Peripheral Node Power Supply Current
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Connection Interval 1 (ms)**} & \multirow{2}{*}{**Connection**} & \multicolumn{2}{c}{**Average Current**} & \multicolumn{2}{c}{**Maximum Current:**} \\ \cline{3-5}  & & \(p\)-Value (Significant) for:** & \multicolumn{2}{c}{**\(p\)-Value (Significant) for:**} \\ \cline{3-5}  & & **Event Length** & **Event Length** & **Event Length** & **Event Length** & **Event Length** \\  & **2500 \(\mu\)s** & **5000 \(\mu\)s** & **7500 \(\mu\)s** & **2500 \(\mu\)s** & **5000 \(\mu\)s** & **7500 \(\mu\)s** \\ \hline \multirow{3}{*}{10} & 20 & 0.819 & 0.819 & 0.819 & 0.002 (*) & 0.003 (*) & 0.001 (*) \\ \cline{2-5}  & 30 & 0.121 & 0.160 & 0.160 & 0.023 (*) & 0.888 & 0.900 \\ \cline{1-1} \cline{2-5}  & 40 & 0.006 (*) & 0.001 (*) & 0.005 (*) & 0.900 & 0.472 & 0.005 (*) \\ \cline{1-1} \cline{2-5}  & 50 & 0.001 (*) & 0.005 (*) & 0.001 (*) & 0.019 (*) & 0.066 & 0.007 (*) \\ \cline{1-1} \cline{2-5}  & 100 & 0.001 (*) & 0.001 (*) & 0.001 (*) & 0.900 & 0.681 & 0.324 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Nemenyi post hoc pair-wise statistical results (\(p\)-values) vs. connection interval for current consumption. (*) denotes \(p\leq 0.05\).
## 4 Experiments
### Performance Comparison
#### 4.2.3 Performance Comparison of Adversarial Training Recommendation Models  
The ATKGRM and three baseline adversarial training recommendation models proposed in this paper are implemented in MovieLens-1M and Last. FM. The comparison of the baseline models is shown in Table 5.  
Three types of adversarial training recommendation models are introduced below.  
* IRGAN [33] is a pioneer in the field of recommendation using adversarial training. The discriminator in the IRGAN is used to distinguish whether the items from the generator conform to the user's real preference distribution.
* CFGAN [34] is the first model to use a generator to generate a continuous vector instead of a discrete item index.
* GCGAN [35] combines GAN and graph convolution and can effectively learn the main information by propagating feature values through a graph structure.  
By analyzing the comparison in Table 5 (or Figure 4), we can conclude that on the MovieLens-1M dataset, compared with the optimal baseline adversarial training recommendation model CFGAN, the Recall@5 and the Recall@20 of ATKGRM are increased by 2.5% and 7.2%, respectively. On the Last. FM dataset, compared with the baseline adversarial training recommendation model GCGAN, the Recall@10 of ATKGRM is increased by 11.9%. Moreover, compared with the model GCGAN, the Recall@20 of ATKGRM is increased by 11.7%. It is worth noting that we removed the user and item domain information in the model GCGAN for more accurate comparison. Among them, ATKGRM-KG means that the knowledge graph information is eliminated based on ATKGRM. ATKGRM-KG is better than IRGAN, CFGAN, and RSGAN, which can show the advantages of this adversarial  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**Last. FM**} \\ \cline{2-5}  & **Recall@5** & **Recall@20** & **Recall@10** & **Recall@20** \\ \hline IRGAN & 0.072 & 0.166 & 0.1247 & 0.1823 \\ CFGAN & 0.108 & 0.272 & 0.1335 & 0.2023 \\ GCGAN & 0.0785 & 0.1377 & 0.1369 & 0.1661 \\ ATKGRM-KG & 0.1103 & 0.2846 & 0.1481 & 0.2214 \\ ATKGRM & 0.1107 & 0.2916 & 0.1532 & 0.2259 \\ Improve/\% & 2.5\% & 7.2\% & 11.9\% & 11.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of adversarial training models.  
training model, and ATKGRM is better than AKTGRM-KG, which can show the advantages of using a knowledge graph.
## 4 Results and Analysis
### The Combustion Fit Results of Engine Nominal Operating Point
#### 4.2.2 Each Cylinder Pressure Signals  
The combustion fitting results discussed in the previous sections are based on the four-cylinder average pressure signals measurement in order to eliminate the measurement fluctuation effect. In fact, for the 4-cylinder diesel engine, four cylinders behave different performance to some extent in particular for the pressure signals but not too much due to engine operating balance demand. Table 6 shows the fitting results of the four cylinders respectively. The Seiliger parameters \(a\) and \(b\) are slightly different for the four cylinders, referring to the heat input ratio, there is maximum 2.22% difference for Seiliger parameter \(a\) (cylinder 1 and cylinder 2) and maximum 1.24% difference for Seiliger parameter \(b\) (cylinder 1 and cylinder 4).  
As to Seiliger parameter \(c\) and \(n_{exp}\), there is much discrepancy such as 3.97% of Seiliger parameter \(c\) and 5.2% of Seiliger parameter \(n_{exp}\). This does not apply to cylinder 3, which has irregular cylinder fitting results. However, the fitting results of averaged in-cylinder pressure signals is not much affected by that of cylinder 3 and closed to the result of cylinders 1, 2 and 4. Although there are irregular cylinder fitting results, which are probably caused by the engine operating in reality or a numerical calculation procedure, the averaged in-cylinder pressure signals are relatively reliable to obtain the fitting results in comparison with reality.