esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test and Analysis
### Output Power  
To obtain the maximum value of the power output, a series of different resistances were tested as load resistance paralleled with PVDF film under different flapping frequencies. According to Equations (16) and (17), the output RMS power values were studied. The fitting curves of output RMS power variations for different load resistance under the condition of 5, 7.5, 10, 12.5 and 15 Hz flapping frequency were plotted on Figure 9. In order to obtain more details between output power and load resistance, the output power curves adopted cubic polynomial fitting. It showed that there were no obvious changes for output power in low flapping frequency phase and the RMS output power increased rapidly with the frequency increase without considering resistance. The effect of flapping frequency on output power was much greater than that of load resistance. As a result, it was of vital importance to enhance flapping frequency for output power improvement. As seen in Figure 9, the maximum RMS output power could be up to 21 \(\upmu\)W with 15 Hz flapping frequency and 0.4 M\(\Omega\) corresponding matching load resistance, which was sufficient to drive certain miniature electronic components, such as LED lights.  
\[P_{RMS}=\frac{U_{RMS}^{2}}{R_{load}} \tag{17}\]  
As seen in Figure 10, when the frequency was lower than 12.5 Hz, the open-circuit voltage and the output power versus frequency were linear and quadratic respectively. Equations (14) and (15) were verified by the test from another point of view. When the flapping frequency exceeded 12.5 Hz, the growth rate for both voltage and power was declining, it was due to the reason that the deformation amplitude differed at different frequencies. When the flapping frequency was in a high phase, the PVDF wing could not  
Figure 8: Payload and root mean square (RMS) voltage versus flapping frequency.  
Figure 9: The RMS output power versus load resistance for different frequencies.  
finish the maximum deformation in chord and wingspan direction in one flapping cycle, causing electric data to be adversely affected.
## 3 Results and Discussion
### Rsid\({}^{\text{TM}}\)_Urine and RSID\({}^{\text{TM}}\)_Reader Experiments_  
Assuming 100% extraction efficiency, 100 \(\upmu\)L of urine on a sterile cotton swab extracted in 1 mL of the RSID\({}^{\text{TM}}\) Urine buffer will produce an extract that contains 100 nL of urine per microliter of extract. In order to generate 1, 5 and 10 \(\upmu\)L test volumes of urine, 20, and 100 \(\upmu\)L of urine extract were adjusted to a total volume of 200 \(\upmu\)L with RSID\({}^{\text{TM}}\) Urine buffer (for the 10 \(\upmu\)L urine sample, 200 \(\upmu\)L of extract was used directly). Two different Readers were evaluated in this study, each with two sets of RSID\({}^{\text{TM}}\) cassette replicates. All results were concordant.  
Figure 5 shows results of RSID\({}^{\text{TM}}\) Urine as they appear in a digital photograph (Nikon's Coolpix 5600) and in the digital images generated by the RSID\({}^{\text{TM}}\) Reader. Extraction negative controls (Figure 5, cassettes 1 and 2) and blood-saliva-semen cross-reactivity controls (Figure 5, cassettes 9 and 10) were scored as \"negative\" by both RSID\({}^{\text{TM}}\) Readers tested. Samples with calculated 1, 5 and 10 \(\upmu\)L of urine were read as \"positive\" by both RSID\({}^{\text{TM}}\) Readers tested (Figure 5, cassettes 3-8).  
Cassettes 3 and 4, on which a calculated 1 \(\upmu\)L of urine were tested had very light positive signal, which was expected since 1 \(\upmu\)L of urine is at the detection limit of RSID\({}^{\text{TM}}\) Urine test [5]. To measure reproducibility of RSID\({}^{\text{TM}}\) Reader, cassette 3 was analyzed three times on both RSID\({}^{\text{TM}}\) Readers. Both RSID\({}^{\text{TM}}\) Readers detected signal in all three replicas.  
Figure 4: Results of RSID\({}^{\text{TM}}\) Semen and RSID\({}^{\text{TM}}\) Reader Experiments. Results of two replicates for each analyzed semen volume from one Reader are shown. Cassette number and calculated volume of semen added to each cassette are shown **(top)**. Digital camera capture of cassettes and RSID\({}^{\text{TM}}\) Reader images are shown below, respectively. **Top**—Digital photograph of the cassettes (C—Control; T—Test lines) with analyzed semen volumes indicated above. **Bottom**—Images taken from RSID\({}^{\text{TM}}\) Reader. RSID\({}^{\text{TM}}\) Reader results (positive [+] or negative [–]) are indicated under each RSID\({}^{\text{TM}}\) Reader image. Note: Camera capture or RSID\({}^{\text{TM}}\) Reader images are slightly less sensitive than visual observation of the cassettes.
## 4 Simulation Results
### Preprocessing of Data and Parameters  
#### 4.1.1 Mechanism Model  
The values of parameters in three blocks are shown in Table 6. For unascertained parameters, the initial value is considered. According to the thermodynamic properties of saturated water and saturated vapor, functions \(Tp(\cdot)\) can be written as  
\[P_{dr}=A_{dr}\cdot\rho_{v}^{3}+B_{dr}\cdot\rho_{v}^{2}+C_{dr}\cdot\rho_{v}+D _{dr}, \tag{17}\]  
\[H_{v}=A_{v}\cdot\rho_{v}^{3}+B_{v}\cdot\rho_{v}^{2}+C_{v}\cdot\rho_{v}+D_{v}, \tag{18}\]  
\[H_{wv}=A_{wv}\cdot\rho_{v}^{4}+B_{wv}\cdot\rho_{v}^{3}+C_{wv}\cdot\rho_{v}^{2 }+D_{wv}\cdot\rho_{v}+E_{wv}, \tag{19}\]  
\[\rho_{w}=A_{w}\cdot H_{w}^{3}+B_{w}\cdot H_{w}^{2}+C_{w}\cdot H_{w}+D_{w}, \tag{20}\]  
Figure 12: Time sequence propagation structure of the network for mechanism model.  
where \(A_{dr}=1.104\times 10^{-6}\), \(B_{dr}=-8.206\times 10^{-4}\), \(C_{dr}=0.2265\), \(D_{dr}=-0.2319\), \(A_{v}=2.331\times 10^{-5}\), \(B_{v}=-6.728\times 10^{-3}\), \(C_{v}=-2.151\), \(D_{v}=2860\), \(A_{uvv}=-7.772\times 10^{-7}\), \(B_{uv}=4.381\times 10^{-4}\), \(C_{uvv}=-0.102\), \(D_{uv}=14.12\), \(E_{uv}=870.3\), \(A_{w}=-1.458\times 10^{-7}\), \(B_{w}=4.729\times 10^{-4}\), \(C_{w}=-0.8501\) and \(D_{w}=1355\). Then, the derivatives \(\partial P_{dr}/\partial\varphi_{v}\), \(\partial H_{v}/\partial\varphi_{v}\) and \(\partial\rho_{w}/\partial H_{w}\) in gradient terms can be easily derived. The range of time-varying parameters, inputs and outputs are shown in Table 7. It should be noted that the range such as \([a\times 10^{\pm c},b\times 10^{\pm c}]\) is written as \([a,b]^{E\pm c}\) for simplicity. In the training processes, the positive and negative characteristics of parameters affect the direction of gradient terms. Therefore, for parameters with different ranges, different normalization ranges and methods need to be adopted to retain their positive and negative signs. In general, the normalized interval of parameters can be divided into three categories, including \([0,1]\), \([-1,1]\) and \([-1,0]\). First, for parameter with interval \([a,b]\), where \(a\geq 0\) and \(b>0\), the normalized interval is set as \([0,1]\). The normalization method can be written as \(\overline{x}=(x-a)/(b-a)\), where \(\overline{x}\) is normalized value of \(x\). Second, for parameter with interval \([a,b]\), where \(a<0\), \(b>0\) and \(a=-b\), the normalized interval is set as \([-1,1]\). The normalization method can be written as \(\overline{x}=x/b\). Third, for parameter with interval \([a,b]\), where \(a<0\) and \(b<0\), the normalized interval is set as \([-1,0]\). The normalization method can be written as \(\overline{x}=-(|x|-|b|)/(|a|-|b|)\). In Table 7, the normalized (std.) range of parameters such as \(w_{p214}^{2}\), \(w_{p24}^{2}\), \(w_{p31}^{5}\) and \(b_{ho1}^{5}\) is \([-1,1]\), and that of other parameters is \([0,1]\). Moreover, the learning rate \(\eta\) is set to 0.001 and the threshold of gradient is set to one.
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 2 Modeling of the Proposed System
### Photovoltaic System (PV)  
A simplified model based into account ambient temperature and solar irradiation is employed in this study to compute the energy generated by the PV panels \(\mathrm{PV_{p}(t)}\), as indicated in the equation below [28; 29].  
\[\mathrm{PV_{p}(t)=N_{PV}\times PV_{rat}^{P}\bigg{(}\frac{R_{int}(t)}{R_{STC}} \bigg{)}\bigg{[}1+\gamma_{T}\bigg{(}(R_{int}(t)\bigg{(}\frac{T_{nor}\ -20}{0.8}\bigg{)}+T_{amb}(t))-\ T_{STC}\bigg{)}\bigg{]}\cdot n_{W}\ \eta_{PV} \tag{1}\]  
where, \(\mathrm{PV_{rat}^{P}}\) indicates the rated power of the PV panel at standard test condition (STC) [kW], \(R_{int}(t)\) is the intensity of solar radiation at time (t), \(R_{STC}\) denote the intensity of solar radiation at standard conditions [1000 W/m\({}^{2}\)], \(\mathrm{N_{PV}}\) is the number of PV units, \(\gamma_{T}\) is the PV module temperature coefficient [%/\({}^{\circ}\)C], \(\mathrm{\eta_{{}_{W}}}\) is the wiring efficiency, \(\mathrm{\eta_{PV}}\) is the PV module efficiency, \(\mathrm{T_{nor}}\) is the cell temperature under at normal operating conditions, \(\mathrm{T_{amb}(t)}\) denote the ambient temperature (\({}^{\circ}\)C), \(\mathrm{T_{STC}}\) denote the cell temperature under standard operating conditions (\({}^{\circ}\)C). The technical specifications of the PV panel modeling are shown in Table 1.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 3 Channel Characterization Metrics
### Correlated Color Temperature  
CCT refers to the color appearance of light, expressed in Kelvin (K), ranging from warm hues (lower CCT) to cool tones (higher CCT). In indoor applications, CCT is critical for optimizing visual comfort, task performance, and influencing psychological wellbeing. Appropriate CCT settings can create conducive lighting environments, improve productivity, and ensure accurate color representation, particularly in professional settings such as offices, retail spaces, and healthcare facilities. Additionally, tunable CCT in modern lighting systems supports energy efficiency and enhances user wellbeing. The correlated color temperature is calculated from the spectral power distribution received on the active area of the photodetector \(P_{R}(\lambda)\). The \(P_{R}(\lambda)\) is represented in the CIExy 1931 color space using three coordinates \((x,y,z)\)[21]. The CCT is calculated using the method presented in [22].
## 5 Results and Discussion
### Multilayer Perceptron Model (MLP)
Based on the minimum value of the arithmetic mean of this relative accuracy criterion for the three measurements, the final model was selected. According to [48], machine learning models are most often trained with 70% of the total data set, while the remaining 30% of the set is reserved for testing, i.e., to assess the accuracy and quality of the model. Additionally, in [49] the division of data in the mentioned ratio showed a good influence on the accuracy of the classifier. The random forest model presented in [50] showed the lowest mean standard error when dividing the data in the ratio of 70%:30%. The selection of this ratio when creating predictive models is one of the inevitable steps that affects its performance. Based on the results of multiple tests of 30 different model variants in Table 7, it is concluded that the best performance is shown by the MLP model variant in which the division of the total data set of 64,301 input/output vector was performed in the ratio 70%:30%. The average RE of the selected model for the output variable _Cell Downlink Average Throughput_ is RE = 0.104 which means that the degree of accuracy is equal to 89.6%. For the variable _Average User Downlink Throughput_, the average relative error has the value RE = 0.120, i.e., the degree of accuracy is 88%. Table 7 also shows that each hidden layer of the architecture of the selected model consists of 20 neurons, whose output function has the shape of the hyperbolic tangent _tanh x_ (Figure 5). The output function of the two neurons in the output layer for the selected, but also for all tested variants of the MLP model, is linear or _Identity_. Figure 5: Output function of neurons shape of hyperbolic tangent _tanh x_. Table 8 shows the variants of the tested MLP models that have the lowest average values of testing error by individual ratios of the training data set and the testing data set. This table is a summary of Table 7. Based on Table 8, it can be concluded that as the percentage of training data increases, the average relative prediction error decreases, e.g., it is obvious that for a ratio of 50%:50% with Hyp.Tg. and with 20 neurons in each hidden layer, the model has an average RE = 0.115, so the degree of accuracy is 88.5%. If this value is compared with RE = 0.104, i.e., with a degree of accuracy of 89.6%, as much as a model has with a ratio of 70%:30%, it is concluded that by reducing the ratio to 50%:50%, we lose only 1.1% of the performance accuracy of the model. This means that the model can perform prediction quite well even at a ratio of 50%:50%.
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 4 Deep Learning Defect Detection Methods for X-ray Images for Industrial Products
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Target** & **Performance** \\ \hline
[136] & R-CNN & Metal & Can eliminate time-consuming and inconsistent criteria while making judgments more efficient and accurate \\
[124] & Deep Neural Networks & Actual samples from the hard metal production industry & Indicates that the fusion model outperforms the separate models in terms of recall (100\%), precision (60\%), F-score (75\%), and accuracy (88.24\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method** & **Strengths** & **Weaknesses** \\ \hline
[125] & Three-stage Deep Learning & Ability to adapt to different types of patterns; the three-stage approach allows for more accurate and efficient detection of defects & The accuracy of the model can depend on the specific models used in each stage, if the models are not well-suited for the task, the performance may suffer \\
[126] & CNN model with 10 layers & Ability to achieve high classification accuracy & May not work well with other types of images \\
[127] & SVM-based method & Achieved real-time X-ray image analysis and reduced undetected defects and false alarms; can work well with small datasets & SVM’s can be sensitive to the choice of kernel and parameters \\
[129] & Yolov5-based DR & Ability to detect tiny anomalies and improve edge retention by using fast guided filtering & May not work well with other types of images or industries \\
[130] & Lightweight semantic segmentation & The dimension reduction allows for accurate recording of bead toe positions in X-ray images; can be trained to work with different types of x-ray images, such as mammograms or chest x-rays 40.9\% increase in Mean of Average \\ Deep learning with & Precision (mAP) value, can effectively \\
[131] & X-ray images and Feature Pyramid & Detect objects at different scales, which is important for defect detection in \\ Networks (FPNs) & X-ray images as defects can be small and difficult to spot \\  & & Improved curve fitting performance; \\ Faster R-CNN & able to handle multiple defect classes; \\ detection model & can handle images of different scales, with X-ray & which is important for defect detection \\ preprocessing & in X-ray images, as defects can be small & structures that could be mislabeled as defects \\  & & Effective at detecting multiple defects, it works well with X-ray images, by preprocessing them into relief images to make defects easier to identify \\  & & Improved performance and higher classification accuracy compared to \\ Stacked & individual models; ability to effectively identify breakdowns during \\ Generalization & & manufacturing process; the ensemble approach can improve the robustness of the model by combining the strengths of multiple models \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Strengths and weaknesses of different deep-learning techniques for identifying defects in X-ray images.
###### Abstract  
This study develops a novel physics-informed neural network (PINN) framework for predicting steady-state dynamic responses of infinite Euler-Bernoulli (E-B) beams on foundations under moving loads. By combining localized PINN modeling with transfer learning techniques, our approach achieves high-fidelity predictions across broad parameter ranges while significantly reducing data requirements. Numerical results show that the method maintains accuracy with less than half the training data of conventional PINN models (15 target domains) and remains effective with just four domains for approximate solutions. Key findings demonstrate that optimal spatial distribution--rather than quantity--of target domains ensures robustness against noise and parameter variations. The framework advances data-efficient surrogate modeling, enabling reliable predictions in data-scarce scenarios with applications to complex engineering systems where experimental data are limited.  
PINNs; surrogate model; moving load; transfer learning Article
## 2 Materials and Methods
### Network Evaluation  
We evaluated the CNN for the correct identification of a thrombus, thrombus location assessment and thrombus segmentations. An expert neuroradiologist with >15 years of experience (expert 1) and a neuroradiologist in training with >4 years of experience (expert 2) performed the same task on the same test set independently: the experts evaluated the scans for presence of a thrombus and performed manual segmentations of the thrombus if they assumed the presence of a thrombus in the scan. The neuroradiologists were blinded for any clinical information and were not aware of the ratio of non-thrombus versus thrombus scans. We compared the results from the CNN with those of the experts.  
Figure 1: Convolutional neural networks (CNN) architectures for the asymmetry and hyperdense artery sign (HAS) detection network. Both networks have similar architectures. The convolutional layers consist of 5 convolutional layers with max pool layers after the first two convolutions. The asymmetry detection network concatenates the output of the convolutional layer from left and right patch pair, before passing through the dense layers. Dimensions of the feature maps and convolutional kernels are shown at the borders. The number of filters is shown at the forward pass for each convolutional kernel.  
We assessed the sensitivity, specificity and precision for the detection of a thrombus within a NCCT scan. A thrombus was considered as \"present\" if at least one voxel within the NCCT scan was classified as \"thrombus\". The thrombus location was considered correct if at least a single voxel of the segmentation overlapped with the ground truth segmentation. Sensitivity was defined as the ratio of thrombi correctly found \"present\" by the CNN or neuroradiologists and the total number of scans with truly present thrombi according to the ground truth. Specificity was defined as the ratio of thrombi correctly found \"absent\" by the CNN or neuroradiologists and the total number of scans with absent thrombi according to the ground truth. The accuracy of the thrombus location assessment was defined as the percentage of scans with correct thrombus location. To evaluate the thrombus segmentation of the CNN and both neuroradiologists we assessed the agreement of the thrombus volume and mean density by the computation of the two-way intraclass correlation coefficient (ICC) agreement using the 'irr' library from R CRAN repository [24] for thrombus volume and mean density in HU. The ICC of the thrombus volume assessment was computed for all patients within the test set with a proven thrombus. If a thrombus was not correctly detected, we set the volume to zero. For the ICC of the mean thrombus density, we only included the thrombus density measurements for patients with a proven thrombus for which the CNN and both neuroradiologists correctly detected the thrombus. We additionally created scatter and Bland-Altman plots to study the relation between the measurements made by network or neuroradiologists and the ground truth.
## 5 Taxonomy of Technology-Enabled Smart City Applications in 6G Networks
### Internet of Things (IoT)
#### 5.1.2 Internet of Medical Things (IoMT)  
The IoMT is a distributed network of bio-medical sensors and devices that acquire, process, and transmit the bio-medical signals of patients. It integrates the communication protocol of the IoT with medical devices to enable remote patient monitoring and treatment. Its global market is expected to reach $172.4 billion by 2030 from $39.3 billion in 2020, at a CAGR of 15.9% from 2021 to 2030 [82]. The IoMT has several applications, such as the monitoring of patients with chronic diseases, monitoring of elderly people, disease prognosis and diagnosis, medical equipment and drug monitoring, drug anti-counterfeiting, and medical waste management. In the context of a pandemic such as COVID-19, the IoMT can be used for the detection, tracking, and monitoring of infected individuals and the prediction of infections [83]. The IoMT applications require ultra-low latency and high reliability for scenarios such as remote surgery. The tactile and haptic internet is the backbone for such scenarios, whose requirements are not completely fulfilled by the current wireless systems [84]. The tactile internet requires an end-to-end latency of the order of 1 ms and haptic feedback requires a latency of sub-milliseconds [85; 86].  
Figure 15: Growth of Internet of Things (IoT) and non-IoT devices from 2010 to 2025.
## 2 Materials and Methods
### Characterization
#### 2.3.3 Physical Analysis  
The Scanning Electron Microscope (SEM) tests were used to evaluate the morphological structure of the samples were performed in an Ultra-high-resolution Field Emission Scanning Electron Microscopy (FESEM). Before the analysis, the polyureas were covered with a thin film of Au-Pd (80-20 wt.%), using a high-resolution sputter coater. Secondaryelectron images, i.e., topographic images, were obtained at an acceleration voltage of 10 keV. Atomic contrast images were obtained using a Backscattering Electron Detector (BSED) at an acceleration voltage of 15 keV. After these tests, the samples were analyzed between 0 and 10 keV in the Energy Dispersive X-ray (EDX) to determine the elementary composition of the polyureas.  
The density of the polyureas was determined following the standard ISO 1183, using the immersion method which determines the density of solid substances. It was possible to determine the density of the samples using specimens with 10 \(\times\) 10 mm, using an analytical balance and distilled water as the immersion liquid.  
For the water absorption test, the samples had dimensions of 10 \(\times\) 10 \(\times\) 2 mm, which were first placed in an oven for 24 h at 50 \({}^{\circ}\)C, following the standard ASTM D570. Subsequently, after weighing, the samples were immersed in distilled water for 24 h and weighed again after that period had passed. The percentage of water absorption was determined using Equation (1):  
\[\%\ \text{Water absorption}=(\text{mf}-\text{mi})/\text{mi}\times 100 \tag{1}\]  
where mf and mi represent the final and initial mass of the samples tested, respectively.  
In the dimensional stability test, performed according to the standard ASTM D1204, the dimensions of a specimen of the polyureas were registered using reference marks. The sample was then placed in an oven for 24 h, at 70 \({}^{\circ}\)C. After that period, the samples were exposed to room temperature for one hour then a new measurement was made.