esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Nutrient Content and Energy Value of SDexF  
All tested features regarding the nutrient content in SDexF are presented in Table 5 as mean values along with standard deviation. For a complete picture of the nutritional value, the table also includes the average total fibre content (discussed in Section 3.1). The energy value of SDexF was 296.66 kcal/100 g (1238.56 kJ/100 g), which is not a very high amount, considering that SDexF was added in the amount of 5 g to vegetable and fruit mousses, which gives a value of approximately 60 kcal. The starch content in SDexF was 29.06 g/100 g. The amount of fat was below the Limit of Quantification (LOQ), which was 0.04 g/100 g. The fatty acid content, including saturated fatty acids (SFA), monounsaturated fatty acids (PUFA), trans fatty acids (TFA), omega-3 fatty acids and omega-6 fatty acids was below 0.06 g/100 g. The nitrogen content was 0.013 g/100 g and calculated on this basis as protein content was 0.08 g/100 g. These parameters prove that SDexF contains small amounts of fat and protein but contains 55.84 g/100 g carbohydrates. Total ash content was also tested and amounted 0.39 g/100 g. The sodium chloride content was as low as 0.1 g/100 g.
# Selection of a Photovoltaic Panel Cooling Technique Using Multi-Criteria Decision Analysis
## 2 Methods and Criteria
### Technology Feasibility
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**S.No**} & \multirow{2}{*}{**Cooling Method**} & \multirow{2}{*}{\begin{tabular}{c} **Temperature** \\ **Drop (°C)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Panel Area and** \\ **Type (cm \(\times\) cm)** \\ \end{tabular} } & \multirow{2}{*}{**Peak Power**} & \multirow{2}{*}{
\begin{tabular}{c} **\% Efficiency** \\ **Wh** \\ \end{tabular} } & \multirow{2}{*}{**Net Power**} & \multirow{2}{*}{**Reference**} \\  & & & & & & & \\ \hline
1 & Aluminum fins & 12.5 & \(50\times 50\) & 20 & 4\% & 40 & [14] \\ \hline
2 & Heat pipe cooling & 14.2 & 1240 \(\times\) 541 & 80 & 8\% & 80 & [25] \\ \hline
3 & Phase change material & 23 & 48.5 \(\times\) 34.5 & 20 & 10\% & 100 & [26] \\ \hline
4 & Thermosiphon cooling & 23 & 1240 \(\times\) 541 & 80 & 8\% & 80 & [27] \\ \hline
5 & Thermosiphon/clay pot cooling & 28 & 43 \(\times\) 43 & 20 & 10\% & 100 & [28] \\ \hline
6 & Forced convection fan cooling & 22 & 88 \(\times\) 88 & 55 & 7\% & 70 & [29] \\ \hline
7 & Water spraying & 40 & 75 \(\times\) 75 & 50 & 15\% & 150 & [30] \\ \hline
8 & Evaporative cooling & 40 & 75 \(\times\) 75 & 50 & 15\% & 150 & [16] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical performance of different cooling methods. The temperature of the panel is different in different climatic regions as well as in summer and winter conditions. The temperature of the PV cell (T\({}_{\rm C}\)), which is given below in Equation (4), depends on the ambient temperature (T\({}_{\rm a}\)) and solar radiation intensity (I\({}_{\rm T}\)). NOCT is the nominal operating temperature of the PV cell, which is 45 \({}^{\circ}\)C [32]. \[\rm T_{C}=T_{a}+\left[\frac{I_{T}}{800}(NOCT-20)\right] \tag{4}\]
## 3 UAV 6G Networks
### 6G Applications
#### 3.1.2 Wearable Technology Based Futuristic Applications  
Brain computer interface, wearable clothing and technology are futuristic ideas that demand robust security for sharing the data. Current wireless networks including 5G are not capable to utilize numerous future prospects that are beyond the traditional medical ways.  
XR Apps with Multi-Sensory  
Extended reality multi-sensory applications are designed in a way for the provision of user experience that is entirely enchanting by amalgamating the reception from human sensory, environment and, human body moves, and several data originators. In view of the above, the aspect of data integrity is mandatory for such applications, as any malicious attack can be devastating and can spoil the user experience.  
Autonomous Systems Connected through a Wide Range Network  
Networked together autonomous devices including self-driven vehicles, delivery methods based on drones and robots based on autonomous technology are part of ancillary field, where 6G has potential uses. These applications require all three 5G service classes at the same time. In order to meet this end, 5G slicing cannot be a possible solution.  
Support for Vertical Domains  
The KPIs identified by 3 GPP against secondary and core QoS for vertical commerce, in which identical services and artifacts are offered, include energy, automation, health and manufacturing. In the near future, 5G will reveal its inability to cater the ever increasing number of networked devices in vertical industries, when we discuss mMTC.
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 2 Electrical Connection Faults
### Solder Ball  
Finally, consider the effects on circuit performance of cracks in solder ball. Vibration, mechanical shock, and other environmental stresses may cause solder ball cracking. As shown in Figure 6, for the solder ball crack area, the connection mode of metal-metal becomes metal-void-metal, which results in a capacitance effect in the equivalent connection impedance network. For a solder ball area without a crack, when current flows through the metal, the bulk resistance and skin effect of the solder ball produce a resistance and inductance effect in the equivalent connection impedance network.  
As the degradation degree of a solder ball increases, the crack area increases, which leads to the increase of connection resistance \(R_{3}\), capacitance \(C_{3}\) and inductance \(L_{3}\). Due  
Figure 4: Equivalent connection impedance network of bonding area.  
Figure 5: The normal and fault values of the connection resistance of bonding wires in the experimental testing and model simulation.  
Figure 6: Equivalent connection impedance network of a cracked solder ball.  
to the capacitance effects arising from solder ball cracks, such faults mainly affect the lower frequency components of a transmitted signal. Similarly, in our experiments, the faulty solder balls were produced using an accelerated test, and the maximum measured fault connection resistance was as high as 123 m\(\Omega\). However, most of the observed values were not that high. In order to simulate the most realistic configuration for engineering applications, a lower value was selected. Accordingly, in the current simulation model, the maximum connection resistance for a faulty solder ball was chosen as 105 m\(\Omega\), which is representative of what was generally observed in the experimental tests [15]. In addition, the maximum connection resistance of a normal solder ball in the simulation model is 5 m\(\Omega\), which is also representative of what was obtained from experiment, as shown in Figure 7.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Activation Functions in Artificial Neural Networks (ANNs)  
In our discussion of ANNs, it is crucial to delve deeper into the role of activation functions, which play a pivotal role in the network's ability to capture non-linear relationships in the data. Activation functions introduce non-linearity to the model, enabling ANNs to learn from an error and make necessary corrections, which is essential for deep learning.  
One of the most commonly used activation functions is the Rectified Linear Unit (ReLU). In our study, we employed ReLU due to its computational efficiency and ability to prevent the vanishing gradient problem, which can be an issue with other activation functions. Mathematically, ReLU is defined as:  
\[f(x)=max(0,x)\]  
This means that, if the input is positive, it will return the input value, and if it is negative, it will return zero. The simplicity of this function allows models to train faster and requires less computational resources compared to other non-linear functions.  
However, it is worth noting that, while ReLU is beneficial in many scenarios, it is not without its challenges, such as the dying ReLU problem, where neurons can sometimes get stuck during training and stop updating. To mitigate this, variants like Leaky ReLU and Parametric ReLU have been introduced.  
Figure 5: Comparision of learning curves.  
In our analysis, the use of ReLU as an activation function in the hidden layers of our ANN model allowed us to capture the non-linear patterns in the water-quality data effectively. By introducing non-linearity at each layer, our network could learn complex relationships and provide more accurate predictions.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 5 Experimental Validation  
A 125 V, 50 Hz inverter prototype with the proposed grid impedance compensator was built for demonstration purposes and evaluate the system at the scaled-down laboratory level. Figure 17 shows the inverter prototype, where the volume of the compensator is much smaller than the GC-VSI owing to its extremely low volt-ampere (VA) rating. The design parameters are tabulated in Table 1. The inverter is supplied by an external DC power supply CHROMA 62050H-600S, and controlled by the digital controller, Texas Instruments TMS320F28379D. The experimental inverter setup is shown in Figure 18, where the inverter is connected to a 220 V, 50 Hz power grid through an isolator for the safety reason. The leakage inductance of the isolator is 0.65 mH. In Figure 18, an optional \(R_{d}-C_{d}\) branch is connected in shunt at the PCC to smooth the PCC voltage, which is usually included in the EMI filter. Because the LV-VSC is in series with the utility before the inverter is enabled, it will be activated after the dc capacitor voltage increases gradually to the reference voltage. Thereafter, the inverter will get involved and reach the steady-state operation.  
Figure 19 presents the key waveforms of the inverter system. As seen, the dc capacitor voltage, \(u_{\text{dc}}\), of the LV-VSC is regulated at 30 V, which is superposed at the inverter output voltage, \(u_{o}\). The grid current, \(i_{o}\), is regulated in synchronous with the PCC voltage, \(u_{\text{pcc}}\). Owing to the full feedforwarding mechanism (as shown in Figure 8), strong harmonic rejection performance against grid harmonic distortion is obtained for the grid current as indicated in Figure 19.  
Figure 16: Simulation waveforms of the inverter outputs with traditional current control scheme (CASE I).
## 3 Experiment Results and Discussion
### Influence of Train Speed on Vibration Velocity  
Figure 11 refers to variations of the pile-raft foundation's velocity with the train speed. It can be seen that the present vibration velocity tends to increase with the increasing train speed. The increasing rate at the track slab, roadbed, road shoulder, slope toe, and the ground soil is about 1.38, 0.135, 0.115, 0.133, and 0.104 mm/s per 10 km/h. Note in Figure 11a that the vibration velocity at the track slab (i.e., V1) is sensitive to the train speed compared to that at the road bed, road shoulder, slope toe, and ground surface (i.e., V2, V3, V4, and V12), respectively, due to the higher stiffness of the structure. Similar variations can be found in [PERSON]'s study [26]. As the highest peak frequency of the M-shaped wave simulated in the model loading test was 30 Hz, the maximum train speed in the model test was 270 km/h, and the dynamic response analysis of the track embankment and pile-raft foundation at higher speeds can be achieved by numerical simulation.  
The relationship between the vibration velocity and train speed at the locations V5, V6, V7, and V11 is shown in Figure 12. It can be seen that that the vibration velocity response of the roadbed, raft, cushion, and bottom of the subsoil increases with the increase of the train speed. The growth rate is 0.178, 0.098, 0.061, and 0.0174 mm/s per 10 km/h, respectively. Additionally, from Figure 12, it can be seen that the vibration velocity's growth rate of  
Figure 11: Relationship between the vibration velocity and train speeds in the model test. (**a**) V1, V2, V3, V4, V12, (**b**) V2, V3, V4, V12.  
the roadbed (V5) is 0.44 mm/s per 10 km/h when the train speed is increased from 225 to 270 km/h, which is higher than the others, implying that the vibration response at the track and roadbed is more sensitive to the high train speed. As expected, the velocity's growth rate of internal vibration is comparably lower than that of the surface. The upper vibration waves are reflected and attenuated by the isolation of the raft structure, resulting in a lesser vibration response of the subsoil under the raft. The sensitivity of the vibration velocity response of the foundation soil to the train speed is much lower than that of the upper structures, such as the track slab, roadbed, and embankment.
## 1 Introduction
Miniaturized palladium (Pd) nanowire hydrogen sensors [7] that can be used at room temperature have good sensitivity to H\({}_{2}\) concentration, thanks to their large surface-to-volume ratio. These sensors are good candidates for ultra-low power (ULP) hydrogen sensing, as they are very low power, while maintaining a small form factor. As these sensors are very sensitive to temperature, temperature effects should be compensated to increase measurement accuracy. A grid of 14 Pd nanowires, fabricated on a silicon wafer, has been used for gas sensing [8]. Half of these sensors are only sensitive to temperature and have been used as reference nanowires. The remaining sensors are sensitive to both temperature and H\({}_{2}\) concentration and have been used as sensing nanowires. As reference and sensing nanowires have a similar temperature dependence, temperature cross-sensitivity is cancelled by deploying a differential approach in the readout circuit. The readout circuit proposed in [9] has been used to measure the change in conductivity of these nanowires upon hydrogen exposure. Upon hydrogen exposure, the conductance change of sensing nanowires in comparison with reference nanowires is measured and converted to a digital value using an analog-to-digital converter. This paper makes the following contributions: (a) a new solar energy harvester circuit is proposed that scales the power consumption and performance of a wireless sensing platform, according to the harvested solar energy. This circuit reconfigures the power and speed of a fully integrated gas sensor interface circuit and data transmission duty cycle, according to the energy stored in the battery; (b) we present the design and implementation of an energy-efficient wireless sensing platform that uses the proposed solar energy harvester circuit to guarantee autonomous operation of the proposed hydrogen gas sensor. System level design parameters that affect autonomous operation of this sensor, including selection of the appropriate energy storage option and wireless data transceiver, have been discussed thoroughly. The remainder of this paper is organized as follows. Section 2 presents the system architecture, including the realized integrated circuit and required external components. Section 3 presents the system implementation of the realized integrated circuits. In Section 4, measurement and simulation results have been discussed to evaluate the performance and autonomous operation of the proposed hydrogen sensor, and finally, Section 5 concludes the paper.
## Materials and Methods  
### Greenhouse and Chamber Drying Experiments  
The original technology for Prosek wine making involves drying the grapes; however, different drying methods are currently used in Croatia (sun-drying, chamber-drying, greenhouse-drying, etc.). To compare volatile compounds formed in dried grapes under two controlled conditions, we set up a drying experiment in the greenhouse that is a different technique from the direct sun-drying method and the chamber-drying one.  
The experiments were conducted with the native grapes of 'Marastina' cultivar (_Vitis vinifera_ L.) grown on the peninsula Peljesac, Croatia (Dalmatia). A total of 300 kg of grapes were manually harvested at the technological maturity and partially dried in a single layer under the controlled greenhouse conditions (Schwarzmann, volume of 225 m\({}^{3}\)). Only grapes with visually good health status were used for the experiment. Daily temperature in the greenhouse was between 17 and 37 \({}^{\circ}\)C, and indoor humidity was \(55\pm 13\%\).  
At the same time, a total of 20 kg of grapes were uniformly distributed in a single layer in a hot-air chamber SP-440 C (Kambic, Semic, Slovenia, internal dimension 1000 (W) \(\times\) 800 (H) \(\times\) 550 (D) in mm) at a constant temperature of 50 \({}^{\circ}\)C \(\pm\) 0.4 \({}^{\circ}\)C in two repetitions. Based on the study of [PERSON] et al. [19], chamber-drying at 50 \({}^{\circ}\)C had advantages over the sun-drying and chamber-drying at 40 \({}^{\circ}\)C in terms of the aroma, flavor, and color of the must from 'Pedro Ximenez' grapes and therefore it was selected as the investigated temperature of the chamber-drying experiment in the study. The grapes were dried until the sugar content reached around 36 \({}^{\circ}\)Brix, which took 22 days in the greenhouse, and 3 days in the chamber. Randomly chosen samples of grape were collected from at least 10 different points every other day in the greenhouse and every day in the chamber to determine the progression of grape drying. Three representative samples of grapes were collected for the analyses of aroma compounds at the beginning and at the end of the dehydration process and squeezed by horizontal presses to obtain juice. Analysis of volatile compounds was performed one month after storage of juice samples at -80 \({}^{\circ}\)C.
## 3 Environmental Vibration Test and Linear-Elastic Modeling
To assess the impact of the presence of RC walls and infilled frames compared to a bare frame on the fundamental period, these effects have been evaluated and presented in the Table 4. In Figure 7, the mode shapes of the refined three-dimensional model are plotted. The modal analysis results provide insight into the influence of RC walls and infilled masonry walls on the fundamental periods and corresponding frequencies of the hospital building. For the bare frame model, the fundamental periods are notably longer, with values of 0.244 s, 0.242 s, and 0.226 s for the first three modes, corresponding to frequencies of 4.10 Hz, 4.13 Hz, and 4.42 Hz, respectively. When RC walls and infilled masonry walls are included in the model (referred to as the \"full model\"), the fundamental periods reduce to 0.214 s, 0.175 s, and 0.151 s for the first three modes, indicating increased stiffness and a corresponding rise in frequencies to 4.66 Hz, 5.71 Hz, and 6.59 Hz. The reduction in the fundamental period proves that structural stiffening due to RC and masonry infill walls has resulted in great changes in dynamic characteristics compared to a bare frame. Furthermore, the overall change in mode shapes displayed in Figure 7 leads to the understanding that adding these elements means changing not only frequencies but also deformation patterns along the building and enhancing lateral stiffness. The obtained results point out the relevance that exists in the true modelling of such elements for the correct prediction of dynamic behaviour in a building and for proper seismic performance assessment.
## 3 Data Acquisition and Processing
### Data Processing and Analysis
#### 3.2.3 Data Validity Test  
Factor analysis was used to test the structural validity of the sample data, examining whether the four latent variables related to outpatient attraction across different types of hospitals are strongly correlated. Prior to the analysis, the Kaiser-Meyer-Olkin (KMO) measure and [PERSON]'s sphericity test were conducted to assess the suitability of the sample data for factor analysis. A KMO value closer to 1 indicates stronger suitability. The results show that the KMO values for the two samples are 0.679 (general hospitals) and 0.750 (specialized hospital), and [PERSON]'s sphericity tests were significant at the 0.01 level, indicating that the data meet the prerequisites for factor analysis.
## 4 Examples
### Application for Random Forest Based Discrimination Method  
The Random Forest-Based Discrimination (RFBD) method can be described with matrices, which results in the proposed method that can be used for RFBD learning processes, as shown in Figure 6. In Figure 6, majority logic means that the final output result is determined by the majority of the outputs of a plurality of decision trees.  
Figure 5: Example of mind map for house detection from remote sensing satellite images.  
Figure 6: Application of the proposed GCN and GNN for RFBD learning processes.
## 2 Materials and Methods
### Prediction Models
To summarise, the input parameters used to construct the RF Machine Learning model included in situ SST and SSS data, along with the red, blue, green, and infrared bands from Sentinel-2 satellites. The model was subsequently developed using the Pandas library in Python (v.3.13). The'sklearn.ensemble' module from the scikit-learn Python Machine Learning library provided a built-in function needed to create the random forest regressor model. Once the model was imported, the training data was fitted into the model to enable the algorithm to learn from the data and construct the model. The'sklearn.model_selection' module was utilised to implement the training-to-testing split ratio as the validation method. Consequently, predictions were made on the testing data to compare the predicted values with the actual in situ values, and the performance of the model was assessed using the'sklearn.metrics' module to obtain the PCC and RMSE performance metrics. Lastly, the output parameters of the model included the predicted SST and SSS, along with the corresponding longitude and latitude of the predicted points. The complete predictive modelling process is depicted in Figure 7.  
Figure 6: SSS and SST model data obtained from Marine Copernicus Data Hub used for validation. Figure 7: Complete predictive modelling process.