esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Many severe weather systems that have impacted China in the past have been linked to the dynamic and thermodynamic influences of the QTP [46; 47; 48; 49]. Simulation studies have indicated that assimilated atmospheric moisture, temperature, and pressure information over the QTP and surrounding areas is very important and useful in the forecasting of precipitation in downstream areas [50]. However, as shown in Figure 1, the spatial distribution of operational radiosonde stations, while reasonably adequate in eastern China, is rather sparse in western China, especially over the QTP. As noted above, COSMIC RO can provide atmospheric temperature, pressure, and humidity profiles in all weather conditions, thereby overcoming the lack of operational radiosonde soundings with high spatiotemporal distributions, especially in the QTP and desert areas. Although some studies have compared COSMIC RO and radiosonde observations [4; 11; 51; 52; 53], they have focused on temperature, and few comparisons of pressure and humidity have been reported over China, especially for the QTP region. In this study, using radiosonde soundings with a high vertical resolution of \(\sim\)10 m, we investigate the discrepancies in atmospheric temperature, pressure, and vapor pressure profiles obtained from COSMIC RO data compared to radiosonde observations in China. This paper is organized as follows. In Section 2, we briefly describe the instruments, measurements, and methods employed. Section 3 compares the COSMIC atmospheric wet profiles with matched radiosonde soundings in China. The results are presented and discussed in Section 4, and Section 5 summarizes our findings.
## 3 Experimental Validation  
Based on the developed system and algorithm, the feasibility tests are performed. First, the temperature effects on the electrical impedance and DI, which is one of the biggest error sources in the making-decision for crack detection [44], are investigated. Then, three-point bending tests are conducted for validating the proposed crack localization algorithm.
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
## 3 Developed System
### Implementation of the Proposed System
#### 3.5.2 Firmware
Additionally, a copy of the program memory data rows is stored for recovery purposes in case of failure. With the firmware update data structure prepared, the next step involves formatting each of the rows to be updated. This formatting consists of erasing the contents of these rows and filling them with the hexadecimal value 0 xFF. Subsequently, each row of the program memory is updated with the data from the received records. Finally, a verification of the updated rows is performed to ensure that the content written into the program memory exactly matches what was intended. This involves verifying whether the program memory  
Figure 14: Screenshot of the data log from the TX pin of the RA-07H radio transceiver, Port 7. Figure 15: Screenshot of the data log from the TX pin of the RA-07H radio transceiver, Port 8.  
content specified in the update record addresses corresponds to the received update record data. If the verification is successful, the update process signals Success; otherwise, it signals Failure and restores the previous firmware version. Figure 16 graphically illustrates the described update process. As depicted in Figure 16, the update process is finalized with the transmission of the update status by the end device. The update message includes the filename being updated and its status, either success (Code 3) or failure (Code 4). Figure 17 illustrates the message sent by the microcontroller to the RA-07H radio transceiver and its reception by The Things Network server through Port 9. The network server, upon receiving the firmware update status message, forwards it to the application server. There, the middleware interprets the message and update the state of each firmware update record from 'update' to the state indicated by the update message, either 'updated' or 'failure'. Figure 16: Graphical representation of the firmware update process during runtime. Figure 10:0 Sending the firmware update status message. From the analysis of the debug report of the UART TX and RX pins of the microcontroller and the RA-07H radio transceiver, correlated with the data traffic on the network server, it was observed that the time required for the update process depends on the number of records to be updated due to the characteristics of the LoRaWAN network. It is also noted that the firmware update occurred during runtime without the need to restart the end device after completion of the update and without any perceived downtime, as depicted in Figures 18 and 19.
## 2 Materials and Methods
### Synthesis and Application of the Coatings  
TiO\({}_{2}\) water-based sol (0.1 M), hereafter called N-TiA, was synthesized from titanium (IV) oxysulphate (TiOSO\({}_{4}\), Sigma-Aldrich, St. Louis, MO, USA) as a Ti precursor as reported in [PERSON] [63]. Briefly, 1 \(\times\) 10\({}^{-2}\) mol of TiOSO\({}_{4}\), hydrolyzed in distilled water at 40 \({}^{\circ}\)C, were subjected to precipitation by slowly dripping ammonium hydroxide (NH\({}_{4}\)OH, 26%, [PERSON], Milan, Italy) into the solution until the pH \(\approx\) 7. The white precipitate was repeatedly washed with distilled water to eliminate the residual SO\({}_{4}\)\({}^{2-}\) ions (as checked by a BaCl\({}_{2}\) test). The resulting powder was suspended in distilled water (100 mL), followed by peptization with nitric acid (HNO\({}_{3}\) 65% [PERSON]) until the pH was down to 1.3-1.5. The suspension was left under vigorous stirring at room temperature for about 30 min until it became clear. The sol was then refluxed at 100 \({}^{\circ}\)C for 2 h to induce crystallization. The colorless final sol is slightly opalescent and does not show precipitates.  
The organically modified silica coating (hereafter called TP-Hy) was prepared by using tetraethyl orthosilicate (TEOS, Sigma Aldrich) and hydroxyl-terminated polydimethylsiloxane (PDMS, Sigma Aldrich) as starting materials. Ethanol (EtOH, Sigma-Aldrich) and de-ionized water were used as solvents, while oxal acid dihydrate (OxA, Sigma-Aldrich) was used as the catalyst [32]. The procedure was as follows: TEOS was hydrolyzed in a solution containing EtOH, H\({}_{2}\)O and OxA in the molar ratio TEOS:EtOH:H\({}_{2}\)O:OxA of 1:15:15:0.00015 under vigorous stirring at room temperature for 140 min. PDMS in a ratio of TEOS:PDMS = 1:0.1 (_v/v_) was then added to the previous solution and refluxed for 24 h.  
The compatibility and effectiveness of the innovative self-cleaning surface treatment were preliminarily verified on samples of raw _Lecce stone_. Before the application of the coatings, 5 \(\times\) 5 \(\times\) 1 cm\({}^{3}\) samples were dried at 60 \({}^{\circ}\)C in a ventilated oven for 24 h, kept 3 h in a dry atmosphere and then weighed. The procedure was repeated until constant weight was achieved according to UNI 10921:2001 [77]. Then the TP-Hy hydrophobic treatment was applied by brush directly on the stone samples, and three layers of N-TiA or of commercial P25 (for comparison) sols (0.1 M) were added. The P25 (pH \(\approx\) 5) was applied as prepared, while N-TiA, before application, was repeatedly washed until a final pH \(\approx\) 5 and re-suspended in distilled water [63]. The amounts of TP-Hy and TiO\({}_{2}\) coatings applied to the stone surface per unit area were held constant as accurately as possible. The stone samples were then stored in a desiccator at a temperature of 23 \(\pm\) 1 \({}^{\circ}\)C.
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
## 3 Numerical Experiments
### The Surge Waves in Tailrace Canal of a Hydropower Station  
The surge waves in the tailrace canal of a hydropower station caused by load rejection were simulated by the 1D-2D coupled model. The tailrace canal had a constant rectangular cross-section with a length of 100 m, a width of 10 m, a flat bottom, and a Manning roughness coefficient of 0.015. The upstream boundary was a given discharge, which decreased linearly from 600 m\({}^{3}\)/s to 0 m\({}^{3}\)/s within 8 s. The downstream boundary was a constant water depth of 15 m. The lateral boundaries of the 2D domain were treated as no-slip wall boundary conditions. As depicted in Figure 6, the 2D submodel and 1D submodel were used to simulate the upstream half part and downstream half part of the domain, respectively. The interface was at \(x\) = 50 m, which was also at the outer boundary of the 2D domain. The lattice spacing was 0.2 m, the time step was 0.01 s, and the relaxation parameters were \(\tau_{1}=0.6\) and \(\tau_{2}=0.8\).  
The discharge and water depth histories of the 1D-2D coupled model and commercial software Delft3D were compared in Figure 7. The results agreed well with each other, and the proposed method had a better ability to capture the steep inflection point of the surge, indicating a smaller numerical diffusivity.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Influence of Structural Parameters on Vibration in Integrated Bearings
#### 2.2.3 Effect of Cage Pocket Clearance on Vibration of Integrated Bearings  
Cage pocket clearance significantly impacts the vibration characteristics of integrated bearings. The integrated bearings referenced in this study utilize a crowned cage made of nylon material. Figure 12 illustrates the relationship between cage pocket clearance and bearing vibration when both the inner and outer raceway curvature radius coefficients are set at 0.529. As shown, for the crowned cage, an increase in cage pocket clearance initially leads to a decrease in the vibration acceleration level and vibration velocity of the integrated bearings, followed by an increase. The optimal vibration characteristics are observed when the cage pocket clearance is between 0.045 and 0.055 mm.
# Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement
## 2 Principle and Experiment
### Measurement System and Data Acquisition  
The experimental device is shown in Figure 2. Three single-axis precision rotary tables are used, namely, continuous, PI and RPI, to provide the step by step standard rotary angle values of three axes, with accuracies of \(\pm 4^{\prime\prime}\), \(\pm 2^{\prime\prime}\), and \(\pm 1^{\prime\prime}\), respectively. Four eddy current sensors are employed and asymmetrically located by the fixture. The sensor probe is fixed by the nut through the arc plate, and the sensor array probe points to the ball center of the spherical joint. The position of the sensor is shown in Figure 3. The angles between the sensors are \(60^{\circ}\), \(75^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\). The arc-shaped plate has a ring groove, and its pitch angle ranges from \(120^{\circ}\) to \(150^{\circ}\). The rotation deviations of the ball head within the rotation range of the X, and Y axes and the ball joint rod are \(39~{}\upmu\)m, \(38~{}\upmu\)m, and \(23~{}\upmu\)m.  
Figure 1: Schematic diagram of measurement system.  
Figure 2: (**a**) Displays the eddy current sensor array; (**b**) displays the three-dimensional rotary table.  
The output value of the eddy current sensor is the voltage signal. The data is collected by an NI acquisition card and stored in an Excel table by LabVIEW software. The standard angle of three-axis rotation is provided by the three-dimensional rotary table. The neural network was used to train the measurement model, with the collected eddy current voltage as the input and the three-dimensional rotation angle as the output. During the experiment, three standard angle values provided by the three rotary tables and the output values of the eddy current sensor are mapped into data groups one by one to facilitate the subsequent artificial neural network model training.
# Comprehensive Optimization of Air Quality in Kitchen Based on Auxiliary Evaluation Indicators
## 2 Description of Numerical Method
### Application of the Auxiliary Evaluation Index
#### 2.3.1 Initial Definition and Limitations  
Initially, Q is defined as the arithmetic average of several normalized air quality indicators. Each indicator's measured value is divided by its standard upper limit, resulting in a dimensionless summation index. The formula is expressed as follows:  
\[Q=\frac{1}{4}\left(\frac{100\%}{20\%}+\frac{PMV}{1}+\frac{CO_{2}}{700}+\frac {COFP}{50}\right) \tag{7}\]  
Although this approach provides a straightforward method for integrating diverse metrics, it has several limitations. Firstly, the variations in individual indicators affect the overall Q value inconsistently. Secondly, the negative PMV values, which indicate thermal discomfort, are not adequately accounted for. Thirdly, assigning equal weights to all indicators fails to reflect their relative importance.
## 3 Performance Analysis of System
### BER Performance Analysis of GCI-DCSK SWIPT
#### 3.2.2 BER of Modulated Bits  
The decoding of the modulated bits is divided into two steps: distinguish the active carriers and decode the modulated bits from the active carriers. The modulated bits will be recovered incorrectly if an error occurs in any step. In the first case, the active carriers are determined correctly, but the modulated bits are wrongly decoded. The BER in this case is formulated as  
\[P_{m}^{1}=\frac{1}{2}\text{erfc}\bigg{[}\frac{2\beta}{(\beta-\beta_{1})\delta }+\frac{\beta^{2}}{2(\beta-\beta_{1})\delta^{2}}\bigg{]}^{-0.5}. \tag{24}\]  
In the second case, \(\lambda\) (\(1\leq\lambda\leq p_{2}\)) inactive carriers are detected as active carriers, and the modulated bits will be detected by the inactive carriers with the probability of correct detection being 0.5. So the average BER in this case is formulated as \[P_{m}^{2}=\frac{\sum_{i=1}^{P_{2}}C_{p_{2}}^{i}[0.5i+p_{m}^{1}(p_{2}-i)]}{p_{2} \sum_{j=1}^{P_{2}}C_{p_{2}}^{j}}. \tag{25}\]  
Finally, the BER of the modulated bits is given by  
\[P_{m}^{BER}=(1-P_{i}^{SER})P_{m}^{1}+P_{i}^{SER}P_{m}^{2}. \tag{26}\]
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery  
### Thermal Hazards of Typical Battery Components  
In general, a LIB is mainly composed of electrodes, electrolytes and a separator. The electrodes can further be divided into the cathode and the anode. The thermal hazard of the LIB usually results from the destructive reactions of battery components such as the decomposition of electrode/electrolyte, the reaction between electrodes, the reaction between electrode and electrolyte, etc. In order to gain a comprehensive understanding on the thermal hazards of the LIB, the hazards of typical battery components will be discussed following.
## 3 Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour Model Design
### Edge Contour-Guided Feature Fusion Residual Network
The outputs of these two convolutional layers are then subjected to element-wise multiplication and addition with scale 1, yielding the fused output result R1 with a feature size of (56, 56, 256). In Stage 2, the backbone network consists of four bottleneck residual blocks, resulting in an output feature map scale 2 of dimensions (28, 28, 512). The feature map of the bone marker is then combined with scale 2 to produce the fused feature R2 with dimensions (28, 28, 512). Stage 3 comprises six bottleneck residual blocks, generating the feature map scale 3, which, after SPADE model processing, yields the fused feature R3 with dimensions (14, 14, 1024). In Stage 4, with three bottleneck residual blocks, outputs scale 4 with dimensions (7, 7, 2048) and the fused feature R4 has the same dimensions (7, 7, 2048). Figure 6: Edge contour-guided feature fusion Resnet network. On the basis of edge contour guided Resnet50, a multi-scale fusion strategy is introduced to enhance the shallow detail features of bone marker images. While preserving the deep feature R4, additional fusion features of different scales are incorporated with dimensions being (56, 56, 256), (28, 28, 512), (14, 14, 1024), and (7, 7, 2048), respectively. The aforementioned four feature maps undergo adaptive average pooling layers to obtain features with dimensions (1, 1, 256), (1, 1, 512), (1, 1, 1024), and (1, 1, 2048). Subsequently, these five layers of feature vectors are concatenated along the channel dimension, yielding a fused feature with dimensions (1, 1, 3840). This fused feature is then processed through a fully connected layer (fc) for output with the incorporation of a Dropout layer in the fully connected layer to prevent overfitting.
# Degradation of Hydraulic System due to Wear Particles or Medium Test Dust
## 3 Results
### Gear Pump
#### 3.2.2 Surface Inspection of Hydraulic Gear Pump  
Wear on three gear pump housings from a production series was studied. The first was a new pump that was examined. The second pump was tested for 624 h of operation with wear particles. The third pump was tested for 5.3 min with medium test dust. The hardness of the pump housing, which was a type of aluminum alloy, was measured between a minimum of 67 HV\({}_{0.5}\) and a maximum of 85 HV\({}_{0.5}\) for all three housings. Internal leakage was present where there were gaps between the pressurized and unpressurized (suction) sections. Wear of the pump housing was observed on the pressurized and suction sides. Figure 10 shows the wear of the housing on the suction side where the gear was in contact with the housing. The width of the worn band was measured as 9.55 \(\pm\) 0.05 mm. The width of the gear that was in contact was 10.00 \(\pm\) 0.05 mm.  
The undamaged surface of the pump on the suction side tested with wear particles is shown in Figure 11(a1) and the surface tested with test dust is shown in Figure 11(b1). Figure 11(a1,a2) show the undamaged surface and the boundary between worn and undamaged surfaces of the housing tested with wear particles. There are some horizontal scratches perpendicular to the sliding direction due to wear particles caught between the housing and the side plate (Figure 11(a1,a2)). These particles erode the surface due to the pressure difference. No such horizontal scratches can be seen on the unwor  
Figure 10: Wear of the housing on the suction side of (a) pump tested with wear particles and (b) pump tested with medium test dust.  
Figure 9: Volumetric efficiency of hydraulic gear pump tested with test dust.  
tested with test dust (Figure 11(b1,b2)) since the test was very short (5.3 min). On the worn surface (Figure 11(a3)), scratches can be seen in the same direction as during sliding, indicating a two-body abrasion where the gear abraded the housing. In Figure 11(b3), there are more scratches in random directions, indicating a three-body abrasion mechanism. The particles that are moving with the fluid are as small, or smaller, as the clearance between the gear and the housing. These are critical size particles, which provide the most damage [18]. Particles also have the ability to get stuck in the softer material (in this case, housing) and then abrade the counter part of the tribological pair, which, in this case, are the gears. Mechanisms such as micro-cutting, micro-ploughing, and micro-rolling occurred.  
Figure 11: Section side of the pump housing: (**a1,b1**) unworn surface, (**a2,b2**) border between unworn and worn surfaces, and (**a3,b3**) worn surface.
# Damage Analysis of Reinforced Concrete Columns Based on Fiber Beam-Column Element
## 5 Discussion  
The RSAPS platform can accurately simulate the nonlinear mechanical properties of reinforced concrete members [10; 19; 22], but it is still insufficient in the damage analysis of members considering the damage of steel and concrete materials. This study combined the fiber element model with the damage constitutive model of concrete and steel bars. Then, the RSAPS platform can consider the damage behavior of steel and concrete materials, and accurately simulate the nonlinear mechanical properties of members. The RSAPS platform can be applied to the seismic damage analysis of reinforced concrete members, which provides a basis for revealing the failure mechanism of members.  
The RSAPS platform can display the damage distribution and material damage of the members. However, it cannot provide the damage index of the members. In future research, the damage model of members based on the damage of steel and concrete materials will be studied.  
The fiber element model used in this paper does not consider the influence of shear deformation, and can only be used to simulate the mechanical properties of columns dominated by flexural failure. Some fiber element models [12; 32] considering shear deformation have been proposed. In future research, the multi-dimensional damage constitutive model can be further combined with the fiber element to consider the damage of the members while considering the shear damage effect.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Application of Machine Learning Regression Models
#### 2.4.3 Hyperparameter Tuning  
Following the selection of the best feature combination, the ML models were applied using hyperparameter tuning. This method may significantly improve the performance of ML regression models [50] by adjusting each model's internal parameters, such as the learning rate and tree depth. In this study, a systematic approach for hyperparameter tuning was employed, utilizing the GridSearchCV method with a 5-fold CV. Again, 5-fold cross-validation further ensures robustness by splitting the dataset into five subdivisions, using four for training and one for validation in a rotating fashion. This iterative process allows the model to be trained and validated multiple times, always withholding the testing data from the algorithm, providing a robust assessment of its performance across various hyperparameter settings. The method examines several hypotheses and identifies the optimal hyperparameters, based on the performance of the models, considering the \(R^{2}\) results. This approach aims to maximize the predictive accuracy of the ML regression models. The specific hyperparameters applied in the implementation of the regression models are identified in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of**} & \multicolumn{2}{c}{**RFR**} & \multicolumn{2}{c}{**XGBR**} & \multicolumn{2}{c}{**GBR**} & \multicolumn{2}{c}{**BR**} & \multicolumn{2}{c}{**ABR**} \\ \cline{2-11}  & **NE** & **MD** & **NE** & **MD** & **NE** & **MD** & **NE** & **MS** & **NE** & **LR** \\ \hline Irrig. & 100 & 3 & 100 & 3 & 100 & 3 & 100 & 0.5 & 100 & 1.0 \\ CD & 100 & 3 & 100 & 3 & 300 & 3 & 100 & 1.0 & 100 & 0.1 \\ \hline VI—PS & 100 & 5 & 100 & 5 & 100 & 3 & 100 & 1.0 & 300 & 0.1 \\ VI—S2 & 200 & 7 & 100 & 7 & 100 & 3 & 200 & 0.75 & 100 & 1.0 \\ VI—L8 & 100 & 5 & 200 & 7 & 100 & 3 & 100 & 1.0 & 300 & 0.1 \\ Irrig. and CD & 100 & 7 & 100 & 7 & 100 & 3 & 100 & 1.0 & 200 & 0.1 \\ Irrig. and VI—PS & 200 & 3 & 100 & 5 & 300 & 3 & 200 & 1.0 & 100 & 0.1 \\ Irrig. and VI—S2 & 300 & 5 & 100 & 5 & 300 & 3 & 300 & 1.0 & 300 & 1.0 \\ Irrig. and VI—L8 & 200 & 5 & 200 & 5 & 200 & 3 & 200 & 1.0 & 300 & 0.1 \\ CD and VI—PS & 200 & 7 & 200 & 5 & 300 & 3 & 200 & 1.0 & 100 & 1.0 \\ CD and VI—S2 & 200 & 9 & 200 & 3 & 300 & 3 & 300 & 1.0 & 300 & 1.0