esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sorption Experiments
#### 2.3.2 Isotherm  
The sorption isotherm experiments were carried out in 12 mL flasks at 25 \({}^{\circ}\)C by placing 0.10 g of non- and acid-modified clinoptilolite-rich tuffs with 10 mL of Pb(II) aqueous solution at different initial concentrations ranging from 10 to 500 mg L\({}^{-1}\). The mixtures were agitated in an orbital shaker at 150 rpm for 24 h and after this time, the samples were centrifuged at 4500 rpm for 2 min and the metal concentrations of the supernatants were analyzed by atomic absorption spectrometry (AAS).  
In this work, Langmuir and Freundlich models were applied to describe the equilibrium metal adsorption. The lineal forms for these models are expressed by Equations (4) and (5), respectively [33],  
\[\frac{C_{e}}{q_{e}}=\frac{1}{q_{m}K_{L}}+\frac{C_{e}}{q_{m}} \tag{4}\]  
\[\ln q_{e}=lnK_{F}+\frac{1}{n}lnC_{e} \tag{5}\]  
where \(q_{m}\) (mg g\({}^{-1}\)) is the monolayer adsorption capacity of the adsorbent, \(q_{e}\) is the amount of solute adsorbed at equilibrium (mg g\({}^{-1}\)), \(C_{e}\) (mg L\({}^{-1}\)) is the equilibrium solute concentration, \(K_{L}\) (L mg\({}^{-1}\)) is the adsorption constant related to the energy of sorption. \(K_{F}\) (mg g\({}^{-1}\))(L mg\({}^{-1}\))\({}^{1/n}\) and 1/\(n\) are empirical constants of the Freundlich isotherm.  
In order to compare the results obtained for the Pb(II) sorption by the different acid-modified zeolitic materials considering each initial concentration from the isotherm, the partition coefficients (PC) were calculated as follows:  
\[\mathrm{PC}=q_{e}\Big{(}\mathrm{mg\ g}^{-1}\Big{)}/C_{e}(\mathrm{\mu M}) \tag{6}\]
## 5 Conclusions
Furthermore, no wind speed-up is observed near the PV modules, which supports safe system operation. From the perspective of thermal safety for electrical equipment, the 10\({}^{\circ}\) symmetrical PV system offers the most balanced solution in terms of ventilation velocity, airflow distribution in the transformer room, PV back surface cooling, and structural wind load risks. It provides the best ventilation performance among all PV-equipped cases (only a 0.6% reduction compared to the no-PV baseline and a 14.6% improvement over the horizontal PV system), the least indoor stagnation, smooth PV backflow ventilation, and no excessive structural burden from wind pressure, ensuring high operational safety. The conclusions are as follows:  
1. The 10\({}^{\circ}\) symmetrical PV system offers the most balanced solution. Although the symmetrical pitched roof installation has relatively lower power output, it provides the most stable and reliable ventilation performance without introducing additional wind load risks. The 10\({}^{\circ}\) symmetrical PV system offers the most balanced solution in terms of ventilation velocity, airflow distribution in the transformer room, PV back surface cooling, and structural wind load risks, showing only a 0.6% reduction compared to the no-PV baseline and a 14.6% improvement over the horizontal PV system. While the symmetrical roof installation results in lower power generation, it provides the most reliable ventilation effect and eliminates concerns about the additional wind load. 2. The traditional horizontal PV installation method presents certain drawbacks for both natural ventilation in the main transformer room and heat dissipation from the rear side of the PV modules. In the windward condition, airflow velocity at the bottom ventilation zone is reduced by 13.2% and by 11.8% in the leeward condition. Therefore, when applying PV systems in semi-outdoor substations, it is essential to integrate wind environment assessments into the design process. However, this installation method is not affected in terms of power generation performance. 3. The unilateral pitched installation method requires a comprehensive evaluation of multiple factors. It is most suitable for environments with a consistent prevailing wind direction, where a windward layout can achieve dual optimization of power generation and natural ventilation. Compared to the horizontal PV system, ventilation performance improves by more than 10%, and the ventilation loss relative to the no-PV condition remains below 4%. For south-facing orientations, the power generation loss remains under 4%. However, this layout can lead to higher wind loads, increasing installation costs, and may also hinder the airflow entering the ventilation openings in windward configurations.
# Analysis of Flow Field Characteristics in the Three-Phase Jet Fire Monitor Head
## 3 Results and Discussion
### Analysis of the Influence of the Powder-Pipe Bending Angle on the TPJFM IFF
As shown in the velocity distribution at the outlet in Figure 10, with the increase in \(A_{p}\), the velocity at the upper part of the nozzle outlet gradually increases, and the overall velocity distribution becomes more uniform. At \(A_{p}\) = 90\({}^{\circ}\), there is a significant velocity difference between the upper and lower parts of the nozzle outlet. Calculating the average velocity along the straight line through the XY plane of the nozzle outlet cross-section  
Figure 8: TKE contour map of cross-sections with different \(A_{p}\). shows that at \(A_{p}\) = 90\({}^{\circ}\), the average upper velocity is 38.72 m/s, and the average lower velocity is 39.97 m/s, with a difference of 1.25 m/s. At \(A_{p}\) = 120\({}^{\circ}\), the velocity difference between the upper and lower regions decreases significantly, with the upper velocity averaging 39.37 m/s and the lower velocity averaging 39.58 m/s, a difference of 0.21 m/s. The velocity difference reduces by 83.2%, significantly improving the uniformity of the velocity distribution at the nozzle outlet. As \(A_{p}\) increases from 90\({}^{\circ}\) to 120\({}^{\circ}\), the cross-section of the powder pipe along the XZ plane changes from circular to elliptical, accelerating the flow velocity near the inlet side of the powder-pipe bend and reducing the width of the wake region. The average TKE at the nozzle outlet decreases by 20%, and the \(\Delta p_{o}\) across the IFF decreases from 876.65 kPa to 861.53 kPa, a reduction of 1.73%. Thus, within a certain range, increasing \(A_{p}\) can effectively reduce flow losses. Although the increase in \(A_{p}\) results in a small increase in the uniformity index \(\gamma_{o}\) of the nozzle, the velocity difference between the upper and lower parts of the IFF decreases from 1.25 m/s to 0.21 m/s, an 83.2% reduction, significantly improving the uniformity of the velocity distribution in the IFF.
## 3 Preliminary Knowledge
### Training-Aware Quantization  
Training-aware quantization conducts quantization during training. It uses floating-point numbers to save fixed-point parameters during training and uses fixed-point parameters directly during the final inference. The quantization parameters in the network are learned and updated during the training process.  
The forward propagation of quantization during training is as follows. It first multiplies the input x and weight w by the scale and then converts it to \(x^{\prime}\) and \(w^{\prime}\) through the round function for calculation (formulas (1) and (2)):  
\[x^{\prime}=q(x) \tag{1}\]  
\[w^{\prime}=q(w) \tag{2}\]  
In backpropagation, since \(x^{\prime}\) is a discrete value, the second term in (formulas (3) and (4)) is non-derivable. To solve this problem, researchers proposed a straightforward estimator [16] (STE), as shown in Figure 2. The stepwise function in Figure 2 represents a common uniform quantization method, and its gradient is normally difficult to find. STE is actually a sign function that takes 1 when the gradient of the stepwise function is greater than or equal to 0, so that the gradient here can be regarded as 1. By doing so, the quantization function becomes an identity mapping f(x) = x for backpropagation.  
\[\frac{dL}{dx}=\frac{dL}{dx^{\prime}}\times\frac{dx^{\prime}}{dx} \tag{3}\]  
\[\frac{dL}{dw}=\frac{dL}{dw^{\prime}}\times\frac{dw^{\prime}}{dw} \tag{4}\]  
In short, the discrete values in the neural network will lead to a performance drop, caused by its low number of bits. Moreover, the inherent discrete nature of the quantized model hinders the conventional backpropagation calculations, which assume the weights are continuous. Although mandatory fitting methods could train a model in a discrete manner, it makes the training unstable.
## 3 System Functional Modules
### Sensor Node Module Hardware and Software Design
#### 3.2.3 Software Implementation of Node Modules  
(1) Data Format  
Before they are sent to the base station, the data are collected through nodes in certain format, which is shown as Table 1.  
(2) Node Working Process  
Once a node module is connected to the supply, first the battery voltage is checked. Any voltage under 3.5 V is considered insufficient. To ensure that the battery will not malfunction due to over discharge, the system will set the next starting time as 2 hours later, and immediately enter a dormant state; when the battery voltage is normal, the node will send networking information to the base station.  
To reduce the communication conflicts resulting from nodes simultaneously sending networking information, the nodes will delay sending the information based on their own serial numbers (such as Node 1 sending the network information after 5 ms, Node 2 sending information after 10 ms, _etc._). If a node does not receive the confirmed information from the base station by sending network information within 500 ms, the system will judge the networking times; once it is exceeded 5 times, the system will stop networking and directly go into a state of hibernation.  
If the node network is successfully built, the current time and the parameters are obtained from the confirmed information sent through the base station networking to update the node configuration information and the current time; then it starts sampling the sensors and recording the sampling time; the node packs the sampling data, transfers it to the station and waits for its confirmation; if it does not  
\begin{table}
\begin{tabular}{l c l} \hline \hline
**Name** & **Length (Byte)** & **Meaning** \\ \hline Message header & 1 & Regular byte 0 XD7 \\ Serial number of nodes & 1 & The serial number of nodes (set by users) \\ Message numbering & 1 & It’s used to identify message currently delivered \\ CRC check code & 2 & Data CRC check code \\ Data length & 1 & Data length (excluding the first three items and its own length) \\ Sensor state & 1 & Whether the sensors are valid \\ Sensor data & N & The data of sensors are concerned about temperature, humidity and battery voltage \\ Sampling time & 6 & The time when data are sampled \\ \hline \hline \end{tabular}
\end{table}
Table 1: node sent to the base station data format.  
Figure 6: The composition of the node power management system.  
receive the confirmed information from the base station by sending the data within 500 ms, which means the data delivery failed, and if it fails to send data after three tries, then the network breaks down and the system will go into hibernation (Figure 7).
## Appendix A
### Grasp Configuration  
The grasp configuration system relies on detailed feature extraction and preprocessing steps for effective prediction. Key features include:  
* Object FeaturesThe system extracts the object category (e.g., apple, bottle), grasping area, and surface area from the object detection pipeline. Figure A5 shows the grasping area (red line) of the detected objects. These features provide a foundation for predicting the grasp configuration. The shortest segment of the object to be grasped is also identified to ensure precision in manipulation.  
* Grasping Ratio To quantify the ease of grasp, the grasping ratio is calculated as: \[\text{Ratio}=\frac{\text{Grasping Area}}{\text{Surface Area}}\] (A1) where Surface Area > 0. This metric captures the proportional relationship between the graspable area and the overall surface area, offering insights into the grasping ability of the object.  
* Stability Parameter A stability feature is introduced to classify objects into one of three discrete stability levels--low, medium, or high. These levels are assigned numerical values of 0, 1, and 2, respectively, to facilitate machine-learning compatibility.  
* Feature Normalization Numerical features, such as grasping area, surface area, and grasping ratio, are normalized using standard scaling techniques to ensure uniformity across feature magnitudes, preventing any one feature from disproportionately influencing the model. The object category is label-encoded into numerical values to enable its use within the machine learning framework.  
The predictive model is a pre-trained Random Forest classifier designed to output the grasp configuration \(\mathrm{g}_{\mathrm{conf}}\in\{2,3,5\}\) representing the number of fingers required for grasping. The model uses a feature vector combining the encoded object category, normalized numerical features, and mapped stability values. Trained on a labelled dataset of diverse objects, the Random Forest classifier ensures robustness and adaptability to various geometries and sizes. Once an object is detected by the robotic vision system, the extracted features are processed and fed into the trained model. The system predicts the optimal grasp configuration, directing the robotic manipulator to execute the most appropriate grasp. Users can adjust the stability feature to achieve grasps with low, medium, or high stability. If stability data is unavailable, the system randomly assigns stability levels before making predictions. This integration into the perception-action pipeline ensures seamless and efficient robotic operation. Below Figure A6 shows the training results of the Random Forest Classifier Model.
## 3 Results
### Effect of Slit Distance on the Frequency Response of Slitted Beams  
Optimizing the dynamic behaviour and energy harvesting efficiency of slitted beams using PZT requires examining the effect of slit length on the frequency response of the beams. The coupling effect between the resonance modes can be strongly influenced by the distance between the fixed end and the beginning of the slit. It is essential to comprehend how different slit distances impact the resonance peaks and coupling strength when building beams to optimize the amount of energy captured from ambient vibrations. We investigated the connection between slit geometry and dynamic response for three slit lengths: 67 mm, 92 mm, and 117 mm.  
Figure 9 shows the results for three different slit lengths, namely, 67, 92, and 117 mm. Obviously, the frequencies of the first resonance peaks increase with a slit length of 117 mm and are well separated, and the anti-resonance dip becomes more pronounced. However, regarding the second resonance peaks, two observations can be made: First, the separation between the first and second peaks decreases with a decrease in slit length; secondly, the spread in the frequencies of the second peaks is much less than for the first peaks, again indicating stronger coupling, and the anti-resonance becomes less prominent. As would be expected for less slit length (67 mm), when the situation approaches that of the standard beam, the response approaches that of a standard beam without a slit. This trend suggests that the dynamic response and energy harvesting capability of the beam can be greatly affected by optimizing the slit distance.  
The optimization of the dynamic response of the slitted cantilever beam through the meticulous modification of slit parameters greatly enhances its energy collecting capability. The inclusion of slits in the system results in mode coupling, therefore expanding the range of operating frequencies. This phenomenon enhances the efficiency of energy capture of the harvester from a broader range of ambient vibrations. Through the manipulation of the slit width, length, and distance from the fixed end, together with the adjustment of the tip mass, it is possible to shift and amplify the resonance peaks, thus resulting in increased energy production. The slitted beam is more effective in energy harvesting applications due to its improved design, which enables better adaptation to varied vibrational conditions. These structural changes increase the slitted beam's total energy conversion efficiency, as confirmed by the results of FEA simulations and the analytical model. This suggests a viable way to improve the functionality of piezoelectric energy harvesters.  
Figure 8: Frequency response function of slitted beam with PZT layer for three slit widths.
## 4 Case Study
### Evaluating and Ranking of Suppliers  
This section describes the step-by-step priority assessment explained in Section 3. Hereafter, firm is referred as a node to actively depict the network system perspective. The proposed bicriterion network resilience model aims to develop a systematic and visual assessment of a supply network system based on dual objectives of maximizing firm's resilience performance and minimizing firm's exposure to structural design driven vulnerability. Researchers state that a supplier relationship can no longer be viewed as a chain-like linear relationship, but as a complex network of relationships. Consistent with this view, this study is based on a network model, but it differs in terms of defining and integrating the resilience value (based on SC capabilities) and the network value (attributes of network structures). In this section, the adopted resilience attributes are explained and then the types of network attributes considered for this study are elaborated. Then, we provide a step-by-step order of prioritization with a case example based on the concordance-discordance approach introduced by [PERSON] et al. [78]. Finally, a decision support system is provided as a means to distinguish the firms that require immediate attention from a risk assessment perspective.  
A fundamental issue with the BNR model is that the two criteria (resilience and network values) have completely different and often conflicting aspects because the resilience value is based on the subjective judgment of resilience, while the network value is based on the quantitative interpretation of network positional relationships. Our aim is to propose a network resilience model that can achieve two goals: (i) understand the suppliers' current level of resilience and (ii) prioritize the suppliers (nodes) to maintain a balanced level of network resilience.
## 3 Design of SIRAC
### Combining the SI Algorithm and RAC  
Figure 6 shows the architecture of SIRAC, which includes SI algorithm and RAC. The SIRAC can simultaneously meet trajectory tracking and disturbance rejection requirements. The desired input \(u_{d}\) and state \(x_{d}\) can be calculated off-line based on the desired landing trajectory while using the SI algorithm. The RAC is used to robust against the wind disturbances. The SI drives the output trajectory of the aircraft to the desired trajectory.  
Now, the algorithm of the SIRAC is designed, as follows.  
1. Calculating \(y_{d}=[h_{d},V_{d}]^{T}\).
2. Knowing \(y_{d}\), solve Equation (29) and Equation (34) using SI algorithm to find the \(u_{d}\) and \(x_{d}\).
3. Knowing \(u_{d}\) and \(x_{d}\), solve \(z_{d}=C_{m}x_{d}+D_{m}u_{d}\).
4. Knowing \(z_{d}\), solve \((z_{d}-z)\) for the input of RAC: \(\left[\Delta h,\Delta\dot{h},\Delta V,\Delta\dot{V},\Delta q,\Delta\theta \right]^{T}\).
5. Knowing \((z_{d}-z)\), solve Equation (36) for the output of RAC: \(\left[\delta_{cc},\delta_{lc}\right]^{T}\).
6. Knowing \(\left[\delta_{\alpha c},\delta_{lc}\right]^{T}\), solve \(u_{d}+\left[\delta_{cc},\delta_{lc}\right]^{T}\) for the input of the aircraft: \(u\).
7. Knowing \(u\), solve the system equation of aircraft for \(z\).
8. Iterate over Step 3 to 7 until the landing process is done.
## 4 Results
### Implementation Details  
The training sets obtained by data augmentation were preprocessed by a series of common data enhancement methods. For the purpose of spectral augmentation, we performed random cropping of both the image and its corresponding mask, altering size and aspect ratio. This approach was adopted to enhance the diversity of the dataset. Subsequently, due to constraints posed by GPU memory, all the images were resized to dimensions of 256 \(\times\) 256 \(\times\) 3 prior to being inputted into the networks. Afterwards, all the compared models were trained with the same training environment. Specifically, the networks were optimized by the Adam algorithm [37], whose moments of 0.9 and 0.999 correspond to the two parameters, in Windows 10 with one GTX3060 (memory 12 GB) that allows for a batch size of 2 images. All experiments were performed using PyTorch 1.11.0.  
The learning rate was initially set to be 0.0001 and reduced by a factor of 0.1 every 30 epochs. Since the raw size datasets were divided into many patches, the model needed a significantly higher number of iterations within each epoch across those three datasets. Moreover, we used the fine-tuned strategy in our network, which can accelerate the convergence. Training is conducted by optimizing structure loss function employing the Adam algorithm. Therefore, the last part of the proposed network utilized Sigmoid activation to output the results. Then, structure loss with binary cross entropy loss and the weighted IoU loss function [17; 25] can be described as:  
\[StructureLoss=BCELoss+WeightIoULoss \tag{6}\]  
\[BCELoss(p,m)=-\
icefrac{{1}}{{N}}\sum\limits_{i=1}^{N}\left[m_{i}\cdot\log(p_{ i})+(1-m)\log(1-p_{i})\right] \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Training Set** & **Validation Set** & **Test Set** \\ \hline Deep Globe & 780 & 111 & 222 \\ Massachusetts & 1108 & 14 & 49 \\ GF-2 & 170 & 10 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets Allocation Details.  
\[WeightIoULoss(p,m)=1-\frac{\sum_{i=1}^{N}\left(p_{i}\times m_{i}\times w_{i}\right)+ \varepsilon}{\sum_{i=1}^{N}\left(p_{i}+m_{i}+p_{i}\times m_{i}\right)\times w_{ i}+\varepsilon} \tag{8}\]  
In Equations (7) and (8), \(N\) represents the number of samples. \(m\) represents the label of the sample index \(i\)th, usually 0 (negative category) or 1 (positive category). \(p\) represents the model predicted value of sample \(i\), usually between 0 and 1, indicating the model's probability estimate that the sample belongs to a positive category.  
Figure 3 shows the convergence tendency of loss function values in the process of the training. Evidently, the error exhibits a gradual reduction over time. In Figure 3b, the validation loss value becomes stable after the 30 epochs among the three datasets.