esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Method
### Feature Fusion  
As shown in Figure 2, we concatenate the feature vectors \(f_{rgb}\) (with dimension \(d_{1}\)) generated by the EFEM with the feature vectors \(h_{n}\) (with dimension \(d_{2}\)) generated by the IREM. The concatenated feature vector is then nonlinearly transformed using a fully connected layer (FC layer) to map it to a feature vector of dimension \(d_{3}\), and it is further subjected to a probabilistic operation called Dropout, which randomly discards part of the node outputs during the training process with a retention rate of \(p\). The final feature vector output is denoted as \(f_{total}\):  
\[f_{total}=Dropout\Big{(}\sigma\Big{(}W\Big{[}f_{rgb};h_{n}\Big{]}+b\Big{)},p \Big{)} \tag{11}\]  
where \([:]\) denotes column-wise concatenation, \(W\in\mathbb{R}^{d_{3}\times(d_{1}+d_{2})}\) is the weight matrix of the fully connected layer, \(b\in\mathbb{R}^{d_{3}}\) is the bias vector, \(\sigma\) denotes the activation function, and \(Dropout(\cdot,p)\) denotes the Dropout operation, where each element is retained with a probability of \(1-p\).  
Figure 6: GRU Structure.  
Finally, the feature vector \(f_{total}\) is classified into \(m\) classes using a softmax classification layer:  
\[P(y=c|f_{total})=\frac{e^{W_{c}^{T}f_{total}+b_{c}}}{\sum_{i=1}^{m}e^{W_{f}^{T}f_{ total}+b_{i}}} \tag{12}\]  
where \(W_{c}\in\mathbb{R}^{d_{3}}\) and \(b_{c}\) correspond to the weight vector and bias associated with class c, respectively. \(P(y=c|f_{total})\) represents the probability of belonging to class \(c\), and given the feature vector \(f_{total}\), the sum of probabilities across all classes is equal to 1.
## 3 Results and Discussion
### X-ray Diffraction (XRD)  
The study to determine the different mineral phases present in the PAAZ-01 and PAAZ-04 samples (Figure 4) established the dominant presence of neoforming mineral species such as mordenite and smectite, accompanied by secondary phases such as halolyosite, illite and quartz, which were formed by a hydrothermal process that caused  
Figure 3: Microphotographs of samples of unaltered pyroxenic andesites (**a,b**) obtained with Obj x.2.5 and crossed nicols (Nx).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{Sample} & \multicolumn{6}{c}{(\% weight)} & \multirow{2}{*}{LOI} \\ \cline{2-2} \cline{4-9}  & & SiO\({}_{2}\) & Al\({}_{2}\)O\({}_{3}\) & CaO & Na\({}_{2}\)O & K\({}_{2}\)O & MgO & Fe\({}_{2}\)O\({}_{3}\) & TiO\({}_{2}\) \\ \hline PAAZ-01 [1] & 65.17 & 13.19 & 0.88 & 3.92 & 2.99 & 2.09 & 1.60 & 0.119 & 11.70 \\ \hline PAAZ-02 & 64.49 & 13.57 & 0.96 & 2.94 & 2.36 & 2.10 & 1.40 & 0.113 & 11.50 \\ \hline PAAZ-03 & 61.20 & 14.70 & 2.58 & 1.63 & 1.69 & 3.17 & 4.30 & 0.47 & 3.40 \\ \hline PAAZ-04 & 62.40 & 13.82 & 2.45 & 2.78 & 2.26 & 2.44 & 4.70 & 0.49 & 9.75 \\ \hline UPA-01 [2] & 52.30 & 18.20 & 10.20 & 2.02 & 0.59 & 5.08 & 9.68 & 0.80 & 0.70 \\ \hline UPA-02 & 51.90 & 19.10 & 10.14 & 2.01 & 0.60 & 5.11 & 9.66 & 0.77 & 0.60 \\ \hline \hline \end{tabular}  
* Pyroxenic andesite altered to zeolite, [2] Unaltered pyroxenic andesite.  
\end{table}
Table 3: Chemical composition of the samples studied by X-ray fluorescence (XRF).  
particularly intense zeolitization and smectization [31]. Section 3.1 describes in detail how the neoforming minerals have replaced the original minerals. On the other hand, [PERSON] et al. [32] describe these isomorphic and pseudomorphic substitution processes as an enthronement of new mineral varieties. This process is interpreted in this work as a diffusive event of bidirectional character by which the hydrothermal solutions captured ions from the host rock (original pyroxenic andesite) and provided foreign ions that created mordenite and smectite. As can be seen in Figure 4, there are some compositional and morphological differences in both samples. The PAAZ-01 sample presents more pronounced and intense peaks that show a high crystallinity of the mineral species present, mainly mordenite, while in the PAAZ-04 sample, the intensity of these peaks tends to attenuate significantly. Another detail to highlight in this last sample is the presence of amorphous phases, possibly produced by the formation of glass in the places where the hydrothermal solutions, still hot, experienced rapid cooling [28]. Another interpretation could be that this amorphous phase was deposited during the pyroclastic events that took place in this region during the Neogene period [33].
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 3 Results
### Distribution of the Harvest Index  
The HI is a key parameter in the model that affects how accurately crop yield predictions are made. In this work, the HI was calibrated for each county-level district using the estimated GPP and statistical yield. Figure 3a illustrates the average HI for each county, from 2001 to 2019, demonstrating the significant regional variation in the HI. The difference between the different counties even exceeds 0.4. The HI displays the spatial characteristics, which are low in the south and high in the north. In general, HB and SD are higher than in HN, JS, and AH, and the mean values for the HI are, respectively, 0.46, 0.48, 0.41, 0.39, and 0.37. The HI distribution range consists primarily of the intervals 0.3-0.4 and 0.4-0.5, accounting for more than 77% of the total, followed by 0.5-0.6, accounting for 17.19%, and  
Figure 2: Changes in GPP from 2001 to 2019. The black dots, red dots, and blue dots linked by solid lines represent the average GPP, total GPP, and planting area, respectively.  
the sum of less than 0.3 and greater than 0.6 being less than 6%. The calibrated HI values in this investigation are consistent with the findings in earlier studies, proving that this approach can successfully invert the HI. For instance, the HI fluctuation range for the main maize-growing regions in China (2009-2016) was 0.25-0.67, and the values from the HI in the NCP area were 0.32-0.64, according to the field investigation by [PERSON] et al. [55]. The corrected HI can be used to convert the GPP to maize output.  
Furthermore, we explored the volatility of the HI over time. Figure 3b shows the coefficient of variation (CV) in the county-level HI from 2001 to 2019. Only a few county-level administrative regions have a CV in the HI that is greater than 20%, indicating a small degree of variation in the summer maize HI from 2001 to 2019.
# A Mixed Method with Effective Color Reduction
## 2 Materials and Methods
### Overview of the BS+ATCQ Algorithm
#### 2.1.3 The BS+ATCQ Method  
The solution described in [46] applies the ATCQ method to the solution obtained by BS (Algorithm 5). BS defines an initial quantized palette and a partition of the pixels of the original image. This information is used to define an initial tree of ants and ATCQ operations improve the quantized palette.  
First, BS creates a binary tree with \(q\) leaves, denoted \(\{l_{1},...,l_{q}\}\). The leaf \(l_{k}\) represents the subset \(C_{l_{k}}\) of pixels of the original image and defines a color of the quantized palette, \(color_{l_{k}}=m_{l_{k}}/N_{l_{k}}\), where \(m_{l_{k}}\) and \(N_{l_{k}}\) are computed by Equations (1) and (2), respectively.  
Before applying ATCQ, the leaves of the binary tree are used to define \(q\) nodes in the second level of the tree of ants, \(\{S_{1},...,S_{q}\}\). The leaf \(l_{k}\) is used to initialize the three variables associated with the node \(S_{k}\): \(nc_{k}\) and \(sum_{k}\) take the value of \(N_{l_{k}}\) and \(m_{l_{k}}\), respectively, while \(\epsilon_{k}\) is computed by Equation (11), which computes the similarity between the color of the leaf \(l_{k}\) and each pixel \(p_{i}\) in the subset \(C_{l_{k}}\).  
\[e_{k}=\sum_{p_{i}\in C_{l_{k}}}Sim(p_{i},color_{l_{k}}) \tag{11}\]  
At this point, the tree of ants includes the root node and the maximum number of children allowed for that node (\(q=Q_{max}\)). Therefore, this tree represents an initial quantized palette with \(q\) colors. Then, the ATCQ operations are applied in order to connect all the ants to the tree. When these operations conclude, the quantized image is generated in the same way described for ATCQ.  
```
1:Apply BS
2:for each leaf \(l_{k}\) of the binary tree do
3: Create node \(S_{k}\) in the second level of the ATCQ tree
4: Set \(nc_{k}=N_{l_{k}}\), \(sum_{k}=m_{l_{k}}\)
5: Compute \(\epsilon_{k}\) by Equation (11)
6:endfor
7:Apply ATCQ
8:Generate the quantized image
```  
**Algorithm 5** Binary splitting + ATCQ algorithm.  
The computational results reported in [46] show that the image generated by BS+ATCQ is always better than the result of BS or ATCQ applied independently. In addition, this method consumes less time than other methods applied to the same problem.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Route Discovery Time  
Route discovery time is a period starting when the source node begins the route discovery process and ending when two node-disjoint paths are discovered and established.  
When a frame is transmitted from a source to a destination in a network, the latency of delivering the frame is calculated as follows [19]:  
\[\tau_{latency}=\tau_{prop}+\tau_{q}+\tau_{tx}+\tau_{proc}. \tag{13}\]  
where \(\tau_{latency}\) is the latency, \(\tau_{prop}\) is propagation time, and \(\tau_{q}\), \(\tau_{tx}\), \(\tau_{proc}\) are queueing time, transmission time, and processing delay, respectively at nodes in the path between the source and the destination.  
Assume that the queueing time, transmission time, and processing delay at nodes in the network are the same. Let \(t_{node}\) be the processing time at a node, including queueing time, transmission time, and processing delay, and let \(t_{prop.}\) be the propagation time between two adjacent nodes in the network.  
Therefore, the latency of delivering a frame through a path from the source to the destination can be determined as follows:  
\[\tau_{latency}=n_{hop}\times t_{prop.}+\left(n_{hop}+1\right)\times t_{node}, \tag{14}\]  
where \(n_{hop}\) is the hop counts of the path between the source and the destination.
## 3 Improved Artificial Potential Field Method Based on Joint Space
### Joint Angle Constraints  
Typically, each joint of a robotic arm has angle limitations. To ensure that the joint angles of the robotic arm do not exceed their respective limits during obstacle avoidance, the algorithm proposed in this paper must satisfy the following requirements.  
Taking an n-degree-of-freedom robotic arm as an example, we assume that the joint angle limits for each joint of the robotic arm are in the range of [\(\theta_{i\_min}\), \(\theta_{i\_max}\)].  
Solution 1: In the first step of the algorithm, the initial and target positions of the robotic arm can be transformed from Cartesian space to joint space using inverse kinematics. \(q_{start}\) can be assumed to be \([\theta_{1},\cdots,\theta_{n}]\) and \(q_{goal}\) can be assumed to be \([\theta_{1}^{*},\cdots,\theta_{n}^{*}]\). Due to the existence of multiple solutions in inverse kinematics, it is necessary to select an appropriate solution in this step, ensuring that \(\theta_{i\_min}<\theta_{i}<\theta_{i\_max}\) and \(\theta_{i\_min}<\theta_{i}^{*}<\theta_{i\_max}\).  
Solution 2: The algorithm imposes limitations on the magnitude of attraction \(Fatt\) and repulsion forces \(Frep\) during the computation (such as \(F_{att}\leq 0.1\), \(F_{rep}\leq 0.1\)). This serves to prevent the joint angles from exceeding the constraint range.  
Solution 3: During obstacle avoidance, it is possible that the strong repulsive force from an obstacle may cause one of the joint angles of the robotic arm to exceed the specified limit. In the case depicted in Figure 7, we can appropriately reduce the repulsive force by adjusting the repulsion coefficient \(k\). This allows the robotic arm to complete the obstacle avoidance task while ensuring that the joint angles remain within the allowed range.  
Figure 6: The repulsive force of a virtual obstacle to joint.
## 3 Results and Discussion
### Typical Profile Analysis
#### 3.2.2 Typical Point Characteristics
The von Mises stress increase values of the bottom plate measurement point U1 are all negatively increasing, the negative increase value of the measurement point in the MS4 profile is the largest, and the values of the measurement points of the four working conditions demonstrate a similar amount of change; the negative change extreme value of the von Mises stress of the measurement point of the excavation using the CD blasting method is the largest, and it is \(-\)1634.97 kPa. The negative change extreme value of the von Mises stress of the measurement point U5 is negatively increasing in the MS2 profile, and it ranges from \(-\)139.74 to \(-\)164.41 kPa in the MS1, MS3, MS4, and MS5 profiles, which are negatively increasing; however, a positive increase was seen in the MS1, MS4, and MS5 profiles, and the values of the four working conditions are similar; the negative value of the von Mises stress at the upper and lower step static method excavation point U5 is the largest, which is \(-\)4150.37 kPa; the positive value of the von Mises stress at the CD static method excavation point U5 is the largest, which is 2496.89 kPa. The extreme value of von Mises stress at the left and right sidewall tunnel structure measurement points is similar in different calculation conditions, with the negative change of the measurement point value in the MS2 and MS4 profiles being prominent, and the negative change of the measurement point value in the MS2 profile being the largest; the extreme value of the measurement point in the CD static method excavation is the largest, whereby the extreme value of the measurement point L1 is \(-\)4696.90 kPa and the extreme value of the measurement point R1 is \(-\)4366.50 kPa. The extreme value of von Mises stress at the measurement point far away from the tunnel structure is similar, with the extreme value of negative change at the measurement point of the MS2 profile being the largest and the extreme value of the measurement point of the CD blasting method being the largest;  
Figure 21: Comparison curves of von Mises stress extremes at typical profile measurement points of existing tunnels with different working conditions: (**a**) arch; (**b**) base plate; (**c**) left wall; and (**d**) right wall. the extreme value of the measurement point L5 is \(-398.64\) kPa and the extreme value of the measurement point R5 is \(-360.02\) kPa.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions
### Artificial Intelligence and Machine Learning Integration  
The application of ML and deep learning techniques in indoor localization holds significant promise for improving accuracy and adaptability. Algorithms such as LSTM networks and SVR can process large datasets and mitigate issues like NLOS conditions and multipath interference [36; 60]. Future research should focus on optimizing these models to reduce computational complexity and enhance real-time performance, particularly in dynamic environments like hospitals, warehouses, and military zones.
## 4 Research Results
### Service Quality Performance Matrix Results  
The average value and index of each question were calculated through Formulas (1) and (2), as shown in Table 3, and the relevant values were introduced into the SQPM, as shown in Figure 9. The service elements in the priority improvement area were items 1-16 and 20, indicating that the demand for these services exceeds satisfaction, and older adults are unsatisfied with them. Therefore, they need to be improved. The service elements in the improvement area were items 17 and 19, indicating that the demand for these services is equal to the satisfaction, and they are accepted by older adults. Therefore, service providers can list surplus resources as secondary improvement items. The service elements in the maintenance area were items 15 and 18, indicating that satisfaction for these services is higher than the demand, and older adults are happy about them and highly satisfied. Therefore, service providers can maintain the current service quality.  
Figure 8: Importance–satisfaction (I-S) model results.
## 4 Results and Discussion
### The Effect of H/C, O/C, ER, and T on Producer Gas Composition
Based on the simulation above, the biomass gasification process should be carried out under high O/C but low H/C to obtain the maximum CO and CO\({}_{2}\). Both O/C and H/C should be high to obtain the maximum CH\({}_{4}\) and H\({}_{2}\). This is in line with the simulation shown in Figure 5, which demonstrates increasing CH\({}_{4}\) and H\({}_{2}\) with H/C. In contrast, the CO and CO\({}_{2}\) decrease. Increasing CO, CO\({}_{2}\), CH\({}_{4}\), and H\({}_{2}\) is simulated with increasing O/C. H\({}_{2}\) and CH\({}_{4}\) are produced from endothermic reactions, and the production may continue with an increase in the H/C ratio during the gasification process. Therefore, biomass that is rich in hydrogen content may produce more H2 or CH4. [PERSON] et al. (2017) found that the hydrogen content of a biomass lowers CO and CO\({}_{2}\), but increases H\({}_{2}\) and CH\({}_{4}\). Exothermic reaction may occur when the biomass is rich in oxygen content. Increasing the O/C ratio would increase the compositions of CO, CH\({}_{4}\), and H\({}_{2}\). Similar findings are reported in other studies ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019). Aside from the processing parameters inside a reactor, the compositions of producer gas are also affected by the properties of biomasses. Biomass usually consists of cellulose (30-60%), hemicellulose (15-25%), and lignin (20-40%). On the Van Krevelen diagram (Figure 6), lignin has the lowest H/C and O/C ratio, whereas cellulose has the highest ratios ([PERSON] et al., 2017). Biomasses rich in hemicellulose and lignin are usually selected for gasification ([PERSON] et al., 2017; [PERSON] et al., 2019). The C-H-O ternary diagram indicates that a biomass with a high H/C ratio produces more CH\({}_{4}\) and H\({}_{2}\), whereas a biomass with a high O/C ratio produces more CO and CO\({}_{2}\)([PERSON] et al., 2017). Figure 4: Simulation on the effects of ER and T on the gas composition of (**a**) CO, (**b**) CO\({}_{2}\), (**c**) CH\({}_{4}\), and (**d**) H\({}_{2}\) at conditions H/C = 1.494, O/C = 0.689, and N/C = 0.0198, built using Equations (48)–(51).
## 3 Results and Discussion
### No Turbidity Interference
#### 3.1.3 Comparison of FDPE_PLS and PE_PLS  
We performed two experiments without turbidity interference. The first was using FDPE_PLS to establish the model by five extreme points in the first derivative spectrum. The second was using PE_PLS to establish the model by three extreme points in the original spectra. The calibration and verification sets were used to verify the performance of the two COD prediction models. The performance of the two models were compared, and the result is shown in Table 6.  
It can be seen from Table 6 that the performance of FDPE_PLS is better than PE_PLS. FDPE_PLS has lower RMSE and average relative error, which means the prediction model established by five extreme points in the first derivative spectrum has higher accuracy. So FDPE_PLS was chosen as the modeling method in this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**PE\_PLS**} & \multicolumn{2}{c}{**FDPE\_PLS**} \\ \hline  & **Calibration Set** & **Validation Set** & **Calibration Set** & **Validation Set** \\ \hline \(R^{2}\) & 0.9987 & 0.9988 & 0.9988 & 0.9995 \\ \hline RMSE & 0.3219 & 0.4043 & 0.3051 & 0.2919 \\ \hline Average relative error & 1.6984\% & 1.6107\% & 1.2198\% & 1.036\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison between PE_PLS and FDPE_PLS.  
Figure 13: Fitting between predictive values and actual values in the calibration set and validation set in PE_PLS.
# Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm
## 2 Construction of Evaluating Indicator System and Grading Standards
### Preprocessing of Data
#### 3.3.2 Detection and Replacement of Outliers  
An outlier, also known as an anomaly, refers to one or several values in the data that differ significantly from other values. In machine learning, outliers may increase the bias and variance of the model, thus affecting the performance and accuracy of the model. Thus, it is necessary to detect and replace outliers.  
The commonly used methods for outlier detection include \(Z\)-score, box plot, local outlier factor, isolated forest, etc. This article uses the box plot method [29] to screen outliers. This method is relatively simple and easy to use and can display the distribution of data in an intuitive manner. At the same time, it is also sensitive to and able to detect outliers. Based on this method, this article detected outliers in the collected cases, and the results are shown in Figure 4.  
From the box plot in Figure 4, it can be seen that there are outliers in each hazard level of the evaluating indicator. After detecting outliers, it is necessary to replace them reasonably. In machine learning, data replacement generally selects the mean, mode, median, etc. In this article, we choose to replace the mode of each evaluating indicator under each hazard level for discrete variables and replace the average value of each evaluating indicator under each hazard level for continuous variables.
## 4 Experiments  
The aim of these experiments is to systematically evaluate the functionality of the proposed method and understand the effect of the fitness function's weights on exploration performance. We designed our experiments as an ablation study to isolate the impact of each core criterion. For the first experiment, we tested extremal configurations where one weight is set to 1 and the others to 0 to observe the behavior of each criterion in isolation. We also tested a balanced configuration where all criteria are weighted equally. For subsequent experiments in more complex environments, we tested combinations informed by these initial results.  
The results were obtained in both simulated and real-world environments. The simulations were conducted using Gazebo (v11.15.1), while the physical experiment utilized an ABB IRB 4600 robotic arm (ABB Ltd., Zurich, Switzerland). Both the ABB IRB 4600 (used in experiments 1, 2, and 4) and the ABB IRB 2400 (used in experiment 3) were controlled via ROS (noetic). In all experiments, a Kinect v2 depth camera (Microsoft Corp., Redmond, WA, USA), with a maximum range of 5 m, was mounted on the robot flange to provide visual input. The simulated camera model was configured to match the specifications of its real-world counterpart.  
The arm trajectory was planned by the RRTConnect [23] algorithm and the candidate's position was transformed into the robot's frame by the inverse kinematics IKFast [24]. If the inverse kinematics calculation or the trajectory planning failed, the next best candidate was chosen.  
To generate a trajectory, the candidate pose was first converted into multiple invariant orientations around the Z-axis. For each pose variant, the inverse kinematics solver attempted to find all valid joint configurations using MoveIt's IK interface with collision checking from the planning scene. The evaluation took place in four different environments with different complexities.  
The experimental scenarios were designed to reflect typical industrial applications and to incrementally increase in complexity. Our first experiment modeled a common use-case where a robot started in a safe, open 'home' position. Subsequent experiments introduced more clutter and confinement to systematically test the planner's limits, culminating in a final validation on a physical robotic platform.