esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Challenges and Limitations in Yield Prediction Models  
The primary difficulties encountered by statistical and ML models arise from the complex interplay of factors influencing crop yield. These factors can be broadly categorized into three main groups: (1) genetic factors, which include crop variety, seed quality, and plant resistance traits; (2) environmental factors, such as temperature, precipitation, and biotic stressors (e.g., pests and diseases); and (3) management practices, including planting date, irrigation, fertilization, pest control strategies, and soil health management (e.g., nutrient management, organic matter content). Each of these categories plays a crucial role in determining yield outcomes, and their interactions add further complexity to the prediction models [18]. The use of remote sensing in data-driven models introduces additional variables, such as the choice of satellite sensors and the spatial, temporal, and spectral resolution. Other factors include the vegetation indices used and the quality and timing of measurements. Due to this wide range of variables involved, yield forecasting models, while effective in specific regions, often underperform when applied to different areas [19]. Moreover, many of the crop yield models are designed for application at the national or regional level [2; 7; 20], as coarse-scale yield data are more readily available, while field-scale ground-truth estimates are much harder to obtain.  
However, the recent scientific literature highlights that these models, while effective on a broader scale, often underperform when applied at the field scale [21; 22; 23]. This is because, when transitioning from coarse to fine scales, new challenges emerge. First, parameters validated on a coarse scale are often not adaptable to finer scales [24]. Most importantly, among the numerous factors influencing crop yield, some are specific to individual fields. Elements such as soil type, topography, microclimate variations, and agricultural practices significantly affect yield predictions [25; 26], especially in regions with a highly fragmented agricultural landscape, such as Italy [27; 28]. Depending on these factors, the forecasting performance of the field-scale models can vary significantly.
## 4 Proposed Solution
### Application Constraints  
The flow \(f_{k}\) must be fully received by the destination \(f_{k}.dest\) within the deadline \(f_{k}.d\). The maximum end-to-end latency constraint is defined as follows:  
\[\forall(v_{a},v_{b})\in R_{k},v_{a}=f_{k}.src,\forall(v_{x},v_{y })\in R_{k},v_{y}=f_{k}.dest: \tag{18}\] \[f_{k}.l\triangleq(\varphi[k,x,y]+\omega[k,x,y]\times f_{k}.prd) -(\varphi[k,i,j]+\omega[k,i,j]\times f_{k}.prd)\] \[f_{k}.l\leq f_{k}.ddl-f_{k}.td\]  
Here, we define \(f_{k}.l\) as the communication latency of a flow \(f_{k}\) from the source to destinations.
# Identification and Isolation Techniques for Plant Growth Inhibitors in Rice
## 6 Current Status, Existing Limitations, and Future Perspectives
### Current Status and Existing Limitations
#### 6.1.1 Extraction and Separation Techniques
Solid-phase extraction (SPE), or SPME (an optimized type of SPE), is a sophisticated, accurate, economical, non-exhaustive, and robust technique. This can reduce labor requirements for extraction by combining all steps of extraction into one. Additionally, it has been demonstrated as a highly efficient technique by means of its direct integration with separation and analysis systems such as chromatography systems. Despite the numerous advantages and extensive applications of SPME in both agriculture and medicine fields, this technique still has some limitations. For instance, although SPME seems to be the most effective tool for analyzing volatile and semi-volatile organic compounds, for non-volatile and high-molecular-weight compounds, it is less effective, which remains a major concern that needs to be tackled.
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 3 Case Analysis  
In order to verify the validity and reliability of two established combination models, the traffic flow time series of a highway in southwest China was taken as an example. The traffic flow data from January 2014 to December 2018 were chosen as the historical fitting data; the data from January 2019 to December 2019 were used as the forecast comparison sample.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 3 Wavelet Packet Energy Entropy
This information can be used to show the change of the frequency characteristic when applied in the fault diagnosis. After the frequency band of the vibration signal with the fault has been subdivided into several small segments, the energy of the fault vibration signal changes with the frequency distribution. The wavelet packet energy entropy (WPEE) is proposed to illustrate this change. The signal is decomposed by the \(J\)-level wavelet packet and its total energy is decomposed into \(J\) non-overlapping frequency intervals. Therefore, the energy of each decomposed segment can be regarded as the characteristic information of the original signal by measuring the distribution of the signal energy in each frequency band. Assume that \((j,i)\) is the \(i\)th node in the \(j\) level and that the corresponding reconstructed signal is expressed as \(S_{j,i}\), the energy of which is denoted as \(E_{j,i}\). The energy calculation formula corresponding to node \((j,i)\) is as follows:  
\[E_{j,n}=\int|S_{j,n}(t)|^{2}dt=\sum_{k=1}^{n}|S_{j,n}[k]|^{2}, \tag{15}\]  
where \(j\) is the decomposition level, \(n\)\(=[0,1,\ \cdots,2^{j}-1]\) is the \(n\)th node on the \(j\)th level, and \(S_{j,n}\) is the reconstructed signal. The total energy of the original signal can be defined as:  
\[E_{j}=\sum_{n=0}^{2^{j}-1}E_{j,n}. \tag{16}\]  
The proportion of the reconstructed signal energy corresponding to each node in the total energy is  
\[p_{j,n}=\frac{E_{j,n}}{E_{j}}, \tag{17}\]  
in which \(p_{j,n}\) represents the wavelet packet energy distribution in the relevant wavelet packet node. Finally, according to the definition of the Shannon entropy, the WPEE can be calculated as follows:  
\[Entropy_{j,n}=-p_{j,n}ln\Big{(}p_{j,n}\Big{)} \tag{18}\]  
\[WPEE_{j}=-\sum_{n}Entropy_{j,n}. \tag{19}\]  
The relative WPEE of each sub-signal can be calculated as follows:  
\[WPEE_{j,n}=Entropy_{j,n}/WPEE_{j}. \tag{20}\]
## 3 Proposed Method for Attribute Weighting
### The Description of Principal Component Analysis and the Entropy Method
#### 3.2.2 Entropy Method  
The Entropy Method is a commonly utilized objective weighting tool in decision analysis involving multiple criteria [31]. Rooted in the concept of Entropy from thermodynamics, as introduced by [PERSON], this method quantifies the variability of criteria [32]. Entropy measures the uncertainty or variability present in the data, offering crucial insights into the diversity of criteria within the dataset. Its primary objective lies in calculating criterion weights based on the inherent diversity of the dataset [33]. Essentially, this approach involves the use of the decision matrix and the execution of various algebraic operations to derive attribute weights from informed judgments, harnessing the information and specific characteristics of the data [34]. The subsequent section outlines the step-by-step calculation process of the Entropy Method.  
Step 1. Construction of the Decision Matrix  
The decision matrix is a matrix that reflects the information for each alternative concerning each criterion. Consider the data matrix with \(n\) variables and \(m\) alternatives, where each element \(d_{ij}\) represents the rating of alternative \(i\) on criterion \(j\).  
Step 2. Normalization of the Decision Matrix  
In this step, a normalization technique is applied to ensure data is on a consistent scale. The specific technique used depends on the nature of the problem. Consider the normalized data matrix, \(N\), obtained through this normalization process.  
Step 3. Entropy Calculation [35]  
This step involves computing the Entropy for the normalized data matrix, \(N\), using [PERSON]'s Entropy Equation (7).  
\[e_{ij}=-\frac{1}{\text{ln}(n)}{\sum_{k=1}^{n}{\frac{n_{ik}}{n}\text{ln}\Big{(} \frac{n_{ik}}{n}\Big{)}}}, \tag{7}\]  
Here, \(n_{ik}\) represents the normalized value of alternative \(i\) on criterion \(j\).  
Subsequently, the Entropy for each criterion is calculated using the Equation (8).  
\[H_{j}=\frac{1}{\text{m}}{\sum_{i=1}^{m}{e_{ij}}}, \tag{8}\]  
Step 4. Criterion Weight Determination  
The final step involves determining the weights of the criteria, as depicted in Equation (9).  
\[W_{j}=\frac{1-H_{j}}{m-\sum_{k=1}^{m}{H_{k}}}, \tag{9}\]  
Here, \(W_{j}\) represents the weight of criterion \(j\), and \(H_{j}\) is the Entropy of criterion \(j\).  
The outcome yields the weights for each variable within the dataset, effectively presenting them in a ranked list. These weights can be utilized to assign priorities and make more consistent decisions.
## 3 Blasting Vibration in the Arch Cover Method
### Blast Pressure Induced by Rock Blasting  
The blast pressure induced by rock blasting follows an exponential attenuation pattern or an approximate triangular pattern. The time history of the blast hole pressure \(P\) described by a triangular pattern is straightforward and convenient, as shown in Figure 5. The main parameters are the peak blast pressure \(P_{\rm e}\), the peak blast pressure rising time \(t_{\rm a}\), and the vibration period \(T\). Many published papers refer to peak blast pressure attainment time  
Figure 4: Layout of blast holes.  
within several milliseconds. However, there are also blast hole measurements that indicate the peak pressure rising time is between 20 and 150 \(\upmu\)s, depending on the explosive type and confinement [28].  
To estimate the peak blast pressure in the blast wave propagating from a cylindrical explosive, [PERSON] and [PERSON] [29] and [PERSON] [30] proposed the following simplified equation derived by the equation of state for an ideal gas:  
\[P_{\text{d}}=\rho_{\text{e}}D^{2}/(1+\gamma) \tag{3}\]  
where \(P_{\text{d}}\) is the detonation pressure; \(D\) is the detonation velocity; \(\rho_{\text{e}}\) is the density of the unreacted explosive; \(\gamma\) is the specific heat ratio.  
The well-known expression of the detonation pressure for many explosives is given by assuming \(\gamma=3\)[31].  
The detonation pressure produces a gas pressure, often called explosion pressure, in the reaction portion of the explosive column. The explosion pressure is exerted on the blast hole walls by expanding gases after the chemical reaction is completed. [PERSON] and [PERSON] [32] proposed that the explosion pressure is approximately one-half the detonation pressure:  
\[P_{\text{e}}=P_{\text{d}}/2=\rho_{\text{e}}D^{2}/8 \tag{4}\]  
The explosion pressure is also called full coupled blast hole pressure in the case of the charge completely filling the blast hole. However, an empty space exists between an explosive column and the blast hole wall using the decoupling technique. [PERSON] and [PERSON] [33] provided a calibration equation to consider the decoupling effect.  
\[P_{0}=P_{\text{e}}(d_{\text{c}}/d_{\text{b}})^{2\lambda} \tag{5}\]  
where \(d_{\text{c}}\) is the explosive diameter; \(d_{\text{b}}\) is the blast hole diameter; \(\lambda\) is the explosive's adiabatic expansion constant, assuming 1.5 for average adiabatic exponent. Table 2 lists the explosives properties for this project.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 4 Our Proposed Solution
### Adaptive Spreading Factor  
To maintain a stable connection among nodes and limit the number of lost packets, we have created a method that allows the nodes to instruct the network to switch to a higher spreading factor to avoid further errors. In our experiments, we have chosen a threshold of three errors before the considered node increases its spreading factor and informs its neighbors to do the same. Whenever a node receives a message about increasing the spreading factor, it propagates the information to its other neighbors, thus eventually notifying all nodes. Clearly, the threshold for increasing the spreading factor can be easily modified according to the rate at which messages are sent out by nodes, or even considering other networking conditions, in order to be more reactive or more conservative.  
Since one or more nodes may not correctly receive the notification to switch to a different spreading factor, our system periodically selects a node to temporarily change its spreading factor to other values to broadcast a message instructing all potential listeners to use the newer spreading factor. This system would allow defective nodes left behind to join the network again.  
In our solution, nodes can only use spreading factors 7, 10 or 12 since we have empirically noticed that moving only to the next greater value, for example, from 7 to 8, does not substantially improve the connection health and would result in requiring multiple spreading factor increases, wasting additional time. Furthermore, using only a selected few options could avoid interference with other nearby applications using LoRa with different spreading factor values.  
Since using a high spreading factor has drawbacks, nodes also have the ability to reduce the error counter in a similar way to what is performed for their increments; hence, the spreading factor can be decreased after a certain number of successful message deliveries or after a certain time with no errors. The notice to switch to a lower value is sent out via flooding, identically to the one for increasing it.
## 4 Results and Discussion
### Experiment on the Effect of the Number of Consecutive ECG Beats  
In this section, the effect of ECG beat detection methods, i.e., manual (using annotated files) vs. automatic Hamilton's method, and the effect of the number of ECG beats on the accuracy, will be elaborated. Table 4 shows the results for various numbers of ECG beats for two ECG beat detections and two ECG databases.  
With manual ECG beat detection, 100% authentication accuracy can be obtained, starting from eight beats and above for the ECG-ID database and from six beats and above for the MIT-BIH database. This could be because the number of the persons to be identified from the ECG-ID database (90 persons)  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**P\#** & \begin{tabular}{c} **Beat** \\ **Detection** \\ \end{tabular} & **DSC** & **Res** & **Database** & **\#Layer** & \begin{tabular}{c} **\#Floating** \\ **Points** \\ \end{tabular} & \(t_{train}\) **(ms)** & \begin{tabular}{c} **\#** \\ **(ms)** \\ \end{tabular} & \
\begin{tabular}{c} **\#** \\ **Beats** \\ **Per ID** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline
1 & Manual & No & Yes &
\begin{tabular}{c} ECG-ID \\ MIT-BIH \\ \end{tabular} & 28 & 55,034 & 29,301 & 75 & 8 & 100 \\ \hline
2 & Manual & Yes & No &
\begin{tabular}{c} ECG-ID \\ MIT-BIH \\ \end{tabular} & 22 & 33,482 & 29,808 & 75 & 8 & 97.78 \\ \hline
3 & Manual & Yes & Yes &
\begin{tabular}{c} ECG-ID \\ MIT-BIH \\ \end{tabular} & 28 & 33,482 & 27,001 & 70 & 8 & 100 \\ \hline
4 & Hamilton & Yes & Yes &
\begin{tabular}{c} ECG-ID \\ MIT-BIH \\ \end{tabular} & 28 & 30,752 & 102,464 & 71 & 5 & **97.89** \\ \hline
5 & Hybrid & Yes & Yes &
\begin{tabular}{c} ECG-ID \\ MIT-BIH \\ \end{tabular} & 28 & 33,482 & 27,001 & 70 & 10 & 98.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental Results for Various CNN Models.  
Figure 5: Confusion matrix of the softmax layer (output layer) using single and multiple ECG beats for ECG-ID and MIT-BIH databases.  
is almost twice the number of the patients to be identified from the MIT-BIH database (48 patients). In contrast, with [PERSON]'s automatic detection, the authentication accuracy for both ECG-ID and MIT-BIH cannot reach 100%. This can happen because not all the ECG beats detected by [PERSON]'s method are good beats, so they might require further preprocessing. Nevertheless, manual ECG beat detection (using annotated files) is only feasible in the lab or offline authentication, as the beat detection need to be verified manually by cardiologists. Therefore, the optimum numbers of ECG beat using [PERSON]'s beat detection is seven and five for ECG-ID and MIT-BIH databases, respectively (the accuracy is highlighted in bold in Table 4).
## 2 Pointing Strategy and Model Establishment
### Disturbance Analysis and Descriptions
#### 2.3.3 Gravity Gradient Torque  
Each small mass element within the MOSA experiences the gravitational force of the Earth. Due to the non-coincidence of the rotation center of the telescope pointing mechanism with the center of mass of the spacecraft, the resultant gravitational force acting on the entire MOSA does not pass through the center of mass of the spacecraft. The torque caused by the gravitational gradient is referred to as the gravity gradient torque. The  
Figure 6: The noise curve. (**a**) Solar radiation pressure noise. (**b**) Micro-propulsion thruster thrust noise.  
presence of the gravity gradient torque can degrade the tracking precision and stability of the telescope pointing system. The torque model can be expressed as follows:  
\[\mathbf{D}_{\mathrm{G}}=\frac{3\mu}{\left|\tau\right|^{5}}\mathbf{r}\times(J_{t} \cdot\mathbf{r}), \tag{11}\]  
where \(\mu\) is the gravitational constant of the Earth, and \(\mathbf{r}\) is the vector pointing from the center of the Earth to the center of the probe.
## 5 Establishment of a Real-Time Salt Contamination Monitoring System  
### System Architecture  
Based on previous verification and analysis, the LSTM algorithm exhibits the best performance. Therefore, to establish a real-time salt contamination monitoring system for insulators on the transmission line, this study utilizes the LSTM algorithm to create a cloud monitoring platform for leakage currents in insulators. The platform architecture allows the saving of weather parameters to the database. After storage, the real-time leakage current can be estimated. Subsequently, the real-time weather and predicted leakage current are transmitted to the monitoring webpage. This provides maintenance personnel with the ability to observe and monitor estimated leakage current results through computers or mobile phones. In this study, an economical and effective maintenance strategy can be formulated based on the predicted leakage current, leading to significant savings in maintenance and labor costs.  
Furthermore, to assess the salt contamination or pollution level of the insulator, this study examines the relationship between leakage current and discharge phenomena when the insulator surface is contaminated. As depicted in Figure 1, when the leakage current exceeds 1 mA, the insulator's surface initiates a small spark discharge. Therefore, the proposed monitoring system's threshold values can be set at 0.5 mA and 1 mA. Additionally, in accordance with the estimated leakage current, this study utilizes a 30-min interval. The system calculates the number of leakage current pulses surpassing 0.5 mA and 1 mA within this time frame. If there are more than 10 instances of leakage current pulses exceeding 0.5 mA in a 30-min interval, it indicates a gradual deterioration in the insulator's insulation condition. Although there is no immediate risk of flashover, from a maintenance standpoint, the pollution level of the insulator will transition from Normal to Warning.  
When there are more than five instances of leakage current pulse greater than 1 mA within 30 min, it indicates that the insulator has poor insulation and is beginning to generate small spark discharge. If the current pollution or environmental conditions are more severe, a flashover is very likely to occur. The pollution level of the insulator will then change to Danger. At this point, washing of the insulators or other maintenance is recommended. Table 6 shows the predicted leakage current and pollution level of the insulator.
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 4 Results
### Ablation Studies
#### 4.5.1 Effectiveness of Different Components  
As shown in Figure 4, We conducted ablation experiments to understand the impact of different modules on the model, and considered the following five main models: (1) The baseline (BL) model (DFormer-S encoder and ham decoder structure, No. 1); (2) The ORGM and SFRRM modules added to the BL model (No. 2); (3) The ORGM and RFRRM modules added to the BL model (No. 3); (4) The ORGM, SFRRM, and RFRRM are integrated with the BL model (No. 4); (5) The proposed full model (No. 5). The results of these experiments are summarized in Table 5, and the ablation experiments in this section were all completed on the NYUDepthv2 dataset. the BL model achieves 78.24 on Acc, 66.6 on mAcc, and 53.5 on mIoU. Compared with BL, after adding ORGM and SFRRM, Acc slightly decreased by 0.14, but mAcc increased by 0.65 and mIoU increased by 0.3. After integrating the ORGM and RFRRM modules, the No.3 Model improved by 0.04, 0.88, and 0.57 on these three metrics compared to the BL model. When using ORGM, SFRRM, and RFRRM modules simultaneously, the No. 4 model performs 0.11, 1.17, and 0.69 better than the BL model on these three metrics. Notably, using the No. 5 model with all modules has improved the BL model by 0.31, 1.79, and 0.95 on these three metrics, which clearly verifies the effectiveness of each proposed component. Furthermore, it should be noted that the RFRRM significantly improves the detection performance compared to the SFRRM module, indicating that the RGB features extracted in the two-stream network encoder are more abundant and effective. However, from the No. 4 experiment using both SFRRM and RFRRM simultaneously, we can see that using both modules is more effective than using one module alone. This suggests that relying on a single module for extracting either RGB or spatial features is insufficient, and richer features can be extracted by employing both modules simultaneously. Note that the prediction results obtained after adding the SSFM module are closer to the results of the GT graph, indicating that the SSFM module can suppress noise features and enhance effective features through attention.