esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Factors Governing Survival and Occurrence of Opportunistic Pathogens in Premise Plumbing Systems
### Flow Patterns and Reducing Water Age  
According to [PERSON] et al. [6], water flow patterns control the duration and timing of stagnation events, internal pipe velocities, presence of stagnation zones, and use of recirculation pumps. Water flow patterns in premise plumbing can influence the formation and shearing of biofilms, the transport or nutrients and disinfectants, temperature profiles throughout the system, the rate of bulk water chemical reactions including disinfect decay, and, therefore, the occurrence of OPPPs [4]. Inherent to the number of influential factors, it is difficult to establish direct correlations between the effects of flow variations on the growth and survival of OPPPs.  
Understanding how to reduce water age within a distribution network is imperative to ensuring the quality of drinking water supplied. To facilitate this, additional research is required to determine with greater certainity what range of water ages are problematic and under what circumstances. Specifically, literature directly describing the relationship between water age, and the occurrence of opportunistic premise plumbing pathogens such as legionella spp. is lacking. Exceeding this knowledge deficiency may result in a wider range of preventative and remedial actions available to lessen the effects of high water age in distribution systems. Methods currently available during design and operation include regular flushing, correct sizing, and the elimination of dead ends.  
Given that water age is a function of demand, there would likely be substantial benefit in performing comprehensive modelling of occupant behaviour and consumption. This should extend to assessing the accuracy and validity of future demand prediction for the sizing of new builds. [PERSON] et al. [21] recommends an increased monitoring of green buildings to identify and resolve problems as they are encountered and hence establish any trends. Then, this information might be incorporated into building codes and standards outline or circumvent any negative and unintended consequences involved.  
For example, these codes and standards could mandate size requirements for plumbing system volumes according to demand--this might be validated by the knowledge that a higher premise plumbing water age will result whenever there are significant reductions to potable water demand without proportionally reducing the total system size. This approach would be most suited to the design and construction phases and would be extremely difficult, if at all possible, beyond such times. For existing pipe networks within existing structures, flushing is likely to be a more effective solution [11]. This strategy seems counterproductive to green building principals and offers the impression of water and energy wastage. Although water can be recovered for use in non-potable applications, some must be expended, especially if flushing is combined with thermal shock treatment.
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 2 Related Works
### Probabilistic Localization Methods  
As the procedure of obtaining the target positions in the NLOS condition can be well approximated to solve a nonlinear system, several algorithms [25; 26; 27; 28] have been developed to find the solutions with high accuracy. The scheme in [25] classified the area covered by the BS signals into LOS and NLOS zones by employing support vector classification and trigonometric approximation. In [26], the model of target locations was expressed as a probability distribution function. By processing the previous localization results with the algorithm known as two-step weighted least-squares, reduced NLOS error was achieved. The authors of [27; 28] advanced the conventional particle filter (PF) algorithms to achieve enhanced computational efficiency and reduced complexity when solving the nonlinear localization systems.
## 2 Materials and Methods
### Data Pre-Processing, Model Validation, and Performances Metrics  
One of the significant aims of any AI model is to ascertain that the model conforms to acceptable data based on the model's evaluation metrics utilized to obtain a dependable and strong computed outcome of the unknown data. Nevertheless, overfitting and local minima problems occur in the data validation. Hence, the performance of the learning phase might be unsatisfactory. This is especially true when the analysis deals with a relatively small dataset, as in this study. Various validation methods can be employed, including cross-validation (k-fold), hide-out, and leave one out. Here, the k-fold approach was used to repeal overfitting issues. With respect to this study, the data were split into 70% and 30% for calibration and validation, respectively. The results of the tribological experiments were pre-processed and normalized according to Equation (16). Data normalization was performed prior to model training. Data normalization enhances the prediction accuracies of the models. The current work introduced the SVR model coupled with PSO and HHO models  
Figure 6: Flow chart for the proposed hybrid models [39].  
to predict the abrasive tribological behaviors of PTFE matrix composites. The prediction of tribological behaviors is important. However, the creation of a reliable model is often challenging and difficult given the nature of the dataset obtained from the experiments.  
Normalized data:  
\[y=\left(\frac{x-x_{min}}{x_{max}-x_{min}}\right) \tag{19}\]  
where \(y\) = normalized data, \(x\) = is the experimental data, and \(x_{max}\)_and_\(x_{min}\) are the maximum and minimum experimental data, respectively.  
For the purpose of appraising the prediction accuracies of the models, the frequently used evaluation metrics of determination coefficient (R\({}^{2}\)), correlation coefficient (R), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used. These are mathematically given in the equations below.  
\[R^{2}=1-\frac{\sum_{i=-1}^{N}{(x-y)^{2}}}{\sum_{i=1}^{N}{(x-\hat{x})^{2}}} \tag{20}\]  
\[R=\frac{\sum_{i=-1}^{N}{(x-\hat{x})(y-\hat{x})}}{\sqrt{\sum_{i=1}^{N}{(x-\hat{ x})^{2}}\sum_{i=1}^{N}{(y-\hat{y})^{2}}}} \tag{21}\]  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}{(x-y)^{2}} \tag{22}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}{(x-y)^{2}}}{N}} \tag{23}\]  
\[MAPE=\frac{1}{N}\left[\sum_{i=1}^{N}{\frac{x-y}{x}}\right] \tag{24}\]  
where \(x\), \(y\), \(\hat{x}\) and \(\hat{y}\) = actual, predicted, average actual, and average predicted values, respectively.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 1 Introduction
With the input of the video stream image captured by the monitoring system in order to the embedded equipment for processing, it can realize the remote monitoring of conveyor belt operation with improved accuracy and avoidance of interference from harsh and complex environments. The algorithm, which is based on a multi-scale feature fusion network proposed in this paper, has the following characteristics:  
(1) Multi-scale fusion extracts global and local edge features effectively in a network structure;  
(2) The traditional convolutions are replaced with depthwise separable convolutions for network compression, which improves detection speed and performance to meet the requirements of various production scenarios;  
(3) A new weighted loss function is presented to optimize the network, which has a greater improvement in accuracy;  
(4) The experimental results show that the proposed method can achieve 78.92% in terms of pixel accuracy and the processing speed is 13.4 FPS with the error of less than 3.2 mm.
However, the exposure limits mentioned in ISO 2631-5:2004 remained the same in the new ISO 2631-5:2018. According to ISO 2631-1, and Directive 2002/44/EC, the daily (8-h) equivalent exposure action value and exposure limit value for the whole body are 0.5 and 1.15 ms\({}^{-2}\), and for hand-arm vibration are 2.5 and 5.0 ms\({}^{-2}\), respectively. Furthermore, ISO 2631-5 scaled the probability of adverse health effects due to WBV (daily compressive dose (SA) of the lumbar spine) into three categories, namely, low (<0.8 MPa), moderate (0.8 to 1.2 MPa), and high (>1.2 MPa) [16; 18]. Moreover, attached or hitched implements also play one of the major roles in vibration development. For example, the usual range of WBV of tractorswith implements is 0.5-1.49 ms\({}^{-2}\)[32; 33; 34]. According to ISO 20816-1, the vibration severity of class II medium machines is categorized into four classes: good (0.28-1.12 mms\({}^{-1}\)), satisfactory (1.8-2.8 mms\({}^{-1}\)), unsatisfactory (4.5-7.1 mms\({}^{-1}\)), and above this limit is unacceptable [26; 35]. Analysis of vibration exposure of an up-land crop translpanter is essential for ensuring proper transplanting and minimizing the adverse health effects on the operator caused by driving surface roughness, forward speed, operating status, and load conditions. The considered onion transplanter of this study was under development, and consisted of an operator-driven four-wheel vehicle and a mounted onion transplanter. The selected vehicle for this onion transplanter is usually used as a rice transplanter, where the mud (soft soil) acts as padding and minimizes the vibrational exposure significantly. Although several studies were conducted to evaluate the vibrational effects on machine performance and the operator's health in automobiles and large off-road vehicles such as tractors [6; 12; 15; 16; 36], few studies have investigated the vibrational exposure of upland crop transplanters, especially riding-type [37; 38; 39; 40; 41], which might adversely affect the performance of crop production as well as endanger the health of the operator. Regarding these issues, the objective of this study was to measure and evaluate vibrational effects of a 12-kilowatt self-propelled riding-type automatic onion transplanter for the operator's comfort and transplanting performance.
## 2 Data Collecting and Description  
To accurately predict the FV using simple ML and HML models in a frequent manner, it is important to utilize input variables that are routinely measured several times per hour. Therefore, the FD (pounds per cubic foot, pcf) and the MFV (sec/quart) were selected as inputs for the simple ML and HML models based on prior studies showing their significant impact on FV [10; 17; 18; 19; 24; 26; 30; 31].  
* The FD has a direct impact on downhole hydrostatic pressure, which is exerted by the fluid column in the wellbore. The FD influences the FV in several ways. Higher FD increases the FV due to higher hydrostatic pressure, which pushes more fluid through the filter. Additionally, particle-fluid interactions affect cake formation and filtration rate, while changes in temperature and pressure alter the fluid's properties, affecting its ability to filter [11; 12; 27; 28; 32].
* The MFV measures the viscosity of DFs under realistic conditions. This test estimates the time it takes for a fixed volume of fluid to flow through a narrow orifice, which is influenced by the Reynolds number, a measure of the drag coefficient. A higher MFV indicates a thicker, more resistant fluid that flows more slowly, resulting in a lower ability to infiltrate pores, thereby resulting in a lower FV [11; 28; 33; 34].  
The 17-well compiled dataset (1260 data records) includes FV, FD, and MFV measurements for the WBDFs employed. Table 1 summarizes the dataset statistically. This database is applied for developing HML for accurate semi-real-time FV prediction.
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 3 Method
### Residual Large Kernel Separable Attention Block (RLSKAB)  
The LSKA proposed by [PERSON] et al. [42] represents an innovative integration of traditional convolution and self-attention mechanisms embedded within the visual attention network (VAN) [43]. Its architecture is detailed in Figure 3a. While self-attention excels at capturing long-range dependencies, its high computational complexity and lack of channel adaptability hinder practical applications in image processing. Similarly, large-kernel  
Figure 2: Structure of the Residual Large Kernel Separable Feature Distillation Module (RLSKFDM).  
convolution expands the receptive field but introduces significant computational overhead and parameter growth. To address these challenges, LSKA decomposes large-kernel convolution into multiple simple convolutional layers, incorporating self-attention principles to reduce both computational complexity and parameter count. This approach enhances network expressiveness while maintaining effective modeling of long-range dependencies.  
The RLSKAB structure proposed in this study is illustrated in Figure 3b. This module builds upon the attention block of the VAN network, integrating the architectural logic of shallow residual blocks from residual feature distillation networks. By leveraging large separable kernel attention mechanisms, RLSKAB enhances feature extraction capabilities and strengthens the modeling of global-local contextual information. Additionally, the module incorporates the Hard Swish activation function after residual skip connections, which boosts the network's non-linear representation capacity while incurring minimal computational overhead. This design ensures superior performance with reduced resource consumption, making it particularly well-suited for remote sensing image super-resolution reconstruction tasks.  
For a single RLSKAB, assuming the input feature is \(F_{RLSKAB\_\text{-}\text{In}}\), its output feature \(F_{RLSKAB\_\text{-}\text{out}}\) can be expressed as  
\[F_{\text{Conv}}=\text{Conv}_{\mathbf{1}\times\mathbf{1}}\big{(}BN\big{(}F_{RLSKAB \_\text{-}\text{in}}\,\big{)}\big{)}, \tag{10}\]  
\[F_{LSKA}=\text{LSKA}(GeLU(F_{\text{Conv}}\,)), \tag{11}\]  
\[F_{RLSKAB\_\text{-}\text{out}}\,=HS\big{(}\text{Conv}_{1\times 1}(F_{LSKA})+F_{ RLSKAB\_\text{-}\text{in}}\,\big{)}, \tag{12}\]  
where \(F_{\text{Conv}}\) and \(F_{LSKA}\) represent the output of the first layer \(3\times 3\) convolution and the output of the large separable kernel attention, \(\text{Conv}_{1\times 1}\) represents the convolution operation, \(BN\) represents the Batch Norm standardization, \(GeLU\) and \(HS\) represent the GeLU activation function and the Hard Swish activation function, respectively. Based on the integration of VAN's attention mechanism and RFDN logic, RLSKAB effectively expands  
Figure 3: Structure of different blocks. (**a**) LSKA in VAN. (**b**) RLSKAB.  
the receptive field through LSKA and improves the perception of the global information of remote sensing images.
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
## 2 Multi-GNSS Uncombined Model
### ISB Parameter Processing Model  
Typically, ISB parameters can be modeled using white noise, random constant and random walk processes. The white noise model is expressed as follows [22]:  
\[ISB_{r}^{S}(k)\sim N\Big{(}0,\sigma^{2}\Big{)} \tag{10}\]  
where \(k\) represents the epoch number. The advantages of the white noise model are (a) ISB parameters are irrelevant between epochs. (b) It is advantageous to adopt the white noise model when we are completely unaware of how the actual ISB parameters change or what constraint model to add [19].  
The random constant model is relatively simple, i.e., an ISB parameter can be considered as a random constant and only one value is estimated in a day, which is expressed as follows:  
\[ISB_{r}^{S}(k)=ISB_{r}^{S}(k-1) \tag{11}\]  
The random walk model [23] can be represented as:  
\[ISB_{r}^{S}(k)=ISB_{r}^{S}(k-1)+\varepsilon_{ISB},\hskip 14.226378 pt\varepsilon_{ ISB}\sim N\Big{(}0,\sigma_{\varepsilon_{ ISB}}^{2}\Big{)} \tag{12}\]  
The random walk model retains the estimate of the last epoch, and the variance increases linearly over time. After an experimental comparison of ISB parameters of G + C + R + E + J systems, [PERSON] et al., suggested that the white noise model or random walk model can be adopted in the PPP uncombined model process for a multi-system [19]. In this study, the white noise model is used to estimate ISB parameters.
## 3 Results and Discussions
### Heat Dissipation Analysis
This analysis highlights the unique thermal properties of materials, providing insightful data on their suitability for applications where thermal management is critical. Figure 8: Shielding chassis’s thermal analysis: (**a**) metal; (**b**) carbon fiber; (**c**) nickel-plated carbon fiber.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 4 Locata Applications
### Indoor/Outdoor Vehicle Tracking  
Locata's application on vehicle tracking and precise positioning were conducted both indoors and outdoors. In the beginning, static/kinematic positioning was implemented through an integration of GPS and Locata, due to the lack of redundancy of Locata measurements. Tests of using prototype Locata technology demonstrated that outdoor static and kinematic positioning accuracy were at the sub-centimetre-level and centimetre-level, respectively. Due to errors that were induced by possible multipath and obstructions, indoor kinematic positioning accuracy was reduced to the sub-metre-level [10; 17; 39; 61]. Later in [62], the feasibility of using Locata for industrial machine guidance was demonstrated through a trial at BlueScope Steelworks under an extremely severe multipath environment, where the positioning results were with sub-centimetre precision and centimetre-level absolute accuracy. More recently, fault-tolerant decentralised GNSS/Locata/INS integration algorithms were investigated for land vehicle navigation [46]. To mitigate the multipatheffects, VRay antenna was designed and applied for vehicle tracking, and investigations regarding the positioning algorithms were detailed in [31; 32].
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 4 Evaluation, Result and Analysis
### Evaluation Metrics  
When evaluating the performance of a model, the evaluation metrics are important. We utilized four evaluation metrics that have been used in several studies: accuracy, recall, precision, and F1 score. Equations (5)-(8) show the method for calculating these metrics  
\[Accuracy=\frac{TP+TN}{(TP+FN+FP+TN)} \tag{5}\]  
\[Recall=\frac{TP}{(TP+FN)} \tag{6}\]  
\[Precision=\frac{TP}{(TP+FP)} \tag{7}\]  
\[F1\ Score=\frac{2\times Recall\times Precision}{(Recall+Precision)} \tag{8}\]  
True positive (TP) is when the model correctly classifies something as positive, and true negative (TN) is when the model correctly classifies something as negative. False positive (FP) is when the model incorrectly classifies something as positive when it was negative, and false negative (FN) is when the model incorrectly classifies something as negative when it was positive.  
As previously mentioned, the ISCX 2016 VPN/Non-VPN data are highly imbalanced between classes. To account for the potential bias in the results due to the imbalance between the different categories of data, we used macro average [36]. Macro average calculates the average value of precision, recall, accuracy, and F1 scores for each category to provide a more comprehensive and unbiased assessment across all categories.
## 3 Results and Discussion
### Validation  
After the identification of the optimal levels, the final phase in the GRA is the prediction and validation of the performance enhancement of the outputs. The predicted GRG was calculated as per Equation (7). Validation trials were performed to verify the results of the investigation. The optimized levels of M\({}_{L}\), V\({}_{L}\), \(\mu\), and T\({}_{s}\) were found to be 8.5 \(\times\) 10\({}^{-4}\) g, 4.1 \(\times\) 10\({}^{-4}\) mm\({}^{3}\), 2.0 \(\times\) 10\({}^{-1}\), and 1.5353 \(\times\) 10\({}^{-6}\) mm\({}^{3}\)N\({}^{-1}\)m\({}^{-1}\), respectively. It can also be implied from Table 9 that the findings of the validation stage are consistent with the computed values. In addition, an enhancement of 52% in the GRG was also achieved.
## 3 Proposed Identification Methods
### Proposed Hybrid Methods
#### 3.5.1 HPSO-QN Sequential Method  
In the HPSO-QN sequential method (SM), PSO is utilized to find the _gBest_ solution. The pseudo-code of the HPSO-QN SM algorithm is presented in Algorithm 3. In the same iteration, this solution serves as the starting point for the QN method. If the QN method's solution is better than that of the PSO, the _gBest_ solution from PSO is replaced with the solution from the QN method. This method improves the _gBest_ solution obtained from the PSO algorithm with each iteration by balancing the exploration of the search space by PSO and the exploitation of the local minima by the QN method, thus enhancing both the local and global searches in each iteration.
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 4 An RB RAIM Based on a Robust MM Estimation
### Robust MM Estimation
#### 4.2.2 Equivalent Weight Function of the M Estimation  
The M estimation is based on the minimization of a score function of residuals that aim at a result not influenced by outliers. There are more than 10 kinds of score function and the following four representative functions (Cauchy, Huber, Tukey and IGG III) were selected for comparison, as shown in Table 1.  
A monotone-based M estimator is then applied, which is not as aggressive as the previous one, to neglect some observations but not decrease the efficiency. Therefore, the M estimate after the initial LTS almost always uses the Huber score function. Otherwise, when using a hard-redescending function, such as the Tukey or the IGG III, concatenate an aggressive initial estimate with an also-aggressive posterior one. The Huber function was found to be the best choice of the above four and was adopted in this study, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Function** & **Parameter** \\ \hline Cauchy & \(\overline{p}_{i}=\frac{1}{1+\left(\frac{1}{k}\right)^{2}}\) & \(k=2.3849\) \\ Huber & \(\overline{p}_{i}=\begin{cases}1,&|v_{i}|<k\\ \frac{k_{0}}{|v_{i}|},&|v_{i}|\geq k\end{cases}\) & \(k=1.3450\) \\ Tukey & \(\overline{p}_{i}=\begin{cases}\left(1-\left(\frac{p}{k}\right)^{2}\right)^{2},&|v _{i}|<k\\ 0,&|v_{i}|\geq k\end{cases}\) & \(k=4.6851\) \\ IGG III & \(\overline{p}_{i}=\begin{cases}1,&|v_{i}|<k_{0}\\ \frac{k_{0}}{|v_{i}|},&\left(\frac{k_{0}-|v_{i}|}{k_{1}-k_{0}}\right)^{2},&k_{0 }\leq|v_{i}|<k_{1}\\ 0,&|v_{i}|\geq k_{1}\end{cases}\) & \(k_{0}=1.0\sim 2.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four representative score functions.  
Figure 3: Flow chart of the least trimmed squares (LTS) estimator.