esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous Review Papers  
A number of review or survey papers discussed various VRPs from different scopes and perspectives. However, only a handful of survey papers focused on the scope of the disaster and humanitarian operations. Only three review papers are found addressing VRPs in terms of the application for humanitarian operations excluding our preliminary work [6]. Both [7; 8] focused solely on the delivery problem, while [9] looked into the two-stage stochastic programming modelling approach of the humanitarian operations. Recent work conducted by [10] focused on casualty management while using OR techniques, which also includes supply and rescue operations, among others, but not limited to only routing problems. Beyond the humanitarian operation's scope, but related to this study, ref. [11] addressed the eco-friendly, unmanned bus routing problem, while [12] proposed and reviewed various RVRP within the field. Ref. [13] discussed the 50 years of progress in VRP since [14], and the same focus is given in [15], presenting a comprehensive study of 277 papers from the year 2009 to 2015, extending the work of [16]. Meanwhile, Ref. [17] discussed the stochastic and dynamic VRPs for various applications. In general, most of the aforementioned review papers (except for [10; 15]) only surveyed a moderate number of papers and did not explicitly present the methodology of their review, such as the years in which the reviewed papers are published. Instead, they mainly focused on the findings. Having said that, these review papers all echoed the same suggestions, which include:  
* the need to address the real-world problems,
* the need for the RVRP, and
* the importance of stochastic and dynamic elements of the studied problem.  
This suggests the persistent gap in RVRP or that the existing work may not meet the criteria of a real-world problem despite the theoretical connotation given. Nonetheless, Ref. [17] highlighted the emerging trend of RVRP, which is strongly related to the stochastic and dynamic elements of routing problem. The advancement of computation power could be one of the key catalysts. Finally, all of the mentioned review papers in this section are presented in Table 1, highlighting the focus of their research as well as the setting of their survey in terms of the year of publication and number of papers reviewed.
## 1 Introduction
It has a relatively fast training speed, simpler hyperparameter settings, strong regularization ability, better ability to handle overfitting problems, and improved performance with noisy data and nonlinear problems. The objective of this study is to develop a productivity ratio prediction model for perforated wells by integrating the particle swarm optimization (PSO) with the least squares support vector machine (LSSVM). Specifically, the LSSVM technique is employed to establish the productivity ratio prediction model for perforation wells, while the PSO algorithm is utilized to optimize the parameters within the LSSVM algorithm. In comparison to other parameter optimization methods and machine learning algorithms, the combination of the PSO algorithm and the LSSVM technique proves to be a viable approach for predicting the productivity ratio of perforated wells. Three-Dimensional Finite Element Analysis for Evaluating the Productivity Ratio of Perforated Wells  
The productivity ratio (PR) is defined as the ratio of the steady-state productivity of a perforated well to the steady-state productivity of an ideal open-hole well under the same conditions. The mathematical expression is as follows:  
\[\text{PR}=\frac{Q}{Q_{0}}, \tag{1}\]  
where PR is the productivity ratio of the perforated well, \(Q\) is the productivity of the perforated well, m\({}^{3}\)/d, and \(Q_{0}\) is the productivity of the ideal open-hole well, m\({}^{3}\)/d, which can be calculated using the following theoretical formula:  
\[Q_{0}=\frac{2\pi Kh(p_{e}-p_{w})}{\mu lnr_{w}/r_{e}}, \tag{2}\]where \(K\) is the formation permeability, \(h\) is the formation thickness, \(p_{w}\) and \(p_{e}\) are the bottom hole pressure and external boundary pressure, respectively, \(\mu\) is the fluid viscosity, and \(r_{w}\) and \(r_{e}\) are the radius of the wellbore and the circular formation, respectively. Due to the asymmetry of the perforation hole arrangement and the complexity of the flow paths, the productivity of perforated wells cannot be accurately determined using analytical methods. Therefore, numerical methods, specifically the finite element method, are employed in this study to calculate the productivity of perforated wells under varying perforation parameters.
## 3 Proposed Model  
The proposed model is illustrated in Figure 9 and comprises two processes (i.e., training and testing). In Figure 9, the dashed line is the stream of the signal in the training process, and the solid line is the stream of the signal in both the training and testing processes. In the training process, after target estimation, we achieved the error signal \(e[j,k]\), which was used to train the neural network. In this process, the neural network uses the signal \(y[j,k]\) as the input and the signal \(e[j,k]\) as the output (label). The signals \(s[j,k]\), \(r[j,k]\), \(a[j,k]\), and \(e[j,k]\) do not appear in the training process, as shown in Figure 5.  
In the testing process, in Figure 9, the signal does not go through the path indicated by the dashed line but only through the one denoted by the solid line. The original data \(u[k]\) is magnetized (i.e., modulated) into the signal \(a[j,k]\in\{-1,1\}\), and the data are stored in the BPMR channel. When the data are read back, the readback signal \(y[j,k]\) is affected by the additive noise, TMR, and media noise. The received signal is equalized by the equalizer \(\mathbf{F}\) and converted into the desired signal \(d[j,k]\). However, the readback signal \(y[j,k]\) that is converted to \(z[j,k]\) is not the same as the desired signal \(d[j,k]\). The signal \(z[j,k]\) goes through the serial detection, which includes a horizontal detector (inner detector)  
Figure 8: Learning curve of the network with three hidden layers.  
Figure 9: Our proposed system model.  
and a vertical detector (outer detector). The neural network extracts the error signal \(\hat{e}[j,k]\) from the received channel output signal \(y[j,k]\), and \(\hat{e}[j,k]\) is slightly clearer between the desired signal \(d[j,k]\) and the received signal \(y[j,k]\). The noise information \(\hat{e}[j,k]\) from the neural network is supplied after the inner detection to create the soft output \(r[j,k]\) for the outer detector and improve the bit error rate (BER) performance of the systems. The serial detection output \(\hat{a}[j,k]\) is demodulated to recover the original signal \(\hat{a}[j,k]\).
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 3 Proposed Methodology
### A Stochastic Approach  
A very common scenario used to detect earthquake events involves signals from low-cost sensors, placed in several areas near roads and a methodology that allows the identification of earthquake events in such data. In our context, a seismic event is defined as the transition from a state of pure noise to a seismic signal. The current methodology we use to detect earthquake events, as presented in [27], is a stochastic differential model that employs a sliding window technique on the time-series data of the sensor. This window is incrementally moved through the dataset at predefined intervals. Within each window, the data undergo a transformation process, the variance function of the transformed data is computed, and its shape is assessed in relation to a power law distribution.  
If the shape of the variance function closely aligns with the characteristics of a power law distribution, it is postulated that this is indicative of the data window predominantly representing noise. In this case, the model proceeds to the next window. However, when the shape of the variance function diverges significantly from the expected power law shape, the algorithm terminates and signals the detection of a seismic event.  
In our case, we applied this algorithm to a dataset obtained from our low-cost seismic monitoring system. The successful detection of a seismic event was determined based on the algorithm's ability to identify an outbreak near the initiation of the seismic event, while not focusing on the detection of the whole event.  
It is worth noting that, although this method demonstrated competency in identifying seismic events in its original presentation, it faced challenges in distinguishing between  
Figure 1: Architectural diagram of the proposed methodology.  
seismic events and events triggered by external factors, such as passing vehicles. This limitation was discussed as a key factor in the interpretation of the results.
## 5 Conclusions  
In this paper, we thoroughly studied the modeling and design of the proposed system, developed the OST-SMC controller, and successfully analyzed the system's stability using the Lyapunov stability theory. Simulations were implemented in an environment combining MATLAB/Simulink and CarMaker, and comparative experiments were conducted. Based on the simulation results, we concluded that the proposed control law effectively addressed the tracking problem with high performance. This demonstrates that the vehicle accurately followed the desired speed, a critical requirement for autonomous driving. Furthermore, the proposed method successfully attenuated the chattering issue inherent in first-order sliding mode controllers. It also proved its robustness to uncertain parameters and external disturbances from the road. In future work, we aim to advance this approach by designing an adaptive super-twisting control law and analyzing frequency response for the motion control of autonomous vehicles. Additionally, we plan to integrate the engine output map into the overall control framework to develop a more holistic model that accounts for both  
Figure 10: Tracking performance comparison of OST-SMC vs SMC.  
vehicle dynamics and engine behavior. We will also apply the proposed method to real vehicles to verify its effectiveness in real-time applications. Furthermore, we recommend exploring different scenarios, such as evaluating the system's performance on slippery roads with low friction coefficients, for those interested in further research on this topic.  
Conceptualization, K.L.G. and W.Y.C.; methodology, K.L.G. and W.Y.C.; software, K.L.G.; validation, K.L.G. and W.Y.C.; formal analysis, K.L.G. and W.Y.C.; investigation, K.L.G. and W.Y.C.; resources, K.L.G.; data curation, K.L.G.; writing--original draft preparation, K.L.G.; writing--review and editing, K.L.G. and W.Y.C.; visualization, K.L.G. and W.Y.C.; supervision, W.Y.C.; project administration, W.Y.C.; funding acquisition, W.Y.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation (NRF), Korea, under Project BK21 FOUR (Smart Robot Convergence and Application Education Research Center), NRF, Korea, grant funded by the Korea government (MSIT) (No. RS-2023-00213640, Accurate Autonomous Tracking Control by Unmatched Disturbance Compensator with Reference Re-design Filter) and partly supported by the Technological Innovation R&D Program (S3430140) funded by the Ministry of SMEs and Startups (MSS, Korea).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The author appreciates [PERSON] for pre-reviewing the proposed formulation of this paper before submission.  
The authors declare no conflicts of interest.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 3 Method
### Classification Models
#### 3.3.2 Support Vector Machine  
The support vector machine (SVM), abbreviated as SVM, is a supervised learning algorithm used for classification and regression analysis. Its main idea is to find a hyperplane that effectively separates data points of different classes and maximizes the margin (i.e., the distance from the points closest to the hyperplane to the hyperplane). The dataset for apple blue mold disease has a relatively small sample size, and SVM is capable of finding a more generalizable classification decision boundary in situations where the sample is limited [36]. In datasets with a small number of samples, the impact of noise and outliers on the model may be more significant. However, since SVM focuses on data points near the support vectors, it is relatively insensitive to noise and outliers far from the support vectors. This characteristic enables SVM to better handle exceptional cases in datasets with a small number of samples [37].
## 5 Establishment of 3D Geological Model  
The construction of a 3D geological model is an integral component of reservoir modeling and simulation, initiated by developing a lithofacies model [45]. Subsequently, under the guidance of this lithofacies model, a reservoir parameter model is formulated to anticipate the physical attributes of inter-well reservoirs. To enhance the precision of geological modeling, the stratigraphic structure pattern was utilized as a reference framework. The marker layer, sedimentary cycle, and lithology combination served as the foundation for comparative analysis, enabling the execution of detailed stratigraphic correlation and the establishment of an accurate isochronous stratigraphic framework. According to the sedimentary cycle and lithology combination, the interior of the Chang 7 member was divided into three sand groups and five small layers. The lithology of Chang 7\({}_{2}\) and Chang 7\({}_{3}\) is different, the characteristics are obvious, and the boundary is easy to identify. Chang 7\({}_{1}\) and Chang 7\({}_{2}\) are divided according to the sedimentary cycle, and the top of the cycle is argillaceous and carbonaceous mudstone. The precise isochronous stratigraphic framework aims to follow the principle of isochronous modeling and apply the combination of deterministic modeling and stochastic modeling in geological modeling. Considering the dominant control of lithofacies on the distribution of sand bodies and reservoir physical properties in the area, the authors established the lithofacies model for the target layer within the study area prior to constructing the reservoir physical properties model.
## 2 Materials and Methods
### Optimization Models  
Model M-1 (Equation (10)) was used to obtain the minimum _NPC_ of every unit of the logistics center for Action 1 (see details in Section 2.5.1). This model minimizes the _NPC_ of a logistics center unit, subject to Equations (3)-(7), by varying the contracted powers (\(P_{c}\)) (one for each charging period of the 3.0A tariff) between the specified lower (\(P_{c}^{L}\)) and upper (\(P_{c}^{L}\)) bounds. Later, the minimum NPCs are added up to represent the total net present cost of the project over the analyzed timeframe.  
\[\begin{split}&\underset{P_{c}(i)}{\text{\emph{min}}}\ \text{\emph{NPC}}\ (\text{\emph{Eq.}\ (\ref{eq:1})})\\ &\text{subject to Equations (\ref{eq:2}) to (\ref{eq:3})}\\ & P_{c}^{L}\leq P_{c(i)}\leq P_{c}^{L}\\ & P_{c(i)}\in\mathbb{R}\ \ \ \ \forall i\in\{P1,P2,P3\}\\ \end{split} \tag{10}\]  
Additionally, model M-1 was used in Action 2 (see details in Section 2.5.2), where the minimized _NPC_ does not represent the values of the separated units, but the combined cost of all eleven units studied in this project, considered as a single load from an electrical point of view. Equations (2)-(6) and the lower (\(P_{c}^{L}\)) and upper (\(P_{c}^{U}\)) bounds were conveniently modified to correspond to the combination of the units.  
Due to the nonlinearities of the model M-1, it was solved using the TRNSYS18 Simulation Studio [16] and its optimization library component, the GenOpt Generic Optimization Program [17] in a single-objective optimization (SOO) process based on the NPC with a LCC perspective. This approach can be found in similar studies using multi-objective optimization (MOO) processes as performed by [PERSON] et al. [24] and [PERSON] et al. [25].  
Finally, model M-2 (Equation (11)) was implemented for Action 3 (see details in Section 2.5.3), where _NPC_ is minimized subject to Equations (2)-(7) and additional energy balances provided by the simulation software. The decision variable in model M-2 is the number of PV panels, which were installed in strings of seven individual panels connected in series. This distribution is advisable for security purposes and to adequate the DC voltage to the input range of market inverters (e.g., the Solar Inverters List published by NATA, National Association of Testing Authorities [26]).  
Due to the strong nonlinearities of the optimization model M-2, which additionally contains implicit equations evaluated by the simulation software, the model can be solved using a metaheuristic algorithm based on particle swarm implemented in the optimization tool, GenOpt (see Figure 11).  
\[\begin{array}{l}\underset{n_{PV}}{min}\text{\emph{NPC}}\ (\text{Eq.}\ (1))\\ \text{subject to Equations (2) to (7)}\\ \text{energy balances}\\ \text{n}_{PV}^{L}\leq n_{PV}\leq n_{PV}^{II}\\ \text{n}_{PV}\in\mathbb{N}:7|n_{PV}\end{array} \tag{11}\]
## 5 Simulation Experiment
### Parameter Design  
The main experimental parameters that affect the performance of the MISSA include population size, number of iterations, and crossover probability. In order to determine the optimal parameter combination, this paper adopted the Taguchi orthogonal experimental method. A 3-factor 4-level experiment was designed:  
Population size: [40, 60, 80, 100]  
Number of iterations: [40, 60, 80, 100]  
Crossover probability: [0.6, 0.7, 0.8, 0.9]  
For the IGD index (the smaller the better), key parameter effects were screened through 16 sets of experiments. Signal-to-noise ratio (SNR) is the core indicator used in Taguchi  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Quantity of Workpieces** & **Quantity of Machines** & **Delivery Date (min)** & **Scheduling Cycle** \\ \hline MK01 & 10 & 6 & 35, 30, 30, 35, 35, 30, 30, & 34 \\ MK02 & 10 & 6 & 20, 20, 20, 20, 20, 15, 20, & 28 \\  & & & 15, 20 & \\  & & & 200, 180, 170, 180  
method to measure the performance of parameter combinations. Calculate the SNR for each set of parameters using the following formula. Each experiment was repeated 10 times (i.e., \(n=10\) in the formula) to eliminate random errors:  
\[SNR=-10\text{log}_{10}\frac{1}{n}\sum_{i=1}^{n}IGD_{i}^{2} \tag{17}\]  
The results are shown in the Table 4:  
The parameter levels with the highest average SNR are: population size of 100 (80.2 dB); 100 iterations (79.6 dB); The crossover probability is 0.8 (78.9 dB).  
To verify the above conclusion, the IGD index of the solution results was analyzed on the MK03 case. According to the experimental results shown in Figure 7, the optimal parameters for the MISSA algorithm were ultimately determined to be: population size: 100, number of iterations: 100, crossover probability: 0.8. Table 5 lists the initial parameter values for different algorithms for comparison of their performance.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Level** & **Average SNR (dB)** & **The Best** \\ \hline \multirow{4}{*}{Population size} & 40 & 69.7 & \multirow{4}{*}{} \\  & 60 & 71.3 & \multirow{4}{*}{} \\  & 80 & 75.4 & \multirow{4}{*}{} \\  & 100 & 80.2 & \(\surd\) \\ \hline \multirow{4}{*}{Number of iterations} & 40 & 70.1 & \multirow{4}{*}{} \\  & 60 & 73.3 & \multirow{4}{*}{} \\  & 80 & 76.3 & \multirow{4}{*}{} \\  & 100 & 79.6 & \(\surd\) \\ \hline \multirow{4}{*}{Crossover probability} & 0.6 & 74.9 & \multirow{4}{*}{} \\  & 0.7 & 76.4 & \multirow{4}{*}{} \\  & 0.8 & 78.9 & \(\surd\) \\ \cline{1-1}  & 0.9 & 78.8 & \\ \hline \end{tabular}
\end{table}
Table 4: Average SNR values at various parameter levels.  
Figure 7: IGD Value Main Effect Plot.
## 1 Introduction
The capacity drop phenomenon was also confirmed by [PERSON] and [PERSON] [5]. Since then, many empirical studies have been conducted to quantify the capacity drop ranging between 0.5% and 35% [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. In consideration of overall traffic delays, the rate of queue discharge is of significant importance. Numerous research was already conducted about the capacity drop, but some significant features are still unclear. The extent of how the queue discharge rate reduces downstream on the onset of rainfall and whether it has a relationship with traffic flow variables is still unknown. There have been a significant amount of studies that look into the effect of weather conditions on the pre-breakdown flow rate at a given location [16; 17; 18; 19; 20] but not on the queue discharge rates. This current study will then exhibit additional empirical observations to forward an understanding of the capacity drop. Findings can be used in control principles mitigating congestion and to further understand traffic processes. This study would also answer the gap in previous research on the capacity drop, which excludes the effects of rainfall since most studies about the capacity drop were conducted during clear conditions, albeit there are a few exceptions.
# Three-Dimensional Division of Visible Light Communication Irradiation Area
###### Abstract
The above research on VLC channels is very important to more deeply understand and improve the communication performance of these systems. In comparison with the above studies, our work focuses on an LOS channel to study the visible light irradiation range in the LOS channel and to quantitatively analyze the volume share of the optical signal reception area in the communication channel. We simulated a VLC system with fixed positions at both ends of the transceiver, based on a theoretical LOS channel model [41; 42; 43]. We discuss the effects of five different indicators on the communication system, namely communication distance, transmitter half angle, irradiance half angle at the detector, irradiance half angle at the source, and detection surface area. We built a 10 m indoor LOS channel based on an IM/DD modulation VLC system with the position of the transmitter and the receiver not fixed. We established a 3D model of an entire communication channel using comparative experiments of different lampshade heights, and divided it into three different communication areas. Further analysis showed that when the transmitter angle was fixed, the longest communication distance varied non-linearly with the change in the lampshade height. At the same time, the volume ratio (VR) of areas I and III was close to 20\(\sim\)30% and that of area II was the highest, reaching more than 50%, which was the best signal receiving area obtained. The main contributions of this paper are as follows: (1) A three-dimensional BER distribution model of VLC under a conical lampshade was established; (2) The optimal signal reception position in the illuminated area was determined. The rest of the article is organized as follows: Section 2 introduces the theoretical model of VLC; Section 3 illustrates the theoretical simulation and analysis of the VLC; Section 4 explains the VLC experiment setup, presents the experimental results, and provides a discussion. A summary of the work is given in Section 5.