esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bi-Objective Model for APP with Production in Advance and Backordering
### Objective Functions  
According to the literature [20], the total production cost and stability in the workforce are mainly considered, where the total production cost mainly includes the product production cost, raw material cost, product inventory cost, labor cost, BC, and LSC.  
The total production cost in period \(t\) can be calculated by Equation (7).  
\[C_{1t}=\sum_{i=1}^{I}PP_{it}\cdot PC_{i} \tag{7}\]  
Total demand of raw material \(j\) and the total raw material cost in period \(t\) can be calculated by Equations (8) and (9).  
\[RM_{jt}=R_{ji}\cdot PP_{it} \tag{8}\]\[C_{2t}=\sum\limits_{i=1}^{I}PP_{it}\cdot PR_{it}=\sum\limits_{i=1}^{I}\sum\limits_ {j=1}^{J}PP_{it}\cdot R_{ij}\cdot RC_{it} \tag{9}\]  
Using Equation (10), the total inventory cost in period \(t\) can be calculated based on Equation (1).  
\[C_{3t}=\sum\limits_{i=1}^{I}CI_{it}\cdot CK_{i} \tag{10}\]  
If the working hours required for production are less than the maximum labor hours of regular time for \(k\) type worker, \(\sum\limits_{i=1}^{I}PP_{it}\cdot PW_{ik}\leq W_{tk}\cdot WR\), then the total labor hours for regular time \(WRT_{tk}=\sum\limits_{i=1}^{I}PP_{it}\cdot PW_{ik}\) and the total labor hours of overtime \(WOT_{tk}=0\). Otherwise, \(WRT_{tk}=W_{tk}\cdot WR\) and \(WOT_{tk}=\sum\limits_{i=1}^{I}PP_{it}\cdot PW_{ik}-W_{tk}\cdot WR\). Thus, total labor hours for \(k\) type worker of regular time and overtime can be written as Equations (11) and (12).  
\[WRT_{tk}=\min\Biggl{\{}\sum\limits_{i=1}^{I}PP_{it}\cdot PW_{ik},W_{tk}\cdot WR \Biggr{\}} \tag{11}\]  
\[WOT_{tk}=\max\Biggl{\{}\sum\limits_{i=1}^{I}PP_{it}\cdot PW_{ik}-W_{tk}\cdot WR,0\Biggr{\}} \tag{12}\]  
The total labor cost in period \(t\) can be calculated by Equation (13).  
\[C_{4t}=\sum\limits_{k=1}^{K}(WH_{tk}\cdot WHC+W_{tk}\cdot WI_{k}+WRT_{tk}\cdot W RC _{k}+WOT_{tk}\cdot WOC_{k}) \tag{13}\]  
The BC and LSC can be calculated according to Equations (5) and (6). Then, the first objective function that aims to minimize the total production cost is defined in Equation (14).  
\[\min Z_{1}=\sum\limits_{t=1}^{T}(C1_{t}+C2_{t}+C3_{t}+C4_{t}+C5_{t}) \tag{14}\]  
The diversity of products and fierce competition make the stability of the manufacturing industry more important than ever. Therefore, in order to improve the adaptability of enterprises and reduce production costs, we need to consider the stability of workers. Hence, the sum of changes in the number of workers is used to measure its stability according to Equation (15).  
\[\min Z_{2}=\sum\limits_{t=1}^{T}\sum\limits_{k=1}^{K}(WH_{tk}+WL_{tk}) \tag{15}\]  
where for period \(t\), the hired \(k\) type worker number, \(WH_{tk}=\max\{W_{tk}-W_{tk-1},0\}\), and the number of laid off \(k\) type workers, \(WL_{tk}=\max\{W_{tk-1}-W_{tk},0\}\).
## 2 Materials and Methods
### NDVI Measurements
The Terra platform crosses the equator in the morning from north to south, while  
Figure 2: Sample SPOT image (**A**), showing field sampling sites (yellow dots) for the Pinhorn grazing reserve in 2009. (**B**) A detailed image of these sites, along with the single MODIS pixel (parallelogram) best corresponding to the centermost field sampling site. Each sampling site contained several 1-m diameter optical sampling plots (circles) (**C**) coincident with biomass harvesting sites (rectangles) (**C**) that were used for NDVI calibration against biomass. Each sampling site also included a 1-lactare (100 m \(\times\) 100 m) sampling grid (**C**) that was used for comparison of ground NDVI to satellite NDVI measurements. See Sections 2.2–2.4 for field sampling protocols. the Aqua platform crosses the equator in the afternoon and travels in the opposite direction (south to north). Since our goal was to compare these two detectors, they were treated as two separate sensors rather than combined in our analysis. The data used in this study were derived from a single MODIS pixel (250m) containing the monthly plot sampling locations for each site. These products were 16-day composites created with a combination of bidirectional reflectance distribution function (BRDF) and maximum value composite (MVC) methods, which tend to select the least atmospherically-contaminated (most cloud-free) values, enhancing data accuracy and quality [37]. Images by the Systeme Pour l'Observation de la Terre (SPOT) satellite network were georeferenced, atmospherically corrected, and processed to reflectance by the Alberta Terrestrial Imaging Center, using identical ground-control points for 2009 and 2010. NDVI was subsequently extracted as follows:  
\[\mathrm{NDVI_{S}}=\frac{\left(\mathrm{Band\ 3\ -\ \mathrm{Band\ 2}}\right)}{ \left(\mathrm{Band\ 3\ +\ \mathrm{Band\ 2}}\right)}. \tag{3}\]  
The SPOT orbit is polar, circular, sun-synchronous, and phased, so that imagery over a particular area of the Earth is produced at the same time of day, at a constant altitude, every 26 days [23]. The SPOT imagery used in this study was obtained from the SPOT 4 (Summer 2009) and SPOT 5 (summers of 2009 and 2010) platforms and have a 10 m spatial resolution. Figure 2 illustrates the different sampling scales of SPOT, MODIS, and ground sampling, leading us to derive a scaling methodology for relating ground sampling to the most closely matching satellite pixels. In each case, NDVI was extracted from a satellite pixel most closely matching each ground sampling location and all NDVI values were aggregated (averaged) by date and site (Pinhorn or Sounding Creek) for the summary evaluations shown in this study. The effect of data aggregation on the resulting analyses is further discussed in Figure S1 (Supplementary Materials).
## 2 Soil Salinization  
Salinization occurs when water soluble salts, such as sodium (Na\({}^{+}\)), chloride (Cl\({}^{-}\)), potassium (K\({}^{+}\)), sulfate (SO\({}_{4}\)\({}^{2-}\)), magnesium (Mg\({}^{2+}\)), carbonate (CO\({}_{3}\)\({}^{2-}\)), bicarbonate (HCO\({}_{3}\)\({}^{-}\)), and calcium (Ca\({}^{2+}\)), accumulate in the soil [6]. The most common salt species in saline soils is sodium chloride, which has a negative impact on plant production or causes death. When the electrical conductivity (EC) of the saturation soil paste is 4 dS m\({}^{-1}\) (about 40 mM NaCl), the soil is categorized as saline [25; 26].  
The entire amount of salt-affected land on the planet is estimated to be 900 million hectares, or 6% of the total land mass [27]. Soil salinization is limiting the amount of land that can be utilized for agriculture by 1-2% every year, with arid and semi-arid areas being the worst impacted [6]. According to the Food and Agricultural Organization (FAO), the salinization of arable land will lead to 30% land loss over the next 25 years and up to 50% by 2050 if preventive measures are not adopted [28].  
In particular, the majority of salt-affected areas are found in arid and semi-arid regions of the globe. The majority of nations impacted by salinity are found in sub-Saharan Africa,and Central and Middle East Asia. Soil salinization is a major problem in developing nations with dry climates (such as India, Bolivia, Pakistan, Egypt, Peru, Tunisia, and Morocco); industrialized nations, however, are less affected by salt but are not insusceptible. Salinization has already harmed crop output in 20-30% of irrigated farmland, with an additional 1.5 million hectares being damaged each year [29].  
Soil salinity is becoming more of a concern throughout the world, resulting in soil deterioration and a global yearly agricultural production loss of more than USD 12 billion owing to this phenomenon [30,31].
## 3 Results and Discussion
### Historical Storm Surge Event, January 2019
#### 3.1.5 HEC-RAS Model Validation  
The validation of the HEC-RAS model of the Vistula Lagoon was possible thanks to the comparison of the calculated water stages at Nowakowo gauging station with IMGW-PIB observations and earlier numerical simulations [11].  
The time series plot in Figure 7 shows the observed water levels alongside the two modeled predictions. Both models follow the observed trend well, with minor deviations. For this episode, both our own model MODEL_MSZ and HEC-RAS underestimate the results of water table elevation calculations, especially in the second part of the event.  
The statistical performance metrics for the two models are presented in Table 1. The statistical analysis of the modeled water surface elevation at Nowakowo reveals that both computational models exhibit strong accuracy in replicating observed values. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) indicate that both models provide similar levels of precision, with HEC-RAS performing slightly better. The Nash-Sutcliffe Efficiency (NSE) values, exceeding 0.93 for both models, confirm their high reliability, as NSE values above 0.9 denote an excellent fit between modeled and observed data. However, a slight systematic underestimation of water levels is observed in both models, as reflected in the bias values, which are negative and of a similar magnitude. This suggests that while the models capture the overall trend effectively, they tend to predict water surface elevations as marginally lower than the actual measurements.  
Additionally, the scatter plots in Figure 8 show a strong correlation between observed and modeled values for both models, with most points clustering around the 1:1 reference line. This confirms that both models perform well, with minor underestimation trends.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Model\_MSZ** & **Model\_HEC\_RAS** \\ \hline MAE (m) & 0.1126 & 0.1088 \\ RMSE (m) & 0.1283 & 0.1243 \\ NSE & 0.9300 & 0.9343 \\ Bias (m) & \(-\)0.0994 & \(-\)0.1051 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical metrics for hydrodynamics models.  
Figure 7: Observed vs. modeled water stage elevation at Nowakowo.  
The scatter plot in Figure 9 comparing Model_MSZ and HEC-RAS demonstrates a strong correlation, suggesting that both models yield comparable water surface elevation results. The majority of the data points align closely along the 1:1 reference line, confirming that the two computational approaches follow a similar pattern in simulating water levels. However, small deviations are noticeable, which may indicate systematic differences in their numerical formulations, parameterizations, or sensitivity to boundary conditions.  
To sum up the validation of the HEC-RAS model, it can be stated that the simulation results of the historical storm event that occurred in the Vistula Lagoon in January 2019 sufficiently well represent the actual course of the phenomenon. This means that the HEC-RAS model is well suited for modeling wind-driven storm surges in shallow water bodies, such as lagoons.
## 2 Preliminaries
### Dynamic Graph Connectivity
#### 2.2.3 Level Truncation  
Maintaining HDT's multi-level structure may require several expensive dynamic memory allocation operations. Although this expense could be reduced by allocating the space for all levels in advance, this would be wasteful, since the higher levels tend to be sparsely populated.  
[PERSON] et al. (2016) showed that _level truncation_--i.e., putting a limit on the number of levels in the data structure--improves HDT's performance in practice. An extreme version of this idea is to disable edge promotion entirely and use a _single_ level: Level 0. That is, we maintain a single spanning forest \(F\) of the graph, where each tree in \(F\) is represented using an ET tree. Deleting non-tree edges is, again, trivial. Deleting a tree edge \(e=(u,v)\) splits the tree \(T\) in \(F\) containing \(e\) into two trees \(T_{u}\) and \(T_{v}\). Suppose \(T_{u}\) is the smaller tree. For each node \(v\) in \(T_{u}\), we scan the edges incident on \(v\) to determine if any of them is a replacement edge. This requires two queries to the ET trees containing the endpoints of each edge. If a replacement edge \(f\) is found, we re-link \(T_{u}\) and \(T_{v}\) using \(f\) and stop.  
In the rest of the paper, we refer to the version of HDT where edge promotion is disabled as \(\mathrm{HDT}(0)\). The following observation is easy to verify.  
**Proposition 1**.: _Let \(G\) be an \(n\)-node graph. Then, \(\mathrm{HDT}(0)\) handles any sequence of \(q\) edge deletions in \(G\) in \(\mathcal{O}(q+s\log n)\) time, where \(s\) is the total number of edge scans performed over the entire sequence._  
Note that Proposition 1 holds regardless of whether one scans the edges incident on the smaller component or those incident on the larger component. We shall nevertheless assume that the smaller component is the one whose edges are scanned, since in practice, that component tends to have fewer incident non-tree edges.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Logistics Administrative Management
#### 4.3.1 Forecasting and Demand Planning  
This case study involves a small-sized processed food factory producing freeze-dried snacks from local fruits and vegetables. The company sells through online channels and modern trade outlets. Previously, the factory did not forecast demand, resulting in stockouts, expired inventories, and product returns from retailers.  
Basic forecasting models were initially introduced [39], but accuracy was unsatisfactory with Root Mean Square Error (RMSE) values under 60%. Subsequently, advanced forecasting tools using data mining techniques (RapidMiner and Python) were implemented (see Figure 5). Using two years of historical sales data as a training set, the new model achieved 80% accuracy (RMSE).  
This improved forecasting accuracy enabled better planning, sourcing, and communication, resulting in a 16.3% cost saving.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks
### Blockchain as a Security Mechanism in V2X
#### 6.2.2 Challenges of Implementing Blockchain in V2X  
The use of blockchain in vehicular networks is affected by several challenges and we discuss some of them here. Several new or modified blockchain have been proposed, such as [70; 71], to reduce the average puzzle computation time and lower the block message delivery delays in a geographically bounded area, as shown in Table 7. In Ref. [71], a regional blockchain has been proposed to attain 51% attack success probability in terms of several control parameters such as numbers of good vehicle and malicious vehicles, message delivery time, and puzzle computation time that guarantees the stability of blockchain. Similarly, [PERSON] et.al [70] proposed self-managed and self-organized blockchain-based VANET using concept of an Ethereum blockchain, smart contract, and challenge-based authentication mechanism.  
If we try to implement a blockchain using existing vehicular networks, the PoW Puzzle during the mining phase consumes a significant amount of time that is not suitable for time-critical message dissemination. The blockchain creates a high requirement for computing power and storage capacity for vehicular nodes. The blockchain for vehicular networks still faces challenges, such as timely dissemination of newly mined blocks, message immutability etc., due to the dynamic topology, large geographic areas, and high mobility of vehicles.  
Some research has been done in vehicular networks by integrating edge-cloud computing and 5G cellular networks to provide safety, security, privacy and performance improvements in the absence of blockchain technology, as outlined in Table 7. The authors in [72] proposed a new privacy-preserving protocol to improve the security in vehicular networks, by applying the concept of edge-computing based on 5G cellular networks. Their scheme provides an effective privacy preservation technique with an enhanced class security scheme to improve data transmission for road surveillance system. Similarly, the authors in [73] propose 5G-enabled Software Defined Vehicular Networks (5G-SDVNs), where specified SDN functionality is used along with MEC to handle vehicle groups in 5G networks. The authors used an edge-based data dissemination protocol for VANETs in order to improve connectivity, improve resource utilization, reduce communication overhead, and reduce latency. However, the information exchanged between the vehicles is not transparent and traceable. The exchanged information is not resilient and it can be modified by the malicious vehicles. Thus, integrating edge and cellular V2X networks without using blockchain techniques cannot fully satisfy the security requirements of the vehicular networks.
# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
## 12 Architecture Performance Comparisons
### Activation Function  
In the context of neural networks, an activation function introduces non-linearity to the model. It aids the network in discovering intricate relationships and patterns within the data, empowering it to make precise predictions. ReLU, Softmax, Leaky ReLU, Parametric ReLU (PReLU), Exponential Linear Unit (ELU), and ThresholdedReLU have been tested in the development of the architecture as depicted in Table 7. Using the ELU activation function was found to have the highest accuracy, with a slight decrease in execution time.  
\begin{table}
\begin{tabular}{|c c c c c|c c|} \hline
**Architecture** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Confusion Matrix** & **Execution Time (s)** \\ \hline With 0.2 dropout rate & 0.94452 & 0.95610 & 0.946391 & 0.95122 & [66,026 & 4063] & 706.858 \\  & & & & [5013 & 88,498] & & \\ \hline With 0.5 dropout rate & 0.94671 & 0.96424 & 0.94168 & 0.95283 & [66,824 & 3265] & 689.515 \\  & & & & [5453 & 88,058] & & \\ \hline With 0.7 dropout rate & 0.94500 & 0.97129 & 0.93131 & 0.95088 & [67,515 & 2574] & 700.178 \\  & & & & [6423 & 87,088] & & \\ \hline
**Without dropout layer** & **0.94699** & **0.96456** & **0.94187** & **0.95308** & [**66,853** & **3236**] & **936.976** \\ \hline \end{tabular}
\end{table}
Table 6: Performance Comparisons among different Dropout Rates.  
\begin{table}
\begin{tabular}{|c c c c c c|c c|} \hline
**Architecture** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Confusion Matrix** & **Execution Time (s)** \\ \hline
2 Conv1D and max-pooling layers & 0.94333 & 0.95843 & 0.94169 & 0.94999 & [5452 & 88,059] & 629.896 \\ \hline
3 Conv1D and max-pooling layers & 0.946937 & 0.96358 & 0.94279 & 0.95307 & [66,757 & 3332] & 795.653 \\  & & & & [5349 & 88,162] & & \\ \hline
5 Conv1D and max-pooling layers & 0.94676 & 0.97074 & 0.93504 & 0.95256 & [67,454 & 2635] & 964.9478 \\  & & & & [6074 & 87,437] & & \\ \hline
**4 Conv1D and max-pooling layers** & **0.94699** & **0.96456** & **0.94187** & **0.95308** & **[66,853** & **3236**] & **936.976** \\ \hline \end{tabular}
\end{table}
Table 5: Performance Comparisons among a range of Convolution Layers.
## 3 The Proposed Architecture  
### The Satellite Network Infrastructure  
In order to develop an effective knowledge-based cognitive architecture, we start with understanding the domain in which the architecture will be operating- a satellite network infrastructure. The satellite network infrastructure for this study is made up of sub route groups, \(G_{i}\in\{G_{1},\ G_{2},\ G_{3},\ldots\ldots G_{N}\}\), and each sub route group \(Gi\), is linked to several networks \(G_{i}N_{k}\in\{G_{i}N_{1},\ G_{i}N_{2},\ G_{i}N_{3},\ldots\ldots G_{i}N_{N}\}\), where \(Nj\) is a network of terminals, represented as \(G_{i}N_{k}T_{j}\in\{G_{i}N_{k}T_{1},\ G_{i}N_{k}T_{2},\ G_{i}N_{k}T_{3}\ldots \ldots G_{i}N_{k}T_{N}\}\), as shown in Figure 2.  
Together with the application data packet to be sent, each terminal, \(G_{i}N_{k}T_{j}\), sends status data packets with a set of attributes, such as id, downstream signal to noise ratio, upstream signal to noise ratio, temperature of the terminal, latitude and longitude, etc. As shown in Figure 2, the status data packets are received at a base station for network monitoring purposes. Each sub route group, \(G_{i}N_{k}\), transmits high level statistics data with a larger set of attributes, including the attributes of the sub route group, such as id, maximum transmit power of the group, IP-address, customer id, transmit carrier frequency, receiving carrier frequency, and satellite name, among other variables. Each terminal, \(G_{i}N_{k}T_{j}\), in a sub route group, \(G_{i}N_{k}\), can transmit and receive data. Figure 2 illustrates the hierarchical architecture of the satellite network.
## 3 Results and Discussion
### Calculation Parameter Setting
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & \(\text{C}_{\text{n}}{}^{2}\) & \(\text{D}/\text{r}_{0}\) & **jitter** & \(\text{\beta}_{0}\) & **P** & \(\text{N}_{\text{D}}\) \\ \hline S1 & \(5.0\times 10^{-15}\) m\({}^{-2/3}\) & 4.0 & 0 & 1 & - & - \\ S2 & \(5.0\times 10^{-14}\) m\({}^{-2/3}\) & 16.1 & 0 & 1 & - & - \\ S3 & \(5.0\times 10^{-15}\) m\({}^{-2/3}\) & 4.0 & 5 \(\mu\)rad & 5 & - & - \\ S4 & \(5.0\times 10^{-14}\) m\({}^{-2/3}\) & 16.1 & 10 \(\mu\)rad & 5 & - & - \\ S5 & \(1.0\times 10^{-14}\) m\({}^{-2/3}\) & 6.13 & 5 \(\mu\)rad & 5 & 10 kW & 25.5 \\ S6 & \(1.0\times 10^{-14}\) m\({}^{-2/3}\) & 6.13 & 5 \(\mu\)rad & 5 & 50 kW & 127.3 \\ S7 & \(1.0\times 10^{-14}\) m\({}^{-2/3}\) & 6.13 & 5 \(\mu\)rad & 5 & 100 kW & 254.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scenarios used in the wave optics simulations. where \(k=2\pi/\lambda\) is wave number, \(C_{n}^{2}(z)\) is the turbulent refractive structure parameter at position \(z\), and \(L\) is the transmission distance. In the case of a horizontal path in which the refractive index structure parameter, \(C_{n}^{2}\), is assumed as a constant, [PERSON]'s parameter can be expressed as \(r_{0}=(0.16C_{n}^{2}k^{2}L)^{-3/5}\). The intensity of the thermal blooming effect is described by the Bradley-Herrman thermal distortion parameter, \(N_{D}\)[10], which is defined as:  
\[N_{D}=4\pi\sqrt{2}C_{0}\lambda^{-1}\int\limits_{0}^{R}\{\alpha(z)(P\exp(-\int \limits_{0}^{z}dz^{\prime}\xi(z^{\prime})))/\big{[}|\overset{\cdot}{v}+z\overset{ \cdot}{\omega}|D(z)|\big{]}\}dz, \tag{14}\]  
where \(C_{0}=1.66\times 10^{-9}\) m\({}^{3}\)/J, \(P\) is the laser emission power, \(\alpha\) is the absorption coefficient, \(v\) is the wind speed along the propagation path, \(\omega\) is the angular velocity of light beam scanning, and \(\xi\) is the atmospheric extinction coefficient. Results of long-exposure average can help to reveal the statistical average effects of spot position wandering and spreading on the overall energy spread. The long-exposure results in the simulation were averaged over an ensemble of 60 statistical independent random realizations.