esa-sceva/satcom-chunk-collection huggingface dataset:
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 4 Evaluation Results and Discussion
### Simulation Set up  
Our simulation scenario consists of a total of 100 nodes placed randomly in a 600 \(\times\) 600 m area, each node has a transmission range of 50 m. The topology setup is shown in Figure 6 below. There is one sink placed in the center (node ID: 1 with dot pattern in Figure 6) and 11 IDS cluster heads (the grey nodes) to cover the operation of the remaining 88 nodes in the network (the white nodes). Every node sends packet to the sink at the rate of 1 packet every 60 s. Data specified in Section 3.2.2 is collected and sent to the cluster heads every 2 min. We implement the specification-based module from Section 3 in the IDS cluster heads to analyze the collected trace data. The thresholds are set up based on analyzing the statistical data in the trace files with the following values: \(threshold_{DIS\_count}=3\), \(threshold_{fault}=2\), and \(threshold_{instability}=10\).  
We implemented each of the five types of attacks as discussed in Section 2 in random positions of 88 normal senders. For each of the attack scenario, there will be one random node in the 88 normal nodes to be injected the relevant attack code. The attacks are initiated after 3 min when the topology establishment phase is done. The summary of the simulation parameters is shown in Table 3 below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Threshold** & **Meaning** \\ \hline \(threshold_{DIS\_count}\) & Alarm if a node visit the DIS state more than \(threshold_{DIS\_count}\) in the monitoring time. \\ \hline \(threshold_{fault}\) & Alarm if a node not updating info from the neighbours \\ \hline \(threshold_{instability}\) & Alarm if node visit the instability states \(S1\), \(S5\), \(S6\), \(S7\), \(S8\) \\ more than a \(threshold_{instability}\) in the monitoring time \\ \hline \hline \end{tabular}
\end{table}
Table 2: The thresholds used in the algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Simulation platform & Cooja Contiki 2.6 \\ Number of nodes & 99 senders, 1 sink \\ Number of cluster head & 11 \\ Number of attackers & 1 \\ Traffic model & Constant bit rate \\ Sending rate & 1 packet every 60 s \\ IDS require info every & 2 min \\ Simulation run time & 30 min \\ \hline \hline \end{tabular}
\end{table}
Table 3: The simulation parameters.
## 3 Materials and Methods
### System Architecture
#### 3.2.2 Prediction Model  
An integrated prediction model based on the combination of EMD and LSTM is proposed to maximize the prediction effectiveness and minimize the complexity of calculations. The proposed model is shown in Figure 6 consisting of the following four steps:  
* Firstly, the EMD algorithm was applied to decompose the original stock closing price time series into several independent IMF components and one residual component.
* Secondly, the news sentiment score and the principal component from the feature engineering part were included as input features to the model.
* Thirdly, the LSTM model was used as the prediction tool for each IMF component. Consequently, the corresponding components acquired the prediction values. The LSTM was trained individually by each IMF; thus, the network parameters, epoch and batch size are specially tuned for each IMF. This is the significant difference that makes a hybrid EMD-LSTM model better than a single LSTM model.
* Finally, each predicted IMF was combined using (4) to get the final predicted stock closing price after obtaining the predicted results of the IMFs. Then, the results were compared with other models using performance metrics.  
Figure 5: Technical Indicator Component Creation.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
###### Abstract  
This paper presents a general analytical solution for the problem of locating points in planar regions with an arbitrary geometry at the boundary. The proposed methodology overcomes the traditional solutions used for polygonal regions. The method originated from the explicit evaluation of the contour integral using the Residue and Cauchy theorems, which then evolved toward a technique very similar to the winding number and, finally, simplified into a variant of ray-crossing approach slightly more informed and more universal than the classic approach, which had been used for decades. The very close relation of both techniques also emerges during the derivation of the solution. The resulting algorithm becomes simpler and potentially faster than the current state of the art for point locations in arbitrary polygons because it uses fewer operations. For polygonal regions, it is also applicable without further processing for special cases of degeneracy, and it is possible to use in fully integer arithmetic; it can also be vectorized for parallel computation. The major novelty, however, is the extension of the technique to virtually any shape or segment delimiting a planar domain, be it linear, a circular arc, or a higher order curve.  
cauchy theorem; residue theorem; Jordan curve theorem; generalized polygons; complex calculus; parametric curves; Bezier segments; winding number; negative real axis intersection Article
## 4 Numerical Results and Discussion
### Number of LSTM Layers  
This subsection shows the tests obtained by considering 40, 5, and 2 hidden layers in the LSTM, with an accuracy of 100%, 99.99%, and 96%, respectively. The training process took 16, 10, and 8 min, respectively. The process for the 40-layer LSTM is reported in Figure 7, while the two-layer one is reported in Figure 8.  
Figure 8: The training process of the 2-hidden-layer LSTM.  
Figure 7: The training process of the 40-hidden-layer LSTM.  
Although the results in terms of expanded nodes were better in terms of both expanded nodes and solved LMIs with respect to the SVM-enhanced planning, the higher computational cost of LSTM-based classification exceeded this benefit. Namely, while the number of node expansions was reduced to 141 and the solved LMIs were 3651, the classification process brought an extra burden of roughly four times more computational effort.
# Novel Processing Methods of Low-Clinker Multi-Component Cementitious Materials--A Review
## 3 Magnetic Field Treatment (MF-T) Methodology  
Magnetic Field Treatment (MF-T) can improve water's properties by causing the separation of hydrogen nuclei (protons) and water \"clusters\" into single or smaller molecules due to strong magnetic forces [72; 73; 74; 75]. The effectiveness of MF-T depends on the duration of exposure to the MF-T (water flow velocity), magnetic flux density, and volume of water exposed to the field [73; 74]. Magnetized water (M-Water) is formed when water passes through a permanent MF-T, leading to molecular structural changes and new characteristics [72; 74]. The M-Water layer surrounding cement particles becomes thinner, reducing the need for water during mixing and modifying the physical and chemical properties of M-Water, including surface tension, conductivity, pH, density, volatility, and the ability to alter dissolved substances [73; 74; 75]. M-Water also exhibits a lower viscosity than tap water due to a decreased bond angle (from 104.5\({}^{\circ}\) to 103\({}^{\circ}\)) [72; 74; 76].  
The relationship between MF-T and the surface tension coefficient (\(\sigma\)) of M-Water is not monotonic, as shown in Figure 5[77]. The optimal MF-T is 300 mT, and both higher and lower intensities lead to an increase in the \(\sigma\) of M-Water, indicating an enhanced magnetization effect. The duration of magnetization varies with the MF-T intensity; stronger MF-T requires shorter treatment times, such as 3 min at 1000 mT and 20 s at 1500 mT [77].  
Obtaining M-Water with lower ST is challenging at very high MF-T, and excessive magnetization may even lead to an increase in \(\sigma\), as reported in [PERSON] and [PERSON]'s research [78]. The \(\sigma\) of tap water decreases considerably as the treatment duration increases, reaching a minimum of a 9% decline at 13 min with an MF-T of 1000 mT [79].
## 5 Factors Affecting the Anaerobic Digestion Process and Biogas Production
### Temperature  
As this is one of the critical parameters influencing AD, temperature influences the activity of enzymes and co-enzymes and the methane yield and digestate (effluent) quality [84; 85]. Anaerobic bacteria generally grow at three temperature ranges, namely the psychrophilic (10-30 \({}^{\circ}\)C), mesophilic (30-40 \({}^{\circ}\)C) as well as thermophilic (50-60 \({}^{\circ}\)C) range [86; 87]. Generally, AD performance increases with an increasing temperature [88]. There has been an emphasis on the advantages of the thermophilic operation, which has high metabolic rates, higher rates of destroying pathogens, and higher specific growth rates, collectively leading to higher biogas production [85; 86; 87; 88]. [PERSON], et al. [89] demonstrated that ammonia accumulation inhibition affects thermophilic digestion less than mesophilic digestion.  
Biogas production under thermophilic conditions (55 \({}^{\circ}\)C) has been reported to give more than double the amount produced under psychrophilic conditions (15 \({}^{\circ}\)C) by [PERSON], et al. [90]. Furthermore, other studies show that phosphorus assimilation and organic nitrogen degradation increase with temperature too [85]. Thermodynamically higher temperatures benefit endergonic reactions, such as the breakdown of propionate into acetate, CO\({}_{2}\), and H\({}_{2}\), though that is not favorable to exergonic reactions, such as hydrogenotrophic methanogenesis [84]. Further, the temperature may influence the passive separation of solids with considerable improvement under thermophilic compared to psychrophilic conditions [91]. There are, however, some shortfalls in thermophilic conditions, for instance, being sensitive to environmental changes compared to the mesophilic process [92; 93].
## 3 Results and Discussion
### Biogas Yield
The cause of this condition can be associated with the content of other substances from the FM and WSC plants in the silage, because the silage produced from FM contained demonstrably less lignin and other vegetable fiber than the silage produced from WSC. Fiber, namely, cellulose, hemicellulose and lignin, is a substance that is difficult to biodegrade [45]. Thus, it is theoretically possible that the lower content of substances with the worse degradability (CF and ADL) contributed to the higher biogas yield from the FM silage compared with WSC and, thus, compensated  
Figure 4: Biogas yield during 35 days of the assay (average values for every day of measurement (\(n=3\)) for one measurement, \(\pm\) SD). FM = fodder mallow; WSC = white sweet clover; FM + WSC = fodder mallow + white sweet clover. for the lower content of starch. In contrast, the WSC silage exhibited the highest contents (\(p<0.05\)) of CF and ADL, which could have been the reason for the lowest yield of biogas in this variant. If we look at these values in a greater detail, we can see that the FM variant contained 11% less CF and 2.4% less ADL. The WSC variant contained only 1.24% more starch, i.e., readily degradable substances. It is, therefore, possible that the higher content of readily degradable, carbonaceous substances does not have such an effect compared with the content of worse, degradable substances.
## 4 Methodology
### Features  
In each experiment and for each temporal batch generated, the four graph models presented in Section 4.1 were created. Then, from each graph model, four dataframes (two-dimensional labelled data structure or data table with samples as rows and extracted features as columns) were created extracting several features. All the features are presented in detail in [10]:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**batch\_size = 60,000**} \\ \hline
**\#** & **\#** & **\#** & **\#** & **\#** & **\#** & **\#** \\
**batch** & **Ex** & **Gmb** & **Mrk** & **Pool** & **Mxr** & **eWal** \\ \hline
1 & 122 & 51 & 18 & 14 & 30 & 12 \\
2 & 104 & 46 & 12 & 12 & 12 & 11 \\
3 & 81 & 27 & 9 & 9 & 1 & 10 \\ \hline \multicolumn{6}{c}{**batch\_size = 30,000**} \\ \hline
**\#** & **\#** & **\#** & **\#** & **\#** & **\#** & **\#** \\
**batch** & **Ex** & **Gmb** & **Mrk** & **Pool** & **Mxr** & **eWal** \\ \hline
1 & 121 & 50 & 14 & 13 & 18 & 12 \\
2 & 109 & 40 & 17 & 11 & 20 & 12 \\
3 & 102 & 46 & 12 & 11 & 11 & 10 \\
4 & 89 & 27 & 6 & 7 & 3 & 11 \\
5 & 74 & 23 & 5 & 6 & 1 & 9 \\
6 & 73 & 22 & 7 & 6 & 1 & 8 \\ \hline \multicolumn{6}{c}{**batch\_size = 20,000**} \\ \hline
**\#** & **\#** & **\#** & **\#** & **\#** & **\#** & **\#** \\
**batch** & **Ex** & **Gmb** & **Mrk** & **Pool** & **Mxr** & **eWal** \\ \hline
1 & 119 & 50 & 14 & 13 & 13 & 11 \\
2 & 110 & 41 & 15 & 10 & 18 & 12 \\
3 & 104 & 39 & 10 & 11 & 11 & 10 \\
4 & 100 & 42 & 12 & 10 & 11 & 10 \\
5 & 90  
* _Entity dataframe_ contains all features extracted from the entity-transaction graph and contains a total of seven features.
* _Address dataframe_ contains all features extracted from the address-transaction graph and contains a total of seven features.
* _1_motif dataframe_ contains the information directly extracted from the _1_motif_ graph and contains a total of nine features.
* _2_motif dataframe_ contains information gathered from the _2_motif_ graph and contains a total of 18 features.  
In order to compare information from different batches, and in order to reduce the complexity of the models, the features (excluding the ones related to an amount or balance) were normalized on a range between 0 and 1. This normalization was computed with respect to the minimum and maximum values in the corresponding batch. This process allow us to re-use these models with other batch sizes and to use them with input data from other sources/blockchains in future studies.  
Let \(X\) be the value to be normalized, \(X_{\text{min}}\) the minimum and \(X_{\text{max}}\) the maximum of the considered feature in a certain batch, then the normalized value \(X_{\text{norm}}\) was computed using Equation (3):  
\[X_{\text{norm}}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}}. \tag{3}\]
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 3 Developments Related to the Extraction of Soil Properties from Remote Sensing Data
### Trends in French Remote Sensing Research in Relation to Threats to Soil
The most striking trend is the almost exponential number of articles on soil carbon loss. As explained above, this trend relates to both policy issues and advances in the feasibility of monitoring SOC. Similarly, we observe a large increase in soil sealing studies, related to population growth, soil sealing, and land-use issues, as well as the opportunities provided by new high-resolution, time-frequency satellites. The increase in soil erosion and contamination studies also relates to global issues and advances offered by new sensors. In comparison, studies on soil salinization remain rather stable; biodiversity is slowly emerging, while acidification and compaction are almost negligible. Figure 3: Percentage of articles per soil threat (out of 857 articles where threats were explicitly mentioned).
## 2 Materials and Methods
### Quantitative Analysis of Total Flavonoid Content  
In order to determine the total flavonoid content in the ethyl acetate, acetone, methanolic, and aqueous extracts of _C. axillaris_ fruits, a well-established method involving aluminum chloride (AlCl\({}_{3}\)) was employed. The quantification protocol used was adapted from a previous research article [43], with necessary modifications made. To create a standard calibration curve, a sequential serial dilution technique was used to prepare six different concentrations of quercetin dissolved in ethanol. The concentrations ranged from 800 \(\mu\)g/mL to 25 \(\mu\)g/mL in a decreasing order of concentration. Four different plant extract solutions with a concentration of 1 mg/mL were prepared using ethanol as a diluent. The preparation involved mixing 2 mL of the plant extract solution with 8 mL of distilled water in a glass test tube. Next, 0.6 mL of a 5% NaNO\({}_{2}\)solution was added to the mixture, which was continuously agitated for 5 min. After that, 0.6 mL of 10% AlCl\({}_{3}\) solution was added, and the resulting mixture was incubated for 5 min. To this, 4 mL of a 1 M NaOH solution was added, and the final mixture was incubated for approximately 30 min at room temperature. An analogous process was conducted to prepare a blank sample, except that 2 mL of ethanol (the diluent) was used instead of the plant sample solution. Subsequently, all the standard, plant sample, and blank solutions were subjected to analysis using a UV spectrophotometer (1900i double-beam spectrophotometer, Tokyo, Japan), specifically at a wavelength of 415 nm. To ensure precise and dependable measurements, this analysis was carried out in triplicate for each solution [43].
## 5 Computational Algorithm  
The computation is carried out using a pile group cyclic torque computational program (PGCYT). The flowchart is shown in Figure 2. The step-by-step algorithm for obtaining the cyclic degradation parameter of flow-controlled geomaterial is as follows:  
1. Using the input parameters for geomaterial medium, pile group and cyclic loading, the analysis is carried out under cyclic torsional loads.
2. The torsional and lateral spring stiffnesses of the pile group are determined using Equations (9)-(10), assuming displacement and twists as the function of time for the first cycle (\(N=1\)).
3. The stress-strain response of geomaterial is obtained from the hyperbolic model using Equation (11).
4. The lateral and torsional spring stiffnesses for geomaterial at a node related to parameters inside the yield surface are determined using Equation (18) and Equation (21), respectively.
5. The lateral and torsional spring stiffnesses for geomaterial at node-related yield flow parameters are determined using Equation (19) and Equation (22), respectively.
6. The nodal displacements and twists are evaluated using Gauss elimination for all elements using Equation (24).
7. The values of \(\tau\) and \(\tau_{u}\) are evaluated for all elements using Equations (16) and (17).
8. The degradation parameter for lateral and torsional geomaterial stiffnesses has been computed.
9. Using reduced values of the geomaterial stiffness, analysis is again carried out following the repeating steps (a-h), and the procedure is repeated until the desired convergence is achieved.  
* The distributions of twist, plastic strain, and peak shear stress are computed for the pile group.  
Figure 2: Flowchart for computation program “PGCYT”.
# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 2 Background and Related Work
### Background
#### 2.1.3 P&D Phases  
[PERSON] and [PERSON] [13] describe P&D as demonstrated in Figure 2.  
Figure 1: Google trend of cryptocurrency pump and dump.  
The P&D event consists of three main phases: accumulation, pump, and finally, dump. In the accumulation phase, the offenders who are looking to create a P&D event start buying the targeted coin in a way that does not lead to an increase in coin price significantly because it may take days to months based on the volume of the coin. Therefore, in this phase, a significant percentage of available coins will be stored in the wallet of those offenders. Then, in the pump phase, the offending use of social media, such as Twitter, Telegram, and Reddit, affects cryptocurrency investors the most. Spreading rumors trying to convince people that the target coin will pump due to multiple factors, for instance, if the coin is not listed in well-known crypto exchanges, they will spread the rumor that it will be listed in Binance [14]. Therefore, some investors will be deceived by this news and start buying that coin, which will inflate the price of the coin. The last phase is a dump in which offenders will start to sell their coins after a profit of several hundred percent, and at one point, they will sell as much as they can, thus creating the peak point. Then, the price will drop below the price before the start of the event. Most of those who bought in the pump phase will be left with useless bags.  
There are too many examples of P&D, such as the one mentioned by [15], which happened on BVB coin, or the DATA/BTC in September 2019, mentioned in [16]. We have added one of the most recent P&Ds faced while conducting this survey. This P&D event was organized by a group in Telegram messenger named \"Binance Crypto Pumps Signals\" [17], with more than 400,000 members. In this group, they organized P&D events in three exchanges of Poloniex [18], Kucoin [19], and Binance. One of the latest P&Ds of this group was on BAR/USDT. They started sending messages in the Telegram group; the first message usually contains data about the date, time, exchange, pairing, and target. After this announcement, they sent some messages about their previous pumps and profits from which members gained to convince members of the channel to participate in this event. They also sent messages clarifying how to buy and sell coins in that specific exchange. Finally, they started to send countdown messages, and, at the announced time, sent a picture that contained the currency name in it. The screenshots are shown in Figure 3. Their last pump occurred on Poloniex Exchange, and the coin was BAR with a pairing of USDT. The chart is shown in Figure 4.  
Figure 2: The three phases of P&D [13]. Each color indicates one phase that will be described in the following section.
## 1 Introduction
The system contributes to the fields of robotic control, autonomous navigation, and interactive machine learning by lowering the barrier to entry for novice users while maintaining flight safety and environmental awareness. Nonetheless, the system is not without limitations. Challenges such as occasional gesture misclassification, command latency, and sensitivity to lighting conditions remain areas for improvement. A recent overview of the UAV field emphasizes the growing need for multimodal and adaptive drone interfaces, underscoring the relevance and timeliness of this research [22]. Future efforts will aim to enhance gesture recognition reliability, reduce latency through software optimization, and increase system usability through better camera modules or dual-view configurations. Also, scaling the platform to support multi-drone coordination and adapting it for outdoor environments using GPS or SLAM-based localization would further expand its applicability and impact.