esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area  
The municipal waste landfill in Tychy is divided into two parts: a closed and reclaimed landfill (I), and the currently operating landfill sites ((II), (III)) along with the surrounding infrastructure. It covers an area of 12.7 ha (Figure 1).  
The disused landfill, with an area of 3.5 ha, was used before 1988 as a landfill for building materials and later transformed into a municipal landfill. The landfill was closed due to the lack of an appropriate lining system to prevent the migration of pollutants [10]. The active part of the landfill consists of two sites, which were built in the years 1994-2004.  
The landfill complex has been monitored since 1995. Originally, the observation network monitoring the Quaternary aquifer consisted of 14 piezometers (P1-P14) located at the front of the old and new municipal landfills. In the following years, this network was systematically rebuilt. In 2000, 2 piezometers (P15 and P16) were made, located north of the landfills in the zone of groundwater inflow (up gradient). In the following years, the expansion of the active system and the accompanying infrastructure resulted in the liquidation of some of the piezometers (P5, P6, P7, P8, P11, P12, P13, and P14) and the drilling of 2 new single piezometers (P5 and P18), as well as two pairs of nested piezometers measuring the upper (P17 and P19) and lower parts of the aquifer (P19 and P19A) (Figure 1).  
In 2011, 3 piezometers were removed: P11, P12, and P13. In 2017, the P8 piezometer was destroyed [43]. Currently, the observation network for local groundwater monitoring of these landfills consists of 14 piezometers: P1, P2, P3, P4, P5', P9, P10, P15, P16, P17, P17A, P18, P19, and P19A (Figure 1).  
The municipal landfills are located within the Fore-Carpathian depression in the central part of the Upper Silesian Coal Basin. The upper part of the Upper Carboniferous in this region contains layers from the Upper Silesian sandstone series. Locally, on the Upper Carboniferous, there are eroded patches of Triassic sediments [44]. Carboniferous and Triassic sediment are covered by thick (up to 80 m) Miocene clay formations. On the Miocene, there are Quaternary sediments (sands, gravels, and clays) with a thickness below 17.0 m [45] (Figure 2).  
The hydrogeological profile consists of three aquifers--Quaternary (Pleistocene), Triassic (locally), and Carboniferous. Locally, groundwater exists in the interbedding of the Miocene sediments. The Quaternary aquifer is locally separated by poorly permeable sediments. The lower aquifers are protected by very poorly permeable Miocene layers of a thickness of 80 m [46]. The groundwater flows south in the research area (Figure 3).  
Figure 1: Location of the research area along with the observation network system.  
Figure 2: Geological cross-section of the study area (based on [10]).
## 3 Core Algorithm Analysis  
### Representation of 4D Trajectory and Conflict Detection  
The continuous trajectory of UAV can be modeled as a sequence of discrete points (waypoints) expediently for computer processes. There is a direction between every two adjacent waypoints due to the velocity vector. The position of 3D and corresponding time--called spatial-temporal information--makes up the 4D trajectory of each UAV. For \(UAVi\,(i=1,2,...,n)\), its dynamic characteristics can be described in a Cartesian coordinate system [20]. The region formed by \(x\) and \(y\) axes indicates the horizontal plane, and \(z\) stands for the altitude.  
\[\begin{array}{c}p_{t}^{i}=\left[\begin{array}{c}x_{t}^{i}\\ y_{t}^{i}\\ z_{t}^{i}\end{array}\right],\,\,\,\,
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 3 Method
### Priority Experience Replaying  
The motivation of introducing priority experience replaying is to optimize the training process and solve the problems in traditional experience replaying. The traditional replaying method treats all empirical samples equally, but in fact, some experience is more important for model learning. Priority experience replaying introduces a mechanism that can focus more frequently on learning those experiences that have a greater impact on the model, thereby improving the efficiency of training and the performance of the model. In order to overcome these problems, we introduce a random sampling method [40] between pure greedy priority and uniform random sampling. We ensure that the probability of being sampled in the priority of a transition is monotonic, and that the probability is non-zero even for the transition of the lowest priority. Specifically, we define the probability i of the sampling transition as follows:  
\[P(i)=\frac{P_{i}^{\alpha}}{\sum_{k}p_{k}^{\alpha}} \tag{37}\]  
where \(P_{i}>0\) is the priority of conversion i. The index \(\alpha\) determines how much priority is used, and \(\alpha=0\) corresponds to the uniform case.  
The first variant we consider is a direct, proportional priority, where \(P_{i}=\lvert\delta_{i}\rvert+\epsilon\), and \(\epsilon\) is a small positive constant that prevents the transition edge from not being revisited once the error is zero. The second variant is an indirect, rank-based priority, where \(P_{i}=\frac{1}{rank(i)}\), and \(rank(i)\) is the level of data i sorted by \(\lvert\delta_{i}\rvert\) in the experience pool. In this case, \(P\) becomes a power-law distribution with exponent \(\alpha\). In error, both distributions are monotonic, but the latter is more robust because it is not sensitive to outliers.  
In order to effectively sample from the distribution in Equation (37), the complexity cannot depend on the capacity \(N\) of the empirical pool. For rank-based variants, we can approximate the cumulative density function using a piecewise linear function with k equal probability segments. The segment boundaries can be calculated in advance (they only change when \(N\) or \(\alpha\) changes). At runtime, we sample a fragment and then uniformly sample it in the transformation. This is especially effective when combined with minibatch-based learning algorithms: selecting \(k\) as the size of the minibatch and sampling a transition from each segment precisely is a form of stratified sampling that has the additional advantage of balancing the minibatch. The proportional variables are different, and also allow effective implementation based on the \"sum-tree\" data structure (where each node is the sum of its child nodes, and the priority is the leaf node), which can be effectively updated and sampled.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 4 Materials and Method
### Recommended Models for Attack Detection  
In this section, attacks against the testbed containing the SCADA system were detected. For this purpose, a deep learning-based model and a machine learning-based model were applied to the previously prepared dataset. The analysis results obtained were compared with each other according to particular metrics.  
In order to achieve successful results in proposed models, data were preprocessed, and experiments were performed. Innovative and different approaches were proposed for the cybersecurity of a physical testbed containing a SCADA system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual Values**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{**Predictive Values**} & **Positive** & TP & FP \\ \cline{2-4}  & **Negative** & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion Matrix.  
4.5.1. Preparing the Data and Transmitting Them to the Proposed Models  
This section concerns turning the dataset into an analyzable state and designing the appropriate models, which Figure 4 summarizes. Several data pre-processes were determined to make the dataset analyzable. The primary operations were deleting attributes and instances deemed unnecessary or containing too many null values. The next step was completing the attributes containing missing data using the mean method. Another process is to convert data types to the same type using categorization processes. In the end, a dataset with 25 features was obtained.  
After preprocessing, the dataset was divided into parts for training, validation, and testing in the data fragmentation stage. The split ratios here were kept constant in the models used. The obtained training and validation data were combined and sent to the proposed models. Tests were carried out on the model using the test data, and analysis results were obtained for training, validation, and test data. According to the results, the attack-detection success of the proposed model was evaluated. These stages were essential for obtaining the most suitable model which achieved the highest success rate in detecting the attack.  
Figure 4: Processing the data and delivering them to the proposed models.
## 2 Methodology  
### Description of the SORC System  
Exhaust gases are recovered from a Jenbacher JMS 612 GS-N engine, which is located in an industrial plant for the transformation of plastic packaging in Barranquilla-Colombia. The engine operates with natural gas as fuel with a flow rate of 120 L/min at a pressure of 116.36 kPa, and a temperature of 38.9 \({}^{\circ}\)C by mean of an ORC configuration. The engine operates at 1482 rpm in island mode, with a mechanical power of 1758.77 kW, where the exhaust gases generated by the engine are in a temperature of 435. 1 \({}^{\circ}\)C, pressure of 102.30 kPa, and mass flow of 2.77 kg/s. The integration of the SORC system with the gas engine allows one to increase the overall thermal efficiency of the prime mover, as a consequence of the thermal load reduction transferred to the environment, which mean better use of energy [32] as shown in Figure 1.  
Basically, the physical structure of the system shown in Figure 1 is constituted as follows: The exhaust gases from the natural gas engine (state 10) transfer energy by heat with the thermal oil in the shell and tube heat exchanger (ITC1), and subsequently are evacuated to the environment (state 11). The thermal oil at high pressure (state 3 AT) circulates through the thermal oil circuit through the energy supplied by the Pump 1 (B1) and allows the indirect evaporation of the organic fluid to avoid an unsafe operating temperature in the ORC system. The hot thermal oil (stream 1 AT) serves as a thermal source to evaporate the organic fluid in the compact plate heat exchanger (ITC2) to obtain steam superheated (state 1 ORC), which is the highest pressure and temperature of the ORC. Therefore, in this thermodynamic state of the cycle, the fluid is used to convert the thermal energy to electric power through the turbine (T1). The thermal process in the ITC2 is carried out in three zones, named preheating (Zone 1), evaporation (Zone 2), and overheating (Zone 3). As the organic fluid in the turbine (T1) from the evaporator (ITC2) expands, an energy conversion process occurs in the generator (G). The expansion of the fluid is given until the lowest pressure of the cycle to the condenser (ITC3), where all the mass is condensed (state 3 ORC). The condensation process happens in two phases, called condensation process (from state 1A to state 1 gA), and cooling process (state 1 gA to state 2A). Then, the organic fluid pressure is elevated to the evaporating pressure in the ITC2 exchanger with the pump (B2), completing the SORC cycle [18; 31; 33; 34].  
Figure 1: Physical structure of the waste heat recovery (WHR) system coupled with a natural gas engine.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
###### Abstract  
Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics.  
hexapod robot; kinematics; gait pattern generation +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion  
### Open Loop Conditions  
As presented in Figure 14, at open loop conditions both 2L_FOC and 3L_FOC torques follow perfectly their references; however, the 2L_FOC presents more torque ripples, traduced by important audible noise and important current distortions (Figure 15), which is totally eliminated in the 3L_FOC.Moreover, as shown in Figure 16, the 3L_FOC has better current THD than the 2L_FOC.  
Figure 14: Comparison between **(a)** 2L_FOC and **(b)** 3L_FOC in open and closed loop conditions in terms of Torque ripple.  
Figure 13: Photography of the experimental platform.
## 2 Materials and Methods
### mQSO Algorithm  
The multi-swarm Quantum Swarm Optimization algorithm is a well-known approach for dynamic optimization proposed in [13]. The main features of the mQSO are the usage of several swarms at the same time, application of exclusion, anti-convergence, and charged and quantum swarms. The swarm is divided into several sub-swarms with the aim to let every small swarm seek and track different local optima. However, a simple division into several small swarms is not enough: if there will be no interaction between sub-swarms, then some of them may converge to the same local optimum.  
In mQSO, two forms of swarm interaction are applied: exclusion and anti-convergence. The exclusion mechanism makes sure that two or more swarms are not clustering around the same peak. To prevent this, a competition between swarms is used, in particular, when their swarm attractors, i.e., best solutions, are within the exclusion radius \(r_{excl}\), the swarm, which is further from the optimum, is excluded.  
The anti-convergence mechanism works as follows: when all of the swarms have converged to their corresponding local optima, the worst swarm is reinitialized, thus, some part of the total population is always searching for new local optima. The anti-convergence implements global information sharing between all swarms. The convergence to the local optimum is detected when the neutral swarm size is smaller than the predefined convergence radius \(r_{conv}\).  
The mQSO starts by randomly initializing \(N_{s}\) swarms of \(N_{p}\) particles each within the \(D\)-dimensional search space with positions \(x_{nij}\), \(n=1,2,\ldots,N_{s}\), \(i=1,2,\ldots,N_{p}\), \(j=1,2,\ldots,D\). In the implementation in this study, the mQSO uses two types of particles: neutral and quantum, but not charged, as used in [10]. The update rule for the neutral particles is as follows:  
\[\begin{array}{c}\overrightarrow{u_{ni}}\gets w\Big{[}\overrightarrow{ u_{ni}}+c_{1}\overrightarrow{\epsilon_{1}}\odot\left(\overrightarrow{p_{ng}}- \overrightarrow{x_{ni}}\right)+c_{2}\overrightarrow{\epsilon_{2}}\odot \left(\overrightarrow{p_{ni}}-\overrightarrow{x_{ni}}\right)\Big{]},\\ \overrightarrow{x_{ni}}=\overrightarrow{x_{ni}}+\overrightarrow{u_{ni}}, \end{array} \tag{4}\]  
where \(i=1,2,\ldots,N_{p}\) is the particle index in the population, \(\overrightarrow{u_{ni}}\) is the velocity of i-th particle in \(n\)-th swarm, \(n=1,2,\ldots,N_{s}\), \(\overrightarrow{x_{ni}}\) is current particle position, \(\overrightarrow{p_{ni}}\) is the personal best position, \(\overrightarrow{\epsilon_{1}}\) and \(\overrightarrow{\epsilon_{2}}\) are random vectors in \([0,1]^{D}\), \(c_{1}\) and \(c_{2}\) are social and cognitive parameters, \(w\) is the inertia factor, \(p_{ng}\)--is the global best solution, \(\odot\) denotes the element wise product. The quantum particles in mQSO are sampled within the \(D\)-dimensional ball of radius \(r_{cloud}\) around the best particle as follows:  
\[\vec{x_{ni}}\in B_{n}(r_{cloud}). \tag{5}\]  
The quantum local search is performed for \(N_{q}\) steps for all the best solutions \(\vec{p_{ng}}\) in each swarm \(n\).
# Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism
## 5 Stochastic Game Formulation  
To model the interaction between cooperative and selfish users, we define a finite stochastic game between a group of cooperative users \(S_{M}:=\{1,\ldots,M\}\) and a group of non-cooperative users \(S_{N}:=\{1,\ldots,N\}\). All users access a shared wireless channel using the SAZD mechanism. Furthermore, we consider that the group \(S_{M}\) plays a cooperative game with all users, and therefore they attempt to optimize the overall system's performance, whereas each user in the group \(S_{N}:=\{1,\ldots,N\}\) attempts to optimize his own performance.  
We summarize the components of the game as follows:  
* Players: The sets of cooperative and non-cooperative players are defined, respectively, as \(S_{M}\) and \(S_{N}\). In what follows, we refer to the players as users.
* Strategy space: The set of strategies is the set of users' actions. For each user \(i\), we define the set of pure strategies as \(A_{i}=\{T,W\}\), where T represents the action \"Transmit\", and W is the action \"Wait\". Thus, at a given time slot, a user holding a packet can choose one action in \(A_{i}\). Furthermore, we define the mixed strategies as the set of all the distributions over \(A_{i}\), which is \(\phi_{i}=\{q_{c}^{i},1-q_{c}^{i}\}\) for a cooperative user \(i\), and \(\psi_{j}=\{q_{nc}^{j},1-q_{nc}^{j}\}\) for a non-cooperative user \(j\).
* Utility: The utility function corresponds to the user's level of satisfaction, which can be, in the case of our study, the average throughput, the access delay, or any other performance metric of interest. Let \(u_{i}:\psi_{i}\times\psi_{-i}\rightarrow\mathbb{R}\) denote the utility function of user \(i\) in \(S_{N}\). \(u_{i}\) depends on \(q_{r}^{i}\) the transmission probability of user \(i\) and the vector \(q_{r}^{-i}=[q_{r}^{1},q_{r}^{2},\ldots,q_{r}^{i-1},q_{r}^{i+1},\ldots]\) of others' transmission probabilities. Thus, each non-cooperative user possesses his own utility function. On the other hand, let \(U_{S}\) be the common utility function of all cooperative users among the set \(S_{M}\), which corresponds to the overall system performance.
* Game information: We assume that all players share a common knowledge, which is: the total number of players in the game, their own strategy space, and the strategy space of others, their utility, and the utility of others. On the other hand, we assume that cooperative players do not have the knowledge of the existence of selfish players among them. As a result, they behave cooperatively assuming that others will behave similarly. However, selfish users assume that everyone in the game is selfish, and therefore they behave selfishly.  
In the mixed game defined in this paper, cooperative users are interested in maximizing the overall utility function (e.g., the system throughput), whereas selfish users maximize their own utility functions.
# Infectious Diseases Associated with Exposure to Pollutants in a Local Population from Mexico
## 3 Results  
### Comparison of Sociodemographic Characteristics  
Sociodemographic characteristics across the different study zones, Zones 1, 2, and 3 in the locality of Acatzingo de Hidalgo and Zone 4 in the locality of Progreso de Juarez, were compared. The study group was significantly represented (\(p=0.044\)) by individuals aged between 0 and 29 years (60%), with a smaller proportion (10.1%) comprising adults over 60 years old across all four study zones. The gender distribution was equal, with 58.1% females and 41.9% males, and the predominant occupations were students (47.3%), housewives (24.2%), and farmers (19.3%). Tobacco and alcohol use were low (10.1%) in the population, with significant differences observed among the studied zones (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Zone 1**} & \multicolumn{2}{c}{**Zone 2**} & \multicolumn{2}{c}{**Zone 3**} & \multicolumn{2}{c}{**Zone 4**} & \multirow{2}{*}{\multirow{2}{*}{\(p\)-Value**}} \\  & & **n = 169** & & **n = 91** & & **n = 80** & & **n = 85** & \\ \cline{3-10}  & & **Frequency** & **\%** & **Frequency** & **\%** & **Frequency** & **\%** & **Frequency** & **\%** & \\ \hline \multirow{3}{*}{Age} & 0–29 years & 103 & 60.9 & 54 & 59.3 & 57 & 71.3 & 41 & 48.2 & \\  & 30–59 years & 49 & 29.0 & 27 & 29.7 & 21 & 26.3 & 30 & 35.3 & 0.044 \({}^{*}\) \\  & \textgreater{}60 years & 17 & 10.1 & 10 & 11.0 & 2 & 2.5 & 14 & 16.5 & \\ \hline \multirow{3}{*}{Sex} & Women & 97 & 57.4 & 53 & 58.2 & 52 & 65.0 & 45 & 52.9 & \\  & Men & 72 & 42.6 & 38 & 41.8 & 28 & 35.0 & 40 & 47.1 & \\ \hline \multirow{3}{*}{Occupation} & Farmer & 32 & 18.9 & 16 & 17.6 & 10 & 12.5 & 24 & 28.2 & \\  & Businessman & 7 & 4.1 & 6 & 6.6 & 5 & 6.3 & 7 & 8.2 & \\ \cline{1-1}  & Housewife & 39 & 23.1 & 25 & 27.5 & 15 & 18.8 & 24 & 28.2 & \\ \cline{1-1}  & Retired & 0 & 0 & 2 & 2.2 & 2 & 2.5 & 2 & 2.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Social and health indicators in studies sites.  
The presence of infectious diseases caused by bacteria, fungi, and viruses showed highly significant differences (\(p\) = 0.001) among the study zones, with high frequencies in Zone 2 (56%, 51/91), Zone 3 (63.7%, 51/80), and Zone 4 (51.8%, 44/85), and a lower frequency in Zone 1 (5.9%, 10/169). Therefore, a total of 156 reported cases were documented. Additionally, significant differences were observed in the presence of chronic degenerative diseases (\(p\) = 0.005), with diabetes mellitus type II (15.3%, 65/425) being the most prevalent, followed by arterial hypertension (4.0%, 17/425) in the studied areas (Table 1).