esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods
### Methodology
#### 2.3.4 ARIMA Model  
The ARIMA (AutoRegressive Integrated Moving Average) model functions by detecting patterns in past data and extrapolating these trends into the future. The ARIMA model is used to forecast the SPEI, a drought index that captures the effects of both precipitation and temperature on water demand. The ARIMA(p,d,q) model combines autoregressive AR(\(p\)), differencing I(\(d\)), and moving average MA(\(q\)) components. The autoregressive AR(\(p\)) component of the model entails using past values of the variable to predict its current value. The integrated I(\(d\)) component involves transforming the data to make them stationary. The moving average MA(\(q\)) component models the error term as a composite of error terms that occurred at the same time and in the past. p, d, and q are the parameters corresponding to the three models [19]. Mathematically, it can be represented or described as follows:  
\[C(t)=\varphi_{0}+\sum_{i=1}^{p}\varphi_{i}C_{t-i}+\varepsilon_{t}+\sum_{i=1}^{ q}\gamma_{i}\varepsilon_{t-i} \tag{6}\]  
where C(t) represents the reconstructed component time series formed after the SE algorithm; \(\varepsilon_{t}\) represents the current period random error disturbance; \(\varphi_{i}\) and \(\gamma_{i}\) represent the model parameters; p denotes the quantity of autoregressive terms; and \(q\) denotes the amount of terms in the moving average.
# Varietal Authentication of Extra Virgin Olive Oils by Triacylglycerlycerols and Volatiles Analysis
## Results and Discussion
### Discriminant Analysis
According to standardized coefficients, oleic and linoleic acids for FA had the greatest impact on the discrimination for functions 1 and 2. As regards the volatiles, ethanol and 1-penten-3-one had the greatest impact on the discrimination for function 1. The values showed the impact of each variable on the discriminant function after \"standardizing\", putting each variable on the same platform. Figure 2 shows the plot of the first two discriminant functions, using FA percent composition of monovarietal EVOO samples, while Figure 3 shows the plot of the first two discriminant functions, using volatile percent composition. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable of FA**} & \multicolumn{3}{c}{**Function**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline C16:0t & 7.80845 & 2.462617339 & 1.78735183 \\ C16:1t & \(-\)5.0273175 & 2.482626305 & 1.12018377 \\ C18:0t & 2.9099661 & 1.892928834 & 0.80540848 \\ C18:1t & 16.271798 & 8.045815225 & 4.48269249 \\ C18:2t & 13.561999 & 12.83014703 & 9.61389748 \\ C18:3t & \(-\)0.4295152 & 0.498737036 & 1.051572 \\ C16:1 sn-1 & 2.2615419 & \(-\)0.769331234 & \(-\)1.38994605 \\ C18:2 sn-1 & \(-\)1.4760586 & -1.070614593 & \(-\)1.77232348 \\ C18:3 sn-1 & \(-\)0.1162137 & \(-\)2.22366257 & 0.19232926 \\ C18:1 sn-2 & 0.1016169 & 9.43991337 & 5.33667001 \\ C18:3 sn-2 & 0.2085916 & \(-\)1.495984501 & 0.82082452 \\ \hline
**Variable of volatiles** & **1** & **2** & **3** \\ \hline ethanol & 23.335 & \(-\)1.046 & \(-\)0.192 \\ pentanal & \(-\)1.182 & 1.107 & 0.918 \\ \(n\)-decane & 0.741 & 1.864 & 0.847 \\
3-ethyl-1,5-ocatadien & \(-\)18.622 & \(-\)0.787 & \(-\)1.674 \\
1-penten-3-one & 10.969 & \(-\)0.711 & 1.736 \\ _trans_-2-pentenal & 0.404 & 1.228 & \(-\)1.263 \\ \hline \hline \end{tabular}  
* t, total FA% content in TAG fraction; _sn_-1, FA percent content in _sn_-1 position of TAG fraction; _sn_-2, FA percent content in _sn_-2 position of TAG fraction.  
\end{table} 
Table 5: Standardized canonical discriminant function coefficients obtained from LDA analysis using total and positional TAG acidic compositions of monovarietal EVOO samples. Figure 2: Discriminant function plot of the first two functions obtained using total and intrapositional FA percent composition of monovarietal EVOO samples (DA, Dolce Agogia; FR, Frantoio; LE, Leccino; MO, Moraiolo). In the two-dimensional space defined by the first two discriminating functions, the samples belonging to the same cultivar are well discriminated, even if the Dolce Agogia and Moraiolo groups are better concentrated around the centroid of the group. However, the results of the classification show that the samples of all groups are correctly classified.
# Conceptual Design of an Extended Reality Exercise Game for the Elderly
## 3 VRrowing Conceptual Design
### System Design
#### 3.2.1 System Architecture  
To simplify the equipment and operations, provide a rich gaming experience, ensure low game investment, and be suitable for home use, the _VRrowing_'s client will adopt the mode of \"Play Device\" plus \"User Station\" and use the Internet to link with the system server, as shown in Figure 1. We propose a large screen/HMD for the game presentation to provide better immersion effects, potentially improving the game experience and increasing continuous use and frequency. However, the display device is not limited to a large screen or HMD. _VRrowing_ could also support other display devices, such as TVs or laptops.  
The hand controller is used for the upper body movement monitoring and the interaction between the player and the system, while the motion sensors (such as SlimeVR, Tundra, AprilTag, Vive, HaritoraX, and KinectToVR) are used to track and record the lower body movement (including legs and waist). The sensors are based on inertial measurement unit (IMU) sensors and connected to _VRrowing_ by the Internet. To track all of the lower body movement, both thighs, both ankles, and the waist all have a wearable sensor. Each sensor tracks its own rotation in space. Then SlimeVR, for example, calculates the joint  
Figure 1: The system architecture of _VRrowing_.  
angles and estimates limb positions by players' headset location and proportions, thereby obtaining the lower body movement.  
Although _VRrowing_ has several exercise activities and gamified tasks with standing or sitting postures, the necessary space required is suitable for home use. Users need a space with the arms out as the diameter in the sitting posture. Even in a standing posture, the extra required space compared to sitting is acceptable for home use. Considering the feasibility and safety of using _VRrowing_ in small rooms, the required area of body movement is one step for each side.
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 4 Sensor Experiment and Discussion
### Hysteresis Measurement  
The hysteresis loops of the FeNi and FeCo were characterized by using the resistance strain gauge method [20], as depicted in Figure 12. The horizontal and vertical axes in the graph stand for external magnetic field intensity and magnetostrictive coefficient respectively. The curves 1 and 2 represent the corresponding changes in the magnetostriction of the FeNi coating at increasing and decreasing magnetic field intensity, respectively. Meanwhile, the curves 3 and 4 characterize the relationship between the magnetostrictive coefficient of the FeCo and the external magnetic field. It is clear that the hysteresis loop of FeNi and FeCo were distinctly different. Owing to the week remanence and hysteresis effect, the FeNi alloy exhibits relatively narrow and steep hysteresis loop over the FeCo, which means the FeNi coated sensor will offer better symmetry in current sensing. That is the reason why the FeNi was favored as the magneticsensitive interface for magnetic sensing.  
Figure 11: The baseline noise measurement of the developed sensor.  
Figure 12: The measured magnetostrictive curves for FeCo and FeNi.
# Design for Additive Manufacturing and for Machining in the Automotive Field
## 3 Results  
### Topology Optimization and FEA  
The geometry resulting from the TO is shown in Figure 1b, which also includes the DS for the bracket and a preliminary prediction of the stress distribution. From Figure 1b, it is also possible to note that, in spite of adjusting the element density [30], the output model from the TO was still very coarse in terms of surface finish and geometric accuracy.  
This output was completely redesigned to a parametric CAD model. In principle, this procedure is more time-consuming than smoothing the TO output. However, unlike STL files, parametric models can be easily modified as many times as required by simply changing the numerical parameters. Following a recursive process, the CAD model based on the geometry from TO was validated by FEA. In particular, a stress analysis was performed in order to verify that the minimum structural safety coefficient was not lower than 2. The process required three iterations to reach the final configuration, which fulfilled all the functional and structural constraints.  
The results of the FEA for Von Mises stress and displacement on the redesigned component are shown in Table 1. As a term of comparison, the table includes the same results for the original bracket geometry. The comparison allows observing that the optimized bracket guarantees a decrease by 36% of the maximum displacement, together with a slight reduction in the maximum stress (\(-4\%\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Original Bracket** & **TO Bracket** \\ \hline \hline \end{tabular}
\end{table}
Table 1: FEA results for displacements and Von Mises stresses for the original and redesigned bracket.
# A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network
## 3 Proposed SEFR Protocol
### Methodology of SEFR
The data encryption \(E_{c}\) towards the cloud server from the fog node is performed using Equation (9). \[E_{c}\equiv D^{e}(mod\,s) \tag{9}\]  
where \(D\) is representing the data from fog nodes. At the receiving end, the encrypted data \(E_{c}\) is decrypted by the cloud server using Equation (10). \[D\equiv E_{c}^{d}(mod\,s) \tag{10}\]
###### Abstract  
Recently, the rapid advancement in technologies of modern intelligent objects has led to a new network paradigm, called the Internet of Things (IoT), in which every networked and automated object has been connected in a pervasive manner. New types of IoT-based application services are thus presented. In a healthcare oriented environment, the usage of IoT has brought opportunities for assisting physicians (or nurses) to provide on-demand and real-time body-care services to patients with higher accuracy and better efficiency. However, while IoT-oriented techniques deliver such advantages, they may encounter system security vulnerabilities and patient privacy threats not seen in the past. In this paper, we propose a robust IoT-based healthcare system, called BSNCare+, in which body sensor networks (BSNs) are adopted as the underlying communication architecture. In the proposed healthcare system, we exploit lightweight crypto-primitives to construct a secure communication mechanism that does achieve data confidentiality and entity authentication among intelligent body sensors, the mobile gateway and the backend BSN-Care server. In addition, we evaluate the performance of the proposed healthcare system using the Raspberry PI series platform. The results show the practicability and feasibility of BSNCare+.  
authentication; healthcare; Internet of Things (IoT); privacy; security +
Footnote †: journal: Applied Sci. 2016
# A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity
## 3 The Construction of Initial and Target Topology Coordinate Systems
Finally, the initial relative coordinate matrix \(\mathbf{X}\) can be obtained by  
\[\mathbf{X}=\mathbf{U}\sqrt{\mathbf{\Lambda}}. \tag{20}\]The distance matrix \(\mathbf{D}\) of target topology is a symmetric matrix, where \(d_{lm}=d_{ml}\), \(d_{ll}=0\), for all \(l,m=1,2,\ldots,N\), and is expressed by  
\[\mathbf{D}=[d_{lm}]_{N\times N}=\left[ \begin{array}{cccc}d_{11}&d_{12}&\cdots &d_{1N}\\ d_{21}&d_{22}&\cdots&d_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ d_{N1}&d_{N2}&\cdots&d_{NN}\end{array} \right], \tag{21}\]  
According to Algorithm 1, replacing the input distance matrix \(\mathbf{\Delta}\) with the distance matrix \(\mathbf{D}\) of the target topology, the output of Algorithm 1 will be the target topology coordinate matrix \(\mathbf{Z}\). The target coordinates of the target topology shape can be obtained from \(\mathbf{Z}\).
# Role of Nicotinamide in Genomic Stability and Skin Cancer Chemoprevention
## 2 The Influence of NAD+ Status on Genomic Stability and Cell Senescence  
Endogenous (e.g., oxidative metabolism, spontaneous hydrolytic reaction, DNA replication errors) and exogenous (e.g., UV and infrared radiations, chemicals) insults induce DNA damage, such as strand breaks, depurination, depyrimidination, crosslinks, and modified bases [8]. Genome stability is maintained by DNA repair systems (e.g., base excision repair (BER), nucleotide excision repair (NER), mismatch repair systems [9]. Following DNA damage, the cells undergo transient arrest to repair modifications through DNA repair systems, and then re-enter the cell-cycle [10]. The efficiency of mechanisms involved in genomic stability may depend on energy status. Indeed, several proteins, which contribute to repair DNA damage, such as DNA ligases (e.g., DNA ligase I, III), chromatin remodelers (e.g., CSB, Swi2/Snf2) and histone-modifying enzymes (e.g., ATM kinase, DNA-PK) are ATP-dependent enzymes [11]. Incomplete DNA repair leads to an accumulation of DNA damage that, in turn, leads to persistent DNA damage response, telomere attrition, and genomic instability. Persistent DNA damage has immediate effects, driving cells towards apoptosis or senescence, as well as long-term consequences resulting in aging or cancer [8; 10]. Apoptosis and senescence are two tumor-suppression mechanisms that eliminate the cells at risk for oncogenic transformation. Apoptotic cells are quickly eliminated. Conversely, senescent cells remain viable in the tissue although they may undergo several changes, including growth arrest, modification of gene expression and morphology, and acquisition of a senescence-associated secretory phenotype (SASP) that is characterized by secretion of pro-inflammatory cytokines, growth factors, and proteases [12]. SASP allows the removal of damaged cells by recruiting immune cells, and promotes tissue renewal by mobilization of nearby progenitor cells. Thus, cell senescence protects the organism from developing cancer.
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination
### Traditional Method (TM)  
The TM is an exhaustive search method to determine optimal GNSS satellite constellations/subsets after checking the quality of all possible GNSS satellite subsets. The TM uses the WGDOP (or GDOP) as a quality criterion. It checks the WGDOP value of all possible GNSS satellite subsets to define the optimal GNSS satellite subset with the lowest WGDOP value [21]. Checking the quality of all possible satellite subsets delivers the optimal satellite combination and, therefore, the TM can be considered the ground truth. However, it becomes time-consuming when the number of satellites increases because the number of possible satellite subsets increases dramatically. For instance, in the cases of 15 and 25 available GNSS satellites, the possible satellite combinations of 10 satellites are about 3 thousand and more than 3 million, respectively.
## 1 Introduction
Therefore, we use the Skip-Gram model to learn POI embeddings and combine it with Lite-GRU to learn user embeddings with temporal data processing capabilities, ultimately generating high-quality embedding vectors. * **VAE Module:** To tackle the high dimensionality and noise problems of LBSN data, this paper introduces VAE, which can extract useful features from high-dimensional noisy data, reduce dimensionality, and mitigate noise interference, thereby enhancing the model's robustness. Considering that VAE can better capture complex nonlinear relationships while maintaining global topological structures, it overcomes the linear limitations of Principal Component Analysis (PCA), avoids the overfitting issues that may arise with AutoEncoder (AE), and is more efficient than t-Distributed Stochastic Neighbor Embedding (t-SNE) when handling large-scale datasets. * **Edge-Level Attention:** In response to the challenge of effectively parsing the complex network structure in LBSNs, where traditional methods fail to fully leverage edge properties and weights to accurately extract features and propagate information, this paper introduces an edge-level attention mechanism. By considering the properties and weights of edges, it learns the importance of different types of edges to enhance information propagation and feature extraction. Additionally, by combining node residual connections and multi-head attention, the node aggregation and propagation process is further optimized, effectively integrating the diverse information contained in different types of nodes and edges to obtain better node representations. The rest of this paper is organized as follows: Section 2 introduces related work; Section 3 presents the research problem and relevant definitions; Section 4 provides a detailed description of the GEVEHGAN model; Section 5 designs experiments and analyzes the results; Section 6 concludes this paper's findings.
# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 3 Mobile Robot and Industrial Robot Design
### Software of Industrial Robot  
An increase in the usage of ROS applications in industrial robotics, and its applications on industrial robotic arms, has contributed to the development of the ROS industrial (ROS-I) platform [38, 39]. In addition to the benefits of the standard ROS platform previously mentioned, ROS-I provides us with supplementary tools and capabilities specific to industrial robotic arms. One of the biggest advantages of using the ROS-I platform is the programming of the robotic arm in conventional programming languages, such as Python and C++, instead of the native language of the controller, which is company dependent. The ROS packages used in our work to communicate and control the industrial robot are as follows:  
* _abb_driver_ that enables the communication between personal computer and ABB IRC5 industrial robot controller for robot control. The messages being exchanged contain information about the condition of the robot, such as the position of the robot's wrists.
* _paletizer_ package, developed for sorting/palletizing industrial crates, as well as to communicate with the rest of the system, e.g., OPIL, from which it receives the commands for palletization and reports on the state of the task.
* _abb_irb140_unal_, the package that provides information about the physical representation of robots, such as URDF and SRDF records.  
For controlling the ABB industrial robotic arm, the abb_driver is used [40] (ROS driver for the ABB industrial robots), as the driver is made for specific hardware. The part of the driver located on the industrial controller is written in the programming language RAPID (the native language of the controller), while the part of the driver located on the personal computer is written in C++. The block diagram of communication between two applications/two devices is shown in Figure 8.  
Some of the additional features that ROS-I provides in the form of additional packages are an extended set of robotic arm models that the ROS driver can control (abb_experimental package [41]). The experimental package expands the capabilities of the ROS driver and adds the models and parameters of the robotic arms. The most important tool provided using the ROS-I platform is the MoveIt package [42]. The MoveIt package gives us the possibility of creating a functional robot from a CAD model, i.e., generates the required universal robot description format (URDF) and semantic robot description format (SRDF) files to define the parameters of the robotic arm. Additionally, an additional package for a visualization RViz provides us the visualization of 3D models of robots, as well as their movement and coordinate systems.
# Seismic Risk in Alborz: Insights from Geological Moment Rate Estimation and Fault Activity Analysis
## 3 Results and Discussion
Considering that deformation and stress were previously estimated from geodetic measurements [42,43] in the Central Alborz area, unlike Zagros, it is considered to have more seismic nature, so it is possible that 1 mm of missing shear stress is accumulating in the central part of the Mosha fault or the eastern part of the fault zone north of Tehran; in other words, this part of the Mosha fault could be a single fault. It can be considered locked with a relatively high probability of seismic failure in the future evolution of its tectonic earthquake [44]. Also, according to the results of this research, which estimates the risks of the eastern faults of Tehran to be higher, it seems that the studies of [38,44,45,46] also confirm the same opinion. This study argues that the Sorkh-e Hesar and Ghasr-e Firuzeh fault systems include a system that has not been introduced and show that we need to focus on the Mosha-Parchin system in studies, so according to the results of this study, the shown stress accumulation in this fault system can be attributed to the fault locking of this system northeast, east, and southeast of Tehran and the high seismic potential in this fault that ought to be the focus of studies. To calculate the geological moment rate, several parameters including fault length, fault dip, fault slip rate, and thickness of the seismic layer in the region are required. Assuming a constant seismic layer thickness throughout the seismotectonic zone, the seismic moment rate can be obtained using Equation (9) [47]:  
\[\mathrm{M}=\mu\sum\left(\mathrm{Li.Hs.Si}\right)/\left|\mathrm{Cos}\ \delta_{1}\right| \tag{9}\]  
where \(\mu\) is the shear coefficient (with a value of 3 \(\times\) 10\(\cdot\)10 N/m\({}^{2}\)), Li is the fault length, Si is the fault slip rate, \(\delta_{1}\) is the fault dip, and Hs is the seismic thickness. In this study, considering the depth of the Alborz earthquakes, a seismic layer thickness of 18 km was assumed, and the absolute value of the cosine function was used to ensure that the calculation is positive. Consequently, the geological moment for each main fault (Table 7) and the average geological moment for the region (\(\sim\)5.18218 \(\times\) 10\({}^{17}\) Nm/y) were calculated. Based on the calculations, the Eshkabad, Taleqan, and Mayamey faults exhibit the highest geological moment rates (Table 7; Figure 6). Figure 5: The seismic hazard map of the area. The Central Alborz and the western part of the Eastern Alborz show high seismic hazards.