esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Detailed Operators
### Subsystem Division Based on Failure Decoupling
In the other words, if \(a_{ik}=a_{ij}=1\), \(a_{ij}=1\). Hence, there is a simple calculation method for the reachability matrix:1. Build the adjacency matrix \(A=\big{(}a_{ij}\big{)}_{n\times n}\) based on the directed graph and its transposed matrix \(A^{T}=\big{(}a_{ji}\big{)}_{n\times n}\). 2. Analyze the \(y\)th column of the adjacency matrix \(A\) and \(A^{T}\) (\(k=1,\,2,\,\ldots,\,n\)). If the element \(a_{xy}\) of \(A\) is 1 and the \(a_{ry}\) of \(A^{T}\) is 1, set the all the \(a_{xy}\) of \(A\) to be 1 with the adoption of permutation and combination. Establish its derivative matrix represented as \(M\). 3. Obtain the transposed matrix of \(M\), denoted as \(M^{T}\). 4. Process the matrix \(M\) and the matrix \(M^{T}\) according to the above iterative criterion. Stop the iteration until the elements in \(M\) do not change as the number of iterations increases. 5. Replace all the elements in the leading diagonal of \(M\) by 1 to get the reachability matrix \(\Phi\). The product subsystem division is obtained via module failure decoupling based on the decomposition of reachability matrix. If \(L_{i}\) (\(i=1,\,2,\,\ldots,\,k\)) are the subsystems divided of the set \(F=\bigcup_{i=1}^{k}L_{i}\) and \(L_{i}\cap L_{j}=\oslash(i,j)\). Based on the reachability matrix, \(R(M_{i})\) is the set consisting of the modules that the module \(M_{i}\) can reach; the set \(A(M_{i})\) consists of the modules which can reach the module \(M_{i}\); \(C(M_{i})\) is the set consisting of the common elements of \(R(M_{i})\) and \(A(M_{i})\). \(B(M_{i})\) is a set decided by \(A(M_{i})\) and \(C(M_{i})\). \(B(M_{i})=C(M_{i})\) if \(A(M_{i})=C(M_{i})\), otherwise \(B(M_{i})=\oslash\). The subsystems divided can be got by the above failure decoupling, and the detailed operators can be found in Section 4.
# UAV Formation Trajectory Planning Algorithms: A Review
## 5 Local Trajectory Planning Algorithm  
The local trajectory planning algorithm belongs to the dynamic programming algorithm. According to the UAV sensors in the UAV formation, the current location information  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Challenge** & **Optimization Criteria** & **Method** & **Dimension** \\ \hline C. [PERSON] and [PERSON] [120] & Trajectory & Dynamic step size, Adaptive learning & NN & 3D \\ [PERSON]. [PERSON] et al. [121] & Trajectory & Supervised learning & ANN + A* & 2D \\ [PERSON] et al. [122] & Environment & Multi-agent & Deep-Sarsa & 3D \\ [PERSON] et al. [123] & Environment & STATI & MADDPG & 2D \\ [PERSON] et al. [124] & coverage problem & Adaptive, Information sharing & DMUCRL & 2D \\ [PERSON] et al. [125] & Environment & Attention network & AM & 2D \\ [PERSON] et al. [126] & Environment, Trajectory & SSA & B-spline curve & BNN & 3D \\ [PERSON] et al. [127] & MEC & Low complexity & MADDPG & 2D \\ [PERSON] et al. [128] & Communication & CMDP & cDQN & 3D \\ [PERSON] et al. [129] & Collect Data & Dec-P/MDP & DDQN & 2D \\ [PERSON] et al. [130] & Time & Note dynamic clustering & PSO + IA-DRL & 2D \\ \hline \end{tabular}
\end{table}
Table 7: A summary of the content of the survey about machine learning algorithms.  
Figure 24: A Deep Reinforcement Learning Model.  
and local obstacle information are collected in real time so as to dynamically plan the optimal trajectory from the starting point to the target point. The algorithms for UAV formation local trajectory planning usually include the artificial potential field method (APF), dynamic window approach (DWA), mathematical optimization algorithm (MOA), and Model Predictive Control (MPC).
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
### Background  
The surface of greenhouses in the world exceeds 700,000 ha, being concentrated mainly in Asia, in the Mediterranean basin, and in central and northern Europe [15]. In this order of ideas, world exports of tomatoes exceeded 6.9 billion euros in 2013, with the EU, the main sector of world trade in fresh tomatoes, with 57.8% in 2013 (volume in kg) and 65.7% of turnover (volume in euros). Spain is the main supplier, with 23.78% and 21.7% of EU imports in tons and euros respectively. Of the 27,000 ha of greenhouses in the province of Almeria, 10,232 ha are destined to fresh tomato crops, the main crop in the greenhouses of Almeria with a total production of 958,462 tons, which represents 83.2% of the surface and 61% of the total production of Andalusia having the first place in the national production. Regarding exports, its main markets are in the EU, especially Germany, France, the Netherlands and the United Kingdom [16; 17]. Due to this relevance, our research is focused on tomato greenhouses. Accordingly, the most widespread empirical propagation models in the presence of vegetation are those shown in Table 1. However, because they are general, the predictions that the aforementioned models show could be improved with values closer to the real ones, helping to contribute to better planning in the deployment being this the main object of our study.  
According to the literature reviewed we have found similar research that studies the influence of foliage on radio path losses for WSN planning orchards [18] and a study that improves the path loss model for wireless sensor networks in mango greenhouses [19], others based on deployment of WSN over tomato greenhouses for monitoring environmental variables [20], and precision agriculture [21], but none of the reviewed literature was related to radio wave propagation models for tomato production greenhouse as in our work.
# Volumetric Modulated Arc Therapy for Radiosurgery of Brain Metastases: A Single-Center Study
## 3 Results
### Survival Analysis  
Survival analysis according to the Kaplan-Meier model is shown in Figure 5. The median survival time (MST) was 7.72 months (95% confidence interval: 5.9-9.49 months). OS rates were 59.5% at 6 months, 35.5% at 12 months, and 16.5% at 24 months. The log-rank test results of the Kaplan-Meier model for all factors are shown in Figure 5. Significant differences in survival were found for the following variables: (a) KPS; patients with KPS \(\geq\) 70 had a median survival time (MST) of 12.06 (95% CI, 7.3-16.8), as opposed to those with KPS \(<\) 70 who had an MST of 2.2 (95% CI, 1.12-3.28) (\(p<\) 0.001); (b) DS-GPA Class; patients were divided into four groups: Class I (DS-GPA 0-1.0; \(n\) = 38), Class II (DS-GPA 1.5-2.0; \(n\) = 43), Class III (DS-GPA 2.5-3.0; \(n\) = 32), and Class IV (DS-GPA 3.5-4.0; \(n\) = 8). Due to the smaller sample size of the last two groups, for survival analyses, they were grouped into one group (Class III-IV, \(n\) = 40). Thus, the MSTs of the patients grouped according to DS-GPA were Class I at 2.56 months (95% CI 0.67-4.44), Class II at 7.75 months (95% CI 5.55-9.94), and Class III-IV at 14.09 months (95% CI 9.15-19.03) (\(p<\) 0.001). (c) Age; the MST for patients \(<\)65 years was 8.58 months (95% CI 4.3-12.86), while those \(\geq\)65 years presented a MST of 6.28 months (95% CI 2.95-9.6); (d) presence of PFBMs; the MST of the patients with the presence of PFBM was 14.39 (95% CI 5.09-23.69; \(n\) = 29), while that of those without was 6.44 (95% CI 4.65-8.24; \(n\) = 92) (\(p\) = 0.029); (e) the MST of patients with BMs of breast cancer origin was 19.42 months (95% CI 16.52-22.32), while that of those with lung cancer was 7.52 (95% CI 5.75-9.3) (\(p\) = 0.015). In contrast, no statistically significant differences were found between median survival times regarding seizures (\(p\) = 0.96) and the presence of extracranial BMs (\(p\) = 0.28). Finally, univariate and multivariate Cox regressions were performed (Table 2). In the univariate analysis, significant differences were found in the hazard ratios of the following variables: KPS (HR\({}_{\text{KPS}<70}\) = 4.15; \(p<\) 0.001), DS-GPA (HR\({}_{\text{DS-GPAII}}\) = 0.58, \(p\) = 0.018; HR\({}_{\text{DS-GPAII-IV}}\) = 0.32, \(p<\) 0.001), age (HR\({}_{\geq 65}\) = 1.6; \(p\) = 0.014), PFBMs (HR = 0.62; \(p\) = 0.03), and primary tumor type breast/lung (HR\({}_{\text{breast}}\) = 0.51; \(p\) = 0.017), no significant differences were found between BM-related epilepsy (\(p\) = 0.96), cumulative tumor volume (\(p\) = 0.19), and ECM (\(p\) = 0.29). In multivariate analysis, only KPS (HR\({}_{\text{KPS}<70}\) = 2.59; \(p\) = 0.001) and DS-GPA (HR\({}_{\text{DS-GPAII}}\) = 0.55, \(p\) = 0.022; HR\({}_{\text{DS-GPAII-IV}}\) = 0.38, \(p\) = 0.006) were statistically significant.
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 5 NAD+ Biosynthesis
### The Salvage Pathway  
Taking into account that the half-life of NAD+ is approximately 4-10 h and that the average daily intake of vitamin B3 is 15 mg, which does not cover physiological needs (for example, the liver needs approximately 8 g per day), cells have no choice but to recompose the NAD+ molecule through the salvage pathway [3]. Nicotinamide (NAM), the catabolic product released by NAD+-consuming enzymes, is recycled in two stages. The first step is the rate-limiting reaction of the pathway catalysed by NAMPT, transforming NAM to nicotinamide mononucleotide (NMN) in the presence of magnesium (Mg\({}^{2+}\)) and phosphoribosyl pyrophosphate (PRPP). In the second stage, the enzyme NMNAT reconstitutes NAD+ from NMN [38; 39; 40; 41; 42; 43]. This pathway, the salvage pathway, is the main source of NAD+ generated in mammalian cells due to its high production efficiency, and less than 1% NAD+ is lost daily due to efficient recycling. This might explain why cells are completely dependent on this pathway [1; 3; 7; 42; 43].
# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 3 Method and Datasets
### Description of the Proposed Method
#### 3.2.3 Detection of Abnormal Behavior  
Due to the symmetrical placement of P1-P4 in the abstracted space, the information about the relative weights of the BMPs in the general structure of activities is encoded in the position of the centroid of the image representing the activity map.  
Starting from this observation, we propose the following method to detect time intervals with unusual/atypical activity (see Figure 7):  
Step 1: Manually select an interval (e.g., a week) from the past, with known normal activity. Using the sensor data, build activity maps for each one hour time slice, for all the days in the reference interval. The total number of activity maps for the reference interval will be: _Number_of_time_slices_per_day_\(\times\)_Number_of_days_.  
Step 2: Compute and plot the centroids of the images containing the activity maps for each time slice, across the entire reference interval.  
Step 3: Find the smallest bounding circle (SBC) \(C(x_{c},y_{c},R)\) that encompasses the centroid points of the activity maps for the reference interval, where \((x_{c},y_{c})\) are the coordinates of the center, and \(R\) is the radius. (There are 24 such circles for the reference interval, one for each one hour time slice.) In this study, we have used the algorithm described in Ref. [45] to determine the SBC.  
Step 4: Build activity maps starting from the sensor data recorded in the past hour. For each new activity map, compute the centroid \((x_{i},y_{i})\) of the respective image, along with two additional parameters, as follows:  
The overall activity level for the respective time slice:  
\[AL=\sum_{i=1}^{4}\lvert\{P_{i}\}\rvert \tag{4}\]  
where \(\{P_{i}\}\) is the set of events reported by the sensors, associated with the place \(Pi\), and \(\lvert\{P_{i}\}\rvert\) is the cardinal of this set.  
Figure 6: A fragment of an activity map for 6 days.  
The measure of the activity deviation (\(AD\)) is a function of the Euclidean distance between the current centroid (\(x_{i}\),\(y_{i}\)) and center of the reference SBC (\(x_{c}\),\(y_{c}\)).  
\[AD=\sqrt{\left(x_{i}^{2}-x_{c}^{2}\right)+\left(y_{i}^{2}-y_{c}^{2}\right)}-R \tag{5}\]  
With this measure, activity maps with \(AD\leq 0\) are considered to reflect normal activity, while those with \(AD>0\) are reported as unusual or deviant activity (see Figure 7d), after an additional validity check described in the next subsection.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 7 Conclusions
#### Future Work  
As future work, we plan to develop non-linear models for the proposition dynamic and time-varying systems. This will allow the solution developed in our work to be implemented in real operation networks without change in their cores.  
We intend to refine the proposed solution by evaluating other selection-based strategies for slices that constitute the core of the framework, such as genetic algorithms, deep learning, and utility function, among others.  
We also intend to implement our refined solution for a mobile device, e.g., Vehicle, UAV, Robot. Our main goal will be to assess the solution at various points in the 5G network architecture, and thus statistically verify if there is a significant performance difference in the NSSF according to the implementation model.  
Furthermore, we intend to perform experiments evaluating the integration between market orchestration tools, such as ONAP, EMCO, and OpenNESS. The integration model should be flexible, facilitating deployment anywhere in the network, without the need for changes to equipment and VNFs currently operating.  
Conceptualization, D.C.d.S., J.O.R.B.J., M.A.F.d.S. and C.d.C.M.; methodology, D.C.d.S., M.A.F.d.S., C.d.C.M. and R.M.S.; software, D.C.d.S. and M.A.F.d.S.; validation, D.C.d.S., J.O.R.B.J. and M.A.F.d.S.; formal analysis, C.d.C.M., G.B., C.E.C. and R.M.S.; investigation, D.C.d.S., J.O.R.B.J., G.M.M. and M.A.F.d.S.; resources, D.C.d.S. and M.A.F.d.S.; data curation, D.C.d.S., J.O.R.B.J. and M.A.F.d.S.; writing--original draft preparation, J.O.R.B.J.; writing--review and editing, D.C.d.S., J.O.R.B.J., G.M.M. and M.A.F.d.S.; visualization, C.d.C.M., G.B. and C.E.C.; supervision, R.M.S.; project administration, J.O.R.B.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.
# RPKI Defense Capability Simulation Method Based on Container Virtualization
## 6 Experimental Verification and Analysis
### Experimental Environment  
The experiments in this paper were conducted via the Mitaka version of OpenStack. A total of four DELL R730 (Dell Technologies, Round Rock, TX, USA) servers were used in this experiment, all running CentOS 7. The specifications for the nodes are as follows: the control node has an Intel Xeon E5-2609 v4 1.70 GHz processor, 128 GB RAM, and 2 TB hard disk space; the network node has an Intel Xeon E5-2620 v4 2.10 GHz processor, 64 GB RAM, and 1 TB hard disk space; compute node 1 has an E5-2620 v4 2.10 GHz processor, 128 GB RAM, and 1 TB hard disk space; and compute node 2 has an E5-2609 v4 1.70 GHz processor, 64 GB RAM, and 2 TB hard disk space (Intel Corporation, Santa Clara, CA, USA). The Docker version on all compute nodes was 18.03.1-ce. All the physical nodes were interconnected through an Ethernet switch.
## 2 Material and Methods
### Methods and Tools  
The research project was approved by the Bioethics Committee of the Opole Medical Chamber (Resolution No. 237 of 13 December 2016) and was in accordance with the Declaration of Helsinki guidelines regarding the conduct of clinical trials on humans.  
The study was performed using a 16-channel EMG system (Noraxon, DTS, Desktop Direct Transmission System, Scottsdale, AZ, USA) with a 16-bit sampling accuracy at 1500 Hz. The MyoResearch XP Master Edition for DTS Noraxon system was used for data analysis. To synchronize the EMG system a wireless unit (a 3-axis wireless DTS 3D accelerometer sensor with the nominal output range of \(\pm\)6 g, sensitivity of \(\pm\)0.67 V/g, and bandwidth of 5 Hz-1.8 kHz) was used to transmit the EMG signal directly to a PC. The EMG signals were subjected to data rectification.  
The sequence of bioelectrical muscle activity was determined by manually selecting the onset and offset of the raw recording of a particular muscle activity while observing the EMG signal waveform [16]. The study protocol followed the SENIAM project guidelines [17]. The series of fencing actions was divided into three attempts in a single set of direct thrusts to a visual stimulus.  
Muscle activity time was assessed using the three attempts performed in a single set. The attempts were divided into single sequences. The activity time was calculated as the percentage of the three attempts divided by the time of the entire action. The fencers performed thrusts on the coach's torso in response to a visual stimulus, which was a quick motion of the coach's blade from parry quarte to parry site. An accelerometer was attached to the knuckle guard of the coach's weapon to accurately measure the movement timing.  
The next measure was complex reaction time (CRT) calculated as the interval between the first significant change in the accelerometer signal and the highest EMG value.  
Wavelet coherence was used for the analysis of nonstationary signals. The coherence is computed using the analytic Morlet wavelet. The wavelet coherence of two time series \(x\) and \(y\) is:  
\[\frac{\left|S\big{(}C_{x}^{*}(a,b)C_{y}(a,b)\big{)}\right|^{2}}{S\big{(}\big{|} C_{x}(a,b)\big{|}_{2}\big{)}\times S\big{(}\big{|}C_{y}(a,b)\big{|}_{2}\big{)}} \tag{1}\]  
where \(C_{x}(a,b)\) and \(C_{y}(a,b)\) denote the continuous wavelet transforms of \(x\) and \(y\) at scales \(a\) and positions \(b\), respectively. The superscript \({}^{*}\) is the complex conjugate and \(S\) is a smoothing operator in time and scale.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 2 Materials and Methods
### Experimental Measurements
_Iperf_ (v2), which has both client and server functionality, allows creation of data streams to measure the throughput between the two ends in one or both directions. During the experimentation, three MikroTik RB433 UAH devices have been used: one of the RB433 UAH devices acted as an _iperf_ server, while the other ones acted as clients. The _iperf_ server accepts multiple clients simultaneously. In our case, we have used three concurrent User Datagram Protocol (UDP) threads during the experimentation, and we have measured communication parameters such as bandwidth, transferred bytes, packet error rates (PER) and jitter. The choice of UDP is made for providing quick and easy communication between vehicles; it is more preferable to resend the same message several times than having to manage the confirmation of receipt by Transmission Control Protocol (TCP). Figure 5 depicts the devices used to perform the tests, where (a) shows the laptop used to store the _iperf_ traces, (b) shows the AC/DC inverter plugged into the vehicle, (c) (h) and (i) show the antennas placed on the roof of the vehicles, (d) is the portable spectrum analyzer used to analyze the communication V2V and V2I and (e) (f) and (g) show the location of the antennas used at the infrastructure (streetlight). To provide and ensure VANET connectivity, we have selected the proactive distributed protocol BATMAN-ADV [33], which is the acronym of Better Approach To Mobile Adhoc Networking Advanced. It is a routing protocol for multi-hop mobile Adhoc networks that does not manage a centralized knowledge of the wireless network and does not need to consolidate information concerning network changes in every node in the network, since it does not try to determine the entire route at sending time. Each packet passing through each node of the network gets the next best jump at that time for its destination. Data is passed to the next neighbor at a given mac-address until it reaches its destination. We have placed the BATMAN-ADV protocol on each RB433 UAH device, and using _iperf_, we have analyzed communication flows between two static vehicles (V2V) and between a static vehicle and the infrastructure (V2I), as shown in Figure 3. Figure 6 shows the communication stack implemented for the experimental measurements. Network parameters obtained by the experimental measurements are presented in Section 5. Figure 4: Two RB433 UAH MikroTik device, with two Ubiquitin XtremeRange2 and an Ubiquitin XtremeRange5 cards and the corresponding antenna.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 4 Case Study  
This section validates the effectiveness of the CNN-MHSA-LSTM-GAM-CAM hybrid model on a real distributed network load dataset through comprehensive comparative experiments. In addition, the effects of each attention module are analyzed, revealing the potential of the attention mechanism in short-term load forecasting.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 5 Experiment and Results  
The proposed fault analysis method for DTU was implemented on a realistic DTU fault database released by the State Grid Corporation of China.
## 3 Results and Discussion
### MW-Based Plant Prototype  
According to the simulation evidence, the prototype was sized and designed with the aim of providing the continuous disinfection of shelled almonds: a specific mass flow rate of at least 2 kg/min reaching 60 \({}^{\circ}\)C on the almonds' surface temperature after the treatment.  
The prototype consisted of a shielded treatment chamber containing a polypropylene tube (length: 3000 mm; inner diameter: 90.8 mm) in which the almonds were irradiated by MW energy. A rotating metallic helix inside the tube allowed the almonds to pass easily from inlet to outlet. The rotating helix promoted constant mass mixing, providing a much more uniform distribution of the electromagnetic field for the almonds inside the treatment chamber. The helix was driven from an electric motor of 0.55 kW with reducer and integrated with variable frequency drive (VFD). The MW treatment chamber is a stainless steel reverberation chamber with an irregular shape to limit the possibility of stationary waves and hot spots. According to the numerical simulation carried out, the microwave power supply of the pilot plant consisted of five magnetron generators connected to the treatment chamber by standard WR340 rectangular waveguides. They operate at 2.45 GHz and can provide a variable output power up to a maximum of about 1.5 kW. They are equally spaced along the treatment chamber and each one is powered by a TM060 generator connected to a SM1180T power supply. The plant is equipped with a loading hopper of 40 L. The electric motor and magnetron generators can be turned on and off at different times. The process parameters, such as temperature, MW power, and speed, were constantly monitored using a PLC-based electronic control and management system.  
Figure 7a shows the pilot plant prototype and Figure 7b a detail illustrating the magnetron generator (MW source) with the corresponding power supply, control, and cooling systems.