esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model 1 Results  
This section presents the results obtained after modeling the soiling losses with ANN, but without including the short-circuit current of the unmaintained panel as an input variable. Figure 8 compares the real and estimated losses, and the data is in chronological order, so it is also possible to see how soiling has been accumulating from July to February.  
The above graph shows how the soiling accumulated with upward trends as well as sudden drops; these were probably due to rain on both panels. To be clear, if both panels were totally clean, the losses caused by soiling would be 0. In spite of the ups and downs and the great amplitude, in the middle of the graph, a very pronounced upward trend could be seen (possibly caused by episodes of Saharan soiling) and how, suddenly, the values fell back down, possibly due to rainfall. After that, a stable trend was maintained, synonymous with a soiling-free atmosphere. In this way, it was possible to perceive those periods during which there was more accumulated soiling, as well as the period in which it was the highest. Consequently, the modeling has proven satisfactory, even in the absence of the short-circuit current. Figure 9 shows a scatter plot where the real losses are represented against the estimated losses.  
Figure 7: Real losses without Isc.  
Although not a perfect correlation, the two represented variables followed a positive linear trend. The point cloud follows this trend to a greater extent even if there are numer  
Figure 8: Comparison of real versus estimated losses without Isc.  
Figure 9: Scatter plot of actual vs. estimated losses without Isc.  
ous points that move away from it, indicating a slight overestimation of the cases. The cloud is denser at the beginning, where most of the values are concentrated. This means that most losses were found in the range from 0% to 6%, approximately, with the point cloud being diluted at higher values. Table 7 presents the parameters obtained from the network validation.  
The table shows how the MBE and nMBE values determined that the model had not been overestimated, given that their values were close to zero. Likewise, the RMSE value and its normalization showed a 1.60% error in losses due to soiling, while the normalized error in percentage terms shows that it was below 12% for all the cases analyzed. Nevertheless, this standardized parameter was still within fairly acceptable limits since the estimate was considered good at between 10% and 20%. The result of the \"r\" correlation coefficient was very satisfactory, with a strong relationship between the two losses.
## 3 Proposed Methodology
### Data Transmission  
The data transmission phase comprises three subphases;  
1. The data sensed with encrypted and authenticated format transmitted by the member node to CH and when not linked to any route, can sleep so that energy is saved.
2. The received data are aggregated and compressed by CH to generate a new signal, which is then transmitted to BS through the route specified. (It is considered that node j is subsequent hop in routing table.)
3. Base station uses an exclusively shared key for decryption and authentication of the data received.  
These subphases are observed with the form as given in Equations (13) and (14).  
\[x\to CH:DATA|ID_{x}|E(K_{xch},d_{x}) \tag{13}\]  
\[MAC(K_{xch},DATA|ID_{x}|E(K_{xch},d_{x})) \tag{14}\]  
Once the data are received, they are aggregated by CH and then forwarded to BS by Equation (15).  
\[CH\to BS:AGGR\_DATA|ID_{ch}|ID_{j}|E\Big{(}K_{jch},seq_{no}\Big{)}|E(K_{ chts},d_{ch})\Big{|}MAC(K_{chts},AGGR\_DATA|seq_{no}|E(K_{chts},d_{ch}) \tag{15}\]  
AGGR\_DATA specifies the type of the packet, ID\({}_{\text{ch}}\) and ID\({}_{\text{j}}\) denote the previous and next hop in the path, respectively; the packet reply is verified with the encrypted sequence number when AGGR\_DATA packet is received by any node with identical sequence number, then packet is dropped. D\({}_{\text{ch}}\) represents encrypted data for BS and MAC, which supports maintenance of the packet integrity and authentication. The following operations are performed by the node receiving this packet: (1) ID of the next hop is checked; when similar to its ID, the sequence number is decrypted. (2) The packet sequence number is verified in the receiver packet table, if not found, packet type and the sequence number is entered or else left out. (3) The entry of the subsequent hop is modified with the ID of the subsequent hop nodes and that of the preceding hops with its ID. (4) The sequence number is encrypted using the pairwise key of subsequent hop as well as then forwarded again. In this manner, data reach the BS through the route specified and BS uses an exclusively shared key (K\({}_{\text{chbs}}\)), which evaluates the data efficiency.
## 3 Experimental Setup
### Data Acquisition Process  
The animals were acclimatized to the experimental circumstances in the inhalation chamber for two days after they had fully recovered from surgery before being tested. The animals were subjected to the same conditions as the habituation days during the testing days, and their EEGs were recorded. Baseline activity was monitored for 30 min before treatment. Animals were divided into three groups to receive the inhalation of either 200 microliters of lavender EO or 200 microliters of distilled water (control), or an intraperitoneal injection of 15 mg/kg diazepam (positive control). Post-drug recording was performed for 180 min after treatment. Values of EEG parameters were normalized with baseline levels and are expressed in % baseline. The overall experimental protocol setup for EEG recording is shown in Figure 1.  
An airtight cylindrical plastic chamber was used as the inhaling chamber. The diameter was 25 cm, and the length was 50 cm (height). It was perforated with two small openings for intake and outlet (opposite to each other) having air tubing connections. The inlet tubing was connected to a fan delivering ambient air and/or volatile molecules into the chamber. The outlet tubing was for outflow ventilation. The fan engine was powered by direct current and required 1.92 W of electricity. The rotation speed was 4000 rpm with an air flow of 0.41 cmm (cubic meters per minute).  
EEG and EMG Signal AcquisitionEEG and EMG signals were collected with low-pass 50 Hz and high-pass 0.1 Hz filters, and were sampled at 400 Hz by a PowerLab/4 SP system (AD Instruments) with 12-bit A/D, and then stored in a PC through the LabChart 7 software. The EEG and EMG signals were handled using band-pass digital filters that ranged from 0.78 to 45.31 Hz and from 1 to 100 Hz, respectively. Standard LFP analysis, the frequency analysis, was performed using the Fast Fourier transform (FFT) function in LabChart 7.
## 2 Device Design and Structure
A second Bragg grating with lower reflectivity is placed at the laser's output side. In this case, only four periods have been used. Figure 2b displays the simulated transmission of both components. The highly reflective Bragg mirror presents a spectrally flat reflectivity of 90% with a stop band of \(\Delta\lambda\) = 100 nm centered at \(\lambda\) = 1540 nm. For the output reflective Bragg mirror, we observe a reflectivity spectrum centered at the same wavelength with a maximum reflectivity of 20%, although it shows a much wider stop band that extends over a range of \(\Delta\lambda\) = 300 nm. In any case, only the wavelength range that overlaps with the optical gain bandwidth of the MQWs will be relevant for the laser design (typically from \(\Delta\lambda\) = 1500 nm to 1600 nm). The co-integrated, widely tunable laser-SOA booster has been fabricated by wafer molecular bonding of a 100 mm III-V epilayer on a previously patterned 200 mm SOI wafer containing the Si photonic circuitry. A thin chemically polished silicon dioxide layer is used as a bonding interface. After InP substrate removal, III-V waveguides were patterned and cladded with a BCB polymer. Then, via definition and device metallization was carried out. A detailed description of the device fabrication can be found elsewhere [2; 37]. Finally, the co-integrated device was cleaved at the SOA edge (see Figure 1). An anti-reflecting coating was deposited at the output facet to minimize optical back-reflections.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 3 Results
### Field Tests
#### 3.2.2 Movement Data Analysis  
The streaming data from the person walking through the path is shown in Figure 12. Samples are stitched together to make picturing the activity easier. Same activity is repeated over the span of several hours. In Figure 13, we can see the result of some burst-reports that were interpolated to avoid idle periods between bursts but clearly reflects that the same activity is taking place continuously.  
Figure 11: Range and elevation profile.  
Figure 10: Data reports received. Distance from gateway to farthest point is 12 km.
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
## 2 Impedance Measurement Method
This method flows current \(I_{d}\) through probes P1 and P4 on the outside and measures the voltage through probes P2 and P3 on the inside. The symbol \(Z_{11}\) represents the tree impedance formed between nodes \(N5\) and \(N6\), and the symbol \(Z_{12}\) represents the tree impedance formed between nodes \(N7\) and \(N8\). Since the input impedance of a voltmeter is ideally infinite, the voltmeter between nodes \(N2\) and \(N3\) can be modeled as disconnected. Therefore, the path from \(N6\) through \(N2\) and \(N3\) to \(N7\) is broken, and no current flows along this path. Then, the current \(I_{d}\) supplied from the current source flows entirely through \(Z_{\text{c1}}\), \(Z_{\text{i1}}\), \(Z_{\text{t}}\), \(Z_{\text{t2}}\), and \(Z_{\text{c4}}\). Between nodes \(N6\) and \(N7\), the voltage \(V_{t}\) generated by the known current \(I_{d}\) and the measurement target \(Z_{\text{t}}\) is formed. Since no current flows between nodes \(N6\) and \(N2\), the voltage at node \(N6\) is equal to the voltage at \(N2\). Likewise, since no current flows between nodes \(N7\) and \(N3\), the voltage at node \(N7\) is the same as the voltage at \(N3\). As a result, the voltage \(V_{c}\) measured at nodes \(N2\) and \(N3\) becomes the same voltage as the voltage \(V_{t}\) formed across \(Z_{\text{t}}\). The impedance calculated from the applied current \(I_{d}\) and the measured voltage \(V_{c}\) is related only to the tree impedance \(Z_{\text{t}}\). Therefore, using the four-point measurement method, accurate impedance measurement is possible, excluding the influence of contact impedance. Contact impedance specifically includes the electrode polarization impedance at the interface with the ionic solution for metal electrodes carrying current [14]. Since this polarization impedance occurs at the electrode through which the current flows, the influence of polarization impedance can be avoided by using a four-point measurement method that measures the voltage at a separate electrode [15; 16; 17]. Even if a one-time measurement is repeated, the measurement reproducibility is good because the contact impedance value between the probe and the object is not reflected in the measurement value. The four-point measurement method is used in the semiconductor field to accurately measure the surface resistance of wafers without probe contact resistance error [18]. Therefore, the TPAM system being developed must basically have a four-point impedance measurement function.
## 2 Prediction of Wind-Power probability Density Based on Quantile Regression LSTM
### Wind-Power Probability Density Prediction Process Based on Quantile Regression LSTM  
(1) Normalize the obtained historical wind power data, as shown in Formula (16), to reduce the training time and improve the accuracy of the model.  
\[X_{t}=\frac{X_{t}-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{16}\]  
where \(X_{t}\) is the sample data at a time \(t\), and \(X_{\text{max}},X_{\text{min}}\) are the maximum value and minimum value of all samples, respectively.  
(2) Divide the training set and the test set, determine the parameters of each layer in the LSTM network, and preset the required number of loci.  
(3) The test set is input into the trained LSTM model for power prediction, and the power prediction results under each quantile are obtained.  
(4) The kernel density estimation is performed on the prediction results at each quantile obtained by training, and the probability density function at each prediction point is calculated.  
The specific prediction process is shown in Figure 2.
# Hyperlipidemic Rabbit Models for Anti-Atherosclerotic Drug Development
## 3 Hyperlipidemic Rabbit Models for the Development of Drugs
### Fibrates  
Fibrate is an agonist of the peroxisome proliferator-activated receptor \(\alpha\), which up-regulates the expression of the lipoprotein lipase, apo A-I, and apo A-II genes in the liver [70]. Fibrates are known as potent TG-lowering drugs for humans because they can reduce TG-rich lipoproteins such as CM, VLDL, and their remnants. In both humans and rodents, fibrates significantly decrease plasma TG, but this effect is either absent or only slight in rabbits [71; 72]. In spite of this, [PERSON] et al. reported that fibrate can exert an anti-atherosclerotic effect in cholesterol-fed NZW rabbits [73]. [PERSON] et al. evaluated the effects of fenofibrate on atherosclerotic plaque regression using magnetic resonance imaging (MRI). Atherosclerosis was induced by balloon injury and nine-month 0.2% cholesterol diet feeding in NZW rabbits. The baseline established  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
atherosclerotic burden was assessed by MRI, and then the rabbits were treated with fenofibrate for six months. Fenofibrate treatment did not change plasma LDL-C levels but increased plasma HDL-C levels. MRI analysis showed that fenofibrate treatment led to an 11% reduction in the aortic lesions compared to the pre-treatment baseline area [74]. [PERSON] et al. examined the effect of fenofibrate on plaque thrombogenicity and plaque stability in cholesterol-fed NZW rabbits with balloon injury. Fenofibrate significantly decreased tissue factor expression (42% reduction) and plaque cholesterol content (45% reduction) in the iliac artery of rabbits [75].
## 2 Wind Power, Photovoltaic Power and Load Power Forecasting Model  
### Maximal Information Coefficient  
The MIC can measure the degree of correlation between any two random variables. In this study, MIC is used to quantify the correlation between the input features and the true value of the target output. The core idea is that if two variables are correlated, dividing their scatter plot into grids allows calculating their mutual information (MI) based on the approximate probability distributions within each grid. The normalized MI can quantify the correlation between any two variables.  
Given two random variables \(X\) and \(Y\) and their sample pairs \(D=\{(x_{i},y_{i}),i=1,\ldots,N\}\), where \(N\) is the number of samples, the calculation steps of MIC are as follows [22].  
First, the sample space is divided into an \(m\times n\) grid \(G\). Second, the empirical joint probability density function \(p(x,y)\) and empirical marginal probability density function \(p(x)\) and \(p(y)\) are estimated. Then, the MI is calculated as  
\[\text{MI}(X,Y|D,G)=\sum_{x\in X}\sum_{y\in Y}p(x,y)\log_{2}(\frac{p(x,y)}{p(x) p(y)}) \tag{1}\]  
where \(\text{MI}(X,Y|D,G)\) denotes the MI between \(X\) and \(Y\) with sample set \(D\) and grid \(G\). There are various partitioning methods for the same grid size \(m\times n\). Among all possible grids, the maximal MI is defined as  
\[\text{MI}^{*}(D,m,n)=\max_{G}\text{MI}(X,Y|D,G) \tag{2}\]  
where \(\text{MI}^{*}(D,m,n)\) denotes the maximal MI.  
Normalize the maximum MI into an interval [0, 1], as follows:  
\[\text{NMI}^{*}(D,m,n)=\frac{\text{MI}^{*}(D,m,n)}{\log\min\{m,n\}} \tag{3}\]  
where \(\text{NMI}^{*}(D,m,n)\) represents the normalized maximal MI.  
Among all the grids satisfying \(m\times n<k(N)\), the \(\text{NMI}^{*}(D,m,n)\) can be calculated. Then the MIC is the maximal \(\text{NMI}^{*}(D,m,n)\) over all possible grids, as follows:  
\[\text{MIC}(X,Y)=\max_{m\times n<k(N)}\{\text{NMI}^{*}(D,m,n)\} \tag{4}\]  
where \(k(N)\) is normally set at \(k(N)=\text{N}^{0.6}\)[23].  
The value of MIC is between 0 and 1. The larger the MIC, the stronger the correlation between the two random variables.
# Functional Evaluation of a Novel Multi-Axial Alveolar Distractor--Preliminary In Vivo Animal Study
## 2 Materials and Methods  
### Multi-Axial Alveolar Distractor Design Concept and Mechanical Tests  
The most important design concept in the previously proposed multi-axial alveolar distractor is that it uses a ball and socket joint mechanism wielded to the base bone plate up to 60\({}^{\circ}\) toward the buccal/lingual and mesial/distal sides intra-operatively (Figure 1). The ball and socket joint can be relocked by tightening the hex nut after vector adjustment according to the pre-operative plan. The base bone plate and transport plate can be fixed onto the residual bone and the transport segment can be vectored through activating the transport screw with 0.25 pitch and 13 mm distraction height. The alveolar distractor and screw were manufactured with Ti6 Al4V alloy by the ISO13485 quality management systems company (HUANG LIANG Biomedical Technology Co., Ltd., Luzhu Dist., Kaohsiung City, Taiwan) (Figure 1 top-left). The mechanical functional tests were performed to evaluate the effectiveness and safety of this new multi-axial alveolar distractor in a previous study [18].  
Figure 1: The new developed multi-axial alveolar distractor consists of an extra-osseous system with a ball and socket joint mechanism wielded to a base bone plate, transport plate and activating transport screw. The upper left part shows the physically manufactured distractor made of Ti6 Al4V alloy.
## 3 An Architecture for Self-Organization  
The architecture for self-organization in WMN was projected to adapt to the context of the gateway nodes. The Figure 2 shows the use of the self-organization architecture in a typical scenario. The gateway nodes are connected to the wired Ethernet network providing access for the Internet to mesh routers. The mesh routers, forms the backbone of network using the 802.11 standard. The communication between the client nodes and mesh routers are made through the standard 802.11 g. This process is classified as single radios, because the backbone and the connection among clients' nodes are made by using the same network interface card (NIC). In the agents' layer two types of agents were developed and these are: NS (_Network-Size_) and NM (_Network-Monitoring_). In the self-organization process was integrate the capacity of Self-X (configuration, optimization, healing and protection) in each node-gateway in mesh. The clients' nodes are associated with the mesh router, according to the covering area. The definition of WMN can be represented through an unidirectional graph G = (R,L), where R is the group of mesh routes, and L is the group of connections between two nodes gateways x,y. Some nodes x can maintain a connection with the node _gateway_ g and can be connected to the Internet.  
Each client node can establish bi-directional connections with node gateway x, as it can be observed in the Figure 2.  
The clients nodes can be so much mobile, as fixed and they differ from the type of the device: class of mobile devices (PDA's, cellular telephones and laptops) and class of fixed devices (computers desktop, servants and node-gateways).  
For a better explanation about the logic interaction among the modules involved in this work, it grew the architecture based in layers. The approaches based on layers guarantee flexibility and simplicity in the development, in the sense that all of the involved functions are well defined and treated in an independent way. This is an advantage regarding the detection of isolated flaws, as it can be seen in the Figure 3. In this same Figure it is observed that the self-cure capacity and self-protection that are in tones of dark ash, they were not implemented in the mark of this work, they were only implemented together self-optimization capacity and self-configuration with NS_Agent.  
The details of each layer are described in the following sections and they stand out in a conceptual way in the interaction among all of the proposed layers.
# Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network
###### Abstract  
Non-invasive conveyor belt diagnostics in damage detection allows significant reductions of the costs related to belt replacement, as well as the evaluation of belt usability and wear degree changes over time. As a result, it increases safety in the location where the belt is used. Depending on the location of a belt conveyor, its length or the type of the transported material, the belt may undergo wear at different rates, albeit the wear process itself is inevitable. This article presents an artificial intelligence-based approach to the classification of conveyor belt damage. A two-layer neural network was implemented in the MATLAB programming language, with the use of a Deep Learning Toolbox set. As a result of the optimization of the created network, the effectiveness of operation was at the level of 80%.  
neural networks; conveyor belts; magnetic method; diagnostics; automatic detection 1
## 3 Results and Discussion
A parameter that provides more direct information on the relative changes of the average Mn oxidation state is the edge energy E\({}_{0}\). The change in E\({}_{0}\) for the GDE before and after partial O\({}_{2}\) starvation as a function of ORR/OER is shown in Figure 4d. It can be noticed that the value of E\({}_{0}\) decreases by about 5 eV and 3 eV for ORR and OER when comparing before and after O\({}_{2}\) starvation, respectively. We used an empirical linear relationship between Mn K-edge energy, E\({}_{0}\), and the Mn oxidation state [3, 19] to investigate the oxidation state of the MnO\({}_{2}\) component under starvation conditions (Figure 5). For the calibration, standard Mn compounds MnO (+2), Mn\({}_{3}\)O\({}_{4}\) (+2.7 [3]), Mn\({}_{2}\)O\({}_{3}\) (+3), and pyrolusite MnO\({}_{2}\) (+4) were utilized. The same figure also shows the Mn valence of \(\alpha\)-MnO\({}_{2}\) in its pristine state, which is +3.9. According to the linear relationship, the partial O\({}_{2}\) starvation lowered the valence of Mn to about +3 during ORR and +3.5 during OER. Instead, the Mn oxidation state of GDE before O\({}_{2}\) starvation increased slightly compared with the pristine \(\alpha\)-MnO\({}_{2}\) sample, and its value exhibits minor changes between ORR and OER states, thus indicating good reversibility.