esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 HSI Procedure
### Preprocessing
#### 2.2.6 Mean Centering (MC)  
MC is one of the preprocessing methods that reposition the origin at zero by subtracting a variable mean from the variables, which does not change the variance of such a variable. Such a transformation would be helpful for interpreting deviations from the mean, and most importantly for mitigating problems in statistical models that present multicollinearity, for instance, moderated regression and structural equation models. Because MC only mitigates micro-collinearity and does not comprehensively solve the correlations between the variables, its efficiency in managing the collinearity in complex models can be debated [16; 64].  
MC can also enhance the performance of calibration models when combined with other preprocessing methods applied to HSI. An example was provided by [PERSON] et al.,who, during the development of a rapid non-destructive analytical method for predicting nutritional content in food products, de-applied MC in a combined manner with SNV and Savitzky-Golay first and second derivatives. Their analysis results indicated that Ridge regression, after MC preprocessing, provided high accuracy in the prediction of protein and moisture content; however, for carbohydrate and ash content, the model was slightly less effective in terms of accuracy [64]. Further improvement can be obtained simply by increasing the sample size or by combination with MSC to reduce the effect of external noise, and this would increase the model's accuracy for better prediction in hyperspectral food analysis data [39]. In addition, since MC showed effectiveness in data extraction to evaluate food composition, suggesting the effect of this preprocessor in detecting the compositions of useful products such as dairy and eggs.
## Materials and Methods
### Protein Extraction and Western Blot Analysis  
The protein extraction was performed from 50 mg samples with Tissue Protein Extraction Reagent (TPER) (ThermoFischer Scientific, Rockford, IL, USA). All samples were diluted to 50 \(\upmu\)g protein/well with TPER reagent. After denaturation with 1:1 Laemmli buffer (Bio-Rad Laboratories, Hercules, CA, USA), proteins were separated by SDS-PAGE electrophoresis on Mini-PROTEAN TGX Stain-free Precast Gels (Bio-Rad Laboratories, Hercules, CA, USA) with Tris/Glycine/SDS premixed electrophoresis buffer (Bio-Rad Laboratories, Hercules, CA, USA) at 90 V for 1.5 h. After that, the proteins were transferred onto 0.20 \(\upmu\)m polyvinylidene dilluoride (PVDF) membranes (Bio-Rad Laboratories, Hercules, CA, USA) at 25 V for 5 min with a Bio-Rad Trans-Blot Turbo Transfer System (Bio-Rad Laboratories, Hercules, CA, USA). The next steps (membrane blocking, incubation antibodies with chromogen solution) were performed using the WesternBreeze Chromogenic kit (Invitrogen, Carlsbad, CA, USA), and the membranes were processed according to the manufacturers' instructions. The primary antibodies, anti-TNF-\(\alpha\) rabbit polyclonal antibody (Abcam, Cambridge, UK) and anti-PCNA rabbit polyclonal antibody (Santa Cruz Biotechnology, Santa Cruz, CA, USA), at a dilution of 1:150 were used overnight for membrane hybridization. The secondary antibodies used were those provided with the WesternBreeze kits. Blots were scanned using Bio-Rad ChemiDoc MP Imaging System (Bio-Rad Laboratories, Hercules, CA, USA). Densitometry of protein bands was carried out using the ImageJ 1.42 software (NIH Bethesda, MD, USA), and values were expressed as a percentage of the total size and density of all bands (intensities).
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
## 2 Study Area and Data Source
### Additional Ebro Hydrographic Confederation Data  
The Hydrological Planning Office of the CHE has been tracking air flights during flood episodes since the extraordinary flood that took place in February 2003. Flights are scheduled according to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Mission Identifier** & **Date of Capture** & **Hour of Capture** \\ \hline
**A** & Sentinel-1A & 6 April 2018 & 18:03 \\ \hline
**B** & Sentinel-1B & 13 April 2018 & 17:55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the Sentinel-1 SAR images used in the study.  
the time of the maximum flood peak as long as weather conditions allow it. Through these flights, oblique and vertical aerial photographs, as well as videos, are obtained, all in high resolution. This information provides the basis for the cartographic restitution of orthophotographs of the National Aerial Orthotopography Program (PNOA, acronym in Spanish) included in the General Spanish National Plan for Territory Observation (PNOT, acronym in Spanish) and to obtain, by digitization, the flood areas corresponding to each flood. This favors a greater understanding of the water behavior of the Ebro river over time.  
For the study area, a vertical aerial photograph corresponding to a part of the section that the CHE called \"Ribera Alta\" was used (Figure 3). It covers a region that goes from the village of Novillas to Alagon and was captured on 14 April 2018.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 2 Background
### DTN Characteristics and Key Management  
Network environments characterized by intermittent connectivity, network heterogeneity, and large delays are called \"challenged networks\". DTN is a computer networking architecture that aims to address the technical issues present in challenged networking environment, as well as specify the necessary components for interconnecting heterogeneous networks. The term DTN stems from [PERSON]'s paper [11], which introduced an architecture generalized from design work for the InterPlanetary Networking (IPN), which in turn addressed networking challenges in deep-space communications.  
The two main challenges addressed by DTNs are related to (a) long propagation delays and (b) intermittent connectivity, implying the lack of a continuous end-to-end path. Under such restricted and harsh networking conditions, traditional internetworking protocols (e.g., TCP/IP) are neither applicable nor suitable [12]. Networks where DTN architectures may apply include:  
* _Deep space networks [13]_--They are characterized by extremely long delays that typically cause memory and/or storage exhaustion.
* _Sensor-based networks [13]_--Their idiosyncrasies include extremely low end-node power, memory, and CPU capability.
* _Terrestrial wireless networks [14]_--This silk of networks is characterized by high mobility and changes in signal strength.
* _Vehicular AdHoc networks [15]_--They exhibit mobility, self-organization, distributed communication,_ and road-pattern restrictions.
* _Satellite networks [16]_--They are characterized by long delays and high rate of packet loss.
* _Underwater acoustic networks (undersea networks) [3, 17]_--They demonstrate spatial coverage and high density of nodes.
* _Rural area DTN [18]_--They are distinguished by opportunistic behaviour, sporadic and isolated message transmission.  
The constraints under which such challenged networks function has also severe effects on the security protocols, and therefore traditional solutions cannot be directly applied. The need for secure communications in open networks like DTNs is higher than ever. However, until recently, security was not considered to be an issue for DTNs in space missions. Moreover, the authors in [2] propose a practical mechanism to evaluate security protocols, including key exchange ones in DTNs. This is done by considering node credentials and network topology. Such a method could help in identifying the most efficient key management scheme in terms of delay for experimentally tested scenarios.
# SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
## 6 Implementation  
To verify the effectiveness of our attack, we carried out two attacks targeting SGX open source projects: mbedTLS-SGX, an enclave that provides various encryption and certificate-related algorithms [24], and Graphene-SGX, an enclave that helps non-SGX programs to be run in SGX [25].
## 4 Discussion  
We utilized two polymer gel dosimeters to measure FWHM, scanning positions, and Bragg peak range of the pencil beam. The deviations of Bragg peak range between the measurement and MC simulation were 0.13% and \(-\)0.53% for MAGAT-f and PAGAT gel, respectively. An approximate WET was determined for the container bottom. FWHM at various depths was measured with two polymer gels. As a whole, FWHM tended to increase with the depth within both MAGAT-f and PAGAT measurements. The fluctuation of FWHM at different depths could possibly be attributed to the proton scattering from the solid water slabs positioned at the irradiation path. Drifting protons measured by the beam monitor could be another factor. FWHM of the beam spot was correlated to the standard deviation, but the slope slightly diverged from the expected results. The discrepancies of scanning positions between the measurement and the theoretical value were 7.79% and 4.51% for MAGAT-f and PAGAT, respectively. The position monitor in the nozzle can measure the scanning positions and record them in the machine log file. By analyzing the machine log file recorded by the beam monitor, the position error can vary up to 1 cm because the facility was still under commission. Further studies are needed to measure the spread-out Bragg peak (SOBP) and the dose distribution of real patient plans with polymer gel dosimeter.  
Reproductivity of a polymer gel dosimeter can cause errors in the measurement. [PERSON] et al. [15] investigated the reproducibility of a 3D polymer gel dosimeter (polyacrylamideamide gelatine gel). A high dosimetric precision (3.1%) was found for the intra-batch study. For the inter-batch experiment, the precision was 4.3%. [PERSON] et al. [16] analyzed the detailed uncertainty of MAGAT and PAGAT gels. The overall accuracy was well within 5% when a careful scanning set-up was designed [16]. In our work, the standard deviation of R\({}_{2}\) based on the MRI images ranged from 0.0093 to 0.2778 s\({}^{-1}\), peaking at 14.5 Gy. During the experiments, the percentage of gel and the storage and scanning temperature were carefully controlled. The reproducibility was under investigation by fabricating the gel at different batches to obtain the dose response after proton irradiation. Moreover, obvious saturation was found when the dose was over 20 Gy. The percentage of each ingredient of polymer gel was under investigation in order to increase the response sensitivity. There was a 20% underestimation at the Bragg peak measured with a PRESAGE(r) dosimeter [17]. An empirical-correction-based method was demonstrated for reconciling the LET dependence of Gafchromic EBT film in the proton depth dose curve measurement [18]. Further investigation should be focused on quantifying the effect of LET dependence on the polymer gel dosimeter for appropriate correction.
# A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
## 2 Hybrid Unsupervised Exploratory Plots
### Classical Multidimensional Scaling  
Multidimensional scaling (MDS) [42] is a set of methods that project high-dimensional data into a low-dimensional space using distances or dissimilarities. Classical MDS (CMDS) is a member MDS that shows the structure of distance-like data as a geometrical image [43]. Generally, MDS's input is not a dataset, but the similarities of a set of items instead. CMDS uses a single distance matrix of Euclidian type as input.  
CMDS is also known as Principal Coordinates Analysis (PCoA) [44], Torgerson Scaling or Torgerson-Gower scaling. It is often used to visualize data when only their distances or dissimilarities are available, but in this research, in order to extend and validate HUEPs with this family of methods, the original dataset has been reduced to a distance matrix. This matrix (pairwise distance between pairs of observations) creates a new configuration of points using the following metrics:  
* Squared Euclidean;
* Standardized Euclidean (seuclidean): each coordinate difference between observations is scaled by dividing by the corresponding element of the standard deviation;
* Cityblock;
* Minkowski;
* Chebyshev: maximum coordinate difference;
* Cosine: one minus the cosine of the included angle between points;
* Correlation: one minus the sample correlation between points;
* Hamming, which is the percentage of coordinates that differ;
* Jaccard: one minus the Jaccard coefficient, which is the percentage of non-zero coordinates that differ;
* Spearman: one minus the sample Spearman's rank correlation between observations.
## 3 Results
### Quantitative Analysis for Precipitation Forecast
Figure 15: **(a)** RMSE (black line) and PPMCC (black dashed line) and **(b)** SRCC of C1, C2 and C3 experiments for the 6-h accumulated precipitation (mm) against the corresponding AWS observations during 0500–1100 UTC (1300–1900 LST). The spatial variability of the SRCC between the forecasted and observed 6-h accumulated precipitation (Figure 15b) revealed that a positive correlation between forecasted and observed 6-h accumulated precipitation across the study area was found in all C1, C2 and C3 experiments. Compared with the group C1, the C2 group exhibited a much higher correlation between forecasts and observations in the experiments of DA02, DA03, and DA04, but not DA01. The higher correlation (SRCC values closer to 0.5) with statistical significance (\(p<0.05\)) was observed in DA02, DA04, CDA02 and CDA03. Overall, RMSE, PPMCC, and SRCC analyses demonstrated that the simulations with data assimilation under C2 and C3 experiments performed better than those without data assimilation under C1 experiments. C2 experiment DA04 and C3 experiments performed best among all the experiments, implying that the performance of precipitation prediction gradually increased as the implementation time of data assimilation became closer to the target period of 0500 to 1100 UTC, that is, the occurrence of ATs.
## 4 Methodology
### Performance Indexes  
The operation of wind turbines on offshore semi-submersible platforms presents significant differences with respect to onshore wind parks and also to offshore wind parks with fixed foundations. Specifically, the displacements of the semi-submersible platform interact with the wind turbine, creating fluctuations that alter the aerodynamic state in which the system operates, with a great impact on electricity production. Moreover, these oscillations may also change the mechanical loads, altering the circumstances for fatigue to appear. In this sense, a controller able to cope with these interactions may significantly improve the profitability of the system, as vibrations play a key role in it [47].  
As a key element of the methodology, this work employs three performance indexes already used to evaluate developed controllers for wind turbines in semi-submersible platforms [45]. In contrast with graphical results, e.g., [48; 49], the indexes summarize the results using a performance rating, thus making it easier for the reader to understand the contributions of the different controllers and the operation of the system. As [38; 43; 50] show, two key measurements are the speed tracking error and power generation. Furthermore, [51] remarks on the relevant role of aerodynamics and loads on wind turbines. Nevertheless, there is no consensus about the definition of the performance evaluation indexes, with the mean and standard deviation often being employed. The indexes used in this work record the influence of a controller on the mechanical loads, on the efficiency in electricity generation, and on the behavior of the wind turbine compared with its nominal operation, i.e., the speed reference tracking error. According to this, for the sake of clarity, the definitions of the indexes are given as follows:  
* MI (Mechanical loads index). It estimates the impact of the evaluated controller on the lifespan of the platform using the reaction moment, \(M_{Base,i}\), considering a rigid joint at the base of the turbine: \[\text{MI}= \frac{1}{\text{N}}\sum_{\text{i}=1}^{\text{N}}|M_{Base,i}|.\] (9)
* GI (Generation index). It provides a measure of the performance on energy harvesting using the difference between the generated power \(\text{P}_{\text{i}}\) and the nominal power \(\text{P}_{\text{nominal}}\): \[\text{GI}= \frac{1}{\text{N}}\sum_{\text{i}=1}^{\text{N}}|\text{P}_{\text{i}}- \text{P}_{\text{nominal}}|.\] (10)
* Spl (Speed index). As a performance indicator, this index calculates the difference between the speed of the generator \(\omega_{\text{i}}\) and its nominal value \(\omega_{\text{nominal}}\): \[\text{SpI}= \frac{1}{\text{N}}\sum_{\text{i}=1}^{\text{N}}|\omega_{\text{i}}- \omega_{\text{nominal}}|.\] (11)  
All these indexes are calculated as the average of the absolute values for every time step i. Thus, a lower value means a better controller performance.  
Figure 3: The integration process of the stand-alone software tools.
## 2 Data and Methods
### Data Processing
#### 2.3.2 Satellite Data Processing  
Due to the differences in temporal cycles, temporal/spatial resolutions, coverage, and storage formats of each satellite product, comprehensive processing is required. Firstly, the ArcGIS10.8 software iterator tool was used for batch format conversion to create TIFF format satellite product data sets. Secondly, the ArcGIS10.8 software iterator tool was used for mask clipping to obtain coverage consistent with the study area. Subsequently, the ArcGIS10.8 software was used for resampling to obtain a 2020 satellite product data set with consistent spatial resolution (0.05\({}^{\circ}\)) in the study area. Finally, the ArcPy10.8 tool and MATLAB2022a software were used for batch extraction of the SIF satellite product raster attribute values corresponding to each site. We set that the SIF satellite products are suitable for arid regions if the spatial attributes of SIF in each ecosystem are reasonable and exhibit annual averages close to the measured GPP.
# Capturing Features and Performing Human Detection from Human Gaits Using RFID
## Materials and Methods
### System Design
#### 2.3.1 System Overview  
As shown in Figure 5, RF-Detection is mainly composed of three modules: data collection module, signal preprocessing module and deep learning module.  
First, we obtain the raw phase and Received Signal Strength Indicator (RSSI) data captured by the RFID tag through the antenna. After that, we perform preprocessing operations on the data. In addition to conventional phase unwrapping, normalization and other operations, we also perform segmentation and processing on the human motion signal, and we expand the data and other operations to improve the robustness and diversity of the data. After data preprocessing, it will be sent to the deep learning module. Specifically, this module is mainly composed of a Visual Geometry Group (VGG) neural network. The preprocessed data is sent into the module to extract the walking behavior of the persons first, so as to obtain their walking characteristics, and constantly adjust the weight of the convolution filter to achieve satisfactory results. Finally, the classification and identification are carried out through the output layer, with the specific process shown in Figure 6.  
Figure 4: Phase and amplitude of two volunteers as they pass continuously: (**a**) Phase presentation of two volunteers under different experiments; (**b**) Amplitude presentation of two volunteers under different experiments.  
Figure 5: RF-Detection System.
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 5 Rf Stray Currents Simulations
### Minimization of RF Stray Currents  
Based on the observations made in Sections 4.1 and 4.2, simulations were carried out with the model presented in Section 5.1, comparing the RF stray current through ISEG output filter. All the simulations are carried out with the same ISRF output conditions; only the CVD configuration is changed.  
We can see from Table 2 that the main contribution of RF stray currents when CVDII is installed comes from differential mode currents, due to the connection of one CVD leg to the inner conductor of the RF coaxial line. Since the CVDII outer leg connection does not contribute significantly to the circulation of RF stray currents, we can make a consideration for the further reduction of RF stray currents through ISEPS components, when the CVD is connected at the primary of ISRF transformer: a capacitor can be installed between the outer conductor of the coaxial line and HVD to shunt directly inside ISRF cubic the  
Figure 7: RF stray current resonances in SPIDER transmission line conductors and ISEG filter, in configuration with CVD installed at the secondary side of ISRF transformer. Two transmission line lengths are considered in the simulation, 43 and 100 m.  
common mode current caused by the stray capacitance between primary and secondary of the RF transformer (see Figure 8).  
Figure 9 shows that the shunt capacitor can further reduce the electrical stress caused by the circulation of RF stray currents on ISBI, ISPG and ISEG output filter components.
# Evaluating LoRaWAN Connectivity in a Marine Scenario
## 2 Material and Methods
### Experimental Set-Up
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gateway Id.** & **Position Id.** & **Latitude** & **Longitude** & **Altitude (asl, m)** \\ \hline LG308 1 & Crevari & 44.42446 & 8.73098 & 168 \\ LG308 2 & Mount Gazzo & 44.44219 & 8.84801 & 417 \\ LG308 3 & Mount Fasce & 44.40780 & 9.01618 & 540 \\ LG308 4 & Righi & 44.42564 & 8.93563 & 290 \\ LG308 5 & Scarpino & 44.44416 & 8.86392 & 396 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Position of the gateways used for tracking the passenger vessel. Figure 3: Working area of the research cruise ICOS20 with, superimposed, (blue squares) the positions of the gateways, (black square) the position of the W1M3A observatory, and (red line) the track of R/V Dallaporta. The following metrics have been considered to assess the performance of the transmission: packet loss ratio, packet reception rate (\(PRR\)), Expected Signal Power (\(ESP\)), \(RSSI\) and \(SNR\) values as available from gateways. The packet loss ratio, the \(ESP\) and \(PRR\) have been defined through the following equations:  
\[Packet\ Loss\ ratio=1-\frac{Number\ of\ packets\ received}{Number\ of\ packets\ sent} \tag{9}\] \[ESP=RSSI+SNR-10\cdot\log_{10}\bigl{(}1+10^{0.1\ SNR}\bigr{)}\] \[log_{10}(PRR)=PL\cdot log_{10}(1-BER)+log_{10}(P_{5})\]  
where \(BER\) is the bit error rate, and \(P_{5}\) is the probability of successful preamble reception. The term \(P_{5}\) considers that the channel is not a constant additive white Gaussian noise channel but behaves like a slow fading Rayleigh channel, and it is calculated as in [36]. \(BER\) is calculated as proposed in [37] through the following set of equations:  
\[BER=Q\left(2\cdot\frac{\log_{12}(SF)}{\sqrt{2}}.\frac{E_{b}}{N_{ b}}.\left(\frac{4}{4+CR}\right)\right) \tag{10}\] \[\frac{E_{B}}{N_{o}}=SNR+10\cdot\log_{10}\left(\frac{BW}{R_{b}}\right)\] \[R_{b}=SF\cdot\frac{BW}{2^{SF}}\cdot\left(\frac{4}{4+CR}\right)\]