esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Dissipation and Stress Equilibrium Behavior of Granite under Dynamic Impact
## 4 Dynamic Behavior  
### Strain Character  
As shown in Figure 10, there are stress-strain curves with different length-diameter ratios of 0.6, 0.8, 1.0, 1.2, and 1.4 and stress history curves with different loading strain rates of 63.8 s\({}^{-1}\), 109.8 s\({}^{-1}\), 156.8 s\({}^{-1}\), and 253.2 s\({}^{-1}\). Under the dynamic load, the rock presents four stages of microcrack closure, elastic, plasticity, and failure, respectively. The stress-strain curves of specimens with length-diameter ratios of 0.6 and 0.8 are similar. First, the upper concave part of the early curve is the microcrack closure stage, and the curve rises slowly. Second, the curve appears in a linear growth phase and the rock undergoes elastic deformation. Then, the slope of curve decreases due to plastic deformation of rock. When the peak strength is reached, the slope of the curve negatively correlated with the rock damage [16]. After the peak strength, depending on the length of the specimen, faster curve falling and more brittleness can be present [17].  
Figure 9: Energy dissipation rate–stress equilibrium time curve (**a**) \(v\) = 8 m/s (**b**) \(v\) = 10 m/s (**c**) \(v\) = 12 m/s (**d**) \(v\) = 14 m/s.  
Figure 10: _Cont_.  
Under the same strain rate of 156.8 s\({}^{-1}\), the block size characteristics of granite specimens are shown in Figure 11. It is clear from the figure that the fragmentation of granite specimens with length-diameter ratios of 1.2 and 1.4 is large, while the fragmentation of granite specimens with length-diameter ratios of 1.0 is very small. The fragmentation of the specimens with length-diameter ratios of 0.6 and 0.8 is relatively uniform and the fragmentation size is moderate.  
According to the stress-strain curves of several groups of tests with different length-diameter ratios, the overall stress-strain variation law is basically the same and the stress increases approximately linearly before reaching the peak. With the decrease of the length-diameter ratio, the peak strength is reached more slowly. This means that a rock with a small length-diameter ratio can maintain a longer stress equilibrium state before failure under the impact test. Before the stress of the specimen reached the peak, the curve fluctuated under different degrees, which was caused by the different duration of uniform stress inside the rock and the uniform deterioration of stress after the microdeformation and failure of the rock mass.
## 2 Modelling
### Distance between Ellipsoids  
Minimal distance \(\Delta D_{min}\) between two ellipsoids \((\mathbf{A},\mathbf{K})\) and \((\mathbf{A}^{\prime},\mathbf{K}^{\prime})\), is a solution of the minimisation problem:  
\[\Delta D_{min}=min|\mathbf{X}-\mathbf{Y}|,\mathbf{X}\in S,\mathbf{Y}\in S^{ \prime}, \tag{4}\]  
where \(S\) and \(S^{\prime}\) are the surfaces of ellipsoids. The equation 4 cannot be solved analytical, but there is a variety of numerical methods. All methods may be divided into geometrical [24; 25] and algebraical [26; 27].  
Here we will use the geometrical approach developed by [PERSON] and [PERSON] [25]. The idea of the method is to construct two balls completely inside each ellipsoid (see Figure 1).  
Then we check whether the line segment \([c_{1},c_{2}]\) between the two centers is entirely contained in \((\mathbf{A},\mathbf{K})\cup(\mathbf{A}^{\prime},\mathbf{K}^{\prime})\). If it is, then the two ellipsoids have a nonempty intersection and the distance \(\Delta D_{min}=0\); otherwise, we continue and compute new point \(\mathbf{X}(k+1)\) as the intersection of the line segment \([\mathbf{c}_{1},\mathbf{c}_{2}]\) with the boundary \(S\), and also \(\mathbf{Y}(k+1)\) as the intersection of \([\mathbf{c}_{1},\mathbf{c}_{2}]\) with the boundary \(S^{\prime}\). As the accuracy criteria the angles between segments \(\angle([\mathbf{c}_{1},\mathbf{c}_{2}],[c_{1},\mathbf{X}(k)])\) and \(\angle([\mathbf{c}_{1},\mathbf{c}_{2}],[\mathbf{c}_{2},\mathbf{Y}(k)])\) can be used.
## 3 The Proposed Scheme
### Preliminary  
In this subsection, we provide some definitions in our proposed scheme.  
* \(n\): A large prime number that forms a finite primes field with an order less than \(n\).
* \(l\): A security parameter that defines the hashed messages' length.
* \(V\): The current medical organization.
* \(W\): The patient's desired medical organization.
* \(U\): A patient making an authentication request with a server \(V\) in a health information system.
* \(S\): A server that accepts the registration of the patient request, the login request, and the password modification request in the health information system of hospital \(V\).
* \(UID_{i}\): A patient's real identity computed from social security numbers, where \(i\in\{U\}\) in the certification.  
* \(ID_{i}\): A registration local identity that can link the \(UID_{i}\) of user \(i\in\{U\}\).
* \(H_{1}\), \(H_{2}\): Two secure hash functions that each maps \(Z_{n}^{*}\rightarrow\{0,1\}^{I}\) with collision-resistance and outputs the same \(l\)-bits hash strings.
* \(pw_{U}\): A initial password that is chosen by a server \(V\) when a patient \(U\) has remotely registered on the server for the first time.
* \(E_{k_{i}}\): A symmetric key encryption function for the party \(i\) under the symmetric key \(k_{i}\), where \(i\in\{U,V\}\).
* \(D_{k_{i}}\): A symmetric key decryption function for the party \(i\) under the symmetric key \(k_{i}\), where \(i\in\{U,V\}\).
* \(ASE_{pk_{i}}\): An asymmetric key encryption function for the party \(i\), where \(i\in\{U,V\}\).
* \(ASD_{k_{i}}\): An asymmetric key decryption function for the party \(i\), where \(i\in\{U,V\}\).
* \(EHR_{i}\): A patient electronic health record (EHR) in one hospital organization, where \(i\in U\).
* \(Bio_{i}\): A biometric information value that is chosen uniformly from the party \(i\), where \(i\in\{U\}\).
* \(Date\): A patient EHR migration limitation date period.
* \(Cert_{i}\): A migration certification of the party \(i\) with \(ID_{i}\) registration and public keys for this \(ID_{i}\), where \(i\in\{U,V\}\).
* \(HCA\): A hospital certification authority (HCA) that helps the patient to generate the patient migration permission signature to another hospital or medical center in the public key infrastructure (PKI).
* \(Agree_{i->j}\): A patient-delegated EHR migration agreement document whereby the patient agrees that its own patient files can migrate from current hospital \(i\) to the desired one \(j\), where \(i,j\in\{V,W\}\).
## 5 Integration of Sorption Systems On-board  
As pointed out in the introduction and in the previous section, the integration of sorption systems was considered only under the assumption that the waste-heat driven system was the only one providing cooling to the cold room, and only temperature levels from \(-18\)\({}^{\circ}\)C to \(+10\)\({}^{\circ}\)C were considered, but without any explicit consideration of the refrigeration units installed on-board. Indeed, the features required of the refrigeration system are diverse, depending on the mode of catching fish and the circuit between catch and consumption. When not consumed locally, fish can be processed in different ways: frozen as fresh catch, or fillets, prepared dishes, and canning. Storage and processing methods depend also on the estimated period of preservation: for a short time (from a few hours up to a few days) the fish could be stored in ice flakes or cold rooms, otherwise, freezing preservation is mostly used [34]. Therefore, two main temperature levels are needed: ice production or preservation of fish in cold rooms--between \(-5\)\({}^{\circ}\)C and \(0\)\({}^{\circ}\)C and freezing at \(-40\)\({}^{\circ}\)C or \(-21\)\({}^{\circ}\)C. Hence, it is clear that different sorption systems and, as already discussed, heat sources (i.e., exhaust gases and cooling water  
Figure 8: The CaCl\({}_{2}\)/ammonia prototype [16].  
of the engine) are needed to optimize the refrigeration process. In the following sections, different cases will be discussed.
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 4 Results and Discussion
### Gear Wear Monitoring
Based on the above analysis, it can also be concluded that the MSB-SE in conjunction with the MSB-SEc can reveal more overall information for the gear transmission system. The indicator \(R\) developed based on MSB-SE effectively shows the severity of the gear wear, and is sensitive to the changes in the gear wear. It can also be concluded that the tooth wear of the gear on the first shaft is significantly more severe and the other gears are relatively healthy. These findings can be used to achieve a high accuracy in predicting the fault advancement for implementing timely maintenance. The authors of this paper have tried different methods, such as the popular time synchronous average, wavelet transforms, and different indicators such as amplitude of tooth meshing harmonics, ER and FM4. The similar monitoring results as presented in Reference [2] were obtained, none of these indicators is reliable for gear wear monitoring. The averaged logarithmic ratio (ALR) indicator based on root mean square (RMS) proposed by [PERSON] et al. [2] is successful in gear wear evaluation, and will be examined in further study.
# A Compact Sequentially Rotated Circularly Polarized Dielectric Resonator Antenna Array
###### Abstract  
In this study, a compact \(2\times 2\) circularly polarized (CP) sequentially rotated (SR) dielectric resonator antenna (DRA) array operating in the IEEE 802.11a band is presented. To acquire the CP radiation, an elliptical slot (ES) was introduced to couple a rectangular dielectric resonator (RDR). The ES generates two resonant frequencies corresponding to the dominant even and odd modes. The SR feeder is made of four quarter-wavelength microstrip transformers to reduce the input impedance of the elements and, consequently, maximize the power transferred to each element. Experimental and simulation verifications were conducted on a \(54\times 50\times 0.813\) mm\({}^{3}\) prototype to evaluate the performance of the proposed antenna array, which achieved a VSWR \(<-10\) dB bandwidth of 1 GHz (5.1-6.05 GHz) and axial ratio (AR) \(<3\) dB of 0.95 GHz (5.1-5.85 GHz). The agreement between the simulated and measured results confirmed the validity of the proposed design.  
antenna array; circular polarization; elliptical slot; sequential rotation; wideband +
Footnote †: journal: Applied Sciences  
employed a fractal cross-slot to excite four DRs and achieved an axial bandwidth ratio of 18%. The length and width of the proposed array were 100 and 84 mm, respectively.  
In this study, a novel approach is proposed to design an SR CP DRA array for the IEEE 802.11a band. In Section 2, we describe the design and analysis of elliptical aperture-coupled rectangular ceramic blocks to obtain CP radiation. The proposed four-quarter-wavelength transformer feed network topology is described in Section 3. In Section 4, the simulated and measured results are stated to authenticate the feed network performance. Finally, Section 5 concludes this paper.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Evaluation with Real Datasets
#### 5.2.2 Detection Results of Inside and Outside Nodes  
The numbers of internal and external node count computed for the three different methods are shown in Figure 8. Here, a two-day dataset is used to observe the performance in greater detail. The proposed method uses the variable lengths of both \(Q\) and \(R\). The range of the lengths is 2\(\sim\)10. In Figure 8, the first bar generated by the observation method indicates that 14 out of 21 nodes are inside the emergency room and that seven nodes are outside the area. This result is generated when the observation is applied to the log data generated by the mobile asset tracking system when the evening group used it on 15 July. The second bar on that date is generated by the battery estimation method. Its result is equal to that of the observation. However, their results are different from the counts in the fourth bar (written by the nurses on that date), since those methods cannot actively identify any failure nodes. Given that a mobile node can leave the emergency room, the observation incorrectly considers the mobile nodes, which fail while within the emergency room as nodes that left the monitoring area (_i.e._, left nodes). The battery lifetime estimation method also incorrectly considers the failed nodes with sufficient battery power as left nodes. On the other hand, the proposed method using trend analysis successfully detects the three failed nodes and four left nodes and then changes the number of inside and outside assets (the third bar on that date). Although a mobile node reports its data every 30 s, four left nodes report their data at least once when outside the monitoring area while they wait for an elevator before moving to a different department. This additional report from an outsider node (_i.e._, left node) allows the system to correctly classify all left nodes properly. Unlike these nodes, the three failed nodes do not commonly satisfy the condition \(Q_{w}<T_{nc}\), because they failed inside the monitoring area (_i.e._, emergency room). The classification result from the proposed scheme  
Figure 7: Average neighbor count in the simulation and in the emergency room.  
successfully matched the observations made manually by the nurses; thus, this result indicates that the classification process was successful. The result for 17 July is also similar to that of the observations for the 15 July data. By observing the results generated by the proposed method and the nurse's reports, it is clear that the classifier proposed in this work identifies all of the failed nodes that the other approaches cannot identify.
## 3 Antenna Characterization  
### Antenna Measurement Setup  
The antenna characterization was conducted in an anechoic chamber equipped for millimeter-wave measurements up to 110 GHz. The measurement system consists of two signal generators and a microwave receiver as shown in Figure 3. A frequency multiplier connected to the signal generator transmits the test signal in the frequency range from 50 to 75 GHz. The LWA is connected to the transmitter. After 4 m of wireless transmission, a horn antenna connected to a harmonic mixer receives the test signal emitted by the LWA. The gain of the LWA (here: antenna under test (AUT)) is calculated through the difference in path loss to a calibrated horn antenna with known gain. Thereby, the frequency response of the measurement system and especially of the external multiplier is cancelled out.  
For the measurements, the antenna assembly depicted in Figure 4 was utilized, which shows the fabricated PCB contacted by coaxial end launch connectors (ELCs), which are presented in [20]. The ELCs also screw the PCB to brass brackets, which provide mechanical fixtures. A coupled line (CL) transition is used between the SIW LWA and the grounded coplanar waveguide (GCPW), which is interfaced by the ELC. The CL transition further performs impedance matching between the  
Figure 2: Dispersion diagram of the unit cell of the LWA based on Eigenmode simulations. The frequencies of the Eigenmodes are plotted versus the normalized phase change for the right-handed (RH) region (circles, purple) and the left-handed (LH) region (diamonds, green).  
waveguides and prevents a DC short for the GCPW interface, allowing for integration of photodiodes and/or amplifiers. Since the SIW LWA is symmetric in its longitudinal axis, its two ELC ports inherit the same radiation characteristics, albeit mirrored. In the setup, one port is utilized for feeding the signal, and the other is terminated by a matched load to avoid back reflections.  
For evaluating the losses of the SIW LWA, a vector network analyzer (VNA) was employed to measure the 2-port scattering parameters. Thru-Reflect-Line (TRL) test structures were measured in advance to de-embed the connector and adapter losses and evaluate waveguide losses.
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 4 Results and Discussions
### Raw Data Collection and Data Standardization  
By acting on the pneumatic valve (V1, V2, and V3) closures and setting the inlet water pressure (S1) to 5.5 bar, the tank pressure (S5) to 1.3 bar, and the tank level (S7) to 300 mm, the standard plant behaviour has been achieved. Then, all the anomalies were reproduced by acting on the shut-off valve (VM) closures, as described in Table 3. The data are collected in a single database containing physical quantities of different types of different scales. Indeed, for each sampling, flow, pressure, and level measurements characterize the fluids circulating in the system. For this very reason, before starting the training phase of the SOM, the data are standardized through the z-score method, where a random variable X, with a mean \(\gamma\) and variance \(\sigma^{2}\), is transformed to a random variable Z with mean 0 and variance equal to 1 [52]. The calculation involves subtracting from X, the variable of interest, its mean \(\gamma\) and dividing it by the standard deviation \(\sigma\) (Equation (26)).  
\[Z=\frac{X-\mu}{\sigma} \tag{26}\]  
Figure 7: All steps of the SOM artificial neural network.
## 5 Insights and Future Directions
### Novel Protocol Design  
Each routing survey evaluates and analyzes a variety of routing protocols. Such an analysis is beneficial in deducing proper guidelines for devising new routing protocols as per corresponding area of routing in WSNs. The routing protocols designed on the basis of those common guidelines may show similarity in their design pattern which may make their analytical and experimental comparison simpler. Therefore, the routing-related surveys in WSNs should consider this aspect seriously. It would be more appreciated, if a general routing protocol design framework would be devised that may provide proper insights and guidelines for designing a general routing protocol in WSNs. Such a framework may help in handling the challenge of variability among routing protocols in the context of simulation setup, operational framework and comparison with _state-of-the-art_ approaches.
## 2 Materials and Methods  
### The Determination of the Dust Episode Case  
The DAEC of 16 to 18 June 2016, which is studied here, was identified using a satellite algorithm, based on key aerosol optical properties that are a measure of aerosol load, size, and absorptance. Specifically, the algorithm, which is described in detail in [39], uses level-3 daily \(1^{\circ}\times 1^{\circ}\) gridded spectral Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua Collection 6.1 and corresponding Aerosol Index (AI) data from the Ozone Monitoring Instrument (OMI) Aura OMAERUV databases. The algorithm, which ran over the broader MB for the study period 2005 to 2019 [39], operates on a daily basis and on a pixel level to first identify the presence of DAs over the specific pixel and on the specific day. The selected study region (broader MB extending from 9.5\({}^{\circ}\) W to 39.5\({}^{\circ}\) E and from 29.5\({}^{\circ}\) N to 47.5\({}^{\circ}\) N) is shown in Figure 1, where a smaller region (yellow frame), over which the 16-18 June 2016 dust export took place and regional averaging was applied to DRE values (see Section 3.5), is also displayed. The algorithm operates whenever the following specific criteria are met: (i) the Angstrom Exponent (a), calculated using the original MODIS spectral AOD information, is less than 0.4 (\(\alpha\leq 0.4\)), and (ii) the AI is greater or equal to 1 (AI \(\geq\) 1). In those cases, i.e., when dust dominates in the atmosphere overlying the specific pixel,another threshold is applied to ensure the occurrence of a strong or an extreme DA Episode (DAE), which is the case whenever AOD\({}_{\text{mean}}\) + 2 STDV \(\leq\) AOD \(\leq\) AOD\({}_{\text{mean}}\) + 4 STDV and AOD \(\geq\) AOD\({}_{\text{mean}}\) + 4 STDV, respectively. Then, if at least 30 pixels undergo strong or extreme DAEs on a specific day, this day is considered to be a DAE Day (DAED). For each identified DAED, the algorithm outputs information about the pixel-level dust loading (AOD) and the number of DAEs. Three such DAEDs, on 16, 17, and 18 of June 2016, were identified by the satellite algorithm, yielding the investigated DAEC in this study. Note that the examined DAEC in this study is one of the 98 DAECs (with duration of n (n \(\geq\) 1) sequential DAEDs) consisting of the 166 overall DAEDs identified by the algorithm from 2005 to 2019 [39]. This examined DAEC was selected because: (i) it took place in summer, when the cloud fraction (CF) over the MB, and especially over its southern parts, is low, while the incoming solar radiation is high; (ii) its 3-day duration enables the observation of the evolution of the dust export from North Africa in parallel with the evolution of cloudiness and temperature fields; and (iii) it has extended spatial coverage of the MB, covering 8% of its area. Thus, the selected DAEC, apart from being intense, is also long and spatially extended.
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 1 Introduction
Figure 1b,c shows three different light sources, including point, area, and ambient lights. Note that the light source in (a) is an example of the area light source with the radius \(d\). Since our image-space model does not require notable extra computation time, ISMOS easily enabled us to manipulate multiple light sources. The multiple directional light sources were difficult to apply in the previous image-space approaches because the hemispheres centered at the pixels were not directional. Besides, we applied an opacity threshold for the shading effect estimation on the opaque pixels to make the inner volume structures stand out, which allowed us to create renderings accommodating multiple transparent layers with different opacity values. We also handled transparent objects efficiently by applying a transmittance approximation with a smooth function, which can be easily performed in a volume-rendering pass, as presented in Figure 2. The approximated functional form of the transmittance describes the opacity values, which allowed us to represent all opacity values over the depth with only one parameter per pixel. Figure 3 presents the rendering pipeline. We extended the conventional volume rendering with frame buffers to simulate various occlusion shading models. Our major contributions in this work include:  
* An interactive approximate image-space multidirectional occlusion shading model applicable to the conventional direct volume-rendering pipeline without preprocessing and additional GPU memory;
* The simulation of light sources, including ambient occlusion and soft and hard shadows as a unified model;
* The handling of multiple directional lights with only a minor additional overhead;
* A multi-layered view for transparent scenes.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 3 Proposed Approach: Stpa-Rl
### System Environment Modeling
#### 3.1.4 Hazard Reclassification and Prioritization  
Hazards are defined as potential threats within the system and must undergo a process of reclassification and prioritization. Hazards identified by STPA are reclassified using insights from RL simulations, potentially revealing new unsafe conditions. UCAs are used as conditions for reaching hazardous states. Based on the state conditions, the severity of the hazards is assessed, and a priority is assigned. Hazards are prioritized based on their frequency of occurrence. The hazards are further detailed and applied to the control algorithm. The hazards can be represented as states that lead to undesirable outcomes.  
Let \(H\) be the set of hazardous states \(H=\{h_{1},\ldots,h_{k}\}\), where each \(h\) represents the state reached hazard.