esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 2 Materials and Methods
### Microscope and Accessories  
* Compound transmission microscope Brunel Westbury SP30, monocular, with quadruple revolving nosepiece equipped with \(\times\)4, \(\times\)10, \(\times\)40, \(\times\)100 (oil) DIN standard parfocal objectives and eyepiece with \(\times\)10 magnification for reaching a magnification range from \(\times\)40 to \(\times\)1000. It includes: (A) a mechanical stage with motion along the X-Y axes and Vernier reference scales; (B) a coarse and fine focus mechanism consisting of a rack and pinion for motion along the Z axis with focus stop; (C) a substage Abbe achromatic condenser with independent focus control (N.A. 1.25); (D) an iris diaphragm and filter carrier; and (E) controlled LED illumination.
* Additional objectives with magnifications of \(\times\)20 and \(\times\)60 (N.A. 0.85) to provide the required value range. These objectives substitute respectively the \(\times\)10 and \(\times\)100 (oil) objectives originally included.
* Digicam CCTV Adapter. This is mounted in the eyepiece to provide a secure attachment to an external digital camera by a C-mount adapter (see the details in Figure 3).
* Digital camera coupled to the microscope eyepiece to capture digital images of diatoms. Three models were evaluated: (1) UI-1240 LE-C-HG (by IDS) CMOS USB2.0 color camera with a resolution of 1280 \(\times\) 1024 pixels (1.3 Mega pixels), 25 fps (frames per second), and 5 \(\upmu\)m pixel size; (2) Toupcam LCMOS LP605100A (by ToupTek Europe) CMOS USB2.0 color, res. \(w\times h\) of 2592 \(\times\) 1944 pixels (5 Mpx) and 2.2 \(\upmu\)m pixel size; and (3) DMK 72 BUC02 (by The ImagingSource) CMOS USB2.0 monochrome with res. \(w\times h\) of 2592 \(\times\) 1944 pixels (5 Mpx) and 2.2 \(\upmu\)m pixel size. The digital camera should be selected carefully having in mind whether it will be used with the provided proprietary software or it is necessary to develop customized software. In the latter case--as our case--the camera should provide an open driver and/or an SDK (software development kit) for the target operating system (e.g., Linux) where the user programs must run.
## 5 Conclusions  
The impact mechanism of hot water flow rate on the performance of SASCHCS is studied and the corresponding decision criterion is presented based on the annual data of a typical high-rise office building in subtropical Guangzhou. The primary findings are summarized below:  
1. The useful heat of collectors is beneficial for the increase of the collector flow rate due to the enhancement of heat transfer. Simultaneously, the improvement of amount in solar heat slightly increases the hot water temperature of generators and the consequent rise of COP in absorption subsystems. Therefore, the daily energy saving goes up by 21% based on the August data when  
Figure 11: Daily energy saving for different generator flow rate based on data from Apr to Oct.  
the collector flow rate enhances from 1 m\({}^{3}\)/h to 10 m\({}^{3}\)/h. It is noteworthy that such growth becomes tiny for the collector flow rate exceeding 4 m\({}^{3}\)/h.
2. Despite the increase of generator hot water flow rate decreasing the COP of absorption subsystems owing to the higher generator consumption, such drawback is offset by the enhanced amount of solar heat through the improvement of collector efficiency. Consequently, the daily energy saving grows by 37.5% based on the August data as the flow rate of generator hot water goes up to 10 m\({}^{3}\)/h. Similarly, it is displayed that the energy saving enhances slightly for the generator hot water flow rate that reaches 6 m\({}^{3}\)/h.
3. The optimal flow rate of hot water is derived based on the trade-off of energy saving and consumption in water pumps. It is exhibited that the optimal collector and generator flow rate are 3.6 m\({}^{3}\)/h (13.33 kg/(h m\({}^{2}\))) and 5.2 m\({}^{3}\)/h (19.26 kg/(h m\({}^{2}\))), respectively, for the annual operation. The generator hot water flow rate to the collector one approaches 1.44 in the optimal case. Moreover, the peak annual specific energy saving of SASCHCS is 32.73 kW h/m\({}^{2}\).  
[PERSON] contributed to writing and analysis, [PERSON] contributed to conceptualization, [PERSON] contributed to editing, [PERSON] contributed to validation. All authors have read and agreed to the published version of the manuscript.  
This work is supported by: (1) Natural Science Foundation of Guangdong Province under the contract No. 2018A030313310, (2) Guangzhou Municipal Science and Technology Project under the contract No. 201904010218, (3) State Key Laboratory of Compressor Technology under the contract No. SKL-YSJ201806, and (4) Key Laboratory of Efficient and Clean Energy Utilization of Guangdong Higher Education Institutes under the contract No. KLB10004.  
The authors declare no conflict of interest.
# A Tutorial on Retroreflectors and Arrays Used in Satellite and Lunar Laser Ranging
## 7 Lunar Laser Ranging
If the Apollo reflector arrays are pointed at the center of the Earth, the maximum beam incidence angle on the array from any Earth station (ignoring lunar libration) is  
\[\theta_{inc}=atan\left(\frac{R_{E}}{R_{EM}}\right)=0.95\;deg \tag{15}\]  
and the unspoiled cube diameter for which the cross-section falls to half its peak value is \(D_{1/2}=40.6\) mm = 1.6 inches, but the typical manufacturing tolerances are 0.5 arcsec for dihedral angles and \(\lambda/10\) for surface flatness. Apollo 15 has a flat array of three hundred 38 mm fused quartz cubes, each with an unspoiled peak cross-section of \(5.8\times 10^{7}\) m\({}^{2}\). Thus, the theoretical array cross-section, ignoring manufacturing tolerances  
Figure 11: Images and locations of current lunar retroreflector arrays. and local environmental effects, is \(\sigma\cong 300(0.5)(5.8\times 10^{7}\) m\({}^{2})\)= \(8.7\times 10^{9}\) m\({}^{2}\). According to [PERSON] [12], polarization losses due to the uncoated TIR faces reduce the cross-section by a factor of four, resulting in \(\sigma=2.2\times 10^{9}\) m\({}^{2}\), but the International Laser Ranging Service (ILRS) lists a slightly lower Apollo 15 array cross-section of \(\sigma=1.4\times 10^{9}\) m\({}^{2}\). For the first 30 years following the Apollo 11 landing, the vast majority of the LLR data set was provided by three stations equipped with meter class telescopes, i.e., (1) the 2.7 m MLRS telescope at the University of Texas McDonald Observatory, (2) the 1.5 m CERGA LLR telescope in Grasse, France, and (3) the 1.8 m University of Hawaii telescope on Mt. Haleakala. The latter site was decommissioned by NASA in 1992, but, since that time, two additional LLR sites have been added: (4) MLRO, Matera, Italy, and (5) the 3.5 m Apollo telescope located at Apache Point, New Mexico, USA. The large 3.5 m telescope at the Apache Point site resulted in multiphoton returns and unprecedented lunar-range precisions at the 1 to 3 mm level [13]. Since the turn of the century, more efficient, single, large retroreflectors have been proposed by various international teams to replace the large Apollo 15 array on future lunar-landing missions [14; 15; 16; 17; 18; 19]. Whereas each of the 300 38 mm-diameter retroreflectors in the Apollo 15 array currently contributes about 0.33% to the overall signal seen by the ground stations due to the wide divergence angles of their reflected beams, no dihedral angle is required for small-diameter reflectors (<150 mm for coated and <100 mm for uncoated and hollow reflectors). However, since larger-diameter retroreflectors produce low-divergence reflected beams, they required dihedral angles of 0.20, 0.25, and 0.35 arcsec for coated, uncoated, and hollow reflectors, respectively. More recent publications provide updated analyses of these larger lunar reflectors [16; 17].
# Reactive Balance Control for Legged Robots under Visco-Elastic Contacts
## 6 Results
### Test D: All Controllers
#### 6.7.1 Soft Contacts  
In the noiseless simulations both TSID-Flex-K and TSID-Adm perform well, meaning that they achieve a good trade-off between state tracking and control effort. For lower feedback gains (i.e., higher position tracking errors) TSID-Flex-K and TSID-Adm achieve very similar performance, whereas for higher feedback gains they differ because of the ways in which they handle friction cones.  
The unavoidable force tracking delay of admittance-control strategies is typically seen as an issue in the literature [3; 8], because it clashes with the assumption of second-order dynamics. However, if we accept a fourth-order dynamics for our system, we can see that this delay is not at all incompatible with good CoM tracking (as long as gains are properly tuned), as shown for instance in Figure 7. The only downside of this delay is that it may lead TSID-Adm to violate friction cone constraints. Indeed, even though TSID-Adm constrains the desired forces inside friction cones, real forces may violate these constraints because of the tracking delay.  
Adm-Ctrl performs reasonably well too, but less than TSID-Flex-K and TSID-Adm. On the contrary, TSID-Rigid is unstable for all the tested gain sets, except one, and even in this one case its performance is inferior to the other controllers (see Figure 2).  
In the noisy simulations, things change remarkably. First, the performance of TSID-Flex-K and TSID-Adm gets worse, especially in terms of CoM snap, and in particular for lower feedback gains. Nonetheless, they remain the best controllers overall. Adm-Ctrl is only slightly affected by the introduction of noise, showing a remarkable robustness. Surprisingly, TSID-Rigid performs much better than in the noiseless case, showing a stable behavior with 3 gain sets, and getting a performance that is much closer to the Pareto front. To understand why, we have performed another test removing Coulomb friction (see Figure 8). This test shows that Coulomb friction was the cause of the improved stability of TSID-Rigid, and of much of the performance deterioration of the other controllers.  
Figure 7: TSID-Adm, soft contacts (\(\alpha=0.01\)), noiseless simulation and low feedback gains (\(w_{u}=10^{-6}\)). CoM tracking is good despite the significant delay in force tracking.
# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
## 6 Discussions  
The confusion matrices for the AE and 3-SgOut methods are given in Tables 2 and 3. The performance results including three measures, which are computed from the confusion matrices, are represented in Table 4.  
It can be seen that the accuracy of both methods is over 99% and the precision is equal to 100%, while the recalls are 96.62% and 17.99% for AE and 3-SgOut, respectively.  
Accuracy is the ratio of correctly detected (as normal or abnormal) samples to the total samples. Recall shows the method's ability to correctly detect the samples with anomalies. Precision gives the percentage of truly detected samples with anomalies out of all detected samples with anomalies.  
Due to the fact that the number of samples with anomalies in the dataset is less than 1%, the accuracy rate is not meaningful for the performance comparison of the methods. The other two metrics, precision and recall, provide important information about method performance. Both methods have 100% precision ratios. That means the methods have no false positive error.  
The recall of the 3-SgOut is 17.99% while it is 96.62% for the AE. Based on the recall ratios of the methods it is concluded that 3-SgOut detects only 17.99% of total anomaly samples as anomalies while the AE correctly detects them with a 96.62% ratio.  
The 3-SgOut method does not have a training phase and it simply detects the outliers based on the mean and the standard deviation of the data. The results of 3-SgOut show that the detection of anomalies in the temperature data cannot be performed effectively by just finding the outliers.  
When AE and 3-SgOut are compared in terms of cost and performance according to the above-mentioned outcomes, the findings can be summarized as follows: the computational and time cost of 3-SgOut are much lower than those of the AE because 3-SgOut relies on only two basic statistical quantities, mean and standard deviation; the performance of the AE for detecting the anomalies in the data is much higher than that of 3-SgOut because the recall ratio of the AE is 78.63% larger than that of 3-SgOut.
## 3 Experimental Verification
### Eikonal Equation
Figure 18: Prediction results of PIDeepONet and MulSPIDeepONet under two different radii compared with the analytical solutions. (**a**,**d**) are the real solutions \(s(x)\) of numerical simulations for any arbitrary test variable function (circular boundary centered on the origin, with different radii), (**b**,**e**) are the prediction results given by the baseline model (PIDeepONet) and proposed method (MulSPIDeepONet), respectively, and (**c**,**f**) are the absolute errors between the prediction results and the numerical solution. This experimental result again proves that, through its network enhancement strategy, the proposed method has an improved ability to identify the independent variable characteristics of the solution function by integrating the characteristics of the trunk network and the branch network. This unique network architecture and feature processing strategy give MulSPIDeepONet better generalization ability and solving effect in dealing with PDEs, which verifies the effectiveness of the network structure design and feature fusion strategy proposed in this work. In this section, three different PDE systems have been solved and analyzed. The proposed MulSPIDeepONet indirectly constructs the mapping relationship between the variable function and the solution function and enhances the feature recognition of the solution function to the independent variables. Thus, it has stronger ability to solve PDEs, especially systems with large gradient changes, moving contact discontinuities, and large changes in physical characteristics. The reasons are as follows. First, the baseline model completely separates the trunk network and the branch network, such that the information between the independent variables of the variable function and the solution function cannot be fully utilized. This results in the loss of available information. In contrast, the proposed method accurately captures the trend of changes in physical characteristics. Second, MulSPIDeepONet enhances the identification of the independent variable information in the solution function, which helps prevent the loss of information as the network layer deepens during training. Third, the potential variables are used to encode the information bottleneck between input and output. Therefore, this network structure exhibits unique advantages in capturing complex physical phenomena, dealing with regions with drastic gradient changes, and accurately reflecting discontinuities in the system.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 3 Materials and Methods
### Training Data Extraction and Validation  
In order to create the training datasets for the classification algorithms, 20 Sentinel-2 MSI images [36] with cloud cover lower than 30% were considered before and after the fires of the summer of 2017, which mainly occurred between 30 January and 14 February (Table 1). The MSI sensor has 13 spectral bands with frequencies spanning from the Visible- and Near-Infrared (VNIR) to the short-wave infrared (SWIR). It has a spatial resolution of 10 m, 20 m and 60 m and 5 days temporal resolution [37]. Data were downloaded from Copernicus open-access hub platform ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)), accessed on 18 August 2021) and atmospherically and topographically corrected to obtain surface reflectance (20 m, L2A level) using Sen2 cor algorithm [38].  
The creation of the dataset was developed by a human expert using false color composite over Sentinel-2 images (Figure 2A), photo interpretation criteria and spectrum analysis to distinguish between burned and unburned areas (Figure 2B).  
Figure 1: Study area.  
Based on the image analysis, two datasets were created:  
* POST: pixels values from a post-fire image.
* DELTA: pixels values composed by the difference between a pre-fire and a post-fire image.  
For the POST dataset, 100 burned and unburned polygons were built, representing 5.98% of the study area. In the other case, for the DELTA dataset, 80 burned and unburned polygons were selected, adding up to near 2.30% of the study area. In order to ensure the consistency of the datasets, the training polygons were overlapped with several spectral indices that have shown the best response in burned area detection [39; 40] (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sentinel-2 Tiling Grid ID** & **Cover Region** & **Date** & **Time** \\ \hline  
U & Bio-Bio & 6 January 2017 & 14:27:41 UTC \\
19 HBU & Bio-Bio & 15 February 2017 & 14:28:51 UTC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-2 images ID Tiles, cover region, date and time of sensing.  
Figure 2: Infrared false-color composite, red and black tones, show unburned and burned vegetation, respectively (**A**). Spectral signature of unburned and burned vegetation (**B**).
## 1 Introduction
Furthermore, in [42], a close relation between the twist symmetry and the existence of a broad pass-band was found in periodically loaded cylindrical waveguides. However, despite the numerous interesting properties of twist symmetries, to the authors' knowledge, only one work was aimed at providing physical insight into twist symmetries [46]. There, the multimode technique was employed to demonstrate that the coupling of the higher order modes is important to explain the different response in twist-symmetric structures with respect to non-twist-symmetric structures. In the present work, a mode-matching formulation is presented for the dispersion analysis of periodically loaded coaxial transmission lines with twist symmetries. The formulation is validated using the commercial software CST Microwave Studio [47] and is used to provide new insights into the physics of twist symmetries. Specifically, we build on the study in [46], and we discuss in detail how the different higher order coaxial line modes (TMand TE) are linked to the order of the twist symmetry. Furthermore, it is demonstrated that the importance of the different coaxial line modes changes across the pass-band. Figure 1: Illustration of a twist symmetry of order \(\xi=4\). The angle of the consecutive rotation here is \(\pi/2\).
# Cybersecurity Risk Assessment: A Systematic Mapping Review, Proposal, and Validation
## 4 Analyze and Discuss the Results
### Cybersecurity Risk Assessment Tools (Reference Models and Applications)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool Name** & **Base Model** & **Type of Tool** \\ \hline ADAMANT [35] & ISO/IEC 27001, NIST 800-30 & Qualitative \\ OSCAD-Ciras tool [25] & ISO/IEC 27001, ISO/IEC 31000 & Qualitative \\ SGIS toolbox [26] & ISO/IEC 27001, NIST 800-30 & Qualitative \\ MST risk analysis [41] & ISO/IEC 27005, NIST 800-30, Octave & Quantitative \\ TISRIM [24] & ISO/IEC 27005 & Qualitative \\ Teng algorithm [46] & ISO/IEC 27005 & Quantitative \\ [PERSON] model [42] & ISO/IEC 27000 series & Quantitative \\ Mobile risk calculator [32] & NIST 800-30, RISK watch, CORA & Quantitative \\ Nessus [27, 29, 43] & NIST 800-30, HIPAA, PCI & Qualitative \\ CSRAS [23] & NIST 800-30 & Qualitative \\ CYRVM [39] & NIST 800-30 & Quantitative \\ Cybersecurity evaluation tool CSET [44] & NIST CSF, ISO/IEC 27001 & Qualitative \\ Patch management [47] & NIST CSF, ISO/IEC 27001 & Qualitative \\ AWWA [45] & NIST CSF & Qualitative \\ CSAT [38] & NIST CSF & Qualitative \\ Asset assessment methodological tool [21] & EBIOS, MAGERIT & Qualitative \\ RACOMAT [30] & ISO/IEC 31000 & Qualitative \\ Data protection impact assessment tool [28] & CARAM & Quantitative \\ MVS visualization platform [22] & ITSEC & Qualitative \\ Tritilanumt, Suratose tool [27] & OWASP & Qualitative \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Risk assessment tools with an underlying risk assessment model extracted from primary studies. tool is focused on the telecommunications sector, and the SGIS toolbox is focused on the energy sector. A total of 15 of 35 tools can be considered to target all types of risks. Nineteen of the tools are focused on a specific type of risk. For example, the data protection impact assessment tool only focuses on risk assessment for cloud services relating to account hijacking or insider threats. Another example is the R. Riesco model, which is focused on risks derived from the cyber-defense field, such as phishing or watering hole attacks, and can be considered for any sector or industry since it is general purpose. A third example is Tritilanunt, [PERSON]'s tool that, despite being focused on all types of risks, and having an application in the medical sector, emphasizes risks related to information privacy and exposure of personal information. Likewise, many vulnerability analysis tools such as Eye retina, Nessus, Nikto, or N circle are focused on more technical risks such as denial-of-service, man-in-the-middle, or password attacks that can affect all types of organizations in the same manner. Finally, there are tools, such as CYRVM, MST risk analysis, J. Hu model, or SGIS toolbox, that focus on risks related to networks and that seek to identify risks related to packet sniffing attacks, ping sweeps, and port scanning. Summarizing the results of this subsection: (1) We can see that the ISO 27000 family and the NIST 800-30 are risk assessment models used as reference for qualitative and