esa-sceva/satcom-chunk-collection huggingface dataset:
# Cybersecurity Risk Assessment: A Systematic Mapping Review, Proposal, and Validation
## 6 Validation of the Model Proposal and Results
### Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Relevant Expert Information** & **Answers** \\ \hline  & 29.6\% ISO 27000 family \\  & 14.8\% COBIT 2019 \\  & 14.8\% Independent model \\ Most used models & 11.1\% NIST CSF \\  & 7.4\% NIST 800-30 \\  & 7.4\% MITRE \\  & 3.7\% PCI-DSS \\ \hline  & **Assets identification:** \\  & 46\% Essential, 21\% Very relevant, 29\% Relevant, and 4\% Almost irrelevant \\  & **Threat identification:** \\  & 39\% Essential, 32\% Very relevant, 29\% Relevant, and 0\% Almost irrelevant \\ Risk assessment sub-stage relevance. & **Identification of vulnerabilities:** \\  & 39\% Essential, 29\% Very relevant, 32\% Relevant, and 0\% Almost irrelevant \\  & **Identification of impact and probability:** \\  & 36\% Essential, 28\% Very relevant, 32\% Relevant, and 4\% Almost irrelevant \\  & **Identification of countermeasures and calculation of residual risk:** \\  & 25\% Essential, 39\% Very relevant, 29\% Relevant, and 7\% Almost irrelevant \\ \hline Type of calculation preferences & 61\% Quantitative \\  & 49\% Qualitative \\ \hline Use of risk assessment tools & 52\% No \\  & 48\% Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Opinions about risk assessment variables. Likewise, 96% of the experts considered it relevant to value the percentage of loss of an asset, as performed by the SANS [52], while 50% only quantified the percentage loss of assets under attack. Another relevant point is that the SANS Institute ARO variable was accepted by 85% of the experts as a good way of calculating probability. Finally, all experts considered it relevant to measure the effectiveness of countermeasures and subsequently calculate residual risk. Most of them use some metric to measure the effectiveness of a countermeasure in their organizations. In addition, the ratio of the proposal was assessed by about 90% of the participants as adequate. The final evaluation of the experts with the presentation of all the variables of the proposed cybersecurity risk assessment model is shown in Table 16. More than 90% of the experts agreed with the proposed quantitative and qualitative relationships. Likewise, the comments of the experts were generally positive. Therefore, it is considered that there was adequate acceptance of the proposal by the experts. The acceptance covers both the variables proposed in the quantitative and qualitative models.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 2 FSO Communication Channel Effects and Their Mitigation Techniques
### FSO Communication System: Temporal Challenges
Furthermore, solar effects on optical waves at the 1550 nm range can be mitigated using appropriate optoelectronic devices/filters. In conjunction with the environmental (channel) state, the dynamic alteration of laser energy strength with an appropriate adjustable control system will offer a more effective and stable FSO data link while maintaining the given transmission speed and BER. The requirement of transmitting power is less in perfect weather, while in bad weather conditions, it is quite large. The findings of many theoretical, methodological, and experimental research on atmospheric turbulence intensity variations, as well as in-depth discussions, can be found in [49; 50; 51].
# Autonomous Intersection Management by Using Reinforcement Learning
## 5 Conclusions  
In this study, we suggested an autonomous intersection management system (DRLAIM) inspired by deep reinforcement learning to develop an intersection-crossing strategy. DRLAIM establishes accelerated control of every vehicle, ensuring safety by preventing collisions. We examined eight different reward functions, and the experiment results indicate that the intersection control model with reward function 7 had an 83 percent improvement in throughput. In the future, we can consider simulating different types of intersections, such as roundabouts, to assess the suitability of DRLAIM for other types of intersections. Furthermore, we can consider using an environment with multiple intersections to simulate the interactions between different intersections, i.e., whether the decision made for one intersection would affect adjacent intersections or not. Finally, we can design a strategy that can be applied to autonomous vehicles and incorporate pedestrians and cyclists in an intersection.  
Conceptualization, P.-A.H.; methodology, W.-L.C.; formal analysis, P.K.; investigation, P.K.; data curation, W.-L.C.; writing--review and editing, P.K. and P.-A.H.; supervision, P.-A.H. All authors have read and agreed to the published version of the manuscript.. This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Inflow Rate (Vehicles/min)** & **100** & \multicolumn{2}{c}{**75**} & \multicolumn{2}{c}{**50**} & \multicolumn{2}{c}{**25**} \\ \hline
**Method** & **DRLAIM** & **FFS** & **DRLAIM** & **FFS** & **DRLAIM** & **FFS** & **DRLAIM** & **FFS** \\ \hline
**AQL (vehicles)** & 46.03 & 46.07 & 25.80 & 21.73 & 13.12 & 7.75 & 5.79 & 0.63 \\ \hline
**AWT (s)** & 38.77 & 39.27 & 30.84 & 34.37 & 24.94 & 26.88 & 23.56 & 7.74 \\ \hline
**Throughput (vehicles)** & 211.70 & 222.00 & 180.10 & 197.20 & 126.30 & 148.40 & 73.50 & 79.10 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison with FFS method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**EpochsPerformance Indicator** & **Total Reward** & **AQL (Vehicles)** & **AWT (s)** & **Throughput (Vehicles)** \\ \hline
**25** & \(-\)11,711.5 & 52.12 & 34.45 & 168.3 \\ \hline
**50** & \(-\)10,239.0 & 42.83 & 31.71 & 211.3 \\ \hline
**100** & \(-\)10,443.0 & 50.69 & 33.13 & 167.3 \\ \hline
**200** & \(-\)12,484.4 & 63.79 & 44.57 & 176.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average performance of model trained with different number of epochs using action time step 0.5 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**EpochsPerformance Indicator** & **Total Reward** & **AQL (Vehicles)** & **AWT (s)** & **Throughput (Vehicles)** \\ \hline
**25** & \(-\)4839.8 & 45.37 & 29.76 & 197.3 \\ \hline
**50** & \(-\)5115.4 & 45.57 & 31.67 & 191.6 \\ \hline
**100** & \(-\)5669.1 & 44.69 & 31.48 & 201.2 \\ \hline
**200** & \(-\)6524.5 & 50.11 & 37.72 & 189.5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average performance of model trained with different number of epochs using action time step 1 s.
## 6 Conclusions  
A coupling model of the GRIN fiber probe and HC-PCF has been established and a method to calculate the coupling efficiency of the model has been deduced. An optimization problem was proposed as a guideline for the design of the probe to obtain the longest working distance at a specified waist radius. A series of specified probe samples were fabricated and a coupling efficiency experiment was carried out. Image processing was implemented before the experiment to correct the results. In addition, the relationship between the coupling efficiency and working distance was discussed. The theoretical and experimental results show that the maximum coupling efficiency of the probe is obtained when the coupling distance is equal to the working distance of the probe. Although the coupling efficiency of the GRIN fiber probe at a very short distance is lower than that of SMF, the coupling efficiency of the probe is obviously higher than that of SMF with the increase of coupling distance. Therefore, the probe coupled with HC-PCF can obtain a higher coupling efficiency at a longer distance. The longer distance can improve the response time of the system and the higher coupling efficiency can improve the sensitivity of the system. Because of the contradictory relationship between the maximum coupling efficiency and the working distance of the GRIN fiber probe, some theoretical and experimental analyses are necessary in the future to select the optimized GRIN fiber probe for gas sensing.  
[PERSON] and [PERSON] conceived and designed the experiment; [PERSON] performed the experiment; [PERSON] and [PERSON] supervised the entire work; [PERSON] and [PERSON] wrote the paper.  
The authors acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 61773249, No. 41704123), the Natural Science Foundation of Shanghai (Grant No. 16 ZR1411700), and the Science and Technology on Near-Surface Detection Laboratory (Grant No. TCGZ2018A007).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Statistical Analysis
#### 3.1.2 Producer  
Similarly to the grouping by the type of communication, violin charts with the division into the producers are presented (Figure 4). Due to the sensitivity of the data, the numbers were used instead of the brand names. Analyzing violin plots, it is worth noting that the curves of the Kernel Density Estimator (KDE) represent the probability density, not the cardinality of the considered set. In other words, the area under the curve equaled 100%. More information about how the KDE was created can be found in [33]. A single violin in the presented plot describes two sets: left-hand without and right-hand with failures (blue and orange, respectively), which are not in the same scale in terms of absolute numbers. To interpret the data, the locations and number of picks, as well as the shape of the curve itself need to be compared.  
In particular, Manufacture Nos. 2 and 4 had their maxima without failure above consumption of 20,000 kWh, which suggests that purchases were abandoned. In the case of Producer No. 10, the failures were slightly delayed in relation to the number of new meters, but it was difficult to draw  
Figure 4: Consumption and failures by producer.  
Figure 3: Stationary probability distribution.  
any more specific conclusions here. It was notable that the KDE curves of failure were in general longer and had relatively small maxima, which indicated that the occurrence of failure was random for almost any manufacturer.  
Table 3 is the complement of the violin charts. The failure results presented here were certainly influencing the Rapp's purchasing policy. On the one hand, we had a strong leader (Producer No. 16), which maintained the failure rate of its devices at a relatively low level of 13%. On the other hand, we observed diversification and an attempt to become independent of this producer. The reason to withdraw the meters of Producer No. 2 was the high failure rate of over 90%, even in a few years.  
The above observation strongly supported the thesis that statistical analysis performed on a big dataset can create significant savings and should be used in the process of designing and maintaining smart buildings.
## 2 Data and Methods
### Equipment and Methods
The procedure for field calibration is to use the Trios sensors and micro-spectrometer to simultaneously measure diffuse reflectors with different reflectance values against a target cloth, and to obtain the simultaneously measured Trios radiance, irradiance and micro-spectra with their corresponding DN values. The formula for field-calibrated radiance is presented in Equation (2) and for field-calibrated irradiance in Equation (3). \[L_{\text{Trios}}=gain\times DN_{STS-VIS}+offset \tag{2}\]  
\[Ed_{\text{Trios}}=gain\times\widetilde{DN}_{STS-VIS}+offset \tag{3}\]  
In Formula (2) above, \(L_{\text{Trios}}\) is the radiance of the different diffuse reflection plates measured by Trios, and \(DN_{STS-VIS}\) is the DN of the different diffuse reflection plates measured by the micro-spectrometer. \(Ed_{\text{Trios}}\) in Formula (3) is the irradiance of the different diffuse reflectors measured by Trios and \(\widetilde{DN}_{STS-VIS}\) is the the DN of the different diffuse reflection plates measured by the micro-spectrometer. We used the micro-spectrometer to obtain the R_rs_ of plateau lakes as follows: firstly, the DN values obtained with the micro-spectrometers (a)-1, (a)-2 and (a)-3 are converted into radiance and irradiance according to Formulas (2) and (3), and then the R_rs_ of the micro-spectrometer is calculated according to Formula (1). The micro-spectrometer needs to be physically connected to the micro-computer in the unmanned ship using a USB cable; additionally, we also need to use professional software to identify the sensor, and only after the identification is passed can the data \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Trios** & **STS-VIS** \\ \hline Wavelength (nm) & 200–1100 & 350–810 \\ \hline Optical Resolution (nm) & 3.3 nm & 2.2 nm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter table of Trios and STS-VIS. Figure 1: (**a**) Ocean Optics micro-spectrometers (STS-VIS); (a)-1 sensor for measuring sky radiance; (a)-2 sensor for measuring water radiance; (a)-3 sensor for measuring downward irradiance. (**b**) Ocean Optics and Hygirrel fiber. (**c**) Cosine Correctors. (**d**) Trios. (**e**) Diffuse reflectance plates and target cloth (99%, 95%, 30%, 20%, 5%, and 2%). acquisition parameters be set. The main parameters to be set are integration time, multiple scan averaging, sliding average width and other parameters. It is also necessary to set the address where the spectrum file is to be saved. Once the settings are complete, the micro-spectrometer can be used for water spectrum acquisition.
## 3 Results and Discussion
### Microstructure  
Figure 10 shows the morphologies, structures, and phases of the geopolymers with a curing time of 28 days, which is when significant changes and differences between the samples are observed. The NaOH produces the phase dissolution of the FA, and forms reaction products with a homogeneous and compact matrix that does not occur in the starting material, the main characteristic of which is its spherical shape (Figure 10a) [48]. In the micrographs corresponding to the geopolymers, cavities left between the reaction products can be observed, which are mostly gels formed by long chains of silicates and aluminates balanced by the sodium ions of the NaOH used as solution. These results consistent with other investigations [49; 50]. The new structures are a consequence of alkaline attack and thermal curing during the process of obtaining the geopolymers [51].  
Likewise, through these images it is also possible to observe the evolution of the reaction products with different particle sizes of FA-1.  
In Figure 10b there are fewer crystalline phases and a heterogeneous matrix without defined structures which is related to a low crystallinity; as the particle size decreases (Figure 10c,d), the number of crystalline products increases and is consistent with zeolitic structures attributed to the faujasite and type Y-zeolite. These structures are present in the sets G1, G2, and G3, but with higher intensity in G3 (Figure 5) inside an amorphous matrix attributed to the formation of gels in the activation and geopolymerization process [51].  
This influence on the achievement of the reaction products is observed in the displacement of the bands of the FT-IR spectra and in the creation of new crystalline phases that also appear in XRD.  
Figure 10: SEM micrographs of: (**a**) FA-1, (**b**) G1-28, (**c**) G2-28, (**d**) G3-28.
# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 4 Experiments
### An Ablation Study  
To verify the effectiveness of each component of our method, we designed a series of variants:  
* Ours w/o depth: The original model without depth maps;
* Ours w/o multi-scale FE: The model with depth maps but without multi-scale feature extraction;
* Ours w/o spatial fusion: The model with depth maps and multi-scale feature extraction but without spatial fusion;
* Ours: The complete model with depth maps, spatial fusion, and multi-scale feature extraction.  
We trained these variants using the same training data and evaluated their performance on the test dataset. The evaluation results are shown in Table 3. The results indicate that our model w/o depth performs poorly in dehazing due to the lack of critical information. Our model w/o multi-scale FE, with the assistance of depth information, provides more natural results in regions with significant depth variations. However, due to the lack of long-range context and spatial fusion strategies, the network exhibits issues while handling dense haze, with poor texture recovery and lighting that is inconsistent with the surrounding regions. Our model w/o spatial fusion restores the textures in hazy regions better but still suffers with inconsistent lighting in the surrounding areas. In contrast, our complete model delivers more realistic and natural results when handling various complex and extreme scenarios.
# Special Issue \"Reinforced Concrete: Materials, Physical Properties and Applications\"
## 3 Bearing Capacity near Support Areas of Continuous Reinforced Concrete Beams and High Grillages  
The authors of research [2] present a calculation method based on experimental data for bearing capacity of load-bearing reinforced concrete monolithic continuous sections of strip beams that connects depressed or driven reinforced concrete piles into a single foundation structure. Experimental study results, experimental tensile shear force comparison with the calculation results obtained by the method of design codes, as well as the stress-strain state of a continuous-beam system under extreme load conditions modelling was the rationale for the authors' method. The paper shows \"the physical aspect of the phenomenon and significantly refines the physical shear model. The authors evaluated the influence of structural factors in solid research elements, and on the basis of this\" [2], the Lira-SAPR PC simulated the work of the studied elements under the action of a transverse load to investigate the stress-strain state and confirm their failure pattern, adjusted in the physical model by the finite element method in non-linear setting. A calculation-bearing capacity model, which can more accurately determine their strength, is proposed for continuous reinforced concrete beam sections near the supporting and high grillages.  
The authors thus present a very important study in terms of structural mechanics and building structures. Engineering solutions are outlined, supported by fundamental scientific new knowledge that relate to reinforced concrete beams and grillages, and a considerable step is taken towards studying the operation of reinforced concrete structures.  
Simplified 2D Finite Element Model for Calculation of the Bearing Capacity of Eccentrically Compressed Concrete-Filled Steel Tubular Columns  
The authors of [3] create a simplified two-dimensional numerical FEM model for calculating the loading capacity of eccentrically compressed steel tubular columns with concrete filling. \"Steel Tubular Concrete Columns (CFST) are widely used in construction due to their effective resistance to compression and bending at the same time. However, at present, there is no generally accepted effective calculation method that considers both the materials nonlinearity and lateral compression. A finite element method for the analysis of RCSTC in a physically nonlinear setting is proposed by reducing a three-dimensional problem to a two-dimensional one based on the flat sections hypothesis\" [3].  
From the point of view of structural mechanics, a lot of work has been conducted on numerical and analytical studies. In addition, it should be emphasized that pipe-concrete columns are of particular interest--these are unique structures that, due to the resulting clip effect, are of particular interest due to the large reduction in working sections and the possibility of obtaining columns with a very high bearing capacity. This makes them applicable to objects of any level of responsibility.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 1 Introduction
* Implementation of Integration Service: One approach to realizing the integration between MEC and 5G technology is implementing a specific service. This service can be provided using a dedicated communication API. * Expansion of MTS API: The original MTS API was not designed to route application traffic to the 5 GC. Therefore, to enable traffic transfer between MEC and the 5G network, it is necessary to enhance this API by creating a new specific service. * Validation of the API in a Test Environment: The proposed API was validated in a testbed environment using a simulated MEC framework and a 5 GC. This article is structured as follows: Section 2 presents the fundamental concepts necessary for understanding this work. Section 3 provides a literature review related to this work. Section 4 describes the proposal of this work, including its architecture, implementation details, and operation. Section 5 presents the use cases used for comparison purposes, the testbed environment, evaluation metrics, and the collected results, discussing their implications. Section 6 concludes the article.
# Traceability of Surgical Instruments: A Systematic Review
## 2 Traceability Surgical System Architecture Overview
### Layer 2  
This layer plays a crucial role in orchestrating information exchanges [26]. It acts as the system's brain, facilitating the exchange of information between the different components of the traceability system. It ensures automated monitoring, precise recording, and dynamic data management associated with each instrument. It allows real-time tracking of surgical instruments, automatically records related events, and archives this information, including any modification or update, in a structured manner. The first step is to register each surgical instrument in the database. Each record includes relevant information such as the unique identifier (ID), specific instrument function, sterilization data, and, optionally, an image of the instrument for visual identification. Sophisticated algorithms are implemented with the centralized database for real-time monitoring, anomaly detection, and proactive problem management.