esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Comparison with State-of-the-Art Methods  
The proposed approach was compared with three conventional methods: bicubic interpolation low-rank and total-variation regularizations [9] and non-local up-sampling [2]. We compared our results with the CNN-based MRI SR method: single contrast super-resolution CNN (SCSR) [13] and residual-learning network (ReCNN) [11]. The SCSR was proposed for multi-contrast MRI super-resolution, and we extracted the output of its single contrast subnet for comparison. Another compared method is the most recent CNN-based method, namely ReCNN [11], which is designed for 3D MRI super-resolution. To compare our method with ReCNN, we performed the experiments according to the baseline network of [11] and trained the corresponding 2D CNN from the scratch.  
To evaluate the performance of DGGRN, we trained a residual network (ResNet) with the same training data and eight Resblocks from scratch. Experiments were performed on BrainWeb and NAMIC. PSNR and structural similarity (SSIM) were used as quantitative measures. Higher PSNR values indicate the reconstructed version is more faithful to the ground-truth image, while higher SSIM values indicate that more accurate image structures are preserved. MATLAB functions were used for the evaluation.  
The quantitative results of different methods are reported in Table 1. Compared with SCSR, our method yields PSNR values that are higher by approximately 1.6 dB on BrainWeb, 0.6 dB on NAMIC and 0.4 dB on IXI. DGGRN performs more competitively on the BrainWeb dataset. Our method outperforms ReCNN, which is a CNN with residual learning, for all three test sets. SSIM value corresponds to the perceptual quality of the structural similarity. The SSIM values of DGGRN are higher than other methods on real data of NAMIC and IXI. Thus, it is not trivial to embed image gradients into the CNN models.  
Figure 4 presents the reconstructed images of DGGRN and other methods with an upscaling factor of 4 on the three test sets. The bicubic method tends to produce blurry images with unexpected artifacts. SCSR and ResNet present more visually appealing results than bicubic method; however, they lose subtle details in some local regions. DGGRN restores more accurate image high-frequency details of edges and textures areas and recovers more informative structural details than the above methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Upscaling Factor** & **Bicubic** & **LRTV** & **NMU** & **SCSR** & **ResNet** & **ReCNN** & **Ours** \\ \hline \multirow{4}{*}{BrainWeb} & \multirow{2}{*}{3} & 21.51 & 24.81 & 27.50 & 32.99 & 33.25 & 32.86 & **34.32** \\  & & 0.827 & 0.904 & 0.952 & 0.984 & 0.985 & 0.973 & **0.987** \\  & & 18.3 & 21.67 & 21.54 & 26.07 & 27.71 & 26.10 & **27.62** \\  & & 0.664 & 0.820 & 0.811 & 0.922 & 0.938 & 0.925 & **0.942** \\  & & 16.37 & 19.36 & 19.33 & 21.31 & 22.11 & 21.03 & **23.11** \\  & & 0.525 & 0.697 & 0.682 & 0.776 & 0.803 & 0.771 & **0.849** \\ \hline \multirow{4}{*}{NAMIC} & \multirow{2}{*}{3} & 28.70 & 31.98 & 33.95 & 36.86 & 37.00 & 36.64 & **37.21** \\  & & 0.850 & 0
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 4 Proposed Scheme-I (Key Recovery System Using Signcryption)
### Key Pair Generation Phase  
In this phase, \(Part_{i}\) receives a partial private key from KGC and uses it to generate full private key \(sk_{i}\) and public key \(pk_{i}\).  
* **Step 1:** KGC generates parameters \(w_{i},t_{i}\in\mathbb{Z}_{p}^{*}\) for participant \(Part_{i}\) through the following operation and sends them to \(Part_{i}\) through a secure channel. \[X_{i}=g^{x_{i}}\] (1) \[d_{i}=x_{i}+sH(ID_{i},w_{i})\ mod\ q\] (2)
* **Step 2:** Participant \(Part_{i}\) who receives \(X_{i},d_{i}\) from KGC, selects Random numbers \(z_{i},v_{i}\in\mathbb{Z}_{p}^{*}\) and sets \(Part_{i}\)'s private key \(sk_{i}\).
* **Step 3:** Participant \(Part_{i}\) generates \(Z_{i},V_{i}\) and sets public key \(pk_{i}\). \[Z_{i}=g^{z_{i}}\] (3) \[V_{i}=g^{v_{i}}\] (4)
## 3 Engineering Geological Model
### Numerical Test Scheme  
This study mainly focused on the influence of the rock joint, dip angle of the rock stratum, and slope angle on the stability of the interbedded anti-dip rock slope. The rock slope includes interbedded mudstone strata and brown shale strata, and the thickness of each mudstone and brown shale layer is 3 m and 1.5 m, respectively. The specific numerical test scheme is shown as follows:  
1. Effect of a joint in the rock layers on the stability of the interbedded anti-dip rock slope. A slope-free surface was designed with an irregular step shape based on the true excavation sequence (Figure 1), and the dip angle of each rock layer was set to 20\({}^{\circ}\). For the model containing joints, joints with a 70\({}^{\circ}\) inclination angle and a 3 m length were located in the vicinity of the free slope surface, which was perpendicular to the bedding plane (Figure 2a). Furthermore, one slope without joints was designed to compare the failure characteristics (Figure 2b).
2. Effect of the inclination angle of the rock layers on the stability of the anti-dip interbedded rock slope. The irregular slope-free surface was simplified into a straight line in the numerical model, and the inclination angle of the rock stratum was set to 20\({}^{\circ}\) or 70\({}^{\circ}\) (Figure 2c,d).
3. Effect of the slope angle on the stability of the anti-dip interbedded rock slope. The slope angle was designed to be 60\({}^{\circ}\) (Figure 2d) or 80\({}^{\circ}\) (Figure 2e) in this scheme.
## 3 Results
### Post-Synthesis Simulation Results: Resources Utilization of the Encryption/Decryption Systems
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation** & **Resource** & **Utilization** & **Utilization [\%]** \\ \hline Complete decryption system & LUT & 29,111 & 10.62 \\ (350 MHz packet rate) & FF & 4339 & 0.79 \\  & BUFG & 1 & 0.25 \\ \hline Complete decryption system & LUT & 29,156 & 10.64 \\ (23.4 MHz packet rate) & FF & 4339 & 0.79 \\  & BUFG & 1 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hardware resource utilization related to the complete decryption scheme, including all the blocks to test the decryption algorithm, both when the encrypted packets are received in input on each rising edge of the 350 MHz clock signal and when they are provided every 42.7 ns (i.e., 23.4 MHz); also, the resource utilization of only the decryption block are reported. attributable to the multiplication of the Inverse Mix Columns operation carried out in the decryption block, because involve a large number of values such as 0x009090909, 0x0B0B0B0B0B, 0x0D0D0 DD, 0x0 EOEOE0E; such multiplicative constants require the storing of numerous intermediate values inside the LUT, occupying more hardware resources and consuming more power [34]. For this reason, several strategies were proposed in the scientific literature for reducing resource utilization and power consumption [35; 36]. However, since the area occupation requirement is not as stringent as the encryption/decryption speed for the specifications of the developed project, the implementation choice fell on obtaining the data packets in the shortest possible amount of time at the expense of a greater chip's area occupation.
## 3 Results and Discussion
### Accuracy  
The measurement results for each spiked sample are shown in Table 4; the real Ouzo sample without spike was measured three times and gave an average value, \(x_{0}\) = 0.62 mg/L DEHP.  
Then, the correlation diagram, Figure 8, is constructed, in which the experimental values obtained from the method under validation, \(x_{i}\) (\(y\)-axis), are presented in terms of the corresponding known concentrations of the samples, \(\mu_{i}\) (\(x\)-axis). Additionally, the regression line is plotted using the least squares method. Finally, with the help of Excel we obtain the regression analysis table, a part of which is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{\(\mathbf{X_{i,sp}}\)} & \multicolumn{2}{c}{**Known Concentration,**} & \multirow{2}{*}{**Method under Validation, \(\mathbf{x_{i}}\)**} \\  & \(\mu_{i}\) (\(\mu_{i}\) = \(x_{i}\), \(\mathbf{sp}\) + \(x_{0}\), \(x_{0}\) = 0.62)** & \\ \hline
0.3 & 0.92 & 0.94 \\ \hline
0.7 & 1.32 & 1.35 \\ \hline
1.1 & 1.75 & 1.70 \\ \hline
1.5 & 2.12 & 2.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of the method.  
Figure 7: Sensitivity plot for the linearity check of the method.  
To check the accuracy of the method, the following hypothesis tests were performed:  
* For the slope b = 0.9875: H\({}_{0}\): b = 1 vs. H\({}_{\rm A}\): b \(\
eq\) 1, to ascertain whether the value of b is statistically equal to monad, the 95% confidence interval of b should contain monad. From the regression analysis table, Figure 8 (below), CI 95% = (0.863; 1.112). Since CI includes monad we accept the null hypothesis, H\({}_{0}\): b = 1. Therefore, we conclude that there is no \"proportional\" systematic error.
* For the intercept a = 0.0315: H\({}_{0}\): a = 0 vs. H\({}_{\rm A}\): a \(\
eq\) 0, to ascertain whether the value of a is statistically equal to zero, the 95% confidence interval of a should contain zero. From the regression analysis table, Figure 8 (below), CI 95% = (\(-\)0.167; 0.230). Since CI includes zero, we accept the null hypothesis, H\({}_{0}\): a = 0. Therefore, we conclude that there is no \"standard\" systematic error [9].
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge
### LoRa PHY Layer
#### 2.2.3 Channel Activity Detection Mechanism  
LoRaWAN provides channel activity detection (_CAD_) mechanism for carrier sensing. _CAD_ was designed to quickly detect the presence of LoRa preamble in the channel with the best possible power efficiency and concludes two phases: reception phase and processing phase. As _CAD_ mode reception phase begins, a LoRa device switches its radio to receive mode on a preconfigured SF and captures all symbols present in the channel. During the signal processing phase, the LoRa radio modem seeks a correlation between the received waveform of symbols and the ideal waveform of preamble symbols. Once the comparison process is complete, the CAD_Done interrupt is activated, and the system returns to standby mode. If the preamble matches with the ideal waveform, then the CAD_Detected interrupt is activated and the device is ready to receive the data payload [31]. The required _CAD_ time can be calculated as  
\[\mathrm{T}_{CAD}=\frac{2^{SF}+32}{BW}(\mathrm{sec}) \tag{12}\]
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Enzymes** & **CON** & **GUA** & **WBV-E** & **GUA+WBV-E** & \(p\) & \(\varepsilon^{2}\) \\ \hline Amylase (\(\mu\)Kat/L) & 0.79 \(\pm\) 0.08 & 0.77 \(\pm\) 0.14 & 0.075 \(\pm\) 0.10 & 0.80 \(\pm\) 0.06 & 0.8384 & 0.0470 \\ AST (\(\mu\)Kat/L) & 2.20 \(\pm\) 0.25 & 2.45 \(\pm\) 0.21 & 2.30 \(\pm\) 0.49 & 2.38 \(\pm\) 0.30 & 0.5325 & 0.1465 \\ ALT (\(\mu\)Kat/L) & 1.02 \(\pm\) 0.12 & 1.13 \(\pm\) 0.21 & 1.12 \(\pm\) 0.10 & 1.21 \(\pm\) 0.10 & 0.3990 & 0.1640 \\ ALP (\(\mu\)Kat/L) & 2.20 \(\pm\) 0.30 & 1.92 \(\pm\) 0.29 & 2.16 \(\pm\) 0.27 & 2.30 \(\pm\) 0.21 & 0.3064 & 0.2007 \\ Lipase (\(\mu\)Kat/L) & 0.11 \(\pm\) 0.01 & 0.11 \(\pm\) 0.00 & 0.11 \(\pm\) 0.01 & 0.12 \(\pm\) 0.01 & 0.6838 & 0.0830 \\ CK (\(\mu\)Kat/L) & 31.97 \(\pm\) 1.03 & 31.56 \(\pm\) 6.74 & 26.73 \(\pm\) 4.74 & 28.58 \(\pm\) 7.41 & 0.6170 & 0.0411 \\ \hline \hline \end{tabular} \(\mu\)Kat/L, microkatal; CON, control; GUA, group treated with guarana; WBV-E, group exposed to mechanical vibration generated in platform; GUA + WBV-E, group treated with guarana and exposed to mechanical vibration. Values are presented in means \(\pm\) SD and \(\varepsilon^{2}\), epsilon squared.  
\end{table} 
Table 3: Enzymes concentrations in animals submitted to some interventions. The effect of guarana extract and/or WBV on stool consistency was evaluated for 6-weeks of treatments. The stool consistencies of the rats are shown in Table 5. The consistency of the stool among the animals that received guarana (GUA and GUA+WBV-E groups, Type 4) was altered (\(p<0.05\)) as compared with the animals that did not receive guarana (CON and WBV-E, type 2). The \(\varepsilon^{2}\) was 0.6016 (a moderate relationship).
# Towards the Internet of Smart Trains:
## 2 Communication Systems in Railway Scenarios
### Train-to-Infrastructure Connection  
This scenario requires two types of links among the Access Point (AP) transceivers located in the train and the fixed network infrastructure. These links must be bidirectional, with high data rates and latencies lower than 100 ms while traveling at speeds up to 350 km/h or even higher [3]. Jointly with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristics** & **Urban** & **Urban/Inter-City** & **Inter-City** & **High-Speed** \\ \hline Maximum speed (kph) & s \(\leq\) 70 & 70 \(<\) s \(\leq\) 160 & 160 \(<\) s \(<\) 250 & \(\geq\)250 \\ Line length (km) & 1 \(\leq\) 20 & 20 \(<\) 1 \(<\) 100 & 100 \(\leq\) 1 \(<\) 250 & 1 \(\geq\) 250 \\ Parallel tracks (units) & 1 & 2 & 3 & 4 \\ Rolling stock & Single & Similar & Mixed & Very Mixed \\ Stock types & 1 & 2-4 & 5–8 & 9+ \\ Train stations & 1-5 & 6-20 & 21-50 & 51+ \\ Operators & 1 & 2 & 3–5 & 6+ \\ Passengers (per km of line) & \(n\) \(<\) 100,000 & 100,000 \(\leq\) \(n\) \(<\) 200,000 & 200,000 \(\leq\) \(n\) \(<\) 500,000 & \(n\) \(\geq\) 500,000 \\ Range of services & Single & Small diversity & Multiple variances & Extremely varied \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the different line types.  
Figure 2: Railway communications scenarios (Renfe AVE train and train station pictures are under Creative Commons License). Color meaning: pink (train-to-infrastructure communications), blue (inter-car communications), light-green (intra-car communications), yellow (communications inside the station), purple (infrastructure-to-infrastructure communications), and dark green (wireless sensor networks).  
an availability of 98-99% mandatory to comply with Reliability, Availability, Maintainability and Safety (RAMS) requirements.  
Several works exist in the literature related to the characterization of train-to-ground wireless links [7]. Generally, train-to-infrastructure systems communicate with wayside units using GSM-R or IEEE 802.11. For instance, the effect that structures like viaducts, bridges and terrain cuttings can cause in GSM-R has been analyzed exhaustively in the literature [8]. Furthermore, in the case of high-speed scenarios, [PERSON] et al. [9] present a survey on channel measurements and models.
# Analysis of the Current State of Automation of Hazard Detection Processes in BIM in Slovakia
## 2 Materials and Methods
### Preparation of Survey Questions and Syllabus for the Interview  
An electronic questionnaire was prepared from acquired theoretical knowledge of hazard detection in BIM [15; 17; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 51], and practical knowledge with BIM management based on experience in the construction industry in Slovakia. It was composed of 33 questions connected by logical links, so that it was possible to exclude inappropriate respondents (e.g., if they did not perform preparation and control of HS on their projects or did not use BIM on any project). The logical links also made it possible to analyze the reasons for some of the answers, e.g., reasons for not using BIM for HS purposes or, conversely, to specify how companies performed a given process.  
The questionnaire consisted of six sections:  
1. Introduction, containing questions concerning the job position, confirmation of HS inspections on the construction site and of the use of BIM.
2. Detection of hazards.
3. Software used for hazard detection.
4. Transfer of information about hazards to the person responsible.
5. The current situation in occupational health and safety control on the construction site.
6. The importance of the topic and its future.  
The video interview was used to explain the research topic and the purpose of the survey and to eliminate technical problems with the electronic questionnaire. It also allowed the analysis of specific questions and answers and developed the discussion, which led to further information.  
The interview guidelines were:  
1. Introduction--explanation of the topic and goals of the research.
2. Explanation of the chapters, scope and duration of the electronic questionnaire.
3. Explanation of the functions of the electronic questionnaire (e.g., logical links, moving to another question).
4. Execution of the questionnaire--explanation of questions if needed and detailed discussion of specific questions.
5. Final discussion.  
In this paper, only questions related to hazard detection, found in units 1-2 and partially in 6, interpreted in the form of graphs and tables processed using Microsoft Excel software, were analyzed (the published order of questions and survey results differs from the original order in the questionnaire).
## 3 Results
### FTIR Analysis of Carbon Sources  
In all the spectra, the characteristic PS peaks (at 1452 and 1492 cm\({}^{-1}\)) correspond to C-H and -CH\({}_{2}\) stretching bands that were similar to [PERSON] et al. [42]. Additionally, the broad peak of OH stretching (3400-3600 cm\({}^{-1}\)) and the peak absorptions in the regions of the carbonyl bands (1710-1730 cm\({}^{-1}\)) could be identified by FTIR spectroscopy. The region with maximum readings between 1710 to 1730 cm\({}^{-1}\) (Figure 3) corresponds to the stretching vibrations of the C=O bonds originating from different functional groups of not only carboxylic acids, but also ketones or aldehydes, whose absorption bands may overlap [43].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Acid Number (mg KOH/g)** & **Process Conditions** \\ \hline PS0 & 22.0 & N/A \\ PS1 & 39.0 & 60 \({}^{\circ}\)C, O\({}_{3}\)/O\({}_{2}\), 20 h \\ PS2 & 33.7 & 80 \({}^{\circ}\)C, O\({}_{3}\)/O\({}_{2}\), 20 h \\ PS3 & 29.5 & 100 \({}^{\circ}\)C, O\({}_{3}\)/O\({}_{2}\), 20 h \\ PS4 & 23.6 & 180 \({}^{\circ}\)C, O\({}_{3}\)/O\({}_{2}\), 20 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conditions and acid number results of thermal degradation procedure on PS0 flakes.  
Figure 3: FTIR results of PS0–4 samples.  
What is worth noting is that after the thermal oxidation pretreatment process, the changes in the intensity of the OH and C=O bands for the samples tested were observed. The treatment of PS samples at 60, 80, and 100 \({}^{\circ}\)C show, in general, higher absorbance in the hydroxyl and carbonyl regions than the original material prior to oxidation. The PS samples after thermal oxidation at 180 \({}^{\circ}\)C present spectra with an absorbance profile similar to that recorded for PS0 before the pretreatment process. Therefore, PS1, PS2 and PS3 samples are better candidates for sources of activated carbon for selected bacteria strains. These results seem to be corroborated by the titration values presented earlier in Table 1.
# Track Modulus Assessment of Engineered Interspersed Concrete Sleepers in Ballasted Track
###### Abstract  
Ballasted railway track is typically constructed using sleepers that are manufactured from a common material type within a given length of track. Timber and concrete are the two most common sleeper materials used internationally. Evidence from historical installations of interspersed concrete sleepers in timber sleeper track in North America has indicated inadequate performance, due largely to the heterogeneity in stiffnesses among sleepers. Theoretical calculations reveal that interspersed installation, assuming rigid concrete sleepers and supports, can result in rail seat forces more than five times as large as the force supported by the adjacent timber sleepers. Recently, engineered interspersed concrete (EIC) sleepers were developed using an optimized design and additional layers of resiliency to replace timber sleepers that have reached the end of their service lives while maintaining sleeper-to-sleeper stiffness homogeneity. To confirm that the concrete sleepers can successfully replicate the stiffness properties of the timber sleepers installed in track, field instrumentation was installed under revenue-service train operations on a North American commuter rail transit agency to measure the wheel-rail vertical loads and track displacement. The results indicated that there are minimal differences in median track displacements between timber (2.26 mm, 0.089 in.) and EIC sleepers (2.21 mm, 0.087 in). Using wheel-load data and the corresponding track displacements associated with each wheel load, track modulus values were calculated using the single-point load method based on beam on elastic foundation (BOEF) fundamentals. The calculated values for the track modulus indicated similar performances between the two sleeper types, with median values of 12.95 N/mm/mm (1878 lbs./in./in.) and 12.79 N/mm/mm (1855 lbs./in./in.) for timber sleepers and EIC sleepers, respectively. The field results confirmed the suitability of the new EIC sleeper design in maintaining a consistent track modulus for the location studied, thus evenly sharing loads between and among sleepers manufactured from both concrete and timber.  
track modulus; ballasted track; field measurement; interspersed concrete sleeve; timber sleeper; concrete sleeper; wheel-rail vertical load +
Footnote †: journal: Applied Sciences