esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Categorization of Digital Applications in the Energy Sector
## 4 Categorization of Digital Applications
### Processes Optimization  
Besides supporting the balancing of the energy system, digital applications offer great potential to optimize internal processes. Some of the process optimizations are specific to the energy sector while others can be observed across different sectors.  
Data analytics and machine learning can improve the understanding of correlations and the ability to identify the root cause of anomalies and thereby help to define predictive maintenance strategies [7; 8; 34; 35; 36; 37]. While predictive maintenance can avoid costs and downtime for any equipment, it is, in particular, relevant for assets with high availability requirements and assets which are difficult to access, such as offshore wind turbines. If data is captured and analyzed thoroughly a digital twin of equipment units, entire assets, and even whole systems can be created. Digital twins help to optimize operations and maintenance activities in line with overall objectives [35; 36]. These objectives can be, for example: increasing a power plant's primary energy efficiency, deferring grid investments, reducing energy consumption or increasing expected lifetime. Besides that, digitalization can help to improve the preparation for and the documentation of construction and maintenance work via delivering upfront information on potential issues, relevant (virtual-/augmented-reality based) instructions, and material and tool lists [8; 36]. A digital document management system can increase accessibility of documents and decrease administrative costs [36]. Many repetitive tasks can be automated based on digital solutions (RPA, robotic process automation). In particular, administrative tasks as well as some regulatory reporting can be automated [36]. The automation of more complex and less standardized tasks might require the application of machine learning. Connecting internal processes and IT systems with customer/supplier/partner processes and IT systems can offer benefits. The benefit of automated processes and connected IT systems is mostly an efficiency increase but they can also lead to higher process quality [36]. Non-energy related processes such as, for example, supply chain [43], human resources/recruiting [44], strategy definition [45], controlling and accounting [46], and legal can also greatly benefit from digitalization in terms of process efficiency or quality of the output.
# An Innovative and Efficient Method for Reverse Design of Wheel-Rail Profiles
## 1 Introduction
This paper presents a reverse design method for wheel-rail profiles with the rolling radius difference as the target. The method consists of three parts, i.e., the wheel, symmetric rail and non-symmetric rail profile designs. Expected wheel profiles (rail profiles) can be designed based on given rail profile shapes (wheel profile shapes) and rolling radius difference curves and according to the dichotomy and loop iteration method. Compared with the above-mentioned methods, this is a reverse design method deduced from the mapping relation between a rolling radius difference and a wheel-rail profile gradient. According to this relationship, the wheel profile (rail profile) satisfying the target rolling radius difference can be obtained by adjusting the wheel profile (rail profile) gradients. In addition, with this relationship, we can better understand the effect of profile changes on the contact geometry. The designed wheel-rail profile formed by a multi-sectional quadratic curve can be directly output. Dichotomy is used during the numerical calculation in order to improve the calculation efficiency.
## 7 Conclusions  
This study proposed a slope safety factor prediction model (ISSA-BP) based on an improved sparrow search algorithm to optimize the BP neural network, and it was optimized in two aspects: the introduction of dynamic weight factors and the ability to coordinate local mining and global searches. Combined with the reverse learning strategy, to obtain the reverse solution, the search range of the algorithm was expanded and its shortcoming of being easily trapped in a local area was improved. Based on 352 sets of slope examples, three machine learning methods were used to predict the safety factor of the slopes.  
Through the calculation of evaluation indicators, the root mean square error of the ISSA-BP prediction model was 1.64%, the average absolute error was 0.0215, and the coefficient of determination was 0.99; compared with the BP neural network, the error was reduced by 78%. The BP neural network model optimized by the improved sparrow algorithm showed higher accuracy.  
Finally, using three models to predict the slope safety factor at Tianbao Port in Wenshan Prefecture and comparing it with the actual value, the result of the ISSA-BP model's operation was the closest to the actual safety factor, which verifies the experimental results and provides a basis for establishing the Tianbao border Police Station.  
Experimental data collection, Y.W.; model development and code, Y.W. and H.D.; analysis of the results, Y.W.; project management, Y.W., J.L., Q.Z. and X.Z.; supervision, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanxi Natural Science Foundation (201901D111050) and Dongna Liu (grant number: NSFC 41802191).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \(\mathbf{\gamma}\) & \(\mathbf{c}\) & \(\mathbf{\varphi}\) & \(\mathbf{\alpha}\) & \(\mathbf{H}\) & \(\mathbf{ru}\) & **Predicted FOS** \\ \hline BP & 18.5 & 9.50 & 26.50 & 30 & 70 & 0 & 1.349 \\ PSO-BP & 18.5 & 9.50 & 26.50 & 30 & 70 & 0 & 1.406 \\ ISSA-BP & 18.5 & 9.50 & 26.50 & 30 & 70 & 0 & 1.231 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction results of the three neural network models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dangerous Sliding Center (m)** & **Dangerous Sliding Radius (m)** & **Sliding Force (kN)** & **Anti-Skid Force (kN)** & **Safety Factor** \\ \hline (131.69, 51.34) & 83.85 & 19,938.40 & 24,235.51 & 1.216 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation results of composite soil slope stability.  
Data Availability Statement: The data used to support the findings of this study are available from the corresponding authors upon request.  
Thanks to the reviewers for their careful suggestions and the academic editors for their sincere affirmation. Thanks to everyone who helped with the experiments.  
The authors declare no conflict of interest.
## 3 Results
### Grape Yield per Vine and Must Parameters
#### 3.3.1 Fermentations at Location M  
Both types of AF were compared in two consecutive vintages (2020, 2021) for location M. The changes in the yeast cell concentrations and yeast species diversity during spontaneous (sAF) and inoculated (iAF) AF of the Merlot mash are shown in Figure S2. The initial yeast concentration varied between 4.8 and 7.2 log (CFU/mL) depending on the vintage. The duration of both AFs in the 2020 vintage was shorter (14 days for iAF and 18 days for sAF) than in the 2021 vintage (21 days for both AF), most likely due to a higher initial yeast concentration in the 2020 vintage mash (6.7 (sAF) and 7.2 (iAF) log (CFU/mL) in 2020 vs. 4.8 (iAF) and 5.8 (sAF) log (CFU/mL) in 2021). Similar kinetics of AF were observed for the sAF and iAF for each vintage, although the initial yeast population in the iAF was significantly higher in 2020 and significantly lower in 2021 (\(p\leq 0.05\)) (Figure S2, upper plots). The dynamics of the yeast populations during the two AFs were assessed by colony morphology and microscopic examination and should serve to group the yeasts into genera. Identification as a species would only be a guess. The highest species diversity was observed at the beginning of the two AFs in the two consecutive years (day 0, Figure S2), with _Hanseniispora_ sp. dominating the yeast population. The abundance of _Hanseniispora_ sp. on day 0 was higher in the sAF compared to the iAF in both vintages (71.5% vs. 53.6% in 2020, 91.2% vs. 76% in 2021), which can be attributed to the inoculation of the commercial strain _S. cerevisiae_ (_Saccharomyces_ spp.) in the mash of the iAF (Figure S2, middle and bottom graphs). However, yeasts of other genera were present in lower percentages, with the species diversity and abundance differing between vintages. Yeasts of the genus _Saccharomyces_ spp. (8.1% (2020) to 9.8% (2021) in sAF, 19.7% in the iAF of both vintages) and _Pichia_ spp. (4.0% (2020) and 0.6% (2021) in sAF; 3.3% (2020) and not detected (2021) in iAF) were also isolated from the must of both vintages on day 0. Yeasts of the genus _Starnerella_ were only detected in the 2020 vintage (32.5% in sAF, 6.2% in iAF) and of the genus _Metschnikovia_ in the 2021 vintage (0.17% in sAF) (Figure S2, middle and bottom graphs). As alcoholic fermentation progressed, the species diversity decreased in both AFs. However, in the sAF of both vintages, _Hanseniispora_ sp. yeasts dominated in the first six days, while _Saccharomyces_ spp. yeasts developed thereafter and dominated until the end of the sAF. The main difference between the two vintages in the sAF was that the yeast _Hanseniispora_ sp. was also detected at the end of this AF of the 2020 vintage (days 14 and 18). In contrast to the sAF, yeasts of the genus _Saccharomyces_ already predominated in the iAF of both vintages on the third day (Figure S2, lower diagrams).
## 2 Materials and Methods
### OCE Results Processing Method  
To obtain the elastography results from the vibrational optical-coherence elastography, a mathematical model was proposed by our research group [19]. For each scanning position, the A-scan was repeated 512 times, and the phase difference between the first scan with each of the rest scans was obtained. Thus, fast Fourier transform (FFT) was applied to the phase difference; the phase FFT amplitude as function of depth was procured. The phase FFT amplitude was prompted by the vibration stimulation, and directly related to the sample strain (\(\varepsilon\)) under the stress (\(\sigma\)), the higher elasticity material with a lower phase FFT amplitude and lower elasticity material with a higher phase FFT amplitude. After that, a linear polynomial model was applied to the phase FFT amplitude, by which the slope of phase FFT amplitude was calculated. As the phase FFT amplitude is directly correlated with the sample strain, the ratio of slopes from different elasticity samples equals the strain ratio for different elasticity samples. In this model, the sample and the reference material were considered as an elastic material and the vibration amplitude was uniformly transmitted to the sample and reference material, which means the stress applied on reference material and sample was the same. Therefore, the strain ratio of sample and reference material was inversely proportional to the ratio of sample elasticity and reference material. Sample elasticity was estimated by using the reference material's known elasticity multiplied by the ratio of reference material strain and sample strain, which can be expressed as Equations (3) and (4):  
\[\frac{E_{s}}{E_{R}}=\frac{\sigma_{s}/\epsilon_{s}}{\sigma_{\kappa}/\epsilon_{ \kappa}}=\frac{\sigma_{S}\varepsilon_{R}}{\sigma_{R}\varepsilon_{S}}\approx \frac{\varepsilon_{R}}{\varepsilon_{S}} \tag{3}\]  
\[E\approx E_{R}*\frac{\varepsilon_{R}}{\varepsilon_{S}} \tag{4}\]  
where \(E_{S}\) denotes sample elasticity, \(E_{R}\) denotes reference material elasticity, \(\sigma_{S}\) denotes sample stress, \(\sigma_{R}\) denotes the stress of reference material, and \(\varepsilon_{S}\) and \(\varepsilon_{R}\) are the strain of sample and reference material, respectively. In the application of OCE, the constant stress field is assumed to be applied to the sample and reference material. Thus, the stress applied to sample and reference material can be considered the same.  
In this work, the reference material was fabricated using a silicone material (Ecoflex 00-30; Smooth-on Inc., Macungie, PA, USA), according to the manufacturer's manual with a thickness of 0.5 mm, and 20 mm in length and 15 mm in width. The elasticity was evaluated as 170.96 kPa with a mechanical compression test.
## 4 Experiments and Results
### Experiments and Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Length** & **Method** & **Noise Reduction** & **Accuracy (\%)** \\ \hline
2000 & CZE-Linear-SVM [50] & - & 17.75 \\ \hline
2000 & CZE-Polynomial-SVM [50] & - & 12.73 \\ \hline
2000 & CZE-Rbf-SVM [50] & - & 15.71 \\ \hline T-to-T wave & CZE-Linear-SVM [50] & - & 14.37 \\ \hline T-to-T wave & CZE-Polynomial-SVM [50] & - & 12.16 \\ \hline T-to-T wave & CZE-Linear-SVM [50] & - & 13.19 \\ \hline
2000 & CZE-Linear-SVM [50] & Band Pass Filter & 0.98 \\ \hline
2000 & CZE-Polynomial-SVM [50] & Band Pass Filter & 0.98 \\ \hline
2000 & CZE-Rbf-SVM [50] & Band Pass Filter & 1.30 \\ \hline
2000 & EECGNet-1 [51] & - & 55.40 \\ \hline
2000 & EECGNet-2 [51] & - & 45.68 \\ \hline
2000 & EECGNet-3 [51] & - & 39.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experimental results of other methods when training and validating with data from different sessions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Patch-Size** & **Filter** & **Hist Block** & **Block** & **Accuracy** \\ \hline
1 & 3 & 6 & 3, 3 & 0.5 & 49.17 \\ \hline
2 & 3, 3 & 3, 3 & 3, 3 & 0.5 & 47.94 \\ \hline
2 & 3, 3 & 6, 6 & 3, 3 & 0.5 & 54.44 \\ \hline
2 & 3, 3 & 3, 3 & 7, 7 & 0.5 & 48.44 \\ \hline
3 & 3, 3, 3 & 4, 4, 4 & 3, 3 & 0.5 & 55.37 \\ \hline
3 & 7, 7, 7 & 6, 6, 6 & 7, 7 & 0.3 & 47.33 \\ \hline
3 & 7, 7, 7 & 6, 6, 6 & 7, 7 & 0.5 & 47.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Experimental results of EECGNet [51] under various parameters.
# Design of a Modular Plantar Orthosis System through the Application of TRIZ Methodology Tools
## 2 Methodology
### Phase 1. Application of the Concept of Ideality
#### 2.1.1 Stage 1. Formulation of the System's Ideality  
The main design requirement for any new or improved system arises from the concept of ideality. TRIZ establishes that a system reaches its ideality when it performs each of the functions required of it and reduces the costs incurred in providing those functions [15]. Determining the ideality of a system is the first step in the deployment of TRIZ [23].  
Based on the above, a review of literature related to lower extremity conditions was conducted to establish the role plantar orthosis system should play [7; 24; 25; 26], considering comments from orthopedists and patients. It was stipulated that it should re-align the axes of the lower extremities and redistribute plantar pressure according to the specific condition of the patient [27]. On the other hand, it was determined that the standardization of manufacturing processes in prefabricated systems makes them the option for performing the function according to orthopedists' prescription and customers' comfort at a minimum cost.
## 5 State of the Art
### Surveillance and Reconnaissance  
Surveillance and reconnaissance are two of the most popular use cases in which both the military and civilian domains cooperate. The rapid emergence of connected objects and the standardization of those related to UAVs as well as artificial intelligence through computer vision has created an ecosystem favorable to the integration of several surveillance sensors, such as RGB and thermal cameras. [PERSON] et al. [27] showed that UAV surveillance is more advantageous than the conventional surveillance technique based on fixed CCTV cameras during reconnaissance patrols. In addition to minimizing the risk of casualties, it allows for the rapid and wide exploration of surveillance areas.  
Multi-UAV systems are more suitable for surveillance and reconnaissance over a large area because the scalability of the number of UAVs per swarm favors the coverage of large areas and intelligence in less time. [PERSON] et al. [26] presented a path planning system for real-time surveillance employing cooperative multi-UAVs. Since the main challenge of this work is similar to the problem of cooperative motion planning, they used a swarm of UAVs that need to control, monitor, and periodically generate mosaic maps of their respective mission areas.  
Similarly, [PERSON] et al. [29] presented a surveillance mission planning based on a cooperative multi-UAV system for space coverage optimization to reach suitable sensing locations. The proposed solution works in two phases. Firstly, they used the Rapid Exploration Random Tree algorithm to quickly explore the space until the sensing locations were found. Then, they used Particle Swarm Optimization to maximize the gathered information about the reached sensing locations.  
The fields of application of multi-UAV systems in surveillance are varied [30,67]. One of them [67] integrated the Oriented Line Segment Coverage Problem (OLSC) model to improve intra-swarm cooperation. Each drone is fitted with an onboard camera that provides large coverage of the space in order to detect and identify the size and direction of targets. The architecture of the proposed model is centered around a GCS that plansthe missions and a master UAV that controls and coordinates the movements of the slave UAVs to cover all the targets. Following the same work, [PERSON] et al. [30] have developed a GCS for surveillance missions with multi-UAV. The proposed system is decentralized, allowing the dispatching of tasks to the UAVs. Each UAV displays in real time its visualization operation in the 3D environment. They demonstrated the reliability of the system by using two quadrotors equipped with cameras.
## 1 Introduction  
Tempcore(tm) is the trademark for the heat treatment route that was developed in the 1970s by CRM Group and has since been widely used for the manufacturing of reinforcing steel bars (rebars) [1, 2]. It is a quench and self-tempering thermo-mechanical process wherein the hot rolled rebars come out of the last hot-rolling mill at a temperature above A\({}_{\text{c3}}\) at which the rebars are still in their austentic state and then are rapidly quenched with water sprayed onto their surface at high volumetric flow rate. The outer layer of the rebar transforms from austenite to martensite up to a certain depth. The time of quenching and the volume of water sprayed are controlled to achieve the desired thickness of the outer martensitic layer for a given diameter of the rebar, and below this depth the core is still austenitic. The rebar is then left to cool in the cooling bed, where the core transforms to a ferrite and pearlite mixture while the heat dissipating from the core tempers the martensitic layer, hence the name Tempcore. The process is schematically presented in Figure 1. This composite nature of the rebar with an outer hard layer and a ductile inner core is achieved with a relatively simple heat treatment process that produces high-strength rebars that are bendable and weldable without the need for micro-alloying with V or Nb [1, 2, 3].  
The Tempcore(tm) process often has been investigated from the point of view of strength and corrosion resistance [4; 5; 6; 7], fatigue behavior during service [8], behavior at high temperatures [9], and effects of their geometry [10]. Very few studies could be found in the literature that investigate the residual stresses that are generated during the manufacturing process itself. The research project from which this report is extracted aims at understanding the links between fatigue performance and residual stresses in the rebars, with the goal to enhance their fatigue life. It is well established that macroscopic compressive surface residual stresses enhance the fatigue performance of the material by closing fatigue cracks that usually initiate near the surface. On the other hand, tensile residual stresses lead to premature failure of parts by aiding crack growth and could also lead to stress corrosion cracking [11; 12]. In service, the residual stresses are superimposed with the external load stresses and can be either beneficial or sometimes catastrophically detrimental by lowering the stresses at which cracks initiate to dangerously low levels [13].
# MCST Scheme for UAV Systems over LoRa Networks
## 6 Numerical Simulations
### Simulation Scenarios and Settings
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Network coverage size & 1 km \(\times\) 1 km \(\times\) 150 m \\ \hline Transmit power (\(P\)) & 20 mW \\ \hline Frequency (F) & 920 MHz [8] \\ \hline Channel bandwidth (\(B\)) & 400 kHz [8] \\ \hline Attenuation constant (\(\alpha\)) & 2.34 [8] \\ \hline Shadowing parameter (\(\sigma\)) & 5.06 dB [25] \\ \hline RSSI, Basic rate (\(R_{b}\)) & –98 dBm, 200 kbps \\ \(-\)117 dBm, 20 kbps [8] \\ \hline RTS size & 120 bits \\ \hline CTS size & 88 bits \\ \hline STS size & 96 bits \\ \hline ACK size & 88 bits \\ \hline Preamble time & 401.41 ms \\ \hline Number of simulations & 10,000 times \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters and Settings. where \(L_{f}=D_{f}+D_{prop}\) is the transmission latency, \(L\) of \(f\)th BRF transmission. The achievable BRF throughput (\(S_{a}\)) is the number of bits that can be transmitted from the source node to the destination node of a BRF transmission in one second and is computed as:  
\[S_{a}=\frac{1}{F}\sum_{f=1}^{F}\frac{M}{L_{f}} \tag{8}\]  
Meanwhile, the achievable energy consumption (\(E_{a}\)) is the average of the total energy consumed for transmitting both the control messages and DATA packets from the source node to the destination node and is computed as:  
\[E_{a}=\frac{1}{F}\sum_{f=1}^{F}P\times L_{f} \tag{9}\]  
where \(P\) is the transmit power of a transmitting node.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 6 Results and Discussion
### Query Forwarding Behavior Within Mini-Datacenter  
Several experiments have been performed to analyze and justify the proposed architecture to be suitable for query flow under various buffer occupancies, by measuring the reliability of transmissions as the evaluation metric.