esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Analytical Parameters  
The parameters measured during the process were: pH, TS, VS, lignocellulosic composition (cellulose, hemicellulose and lignin), C/N ratio, protein, dissolved organic carbon (DOC), NH\({}_{4}\)\({}^{+}\), biogas yield and composition, phenolic compounds and dissolved lignin.  
For the steam-exploded solid and liquid fraction: The TS and VS were measured following the standardised methods of the American Public Health Association (APHA) 1998. The carbon and nitrogen contents were measured using a CHN analyser (2400 CHNS/O Series II System, Perkin Elmer, Waltham, MA, USA). Crude protein was calculated as a percentage of Nitrogen \(\times\) 6.25 [32].  
For the steam-exploded solid fraction: The lignocellulosic composition in the solid fraction was measured following the detergent system method [33] and by using a fibre analyser (ANKOM Technology, A-200, New York, NY, USA).  
For the steam-exploded liquid fraction: DOC was measured using a total organic carbon analyser (TOC-L-CPH/CPN, Shimadzu, Japan) after filtering the liquid fraction through a 0.45-\(\mu\)m glass fibre filter (Advantec, GC-50, 47 mm, Ehime, Japan). The pH was measured using a pH metre (HORIBA, B-212, Kyoto, Japan). The Folin\(-\)Ciocalteau method was used to measure the phenolic compounds in the liquid fraction. The dissolved lignin concentration in the liquid fraction was measured based on the same method described by [34], which involves acid precipitation of the dissolved humic fraction and dissolved lignin (both were accounted into the lignin fraction) by measuring soluble chemical oxygen demand (sCOD). Instead of sCOD, this study measured DOC.  
For the AD digested effluent: The NH\({}_{4}\)\({}^{+}\) concentration in the digested effluent was measured using HPLC (HPLC; Column IC I-524A, Guard column IC IA-G; Column IC YS-50, Guard column IC YS-G from Shodex Company for the anion and cation analyses).  
Biogas composition: The biogas composition was monitored using a gas chromatograph (SHIMADZU, GC-2014, Kyoto, Japan) equipped with a packed column ([PERSON], Shincarbon ST 6.0 m long, 3 mm ID., Japan) and a thermal conductivity detector. The temperatures of the injector and detector were maintained at 120 \({}^{\circ}\)C and 260 \({}^{\circ}\)C, respectively. The column temperature was gradually increased from 40 \({}^{\circ}\)C to 250 \({}^{\circ}\)C, and helium was used as a carrier gas with a flow rate of 40 mL min\({}^{-1}\).
# Power Sharing Control in a Grid-Tied DC Microgrid: Controller Hardware in the Loop Validation
## 2 MGIS: Description, Modeling, and Design  
The topology used for the implementation of the MGIS shown in Figure 1 includes a single-phase HFBC made up of a power stage; switches \(Q_{1}\), \(Q_{2}\), \(Q_{3}\) and \(Q_{4}\); a capacitor \(C\); and an inductor \(L\) that has an associated resistance \(r\). This topology is typical for the interconnection of distributed generation systems and \(\mu\)Gs with the MG [42]. Additionally, the proposed topology includes a PA in the DC side, which represents the main source of energy that feeds the DC load, \(R\). When the energy generated in the PA is greater than that consumed by the DC load, the rest of the energy is transferred to the MG. In the event that the energy generated by the PA is not enough to satisfy the demand of the DC load, the necessary energy is extracted from the MG. \(i_{pp}\) represents the electric current supplied by the PA; \(i_{R}\) is the current of the DC load \(R\); \(i_{c}\) is the current in the capacitor \(C\); \(v_{dc}\) is the DC bus voltage, which, in turn, represents the DC side voltage of the HFBC; \(i_{dc}\) is the DC side current of the HFBC; \(v_{ab}\) is the AC side voltage of the HFBC; \(i_{a}\) is the AC side power of the HFBC, which, in turn, corresponds to the electric current of inductor \(L\); and \(v_{g}\), which corresponds to the voltage of the MG.  
The proposal is based on the design of a platform to evaluate the controller's operation used in power sharing between a \(\mu\)G and the MG in a stage prior to its implementation in the field, eliminating the risk of damage to the real device. An isolation transformer is not contemplated in the proposed topology; however, it is recommended to include it in its physical implementation.
## 2 Related Work
In [16] the authors discuss in a qualitative manner the challenges and limitations of FRER. In [17] the authors study the capability of tolerating the faults caused by single event upsets (a specific type of temporary fault) using a duplicated communication path, showing that an adequate schedulability analysis is required to tolerate all faults even with redundant paths. Finally, in [18] the authors present a simulation model for redundancy management and validate the design of the redundancy mechanism using fault injection. Nevertheless, to the best of the authors' knowledge there are no works devoted to evaluating the reliability of TSN-based networks in a quantitative manner. In this article we present three formal models based on the PRISM probabilistic model checker. PRISM has extensively been used to evaluate the reliability of systems, protocols and networks. Some examples of this are [19], where the authors use PRISM to model and evaluate the reliability of a group membership protocol for dual-scheduled time division multiple access networks; or [20], where the authors study the reliability achievable when using fault tolerance mechanisms in an architecture specifically designed for highly reliable adaptive distributed embedded systems. To the best of the authors' knowledge, this is the first article that presents a formal model based on the PRISM probabilistic model checker and a parametric sensitivity analysis to evaluate the reliability achievable by standard TSN and the PTRF mechanism in the presence of temporary faults.
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 4 The Necessity of Considering the Uncertainties in Dam Deformation  
In order to verify whether the interval prediction paradigm can have a positive impact on the performance of dam deformation prediction models compared to the point prediction paradigm, in this section, the deformation PIs constructed by the interval prediction models are transformed into deterministic prediction results for comparison with deterministic prediction.  
Figure 11: The visualization results of SCQP-based models on _PICP_, _MPIW_, and _CWC_: (**a**) _PICP_, (**b**) _MPIW_, and (**c**) _CWC_.  
prediction models. The process of converting PIs into deterministic prediction results can be expressed as follows:  
\[\hat{Y}(X_{i})=[\frac{LB^{\alpha}(X_{i})+UB^{\alpha}(X_{i})}{2}] \tag{24}\]  
It should be noted that the prediction results of both the Bi-SCLSTM and CIE interval prediction come from the Bi-LSTMs based on the loss function of mean square error, and therefore their results are the same and represent the point prediction model. Figure 12 demonstrates the comparison of the result curves by interval prediction methods.  
According to Figure 12, the deterministic prediction results transformed from the deformation PIs are very close to the actual deformation. The \(R^{2}\) of the Bi-SCQLSTM and Bi-QLSTM are 0.975 and 0.952, respectively, which is significantly higher than the \(R^{2}\) of the Bi-LSTM and GPR (see Figure 13). Furthermore, the _MAE_ and _RMSE_ of the Bi-SCQLSTM are much smaller than those of the other models, only 1.003 mm and 1.316 mm, which indicates that the Bi-SCQLSTM has the best performance in both local error and overall error control. Compared with traditional point prediction models, these methods are capable of producing prediction results that are closer to the observed deformation, and the proposed Bi-SCQLSTM still demonstrates superior performance in point prediction experiments, with its advantages primarily reflected in the following two aspects. First, the Bi-SCQLSTM constructs high-quality deformation PIs that accurately capture and explain the inherent uncertainty of dam deformation under the synergistic influence of internal and external factors. In other words, the model successfully captures subtle perturbations in the periodic trends of dam deformation during training, leading to more significant improvements in _MAE_ and _RMSE_ compared to \(R^{2}\). Second, as evidenced by the MPIW of different interval prediction paradigms (Table 1), PIs constructed by the Bi-SCQLSTM exhibit significantly narrower widths than those of other models. This phenomenon can be attributed to the proposed model's stronger robustness against the adverse effects of mapping randomness, resulting in deformation PIs with consistently shorter coverage widths. This reduction in uncertainty is also partially reflected in the output point prediction results.  
Figure 12: Point prediction results of interval prediction models.
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 2 Materials and Methods
### Near-Infrared Modeling Methods  
#### 2.4.1 Spectral Data Preprocessing  
Raw near-infrared spectral data contain valuable information about chemical composition, but they can also be influenced by interferences such as background noise, baseline drift, and molecular scattering. To mitigate these interferences and enhance the predictive accuracy of the model, preprocessing of spectral data is a crucial step prior to modeling [30; 31]. In this study, several preprocessing techniques were employed, including Savitzky-Golay first derivative (FD), Savitzky-Golay smoothing (S-G-S), standard normal variate (SNV), and unit vector normalization [32]. Each method has unique characteristics and yields different effects: the FD effectively removes baseline drift and background interference, the S-G-S improves the signal-to-noise ratio, the SNV corrects for light scattering, and normalization mitigates the effects of path length variations or sample dilution on the spectra.
## 4 Discussion
### Strengths and Limitations  
This study has limitations. First, the number of athletes enrolled is not high. However, a strength is that studied athletes were very similar according to training characteristics (day/week of training, Km/week, years of training). Moreover, each subject served as a control for him/herself, increasing the statistical power, and reducing the effects of confounding factors.  
Second, insulin was not directly assessed in these subjects. This important issue merits further investigation in future studies to understand the link between species of lipids,inflammatory markers and insulin resistance (IR) during exercise. However, all our subjects had no history of IR and type 2 diabetes (T2D) and they showed normal body mass index; therefore, this relationship could be negligible. Nonetheless, it will be interesting to assess exercise effects on lipid-related biomarkers in IR, T2D or obese subjects in future studies.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Discussion  
With the emerging applications of URLLC and the fast development of the sensor-based application, a Cluster-based multiple-access scheme named Cluster-based Orthogonal Frequency Subcarrier based Multiple Access (C-OFSMA) is proposed with 5G URLLC's high requirement adaptation for intra-vehicular data transmission. The URLLC demanded high reliability, approximately 99.999% of data transmission within extreme short latency of less than 1 ms. We consider a short-range uplink communication system where the non-periodic sensors are ubiquitously distributed around the receiver. The Cluster divide, Bandwidth allocation strategy, random channel, and frequency selection are mentioned in the system model in Section 3. When transmitting a single packet from the duplications, a frequency band is first selected. Afterwards, a subcarrier channel \(f_{set_{x}}\) is also picked randomly. In our proposed system, a diverse number of clusters, frequency band, and subcarrier channels are included to ensure higher reliability of the packet transmission. The C-OFSMA implies that increasing the diversity causes decreasing the minimum subcarrier demands in each frequency band. Furthermore, analyzing the impacts of diverse packet duplication in minimum subcarrier detection and reliability response. Therefore, the C-OFSMA access scheme incorporated the packet diversity principle to ensure the URLLC's expected 99.999% reliability and random operation allows short-latency within 1 ms packet transmission.
## 6 Simulation Results
### Economic Analysis through One Year
#### 6.2.3 Violations  
It is difficult to assign a monetary value to the PRR violations since this is unique for each agreement. However, PRR violations throughout the year are presented in this work to serve as an indicator of the accuracy and performance of each PRRC method. Figure 23 shows the cumulated measurement of the minutes when the output PRR was greater than \(\text{PRR}_{\text{max}}\). It is noted that most of the PRR violations in Coto Laurel occurred during the summer, which is characterized for having the highest solar radiation profiles according to Figure 8. The main cause of these violations over the year was because the ESS reached its minimum (30%) or maximum capacity (100%). This implied that the ESS was unable to support the PRRC. However, the PRR violations were relatively small, considering that the RS was the PRRC method with the most violations throughout the year (84 min). The PDS and the SOLPF were the PRRC methods that did not show any PRR violations throughout the year.
## 2 Related Work
** While it fundamentally belongs to the category of feature-based methods, we categorize it separately because of its prevalence in the existing work and significant methodological and conceptual differences from conventional feature-based methods.On one hand, the existing DL-based methods can be classified into time-domain waveform-based ones and transform-domain image-based ones, according to the types of data fed into the neural networks. On the other, they can be classified based on the different neural network architectures that they are built on. In particular, long short-term memory (LSTM) networks are employed to extract temporal features from input signals to improve classification accuracy [23; 24]. Convolutional neural network (CNN)-based automatic modulation classification approaches are proposed in [21; 22]. The authors in [25; 27] propose hybrid CNN-LSTM models combining CNN's spatial feature extraction and RNN's temporal sequence processing capabilities for time-dependent signals. Ref. [26] adapts a convolutional long short-term deep neural network (CLDNN) to AMC issues, while [28] further proposes a multi-channel CLDNN (MCLDNN) approach. Leveraging the residual network (ResNet), a parallel architecture integrating residual network (ResNet) modules with gated recurrent units (GRUs) and cascaded LSTM networks with two-layer ResNets is developed in [29; 30]. A three-stream deep learning framework is designed in [28], leveraging the independent/joint features of in-phase/quadrature (I/Q) components through multi-channel inputs to enhance spatiotemporal feature fusion. In [31], modulation classification is carried out by adopting a multi-channel input strategy and building a multi-scale neural network (MSNN) with multi-head self-attention and bidirectional GRUs. The above existing works show good examples of AMC. However, the number of works focusing on AMC for OTFS systems is very limited. In particular, although existing deep learning-based works demonstrate the power of applying deep learning techniques in the field of AMC, determining how to leverage deep neural networks to enhance the accuracy of AMC for OTFS is still an urgent issue to tackle. In [33], an AMC approach for OTFS systems is proposed by designing a hybrid CNN and LSTM network with a residual stack to process I/Q symbols. However, it primarily relies on time-domain features of the received signal, while the intrinsic DD-domain features of OTFS signals remain unexploited. Moreover, although embedded-pilots are harnessed in channel estimation in OTFS systems [34; 35], as far as we are aware, there is no AMC-related work incorporating them to boost the performance.In this paper, we propose an AMC approach for OTFS systemsby building a dual-stream CNN-based framework to incorporate both time-domain and DD-domain information and by developing a differentiated embedded pilot insertion scheme. The experimental results demonstrate that the proposed approach can achieve high accuracy even under extreme conditions.
## 2 Materials and Methods
### Variables  
Medical professionals recorded muscle injuries during the regular daily medical checkpoints, occurring right before or after the training session or match. All injuries were reported to the internal database and were immediately shared among the different compartments of the club. Muscle injuries were recorded and classified either as (i) functional muscle injuries represented as delayed onset muscle soreness (DOMS) or (ii) structural injuries i.e., ruptures (a partial or complete tear of muscle fibers). The accuracy and the consistency of the clinical diagnosis were obtained with the use of ultrasound imaging. Permission for return to play was granted by the medical doctor once the ultrasound imaging showed a total healing of the tissue, with a pain-free contraction of the muscle and an appropriate overall fitness status evaluated by the whole medical and coaching staff. A reinjury was defined as an injury to the same location and categorized as the same type after the player had returned to full participation following the initial injury. Injury severity, expressed as a total number of days in absence from training, was presented separately for DOMS and rupture injuries, respectively. Football exposure was calculated for all the observed seasons and was reported as the number of training and match hours. The total player match exposure time in hours for a team was given by (\(N_{\text{m}}\) * \(P_{\text{m}}\) * \(D_{\text{m}}/60\)) where \(N_{\text{m}}\) is the number of team matches played, \(P_{\text{m}}\) is the number of players in the team, and \(D_{\text{m}}\) is the duration of the match in minutes. Similarly, the total training exposure time in hours is given by (\(P_{\text{t}}\) * \(D_{\text{t}}/60\)), where \(P_{\text{t}}\) is the number of players attending a training session and \(D_{\text{t}}\) is the duration of the training session in minutes. Exposure time was adjusted for the 2019/2020 season, where there was a complete cessation of training due to the COVID-19 lockdown. In particular, muscle injury incidence was calculated as the number of muscle injuries sustained per 1000 h of the training and match exposure, respectively. Also, injuries were classified as new one or a reinjury, according to the affected body region, contact nature of injuries, and training/game occurrence.
## 3 Results and Discussion
### Comparison between Strength Properties of OMGPC
#### 3.3.2 Relation between Compressive, Flexural, and Splitting Tensile Strengths  
The relationship between the compressive, flexural and splitting tensile strength of OMGPC containing RESIN8 is presented in Figure 15. The relationship between the two strengths of conventional concrete was established in ACI committee 318 [83] and Eurocode EN 1992 [84], as depicted in Equations (5) and (6), respectively. [PERSON] et al. [68] obtained a relationship between the compressive and flexural strengths of convectional concrete containing waste plastic aggregate from the data gathered from various studies performed by different authors, as shown in Equation (7).  
\[\text{f} =0.62\lambda\text{ }_{\text{c}}^{0.5} \tag{5}\] \[\text{f} =0.30_{\text{c}}^{0.667}\] (6) \[\text{t} =0.09_{\text{c}}^{0.97} \tag{7}\]  
where \(\lambda\) = is a modification factor, \(\lambda\) for normal and lightweight concrete = 1, and \(\text{f}\) = flexural strength (MPa).  
The compressive and flexural strength relationship of the OMGPC containing RESIN8 obtained in this study is presented in Figure 15 and Equation (8). The correlation coefficient (R\({}^{2}\)) for OMGPC containing RESIN8 is 0.9063, which indicates a good alignment between the model and data, with the closest R\({}^{2}\) to 1 symbolising good correlation. Equation (9) presents the relationship between the compressive and STS of OMGPC containing RESIN8 with an R\({}^{2}\) of 0.9466, as depicted in Figure 15. In contrast, the power model equation for predicting the flexural and compressive strength relationship of OMGPC containing FWG  
Figure 14: Relationship between the \(\text{c}\) and the hardened unit weight of OMGPC containing RESIN8 and FWG.  
has an R\({}^{2}\) of 0.3759, which is not a good fit for strength prediction; hence, the graph is not presented in this study.  
\[\begin{array}{rl}\text{\tiny{f}}&=0.1557^{0.9783}_{\text{\tiny{c}}}\\ \text{\tiny{t}}&=0.005^{1.8702}_{\text{\tiny{c}}}\end{array} \tag{8}\]  
Figure 16 presents the relationship between the compressive and flexural strength of OMGPC and previous studies on concrete incorporating fine waste plastic [85; 86]. The power model equation is depicted in the figure. However, there is variation in the strength predictive equations obtained by various authors, re-establishing that the elements that affect strength properties are slightly different. Further, the relationship between the compressive strength and STS of OMGPC incorporating RESIN8 and some of the previous studies on convectional concrete incorporating fine waste plastic is presented in Figure 17 [82; 87].
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
## 4 Results and Discussion
### The Exergetic Efficiency of the ISCC Power Plant  
The exergetic efficiency of the ISCC power plant was calculated for different solar heat power inputs. The comparison was implemented at three different ambient temperatures: 5, 20, and 35 \({}^{\circ}\)C.  
The ISCC power plant energetic efficiency for solar heat inputs 0 MW, 50 MW, and 75 MW at different ambient temperatures is depicted in Figure 9. The exergetic efficiency of the ISCC power plant was calculated based on the design condition data for the different solar heat power inputs.  
Figure 9 reveals that the exergetic efficiency of the ISCC power plant is inversely proportional to the ambient temperature, where it decreased from 47.2% to 46% with increasing ambient temperature from 5 \({}^{\circ}\)C to 35 \({}^{\circ}\)C for solar heat input equal to 75 MW. In addition, it decreased from 48.2% to 46.58% when the ambient temperature increased from 5 \({}^{\circ}\)C to 35 \({}^{\circ}\)C for solar heat input equal to 50 MW.  
Figure 9 also illustrates the exergetic efficiency of the combined cycle regime (solar heat input equal to 0 MW) at different ambient temperatures. In the absence of the solar field, the exergetic efficiency of the plant reached 49.18% and 47.21% at ambient temperatures 5 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, respectively. This demonstrates that the exergetic efficiency of the ISCC power plant in Kureimat has higher efficiency under the combined cycle regime than under the ISCC regime, as shown in Figure 9. This may be due to the existence of the solar field, which needs precise design optimization of solar energy integration in a CCPP.  
Figure 8: Exergetic efficiency of different components of the ISCC at different ambient temperatures for solar heat input equal to 75 MW.  
Like the overall thermal efficiency, the exergetic efficiency of the ISCC power plant decreased with increasing ambient temperature, mainly at ambient temperature 35 \({}^{\circ}\)C. This may be due to the sharp decrease in the exergetic efficiency of the condenser and the solar field with increasing ambient temperature, as shown in Figures 6-8. These figures also show that the exergetic efficiency of the gas turbine and the HRSG decreased with increasing ambient temperature, and that also affected the exergetic efficiency of the ISCC power plant, as shown in Figure 9.