esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Isolation Devices for Protecting RC Buildings: The Frangipane School in Reggio Calabria
## 4 Finite Element Numerical Analysis
### Modelling Strategy  
The modelling strategy consisted of the use of 1D beam elements with nominal geometrical properties according to Table 1 and lumped plasticity hinges to include the effect of possible degradation and damage evolution. More precisely, the mechanical characterization was carried out based on Eurocode 8 (EC8) [35] and as in Figure 8.  
Compared to literature and practice assumptions, a basic and conventional but consolidated modelling approach was used for the Frangipane school building in terms of load-bearing members and seismic devices, which have both a primary role for seismic assessment purposes.  
According to several research efforts, the use of 1D elements and lumped mass plasticity models for RC frame structures in seismic conditions is rather efficient in computational cost and robust in terms of the accuracy of results. Comparative non-linear numerical analyses reported in [36] for a five-story RC frame showed, for example, a rather good correlation of seismic performances for FE models based on lumped plasticity or finite length hinges approaches. The results of extended parametric analyses proposed in [37] for FE models based on lumped plasticity or distributed plasticity formulations, on the other side, gave evidence of scattered predictions in terms of the inter-story drift ratio and maximum base moment from a comparison of plasticity formulations on selected buildings. When possible, however, empirical expressions and constitutive laws calibrated to experimental data should be preferred to conventional plastic models, which are already implemented in commercial codes [38].  
\begin{table}
\begin{tabular}{c c c} \hline  & **Beams** & **Pillars** \\ \hline
**Story** & \(f_{c,m}\) **[MPa]** & \(f_{c,m}\) **[MPa]** \\ \hline F & 25.81 & - \\ PT & 26.54 & 20.52 \\ \(1^{\circ}\) & 18.10 & 11.00 \\ \(2^{\circ}\) & 21.39 & 9.37 \\ \(3^{\circ}\) & 17.62 & 7.78 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of concrete, based on in-field and laboratory experiments (average values).  
Similar attention should also be spent on the mechanical characterization of seismic isolation devices and the use of experimentally derived mechanical features for seismic analyses [5]. In the specific application of friction devices as in the present study, literature efforts proved that both static and dynamic friction phenomena, which are intrinsic to selected devices (but disregarded for the static component in most commercial codes), could have additional effects on predicted seismic response of the RC frame components [27]. Such a calibration process--for plasticity formulations and static friction terms--would result in additional precision for the behaviour assessment of load-bearing elements in ordinary RC buildings but structural systems in general, which are located in seismic areas, including base isolation technologies.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 7 Conclusions  
In this paper, we proposed a low-complexity SeCNN-OTFS method to reduce the computational cost of signal detection in OTFS systems. By replacing conventional convolutional layers with depthwise separable convolutions, the proposed model achieves approximately an 81% reduction in parameter count compared to the 2D-CNN framework. To enhance feature representation while maintaining computational efficiency, we designed a lightweight SeparableBlock architecture, which incorporates global residual learning in the identity branch and recursive learning in the residual branch. Furthermore, a channel attention mechanism was introduced to adaptively highlight salient features while suppressing irrelevant responses, thereby improving robustness under noisy and high-mobility channel conditions. Simulation results confirm that the proposed SeCNN-OTFS achieves competitive BER performance, while significantly reducing model complexity. These findings demonstrate that SeCNN-OTFS is a promising and scalable solution for real-time wireless communication systems, particularly in resource-constrained scenarios such as satellite or IoT communications.  
Conceptualization, Y.W.; methodology, Y.W.; validation, Y.W. and Z.Z.; formal analysis, Y.W.; investigation, Y.W. and Z.Z.; resources, Z.C.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, H.L. and T.Z.; visualization, Y.W.; supervision, Z.C. and H.L.; project administration, Y.W. and Z.C.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Project of Ministry of Science and Technology (Grant D20011).  
Not applicable.  
The dataset in this paper is obtained from Matlab.  
The authors declare no conflicts of interest.
## 4 Model Training and Experimental Results
### Experiment Based on Publicly Available Dataset
#### 4.1.1 Ablation Experiment  
We conducted comparative experiments using four distinct network architectures: the ResNet50 model, the ACResNet model (which integrates ACmix into ResNet50), the InResNet model (introducing inception-attention into ResNet50), and the InRes-ACNetmodel. We employed the cross-entropy loss function for forward propagation, a standard approach in addressing multi-classification problems, as illustrated in Equations (5) and (6).  
\[loss=\frac{1}{S}\sum_{i}^{S}loss_{i} \tag{5}\]  
\[loss_{i}=-\sum_{c=1}^{M}y_{ic}\log(p_{ic}) \tag{6}\]  
where \(loss\) is the average loss rate; \(loss_{i}\) is the loss rate of the \(i\)th sample; \(S\) is the total number of samples; \(M\) is the number of classes; The value of \(y_{ic}\) can be 0 or 1, it is 1 if the true class of the \(i\)th sample is to class \(c\), 0 otherwise; \(p_{ic}\) is the predicted probability that the observed sample \(i\) belongs to class \(c\).  
The results of the ablation experiments are presented in Table 1. The ResNet50 model demonstrated an accuracy of 82.71% in the sEMG gesture recognition test, attributed to its robust feature extraction capabilities embedded in the deep convolutional structure. The ACResNet model achieved an increased accuracy rate of 85.98%, owing to the incorporation of the ACmix module, which facilitated more precise information integration, thereby enhancing the model's ability to comprehend and characterize image content.  
The InResNet model demonstrated a slight improvement in accuracy to 86.31%. This enhancement is attributed to the integration of the inception-attention module, which enables the model to capture multi-scale feature information. The InRes-ACNet model, combining the inception and ACmix modules, achieved a notable accuracy of 87.82% in sEMG gesture recognition, significantly surpassing other individually enhanced models. Compared with the ResNet50, the recognition accuracy of the ACResNet and InResNet models increased by 3.24% and 3.6%, respectively. The InRes-ACNet model's accuracy improved by 5.11%, with only a 0.23 M increase in the number of parameters. As indicated by the parameter values in Table 1, the InRes-ACNet model effectively extracts both local details and global information of sEMG signals, thanks to its improved module structure, while maintaining parameter efficiency, thus significantly enhancing model performance.  
In experiments, it was demonstrated that both the inception-attention module and the ACmix module individually enhance the model's recognition performance. The InRes-ACNet model, which integrates these two modules simultaneously, exhibits a superior classification performance in sEMG gesture recognition tasks.
## 7 Effects of Roasting on Lipid Content
### Pistachios  
A similar trend was observed in pistachios. [PERSON] et al. (2017) reported that raw pistachios contained 7.2% saturated fatty acids, 60.8% monounsaturated fatty acids (MUFAs), and 23.4% linoleic acid. After roasting, the saturated fatty acid content slightly increased to 7.6%, while the MUFA content rose to 61.5%. The linoleic acid content also exhibited a minor increase, reaching 23.9% in roasted pistachios [80].  
[PERSON] et al. (2016) investigated the yield and quality of oil extracted from raw and roasted pistachios using cold pressing. Pistachios were roasted using two methods: a conventional oven at 160 \({}^{\circ}\)C for 20 min and a microwave oven at 1 kW for 6 min, achieving an internal kernel temperature of 130 \({}^{\circ}\)C. Oil yield from cold pressing was 25.3% for raw pistachios, 24.6% for conventionally roasted pistachios, and 23.2% for microwave-roasted pistachios. Compared to Soxhlet extraction, the cold pressing yields were 31.5%, 33.2%, and 32.9%, respectively. Chemical characterization of the oils revealed no significant differences. Oleic acid constituted 55.3%, 55.8%, and 56% of the total oil content in raw, conventionally roasted, and microwave-roasted pistachios, respectively. The linoleic acid contents were 31.3%, 30.6%, and 30.4% of total fatty acids in the three oil samples, respectively [89].  
[PERSON] et al. (2015) observed a similar trend in pistachios. Consistent with the findings for hazelnuts and almonds, the oleic acid content decreased slightly from 53.8% in raw pistachios to 53.2% in pistachios roasted at 185 \({}^{\circ}\)C for 21 min. The linoleic acid content showed a minor increase, from 30% in raw pistachios to 30.5% in roasted pistachios. The palmitic acid content also increased slightly, from 10.9% in raw pistachios to 11% in roasted pistachios [88].  
[PERSON] et al. (2022) studied lipid oxidation in hazelnut oil. At the beginning of the experiment, the initial levels of PUFAs, MUFAs, and short-chain fatty acid (SFAs) were 136 mg/g, 881 mg/g, and 78 mg/g, respectively. After 40 days of oxidation at 60 \({}^{\circ}\)C, the levels of PUFAs decreased to 65 mg/g, MUFAs decreased to 727 mg/g, and SFAs increased to 72 mg/g. Furthermore, the authors observed that lipid oxidation was slow during the first 15 days, followed by a period of accelerated oxidation. This initial delay is attributed to the protective role of antioxidants, which gradually became depleted, leading to increased lipid oxidation [90].  
In conclusion, consistent with the findings of [PERSON] et al. (2015), roasting processes do not typically induce significant alterations in the fatty acid composition of nuts, and no detectable increase in trans fatty acids was observed [88].
###### Abstract  
The purpose of this study was to determine the annual energy consumption that can be attributed to heating, ventilation, and air conditioning (HVAC) systems' mixing temperature error. To develop a mixing temperature prediction model for a single-duct variable air volume (VAV) system, the mixing temperature was measured using 15 temperature sensors installed in an HVAC mixing chamber as well as the existing air handling unit's (AHU) mixing temperature sensor. The mixing chamber was modeled using computational fluid dynamics (CFD), and a coefficient of variation of the root-mean-square error of 7.927% indicated that the model was reliable. Next, CFD simulation cases were formulated, and the temperature distribution of the mixing chamber was analyzed. This revealed that the amount of outdoor airflow input and the change in the temperature distribution of the mixing chamber were directly proportional to each other and that the mixing temperature measurements for the mixing chamber were not accurate. The mixing temperature prediction model was developed through multiple regression analysis and was successfully applied and verified. Compared with the measurements provided by existing mixing temperature sensors, the mixing temperature prediction model indicated an absolute error of 0.008-0.42 \({}^{\circ}\)C, confirming the model's prediction performance.  
computational fluid dynamics (CFD); variable air volume (VAV) system; air handling unit (AHU); mixing chamber; mixing air temperature; prediction model
# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 2 Materials and Methods  
### Proposed Methods  
In this study, the authors aimed to detect arcing defects in switchgear using DL techniques in both the time and frequency domains. To do so, the researchers followed a multi-step process depicted in Figure 1. Firstly, sound data was collected and transformed into images using Mel-Spectrogram and supplemented with gathering the dataset of switchgear faults (arcing with non-arcing). The dataset was then split into three phases: training, validation, and testing. To achieve high accuracy, features were extracted using two methods: 1D-CNN and the classification algorithm (LSTM) for the hybrid model. The results of the hybrid model were compared to those of the lone models (1D-CNN and LSTM) to identify arcing faults in both the time and frequency domains. This approach can be useful for identifying faults in switchgear to prevent bigger losses and improve the overall performance of the equipment.
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
## 2 Theoretical Model for Melting Curve (Liquidus) of a Mixture
### Example: The Mo-W System  
As an example of the practical use of our new analytic model, let us consider a mixture of molybdenum and tungsten which, according to [18], form a continuous solid solution without any eutectics. According to studies in the literature [19], the melting curves of Mo and W are, respectively, \(T_{m}^{\rm Mo}(P)=2896\,(1+P/36.6)^{0.43}\) and \(T_{m}^{\rm W}(P)=3695\,(1+P/41.8)^{0.50}\). Hence, in Equation (8), \(T_{\ell,1}(0)=2896\), \(T_{\ell,1}^{\prime}(0)=34.0\), and \(T_{\ell,2}(0)=3695\), \(T_{\ell,2}^{\prime}(0)=44.2\). Then, the best fit of the form (8) with the above parameters to the experimental data of Ref. [18] brings up \(C=-0.4\). In Figure 1, the resulting \(T_{\ell}(x)\) is compared to the liquidus of Mo\({}_{1-x}\)W\({}_{x}\) from experiments.  
In this case of Mo-W, a variation in \(C\) by as much as \(60\%\), \(C=-0.4\pm 0.24\), causes a shift in the model liquidus within \(\sim 50\) K, or \(\sim 1.5\%\) only. Specifically, the largest deviations from the \(C=-0.4\) liquidus are \(+49.7\) K at \(x=0.564\) with \(C=-0.64\), and \(-51.4\) K at \(x=0.563\) with \(C=-0.16\), compared to \(T_{\ell}=3392.0\) K at \(x=0.57\) with \(C=-0.4\). Thus, the exact knowledge of the value of \(C\) may not be really necessary for the model to produce the liquidus of a mixture in good agreement with experiment.  
Figure 1: The ambient phase diagram of the Mo-W system: comparison of the liquidus curve produced by the theoretical model presented in this work to the experimental datapoints of Ref. [18] (open circles).
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 3 Materials and Methods
### Preprocessing of CT Image Data
#### 3.3.1 Dataset Splitting and Preprocessing  
In the initial phases of dataset splitting and pre-processing, we divided the MSD Task03 liver dataset into three parts to facilitate training, testing, and validation. The dataset comprises 3D slices in DICOM format, which we transformed into the NIFTI file format using the dicome2 nifti library. This conversion was necessary to ensure compatibility with the Monai framework. To ensure balanced distribution and facilitate model evaluation, we allocated 70% of the data to the training set, 15% to the testing set, and the remaining 15% to the validation set. We employed various cross-validation techniques during training to prevent overfitting and enhance the model's generalization ability. Additionally, we applied a transformation to select the key work of either the image or label or both to diversify and strengthen the training dataset. This approach aimed to avoid pattern memorization by the model and subsequently improve its generalization ability. We incorporated dropout and L2 regularization techniques within the model architecture to prevent overfitting. Using dropout layers, we randomly deactivated neurons during training, thus reducing co-adaptation and enhancing generalization. The L2 regularization technique penalized large weight values, thereby preventing the model from fitting noise in the training data. We recorded the training loss, training metric, testing loss, and testing metric for each epoch, implementing early stopping to halt model training when the dice loss ceased decreasing or began increasing. Our dataset splitting and pre-processing pipeline ensured the robustness of training and validation of the ResUNet architecture, preventing overfitting and generating reliable segmentation results.
# Feedback Characterization for Enhanced Heat Recovery from Composing Processes: A Review
###### Abstract  
Compost Heat Recovery Systems (CHRS) sustainably capture heat from composite waste biomass, helping reduce greenhouse gas emissions and fossil fuel reliance. The choice of feedstock affects the performance of CHRSs as it controls the microbial activities and the amount of heat generated. This review evaluates plant-based, animal-derived, and non-agricultural feedstocks to optimize CHRS energy recovery. A systematic review of 244 studies, published from 1996 to 2023 and available on Scopus, Web of Science, and external databases, categorized feedstocks based on properties like carbon-nitrogen ratio (C/N), moisture content, bulk density, and heating value to assess their impact on energy recovery and compost quality. The review followed the PRISMA guidelines, excluding irrelevant documents and those that lacked quantitative data. Animal-based materials, which have high levels of moisture and nutrients, such as nitrogen (14.50-32.20 g/kg TS) and phosphorus (13.0-13.5 g/kg TS), promote rapid growth of microbes and consistent heat production supported by their stable carbon content (353.8-450.0 g/kg TS) and optimal C/N ratios (5.90-28.90). On the other hand, plant-based materials that are rich in volatile solids (327.2-960.0 g/kg TS) and lignin (36.7-290.0 g/kg TS) offer a steady and prolonged release of heat but decompose more slowly.  
Keywords:compost heat recovery systems; thermocompost; waste-to-energy ratio; feedstocks; energy recovery; agricultural waste +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Lesion Segmentation
#### 2.1.1 Unsupervised Method: Energy Minimization Based Algorithm  
Unsupervised methods do not require a labelled training dataset. One of the main advantages they have is the low computational cost [29]. Another one is that they do not need a large database as no training is performed. In contrast, its performance could not be robust for low quality images, or some interaction with the user is needed to achieve a good performance.  
There are many state-of-the-art unsupervised algorithms in the literature and some of them have been explored in this paper (edge-based active contour, region-based active contours, segmentation based on convex optimization). However, for the final analysis an energy minimization method was chosen because it is less dependent on the parameter setting. In this kind of algorithm, an energy measure, which includes region and boundary information, is minimized to solve the segmentation problem. Over recent years, energy minimization algorithms based on convex relaxation have been developed [29; 30]. This paper presents an algorithm using convex relaxation based on a previous work by the authors [31; 32]. The original idea was proposed by [PERSON] and [PERSON] [33]. It consists of posing the problem of segmentation as a problem of minimization of a convex energy function. In this energy function, the distance between the histograms of each region within the image and histogram models is minimized.  
Two histogram models were defined for each dermoscopic image, one for the foreground (the lesion) and the other for the background (the skin). To generate these two histogram models, the algorithm requires a manual selection of a partial part of each region (Figure 1).
# Train Scheduling with Deep Q-Network: A Feasibility Test
## 6 Conclusions  
In this paper, we consider a train scheduling problem in which both local and express trains are scheduled. For a given target schedule on a unidirectional single-track railway, we decompose the problem into separate subproblems in which a single express train and its preceding local trains are to be scheduled. We formulate each subproblem as a Markov decision problem and solve it with deep reinforcement learning technique.  
We demonstrated the performance of the proposed method over 150 instances which are further divided into three groups with respect to the number of local trains. For the instances with a small number (2 and 3) of local trains, the proposed method is compatible with the exact algorithm (e.g., branch-and-bound or branch-and-cut methods). However, for larger instances, it still requires an improvement to obtain better schedules.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \# & **MIP** & **DQN** & **Gap (\%)** & **\#** & **MIP** & **DQN** & **Gap (\%)** \\ \hline
1 & 11,186 & 11,942 & 6.76 & 26 & 11,278 & 12,034 & 6.70 \\
2 & 9248 & 9248 & 0.00 & 27 & 19,554 & 29,198 & 49.32 \\
3 & 18,4  
The advantage of the proposed method is that the learned policy can be used for any instances regardless of the number of local trains. Another advantage is its possible extension to use more complex objective function (e.g., energy consumption, passenger crowdedness, etc.) with alternative forms of rewards. However, this possibility is still conceptual and therefore empirical validations are needed as one of the future studies.  
The obvious next step for future research is to devise an effective exploration strategy by which the agent can approximate the \(Q\)-value for diverse states. Furthermore, we do not have enough experiments on possible values of hyperparameters such as the architecture of the neural network and the parameters related to its training (e.g., learning rate). Therefore, more careful investigations, for example, Bayesian optimization, are required.  
Conceptualization, S.O. and Y.M.; methodology, Y.M.; software, I.G. and Y.M.; validation, I.G., S.O., and Y.M.; formal analysis, Y.M.; investigation, I.G.; resources, S.O.; data curation, I.G.; writing--Original draft preparation, I.G.; writing--Review and editing, Y.M.; visualization, I.G.; supervision, Y.M.; project administration, S.O.; funding acquisition, S.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Railroad Research Institute grant number PK2002B2.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.