esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
###### Abstract  
Our objective is to test the published models of partonic energy loss, particularly those describing the energy loss mechanisms of quarks traversing nuclear matter, within the framework of semi-inclusive deep inelastic scattering. Our methodological approach focuses on quantifying the quark energy loss in cold matter by analyzing the positive pions (\(\pi^{+}\)) produced in various nuclear targets, including deuterium, carbon, iron and lead, while our first approach only includes deuterium and carbon. Before normalizing the pions' energy distribution to unity to perform a shape analysis, acceptance corrections were performed to account for the detector's efficiency and ensure accurate comparisons of the spectra. By normalizing the energy spectra of \(\pi^{+}\) produced from these distinct targets and based on the Baier-Dokshitzer-Mueller-Peigne-Schiff theory, which posits that quark energy loss depends only on nuclear size, it is assumed that the energy distributions of the targets will exhibit similar behavior. For this normalization, an energy shift between these distributions, corresponding to the quark energy loss, is identified. To ensure accuracy, statistical techniques such as the Kolmogorov-Smirnov test are used. The data used to test and explore the analysis technique and method were from the CLAS6 EG2 dataset collected using Jefferson Lab's CLAS detector.  
Keywords:hadronization; semi-inclusive deep inelastic scattering; cold QCD matter; radiative energy loss +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Micro- and Macro-Mechanical Analysis Models for Fiber-Reinforced Composites
It introduces a contact coefficient \(C\) to characterize macro elastic mechanical properties of composite laminates. When \(C=0\), it will denote the series model. Instead, when \(C=1\), it will denote the parallel model. The empirical formula for the contact coefficient \(C\) can be also studied. \[C=0.4V_{f}-0.025\text{ }or\text{ }C=0.2 \tag{5}\]  
Additionally, a typical quantification model derived by [PERSON] [28] is given below. \[ \begin{array}{l}E_{1}=V_{f}E_{11}^{f}+V_{m}E_{m}\\ E_{2}=\frac{(V_{f}+V_{m}a_{11})(V_{f}+V_{m}a_{22})}{(V_{f}+V_{m}a_{11})(V_{f}^{ \prime}S_{2}^{\prime}+a_{22}V_{m}s_{22}^{m})+V_{f}V_{m}(s_{21}^{m}-S_{21}^{ \prime})a_{12}}\\ v_{12}=V_{f}v_{f}+V_{m}m\\ G_{12}=G_{m}\frac{(G_{12}^{f}+G_{m})+V_{f}(G_{12}^{f}-G_{m})}{(G_{12}^{f}+G_{m} )-V_{f}(G_{12}^{f}-G_{m})}\end{array}  \tag{6}\]  
where \(a_{11},a_{22},a_{12},S_{11}^{f},\cdots,S_{12}^{m}\) are functions of material properties for constituents including the fibers and matrix. They can be calculated through the following formulas. \[a_{11}=E_{m}/E_{11}^{f},a_{22}=0.5(1+E_{m}/E_{22}^{f}),a_{12}=(S_{12}^{f}-S_{12 }^{m})(a_{11}-a_{22})/(S_{11}^{f}-S_{11}^{m}) \tag{7}\]  
Additionally, the compliance matrixes of the fiber and matrix materials are listed as  
\[[S_{ij}]^{f}= \begin{bmatrix}\frac{1}{E_{11}^{f}}&-\frac{v_{12}^{f}}{E_{11}^{f }}&-\frac{v_{12}^{f}}{E_{11}^{f}}\\ -\frac{v_{12}^{f}}{E_{11}^{f}}&\frac{1}{E_{22}^{f}}&-\frac{v_{23}^{f}}{E_{22}^ {f}}\\ -\frac{v_{23}^{f}}{E_{11}^{f}}&-\frac{v_{23}^{f}}{E_{22}^{f}}&\frac{1}{E_{22}^ {f}}\end{bmatrix} ,[S_{ij}]^{m}= \begin{bmatrix}\frac{1}{E_{m}}&-\frac{v_{m}}{E _{m}}&-\frac{v_{m}}{E_{m}}\\ -\frac{v_{m}}{E_{m}}&\frac{1}{E_{m}}&-\frac{v_{m}}{E_{m}}\\ -\frac{v_{m}}{E_{m}}&-\frac{v_{m}}{E_{m}}&\frac{1}{E_{m}}\end{bmatrix}  \tag{8}\]  
Therefore, as a conclusion, macro elastic mechanical parameters of the composite lamina can be viewed as functions of the material properties of constituents. Furthermore, when micro input parameters, including elastic mechanical properties of constituents, volume fractions and model parameters, are dealt with as uncertain parameters, macro elastic mechanical parameters of composites are also uncertain. One can make use of some statistical or non-statistical methods to quantify them from the perspective of theoretical analysis or experimental studies. As a result, the uncertainty quantification of composites will play an important role in the structural response analysis of composite laminates.
# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 2 Contributed Papers
### Monitoring Ecohydrological Processes  
In this category, one paper is published. [PERSON] et al. [15] investigated how water transfer in arid northwestern China influences the wetland ecosystem via surface and groundwater interactions. In arid and semiarid regions, water transfer is a useful way to prevent vegetation degradations and maintain healthy ecosystems. This study analyzed the spatiotemporal pattern of vegetation coverage before and after water transfer in Qingtu Lake and the surrounding area. The results show that water transfer from the upstream contributes to the expansion of water bodies and vegetation, particularly for the condition of fractional vegetation coverage of 30-50%. The groundwater and soil water content increase can remain at high levels for the following months after water transfer, suggesting that the transferred water can be stored as ground water or soil water due to the strong surface and subsurface interactions, which provide water use for vegetation in the following year.
## 2 Models and Methods
### Numerical Model
#### 2.2.4 Initial Condition  
To solve the parental equation for unsteady flow using the LES (Large Eddy Simulation) turbulence model, an initial condition was required. In the beginning for \(t=0\), the velocity vector at each point is equal to the velocity vector calculated in the steady-state using the \(k-\omega\) SST turbulence model (45). Pressures at each surface element were calculated every two computational time steps.  
The maximum frequency related with the Nyquist frequency was 25000 Hz. These sound frequencies cover the full range of human hearing.  
\[\begin{array}{ll}\forall_{i,j,k\in N^{+},}&u_{i,j,k}(t=0)=u_{i,j,k}^{k-\omega}(t =\infty)\\ \underset{\begin{subarray}{c}i\leq i_{max},\\ j\leq j_{max},\\ k\leq k_{max}\end{subarray}}{\forall}&\end{array} \tag{45}\]  
where:  
\(i\)--grid index on the x axis  
\(j\)--grid index on the y axis  
\(k\)--grid index on the z axis.
## 4 Discussion
Future multicenter studies involving patient cohorts of a larger size are required to provide a more comprehensive assessment of _EGFR_ mutation status, considering potential ethnic differences. Although the mutation status predicted by radiomics may not currently directly influence patient management, its potential utility lies in guiding subsequent molecular testing strategies, particularly when tissue availability is limited. Furthermore, algorithms with greater predictive ability can be developed for other mutations. Through the identification of highly relevant mutation tests based on radiomics predictions, clinicians can prioritize and tailor further molecular analyses, thereby reducing test costs. Moreover, a negative initial test result in a tumor predicted by radiomics to harbor a specific mutation can prompt clinicians to repeat the test, potentially detecting mutations with low allele frequencies and ensuring that patients do not lose the opportunity for targeted treatment. Radiomic features, such as wavelet transforms, capture both spatial and frequency information, providing a quantitative measure of tumor heterogeneity. These features may correlate with variations in tissue density and morphology, offering insights into the biological behavior of tumors. By interpreting these features in the context of tumor biology, we can bridge the gap between machine learning and clinical relevance [27]. In our study, areas of higher texture complexity indicated more aggressive tumor phenotypes. Additionally, our study indicated a Skewness feature, which can be interpreted as the asymmetry in the distribution of voxel intensities in a region of interest (ROI). This was also interpreted as being related to heterogeneity in the tumor tissue. In this study, Wavelet-LLL/HLL represents the decomposition into low (L) and high (H) frequency components in different directions. The texture in different levels in the image, correlating with structural heterogeneity in the tumor, also indicated an _EGFR_-positive state. The high kurtosis indicates that there are extreme values which may be linked with the irregular tissue composition in the tumor. The current NCCN guidelines recommend minimally invasive surgery in suspected cases of stage 1A lung carcinoma. However, studies have revealed relationships between _EGFR_ mutations and increased risks of brain metastasis when the cancer is diagnosed and after curative resection [28; 29; 30]. The determination of radiomics signatures on CT images for _EGFR_ mutations may indicate the need for preoperative brain MRI in individuals with Stage 1A (Tabc, N0) lung cancer. To summarize, the features that were extracted in this study are correlated with tumor aggressiveness as well as more heterogeneous textures and higher energy values. Features like kurtosis and range provide insights into the variability of tissue composition within the tumor, which may be linked to differences in cellular structure or necrosis. Finally, high-intensity homogeneous zones might reflect necrotic or well-vascularized regions in the tumor, offering clues about the tumor's viability or growth patterns.
## 2 Inter-Cell Scheduling Model Based on TSCS-BD  
### Problem Description  
A large-scale inter-cell scheduling problem is an optimization problem and considers that \(n\) parts are scheduled to be processed on \(m\) machines located in \(u\) cells. Most parts can be processed in a designated cell, and there is no inter-cell move. Only exceptional parts need to be transported to other cells due to their manufacturing cell's inability to meet the processing requirements for some of the processes. Additionally, some machines in different manufacturing cells have identical capacities. These exceptional parts have several candidate inter-cell processing routes, which is known as inter-cell manufacturing with flexible routes.  
The problem considered in this paper is subject to the following assumptions:  
1. All parts and machines are released at zero moment.
2. The processing route of a part consists of multiple operations that have sequence constraints.
3. For any operation, there is at most one machine in a cell that can process it.
4. Once an operation has started to be processed on a machine, it must not be interrupted until it is finished.
5. Operation of a job can be performed by only one machine at a time, and each machine can perform only one operation of any job at a time.
6. Exceptional parts are allowed to be transported to other cells and returned to the original manufacturing cell for subsequent processing.
7. Processing routes and processing time are known, and transportation time for parts in a cell is ignored.
8. Inter-cell transport capacity is adequate with no wait time.
## 3 Experimental Results and Discussion
### Effect of Cutting Direction and Joint Spacing on Cutting Performance  
In jointed rock mass excavation, joint spacing is one of the key factors influencing the performance of mechanical excavators. Additionally, in the case of roadheaders, where the cutting direction remains constant during excavation, the angle between the joint plane and the cutting direction also acts as a factor affecting performance. In this study, the influence of cutting direction and joint spacing on the cuttability of a conical pick was investigated, focusing on \(FC_{m}\), \(FC_{p}\), and \(SE\). Additionally, the variation in the optimal ratio of line spacing to cutting depth (\(s/p_{\text{opt}}\)), where cutting efficiency is maximized, was observed in relation to cutting direction and joint spacing. The results of the rock cutting tests are listed in Tables 3-6.  
Figure 11: Idealized tensile fracture under the conical pick: (a) midpoint between joint planes; (b) near the joint plane.  
Figure 12: Jointed rock mass specimen after cutting at an \(\alpha\) of 90\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(p\) (mm) & **s (mm)** & **s/p** & \(FC_{m}\) (kN) & \(FC_{p}\) (kN) & \(SE\) (MJ/m\({}^{3}\)) & \(s/p_{\text{opt}}\) \\ \hline
3 & 3 & 1 & 1.57 & 3.18 & 178.0 & 4.13 \\  & 6 & 2 & 1.65 & 4.14 & 96.9 & \\  & 9 & 3 & 1.99 & 5.20 & 78.2 & \\  & 12 & 4 & 2.19 & 5.29 & 72.9 & \\  & 15 & 5 & 2.53 & 6.14 & 80.8 & \\  & 18 & 6 & 2.73 & 6.75 & 95.8 & \\
6 & 6 & 1 & 3.32 & 6.93 & 108.3 & 4.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rock cutting results of the intact rock.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\alpha\) (Deg.) & \(p\) (mm) & \(s\) (mm) & \(s\)/\(p\) & \(s\)/\(J_{s}\) & \(FC_{m}\) (kN) & \(FC_{p}\) (kN) &
\begin{tabular}{c} SE \\ (M)/m\({}^{3}\)) \\ \end{tabular} & \(s\)/\(p_{\rm opt}\) \\ \hline
0 & 6 & 6 & 1 & 0.07 & 2.07 & 5.87 & 63.4 & 3.88 \\  & & 9 & 1.5 & 0.1 & 2.64 & 6.81 & 58.7 & \\  & & 12 & 2 & 0.13 & 2.83 & 7.38 & 43.8 & \\  & & 18 & 3 & 0.2 & 3.97 & 9.53 & 47.6 & \\  & & 24 & 4 & 0.27 & 4.10 & 10.55 & 44.8 & \\  & & 36 & 6 & 0.4 & 5.90 & 12.69 & 52.0 & \\
9 & & 9 & 1 & 0.1 & 3.73 & 10.12 & 52.2 & 3.38 \\  & & 12 & 1.33 & 0.13 & 4.53 & 12.03 & 42.8 & \\  & & 18 & 2 & 0.2 & 4.91 & 12.79 & 37.9 & \\  & & 24 & 2.67 & 0.27 & 5.90 & 15.85 & 36.3 & \\  & & 36 & 4 & 0.4 & 8.38 & 16.22 & 42.3 & \\  & & 48 & 5.33 & 0.53 & 6.85 & 16.10 & 47.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Rock cutting results of the jointed rock mass with a joint spacing of 90 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\alpha\) (Deg.) & \(p\) (mm) & \(s\) (mm) & \(s\)/\(p\) & \(s\)/\(J_{s}\) & \(FC_{m}\) (kN) & \(FC_{p}\) (kN) &
\begin{tabular}{c} SE \\ (M)/m\({}^{3}\)) \\ \end{tabular} & \(s\)/\(p_{\rm opt}\) \\ \hline
0 & 6 & 6 & 1 & 0.07 & 2.07 & 5.87 & 63.4 & 3.88 \\  & & 9 & 1.5 & 0.1 & 2.64 & 6.81 & 58.7 & \\  & & 12 & 2 & 0.13 & 2.83 & 7.38 & 43.8 & \\  & & 18 & 3 & 0.2 & 3.97 & 9.53 & 47.6 & \\  & & 24 & 4 & 0.27 & 4.10 & 10.55 & 44.8 & \\  & & 36 & 6 & 0.4
## 3 Methodology  
The objective of the domain ontology construction methodology proposed in this research is to adapt the most preeminent practices that have already been introduced in the study of ontology construction. In the design and development of this methodology, the tasks were classified into various phases regarding cluster-based ecological and confined domain ontology construction in structured knowledge management. These phases are as follows: (1) Pre-conceptualization: this phase comprises activities that must be performed before the identification of terms for the construction of a domain ontology; (2) Conceptualization: this phase involves the identification of terms and/or concepts from a data source; (3) Pre-processing: in this phase, numerical values are computed for the textual terms/concepts available in the dataset; (4) Concept clustering: the aim of this phase is to cluster the instances and concepts through numerical values that were already computed in the pre-processing phase; and (5) Post-conceptualization: this phase consists of activities such as localization, verification, implementation, integration, evaluation and documentation. This ontology construction scheme comprises a combination of mechanisms, namely, adapted ontology development methodology [31], concept clustering, and concept extraction mechanisms (see Section 3.3). The proposed scheme is illustrated in Figure 2. A detailed description of the phases and activities involved in the proposed scheme is discussed below.
# A Parallel Multimodal Integration Framework and Application for Cake Shopping
## 3 Results
### User Study  
We conduct user studies to compare our multimodal shopping system (MMS) with the traditional 2D shopping system (2 DS). The 2D shopping system is built using a PyQt5 module. It shows cake information through text and images (see Figure 9). We invite 10 participants and make them use MMS and 2 DS separately to purchase the desired cakes. The order of using the system is randomized. Before the MMS group, participants complete five conversations to become familiar with the system. After the experiment of each group, participants complete a system evaluation questionnaire, which consists of four items: \"I can quickly select the cake I want\" (Efficiency), \"I find the shopping process enjoyable\" (Pleasure), \"I feel like I am shopping at a real cake store\" (Immersion), and \"I would be willing to shop in this way\" (Likability). Each item has five levels (1 = totally disagree to 5 = totally agree). All participants are students. Five of them are male. The average age is 24.4 (SD = 1.35). They are naive to the purposes of the experiment.  
Figure 10 shows the mean scores of four items for the MMS and 2 DS. The error bars indicate 95% within-subject confidence intervals. It can be seen that 2 DSs have higher scores in terms of Efficiency and Likability. MMS has higher scores in terms of Pleasure and Immersion. We perform a paired samples \(t\)-test on each item for two groups. The results show statistically significant differences between the MMS and 2 DS in terms of Efficiency (\(t(10)=-6.708\), \(p=0.000<0.05\)), Pleasure (\(t(10)=6.128\), \(p=0.000<0.05\)), Immersion (\(t(10)=2.400\), \(p=0.040<0.05\)), and Likability (\(t(10)=-2.535\), \(p=0.032<0.05\)). The analysis above indicates that our multimodal system offers users immersive shopping  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Overall** & **2-Modals** & **3-Modals** & **4-Modals** & **5-Modals** \\  & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** \\ \hline RW & 0.844 & 0.763 & 0.790 & 0.854 & 0.968 \\ MV & 0.684 & 0.721 & 0.677 & 0.641 & 0.698 \\ LW & 0.686 & 0.709 & 0.690 & 0.669 & 0.677 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation for multimodal fusion method. We compare our relative weighted fusion (RW) to majority voting (MV) and linear weighted fusion methods (LW) in terms of fusion accuracy. 2-modals means two-modality fusion experiments, and so on for 3-modals, 4-modals, and 5-modals.  
experiences. The multimodal interaction also enhances the entertainment. However, compared to 2D text-based shopping, our system is not efficient. This inefficiency affects how much participants like the multimodal system. We conduct an interview after the experiments to gather their opinions. One key observation is that participants found the integrated device challenging to use. The need to navigate and coordinate between devices resulted in a perceived lack of efficiency. The device calibration and wearing prior to the experiment also added to the sense of complexity.
## 4 Bearing Fault Detection Using YAMNet and Transfer Learning  
The present work investigates the capabilities of pre-trained SED networks in fulfilling bearing fault diagnosis tasks. Namely, a TL approach was undertaken with the aim of transferring the knowledge held by YAMNet layers to REB spectrograms classification. To this end, the Case Western Reserve University (CWRU) [30; 62] bearing dataset was examined, representing a standard reference in this field. The dataset was pre-processed to comply with YAMNet architecture and it was split for training, validation, and test phases. In this context, different labelling options were considered. Afterwards, the network was fine-tuned for dealing with fault diagnosis scenarios characterized by restricted datasets. Finally, the model was tested under new data.
# Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning
## 3 Related Work  
The study developed by [PERSON] [25] demonstrates its use in order to obtain the DT to both generate normal data in order to simulate the system to be studied and later use it to detect anomalies.  
In the study carried out by [PERSON] [26], authors apply a model-based approach to generate the characteristics of the photovoltaic system studied. This type of approach presents the drawbacks of the need to have a level of expert knowledge in the photovoltaic system, and it does not take advantage of the availability of information provided by SCADA systems.  
Studies based in deep learning methods are gained more attention. Other studies like [PERSON] [27] or [PERSON] [28], authors present approaches based on Deep Learning models (the first based on Artificial Neural Networks and the second on Variational Recurrent Autoencoders), with the aim of obtaining models to detect anomalies in photovoltaic systems. These studies obtained good results but are limited to uni-variable series, which does not capture all the possible relationships between the signals of the system to be studied.  
[PERSON] [20] carried out a study of the use of a DT for fault diagnosis in a PVSF but through a mathematical modeling. Unfortunately, there are very few studies that carry out an analysis using a data-based approach.  
In our study we intend to carry out the approach from a multi-variable perspective in order to be able to obtain the DT of a PVSF inverter using an autoencoder architecture by combining convolutional and recurrent layers to better capture the relationships between the time series obtained from the inverter. The contributions of this paper are summarized as follows:  
* Obtain a DT from a PVSF using a data driven approach.
* Using the obtained DT for anomaly detection using two different methods.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 6 Limitations and Future Research Directions  
### Training Efficiency of the Method  
To evaluate AMMNet's computational efficiency, we compared its training time with that of other change detection models (DTCDSCN, MSCANet, AMTNet). While AMMNet excels in accuracy, its complex network structure results in longer training times. Therefore, comparing the training efficiency of different models is essential for a more comprehensive evaluation of their practical application. Table 5 compares the training times of the models under the same experimental conditions. Figure 16 presents a more intuitive comparison of the training times in the form of a bar chart based on the data from Table 5.  
Although AMMNet's training time (267 min and 21 s) is significantly longer than that of other models, this is partly due to its complex structure, which includes the high-frequency attention module (HBAM), spatio-temporal disparity attention module (SBM), and foreground attention module (FAM), resulting in higher accuracy and better detection performance. Increased model complexity inevitably lengthens the training time, but the additional computational overhead results in significant accuracy improvements, particularly in core metrics such as the IoU, precision (Pre), and recall (Rec).  
Figure 16: A bar chart comparing the training times of different methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Training Time** \\ \hline DTCDSCN & 155 m 21 s \\ MSCANet & 200 m 21 s \\ AMTNet & 260 m 21 s \\ AMMNet & 267 m 21 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the training times.  
However, the longer training time remains an area for optimization. Therefore, in future work, we aim to optimize the network structure and reduce computational complexity through a lightweight model design to shorten the training time and enhance scalability in practical applications.