esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PCBs in Coregous migratorius  
We studied Baikal omul, _C. migratorius_, as a bioindicator of PCBs in Baikal water. In the course of biomonitoring, we collected and analysed 46 individuals of the _C. migratorius_ population of Lake Baikal. The analysis revealed that the concentration of PCBs in fish specimens depends on the season and year of sampling (Figure 3). In the investigated specimens, we detected from 30 to 47 PCB congeners, including seven indicator congeners. The average concentrations and statistically significant range of the detected \(\Sigma_{7}\)PCBs in C. _migratorius_ were 5.6 and 4.9-6.3 ng/g (ww), respectively.  
Biomonitoring of PCBs in the lake water revealed five dl-congeners: 105, 114, 123, 126, and 156, which were present in the water below the LOQ of the method used to analyze the water. The total concentrations of dl-congeners in _C. migratorius_ ranged from 1.3 to 1.7 ng/g (ww). The most toxic congener, 126 (the toxicity equivalence factor, TEZ\({}_{2005}\) is 0.1), was found in single individuals of the older age group (4 out of the 46 investigated individuals), reaching 0.5 ng/g (ww).  
The age of fish is a critical biomonitoring factor, since the concentration of PCBs in individuals of the older age group increases [31]. During the biomonitoring, we chose individuals of _C. migratorius_ aged from three to seven years. In this group of fish, the level of concentration of \(\Sigma_{7}\)PCBs changes insignificantly: \(\Sigma\)?PCBs were 3.9 (3 year), 4.9 (5 year) and 5.4-5.6 (6-7 years) ng/g (ww), and the proportion of congeners with a high degree of chlorination (153, 138, 180) increased. In the muscle tissues of the individuals of the older age group (8 years, 2 specimens), the number of PCBs reached extreme values: \(\Sigma_{7}\)PCBs to 32 ng/g (ww) and \(\Sigma_{\text{dl}}\)PCBs to 9.0 ng/g (ww).
# A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics
## 1 Introduction  
A report by EY in [1] discussed how businesses in supply chain identify the need for managing sustainability but lack the technological innovation, and often, business case identification for achieving this cost effectively. In terms of the technology requirements, the concept of Supply Chain 4.0, as described in [2], constitutes emerging technologies such as the Internet of Things (IoT), cloud computing, big data analytics, machine learning, etc., applicable within procurement, warehousing, manufacturing, logistics and transportation, which provide tools capable of improving performance and resilience, and reducing costs, in supply chain management. A technology that combines the application of these emerging technologies is a digital twin (DT). Digital twins (DTs) can be seen as a replica of a physical asset in the digital space, with synced operations and data, in real time, suitable for simulations and applying analytics [3]. Business-case-wise, this work focuses on sustainable logistics through the application of a data-driven DT framework to support logistics sustainability management, which can be described as an integration of eco-social governance with supply chain processes, balancing profitability and sustainability for organizational growth [1, 2]. We use our vast amount of data as a valuable asset [4] to further enhance our transport managers' capacity to improve the business using digitaltools [5]. In our case study, we focus on logistics, where costs and emissions can be reduced by optimizing operations.  
One of the most prevalent challenges in logistics is empty running. The report in [6] defined empty running as \"trips by a transport vehicle without any freight loaded\" [6]. This work proposes a solution that leverages the distributed digital framework discussed in [7] to stream real-time location and carbon footprint data of the fleet and incoming job orders from the enterprise resource planning (ERP) system to offer recommendations for the optimized planning of pick-ups and drop-offs of orders to minimize empty runs, which constitutes a costs and emissions reduction. The recommendation system is based on a reinforcement learning with human feedback model and a traveling salesman problem algorithm for route optimization. The DT framework provides a solution architecture for handling time and resource constraints efficiently, using historical data and real-time data, along with data, edge, and cloud platforms.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
###### Abstract  
The article is dedicated to the control of magnetorheological dampers (MR) included in a semi-active suspension of an all-terrain vehicle moving along a rough road profile. The simulation results of a half-car model and selected feedback vibration control algorithms are presented and analysed with respect to the improvement of driving safety features, such as road holding and vehicle handling. Constant control currents correspond to the passive suspension of different damping parameters. Independent Skyhook control of suspension parts represents the robust and widely used semi-active algorithm. Furthermore, its extension allows for the control of vehicle body heave and pitch vibration modes. Tests of the algorithms are carried out for a vehicle model that is synthesised with particular emphasis on mapping different phenomena occurring in a moving vehicle. The coupling of the vehicle to the road and environment is described by non-linear tire-road friction, rolling resistance, and aerodynamic drag. The pitching behaviour of the vehicle body, as well as the deflection of the suspension, is described by a suspension sub-model that exhibits four degrees of freedom. Further, three degrees of freedom of the complete model describe longitudinal movement of the vehicle and angular motion of its wheels. The MR damper model that is based on hyperbolic tangent function is favoured for describing the key phenomena of the MR damper behaviour, including non-linear shape and force saturation that are represented by force-velocity characteristics. The applied simulation environment is used for the evaluation of different semi-active control algorithms supported by an inverse MR damper model. The vehicle model is subjected to vibration excitation that is induced by road irregularities and road manoeuvres, such as accelerating and braking. The implemented control algorithms and different configurations of passive suspension are compared while using driving-safety-related quality indices.  
all-terrain vehicle model; tire-road friction; vehicle wheel slip; driving safety; road holding; vehicle handling; vibration control; semi-active suspension; magnetorheological damper
## 2 Materials and Methods  
### Participants  
Participants were voluntarily recruited through university advertisements and social media. Eligible individuals were females aged 18 to 25 years with a BMI of 25 kg/m\({}^{2}\) or higher. Inclusion criteria required stable body weight during the previous three months and the absence of metabolic, hormonal, orthopedic, cardiovascular, or infectious diseases, as well as no regular medication use. Stable body weight during the preceding three months was assessed via self-report during screening, with participants excluded if they reported any change exceeding \(\pm\)2 kg. To reduce potential confounding, participants were instructed to maintain their habitual diet and refrain from engaging in any additional structured physical activity beyond their daily routine during the intervention period. Individuals who did not match these criteria were excluded from the study.  
Following recruitment, eligible participants (N = 125) were assigned to four groups using a computer-generated randomization process via SPSS software (version 26). A simple randomization procedure with a 1:1:1:1 allocation ratio was applied, without stratification or blocking. The randomization sequence was generated by an independent researcher not involved in recruitment or assessments to ensure concealment and prevent selection bias. Group assignments were disclosed only after baseline measurements were completed.  
The study was approved by the Ethics Committee of the State University of Novi Pazar (Approval No. 4957/24) and was registered in the Open Science Framework (OSF) registry (registration DOI: [[https://doi.org/10.17605/OSF.IO/5](https://doi.org/10.17605/OSF.IO/5) HECX]([https://doi.org/10.17605/OSF.IO/5](https://doi.org/10.17605/OSF.IO/5) HECX) (accessed on 4 June 2025). All participants provided informed consent and completed the International Physical Activity Questionnaire (IPAQ) before the experiment [35]. No statistically significant baseline differences were observed between the experimental and control groups in terms of age, height, weight, BMI, waist circumference, or body fat percentage.  
Out of 125 participants who were initially randomized, 111 completed the intervention and participated in both baseline and post-intervention assessments. The final distribution of participants was as follows: mix aerobic (E1, n = 27), kickbox aerobic (E2, n = 28), step aerobic (E3, n = 27), and control (C, n = 29). A total of 14 participants did not complete the study: nine were excluded due to low attendance (defined as participation in less than 75% of the scheduled sessions), and five withdrew voluntarily, citing minor illness or scheduling conflicts (Figure 1). These dropouts were evenly distributed across the groups and did not affect the randomization balance. No adverse events related to the interventions were reported.
# Bending Behavior of Corroded H-Shaped Steel Beam in Underground Environment
## 1 Introduction
The residual bearing capacity of a corroded steel beam determines when it should be replaced. However, there is little research on this issue at present. In this study, twelve tensile samples and nine H-shaped steel beams were corroded in a simulated coal mining environment in the laboratory. Then, the law governing the degradation of the mechanical properties of corroded steel was investigated. Finally, the residual bending capacity of a corroded H-shaped steel beam was investigated by carrying out a four-point bending test, and a model for calculating the bearing capacity was established.
# Aerospace
## 3 Aerocapture Vehicle Design  
The aerocapture vehicle design used in the study is a scaled-up version of the Adaptable, Deployable Entry and Placement Technology (ADEPT) Sounding Rocket 1 (SR-1) flight test article [33; 34]. ADEPT is particularly well suited for small satellites as secondary payloads because the stowed configuration minimizes the volume footprint on a host orbiter. The SR-1 test article shown in Figure 4 has an entry mass \(m=11\) kg, stowed diameter of 0.235 m, and a deployed diameter of 0.688 m. The current best estimate of the payload mass fraction (ratio of useful spacecraft mass to total entry mass) is 0.60. The drag coefficient \(C_{D}\) for the configuration is 1.50, resulting in a ballistic coefficient \(\beta=m/C_{D}A\)\(\simeq 20\) kg/m\({}^{2}\). The scaled-up version of the SR-1 article used in this study has a stowed diameter of 0.5 m and a deployed diameter of 1.5 m. The vehicle design uses the same \(\beta_{1}=20\) kg/m\({}^{2}\) to maintain comparable aerodynamic performance as the test article. This constrains the maximum entry mass \(m=\beta_{1}C_{D}A\) to 53 kg. Assuming a payload mass fraction of 0.60 and allocating an additional 3 kg for the separation mechanism, the vehicle mass after the ADEPT jetfusion is 29 kg. The vehicle post-jettison of the drag skirt has the same diameter as the stowed configuration (\(D=0.5\) m), resulting in \(\beta_{2}=150\) kg/m\({}^{2}\) assuming \(C_{D}=1.0\) for the spherical nose cap. This gives a ballistic coefficient ratio \(\beta_{2}/\beta_{1}=7.5\) before and after jettison, which will be shown later to provide sufficient control authority and keep the peak heat rate within the tested limit of the carbon-cloth TPS at both Mars and Venus [35].
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 3 Methods
### Model Evaluation Metrics  
This paper uses mean absolute error (_MAE_), root mean squared error (_RMSE_), and coefficient of determination (R\({}^{2}\)) as the evaluation indexes to evaluate the experimental accuracy of the model. Both _MAE_ and _RMSE_ are used to measure the difference between the predicted and measured values of the model. _MAE_ is a measure that is insensitive to outliers. Since squaring is present, the _RMSE_ is more sensitive to large errors. When the _MAE_ and _RMSE_ are smaller, the error is smaller, and the model inversion effect is better. The R\({}^{2}\) intuitively reflects the model inversion effect, and its general range is 0 to 1. When R\({}^{2}\) is closer to 1, the model inversion effect is better. The calculation formula of MAE, RMSE and R\({}^{2}\) are shown in Equations (11)-(13):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}\rvert \tag{11}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}\right)^{2}} \tag{12}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}\right)^{2}}{\sum_{i=1}^{n} \left(\overline{y}-y_{i}\right)^{2}} \tag{13}\]  
where n denotes the number of samples, y\({}_{i}\) denotes the measured value of Chl-a concentration, \(\hat{y}\) denotes the projected value of Chl-a concentration, and \(\overline{y}\) denotes the mean value of the measured value of Chl-a concentration.
# Forecasting the Distortion in Solar Radiation during Midday
## 3 Results and Discussion  
This section presents and discusses the results of the proposed approach. The proposed approach was applied over a data set of per-minute readings of global solar radiation for one year (2019) for two locations: Riyadh city and Al-Kharj city.  
The calculation was conducted using Matlab. The results of some days for some months are presented in this section. The values of the constants: \(k\), \(w_{1}\), \(w_{2}\), and \(w_{3}\) are 10, 2, 2 and 0.5, respectively.
## 3 Aggregated Fraction Model and Generalized Model: Comparison
### Reasons to Use the Generalized Model
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Data Set** & \(|T|\) & \(d\) & \(|J|\) & \(|R|\) & \(|P|\) & \(L_{rt}\) & \(W_{jr}\) & \(p_{min,ir}\) & \(p_{max,ir}\) & \(e_{r}\) \\ \hline \(inst\_j30\_r5\) & 35 & 1 & 30 & 5 & 30 & \([0.0,70.0]\) & \([30.0,50.0]\) & \([1.0,5.0]\) & \([6.0,10.0]\) & \([1.0,4.0]\) \\ \(inst\_j30\_r10\) & 35 & 1 & 35 & 10 & 30 & \([0.0,70.0]\) & \([30.0,50.0]\) & \([1.0,5.0]\) & \([6.0,10.0]\) & \([1.0,4.0]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Instance dataset parameters. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Data Set** & **Min** & **Q1** & **Median** & **Mean** & **Q3** & **Max** \\ \hline \(inst\_j10\_r5\) & 0.19 & 0.45 & 0.51 & 0.53 & 0.59 & 0.87 \\ \(inst\_j15\_r5\) & 0.25 & 0.46 & 0.55 & 0.55 & 0.61 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(X(I)\) value summary for instances of datasets \(inst\_j10\_r5\) and \(inst\_j15\_r5\), with time limit equal to the optimal solution time for the aggregated fraction model. difference in solution quality for the case when there are many resource types required to implement each job. For datasets \(inst\_j30\_r5\) and \(inst\_j30\_r10\) we set a 5 min time limit. In Table 8 we present the results for these datasets. We note that we compare suboptimal solutions, obtained in a given time limit. We point out that in the same time the generalized model provides better solutions in all cases. Worst-case ratio of generalized model solution objective to aggregated model solution value is around 0.6. It is reached by the flexible solution structure, even with worse performance evaluated by reached relative gap. We illustrate the gap values in Table 9 to make conclusions about the real optimal objective function value. The aggregated fraction model has a low relative gap in all cases. No significant progress can be achieved by the aggregated fraction model with higher time limits, so it cannot outperform generalized model in solution quality. Computational experiments confirm that we can achieve better solutions within the same solution time limit with the same solver if we apply the generalized formulation approach.
# Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification
## 1 Introduction
While previous work employed the Frobenius norm for solving the optimization problem to obtain sparse coefficients, this work utilizes the L1-norm as the optimizer. This change results in a sparse representation, minimizing the number of features required. The organization of the work is as follows. In Section 2, we review related work on feature extraction based on coding and our previous work on sparse coding for hand analysis. Section 3 details sparse coding feature extraction based on dictionary learning. In Section 4, we present the neural network classification used to evaluate the proposed technique. Section 5 describes the details of the dataset and the obtained experimental results. The discussion and limitations are presented in Section 6, while the conclusions are summarized in Section 7.
## 3 VLC for Underwater Communication
### Configurations of the Optical Link
#### 3.2.4 Non-Line-of-Sight (NLOS) Setups  
It employs diffused light beams reflected off surfaces like the sea surface or seabed to overcome LOS obstructions, enabling flexible alignment and point-to-multipoint communication [22; 24]. However, challenges include path loss, signal dispersion, and interference from sea surface turbulence [22; 24].
## 3 Results of Global-Local Analysis
### Results
#### 3.3.6 Computation Time  
One of the main objectives of this type of methodology corresponds to a reduction in the size of the problem, as shown in Table 1, as well as a reduction in the computation time. For this study, even with a small and simple structure, such as the DCB test, some time savings were achieved. In Table 4, the computation times are listed for all the cases analyzed, and the % savings are presented. As a reference, the computation time for the monolithic model with the most refined mesh is used.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Monolithic** & **Case** & **Element Size [mm]** & **Global/Local Model [mm]** & **Error** \\ \hline \multirow{6}{*}{Case I} & & 1 & 4.9767 & 11.65\% \\ \cline{2-5}  & & 0.625 & 4.3437 & 2.55\% \\ \cline{2-5}  & & 0.5 & 4.4717 & 0.32\% \\ \cline{2-5}  & & 1 & 4.9899 & 11.95\% \\ \cline{2-5}  & & 0.625 & 4.3576 & 2.23\% \\ \cline{2-5}  & & 0.5 & 4.4852 & 0.62\% \\ \hline \multirow{6}{*}{Case III} & & 1 & 4.9926 & 12.01\% \\ \cline{2-5}  & & 0.625 & 4.3247 & 2.97\% \\ \cline{1-1} \cline{2-5}  & & 0.5 & 4.4858 & 0.64\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparative crack tip displacement between analysis methods with different element size meshes.  
Even if the strategy is applied on a global/local problem which is smaller in terms of total nodes, the different stages of the methodology require multiple communication between the models, increasing the computation time. Nevertheless, in this case, the size of the local problem is on the same order of magnitude as the monolithic model, whereas for a real case with larger structures, the local problem could be a few orders of magnitude smaller than the monolithic model, implying a larger reduction in computing time.
# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
## 2 Materials and Methods  
### Study Area  
The study area is located in Zhuozhou, Gabeidian and Dingxing County of Baoding City, Hebei Province, China, which ranges from 115 deg29'E, 39 deg57'N to 116 deg24'E, 39 deg35'N, as shown in Figure 1. This area is located in the North China Plain and has a temperate continental monsoon climate. The average annual temperature is approximately 9 degC-15 degC, the annual solar availability rate is approximately 60%, the annual precipitation is about 550 mm, and the water and heat conditions are suitable for growing crops. Corn and winter wheat are the main crops; others include soybean and cotton.  
The summer corn is mostly planted before the winter wheat is harvested in the study area. Generally, the summer corn emerges in mid-June, and this is also the harvesting time of winter wheat. It enters the jointing stage in the early July, and enters the heading stage in the early August. From the late August to early September, it enters the milk-ripening period, and reaching the maturity stage in the middle of September [46; 47; 48]. This is the whole phenological period of summer corn in the study area, which is used to determine the time ranging of acquisition dates for multi-source remote sensing images.
## 6 Conclusions  
In this paper, we studied the IRS-assisted ISAC framework, wherein the IRS is exploited to establish virtual links in NLoS areas for enhancing radar sensing performance and communication data rate. We aim to improve the system's transmission and energy efficiency through joint task offloading and resource allocation under constraints of transmit power budget, sensing quality, and tolerable latency of offloading. Specifically, transmit beamforming, IRS phase shift, and task offloading are jointly designed, and the weight coefficient is leveraged to control the balance between performance and overhead. The PER DDPG-based and TD3-based algorithms are developed for the complex optimization problem. Numerical results demonstrate that the proposed algorithms have better performance than the baseline scheme. In addition, the simulation shows that the system performance is related to the transmit power, the number of IRS components, and the number of users. In practical applications, we can optimize system performance by setting parameters reasonably. In future work, we will combine the distributed DRL algorithm and federated learning framework to improve the efficiency and scalability of the joint optimization scheme in large-scale networks. Meanwhile, extended to multi-IRS scenarios, our proposed method suffers from the action space explosion problem caused by the exponential increase in intermediate channel coefficients. Therefore, the meta-reinforcement learning can be adopted to decompose the cascaded channel, and reduce the solution complexity and computational overhead. Moreover, future experiments will focus on implementing and testing the proposed strategies in real environments, striving to translate the theoretical potential into practical gains.  
Conceptualization, L.Y. and Y.W.; methodology, L.Y.; software, L.Y.; validation, X.W., Y.W. and L.Y.; formal analysis, X.W.; investigation, Y.W.; writing--original draft preparation, L.Y.; writing--review and editing, L.Y., Y.W. and X.W.; supervision, Y.W. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** The datasets used in this paper available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.