esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application Cases
### Collection and Processing of Representative Samples  
A total of 69 different types of earthquake science experience facilities were identified through field surveys and online searches, and images of them collected. (The sample cases were sourced from China). To ensure the comparability of the representative samples, expert reviews were conducted to select samples, removing those with blurred images, similar shapes, and those not in use. This ensured that the samples were representative, reliable in source, with clear morphological features, and presented from consistent angles. Additionally, complex background environments in the images were removed to avoid user distractions during later experiments. Ultimately, 35 representative samples were obtained, as shown in Figure 2, denoted as \(S=\{S_{1},S_{2},\cdots,S_{35}\}\).
# Opportunities for Machine Learning in District Heating
## 2 District Heating Networks
### Operational Challenges in Adapting District Heating Networks
#### 2.1.3 Heat Transfer  
In the building substations, the flow rate from the DH network is controlled both to power the building's space heating circuit and the domestic hot water production circuit. This is typically done by means of a traditional PI-controller which makes sure that the supply temperatures in those circuits meet their set points. The set points, in turn, are often calculated based on a heating curve together with an outside temperature measurement (space heating circuit) or is a fixed value (domestic hot water circuit). This heat transfer process often results in inefficiencies if not properly managed.  
Optimal control in building substations is often referred to as Demand Side Management (DSM). By switching from the rule-based heating curve to a more advanced control, the flow rate (and as a result, also the heating power) to the building can be temporary reduced or increased. Since buildings contain a lot of thermal mass (concrete, furniture, etc.), this can be done up to a few hours, without thermostats or tenants noticing. This is a powerful tool, since it actually uses the buildings as short-term thermal energy storage. By doing this in a coordinated way between the buildings connected, it is possible to shift the demand profile of a DH network to a desired shape. Since DH networks are demand driven, the production will follow this demand profile.  
When it comes to analytics, it should be emphasised that a substation consists of a large number of components (heat exchangers, valves, pumps, control units, etc.), and that those components can always fail, often without the consumer or the DH network operator noticing it. A study [21] claims that three quarter of the substations work sub-optimally. The result of these errors always is a bad cooling of the DH water in the substation, and therefore a higher return temperature back to the network than necessary. Such faults in substations constitute an important source of inefficiencies and this has led to increased interest in fault detection.
## 1 Introduction  
### Quality Management  
Quality management based on result-oriented performance is being used by maintenance managers regarding business analytical approaches [1]. 'Result-oriented' is a term used to express a process and activity plan that focuses on the outcome rather than the process used to deliver a service, such as maintenance services during the operation process. While focusing on the results, it also maintains the required process orientation for quality results and, in particular cases, even contributes to improving the process itself [2]. Integrated quality systems are considering the optimized process for inspections and maintenance activities in terms of quality assurance connected with quality control, which is a gap in maintenance decision-making and research [3, 4]. On the other hand, cost quality trade-offs are required when decision-makers seek to reduce maintenance cost and maximize quality or safety [5]. Meanwhile, in recent research, the inspection strategies are founded on the relevant failure probability [6]. For example, objects that have a low probability of a defect may use a sample inspected or without inspection as an appropriate inspection strategy [7], but a high uncertainty after the pre-inspection should be checked [8; 9; 10; 11]. Therefore, the classification of structural elements and their defects is vital since risky zones exist in bridges and inspectors must not miss them in favor of safe elements in their visual tests and maintenance activity.  
Consequently, safety enhancement for risk mitigation in operation procedures based on structural element prioritization is one of the most important results and objectives for inspection planning and maintenance management. For this manner, finding the intervention action priority, as well as the inspection and maintenance method, is of significant importance. Meanwhile, the degradation model and life cycle cost based on durability prepare a row database for decision making based on the priority of elements to planning the maintenance activities [12; 13; 14]. According to recent research results, risk analysis and the value of information can be applied using decision trees together with Bayesian inference for optimization and updating [15]. Regarding the database measurements, data noise and outliers may be reduced using the Principal Component Analysis (PCA) to obtain valuable parameters for the decision support system [16]. The PCA is important for maintenance decision-makers because the parameter weights will modify and correct according to reality. To that aim, a visual inspection may provide the raw data for the first step of multi-quality control and complement it with an accurate structural investigation based on structural health monitoring (SHM) tools [17]. In addition to PCA, other outlier classification methods have been noted in recent research for SHM data analysis such as anomaly detection, the Bayesian dynamic linear model, and the Bayesian dynamic linear model [18].
## 5 Conclusions  
The assessment and estimation of water demand for agriculture is crucial to improve water resource management in a given region. The final objective was to determine the water demand for agriculture in the Boufakrane River watershed through several steps. First, a map of the different crop types was produced using the SVM and RF machine learning algorithms, based on field data combined with the high spatial resolution Google Earth images. Five crop types were mapped, including cereals, other crops followed by cereals, vegetables, olive trees, and fruit trees. Then, the evaluation of the classification map was made based on the Kappa index and the overall accuracy. Finally, the satellite data were combined with climate, soil, and crop data before being used as inputs for CROPWAT software to estimate the water requirements for agriculture.  
The mapping results showed a strong potential of high-resolution satellite data in agricultural species mapping. The evaluation of the two classifiers used (RF and SVM) showed a Kappa index higher than 0.67 and an overall accuracy exceeding 83%. The irrigation requirements showed that the other crops followed by cereals, olive trees, and fruit trees were the three types of crops that required a very large quantity of water, exceeding 500 mm. Vegetables required an amount of about 450 mm; the demand of cereals did not exceed 200 mm.  
The method developed in this work facilitates estimations of the agriculture water demand in the study area, thereby promoting sustainable water resource management. Through this study, we recommend a combination of these methods with existing real data for the implementation of a system for the quantification of water resources forcrops throughout Morocco, which would allow validation using global crop yield data. It was also an opportunity to see the link between water demand and known groundwater reserves and existing data on actual evapotranspiration in this area.  
Conceptualization, M.E.H., A.E. and A.V.R.; Data curation, M.E.H., A.E. and A.V.R.; Formal analysis, M.E.H., A.E. and A.V.R.; Funding acquisition, A.E. and A.V.R.; Investigation, A.E. and A.V.R.; Methodology, M.E.H., A.E. and A.V.R.; Project administration, A.E. and A.V.R.; Resources, A.E. and A.V.R.; Software, M.E.H.; Supervision, A.E. and A.V.R.; Validation, M.E.H., A.E. and A.V.R.; Visualization, M.E.H.; Writing--original draft, M.E.H., A.E. and A.V.R.; Writing--review and editing, M.E.H., K.F.-L. and J.A.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The authors would like to thank the Thematic Project 4, Integrated Water Resources Management of the Institutional University Cooperation, and VLIR-UOS for the financial support, equipment and mission at KU Leuven, Belgium.  
The authors declare no conflict of interest.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
An in-depth analysis of these various facets of the mmWave hybrid beamforming landscape provides valuable insights into the design considerations, trade-offs, and opportunities in deploying efficient and scalable hybrid beamforming solutions for next-generation wireless communication networks. Figure 4 illustrates the summarized view of the in-depth analysis of the Mm-wave hybrid beamforming landscape.
## 5 Environmental Factors  
### Geographical Variation of the Essential Oil Composition  
The essential oil composition of thyme varies depending on geographical location; this variability has been presented in several papers. [PERSON] et al. [95] conducted a study on _T. citridorus_ grown in the semi-arid continental climate of southeastern Anatolia, Turkive, revealing variations in essential oil composition during different phenological stages. Major compounds identified included terpinolene, \(\alpha\)-terpineol, linalool, bornyl acetate, and borneol. In contrast, _T. citridorus_ cultivated in Iran was dominated by geraniol, geraniol, and neral [98].  
Further insights into the geographical influence on thyme essential oils were provided by [PERSON] et al. [99], who analyzed _Thymus vulgaris_ essential oils from Morocco and France. Variances in major constituents, such as borneol, \(\alpha\)-terpinol, and carvacrol (31.04%, 15.16% and 7.13%, respectively) in Moroccan oils compared to thymol, ortho-cymene, and \(\gamma\)-terpinene (35.77%, 17.23% and 8.05%, respectively) in French oils, were observed. Additionally, the anti-microbial and free radical scavenging activities were found to be higher in _T. vulgaris_ cultivated in France. [PERSON] et al. [100] analyzed different chemotypes of _Thymus vulgaris_ essential oils and identified 20 distinct chemotypes based on their compositions. Specifically, the essential oil from Nyons, France, exhibited the linalool chemotype, whereas the oil sample from Jablanicki, Serbia, displayed the geraniol chemotype. Additionally, the essential oil from the Pomoravje District, Serbia, was characterized by the sabinene hydrate chemotype, while that from Richerenches, France, evidenced the thymol chemotype. Lastly, the types of essential oils in thyme differ not just between countries but also among nearby regions. This suggests that environmental factors have a substantial impact on these differences.
# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 3 Results  
### Experimental Setup  
To validate the effectiveness of the proposed shared control architecture, experiments of human-robot co-manipulation tasks were conducted using a 7 DOF Kinova Jaco2 manipulator ([[https://www.kinovarobotics.com/product/gen2-robots](https://www.kinovarobotics.com/product/gen2-robots)]([https://www.kinovarobotics.com/product/gen2-robots](https://www.kinovarobotics.com/product/gen2-robots)), accessed on 25 July 2025) equipped with a Bota Systems Rokubimini force/torque sensor ([[https://www.botasys.com/force-torque-sensors/rokubi](https://www.botasys.com/force-torque-sensors/rokubi)]([https://www.botasys.com/force-torque-sensors/rokubi](https://www.botasys.com/force-torque-sensors/rokubi)), accessed on 25 July 2025). Four non-invasive Delsys Trigno Avanti ([[https://delsys.com/trigno-avanti/](https://delsys.com/trigno-avanti/)]([https://delsys.com/trigno-avanti/](https://delsys.com/trigno-avanti/)), accessed on 25 July 2025) sensors with a frequency of 1.78 kHz were also used; these are active sensors that already have a pan-integrated pre-amplification circuit that allows the reduction in input noise, such as parasitic voltages, due to capacitive couplings or the possible displacement of the electrodes. Following Seniam guidelines ([[http://seniam.org](http://seniam.org)]([http://seniam.org](http://seniam.org)), accessed on 29 July 2025), sensors were placed on the dominant arm of the human operator, as much as possible in the center of the following muscles: the biceps and triceps brachii, flexor carpi radialis and extensor carpi ulnari muscles, as shown in Figure 3. The streaming of EMG data was performed through a Python v3.8 GUI on a Windows PC, while the control software and the SVM classifier, developed using Python and the Scikit-learn library [42], were implemented in the ROS (Robotic Operating System) framework running on Linux. To allow the two workstations to communicate, TCP/IP sockets were used.  
Figure 2: Finite state machine. Starting from the _no-contact_ mode, the FSM switches to a different control mode based on the class triggered by the human operator and the time it is maintained.
## 4 Results
### Comparative Analysis of Local Search Operators  
This section focuses on comparing the local search operator across different configurations to assess its performance and impact on solving the Multi-Demand Multidimensional Knapsack Problem. The local search operator, described in Section 3.3, is evaluated under the following four distinct configurations: Addition only, Drop only, Swap only, and a mixed strategy combining all three.  
To facilitate the comparison, each instance was executed 30 times, where the _max_value_ represented the best value obtained across these 30 runs, and the _avg_max_value_ corresponded to the average of the maximum values achieved in these 30 executions. Summary tables are constructed for the four types of instances studied, detailing key metrics such as the maximum value achieved (_max_value_), the average of the maximum values (_avg_value_), the average computational time (_avg_time_), and the standard deviation of the computational time (_std_time_) for each configuration across all instances.  
Furthermore, violin plots are used to visually represent the distribution of results, providing insights into the variability and concentration of the obtained values. Meanwhile, the scatter plots compare the %GAP of average and maximum values against the best-known solutions, where %GAP stands for \"percentage gap\", defined as %GAP = \(\left(\begin{array}{c}\text{Best Known Value}-\text{Obtained Value}\\ \text{Best Known Value}\end{array}\right)\times 100\%\), quantifying the relative difference between the obtained solution and the best-known solution. These plots illustrate how each configuration performs as the instance size or solution quality changes. To rigorously assess the statistical significance of the observed differences among the configurations, we employed the Kruskal-Wallis test [25], a non-parametric method suitable for comparing multiple groups without assuming a normal distribution. Whenever significant differences arose, the post hoc [PERSON]'s test [26] would be used to pinpoint the specific operator pairs that exhibited statistically significant differences.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 2 Materials and Methods
### Attitude System of SNUGLITE
#### 2.3.3 Attitude Estimation Analysis Method
The rotation matrix is comprised of two parts, as shown in Equation (24):  
\[R^{\text{body}}_{\text{ECI}}=R^{\text{body}}_{\text{local}}\cdot R^{\text{local}} _{\text{ECI}} \tag{24}\]  
where \(R^{\text{local}}_{\text{ECI}}\) is a rotation matrix from the ECI frame to the local orbital plane frame and \(R^{\text{body}}_{\text{local}}\) is a rotation matrix from the local orbital frame to the body frame. To generate \(R^{\text{local}}_{\text{ECI}}\), the equinox-based transformation method is utilized with Earth orientation parameters provided by the International Earth Rotation and Reference System [23]. For the rotation matrix from the local frame to the body frame, \(R^{\text{body}}_{\text{local}}\), the estimated attitude of quaternion parameters, calculated in the attitude system of the CubeSat and denoted as 'estimate attitude' in the full beacon, is utilized in Equation (25):  
\[R^{\text{body}}_{\text{local}}=\left[ \begin{array}{ll}q_{0}^{2}+q_{1}^{2}-q_ {2}^{2}-q_{3}^{2}&2(q_{1}q_{2}+q_{0}q_{3})&2(q_{1}q_{3}-q_{0}q_{2})\\ 2(q_{1}q_{2}-q_{0}q_{3})&q_{0}^{2}-q_{1}^{2}+q_{2}^{2}-q_{3}^{2}&2(q_{2}q_{3}+ q_{0}q_{1})\\ 2(q_{1}q_{3}+q_{0}q_{2})&2(q_{2}q_{3}-q_{0}q_{1})&q_{0}^{2}-q_{1}^{2}-q_{2}^{2 }+q_{3}^{2}\end{array} \right] \tag{25}\]  
The performance analysis of the attitude estimation of the SNUGLITE CubeSat was conducted using the two equations below:  
\[\delta\hat{\mathfrak{s}}=\hat{\mathfrak{s}}^{\text{body}}_{\text{meas.}}- \hat{\mathfrak{s}}^{\text{body}}_{\text{estm.}} \tag{26}\]  
\[\delta\hat{\theta}=\arccos\left(\hat{\mathfrak{s}}^{\text{body}}_{\text{meas. }}\cdot\hat{\mathfrak{s}}^{\text{body}}_{\text{estm.}}\right) \tag{27}\]  
where \(\delta\hat{\mathfrak{s}}\) is an error vector between the reference sun vector and the estimated sun vector, and \(\delta\hat{\theta}\) is the angle between two vectors.
## 3 Results and Discussion
### Cytocompatibility of Scaffolds  
The cytotoxicity of the MBG and EGCG-MBG scaffolds was evaluated by the CCK-8 assay. Figure 7 shows the effect of these scaffolds on cell viability. The survival rate of the MBG scaffold was 90.15% and 97.44% at 24 and 48 h, respectively. Whereas, the EGCG-MBG scaffold displayed a higher proliferation than the MBG scaffold (104.36% and 107.52% for 1 and 2 days, respectively). The cell proliferation results of MG63 behavior showed a significant difference between MBG and EGCG-MBG groups (\(p<0.05\)) [27]. Therefore, the proper concentration of EGCG (200 ug/mL) can prevent cell apoptosis and directly enhance cell proliferation. It could be speculated that the chemical structure of EGCG with MBG scaffolds would remove the free radicals quickly and protect the cell viability.
# Mechanical Design and Gait Optimization of Hydraulic Hexapod Robot Based on Energy Conservation
## 6 Conclusions  
This paper presents the mechanical design and gait optimization of the hydraulic hexapod robot based on energy conservation. It aims to address the problem of building up a framework for the modeling of the hydraulic walking robot and provides an effective design process based on the proposed framework. A two-stage process was adopted for modeling and optimizing the structural and gait parameters of the HHR. In the modeling stage, a step-by-step study is introduced to build geometric, kinematic, static, foot trajectory, and hydraulic driving system models. By establishing the above model, it is possible to predict the walking state and energy consumption of the robot. The optimization stage shows the procedure to formalize and to solve a highly nonlinear multi-constraint multi-objective optimization. In order to optimize the mechanical structure and gait parameters simultaneously, the objective function contains three parts: flow rate, maximum pressure, and stability margin. Due to the complexity of the problem, the archive-based micro genetic algorithm (AMGA) is applied to find the optimal solution. The AMGA had a limited optimization range for each iteration but is more efficient when it gets close to the optimal value. In the optimal solution, the energy consumption of the HHR reduced 44.6% in comparison with the original mechanical structure and foot trajectory. The virtual prototype based on co-simulation of MATLAB and ADAMS is employed to prove the theoretical result is reasonable and reliable.  
Further works in this design process will focus on improving the motion ability of the robot. More operation conditions can be considered and optimized in the gait calculation section, such as turning gait, uphill gait, stair gait, and motion on uneven ground.  
Figure 17: The cylinder pressure of leg 1.  
Figure 18: The total flow rate of the HHR.  
**Author Contributions:** Conceptualization, S.Z., B.J., and Y.C.; methodology, S.Z. and B.J.; software, Y.C.; writing, S.Z.; supervision, B.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the National Natural Science Foundation of China under grant 51521064 and Ningbo Municipal Bureau of Science and Technology under grant 2019B10052.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Proposed System and Methodology
### Signal Denoising
During the training of the DAE, a reconstructed spectrogram \(z\) obtained from the noisy signal spectrogram \(\hat{x}\) is compared to the non-noisy target \(x\). The non-noisy and noisy version of each input are obtained either by measuring the \(T(s_{nm})\) and \(R(s_{nm})\) signals in a physical environment or from a radio simulation environment, or even by artificially corrupting non-noisy data. In our proposed DAE model, convolutional layers are used where the parameters \(W\) and \(b\) of each images batch are shared among all the locations to provide spatial locality. In this way, we put together the advantages of DAEs and the low complexity of the CNN paradigm. The resulting architecture is a Convolutional Denoising Autoencoder (CDAE). In general, CDAEs are very effective in signal processing [60] and in image processing perform better than classical DAEs [61]. Figure 4 shows the process of the proposed CDAE for signal denoising. The effectiveness of using DAEs for reconstructing spectrograms of corrupted signals in unlicensed bands had been reported in our recent works [62; 63]. However, it was only applied for denoising two Wi-Fi protocols in light noise scenarios. In this paper, we expanded our analysis substantially, using CDAEs to denoise multi-protocol signals, including unlicensed-LTE, in view of performing protocol identification. Furthermore, we assessed the effectiveness of CDAE for various propagation models and SNR values, with severe degradation in signal reception in harsh environments. We tuned the CDAE architecture so that it _generalizes_ with consistent performance across all signals operating in the unlicensed spectrum and all noise levels and harsh environments. The architecture of our CDAE is explained in detail in Section 4.4. Figure 4: Schematic view of the operation for signal denoising using a Convolutional Denoising Autoencoder (CDAE). The input is noisy data (spectrograms of the received signal under various channel propagation scenarios). The encoder and decoder consist of multiple convolutional layers: the output is compared to the clean signal’s spectrogram. By optimizing the loss function, the network finds a denoised representation [62].