esa-sceva/satcom-chunk-collection huggingface dataset:
# Rio Madeira HVDC System: Commissioning of the Ground Electrode for Bipole 2 at Porto Velho
## 5 Electrode Commissioning
### Soil Surface Potential Profiles  
This survey consisted of the sequential measurement of potential differences on the soil surface in the West and South directions, starting at the switching house busbar. The measurements were performed with a DC voltmeter and a pair of non-polarizing Cu/CuSO\({}_{4}\) half-cells. The soil surfaces where the half-cells were placed were previously cleaned of weeds, rock fragments, etc. The porous contact of the half-cell with the ground was facilitated by the moist soil due to the rainy weather during the tests.  
The potential difference from the busbar to the grounding ring of the switching house was first measured, and then the soil surface potentials were measured according to the following intervals:  
* 100 m, from the grounding ring of the switching house to the wells' perimeter;
* 10 m, from the well perimeter to the site fence;
* 100 m intervals away from the site fence.  
Figure 6 presents the two measurement directions, West and South, and Figure 7a presents the corresponding soil surface potential curves (blue and red), which allowed for the construction of the average profile (green) for the first 2 km from the site perimeter. The three curves are referenced to the nominal current of 2625 A.  
Figure 6: The two soil-surface potential survey directions–West and South (the West line was limited by a river).  
Figure 7b presents the three previous potential curves converted to resistance curves, adjusted for the calculated electrode resistance of 0.35 \(\Omega\). The average curve (green) corresponds to the expected potential profile for the 1D geoelectric model, which was adopted for the electrode design.  
This set of mirrored curves allows for the following findings:  
* Figure 7a--as expected, the equipotential curves produced by the injection of DC currents into the ground through the electrode are asymmetric, due to the asymmetry of the electrode perimeter and to the anisotropy of the ground structure;
* Figure 7b--2 km away from the electrode perimeter, the upward trend in the resistance curves is still strong, not allowing for the determination of the electrode resistance through the fall-of-potential method (which requires the stabilization of the apparent resistance curve on the electrode resistance).
## 2 Requirements Specifications and Measurements Gaps
### Ocean Surface Currents  
This variable presents a gap in the required revisit time. The required revisit time is 3 h, and the required spatial resolution is 1-25 km, with a speed accuracy of 5 m/s and 10\({}^{\circ}\) in direction. A federation of three missions, Sentinel-3 (radar altimeter SRAL) and a specific combination with a constellation of Global Navigation Satellite Systems-Reflectometry (GNSS-R) instruments [12; 13], microwave radiometer, altimeter radar [14] and SAR on small platforms, could improve the accuracy and the revisit time efficiently.
# Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm
## 2 5G NR Time Service Signal Model  
### Generation of 5G NR Time Service Signal  
The system principle diagram for the generation of the 5G NR time service signal is shown in Figure 1.  
The 5G NR time service intermediate frequency time signal \(S_{1}(t)\) can be expressed as:  
\[S_{1}(t)=d(t)pn(t) \tag{1}\]  
The signal \(S_{1}(t)\) represents the 5G NR time service intermediate frequency signal generated by the spreading modulation of the data signal with a pseudo-random spread spectrum code. Here, \(d(t)\) represents the data code, \(pn(t)\) represents the pseudo-random spread spectrum code generated by the pseudo-code NCO. After generating this intermediate frequency signal, it needs to be modulated to the corresponding 5G frequency band so that the signal can be transmitted in a manner that appears submerged within the 5G frequency band.  
The 5G NR time service signal \(S_{2}(t)\) can be expressed as:  
\[S_{2}(t)=S_{1}(t)c(t) \tag{2}\]  
\[c(t)=cos(\omega t+\varphi) \tag{3}\]  
where \(S_{2}(t)\) represents the 5G NR time service intermediate frequency signal modulated to the corresponding 5G frequency band, \(S_{1}(t)\) represents the 5G NR time service signal, \(c(t)\) represents the cosine carrier signal with frequency \(\omega\) and initial phase \(\varphi\).
## 4 Summary and Conclusions  
Standard classification technique seldom incorporates domain knowledge associated with the physical properties of rice grain. Hence, a knowledge-based neuro-fuzzy classification technique was proposed in this study for grading of basmati rice grains. This technique takes into account the physical properties of grain devised from an image-based method to classify whole and broken grain.  
A novel image processing technique was adopted for morphological feature extraction followed by ANFIS model building for discrimination of grains. The classification accuracy for the test images were \(>\)98.6%, which comparatively better than standard SVM and KNN classifier (\(<\)95%). Moreover, the proposed ANFIS classification results seem to be more reliable than the results obtained from SVM and KNN, since it deals with uncertainty in output. It is important to note that the standard technique does not take into account any domain knowledge associated with grain physical properties. In fact, the physical properties are essential for grain grading and characterization as analyzed in this study.  
The milling efficiency was estimated in terms of percentage of whole grain or head grain and it was 77.27% for the test sample. However, the colour and texture based quality and grading was not considered during feature selection. The overall results of the adopted methodology were promising in terms of classification accuracy and efficiency. This work can be extended to discrimination of different rice varieties for determining the degree adulteration. Furthermore, the real-time image processing based grading can be addressed equally. This can be extended for optimization of milling machine parts characteristics during milling operation (roller parameter-speed, the gap between rollers) for process automation using micro-controller units.  
This research received no external funding.  
The author would like to thank Rice Processing Unit, Department of Postharvest Engineering, Bidhan Chandra Krisish Viswavidyalaya, India for providing sample images of basmati rice.  
The authors declare no conflict of interest.
## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Admission Filter  
SSDs have a limited lifespan, which necessitates minimizing their write/erase cycle frequency to reduce wear-related operational costs. To decrease the write load on SSDs while ensuring that cache hit rates in the network remain optimal, careful selection of replicas to be inserted into the SSD cache is required. To achieve this, we leverage statistical information on replica popularity and consider the cache redundancy characteristics in ICN. We have designed a cache admission filter based on probabilistic insertion, which effectively reduces both SSD write operations and cache replacement rates. At the same time, by filtering out content with low cache utility, the overall cache hit rate is improved.  
In our design, the write load on the SSD cache consists of two main components. One part of the load comes from cache hits in the DRAM virtual queue (\(Q_{v}\)). Specifically, when a request hits \(Q_{v}\), it indicates that the corresponding replica has a short request interval, which typically suggests that the replica has high popularity or strong temporal locality. As a result, when the data for this replica are returned, we directly insert them into the SSD cache. The other part of the load comes from the cache replacement process in the DRAM cache queue (\(Q_{d}\)). The SSD cache admission filter checks these replicas and, based on the filtering results, probabilistically inserts them into the SSD cache. The following sections will provide a detailed description of the three main processes of the SSD cache filter: popular replica selection, redundant replica filtering, and cache load tuning.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 4 Solution Approach  
The proposed formulation includes nonlinear terms. Hub-and-spoke location transportation network design is an NP-hard problem, thus the proposed model in this paper. We propose some techniques to linearize the formulation, as well as tightening rules to have a tighter formulation. These techniques are presented in full in Appendices A and B.  
Once the linearization techniques (Appendix A) are applied and the tightening rules are implemented, the model could be solved for very small instances only (up to nine nodes) using commercial software packages (GAMS), but the solver may not accommodate larger-sized problems. Thus, a Benders decomposition (BD)-based method is developed to tackle the problem for larger instances. In addition, the methodology is further enhanced using e-optimality and warm-start techniques.  
A common approach to tackle large-scale optimization problems is to decompose the problem into smaller problems that are typically easier to solve so that iteratively solving these smaller problems leads to finding the solution to the original, large-scale problem. Benders decomposition (BD) is a common approach in the literature of MIP models that have been extensively used for large-scale MIPs. In general, the integer variables are modeled as the master problem, and the remaining LP model would be the sub-problem. By solving the LP sub-problem to optimality, more information about the problem is obtained and added to the master problem, which needs to be resolved, using the new information from the sub-problem. This procedure repeats until no further improvement in the solution is observed.  
As reported by [PERSON] and [PERSON] [26] and [PERSON] and [PERSON] [50], the convergence of the BD is not easily accessed, especially as the size of the problem instance increases. Since the initial development of the BD by [PERSON] in 1963, it has been successfully applied in several research articles, and several improvements have been developed to overcome the shortcomings of the original method. Interested readers can refer to [PERSON] et al. [51] for further information.  
In our case, we implement two accelerating techniques as described in the following sections to boost the algorithm and obtain the solution in a reasonable amount of time for medium-sized instances.
# Grasp Detection Combining Self-Attention with CNN in Complex Scenes
## 5 Experiments
### Ablation Study  
In Subsection B, the parameter configurations of the transformer layers in the model are described in detail. To further explore the impact of different configurations on grasp detection, we conducted experiments on the Cornell dataset and VMRD separately; the detailed experimental results are shown in Tables 4 and 5. The P, H, M, and L represent patch size, hidden size, MLP size, and layer, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Method** & **Accuracy (\%)** & **Time (ms)** \\ \hline Zhang [24] & ROI-GD & 68.2 & 110 \\ Zhang [25] & ROI-ResNet & 70.5 & 154 \\ Park [26] & OD, GD, reasoning & 74.3 & 30 \\ Kumra \({}^{\star}\)[7] & GR-ConvNet & 67.7 & 2.8 \\ \hline
**Ours** & Trans-CNN & 72.2 & 5.2 \\ \hline \hline \end{tabular} The runtime results for the method \({}^{\star}\) are tested by ourselves; the other methods are referred to in [26].  
\end{table}
Table 3: Accuracy of different methods on the VMRD.  
Figure 9: The detection results on VMRD.  
On Cornell: Five parameter configuration sets were tested on the Cornell dataset to verify the impact of different parameters on the proposed model. For a small dataset such as Cornell, the accuracy of the model was not improved significantly; with the growing parameters, the transformer-based feature extraction structure with small-scale parameter configuration can achieve excellent grasp detection accuracy.  
On VMRD: For the larger and more complex dataset, the VMRD, the smaller models hardly fit well. Just stacking transformer layers singularly may not improve the performance further when the hidden size and MPL size are not large enough. The key to improving performance is to scale up the size, but that made the model bloated. We failed to achieve good results in the case of small size H and M (H = 256, M = 512). Instead, with increasing size, the proposed model achieved better detection accuracy, while the 2D Gaussian-based grasp representation also played a positive role.
## 2 Materials and Methods
### Demonstration of the Model's Potential for Application as a Decision Support Tool
#### 2.2.8 Simulated SIT Scenarios  
All simulations covered one annual cycle, from 1 January until 31 December. The following scenarios were simulated for each of the three hypothetical sites (A, B, and C):  
1. NO IPM--this simulation was used to illustrate the potential of the medfly population in local conditions in the absence of any control measures. The scenario served as a reference (control). The simulated results were also used to determine SIT-opportunity windows--periods and places in which the next cohorts of new SIT-vulnerable generations of adult medflies emerge. The established SIT opportunity windows were then used to set the timing and duration of the simulated SIT releases in all scenarios presented below.
2. Three SIT implementation scenarios with varying rates of releases of sterile males that emerged from a batch of 50,000, 100,000 or 200,000 of weekly supplied irradiated male pupae were used. All males released at once, in weekly intervals, were distributed uniformly by low-flying drones over all the plots that contain fruit trees.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sterile Male Quality Parameter** & **Assumed** & **Number of** \\  & **Value** & **Individuals** \\ \hline PUPAE: base cohort of irradiated male pupae & & 1000 \\ ADULT males: emergence rate & 85\% & 850 \\ Active fliers at the release time & 85\% & 723 \\ Time to maturity post-release & 2 days & \\ Daily mortality rate in the field (assumed & & \\
50\% survival after 4 days) & 20\% & \\ Effective fliers surviving in the field until maturity & & 462 \\ Mature fliers joining wild male calling groups (leks) & 90\% & 416 \\ Sterile to wild mating competitiveness & 30\% & \\
**Effectively competitive sterile males from** & & **125** \\
**1000 irradiated pupae** & & \\ Female re-mating chance (mating with a sterile and & & \\ then wild male) & 5\% & \\ \hline \end{tabular}
\end{table}
Table 1: The sterile male quality parameters and their assumed values.  
3. Three scenarios of bi-weekly release--the same numbers of weekly used batches of irradiated pupae (50,000, 100,000 or 200,000) were used, but the \"base\" weekly batch of pupae was divided into two parts, and the sterile males emerging from each part were released separately at set intervals of 3-4 days (twice a week).
4. Spot ground release--three scenarios where the sterile males emerging from 50,000, 100,000 or 200,000 of weekly supplied irradiated male pupae were released once a week on the ground at 100 fixed release points arranged into a grid spaced at approximately 50 m.
# Improved Bio-Oil Quality from Pyrolysis of Pine Biomass in Presurized Hydrogen
## 3 Results and Discussion
### GC-MS Analysis of the Bio-Oil  
The characteristics of pyrolysis bio-oils in nitrogen and hydrogen atmosphere were further compared. The pyrolysis oil under different atmospheres was analyzed by GC-MS (The peak area of bio-oil components is the percentage of the peak area based on the total components identified), and the results are shown in Figure 6 and Table 2. It should be noted that GC-MS analysis cannot identify all the compounds present in the bio-oil because only volatile compounds could pass through the GC column and be detected. The compounds with a boiling point lower than 400 \({}^{\circ}\)C accounts for about 80% of the bio-oil (Figure 5); based on the analysis conditions of GC-MS used, about 80% of the bio-oil components may be detected via GC-MS.  
Figure 5: Weight loss rate of pyrolysis bio-oils in different atmospheres at different temperature ranges. Figure 6: GC-MS analysis of the pyrolysis bio-oils under different atmospheres. Based on the analysis and comparison of GC-MS results, the composition of pyrolysis bio-oil obtained from pine wood in a hydrogen atmosphere is significantly different from that in a nitrogen atmosphere. The sum of ethyl acetate (peak area percentage 19.1%) and methyl octadecanoate (peak area percentage 15.42%) generated in 25.80 and 28.19 min accounts for approximately 34.5% of the peak area of pyrolysis bio-oil in hydrogen. This ester has good flammability and is more conducive to fuel ignition and complete oxidation. In addition, long-chain esters are mainly used as lubricants, anti-adhesion agents, and flow aids. It can be used as flow aid direct tablet pressing, filter aid, clarifier and defoamer, and as suspension aid and thickener or liquid preparations. The alkali and water resistance are improved by adding coatings and painting [8]. In industry, the industrial prices of ethyl hexadecanoate and methyl octadecanoate in 2020 are 18,000 and 10,500 yuan/ton, respec
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 5 Conclusions  
This letter proposes an active RIS-assisted uplink NOMA technique using the MADDPG framework, aiming to minimize the sum of the expected RSE errors during wireless transmission. By leveraging the capabilities of an active RIS and the multi-agent learning capability of MADDPG, the proposed method dynamically optimizes the resource allocation to enhance the sensing and transmission performance in wireless networks. The simulation results validate that the proposed algorithm achieves notable improvements in minimizing the sum of the RSE errors, outperforming the conventional algorithms by approximately 87% to 200%. These results demonstrate the effectiveness of combining intelligent reflecting surfaces with reinforcement learning for joint communication and sensing design. Despite these promising results, the proposed approach also has several limitations. The offline training phase of MADDPG can be computationally intensive, especially with many agents, and the convergence behavior may be sensitive to hyperparameter tuning. In future work, we plan to explore more lightweight and scalable learning strategies while also evaluating additional performance metrics such as the convergence speed, latency, and energy efficiency. Moreover, extending the framework to support mobile agents and distributed multi-cell deployments will enhance its applicability further in real-world WSN scenarios.  
Conceptualization, R.L. and L.X.; methodology, R.L. and L.X.; software, R.L.; validation, L.X.; formal analysis, R.L. and L.X.; investigation, R.L.; resources, R.L.; data curation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, R.L. and L.X.; visualization, R.L. and L.X.; supervision, R.L.; project administration, L.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 3 Results
### Analytical Results
#### 3.3.4 Pigment Pickup and Cleaning Cycle  
The decision on the number of necessary cleaning cycles is guided by meticulous observations of the sample and quartz tissue after each treatment. Table 9 presents images highlighting the need for a second cleaning cycle, due to the presence of numerous yellow paint residues after the first cycle (zone a), and the efficacy of this additional cycle has been validated through Py-GC/MS analysis, as depicted in Figure 8.  
The analysis of the quartz tissue after the initial cleaning cycle illustrates fragments of the acrylic binder (peaks 1 and 2 denoting MMA and nBMA, respectively) and the yellow pigment PY74 (peak 3, 2-methoxy-benzemanine; peak 4, 1-isocyanato-2-methoxy-benzene; peak 5, 2-methoxy-4-nitro-benzenamine). After the second cleaning cycle, the pyrogram exclusively reveals the blue pigment, with the marker peak 3 (1,2-benzenedicarbonitrile), without traces of the yellow pigment. This thorough process resulted in a 38/45 overall empirical evaluation for this cleaning case, attesting to its satisfactory outcome, although the presence of evident traces of the blue pigment in the ablated material indicates a certain invasiveness of the laser on the paint layer to be preserved.  
Figure 7: Micro–ATR–FTIR spectrum of AlkY (unaged), AlkY (aged), and aged AlkY–AlkB cleaned with QS laser.
# Anthropometric Values in Spanish Elite Soccer: Differences between Divisions and Playing Positions
Another of the most relevant tools in the study of body composition through anthropometry is the somatotype, defined as the quantification of the shape and composition of the human body through the numerical qualification of three components, using different anthropometric equations and measurements [20]. Derived from the somatotype, the somatocard is the expression of the three components (endomorphy, mesomorphy and ectomorphy) in a graphic representation. It is useful in the field of sports to be able to place the somatotype of the athlete being evaluated against the somatotype of the sport he/she practices, based on a comprehensive collection of data. In fact, due to the easy visual interpretation of the somatocard, it is frequently used in soccer teams to detect the differences in the somatotype of the players of the same squad, and thus be able to customize a diet or training style in those players who require it [15; 20; 21]. Although this study focuses on the use of anthropometry, there are two methods frequently used in soccer to assess body composition: bioimpedance and dual X-ray densitometry. Bioimpedance is a non-invasive and easy-to-apply method based on measuring the body's resistance to the flow of electric current, while dual X-ray densitometry is an indirect method used to measure MM, FM, and bone mineral density through photon attenuation [9]. Spain's professional soccer league and its national team are considered among the best in the world [22]. However, there are few published studies that evaluate the body composition of these players through anthropometry [6; 23; 24; 25]. Therefore, there is a need to have a description of the Spanish soccer player at the kianthropometric level in the different levels of play. The aim of this study was to describe the anthropometric and body composition profiles of professional soccer players in the second, second B, and third divisions during the 2019-2020 season in order to compare the anthropometric parameters of players between positions and competitive divisions. Consequently, the initial hypotheses were:  
**Hypothesis (H1). **_Higher-category soccer players will have lower values for FM and sum of skinfold, and higher values for MM and bone mass (BM)._  
**Hypothesis (H2). **_At lower competition levels, there will be more differences between playing positions._