esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model
### DMPF Scheduler Module
#### 4.1.2 Resource Monitor  
Let's define graph \(G=(V,E)\) before describing the Resource Monitor Component (RMC). Where \(V\) is the set of DASH clients, and HTTP-media servers; and set \(E\) represents the edges of \(G\). RMC uses RESTful APIs to obtain the available bandwidth of the links from the SDN controller. The measured values are stored in a two-dimensional array \(B\), in which \(B_{ij}\) indicates the available bandwidth between \(i\) and \(j\in V\). The connectivity among DASH clients and HTTP-media servers can easily be inferred from \(B\). In fact, this component constructs graph \(G=(V,E)\) and the available bandwidth among the nodes in \(V\).
## 1 Introduction
### Related Works
Although the studies mentioned above lay a foundation for the tolerance analysis considering surface deviations, they fail to handle the assembly of the multiple mating planes that are generally present in mechanical products. To enable the actual assembly circumstances of mechanical parts to be covered by tolerance analysis, attention has been paid to computing the PODs of multiple non-ideal surfaces. For instance, [PERSON] [29] considers that the assembly of multiple mating surfaces is a process over which each mating surface mates in order iteratively, and the Euclidean distance between reference points picked on two assembled parts converges progressively. However, the mating surfaces cannot interfere with each other. Consequently, a computing framework with the convergence of the Euclidean distance between reference points as the search target has been proposed. According to displacement conditions (e.g., non-interpenetration and displacement boundary conditions) and load conditions (e.g., force balance and moment balance conditions) that should be satisfied if parts are assembled with multi-mating surfaces, [PERSON] and [PERSON] proposed a linear-complementarity-condition-based computing approach [30]. Under the condition of deviations of multiple mating surfaces, both of the approaches have been verified in the tolerance analysis based on non-ideal surfaces. However, the concepts of assembly employed by the two approaches do not cover different types of positioning (e.g., incomplete positioning and over-constrained positioning). Consequently, they cannot solve the problem of POD computation considering both the effects of surface deviations and different types of positioning. As shown, to avoid the effects on the accuracy of tolerance analysis caused by only considering the size and position errors but ignoring the form errors, the problems of PODcomputation considering surface deviations have gained much attention. However, two problems remain unsolved in the existing literature:  
* Although much attention has been paid to the problems of the POD computation of a single mating plane, the problem of the POD computation of multiple mating planes has still been rarely researched. * Two types of approaches have been proposed to compute the PODs of multiple surfaces, but they only take into account surface deviations and neglect multiple types of positioning, which are not included in the employed concepts of assembly.
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
## 3 Model Validation
### Effect of Loss Factor  
The loss factor of metallic materials is generally low at room temperature and varies with temperature and frequency [37; 38]. To simplify the calculation, we consider the loss factor for the entire frequency band with a fixed value. Figure 14 presents the results for \(T_{2}=0\)\({}^{\circ}\)C and \(T_{1}=-50\)\({}^{\circ}\)C. When the sound waves are incident vertically, various loss factors affect the STL. As shown in Figure 14, the influence of the loss factor is also reflected in the trough position of the STL. Moreover, with an increase in the loss factor, the amplitude at the trough position increases gradually; in the non-valley position, the influence of the loss factor is less prominent.
# Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
## 4 Experiments
### Comparison with the State of the Art  
In order to verify the effectiveness of the improved LSTM quantization method, comparison experiments with related works are carried out.  
Experiments are performed on character-/word-level language modeling. We compare the full-precision LSTM and popular state-of-the-art quantized LSTMs, including (i) 1-bit LSTMs, binarized using BinaryConnect (BCN) [25], binary weight network (BWN) [26], and loss-aware binarization (LAB) [27]; (ii) 2-bit LSTMs, ternarized using ternary weight networks (TWN) [28] and loss-aware ternarization with approximate solutions (LAT) [29]. Analogous to BinaryConnect, we also include a baseline called TerConnect1, which ternarizes weights to \(\{-1,0,+1\}\) using the same threshold as TWN but does not scale the ternary weights. (iii) Multi-bit LSTMs: For simplicity of notation, we denote all the compared multi-bit quantization methods, including uniform [30], balanced [31], greedy [32], refined greedy [32], and alternating LSTM [33] quantization as Uniform, Balanced, Greedy, Refined, and Alternating, respectively. Above all, we apply these methods to quantize the full precision pre-trained weight (activation is not quantified) of the single LSTM layer.
## 7 Conclusions
### Future Research Directions  
New research questions have arisen along this work leaving, as a result, several opportunities to improve the developed system. Although the environments employed in simulations are proposed as benchmarks, it would be beneficial to check the validity and performance of the proposed approach on a broader variety of scenarios. Large office-like environments would be exciting to put the system into more realistic situations like mapping buildings where larger fleets could be employed, too. Since the robot model defined can support robots with different characteristics, exploiting heterogeneity could be a promising research direction. Integrating a fleet with heterogeneous robots (e.g., different in size, sensory, and motion capabilities) could enhance the skills of the fleet. For instance, given their greater mobility, UAVs could help the fleet to keep connected by playing a relay role, while small terrestrial robots could be the key to get into access-restricted spaces. At last but not least, executions on real systems are also planned. Despite the goodness of any simulator, many important details escape their scopes. The proposed approach is designed to serve as a solution for real-world applications so that it is imperative to verify its feasibility in real scenarios. In such a case, localisation and mapping errors cannot be ignored. Both simultaneous Localisation and Mapping (SLAM) algorithms and the sensory and motor devices should be carefully studied to limit the influence of this kind of errors on the high-level decision components. Regarding the equipment availability of the involved laboratories, the candidate platforms would be either IRobot or KheperaIII units.  
Conceptualisation, Methodology, Software, Validation, Analysis and Writing-original draft preparation, F.B.; Writing reviews and Supervision, E.G., C.C. and P.M.  
This research was partially funded by the Comision Sectorial de Investigacion (CSIC-UdelaR) through its mobility program MIA.
###### Abstract  
Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400-1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of \(R_{p}~{}=~{}0.966\) and \(RMSEP~{}=~{}5.105\%\), which is better than those of successive projections algorithm (\(R_{p}~{}=~{}0.932\) and \(RMSEP~{}=~{}7.329\%\)), and the uninformative viable elimination algorithm (\(R_{p}~{}=~{}0.928\) and \(RMSEP~{}=~{}7.416\%\)). The results obtained by BFWA were more stable, with a smaller standard deviation of \(R_{p}\) and \(RMSEP\) than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying.  
hyperspectral imaging; wavelength selection; vegetable soybean; binary firework algorithm; PLSR +
Footnote †: journal: Applied Soft 2019, 9, 331; doi:10.3390/app9020331
## 3 Path-Planning Design and Dynamic-Obstacle Avoidance Strategy
### Trajectory Optimization Based on the Resultant Path of 3D JPS Algorithm  
The 3D JPS algorithm is a path planning algorithm based on a discrete 3D grid map. The paths obtained directly from the 3D JPS algorithm still cannot escape the limitations imposed by the search direction. This will cause the 3D JPS algorithm to ignore the possibility of any search directions during the search process, making the path appear unnecessarily twisted and unsmooth. In this paper, the set of path nodes of the 3D JPS algorithm is optimized by the parent node transfer law, and the redundant nodes are removed to realize  
Figure 8: Three-dimensional JPS algorithm extension method. (**a**) Same-plane linear extension. (**b**) Same-plane diagonal extension. (**c**) Body diagonal extension.  
the JPS algorithm for any-angle path planning. Meanwhile, the minimum-snap seventh-order polynomial-interpolation method is introduced to further ensure the continuity and smoothness of information such as path node position, velocity, acceleration, and jerk function of the drone in the flight process. The trajectory optimization framework based on the 3D JPS algorithm for path generation is shown in Figure 9.
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### RoBERTa-DCNN-GR: An Extractive Summarization Model
#### 4.1.1 RoBERTa-wwm-ext Pre-Trained Model  
RoBERTa [61] is a robustly optimized BERT pre-training approach developed by researchers from Facebook AI and the University of Washington in 2019. Compared with BERT, RoBERTa introduces dynamic masking to dynamically alter masked tokens during pre-training, which increases the randomness and diversity of the input data, enabling it to learn deeper linguistic representations. Furthermore, RoBERTa employs substantially larger training corpora and extends pre-training durations, allowing the model to learn intricate linguistic patterns and subtleties while significantly improving its ability in context-aware semantic granularity recognition.  
Figure 2: Length distribution of maritime judgment documents.  
RoBERTa-wwm-ext [62], developed by the joint laboratory of HIT and iFLYTEK Research (HFL), is a Chinese pre-trained language model based on RoBERTa. The \"wwm\" in its name stands for whole word masking--a strategy that masks the entire word instead of individual characters during pre-training, thereby improving the model's understanding of the whole word. The \"ext\" suffix indicates an expanded corpus. Compared to RoBERTa, RoBERTa-wwm-ext extends its pre-training dataset beyond Chinese Wikipedia to encyclopedia, news, and Q&A web, totaling 5.4 billion tokens. This enriched data coverage significantly enhances the model's semantic precision and its cross-domain generalization capabilities.  
Given the superior capability of RoBERTa-wwm-ext in Chinese text processing, this paper employs the model to generate contextualized sentence embeddings from Chinese maritime judgment documents, thereby providing a foundation for downstream tasks including sentence classification and extractive summarization for judicial texts.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 4 Results
### Step 5: Mitigating Strategies
However, the risk of introducing a design non-compliant with the regulations would have implied worse consequences in terms of costs and time. It is important to enlighten that, even though the case study is an experimental low-cost mission, strategies to be adopted during the design phase must not neglect current missions and past experience (point 16 is inspired from the [PERSON] experience [42]).
## 4 Results and Discussion
### Comparison between Prediction Results and Actual Data for Four Models  
Figure 10 shows the comparison between prediction results and actual data for the four models' evaluation results of PL5, in the intervals of 10 min, 20 min, and 30 min under the four models with MLR (in red), SVR (in green), LSTM (in dark red), and improved LSTM (in blue). Among the four prediction models, LSTM with weather influence feature performed the better for the parking data from PL5. From the comparison above, the improved LSTM model using the weather feature showed more accuracy in its prediction of parking occupation. The experimental results show that the MLR model is not fitting well in the time period when the wave peaks. The result of the SVR model indicates that it can only predict a relatively stable occupancy period, i.e., in a prediction interval of 10 and 20 min, and the model has a large error for intervals of 30 min. The simple LSTM model shows a periodic line change like a sine wave. It presents the same parking occupation trend every day, since it does not contain the weather feature.  
Figure 9: Relation between parking occupancy rate and time for four week.
## 2 Materials and Methods  
### Experimental Design  
The research was conducted using highly contaminated [6] soil (soil P) subjected to long-term anthropoppressure related to the intensive activity of Zn and Pb smelting industry. Soil was sampled from Upper Silesia region in southwestern part of Poland (Piekary Slaskie: 50.3789\({}^{\circ}\) N, 18.9270\({}^{\circ}\) E), from an area exposed to strong impact of dust deposition (Cu, Zn, Pb, Cd, Ni, Cr). Collected soil was identified as loamy sand, characterized by 60 g kg\({}^{-1}\) of organic matter content and alkaline pH at level of 7.6. The concentration of individual metals in soil was diversified with predominant content of total Zn and Pb and significantly lower concentrations of other metals, such as Cd, Cu, Ni, Cr (Zn = 4090 mg kg\({}^{-1}\), Pb = 2200 mg kg\({}^{-1}\), Cd = 146 mg kg\({}^{-1}\), Cu = 28.8 mg kg\({}^{-1}\), Ni = 12.0 mg kg\({}^{-1}\), Cr = 10.8 mg kg\({}^{-1}\), respectively). However, soil contamination with Cd was still extremely high when compared to background values in uncontaminated soils. Additionally, the uncontaminated [6] soil (soil UP) of similar texture was collected from the IUNG-PIB Experimental Station, located in eastern area of Poland (Osiny: 51.4642\({}^{\circ}\) N, 22.0712\({}^{\circ}\) E), as \"blank\" soil in the experiment with earthworms to determine reference-metal bioaccumulation. Soil UP was specified as loamy sand with 19.7 g kg\({}^{-1}\) of organic matter and pH value at 6.9.  
The research was conducted based on the microcosm laboratory experiment in which two main parameters, solubility and bioavailability of TE, were analyzed. For this purpose, the 1.5 kg of soil P was amended by one of exogenous organic or mineral materials (soil amendments) described in Section 2.2. The soils thoroughly mixed with the amendments were incubated for 30 days at 20-22 \({}^{\circ}\)C, at moisture equivalent to 70% of the field water holding capacity (FWHC). After incubation, the samples were divided into 3 sub-samples (replicates) by placing 0.5 kg portions of soil in 1-liter glass jars. Earthworms were then introduced into each jar (5 individuals per jar) and stored in the dark at room temperature (20 \({}^{\circ}\)C) for 28 days (4 weeks). Soil moisture was adjusted to 70% of FWHC every three days.  
The aim of the experiment was to determine the total and bioavailable metal fraction that was absorbed directly by earthworms. Therefore, after the end of the bioassay period, the survival and change in weight of earthworms, as well as soil physicochemical properties, were evaluated (Sections 2.2.1 and 2.2.2).
## Materials and Methods
### Milk Sample Preparation and Udder Health Trait Determination  
Teats were disinfected with 70% ethanol before milking and the first five squirts of milk were discarded. A quantity of 10 mL milk per udder quarter was examined for specific mastitis pathogens and somatic cell count in the laboratory of Landesbetrieb Hessen, following the DVG guidelines [25]. The specific mastitis pathogens were classified into major pathogens (MAJOR) including _Aerococcus sp., Aesculin hydrolyzing streptococci, Candida Krusei, Enterococcus sp., Escherichia Coli, Lactococcus sp., Staphylococcus aureus, Streptococcus dysgalacticiae, Streptococcus uberis,_ mold fungus, and _Proteus sp.;_ minor pathogens (MINOR) included _Coagulase-negative staphylococci_ and _Corynebacterium sp._ The following pathogens were additionally analyzed separately: _Coagulase-negative staphylococci_ (CNS), _Corynebacterium sp._ (COR), _Aesculin hydrolysing streptococci_ (AESC), and _Aerobic bacilli_ (AER). The pathogens were defined as binary traits. A cow received a score of 1 for the overall group definition (MAJOR or MINOR) or for CNS, COR, AESC, or AER when the respective pathogen was detected in at least one udder quarter, otherwise a score of 0 was assigned.  
At the individual cow level, 32.9% and 12.6% of all samples were culturally negative in the compost and in the cubicle system, respectively. The prevalence of minor pathogens was 57.2% in compost and 80.6% in cubicle. A fraction of 16.7% of cows kept in compost and 6.1% of cows kept in cubicle had infections with AER. The prevalence of AESC was 5.0% in compost and 4.8% in cubicle.  
Microscopic differential cell counting from a 50 mL sample was conducted in the laboratory of the Institute of Animal Breeding and Genetics, University of Giessen, according to the protocols by [PERSON] et al. and [PERSON] [26; 27]. We considered LYM, MAC, PMN, and the PMN subgroups sN and bN. The sum of these cell fractions was 100%. The individual cell fractions were defined as percentages in the subsequent analyses. Quarters with less than 30 counted cells were excluded from the study. Mean values for LYM were 82.0% in compost and 79.4% in cubicle. The mean value for MAC in compost was 47.6% and 49.4% in cubicle. Mean values for PMN were 21.6% in compost and 20.4% in cubicle.
# Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter
## 4 Discussion
To ensure the altimetric accuracy of the DInIRA, the system noise needed to be limited by the carrier. In addition, the baseline length could be increased as much as possible while confirming the altimetric ambiguity and the phase coherence. Moreover, a longer baseline could alleviate the stringent requirement for a systematic phase noise level and data processing accuracy, allowing the DInIRA to achieve better performance. To obtain a more uniform high-accuracy inversion capability of ocean waves in a wide swath range, the distributions of the incidence angle of the Ku and Ka bands' antennas could be adjusted. Although the campaign topped the data limitation to some extent, it could only prove that the OWPs could be inversed well under the low sea state conditions in the Ku/Ka dual-band data. The inversion capability of the ocean waves using the airborne Ku/Ka dual-band data under the worse sea state conditions is still open to discussion. In the future, further study will focus on the data fusion using the dual-band data to improve the accuracy of ocean wave inversion, and apply this technology to the satellite altimetry.
## 4 Experiments
### Case Study
#### 4.4.3 Qualitative Ablation Results  
To better present the experimental results visually before and after ablation, we perform a detailed case study in the same trajectory. There are five experiment results on course, speed, sailing distance, and Semantic Location Vector. Moreover, the results are displayed in the five different images. To be precise, we present the visual results of four trajectories on a single image, including input track points, label trajectories, and prediction results before and after ablation. Specifically, in each image, it is clear that the yellow curve and the real trajectory almost overlap.  
Figure 7 displays the difference with and without the course, speed, and sailing distance in a PESO model. We notice that the prediction results have serious problems in direction and distance without course, speed, and sailing distance of a ship. That is because the model cannot rely only on longitude and latitude to judge the following status. Figure 8 denotes the comparison result with and without course features in a turning corner. The prediction by PESO without a course is in the wrong direction and getting far away from the real one and the yellow one. This is probably because PESO, without course, is unable to identify the sailing direction of the vessel. Figure 9 denotes the comparison result of speed. We suppose that the speed feature helps the model recognize current sailing status. Without speed, the model can easily make a wrong estimation on each timestamp. The PESO model without distance is unable to grasp the sailing distance in every timestamp and makes incorrect predictions in Figure 10. Figure 11 shows the visual comparison results of the Semantic Location Vector. The Semantic Location Vector maintains the spatial correlation between historical and predicted track points. Without the Semantic Location  
Figure 5: The visual comparison of different baselines and PESO. The difficulty of prediction increases gradually from (**a**–**c**). PESO outperforms other models in all scenarios.  
Figure 6: The visual comparison of different structures of PESO. The difficulty of prediction increases gradually from (**a**–**c**). As we can see from the figures, PESO (with LSTM–LSTM) can obtain the best prediction results.  
Vector as guidance information for the decoder, the model can easily generate inaccurate predictions due to poor spatial correlation.  
Figure 8: The influence of the COG information. The yellow and green lines are prediction results of PESO with and without COG information, respectively.  
Figure 7: The influence of the sailing status information. The yellow and green lines are prediction results of PESO with and without sailing status information, respectively.  
Figure 9: The influence of the SOG information. The yellow and green lines are prediction results of PESO with and without SOG, respectively.  
From these ablation studies, the accuracy and robustness of the predicted trajectory by PESO are beyond doubt. Moreover, the conducted experiments illustrate that PESO is always capable of predicting satisfying track points.