esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Tasks (o)**} & \multicolumn{4}{c}{**(Auxiliary Modules)–Unitary Processing Times in Seconds]**} \\ \cline{2-5}  & **RMT A** & **RMT B** & **RMT C** & **RMT D** & **RMT E** \\ \hline
1 & (1,2)–[0.12] & & (1)–[0.072] & & \\
2 & & (1)–[0.102] & & & (1,3)–[0.108] \\
3 & & & (1,2)–[0.078] & & \\
4 & & & (3)–[0.09] & (1,3)–[0.108] \\
5 & & (1,2)–[0.12] & & (1,2)–[0.108] & \\
6 & (2,3)–[0.072] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Compatibility data among tasks, RMTs, and modules. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cell Id.** & **RMC \#1** & **RMC \#2** & **RMC \#3** \\ \hline RMC \#1 & - & 17 & 16 \\ RMC \#2 & 17 & - & 6 \\ RMC \#3 & 16 & 6 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Travel times among RMCs (minutes). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**OCRA Parameters** & \\ \hline Auxiliary module type (m) & 1 & 2 & 3 \\ \hline \(FMAM_{mr}\) & 0.35 & 1 & 1 \\ \(PMAM_{mr}\) & 0.7 & 0.6 & 1 \\ \(RPMA_{mr}\) & 1 & 0.7 & 1 \\ \(AMAM_{mr}\) & 0.95 & 0.8 & 1 \\ \(NTA_{mr}\) & 34 & 94 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OCRA parameters for the ergonomic risk assessment (assembly tasks). The OCRA parameter values are specific for each auxiliary module type and they do not depend on the RMTs on which the auxiliary modules are installed. Moreover, the OCRA method includes two additional multipliers for considering the presence of recovery periods during the workshift, i.e., \(REC\), and the overall duration of the repetitive tasks, i.e., \(DM\). Specifically, \(REC\) is the recovery period multiplier and it investigates the distribution of the breaks during the workshift. \(REC\) is equal to 1 when the distribution of the breaks during the workshift allows the restoration of the musculoskeletal function of the workers' muscle and tendon groups. The duration multiplier \(DM\) is higher than 1 when the total time of repetitive tasks during the workshift is lower than eight hours. In the reference case study, \(REC=1\) and \(DM=2\). Finally, \(NTA_{mr}\) and \(NTD_{mr}\) refer to the number of technical actions required to perform each assembly/disassembly task. Globally, the set of the input data leads to 78,960 decision variables and 168,226 constraints. The model is coded in AMPL language and processed adopting the solver Gurobi Optimizer(r) v4.0.1.0 on an Intel(r) CoreTM i7 CPU @ 2.40 GHz and 8.0 GB RAM workstation. The solving method to build the Pareto frontier is the Normalized Normal Constraint Method (NNCM) presented by [PERSON] et al. [27]. Starting from these data, 150 points of the Pareto frontier are computed. The solving time is approximately of about 3 min per point. The key results are described in the following Section 4.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Signal Filtering  
#### 3.3.1 Rhythmic Motion Noise Suppression  
A copy of the extracted rPPG signal as well as the head-orientation signals are converted to the frequency domain using Fast Fourier Transform. The three resulting head-orientation spectra (one each of pitch, roll, and yaw) are combined into one via averaging. This is then subtracted from the raw rPPG spectrum after amplitude normalization. This way, the frequency components having a high value in the head-orientation spectrum are attenuated in the rPPG spectrum. Subsequently, the frequencies outside of the human heart rate range (0.7-4 Hz/42-240 bpm) are removed from the spectra.
# CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification
## 3 Methodology
### Feature Extraction  
We use the pre-trained SpanBert as our feature extractor. SpanBert is built based on the encoder of transformers with powerful linguistic representation and feature extraction capabilities, and it uses Span mask to pre-train bidirectional transformers to generate deep bidirectional linguistic representations. We first send the whole sentence and the relation span of the sentence to SpanBert, obtain the corresponding two feature vectors, use the \"[cls]\" token in the vector as the global semantics of the whole vector, and then extract the entity vector by the mask extractor. Suppose a sentence has the following form:  
\[\text{S}=\left(\cdots,\textbf{x}_{i},\cdots,\textbf{x}_{j},\cdots\right) \tag{1}\]  
The feature extraction process can be summarized as the following equation:  
\[\text{span}=\left([cls],x_{i},\cdots,x_{j}\right) \tag{2}\]  
\[\text{s}=W_{1}\big{[}\text{GELU}(B\left(\text{span}\right))\big{]}+b_{1} \tag{3}\]  
Figure 1: This is an overview of our model. When a sentence is an input, we extract the span of the relationship between entities by pre-processing and then send the span to BERT to extract features along with the sentence piece by piece. The features extracted from the sentence are passed through a mask extractor to extract the entity vector; eventually, \(w\), \(e1\), \(e2\), and \(s\) are fed together into the prediction structure for predictive classification.  
\[\mathrm{w}=W_{2}[\mathrm{GELU}(B(S))]+b_{2} \tag{4}\]  
\[\mathrm{entity\,\,feature}\,\,=\mathrm{Mask}\big{(}\mathrm{B}(S),\,\,\mathrm{ entity\,\,mask}\,\big{)} \tag{5}\]  
where \(S\) represents a sentence, _span_ represents a relational span, \(B\) represents the SpanBert model, \(w\) represents the feature vector of the whole sentence, \(s\) represents the feature vector of the relation span, \(entity_{feature}\) represents the feature vector of the entity, \(entity_{mask}\) represents the entity's mask vector, and Mask represents the mask extractor. \(w_{1}\), \(w_{2}\) are the weight matrices and \(b_{1}\), \(b_{2}\) are the biases.  
The masking mechanism is a frequently used method in deep learning. The use of masking can be seen as covering a film on top of the original tensor, and this film can mask the elements we do not want to see, so as to achieve the purpose of masking or selecting some specific elements. The purpose of our mask extractor is to extract the entity vector we need from the feature vector of the whole sentence. This is achieved by performing a matrix multiplication operation with the designed mask vector and the feature vector of the whole sentence to finally obtain the entity vector we need. The mask extractor is shown in Figure 2.  
\[\mathrm{Mask(x,y)}=\frac{\mathrm{Matmul(x,y)}}{\mathrm{length}},x=[x_{1},x_{2},\cdots,x_{n}],y=[y_{1},y_{2},\cdots,y_{n}] \tag{6}\]  
\[\mathrm{length}\,\,=\sum_{i=0}^{n}y_{i}(y_{i}=0\|y_{i}=1) \tag{7}\]  
where x and y are vectors, and length represents the number of tokens that constitute the entity.
## 4 Discussion
### Practical Applications  
* Users of the K-AI Wearable Tech (with 50 Hz sampling frequency) may consider this tool as a valid alternative to radar for evaluating a sprint acceleration force-velocity profile.
* The force-velocity profile variables obtained show a good inter-unit reliability, meaning that GPS devices can be used interchangeably to measure force-velocity profiles.  
Figure 7: Typical errors in % (on the left panel) and SWC in % (on the right panel) for P\({}_{\rm max}\), F\({}_{0}\), V\({}_{0}\), V\({}_{\rm max}\) and \(\tau\), for the two GPS units tested here, and for the GPS unit tested by [PERSON] et al. (2016).  
* The force-velocity profile variables obtained show a good inter-trial reliability for both radar and GPS, but it is important to consistently use the same measurement device, either radar or GPS, to ensure valid comparisons.
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 5 Policy Gradient for MA-Stream
### Evaluation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Policy**} & \multirow{2}{*}{**Total Reward**} & \multicolumn{3}{c}{**User Average**} \\ \cline{3-5}  & & **Users** & **Stall Ratio** & **Reward** \\ \hline \multirow{2}{*}{**“Even\"**} & \multirow{2}{*}{7796.12} & 1, 2, 3 (HD) & 0.39 & 0.67 \\  & & 4, 5 (LD) & 0.08 & 0.93 \\ \hline \multirow{2}{*}{**“Adaptive\"**} & \multirow{2}{*}{8251.66} & 1, 2, 3 (HD) & 0.18 & 0.86 \\  & & 4, 5 (LD) & 0.28 & 0.77 \\ \hline \multirow{2}{*}{**MAPG-finite**} & \multirow{2}{*}{8441.75} & 1, 2, 3 (HD) & 0.24 & 0.80 \\  & & 4, 5 (LD) & 0.11 & 0.91 \\ \hline \multirow{2}{*}{**“Low Dev\"**} & \multirow{2}{*}{8263.49} & 1, 2, 3 (HD) & 0.22 & 0.82 \\  & & 4, 5 (LD) & 0.20 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reward breakdown for fairness function on 2500 KB/s download link. Figure 8: Average download bandwidth for all tested users based on the MAPG-finite strategy with the fairness reward function on 2500 KB/s download link. MAPG-finite assures the same average bandwidths for users with HD preferences, and lower bandwidths for users who desire lower bitrates. max bitrate of the video to avoid a high stall time. When the number of residue cached chunks is more than three, the agent starts to request for the max bitrate, and, thus, a better display quality is obtained. Comparing Figure 9 with Figure 6, all policies receive higher rewards under ABR due to the benefits of bitrate adaptation. We note that MAPG-finite still outperforms the \"Even\" policy by 21.94%, the \"Adaptive\" policy by 41.25%, and the SARSA policy by 37.11%. In this evaluation, we choose the buffer-based strategy for ABR due to its efficiency for implementation. According to the numerical results, our proposed MAPG-finite is able to adapt well to a dynamic bitrate environment. We are aware that new ABR policies--some are engined by RL algorithms themselves--have been proposed and evaluated [36; 38; 39; 40; 41] to improve the streaming quality. The key aim of the evaluation was to show that the proposed framework can work on ABR streaming strategies, not to compare the different streaming strategies. Thus, any streaming algorithm can be used in our evaluations and our results show that an efficient bandwidth distribution among multiple agents can be achieved with the proposed algorithms, where each agent uses any of the ABR/CBR streaming algorithms.
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 3 Problem Formulation
### Multi-Hop Problem Formulation  
The previous one-hop scenario makes it clear that it places strict requirements on the location and count of donor distributions. Recognizing that achieving a practical donor distribution is not always feasible, we propose a multi-hop problem model inspired by the single-hop problem scenario. The inherent multi-hop problem involves determining the most optimal node placement to ensure efficient data transfer via node-to-node communications whilst minimizing the number of nodes deployed.
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 4 Experimental Studies
### Walking in the Open Area  
The open area test was performed to evaluate the proposed infrastructure on a circular observation deck in Macau, China, as shown in Figure 4. The radius of the circular deck is approximately 30 m. The distance between the smartphones and the reference station is approximately 6 km. There are some buildings approximately 1 km away. As shown in the figure, a pedestrian holding the pole walked along the specific tracks from 3:00 p.m. to 4:00 p.m. on 30 November 2016.  
Figure 5 shows the trajectories using different methods. As shown, the pedestrian walked along three pre-defined routes: a portion of a circle sector, a small circle, and a rectangle. In the figure, the red trajectory represents the ground truth, and the blue and green trajectories represent GNSS and DGNSS solutions in the smartphone, respectively. There was a significant bias between the standalone GNSS solution and the ground truth. Figure 5 shows that most of the system errors were removed using the proposed infrastructure.  
Figure 4: Experiment on an observation deck in Macau, China.  
Figure 5: Positioning results in the open area, the trajectories of real-time kinematic positioning (RTK) receiver (red line), GNSS (blue line) and DGNSS (green line) from the Unistrong smartphone.  
We also compared the positioning error of GNSS with that of DGNSS in the smartphone. Table 2 shows that the horizontal average positioning error is 2.3 m and the RMS error is 2.6 m when using standalone GNSS in the smartphone. With the proposed DGNSS, the horizontal average positioning error is reduced to 0.8 m and the RMS error is reduced to 1.5 m.  
The performance of the proposed infrastructure was also assessed in different types of smartphones. As shown in Figure 6, the DGNSS trajectory of the Unistrong smartphone is shown as the blue line, while that of HTC is shown as a black line. The positioning accuracy is found to be improved in both smartphones. The DGNSS performance in the Unistrong smartphone is better than that on the HTC smartphone. The difference can be caused by a number of factors, such as different algorithms and models employed in smartphone GNSS chipsets.
### Context and Motivation  
The construction of more sustainable buildings has become an urgent necessity in the face of global warming. In this context, medical centers play a fundamental role, especially in rural areas with difficult access, as is the focus of this case study. The healthcare sector, due to the intensive use of medical equipment, ventilation and air conditioning systems, and the high volume of activity, presents great potential to reduce its carbon footprint through the implementation of sustainable construction practices. In this regard, the Puntahacienda community proposed to build a medical center using plastic bottles collected and recycled by all its members. These containers were then filled with dry sand to finally form the walls and other components instead of traditional blocks or bricks. This infrastructure is innovative, but the most interesting aspect is the organization of its inhabitants in their search for better conditions for healthcare services. To provide the best community services, renewable energy systems using solar panels were included, and the system was designed to include a heat pump and thermal storage. In the face of climate change, it is imperative to rethink how we design and operate infrastructure so that it not only meets the medical needs of the population but also contributes to the global fight against global warming, promoting a healthier and more sustainable future.  
The motivation for building sustainable medical centers, as presented in Figure 1, lies in several intertwined factors. First, healthcare facilities must ensure a comfortable and efficient environment for both patients and medical staff. Thermal comfort and air quality are essential for a suitable environment for patient recovery and the well-being of healthcare workers. By integrating passive design solutions, such as adequate insulation, natural light utilization, and ventilation control, buildings can maintain a suitable environment without relying heavily on energy-intensive mechanical heating and cooling systems. Furthermore, the incorporation of renewable energy, such as solar panels in this case, and efficient heating systems, such as heat pumps, reduces energy consumption and also improves the medical center's resilience by avoiding power outages, which is crucial in emergency situations.  
Ultimately, the motivation for building more sustainable buildings in the medical field is twofold: to combat global warming and to improve the health and well-being of both patients and professionals.
# Analytical Framework of CP-Free Multiuser OFDM System for Coordinated Multi-Point at mmWave
## 1 Introduction
### Related Works
In this study, CHT technique is introduced with the help of the Hadamard matrix in addition to BD precoding for multiuser interference (MUI) reduction. Moreover, to the best of authors' knowledge, the CP-free multiuser OFDM system utilizing the CoMP-JT transmission method has not been well investigated. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Schemes** & **Merits** & **Demerits** \\ \hline
[13] & mMIMO & Low power consumption & Single bit analog-to-digital converter \\
[14] & mMIMO & Low computational complexity and latency & \(\times\) \\
[15] & MIMO & Medium computational complexity & \(\times\) \\
[16] & MIMO & Improved short-term memory and & \(\times\) \\  & & interference cancellation & \\
[17] & SISO & Low computational complexity & Difficulty in channel estimation \\
[18] & MIMO & \(\times\) & High complexity \\
[19] & MIMO & Low power consumption, cost, and low complexity & \(\times\) \\
[20] & MIMO & Low complexity & \(\times\) \\ This paper & MIMO & Low computational complexity and cost & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related technologies, merits, and demerits.
# ISAR Image Quality Assessment Based on Visual Attention Model
## 4 Discussion of the Method
### Attention Heatmap Analysis  
From the attention heatmap in Figure 6, it can be seen that after the Gram-T extracts the features, the CAB module and IRAB module play a constraining role in the attention weights. We selected four representative ISAR images: the first and second images are of good quality, the third image is slightly blurry, and the fourth is cluttered. The attention high-response area focuses on salient objects, which is consistent with human eye judgment. Therefore, the model is conducive to improving assessment performance.
## 7 Discussion
### Occurrence State of Trace Elements in Cassiterite and Its Indicating Significance
[40] pointed out that Sn plays an important role in the enrichment process of In. Tin and indium are co-enriched and migrated in ore-forming fluids and separated in the mineralization and precipitation stage. It is especially obvious in skam deposits and epithermal deposits related to granite (hydrothermal vein type) [41]. Figure 9: Binary diagrams of trace element content in cassiterite in the Shuangjianzishan deposit. (**a**) Nb versus Ta; (**b**) Zr versus Hf; (**c**) W versus U; (**d**) Sc versus V; (**e**) Fe versus Ga; (**f**) Co versus Ni. Nb-Ta and Fe-Mn usually appear in pairs and enter the cassiterite crystal lattice together. Cassiterite often replaces Sn\({}^{4+}\)\(\leftrightarrow\) Fe\({}^{2+}\) + Mn\({}^{2+}\) + Nb\({}^{5+}\) + Ta\({}^{5+}\) in pegmatic and high-temperature quartz veins, while Fe\({}^{3+}\) + Nb\({}^{5+}\) + Ta\({}^{5+}\)\(\leftrightarrow\) Sn\({}^{4+}\) is replaced in middle-low-temperature quartz veins [42]. However, cassiterite in the Shuangjianzishan deposit is rich in Fe and poor in Nb and Ta. It is speculated that Fe may enter the cassiterite lattice through Fe\({}^{4+}\) + OH\({}^{-}\)\(\leftrightarrow\) Sn\({}^{4+}\) + O\({}^{2-}\) instead of the above two replacement reactions, which also explains the reason why cassiterite is poor in Nb and Ta. [PERSON] et al. [43] thought that cassiterite of hydrothermal origin was generally poor in Nb and Ta, especially cassiterite in quartz veins, and (Nb, Ta)\({}_{2}\)O\({}_{5}\) was generally less than 1%. Nb\({}^{5+}\) and Ta\({}^{5+}\) have similar ionic charge and ionic radius, and ZrO\({}_{2}\), HfO\({}_{2}\), and SnO\({}_{2}\) have the same structure, so the contents of Nb, Ta, Zr, and Hf and the ratio of Nb/Ta and Zr/Hf have a clear indication for the genesis of cassiterite, and Nb-Ta and Zr-Hf in cassiterite show obvious positive correlation characteristics (Figure 9,b). The mass fraction of Zr and Hf in cassiterite of high-temperature hydrothermal origin is high. The (Fe + Mn)/(Nb + Ta) indices of cassiterite in the Shuangjianzishan deposit are all less than 2, indicating that it has the characteristics of cassiterite in hydrothermal deposits [44]. The Nb/Ta ratio of cassiterite in the Shuangjianzishan deposit ranges from 9.17 to 201.66, with an average value of 81.51, suggesting that it is related to magmatic hydrothermal solution (Nb/Ta ratio > 1), suggesting that ore-forming materials may mainly come from magma. There is a positive correlation between Sc-V and Co-Ni (Figure 9,f), indicating the existence of isomorphic substitution reactions between Sc-V, Co-Ni, and Sn.
# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## 4 Validation Activities at Reference Sites
### Validation Efforts in HiWATER
In [148], an evaluation of the MODIS NDVI products (MOD09 GQ and MYD09 GQ) was performed across six land use types over nearly one complete growing season. The spatial heterogeneity and scale effects of the NDVI were investigated. The study reported that spatial heterogeneity was commonly found in MODIS data at a resolution of 250 m for different land types. Upscaling of in situ NDVI data based on high spatial resolution satellite imagery can noticeably enhance the validation accuracy of NDVI estimates at the pixel level. In addition to the WSNs, a thematic experiment within HiWATER was the multi-scale observation experiment on evapotranspiration (MUSOEXE), which established a flux observation matrix in the middle reaches of the HRB from May to September 2012. This observation matrix was composed of 22 EC systems, eight LAS systems and 21 automatic meteorological stations (AMS) that were used to deliver footprint-scale measurements (Figure 3) [68,72]. Multi-scale (e.g., on the order of 100 m to 1-2 km) observations of meteorological elements and land surface parameters were collected for validating regional ET over heterogeneous surfaces. In addition, these comprehensive measurements were beneficial for identifying scale effects and providing ground truth that coincides with the development of the remote sensing models and scaling approaches for validation purposes. As demonstrated in [69,70], [PERSON] et al. validated corn transpiration and soil evaporation estimated by the two-source energy balance model (TSEB) at site and field scales based on the EC and stable oxygen and hydrogen isotope measurements. In [73], an innovative validation framework including quantification of the spatial heterogeneity, optimisation of the ground sampling strategy, multi-scale measurements, upscaling theory, uncertainty analyses, and validation method (direct validation, indirect validation and cross validation) was proposed to validate ET RSPs at different scales. In conclusion, as a comprehensive remote sensing experiment, HiWATER relicitously connects validation theories with detailed validation exercises.