esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Particle Size Distribution Analysis  
Considering the diverse nature and the different operations performed on all the material used during the trials, a different particle size distribution was expected. Thus, to be able to compare slag and lime neutralization trials, a particle size distribution analysis (PSD) was performed by laser diffraction in air media. The analysis is presented in Figure 5.  
It is noticeable that the slag samples that had been crushed, such as O2 and S2, were remarkably coarser than the rest of the samples. In addition, they had a much narrower PSD. On the other hand, the O1 sample presented values across the whole spectrum while remaining on average finer than the crushed samples. The wide range is to be attributed to the fact that the O1 sample was a slag generated by water quenching, and its nature was not artificially modified by any crushing technique. The PSD of sample S1 was in between both crushed samples and the O1 sample. This is to be attributed to the metal extraction process performed at SMT, which requires grinding as one of the steps. Lime samples presented an even finer particle size distribution than O1 and S1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Description** & **Drying** & **Crushing** & **Sieving (1 mm)** \\ \hline O1 & OTK Landfill slag & \(\checkmark\) & - & \(\checkmark\) \\ O2 & EAF slag & - & \(\checkmark\) & \(\checkmark\) \\ S1 & SVK Landfill slag & \(\checkmark\) & - & \(\checkmark\) \\ S2 & AOD slag & - & \(\checkmark\) & \(\checkmark\) \\ OL & OTK Lime & - & - & - \\ SL & SMT Lime & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of materials used and how they were processed before trials.  
Figure 5: Particle size distribution (PSD) analysis of the investigated materials.
## 2 Numerical Modeling
### Verification of the Numerical Model  
It is crucial to verify the computation model in two ways. The first is to perform a grid independence study to ensure that the obtained results are not affected by the grid size or number. In the second verification process, the numerical results are compared with the experimental or numerical data obtained from the literature. Two grid distributions were tested, as well as two numerical models. The Ansys Fluent software package was used to generate the computational domain and results. The two most widely used models, which are the k-\(\varepsilon\) and k-\(\omega\) models, were compared to determine and select the model that is more suitable for the current application.
## 2 Materials and Methods
### Assessment of AC Entersorbent Efficacy in Animal Model with ARS  
#### 2.2.1 Animal Model  
Two weeks before the start of the study (approved by IEPOR Committee on Bioethics, protocol No 4 dated 16 April 2015), thirty male Wistar rats (six weeks old) with body weight \(\cong\)140 g were individually housed for the purpose of determining oat porridge consumption levels. All animals had ad libitum access to standard food and water in accordance with the provisions of the European Convention for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes (Strasbourg, 1986). During the adaptation period, animals were fed additionally to standard food with thick oat porridge from rolled oats flakes (cooked in 1 L of water with 1 teaspoon of unrefined sunflower oil, after good thickening and solidification, 50 g of carrot and 50 g of apple finely grated were added). Animals were randomly allocated into 3 groups: 1--irradiated rats--Ir. control (n = 10); 2--irradiated rats receiving AC enterosorbent -Ir+AC (n = 10); 3--healthy rats--H. control (n = 10). The daily dose of AC was 1.2 g per 1 kg of rat's body weight, mixed in a small amount (teaspoon) of oat porridge. This dose consumption was controlled visually because each rat was in separate cage and the preliminary withdrawal for 2-3 h of habitual food ensured AC daily dose consumption. AC was fed to rats for 3 days before irradiation, on irradiation day and for 9 days post- irradiation. Both control groups received only oat porridge by the same scheme as group 2. Half the animals of each group (5 rats) were sacrificed under general chloroform anesthesia for blood and organ collection on the 1 st day post-irradiation (post-Ir), second half (5 rats)--on the 9 th day post-Ir.
# Inferring Urban Social Networks from Publicly Available Data
## 4 Characterization of the Urban Social Network
### Connectivity, Communities, Degree
**(b)** Size of the largest 50 clusters (Louvain). **(c)** Degree distribution with Lognormal fit. **(d)** Geographical distance between adjacent vertices. **(e)** Mean and max intra-cluster geographical distances. **(f)** Mean distance to all other vertices vs. Degree. sponding to degrees \(\geq\mu\). The fit looks indeed accurate when \(f_{u}\sim 1+\mathcal{LN}(\ln(2),0.25)\), whereas when \(f_{u}\equiv 1\) the tail of the distribution is much shorter. The impact of the exponent \(\beta\) is slightly but consistently visible in all plots: when \(D=d^{-2}\), the distribution is more skewed than when \(D=d^{-0.5}\), with a larger portion of loosely connected vertices compensated by the presence of greater hubs. This phenomenon is more visible in the other cities (see Appendices C and D), where the territory is proportionally wider. The rationale is that a weaker dependence on the distance pushes individuals living in central and denser areas to connect to peripheral vertices, that would otherwise remain isolated.
# Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications
## 4 Conclusions  
The design, realization and measurement of a thin lightweight absorbing material for space applications were presented. The behavior of the absorber with respect to incidence angle was emphasized during the design procedure and wide-angle performances for oblique incidence were shown to be particularly challenging due to increasing scan and mismatch phenomena. The problem was addressed by dividing the initial surface into two sectors, each being optimized for a narrower angular range. A specific measurement setup was proposed to show that the whole structure effectively showed performances expected by each separate sector when impinged with a proper incident angle. The measurements were performed in anechoic chamber. The absorber achieved a reflection coefficient inferior to \(-11.5\) dB, corresponding to an absorptivity above 0.965, over the 14% frequency band for [2 GHz, 2.3 GHz] for TE and TM polarizations for angles of incidence varying from 35\({}^{\circ}\) to 65\({}^{\circ}\).  
The concept of a multi-sector absorber, demonstrated for two sectors in the current study, can be extended to more sectors in order to improve the performances. At the limit, the absorber could be continuously tapered in order to obtain a gradient FSS-absorber which would probably lead to better performances, although at the cost of simplicity.  
Concept and design, O.R. and A.C.L. and X.B.; Investigation, O.R. and A.C.L.; Simulations, O.R.; Measurement, O.R. and A.C.L.; Draft preparation, O.R. and A.C.L.; Review, X.B., K.E. and N.C.; Supervision, K.E. This research was funded by the Centre National d'Etudes Spatiales (CNES). The authors declare no conflict of interest.
## 4 Experiments and Analysis of Results
### Analysis of Price Level Quantities  
For the MGMI-CEHCN model, the number of price levels determines whether the price granularity information can be fully embedded adequately. We selected different \(\theta\) values for the three datasets, to conduct relevant experiments and investigate their impact on the model performance. As shown in Figure 6, the optimal value for the number of price levels \(\theta\) varied across datasets, from 10 on the Cosmetics dataset, to 100 on the Diginetica  
Figure 4: The effect of the number of channels of multiple interests.  
Figure 5: The effect of parameter size on contrastive learning.  
buy dataset, to 50 on the Amazon dataset. We hypothesized that the diversity of user pricing preferences across datasets led to this variation. Of course, we can also observe that the three datasets were designed to obtain the best performance when the number of price levels is few or many. We believe that when 0 is set to few, the model cannot fully learn the user's price preference and can only roughly obtain a high or low price; whereas when 0 is set to many, this will result in goods with similar prices being assigned to different price levels, which in turn leads to performance degradation.
## 3 Results
### Range of Motion  
Figure 5 shows the difference between the ROM of the spine with posterior spinal fusion and that of the intact model when the T12 segment is fractured. ROM of the intact model was a flexion of 13.4\({}^{\circ}\), extension of 11.1\({}^{\circ}\), lateral bending of 11.2\({}^{\circ}\), and axial rotation of 6.6\({}^{\circ}\). A retrospective study investigated the ROM in patients with thoracolumbar vertebral fractures and found that the ROM in those who underwent spinal fixation surgery in the adjacent segments was reduced by 50% compared to normal ROM [50]. Subsequent studies demonstrated that the ROM of the thoracic and lumbar spine in patients undergoing surgical treatment for vertebral fractures was lower than that healthy people, but the underlying mechanism was not identified [51]. In this study, when a PS was used, the average ROM decreased by 2.05\({}^{\circ}\) compared to that of the normal spine, and when a CS was used, the average ROM decreased by 3.875\({}^{\circ}\); the finding is consistent with that in the above clinical study. These results suggest that the fixation mechanism of the fractured spine directly limits the motion of the spine. In flexion, the T11-L1-L2 fixation using a PS leads to the most common result among all cases. The value of extension is the same as that of flexion, and only the reverberation is the opposite. However, the most common result in the intact model was T10-T11-L1 fixation using a CS, and the largest difference of the ROM was observed with T10-T11-L1-L2 fixation using a CS. T11-L1-L2 fixation using a CS exhibited the lowest ROM in lateral bending. In addition, the average ROM was 4.9\({}^{\circ}\) lower than that of the intact model, and this result corresponds to the smallest average value among all motions. In the axial rotation, the ROM of T11-L1-L2 fixation using a PS was similar to that in the intact model. Overall, T11-L1-L2 fixation using a PS led to similar ROM to that in the intact model, and the largest difference in ROM was observed in T11-L1-L2 fixation using a CS.  
Figure 4: ROMs and intervertebral disc (IVD) pressure in the present study and extant study: (**a**) Data from eight previous studies and the present study on ROM with respect to motion. Black dotted lines represent the median curves of 10 L1-5 specimens [44,48]. The red ranges denote the range of results for a moment corresponding to 7.5 Nm. (**b**) Data from eight previous studies and present study on intradiscal pressure between L4 and L5 with respect to flexion. Black dotted line and red ranges denote the median relationship for 15 L4-5 segments, and its range of results for 0 N, 300 N, and 1000 N [44,49].  
Figure 5: Comparison of the difference between the ROM result and intact model for all cases: (**a**) Maximum ROM for each motion based on the fixing method, (**b**) Difference value between ROM of the intact model and ROM based on each fixing method.
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 6 Conclusions  
In this article, we have introduced a novel multi-model for the SED challenge in urban environments. Because there are several sound event categories which need to be detected and identified in the urban SED task, our main focus in this article was to divide the dataset into two subclasses of audio events prevalent in urban environments. For this, all the machine-generated (i.e., artificial) sound events were placed in the first subclass, while natural sounds such as human conversation were categorized in the second subclass. To achieve this, we employed two distinct features (MFCC and RASTA-PLP spectra) for the two subclasses rather than a single feature for both subclasses. We ran multiple experiments with various ranges of erosion and dilation masks during the post-processing stage. We conclude that continuous noises, such as automobile sounds (excluding brakes), require a modest size of filter mask. As there may be pauses in speech, sounds such as people speaking require a larger size of filter mask. Furthermore, our proposed stacked multi-model (S-CRNN) was able to accurately divide and classify the artificial and natural sound datasets with a high F1-score and low ER. Our future work will focus on developing a lightweight S-CRNN capable to operate well with embedded systems for a number of real-world applications, including robotics, voice enhancement, etc., that need low power and l ow cost. We will try to utilize the real time factor (RTF) in order to determine whether our proposed model will be effective in real-time.  
Figure 12: Overall F1-score comparison using S-CRNN Classifier.  
**Author Contributions:** Conceptualization, M.S.K. and M.S.; methodology, M.S.K., M.S. and A.K.; software, M.S.K. and M.S.; validation, M.S., A.K. and M.A.; formal analysis, M.S.K. and L.H.; investigation, M.S. and W.I.; resources, M.S.K. and M.S.; data curation, M.S.K.; writing--original draft preparation, M.S.K., A.A. and E.T.E.; writing--review and editing, M.S.K., M.S., W.I. and A.K.; visualization, M.S.K. and M.S.; supervision, M.S.; project administration, M.S. and L.H.; funding acquisition, A.A. and E.T.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### The Effect of RGB Saturation on Mean Average Precision  
The results in Table 12 showed that all the mAPs obtained are higher than the COCO 2017 validation dataset of 22.2% for SSD MobileNet V2. As expected, Enhance 3.5 has the biggest gain in mAP when compared to the control group of Enhance 1.0, as the features are more distinguishable. Enhance 3.5 has the highest increase with +7.06% over the control group and a nearly 20% increase over the COCO 2017 validation dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **Non-Quantized** & **Quantized** & **Edge TPU** & **Edge TPU Max** \\  & **(\%)** & **(\%)** & **Standard (\%)** & **(\%)** \\ \hline Blue cube & 98 & 94 & 97 & 97 \\ Blue cylinder & 88 & 88 & 91 & 91 \\ Red cube & 93 & 94 & 94 & 93 \\ Red cylinder & 93 & 89 & 88 & 88 \\ Yellow cube & 93 & 94 & 92 & 94 \\ Yellow cylinder & 91 & 89 & 91 & 89 \\ \hline Overall & 92.67 & 94 & 97 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Detection scores with Learning Rate 0.08.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **Non-Quantized** & **Quantized** & **Edge TPU** & **Edge TPU Max** \\  & **(\%)** & **(\%)** & **Standard (\%)** & **(\%)** \\ \hline Blue cube & 96 & 94 & 94 & 94 \\ Blue cylinder & 96 & 95 & 96 & 96 \\ Red cube & 97 & 96 & 96 & 97 \\ Red cylinder & 95 & 95 & 95 & 94 \\ Yellow cube & 97 & 97 & 91 & 97 \\ Yellow cylinder & 91 & 93 & 91 & 94 \\ \hline Overall & 95.33 & 95.00 & 93.83 & 95.33 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Detection scores with Learning Rate 0.16.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **Enhance 1.0 (\%)** & **Enhance 1.5 (\%)** & **Enhance 2.5 (\%)** & **Enhance 3.5 (\%)** \\ \hline Blue cube & 29.98 & 31.71 & 35.1 & 34.02 \\ Blue cylinder & 37.8 & 37.33 & 35.36 & 27.9 \\ Red cube & 27.9 & 53.7 & 48.79 & 54.95 \\ Red cylinder & 29.06 & 36.07 & 41.79 & 42.4 \\ Yellow cube & 44.16 & 48.26 & 52.05 & 54.39 \\ Yellow cylinder & 40.94 & 42.28 & 38.27 & 38.54 \\ Overall & 34.97 & 41.56 & 41.89 & 42.03 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Mean Average Precision with Enhanced Saturation Level.  
As shown in Figure 8, all enhanced datasets including the control group have a higher mAP than the COCO 2017 validation dataset of 22.2% as a benchmark, which indicates improved object detection for our pick-and-place system.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 3 Coding Based Batching and Caching in CoFi  
In this section, we introduce the major design issues in CoFi, i.e., coding based batch transmission and MAC layer caching. We begin with the basic idea of randomized network coding and how it can be applied to simplify both mechanisms.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{\(\theta\) = 0.01} & \multicolumn{3}{c}{\(\theta\) = 0.1} & \multicolumn{3}{c}{\(\theta\) = 1} \\ \cline{3-10}  & & **Y** & **U** & **V** & **Y** & **U** & **V** & **Y** & **U** & **V** \\ \hline \multirow{8}{*}{Solid} & Basketball & 2.68\% & 2.47\% & 3.08\% & 0.59\% & 0.51\% & 1.17\% & 1.27\% & 1.29\% & 1.69\% \\  & Dancer & 2.92\% & 2.70\% & 3.73\% & 2.71\% & 2.52\% & 3.40\% & 1.31\% & 1.30\% & 1.81\% \\  & Longdress & 8.25\% & 11.55\% & 11.15\% & 7.28\% & 10.20\% & 9.85\% & 2.95\% & 3.93\% & 3.80\% \\  & Loot & 11.94\% & 11.96\% & 11.92\% & 10.58\% & 10.74\% & 10.81\% & 4.54\% & 6.15\% & 5.88\% \\  & Redandblack & 8.80\% & 9.54\% & 10.60\% & 7.87\% & 8.47\% & 9.59\% & 3.07\% & 3.25\% & 3.87\% \\  & Soldier & 12.75\% & 12.82\% & 12.47\% & 11.39\% & 11.53\% & 11.27\% & 4.53\% & 5.53\% & 5.53\% \\  & Thaidancer\_v & 12.33\% & 18.32\% & 18.25\% & 10.99\% & 16.33\% & 16.27\% & 11.61\% & 16.91\% & 17.46\% \\  & Queen & 3.32\% & 4.48\% & 4.67\% & 2.39\% & 3.32\% & 3.56\% & 0.76\% & 0.46\% & 0.80\% \\  & Facade & 1.16\% & 2.03\% & 2.02\% & 0.96\% & 1.68\% & 1.69\% & 0.35\% & 0.50\% & 0.64\% \\ \hline \multirow{8}{*}{Dense} & Longdress\_v & 0.19\% & 0.21\% & 0.25\% & \(-1.61\%\) & \(-1.99\%\) & \(-1.89\%\) & 60.96\% & 71.45\% & 69.42\% \\  & Loot\_v & \(-2.78\%\) & \(-5.77\%\) & \(-5.25\%\) & \(-5.04\%\) & \(-7.16\%\) & \(-6.64\%\) & 189.75\% & 357.43\% & 337.41\% \\ \cline{1-1}  & Redandblack\_v & 1.09\% & 0.49\% & 1.66\% & \(-0.91\%\) & \(-1.57\%\) & \(-0.72\%\) & 82.60\% & 97.15\% & 90.60\% \\ \cline{1-1}  & Soldier\_v & 1.28\% & \(-1.71\%\) & \(-1.93\%\) & \(-1.35\%\) & \(-3.63\%\) & \(-3.77\%\) & 93.64\% & 154.82\% & 165.47\% \\ \cline{1-1}  & Boxer\_v & \(-5.23\%\) & \(-9.09\%\) & \(-9.06\%\) & \(-7.34\%\) & \(-10.42\%\) & \(-10.45\%\) & 136.58\% & 196.47\% & 199.10\% \\ \hline \multicolumn{2}{c}{**Average Results**} & 4.19\% & 4.29\% & 4.54\% & 2.75\% & 2.89\% & 3.15\% & 42.42\% & 65.47\% & 64.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparisons between the proposed sparsity-related optimization parameter and the fixed parameter \(\theta\) = 0.01, 0.1, and 1 on point cloud attribute lossy compression.
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 6 Conclusions  
The tri-satellite TDOA localization uses the intersection of two TDOA LOP to determine the location of the interference source. However, if there are two intersection points between the two TDOA LOP, it is difficult to determine the correct point location. This paper proposes a mirrored point elimination method based on multi-moment measurement. This method uses the TDOA measurement data at multiple times to determine the area where the interference source is located in reality based on the convergence of the TDOA LOP at multiple moments. Then, the result is used as the initial value of the Newton iteration method to solve the single-time positioning equation. The location results with a high precision can be obtained after removing the mirrored points. The simulation experiment analyzes the influencing factors on the positioning performance, and verifies the robustness under the larger measurement error and the deviation of the prior initial value.  
Meanwhile, the location accuracy of the proposed method is verified in real testing. Compared with the conventional method, the proposed method can effectively and quickly obtain higher accuracy. The ability of the algorithm is confirmed and can provide guidance on the real tri-satellite location projects.  
All authors contributed extensively to this paper. [PERSON] provided the main idea and wrote the paper; [PERSON] and [PERSON] review and edit the paper, [PERSON], [PERSON] and [PERSON] modified the manu-script and provided many valuable suggestions; [PERSON] and [PERSON] conceived and designed the experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Central Government Guides Local Technology Development Funds, Grant No. 206Z0701G.  
Not applicable.  
The authors wish to thank the anonymous reviewers for their valuable comments on improving this paper.  
The authors declare no conflict of interest.
# Context-Based Energy Disaggregation in Smart Homes
## 5 Experimental Results
### User Presence Conditioning  
The second conditioning is analyzed below. Figure 9 shows the real aggregate consumption trace of a Washing Machine with the same LCD-TV trace that has been analyzed above; the graphic shows a portion of washing cycle with a high consumption phase (corresponding to the water heating phase) in the middle. In this case, the LCD-TV disaggregation is a little worse (demonstrating that depending on the device traces combination, disaggregation precision can change) and this kind of conditioning, both in the single (Figure 10) and double (Figure 11) interval version, does not introduce relevant improvements with respect to the algorithm by [PERSON] and [PERSON] [9]. This is due to the fact that in this case the TV usage lasts for a very long period, probably longer than the user presence observation interval. Figures 10 and 11 shows an improvement in the Washing Machine disaggregation, obtained through the _Single Interval Conditioning_ and the _Double Interval Conditioning_, respectively. The washing phase, which is characterized by low power consumption, is difficultly distinguishable; the basic algorithm, in fact, confuse it for a PC-Desktop execution (Figure 12). Although even with the _Single Interval Conditioning_ few errors are encountered, a portion of washing machine consumption is well-assigned (Figure 10). The graphical results are confirmed by the precision percentage shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **AFAMAP [9]** & **USC** \\ \hline Refrigerator & 27.88\% & 30.77\% \\ LCD-TV & 99.28\% & 100.00\% \\ PC-Desktop & 50.99\% & 74.07\% \\ Coffee Maker & 36.14\% & 77.21\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision results obtained with [PERSON] [9]’s Additive Factorial Approximate MAP (AFAMAP) algorithm in basic and with _Usage Statistics Conditioning_ version.  
Figure 8: Power consumption trace disaggregated with the _Usage Statistics Conditioning_ (_USC_)—test case 1.  
Figure 11: Power consumption disaggregation obtained with the _UP Double Interval Conditioning_.  
Figure 10: Power consumption disaggregation obtained with the _UP Single Interval Conditioning_—test case 2.  
Figure 9: Aggregate power consumption trace—test case 2.