esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Macrozoobenthos Diversity and Habitats Type
Multidimensional scaling (MDS) ordination of macrozoobenthic data readily distinguished the bootstrap-like samples from the three benthic habitats, particularly those from A and B, as assemblage dissimilarity was higher between these two (75%) than between B and C (58.5%). Habitat A--circalittoral mud and sandy mud with _Upogebia pusilla_ accounted for a diversity of 44 taxa and an average density of 4806 indv.m\({}^{-2}\), characterized mainly by _U. pusilla_, which reached up to 225 indv.m\({}^{-2}\) (mean: 56 indv.m\({}^{-2}\)). Dettrivorous annelids (_Heteromastus filiformis, Proisopio maciolekae_, and oligochaetes) numerically dominated this habitat (4067 indv.m\({}^{-2}\)). Habitat B--circalittoral mud with _Abra nitida_, _Pitar rudis_, _Spisula subtruncata_, _Acanthocardia paucicostata_, _Nepththys hombergii_, and _Mytilus galloprovincialis_ beds was characterized by 26 taxa, among which, besides opportunistic oligochaets and polychaets species (45% of total density), there were _N. hombergii_ and _H. filiformis_, typical mollusks association of this habitat dominated, _A. nitida_ (1481 indv.m\({}^{-2}\)), _A. paucicostata_ (23 indv.m\({}^{-2}\)), _Polititatges aureus_ (16 indv.m\({}^{-2}\)), _S. subtruncata_ (15 indv.m\({}^{-2}\)), and _P. rudis_ (8 indv.m\({}^{-2}\)). The mollusks biomass represented 95% of the total. Figure 7: Shade plot of square root transformed abundances for the abundant species. Species clustering based on the index of association. Habitat A—circalittoral mud and sandy mud with _Upogebia pusilla_ (up to 30 m depth); habitat B—circalittoral (20–45 m) mud with _Abra nitida_, _Pitar rudis_, _Spisula subtruncata_, _Acanthocardia paucicostata_, _Nepththys hombergii_, and _Mytilus galloprovincialis_ beds; habitat C—circalittoral mud dominated by _Melima palmata_. Habitat C--circalittoral mud dominated by _Melinna palmata_--as a result of hydrodynamics, this habitat covered the largest area in the investigated perimeter. This habitat was characterized by a species richness of 37 taxa that recorded an average density of 2544 indv.m\({}^{-2}\), distinguished from habitat B through the dominance of deposit feeder polychaee _M. palmata_, which reached up to 2304 indv.m\({}^{-2}\) (mean: 930 indv.m\({}^{-2}\)). The engineering species _M. palmata_, with 37% of the total average density, along with the oligochaete _H. filiformis_ and _A. nitida_, with 82%, build characteristic habitats and community associations.
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 4 Experimental Results
### Experimental Settings  
**Dataset**: We carried out the experiments on the ImageNet classification dataset [40], which contains 1.2-million training images, 50-thousand validation images, and 100-thousand test images in 1000 categories.  
**Training strategy:.** Within the primary framework of our study, our objective was to enhance the model robustness against quantization. We attempted to accomplish this by first pretraining a more-extensive model, followed by implementing quantization, thereby yielding superior outcomes.  
We used pre-trained weights to initialize the training process following the training schemes outlined in the timm repository [41; 42]. With our initialization schemes, the training steps were much fewer than training from scratch. Additionally, for the quantization process, we disabled the learning rate scheduler, allowing the pre-trained weights to be fine-tuned with a relatively small initial learning rate.  
We fine-tuned ResNet-50-SANA with an initial learning rate of 0.00003, a batch size of 256, a weight decay rate of 0.0001, and a drop path rate of 0.05. We augmented the training data using Random Augmentation [43] with a magnitude of seven and a noise deviation of 0.5, Label Smoothing with a parameter of \(\alpha=0.1\), MixUp [44] with a parameter of \(\alpha=0.2\), CutMix [45] with a parameter of \(\alpha=1.0\), and Horizontal Flip with a probability of 0.5. For the validation data, we also apply Random Resized Cropping (RRC) with a parameter of 0.95 (to keep 95% of the original image pixels). The binary cross-entropy (BCE) loss function and LAMB optimizer were also used to calculate the model error and find the best parameters.  
To fine-tune our EfficientNet-B2-SANA model, we followed the recommended hyper-parameters and set the learning rate to 0.00001, the batch size to 256, the weight decay rate to 0.00001, the dropout rate to 0.3, the drop path rate to 0.2, and the model weights' moving average decay rate to 0.9999. We used RMSprop [46] with a parameter of \(\epsilon=0.001\) for optimization. For data augmentation, we applied Random Augmentation with an integer magnitude of nine and a noise deviation of 0.5. Additionally, we used Random Erasing [47] with a probability of 0.2 for further improvement.
## 3 Results
### Age and Gender  
Multivariate analysis of variance showed that age (F (6, 426) = 3.85, sig = 0.001, Eta = 0.05) and gender (F (1, 426) = 2.85, sig = 0.000, Eta = 0.07) had a significant effect on PS. There was no significant age difference in the basic homework. The results showed that there was no significant age difference in the basic tasks. The greatest PS belonged to 8-year-old children and the lowest to adults. Significant differences were found in PS between both genders in both the AP and ML directions in the TOD, TCD, SOD, SOS, SCD, and SCS tasks (\(p<0.05\)) (Tables 2 and 3). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Age (year)**} & \multirow{2}{*}{\(n\)} & \multicolumn{2}{c}{**Height (m)**} & \multicolumn{2}{c}{**Body Mass (kg)**} & \multicolumn{2}{c}{**BMI (kg/m\({}^{2}\))**} \\ \cline{3-8}  & & **Women** & **Men** & **Women** & **Men** & **Women** & **Men** & **Women** & **Men** \\ \hline
7 & 70 & 1.23 (0.05) & 1.22 (0.05) & 23.75 (3.57) & 22.85 (2.70) & 15.47 (1.78) & 15.26 (1.08) \\
8 & 70 & 1.27 (0.06) & 1.29 (0.06) & 24.79 (4.81) & 26.64 (4.44) & 15.13 (2.28) & 15.78 (1.82) \\
9 & 70 & 1.33 (0.07) & 1.34 (0.06) & 30.39 (6.86) & 29.44 (6.10) & 16.79 (2.51) & 16.25 (2.43) \\
10 & 70 & 1.41 (0.04) & 1.40 (0.06) & 32.49 (5.80) & 33.42 (7.03) & 16.24 (2.50) & 16.98 (2.91) \\
11 & 70 & 1.46 (0.04) & 1.47 (0.06) & 37.77 (5.19) & 38.32 (7.46) & 17.59 (2.42) & 17.60 (2.55) \\
12 & 70 & 1.52 (0.06) & 1.48 (0.05) & 41.59 (9.55) & 39.12 (5.98) & 17.85 (3.33) & 17.77 (2.01) \\ Adult & 20 & 1.62 (0.06) & 1.82 (0.07) & 52.32 (6.73) & 84.26 (10.3) & 19.94 (2.74) & 24.67 (2.70) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic characteristics of the participants. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Task** & & **7 Years** & **8 Years** & **9 Years** & **10 Years** & **11 Years** & **12 Years** & **Adult** \\ \hline TOD & Men & 0.79 (0.34) & 0.78 (0.23) & 0.68 (0.30) & 0.80 (0.54) & 0.68 (0.39) & 0.69 (0.27) & 0.80 (0.24) \\  & Women & 0.89 (0.51) & 0.91 (0.60) & 1.05 (0.62) & 1.15 (0.79) & 0.99 (0.64) & 0.92 (0.60) & 0.92 (0.67) \\ Sig. & & 0.29 & 0.24 & 0.00 & 0.03 & 0.01 & 0.04 & 0.60 \\ T & & 1.06 & 1.19 & 3.16 & 2.12 & 2.05 & 2.07 & 0.53 \\  & Men & 0.88 (0.32) & 0.90 (0.29) & 0.77 (0.24) & 0.84 (0.34) & 0.79 (0.37) & 0.79 (0.28) & 0.65 (0.33) \\  & Women & 0.77 (0.22) & 0.83 (0.35) & 0.90 (0.40) & 0.81 (0.31) & 0.83 (0.74) & 0.79 (0.33) & 0.54 (0.16) \\  & & 0.10 & 0.37 & 0.11 & 0.68 & 0.77 & 0.99 & 0.38 \\ T & & 1.67 & 0.89 & 1.62 & 0.41 & 0.29 & 0.05 & 0.89 \\  & Men & 0.85 (0.20) & 0.90 (0.34) & 0.80 (0.24) & 0.79 (0.27) & 0.84 (0.49) & 0.80 (0.34) & 0.90 (0.38) \\ TCD & Women & 0.87 (0.40) & 0.88 (0.49) & 1.01 (0.46) & 1.07 (0.64) & 0.97 (0.56) & 0.94 (0.35) & 0.68 (0.24) \\ Sig. & & 0.84 & 0.87 & 0.01 & 0.20 & 0.32 & 0.10 & 0.04 \\ T & & 0.19 & 0.16 & 2.39 & 2.41 & 1.01 & 1.65 & 2.34 \\  & Men & 0.91 (0.15) & 0.93 (0.24) & 0.84 (0.
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 3 Results and Discussion
### Prediction Results by MLP Model  
Figures 8 and 9 show the prediction results by the MLP model for the 1-year and 10-year periods, respectively. The predictions were made for 1 year and 10 years using the 180-day and 5-year sets of input data, respectively. The model can only make reliable predictions for periods equivalent to the amount of input data, i.e., for the 10-year prediction, 5 years of input data were required, and for the 1-year prediction, 180 days of input data were required. The predicted strength was 4.34 N/mm\({}^{2}\) for 1 year and 3.73 N/mm\({}^{2}\) for 10 years. In both cases, the results were predicted for the input data with and without omission of the highly fluctuating 0-28-day data. The reason for this is to obtain results closer to those of the actual experiments. As can be confirmed in Figures 8 and 9, the predicted results for 0-28 days included data-produced results that deviated from the actual results in the initial phase. The reason for this is that the method learns the input data, recognizes the patterns of fluctuations for all the data, and determines the balanced prediction results. If the highly fluctuating part is omitted in the input phase, then its influence on the predicted results will decrease. However, omitting the highly fluctuating part will result in lower predicted results than the actual compressive strength after a certain period. Meanwhile, the results in Figure 8 were observed to be the best fitting to the actual data, with a very small deviation even when the 0-28 days data were included. This is due to the higher amount of input data, and likewise in other models, proportional to the amount of input data. Therefore, the influence of the initial fluctuating part became less. The model was found better than other methods, suggesting that the MLP might be the best prediction model for this study. This study used actual data to compare with predicted results so that the best model can be selected. However, if the actual data are not available, then, based on the trend in the predicted data, the data obtained by omitting the highly fluctuating input data appear more reliable and accurate.
# An NDN Cache Management for MEC
## 2 Named-Data Networking Architecture
### NDN Cache Management Limitation  
Most CMSs commonly propose to cache popular content on RsEN. Such schemes expect that many _Interests_ generated in RsEN can be responded to within the RsEN. So, they also expect that the number of _Interest/Data_ transmitted to the core network can be reduced. However, such an expectation has some unrealistic issues.  
First, such schemes generally assume that the requested content is popular such that many users who have geographically dispersed locations in RsENs enjoy them. However, that is a very unusual case. For example, YouTube statistics published in 2019 show that more than 1.9 billion users watch YouTube content a month, 250 million hours of YouTube viewed per day on TV screens, and the most popular videos generate over 3 billion views. However, considering the average cumulative views of each video, most videos generate less than 10,000 views [14; 15; 16; 17; 18]. This means that most YouTube videos are viewed only by 0.0005% of users. Hence, the number of RsENs that have cached the requested content may be smaller than expected. In this case, most _Interests_ will finally be transmitted to content providers. Additionally, although some contents have been cached in RsENs, most caches in RsENs may not be used if the cache hit rate of the nodes in RsENs is relatively low. Moreover, if a cache hit  
Figure 2: WAVE Procedure.  
rate of caches is low, the caches can be deleted from the RsEN over time due to the cache management policy of the RsEN provider.  
Second, such schemes overlook a practical fact that the providers of RsENs do not have any responsibility to cache the transmitted content. Instead, as shown in the case of the CDN (Content Delivery Network) service, it is generally accepted that content providers pay a cost for caching content. Hence, it cannot be guaranteed that RSEN providers cache the received content generated by publishers who are not members of their RsENs.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 3 Materials and Methods
### Lighting Control Based on Rules
#### 3.3.2 Lighting Control Rules for subway environment  
Generally, the distribution of passenger flow in a metro station can fluctuate significantly. When the number of passengers in a station is small, dimming all LEDs to a 100% level is inefficient and also unnecessary, but dimming down some of the LEDs is unsafe and can be uncomfortable for passengers. Therefore, the solution is to track the routes of passengers and control the lighting accordingly while considering both energy saving and user comfort.  
The open-loop control method can be used in the subway environment in the same as in the office environment. However, daylight harvesting cannot be implemented, and the dimming level of a LED in the subway environment depends on a user's route. The two most common walking scenarios in a subway station, i.e., walking straight forward and passing an intersection, which are shown in Figure 8a,b, respectively. The design principle is that the illuminance change cannot be detected by a passenger or is hardly noticeable. Therefore, the illuminated area on a route should be long enough to cover most of the FOV of a passenger. In Figure 8a, the dimming level of the LED right above the passenger is 100%, as well as the dimming levels of the two LEDs ahead and behind of the passenger. The third LEDs in both directions are dimmed to 70%. The remaining LEDs are dimmed down to the default level of 10%. The advantage of this control strategy is that with the help of the symmetry of the illuminated area, the strategy can be realized based on the occupancy detection result without knowing the passenger movement direction, which could cost extra equipment and programs to be determined. The forwarding count in the request frame starts from three, and its value is reduced by one every time it is transmitted to a neighbor. When a sensing module receives from its neighbor a request frame whose forwarding count is equal to one, the dimming level of the associated LED is set to 70%. In Figure 8b, LEDs nearest to the crossroad are allocated to one group. If any LED in the group is triggered by a passenger, LEDs at the other three directions are automatically dimmed to 100%. The second LEDs in four directions are dimmed to 70% according to the received request frame. Turning on LEDs in all directions is conducted because it is difficult to predict the wanted direction of a user.  
Figure 7: Subdivision of Zone B according to the distance to the window.
## 3 AR/VR Hardware and Software Requirements
### Low-Latency Displays and Headsets for AR/VR
The liquid crystal displays (LCDs) of headsets are being replaced with the Texas Instruments DLPs as the projectors for AR/VR displays. A new strategy to determine total system latency was introduced in [68] to measure the cognitive latency using video see-through devices when it becomes hard to instrument the input to measure its corresponding output signals. It was verified in [68] that the delay of humans performing a rapid motor task using different video see-through devices and also in front of a computer would correspond to the system latency. A hardware instrumentation-based technique for benchmarking has been used to measure some types of latency. Such a new form of latency measurement through human cognitive performance can be reliable and comparable to hardware-instrumentation-based measurement. A standardized canonical test was introduced to perform cognitive latency tests to measure multiple latencies [69] successfully, which included the photon-to-photon latency and the tracking latency. The test was performed on four HMDs, where the first device was an ad hoc system, known as Prism, and the rest of them were the Oculus Quest, which is an untethered VR system, the Oculus Rift S, and the Valve Index, which are both tethered VR HMDs. The Prism ad hoc system [70] with a pair of color cameras connected to an Acer Windows Mixed Reality device with a display resolution of 1440 \(\times\) 1440 per eye at 90 Hz was designed to generate a close-to-optimal see-through low-latency AR experience and minimize the amount of re-projection between the cameras and the display. The pair of cameras attached to the Prism device had sensors configured to capture video at 90 Hz at a resolution of 1704 \(\times\) 1440 pixels, which were custom-built using Omni Vision's 4689 sensors. The latency of the system was measured with a hardware-instrumentation-based method based on a sub-millisecond accuracy clock. To measure cognitive latency, a rapid task similar to [71] was performed several times until the completion of 10 error-free trials for each HMD. It was found that the performance of the users directly impacted the latency, as the standard deviation of the error had a direct impact on the users' behavior due to the occurrence of an error arising from releasing a button too early.
## 4 Results and Discussion
### Glacial Lake Mapping Results in the Study Area  
In order to test the performance of the Sentinel-1A/1B data with the developed median-composite based segmentation method over large areas, experiments covering the whole study region of the southeastern Tibetan Plateau were carried out, as shown in Figure 7. Three sub-regions that were characterized by different environmental conditions were selected as typical examples for more local details. The glacial lakes were formed in the junction area of the Salween and Brahmaputra Basins, and were more densely distributed in the Brahmaputra Basin. Almost all the glacial lakes in this region  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Class** & \begin{tabular}{c} **Commission** \\ **Error (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Omission** \\ **Error (\%)** \\ \end{tabular} & **Kappa** &
\begin{tabular}{c} **Overall** \\ **Accuracy (\%)** \\ \end{tabular} \\ \hline SVM & Water & 4.86 & 6.89 & 0.85 & 92.13 \\ Land & 5.65 & 6.94 & & \\  & Water & 7.62 & 8.95 & 0.79 & 88.87 \\ ISODATA & Land & 8.49 & 9.14 & & \\ Proposed & Water & 1.03 & 2.72 & 0.95 & 96.54 \\ method & Land & 0.38 & 3.91 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of classification accuracy of three different methods.  
Figure 6: Seasonal changes in the number and area of glacial lakes from May to October 2018.  
could be categorized into an unconnected glacial lake type, namely lakes that were not in directly contact with current glaciers, due to the accelerating retreat of glaciers induced by the warm and humid climate. Some of these glacial lakes were impounded by block or debris dams [35], exhibiting a clear boundary in the SAR intensity image. However, their confusion with the terrain shadow areas (e.g., yellow ellipse in Figure 7a) must be considered because of the very low backscattering intensity that is similar to that of water. In this study, terrain shadows are effectively resolved using a threshold applied on slopes derived from DEM and the meaningful median estimator for the intensity values. Other lakes, including a large number of small glacial lakes, were embedded in undulating landscapes or impounded by rock barriers [36], filling with water due to both abundant rainfall and glacier meltwater (Figure 7b,c). These results suggest that the combination of the time series of Sentinel-1A/1B SAR imagery and the median composite mapping method would provide reliable and accurate information of the temporal evolution of glacial lakes over the large and rugged alpine areas, and allow for the identification of potentially dangerous glacial lakes.
# Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines
## 4 Update of the Digital Twin with Information Acquired in Inspections
The point cloud was acquired with a Reson 7125 multi-beam echosounder (MBES). The pipeline position was automatically detected using a convolution neural network (CNN), which was trained to detect pipeline profiles from a cross-section of the point cloud. Ground truth data was generated from historical surveys, where the pipeline position was placed manually by a qualified data processor. The ground truth contains a pipe diameter marker, representing the position. Using the pipe and terrain model, the free-span and burial events can then be computed automatically. The web-based Roames Pipe Inspection tool can be used to inspect and adjust machine-learning-based point classification and pipeline positioning. The neural network-based pipe modelling positions the pipe within the correct location relative to the pipe points. The results of the neural network quantitative analysis showed a 97.2% accuracy in detecting pipe burials. The observed mean error in pipeline position was 0.46 cm. While these new developments provide the operators with the desired deliverable, the large volume of information presents new challenges for inspection contractors and their onboard data processors working in a remote environment. Ongoing technological advancements in satellite communications present new opportunities for data to be transferred off the vessel in near real-time. This allows data processing to be performed securely in the cloud environment and validated by a global team of experts in offices around the world. It is noted that the construction of the digital twins shall be started at the design phase of the offshore structure. The digital twin model must be updated with as-built and as-installed conditions and could be updatable in a fast way for accidental damages, permitting a quick evaluation of the asset safety and providing information.
# Multi-NETVIS: Visual Analytics for Multivariate Network
## 4 Research Methods
### Louvain-3D FDA Layout
Community network layout and layout constraint specification: we use a 3D force-oriented layout for the community network corresponding to each virtual node; the location of a virtual node is applied as the layout center for its community, with half the number of nodes in each community used as the layout space radius of that community network. We constrained the layout results of the community network according to the specification, adjusting the node position to prevent the the community network layout structure from overflowing the community scope. The layout constraint specification is shown in Algorithm 1. ```
0: Coordinates \((x_{v},y_{v},z_{v})\) of node V; center coordinates \((x_{cv},y_{cv},z_{cv})\) and sphere radius \(R\) of the drawing region
0: The regular layout
1:\((\min x,\min y,\min z)=[\min(x_{v}),\min(y_{v}),\min(z_{v})],v\in V\)
2:\((\max x,\max y,\max z)=[\max(x_{v}),\max(y_{v}),\max(z_{v})],v\in V\)
3:\((r_{x},r_{y},r_{z})=[R/(\max x-x_{cv}),R/(\max y-y_{cv}),R/(\max z-z_{cv})]\)
4:if\(pos(v)\) not in the sphere then
5:\((x_{v},y_{v},z_{v})=[(x_{v}-\min x)\cdot r_{x},(y_{v}-\min y)\cdot r_{y},(z_{v }-\min z)\cdot r_{z}]\)
6:endif
```  
**Algorithm 1** Layout constraint specification  
Adding association edges between communities: after completing the trunk layout and community layout, we add the association edges between the different community nodes to gain the complete network structure.