esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The edges of the water body are the main zones where incorrect classification occurred since these environments are very complex (e.g., there were widely distributed shallows, sand, and plankton) in the present study (Figure 5). The zones of the commission of water mainly appeared as a light-green and bright white color in OLI (Figure 10a). There were sparse emergent aquatic plants or plankton in the light-green zones, whose spectral characteristics were similar to the vegetation (Figure 11). Thus, these zones might be categorized as vegetation in the OLI images. However, in these zones, the main return of the radar pulse came from the water due to the low density of vegetation. Thus, these zones were categorized as water in the SWI images, which should be regarded as correct. The bright white zones were sand with high water content and low surface roughness; therefore, these bright white zones were wrongly categorized as water in the SWI image.  
We cannot determine whether the omission water (Figure 10b) was part of the water body because its spectral characteristics were different from those of normal water (Figure 11). However, they were categorized as water in the MNDWI image. Therefore, further study is required to investigate this.  
Compared with the original images of S1A, SWI images have the ability to enhance the difference between water and non-water. Furthermore, high speed, efficiency, and classification accuracy are regarded as being advantages of the SWI threshold classification method for extracting water from SWI imagery, as shown in Table 3.  
Table 3 also shows that the area of water can be extracted with the highest QA of 99.57% using the SVM method with SWI data. However, when compared to the similarly performing TCM method (QA: 98.84%, see Table 3), it is more time consuming to analyze inundation extent using SVM.  
Compared to the OLI and MODIS images, SWI images have high effective spatiotemporal resolution. The revisit time of OLI is 16 days, and the frequency of cloudy or rainy weather is very high in the south of China. Therefore, there is a lack of useful OLI data when detecting the dynamic changes in Poyang Lake (Figure 8).  
Although MODIS has a revisit time of one day, its spatial resolution is too coarse to identify the details of the water and small water bodies. Poyang Lake is composed of a number of lakes with areas of less than 1 km\({}^{2}\) that are easily omitted from MODIS. The annual water surface area of Poyang Lake extracted from MODIS ranged from 700 km\({}^{2}\) to 2700 km\({}^{2}\) during 2000 to 2010 [6]; however, the water surface area ranged from 1700 km\({}^{2}\) to 3700 km\({}^{2}\) in the present study. Additionally, the quantity accuracy from MODIS was 67.56%, and meaning that water surface area of 537 km\({}^{2}\) wasn't detected by MODIS in sub-region in the Figure 9. It may be possible to improve the quantity accuracy by other advanced methods, although we still believe that MODIS cannot precisely identify details of the water and small water bodies, especially in areas where water changes are severe.
## 3 Results
### Effect on NO Emissions  
NO emissions during operation at certain air-to-fuel ratios can be found in Figure 7. The following points can be made:  
* An increase in air-to-fuel ratio results in higher NO emissions. This is a well-known phenomenon within combustion chemistry.
* The sand reference has the lowest NO emissions, meaning that no improvements could be verified by using LD-slag as bed material.
* Of the experiments that involved LD-slag, those with lowest CO emissions also had the lowest NO emissions.  
A positive effect of up to 30% reduction of NO have previously been reported in an OCAC campaign with partial substitution of sand with ilmenite [10]. However, similar effects have not  
Figure 6: Measured concentrations of CO (mg/Nm\({}^{3}\), at 6% O\({}_{2}\)) at position kh2 in the convection path as a function of the air-to-fuel ratio.  
been seen in subsequent campaigns with ilmenite and manganese ore [14]. Instead, the use of oxygen-carrying bed material typically has resulted in a modest increase in NO emissions, to a similar degree as is reported for LD-slag here. This is not unexpected, since the presence of an oxygen carrier should increase the availability of oxygen in the boiler, which in turn could be expected to result in higher NO emissions.
## 5 Conclusions  
For the first time, we demonstrate Ge p-i-n PDs integrated with polymer MLs for an optical communication receiver at 850 nm wavelength through an optimal method of  
Figure 14: (**a**) HTOL reliability test at \(175\ ^{\circ}\)C showing dark current vs. duration up to 2000 h. (**b**) HTS reliability test at \(135\ ^{\circ}\)C showing QE variation for all samples after 2000 h.  
photon-acid diffusion. The unique property of this device, i.e., it is capable of absorbing light with a larger effective optical window, overcomes the limitations between bandwidth, dark current, and QE for a given detector area. A typical device fabricated here features a high responsivity of 0.6 A/W, a low dark current of 138 nA, a high 3 dB bandwidth of 21.4 GHz, and a large effective optical aperture of 54 \(\upmu\)m. Moreover, all fabricated devices have passed the reliability tests in accordance with the standards in the document Telecordia GR-468-CORE. Moreover, the demonstrated devices here can reach a 3 dB bandwidth of \(>\)35 GHz when the PD diameter is scaled down to 20 \(\upmu\)m while maintaining the same large effective optical aperture, which may be applied for 400 Gbps pulse-amplitude modulation four-level (PAM4) application using a four-channel photo-receiver. All these results show great promise in utilizing the demonstrated Ge PD integrated polymer MLs for future high-speed optical communication systems.  
Conceptualization, N.N. and Y.-H.L.; methodology, Y.-H.L., C.-P.L., P.-W.C., C.-T.T., C.-C.L. and T.-T.W.; software, C.-C.L. and Y.-H.L.; validation, Y.-H.L., C.-P.L., P.-W.C., C.-T.T., C.-C.L. and T.-T.W.; formal analysis, Y.-H.L. and C.-C.L.; investigation, Y.-H.L. and C.-C.L.; data curation, Y.-H.L. and C.-C.L.; writing--original draft preparation, Y.-H.L.; writing--review and editing, N.N. and L.W.; visualization, Y.-H.L.; supervision, N.N. and L.W.; project administration, N.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Y.-H.L., C.-C.L., P.-W.C., C.-T.T., T.-T.W., and N.N. are shareholders of Artilux Inc., a private company that makes SWIR 2D/3D sensors, imagers, and photonic integrated circuits.
## 2 CNN Deployment in Automotive
### CNN-Based Object Detection  
CNNs belong to the family of deep neural networks (DNNs), characterized by artificial neurons organized in layers. They possess distinctive attributes setting them apart from other types of neural networks, including local receptive fields, spatial subsampling, and shared weights. CNNs facilitate cooperation between multiple sequential stages, involving convolution, padding, pooling operations, and the introduction of nonlinearities via activation functions (Sigmoid, Softmax, TanH, ReLU, Leaky ReLU) and fully connected layers (FCLs). Each convolutional layer has an input and output called feature maps, which are 2D arrays of color channels for images and 3D arrays for videos, and 1D arrays for audio inputs. Throughout the network's processing of input data, new features are extracted at each output stage, and convolutional operations detect more distinctive features in subsequent layers. [PERSON] et al. [18] offer a comprehensive definition of all architectural components of a CNN, including their mathematical equations, and employ the reference architecture of AlexNet [19].  
Object detection plays a pivotal role in enabling self-driving cars and ADASs to perceive their environment and make well-informed decisions. Various detection algorithms, including deep CNN approaches, have been proposed for this purpose. However, these models often pose significant computational demands, rendering them unsuitable for deployment on embedded platforms with limited resources. Hence, there exists an urgent necessity to develop a more compact neural network model capable of addressing this computational challenge. The primary challenge in this context is to ensure the successful transplantation of the object detection network model onto an embedded platform without compromising accuracy and stability, which is crucial for the safe and reliable operation of self-driving cars and ADASs.  
Object detection algorithms are prominently categorized into either one-stage or two-stage algorithms. Only one-stage algorithms possess the capability to operate efficiently under the constraints of embedded systems in real-time. In contrast, two-stage algorithms, exemplified by Faster R-CNN [20], demand more computational resources, rendering them unsuitable for deployment on embedded platforms. Presently, SSD (Single Shot Detector) [21] and YOLO (You Only Look Once) [22] stand out as two widely utilized one-stage object detectors. According to [PERSON] et al. [21], YOLO may not be as effective in detecting dense multiple targets and large objects, while the SSD detector is deemed more suitable for effectively detecting a large number of objects with varying scales and types.
# WSN Nodes Placement Optimization Based on a Weighted Centroid Artificial Fish Swarm Algorithm
## 3 Weighted Centroid Fish Swarm Algorithm
### Procedure of the Weighted Centroid Fish Swarm Algorithm
#### 3.1.2 Binary Coding  
Binary coding is intuitive and convenient for the optimal sensor placement problem [13]. Let \(n\) denote the initial number of candidate sensors and \(s\) denote the final number of sensors determined. The binary coding for a solution can be expressed as \(\mathbf{X}=(x_{0},x_{1},\ldots,x_{j},\ldots,x_{n})^{\text{T}}\), \(j\in\{1,2,\ldots,n\}\). If \(x_{j}=1\), there is a sensor on that DOF. If \(x_{j}=0\), there is no sensor on that DOF. The condition \(\sum\limits_{j=1}^{n}x_{j}=s\) is satisfied.  
The conversion between binary coding \(\mathbf{X}\) and modal matrix \(\mathbf{\phi}_{s\times\text{m}}\) is the process of removing the rows of \(\mathbf{\phi}_{n\times\text{m}}\). In this process, remove those rows of \(\mathbf{\phi}_{n\times\text{m}}\) corresponding to those \(\{x_{j}|x_{j}=0,j\in[1,2,\ldots,n]\}\) in \(\mathbf{X}\). For convenience, this process is represented by Equation (4):  
\[\mathbf{\phi}_{s\times m}=g(\mathbf{\phi}_{n\times m},\mathbf{X}_{n\times 1}) \tag{4}\]  
where the modal matrix \(\mathbf{\phi}_{s\times m}\) is a solution for placing \(s\) sensors in optimal sensor placement; \(\mathbf{X}_{n\times 1}\) is the binary coding corresponding to a solution.  
For a simple example, substituting \(n=4\), \(m=2\), \(s=3\) and \(\mathbf{X}_{4\times 1}=(1,0,1,1)^{\text{T}}\) into Equation (4), and take \(\mathbf{\phi}_{4\times 2}=\left[\begin{array}{cc}1&5\\ 2&6\\ 3&7\\ 4&8\end{array}\right]\) as the input modal matrix of function \(\mathbf{g}\), the output modal matrix of \(\mathbf{g}\) is  
Figure 1: Visual and Step of an artificial fish. \(X_{n1}\), \(X_{n2}\): Locations of two fish within Visual\[\phi_{3\times 2}=g\left(\left[\begin{array}{cc}1&5\\ 2&6\\ 3&7\\ 4&8\\ \end{array}\right],\left[\begin{array}{c}1\\ 0\\ 1\\ 1\\ \end{array}\right]\right)=\left[\begin{array}{cc}1&5\\ \mathbf{X}&\mathbf{X}\\ 3&7\\ 4&8\\ \end{array}\right]=\left[\begin{array}{cc}1&5\\ 3&7\\ 4&8\\ \end{array}\right]\text{ and can be easily obtained by deleting the second row of }\phi_{4\times 2}.\]
## 5 Discussion and Conclusions  
### Power Inefficiencies  
The data presented in Table 3 indicate a clear need to optimize both container and virtualization environments on ARM Edge devices, as the range of watt-to-operation requirements will place a greater demand on both power requirements, potentially affecting battery life, and heat output.  
Moreover, this also demonstrates the similarities between containerization and virtualization environments when considering power consumption, allowing greater freedom for software deployment.
## 9 Monitoring Applied to Aquaculture and Fishery Productions
As for tank aquaculture, various remote sensor systems were proposed to have a conveyed gathering of sensor hubs organized together and be able to exchange the crude information up to a base station through an IoT network [169; 170]. Using Arduino-like hardware and a few simple probes, automatic farming systems were developed based on IoT platforms (Figure 7). All IoT systems for aquaculture are to be considered smart systems based on intelligent sensors, intelligent processing, and intelligent control. Their functions consist of data collection, real-time image acquisition, wireless transmission, intelligent processing, warning messages, and auxiliary decision making [171; 172; 173]. Any aquaculture IoT monitoring system fundamentally consists of water quality monitoring stations, including meteorological stations, water quality control stations, and on-site and remote monitoring centers. These structures are supported by a central cloud-processing platform [174]. The water monitoring stations are provided with monitoring sensors and take advantage of wireless data collection terminals. Local data are collected in ponds and transmitted to monitoring centers. In particular, such water quality parameters as dissolved oxygen, pH, and ammonia concentration are key elements to allow prompt answers to production issues. Generally, weather stations are also used to acquire in real time such meteorological data as wind speed, wind direction, and air humidity. The system analyzes the relationships between the water quality parameters and weather changes to predict water quality trends and ensure the optimal water quality in the culture tanks. The tank controllers include independent control terminals, an electrical control box, aerators, and other equipment,  
Figure 7: Block diagram of the system proposed in [169] for IoT control of aquaculture productions (modified). with the control terminal receiving wireless instruction from the control equipment. On-site and remote monitoring centers based on wireless sensor networks and the Global System for Mobile Communications (GSM) central servers with central cloud processing platforms are included, favoring an intelligent control algorithm for water quality to achieve data acquisition, smart data processing, alarms, and their mailing to human managers [175]. Central cloud processing platforms provide the basis for decision making for farmers by providing a variety of models and algorithms of quality monitoring, feeding, and pond management [176]. These strategies reduce the risks of product losses, reduce the pollution of local environments by increasing the efficiency of culture procedures, and also reduce the need for using drugs, with obvious advantages for local environments.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.2 NACP LiDAR-Based Biomass Estimates, Boreal Forest Biome, North America, 2005-2006  
Using the same method as [PERSON] et al. [85], [PERSON] et al. [88] mapped the boreal forest AGB at a 500 m spatial resolution for North America with field measured biomass, airborne LiDAR and GLAS data. Sampling and model uncertainties of generated biomass were also quantified using the method of [PERSON] et al. [85].
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## Case Study
### Analysis of Influencing Factors of Travel Time  
Freeway travel time is determined by various factors, such as weather, traffic accidents, holidays, and weekdays. However, owing to limitations of the sample data, only traffic accidents and holidays were considered in this paper.
## 2 Materials and Methods
### Sensitivity Analysis Using the Kriging Method and GIS  
In the Al-Baha region, georeferenced data were gathered from 20 sites (S1 to S20). Wind speed, soil type, slope, surface roughness, and dust deposition rates (ton ha\({}^{-1}\)) were among the variables. Spatial data on land-use practices; topographical features; and meteorological parameters, such as wind speed and precipitation, were combined with field data on wind erosion and dust deposition. We used Geographic Information Systems (GISs) and the Kriging interpolation method to evaluate the susceptibility of several sites in the Al-Baha region to wind erosion, wind surface disturbance, and dust deposition. We were able to map and examine spatial changes throughout the research region thanks to this methodology, which gave us insights into the environmental factors influencing soil stability.  
Layered maps of the area were produced using GIS, showing the geographical distribution of dust deposition, wind surface disturbance, and wind erosion at each location. A complete spatial context was provided by each layer's alignment with elevation, slope, and topographical-feature data. Digital Elevation Models (DEMs) obtained from satellite data were used to map topographical features like slope and elevation. This research assisted in identifying regions with steep slopes or high altitudes that are more vulnerable to wind erosion.  
In unsampled areas, Kriging was used to forecast the sensitivity of wind erosion and dust deposition. This geostatistical method computes the spatial autocorrelation between neighboring points using known sample points (S1 to S20) to estimate values in surrounding areas. To carry out the Kriging analysis, several GIS modules were utilized, including ArcGIS Geostatistical Analyst. To determine which model best fits the data on the spatial distribution of dust and wind disturbances, we investigated a variety of models, including spherical and exponential ones. Cross-validation techniques were employed to validate the Kriging model and evaluate the precision of predicted values in comparison to observed values at sample points. Kriging produced continuous surfaces that showed different susceptibilities to dust deposition and wind erosion. These were plotted on maps to show the locations at high risk.  
Based on the sites' anticipated susceptibility to dust deposition and wind erosion, they were categorized into sensitivity zones. Elevated winds, little foliage cover, and steep inclines were used to identify high-sensitivity locations. High-, medium-, and low-sensitivity zones were clearly distributed spatially in the final sensitivity maps. This classification helps identify the most vulnerable locations and directs efforts toward sustainable land management and wind erosion reduction.
## 5 Discussion
### Constraints and Prospective Advancements  
The present investigation provides valuable perspectives on the effectiveness of the CNN-ResBiGRU model in identifying physical exercises performed in a gymnasium setting utilizing multimodal sensor data. Nonetheless, it is imperative to acknowledge the constraints of our endeavor and pinpoint potential avenues for future exploration.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Accuracy (\%)**} \\ \cline{2-6}  & **Accelerometer** & **Gyroscope** & **IMU** & **EMG** & **IMU + EMG** \\ \hline InceptionTime [42] & 91.56\% & 90.42\% & 77.50\% & 77.50\% & 86.84\% \\ DeepConvLSTM [43] & 89.29\% & 84.31\% & 88.62\% & 78.30\% & 90.46\% \\ ResNet [44] & 79.24\% & 84.04\% & 95.95\% & 77.45\% & 92.85\% \\ \hline CNN-ResBiGRU & 95.69\% & 95.58\% & 96.96\% & 91.53\% & 97.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of comparison with state-of-the-art models.  
Our work is built upon the Myogym dataset, a comprehensive and widely used dataset in the industry. While this dataset is a standard for comparison, it may not fully represent the range of gym activities and user demographics in real-world scenarios. To address this, further investigation might examine the applicability of our results by assessing the suggested CNN-ResBiGRU model on supplementary datasets, thereby enhancing the validity of our findings.  
Another constraint is the exclusive emphasis on data obtained from a solitary wearable device, namely the Myo armband, which is worn on the right forearm. Although this device offers comprehensive IMU and EMG data, the effectiveness of the suggested model might be affected by the precise positioning and setup of the sensors. Subsequent research might explore the influence of other sensor placements on identification precision, such as the wrist, upper arm, or lower body. In addition, investigating the use of numerous wearable devices or including data from other sensory modalities, such as heart rate or breathing, can improve the strength and flexibility of the identification system.
# Research on the Settlement Patterns of Tunnel-Surrounding Rock Under Groundwater Conditions
## 5 Conclusions  
This study addresses the issue of groundwater's influence on tunnel-surrounding rock deformation under complex geological conditions, through the combination of field monitoring and numerical simulation. A comparative analysis of settlement curves and numerical values under groundwater and non-groundwater conditions reveals the deformation patterns of tunnel-surrounding rock. This analysis shows that groundwater significantly impacts surrounding rock deformation. The following main conclusions are drawn:  
1. The settlement rates after tunnel excavation demonstrate a clear \"steep decline--gradual decline--stable\" evolution trend, stabilizing around 35 m once excavation reaches the monitoring cross-sections. Rising groundwater levels reduce the effective stress in the soil, accelerating plastic deformation and resulting in an earlier stabi  
Figure 11: Action mechanism of groundwater on settlement.  
lization point. Conversely, falling groundwater levels strengthen the soil skeleton, delaying plastic deformation development and resulting in a later stabilization point. (2) The numerical simulation settlement curves closely align with the field monitoring curves, both exhibiting a three-phase trend of \"steep, gradual, stable\". The error rates for vault settlements range from 0.35% to 2.86%, and those for ground surface settlements range from 3.47% to 5.48%. Both error rates fall below 20%, indicating the simulation's reliability and its good correlation with the monitoring results. (3) Groundwater significantly influences the deformation patterns of tunnel-surrounding rock, particularly in strata such as loess and mudstone, which soften upon contact with water. Deformation quantities in these strata are significantly greater under groundwater influence than in conditions without groundwater. Under groundwater influence, the maximum vault settlement displacement is 3.28 times greater than that without groundwater influence, and the maximum ground surface settlement displacement is 1.8 times greater. This indicates that groundwater significantly amplifies rock deformation, warranting special attention in similar engineering projects.  
Not applicable.  
The data that support the findings of this study are available upon reasonable request from the corresponding author, [[PERSON], [EMAIL_ADDRESS]].  
The authors declare no conflicts of interest.