esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments  
### Datasets Description  
The Gaofen-1 satellite (GF-1) adapts to multiple spatial resolutions, multiple spectral resolutions, and the integrated demand for multi-source remote sensing data, and it can realize the measurement, control, and management of the time period outside the country. The satellite carries two 2 m-resolution panchromatic/8 m-resolution multispectral cameras and four 16 m-resolution multispectral cameras, with 2 m high-resolution realizing an imaging width of more than 60 km and 16 m resolution realizing an imaging width of more than 800 km. Gaofen-2 satellite (GF-2), as China's first wide-format civil remote sensing satellite with a resolution of sub-meter, has broken through the sub-meter-level and large-width imaging technology. The satellite carries two high-resolution 1-m panchromatic and 4-m multispectral cameras to achieve spatial imaging. The positioning accuracy of the satellite without control points reaches 20-35 m, and it also has intelligent on-board autonomous management capabilities. Examples of Gaofen-1 and Gaofen-2 images are shown in Figure 4.  
We selected remote sensing images from different satellites for experimentation and validation as follows: GF-1 satellite remote sensing images were used as experimental data, with the United States as the target region. The total area of the United States is 9.37 million square kilometers. A total of 1786 views of the remote sensing image were selected to complete the coverage of the United States, and the imaging time span was from 1 March 2021 to 30 September 2021. The validation data used GF-1 satellite remote sensing images corresponding to Hebei 2 and Inner Mongolia regions, and GF-2 satellite remote sensing images corresponding to Hebei 1 region.  
To understand the difficulty of the problem, we counted the number of elements and subsets of the experimental and validation datasets, which directly affects the computational complexity; the more elements and subsets, the more complex the problem usually is. The statistical results are shown in Table 1.
## 2 Related Work  
### RL-Based Mapless Navigation  
Recent advances have proposed sensor-level approaches (e.g., a single monocular camera [18], a 2D LiDAR and a monocular camera [19], and a depth camera [20]) to address the navigation problem, in which a single robot can be trained to obtain the capability of avoiding collisions with obstacles. A critical issue of applying RL-based approaches to solve navigation tasks is that a considerable amount of sensor-level data are required to train a navigation policy. Thus, most work focuses on transferring the learned policy in simulations to real scenarios [21; 22].  
To reduce the training time, the parallel and distributed paradigm [23; 24] can be applied to more efficiently collect samples by multiple agents in parallel. In synchronous RL algorithms (e.g., Parallel Advantage Actor Critic (PAAC) [25]), each agent explores its own environment and collect samples, and then the global model can be updated synchronously. In asynchronous RL algorithms (e.g., Asynchronous Advantage Actor Critic (A3C) [26]), a coordinator is required to update the global model without waiting for all agents to send the gradients. However, privacy protection and communication consumption need to be taken into consideration for parallel RL methods.  
Moreover, we also find that the testing environment for most work is almost the same as the training settings such as in [22; 23]. A potential problem that still needs to be investigated is whether the learned policy can be flexible and resilient enough when the robot encounters an unknown and unfamiliar environment. Standard episodic RL algorithms often struggle with the generalization and adaptability of out-of-distribution observations [27]. For navigation tasks, UGVs may face a variety of never encountered obstacles in practice, and we cannot enumerate all the scenarios during the training procedure [28].
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 7 Conclusions  
In this paper, we propose a communication optimization scheme for accelerating distributed deep learning systems. We perform compression in units of layers of the deep learning model. Therefore, our proposed compression scheme can perform compression more efficiently than comparing all gradients with a threshold. Furthermore, to reduce the overhead of calculating the threshold, we use the L1 norm of each hidden layer rather than using gradients when calculating the threshold. We implement a distributed deep learning environment using TensorFlow and MPI  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Layer Dropping** & \begin{tabular}{c} **Worker** \\ **Compute (sec)** \\ \end{tabular} & \begin{tabular}{c} **Parameter Server** \\ **Compute (sec)** \\ \end{tabular} & \
\begin{tabular}{c} **Communication (sec)** \\ \end{tabular} &
\begin{tabular}{c} **Total (sec)** \\ \end{tabular} \\ \hline
1 Gb/s & No & 0.388 & 0.073 & 3.457 & 3.918 \\  & Yes & 0.408 & 0.085 & 0.182 & 0.675 \\ \hline
56 Gb/s & No & 0.222 & 0.048 & 1.951 & 2.222 \\  & Yes & 0.230 & 0.060 & 0.375 & 0.665 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average execution time per iteration.  
Figure 14: Communication time and computation time in a high bandwidth network.  
To verify the performance of the proposed scheme according to the network performance, experiments are performed in both a 1 Gbit Ethernet environment and a 56 Gbit environment. We show through experiments that our compression method works efficiently not only in a low bandwidth network, but also in a high bandwidth network. In the future, we will conduct experiments by applying it to various large-scale deep learning models and optimizers.  
Conceptualization, J.L., H.J., and B.N.; methodology, H.J., B.N., and J.L.; software, H.C., H.J., and N.B.; writing, original draft preparation, H.C. and J.L.; writing, review and editing, H.C., J.S.S., and J.L.; supervision, J.L. and J.S.S.; project administration, J.L. and J.S.S.; funding acquisition, J.L. and J.S.S. All authors read and agreed to the published version of the manuscript.  
This research was supported by the Next-Generation Information Computing Development Program (2015M3C4A7065646) and the Basic Research Program (2020R1F1A1072696) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT, the GRRC program of Gyeong-gi province (No. GRRC-KAU-2020-B01, \"Study on the Video and Space Convergence Platform for 360 VR Services\"), the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01423), and the National Supercomputing Center with supercomputing resources including technical support (KSC-2019-CRE-0101).  
The authors declare no conflict of interest.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Technologies
#### 3.1.4 Wi-Fi  
In a Wi-Fi environment, computers, tablets, cellphones, and other devices may all be connected to the internet using Wi-Fi devices. Doing so establishes a network by enabling information interchange between these devices and several others. The communication range of Wi-Fi is broad, and it follows IEEE 802.11 standards. In order to enable more devices to connect to the network from farther away, an access point expands the bandwidth flowing from a router.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Methods** & **Data** & **Limitations** \\ \hline
[43] & Neuro-Fuzzy Inference and Multi-Kernel Learning & KEGG Metabolic Dataset & Evaluating parameters are not comparable. \\ \hline
[45] & CNN, BiGRU, BiLSTM Ensemble & Cardiac Disease Dataset & The dataset used in this approach is not a standard dataset \\ \hline
[46] & Gradient Boosting, XG Boost, Random Forest Ensemble & Hungarian, Cleveland, Z-Alizadeh Sani Dataset & The complexity and price of the model are increased by stacking three models. \\ \hline
[47] & Generalized Discriminant Analysis and Fisher Method & SR-CAD and NSR-CAD & There is lack of training on large datasets. Training on heart rate variability needs to improve. \\ \hline
[48] & Random Forest, KNN, Decision Tree, Naive Bayes & Hungary, Cleveland, VA Long Beach, Switzerland Datasets & There should be more experimentation with model combinations and feature choices. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Existing studies on cardiac disease.
## 4 Integrated Positioning
### Baseline Filtering  
The epoch-by-epoch baseline solution in Section 4.2 is further processed to obtain the filtered estimates for the center of the MSS (Figure 1a). Unlike the previous method in [8], which uses constrained nonlinear filtering for antenna positions, the integrated method in Section 4.2 yields estimates of the center position, which is assumed to be stationary for a rotating, leveled frame. Hence, dead reckoning (linear) Kalman filtering yields the filtered estimates for the stationary center position. The kinematic equation reads:  
\[\beta_{i}=\beta_{i-1}+v_{i-1}^{\beta} \tag{40}\]  
where the state vector \(\beta_{i}=b_{i}^{{}^{\prime}12}\) consists of position components. The process noise \(v_{i-1}^{\beta}\) has a zero mean normal distribution with variance-covariance matrix \(Q_{v^{\beta}v^{\beta},i-1}\), which is given as:  
\[Q_{v^{\beta}v^{\beta},i-1}=\mathrm{diag}\left([\sigma_{b_{x}}^{2},\ \sigma_{b_{y}}^{2},\ \sigma_{b_{z}}^{2}]\right) \tag{41}\]  
with \(\sigma_{b_{x}}\), \(\sigma_{b_{y}}\) and \(\sigma_{b_{z}}\) the process noise standard deviations. Since the center position of the rotating frame is stationary, a dead reckoning filter is used (_i.e._, \(\sigma_{b_{x}}=\sigma_{b_{y}}=\sigma_{b_{z}}=0\), which is equivalent to the recursive least-squares estimation of constant parameter vector \(b^{{}^{\prime}12}\)). The observation model reads:  
\[\xi_{i}=\beta_{i}+w_{i}^{\beta} \tag{42}\]  
with \(\xi_{i}\) given by \(\tilde{b}^{{}^{\prime}12}(\tilde{Z}^{\prime})\) at epoch \(i\). Observation noise \(w_{i}^{\beta}\) is assumed to have a zero mean normal distribution with covariance matrix \(Q_{w^{\beta}w^{\beta},i}\), which is given by \(Q_{\tilde{b}^{{}^{\prime}12}(\tilde{Z}^{\prime})\tilde{b}^{{}^{\prime}12}( \tilde{Z}^{\prime})}\) at epoch \(i\).
## 3 Materials and Methods
### Landslide Conditioning Factors
In Austria--as earthquakes are relatively rare--rainfall is the main triggering factor for landslides and a critical factor in LSM. Rainfall features can vary by weather situations and topographical features, which can cause substantial temporal and spatial variances in a rainfall event. Annual precipitation data were classified into four classes: 0-600 mm, 600-1200 mm, 1200-1800 mm, and \(>\)1800 mm. Distance to drainage is a hydrological process of slope that influences LSM: the closer to drainage, the more erosion. Drainage delivers water that causes material saturation, resulting in landslides into the valley [48]. The study area was classified into five classes, namely the intervals of 0-500 m, 500-1000 m, 1000-1500 m, 1500-2000 m, and \(>\)2000 m.  
Distance to roads is a crucial anthropogenic factor for the occurrence of landslides [49]. The distances to roads were classified into the five buffer zone classes of 0-200 m, 200-400 m, 400-600 m, 600-800 m, and \(>\)800 m.  
Active faults play a significant role in landslides [50]. Firstly, they are the source of occurrences of earthquakes, and secondly, active faults have vital roles in cracking of stones, which in turn creates instability. This disjointed formation of geology leads to reductions in the shear resistance of the rock strength and triggers the occurrence of landslides. The distance to faults was classified based on the distance of landslides (Euclidean distance) from the fault and classified into the following five classes: 0-1000 m, 1000-2000 m, 2000-3000 m, 3000-4000 m, and \(>\)4000 m distances. Figure 2: _Cont._
## 3 Method and Material  
### Polygeneration System Simulation  
In this section, the material and energy metabolism paths of steel, chemical, building materials, and power industries will be identified first, and then the simulation of polygeneration systems will be modeled based on the bottom-up modeling method. The modeling process follows the chain of \"supplier--waste/by-products--technologies--adopter\", where the iron and steel industry are the suppliers in this study and the chemical, building materials, and power industries are the adopters. The production of wastes and by-products in the iron and steel industry is shown in Figure 1(44,45).  
It can be seen from Figure 1 that there are eight types of waste/by-products in the steel industry, which are coke sensible heat, sintering waste heat, blast furnace gas residual pressure, coke oven gas, blast furnace gas, desorbed gas, blast furnace slag, and converter slag, respectively. According to the utilization approaches of these waste/by-products and the matching and applicability issues of raw materials, processes, and technologies in the polygeneration system, 4 polygeneration paths are designed in this study, and a total of 20 technologies (No. T1-T20) are identified in this study to address these waste/by-products [46; 47; 48], as follows:  
* Path 1: Coke waste heat/Coke oven gas/Desorbed gas \(\rightarrow\) T1-T9 \(\rightarrow\) Power/ Chemical products.
* Path 2: Sintering waste heat \(\rightarrow\) T10 and T11 \(\rightarrow\) Power.
* Path 3: Blast furnace gas/Blast furnace residual pressure \(\rightarrow\) T12-T15 \(\rightarrow\) Power/ Chemical products.
* Path 4: Blast furnace slag/Converter slag \(\rightarrow\) T16-T20 \(\rightarrow\) Building materials. The specific information was shown in Figure 2.  
* Path 1: Polygeneration system of iron and steel industry, chemical industry, and power industry based on coking process. In this path, coke waste heat and coke oven gas generated in the coking process would be used for power generation and chemical product production, including coal tar, sulfuric acid, methanol, ethanol, and methyl methacrylate (MMA).
* Path 2: Polygeneration system of iron and steel industry and power industry based on sintering process. In this path, sensible heat from the sintering process would be used for power generation and directly converted into coupling-driven kinetic energy.
* Path 3: Polygeneration system of iron and steel industry, chemical industry, and power industry based on ironmaking process. In this path, the blast furnace gas, blast furnace top pressure, and other by-products generated in the ironmaking process would be used for power generation or the preparation of carbon monoxide (CO).  
It can be seen from Figure 2 that a total of 9 technologies are included in polygeneration path 1, i.e., T1-T9. Similarly, there are 2, 4, and 5 technologies embodied in polygeneration path 2 (T10, T11), polygeneration path 3 (T12-T15), and polygeneration path 4 (T16-T20), respectively.
# Evaluation of Rock Brittleness Index under Dynamic Load
## 3 Results
### Applicability Analysis of Brittleness Index under Dynamic Load  
The rationality of the existing brittleness index to measure the brittleness of rock under static load has been verified by a significant number of tests, but its rationality for measuring the brittleness of rock under dynamic load still needs to be explored. According to the test results, the brittleness of rock under dynamic load was significantly improved compared with that under static load from the perspective of qualitative analysis. Therefore, an index that can quantitatively measure the brittleness of rock under dynamic load should have  
Figure 5: Rock failure under different loading modes.  
the same law. Through the dynamic and static tests which we carried out, 11 brittleness indices of each specimen were calculated, and the average value of the parallel specimens as taken as the brittleness index value for each type of rock. The calculated results and the calculation results are shown in Table 3. Among these indices, the smaller the index values of \(B_{9}\) and \(B_{11}\), the stronger the brittleness; in contrast, the larger the index values of the other indices, the stronger the brittleness.  
It can be seen that when the static load changed to a dynamic load, the brittleness value calculated by brittleness indices \(B_{4}\), \(B_{6}\), and \(B_{7}\) weakened instead. \(B_{4}\) was determined by the stress reduction degree and stress reduction rate at the post\(-\)peak stage of the stress-strain curve. However, in the SHPB test, the residual strength was generally zero and the correctness of the post\(-\)peak curve is still controversial. Therefore, \(B_{4}\), which determines rock brittleness only through the post\(-\)peak brittleness characteristics, was not suitable for measuring rock brittleness under dynamic load. \(B_{6}\) had the same problem. \(B_{7}\) was determined based on the relationship between elastic strain energy and total energy before peak. Only the energy variation characteristics of the pre\(-\)peak stage were considered, and the effects of compressive strength and elastic modulus were ignored, which resulted in incorrect results.  
The brittleness of the rock samples was reflected not only in the pre\(-\)peak stage, but also in the post\(-\)peak stage. In the pre\(-\)peak stage, the more brittle the rock, the larger the elastic modulus and the smaller the unrecoverable strain. In the post\(-\)peak stage, the more brittle the rock, the greater the reduction in stress and the faster the reduction rate. Since \(B_{8}\) and \(B_{10}\) take into account pre\(-\)peak and post\(-\)peak brittleness characteristics and are more comprehensive than other indices, the subsequent studies in this section will mainly use \(B_{8}\) and \(B_{10}\) to explore the brittleness response characteristics of rocks.
# Extending Battery Lifetime by Avoiding High SOC
## 5 Results
### Drive Cycle Study  
The degradation of the battery as function of years for the three vehicles while running 90% DOD for the WLTC drive cycles are shown in Figure 9. Important to note is that these results only represent the impact of ageing due to driving and no consideration has been taken to the calendar ageing. The actual battery lifetime will therefore be shorter than the shown results. However, here the focus is on the comparison of the impact from different types of driving, calendar ageing will be investigated in Section 5.4.  
The results show, as expected, that Vehicle 3 with the highest C-rate has the shortest lifetime in all three cycles. As the C-rate is reduced, this difference in lifetime is also reduced.  
For the city driving, WLTC medium, see Figure 9a, all vehicles reach close to the same lifetime. Vehicles 1 and 2 show almost the same ageing trajectory. As expected, this is also the drive cycle that provides the longest lifetime.  
Figure 8: Model result at 30 \({}^{\circ}\)C starting at different SOC levels in **(a)** 10% Depth of Discharge (DOD) with 0.5C, 1C and 2C; and **(b)** 20% and 50% DOD with 1C.  
The highway driving, Figure 9c, is the case showing the most aggressive ageing. Vehicles 1 and 2 manage to meet the common set warranty level of eight years [10], though not Vehicle 3.
## 4 Discussion  
Various experimental tools tracked the spatial-temporal occurrences of the deformation waves in the deformed red sandstone samples during their loading. The generation of deformation waves in the rock samples was found to be related to the regions of elastic stress concentration of varying scales [42]. Under the three loading rates, the deformation waves' propagation rates were 3.0 \(\times\) 10\({}^{-5}\), 5.9 \(\times\) 10\({}^{-5}\), and 10.4 \(\times\) 10\({}^{-5}\) m/s, respectively. They exceed, by one order of magnitude, the axial loading rates, i.e., the boundary advance speeds, of 0.16 \(\times\) 10\({}^{-6}\), 0.8 \(\times\) 10\({}^{-6}\), and 1.6 \(\times\) 10\({}^{-6}\) m/s for the loading rates of 0.1, 0.5, and 1 mm/min, respectively. Thus, the propagation rate of deformation waves is significantly higher than that of the mass particles. The local thickening and uplift caused by boundary advancement continue to propagate with the help of elastic force and inertial force. Thus, the matter particles' initial high displacement rate under the elastic force's action tended to decrease due to viscous resistance. As the flow rate decreased, the subsequent substance deformation was accumulated, leading to local bulging and increased elastic potential. This would further accelerate the matter particles' displacement velocity. Such alternation of high and low velocities is manifested as wave propagation. It can be considered that this is a kind of deformation wave that contains the migration of matter particles which is essentially the particle momentum waves proposed by [PERSON] [43].  
The deformation wave propagation is a process of mutual conversion between the elastic and kinetic energies under the boundary-driven condition and a process of adjustment for the imbalance between the driving force and viscous force inherent in solids. The situation where the driving boundary induces the deformation wave propagation is known as \"boundary-driven wave generation\" or \"external-driven wave generation\". Viscous deformation arises when the elastic stress is relaxed in regions of stress concentration [44]. The elastic strain concentration is restricted by viscous deformation (relaxation). Moreover, since the viscous deformation is usually induced near the regions of stress concentration, the distribution of centers of viscous deformation (flow channels) in the samples coincided with those of the regions of elastic strain concentration.
## 4 Open Issues
### Service-Oriented Architecture  
The concept of service-oriented architecture (SOA) [6, 13-15] is by no means new and has been widely used in for example the web services domain. However, many of its elegant potentials have not ever been explored in WSANs, though SOA will undoubtedly have a major impact in many branches of technology [16]. SOA is an architectural style encompassing a set of services for building complex systems of systems. It can be regarded as a model in which a system is decomposed into smaller, distinct units that are able to provide certain functionality. As an architectural evolution and a paradigm shift in systems integration, SOA enables rapid, cost-effective composition of interoperable, scalable systems based on reusable services exposed by these systems. This is particularly useful for QoS provisioning in WSANs that are integrated into large-scale cyber-physical systems in which multiple applications run on diverse technologies and platforms.  
Identifying and specifying services are crucial for exploiting SOA in WSANs. A large number of questions need to be answered in this respect. For example, how many categories of services should be classified in the context of WSAN? What are the functionality, interface, and properties of each service? What are its quality levels relevant to performance requirements? In particular, how to deal with the difference between sensors and actuators when specifying services?