esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Results and Discussion
### Seismic Performance Analysis  
#### 3.2.1 Experimental Results of Seismic Performance Indicators  
(1) Hysteretic Curves and Skeleton Curves  
The force-displacement hysteretic models of all specimens during the loading process are shown in Figure 12a-i. The hysteretic curves of all unreinforced specimens exhibited a \"bow\" shape with a noticeable pinching effect. Meanwhile, those of all strengthened specimens presented an \"inverse S\" shape, indicating that the bond-slip between steel bars and concrete existed in the loading process. Generally, for the strengthened specimens, at the initial loading stage, the lateral force was approximately proportional to the lateral displacement before the internal longitudinal rebars yielded, and the residual deformation, hysteresis loop area, and energy dissipation were small, reflecting the elastic state of the pier columns. With the increase in lateral displacement until reaching the peak point, the hysteresis loop became less steep, owing to the development of concrete cracks and slippage between the steel bars and concrete. The residual deformation and energy consumption increased step by step, and the stiffness gradually degraded, reflecting the elastic-plastic stage of the pier columns. After reaching the peak load, the energy-dissipation capacity of the specimens continued to improve; however, the bearing capacity and stiffness rapidly degraded until the specimens were damaged.  
Comparisons of the skeleton curves of all specimens are shown in Figure 12j. (a) Those between reinforced specimens (i.e., RP-0 to RP-5) and unreinforced specimens (i.e., UP-1 to UP-3) indicate that the bearing capacity and initial stiffness of reinforced specimens were much higher than those of unreinforced specimens; however, the peak and ultimate displacement of reinforced specimens are generally lower than those of unreinforced specimens. The comparison between UP-1 and RP-1, UP-2 and RP-4, and UP-3 and RP-5 shows that IPCSAM can effectively improve the bearing capacity and initial stiffness of the pier column. (b) The comparison between RP-0 and RP-1 shows that SSAWC with a self-stress of 1.6 MPa can effectively improve the bearing capacity and initial stiffness of the pier column. (c) The comparison between RP-1 and RP-2 indicates that a 0.25% increase in the reinforcement ratio of LCSS can effectively improve the bearing capacity of the pier column. (d) The comparison between RP-1 and RP-3 shows that initial damage has a relatively small impact on the bearing capacity and initial stiffness of the pier column. (e) The comparison between RP-1, RP-4, and RP-5 indicates that the larger the length-diameter ratio of the reinforced specimen, the greater the bearing capacity and initial stiffness, and the smaller the peak and ultimate displacement.  
Figure 12: _Cont._Figure 12: Hysteretic and skeleton curves of specimens (**a–j**).
## 2 CFD Simulations  
### Governing Equations  
The present study analyzes the transient behavior of thin liquid film formation near a heated probe tip. We used the commercial code of ANSYS Fluent (v.17.2) to solve the conservation equations of mass and momentum as follows:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho\overset{\rightarrow}{v})}{ \partial x}=0 \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho\overset{\rightarrow}{v})+\
abla\cdot(\rho \overset{\rightarrow}{v}\overset{\rightarrow}{v})=-\
abla p+\
abla\cdot( \overset{\rightarrow}{\tau})+\rho\overset{\rightarrow}{g} \tag{2}\]  
where \(\rho\) is the density, \(v\) is the velocity vector, \(p\) is the pressure, \(\tau\) denotes the stress tensor, and \(g\) is the gravitational acceleration. The enthalpy-based energy equation considering the solidification and melting model is as follows [20; 21; 22; 23]:  
\[\frac{\partial}{\partial t}(\rho H)+\
abla\cdot(\rho\overset{\rightarrow}{v} H)=\
abla\cdot(k\
abla H) \tag{3}\]  
where \(H\) indicates the enthalpy and \(k\) is the thermal conductivity. The enthalpy, \(H\), is defined as the sum of the sensible enthalpy, \(h\), and the latent enthalpy, \(\Delta H\), as follows:  
\[H=h+\Delta H \tag{4}\]  
\[h=h_{ref}+\underset{T_{ref}}{\overset{T}{T}}c_{p}dT \tag{5}\]  
\[\Delta H=\beta L \tag{6}\]  
where \(h_{ref}\) is reference enthalpy, \(T_{ref}\) is reference temperature, \(c_{p}\) is the specific heat, and \(L\) is the latent heat. The liquid fraction, \(\beta\), is estimated by:  
\[\beta=\left\{\begin{array}{cc}0&T<T_{S}\\ 1&T>T_{L}\\ \left(T-T_{S}\right)/\left(T_{L}-T_{S}\right)&T_{S}<T<T_{L}\end{array}\right. \tag{7}\]  
where \(T_{S}\) denotes solidus temperature at which the solidification begins, and \(T_{L}\) represents liquidus temperature at which the melting begins. However, Equation (7) can be used for the case where the values of \(T_{S}\) and \(T_{L}\) are different (e.g., alloys and multiple substances). In the case of a pure substance (e.g., water/ice), \(T_{S}\) is equal to \(T_{L}\). Therefore, \(\beta\) should be expressed based on the enthalpy as follows:  
\[\beta=\left\{\begin{array}{cc}0&H<H_{S}\\ 1&H>H_{L}\\ \left(H-H_{S}\right)/\left(H_{L}-H_{S}\right)&H_{S}<H<H_{L}\end{array}\right. \tag{8}\]  
where \(H_{S}\) indicates enthalpy of the solid at which the phase change begins, and \(H_{L}\) represents enthalpy of the liquid. The liquid fraction, \(\beta\), is zero at the solid phase where the temperature is below 273 K. When the temperature of the solid phase increases to 273 K, the enthalpy of the material, \(H\), starts to increase from \(H_{S}\) to \(H_{L}\). The liquid fraction, \(\beta\), changes with corresponding enthalpy, \(H\).
## 5 Digital Image Frame Relay Transmission (DIFRT)
### Image Compression and Decompression  
The image compression and decompression are essential in digital image transmission. Because a large amount of uncompressed raw image data will take a lot of transmission time and even cause severe channel congestion. Therefore the image compression is necessary. In the research, the image is compressed and decompressed by the MATLAB platform. Moreover, the discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) methods are applied in image compression and decompression. DCT is suitable to compress data or images, and it can convert the spatial domain of signals into the frequency domain with good de-correlation performance. Generally, in the spatial domain, the correlation of image data is large. If the image data signal is converted from spatial domain to frequency domain, it will not affect the phase. Whereas the distribution of energy changes obviously from a relatively uniform distribution to concentration on a few low-frequency components. Some small components can be discarded directly without the degradation of image quality for efficient compression [35; 36; 37].  
In MATLAB, image compression is processed with DCT function DCTMTX. The direct calculation of DCT is very complicated, hence we use the function BLKPROC to divide the original image into \(4\times 4\) pixel blocks in order to improve the conversion efficiency of DCT. Since the coefficient matrix of the DCT output owns a large frequency range, however, this frequency range does not substantially contain any image information. Therefore, the frequency coefficient can be simplified by quantization. Here, we adopt a \(4\times 4\) mask matrix to quantize the DCT frequency coefficient. At the receiver, the original image information can be finally reconstructed by inverse quantization and IDCT decompression.
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 3 Methodology
### Experimental Setup
Therefore, every anchor returned at least one measurement from the beginning of the recording of the data file for the first valid RSSI vector to be recorded. 2. After the first complete RSSI vector was recorded, as described above, every new RSSI value from an anchor was added to the 1-element buffer and the contents of the buffer recorded as a new RSSI vector. This simulated the behavior of systems that attempt a localization step every time a new RSSI measurement is received from an anchor. 3. Because the accelerometer sampling rate was higher than the RSSI sampling rate, the accelerometer measurement closest in timestamp to the latest timestamp of the RSSI measurements in the buffer was recorded to be the accelerometer measurement corresponding to that RSSI vector. A total of 57,489 vectors (approximately 11,000 per location after minor resampling) were obtained as a dataset in this manner.
###### Abstract  
Rising temperature, drought and more-frequent extreme climatic events have been predicted for the next decades in many regions around the globe. In this framework, soil water availability plays a pivotal role in affecting vegetation productivity, especially in arid or semi-arid environments. However, direct measurements of soil moisture are scarce, and modeling estimations are still subject to biases. Further investigation on the effect of soil moisture on plant productivity is required. This study aims at analyzing spatio-temporal variations of a modified temperature vegetation wetness index (mTVWI), a proxy of soil moisture, and evaluating its effect on gross primary production (GPP) in forests. The study was carried out in Europe on 19 representative tree species during the 2000-2010 time period. Results outline a north-south gradient of mTVWI with minimum values (low soil water availability) in Southern Europe and maximum values (high soil water availability) in Northeastern Europe. A low soil water availability negatively affected GPP from 20 to 80%, as a function of site location, tree species, and weather conditions. Such a wetness index improves our understanding of water stress impacts, which is crucial for predicting the response of forest carbon cycling to drought and aridity.  
Crown defoliation; drought; Gross Primary Production; modified Temperature Vegetation Wetness Index; MODIS; Soil Moisture 2019 7 42; doi:10.3390/clf7030042
# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 4 Results and Discussion
Our method used two Kinects, although we aim to reduce the number of Kinects; later, we measured the processing times, fps and the number of triangles. The fps rate is highest and the processing time is faster. Next, we performed the integration process by sending the data over the network. Figure 10: The real-time 3D reconstruction result from each stage. Based on Table 1, our method used two Kinects compared to five Kinects, and the average numbers of triangles is significantly different. Our processing rate in frames per second produced higher than 78 fps compared to [PERSON] et al. 's [30] findings. After the 3D-textured model of the local user has been reconstructed, the 3D data such as mesh and texture along with the audio of the local user will be compressed and transmitted across the network to the remote site after being assembled in a UDP packet. When the packet is received in the remote client site, the packet will be disassembled and the data will be decompressed before being displayed on the remote user's PC. The experiment will not use green screen settings as it did in the present case [34] in order to ensure that the device is used in practical situations of natural environments. Figure 11 shows the results of the 3D telepresence where a remote user can view and interact with a reconstructed 3D textured model of a local user sent to the remote location over the network. We have tested displaying the reconstructed 3D textured model using a small-scale Z-hologram. Our method has been loaded into the telepresence system to present the 3D presence of user representation in real-time, and we compare our results with [PERSON] [13], whose work has performed real-time 3D reconstruction and was loaded into telepresence, and who also recorded their findings after the compression process in a color image, UV and compression data from client to server. We also performed the same process and recorded our findings. Table 2 shows the comparison; based on compression from raw data to final data, there are significant differences, especially when we requested that the server perform the telepresence and communicate with client user. However, the texture mapping required an improvement in real-time. With UV mapping compression we cannot compare with Cordova-Esparza [13], as they have not covered the UV compression in their method. However, we still present our UV compression results in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Number of Kinects** & **Processing Time** & **Processing Rate** & **Average Number** & **Average Number** \\  & **Kinects** & **(ms)** & **(fps)** & **of Vertices** & **of Triangles** \\ \hline This research & 2 & 27 & 78 & 2274 & 12,303 \\ \hline [PERSON] et al. [30] & 5 & 78 & 13 & 228,581 & 445,447 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The processing time records, the results in a comparison with previous work. Figure 11: The initial result of displaying the (**a**) reconstructed 3D model of the user (**b**) in Z-hologram.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Implementation of Image-Based Devices and Systems
#### 4.2.4 UAV Photogrammetry for Displacement Monitoring  
UAV-based photogrammetry is widely employed to create high-resolution topography for researching surface processes because to recent developments in the use and accessibility of unmanned aerial vehicle (UAV) platforms and the development of user-friendly picture processing tools [60]. Utilizing a drone for UAV photogrammetry is quite a complex task as there are very few practical case studies about it for SHM; however, it is a convenient strategy and much more efficient compared to conventional SHM.  
Founded from the study of [11], the crucial inspection and maneuvering characteristics, such as the capacity to hover and reach higher heights of the structure, auto take-off, and landing, are taken into consideration while developing a multirotor unmanned aerial vehicle (MUAV) or multirotor drone system. A camera is attached to a drone with a carbon fiber frame and glass epoxy to sustain stable flight under harsh environments (or atmosphere). It can fly for a total of 20 min with a maximum payload of one hundred grams. The drone is then connected to a transmitter to control the movement of the drone, particularly for hovering to capture a stable image from a region of interest. The elevation of the drone concerning the region of interest may vary depending on the researchers. Additionally, the cost of drones shall be considered a key factor in this type of SHM. Two different drones [60] may be used to capture the dam images. The heavy-lift UAV has two batteries, but the low-cost UAV only has one. As a result, the heavy-lift UAV can fly for a longer period and is thus more suited for photogrammetry of huge regions of interest, such as dams.
# Extensive Benchmarking of DFT+\(U\) Calculations for Predicting Band Gaps
## 2 Methods and Materials
### Materials  
To cover a sufficiently representative set of materials, we have investigated binary oxides, binary nitrides, binary sulfides, oxynitrides, and oxysulfides. Metals in these compounds were limited to \(d^{0}\) (Sc\({}^{3+}\), Y\({}^{3+}\), Ti\({}^{4+}\), Zr\({}^{4+}\), Hf\({}^{4+}\), V\({}^{5+}\), Nb\({}^{5+}\), Ta\({}^{5+}\), Mo\({}^{6+}\), W\({}^{6+}\)) and \(d^{10}\) (In\({}^{3+}\), Ge\({}^{4+}\), Sn\({}^{4+}\), Pb\({}^{4+}\)) cations to tailor the study towards photocatalytic materials [99]. Using the data from the Materials Project [103], we created combinations of O, N, S, and these \(d^{0}\) and \(d^{10}\) cations. This resulted in an extensive list of materials which was paired down using criteria of unit cell size and experimental band gap: the number of atoms per unit cell was limited to \(<20\) and an experimental band gap had to be available in the literature for comparison. Table 1 shows the resulting list, containing 8 oxides, 2 nitrides, 6 sulfides, 3 oxynitrides, and 1 oxysulfide. These materials are all \(d^{0}\) or \(d^{10}\) closed-shell systems due to the oxidation states of the TM and group III-IV elements, as listed above.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Formula** & **Space Group** \\ \hline
1 & TiO\({}_{2}\) & \(P4_{2}/mmn\) \\
2 & ZrO\({}_{2}\) & \(P2_{1}/c\) \\
3 & HfO\({}_{2}\) & \(P2_{1}/c\) \\
4 & V\({}_{2}\)O\({}_{5}\) & \(Pmmn\) \\
5 & Ta\({}_{2}\)O\({}_{5}\) & \(C2/c\) \\
6 & WO\({}_{3}\) & \(Pm3m\) \\
7 & SnO\({}_{2}\) & \(P4_{2}/mmn\) \\
8 & PbO\({}_{2}\) & \(P4_{2}/mmn\) \\
9 & InN & \(P6_{9}mc\) \\
10 & Sn\({}_{3}\)N\({}_{4}\) & \(Fd3m\) \\
11 & TS\({}_{2}\) & \(P3m1\) \\
12 & ZrS\({}_{2}\) & \(P3m1\) \\
13 & HfS\({}_{2}\) & \(P3m1\) \\
14 & MoS\({}_{3}\) & \(P2_{1}/m\) \\
15 & Sc\({}_{2}\)S\({}_{3}\) & \(Fddd\) \\
16 & SrS\({}_{2}\) & \(P3m1\) \\
17 & NbNO & \(P2_{1}/c\) \\
18 & TaNO & \(P2_{1}/c\) \\
19 & Ge\({}_{2}\)N\({}_{2}\)O & \(Cmc2_{1}\) \\
20 & Y\({}_{2}\)SO\({}_{2}\) & \(P3m1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical formula and space group for each of the materials in this benchmark study.
## 4 Processing of the Measured Data
### Quantitative Evaluation of the Classification Results  
Accurate and reliable evaluation of the classification outputs is essential for an objective assessment of the effectiveness of the proposed methods. This subsection focuses on the quantitative evaluation of the classification results based on standard metrics: precision, recall, and F-score. These metrics provide a comprehensive view of the performance of classification algorithms and enable the comparison of various approaches [45; 46].  
_Precision_ represents the proportion of correctly classified positive examples out of the total predicted positive cases. It is calculated using the formula:  
\[Precision\ =\ \frac{TP}{TP+FP} \tag{1}\]  
where \(TP\) denotes the number of true positives in the extracted results, and \(FP\) represents the number of false positives in the extracted results. _Recall_ measures the classifier's ability to identify all relevant positive cases. It is defined by the following formula:  
\[Recall\ =\ \frac{TP}{TP+FN} \tag{2}\]  
where \(FN\) denotes the number of false negatives in the ground truth. The _F-score_ is the harmonic mean of precision and recall, providing a balanced performance metric for classification. This indicator enables an effective comparison of different classification methods and helps identify the optimal solution for a given problem. It is defined as follows:  
\[F_{score}\ =\ 2.\frac{Precision.\ Recall}{Precision+Recall} \tag{3}\]  
When analyzing results, it is crucial to assess the values of all three metrics together. High precision may indicate a low number of false positive cases, while high recall demonstrates the classifier's ability to correctly identify the most relevant cases. The F-score evaluates the trade-off between these two metrics and provides a comprehensive view of the model's performance.
# A Survey on Memory Subsystems for Deep Neural Network Accelerators
## 8 Off-Chip Memory Architectures in DNN Accelerators  
We have reviewed different on-chip memory architectures in DNN accelerators in the previous sections. Three off-chip memory organizations are reviewed in this section.  
The access to off-chip memory imposes a big limitation on the energy efficiency of CNN (convolution neural networks) accelerators. One technique to decrease DRAM access is data reuse, but specific data reuse can minimize memory access for only the respective data type. Therefore, sticking to one data type does not solve the issue for the entire network and will cost for reuse of other data types. The authors of [32] offer a promising layer partitioning and scheduling scheme, called Smart Shuttle. An analytical framework is presented to measure DRAM access volume and find suitable sectionalization and scheduling arrangement for every layer.  
Another methodology named ROMANet, for promising DRAM access energy savings is proposed in [33]. It empowers effective reuse off-chip memory access management and data arrangement. DRAM energy access shows dependency on number of DRAM accesses, row buffer misses and conflicts. The authors of [33] offer an analytical model to optimize these parameters. The data mapping in off-chip DRAM improves throughput and reduces DRAM access energy. Along with it, the data mapping in on-chip SRAM buffers supports bank-level parallelism. Figure 35 shows the flow of operation of ROMANet methodology.  
Figure 34: NDPX architecture block diagram [19].  
Unlike conventional DNNs, capsule networks (CapsNets) are capable candidates that offer multidimensional capsules rather than one-dimensional neurons. However, the memory requirements are likewise high, triggering necessity of energy efficient on-chip memories. The authors of [34] analyze design challenges for on-chip memories deployed in CapsNets accelerators. DESCNet, a multibanked memory architecture, is proposed, which is based on dataflow mapping and respective memory access patterns of various steps of CapsNet inference.
# A Near-Far-Field Model for Bubbles Influenced by External Electrical Fields
## 2 Mathematical Model
### Near-Field Solver: System of Ordinary Differential Equations with Boundary Conditions  
For the near-field, we have to solve a BVP for ODEs, see [30; 31; 32].  
We assume the following nonlinear ODE, given as:  
\[\frac{d\mathbf{y}}{dt}=\mathbf{f}(\mathbf{y},t),\ t\in[a,b], \tag{18}\] \[B_{a}\mathbf{y}(a)+B_{b}\mathbf{y}(b)-\mathbf{d}=0, \tag{19}\]  
where \(\mathbf{y},\mathbf{d}\in\mathbf{R}^{n}\), \(B_{a},B_{b}\in\mathbf{R}^{n\times n}\) and \(\mathbf{f}:\mathbf{R}^{n}\times\mathbf{R}^{+}\rightarrow\mathbf{R}^{n}\), see [32]. For example, we can rewrite our boudary value problem (5)-(7) into this vectorial notation.  
The boundary value problem (18)-(19) can be solved with a multiple shooting algorithm, which is given in the following approach. We have a mesh \(a=s_{1}<s_{2}<\ldots<s_{m}=b\), on each mesh interval \([s_{i},s_{i+1}]\) with \(1\leq i\leq m-1\). Then, we apply the following steps to solve the boundary value problem (BVP).  
We solve the BVP in the following five steps:  
* For the grid-points \(\tilde{\mathbf{s}}_{1},\ldots,\tilde{\mathbf{s}}_{m}\in\mathbf{R}^{n}\), we denote by \(\mathbf{y}(s;s_{j},\tilde{\mathbf{s}}_{j})\) the solution of the initial value problem \[\frac{d\mathbf{y}}{dt}=\mathbf{f}(\mathbf{y},t),\] (20) \[\mathbf{y}(s_{j};\tilde{\mathbf{s}}_{j})=\tilde{\mathbf{s}}_{j}.\] (21)We approach to the grid and define a piecewise function \(\tilde{\mathbf{y}}\) with  
\[\tilde{\mathbf{y}}(s)=\mathbf{y}(s;s_{j},\tilde{\mathbf{s}}_{j}), \text{ for }s_{k}\leq s\leq s_{k+1},\ 1\leq j\leq m-1, \tag{22}\] \[\tilde{\mathbf{y}}(s_{m})=\tilde{\mathbf{s}}_{m}. \tag{23}\]
3. Then \(\tilde{\mathbf{y}}\) solves the boundary value problem (18)-(19), if and only if: \[\mathbf{y}(s_{j+1};s_{j},\tilde{\mathbf{s}}_{j})=\tilde{\mathbf{s }}_{j+1},\text{for }1\leq j\leq m-1,\] (24) \[B_{a}\tilde{\mathbf{s}}_{1}+B_{b}\tilde{\mathbf{s}}_{m}-\mathbf{ d}=0,\] (25)
4. Therefore, we have to solve the fixpoint problem, which is given by the initial value problem (20)-(21) and the conditions (24)-(25) and defined as: \[\mathbf{F}(\tilde{\mathbf{s}}_{1},\dots,\tilde{\mathbf{s}}_{m})=0,\] (26) which can be solved by linearization or nonlinear solver methods, e.g., [PERSON]'s method.  
The multiple shooting algorithm allows us to compute the boundary value problem also with respect to the Neumann-boundary conditions. see [32]. The algorithm is a rewriting of the underlying BVP into an initial value problem (IVP) plus additional conditions. Based on the nonlinearity of the differential equation system, we have to apply linearization schemes or nonlinear solver schemes. In the Section 6.1, we apply the multiple shooting algorithm with linearization and additional solver schemes to approximate the nonlinear differential equation-system.