esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 3 System Model and Problem Formulation  
In this section, we present the system model and problem formulation of UAV-assisted wireless rechargeable sensor networks (UAV-WRSNs). For the sake of clarity, Table 2 lists the symbols used in this paper.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**References** & **Optimization Objective** & **Optimization Scheme** & **Learning-Based** & **Charging Mode** \\ \hline \multirow{2}{*}{[20]} & Minimize UAV energy consumption & \multirow{2}{*}{DRL} & \multirow{2}{*}{Yes} & \multirow{2}{*}{One-to-multiple} \\  & while maximizing UAV throughput & & & \\ \hline \multirow{2}{*}{[32]} & Minimize hovering points and flying & \multirow{2}{*}{Particle swarm optimization} & \multirow{2}{*}{No} & \multirow{2}{*}{One-to-multiple} \\  & distance of UAV & & & \\ \hline \multirow{2}{*}{[33]} & Maximize energy efficiency & \multirow{2}{*}{Polynomial-time approximation} & \multirow{2}{*}{No} & \multirow{2}{*}{One-to-one} \\  & & & \\ \hline \multirow{2}{*}{[34]} & Minimize trajectory path while & \multirow{2}{*}{Ant colony algorithm} & \multirow{2}{*}{No} & \multirow{2}{*}{One-to-one} \\  & maximizing network lifetime & & & \\ \hline \multirow{2}{*}{[35]} & Maximize energy of nodes & Geometry-based algorithm & \multirow{2}{*}{No} & \multirow{2}{*}{One-to-one} \\ \hline \multirow{2}{*}{[40]} & Minimize the overall data packet loss & DRL & & \\ \hline \multirow{2}{*}{[41]} & Minimize charging path & DRL & & \\  & Dynamic routing & & \\ \hline \multirow{2}{*}{[42]} & Minimize death time of nodes and & \multirow{2}{*}{DRL} & \multirow{2}{*}{Yes} & \multirow{2}{*}{One-to-one} \\  & UAV energy consumption & & & \\ \hline \multirow{2}{*}{[43]} & Minimize node death rate & DRL & & \\ \hline \multirow{2}{*}{D-JERDG} & Minimize node death rate and flight & \multirow{2}{*}{DRL} & \multirow{2}{*}{Yes} & \multirow{2}{*}{One-to-multiple} \\  & energy consumption while & & & \\ \multicolumn{2}{c}{maximizing UAV throughput} & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related works with D-JERDG.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Notations** & **Definitions** & **Notations** & **Definitions** \\ \hline \(k\) & total number of nodes & \(D_{i}(t)\) & buffer length of node \(i\) \\ \(m\) & number of CH nodes & \(\varphi_{u}(t)\) & speed of the UAV \\ \(h\) & number of CM nodes & \(\theta_{u}(t)\) & yaw angle of the UAV \\ \(Q_{max}\) & buffer capacity of nodes & \(\alpha\) & weight coefficient of CM nodes \\ \(E_{max}\) & battery capacity of nodes & \(R_{c}\) & maximum charging distance \\ \(RE_{i}(t)\) & remaining energy of node \(i\) & \(R_{d}\) & maximum data transmission distance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation and definition.
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 4 Shear Modulus of Sand with Different Saturations
### The Effect of Saturation on G/\(G_{max}\)-\(\gamma\)  
The Hardin-Drnevich hyperbolic model was used to normalize the \(G\)-\(\gamma\) decay curve to obtain the \(G\)/\(G_{max}\)-\(\gamma\) relationship curve. To investigate the effect of saturation on the decay characteristics of shear modulus with shear strain, the \(G\)/\(G_{max}\)-\(\gamma\) curves at different net stresses (100 kPa, 200 kPa, 300 kPa and 400 kPa) with different saturations (0, 0.17, 0.4, 0.65 and 1) were selected and summarized, as shown in Figure 9.  
Figure 9a-d show that the normalized modulus \(G\)/\(G_{max}\) decreases with increasing shear strain and the smaller the net stress, the faster the decay in the modulus with strain. At the same shear strain, the normalized modulus \(G\)/\(G_{max}\) is not monotonically related to the change in saturation and reaches a maximum at saturation near 17%. Under the same net stress condition, the \(G\)/\(G_{max}\)-\(\gamma\) decay curve gradually increases with the decrease in saturation and it decreases when the saturation is less than 17%. The \(G\)/\(G_{max}\)-\(\gamma\) decay curves for dry sand coincide with or are slightly higher than those of saturated sand (\(S_{r}=1.0\)). It shows that the \(G\)/\(G_{max}\)-\(\gamma\) decay curve firstly rises and then falls in a certain range with the decrease in saturation under the dehumidification path. This means that with the increase in matrix suction caused by the decreasing saturation, the inhibitory effect  
on the decay of the dynamic shear modulus of the sand reaches a maximum value at a certain characteristic water content state.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 2 Methods
### Decoder  
The complexity and diversity (small scale, high similarity, and mutual occlusion) of man-made objects on the ground poses great challenges to the real-time segmentation of remote sensing urban imagery. Meanwhile, aggregating global semantic information is an effective solution. In order to obtain accurate global context information, the existing mainstream methods were modified by adding an attention mechanism to the CNN [2,33] or using Transformer as the main encoder [34]. However, the former cannot eliminate the convolution operation completely and the latter imposes a great computational overhead while losing the features' position information. As shown in Figure 1, we used three DTBs and a feature aggregation module to construct an efficient decoder in the proposed model. Consequently, our model can accurately capture multi-scale global context semantic information while maintaining high efficiency by constructing such a lightweight and hierarchical structure in the decoder.
###### Abstract  
The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, _grant-free_ (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.  
random access protocol; grant-free; beyond 5G; reinforcement learning; massive MIMO +
Footnote †: journal: Sensor and Actuator Networks
## 3 Results
### Sensors Response toward Target Gases  
As reported in Section 3.1, the MOS sensors BIOS1 and BIOS2 were chosen since the first had a more generic response, while the latter showed higher responses when exposed to short chain alcohols. These findings were further justified by the different composition and surface structure of the respective MOS layers, as seen in Section 3.2.  
Since from SPME-GC/MS, it emerged that the most common vapor released by films, other than acetic acid, is butanol (data not shown) the sensor duplex responses towards acetic acid and butanol were investigated and are reported in Figure 4.  
Figure 3: Scanning electron microscopy (SEM) analysis (inset-energy-dispersive X-ray spectra (EDX)) and Raman spectroscopy of samples BIOS1 (**a**) and BIOS2 (**b**).  
As can be seen, the two sensors show different response trends when exposed to the two target volatiles. BIOS1 seems to detect lower concentrations of acetic acid compared to BIOS2 and reaches saturation faster, while both sensors seem to be quite responsive to low concentrations of butanol.  
It should be noted that neither of the two sensors show a linear range of response, having instead a response type more like negative exponential (or quasi-logarithmic for the curves reporting percentage reduction compared to baseline), which is common for resistive sensors like the ones employed in this work.
# An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition
## 3 Proposed Method
### Preprocessing
#### 3.1.1 Remove Redundant Points  
Any given in-air handwritten character \(P\) can be represented by its coordinate sequence as  
\[P=[(x_{1},y_{1}),\ldots,(x_{t},y_{t}),\ldots,(x_{T},y_{T})] \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Types** & **Limitations** \\ \hline Linear normalization [11] & Preprocessing & Neglecting the density of handwritten character sequence coordinates can lead to severe shape distortion \\ \hline Nonlinear normalization [9] & Preprocessing &
\begin{tabular}{c} Unable to correct skew, local width or height \\ imbalances in handwritten characters \\ \end{tabular} \\ \hline Pseudo-2D normalization and line density projection & Preprocessing & Usually dealing with image features \\ interpolation [11] & Preprocessing & Usually dealing with sequence features \\ \hline Coordinate normalization [24] & Preprocessing & Usually dealing with sequence features \\ \hline Eight-directional feature maps [10] & Feature extraction & Usually applied to OLHCCR \\ \hline Higher-order directional features. [25] & Feature extraction & Usually applied to IAHCCR \\ \hline Learning-vector-quantization-technique-based & Classifier & High computational cost and storage consumption \\ multi-level classifier [27] & Classifier & Low recognition accuracy and high storage cost \\ \hline MQDF [25] & Classifier & Low recognition accuracy and high storage cost \\ \hline LSRC [28] & Classifier & Difficulty obtaining discriminative features and low recognition accuracy \\ \hline Locality-sensitive sparse representation toward & Classifier & Low recognition accuracy and high storage cost \\ optimized prototype classifier [29] & Classifier & Relying on manual feature extraction and large amounts of data \\ \hline Nine-layer convolutional neural network model & & \\ combined with data-augmentation technology [23] & Classifier & Less efficient computational structure \\ \hline End-to-end recognizer based on & Classifier & It has difficultly effectively learning the global spatial features of sequences \\ \hline RNN system with two new computing & Classifier & \\ architectures added [4] & Classifier & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related works.  
where \(x_{t}\) and \(y_{t}\) are the XY coordinates of the \(t\)th point of \(P\); and \(t=1,\ldots,T\), \(T\) is the number of the coordinate points of \(P\). For the coordinate sequence of the character, except for the first point, if the Euclidean distance between the \(t\)th point \((x_{t},y_{t})\) and its adjacent point \((x_{t-1},y_{t-1})\) is less than the given threshold \(L\), i.e.,  
\[\sqrt{(x_{t}-x_{t-1})^{2}+(y_{t}-y_{t-1})^{2}}<L \tag{2}\]  
then the point \((x_{t},y_{t})\) is deleted as a redundant point. In the following experiments, \(L\) varies corresponding to different handwritten character \(P\), and can be computed by \(0.015\times max\{h,w\}\), where \(h\) and \(w\) are the space height and width of \(P\), respectively.
## 2 SIR-Based Performance Analysis
### The Outage Probability of the Output SIR
Also, the case analyzed in [23; 25] for Pout in the presence of Rician fading and Nakagami-\(m\) CCI will be obtained from (15) by setting the parameter \(m\) to be \(1\), \(\kappa_{y}\) to be \(0\), and \(\mu=m\). To observe the influence of the parameters of fading and CCI on the Pout, we plotted two figures with several graphs using Wolfram Mathematica and Origin. The correlation between \(L\) input branches in the SC receiver is assumed as minimal. Figures were created using Wolfram Mathematica and Origin. We took the next values for the following parameters: \(m_{i}=m\), \(\mu_{i}=\mu\), \(\Omega_{i}=\Omega\), \(s_{i}=s\). From Figure 4, one can see that Pout increases when \(\kappa_{x}\) decreases. The Pout decreases as the fading parameter \(m\) increases, and system performance is better. It can be seen from Figure 5 that Pout does not change significantly when changing the parameters \(\kappa_{y}\) and \(\mu\). A larger value of the number of input diversity branches in the SC receiver improves the system performance by decreasing the Pout. This impact is also presented in Figure 5. It can be noticed that the maximum benefit is obtained when \(L\) changes from \(1\) to \(2\), and then the gain decreases. Below the figures, we show tables with the number of required additions to achieve accuracy to the 5 th decimal place in the series which present the Pout in (15). From Table 1, one can notice how it is necessary to sum a maximum of 16 terms to achieve that accuracy for the corresponding values of the system parameters. Figure 4: Outage probability of multi-branch SC receiver depending on SIR versus different values of fading parameters \(\kappa_{x}\) and \(m\). Figure 5: Pout of multi-branch SC receiver versus SIR considering different values of CCI parameters \(\kappa_{y}\) and \(\mu\), and number of branches \(L\). It is visible from Tables 1 and 2 that for all values of BX fading parameters and \(\kappa\)-\(\mu\) CCI parameters, for all \(z\), the number of additions does not exceed 9. One can notice from Table 2 that for \(z=-10\) dB it is only 5, while for \(z=0\) dB and \(z=10\) dB it is slightly higher and increases to 9, depending on other parameters values.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 4 Results and Discussion
### Cycling Test
#### 4.2.2 Effect of Varying Rectified Voltage  
From a different perspective, the output power against the rectified voltage is presented in Figure 22. It is found that, through both circuits, the output power shows a positive correlation with the rectified voltage (implying different cycling speed). In the cycling test, highest output power achieved by H-Bridge and FBR circuit is 10.1 \(\upmu\)W and 0.61 \(\upmu\)W, respectively, at 80 RPM. In other words, for the same input voltage/energy level, the output power of FBR circuit is only about 6% of the H-Bridge circuit.
## 2 Materials and Methods
### Virtual Measurements
Figure 3: Sagittal cross-section of spatial distribution of the E-field near an insulated human body model: sagittal direction of EMF propagation (from front to back of the model), vertical E-field polarization (from top to bottom); linear scale; orange color applied at 10 V/m level. equipment with E-field probe with a geometrical center in a considered location near the human body. The calculated E-field strength was averaged over the volume of a sphere with a 50 mm diameter, as the dimensions of the simulated measurement probes. Manufacturers usually do not provide data on the exact dimensions of the measuring sensors in PEMs. The 50 mm diameter for simulated E-field averaging at the sensors location and probes of other equipment used in the environmental EMF measurements was chosen arbitrarily, taking into account the actual dimensions of various equipment. Additionally the point values of E-field strength simulated at distance 5, 10, 15, 20 mm to the body were determined for analyzed locations of the ball-probes measurement--to model the potential use of future small-dimension sensors in distributed measurement system. The numerical simulations were performed using a finite-difference time-domain (FDTD) solver of Sim4 Life software v7.2.4 (Zurich Med Tech, Zurich, Switzerland) and an anatomical male model (Duke v3.1--age: 34 years old, height: 1.77 m, weight: 70.2 kg, BMI ratio: 22.4, resolution: 2 \(\times\) 2 \(\times\) 2 mm, mapping of over 300 structures, organs and tissues of the human body). Duke's anthropometric properties are close to the 50 th percentile of male population used in ICRP 110 Adult Reference Computational Phantoms [31]. Dielectric parameters and densities of particular body tissues, of which the Duke numerical model is composed of investigated frequencies, were provided from the IT'IS Database for thermal and electromagnetic parameters of biological tissues based, among others, on the widely recognized and used data developed by [PERSON] et al. [32; 33]. The model was placed in a free space (analyzed area with a volume of 4 \(\times\) 4 \(\times\) 4 m) with boundaries at a 1.7-1.9 m horizontal distance from the body model, and a 1.1 m vertical distance from the top and the bottom in the case of the model insulated from the ground. The numerical models of exposure scenarios were composed of approximately 510 million voxels, with the dimensions of a single voxel in the vicinity of the human body model in which the distribution of EMF was analyzed being 5 \(\times\) 5 \(\times\) 5 mm, which met the requirement of the EN IEC 62232 standard for this type of numerical calculations, which says that the voxel size should not be larger than 1/10 of the wavelength (for a maximum considered frequency of 3.6 GHz, the wavelength in air is approximately 83 mm) [30]. Convergence level (CL), which is quantity of accuracy of simulation and defined as the difference between the last two estimations in the frequency domain, was selected \(-\)50 dB (the medium in the used software settings).
# Crack Propagation Behavior of Fused Silica during Cyclic Indentation under Incremental Loads
## 2 Experimental Details  
A fused silica plate was used as specimen, the major material properties of which (provided by the manufacturer) are listed in Table 1. As the indenter tip geometry affects the brittle fracture of glass materials [11; 23; 24; 25; 26], in this study, a Berkovich indenter and a cube-corner indenter made of single-crystal diamond were used for comparison. The geometries of the indenters are illustrated in Figure 2. The indentation tests were performed on a nanoindentation instrument ENT-1100a (Elionix Inc., Tokyo, Japan).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Properties** & **Values** \\ \hline Vickers hardness [GPa] & 8.9 \\ Young’s modulus [GPa] & 74 \\ Poisson’s ratio & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major material properties of the fused silica sample.  
Figure 1: Schematic diagram of incremental cyclic loading in precision grinding of a hard brittle material.  
Figure 3 illustrates the control graph of indentation load during incremental-load cyclic nanoindentation. At each indentation cycle, a constant loading/unloading rate was used, and the load was held for 1 s at the maximum load. The maximum load was increased by a constant step \(\Delta P\) between each indentation cycle and reached the final maximum load at the last cycle. After unloading of each cycle, the indenter tip was pulled up a few microns above the indented surface before the subsequent cycle started. To investigate the brittle fracture in micron and submicron-level, small maximum loads of 50 or 100 mN were applied. The number of indentation cycles were set to 2, 5, or 10 to comparatively examine the cyclic effects. For example, in incremental-load cyclic nanoindentation at a final maximum load of 100 mN with 10 cycles, the maximum load increased by \(\Delta P\) = 10 mN between each cycle. For comparison, single nanoindentations were also carried out. To confirm reproducibility, the indentation tests were performed 10 times for each condition. Table 2 shows the indentation conditions.  
Figure 3: Control graph of indentation load for incremental cyclic nanoindentation.  
Figure 2: Indenter geometries used in this study: (**a**) Berkovich indenter; (**b**) cube-corner indenter.  
The indents were observed by using scanning electron microscopy (SEM), and the topography of the indent surface was examined by atomic force microscopy (AFM). Moreover, temporal analysis of deformation and brittle fracture during loading/unloading processes were performed based on the real-time recorded load-displacement curves.
## 2 Data and Methods
### Satellite Data
#### 2.2.1 Hy-1c/hy-1d  
HY-1C satellite was launched in 2018, and HY-1D satellite was launched in 2020 [17]. Both satellites are equipped with a Chinese Ocean Color and Temperature Scanner (COCTS), Coastal Zone Imager (CZI), Ultraviolet Imager (UVI), Satellite Calibration Spectrometer (SCS), and Automatic Identification System (AIS). In this study, the efficiency of CZI in the inversion of marine chlorophyll-a concentration is discussed. In addition, the CZI sensor has a width of 1000 km, a resolution of 50 m, and an imaging observation frequency of three days at a time (Table 1), which is mainly used for watercolor monitoring of coastal water bodies [18; 19; 20].  
Figure 2: Topographic map of the study area. (**a**) Coastal map of Zhejiang Province. The red boxes in (**a**) are typical large yellow croaker cage aquaculture areas. (**b**–**e**) represent the locations of Zhoushan City in Zhejiang Province, Da Chen Island in Taizhou City, Dongtou District in Wenzhou City, and Nanji Island in Wenzhou City, respectively.
## 2 Materials and Methods
### Selecting Variables  
Three data sources with different spatial resolutions (UAV imagery with 0.03 \(\times\) 0.03 m\({}^{2}\), PlanetScope imagery with 3 \(\times\) 3 m\({}^{2}\), and climate data with 1 \(\times\) 1 km\({}^{2}\)) were used to develop the plant density prediction model. Regression analysis using statistical and machine learning methods was used for the model. The selection of variables, both for response and predictors, is crucial in regression analysis. The response variable for the regression model was the soybean plant density derived from the number of plants counted by the YOLOv3 best model (Section 2.3). For the predictors, PlanetScope imagery data (spectral reflectance and NDVI) and climate data (precipitation and temperature) were selected. The first predictor, the spectral reflectance values of all PlanetScope bands, was used from every pixel in the observation field to examine the potential of each band in explaining plant density. Second, NDVI values calculated from the spectral reflectance value of PlanetScope imagery was used in the regression analysis. Moreover, we also minded the temporal resolution of the satellite imagery for increasing the capability of regression models in estimating plant density. Spectral reflectance and NDVI data were selected every ten days, calculated from the sowing dates (10, 20, 30, and 40 DAS) to reflect the growing process of the plants, as time series data could be valuable information for detecting plant density in the later growth stages. The last predictor variables were derived from climate data with the following details: (1) seven-day precipitations before and after the sowing date calculated from daily values; (2) ten-day cumulative temperatures at 10, 20, 30, and 40 DAS, calculated from the daily mean air temperature. All data from YOLOv3 model, PlanetScope imagery, and climate data were transformed into averaged values within each 3 \(\times\) 3 m\({}^{2}\) grid cell to match pixels area of PlanetScope. Details about the predictor variables are summarized in Table 2. The data were divided into training and test data sets by randomly selecting the observation fields. In this process, we also considered the potential effect of different years on plant density, as the climate condition varied largely from year to year; thus, the training and test datasets were not divided by the years. Training data were obtained using PlanetScope pixels in Fields 1, 4, 5, 6, 7, 10, and 12 (\(n=6600\)), while pixels in the other five fields were used as test data (\(n=5839\)).  
Figure 4: Examples of preprocessing multitemporal NDVI of PlanetScope by smoothing spline in a certain pixel for Fields 1, 6, and 9. Points represent observed NDVI values at a certain day after sowing (DAS) derived from PlanetScope imagery, and the lines represent the estimated curves from the smoothing spline.