esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Data and Pre-Processing  
#### 2.2.1 Climate Data  
In this study, we used two climatic variables, i.e., temperature and precipitation, to calculate SPEI. Monthly data of 37 meteorological stations during 2000-2020 were obtained from the National Meteorological Information Center of China ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 30 May 2021)), which were consistent with the time period of MODIS data. To maintain data consistency for subsequent calculations, both SPEI data (calculated from meteorological data) and MODIS LAI, ET, GPP data were removed seasonality in the same way. The details for the operation are given in the Section 2.3.
## 4 Simulation Results and Analysis
### Two-DimensionalSimulation Parameters and Results
The localization errors for DOI values of 0, 0.05, and 0.1 were compared as shown in Figure 8. In Figure 8a, the proportion of beacon nodes increases from 10% to 40%, and in Figure 8b, the communication radius increases from 20 m to 50 m. The impact on the classical DV-Hop algorithm was more significant, with the localization error increasing by approximately 10%. In contrast, the proposed algorithm was less affected, with the localization error increasing by only about 2%. These results collectively demonstrated that the proposed algorithm exhibited stronger robustness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & **Rectangular Area** & **C-Shaped Area** & **S-Shaped Area** & **O-Shaped Area** & **X-Shaped Area** & **H-Shaped Area** \\ \hline DV-Hop & 1.1 & 1.6 & 1.7 & 1.7 & 1.4 & 1.4 \\ Anneal-DV & 15.2 & 16.2 & 18.2 & 18.5 & 15.6 & 16.3 \\ PSO-DV & 11.1 & 12.9 & 13.5 & 14.6 & 13.2 & 13.4 \\ SSA-DV & 15.1 & 16.5 & 16.7 & 21.8 & 18.1 & 17.6 \\ IPSA-DV & 19.7 & 21.4 & 22.2 & 25.6 & 21.2 & 23.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The computational complexity comparison in 2D scenarios. Figure 7: Comparison of localization error under different communication radii in 2D scenarios. (**a**) Rectangular area. (**b**) C-shaped area. (**c**) S-shaped area. (**d**) O-shaped area. (**e**) X-shaped area. (**f**) H-shaped area.
## 3 The Proposed ReSiNet for Low-Memory Devices
### Time Complexity, Success, and Challenge of ReSTiNet
#### 3.3.1 Time Complexity  
In the proposed algorithm, some operations occur only once and their time complexity is \(O(1)\). However, in ReSTiNet, different methods have iteration, and their time complexity is \(O(n^{2})\). Therefore, the time complexity of our proposed algorithm is as follows: \(O(1)+O(n^{2})=O(n^{2})\). Therefore, we define this algorithm as having a Quadratic TimeComplexity to indicate that as the size of the input increases, the amount of time needed to run it increases accordingly. Informally, Quadratic Time Complexity represents an algorithm for which its performance is directly proportional to the squared size of the input data set.
# Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution
## 3 Principle Analysis  
### Traditional Analytical Model and Its Limitations  
The change of the air gap (\(\delta\)) between the armature and the sensor probe leads to the change of the inductance of the inductive sensor, which is based on the principle of electromagnetic induction. The traditional inductance calculation model [9] is given by,  
\[L=\frac{N^{2}}{R_{\mathrm{m}}} \tag{1}\]  
where \(N\) is coil turns of the inductive sensor and \(R_{\mathrm{m}}\) is the reluctance of the air gap, which is given by,  
\[R_{\mathrm{m}}=\frac{2\delta}{\mu_{0}S} \tag{2}\]  
where \(\mu_{0}\) is the vacuum permeability; \(\delta\) is the air gap length between sensor probe and armature; \(S\) is the cross-sectional area of the core. Substituting (2) into (1), yields,  
\[L=\frac{\mu_{0}N^{2}S}{2\delta} \tag{3}\]  
For the differential inductive sensor shown in Figure 2, the differential inductance (\(\Delta L\)) is given by,  
\[\Delta L=L_{1}-L_{2} \tag{4}\]  
where \(L_{1}\) and \(L_{2}\) are the inductance values of a pair of probes with differential arrangement, respectively. According to Equation (3), \(\Delta L\) can be equivalent to,  
\[\Delta L=\frac{2L_{0}\delta_{0}\Delta\delta}{\delta_{0}^{2}-\Delta\delta^{2}} \tag{5}\]  
where \(\delta_{0}\) is zero gap, \(\Delta\delta\) is the armature displacement \(x\), and \(L_{0}\) is the zero position inductance when the armature is located in the center between two probes. \(L_{0}\) can be obtained from Equation (3), that is,  
\[L_{0}=\frac{\mu_{0}N^{2}S}{2\delta_{0}} \tag{6}\]  
when using the above model to design a large range inductive sensor, there are at least the following two limitations:  
1. Ignoring the leakage inductance, the total inductance of the inductive sensor under different air gap cannot be estimated correctly, resulting in large deviation in the design of parameters of the sensor;
2. The fringing effect are not considered. When a large range differential inductive sensor is used to measure the armature displacement with transverse parasitic motion, the above model cannot guide the design of the armature size.
## 5 Results and Discussion
### Validation
Figure 6 shows the time series of surface meteorological variables averaged for 8 observation stations on 17 April 2018, 11 April 2020, and 15 April 2021. In \(T_{2m}\) (Figure 6a), OPTM simulated the closest to the observations for the sea breeze circulation. In \(RH_{2m}\) (Figure 6b), EXP3 and EXP4 were closer to observation, and all experiments showed dry bias until 0600 UTC (15 LST) when the sea breeze was the strongest: OPTM was the closest to observation after 0600 UTC, and most experiments showed dry bias on 17 April 2018. Diurnal variations in \(RH_{2m}\) were well represented and was inversely related to \(T_{2m}\), as expected. The EXP2 appeared close to observation and showed dry bias until 0500 UTC (14 LST) when the sea breeze was the strongest, whereas OPTM was the closest to observation after 0500 UTC on 11 April 2020. All the experiments except for EXP4 showed similar trends, and OPTM was closest to the observation on 15 April 2021. Figure 6: Time series of the surface variables averaged at the eight meteorological stations for different validation dates: (**a**) 2 m temperature, (**b**) 2 m relative humidity, (**c**) 10 m wind speed, and (**d**) 10 m wind direction, on 17 April 2018, 11 April 2020, and 15 April 2021, respectively. However, moist bias became large after 0400 UTC (13 LST) when the sea breeze was strong. In \(WS_{10m}\) (Figure 6c), OPTM and EXP1 were closest to observation: compared to other meteorological variables, the pattern of overestimation was more significant, especially on 15 April 2021, which is common in the wind speed prediction using WRF [61; 64]. In \(WD_{10m}\) (Figure 6d), at the onset time of the sea breeze, the observations showed a change in the wind direction, from south-southwest to east at 0000 UTC (0900 LST) on 17 April 2018, from southwest to southeast at 0800 LST on 11 April 2020, and from southwest to southeast at 0000 UTC on 15 April 2021. The OPTM simulated well the onset time of the sea breeze on 17 April 2018 and 15 April 2021; however, on 11 April 2020, the sea breeze onset time was delayed by 1 h in OPTM and by 2 h in the other experiments. Therefore, OPTM captured well the timing of changing from sea breeze to land breeze in all the three cases.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 2 Resonant Properties of CSRR Structures  
This section provides some physical insights into CSRR. A comparison between circular and rectangular CSRR is also presented.  
The CSRR structure in Ref. [29] is shown in Figure 2a, where the resonator uses three rectangular split rings. In the middle of the CSRR, a via hole is drilled to cater a glass tube. The main parameters of this structure are given in Table 1. The initial values of these parameters are chosen so that the outer ring is half wavelength at the resonant frequency. The final values are obtained through parameter sweep in HFSS software. Such a structure can be modelled as a hybrid LC resonator [33; 34]. The corresponding values of each lumped element can be retrieved from full wave simulations, and the extracted values are given in Table 2. The response of the LC resonant circuit is evaluated using both circuit method and full wave simulation. It has to be mentioned that the central frequency is largely determined by the length of the split ring. Changing the length will modify the central frequency. To make a comparison to the design in Ref. [29], we chose the diameter of the circular ring to be the same as the side length of the rectangular ring.  
Figure 1: Circular CSRR (**a**) a 3D illustration; (**b**) Top view; (**c**) Rear view.  
According to the equivalent circuit diagram, the resonant frequency of the structure can be deduced as [35]  
\[f_{\mathrm{r}}=\frac{1}{2\pi\sqrt{L_{\mathrm{r}}(C_{\mathrm{c}}+C_{\mathrm{r}})}} \tag{1}\]  
The equivalent circuit model gives good prediction to the resonant frequencies, compared to the full wave simulated results by commercial software HFSS, as shown in Figure 3. The resonant dips of the circular CSRR are several dB larger than that of the rectangular CSRR. The field distribution around the resonator is plotted in Figure 4. It can be seen from the scale bar that the peak field of the circular CSRR is 7 dB higher than that of the rectangular CSRR, indicating that better interaction between fields and sample can take place for circular CSRR. This may explain why circular CSRR has deeper resonance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **a** & **b** & **c** & **d** & **e** & **f** & **g** \\ \hline Values (mm) & 0.45 & 0.3 & 0.4 & 0.3 & 0.3 & 7.3 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Main Parameters for two types of CSRRs shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Rectangular CSRR** & **Circular CSRR** \\ \hline \hline \(L\) & 5.512 nH & 5.048 nH \\ C\({}_{\mathrm{c}}\) & 1.563 pF & 1.210 pF \\ C\({}_{\mathrm{r}}\) & 2.231 pF & 2.187 pF \\ L\({}_{\mathrm{r}}\) & 1.141 nH & 0.838 nH \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Lumped Parameters for the Equivalent Circuits.  
Figure 2: CSRR based on (**a**) rectangular structures; and (**b**) circular structures.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 3 Algorithms for Phase Distribution Optimization  
As mentioned above, the sidelobe levels increase when considering the phase information and neglecting the magnitude information on the PMS aperture. Indeed, the considered PMS only controls the phase of the reflected field and the magnitude information of the aperture field is lost. Therefore, the bare FT-based beamforming method  
Figure 4: Normalized project pattern and reconstructed electric field pattern for the single-beam (**a**,**b**) and the dual-beam (**c**,**d**) cases in the \((u,v)\)-space, as produced by a \(10\times 3\) PMS. (**a**) Normalized project pattern at 5 GHz, single-beam at \((\theta_{0_{1}},\varphi_{0_{1}})=(60.2^{\circ},221.1^{\circ})\). (**b**) Normalized reconstructed electric field pattern at 5 GHz, with \((\theta_{0_{1}},\varphi_{0_{1}})=(60.2^{\circ},221.1^{\circ})\). (**c**) Normalized project pattern at 10 GHz, dual-beam at \((\theta_{0_{1}},\varphi_{0_{1}})=(30^{\circ},45^{\circ})\) and \((\theta_{0_{2}},\varphi_{0_{2}})=(30^{\circ},225^{\circ})\). (**d**) Normalized reconstructed electric field pattern at 10 GHz, with \((\theta_{0_{1}},\varphi_{0_{1}})=(30^{\circ},45^{\circ})\) and \((\theta_{0_{2}},\varphi_{0_{2}})=(30^{\circ},225^{\circ})\).  
needs to be complemented by a numerical optimization step that adjusts the aperture phase distribution in order to overcome this shortcoming.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 3 LWM2M Proxy
### Create  
The Proxy sends an error message in response to a Create request when the latter is not well-formed, i.e., if all the mandatory resources are not present in the conveyed message payload, or if the message payload conveys an Object Instance ID in conflict with one already present (see Figure 4, step 2). In this latter case, the Proxy checks the correctness of the conveyed Object Instance ID using the list of Object Instances stored for each Client.
###### Abstract  
Composites designed by employing metal/dielectric composites coupled to the components of the incident electromagnetic (EM) fields are named metamaterials (MMs), and they display features not observed in nature. This type of artificial media has attracted great interest, resulting in groundbreaking theory that bridges the gap between EM and photonic phenomena. Practical applications of MMs have been delayed due to the high losses related to the use of metallic composites, on top of the challenges in manufacturing nanoscale, three-dimensional structures. Novel materials--for instance, graphene or transparent-conducting oxides (TCOs), employed for the production of multilayered MMs--can significantly suppress undesirable losses. It is worthwhile noting that three-layered nanocomposites enable an increase in the frequency range of the surface wave. This work analyzes recent progress in the physics of multilayered MMs. We deliver an outline of key notions, such as effective medium approximation, and present multilayered MMs based on the three-layered structure. An overview of graphene multilayered MMs reveals their ability to support Ferrell-Berreman (FB) modes. We also describe the tunable properties of the multilayered MMs.  
hyperbolic metamaterial; transparent conducting oxides; graphene; surface plasmon polaritons 1
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 3 Results
### ASPP Design Choices  
To evaluate the effect of different branches in ASPP, we compare the performance of different structures after removing different branches in ASPP, as shown in Table 1. After removing image-level features, the classification performance of the structure is better than that of the original network and other variant structures, and an observation that validates our choice to remove image-level features from ASPP structure to improve network performance with respect to radar image classification.  
Using different input feature dimension values, we further demonstrate the positive effect of reducing the ASPP input feature dimension from 2048 to 512 (Table 2). The OA and Kappa values are optimal with 512 dimensions; other dimension values resulted in lower classification performance because of feature redundancy (higher dimension), or loss of image details (lower dimension). With this improved ASPP structure, we have improved the classification performance over that of the original ASPP structure.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Color Histogram  
The color histograms were constructed from the three RGB and three HSV channels, with each channel represented by a 32-bin histogram without background suppression. Consequently, the color histogram vector comprised 192 elements. Preliminary results indicate that a training set of merely five images achieved a classification accuracy of 76%, while increasing the training set to 160 images enhanced the accuracy to 92%, as detailed in Table 8. These findings are encouraging; however, it is critical to validate these results with a more extensive dataset and images captured under various weather conditions to ensure robust color classification.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline
**SVM** & precision & 53.69 & 55.89 & 55.60 & 57.27 & 59.18 & 59.51 \\  & recall & 52.29 & 55.34 & 55.83 & 58.15 & 60.20 & 60.69 \\ \hline
**RF** & precision & 60.65 & 61.08 & 57.69 & 60.55 & 62.45 & 63.57 \\  & recall & 60.00 & 59.49 & 57.29 & 60.80 & 62.58 & 63.97 \\ \hline
**KNN** & precision & 54.89 & 56.85 & 58.45 & 60.70 & 64.86 & 65.69 \\  & recall & 57.10 & 56.77 & 58.68 & 61.20 & 64.25 & 65.44 \\ \hline
**ANN** & precision & 50.35 & 58.43 & nan & 58.88 & 63.79 & 66.08 \\  & recall & 48.92 & 55.82 & 22.53 & 57.50 & 63.81 & 65.88 \\ \hline
**NB** & precision & 57.95 & 61.65 & 61.54 & 62.61 & 63.70 & 63.06 \\  & recall & 56.77 & 60.34 & 61.25 & 63.26 & 64.21 & 63.58 \\ \hline
**GBM** & precision & 55.67 & 60.28 & 57.98 & 60.56 & 61.10 & 63.38 \\  & recall & 53.06 & 56.84 & 56.63 & 60.14 & 60.95 & 63.38 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The distance transformation features’ results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline
**SVM** & precision & 48.00 & 59.97 & 66.27 & 69.05 & 71.55 & 70.07 \\  & recall & 43.16 & 54.59 & 63.40 & 67.72 & 70.95 & 69.17 \\ \hline
**RF** & precision & 64.42 & 71.41 & 72.69 & 74.79 & 76.52 & 78.68 \\  & recall & 63.74 & 70.82 & 72.64 & 74.78 & 76.59 & 78.53 \\ \hline
**KNN** & precision & 33.56 & 48.78 & 51.91 & 56.04 & 58.29 & 60.41 \\  & recall & 31.55 & 47.93 & 52.40 & 55.58 & 58.29 & 59.71 \\ \hline
**ANN** & precision & nan & nan & nan & nan & 47.27 & 48.63 \\  & recall & 19.49 & 30.34 & 19.13 & 30.62 & 39.05 & 44.75 \\ \hline
**NB** & precision & 65.78 & 70.22 & 70.41 & 72.61 & 70.09 & 71.48 \\  & recall & 62.53 & 67.82 & 68.06 & 71.16 & 62.62 & 64.41 \\ \hline
**GBM** & precision & 66.64 & 67.56 & 70.92 & 72.32 & 75.74 & 79.44 \\  & recall & 65.08 & 63.84 & 69.38 & 71.78 & 75.71 & 79.31 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The shape features: the results of area, Hu, and distance transformation features’ combination.
###### Abstract  
To improve the peak power and extinction ratio and produce ultra-short pulses, a novel approach is presented in this paper offers a highly effective modulated method for a gain-switched semiconductor laser by using step-pulse signal modulation. For the purpose of single pulse output, then the effects on the output from the gain-switched semiconductor laser are studied by simulating single mode rate equation when changing the amplitude and width of the modulated signal. The results show that the proposed method can effectively accelerate the accumulation speed of the population inversion and we can acquire the output pulse with higher peak power and shorter width. Compared with the traditional rectangular wave modulation, this method is advantageous to obtain a high gain switching effect by increasing the second modulation current and reduce the pulse width to saturation at the best working point. It can be incorporated as a practical and cost-effective approach for many fields which need high extinction ratio short pulse, such as the optical time domain reflectometry.  
Keywords:gain-switched semiconductor laser; rate equation; step modulation; simulation; curve fitting +
Footnote †: journal: Applied Physics Letters
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 3 System Model and Problem Formulation
### Channel Model  
#### 3.3.1 Air-to-Air Channel Model  
The balloon-to-balloon channel is virtually unobstructed and dominated by line-of-sight (LoS) links, so the propagation loss between balloon \(n\) and balloon \(i\) lends itself to free-space propagation loss (FSPL),  
\[\mathbb{L}_{LoS}^{n,i}=(\frac{4\pi d_{n,i}f_{c}}{c})^{2}, \tag{8}\]  
where \(d_{n,i}\) represents the distance between neighboring balloons \(n\) and balloon \(i\) and \(d_{n,i}\geq d_{min}\). \(d_{min}\) is the minimum safe distance to ensure that balloons do not collide. \(f_{c}\) denotes the carrier frequency, and \(c\) is the speed of light.
# Robots in Partially Known and Unknown Environments:
## 3 Problem Description
### Mathematical Model  
The mathematical model presented in this section is based on [27]. The problem variables enable us to define the route coordinates as follows:  
\[x_{i}=i\text{-th coordinate $x$ of the robot},\quad\forall\;i\in\mathbb{N},\;x\in\{0, \dots,l\}. \tag{1}\]  
\[y_{i}=i\text{-th coordinate $y$ of the robot},\quad\forall\;i\in\mathbb{N},\;y\in\{0, \dots,m\}. \tag{2}\]  
The main parameters of the model are related to obstacles. Information related to the presence of obstacles in grids is expressed as  
\[O_{x,y}=\begin{cases}1&\text{if there is an obstacle in $(x,y)$}\\ 0&\text{if there is no obstacle in $(x,y)$}\end{cases}\quad\forall\;x\in\{0, \dots,l\},\;y\in\{0,\dots,m\}\]  
Given that we can work with partially known environments, we consider a parameter to represent obstacles before the algorithm execution. Known obstacles will be represented using a binary matrix with \(l\times m\) resolution, where \(l\) is the grid width and \(m\) is the grid height.  
\[Known\_O_{x,y}=\begin{cases}1&\text{if there is an obstacle in $(x,y)$}\\ 0&\text{if there is no obstacle in $(x,y)$}\end{cases}\quad\forall\;x\in\{0, \dots,l\},\;y\in\{0,\dots,m\}\]  
The main constraints of the problem studied are listed below:  
Figure 1: Environment grid. The robot can use one cell at each step. Black cells represent obstacles, and white cells represent free spaces to transit.  
Figure 2: Possible directions for the robot in the grid.  
* A robot should change position at each step. \[(x_{i},y_{i})\
eq(x_{i+1},y_{i+1})\] (3)
* A robot should not use a grid when there is an obstacle. \[O_{(x_{i},y_{i})}\
eq 1,\ \forall\ (x_{i},y_{i})\] (4)  
The main objective of the problem is to find a collision-free path of minimal length. The length of the path is computed by Euclidean distance. The objective can be represented as  
\[\text{Minimize}\ L(P)=\sum_{i=1}^{n-1}\|P(i)-P(i+1)\| \tag{5}\]  
where \(P\) represents the constructed path, each \(P(i)\) represents the i-th position of the robot during the path, and \(n\) is the number of positions of the path. The norm operator computes the Euclidean distance between two positions in sequence. Each position is described by its horizontal and vertical coordinates on the map.