esa-sceva/satcom-chunk-collection huggingface dataset:
# Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
## 2 Materials and Methods
### Training Mode of Hand Rehabilitation Robot
#### 2.4.2 Active Training Mode  
Active Repetitive Motion (ARM) rehabilitation therapy has a human-computer interaction system that allows patients to actively participate in rehabilitation training. It senses the motion parameters of the rehabilitation hand and joins the control session while displaying them on the interactive interface. The main research results include the Hand Mentor TM rehabilitation hand designed by the Deaconess Health System in the United States and the ARM rehabilitation hand developed by the University of Victoria in Canada. The Hand Mentor TM was designed by Deaconess Health System [117]. In addition to continuous passive flexion and abduction training, this rehabilitation hand has the ability to sense the pressure caused by finger flexor spasms and compulsively induce finger movement training. The size of the glove can be adjusted to fit most adult left and right hand sizes. The device is stable and reliable and is one of the more mature hand rehabilitation devices already in use in U.S. rehabilitation medical institutions.
# Multi-Condition Constrained Pedestrian Localization Algorithm Based on IMU
## 3 Results
### Parameter Setting  
For the pedestrian positioning system designed in this paper, some additional details regarding the basic parameters and initial settings for the Kalman filter are provided in Table 1. Among them, the known height and latitude of the experimental location allow for the precise calculation of the local gravitational acceleration, which greatly enhances the accuracy of the positioning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Type** & **Parameter Name** & \begin{tabular}{c} **Specific Numerical Values/** \\ **Calculation Formulas** \\ \end{tabular} \\ \hline \multirow{3}{*}{Basic Settings} & \multicolumn{2}{c}{Sampling Frequency (Ts)} & \(1/100\) (s) \\ \cline{2-3}  & Height & 825 (m) \\ \cline{2-3}  & Latitude & 38.1 (\({}^{\circ}\)) \\ \hline \multirow{3}{*}{\begin{tabular}{c} Kalman filter \\ setting \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} Diagonal elements of the \\ covariance matrix Q \\ of process noise \\ \end{tabular} } &
\begin{tabular}{c} Accelerometer noise \\ \end{tabular} & \(2\times 10^{-4}\times 9.8\times\sqrt{1/7\text{s}}\times\left[1\;1\;1\right]^{\prime}\) \\ \cline{2-3}  & & Gyroscope noise & \(0.01\times\left(\pi/180\right)\times\sqrt{1/T\text{s}}\times\left[1\;1\;1\right] ^{\prime}\) \\ \cline{2-3}  & & Accelerometer bias noise & \(0.000001\times\left[1\;1\;1\right]^{\prime}\) \\ \cline{2-3}  & & Gyroscope bias noise & \(0.0000001\times\left(\pi/180\right)\times\left[1\;1\;1\right]^{\prime}\) \\ \cline{2-3}  & & Gyroscope bias noise & \(0.0000001\times\left(\pi/180\right)\times\left[1\;1\;1\right]^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed parameter setting information.  
Figure 10: Experimental Site and Path. (**a**) Satellite Map Overview. (**b**) Experimental Path.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial intelligence \\ ANN & Artificial neural network \\ ARIMA & Auto-regressive integrated moving average \\ BD & Big data \\ CML & Commercial microwave link \\ CNN & Convolutional neural networks \\ CS & Compressed sensing \\ CSI & Channel state information \\ CV & Coefficient of variation \\ DL & Deep learning \\ DSD & Drop size distribution \\ DT & Decision tree \\ GDA & Gaussian discriminant analysis \\ GIS & Geography information system \\ ICT & Information and communication technology \\ IDW & Inverse distance weighting \\ ITU-R & Radiocommunication sector of International Telecommunication Union \\ LR & Logistic regression \\ LSTM & Long short-term memory \\ MR & Measurement report \\ NLA & Nearby link approach \\ NSE & Nash-Sutcliffe efficiency \\ OK & Ordinary kriging \\ PNN & Probabilistic neural network \\ QoS & Quality of service \\ QPE & Quantitative precipitation estimation \\ RG & Rain gauge \\ RMSE & Root mean square error \\ RNN & Recurrent neural network \\ RSL & Received signal level \\ SST & Synthetic storm technique \\ STFT & Short-time Fourier transform \\ SVM & Support vector machine \\ TD-LTE & Time division long-term evolution \\ TN & Time normalization \\ TRMM & Tropical rainfall measuring mission \\ WAA & Wet antenna attenuation \\ WCN & Wireless cellular network \\ \end{tabular}
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 6 Limitations and Future Work
### Detection Accuracy  
The success of FusionTrack is mainly attributed to the significant improvement in the association accuracy. On the other hand, although the detector is aligned with COCO pretrained models and tuned individually before training on the tracking task, its detection performance does not improve much. We only see a +1.5 in detection accuracy compared with MOTR. This suggests that the data augmentation and training pipeline can be improved to better train the detection branch in future works.
## 4 Review: Slant Links Rain Fade Models
### Statistical Models
#### 4.2.4 PEARP Model  
The PEARP model [92] is based on the probability of exceeding the attenuation threshold level. The steps of the PEARP model are shown in Algorithm 13. Validated information for the PEARP model was not found. The probabilistic weather forecasts could be useful for transmitting data maintaining a quality link in the temperate region. Hence, it can help to maximize the economic value of transmitted data at a high frequency. However, such information for the tropical region is not available yet. Algorithm 13 shows the procedure for this model.  
Advantages  
Useful for setting several types of rainfall rate based on the distinct threshold level. The model allows getting the benefit of ensemble weather forecasts using satellite communications.  
```
1 Determine rain height /* Equation (41) */
2 while\(H_{R}-H_{s}\leq 0\)do
3 if\(\theta\geq 5^{\circ}\)then
4\(L_{S}\leftarrow\frac{(H_{R}-H_{S})}{\sin\theta}\)
5 if\(\theta<5^{\circ}\)then
6\(L_{S}\leftarrow\frac{2(H_{R}-H_{s})}{\left(\sin^{2}\theta+\frac{2(H_{R}-H_{s})}{ \left(\sin^{2}\theta+\frac{2(H_{R}-H_{s})}{K_{\theta}}\right)^{1/2}+\sin\theta}\right.\)
7 Compute \(L_{G}\gets L_{s}\cos\theta\)
8 Compute \(\gamma_{R}\gets k(R_{0.01})^{\alpha}\) /* Use input rainfall rate */
9 Compute \(r_{0.01}\leftarrow\frac{1}{1+0.78\sqrt{\frac{L_{G}T_{R}}{T}}-0.38\left(1-e^{-2L _{G}}\right)}\)
10 Compute \(\zeta\leftarrow\tan^{-1}\left(\frac{H_{R}-H_{s}}{L_{G}T_{0.01}}\right)\)
11 if\(\zeta>\theta\)then
12\(L_{R}\leftarrow\frac{L_{G}T_{0.01}}{\cos\theta}\)
13 else
14\(L_{R}\leftarrow\frac{(H_{R}-H_{S})}{\sin\theta}\)
15 if\(|\varphi|<36^{\circ}\)then
16\(\chi\gets 36^{\circ}-|\varphi|\)
17 else
18\(\chi\gets 0\)
19 Compute \(v_{0.01}\leftarrow\frac{1}{1+\sqrt{\sin\theta}\left(31\left(1-e^{-(\theta/(1+ \chi))}\right)\frac{\sqrt{L_{B}T_{R}}{T}}-0.45\right)}\)
20 Compute \(L_{eff}\gets L_{R}\cdot v_{0.01}\)
21 Compute \(A_{0.01}\leftarrow\gamma_{R}\cdot L_{eff}\) /* Compute range between 0.001% to 5% */
22 if\(p\geq 1\%\) and \(|\varphi|\geq 36^{\circ}\)then
23\(\beta\gets 0\)
24 if\(p<1\%\) and \(|\varphi|<36^{\circ}\) and \(\theta\geq 25^{\circ}\)then
25\(\beta\leftarrow-0.005(|\varphi|-36^{\circ})\)
26 else
27\(\beta\leftarrow-0.005(|\varphi|-36^{\circ})+1.8-4.25\sin\theta\)
28 Compute \(A_{p}\gets A_{0.01}\left(\frac{p}{0.01}\right)^{-(0.655+0.033\ln(p)-0.045 \ln(A_{0.01})-\beta(1-p)\sin\theta)}\) Return \(A_{p}\)
```  
**Algorithm 12**ITU-R P.618 Model [45]  
Limitations  
This model has low horizontal and temporal resolutions.
# Simulation and Design of Three 5G Antennas
## 5 Design of an Eight-Element MIMO Array Antenna for Mobile Terminals with a Metal Frame
### Design and Simulation of an Eight-Element MIMO Array Antenna  
Based on the \"ring-slot\" MIMO antenna structure proposed in Section 4, research has been conducted on an eight-element MIMO array antenna suitable for mobile terminals with a metal frame to achieve improved antenna performance. This antenna operates in the LTE band 42 (3.4-3.6 GHz) frequency range, with a center frequency around 3.5 GHz. A substrate with a relative dielectric constant of 4.4, loss tangent of 0.02, and a thickness of 0.8 mm made of FR4 material is chosen as the system circuit board, with dimensions of 145 mm \(\times\) 75 mm \(\times\) 0.8 mm. A 0.6 mm-thick copper strip is placed around the perimeter of the dielectric substrate to simulate the metal frame within a mobile terminal.  
In this design, eight-element MIMO array antennas are composed of four sets of dual-element \"ring-slot\" MIMO antenna modules. These modules are arranged along the longer sides of the system board; block 1 (Ant1, Ant2) and block 2 (Ant3, Ant4) are placed on one longer side, while block 3 (Ant5, Ant6) and block 4 (Ant7, Ant8) are symmetrically  
Figure 40: The radiation patterns of the dual-element “ring-slot” antenna at a frequency of 3.5 GHz in the E-plane and H-plane. (**a**) E-plane radiation pattern and (**b**) H-plane radiation pattern.  
positioned on the other longer side. As shown in Figure 41, this is the schematic diagram of the eight-element MIMO array antenna structure.
## 3 Data Description
### Application of the DBN Model to Predict GPR Amplitude  
The applications of a predictive model using the DBN method have been widely applied among researchers in addressing both unsupervised problems [33, 34, 35, 36] and supervised issues [37, 38, 39]. In this research, a supervised DBN model was used to predict the GPR amplitude from the concrete structures using various inputs. A comprehensive database consisting of 288 experimental records obtained from the previous publication was used for training and testing the proposed DBN model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Sym.** & **Unit** & **Category** & **Min** & **Mean** & **Max** & **SD** \\ \hline Temperature & T & \({}^{\circ}\)C & Input & 5 & 22.5 & 40 & 11.5 \\ Relative humidity & RH & \% & Input & 55 & 70 & 85 & 12.3 \\ Chloride level & CL & \% & Input & 0 & 0.05 & 0.1 & 0.03 \\ Corrosion status & CS & - & Input & 0 & 0.5 & 1 & 0.5 \\ Signal amplitude & A & dB & Output & \(-5.17\) & \(-1.63\) & 1.04 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the experimental data.  
Figure 4: Histogram of output parameters.
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
## 3 Symbolic Analysis Approach
### Maintaining Symbolic Expression  
Symbolic expressions are a symbolic store that associates program variables with expressions over concrete and/or symbolic values [14].  
A DSP program can be expressed as a DAG (i.e., directed acyclic graph) where the nodes are operators, and the edges represent the input-output relationships of operators. The symbolic analysis happens from the first operator to the end operator along the DAG. During the symbolic analysis process, the input parameters of each operator will be expressed as symbolic expressions, and the output results can also be expressed symbolically according to the semantic constraints of operators and UDFs.  
On top of how the symbolic expressions pass from input to output within one operator, we will also consider the mechanism about symbolic expression passing from upstream operators to downstream operators. During the symbolic analysis, we will ensure that the output symbolic expressions of upstream operators are passed into the adjacent downstream operators.  
The symbolic expressions before analyzing one operator's body are generated based on its upstream operators and its parameter, shown in Algorithm 1.  
```
Input:\(\{\sigma_{1},\sigma_{2},..\sigma_{N}\}\) where \(\sigma_{i}\) is the symbolic expression from the \(i\)-th upstream operator Output: Symbolic expression \(\sigma_{current}\) for this operator \(O_{i}\) init\(\sigma_{current}\gets NULL\) ; for\(i\gets 1\)to\(N\)do \(\sigma_{current}\gets\sigma_{current}\cup\sigma_{i}\);  end for if\(\sigma_{current}=NULL\)then \(\sigma_{current}\gets SPF(O_{i})\) {SPF is the symbolic execution engine that takes the parameters in the i-th operator as input. We can get the symbolic expressions from its results } ;  end
```  
**Algorithm 1**Generating symbolic expression before analyzing one operator.  
The symbolic analyzing engine (e.g., SPF in our study) will maintain these symbolic expressions during symbolic executing along the operator body, and generate PCs once branches are met. The PCs are also maintained automatically by the symbolic analyzing engines. After finishing executing all programs, we can get all PCs, from which we can infer how the input data looks like if we want to reach some program path. By solving all these PCs and generating representative samples, we can have test data to cover all JDU paths.
## 3 Validation Method for the Controller
### Considering Ocean Currents as Disturbances  
The forces of the ocean currents can be considered constant and irrotational according to [PERSON] (1977); and can be implemented using relative velocity, which is the difference between the real velocity and the ocean current velocity  
\[\
u_{rel}=\
u-\
u_{oc}, \tag{28}\]  
where \(\
u\) is the vehicle velocity and \(\
u_{oc}\) is the ocean current velocity.  
The generalized vector for an irrotational ocean current velocity is given by  
\[\
u_{oc}=[u_{oc},v_{oc},w_{oc},0,0,0]^{T}, \tag{29}\]  
where \(u_{oc}\) is an ocean current velocity from the north, \(v_{oc}\) is the ocean current velocity from the east, and \(w_{oc}\) is the ocean current velocity from below.  
Defining the angles \(\alpha_{oc}\) as the angle of attack, and \(\beta_{oc}\) as the slide slip angle, every element of the velocity vector can be calculated as  
\[u_{oc}=\
u_{oc}cos\alpha_{oc}cos\beta_{oc},\]  
\[v_{oc}=\
u_{oc}sin\beta_{oc},\ \ \text{and}\]  
\[w_{oc}=\
u_{oc}sin\alpha_{oc}cos\beta_{oc}, \tag{30}\]  
Since the velocity vector is expressed in the Earth-Fixed frame, Equation (4) can be modified resulting in  
\[M\dot{\
u}_{rel}+C(\
u_{rel})\
u_{rel}+D(\
u_{rel})\
u_{rel}+g(\eta)=\tau, \tag{31}\]  
[PERSON] et al. (2019) performed some experiments where high ocean currents affecting a UUV were measured. Taking them into account and for purposes of the simulations presented in this work, the following ocean currents will be included as constants  
Figure 4: Parameterized desired spiral trajectory of the Unmanned Underwater Vehicle (UUV) BlueROV2.  
\[u_{oc}=\ 0.75\ \frac{\text{m}}{\text{s}},\] \[v_{oc}=0.25\ \frac{\text{m}}{\text{s}},\ \text{ and}\] \[w_{oc}=\ 0.25\ \frac{\text{m}}{\text{s}}, \tag{32}\]
# FASTSET: A Fast Data Structure for the Representation of Sets of Integers
## 1 Introduction  
We describe a data structure for storing and updating a set \(S\) of integers whose elements are values taken in the range \(E=\{0,\ldots,N-1\}\) with \(N\) a given integer. Typically, the \"superset\", or \"universe\", \(E\) corresponds to the indices of the \(N\) elements of a problem instance, and we are interested in storing, using and updating subsets of the universe.  
A set of integers is a very basic mathematical object and it finds use in uncountably many applications of computer programs. Performing the basic set operations (i.e., insertion, deletion, membership test and elements enumeration) in the most effective possible way would benefit all algorithms in which sets of integers play a relevant role (just to mention one example, consider the algorithms operating on graphs, for which the universe is the set of nodes/edges of a graph and the procedure needs to store and update a subset of nodes/edges). The goal of the paper is to provide optimal set primitives such as those mentioned above.  
The literature on algorithms and data structures is very rich, and various possible representations and implementations of such sets are discussed in many textbooks, among which there are some classics, such as [1; 2; 3]. The implementations of the sets fall mainly in two categories: (i) those for which operations such as insertion, deletion and membership test are fast but other operations, such as listing all the elements of the set, performing union and intersection are slow; (ii) those for which operations such as insertion, deletion and membership test are slow but other operations, such as listing all the elements of the set, performing union and intersection are fast. In the first category, we recall the _bitmap representation_ of the set (which uses an array \(v\) of \(N\) booleans, where \(v[i]=\texttt{true}\) iff \(i\in S\)) and some forms of _hash tables_ using either optimal hashing or buckets [4]. In the second category, we can represent a set by an array of size \(N\) in which only the first \(|S|\) entries are filled and contain the elements of the set, a structure called a _partially filled array_ (PFA). Usually the PFA is unsorted (maintaining the elements sorted can speed-up operations such as the membership test, but it slows down insertion and deletion) Another option is to store the elements in a _linked list_, again unsorted. Finally, one can resort to some tree-like structures, such as the _AVL trees_, a self-balancing type of binary search trees [5]. For these structures, insertion, deletion and membership tests are reasonably fast (butnot optimal), but the trade-off is that some other operations are slowed down by the overhead paid in order to maintain the structure sorted.
# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 3 Methodology
### Basic Idea  
While the Zero-DCE method utilizes a simple quadratic light-enhancement curve, achieving satisfactory image enhancement necessitates multiple iterations. However, these iterative applications still leave room for significant improvement in terms of computational efficiency. To address this, we posit that low-light image enhancement can be mathematically formulated as a direct curve mapping process, formally defined through Gamma transformations. Building upon the principles of Gamma correction, we redesign a specialized curve tailored specifically for low-light image enhancement. Consequently, our primary objective is to develop a dedicated curve that is capable of accomplishing the enhancement task through a single-step direct-mapping process.
## 3 Experimental Data  
### Experimental Data Information  
In this paper, the same dataset from the ISPRS 2D and 3D Semantic Labeling Contest was used, including airborne LiDAR data and IR-R-G images from the German Vaihingen dataset (Figure 3). The dataset was divided into two areas, namely, the training set and the test set. In addition, the dataset was divided into nine categories, including power line, low vegetation, impervious surfaces, cars, fence/hedge, roof, facade, shrub, and tree. Each point in the dataset contained the 3D coordinates, return_number, number_of_returns, and intensity. According to statistics, the training set contained753,859 points and the test set contained 411,721 points [31].  
In order to improve the robustness and generalization of the network using another data check, we took test data from Sha City in Hubei Province, which contained 1,954,659 laser points in total. The \(X\)-axis span was 904.55 m, and the \(Y\)-axis span was 789.47 m. The relative altitude of the aerial images, which were obtained with an RCD-105 35 mm focal length camera, was 1100 m, and the point-cloud density was 1.34 pts/m\({}^{2}\), as shown in Figure 4. Raw data include the XYZ information of the point cloud. The original point cloud data and remote sensing images are fused to obtain the point cloud data with RGB information. We used CloudCompare ([[http://www.cloudcompare.org/](http://www.cloudcompare.org/)]([http://www.cloudcompare.org/](http://www.cloudcompare.org/)), accessed on 12 February 2020) tools to carefully label datasets to facilitate precision in subsequent comparisons.
## 4 Results and Discussion
### Topology Optimization and Design Parameters
Based on the topology optimization of the square end plate depicted in Figure 4c, three parameters were derived: (1) volume removal between the bolts, (2) fillet formation, and (3) thickness reduction of the top region. Before proceeding with the parametric stress and strain analysis derived from the topology optimization of the square end plate, seven types were designed like the circular end plate, as illustrated in Figure 5b. The designed square end plate consists of four letters in total, in the order of removed parameters: the first letter from the left is the bolt, the second letter is the fillet, and the third and fourth digits are the thickness. The first letter is depicted in Figure 4c as a red circle, where the symbol \"C\" was completely removed, and the part where nothing was removed is indicated by the symbol \"N\". The second digit is indicated by the symbol \"F\" in the part where the symbol fillet is formed and the symbol \"N\" in the part where nothing is removed. Finally, the third and fourth digits are marked with the symbol \"00\" where nothing was removed, \"10\" where the part to be removed is 10 mm deep, and \"20\" where the part to be removed is 20 mm deep.