esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Overview
### Cell Training Data
#### 3.2.1 Calibration  
In this paper, we fuse crowdsourcing training data collected by multiple devices, which is the most promising solution for reducing the site survey labour consumption [43; 44]. Most existing localisation systems assume that the device contributed for the training data collection is the same in positioning phase. However, every mobile user may become a potential contributor for the fingerprint database construction and the participating devices are usually different, which causes new challenges pertaining to cross-device fingerprint database construction. In addition, different devices have different sensor specifications and varying readings even at the same locations [45], thus a calibration process is essential in the crowdsourced radio map construction by fusing the RSS radio maps from different devices.  
To support different devices and make the fingerprints of diverse devices compatible with each other, the calibration step is performed prior to the positioning phase. The relation in RSS values between two different devices at the same location appears to be linear, as discussed in [46]. In this case, device calibration is conducted by means of data fitting methods that create a linear transformation from the new device to the reference device. The adjusted RSS are then fused as crowdsourced training data.  
In this paper, we implement the multivariate linear regression model [47] to match the signal strengths measured by the new device with the radio map constructed by the reference device. Calibration data collection is simple: the user carries the reference device and the devices that need to be calibrated; walks freely inside the area of interest; and collects data from all visible APs in the environment at the same time. Given a linear mapping with parameters \(a_{mregress}\) and \(b_{mregress}\), the signal strength values reported by Client X are mapped to the RSS values reported by Client Y. The linear regression model is expressed as:  
\[DeviceY_{RSSintensity}=a_{mregress}\times DeviceX_{RSSintensity}+b_{mregress} \tag{2}\]where \(DeviceX_{RSSintensity}\) is the RSS readings from Device \(X\) that needs to be calibrated, while \(DeviceY_{RSSintensity}\) is the RSS readings from the reference Device Y. \(a_{moregress},b_{moregress}\) are the calibration coefficients calculated by the linear regression model.  
To find the linear fit between the two signal strength intensity, the user carries all devices freely walking around the test area and collects the RSS at the same time. In Figure 3, we can see the signal strength from all other devices almost follows a linear match with the reference signal strength intensity at every location. Figure 4 shows a calibration example between Device 1 and Device 7, where Device 7 is used as the reference device. The device specification is described in Table 1 in the Experimental Section 4.  
Figure 4: Multivariate linear regression between Device 1 and Device 7.  
Figure 3: Heterogeneous devices measurements.
# A Proposal for a Standardized Approach to Inducing Low Energy Availability in Athletes
## 4 Discussion
### How Much (or Little) Is Enough?  
There is still an unanswered question about the threshold value for LEA in men and whether there is a threshold at all. The reason for inconsistency in finding a threshold in men could be due to different approaches to measuring LEA. We propose a two-way model to approach this issue in a systematical way. The first approach is to lower EA in previously healthy individuals to a set value of 30, 25, 20, 15, 10 or even 5 (Figure 3). Pioneer work in females has been done in similar manner [5], but the lowest EA in that research was 10. Since males appear to have a greater tolerance for reduced EA, a value of 5 might be necessary to detect any changes in health or performance markers. Another factor is that in research by [PERSON] an [PERSON] [5], participants were sedentary women. In athletes (especially male), longer and more severe restrictions in EA might be necessary.  
There is another notion that there might not be any cutoff value for LEA at all [1]. If there truly is a spectrum (but not a single threshold) where LEA in men can occur, this could be explained with reduction in EA that is more personalized to the athlete. This is why the second approach is to set a proportional reduction in EA--a reduction of 75%, 50%, 25% or even 10% from the baseline EA value (Figure 3). This would produce various values of EA, but reduction would be more individualized to the athlete. This could produce not a single value of LEA but a range of values where progression of lowering EA would elicit severity of undesired effects.
### WSN-Based Upper Limb Rehabilitation Systems  
WSN-based Rehabilitation Systems for the Upper Limb have been introduced in the literature since 2010, indicating that WSN rehabilitation applications is a recent field of research. The sensor nodes that are attached to human body segments, forming a Body Area Network, consist of specific sensor units [10]. Individual accelerometers, gyroscopes and magnetometers or directly a unified Inertial Measurement Unit (IMU) can be integrated into these nodes. These systems rely on both custom-made sensor nodes [11, 12, 13] and off-the-shelf items [14, 15].  
These sensors nodes collect data, such as accelerations or angular velocities, in order to extract information about the position, the motion and/or the direction of body segments. Apart from the IMU, the optical linear encoder (OLE) [16] is another type of sensor that is met in the literature regarding WSN-based rehabilitation applications. It can provide direct information about a joint angle (only 1 DoF), but it is necessary to be combined with an accelerometer at least, to derive further information for the rest of the rotations, which are performed by this joint [17, 18].  
Another fundamental point in an upper limb rehabilitation application is the networking protocol that is going to be implemented. Within the literature, Wi-Fi [19], Bluetooth [14], IEEE 802.15.4 [11], ZigBee [20] and MiWi [13] are proposed, affecting in a varying degree the energy consumption of the total system, as well as the reliability of the sensors' data transmission. Depending on the particular application's objectives, either Time Division Multiple Access (TDMA) scheduling algorithms for node synchronization are used [15, 20] or Carrier Sense Multiple Access/Collision Detection (CSMA/CA) for collision avoidance [21]. As far as the packet loss issue is concerned, due to the high sampling rate of the sensor, a lost data packet does not significantly reduce the accuracy of the collected data, since the missing information can be restored from neighbor sensors' data using sensor fusion methods.  
The interdisciplinary research field of rehabilitation procedure supervision using WSNs appears to be emerging and promising for the future. The achievements and complete rehabilitation solutions developed by relevant companies, such as Libelium [22], Deltason [23], Xsens [24], The IoT Marketplace [25] and Shimmer [26] indicate a rising interest in this field.
## 3 Results and Discussion
### Spatiotemporal Variation of Land Use/Land Cover  
#### 3.2.1 LULC Spatial Distribution in Different Study Years  
The spatial distribution of different LULC types were mapped in Figure 4, and the related LULC area combined with their area changes from 1987 to 2016 were counted in Tables 5 and 6. The area of construction land increased from 189.5 km\({}^{2}\) in 1987 to 497.4 km\({}^{2}\) in 2016, an increase of 162.5% during the 29-year period. The areas of natural surfaces, i.e., farmland  
The LULC distribution transitioned from a pattern dominated by farmland in 1987 to construction land in 2016. The construction land was detected to have a continuous area expansion from 1987 to 2016. It covered 27.8% of the study area in 1987 but expanded to 43.0% in 1996, 61.6% in 2007 and 72.9% in 2016. It was mainly distributed in the city core along the Yangtze River and Han River in 1987 (Figure 4a) and gradually sprawled to the third ring road in 1996 and 2007 (Figure 4b,c). In 2016, the majority of the study area was occupied by construction land (Figure 4d). As a result, farmland was gradually encroached, with its area coverage decreasing from 38.8% in 1987 to 25.2% in 1996, 18.6% in 2007 and 7.5% in 2016. Rapid urban expansion led to a drastic increase in the area of construction land and decrease of natural resources.
## 3 System Model and Description
### Network Configuration and Data Transmission  
In this phase, SPF-Three finds the potential-forwarder neighbors from the neighboring table with the shortest path. BFS-SPF-Three performs the opportunistic routing to find the best shortest path with minimum energy overhead. The schemes with their algorithms are discussed in detail below:
# High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images
## 3 The Proposed Method
### The Embedding Procedures  
In this part, we present the procedures of concealing confidential data into the compressed image using a hidden position matrix which was generated by combination theory. Our proposed embedding procedure is explained in depth as follows.  
Input: An original image _TI_, with R width and S height, and confidential data \(S\).  
Output: Compressed stego image _TI'_.  
Given an original image _TI_, with R width and S height, segment into the n\(\times\)n non-overlapping block image.  
Step 1. Compress the original image block to obtain the bitmap value, _Bm_, according to Section 2.1.  
Step 2. Embed the confidential data \(S\) into the bitmap _Bm_ according to Section 2.2.  
An example is given to further describe the proposed scheme for better understanding.  
Input: Suppose we have a block size of 4 \(\times\) 4 pixels of an image. The block consists of the original pixel values (44, 61, 72, 58, 74, 65, 79, 62, 127, 93, 75, 56, 112, 117, 103, and 60)]. The confidential data stream S that must be concealed is (1101)\({}_{2}\).  
Step 1. Compress the original image block by using the AMBTC method. Figure 3 is the AMBTC encoding of the given pixel value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON] et al.’s Scheme**[22] & **[PERSON] et al.’s Scheme**[23] & **[PERSON] et al.’s Scheme**[24] \\ \hline
**Complex Block** & **difference value D** = \(\left|\left|\begin{array}{c}\textbf{1}\textbf{1}\textbf{1}\textbf{1}\textbf{1} \textbf{1}\textbf{1}\textbf{1}\textbf{1  
Step 2. Embed the confidential data bits into a bitmap by implementing combination theory as explained in Section 2.1. Figure 4 depicts the secret matrix generation which a multiplication operation between bitmap and secret position matrix P. Therefore, Figure 5 shows the secret matrix calculation of each A, B, C and D in the new secret position matrix P\({}^{\prime}\). For every sum value of A, B, C and D, which are 3, 3, 4 and 6 respectively the mod 2 operation is managed for resulting \(\mathbf{S^{r}_{r}}\) as \(\mathbf{S^{r}_{r}=(1100)}\).  
Moreover, as \(\mathbf{Sr\
eq S^{r}_{r}}\), \(\mathbf{1101\
eq 1100}\) then \(\mathbf{S^{r}=(ABCD)}\) ((1101) \(\oplus\) (1100)) = (ABCD) (\(\circ\)) (0001) = **D**. Figure 6 depicts the final result of the embedding procedures, which consisting the original bitmap, stego bitmap, reconstructed original image and reconstructed stego image.
## 3 Results
### ROC Curves  
The ROC curves were constructed for each task, using the measured values obtained from the Fahrenheit recordings as the dependent variable, and the BRS assessment as the independent variable. AUC analysis indicated that all tasks had significant AUC values (Figure 4 and Table 3). The cutoff value for each measured value of the tasks recorded with Fahrenheit were calculated from the coordinate points on the ROC curve using the Youden index. These results were highly accurate for the following three tasks: (A) finger mass flexion, (B) finger mass extension, and (C) thumb radial abduction, and moderately accurate for the following six tasks: (D) thumb-index finger pinch, (E) cylindrical grasp, (F) spherical grasp, (G) index-middle finger flexion, (H) thumb-index finger extension, and (I) finger mass abduction/adduction (Figure 4). The comparison of the ROC curves of the Fahrenheit and the therapists' BRS assessments, using [PERSON]'s test, only showed statistically significant differences in (F) spherical grasp (Z = 1.9739, \(p\) = 0.0484), but not in the other tasks (Z = -1.4434-1.946; all _p_-values > 0.05).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Tasks**} & **Number of Patients** & **Mean Values \(\pm\) SD Recorded** & \multirow{2}{*}{**T**} & \(p\) \\  & **Allocated Each BRS** & & **with Fahrenheit (BRS Score** & **Value** & **Value** \\  & **Score (0/1)** & & **Assigned as 0/1)** & \\ \hline (A) Finger mass flexion & 4/28 & 0.00 \(\pm\) 0.00/0.58 \(\pm\) 0.18 & 11.46 & \textless{}0.001 \\ (B) Finger mass extension & 5/27 & 0.00 \(\pm\) 0.00/0.74 \(\pm\) 0.17 & 19.00 & \textless{}0.001 \\ (C) Thumb radial abduction & 7/25 & 0.00 \(\pm\) 0.00/0.81 \(\pm\) 0.32 & 10.80 & \textless{}0.001 \\ (D) Thumb-index finger pinch & 8/24 & 0.22 \(\pm\) 0.40/0.77 \(\pm\) 0.22 & 2.73 & 0.005 \\ (E) Cylindrical grasp & 6/26 & 0.27 \(\pm\) 0.11/0.87 \(\pm\) 0.31 & 3.91 & \textless{}0.001 \\ (F) Spherical grasp & 8/24 & 0.10 \(\pm\) 0.29/0.83 \(\pm\) 0.38 & 4.58 & \textless{}0.001 \\ (G) Index-middle finger flexion & 22/10 & 0.33 \(\pm\) 0.45/0.80 \(\pm\) 0.31 & 2.64 & 0.007 \\ (H) Thumb-index finger extension & 11/21 & 0.23 \(\pm\) 0.42/0.85 \(\pm\) 0.26 & 3.67 & \textless{}0.001 \\ (I) Finger mass abduction/adduction & 12/20 & 0.27 \(\pm\) 0.42/0.84 \(\pm\) 0.30 & 3.56 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of measurements in each task recorded with _Fahrenheit_.  
Figure 4: Receiver operating characteristic (ROC) curves for each of the nine tasks. The light blue curve was estimated by three therapists (insufficient movement = 0, well-accomplished movement = 1). The orange curve was determined using the values determined with _Fahrenheit_, and the therapist’s clinical judgment.
## 2 The Proposed Timing Mismatch Calibration Technique
### The Model of TIADC in the Frequency Domain  
In an M-channel TIADC system, the sampling instances \(t_{k}\) of each sub-ADC can be written as [22]  
\[t_{k}=(nM+k)\cdot T_{s} \tag{1}\]  
where \(T_{s}\) represents the sampling period of the TIADC system and \(k\) represents the channel number (\(k=0,1,\cdots,M-1\)). The flow chart and the sampling sequence diagrams of the M-channel TIADC system are shown in Figure 1.  
However, due to the existence of timing mismatches, the \(k\)-th channel transfer function can be written as [18]  
\[H_{k}(j\omega)=e^{i\omega(k+\gamma_{k})} \tag{2}\]  
where \(\gamma_{k}\) denotes the timing mismatch of \(k\)-th channel. The block diagram of sampling in an M-channel TIADC is shown in the Figure 2. Therefore, the discrete Fourier transform (DFT) of \(k\)-th channel outputs can be written as [18]  
\[Y_{k}(j\omega)=\frac{1}{M}e^{-j\omega}\sum_{i=0}^{M-1}H_{k}\bigg{(}j\bigg{(} \omega-\frac{2\pi i}{M}\bigg{)}\bigg{)}X\bigg{(}j\bigg{(}\omega-\frac{2\pi i }{M}\bigg{)}\bigg{)} \tag{3}\]  
where \(X(j\omega)\) is the DFT of the ideal input signal. Hence, the outputs of TIADC can be written as  
\[Y(j\omega)=\sum_{k=0}^{M-1}Y_{k}(j\omega)=\hat{X}(j\omega)+\sum_{i=1}^{M-1}Q_{ i}(j\omega) \tag{4}\]with  
\[\hat{X}(j\omega)=\frac{1}{M}\sum_{k=0}^{M-1}e^{i\omega\gamma_{k}}\cdot X(j\omega) \approx X(j\omega) \tag{5}\]  
\[Q_{i}(j\omega)=\underbrace{\frac{1}{M}\sum_{k=0}^{M-1}e^{-j\frac{2\pi i\hat{ \omega}}{M}}e^{i(\omega-\frac{2\pi i\hat{\omega}}{M})\gamma_{k}}}_{C_{i}}X \left(j\left(\omega-\frac{2\pi i}{M}\right)\right) \tag{6}\]  
where \(\hat{X}(j\omega)\) denotes the desired signal, \(Q_{i}(j\omega)\) denotes the spurious signals caused by timing mismatches and \(C_{i}\) denotes the modulation coefficient. The spectrum of an M-channel TIADC with timing mismatches is shown in the Figure 3a. Note that \(Q_{i}(j\omega)\) is the amplitude modulation for the frequency shift replication of input signals. If the time skew errors are identical or zero, the output signal of each sub-ADC is identical and \(Q_{i}(j\omega)\) can be eliminated perfectly. Considering the existence of manufacturing differences and changes in voltage or temperature, the timing mismatch \(\gamma_{k}\) is different in each sub-ADC. Thus, the key challenge is to eliminate these spurious signals.  
Figure 1: (a) The flow chart and (b) the sampling sequence diagram of the M-channel TIADC system.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Magnetic-Model-Free Method Position Sensorless Methods
#### 3.2.1 Additional Component-Based Methods  
A circuit is designed to measure the voltage required for rotor position estimation, and the rotor position can still be estimated under the premise of considering self-inductance and inductance. The resonant circuit has a good real-time rotor position estimation, calculated as the resonance peak as shown in (9) [139]. However, the real-time performance of the rotor position is not ideal. To reduce the predefined inductance parameters, the method based on the bootstrap circuit using bootstrap circuit can effectively detect the initial position of the rotor [140,141].  
\[U_{R}=\frac{U_{k}}{1+jQ[\left(f_{1}/f_{0}\right)^{2}-1]} \tag{9}\]  
where \(f_{0}=1/(2\pi\sqrt{LC})\), \(Q=1/(2\pi f_{1}RC)\), \(R\) and \(C\) represent the resistance and capacitance in the circuit respectively, and \(L\) is the inductance of the characteristic position in the motor.  
As shown in Figure 10, a method based on series inductive coils was proposed [142]. The excitation winding detection coils are independently wound on the stator teeth. According to the different structure of the winding, there are three structures, NNNN, NNSS, NXSX, which are designed to estimate the position of the rotor with the corresponding signal conditioning circuit. Since it is not affected by the winding, this method has the advantages of high detection accuracy, independent control algorithm, and wide speed range. However, it will also increase the risk of the system due to the addition of new accessories.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method
### Intelligent Optimization Methods
#### 4.2.4 Sparse Array Optimization Design Based on Hybrid Algorithm
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**PSLL (dB)**} & \multirow{2}{*}{**PSLL (dB)**} & \multirow{2}{*}{**Filling**} & \multirow{2}{*}{**Directity**} \\  & & **Rerferenced** & & & & & & \\  & & **Array Elements** & & & & & \\ \hline An efficient approach for the synthesis of large sparse planar array [30] & MEPM & \multirow{2}{*}{196} & \(100\) & \(-30.0\) dB & \(-25.11\) dB & \(0.51\) & \(20.05\) dB \\  & & & \(121\) & & \(-29.96\) dB & \(0.62\) & \(22.39\) dB \\ \hline  & & \(144\) & \(49\) & \(-17.60\) dB & \(-19.54\) dB & \(0.34\) & / \\  & & \(144\) & \(69\) & \(-19.40\) dB & \(-23.20\) dB & \(0.48\) & / \\ Synthesis of Thinned Planar Arrays Using 0–1 Integer Linear Programming Method [31] & \multirow{2}{*}{0–1 ILP} & \(144\) & \(76\) & \(-23.07\) dB & \(-24.26\) dB & \(0.53\) & / \\  & & \(144\) & \(88\) & \(-18.65\) dB & \(-23.77\) dB & \(0.61\) & / \\  & & \(256\) & \(128\) & \(-26.72\) dB & \(-31.04\) dB & \(0.50\) & \(25.80\) dB \\  & & \(256\) & \(128\) & \(-25.30\) dB & \(-30.95\) dB & \(0.50\) & \(25.42\) dB \\  & & \(256\) & \(128\) & \(-25.84\) dB & \(-29.74\) dB & \(0.50\) & \(25.15\) dB \\ \hline Research and application of large aperture ultra-sparse array synthesis algorithm [73] & ADMM & \multirow{2}{*}{400} & \multirow{2}{*}{129} & \(-20.0\) dB & \(-20.2\) dB & \(0.32\) & / \\ \hline Synthesis of maximally-sparse & & & & & & & \\ square or rectangular arrays through compressive & & & & & & & \\ sensing [90] & & & & & & & \\ \hline Application of compressed & & & & & & & \\ sensing theory to sparse array & & & & & & \\ orientation graph synthesis [87] & & & & & & & \\ \hline Synthesis of pattern & & & & & & & \\ reconfigurable sparse arrays & & & & & & \\ with multiple measurement & & & & & & & \\ vectors ROCUSS method [84] & & & & & & & \\ \hline Near-field pattern synthesis for & & & & & & & \\ sparse focusing antenna arrays & & & & & & \\ based on Bayesian compressive & & & & & & \\ sensing and convex & & & & & & \\ optimization [91] & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative indications obtained from the literature.
## 3 Indoor Positioning Systems
### Systems Based on Radio Frequency
#### 3.1.2 Range-Free
Regardless of the origin of the fingerprints, the main disadvantage of the method is the effort required to collect samples to compose the database, which increases the cost and reduces the scalability.
# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
## 4 Branching Acceleration for Unit Commitment
### The Branching Acceleration for UC Framework
#### 4.3.1 Neural Network as Classifier  
This study employs the multi-layer perceptron (MLP), a type of neural network, as the binary classifier for the enhanced pruning policy. The input consists of 10 features introduced from Section 4.2, excluding the solution of the relaxed problem at the root node. The output comprises the action probabilities for preserving (optimal node, \(r_{n}=0\)) and pruning (non-optimal node, \(r_{n}=1\)).  
In each layer of the \(L\)-layer MLP, the inputs are processed by multiplying them with a learned weight matrix, adding a bias term, and then applying a nonlinear activation function element-wise. Specifically, the inputs for the \(l\)-th layer are computed as follows,  
Figure 2: Computational process of branching acceleration for UC.  
\[d^{l}=\text{Relu}\left(V^{l}d^{l-1}+b^{l}\right), \tag{17}\]  
where \(V^{l}\) is the weight matrix for the \(l\)-th layer; \(b^{l}\) is the bias vector for the \(l\)-th layer; \(d^{l}\) denotes the output of the \(l\)-th layer (\(l=1,\;\dots,\;L\)), and \(d^{0}\) are the input features. \(\text{Relu}(\cdot)\) is the rectified linear unit function, i.e., \(\max(0,\cdot)\).  
The output \(e\) of the last layer indicates the probability of each class:  
\[e_{i}=\frac{\exp\left(d_{i}^{l}\right)}{\sum\limits_{j=1,2}\exp\left(d_{j}^{l }\right)},i=1,2, \tag{18}\]  
where \(e_{i}\) denotes the \(i\)-th component of the vector \(e\).  
In the training phase, the loss function is the weighted cross entropy:  
\[\ell=-\sum\limits_{j=1,2}o_{j}\cdot q_{j}\log\left(e_{j}\right), \tag{19}\]  
where \(q\) is the class label; specifically, \(q=(1,\;0)\) corresponds to the class prune, while \(q=(0,\;1)\) denotes otherwise; \(o\) represents the weight assigned to each class, which is manually adjusted.  
In the process of label acquisition, we observed a significant imbalance between the number of optimal nodes and non-optimal nodes, with the former being much smaller. To address this imbalance, we assigned a higher weight \(o_{1}\) to the samples of optimal nodes. The weight for non-optimal nodes was set to \(o_{2}\). Specifically,  
\[o_{1}=\frac{|\Omega^{\text{non}}|}{|\Omega^{\text{tra}}|}, \tag{20}\]  
\[o_{2}=1-o_{1}, \tag{21}\]  
where \(\Omega^{\text{non}}\) and \(\Omega^{\text{tra}}\) are the set of non-optimal nodes and the set of all nodes in the training dataset, respectively; \(|\cdot|\) is the number of nodes in the set.