esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Principle
### RGB-LED-ID Feature Extraction and Selection
#### 3.3.4 Step 4: Get the Skewing  
The three images obtained from the separation are placed in the same coordinate system, which is shown in Figure 12. Set a vertical and downward vector passing through the image center. In the first image (R), the ordinate of the center line of the \(i\)th stripe that intersects this vector is \(d_{1}\). In the second image (G), the ordinate of the center line of the last stripe that intersects this vector is \(d_{2}\) in (0, \(d_{1}\)). In the third image (B), the ordinate of the center line of the \(i\)th stripe that intersects this vector is \(d_{3}\) in (0, \(d_{2}\)). Order:  
\[\lambda_{i}=\frac{d_{1}-d_{2}}{d_{1}-d_{3}} \tag{5}\]  
In order to reduce the error, again, take the vector vertical downward. In the first image (R), the ordinate of the center line of the \(j\)th stripe that intersects this vector is \(d_{3}^{\prime}\). In the second image (G), the ordinate of the center line of the first stripe that intersects this vector is \(d_{2}^{\prime}\) in (0, \(d_{3}^{\prime}\)). In the third image (B), the ordinate of the center line of the first stripe that intersects this vector is \(d_{1}^{\prime}\) in (0, \(d_{2}^{\prime}\)). Order:  
\[\lambda_{j}=\frac{d_{3}^{\prime}-d_{2}^{\prime}}{d_{3}^{\prime}-d_{1}^{\prime}} \tag{6}\]  
The skewing coefficient is defined as:  
\[k_{\lambda}=\frac{\sum_{i=2}^{m}\lambda_{i}+\sum_{j=2}^{n}\lambda_{j}}{m+n} \tag{7}\]  
In general, \(m\), \(n>5\) in the above equation, but if the number of stripes is small due to a low frequency or a long distance, \(m\), \(n\) and \(i\), \(j\) can be reduced simultaneously.  
Moreover, for LED5 and LED6, the number of image stripes separated by the receiver is different due to the different frequencies of the three channels at the transmitter. In this case, periodic chaos exists when the phase difference is extracted. Therefore, in the system composed of a transmitter like LED5 or LED6, only five effective features except skewing can be extracted.  
The above is the process of feature extraction. However, there is a situation that needs to be considered: when the natural light or other non-system light in the environment is very strong, the contrast between the bright and dark stripes of the LED project in the image captured by the sensor will be reduced. This problem can be solved based on our previous work [16]. An algorithm named contrast limited adaptive histogram equalization (CLAHE) was proposed in Ref. [16]. It can enhance the contrast of the image itself when there is interference from external light.  
Figure 11: Get the duty-ratio of the bright stripes and the number of stripes.
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 4 Discussions
### Experimental Environment and Datasets
#### 4.1.1 Real Datasets  
We use the following 2 real datasets. The first is MSWEB in the UCI KDD package [24]. This dataset is a week of access logs in the virtual area of Microsoft portal. Each record represents a set of areas accessed by a user session. MSWEB is a dense dataset, with a total of 32,711 records. The number of independent elements is 285 and the maximum length and minimum length are 35 and 1, respectively.  
The second dataset is DBLP, which is a snapshot extracted from the famous DBLP bibliography [25]. The raw data is in XML format and we extract the author and editor field of the first 1 million publications to make a set dataset and remove the duplicate elements in each set. DBLP is a large and sparse dataset. The number of independent elements is 283,885, and the maximum length and minimum length are 176 and 1, respectively.
## 3 Performance Analysis
### Capacity Analysis with Adaptive Transmission
#### 3.2.2 Optimal Rate Adaptation with Constant Transmit Power (ORA)  
In the ORA technique, node \(S\) only alters its transmission rate with the variations of the total end-to-end SNR. The average channel capacity using this scheme is given by [2] (Equation (24)):  
\[\overline{C}_{ORA}=\frac{B}{2\ln(2)}\int_{0}^{\infty}\ln(1+\gamma)f_{\gamma_{ l}}(\gamma)d\gamma. \tag{14}\]Plugging Equation (6) into Equation (14), we obtain:  
\[\overline{C}_{ORA}= \sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\sum_{n=0}^{N_{1}\mu_{1}+i-1 N_{2}\mu_{2}+j-1}\sum_{l=0}^{\infty}\frac{\xi_{1}\xi_{2}\Delta\Pi\Psi B}{2\ln(2)}\] \[\times\Bigg{[}(\psi_{1}+\psi_{2})\int_{0}^{\infty}\frac{\ln(1+ \gamma)\gamma^{n+l}}{e^{(\psi_{1}+\psi_{2})\gamma}}d\gamma-(n+l)\int_{0}^{ \infty}\frac{\ln(1+\gamma)\gamma^{n+l-1}}{e^{(\psi_{1}+\psi_{2})\gamma}}d \gamma\Bigg{]}. \tag{15}\]  
Furthermore, solving the integrals of Equation (15) with the help of [47] (Equation (A.3)), one can obtain the average channel capacity using the considered technique as:  
\[\overline{C}_{ORA}= \sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\sum_{n=0}^{N_{1}\mu_{1}+i- 1N_{2}\mu_{2}+j-1}\sum_{l=0}^{\infty}\frac{\xi_{1}\xi_{2}\Delta\Pi\Psi B}{2 \ln(2)}\frac{e^{(\psi_{1}+\psi_{2})}}{(\psi_{1}+\psi_{2})^{n+l}}\left[\sum_{ p=0}^{n+l}\Gamma\big{(}-p,(\psi_{1}+\psi_{2})\big{)}\right.\] \[\times\left.(\psi_{1}+\psi_{2})^{p}\Gamma(n+l+1)-\sum_{q=0}^{n+l- 1}\Gamma\big{(}-q,(\psi_{1}+\psi_{2})\big{)}\Gamma(n+l)(n+l)(\psi_{1}+\psi_{2 })^{q}\right]. \tag{16}\]
## 2 Overview of InSAR Methodology  
One of the principal applications of the SAR technology is represented by the SAR interferometry (InSAR) technique [13; 14; 15; 16]. It relies on the measurement of the phase difference between two (or more) complex-valued SAR images, acquired from different orbital positions and/or at different times. In principle, different distributions of the receiving/transmitting antennas define distinctive interferometric configurations. In particular, conventional mono-static InSAR configurations involve the use of two antennas observing the investigated scene:  
* at the same time and from different positions, spaced in the across-track direction (across-track interferometry);
* at different times and from the same position (along-track interferometry) [33; 34];
* at different times and from different orbital positions (repeat-pass across-track interferometry).  
Bi-static and multi-static InSAR interferometry configurations, where receiving and transmitting antennas are on board different moving platforms, have also been explored [35; 36; 37]. Such field of research, within InSAR framework, represents one of the most promising lines of development of InSAR technology.
## 3 Methodology
### Model Evaluation and Forecasting Performance Measures  
Model evaluation is crucial when comparing forecasting models, as its choice may affect the final results in relevant way. As customary, we use the walk-forward validation method for the evaluation of our statistical models, whereas for deep learning ones we use train-test validation. Moreover, for each member of the two categories of forecasting methods we measure the forecasting performance by using three different forecasting error measures in a multi-step forecasting scenario.  
* Mean Absolute Percentage Error (MAPE), which is scale-independent forecasting measure and is defined as: \[\text{MAPE}=100\cdot\sum_{t=1}^{N}\frac{|y_{t}-\hat{y}_{t}|}{y_{t}}\] (5) where \(y_{t}\) is the observation at time \(t\) and \(\hat{y}_{t}\) is the corresponding predicted value. In simpler terms, MAPE is the average error in percentage of the observation we are trying to forecast. MAPE is thus scale-independent, so it represents the perfect fit for our grid dataset composed of thousands of cells where each cell may have levels of mobile activity in a very different scale.
* Root Mean Squared Error (RMSE), a widely employed error measure defined as: \[\text{RMSE}=\frac{1}{N}\sum_{t=1}^{N}(y_{t}-\hat{y}_{t})^{2}\] (6)
* Maximum Absolute Error (MAXAE) considers the maximum error (in absolute value) that a given predictor can make, thus taking into account the worst-case scenario. \[\text{MAXAE}=\max_{t}|y_{t}-\hat{y}_{t}|\] (7)  
There are of course other forecasting measures such as MAE (Mean Absolute Error) which is both popular and simpler to understand. However, in our case MAE would result misleading and this for two reasons. The first one is that there are \"calm\" periods in our data which are easy to predict but there are also periods which contain high variability. By using MAE we would not be able to show the distribution of the error in particular for high variability observations. This is also the reason we consider the MAXAE as an additional measure of performance which is particularly good in showing the maximum error of forecast. Finally, we compute each of these three forecasting measures for each one of the cells, thus we use a boxplot graphics to show their distribution along the cells.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 3 Channel Estimation in the RIS-Assisted Communication Systems
### MIMO Systems
#### 3.3.2 Two-Stage Based Cascaded Channel Estimation for a Multi-User System  
In [25], they propose a two-stage method for the uplink cascaded channel estimation without using a typical user in an RIS-assisted MU-MIMO cell system. Specifically, in the first stage, an ambiguous shared RIS-BS channel is constructed with all users jointly, to differentiate the multi-user gain and reduce propagation errors. In the second stage, each user estimates its channel together with the RIS and obtains full CSI of the cascaded channel and analyzes the required pilot overhead. For more details, see Algorithm 1 in [25].
Sensors_**2013**, _13_, 5406-5447; doi:10.3390/s130505406  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Review  
A Survey of Body Sensor Networks  
[PERSON] 1,2,*, [PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 2  
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian 116024, China;  
E-Mails: [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (W.W.)  
School of Software, Dalian University of Technology, Economy and Technology Development Area, Dalian 116620, China; E-Mails: [EMAIL_ADDRESS] (G.Z.);  
[EMAIL_ADDRESS] (G.H.)  
Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (Q.L.); Tel.: +86-138-8941-5322 (X.L.); +86-133-3226-5366 (Q.L.);  
Fax: +86-411-8470-4005 (Q.L.).  
Received: 19 December 2012; in revised form: 12 April 2013 / Accepted: 19 April 2013 /  
Published: 24 April 2013  
**Abstract:** The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives.  
**Keywords:** body sensor network; sensor; data fusion; network communication; practical application of BSNs
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 5 Discussion
### The Developed Protocol Ensures Privacy Preservation  
A user transmits and receives data by using a blockchain account during the data transmission. The user account in the blockchain is private and cannot be linked to a real person. As a result, blockchain anonymity has the ability to protect public data from revealing an entity's real identity. Furthermore, no information about the patient will be revealed throughout the keyword search procedure. The private blockchain cannot derive the patient's true identity from the EHR's encrypted text and re-encryption key through proxy re-encryption.
# Enhancing Vibration Isolation Performance by Exploiting Novel Spring-Bar Mechanism
## 3 Force Transmission and Power Flow Analysis
### Non-Periodic Response
A positive Lyapunov exponent indicates the occurrence of chaotic motions. For a system with \(\alpha=-0.7,\ \beta=1.5,\ \xi=0.015,\ \Omega=0.3\) and the initial conditions \(\left(X_{0},\ \dot{X}_{0}\right)=\left(0,\ 0\right)\), the time histories of the steady-state response are obtained by using numerical integrations. A Fourier transformation of the response was performed, and the results shown in Figure 15a demonstrate a broadband frequency spectrum. The largest Lyapunov exponent obtained was 0.049, suggesting that the response is of a chaotic nature. In this situation, the response does not repeat itself at a regular interval, as shown in Figure 15b. As a result, the use of force transmissibility, traditionally defined as the ratio of the amplitudes of the transmitted and the excitation forces, is not suitable. Additionally, when chaos occurs, assessing the performance of the isolator via approximations by only considering the response amplitude can result in large inaccuracies. In contrast, time-averaged power flow variables can provide good performance indices by taking into account of the long-term dynamic behaviour of the system. As shown in Figures 13 and 14, the use of time-averaged input power \(\overline{\rho}_{\text{in}}\) allows quantitative comparisons of the vibration levels associated with periodic and chaotic responses. It should be noted that for a robust functioning of \(\overline{\rho}_{\text{in}}\) as a performance index, the effects of averaging time on the time-averaged input power should be examined [29]. For this purpose, an investigation is conducted on the dependence of the time-averaged input power of a chaotic response on the averaging time. The results shown in Figure 15c suggest that the variations of \(\overline{\rho}_{\text{in}}\) are confined to a 4 dB band when the averaging time  
Figure 15: Dynamic response at \(\Omega=0.3\) with \(\alpha=-0.7,\ \beta=1.5,\ \xi=0.015,\ F_{0}=0.01\). (**a**) Frequency spectrum, (**b**) instantaneous input power, and (**c**) time–averaged input power. \(\tau_{p}\) is larger than 10 excitation cycles (10\(T\)) and to a 2 dB band when \(\tau_{p}>100T\). As \(\tau_{p}\) increases further, the changes in \(\overline{p}_{\text{in}}\) become smaller, and the time-averaged input power evolves asymptotically to 161 dB. The asymptotic characteristic of \(\overline{p}_{\text{in}}\) can arise from the fact that chaotic responses are bounded in the phase space [42]. This asymptotic behaviour enables quantitative measurements of vibration levels associated with chaotic responses and consequently demonstrates the advantage of using power flow variables as the performance indicators of nonlinear vibration isolators.
## 2 Mathematical Modeling and Theoretical Foundations
### Frequency Domain Convolutional Blind Source Separation
Furthermore, \(x_{i}\) represents the \(i\)th observation vector composed of \(K\) frequency points \(\left[x_{i}^{(1)},x_{i}^{(2)},...,x_{i}^{(K)}\right]^{T}\), and \(x^{(k)}\) denotes the observation vector composed of \(M\) observation signals at the \(k\)th frequency point \(\left[x_{1}^{(K)},x_{2}^{(K)},...,x_{M}^{(K)}\right]^{T}\). Furthermore, \(H^{(k)}\equiv\left\{h_{ij}^{(k)}\right\}\), this implies that \(h_{ij}^{(k)}=\left[h_{ij}^{(1)},h_{ij}^{(2)},...,h_{ij}^{(K)}\right]\) is denoted as the element of the \(i\)th row and \(j\)th column of the \(k\)th mixing matrix. Note that we use lowercase bold letters for vector variables and uppercase letters for matrix variables, and each value of the superscript \(k\) is a frequency point and \(K\) is the number of frequency points. If the separation filter matrix exists, i.e., the inverse or pseudo-inverse matrix of the mixing matrix at each frequency point exists, the separated source signal is:  
\[\textbf{Y}^{(k)}=\textbf{W}^{(k)}\textbf{X}^{(k)}, \tag{6}\]  
where \(\textbf{W}^{(k)}\) is the separation matrix in the frequency domain. The purpose of BSS is to find the corresponding separation matrix \(\textbf{W}^{(k)}\) from the observed signal \(\textbf{X}^{(k)}\) to achieve the extraction of the source signal. The frequency-domain separated signal is transformed to the time-domain by ISTFT, which is the estimation of the source signal.
## 3 VR Conferencing
### Current Challenges  
Nowadays, the technology of building virtual reality conference rooms is still incomplete and faces many challenges [26].  
The challenge of network delay still exists. In contrast to a traditional video conference, there is no need to transmit the video stream of each participant, so the total bandwidth is reduced. However, to obtain the above spatial audio effect, it is necessary to transmit the audio stream of other participants to each participant while the system simultaneously updates the position, posture, and other information of all other participants for all users. The bandwidth demand is therefore still very large. Thus, a feasible scheme [24] is needed to reduce the bandwidth of audio transmission.  
Another challenge is the lack of avatar authenticity. The user's impression of an avatar's posture and animation is still quite different from the real situation. The methods for avatar generation include avatars based on the user's real image and pure virtual avatars. An avatar based on the real image has a more realistic visual effect, but expensive equipment is required to achieve the ideal effect. Although completely virtual avatars cannot show the real appearance of participants, they tend to be more integrated with the virtual environment, and the body animation is more natural [33].  
There is no available solution for interaction design. Traditional video conference is the mainstream form at present and will likely remain so for a long time. One reason is that its interaction design is mature, and we can experience similar interaction modes even using different video conferencing software, thus it could save us time in getting familiar with new software. In such circumstances, virtual reality conferencing has a long way to go [34]. Moreover, unlike familiar operations on computers, participants usually need more time to get familiar with their interaction in a virtual reality environment.  
How to attract more people to virtual reality conference rooms is also a critical challenge. In [8], it was mentioned that the virtual conference itself reduces the opportunities for interaction between people. Another viewpoint is that important meetings should be face to face, and people can better prepare for the meeting by taking advantage of the long-distance travel time [35].
# Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
## 2 Materials and Methods
### Methodology Design  
#### 2.3.1 Motivation  
Our proposed method is targeted at PM sensor in-field calibration to reduce deviations against relative humidity and temperature. Because the interference mechanism is difficult to describe under practical working conditions and individual differences exist among sensors, it is challenging to determine a global regression model to cover the  
Figure 1: In-field calibration is conducted when the sensor is in close proximity to the station. The station data can be regarded as ground truth and a regression model is to be obtained from sensor data to ground truth. The system for our research is deployed in Anji County, near Huzhou City in Zhejiang Province.  
whole sensor range and all common temperature and humidity situations. Even some methods such as ANN-based learning can fit any non-linear model theoretically, high complexity, generalization performance risk and large requirement on data amount limit their advantages.  
Therefore, an intended in-field calibration model for PM sensor can compensate for deviation even with non-linear characteristics caused by relative humidity and temperature at a light calculation cost, together with interpretability and adaptability. Inspired by segmented linear regression, although it is difficult to directly find a global regression model to compensate varied deviation, linear regression still works in some local domains [26]. In fact, samples from sensory data can be divided into several parts and linear regression performs well in each part. The division can be realized via clustering algorithm adaptability [27].
# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 3 Methodology
### Multi-View Data Augmentation  
Due to the difficulty in collecting foreign object data on coal conveyor belts. We apply various geometric transformations [33] to simulate different camera angles and enhance the model's generalization ability, as shown in Figure 3.  
Figure 2: Overview of CAFOD: Deformable DETR Enhanced with MVDA (Viewpoint Invariance), CFP (Context Awareness), CBAL (Belt Focus), and VFL (Accuracy Improvement) modules.  
We define four main transformation matrices. They are perspective \(P\), scaling \(S\), rotation \(R\), and translation \(T\), shown in Equation (1). \(P\) represents the perspective transformation matrix, controlling the perspective effects in the \(x\) and \(y\) directions, with parameters \(P_{x}\) and \(P_{y}\) randomly generated in a \([-0.001,0.001]\) range. \(S\) is the shearing transformation matrix, where \(S_{x}\in[-10^{\circ},10^{\circ}]\) and \(S_{y}\in[-10^{\circ},10^{\circ}]\) define the shearing strengths along the respective axes. \(R\) represents the rotation transformation matrix, responsible for rotating the image by an angle \(\theta\), where \(\theta\in[-15^{\circ},15^{\circ}]\). \(T\) represents the translation transformation matrix, which shifts the image in the \(x\) and \(y\) directions. The translation distances \(T_{x}\in[-0.1w,0.1w]\) and \(T_{y}\in[-0.1h,0.1h]\) are randomly generated within 10% of the image width (\(w\)) and height (\(h\)). We combine these transformation matrices to form a total transformation matrix \(A_{aug}\), in Equation (2).  
\[\begin{split} P&=\begin{bmatrix}1&0&0\\ 0&1&0\\ P_{x}&P_{y}&1\end{bmatrix},\\ S&=\begin{bmatrix}1&\tan(S_{x})&0\\ \tan(S_{y})&1&0\\ 0&0&1\end{bmatrix},\\ R&=\begin{bmatrix}\cos(\theta)&-\sin(\theta)&0\\ \sin(\theta)&\cos(\theta)&0\\ 0&0&1\end{bmatrix},\\ T&=\begin{bmatrix}1&0&T_{x}\\ 0&1&T_{y}\\ 0&0&1\end{bmatrix}.\\ \\ A_{\text{aug}}&=P\cdot S\cdot R\cdot T\\ X^{\prime}&=A_{\text{aug}}\cdot X\\ B^{\prime}&=A_{\text{aug}}\cdot B\\ \end{split} \tag{1}\]  
The expression \(A_{\text{aug}}\cdot X\) refers to applying the affine transformation to each pixel coordinate of the image individually, rather than performing a direct matrix multiplication on the entire image. \(X^{\prime}\) and \(B^{\prime}\) are the transformed image and the corresponding bounding box.  
Figure 3: Multi-view data augmentation simulates the perspective changes of different cameras. The data augmentation can provide more differentiated features from different angles to improve the model’s adaptability to different cameras.  
Applying the transformation to each image in the dataset \(D\), we obtain the enhanced dataset \(D^{\prime}\), providing the model with more differentiated features from different perspectives and improving the model's adaptability to multiple cameras.