esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Bottleneck Identification of Manufacturing Resources in Complex Manufacturing System
## 3 Material and Methods
### Dynamic Bottleneck Identification Method
#### 3.3.2 System-Wide Bottleneck Identification  
The bottleneck detection algorithm can identify the bottleneck of the subsystem in the CMS, but it cannot obtain the overall bottleneck status of the system. The system-wide bottleneck is critical to production management, so a multi-index real-time bottleneck degree calculation method (CFBI) is proposed to calculate the system bottleneck, as shown in formula (16). This method not only considers the independent characteristics of the machine (workload and capacity) but also considers the interaction between the machines (congestion and starvation caused by upstream and downstream machines) and improves it based on [PERSON] et al. [7].  
\[g_{ij}=\omega_{1}f_{1}(t)+(1-\omega_{1})f_{2}(t) \tag{16}\]  
where \(g_{ij}\) represents the shifting bottleneck degree of station \(i\) in stage \(j,f_{1}(t)\) represents the effective working time function, \(f_{2}(t)\) represents the machine state function, and \(\omega_{1}\) represents the system influence weight coefficient.  
\[f_{1}(t)=\frac{a_{i}}{a_{\min}} \tag{17}\]  
where \(\mathrm{a}_{i}\) represents the duration of the effective state in station \(i\) and \(a_{\min}\) represents the minimum value of the duration of the effective state of all machines in a particular stage.  
\[a_{i}=\sum_{j=1}^{n}l_{ij}+\sum_{j=1}^{n}P_{ij}+R_{i}+M_{i} \tag{18}\]  
where \(l_{ij}\) represents the preparing time of the task(part) \(j\) on station \(i\), \(P_{ij}\) represents the processing time of the task(part) \(j\) on station \(i\), \(R_{i}\) represents the downtime of station \(i\), and \(M_{i}\) represents the time to replace the required tooling for station \(i\).  
Assume the set of effective working times for all machines can be denoted as:  
\[A=\{a_{1},a_{2},a_{3},\cdots,a_{n}\} \tag{19}\]  
\[a_{\min}=\min\{a_{1},a_{2},a_{3},\cdots,a_{n}\} \tag{20}\]  
\[f_{2}(t)=\frac{(B_{i-1}-B_{i})+(S_{i+1}-S_{i})}{Z_{i}} \tag{21}\]  
where \(B_{i}\) represents the blockage time of station \(i\), \(B_{i-1}\) represents the blockage time of the adjacent upstream station, \(S_{i}\) represents the starvation time of station \(i\), \(S_{i+1}\) represents the starvation time of the adjacent downstream station \(i+1\), and \(Z_{i}\) represents the length of the current time window.  
Assume the set of shifting bottleneck degrees for the \(i\)th machine can be denoted as:  
\[G_{i}=\{g_{i1},g_{i2},g_{i3},\cdots,g_{in}\} \tag{22}\]  
Figure 9: Example of subsystem bottleneck status.  
The DBI-BS method is validated in a complex system composed of a simulated discrete manufacturing shop. The workshop is equipped with a cyber-physical system (CPS) that can collect real-time production data and uses the simulation software AnyLogic to build a one-to-one simulation model according to the structure of the physical workshop.
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 3 EconLedger: Rationale and Architecture
### System Design Overview
#### 3.1.1 IoVT Application  
The upper-level IoVT application utilizes an EconLedger fabric to enable decentralized video analytic services and information visualization at the edge. All devices and users must be registered to join the IoVT system as required by the permissioned network, which can provide basic security primitives such as public key infrastructure (PKI), identity authentication [30], and access control [31], etc. Real-time video streams generated by cameras are transferred to on-site/near-site edge devices for lower level analytic tasks, such as object detection and situational contextual features extraction. Thus, cameras associated with edge devices act as IoVT service units at the network of edge. Then, IoVTservice units send raw video data and extracted contextual information to the information visualization unit, which provides video recordings and smart applications for authorized users.  
To prevent visual layer attacks, IoVT service extracts ENF signals from video streams as an environmental fingerprint, which is stored into DDB and secured by _EconLedger fabric_. At any given time instant, variation trends of ENF-containing multimedia signals from all synchronous cameras on the same power grid are almost identical. Therefore, using ENF fluctuations recorded on EconLedger laid solid ground truth for video authenticity verification. By calculating correlation coefficients among ENF signals extracted from video recordings with an agreed ENF estimate recorded on distributed ledger, the information visualization unit verifies whether or not live/offline video streams are generated by cameras within the same power grid [32; 33].
## 3 Methodology
### Multi-Channel Convolutional Neural Network Architecture
The training set image represented as (\(w_{1}\), \(x_{1}\), \(y_{1}\), \(z_{1}\)), (\(w_{2}\), \(x_{2}\), \(y_{2}\), \(z_{2}\)), (\(w_{3}\), \(x_{3}\), \(y_{3}\), \(z_{3}\)),..., (\(w_{n}\), \(x_{n}\), \(y_{n}\), \(z_{n}\)), including \(w_{i}\in R^{200\times 200}\), \(x_{i}\in R^{12}\), \(y_{i}\in R^{3}\), \(z_{i}\in R^{12}\). \(w_{i}\) is a third-order tensor with a size of 200 \(\times\) 200 for each image. \(x_{i}\) is a 12-dimensional binary vector, and each dimension corresponds to 12 categories of images, the 12 categories are listed in Table 2, including women's wear, onnaments, etc. Among them, others represents the item category that does not belong to the above 11 categories; \(y_{i}\) uses RGB color mode; \(z_{i}\) is a 12-dimensional binary vector, and each dimension corresponds to 12 styles of images, the 12 stylesare listed in Table 3, including simple, retro, etc. Among them, others represents the item styles that does not belong to the above 11 styles. The expression of \(x_{i}\) and \(z_{i}\) using one-hot encoding is shown in Tables 2 and 3. \(x_{i}\), \(y_{i}\) and \(z_{i}\) respectively, represent the category, color, and style of the image, such as when \(x_{m}\) = (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), \(y_{m}\) = (0, 0, 0) and \(z_{m}\) = (0, 0, 0, 1, 0, 0, 0, 0, 0, 0), the image \(w_{m}\) is a black Korean women's image. All images in the graphic library are classified according to category, color, and style using an MCNN network, and corresponding images in the database are marked. When a user uploads an interested item image, the MCNN determines the features of category, color, and style of the image \(A=(x_{i},y_{i},z_{i})\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & \(\mathbf{x_{i}}\) & **Category** & \(\mathbf{x_{i}}\) \\ \hline women’s wear & (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) & ornament & (0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0) \\ makeup & (0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) & bag & (0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0) \\ men’s wear & (0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0) & sport & (0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0) \\ appliance & (0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0) & furniture & (0, 0, 0, 0, 0, 1, 0, 0, 0) \\ mother-child & (0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0) & shoes & (0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0) \\ digital & (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0) & others & (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Category one-hot encoding form. Figure 3: The architecture of the MCNN.
## 3 Model and Methodology
### Path Risk Constraint  
When UAVs are operating in urban airspace, there is a chance of failure, which leads to the possibility of hitting people on the ground and may cause fatalities. In this way, for a planned UAV path, the risk imposed on people on the ground should be as low as possible, especially when a ground risk map is provided in advance. To guarantee the safety of ground people, the risk caused by the UAV's operation route should be quantized in the planning model first. As shown in Figure 4, there is a potential UAV path, which is expressed as a red line. This potential path would go through different risk grids on the ground, resulting in different risk levels. The size of each grid on the ground is \(1\) km \(\times\) 1 km, which is the same as described in Figure 1. In Figure 4, \(R_{i,j}\) means the risk value of each grid \(G_{i,j}\). It is supposed that these values can be determined and given by the existing risk estimation methods [30], which are considered to be used directly.  
In order to quantize the risk caused by the planned UAV path, a path box is defined in each risk grid where the UAV path goes through, as described in Figure 4. The path boxes are denoted as a number of red rectangles with dash lines. A path box covers the complete UAV path segment in a risk grid on the ground. As shown in the example in Figure 4, the total number of the path boxes is 9, which can be denoted as \(B_{k}\) (\(k\) = 1, 2,..., 9). Suppose the length of a UAV path segment in a path box \(B_{k}\) is denoted as \(L_{Bk}\) (\(k\) = 1, 2,..., 9). Actually, the accurate length of a path segment in a path box can be calculated easily once the UAV path is generated. Correspondingly, the risk of a path segment in each path box can be obtained by multiplying the length of a path segment and the risk of a specified path box. As a result, the total path risk constraint can be mathematically quantized using Equation (2) by adding the risk values of all the path segments in all the path boxes.  
\[J_{R}=\sum_{k=1}^{N}R_{Bk}\cdot L_{Bk} \tag{2}\]  
In Equation (2), \(J_{R}\) is the quantized risk constraint, \(R_{Bk}\) is the grid risk value in which a path box \(B_{k}\) is located, \(L_{Bk}\) is the length of a path segment divided by a path box \(B_{k}\), and \(N\) is the number of path boxes on the whole UAV path.  
Figure 4: Quantization of path risk constraint.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
###### Abstract  
This study aims to enhance elderly fall detection systems by using the YOLO (You Only Look Once) object detection algorithm with pose estimation, improving both accuracy and efficiency. Utilizing YOLOv7-W6-Pose's robust real-time object detection and pose estimation capabilities, the proposed system can effectively identify falls in video feeds by using a webcam and process them in real-time on a high-performance computer equipped with a GPU to accelerate object detection and pose estimation algorithms. YOLO's single-stage detection mechanism enables quick processing and analysis of video frames, while pose estimation refines this process by analyzing body positions and movements to accurately distinguish falls from other activities. Initial validation was conducted using several free videos sourced online, depicting various types of falls. To ensure real-time applicability, additional tests were conducted with videos recorded live using a webcam, simulating dynamic and unpredictable conditions. The experimental results demonstrate significant advancements in detection accuracy and robustness compared to traditional methods. Furthermore, the approach ensures data privacy by processing only skeletal points derived from pose estimation, with no personal data stored. This approach, integrated into the NeuroPredict platform developed by our team, advances fall detection technology, supporting better care and safety for older adults.  
vision-based fall detection; YOLOv7; pose estimation; elderly; real-time alert; privacy-preserving +
Footnote †: journal: Future Internet
## 5 Case Analysis
### Result Analysis  
Using the data in Table 1, simultaneously, Equations (9) and (21) calculate the theoretical water inflow to be 2.96 m\({}^{3}\)/d/m, and the measured water inflow to be 3.41 m\({}^{3}\)/d/m, with an error of 13.2%. If the influence of excavation disturbance and ground stress on the permeability coefficient of surrounding rock is not considered, the calculated theoretical water inflow is 2.48 m\({}^{3}\)/d/m, and the error from the actual measurement is 27.3%. That is to say, considering the influence of ground stress and excavation disturbance on the permeability coefficient of surrounding rock, the calculation error of water inflow can be reduced from 27.3% to 13.2%, which improves the accuracy of water inflow prediction and it verifies the rationality of the model constructed in this article and the correctness of the formula derivation to a certain extent. The prediction error is mainly caused by the approximate replacement of the equivalent circle and the actual seepage near the tunnel is a two-dimensional flow, which is caused by ignoring the vertical seepage in the calculation.
## 2 GTLR-BiSAR Imaging Geometry and Signal Properties
### GTLR-BiSAR Imaging Geometry of Sliding Spotlight and TOPS Modes  
As shown in Figure 1, the coordinate frame \(O\)-\(XYZ\) is utilized to explicate the geometry of the GTLR-BiSAR system. We assume the \(X\)-axis is the range direction of the illuminated scene. The \(Y\)-axis is the nadir of the LEO satellite tract, which is parallel to the flight direction. We assume it as the azimuth direction. The \(Z\)-axis is determined by the right-hand Cartesian coordinate system, which is vertical to the illuminated scene plane and points to the Earth center inversely. It can be used to express the orbit height of the LEO satellite. The coordinate origin \(O\) is the starting nadir point of the LEO satellite.  
The GEO transmitter is geostationary with the capability of monitoring the earth continuously. The main beam of the LEO antenna is steered to point to a fixed point called virtual rotation center, Figure 1a describes the SS mode of the LEO receiver, which is able to achieve high spatial resolution by extending the synthetic aperture time. Figure 1b describes the TOPS mode of the LEO receiver, which is able to provide excellent coverage performance by increasing the beam forward and backward steering angle. The black dashed lines \(L_{1}\) and \(L_{2}\) are parallel to the \(Y\)-axis, they are the far and near edge lines of the illuminated scene, respectively. The red dashed line \(L_{3}\) is the center line of the swath. The red cross stars \(T_{1}\), \(T_{2}\) and \(T_{3}\) scatter along \(L_{3}\) (\(T_{3}\) is half red and half green because it is the intersection of \(L_{3}\) and \(L_{4}\)). These targets along \(L_{3}\) are characterized with the same Doppler FM rate because of the minimal range from these targets to the moving station, i.e., the LEO receiver, are all the same [23]. However, the bistatic ranges of these targets are different, their echoes are received in different range gates. The green cross stars \(T_{3}\), \(T_{4}\) and \(T_{5}\) scatter along \(L_{4}\). These targets are characterized with the same bistatic range (at a certain time). However, the FM rates of these targets confined in the same range gate differ from each other because their minimal ranges to the LEO receiver are different [23]. That is the main reason why the traditional algorithm fails to obtain a well-focused image, which is generally called the spatial variance problem in the BiSAR system. In Section 4, we will describe how to correct the spatial variance problem in detail.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 5 Conclusions  
The method with EEMD-based KICA has been proposed to improve the efficiency of fault-feature extraction of a single-channel vibration signal of rotating machinery in this paper. Its advantage is that it can estimate the number of fault signals of a single-channel vibration signal and extract the fault features clearly. This method only needs to input a single-channel signal, and the outputs are fault signals and number, without artificial selection. Through the experiment of faulty gear, a faulty rolling bearing and a faulty shaft, the results show that the proposed method makes the fault features more clear compared with wavelets, EMD, VMD and EEMD, which demonstrates its effectiveness for fault-feature extraction of rotating machinery.  
Because this paper mainly studies the effectiveness of the new proposed method, mainly for offline signals, future studies will conduct online signal research, which requires attention to the problem of time consumption. Finally, this fault diagnosis method of rotating machinery is worth being evaluated with large industrial equipment.  
Conceptualization, H.S. and L.F.; Methodology, H.S.; Software, L.F.; Resources, H.S.; Data Curation, L.F.; Writing-Original Draft Preparation, L.F.; Writing-Review & Editing, H.S.  
This research was funded by the Natural Science Foundation of China grant number U1708255.  
The authors would like to acknowledge the Natural Science Foundation of China \"Research on reliability theory and method of total fatigue life for large complex mechanical structures\".  
The authors declare no conflict of interest.
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 5 Technical Challenges
### User Identification and Secure Communication  
Each user is identified by the credentials stored in and used by their device. To identify each device and verify that it is the source of a message asymmetric cryptography is usually being used. Each physical user has their credentials (i.e., an email or a username) that are used by their device as their identity along with one or more private/public key pairs associated with it. The device signs each message with its private key, producing a digital signature. The signature of a message m is the hash of the message encrypted using the private key. This signature can be verified later with the user's public key. In our protocol, we use SHA-256 as our hashing function h() and the RSA asymmetric cryptosystem with a key-size of 1024 bits for signatures.  
Figure 3: A simple hash chain, created by successively applying a hash function.  
In most proof of location protocols, the entity responsible for managing the entities that participate in the system is the certificate authority (CA), who can also perform signature verification. Other protocols like [3; 18] use a blockchain. The identity of the devices and the validity of the information that they submit is checked by distributed nodes that come to a consensus. It is also possible that each node self-signs their messages. This means that there is no CA responsible for associating its keys with its identity and performing signature validation [14]. The other nodes with which it communicates decide whether they will trust it or not.  
The asymmetric cryptosystem is suitable for signature verification, as only the hash of the message has to be encrypted. However, it has a limit in the amount of data it can encrypt, making it unsuitable for longer message encryption [25]. Thus, for the secure encrypted communication between the nodes, we use symmetric key encryption. The sender A generates a random symmetric key k and encrypts the message m with that, producing the encrypted message k(m). It also encrypts k with his private key p\({}_{\text{A}}\), producing p\({}_{\text{A}}\)(k). It sends the two encrypted information k(m)\(|\)p\({}_{\text{A}}\)(k) to the receiver B. The receiver decrypts the symmetric key k using [PERSON]'s public key P\({}_{\text{A}}\). Finally, it uses k to decrypt the message m. For symmetric encryption, we use the AES algorithm.
# Magnetic Levitation Belt Conveyor Control System Based on Multi-Sensor Fusion
## 3 Method
### Extended Kalman Filtering Principle  
The Kalman filter is an optimized autoregressive data processing method. It can estimate the next state according to the estimated value of the previous state under the action of the system noise, measured value, and measured noise. It can then obtain the best estimated value of the next state. Since the Kalman filter is only applicable to linear systems and cannot directly solve the estimation problem of nonlinear systems, the extended Kalman filter [29] was adopted for this system. First, a Taylor series was used to linearize the system, and then the discrete Kalman filter algorithm was used to estimate the displacement state.  
The system model is expressed in the following general form of the state procedure and the observation equation of a nonlinear discrete system:  
\[\left\{\begin{aligned} x_{k}&=f(x_{k-1},u_{k-1}, \omega_{k-1})\\ z_{k}&=h(x_{k},\
u_{k})\end{aligned}\right. \tag{12}\]  
where \(x_{k}\) is the state quantity, \(z_{k}\) is the measurement value, \(u_{k}\) is the input vector, \(\omega_{k}\) is the system process white noise obeying the Gaussian distribution, \(\
u_{k}\) is the system measurement white noise obeying the Gaussian distribution, and \(Q\) and \(R\) are their respective covariance matrices. Their probability distributions are \(P(w)\in N(0\),\(Q)\) and \(P(v)\in N(0\),\(R)\).  
The specific steps of extended Kalman filter estimation are as follows:  
\[\left\{\begin{aligned} \hat{x}_{k}&=f(\hat{x}_{k-1},u_{k-1},0)\\ P^{-}&=A\hat{P}_{k-1}A^{T}+WQ_{k}W^{T}\\ K_{k}&=P_{k}^{-}H^{T}\big{[}HP_{k}^{-}H^{T}+VRV^{T} \big{]}^{-1}\\ \hat{x}_{k}&=\hat{x}_{k}^{-}+K_{k}[z_{k}-h(\hat{x}_{ k},0)]\\ P_{k}&=(I-K_{k}H)P_{k}^{-}\end{aligned}\right. \tag{13}\]  
where \(A\) is the state transition matrix after partial derivation, and \(H\) is the measurement matrix after partial derivation.  
Figure 6: Calibration method.
## Abbreviations  
AOI or ROI spatial Area Of Interest or Region Of Interest  
ASL Above Sea Level  
CHM Canopy Height Model  
CCD Charge-Coupled Device  
DBH Diameter was measured at Breast Height  
DN Digital Number  
DTM Digital Terrain Model  
EGM96 Earth Gravitational Model 1996  
FOV Field Of View  
FLEX FLUorescence EXplorer  
FLEX-US Collaborative ESA/FLEX and NASA/GSFC airborne campaign in the USA  
GEP Gross Ecosystem Production  
G-LiHT Goddard's Lidar, Hyperspectral, and Thermal airborne system  
GSFC NASA Goddard Space Flight Center  
GPP Gross Primary Production  
GPS Geographic Positioning System  
HyspIRI Hyperspectral InfraRed Imager  
INS Inertial Navigation System  
LAI Leaf Area Index  
LaRC NASA Langley Research Center  
LiDAR Light Detection And Ranging  
LP Loblolly Pine  
LUE Light Use Efficiency  
LWIR LongWave InfraRed  
MERIS Medium Resolution Imaging Spectrometer  
MIR Middle InfraRed  
MTCI MERIS Terrestrial Chlorophyll Index  
NC North Carolina (USA)  
NDVI Normalized Difference Vegetation Index  
NEP Net Ecosystem Production and ecosystem respiration  
NIR Near InfraRed  
NIST US National Institute of Standards and Technology  
NETD Noise Equivalent Temperature Difference  
PARi incident Photosynthetically Active Radiation  
PRI Photochemical Reflectance Index  
RGB Red Green Blue  
ROI Region Of Interest  
SEM Standard Error of the Mean  
SD Standard Deviation  
SIF Solar-Induced chlorophyll Fluorescence  
SNR Signal to Noise Ratio  
SV Singular Vector Decomposition method  
SWIR ShortWave InfraRed  
SZA Solar Zenith Angle  
TChl Total canopy Chlorophyll  
TIN Triangulated Irregular Network  
VDR Vertical Distribution Ratio (VDR)  
VNIR Visible to Near InfraRed  
VSWIR Visible to ShortWave InfraRed