esa-sceva/satcom-chunk-collection huggingface dataset:
# Dark-Center Based Insulator Detection Method in Foggy Environment
## 2 Construction of Foggy Insulator Datasets CPILDs  
Since there is no publicly available insulator dataset for foggy environments, this paper needs to construct a foggy insulator target detection dataset first, called CPILDs.  
In the non-sky region of most color images, the image pixels are always low in one or more RGB channels, which is called the dark channel prior, as shown in Equation (1).  
\[J^{dark}(x)=\min_{y\in\Omega(x)}\left(\min_{c\in\{r,g,b\}}J^{c}(y)\right)\to 0 \tag{1}\]  
where \(J^{c}\) denotes each channel of the color image, \(J^{dark}\) denotes the output grayscale image, and \(\Omega(x)\) denotes the pixel-centered filter window. The minimum values of the RGB component of each pixel were found and stored in a grayscale map of the same size as the original image. Then, a minimum value filter is applied to the grayscale image.  
However, white fog would make the minimum values of its three channels larger. Defogging the image can make the value smaller and the image clearer. Therefore, a foggy image generation model is proposed as follows:  
\[I(x)=J(x)t(x)+L(1-t(x)) \tag{2}\]  
where \(J(x)\) is the original fog-free image, \(L\) is the brightness of the atmospheric light component, and \(t(x)\) is the transmittance. The value of luminance \(L\) ranges from 0 to 1, indicating the color of the added fog, and the larger the luminance \(L\) is, the whiter the fog is. The transmittance \(t(x)\) is within the range [0, 1], indicating the ratio of the original image and the fog in the output image. An appropriate transmittance \(t(x)\) needs to be set for each pixel in the image as Equation (3).  
\[t(x)=\exp\left[-D\left(-0.04\sqrt{\left(w-w_{c}\right)^{2}+\left(h-h_{c}\right) ^{2}}+s\right)\right] \tag{3}\]  
where \(\left(w_{c},h_{c}\right)\) is the image center, which is chosen as the fogging center. \(s\) is the fogging size, which is the square root of the maximum value of the image width and height. \(D\) is the fog thickness coefficient. From Equation (3), it can be seen that the larger \(D\) is and the smaller \(t(x)\) is, the heavier the fog is and the smaller the light transmission is. Therefore, various fog environments can be simulated by setting different parameters of brightness \(L\) and the fog thickness coefficient.  
As mentioned above, the CPILD public dataset is constructed by setting the brightness \(L\) between [0.7, 1.0] and the random fog thickness coefficient \(D\). The CPILDs dataset contains 8480 fogged images and clear images, with two types of target detection label information. We divided training sets and test sets to 9:1 without additional validation sets. To further ensure randomness, the training and test sets were halved for the experiments, and a total of 4240 images were finally obtained, 3816 in the training set and 424 in the test set. A part of the generated foggy insulator images is shown in Figure 1.
## Appendix A Full-State EKF Model  
Using the full-state IMU equations as the system propagation equation, the state vector includes the attitude, velocity, position, inertial sensor biases, and GNSS receiver clock offset and drift for the full-state EKF. The EKF is described as a system propagation equation (Equation (A1)) and a measurement update equation (Equation (A2)). In the system state propagation step, the IMU measurements of the specific force \(\overset{\rightarrow}{f}_{ib,meas}\) and angular rate \(\overset{\rightarrow}{\omega}_{ib,meas}^{b}\) are considered as the control input (All the nomenclatures can be seen in Appendix D). In the measurement update step, the GNSS pseudo-range and pseudo-range rate measurements are used for the measurement update:  
\[\hat{X}_{k}^{-}=\Phi_{k}\hat{X}_{k-1}+\Gamma_{uk}\overset{\rightarrow}{u}_{ k}+\Gamma_{uk}\overset{\rightarrow}{w}_{k}\] (A1)  
\[\hat{X}_{k}=\hat{X}_{k}^{-}+K_{k}\bigg{(}\overset{\rightarrow}{Z}_{k}-H_{k} \hat{X}_{k}^{-}\bigg{)}\] (A2)  
where \(\hat{X}_{k}^{-}\) is the predicted state vector at epoch k, \(\hat{X}_{k}\) is the measurement updated state vector at epoch k, \(\Phi_{k}\) is the dynamic state transition matrix at epoch k, \(\Gamma_{uk}\) is the input control-driven matrix at epoch k, \(\Gamma_{uk}\) is the process noise matrix at epoch k, \(\overset{\rightarrow}{u}_{k}\) is the control input vector of the IMU measurements of the specific force and angular rate, \(\overset{\rightarrow}{w}_{k}\) is the process noise vector at epoch k, \(K_{k}\) is the Kalman gain at epoch k, \(\overrightarrow{Z}_{k}\) is the estimate of the measurement produced by the states estimated by the Kalman filter, and \(H_{k}\) is the measurement matrix at epoch k.
# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
###### Abstract
Furthermore, the recommendation system based on matrix factorization suffers from the limitation of the simple linear inner product. In addition, during the inner product of traditional matrix factorization, the results of all dimensions are accumulated with the same weight, which could be seen as the connection weights all being 1, to get the final scalar result. Both of these make it non-effective to model the complex non-linear relationships between the user and the item. To solve these ends, [PERSON] et al., proposed the generalized matrix factorization (GMF) [7], which utilizes the neural network to endow MF with non-linear learning capability through the activation function and combines the values of different dimensions with different weights by introducing weight matrix. GMF is empowered with the ability to model the second-order non-linear interaction between users and items. The expression ability of the model gets significant improvement. Despite all these advantages, it is not effective for GMF to capture the high-order interaction between users and items that contains richer information because of the shallow network structure, which may limit the performance of the recommendation system. Therefore, hidden layers are introduced on the second-order interaction obtained by the non-linear element-wise product in IFDNAMF, by the deep neural network, we could model the complex non-linear high-order interaction between the user and the item. The main contributions of our proposed approach can be summarized as follows. 1. We first propose a new recommendation model, the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, in which we introduce the auxiliary information to assist the model in describing the user features and item features more comprehensively and specifically;
2. Then, we propose a new method of information fusion, in which the inner product between the different information domains is adopted to learn the cross-features,so that the IFDNAMF could obtain the \"and\" relationship containing the higher-value information between the auxiliary information. Meanwhile, to distinguish the importance of diverse cross-features on recommendation results, we also introduce the attention mechanism to learn the weights of different cross-features;
3. Finally, we conduct extensive experiments on two datasets: MovieLens and Book-crossing. The experimental results demonstrate the outstanding performance of IFDNAMF.
# Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
## 2 Development of the Algorithm
### YOLOv7 Algorithm  
#### 2.3.1 Technical Details  
Figure 4 illustrates the technical methods employed to ensure the efficient and precise identification of trolley codes during regular operational circumstances. The dataset underwent initial manual annotation using Labellmg, followed by training the YOLOv7 network to recognize trolley codes. The YOLOv7 model underwent a channel pruning algorithm to eliminate redundant channels and weights of its parameters. This process aimed to simplify the model's structure and reduce its parameter count, while preserving its accuracy.  
Figure 3: An image extracted from the dataset of workpieces featuring small and large holes is presented in Figure (**a**). The image showcases the annotated results of the small and large holes on sample 10111. (**b**) presents the identification and coordinates of the small and large holes as indicated in (**a**).
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 2 Related Work
A case study is also analyzed to illustrate the minimal measures that organizations require to build network security [33]. This study presents a hybrid shorthand approach that combines the LSB steganography technique with the RSA and Caesar Cipher encryption algorithms. This combination aims to increase the security of protected information and ensure the confidentiality of secret communications. The effectiveness of the proposed scheme has been demonstrated through experimental results, which have shown that the quality of the cover image remains intact and is indistinguishable from the original image while providing a high level of security [34]. As a result of the tremendous advances in digital communications and media, 3D models have become the core of various applications in diverse fields. Consequently, these models can be utilized in steganography, acting as secure and innocent host media for sheltering covert data. In this paper, the Blowfish encryption algorithm is performed on confidential data for higher security [28; 35; 36; 37; 38]. The work described in the related works fails to include the benefits of LSB and AES advantage. Therefore, we combined and developed a hybrid approach.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 2 Materials and Methods
### Hatch Filter  
While the pseudorange measurements are unambiguous but imprecise, the carrier phase measurement is very precise but ambiguous. One method to apply carrier phase measurements without explicitly solving the integer ambiguity problem is to use those measurements to smooth the pseudorange measurements--so-called Carrier-Smoothed Code (CSC) techniques. One of these techniques is the Hatch Filter.  
The time-differencing of the carrier phase measurement, in its error-free model, is given by [19]  
\[\Phi_{r}^{s}(n)-\Phi_{r}^{s}(n-1)=r_{n}-r_{n-1}+\lambda N_{r}^{s}-\lambda N_{r }^{s}=r_{n}-r_{n-1} \tag{25}\]  
where, by means of differencing, the integer ambiguity is cancelled out. Then, the weighting factors \(W(n)\) for the Hatch Filter are given by  
\[W(n)=W(n-1)-\gamma \tag{26}\]  
The weighting factor must be initialized in its first iteration, which can be accomplished by using the pseudorange measurement in this iteration, corresponding to making \(W(1)=1\). Finally, the Hatch Filter equation is  
\[\rho_{s,k}=W(n)\rho_{k}+(1-W(n))(\rho_{s,k-1}+\Phi_{k}-\Phi_{k-1}) \tag{27}\]  
where \(\gamma\) is the averaging constant and defines the averaging interval for the filter. The averaging constant is usually set as 0.01 or 0.02 for smoothing intervals of 100 or 50 s respectively, for measurements at the rate of 1 Hz. It should be noted that, since this filter uses the time-differencing of the carrier phase measurements, it must be reset whenever a cycle slip occurs to account for changes in the integer ambiguity.
## Materials and Methods
### Remote Terminal Unit  
The orientation of the heliostat is controlled by an RTU based on an MCU. The block diagram of the RTU is shown in Figure 5. The RTU consists of an MCU, an RTC, an XBee RF module, a Bluetooth radio module, an analog thumb joystick, a \(20\times 4\) liquid crystal display (LCD), two motor driver circuits for the two DC motors, two rotary encoders, and a programming port ICSP (In-Circuit Serial Programming).
# Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption
## 2 Literature Review
[18] presented an electric vehicle routing problem with time windows and random waiting times at charging stations, and proposed a two-stage simulation-based heuristic algorithm based on adaptive large neighborhood search (ALNS). The results show that uncertainty in waiting times at charging stations can have a significant impact on route plans. [PERSON] et al. [19] compared various EVs in terms of battery capacity, unit energy consumption rate, and load capacity, and then built an EVRP model for routing multiple types of EVs. [PERSON] [20] considered the EVRP with nonlinear charging time, aiming to minimize the total travel and charging times without approximation of the charging time function. However, most of the existing research on EVRP has been conducted under a number of assumptions, such as: (1) an EV travels at a constant speed, and (2) the energy consumption process of an EV is linearly related to its traveled distance. Few researchers have considered a dynamic speed or a nonlinear energy consumption process when EVs are in operation. [PERSON] and [PERSON] [21] established an EVRP based on a nonlinear energy consumption model that incorporates speed, gradient and cargo load, to optimize multiple objectives of distance, labor, and energy. However, the vehicle speed remains the same between any two nodes. [PERSON] et al. [22] also proposed a nonlinear relationship between speed and energy per unit distance, and built an EVRP under the condition of speed changing over time. [PERSON] and [PERSON] [23] proposed a nonlinear charging and load-dependent discharging EVRP that allows multiple visits to charge stations without duplicating nodes. [PERSON] et al. [24] developed a new comprehensive model of EVRP that considers an electric energy consumption function considering energy losses, a nonlinear charging function technique with piecewise linearization, efficient visits to charging stations, and continuous decision variables for speed, payload, travel time, and recharging. Therefore, to address the gaps in the above work, in this paper, EVs' typical driving cycles in operation are constructed, and a nonlinear electric energy consumption model based on the driving cycles of suburban and urban areas is developed, with consideration of speed, acceleration, and load. These electric energy consumption rules are then incorporated in a new EVRP with customer time windows, which aims to provide a more realistic routing scheme for EVs operating under real-life conditions.
## 2 Determination of Vertical Vibration Load from a Train
### Simulation of Vertical Vibration Load from a Train  
A single carriage of a train is equipped with two bogies and four wheelsets [19]. The mass of a train body is distributed symmetrically from front to back and from left to right, so a side of a single bogie can be selected for analysis. As shown in Figure 1, a train was simplified as a model with a single degree of freedom composed of mass, spring, and damping elements. In Figure 1, \(m_{1}\) is 1/4 of the total mass of a carriage and its accessory, \(m_{2}\) is the mass of a wheelset, \(u_{1}\) and \(u_{2}\) are the vertical vibration displacements of the carriage and the wheelset, respectively, \(k\) and \(c\) are the suspension stiffness and suspension damping, respectively, and \(P(t)\) is the interaction force between wheel and rail. \(m_{1}\) and \(m_{2}\) take values of 6755 kg and 1420 kg, respectively.  
As shown in Equation (5), an equation of motion balance of vertical wheelset was established according to Figure 1.  
\[k(u_{1}-u_{2})+c\big{(}\dot{u}_{1}-\dot{u}_{2}\big{)}+m_{1}\ddot{u}_{1}=0 \tag{5}\]  
Let the relative displacement of a carriage and a wheelset be \(u_{r}\) which is equal to \(u_{1}-u_{2}\). Equation (5) can be rewritten as Equation (6).  
\[ku_{r}+ci\dot{u}_{r}+m_{1}\ddot{u}_{r}=-m_{1}\ddot{u}_{2} \tag{6}\]  
Figure 1: A simplified model of the vertical vibration of a train [19].  
Neglecting the vertical bounce between wheel and rail, the vertical vibration acceleration of the wheelset, \(\tilde{u}_{2}\), and the vertical vibration acceleration of rail, \(a(t)\), can be regarded as equal. Thus, Equation (6) can be rewritten as Equation (7).  
\[ku_{r}+ci\dot{u}_{r}+m_{1}\tilde{u}_{r}=-m_{1}[\sum_{n=0}^{\frac{N}{2}-1}\left(C _{n}cos\ nwt+D_{n}sin\ nwt\right)] \tag{7}\]  
On the basis of Equation (7), \(\tilde{u}_{r}\) can be solved.  
According to D'Alembert's principle, the interaction force \(P(t)\) between the wheels and rails of a train can be calculated according to Equation (8).  
\[P(t)=m_{1}g+m_{2}g+m_{2}\tilde{u}_{2}+m_{1}\left(\tilde{u}_{2}+\tilde{u}_{r}\right) \tag{8}\]  
In the straight section of the entrance and exit lines in the throat area of a metro depot, the load of vertical vibration from a train can be calculated according to the method above, and calculation results are shown in Figure 2.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
## 3 Dataset and Processing
### GNSS Data Processing  
The integrated satellite positioning service system of Yunnan province is put into network operation at the end of 2015, and there are 220 base stations in uniform distribution. As shown in Figure 3, 8 CORS stations in Kunming city are selected, the data sampling interval is 30 s, and 5 IGS stations in China and its surrounding areas are combined, namely BJFS, CHAN, CUSV, HYDE and ARTU. The aim is to introduce the IGS station coordinates and frame into the baseline solution process and reduce the calculation error caused by the inaccurate station coordinates. GAMIT/GLOBK software developed by Massachusetts Institute of Technology is used for baseline solution and adjustment. Due to the lack of data of CORS related sites in 2018, only the single-day solution time series of CORS stations in Kunming city during January 2019-December 2020 and the accurate three-dimensional coordinates under the ITRF2014 framework is obtained.  
In the vertical time series of each station, although high-precision data processing software is adopted, there is noise pollution in both the collection and mathematical modeling of the original data. In order to better eliminate noise pollution and reduce errors, the nonlinear low-frequency reconstruction of the original time series obtained from the station is carried out. The Chebyshev function is used as the basis function to construct a low-frequency curve for each observation time series, and then the curve obtained in  
Figure 4: (**a**) GACOS data corresponding to the main image; (**b**) Standard deviation of atmospheric delay phase.  
the above step is the baseline for gross error detection. The threshold value of gross error detection and elimination is set as 3 times the standard deviation to obtain the time series after gross error detection and elimination, and then linear term separation and nonlinear term low-frequency reconstruction were carried out. The time series of vertical deformation of CORS station is obtained by adding the nonlinear term, constant term and linear term after low-frequency reconstruction. Limited by space, only 3 CORS station time series are shown in this paper, as shown in Figure 5. Red represents the original time series, blue represents the low-frequency reconstruction terms, black represents linear changes, and green represents residuals. It can be seen from the figure that the time series presents certain rules in the same time period.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## 3 Simulation Results and Analysis
### Optimal Length of a Time Block in the BTB-TDMA  
The performance of the BTB-TDMA is dependent on the length of time block. For fair comparison, it is necessary to find the optimal length of time block to minimize the collision rate (\(CR\)) and to concurrently optimize the \(CD\) and the \(CU\) of the BTB-TDMA. The \(CR\) is defined as the ratio of the total number of transmitted data packets to the number of collided packets. Figure 7 shows the \(CR\), the \(CD\), and the \(CU\) of the BTB-TDMA according to the change of \(N\). The horizontal axis is the length of time block, and \(R\) and \(Vmax\) are fixed at 1500 m and 5 m/s, respectively. In order to show how the \(CD\) and the \(CU\) of the BTB-TDMA are influenced by the collision due to the asymmetric propagation delay, we show the two cases of the BTB-TDMA in Figure 7:  
1. The asymmetric propagation delay is considered in the BTB-TDMA1.
2. The BTB-TDMA2 is the original BTB-TDMA that assumes symmetric propagation delay.  
The \(CR\), the \(CD\), and the \(CU\) of the BTB-TDMA1 and the BTB-TDMA2 are referred to as \(CR_{BTB1}\), \(CD_{BTB1}\), \(CU_{BTB1}\), \(CR_{BTB2}\), \(CD_{BTB2}\), and \(CU_{BTB2}\), respectively. It can be seen from Figure 7a that \(CR_{BTB1}\) decreases as \(\tau_{b}\) increases, because larger \(\tau_{b}\) leads to greater guard time in a time slot, and that the slope of \(CR_{BTB1}\) is decreased as \(\tau_{b}\) increases. Therefore, the \(CD_{BTB1}\) and the \(CU_{BTB1}\) are improved by the increase of \(\tau_{b}\) because of the reduced collision. However, if \(\tau_{b}\) is larger than a certain value that is optimum, the loss due to the increased idle time becomes larger than the advantage of collision avoidance, and thus, the \(CD_{BTB1}\) and the \(CU_{BTB1}\) are degraded, as shown in Figure 7b,c. The optimum \(\tau_{b}\) is dependent on network conditions such as \(N\), \(R\), and \(Vmax\). In the following sub-sections, we apply optimum \(\tau_{b}\) suitable for a specific network condition. In Figure 7b,c, the \(CU_{BTB1}\) is smaller than the \(CU_{BTB2}\), and the \(CD_{BTB1}\) is greater than the \(CD_{BTB2}\), which shows that the \(CU\) and the \(CD\) of the BTB-TDMA are degraded due to the collision induced by the asymmetric propagation delay. These performance degradations of the BTB-TDMA are increased as \(N\) increases.  
Figure 6: Performance degradation by the transmission order in which 10 nodes move at the maximum speed of 7 m/s.
## 2 Materials and Methods
### Data Collection  
#### 2.3.1 Clinical Markers of Muscle Mass and Strength  
Body mass was measured to the nearest 0.1 kg using an electronic scale (Tanita electronic scale BWB-800 MA, Tokyo, Japan). Stature was measured to the nearest 0.005 m using a Harpenden stadiometer (Holtain Ltd., Crumych, Pembs, UK).  
Total body composition was assessed using a DXA scan on a QDR Explorer fan-beam densitometer (Hologic Inc., Horizon C DXA System, Bedford, MA, USA). Quality control of the DXA scan was performed daily with an encapsulated spine phantom (Hologic Inc., PDA/QDR-1, Bedford, MA, USA) to check for possible baseline drifts. Prior to scanning, participants were asked to empty their bladder, wear underwear only, and remove any metallic objects and reflective materials. The total-body DXA scan lasted about 7 min and was carried out and analyzed by the same trained technician (to ensure consistency) in accordance with \"The Best Practice Protocol for the assessment of whole-body composition by DXA\" [17].  
Isometric grip strength test of the dominant hand was conducted by using the Jamar hand dynamometer (Model 5030 J1, Sammons Preston Rolyan, Bolingbrook, IL, USA). The protocol was designed following the proposal of the American Society of Hand Therapists [18]. Participants were positioned in a sitting position, with their shoulder adducted and neutrally rotated, elbow flexed to 90 degrees, forearm mid-prone, and wrist between 15 and 30\({}^{\circ}\) of dorsiflexion and 0-15\({}^{\circ}\) of ulnar deviation. The instrument had been placed in the hand with the handle placed in the second position. The operator was positioned in front of the subject to set the peak needle to zero. All subjects performed 3 trials squeezing as hard as possible. Each trial consisted of at least 3 seconds of isometric contraction, with 30 seconds of recovery between trials [18]. The operator read the grip strength measure and recorded the result to the nearest 1 kg. Before testing, familiarization with 2 trials of submaximal effort was conducted.