esa-sceva/satcom-chunk-collection huggingface dataset:
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 5 Experimental Results
### Path Optimization Based on Passenger-Carrying Probability  
#### 5.4.1 Description of the Two-Stage Passenger-Seeking Problem  
In reality, taxi drivers begin searching for passengers from their drop-off points, choosing an area (hotspot area) where they believe they are most likely to find passengers as their destination based on their experience. They also select road segments where they are more likely to find passengers. If they pick up a passenger on the way to the destination, it is considered a successful search, as illustrated in Figure 13. This study abstracts this problem into an undirected network path problem from the starting point to the destination. The areas around the starting and ending points are large drop-off and pick-up hotspot areas, respectively, and the path should pass through various smaller pick-up hotspot areas as much as possible. Unlike traditional passenger search path optimization models, the model constructed in this study stops the search as soon as the driver finds a passenger on the path without necessarily reaching the destination. Therefore, it fundamentally aims to optimize the path to increase the probability of finding passengers.  
This study subdivides the taxi driver's passenger search process into two states: the first state is the search process from the drop-off area to the first passenger search area; the second state is the process from the first passenger search area to the second passenger search area [39; 40]. When taxi drivers begin their search for passengers, they first need to identify several passenger hotspot areas that are relatively close to them during the current time period. Then, they rank these areas according to their preferences and proceed to each area in sequence or search for passengers while en route.
## 4 Discussion  
The focus of the study presented in this paper was to determine the optimal combination of key parameters affecting the working performance of the combined fertilizer application device of the orchard stable fertilizer deep applicator. The motion of the scraper and the force of the spiral blade in the process of fertilization were analyzed. In addition, the influence of key experimental factors on the comprehensive qualified rate of fertilization was analyzed through simulation tests. Finally, field experiments were carried out to examine the accuracy of the simulation tests. Although a variety of manure fertilization devices have been studied at the national level, there are still some problems in the research of key components of manure fertilization devices for specific working environments in orchards. The combined fertilization device proposed in this paper has a comprehensive qualified rate of more than 90%, which can meet the requirements of orchard fertilization.  
In the process of verification test, the fertilizer arch phenomenon appeared. The author analyzed the influence of the motion parameters and structural parameters of the combined fertilizer application device on the performance of the fertilizer application device, but without considering the effect of fertilizer moisture content and fertilizer tank structure on the performance of the fertilizer application device, which needs to be analyzed in subsequent research.  
Compared with the fertilizer application device currently in existence, the proposed combined fertilizer application device has the advantages of adapting to a specific orchard environment, and the comprehensive qualified rate of fertilization reaches more than 90%. However, compared with the single structure of the fertilization device, the power consumption was increased. As a result, the power consumption of the fertilization device must be studied and analyzed in subsequent research to further enhance the working efficiency and minimize the power consumption.
## 3 Experimental Results
Although the measurement capability of our multi-angle stitching DHM has been demonstrated by the above experimental result, the precision of system is restricted by the freedom stage with manual operation. If a more advanced motion stage can be used in the measurement system, this system can be further enhanced. What is more, the acquisition speed of system can be further improved by some spatial multiplexing method [19; 20] which can achieve holograms efficiently.
## 3 Average PER in Turbulent Channels  
### Channel Model  
Several applications of OWC systems, including terrestrial, ground-to-space, and underwater high-speed connections, require the transmission of the optical beams through long lengths of turbulent media (i.e., the atmosphere or water volumes). This transmission introduces fluctuations in the received OSNR \(\lambda\), which negatively affect the OWC system performance. Since the media response is stochastic, \(\lambda\) follows the channel fluctuations and becomes a RV that is distributed according to their intensity (weak, moderate, or strong). The system performance is then summarized by an average PER, which is calculated from the instantaneous PER, presented earlier, and the distribution function of the OSNR fluctuations \(f_{\lambda}(y)\), following  
\[\overline{P}_{e}=\int\limits_{y}P_{e}\left(k,y\right)\,f_{\lambda}(y)\,dy\,. \tag{11}\]  
The impact of turbulence can be partially alleviated by introducing multiple receivers that are spatially separated by a few cm and operate in parallel, as shown in Figure 1. The electrical signals from all \(\ell=1,\ldots,L\) receivers are added prior to detection in a simple EGC architecture, which achieves almost-optimal signal combining in \(\chi^{2}\) statistics [14]. The sum of \(\chi^{2}\) RVs (either central or non-central) also follows a \(\chi^{2}\) distribution with parameters  
\[\begin{split} k_{ege}&=\sum\limits_{\ell=1}^{L}k_{ \ell}\,,\\ \lambda_{ege}&=\sum\limits_{\ell=1}^{L}\lambda_{ \ell}\,,\end{split} \tag{12}\]  
where \(k_{\ell}\) and \(\lambda_{\ell}\) denote the degrees of freedom and instantaneous OSNR, respectively, at receiver \(\ell\). For identical receivers \(k_{\ell}=k\), we find that the EGC output is either a non-central or a central RV with \(k\,L\) degrees of freedom, depending on whether the PPM slot contains the symbol energy or not. In the  
Figure 2: Probability of error (PER) vs. \(E_{b}/N_{0}\) for \(Q=2,16,256\). The plot lines and points correspond to the exact and approximated results, respectively.  
non-central case, the combiner OSNR equals the sum of the individual receiver OSNRs that are not equal, but depend on the instantaneous fading conditions of each transmission path. As a result, the instantaneous EGC PER can be readily derived from the single-receiver case by using  
\[P_{e,egc}=P_{e}\left(k\,L,\lambda_{egc}\right) \tag{13}\]  
and the average PER is obtained from  
\[\overline{P}_{e,egc}=\int\limits_{y}P_{e}\left(k\,L,y\right)\,f_{\lambda_{egc} }\left(y\right)dy\,, \tag{14}\]  
where \(f_{\lambda_{egc}}\left(y\right)\) is the distribution function of the summed OSNR fluctuations.
## 2 Message Replication in OppNets for Sensed Data Collection
### Existing Message Replication Techniques
#### 2.3.2 Multiple-Copy Replication  
Multiple-copy replication, on the other hand, associates messages with a replication quota (i.e., number of permitted replicas) that may either be fixed or variable.
###### Abstract  
This paper presents a new algorithm for assessing the reliability of three-dimensional (3D) slope stability considering the spatial variability of soil based on the Particle Swarm Optimization (PSO) algorithm. First, a 3D random field is generated using the Karhunen-Loeve (K-L) expansion method. Then, the simplified Bishop method of limit equilibrium is coupled with the PSO algorithm to calculate safety factors of the slope. Finally, the failure probability of the slope is determined using the Monte Carlo Simulation method. After validating the rationality of the proposed method through a typical case study, this paper offers an in-depth examination of how soil spatial variability affects the stability of 3D slopes. It is observed that, given identical soil correlation lengths, slope geometric parameters, and failure surface widths, the failure probability is positively correlated with soil spatial variability parameters, while the mean safety factor demonstrates an inverse relationship with these variability parameters. Additionally, the failure probability tends to increase as the soil correlation lengths increase, and it also escalates with the expansion of the failure surface width. In contrast, the mean safety factor exhibits an upward trend with the augmentation of the horizontal correlation length, while it diminishes progressively as the vertical correlation length grows, and it also shows a decline with the widening of the failure surface width. The proposed algorithm significantly improves computational efficiency while ensuring accuracy, making it suitable for the reliability analysis of three-dimensional slopes.  
three-dimensional slope; soil spatial variability; particle swarm optimization algorithm; limit equilibrium method; safety factor; failure probability
## 2 Materials and Methods
### Data Collection and Processing  
Two major processes were considered namely the calibration and validation methods. The transplanted rice samples plots were operated as two sets; Group A (23 ha) for the rice plots using the machine and Group B (3 ha) for the rice plots that were manually transplanted. Rice plot vector boundaries were created using the geo-reference Google Earth images to fit with a geometric corrected HJ1A/B image and the topographic map (1:50,000) of the Royal Thai Survey Department(RTSD) [50; 51]. At least 20 Ground Control Points (GCPs) were selected on the image and topographic map. Image rectification was based on a first-order polynomial transformation with RMSE of about 0.2 pixel. A flow chart showing the methodology used in this study is presented in Figure 2.
## 4 Performance Evaluation
### Network Lifetime Prolongation and Coverage Preservation
If the sink is placed at the center of the CWSN, the remaining energy will be distributed over the field evenly, which can be seen in Figure 13. Since the average distance between nodes and sink in Figure 13 is less than that in Figure 12, the total remaining energy in Figure 13 is higher than that in Figure 12. Hence, the network lifetime in Figure 13 is longer than that in Figure 12. In addition, the energy congregated in the surrounding regions of the sink as time passes because the distance between these nodes and the sink is shorter. In Figure 13, some light red regions appear at the 500\({}^{\text{th}}\) round, which implies that some of the active nodes have consumed much energy. At the 1500\({}^{\text{th}}\) round, the original light red regions extended outward, and their centers become yellow-green representing lower remaining energy. After the 1,500\({}^{\text{th}}\) round, we observe the same phenomenon in the extension of light regions. This is because the nearby nodes were awakened to replace the original dying ones. Thus, these awakened sensor nodes will start consuming energy and result in the extension of light regions. Figure 12: Spatial distribution of the remaining energy for the CWSN during simulation. The dark red regions indicate that the regions reserve more energy, and the dark blue regions preserve less remaining energy. Through the proposed CoCMA, preserving the sensing coverage and prolonging the lifetime can be carried out successfully in the CWSN. In CWSNs applying the CoCMA, the position of the sink will affect the performance of the CWSN according our simulation results. The spatial distribution of energy is more even if the position of the sink approximates the geometric center of the CWSN. This is caused by the innate restriction in CWSNs. We will keep studying the related issues in the future.
## 3 Proposed Method
### ColorNet
#### 3.1.1 Acd  
The structure of ACD is shown in Figure 3. The left half is the residual encoder and the right half is the decoder. Different from SCD, in the encoder, we add residual blocks to the downsampling layer to improve the feature extraction ability of the encoder, while the decoder uses only an upsampling layer. The main feature of encoder is the short skip connections between the residual blocks, so that the output of the previous block combined with the output of the current block is used as the input of the next block. The purpose of such a structure is to improve the feature extraction ability of the encoder by allowing the information to enter deeper into the model and learn more non-redundant information. The decoder is consistent with the decoder in the original UNet. Meanwhile, an improved encoder is proposed based on raw LinkNet. Specifically, one less downsampling layer than LinkNet is used in the encoder and bilinear interpolation is used for upsampling instead of deconvolution. The skip connection is used between the encoder and the decoder.  
The initial block of the encoder is a convolution block with a kernel size of \(7\times 7\) and a stride of \(1\). Behind the initial convolution block, a max-pooling with a stride of \(2\) is used to downsample the input images. The later portion of the encoder consists of \(4\) residual blocks. Each residual block is shown in detail in Figure 4a. Here, Conv means convolution and \(/2\) means downsampling by a convolution with stride of \(2\). In addition, for encoder training, we adopt transfer learning, initializing the encoder with ImageNet [33] pretrained weights.  
Figure 3: ACD structure diagram.  
Figure 2: Overview of our ColorNet structure.  
The decoder consists of 6 convolution blocks; the first 5 convolution blocks remain the same as the original UNet, which is computationally efficient. Each block firstly upsamples the input feature maps by bilinear interpolation then concatenates with the cropped feature maps from the residual encoder. Layers within these decoder blocks are shown in detail in Figure 4b. * 2 means upsampling by a factor of 2, which is achieved by bilinear interpolation. Finally, behind the last upsampling block, a \(1\times 1\) convolution followed with a tanh(x) activation layer, which is suitable for generating images [26], is used to generate the final RGB image. Moreover, we use batch normalization between each convolutional layer, which is followed by ReLU non-linearity.
# Online Phase Measurement Profilometry for a Fast-Moving Object
## Principle  
### Principle of PMP  
The sinusoidal grating is designed to parallel to the moving direction, and the projector projects the sinusoidal grating onto the surface of the object, the deformed pattern \(I(x,y)\) captured by the camera is:  
\[I(x,y)=A(x,y)+B(x,y)\cos(\varphi(x,y)+\delta) \tag{1}\]  
where \(A(x,y)\) represents the background intensity of the deformed pattern, \(B(x,y)\) reflects the contrast of the deformed pattern, \(\varphi(x,y)\) is the phase modulated by the height of the object and \(\delta\) is the shifting phase. Then in \(N\) (\(N\geq 3\)) step PMP, the \(n\)-th corresponding deformed pattern is:  
\[I_{n}(x,y)=A(x,y)+B(x,y)\cos(\varphi(x,y)+\frac{2n\pi}{N}) \tag{2}\]  
The phase \(\varphi(x,y)\) is calculated by Equation (3):  
\[\varphi(x,y)=\arctan\left[\frac{\sum_{n=1}^{N}I_{n}(x,y)\sin(\frac{2n\pi}{N}) }{\sum_{n=1}^{N}I_{n}(x,y)\cos(\frac{2n\pi}{N})}\right]. \tag{3}\]  
Because \(\varphi(x,y)\) is wrapped in \((-\pi,\pi]\) due to the arctan function, it should be unwrapped to be the continuous phase \(\Phi(x,y)\) by using the rhombus phase unwrapping algorithm [19], and the object height distribution \(h(x,y)\) is reconstructed by the phase-to-height mapping algorithm [20].  
\[\frac{1}{h(x,y)}=a(x,y)+b_{1}(x,y)\frac{1}{\Phi(x,y)}+b_{2}(x,y)\frac{\Phi_{C }(x,y)}{\Phi(x,y)} \tag{4}\]  
where \(a(x,y)\), \(b_{1}(x,y)\), \(b_{2}(x,y)\) can be obtained by plane calibration, \(\Phi_{C}(x,y)\) is the phase of the reference plane, it can be obtained by measuring the reference plane in advance.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 2 Related Works
### SLAM with Deep Features  
With the widespread application of deep learning in computer vision, researchers have found that deep-learning-based local feature extraction methods exhibit significant advantages over traditional handcrafted approaches. For example, the Superpoint model, introduced by DeTone in 2019, utilizes a fully convolutional network for extracting feature points and descriptors, showcasing superior performance and robustness over traditional methods [18]. GCN-SLAM improves ORB-SLAM2's local feature extraction using the GCNv2 network, enhancing the system's tracking performance [19]. In 2021, LIFT-SLAM, integrating the LIFT network with ORB-SLAM and fine-tuned on the KITTI dataset, achieved a substantial improvement in system accuracy via automatic threshold adjustments [20].  
In terms of global feature extraction, the limitations of traditional bag-of-words methods become increasingly apparent in complex environments. DXSLAM emphasizes the inefficacy of traditional bag-of-words descriptors in accurately depicting spatial relationships between objects, leading to loop detection errors and ultimately resulting in decreased performance of the SLAM system [5]. NetVLAD generated high-dimensional vector descriptors for images through end-to-end trained CNN architecture, showcasing superior performance compared to traditional methods [21]. It finds wide application in the SLAM domain, as evidenced in DOOR-SLAM and [PERSON]'s research, demonstrating enhanced robustness to lighting variations [22; 23].  
Despite the excellent performance of deep-learning-based feature extraction, incorporating both local and global feature extraction in SLAM requires higher computational costs. Some studies attempt to fuse local and global feature extraction to enhance efficiency. In 2020, HFNet achieved a balanced performance and real-time capability by combining MobileNet with Superpoint and NetVLAD [24], optimized through model distillation. DX-SLAM combines HFNet with ORB-SLAM2, effectively improving the system's tracking accuracy and loop detection performance. The authors also found that DX-SLAM has a certain adaptability to dynamic environments [5]. The state-of-the-art HFNet-SLAM completely replaces the feature extraction part of ORB-SLAM3. While its performance surpasses that of ORB-SLAM3, it exposes issues in descriptor loss leading to tracking failure in frame rotation scenarios [7]. Despite the advancements in accuracy and robustness with deep feature-based SLAM methods, challenges remain to be addressed.  
Our work builds on the use of HFNet for local and global feature extraction by adding a new extraction and matching mechanism. By applying dynamic modules to calculate optical flow vectors, we roughly identify the frame's rotation center and potential rotation angles. This allows the system to select different descriptors for matching. This approach overcomes the issue of rotation loss that is encountered when solely using deep-learning-based descriptors.
# CDA-Net for Automatic Prostate Segmentation in MR Images
## 3 Methods
### RAS-Faster RCNN for ROI Extraction
#### 3.1.2 Sequence Correlation Processing  
Since Faster R-CNN was initially applied to object detection tasks in natural images, in the detection results of RA-FasterRCNN, multiple objects or lack of objects appeared in individual slice images (Figure 4a,b). However, this situation should not exist for the task of prostate localization. Thus, this paper utilizes the uniqueness of the organ and the slight variation of the organ position in the same sequence of images and introduces sequence correlation processing to improve organ localization. Taking a sequence of MR images as an example, the detailed description is the following three steps:  
**Step 1:** The object proposals with the credibility of RA-FasterRCNN detection results greater than a certain threshold are retained. In this paper, the threshold is set as 0.80 after repeated experiments.  
**Step 2:** For the multi-box phenomenon shown in Figure 6a: first, we take advantage of the uniqueness of the organ and the slight variation of the organ position in the sequence images to firstly select the slice images which have a single reliable organ bounding box in the sequence images. Nine key points (including the four vertices, the midpoint of each boundary and the center point of the organ proposal) of these slices are utilized for spatial curve fitting based on sequence direction. The fitting method is the least-square polynomial fitting. Then, the fitted spatial curves and the minimum spatial Euclidean Distance are applied to screen and determine the object bounding boxes closest to the fitted position in the slice images with multiple prediction boxes as the final position of the organ. Figure 7 is a schematic diagram illustrating the above process. And Equation (3) describes the mathematical formula basis for screening. It is worth noting that the spatial curve parameters are updated after each screening.  
\[b^{*}=\underset{b}{\text{argmin}}\sum_{j=1}^{9}{((y_{j}^{*}-y_{j}^{b})^{2}+{( z_{j}^{*}-z_{j}^{b})}^{2})}, \tag{3}\]  
where \(b^{*}\) indicates the final organ bounding box selected in a slice image, \(y_{j}^{*}\) and \(z_{j}^{*}\) denote the fitting prediction values of the \(y\)-axis and \(z\)-axis at the \(j\) - th key point, \(y_{j}^{b}\) and \(z_{j}^{b}\) represent the coordinate values of the \(j\) - th key point of the object box \(b\) predicted by the network on the \(y\)-axis and \(z\)-axis.  
**Step 3:** For the missing object phenomenon, as shown in Figure 6b: the final updated spatial curve is used to fit the organ position in the missing detection images.
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
## 4 Discussion  
Table 3 compares some of the performances of the free-space optical communication system, mainly including the response time of the receiving end, whether frame synchronization is required, and whether mechanical alignment is required. It is obvious that our system does not need frame synchronization when realizing point-to-multipoint free-space optical communication, greatly simplifying the demodulation circuit, and does not need to perform mechanical scanning to lock the direction of data transmission, demonstrating certain advantages compared with other free-space optical communication systems. In addition, compared with point-to-multipoint free-space optical communication systems that require mechanical alignment, our system has obvious advantages in its response time at the receiving end. Although the point-to-point free-space optical communication system has a fast response speed, its function is singular and it cannot complete point-to-multipoint data transmission, so our system has certain advantages in realizing point-to-multipoint free-space optical communication.