esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Routing Protocols
### Proposed Two-Level Phantom with a Pursue Ring Protocol (PhaP)
#### 4.1.4 PHASE 3: Packet Routing from \(P_{Nsec}\) to Sink Node  
After packets arrive at the \(P_{Nsec}\), they are forwarded to the sink node using the directed random-walk routing strategy. At the \(P_{Nsec}\), the destination node is the sink node as shown in Algorithm 1.  
```
1:\(\text{sink\_node\_location}\) = (0,0)
2:network initialization using same algorithm as in Reference [3]
3:generate \(X\)-\(Y\) coordinate centered at the sink_node
4:create \(P_{ring}\) according to Figure 1
5:compute \(\theta\) according to Figure 3
6:if (0\({}^{\circ}\leq\theta<\pi/2\))
7:assign node into \(P_{rNE}\)
8:elseif (\(\pi/2\leq\theta<\pi\))
9:assign node into \(P_{rNW}\)
10:elseif (\(\pi\leq\theta<3\pi/2\))
11:assign node into \(P_{rSW}\)
12:elseif (\(3\pi/2\leq\theta<2\pi\))
13:assign node into \(P_{rSE}\)
14:endif
15:Phase 1: Selection of \(P_{Ngt}\) and packet routing from source node to \(P_{Ngt}\)
16:sensor node detect asset, becomes _source_node_
17:\(source\_node\) select \(P_{Ngt}\) using same algorithm as in Reference [20]
18:Packet routing (_source_node_, \(P_{Ngt}\)) // source node send packet to \(P_{Ngt}\) using directed random-walk routing
19:\(\text{basic\_threshold}\) = \(T_{P}\)
20:\(P_{Ngt}\) generate \(R_{N}\) between [0, 1]
21:if (X-coordinate_of_\(P_{Ngt}<X\)-coordinate_of_sink)
22:if (\(R_{N}<T_{P}\))
23:select \(P_{Nsec}\) from \(P_{rNW}\)
24:else
25:select \(P_{Nsec}\) from \(P_{rSE}\)
26:endif
27:elseif (X-coordinate_of_\(P_{Ngt}\geq X\)-coordinate_of_sink)
28:if (\(R_{N}<T_{P}\))
29:select \(P_{Nsec}\) from \(P_{rNE}\)
30:else
31:select \(P_{Nsec}\) from \(P_{rSW}\)
32:endif
33:Packet routing (\(P_{Ngt}\), \(P_{Nsec}\)) // \(P_{Ngt}\) send packet to \(P_{Nsec}\) using directed random-walk routing
34:Packet routing from \(P_{Nsec}\) to sink
35:Packet routing (\(P_{Nsec}\), \(sink\)) // \(P_{Nsec}\) send packet to sink using directed random-walk routing
36:function Packet routing (_sender_node_, _target_node_) // directed random-walk routing
37:\(sender\_node\) generate \(\text{SDR}_{\text{N}}\)
38:while (\(\text{DR}_{N}\)!= target_node)
39:\(sender\_node\) = \(\text{DR}_{\text{N}}\)
40:\(sender\_node\) generate \(\text{SDR}_{\text{N}}\)
41:\(sender\_node\) select \(\text{DR}_{\text{N}}\)
42:endwhile
43:endfunction
```  
**Algorithm 1**Proposed algorithm for PhaP protocol
## 2 3D FEM Model  
### Introduction Remarks  
Over recent decades, computational technology advancements have revolutionized geotechnical and structural engineering, enabling more complex analyses. The finite element method (FEM), with its flexibility and ability to model complex material behaviors and interactions, became indispensable in SSI research. Today, 3D FEM in SSI analysis not only allows for realistic load scenario modeling but also integrates geotechnical research data and building construction. This tool enables detailed analysis of unique construction geometries, diverse soil conditions, and their interactions. It also facilitates exploring new building materials and construction techniques.  
A primary challenge in implementing 3D FEM models is ensuring accurate input data, which is crucial for analysis reliability. This includes precise material property modeling of soil and structures. In the context of our research on the transmission of vibrations from the ground to buildings, the choice of the Ansys Mechanical APDL [26] software as the simulation tool is pivotal. This software stands out for its advanced finite element modeling (FEM) capabilities, which are essential for accurately depicting the complex geometries of buildings and the heterogeneous nature of soil. The analyzed numerical models were crafted using ANSYS APDL (ANSYS Parametric Design Language), a versatile tool for finite element analysis. ANSYS APDL is particularly suitable for this type of modeling due to its powerful parametric capabilities, which allow for the customization and automation of the simulation process. Its ability to accurately simulate the physical behavior of structural elements under various loads and constraints makes it an excellent choice for predicting the performance of building structures.
# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 2 Materials and Methods
### Individual Quantitative Evaluation of Road Performance  
#### 2.2.1 Analysis of Influencing Factors of Roads  
Road and its subsidiary factors [25; 37; 38], including peripheral environment, traffic laws and regulations, driver factors and other dynamic factors, whose varied details are listed in Table 2, can reflect road performance in different extents, and then affect user path selection results.
## 3 Current Techniques for OP Study
### Magnetic Resonance Imaging Techniques  
Magnetic resonance imaging (MRI) is a non-ionising technique that provides indirect information on the 3D organisation of bone by measuring the signal coming from the hydrogen atoms contained in the water and the fat compartments of the bone marrow. Thanks to the application of a magnetic field in combination with a radio frequency (RF), the hydrogen atoms can be excited and upon the removal of the RF radiation, the hydrogen atoms relax and emit an RF signal which is detected by RF coils [10]. These RF impulses provide information on the location and the microenvironment of hydrogen atoms [12]. Consequently, information on bone structure can be obtained indirectly by the MRI signal after an image post-processing phase, as visible in Figure 9.  
Figure 8: Histomorphometric images of a portion of trabecular bone of a healthy postmenopausal woman (**left**) and of a woman affected by postmenopausal osteoporosis (**right**) [51]. The reduction in the trabecular bone volume is evident. This image is licensed under a Creative Commons Attribution–Non-Commercial 4.0 International Licence.  
However, the low amount of water contained in the bone and the presence of the surrounding tissues causes a low signal-to-noise ratio (SNR) in MR images. For this reason, MRI is usually employed at peripheral sites (e.g., distal radius, distal tibia and calcaneus) [10,12]. Furthermore, the SNR is negatively influenced by the voxel size, consequently limiting the analysis resolution to around several hundreds of micrometres [54].  
One of the potential strategies exploited to improve the SNR and thus the analysis outcomes involves the reduction of the RF detector size. For this reason, high-resolution MR imaging has been developed by using clinical MR scanners combined with specially designed coiled detectors. With this technique, it is possible to work with in-plane resolutions up to 110 \(\upmu\)m that allow for resolving the bone trabecular structure and assessing its main architectural features (thickness, separation, number, anisotropy and connectivity degree) [55]. However, since the resolution is close to the trabecular dimension, partial volume effects can occur (for a more detailed explanation of partial volume effects, see Section 4.1.1) [54].  
Another new magnetic-based technique, still at the experimental stage, is solid-state MRI. By the use of custom-made coils, it detects the resonant signal of phosphorous atoms present in the mineral phase of bone, in order to determine the bone tissue mineral density. This method has shown promising results in the assessment of bone density in animal models, but presents some limitations for clinical use, due to the long imaging times (around 1 h) and strong magnetic field applied (at least 4.7 T) [10]. In particular, longer acquisition times increase the probability of involuntary motions in vivo, leading to artefacts and image corruption [54].  
In general, MRI techniques require expensive equipment and a complicated post-processing phase, reducing their availability as routine OP diagnosis tools in clinical applications.
## 3 Processing Method
### Machine Vision Algorithm  
The convolutional neural network has been used for training of the model. The trained network can be divided into two parts (i.e., the down-sampling or contracting path and the up-sampling or expanding path) [58]. The architecture of the CNN model used in this research can be represented by Figure 7.  
The down-sampling or contracting path is composed of blocks, which are comprised of \(3\times 3\) Convolution layer and a \(2\times 2\) Max Pooling layer with an activation function at each layer [50]. The input layer is normalized by adjusting and scaling the activations or batch normalization. The batch normalization improves the speed, performance, and stability of neural networks [59]. The trained deep learning model in this research draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini batch. The network uses max-pooling layers, which reduce the height and width information while keeping the number of channels of the input matrix constant. These layers are intended to increase the resolution of the output. Pooling layers can work with different methods, including maximum, average, or median layers. The contracting path captures the context of the input image in order to perform segmentation. This coarse contextual information is transferred to the expanding path using skip connections.  
Figure 6: **(a)** Original radargram; **(b)** clutter-gram (registered), **(c)** normalized difference, **(d)** 3-Band composite image for training (generated from images **(a–c,e)** labelled discontinuity superimposed on training image.  
The expanding path is composed of the block with \(3\times 3\) convolution layers, deconvolution layers and a concatenation operation on the cropped feature map from the contracting path. The purpose of expanding the path is to enable precise localization. The workflow for training and testing the image dataset is shown in Figure 8.  
Figure 8: Flowchart showing CNN Model training and testing Procedure.  
Figure 7: CNN architecture uses convolutional encoding and decoding units. It takes an image as an input and produces the segmentation as an output.
## 5 Summary and Conclusions  
This study evaluated the eight-day and annual GPP, ET, and eWUE from the uncoupled MODIS product and the coupled PMLv2 product against the in-situ observations for C4 maize and C3 soybean at five cropland sites from the FLUXNET2015 and AmeriFlux datasets. The main conclusion of this study can be drawn as follows: (1) For GPP estimates, the PMLv2 product showed paramount improvements for C4 maize and slight improvements for C3 soybean, relative to the MODIS product. (2) For ET estimates, the PMLv2 product exhibited similar performances to the MOD16 product for C4 maize and C3 soybean. (3) For eWUE estimates, the coupled PMLv2 product could achieve higher-accuracy eWUE estimates than the uncoupled MODIS product at both eight-day and annual scales. Taking the result at an eight-day scale for example, compared to the MODIS product, the PMLv2 product could reduce the RMSE from 2.14 g C \(\text{Kg}^{-1}\) H\({}_{2}\)O to 1.36 g C \(\text{Kg}^{-1}\) H\({}_{2}\)O and increase the \(\text{R}^{2}\) from 0.06 to 0.52 for C4 maize, as well as reduce the RMSE from 1.33 g C \(\text{Kg}^{-1}\) H\({}_{2}\)O to 0.89 g C \(\text{Kg}^{-1}\) H\({}_{2}\)O and increase the \(\text{R}^{2}\) from 0.05 to 0.49 for C3 soybean. (4) Despite the outperformance of the PMLv2 product, both two products failed to differentiate C4 and C3 crop species in their model calibration and validation processes, leading to a certain degree of uncertainties in eWUE estimates.  
The authors declare no conflict of interest.
###### Abstract  
Accurate and efficient object detection in UAV images is a challenging task due to the diversity of target scales and the massive number of small targets. This study investigates the enhancement in the detection head using sparse convolution, demonstrating its effectiveness in achieving an optimal balance between accuracy and efficiency. Nevertheless, the sparse convolution method encounters challenges related to the inadequate incorporation of global contextual information and exhibits network inflexibility attributable to its fixed mask ratios. To address the above issues, the MFFCESSC-SSD, a novel single-shot detector (SSD) with multi-scale feature fusion and context-enhanced spatial sparse convolution, is proposed in this paper. First, a global context-enhanced group normalization (CE-GN) layer is developed to address the issue of information loss resulting from the convolution process applied exclusively to the masked region. Subsequently, a dynamic masking strategy is designed to determine the optimal mask ratios, thereby ensuring compact foreground coverage that enhances both accuracy and efficiency. Experiments on two datasets (i.e., VisDrone and ARH2000; the latter dataset was created by the researchers) demonstrate that the MFFCESSC-SSD remarkably outperforms the performance of the SSD and numerous conventional object detection algorithms in terms of accuracy and efficiency.  
U
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 1 Introduction
### Mobile Robots Localization in Industry 4.0 Concept  
Industry 4.0 and Logistics 4.0 utilize solutions that connect different production phases. Mobile robots are ideal for this purpose, as they can be integrated with an enterprise resource planning (ERP) system, enabling further automation. For instance, when the sales department places an order, the mobile robot receives a list of routes to execute sequentially. The industry employs autonomous mobile robots (AMRs), mobile manipulators (MMs), and automated guided vehicles (AGVs) for various tasks. However, the navigation methods for these vehicles differ significantly [34; 35; 36]. AGV systems follow predetermined routes and are guided by sensors, magnetic lines, or wires. They require complex and costly building interventions. Fault-free navigation and positioning are crucial for AMR robots to carry out tasks in various locations due to their flexibility compared to AGVs. The literature on mobile robot localization systems for the Industry 4.0 concept (see [37; 38; 39; 40] for example, and quoted in the literature) presents standard solutions, such as trilateration and triangulation methods. To solve the navigation problem, a different approach can be taken by using visible light positioning with specially modulated LED lighting [41]. Another option is to use a system based on radio frequency identification (RFID) in the ultra-high frequency (UHF) band to determine the location of pallets carried by forklifts within a warehouse [42]. An effective localization strategy involves processing location measurements to achieve accurate recognition beyond line of sight. This can be accomplished using an enhanced particle filter based on a genetic algorithm [43]. For critical and demanding IoT device applications that require fast and reliable low-latency end-to-end (E2E) communication, the use of programmable intelligent space (PIS) is proposed as an alternative to implement atocells [44].
## 6 Results
### Familiar Then Stranger Mode  
In this mode, we experiment with the same nine scenarios in the stranger and then the familiar mode. Following, we present the simulation results according to movement models.  
* Random way movement model  
Figure 15 shows the results for the random way movement model. Epidemic and Prophet protocols give almost the same results. In fact, the Prophet algorithm is an extended version of the Epidemic protocol, aiming to limit its continuous forwarding scheme. The privacy enhancement starts at around 30% for small networks and decreases to stabilize at around 10%. For the Prophet, privacy has improved by more than 20% for small-size  
Figure 14: The result of stranger mode with working day model.  
networks and decreased until reaching 10% for larger networks. Spray and Wait protocol controls the message copies, reduces forwarding opportunities, and eliminates unsuitable candidates. Thus, its enhancement rate for privacy rises and stabilizes at 20%, independent of the network size. For network efficiency, Epidemic and Prophet increase the delivery time while Spray and Wait achieves the highest packet delivery ratio but slows the transmission and generates the highest ATT.  
* Bus movement model  
Figure 16 supports the results of the previous movement models. The privacy-aware Spray and Wait protocol gained 20% better privacy enhancement. Epidemic and Prophet behave mostly the same for network efficiency, while the Spray protocol has the highest PDR.  
Figure 15: The result of familiar mode with the random way model.  
* Working day movement model  
Figure 17 shows that the privacy enhancement in Epidemic overpasses 20%, rises to 50%, and then slows down to 20% for larger networks, while in Prophet, it gains up to 60%. The Spray and Wait protocol is improved by 20% in a small network compared to the normal routing, by 40% with medium size, and back to 20% with a larger network and, consequently, has the longest delivery time.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 2 Basic Theory
### Introduction to LSTM Principles
#### 2.3.1 LSTM Section  
Input gate: determines how much of the current input is written to the cell state. The formula is as follows:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{5}\]  
where \(i_{t}\) denotes the input gate vector at the moment t, which controls the amount of information in the new input; \(\sigma\) denotes the sigmoid activation function, which compresses the value between 0 and 1; \(W_{i}\) denotes the weight matrix of the input gate; \(h_{t-1}\) denotes the hidden state vector at the moment \(t-1\); \(x_{t}\) denotes the input vector at the moment t; and \(b_{i}\) denotes the deviation of the input gate vector.  
Forgetting gate: based on the current input and the previous hidden state, it is decided which information in the cell state needs to be forgotten. The formula is as follows:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{6}\]where \(f_{t}\) denotes the forgetting gate vector at the moment t, which controls the degree of retention of past information; \(W_{f}\) denotes the weight matrix of the forgetting gate; and \(b_{f}\) denotes the bias vector of the forgetting gate.  
Cell state: Divided into candidate cell state and cell update state, the candidate cell state provides possible new information for cell state updating, while the cell update state is responsible for storing and transmitting long-term dependent information. The formula is as follows:  
\[\widetilde{C}_{t}=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{7}\]  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\widetilde{C}_{t} \tag{8}\]  
where \(\widetilde{C}_{t}\) denotes the candidate cell state vector at the moment \(t\) for updating the cell state, tanh denotes the hyperbolic tangent activation function that compresses the value between \(-1\) and \(1\), \(W_{C}\) denotes the weight matrix of the cell state, \(b_{C}\) denotes the bias vector of the cell state, \(C_{t}\) denotes the cell state vector at the moment t that stores the long-term information, and \(C_{t-1}\) denotes the moment \(t-1\) of the cell state vector.  
Output gate: determines which parts of the cell state are output. The formula is as follows:  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{9}\]  
\[h_{t}=o_{t}\cdot tanh(C_{t}) \tag{10}\]  
where \(o_{t}\) denotes the output gate vector at the moment \(t\), which controls the amount of output information; \(W_{o}\) denotes the weight matrix of the output gate; \(b_{o}\) denotes the bias vector of the output gate; and \(h_{t}\) denotes the hidden state vector at the moment \(t\), which is also the output of the LSTM unit.  
Fully Connected Layer: the LSTM outputs are converted to predictions through the fully connected layer. The formula is as follows:  
\[y=Wx+b \tag{11}\]  
where \(W\) is the weight matrix, \(x\) is the input, and \(b\) is the bias.
# Load Transfer Efficiency Based on Structural Deflection Assessment of the Precast Floating Track  
[PERSON]  
1 Department of Transportation System Engineering, KRRI School, University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea; [EMAIL_ADDRESS]  
[PERSON]  
2  
Correspondence: [EMAIL_ADDRESS]; Tel.: +82-10-3895-91121112112121121211211212112121121211212112121121  
of the station [6]. So, it is more effective to find countermeasures that address the source of the noise, which are more economically and intrinsically efficient for existing railway stations [7].  
A floating slab track is one way to minimize the ground-borne vibration noise by blocking the vibration transmitted from the vehicle-track interaction [8]. This type of track is generally made of the continuous rail, mounted on the massive concrete by the fastening devices and forming the mass-spring-systems (MSS). The combination of the panel's weight with a dead load of superstructures (rails, fastening systems, and sleepers) created the dynamically active mass [9]. Under this type of track, the anti-vibration device such as the glass fiber, rubber bearing, or coil springs [10] is installed as a substructure to disconnect the track from the ground and decrease the magnitude of the load generated by the wheel-rail, and this method is widely used and accepted for railway tracks in Korea and around the world [11; 12; 13; 14; 15; 16; 17; 18]. In floating slab tracks, the rails are usually used as the connection between panels instead of dowel bars to reduce the construction cost. To simulate the performance of this type of track system, the easiest way is to consider it as a single degree of freedom as shown in Figure 2 with F is load, m is the mass of the track structures, k is the stiffness of substructure and c is the damping factor of the system. The biggest deterrent of this type of railway structure is the huge initial construction cost. However, recent studies about the life cycle cost of railway structures pointed out that this type of track can be an alternative method of the ballasted track or conventional concrete track with several advantages such as lower maintenance cost, rapid construction, and lower structure height [19].  
Figure 1: The structural vibration transfer path.  
Figure 2: Single degree of freedom scheme.  
In this study, we determined whether a precast floating track, which was manufactured in a factory, transported, and installed at a construction site, could be a suitable solution to reduce the noise and vibration problems. Our study utilized a precast floating slab track structure, which is a newly developed structure, unlike existing conventional track structures. We assessed the precast floating track panel structure using the finite element method (MIDAS CIVIL program) and structural assembly tests to verify its structural performance. The goal of these tests was to evaluate the displacement of the rail and panel to verify the safety of this system through the load transfer efficiency.