esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight TA-YOLOv8 Method for the Spot Weld Surface Anomaly Detection of Body in White
## 4 Experiment
### Evaluation and Analysis
On the same dataset, the modified YOLOv8 achieved a 3.3% increase in [EMAIL_ADDRESS], a 25% reduction in parameter count, and over 10% decrease in computational complexity. Figure 10 shows the validation sample results for model testing. This figure includes the detection results of weld spot samples and the corresponding heat map visualization. In the heat maps, red areas represent regions identified by the model as defective, while blue areas indicate normal regions. This color contrast provides an intuitive way to observe the areas the model focuses on, especially in response to defects. This indicates the model's high accuracy in detecting even minor defect locations. Additionally, the heat maps from different weld spot images demonstrate strong generalization ability, making the model suitable for various weld surface conditions. The middle five heat maps represent different feature extraction stages. This visualization demonstrates that, under a multi-cross layer network feature extraction and fusion approach, as the extracted feature information progresses to higher levels, the confidence increases gradually from low to high. The model successfully identifies and concentrates on weld spot defects, such as the edges of the welds and areas where cracks or depressions might be present. The model effectively captures the location of welding defects through highlighted (green) regions. In practical production, some welding spots may be overlooked during manual inspection. To assess the model's generalization capability, specific weld point samples were intentionally left unannotated before training. The trained model is subsequently used to predict these samples, and the results demonstrate that it effectively identifies the missed welding spots, as shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **[EMAIL_ADDRESS]** & **Parameters (M)** & **GFLOPs** & **FPS** \\ \hline YOLOv5 [29] & 79.65 & 13.33 & 24.6 & 135 \\ YOLOv5 + SPPF & 81.21 & 13.35 & 24.6 & 130 \\ YOLOv7 [41] & 84.87 & 12.56 & 28.2 & 105 \\ \hline YOLOv8 + SPP [42] & 83.42 & 12.77 & 27.3 & 162 \\ YOLOv8 + SPPF & 83.50 & 12.87 & 27.3 & 104 \\ YOLOv8 + SimSPPF [43] & 83.18 & 12.74 & 27.2 & 121 \\ YOLOv8 + BiFPN & 88.90 & 9.65 & 28.8 & 118 \\ YOLOv8 + SE & 89.32 & 7.40 & 30.1 & 97 \\ YOLOv8 + CBAM & 90.55 & 7.39 & 25.6 & 90 \\ YOLOv8 + EMA & 89.30 & 8.28 & 25.2 & 68 \\ YOLOv8 + Biform [44] & 91.3 & 8.13 & 25.7 & 59 \\ YOLOv8 + LSK attention & 91.1 & 10.15 & 26.0 & 73 \\
**WSDDM** & **90.13** & **11.39** & **25.8** & **98** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the performance with different modules.
## 3 Experimental Results
### Evolution of the Crack Network under an SSR of 0.4  
Figure 8 shows the initiation and propagation process of the crack network during crater blasting under an SSR of 0.4. Under the coupling of dynamic and static loads, similar to the experimental results of under an SSR of 0.2 and 0.3, only an initial radial crack that was parallel to the static stress was observed first at the centre of the blast hole at 150 us (see Figure 7b). At 240 us, the initial circumferential cracks started to appear on the left and right sides of the blast hole, accompanied by a small amount of blasting dust that was starting to leak out (see Figure 7c). At 300 us, the second circumferential cracks also appeared on the left and right sides of the blast hole (see Figure 7d). Due to the effect of static stress, the initial and second circumferential cracks continued to propagate along the direction of the static stress. At 400 us, the initial circumferential crack gradually closed. At the same time, two radial cracks appeared outside the initial circumferential cracks that were parallel the static stress (see Figure 7e). After 500 us, the second circumferential crack started to bend and close and the blasting fragments began to fly outwards.
## 3 System Model
### Energy Consumption Model  
In this paper, we used a simple energy model as adopted in [22; 24]. The required energy for transmitting \(l\) bits data over distance \(d\) is as follows:  
\[E_{T}=(\varepsilon_{\mathit{elc}}+\varepsilon_{f_{S}}\times d^{2})\times l \tag{2}\]  
where \(\varepsilon_{\mathit{elc}}\) is the consumed energy per bit by the transmitting and receiving circuit, and \(\varepsilon_{f_{S}}\) is the free space loss coefficient. It is noted that since the communication range between the receiver and the sender is limited to the radius \(R\) and \(R<d_{th}\)[24] (\(d_{th}\) is the threshold distance), thus the free space \(e_{f_{S}}(d^{2}\) power loss) channel model is employed in this model.  
Meanwhile, the energy consumption for a node receiving \(l\) bits data over distance \(d\) is given by:  
\[E_{R}=\varepsilon_{\mathit{elc}}\times l \tag{3}\]  
Thus, the energy consumption of node \(i\) receiving and forwarding \(l\) bits data is expressed as:  
\[E(i)=E_{T}(i)+E_{R}(i) \tag{4}\]  
EBRA-NLE: DS Evidence Theory-Based Energy Balanced Routing Algorithm for Network Lifetime Enhancement  
DS evidence theory was proposed by [PERSON] in 1970s [11], which can fuse multiple information and its fusion results are in line with people's thinking habits. Therefore, DS evidence theory is widely used in artificial intelligence, detection, and diagnosis etc., [25]. DS evidence theory establishes a one-to-one correspondence between propositions and sets, which can transform the uncertainty of propositions into the uncertainty of sets. In this paper, we apply DS evidence theory to routing selection in WSNs. First of all, we consider the residual energy of nodes, the shortest data transmission path and the buffer proportion, so as to establish \"energy balance factor,\" \"relay coefficient,\" and \"buffer idleness\" as the evidences of DS fusion rules. Then DS fusion rules are  
Figure 1: The topology of wireless sensor networks (WSN).  
applied to fuse the three indexes of corresponding belief function, thus the results of comprehensive evaluation are obtained. It should be noted that the belief function is obtained by sinusoidal mapping of membership function, so as to reflect the importance of each index in the fusion process and make the optimal routing objectively.  
DS evidence theory is an effective method to fuse subjective uncertainty information, and its fusion decision process [26] is shown in Figure 2. Where \(n\) is the number of forward neighboring nodes of node \(i\), that is \(n=\)\(|FN(i)|\). \(m_{a}(A_{1}),\ldots,m_{a}(A_{n})\) is the belief allocation of \(n\) forward neighbors under different index \(a.m(A_{1}),\ldots,m(A_{n})\) is the fused belief allocation of \(n\) propositions of node \(i\). When calculating the probability allocation of each neighbor, it is necessary to establish attribute indexes of evaluation nodes, which will be described in the next section.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 5 Conclusions
* The LEO augmentation improves the ambiguity-fixed formal horizontal precision and reduces the ambiguity-fixed HPLs. The above findings are expected to attract more attention in the field of the LEO-augmented PPP-RTK positioning, possibly with the overbounding parameters refined, with more efficient bias propagation strategies investigated and with real navigation signals from LEO satellites tested in the future. [PERSON] designed the work, processed the data and wrote the paper, [PERSON] contributed to the data acquisition, [PERSON]-M., [PERSON] and [PERSON] contributed to the revision of the paper. All authors have read and agreed to the published version of the manuscript. This research is funded by the National Time Service Center, Chinese Academy of Sciences (CAS) (No. E167 SC14), the CAS \"Light of West China\" Program (No. XAB2018 YDYL01), the National Natural Science Foundation of China (No. 12073034), and the Australian Research Council--discovery project (No. DP 19010244) \"Tracking Formation-Flying of Nanosatellites Using Inter-Satellite Links\". The multi-constellation precise orbital products are available at [[http://ftp.aiub.unibe.ch/CODE_MGEX/CODE](http://ftp.aiub.unibe.ch/CODE_MGEX/CODE)]([http://ftp.aiub.unibe.ch/CODE_MGEX/CODE](http://ftp.aiub.unibe.ch/CODE_MGEX/CODE)) (accessed on 21 March 2022). The authors declare no conflict of interest.
# Integrated Microwave Photonics for Wideband Signal Processing
###### Abstract  
We describe recent progress in integrated microwave photonics in wideband signal processing applications with a focus on the key signal processing building blocks, the realization of monolithic integration, and cascaded photonic signal processing for analog radio frequency (RF) photonic links. New developments in integration-based microwave photonic techniques, that have high potentialities to be used in a variety of sensing applications for enhanced resolution and speed are also presented.  
Keywords:microwave photonics; integrated microwave photonics; photonic signal processing +
Footnote †: journal: _photonics_
###### Abstract  
As a result of rapid urbanization and population movement, flooding in urban areas has become one of the most common types of natural disaster, causing huge losses of both life and property. To mitigate and prevent the damage caused by the recent increase in floods, a number of measures are required, such as installing flood prevention facilities, or specially managing areas vulnerable to flooding. In this study, we presented a technique for determining areas susceptible to flooding using hydrological-topographic characteristics for the purpose of managing flood vulnerable areas. To begin, we collected digital topographic maps and stormwater drainage system data regarding the study area. Using the collected data, surface, locational, and resistant factors were analyzed. In addition, the maximum 1-h rainfall data were collected as an inducing factor and assigned to all grids through spatial interpolation. Next, a logistic regression analysis was performed by inputting hydrological-topographic factors and historical inundation trace maps for each grid as independent and dependent variables, respectively, through which a model for calculating the flood vulnerability of the study area was established. The performance of the model was evaluated by analyzing the receiver operating characteristics (ROC) curve of flood vulnerability and inundation trace maps, and it was found to be improved when the rainfall that changes according to flood events was also considered. The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.  
flood vulnerability; spatial analysis; logistic regression; ROC analysis; flood detection Article
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 2 Evolution of the Proposed Modified Quadratic Boost Converter
### Quadratic Boost Converter with Single Voltage Lift Cell  
In this modified QBC shown in Figure 3, the additional capacitor C\({}_{2}\) is connected in series with inductor L\({}_{2}\), which plays a significant role in boosting the output voltage as well as reducing the voltage stress across the switch. In this topology, when the switch is on, the capacitor C\({}_{1}\), which transfers the energy to the inductor L\({}_{2}\), also charges the capacitor C\({}_{2}\) through the diode D\({}_{2}\) at the same time the inductor L\({}_{1}\) is energized by the input voltage. During the second mode of operation, both the inductor L\({}_{1}\), L\({}_{2}\), and capacitor C\({}_{2}\) are involved in boosting the output voltage. The output voltage of this modified topology is higher than the conventional quadratic boost converter, followed by the voltage gain formula \(\frac{(2-d)}{(1-d)^{2}}\). The voltage stress of this proposed topology is reduced significantly to a certain limit which is another advantage over the CQBC.
## 4 Analytical Model and Evaluation  
We have conducted a probabilistic analysis of the SDMob architecture. This model can guide us through the reasons why SDMob may lose or gain advantages in specific scenarios. In a previous work [46], we focused on comparing RPL with SDMob using the analytical evaluation. Now we aim at evaluating the impact of parameters such as path loss variance and the MN's velocity, and compare the results from the analytical model with simulation results.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 4 Proposed Method
### Arria 10 FPGA Description  
The Arria 10 GX 1150 FPGA board used in this paper adopts TSMC 20 nm technology and has rich design resources. The adaptive logic module (ALM) is the basic component of the FPGA logic structure and 427,200 ALMs can be equivalent to 1,150,000 logic elements (LE) [22], so this FPGA board is named GX 1150. One thousand five hundred and eighteen precision adjustable DSPs (Digital Signal Processings) have encapsulated as hardcore IP (intellectual property) for developers to call, supporting high-performance multiplication and addition operations. Memory can be divided into two types; one is the M20k module, the size of a single module is 20 KB; the other is memory logic array block (MLAB), the size of a single module is 0.64 kb. The M20k module is the best choice for larger memory arrays and provides a large number of independent ports. MLAB is the best choice for the wide and shallow memory array, which can realize shift register and a small FIFO (first in first out) buffer.
## 3 Experiment Results
As can be seen, the defect regions are not distinguishable from those created by the hub geometry, so subsequent steps are unable to extract the defects correctly. The smoothing operator was resized to have an r of 19, and the small threshold \(T_{s}\) was raised to 5, to have produced the initial segmentation result as shown in Figure 7c. As can be seen, the defects are extracted, distinguishable from interference. Continuing from Figure 7c, with the large threshold \(T_{b}\), the area \(M_{s}\), and the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Area** & **Diameter** & **Less than \(M_{s}\)?** & **Less than \(M_{d}\)?** & **A Real Defect?** \\ \hline
1 & 57,867 & 546.7 & N & N & N \\
2 & 1563 & 90.8 & Y & Y & Y \\
3 & 4711 & 149.1 & N & N & N \\
4 & 462 & 159.2 & Y & N & N \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of preliminary defects. Figure 6: Another result of proposed method: (**a**) original image; (**b**) result of proposed method. diameter \(M_{d}\) unchanged, the remaining steps of the segmentation process were performed, to have produced the final defect segmentation result as shown in Figure 7d, in which the defects are marked on the original image. The defects are accurately segmented out.
# Crowdsourcing Framework for QoE-Aware SD-WAN +
## 4 Proposed Framework
### Data Summarization  
The issue of scalability is an important challenge in the use of crowdsourcing for QoS control. Certain streaming content services may have millions of users at any time, and receiving and analyzing feedback from them in real time could become a bottleneck. In order to address this issue, we add two elements in our design. Firstly, an intermediary data summarization layer is placed between the end-user tier and SD-WAN to implement a hybrid fog computing operation. The nodes in this layer are responsible for receiving feedback from users in their region, summarizing the feedback, and sending it back to the network controller(s). For instance, this feedback could include the number of dislikes received over a reporting period. The number and location of these intermediary nodes can be optimized to accommodate any processing limits at the controllers.  
Secondly, a minimalist approach is used in the design of the user feedback data. As discussed in Section 3, our framework relies on negative feedback (dislike button) that is only given when there is a problem. Therefore, no feedback is expected in all regions at all times as long as the streaming quality is acceptable. When a congestion or failure scenario causes a spike of negative feedback in a region, the intermediary-layer server in that region will collect the feedback and send a summary report to the central controller. This approach will allow for service scalability to a wide area network.  
It must be noted that the QoE response time is inherently different from QoS responses. In the case of user-initiated QoE, the feedback is controlled by user actions that typically span a few seconds. QoE-correcting actions from the controller can also be executed within a similar time scale; as such, the limiting impact of propagation delays is less significant in QoE-aware services, as opposed to QoS-aware systems.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 4 Simulation Results
### Simulation Settings  
In the simulation, we considered the MIMO-OFDM system with the parameters shown in Table 1. To model the 5G channel, we used the fading multi-path model channel with the TDL-C Power Delay Profile [33], and the 5G channel was generated using the 5G Matlab toolbox as mentioned in Section 2. The parameters used for the FDNN model, CNN model, and bi-LSTM model are given in Tables 2-4, respectively. In order to train and test the FDNN model, a set of data with \(245,760\) realizations was gathered. We used 70% of the data for training, 15% as the validation set, and 15% of the data for testing. For the CNN model and bi-LSTM model, we used a data set of 10,000 realizations with the same proportions for the training set, validation set, and test set as the FDNN. The parameters for training those models are shown in Table 5.  
All the proposed DL-based channel estimation methods were implemented on an computer with an Intel Core i5-10400 CPU @2.90 GHz, an NVIDIA GeForce GTX 1050 Ti Ti 16 GB memory. Matlab 2021a was used for the Monte-Carlo simulations.
## 3 Results and Discussion
### Penetration Resistance Analysis
#### 3.1.2 Penetration Resistance of Sand  
The sand stratum was simulated using the Mohr-Coulomb constitutive law to represent drained conditions, with elastic modulus E = 25 MPa (within the typical 10-50 MPa range for dense sand), Poisson's ratio \(\mu\) = 0.3, and interface friction coefficient \(\alpha\)-tano (\(\alpha\) = 0.5) ([PERSON] et al., 2018).  
The convergence analysis of the grid size (Figure 15a) and penetration speed (Figure 15b) confirmed that the CEL numerical solutions achieved convergence at a refined zone size of 0.03 \(L\) and a penetration speed of 0.2 m/s. These results correspond well to the centrifuge test data. Theoretical calculations incorporating soil backfill exhibited a moderate correlation with the experimental results at penetration depths of <2 m, while discrepancies progressively increased beyond a 2.5 m depth.  
Based on the stress distribution cloud diagram of typical calculation insertion depth shown in Figure 16, the stress distribution range of sand is much smaller than that of clay. The displacement vector diagram (Figure 17) revealed distinctive dilatant behavior in sand. Shallow penetration (0.6 m): Rapid backfill occurred, with the collapsed cavity diameter limited to 1.2 \(L\). Intermediate-to-deep penetration (\(\geq\)1.3 m): Nonlinear growth in penetration resistance was observed, accompanied by minimal surface heave (<5% \(L\)).  
Figure 12: Variation curves of spudcan penetration resistance with depth in clay: (**a**) grid size convergence analysis; (**b**) penetration rate convergence analysis.  
Comparative analysis demonstrated that penetration resistance in sand substantially exceeded that in clay, exhibiting the following distinctive characteristics:  
1. Compared with clay, the shear failure zone in sand was 42% smaller, indicating that energy dissipation via particle rearrangement governed bearing capacity development.
2. Sand demonstrated nonlinear resistance increases with depth, particularly beyond the critical 0.8 m threshold, reaching the design ultimate bearing capacity of 4400 t at a 1.8 m depth. This response was controlled primarily by interparticle friction and dilatancy effects. Conversely, clay soil exhibited resistance deceleration beyond a 6 m depth, accumulating only 1600 t at a 16 m depth due to bearing capacity plateau effects induced by remolded softening.
3. The soil disturbance zone surrounding the spudcan was significantly more extensive in clay than in sand. In contrast, sand displayed markedly earlier backfill initial  
Figure 13: The cloud diagram of Mises stress for clay at typical penetration depths (Pa).  
tion during penetration. These differential responses necessitate distinct analytical approaches for accurate geomechanical modeling, requiring comprehensive incorporation of backfill effects to ensure the accuracy in theoretical calculations.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 3 Background: iSDVN System
### Routing  
The four types of routing can be described as follows: unicast routing, multicast routing, broadcast routing, and geocast routing [10; 91]. One-to-one communication describes SDVN unicast routing, which routes packets between a source and a destination node. SDVNs use multicast routing to send packets from one source to a group of nodes, also known as grouping or one-to-many routing [92]. Multicasting is widely used in defense and military applications. In this routing type, packets are broadcast across the network from a source node to multiple target nodes connected to the network. It is a popular method of ensuring human safety during disasters. Packets can be geocast to a set of nodes within a particular geographical region or geocast area using geocast routing. Geocast group members are identified by location in a geographic area, while membership in a multicast group can occur anywhere in an ad hoc network [93]. To reduce latency between nodes in an SDVN, researchers have developed several routing protocols, which are categorized as follows:  
1. _Position-based routing protocol_--Using the Global Positioning System (GPS) and the destination IP address, the source node routes a packet using the destination's geographical location and IP address.
2. _Topology-based routing protocol_--In this protocol, packets are forwarded from the source to the destination node based on network connectivity information. Proactive routing methods, reactive routing methods, and hybrid routing methods are three types of proactive routing. 1. _Proactive routing_: For this protocol, the shortest-path algorithm determines the path and stores it in the routing table. During periodic updates, this table is shared with neighbors. 2. _Reactive routing_: Route discovery begins when a node discovers that it needs to communicate with another node. This is called \"on-demand routing.\" This protocol has the advantage of reducing network traffic. 3. _Hybrid routing_: In this protocol, networks are classified as local or global, and proactive and reactive routing methods are combined to reduce routing overhead and delay for local and global networks.  
3. _Broadcast-based routing protocol_--In the broadcast domain, broadcast routing sends packets to every node in the vehicular network.
4. _Cluster-based routing protocol_--For communication purposes, clusters are created in a network based on parameters such as velocity and direction. The cluster head manages inter-cluster and intra-cluster communication. The cluster head creates a virtual network infrastructure to enable scalability when performing intra-cluster communication using a direct path.
5. _Geocast-based routing protocol_--A mobicast message is used to communicate between vehicles in a region termed the _zone of relevance_.