esa-sceva/satcom-chunk-collection huggingface dataset:
# DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation
## 4 Experimental Results
### Ablation Experiments  
In this subsection, we performed ablation experiments to demonstrate the superiority of the DSA module and the two units called C-S and S-C. We compared the proposed DSA module with SENet, STN, CBAM, and DANet [41]. We embedded these attention modules between ResNet-18 and FPN, and the results are shown in Table 4. Figure 7 displays the segmentation results of embedding different attention modules.  
In order to demonstrate the contribution of the C-S and S-C units, we proposed two variant structures: C-S Unit Only and S-C Unit Only. As shown in Figure 3, the C-S Unit Only structure utilizes the C-S unit to process \(X_{1}\), and the processed result is aggregated with \(X_{2}\) by a 'channel shuffle' operator. On the contrary, the S-C Unit Only structure uses the S-C unit to process \(X_{2}\), and the results are aggregated with \(X_{1}\).  
Figure 6: The segmentation results of Mask R-CNN, YOLACT, Polar Mask, SOLOV2, and DSA-SOLO. The red box shows the instance category and the segmentation precision, which is enlarged in the upper left corner of the images. (**a**,**b**) shows the results of two different targets.  
The results are shown in Table 5. Both C-S and S-C units can improve the accuracy of SOLOv2, the experimental results demonstrating the effectiveness of the two proposed units. Specifically, the C-S unit has limited improvement on accuracy, but it improves the speed of SOLOv2. The S-C unit improves the segmentation accuracy, but the speed is affected. The segmentation accuracy is obviously improved when the two units are used simultaneously.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 5 Implementation of On-Board Systems
### Active Approach  
#### 5.1.1 Hardware Configuration  
The configuration adopted in the active on-board system is shown in Figure 12: a single focused transmitter and four pairs of receivers are fixed to a frame which is in turn attached to a cart [40; 45; 47]. The lower active surface of the transducer is separated from the upper surface of the rail; their distance is known as _lift-off_ and is on the order of a few centimeters for both the transmitter and the receivers. The transmitter feeds the top of the rail surface with a narrowband toneburst signal (a Hanning-windowed sinusoid); it has a tunable repetition rate (representing the frequency of the excitation and related to spatial resolution). This results in a (repeated) multimodal guided wave that propagates symmetrically with respect to the excitation line. The receivers detect the guided waves leaking into the surrounding air; for this reason, they are tilted according to Snell's law.
# Collision Study on New Aluminum Alloy W-Beam Guardrail
## 7 Conclusions  
Based on the comparative analysis of the finite element models of three types of W-beam guardrails under the same collision conditions, with the traditional steel guardrail as reference, and through collision simulation experiments, the differences in key indicators such as maximum dynamic deformation, vehicle acceleration, and guardrail energy absorption were compared. The following conclusions were drawn:  
1. According to theoretical derivation, the collision deformation of waveform beams is inversely proportional to the square root of their bending stiffness. After optimization of the waveform beams, their cross-sectional moment of inertia increased by 28.6%, and the deformation in finite element analysis decreased by 12.8%, confirming the accuracy of this law.
2. Under the condition of the same guardrail material, compared with the traditional guardrail, the material cost of the new W-beam guardrail increased by 8.1%, while the maximum dynamic deformation decreased by 12.8%, and the energy absorption increased by 11.6%. This reduces the risk of serious injury to occupants when the vehicle runs off the road in severe collisions. This indicates that optimizing the linear shape of the guardrail section to increase its moment of inertia can effectively improve the protective performance of the guardrail. This optimization strategy provides a valuable reference for guardrail design and is expected to promote the improvement of guardrail safety performance in practice.
3. Compared with the traditional steel guardrail, the new aluminum alloy guardrail re-duces the vehicle's center of mass acceleration by 33.6% and increases the energy absorption by 26.0% after collision. It exhibits better toughness and energy absorption capacity during a collision, better mitigating the impact force generated by the vehicle collision and providing better protection for occupants.  
Conceptualization, L.W., J.L. and Z.T.; methodology, L.W. and Z.T.; software, L.W.; validation, X.H. and Z.T.; formal analysis, R.L.; investigation, D.C.; resources, X.H.; data curation, L.W.; writing--original draft preparation, L.W.; writing--review and editing, all authors; visualization, J.L. and Z.T.; supervision, R.L.; project administration, X.H.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Evolution of Flow and Streaming in Exponential Variable Cross-Section Resonators
## 4 Results and Discussion
### Effect of Driving Strength
#### 4.3.4 Optimal Driving Strength  
Table 1 lists the increments of \(\Delta P\), \(|u_{\text{max}}|\), and \(\Delta T\) and their growth ratios with each increment of \(l\) = 100 \(\upmu\)m. Here, \(\Delta P\ =\ P_{\text{max}}-P_{\text{min}}\) and \(\Delta T\ =\ T_{\text{max}}-T_{\text{min}}\) are the peak-to-peak pressure and temperature amplitude, respectively, and \(|u_{\text{max}}|\) denotes the absolute value of the maximum axial flow velocities.  
Although the values of \(\Delta P\), \(\Delta T\), and \(|u_{\text{max}}|\) all increase with increasing driving strength, their growth ratios do not always increase (some values are negative). From Table 1, it can be easily found that there is a sudden decrease for the increments of \(\Delta P\) and \(|u_{\text{max}}|\) but a sudden increase for the increments of \(\Delta T\) under the condition of Case D to Case E, although they keep increasing from Cases A to Case D. Therefore, \(\Delta P\) and \(|u_{\text{max}}|\) have maximal increments when \(l\) increases from 300 to 400 \(\upmu\)m and minimal increments when \(l\) increases from 400 to 500 \(\upmu\)m. Moreover, the biggest growth ratios for \(\Delta T\) occur when \(\Delta P\) and \(|u_{\text{max}}|\) have the smallest growth ratios, implying a more rapid increase of energy dissipation under the condition change from Case D to Case E than other condition changes.  
This suggests that the optimal exponential resonator has the biggest ratio of output work to input work when the driving displacement amplitude is between 300 and 400 \(\upmu\)m. Furthermore, if the driving amplitude is larger than 400 \(\upmu\)m, the sound pressure amplification ability will become significantly weak, owing to the acceleration of energy dissipation.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 3 Methodologies
### A New Method of Code Generation for Safety-Critical Software
#### 3.2.2 Code Generation Based on Overall Software Requirements  
After the requirements are clarified, the research moves on to the code generation phase. Given the overall requirements of the domain, a straightforward approach is to attempt code generation directly from these overall requirements. This raises the first research question addressed in this paper:  
**RQ1:**: _Can LLMs generate software code that meets the functional requirements directly from the overall requirements of safety-critical software?_  
To verify this question, the overall software requirements are used as prompts and input into GPT-4. GPT-4 will auto-generate the corresponding code. The correctness of the generated code is then validated through manual code review and testing. This approach adopts the \"Prompt-simple\" method.
## 4 Experimental Results
### Error Analysis and Performance Enhancement
As shown in Figure 14, a better segmentation result was obtained. Next, the OCR system was re-evaluated using the rectified dataset. Table 9 presents the new evaluation results of the proposed models with the 13,070 character classification task. It may be observed that the recognition accuracy was significantly improved after projection rectification. The model performance was further improved by incorporating the rectified real dataset into the learning process for both the mixed data model and the transfer learning model.
###### Abstract  
This research proposes a hybrid path planning framework for intelligent inland waterway Unmanned Surface Vehicles (USVs), which integrates the enhanced BIT* (Batch Informed Trees) algorithm with the TD3 (Twin Delayed Deep Deterministic Policy Gradient) deep reinforcement learning method. To address the limitations of traditional path planning algorithms in dynamic environments, the proposed BIT*+TD3 model leverages the BIT* algorithm to generate initial paths in static environments through elliptical informed sampling and heuristic search. Simultaneously, it utilizes the TD3 algorithm to dynamically optimize these paths through twin Critic networks and delayed policy updates. This research designs a novel reward mechanism aimed at minimizing turning angles, smoothing speed transitions, and shortening path lengths. Furthermore, it incorporates a hydrodynamics-based energy consumption model and multi-threaded parallel computation to enhance computational efficiency. Experimental validation demonstrates that, compared to traditional methods, this model exhibits significant improvements in obstacle avoidance success rate, safe distance maintenance, convergence speed, and smoothness. By bridging sampling-based planning methods with deep reinforcement learning methods, this research advances autonomous navigation technology and provides a scalable and energy-efficient solution for maritime applications.  
Keywords:batch informed trees; twin delayed deep deterministic policy gradient; unmanned surface vehicles; deep reinforcement learning
## 3 Algorithm Description
### Model Setup
The reward function is formulated in Equation (3), where \(w_{1},w_{2}\) are the weighting coefficients that balance the objectives of load balancing and delay minimization. If the neighboring node is the destination node, the reward is set to \(q_{max}\) to prioritize rapid data delivery to the destination. \[reward_{j}=\left\{ \begin{aligned} & q_{max}&& N_{j}\text{ is the destination}\\ & q_{max}-w_{1}*g_{j}-w_{2}*D_{ij}&& otherwise \end{aligned} \right. \tag{3}\]  
\[g_{j}=q_{receive}+q_{send}+q_{occupied} \tag{4}\]
## 4 Proposed Scheme
### Step 3: Establishing Final Trajectory for the Vehicle  
This step involves establishing the finial trajectory for the vehicle by selecting the nearest unvisited node in \(S_{v}\) one at a time using a greedy approach named NJNP [35]. NJNP always selects the nearest sensor from the waiting list as the next visiting node. Figure 5 illustrates an example of this process, with the finial trajectory of the vehicle being \(O_{1}\)\(\rightarrow\)\(v_{1}\)\(\rightarrow\)\(u_{1}\)\(\rightarrow\)\(u_{2}\)\(\rightarrow\)\(u_{3}\)\(\rightarrow\)\(u_{4}\)\(\rightarrow\)\(O_{1}\).  
Algorithm 4, which is described as follows, defines the process for establishing the vehicle's trajectory. The computational complexity of this algorithm is \(O(n^{2})\).  
Input: A requesting nodes set \(S_{d}\), the center of outer circle \(O_{1}\)  
Output: The trajectory \(\prod\).  
Step 1: Set \(\prod=\varnothing\), \(v=O_{1}\).  
Step 2: Choose a node \(u\) with the nearest distance in set \(S_{d}\)  
\(\prod\leftarrow\prod\cup\{u\}\)  
\(S_{d}=S_{d}\) / \(u\)  
\(v=u\)  
Step 3: if \(S_{d}\
eq\varnothing\)  
Go to Step 2  
Step 4: Output \(\prod\).  
**Algorithm 4. Establishing trajectory for the vehicle**  
Finally, Figure 6 presents a flowchart of the LDFM scheme proposed in this work. The proposed LDFM scheme consists of Algorithms 1-4. Note that the time complexities of Algorithms 1-4 are \(O(n)\), \(O(n^{2})\), \(O(n^{2})\) and \(O(n^{2})\), respectively, where \(n\) is the number of sensor nodes deployed in the network. Therefore, the complexity of the proposed algorithm is \(O(n^{2})\).  
Figure 5: The final trajectories of the vehicle and drones are as follows: \(O_{1}\)\(\rightarrow\)\(v_{1}\)\(\rightarrow\)\(u_{1}\)\(\rightarrow\)\(u_{2}\)\(\rightarrow\)\(u_{3}\)\(\rightarrow\)\(u_{4}\)\(\rightarrow\)\(O_{1}\) for the vehicle (blue arrow), with \(v_{2}\), \(v_{3}\), \(v_{4}\), \(v_{5}\), \(s\) being assigned to drones (green arrow).  
Figure 6: Flowchart for the proposed LDFM scheme.
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 1 Introduction
The goal of precision fish farming is to improve the accuracy, precision, and repeatability of farming operations. The significant expansion of aquaculture poses both risks and opportunities that require careful evaluation and appropriate measures for adaptation. Aquaculture procedures are complex due to the presence of numerous environmental drivers, such as temperature, extreme events, and harmful algal blooms, their interactions with production systems, and knowledge gaps about fish responses [1; 12]. To support and facilitate decision making in this context, Decision Support Systems (DSSs) have been proposed as appropriate tools. DSSs are computer-based information systems designed to aid the decision-making process in complex situations, using data, models, and algorithms to generate insights and provide users with timely and accurate information to help them evaluate and compare alternative options. Typically, the decision-making process with a DSS involves several steps, including data gathering, analysis, and visualization, scenario analysis, and evaluation of alternatives. In addition, a DSS must provide decision makers with supporting information to enable strategic decisions for promoting industry sustainability in the coming decades. Furthermore, a DSS must achieve a balance between containing sufficient information and being user-friendly for accessibility [13]. The goal of this study is to examine how satellite technology can assist in addressing the issues that arise from the rapid growth of fish farming. The main objectives are as follows: (a) to determine which parameters have an impact on aquaculture processes and how remote sensing can aid in their assessment, (b) to establish what indicators could be used as \"warning signs\" and their corresponding appropriate thresholds, and (c) to evaluate the system's performance in real-life scenarios. This paper presents a web-based platform called \"Aquasafe\" which serves as a decision support system that constantly monitors fish farming operations and provides early warnings for potential risks. It combines satellite and _in-situ_ data to give comprehensive insights and alerts to farm operators. The platform offers a combination of satellite data, _in-situ_ measurements, and meteorological information, providing forecasts and alerts to aid in the effective management of fish farming operations. Ultimately, the system is intended to aid in the effective management of fish farming operations by providing valuable, accurate information about current conditions and production.
## 3 Field Trialed System
### Cyber and Network Security  
This section describes the applied security solutions that fulfil the objectives for security architecture, which are presented in Table 2.  
The baseline security--confidentiality and authenticity--of communications is based on standard security architectures [39, 47] for access and backhaul networks. Radio access is secured with appropriate security protocols for 3 GPP or Wi-Fi networks. Key distribution is based on physical SIM in the case of 4G and 5G networks and on passwords, which can be shared with QR codes, for example, in the case of Wi-Fi in the satellite bubble. Further, different application-layer security protocols were utilized to provide additional security, such as SSH for CAN and TLS for sensor communication and web-based information services.  
Communication between different private networks, which were inter-connected with a tactical core network, was secured with frequency hopping and adaptive modulation [41] and connections that spanned over the internet were secured with IPsec based VPN. The internet and other external services were behind firewalls, which were hosted within the tactical core network or outsourced for commercial service provider.  
Custom security needs in smart farming and remote-controlled tractor scenarios include fine-grained access control mechanisms to segregate users and devices, thus protecting assets and minimizing the threat of malware. Custom security needs in remote-controlled tractor scenarios emphasize the trustworthiness, reliability, and integrity of the safety-critical components as well as the availability of low latency communication needed for navigation.
## 3 Optical Receiver Filter Equation-MGF Optimisation Procedure
### MGF-Based Model for Time-Invariant Rx System  
To develop an MGF model for the Rx system, it would be a good idea to evolve it through three optical receiver operating environments. The first Rx model environment includes the main signal \(r_{sig}(t)\) without AGTN noise (use case I). The second Rx model environment incorporates the main signal \(r_{sig}(t)\) with white AGTN noise (use case II). The third Rx model environment covers \(r_{sig}(t)\) with colored AGTN noise (use case III).
# FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode
## 2 Current Distribution Calculation Model for Frozen Soil  
Figure 1 is the schematic diagram for three-layer frozen soil, where set \(\mathbf{I}\) is the current flowing into the ground. The entire ground can be simply divided into two parts, one of which includes the point current source, and the second is the area that does not contain the point current source [15; 16; 17]. The current distribution in region 1 satisfies the Poisson equation, and it satisfies the Laplace equation in regions 2-6.  
Besides, each region has different soil parameters in Figure 1, and the conductivity is in the order of \(\sigma\)1-\(\sigma\)6. In regions 2-6, where there is no point current source, the electric field equation is as follows. Here, \(V\) is the electric potential.  
Figure 1: Mixed-layer soil model. The resistivity of each layer of soil in regions 1–4 is different, and the grounding electrode is buried in area 1. Region 5 is a mountain that may exist in the surrounding environment, and region 6 is a lake, river, etc.  
\[\
abla^{2}V=0 \tag{1}\]  
Since there is a current source in region 1, the equation in this field must be considered separately. First, define the unit point charge density function as follows. \(\rho\) is the unit point charge density.  
\[\rho(x)=\delta(x-x^{\prime}) \tag{2}\]  
This function is the charge density of a unit point at point \(x^{\prime}\). Further, \(\delta(x)\) is defined as follows:  
\[\left\{\begin{array}{l}\delta(x)=0\cdots(x\
eq 0)\\ \int\limits_{v}\delta(x)dv=1\cdots(x=0)\end{array}\right. \tag{3}\]  
In the area where a point current source exists, the following relationship can be obtained. Here, \(I\) is the current, \(J\) is the current density, \(s\) is the surface through which the current flows, and \(\Omega\) is the calculation area.  
\[I=\oint\limits_{S}\overset{\rightarrow}{J}\cdot d\overset{\rightarrow}{s} =\int\limits_{\Omega}\
abla\cdot\overset{\rightarrow}{J}d\Omega \tag{4}\]  
For  
\[I=\int\limits_{\Omega}I\delta(x)d\Omega \tag{5}\]  
The following formula can be derived:  
\[\
abla\cdot\overset{\rightarrow}{J}=I\delta(x) \tag{6}\]  
Then, the electric field equation in region 1 can be written as follows. Here, \(\rho_{I}\) is the soil resistivity.  
\[\
abla^{2}V=-\rho_{I}I\delta(x) \tag{7}\]  
At the outer boundary: \(V=0\). The surface above region 1 and region 1 has a relationship: \(dv/dn=0\), where \(n\) is the direction of the outer normal, pointing from region 1 to the outside. Assuming that the soil labels in different layers of the model are \(i\) and \(j\), the boundary conditions are  
\[V_{i}=V_{j} \tag{8}\]  
\[\frac{1}{\rho_{i}}\frac{dV_{i}}{dn}=\frac{1}{\rho_{j}}\frac{dV_{j}}{dn} \tag{9}\]  
Using the abovementioned electric field equations and boundary conditions based on the finite element Galerkin method, the differential equation of the soil electric field can be obtained using the weighted residuals method.
## 2 Conceptual Reliability-Based Decision Support Framework
### Service Quality Perceived by End-User  
One of the benefits from social PPPs is enhancing people's well-being by the early provision of new facilities [34]. To better achieve potential social PPPs benefits, improved communications among stakeholders is essential [35; 36]. The mismatch between the contracts being managed remotely from the end-user makes integrated communication more difficult, particularly when the contracts focus on technical and economic concerns [37; 38; 39], risk analysis [40; 41; 42] and benefits as compared to traditional procurement [43; 44].  
Figure 2: Conceptual reliability-based decision support framework.  
In traditionally procured social infrastructure, long-term operational issues are managed by government, often those intimately involved in operations. In PPPs, the focus on value for money (VFM) natural drives to the capability and innovation of the private sector in managing risk, design, construction and operations (as a third party), maintenance and creating cash flows [2]. Some practitioners in the PPPs are even unclear whose expectation they need to satisfy [17]. [PERSON] et al. [17] considered this and demonstrated the deficiency of am end-user's perception during the PPPs evaluation through the use of several cases studies. [PERSON] et al. [28] evaluated the impact of innovations on long-term outcomes and verified that the end-user's involvement positively influences PPPs' project performance.  
The end-user's perception of service quality (_PSQ_, termed with variable '\(P\)') is a function of the relationship between expected service, thus the expectation the end-user has for the service to be provided, and the perceived service, thus the end-user's perception of the actual service that has been provided [16; 45]. It is viewed in terms of a process of 'expectation disconfirmation', where the satisfaction is based mostly on whether it meets or exceeds expectation. The perceived service quality leads to an understanding for improving service quality because it captures the relative information in influencing the end-user's overall perceptions of service [45]. The end-user's perceived service quality can reflect their expected service to a degree [46]. Specific social infrastructure assets require tailored measures to reflect the end-user's perceived service quality, but three generic categories exist being: the space quality, operation, and maintenance of such a service. These categories give rise to represent the end-user's level of satisfaction \(P\) in the way of multicriteria weight analysis, i.e., the sum of weighted satisfaction of each category.