esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Existing Pathfinding Techniques
### Informed Search or Heuristic Approach Algorithms
#### 3.2.3 D-Star Lite Algorithm (Dynamic A*)  
Found in 1994 by [PERSON], the D* or \"Dynamic A*\" Algorithm is an incremental heuristic search Algorithm that can be used for finding the shortest path in situations where the environment maybe unknown or partially unknown. Earlier, most of the pathfinding Algorithms were developed in ways where the environment model was considered to be accurate and complete [50]. However, in the real world, there exists many situations where the environment would change suddenly without any warnings or there exists information which may be incomplete or even non-existent [51].  
Machines which are automated that have the ability to operate in unknown or partially unknown environment, for example, planetary exploration robot or character in video games, require a method that has the capability of fast and efficient re-planning helping them to move more intelligently in a time of critical situations or difficult terrains [52]. Similar to the A* Algorithm, the D* Algorithm 5 creates a list of nodes for evaluation termed \"OPEN list\". There are several different states of nodes such as NEW, RAISE, LOWER, OPEN, and CLOSED [53]. The NEW state refers to the fact that the current node was never kept in the open list. OPEN state means that the node is in the open list [54]. The CLOSEDstate means it is no longer kept in the open list [55]. The RAISE state refers to the fact that the cost of the node was higher than the previous time it was kept on the open list [56]. Similarly, LOWER state means that the cost of the node was lower than the previous time it was kept on the open list [57].  
```
Input: Set of all vertices. Output: Search Path sequence vertices S_DSRI] Step 1: Set d[s] = 0, S_DSRI] = \(\mathbf{\Phi}\), where s is the source vertex and S_DSR is an array having all the visited vertices Step 2: For all vertices v except s, set d[v] = \(\mathbf{\infty}\) Step 3: Find \(\mathbf{q}\) not in S_DSR such that d[q] is minimum; where d[q] is determined by function f(q) as follows: \[f(q)=\left\{min_{q^{\prime}\in\text{Pred}(\mathbf{q})}(g(q^{\prime})+c(q^{ \prime},q))\right.\qquad\text{if }q=s\] \[\qquad\text{otherwise}\]

where f(q) determines the next move based on the g-values. Step 4: Check if its g-value is equal to its f(q) value then it is locally consistent, otherwise it is locally inconsistent. Step 5: The g-values of all vertices are equal to their start distances \(\Longleftrightarrow\) all vertices are locally consistent Step 6: To estimate the distance to the goal, heuristic function, h(q_q_goal) is computed. The heuristic estimate h(q_q_goal) of the distance between vertices q and q_goal must satisfy \[h\left(q,q_{goal}\right)\left\{\begin{aligned} &=0&& if\ q=q_{goal}\\ &\leq c(q,q^{\prime})+h()&&\qquad\text{otherwise} \end{aligned}\right.\] for all vertices q \(\in\mathbf{Q}\) and q\({}^{\prime\prime}\in\text{Succ}(\mathbf{q})\) Step 7: Repeat Steps 3 to 6 until all the vertices are in S_DSR
```  
**Algorithm 5**D* Algorithm.
## 4 Discussion  
Referring to the plots in Figure 8, our method is superior to the other five methods in terms of ASA and USE. The LSC, ERS, and our method exhibit high performance on BR. However, our approach is more competitive at a lower set number of superpixels. As a preprocessing step, generating a lower number of high-quality superpixels is more helpful for subsequent tasks. From Figure 8 and Table 2, it can be seen that the proposed method is comparable to the other five representative methods in terms of boundary adherence.  
Figure 10: The metrics value comparison of our seed sampling strategy and grid initializations.  
Figure 9: Visual comparison of superpixel segmentation results using different algorithms.  
Figure 9 shows the visual results of superpixel segmentation using these algorithms. Enlarge the local segmentation results for detailed inspection. Visually, our method had the most satisfactory segmentation results for different types of images. SLIC, SNIC, and our method showed the best compactness. However, compared with the proposed algorithm, SLIC and SNIC had weaker boundary adherence. The boundary adherence of ERS was very competitive, but the compactness of ERS was not satisfactory. LSC produced competitive results and performed well in compactness and boundary adherence. LSC introduces global information to generate superpixels, which makes it requires 20 iterations. From Figure 8d, LSC took longer to run than our method. In addition, our method can exceed the performance of LSC by adjusting parameters, but it sacrifices compactness. Superpixels generated by FH and the proposed method exhibited content sensitivity. Due to the lack of compactness constraints, the superpixels produced by FH were extremely irregular and under-segmented in content-rich areas. The results show that the algorithm achieves good boundary adherence while ensuring good compactness.
# Postharvest Reduction of _Salmonella enterica_ on Tomatoes Using a Flagmonic Acid Emulsion
## 2 Materials and Methods
### Preparation of Wash Systems  
Treatments were tested against _Salmonella_ with and without 0.5% (\(w/v\)) organic load (OL). The OL was made by pureeing grape tomatoes and then adding the puree to the wash treatments. The treatments of 200 ppm free chlorine, 80 ppm PAA, 30 mM PEL and 50 mM PEL were each tested with and without added OL; water and NR controls were examined without OL. Autoclave-sterilized deionized water was also examined to simulate washing without a sanitizer present. All treatment rinses were prepared in 500 mL volumes. The chlorine sanitizer was made by adding Clorox Regular Bleach (The Clorox Company, Oakland, CA, USA; EPA registration number 5813-1) to sterile, deionized water to a free chlorine concentration of 200 ppm, which was verified with an Ultra High Range Chlorine Portable Photometer (Hanna Instruments, Woonsocket, RI, USA) immediately prior to use. The pH of the chlorine solution was also adjusted to 7.0 (\(\pm 0.02\)) using 1 M citric acid.  
PAA solution was made by mixing Sanidate 5.0 (BioSafe Systems, East Hartford, CT, USA) in sterile, deionized water. The final concentration of 80 ppm PAA was verified using a PAA Test Kit (T5 CTK7500-Z, Thomas Scientific, Swedesboro, NJ, USA). To simulate field packing, a NR control was also examined; this evaluated the level of _Salmonella_ inoculated onto tomatoes that received no treatment. A sterile water control was used to simulate washing without a sanitizer in the system. The pH values of each treatment solution with and without organic load were measured immediately prior to the introduction of inoculated tomatoes (Accumet AB250 pH/ISE meter, Thermo Fisher Scientific, Waltham, MA, USA) to determine the extent to which the acidic tomato puree (OL) affected the pH of the solutions (Table 1).
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 1 Introduction
### Multitenant Energy Management by TowerCo
This functionality is concealed from the operators, has an independent life-cycle management, and operates in the service infrastructure layer. The coordination functionality becomes an infrastructure service that the TowerCo provides to the logical RANs. In Figure 7, we show the addition of the coordination functionality as a service infrastructure provider's (SIP) network function (NF) to the original baseline in Figure 4.  
The rest of the paper is organized as follows. In Section 2, we look at related works. In Section 3, we present a description of the problem we look to solve along with our contributions to this area. The system model that includes the deployment aspects and energy cost distribution is introduced in Section 4. The AI system implemented to assist each MNO in managing their independent logical RAN instances is discussed within Section 5. Finally, details about simulation parameters, assumptions, setup, results, and a comparison to other works can be found in Section 6. Conclusions along with the proposed future directions are presented in Section 7.
# Computational Modelling of Rectangular Sub-Boundary Layer Vortex Generators
## 1 Introduction
When the angle of attack increases, both lift and drag coefficients rise up to values higher than the ones reached in steady state conditions. The concept of micro vortex generators was most probably first introduced by [PERSON] [18]. In that study, wave-type micro VGs with a height of 27% and 42% of the local boundary layer (BL) thickness were mounted on an airfoil to decrease trailing edge noise by suppressing the formation of a Karman vortex street and by reducing the velocity deficit in the airfoil wake. Since the late 1980s, these devices appeared in the literature under different names such as sub-boundary layer vortex generator (SBVG), presented by [PERSON] et al. [19], the submerged vortex generator of [PERSON] et al. [20], the low-profile vortex generator [PERSON] et al. [21] and the micro vortex generator ([PERSON] [22]). As stated in the review of [PERSON] et al. [23], the potential applications of VGs and SBVGs include control of leading edge separation, shock-induced separation and smooth surface separation. Vortex generators are applied on wind turbine blades with the major aim to delay or prevent the separation of the flow and to decrease the roughness sensitivity of the blade. They are usually mounted in a spanwise array on the suction side of the blade and have the advantage that they can be added as a post-production fix to blades that do not perform as expected. Therefore, adding VGs is a straightforward solution to improve the rotor performance ([PERSON] et al. [24]). The main contribution of this work is the computational study of the primary vortex generated by three different rectangular VGs, where it has been found that the highest vane is the most suitable actuator for flow control applications. The goal of this study is to investigate how well the simulations can reproduce the physics of the flow behind a rectangular VG to characterize the primary vortex generated by the vane mounted on a flat plate with three different device heights. The aspect ratio (AR)of the vane defined as the relation between the height and length of the VG will consequently vary, maintaining the angle of attack constant. The baseline VG has an aspect ratio of AR\({}_{\rm H}\) = 2.5, and two height variations are also investigated, those corresponding to AR\({}_{\rm H2}\) = 2 and AR\({}_{\rm H1}\) = 3, as sketched in Figure 1. For this purpose, computational simulations have been carried out using the RANS (Reynolds averaged Navier-Stokes) method and at a Reynolds number Re = 2600. The case consists of a single vortex generator on a flat plate with the angle of attack of the vane to the oncoming flow \(\beta\) = 18\({}^{\circ}\). The flow over the flat plate without the VG has been previously simulated to calculate the boundary layer thickness at the VG position.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 3 Switchable Receiver Front-End Architecture: HARRF LNA and SPDT Integration
### Transmission Line Integration  
To optimise integration, the transmission line impedance transformation is given by:  
\[Z_{TL}=Z_{0}\bigg{(}\frac{Z_{LNA}+jZ_{0}\tan(\beta l)}{Z_{0}+jZ_{LNA}\tan( \beta l)}\bigg{)}, \tag{11}\]  
where:  
* \(Z_{TL}\) is the transmission line impedance,
* \(\beta\) is the propagation constant,
* \(l\) is the transmission line length.  
The phase shift introduced by the transmission lines is given by:  
\[\phi=\beta l=\frac{2\pi}{\lambda}l, \tag{12}\]  
where \(\lambda\) is the wavelength of the operating frequency.
# Site-Specific Earthquake Ground Motions for Seismic Design of Port Facilities in Indonesia
## 3 Results and Discussion
### Comparison of the Proposed SA and That in the SNI  
Regarding site class SD, the proposed SA was larger than that of the SNI in the period range of 0.2-2.0 s, which is the natural period range for most port facilities. The proposed SA showed a maximum value of 1.28 g in the period range of 0.2-0.8 s, whereas the maximum SA of the SNI was 0.93 g. The maximum value of the proposed SA was larger than that of the SNI by a factor of 1.38. Figure 5 shows that the SA at the bedrock was smaller than that of the SNI in the period range of 0.3 s or longer. This disagreement in the SAs suggests that the SNI underestimated the amplification factor of the EGMs for site class SD.  
For site class SE, the difference between the two SAs was not obvious in the period range of 1.0 s or shorter. However, the proposed SA was larger than that in the SNI in the period range of 1.0 s or longer. At 1.5 s, the proposed SA (=0.65 g) was larger than that in the SNI (=0.54 g) by a factor of 1.20. The natural frequency of the shallow subsurface becomes lower because of the nonlinear soil characteristics during massive earthquakes. The change in the natural period is remarkable for the soft soil condition (i.e., for site class SE). The disagreement in the SAs in the long period range is attributed to the fact that we appropriately considered the nonlinear soil characteristics in the analysis. For both site classes, a reasonable seismic design of the port facilities was made possible by using the proposed SAs.
# Enhanced Nondestructive Testing Using Pulsed Eddy Current and Electromagnetic Ultrasonic Techniques
## 2 Pulsed Eddy Current and Electromagnetic Ultrasonic Testing  
### Principles of Pulsed Eddy Current Testing  
The principle of pulsed eddy current detection [12], as depicted in Figure 1, involves applying pulse excitation to an excitation coil positioned on the surface of the test piece. This excitation generates an alternating magnetic field at the middle of the coil, which, in turn, induces eddy currents on the surface of the test piece. The eddy currents create an induced magnetic field that counteracts changes in the original alternating magnetic field, affecting parameters such as the voltage or impedance in the detection coil. When a defect affects the distribution of these induced eddy currents, it alters the test coil's parameters differently compared to an intact area. Thus, defect characteristics can be determined based on these variations. This technique is primarily effective for identifying surface and near-surface defects.  
The voltage change at the test coil can be expressed as follows:  
\[\Delta V=\sum_{i=1}^{N}j\omega\int\limits_{S_{i}}\Delta B\cdot ds=\sum_{i=1}^{N} j\omega\oint\limits_{c}\Delta A\cdot dl, \tag{1}\]  
where \(N\) is the number of coil turns, \(\omega=2\pi f\), \(f\) is the excitation signal frequency, and \(\Delta B\) and \(\Delta A\) represent the variations in magnetic induction intensity and magnetic vector potential, respectively. The symbol \(S_{i}\) represents the plane area surrounded by the coil turns, and \(c\) represents the coil turns. This equation reveals that the voltage change is directly proportional to the number of coil turns and the frequency of the excitation signal. Pulsed eddy current testing exhibits a phenomenon known as the skin effect, where the distribution of induced eddy currents is uneven and decays rapidly with depth. The expression for the skin depth of a pulse square wave is as follows:  
\[\delta=\sqrt{\frac{\Delta}{\pi mn\mu\sigma}},n=1,3,5,7,\ldots, \tag{2}\]  
where \(\mu\) is the material permeability, \(\sigma\) is the electrical conductivity, \(m=\Delta/T\) is the pulse square wave duty ratio, and \(T\) represents the pulse period. Typically, three times the calculated skin depth is considered the effective maximum depth for detecting defects using this method. As the frequency of the excitation signal increases, the maximum depth of detection decreases, rendering pulsed eddy current testing less effective for identifying deep defects within materials.
## 4 Results
### Model Evaluation Phase
#### 4.2.1 Pre-Fine-Tuning Evaluation  
In the initial phase of our research, we deployed two baseline models from the GPT Omni family--gpt-4o and gpt-4o-mini--to assess their capabilities in zero-shot fake news detection and classification. The results, as summarized in Table 3, were unexpectedly low. The GPT-4o model achieved an accuracy of only 12.3%, while the smaller GPT-4o-mini outperformed it with an accuracy of 24.3%, nearly doubling the prediction accuracy of its larger counterpart. This unexpected performance gap raised questions about the models' ability to handle this task without prior training. Initially, we hypothesized that the complexity of the prompt might have contributed to the models' underperformance. To test this, we created two additional, simplified prompts intended to clarify the task for the models. However, even with these modifications, the classification results remained low, suggesting that the prompt complexity was not the primary issue.  
This led us to reconsider our expectations for zero-shot capabilities in detecting fake news. In this task, distinguishing false news from legitimate information often requires subtle contextual understanding, which an untrained model might lack. This is particularly relevant given that even human readers sometimes struggle to identify fake news without prior knowledge of the topic. This reflection underscored a key limitation in the zero-shot  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Resources** & **Training Loss** & **Validation Loss** &
\begin{tabular}{c} **Training Time** \\ **(Seconds)** \\ \end{tabular} & **Training Cost** \\ \hline ft:gpt-4o & API & 0.0021 & 0.0037 & 3353 & USD 54.30 \\ ft:gpt-4o-mini & API & 0.0081 & 0.0075 & 1779 & USD 6.52 \\ ft:bert-adam & Tesla V100-SXM2-16 GB & 0.0294 & 0.0386 & 877 & USD 2.54 \\ ft:cnn-adam & Tesla V100-SXM2-16 GB & 0.6253 & 0.5884 & 47.90 & USD 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fine-tuning metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline base:gpt-gpt-4o-2024-08-06 & 0.123 & 0.123 & 0.123 & 0.123 \\ base: gpt-4o-mini-2024-07-18 & 0.243 & 0.1969 & 0.243 & 0.2123 \\ ft:gpt-4o & 0.986 & 0.9861 & 0.986 & 0.986 \\ ft:gpt-4o-mini & 0.986 & 0.9861 & 0.986 & 0.986 \\ ft:bert-adam & 0.975 & 0.9758 & 0.975 & 0.975 \\ ft:cnn\_adam & 0.586 & 0.6334 & 0.586 & 0.5457 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of model performance metrics.  
application of LLMs for nuanced classification tasks and pointed to the need for more targeted fine-tuning. Consequently, these findings provided a strong motivation to pursue further fine-tuning of the models, enabling them to develop the specific knowledge and context needed to excel in fake news detection tasks.
# Anti-Slip Gait Planning for a Humanoid Robot in Fast Walking
The deviation of motion state between the model and the real robot grows with the growing of the leg's mass and the deviation also grows with the increasing of the walking speed, which adds difficulties to control strategies. Therefore, in this paper, learning from the achievements of predecessors, we proposed a 3D three-mass model. In our model, we regarded both trunk and thighs as an inverted pendulum, and the shanks and feet as mass-points under no constraints with the trunk. Then we used this model to calculate the friction constraints between the ground and the sole, as well as the pattern of the swing leg conveniently with enough precision. Hip trajectory directly affects the stability of the robot. [PERSON] et al. proposed a method to plan the hip trajectory by traversing the search with 2 key parameters [20]. However, this method requires repeated calculations to obtain a higher stability gait, which is complicated and not practical. The preview control method proposed by [PERSON] directly takes the zero-moment point (ZMP) Equation as a constraint condition, and then obtains a smooth trajectory of CoM that takes into account both the robot's stability and the CoM's acceleration increment by means of quadratic optimization [21]. But it relies heavily on the accuracy of modeling, so it is very effective on humanoid robot with light legs but has poor performance in robots with heavy legs. However, as described above, under the current technical conditions, an increase in leg mass due to an increasing demand in driving power is unavoidable. Therefore, in this paper, we improved the preview control method based on the 3D three-mass model. We added compensation to the reference ZMP, and finally obtained a hip trajectory that enables the robot to achieve a large stability margin. With the trajectories of swing leg and hip, we obtained the trajectory of every joint of legs by using inverse kinematics, which means complete gait planning. Simulation experiments have shown that both the motion of the swing leg and the ZMP trajectory have an effect on the rotational slip. The gait planning method proposed in this paper can effectively avoid the robot's rotational slip generated during fast walking. The actual ZMP follows well for the reference ZMP, thus ensuring the stability. The rest of this paper is organized as follows: Section 2 proposes the 3D three-mass model; in Section 3, friction constraint is calculated and trajectory of the swing leg is planned; Section 4 records the planning of hip trajectory; and the simulations and conclusions are presented in Sections 5 and 6 respectively.
## 5 Simulation and Discussion
### Day-Ahead DRO Model Simulation Analysis
#### 5.2.5. DRO Method Evaluation  
To verify the effectiveness of the DRO method, Case 4 is compared with the scheduling results from stochastic optimization (SO) and robust optimization (RO) in the day-ahead phase, as shown in Table 7. From the table, it is evident that the models exhibit varying performance in terms of cost, carbon emissions, and other key indicators. The SO model shows the lowest total cost by assuming probability distributions for uncertainty variables. However, because it relies on historical data to build the probability distribution, it may overlook sudden events, which introduces a higher risk in practical applications. In contrast, the RO model addresses uncertainty by considering the worst-case scenarios. While it ensures system stability, its overly conservative strategy leads to significantly higher total costs than the SO model, resulting in inefficient resource usage and increased operating costs and carbon emissions.  
The DRO model combines the advantages of both SO and RO, effectively addressing uncertainty while avoiding the excessive conservatism of RO and reducing the risks that may arise with SO. The DRO model shows a total cost slightly higher than SO but noticeably lower than RO, indicating that it strikes a better balance between cost and stability while ensuring robustness, demonstrating superior optimization performance.