esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### DUB6W180 Airfoil (Test Case 2)
#### 4.3.2 Flow Transition Localization
The most distinct expression of two plateaus including a steep step between them exists in the PC 2, even though the high gradient in the classical image processing result is sufficient for an auto-centering of the intensity gradient approximation. In conclusion, the PCA is able to reduce the influence of systematic gradients within the flow regimes due to reflection artefacts or spatial temperature gradients, allowing for a flow transition localization with the approximation of the intensity profile. However, the random and systematic errors for an approximation of the intensity gradient profile are on the same order of magnitude for each image processing method. Only if the approximation of the intensity is preferred does the PCA have advantages for locating the flow transition with reduced errors. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{\(x_{\text{tr}}\)/\(c\) in \%} & \multicolumn{3}{c}{\(\sigma_{\text{rand}}\)/\(c\) in \%} & \multicolumn{3}{c}{\(\sigma_{\text{syst}}\)/\(c\) in \%} \\ \cline{2-10}  & **Mean** & **std** & **PCA** & **Mean** & **std** & **PCA** & **Mean** & **std** & **PCA** \\ \hline Intensity approximation & 48.9 & 46.6 & 43.8 & 0.50 & 0.74 & 0.21 & 4.36 & 2.05 & 0.75 \\ Intensity gradient approximation & 45.0 & 43.6 & 43.8 & **0.13** & 0.17 & 0.20 & **0.52** & 0.93 & 0.76 \\ \hline \end{tabular}
\end{table} 
Table 3: Located flow transition position \(x_{\text{tr}}\) and corresponding random and systematic error \(\sigma_{\text{rand}}\) and \(\sigma_{\text{syst}}\) by means of both localization methods for each image processing method’s resulting flow visualization from the DU96W180 test case 2 at a \(\text{Re}=3\times 10^{6}\). Temporal mean value (mean), temporal standard deviation (std) and PCA. All values are normalized to the chord length \(c\). Figure 13: Comparison of the located laminar turbulent flow transition line by means of the temporal mean value (red), temporal standard deviation (blue) and PCA (green) flow visualization of the image series. Flow transition localization based on the approximation of the intensity profile (top) and approximation of the intensity gradient profile (bottom). The intensity gradient approximation method successfully locates the flow transition in each image with minimal random and systematic error due to the existence of a high gradient. The localization based on the intensity profile approximation in the classical image processing methods, however, has a larger systematic error. This error is minimized if the PC flow visualization is used for the localization.
## 2 Related Work
### Satellite Network Resource Allocation
This study stressed the importance of channel conditions and network density in satellite network task design and data transmission strategies, while also exploring ways to boost network performance through space-ground network integration. [PERSON] et al. [8] proposed a channel-aware task scheduling method to optimize resource allocation in broadband data relay satellite networks. The study took into account the effects of channel conditions on task scheduling and data transmission efficiency, highlighting the importance of considering both channel conditions and network density when developing satellite network tasks and transmission strategies. Moreover, emerging technologies in the field of artificial intelligence provided novel solutions for efficient resource allocation in resource constrained networks. [PERSON] et al. [9] investigated the application of intelligent algorithms to address complex network capacity management challenges, with the aim of improving data transmission efficiency and task scheduling. Their work offered a new perspective on satellite network resource optimization, particularly by demonstrating how deep reinforcement learning could be leveraged to improve network performance and resource utilization. Similarly, [PERSON] et al. [10] introduced a reinforcement-learning-based method to optimize satellite network capacity management in response to evolving network environments, thereby enhancing overall network performance. This method facilitated more efficient resource allocation and network utilization in dynamic conditions. [PERSON] et al. [11] examined the data transmission challenges in distributed satellite cluster networks and proposed a distributed robust planning approach, which enhanced the performance and robustness of distributed satellite networks when faced with randomness and uncertainty. [PERSON] et al. [12] proposed a state-action-reward-state-action (SARSA)-based actor-critic reinforcement learning (SACRL) resource allocation strategy. Under the above two processes, this algorithm can optimize the multi-dimensional resource allocation of Satellite Internet of Remote Things Networks (SloRTNs) and the data scheduling of the Inter net of Remote Things (IoRT). The advantage of cloud computing is that it can quickly obtain computing resources and flexibly adjust resource allocation according to business needs. By combining cloud computing technology, researchers can achieve better resource allocation strategies in different networks, such as vehicle networks [13] and wireless sensor networks [14].
# Manufacturer Channel Encroachment and Evolution in E-Platform Supply Chain: An Agent-Based Model
## 4 Simulation and Results
### The Effect of Consumer Channel Preference  
In the benchmark (Scenario A), consumers select two manufacturers in each of the e-commerce platform and direct sales channels, obtain the price information and evaluate utility. Under scenario C, we consider the existence of the channel preference, i.e., consumers will be willing to access more information about products from e-commerce channels in the _GetInfo_ state. This is also common in reality. Large e-commerce platforms have well-developed customer service and after-sales service, and some have integrated logistics, which can give consumers a better experience. When consumers have a channel preference, they will choose three (x = 3) manufacturers from the e-commerce platform and one (y = 1) manufacturer from the direct sales channel to evaluate and make a purchase decision.  
**Observation 2**.: _Consumer channel preference has an impact on the number of dual-channel manufacturers (NDM) and product prices._  
From Figure 8, it is clear that the distribution of the NDM differs significantly between scenarios A and C. Out of one hundred experiments under scenario C, there are eight times where only the single channel exists, which is not the case in the benchmark scenario. When there is a channel preference among consumers, it means that channel competition becomes more aggressive. E-commerce retail platforms have a channel advantage and can gain more exposure relative to direct sales channels. The viability of the direct sales channel is squeezed, so there is an abandonment of channel encroachment by all manufacturers.  
Figure 7: (a) Sales quantity in A and B; (b) consumer utility in A and B. (\(t\) = 100).  
In terms of price, as shown in Figure 9, all prices have decreased under scenario C. Manufacturers will cut retail prices to convert more views into purchases to earn larger profits when the retail channel has more exposure than the direct channel. In the case of the direct channel, the manufacturer needs to use a low-price strategy to attract more consumers to overcome the channel's disadvantage in the face of aggressive competition. Again we further validate these findings through two-sample \(t\)-tests, as shown in Table 5. According to the results of the statistical tests, all four prices in the two scenarios are significantly different and are lower in Scenario C. The effect of channel preference on product sales and consumer utility is not very significant, as can be seen in Figure 10. However, through further analysis we find a significant reduction in sales in the direct sales channel. It is reasonable that consumer channel preferences influence demand for direct marketing channels. For consumers, the presence of channel preference means that the retail channels on e-commerce platforms have more exposure and are more likely to be selected. However, for the supply chain, demand is shifting from the direct sales channel to the retail channel. There will be no significant change in overall sales volume or consumer utility.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 4 An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
### An Efficient Heuristic for EOS Integrated Scheduling  
In this section, we present an efficient heuristic for the EOSIS problem. We first introduce its general framework and then several main components.
## 5 Results and Discussion
### Investigating Error Sources
#### 5.4.2 Multi-path  
Eliminating multi-path was an important factor in the validation process because if satellite signals were recorded by the receiver but due to multi-path, the value of using a GNSS receiver as the validation tool would be minimised. This is because the incoming signals may not represent satellites in line of sight (Figure 11a) but rather a signal reflecting off a building that originated from a satellite that was hidden from the view of the observer (Figure 11b). Traditional Antenna Location Strategies [1] are not suitable for testing the GNSS prediction methodology for obstructed terrain, and therefore a survey-grade receiver like the R8 was used in these validation tests because it was designed to reduce multi-path. The R8 is capable of unsmoothed, unfiltered pseudorange measurements for low loise contributing to a low multi-path error. Signals follow a spatial pattern response with high gain in the direction of the assumed, \"true\" signals and attenuation in the direction from which secondary signals can be expected to arrive. For these tests, we assume that, because hardware specifically designed to minimise multi-path was used, it could be eliminated as a significant error source. The validation results justify this assumption as in no test did \(Measured_{\text{obs}}\) exceed \(Predicted_{\text{obs}}\), something that would have been likely to occur in tests with a high \(Total_{\text{sat}}\) and low \(Predicted_{\text{obs}}\), such as Points 7, 8 and 9. Additional identifiers for each satellite will also be added in future iteratinos of the methodology, helping to assess the influence of multipath in the tests.
## 4 Grating Coupler
### Structure of the Grating Coupler  
Figure 7 shows the structure of the grating coupler which is composed of the grating part and taper part. In the grating part, width of the non-etched area is \(p\), width of the etched area is \(q\), grating period \(\Lambda=p+q\), duty cycle \(d=p/\Lambda\), and width of the grating part is \(b\). In the taper part, \(L_{\text{t}}\) denotes its length. Height of the grating coupler along the \(y\) direction is 250 nm, which is consistent with the height of the silicon nitride (SiN) core.  
Coupling efficiency of the grating coupler (\(\eta_{\text{g}}\)) can be calculated as follow:  
\[\eta_{\text{g}}=\eta_{\text{g1}}\cdot\eta_{\text{g2}} \tag{5}\]  
where \(\eta_{\text{g1}}\) denotes the coupling efficiency between the SMF and grating part, \(\eta_{\text{g2}}\) denotes the transmission efficiency of the taper part. To improve the coupling efficiency of the grating coupler, parameters in the grating part and taper part must be optimized.
## 4 Results on a Real Case: Proof-of-Concept Validation
### Results of the Path Planner System
#### 4.2.4. Path Planning Using Processed Satellite Imagery
The first robot moves to the closest corner and then navigates through the grove, and the second robot moves to the closest opposite corner (located at the last row) and then also navigates through the grove but in the opposite direction (concerning the first robot). The path is coloured depending on the distance to the initial position (blue is used to denote low distances, and red is used to denote high distances). However, the robotic systems will not perform all the paths because they will arrive at a point (stop point marked with a black x-cross) which has been already reached by the other robotic system and they will stop there (see Figure 14c,d). The colour between the initial point (red x-cross) and the stop point (black x-cross) graphically denotes the relative distance. Finally, Figure 14d shows a composition of the path done by each robotic system into one image. Similar examples are shown in Figures 15 and 16, where the robots are not initially close to the corners. In the former example, the path between the initial position and the corners are not marked as \"visited\" because the robotic system navigation starts when the robot gets to the first intermediate point. In the latter case, we show an example where the robots stop when they detect that they have reached the same location. Figure 12: Distance images: (**a**) Parcel satellite image with the final position (blue x-cross), (**b**) a representation of the normalized _Distance_ matrix image, and (**c**) the modified representation. Figure 13: Example of a path planned for a robotic system. Figure 16: Determining path planning for a team composed by two robots (III). (**a**) real path for robot\({}_{1}\), (**b**) real path planning for robot\({}_{2}\), (**c**) superposition of (a) and (b). Figure 14: Determining path planning for a team composed by two robots (I). (**a**) path planning for robot\({}_{1}\), (**b**) path planning for robot\({}_{2}\), (**c**) real path for robot\({}_{1}\), (**d**) real path planning for robot\({}_{2}\), (**e**) superposition of (c) and (d). Figure 15: Determining path planning for a team composed by two robots (II). (**a**) real path for robot\({}_{1}\), (**b**) real path planning for robot\({}_{2}\), (**c**) superposition of (a) and (b).
# Toward an SDN-Based Web Application Firewall: Defending against SQL Injection Attacks
## 8 Performance Evaluation and Comparison  
Signature-based WAFs can efficiently detect and prevent security threats, but their effectiveness depends on the accuracy of the rules or signatures in the database. Therefore, the detection capability we have previously defined for both traditional WAFs and SDN-based WAFs is just an indication of the detection possibility and should not be considered as a measure of comparison. In order to determine which approach is more efficient for SDN-based WAFs, we conducted performance measurements in terms of TCP ACK latency and CPU usage overhead (effect on the controller's efficiency).  
We measured the time it takes to send ACK packets to port 80 of the web server and calculated the average latency over a ten-minute period. We also measured the average CPU usage of the controller over the same time period. Our results show that the developed SDN-based modules outperform ModSecurity in terms of TCP ACK latency. This means that packets in traditional WAFs require longer processing times, as they pass through the controller for processing and routing, then go to the WAF for inspection, and finally to the web server for request processing. On the other hand, in the SDN-based WAF, the stages of determining the route and inspection are combined in the controller, resulting in a faster packet arrival. However, ModSecurity performed better in terms of the CPU usage overhead on the controller, as the controller only directs and does not inspect them. Therefore, it can be concluded that SDN-based WAFs are better when the packet transfer speed is of importance to the network administrator, but traditional WAFs are better when the processing capacity of the controller is a priority. Table 2 summarizes these evaluation results.  
Note that detecting SQL injection attacks after route establishment necessitates forwarding all packets to the controller, introducing notable overhead. However, as demonstrated earlier, the overhead is less than that incurred by using a third-party WAF, making it acceptable in some scenarios. Additionally, although controller vulnerability is a valid concern [38; 39], it is beyond the scope of our paper and merits further exploration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **TCP ACK Latency** & **CPU Usage** \\  & **(Avg)** & **(Avg)** \\ \hline Traditional WAF (ModSecurity) & 50.6 ms & 7.02 \% \\ \hline SDN-based WAF (signatures) & 43.6 ms & 14.78 \% \\ \hline SDN-based WAF (regular expression) & 35.5 ms & 7.046 \% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results.  
Figure 8: Detected payload record.
###### Abstract  
The aim of study was to evaluate the effects of interval training performed in hypoxia on aerobic capacity and second ventilatory threshold in young, untrained men. Participants (n = 48) were randomly divided into a control group and two groups performing the same interval training (three times a week for 4 weeks) in normoxia (200 m asl) (NT) and in hypoxia (HIT) (3000 m asl, \(\text{FIO}_{2}=14.4\%\)). In the incremental test, maximal oxygen uptake (\(\text{VO}_{2}\text{max}\)) was measured and the first (VT1) and second (VT2) ventilatory thresholds and the maximal power output (Pmax) were determined for each participant. The training workloads of the efforts corresponded to the workload at VT2 (effort) and VT1 (active recovery). Training in both normoxia and hypoxia significantly increased absolute \(\text{VO}_{2}\text{max}\) (\(p\) = 0.02, ES = 0.51 and \(p\) = 0.002, ES = 0.47, respectively). In comparison to NT, only IHT significantly (\(p<0.001\); ES = 0.80) improved Pmax, as well as power at VT2 (\(p\) = 0.02; ES = 0.78). The applied IHT was effective in improving Pmax and power at VT2, which was not observed after training in normoxia.  
hypoxia; interval training; maximal oxygen uptake; anaerobic threshold; training; performance
## 4 Proposed 3D-UWSN Design  
The 3D-UWSN is a network model designed for efficient data gathering and immediate transmission of event messages. Events are the emergency accidents that happen in the underwater environment, and they are detected by the sensors. This kind of sensed information is sensitive, and it needs to be forwarded faster to allow for corresponding countermeasures. The proposed network design is operated through the processes of clustering, routing and data gathering by different algorithms. This section is composed of the network model, clustering, AUV movement for data gathering, inter-cluster routing and event message delivery.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 2 Preliminaries
### Knowledge Graph Embedding
#### Translation Based Models  
Translation based models assume entities and relations are both represented as vectors. They measure the plausibility of a fact as the distance between two entities, usually after a translation carried out by the relation. For a true triple, after being translated by the relation, the head entity is close to the tail entity in the embeddings' vector space.  
TransE: TransE [17] is the most representative translation based model. Entities and relations are represented as vectors in space \(\mathbb{R}^{d}\). For a triple \((h,r,t)\), \(\mathbf{h}\), \(\mathbf{t}\), and \(\mathbf{r}\) are denoted as vectors of entities \(h,t\) and relation \(r\) respectively. The score function of TransE is then defined as the negative distance between \(\mathbf{h}+\mathbf{r}\) and \(\mathbf{t}\), i.e.,  
\[f(h,r,t)=-\|\mathbf{h}+\mathbf{r}-\mathbf{t}\|_{1/2}. \tag{1}\]  
The score of \((h,r,t)\) is expected to be large if it holds. The training process of TransE is to minimize a pairwise ranking loss function as follows:  
\[\sum_{(h,r,t)\in\mathcal{T}}\sum_{(h^{\prime},r^{\prime},t^{\prime})\in \mathcal{T}^{\prime}}\max(0,\gamma-f(h,r,t)+f(h^{\prime},r^{\prime},t^{ \prime})),\]  
\[\mathcal{T}^{\prime}=\{(h^{\prime},r,t)|h^{\prime}\in\mathcal{E}\wedge h^{ \prime}\
eq h\wedge(h,r,t)\in\mathcal{T}\}\]  
\[\cup\{(h,r,t^{\prime})|t^{\prime}\in\mathcal{E}\wedge t^{\prime}\
eq t\wedge(h,r,t)\in\mathcal{T}\}, \tag{2}\]  
where \(\mathcal{T}^{\prime}\) is a set of sampled negative examples and \(\gamma>0\) is a margin hyperparameter separating positive examples from negative ones. Here, the observed triples in \(\mathcal{T}\) are treated as positive examples, and the negative ones are generated by corrupting either the head entity or the tail entity of observed triples with the random entities sampled uniformly from \(\mathcal{E}\).
# Hyperspectral Imaging Techniques for the Study, Conservation and Management of Rock Art
## 2 Materials and Methods
### Data Acquisition  
#### 2.3.1 Hyperspectral Imaging System  
The hyperspectral imaging system used in this study consists of a Specim V10E spectrograph (Spectral Imaging Ltd., Oulu, Finland) covering a spectral range of 400-1000 nm, a sCMOS camera (CL-30, effective resolution \(1312\times 728\) pixels by 12 bits), an objective lens (Cinegon 2.4/30 mm focal length), a rotating scanner light and data acquisition (DAQ) spectral software.  
The 2.8 nm spectral resolution was reduced to 5.6 nm by applying binning to improve the signal-to-noise ratio. The data were recorded as a hypercube with two-dimensional spatial images and wavelength bands as the third dimension, and 214 spectral bands were extracted from each pixel to form a relatively continuous spectral curve.
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 2 Related Work
### DCMST Methods  
The minimum spanning tree (MST) is a classic topological technique used in transportation, communication, and other fields. This technique is mainly used to solve the shortest path problem in the case of full connectivity. Many network optimization and operational research problems have been solved based on the MST. Specifically, with the application of MSTs in many fields, it has become an increasingly popular research topic, and many effective methods have been proposed. The MST algorithm was first formulated in [15], with many additional contributions in the early 20 th century. The MST was first used in electrical network planning. An example of an electrical network that served 40 cities was proposed is the first application of the MST algorithm [16]. However, due to differing requirements, the MST approach is often limited in solving practical problems in different research fields.  
In many studies, a more effective algorithm is the DCMST. The DCMST technique has been widely applied to optimize transportation networks, electrical wire networks and other networks in which the lengths of the connections between nodes should be minimized [17]. Degree constraints (which are a spanning tree) are based on the constraint values of nodes and the smallest weight. If a node is out of commission, the degree constraint mechanism is used to prevent the node from becoming invalid. The DCMST algorithm can be introduced to establish a network with the maximum number of crossing paths.  
The DCMST method is NP-hard [18], and it is based on network searching. A randomized primal method (which is a novel tree construction algorithm) was proposed and used to establish a low-overhead degree-constrained tree for stochastic iterative search techniques and builds [19]. Network design problems were proposed to represent spanning trees with sets of edges. Based on black-box optimization techniques, an encoding method was used to initialize a tree [20]. However, the algorithm for a single tree is not applicable as the complexity of the network grows. Node-depth encoding was proposed for forest representation. This approach is based on the union of the encodings of all trees in a forest. The union includes an array of pointers, and each pointer comprises a tree consisting of linear lists containing the tree nodes and their depths [21]. A node-depth-degree representation was proposed to generate and manipulate a set of spanning forests [22]. This structure improved the average run time of previous node-depth algorithms.