esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 5 Future Directions  
In this section, we identify two problems associated with the application of the current scheduling scheme in practical scenarios. Furthermore, we propose potential solutions to address these issues.  
The rollout scheme exhibits polynomial complexity, as demonstrated by the aforementioned complexity analysis. However, the computational complexity of the rollout algorithm remains excessively high in certain cases. On one hand, when UAVs experience rapid movements, it becomes crucial to expedite the calculation of time slot scheduling. On the other hand, while the numbers of clusters and UAVs are typically not extensive, the RIM algorithm can effectively manage the resulting increase in complexity as the number of UAVs increases. Nevertheless, the analysis of computational complexity reveals that the RIM algorithm faces challenges in efficiently handling scenarios with large scheduling cycles. In such cases, a UAV scheduling scheme with lower complexity becomes essential.  
One potential approach to mitigate the complexity associated with the rollout algorithm is to replace the entire basic heuristic strategy with imprecise interference estimates. Specifically, instead of exclusively executing the greedy algorithm from the current stage to a destination node during the computation of node values in a stage, only a restricted number of steps are evaluated. In the context of this article, the greedy algorithm can be utilized to schedule a limited quantity of UAVs after the current stage, evaluating the partial interference caused by the scheduled UAVs. This strategy can effectively reduce complexity within the rollout algorithm.  
In real-world scenarios, the position information of UAVs is often imperfect, resulting in inaccurate interference information among them. Consequently, there is a pressing need to evaluate the uncertainty of the interference information and develop a resilient UAV scheduling strategy. A pertinent research question to address is how to design RIS weights to mitigate interference when the UAV location information is inaccurate. While a wider beam formed by the RIS can enhance the reliability of UAV coverage, it also increases the likelihood of interference between UAVs. Additionally, when the position of the UAV is uncertain, it is imperative to adjust the value of the UAV interference matrix to account for the interference error caused by the positional uncertainty. This adjustment will enhance the robustness of the UAV scheduling scheme.
# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
## 3 Methodology  
The popular open-source platform Arduino is used for creating and developing electrical products. A broad range of sensors is used for collecting data and generating datasets. Among the typical sensors used with Arduino are:  
* Temperature and Humidity Sensors: DHT11 and DHT22 are two common sensors used to monitor temperature and humidity in the environment. To provide precise measurements, these sensors make use of digital signals.
* Light Sensors: For measuring the light level in the surrounding area, photodiodes and photoresistors may be used, such as the LDR (Light Dependent Resistor). Using these sensors, we performed the following processes to create a dataset:  
1. Select the sensors necessary for the project and attach them to the Arduino board in line with the wiring diagrams provided by the sensors.  
2. Create an Arduino sketch and upload it to read data from the sensors. To simplify this process, utilize the built-in features or libraries unique to each sensor.
3. Collecting the sensor data regularly may transfer it to a computer or other storage device for long-term preservation, or you can temporarily store it in the Arduino's memory.
4. Analyze and delete the information gathered. In this stage, outliers may be eliminated, noise may be filtered, or raw sensor results may be converted into useful units (for example, translating ADC values to temperature in Celsius).
5. To make analysis and visualization easier, arrange the cleaned data into an organized format, such as a CSV file or a relational database. Then, we can utilize this information for various things, including developing machine learning models, keeping an eye on trends, or generating predictions.  
The next subsection describes the sensors used with the Arduino, followed by the generation of the dataset.
# Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence
## 2 Related Work
### Multimodal Complementarity  
Early multimodal approaches focused on fusion by simply splicing text features and image features. Multimodal complementarity research methods usually consider visual information as a complement to the textual content of fake news. Such methods firstly use text encoder and visual encoder to extract text features and image features respectively, and subsequently realize multimodal fusion by simple feature splicing.  
Inspired by adversarial networks, [PERSON] et al. [3] proposed an end-to-end fake news detection framework called Event Adversarial Neural Network (EANN). The multimodal feature extractor is first responsible for extracting textual and visual features from the posts. Subsequently, the feature vectors of the two modalities are spliced to form a fusion feature representation. The fused features are not only input to the fully connected classifier for news authenticity discrimination, but also used as input to the event discriminator for recognizing news event labels. [PERSON] et al. [4] further proposed an end-to-end fake news detection framework called MetaFEND. The framework achieves the unification of cross-event generalized representation learning and event-specific modeling through the synergistic fusion of meta-learning and neural process. The framework requires only a small number of labeled samples to quickly adapt to unexpected events, which significantly improves the performance of fake news detection in data-scarce scenarios. [PERSON] et al. [5] improved the complementary framework into a fake news detection framework called Multimodal Variational Autoencoder (MVAE). The framework uses Bi-LSTM and VGG19 to obtain textual representation and visual representation respectively and splices both as shared representation. The encoder encodes the text and image information into shared latent vectors, and the decoder maps the latent vectors back into the text and image space to reconstruct the original input. [PERSON] et al. [6] proposed a multimodal detection framework called SpotFake+. This framework introduces BERT [7] pre-trained language model for text feature encoding and VGG19 [8] model for image feature encoding. Finally the two features are spliced and input to the classifier.  
Although this method of multimodal complementarity fuses the information of two modalities, text and image, the simple splicing fusion method ignores the complex semantic associations between modalities, so it limits the performance of detection to some extent.
## 3 Analysis of Study Methods
### VR Devices  
In studies that use 360-degree video for VR, three studies used the Samsung Gear VR device with a smartphone, one study used an Oculus Rift headset, and one study used an HMD but did not specify the model of the headset. Thus, there is a variety of VR devices with the most common being the Samsung Gear VR.  
In studies that use virtual environments for VR, three studies used the HTC Vive, two studies used the Oculus Rift headset, and one study used the CAVE VR setup. Thus, there is a variety of VR devices used, with HTC Vive being slightly more popular than the Oculus Rift headset.
# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 4 Validation and Results
### Built Cultural Heritage Detection Using Photogrammetry Software  
To validate our framework results, we used state-of-the-art commercial photogrammetry software to develop a workflow for the detection of built cultural heritage in line with the scientific literature on thermal photography outside the archaeological world [48]. In the preprocessing stage, the developed workflow converts the collected thermal frames into the grayscale and resizes them using Algorithm A1. The RGB frames are also cropped using Algorithm A2 to match the size of the thermal frames, then merged with them to produce single 4-band TIFF images. Merging thermal and RGB frames is implemented using Algorithm A3. Matlab (R2019B) is used at this stage. The images are then processed using  
Figure 13: Proposed user interface displaying the detected built cultural heritage during a selected inspection flight and providing the ability to switch between the different views to be able to analyze anomalies in RGB, depth, and thermal scales: (**a**) displays the main view for inspection selection and shows the detected built cultural heritage while (**b**) shows the analysis report of the selected structure over time.  
a proprietary photogrammetry software (Agisoft Metashape 1.3.0), from the stitching and 3D reconstruction to the extraction of single thermal orthophoto. The workflow developed is illustrated in Figure 14.  
By comparing the results obtained from both approaches, it is evident that we managed to cost-effectively replicate the mosaicking and alignment functionalities of state-of-the-art commercial photogrammetry software while adding a new depth-based modality and providing a more convenient user interface for temporally monitoring changes in multimodal views of archaeological sites.  
The framework proposed is transferable and applicable for other research applications. It facilitates the process of obtaining and comparing RGB, depth, and thermal orthomosaics from flight missions carried out throughout the day, specifically after sunset and before sunrise, in each survey area. It can be also used to evaluate the thermal variations occurring when varying the recording time within a day to identify the best time frame in which the thermal anomalies of the soil are most evident. We have tested our proposed approach in Mount Kassar site (Castronovo di Sicilia, PA). Results showed that during the short period before sunrise, the variations in thermal radiance are greater, and consequently the anomalies of the ground are more evident as shown in Figure 15.  
Figure 14: Graphic representation of the workflow developed for the processing of aerial thermal images with proprietary software: (1) Convert and resize the thermal images; (2) Crop the RGB images; (3) Merge thermal and RGB images; (4) Photogrammetric phase; (5) Extract thermal orthophoto in Metashape.  
Figure 15: Comparison of thermal orthophotos obtained from missions carried out at different times of the day (Monte Kassar, Castronovo di Sicilia, PA, USA).
## 8 Calibration of Instrumentation for Aerodynamic Testing
### Scalability  
The scalability issue is, in principle, very important for practice. This issue has been elaborated in extenso in a number of previously published papers, e.g., [33; 34; 36]. It has been found in the literature that blind calibration algorithms that are compatible with the ones analyzed in this paper show a linear increase in the number of required operations, with the number of nodes, \(N\), provided the sizes of in- and out-neighborhoods of all the nodes remain bounded. Formally speaking, it appears that the network topology is irrelevant for the convergence points of the algorithms (in the sense of the given theorems), as long as the graphs satisfy Assumption A3. However, the graph connectedness influences the convergence rate to consensus, which is very important in practice. Obviously, an  
Figure 12: Time evolution of corrected gains; algorithm with reference and instrumental variables; multichannel pressure scanner. Different colors represent different node’s estimates.  
increase in the connectedness degree of the graph leads to an increase in the convergence rate. However, in the case of typical applications of the designed instrument for pressure sensing, we do not have problems of this kind, since we do not expect more than 10 complete instruments to be present, even in the most complex experiments we can envisage (one instrument can contain more sensors, depending on the end users' specifications). In practice, scalability is a more important and acute issue in large sensor networks dedicated to different large space monitoring tasks.  
**Remark 14**.: _Sudden changes in the input signal may exist in aerodynamic pressure-sensing applications. This aspect is adequately covered by the instrument's design, i.e., by choosing a sufficiently high sampling rate, and by using the type of sensors capable of withstanding high over-pressure values. However, the presented algorithms deal with offset and gain variations that are--by several orders of magnitude--slower than the input signal._
# Protection Strategy for Edge-Weighted Graphs in Disease Spread
## 4 Simulation of Disease Spread with Protection and Analysis Results
### Simulation of Disease  
In this work, we use a graph-based SIR model in the same way as in [10], that is, each individual is represented by a vertex in an edge-weighted graph. At time \(t\), each vertex \(v_{i}\) is in a state \(v_{i}^{t}\) belonging to \(\mathcal{S}=\{0,1,-1\}\), where \(0,1\) and \(-1\) represent the three discrete states: susceptible (S), infected (I) and recovered (R). Let \(G\) be an edge-weighted graph. We set  
\[N_{I}(v_{i})=\{v\in N(v_{i}):v\in I\}. \tag{3}\]  
At time \(t+\Delta t\), the vertex \(v_{i}\) will change of state according to probabilistic rules:  
1. The probability \((P_{I}(v_{i}))\) that a susceptible vertex \(v_{i}\) is infected by one of its neighbors is given by \[P_{I}(v_{i})=\sum_{v_{j}\in N_{I}(v_{i})}\rho\Delta t\cdot w_{ij},\] (4) where \(\rho\) is a purely biological factor and representative of the disease and \(w_{ij}\) is the weight of the edge \(e_{ij}\). Notice that the expression (4) can be deduced from the infection model called \(q\)-influence, assuming \(q=\rho\). (see [45; 46]).
2. The probability \((P_{R}(v_{i}))\) that an infected vertex \(v_{i}\) at time \(t\) will recover is given by \[P_{R}(v_{i})=\delta\Delta t,\] (5) where \(\delta\) is the recovery rate.  
Moreover, we assume that the disease is present for a certain period of time and when individuals recover, they are immune.  
The initial population contains one infected node and all the simulations considering \(\delta=\dfrac{1}{15}\). For each network, it was consider a different \(\rho\), so in each case the percentage of population affected is similar. Table 1 shows the different \(\rho\) for each test network.  
Finally, 2000 simulations on each network of disease were performed. Figure 2 shows the results.  
In [PERSON] authors and CAG-mat72 networks the DIL-W\({}^{0}\) ranking performs the best with respect to the minimum peak of the infected curve, respectively. Furthermore, DIL-W\({}^{0.5}\) ranking comes second in both networks. The Strength and Laplacian rankings perform the best with respect to the minimum peak of the infected curve in Zachary karate club network. The DIL-W\({}^{0.5}\) and DIL-W\({}^{1}\) rankings are in fourth and fifth place respectively. However, the computational complexity of Laplacian and Strength is \(O(n\cdot\max_{p\in V}(deg(v))\) and \(O(m)\) respectively, while DIL-W\({}^{\alpha}\) has a computational complexity of \(O(n\cdot<k>)\) (see [30] or [27]), where \(n\) is the total number of vertices of the graph, \(m\) is the total number of edges of the graph, and \(<k>\) is the average degree of vertices in the graph. In Wild bird network, DIL-W\({}^{1}\) performs better in the same criterion.  
On the other hand, regarding the decrease of the infected curve, DIL-W\({}^{\alpha}\) performs the best in Wild bird, Sandy authors, and CAG-mat72 networks. The Laplacian is better on the Zachary network. The above is summarized in the survival rate. In Table 2, we can see the survival rates obtained according to the protections applied to each network.
# Toward the Utilization of 3D Blading in the Cantilevered Stator from Highly Loaded Compressors
## 3 Effects of 3D Blading on the Cantilevered Stator
### Effects of the Compound Sweep and Dihedral  
According to the former analysis, the mechanism of 3D blading in the cantilevered stator is summarized in Figure 24. The forward sweep can enhance the hindrance of the leakage flow on the low-energy fluid near the endwall, while flow separation on the blade suction side is exaggerated slightly. Increasing the sweep height facilitates a uniform separation along the span without changing the endwall flow significantly, whereas excessively large sweep angles lead to a large-scale separation on the blade suction surface and harm the total effect. On the other hand, as shown in Figure 24b, the positive dihedral will not only push the trajectory of the leakage vortex away from the blade suction surface but also promote the radial migration of the low-energy fluid at the hub corner. The utilization of large dihedral heights will elongate the blade wake and induce the radial transportation of the leakage flow, while the excessive dihedral angle will damage the performance at the midspan.  
In consideration of the effects above, a novel cantilevered stator with comprehensive sweep and dihedral is designed, whose aerodynamic performance is evaluated via numerical simulation. As shown in Figure 25, the 3D cantilevered stator employs a relatively high sweep height with a moderate sweep angle, and the dihedral is designed to have a low height and a moderate angle. Note that the blade tip also adopts a forward sweep to balance the radial pressure gradient.  
Figure 23: Comparison of the streamwise leakage mass flow rate under different dihedral angles (\(\varphi=0.46\)).  
Figure 24: The mechanisms of sweep and dihedral on the cantilevered stator: (**a**) sweep; (**b**) dihedral.  
Figure 26 presents the pressure rise and loss characteristics of the 3D cantilevered stator; results of the orthogonal blade, the bare sweep scheme (no dihedral), and the bare dihedral scheme (no sweep) are also provided for the convenience of comparison. Figure 26 implies that the combination of the sweep and dihedral will intensify the beneficial effects, as the \"sweep + dihedral\" scheme has the highest static pressure ratio and the lowest total pressure loss over the whole operating range. At the near-stall point, the static pressure rise coefficient and the total pressure loss coefficient are increased and decreased by 25.5% and 11.1%, respectively. Given the remarkable improvement over the baseline case, it is safe to say that the working mechanism summarized above is correct, and the redesign is successful. The following section will outline the experimental methods used to validate the conclusions.
## 3 Sky vs. Earth Observations with Antenna Arrays  
Radio astronomers and the remote sensing community are both using aperture synthesis to obtain images of the sky or of Earth's surface at radio frequencies from interferometric measurements provided by antenna arrays. However, although for historical reasons the latter have been inspired by the former, there are few differences that deserve attention. This is particularly true when focusing on the relation between the extent of the synthesized field of view and the spacing between the elementary antennas of the arrays. Indeed, regardless of the angular resolution, which depends on the longest separation between antenna pairs, the key parameter of an aperture synthesis interferometer in radio astronomy is the extent of the synthesized field of view, whereas in Earth remote sensing the key parameter is the shortest spacing between the antennas. Although it is well-known that both are closely related, it is explained hereafter why the remote sensing community deduces the extent of the synthesized field of view from the antenna spacing, and why the radio astronomers proceed in the opposite way.
## 3 Experimental Method  
A series of experiments with constant power input were performed in order to determine the temperature history of the setup and PCM packages during the charging and discharging processes. An overview of the performed experiments is given in Table 4. Two series of experiments were performed. In the first series, no thermal spreader was used while in the second series, the thermal spreader was placed on top of the heat source. For each experiment performed without the thermal spreader, a constant power input, \(P\), of 5 W was applied for 60 min (\(t_{on}\)) after which it was turned off for 120 min (\(t_{off}\)) to let the system cool down naturally. The power dissipations of SoC in modern tablet computers ranges from 2 to 6 W [39; 40; 41]; therefore, a power input of 5 W was chosen as an appropriate value. A charging time of 60 min was defined since it was observed that with a longer charging time, the heater temperature exceeded 120 \({}^{\circ}\)C, which could have caused damage to the PCM package. For the experiment with the thermal spreader, the same power input of 5 W was chosen. It was observed that the increase in temperature of the heater was reduced remarkably with the addition of the thermal spreader. Therefore, it was possible to increase the charging time to 120 min without any risk of damaging the PCM package. The PCM package was positioned in the middle of the base. The cover was mounted using four screws, one at each corner. If the thermal spreader was used (aluminum), it was placed between the heater and the lower surface of the PCM package.
## 3 Results and Analysis  
Based on the established numerical calculation model of the cage, simulations and calculations were conducted in conjunction with the computational scenarios outlined in Section 2.4. By analyzing and statistically evaluating the relevant data results, this study delves into the response characteristics of the cage under irregular waves from three perspectives, i.e., the cage motion, the mooring forces, and the pontoon deformation. This analysis is facilitated through the examination of response time history curves, comparisons with wave height time histories, and spectral density statistics.
## 5 Policy-Based Methods  
Value-based methods, such as DQN, DDQN, and D3 QN, are effective for discrete action spaces, offering stability and scalability in robotic navigation tasks with well-defined action sets. However, they struggle with continuous control due to the need for discretization. Policy-based methods, on the other hand, are designed for continuous action spaces, making them ideal for controlling mobile robots with differential drive or other complex dynamics.  
Policy-based methods directly optimize the policy \(\pi(a|s;\theta)\) by maximizing the expected cumulative reward:  
\[J(\pi_{\theta})=\mathbb{E}_{\pi_{\theta}}\left[\sum_{t=0}^{\infty}\gamma^{t}r (s_{t},a_{t})\right]. \tag{37}\]  
The policy parameters \(\theta\) are updated using the gradient of the objective, known as the policy gradient:  
\[\
abla_{\theta}J(\pi_{\theta})=\mathbb{E}_{\pi_{\theta}}[\
abla_{\theta}\log \pi_{\theta}(a|s)Q^{\pi}(s,a)]. \tag{38}\]  
An example of a purely policy-based method is the REINFORCE algorithm [25], which uses Monte Carlo estimates of the return \(G_{t}\) to compute the policy gradient. These methods do not utilize a value function to evaluate actions or guide policy updates, relying solely on sampled returns.