esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of a Solar-Assisted Air-Conditioning System Applied to a Remote School
## 1 Introduction
[34], in their review of the state of the art of these systems, identified that up to 50% primary energy can be saved by using hybrid collectors in absorption machines compared to mechanical vapor compression equipment for cooling. The hybridization of mechanical vapor compression technologies was even proposed, working in combination with absorption technologies to improve the system efficiency [35; 36]. It should be noted that very few studies mentioned the benefits of this type of solar-assisted cooling system in applications with difficult access to electricity, such as isolated communities. By using an energy resource that can be exploited in an excellent way in most of the world's territory, it makes its implementation attractive in this type of scenario where there is no electricity supply. This type of application presents factors that affect the operation of the system, different from those that would be expected in absorption systems installed in towns or cities with access to continuous fresh water and electricity services. However, as mentioned in the previous works, a detailed analysis of the engineering of these systems must be carried out so that they become both technically and economically attractive. This work presents the simulation study of an absorption air-conditioning system driven by a solar thermal collector field, installed in the school of Puertecitos, an off-grid community with a high level of social marginalization. The system was simulated using the TRNSYS software in order to determine its performance under the typical operating conditions of a school in Mexico, as well as to determine the effect of different distinctive factors of a remote community, such as the scarcity of clean water and electricity. The aim of this simulation study was to determine the best operating conditions prior to system start-up, to establish the requirements for external heating and cooling services, and to quantify the freshwater requirements for the proper functioning of the system. This study allows establishing the operation and maintenance strategies, as well as the operating limits, of the cooling system under a real scenario that was not studied in the literature.
## 2 Literature Review Methodology
### Detailed Exploration of Category Backgrounds
#### 2.1.3 Modulation Recognition  
Figure 4 shows an AI-powered modulation recognition system for UWA communication. The transmitter encodes and modulates the data before sending it through an underwater channel affected by noise, Doppler shifts, and attenuation. At the receiver, ML/DL models perform channel estimation and recover the original signal. An RL-based recognition block identifies the modulation type by learning from previous transmissions, lowering BER, and ensuring reliable communication in both single and multi-carrier setups [9; 44; 45; 46; 47; 48].  
Modulation recognition enables underwater receivers to identify signal types automatically [49; 50; 51; 52]. Intelligent detection improves efficiency and reduces errors in dynamic underwater environments. DL models such as CNNs and RNNs help in classifying modu  
Figure 4: A typical UWA transceiver using ML/DL for modulation recognition.  
Figure 3: A typical UWA transceiver using ML/DL for adaptive modulation.  
lation types by analyzing received signals and extracting features. These systems match signals to known patterns like BPSK, QPSK, QAM, FSK, DSSS (Direct Sequence Spread Spectrum), and OFDM. They operate across 1-30 kHz bandwidths and support distances from hundreds of meters to several kilometers. Metrics like cross-entropy (CE) loss, accuracy, and precision assess model performance [53; 54; 55; 56].
## 3 Numerical Study
To assess wind effects on a tall building, it is crucial to examine the first three modes, as illustrated in Figure 4. This focus is due to the fact that wind forces in tall buildings are not commonly excited by higher-order modes since wind forces do not exert force in one direction up to a certain height and then shift to another direction for the remainder of the structure. A modal analysis was conducted on the HFM to explore the building's dynamic behavior. The results of the eigenvalue analysis are depicted in Figure 4. The first and second eigen-mode is characterized mainly by a dominant bending effect, with minimal torsional impact, representing a nearly pure bending scenario. In contrast, the third eigenmodes exhibit translation in the y-direction combined with torsional rotation along the building's Z-axis. Figure 4: Mode shapes of the high-fidelity model: (**a**) 1 st mode bending; (**b**) 2 nd mode bending; (**c**) 1 st mode in torsion. After creating the HFM of the building, a low-fidelity model (LFM) consisting of beam elements can be established by considering the characteristics of the building. Dynamic simulations with the LFM require less computational time to complete. To create the LFM, it is necessary to ensure that this model has attributes similar to those of the detailed building model, such as eigen-frequencies and mode shapes. Timoshenko beam theory was used, as elaborated in Section 3. The top-floor accelerations are presented for two angles of the wind in Tables 1 and 2. Time statistics of the two models are compared in the table. Mean, standard deviation (std), mean + 3 sd, maximum, and the 99% values are compared for the two models for two angles of the incoming wind. The base reactions are presented in Tables 3 and 4. It can be seen that the approximate model can capture the base reactions and the top-floor acceleration with reasonable accuracy. The mean of the top-floor acceleration is almost zero, and extreme statistics are of importance. Further, the time series of accelerations and base reactions are presented in Figures 5-7. The computations were performed on the SuperMUC-NG system equipped with an Intel Xeon Platinum 8174 processor using 8 nodes and each node has 48 cores per node (Intel Corporation, Santa Clara, CA, USA). The computational time for both analyses was compared, and the high-fidelity model required approximately 13,824 CPU hours, whereas the low-fidelity model completed the analysis in approximately 144 CPU hours. The LF model demonstrated a significant computational saving, using only 1% of the resources required by the HF model. Table 5 compiles a few of the Qols that are interesting to each load case, and the % of the difference is presented from HFM. In both acceleration and base reactions, the low-fidelity model was able to capture the responses accurately to less than 3%. In almost all cases, the LFM underpredicts the responses. However, this difference is expected and is considerably justified considering the computational saving it presents.
## 3 HCV proteins that attach to lipid droplets
### Properties of the HCV NSSA protein  
NS5A is a phosphoprotein that is detected in basally phosphorylated (56 kDa) and hyperphosphorylated (58 kDa) forms. It is composed of three domains (DI-DIII) that are separated by low complexity sequences LCSI (between DI and DII) and LCSII (between DII and DIII), which are trypsin-sensitive [65]. Domains DI and DII are essential for HCV RNA replication while recent studies have demonstrated that DIII plays a key role in virion assembly [21, 24, 66-68]. The crystal structure of DI suggests that NS5A forms a dimer that contains a groove for binding RNA at the interface between the monomers [69]. The N-terminal end of DI also has an amphipathic helix that anchors the protein to membranes [70, 71]. In contrast to DI, there is no structural information available for either DII or DIII.  
Compared with core protein, NS5A is less readily detected on lipid droplets and is more broadly distributed on the ER membrane [72]. There is limited information regarding the motifs that are needed for attachment of NS5A to lipid droplets. However, it has been noted that mutation of residues between amino acids 99-104 in DI impairs detection of NS5A on droplets [73]. These residues are located in a segment of random coil at the junction of two subdomains in DI, which is relatively proline-rich. Moreover, mutations in DIII that do not disrupt lipid droplet targeting reduce the extent of co-localization between NS5A and core on droplets [21]. Although these data indicate that NS5A does not require core for lipid droplet attachment, additional studies are required to identify in greater detail the droplet-targeting elements in the protein.
## 3 Experiments and Results
### Computational Complexity and Inference Efficiency Analysis  
To assess the real-time deployment potential of the proposed model, we benchmarked the computational performance of seven object detection architectures--YOLOv11-OAM, YOLOv11, YOLOv10, YOLOv9, YOLOv8, SSD, and Faster R-CNN--on an NVIDIA RTX 4090 GPU with an input resolution of 1024 \(\times\) 1024 using FP16 precision. Three key metrics were evaluated: parameter count (M), floating-point operations (GFLOPs), and inference speed measured in frames per second (FPS).  
As shown in Figure 13, YOLOv11-OAM achieved an inference speed of 65.23 FPS, with 20.06 million parameters and 67.21 GFLOPs. Although the model introduced slightly higher complexity compared with the baseline YOLOv11 (66.26 FPS, 16.49 M, 64.01 GFLOPs), its inference efficiency remained nearly unchanged. This demonstrates the computational lightweight nature of the ODConv, Detect_ASFF, and MPDIoU modules.  
Among all compared models, YOLOv11-OAM offered the best balance between accuracy and efficiency. YOLOv8 and YOLOv9 achieved 61.96 FPS and 49.04 FPS, respectively, while YOLOv10 showed reduced efficiency (43.88 FPS) due to its larger parameter size. SSD  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]\%** & **Recall/\%** & **F1 Score/\%** & **[EMAIL_ADDRESS]\%** & **Precision/\%** \\ \hline Faster R-CNN & 78.38 & 76.55 & 73.7 & 51.99 & 71.19 \\ SSD & 84.228 & 78.17 & 78.3 & 62.1 & 78.41 \\ RetinaNet & 83.06 & 77.71 & 77.2 & 61.20 & 76.77 \\ YOLOv8 & 90.95 & 84.9 & 84.6 & 70.61 & 84.3 \\ YOLOv9 & 90.49 & 84.06 & 84.1 & 69.92 & 83.88 \\ YOLOv11-OAM & 93.15 & 87.87 & 88.5 & 74.08 & 87.41 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different models under various evaluation metrics.  
Figure 13: Inference speed and complexity comparison of object detection models.  
and Faster R-CNN were significantly slower (<15 FPS), despite having similar or higher GFLOPs, underscoring their lower runtime efficiency.
## 2 Data Collection
### GNSS Stations with 30 s Sampling Rate Data  
In April 2015, four stations from BETN were recording 30 s sampling rate data: CHDU in Chengdu, LEID in The Netherlands, PFTP in Australia, and JOHA in South Africa. Meanwhile, MGEX has about 60 stations which can record BDS and GPS observations at 30 s intervals. In addition to selected 13 stations sampling at a 1 s rate and 68 stations sampling at a 30 s rate (shown in Figure 1) were added as reference tracking data to implement the precise orbit determination (POD) procedure to obtain higher precision satellite orbits.
# Post-Quantum Migration of the Tor Application
## 4 Implementation
### Local Network Benchmark  
The average bandwidth connectivity between devices was measured to be 93.8 Mb/s using the iPerf network performance measurement tool [55]. The network was configured as described in Section 3.2; Chutney [10], a Python tool for testing the Tor network locally, was used to generate torrc configuration files for each node (Listings A2 and A3).  
The torrc files were slightly adapted, but most of the settings remained identical to Chutney's templates. Listings A4 and A5 display the used parameters for directory authorities and guard relay. For instance, due to the size of the network setting, PathsNeededToBuildCircuits was decreased to allow more flexible circuit building. The client configuration also pointed to the local directory authorities and contained the setting for testing networks, which adjusted default values, as described in \"man tor\" (Listing A6). The conflux setting was disabled, and the nodes were specified in the configuration to facilitate the onion.  
If accessing the internet through the local Tor nodes, from the browser's point of view, the timing displayed is heavily influenced by external variables such as broadband and thus is not relevant to evaluating the Tor implementation itself. However, considering the timing difference between the same test request made from Firefox to the internet directly and the Tor browser going through the local nodes, it can be inferred that passing through the established Tor channel only adds a few hundred milliseconds (Figure A7). Tor's traffic is mostly web [3]; measuring the time taken to visit an internet resource adds a relevant data point of the overhead introduced by the class. The laptop was configured to perform NAT between its ethernet and WLAN interfaces, and its IP was set as the default gateway on all nodes. DNS points to the Google 8.8.8.8 address.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 2 Study Area and Dataset
### Study Area
To prevent and control landslide disasters, the Hong Kong government has taken a series of measures, such as establishing the Geotechnical Engineering Office, specifically responsible for the design and maintenance of slopes; strengthening geological surveys and monitoring and early warning systems; and raising public awareness and prevention consciousness regarding landslide disasters. These measures have reduced the occurrence and losses caused by landslide disasters to some extent, but continuous reinforcement and improvement are still necessary.
## 2 Distributed Optical Fibre Sensors
### Raman-Based Distributed Sensing
Medium-range systems, operating with multimode fibres, high-performance laser sources at 1064 nm, and silicon photodetectors, cover distances up to 15 km, limited by nonlinear optical effects. Ultimately, long-range systems, operating at 1550 nm, employ mainly single mode fibres over distances larger than 15 km; in this case, the detection requires devices operating at long wavelengths, such as InGaAs photodetectors. Figure 3: The principle of operation of a Raman-distributed temperature sensor (DTS) exploiting the ratio of the anti-Stokes to the Stokes band intensities detected using an OTDR scheme. It is worth mentioning that improved and alternative designs have been proposed over the years, including commercially--e.g., photon-counting Raman OTDRs [53; 54; 55; 56; 57], pulse compression/coding schemes [58; 59; 60; 61], or incoherent Raman OFDRs, in respect of which we direct the reader to the corresponding references. Remarkably, in [62], a resolution of 0.09 \({}^{\circ}\)C in a 1100 m-long fibre with 0.39 m spatial sampling was achieved with direct detection approach. To conclude this section, we report in Table 2 the performance achieved by some commercial interrogators. Please note that the reported values of resolution are obtained only after proper calibration. In fact, the calibration addresses two fundamental issues of Raman-DTSs: the differential attenuation between anti-Stokes and Stokes Raman signals that significantly impairs system performance and the normalisation of the signals from the sensing fibre [63]. The most simple approach proposed by most of the commercial Raman-DTSs consists of introducing a correction factor calculated from the measurement of temperature at the beginning of the fibre and at its remote end (if accessible) using other temperature sensors (see Figure 4a). This is the common approach used for single-ended deployment (i.e., with the fibre extending from the interrogator, with only one connection to the instrument). Other calibration approaches requiring different fibre layouts (e.g., duplexed single-ended and double-ended configuration; see Figure 4) can be implemented for better accuracy and robustness [64].
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Vertical Structure of Rainfall  
PR detection could penetrate the precipitation from top to bottom and had a high vertical resolution of 250 m. Therefore, it was very suitable to investigate the vertical structure of rainfall with PR data. The maximum height to which the rainfall develops (hereinafter referred to as precipitation height or rainfall height) as well as the correspondence to the rainfall rate could be analyzed (Table 8).
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 6 Results and Evaluation
### Latency and Jitter
#### 6.2.2 SEED Internet Emulator Network  
In Table 5, we can observe the results based on the internal communication of the Internet Emulator platform.  
The latency results demonstrated excellent delay times, all being around 1 ms on average. Although there have been limited studies performed using the SEED Internet Emulator, a study by [PERSON] et al. [55] using Programming Protocol-independent Packet Processors (P4) as an emulated SDN model demonstrated achieving active queue management in the data plane, allowing for low latency at the edge and core levels. Additionally, [PERSON] et al. [56] highlighted achieving low latency and high availability using P4 through programmable data planes in SDN. Moreover, [PERSON] et al. [57] conveyed that low jitter results were achievable through programmable data planes in the P4 model for the data center application, following the same structure as the models in this paper. Furthermore, we can observe that Mininet [58] can also provide low latency and jitter results through programmability in the data plane, as shown in our results.
## 3 Path Optimization
### A* Algorithm  
A grid-based method is used to construct the map model for path planning. The distance between the robot and each edge of the obstacle is calculated, determining the distance from the center of mass to each edge of the obstacle polygon, denoted as \(Distk=\{d_{1},d_{2},\ldots,d_{n}\}\). The existing Bowyer-Watson triangulation method is applied to the vehicle's motion environment to perform triangulation, obtaining a set of triangles D after removing obstacles. The existing A* algorithm is then used for pathfinding, which is finding an initial feasible path from the starting point to the goal. An existing path smoothing algorithm is applied to the feasible path found to ensure that the vehicle does not experience rigid impacts at corners. As shown in Figure 4, the path from the start point (0, 0) to the goal point (0, 3) is very smooth, especially at the corners. This guarantees that the vehicle can quickly navigate through the turn while minimizing speed loss, making it capable of handling extremely narrow corner environments.  
Figure 4: The path planning algorithm is represented by the A* algorithm (depicted by the fine black line) for the path curve. The green indicates the robot’s position and orientation, while the black represents the non-traversable areas.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Transport Layer
#### 5.4.4 Distributed Caching  
Due to the structure of multi-hop networks, lost packets cause expensive retransmissions along every hop of the path between the sender and the receiver [39]. To alleviate this problem, a distributed caching system is used in HARE, so that parents acknowledge the correct reception of packets from children and cache their data until it is properly received in the GW.  
As it can be seen in Figure 8, nodes \(N_{12}\) and \(N_{13}\) can go to sleep after the first transmission window, because their data packets have been acknowledged by node \(N_{6}\), which will cache them in memory together with its own data to be sent in the next transmission window.
## 3 Materials and Methods
### Methods of Improvement
#### 3.1.3 Boosting Algorithms  
Boosting is distinct from both bagging and stacking ensemble techniques. As illustrated in Figure 3, in boosting, models are sequentially integrated into the ensemble. Each subsequent model strives to rectify the predictions of its predecessor. The overarching aim of this method is to evolve a robust learner through successive iterations. What differentiates boosting from techniques such as bagging is its inherent capacity to learn iteratively from prior classifiers, progressively focusing on misclassified elements. Contrarily, in bagging, each iteration uses a separate set, thus lacking this accumulative 'learning' aspect.  
In the context of our work, the blending of boosting with deep learning mirrors our approach in stacking. Firstly, feature extraction through transfer learning is carried out before introducing the models into the boosting framework. As with previous methods, VGG-16 and Inception are the chosen architectures for this feature extraction. While we have previously touched on the strategy of amalgamating weak learners to cultivate a stronger one, here, our attention is on adaptive boosting (AdaBoost), which holds the distinction of being among the earliest boosting techniques effectively utilized in classification problems.  
Figure 3: Example of the boosting algorithm, where the red and blue dots represent the normal and abnormal cases, and the green and red boxes represent the correct and incorrect predictions, respectively. As the model progresses, the decision tree classifier represents the weak learners, and the faded blue and red colors represent the images that have less weight because they have already been predicted correctly. In the same fashion as the stacking method, the first part represents transfer learning for feature extraction so the data set can be transformed into a more manageable state for ML algorithms. This process will iterate until the complete training data fit without error or to a specified number of estimators, which in our case is set to 200.  
Referencing Figure 3, AdaBoost employs a decision tree as its weak classifier. This choice is not arbitrary; the decision tree is a frequently favored weak classifier for AdaBoost. During the training phase, the user is required to define the iteration count. In our study, this count is set to 200, meaning that the process will iterate until either the entire training data set fits without error or until it reaches this maximum count.