esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data Processing Framework for Polar Performance Diagrams
## 3 The hrosailing Framework
### Representation for Different Purposes  
The established approach to represent polar diagrams is via a table of boat speeds where the rows and columns are indexed by certain wind speeds and wind angles. These kinds of polar diagrams are compatible with a range of other tools and software, but are hard to adjust to real-world data. For example, if we obtained very good measurement data for some wind speeds and angles and very poor data for others, this information would possibly be lost in a table.  
Another possibility is just to save triplets of \((\text{TWA},\text{TWS},\text{BSP})\) data points that are supposed to be of good quality (for example generated by filtering and interpolating). This representation reflects the quality of the underlying data very well.  
As a result of a regression analysis, or to ensure certain theoretical behaviours of your polar diagram, it may be favourable to represent your polar diagram as a function \(\text{BSP}(\text{TWA},\text{TWS})\) of a given type (approximating the real underlying function).  
The three kinds of representations discussed above are included in the hrosailing framework as PolarDiagramTable, PolarDiagramPointCloud and PolarDiagramCurve, respectively.  
Each of these formats has some advantages and disadvantages. We can compare these by the following criteria:  
1. Can a human (trained in sailing), with a printed version of the raw data, easily use the polar diagram to suggest optimal steering of the corresponding vessel?
2. Can a human (not trained in programming) at least read and understand a printed version of the data?
3. Can the data be used in conjunction with other software such as OpenCPN [19]?  
Figure 6: A visual representation of the hrosailing framework. Red boxes with rounded corners indicate data, yellow boxes indicate framework components. Solid arrows from multiple objects to a single indicate the possibility to compute the object at the end by using the objects at the starting points. Dashed arrows indicate the “part of” relation.  
* Is the computation of a BSP value for given (TWA, TWS) values independent of interpolation methods?
* Can the polar diagram easily be adapted when new information is given?
* Are the respective polar plots smooth and therefore (possibly) more realistic?
* Is there nearly complete information for a collection of slices?
* Theoretically, is the polar diagram capable to adapt to (probably incomplete) real world measurement data?  
A complete comparison with respect to these criteria is given in Table 1. By changing the sails, the polar diagram of a vessel also changes drastically. Many vessels can switch between different sets of sails for different weather conditions. In order to understand the overall behaviour of a vessel, a collection of polar diagrams for each sets of sails is desirable. This is also included in the hrosailing framework as PolarDiagramMultisails.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 5 Graph-Based RA in D2D Networks
### Graph Coloring-Based RA Methods  
RA in D2D-based information relaying in a mmWave vehicular scenario (the sub-6 GHz spectrum is used for overhead communications with small-cell BSs) is explored in [59]. Each D2D pair constitutes a vertex, and the potential interference between two links forms an edge. Channel allocation is performed through graph coloring, ensuring that all neighboring links operate on separate channels. Choosing a color (channel) is also facilitated by the DUEs sharing information about the interference among themselves. The BSs also receive feedback from all nodes to allocate independent channels for overhead exchange between the users. Relatively stable high throughput, with greater gains for shorter inter-BS difference, has been reported. The authors of [60] explore RA in underlay D2D communications based on full-duplex (FD) transceivers, i.e., they are equipped with separate radio frequency circuits for transmission/reception, and can then use the same channels for both UL and DL. Hence, multiple RBs may be allocated to each link, and a RB may be assigned to different links simultaneously. The scenario for which the RA solution is applied is composed of a variable number of DUEs and CUEs within the coverage of a single macro BS. The most significant challenge in FD communications is the resulting self-interference between the transmitting and receiving antennas. An interference graph illustrates the network as follows: the CUEs DL and UL links are considered as separate sets and, together with the DUEs, are denoted as vertices. They are all connected via edges, the weights of which are the interference levels. Vertices have four attributes--type (UL/DL CUE, DUE), vector of the received signal powers on each RB, vectors of colors that show which RBs are allocated to this vertex, and vectors of permitted transmission powers for each RB. The coloring is performed on the basis of the achievable throughput and the interference for a group of neighboring vertices. Simulations show fast convergence, a linear increase in \(R_{T}\) with the number of nodes, and stable fairness. The proposed algorithm is highly dependent on the self-interference index, which is subject to the transceiver's design. An alternative graph structure [58] that considers non-adjacent interference sources is devised for a D2D-based IoT network composed of a variable number of CUEs and DUEs, with a fixed number of channels. A bidirected graph is composed of the CUEs and DUEs as vertices, and the interference between every pair of them is illustrated via edges. The graph coloring is performed by first assigning unique channels to the CUEs, followed by allocating resources for each DUE, such that its potential interference to other unassigned DUEs may be minimal (the interference level at each edge includes the cumulative unwanted signal power of all other vertices in the graph). The algorithm aims to distribute the channels
###### Abstract
Due to the uncertainty of flight time history characteristics, methods that use theoretical fundamentals to solve the distance between two maneuvering profiles, such as principal component analysis [14], neural networks [15; 16], or other types of machine learning algorithms [17; 18], are unstable when applied to the classification and statistics of such yaw angle data. This article presents the development of Phase-Sensitive Detection (PSD) statistical algorithms to calculate the number of 360\({}^{\circ}\) hovers, and S turns in a flight profile. Upon removing redundancy in the median-crossing marks of yaw angle data, the number of hovers can be deduced; meanwhile, through the layering function of PSD and layer-by-layer decomposition of the yaw angle signal, the number of S turns can also be obtained. Finally, the effectiveness of the statistical algorithms presented in this article was verified through a practical flight example.
## 6 Hybrid 3D Convolutional-Transformer Model
### Local Feature Extraction Using 3D-ResNet Pre-trained Model  
This stage represents the local feature extraction phase within the 3D-ResNet Backbone, a deep learning model architecture commonly applied in video processing tasks to extract spatiotemporal features from video frames using a pre-trained 3D-ResNet architecture. This method is useful for a variety of video analysis applications, including action detection, video categorization, and more. In this paper, we employ this pre-trained model to recognize GMMs. We load a pre-trained 3D-ResNet model (**r3d_18**) and replace the fully connected layer with an identity layer (**'nn.Identity0), essentially transforming the model into a feature extractor. Here is a detailed description of each component within this step:  
* Convolutional Layer (3D): This layer performs 3D convolution on the input video frames. Unlike 2D convolution, which only functions on spatial dimensions (height and width), 3D convolution works on both spatial and temporal dimensions (height, width, and depth/time). This layer is responsible for extracting spatiotemporal characteristics from video frames. The implementation details of the Con3D layer are illustrated in Table 2.
* **Batch Normalization (3D):** This layer normalizes the Conv3D layer's output for each mini-batch. It helps to accelerate the training process while also boosting the neural network's performance and stability. Batch normalization for 3D data is applied across the feature maps while preserving temporal coherence. Here, the input channels are equal to 128, which matches the output channels of the previous Conv3D layer.  
* **The Rectified Linear Unit (ReLU):*
* This is an element-wise activation function that adds non-linearity to the model. It outputs the input straight if it is affirmative otherwise it returns zero. This enables the network to learn more complicated patterns. The Inplace is set to True in order to alter the input immediately and save memory.
* **Max Pooling (3D):*
* This layer down-samples in both the spatial and temporal dimensions, lowering the dimensionality of the feature maps while maintaining the most important features. Max pooling reduces the computing effort while controlling overfitting by offering an abstracted form of the representation (see Table 3).
* **The 3D Convolutional Layer:** This layer reduces the number of channels from 128 to 3, preparing the output for the next stage. The input channels are equal to 128, the output channels are equal to 3 and the Kernel Size is equal to (1, 1, 1), indicating point-wise convolution.
# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 2 Related Works  
The main focus of this paper is on the research of the application of autonomous navigation technology in the field of aerial refueling. In response to the application challenges in the field of aerial refueling discussed in the first chapter, we have summarized the latest status and the analysis of the advantages and disadvantages of relevant technologies at home and abroad. Most of the latest solutions involve combining tracking and detection based on deep learning. However, their effectiveness is limited by the respective shortcomings of tracking and detection. To meet real-time requirements, one-stage methods are generally chosen in detection because of their fast detection speed, but they have high omission rates. Examples include YOLO (You Only Look Once) [4]. In tracking methods, although mean shift algorithms have low complexity and are insensitive to slight deformations and occlusions, they have excessive demands on the speed of target movement. While Kalman filter algorithms establish target motion models without requiring a specific speed, they have poor target feature discrimination and require the motion state to follow a linear Gaussian distribution. Particle filter algorithms effectively address occlusion issues but require a large number of samples. Sparse coding methods are robust against partial occlusions and lighting conditions but involve high computational costs.  
Based on the analysis of the advantages and disadvantages of the various methods mentioned, we have selected the most suitable basic algorithm module for the aerial refueling field for optimization and enhancement.  
An adaptive clustering multi-classifier-based method for dynamic target detection and tracking was proposed, employing adaptive classification training tailored to varying feature responses. The decision function was utilized to weigh the detection and tracking outcomes of the multi-classifiers, and depth was estimated based on the outer ring of the refueling drogue. The scoring mechanism was updated in real time according to the information and changes in the motion state. The decision function was employed to perform a weighted fusion of the detection and tracking results from multiple classifiers. The scoring mechanism was updated in real time based on two parameters: the depth estimation information and the motion state of the outer ring of the refueling drogue.  
A primary classifier for the target state determination module was developed to select the optimal target detection. The missing portions of the target were estimated using a positioning area completion mechanism. This approach effectively addresses the challenges of dynamic target detection and tracking loss under complex conditions such as motion blur, target appearance deformation, and occlusion.
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
###### Abstract  
We present a three-dimensional (3D) intersection traffic management platform for small autonomous Unmanned Aerial Vehicles (UAVs), particularly quadcopters, in urban airspace. Assuming many autonomous UAVs are approaching a shared airspace, where UAVs have varying sources and destinations, we propose a system model for a 3D intersection that aims to provide safe and systematic management of UAVs. We also devised a scheduling scheme to ensure that the intersection is efficiently utilized and that there are no collisions among the UAVs in the intersection. The scheduling scheme applies the reservation-based approach, which is sensitive to the sequence of the UAVs in scheduling, thus genetic algorithm is used to determine the best sequence of the UAVs. Simulations were performed to evaluate the efficiency of the system. We also show through the simulations that our scheduling scheme reduces the UAVs' average time in the system by 27 percent compared with when the UAVs are scheduled in a first-come, first-served manner for the highly crowded intersection.  
unmanned aerial vehicles (UAVs); 3D intersection; intersection traffic control; reservation-based scheduling; genetic algorithm Article
# Complex Network Modeling and Analysis of Microfracture Activity in Rock Mechanics
## 2 Experimental Methods and Testing Procedures  
### Samples and Test Equipment  
The original rock samples were sourced from fresh rock at the Yebatan Hydropower Station dam site, situated 75 km west of Baiyu County in the upper reaches of the Jinsha River. The dam site is characterized by a deeply incised valley with steep slopes and narrow width, typical of a high mountain and deep valley topography (Figure 1). The tectonic and gravity stress superposition at the dam site has resulted in high initial ground stress, with a surrounding rock strength ratio of 4-5 and a maximum measured in-situ stress of 37.57 MPa. The rock samples are middle-acid intrusive granites of the Variscan Age (\(\gamma\)\(\delta_{3}\)\({}^{\text{4}}\)), exhibiting semi-idiomorphic fine-medium-fine granular and massive structures. Their main mineral components are plagioclase and quartz, which are generally hard and brittle, with a Mohs hardness of 6-7 and strong weathering resistance (Figure 2).  
According to the current standard \"Rock Test Regulations for Hydropower Engineering\" (DL/T5368-2007 [39]), the diameter of the cylinder should be 48-54 mm, and the height-to-diameter ratio of the sample should be 2.0-2.5 mm. In this study, the rock blocks were processed into complete cylindrical samples according to this specification. The end diameter of the standard sample was D = 50 mm, and the height along the length was h = 100 mm. Some of the rock samples and their schematic diagrams are shown in Figure 3.  
Figure 1: Topography and geomorphological map of dam site area.  
Figure 3: Rock sample photo and schematic diagram.  
Figure 2: (**a**) Rock mineral identification and (**b**) mineral composition proportions.  
Rock triaxial acoustic emission tests were performed on an MTS815 rock testing machine(The MTS815 rock testing machine is manufactured by MTS Systems Corporation, which is headquartered in Eden Prairie, Minnesota, USA) at the Laboratory of Rock Mechanics, College of Water Resources and Hydropower, Sichuan University (Figure 4). The rock test system is composed of a loading system, a measuring system, and a controller. The acoustic emission system used for monitoring was the DiSP Acoustic Emission Test System, which has 16 channels. The test system mainly consisted of the following parts: an acoustic emission card (PC), an acoustic emission host system, an acoustic emission sensor, an acoustic emission preamplifier, and acoustic emission processing software.
## 1 Introduction  
Ad hoc networks are becoming an essential part of our modern technological infrastructure, expanding the range of available applications and their characteristics. On the other hand, thanks to technological advances, unmanned aerial vehicles (UAVs) offer a wide range of possibilities (extending wireless coverage, use in agriculture, search and rescue, fire surveillance, etc.). The creation of an ad hoc network with UAVs also offers a significant advantage over other networks due to the high mobility of its nodes and their great versatility. This type of network is known as a Flying Ad Hoc Network (FANET) and is considered a subtype of Mobile Ad Hoc Networks (MANETs) or Vehicular Ad Hoc Networks (VANET). Due to the particular characteristics of UAVs, they bring new challenges for obtaining node mobility models, routing protocols, energy management, etc. Indeed, FANETs present high mobility of their nodes, both in speed and 3D mobility, frequent changes of network topology, or intense energy and weight restrictions. Consequently, it is essential to carry out a study of the different protocols for FANETs to identify the best ones to minimize the impact of the UAVs' characteristics and create a dynamic, agile, and efficient FANET network. A survey of mobility models, positioning algorithms, and propagation models can be found in [1, 2, 3].  
In this work, after analyzing the types of routing protocols designed for FANETs, we focus on comparing, through a real deployment of a FANET using the IEEE 802.11 (WiFi) standard in the 2.4 GHz and 5 GHz bands, the performance offered by several routing protocols. We believe that a real deployment can provide much more reliable results than those obtained by simulators. Therefore, we consider that the highlight of this article is the actual deployment of the FANET, something that, as we will see, very few articles do, which limits their investigation to simulated experiments. The chosen protocols are Optimized Link-State Routing (OLSR), Better Approach to Mobile Ad Hoc Networking Advanced (BATMAN-ADV), and Babel. They are compared in terms of throughput and packet losses in a network composed of several UAVs and an intermediate relay node. We chose these proactive routing protocols because of their high mobility range, low latency, widespread use in ad hoc networks, and good power consumption; similarly, they have relatively low complexity and computational demands, allowing us to simplify and automate some of the challenges mentioned in [1; 2; 3]. The results highlight the importance of the correct choice of routing protocol for FANETs, showing that Babel achieves higher performance in the studied metrics whereas BATMAN-ADV and OLSR show lower performance.  
The rest of the article is organized as follows: in Section 2, we include a review of state-of-the-art case studies with FANETs, and we break down the different types of routing protocols proposed for ad hoc networks and FANETs. The FANET deployed for this study and the tools used are detailed in Section 3. The results are discussed in Section 4. Finally, conclusions and future works are presented in Section 5.
## 2 Case Studies and Operation Modes of the System
### Control Strategy  
The annual performance of the system was calculated considering a rule-based control strategy. Independent of the actual heating or cooling demand, when one of the sensible DHW storage energy levels falls below a lower limit the system switches to DHW generation mode. Depending on the SoC of the RPW-HEX, either operating mode (c) or (d) is triggered. If (c) is triggered, the RPW-HEX is discharged until it is empty (SoC = 0). Note that if one DHW storage triggers the DHW generation mode, all storage will be charged at least to the upper limit to minimize number of switches between heating/cooling and DHW mode. This is also true if the RPW-HEX cannot provide the required energy to charge all DHW storages. In this case, the system switches from operating mode (c) to (d) to complete the charging of the DHW storage. The DHW storage devices are charged sequentially, one after another, up to the upper limit (starting from the storage with the lowest SoC). In addition, it is possible to overcharge the DHW storage devices, above the upper limit, up to a maximum limit if the RPW-HEX is fully charged and it would be beneficial from a systemic point of view to discharge the RPW-HEX. In this case, the DHW storage devices are charged in parallel and not one after another.  
If there is heating or cooling demand from the building and the RPW-HEX SoC \(<\) 100%, operating mode (a) or (b) are triggered. To avoid constant switching between the heating and cooling mode, e.g., relevant during the transition season or for fast changing external conditions, heating or cooling operation was only triggered after a certain amount energy requirement for heating or cooling, respectively, had been accumulated in the building.
# Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction
## 4 Evaluation Metrics  
Following standard practice for event coreference systems evaluation, the most common evaluation metrics for event coreference resolution can be used to evaluate Arabic event coreference systems, such as MUC [31], B-Cubed [32], CEAF [33], and BLANC [34], all of which report results in terms of recall (R), precision (P), and F-score (F). Additionally, the CoNLL score [35] can be used for Arabic event coreference evaluation, which is the unweighted average of the MUC, B3, and CEAF F-scores.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 3 GNSS-R Measurements  
This paper defines a new approach to classifying wetland inundation based on GNSS-R observables and ancillary data by employing a classification approach based on the MDTR algorithm. In this section, we define the processing required for the GNSS-R measurements to be comparable to the reference dataset and the different GNSS-R observables built from the measurements to be used in the classification algorithm.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
###### Abstract  
Low-density parity-check (LDPC) codes have the potential for applications in future high throughput optical communications due to their significant error correction capability and the parallel decoding. However, they are not able to satisfy the very low bit error rate (BER) requirement due to the error floor phenomenon. In this paper, we propose a low-complexity iterative decoding scheme for product codes consisting of very high rate outer codes and LDPC codes. The outer codes aim at eliminating the residual error floor of LDPC codes with quite low implementation costs. Furthermore, considering the long simulation time of computer simulation for evaluating very low BER, the hardware platform is built to accelerate the evaluation of the proposed iterative decoding methods. Simultaneously, the fixed-point effects of the decoding algorithms are also be evaluated. The experimental results show that the iterative decoding of the product codes can achieve a quite low bit error rate. The evaluation using field programmable gate array (FPGA) also proves that product codes with LDPC codes and high-rate algebraic codes can achieve a good trade-off between complexity and throughput.  
low-density parity-check codes; error floor; product codes; field programmable gate array +
Footnote †: journal: Electronics Electronics