esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Probability of Chip Error in Coherent O-QPSK
### Without Pulse Shaping  
When we take the case of no pulse shaping into account, the shaping function, \(h(t)\), is a non-return to zero (NRZ) rectangular pulse, whose expression is:  
\[h(t)=\begin{cases}1&-T_{c}\leq t<T_{c}\\ 0&\text{otherwise}.\end{cases} \tag{9}\]  
Accordingly, the complex envelope of the received signal in Equation (8) on the Q\({}_{d}\)-I constellation plane can be written as:  
\[\tilde{r}(t)=\Bigg{(}\pm\sqrt{\frac{C}{2}}\pm j\sqrt{\frac{C}{2}}\Bigg{)}+ \Bigg{(}\pm\sqrt{\frac{I}{2}}\pm j\sqrt{\frac{I}{2}}\Bigg{)}h(t)\exp(j\phi_{I} )\ \ -T_{c}\leq t<T_{c},\ \ 0\leq\phi_{I}<2\pi \tag{10}\]  
where \(C\) and \(I\) represent the energies of the useful and interferer signals, respectively. \(\phi_{I}\) is the carrier phase of the interferer. Since the demodulation is coherent, perfect knowledge of the carrier phase and symbol timing is assumed for the useful signal. These parameters are supposed to be uniformly distributed random variables for the interfering signal. The pulse shaping function is constant throughout a chip duration; thus, random symbol timing of the interferer, \(t\), does not give any contribution to the performance. On the contrary, the presence of an interferer carrier phase has a significant impact on the received signal, because \(\phi_{I}\) rotates the interfering signal on the Q\({}_{d}\)-I domain, as indicated in the second addend of Equation (10). The geometric representation of Equation (10) on the Q\({}_{d}\)-I constellation plane is depicted in Figure 1.  
With respect to the transmitted symbol, the first addend in Equation (10) will be one of the constellation points on the Q\({}_{d}\)-I domain, as shown in Figure 1b, while the second addend in Equation (10)  
Figure 1: The impact of the interferer. (**a**) Useful signal; (**b**) Q\({}_{d}\)-I domain representation of useful signal; (**c**) Interferer signal; (**d**) Q\({}_{d}\)-I domain representation of interferer signal; (**e**) Q\({}_{d}\)-I domain representation of received signal.  
represents the interfering signal, which can be any of the points shown in Figure 1d, but rotated according to the interferer carrier phase, \(\phi_{I}\). The final sum in the equation can be interpreted as in Figure 1e; the sampling point of the received signal is located in one of the quadrants in the Q\({}_{d}\)-I plane, in agreement with the transmitted useful symbol, but, due to the presence of the interferer, it is shifted to a random location over the circle with radius \(\sqrt{I}\) and the center at \(\left(\pm\sqrt{\frac{C}{2}},\pm j\sqrt{\frac{C}{2}}\right)\). The amplitude of the useful signal on the Q\({}_{d}\)-I plane is \(\sqrt{C}\), and the amplitude of the interferer signal is \(\sqrt{I}\). In the following parts, \(P_{c}\) and \(P_{s}\) will denote the probability of chip error and the probability of O-QPSK symbol error, respectively. If we consider an ideal maximum likelihood demodulator [26], three distinct cases need to be investigated.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 5 Real-Time UWB Indoor Positioning Systems (RTIPSs)
### Indoor Positioning Systems for Unmanned Aerial Vehicle Applications
Their work aimed to improve localization accuracy by combining the VIO with the ultra-wideband (UWB) positioning technology. A commercial UAV controlled by the Cube Pixhawk2 (CubePilot Pty. Ltd., Breakwater, Australia) and equipped with a 3 DM-GX5-25 (LORD Corporation, Williston, VT, USA) IMU and the Intel RealSense D455 depth camera (Intel Corporation, Santa Clara, CA, USA) was used for practical system testing. The LinkTrack S (SZ Nooploop Technology Co., Ltd., Shenzhen, China) UWB-based IPS, which is based on the TDoA ranging approach, was used, including four fixed anchors and a movable tag. The experimental test results showed that the proposed method achieved an accuracy of 41.4 cm, which was better than the individual performance of UWB (46.3 cm) and VIO (1.506 m). [PERSON] et al. [141] proposed an energy-efficient 3D indoor RTLS for UAVs. They used UWB ranging with wake-up radio (WUR) technologies to minimize the modules' power consumption during data exchange between the tags on the UAVs and the anchors. Their approach was composed of a lightweight positioning algorithm, optimal flight strategy, and ranging error correction algorithm. The system hardware consisted of the Crazyflie 2.1 (Bitcraze AB, Malmo, Sweden) UAV with the STM32F405 Cortex-M4 (STMicroelectronics, Geneva, Switzerland) microcontroller, which enables the flight control, the positioning algorithm implementation, and the integration of BMI088 and BMP388 (Bosch Sensortec GmbH, Reutlingen, Germany) IMU units and pressure sensors, as well as the UWB tag for positioning and localization. A custom-designed UWB-Wakeup shield based on the Qorvo DW1000 transceiver was used for UWB anchors and tag implementation at a 6.8 Mbit/s sample rate. The UWB-Wakeup shield also had the CC1200 (Texas Instruments Incorporated, Dallas, TX, USA) radio transceiver for sending on-off-key wake-up signals to the WUR receivers at 868 MHz. The system testing results showed that the localization error was within 28 cm at 1.2 ms MCU processing time, and it was improved by an additional 25% with the error correction algorithm. The energy consumption was determined to be 24 mJ at the sensor node, which is 50 times less compared to a traditional system with the same localization accuracy. Other UAV-based IPSs were proposed in [142; 143; 144; 145; 146; 147; 148].
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 4 Preliminary Construction: TSBT-ORAM
### Data Eviction Process
#### 4.3.1 Basic Idea  
The basic idea of data eviction is as follows. For each non-leaf layer \(l\), up to two evicting nodes \(v_{l,x}\) and \(v_{l,y}\) are randomly selected (note that the top layer has only one node, and thus, only one node is selected); one data block is evicted from each selected node to one of its two child nodes, while a dummy block is evicted to the other child node. In the following, we focus on the eviction operations involving \(v_{l,x}\), as the operations involved \(v_{l,y}\) are similar. Note that, \(v_{l+1,2x}\) and \(v_{l+1,2x+1}\) are the child nodes of \(v_{l,x}\).  
Figure 2 shows an example of the eviction process, where circled nodes are selected to evict data blocks to their child nodes. Let us consider how node \(v_{2,2}\) evicts its data block. The index block in the node is first retrieved to check if the node contains any real data block. If there is a real block \(D_{e}\) in \(v_{2,2}\) and \(D_{e}\) is mapped to leaf node \(v_{5,20}\), \(D_{e}\) will be obliviously evicted to \(v_{3,5}\), which is \(v_{2,2}\)'s child and is on the path from \(v_{2,2}\) to \(v_{5,20}\), while a dummy eviction is performed on another child node \(v_{3,A}\). Otherwise, two dummy evictions will be performed on nodes \(v_{3,A}\) and \(v_{3,5}\).  
For obliviousness, the eviction process should further meet the following requirements:  
* which real data block is evicted from an evicting node should be hidden;
* which one of two child nodes of an evicting node that receives the evicted real data block should be hidden.  
To meet these requirements, the design of TSBT-ORAM involves the following two aspects:  
* For each evicting node (e.g., \(v_{l,x}\)), the position where the evicted data block resides should be hidden from any server.
* For each receiving node (e.g., \(v_{l+1,2x}\)), each position that can be used to receive the evicted data block should be selected with an equal probability. In other words, each position of the receiving node should have an equal probability to be written during data eviction. This way, the behavior of a receiving node is independent of whether it receives a real or dummy block.
## 5 Expertise Retrieval
### Measuring Similarity Between Mobile Apps' Descriptions
#### 5.1.1 Semantic Similarity Models  
Two main models are explored to measure the semantic similarity between mobile app descriptions, which are Sentence-BERT (SBERT) and Doc2 Vec. The Sentence Transformer SBERT, a state-of-the-art model, was chosen due to its efficient architecture that can be best utilized in information retrieval tasks and proved to outperform BERT [75]. Doc2 Vec was further chosen for exploration, in addition to SBERT, since it is particularly designed for long texts such as paragraphs or even complete articles. Moreover, they were selected to compare a contextualized SBERT model against a non-contextualized Doc2 Vec model [76]. A brief description of the two models is provided:  
**SBERT**. SBERT is a framework for generating fixed-length contextualized and meaningful high-quality embeddings for sentences and paragraphs. With SBERT, it was evident that the complexity of finding semantically similar sentences in a collection of nearly 10,000 sentences was reduced dramatically compared to BERT [75], making it a suitable choice for semantic similarity and information retrieval tasks.  
**Doc2 Vec**. Doc2 Vec is an unsupervised algorithm which is an improvement of the Word2 Vec model. It generates a dense fixed-length embedding for an entire document of any size, whether sentences, paragraphs or even long documents. Doc2 Vec can generate an embedding which represents the general meaning of a document [77].  
The model with the highest reliability in detecting similar app descriptions is chosen to be applied in the design of the proposed crowd selection approach, particularly in the expertise retrieval component.
###### Abstract  
The temperature dependence of the fracture toughness \(J_{C}\) of a three-dimensional (3D) random fibrous (RF) material, with a porosity of 87% along the through-the-thickness (TTT) direction, was investigated using experiments and the finite element method (FEM) in this study. The temperature considered ranges from 299 to 1273 K. The experimental observations revealed the fracture toughness \(J_{C}\) with crack length-to-width ratios of 0.4 and 0.5, which increased from 47.32 to 328.28 J/m\({}^{2}\) and from 44.92 to 280.09 J/m\({}^{2}\), respectively, as the temperature increased. Then, a 3D FE model, considering the meso-morphology characteristics of the 3D RF material, was developed to simulate a size-scaled compact tension (CT) specimen with a single edge crack. Using the elastic modulus and the fracture strength of the silica fibers at room temperature, we verified the effectiveness of the FE model, then predicted the fracture strength of the silica fibers and the bonding between the fibers at elevated temperatures. In addition, our developed FE model proved to successfully simulate the fracture toughness \(J_{C}\) from 299 to 1273 K and reveal the deformation mechanism of the 3D RF material at different temperatures.  
Keywords:random fibrous materials; fracture toughness; J-integral; finite element (FE) model; elevated temperature +
Footnote †: journal: Applied Sciences
# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 2 Materials and Methods
### Data Collection and Processing Procedure Development
#### 2.2.3 During the Flight  
The data collection stage was conducted using an Odroid XU4 Mini Computer with the Ubuntu 1404 LTS distribution of the Linux operating system [32]. Hardware communications are established via an Indigo-based ROS, since this framework runs on a Linux operating system [38].  
The ROS is a framework of processes (also known as nodes) developed by Willow Garage and commonly used as a meta-operational system for robots, providing services such as hardware abstraction, low-level device control, message transmission between processes, packet management, etc. Its architecture is mainly based on the publish-subscribe network pattern, in which multiple nodes subscribe to a master node that manages communication between them. Users can establish a distributed network of nodes that communicate through messages that travel on buses known as topics [26].  
Here, an open-source ROS package was used to acquire data and generate point clouds from the UDP Ethernet packages provided by the VLP-16 [36]. This ROS package contains the velodyne_driver package, which offers essential sensor management via the velodyne_node ROS node, capturing LiDAR data and publishing them in raw form velodyne_msgs/velodyneScan messages in the velodyne_packets topic. Then, execution is conducted using the vdump command, which saves raw data in pcap format.  
Figure 7 below illustrates the communications scheme for acquiring LiDAR system data and position and orientation data using the DJI_SDK ROS package, which provides a ROS interface to the DJI-embedded SDK. This allows us to control the M600 Pro platform through ROS messages and services. This package is based on the main DJI SDK library in C++ [33]. During ROS node execution, dji_sdk publishes orientation data as geometry_msgs/QuaternionStamped messages in the/dji_sdk/attitude topic and the position data as sensor_msgs/NavSatFix messages in the/dji_sdk/gps_position topic. These data are recorded using the rosbag command and are later converted to csv format.  
Figure 6: Marking of variables obtained by the total station.  
After feeding and starting all system components, the system must interface with the Mini Computer via the SSH protocol using the PuTTY software and activate the VNC graphical display connection to start collecting data. To guarantee synchronization, the system clock must be updated through NTP services. In addition, the VLP-16 webserver page must be accessed to check whether the GPS PPS option is Locked. Next, the ROS commands must be executed to start storing data and perform the scheduled flight. Finally, an FTP connection must retrieve the pcap and csv files and start the georeferenced point cloud generation stage.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods
### Detection Based on MNet  
#### 3.3.1 CenterNet  
CenterNet [22] is a single-stage detection model without anchor boxes, which we use as the base detection model. In CenterNet, an input RGB image \(I\in R^{H\times W\times 3}\) is passed  
Figure 2: Examples of easy and hard samples: (**a**) simple samples; (**b**) occlusion samples; (**c**) motion blur samples; (**d**) background integration samples.  
through the feature extraction network backbone to obtain the feature map \(A\in R^{\frac{H}{r}\times\frac{W}{r}\times 3}\), where \(r\) is the downsampling multiple of the feature map relative to the input, which is generally determined by the backbone. For example, in the VGG, the feature output from the image input to the final stage will be downsampled 5 times, while in the ResNet [33], it will be downsampled 4 times. The shared feature map will be processed by different branches to obtain the corresponding key point heat map, position offset, and size (width and height). The heatmap \(heatmap\in R^{\frac{H}{r}\times\frac{W}{r}\times C}\) represents the confidence prediction of the object's potential location, \(C\) represents the total number of categories to be detected, and the value at each position represents the probability score of the object there. The center point position offset \(offset\in R^{\frac{H}{r}\times\frac{W}{r}\times 2}\) represents the deviation between the predicted position and the real position when a key point in the heatmap is mapped back to the input. This deviation is caused by rounding the key point position after downsampling and is optional. The size \(wh\in R^{\frac{H}{r}\times\frac{W}{r}\times 2}\) represents the width and height of the object pointed to by each point in the heat map. During training, it is necessary to create a corresponding object label for each sample according to the central point confidence \(heatmap\), \(offset\), and \(w\) and \(h\) according to the output dimensions.  
The advantage of CenterNet is that it is simple and efficient and has excellent detection performance on large and medium objects in general data sets. However, its limitation is that it is not fully designed for small object detection and cannot effectively retain the feature information of small objects. In addition, sports tasks in specific competition field scenarios are also temporarily unavailable. We combined small object detection and improved the result from the perspective of multiscale feature extraction to enhance the small object detection capability of moving videos.
## 2 System Model
### Energy Harvesting Model  
The communication scenario under consideration in this paper is mainly applied to environmental monitoring, and each EH-ST can harvest different amounts of energy units from the ambient energy sources. For instance, for a solar source, EH-STs in the direct sunlight situation will harvest more energy than those in a cloudy situation. Therefore, we assume the energy harvester of EH-ST \(u\) acquires\(B_{u,k}\) energy units from the environment within the duration of frame \(k\). Based on the energy harvesting models mentioned in References [22; 24; 25], the energy arrival process in this paper is assumed to follow Poisson distribution during one frame transmission time with parameter \(b_{k}\), which represents the stochastic and intermittent nature of the ambient energy sources. Thus, the probability of \(B_{u,k}\) energy units arrived within the duration of frame \(k\) is:  
\[\beta\left(B_{u,k},b_{k}\right)=\frac{b_{k}^{B_{u,k}}exp\left(-b_{k}\right)}{B _{u,k}!}. \tag{6}\]  
The energy arrival process of the components of \(B_{k}=\left(B_{1,k},B_{2,k}\ldots,B_{U,k}\right)\) is considered to be identically and independently distributed (i.i.d) over time and all EH-STs. \(\overline{\beta}\) denotes the average energy units harvested within a long period of time, which reflects the average long-term EH rate, and \(\overline{\beta}=\mathbb{E}\left[B_{u,k}\right]\).
# GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization
###### Abstract  
Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. While graph condensation has recently emerged as a promising approach through which to reduce training cost by compressing large graphs into smaller ones and for preserving most knowledge, its capability in treating different node subgroups fairly during compression remains unexplored. In this paper, we investigate current graph condensation techniques from a perspective of fairness, and show that they bear severe disparate impact toward node subgroups. Specifically, GNNs trained on condensed graphs become more biased than those trained on original graphs. Since the condensed graphs comprise synthetic nodes, which are absent of explicit group IDs, the current algorithms used to train fair GNNs fail in this case. To address this issue, we propose **G**raph **C**ondensation with **A**dversarial **R**egularization (**GCARe**), which is a method that directly regularizes the condensation process to distill the knowledge of different subgroups fairly into resulting graphs. A comprehensive series of experiments substantiated that our method enhances the fairness in condensed graphs without compromising accuracy, thus yielding more equitable GNN models. Additionally, our discoveries underscore the significance of incorporating fairness considerations in data condensation, and offer invaluable guidance for future inquiries in this domain.  
Keywords:data distillation; graph neural networks; fairness; adversarial learning +
Footnote †: journal: Applied
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 1 Introduction
Section 2 describes the operating principles of the analyzed protection algorithms, detailing all the formulas used for fault detection. It is followed by Section 3, which explains and tests the proposed algorithms through software simulations. Then, Section 4 reports the findings of the study and the main contributions of the article. Finally, Section 5 concludes the paper.
## 2 Materials and Methods
### Statistical Analysis  
Patients were separated into CTRCD+ and CTRCD\(-\) groups. Therefore, statistical analysis was performed comparing both groups. Baseline, intermediate, and end-of-treatment (EOT) scans were treated as separate study subgroups. Continuous variables are represented as mean \(\pm\) standard deviation. Differences in continuous variables were studied by means of the Mann-Whitney U-test and Benjamini Hochberg correction procedure for multiple comparison (only \(p\)-values \(<\) 0.05 after Benjamini Hochberg correction were retained). Differences over time between baseline, intermediate, and EOT studies were studied by means of the Kruskal-Wallis test with post-hoc analyses, where applicable (\(p\)-values \(<\) 0.05 were eligible for post-hoc analysis). Differences in categorical variables were studied with Chi-squared test. Statistical analyses were performed in SPSS software version 19.00 (IBM Corp., Armonk, NY, USA). Radiomic features with an intraclass correlation coefficient (ICC) above 0.82 that were identified in a previous study [33] were defined as robust indices and included in the analysis. This threshold was set at the midpoint of the range interval [0.75-0.90] revealing good feature robustness [39; 40] with respect to variations in voxel size and gray level discretization.  
A kNN model-based imputer (1-NN) was utilized to impute the missing values by means of the Orange 3.31 software (Bioinformatics Laboratory at the University of Ljubljana, Slovenia; [[https://orangedatamining.com/](https://orangedatamining.com/)]([https://orangedatamining.com/](https://orangedatamining.com/)), accessed on 11 November 2024). A decision was made to remove the features with more than 10 instances exhibiting missing values.