esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformer: An OS-Supported Reconfigurable Hybrid Memory Architecture
## 3 Design and Implementation
### Page Migration
#### 3.3.1 Page Migration in the Parallel Architecture  
For the parallel hybrid memory, our architecture migrates the NVM pages to DRAM when the number of access counts exceeds the threshold of page hotness, and the NVM pages is reclaimed after the page migration. In the Linux kernel, a _move_pages_ system call is implemented. It provides users with the opportunity to migrate pages between different _non-uniform memory access_ (NUMA) nodes. Based on the _move_pages_, Transformer can perform page migration between different NUMA nodes.  
Figure 5 shows the process of page migration in the parallel architecture. First, the operating system allocates a new DRAM page to accommodate the migrated NVM page. Since the Linux kernel provides _alloc_pages_node_ and other similar functions, we can directly use these functions to allocate pages in DRAM or NVM. Second, all page table entries mapping to this NVM page should be unmapped in the page table according to the table entries in the reverse mapping table. After unmapping, the access permission information, and the data of the NVM page will be copied to the newly allocated DRAM page. Third, we update the page table entries to establish a mapping between the virtual and physical addresses of the newly allocated page, and invalidate the corresponding entry in the TLB. Finally, the original NVM page is reclaimed.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 7 Applications of Wearables in Sports
### Measurement and Monitoring of Athletic Performance
Measuring and monitoring performance and physiological data often involve various measurements such as acceleration, angular velocity, temperature, heart rate, etc. [56]. Instead of monitoring a player's in-game performance, these devices measure the player's bodily performance during the game. The term physiological data in sports has a broad definition that refers to the measurement and monitoring of physical and physiological characteristics [11]. Examples of WTs that collect physiological data include heart rate and sleep monitors, as well as devices that can wirelessly collect electroencephalogram (EEG) and electrocardiogram(ECG) data [145]. Commercially available wearable sensors worn on the body, such as the motusBASEBALL used in baseball, the KINEXON Precision Technologies sensor used in sports such as handball, football, basketball, and ice hockey, and Catapult systems (including different versions such as OptymEye, Catapult ClearSky T6), are used to obtain biometric data from athletes. Physiological data enables the tracking of physical and physiological information for assessing performance and recovery in sports [146]. It provides more objective measurements than subjective self-reporting [145]. Professional and amateur organizations use WTs that measure physiological data, such as heart rate and body temperature, to gain a competitive advantage [147]. [PERSON] [148], noted that professional sports teams commonly use athlete physiological data to monitor player health, fitness, and performance; understand player load; educate coaches (and players) about the effects of training on players; and design appropriate training and rest intervals. Furthermore, the collected data can be used to improve players, prevent injuries, monitor progress, and optimize performance through injury rehabilitation. Teams that physiological data obtained from WTs will significantly impact athletic performance, create competitive advantages, enhance fan experience, and generate new revenue streams [103]. For example, the International Tennis Federation is one of the first sports federations to allow players to wear sensors on their bodies and review critical information during set breaks [149]. Major League Baseball is also one of the first sports leagues to approve wearable physiological devices for use during games [150].
## 4 Application
### Non-Clinical Application
#### 4.2.2 Fitness Monitoring  
Exercise is good for health. However, over-exercise can have severe adverse health effects including death due to heart attack. Therefore, it can be important to monitor HR and RR to assess the health status of the exerciser and customise their training program according to their changing vital signs. Monitoring the physiological state using iPPG while doing exercise can be a convenient solution to overcome the adverse effects of over-exercising. In [PERSON] et al. (2015); [PERSON] et al. (2015); [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2019), iPPG was applied to monitor HR and RR during exercise, considering various fitness devices such as a stepping device, a treadmill, a bike, a hand bike, and a synchro-device.
###### Abstract  
The rise of knowledge graphs has been instrumental in advancing artificial intelligence (AI) research. Extracting entity and relation triples from unstructured text is crucial for the construction of knowledge graphs. However, Chinese text has a complex grammatical structure, which may lead to the problem of overlapping entities. Previous pipeline models have struggled to address such overlap problems effectively, while joint models require entity annotations for each predefined relation in the set, which results in redundant relations. In addition, the traditional models often lead to task imbalance by overlooking the differences between tasks. To tackle these challenges, this research proposes a global pointer network based on relation prediction and loss function improvement (GPRL) for joint extraction of entities and relations. Experimental evaluations on the publicly available Chinese datasets DuIE2.0 and CMeIE demonstrate that the GPRL model achieves a 1.2-26.1% improvement in F1 score compared with baseline models. Further, experiments of overlapping classification conducted on CMeIE have also verified the effectiveness of overlapping trial extraction and ablation experiments. The model is helpful in identifying entities and relations accurately and can reduce redundancy by leveraging relation filtering and the global pointer network. In addition, the incorporation of a multi-task learning framework balances the loss functions of multiple tasks and enhances task interactions.  
knowledge graph; joint entity and relation extraction; overlapping triplets; multi-task learning +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion
### Experimental Results and Comparative Evaluation  
#### 4.3.1 Performance of Device Identification  
To assess the recognition effectiveness of the enhanced YOLOv8_Adv model on different devices, this section will analyze its classification results using a confusion matrix. The classification results are shown in Figure 10.  
The results of the experiment reveal that the YOLOv8_Adv model shows a remarkable performance in classifying infrared images for substation electrical equipment, with an average accuracy rate of 96%. The model attained high accuracy across various equipment categories, verifying its robustness and effectiveness. Specifically, the classification accuracy for the isolator and isolation switch 1 reached 100%, demonstrating its exceptional precision and reliability in recognizing these devices and effectively handling complex infrared backgrounds. For the circuit breakers, the model achieved an accuracy of 95%, while for the bushings, it reached 94%, showing that YOLOv8_Adv maintains strong recognition performance for medium-sized devices with a low error rate. For the isolation switch 2 and insulators, the model's accuracy reached 96% and 97%, respectively, further demonstrating its accuracy in detecting smaller devices.  
However, the experiment also revealed some common types of errors, particularly false positives and false negatives. Specifically, the current transformer is occasionally misclassified as an isolator, which appears as a false positive in the confusion matrix. The underlying reason for this might be the similarity in infrared features between the current transformer and the isolator, making it difficult for the model to distinguish between the two devices. These errors typically occur when the model mistakenly classifies an actual current transformer as an isolator, thereby incorrectly assigning it to the isolator category.  
On the other hand, false negative errors were also observed in the experiment, particularly when multiple different devices were misclassified as background. These errors indicate that the model failed to recognize some devices, possibly due to interference from the background or the small size of the devices, which makes them harder to detect effectively in the image. Such misclassifications reflect the model's tendency to lose track of targets in more complex or occluded scenarios.  
Figure 10: Confusion matrix.  
These results indicate that the YOLOv8_Adv model, with optimized backbone, neck, and head structures, has effectively enhanced accuracy and recall in small device recognition, especially in complex infrared backgrounds with multiple-object interference, while maintaining high detection precision. Overall, the YOLOv8_Adv model meets the high-precision requirements of real-world engineering tasks in the infrared detection of electrical equipment, making it particularly suitable for infrared image monitoring in substations and facilitating the automated monitoring and maintenance of equipment conditions.
# Fault-Tolerant Visual Secret Sharing Schemes without Pixel Expansion
## 3 Proposed Scheme
In a perfect stacking, the resulting transmittance for a white secret pixel is \(1/2\), and because the design of the pattern for a black pixel is accordingly complementary, the resulting transmittance for the black secret pixel is zero. We also analyze the stacking results with different shifts: a one-pixel shift when \(n\) is three or four and up to a two-pixel shift when \(n\) is five or six. Besides, we also calculate the results for a one-pixel diagonal shift, that is a one-pixel right shift plus a one-pixel down shift. All of the results show that there are differences between the transmittance for the black and white pixels of the stacked image. When the transmittance for a white pixel of a secret image differs from that for a black pixel of a secret image, the original secret image can be recognized. Hence, the following theorem can be concluded. Theorem 1. The proposed schemes are the fault-tolerant VSS schemes.
## 3 Mathematical Background
### Dual Quaternion Regression  
To find the regression coefficients or weights, \(\mathbf{w}=[w_{1},\,w_{2},\,...,\,w_{n}]\), of the dual quaternions \(\hat{\mathbf{Q}}=[\hat{\mathbf{q}}_{1},\,\hat{\mathbf{q}}_{2},\,...,\,\hat{ \mathbf{q}}_{n}]\) that yield some desired dual quaternion \(\hat{\mathbf{q}}_{d}\), the following cost function is optimized:  
\[DQreg(\hat{\mathbf{q}}_{d},\,\hat{\mathbf{Q}})=\min_{\mathbf{w}}DQdist(\hat{ \mathbf{q}}_{d},\,\hat{\mathbf{q}}_{r}), \tag{13}\]  
where  
\[\hat{\mathbf{q}}_{r}=DQLC(\hat{\mathbf{Q}},\,\mathbf{w}), \tag{14}\]  
which can be optimized using a generic optimization algorithm, such as MATLAB's FMIN-CON optimization algorithm. The Dual Quaternion Linear Combination (DQLC) function is defined as:  
\[DQLC(\hat{\mathbf{Q}},\,\mathbf{w})=DQLC(\hat{\mathbf{q}}_{1},\,...,\,\hat{ \mathbf{q}}_{n},\,w_{1},\,...,\,w_{n}), \tag{15}\]  
\[=\prod_{i=1}^{n}\mathbf{r}_{i}^{w_{i}}+\frac{e}{2}\sum_{i=1}^{n}w_{i}\mathbf{ t}_{i}\prod_{i=1}^{n}\mathbf{r}_{i}^{w_{i}}. \tag{16}\]
## 3 Human-Machine Interfaces for Data Acquisition and User Feedback
### Data Acquisition and Processing
#### 3.1.3 Types of Processors  
Another choice to be made when designing the systems is the ways the data are going to be processed and analyzed (see Table 3). Several papers chose a smartphone as the sole processor, thus having limited treatment and energy capacities, but a cheap and easily accessible solution. Another option is to use a remote server to analyze the data, a solution with high computation power but with important risks of failure due to connection issues, especially when navigating indoors. Among the papers analyzed in this survey, the most common method was the addition of another wearable device, such as a tablet or a laptop, to act as the processor. This solution offers better computational power than a smartphone, and no connection issues; however, this option may cause a potential loss of comfort for users when carrying the system.  
When acquiring data, smartphones can be carried in the hand or worn on the body with a specific outfit (see Table 4). Despite being natural, occupying one hand with a phone may cause annoyance in everyday life activities, not to mention the risk of accidental dropping, or the risk of theft. Wearing the device on the body may be more comfortable, but localization must be chosen carefully to avoid social stigma: [PERSON] et al. [38] and [PERSON] et al. [16] opted for a system worn on a belt, while [PERSON] et al. [37] chose a specific headset to place the smartphone on the head.
## 3 Methodology
### Transfer Learning and Feature Extraction  
Deep learning methods are effective for extracting information from visual data. Transfer learning is often preferred, especially in deep learning studies where the size of the dataset is relatively small. In this study, ResNet-50, DenseNet-201, and EfficientNet-B0 have been used for the extraction of significant features from the original dataset. These transfer learning models, which are widely recognized to demonstrate effective and reliable performance [35; 36; 44; 45; 46] for image classification tasks, have strong feature extraction and  
Figure 3: Image capturing system for chickpea seeds [43].  
Figure 2: Certified chickpea varieties used in the study.  
non-linear fitting capabilities. The 1000 most discriminative features have been extracted from the final fully connected layer of each respective model.  
ResNet is a deep learning architecture introduced by [PERSON], [PERSON], [PERSON], and [PERSON] in their paper titled \"Deep Residual Learning for Image Recognition\" that addresses the degradation problem [47]. In order to reduce the vanishing/exploding gradient problem and facilitate the learning of deep networks, ResNet includes residual blocks connected by shortcuts (skipping one or more layers) that transfer the convolution block input directly to the output before activation [43; 47]. This network can be used to extract more information from the original data [47; 48]. The ResNet-50 (a 50-layer ResNet) architecture has been employed in this study. It is one of the most widely used deep neural networks [49; 50] and has been proven to be highly accurate for seed classification [35; 51; 52].  
DenseNet was introduced in 2017 with the paper titled \"Densely Connected Convolutional Networks\" [53] and is a stack of dense and transition blocks [54]. DenseNet contains dense connections between convolutional layers to increase network performance and helps to transmit crucial data from the first layer to the final layer [55]. The numerical definitions in the name of DenseNet network architectures indicate the model depth, in other words, the number of layers [38]. The DenseNet-201 architecture has been used in this study for its capability of deep feature extraction and remarkable performance [50; 55; 56].  
EfficientNet model family was developed by [PERSON] and [PERSON] in their 2019 paper \"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks\". EfficientNet network architecture proposes a compound scaling method to achieve much better performance with fewer parameters and lower computational costs (efficiency). This method uniformly scales the resolution, width, and depth dimensions of CNNs [57]. In this study, we have experimented with the EfficientNet-B0 scaled-up version, which was particularly chosen for its suitability for resource-constrained environments [57; 58] considering the practical needs of agricultural applications.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
###### Abstract
In [21], authors have investigated machine learning applications in various fields of FANETs including routing, flight trajectory selection, relay, and recharge. In [22], authors have reviewed and studied Q-learning-based position-aware routing methods. In [23], authors have examined the application of machine learning techniques to improve UAV communications. Finally, in [24], issues and challenges related to FANETs such as mobility models, communications, architecture, and applications have been studied. Our research shows that the number of review papers in the field of RL-based routing protocols in FANETs is very low. This issue proves the need for more research in this field, to familiarize researchers with future research directions and challenges in this field, and to find a suitable view of how to design a RL-based routing method in FANETs. In this paper, a detailed classification of RL-based routing methods has been presented. According to this classification, routing protocols are categorized based on a RL algorithm (traditional reinforcement learning and deep reinforcement learning), routing algorithm (routing path, network topology, data delivery method, and routing process), and the data dissemination process (unicast, multicast, broadcast, and geocast). Then, the state-of-the-art RL-based routing methods are studied and reviewed, and their advantages and disadvantages are expressed. The contributions of this paper are presented as follows:* This paper proposes a detailed classification of reinforcement learning-based routing methods in FANETs. This classification includes three main parts: learning algorithm, routing algorithm, and the data dissemination process. * This paper introduces and compares the state-of-the-art RL-based routing methods in FANETs according to the suggested classification. Furthermore, it studies and reviews the most important advantages and disadvantages of each scheme. * Finally, this paper expresses some challenges and open issues related to RL-based routing methods in FANETs and presents future research directions. The rest of our paper is as follows: Section 2 presents the related works in this area. Section 3 describes fundamentals of reinforcement learning (RL) in summary. Section 4 introduces flying ad hoc networks, communications, unmanned aerial vehicles (UAVs), and their applications, and focuses particularly on the routing process and its challenges in FANETs. In Section 5, we present a detailed classification for reinforcement learning-based routing algorithms in FANETs. Section 6 reviews the state-of-the-art RL-based routing schemes in FANETs. Section 7 discusses RL-based routing schemes generally. Section 8 presents the most important challenges and open issues in the RL-based routing schemes. Finally, Section 9 concludes this paper.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 3 Experiments  
### Datasets  
The MVTec AD dataset is a comprehensive benchmark dataset for evaluating anomaly detection algorithms in industrial inspection scenarios. It consists of over 5000 high-resolution color images across 15 different object and texture categories. The dataset includes both normal images for training and validation, as well as anomalous images for testing, with various types of real-world defects such as scratches, dents, and contaminations. The objects and textures in the dataset exhibit a range of complexities and challenges for anomaly detection, making it a valuable resource for developing and assessing the performance of unsupervised and semi-supervised anomaly detection methods in the context of manufacturing and quality control [5; 6].  
The MVTec-3D AD dataset comprises 4147 high-resolution industrial 3D scans across 10 object categories. It includes anomaly-free scans for training and validation, as well as test samples with various real-world anomalies like scratches, dents, and contaminations. The objects range from those with natural variations (bagel, carrot, cookie, peach, and potato) to standardized but deformable ones (foam, rope, and tire) and rigid ones (cable gland, and dowel). The dataset is designed for unsupervised anomaly detection in industrial inspection scenarios, featuring 41 types of anomalies [40].
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 1 Introduction  
Oceanography requires the use of a wide variety of instruments, tools, and platforms to study the planet's water resources. Unmanned underwater vehicles (UUVs), which are piloted with specialized human resources, support and perform special oceanographic operations. In the category of UUV, underwater gliders are autonomous underwater vehicles (AUV) that perform water column sampling for long periods of time and long range transects; gliding in the water at low speed and communicating with the pilot in the surface via satellite to evaluate their performance and to adjust the instructions for the next operation cycle [1].
## 2 Materials and Methods
### Analysis Methods
#### 2.3.4 Microbial Community Analysis by Illumina MiSeq Sequencing  
To assess the complete microbial community structures, anaerobic biofilm samples were collected from the AWFR system on days 15, 90, 180, 270 and 340. The total DNA from the anaerobic biofilm samples was extracted using the OMEGA Soil DNA Kit D5625-01 (Omega Bio-Tek, Norcross, GA, USA) based on the manufacturer's protocol. The quality and quantity of the extracted DNA were measured by a Nanodrop 1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA).  
For gene libraries construction, the V3-V4 regions of 16S rDNA genes were PCR-amplified using BAC319F/806R (5\({}^{\prime}\)-ACTCCTACGGAGGGCAGC-3\({}^{\prime}\)/5\({}^{\prime}\)-GGACTACHVGGGTWTCTAAT-3\({}^{\prime}\)) and ARC349F/806R (5\({}^{\prime}\)-GYGCASCAGKCGMGAAW-3\({}^{\prime}\)/5\({}^{\prime}\)-GGACTACHVGGGTWTCTAAT-3\({}^{\prime}\)), respectively. PCR reactions were conducted in a total volume of 25 uL mixture containing 12.5 uL Premix Ex TaqMan Hot Start Version (TaKaRa, Dalian, China), 2.5 uL of each primer (1 uM), 25 ng template DNA and ddH\({}_{2}\)O. The bacterial PCR amplification was performed under the following conditions: initial denaturing at 98 \({}^{\circ}\)C for 30 s, 30 cycles of denaturing (98 \({}^{\circ}\)C for 30 s), annealing (56 \({}^{\circ}\)C for 30 s), and elongation (72 \({}^{\circ}\)C for 45 s), and a final extension step at 72 \({}^{\circ}\)C for 10 min. Archaeal community PCR amplification condition was similar to the bacterial community except that the cycle's number was 35. The PCR products were confirmed by 2% agarose gel electrophoresis and purified using the AxyPrepDNA Gel Extraction Kit (Axygen, Union City, CA, USA) following the manufacturer's protocol. Finally, the purified amplicons were pooled in equimolar and paired-end sequenced using the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA).  
After sequencing, the raw sequences were filtered for quality, trimmed and processed based on the Mothur (version 1.30.1, [[http://www.mothur.org](http://www.mothur.org)]([http://www.mothur.org](http://www.mothur.org))) analytic pipeline. Briefly, the adapters, barcodes and primers were trimmed. The sequence reads containing ambiguous base calls, with homopolymers >6 bp and shorter than 200 bp were removed. Chimeras detected by UCHIME (version 4.2.40, [[http://drive5.com/usearch/manual/uchime_algo.html](http://drive5.com/usearch/manual/uchime_algo.html)]([http://drive5.com/usearch/manual/uchime_algo.html](http://drive5.com/usearch/manual/uchime_algo.html))) were filtered out. The resulting high quality sequences were clustered into operation taxonomic units (OTU) at 97% similarity level by Mothur. Representative sequences selected for each OTU were assigned taxonomy using a Ribosomal Database Project (RDP) classifier with a confidence threshold of 80%. The raw sequencing data were deposited to the National Center for Biotechnology Information (NCBI) Short Read Archive (Accession number: SRP101990).