esa-sceva/satcom-chunk-collection huggingface dataset:
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 1 Introduction
In this paper, both the number and the position are considered to select a set of SPs to maximize the coverage rate of the sensors as well as minimize the overlapped coverage rate. We firstly illustrate the coverage problems in WSN, and then we figure out the suitable number of rendezvous points according to the transmission range. After that, the PSO algorithm is utilized to search the optimal position for the SPs. Finally, the shortest loop will be scheduled by the ACO algorithm for the mobile collector to traverse all the SPs. Most of sensors adopt single hop communication and only a few sensors especially some outliers inevitably utilize multiple hops communication. The remaining sections of this paper are organized as follows. Section 2 illustrates some similar work related to our presented schema. The system model which contains the network model and energy model is demonstrated in Section 3. In Section 4, the coverage problem in WSN is analyzed and our presented TRP-MC is described in detail. Simulations are conducted and the results are analyzed in Section 5. Section 6 gives a discussion, and Section 7 concludes this paper. Figure 3: Rendezvous-based schema with a mobile data collector. Figure 2: Data mule-based schema with a mobile collector.
## 6 Potential for Recovery  
When vulnerable children receive a diagnosis of secondary osteoporosis, there is not always the need to treat with bone-targeted therapy because of the pediatric skeleton's ability to undergo recovery in both bone mass/density and shape. Case in point, the growing skeleton has the potency to reconstitute normal heights of vertebral bodies following a vertebral collapse, a phenomenon known as \"vertebral body reshaping\" [34]. This has been illustrated in children with acute lymphoblastic leukemia and children with inflammatory bowel disease [8; 23]. For example, many children with acute lymphoblastic leukemia will undergo vertebral body reshaping following vertebral fractures because most are diagnosed at a young age (and have significant residual growth potential), and the disease (and its treatment) are usually transient. In a series of children with acute lymphoblastic leukemia, the cumulative VF incidence over six years was 32.5% [8], and complete vertebral body reshaping occurred in 77.3% of these children. Notably, the children in which the reshaping was incomplete were older (and had less residual growth potential) and had more severe degrees of vertebral collapse.  
Pediatric patients with inflammatory bowel disease, in general, are older at diagnosis, and the disease is chronic with exacerbations. Vertebral fractures have been reported at diagnosis [59]. The direct effects of chronic inflammation, use of glucocorticoids, delayed puberty, and poor nutrition are contributing factors causing secondary osteoporosis. Optimizing disease control can help reshape vertebral fractures and specific bones and may be the only therapeutic intervention needed for the effective reshaping of vertebral bodies [59].  
At the opposite end of the spectrum are children with chronic diseases and persistent risk factors for osteoporosis (i.e., due to presently incurable conditions), with the need for continuous treatment with glucocorticoids, for example, boys with Duchenne muscular dystrophy. In these children, there is no capacity for spontaneous vertebral body reshaping. For this reason, contemporary care includes monitoring for signs of osteoporosis at the time of initiation of glucocorticoid treatment and starting osteoporosis treatment at the first sign of a low-trauma long-bone fracture or VF [7; 11; 32; 60]. In such children, strategies are currently being considered for the prevention of first-ever fractures.  
These three different clinical scenarios show the importance of assessing whether the vulnerable child with a fragility fracture needs osteoporosis therapy. It is important to recognize that younger age, transient risk factors, normal growth and puberty, and less severe vertebral collapse are key factors for recovery without needing bone-targeted intervention (i.e., bisphosphonates).
## 2 Materials and Methods
### Applied Methods
#### 2.4.2 Preprocessing  
The preprocessing phase was meticulously applied to all three datasets, with tailored approaches for each based on their specific characteristics. The BiLSTM, BiGRU, and proposed GRU-GAN models were trained and evaluated using all three datasets. For dataset 1, we addressed the class imbalance by implementing the SMOTE technique, which was used to generate synthetic samples of benign API call sequences, preserving their unique characteristics. This approach reduced the risk of overfitting and improves the models' ability to learn from both malware and benign classes equally. Subsequently, the balanced dataset was split into training, validation, and test sets.  
Datasets 2 and 3, which primarily contained string features, underwent a more complex preprocessing process. We initiated this process with tokenization, decomposing the API call strings into discrete tokens. This crucial step enabled our deep learning models to effectively interpret and process the structural elements of API calls, enabling the identification of patterns and relationships within the API call sequences. Following tokenization, we implemented sequence padding to standardize the length of API call sequences across the datasets. This uniformity in input dimensions is important for efficient batch processing in neural network architectures, optimizing both training and testing processes by allowing the models to handle diverse API calls within a consistent computational framework. The next step involved one-hot encoding vectorization, which transformed the tokenized and padded sequences into numerical representations. This vectorization technique was chosen for its broad compatibility with various deep learning libraries and its adaptability in handling new categories from previously unseen API calls. To further refine the input data, we applied Min-Max normalization, scaling each feature to a specific range from 0 and 1.  
For datasets 1 and 2, we performed a percentage split: 70% for model training, 15% for validation, and 15% for testing on each dataset. This stratification ensured a robust evaluation approach. Notably, we further enhanced the rigor of our evaluation process by utilizing dataset 3 as an additional test set. The resulting preprocessed data served as the input data for training and evaluating the various models discussed in subsequent sections of this paper.
## 3 Environment Characterization
### Industrial Environment  
The industrial environment presents several challenges that affect the effectiveness and the reliability of communication systems. The AMI applications include the use of various sensors, communication devices, and control systems that enable the monitoring, control, and optimization of energy production, transmission, and consumption. Therefore, the main challenge of AMI applications in the industrial environment is the presence of electromagnetic interference (EMI) which is caused by various sources, such as electrical motors and welding equipment. Consequently, reliable and secure communications are designed to provide access control to ensure data confidentiality and integrity.  
In AMI applications, on the one hand, PLC technology enables communication between SMs and the DC in order to collect accurate and real-time energy consumption data. However, it faces challenges due to electrical noise, interferences from other devices, and varying signal strengths caused by the different lengths and qualities of power lines [48; 49]. On the other hand, RF technology enables communication without the need for physical connections or cables, reducing the cost and complexity of installations. Moreover, IEEE 802.15.4g operates over longer distances, making it suitable for large industrial facilities or remote locations. However, it faces challenges in industrial environments, such as signal interference from other devices, multipath signal propagation, and attenuation due to more obstacles being present, such as walls and metal structures [3; 50].  
Overall, the specific choice of the communication technology for AMI applications depends on several factors, such as the specific requirements of the industrial environment, the availability of power lines, and the overall cost and performance of the communication system. In this case, the range between the SM and DC can be reduced by the presence of machinery, equipment, or other structures that can obstruct the transmission of wireless signals. In general, in an industrial environment, the distance is likely to be less than in an urban environment, and may require more sophisticated communication technologies to ensure reliable and efficient communication.
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
## 4 Test and Analysis of the Microenvironment Monitoring System for Forest Ecology
### Data Analysis
The fire weather was Grade I or II when precipitation lasted for a brief period, suggesting lower risks of forest fire occurrence and spread and effectively forecasting the fire weather grade in this region. Figure 19: The curve of rainfall in Hulun Buir. Figure 20: The curve of the forest fire weather grade in Hulun Buir.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 3 Operator's Dilemma in the Unlicensed Spectrum
### Preliminary of the Unlicensed Spectrum Game  
The unlicensed spectrum game works as follows. One operator chooses either LTE-itself or LTE with LBT, as does the other operator simultaneously. These choices result in one of the four possible outcomes shown in Figure 3 together with two WiFi networks. For WiFi networks, their payoffs are accordingly given by the action of two LTE operators.  
_Case I_ If both LTE operators egoistically use the co-channel of the unlicensed spectrum as LTE-itself, both operators get \(U_{f}\) corresponding to severe unknown interference. As a result, two LTE operators are punished due to mutual defection, e.g., not using LBT; also, two WiFi networks have no opportunity to communicate. _Case II_ If both LTE operators use proper LBT schemes, all networks can share the unlicensed spectrum harmoniously by getting a meaningful portion of time slots for reliable communications. Thus, \(U_{sh}^{LTE}\) and \(U_{sh}^{WiFi}\) are the rewards for mutual cooperation. _Case III_ If LTE operator 2 plays as LTE with LBT and the other sticks to LTE-itself, then operator 1 takes \(U_{s}\), but operator 2 as well as two WiFi networks have \(U_{w}\). Finally, _Case IV_ is the opposite of _Case III_.
## 4 Result and Discussion
### Computational Time  
The proposed method also compares the computation time of each predictor selection. Table 14 describes the computation time of each predictor selection. Overall, the computation time of each predictor selection with both sample ratios is nearly the same. However, when zooming further, all splits predictor with sample ratio of 70/30 computed the processing code at the fastest rate with 11.622430 s while curvature predictor at sample ratio of 80/20 resulted at the slowest rate.
# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Design Optimization and Synthesis
For the combination of linear and non-linear natures of geometrical parameters and design specifications of antennae, deep CNN was used for the fast and accurate design of the antenna [66]. In an earlier study [67], modeling multiband antennae using three ML algorithms with increased accuracy and performance was reported. [PERSON] et al. [68] described a directional antenna design scheme using multi-objective GA to reduce the number of the antenna side lobes and lobe levels, which eventually aids directivity and security in wireless communication. [PERSON] et al. [69] proposed a Dual-Band Global Navigation Satellite System (GNSS) micro-array antenna design optimization method to achieve higher Right-Hand Circular Polarized (RHCP) gain and lower Left-Hand Circular Polarized (LHCP) gain.
## 4 Deconvolution
### Other Models and Applications
In practical applications, these techniques can complement each other and be used in combination to improve the effectiveness of signal processing and the accuracy of fault diagnosis. For instance, blind deconvolution can be used first to estimate the system's response function and the original excitation signal, and then other techniques can be employed for fault feature extraction and diagnosis.
###### Abstract  
Some challenges when implementing the optimized link state routing (OLSR) protocol on real-life devices and simulators are unmanageable: link quality, rapid energy depletion, and high processor loads. The causes of these challenges are link state processing, unsuitable multipoint relay (MPR) nodes, and information base maintenance. This paper proposes a structured, energy-efficient link sensing and database maintenance technique. The improved OLSR in the paper replaces the OLSRv2's HELLO, HELLO, and Topology Control (TC) message sequence with a new sequence. MPR nodes are not mandated to broadcast TC messages if the number of nodes and their OLSRv2 addresses remain unchanged after subsequent broadcasts or if no node reported 2-hop symmetric connections. The paper further proposes an MPR selection technique that considers four parameters: node battery level, mobility speed, node degree, and connection to the base station for optimum relay selection. It combines the four parameters into one metric to reduce energy dissipation and control routing overhead. The modifications were implemented in NS-3, and the simulation results show that our improved OLSR outperforms the existing OLSR, OLSRv2 and other improved routing protocols in energy consumption, routing overhead, the packet delivery ratio and end-to-end delay, as compared to the related literature.  
algorithm; device-to-device; energy; quality of service; OLSR +
Footnote †: journal:  
0024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
The magnetohydrodynamic (MHD) angular rate sensor (ARS) with low noise level in ultra-wide bandwidth is developed in lasing and imaging applications, especially the line-of-sight (LOS) system. A modified MHD ARS combined with the Coriolis effect was studied in this paper to expand the sensor's bandwidth at low frequency (<1 Hz), which is essential for precision LOS pointing and wide-bandwidth LOS jitter suppression. The model and the simulation method were constructed and a comprehensive solving method based on the magnetic and electric interaction methods was proposed. The numerical results on the Coriolis effect and the frequency response of the modified MHD ARS were detailed. In addition, according to the experimental results of the designed sensor consistent with the simulation results, an error analysis of model errors was discussed. Our study provides an error analysis method of MHD ARS combined with the Coriolis effect and offers a framework for future studies to minimize the error.  
magnetohydrodynamic; Coriolis effect; low frequency expansion; angular rate sensor +
Footnote †: journal: Sensors
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 2 Models and Methods
### Improved YOLOv8
#### 2.2.2 Parameter-Free Attention Mechanism SimAM  
Fabric defects vary in shape and size, and the background often has a color similar to the target, which affects the extraction of semantic features. To address this issue, this study introduces the SimAM attention mechanism [16] to enhance the network's ability to extract target features while reducing the attention paid to irrelevant features. Unlike existing attention modules such as BAM and CBAM [17], which can lose important information when distributing weights based on channels and spatial dimensions, SimAM can infer three-dimensional attention weights for intra-layer feature maps without increasing network parameters. It does this by calculating the separability of features to identify key features rich in information, thus reducing model complexity and computational cost. Its structure is illustrated in Figure 3.  
In the SimAM attention mechanism, adjacent neurons exhibit different firing patterns and produce a spatial inhibition effect to achieve better attention, usually being given higher priority during task processing. SimAM evaluates the importance of each neuron by defining an energy function, measuring the linear separability between neurons. The energy function for defining neuron importance is formulated as shown in Equations (1)-(4).  
\[e_{t}^{*}=\frac{4(\sigma^{2}+\lambda)}{\left(t-\mu\right)^{2}+2\sigma^{2}+2\lambda} \tag{1}\]  
\[\mu=\frac{1}{M}{\sum\limits_{i=1}^{M}{x_{i}}} \tag{2}\]  
\[\sigma^{2}=\frac{1}{M}{\sum\limits_{i=1}^{M}{(x_{i}-\mu)^{2}}} \tag{3}\]  
\[\overline{X}=sigmoid(\frac{1}{e_{t}^{*}})\times X \tag{4}\]  
In the formula, the following applies:  
\(e_{t}^{*}\) represents the energy function for each channel.  
\(\sigma^{2}\) is the variance of each channel in X.  
\(\lambda\) is a hyperparameter.  
\(t\) is the target neuron of the input feature.  
\(\mu\) is the mean of each channel in X.  
\(M\) is the number of energy functions.  
\(\overline{X}\) denotes the enhanced features.  
\(X\) represents the input features.  
In this study, some fabric defect features occupy a relatively small portion of the image and are surrounded by a significant amount of irrelevant feature information. The similarity between adjacent pixels in the image is strong, while the similarity between distant pixels  
Figure 3: SimAM structure.  
is weak. By introducing SimAM at the end of the YOLOv8 backbone, the features can be better refined, the network's ability to extract global features is enhanced, and redundant background information is effectively removed. This reduces the interference of irrelevant and complex fabric textures on defect detection.
## 3 Method
### Pose Refinement Process
#### 3.1.2 Outlier Detection Model and Correction Algorithm  
The outlier detection model uses a single-layer Bi-LSTM (Bidirectional Long Short-Term Memory) network and, when an outlier is detected, it is indicated in the output value of the corresponding frame. This model can be used for all joints of the body to detect outliers. Figure 5 shows the changes in coordinates by frame and visualizes the spike point on the graph. This graph confirmed that when outliers occur, the coordinate changes are abnormally large compared to previous frames. As seen in Figure 3, poses can be estimated at a certain level of accuracy in actions with slower speeds, but the proportion of inaccurate coordinate estimations increases as the action speed increases. The data input into the Bi-LSTM network is the coordinate change amount for each frame, calculated as joint coordinate distance changes between frames using the Euclidean distance method. Each piece of data contains a change amount for one joint. As the change amount value range differs depending on the video size, it is divided by the maximum value of each piece of data for normalization between 0 and 1. The network's maximum input length is also set, with padding values of 0 provided for shorter cases. The proposed algorithm includes steps to remove outliers from estimated coordinates and interpolate missing coordinates. The (b) network is a frame-by-frame outlier detection model using the Bi-LSTM model, which outputs the occurrence of outlier data in the coordinates estimated by the pose estimation model. For outlier data collection, we used a data generation technique that mimics outliers based on data containing the correct joint coordinate labels. Outliers in existing pose estimation models occur when the joint coordinates are estimated to be a certain arbitrary distance x, y away from the actual correct coordinates. To mimic this, we used the generated data in the previous step. The amplification and attenuation of the coordinates was applied to randomly selected frames so that the model could capture changes in consecutive frames.
## 3 Definition and Theory Analysis
### Definition  
IEEE (Institute of Electrical and Electronics Engineers) 802.11 is the standard MAC protocol of wireless networks. It includes RTS/CTS (Request To Send/Clear To Send) and basic access mechanisms for data transmission in DCF mode [23].  
In the RTS/CTS scheme, the RTS frame is sent to the reserve channel when the TCP source wants to transmit the data packet. If the receiver successfully receives the RTS frame, it returns a CTS (Clear To Send) frame. After that, the data and the ACK (Acknowledgment) frame will be transmitted respectively. Only the RTS frame may have a collision in this mechanism. Since the RTS frame has no data and is a short frame, it avoids long data frame collision and the hidden terminal problem; while in the basic access scheme, the data will be sent directly once nodes access the channel instead of sending RTS.  
In the DCF (Distributed Coordination Function) mode, each node uses the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) mechanism to compete for wireless channel. Firstly, the sender will detect the channel. If the idle time lasts for the DIFS (Distributed Inter-frame Spacing) time interval and a random backoff time, RTS or the data frame will be sent. If no CTS or ACK is received within a specified period, this frame is considered lost. Then, the transmitter will double the size of the contention window and choose a new backoff timer.  
The model of MAC layer service delay is shown in Figures 1 and 2. No matter which way it works, we define the frame transmission efficiency \(R_{FTE}\) as the ratio of successful transmission delay to the frame service delay in the MAC layer, that is  
\[R_{FTE}=\frac{T_{SL}}{T_{FSD}} \tag{2}\]  
where \(T_{SL}\) is the delay of data successful transmission, which refers to the interval from the start of sending data to the time ACK is received. \(T_{FSD}\) is the total frame service delay from listening on the channel for transmitting this frame to this frame being transmitted successfully.  
From the above definition, \(R_{FTE}\) can reflect the state of channel congestion. When the channel is in a congested state, \(R_{FTE}\) is smaller. On the contrary, \(R_{FTE}\) is larger when the network communication is in good condition.
## 2 Materials and Methods
### Nanocomposite Characterization  
The synthesized materials were characterized using different techniques. The materials morphology and size were determined using Transmission Electron Microscopy (TEM, JEOL JEM-1010, JEOL USA, Inc., Massachusetts, USA) as well as Scanning Electron Microscopy (SEM, Zeiss Ultra plus 55, Oberkochen, Germany). The X-ray diffraction (XRD, Bruker D5005, Bruker Co., Massachusetts, USA) was used to determine the crystalline nature and phase purity using monochromatic Cu radiation K\({}_{\alpha}\) = 0.15418 nm. Raman spectroscopy was used to study the material structural defects. Fourier transform infrared (FTIR, a Nicolet 6700, Thermo Fisher Scientific, Massachusetts, USA) spectroscopy was used to study the chemical groups in the range of 400 cm\({}^{-1}\) to 4000 cm\({}^{-1}\). X-ray photoelectron spectroscopy (XPS) was used to study the chemical state of the samples. A vibrating sample magnetometer (MicroSense EV9, MicroSense, LLC, Massachusetts, USA) was used to study the sample magnetic properties.