esa-sceva/satcom-chunk-collection huggingface dataset:
# Sigma Routing Metric for RPL Protocol
###### Abstract  
This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption.  
L 1 1 2018 1  
Routing into such networks has been considered a challenge, which is why it is the focus of many research and global standardization groups. These groups lead directly to the design of the routing protocol IPv6 for LLN, called the \"IPv6 Routing Protocol for Low-Power and Lossy Networks\" (RPL) [12; 13], standardized by the IETF and presenting itself as the front runner in the LLN. The contributions of this work are the following:  
* During the problem study of the link estimation, some implementation problems of RPL in the Contiki system were found. These implementation problems reduced the performance unnecessarily, which forced our experimental study to focus also in solving these problems.
* SIGMA-ETX was designed and implemented to allow RPL to test the link quality through bidirectional traffic with only simple modifications, without violating the standard, and to verify its improvement in performance through test simulation.  
The rest of this paper is organized as follows: Section 2 defines the RPL protocol, Section 3 presents related works, Section 4 presents the proposed SIGMA-ETX metric model, Section 5 explains the design and simulation of the network, Section 6 describes the result of the simulation, Section 7 discusses RPL issues, and Section 8 offers concluding remarks.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for High-Throughput and Spatial Multi-Omics Integration
#### 2.3.1 SM-Omics: Automating High-Performance Spatial Transcriptomics and Proteomics Data Analysis  
Designed to process high-throughput spatial multi-omics data, M-Omics integrates spatial transcriptomics and antibody-based proteomics to reveal molecular heterogeneity across tissue regions [24]. For instance, it has been applied in glioblastoma research to map gene expression in tumor microenvironments. However, its command-line nature may pose accessibility challenges for smaller labs with limited bioinformatics expertise. Rather than offering a web-based interface, SM-Omics relies on automated pipelines to handle large-scale datasets. Through the integration of spatial transcriptomics with antibody-based proteomics, gene expression and protein abundance can be mapped within tissue samples, offering insights into the molecular heterogeneity of complex biological systems such as tumors or brain tissue. This spatial integration provides a deeper understanding of tissue-specific biological processes by revealing how molecular features vary across different regions.  
A basic advantage of SM-Omics lies in its ability to manage high-performance data through automated workflows. By using tools like Snakemake and Nextflow, multi-step processes for large datasets are automated, minimizing manual intervention and making the platform scalable and efficient for labs equipped with high-performance computing resources. The reliance on automated pipelines makes the platform well-suited for large-scale spatial multi-omics data processing. However, the absence of a user-friendly web interface means the platform is best suited for researchers with bioinformatics expertise comfortable with command-line tools. Moreover, the high computational demands for processing such data require substantial resources, potentially limiting accessibility for smaller research groups without dedicated infrastructure.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
###### Abstract  
In this work, we present power and quality measurements of four transmissions using different emission technologies in an indoor environment, specifically a corridor, at the frequency of 868 MHz under two non-line-of-sight (NLOS) conditions. A narrowband (NB) continuous wave (CW) signal has been transmitted, and its received power has been measured with a spectrum analyzer, LoRa and Zigbee signals have also been transmitted, and their Received Signal Strength Indicator (RSSI) and bit error rate (BER) have been measured using the transceivers themselves; finally, a 20 MHz bandwidth 5G QPSK signal has also been transmitted and their quality parameters, such as SS-RSRP, SS-RSRQ and SS-RINR, have been measured using a SA. Thereafter, two fitting models, the Close-in (CI) model and the Floating-Intercept (FI) model, were used to analyze the path loss. The results show that slopes below 2 for the NLOS-1 zone and above 3 for the NLOS-2 zone have been found. Moreover, the CI and FI model behave very similarly in the NLOS-1 zone, while in the NLOS-2 zone, the CI model has poor accuracy in contrast to the FI model, which achieves the best accuracy in both NLOS situations. From these models, the power predicted with the FI model has been correlated with the measured BER value, and power margins have been established for which LoRa and Zigbee would each reach a BER greater than 5%; likewise, \(-18\) dB has been established for the SS-RSRQ of 5G transmission.  
5G; LoRa and Zigbee transmission; BER and RSSI; indoor corridor; log-distance model; propagation at 868 MHz; QoS link measurements +
Footnote †: journal: _Sensors_**2023**, 23823, [[https://doi.org/10.3390/s23063283](https://doi.org/10.3390/s23063283)]([https://doi.org/10.3390/s23063283](https://doi.org/10.3390/s23063283))
# Application Layer Protocol Identification Method Based on ResNet
## 3 Materials and Methods
### Model Training and Protocol Recognition  
We utilize the pre-trained model of ResNet for protocol feature training and target classification recognition. ResNet is proposed by [PERSON] (2015) in 2015. It is a deep convolutional neural network architecture. It addresses the issue of gradient vanishing that commonly occurs in traditional deep neural networks when increasing the network depth, leading to training difficulties or suboptimal performance. The ResNet18 network model is illustrated in Figure 5.  
Figure 4: These three images primarily showcase the pictures generated using the RGB image generation algorithm, where (**a**) represents the DNS protocol, (**b**) represents the SMTP protocol, and (**c**) represents the HTTP protocol.  
Figure 5: This image primarily introduces the ResNet18 network model.  
The core idea of ResNet is to introduce the concept of residual blocks. Residual blocks enable the network to learn the difference between the residual function F and the identity mapping and add it to the input feature map, forming a skip connection. This aids in gradient propagation, making the network easier to train.  
Due to the input size of ResNet being an RGB image of 224 \(\times\) 224 while our input data is a composite feature map of 28 \(\times\) 28, we need to resize the input data to meet the requirements of the model. We use the transform.Compose function in PyTorch to create an image preprocessing pipeline and the transforms.Resize ((224, 224)) method to resize the 28 \(\times\) 28 composite feature map to a 224 \(\times\) 224 RGB image.
# Overset Grid Assembler and Flow Solver with Adaptive Spatial Load Balancing
## 3 Donor Search  
The donor search categorizes all mesh cells into certain types to be fed to the flow solver which invokes different algorithms based on the type of cells. The definitions of cell types are listed below.  
* Field or computational cell on which governing equations are solved.
* Receptor cell on which governing equations are not solved but flow variables are interpolated from a (donor) field cell.
* Mandatory receptor cell which is located next to an intergrid boundary submerged inside another mesh. Since the boundary condition cannot be applied intrinsically, it is mandatory to interpolate flow variables.
* Hole cell on which neither governing equations are solved nor flow variables interpolated.
* Orphan cell is a mandatory receptor cell whose donor cell is also a receptor cell. It can be avoided by converting the type of donor cell from receptor to field cell.  
Figure 2 shows several cell types of structured Cartesian and unstructured overset mesh after donor search.  
Figure 2: A structured Cartesian mesh (**left**) and an unstructured overset mesh (**right**) after donor search. Gray: field cell; orange: hole cell; light blue: receptor; blue: mandatory receptor.  
Since meshes in a mesh system have no face-to-face connectivity with each other, flow variables are interpolated across intergrid cells of different mesh blocks. Regardless of the order of interpolation, interface fluxes will not be conserved in the whole mesh system due to non-conforming faces of intergrid cells. Conservation of fluxes can be maintained by regenerating mesh in intergrid region so that intergrid faces of mesh blocks conforms perfectly [14; 15] or by using auxiliary mesh in the intergrid region [16]. In this work, variables are interpolated non-conservatively.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 4 Experiments
### Image Reconstruction  
To quantify the comparison of the five algorithms in image reconstruction, we calculate reconstruction errors of these algorithms. Suppose \(\mathbf{Z}_{i}\in\mathbb{R}^{h\times w}\) is the reconstruction image of \(\mathbf{X}_{i}\) by one of the five algorithms, \(i=1,2,...,n\). Then, the average reconstruction error is  
\[\frac{1}{n}\sum_{i=1}^{n}\|\mathbf{X}_{i}-\mathbf{Z}_{i}\|_{F}, \tag{19}\]  
where \(\|\cdot\|_{F}\) denotes the Frobenius norm. The reconstruction result of PCA is shown in Equation (5). It should be further reshaped into a \(h\times w\) matrix in order to obtain the corresponding reconstruction image \(\mathbf{Z}_{i}\). For the four 2D algorithms, i.e., 2 DPCA, A2 DPCA, \((2\text{D})^{2}\)PCA and DB2 DPCA, their definitions of \(\mathbf{Z}_{i}\) are shown in Equations (9), (13), (15) and (17), respectively. Note that for the two bilateral algorithms, the same number of features are extracted from both laterals.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
###### Abstract  
Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify 'out-profiled' consumers--households that exhibit atypical energy-usage behaviors--by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development.
## 7 Technical Considerations for Implementing 6G Technology
### Dimensions of a Cell in 6G Networks  
To overcome the limitations of THz wave propagation, the dimensions and structure of 6G cells must be reimagined:  
* Smaller Cells: Due to limited propagation distances, 6G networks will rely on smaller cells (pico- and femtocells) to ensure adequate coverage and reduce signal attenuation.
* Three-Dimensional (3D) Network Design: Unlike traditional 2D cellular networks, 6G will integrate terrestrial, aerial, and satellite communication layers to provide seamless global coverage. This three-dimensional architecture ensures connectivity in rural and remote areas while supporting high mobility scenarios like in-flight internet and smart transportation systems [261].
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 1 Introduction
[PERSON] [27] proposed a dynamic model to predict the number of pins transferring load in RV reducers with assembly clearance, aiming to improve transmission performance. The above studies provide valuable theoretical and modeling support for analyzing the TE of RV reducers using gear geometry design, virtual modeling, and related methods. However, accurately identifying the TE composition and quickly locating the least reliable components remain critical for improving production efficiency and engineering outcomes. To address this, this paper employs experimental TE analysis, error source tracing, and Fourier analysis to identify and characterize TE components in RV reducers. The remainder of this paper is organized as follows: Section 2 examines the motion transmission characteristics of each RV reducer component. Section 3 calculates the motion frequency characteristics of these components. Section 4 introduces the experimental method for TE measurement. Section 5 analyzes and discusses the TE results of two RV reducer models. Finally, Section 6 summarizes the key conclusions of this study.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
###### Abstract  
Nonlinear active control is very important in many practical applications. Many well-known nonlinear active noise control algorithms may suffer from high computational complexity and low convergence speed, especially in the nonlinear secondary path case. Thus, it is still an actively researched topic for reducing complexity and improving the convergence rate. This paper presents a low-complexity Volterra filtered-error least mean square algorithm when taking a decomposable Volterra model into account for active control of nonlinear noise processes, which is referred as DVM-FELMS. The computational complexity analysis shows that the proposed DVM-FELMS algorithm can significantly reduce the nonlinear active noise control system's complexity. The simulation results further show that the proposed algorithm can achieve promising performance compared with the Volterra-based FELMS algorithm and other state-of-the-art nonlinear filters, while the decomposable error of the Volterra kernel may be introduced inevitably. Moreover, the proposed DVM-FELMS algorithm shows a better convergence rate in the broadband primary noise case due to fewer parameters used in each sub-filter.  
low-complexity; active noise control; nonlinear; adaptive Volterra filter; Kronecker product decomposition +
Footnote †: journal: Applied Sciences
# Unfolded Algorithms for Deep Phase Retrieval
## 4 Numerical Results and Discussion
### Performance of the Proposed UPR-SPARTA
In order to numerically validate this claim, we perform a per-layer analysis of the proposed UPR-SPARTA framework in terms of the achieved relative MSE. Note that such an analysis is possible due to the model-based nature of the proposed approach, which results in interpretable deep architectures as opposed to conventional black-box data-driven approaches. Figure 2 demonstrates the relative MSE versus the number of iterations/layers for the case of recovering a \(k=5\) sparse signal \(\mathbf{x}\in\mathbb{R}^{n}\), where \(n=m=300\). Observing Figure 2, it can be deduced that the proposed methodology achieves a far lower relative MSE much faster than the standard SPARTA. Our final experiment in this part is an analysis of the ESR versus the sparsity level (\(k\)) when the signal dimension and number of measurements are set to \(n=m=150\). Figure 3 illustrates the ESR metric versus the sparsity level (\(k\)) for the considered scenario. It can be observed from Figure 3 that the proposed methodology achieves far superior performance to that of the baseline SPARTA algorithm. Figure 1: Empirical success rate versus the measurement-to-signal length ratio (\(m/n\)) for \(\mathbf{x}\in\mathbb{R}^{n}\), with \(n=100\) and \(k=5\) nonzero entries. Figure 2: Convergence behavior of the proposed UPR-SPARTA as compared to the original SPARTA algorithm for the case of \(\mathbf{x}\in\mathbb{R}^{n}\), where \(n=m=300\).
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### In the Livestock  
A wireless detection system helps to keep livestock healthy with a minimum use of resources. The system detects the temperature and pH level inside the rumen of cows. The data are wirelessly transmitted to an external receiver node via an encapsulated measuring probe referred to as bolas. The finding of this application is developing a wireless rumen checking system for the detection of sub acute rumen acidosis by the quasi-continuous measurement of transmission/indication of the pH-value of the rumen of dairy cows [39; 40]. Table 4, represent the examples of real-time applications for livestock that utilize WSNs.  
[PERSON] et al., 2007 deployed the sensors among cattle for understanding their mood, stress, hunger, etc., in Queensland, Australia. They deployed Radio-Frequency Identification (RFID) tags on their ears for obtaining all of the information in real time.  
[PERSON] et al., 2014 deployed the sensors to eradicate the theft of stock for cattle in Johannesburg, South Africa. They incorporated the Continuous Time Markov Process for the movement of cows. The sensor provides the speed of mobility of the cow which is used as a reference for any movement of the animal. They successfully stopped the theft of animal stock in farms, especially in South Africa and Africa.  
[PERSON] et al., 2017 investigated the tracking of animal's movement for the prevention of theft in Nairobi, Kenya. The authors studied how to recognize and track the preferred animal's location and send the positioning data in a periodic manner to a database. Data from the database was sent on a demand basis via the internet and short messaging system or texts. They designed a sensor module which consists of Worldwide Interoperability for the Microwave Access (WiMAX) gateway, WiMAX base stations, and data collection centers.
## 4 Results and Analysis  
### Settlement  
The settlements of the ground and surface of the embankment over time after the construction of the reinforced backfill are plotted in Figure 7, and the circled numbers indicate the work schedules stated in Table 3. Settlement measurements were performed for 341 days and for 252 days for the ground and surface, respectively, after the reinforced backfill was completed. The surface settlement changes considerably immediately after the completion of the reinforced backfill construction and converges over time. In particular, the maximum surface settlement is 23.1 mm less than the allowable residual settlement stated in the Korean Railway Design Standard for roadbed construction (30 mm).  
The difference in settlements of the ground and surface of the backfill was considered as the net settlement of the backfill. The variation of net embankment settlement and rainfall over time is plotted in Figure 8. Immediately after the completion of the reinforced backfill construction, a compression settlement of 4.5 mm occurred and exhibited a stable condition (1.82-3.09 mm) prior to the wall's construction. The rate of total net settlement of the reinforced backfill was 0.12%. Moreover, additional settlements of 4.06 mm and 1.19 mm occurred owing to the construction of the rigid facing wall and heavy rainfall, respectively. Because the reinforced backfill supports the wall in the RSR structure, additional settlement occurs under the weight of the wall. During heavy rainfall, additional settlement occurred as the soil particles were relocated by the water penetrating through the reinforced backfill [26; 27]. Considering the reinforced backfill height of 6.8 m, the rates of each additional settlement were 0.06% and 0.02%, respectively. The low compressibility of the RSR when using cement-treated gravel as a backfill was confirmed.