esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 6 LoRa Performance: Using Error Correction and Filtering
### LoRa with Forward Error Correction and Filtering  
In addition to incorporating forward error correction, IIR and FIR filters are added immediately after the removal of the signal carrier. This integration aims to improve the performance of LoRa technology in industrial settings. These filters are designed using the Butterworth approximation method, which enables specific filter characteristics to be obtained to meet design requirements. First, the specifications of these filters are determined, including parameters such as normalized cutoff frequency, sampling frequency, and optimal filter order, as shown in Table 3.  
The optimum order of the two filters is obtained by using a loop to test different IIR filter orders (up to 30) and calculating the error produced between the filtered signal and the clean signal (at the transmitter) for each order. The optimum order is then determined by selecting the one with the lowest error value.  
Figure 18: PER over a fading channel with the industrial noise in LOS configuration for the MtO mode, using the error correcting code. **(a)** PER for a 4/5 coding rate; **(b)** PER for a 4/8 coding rate.  
The FIR and IIR filters designed are both characterized by their stability. The FIR filter has an optimum order of 18 and uses 19 coefficients, while the IIR filter has an order of 10 and uses 21 coefficients. A notable distinction between the two is the nature of the phase: the FIR filter has a linear phase, while the IIR filter has a non-linear phase, meaning that the different frequencies of the input signal are shifted temporally in a constant manner for the FIR filter, while a frequency-dependent temporal delay occurs for the IIR filter as shown in Figures 20 and 21. These characteristics must be considered when selecting the appropriate filter for specific application requirements, as they influence filtering accuracy and the time delay of the various input signal frequencies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Sampling frequency & \(10^{6}\) Hz \\ Normalized cut-off frequency & 0.125 \\ Optimum filter order & IIR filter & 10 \\ FIR filter & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: IIR and FIR filters simulation parameters.  
Figure 19: PER over a fading channel with the industrial noise in NLOS configuration for the MtO mode, using the error correcting code. (**a**) PER for a 4/5 coding rate; (**b**) PER for a 4/8 coding rate.  
Figure 20: Magnitude Response (dB) and Phase Response (radians) for RIF Filter.  
The 4/5 coding rate does not produce a significant gain in all the above configurations, which is why the simulations of adding these filters are carried out under the same conditions, exposing only the results obtained for this coding rate.
# Small Dam Drainage with Nonwoven Geotextile after 40 Years of Exploitation
## 4 Conclusions  
The conducted measurements both from the field tests and calculations using HYDRUS (2D/3D) software allow one to formulate the following conclusions:  
1. The drainage with nonwoven geotextile after 40 years of operations still works properly. During this time, the permeability coefficient was reduced more than four-fold, but it is still many times higher (more than 18 times) than the permeability coefficient of earth dam soil.
2. Both our own research and that of other authors indicate that a significant change in permeability (reduction of it, about from three to four times) of nonwoven geotextile comes after the first 10 years of drainage work. Additionally, with the determination coefficient equal to about R\({}^{2}\) = 0.75, it can be concluded that the permeability of nonwoven geotextile is reduced by about 6% per year of operation.
3. The main reason for reduction of geotextile permeability is mechanical clogging caused by fine sand which builds structures called bridges (a kind of filter cake).
4. Symptoms of chemical clogging noted on the air side of drainage did not worsen the permeability.
5. Simulations performed using HYDRUS (2D/3D) software showed that at the maximum water level in the reservoir equal to 3.32 m, water supply to the drainage is higher from upstream than from downstream. Particularly, water supply from above to the drainage is absent.
6. HYDRUS (2D/3D) simulations allow one to predict changes of hydraulic characteristics of geotextiles and protected soil under the drainage pipe in case in situ research is unavailable without structural damage.  
Conceptualization, J.N., and P.Z.; methodology, J.N.; validation, J.N., P.Z.; investigation, J.N. and P.Z. and F.N.; data curation, F.N.; writing--original draft preparation, P.Z. and J.N.; writing--review and editing, P.Z. and J.N.; visualization, J.N. and P.Z.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 General Classification of Modern Haptic Interfaces
### Pneumatic Haptic Interfaces  
Using pneumatic actuators, similar to those used in hydraulic feedback-based systems, is another method for generating haptic feedback. Indeed, a pneumatic interface is composed of a pipe or cavity system containing gas pressured by a compressor to cause shape deformation. The analysis of compressed gas at rest and/or during motion and its application to the device's design are the core principles of pneumatics, as described in [33]. In a pneumatic system, working energy (kinesthetic energy and pressure) is held in the potential status by air pressure, allowing air expansion. The expanding air is the fundamental element of the tactile and kinesthetic interfaces. Pneumatic actuation has medical uses in addition to its traditional uses in industrial automation and robotics. To overcome the drawbacks of commercial haptic devices, pneumatics researchers use the benefits listed below:  
Figure 4: Examples of hydraulically driven tactile interfaces. Illustration of a hydraulic glove with a joint-stretching mechanism providing kinesthetic feedback (**a**). A diagram of a hydraulic actuator that changes the cavity to provide out-of-plane displacement for tactile feedback (**b**) [31]. An electrostatic mechanism controls the hydraulic actuators leading to dielectric normal and lateral motion as a result of cavity deformation (**c**) [31].  
* Cheap and high responsivity;
* Compact and lightweight systems;
* No constraints on output size or design;
* No recirculation lines, unlike a hydraulic system;
* Simple pressure and speed adjustment
* Appropriate for a spotless workplace;
* High power-to-weight ratio;
* A safe usage.  
These advantages have resulted in the widespread adoption of pneumatic methods in the haptics field. However, because of specific pneumatic actuator restrictions, the development of a pneumatic tactile system involves a methodical design process. The design of a pneumatic haptic glove that uses the tube's change in air pressure to control hand gestures is shown in the image below. Because of their resemblance to human skin and durable working properties, polymer-based pneumatic actuators are commonly utilized in wearable tactile interfaces. To provide lifelike tactile feedback, a haptic device's directional indications and diverse mechanical sensations are crucial.
## 6 Conclusions  
The indisputable wide use of EDC indicates that the mean reaction rate is reasonably modeled. The fine-structure reactor model and the energy cascade model are outlined and summarized in this work in detail. The limited Reynolds number for the validity of EDC is discussed. The dependence of the mean reaction rates on the primary EDC constants, \(C_{\rm{D1}}\) and \(C_{\rm{D2}}\), and the secondary constants, \(C_{\rm{\gamma}}\) and \(C_{\rm{\tau}}\), are presented. The combinations of the primary EDC constants, i.e., \(C_{\rm{D1}}\) and \(C_{\rm{D2}}\), are validated using detailed experimental data from Sandia Flame D. Results of case 8 using the EDC constants of \(C_{\rm{D1}}=0.1357\), \(C_{\rm{\rm{D2}}}=0.11\), \(C_{\rm{\gamma}}=1.4549\), and \(C_{\rm{\tau}}=0.1915\) show promising improvement with respect to the original EDC constants. Summary conclusions may be drawn as follows:  
1. The fine-structure reactor model and the energy cascade model are outlined and summarized. From the original work of [PERSON] [1, 2], the original values of the primary EDC constants \(C_{\rm{D1}}\) and \(C_{\rm{D2}}\) and the secondary EDC constants \(C_{\rm{\gamma}}\) and \(C_{\rm{\tau}}\) are presented.  
2. The limiting Reynolds number for the validity of EDC is concluded. Thus, the turbulent Reynolds number should be larger than \(C_{\chi}^{4}\) with different combinations of the primary EDC constants \(C_{\text{D1}}\) and \(C_{\text{D2}}\), or the EDC constants need to be adjusted according to the criteria. 3. The secondary constant, \(C_{\tau}\), should be less than unity with the proposed EDC constant combinations because it is the time-scale ratio of the fine structure to the Kolmogorov scale. The upper limit of \(C_{\alpha}\) is determined from the unity \(C_{\tau}\). 4. The mean reaction rate is expressed as a function of the secondary constants, and its dependence on the EDC constant is presented. The dependence shows that decreasing the mean reaction rate requires deceases in \(C_{\gamma}\) or \(C_{\alpha}\), and vice versa. 5. Comprehensive comparison of the predictions of axial velocity, Reynolds stress, temperature, and major and minor species with experimental data shows that case 8, with the secondary constant \(C_{\alpha}=6\) or the primary constants \(C_{\text{D1}}=0.1357\) and \(C_{\text{D2}}=0.11\), can properly reproduce the Sandia flames D.  
Conceptualization, Y.Y. and C.W.; methodology, D.H.; validation, D.H. and H.M.; formal analysis, D.H.; investigation, D.H. and H.M.; writing--original draft preparation, D.H., H.M. and H.L.; writing--review and editing, C.W., Y.Y. and H.M.; supervision, Y.Y.; project administration, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Jiaotong University, KMGY220006530.  
The authors declare no conflict of interest.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 6 Service-Oriented Middleware (SOM) Architectures Approaches for WSNs  
The SOM architecture is the best platform to develop WSN applications to address hardware challenges such as QoS, security, and heterogeneity. The following is a brief description and summary of the selected approaches that are considered SOM architecture for WSNs.  
An open sensor middleware model based on the SOA for WSNs should have the ability to integrate, in real time, context data with flexibility, reusability, programming abstraction, and simplicity. In addition, many studies consider the network-embedded devices in different applications, such as managing enterprise architecture [42], smart home and industrial applications. These applications can be classified into two categories: SOA-ready devices and SOA not-ready devices [43]. The issue of integrating WSNs into IP-based networks and Internet is addressed in [43]. It provides solutions for implementing SOA based on SOA not-ready devices. A micro SOA model is implemented based on \(\upmu\)IP protocols that only use Hyper Text Transfer Protocol (HTTP) philosophy instead of HTTP protocols [43]. The exchanged data can be between network devices on the same layer or between the embedded and middleware layers through efficient lightweight protocol called JavaScript Object Notation (JSON) (instead of XML format) [43]. JSON can reduce overhead and power consumption, request size, and complete request time. The \(\upmu\)SOA uses the middleware layer. The middleware layer manages access to WSNs by filtering and protecting the system. The filter mechanism removes unnecessary information from any HTTP request. Other mechanisms the middleware provides are security, domain name services, and authorization. However, this middleware does not address the issue of a heterogeneous network [43]. Similarly, the middleware can be designed based on a function block programming abstraction for a WSN that enables the operations to be done in a dynamic environment to reduce overhead and complexity. These features are completed by applying SOA with a Mobile Agent (MA) [44].
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 3 An Optimization Problem to Maximize SE  
Based on the aforementioned analyses, an optimization problem is formulated to maximize the SE of OBON system in this section. The objective function and constraints are introduced one-by-one.
###### Abstract  
Vital parameters are key indicators for the assessment of health. Conventional methods rely on direct contact with the patients' skin and can hence cause discomfort and reduce autonomy. This article presents a bistatic 24 GHz radar system based on an interferometric six-port architecture and features a precision of 1 um in distance measurements. Placed at a distance of 40 cm in front of the human chest, it detects vibrations containing respiratory movements, pulse waves and heart sounds. For the extraction of the respiration rate, time-domain approaches like autocorrelation, peaksearch and zero crossing rate are compared to the Fourier transform, while template matching and a hidden semi-Markov model are utilized for the detection of the heart rate from sphygmograms and heart sounds. A medical study with 30 healthy volunteers was conducted to collect 5.5 h of data, where impedance cardiogram and electrocardiogram were used as gold standard for synchronously recording respiration and heart rate, respectively. A low root mean square error for the breathing rate (0.828 BrPM) and a high overall F1 score for heartbeat detection (93.14%) could be achieved using the proposed radar system and signal processing.  
continuous wave radar; remote sensing; six-port interferometry; vital parameter measurement 29 March 2019; 19, 2492; doi:10.3390/s19112492
## 2 Materials and Methods
### Validation Experiment for Encounter Coordination  
Based on Tables 1 and 2, a dual-USV environment was created, and USV head-on encounter and cross-encounter situations were set up to compare the traditional fusion algorithm and the optimized algorithm with an encounter coordination strategy, as shown in Figures 13-16.  
Figure 11: Global path of optimized algorithm.  
Figure 14: Dynamic window during encounter with optimization algorithm (head-on encounter situation).  
Figure 12: USV trajectory and end position heading of optimized fusion algorithm.  
Figure 13: Dynamic window during traditional algorithm encounter (head-on encounter situation).  
In Figures 13-16, in the traditional algorithm without an encounter coordination strategy, the use of the DWA algorithm fails to achieve the desired avoidance maneuver in an encounter situation. In the simulation experiment results of Figures 13 and 15, the USVs collide, and the navigation task cannot be completed. In contrast, the optimization algorithm with an encounter coordination strategy provides good safety and applicability by setting the priority of avoidance, thereby allowing one of the USVs to slow down and avoid a collision to achieve safe navigation while also complying with the actual rules of safe maritime navigation. In Figures 17 and 18, under the optimization algorithm, the USV does not take a large angle maneuver during the encounter and can choose an efficient cooperative collision avoidance path with a shorter distance, which allows the USV to economize on fuel in practical situations.
# Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
## 2 Related Works
In addition, acquisition can easily be postponed if the weather conditions are not ideal. When it comes to GIS, the resolution is dependent on the size of the raster cell, which is inversely proportional to the resolution; this makes it so that one must find a balance between processing time and storage [52]. By using a UAV, visual information that has better resolution can be obtained, allowing the system to detect smaller gaps, during cloudy and non-cloudy days, at any time, without having to wait for the satellite to pass that region; and since the UAV is equipped with a multi-spectral camera, the maps can be generated instantly and later be processed. This paper presents the results of training and testing Tiny-YOLO, YOLO, Tiny-YOLOv2, Tiny-YOLOv3, Tiny-YOLOv4, and Mask-RCNN with six different datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge (NDRE), Normalized Difference Water Index (NDWI), RGB (Red-Green-Blue), Gray-Scale (GS), and Color Infrared (CIR). When choosing the networks, it was important to take into account the limited computational power of UAVs. Regardless, this method was prioritized over other methods--for example, GIS and satellite imaging with cloud processing, because UAV allows the system to be used at any time, independently of weather conditions, unlike satellite imaging. The chosen networks all have the capability of being deployed in the final system due to being light in comparison with their full network counterparts.
## 2 Materials and Methods
### Network Model Construction  
#### 2.2.1 Structure of the YOLOv8-Seg Network  
The YOLO (You Only Look Once) series of algorithmic frameworks stand out among various detection methods due to their rapid detection capabilities and high precision [25]. With continuous updates and iterations of the model framework, the YOLO series has become a popular real-time object detection model, extensively used in precision and automated agriculture for the detection and segmentation of crops, pests, and weeds.  
In 2023, the Ultralytics team introduced the latest YOLOv8 ([[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)]([https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics))) object detection algorithm, evolving from YOLOv5, as a single-stage anchorless detection framework. The overall network structure is divided into four main components: Input, Backbone, Neck, and Head. The Input section, as the interface, is responsible for scaling input images to the dimensions required for training. It features modules such as mosaic data augmentation, adaptive anchor calculations, adaptive image scaling, and Mixup data enhancement. The Backbone is an enhancement over the YOLOv5 model, adopting ELAN's design principles by replacing the C3 structure with a more gradient-rich C2f structure, enhancing feature extraction through additional skip connections and split operations, and varying channel numbers across different model scales to maintain lightness while capturing more gradient flow. The Neck intensifies feature integration across dimensions, following the Feature Pyramid Network (FPN) [26] and Path Aggregation Network (PAN) [27] architectures, with convolution operations in the upsampling phases removed in layers 4-9 and 10-15 compared to YOLOv5. The Head section employs the current mainstream decoupled structure (Decoupled-Head), separating the classification and detection heads. It replaces the traditional anchor-based approach with an anchor-free method, as shown in Figure 2. The original Objectness branch is removed, leaving only the decoupled classification and regression branches. Moreover, the regression branch utilizes the integral form representation proposed in Distribution Focal Loss, allowing each independent branch to focus more on its respective feature information. YOLOv8 instance segmentation (YOLOv8-seg), an extension of the YOLOv8 model for instance segmentation, enhances the base target detection model by incorporating the YOLACT [28] network to achieve pixel-level instance segmentation. The model outputs masks, class labels, and confidence scores for each object located in the image.  
Figure 2: The YOLOv8-seg network structure diagram and part of the network details.
## 1 Introduction
Recording and storing such information can reduce the risk of losing several active satellites, which could go off track due to maneuvers occurring in between tracks. Therefore, detecting and recording the information of all maneuvers by using the TLE data of satellites with no operational data are important. Although various algorithms and methods can detect the maneuvers of a satellite, the best achieved detection reliability is 95%, which includes a 6% false detection. Just using TLEs to detect maneuvers can lead to false results as the accuracy of TLEs can be limited, but advancements have been proposed. However, combing SGP4 to find the propagated Keplerian elements and compare them with the observed Keplerian elements from the TLEs can improve the existing accuracy rate from 95% to 100%. Hence, this study proposes a new method to detect a satellite's maneuver information by comparing the data obtained using TLEs and SGP4.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 6 Conclusions  
In this paper, we propose a Smart Resilience in an NG-EPON AI-Enhanced fault tolerance system, trained on an NG-EPON topology to detect and localize faulty branch anomalies. The topology used for simulations consists of 32 ONUs connected to a central OLT, with fiber distances ranging from 10 to 20 km, representing a typical NG-EPON deployment. Faulty branch anomalies are detected using a combination of OTDR trace analysis at the central office and BER analysis at the ONUs, and the AI model identifies faults within the network. The proposed architecture and RDBA mechanisms perform effectively under different scenarios, including normal, one fault, and three faults. We validated the performance of the proposed method using simulation-based evaluation data derived from NG-EPON systems and the OPNET simulator. Our AI model is based on a neural network with three hidden layers, trained using datasets generated from OTDR traces and eye diagrams. Our simulations demonstrate that the proposed architecture and mechanism can maintain the system's performance even in the presence of faults. In future work, we aim to enhance our AI model's capability to operate in a more complex and autonomous environment, improving its ability to adapt dynamically to real-world, large-scale NG-EPON topologies. Furthermore, we will include a more detailed simulation-based evaluation scenario to fully quantify the benefits and limitations of our model in real-time fault scenarios.  
Conceptualization and methodology, A.T.L., R.K. and C.-H.T.; formal analysis A.T.L. and R.K.; validation and editing A.T.L., I.-S.H. and C.-H.T.; supervision and funding, I.-S.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Science Council under grants NSTC 113-2221-E-155-055.  
Not applicable.  
Data Availability StatementData are contained within the article.  
We would like to express our sincere gratitude to [PERSON] for her invaluable contribution to our research. [PERSON] provided the critical data that formed the foundation of our study, and her support and insights were instrumental in the completion of this work. We are deeply appreciative of her assistance and dedication.  
The authors declare no conflicts of interest.