esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Relaying with network coding forms a basis for a variety of collaborative communication systems. A linear block coding framework for multi-way relaying using network codes introduced in the literature shows great promise for understanding, analyzing, and designing such systems. So far, this technique has been used with low-density parity check (LDPC) codes and belief propagation (BP) decoding. Polar codes have drawn significant interest in recent years because of their low decoding complexity and good performance. Our paper considers the use of polar codes also as network codes with differential binary phase shift keying (DBPSK), bypassing the need for channel state estimation in multi-way selective detect-and-forward (DetF) cooperative relaying. We demonstrate that polar codes are suitable for such applications. The encoding and decoding complexity of such systems for linear block codes is analyzed using maximum likelihood (ML) decoding for LDPC codes with log-BP decoding and polar codes with successive cancellation (SC) as well as successive cancellation list (SCL) decoding. We present Monte-Carlo simulation results for the performance of such a multi-way relaying system, employing polar codes with different lengths and code rates. The results demonstrate a significant performance gain compared to an uncoded scheme. The simulation results show that the error performance of such a system employing polar codes is comparable to LDPC codes with log-BP decoding, while the decoding complexity is much lower. Furthermore, we consider a hard threshold technique at user terminals for determining whether a relay transmits or not. This technique makes the system practical without increasing the complexity and can significantly reduce the degradation from intermittent relay transmissions that is associated with such a multi-way relaying protocol.  
c 2024 4 313-337. [[https://doi.org/10.3390/network4030015](https://doi.org/10.3390/network4030015)]([https://doi.org/10.3390/network4030015](https://doi.org/10.3390/network4030015))  
12 June 2024  
Revised: 30 July 2024  
Accepted: 31 July 2024  
Published: 8 August 2024
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Our Dataset  
The dataset used in this study is summarized in Table 2. It comprises bug reports collected from multiple open-source projects, including Apache [2], JDT [32]/Platform [33], and KDE [34]/Core [35]/Firefox [36]/Thunderbird [37], covering a total of 858,095 bug reports.  
The bug reports were sourced from various projects, each with its own set of statuses, reflecting the diverse ways different platforms manage and categorize bug reports:  
* Apache: Included 10 statuses such as FIXED, INVALID, and DUPLICATE.
* JDT and Platform: Shared 7 statuses, including FIXED and NOT_ECLIPSE.
* KDE: Contained the most diverse categorization, with 13 unique statuses such as NOT A BUG, INTENTIONAL, UPSTREAM, and DOWNSTREAM.
* Core, Firefox, and Thunderbird: Incorporated 10 statuses, including INACTIVE, SUPPORT, and EXPIRED.  
The large volume and diversity of the dataset allowed for comprehensive training and evaluation of the proposed model. The inclusion of various projects ensured that the model could generalize across different bug-reporting conventions and handle a wide array of statuses. This diversity was instrumental in improving the robustness of duplicate bug detection, as it tested the model's ability to identify patterns across heterogeneous data. The dataset's detailed categorization also facilitated an in-depth analysis of the model's performance, ensuring that it could accurately detect duplicate bug reports across multiple domains and status labels.
## 3 Results
### Quantifying GPP over Texas and the Great Plains Using the Multiple Linear Regression Approach
Figure 14 indicates that BESS GPP values are typically lower than MLR GPP presented in this study and are slightly higher than MODIS GPP, which is likely related to the fact that the BESS algorithm considers the differences in C3/C4 photosynthetic pathways, while MODIS algorithm uses the same LUE\({}_{max}\) across all vegetation types [92]. BESS is also capable of capturing the drought propagation in water stress years. It can be noted that in 2011 Texas experienced a significant decrease in GPP (below 100 gC/m\({}^{2}\)/month), which is also noticeable in 2012. GPP over the Great Plains in 2012 demonstrated a decrease, which is especially pronounced in July-August 2012 as compared to 2007 and 2011 (Figure 14h,i). FLUXNET GPP observations in a time series at select stations have also been included in the comparison (Figure 15). Oklahoma-based US-ARM, US-AR1 and US-AR2 stations show a characteristic decline in GPP over the second half of 2011 and recovery in 2012. US-Ne3 station located in Nebraska (central portion of the Great Plains) demonstrates an opposite pattern: an increase in GPP is noted in summer of 2011, with a decrease in summer 2012, which agrees well with the MLR GPP. Figure 13: Gross primary production (gC/m\({}^{2}\)/month) from MOD17A2 data product in summer 2007 (**a–c**), summer 2011 (**d–f**), and summer 2012 (**g–i**).
# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
###### Abstract  
Current waste sorting mechanisms, particularly those relying on manual processes, semi-automated systems, or technologies without Artificial Intelligence (AI) integration, are hindered by inefficiencies, inaccuracies, and limited scalability, reducing their effectiveness in meeting growing waste management demands. This study introduces a prototype waste sorting machine that integrates an AI-driven vision system with a Programmable Logic Controller (PLC) for high-accuracy automated waste sorting. The system, powered by the YOLOv8 deep learning model, achieved sorting accuracies of 88% for metal cars, 75% for paper, and 91% for plastic bottles, with an overall precision of 90%, a recall of 80%, and a mean average precision (mAP50) of 86%. The vision system provides real-time classification, while the PLC manages conveyor and actuator operations to ensure seamless sorting. Experimental results in a controlled environment validate the system's high accuracy, minimal processing delays, and scalability for industrial recycling applications. This innovative integration of AI vision with PLC automation enhances sorting efficiency, reduces ecological impacts, and minimizes labor dependency. Furthermore, the system aligns with sustainable waste management practices, promoting circular economy principles and advancing the Sustainable Development Goals (SDGs).  
waste sorting; machine vision; sustainability; PLC; green technology; artificial intelligence
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 1 Introduction
Although several approaches have been developed to address the intersections between two of these sub-fields (see areas \(d\), \(e\), and \(f\) in Figure 1), the interception among these three sub-fields is not well explored. The works proposed in [12,13] have addressed the intersection among the three sub-fields (see Figure 1, area \(x\)) in order to estimate the number of clusters in a data stream clustering using the MapReduce model. In [12], the micro-batching approach was shown to be a feasible alternative for linear and sequential data scans that limits processing scalability, while [13] introduced a batch stream approach able to take advantage of MapReduce's parallel processing. The clustering algorithms proposed in [13] were shown to be feasible and efficient when clustering hyperspherical-distributed data streams, but  
Figure 1: Relationship between the main data clustering sub-fields: estimation of the number of clusters \(k\) (**a**), data stream (**b**), and MapReduce (**c**). mented with against normally distributed high-speed data streams. Although the clustering algorithms proposed in [13] were shown to be feasible under the assumption of spherical clusters, the problem is that the \(k\)-means algorithm could merge or split clusters, producing inaccurate results [14] when considering Gaussian-distributed clusters, especially when the data present a high variability. Over this observed problem, the present work shows that an algorithm designed to handle clusters with normal distribution can have better accuracy, even in high-speed data streams and distributed systems. Therefore, this work extends the work presented in [13], with the primary goal to cluster distributed batch streams of clusters with a dynamic estimation of their number in a scalable framework. Despite its imminent importance, it is a poorly explored sub-field considering the increasing data generation rates. In [14], for example, the authors highlight the need for a variety of algorithms combined with MapReduce for clustering data streams. The new contributions of the present paper are as follows: (i) we revisit and extend three evolutionary algorithms capable of automatically estimating the current number of clusters of a data stream, (ii) we propose three novel algorithms, based on data projection and goodness-of-fit hypotheses, for clustering batches of streamed data, and (iii) we present a new and improved set of experiments in a variety of application scenarios, allowing us to compare and analyze all presented algorithms. This paper is organized as follows: we present work related to our proposal in Section 2. Section 4 introduces the proposed algorithms. Experimental tests and result analysis are given in Section 5. The conclusions of this work are presented in Section 6.
## 1 Introduction
Due to their long geological age and various complex tectonic effects, intact rocks inevitably contain a certain number of defects such as microcracks and micropores; therefore, intact rock can be considered as an initial damaged medium [31; 32; 33]. Considerable studies have revealed that in the process of static loading, the microcracks in the intact rock experience the stages of closure, development, extension, and interactive penetration [34; 35; 36], and the wave impedance of intact rocks is strongly affected by the initial stress given the stress sensitivity of the wave velocity and density. Consequently, microcracks within the intact rock enter different evolution stages under different initial stresses, leading to changes in wave impedance, which in turn have an influence on the stress wave propagation. The variation in the quantity of microcracks inside the intact rock under initial stress causes changes in the macroscopic mechanical properties of the rocks, which is usually named initial damage [37; 38]. In the progressive destruction process of rocks under static loading, the closure effect of the microcracks at the initial loading stage can significantly affect the deformation characteristics of rocks, and the current research on the damage of intact rocks has rarely considered the compaction stage of the initial void. For a porous medium with natural defects such as rocks, when the porosity of rocks is high, the compaction stage of the initial microcracks is even more non-negligible. However, related research has been rarely reported. This paper presents a numerical exploration of blasting stress wave propagation in the initial stressed jointed rock mass. Firstly, based on the variation of the stress wave velocity in the intact rock under different equal biaxial static loading in the model test, the initial damage variable was determined, and the Mohr-Coulomb elasto-plastic constitutive model of the rock considering initial damage was established and subsequently implemented in the UDEC. Then, the feasibility of combining the developed model and the BB (Bandis-Barton) model which characterizes the nonlinear deformation of the joints to simulate stress waves across the jointed rock mass under initial stress was validated by comparing the numerical results with the model test results. Finally, further parameter studies were carried out through the UDEC to investigate the effect of the initial stress, angle, and number of joints on the transmission of blasting stress waves in the jointed rock mass.
# User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning
## 6 Conclusions  
In this paper, we have proposed a user pairing scheme in NOMA-based satellite networks with delay QoS constraints. With the objective of maximizing the sum effective capacity without degrading the performance of the delay-sensitive user, the user pairing problem was formulated. In particular, we designed the power allocation strategy to make sure that the performance of the delay-sensitive user achieved with the NOMA scheme was not less than that achieved with an OMA scheme. Based on this, the DRL algorithm was adopted to select a user from many users to pair with the delay-sensitive user and form a NOMA group. Simulation results have been provided to validate those performance analyses, show the effects of key parameters on system performance and the user selection strategy, and demonstrate that the DRL algorithm can significantly improve the system performance by finding the optimal action for each state.  
Conceptualization, Q.Z. and K.A.; methodology, X.Y. and K.A.; validation, X.Y. and K.A.; investigation, Q.Z., H.X., and Y.W.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y., K.A., and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Natural Science Foundation (No. 2020 GXNSFBA15 9051), the China Postdoctoral Science Foundation (No. 2020M681457), and the Scientific Research Foundation of Beibu Gulf University (No. 2019 KYQD40).  
Not applicable.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 1 Introduction
The proposed method was experimentally applied to Japan Aerospace Exploration Agency (JAXA) housekeeping data of Small Demonstration Satellite 4 (SDS-4) and validated over two years. Finally, different ML-based approaches have also received considerable attention for anomaly detection. For example, techniques based on Principal Component Analysis (PCA), such as the technique presented by [PERSON] et al. [10], have been used to improve efficiency by reducing the computational complexity and improving memory utilization overhead while maintaining high accuracy. The works presented by [PERSON] et al. [11], [PERSON] et al. [12], and [PERSON] et al. [13] also used PCA for feature extraction and fault detection in telemetry data. The k-nearest neighbor (kNN), Recurrent Neural Networks (RNN), autoencoder, and One-Class Support Vector Machine (OC-SVM) have also been studied with spacecraft (Suzaku) electrical power system data [14]. The term \"anomaly\" refers to any point or value within the dataset that produces patterns of oddity, novelty, fault, deviation, or exceptions. Hence, the activity of identifying anomalies from normal data is known as anomaly detection. This paper presents a comparative study of different ML techniques to detect anomalies in CubeSat telemetry data, considering system limitations such as computational power, memory footprint, and communication window. The approach considers both pre-processing methods and ML models. We applied the process to a solar panel's dataset generated by BIRDS-3 (NepaliSat, Raavana, Uguisu) and BIRDS-4 (Tsuru) satellites. It is important to note that this is the first utilization of such data. In summary, the contributions of this work are as follows:  
* Analysis of the novel solar panel dataset collected from four CubeSats. * Analysis of five different ML models based on their classification scores, execution times, model sizes, and power consumption. * The proposal of ML model candidates for solar panel anomaly detection on CubeSat systems. The contributions of this work to the research community are the findings showing that specific ML models, i.e., linear models, are most suitable for solar panel anomaly de tection onboard CubeSats, given the lack of attitude control and constrained computational capability of future satellite projects. The rest of the paper is organized in the following way: Section 2 introduces the satellite system and the dataset. Section 3 describes the methods utilized. Section 4 provides the results obtained. Section 5 presents analysis and discussion regarding the results of the experiments on ML modelling and future works. Finally, Section 6 summarizes the outcome of the research and proposes the best model to detect the anomalies within the dataset.
## 2 Materials and Methods
### Assessment of Double-Poling Capacity  
A specially designed double-poling ergometer (SkiErg, Concept 2, Morrisville, VT, USA) was used for all double-poling capacity tests. Power is generated by pulling cords that spin a wind resistance flywheel. The deceleration rate of the flywheel (drag factor) is controlled by a damper located on the flywheel. Therefore, the work required to accelerate the flywheel during each stroke is predetermined. For all performance tests, the damper settings were set at 100% of body weight to account for the hypothetical importance of body weight for endurance performance. Similar set-ups were utilized in previous studies [43]. Participants were instructed to choose a position that mimicked their habitual positions during double poling on skis.  
Given the hypothetical significance of body weight for endurance performance, mean power was reported in both absolute (watts) as well as standardized values (watts per kilogram of body mass).
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 4 Experiments
### Experimental Evaluation Metrics  
In the context of remote sensing object detection, each image is likely to include objects belonging to various categories at distinct spatial locations, requiring simultaneous evaluation of a model's classification and localization performance. Because the classification criteria in traditional image processing cannot be directly applied to object detection tasks, the mean average precision (mAP) was used to measure the quality of detectors. Specifically:  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}},\mathrm{R}=\frac{ \mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}, \tag{15}\]  
where \(TP\) stands for true positive, \(FP\) stands for false positive, and \(FN\) stands for false negative. An excellent detector is expected to achieve very good performance in both recall and precision; therefore, it is important to comprehensively consider the two factors \(P\) (precision) and \(R\) (recall). A better evaluation metric can be obtained by constructing the P-R curve for each category and calculating the average value of the area under the curve. For a dataset with \(N_{c}\) categories, the definition of mAP is as follows:  
\[mAP=\frac{1}{N_{C}}\sum_{i=1}^{N_{c}}\int_{0}^{1}P_{i}(R_{i})d_{i}. \tag{16}\]  
To comprehensively evaluate the detector's performance, we introduce a multi-dimensional evaluation metric system in which computational complexity is quantified by floating-point operations (FLOPs), while the storage requirements and structural scale of the model are measured using the number of model parameters (Params).
## 7 Experimental Demonstration
### Load Shedding Attack--With a Governor  
Suppose in DRAS 1 area, 20% of generation loss (32.6 MW) occurs, and DRAS 1 sends load shedding signals to reduce 30% of the load on bus 5. Since commands are not originated from PU, generation loss might occur in the same area where the command is issued. An adversary sends load shedding commands on bus 5, 6, and 8 to reduce the load by 20%, 15%, and 20%, respectively. The main purpose of the attacker is to disconnect customers from the grid and increase the cost of the utility in terms of customer dissatisfaction and customer reconnection. DRAS 1,2,3 governors provide local safety by verifying safety and the requirement policy (Rule 6).  
DRAS 1 governor requirement policy: This checks whether there is any contingency in area 1; if yes, the total load shed should be in the range of total generation loss of 20 to 30% according to Rule 6.1. In this case, 50% of the load drop commands are issued on bus 5 in response to 20% of generation loss in zip code 1; it will only allow 30% of the load to drop, and it allow commands accordingly. The rest is discarded. In the simulation, as an administrator, we issue 30% load drop commands, but in reality, the administrator will issue commands that they think is the incorrect response for the system and, accordingly, they will adjust the Z value. DRAS 1 governor safety policy: this checks the total load to be dropped; in this case, it is within limits of the safety policy according to Figure 5. Since the 30% load drop is below the threshold, it will be allowed. DRAS 2 and 3: since there is no generation loss in these areas and the commands are not originated from PU, DRAS 2 and 3 governors will discard these commands. Figure 10 presents the area frequency curve where attack commands are blocked by the governor, wherein DRAS 1-relevant commands are allowed (allowed by DRAS 1) and irrelevant 15% and 20% load drops are blocked by DRAS 2 and 3, respectively.  
If commands had originated from PU, although load shedding occurs in zip code 1, generation loss would have occurred in another area. PU issues the command to DRAS 1. The PU governor would verify and sign the command using Rule 2.1 and forward it to DRAS 1. The DRAS 1 governor verifies the sign of the PU governor and the total load needs to be shed using Rule 6.1. If the commands are valid, it verifies whether it is safe to execute commands using the safety property rule (Rule 6.2). If it is safe, it will allow commands. If the governor is not present, the grid destabilizes in Figure 9; otherwise, it occurs as in Figure 10.