esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
In this section, the simulation results of the proposed model in terms of BER and SER are discussed. The BER and SER represent the average error rate for the two UEs. Both UEs data are first demodulated separately, and the error rate is calculated by \(error\_rate=(user\ 1+user\ 2)/2\). For BER, \(error\_rate\) refers to the wrong demodulated bit at the receiver. For SER, \(error\_rate\) refers to the wrong classification of the received symbol. In Figure 5, the BER curve is compared with the CNN-based demodulation against the conventional demodulation system. It is evident from the figure that CNN-based demodulation performs better than the traditional one. At SNR 0 dB, the model provides less accurate results than the conventional system because of the low SNR value. However, as the SNR values increase, the model performance improves. From SNR 5 dB, the model outperforms the conventional demodulation system. The BER for CNN-based demodulation has a similar trend to the conventional system up to 12 dB; after this point, BER improves dramatically for CNN-based demodulation. This indicates the efficient demodulation capability of the proposed CNN model. In Figure 6, SER versus SNR is plotted for the proposed model. The model shows better SER compared to the conventional demodulation scheme. After the SER 15 dB, the difference becomes clearer toward the higher SNR. Thus, the proposed CNN model can be used for improving BER and SER for IRS-based wireless communication.  
Figure 4: Proposed Demod-CNN model training and validation progress for 50 epochs.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 3 System Diagram  
Figure 1 is a system diagram that illustrates the phases of model searching and operation of the classification model. Assuming the diagram can be folded along the dashed line, the first three boxes at the emitter and the last three boxes at the receiver represent human activity that could be automated. The organic samples consist of chromosomes with four genes represented by four symbols, Adenine (A), Cytosine (C), Guanine (G), and Thymine (T), which are passed through thermocoles or undergo amplification of the deoxyribonucleic acid (DNA), separation and delivery to the next block. The \"(+)\", \"(-)\", and \"Ladder\" states of the receiver's reverse function, designated by the listing symbol, reveal the genes that contain COVID-19.  
At the entrance of the second block, the resulting DNA samples are loaded into the second block, called \"Electrophoresis\", which allows the generation of images of gels. The inverse block called \"Artificial Intelligence Classification\" represents the best model for classification.  
In the third block, called \"Computer Vision Processing\", the image from the previous block is filtered and delivered to the next block for numerical representation. On the receiving side, the reverse process is called \"Computer vision interpretation\", where a numerical input is converted into an image for interpretation.  
The remaining issues are associated with the communication process. Galois Field Mapping/Galois Fields Demapping is a function that converts images to polynomials in the emissor and polynomials to images in the receiver. In the fifth block, called \"CSK/QAM modulation\", each of the numbers is calculated as the centroid of the CSK modulation and mapped into a QAM constellation, and these signals are sent over the MIMO channel. In the receiver, the block called \"CSK/QAM demodulation\" takes the QAM signals and converts them into numbers which are delivered to the Galois Fields Demapping block.  
Figure 1 depicts a massive array of LEDs and photodetectors inside a box that represents the VLC/FSK MIMO channel. The segments connecting the antennas represent electromagnetic waves propagating in air molecules as photon jets or sine waves with an amplitude, frequency, and phase.
###### Abstract  
An increasing number of industrial plants integrate the anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW) with a subsequent composing phase. To improve the plant productivity, a fraction of OFMSW digestate can be converted into a carbonaceous material, called hydrocar (HC), through Hydrothermal Carbonization (HTC), and then composed together with the OFMSW digestate itself, to produce \"hydrochar co-compost\". The aim of this paper is to present the design and assembly of batch bioreactors, built in-house to investigate the co-composting process of OFMSW digestate and its HC, and to provide some preliminary results. The OFMSW digestate from an industrial plant was carbonized at 200 \({}^{\circ}\)C for 3 h in a 2 L HTC reactor, to produce wet HC after filtration. The ratio of OFMSW digestate and green waste (1:1) used as bulking medium was reproduced in four bioreactors with an increasing percentage of HC substituting the OFMSW digestate (0, 25, 50, 75%). The bioreactors managed to effectively compost the solid wet biomasses in a wet environment with temperature and oxygen control, while measuring online the oxygen consumption and thus the dynamic respirometric index (DRI). The DRI24\({}_{\text{max}}\) measured with AIR-I solid respirometer (standardized offline measurement) started from values above 800 mg O\({}_{2}\) kg\({}_{\text{ys}}\)\({}^{-1}\) h\({}^{-1}\) before composite and dropped at the end of the process to values in the range 124-340 mg O\({}_{2}\) kg\({}_{\text{ys}}\)\({}^{-1}\) h\({}^{-1}\) for the four mixes, well below the recommended limit of 500 mg O\({}_{2}\) kg\({}_{\text{ys}}\)\({}^{-1}\) h\({}^{-1}\) for high-quality compost stability. These offline DRI values were confirmed by the online DRI measurements. This research is part of the international C2 Land Project funded by the European Institute of Innovation and Technology Climate Knowledge and Innovation Community (EIT Climate-KIC), which is greatly acknowledged.  
hydrochar; co-composting; hydrothermal carbonization; integrated plant enhancement; bench-scale bioreactor design; dynamic respirometric index; C2 Land project
## 3 Excavation Technologies  
Excavation technologies (ETs) are commonly used installation or replacement techniques that involves digging trenches, laying pipes, and restoring the surface of the ground. They are mainly used to rehabilitate collapsed or damaged pipelines. ET is highly disruptive both environmentally and socially, requiring closing roads to traffic, reducing road capacity and increasing congestion, increasing pollution, and limiting access to homes and business [4]. As the ground becomes more crowded, ET is becoming less feasible. In the last twenty years, technological improvements in trenchless methods have provided more environmentally friendly alternatives to ET, demonstrating less environmental impact and increased efficiency for the latter [27].  
Many publications have cited that ET has more carbon dioxide emissions compared to trenchless methods. ET produces large emissions through excavation and refilling processes, as well as by causing traffic congestion. For instance, for one project in New Mexico, it was calculated that ET resulted in 79% more emissions than pipe bursting, and yet ET required double the time to finish [2]. On the same note, carbon emissions from ET during the installation phase were found to be 72.6% higher compared to pipe bursting [28]. Another study showed that ET emitted six times the amount of carbon emissions than tunnel boring machines (TBM), which can be considered a greener technology in place of ET [29].  
In addition to emissions, ET creates broader ecological impacts. A comparative evaluation of ET and horizontal directional drilling (HDD) techniques found evidence that HDD techniques reduced ecological impacts by 66.2% [30]. LCAs consistently report greater environmental damage from ET in all categories, including higher greenhouse gas emissions, energy consumption, and waste production. For instance, ET consumes the most energy during backfilling and paving phases, increasing its ecological footprint compared to CIPP and TBM methods [9]. Moreover, a case study in Pasadena found that ETs' environmental costs were 75% higher than those of trenchless CIPP, further emphasizing its unsustainable nature [4].  
The consistent findings across studies underscore the need to shift from ET to trenchless methods, which provide more sustainable and environmentally friendly solutions. Tools such as LCA and Environmental Value Engineering (EVE) can be of use in making decisions on pipeline installation and rehabilitation projects to minimize ecological and social impacts. The use of trenchless technologies can secure major reductions in energy consumption, carbon emission, and environmental costs in construction operations, with the potential to deliver more sustainable infrastructure development.
## 2 Non-Absorbable Membranes
### Non-Absorbable Membranes of Synthetic Polymers  
From the category of synthetic polymers, polytetrafluoroethylene (PTFE) is an example of a material used in non-absorbable membranes. According to its structure, PTFE can be divided into two types: expanded PTFE (e-PTFE) and high density PTFE (d-PTFE) [24].  
e-PTFE was developed in 1969 and became a standard material in the 1990s. Its structure is bi-layered, with pores between 5 and 20 microns in size. One side presents an open microstructure of 1 mm thickness, with 90% porosity, which delays epithelial growth, and on the other side there is a 0.15 mm thick membrane with 30% porosity, which generates space for new bone [36]. The main disadvantages of the e-PTFE membrane are the higher exposure rate and, of course, the need for a second surgical time to remove it from the site, a feature common to all non-absorbable membranes. Subsequently, in 1993, a high-density PTFE membrane (d-PTFE) was developed with pores smaller than 0.2 microns in diameter [36]. Research focused on d-PTFE has shown that surgical removal was easier for the d-PTFE membrane than for e-PTFE analogues, which could lead to fewer disruptions to the underlying tissue [12]. Due to its high density and micro-porosity, bacterial infiltration is much reduced [24]. [PERSON] et al. reported that d-PTFE completely blocks the penetration of food and bacteria; thus, even if the membrane becomes exposed in the oral cavity, it maintains its functionality [37].  
As both e-PTFE and d-PTFE had reduced mechanical stiffness, titanium mesh was introduced into their structure; this mesh or reinforcement is malleable for good adaptation to the receiving site [7]. Titanium-reinforced e-PTFE membranes have demonstrated higher space holding capacity and better stability than plain e-PTFE [38]. The Gore-Tex membrane (W.L. Gore & Associates, Flagstaff, AZ, USA), which is made of e-PTFE, has been widely used in clinical treatment and has become a first choice-material for GTR and GBR. It is also widely used for general surgery, neurosurgery and cardiovascular surgery [39].
# BoxRF: A New Machine Learning Algorithm for Grade Estimation
## 5 Conclusions  
A new grade estimation method, BoxRF, was proposed in this paper. This method combines the strengths of classical estimation methods and machine learning methods. It mixes the concepts of search radii and direction and estimation based on inverse distance methods, with the robustness of RF methods that comes from forming numerous random groups of data. The method was compared to four machine learning algorithms, NN, KNN, XGB, and SRF. Among the four machine learning algorithms, SRF was the top competitor as it performed the best by far.  
BoxRF performed much better (R\({}^{2}\) = 0.751) than SRF (R\({}^{2}\) = 0.696) in the modeled dataset. The box dimensions that performed the best were sized similarly to the length of ranges indicated by variogram modeling. With performance explained by concepts that are well understood (variograms), the BoxRF method engenders trust in AI. Numerous combinations of hyperparameters performed similarly well, implying the method is robust. The superiority of BoxRF over SRF in this dataset is encouraging as it opens the possibility of improving machine learning by incorporating domain knowledge (principles of geology, in this case).  
Conceptualization, R.G.; methodology, R.G. and I.A.; software, R.G. and I.A.; validation, R.G., I.A. and N.S.; data curation, N.S.; writing--original draft preparation, I.A. and R.G.; writing--review and editing, I.A. and R.G.; visualization, I.A.; supervision, R.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets presented in this article are not readily available as they are part of an agreement between the University of Utah and Erdenet Mining Corporation.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI. & Artificial intelligence \\ BH & Box height \\ BL & Box length \\ BW & Box width \\ CV & Cross-validation \\ FH & Predict from first hit only (1 means yes) \\ kNN & K-nearest neighborhood \\ ML & Machine learning \\ MS & Minimum number of samples in a box for BoxRF \\ NN & Neural networks \\ NR & Number of times to reuse a sample \\ RF & Random forest \\ SRF & Scikit-learn random forest \\ T & Trees \\ XGBoost & Gradient boosting tree method \\ \end{tabular}
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
###### Abstract  
Leaf vein segmentation is crucial in species classification and smart agriculture. The existing methods combine manual features and machine learning techniques to segment coarse leaf veins. However, the extraction of the intricate patterns is time consuming. To address the issues, we propose a coarse-to-fine two-stage hybrid network termed TTH-Net, which combines a transformer and CNN to accurately extract veins. Specifically, the proposed TTH-Net consists of two stages and a cross-stage semantic enhancement module. The first stage utilizes the Vision Transformer (base version) to extract globally high-level feature representations. Based on these features, the second stage identifies fine-grained vein features via CNN. To enhance the interaction between the two stages, a cross-stage semantic enhancement module is designed to integrate the strengths of the transformer and CNN, which also improves the segmentation accuracy of the decoder. Extensive experiments on the public dataset LVN are conducted, and the results prove that TTH-Net has significant advantages over other methods in leaf vein segmentation.  
leaf vein segmentation; two stage; transformer +
Footnote †: journal: Applied sciences
## 4 Experiment
### Experimental Results and Analysis  
#### 4.5.1 Model Performance Comparison  
One of the primary metrics chosen for evaluating the performance of the proposed method against baseline models is Earth Mover's Distance (EMD). This section provides a comparative analysis based on the EMD metric for all the models.  
The results, as presented in Table 1, indicate that the proposed model outperforms the baseline models in terms of the EMD score. Notably, the EMD score for the proposed model is lower than that of the 3D-R2N2, AtlasNet, and Occupancy Networks, suggesting a more accurate 3D reconstruction from the 2D images.  
It is essential to understand that, while the EMD score offers valuable insights into the performance of the models, the specific application context and other qualitative factors also play a crucial role in determining the effectiveness of the 3D reconstruction.
## 3 Experiments and Results  
We demonstrate that SBA-RoPE effectively extends the context window of Large Language Models (LLMs) with just 10,000 fine-tuning data points, a quantity negligible compared to the data volume used in model pre-training stages. We evaluated the model's perplexity on long texts and conducted passkey retrieval experiments, proving that SBA-RoPE surpasses all previous methods for extending the context window.
# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
## 4 Experiment Results and Analysis  
### Urban Taxi GPS Data  
In order to verify the effectiveness of the proposed noise K-means clustering algorithm, five taxi GPS datasets at home and abroad are used as shown in Table 1. Taxi GPS data mainly refer to the vehicle position, direction, and speed information that is regularly recorded by the vehicle via the on-board global positioning system during travel. At present, many taxi GPS datasets exist for many cities across the world. The cities in this study, i.e., Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China), are representative cities for different continents and countries and are large-scale cities. The DB index [47], PBM index [48], SC (silhouette coefficient) [49] and SSE (sum of squares for error) are directly used to evaluate and analyze the clustering results. These evaluation methods are directly related to the number of clusters. Among them, DB index is mainly used to evaluate the performance of the noise K-means clustering algorithm. If the value of DB index is smaller, the similarity between clusters is lower and the clustering result is better. The PBM index is still used to evaluate the quality of clustering structure, where it describes clustering results and object attribution by defining quality. If the value of the PBM index is higher, the clustering effect is better. SC evaluates the clustering results by clustering the cohesion and separation. If the value of SC is greater, the clustering effect is better. SSE is used to evaluate the error probability distribution state and object attribution clustering. If the value of SSE is smaller, the clustering effect is better. The evaluation results and excellent procedures directly affect the effectiveness of urban hotspot discovery.  
In Table 1, the GPS points are distributed in city areas and some are hotspots; however, it is very difficult to capture the hotspots from GPS datasets.
# Real-Time Detection of DDoS Attacks Based on Random Forest in SDN
## 3 Materials and Methods  
### Real-Time Computing  
#### 3.1.1 Edge Computing  
Edge computing is primarily designed to solve the problems that arise in centralized cloud computing architectures. Unlike cloud computing, which is weak in real time and bandwidth constrained, edge computing distributes computing, storage, and network resources on edge nodes closer to the end devices to achieve a sinking of computing power, nearby real-time computing, reduced data transmission from the cloud, and lower data propagation latency. The nodes of edge computing can be smartphones, routers, IoT, and other devices. Distributed edge computing can not only improve data transmission speed, reduce latency, reduce bandwidth usage, and enhance data privacy, but also meets the demand of connected devices for low latency, high bandwidth, and high traffic collaboration. In a network, latency can be divided into sending latency, propagation latency, queuing latency, and processing latency. In addition, these delays add up to the total node delay [23]. This is shown in Figure 1 and Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Definition** & **Formula** \\ \hline Send Delay & The time taken by a host or router to send a data frame. & Send delay \(=\frac{\text{length of data frame (b)}}{\text{channel bandwidth (b/s)}}\) \\ Propagation delay & The time it takes for an electromagnetic wave to travel through the channel. & Propagation delay \(=\frac{\text{channel length (m)}}{\text{propagation rate (m/s)}}\) \\ Processing delay & The time taken by a host or router to process a received packet. & Propagation delay \(=\frac{\text{channel length (m)}}{\text{propagation rate (m/s)}}\) \\ Queuing delay & The time spent waiting for a packet to be queued in the router’s input queue or output queue. & Propagation delay \(=\frac{\text{length of data frame (b)}}{\text{channel bandwidth (b/s)}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time delay definition in the network.  
They are related as in Equation (1):  
\[T_{total}=T_{send}+T_{tran}+T_{queu}+T_{proc} \tag{1}\]  
DDoS attack detection via distributed edge devices can take detection that should be done at the server and detect it earlier. This not only relieves the huge pressure on network bandwidth and servers caused by massive amounts of data, but also reduces the propagation latency of the data. By splitting the processing of large-scale traffic through distributed edge devices, the amount of data handled by a single edge device is reduced, which not only reduces the queuing delay of data at a single edge device but also reduces its processing delay and sending delay.
## 2 Existing Solutions and the Combined Approach of AFF_CGE
### Existing Solutions
#### 2.1.2 Side Channel-Based Detection  
Although encryption conceals the payload of network packets, side-channel data such as packet size and direction remain accessible from the encrypted connection. To address this, some researchers have combined the statistical characteristics of traffic--such as packet size and direction--with machine learning techniques to classify network traffic.  
[PERSON] et al. [9] proposed a detection method for Secure Sockets Layer (SSL) traffic, which is challenging to analyze at the traditional TCP/IP level. Their approach involves reorganizing SSL records from multiple IP packets into a series of unencrypted data, which are then used to classify SSL flows for detecting malicious traffic. [PERSON] et al. [10] introduced a model based on Record Length and Frequency Domain Analysis (REDA) for detecting encrypted traffic. This model leverages the characteristics of the TLS protocol and employs the Discrete Fourier Transform (DFT) [11] to extract frequency domain features from the reconstructed TLS Record Length Sequence (TRLS), facilitating the detection of TLS flows. [PERSON] et al. [12] developed a machine learning-based framework for identifying encrypted malicious traffic across various scenarios. Their approach involves manually selecting traffic statistical features, extracting these features from raw traffic, and then using a machine learning classifier to categorize the traffic.  
However, as modern encrypted traffic becomes increasingly diverse and complex, these methods often struggle with generalization in practical applications due to their heavy reliance on expert knowledge and experience.