esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The most frequent etiological cause of scoliosis is idiopathic, neuromuscular is the second most frequent cause and congenital anomalies are the least frequent causes of scoliosis [1, 2, 3]. It is estimated that the prevalence of idiopathic scoliosis among patients aged 10 to 16 years is 2-3% [1]. Scoliosis causes multiple trunk deformations that can affect a person's perception of their body, and body image reflects a dynamic representation that changes according to actions, emotions and feelings, ultimately being an important factor when assessing health-related quality of life [4]. The proportion of individuals that require surgery is higher in neuromuscular and congenital scoliosis than in idiopathic scoliosis [5, 6]. The socioeconomic impact of scoliosis is significant, affecting the individual, their families and health professionals. The average cost of surgery for idiopathic scoliosis correction varies, costing approximately US 550,000 [7]. For different types of scoliosis, early diagnosis might prevent more aggressive treatment and reduce the rate of complications [1, 5, 6, 7, 8]. School-based screening protocol using clinical examinations could be a reasonable option in the early diagnosis of adolescent idiopathic scoliosis due to its high specificity [9].  
The most commonly used, available and cost-effective complementary diagnostic test for detecting scoliosis is spinal X-ray examination [8, 10]. In the preoperative planning stage, it is common to use radiographs obtained with and without traction or bending films to observe curve flexibility [5; 6]. Surgical planning must consider the perioperative clinical risks, need for osteotomies, quantity and quality (screws, hooks, tapes, cages, rods) of implants, possibility of reoperation and alignment of the possibilities of deformity correction with the expectations of the family. All complementary examinations must be ordered with rationality regarding changes in the surgical plan or therapy with respect to their additional cost and patient safety, especially in the case of examinations involving ionizing radiation [7; 10; 11].  
There are studies in the literature associating the preoperative flexibility of the scoliotic curve with the degree of correction obtained in the postoperative period [12; 13]. However, there are few reports on whether lower preoperative curve flexibility, i.e., greater spinal stiffness, is associated with a higher rate of late mechanical complications. This information is important for planning and therapeutic decision-making by both the multidisciplinary team and the family.  
The present study aims to analyze whether greater preoperative curve flexibility is related to greater deformity correction and fewer late mechanical complications.
## 1 Introduction
We also implement feature selection using Lukasiewicz similarity measure (LS), Luca-measure fuzzy entropy (LFE), and Yeo-Johnson power transform, which is a novel feature optimization technique. Finally, locomotion activity recognition is performed by a multilayer perceptron (MLP). The chief contributions of this paper are as follows:  
* We implement a feature selection framework based on Lukasiewicz similarity measure (LS) and Luca-measure fuzzy entropy (LFE). Quality features produce more accurate results. * To optimize the selected features, the Yeo-Johnson power transform is implemented, which brings the data into a sophisticated shape and consequently supports better classification. * We implement a multilayer perceptron (MLP) and tune its parameters for the final classification of human locomotion activities. * We introduce novel features to the field of RS-HLAR in the shape of Parseval's energy and auto-regressive coefficients that have been popularly used in EEG signal and acoustic signal processing, respectively. The rest of the article is formatted as follows: Section 2 contains the related work on the field of RS-HLAR. Section 3 comprehensively covers the materials and methods used in this study. Section 4 shows the results for all the datasets used in the proposed SSRDC system and compares the system's performance with the available state-of-the-art models. Section 5 discusses the results in a broader perspective and highlights the strengths and limitations of the system, while Section 6 concludes the research article and describes the future plans.
# Chord Recognition Based on Temporal Correlation Support Vector Machine
## 5 Experimental Results and Analysis
### Corpus and Evaluation Results  
For evaluation, we use the MIREX'09 Audio Chord Estimation task dataset which consists of 12 Beatles albums (180 songs, PCM 44 100 Hz, 16 bits, mono). Besides the Beatles albums, in 2009, an extra dataset was donated by [PERSON], which consists of 38 songs from Queen and [PERSON] [21].  
This database has been used extensively for the evaluation of many chord recognition systems, in particular those presented at MIREX 2013 and 2014 for the Audio Chord Estimation task. The evaluation is conducted based on the chord annotations of the Beatles albums provided by [PERSON] and [PERSON] [49], and the chord annotations of Queen and [PERSON] provided by [PERSON] [21].  
According to [50], chord symbol recall (CSR) is a suitable metric to evaluate chord estimation performance. Since 2013, MIREX has used CSR to estimate how well the predicted chords match the ground truth:  
\[CSR=\frac{t_{E}}{t_{A}} \tag{4}\]  
where \(t_{E}\) is the total duration of segments where annotation equals estimation, and \(t_{A}\) is the total duration of the annotated segments.  
Because pieces of music vary substantially in length, we weight the CSR by the length of the song when computing the average for a given corpus. This final number is referred to as the weighted chord symbol recall. In this paper, the recognition rate and CSR are equivalent for a song and the recognition rate and weighted chord symbol recall are equivalent for a given corpus or dataset.  
In the training stage, we randomly selected 25% of the songs from the Beatles albums to determine the parameters \(C\) and \(\gamma\) for the SVM kernel and the state transition probability matrix \(A\). For SVM, the training dataset is composed of the PCP features of labeled musical fragments, which are selected from the training songs. The average estimation accuracies or recognition rates are reported.  
First, we compare the recognition rates of SVM with PCP and EPCP features. The comparison between the ground truth and estimated chord is shown in Figure 7 for the example song (the Beatles song \"Baby It's You\"). The top and bottom figures show the results using the SVM method with PCP features and EPCP features, respectively. The ground truth chord is represented in pink and the estimated chord labels are in blue. Figure 7 indicates that EPCP improves the recognition rate. In Figure 8, the recognition rate using SVM with PCP features is 70.15%, while that of TCSVM with the same features is 75.07%. The top image of Figure 6 shows less reliable estimated chords at the times when the chords change. The bottom image considers the inter-frame temporal correlation of chord features and shows more stable estimated chords even when the chords change.  
Second, we compare the recognition rates of SVM and TCSVM with different features. The recognition results of the TCSVM method with ELPCP are superior, as shown in Figure 8. Because EPCP and ELPCP consider the temporal correlation of music, the rates show few differences between the SVM and TCSVM.
## 3 Design and Fabrication of Diversion Function Pipe Segment Model Test Box  
### Working Principle of Diversion Function Pipe Segment  
To solve issues such as the destruction of groundwater transport channels due to the barrier effect of shield tunneling, the accumulation of upstream water, and the increase in water pressure difference on both sides of the tunnel, a novel pipe segment with a diversion function has been designed. The guide function pipe segment mainly consists of pipe segments, diversion pipes, and distribution pipes. The diversion pipes are pre-embedded inside the concrete pipe segments, and they are used to connect the collection pipes with the distribution pipes. The collection and distribution pipes are used to divert water from one side of the pipe segment to the other. The diversion pipe and distribution pipes form a transportation channel for water, as shown in Figure 2.  
Groundwater can flow into the pipe segments through the collection pipe, and then to the ground via the opposite side's collection pipe, repairing the flow function of groundwater obstructed by the shield-laid pipe segments. The flow of groundwater is supplemented, and the water pressure difference on both sides of the tunnel is reduced, protecting the aquifer system and the ecological environment of groundwater, alleviating unsymmetrical pressure on the pipe segments, as shown in Figure 3.  
Figure 1: Shield tunneling through upper and lower impermeable water transport interlayers (pebbly strata).  
Figure 2: Operating concept of diversion function pipe segments.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 4 Modulation Technology in VLC System
### Color Shift Keying (CSK) Modulation Technique  
The blue LED chip excites the yellow phosphor to produce white light, but its switching speed capability slows down. Therefore, green, blue, and red are used to achieve white light through light mixing technology. CSK is mentioned in IEEE 802.15.7 and lower data rate is used in other modulation schemes [123]. The modulation of CSK is performed using the intensities of the three colors of RGB LED. CSK is sampled in the color space chromaticity diagram [114]. It maps the eye-perceivable color to two chromaticity factors, x and y. Table 4 illustrates the seven bands of human-visible wavelengths, the centers of which are marked in Figure 13. Where \(k\) is the number of bits (\(k=\log_{2}M\)), \(M\) is the modulation order and \(N_{0}\) is one-sided noise power spectral density, the bit error probability (BEP) of a 4-CSK three color LEDs (Trichromatic LEDs, TLED) can be determined by Equation (18) [124]:  
\[BER_{4-CSK}=\frac{1}{2k}Q\bigg{(}\frac{0.8157}{\sqrt{2N_{0}}}\bigg{)}+\frac{1} {2k}Q\bigg{(}\frac{0.817}{\sqrt{2N_{0}}}\bigg{)} \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wavelength (nm)** & **Code** & **Center (nm)** & **(x, y)** \\ \hline
380–478 & 000 & 429 & (0.169, 0.007) \\
478–540 & 001 & 509 & (0.011, 0.733) \\
540–588 & 010 & 564 & (0.402, 0.597) \\
588–633 & 011 & 611 & (0.669, 0.331) \\
633–679 & 100 & 656 & (0.739, 0.271) \\
679–726 & 101 & 703 & (0.734, 0.265) \\
726–780 & 110 & 753 & (0.734, 0.265) \\ \hline \hline \end{tabular}
\end{table}
Table 4: CSK in code and chromaticity coordinates. © 2015 IEEE. Reprinted, with permission, from [114].  
Figure 12: Spectrum of multicarrier modulation scheme **(a)** frequency division multiplexing and **(b)** OFDM, **(c)** a block diagram of OFDM system using N-point FFT and IFFT. From: [119], Copyright © 2015 and Imprint. Reproduced by permission of Taylor & Francis Group through PLSclear.
## 3 Malware Detection and Categorization
### Malware Categorization  
Malware categorization is a critical problem in the field of cybersecurity. In the malware categorization scenario (as seen in Figure 2), MalwD&C not only detects if a downloaded executable file is benign or malware but also determines the specific family to which the malware belongs. This information is important for understanding the characteristics and behaviour of different malware families and developing effective countermeasures and prevention strategies.  
However, publicly available datasets for this problem are limited [14; 17], as cybersecurity companies and individuals often treat their collected labelled PE malware samples as private and do not share them with the public. This makes it challenging for researchers to develop and evaluate machine learning-based approaches for malware categorization. The proposed MalwD&C approach addresses this challenge by providing a lightweight and accurate solution for detecting and categorizing PE malware on Windows-based computer systems. In this scenario, anytime any executable file is downloaded, MalwD&C checks if it is a benign or malware application. If the file is predicted to be a malware application, it checks to which family this malware belongs. Then, it labels that malware file according to its family and saves it as a record for future analysis.  
Figure 1: Malware detection scenario visualization.  
In this scenario, the classifier outputs the label of the testing sample for all the available classes. As only 14 classes had more than 1000 samples, we chose them for our analysis. Figure 3 illustrates the distribution of the available samples for each of the 14 classes.
## 5 Summary  
This paper presents a clustered routing algorithm, LE-KCR algorithm, based on the K-means technique for UWSNs. The proposed algorithm selects cluster-heads by regarding K-means, the relative position of nodes, distance to sink, and remaining energy. The introduction of the gap statistic can automatically select clustering numbers. Through the dual-hop strategy, the edge nodes far away from the cluster-head can also access the network without consuming abundant energy. In addition, an appropriate number of anchor nodes is set, which can not only transmit underwater information to the sea but also locate other nodes. This makes the proposed algorithm implementable when regarding node mobility. We investigate the performance of the proposed LE-KCR algorithm. The results show that the proposed LE-KCR algorithm obviously gains the advantage in reducing energy consumption and the number of dead nodes.  
Methodology, L.L. and Y.Q.; software, L.L.; validation, L.L. and Y.Q.; investigation, L.L. and Y.Q.; resources, L.L. and Y.Q.; data curation, L.L.; original draft preparation, L.L.; review and editing, L.L., Y.Q. and J.X.; supervision, J.X. and Y.Q.; funding acquisition, Y.Q. All authors have read and agreed to this version for publication. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The death rate of nodes vs. different number of nodes.  
**Funding:** This research was funded by the Natural Science Foundation of China (61971378, 61705190), Fundamental Research Funds for the Central Universities, Southwest Minzu University (2021 XJTD01).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Methods Based on Filtering
#### 4.2.2 Frequency Analysis  
To address the limitation of spatial filtering methods (i.e., the kernel cannot be found in the defect image), the frequency domain analysis method was derived. This method firstly converts the image into frequency domain signals using the Fourier transform, and secondly performs a filtering analysis of the signals, and finally converts the signals back to the spatial domain to be stored by inverse Fourier transform. Three articles [25; 37; 38] were published successively, outlining the methods of frequency domain analysis for the defect detection of cold-rolled and hot-rolled steel. Among them, the method proposed in [PERSON] [25] is the most effective, with a detection rate of up to 92.68%. They proposed a method of fast Fourier transform (FFT) combined with a local border search algorithm (LBSA) for the detection of hot-rolled steel strips.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 5 Implementation
### Performance Optimization  
#### 5.3.1 DPDK  
In order to achieve high throughput and low latency, this paper uses a high-performance packet I/O library-DPDK [29], which is a data plane development tool kit.  
In the traditional Click architecture, the FromDevice element, which acts as a sniffer function, sends packets captured from the NIC to the kernel thread in a hardware interrupt and then processes the packet in the protocol stack. Compared with the traditional mode, DPDK has the following advantages: (1) DPDK abandons the traditional interrupt mode and reads the data packet from the network card by polling, avoiding the interrupt overhead. (2) DPDK allocates large memory pages to replace normal memory and reduce cache-miss. (3) Using user space I/O technology (UIO) to intercept interrupts, thus bypassing the subsequent processing of the kernel protocol stack, so as to greatly improve network performance.  
The use of the DPDK causes the system to no longer use the Click buffer, and it can no longer use Click's packet object, instead of using the DPDK packet processing mechanism. Placing the DPDK in the enclave increases the size of the TCB (approximately 516K) [6], so for reasons of TCB size and performance, we place the DPDK in an untrusted area outside the enclave.
## 3 Results
### Nutritional Profile
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type of Beans** & **Energy** & **Protein** & **Carbohydrates** & **Sugar** & **Fibers** & **Fat** & **Saturated** & **Unsaturated** & **References** \\ \hline _Lima beans_ & 392.28 & 22.47 & 72.75 & 3 & 21.34 & 1.27 & 0 & 1.27 & [18; 33] \\ \hline  & 115 & 19.35 & 61.65 & 2 & 6 & 1.14 & 0 & 1.14 & \\ \hline _Black-eyed beans_ & 334.61 & 16.59 & 62.81 & 21.92 & 7.82 & 1.89 & 0 & 1.89 & [32; 34] \\ \hline  & 99 & 6.61 & 17.75 & 10.25 & 7.5 & 0.45 & 0 & 0.45 & \\ \hline _Soybeans_ & 466 & 36.49 & 30.2 & 7.3 & 22.9 & 19.94 & 4.28 & 15.66 & [35; 36; 37] \\ \hline  & 327 & 38 & 21.16 & 3.6 & 17.5 & 1.22 & 0.14 & 0.53 & \\ \hline _Garbanzo beans_ & 378 & 20.47 & 62.95 & 10.7 & 12.2 & 6.04 & 0.603 & 4.108 & [32; 38] \\ \hline  & 164 & 8.86 & 27.42 & 4.8 & 7.6 & 2.59 & 0.269 & 1.739 & \\ \hline _Faru bean_ & 301 & 22.7 & 30.87 & 14.98 & 15.89 & 1.4 & 0 & 1.4 & [32; 39; 40; 41] \\ \hline  & 116 & 8.17 & 20.68 & 14.98 & 5.7 & 0.38 & 0 & 0.38 & \\ \hline _Mung beans_ & 391.37 & 26.30 & 68.39 & 8.47 & 27.85 & 1.4 & 0.025 & 1.375 & [18; 42; 43; 44] \\ \hline  & 374 & 10.58 & 54.6 & 5.88 & 17.15 & 1 & 0.025 & 0.975 & \\ \hline _Adzuki beans_ & 386.07 & 26.95 & 67.84 & \textless{}0.1 & 17.75 & 0.77 & 0.2 & 0.57 & [18; 45] \\ \hline  & 329 & 20 & 63 & \textless{}0.1 & 13 & 0.5 & 0.2 & 0.3 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._micronutrient profile, beans have a high content of phytochemicals [67; 68] that enhance overall health [59]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type of Beans** & **Fe (mg)** & **Zn (mg)** &
\begin{tabular}{c} **Mg** \\ **(mg)** \\ \end{tabular} & **P (mg)** & **Ca (mg)** & **Cu (mg)** & **K (mg)** & **Na (mg)** & **References** \\ \hline _Black beans_ & 5.34 & 3.37 & 180 & 522 & 191 & 1.12 & 1540 & 75 & [19] \\ \hline  & 2.10 & 1.12 & 70 & 140 & 27 & 0.209 & 355 & 2 & \\ \hline _Kidney beans_ & 523 & 3.29 & 164 & 612 & 98 & 0.865 & 1490 & 3 & [19] \\ \hline  & 144 & 1.07 & 45 & 142 & 28 & 0.242 & 403 & 2 & \\ \hline _Navy beans_ & 5.29 & 3.31 & 180 & 523 & 229 & 1.14 & 1470 & 0 & [19] \\ \hline  & 2.36 & 1.03 & 53 & 144 & 69 & 389 & 389 & 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mineral contents of twelve bean varieties (mg per 100 g dry weight at the first row and per 100 g cooked at the second row).
## 2 Related Works
MMS-DSR is designed keeping these technological advancements in mind, aiming to provide a routing protocol that is not just optimized for today's networks but is future-proof and adaptable for the next generation of mobile communication. The work [28] attempts to optimize the bandwidth in the DSR routing protocol during data communication in MANET. The paper purposes a modified Dynamic Source routing protocol which is a Systematic Analysis Dynamic Source Routing protocol (SA-DSR). While SA-DSR introduces important enhancements to bandwidth optimization in traditional DSR it does not incorporate advanced communication technologies like beamforming or MU-MIMO which implies that this approach is not very suitable for high-mobility environments. In light of the above, MMS-DSR presents a transformative approach that synergistically integrates machine learning, advanced optimization, and multi-metric evaluation. More importantly, it does so while natively considering the capabilities and potential of 6G technologies, thereby distinguishing itself as a pioneering solution in the field of MANET routing protocols optimized for smart cities. Finally, in order to understand the research gap in the existing literature and highlight the contributions of the present proposal, Table 2 depicts a table with checkboxes locating the most relevant references analyzed in this work and our research direction. The table shown in Table 2 also categorizes each of the analyzed and discussed works based on their topic by evidencing that our work attempts to handle different topics simultaneously.
## 4 Case Study  
We apply the framework presented in Section 3 to a case study in last-mile logistics, focusing on the design of an innovative delivery system. In this application setting, the objective is to determine optimal locations for parking slots (which serve as transshipment points) and to establish efficient vehicle routes and schedules. This decision-making process is semi-structured, as it involves balancing the preferences of multiple stakeholders and addressing conflicting objectives. In this case study, the role of the LLM was twofold: (1) to serve as a constructive heuristic for the location selection component, embedding stakeholder preferences, and (2) to adjust the parameters of a multi-objective metaheuristic [31] for optimizing vehicle routing and scheduling.