esa-sceva/satcom-chunk-collection huggingface dataset:
# Seatbelt Detection Algorithm Improved with Lightweight Approach and Attention Mechanism
## 3 Methodologies
### GM-YOLOv7 Algorithm Model
#### 3.2.1. Slim Backbone Network Based on the Ghost Module  
The Ghost module, as shown in Figure 3a, is a lightweight convolutional neural network proposed by Huawei Technology in 2020 that offers significant optimizations in terms of parameter and computational complexity. This module transforms traditional convolution into two distinct steps. In the initial phase, the minority vanilla convolutions are employed to generate intrinsic feature maps. The subsequent phase utilizes a variety of inexpensive linear operations to produce more Ghost features. Eventually, the output of the Ghost module, a new and enriched feature representation, is formed by concatenating the intrinsic features and Ghost features.  
During the standard convolutional process, the needed quantity of FLOPs can be formulated as follows:  
\[FLOPs_{C}=c\times k^{2}\times n\times h^{\prime}\times w^{\prime} \tag{1}\]  
Figure 3: Schematic diagram of Ghost module. (**a**) Ghost module; (**b**) G-ELAN module.  
Figure 2: The overall architecture of GM-YOLOv7.  
where \(c\) is the quantity of input channels, \(k\) is the kernel size of convolution, \(n\) is the quantity of channels for output, and \(h^{\prime}\) and \(w^{\prime}\) are the height and width of output data. On the other hand, the FLOPs of the Ghost module are depicted in Equation (2):  
\[FLOPs_{G}=c\times k^{2}\times n/s\times h^{\prime}\times w^{\prime}+n/s\times d^ {2}\times(s-1)\times h^{\prime}\times w^{\prime} \tag{2}\]  
where \(d\) is the kernel size of the linear transformation and \(s\) is the quantity of similar information produced by linear transformation of \(m(m\leq n)\) original feature maps. By comparing Equations (1) and (2), it can be clearly seen that the collective quantity of FLOPs and computing overheads in the Ghost module were greatly reduced without varying the dimension of the output.  
In this study, the Ghost module is employed to swap out the vanilla convolution in the Backbone, reducing model volume and computational overheads while maximizing the effectiveness of feature extraction. In addition, as shown in Figure 3b, this paper introduces a novel G-ELAN, including the hierarchical residual connections and substantial Ghost modules. The architecture of G-ELAN can extract abundant feature information through diverse levels and scales, thus improving inference velocity. Consequently, the utilization of the Ghost modules and G-ELAN blocks substantially lowers the quantity of traditional 3 \(\times\) 3 convolutional layers that exist in the initial YOLOv7-tiny, resulting in an improvement in the overall efficiency and effectiveness.
## 4 Information Fusion and Residual Life Estimation  
In this section, different posterior distributions corresponding to each prior distribution are firstly obtained by fusing field data. Then the Bayesian credible interval method is used to complete a consistency test, which is essential for guaranteeing that all the fused data are from the same population. Furthermore, the residual life is estimated after integrating all the information.
## Extraction and Segmentation of Sputum Cells for Lung Cancer Early Diagnosis  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 Department of Electrical and Computer Engineering, Khalifa University, Sharjah, UAE;  
E-Mails: [EMAIL_ADDRESS] (F.T.); [EMAIL_ADDRESS] (N.W.);  
[EMAIL_ADDRESS] (H.A.-A.)  
2 Department of Computer Engineering, University of Osnabrueck, Osnabrueck, Germany  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +971-(0)6-5611333; Fax: +971-(0)6-5611789.  
_Received: 12 November 2012; in revised form: 14 August 2013 / Accepted: 15 August 2013/ Published: 21 August 2013_  
**Abstract:** Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its symptoms can be found exclusively in advanced stages where the chances for patients to survive are very low, thus making the mortality rate the highest among all other types of cancer. The present work deals with the attempt to design computer-aided detection or diagnosis (CAD) systems for early detection of lung cancer based on the analysis of sputum color images. The aim is to reduce the false negative rate and to increase the true positive rate as much as possible. The early detection of lung cancer from sputum images is a challenging problem, due to both the structure of the cancer cells and the stained method which are employed in the formulation of the sputum cells. We present here a framework for the extraction and segmentation of sputum cells in sputum images using, respectively, a threshold classifier, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with other competitive techniques via a series of experimentation conducted with a data set of 100 images. The extraction and segmentation results will be used as a base for a CAD system for early detection of lung cancer which will improve the chances of survival for the patient.  
**Keywords:** lung cancer detection; image segmentation; sputum cells; threshold algorithm; bayesian classification; mean shift algorithm
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 1 Introduction
In contrast to existing approaches, the proposed method offers the following contributions:  
* In many of the existing DPC-related literature studies, such as the work by [PERSON] and [PERSON] (2014) [7], as well as by [PERSON] and [PERSON] (2021) [8], density estimation is typically performed using a fixed cut-off distance approach. However, this method raises concerns regarding the reliability of density estimation, particularly when dealing with small-scale datasets. Hence, in this study, we depart from relying on the parameter dc within the DPC algorithm and instead employ the mean-shift algorithm to locate local density peaks. This strategic shift ensures that our algorithm is no longer influenced by the parameter dc, thus facilitating more accurate identification of core objects. By utilizing this approach to locate density peaks, we can precisely and efficiently identify cluster cores and even define the number of clusters as needed. This method significantly enhances the reliability and applicability of the clustering algorithm. * The DPC algorithm is sensitive to parameter selection, requiring users to specify a neighborhood distance threshold. This threshold is crucial in determining both the number of peak density points and the quality of clustering results. In many existing DPC methods, such as those described by [PERSON] et al. (2019) [9], commonly used distance metrics like the Euclidean distance are typically employed to assign labels to each data instance based on their proximity to the nearest cluster core. However, this label propagation method is often limited to spherical clusters. This paper introduces a novel adaptive distance metric approach aimed at identifying not only spherical clusters but also clusters of various shapes and densities. The core concept of this adaptive distance measurement involves automatically adjustingdistances based on the data's density. It accomplishes this by expanding distances between sparse points and contracting distances between dense points, enabling more precise identification of density peaks. Moreover, this approach allows us to discover clusters with complex non-spherical shapes. It enables our algorithm to adapt to clusters of different densities and shapes, thereby enhancing its practical applicability in practical scenarios.
###### Abstract  
In recent years, video research has dealt with high-frame-rate (HFR) content. Even though low or standard frame rates (SFR) that correspond to values less than 60 frames per second (fps) are still covered. Temporal conversions are applied accompanied with video compression and, thus, it is of importance to observe and detect possible effects of typical compressed video manipulations over HFR (60 fps+) content. This paper addresses ultra-high-definition HFR content via Hurst index as a measure of long-range dependency (LRD), as well as using Legendre multifractal spectrum, having in mind standard high-efficiency video coding (HEVC) format and temporal resolution recovery (TRR), meaning frame upconversion after temporal filtering of compressed content. LRD and multifractals-based studies using video traces have been performed for characterization of compressed video, and they are mostly presented for advanced video coding (AVC). Moreover, recent studies have shown that it is possible to perform TRR detection for SFR data compressed with standards developed before HEVC. In order to address HEVC HFR data, video traces are analyzed using LRD and multifractals, and a novel TRR detection model is proposed based on a weighted k-nearest neighbors (WkNN) classifier and multifractals. Firstly, HFR video traces are gathered using six constant rate factors (crfs), where Hurst indices and multifractal spectra are calculated. According to TRR and original spectra comparison, a novel detection model is proposed based on new multifractal features. Also, five-fold cross-validation using the proposed TRR detection model gave high-accuracy results of around 98%. The obtained results show the effects on LRD and multifractality and their significance in understanding changes in typical video manipulation. The proposed model can be valuable in video credibility and quality assessments of HFR HEVC compressed content.  
video; high frame rate (HFR); temporal resolution recovery; change detection; constant rate factor; multifractals +
Footnote †: journal: Applied Sciences
# Applicability of Human Thermophysiological Model for Prediction of Thermal Strain in PPE
## 3 Results
### Testing of the Physiological Strain in Climatic Chamber  
Thermal strain is an important limiting factor for the performance of the activity in PPE. Thermal strain was monitored by a set of more than 130 tests in the climatic chamber with human probands wearing various protective ensembles, as shown in Figure 1. Based on continuous on-line monitoring of physiological functions such as heart rate \(HR\) and body core temperature \(T_{re}\), a considerable variability of the results was observed in various probands even under the same test conditions.  
Heat stress management is highly individual and depends significantly on the associated body composition (here assessed by the BMI) and physical fitness (here assessed using VO2 max). The individuality of the subjects was already evident at the beginning of the test, when differences between the probands regarding the initial \(T_{tr,0}\) were up to 1 \({}^{\circ}\)C (36.9 \(\pm\) 0.5 \({}^{\circ}\)C). The different initial \(T_{tr,0}\) temperature and subjective perception of thermal comfort resulted in different test termination times. While some probands managed to perform the tests always until reaching the limit \(T_{re,lim}=38.5\)\({}^{\circ}\)C or \(HR_{lim}=220\) \(-\) age, regardless of different conditions or PPE, other probands repeatedly terminated when the body core temperature reached around 38.1-38.2 \({}^{\circ}\)C, and the higher temperature was subjectively unbearable. The observed results of the physiologic strain tests (maximum test times and body core temperature \(T_{re}\) achieved under the given conditions) are summarized in Table 5.  
In many tests at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, probands quit on their own request before reaching the heart rate limit or body core temperature limit because of intolerable discomfort (headache, intolerable heat, back pain, breathing difficulties, etc.), as summarized in Table 5.  
Obviously, for the temperatures \(-\)10 \({}^{\circ}\)C and 5 \({}^{\circ}\)C, overheating is not a demanding issue. Most of the tests were terminated due to the time limit (125 min), and in the vast majority of tests, the maximum body core temperature measured at the end of the test was below 38.2 \({}^{\circ}\)C. A few test cases deploying the SCBA had to be terminated due to the exhausted capacity of cylinders, even after successively using two of them, or due to other reasons (e.g., too cold, freezing hands). However, under more strenuous conditions (long-term hard work) in PPE, the apparent slow increase of the body core temperature should not be ignored.  
The resulting values measured with different probands were averaged including standard deviations and compared with the simulation calculations performed using the PHS and FMTK thermophysiological models.
###### Abstract
Biomechanical and neuromuscular characteristics of the stationary cycling movement have been suggested as possible explanations and include the following: (1) limited improvement of knee-joint stiffness due to limited knee range of motion (ROM) during cycling; (2) unobserved improvements in a number of components crucial for global motor function (e.g., neuromuscular control) from the lack of collaborative whole-body work; (3) restricted movement variability that limits the improvement of balance and coordination effectively due to the closed-chain nature of stationary cycling that elicits constraints on the movement produced in the lower limbs; and (4) reported difficulty in mounting/dismounting the stationary bicycle as a possible barrier for adherence to stationary cycling HIIT programs [8; 9; 10; 11; 12]. Increased hip flexion angle gait (HFgait) is an exercise modality that has been shown to result in similar energy expenditures (<1 MET difference) to treadmill running for the same heart rate [13]. In this exercise modality, individuals walk on a treadmill and increase intensity (metabolic cost) by increasing hip flexion while walking. HFgait also removes the flight phase of gait associated with running and reduces tibia peak positive accelerations (PPAs) at heel contact [13; 14]. The resulting activity is an open chain movement that involves the whole body (Figure 1) and promotes coordination between the body segments involving many degrees of freedom. Therefore, HFgait might be a valid alternative to cycling for specific clinical populations that are limited to this exercise modality while providing a more complex movement than cycling and simultaneously reducing the joint loading component of running associated with knee pain. Although the metabolic cost of this activity has been investigated, the movement pattern associated with this exercise modality has yet to be investigated. Additionally, how this exercise modality compares kinematically to stationary cycling, the most commonly used exercise modality in populations with lower-extremity pain, is unknown. Understanding the kinematic differences between HFgait and cycling will provide an insight into possible injury mechanisms and highlight biomechanical characteristics of this modality that might be beneficial to specific populations. This may inform exercise prescription and support the adoption of HFgait as an alternative exercise modality. The purpose of the current study is to compare the lower limb kinematic between HFgait and cycling. We hypothesized that lower limb kinematics during HFgait are different from cycling in ways that might be beneficial for clinical populations with lower limb injury. We investigated three-dimensional hip-, knee-, and ankle-joint angles during cycling and HFgait from a discrete and continuous perspective. Additionally, we investigated the kinematics of HFgait and cycling by implementing a phase offset optimization to remove the timing component of the differences between the two patterns.
## 3 Results and Discussion
### Biodiesel Production by Enzymatic Transesterification (First Stage)
#### 3.2.2 Analysis of Methyl Esters in the Biodiesel  
This analysis was applied to the biodiesel obtained in the experiment E1 because of the high production yield. Figure 6 shows the GC-MS spectra of the biodiesel from WFO, in which FAMEs were identified by the retention time data over 60 min of samples running. The sample was directly injected in the equipment under the operating conditions defined in Appendix A. The peak at retention time  
Figure 4: Copper corrosion test for biodiesel.  
Figure 5: ASTM copper strip corrosion standards.  
of 31.21 corresponds to methyl palmitate (C16:0) with a composition of 20.25% (see Table 6). It was identified a substance of methyl oleate (C18:1) in methyl ester with the retention time of 33.30 and the composition of 17.91%. The results of GC-MS analysis indicate that there is a substance of methyl linoleicalidate with the retention time of 21.43 and composition of 2.80%. The peak at 21.58 corresponds to methyl tridecanoate with composition of 1.90%. These results indicate that 42.86% of the sample represents methyl esters in the biodiesel. However, the fatty acids content in WFO was only 49.2% representing the total amount suitable for transesterification. In this sense, the conversion of fatty acids into methyl esters were 87.11% \(\frac{total\leavevmode\
obreak\ bditised\leavevmode\
obreak\ (g)}{total\leavevmode\
obreak\ WFO \leavevmode\
obreak\ (g)}\).  
Different FAMEs profiles are available in literature for biodiesel from WFO and other sources (e.g., palm oil, sunflower oil). For example, [PERSON] et al. [65] determined the FAMEs profile of the waste cooking oil biodiesel and reported highest contributions for methyl oleate (11.24 of reaction time and 37.59% of composition), followed by methyl palmitate (6.78 of retention time and 25.14% of composition). [PERSON] et al. [66] performed GC-MS analysis to determine FAMEs composition of biodiesel from industrial wastes of palm oil processing and identified a substance of methyl hexadecanoate in methyl ester with the retention time of 19.74 and the composition of 12.87%. Also, a sub-stance of methyl octadecanoate was identified with the retention time of 21.63 and the composition of 5.71%.  
[PERSON] et al. [29] reported a reaction yield of 85.49%, while [PERSON] et al. [8] reached a value of 50% using the same enzyme as a catalyst. These results were significantly lower than those obtained in this work. [PERSON] et al. [8] also performed the analysis with alkaline catalyst and obtained yield of 89.4%, which is higher than 87.11% (see Table 7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**FAME** & **Retention Time** & **Composition (\%)** \\ \hline Methyl palmitate & 31.21 & 20.25 \\ Methyl linoleicalidate & 21.43 & 2.80 \\ Methyl oleate & 33.30 & 17.91 \\ Methyl tridecanoate & 21.58 & 1.90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fatty acids composition.  
Figure 6: Gas chromatography–mass spectrometry spectra of biodiesel.
# PrimaVera: Synergising Predictive Maintenance
## 4 Predictive Maintenance Process Model
### PrimaVera Approach
#### 4.1.1 Data Acquisition  
In practical settings selection of suitable sensors for implementing a predictive maintenance solution pose a challenge [10]. To overcome this, a decision support tool will be realised that advises on the most appropriate sensing techniques, spatial sensor placement and optimal sensing strategy to monitor an asset. Though there is a lot of work on optimal sensor placement [12; 13; 94], an overarching decision support tool which takes all facets of predictive maintenance into account is still lacking. Input to such a decision support tool will include critical components together with their failure modes, required accuracy and resolution, cost factors and expert domain knowledge. Implementing an optimal sensing strategy will aid in the mitigation of data uncertainty.  
One of the case studies being analysed in the PrimaVera project is a sludge dredger's propulsion system. A limited amount of recorded failure data is available of this system, only eight clearly labelled eminent failures are present. In order to obtain accurate prognostic models, more failure data are required. Therefore, various methods to acquire more failure data will be evaluated. First method is a model based approach to gather more failure data. A computational white box model is created based on a qualitative functional decomposition of the system. Second method is the use of a scaled physical model of a propulsion systems which purposefully has damaged components installed such as faulty bearings. Third method is the use of public available data sets from similar systems such as the Machinery Fault Database [95] to evaluate the feasibility of transfer learning. Transfer learning allows the domains, tasks and distributions to be different for training and testing [96].  
Figure 1: Predictive maintenance management process model.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 7 Conclusions and Future Work  
Vehicle-to-vehicle communication plays a crucial role in autonomous driving, enabling essential applications, like driver safety, vehicle localization, and automated navigation. Nevertheless, because of the limited transmission range, vehicles may require multi-hop transmissions to establish communication with one another. In this paper, we have proposed an on-demand multi-rate-aware routing protocol called MARV, which selects adequate routing paths depending on the particular packet size. By selecting the adequate path depending on the packet size, MARV gains higher network throughput, lower delay without much control increase.  
In our evaluations, we have taken into account network throughput, end-to-end delay, and routing overhead. However, exploring additional parameters, such as transmission power and thermal noise, can be an intriguing issue, as they have potential impact on the  
Figure 12: Network throughput vs. time (traffic load) when TCP is used.  
Figure 13: Overall network throughput, normalized routing overhead, and end-to-end delay for 10 FTP (TCP) flows.  
transmission range of vehicles. Additionally, real-world scenarios will improve the quality of the performance evaluation. We plan to implement our proposal in real-world settings, such as vehicle mobility, high-density traffic, sudden obstacles, and outdoor wireless, that consider factors such as interference and hardware constraints.  
Computation power and memory is a concern when we add more intelligence to the routing algorithm. However, MARV makes minor modifications to the original routing algorithm, so we do not expect much higher computation and memory increase. However, if we are to extend MARV to mobile devices, instead of vehicles, then it would be beneficial to study the effect on computation and memory. We leave this as future work.  
Real-world experiments by implementing MARV in real vehicle systems will render new challenges and interesting discussions. We leave the real-world implementation as future work.  
This work was in part supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2021R1F1A1063640).  
Data sharing is not applicable to this article.  
The author declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} V2V & Vehicle-to-Vehicle \\ BER & Bit-Error Rate \\ MAC & Medium Access Control \\ PHY & Physical \\ MARV & Multi-Path Ad Hoc Routing Protocol for Vehicular Networks \\ ARF & Auto Rate Fallback \\ RBM & Receiver-Based Auto Rate \\ RTS & Request-to-Send \\ OAR & Opportunistic Auto Rate \\ MAS & Multi-Rate-Aware Sub-Layer \\ MTM & Medium Time Metric \\ CAIDA & Cooperative Association for Internet Data Analysis \\ MTU & Maximum Transfer Unit \\ RREQ & Route Request \\ CBR & Constant Bit Rate \\ FTP & File Transfer Protocol \\ \end{tabular}