esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Lower back pain is a major medical issue that affects about 80% of all adults worldwide [1] and is the second most common symptom for patients visiting a physician for a general illness [2]. Due to the high prevalence, there have been many studies on the characteristics of the spine, demonstrating that determining the physiological load on the lumbar spine is crucial for understanding lumbar spine disease and for effective lumbar spine surgery and injury prevention [3; 4].  
Computational modeling of the passive ligamentous spine is widely used to assess various biomechanical behaviors [5; 6; 7; 8; 9; 10]. Currently, finite element (FE) modeling of the spine is frequently used to assess deformation and stress distribution within the joint structure of the spinal system [11; 12; 13; 14; 15]. To establish valid spinal models, accurate mechanical properties are required for each component and their representations.  
Among the important tissues, the mechanical properties of the ligaments were largely investigated: The mechanical properties of the capsular ligament (CL), anterior longitudinal ligament (LL), ligamentum flavour (LF), posterior longitudinal ligament (PLL), intertransverse ligament (ITL),interspinous ligament (ISL), and supraspinous ligament have been reported based on in vitro tube tensile testing, and their effects on lumbar spine biomechanics have been investigated [16; 17; 18; 19; 20].  
Recently, studies have investigated the effects of ligament properties on FE model responses. For example, when the strength of the ligaments varied considerably within the model, momentary sharing and internal forces within the disc and joints were affected [21; 22; 23; 24]. Discs have also been modeled with both linear [25; 26] and nonlinear properties [27; 28; 29; 30]. However, the modeling of the muscles has often been ignored, being replaced by corresponding external loads on the spinal bone. In a recent study, more realistic muscle load conditions were assessed using a combined musculoskeletal and FE model of the lumbosacral spine [31]. However, the effect of the passive support from adjacent spinal muscles was not considered.  
This study thus aims to investigate the effect of passive support from the psoas major, quadratus lumborum, and erector muscles on the range of motion (RoM) and intradiscal pressure (IDP) of the lumbar spine. We also investigate the effects of the stiffness of each component. We constructed a lumbar spine model that incorporated these muscle groups by adding computed tomography (CT)-based muscle elements to a conventional passive ligamentous spine model verified using data from the literature [32]. The effects of the passive support from the adjacent spinal muscles on the RoM and IDP of the lumbar spine were tested under various load conditions by comparing the results of the models with and without muscle parts.
# Systematic Review on the Use of CCPM in Project Management: Empirical Applications and Trends
## 4 Discussion
The identification of implementation gaps, the emphasis on integration with digital technologies (e.g., real-time monitoring platforms [11]), and the recognition of project success as a multidimensional and multistakeholder phenomenon [76] demonstrate a mature and forward-looking perspective. Additionally, the synthesis of results around the main project objectives--time, risk, and cost reduction--provides project managers with evidence-based arguments for adopting CCPM strategies in diverse and dynamic environments. Finally, the study offers a solid foundation for future research agendas by highlighting opportunities for the empirical validation of generic models, investigating organizational readiness factors, and developing implementation frameworks tailored to high-variability environments. A particular emphasis should be placed on the research gap regarding ETO (Engineer-to-Order) systems, which represent firms that are continuously engaged in projects focused on developing new products and services. These environments, characterized by high customization, volatility, and resource interdependence, represent a promising field for exploring the applicability and necessary adaptations of CCPM. Despite the rigorous methodology adopted in conducting this systematic review, the study presents some limitations that should be acknowledged. The first concerns the heterogeneity of the analyzed studies, both in terms of research methods and application contexts. While this diversity enriches the discussion, it also hinders direct comparison of the results and limits the potential for more precise generalizations. A second relevant limitation is the predominance of modeling and simulation-based approaches, which, although contributing to the technical advancement of CCPM, have limited empirical validation. Many of these studies are developed in controlled environments, with little or no documentation of practical implementation, which reduces their direct applicability to real-world project settings. Furthermore, most of the reviewed studies do not provide detailed information about their organizational context, such as sector, company size, or project management maturity level. This lack of contextual data constrains the extrapolation of findings and the development of actionable guidelines for practitioners aiming to implement CCPM in specific environments.
## 1 Introduction
(2019) analyzed AE signals during joint cyclic shear, and the results showed that the accumulated AE energy gradually decreased with the increase in the number of cycles. It indicated that a high normal stiffness could significantly increase the number of AE signals. However, the relationship between failure modes of joint surfaces and AE characteristics has not been further discussed. Additionally, under the action of vibrant tons such as quake, blasting, and mechanical resonance, the architectural plane will create shear motion at various rates ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019). According to [PERSON] et al. (2019), for high-level nuclear waste repositories and dam construction, more stringent design criteria should be required, and the rate-dependent mechanical behaviors of rock joints need to be taken into account. Some previous researchers have examined the factors which impact the rate-dependent shear habits of joints, including shear rate range ([PERSON] et al., 2019), rock type ([PERSON] et al., 2019), and normal loading rate ([PERSON] et al., 2019). The shear rate affects not only the shear strength of the rock joint but also the failure modes of the joint during shear. [PERSON] et al. (2019); [PERSON] et al. (2019) investigated the rate-dependent shear actions of rough joints during shear. The outcomes suggested that the damaged areas of joints decrease with increasing shear rate, and the corresponding cumulative AE signals are inversely proportional to the shear rate. However, the above studies were only carried out under CNL boundary status. As aforementioned, the evolution of AE characteristics for the sheared joint under CNL condition has been widely studied. However, experimental tests that consider the effect of normal stiffness and shear rate on AE characteristics of joints have yet to be conducted. In the present study, a series of CNS direct shear tests were performed on artificial plaster joints. The effects of normal stiffness and shear rate on the mechanical behavior and AE characteristics of joints are discussed.
## 1 Introduction
### Research Problem and Questions  
A number of studies have surveyed the application of trust models in WSNs. For instance, [PERSON] et al. [19] explored IoT and RPL (routing protocol for low-power lossy networks) security. The authors analyzed the plausible attacks and suggested the use of trust models for routing protocol security. [PERSON] et al. [20] conducted a review of trust assessment models for WSNs as part of an IoT security framework. Trust management techniques were examined by [PERSON] et al. [21], who emphasized the potential of incorporating modern technologies like blockchain and machine learning (ML). [PERSON] et al. [22] focused on underwater WSN (UWSN) security and provided a classification of the security methods used. A recent review of trust models and approaches in IoT provides a managerial perspective on IoT security, including WSN security [23].  
In empirical research, studies have focused on specific DoS attack scenarios. For example, [PERSON] et al. [24] proposed a lightweight trust-based security system for preventing distributed DoS (DDoS) attacks affecting RPL. The energy-efficient trust management and routing system developed by [PERSON] et al. [4] mitigates selective forwarding and new flow attacks in SDWSNs. Additionally, most trust models are only evaluated through simulations, and their application and feasibility in real-world scenarios are unknown. Therefore, there is a need to develop a better understanding of the challenges facing the development of feasible implementation applicable to the real-world trust models that can effectively address a broad spectrum of DoS attacks and protect the integrity of the services provided by WSNs.  
This study seeks to establish a foundation for creating robust trust models that can identify and protect WSNs from a range of different types of DoS attacks by identifying the requirements for trust evidence gathering and the feasible trust evaluation approaches. This study examines the literature on trust models in WSNs to address the following research questions:  
RQ1: What types of DoS attacks in WSNs can be addressed using trust models?  
RQ2: What trust evidence is required to identify DoS attacks effectively?  
RQ3: What approaches can be used to extract the required trust evidence?  
RQ4: What methods for trust evaluation can be used?  
RQ5: What are the key challenges to applying trust models for the defense of WSNs against DoS attacks?
## 4 Results and Discussion
### Performance Analysis
#### 4.1.8 Performance Metrics Comparisons: (Accuracy, Precision, Recall, and Delay)

Table 4: Performance metrics comparisons with respect to neural networks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Detection Rate**} & \multicolumn{2}{c}{**Authentication Time**} & \multicolumn{2}{c}{**Delay**} \\ \hline \multirow{3}{*}{**\% of Attacks**} & \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**Methods**} \\ \cline{2-7}  & **ML-IDP** & **BSHNN** **(\%)** & **Mobile Users** & **ML-IDP** & **BSHNN** **(s)** & **Number of** **(s)** & **ML-IDP** & **BSHNN** \\ \hline
4 & 92.87 & 94.75 & 10 & 9.76 & 5.22 & 1 & 0.001 & 0.001 \\ \hline
8 & 93.23 & 95.18 & 20 & 10.25 & 6.14 & 2 & 0.04 & 0.05 \\ \hline
12 & 93.45 & 96.15 & 30 & 15.76 & 8.27 & 3 & 0.05 & 0.04 \\ \hline
16 & 93.89 & 96.44 &The significant achievement of BSHNN is attributed to the proposal of the mud ring algorithm as an optimized switch selection process. The very selection is far superior to conventional and popular [PERSON]'s algorithm. Thus, in Table 7, a comparison with respect to the performance attributes such as bandwidth, jitter, and delay has been presented. As can be readily observed, a sufficient improvement of bandwidth under a reduced jitter is presented by MRA. This is a significant achievement given that the success of BSHNN, as established in previous sections, heavily depends on the effectiveness of the MRA algorithm. MRA's dynamic switch selection is crucial for 5G's dynamic data flows, unlike [PERSON]'s deterministic approach, which lacks flexibility and is less responsive to real-time network changes. MRA's load balancing prevents flow table overloading, demonstrating its superior performance over [PERSON]'s algorithm [36], efficient resource use, and dynamic adaptability. The BSHNN method outperforms existing methods in all evaluation metrics, achieving higher detection rates, lower authentication times, and increased throughput. Its advanced neural network architecture accurately learns and classifies data patterns, improving authentication and data processing speeds. Its optimized processing and decision-making capabilities enhance network performance and security compared to ML-IDP, CNN, and SEE-BF methods. Table 8 shows the performance comparison of detection rate, authentication time, and throughput with existing methods such as ML-IDP, CNN, and SEE-BF approaches. The BSHNN algorithm implemented in the NS-3 network simulator shows significant improvements in intrusion detection for 5G SDN environments. It achieves a high detection rate of 96.41% and reduces authentication time to 16.2 s for 50 mobile users. It also ensures minimal delay and a high throughput of 428 Mbps, surpassing traditional methods of data transmission. BSHNN maintains data integrity and accurately identifies network events, with precision and recall values of 96.40% and 97.53%, respectively. Its integration with blockchain technology enhances decentralized authentication and authorization for secure 5G operations.
###### Abstract
The accuracy, recall, and specificity of the MobileNet algorithm were 84.6%, 82%, and 88.54%, respectively, for the testing set. Although the accuracy of MobileNet is lower than VGG16, its testing time was seven times faster. Therefore, MobileNet has the potential to be used for identifying damaged boxes in a ULS system and could be a reliable and efficient way to handle parcel boxes in metropolitan areas. This paper contributes to developing a more eco-friendly and efficient system for parcel delivery in urban areas, which could help address the challenges of increasing parcel volumes and environmental concerns. These are various research works that involve the application of machine learning algorithms in different areas. The first paper presents a new CoAP-IoT anomaly detection dataset for machine learning algorithm validation. The second paper introduces an effective motion-tracking scheme for machine-learning applications in noisy videos. The third paper uses a hybrid model of Swin Transformer and Cascade R-CNN, focusing on pine larva detection. The fourth paper proposes a YOLOv5-based intelligent parcel storage system for parcel classification and positioning. The fifth paper presents the design and analysis of a dual-band high-gain THz antenna array for THz space applications. The sixth paper proposes a dual-band wearable antenna design and SAR analysis for WLAN applications. The seventh paper introduces a novel feature selection method for IoT intrusion detection using machine learning. The eighth paper evaluates the performance of using the YOLO deep learning framework in the Nvidia Jetson platform. Lastly, the ninth paper focuses on the image classification of parcel boxes in the underground logistics system using CNN MobileNet. All these works aim to solve real-world problems by leveraging machine learning algorithms and techniques, making them valuable contributions to the field of artificial intelligence and its applications. However, there are still some challenges regarding the implementation of each research work as a future work. The authors declare no conflict of interest.
# Deep-Feature-Based Approach to Marine Debris Classification
###### Abstract  
The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, utilized as feature extractors according to three different extraction schemes for the identification and classification of underwater marine debris. We compare the performance of a neural network (NN) classifier trained on top of deep CNN feature extractors when the feature extractor is (1) fixed; (2) fine-tuned on the given task; (3) fixed during the first phase of training and fine-tuned afterward. In general, fine-tuning resulted in better-performing models but is much more computationally expensive. The overall best NN performance showed the fine-tuned Inception-ResNetV2 feature extractor with an accuracy of 91.40% and F1-score 92.08%, followed by fine-tuned InceptionV3 extractor. Furthermore, we analyze conventional ML classifiers' performance when trained on features extracted with deep CNNs. Finally, we show that replacing NN with a conventional ML classifier, such as support vector machine (SVM) or logistic regression (LR), can further enhance the classification performance on new data.  
deep learning; marine litter classification; feature vectors; transfer learning; computer vision +
Footnote †: journal: Applied Sciences
## 4 Data Processing
### Between-Class Comparison
#### 4.4.2 Chromatographic Alignment  
Chromographic alignment can significantly reduce errors that occur in across-classes analysis. Retention time locking technique is used to keep the similar retention time after the column replacement [186]. However, even within one study, when the hardware remains the same, because the instrumental conditions change within a long measurement sequence, retention times can vary to some degree among analyses. These retention time shifts usually result from degradation of the stationary phase, unstable carrier gas pressure, oven temperature program, and hardware maintenance. In large-scale studies, unstable chromatography may cause a peak to be detected at different retention times in different measurements. Oscillations of the MS detector can cause peak deconvolution differences on those peaks. Consequently, a peak has different retention times and is quantified in different ion channels. This can create problems for peak rescaling and peak area comparison within an omic study. Therefore, data alignment is a necessary step for cross-class analysis.  
This alignment is performed either before peak detection on pixel/tile level or after the peak detection on peak table level. In the former case, alignment is performed on the entire chromatogram. Various alignment algorithms are available including wraparound correction, such as the PARAFAC-based modulation time shift correction, the windowed rank minimization, and the indexing schemes cylindrical mapping [187; 188; 189]. The alignment can be achieved from a pixel/tile perspective. With binning and tiling schemes of the chromatogram, the overall size of the data files is reduced [191]. The computer can focus on processing those chromatographic fragments that have significance, thus saving overall data processing time [192]. In the peak table-based alignment process, all peaks detected in different positions or with different mass spectra throughout the processed chromatograms are assigned an identifier. This approach can be completed manually or automatically [193; 194].
# Two Polarization Comb Dynamics in VCSELs Subject to Optical Injection
## 3 Theoretical Bifurcation Analysis
### Influence of Spin-Flip Relaxation Rate  
Further insight into the two polarization comb dynamics is provided in Figure 12, where bifurcation diagrams are shown for fixed \(\Delta v_{x}=-9\) GHz when varying the injection strength together with the spin-flip relaxation rate \(\gamma_{s}\). The blue and red colors in Figure 12 correspond to the bifurcation diagrams of X-PM and Y-PM, respectively. When \(\gamma_{s}\) is small (Figure 12a), the two polarization modes are excited at very low injection strengths. Most importantly, the comb lines start to progressively appear in each polarization mode as injection strength increases. Interestingly, when we keep increasing the spin-flip relaxation rate \(\gamma_{s}\), the VCSEL output first shows the complex dynamics in the dominant polarization mode, which bifurcates to the excitation of the depressed polarization mode accompanied by comb generation, as shown in Figure 12b. The bifurcation sequence remains quite similar when further increasing the spin-flip relaxation rate. Still, the size of the complex dynamics region in X-PM increases, therefore resulting in a decrease in the size of the two combs region, as seen in Figure 12b-d. The size of the comb regions remains fixed at a high value of \(\gamma_{s}\), as seen in Figure 12c,d. Unlike the case of Figure 12a,b, where the two polarization combs take place smoothly, the two comb appearance is abrupt when the spin-flip relaxation rate is large in Figure 12c,d. The two polarization comb dynamics bifurcates to a single polarization comb at large injection strength for each bifurcation diagram. It is worth observing that cascade harmonics characterizes bifurcation to single polarization comb dynamics at low \(\gamma_{s}\). In contrast, the two complex polarization dynamics result in the single polarization comb at significant \(\gamma_{s}\). Once the single polarization mode operation is achieved in VCSEL, bifurcation scenarios remain similar whatever the value the spin-flip relaxation rate is. In addition to the spin-flip relaxation rate, we have checked that the two polarization comb dynamics are observed in a wide range of SFM parameters including linear dichroism (\(\gamma_{s}=-0.1\) ns\({}^{-1}\) to \(\gamma_{s}=-0.8\) ns\({}^{-1}\)), injection current (\(\mu=2.29\) to \(\mu=5.29\)), linewidth enhancement factor (\(\alpha=1\) to \(\alpha=5\)) and linear birefringence (\(\gamma_{p}=1\) GHz to several hundred of GHz).
# Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles
## 4 Stopping Distance and Braking Dynamics
### Mass of the Vehicle  
In this section, we present the effect of the mass of the vehicle on the braking distance. From [PERSON]'s Second Law of motion, the force (braking force since the vehicle concerned is already in motion) is the product of mass and acceleration (or, in this case, deceleration). The braking distance of a vehicle increases proportionally with its mass. Assume a force of 1500 N is applied by the brakes to stop a moving car with a mass of 750 Kg, the car will decelerate at 2 m/s\({}^{2}\), but if the same force (F = 1500 N) is applied to stop a moving car with  
Figure 21: Minimum allowed distance for a given speed of the following vehicle (**A**) to avoid a collision, assuming that the followed vehicle (**B**) stops suddenly.  
Figure 20: Maximum speed of the following vehicle (**A**) given the distance to avoid a collision, assuming that the followed vehicle (**B**) stops suddenly.  
a mass of 1500 Kg, it will decelerate at only 1 m/s\({}^{2}\). When the car's mass is doubled and the same braking force is applied, the car is now slowing down at half rate. The car with double mass will take twice as long to come to a complete halt.  
The braking distance (m) and mass of the vehicle (Kg) are described in Figure 22 at different speeds (km/h). At a given speed, the braking distance required for a vehicle to come to a complete halt varies directly with the mass of the vehicle. For example, a vehicle moving at 140 km/h can stop within a 50 m braking distance if it has a mass of less than 1000 Kg, whereas it requires around 375 m if it has a mass of 2000 Kg. A car moving with 20 km/h can halt at 50 m braking distance if its mass is 1500 Kg and needs only 25 m (half of the previous) to halt if it has 1250 Kg and so on.  
The speed of the vehicle (m) and the braking distance (m) at different masses of the vehicle (Kg) are presented as shown in Figure 23. At a given mass, the braking distance directly correlates with the speed. A lighter vehicle requires a shorter braking distance compared to a heavier vehicle moving at the same speed. For example, if a 100 m braking distance is given, a 500 Kg car will speed up to around 140 km/h and still be able to brake safely, but a heavier car (say 10,000 Kg) will only speed at around 100 km/h for it to avoid the collision.