esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Concurrency
#### 4.1.1 Test Pipeline  
We evaluate the concurrency tools of the REIP SDK using a video processing pipeline (Figure 4a) consisting of data acquisition with two camera blocks (5 MP, 14.4 fps) followed by real-time object and motion detection blocks, and the corresponding image writer block.  
Different variations of the video pipeline were executed on the NVIDIA Jetson Xavier NX platform (up to 6 CPUs with 8 GB of RAM and 384 CUDA cores) for 30 s, and the number of frames processed by each block were measured (Table 2). Object and motion  
Figure 4: Data processing pipelines used to benchmark REIP SDK (left) and comparison of different serialisation strategies. (**a**) Video processing pipeline for investigation of REIP SDK performance overhead in different configurations (Table 2). (**b**) Audio processing pipeline for comparison of the overall performance of REIP SDK on different platforms (Table 3). (**c**) Data throughput between two blocks as a function of buffer size and serialisation strategy for inter-process communication on Jetson Xavier NX. Pickle serialisation has the lowest overhead for small buffers whilst Plasma method provides the highest data throughput for larger buffers.  
detection blocks use the 'latest' connection strategy, while image writer blocks process every frame provided to them by object or motion detection blocks. The motion detection block is outputting one difference image for every two consecutive input frames. The object detection block is next overlaying the bounding boxes of the detected objects on every processed frame and outputs it to the image writer block. We also report the number of lost frames that have not been pulled by the camera block in time for scenarios were the system gets overloaded, as well as any frames still left in the input queues of the image writer blocks. For stereo configurations, object and motion detection blocks are alternating their input on every call of the _process_ function.
# HI-Sky: Hash Index-Based Skyline Query Processing
## 4 Performance Evaluation  
The results of the performance evaluation of HI-Sky are detailed in this section. The experimental environment is described first, and is followed by experimental results along with a description of the state-of-the-art methods used for comparison against our method.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 4 Results and Discussion
### Throughput Performance
#### 4.2.1 Node Throughput for AODV Routing Protocol  
To evaluate whether the observed differences in the protocol performance metrics are statistically significant, we conducted an ANOVA test at a 95% confidence interval. The results indicate that the protocol selection has a statistically significant impact on the throughput (\(p<0.05\)), end-to-end delay (\(p<0.01\)), and packet delivery ratio (\(p<0.05\)). This analysis confirms that the performance variations observed in our simulations are not random fluctuations but meaningful differences attributable to the design and behavior of the routing protocols under study. By incorporating statistical testing, we ensure that our conclusions are robust and applicable to real-world MANET deployments. The throughput performance of the laptop nodes utilizing the AODV routing protocol is illustrated in Figure 7. Throughout the simulation, the throughput stabilizes at approximately 1000 kbps, demonstrating the protocol's capacity to efficiently manage data transmission across heterogeneous nodes. The consistent performance across different laptop hosts highlights AODV's effectiveness in maintaining robust communication in varied network conditions.  
The throughput behavior of the mobile nodes for the AODV routing protocol is shown in Figure 8. The graph shows a gradual increase in throughput over time, eventually stabilizing between 30,000 and 40,000 kbps for most nodes. The variations in performance across the different nodes reflect the protocol's ability to manage varying mobility patterns and network dynamics, demonstrating its effectiveness in maintaining high throughput under heterogeneous conditions.  
Figure 7: AODV’s throughput for laptop nodes.  
AODV's throughput performance for fixed nodes is shown in Figure 9. Throughout the simulation, the throughput shows a steady increase, eventually reaching approximately 20,000 kbps for both fixed nodes. The higher throughput for Node 0 compared to Node 1 indicates the influence of the individual node positioning or link quality, emphasizing the importance of network topology in static MANET environments.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison
#### 6.3.2 End-to-End Delay  
Figure 8 shows the end-to-end delay of the proposed schemes and the existing WDFAD-DBR scheme. A-DBR overcomes the void hole problem by adjusting its transmission range, which results in minimum packet drop and reduced end-to-end delay. In the proposed scheme C-DBR, the network is split into clusters to minimize the transmission distance, which helps with reducing the end-to-end delay. The CHs aggregate the sensed information and forward it towards the sink. The proposed scheme CA-DBR avoids collision, which reduces the end-to-end delay. The proposed scheme B-DBR uses backward transmission when it finds a void hole to continue data forwarding.  
WDFAD-DBR has high end-to-end delay because it uses holding time, which increases its end-to-end delay. The proposed schemes outperform WDFAD-DBR, as void hole probability still exists in this protocol. The packet drop is due to void hole occurrence, which results in increased end-to-end delay in WDFAD-DBR.  
End-to-end delay of of RE-PBR is moderate as compared to the proposed schemes B-DBR and C-DBR, whereas almost the same with A-DBR. This is because the aforesaid techniques use backtracking and cluster-based approaches, which take time to process the route from source to destination, while the later proposed scheme has the mechanism of transmission power adjustment instead of alternate route, which enables it to reduce delay.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 4 Analysis of Eavesdropping Attacks in UASNs
### Eavesdropping Success Condition  
We now define the eavesdropping success condition that determines whether an eavesdropper can successfully wiretap the communication between a transmitter and a receiver. It is obvious that the eavesdropper can tap information implying that the eavesdropper can establish a link with a node. Thus, the eavesdropper can successfully tap information if and only if it fulfill Equation (13). Combining Equations (2), (12) and (13), we can have  
\[k\times 10\log d+d\times 10\log\alpha(f)+10\log N(f)-10\log AG\leq 10\log P_{t}- \Delta_{0} \tag{16}\]  
where \(AG=1\) when networks with an isotropic eavesdropper and \(AG=M\) when networks with an array eavesdropper. We observe that left-hand-side (LHS) of Equation (16) is an increasing function of the distance \(d\). If we let LHS of Equation (16) be equal to right-hand-side (RHS), we then can obtain the _maximum eavesdropping distance_\(d_{\max}\), within which an eavesdropper can wiretap a transmission successfully.  
Generally, it is _non-trivial_ to obtain the exact expression of \(d_{\max}\) since Equation (16) is a transcendental function of \(d\). However, we observe from Equation (16) that it is not difficult to obtain the numerical results of \(d_{\max}\) when the frequency \(f\), environment parameters (spreading factor \(k\), and wind speed \(w\)) and the transmission power \(P_{t}\) are given.  
Thus, we calculate \(d_{\max}\) with different frequency \(f\), \(k\) and \(w\) with the threshold of signal-to-noise \(\Delta_{0}=20\,\)dB [38] and \(P_{t}=100\) dB considering an isotropic eavesdropper and an array eavesdropper (\(M=10\)), respectively. The numerical results are presented in Figure 5. Specifically, we can see from Figure 5 that an isotropic eavesdropper (red dash lines) have higher \(d_{\max}\) than an array eavesdropper (blue solid lines). Besides, \(d_{\max}\) varies with different signal frequency \(f\) when environment parameters (spreading factor \(k\), and wind speed \(w\)) are fixed. Furthermore, as shown in Figure 5, when the spreading factor \(k\) is fixed, increasing wind speed \(w\) leads to decreasing \(d_{\max}\). This is the result of the higher noise when wind speed \(w\) is higher. It can also be seen from Figure 5 that with the fixed wind speed \(w\), \(d_{\max}\) decreases with the increased spreading factor \(k\). The reason is that increasing spreading factor \(k\) leads to higher absorption of the acoustic signal.
###### Abstract  
In order to investigate the compaction characteristics of graded crushed stone under repeated utilization, it is necessary to improve the utilization rate of road recycling crushed stone and maintain its strength and stability during recycling. In the present study, repeated compaction characteristic curves of graded crushed stone were developed using repeated compaction and screening of the graded crushed stone. The correlations between the fractal dimension of particle size distribution and repeated compaction times, water content, and dry density were analyzed. The experimental results indicate that both the maximum dry density and the optimal water content increase as the repeated compaction times increase. The fractal dimension of the particle size distribution of the graded crushed stone used in this test is 2.33 to 2.57. The obtained results show that as the fractal dimension increases, the maximum dry density of the graded crushed stone increases. At a constant repeated compaction time, the fractal dimension of the particle size distribution of graded crushed stone increased and then decreased as the water content increased. In summary, the compaction performance of the structural layer of graded crushed stone can be tuned by adjusting the fractal dimension of the particle size distribution of the recycled pavement structural layer in practical applications. Moreover, the optimal graded materials can be prepared under the guidance of the fractal dimension, thereby achieving an optimized working performance of the recycled pavement structural layer.  
Keywords:road engineering; fractal dimension; compaction test; graded crushed stone; dry density +
Footnote †: journal: Applied Sciences
## 1 Introduction
Obviously, the deviation of the noise model means the estimation error of the noise distribution, which will lead to the degradation of channel estimation and signal detection performance. Figure 1: Time-varying multipath channel with clustered-sparse structure estimated in a lake communication trial. To tackle the challenge of estimating the time-varying UWA channel with excessive delay spread and complex noise distribution, we adopted Bayesian learning to obtain the multipath and noise distribution characteristics of the UWA channel, based on which Bayesian channel estimation is derived to improve the performance of pilot-based channel estimation. The main contributions of this work are summarized as follows:  
* To estimate the time-varying multipath channel with colored noise, we propose a novel Bayesian learning-based channel estimation architecture for UWA-OFDM systems. Specifically, a clustered-sparse channel distribution model is constructed to characterize the delay power spectrum and temporal correlation of each cluster in the multipath channel, and a noise-resistant channel measurement model is constructed to reduce the noise disturbance. By learning the model hyperparameters, the Bayesian channel estimation based on the two models can be iteratively optimized. * To obtain the clustered-sparse distribution, we propose a partition-based clustered-sparse Bayesian learning (PB-CSBL) algorithm. Through the cluster partition, different clusters can learn different channel correlation coefficients, and thus the inter-cluster interference of the multipath channel can be suppressed. * To lessen the effect of strong colored noise, we propose a noise-corrected clustered-sparse channel estimation (NC-CSCE) algorithm. Based on the iterative symbol decision and noise correction, the more accurate hyperparameters of the models can be obtained, which can improve the accuracy of the Bayesian channel estimation. The rest of this paper is organized as follows. Section 2 discusses the related works. In Section 3, the system architecture and channel models are constructed. Then, the Bayesian learning-based clustered-sparse channel estimation method is designed in Section 4. In Section 5, the noise-corrected clustered-sparse channel estimation method is presented. The evaluation and result analysis are shown in Section 6. In Section 7, conclusions are given.
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 2 Materials and Methods
### IBS Deposition of ZnO-CF\({}_{x}\) Nanocomposites  
A \"customized\" dual-ion-beam system with ion-beam-assisted growth, already described elsewhere [3; 45], was used to deposit the zinc oxide-fluoropolymer (ZnO-CF\({}_{x}\)) nanocomposites. In order to improve the ZnO's stoichiometry, the deposition of all of the nanocomposites was assisted with a low energy ion beam fluxing O\({}_{2}\) at 3 sccm (standard cubic centimeters per minute) in the assistance gun. Growth rates (\(r\)) of each component of the nanocomposite material were separately evaluated by means of a quartz microbalance sensor placed close to the substrate. The film's thickness was 150 nm unless otherwise stated. Nanocomposites were prepared at three different ZnO volume fractions (\(\phi\) = 0.05, 0.10, 0.15) [45]. Details about \(\phi\) calculations are reported in Equation (S1).
# Digital Communication Forensics in 6G and beyond Networks
## 2 Literature Review
### Cloud Digital Forensics  
The difficulties that forensic investigators encounter when collecting evidence from the cloud computing environment are covered in [29]. The authors give a fictitious case study that looks at the importance of using both a partial and complete strategy for data collecting as well as how each approach impacts the time it takes to conduct a forensics investigation and the accuracy of the results. The investigative framework proposed by [PERSON] and [PERSON] is utilized to examine the crime covered in the case study. The study also offers statistical analysis findings to assist investigators in choosing the best techniques for evidence acquisition.  
The authors in [30] proposed a logging scheme called eCLASS, which preserves log confidentiality by taking into account edge-cloud properties when collecting logs from distributed edge nodes. The suggested scheme takes on issues with cloud forensics, like collecting user activity logs in virtualized and multitenancy environments. A security analysis and performance evaluation that demonstrate the effectiveness of the eCLASS system are also discussed.  
In order to combat cloud crime, it is now urgently necessary to develop powerful cloud computing systems digital forensics architectures and frameworks. A DDoS attack or DDoS malicious concept is used in this research to disrupt the targeted server or services by flooding their network resources with Internet traffic. DDoS attacks are efficient because they use a variety of ignored computers as attack traffic sources. Computers and other network resources, such as IoT devices, can be installed in the machines [31]. Table 1 provide a comparison of the discussed DF solutions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref. No** & **Type of Networks** & **Issues** & **Algorithm Used** & **Parameters/Metrics** \\ \hline \multirow{4}{*}{[22]} & Wireless local area & The main challenge in WLAN DF is & & & Effectiveness \\  & wireless local area & intercepting and preserving all mobile station & & & Filter control \\  & networks & communications to conduct a proper digital & & & Flow graphs \\  & & forensics investigation on them. & & & Integrity \\  & & & & & Transparency \\  & & framework in IoT and social systems & & technology & Traceability \\  & & Model of the digital forensics procedure for & & & Modeling \\
24 & Business networks & & mobile business devices & & PDDS & Business data \\  & & Smart technology & & & \\
25 & 5G HetNets & Consumer electronics at the periphery of 5G & & & \\  & HetNets using deep learning-enabled DF
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Print Quality Problems  
#### 3.3.1. Walls Not Touching  
Walls are either partially touching or not touching at all. If the walls do not fuse, the printer is most likely encountering under-extrusion due to a thin wall problem limited by the nozzle size, and/or a loose belt (Figure 11). Solutions include the following:  
1. Resolving the under-extrusion issue (refer to 3.2.1: Under-extrusion).
2. Ensure that the wall thickness is in relation to the size of the nozzle. The wall thickness should be a multiple of the diameter of the nozzle thickness. For example, a 0.4 mm nozzle should have a wall thickness of 0.4 mm, 0.8 mm or 0.12 mm. If a 0.10 mm wall is used, two perimeters walls of 0.4 mm, giving a total of 0.8 mm will be created. This results in a 0.2 mm gap between the walls.
3. Walls partially touching can also be due to a loose belt. Ensure that the belts are tightened.
# OTFS-Based Handover Triggering in UAV Networks
## 6 Mathematical Analysis of the Channel Estimation Error Effect
### LS-Based OTFS DD Channel Estimation  
LS is the simplest OTFS channel estimation technique, where LS is used to estimate the DD channel coefficients from received symbols corresponding to specific embedded pilots in the DD domain, as given in [27]. As a single pilot \(s_{DD}[l^{\prime},k^{\prime}]\) is diffused over the whole OTFS symbol in the DD domain, a single embedded pilot can be used for estimating the whole channel, as given in [27]. Thus, by setting \(s_{DD}[0,0]=1\) at \(l^{\prime}=k^{\prime}=0\), and multiplying both sides of (22) by \(MN\), \(\hat{h}_{DD}[l,k]\) can be written as follows [27]:  
\[\hat{h}_{DD}[l,k]=h_{DD}[l,k]+G_{DD}[l,k], \tag{40}\]  
Using (39), \(MSE_{\mathcal{H},LS}\) can be evaluated as follows:  
\[MSE_{\mathcal{H},LS}=\frac{1}{MN}{\sum_{l=0}^{M-1}{\sum_{K=0}^{N-1}}}{\left|G _{DD}[l,k]\right|^{2}}, \tag{41}\]  
which results in the following:  
\[MSE_{\mathcal{H},LS}=\frac{1}{N}{\sigma_{g}^{2}} \tag{42}\]  
The detailed derivation of (42) can be found in Appendix A.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 2 Related Work
### Conventional Machine Learning Methods  
In the early era of artificial intelligence, researchers employed conventional machine learning methods due to a lack of data availability and computational resources. For instance, [PERSON] et al. [18] presented a real-time mobile-enabled healthcare system called the m-skin doctor for skin cancer detection. The system uses computer vision and image processing techniques including Gaussian filter noise removal, grab-cut algorithm segmentation, and a support vector machine (SVM) for classification. The system achieved 80% sensitivity and 75% specificity; however, its limited sensitivity and specificity rates restrict its real-world implementation. [PERSON] and [PERSON] [19] presented an adaptive machine with an ensemble SVM skin cancer classification. This ensemble model achieved a higher performance with reduced inference speed and cost. Their model was evaluated using the PAD-UFFS-20 dataset. [PERSON] [20] proposed an automated skin lesion segmentation approach for dermatologists that used dermoscopic imaging to detect and classify skin lesions. This approach involves two steps: preprocessing and segmentation. In the preprocessing step, unnecessary detail noise is removed, while the GrabCut algorithm is used for segmentation.  
The k-means clustering algorithm and color features from the training images improve the boundaries of the segments. This method enhances the diagnostic capability of dermatologists and aids in the early detection and classification of skin cancers. [PERSON] et al. [21] proposed a deep-learning-based system for the early detection of melanoma lesions. The system uses clinical images, preprocessed to reduce illumination and noise effects, and a pre-trained CNN to distinguish between melanoma and benign cases. The experimental results of the developed system outperformed state-of-the-art methods in diagnostic accuracy, making it a valuable tool for dermatologists in the early diagnosis of melanoma. Initially, conventional methods were considered milestones in computer-vision-based methods for skin cancer detection. However, conventional methods require handcrafted feature engineering, exhibit limited performance, have high false rates, and require domain experts.
## 4 Experimental Results
### Comparison with Other Traditional Methods
_Mon_ is calculated as follows:  
\[Mon=\ \left|\frac{No.\ of\ df>0}{N-1}-\frac{No.of\ df<0}{N-1}\right|df=\ \frac{HI_{i+1}-HI_{i}}{i}\quad 1 \leq i\leq N \tag{13}\]  
where \(df\) is the difference in the values of any two adjacent points in \(HI\) curve and \(N\) is the total number of \(HI\) values. _Rob_ reflects the tolerance of the \(HI\) to random fluctuations, which may arise due to faulty sensors, variations in operating conditions, or unexpected events. _Rob_ is defined as  
\[Rob=\ \frac{1}{N}\sum_{i=1}^{N}\exp\left(-\left|\frac{HI_{i}-HI_{i}^{T}}{HI_{i} }\right|\right) \tag{14}\]  
where \(HI_{i}^{T}\) is the mean trend value of the \(HI\). Figure 11: Constructed \(HI\) in various methods: (**a**) root mean square (RMS), (**b**) variance, (**c**) approximate entropy (ApEn), (**d**) kurtosis, (**e**) skewness, and (**f**) EMS-SVD-K-medoids. Similarly, _Corr_ measures the degree of linear correlation between the _HI_ and time. It is expected that a good _HI_ gradually increases by time. In a strong positive correlation, the _Corr_ value is close to 1 and vice versa. _Corr_ is defined as  
\[Corr=\ \frac{\left|\sum_{i=1}^{N}\left(Hl_{i}-\overline{HI}\right)\left(i- \left(\sum_{i=1}^{N}\frac{i}{N}\right)\right)\right|}{\sqrt{\sum_{i=1}^{N} \left(Hl_{i}-\overline{HI}\right)^{2}\sum_{i=1}^{N}\left(i-\left(\sum_{i=1}^{N }\frac{i}{N}\right)\right)^{2}}}\ \ \ 1\leq i\leq N \tag{15}\]  
Here, \(\overline{HI}\) is the mean value of all the _HI_ values. Table 2 presents _Mon_, _Rob_, and _Corr_ values for the proposed _HI_ and the traditional health indicators mentioned earlier. The results in Table 2 show that all _Mon_, _Rob_, and _Corr_ of the current model are higher than those in other models. The obtained results demonstrate that the proposed model is superior to other models, and it yields a better _HI_.