esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent decades, memory-intensive applications have experienced a boom, e.g., machine learning, natural language processing (NLP), and big data analytics. Such applications often experience out-of-memory (OOM) errors, which cause unexpected processes to exit without warning, resulting in negative effects on a system's performance and stability. To mitigate OOM errors, many operating systems implement memory compression (e.g., Linux's ZRAM) to provide flexible and larger memory space. However, these schemes incur two problems: (1) high-compression algorithms consume significant CPU resources, which inevitably degrades application performance; and (2) compromised compression algorithms with low latency and low compression ratios result in insignificant increases in memory space. In this paper, we propose QZRAM, which achieves a high-compression-ratio algorithm without high computing consumption through the integration of QAT (an ASIC accelerator) into ZRAM. To enhance hardware and software collaboration, a page-based parallel write module is introduced to serve as a more efficient request processing flow. More importantly, a QAT offloading module is introduced to asynchronously offload compression to the QAT accelerator, reducing CPU computing resource consumption and addressing two challenges: long CPU idle time and low usage of the QAT unit. The comprehensive evaluation validates that QZRAM can reduce CPU resources by up to 49.2% for the FIO micro-benchmark, increase memory space (1.66\(\times\)) compared to ZRAM, and alleviate the memory overflow phenomenon of the Redis benchmark.  
memory compression; memory-intensive application; NLP; OOM; QAT Article
## 3 Results
### Mechanical Strength
In the view of the flexural strength results of all the composites, the composite type with 0.3% MWNT and 5% NS (corresponding to a strength of 8.7 MPa) achieved the largest enhancement of 52% compared to the control with no MWNTs and NS. This signified that a prominent enhancement in the flexural strength could be accomplished by the simultaneous utilization of MWNTs and NS. The enhancements of the compressive and flexural strengths were achieved by incorporation of the appropriate amounts of MWNTs and NS, but additions of MWNTs and NS were detrimental to the mechanical strength of the composites when both nanomaterials were excessively utilized.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.9 Residual Attention UNet  
In 2020, [PERSON] et al. developed a 3D hybrid deep attention-aware network to extract liver/tumors from CT scans, utilizing a modified residual attention U-Net (RA-UNet). This approach integrated U-Net's ability to capture multiscale attention information, merging low-level and high-level features. They introduced the attention residual mechanism to medical imaging, optimizing the model with fewer parameters than typical approaches. Their work expanded the U-Net framework to include 3D liver/tumor segmentation tasks. The system featured a two-stage process: initial liver localization by RA-UNet-I, and precise liver segmentation and tumor lesion identification by RA-UNet-II. The model was trained on both the LiTS and the 3 DIRCADb datasets. The training process is notably time-intensive, primarily due to the use of 3D convolutions. Their result presented a Dice score of 0.595 and 0.830 for tumor segmentation on LiTS and 3 DIRCADb datasets, respectively [20].
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 5 Analysis and Discussion of Experimental Results
### Cost Effectiveness  
IWT scheme permits fast implementation of wavelet transforms with half calculation time as compared to standard hiding techniques. IWT works on integer numbers so takes very less time to compute the coefficients. It analyzes the input signal at different frequencies with varying resolutions, known as multi-resolution analysis. It also allows reversible IWT as compared to conventional DWT which presents errors because of floating-point processes. It can efficiently reconstruct the original signal because of lossless compression and its integer nature. It analyzes discontinuity and sharp spikes in the signal. It works equally in the time and frequency domains, and in the time domain discontinuities, can be analysed easily. It is cost efficient because of its integer nature and simpler hardware requirements.
## 3 Results  
Five different metrics for evaluation were used so the results can be easily compared to related works. All metrics measured are compared to masks labelled by human expert which are used as ground truth. The first used metrics is pixel accuracy, its equation is (1) where \(N_{\text{TP}}\) stands for true positive pixels or voxels, \(N_{\text{TN}}\) true negative, \(N_{\text{FP}}\) false positive and \(N_{\text{FN}}\) false negative.  
\[A_{P}(X,Y)=\frac{N_{\text{TP}}+N_{\text{TN}}}{N_{\text{TP}}+N_{\text{TN}}+N_{ \text{FP}}+N_{\text{FN}}} \tag{1}\]\[D(X,Y)=\frac{2*|X\cap Y|}{|X|+|Y|} \tag{2}\]  
\[I_{oU}(X,Y)=\frac{|X\cap Y|}{|X\cup Y|}=\frac{|X\cap Y|}{|X|+|Y|-|X\cap Y|} \tag{3}\]  
Dice coefficient [25] is expressed by Equation (2) and intersection over union [26], also known as Jaccard index, is expressed by Equation (3). \(X\) and \(Y\) stand for a set of positive pixels/voxels on first and second compared mask. We used Visceral segmentation tool [26] for computing the results in all metrics.  
The training of the segmentation neural networks used in this paper is relatively computationally demanding. For this reason, we compare the resulting architectures also from the point of view of computational time needed for training and computational time needed for prediction. Results can be seen in Table 3. As it is obvious from the table, the computational time increases with the complexity of the model and image data resolution, on the other hand it is in all the cases below one minute during application of the model to data.
## 2 Background
### Model Evaluation Indicators
#### 2.4.2 ROC Curves  
Receiver Operator Characteristic Curve is a comprehensive indicator of the continuous variable of sensitivity and specificity. It is used as a graph to show the relationship between two degrees. The vertical coordinate of the curve is the true positive rate, or so called sensitivity:  
\[TPR=TP/(TP+FN) \tag{3}\]while the horizon coordinate of the curve is the false positive rate, or so called 1-specificity:  
\[FPR=FP/(TN+FP) \tag{4}\]  
In medical research, the ROC curve can easily visualize the ability to recognize diseases at any threshold, and can select the optimal diagnostic threshold. It can be used to compare the ability of two or more different diagnostic tests to identify diseases. AUC (Area under Curve) is the area under the ROC curve, between 0.1 and 1. AUC as a numerical value can intuitively evaluate the quality of the prediction model, and the more the AUC value tends to 1 the better the prediction effect is.
# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 4 Experimental Analysis
### Experiments  
#### 4.3.1 Ablation Study  
To validate the detection improvement effect of the above methods, experiments based on the YOLOv7 network structure were conducted using the UA-DETRAC dataset.  
We trained four different models and analyzed the effects of different combinations of improvements. The experimental results are presented in Table 1. After integration of the CBAM attention mechanism, mAP increased by 1.7%. This suggests that the addition of channel and spatial attention modules can generate more specific features. However, the increase in computational complexity resulted in a decrease of 21 FPS in the output. After replacing the ELAN structure with the CLAN structure, the mAP increased further by 0.1% and the FPS increased by 14, demonstrating that this structure can significantly reduce computational complexity without significantly affecting model accuracy. Finally, substituting the CIoU with the WIoU v3 resulted in a 3% increase in mAP with only a 7 decrease in FPS compared to the original YOLOv7.  
We also compared the performance of the original model and the enhanced model under different weather and lighting conditions, as well as their precision, recall, number of parameters, and computational complexity. The results, as shown in Table 2, demonstrate that ETS-YOLOv7 reduces the number of parameters by approximately 14% and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CBAM** & **CLAN** & **WIoU-v3** & **[EMAIL_ADDRESS]:95(\%)** & **FPS** \\ \hline  & & & 87.2 & 156 \\ \(\surd\) & & & 88.9 & 135 \\ \(\surd\) & \(\surd\) & & 89.0 & 149 \\ \(\surd\) & \(\surd\) & \(\surd\) & 90.2 & 149 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact of different components on detection effectiveness.  
Figure 5: Results of dataset distribution visualization. (**a**) Distribution of different weather; (**b**) distribution of different detection targets.  
the computational complexity by about 23%, while improving both precision and recall. Under four different weather and lighting conditions, the mAP of ETS-YOLOv7 consistently outperforms that of the original model, with the most significant improvements observed under cloudy and sunny conditions, showing increases of 4.4% and 3.6%, respectively. This experiment demonstrates that, regardless of image blurring caused by windy weather or brightness changes due to lighting, the proposed ETS-YOLOv7 can effectively detect and classify vehicle targets, showing robustness to varying lighting intensity and weather conditions.
# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 4 Results
### Discussion  
In this study, the proposed deep learning framework primarily comprises three important steps, including RoI segmentation, selection of most important features, and classification of AD classes. The fuzzy-based superpixel-clustering algorithm effectively adapts to variations in the contrast and intensity of sMRIs. This adaptability is vital in capturing subtle transitions between diseased and healthy regions, which have overlapping properties. After the segmentation of RoI regions, hybrid feature extraction (combination of PLTP, ResNet-50, and VGG-16 models) is accomplished to capture features from segmented RoI images. Furthermore, the dimensions of the extracted features are decreased by introducing the MGTO algorithm.  
As denoted in Table 7, the selection of most informative features by the MGTO algorithm decreased the processing time of this framework on all three datasets (ADNI, real time, and AIBL). In comparison to the conventional deep learning models, the proposed MGTO-CapsNet model required a minimal processing time of 42.16, 60.33, and 32.55 s on the ADNI, real-time, and AIBL datasets. The selected informative features are finally passed as input into the CapsNet model for classifying the types of AD classes. The superiority of the fuzzy-based superpixel-clustering algorithm and MGTO-CapsNet model is demonstrated in Tables 1-4.
## 3 Probabilistic Seismic Demand Model and Definition of Limit States
### Definition of Limit States
For the example gantry crane, the fundamental period was around 0.56 s. Therefore, pushover analysis was utilized in this study to identify the limit states of the gantry crane. According to the previous description, five groups of thresholds are shown in Table 2 and the cooked data calculated by Equation (5) are also shown in the last three rows of the same table. The last column shows the thresholds of the deflection ratio. Though they are described by SA, DC, and CP, respectively, they just represent three different working grades, i.e., A7, A6, and A5, respectively. If the deflection ratio exceeds 0.143, the trolley on the girders will suffer from derailment and cannot operate normally. Figure 7 shows the curve developed by pushover analysis for the midspan of the trolley girder. Three limit states are shown in Figure 7. The first limit state, serviceability (SA), is described as \begin{table}
\begin{tabular}{l l l l l l} \hline
**Location** & **Joint** & **Midspan** & **Flexible Leg** & **Rigid Leg** & **DR** \\ \hline Direction & X & & & Y & \\ SA (m) & 0.055 & 0.104 & 0.104 & 0.093 & 0.035(A7) \\ DC (m) & 0.144 & 0.171 & 0.177 & 0.177 & 0.044(A6) \\ CP (m) & 0.513 & 0.555 & 0.981 & 0.926 & 0.050(A5) \\ \hline SA (\%) & 0.368 & 0.297 & 1.742 & 1.552 & 0.100(A7) \\ DC (\%) & 0.958 & 0.488 & 2.958 & 2.952 & 0.125(A6) \\ CP (\%) & 3.422 & 1.586 & 16.358 & 15.436 & 0.143(A5) \\ \hline \end{tabular}
\end{table} 
Table 2: Thresholds in different limit states. DR, deflection ratio; SA, serviceability; DC, damage control; CP, collapse prevention. the state in which the gantry crane behaves linearly and can operate with a little repair after the earthquake. Therefore, 0.297% was taken as the threshold of SA, since it is the end point of the elastic stage. The second limit state is \"damage control\", and the state means that the gantry crane has suffered significant damage, but has not collapsed. Since the gantry crane is a special equipment with high security requirements, the value of 0.488% was conservatively chosen as the threshold. The third state called \"collapse prevention\" represents the state in which the gantry crane begins to collapse. The value of 1.586% was chosen as the threshold for collapse prevention since the slope of the pushover curve is negative after that point. In summary, the thresholds of 0.297%, 0.488%, and 1.586 % were adopted for the SA, DC, and CP of the midspan of the trolley girder in this research, respectively. Besides, the other three groups of limit states were defined by this method, respectively.
## 2 Materials and Methods
### Field Test Set-up  
Field measurements were conducted in a poultry (560 m\({}^{2}\), containing 3056 finisher broilers) and a pig barn (57 m\({}^{2}\), containing 48 finisher pigs) at Flanders Research Institute for Agriculture, Fisheries, and Food (ILVO, Merelbeke, Belgium). Polytetrafluoroethylene (PTFE) tubes of 4 mm inner-diameter were used to transport air samples from the sampling locations to measuring devices. A two-micron PTFE particle filter (Model SP53-T-2T, M&C TechGroup GmbH, Ratingen, Germany) was equipped at the inlet of each tube.  
In the poultry barn, one sampling tube was allocated to each measurement device. The tube inlets were tightened closely together one metre above the litter. A total of ten measurement trials were carried out on the fifth and fourth day before slaughter, and three other trials on the second day after removing all chickens from the barn. Axetris measurements were unavailable during the poultry barn test due to a technical issue. In the pig barn, in total seventeen measurement trials were performed during the first two days after removing the pigs for slaughter. The air samples were taken at a ventilation exhaust 3.2 m above the floor. Two sampling tubes were installed between the measurement point and the measuring devices. T-shaped tube connectors were used at the end of the sampling tubes to split the gas stream. One sampling tube was shared by INNOVA, Picarro, and Gasmet, and the other by Rosemount, Axetris, and the reference method.
# Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention
## 5 Evaluation
### Simulations with Synthetic Workload
In fact, in systems at higher loading, fixed-priority scheduling techniques cannot guarantee all the deadlines, unlike other algorithms, such as Earliest Deadline First [30; 33]. The WFDU allocator is the one that provided better schedulability rates than any other of the proposed algorithms. The FFDU allocator in combination with DM is the algorithm with the worst schedulability rates. The peaks of the function imply scenarios with lower-interference factors, so the schedulability rates are higher. Figure 8 depicts the percentage of tasks that passed the schedulability test over those tasks that were feasibly scheduled. Obviously, if the task set is not schedulable, it makes no sense to apply the schedulability test. As seen in this figure, it is evident that as the number of cores in the system increases, a greater number of tasks fail the schedulability test. This occurs because the definition of \(WCRT^{k(\text{th})}\) is an upper bound, and it is overestimated as the number of broadcasting tasks and the coefficient of interference increase. For FF scenarios, as the schedulability ratio was zero from systems with four cores (from scenario 4 onwards), the schedulability test could not be applied (0 in Figure 8) The WF allocator is the one with which the test provided better results. It is important to note that there is no set that passes the schedulability test and is not schedulable. However, there are task sets that fail the test and are schedulable. Then, the proposed test is sufficient but not necessary. Figure 7: Schedulability ratio with DM and different allocators.
## 2 Mechanical Model and Dynamics Equations  
### Blade-Damper Model  
In accordance with Figure 1, the vibration of the first bending mode of the blade in \(y\) direction is considered in this paper. The vibrations of the blade in the \(x\) and \(z\) directions will be neglected, as they are higher order modes and very small relative to the displacement in the \(y\) direction. The blade is taken as the moving body; the bladed disc is the moving coordinate system. The convective motion, the motion of the moving coordinate system relative to the static coordinate system, is the bladed disc's rotation about a fixed axis. The motions of the blade and damper relative to the bladed disc can be approximated as the linear motion along the \(y\) direction. In Figure 1, \(\overset{\rightarrow}{\omega}\) is the angular velocity of the bladed disc; \(\overset{\rightarrow}{\alpha}\left(\overset{\rightarrow}{\alpha}=d\overset{ \rightarrow}{\omega}/dt=\overset{\rightarrow}{\omega}\right)\) is the angular acceleration. The absolute motion of the blade consists of rigid body rotation and vibration relative to the bladed disc. It is assumed that the rigid damper is mounted on an elastic beam and that there is no friction between the damper and the elastic beam. When the bladed disc is rotating, the damper is pressed on the platform by the normal pressure which contains the centrifugal force, Coriolis inertia force and the component of the damper's gravity. The damper and the platform are not separated during the whole process. The sizes of the damper and the blade are ignored for approximate calculation. Based on the above assumption, the structure of under-platform damper (Figure 1) is simplified as a spring-mass model, which is shown in Figure 2.  
Figure 1: Structure of blade with under-platform damper.  
Two local coordinate systems parallel to the \(\alpha vyz\) coordinate system are defined at the platform and the damper, and are stationary relative to the bladed disc. Displacement of the blade relative to the corresponding local coordinate system is \(y_{1}\), while relative displacement of the damper is \(y_{2}\), both of which are in the \(y\) direction. The equivalent mass of the blade is \(m_{1}\). The total equivalent mass of the damper and the elastic beam given by the energy method is \(m_{2}\). The vibrational stiffnesses of the blade and damper in the \(y\) direction are \(k_{1}\) and \(k_{2}\). The linear damping coefficients of the blade and damper in the \(y\) direction are \(c_{1}\) and \(c_{2}\overset{\rightarrow}{Q_{1}}\) is aerodynamic excitation force. The dry friction force between the damper and the platform is \(\overset{\rightarrow}{f}_{1}\), and its reaction force is \(\overset{\rightarrow}{f}_{1}\)'.(\(\overset{\rightarrow}{f}_{1}=-\overset{\rightarrow}{f}_{1}\)', in \(y\) direction).
# Using Connected Vehicle Data to Evaluate National Trip Trends
## 3 CV Data  
Approximately 500 billion records of December 2022 CV trajectory data for the United States were used for this study. This data was available at 1-3 s frequency and approximately 3-m spatial accuracy. Each CV waypoint had an associated anonymized journey identifier, geolocation, timestamp, speed, heading, ignition status, and vehicle classification code attribute, enabling the identification of a waypoint as an EV, HV, or ICEV waypoint. These attributes together help generate aggregated records, one per CV journey, as outlined by the procedures in the following section. CV waypoints with a missing vehicle classification code value were excluded from the analysis for consistency.
## 4 Experimental Results
### Keyframe Detection Results  
Keyframe detection was performed using the ResNet101v2 transfer learning algorithm. In order to determine the classification success of the dataset divided into two as \"keyframe\" and \"non-keyframe\", the accuracy, precision, recall, and f1-score were calculated. The results of the calculated model evaluation criteria are presented in Table 5.  
Keyframes were extracted from the videos using the weight file obtained as a result of transfer learning. The number of original frames in each video (Nf), the number of frames extracted after the model (Nk), the number of missed keyframes (Nm), the number of correctly extracted keyframes (Na), video compression ratio (CR), computation time, and the evaluation results of the keyframe extraction method are presented in Table 6.
## 3 Results
### Biochar Adsorption Mechanisms Study  
#### 3.2.1. \(\text{NH}_{4}\)+ Biochar Adsorption Capacity  
The effect of the initial concentration of N-NH4+ (C0) on the biochar equilibrium adsorption capacity (Qe) is reported in Figure 4. During the procedure, the pH of the solutions decreased from 6.3 to 5.7 as the N-NH4+ concentration increased from 5 to 100 mg/L. Biochar addition caused the increase of pH to values higher than 7.5. As shown, the adsorption capacity increased linearly as C0 increased, indicating that the saturation of the adsorption sites was not achieved in the investigated C0 range.