esa-sceva/satcom-chunk-collection huggingface dataset:
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 7 Numerical Results
### Basic P2P Network  
These results were obtained using the parameters and values presented earlier. Specifically, \(c=0.02\) (files/s), \(\lambda=1\) (users/s), \(\mu=0.00125\) (files/s), \(\theta=0.01\) (leecher departures per sec), \(\gamma=0.01\) (seed departures per sec), and \(\eta=0.85\). In Figure 8, we can see the number of peers (both leechers and seeds) who go through an initialization phase during which the system is found in a transitory mode where there are few peers, especially seeds, and the file sharing is very inefficient due to the lack of resources. As time passes, the system enters a stable state where the number of peers varies around a clear average value. This is because the number of resources is sufficiently high, and most leechers find either a leecher that can share the missing chunks or seeds that remain in the system. Note that the number of peers continues to vary throughout the realization of the experiment, but it clearly does so around a fixed value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Description** & **Units** \\ \hline K & \(1/10\) & File chunks & size/chunks \\ \hline \(X_{i}\) & - & Infected leechers & peers \\ \hline \(Y_{i}\) & - & Infected seeds & peers \\ \hline \(N_{i}\) & 10 &
\begin{tabular}{c} Initial number of \\ infected seeds \\ \end{tabular} & peers \\ \hline \hline \end{tabular}
\end{table}
Table 3: Added parameters to the chain with infected nodes.  
For this experiment, the average number of leechers obtained was 100, and the average number of seeds was 16, as shown in Table 4.
## 2 Results
### Changes in NMDA Receptors Subunits and NOS Activities Related to Acute SD (Experiment II)  
NOS signalling is known to be a key regulator of the sleep, while NMDA receptors-dependent mechanisms have been implicated in global regulation of the sleep-wake cycle and mechanisms of memory consolidation and brain aging. We set to investigate the impact of age and acute SD on NR1, NR2A, and NR2B expression in the frontal cortex (Figure 4A-C) and NOS isoforms activity in the R and L parietal cortex separately (Figure 5A-C). Table 1 presents indexes of laterality for NOS isoform activities. As can be readily seen, there was a considerable age-dependent reduction of the expression levels of all NMDA receptor subunits tested, with statistically significant differences in all three sets of data (the decrease to 95.5% in NR1, to 92.6% in NR2A, and finally to 96.2% in NR2B). No changes were observed in either young adult or old groups exposed to acute SD (Figure 4A-C).  
In a similar way, assessments and cross-comparison of NOS isoforms in animals of different age groups with and without exposure to acute SD showed considerable age-dependent decrease but only in nNOS (the bilateral drops to 48.8% and 41.6%) and eNOS activity (the significant drop to 58.7% in the L side, that with borderline significance to 65.1% in the R side), with the activity levels of iNOS remaining stable across different age groups (Figure 5A-C). In young rats exposed to acute SD, no changes in the activity of NOS isoforms were found when compared to age-related controls. However, results of ANOVA with repeated measures and of t-test (but not of one-way ANOVA) supported the significant change in iNOS activity in the R side of old rats exposed to acute SD when compared to age-matched controls. Accordingly, lateralization index analysis revealed mild asymmetry in all NOS isoforms in young adult control rats (Table 1). Results of correlation analysis performed on data of experiment II did not support significant changes in young or old rats exposed to acute SD when compared to corresponding age-related controls.  
Figure 4: Expressions of NMDA receptor subunits in the frontal cortex from both hemispheres in young adult and old rats exposed to acute sleep deprivation. Optical density of samples respective NMDA subunit bands (**A–C**) related to that of \(\alpha\)-tubulin; results are presented as means \(\pm\) SEM. Statistical significance (Student’s \(t\)-test) was calculated with respect to young adult controls (*** \(p<0.001\)). SD—sleep deprivation, YCTRL—young adult controls (\(n=12\)), YSD—young adult rats exposed to acute SD (\(n=12\)), OCTRL—old controls (\(n=12\)), OSD—old rats exposed to acute SD (\(n=12\)).
## 4 Experimental Results and Discussion
### Comparison of Results with Previous Works
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Acc.**} & \multicolumn{3}{c}{**Gyro.**} & \multicolumn{3}{c}{**Acc.+ Gyro.**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Accuracy (\%) (\(\pm\)SD (\%))**} & **Loss** & **F1-Score (\%)** (\%)** & **Accuracy (\%) (\(\pm\)SD (\%))** & **Loss** & **F1-Score (\%)** (\%) (\(\pm\)SD (\%))** & **Accuracy (\%) (\(\pm\)SD (\%))** & **Loss** & **F1-Score (\%)** (\(\pm\)SD (\%))** \\ \hline ResNet & 98.60\% & 0.057 & 94.33\% & 91.48\% & 0.751 & 86.73\% & 98.84\% & 0.058 & 95.05\% \\  & (\(\pm\)0.11\%) & (\(\pm\)0.007) & (\(\pm\)0.46\%) & (\(\pm\)9.36\%) & (\(\pm\)1.184) & (\(\pm\)5.69\%) & (\(\pm\)0.16\%) & (\(\pm\)0.014) & (\(\pm\)0.42\%) \\ \hline ResNetSE & 98.65\% & 0.040 & 94.34\% & 96.93\% & 0.133 & 90.65\% & 98.90\% & 0.052 & 95.49\% \\  & (\(\pm\)0.18\%) & (\(\pm\)0.009) & (\(\pm\)0.90\%) & (\(\pm\)0.33\%) & (\(\pm\)0.019) & (\(\pm\)0.54\%) & (\(\pm\)0.09\%) & (\(\pm\)0.004) & (\(\pm\)0.52\%) \\ \hline ResNeAt & 98.12\% & 0.070 & 92.49\% & 94.98\% & 0.225 & 84.65\% & 98.64\% & 0.072 & 94.53\% \\  & (\(\pm\)0.31\%) & (\(\pm\)0.009) & (\(\pm\)1.12\%) & (\(\pm\)0.46\%) & (\(\pm\)0.038) & (\(\pm\)1.59\%) & (\(\pm\)0.16\%) & (\(\pm\)0.006) & (\(\pm\)0.41\%) \\ \hline SEResNet-BiGRU & 98.79\% & 0.021 & 95.03\% & 97.08\% & 0.108 & 89.86\% & 98.92\% & 0.048 & 95.71\% \\  & (\(\pm\)0.11\%) & (\(\pm\)0.004) & (\(\pm\)0.89\%) & (\(\pm\)0.16\%) & (\(\pm\)0.029) & (\(\pm\)0.67\%) & (\(\pm\)0.09\%) & (\(\pm\)0.012) & (\(\pm\)0.43\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recognition effectiveness of DL models on the MobiAct v2.0 dataset by using different sensor data.
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 1 Introduction
### Metamodeling  
When a good balance is struck between efficient exploration of the input space (\"learning\" the behavior of the system) and focusing on potential areas of optimization interest, a metamodel allows to minimize the use of any costly simulation model evaluations, as described in the work of [22]. Additionally, techniques reviewed by [23] such as variable screening, dimensionality reduction, and use of multiple metamodels can further extend the legs of metamodels, increasing the number of scenarios where they can be profitably employed.  
Similar to simulation optimization, methodologies employing metamodels and comparisons between them exist [24]. However, the points of comparison such as between setups, problem types and domains, or specifics of the simulation models used differ between the observed case studies, making it hard to draw fully generalizable conclusions regarding performance or cost characteristics.  
Of course, some domains have long-established and well-explored metamodels. For example, the domain of energy prediction models [25] extends standard surrogate energy prediction models by incorporating household preferences into the underlying simulation model and using an artificial neural network metamodel. In engineering designs, kringing regression is popular, as demonstrated in applications such as vacuum/pressure swing adsorption systems [26]. Polynomial-based surrogates are also seeing advancements in engineering applications [27]. These are not the only options, as metamodeling does not have a \"one size fits all\" methodology. Technique selection comes with various trade-offs. Ref. [28] compares a number of these trade-offs for the scenario of building design optimization while [29; 30] do the same for the water resources modeling case.  
There is a growing number of studies on deep learning to fully exploit the power of neural networks. Deep learning has been applied to many real-world problems successfully, however, two main issues, among others, raised by [31] are relevant to consider. First of all, deep learning techniques require a large amount of data. As one of the goals of this work is a methodology that minimizes the use of expensive simulation model evaluation, gathering this data to train a deep learning system would not be ideal. Secondly, deep learning models can have high memory and computational requirements. As a result, while deep learning can provide a high level of flexibility, when simpler metamodels, where the best is selected from a variety of options, can achieve good final Pareto front performance, the approach that requires less data and incurs lower computational costs might be more appropriate.
## 4 Field Tests and Analyses
### Field Tests
#### 4.2.1 Cutting Test  
The experiment used electronic detonators and an uncoupled continuous charge method. The explosive employed was a No. 2 rock emulsion explosive, with a diameter of 42 mm and a coil diameter of 32 mm. The detonation time was divided into seven sections, with an interval of 100 ms or 200 ms between each section. The charge amount for each hole was distributed according to the explosive consumption, and the detonation was carried out in the following sequence: cutting hole, edge collapse hole, upper collapse hole, bottom plate hole, and surrounding hole. To analyze the cutting effect of combined long and short holes, three separate cutting tests were conducted, and the results of the drilling and groove  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Types of Holes** & **Depth/cm** & **Distance/cm** & **Number of Holes** & **Charge Per Hole/g** & **Detonation Time/ms** \\ \hline Cutting holes & 280 & 30 & 8 & 1800 & 0 \\ Collapsing hole 1 & 260 & 70 & 8 & 1500 & 100 \\ Collapsing hole 2 & 260 & 70 & 10 & 1200 & 200 \\ Bottom plate hole & 260 & 76 & 6 & 1200 & 400 \\ Surrounding holes & 260 & 50 & 17 & 900 & 600 \\ \hline \end{tabular}
\end{table}
Table 6: Design parameters for oblique cutting test 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Types of Holes** & **Depth/cm** & **Distance/cm** & **Number of Holes** & **Charge Per Hole/g** & **Detonation Time/ms** \\ \hline Cutting holes & 280 & 45 & 6 & 1800 & 0 \\ Collapsing hole 1 & 270 & 70 & 8 & 1500 & 100 \\ Collapsing hole 2 & 260 & 70 & 10 & 1500 & 200 \\ Bottom plate hole & 260 & 76 & 6 & 1500 & 400 \\ Surrounding holes & 260 & 50 & 17 & 900 & 600 \\ \hline \end{tabular}
\end{table}
Table 7: Design parameters for oblique cutting test 2.  
Figure 7: Layout of boreholes for three types of blasting tests (unit: cm): (a) combined long and short hole cutting; (b) oblique cutting test 1; (c) oblique cutting test 2.  
cavity forming are shown in Figure 8. It can be observed that the shape of the groove cavity of the long and short eye groove is a regular long cube. The groove depths of the three experiments were 2.3-2.45 m, and the utilization rate of the groove holes exceeded 80%, indicating a good cutting effect.
# An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields
## 4 Experimental Evaluation
### Experimental Results  
#### 4.3.1 Qualitative Analysis  
According to the method proposed in this paper, we have obtained the improved semantic segmentation results. The comparisons on VOC dataset among FCN-8s, DeepLab-v2 [22], and our method are shown in Figure 10.  
Figure 10: Qualitative results on the reduced validation set of PASCAL VOC 2012. From left to right: the original image, SLIC segmentation, FCN-8s results, DeepLab-v2 results, our results, and the ground truth.  
As shown in Figure 10, our results are significantly closer to the ground truth than those by FCN-8s, especially for the segmentation of the object edges in images. By comparing the results obtained by FCN-8s with ours, it can be seen that they are the same color and they have a similar object outline, which indicates that our method inherits feature extraction and object recognition abilities of FCN. In addition, our method can get finer details than DeepLab-v2 in the vast majority of categories, which profits from the boundary optimization of coarse features. From segmentation results of each image in Figure 10, we also notice the following details: (a) All these methods can identify the classification of the single large object and locate the region accurately, due to the excellent extraction ability of CNNs. (b) In the complex scene, these methods cannot work effectively due to the error of extracted features, even employing different post-processing steps. (c) Small objects are possible to be misidentified or missed, due to the lack of the pixels describing the small objects.  
It is worth mentioning that, in some details of Figure 10, such as the rear wheel of the first bicycle, the results of DeepLab-v2 seem better than ours. The segmentation result of DeepLab-v2 almost completely retained the rear wheel of the first bicycle. However, it cannot be overlooked that DeepLab-v2 fails to properly process the front wheels of the two bicycles. In contrast, the front wheels processed by our method are closer to that of ground truth. For the rear wheel missing problem, some analysis is made on the difference among the bicycle body, solid rear wheel, and background. The solid rear wheel has a distinguished color with compassion of the background, but at the same time, the same case exists in the bicycle body and solid rear wheel, leading to a large probability of misrecognition. Additionally, the non-solid wheel is much more common than the solid one in the real scenario; therefore, the CRF model tends to predict the non-solid wheel.  
The proposed semantic segmentation method is implemented on Cityscapes dataset. Some visual results proposed by FCN-8s, DeepLab-v2, and our method are shown in Figure 11. Similar to the results on PASCAL VOC 2012 dataset, the improved method achieves better performance than others.
## 4 The Proposed Community-Based Event Delivery Protocol
### Queue Management  
In opportunistic networks like DTSNs, in order to reach a certain data delivery ratio, queue management scheme is necessary. The goal of the queue management scheme is to properly arrange events, to determine event's delivery order in the buffer queue of sensor and to determine which event should be dropped when the queue is full. It is of great importance to the network performance. The main idea of our queue management scheme is to employ both the event survival time and the event successful delivery time to signify the amount of redundancy and the importance of a given event.
## 3 Representation of Shadow and Caustics
### Methods  
#### 3.1.1 Generation of Shadow with Illusion  
An illusion-based shadow can be represented by increasing the luminance around the area to be shadowed to make the shadow appear darker. To increase the luminance of the environment without inducing visual discomfort, it is necessary to consider the reflection by the real floor surface on which the shadow is cast. The proposed method represents shadows as virtual objects (hereafter, \"shadow inducers\") that increase the luminance around the originally cast shadow areas while taking the reflection of the real floor into account. In this case, the light \(L_{o}\) reaching the observer's eyes is represented as the sum of light \(L_{r}\) produced by the environment and the light \(L_{s}\) produced by the shadow inducer:  
\[L_{o}=L_{r}+L_{s}. \tag{1}\]  
The shadow inducer is generated using the following method. First, the shadow area cast by a virtual transparent object (a) is calculated as a shadow map (c) in a conventional manner [24]. Next, this shadow map is used to generate an amplification ratio (d) for calculating the shadow inducer. This amplification ratio is designed to decrease as the shadow fades to make the increased luminance inconspicuous. It is based on the fact that the human visual system is insensitive to gradual changes in the luminance [25]. Specifically, the amplification ratio \(\alpha(d)\) at a point \(P\) is determined by the distance \(d\) from \(P\) to point \(Q\), which is the closest point on the shadow edge from \(P\), based on the following ramp function:  
\[\alpha(d)=\frac{D-d}{D}, \tag{2}\]  
where \(D\) is the distance from \(Q\) to the outer end point of the shadow inducer on the extension of the line segment \(\overline{QP}\).  
To take the reflection of the real floor into account, the shadow inducer (e) is generated using the luminance of the floor texture (b). Specifically, after correcting the amplification  
Figure 2: Process for generating shadow and caustics.  
ratio \(\alpha(d)\) to \(\alpha^{\prime}(d)\) by applying a coefficient \(k\) that determine brightness of shadow inducer against the real floor, the shadow inducer \(L_{s}\) is obtained by multiplying \(\alpha^{\prime}(d)\) by the real floor texture \(L_{r}\), or the recorded luminance of the real environment, as follows:  
\[L_{s}=\alpha^{\prime}(d)L_{r}, \tag{3}\]  
\[\alpha^{\prime}(d)=k\alpha(d). \tag{4}\]  
In this study, we manually set the 3-D position of the point light source and tuned the coefficient \(k\) empirically while examining the rendering results so that the shadow inducer did not become too bright when compared with the surrounding floor.  
Equation (1) can therefore be modified as follows:  
\[L_{o}=(1+\alpha^{\prime}(d))L_{r}. \tag{5}\]
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 4 Experimental Study
### Baseline Approaches  
We have evaluated our approach against _Eager Maintenance_, _Time To Live (TTL)_, _PageRank_, _Size_, _ChangeRatio_ and _ChangeRate_.  
_Eager Maintenance:_ In eager maintenance, all the updating jobs are performed immediately after the new data arrives. Eager maintenance updates all the materialized views immediately after the query evaluation and each update query has to wait until view maintenance is done. The cost of the view maintenance is normally high [38].  
_Time To Live (TTL):_TTL is a fixed threshold--the age at which the view must be refreshed. Most of the maintenance manager use _TTL_ to captures how old the views from the last query evaluation are. TTL is an important feature that illustrates when the data source was last visited and updated [39].  
\[P_{TTL}(X_{t,c})=t_{i}-t_{last}(X_{t,c}) \tag{4}\]  
As shown in Equation (4), TTL is used by the scheduling strategies to fetch the data source \(c\) in time \(t_{i}\), whereas, \(t_{last}\) is the last update time.  
_PageRank (PR):_Updates the local views based on the ranking of the Linked Data sources. The ranking is calculated based on the number of incoming links and the sources are pre-fetched from the highest PageRank [40]. It is represented as:  
\[P_{PR}(X_{t,c})=PR(X_{c_{highest},t_{update}}) \tag{5}\]  
_Size:_In this policy, _Size_ is determined by checking the Linked Data sources. The priority is given to the largest data source [41]. The formula for _size_ is as follows:  
\[P_{size}(X_{t,c})=|X_{c_{max},t_{update}}| \tag{6}\]  
_ChangeRatio:_In this policy, _ChangeRatio_ counts how many items were updated based on the last known time period [42]. This metric is useful for storing the change history and number of detected changes of the Linked Data as shown in Equation (7).  
\[P_{ChangeRatio}=\sum_{i=no.ofchanges}|X_{c,t_{lastupdate}}| \tag{7}\]  
_ChangeRate:_In this maintenance policy, the local views are updated from the sources with the most to the least changes [15] observed at last known points. It is represented as follows:  
\[P_{ChangeRatio}=\Delta\left(\begin{array}{cc}X_{c,t_{lastupdate}},X_{c,t_{ lastupdate-1}}\end{array}\right) \tag{8}\]
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 4 Deep Learning Defect Detection Methods for X-ray Images for Industrial Products  
Non-destructive testing (NDT) is a method that uses radiography or ultrasound technologies to discover faults without causing damage to the detected objects. It is widely used in engineering industries to detect and evaluate defects in materials of all types. An important technique in non-destructive testing is radiographic testing, which uses X-rays to identify and evaluate flaws or defects, such as cracks or porosities. Defects can appear in X-ray images in many shapes and sizes, making detection difficult. The images are often low contrast and noisy, making identification of defects difficult. The traditional approach for identifying defects in industrial products is for human operators or experts to visually inspect radiographs. However, this method can be subjective and prone to errors. Additionally, the process of examining a large number of images can be time-consuming and may lead to misinterpretations. However, there have been significant advancements in the field of defect detection in recent years, thanks to the emergence of deep learning techniques. As a result, a number of methods for detecting defects have been proposed, which are more efficient and reliable than the conventional approach. This section aims to provide a summary of current research on industrial product defect detection methods using X-ray images. Specifically, it covers the use of deep learning techniques such as convolutional neural networks and generative adversarial networks to analyze radiographic images and identify defects with a high degree of accuracy. These methods have the potential to reduce the subjectivity and human errors associated with the traditional approach, as well as the time required for inspection. Additionally, they can be trained to improve over time with more data, making them more robust and reliable.
###### Abstract  
Rural building automatic extraction technology is of great significance for rural planning and disaster assessment; however, existing methods face the dilemma of scarce sample data and large regional differences in rural buildings. To solve this problem, this study constructed an image dataset of typical Chinese rural buildings, including nine typical geographical regions, such as the Northeast and North China Plains. Additionally, an improved remote sensing image rural building extraction network called AGSC-Net was designed. Based on an encoder-decoder structure, the model integrates multiple attention gate (AG) modules and a context collaboration network (CC-Net). The AG modules realize focused expression of building-related features through feature selection. The CC-Net module models the global dependency between different building instances, providing complementary localization and scale information to the decoder. By embedding AG and CC-Net modules between the encoder and decoder, the model can capture multiscale semantic information on building features. Experiments show that, compared with other models, AGSC-Net achieved the best quantitative metrics on two rural building datasets, verifying the accuracy of the extraction results. This study provides an effective example for automatic extraction in complex rural scenes and lays the foundation for related monitoring and planning applications.  
rural building extraction; deep learning; diverse dataset; attention mechanisms; context collaboration networks; remote sensing +
Footnote †: journal: Medical Sciences