esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Container Effect in Deep Learning Workloads and Implications
## 4 Analysis of Deep Learning Event Traces
### Page-Fault Analysis  
Since page-faults and write-backs are quantified as significant causes of performance penalties in containerized deep learning, we further analyze memory and storage-related events using Linux Fitrace. Specifically, we investigate page-fault situations and their main reasons comprehensively. Table 8 shows the distribution of page-faults, and the files and functions that cause the page-faults. As we see from the table, a majority of page-faults are caused by a small number of specific files. In particular, the main sources of page-faults are found to be shared libraries, garbage collection, and regular expressions for path rules. Page-faults caused by shared libraries occur when an application loads a library file that is not resident in the main executable file. If the library is already in memory, this is a minor fault and only requires a re-mapping by the OS. If not, the library file needs to be loaded from storage and this is called a major fault. Garbage collection can also cause page-faults because garbage collection may result in the deallocation of memory that is still in use. Also, regular expressions for path rules can incur page-faults when complex expressions should be evaluated, which can lead to large memory space allocations.  
To buffer the performance overhead caused by frequent storage flushes in container-based deep learning, NVM (non-volatile memory), also known as persistent memory, can be adopted. For example, eMRAM, a type of NVM for mobile systems, is being developed by Samsung Electronics [32]. If we flush hot dirty pages generated in Table 8 to the NVM, access to slow storage media can be eliminated. This can significantly accelerate the performance of container-based deep learning workloads, which will be discussed further in the next section.
## 3 Results and Discussion
### Initial Stages of STO Film Growth
The dynamics of changes in the thickness of the film and the area of the substrate occupied by the islands allow us to conclude that under these conditions, at deposition times from 20 to 50 s, the stage of the formation of islands as a result of the fusion of small nuclei, the so-called primary coalescence, is realized. At this stage, the main mass transfer is carried out by diffusion of the substance over the substrate in the spaces between the islands. After 50 c of deposition, the island formation stage is replaced by a secondary coalescence stage, when the fusion of the islands begins to form a single structure in the form of a grid. At this stage, the degree of coating of the substrate surface with a condensed substance increases sharply due to a change in the shape of the islands to a flatter one. This stage of film growth is characterized by significant mass transfer over the surface of the accreting island grains [52]. The study of the stages of nucleation and coalescence of strontium titanate films on silicon carbide allows us to draw the following conclusions. In the considered ranges of substrate temperatures and working gas pressures, which ensure condensation at low supersaturation, the process of STO phase nucleation on the SiC surface occurs according to the Volmer-Weber island mechanism. The implementation of this growth mechanism in the STO-SiC system is determined by the mismatch between the hexagonal substrate structure and the cubic film one, and their weak interphase bond [50]. At the same time, weak supersaturation provides conditions for the formation of oriented STO islands, which allows us to expect the growth of a highly oriented film in the future. Figure 1: MEIS spectra of STO films obtained at pressures of 6 and 10 Pa and at substrate temperatures of 800–900 \({}^{\circ}\)C.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.8 Precision-Recall Curve  
The Precision-Recall (PR) curve plots the sensitivity rate (true positives) on the abscissa and the precision metric on the ordinate (positive values correctly predicted) for different probability thresholds. The area under the PR curve, called PR AUC (_Area Under Curve_), allows evaluating the performance of the classifier in terms of balance between _precision_ and _recall_. Inspecting the AUC is advisable when the samples are not balanced, that is, when there are few samples of a positive class. This assumption is not the case for the _dataset_ available in this work since the number of positive samples coincides with the number of negative samples. The greater the area under the curve, the greater the model's performance. In other words, the optimum would be a curve as close as possible to the upper right corner (high recall and high precision).
# Adaptive Relay Selection Scheme by Using Compound Channel
## 3 The Conventional Relay Selection Schemes
### The Frobenius Norm-Based Selection Scheme  
The Frobenius norm is defined as L\({}_{2}\)-norm of the Euclidean norm. In Euclidean space, the Frobenius norm is the distance from origin. The Frobenius norm can be expressed as follows,  
\[\left\|\cdot\right\|_{k}^{2}=\sum_{i=1}^{N_{t}}\sum_{j=1}^{N_{l}}\left|a_{ij} \right|^{2}=tr(\mathbf{H}_{k}^{T}\mathbf{H}_{k})=\sum_{i=1}^{N_{l}}\sigma_{i}^ {2}, \tag{3}\]  
where \(\left\|\cdot\right\|_{k}^{2}\) means square of the Frobenius norm of the \(k\)-th relay. \(a_{ij}\) denotes each channel element of \(\mathbf{H}_{k}\) and \(\left|a_{ij}\right|\) denotes the absolute value of \(a_{ij}\). The Frobenius norm of relay equals the square root of a sum of all \(\left|a_{ij}\right|^{2}\). And the diagonal entry in \(\mathbf{H}_{k}^{T}\mathbf{H}\) is the sum of the squares of each column of \(\mathbf{H}_{k}\). Therefore, the sum of the diagonal entries of \(\mathbf{H}_{k}^{T}\mathbf{H}\) equals the total sum of \(\left|a_{ij}\right|^{2}\). Furthermore, it is same with the sum of an eigenvalue \(\mathbf{H}_{k}^{T}\mathbf{H}\). \(\sigma_{i}^{2}\) means the square of singular value of \(\mathbf{H}_{k}\). (4) presents the singular value decomposition (SVD) of \(\mathbf{H}_{k}\). \(\mathbf{H}_{k}\) can be described as follows,  
\[\mathbf{H}_{k}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{H}, \tag{4}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are unitary matrix. \(\mathbf{\Sigma}\) is a diagonal matrix with singular value. \(\sigma_{i}\) is a component of \(\mathbf{\Sigma}\).  
The sum of an eigenvalue \(\mathbf{H}_{k}^{T}\mathbf{H}\) is same sum of \(\sigma_{i}^{2}\). In other words, the Frobenius norm depends on singular value. The Frobenius norm-based selection scheme calculates (3) for each candidate relays channel and selects the relay with the largest Frobenius norm.
###### Abstract  
There is conflicting evidence on whether concurrent aerobic endurance and resistance training (RT) leads to synergistic enhancements in aerobic capacity and muscular strength or causes interference, limiting performance gains. We developed a concurrent training (CT) intervention, including full-body dynamic RT combined with high-intensity interval training (HIIT), on a cycle ergometer to determine whether a brief CT intervention is beneficial to both muscular strength and aerobic capacity. In an exploratory pilot study, participants (\(n=10\); male = 4) undertook a four-week CT intervention consisting of RT, including six compound movements (bench press, squat, deadlift, Pendley row, squat jumps, and rack pulls), plus cycle HIIT. The pre-/post-intervention improvements were assessed via bench press and leg press 3 RM testing, an isometric mid-high pull, a countermovement jump, and the change in the relative VO2 max. We observed significant (\(p<0.1\)) increases in the bench press (6.4%), leg press (6.7%), IMTP (11.1%), and relative VO2 max (7%) results. Interestingly, the participants with the highest pre-intervention relative VO2 max demonstrated no performance improvements. These pilot test results suggest that CT is an effective strategy that enables synergistic enhancements that can be observed with very low training volumes. This suggests that CT is an effective strategy for improving muscular strength and aerobic endurance in non-elite physically active individuals.  
2024 14 8447 10.3390/app14188447 Article An Exploratory Study on Whether the Interference Effect Occurs When High-Intensity Strength Training Is Performed Prior to High-Intensity Interval Aerobic Training
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 4. Results
### Evaluation Metrics  
We evaluate the performance of object detection using four metrics: Precision (P), Recall (R), Average Precision (AP), and Mean Average Precision (mAP), as shown in Equations (8)-(10) and (11). Recall measures the proportion of actual positive samples detected by the model, while Precision measures the proportion of true positive samples among those predicted as positive by the model. These two metrics are interrelated.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Image** & **Person** & **Bicycle** & **Car** & **Bus** & **Motorbike** & **Total** \\ \hline VOC\_train & 8111 & 13,256 & 1064 & 3267 & 822 & 1052 & 19,561 \\ VOC\_test & 2734 & 4528 & 337 & 1201 & 213 & 325 & 6604 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the used datasets, including VOC_train and VOC_test.  
Figure 7: Example graph of the dataset in this article. The first row of photos are haze-free images from the PASCAL VOC dataset; the second row is a synthetic fog map with fog added. The image in the third row is the clean image after AODs dehazing, which is VOC_train.  
Average Precision (AP) is the area under the Precision-Recall (PR) curve, and for detection algorithms, a higher AP value indicates better performance. The mean of the AP values for K categories is called Mean Average Precision (mAP). Similarly, a higher mAP value signifies better algorithm performance. The IoU = mAP@x mentioned in this paper refers to the Mean Average Precision value at an IoU threshold of x.  
\[P=\frac{TP}{TP+FP} \tag{8}\]  
\[R=\frac{TP}{TP+FN} \tag{9}\]  
\[AP=\int_{0}^{1}P(R)d(R) \tag{10}\]  
\[mAP=\left(\sum AP\right)/K \tag{11}\]  
TP (True Positives) refers to the number of instances where positive samples are correctly identified, while FP (False Positives) indicates the number of instances incorrectly identified as positive samples. FN (False Negatives) represents the number of instances incorrectly identified as negative samples.
## 3 Platform Subsystem Design
### Power Subsystem  
The electrical power subsystem (EPS) of the CubeSat generates power from high-efficiency solar cells that harvest solar energy and convert it into electricity. The excess energy is stored in a rechargeable battery, which is utilized when there is no access to solar power. In most cases, the CubeSats uses the stored energy during eclipses to operate. In addition, the EPS manages the electrical energy by utilizing converters to distribute the power to all subsystems, including the payload. To ensure that the CubeSat operates throughout its mission time, the EPS is designed precisely to meet the power requirements of each component. The Light-1 EPS subsystem consists of three main components: the solar cells, the EPS board, and the battery pack.  
The solar panels contain a triple junction of GaInP/GaInAs/Ge. The solar cells in the solar panels have a maximum power of 1.05 W with an efficiency of 29.5%. The EPS board is responsible for managing and distributing the electrical power. It consists of several electronic circuits that regulate and control voltages to meet the voltage demand of all the subsystems. One of the main features of the EPS board is that it can track the maximum power point (MPPT). The Light-1 EPS board also contains protective features such as overcurrent and overvoltage protection for various components of the board. The EPS includes a distribution unit that employs converters to step down/up the voltage according to the subsystems/load operating voltages. The lithium-ion (Li-ion) battery is used for storing excess power that is used when solar power is unavailable. Light-1 has four Li-ion battery cells with each cell voltage of 3.6 V and 3200 mAh capacity. Light-1 is a 3U CubeSat with two deployable solar panels. The deployable solar panels help in increasing the power generated during sunlight. Light-1 has 22 solar cells distributed across five solar panels of which two are deployable. Figure 6 shows the distribution of the solar cells in each panel.  
\begin{table}
\begin{tabular}{c c} \hline
**No.** & **Component** \\ \hline
11 & Reaction wheels 4W set \\ \hline
12 & S band transceiver \\ \hline
13 & GPS receiver \\ \hline
14 & RF splitter \\ \hline
15 & 3- axes magnetorquers \\ \hline
16 & PMT detector (payload) \\ \hline
17 & SiPM detector (payload) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 4 Implementation
### Libp2p Host
#### 4.5.4 Inherent Challenges  
Libp2p faces some challenges when used in opportunistic networks, mainly due to the dynamism of these topologies. Key among them is adaptability to perpetual changes in the network; the rapid entry and exit of nodes makes it difficult to maintain effective connections and routes, often increasing the time and resources needed to reestablish them when they are lost. Furthermore, peer discovery, which is crucial in such networks, can be very expensive in terms of bandwidth and energy, especially in dense or heterogeneous networks where nodes use different transport technologies.  
Routing challenges also present significant challenges; libp2p algorithms may not be fast and efficient enough in environments where routes change too frequently, potentially introducing latency or redundancy in message propagation. Resource consumption is another major limitation, as libp2p can be demanding in terms of energy and computational capacity, especially on IoT devices that have limited batteries and processing capabilities.  
Due to these limitations, it has been decided to delegate peer discovery to Bluetooth Mesh, while libp2p would be responsible for managing global connectivity and routing in the network. In this way, once a peer is discovered, libp2p would store the Bluetooth Mesh ID in its routing table, as shown in Figure 6.
# A Next-Generation Core Network Architecture for Mobile Networks
## 9 VCNs and Core Network Security
Particular types of traffic can be enabled, e.g., to let the UE act as server or to enable M2M communications, while other types of traffic can be filtered, in order to block some services or to protect the UE and VCN against particular types of attacks and malware. Similarly, as mentioned in Section 7, the infrastructure used for running VCNs may vary depending on the user scenario. For example, private cloud infrastructure, together with strict local security policies and stronger encryption, can be used when a high security level is required (e.g., Government, enterprise), while a public cloud infrastructure (e.g., Amazon AWS), a cellular operator hosting infrastructure, or eNodeBs with hosting capabilities (e.g., edge cloud, fog computing) could also be used for hosting VCNs of other users. Furthermore, in the case of multi-SIM UEs, one user may be associated, at the same time, with multiple core networks hosted in different clouds. If an external hosting infrastructure (either public or private) is used to host some VCNs (see Figure 19), the network operator would only keep control on setting up and tearing down the VCNs, and managing some billing and administration operation. This, together with specific service level agreements with cloud operators, would enable the cellular operator to provide a new class of custom security services while, at the same time, reducing operational cost. Figure 18: Attacks on the new architecture.
## 4 Results and Discussion
### Analysis of the Causes of Groundwater Chemistry Types  
#### 4.3.1 Groundwater Chemistry Control Mechanisms  
A Gibbs plot can be used to illustrate the causal mechanism of water chemical composition [41]. In the Gibbs plot, if the TDS value is high and the Na\({}^{+}\)/(Na\({}^{+}\)+Ca\({}^{2+}\)) or Cl\({}^{-}\)/(Cl\({}^{-}\)+HCO\({}_{3}\)\({}^{-}\)) ratio is close to 1, the ion control mechanism is evaporative crystallization. If the TDS value is medium and the Na\({}^{+}\)/(Na\({}^{+}\)+Ca\({}^{2+}\)) or Cl\({}^{-}\)/(Cl\({}^{-}\)+HCO\({}_{3}\)\({}^{-}\)) ratio is less than 0.5, the ion control mechanism is water rock effect. If TDS value is low and the Na\({}^{+}\)/(Na\({}^{+}\)+Ca\({}^{2+}\)) or Cl\({}^{-}\)/(Cl\({}^{-}\)+HCO\({}_{3}\)\({}^{-}\)) ratio is close to 1, the water chemistry component control mechanism is the rainfall effect [42; 43]. From the Figure 6, it can be seen that the chemical composition of groundwater in the study area is mainly dominated by water-rock action.  
Figure 5: (**a**) Exceedance rate of each component of groundwater, (**b**) pollutant correlation coefficient matrix.
## Materials and Methods
### Measurement of Physical Property Changes for the Sandstone Cores  
Several previous studies confirmed that physical properties, such as the porosity, density, and seismic wave velocity of CO\({}_{2}\) reservoir rock can change after CO\({}_{2}\) injection into the reservoir formation [23; 32; 38; 55]. However, it has not been clearly proved what the main reason is for the property change of the reservoir rock and how the change proceeds so rapidly. It is important to quantify the extent of the rock property change because it can affect both leakage safety and CO\({}_{2}\) storage capacity of the reservoir. Changes in porosity, density, and Primary/Secondary (P/S) seismic velocity for nine sandstone cores were measured at different scCO\({}_{2}\)-core-groundwater reaction times, and the results were compared with those of the \(SR_{\text{RMS}}\) results. To quantify the physical property changes of three sandstones (S1, S2, and S3) due to the geochemical reaction, each core (three cores for each sandstone) was placed in a large stainless steel chamber (1.5 L of capacity), and 1 L of groundwater was added at 100 bar and 50 \({}^{\circ}\)C. Then, scCO\({}_{2}\) was injected into the chamber by a high-pressure syringe pump to fill the empty space of the chamber. The inside pressure of the chamber was maintained at 100 bar by a back-pressure controller and the chamber was coated with a heating jacket at 50 \({}^{\circ}\)C to maintain the CO\({}_{2}\) as a super critical fluid during the whole reaction time. After each core was taken from the chamber and dried in an oven, the physical properties of the core were measured at different reaction times (total 150 reaction days). The control experiment without scCO\({}_{2}\) in the chamber (zero air instead) was also repeated to separate the effects of scCO\({}_{2}\) injection on rock property changes. Dry density, porosity, and seismic velocity of the cores were measured by the \"ASTM/C97\" test, \"ASTM/C97M-09\" test, and \"ASTM/D2845-08\" test referred from the American Society of Testing Materials. A more detailed procedure for the core experiment can be found in [54]. The property values for the three sandstones vs. reaction time were plotted and their changes were also compared with those of \(SR_{\text{RMS}}\) and the cation concentration in groundwater. The schematic of the core experiment to measure the physical property changes of sandstone is shown in Figure 4.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Security
#### 7.3.4 Decentralized Anonymity Schemes  
Due to the nature of D2D communications, which are peer-to-peer, opportunistic, and self-organizing, an anonymity scheme that is independent and not reliant on centralized third parties is required. Additionally, these schemes are required to highlight the problem of anonymity abuse without the presence of a single trusted center. There is an author who proposed distributed privacy scheme in ad-hoc and vehicular networks [84]. However, the issue is not being fully explored in D2D communications.
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 6 Particulate Matter Emission Mitigating Strategies
### Oil and Water Spraying  
Oil and water spraying in poultry housing helps to control indoor PM concentration. Spraying liquid agents, such as tap water, acidic water, electrolyzed water, or a combination of water and soybean or canola oil, over poultry buildings has been studied to lower PM levels and disinfect the houses [24; 72; 73; 74; 75; 76]. According to [PERSON] et al. (2012), Spraying water on top of litter at an application rate of 150 to 600 mL m\({}^{-2}\) reduced PM\({}_{10}\) and PM\({}_{2.5}\) emissions by 18 to 64% in an avary hen house but increased NH\({}_{3}\) emissions by 21 to 65% [76]. High liquid spray dosages decrease PM significantly while increasing NH\({}_{3}\) levels due to the accumulation of litter moisture [112]. A spray dose of pH3 and 25-mL kg\({}^{-1}\) dry litter d\({}^{-1}\) showed a good combination for controlling PM levels in littered CF hen houses without creating unwanted increases in NH\({}_{3}\) emissions [24]. Spraying a liquid agent like electrolyzed water over the litter of CF hen houses has been demonstrated to decrease PM successfully. Cautions should be paid as spraying water may lead to corrosion of metal equipment in poultry facilities [24].  
Research on oil and water spraying shows significant PM and airborne bacteria reductions (Table 9). Total dust, airborne bacteria, and fungi were significantly reduced by soybean oil for 24 h after spraying [113]. Particulate matter removal efficiency of oil and water spray ranges from 18 to 89%. Increased spraying frequency can help reduce PM emissions even further, but it can also make surfaces oily and slippery, posing a safety risk to workers and animals [114].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
* m m\({}^{-2}\),
* mm\({}^{3}\) min\({}^{-1}\) for data log interval of 1 s\({}^{-2}\) g oil day\({}^{-1}\)pig\({}^{-1}\).  
\end{table}
Table 9: Particulate matter reduction with liquid spray.
# High Performance Receiver Design for RX Carrier Aggregation
## Abbreviations  
The following abbreviations are used in this manuscript:  
LNA Low Noise Amplifier LNTA Low Noise Trans-conductance Amplifier FE Front End RX Receiver TIA Trans Impedance Amplifier SISO Single Input, Single Output SIMO Multiple Input, Multi Output CA Carrier Aggregation
## 3 Materials and Methods
### Machine Learning Algorithm
#### 3.5.3 Random Forest Algorithm  
A supervised ML technique known as the RF [15] or random decision forest algorithm is used for classification, regression, and other tasks using decision trees. A randomly chosen portion of the training data is used by the RF classifier to generate a collection of decision trees. It simply consists of a collection of decision trees from a randomly chosen subset of the training set, which is subsequently used to decide the final prediction. The total process is shown in Figure 4. Here, the training dataset is divided into subsets, and from every subset of training data, a DT is made. Finally, by averaging the prediction from every DT, the final decision is made.  
This technique is also called an ensemble technique, as it combines multiple models. The RF algorithm attempts to produce an uncorrelated forest of trees whose forecast by committee is more accurate than that of any individual tree by using bagging and feature randomness when generating each individual tree.