esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 5 Analysis and Discussion
### Evolution over Time  
As can be seen in Table 3, most of the articles found are in the cloud layer (25), followed by fog (8) and edge (2). It is also worth noting that while all articles present research on the evaluation of resource management across the C2T continuum, the survey also includes 19 articles proposing both simulators or extensions to simulators. In total, some 20 journals have published on the topic in the period under study. It is also worth noting that the number of Q1 and Q2 journal publications on the evaluation of resource management across the C2T continuum is increasing year on year. In particular, the introduction of iFogSim [28] in 2017 is directly responsible for stimulating research in subsequent years, a trend that is expected to continue into the future based on the maturity of the underlying CloudSim simulator framework and the established base of users. All other articles identified for both fog and edge computing were published in 2018 and early 2019. While there is a clear transition of research from cloud computing to the edge, it is more correct to say that 'all boats arerising'; there is more research being published on this topic both in cloud computing, and in fog and edge computing. Given the significant multiplier of papers that have been published in the last two years in conferences and as reported in recent surveys [5; 10; 11], one would expect significant increase in journal publications in the near term.  
Table 3 illustrates the dominance of resource allocation as a theme from 2015 to 2019. While this trend is likely to continue, one can see that not only are more journal articles being published on a wider number of resource management mechanisms, but one can see greater focus on resource provisioning and load balancing in 2019. Resource mapping and monitoring remain at relatively low levels.
# Boat-to-Grid Electrical Energy Storage Potentials around the
## 1 Introduction
### The Potentials of Using Vehicle-to-Grid Technology
On the other hand, EV charging without control could not make use of PV energy in an efficient way, leading to a need for power from non-photovoltaic sources [23]. The suitable coordination of charging and discharging EVs involves collective decision-making. The effective central coordination of V2G can prevent pulling demand and excessive charging, which affect battery life [1]. In spite of its numerous benefits, V2G also has its drawbacks, the greatest of which is battery life time [24]. As EV batteries only have a finite number of charging cycles, using EVs for storing energy will decrease the lifetime of their batteries. Therefore, those involved in the operation of V2G systems have to be incentivized to take participate in such programs, since with no compensation for lost battery lifetime VE owners would not be motivated enough [25].
## 3 Test Methods
### Three Calculation Methods of Dielectric Constants
#### 3.2.3 Common-Mid-Point (CMP) Method of 3D GPR  
The CMP means several pairs of antenna excitation points are symmetrically distributed at the center of the test area, called the M point. The R point is called the common midpoint of these excitation points which is at the interface just below the M point. The velocity of the EM waves propagating through the layers is calculated by the distance  
Figure 2: The amplitude of the GPR test. (**a**) Reflection on the surface; (**b**) Reflection on a metal plate.  
between the transmitting and receiving antenna. In other words, the CMP method calculates the velocity of the EM waves propagating through the layers in terms of the distance between the transmitting and receiving antenna, and then calculates the dielectric constant of the EM waves.  
With the principle of CMP, 2D radar with single channel cannot use the CMP method because it only has one antenna with the function of transmit and receive at the same time. 3D-Radar has an antenna array based on different distance and independent multiple frequency transmit receive antenna, which can measure simultaneously. Each receiving antenna receives electromagnetic wave from the transmitting antenna, which is reflected in each layer interface. The CMP detection technique using the 3D-Radar is shown in Figure 3. T and R represent the transmitting antenna and receiving antenna, respectively. With the CMP method, the dielectric constant of each pavement layer can be measured.  
The dielectric constant of the asphalt concrete layer was calculated by the Common Mid-Point Method by Equation (5) [1]:  
\[\varepsilon_{AC}=\frac{c^{2}}{x^{2}}\left({t_{2}}^{2}-{t_{1}}^{2}\right) \tag{5}\]  
where, \(x\) (m) denotes the distance between two antennas, \(t_{1}\) and \(t_{2}\) denote the time of EM wave transits in asphalt concrete layer by receiving and transmitting one-part type antennas and separating type antennas, respectively.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 4 Results
### Comparison with Previous Work  
Table 7 compares the performance of the DenseNet121-based EEG normal-abnormal classification method with previous studies. Our method demonstrated higher sensitivity (75.0%) and accuracy (79.9%) compared to previous work on TUH abnormal EEG classification.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** & **F1 Score** \\ \hline \multirow{3}{*}{Signal Image} & Train & 56.65\% & 83.72\% & 76.39\% & 70.67\% & 65.05\% \\  & Validation & 53.62\% & 79.11\% & 71.07\% & 66.64\% & 61.12\% \\  & Test & 55.83\% & 80.41\% & 69.79\% & 69.40\% & 62.04\% \\ \hline \multirow{3}{*}{Spectrogram} & Train & 77.95\% & 87.79\% & 85.58\% & 83.05\% & 81.59\% \\  & Validation & 75.17\% & 85.38\% & 83.11\% & 80.39\% & 78.94\% \\  & Test & 75.00\% & 83.78\% & 78.95\% & 79.85\% & 76.92\% \\ \hline \multirow{3}{*}{Scalogram} & Train & 83.07\% & 89.17\% & 87.71\% & 86.23\% & 85.33\% \\  & Validation & 74.90\% & 81.20\% & 79.22\% & 78.12\% & 77.00\% \\ \cline{1-1}  & Test & 73.33\% & 82.43\% & 77.19\% & 78.36\% & 75.21\% \\ \hline \hline \end{tabular}  
* Sens: Sensitivity; Spec: Specificity; Prec: Precision; Acc: Accuracy  
\end{table}
Table 6: Performance of the DenseNet121-based classification method after post-processing, evaluated on the training, validation, and test sets, using signal images, spectrograms, and scalograms as inputs.  
Figure 6: Confusion matrix for the test set.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Method** & **Sens (\%)** & **Spec (\%)** & **Acc (\%)** & **BAC (\%)** \\ \hline
[20] & KNN & - & - & 58.2 & - \\
[20] & Random forest & - & - & 68.3 & - \\
[21] & 1D-CNN & - & - & 76.9 & - \\
[21] & 2D-CNN & - & - & 70.39 & - \\
[21] & TCNN-RNN & - & - & 71.48 & - \\
[22] & LSTM & - & - & 79.18 & - \\
[23] & Deep CNN & 71.4 & 86.0 & 79.3 & - \\
[24] & CNN & - & - & - & 71.9 \\
[25] & BD-Shallow & 72–87 & 69–96 & - & - \\ Ours & DenseNet & 75.0 & 83.8 & 79.9 & 79.4 \\ \hline \hline \end{tabular}  
* Sens: Sensitivity; Spec: Specificity; Acc: Accuracy; BAC: Balanced Accuracy; TCNN-RNN: Time-distributed convolutional recurrent neural network; BD-Shallow: Braindecode Shallow ConvNet;  
\end{table}
Table 7: Performance comparison of DenseNet121-based EEG normal–abnormal classification methods with previous works.
## 3 Methodology
### Detection Head Reconstruction  
Most object detection models typically extract features by stacking deeper convolutional neural network layers. Deeper convolutional layers gradually enrich semantic information but simultaneously blur fine-grained details, resulting in reduced accuracy for small object detection. The backbone network of YOLOv7 downsamples the input image three times in total. The network extracts four feature map layers: P2, P3, P4, and P5. The P3, P4, and P5 feature maps are processed through the neck layer and then directed to the small, medium, and large object detection heads in the head layer, respectively, for detecting objects of varying scales.  
However, in the tower crane operating scenario, the cigarette and phone objects are tiny and have extremely weak contrast with the background. The P2 feature map contains richer object underlying feature information. The process involves inputting the P2 feature map to the neck layer and adding a tiny head to enhance detection accuracy and efficiency. Integrating low-level features allows the network to capture finer details of tiny objects, helping to prevent the gradual loss of small object information as the network deepens. As the network depth increases, information about tiny objects is progressively lost. However, the large object detection head is redundant; removing the P5 detection layer improves the accuracy and stability of tiny object detection in the tower crane operating  
Figure 6: HA-PANet structure.  
environment. This adjustment increases the model's adaptability to specific scenarios, focusing on cigarette and phone object sizes while minimizing attention to irrelevant sizes and decreasing computational complexity. The specific improved structure is shown in Figure 7.
# Impact Performance of Helmholtz Self-Excited Oscillation Waterjets Used for Underground Mining
## 2 Research Method
### Numerical Simulation Setup  
Due to axisymmetric nature of flow and geometry, a two-dimensional analysis is deemed to be suitable for numerical simulation, where the axial direction is \(x\), the radial direction is \(y\). The computational domain is shown in Figure 4 with appropriate boundary conditions. The inlet and outlet are pressure boundaries. The fluid flows through the HSEOW nozzle, and the jets are ejected from the downstream nozzle onto the wall surface, where \(S\) is the standoff distance. The radius \(R\) of outlet is prolonged to ensure full development.  
Figure 4: Schematic of computational domain.  
Figure 3: Mass loss as a function of erosion time (\(L_{c}/d_{1}=2\), \(D_{c}/d_{1}=8\), \(\alpha=120^{o}\), \(P_{\mathrm{o}}=20\) MPa).  
The flow inside the nozzle was assumed to be unsteady, controlled by the RANS equations together with the continuity equation. RNG k-epsilon model was adopted to govern the turbulence characteristics and the near-wall treatment was left as the standard wall functions. Pressure boundary conditions were applied on the inlet and outlet. ANSYS fluent was employed in the whole simulation works. The governing equations were discretized by the finite volume method and the second order upwind scheme was adopted for spatial discretization of the convection terms. The SIMPLE algorithm was employed to couple the pressure and velocity. The Schmerr-Sauer cavitation model was used to obtain two-phase flow of HSEOW.  
The equation of vapor volume fraction has a general form  
\[\frac{\partial}{\partial t}(\alpha_{v}\rho_{v})+\
abla\cdot\left(\alpha_{v} \rho_{v}\overset{\rightarrow}{u}\right)=\frac{\rho_{l}\rho_{v}}{\rho}\frac{d \alpha_{v}}{dt} \tag{1}\]  
The net quality source  
The free jet impingement calculated by RNG k-epsilon is compared with the experimental values by [PERSON] and [PERSON] [22] in Figure 6. The mesh independency was proven in Table 4, which indicated that the average velocity of the medium and fine resolution meshes at nozzle outlet is nearly the same. The medium mesh was selected as final grid, which was about 91,000 cells.
## 2 Engineering Background and Calculation Model Establishment
### Original Construction Details  
Based on this bridge system's characteristics and the project's site conditions, the designer suggests constructing the main bridge using the full hall support method. The primary construction process is as follows and shown in Figure 4:  
(1) Leveling the site, setting out the construction line, determining the pile position and constructing an access road within the main bridge while the factory begins processing steel beams; (2) constructing a bored pile and pouring the bearing platform of it; (3) constructing pier columns and connecting pier cap beams; (4) constructing cable towers and installing full hall supports; (5) assembling the steel box girder section of the main beam on the support platform; (6) welding the bridge deck reinforcement and shear nails, pouring the UHPC (ultra-high-performance concrete) pavement layer on the bridge deck; (7) tensioning stay cables; (8) dismantling the full hall bracket; (9) constructing the sidewalk, railing, expansion joint, drain hole and bridge deck pavement; calculating whether or not secondary tensioning is required based on the measured cable force; (10) completing the construction of the entire bridge.  
According to the above construction organization plan, the UHPC bridge deck layer is first completed during the full support stage of the main beam. Then, cable tension is applied. Hence, under the full support construction plan, the pouring sequence of the UHPC layer on the bridge deck will not affect the final internal cable force.
## Mathematical-Model Formulation  
### Problem Description  
This paper considers the problem of energy consumption concerning scheduling, while considering job priority and makespan control for parallel machines, to improve energy consumption and production efficiency for environmental sustainability [9]. Production costs such as overhead and late delivery are also significant and should be mentioned.  
Therefore, the objective of this study is to minimize total energy cost, including considering due-date and lateness constraints. In this context, a set of \(I\) different jobs {\(j_{1}\), \(j_{2}\),..., \(j_{l}\)} were scheduled on \(M\) parallel machines {\(m_{1}\), \(m_{2}\),..., \(m_{M}\)}. We assumed that each job \(j\) had deterministic processing time \(p_{jm}\) on each machine. The jobs could be assigned to any machine. Due to the job characteristics, each machine required different levels of energy consumption \(e_{jm}\) to process each job. All jobs were available at time zero. The completion time of job \(j\) is denoted by \(C_{jm}\), which is the time when the processing of the job was completed on machine \(m\). Only after the machine starting the process could no idle time be inserted into the schedule with no preemption. Additionally, in each time period, only one job could be processed on machine \(m\).  
On the basis of these assumptions and the following notations, a mathematical model is proposed to formulate this problem in order to minimize energy consumption while considering job priority and makespan control. This is defined as the minimization of energy-consumption, late-delivery, and production-overhead costs.
# Development of IoT-Based Real-Time Fire Detection System Using Raspberry Pi and Fisheye Camera
## 1 Introduction
For example, a method using sensors can only detect a local fire, and even if a WSN is built with multiple sensors, a cost problem arises. Image-based analysis can only detect fires within the camera's field of view and it is difficult to locate the fire point. In the case of a thermal imaging camera, there is a disadvantage in that the installation cost of each camera is high. When using drones or satellite images, it is possible to detect a wide area, but it is difficult to detect a fire immediately. Therefore, this study aimed to develop a technology that can detect a wide area at once, process fire detection in real time, and estimate the fire point while applying image-based technology at a relatively low cost. In order to cover a wide area, it is useful to install many inexpensive devices rather than a small number of expensive ones. In addition, it would be useful if the optimal installation point was analyzed to effectively detect forest fires. However, there have been few studies on the detection of a wide area with low-cost devices and sharing the results. In this study, we developed a forest fire detection system that can identify forest fire sites early and estimate the location using a fisheye lens and IoT technology. The developed technique was applied to Mt. Bukjeong near the Samcheok Campus of Kangwon National University in Samcheok City, Korea. The optimal installation point was analyzed using Geographical Information System (GIS) spatial analysis considering the topography of a terrain, and a system was installed to analyze the location of forest fires in real-time by receiving fisheye lens images transmitted from the Raspberry Pi system.
## 3 The Proposed Model
### Singular Value Decomposition  
SVD is an influential tool that takes several applications like pattern detection and data compression. SVD allows robust and reliable matrix factorizes for extracting the algebraic and geometric invariant features of images. The SVD factorization a square/non-square matrices as to 2 orthogonal matrices and singular value (SV) matrix. The spatial domain feature of images of the size \(100\times 100\) is demonstrated utilizing SVD by feature vector group of size \(1\times 100\). It can be predictable for speeding up the computational procedure by removing irrelevant features but preserving as a lot of data as feasible from the images. The SVD of rectangular real complex matrix \(A\) has been formulated as follows [17].  
\[A=U\Sigma V^{T}=\left[\begin{array}{cccc}u_{11}&\cdots&u_{1m}\\ u_{21}&\cdots&u_{2m}\\ \vdots&\ddots&\vdots\\ u_{m1}&\cdots&u_{mm}\end{array}\right]\times\left[\begin{array}{cccc}s_{1} &0&\cdots&0\\ 0&s_{2}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&s_{m}\end{array}\right]\times\left[\begin{array}{cccc}v_{11}& \cdots&v_{1n}\\ v_{21}&\cdots&v_{2n}\\ \vdots&\ddots&\vdots\\ v_{n1}&\cdots&v_{nn}\end{array}\right]_{n\times n}\, \tag{1}\]  
where  
\[UU^{T}=I. \tag{2}\]  
\[VV^{T}=I. \tag{3}\]  
\[s_{1}\geq s_{2}\geq s_{3}\ldots\ldots\geq s_{m}. \tag{4}\]  
where \(A\) refers to the \(m\times n\) matrices, \(U\) implies the \(m\times m\) orthonormal matrices, \(V\) signifies the \(n\times n\) orthonormal matrices, and \(\Sigma\) defines the diagonal matrices of sizes \(m\times n\) that is collected of SVs of \(A\) such that it holds non-negative number.  
The diagonal entry of \(\Sigma\) matrix signifies the SV and it can be superior values related to the entries of \(U\) and \(V\) so that matrix of size \(m\times n\) is decreased to vector of sizes \(n\). The SV is ranked in descending order; a primary entry of SV matrix contains the best substantial data but the final entry at the vector comprises the least significant data. The SV has the energy data but the orthogonal matrices contain the essential data. \(U^{T}\) and \(V^{T}\) are the transpose of matrices \(U\) and \(V\) correspondingly. \(I\) demonstrates the identity matrix. The  
Figure 2: Overall process of OMKHES-MS technique.  
column of \(U\) is termed as left singular vector of \(A\) whereas the column of \(V\) is named as the right singular vector of \(A\).
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 2 Finite Element Simulation
### Purpose and Boundary Conditions  
This study has selected high-lead QFN packages as the research subjects. The primary focus is on investigating the impact of thermal via parameters on die heat dissipation performance, with the aim of minimizing the maximum temperature (T\({}_{\text{max}}\)) of the high-lead QFN package. Simultaneously, an in-depth exploration is undertaken into the response variations of three types of QFN packages (conventional QFN, dual-row QFN, and high-lead QFN) under the influence of thermal vias. All three QFN packages are equipped with 68 leads. Among these, the dual-row QFN package employs a staggered layout for its leads, whereas the high-lead QFN package features lead embedded on its side. Table 1 provides comprehensive geometric dimensions and structural parameters for these three QFN models. To mitigate the influence of environmental factors on the QFN temperature distribution, a larger PCB size has been chosen. The environmental temperature is set at 25 \({}^{\circ}\)C.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 5 Trust Data Gathering
### Trust Parameters
**Task success** may not be very applicable in most network environments, but in environments where the nodes are active and perform tasks, they may offer a good way of monitoring whether a node is compromised. The authors of [33] use task success scores of UAVs to keep track of the number of tasks given, which were completed within the specified time allocation for each UAV. **Memory integrity**, employed in [26], creates a hash of the memory of the node. The memory can be re-hashed and compared to the original hash to ensure that the OS/firmware has not been tampered with. In [26], the hash is immutably stored within the node, and a self-scrutiny method is used for the node to check itself. This method could also be employed in other ways, such as storing the original hashes of the nodes in a cluster head or blockchain, which could be compared to the memory hashes of any node at any time by other nodes in the network. **Other**. Here, some more obscure measures seen in the models are discussed, as well as some methods that are not directly used for measuring trust but can be used to increase the security of the network and authenticate devices. The authors of [11] define three perceptions used to calculate trust based on a probabilistic graph model (this is discussed further in Section 6). These perceptions are as follows:  
* Ability--sensing and reasoning capabilities and influence on others. * Benevolence--a measure of the trustor's belief about how likely the trustee is to assist the trustor. * Integrity--the perceived characteristics of predictability, consistency, honesty and reliability of the CPS. The authors of [34] use a somewhat similar approach, using three attributes modeled by generalized stochastic petri nets (another graphical probabilistic model type). The three attributes modeled here are:  
* Reliability--the probability that the system will not fail. * Availability--the probability that the system services are ready at a given time. * Security--intrusion probability. The authors of [38] define a set of good and bad manners with different impact values on the trust score of the node. These good and bad manners are mostly related to the parameters discussed earlier, such as communications, data quality, resource usage and behavior. Techniques such as blockchains have been used in some models to immutably store publicly accessible data, such as trust values [9; 33]. Authentication methods such as RF-DNA fingerprinting [9] and signature-based intrusion detection systems (IDS) [12] have also been seen to help to authenticate devices on static networks and prevent counterfeiting or sybil-type attacks. These techniques do not affect the trust calculation in the models, but they can be incorporated to add an extra layer to the security of the network.