esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The devices must be usable throughout the whole life of the animals. Despite the importance of complex sensory monitoring, very few publications have reviewed these issues, which is why it was chosen as a topic, highlighting the specific issues of lifelong-monitoring data processing. Historically, the first rumen bolus sensor measurements appeared after the 2000s in experiments. The first measurements were taken on reticulo-ruminal pH and temperature [9; 14; 16]. The experiments were typically performed on fistulated cattle and lasted for a few days. During this period, transmission and/or storage techniques at the given technical level have been developed. These typically did not allow long-term measurements. At this time, the study on the mechanical design of rumen boluses also began, and two types were distinguished: the winged and the weighted mechanic types [3]. Both methods served for stabilizing the position of the sensor, as unlike traditional sensor devices, these sensors are not fixed, which makes it more difficult to process and interpret data. However, these early time sensors (bolus sensors as well as other \"at cow\" sensors) provided only primary information (lying time, average activity, or time spent ruminating, or ECG). It was not even possible to carry out long-term measurements with the technology; however, in 2010 [9], it was reported that no theoretical obstacle for lifelong monitoring exists using bolus methodology. The current trend is sensor fusion or sensor integration [17; 18; 19]. This means that measurements are made simultaneously with several sensors, and these measurements are aggregated by a data-processing system from which not only integral data but integral statements regarding important issues related to the condition or welfare of the animals are gained. Such claims are the estrus, inadequate feeding, symptoms of disease or lameness, and prediction of the time of calving. The aim of this paper is to give an overview of cattle rumen bolus-sensing technologies, and in the following chapters, it will discuss: (i) the areas of application of rumen boluses, (ii) the sensing solutions, (iii) data transfer solutions, (iv) data-processing and artificial intelligence (AI) solutions which are already applied or suggested in rumen boluses.
###### Abstract  
Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4 QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.  
live-video delivery; 5G networks; virtualized content delivery networks; network function virtualization; service function chain deployment; deep reinforcement learning +
Footnote †: journal: _Future Internet_  
service (QoS) and Quality of Experience (QoE) for widely distributed users. CDN uses replica servers as proxies for the content providers' origin servers. Traditional CDN systems are deployed on dedicated hardware and incur, thereby in high capital and operational expenditures. On the other hand, virtualized Content Delivery Networks (vCDN) use Network Function Virtualization (NFV) [5], and Software-defined Networking (SDN) [6] to deploy their software components on virtualized network infrastructures like virtual machines or containers. NFV and SDN are key enablers for 5G networks [7]. Over the last two years, numerous internet services providers (ISP), mobile virtual network operators (MVNO), and other network market players are exploiting 5G networks to augment the spectrum of network services they offer [8]. In this context, virtualized CDN systems are taking the profit of 5G networks to enable higher server consolidation, and flexibility during deployment [9; 10]. VCDNs reduce both capital and operational expenditures concerning CDNs deployed to dedicated-hardware [11]. Further, vCDNs are edge-computing compliant [12] and make possible to act win-win strategies between ISP and CDN providers [13].
## 2 Materials and Methods
### CFD Simulation Method and Parameter Setting  
#### 2.3.1 CFD Simulation Principle  
The flow of fluid follows three major equations, namely, the continuity equation, the momentum equation, and the energy equation. By solving the three equations, the wind field and thermal field of fluid flow can be obtained. In this study, CFD technology is used to simulate the steady-state wind field of the NSC. This study focuses on the wind field characteristics of the NSC, analyzes the impact of wind speed on athletes' performance, and solves the wind speed and wind pressure in the fluid area. Therefore, the continuity equation and momentum equation of the fluid in the study area are established, as shown below.  
Continuity equation:  
\[div(\mathbf{U})=0 \tag{1}\]  
where \(\rho\) represents density; and \(u,v,w\) are velocity vectors in the \(x\), \(y\), and \(z\) directions. Since the CFD simulation performed in this study is a steady simulation, there is no time term in the continuity equation.  
Momentum equation:  
\[div(\rho u\mathbf{U})=div(\eta\mathbf{grad}u)-\frac{\partial p}{\partial x}+S_{u}; \tag{2}\]  
\[div(\rho v\mathbf{U})=div(\eta\mathbf{grad}v)-\frac{\partial p}{\partial y}+S_{v}; \tag{3}\]  
\[div(\rho v\mathbf{U})=div(\eta\mathbf{grad}w)-\frac{\partial p}{\partial z}+S_{w}. \tag{4}\]  
where \(\eta\) is the dynamic viscosity of the fluid. Similarly to the continuity equation, the transient time term of the momentum equation is also removed.  
By performing CFD simulation of the steady state, the wind field distribution information of the NSC can be obtained.
# Blockchain for Future Wireless Networks: A Decade Survey
## 2 Background
### Wireless Networks: An Overview  
The WNs evolved in a short span of time, witnessing explosive growth in the sector of industry, healthcare, science, and technology by pervasively connecting them. Since the 1970s, newer generations of WN have been introduced, which adroitly improve people's quality of life by providing productive services, such as voice calls, multimedia services, remote connections, on-demand, intelligent services, and many more. In 1979, the first cellular WN \(1\)G was introduced, but it had low voice quality, higher interference, and no encryption mechanism was applied for secure communication. Then, with primary progressions, other generations (2G, 3G, 4G, and 5G) of cellular WNs were developed to add value to telecommunication and network service [3]. 2G provides a few imperative mobile call advancements, with encryption mechanisms, such as improving voice quality and reducing cross-talk [4]. On the contrary, 3G networks are faster and capable of transmitting data at a higher rate (maximum download speed 7 Mbps). They facilitated end-users to record video calls, watch TV online, surf the internet, and play online mobile games for the very first time [27]. Moreover, IoT-enabled devices become the center of social connectivity in 3G by using IP-based communication, but it also raises concern for security vulnerabilities [28].  
4G has become the first generation to use long-term evolution (LTE) technology that improves the data rate and QoS of WNs. Moreover, 5G has replaced 4G with various changes, such as enhanced data rates (1 Gbps), low latency (100 ns), mobility range (100-500 km/h), etc., for better network coverage and reliability [29; 30]. The communication latency in 5G has decreased substantially, resulting in fast download and upload speeds. Although 5G networks are becoming a reality, technologists have already started to be engaged with future WNs, i.e., 6G, which anticipates putting greater prominence on wearable technologies, unmanned aerial vehicles (UAVs), 3D networking, and wireless power transfer to amplify people's quality of life [31]. However, the radio resources used by the WNs operators are entirely open to security attacks, and therefore there is a need to explore and examine such attacks to stop them before they jeopardize the WN systems.
## 2 Numerical Simulation
### Computational Domain and Grid Generation  
The computational domain of fluid simulation has a significant influence on the calculation results [22]. It is necessary to eliminate the interference of the computational domain boundary on the flow field around the segment model, and the turbulent wake needs to diverge completely. The computational domain and boundary conditions of a single train arranged on the windward side of the bridge are shown in Figure 2. The airflow inlet of the computational domain is velocity inlet, the wind attack angle is \(\alpha\), and the outlet boundary condition is the zero pressure outlet. The front and rear walls are symmetrical boundaries, and the upper and lower walls are no-slip walls. The width of the bridge model is B and the distance from the train-bridge system model to the inlet and outlet of the computational domain are 6B and 13B, respectively. The distances from the upper and lower walls and front and rear walls of the model are both 6B.  
The grid generation is completed in the commercial software ANSYS ICEM CFD. In order to make the simulation results more accurate, the strategy of hybrid grid and layered encryption is adopted to discrete the computational domain. The tetrahedral grid with better adaptability is used in the area close to the model of the train-bridge system with a wind barrier, and a fine area and extra-fine area are set around the model. The hexahedral grid is employed in the computing domain, except for the fine and extra-fine regions, which can improve the grid quality and computational efficiency. In order to accurately simulate the flow separation, 10 boundary layer grids are established on the model surface of bridge, train, and curved wind barrier, as shown in Figure 3a. The height of the first layer of the boundary layer grids is 0.029 mm, which is calculated based on the dimensionless wall distance \(y^{+}=1\). The surface mesh of the model is shown in Figure 3b, the grid growth factor is set to 1.1, and the total number of grids varies with different wind barriers, but they all exceed 12 million. The grid quality is qualified after an assessment with ICEM CFD, which can guarantee the accuracy of the numerical simulation.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 4 Discussions
### Determination of LD\({}_{50}\)  
In order to obtain the value of LD\({}_{50}\) of the CAP jet to the HEK293 cells, the cell viabilities in Figure 5 are plotted versus the logarithmic values of (\(W/N\)), forming an S-shaped curve, as shown in Figure 6. The fitted value of log(\(W/N\)) corresponding to the cell viability of 50% is 1.54. Thus, the value of LD\({}_{50}\) is 34.67 mJ/cell. Although the obtained LD\({}_{50}\) value in this section is not versatile for other CAP sources and biological materials, this method combining plasma physics and pharmacy provides a novel idea that is easy to conduct and realize for estimating the plasma dosage in plasma medicine. The value of LD\({}_{50}\) plays a critical role as an indicator for studying the plasma biomedical effects provided that various types of CAP sources and cell lines are used in future studies. On the one hand, this dosage equals a 217-s CAP treatment to the HEK293 cells under the operating parameters as listed in Section 2.2. Thus, when studying the sub-lethal effects of the HEK293 cells using the CAP Bio-Med Platform, it would be better to limit the plasma treatment time to be shorter than 217 s under the discharge power of 0.8 W, or control the discharge power to be lower than 0.8 W when the plasma treatment time lasts 217 s or even longer. On the other hand, the LD\({}_{50}\) value of 34.67 mJ/cell is also an important reference datapoint when studying the lethal and/or sub-lethal effects of other cell lines using different types of CAPs.  
The published results concerning plasma dosage can be roughly divided into two types: one is the quantitative definition of plasma dosage [8], and the other skips the definition of plasma dosage but controls specific effects with the help of advanced algorithms such as machine learning [19; 23; 24; 25]. For the first type, the number densities of  
Figure 6: S-shaped curve of the cell viability versus the logarithmic value of (\(W/N\)).  
all the considered RONS should be precisely measured or calculated with great efforts; in particular, whenever the discharge parameters or even the environmental conditions are changed, all the measurements or calculations need to be re-conducted. While for the second type, the design, validation, and optimization of the algorithm with the considerations of complex processes as illustrated in Figure 1 are also a very challenging job. In this study, the measurement of LD\({}_{50}\) value is determined based on the biological effects of the CAP treatment with no considerations on the complicated physical and biochemical processes, as well as specific geometrical configurations and operating conditions of the CAP generators; consequently, it is a time-saving and feasible method for estimating the plasma dosage in practice.
# Deep Error-Correcting Output Codes
## 3 Deep Error-Correcting Output Codes (DeepECOCs)
### Incremental Support Vector Machines (Incremental SVMs)
#### 3.2.1 KKT Conditions  
To take the probabilistic outputs of the base classifiers as new representations of data, we adopt linear support vector machines (linear SVMs) as the binary classifiers (dichotomizers), which solve a quadratic programming problem  
\[\min_{\textbf{w},b,\hat{\mathcal{A}}_{i}}J(\textbf{w})=\frac{1}{ 2}\|\textbf{w}\|^{2}+\lambda\sum_{i=1}^{N}\hat{\xi}_{i}\] \[s.t.\quad\ y_{i}f(\textbf{x}_{i})\geq 1-\xi_{i},\xi_{i}\geq 0,i=1,\ldots,N \tag{1}\]  
where **w** and \(b\) are the coefficients and bias of the binary classifier, \(y_{i}\in\{+1,-1\}\), \(\xi_{i}\)'s are the slack variables, and \(N\) is the number of the training data.  
In the real world applications, the original sample space may not have a hyperplane that correctly divides the two types of samples. This is the need to use a kernel function to map samples from the original space to a higher dimensional feature space.  
Figure 1: 6 coding matrices encoded with different coding strategies. (**a**) one-versus-one. (**b**) one-versus-all. (**c**) DECOC. (**d**) ECOCONE. (**e**) Dense random. (**f**) Sparse random.  
In order to overcome the limitation of off-line binary classifiers in ECOC framework, we use online SVM as binary classifiers [45] and apply it in our experiments. We rewrite the Problem (1) in a Lagrange form,  
\[\min_{0\leq\alpha_{i}\leq C}:J(\mathbf{w})=\frac{1}{2}\sum_{i,j}\alpha_{i}Q_{ij} \alpha_{j}-\sum_{i}\alpha_{i}+b\sum_{i}y_{i}\alpha_{i} \tag{2}\]  
where \(Q\) is a symmetric matrix \(Q_{ij}=y_{i}y_{j}k(\mathbf{x}_{i},\mathbf{x}_{j})\), and \(\alpha_{i}\) is the Lagrange multiplier. The first-order conditions on \(J(\mathbf{w})\) reduce to the KKT conditions:  
\[g_{i}=\frac{\partial J(\mathbf{w})}{\partial\alpha_{i}} = \sum_{j}Q_{ij}\alpha_{j}+y_{i}b-1 \tag{3}\] \[= y_{i}f(\mathbf{x}_{i})-1\begin{cases}>0;&\alpha_{i}=0\\ =0;&0<\alpha_{i}<C\\ \leq 0;&\alpha_{i}=C\end{cases}\] \[\frac{\partial J(\mathbf{w})}{\partial b} = \sum_{j}y_{i}\alpha_{j}=0 \tag{4}\]  
The goal of incremental SVM is to preserve the KKT conditions for all previous training data when we add the unlearned examples into the solution. Based on the partial derivatives \(g_{i}\), the training examples can be partitioned into three different categories: the set \(\mathcal{S}\) of support vectors on the margin (\(g_{i}=0\)), the set \(\mathcal{E}\) of error support vectors violating the condition (\(g_{i}<0\)), and the remaining set \(\mathcal{R}\) of (ignored) vectors.
## 2 Materials and Methods
### Cytotoxicity Assay  
The cytotoxicity of AnTT and PT3 was determined using the yellow tetrazolium salt 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) according to previous studies with slight modification [16,30,32]. Briefly, 100 \(\mu\)L of SW1353 chondrocytes (\(5\times 10^{4}\) cells/mL) were seeded in a 96-well plate with complete media for 24 h. Subsequently, the media were removed and replaced with 100 \(\mu\)L of complete media with a series of concentrations of AnTT or PT3 for another 24 h. At the end of the treatment, 20 \(\mu\)L of MTT solution (5 mg/mL) was added to each well and then further incubated for another 4 h inside a 37 \({}^{\circ}\)C incubator. The optical density (OD) of each well was measured at the wavelength of 570 nM using a Multiskan GO microplate reader (Thermo Fisher Scientific, Vantaa, Finland). The viability of the treated cells was calculated by dividing the OD of the treated group with the OD value of the VC and multiplied by 100%.  
In certain experiments, SW1353 chondrocytes were challenged with MIA in its half-maximal inhibitory concentration (IC\({}_{50}\)) value (31.5 \(\mu\)M) determined from our previous study [16], with/without prior or concurrent treatment of locotrienols. In the preventive model, 100 \(\mu\)L of SW1353 chondrocytes (\(5\times 10^{4}\) cells/mL) were seeded in a 96-well plate with complete media for 24 h. Subsequently, the media were removed and the cells were pre-treated with 100 \(\mu\)L of fresh complete media with a series of concentrations of AnTT or PT3 for another 24 h. Lastly, the media were replaced again with 100 \(\mu\)L of fresh complete media with MIA. For the treatment model, 100 \(\mu\)L of SW1353 chondrocytes (\(5\times 10^{4}\) cells/mL) were seeded in a similar condition as the preventive model for 24 h. Subsequently, the media were replaced with 100 \(\mu\)L of fresh complete media with a series of concentrations of AnTT or PT3 and MIA (IC\({}_{50}\)) for another 24 h. An MTT assay was conducted at the end of the treatment. The viability of cells was calculated as a percentagerelative to the VC, as mentioned previously. The effective concentrations of AnTT and PT3 that proved to be protective against MIA-induced reduction in cell viability were used in the subsequent experiments.
## 2 Related Work
### Traditional ABR-Based Approaches  
Many ABR algorithms have been proposed for DASH, including BBA [10], BOLA [11], ELASTIC [12], MPC [13], ABMA+ [14], Festive [15], PANDA [16], and Pensieve [17]. These ABR algorithms are implemented on the client side of DASH and aim to optimize video streaming by selecting the highest possible bitrates while minimizing rebuffering. However, these ABR algorithms often rely on fixed rules for bitrate selection and streaming orchestration, which can be less effective in highly dynamic environments. In such conditions, accurately characterizing and predicting bandwidth and making decisions that consider the overall environment can be challenging.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 3 Traffic Incident Detection Based on Dynamic Graph Embedding  
Taking into account research questions Q1 and Q2, in this section, we describe the traffic incident detection based on the dynamic graph embedding. The main idea is to utilize the correlation between the multiple traffic time series to construct the graph where the nodes indicate the traffic flow and the weights of the edges is the correlation coefficient between two traffic flows. We set a sliding window to divide the multiple traffic time series into several time intervals and construct the dynamic graph. To quantify the graphs' information, we calculate the graph entropy based on the correlation coefficient matrix. The dynamic graph embedding model is proposed to cluster the graphs. Since there is a stable correlation in the graphs without the traffic incident, the graphs with anomalies are far from the regular graphs. In this case, the abnormal graphs are discriminated and can be detected as a traffic incident.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## The Proposed Scheme  
### Watermark Embedding Process  
The flow diagram of the watermark embedding process is shown in Figure 2.  
The concrete steps are described as follows:  
1. The original image \(I\) is transformed with DWT three times, and each level of DWT is performed sequentially on the original image, \(\text{LL}_{1}\), and \(\text{LL}_{2}\). Finally, \(\text{LL}_{3}\) is obtained for the SVD transform in Step 3. Due to the fact that the LL sub-band has sufficient information, the capacity of watermark embedding is large in the LL sub-band, which can ensure the good imperceptibility of the proposed method.  
Figure 1: Directions of the keypoints.  
Figure 2: Flow diagram of the watermark embedding process.  
* The watermark information is encrypted with the Arnold transform, and the scrambling time is saved as key \(T\).
* According to Equations (5) and (6), SVD is performed on the LL\({}_{3}\) sub-band to get three matrices: both \(U\) and \(V\) are orthogonal matrices, and \(S\) is the diagonal matrix of singular values. After that, singular values of the LL\({}_{3}\) sub-band are altered: \[I_{\text{LL}_{3}}=USV^{\text{T}},\] (5) \[S_{\text{w}}=S+\alpha\times W_{\text{a}},\] (6) where \(\alpha\) is the scaling factor, determining the performance of the watermarking scheme in robustness and imperceptibility, and \(W_{\text{a}}\) is the watermark encrypted with Arnold transform.
* Owing to the fact that the watermark for embedding is an image matrix, \(S_{\text{w}}\) is not a diagonal matrix after Equation (6), which can result in distortion in inverse SVD transform. Thus, SVD is performed on the \(S_{\text{w}}\) again to obtain the watermarked diagonal matrix, which is illustrated by: \[S_{\text{w}}=U_{\text{w}}S_{\text{ww}}V_{\text{w}}^{\text{T}}.\] (7)
* The watermarked sub-band \(I_{\text{LL}_{3\text{w}}}\) is generated by \(S_{\text{ww}}\), \(U\), and \(V\), as shown in Equation (8): \[I_{\text{LL}_{3\text{w}}}=US_{\text{ww}}V^{\text{T}}.\] (8)
* According to the concrete process in Step 1, three-level inverse DWT (IDWT) is performed with other sub-bands to generate the watermarked image \(I_{\text{w}}\).
* After Step 6, SIFT is performed on the watermarked image \(I_{\text{w}}\), and feature points are extracted. Meanwhile, descriptors of the watermarked image are recorded as keys \(P\) for the feature matching in the extraction process. To be more concrete, the coordinates, scales, and orientations of the feature points in the watermarked image are obtained, which are used for RST attack correction.  
In the end, the watermark embedding process is finished.