esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
## 3 The Proposed Watermarking Scheme  
### Adaptive Embedding Factor  
The determination of the watermark embedding factor is a trade-off problem, which needs to be adjusted according to specific applications and image attributes. In the actual operation, it may need to be tested and adjusted many times to find the best embedding factor, so as to ensure a good balance between the watermark effect and the image quality. In digital images, since the probability density function usually conforms to the normal distribution characteristics, the embedding factor is determined by evaluating the similarity between images, and the watermark is embedded into the cover image accordingly, making it difficult to be detected.  
The non-adaptive calculation of the embedding factor is easy to make the watermark image fragile, and it is cumbersome to calculate these values. Therefore, adaptive computing becomes the preferred solution. We use the Bhattacharyya distance between the cover image and the watermark image to adaptively calculate the embedding factor, and normalize the distance by an exponential function to obtain the embedding factor. The specific calculation formula is as follows:  
\[\alpha=\lambda\times\frac{1}{1+e^{d}} \tag{10}\]  
where \(\lambda\) is the parameter to be adjusted, the value range is 0 to 1, and d is the Bhattacharyya distance between the cover image and the watermark image.
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 3 Results
### Results for the Final Conceptual Design Assessment  
The last stage of the design parameters' exploration used a developed virtual prototype and numerical techniques to determine values from the electromagnetics dynamic modeling. The results obtained from previous stages were combined with conclusions derived from the results study, and a new design was developed with the following main parameters: number of poles, 48; number of rotor disks, 3; magnet length [mm], 1800; magnet width [mm], 400; magnet thickness [mm], 50; coil width [mm], 350; coil thickness [mm], 60; and air gap [mm], 40. Other important specifics are as follows: four rows of magnets are used--i.e., the middle wall has two rows of magnets, one on each side; the coils are larger than magnets so as to avoid a magnet overlapping with tangential sides of the coil.  
The performed simulations fully correspond to the previous simulations (rotational speed of 13 rpm and simulation model setup), and they were run for three load cases of the following mechanical input power: 10 MW, 14 MW, and 18 MW. The results are summarized and shown in Figure 8, together with preliminary variants' results, for a better review and comparison.  
Several observations could be outlined, based on the results obtained via the simulations of the final design variant:  
* The preliminary defined design has insufficient efficiency, reaching \(\approx\)75% at a 10 MW mechanical power input;
* The best design variant, III, reaches \(\approx\)96% efficiency at a 10 MW mechanical power input;
* The final design variant also shows consistent efficiency, even at a mechanical power input increased up to 18 MW, reaching \(\approx\)92% efficiency;
* A further feasibility study needs to examine the manufacturability and other technical specifics of the developed conceptual design variant.
## 5 The Query Management Scheme
### System Architecture  
Figure 3 briefly illustrates our system architecture, which consists of four major components: the users, the user applications, the Base Station and the sensor network. Below is a brief description of each component.  
Figure 3: System architecture for the query management scheme.  
1. The Users/Applications Each user uses an application to access underwater sensor networks. The users can send queries and receive query results through the base station with the user applications.
2. The Base Station (BS) In our system architecture, the base station plays a crucial role in organizing queries into sets, receiving sensor data from each node, reorganizing the query results, and transmitting the data to each of the applications. Since the BS assumes an extensive role, it is divided into three units, each of which is enumerated below. The BS has a comprehensive network metadata called a \"network dictionary\" that contains the information of sensors attached to each node and the location of node. With the network dictionary, the Base Station selects the \"target nodes\" for each query--the nodes that collect the data fields requested by each query. Figure 4 is an example of a network dictionary. The BS is provided with infinite processing/transmitting power. 1. The Query Manager (QM) The QM receives queries from the users up to as much as a node can process at a time, arranges them into a query set, and routes the set to targeted sensor nodes. The network dictionary is used to select the target nodes. 2. The Node Manager (NM) The NM receives the query result tuples from the sensor nodes. Using the time stamps on the result tuples, the NM generates a timeline of queries to determine which queries are responded at which moment. It then generates the final response tuple that has a number label to clarify its time sequence, and passes the differently labeled tuples to the Distributor. 3. The Distributor The distributor receives the final tuples from the NM and sends the arranged query results to the user applications. 3. Sensor Nodes Each sensor node receives queries and merges the query results. It sends back result tuple to the BS.  
Figure 4: Example of a network dictionary possessed by a BS.  
Our query management scheme is divided into four phases: the Query Transmission Phase (QTP), the Query Processing Phase (QPP), the Query Response Phase (QRP), and the Response Distribution Phase (RDP). The following subsections provide detailed explanations for each phase.
## 2 Materials and Methods
### Pre-Impact Fall Detection During E-Scooter Riding
#### 2.2.3 Unsupervised Model  
The same sliding-window technique was applied as in the supervised model (Section 2.2.1). The window size was 1 s, corresponding to 60 frames, with a 50% overlap [21]. The input data consisted of 6 features: 3 axes of acceleration and 3 axes of angular velocity, resulting in a data dimension of 6 \(\times\) 60. The data were divided into training and test datasets with a 2:1 ratio, and 20% of the training dataset was used for model validation during training. Normal driving data were used as the normal data, while fall data were considered abnormal. Only the normal data were used for training the model.  
Knowledge distillation is a technique used to transfer knowledge from a large, complex model, known as the \"teacher\", to a smaller, more efficient model, referred to as the \"student\" [22]. The goal is to compress the teacher's knowledge into the student model while preserving its performance and generalization capabilities. The teacher model consisted of a 3-layer CNN encoder with filter sizes of 128, 64, and 64, respectively. LeakyReLU activation functions were applied in each layer, and max pooling was used between layers to prevent overfitting. The decoder also employed a 3-layer CNN with filter sizes of 64, 64, and 128, respectively, and LeakyReLU activation functions were applied. The student model comprised a 3-layer CNN encoder and a 3-layer decoder, with filter sizes of 8, 8, and 4 in the encoder, and 4, 8, and 8 in the decoder, respectively. Similar to the teacher model, LeakyReLU activation functions were used for both the encoder and decoder. Max pooling was applied in the encoder to prevent overfitting.  
Reconstruction error [23] refers to the difference between the input and the output data of an autoencoder and was calculated as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\hat{x}_{i})^{2} \tag{8}\]  
where \(x_{i}\) represents the original value of the input window, and \(\hat{x}_{i}\) represents the value of the reconstructed output window.  
The autoencoder model (Figure 4) was trained only for the normal data from the training dataset, and the maximum reconstruction error observed during training was set as the threshold. Any window data exceeding this threshold were considered anomalous. If two consecutive windows were classified as anomalous, the end frame of the second window was designated as the detected frame, and the data were then classified as a fall. Accuracy, sensitivity, precision, F1 score, and lead time were calculated as Equations (3)-(7).  
Figure 4: Deep learning autoencoder model.
# Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks
###### Abstract  
Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.  
lipreading; temporal convolutional networks; 3D-CNN; ResNet50 +
Footnote †: journal: Applied Sciences
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 3 Mathematical Formulation
### Solar Unit  
The data received from the sun to the array surface is converted to its output power using the equation  
Figure 1: Centralized control micro-grid (MGCC) structure with the electricity market interaction.  
\[P_{PV}=\
icefrac{{G}}{{1000}}\times P_{PV,rated}\times\eta_{pv,conv} \tag{1}\]  
In Equation (1), \(G\) is the radiation power perpendicular to the array surface (W/m\({}^{2}\)) and \(P_{PV,rated}\) represents the nominal power of each array, which is obtained for the cost. \(\eta_{PV,conv}\) is also equivalent to the efficiency of the DC/DC converter installed between each DC array and the corresponding bus. By verifying the vertical and horizontal component of the solar power, at any moment, the power can be transmitted (vertically) onto the surface of the array installed with the angle \(\theta_{PV}\) could be calculated according to (2)  
\[G(t,\theta_{PV})=G_{V}(t)\times\cos(\theta_{PV})+G_{H}(t)\times\sin(\theta_{ PV}) \tag{2}\]  
where \(G_{H}(t)\) and \(G_{V}(t)\) are the horizontal and vertical radiation rates in terms of (W/m\({}^{2}\)), respectively.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 5 Measurement Results  
The captured waveforms were processed one after another according to the algorithm shown in Figure 6. For each waveform file, 10,000 iterations were conducted, which required the record duration to be approximately 2 h 47 min (over 6400 s). First, the synchronization success rate was evaluated for each [channel type, SNR] pair. The results are presented in Table 1. As may be seen, the synchronization procedure performs better in the AWGN channel, while in the fading channel it is strongly related to SNR level. Nevertheless, in each case at least 9000 iterations were successful, which is sufficient for the evaluation of channel estimation.
## 2 Centralized Treatment Plants
### Urban Wastewater Treatments
#### 2.1.3 Membrane Technologies
The water from NF was irrigation- and cooling system-compliant, while, from the UF, only the cooling system was compliant due to the high electric conductivity levels of the effluent [66]. A tertiary treatment technology, a continuous ultrafiltration (c-UF) system with self-cleaning capacity coupled to an ozonation process, was studied from the technical-economic point of view. The secondary effluent was supplied to the c-UF pilot plant at a flow rate of 3.3 m\({}^{3}\) h\({}^{-1}\), and the presence of suspended solids and **CECs** were the main analytical parameters. To extend the filtration operating time, the c-UF plant has a self-cleaning device, a filter placed prior to the c-UF equipment which has a self-cleaning system through a rotating purge arm. The self-cleaning system contributed to a reduction in turbidity of 95-96% and in suspended solids of 90.1-100%. _E. coli_ and total coliforms were completely removed in the permeate stream. The reject stream and wash waters were treated by an ozonation process. All CECs identified in WW were completely removed, except for 2-hydroxybenzothiazole and caffeine, with reduction values of 39.7% and 38.1%, respectively. The reclaimed water met each and every one of the criteria established in the new European regulation (EU 2020/741), and this process was 39.1% more economic with regard to conventional tertiary treatment, an interesting feature for implementation of this technology both in developed and developing countries [67].
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 3 A Coordination Technique for Scalable BFT Consensus
### Step 1. Electing a Prime Node  
The first step is to elect a prime node among all participant nodes. The elected prime node plays a role of interacting with a consensus coordinator. This election step runs each regular \(t\) time (we assumed that all nodes have the same time unit through a logical clock or a physical clock algorithm (e.g., [29; 30; 31])). Once all steps are completed, this prime node election step is carried out again. The algorithm of this step is shown in Algorithm 1.  
Figure 2: Overview of the coordination technique for scalable BFT-based consensus.  
```
1:\(random=\text{Random}(seed)\) % \(N(Node)\)
2:ifrandom equals to \(Node_{i}\)then
3:\(sig_{prime}\) = Signature \((Node_{i},seed)_{sk_{prime}}\)
4:notify (\(sig_{prime}\), \(pk_{prime}\)) to CC
5:endif
```  
**Algorithm 1** [_All Nodes_] Electing Prime Node  
Electing the prime node starts with a _seed_ which is a previous block hash value, and modulates the random number with the seed by total number of nodes \(N(node)\) to get a prime node number. The hash value of a previous block must be the same throughout all nodes because they are already agreed in a previous round. In the case that the result \(random\) from the random algorithm is equal to a unique number of each node \(Node_{i}\) that is assigned before, a node is elected as a prime node. After signing its node number \(Node_{i}\) and \(seed\) with its private key \(sk_{prime}\), it notifies the consensus coordinator \(CC\) with the result of sign \(sig_{prime}\) and its public key \(pk_{prime}\).
# Using Modeling to Select the Type of Microwave Field Emitter for Dense-Layer Grain Dryers
###### Abstract  
The microwave field is used for drying and disinfecting grains during the pre-sowing seed treatment. The use of a microwave field in these installations leads to an increase in their productivity and a decrease in the energy consumed by them. In grain dryers, where the grain moves in a dense layer without being loosened, one of the challenges in using microwave fields is ensuring sufficient uniformity of the field distribution. In this article, waveguide design options that introduce microwave radiation into the grain layer are discussed. The objective of this study was to use application software to find the optimum type of transmitter from the three options presented. A mathematical simulation of the electromagnetic field distribution was performed with the use of CST Microwave Studio software 2019 in order to evaluate and compare horn-type, rectangular, and semicircular waveguides. The data on the standing wave ratio and radiation efficiency of these types of waveguides have been reported. The specific features of the microwave electromagnetic field distribution and radiation power in the output of these waveguides have been described. The results of mathematical simulations revealed that semicircular waveguides with slot-type radiators are preferable for processing dense grain layers.  
microwave electromagnetic field; waveguide; slot-type radiators; standing wave ratio; radiation efficiency
# Analyzing GPU Performance in Virtualized Environments: A Case Study
## 2 Background
### Para- and Full Virtualization  
GPUs in this category are virtualized at the driver and hypervisor levels. Paravirtualization employs customized GPU drivers in the guest OS, whereas full virtualization uses standard drivers. A notable example is Intel GVT-g [7], a technology that utilizes mediated passthrough (MPT) for efficient and isolated vGPU access. This method supports advanced features such as live migration [26] and enhances vGPU density [27; 28], contributing to its comprehensive utility in virtualized environments.  
Although not widely adopted in cloud environments due to competition from proprietary solutions, GVT-g holds significant value for its distinctive technical advantages. It seamlessly integrates with the Linux kernel as an open-source software-based solution, enabling community customization and broader applicability compared to closed-source alternatives. Furthermore, its lightweight design minimizes performance overhead and hardware dependence, making it a cost-effective option for diverse computing scenarios.
## 4 Discussion
### Maximum Strength Evaluation
#### 4.2.3 IRBM--F (Internally Reinforced Brick Member Fixed by Nut)  
In this specimen, from the strain data above, effective use of the reinforcement was confirmed by fixing the SI rods, which resulted in a great improvement in structural performance. As a result, the stress of the bottom rod was expected to reach its tensile strength at the maximum strength point, and that of the top rod was found to exceed its yield strength. Therefore, for the maximum strength evaluation, the stresses of the bottom and top rods were set to be the tensile (\(\sigma_{t}\)) and yield (\(\sigma_{y}\)) strengths, respectively, which were obtained from the material test (See Table 3). It was also noted that when the maximum strength was recorded, the neutral axis depth was very short, and the neutral axis was quite close to the extreme compression fiber of the damaged section since the cross-section had a relatively wide width (430 mm) compared to the depth (210 mm). Therefore, for simplicity, the neutral axis was assumed to be located at the extreme compression fiber, and the force equilibrium condition at the damaged cross-section was set to be as in Figure 12. The maximum strength \(Q_{max,IRBM--F}\) was then evaluated from Equations (6) and (7), where \(\sigma_{t}\) and \(\sigma_{y}\) in Table 3 were substituted for the bottom rod stress (\(\sigma_{tr}\)) and the top rod stress (\(\sigma_{tr}\)), respectively. The calculated \(Q_{max,IRBM--F}\) was 129.43 kN and was shown together with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & \(Q_{max,cal}\) **(kN)** & \(Q_{max,exp}\) **(kN)** & \(Q_{max,cal}\) **/\(Q_{max,exp}\)** \\ \hline UBM & 14.84 & 13.96 & 1.06 \\ \hline IRBM & 14.84  & 27.08 & 0.55 \\ \hline IRBM—F & 129.43 & 126.20 & 1.03 \\ \hline IRBM—FNM & 102.92 & 120.47 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum strength calculation results.  
the test results in Figure 10c. The estimation value was also compared with the experiment value in Table 4. As can be seen in the Figure and Table, the ratio of the calculation to the experiment of the maximum strength was 1.03, and the evaluation result exhibited good accuracy.  
\[M_{max}=A_{br}\times\sigma_{br}\times d_{br}+A_{tr}\times\sigma_{tr}\times d_{tr} \tag{6}\]  
\[Q_{max,IRBM-F(IRBM-FNM)}=\frac{4M_{max}}{l} \tag{7}\]  
where \(A_{br}\) is the total cross-sectional area of the bottom rod (230 mm\({}^{2}\) in this study), \(A_{tr}\) is the total cross-sectional area of the top rod (230 mm\({}^{2}\) in this study), \(\sigma_{br}\) is the stress of the bottom rod, \(\sigma_{tr}\) is the stress of the top rod, \(d_{br}\) is the distance between the extreme compression fiber and the rod position on the bottom side (160 mm in this study), and \(d_{tr}\) is the distance between the extreme compression fiber and the rod position on the top side (50 mm in this study).
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 5 Conclusions  
1. Utilizing the UMESHMOTION subroutine, a conventional energy dissipation model was modified to account for the interaction between temperature and wear. The plastic deformation of Ti-6 Al-4V alloy materials is considered in the development of numerical simulation methods and finite element models of thermal-mechanical-wear coupling. It is possible to obtain the evolution of the contact surface's temperature rise, contact pressure, and wear volume.
2. Wear is reduced when the temperature and plastic deformation of materials are not considered. When the plastic deformation of materials is considered, the temperature of the contact surface is lower and the wear profile is no longer a smooth Hertzian shape.
3. The amount of wear increases with an increase in fretting speed. The temperature in the contact area and near the contact area increases with increasing fretting speed. The temperature of the surface near the non-contact area is the highest at a fretting speed of V.
4. The amount of wear increases with the number of cycles, whereas the rate of increase in wear decreases. The peak contact pressure decreases with the increase in the number of cycles. The peak temperature rise on the contact surface increases with the number of cycles, and its horizontal position moves with the cylindrical specimen.
5. The normal load has an influence on the temperature rise, the contact pressure, and the amount of wear. The wear profile is much less smooth under variable normal loading. Two variable normal loads with the same phase have similar wear profiles and temperature rise distributions.  
Conceptualization, methodology, resources, formal analysis and funding acquisition, L.L. ([PERSON]); validation and writing--original draft preparation, W.Z.; software and data curation, G.L.; visualization and editing, J.W.; investigation and funding acquisition, L.L. ([PERSON]); methodology and project administration, M.X. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Variation law of temperature distribution with normal load.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant Number 51975449) and the Key Research and Development Program of Shaanxi (Grant Number 2021 GY-309). The authors are grateful to other participants of the project for their cooperation.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.