esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Intraparticle to Interparticle Entanglement Swapping Protocols
## 3 Methods
### Entanglement Swapping Protocols
#### 3.1.3 Entanglement Swapping Protocols Based on the Cross-Kerr Medium
The Hadamard's effect is as follows:  
\[H\ket{1}\rightarrow\frac{1}{\sqrt{2}}(\ket{0}-\ket{1}), \tag{15}\]  
and  
\[H\ket{0}\rightarrow\frac{1}{\sqrt{2}}(\ket{0}+\ket{1}). \tag{16}\]  
After photons pass through the half-wave plate placed at 22.5\({}^{\circ}\), \(\ket{\varphi}_{6}^{\prime}\) will become \(\ket{\varphi}_{7}^{\prime}\), and the equation is as follows:  
\[\ket{\varphi}_{7}^{\prime}=\frac{\sqrt{(1-\alpha^{4}-\beta^{4})}}{2}[(\ket{01 }_{102o}+\ket{10}_{102o})(\ket{00}_{152s}-\ket{11}_{152s})+(\ket{01}_{102o}- \ket{10}_{102o})(\ket{01}_{152s}-\ket{10}_{152s})], \tag{17}\]  
Figure 4: The experimental scheme of entanglement swapping based on the cross-Kerr medium. R\({}_{45}\) represents a half-wave plate placed at \(45^{\circ}\), R\({}_{22.5}\) represents a half-wave plate placed at \(22.5^{\circ}\), QND represents quantum nondemolition measurement, PBS represents polarization beam splitter, and D\({}_{\{i|i=1,2,3,4\}}\) represents detectors. where \(1o2o\) and \(1s2s\) represent the orbital and spin angular momentum degrees of freedom of photons 1 and 2, respectively. Next, photons 1 and 2 pass through a PBS, and then four photon detectors \(D_{1},D_{2},D_{3},D_{4}\) are used to detect photons. Based on detected results, the entangled state of the two photons is determined. If \(D_{1},D_{3}\) or \(D_{2},D_{4}\) detectors respond at the same time, the entangled state \(|\varphi\rangle_{7}^{\prime}\) collapses as \(|\varphi\rangle_{8}^{\prime}\), which is shown by the following equation:  
\[|\varphi\rangle_{8}^{\prime}=\frac{\sqrt{(1-\alpha^{4}-\beta^{4})}}{2}(|01 \rangle_{1o2o}+|10\rangle_{1o2o}). \tag{18}\]  
Performing the unitary operation \(\sigma_{x}\) on the orbital angular momentum degree of freedom of photon 2 will provide the desired entangled state. The equation is as follows:  
\[\ket{\varphi}_{3}^{\prime}=\alpha^{2}\ket{00}_{1 so}\ket{00}_{2 so}+\alpha\beta( \ket{00}_{1 so}\ket{11}_{2 so}+\ket{11}_{1 so}\ket{00}_{2 so})+\beta^{2}\ket{11}_{1 so}\ket{11}_{2 so}, \tag{11}\]0  
If \(D_{1},D_{4}\) or \(D_{2},D_{3}\) detectors respond at the same time, the entangled state \(|\varphi\rangle_{7}^{\prime}\) collapses as \(|\varphi\rangle_{10}^{\prime}\), which is represented by the following equation:  
\[\ket{\varphi}_{3}^{\prime}=\alpha^{2}\ket{00}_{1 so}\ket{00}_{2 so}+\alpha\beta( \ket{00}_{1 so}\ket{11}_{2 so}+\ket{11}_{1 so}\ket{00}_{2 so})+\beta^{2}\ket{11}_{1 so}\ket{11}_{2 so}, \tag{11}\]1  
For the same reason, the entangled state desired will be obtained by performing the unitary operation \(i\sigma_{y}\) on the orbital angular momentum degree of freedom of photon 2.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 4 PEAR Solution: Model Design and System Implementation
### PEAR Consumption Model  
In an ISA100.11a network, the system manager allocates cyclic communication opportunities to transmit or receive periodic sensor measurements and management data such as network reports and alarms. While the Tx and Rx rate of sensor data is based on a static configuration and is expected to be the largest source of energy consumption, the frequency and size of management packets are dynamically determined based on the network conditions and events. Moreover, due to physical phenomena such as interference and channel path attenuation, packets can be lost or received with errors resulting in additional retransmitted packets. The PEAR algorithm validates the energy threshold constraints against the cumulative Tx and Rx opportunities used, regardless of their type.  
The ISA100.11a standard specifies dynamic and static energy attributes to enable energy management. The values of the EnergyLeft dynamic energy attribute is determined at network runtime and reflects the remaining battery capacity. The static energy attributes such as EnergyLife, ListenRate and TransmitRate are used by sensor manufacturers to specify the energy design of an ISA100.11a field device. The values of the static attributes are generated to emulate the manufacturer energy design specification. These attributes are the reference for the consumption thresholds and energy profile definition adopted by the PEAR algorithm.  
The actual energy consumption within a time slot (TS) is dependent on the packet data size and the radio module consumption rate in different operating regimes. Since PEAR should adapt to a network that is dynamic and configurable over time, the number and size of the data packets cannot be determined beforehand. To cover the worst consumption case scenario, a publish ISA100.11a data packet with the maximum size of 115 bytes (i.e., 80 bytes for the payload and 35 bytes for the headers) is considered as the reference for consumption and lifetime estimation.  
The Tx/Rx consumption rate is measured on the VN210 radio module [10] factoring in the time spent and current drawn in each operation mode namely, packet processing, clear channel assessment (CCA), Tx/Rx, operation mode switching, and acknowledgement Tx/Rx. Considering as reference a packet with the size of 115 bytes, the average consumption rate is 27.123 mA for packet transmission and 14.913 mA for packet reception. Equivalently, each Tx and Rx of a DPDU drains 7.534 mAh/149.132 nWh and 41.425 mAh/271.24 nWh respectively from the available battery capacity. Based on this consumption model, the sensor lifetime is estimated depending on the battery capacity and the number of DPDU packets per minute.  
For instance, a sensor node having a battery capacity of 25 Ah and maintaining the Tx and Rx consumption rate lower than 135 DPDUs/min is expected to have a three-year lifespan.
## 2 Materials and Methods  
### Installation of VRFB Single Cell  
A VRFB single cell was created to prepare vanadium electrolytes and to investigate battery flow performance. The cell design included two half-cells separated by an ion-exchange membrane. The cell was constructed by a set of components distributed in sandwich layers, consisting of the inclusion of bipolar plates, joints, and carbon paper electrodes, as illustrated in Figure 2. This VRFB cell structure was composed of inlet and outlet channels (Teflon manifold) for the electrolyte and a flow distribution gate, and the vanadium electrolyte was fed by peristaltic pumps. The electrolyte flowed through the manifold, going into the single cells separately. The graphite bipolar plates contributed to the uniform transport of electrolytes and to electron conduction. Teflon flow frames were applied to increase homogenously the electrolyte flow to the electrode. Three-layer carbon paper electrodes (Sigracet SGL 25 AA) are active sites where redox reactions occur. The membrane (Nafion Store, Ion Power) was used to separate the anode and cathode and to transfer protons. The sealing structure of VRFB was utilized to prevent internal and external leakages in the cell. In this work, fluorine-rubber gaskets with the proper features were designed for preventing leakages. The components of our VRFB cell were assembled in the structure under compressive pressure in the following sequence: end plate, manifold, current collector, bipolar plate, gasket, flow frame, electrode, gasket, and membrane. Both sides of the cell were assembled as a mirror sequence. After the system was installed as in Figure 2, the leakage test of the cell was conducted. The specifications of the VRFB used in this work are presented in Table 1.  
Figure 1: Flow pattern of VRFB stack. (**a**) Horizontal VRFB stack; (**b**) Vertical VRFB stack.
## 4 Conclusions  
This study, based on indoor rheological tests, used raw materials of the debris flow from Jiangjiagou as the research subject to obtain the rheological curves of the debris flow slurry at different solid volume concentrations. It discussed the evolution laws of yield stress and viscosity at different solid volume concentrations, constructed a predictive model for the rheological parameters of the Jiangjiagou debris flow based on fitting regression, and thoroughly analyzed the influence mechanism of solid volume concentration on the rheological characteristics of the debris flow slurry. Our conclusions are as follows:  
1. The rheological curves of the debris flow from Jiangjiagou generally conformed to the Bingham fluid model, where shear stress consistently increases linearly with shear rate. The elevation in solid volume concentration continuously increased the rheological curves' upward trend; yet, the increase in shear stress was not particularly significant at low volume concentrations and became more pronounced only at high volume concentration.
2. In a semi-log coordinate system, both the yield stress and the viscosity of the debris flow from Jiangjiagou exhibited a linear relationship with the solid volume concentration, which formed the basis for establishing a predictive formula for the rheological parameters of the debris flow in relation to the solid volume concentration.
3. The increase in solid volume concentration led to a higher solid phase volume fraction in the debris flow, resulting in reduced water content, increased viscosity, inhibited turbulence in the debris flow, a tighter structure, mutual adhesion of coarse and fine particles, greater friction between particle contacts, and hence, a continuous increase in resistance.
4. Typical clay minerals with high content exhibit superior properties in specific surface area, hydrophilicity, expansibility, and surface adsorption capability compared to atypical clay minerals, thereby endowing the debris flow slurry under high volume concentration conditions with strong capture ability of pore water, resulting in a debris flow with strong fluidity and viscosity.
5. The debris flow exhibited a significant shear-thinning effect, which was more pronounced at lower shear rates, greatly reducing the viscous resistance of the debris flow.  
[PERSON] was responsible for planning the experiments, directing the research project, and writing this paper. [PERSON] was responsible for some experiments. [PERSON] was responsible for data sorting. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial supports of the Planned Project of Gansu Science and Technology Department [grant No. 21 JR7 RA310] and the Youth Science Foundation of Lanzhou Jiaotong University [grant No. 2021029].  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 4 Evaluation Indexes
### Evaluation of the Invisibility of Watermark  
The invisibility of the watermark is measured by the quality of the watermarked image. The recovery capability of the algorithm is measured by the quality of the restored image. The higher the quality of the image, the better the invisibility of the watermark will be and the stronger the recovery capability will be. The standard for evaluating the quality of image is the PSNR. Assuming that the size of the image is \(M\times N\), the PSNR is defined as Equation (1):  
\[\text{PSNR }=\ 10\log_{10}\frac{255^{2}}{\text{MSE}}(\text{dB}) \tag{1}\]  
\[\text{MSE }=\ \frac{1}{M\times N}\sum_{i\ =\ 1}^{M}\sum_{j\ =\ 1}^{N}[\mathbf{I}(i,j)-\mathbf{I}^{ \prime}(i,j)]^{2} \tag{2}\]  
where \(\mathbf{I}\) is the original image and \(\mathbf{I}^{\prime}\) is the image after processing. The MSE in Equation (2) represents the mean square error between the two parts. The higher the value of the PSNR, the better the image quality will be.
## 4 Results: Monitoring and Slope Stability
### Site Preparation and Sensor Planning
Radar was set up to scan the images of the slope surface to provide the changes in slope surface behavior. The scanned images can be investigated in connection with the inclinometer data and pore pressure changes, which can be implemented to provide a slope warning system and be used for setting out threshold value.
# HyperIO: A Hypervisor-Based Framework for Secure IO
## 3 System Design
### Implementation Details  
HyperIO is based on a thin hypervisor, which intercepts a minimal set of events. The hypervisor does not emulate the peripheral devices and therefore does not support multiple operating systems' execution. These properties allow the hypervisor to incur an insignificant performance overhead. The hypervisor intercepts only events that are required to achieve its goals. Some events are intercepted only in secure mode; others are intercepted in the regular mode. We can divide the intercepted events into two categories: (a) access to IO ports and (b) access to memory regions. HyperIO intercepts access to IO ports by setting the corresponding bits in the IO-bitmap of the VMCS. Access to memory regions are intercepted by zeroing the EPT entries' access rights.  
HyperIO assumes that the system is equipped with a PS/2 keyboard and monitors port 0x60, the IO port that the operating system uses for communication with the keyboard. Writing to port 0x60 is intercepted in both modes of operation. The operating system configures the keyboard LEDs by writing a sequence of two bytes to port 0x60. The first byte is 0 xED, and the second byte defines the state of the LEDs. Specifically, the scroll lock LED's state is defined by the second byte's least significant bit. HyperIO sets this bit according to the current operation mode, regardless of the operating system's setting.  
When the user presses or releases a key, the keyboard generates an interrupt. The operating system reacts to this interrupt by reading from port 0x60. When in the secure mode, HyperIO intercepts the operating system's attempt to read from port 0x60 and replaces the actual code of the pressed key with the code of the asterisk key.  
During the transition into the secure mode, HyperIO gains control of the display adapter in two steps:  
1. the display mode is selected to be text mode;
2. the memory region that controls the displayed characters is configured to be inaccessible.  
In text mode, the display adapter uses the memory region that resides at physical address 0 xB8000. HyperIO puts in this region the text that it was requested to display. Invideo mode, the display adapter uses a different memory region configured by the operating system. Therefore, the operating system can freely modify the video mode memory region while the secure mode is active without affecting the pixels that are displayed on the screen, while in secure mode, HyperIO prevents malicious modification of the display adapter's video mode by intercepting the operating system's access to the configuration registers. The PCI configuration space reports the location of these ports. Typically, the operating system accesses the configuration registers of the display adapter only during its initialization. We can assume that a benign operating system never accesses the memory regions secured by the hypervisor after the operating system's initialization completes. Therefore, in our current implementation, HyperIO resets the PC when malware attempts to access these memory regions.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 4 Discussion
However, it should be taken into account that the more branches there are in the network, the larger the area it covers, but the distance between the most distant nodes will decrease. Losses also impose limitations on scalability. The maximum acceptable distance between nodes ranges from 10 to 56 m, depending on the connection throughput between nodes, node locations, and central frequency. Thus, by knowing the node locations (outdoors in urban or rural areas or inside residential buildings), the allowable distance between nodes, and the number of nodes that ensure the desired quality of service for each scenario, it is possible to assess the possible distances and areas over which the network can reliably and qualitatively transmit data. Analytical expressions for calculating the values of metrics characterizing the network operation are obtained in this paper. Calculations using these expressions are convenient to perform, for example, in MATCAD or MATLAB environments. In this case, it is possible to immediately build dependencies of performance indicators on the network parameters. Therefore, the computational cost of the study was minimal compared to modeling.
# Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net
## 3 Settings for Simulative Studies
### Indexes of the Quality Assessment  
To evaluate the performance of the GMResUnet, we defined three metrics to evaluate the effect of sparse projection data synthesized into multi-angle projection data, i.e., the difference between the predicted values and the real labeled dataset, as the peak signal-to-noise ratio (_PSNR_), structural similarity (_SSIM_), and the projection data synthesis error [35]. These performance metrics are defined as follows:  
The peak signal-to-noise ratio (_PSNR_) is the ratio of the maximum power of the signal to the noise power of the signal in decibels (dB). It is defined based on the mean square  
Figure 9: Evolution of the loss function with training cycles for 4 sparse projection data. (**a**) Training set; (**b**) validation set.  
error (_MSE_) for the given original image \(I\) of size \(m\times n\) and a noisy image \(K\) to which noise has been added. Its _MSE_ and _PSNR_ are defined as:  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I(i,j)-K(i,j)]^{2} \tag{10}\]  
\[PSNR=10\cdot\log_{10}(\frac{MAX_{I}^{2}}{MSE}) \tag{11}\]  
where \(m\) and \(n\) are the length and width dimensions of the image respectively; _MSE_ is the mean square error; \(I\) is the true value; \(K\) is the predicted value; and \(MAX_{I}\) is the maximum possible pixel value in the image.  
The structural similarity (_SSIM_) is a measure of the similarity of two images. The closer the _SSIM_ value is to 1 means that the two images are more similar. The specific mathematical representation is:  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})\cdot(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2} +\mu_{y}^{2}+c_{1})\cdot(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{12}\]  
where \(x\) and \(y\) are the original and reconstructed images, respectively; \(\mu_{x}\) and \(\mu_{y}\) are the mean values corresponding to the images \(x\) and \(y\), respectively; \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variance of the images, respectively; \(\sigma_{xy}\) is the covariance of \(x\) and \(y\); and \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are the constants used to maintain stability, where \(L\) is the range of pixels of the images \(x\) and \(y\), usually taken as \(k_{1}=0.01\), \(k_{2}=0.03\).  
The relative error represents the error between the generated values of the projection data and the labeled data:  
\[err=\frac{\Delta}{L}\times 100\%=\frac{\sum_{k=1}^{N}|y_{k}^{*}-y_{k}|}{\sum_{k=1 }^{N}y_{k}^{*}}\times 100\% \tag{13}\]  
where \(\Delta\) is the absolute error; \(L\) is the true value; \(y_{k}^{*}\) and \(y_{k}\) are the true and generated values of the \(k\)th pixel of the projection data matrix, respectively, and \(N\) is the total number of pixels of the absorbance data.
# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 2 Methodology
### YOLO (You Only Look Once)
#### 2.2.1 YOLOv3u  
YOLOv3u is an advanced variant of YOLOv3, optimized by Ultralytics for improved accuracy and inference speed. It incorporates the anchor-free, objectness-free split head used in YOLOv8 models. While maintaining the original Darknet-53 backbone and neck architecture from YOLOv3, the aforementioned modification eliminates the need for pre-defined anchor boxes and objectness scores. The updated detection head improves the model's ability to detect objects of varying sizes and shapes.
# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 5 Results  
### Quantitative Results  
For systems trained on natural singing, Tables 1-3 list quantitative metrics related to timbre, timing and pitch models respectively. Examples of different modulation spectra for timbre and pitch are shown in Figures 5 and 6. For systems trained on pseudo singing, Table 4 lists quantitative metrics related to timbre models.  
These metrics show that, for some of the framewise metrics, such as harmonic MCD, our system is slightly behind. For some other metrics, such as the timing errors or aperiodic BAPD, our system is slightly ahead. For systems trained on pseudo singing the differences tend to be a little bigger, we argue that this is due that predicting averages for this kind of data results in good results for these kind of metrics. However, in all metrics based on the modulation spectrum, which considers variations in time, NPSS shows an improvement over the other systems.  
When we compare an example of generated harmonic parameters during a vibrato in the left two subplots of Figure 5, we notice the features predicted by NPSS having more detail than Sinsky-HMM and Sinsky-DNN. In particular the framewise conditioning of harmonic features on F0 in NPSS, causes the harmonic features to modulate along the vibrato, similar to what happens in the reference recording. In the modulation spectrum analysis on the right-hand side of Figure 5, we can see that overall NPSS tends to follow the modulation spectrum of the reference recording a little closer than Sinsky-HMM and Sinsky-DNN in lower modulation frequencies. Compared to especially Sinsky-DNN, NPSS has less rolloff in higher modulation frequencies, indicating less oversmoothing over time. However, all systems have less high frequency modulation spectrum content than the reference recording, indicating none of the systems are able to reproduce all the details of the original signal.  
The analysis of the modulation spectrum of the log F0 predicted by different systems is shown in Figure 6. We can see that overall NPSS matches the modulation spectrum of the reference recording similarly or slightly better than Sinsky-HMM, but notably better than Sinsky-DNN. When we focus our attention to the range of modulation frequencies corresponding to vibratos in this voice, 5-7 Hz, we see that Sinsky-HMM and Sinsky-DNN have a sharp peak at 5 Hz, whereas for NPSS this whole range has increased energy, similar to the reference. This may indicate that NPSS produces a wider range of vibrato rates, similar to a real singer. In Sinsky-HMM and Sinsky-DNN vibrato parameters (rate and depth) are modeled separately from the base F0, which may explain their tendency to produce very controlled, regular vibratos.