esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 3 Visibility-Based Collision Warning System Design  
The target problem in this work is to design a system that can not only predict vehicle collisions, but also enhance the safety of driving by giving early warnings. In prediction systems, there is often a tradeoff between how far into the future you can predict accurately and how far into the future the prediction is actually required for the target application. For example, the average prediction errors in a smart grid system go from 4.58% in the first time slot to 9.67% in the second time slot, and then 12.61%, 14.89%, and 16.32% in the 3 rd to 5 th time slots, respectively. If only 15% error is tolerable, then the only 4 time slot predictions can be considered; however, for proper smart grid operations, predictions for wind speed, irradiance, and load consumption in the future 5 or more time slots may be desirable.  
To solve the above target problem, we propose a Cyber-Physical System (CPS) called Visibility-based Collision Warning System (ViCoWS), whose architecture is illustrated in Figure 2. ViCoWS consists of a physical part and a cyber part. The physical part consists of GPS module, DSRC module, camera, and Electronic Control Unit (ECU). The cyber part consists of 4 types of models, including headway distance prediction model, velocity prediction models (one for Subject Vehicle (SV) and one for Leading Vehicle (LV)), prediction horizon estimation model, and rear-end collision warning model. The future velocities of SV and LV are predicted by the velocity prediction models and the future headway distance between SV and LV is predicted by the headway distance prediction model. Using the two series of future velocities of SV and LV and the headway distances in all time slots in the prediction horizon, a collision risk level is predicted as shown in Figure 3, which uses TTC as defined in Equation (1). Note that the prediction horizon is not fixed in this system, rather it is dynamically changed using the prediction horizon estimation model. For changing the prediction horizon, this model takes human factors into consideration by estimating PRT under different visibility conditions [19]. Under severe visibility conditions, PRT will be estimated to be longer than normal, and thus the prediction horizon will be increased so as to be able to predict future possible collisions and thus enhance driving safety. The different models will be discussed in details in the rest of this section. When the collision risk level is high, the human-machine interface (HMI) will warn the driver.  
Figure 2: Visibility-based Collision Warning System Design.
# Passively Q-Switched KTA Cascaded Raman Laser with 234 and 671 cm\({}^{-1}\) Shifts
## 4 Conclusions  
In summary, a compact passively Q-switched KTA cascaded Raman laser was demonstrated for the first time. A composite crystal of Nd:YAG/Cr\({}^{4+}\):YAG was used for passively Q-switched fundamental laser generation at 1064 nm. The obtained multi-Stokes laser wavelengths range from 1090 to 1200 nm. The output spectra with two different cavity lengths were measured. Two strong lines with similar intensity were achieved with a 9 cm length cavity. One is the first-Stokes at 1146.8 nm with Raman shift of 671 cm\({}^{-1}\), and the other is the Stokes at 1178.2 nm with mixed Raman shifts of 234 cm\({}^{-1}\) and 671 cm\({}^{-1}\). For the setup with a shorter length cavity of 5 cm, some conversions from low order Stokes to higher order Stokes occurred due to the lower loss caused by the shorter cavity length, and the output Stokes waves with high intensity were still at 1146.8 nm and 1178.2 nm. However, the intensity of 1178.2 nm was obviously higher than that of 1146.8 nm. Under an incident pump power of 10.5 W, the measured maximum average output power was 540 mW. The measured pulse width was around 1.1 ns, and the pulse repetition frequency was 14.5 kHz. This compact passively Q-switched KTA cascaded Raman laser can yield multi-Stokes waves, which enrich laser output spectra lines and hold potential applications for remote sensing and terahertz generation.  
Conceptualization, Z.X., Y.D., and H.Z.; validation, Z.X. and S.L.; formal analysis, Z.X., S.L., Z.L., Y.Z., and H.Z.; resources, H.W.; writing--original draft preparation, Z.X. and S.L.; writing--review and editing, Y.D., Z.L., L.C., Y.Z. and H.Z.; supervision, Y.D. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Provincial Natural Science Foundation of China under (Grant No. LY19F050012), the National Natural Science Foundation of China under (Grant No. 62075167 and No. 61905180), the public welfare projects of Wenzhou city under (Grant No. G2020013 and No. S20180015), and the Natural Science Foundation of Fujian Province (Grant No. 2019J01403).  
Not applicable.  
All of the data reported in the paper are presented in the main text. Any other data will be provided on request.  
The authors declare no conflict of interest.
# A Review of Research on Secure Aggregation for Federated Learning
## 3 Privacy-Preserving Mechanisms
### Secure Multi-Party Computation
Each participant \(P_{i}\) receives their respective secret shares as follows: \[m_{i}=f_{1}(x_{i})=m+a_{1}x+a_{2}x^{2}+\cdots+a_{t-1}x^{t-1}.\] (2) \[d_{i}=f_{2}(x_{i})=d+b_{1}x+b_{2}x^{2}+\cdots+b_{t-1}x^{t-1}.\] (3) Participants \(P_{i}\) calculate \(s_{i}\) as follows: \[s_{i}=m_{i}+d_{i}=f_{1}(x_{i})+f_{2}(x_{i})=(m+d)+\cdots+(a_{t-1}+b_{t-1})x^{t -1}.\] (4) The secret \(s=m+d\) can be recovered by using the reconstruction formula for any \(t\) participants. The above secret sharing schemes each present specific advantages and limitations when applied in federated learning. Verifiable secret sharing schemes can detect malicious clients, whereas Shamir's secret sharing and additive secret sharing lack this capability. Shamir's secret sharing supports both addition and multiplication operations and generally provides acceptable computational efficiency. Additive secret sharing is generally more efficient but typically supports only addition; implementing multiplication requires additional mechanisms. Verifiable secret sharing offers strong security against malicious clients, but it typically incurs significantly higher computational and communication overhead.
## 4 Technologies for Nanocellulose Production
### Microwave Pretreatment  
Microwave (MW) pretreatment is a non-conventional thermal pretreatment for the fractionation of LB. Microwaves are non-ionizing electromagnetic, low-frequency waves, with a spectrum between 300 and 300,000 MHz, and a wavelength in the range of 1 mm to 1 m [56; 61]. As microwaves induce dipoles to align with the oscillating electric field, they produce thermal effects that break hydrogen bonds, which results in the disintegration of cellulose chains and thereby the breakdown of cellulose structure [62]. Traditional heating, conduction, convection and radiation cause the heat transfer from the outside of the material to the inside, whereas microwaves directly convert electromagnetic energy into heat, which causes heating at the molecular level. This results in the uniform distribution of heat to the component, and prevents overheating [7]. During the MW pretreatment, the specific polar regions inside the biomass are created, called the internal 'hotspots' that endorse the LB fractionation and swelling.  
To increase the effectiveness of the microwave pretreatment, it can be combined with various chemicals, such as alkali, which are used for biomass pretreatment [14]. In a study, brewer's spent grain was subjected to microwave-assisted alkali pretreatment. The pretreatment resulted in delignification, as the lignin content was reduced to 2.77 \(\pm\) 0.9 g/100 g biomass (acid-soluble lignin) and 9.09 \(\pm\) 0.65 g/100 g (acid-insoluble lignin) from 7.12 \(\pm\) 0.4 g/100 g and 23.36 \(\pm\) 1.2 g/100 g, respectively, for the untreated biomass [63]. Lignin and xylan removal of 42.81% and 74.73%, respectively, was achieved when Pinus bungeana was pretreated with microwave and deep eutectic solvent combined [64]. Microwave pretreatment was used for the delignification of corn cobs prior to nanocellulose production. At the optimal conditions of 180 W power, 12.86% sodium chlorite and 16 min of reaction time, delignification of approximately 97.31% was accomplished [65].
## 2 Materials and Methods
### Procedure  
The experimental procedure consisted of five blocks (Figure 1): 1. resting state with the eyes open, 2. resting state with the eyes closed, 3. stroboscopic stimulation with the eyes closed, and 4. the oddball procedure. Each block was separated by a short rest break. Participants were seated in a dimly lit room at a distance of approximately 60 cm from the computer screen on which experimental stimuli were presented.  
1. Resting state with the eyes open/eyes closed. Participants were instructed to look at the fixation cross at the center of the screen and think of nothing in particular for a period of 120 s. The beginning and the end of a block was announced with a brief sound. In the other condition participants were instructed to close their eyes when they heard a brief sound and think of nothing in particular for a period of 120 s. The end of a block was also announced with a sound which was also a signal to open their eyes.  
Figure 1: Outline of the experimental procedures: (**a**) resting state with the eyes open and eyes closed; (**b**) stroboscopic stimulation with the eyes closed; (**c**) the oddball ERP procedure.  
2. Stroboscopic stimulation with the eyes closed. The stroboscope was located straight above the computer screen. Participants were asked to close their eyes as soon as they heard a brief sound and keep them closed during the whole block. A strobe light was emitted at 16 Hz frequency for 8 s. There were 18 trials with 2 s blank screen breaks.
3. The oddball procedure. Sequences consisting of 80% Xs and 20% Os were presented at the center of the computer screen. The participants had to press one button for X's and another for O's. Each letter was displayed for 100 ms, followed by a 1400 ms blank screen interstimulus interval (ISI).  
Artifacts in the signal, subjected to the analysis process, were naturally appearing artifacts related to person movements such as muscle, eye or heart artifacts and to equipment operating such as line noise, channel noise, etc.
## 4 Experiments
### Experiments on Registration Effectiveness
#### 4.3.4 Comparison between Registration Effectiveness of Transformation Estimation  
Based on our registration framework and our proposed feature descriptor, we compare the registration effectiveness between our accurate transformation estimation and state-of-the-art transformation estimation such as feature matching, clustering, ICP, RANSAC, etc., on the above datasets. Table 2 shows the registration effectiveness of kinds of transformation estimation based on formulas (36) and (37). It is clearly shown that our proposed transformation estimation outperforms other transformation estimations followed by feature matching + clustering and RANSAC, and transformation estimation based on ICP obtains the lowest registration effectiveness in all transformation estimation methods. The core reason that transformation estimation based on ICP obtains the lowest registration effectiveness is that the initial transformation is unknown. So, when the initial transformation is obtained by feature matching, we then obtain a transformation between a pair of point clouds, and it will improve registration effectiveness. At the same time, according to a comparison of the registration effectiveness of transformation estimation between  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Parameter Value** & **Description** \\ \hline \(r\) & 4 mr & The support radius used to detect keypoints \\ \(\varepsilon_{\vartheta}\) & 1.5 & A threshold for a surface variation index \\ \(L_{th}\) & \(\pi/3\) & A threshold for the maximum difference between adjacent included angles \\ \(d_{min}\) & 5 mr & A threshold for the nearest distance \\  & & between a keypoint and a boundary point \\ \(\{r_{1},\dots,r_{s}\}\) & \{3 mr, 5 mr, 7 mr, 9 mr\} & A multi-support radius used to generate a multi-scale feature descriptor \\  & & A threshold for the distance ratio \\  & & between a pair of the first nearest \\ \(\varepsilon_{r}\) & 0.8 & descriptor and a pair of the second \\  & & nearest descriptor \\ \(\kappa\) & 7 & The number of clusters in coarse \\  & & transformer estimation \\ \(\varepsilon_{d}\) & 1 mr & A threshold for determining whether a point is an inlier \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in our experiments.  
our proposed transformation estimation and feature matching + clustering and feature matching + ICP, three steps in our proposed transformation estimation were found to gradually improve registration effectiveness. The transformation estimation based on feature matching can not only obtain the transformation of the point cloud but also obtain a series of correspondences between source keypoints and target keypoints between source feature descriptors and target feature descriptors. These correspondences are applied to coarse transformation estimation based on clustering, which will help us obtain the initial transformation of point cloud registration. Finally, a fine transformation estimation (transformation estimation based on ICP) via the initial transformation can further obtain a more optimal transformation of point cloud registration.
# Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks
## 1 Introduction
### Contributions and Outcomes  
With the increasing number of IoT nodes, connected devices and the huge network traffic, the demand for seamless network connectivity has also increased. Thus, the need is to look for extended network capabilities in the future 6G wireless networks. This paper proposes a dynamic framework to offer massive connectivity in a 6G-enabled IoT network. The novel contributions of the paper are  
* A cell-free IoT network is proposed that supports the enormous wireless nodes with uniform coverage and QoS.
* An interference-aware scheduling algorithm is proposed that offers optimal resource management by associating APs to the nodes optimally using designed pilot allocation.
* The mathematical formulations are obtained for the achieved spectral efficiency under different precoding schemes for different cell-free operations.
* The system is evaluated for performance in terms of spectral efficiency achieved for different number of communication nodes, pilot lengths and precoding methods.
* The proposed cell-free network with the proposed scheduling mechanism is compared with other system models, one incorporating random scheduling and other not incorporating any scheduling.  
The rest of the paper is organised as follows. Section 2 defines the system model along with the mathematical formulations for pilot transmission, channel estimation and data transmission. The precoding schemes used for the proposed model are also explained in Section 2. Section 3 gives the scheduling mechanism adopted for the association of APs and nodes followed by the proposed algorithm. The results are presented in Section 4 with a detailed discussion. Section 5 concludes the paper, giving a direction for the future scope. Table 1 lists the summary of notations used throughout the paper.
# Characteristics of Low-Frequency Acoustic Wave Propagation in Ice-Covered Shallow Water Environment
## 4 Parameter Analysis
### The Influence of Ice Thickness  
The acoustic parameters of an ice layer include ice layer density, thickness, and compressional wave and shear wave velocities. Considering that ice density has little effect on sound propagation, especially on long-distance sound propagation, it is neglected. The influence of ice thickness on sound field distribution is mostly considered here. Since the ice thickness is continuous fluctuation, it is necessary to study the influence of different ice thicknesses on sound propagation. The frequency of the sound source is 50 Hz, the depth of the sound source is 30 m, the depth of the sound receiver is 30 m, the seawater depth is 100 m, and the thickness of the ice layers are 5 m, 10 m, 15 m, and 20 m respectively. The results of numerical analysis are shown in Figures 3 and 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Type** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{**Ice layer**} & Ice thickness (m) & 3 \\  & Ice density (g/cm\({}^{3}\)) & 0.9 \\  & Compressional speed ice (m/s) & 3500 \\  & Shear speed ice (m/s) & 1800 \\  & Compressional attenuation ice (dB/\(\lambda\)) & 0.3 \\  & Shear attenuation ice (dB/\(\lambda\)) & 1.0 \\ \hline \multirow{2}{*}{**Fluid layer**} & Fluid depth (m) & 100 \\  & Fluid density (g/cm\({}^{3}\)) & 1.0 \\  & Compressional speed fluid (m/s) & 1482 \\ \hline \multirow{2}{*}{**Seafloor layer**} & Seafloor density (g/cm\({}^{3}\)) & 2.2 \\  & Compressional attenuation seafloor (dB/\(\lambda\)) & 0.76 \\ \cline{1-1}  & Shear attenuation seafloor (dB/\(\lambda\)) & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model physical parameters.  
Figure 2: Verification of two different methods for the proposed model.  
Figure 4: Contour plots (**a**–**d**) of propagation loss at different ice thicknesses.  
Figure 3: Curves of propagation loss at different ice thicknesses.  
Figures 3 and 4 show the transmission loss curves and contour plots at different ice thicknesses, respectively. The results show that when the distance between the sound source location and the ice layer is constant, the propagation loss increases with the increase of ice thickness. This is primarily because the thicker the ice layer is, the more the energy of compression wave in seawater penetrates into the seafloor, resulting in greater propagation loss.
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Interpolation-Based Scheme  
In quantum steganography, the cost of aiming to have a high embedding capacity is a lower image resolution. For this reason, researchers tried to find diverse ways to regain their image quality. One of the solutions provided by researchers is to employ an interpolation method into steganography and data hiding schemes [39], which is a method for producing high-resolution images from lower ones by resizing them. The previous approaches focused primarily on applying interpolation-based algorithms using classical computing. A recent approach by [PERSON] et al. [12] implemented quantum steganography by embedding confidential messages into a NEQR model image using image interpolation [40].  
Before embedding, the algorithm increases the cover image's size using quantum image interpolation, producing an up-scaled version of it. The scheme then calculates the interpolation error by calculating the difference between the up-scaled image and the former one. Next, the algorithm will calculate the number of qubits to be hidden in each pixel using the formula, which represents the interpolation errors. Then, based on the calculated capacity for each pixel, the confidential message qubits will be hidden accordingly. Each pixel is edited at least once after embedding to produce a steganographic image. The confidential image is later recovered by extracting the initial pixels and deleting the inserted ones using quantum nearest neighbor interpolation.  
The results of following this method have shown an excellent embedding capacity without affecting the image quality compared to relative schemes. Moreover, [PERSON] et al. [12] suggested that with more improvement, the approach could deliver better results, especially for the trade-off between capacity and invisibility.
## 2 Materials and Methods
### Investigation of the Necessary Criteria for an ILT  
First, the existence of the inverse Laplace transforms for the weighting functions must be established. To do this, it is essential that the limits of the weighting functions approach zero as \(s\rightarrow\infty\) and that the limits of the weighting functions multiplied by \(s\) remain finite as \(s\to 0\). These conditions apply when using a normalized Laplace variable \(\zeta\) instead of \(s\). In what follows, the four required limits to confirm the existence of the inverse Laplace transforms for \(W_{1}^{*}(\zeta)\) and \(W_{2}^{*}(\zeta)\) will be examined. Firstly, the limits for \(\zeta\to 0\).  
Figure 3: Logarithm of absolute of weighting function \(W_{1}^{*}\) for real and imaginary arguments for fixed dissipation number.  
\[\lim_{\xi\to 0}W_{1}^{*}(\xi)\zeta \tag{15}\] \[=\lim_{\xi\to 0}\ 8\,Dn\frac{\sqrt{\text{I}_{2}(\sqrt{\zeta})}}{ \sqrt{\text{I}_{0}(\sqrt{\zeta})}}\frac{1}{\sinh\!\left[\sqrt{\frac{\text{I}_{0 }(\sqrt{\zeta})}{\text{I}_{2}(\sqrt{\zeta})}}\zeta Dn\right]}\] (16) \[=\lim_{\xi\to 0}\ 8\,Dn\ 1\ \frac{1}{\infty}\] (17) \[=\lim_{\xi\to 0}\ \frac{8\,Dn}{\infty}=0\] (18) \[\lim_{\xi\to 0}W_{2}^{*}(\xi)\zeta\] (19) \[=\lim_{\xi\to 0}\ 8\,Dn\frac{\sqrt{\text{I}_{2}(\sqrt{\zeta})}}{ \sqrt{\text{I}_{0}(\sqrt{\zeta})}}\frac{1}{\tanh\!\left[\sqrt{\frac{\text{I}_ {0}(\sqrt{\zeta})}{\text{I}_{2}(\sqrt{\zeta})}}\zeta Dn\right]}\] (20) \[=\lim_{\xi\to 0}\ 8\,Dn\ 1\ \frac{1}{1}=8\,Dn \tag{21}\]  
Since \(Dn\) must be finite, the overall limit must also be finite. Next, the two limits as \(\zeta\to 0\) are evaluated.  
\[\lim_{\xi\to\infty}W_{1}^{*}(\zeta) \tag{22}\] \[=\lim_{\xi\to\infty}\ \frac{8\,Dn\sqrt{\text{I}_{2}(\sqrt{\xi})}}{ \sinh\!\left[\sqrt{\frac{\text{I}_{0}(\sqrt{\xi})}{\text{I}_{2}(\sqrt{\xi})}} \zeta Dn\right]}\zeta\sqrt{\text{I}_{0}(\sqrt{\zeta})}\] (23) \[=\lim_{\xi\to\infty}W_{1}^{*}(\zeta)\zeta\ \lim_{\xi\to\infty}\frac{1}{\zeta}\] (24) \[=0\ \frac{1}{\infty}=0 \tag{25}\]  
\[\lim_{\xi\to\infty}W_{2}^{*}(\zeta) \tag{26}\] \[=\lim_{\xi\to\infty}\frac{8\,Dn\sqrt{\text{I}_{2}(\sqrt{\zeta})}} {\tanh\!\left[\sqrt{\frac{\text{I}_{0}(\sqrt{\zeta})}{\text{I}_{2}(\sqrt{\xi} )}}\zeta Dn\right]}\zeta\sqrt{\text{I}_{0}(\sqrt{\zeta})}\] (27) \[=\lim_{\xi\to\infty}W_{1}^{*}(\zeta)\zeta\ \lim_{\xi\to\infty}\frac{1}{\zeta}\] (28) \[=8\,Dn\ \frac{1}{\infty}=0 \tag{29}\]  
The outcomes of these calculations confirm that all the required conditions for an inverse Laplace transformation are satisfied.
###### Abstract  
This paper proposes a charging-aware multi-mode routing protocol (CMRP) to collect data in the wireless rechargeable sensor networks. The routing mechanism in CMRP is not steady but changes according to the energy charging status of sensors. Sensors that cannot replenish their energy efficiency use the routing protocol with less energy consumption. On the contrary, sensors that can replenish their energy use the low propagation delay routing protocol. A novel heuristic chaining mechanism based on multi-level convex hull circle (MCC) is also proposed. Simulation results show that CMRP not only has longer operation time than LEACH and PEGASIS but also has the shortest propagation delay time. The lifetime of CMRP is less than the minimum spanning tree by about 1%, but the propagation delay is shorter than MSTP about 21-28%. In addition, CMRP considers both reducing energy consumption and shortening the propagation delay at the same time. The life-delay rate of the CMRP is close to the optimal results.  
charging-aware multi-mode; data gathering; multi-level convex hull circle; life-delay ratio; wireless rechargeable sensor networks +
Footnote †: journal: Sensors