esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Threshold Queuing Model Based on M/M/1  
The queuing process of vehicles in the connected roadway is represented by an M/M/1 threshold queuing model. According to the characteristics of the threshold queue, each batch of vehicles arrives at the passenger transportation hub at an average interval of \(1/\lambda_{b}\). In the initial stage, the traffic flow is small, the traffic in the connected roadway is in a non-congested condition and the vehicles can drive away from the pick-up zone with passengers faster, and the average time consumed by each batch of vehicles in the vehicle service area is \(1/\mu_{b1}\). With the increase in traffic flow, the length of the vehicle queue in the connected roadway gradually increases. After the number of batches of vehicles in the queue exceeds a certain value \(U_{b}\), the traffic flow in the connected roadway is in a congested condition; at this stage, the time consumed by each batch of vehicles to carry passengers and leave the pick-up zone is also extended to \(1/\mu_{b2}\). With the continuous dissipation of passengers and vehicles, the length of the vehicle queue gradually shortens. After the number of batches of vehicles in the queue is reduced to a certain value \(D_{b}\), the  
Figure 3: Queueing representation of traffic flows. traffic flow in the connected roadway is once again in a non-congested condition. The average time consumed by vehicles in the vehicle service area also decreases back to \(1/\mu_{b1}\). The transition between non-congested and congested conditions is controlled by a low threshold \(D_{b}\) and a high threshold \(U_{b}\). When the number of batches of queuing vehicles is less than \(D_{b}\), the service rate of the vehicle service area is higher and follows the exponential distribution with parameter \(\mu_{b1}\). When the number of batches of queuing vehicles is greater than \(U_{b}\), the service rate of the vehicle service area is lower and follows the exponential distribution with parameter \(\mu_{b2}\). When the number of batches of queuing vehicles is between \((D_{b},\ U_{b})\), two service rates of \(\mu_{b1}\) and \(\mu_{b2}\) exist simultaneously. The condition transition process of the vehicle queue controlled by the threshold is shown in Figure 4.  
According to Markov chain [30], the probability of the queue length when the system reaches the steady state can be obtained. Let \(\pi(i,\ j)\) denote the probability that the queue length of the queue is \(i\) in state \(j\) (\(j=1,2\)), with \(j=1\) indicating that the system is in the non-congested state and \(j=2\) indicating that it is in the congested state. The service load rate of the vehicle service area in the non-congested condition is \(\rho_{b1}=\lambda_{b}/\mu_{b1}\), and the service load rate in the congested condition is \(\rho_{b2}=\lambda_{b}/\mu_{b2}\), then the steady-state probability of the system can be obtained from Equations (3)-(6).
## 5 Discussion
### Theoretical and Experimental PSNR  
To be able to theoretically evaluate the PSNR of the general case, some definitions were introduced to substantiate the analysis of the proposed methods. In Step 2 of Algorithm 5 and Step 3 of Algorithm 6, we calculated the difference between the pixel of the \(x\)-th camouflage image \(C_{x}(i,j)\) and its \"target\" (\(C_{1}(i,j)\oplus\ldots\oplus C_{x-1}(i,j)\oplus C_{x+1}(i,j)\oplus\ldots\oplus C _{n}(i,j)\oplus S(i,j)\)). Because we had no idea about the difference, it was necessary to calculate the expectation value of this difference, and use it to obtain a theoretical low bound of the PSNR of each share in the proposed scheme. For any possible secret and camouflage image, each pixel of the secret and target can be seen as two random values. The expectation value \(E\) for the difference between them is defined as  
\[E=\bigg{(}\sum_{i=0}^{255}\bigg{(}\sum_{j=0}^{255}|i-j|\bigg{)}\bigg{)}/256^{2} \tag{3}\]  
After calculation, we obtained \(E\) to be about 85.332031. If the target pixels were all encrypted in the same single share, we were able to estimate the MSE between this cover image and its share as follows:\[S\_MSE=\frac{\sum_{i=1}^{H}\sum_{j=1}^{W}E^{2}}{H\,\times\,W}=7281.5534. \tag{4}\]  
However, our proposed method selects the minimum among the \(n\) shares as the target pixel for each pixel, so the actual MSE of the proposed scheme is less than or equal to the theoretical MSE (\(T\_MSE\)), which considers the target pixel to be uniformly selected from all \(n\) shares. Therefore, only \(1/n\) pixels of any one share are selected as the target pixels, and \(T\_MSE\) is defined as follows:  
\[T\_MSE\left(\,\frac{\,\sum_{i=1}^{H}\sum_{j=1}^{W}E^{2}}{H\,\times\,W}\right)/n \tag{5}\]  
According to the theoretical MSE, one can obtain the theoretical PSNR (\(T\_PSNR\)) as follows:  
\[T\_PSNR=10\,\times\,\log\!\left(\frac{255^{2}}{T\_MSE}\right)\,(\mathrm{dB}) \tag{6}\]  
Table 6 shows the \(T\_MSE\), \(T\_PSNR\) and experimental PSNR, which is equal to Table 3, for \(n=3\) to 7 of Algorithm 5. Similarly, Table 7 shows the \(T\_MSE\), \(T\_PSNR\), and experimental PSNR, which are quoted from Table 7, for \(n=3\) to 7 of Algorithm 6. From Tables 6 and 7, we can observe that the experimental PSNR in each case is larger than the theoretical PSNR.
## 5 Conclusions  
A new single-phase grounding fault detection model based on PSO-SVM has been proposed in this research. The value of the learning factors is correspondingly increased by increasing the particle swarm algorithm's inertia weight value. Additionally, the optimization speed is expedited by adding a flight-time element to the particle position update formula, which results in an enhanced PSO algorithm. To determine the best parameters, the enhanced PSO algorithm is used to fine-tune the SVM parameters \(C\) and \(g\). Based on the improved PSO-SVM fault detection model, the fault detection accuracy of the simulation and experiment is up to 99.7%, indicating that the proposed method has high reliability.  
Conceptualization, Z.X. and F.D.; data curation, S.R. and Y.L.; formal analysis, Z.X. and D.L.; investigation, S.H. and Y.L.; methodology, S.H. and F.D.; project administration, Z.L.; software, S.H.; supervision, Z.L. and D.L.; validation, S.R.; and writing--original draft, Z.X. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Detection Accuracy** & **Convergence Rate (ms)** \\ \hline
**PSO** & & 25.5 \\
**SVM** & 85.6\% & 15.1 \\
**The traditional PSO-SVM** & 92.3\% & 211 \\
**The improved PSO-SVM** & 99.7\% & 156 \\
**WT** & 80.0\% & - \\
**STFT** & 75.0\% & - \\
**HHT** & 85.7\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Comparison.  
Figure 9: Illustration of model confusion matrix.  
**Funding:** This work was supported by the Science and Technology Project of China Southern Power Grid Co., Ltd. 031900 KC23070046.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author due to data confidentiality requirements.  
**Conflicts of Interest**: Author [PERSON], [PERSON], [PERSON], [PERSON] were employed by the company Dongguan Power Supply Bureau. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods  
### Study Area and Water Quality Data  
Beijing (39\({}^{\circ}\)28\({}^{\prime}\) N-41\({}^{\circ}\)05\({}^{\prime}\) N, 115\({}^{\circ}\)25\({}^{\prime}\) E-117\({}^{\circ}\)30\({}^{\prime}\) E), the capital of China, is located at a central latitude, belonging to the eastern warm temperate monsoon zone with semi-humid continental climate and four distinct seasons. Human activities and climate change have important effects on water quality. The local government faces serious challenges of pollution control and natural resource management of lakes in Beijing, especially for landscape water and drinking water in lakes and reservoirs. As an issue of national concern, numerous studies in lakes have been paid more attention regarding their physical, chemical (e.g., total nitrogen [17], total phosphorus [18], heavy metals [19], etc.), and biological (e.g., phytoplankton [20], aquatic fish [21] and aquatic plant [22] etc.) parameters, as well as the influences of land use [23] and eutrophication [24]. In this research, we focus on water quality parameters of Beihai Lake. Beihai Lake, connecting Qianhai Lake in the north and Zhonghai Lake and Nanhai Lake in the south, is the largest landscape lake around the Forbidden City. The location of Beihai Lake is shown in Figure 1. Water of Beihai Lake comes from Miyun and Guanting Reservoirs, which are very important drinking water areas in Beijing. The water quality parameters of reclaimed landscape water can reach the national water quality standards Class IV at most, which is the minimum standard for industry and recreation [25].  
We used continuous time series water quality monitoring data from the Beijing Water-affair Authority for about 120 h in August 2013. Water quality parameters measured include PH, CHLA (mg/L), NH4H (mg/L), DO (mg/L), BOD (mg/L), and EC (\(\upmu\)s/cm). Basic statistics of the measured water quality variables of Beihai Lake are shown in Table 1 as follows.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 2 Dual In-Wheel Motor Wheel-Leg
### Design of Six-Arm Hydro-Pneumatic Spring Suspension  
The dual in-wheel motor wheelset unit imposes new requirements on suspension design, and the suspension should have the characteristics of modularization [24]. It requires the suspension unit to withstand loads from all directions. Traditional suspensions only have the ability to withstand vertical loads, and longitudinal and lateral loads rely on the rigidity of the chassis or load-bearing body to resist impact. The suspension designed in this study includes six hydro-pneumatic springs and four tension springs. This six-degree-of-freedom suspension can bear loads from multiple directions, enhancing the maneuverability and flexibility of the wheelset unit. Under the action of tension springs, the suspension not only effectively mitigates the instability of differential steering but also equivalently reduces the unsprung mass, increasing the suspension response speed. In addition, tension springs assist in resisting lateral and longitudinal loads. The dual in-wheel motor wheelset and the six-arm hydro-pneumatic spring suspension together form the dual in-wheel motor wheel-leg system, as shown in Figure 2.  
Figure 1: Dual in-wheel motor wheelset unit.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 3 Background  
### Preliminaries of Kinetic Drives  
Seagate recently announced the invention of Kinetic Drives [8; 9]. Figure 1 shows the difference between traditional and Kinetic storage stacks. We can see that traditional hard disk drives are connected via an Serial Attached SCSI (SAS) or AT Attachment (ATA) interface, while Kinetic Drives are accessed via Ethernet connections. Currently, there are two Ethernet ports in a Kinetic Drive, and its storage capacity is 4 TB. Kinetic Drives support a _key_ size up to 4 KB and a _value_ size up to 1 MB for key-value pairs. With Ethernet connections and supported by TCP/IP, data stored in Kinetic Drives can be quickly accessed by users via the IP addresses of the drives using the following built-in APIs.  
* Get(key): Given a _key_, gets the key-value pair.
* GetPrevious(key): Given a _key_, gets the previous key-value pair.
* GetNext(key): Given a _key_, gets the next key-value pair.  
* GetKeyRange(key1, key2): Given _key1_ and _key2_, gets the key-value pairs in the range between them.
* Put(key, value): Given a _key_ and a _value_, stores the key-value pair.
* Delete(key): Given a _key_, deletes the key-value pair.  
With its own CPU, RAM, and built-in LevelDB system [15], the Kinetic Drive manages data by sorting and storing key-value pairs in an order based on _keys_. With a given _key_, a Kinetic Drive can independently operate itself to perform key-value operations and directly returns the result to the user. Hence, we can see that a Kinetic Drive becomes a part of the storage resource pool for users to access data, when it is connected to the Internet.  
In addition, there is a unique feature of Kinetic Drives--P2P operation. Direct data migration via P2P operation is available between two Kinetic Drives because they are accessed via IP addresses. Therefore, a P2P data transfer can be initiated from one Kinetic Drive to another, given the IP address of the destination drive. This direct P2P data transfer does not need any storage server as the intermediary.
# Maximizing the Capacity of Edge Networks with Multicasting
## 5 Multicast Transmission Distance
### Lower Bound on the Transmission Distance  
**Lemma 2**.: _In the complete k-ary multicast tree, the average distance from one SBS without caching the requested content to other SBSs is_  
\[\overline{L(C_{i})}=\frac{Q_{2}}{(m-s)C_{i}} \tag{7}\]  
_where \(Q_{2}\) denotes \(\frac{sh(h-1)(1-k)}{4\left(1-k^{h}\right)\sqrt{\lambda_{c}\lambda_{u}\pi R^{2}}}\)._  
The proof of Lemma 2 can be found in Appendix B.  
**Theorem 2**.: _In the complete k-ary multicast, the system capacity can be represented as_  
\[C=\frac{4 Wm}{Q_{2}\sqrt{n}\sum\limits_{i=1}^{m}\frac{p_{i}}{C_{i}}}. \tag{8}\]  
Figure 2: Two limits of multicasting. Last-hop in the left and complete k-ary in the right.  
Proof of Theorem 2.: The proof is almost identical to that of Theorem 1; it is omitted here for concision.
# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 4 Performance Evaluation  
### Simulation Setup  
The proposed IoT steganography technique was implemented with MatLab. We used a set of 50 wav signals with a confidence ratio of 95%. The signals were sampled at 8, 16, and 44.1 kHz. Each signal was quantized at 16 bits with a duration of 3 to 10 s and a data rate varying between 128 Kbps, 256 Kbps, and 705 Kbps. Stego signals were generated by embedding data at the frequency bands [0.25-4] kHz, [0.25-7] kHz, and [0.5-22] kHz. In each experiment, only one source was used and the results were averaged over all sources.
## 4 Results: State-of-the-Art Approaches
### Semantic Modeling in IoUT
#### 4.1.3 Data and Information Modeling for UUVs
Collecting and analyzing environmental data and information is also a key factor in IoUT, to have an operational advantage (over the enemy, i.e., human or physical phenomena). More specifically, NATO CMRE's EKOE reports that this is critical especially in underwater environments, where this knowledge offers an advantage in undetected vehicles, as well as for the early detection of the enemy [85]. Reaching environmental awareness at high levels supports more efficient prediction and decision-making tasks executed by commanders, but also by a UUV/AUV itself.
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 2 Mathematical Background
### Review of Membership-Mappings-Based Privacy-Preserving Transferable Learning
#### 2.5.3 Differentially Private Source Domain Classifier  
For a given differential privacy parameters: \(d,\epsilon,\delta\); Algorithm 5 is applied on \(\mathbf{Y}_{c}^{sr}\) to obtain the differentially private approximated data samples, \(\mathbf{Y}_{c}^{+sr}=\{y_{sr}^{i+L}\in\mathbb{R}^{p_{sr}}\mid i\in\{1,\cdots,N_ {c}^{sr}\}\}\), for all \(c\in\{1,\cdots,C\}\). Algorithm 6 is applied on \(\{\mathbf{Y}_{c}^{+sr}\}_{c=1}^{C}\) to build a differentially private source domain classifier characterized by parameters sets \(\{\mathcal{P}_{c}^{+sr}\}_{c=1}^{C}\).
## 2 Laser-Driven Very-High-Energy Electron/Photon Beam Radiation Therapy System
### Simulations on Laser Plasma Accelerators  
Electron beams can be produced and accelerated in the injector cell driven by the same laser pulse as that in the accelerator cell, relying on the self-injection mechanism [32,33]. We studied the production of high-quality electron beams with energy of 300 MeV and a charge of \(\sim\)100 pC by means of the particle-in-cell (PIC) simulations. The self-injection electron beam production has been investigated by the 2D PIC simulation code (VORPAL) [34], using the 2D moving window, of which the cell size was 94 \(\times\) 90 \(\upmu\)m\({}^{2}\) and the number of simulation cells 1792 \(\times\) 256, respectively, assuming He\({}^{2+}\) immobile ions and four electrons per simulation cell. Figure 5a,b shows the simulation result of the electron density distribution and the electron energy spectrum, respectively, at the propagation distance \(z=3\) mm for the drive laser pulse with wavelength \(\lambda_{L}=0.8\)\(\upmu\)m, the initial normalized field \(a_{0}=4\), spot radius r\({}_{L}=15\)\(\upmu\)m, and pulse duration r\({}_{L}=27\) fs, which is focused into uniform plasma with electron density \(n_{e}=2\times 10^{18}\) cm\({}^{-3}\). In this example, the focused spot size was chosen from the condition \(k_{{}_{p}}R_{{}_{s}}=k_{{}_{p}r_{L}^{*}}=2\sqrt{a_{{}_{0}}}\) to form a spherical bubble wake with radius \(R_{B}\). For this condition, the accelerated electron beam has energy of 320 MeV, relative rms energy spread of 4%, and charge of 100 pC. As shown in Figure 5c,d, increasing the focused spot radius up to r\({}_{L}=30\)\(\upmu\)m, the electron beam energy decreases down to 210 MeV, while the energy spread becomes significantly worse as the trapped beam charge increases. This may be attributed to longitudinal expansion of the bubble size [33] and beam loading effects.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Case** & **A** & **B** & **C** & **D** & **E** \\ \hline Electron beam energy [MeV] & 50 & 100 & 150 & 200 & 250 \\ Laser wavelength [\(\upmu\)m] & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 \\ Laser energy per pulse [mJ] & 33 & 63 & 92 & 121 & 149 \\ Peak power [TW] & 1.7 & 3.2 & 4.6 & 6.0 & 7.5 \\ Pulse duration [fs] & 20 & 20 & 20 & 20 & 20 \\ Matched spot radius [\(\upmu\)m] & 3.6 & 5.0 & 6.0 & 6.9 & 7.6 \\ Plasma density [\(10^{19}\) cm\({}^{-3}\)] & 2.3 & 1.2 & 0.8 & 0.6 & 0.5 \\ Accelerator length [mm] & 0.2 & 0.5 & 0.9 & 1.4 & 1.9 \\ Charge per bunch [pC] & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of laser plasma accelerators for VHE electron beam radiation therapy system.
# Flavonoids as Promising Multitarget Agents in Alzheimer's Disease Therapy
## 2 Activities of Flavonoids from Natural Sources
### Inhibition of Acetylcholinesteraseerase (AChE)
#### 2.4.3 Flavones
The alkyl-substituted flavones 5\({}^{\prime}\)-geranyl-4\({}^{\prime}\)-methoxy-2\({}^{\prime}\),5,7-trihydroxyflavone (**143**), 5\({}^{\prime}\)-geranyl-2\({}^{\prime}\),4\({}^{\prime}\),5,7-tetrahydroxyflavone (**144**), morusin (**49**), cyclomorusin (**51**), morusinol (**52**), neocyclomorusin (**53**) and kuwanon C, the 2\({}^{\prime}\),4\({}^{\prime}\)5,7-tetrahydroxy-3,8-bis(3-methyl-2-buten-1-yl)flavone (**48**), isolated from the roots of _Morus lhou_, present AChE inhibition characterized by IC\({}_{50}\) values of 10.95, 16.21, 36.40, 31.69, 173.49, 26.69 and 25.06 uM, respectively [83]. 6,8-Diprenyleriodictyl or 3\({}^{\prime}\),4\({}^{\prime}\),5,7-tetrahydroxy-6,8-bis(3-methylbut-2-en-1-yl)flavone (**145**) and 6-phenyljigenin or 4\({}^{\prime}\),5,7-trihydroxy-6-(3-methyl-2-buten-1-yl)flavone (**146**), isolated from the leaves of _Polygonum limbatum_ and the twigs of _Dorstenia barteri_ Bureau var. _multiradiata_, exhibit IC\({}_{50}\) values of 15.03 and 25.73 uM [62], respectively. Thus, the presence of a hydroxy group at C7 is important for the AChE inhibition. Comparing morusin (**49**) with morusinol (**52**), the structural difference consists in the hydration of the prenyl group linked to C3, producing a significant decrease in the activity to inhibit AChE. This result implies that hydrophobicity is important for the inhibition [83]. Considering isoflavones, genistein (**2**), extracted from the roots of _Astragalus membranes_ (Fish.) Bge., exhibits an IC\({}_{50}\) value of 167.00 uM, [57], a weaker AChE inhibition than the corresponding flavone, apigenin (**29**) (IC\({}_{50}\) = 34.43 uM) [49]. Barbi-ggerone (2\({}^{\prime}\),4\({}^{\prime}\)5\({}^{\prime}\)-trimethoxy-6,6-dimethylpyranoisoflavone) (**147**) and 4\({}^{\prime}\),5\({}^{\prime}\)-dimethoxy-6,6-dimethylpyranoisoflavone (**148**), isolated from _Millettia pachycarpa_ Benth, exhibit IC\({}_{50}\) values of 121.60 and 131.17 uM, respectively, indicating that the loss of one methoxy group at ring B reduces the AChE inhibition [98]. Figures 14 and 15 show the chemical structure of flavones **132**-**148** that inhibit AChE with IC\({}_{50}\) values ranging from of 1.65 to 237.74 uM, except for compounds **136**, **139** and **140** that were inactive. Figure 14: Structure of flavones that inhibit AChE and of the inactive flavones **136**, **139** and **140**. Figure 15: Structure of isoflavones **147** and **148** that inhibit AChE.
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 2 Calculation of the Monitoring Point's Coordinate Time Series
### GNSS Monitoring Network Adjustment and Noise Filtering  
First of all, GNSS data is processed in the post-processed kinematic mode to obtain the baseline vectors of the monitoring station to three reference stations epoch-by-epoch. Secondly, we identify and eliminate gross errors of baseline vectors by the misclosures and a minimally constrained least-squares adjustment epoch-by-epoch. Finally, we use the reference points' coordinates obtained from the construction control network as constraints and then process the GNSS monitoring network data with a least-squares adjustment at each epoch based on the qualified baseline vectors and their variance information [28]. Nevertheless, the coordinate time series of monitoring points contains not only the real displacements of super-tall buildings, but also the measurement noise (e.g., GNSS phase noise). The displacement of the monitoring point is characterized by systematic signal even though it may be small over a short time period, but the measurement noise is characterized by strong noise that can be reduced by wavelet filtering. The choice of an appropriate wavelet basis function and the best decomposition level are the main issues in wavelet analysis. The compactly supported orthogonal Daubechies wavelet is widely applied in the field of engineering [29; 30] since it gives accurate results in time-frequency analysis. Regarding the choice of decomposition level, we take into account both the signal change and the data-sampling rate.