esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Practical Applications of VR in CR
### Individual Semi-Inmersive Systems
It consists of a sensor that translates body movements, a virtual reality headset, a database, and a computer that runs the program as a server. In this system, patients start exercising by launching the interface on the computer and accessing the system with existing user records in the database. Patients can view their past exercise scores and can also start exercising directly from the interface. The system can determine compensatory movements, range of motion, time, and the angle relationship between limbs. With the help of the Kinect sensor, patients' body movements are directly translated into games [32]. The visual environment of the game is wirelessly transmitted to the VR headset, and the gyroscope data in the headset are also used to create a faster-responsive and immersive experience. [PERSON] et al. (2018) [33] evaluated the effect of a semi-immersive virtual reality system on promoting functional recovery in patients with multiple sclerosis using the Bioengineering Technology and Systems (BTS) Nirvana. The system created a virtual sensory room that enhances sensory engagement in rehabilitation by increasing visual and auditory feedback. Patients can experience immersive stimulation in different real-world scenarios through interactive exercises on the screen, which can involve moving in different directions, manipulating different objects, or creating specific object categories through dynamic interaction with the virtual environment. A statistical analysis of laboratory data revealed that VR can enhance the enthusiasm and compliance of patients during the rehabilitation process, enabling them to engage in long-term, high-intensity training. Patients may be motivated by the variety of environments and stimuli, potentially reactivating or enhancing neural functions in the brain, including those associated with the cholinergic and dopaminergic systems [34]. From the studies mentioned above, it is evident that a drawback of individual semi-immersive virtual reality rehabilitation therapy systems is that the reduced immersion may limit the effectiveness of certain cognitive rehabilitation exercises.
## 3 System Model
### Data Aggregation Model  
To incorporate data aggregation into the geometric routing model, we adopt the foreign-coding model [65] scheme. Specifically, we assume a node \(i\) is able to compress the data originating at its upstream neighbor \(j\) using its local data. The compression ratio depends on the data correlation between node \(i\) and \(j\), which is denoted by the correlation coefficient \(\rho(i,j)=1-H(i|j)/H(i)\), where \(H(i)\) is the entropy coded data rate of the information at node \(i\), and \(H(i|j)\) is the conditional entropy coded data rate of the same information \(H(i)\) at node \(i\) given the side information \(H(j)\). Examples of correlation models include the Gaussian random field model [66] which assumes that the correlation coefficient \(\rho(i,j)\) decreases exponentially with the distance between nodes, or \(\rho(i,j)=\frac{1}{1+|d(i,j)|}\), and the inverse model [65] which assumes the data correlation is inversely proportional to the Euclidean distance between nodes, or \(\rho(i,j)=exp(-\alpha\times d^{2}(i,j))\), where, \(\alpha\) is data correlation parameters. Higher \(\alpha\) means smaller data correlation, and vice versa.
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 2 Mitochondrial Disorders
### Spinoccerebral Ataxia  
Hereditary spinocerebellar ataxis define a genetically heterogeneous group of autosomal dominantly inherited progressive disorders characterized by loss of balance and coordination. Although the onset of ataxia usually occurs in the third or fourth decade of life [47], several genetic SCAs have been described in children. Usually, imaging is poor, showing mainly cerebellar atrophy in all these forms.  
CACNA1G calcium channel is known to cause adult-onset spinocerebellar ataxia [42]; recent works report cerebellar ataxia associate with mutations in this gene [48; 49]. MRI shows cerebellar atrophy [49] (Figure 9).  
CACNA1A can cause a wide range of disorders, including episodic ataxia type 2 (EA2), familial hemiplegic migraine (FHM1), and progressive spinocerebilar ataxia (SCA6). Mild progressive cerebellar atrophy has been described in mutations of this gene [50].  
In SCA 5, due to mutations, the SPTBN2 gene has been reported early ataxia associated with cerebellar atrophy [51].  
Figure 8: Coenzyme Q\({}_{10}\) deficiency. Brain MRI scan of a 15-year-old girl. MR images demonstrate not specific diffuse cerebellar atrophy and a normal volume of the brainstem.
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 3 Types of Corrosion Inhibitors
### Composite-Based Corrosion Inhibitors
#### 3.4.2 Carbon-Based Corrosion Inhibitors  
Graphene (Gr), graphene oxide (GO), reduced graphene oxide (rGO), CNTs, and CNS are among the carbon derivatives that have been regarded as the best anticorrosive additives due to their high water solubility, good biocompatibility, and low toxicity [67]. According to molecular dynamics simulation, the hydrophobic coating that CNTs can create on the surface of carbon steel via functionalized groups limits the diffusion migration of corrosive media. In order to stop the corrosion of an aluminum current collector used in lithium-ion batteries, [PERSON] and colleagues [68] reported functionalized carbon nanotubes (FCNTs) synthesized using a hydrothermal method and adopted as a corrosion inhibitor of carbon steel in CO\({}_{2}\)-saturated NaCl solution. They showed that functionalizedcarbon nanotubes (FCNTs) can effectively inhibit carbon steel corrosion, and their IE is close to 90% at the concentration of 100 mg L\({}^{-1}\).  
CNSs attracted researchers' interest due to their sphere shape and other unique features. Figure 6 shows encapsulation of CIs on a carbon nanosphere for the development of a carbon-based anticorrosive coating.  
A detailed discussion on the usage of encapsulated CIs is presented later in Section 4 of this article. A unique hollow porous carbon nanosphere made of graphite (GCN@M16) with corrosion inhibitors infused in it was proposed for fabrication and investigation by [PERSON] et al. [69]. The prepared CI strengthened their corrosion resistance capabilities while also acting as a lubricant and empowering them as corrosion inhibitors. The prepared CI was put to the test on copper metal in HCl, a media known to be corrosive. After applying GCN@M16 or M16, the corrosion phenomenon can be suppressed. Figure 7 clearly shows the surface microtopography of copper that has been immersed in HCl solution in the presence and absence of 5.0 wt.% GCN. The copper plates appear to become rougher on the surface when M16 or GCN@M16 is added to the HCl solution, indicating the existence of corrosion as well. This is shown in Figure 7d,e, but there are no longer any obvious deep cracks. The color of each corrosion solution after 30 days is also shown in Figure 7b-e.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion  
### Results after Varying the Packet Size  
The simulation was performed with various simulation durations and numbers of nodes for propagation channels and routing protocols across the IEEE 802.11p stack. The given routing algorithms and transmission models are evaluated, and evaluation metrics such as the packet delivery ratio, throughput, latency, hop count, and energy usage are addressed, predicated on the variable number of nodes and packet size. The following are some situations used to analyze the performance of the network.
# A Review of Research on Dropwise Condensation Heat Transfer
## 3 Dropwise Condensation Heat Transfer Model
### Drop Size Distribution  
After establishing the heat transfer model of a single droplet, the heat transfer rate of the whole surface can be calculated by modeling the drop size distribution. Although [PERSON] [31] and [PERSON] [25] proposed a random distribution mathematical model of droplets on the surface early, the population balance theory proposed by [PERSON] [32] and [PERSON] [33] was more accurate and more widely used, and subsequently improved by [PERSON] [21]. According to this model, the radius of small drops keeps on increasing, but the number of drops on the overall surface is a constant value of the range of \(r_{1}\) to \(r_{2}\). Based on this theory, the formula of small drop size distribution is  
\[\mathrm{n}(r)=\frac{1}{3\pi r_{e}^{3}r_{max}}\bigg{(}\frac{r_{e}}{r_{max}} \bigg{)}^{-2/3}\frac{r(r_{e}-r_{min})}{r-r_{min}}\frac{A_{2}r+A_{3}}{A_{2}r_{e }+A_{3}}\exp(B) \tag{8}\]  
where  
\(A_{1}=\frac{\Delta T}{2\rho H_{3}}\),  
\(A_{2}=\frac{\theta(1-cos\theta)}{4k_{c}sin\theta}\),  
\(A_{3}=\frac{1}{2h_{i}}+\frac{\delta(1-cos\theta)}{k_{coat}sin^{2}\theta}\)  
\(\mathrm{B}=\frac{A_{2}}{\tau A_{1}}\bigg{[}\frac{(r-r_{min}^{2})}{2}+2\tau_{ min}(r-r_{min})+r_{min}^{2}ln(r-r_{min})\bigg{]}+\frac{A_{3}}{\tau A_{1}} [r-r_{min}+r_{min}ln(r-r_{min})]\)  
\(\tau=\frac{3r_{e}^{2}(A_{2}r_{e}+A_{3})^{2}}{A_{1}(11A_{2}r_{e}^{2}-14A_{2}r_{ e}r_{min}+8A_{3}r_{e}-11A_{3}r_{min})}\)  
where \(\tau\) is sweeping period, s.  
The distribution model of Rose [25] is still adopted for the droplets grown by coalescing.  
\[\mathrm{N}(\tau)=\frac{1}{3\pi r^{2}r_{max}}\bigg{(}\frac{r}{r_{max}}\bigg{)} ^{-2/3} \tag{9}\]  
Although the above theory only remodels the small drop size distribution, it significantly improves the accuracy of the overall heat transfer model because most of the heat transfer rate transferred to the condensing surface through the small droplets in the condensation process.  
Substituting Equations (7)-(9) into Equation (2), we can obtain the heat transfer rate \(\Phi_{D}\) through all the droplets, and we can derive the expression of the heat transfer rate of the surface as \(\Phi=\Phi_{F}+\Phi_{D}\).  
Many in-depth studies have been carried out on the heat transfer model and the adsorption theory has been applied to more scientific observation methods, which has been recognized by more and more scholars. However, up to now, the dropwise condensation heat transfer model is generally based on the nucleation sites theory, and no one hasestablished any models based on absorption theory. In addition, there is no applicable and universal heat transfer model for the combination surfaces that have been studied a lot at present, including the influence of droplets between two surfaces with different characteristics and the migration of droplets between surfaces.
## 3 Results and Discussion  
### Principle of the Sensing System  
The goal of this study is to develop a new universal and highly sensitive strategy for the ECL-meditated detection of sequence-specific DNA by coupling Nt.BbvCI-assisted TRA with an RCA reaction for signal amplification and formation of hemin/G-quadruplex. Nt.BbvCI is a nicking endonuclease that exclusively cuts one strand of a double-stranded DNA at the cleavage site \(\mathrm{XXCC}\) \(\blacktriangledown\) TCAGCXX from the 5' end to 3' end. To make this strategy universal, we design the recognition site in the double-stranded DNA of SH-CP/AP and only the SH-CP could be cleaved. As illustrated in Figure 1, the pretreated AuE surface was modified with SH-CP via Au-S bonds and blocked with HT. Then, target DNAs hybridize with the SH-CP and AP strands to form \"Y\" junction structures containing specific recognition sites (in SH-CP/AP hybridization DNA) for the Nt.BbvCI enzyme. The hybridized SH-CP sequences were then cleaved into two fragments by Nt.BbvCI, leading to the release of the targeted DNA and AP due to the instability of cleaved hybridization structures. The released target DNA and AP again hybridized with other un-digested SH-CP sequences and trigger target recycling cycles, resulting in the production of numerous cleaved SH-CPs (on the electrode) with bare binding sites for the padlock probe. Padlock probes ligated with T4 DNA ligase were bound to the digested shorter SH-CPs (on the electrode) via hybridization. Upon the addition of phi29 polymerase and dNTPs, the RCA reaction was isothermally executed at 37 \({}^{\circ}\)C at the 3' end of the shorter SH-CP to generate long single-stranded DNAs with tandem repeats containing G-quadruplex sequences. Finally, hemin/G-quadruplex were formed by incubation with hemin, which led to the consumption of oxygen and resulted in a decrease in ECL emission in the detection buffer. The decrease in ECL intensity can thus be related to the quantity of target DNA in testing samples.  
Figure 1: Illustration of the dual signal amplification strategy for specific DNA detection based on the Nt.BbvCI-assisted recycling amplification (TRA), rolling circle amplification (RCA) reaction and the formation of hemin/G-quadruplex.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Optimization and Design of IRSs
#### 3.3.4 Multiuser MISO/SIMO Scenarios
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **System Model** & **Performance Metric** & **Main Results** \\ \hline
[64] & Downlink, NOMA, NLOS & Rate & Larger rate \\  & NOMA, NLOS & & than OMA systems \\ \hline
[65] & Downlink, LOS, NOMA, downlink & Tx power & Lower tx power \\  & NOMA, downlink & Tx power & than a baseline: random phases \\ \hline
[66] & Uplink, Clustering, NOMA & Correctly detected users & Clustering NOMA with larger number of correctly detected users \\ \hline
[67] & NLOS, NOMA, cell edge & Outage prob. & Analytical results are provided for the IRS-NOMA scheme \\ \hline
[68] & NLOS, mmWave, Gradient descent & Outage prob. & Lower outage prob. than baselines \\ \hline
[69] & Downlink, DRL, Gradient descent & Rate & Comparable performance with [70] \\ \hline
[70] & Downlink, NLOS & EE & Notably higher EE \\  & Gradient descent & & than an AF setup \\ \hline
[71] & LOS, MEC, ML & Tx power & Lower MLE \\  & LOS, MEC, ML & and MLE & than baselines \\ \hline
[72; 73] & LOS, GNN, imperfect CSI & Rate & Higher rate \\  & & imperfect CSI & and MLE & than baselines \\ \hline
[74] & NLOS, imperfect CSI & Rate & Higher system performance than baseline setups \\ \hline
[75] & multi-IRS, NLOS, imperfect CSI & Rate & Higher rate \\  & multi-IRS, NLOS, imperfect CSI & Rate & than baselines \\ \hline
[76] & LOS, Imperfect cascaded CSI, & Tx power & Lower tx power \\  & & cascaded CSI, & than baselines \\ \hline
[77] & CR, downlink, imperfect CSI & Rate & Rate improvement in an IRS-aided CR network \\ \hline
[78] & Downlink, indoor LOS, DL & Rate and MSE between the NN output phases and the optimal phases & Good MSE performance based on the user locations \\ \hline
[79] & NLOS, DL & Rate & Good tradeoff between performance and complexity \\ \hline
[80] & NLOS, FWoL-FPHC & Rate & Higher protection level and rate \\ \hline
[82] & Downlink, Discrete phase shifts, NLOS & Tx power & Good performance with low resolution phase shifters \\ \hline
[83] & Uplink, On/Off IRS & Rate & Larger rates over random strategies \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 2 Methods
### Cluster Validation Indices
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Name** & **Notation** & **Intra** & **Inter** & **Formula** \\ \hline KCE ([PERSON] et al., 2016) & KCE & \(K\times\mathcal{J}_{K}\) & Intra \\ \hline WB-index ([PERSON] et al., 2016) & WB & \(K\times\mathcal{J}_{K}\) & \(\sum\limits_{k=1}^{K}n_{k}\|\mathbf{c}_{k}-m\|_{p}^{d}\) & Intra \\ \hline Calinski–Harabasz ([PERSON], 1970) & CH & \((K-1)\times\mathcal{J}_{K}\) & \((N-K)\times\sum\limits_{k=1}^{K}n_{k}\|\mathbf{c}_{k}-m\|_{p}^{d}\) & Inter \\ \hline Davies–Bouldin ([PERSON] et al., 2016) & DB & \(\frac{1}{n_{k}}\mathcal{J}_{K}^{k}+\frac{1}{n_{p}}\mathcal{J}_{K}^{k}\) & \(\|\mathbf{c}_{k}-\mathbf{c}_{k}\|_{p}^{d}\) & \(\frac{1}{K}\sum\limits_{k=1}^{K}\max\limits_{k\
eq k}\frac{\text{Intra}(k,k)}{ \text{Intra}(k,k)}\) \\ \hline [PERSON], [PERSON], and [PERSON] ([PERSON] et al., 2017) & PBM & \(K\times\mathcal{J}_{K}\) & \(\max\limits_{k\
eq x}(\|\mathbf{c}_{k}-\mathbf{c}_{k}\|_{p}^{d})\times\mathcal{ J}_{1}\) & \(\Big{(}\frac{\text{Intra}}{\text{Inter}}\Big{)}^{2}\) \\ \hline Ray–Turi ([PERSON] et al., 2016) & RT & \(\frac{1}{N}\times\mathcal{J}_{K}\) & \(\min\limits_{k\
eq x}\|\mathbf{c}_{k}-\mathbf{c}_{k}\|_{p}^{d}\) & Intra \\ \hline Wemmert–Gancarski ([PERSON] et al., 2017) & WG & \(\|\mathbf{x}_{i}-\mathbf{c}_{k}\|_{p}^{d}\) & \(\min\limits_{k\
eq x}\|\mathbf{x}_{i}-\mathbf{c}_{k}\|_{p}^{d}\) & \(\frac{1}{N}\sum\limits_{k=1}^{K}max\Big{(}0,\ n_{k}-\sum\limits_{i\in\mathbf{h}} \frac{\text{Intra}(i)}{\text{Inter}(i)}\Big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Internal cluster validation indices.
## 2 PI Assessments  
### PI Coverage Probability  
The PI coverage probability (PICP) is the most important characteristic for the reliability, as it indicates the probability that the targets lie in the constructed PIs. PICP can be calculated as follows:  
\[\text{PICP}=\frac{1}{N_{p}}\sum_{i=1}^{N_{p}}\rho_{i} \tag{1}\]where \(N_{p}\) is the number of the sample data. \(\rho_{i}\) is defined as follows:  
\[\rho_{i}=\left\{\begin{array}{ll}0,&if\quad y_{i}\
otin[L_{i},U_{i}]\\ 1,&if\quad y_{i}\in[L_{i},U_{i}]\end{array}\right. \tag{2}\]  
where \(y_{i}\) is the target value. \(L_{i}\) and \(U_{i}\) are, separately, the lower bound and the upper bound of the PIs. The value of \(\rho_{i}\) depends on whether the target value is covered by the PIs. Therefore, the more target values are covered by the PIs, the higher the PICP and the more reliable the PIs are. The ideal value of the PICP is 100%, which means that all the target values are covered.
## 5 Conclusions  
In this work, we developed an HFI based on an AFT as a component for water energy conversion. To target M\({}_{1}\), M\({}_{2}\), and M\({}_{3}\) of the AFT, hydraulic performance testing and CFD numerical simulation were integrated to optimize the AFT structure. The conclusions are as follows: the \(nRMSE\) between the simulated and measured AFT outlet flow values is 5.1%, revealing the high accuracy of the mesh rotation simulation method with a fixed \(n\). The \(P\) of the AFT increases and then decreases with increasing n, whereas it increases with increasing \(H\). Regarding the distribution characteristics of the AFT output power and internal flow field, the \(P_{\text{max}}\) of D33 is the highest, increasing by 6.7-33.5% compared with those of the other treatments. In the flow field of D33, the reg-lv area is small, whereas the reg-hv and \(v\) areas are large, resulting in high energy conversion efficiency. The measured \(Q_{\text{inj}}\) values of D33, D34, and D44 were 288.6-847.3 L/h, and their \(H_{\text{min}}\) values were less than 0.12 MPa, which was 0.005-0.030 MPa lower than that of the venturi fertilizer injector, whereas \(Q_{\text{inj}}\) increased by 38.7%-461.0% on average. Considering the effect of cavitation on the impeller life, D34 and D44 are more suitable as impeller structure combinations for the HFI. \(P_{\text{max}}\) decreased by an average of 10.7% and 15.1% under each \(H\), and \(Q_{\text{inj}}\) decreased by 1.3% and 7.0%, respectively, compared with D33, but \(NP\) decreased by 30.5% and 37.9% for D34 and D44, respectively. Considering the fertilizer injection efficiency, impeller service life, and additional costs of multiple mold openings in actual production,D44 was ultimately selected as the impeller structure combination for the HFI. In addition, the HFI will be further improved in the future, the replacement of impeller materials will be explored to improve the wear resistance of the AFT, the long-term use reliability of the HFI will be tested for different irrigation environments, and the overall structure design will be changed to improve the long-term use reliability of HFI.  
Conceptualization, Y.M. and C.Z.; methodology, C.Z. and Y.M.; software, C.Z.; validation, Y.M., S.L. and B.Z.; formal analysis, C.Z., Q.Z., S.L., B.Z. and Y.G.; investigation, C.Z.; resources, S.L.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, Y.M. and S.L.; visualization, B.Z. and Y.G.; supervision, J.X. and Y.M.; project administration, Y.M.; funding acquisition, Y.M. and J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Science and Technology Project for Revitalization of Inner Mongolia, China (2022 EEDSKJXM004) and the Research and Development Support Program of China Institute of Water Resources and Hydropower Research (ID0145B042021).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
# Water Use Efficiency Spatiotemporal Change and Its Driving Analysis on the Mongolian Plateau
## 3 Results  
### Temporal and Spatial Trend Analysis of NPP, ET, and WUE  
#### 3.1.1 Temporal Differentiation of WUE in the Mongolian Plateau  
We studied the annual average WUE for different vegetation types in the Mongolian Plateau over 37 years and analyzed the linear trends (Figure 2). The results showed that the annual average WUE of forests was higher than that of grasslands, and the WUE for other vegetation types exhibited a decreasing trend, except for farm-land (\(p<0.05\)) and desert grasslands (\(p>0.05\)). The annual rates of change in WUE in meadow, typical, and desert steppes; broadleaf forests; agricultural land; and coniferous forests were \(-0.005\) gC/mm\(\cdot\)m\({}^{2}\cdot\)year, \(-0.002\) gC/mm\(\cdot\)m\({}^{2}\cdot\)year, \(0.0003\) gC/mm\(\cdot\)m\({}^{2}\cdot\)year, \(-0.004\) gC/mm\(\cdot\)m\({}^{2}\cdot\)year, \(0.002\) gC/mm\(\cdot\)m\({}^{2}\cdot\)year, and \(-0.003\) gC/mm\(\cdot\)m\({}^{2}\cdot\)year, respectively.  
Figure 2: Annual variation in water use efficiency for different vegetation types in the Mongolian Plateau.
## 2 Materials and Methods
### Feature Extraction
#### 2.2.1 Standard Features  
Standard features for the characterization of AE signals were extracted and denoted as follows:  
1. Peak amplitude (\(\upmu\)V) denotes burst signal linear peak amplitude,
2. Burst signal duration (\(\upmu\)s) represents the elapsed time after the first and until the last threshold crossing of a burst signal,
3. Burst signal energy (\(\upmu\)V\({}^{2}\)s) according to EN 1330-9 is the integral of the squared AE-signal over time,
4. Burst signal rise-time (\(\upmu\)s) represents the elapsed time after the first threshold crossing and until the burst signal maximum amplitude,
5. Frequency centroid (Hz) denotes the frequency at which the spectrum has its center of gravity (including only the frequencies and corresponding amplitudes in the range between 0 and 1000 kHz),
6. Frequency of the max. amplitude of the spectrum (Hz),
7. Frequency of the max. amplitude of continuous wavelet transformation (using the complex Morlet wavelet) (Hz),
8. Partial power of frequency spectrum between 0 and 200 kHz (/),
9. Partial power of frequency spectrum between 200 and 400 kHz (/),
10. Partial power of frequency spectrum between 400 and 1000 kHz (/).