esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Glenohumeral Kinematics and scapular Movements at Maximum Flexion and Extension  
The maximum shoulder RoM was lower for flexion on the paretic side (121.4\({}^{\circ}\)\(\pm\) 18.7), with a significant difference (\(p\) = 0.032) compared with the non-paretic side (141.8\({}^{\circ}\)\(\pm\) 17.9). Paretic shoulder abduction (139.7\({}^{\circ}\)\(\pm\) 33.6) was also lower compared with the non-paretic side (171.3\({}^{\circ}\)\(\pm\) 14.2), although not significantly, due to the large variance on the paretic side (\(p\) = 0.081) (Table 2). A significant difference was also found for scapular tilt during maximum shoulder flexion on the paretic side (17\({}^{\circ}\)\(\pm\) 7.4), which was lower compared with the non-paretic side (25.6 \(\pm\) 4.7) (\(p\) = 0.032). The non-paretic shoulders had a degree of maximum flexion and abduction close to that of the controls.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pts**} & \multirow{2}{*}{**Age (Year)**} & \multicolumn{2}{c}{**Follow-Up**} & \multirow{2}{*}{**Paretic Side**} & \multirow{2}{*}{**FMA (0-15)**} & \multirow{2}{*}{**MAS**} & \multirow{2}{*}{**VAS**} \\  & & & & & \\ \hline
1 & 47 & 1.5 & Left & 15 & 0 & 0 \\
2 & 57 & 12.5 & Right & 12 & 0 & 0 \\
3 & 58 & 36.4 & Right & 11 & 0 & 0 \\
4 & 27 & 28.4 & Left & 10 & 1 & 0 \\
5 & 74 & 61.8 & Right & 8 & 0 & 0 \\
6 & 55 & 32.7 & Right & 10 & 0 & 3 \\
7 & 58 & 13.8 & Left & 9 & 0 & 0 \\ \hline \hline \end{tabular} FMA = Fugl-Meyer Motor Assessment; MAS = Modified Ashworth Scale; VAS = visual analog scale.  
\end{table}
Table 1: Patient clinical and general data.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Paretic Side** & **Non-Paretic Side** & **[PERSON] et al., 2020 [29]** \\ \hline RoM MaxFlex * & 121.4 (18.7) & 141.8 (17.9) & 137.6 (9.2) \\ RoM MaxAbd & 139.7 (33.6) & 171.3 (14.2) & 170.3 (13.4) \\ scapular PR MaxFlex & 19.5 (7) & 17.8 (9.4) & \\ scapular TI MaxFlex * & 17.0 (7.4) & 25.6 (4.7) & \\ scapular UD MaxFlex & 33.3 (8.6) & 29.4 (8.7) & \\ scapular PR MaxAbd & 15.5 (8.1) & 16.5 (6.8) & \\ scapular TI MaxAbd & 17.1 (9.2) & 19.1 (8.9) & \\ scapular UD MaxAbd & 29.4 (11.5) & 31.4 (9.7) & \\ \hline \hline \end{tabular}  
* RoM = range of movement; Flex = flexion; Abd = abduction; PR = protection–retraction; TI = anterior–posterior tilt; UD = upward/ downward rotation.
* \(p\) = 0.032 (paretic side vs. non-paretic side).  
\end{table}
Table 2: Glenohumeral kinematics and scapular movements for paretic and non-paretic arms during maximum shoulder flexion and abduction. RoM values are reported as means (SD).
## 2 Background and Related Literature
### Related Literature
[18] employed logo and seal detection, which can be particularly beneficial when document images contain illustrations like logos or figures. In another example, Ref. [7] utilized multiscale smearing techniques to locate text and image blocks, while [21] employed affine invariant hashing for local region identification. These diverse approaches demonstrate the evolving landscape of image feature extraction for document retrieval and classification tasks. In [4], an XY tree deploying index structures for large-scale searches was introduced, and a comparison between locally sensitive hashing and brute force methods was presented. For DIR systems that rely on local regions to represent images, inverted index data structures are commonly employed [21]. Additionally, there is a growing trend of utilizing deep learning approaches in several recent studies [1; 3; 22; 23; 24; 25], reflecting the ongoing evolution and incorporation of advanced techniques in DIR systems. In this study, our approach is built on the previous literature and distinguishes itself by employing an ensemble of DIR systems rather than creating a single DIR system. This choice is because individual DIR systems relying solely on a single image feature generally fall short in comprehensively representing all aspects of document images and are susceptible to errors under specific conditions. We can mitigate errors from individual plans by seeking consensus across multiple individual DIR systems.
###### Abstract  
Battery models are mathematical systems that aim to simulate real battery cell sufficiently accurately. Finding a comprise between complexity, computational effort and accuracy is thereby key. In particular, modelling sodium-nickel-chloride/iron-chloride cells (\(Na-NiCl_{2}/FcCl_{2}\)), as a promising alternative for stationary energy storage, bears some challenges. The literature shows a few interesting approaches, but in most of them the second active material (\(NiCl_{2}\) or \(FcCl_{2}\)) or the entire discharging/charging cycle is not considered. In this work, an electrochemical and thermal model of \(Na-NiCl_{2}/FcCl_{2}\) battery cells is presented. Based on an equivalent circuit approach combined with electrochemical calculations, the hybrid model provides information on the performance of the cell for charging and discharging with a constant current. By dividing the cathode space into segments, internal material and charge flows are predicted, allowing important insights into the internal cell processes. Besides a low calculation effort, the model also allows a flexible adaption of cathode composition and cell design, which makes it a promising tool for the development of single battery cells as well as battery modules and battery systems.  
sodium-nickel-chloride cells; \(Na-NiCl_{2}/FcCl_{2}\) cell; ZEBRA cell; battery modelling +
Footnote †: journal: batteries
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 5 Parameter Optimization for NOMA System with Laser Chaos Decision-Maker and Error Correcting Codes  
### NOMA System with Error Correcting Codes  
Here, we consider introducing error correcting code (ECC) in the system. Error correction capability depends on the type of ECCs. Again, we define the number of bit errors at the \(n\)th user as _bit-error\({}_{n}\)_. Additionally, we define the \(\varphi\) bit-correctable error  
Figure 8: Comparison of the LCDM-NOMA with the C-NOMA and the UCGD-NOMA in BER.  
Figure 7: Visualization of the behavior of the LCDM-NOMA. (**a**) Time evolution of the selected pairing option; (**b**) time evolution of the _system bit error_.  
correcting code for the \(n\)th user as \(ECC_{\varphi,n}\). \(\varphi_{n}\) is the number of correctable bit errors on the \(n\)th user. \(ECC_{\varphi,n}\) takes various values depending on the type of ECCs.  
It should be noted that a user may respond ACK to BS, although bit errors indeed occur. For example, with \(ECC_{0,n}\) for the \(n\)th user, we do not consider error correction because \(ECC_{0,n}\) means 0 bit-correctable error correcting code. That is, for example, in the case of \(bit\)-\(error_{n}=1\) for the \(n\)th user, when \(ECC_{0,n}\) is set, the \(n\)th user responses NACK to BS. Conversely, when \(ECC_{2,n}\) is set, meaning that 2 bit-correctable error correcting code is employed, the \(n\)th user responds ACK to BS when \(bit\)-\(error_{n}=1\). Therefore, we have to investigate the dependency on the type of ECCs.
## 4 Conclusions  
In this paper, the pre-camber seen during bridge cantilever construction is predicted by our established GM-BP combined prediction model, and the main conclusions are as follows:  
1. The GM(1,1) prediction model and the BP neural network model have their own limitations. The prediction accuracy of the former is greatly affected when predicting sample data with a large degree of dispersion; the latter has a better nonlinear fitting ability, but it requires a large amount of sample data, is less stable, and is prone to non-convergence.
2. The GM-BP combined prediction model can effectively improve prediction accuracy. The prediction result of the GM(1,1) model is used as the input of the BP neural network, and the new prediction value is then output through the neural network, which can give full play to the advantages of the two models: it has the characteristics of requiring fewer samples and having a higher prediction accuracy.
3. Our validation using engineering examples shows that the GM-BP combined prediction model has significantly improved the prediction accuracy of pre-camber and that the model's stability is stronger. From the prediction results of the pre-camber of the girder sections 11#-13#, which are to be constructed, compared with those of the GM(1,1) prediction model, the maximum relative error is reduced from 12.81% to 4.52%, and the prediction accuracy is higher; from the prediction results of the whole girder section, the predicted value of the GM-BP model is more in line with the actual value of the pre-camber, and the model's stability is stronger.  
Finally, the performance of the GM-BP combined prediction model largely depends on the choice of its parameter settings. Inappropriate parameter settings may result in suboptimal model performance. Subsequent evaluations and adjustments of the model parameters may be conducted using suitable optimisation algorithms, with the objective of further improving the performance and accuracy of this model.  
This research received no external funding.  
Not applicable.  
Figure 9: Comparison chart of relative errors in model predictions.
## 2 Materials and Methods
### Statistical Analyses  
Prior to statistical analyses, Tmax and RTDmax data were normalized with participants' individual body mass. Normality of the dependent variables was confirmed using a Shapiro-Wilk test (\(p>0.05\)). Descriptive statistics (mean and standard deviation) were calculated for all variables (Tmax, RTDmax, RTD-SF, and Intercept). A 2 \(\times\) 2 analysis of variance (ANOVA) with time (pre-test and post-test) as within-subject factor and group (EMS and VOL) as between-subject factor was applied to each dependent variable separately for the trained and non-trained leg, followed by the Bonferroni post hoc test. The [PERSON]'s effect size (d) was used to quantify the within-group differences as d \(<\) 0.2 (trivial or no effect), d = 0.2-0.5 (small), d = 0.5-0.8 (moderate), d = 0.8-1.3 (large), and d \(>\) 1.3 (very large). [PERSON]'s d effect size (ES) and 90% confidence interval (CI) were computed using the harmonic mean of the SD of the compared conditions and an ES of 0.20 was considered as the minimal value of practical importance. When the 90% CI overlapped substantial positive and negative values, the effect was deemed unclear; otherwise, effects were deemed clear [38]. \(p\)-values \(<\) 0.05 were considered statistically significant. The magnitude comparison analysis was performed using a custom Excel spreadsheet (available from: [[https://www.sportsci.org/jour/03/wghtrials.htm](https://www.sportsci.org/jour/03/wghtrials.htm)]([https://www.sportsci.org/jour/03/wghtrials.htm](https://www.sportsci.org/jour/03/wghtrials.htm)), accessed on 18 September 2023), while other statistical analyses were performed using the software package SPSS (IBM SPSS version 20.0, Chicago, IL, USA).
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 5 Security Analysis
### Formal Proof  
The eCK adversary model [33; 34; 40] is employed for the security proof. The system authentication model is shown in Figure 4. After registration, \(SP_{sp}\) obtains its private and public keys \((k_{sp},PK_{sp})\). The private and public keys of \(S_{s}\) are \((k_{s},PK_{s})\). During authentication and key agreement, they negotiate the session key \(SSK_{s}\) (\(SSK_{sp}\)) by exchanging authentication information \(M_{s}\) and \(M_{sp}\), where \(M_{s}=f_{s}(x_{s}k_{s})\) and \(M_{sp}=f_{s}(x_{sp}k_{sp})\), \(x_{s}\), and \(x_{sp}\) are random ephemeral secrets.
# Study on the Mechanical Criterion of Ice Lens Formation Based on Pore Size Distribution
## 2 Theoretical Model
### Formation of Ice Lens  
When water flows toward the freezing front, it will be frozen directionally and forms a layer of segregation ice [32; 33; 34; 35; 36]. Scholars proposed a large number of theoretical models for describing the formation of ice lens. Through the comparative analysis of the criteria of ice lens formation, it can be seen that the mechanical criterions of ice lens formation have two components: driving force for promoting the formation of ice lens and constraining force for preventing the formation of ice lens. When the driving force equals or exceeds the constraining force, the connection between soil particles will be broken, and a new ice lens will be generated. This criterion can be expressed in a generic form:  
\[\sigma_{\text{\small{por}}}\geq\sigma_{\text{R}} \tag{31}\]  
where \(\sigma_{\text{\small{por}}}\) is the driving force of ice lens formation and \(\sigma_{\text{R}}\) is constraining force of ice lens formation.  
[PERSON] [11] and [PERSON] [37] believed that a new ice lens was formed when the ice pressure equaled or exceeded the sum of the overburden pressure and the separation pressure. [PERSON] et al. [38] showed that a new ice lens forms when the pore water pressure equals or exceeds the sum the effective pressure and cohesive force. [PERSON] [39] shows that the driving force of the ice lens formation generally includes pore water pressure, pore ice pressure, pore pressure, unfrozen water film pressure, and so on. However, the formation mechanisms of these different pressures are not well understood.  
Figure 5: Freezing characteristic curve of soil.
###### Abstract  
A novel approach, using low Earth orbit (LEO) satellite microwave communication links for cloud liquid water measurements, is proposed in this paper. The feasibility of this approach is studied through simulations of the retrieval system including a LEO satellite communicating with a group of ground receivers equipped with signal-to-noise ratio (SNR) estimators, a synthetic cloud attenuation field and a tomographic retrieval algorithm. Rectangular and Gaussian basis functions are considered to define the targeted field. Simulation results suggest that the proposed least-squares based retrieval algorithm produces satisfactory outcomes for both types of basis functions. The root-mean-square error of the retrieved field is around 0.2 dB/km, with the range of the reference field as 0 to 2.35 dB/km. It is also confirmed that the partial retrieval of the cloud field is achievable when a limited number of receivers with restricted locations are available. The retrieval outcomes exhibit properties of high resolution and low error, indicating that the proposed approach has great potential for cloud observations.  
cloud measurement; low Earth orbit (LEO) satellite; satellite communication; tomography; signal-to-noise ratio +
Footnote †: journal: _arxiv.org_
## 1 Introduction
We introduce the mathematical model of the OTFS modulation and Pulse-OTFS signals and derive general expressions for the power spectral density (PSD) and autocorrelation function (ACF) of OTFS modulation, which are equally applicable to the proposed signal. We also discuss the relationship between the proposed signal properties and parameters as well as the connection with BPSK. 3. The navigation performance of Pulse-OTFS is evaluated in comprehensive detail and compared with that of the original OTFS signal, traditional GNSS signal, and Locata signal, i.e., Pulse-BPSK. The results indicate that the carrier power-to-noise density ratio (\(C/N_{0}\) of the proposed signal is about 8 dB lower than that of the GNSS and Locata signals for the same code-tracking accuracy. This means that the proposed signal achieves higher measurement accuracy at the same \(C/N_{0}\). Meanwhile, the acquisition complexity is reduced by at least 89.4%. It also has advantages in terms of compatibility and anti-multipath performance. 4. We implement an experimental platform based on software-defined radio (SDR) and verify the advantage in the fast acquisition and code-tracking accuracy of the proposed signal through a real analog channel. The experimental results are consistent with the theoretical performance analysis. The remainder of this paper is organized as follows. Section 2 introduces the proposed Pulse-OTFS signal model and derives analytical expressions of the ACF and PSD. Section 3 evaluates the navigational performance of the proposed signal. Section 4 verifies the advantages of the proposed signal through an SDR-based experimental platform. Finally, the paper concludes in Section 5.
# Performance Analysis of a Color-Code-Based Optical Camera Communication System
## 2 Proposed OCC System Architecture
### Optimization of Hardware Parameters  
Optimization is required for the monitor at the transmitter to send the coded colors correctly. It was aimed to transmit the data accurately by taking into account whether the test room was bright or dark, the light color of the lamp in the illuminated area, and the effects of daylight when the monitor was placed in front of a window. The 30-square-meter test room was illuminated by an LED luminaire with a luminous efficiency of 120 lm/W, producing a luminous flux of 3000 lumens and a color temperature of 6500 K. Dead pixels and light bleed caused by the manufacturing process of the monitor were also taken into account. During the tests, the dead pixel status of the monitor was inspected and no dead pixels were found. However, in the light bleed test, light bleed was detected from the bottom to the top of the panel. As shown in Figure 10, the negative effect on communication was reduced by angling the screen 5 degrees in the \(z\)-axis. In addition, the brightness of the monitor was reduced by 34 and the contrast value and color saturation were set to the highest value. The blue filter setting of the monitor was maximized to reduce hardware-related problems.  
The maximum pixel count of the monitor used during the tests was 1920 \(\times\) 1080. However, due to light bleed, monitor response time, and hardware issues of the LCD monitor, the monitor screen was optimized and used. The size of the channels, the distance between the channels, and the number of channels were adjusted to achieve the maximum number of channels and maximum distance, which were the objectives of this study. Here, the distance between channels is necessary to optimize the blooming effect, which has a distorti  
Figure 9: Sample code for masking.  
There are many options to determine the color level and size of the channels. The NumPy library was used and the desired efficiency was achieved because it can meet the requests, such as being easily adjustable to the dimensions and positions we want to apply in the channels, easily callable, and compatible with Python-Tkinter. The channels created with the NumPy library were placed on the Canvas. The size and color values of the transmitter channels created with NumPy are shown in Figure 11. Channel sizes were optimized as 40 \(\times\) 40. Color values were chosen between 0 and 255. Due to the angle of the monitor on the \(z\)-axis, the value for the green color was chosen as 200. After optimization, the data were distributed to the channels as packets of three bits each. The first 3 bits were set to the first channel and the last 3 bits to the sixteenth channel.
## 4 Time Continuous Compactness Based _Rwta_
### ReLoss-TCC-Based RWTA Strategy  
The RWTA strategy based on ReLoss-TCC refers to the routing and wavelength-timeslot solution which minimizes the relative change of the TCC in all alternatives. That means it has the least impact on the connectivity status of network resources after the RWTA process. Suppose the request is denoted as \(r(s,d,t_{a},TW,t,l)\), where \(s\) and \(d\) represent the source node and destination node, \(t_{a}\) represents the arrival time of this request, \(TW\) represents the time window, \(t\) represents the number of timeslots required for the request, and \(l\) is the security level. The ReLoss-TCC-based RWTA strategy is shown in Algorithm 2 and specifically includes the following three stages: (i) network initialization and routes configuration (Step 1-5); (ii) find all alternative solutions for the arriving request based on FF strategy (Step 6-17); and (iii) get the final solution based on ReLoss-TCC strategy (Step 18-22).  
```
1 Topology \(G(V,E)\), Source and destination pairs \(\{s,d\}\);
2 For each \(\{s,d\}\) pair, do
3 calculate the shortest paths \(p\);
4 store \(p\) into set \(P\);
5 create map \(\{s,d\}\to p\);
6 End For
7 For each path \(p\) in set \(P\), do
8 find the paths which share links with \(p\), store them into set \(P(p)\);
9 End For
10 Request \(r(s,d,t_{a},TW,t,l)\) arrives;
11 Search the path \(p\) according to the map created in \(\textit{step 2}\);
12 Find the wavelengths of QKCh on path \(p\) that satisfy wavelength consistency constraint;
13 store the wavelengths into set \(W\);
14 If\(W\
eq\varnothing\)
15 For each \(w\) in set \(W\)
16 Search the available timeslots in \([t_{a},t_{a}+TW+t]\);
17 store timeslots into set \(S\);
18 If\(S\
eq\varnothing\)
19 find the available timeslot segments with length t;
20 store timeslot into set WT;
```  
**Algorithm 2** ReLoss (relative loss)-TCC-based RWTA strategy.
## 4 Mobile Head CT Scanner and Rotorcraft Retrofitting
### Design Considerations for Retrofitting Head CT Scanners into Rotorcraft
#### 4.2.3 Actuation Mechanism of the Mobile Head CT Scanner  
Different CT scanners utilize different mechanical actuations for translation during the scanning process; therefore, it is important to first identify the mechanism as it directly influences the complexity and design of the support structure. For example, the translation of the entire body of CereTom is accumulator-driven. For SOMATOM Onsite, the CT scanner consists of a telescopic gantry, where the inner section elongates while the rest of the structure remains stationary during the scanning process [28]. For xCAT, there is no translation involved since it uses cone beam technology which performs a volumetric scan through a single gantry rotation [30]. To ensure the CT scanner's travel is unidirectional and precise, multiple MSU programs were seen installing linear guides to assist the CT scanner during translation [14,31]. Therefore, for CT scanners where the entire structure is designed to travel during scanning, additional mechanical guides must be designed in addition to the support structure, while for CT scanners where only part of the gantry translates, a simple fixed support structure is adequate.