esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 5 Experiment and Evaluation
### Performance Evaluation  
The difference between the ground truth and reconstructed data was used to evaluate the effectiveness of the data reconstruction model after the training process. We can use the aforementioned MSE loss function (24) for evaluation; however, the measurement units of the datasets differ. As a result, for convenience of use, we normalize the loss function into the common metric (%), called an error metric, which can be used to evaluate various forms of various datasets. In this study, the error metric was employed as a reliable method for assessing the performance of all data reconstruction techniques. It was calculated as follows:  
\[\text{error(\%)} =\frac{||\text{Exact data}-\text{Reconstructed data}||^{2}}{|| \text{Exact data}||^{2}}\times 100, \tag{28}\] \[=\frac{\sum_{i=1}^{K}||\hat{Y}_{i}-Y_{i}||^{2}}{\sum_{i=1}^{K}|| \hat{Y}_{i}||^{2}}\times 100,\]  
where the exact data are the ground truths, and the reconstructed data are the outputs of the reconstruction schemes.  
In addition, \(K=\{K_{\text{train}},K_{\text{test}}\}\), where \(K_{\text{train}}\) and \(K_{\text{test}}\) correspond to the number of training and testing data of the training and testing errors, respectively. Therefore, the error value reflects the performance of the system after training and testing. The higher the error value, the worse the system performance. In essence, it is equivalent to the value of the loss function after normalization to enable the objective evaluation of the system performance with all types of datasets.  
For the purpose of evaluating the effectiveness of the proposed model, this study investigates several noise-affected data reconstruction methods.  
* Partial least squares regression (PLSR): PLSR [35] models each relationship in multivariable data using statistical techniques. Based on the detected correlation, it is possible to make predictions for data that are influenced by noise.
* Bidirectional recurrent neural network (Bi-RNN): Bi-RNN [18] can capture temporal dependencies in both the forward and reverse orientations. Bi-RNN can effectively denoise and reconstruct data sequences.
* Convolutional autoencoder (CAE): The CAE [31] inherits the data recovery capabilities of the AE network. Additionally, the combination with a convolutional network helps the CAE handle effectively two-dimensional data.
## 4 Future Perspectives
### GNSS-Reflectometry (GNSS-R)
#### 4.1.3 Future Options for the GNSS-R
Since in this case, a single antenna placed at various pre-defined positions is used (representing the virtual array positions), the amount of data processing is the same as for other conventional temporal frequency processing techniques [119]. Such methods, however, also have an impact on received signal characteristics, such as a high Doppler rate of change (e.g., up to 10 Hz/s instead of 0.65 Hz/s [119]), which implies the design of adapted or new algorithms.
# Considerations of Curvature for a Near-Eye Holo-Video Display
## 2 Flat Screen  
### LiNbO\({}_{3}\) Flat Screen Device Review  
A primitive design for a LiNbO\({}_{3}\) near-eye device might resemble something shown in Figure 2. Light is introduced into a surface proton-exchanged waveguide by either prism or grating coupling. In the opposite direction, a chirped SAW is launched from comb transducers fabricated on the surface of the device. The SAW is guided by the waveguide and interacts with the incoming light for some unspecified interaction length. Using k-vector analysis [22], it can be shown that the momentum of the light will change according to the opposing acoustic k-vectors. The bounds of this interaction are defined by the strength of the acoustic frequency chirp: The highest spatial frequency available defines the greatest deflection possible, and the lowest defines the minimum the light must deflect in order to escape the waveguide.  
We provide a brief overview of the SAW-light interaction in Figure 2. A single-mode waveguide carries polarized light at some initial angle, \(\theta_{1}\), until the light encounters a surface acoustic wave traveling along the top of the waveguide. These waves will interact [22], and the light will escape, or leak from, the waveguide at some angle \(\theta_{2}\). With the correct setup, the leaky light will eventually encounter the back face of the substrate, where a surface-relief grating allows it to escape the substrate at exit angle or \"maximum deflection angle\", \(\theta_{MAX}\). The user's eye would then be set behind this back plane at some set distance, and the total viewing area (or the total area that the user can look into and see an image) would be determined by the region over the device that allows for acousto-optic interaction.  
Figure 1: An example of an edge-exit vs. face-exit device.  
Figure 2: The original design for a face-exiting flat-screen near-eye display.
## 3 Results and Discussion
### Other Compounds--Toluene  
The aromatic hydrocarbons toluene and ethylbenzene as well as the heterocyclic 2-pentylfuran, not belonging to the groups mentioned above, are discussed separately. Toluene was present in all strains of both species at 17.05 \(\pm\) 8.11 vs. 104.5 \(\pm\) 38.8 \(\upmu\)g/g f.w. in _P. eryngii_ and _P. ostreatus_, respectively. Toluene derives from unsaturated fatty acids and has been reported in wild edible species [15], freeze-dried truffle (_Tuber aestivum_) [57], and other food items like cooked beef [58; 59] and fresh Mediterranean fish and shellfish [60]. Toluene exhibited a distinct distribution among _Pleurotus_ volatiles, its concentrations being 3-13 times lower in _P. eryngii_ when compared to _P. ostreatus_ (Figure 1, Tables S1 and S2). Ethylbenzene was detected at low concentrations only in _P. eryngii_. It has been reported in food items like cooked beef [58] and fermented cucumbers [61] but--to the best of our knowledge--not in edible mushrooms. The heterocyclic compound 2-pentylfuran is formed by the autoxidation of linoleic acid [62]; its odor is described as green, earthy and meaty [17; 40]. It was detected at low concentrations in _P. eryngii_ and _P. ostreatus_ (0.17-0.94 \(\upmu\)g/g f.w.), and it did not exhibit any specific distribution pattern. It was identified in _Pleurotus_[40] and other edible mushrooms [2; 15; 17; 50].
## 1 Introduction  
Wireless sensor networks (WSNs), sometimes covering wide geographical areas, are being deployed for a large number of applications, as they have become one of the most suitable technologies for supporting services, such as air quality monitoring, vehicular traffic monitoring, street lighting optimization, smart parking, among others [1]. In these scenarios, WSNs are exposed to time variant conditions, such as changes in the propagation of radio signals, temperature, humidity, wind, interferences and deterioration of the physical components. This random behavior affects even those WSN applications where nodes are static. Thus, the network can face from favorable to adverse conditions, and yet, it must operate uninterruptedly with the best possible performance, providing continuous information services.  
In addition, other factors can impact the services delivered by the networks, such as faults and attacks. For faults, intermittent or dropped radio links can change the network connectivity. For attacks, and in particular if nodes are static, the exposure to threats can be increased, since evasion tactics are not possible [2]. Therefore, an important consideration in the design of sensor networks is the notion of resilience, where the routing protocol plays a key role in the self-configuring capabilities of the WSN to deal with adverse scenarios.  
Among different types of menaces, jamming, an intentionally generated interference to degrade the services offered by a WSN, is one of the most feasible kinds of attacks. Cryptographic mechanisms cannot prevent jamming from affecting a WSN, since the interference can impact on the transmission and reception of legitimate packets. Therefore, an analysis of the effects of jamming can be useful as a way to evaluate the performance of WSNs.  
Some of the parameters studied to identify the jamming nature are: resource limitations, wireless communication medium, the mobility of nodes and absence of infrastructure [3]. Another performance metric is the amount of changes in the radio links (new or dropped links) implying node connections or disconnections and, hence, a constant change on WSN topology (thus, this metric is related to network connectivity). Ultimately, high changes on the availability of routes means that the routing protocol should be able to react quickly to any changes and update routes efficiently. Additionally, when a network is disturbed, node time recovery ought to be measured [3]. Nodes' forwarding packet capability is also essential in a WSN and can be significantly affected by the characteristics of the wireless medium and noise. Nodes that carry a high amount of traffic are likely to exhaust their energy faster, and this causes network failures or traffic spikes generating packet loss. Moreover, the addition of new nodes to the network also causes changes in routing tables and topology [3]. However, it is very important to analyze the whole scenario, because sometimes, node failures or poor connectivity can have similar effects as a jamming attack [4].
## 3 Simulation Analysis
### Optimization Parameter Analysis
#### 3.3.2 Influence of Orifice to Outlet Distance on Static Performance  
It can be seen from Figure 7a that the bearing capacity under the different conditions changes almost uniformly with the eccentricity. When the orifice is closer to the outlet, the deceleration phase of the compressed gas flowing into the atmosphere after throttling is shorter, resulting in increased gas consumption, as shown in Figure 7b.  
\begin{table}
\begin{tabular}{c c c} \hline
**Structural Parameters** & **Initial Value** & **Unit** \\ \hline Air supply pressure, \(P_{s}\) & 0.4 & MPa \\ Bearing length, \(L\) & 60 & mm \\ Journal diameter, \(D\) & 30 & mm \\ Orifice diameter, \(d\) & 0.2 & mm \\ Orifice to outlet distance, \(l\) & 15 & mm \\ Average gas film thickness, \(h_{av}\) & 20 & μm \\ Equalizing chamber diameter, \(d_{a}\) & 2 & mm \\ Equalizing chamber depth, \(h_{a}\) & 0.1 & mm \\ Single-row orifices number, \(n\) & 8 & / \\ \hline \end{tabular}
\end{table}
Table 1: Structural parameters of aerostatic bearing.  
Figure 6: Influence of orifice diameter on (a) bearing capacity and (b) gas consumption.
## 5 Conclusions  
This paper presents a new deep method for DR detection and diagnosis. The main novelty of the proposed method is the fusion of the low-level information (grey level image) with the high-level information (reflectivity and thickness images) using a three-channel data representation to the input of the deep CNN. Different CNNs are investigated, and the superiority of the proposed fusion is indicated using the three-channel data representation over using the traditional image. The average accuracy in the case of low-level gray images is found to be 86%, with an upper limit of 90.1%. However, in the case of high-level fused images, the accuracy is improved to an average of 97.5%. The great improvement in accuracy as well as sensitivity and specificity supports using this technique in DR objective detection, especially when comparing these results with the results of related work. These preliminary results come with the cost of the effort required to extract the reflectivity and thickness maps. In the future, we will investigate using other deep CNN networks. In addition, more data will be tested in order to investigate the reliability of the proposed method.  
S.H., A.E., F.K. and A.E.-B.: conceptualization conceptualization conceptualization and formal analysis. S.H., A.S. ([PERSON]), M.E. and A.E.-B.: methodology. S.H., A.S. ([PERSON]) and M.E.: software development. S.H., A.E., A.S. ([PERSON]), M.E. and A.E.-B.: validation and visualization. S.H., A.E., F.K., R.K.F. and A.E.-B.: initial draft. R.K.F., M.A.M., A.S. ([PERSON]) and A.E.-B.: resources, data collection, and data curation. S.H., A.E., H.S.S. and A.E.-B.: review and editing. A.E., W.M. and A.E.-B.: project administration. M.A.M., A.S. ([PERSON]), W.M. and A.E.-B.: project directors. All authors have read and agreed to the published version of the manuscript.  
This research received external funding from the Academy of Scientific Research and Technology (ASRT) in Egypt (Project No. JESOR 5246).  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of University of Louisville (protocol code 18.0010 and date of approval 24 January 2019).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Related Work** & **Dataset** & **Accuracy\%** \\ \hline Proposed System & same dataset & 97.1 \\ [PERSON] et al. [25] & same dataset & 94.3 \\ [PERSON] et al. [24] & same dataset & 92.0 \\ \hline [PERSON] et al. [27] & different dataset & 93.5 \\ [PERSON] et al. [17] & different dataset & 88.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Related work accuracy comparison.
## 3 Results and Discussion
The impact of acoustic energy was evident in reduction of the processing time; a physical process that builds, enlarges and collapses gaseous and vaporous cavities in an irradiated liquid, hence enhancing the mass transfer and allowing chemical reactions to occur [56; 57; 58]. To the best of our knowledge, this new procedure provides the first example of an efficient and ultrasound-promoted approach for the synthesis of 1,3-diaryl-5-(1-phenyl-3-methyl-5-chloro-pyrazol)-4,5-dihydropyrazoles. This method is the most simple and convenient and would be applicable for the synthesis of different types of nitrogen-containing heterocyclic compounds. The structures of all the synthesized compounds were established by their NMR, IR, MS and analysis elemental. The FT-IR spectra of synthesized 5-pyrazol-4,5-dihydropyrazole derivatives **3** showed bands at stretching frequencies in the range of 1584-1598 cm\({}^{-1}\) and 1488-1502 cm\({}^{-1}\), which are characteristic of -C=N and -C=C groups. No peak appeared in the range of 1650-1750 cm\({}^{-1}\), which indicated the disappearance of the carbonyl group (C=O) of the (_E_)-3-(5-chloro-3-methyl-1-phenyl-1_H_-pyrazol-4-yl)-1-arylprop-2-en-1-one **1**. The \({}^{1}\)H NMR spectrum for compound **3** showed proton signals of the pyrazoline moiety as an ABX-type spin system, and the proton signals were observed as double doublets, due to the spin coupling in the range of 3.17-3.95 ppm. The signal of -CH\({}_{3}\) pyrazole and aryl protons in compound was observed between 2.07-2.79 and 6.43-8.23 ppm, respectively.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
###### Abstract  
This article presents the performance analysis and comparison of turbo codes for several interleavers with short blockchains. According to the symmetry concept, turbo codes consist of two symmetric convolutional codes preceded by an interleaver. Some interleavers can be described by symmetric \(3\times 3\) generator matrices, whereas others are represented through symmetric matrices in which the nonzero terms in the matrix are monomials. Interest in short blockchains has grown over the years because of their use in emerging applications that require the transmission of data units in the order of hundred bits. In this research, interleavers used are random interleaver, semi-random interleaver, quadratic polynomial permutation (QPP) interleaver, and chaotic Lozi map interleaver. For each interleaver, the turbo code is assessed with at least two short data distances; then, the interleavers are compared against each other. The performance of turbo codes is assessed based on bit error rate, particularly with lengths between 64 and 512 bits. Additionally, an interleaver based on Duffing chaotic maps is proposed, whose deterministic nature provides an advantage, and the performance results are presented, achieving excellent BER values for 64 bits. With these results, the effect of the interleaver on turbo codes and particularly on short block length interleavers, is studied.  
turbo code; bit error rate; interleaver; short block length Article Design of Chaotic Interleaver Based on Duffing Map for Turbo Code  
[PERSON]; [PERSON]; [PERSON] [PERSON] Design of Chaotic Interleaver Based on Duffing Map for Turbo Code  
[[http://doi.org/10.3390/sym14122529](http[PERSON]doi.org/10.3390/sym14122529)]([http[PERSON]doi.org/10.3390/sym14122529](http[PERSON]doi.org/10.3390/sym14122529))
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 6 Conclusions  
In this paper, a robust estimation strategy for vehicle motion states is proposed based on the ESMF algorithm and a calculation scheme for tire forces. The estimation results under two classic maneuvers demonstrate the effectiveness of the presented strategy. As an alternative to the expensive and professional sensors measuring the important vehicle motion states, this strategy has great application value in production vehicles where more and more attention is being paid for active safety technology. The innovations of this study are summarized as follows:  
* A calculation scheme with simple structure and good accuracy is designed to replace the complex and commonly used tire models to acquire the tire forces, where no information about road conditions is needed.
* The robust tolerance is offered by the proposed estimation strategy to the vehicle model error and the stochastic disturbance existing in the output of common onboard sensors (so-called unknown but bounded noises). This kind of noise distribution covers the noise condition in a great number of practical situations. Therefore, the presented strategy can be applied on the occasions where the conventional ones are inapplicable.  
For further research, the impact of some vehicle parameters changing during the vehicle state estimation will be taken into account. In addition, the effectiveness of the novel strategy will be verified  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Maneuver**} & \multicolumn{2}{c}{**Running Time (s)**} \\ \cline{2-3}  & **ESMF-Based** & **UKF-Based** \\ \hline DLC & 0.466 & 0.816 \\ Slalom & 1.017 & 1.841 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Running time.  
by the driving test data collected from some production vehicles, where only some standard onboard sensors are equipped and the necessary signals can be directly obtained from CAN bus.  
[PERSON] conceived this work as a review. [PERSON], [PERSON] and [PERSON] wrote the paper. Besides, [PERSON] helped improve the design of system model, and [PERSON] put forward some constructive suggestions. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (61603158 and 51405203), the China Postdoctoral Science Foundation (2017M611711 and 2016M601727), the Six Talent Peaks Project in Jiangsu Province (2016-IXQC-007), the Jiangsu Planned Projects for Postdoctoral Research Funds (1701064C), and the Senior Talent Fund Project of Jiangsu University (16 JDG067).  
The authors declare no conflict of interest.
# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### The Physician Method
#### 2.3.7 The 2010 ACR--EULAR Classification Criteria for Rheumatoid Arthritis  
Very useful for the classification of RA patients in clinical studies are the 2010 ACR--EULAR classification criteria for rheumatoid arthritis, which are presented below. A score of >6 is needed for a diagnosis of rheumatoid arthritis. However, it is important that they are classification and not diagnostic criteria. They are meant to be useful for the classification of RA patients in groups for clinical studies. The diagnosis of RA is based on a combination of clinical, radiographic, and serological findings, and the doctor is still the one who will make the diagnosis of RA [43].  
* Joint involvement
* 0: 1 large joint
* 1: 2-10 large joints
* 2: 1-3 small joints (with or without the involvement of large joints)
* 3: 4-10 small joints (with or without the involvement of large joints)
* 5: >10 joints (at least 1 small joint)  
* 0: negative RF and negative anti-CCP
* 2: low-positive RF or low-positive anti-CCP
* 3: high-positive RF or high-positive anti-CCP
* Acute phase reactants
* 0: normal CRP and normal ESR
* 0: abnormal CRP and abnormal ESR
* Duration of symptoms
* 0: <6 weeks
* 1: >6 weeks  
\begin{table}
\begin{tabular}{c c} Grade 0: Normal joint & No GS-detected SH and no PD signal within the synovium \\ Grade 1: Minimal synovitis & Grade 1 SH and \(\leq\)Grade 1 PD signal \\ Grade 2: Moderate synovitis & Grade 2 SH and \(\leq\)Grade 2 PW signal or Grade 1 SH and a \\ Grade 2 PD signal \\ Grade 3: Severe synovitis & Grade 3 SH and \(\leq\)Grade 3 PD signal or Grade 1 or 2 SH and a \\  & Grade 3 PD signal \\ \end{tabular}
\end{table}
Table 2: EULAR-OMERACT combined scoring system for grading synovitis in rheumatoid arthritis. (GS: gray scale, SH: synovial hypertrophy, PD: power Doppler).
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 7 Discussion
### Integration of User Requirements and Operational Constraints in LWSN Energy Management  
In designing energy management strategies for LWSNs, it is essential to align with the user's requirements and operational constraints, such as network lifetime expectations, coverage demands, and cost limitations. Real-world applications of LWSNs, such as infrastructure monitoring and border surveillance, often require continuous, uninterrupted operation in remote and sometimes inaccessible locations. This necessitates strategies that are both energy-efficient and sustainable under practical conditions.  
Integrating user requirements involves defining specific performance metrics, including desired monitoring frequency, data accuracy, and acceptable latency. Operational constraints, such as environmental factors (temperature and terrain) and maintenance accessibility, should influence the selection of energy-saving techniques. For instance, the adoption of adaptive strategies, such as duty cycling, hierarchical sensing, dynamic power adjustment, etc., can enhance energy efficiency without compromising data quality or network coverage. Furthermore, adopting energy-harvesting methods, where feasible, can ensure a consistent energy supply while employing dynamic power adjustment mechanisms to help meet the diverse energy demands posed by varying environmental conditions. These tailored energy management strategies will ensure that LWNNs remain functional and reliable, meeting the efficiency and practicality required for real-world deployments.