esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
## 3 Results
### Design Specification  
As it was mentioned in the introduction, a rectenna (rectifying antenna) is a combination of a jointly working antenna and an RF-to-DC rectifier. The antenna captures the RF electromagnetic energy, while the rectifier converts the input AC voltage into DC voltage, that can be used for low-power battery-operated devices. Additionally, another component between the RF-to-DC rectifier and the antenna is included, which is an impedance matching network and whose function is to adjust the impedance between these two elements. In this application, the input is the free space, while the output is the antenna port connected to the rest of the harvesting circuit.
## 1 Introduction
By adjusting the detection transducer at the proper angle to maximize the sensitivity to the leaky S\({}_{0}\) mode, the S\({}_{0}\) mode with small dispersion and low amplitude can be detected, along with the A\({}_{0}\) mode with large dispersion and high amplitude. When the propagating distance is not long enough, the S\({}_{0}\) and A\({}_{0}\) mode are not separated. In this case, the large wave packet of the A\({}_{0}\) mode in the time domain caused by its strong dispersion may make it difficult to identify the S\({}_{0}\) mode or other reflections [25; 26]. In addition, using an optical method, e.g., laser deflectometry or interferometry, it is very difficult to detect the S\({}_{0}\) mode generated by pulsed laser irradiation on the surface of plate. This is not only because of the interference introduced by the antisymmetric modes, but also because most of the interferometry and the deflectometry is only sensitive to out-of-plane displacement, while the S\({}_{0}\) mode has low out-of-plane displacement, especially when it is generated by asymmetric excitation [13; 27]. Our previous work presented a method to generate pure non-dispersive S\({}_{0}\) mode using laser lateral generation in a thin aluminum plate in dry conditions [28]. Using this method, the S\({}_{0}\) mode is enhanced and the A\({}_{0}\) mode is restrained effectively. In this paper, we extend our previous work by combining the laser lateral generation method with an existing laser-based ultrasonic detection technique, and realizing detection and localization of bulk defect represented by an artificial notch in an aluminum plate immersed in water. First, pure S\({}_{0}\) mode with low dispersion is generated by pulsed laser laterally irradiating on the side of the aluminum plate. A laser vibrometer is used to detect the out-of-plane displacement of the waves on the surface of the plate. The S\({}_{0}\) mode at specific _fd_ range is selected by calculating the dispersion curves and attenuation relation to ensure the lowest dispersion and attenuation. Then, an underwater aluminum plate with artificial notches was experimentally evaluated using laser laterally generated S\({}_{0}\) mode; an experiment using A\({}_{0}\) mode was also performed for comparison. Utilizing the mode conversion produced by the interaction between S\({}_{0}\) mode and defect, the defect can be detected and localized. Furthermore, owing to the non-dispersion characteristics of the S\({}_{0}\) generated by a lateral laser source, the multiple defects with short spatial intervals can also be detected and localized according to the mode conversion at each defect.
# GaN-on-Si: Monolithically Integrated All-GaN Drivers for High-Voltage DC-DC Power Conversion
## 3 The Proposed Gate Driver for Buck Converter
### Power Switch  
The high-voltage power switch consists of two stacked E-mode transistors. After a trade-off analysis of switching frequency and conduction loss, the gate width of each device was set at 96 mm, consisting of 2 transistors of each 96 fingers with a width of 1000 \(\upmu\)m.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Observation Parameter Variation
#### 3.3.3 Algorithm Implementation  
In this algorithm, \(e\) is the sequence of observation and \(\theta=\theta_{o}\). It solves the recursive sensitivity function Equation (9) in forward matrices \(F^{k}\) for \(k=1,\cdots,t\). The observation matrix is decomposed into the independent \(\tilde{O}\) and dependent \(\tilde{O}\) observation matrices on \(\theta_{o}\). Then, these observation matrices are represented as diagonal matrices in the SMF computation. The detailed steps of filling the contents of the forward matrices \(F^{k}\) are explained as follows. \(F^{1}\) is initialized as a sum of two temporary matrices \(Ftmp_{1}\) and \(Ftmp_{2}\), which are used to compute the coefficients for the independent and dependent parts on \(\theta_{o}\). \(Ftmp_{1}\) is computed as the matrix multiplication of \(\tilde{O}^{1}\) and \(\Gamma^{\prime}\), and a zero vector of size \(n\) states is appended at its end to represent the zero coefficients of degree 1. In the same way, \(Ftmp_{2}\) is computed as the matrix multiplication of \(\tilde{O}^{1}\) and \(\Gamma^{\prime}\), and a zero vector of size \(n\) states is appended in front of it to represent the zero coefficients of degree 0.  
\[F^{1}=Ftmp_{1}+Ftmp_{2}\]  
The remaining matrices \(F^{k}\) of size \(n\times k,2\leq k\leq t\), are computed using recursion. Again, two temporary matrices \(Ftmp_{1}\) and \(Ftmp_{2}\) are used for computing the coefficients as it is explained above. Here, \(Ftmp_{1}\) is computed as the matrix multiplication of the diagonal matrix \(\tilde{O}^{k}\) that is independent of \(\theta_{o}\), \(A^{\prime}\) and \(F^{k-1}\), and a zero vector of size n states is appended at its end. \(F^{k-1}\) represents the recursion in the filtering probability. Likewise, \(Ftmp_{2}\) is computed as the matrix multiplication of the diagonal matrix \(O^{k}\) that is dependent on \(\theta_{o}\), \(A^{\prime}\) and \(F^{k-1}\), and a zero vector of size \(n\) states is appended in front of it. Then, \(F^{k}\) is set as the sum of \(Ftmp_{1}\) and \(Ftmp_{2}\):  
\[F^{k}=Ftmp_{1}+Ftmp_{2}\]
# Remediation of Heavy Metal Contaminated Farmland Soil by Biodegradable Chelating Agent GLDA
## 3 Results and Discussion
### Influence of Leaching Time on the Leaching Effect of Heavy Metal Pb by GLDA-Na  
When the soil was washed with a GLDA-Na solution with a pH of 4 and an unadjusted pH, the removal rate of heavy metals increased over time, and after 120 min, the removal rate of heavy metals tended to be stable (Figures 2 and 3). Therefore, the best leaching time could be selected as 120 min. When the pH was 4, the complexation effect of the GLDA with heavy metals was better and the removal rate was high at 120 min. However, the reduction rate decreased after the time exceeded 120 min; therefore, it was inferred that the complexation of heavy metals with chelating agents basically reached equilibrium and reached the maximum value during 120 min, and the extraction efficiency could not be improved by prolonging the time. The leaching efficiency of the GLDA-Na solution at a pH of 4 was higher than that without adjusting the pH. At 120 min, the leaching efficiency of GLDA-4 on the Huaqiao soil was 51.65%, and that of the GLDA-Na solution without adjusting the pH was 33.65%. At 30 min, the leaching removal rate of the GLDA-4 was 37.15%, which exceeded the highest leaching efficiency of GLDA-Na without adjusting the pH value.  
The soil in the other three sites showed the same trend. It could be seen that to achieve the same efficiency, the leaching time of GLDA-Na with a pH value of 4 is greatly shortened. The stability of metal-chelating agent complexes depends on many factors, including the oxidation state and coordination number of metal ions, as well as the electronic structure and properties of the chelating agents. However, these factors no longer change significantly after a certain period of contact [6].  
Figure 1: The structure of GLDA.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Leacheates** & **SB** & **HQ** & **TL** & **LT** \\ \hline GLDA-4 & 38.74 ab & 51.65 a & 56.70 a & 48.45 a \\ GLDA-5 & 39.91 a & 49.68 a & 48.29 b & 40.06 b \\ GLDA-6 & 37.79 b & 46.84 b & 45.20 c & 33.12 c \\ GLDA-7 & 38.93 ab & 42.48 c & 42.78 d & 27.71 d \\ GLDA-10 & 35.73 c & 43.46 c & 38.89 e & 21.67 e \\ GLDA & 23.98 d & 33.65 d & 30.09 f & 11.40 f \\ CA-4 & 6.96 e & 7.65 e & 5.76 g & 8.13 g \\ CA-5 & 4.47 f & 4.80 f & 3.44 h & 4.60 h \\ CA-6 & 2.07 g & 2.11 g & 1.81 hi & 0.48 i \\ CA-7 & 1.95 g & 2.10 g & 1.65 hi & 0.54 i \\ CA-10 & 0.11 h & 4.40 g & 0.70 i & 0.02 i \\ \hline \end{tabular} According to [PERSON]’s multiple range test (\(p<0.05\)), the means in the same line (same contaminated soil) followed by the same letter were not significantly different.  
\end{table}
Table 3: Leaching removal rate of heavy metal Pb under different treatments (%).
## 3 LED Model Parameter Extraction Methods
### Modified Nelder-Mead Method
* Using points from the parameter space with values much less than those forming a valid parameter set for a typical LED chip decreases the success rate of the parameter extraction process. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Simplex Size**} & **Success Rate with an** & **Success Rate with a** & **Success Rate with a** \\  & **Increased Central** & **Decreased Central** & **Decreased Central** \\  & **Point (10\(\times\)) [\%]** & **Point (10\(\times\)) [\%]** & **Point (5\(\times\)) [\%]** \\ \hline N = 10 & 58.1 & 0 & 15.7 \\ N = 20 & 89.3 & 15.2 & 37.5 \\ N = 30 & 94.5 & 41.4 & 59.9 \\ N = 40 & 95.2 & 55.8 & 72.3 \\ N = 50 & 98.8 & 78.2 & 95.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dependence of the success rate of the modified Nelder–Mead algorithm on the choice of the initial central points. * An initial parameter set with parameter values not too far above the values from the expected solution of the parameter extraction process increases the success rate, involving only a slight increase in the computational cost. * The above heuristic modifications result in high success rates for the parameter extraction, but do not assure high robustness. We found that the success rate of the parameter extraction method using these modifications strongly depends on the initial central vector. However, even without increasing the values in the initial set, a high success rate may still be achieved, as presented in Table 2. A thorough investigation of the failed extraction processes revealed that the process often becomes stuck in a local minimum, resulting in much higher cost function values than the global minimum. To ensure robustness, an additional heuristic approach was added to the initialization step of the parameter extraction process: the number of the initial vectors was increased to 100, covering a significantly extended range, as follows:  
\[\mathbf{x}_{i}[k]=\left[1.001+rand\bigg{(}rand\bigg{(}-\frac{i}{100},-\frac{i-1}{10 0}\bigg{)},rand\bigg{(}\frac{i-1}{100},\frac{i}{100}\bigg{)}\bigg{)}\right] \mathbf{x}_{0}[k], \tag{13}\]  
where \(i\) is the index between 1 and includes 100. The cost function for these points is evaluated, and the 30 points with the smallest cost function values are kept, for which we used the Nelder-Mead algorithm modified with the above-described heuristics. With this addition, the success rate of the parameter extraction process reached 100% in the case of 5200 extractions performed with measurement data from 52 different LED samples of various types, including color LEDs and phosphor-converted white LEDs. The choice of the central point has a much smaller effect than before. The success rate for increased parameter values remains 2600/2600 (50 extraction per LED sample); with 10-fold reduced values, the success rate was 2595/2600, nearly 100% This suggests that the Nelder-Mead algorithm, modified with these heuristics, is now robust enough and likely to work well for other LED types beyond those for which we had archived measurement data from the Delphi4 LED project.
# Numerical Investigation on NOx Emission of a Hydrogen-Fuelled Dual-Cylinder Free-Piston Engine
## 4 Conclusions  
Through the analysis of the NOx emission performance of each engine model, it could be concluded that the free-piston engine possesses remarkable NOx reduction potential compared with a conventional spark-ignition engine.  
The highest NOx reduction could reach 57.37% at an engine frequency of 6 Hz. However, hydrogen, which is considered a high-functioning, environmentally friendly renewable fuel, produces more NOx than gasoline in the free-piston engine, though the range is relatively small, especially at the low engine frequency stage (1.4% at 5 Hz and 10.97% at 11 Hz). Above all, thanks to the fuel specialty of hydrogen, NOx is the only emission that requires consideration in hydrogen engines. Though hydrogen produces more NOx than gasoline, the range is not considerable, and in addition, the engine characteristics of a hydrogen free-piston engine are as good as those of a gasoline free-piston engine.  
The relationship between NOx formation and the equivalent ratio obtained by simulation is as follows: \(\varphi=0.5\) is the obvious demarcation point of the formation of NOx; when \(\varphi<0.5\), it does not affect NOx much; when \(0.5<\varphi<0.88\), NOx increases with the increase of \(\varphi\) and reaches the highest point at \(\varphi=0.88\); when \(0.88<\varphi<1.2\), NOx sharply decreases with the increasing \(\varphi\).  
For lean mixtures (\(\varphi<0.5\)), the contribution of ignition advance timing to NOx production is relatively small; when \(\varphi>0.5\), ignition advance timing could apparently affect NOx production, and the amount increased with the increase in ignition advance timing. With the increased ignition advance timing, the combustion duration is shortened, the 50% heat release point gets closer to TDC, combustion velocity rises and the average highest in-cylinder temperature increases simultaneously.  
Figure 19: Highest in-cylinder temperature with ignition advance timing.  
**Author Contributions:** Conceptualization, A.R. and Y.W.; methodology, C.L. and B.J.; software, C.L. and B.J.; writing--original draft preparation, C.L.; writing--review and editing, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
The temporomandibular joint (TMJ) occupies a central anatomical position in the craniofacial structure, connecting the temporal bone, cranial base, and mandibular condyle [1, 2]. The stomatalgust include the TMJ, teeth, maxilla, mandible, muscles, ligaments, nerves, and vasculature [3]. The TMJ enables mandibular mobility, essential for chewing and speaking through coordinated masticatory and accessory muscle action [1]. Analogue and digital articulators simulate these dynamics to optimize prosthetic treatment by adjusting occlusion and joint movement [2, 4, 5, 6, 7, 8]. Their use improves restoration predictability and alignment, enhancing prosthodonic outcomes [8, 9, 10]. Digital workflows have spurred the development of virtual articulators (VAs), with research focused on their accuracy and clinical utility [9, 11, 12, 13]. VAs offer superior occlusal analysis and jaw movement simulation compared to mechanical systems. Concurrently, electronic axiography provides precise recordings of condylar path movements, aiding biomechanical assessments and prosthodonic planning [14, 15, 16, 17]. Calibrating virtual or mechanical articulators requires two posterior determinants: sagittal condylar inclination (SCI) and transverse condylar inclination (TCI, or Bennett angle) [18, 19]. SCI measures the condylar path angle relative to sagittal and horizontal planes. TCI quantifies the non-working condyle's horizontal deviation during mediotrusive movement (Figure 1). Mean SCI values range between 20 and 33 degrees [20], while the Bennett angle averages 15 degrees, reflecting lateral mandibular displacement [13, 21, 22]. These parameters ensure functional harmony in prosthetic reconstructions. The Modjaw device (MODJAW, Lyon, France) streamlines full-mouth rehabilitation by replacing traditional tools like facebows and mechanical articulators [13; 23]. Expanding on prior research linking condylar path anatomy to anterior guidance during protrusion [24], this study addresses a critical gap: whether SCI calibration should occur during protrusive movements or mandibular opening. Clarifying this distinction will refine articulator programming and prosthetic rehabilitation accuracy, advancing diagnostic and clinical applications.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 7 Conclusions  
\"_In the next ten years, data sciences and software will do more for medicine than all of the biological sciences together.\"_--[PERSON] [28]  
It remains to be seen if the optimistic prediction of [PERSON] (a prominent Silicon Valley technologist and healthcare investor) will come to fruition. However. it is safe to assume that with 5G along with the confluence of other emerging technologies--such as the Internet of Things (IoT), big data, Artificial Intelligence (AI) and Machine Learning (ML)--the healthcare industry will change drastically as these technologies are used to augment human capacity and enhance the effectiveness of human potential. In the near future, 5G technology, when it is finalized, will enable novel healthcare applications while facilitating an ad hoc orchestration of healthcare services by integrating patients, medical practitioners, and social workers through its enhanced Mobile Broadband (eMBB), URLLC and ubiquitous access services. 5G will enable resource pooling of expert human resources through high-performance and reliable telemedicine, including enhanced telemedicine using the tactile Internet with haptic feedback. Personalized healthcare will become possible through advancements in big data, sensor technologies and AI/ML. Furthermore, routine activities of humans including diagnoses of most cases will be managed by AI and ML algorithms. In this way, the overall healthcare systems will be improved, thereby benefiting the global economy.  
The purpose of this paper was to present for the first time a big-picture overview of this impending 5G-enabled healthcare revolution that will be driven by 5G wireless technology and fully supported by other associated technologies. We provide a description of each of the various involved technologies and their potential for healthcare, while providing pointers to existing literature and advances. We also presented a case study on the economic benefits that will be offered by 5G technology-enabled healthcare. Further, we have highlighted the exciting research and implementation opportunities in building this future of 5G-enabled healthcare while also pinpointing the substantial challenges involved and the potential pitfalls.  
All authors contributed equally to this work. [PERSON] suggested the skeleton of the paper and also provided the supplementary material for paper write up. [PERSON] wrote the main paper with the guidance of all other authors. [PERSON] and [PERSON] provided the technical guidance for 5G and related applications with proofreading the paper. All authors provided comments on the manuscript at all stages.  
The authors declare no conflict of interest.
# Bit Independence Criterion Extended to Stream Ciphers
## 2 Preliminaries
### Stream Ciphers and RC4
```
1:\(i\gets 0\)
2:\(j\gets 0\)
3:while Generating Output do
4:\(i\leftarrow(i+1)\mod 256\)
5:\(j\leftarrow(j+S[i])\mod 256\)
6: Swap \(S[i]\) and \(S[j]\)
7: Output \(S[(S[i]+S[j])\mod 256]\)
8:endwhile
```  
**Algorithm 2** RC4 pseudo-random generator  
The weaknesses found can be classified according to the theme they exploit, some of which are:  
1. Weak keys. 2. Key recovery from the state. 3. Key recovery from the key-stream. 4. State recovery attacks. 5. Biases and distinguishes. While the fifth point is the most studied subject in the literature, the third point is the most serious attack made to RC4. The theme that is exploited in this paper has been deeply studied--in particular, [PERSON] and [PERSON] [24] demonstrated that certain related key-pairs generate similar output bytes in RC4. Later, [PERSON] [25] reported colliding key pairs for RC4 for the first time, and then stronger key collisions were found in [26]. For the RC4 stream cipher, several modifications have been proposed; while some modified only certain components or some operations, others completely changed the algorithm (see [42]). It is important to note that even RC4 variants have had a lot of attention in the scientific community (see [43]).
## 7 Future Prospects of Multi-Source Data Fusion and Analysis Algorithms in Smart Cities
### Exploration Direction of Deep Mining and Association Analysis  
At present, there is still much room for improvement in multi-source data fusion algorithms in mining the potential value of data. There are rich intrinsic connections between different data sources, but most of the current analysis methods only stay on the surface and fail to fully reveal their deep-level connections and laws. For example, there may be some spatiotemporal correlation between sensor data and social media data, reflecting the real-time response of urban residents to environmental changes, but existing algorithms are difficult to effectively capture and use this correlation for in-depth analysis.  
Future research should focus on developing more powerful deep mining and correlation analysis technologies [69]. By integrating multidisciplinary knowledge, such as data mining, machine learning, and statistics, complex data models can be constructed to explore hidden patterns and potential relationships between different data types. For example, deep-learning algorithms can be used to jointly analyze massive amounts of sensor data and social media text data to mine the intrinsic connection between changes in residents' emotions and fluctuations in environmental indicators, providing a more forward-looking decision-making basis for urban management. At the same time, attention should be paid to the visualization of correlation analysis results, presenting complex relationships to urban managers in an intuitive and easy-to-understand way--such that they can better understand the urban operation mechanism and formulate more scientific and reasonable policies and plans.