esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Area
#### 2.1.3 DDS-Based Waveform Generator  
The most used method for a digital chirp waveform generator is DDS. Compared to the memory-map method, the DDS method has more usability because it can synthesize the desired signal by using the initial parameter such as chirp rate, reference clock, and length of the phase accumulator.  
Because the initial parameter occupies relatively low memory, the DDS method has minimal dependence on memory. Thus, the SAR system can be miniaturized by reducing the components. However, when the DDS method outputs the signal, the quality of the signal depends on a system clock and bit resolution. If the output signal has low bit resolution, truncation and phase errors can occur. Phase errors cause distortion or ambiguity in the SAR image [12]. These problems result in a failure to meet SAR development goals. Consequently, it is important to generate and transmit accurate signals because SAR uses the transmission chirp waveform as a reference signal for image processing.  
A block diagram of the digital chirp waveform generator is depicted in Figure 1. The memory-map-based chirp generator, illustrated in Figure 1a, loads the data according to the pulse repetition frequency (PRF), whereas the DDS-based chirp generator loads the values corresponding to the accumulated phase from the look-up table (LUT).  
Because most chirp waveform generators adopting digital methods have mechanical issues, we used the MDDS method, considering the hardware specifications for a high-speed, low-cost waveform generator. In this study, we considered two aspects of the MDDS: low-cost wideband waveform and distortion compensation. Furthermore, we developed the waveform generator by applying an improved MDDS method to verify the proposed method.
# Quantification of Optical Chirality in Cellulose Nanocrystal Films Prepared by Shear-Coating
###### Abstract
Other authors used a similar method based on convective-shear assembly to obtain the same alignment effect of CNC on gold and silica substrates [16]. Dip coating of non-sonicated CNC suspensions also produce birefringent films, as reported by our group [17]. To characterize the chiral response of self-assembled CNC films, circular dichroism spectroscopy has been used, but often the strong signal produces saturation in the detector [1]. Furthermore, causality requires that circular birefringence should be determined too. A complete description of the polarization and depolarization properties of a sample is obtained by using the Mueller matrix approach [18]. In this approach, a 4 \(\times\) 4 matrix representing the sample can be determined from measurements in the reflection or transmission modes. Previously, a Mueller matrix approach was used to characterize chiral CNC free-standing films with a mosaic-like texture, that is, films with a wide range in color reflected by each color domain [19]. In this work, we report the fabrication of glass-supported CNC chiral films by shear-coating. The films display an iridescent structural color in areas of cm\({}^{2}\). Mueller matrix measurements at several angles of incidence in the reflection mode, as well as transmission at normal incidence, are used to characterize the optical response of the CNC films. The elementary polarization properties of the films are extracted from the experimental Mueller matrix in transmission mode, by analytical inversion and by regression analysis using Tellegen constitutive equations as well. Immersion of the films in deionized water shows the swelling property and the kinetics of the drying is studied by reflection spectroscopy.
## 3 Method  
### Hardware Design  
The sensor module integrates four TI IWR6843 60 GHz FMCW radar units and twelve VL53L5 CX TOF sensors in a concentric dual-layer layout (Figure 1b). Radar modules are spaced at 90\({}^{\circ}\) intervals on the outer ring, achieving \(\pm\)0.8\({}^{\circ}\) angular resolution for limb motion tracking, while TOF sensors form an inner hexagonal grid with 15 cm baseline spacing to resolve skeletal joint positions. A redundant CAN bus backbone connects the sensor array to an NVIDIA Jetson Nano computing unit, which executes real-time signal fusion at 200 Hz sampling frequency. Mechanical safety interlocks include optical fingerprint authentication and electromagnetic brake relays, ensuring fail-safe mode transitions during structural reconfigurations.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 3 Results
### Validation of Estimated GPP and NPP
#### 3.2.2 Cross Validation Against High Resolution GPP and NPP  
We compared the downscaled GPP/NPP with the high resolution GPP/NPP, which was derived from high resolution FPAR and LAI data by using the MusyQ GPP/NPP algorithm. Specifically, the high resolution (30 m) LAI and FPAR were retrieved by using an instantaneous quantitative model based on the law of energy conservation and the concept of recollision probability from Chinese HJ-1B satellite observations data [54]. Figure 13 demonstrated the comparison of the downscaled GPP against high resolution GPP. In general, a good linear relationship existed between the downscaled GPP and modelled GPP. R\({}^{2}\) was greater than 0.8, and RMSE was less than 1.5 g C-m\({}^{-2}\).d\({}^{-1}\) from June to September. But, we could found that both D_GPP_GLASS and D_GPP_MODIS somewhat underestimated modelled GPP in most times. As for the NPP estimates, as shown in Figure 14, the estimated NPP showed good consistency with the modelled NPP. NPP from downscaled GLASS data (D_NPP_GLASS) and NPP from downscaled MODIS data (D_NPP_MODIS) underestimated modelled NPP in September.  
Figure 12: Validation of GPP against ground measurement: (**a**) Validation of D_GPP_GLASS; (**b**) validation of D_GPP_MODIS; (**c**) validation of estimated GLASS GPP; (**d**) validation of MODIS GPP.
## 1 Introduction
### Motivation  
Machine learning applications have become renowned for their impact in the field of wireless communications. Its adaptation cuts across different fields ranging from health,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Full Meaning** \\ \hline QML & Quantum Machine Learning \\ \hline QoE & Quality of Experience \\ \hline QoS & Quality of Service \\ \hline RAN & Radio Access Network \\ \hline RL & Reinforcement Learning \\ \hline SAGIN & Space–Air–Ground Integrated Network \\ \hline SDNs & Software-defined Networks \\ \hline SIC & Successive Interference Cancelation \\ \hline SL & supervised learning \\ \hline SNR & Signal to Noise Ratio \\ \hline SVMs & Support Vector Machines \\ \hline SWIPT & Simultaneous Wireless Information and Power Transfer \\ \hline TDMA & Time Division Multiple Access \\ \hline THz & Terahertz Frequency \\ \hline UAV & Unmanned Aerial Vehicle \\ \hline UE & User Equipment \\ \hline uHDD & Ultra-High Data Density \\ \hline uHSLLC & Ultra-High-Speed with Low-Latency Communications \\ \hline UL & Unsupervised Learning \\ \hline uMBB & Ultra-mobile Broadband \\ \hline umMTC & Ultra-Massive Machine Type Communications \\ \hline uMUB & Ubiquitous Mobile Ultra-Broadband \\ \hline UNNs & Untrained Neural Networks \\ \hline uRLLC & Ultra-reliable Low Latency Communication \\ \hline UWC & Underwater Wireless Communications \\ \hline VLC & Visible Light Communication \\ \hline VR & Virtual Reality \\ \hline WWWW & Worldwide Wireless Web \\ \hline XR & Extended Reality \\ \hline
1G & First Generation \\ \hline
2G & Second Generation \\ \hline
3G & Third Generation \\ \hline
3 GPP & Third Generation Partnership Project \\ \hline
4G & Fourth Generation \\ \hline
5G & Fifth Generation \\ \hline
6G & Sixth Generation \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._agriculture, defense, and the economy. Recently, ML-enabled approaches have been leveraged to provide solutions for the improved and optimal performance of 6G wireless networks [22]. Although ML has shown promising outcomes with its integration with 6G networks, its performance can be improved by convergence with other enabling technologies [23; 24]. Several studies have highlighted the prospect of converging technologies with ML to optimize the performance of the emerging technology [25; 26; 27; 28]. It is hoped that a similar idea of convergence can be introduced in the future 6G network for optimal network performance and improved KPI while also providing answers to new questions and creating new innovative technologies.
## 2 Experimental
### Mass Transfer Equations  
The absorption rate allows the calculation of the overall mass transfer coefficient for both the packed column and the membrane contactor system, using Equation (1) (adapted from [29]).  
\[r_{\text{CO}_{2}}=F_{a,i}\cdot\frac{p_{attn}}{R\cdot T_{i}} \tag{1}\]  
where \(F_{a}\) is the volumetric rate of absorption of CO\({}_{2}\) (L s\({}^{-1}\)) given by Equations (2) and (3), \(p_{attn}\) is the atmosphere pressure, \(R\) is the gas constant, \(T\) is the temperature and the subscript \(i\) represents either the column or the contactor.  
\[F_{a,column}=\frac{\left(C_{CO_{2,l}}-C_{CO_{2,l}}\right)\cdot\left(F_{air}+F_ {CO_{2}}\right)}{100\cdot 60} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Volumes** & **Species Concentration** \\ \hline NaOH still in solution & \(\begin{array}{l}V_{total}=V_{1}+V_{2}\\ V_{CO_{3}^{-}}=2\cdot V_{2}\\ V_{OH^{-}}=V_{total}-V_{CO_{3}^{-}}\end{array}\) & \(\begin{array}{l}[OH^{-}]=\frac{[HCl]\cdot V_{OH^{-}}}{V_{Sample}}\\ \left[CO_{3}^{2-}\right]=\frac{[HCl]\cdot V_{CO_{3}^{-}}}{V_{Sample}}\end{array}\) \\ \hline NaOH already exhausted & \(\begin{array}{l}V_{CO_{3}^{-}}=2\cdot V_{1}\\ V_{HCO_{3}^{-}}=V_{2}-V_{1}\end{array}\) & \(\begin{array}{l}[CO_{3}^{2-}]=\frac{[HCl]\cdot V_{CO_{3}^{-}}}{V_{Sample}}\\ \left[HCO_{3}^{-}\right]=\frac{[HCl]\cdot V_{HCO_{3}^{-}}}{V_{Sample}}\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Volumes and species concentrations obtained by titration.  
\[F_{a,membrane}=\frac{\left(C_{CO_{2,I}}\cdot G_{I}-C_{CO_{2,II}}\cdot G_{II}\right)}{100 \cdot 60} \tag{3}\]  
where \(C_{CO_{2,I}}\), \(C_{CO_{2,II}}\), \(F_{air}\), \(F_{CO_{2}}\) are the concentrations (vol %) of CO\({}_{2}\) at the inlet (\(I\)) and the outlet (\(II\)), and the flow rates of air and CO\({}_{2}\) of the packed column, respectively; and \(G_{I}\) and \(G_{II}\) are the inlet and outlet total flow gas of the membrane contact, respectively.  
Absorption rates were further used to calculate the overall mass transfer coefficient (\(K_{ov}\), mol atm\({}^{-1}\) m\({}^{-2}\) s\({}^{-1}\)) by means of the following equations for both, the packed column system \(-K_{ov,c}\), Equation (4)- and the membrane contact set up \(-K_{ov,m}\), Equation (5), (adapted from [30]):  
\[K_{ov,c}=\frac{r_{CO_{2}}}{a\cdot A\cdot H\cdot\Delta P_{lm,i}} \tag{4}\]  
\[K_{ov,m}=\frac{r_{CO_{2}}}{a_{eff}\cdot\Delta P_{lm,i}} \tag{5}\]  
where \(a\) is the specific area of packing per unit volume of the column (m\({}^{2}\) m\({}^{-3}\)), A is the cross-sectional area of the tower (m\({}^{2}\)), H is the packing height (m), \(\Delta P_{lm}\) is the logarithmic mean driving force (see Appendix A) and \(a_{eff}\) is the effective gas-liquid contact area (m\({}^{2}\)).  
Finally, the efficiency removal of CO\({}_{2}\) (E, %) was measured according to the following equation [31]:  
\[E(\%)=\frac{CO_{2,I}-CO_{2,II}}{CO_{2,I}}\cdot 100 \tag{6}\]
## 2 The Space-time Radial Polynomial Basis Function Collocation Method
With the consideration of time-marching scheme, we may have the collocation points for the 1D heat conduction problem as shown in Figure 1.  
In this study, we propose a new idea for collocating the source points using two different schemes, namely the inner source collocation scheme and the outer source collocation scheme. The inner source collocation scheme is similar to that in the RBFCM, as depicted in Figure 2a. Different from the collocation scheme in the RBFCM, the center point is regarded as the source point in which the source and the inner points are collocated separately. Besides, the numbers of the source and the inner points are not necessary to be the same. It should be noted that all the collocation points are placed in the space-time domain. The boundary as well as initial conditions can, therefore, be turned into the space-time boundary conditions. Accordingly, the one-dimensional heat conduction initial value problem becomes the two-dimensional boundary value problem such that the conventional time-marching scheme is not applicable in the proposed method. In this article, the idea of the outer source collocation scheme is motivated by the MFS. The conception of the MFS is to approximate numerical solutions through a fundamental solution by means of collocating the source points outside the boundary, where numerical solutions can be approximated adopting the linear combination of the nonsingular functions. Following the similar path, the sources of the outer source collocation scheme are collocated outside the domain, as depicted in Figure 3. The following equation is adopted for collocating the source points for the outer source collocation scheme:  
\[\mathbf{y}_{j}^{s}=\eta r_{j}(\cos\theta_{j},\;\sin\theta_{j}),\;j=1,\ldots,N_ {S}, \tag{14}\]  
where \(r_{j}\) denotes the radiuses of the source points, \(\theta_{j}\) denotes the angle of the source points, \(N_{S}\) is the number of source points, \(\eta\) denotes the dilation parameter and \(\eta>1\). The proposed two novel collocation schemes are investigated and applied to the example applications. Accuracy of the proposed collocation schemes are also discussed in the following section.
# An Improved Fuzzy Vector Signature with Reusability
## 6 Conclusions  
In this paper, we presented an FVS scheme improved upon across all aspects of efficiency and security that is, more strictly speaking, based on the subset-based sampling method [17]. Compared to the original FVS scheme [20], we reduced the size of the signing parameter and the verification key to approximately two-thirds their original sizes and cut the signature size by about half. In addition, we reduced the number of pairings necessary for signature verification to about two-thirds the original number.  
We prove that our FVS scheme is VK-private and SIG-private, meaning that the verification key and signatures generated using user correlated fuzzy (biometric) data do not reveal any information about the fuzzy input data. Additionally, instead of the unforgeability of Reference [20], we define the reusability property which guarantees that a user is able to reuse their fuzzy correlated (biometric) data to generate polynomially-many verification keys, all while still making it infeasible for an adversary to forge a signature without any fuzzy (biometric) data. Under the reusability notion, we can prove that our FVS scheme is reusable, assuming that our FVS scheme is {VK, SIG}-private and the DL assumption holds.  
In the remote authentication protocol of our FVS scheme, a user must receive the signing parameter and transmit a signature in response to a random challenge message. The primary advantage of FVS-based (biometric) authentication is that the transmission cost, including the signing parameter and signature, is determined only by the number of dimensions with respect to the fuzzy (biometric) data, not by the number of entire subsets. Thus, unlike the authentication protocol with a fuzzy extractor, the transmission cost between the user and the authentication server becomes remarkably smaller. However, the disadvantage of our FVS-based authentication scheme is that the server is required to perform pairing operations by the number of entire subsets, which is the worst case scenario. Such a burden may be somewhat alleviated by utilizing the computing power of the server in parallel, but it could be more desirable to build a new FVS scheme that supports efficient batch verification operations in the future.  
Conceptualization, J.H.P.; Formal analysis, I.L., and D.H.L.; methodology, I.L., and M.S.; validation, M.S., and J.H.P.; writing--original draft preparation, I.L.; writing--review and editing, M.S., and J.H.P.; supervision, D.H.L.; project administration, D.H.L.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2016-6-00600, A Study on Functional Encryption: Construction, Security Analysis, and Implementation).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### AFM Analysis
The film thickness (d\({}_{\rm film}\)), roughness, and fit error (mean square error--MSE) were achieved through fitting the experimental spectroscopic ellipsometry (SE) spectra, and their values are presented in Table 2. The roughness increased with the sample's thickness, although the absolute values were very low, revealing a smooth surface, which was also underlined by AFM measurements.
## 4 Discussion
### Extended Yale B Database Analysis and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Occlusion Rate**} & \multirow{2}{*}{\
\begin{tabular}{c} **Other Measurement Model** \\ **Performance Indicators** \\ \end{tabular} } & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} 10\% (Random \\ Occlusion) \\ \end{tabular} } & Precision \textgreater{} 0.7 & 16 & 12 & 16 & 0 & 17 & **30** \\  & Recall \textgreater{} 0.7 & 28 & 19 & 31 & 4 & 24 & **31** \\  & F1 Score \textgreater{} 0.7 & 17 & 15 & 22 & 0 & 16 & **32** \\  & Precision, recall, and F1 Score = 0 & 0 & 0 & 0 & 0 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 8 & 10 & 12 & 0 & 15 & **29** \\
20\% (Random & Recall \textgreater{} 0.7 & 21 & 13 & 24 & 5 & 22 & **28** \\  & F1 Score \textgreater{} 0.7 & 7 & 7 & 12 & 0 & 16 & **31** \\  & Precision, recall, and F1 Score = 0 & 0 & 0 & 0 & 0 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 4 & 4 & 4 & 0 & 16 & **25** \\
30\% (Random & Recall \textgreater{} 0.7 & 21 & 2 & 19 & 2 & 24 & **25** \\  & F1 Score \textgreater{} 0.7 & 2 & 1 & 4 & 0 & 15 & **27** \\  & Precision, recall, and F1 Score = 0 & 0 & 0 & 0 & 0 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 2 & 0 & 2 & 0 & 10 & **17** \\
40\% (Random & Recall \textgreater{} 0.7 & 12 & 1 & 12 & 1 & **29** & 24 \\  & F1 Score \textgreater{} 0.7 & 0 & 1 & 0 & **25** & 17 \\  & Precision, recall, and F1 Score = 0 & 0 & 0 & 0 & 0 & 0 & **0** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The number of categories that identified performance indicators to an extent greater than 0.7 or single categories that were wrongly identified from Extended Yale B datasets for different methods. In summary, from the number of categories for each method with precision >0.7, the proposed method identifies 10%, 20%, 30%, and 40% of faces, vertical occlusion of 10% of faces, and diagonal occlusion of 20% of face images better than other related algorithms.
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 3 DSDVMEC Architecture
On the other hand, the assignment of communication infrastructures (eNBs and RSUs) to SDN Controllers is accomplished by an SDN application named Designator, which receives the decision taken by the Autonomic Module through the Am6 reference point. The decision and assignment/deployment process can be observed in Figure 3. It starts with the Monitor sending _Req. Reg. Flux_ message requesting data about the flow-rules of SDN applications via Rest API. These applications forward the requests to the corresponding controllers using the Northbound Interface, which in turn request the infrastructure services using OpenFlow as the Southbound protocol to reply by sending back a _Resp. Reg. Flux_ Message. Then, the message _Req. Stat. MEC_ is sent to the VIM indicating the request to use the computational resources of VNFs hosting the SDN controllers. The response message _Resp. Stat. MEC_ is sent back to the Monitor. Upon receiving the data, the Monitor forwards them to the aggregator for processing. Next, the Autonomic Manager runs the optimization heuristic to decide on resource allocation to controllers based on the data received from the Aggregator. After the heuristic  
Figure 2: DSDVMEC architecture. execution, detailed in the next section, the message _Req.__Creat.VNF_ to the VIM with the goal of creating new controllers along with their optimized corresponding computational resources. At the end of the process, the message _Resp.__Creat.VNF_ is sent to the Autonomic Manager. In the sequel, the message _Req.__Sol.__Assig_ is sent to the SDN application, Designator, which is responsible to effectuate the assignment of communication infrastructures (eNb and RSUs) to the SDN controllers. The Autonomic Module receives the message _Resp.__Sol.__Assig_. when the allocation is concluded. Finally, the Autonomic Manager solicits to the VIM the exclusion of the controllers not assigned to infrastructures via message _Req.__Del.__NFV_. By doing the exclusions at the end of whole process, the latency of flow-rules due to assignments are reduced since previous ongoing flows of such excluded controllers have already been switched to the new ones.
# A Literature Survey on Offline Automatic Algorithm Configuration
## 4 Other Studies
### Studies on Different Aspects of the Algorithm Configuration Problem
They demonstrated that it is much more important when the configuration budget is low. Additionally, they also showed how a wrong transformation can be detrimental to the configuration process. Algorithm configuration methods themselves are based on many heuristics, and this leads to a deficiency of theoretical guarantees. Recently, [PERSON] et al. [128] focused on this problem and developed a general-purpose configuration optimizer called Structured Procrastination, with guarantees of close to the optimal algorithm configuration within alogarithmic factor of the optimal runtime in a worst-case sense. Moreover, they demonstrated that the gap between the worst-case runtimes of the existing configurators (SMAC, ROAR, ParamILS, GGA, irace) and their solution can be large. The main contribution of their work is that it comes with theoretical guarantees (lower and upper bounds on the runtime), but no empirical illustration is provided. In [129; 130], they proposed a simpler method (LEAPSANDBOUNDS) that improves on [128] by finding an approximately optimal configuration with better runtime guarantees on a broader class of problems. In common, the performance of algorithm configuration methods depends on the definition of parameter search space [131], but Evoca [132] (which is a meta evolutionary algorithm) has demonstrated that it is not sensitive to the parameter's search space definition. [PERSON] and [PERSON] [110] suggested an efficient collaborative approach that associates the tuning process with a parameter search space definition process and so combines Evoca with I-Race and ParamILS. Developed automated parameter configuration methods (ParamILS, GGA, SMAC, and irace are the most popular ones) have their own parameters that affect their performance, and their default values are set manually by their developers. [PERSON] et al. [133] suggested a meta-tuning process and tested this suggested method on the irace algorithm configuration method. Experimental results confirmed the importance of those parameters and also revealed the complex interactions between them. Results also demonstrated that irace default settings are significantly improved with a confidence level of 99%. In [PERSON] et al. [134], they extended the study of irace on several aspects (such as surrogate models used) and they configured 29 important parameters of the SMAC configurator.