esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### General Discussion  
The results from applying the present approach for estimating the occurrence of drought, based on utilizing the satellite-derived TCI and meteorological data, indicate that this approach is an effective tool for reliable assessment of drought hazards. Verification of the performance of the developed DISS drought index, conducted through a comparison of the extent of drought expressed by the area of drought in the analyzed region and the reduction in yield of the main crops revealed that there is a good statistical relationship between these two types of data. The figures of yield reduction obtained from the Central Statistical office suggest that the DISS index can be considered a good drought indicator. The relationship is particularly evident in the case of high yield reductions, as presented in Figures 12 and 13.  
The presented study also revealed that the intensity of drought estimated using the DISS drought index depends on the number of lags of TCI used in the DISS formula. Applying a time lag to the TCI results in a better correlation with the HTC meteorological index, and so the satellite response to atmospheric drought is better described. This is explained by observations that the reaction of vegetation to water shortage differs depending on the type of land cover, i.e., agricultural crops, grasslands, and forests. Applying DISS in versions of three, four, or four lags of TCI allows us to obtain information about drought suitable for particular vegetation environments. Our experience is that the application of three or four lags of TCI in the DISS formula is suitable to detect a water deficit within agricultural land and grasslands, which lasts, according to the HTC, 30-40 days. Application of five lags of TCI in the DISS formula is suitable to detect drought effects within forest areas, which are more resistant to drought, as a water deficit according to the HTC can last, in this case, 50 days. The period of satellite information is always shorter than the meteorological information because of the delayed response of vegetation to a lack of water.  
The important asset of the presented approach is that it considers in the formula of DISS the variability of climatic conditions in the region through incorporation of the median of HTC. Such a solution allows us to adjust the values of the DISS index to local meteorological conditions characterized by levels of air temperature and precipitation.  
Figure 28: Percentage of drought occurrence in the 2001–2019 period (July 3–July 27).
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 6 Encryption Evaluation  
In this study, we conducted a performance evaluation of images with encryption described in Section 5. We used Python to implement the original AES. Moreover, we proposed a method using the linear congruential generator and the Mersenne Twister using the systems and simulators provided by IBM Quantum using IBM Quantum accounts to implement the proposed method through the quantum random walk.  
The experiments were conducted in quantum simulators because the AES proposed in this research is challenging to implement the existing quantum computing environment due to practical restrictions. In addition, IBM quantum computing environments support Qiskit, a quantum programming tool used in this study. However, IBM quantum computing environments with more than six qubits are currently exclusively used collaborative support for joint development. Because the AES described in this research requires more than six qubits, it is practically unavailable to quantum computing environments, so simulation results are reported. Lena, Baboon, and Pepper, which are 256 \(\times\) 256 pixels, are the plain images shown.  
The images in the first row in Figure 20 are plain images; those in the second row are cipher images; and those in the third row are the recovered images from the cipher images.  
Figure 19: Result of quantum random walk.  
We used the number of pixel change rate (NPCR) and the unified average changing intensity (UACI) to test the influence of a 1 px change on the whole image encrypted by any encryption algorithm. The higher the NPCR and UACI values, the better the encryption will be.
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.3 Quadratic Discriminant Analysis (QDA)  
Quadratic discriminant analysis (QDA) closely resembles linear discriminant analysis (LDA), with the notable distinction of utilizing a quadratic decision surface to segregate measurements among two or more classes of objects or events [32]. QDA operates under the assumption that each class adheres to a Gaussian distribution. The class-specific prior is determined by the proportion of data points belonging to the class, while the class-specific mean vector represents the average of the input variables associated with the class.
## 6 Profound Impact Potential on Decision-Making Processes  
Weather and environmental monitoring have always been critical to informing strategic decision-making in both civilian and military domains. With legacy systems such as the Defense Meteorological Satellite Program (DMSP) nearing the end of their operational lifespans, there is growing urgency to modernize satellite constellations and remote sensing capabilities. This section synthesizes major advancements across several programs, notably the Geostationary Operational Environmental Satellite-R (GOES-R) series and the Weather System Follow-on Microwave (WSF-M) satellite program.
## 3 Numerical Results
### Dynamic Convergence  
The magnetostrictive material is Terfenol-D, FGM 1 is SUS304, and FGM 2 is Si\({}_{3}\)N\({}_{4}\). The nonlinear supersonic air flow \(M_{\infty}~{}=~{}2\), i.e., \(U_{\infty}~{}=~{}23,304~{}in/s\), over the outer surface of Terfenol-D control law on FGM plates/cylindrical shells with \(C_{\gamma}\) = 1.4, \(\rho_{\infty}~{}=~{}0.00000678~{}lb/in^{3}\), at altitude 50,000 ft for the numerical GDQ computations including the effect of the advanced nonlinear varied shear correction coefficient. It is very interesting and novel of the nonlinear TSDT thick magnetostrictive FGM plates/cylindrical shells in nonlinear unsteady supersonic flow with a = L, \(h^{*}\) = 1.2 mm, \(h_{1}\) = \(h_{2}\), and \(h_{3}\) = 0.1 mm. At t = 6 s, \(M_{\infty}~{}=~{}2\), \(k_{c}c\) (\(t\)) = 0, advanced \(k_{a}\) values, \(L/h^{*}\) = 5, \(L/R~{}=~{}1\), T = 100 K, \(\overline{T}_{1}\) = 100 K in \(\theta_{L}~{}=~{}60^{\circ}\), i.e., \(a/b~{}=~{}0.5773502,~{}R_{n}\) = 2 are used for the convergence tendencies of middle-plane displacement \(w(a/2,b/2)\) (mm) vs. N = M shown in Figure 2 for \(c_{1}\) = 0.925925/mm\({}^{2}\) that are compared with \(c_{1}\) = 0.0/mm\({}^{2}\) case. The effect of \(c_{1}\) value on the nonlinear unsteady supersonic flow model is much more significant. Thus, \(N\times M\) = 13 \(\times\) 13 grid points can be used in the GDQ calculations.
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model
### Simulator Description
Assigning \(t_{off}\): A random \(t_{off}\) value is assigned, following an exponential distribution, to stations that have completed downloading their \(x_{on}\). The frame counter is updated again. 9. Final Frame Counter Check: As in step 7, check if the frame counter is less than the predefined maximum. If so, repeat step 2; otherwise, the simulation ends. 10. Performance Parameter Calculation: The performance parameters for the simulation are now calculated, including  
* The average instantaneous throughput (\(\overline{X}\))
* The average resource utilization (\(\overline{U}\))
* The average number of active users (\(\overline{Q}\))  
This simulator stands out due to its ability to handle realistic assumptions regarding traffic distribution, MCS selection, and scheduling algorithms. The alternating use of exponential and Pareto distributions for traffic, combined with detailed modeling of radio conditions and user interactions, ensures that the simulator provides highly accurate and reliable results. By incorporating both validation and robustness studies, the simulator outperforms existing tools in its capability to model real-world network conditions while significantly reducing simulation time without sacrificing precision.
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 2 System Model
### Downlink Data Transmission Using Rate-Splitting Scheme
To facilitate the analysis, we rewrite (10) by using (9) and (11) as  
\[y_{k}=\sqrt{\alpha_{\text{D}}P_{c}}\mathbf{h}_{k}^{H}\mathbf{w} _{c}s_{c}+\sqrt{\alpha_{\text{D}}P_{d}}\mathbf{h}_{k}^{H}\mathbf{w}_{k}s_{k}+ \sum\limits_{j\
eq k}^{K}\sqrt{\alpha_{\text{D}}P_{d}}\mathbf{h}_{k}^{H} \mathbf{w}_{j}s_{j}+\mathbf{h}_{k}^{H}\mathbf{n}_{\text{D}}+n_{k}. \tag{13}\]  
From (13), the achievable rates of the common and private message for the \(k\)th user are respectively expressed as  
\[R_{k,c}=\frac{\tau-\tau_{p}}{\tau}\log_{2}(1+\text{SINR}_{k,c}), \tag{14}\]  
and  
\[R_{k}=\frac{\tau-\tau_{p}}{\tau}\log_{2}(1+\text{SINR}_{k,p}), \tag{15}\]  
where \(\text{SINR}_{k,c}\) and \(\text{SINR}_{k,p}\) denote the SINRs of the common and private messages, respectively. The SINRs of the common message and the private message of the \(k\)th user are respectively given by  
\[\mathbf{x}=\sqrt{P_{c}}\mathbf{w}_{c}s_{c}+\sum_{k=1}^{K}\sqrt{P_{k}}\mathbf{ w}_{k}s_{k}, \tag{9}\]0  
and  
\[\mathbf{x}=\sqrt{P_{c}}\mathbf{w}_{c}s_{c}+\sum_{k=1}^{K}\sqrt{P_{k}}\mathbf{ w}_{k}s_{k}, \tag{9}\]1The SE for the system under consideration is obtained as [13]  
\[\mathbf{x}=\sqrt{P_{c}}\mathbf{w}_{c}s_{c}+\sum_{k=1}^{K}\sqrt{P_{k}}\mathbf{ w}_{k}s_{k}, \tag{9}\]2  
where \(R_{c}=\min_{k\in K}R_{k,c}\) since the common message must be decoded at all users.
_Applied_  
_sciences_  
_Receiv_  
**Chemometric Strategies for Spectroscopy-Based**  
**Food Authentication**  
**[PERSON] 1\({}^{\copyright}\), [PERSON] 2,3\({}^{\copyright}\), [PERSON] 4 and [PERSON] 4,**\({}^{\copyright*}\)\({}^{\copyright}\)  
\({}^{1}\) Dipartimento di Scienze Fisiche e Chimiche, Universita degli Studi dell'Aquila, Via Vetoio (Coppito 2, Edificio \"[PERSON]\"), 67100 Coppito (AQ), Italy; [EMAIL_ADDRESS]  
\({}^{2}\) Dipartimento di Chimica, Universita degli Studi di Roma \"La Sapienza\", Piazzale [PERSON] 5, 00185 Roma, Italy; [EMAIL_ADDRESS]  
\({}^{3}\) Department of Food Science, Stellenbosch University, Private Bag X1, 7602 Matieland (Stellenbosch), South Africa  
\({}^{4}\) U. Lille, CNRS, LASIRE, Laboratoire de Spectroscopie pour les Interactions, la Reactivite et l'Environnement, Cite Scientifique, F-59000 Lille, France; [EMAIL_ADDRESS]  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +33-769-47-66-54  
Received: 10 August 2020; Accepted: 15 September 2020; Published: 18 September 2020  
**Featured Application: This review will offer a global overview of the chemometric approaches most commonly used in the field of spectroscopy-based food analysis and authentication. Three different scenarios will be surveyed: data exploration, calibration and classification. Basic and simple descriptions of the main multivariate techniques exploited in such a domain along with a comprehensive outline of their most recent and interesting applications will be provided.**  
**Abstract: In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed--NIR--or Fourier Transform InfraRed--FT-IR--spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.**  
**Keywords: spectroscopy; food authentication; chemometrics; data exploration; calibration; classification; data fusion; curve resolution; analysis of multivariate designed data**
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Irradiation Source Issues
#### 4.3.1 Irradiation Source Issues in TID Tests  
For TID tests, the Co-60 standard radiation source has high penetration and usually test area size is also accurate for even big systems. If another source is used, such as X-rays, constraints from the point of view of both penetration and available test area should be taken into account.
### Verification of the Seasonal Radiative Energy Budget  
Here we examine our assumptions that (i) SEVIRI and MODIS measure the same FRP emitted from simultaneously detected fires; and (ii) that sensor-to-sensor differences in the seasonal radiative energy budgets (see Table 1 and Figure 3) are attributed to SEVIRI omission errors. Instantaneous comparisons between SEVIRI and MODIS measurements of FRP were strictly limited to clusters of contiguous active fire pixels surrounded only by SEVIRI \"non-potential fire pixels\" (_i.e._, cloud-free land pixels), referred to hereafter as \"well observed fires\". The correlation between SEVIRI and MODIS (\(R^{2}=0.61\) for \(n=282\) clusters over all ROIs) shown in Figure 11a is slightly lower than that reported by [PERSON] _et al._[17], but a bias of \(-14.2\) MW (calculated as the mean difference between all observations) and a scatter of 290.4 MW (calculated as the standard deviation of the differences between all observations) agrees quite well with [PERSON] _et al._[17].  
Figure 11b, however, indicates that for well observed fires, SEVIRI does not universally measure less FRP than MODIS. Despite the scatter, SEVIRI tends to measure more FRP than MODIS for low FRP clusters and less FRP than MODIS for high FRP clusters. If comparisons of well-observed fires are limited to MODIS measurements of FRP between 40 MW and 3000 MW then the bias and the scatter reduce to \(-1.3\) MW and 212.0 MW, respectively--agreeing quite well with the analysis and results of [PERSON] and [PERSON] [33].  
A low bias indicates that for aggregations of numerous, simultaneous observations over the entire fire season, and over all ROIs, SEVIRI and MODIS measure on average the same FRP emitted from well observed fires. Interestingly, however, Figure 11b suggests that SEVIRI's overestimation of MODIS at low MODIS measurements of FRP tends to balance out SEVIRI's underestimation of MODIS at high MODIS measurements of FRP. Indeed we find that the summed FRP measured by  
Figure 10: In (**a**) are seasonal SEVIRI errors of omission and commission evaluated for all regions of interest (ROIs) as a function of MODIS scan angle. In (**b**) is the minimum atmospherically corrected FRP detected by MODIS over all ROIs as a function of MODIS scan angle.  
SEVIRI over all well observed active fire pixel clusters detected in all ROI's during the fire season agrees within \(\pm\)5% of that measured by MODIS. Therefore we quantitatively confirm our assumptions and reaffirm the assertion of [PERSON] _et al._[17] that for concurrent and collocated observations, differences between the regional and seasonal radiant heat budgets measured by SEVIRI and MODIS are attributed to SEVIRI omission errors.
## 2 Materials and Methods
### Measurement System Specifications  
Two types of PEMS were used to measure CO2 concentrations in ship exhaust gas. Both types of equipment measure CO2 concentrations using a nondispersive interpolated (NDIR) method, the detailed specifications of which are shown in Table 2. Due to the experimental schedule, SEMTECH DS+ was used to measure ships with SSD, and HORIBA OBS-ONE was used to measure CO2 emission from ships with HSD; both sets of equipment collect one data point per second.  
PEMS is mainly used to measure pollutants in road mobile sources and conduct the real driving emission (RDE) test; its suitability has been proven in many studies [16].  
In addition, in order to minimize errors in the PEMS data, calibration was performed using a calibration gas (CO2 12.33%) before and after each experiment, and the filter inside  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Vessel ID** & **Vessel Type** & **Engine Type** & **Displacement (Ton)** & \begin{tabular}{c} **Vessel size** (L \(\times\) B \(\times\) H)** \\ **(m)** \\ \end{tabular} & **Engine Specification** & **Fuel** \\ \hline SSD 1 & Passenger vessel & Slow speed & 9196 & \(133\times 19.4\times 11.05\) & \begin{tabular}{c} MAN B\&W 6840 ME-B9.5 \\ 6618 kW/146 rpm \\ MAN B\&W 6535 MC \\ 4404 kW/173 rpm \\ \end{tabular} & Bunker A \\ SSD 2 & Passenger vessel & Slow speed & 4701 & \(103\times 15.6\times 9.9\) & \begin{tabular}{c} MAN B\&W 6535 MC \\ 4440 kW/173 rpm \\ \end{tabular} & Bunker A \\ HSD 1 & Passenger vessel & High speed & 595 & \(61.28\times 14\times 3\) & \begin{tabular}{c} CUMMINS MARINE \\ K38 690 kW/1900 rpm \\ \end{tabular} & Diesel \\ HSD 2 & Passenger vessel & High speed & 225 & \(44.87\times 9\times 2.5\) & \
\begin{tabular}{c} YANMAR 12 LAAL-UTN \\ 735 kW/1800 rpm \\ \end{tabular} & Diesel \\ HSD 3 & Fishing boat & High speed & 16 & \(18.35\times 5.84\times 0.93\) &
\begin{tabular}{c} VOLVO D16-MI \\ 401 kW/1800 rpm \\ \end{tabular} & Diesel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental vessel specifications.  
the equipment was replaced for each test. The maximum measuring time was set within 4 h to minimize the span and zero drift according to the manufacturer's recommendations.  
The amount of fuel flow was measured by a Coriolis mass flowmeter (RHM 03L, Rheonik, Maisach, Germany). The fuel was modified to install a fuel flowmeter, and the device was calibrated before and after the experiment to ensure measurement accuracy. The detailed specifications of the fuel flowmeter are shown in Table 3.
## 5 Performance Evaluation
### Computation Overhead
The trade-off between the level 2 and level 1 security of the proposed protocol was that the level 2 secure channel offered more security properties at the cost of doubling the computation cost. In contrast, with a level 1 secure channel, a sensor node required less effort to establish a communication channel, thus reducing the active time of the processor and the transceiver. However, the modified ECQV scheme had to be triggered periodically to mitigate the risk of some attacks.
## 5 Case Study
### Computational Results and Method Comparison  
To validate the effectiveness of the proposed solution approach, two well-established algorithms, NSGA-II [62] and DGWO [63], were selected as benchmark comparators. A series of numerical experiments are conducted to assess and compare the performance of the three algorithms in solving multi-objective optimization problems. The evaluation metrics employed included run time, feasible solution ratio (FSR), constraint violation intensity (CVI), and hypervolume (HV). To mitigate the influence of randomness, each experiment was independently executed 50 times, and the average results are reported. All experiments were performed on a Windows-based computer equipped with an Intel(R) Core (TM) i7-9570H CPU running at 2.60 GHz.  
Specifically, the FSR reflects the proportion of feasible solutions--those that satisfy all imposed constraints--among the total number of solutions generated, thereby indicating that the capability of algorithm in constraint handling. The CVI quantifies the severity of constraint violations in infeasible solutions, providing a nuanced evaluation of the performance under both mild and severe infeasibility scenarios. The HV metric, calculated over the set of feasible solutions, measures both the convergence and diversity of the obtained Pareto front in the objective space. A higher HV value signifies superior quality of the solution set in terms of coverage and optimality.  
As shown in Table 8, DGWO achieved the shortest average runtime (3.3 s), indicating a certain advantage in computational efficiency. DCSA required 3.6 s on average, slightly outperforming NSGA-II, which recorded a runtime of 4.9 s. However, in terms of the three key indicators for solution quality and diversity--feasible solution ratio (FSR), constraint violation intensity (CVI), and hypervolume (HV)--DGWO performed poorly. Its FSR was only 84%, CVI reached as high as 10.1%, and HV was the lowest among the three algorithms,at 0.77. These results suggest that despite its computational speed, DGWO suffers from significant deficiencies in solution stability and distribution uniformity. NSGA-II, on the other hand, demonstrated superior solution quality, with the highest FSR of 91% and the highest HV of 0.93, indicating excellent global search capability in multi-objective optimization. Nevertheless, it also exhibits the longest runtime and slightly inferior convergence performance, as reflected by a CVI of 5.2%. In contrast, DCSA exhibited a well-balanced performance across all evaluation metrics. It achieved the best convergence with the lowest CVI of 4.7%, while maintaining a competitive computational time. Furthermore, its FSR (90%) and HV (0.90) were close to those of NSGA-II, reflecting strong overall effectiveness in both solution quality and stability.  
In summary, DCSA offers a favorable trade-off between computational efficiency, convergence stability, and solution diversity, making it a robust and practical optimization strategy for multi-objective problems.