esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Advantages and Limitations of the Improved GPP Estimation Model  
The improved GPP estimation model considered the atmospheric CO\({}_{2}\) fertilization effect and the difference in CO\({}_{2}\) gradient between the canopy and the interior of the leaf, making the GPP estimates more physical and physiological relevance. The improved GPP estimation model diminished the deviation between the GPP estimates and the observed data and improved the consistency. Therefore, the proposed GPP estimation model is algorithmically superior and the estimated GPP is more consistent with the observed value. Furthermore, the spatial distribution of GPP exhibits a strong correlation with specific land cover types. Notably, regions characterized by abundant vegetation cover, such as forests and grasslands, demonstrate increased GPP, primarily concentrated in the eastern and southeastern regions of the Qinghai-Tibet Plateau. In contrast, the GPP tends to be around zero in the western and northwestern regions of the Qinghai-Tibet Plateau, where the land cover is predominantly desert. The findings are consistent with existing research that shows changes in the underlying surface exert a substantial influence on ecosystems, causing changes in their composition, structure, and function, which encompass material and energy cycles, consequently affecting terrestrial carbon storage [33; 34; 35]. Since the validation data consisted of FLUXNET station data and the data collected from the literature with spatial and temporal representativeness, the improved GPP estimation model validation has more assurance of accuracy, demonstrating the algorithmic superiority of the proposed GPP estimation method in this study.  
Figure 4: Comparisons of long-term (2003 to 2018) inter-annual average GPP between the GPP dataset in this study and MODIS GPP products. (**a–c**) and (**d–f**) present the validation of the GPP dataset in this study and MODIS GPP based on all reference data, FLUXNET data, and the literature-based data, respectively.  
While the meteorological data and atmospheric CO\({}_{2}\) data allowed the model to perform well, there were still other possible environmental variables that had the potential to improve GPP estimation accuracy, such as soil moisture levels, nitrogen content, and mineral composition [36]. In addition, the differences in photosynthetically active radiation between shaded and sunlit leaves, resulting in different photosynthetic capacities, were also not considered in this study [8]. Therefore, more comprehensive considerations of the environmental constraints will be made to improve the reasonableness and accuracy of GPP estimation in future work.
## 3 Results and Discussions
reported that interstitial Ag might increase the formation energy of V\({}_{\text{Te}}\) and suppress the formation of V\({}_{\text{Te}}\) and in turn electron concentration was decreased [21]. The reactions between Agi and the defects are proposed below  
\[\text{Ag}_{\text{s}}+\text{xTe}_{\text{Bi}}^{\circ}+\text{x}e^{\prime}\to \text{Ag}_{\text{i}}^{\circ}+e^{\prime} \tag{2}\]  
\[\text{Ag}_{\text{s}}+\text{yV}_{\text{Te}}^{\circ\circ}+2\text{y}e^{\prime} \to \text{Ag}_{\text{i}}^{\circ}+e^{\prime} \tag{3}\]  
The coefficients of x and y indicate how efficiently the formation of TeBi and V\({}_{\text{Te}}\) was suppressed by Agi [21]. The interaction of Ag and antisite defects was investigated using the formation energy obtained from the first-principles calculations in Figure 5. In the formation energy calculations, we only considered antisite defects because the calculations always confirmed that antisite defects were the major defects. The absolute values of TeBi and BiTe decreased with Ag. The relative reduction in energy after the incorporation of Agi was larger in BiTe than that in TeBi: the reduction was 0.71 and 0.59 eV for BiTe and TeBi, respectively, which means that Agi stabilized BiTe as compared with TeBi. The major defect in the Te-rich condition was TeBi, regardless of Agi. However, the chemical potential of Te (\(\upmu_{\text{Te}}\)) varied with dopants such as Sb and Se, and fabrication condition. The \(\upmu_{\text{Te}}\) region between 0 eV (Te metal) and the cross point of the two lines of TeBi and BiTe means that TeBi is more preferred than BiTe. It should be noted that the region decreased with Agi. Therefore, we also confirmed that Agi acts as a stabilizer of BiTe. In p-type Bi\({}_{2}\)Te\({}_{3}\), it was reported that Ag was substituted for Bi or Sb, which is contrary to the results in this study. The difference in the position occupied by Ag between p- and n-type Bi\({}_{2}\)Te\({}_{3}\) is possibly due to the lattice parameters. The c-axis lattice parameters of the p-type are significantly  
Figure 4: SEM images of the n-type samples (**a**) without Ag; (**b**) with 0.05 wt % Ag; (**c**) and (**d**) are the SEM image used in the EDS composition analysis, and the spectrum obtained from the spot in (c), respectively. smaller than that of the n-type, because the covalent radius of Sb is smaller than that of Bi, and 75% Sb\({}_{2}\)Te\({}_{3}\) is added to Bi\({}_{2}\)Te\({}_{3}\) for p-type transport. Thus, Ag needs more energy to enlarge the unit cell to occupy the interstitial position in the smaller unit cell. We confirmed that the formation energy of AgBi ranged from 1.09 to 1.07 eV as the c-axis varied from 30.468 to 30.497 A, whereas the energy of AgBi ranged from 1.48 to 1.45 eV. Even though Ag\({}_{\text{i}}\) is more stable in the enlarged cell, AgBi is more preferred than Ag\({}_{\text{i}}\) in the reduced cell.
# Demand-Response Control in Smart Grids
## 3 Generic Formal Model for Demand Response
### Provider Agent Model  
The provider agent is responsible for monitoring energy production, responding to demand signals, and making decisions about when to increase or decrease energy production. The provider agent is expected to satisfy the requests of consumer users and coordinate between different agents to ensure the entirety of requested electricity and preserve an equilibrium between the production and consumption of power (according to the consumer agents' requests and constraints).  
\(L_{t}\) denotes the generated capacity in each time slot \(t\in T\), which may differ between time slots. The maximum generated capacity \(L_{t}^{\text{max}}\) in each time slot \(t\in T\) is likewise defined.  
We consider a cost function \(Ct(Lt)\), which represents the cost of the provider agent delivering Lt units of energy in each time slot \(t\in T\). The power cost function is, in general, a monotonically increasing function of the power amount. For example, it is possible to use a quadratic cost function:  
\[\text{C}_{t}(L_{t})=a_{t}{L_{t}}^{2}+b_{t}L_{t}+c_{t} \tag{7}\]  
where \(a_{t}>0\) and \(b_{t}\), \(c_{t}\geq 0\) are pre-determined parameters.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 3 The SUC Concept and Its Realization as an Alternative to the PUF
TA stores the resulting SUC \(T\)-\((x_{i},y_{i})\) pair in a secret pair record for later use. The random, unpredictable, non-repeatable, and unknown bit stream generated by the TRNG is fully and exclusively responsible for generating the SUC. Therefore, the generated SUC in the SoC is similarly unpredictable, non-repeatable, and unknown. Thus, for every time point \(t>0\). \[\text{SUC}_{t}=GENIE(TRNG_{t}) \tag{1}\]  
And for any \(t_{1}\) and \(t_{2}\):  
\[TRNG_{t_{1}}\
eq TRNG_{t_{2}}\rightarrow\text{SUC}_{t_{1}}\
eq\text{SUC}_{t_{ 2}} \tag{2}\]  
With a very high probability. Furthermore, SUC can mathematically be described as:  
\[\text{SUC}_{t}:\{0,1\}^{n}\times\{0,1\}^{k_{t}}\rightarrow\{0,1\}^{n} \tag{3}\]  
where \(n\) and \(k_{t}\) are the SUC input/output size and the bit size of the cipher's secret key, respectively. It is well known that the cardinality of the set of all possible permutations from \(\{0,1\}^{n}\) to \(\{0,1\}^{n}\) is \(2^{n}!\) Therefore, \(\sigma=2^{n}!\) is theoretically the number of all possible ciphers including their key-choices that can be selected as SUCs. Here, the probability \(P_{SUC}\) of every resulting SoC FPGA device having its unique and individual SUC is:  
\[P_{\text{SUC}}=1-\frac{1}{\sigma}\to 1 \tag{4}\]  
In difference to PUFs, SUC is a cipher equivalent to a Pseudo Random Function (PRF). Notice that all \(2^{n}\) possible pairs are selectable with an equal security level. SUC authentication phase:  
Figure 5 illustrates a generic SUC-based identification protocol for verifying an enrolled SoC\({}_{A}\). The proposed protocol may proceed as follows [32]:  
Figure 4: Mutating an SUC into a system-on-chip (SoC) [32]. TA randomly chooses a pair \((x_{i},y_{i})\) from the secret records of SoC\({}_{A}\). Then, the TA sends \(y_{i}\) to SoC\({}_{A}\). 2. The SoC\({}_{A}\) device decrypts \(y_{i}\) by using its SU\({}_{A}\) and sends the plaintext \(x^{\prime}_{i}\) to TA. 3. So\({}_{A}\) is authentic when \(x^{\prime}_{i}=x_{i}\). TA then marks the pair \((x_{i},y_{i})\) as a used pair and never uses it again.
## Materials and Methods
### Sorghum Test on Farm B  
Mycorrhizal and nonmycorrhizal sorghum seeds were sown on 3 June 2014 on four plots, two per treatment, 1.5 ha each. The soil was sandy-silty-loam (sand 57.4%, silt 21.3%, clay 21.3%), alkaline (pH 8.3, 1:5 soil/water extract), with the following characteristics at the beginning of the experiment: total N 1.2 g kg\({}^{-1}\), organic matter 10.0 g kg\({}^{-1}\), total carbonate 70.7 g kg\({}^{-1}\), available P\({}_{2}\)O\({}_{5}\) 3.3 mg kg\({}^{-1}\). The sorghum control treatment (S) was nonmycorrhizal sorghum that received a recommended dose of P\({}_{2}\)O\({}_{5}\) and N (i.e., P\({}_{2}\)O\({}_{5}\) 50 kg ha\({}^{-1}\), N 120 kg ha\({}^{-1}\)), calculated as described previously (expected uptake 3.0 kg N and 1.25 kg P\({}_{2}\)O\({}_{5}\) per t of yield; expected biomass 40.0 t ha\({}^{-1}\)). The full dose of P\({}_{2}\)O\({}_{5}\) was applied at seedbed preparation, while half of the N was applied at the time of sowing and the remaining half at fifth-leaf stage (GS 15). The experimental sorghum test (S + m) was mycorrhizal sorghum that received only half the dose of N (60 kg ha\({}^{-1}\)). Sorghum grew under rainfed conditions, and total rainfall during the crop cycle was 196 mm. Sorghum was harvested at flowering stage (GS 61), on 30 August 2014. The S + m and S forages were ensiled in bales wrapped in polyethylene film. Twenty-eight lactating cows (Italian Friesian cattle) were divided into groups differing only in the use of inoculated (S + m group) or control (S group) sorghum silage (the experimental test was 16 days long). The groups were balanced for number (14), parity (3.0 \(\pm\) 1.6 and 3.0 \(\pm\) 1.8 for S + m and S groups, respectively), days in milk (151 \(\pm\) 30 and 150 \(\pm\) 26.4 days), and milk production (27.0 \(\pm\) 4.7 and (27.3 \(\pm\) 3.7 kg d\({}^{-1}\) per head). Ingredients of the diets (kg per day) fed to lactating buffalo were as follows: mycorrhizal silage (22 and 0 for S + m and S groups, respectively), nonmycorrhizal silage (0 and 22), meadow hay (6 and 6), concentrate (a mixture based on maize meal, soybean meal, sunflower meal, beet pulp, barley meal, wheat flour shorts, flaked maize, hydrogenated palm fat (14 and 14), and water (4 and 4)), and a mix of vitamins and minerals (0.4 and 0.4). The chemical composition of the diets expressed as g kg\({}^{-1}\) of dry matter was as follows: crude protein 162 and 157, ether extract 40.3 and 40.7, neutral detergent fiber 420 and 425, acid detergent fiber 260 and 264, and UFL 850 and 850. Animal handling and milk sampling were like the maize test. Milk yield was 26.8 and 27.3 kg d\({}^{-1}\) per head for the S + m and S groups, respectively.
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 5 Experiment
### Implementation and Measures  
Various experiments were carried out after creating morphologically annotated Amharic text corpora which could be used as benchmarks for developing and evaluating the performance of IR and NLP systems. Python was used for the preprocessing tasks, while indexing and retrieving were performed using Lemur toolkit. The retrieval effectiveness was evaluated automatically using trec_eval tool. Precision-recall curves, precision (P) at a certain level of cut-off (P@5, P@10, P@15 and P@20) and Mean Average Precision (MAP) were used to measure retrieval effectiveness as it is usually the case in IR. Four word embedding models (CBOW and Skip-gram models for stem-based and root-based text representation) were created offline using word2 vec by tuning default parameter values. These models were used for query expansion.
# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 6 Conclusions  
In health services information systems, the relevance of medical data in the diagnostic process is paramount. Most healthcare services rely on external systems to store patient information, making the security of these systems of utmost importance. With the advancements in communication and multimedia technologies, digital elements can be manipulated, copied, and replicated without leaving discernible traces, emphasizing the critical need for robust security measures. The effective transfer of medical information over public networks is significantly evolving, especially in telemedicine, telediagnosis, telesurgery, distance learning, and applications related to private database consultations. Transfer conditions mirror those encountered in electronic commerce, subjecting specific medical images to potential vulnerabilities such as communication errors and lossy compression. Preserving the integrity of medical information within the images is particularly crucial. To address these challenges, this paper introduces a novel blind Fast Discrete Curvelet Transform (FDCuT), Discrete Cosine Transform (DCT), and RSA-based medical image watermarking technique. The proposed algorithm demonstrates superior performance, specifically in terms of Peak Signal-to-Noise Ratio and maximum embedding capacity.  
Conceptualization, A.O. and A.A.T.; methodology, A.O. and T.A.; software, A.O.; validation, A.O., A.A.T
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Evaluation Criteria
#### 2.3.6 Evaluation Parameters/Metrics  
In the classification problems, usually, the target classes are given while the predicted/desired classes are obtained by the prescribed approaches/models and techniques. So, the evaluation parameters are used to see to what extent classification was successful. These are equally applicable to single and multi-label classification scenarios. Nonetheless, in the case of multi-label classification, more sophisticated approaches are used since the interclass boundaries/differences may blur which may result in potential misclassification.  
The evaluation criterion will highlight which scientific documents have used which particular evaluation parameters, for example, accuracy, precision, recall, and F-Measure [59; 60].  
These are calculated by measuring true positive (TP), true negative (TN), false positive (FP) and false negative (FN) metrics, as given in Equations (1)-(4) [59; 60].  
Precision: The number of true positive observations which belong to the total expected positive observations. It is represented by the formula:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
Recall: Represent the number of actual positive cases predicted as being positive. It is represented by the formula:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
Accuracy: This is the percentage of correct predictions the model made. It is represented by the formula:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{3}\]  
F1-Score: This is the weighted average of the recall and the precision. It is represented by the formula:  
\[\text{F1}-\text{Score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{4}\]
## 2 Reconstruction Principle  
### Basic Principle of Sfs  
The basic idea of SFS is that the illuminated patches of object surfaces correspond to the local regions in the gray image [29]. The gray scale of an image is mainly related to the following four factors: the surface orientation, the illumination direction, the direction of the camera relative to the object surface, and the reflection characteristics of the object surface, as shown in Figure 1.  
The normal vectors of surface can be expressed as \(N\) (\(-p\), \(-q\), 1), and the function of the object surface is \(z=f(x,y)\). Hence, the components of the surface gradient in the \(x\) and \(y\) direction can be expressed as:  
\[\left\{\begin{array}{l}p=\partial z/\partial x\\ q=\partial z/\partial y\end{array}\right. \tag{1}\]  
We assumed that the direction vector of the light source is \(L(-p_{0}\), \(-q_{0}\), 1), the intensity of the incident radiance is \(I_{0}\), and the reflection coefficient of the object surface is \(\rho\). \(\theta_{i}\) represents the angle between the normal surface and light source direction. Based on the Lambert reflectance model [30], the reflected radiance \(L_{r}\) can be expressed as follows:  
\[L_{r}=I_{0}\rho\cos\theta_{i}=I_{0}\rho\frac{p_{0}p+q_{0}q+1}{\sqrt{p^{2}+q^{2 }+1}\sqrt{p_{0}^{2}+q_{0}^{2}+1}} \tag{2}\]  
It is well-known that the gray scale of the image is proportional to the reflected radiance of the corresponding point on the object surface [31]. Hence, the gray scale of the image \(E(x,y)\) that corresponds to surface orientation \((p,q)\) can be expressed as follows:  
\[E(x,y)=I_{0}\rho\frac{1+p_{0}p+q_{0}q}{\sqrt{1+p^{2}+q^{2}}\sqrt{1+p_{0}^{2}+ q_{0}^{2}}} \tag{3}\]  
As shown in the previous equation, the surface orientation has two degrees of freedom, and the gray scale of the image can only provide one constraint. Therefore, the SFS algorithm is essentially an ill-problem, and a unique solution cannot be obtained. Although the constraints and restrictive assumptions can be added, they can only guarantee the smoothness of the solution. Based on the rough characteristics of machined surface topography, the existing SFS algorithm is unable to fulfill the reconstruction requirements.
## 6 Empirical Results and Analysis
### Heterogeneity Test
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** \\ \hline
**Variables** & **Eastern China** & **Central China** & **Western China** \\ \hline lnHSRFreq1 & 0.039 & 0.057 ** & 0.332 ** \\  & (1.64) & (2.10) & (3.80) \\ sci\_GDP & 0.247 & 1.175 ** & \(-\)2.174 \\  & (0.58) & (2.51) & (\(-\)1.43) \\ lnbooks\_100\_1 & 0.196 ** & 0.082 & 0.063 \\  & (3.76) & (1.59) & (0.48) \\ lnmobile1 & \(-\)0.021 & \(-\)0.164 ** & \(-\)0.680 ** \\  & (\(-\)1.27) & (\(-\)4.37) & (\(-\)2.18) \\ Fin\_GDP & 0.039 & \(-\)0.024 & \(-\)0.050 \\  & (0.88) & (\(-\)0.49) & (\(-\)0.40) \\ unemp\_Pop & 0.025 & \(-\)0.029 & \(-\)0.539 ** \\  & (0.73) & (\(-\)0.54) & (\(-\)2.55) \\ gyfs\_GDP & \(-\)0.041 & \(-\)0.050 & \(-\)0.217 \\  & (\(-\)1.06) & (\(-\)0.99) & (\(-\)1.49) \\ Constant & \(-\)3.459 ** & \(-\)3.343 ** & \(-\)0.276 \\  & (\(-\)15.62) & (\(-\)13.49) & (\(-\)0.20) \\ Observations & 1150 & 1080 & 600 \\ R-squared & 0.542 & 0.531 & 0.168 \\ Number of cities & 115 & 108 & 60 \\ City FE & Yes & Yes & Yes \\ Time FE & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of the sub-sample regression of eastern, central, and western regions. However, the supply of high-speed rail services has greatly reduced transportation and commuting costs, sped up the development of talents and innovative elements, accelerated the dissemination of various knowledge and information, and formed a green innovation network between cities. As a result, the innovation output of society and enterprises is accelerated, and thus the level of green technology innovation in various western cities is greatly increasing. To further test the heterogeneity of the effect of high-speed rail green technology innovation, this article adds the interaction terms of the high-speed rail service frequency and various control variables based on the baseline model regression. The results are displayed in Table 6. By observing columns (1) to (6), it can be found that only the coefficients of the interaction term between the high-speed railway and government support and public cultural infrastructure are significantly negative, while coefficients of other interaction terms are not statistically significant. This shows that the greater the degree of government support and the higher the level of public cultural infrastructure in a city, the smaller the marginal effect that the city will gain from the construction of high-speed rail. Meanwhile, this implies that cities that lack government support and that have relatively backward public cultural infrastructure can gain more benefits from the supply of high-speed rail services.
# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 5 Results and Discussion
### Comparative Performance Analysis  
In Table 3, we present a comparison between our FLAME-VQA model and several other established models commonly used for video quality assessment which were previously used to evaluate the video quality of the LIVE-YT-HFR dataset. The analysis is based on two key metrics, namely SROCC and PCC.  
While examining the data presented in the table, it becomes evident that FLAME-VQA stands out prominently among the comparative models. It showcases higher SROCC and PCC values than the benchmark models, indicating its robustness across a wide range of scenarios. These results signify the model's ability to capture and replicate human perception of video quality. Among the other models, a few exhibit notable performance as well. GSTI boasts a substantial SROCC of 0.7909 and an almost equally high PCC of 0.791, positioning it as a strong competitor. AVQBits \(\,\)I\(\,\)H\(\,\)0\(\,\)\(\,\)\(\,\)s and AVQBits \(\,\)I\(\,\)M3 also demonstrate commendable correlation coefficients, highlighting their efficacy in assessing  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **SROCC** & **PCC** \\ \hline PSNR & 0.695 & 0.6685 \\ SSIM [16] & 0.4494 & 0.4526 \\ MS-SSIM [17] & 0.4898 & 0.4673 \\ FSIM [18] & 0.5251 & 0.5008 \\ ST-RRED [19] & 0.5531 & 0.5107 \\ SPEED [20] & 0.4861 & 0.4449 \\ FRQM [50] & 0.4216 & 0.452 \\ VMAF [51] & 0.7303 & 0.7071 \\ DeepVQA [32] & 0.3463 & 0.3329 \\ GSTI [13] & **0.7909** & **0.791** \\ AVQBits \(\,\)I\(\,\)M3 [40] & 0.7118 & 0.7805 \\ AVQBits \(\,\)I\(\,\)M4 [40] & 0.4809 & 0.5528 \\ AVQBits \(\,\)I\(\,\)M0 [40] & 0.4947 & 0.5538 \\ AVQBits \(\,\)I\(\,\)H\(\,\)0\(\,\)i\(\,\)s [40] & 0.7324 & 0.7887 \\ AVQBits \(\,\)I\(\,\)H\(\,\)0\(\,\)i\(\,\)f [40] & 0.674 & 0.7242 \\ FLAME-VQA & **0.8961** & **0.9086** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of performance of our fuzzy logic-based model (FLAME-VQA, on the complete dataset) with state-of-the-art models on the LIVE-YT-HFR dataset. The best and the second-best values in each column are marked in boldface.  
Figure 7: Three-dimensional views of the model’s output in correlation with a pair of input parameters: (**a**) Video CRF and Video FPS; (**b**) Video FPS and Video TI.  
video quality. However, some models fall short in comparison to FLAME-VQA. Notably, DeepVQA exhibits lower SROCC and PCC values, of 0.3463 and 0.3329, respectively, indicating a weaker alignment with human perception. Similarly, SSIM and FRQM also display comparatively lower coefficients, suggesting limitations in accurately assessing video quality.  
The performance of FLAME-VQA can be attributed to its utilization of fuzzy logic and its incorporation of four crucial input parameters--video frame rate, compression rate, spatial information, and temporal information. By leveraging these factors within a fuzzy logic framework, FLAME-VQA adeptly captures the intricate interplay of technical attributes and subjective perceptual aspects, resulting in quality assessments that closely resonate with human judgments.