esa-sceva/satcom-chunk-collection huggingface dataset:
# [PERSON]'s Law and Perceptual Features for Face Image Quality Assessment
## 4 Results
### Comparison to the State-of-the-Art Methods  
In this subsection, the proposed method relying on [PERSON]'s law-inspired and perceptual features is compared to several other state-of-the-art NR-IQA methods (BIQI [66], BLIINDS-II [67], BMPRI [68], BRISQUE [69], CurveletQA [70], GM-LOG-BIQA [71], IL-NIQE [72], NIQE [73], OG-IQA [74], PIQE [75], SSEQ [76]) on face images. As already mentioned, the results were obtained using the GFIQA-20k [56] benchmark database. Further, all methods using some kind of machine learning method were evaluated using exactly the same protocol. Namely, the database was randomly divided into training (appx. 80% of images) and test sets (appx. 20% of images). Next, the PLCC, SROCC, and KROCC were computed between the labels of tests and the predicted quality scores. This process was repeated 100 times, and the median PLCC, SROCC, and KROCC values are reported in this study. In contrast, opinion-unaware methods, i.e., IL-NIQE [72], NIQE [73], and PIQE [75], were directly evaluated on the entire database without any prior partition of the database, since these methods do not rely on any machine learning techniques. The numerical results of this comparison are summarized in Table 3. From this table, it can be concluded that the fusion of Benford's law-inspired and perceptual features has proved to be an effective image representation for the estimation of perceptual image quality. Specifically, the proposed BL-IQA is the one from the examined methods that provides the highest median PLCC, SROCC, and KROCC values. Namely, these values are appx. 0.02 higher than those provided by the second-best-performing CurveletQA [70] in terms of the SROCC and KROCC and the BIQI [66] in terms of the PLCC. The numerical results of the comparison to the state-of-the-art methods are visually summarized using radar graphs in Figure 12.  
Figure 11: Ground-truth vs. predicted quality scores scatterplot on a GFIQA-20k [56] test set.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **PLCC** & **SROCC** & **KROCC** \\ \hline BIQI [66] & 0.794 & 0.790 & 0.599 \\ BLIINDS-II [67] & 0.685 & 0.674 & 0.491 \\ BMPRI [68] & 0.673 & 0.662 & 0.481 \\ BRISQUE [69] & 0.721 & 0.718 & 0.527 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison to other state-of-the-art algorithms using GFIQA-20k [56] database. Median PLCC, SROCC, and KROCC values were measured over 100 random train-test splits. The best results are typed in red, the second-best results are typed green, and the third-best results are given in blue.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 4 Implementation and Experimental Analysis
### Experiment Scenarios  
We devised four main experiment scenarios to validate whether the hybrid blockchain system meets its goals of ensuring data integrity, security, and overall performance. These scenarios were designed around a realistic data pipeline involving actual extravehicular activity data.  
In our implementation, EVA mission data were categorized based on sensitivity. Public mission logs, such as task schedules and environmental readings, were processed through the public blockchain. In contrast, private data--such as astronaut health metrics and biometric logs--were stored on the private blockchain. Integrity for sensitive records was ensured through ZKP mechanisms, while synchronization between blockchains was handled using Chainlink's CCIP.  
Each of the four experiments focused on a distinct objective, as follows: verifying public data integrity, enforcing private data access control, assessing ZKP-based verification performance, and comparing system performance with a traditional centralized database. The experimental setup, procedure, and results for each scenario are described below.
## 2 Band-Limited Phase-Only Correlation (BLPOC)  
The Phase-Only Correlation (POC) function is an efficient technique commonly applied on image matching tasks as a measure of similarity between two sequences. However, when the main information of a given signal is clustered in a frequency band, the meaningless phase components of the signal reduce the efficiency of the method. In this sense, the Band-Limited Phase-Only Correlation (BLPOC) eliminates meaningless information by extracting an effective region obtained from the compared sequences [21; 22; 23; 24; 25; 26]. The basic theory of the BLPOC function is described below.  
Given two \(N\)-point 1D sequences (\(f(n)\) and \(g(n)\)), for which the index ranges are \(n=0,\ldots,N-1\), the 1D Discrete Fourier Transform (1D DFT) of each sequence (\(F(k)\) and \(G(k)\), respectively) is calculated as [27]:  
\[F(k)=\sum_{n=0}^{N-1}f(n)W_{N}^{kn}=A_{F}(k)\exp(j\theta_{F}(k)), \tag{1}\]  
\[G(k)=\sum_{n=0}^{N-1}g(n)W_{N}^{kn}=A_{G}(k)\exp(j\theta_{G}(k)), \tag{2}\]  
where \(k=0,\ldots,N-1\), and \(W_{N}=\exp(-j2\pi/N)\); amplitude components are denoted as \(A_{F}(k)\) and \(A_{G}(k)\); and \(\theta_{F}(k)\) and \(\theta_{G}(k)\) denote the phase components. Next, the cross-phase spectrum (\(Rn_{FG}(k)\)) between \(F(k)\) and \(G(k)\) is defined as:  
\[Rn_{FG}(k)=\frac{F(k)\overline{G(k)}}{\|F(k)\overline{G(k)}\|}, \tag{3}\]  
where \(\overline{G(k)}\) denotes the complex conjugate of \(G(k)\). An effective region is calculated to eliminate the meaningless information in the cross-phase spectrum (depending on the input sequence) [19; 26]. Thus,assuming the zero frequency component of the input sequence \(F(k)\) is shifted at the center of the spectrum, the range of the effective region (defined by the limits \(F_{1}\) and \(F_{2}\)) is:  
\[k=F_{1},\ldots,F_{2}, \tag{4}\]  
where \(0\leq F_{1}\leq F_{2}\) and \(F_{1}\leq F_{2}\leq N-1\). Finally, the BLPOC function (\(\hat{r}_{fg}(n)\)) is the 1D Inverse Discrete Fourier Transform (1D IDFT) of \(Rn_{FG}(k)\), which is calculated by:  
\[\hat{r}_{fg}(n)=\frac{1}{L}\sum_{k=F_{1}}^{F_{2}}Rn_{FG}(k)W_{L}^{-kn}, \tag{5}\]  
where \(L\) is the effective size of the region defined as \(L=(F_{2}-F_{1})+1\). The BLPOC function is a measure of similarity between sequences and, in this sense, if \(f(n)\) and \(g(n)\) are the same sequence, the resulting BLPOC function is the Kronecker's delta function (\(\delta(n)\)); on the other hand, if \(f(n)\) and \(g(n)\) are not the same sequence, the resulting similarity is computed through a matching score.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
###### Abstract
The RNS significantly improves the parallelism of scalar multiplication. Therefore, the throughput is very high. However, the core consumes up to 24,200 slices and 276 block RAM's (BRAMs). These designs cannot be deployed on resource-constrained applications, such as wireless-sensor network (WSN) [18], and software-defined networking (SDN) applications [19]. In [19], the authors reported a platform for SDN, which has a wide range of applications in today's world. In this work, [PERSON] et al. integrated a variety of ECC algorithms. However, to date, there are not any designs that can combine multiple ECC algorithms in a single optimized architecture. In this work, we target an ECC processor that integrates multiple ECC algorithms in a single low-cost, area-efficient architecture. The proposed core could perform multiple algorithms without re-synthesizing. The selected algorithms are Ed25519, ECDSA with NIST P-256, NIST P-384, and the NIST P-521 curve. The experimental results show that the design consumes significantly low resources and provides a working frequency comparable with other proposals. In the experiments, we deploy two different configurations of the core. The first one is integrated with two lightweight algorithms, which are Ed25519 and ECDSA with NIST P-256. The second one can operate Ed25519, ECDSA with NIST P-256, NIST P-384, and NIST P-521. The resource usage for the later configuration is 10,259 slices on Virtex-7 devices, and the working frequency achieves 112.2 megahertz (MHz), which can be compared with other EdDSA or ECDSA single-functional architectures. The rest of the paper is organized as follows. Section 2 introduces the ECDSA and EdDSA algorithms and reveals the essential similarities between them. Section 3 explains the optimized architecture of the multi-functional design. Section 4 gives the experimental results under various aspects. Finally, Section 5 concludes the paper.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 3 Optimizing the volume of the residual set
### Dynamic Execution Phase
#### 4.2.2. Contention Transaction Patterns  
Before introducing the fine-grained scheduling design, we first analyze the fundamental dependency patterns within the residual set \(\mathcal{T}\) using two representative examples, as illustrated in Figure 3. \(W(\cdot)\) denotes a write operation, and \(R(\cdot)\) represents a read operation.  
Figure 2: Transaction execution model.  
**Example 1 (Direct dependency).**_Figure 3a illustrates a direct dependency scenario in which transactions \(T_{1}\), \(T_{2}\), and \(T_{3}\) access shared data items \(G_{2}\), \(G_{3}\), and \(G_{4}\) with overlapping write operations. To ensure serializability under such contention, transaction execution can be coordinated by assigning different start times based on dependency order. For instance, by applying proactive delayed data access [28], \(T_{3}\)'s write to \(G_{2}\) can be scheduled first, followed by \(T_{2}\), and finally \(T_{1}\), respecting a write sequence to avoid conflicts._  
**Example 2 (Transitive dependency).**_In real-world workloads, more complex dependency chains frequently arise, as shown in Figure 3b. Here, transitive dependencies exist among \(T_{1}\), \(T_{2}\), and \(T_{3}\). Unlike direct dependencies, simply adjusting the execution order of transactions cannot resolve the scheduling problem. An inappropriate sequence may still result in transaction aborts, which imposes a more sophisticated scheduling solution._
## 3 Results and Discussion
The method is based on finding an exact value of the light extraction efficiency (LEE) by analyzing the L-I curve with an advanced carrier dynamics equation, which obtains the IQE values by comparing the EQEs, i.e., IQE = EQE \(\times\) LEE. The IQEs of the samples were evaluated by our proposed method and the results are shown in Figure 5b. A higher peak value of the IQE in LED A is observed, \(\sim\)56% at 15 mA, compared to \(\sim\)51% at 4 mA for LED B. The IQEs of LED A and LED B at 50 mA are \(\sim\)52% and \(\sim\)38% (a \(\sim\)1.36-fold difference), which corresponds to the light output powers at 50 mA. This observation indicates that the LEEs of LED A and LED B are comparable. A more severe efficiency droop phenomenon (efficiency degradation from the peak of IQE to the IQE at 50 mA) is observed in LED B, i.e., \(\sim\)4% for LED A and \(\sim\)13% for LED B. These efficiency droop behaviors can be explained as follows: polarization sheet charges induced by strain at well-barrier interfaces lead  
Figure 4: Electroluminescence (EL) spectrum dependence on driving current of (**a**) LED A and (**b**) LED B.  
to band bending which makes it potentially easier for carrier overflow from the MQWs to the p-side region [39; 40]. In other words, the carrier overflow is enhanced by the energy barrier reduction by the strain, which results in a decrease in injection efficiency and consequently makes efficiency droop at a high injection current [41]. Therefore, the experimental results in Figure 5b, such as the IQE and efficiency droop behavior, are mainly determined from the piezoelectric field induced by strain. That is, the stronger \(F_{PZ}\) of LED B than that of LED A in Figure 2 results in a more severe efficiency droop and degradation in emission performance. Lastly, measured data of both samples are summarized and shown in Table 1 for the comparison.
###### Abstract  
This paper theoretically analyzes the fundamental performance of a fourth-order steady-state moving object tracking filter, called an \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter. The \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter considers estimations of jerk (time-derivative of acceleration) in the motion of targets. First, regarding the stability conditions of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter, we prove that there are unstable cases even when the conventionally derived stability conditions are satisfied, and then we derive the correct stability conditions. Next, we analytically derive performance indices that indicate the steady-state errors for targets with typical motions. Based on the derived indices, the optimal performance of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter is theoretically analyzed and compared with that of the traditional second- and third-order steady-state tracking filters, i.e., \(\alpha\)-\(\beta\) and \(\alpha\)-\(\beta\)-\(\gamma\) filters. Numerical analyses and simulations are used to verify the advantages and disadvantages of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter over the above-mentioned filters. The practicality of the use of jerk for the tracking filtering problem is revealed in this paper.  
\(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter; jerk tracking; fourth-order tracking filter; stability condition; performance index +
Footnote †: journal: Applied Sciences
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 5 Modeling and Results Discussion
### Scenarios
#### 5.4.2 Modeling a Dense Area Scenario  
We solely take into account a standard eNodeB in this case, which has the five previously described groups and varying average arrival rates. In the meantime, H2H traffic has a steady average arrival rate.  
Given that there are only six resources available to handle fifteen instantaneous requests, Figure 6's results demonstrate that all H2H requests and 81% of M2M requests are fulfilled:
## 4 Results and Discussion
### Predictive Model Results
#### 4.2.1 Data Collection
These types of data, while extremely valuable for developing a highly accurate prediction model, were not accessible due to data privacy policies and authorization restrictions. Accordingly, we created a survey-based dataset that reflects many of the same factors, but we only used data that could be collected directly from students, without needing institutional approval. This limitation reduced the usable dataset to 15 attributes (see Table 9), selected through a feature selection process based on availability, relevance, and survey response quality. \begin{table}
\begin{tabular}{c c} \hline
**Attributes** & **Description** \\ \hline Gender & Student’s gender (“M”: male or “F”: female) \\ \hline Age & Age of students \\ \hline  & Nationality (binary: \\  & “M”: Moroccan \\ Na & “T”: Ichadien \\  & “MA”: Mauritanian \\  & “C”: Congolese) \\ \hline  & Mother’s education (numeric: \\  & 0 -none \\  & 1 -bac \\ Medu & 2 -bac + 2 \\  & 3 -bac + 3 or + 4 \\  & 4 -bac + 5 or plus) \\ \hline  & Father’s education (numeric: \\  & 0 -none \\ Fedu & 1 -bac \\  & 2 -bac + 2 \\  & 3 -bac + 3 or + 4 \\  & 4 -bac + 5 or plus) \\ \hline \end{tabular}
\end{table} 
Table 9: Attributes chosen for the dataset. The feature selection was informed by students' responses concerning factors influencing student dropout. Attributes requiring access to personal or institutional data, such as academic records and financial information, were excluded. The final set of features comprised variables most frequently identified by students as influential in their dropout decisions, ensuring both relevance and representativeness. To optimize model performance and interpretability, only attributes demonstrating strong predictive value were retained, while those exhibiting high redundancy were removed. Following data collection and feature selection, the dataset was prepared for subsequent phases, including data cleaning and data analysis. This preprocessing stage ensured data quality and consistency prior to model training. Thereafter, several classification algorithms were implemented and evaluated to determine their predictive accuracy, with the aim of selecting the most effective model for predicting university dropouts. For the implementation, we used the Anaconda distribution environment. It is a scientific computing distribution with a rich set of machine libraries. It provides support for all operating systems and a wide range of programming language support. The standard installation comes with several applications (JupyterLab, Jupyter Notebook, QtConsole, Spyder, and Glue). We used Jupyter Notebook version 6.5.2 extensively to run all experiments, and the code was written in Python version 3. As shown in Figure 4, the dataset was imported into a Pandas data frame, as the initial step in the analysis workflow.
# Experimental Study on Heat Transfer Performance of Vacuum Tube Heat Collector with Thermal Storage
## 4 Result and Discussion
### Working Condition in Daytime of Summer Season  
As is shown in Figures 10 and 11, experiments were conducted to evaluate the performances of the new PCM energy storage vacuum tube heat collector for two typical days during the period of August 2016. Figure 10 illustrates a sunny day with higher solar radiation and lower outdoor temperature. On the contrary, Figure 11 illustrates a day with lower solar radiation and higher outdoor temperature. Figures 10 and 11 show that the factor of solar radiation has a greater influence on the temperature of the heat collector than that of ambient temperature during the charging process. In Figure 10, the highest collector temperature achieved is about 67 \({}^{\circ}\)C. Yet, the highest collector temperature achieved is about 52 \({}^{\circ}\)C in Figure 11. From Figure 10, it is observed that the temperature rose fastest during the time period of 14:35-15:00. This can also be confirmed by Figure 11. When the average solar irradiation amount is 672 W/m\({}^{2}\), the amount of heat absorbed by PCM in the daytime of the summer season is estimated to be 3300 kJ. When the average solar irradiation amount is 615 W/m\({}^{2}\), the amount of heat absorbed by PCM in the daytime of the summer season is estimated to be 2934 kJ.  
Figure 10: Water temperature versus time by daylight.  
Figure 9: Outlet water temperature versus time at night.  
Figure 12 depicts the outlet temperature variation during the discharging process. The outlet temperature is 58 \({}^{\circ}\)C when the water valve is closed. The transitory flow goes into the U-tube and out after a short period of heat transfer. The heat transfer equilibrium stage is reached and the PCM is fully discharged when the outlet temperature drops to a substantially constant value. During the discharging period, when the flow rate is 35 L/h, it takes 10 min to achieve the heat exchange balance. When the flow rate is 100 L/h, it takes 3 min to achieve the heat exchange balance. It could be noted that the ratio of the flow rate change is inversely proportional to the equilibrium time ratio.
## 5 Conclusions  
This paper proposes an inter-symbol processing method for the problem of estimating the speed of communication radiation sources. This method estimates the speed of moving targets through steps such as symbol segmentation, phase compensation, and inter-symbol FFT transforms. In keeping with the objective of passive-sensory integration, it is distinguished by the fact that the speed of motion of the communication radiation source can be directly determined through inter-symbol processing of the received signals, negating the need to specifically add transmitting and receiving equipment to the communication system. Through theoretical analysis and simulation experiments, it is verified that the speed estimation method of moving communication radiation sources based on inter-symbol processing can extract speed information. The simulation results show that the speed estimation accuracy of the algorithm proposed in this paper can reach 98.5%. Data-assisted phase compensation and inter-symbol spectral analysis-based speed estimation methods for radiating sources employing other PSK modulations (e.g., BPSK and 8 PSK) will be further explored in the future.  
Conceptualization, G.L. and B.C.; methodology, G.L.; software, B.C.; validation, G.L. and B.C.; formal analysis, G.L.; investigation, G.L.; resources, G.L. and B.C.; data curation, G.L. and B.C.; writing--original draft preparation, B.C.; writing--review and editing, G.L. and B.C.; visualization, G.L. and B.C.; supervision, G.L. and B.C.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61671375.  
Not applicable.  
The sharing of the data used in this study is limited due to the privacy restrictions of the project. If necessary, the data can be obtained by contacting the corresponding author.  
The authors declare no conflicts of interest.
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 3 Experiments
### Case Study  
To evaluate the practical value of the prosed method, we applied EACO to detect dynamic communities in the Beijing taxi trajectory dataset. Identifying dynamic communities in human mobility data is useful for understanding the dynamic process of the spatial interactions between different places in a city [4]. These dynamic communities enable data-centric urban planning [39]. The study area of the case study is located within the fifth ring road of Beijing. The selected road network contains 10,919 road segments. 363,323 pairs of OD points were extracted from the trajectories of about 30,000 taxis on 9 May 2016 (from 8:00 to 24:00). For each hour, we constructed a spatially embedded graph. Therefore, a 16-layer snapshot network was obtained. The number of OD pairs per hour is displayed in Figure 9.  
Figure 12: Communities discovered by DYNMOGA: **(a)** 8:00–9:00; **(b)** 9:00–10:00; **(c)** 10:00–11:00; **(d)** 16:00–17:00; **(e)** 17:00–18:00; **(f)** 18:00–19:00.  
Figure 13: Communities discovered by RWLA: **(a)** 8:00–9:00; **(b)** 9:00–10:00; **(c)** 10:00–11:00; **(d)** 16:00–17:00; **(e)** 17:00–18:00; **(f)** 18:00–19:00.  
We further used the average frequency of OD pairs between road segments within the same community [41] to quantitatively evaluate the performance of the three methods. The results in Table 4 show that the average frequencies of OD pairs between road segments within the communities discovered by EACO were higher than those within the communities discovered by the three comparison methods. This indicates that the proposed method performs better in identifying the spatial interaction patterns. In Table 4, we can see that the average frequencies of OD pairs between road segments within the communities discovered by EACO were slightly higher than those within the communities discovered by DYNMOGA and RWLA. Figures 12 and 13 show that the communities identified by DYNMOGA and RWLA were usually spatially dispersed. As [PERSON] et al. [21] noted, these spatially dispersed communities were likely formed by random movements or noise. Therefore, the communities detected by DYNMOGA and RWLA may be not suitable for identifying urban structures and human mobility patterns.  
In Figure 14, the evolution of communities identified by EACO is displayed. We can see that the communities in vehicle movements usually changed over time. The communities in the morning (8:00-10:00) and evening (16:00-19:00) rush hours changed more drastically because the intensity of human activities was very high. In the following section, the dynamic communities in the morning and evening rush hours are analyzed.