esa-sceva/satcom-chunk-collection huggingface dataset:
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 4 Simulation Study
### Test Results of the Packet Latency  
The purpose of the simulations presented in this section is to investigate the impact of data generation frequency and number of forwarding on data latency. All packets sent in this experiment were generated in specific length and generation interval. The packet length for this test was set to 359 Bytes which is the length of packet reported by mobile nodes. The packet generation interval is constant in each run of this test. The minimum generation interval was set as 5 s, and generation interval of each test is increased by 2 s until reached 13 s.  
Figure 7 illustrate the effects of the data generation frequency on the data latency. According to the settings of CLA, every network node can obtain up to two hops of forwarding information. That means if the destination node is within two hops of the sending node, the relay node can even forward it without re-calculation. When there are more than 2 hops, there will always be some relay nodes that can forward the packets directly based on the results of the previous calculation. In contrast, VBF requires a new search for the next hop node based on information such as the radius of pipeline before each forwarding, which exacerbated the data latency in the case of node movement. MFLOOD  
Figure 6: Three different network topologies. (**a**) One hop to Destination. (**b**) Two hop to Destination. (**c**) Three hop to Destination.  
does not require additional calculations to find the next hop. However, a backoff time is added before forwarding for conflict reduction reasons, which can significantly increase the source-to-destination data latency. Thus, the data latency of CLA are better than the VBF and the MFLOOD.
# Closed-Form Solution of the Bending Two-Phase Integral Model of Euler-Bernoulli Nanobeams
## 5 Conclusions  
The accuracy of the nonlocal differential model of Eringen's nonlocal elasticity is questionable in some cases of loading and boundary conditions. The integral model and the two-phase integral model are valid and produce consistent results in all cases, but they have computational difficulties related to integral or integro-differential equations involved.  
In this article, a technique has been presented for constructing closed-form solutions of the governing equations of the two-phase integral model of nonlocal Euler-Bernoulli nanobeams in bending, which find many applications in micro- or nano-electromechanical systems (MEMS or NEMS). The technique is based on the decomposition of the initial fourth-order integro-differential boundary value problem into two second-order boundary value problems and the use of the direct operator method for the exact solution of  
Figure 8: Deflection of clamped beam (CP) under uniform load and various values of \(\xi_{1}\).  
Figure 9: Deflection of clamped beam (CP) under variable load and different values of \(\tau\).  
Volterra-Fredholm inegro-differential equations of convolution type presented in [39]. The procedure is easily programmable to any symbolic algebra system, and an algorithm has been provided.  
Results have been given for three types of boundary conditions and two kinds of transverse distributed loads. It has been shown that the two-phase integral model in all cases predicts a softening effect, which is greater as the nonlocal parameter \(\tau\) increases.  
The technique can be used to solve easily and effectively other similar problems. Its main disadvantage is that because it is based on the Laplace transform, it is limited to classes of functions for which direct and inverse integral transformations are available.  
This research received no external funding.  
Not applicable  
The authors would like to thank the anonymous reviewers for their valuable comments.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FIDE & Fredholm Integro-Differential Equation \\ VFIDE & Volterra-Fredholm Integro-Differential Equation \\ BVP & Boundary Value Problem \\ DBVP & Differential Boundary Value Problem \\ FIDBVP & Fredholm Integro-Differential Boundary Value Problem \\ VFIDBVP & Volterra-Fredholm Integro-Differential Boundary Value Problem \\ \end{tabular}
# Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.3 Land Cover  
The University of Maryland global classification scheme (UMD) [48], available from the MODIS Land Cover Type Product (MCD12Q1) Collection 6, at a 500-metre spatialresolution and annual time step was used [49; 50]. Land cover information was extracted only for the years considered as study cases.  
UMD encompasses 17 different classes based on vegetation cover, the height of the canopy and the mosaics of cultivation [48]. The product has been widely used in different studies and the ultimate Collection 6 presents several refinements, both in the algorithm and resulting map datasets, that strongly improved the accuracy of land cover type detection [51].
# Quantum Private Comparison Based on Four-Particle Cluster State
## 6 Conclusions  
In this paper, we present a QPC protocol that employs four-particle cluster states as quantum resources to determine the equality of private information between two groups of users within a single execution of the protocol. Our approach achieves a qubit efficiency of 50% by enabling the distributed transmission of the quantum sequence. We also incorporate easily implementable quantum technologies, such as local operations on four-particle cluster states and single-particle measurements, which enhance the practicality of our protocol. Security analyses indicate that the protocol is robust against both external attacks and insider threats, ensuring the confidentiality of the participants' private data.  
Conceptualization, M.H.; methodology, M.H.; writing--original draft, M.H.; writing--review and editing, Y.W.; supervision, M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund of Network and Data Security Key Laboratory of Sichuan Province (Grant No. NDS2024-1) and the Gongga Plan for the \"Double World-class Project\".  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Ref. [34]** & **Ref. [35]** & **Ref. [36]** & **Ref. [37]** & **Ours** \\ \hline \multirow{3}{*}{Quantum resource} & Four-particle & Four-qubit & Four-qubit & Four-qubit cluster state and extended & Five-particle cluster state & Four-particle cluster State \\  & cluster state & cluster state & Bell state & & \\ \hline Entanglement swapping & No & No & Yes & No & No \\ \hline Unitary operation & Yes & Yes & No & Yes & Yes \\ \hline Quantum measurement & Single-particle & Single-particle &
\begin{tabular}{c} Bell-basis and \\ extend Bell basis \\ \end{tabular} & Single-particle & Single-particle \\ \hline \multicolumn{5}{c}{The pairs of private} \\ \multicolumn{5}{c}{information compared} & 1 & 1 & 1 & 2 & 2 \\ \hline Qubit efficiency & 25\% & 50\% & 50\% & 40\% & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of our proposal with several existing QPC protocols.
## 4 Conclusions  
In summary, we have investigated the geometric configurations, SCO behavior, and MR effect of PTMCp complexes. Different spin states show different structures. The structures can be classified into the sandwich type and biplane type. In the case of three spin states for each molecule, by varying structures with simulation, the SCO from the IS state to the HS state can be realized in PMnCp and PFecp. For PCoCp, the three spin states are interchangeable. The GMR effects of sandwich molecules on the ground state are simulated, and the GMR ratios are found to be above 84% in devices with electrodes of Ni monoatomic wire and 2 A Ni nanowire. In devices with electrodes of 3.5 A and 4.5 A Ni nanowires, the GMR effect almost vanishes. The transmission spectra and eigenstates of the molecule junctions are calculated to understand the underlying mechanism of spin transport in these systems, we consider that the main reason for the different MR at different sizes of electrodes is the more pronounced difference between molecules and electrodes in the degree of coupling for P and AP types. Metal serves as a conduit in the transportation process, however, its dominant role in achieving perfect SFE has not been demonstrated in P-type small-sized electrode devices. Further exploration may be required for non-zero biased or non-magnetic electrodes. Moreover, by adding multiple adatoms to the contacts between the molecule and the electrodes, the GMR effect can be observed. These results suggest that PTMCp complexes are promising candidates for realistic applications in molecular spintronics and provide some ideas to experimentally enable the observation of more phenomena.  
Writing--original draft, M.Y.; Writing--review & editing, L.Z., W.-L.Y. and X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62274113 and 61674110).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods
### Experimental Setup
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & **SegNet** & **U-Net** & **RefineNet** & **WFCNN-1** & **WFCNN-2** & **WFCNN** \\ \hline A & 78.50\% & 86.40\% & 92.80\% & 94.20\% & 95.60\% & 96.90\% \\ B & 21.50\% & 13.60\% & 7.20\% & 5.80\% & 4.40\% & 3.10\% \\ \hline \hline \end{tabular}  
* A denotes the proportion of correctly classified pixels; B denotes the proportion of misclassified pixels.  
\end{table} 
Table 6: Comparison of model performance statistics on the aerial image labeling dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & **SegNet** & **U-Net** & **RefineNet** & **WFCNN-1** & **WFCNN-2** & **WFCNN** \\ \hline A & 75.58\% & 81.75\% & 82.67\% & 85.48\% & 91.13\% & 94.13\% \\ B & 24.42\% & 18.25\% & 17.33\% & 14.52\% & 8.87\% & 5.87\% \\ \hline \hline \end{tabular}  
* A denotes the proportion of correctly classified pixels; B denotes the proportion of misclassified pixels.  
\end{table} 
Table 5: Comparison of model performance statistics on the GF-6 images dataset. Figure 9: Confusion matrix for different model on the aerial image labeling dataset: (**a**) SegNet, (**b**) U-Net, (**c**) RefineNet, (**d**) WFCNN-1, (**e**) WFCNN-2, and (**f**) WFCNN.
## 3 Proposed Approach
### Task Formulation  
Morphological disambiguation is the process of assigning the most appropriate morphological analysis to a given word form within its context. This task is important for languages with complex morphology, where a single word form can have multiple valid analyses depending on its use in a sentence. Formally, this task can be defined as follows:  
Figure 1: Contrastive learning for morphological disambiguation: aligning context and morphological representations.  
Let \(S=(w_{1},w_{2},\ldots,w_{n})\) denote a sentence, where each \(w_{i}\) is a word. For each word \(w_{i}\), it has a set of morphological analyses, denoted by \(A_{i}=\{a_{i1},a_{i2},\ldots,a_{ik}\}\). Each analysis \(a_{ik}\) can be represented as a tuple \((r_{ikj},p_{ikj},m_{ikj})\), where:  
* \(r_{ikj}\) represents a root/lemma,
* \(p_{ikj}\) denotes its part of speech,
* \(m_{ikj}\) indicates a sequence of grammatical features.  
The objective of morphological disambiguation is to identify the most probable analysis \(a_{i}^{*}\) for each word \(w_{i}\) in the sentence, such that \(a_{i}^{*}\in A_{i}\). This prediction should maximize the overall probability of the sequence of analyses \(A^{*}=(a_{1}^{*},a_{2}^{*},\ldots,a_{n}^{*})\) given the sentence \(S\).  
The probability of the sequence of morphological analyses given the sentence \(S\) is expressed as \(P(A^{*}\mid S)\). This can be factorized using the chain rule:  
\[P(A^{*}\mid S,A)=\prod_{i=1}^{n}P(a_{i}^{*}\mid S,a_{1},a_{2},\ldots,a_{i-1}) \tag{1}\]  
where \(P(a_{i}^{*}\mid S,a_{1},a_{2},\ldots,a_{i-1})\) represents the probability of the analyses \(a_{i}^{*}\) for the word \(w_{i}\), conditioned on the sentence \(S\) and the analyses of the preceding words.  
To approximate these probabilities, various models can be employed: (i) HMMs are used to capture the transition probabilities between analyses and the likelihood of words given the analyses. (ii) Models such as recurrent neural networks (RNNs) [23; 24] and transformers [25] are capable of learning complex patterns and dependencies in the data, providing a better understanding of context.
###### Abstract
Dynamic adjustment of fuzzy weights, informed by real-time student performance, ensures that the system improves its predictions and responses [12]. By continuously refining adaptation thresholds, the system personalizes the learning experience, making UI modifications more relevant to each individual. Without this capability, either students would receive help too early, limiting their challenge, or they must endure struggle too long before the system steps in. The ability to dynamically adjust fuzzy weights facilitates adaptive learning that balances lesson difficulty and lesson support according to student learning. While fuzzy logic and reinforcement learning have been independently applied in adaptive educational systems, prior work (e.g., [11]) has often relied on static fuzzy rules or focused on broad content-level adaptation. These approaches lack the flexibility to dynamically adjust user interface components in real time based on evolving student behavior. This study addresses that gap by proposing a novel combination of fuzzy inference and real-time reinforcement learning to continuously optimize the thresholds that govern UI adaptation. Rather than predefining when support should be shown or challenge increased, the system learns and refines these decisions during usage, resulting in more personalized and pedagogically appropriate UI modifications. This approach offers a distinct contribution to adaptive learning research by enabling fine-grained, self-improving UI personalization, with direct application in programming education where timely, individualized support is crucial. In view of the above, this paper presents an adaptive UI system for Java learning software that uses fuzzy logic with dynamically adjustable fuzzy weights. The system consists of two core modules: a Fuzzy Inference Module that uses triangular membership functions to classify students based on the time they spend on exercises and a Reinforcement Learning Optimization Module that dynamically adjusts fuzzy membership function thresholds. These modules work together to determine UI adaptations such as hints, difficulty levels, and step-by-step guidance. By incorporating reinforcement learning, the system continuously refines its adaptation logic, ensuring that students receive optimal support as they progress. This approach enhances engagement and learning efficiency, demonstrating the value of intelligent and dynamic UI adaptation in modern e-learning environments.
## 3 Results
### SEM and TEM of the Semiconductive Shielding  
The dispersion of nanoparticles in the matrix polymer can be observed by the SEM of Figure 6 and the TEM of Figure 7. Figure 6a-c shows the fracture surface SEM images of composite nanomaterials with an LSC content of 0%, 1%, and 5%, respectively. The white spots in Figure 6 are the LSC nanoparticles. It can be seen from Figure 6b that the nanoparticles are uniformly dispersed in the matrix and the white spots in Figure 6b,c increase as the LSC content increases. Figure 7 shows that, the contrast degree of the carbon black particles in the polymer matrix are light, and the black particles with deep contrast are LSC particles. It can be seen from the figure that the size of the black particles acts at several hundred nm, which matches the SEM image of the LSC particles in Figure 5. Figure 7 shows that carbon black particles fill the matrix polymer and form conductive channels. LSC particles  
Figure 4: X-ray diffraction (XRD) patterns of LSC.  
Figure 5: Scanning electron microscopy (SEM) images of LSC: (**a**) Ball milling (BM)-LSC (0 h); (**b**) BM-LSC (10 h); (**c**) BM-LSC (20 h).  
are uniformly dispersed in the polymer matrix. However, the nanoparticles are prone to agglomeration when the LSC concentration is high.
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 2 New Orthogonal Transforms
### Full Non-Exponential Matrix with Arbitrary \(\beta\) Parameter  
For the non-exponential type of matrix, presented in the previous subsection, for each subsequent group of four elements of basis sequence:  
\[a_{1},a_{2},a_{3},a_{4}|a_{5},a_{6},a_{7},a_{8}|a_{9},a_{10},a_{11},a_{12}|a _{13},a_{14},a_{15},a_{16}|\dots\]  
there is a relation:  
\[\frac{a_{i+4}}{a_{i}}=\beta=const\qquad\text{for}\ \ i=1,2,3,4,\dots,N-4 \tag{7}\]  
The coefficient \(\beta\) can be any positive number. Then, each subsequent group of four elements of the basis sequence may be calculated by multiplying the previous group by \(\beta\) satisfying Condition (4).  
This way, the basis sequence will have the following form:  
\[a_{1},a_{2},a_{3},a_{4},\beta a_{1},\beta a_{2},\beta a_{3},\beta a_{4},\beta^ {2}a_{1},\beta^{2}a_{2},\beta^{2}a_{3},\beta^{2}a_{4},\dots,\beta^{j}a_{1}, \beta^{j}a_{2},\beta^{j}a_{3},\beta^{j}a_{4} \tag{8}\]  
where \(\beta\)--positive real number  
\(j=\frac{N}{4}-1\,\ N=2^{n}\,\ n=2,3,4,\dots\)
## 5 Analysis
### Parameter Configuration Summary
The other four graphs (the ones at the bottom of each figure), represent the correlations of estimated and actual \(\mu_{p}\) and \(\sigma_{p}\) values in the natural scale instead of ratios (not homogenized by project duration). Arguably, the effect of the project size (duration) plays an important role as it gives the impression of obtaining higher correlations than in ratio scale. Still, these graphs show that the empirical expressions proposed in this paper work quite satisfactorily. A final comment concerns the last two conditions of a representative regression analysis: independence of errors (normally distributed residuals) and constant variance of errors across observations (homoscedasticity). Regarding the first condition, we must be aware that our expressions are tested in a benchmark network dataset. The projects in this dataset do not follow a perfectly regular (random) pattern. For example, many of them (approximately one-third, but dependent on each dataset parameter configuration) did not produce a project duration extension at all (\(\mu_{p}=PD\)). This means that, in those cases, there was a clearly dominant critical path, and our expressions anticipated it perfectly. However, although this aspect was intrinsically good (demonstrating that our expressions work as they are supposed to), it also artificially increased the residuals kurtosis (a higher than usual density of \(\mu_{p}/PD=1\) and \(\sigma_{p}/\sigma_{K}=\)1 values). This artificially produced a slight departure from normality in the quantile-quantile (QQ) plots of some datasets (a sample of which is included in Appendix C of the Supplementary Materials for dataset I). Still, all things considered, the QQ plots showed that the residual normality assumption was perfectly tenable. Regarding the homoscedasticity check, the standardized estimates versus standardized residual graphs were also drawn for all datasets (Appendix C in the Supplementary Materials shows some for dataset I). The gunshot pattern found in all of them evidenced an absence of datapoint directionality which eventually confirms that there was hardly any heteroscedasticity. Overall, all regression conditions were met, indicating that our estimates are unbiased and consistent.
## 4 Discussion  
### Comparison of SIF and NDVI in Each Vegetation Type  
The fitted curves of each vegetation type in Northeastern China were obtained by extracting the phenology curves of MODIS NDVI and SIF data (Figure 12). The results demonstrated that the NDVI and SIF curves in each vegetation type exhibited obvious variation characteristics with a complete single-peaked distribution, but there were some differences in the curve morphology, which resulted in significant variations in the phenological nodes that were produced. The NDVI time series curves of all vegetation types entered the spring onset early (except GL) and ended later. The peaks are gentler, especially in CF, BF, and Mix B-C, which have wider peaks, and the maximum values are relatively blurred. In contrast, the peaks of SIF time series curves were narrower in all vegetation types, and their growth peaks were more clearly defined. All of these resulted in longer and flatter LOS\({}_{\text{NDVI}}\) than LOS\({}_{\text{SIF}}\) for all vegetation types. This could be a result of the fact that SIF contains data on the stress states that are represented by both solar radiation and fluorescence efficiency. SIF data can successfully track variations in plant growth conditions over a large range induced by vegetation photosynthetic rate for the same vegetation index of greenness. As a result, SIF may be more sensitive to the inversion of vegetation phenology in Northeastern China.
## 3 Results and Discussion
### Effect of Catalyst Load on Photocatalysis Treatment  
For this experiment, the effect of catalyst load on the output parameters was studied. The catalyst load was varied from 0.3 g/L to 1.5 g/L, while the reaction time and mixing speed were kept constant at 30 min and 60 rpm, respectively. The catalyst load had a minimal effect on the pH (Figure 4a), as no significant change in pH was observed; it ranged from 6.82 to 7.24. The catalyst load removal efficiency results for colour (Figure 4b), turbidity (Figure 4c), and COD (Figure 4d) decreased with an increase in catalyst load. These results concur with the findings of previous researchers [15; 19], who reported that removal efficiencies decreased above the optimum catalyst load. [PERSON] et al. [38] investigated the effect of catalyst concentrations (0.1, 0.5, and 1.0 g/L) and reported that the percentage removal of phenol increased with catalyst loading until 0.5 g/L and thereafter decreased at higher catalyst loading. Furthermore, refs. [16; 17; 19] found that excessive catalyst loading results in turbidity and a blocking effect that reduces light transmission through the whole solution and causes light scattering and a screening effect that decreases the degradation and decolourization percentages.  
It was observed that reaction time and catalyst load, turbidity, and COD removal were the most significant input and output variables, respectively, and therefore these were the parameters used for the optimization. Furthermore, the optimal ranges for catalyst load  
Figure 4: Trends of the effect of catalyst load on photocatalysis treatment parameters: (**a**) The effect of catalyst load on pH; (**b**) The effect of catalyst load on colour removal; (**c**) The effect of catalyst load on turbidity removal (**d**) The effect of catalyst load on COD removal (reaction time of 60 min and mixing speed of 60 rpm).  
and reaction time were determined to be 0.3-0.6 g/L and 20-40 min, respectively. These conditions were applied to the optimization.