esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For the identification of all the indicators that comprehensively characterize the study area in terms of relationships between urban greenery, built heritage and the social fabric, and for the construction of the rules necessary for the classification of urban areas, the support of a pool of urban planning experts was used. Following the approach applied in [7], initially we partition the study area into homogeneous zones provided by census atomic elements called microzones, defined as homogeneous areas of the urban system representing the smallest areas in which census data were collected on the resident population, on the type and number of buildings and properties, on the presence of industrial fabrics and services, etc. Based on the values of the core set of indicators and considering a fuzzy rule set built by domain experts, each microzone is classified into a specific urban green area class (UGA class); to perform this classification we implement a fuzzy rule-based system in the GIS platform. Generally, Mamdani [8; 9] or Tagaki-Sugeno [10; 11] models are applied in the deductive inference processes of a fuzzy rule-based system. Unlike the Mamdani model, in which each rule has a fuzzy output variable, in the Tagaki-Sugeno model it is a crisp variable. GIS-based fuzzy rule systems are proposed in the literature in which the Mamdani model [7; 12; 13], the Tagaki-Sugeno model [14; 15] and a hybrid model [16] are applied. In this research we use the Mamdani fuzzy rule-based model proposed in [7], in which a center of gravity (CoG) method [17] is used in the defuzzification process. After classifying all microzones we use the spatial dissolve operator to aggregate adjoint microzones belonging to the same UGA class, to detect the urban green contexts (UGCs) corresponding to regions of the study area that are homogeneous with respect to the characteristics of the urban greenery and its relations with the buildings and social fabric. The output of our framework is the production of a UGC map in which a thematic map of the UGC detected in the study area is generated. In addition, to evaluate the reliability of the UGC map, based on the concept of fuzzy reliability of the partitioning of a geographic area introduced in [18] and following the process applied for evaluating the reliability of the urban context map proposed in [7], we generate a reliability map in which the reliability of a UGC is calculated as a weighted average of the membership degree of the output class of the microzones spatially included in the UGC. To measure the accuracy of the UGC classification, we compare the results with the UGC map obtained in supervised mode by the domain expert. In Section 2 we introduce our GIS-based framework and in Section 3 a detailed description of the processes implemented is performed. In Section 4 the results obtained applying our framework to the sample study area of Pozzuoli are shown. Final considerations are presented in Section 5.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 4 Proposed Dual Kalman Filter Structure for RTK and Its Rationales
### Dual Kalman Filter Structure
#### 4.2.2 Dual KF Structure Development Strategy  
From the above analysis, we can deduce that the approximately predicted initial value and the large covariance of an AIA state must be refined to correctly resolve integer ambiguities with \(r>3\). Thus, it might be beneficial to remove the problematic integer state and wait for it to be refined before passing it to LAMBDA. A convenient and practical approach is to therefore implement a dual KF structure. The dual KF structure consists of a primary main-KF and temp-KF. The main-KF was primarily used for determining position solutions with resolved integer ambiguities. The temp-KF was operated only when the ratio test failed and at least one AIA existed.  
The dual KF structure-based RTK procedure is illustrated in Figure 11. The proposed KF procedure starts with a main-KF. In a typical RTK implementation, the main-KF passes the ratio test after some initialization time and results in centimeter-level positioning accuracy. In the course of RTK operation, new satellites can appear and be included in the main-KF. As discussed previously, the ratio test may fail with a high probability if an AIA has been introduced to the main-KF. If the main-KF fails the ratio test, the AIA is excluded and re-updated without the AIA state by temp-KF. Now, a temp-KF without AIA is implemented and used for position solutions instead of the main-KF until the main-KF LAMBDA solution with AIA passes a ratio test owing to the convergent state covariance.  
Figure 11: Flowcharts for the proposed dual Kalman filter structure for RTK.  
Figure 12 compares the ratios of the main-KF and the temp-KF. In the figure, the ratio of the main-KF alone drops below three when new satellites are included in the KF filter. However, the ratio of the temp-KF is above three.
## Appendix A Sum-Frequency Generation  
For completeness, in this Appendix we include the calculations to derive Equation (3) in Section 2. Our starting point are the input states to one of the two SFG processes used in the passive transmitter illustrated in Figure 1: \(|\sqrt{\frac{\mu}{2}}(e^{i\theta_{1}}+e^{i\theta_{2}})\rangle_{c_{1},+45^{\circ}}\) and \(|\sqrt{\mu}e^{i\theta_{3}}\rangle_{a_{2},+45^{\circ}}\). Such process is described by the Hamiltonian \(H=i\hbar\chi(c_{1}a_{2}c_{2}^{\dagger}-\mathrm{H.c.})\), where \(c_{2}^{\dagger}\) represents the creation operator for the light wave at frequency \(w_{3}=w_{1}+w_{2}\)[45]. The parameter \(\chi\) is a coupling constant that is proportional to the second-order susceptibility \(\chi^{(2)}\) of the nonlinear material, and H.c. denotes a Hermitian conjugate. When the pump mode at frequency \(w_{2}\) is kept strong and undepleted, then this mode can be typically treated classically as a complex number. With this assumption, we have that the effective Hamiltonian above can now be written as \(H=i\hbar\chi(\sqrt{\mu}e^{i\theta_{3}}c_{1}c_{2}^{\dagger}-\mathrm{H.c.})\). Using the Heisenberg equation of motion, it is straightforward to obtain the following coupled-mode equations:  
\[\frac{\mathrm{d}c_{1}}{\mathrm{d}t}=-\chi\sqrt{\mu}e^{-i\theta_{3}}c_{2},\quad \frac{\mathrm{d}c_{2}}{\mathrm{d}t}=\chi\sqrt{\mu}e^{i\theta_{3}}c_{1}, \tag{17}\]  
which can be solved in terms of initial values at \(t=0\) to yield  
\[c_{1}(t) = c_{1}(0)\cos{(\sqrt{\mu}\chi t)}-e^{-i\theta_{3}}c_{2}(0)\sin{( \sqrt{\mu}\chi t)}\] \[c_{2}(t) = c_{2}(0)\cos{(\sqrt{\mu}\chi t)}+e^{i\theta_{3}}c_{1}(0)\sin{( \sqrt{\mu}\chi t)}. \tag{18}\]  
At the point of complete conversion, \(t_{c}=\pi/(2\sqrt{\mu}\chi)\), we obtain  
\[c_{1}^{\dagger}(t_{c})=-e^{i\theta_{3}}c_{2}^{\dagger}(0),\quad c_{2}^{ \dagger}(t_{c})=e^{-i\theta_{3}}c_{1}^{\dagger}(0). \tag{19}\]  
That is, at time \(t_{c}\) we find that the resulting output state at frequency \(w_{3}\) from the SFG process is given by  
\[|-\sqrt{\frac{\mu}{2}}e^{i\theta_{3}}(e^{i\theta_{1}}+e^{i\theta_{2}})\rangle _{c_{2},+45^{\circ}}. \tag{20}\]
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI-Based Systems in Glaucoma  
Artificial intelligence proves a high capacity for the treatment of glaucoma, for detecting structures related to glaucoma and its functional changes, and for its clinical diagnosis and its prognosis. Typical applications of AI in this domain include (1) detection, (2) diagnosis, and (3) prognosis.
## 4 Phenomenological and Statistical Analysis of Results
### Interpretation of Hysteresis and Backbone Curves  
Figure 7 illustrates hysteresis and backbone curves for each joint specimen, with the hysteresis curves for all five joint specimens resembling each other closely. The hysteresis loops displayed robust fullness and residual deformation post-unloading, typical of reinforced concretes. Despite the similar initial structures, the enduring loading phase revealed a pinching phenomenon. The bearing capacity of the ZP-2 joint decreased at a rapid pace, with recorded positive terminal values significantly lower than the other four specimens. The initial rigidity of ZP-2 was also markedly inferior to the other three new joints. The hysteresis loop area for ZP-2 was notably the smallest. Figure 7f reveals that the backbone curves for all joint specimens were \"S\" shaped, representing the elastic, plastic, limit, and failure stages. ZP-3's elastic phase curve initially displayed a steeper slope than the other four specimens but declined faster post-peak. Consequently, the ductility of ZP-3 was considered inferior due to diminished strength post concrete pouring. A comparison of ZP-4 with ZP-1 indicated minor differences in initial stiffness, with the pre-final-load slope of the curve being relatively consistent. However, ZP-4's final load surpassed that of ZP-1. Continuing to heighten the load led to a decline in joint bearing capacity, with ZP-1 declining at a notably faster rate than ZP-4. Compared to ZP-2, ZP-1 had superior bearing capacity, which decreased at a slower rate than ZP-2 post reaching the final load.  
The improvement of bearing capacity, ductility, and energy dissipation capacity of joints can be effectively achieved by enhancing the post-casting area of concrete and the length of the L-shaped bars.  
Figure 6: Failure modes of each specimen: (**a**) cast-in-site joint, (**b**) ZP-1, (**c**) ZP-2, (**d**) ZP-3, (**e**) ZP-4.  
Figure 7: \(Cont.\)
# A Novel 1-D CCANet for ECG Classification
## 3 Proposed Method
### Hand-Crafted Feature Extraction
#### 3.1.1 One-Dimensional Spectrogram  
For the frequency domain, we use a one-dimensional spectrogram, which is a representation of the spectrum of frequencies of a signal as it varies with time. It is built through a short-time Fourier transform (STFT) of each of the two non-normalized leads \(x_{h}\ (h=1,2)\). A window slides through the signal (with potential overlapping) and computes at each step the squared magnitudes of the STFT of the portion of the signal belonging to the window. The Hamming windowing is used for this process. The spectrogram is then obtained by concatenating, along the time axis, the squared magnitudes acquired for each window. The squared magnitudes obtained for each frequency (up to half of the sampling rate) at each time step can be reported in a matrix where axis 0 and 1 are the frequency and time axis, respectively. Since the range of the squared magnitudes varies significantly, the resulting matrix is rescaled to \([0,1]\) to yield \(X_{h,\text{spec}}\ (h=1,2)\).  
Figure 1: Classification processing pipeline of our method. (**a**) Feature extraction: for each of the two leads \(x_{h}\ (h=1,2)\), hand-crafted features are extracted to build \(x_{h,\text{cat}}\ (h=1,2)\) while \(x_{\text{time}}\), a vector of time-domain features, is built for both leads. (**b**) Part of these features, \(x_{h,\text{cat}}\ (h=1,2)\), feeds the 1-D CCANet-SVD module, which first outputs the neural features \(f_{\text{neur}}\) and then a reduced version of the neural features \(f_{\text{pca}}\). The concatenation of \(x_{\text{time}}\) and \(f_{\text{pca}}\) feeds the classification module. The output is the predicted heartbeat class.  
A weighted average along the frequency axis is performed thus turning the \(X_{h,\text{spec}}\) matrix into a one-dimensional vector, \(x_{h,\text{spec}}\). The equation used is the following (2):  
\[x_{h,\text{spec}}=\frac{1}{S(n)}\sum_{k}\frac{1}{k^{n}}X_{h,\text{spec}}[k], \tag{2}\]  
where \(S(n)=\sum\limits_{k}\frac{1}{k^{n}}\).  
This feature extraction method requires three parameters: the number of samples in the window of the STFT (\(N_{\text{wind}}\) = 64 and 46 for MIT-BIH and INCART respectively), the number of samples in the overlap between two consecutive steps (\(N_{\text{overlap}}\)= 32 and 23 for MIT-BIH and INCART respectively) and \(n\) (0.25 for both MIT-BIH and INCART), the weight parameter in Equation (2). Suitable parameters are found with a greedy search. The feature vector \(x_{h,\text{spec}}\) is of size 10.
# Comprehensive Insights into the Molecular Basis of HIV Glycoprotein
## 3 gp120-gp41 Interaction  
The HIV surface exhibits trimeric Env spikes formed through noncovalent interactions between gp120 and gp41 [140]. During the entry of HIV into host cells, a dynamic structural rearrangement takes place (Figure 5). Initially, gp120 binds to the CD4 receptor on the host cell, causing a conformational change that disrupts the noncovalent interactions between gp120 and gp41 and reveals the coreceptor-binding sites [141]. This disclosure leads to the reorganization of gp41 helices within the Env spikes into open structural conformations, allowing the rest of the Env complex to undergo subsequent conformational changes [29]. In the prefusion state, gp41's hydrophobic core binds tightly to the extended N-terminal domain (NTD) and C-terminal domain (CTD) of gp120, forming the gp41-tryptophan clasp [66; 83]. The rearrangements in the gp41 spike result in the opening of the gp41-tryptophan clasp, facilitating the enlargement of the fusion pore necessary for viral entry by displacing the gp120 termini [66; 142]. Observations indicate that the structural transition from prefusion to postfusion conformations of gp41 is critical for this process. While most HIV strains require two to three Env spikes for entry stoichiometry, constructing the fusion pore may involve one to seven Env spikes [143; 144].  
Numerous studies have investigated the interaction between gp120 and gp41 to elucidate their interaction domains [29; 58; 65; 66; 83; 86; 101; 123; 145; 146]. There is a consensus that the inner domain, N-terminal domain, and disulfide-bonded domain of gp120 form noncovalent interactions with the heptad repeat 1 region of gp41. Specifically, the inner domain of gp120 can modulate its interaction with gp41 and its binding to CD4 [86], while the terminal regions of gp120 primarily interact with the disulfide-bonded regions of gp41 [147]. gp120-gp41 interactions can be disrupted through amino acid substitutions in the disulfide-bonded region of gp41, such as W596A and W610A [148]. Additionally, broad neutralizing antibodies, such as 3 BC315, have been identified to interfere with these interactions. However, the dynamic nature of Env trimers may influence the exposure of epitopes targeted by these antibodies [147; 149; 150].
# Giant Dipole Multi-Resonances Excited by High-Frequency Laser Pulses
## 1 Introduction  
As emphasized in a number of recent publications, high-intensity lasers, ranging from the optical to the X-ray domain, may influence various nuclear processes, e.g., nuclear transitions by electronic [1, 2] or muonic transitions [3], proton emission from nuclei via the nuclear photoeffect in an intense laser field and accompanied by a \(\gamma\)-ray [4], modification of \(\alpha\)- [5, 6, 7] and proton decay rates [8], modification of heavy ion elastic scattering differential cross-sections [9, 10] or the deuteron-triton fusion probability enhancement [11, 12]. However, excepting perhaps the case of elastic scattering of heavy ions, the low photon energies and the insufficiently high power of these types of laser pulses are rather weakly disturbing the quantum states of the atomic nucleus. For this reason, the newly entered-into-operation ELI-N facility intends to use ultra-intense laser fields with intensities reaching up to \(10^{22}\)-\(10^{23}\) W/cm\({}^{-2}\) to produce intense low-energy gamma beams (\(<\)20 MeV) by Compton back-scattering and therefore allow nuclear structure and reactions investigations [13, 14]. On the other hand, there are perspectives to achieve intense X-ray fields produced by free-electron lasers. Intense laser pulses are expected to reach power densities of over \(10^{20}\) W/cm\({}^{2}\) using 9.9 keV photons [15].  
Giant resonances are one of the most remarkable examples of collective excitations in the atomic nuclei [16]. The giant dipole resonance (GDR), discovered 75 years ago, can be excited, for example, by photon absorption by a nucleus. The electric component of the incident photon, homogeneous over the nucleus since the wavelength is much larger than the nuclear radius (\(\lambda\gg R\)), induces a coherent displacement of the proton distribution with respect to the neutron distribution [17]. [PERSON] and [PERSON] proposed a simple description of the dipole mode in spherical nuclei by the reciprocal vibrations of the rigid proton and neutron distributions [18]. Following the original work of [PERSON] on the eigenwibrations of a one-component nuclear liquid drop [19], [PERSON] and [PERSON] operated the extension to two fluids (proton-neutron mixture) interpenetrating each other [20]. The hydrodynamical treatment of protons vibrating against neutrons provided one of the most transparent descriptions of this excitation mode in nuclei, as showed by further developments [21, 22, 23, 24, 25, 26, 27, 28, 29, 30].  
Below, we lay down a framework aiming to describe the coupling of a laser field to a proton-neutron fluid.
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
## 3 Fault Tree Analysis Methodology and AutoFTA 3.9 Software  
Fault tree analysis (FTA) constitutes a systematic fault diagnostic methodology that initiates with the definition of a Top Event representing the most critical system failure scenario [18]. The analysis progresses through a deductive reasoning approach, decomposing root causes in a top-down hierarchical manner. Beginning from the Top Event, the process iteratively identifies all immediate causal factors contributing to each superior event level, continuing downward until reaching fundamental Basic Events that require no further resolution. The fault tree structure interconnects Top Events, Intermediate Events, and Basic Events through standardized logical operators--primarily AND gates and OR gates--to create a visual representation of failure propagation logic and causal relationships. This methodology enables both qualitative identification of system vulnerabilities and critical failure paths, as well as quantitative computation of Top Event occurrence probabilities. Widely implemented across complex engineering systems for reliability assessment, risk prediction, and safety assurance, FTA distinguishes itself through logical rigor, graphical clarity and intuitive representation, and broad applicability across engineering disciplines [19].  
AutoFTA 3.9, developed by Beijing ANNSO Technologies Co., Ltd., represents a comprehensive safety assessment software platform accessible to the broader engineering community. This domestically engineered solution addresses China's critical demand for rapid analytical computation capabilities targeting large-scale complex systems. As a premier integrated fault tree modeling and analysis platform, it finds extensive application across reliability-critical sectors including aerospace, nuclear power, and automotive electronics. AutoFTA 3.9 has established itself as an indispensable instrument for engineers conducting fault diagnosis and implementing risk management protocols.
# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 4 Results
### Objective vs. Subjective Quality Assessment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\mathbf{M_{X}}\) & **APSNR** & **Delta** & **MSAD** & **MSE** & **NQI** & **PSNR** & **SSIM** & **VIF** & **VMAF** & **VQM** \\ \hline \(\mathbf{x}\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Intercept (\(\mathbf{a}\)) & \(-5.58\) & 4.49 & 6.05 & 4.39 & 0.10 & \(-0.33\) & \(-13.48\) & \(-0.38\) & \(-0.38\) & 5.38 \\ Slope (\(\mathbf{b}\)) & 0.25 & \(-4.58\) & \(-1.03\) & \(-0.03\) & 10.07 & 1.02 & 18.53 & 6.92 & 0.05 & \(-1.04\) \\ R-Square (COD) 1 & 0.92 & 0.60 & 0.90 & 0.74 & 0.88 & 0.92 & 0.89 & 0.92 & 0.91 & 0.91 \\ RSS 2 & 4.32 & 21.28 & 5.44 & 13.50 & 6.37 & 4.32 & 5.71 & 4.27 & 4.98 & 4.98 \\ Root-MSE (SD) 3 & 0.36 & 0.79 & 0.40 & 0.63 & 0.43 & 0.36 & 0.41 & 0.35 & 0.38 & 0.38 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 4: The parameters of the quality models based on individual objective metrics. Figure 6: Fitted curves plot with confidence and predicted intervals for linear regression models based on different classical (CL) metrics: (\(\mathbf{a}\)) APSNR, (\(\mathbf{b}\)) Delta, (\(\mathbf{c}\)) MSAD, and (\(\mathbf{d}\)) MSE. appear in the prediction band. In Figures 6 and 7, it can be seen that in the case of better correlated metrics, i.e., with a higher correlation coefficient and smaller RSS and root MSE (SD) values, the confidence bands and prediction confidence bands are correspondingly narrower. Analysing the values of the determination coefficient included in Table 4, it can be seen that the best results are provided by models based on the APSNR, PSNR, and VIF metrics--in each of these cases, the R-Square is 0.92, with a high correlation coefficient (r = 0.97). Good modelling results for these three metrics were also confirmed by the lowest RSS and MSE (SD) values. An interesting case here is the VQM metric, which shows a very high correlation value with the subjective assessment values but a lower coefficient of determination than the three above-mentioned metrics. The question arises about the possibility of creating a quality model combining the features of existing models and thus obtaining an even better mapping of objective parameters to the subjective perception of video quality by users. A machine learning model was used for this purpose. The set of values of all objective metrics analysed here and the corresponding values of subjective assessments were used to train it. Figure 7: Fitted curves plot with confidence and predicted intervals for linear regression models based on different classical (CL) metrics: (**a**) NQI, (**b**) PSNR, (**c**) SSIM, (**d**) VIF, (**e**) VMAF, and (**f**) VQM.
# Differentially Private XGBoost Algorithm for Traceability of Rice Varieties
###### Abstract  
Privacy protection in agricultural traceability has received more and more attention. Most of the existing methods only protect the original data information from the perspective of cryptography and ignore the availability of the protected information. In fact, after data is processed by cryptography, blockchain, and other technologies, it cannot be directly used for machine learning model training. Therefore, differential privacy has great potential value for privacy protection in agricultural traceability, which can enable data to participate in classification tasks under privacy protection. In this paper, we propose an integrated algorithm for agricultural traceability called Differentially Private XGBoost (DP-XGB), which can protect the privacy of the original data during the training process and obtain high model utility under the condition of a small sample size. We inject Gaussian noise into the gradient operator and Hesse operator of the original XGBoost and give the calculation method of the resulting privacy budget. Experiments show that our method can effectively obtain differential privacy guarantees and achieves very high classification accuracy when the noise is small.  
differential privacy; variety traceability; rice data safety; machine learning Article