esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing Methods
#### 2.3.6 Evaluation Factor Selection and Normalization  
Road collapse disasters arise from the combined effects of natural and human-induced factors. Considering the environmental and geological features of the Guangxi Zhuang Autonomous Region, the study area in China, this study selected 10 key evaluation factors essential for risk assessment: slope gradient, aspect, lithology, plan curvature, profile curvature, vegetation coverage, annual precipitation, population density, soil moisture,and distance to roads. These factors were standardized to ensure robust data support for collapse risk analysis, thereby improving the model's predictive precision. Natural factors predominantly include geological and climatic elements (e.g., topography, lithology, and precipitation), whereas human-related factors encompass road infrastructure and population density.  
Standardization Process: To address dimensional disparities and enhance data comparability, all factor datasets underwent a systematic standardization process, outlined as follows:  
1. Data Collection and Preprocessing: Factor data were acquired from Guangxi sources. Slope gradient, aspect, plan curvature, and profile curvature were extracted from a digital elevation model (DEM); lithology data were sourced from geological surveys; vegetation coverage and soil moisture data were derived from remote sensing imagery; annual precipitation was obtained from meteorological records; and population density and distance to roads were calculated using demographic and road network data. All datasets were cleaned and standardized to ensure quality and uniformity.
2. Standardization Method: Given the data distribution and the convolutional neural network (CNN) model's requirements, min-max normalization was employed. This method rescales factor values to the [0, 1] range, maintaining their relative relationships while aligning with the neural network's input specifications [16].
3. Normalization Calculation: For each factor \(X\) (e.g., slope gradient or precipitation), the normalized value \(X_{\text{norm}}\) is calculated using the following formula: \[X_{\text{norm}}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}}\] (5) where \(X_{\text{min}}\) and \(X_{\text{max}}\) denote the minimum and maximum values of the factor across the study area, respectively [26].
4. Data Validation and Adjustment: After standardization, the normalized data were examined to detect and correct outliers or missing values. For factors prone to extreme values (e.g., population density), logarithmic transformation or truncation was applied prior to normalization to reduce skewness and improve model training stability [25].
5. Application of Results: The standardized factors were utilized as input features for CNN training and risk assessment, unifying their scales and enhancing the model's capacity to discern variable interactions [26].  
This rigorous standardization process ensured data consistency, providing a solid foundation for the accuracy and reliability of the subsequent risk prediction model.
## 3 Results  
### The Fungal Data Set  
A total of 103 papers dealing with the fungal deterioration of wall paintings were collected, regarding 107 different monuments grouped in 74 sites. A total of 378 fungal entries were obtained, consisting of 1209 records belonging to 173 genera and 260 species (Table 1). \begin{table}
\begin{tabular}{c c c c} \hline
**Genus** & **Fungal Name** & **References** & **Environment** \\ \hline _Accmoniella_ & _Accmoniella__atra_ & [21] & C-NHE \\ \hline \multirow{6}{*}{_Accmonium_} & _Accmonium campusoverum_ & [37] & NC-HE \\ \cline{2-4}  & _Accmonium charticola_ & [38; 39; 40; 41] & NC-HE, C-NHE \\ \cline{2-4}  & _Accmonium masseei_ & [23; 25] & C-HE \\ \cline{2-4}  & _Accmonium murorum_ & [23; 24; 25; 28; 32] & C-HE, NC-HE \\ \cline{2-4}  & (syn. _Giomusatix murorum_) & [23; 24; 25; 28; 32] & C-HE, NC-HE \\ \cline{2-4}  & _Accmonium rutilum_ & [21] & C-NHE \\ \cline{2-4}  & _Accmonium_ cf. _rutilum_ & [39] & C-NHE \\ \cline{2-4}  & _Accmonium_ sp. & [23; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47] & C-HE, NC-HE, C-NHE \\ \hline _Acrodontium_ & _Acrodontium crateriforme_ & [48] & C-NHE \\ \hline _Acrophialophora_ & _Acrophialophora fusispora_ & [28; 32] & NC-HE \\ \cline{2-4}  & (syn. _A. nainiana_) & [28; 32] & NC-HE \\ \hline _Acrostalagmus_ & _Acrostalagmus luteoalbus_ & [49] & C-NHE \\ \cline{2-4}  & (syn. _Verticillium lateritium_) & [49] & C-NHE \\ \hline _Acrothecium_ & _Acrothecium_ sp. & [50] & O-SPE \\ \hline _Actinomucor_ & _Actinomucor elegans_ & [51] & C-HE \\ \hline _Akanthomyces_ & _Akanthomyces lecaniti_ & [39; 43; 52] & NC-HE, C-NHE \\ \cline{2-4}  & (syn. _Verticillium lecaniti_) & [39; 43; 52] & NC-HE, C-NHE \\ \hline _Allophoma_ & _Allophoma labilis_ & [53] & SC-NHE \\ \hline \end{tabular}
\end{table} 
Table 1: List of the fungal entries retrieved from the different papers grouped by genera, in association with the corresponding references and the environmental categories where they have been registered.
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 4 The Proposed Separable Data Hiding System
### Image Encryption  
In the image encryption phase, by using an encryption key, a content owner encrypts the original uncompressed image to obtain an encrypted image. The encryption procedure is the same as that in the joint system.
## 3 State Evaluation and Fault Prediction of Protection System Based on Digital Twin Technology  
### Workflow of Twin Model Layer  
The twin model layer is the core of the digital twin architecture, integrating datasets, state evaluation, and fault prediction algorithms. Its workflow is shown in Figure 3. The operational data of the protection system over a sufficiently long period of time are collected and collated, its evaluation index (main characteristic quantity), comment set (classical domain), and fault set are selected. A judgment matrix is constructed based on the historical operation. The initial weights are determined by the improved analytic hierarchy process (AHP). The clustering centers of its \(N\) common faults are calculated with sufficient fault samples. The current operation information of the protection system to be evaluated is digitized, and the closeness to the classical domain is determined, and the weight of the characteristic quantities is modified based on the variable weight model according to its real-time state. If the status of the equipment is judged to be \"severe\", a fault prediction is given, and the result is obtained by comparing it with the reference series of the clustering center. Subsequently, the operation and maintenance personnel conduct preventive maintenance on the equipment, and update the fault set and clustering center according to the fault condition. Sections 3.1 and 3.2 describe the methods and principles of state evaluation and fault prediction of the protection system, respectively.  
Relying on the realistic conditions that have been realized and can be achieved, the framework is arranged at a low cost and has a high feasibility for implementation in a realistic environment. As described in Section 2, relying on the secondary monitoring system already developed by the State Grid, it is possible to obtain real-time information on the operation of the protection system equipment. The processing platform of the information and the digital twin model are arranged in the station-side operation and the maintenance management system of the smart station to ensure the reduction of operation, the maintenance and overhaul costs, and to improve the feedback efficiency after maintenance.
# Spatiotemporal Analysis of Urban Expansion in Beijing, China
## 3 Results
### Characteristics of Urban Expansion Space in Beijing  
The fractal dimension can reflect the complexity and tortuosity of urban edges [21]. A fractal dimension value between 1 and 1.5 indicates a simple and well-planned urban boundary, while a fractal dimension value between 1.5 and 2 indicates a trend toward complex and unstable urban boundaries. The dimensions of urban spatial classification in Beijing in 1995, 2000, 2005, 2010, 2015, and 2020 were 1.52, 1.56, 1.58, 1.59, 1.60, and 1.59, respectively, showing a trend of increasing and then decreasing within the theoretical range, indicating that the boundaries of Beijing are gradually becoming more complex, tortuous and unstable.  
As shown in Figure 6, the perimeter of the study area in 1995 was 701.46 km, exhibiting complex boundaries and scattered land use. In 2000, the perimeter of the research area was 922.75 km, an increase of 221.29 km compared to that in 1995. The urban boundary is complex and has no significant change overall. In 2005, the perimeter of the research area was 1193.68 km, which represents an increase of 270.93 km compared to that in 2000, and the urban boundaries were more complex. In 2010, the perimeter of the research area was 1278.4 km, an increase of 84.72 km compared to that in 2005. In  
Figure 5: Urban expansion process in various urban areas from 1995 to 2020.  
2020, the perimeter of the research area was 1430 km, which is basically the same as that in 2015. Moreover, the center point coordinates of the study area patches were obtained for each time period, the horizontal and vertical coordinates of the center of gravity of the study area were calculated, as was the transfer distance. The center of gravity of the research area moved 0.84 km to the northwest between 1995 and 2000 and 0.94 km to the northeast between 2000 and 2005; it basically remained unchanged from 2005 to 2010; it moved 0.62 km to the northeast between 2010 and 2015 and 0.65 km to the northeast between 2015 and 2020. Overall, the center of gravity of the study area has shifted 2.25 km to the northeast.
## 9 A Complete Pattern: WS-Trust
### Implementation  
In this solution, the concept of trust is realized by obtaining a security token from the web service (in our diagram, the Security Token Service) and submitting it to the receiver who in turn validates that security token through the same web service. Upon approval, the receiver establishes a valid trust relationship with the receiver that lasts as long as the security token is valid.  
In order to assure effective implementation, we need to take in consideration the following:  
* To communicate trust, a service requires proof, such as a signature to prove knowledge of a security token or set of security tokens. A service itself can generate tokens or it can rely on a separate STS to issue a security token with its own trust statement.
* Although the messages exchanged between the involved entities are protected by WS-Security; still three issues related to security tokens are possible: security token format incompatibility, security token trust, and namespace differences. The WS-Trust pattern addresses these issues by defining a request/response protocol (in which the client sends _RequestSecurityToken_ and receives _RequestSecurityTokenResponse_) and introducing a Security Token Service (STS) which is another web service.
* Based on the credential provided by the requester, there are different aspects of requesting a security token (RST), each of which has a unique format that the requester should follow:
* The issuance process: formed as _RequestSecurityToken (RequestType, Claims)_.This is our use case Create a security token in the Dynamics section.
* The renewal process: formed as _RequestSecurityToken (RequestType, RenewTarget)_.
* The cancel process: formed _RequestSecurityToken (RequestType, cancelTarget)_. By the way, the cancelled _token is no longer valid for authentication and authorization.
* The validate process: formed as _RequestSecurityToken (RequestType, ValidateTarget)_.  
The WS-Trust specification was created as part of the Global XML Web Services Architecture (GXA) framework, which is a protocol framework designed to provide a consistent model for building  
Figure 7: Sequence Diagram accessing a resource using a token.  
infrastructure-level protocols for web services and applications [32]. It was authored by Microsoft, IBM, Verisign, and RSA Security and was approved by OASIS as a standard in March 2007.
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 2 Methodology
### Clock Prediction Model  
The stability of a satellite clock can be represented by the factors of phase deviation, frequency deviation and frequency drift. A quadratic polynomic model is usually applied for satellite clock offset prediction which is given as [20; 21]  
\[c(t)=a_{0}+a_{1}t+\frac{1}{2}a_{2}t^{2}+\varepsilon(t) \tag{1}\]  
where \(a_{0}\), \(a_{1}\) and \(a_{2}\) are the polynomial coefficients which are corresponding to the clock offset at the initial time, frequency deviation and drift, respectively; \(\varepsilon(t)\) is a generic random process noise. [PERSON] et al. showed that the quadratic polynomic model is suitable for Rubidium (Rb) clocks, while for Cesium (Cs) clocks, the linear polynomic model is better and the quadratic term \(a_{2}\) is unnecessary [22]. In addition to linear and quadratic terms, periodic variations should also be added in the prediction model [23]. Pronounced sinusoidal variations are found in GPS satellite clocks and the period equals the orbital period [23]. The clock prediction model with periodic terms can be expressed as [24]  
\[\left\{\begin{array}{l}c(t)=a_{0}+a_{1}t+\frac{1}{2}a_{2}t^{2}+a_{s}\sin( \frac{2\pi}{T}t)+a_{c}\cos(\frac{2\pi}{T}t)+\varepsilon(t),\quad Rb\\ c(t)=a_{0}+a_{1}t+a_{s}\sin(\frac{2\pi}{T}t)+a_{c}\cos(\frac{2\pi}{T}t)+ \varepsilon(t),\quad Cs\\ a_{s}=A\cos\varphi_{0};\quad a_{c}=A\sin\varphi_{0}\end{array}\right. \tag{2}\]  
where \(A\) is the amplitude, \(T\) is the period of the sinusoid and \(\varphi_{0}\) is the corresponding initial phase. When the satellite clock model is identified, the clock model coefficients \(a_{0}\), \(a_{1}\), \(a_{2}\), \(a_{c}\) and \(a_{s}\) can be determined by fitting to the precise clock estimates.
## 3 Results
### Postural and Environmental Determinants of Work Activity
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**2017** & **SoWD (n = 82)** & \multicolumn{4}{c}{**WaSaD (n = 212)**} & \multicolumn{4}{c}{**Shift Type Effect**} \\ \hline  & \multicolumn{2}{c}{**Fixed Night (n = 33)**} & \multicolumn{2}{c}{**Fixed Early (n = 49)**} & \multicolumn{2}{c}{**Fixed Daytime (n = 126)**} & \multicolumn{2}{c}{**Rotating (n = 86)**} & \multicolumn{2}{c}{**Total (n = 294)**} & \multicolumn{1}{p{42.7 pt}}{\(\chi^{2}\)} & \multicolumn{1}{p{42.7 pt}}{\(p\)} \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Mental & 3 (9.1\%) & 5 (10.2\%) & 5 (4.0\%) & 5 (5.8\%) & 18 (6.1\%) & \multirow{4}{*}{33.408} & \multirow{4}{*}{\(<\)0.001 *} \\ \cline{2-2} \cline{5-8}  & Physical & 23 (69.7\%) & 39 (79.6\%) & 72 (57.1\%) & 33 (38.4\%) & 167 (56.8\%) & & \\ \cline{2-2} \cline{5-8}  & Both & 7 (21.2\%) & 5 (10.2\%) & 49 (38.9\%) & 48 (55.8\%) & 109 (37.1\%) & & \\ \hline
**2019** & \multicolumn{2}{c}{**SoWD (n = 94)**} & \multicolumn{2}{c}{**WaSaD (n = 196)**} & \multicolumn{2}{c}{**Shift type effect**} \\ \hline  & \multicolumn{2}{c}{**Fixed night (n = 36)**} & \multicolumn{2}{c}{**Fixed early (n = 58)**} & \multicolumn{2}{c}{**Fixed Daytime (n = 58)**} & \multicolumn{2}{c}{**Rotating (n = 79)**} & \multicolumn{2}{c}{**Total (n = 290)**} & \multicolumn{1}{p{42.7 pt}}{\(\chi^{2}\)} & \multicolumn{1}{p{42.7 pt}}{\(p\)} \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Mental & 3 (8.3\%) & 6 (10.3\%) & 4 (3.4\%) & 4 (5.1\%) & 17 (5.9\%) & \multirow{4}{*}{27.923} & \multirow{4}{*}{\(<\)0.001 *} \\ \cline{2-2} \cline{5-8}  & Physical & 23 (63.9\%) & 42 (72.4\%) & 66 (56.4\%) & 29 (36.7\%) & 160 (55.2\%) & & \\ \cline{2-2} \cline{5-8}  & Both & 10 (27.8\%) & 10 (17.2\%) & 47 (40.2\%) & 46 (58.2\%) & 113 (39.0\%) & & \\ \hline \end{tabular}
\end{table} 
Table 1: Mean (SD) of age, work seniority and sleep duration in 2017 and 2019 by shift type, and shift types comparisons. The perception of the adoption of postures with upper limbs above the shoulders and the flexion of the trunk was more frequent among night shift workers, and differences between the shifts were statistically significant in both years. The posture with trunk rotation presented a higher frequency among those workers of night shifts, with significant differences in 2019 (Table 3). The frequency of discomfort related to environmental conditions was higher among night and early morning shift workers. The differences among shift types were statistically significant in both data collection moments for all the categories assessed (noise, vibrations, temperature) (Table 4).
## 3 Results
### Stripline Incorporating the Proposed Design Methodology  
#### 3.2.1 Loss Reduction  
The attenuation constant of the stripline incorporating the proposed design methodology, along with its counterparts, is shown in Figure 8. The introduction of air cavities results in an average reduction of 19.40% in attenuation across the frequency band from DC to 40 GHz.
# The Law and Mechanism of the Effect of Surface Roughness on Microwave-Assisted Rock Breaking
###### Abstract  
In physical engineering, a rock surface, whether naturally or artificially formed, is rough. When irradiating rocks, microwaves produce reflections and diffractions on the surface of rough rocks, which significantly affect the absorption of microwave energy by rocks, thus influencing the result of microwave irradiation. In order to explore the influence of rough rock surfaces on the effect of microwave-assisted rock breaking, microwave irradiation tests were carried out on basalt samples with different values of roughness to test the temperature and P-wave velocity of the samples before and after microwave irradiation. Numerical test methods were used to systematically study the influence of rough rock surfaces on microwave irradiation. The results show that, under the same microwave irradiation conditions, the effect of microwave irradiation on rough surface basalt is more significant than that of flat surface basalt. The surface temperature distribution range of flat surface specimens is narrow, the surface temperature range of rough surface specimens is wider and more inhomogeneous, and the maximum surface temperatures of rough surface specimens are much higher than those of flat surface specimens. After irradiation, new macroscopic cracks were generated on the surface of the samples, and the crack propagation of the rough surface samples was more obvious. The decrease in P-wave velocity before and after the irradiation of flat surface samples is small, and that of rough surface samples is larger. The main factors affecting the effect of microwave irradiation on the rough surface are the refraction and reflection of electromagnetic waves, heat conduction, and stress concentration on the surface.  
microwave irradiation; surface roughness; crack propagation; thermal stress; thermal conduction +
Footnote †: journal: Applied Sciences
## 4 Discussion  
The volatile fraction of raw lamb meat was largely dominated by aldehydes, which are secondary products of oxidation and have been considered as markers of fat oxidation during storage time [48]. However, several volatiles that are not directly related to lamb meat flavour were also identified, namely benzothiazole, \(o\)-xylene, toluene, and crown ether-15. Such compounds may be considered as artifacts originating from environmental or external contamination and may not be considered as \"natural\" or unambiguous meat volatiles.  
The evolution of aldehydes with respect to storage time recorded positive [PERSON]'s correlation (\(r\)) (\(p<0.05\)). Respective \(r\) values for the groups hexanal-nonanal, heptanal-nonanal, and octanal-nonanal were 0.82, 0.44, and 0.72, through the 9 days of storage. However, the groups hexanal-nonanal and octanal-nonanal recorded excellent \(r\) values equal to 1 for the first 5 days of storage, in which raw lamb meat retained a fresh character based on microbiological (total viable counts \(<7\) log CFU/g) and sensory data (odour and taste) [18]. This was not the case for the group heptanal-nonanal, in which a perfect negative [PERSON]'s correlation was recorded (\(r=-1\)). The evolution of the aforementioned aldehydes through the 9 days of storage is given in Table 2.  
Specific aldehyde ratios, namely hexanal to nonanal (Hex/Non), heptanal to nonanal (Hept/Non), octanal to nonanal (Oct/Non), and sum of hexanal, heptanal, and octanal to nonanal ratio (Hex + Hept + Oct/Non) (Table 2) recorded strong positive [PERSON]'s correlation with the degree of oxidation values (TBA test) expressed as mg malonic dialdehyde per kg of lamb meat (mg MDA/kg) throughout storage (1.4 mg MDA/kg on day 1, 2.8 mg MDA/kg on day 5, and 3.8 MDA/kg on day 9) [18]. Respective [PERSON]'s correlation values were \(r=1.00\), \(r=0.87\), \(r=0.83\), and \(r=0.89\) (Table 2). It should be stressed that the hexanal to nonanal ratio has been proposed as an indicator of the level of oxidation in olive oil [49], whereas variations in hexanal content with respect to storage time could serve as indicators of flavour deterioration in cooked pork [48]. In addition, octanal and nonanal (among other volatiles) have been proposed as indicators of raw beef quality stored under refrigeration [17].  
Based on the aforementioned, the ratios Hex/Non, Oct/Non, and Hex + Hept + Oct/Non may be proposed as indicators of freshness and degree of oxidation, enhancing the quality of fresh lamb meat stored under refrigeration.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 4 Simulation Results
### Ramp Wind  
Figures 5 and 6 show the responses of the PMSG-WT to ramp wind. Wind speed is shown in Figure 5a. As shown in Figure 5b,c, the proposed NAC provides the smallest tracking error of the mechanical rotation speed \(\omega_{\mathrm{m}}\), compared with the VC, GSPI and FLC. The VC has the biggest tracking error and requires the longest recovery time. It can be explained that the VC is adjusted for a specific operation point of the system and cannot ensure provision of a satisfactory dynamic performance for time-varying operation points. Although the FLC can provide a high tracking performance, the tracking error of \(\omega_{\mathrm{m}}\) still exists. It is because that the FLC requires full state measurements, but the \(\frac{\mathrm{d}V}{\mathrm{d}t}\) in Equation (9) is unknown in the FLC design. The GSPI also achieves better performance than the VC. This is because the GSPI can schedule PI gains frequently under time-varying wind speeds. However, it increases the burden of the controller.  
To keep the extracted wind power at the rated power, the required pitch angle \(\beta_{\mathrm{r}}\) should change with the varying wind speed, as shown in Figure 5d. In Figure 5e,f, to maintain the extracted  
Figure 5: Responses of the PMSG-WT to ramp wind speed. (**a**) Wind speed V. (**b**) Mechanical rotation speed \(\omega_{\mathrm{m}}\). (**c**) Relative error of \(\omega_{\mathrm{m}}\). (**d**) Required pitch angle. (**e**) Power coefficient \(C_{\mathrm{p}}\). (**f**) Mechanical power \(P_{\mathrm{w}}\). (**g**) Active generating power \(P_{\mathrm{m}}\). (**h**) Reactive generating power \(Q_{\mathrm{m}}\).  
wind power around its rated value, the power coefficient \(C_{\text{p}}\) increases when wind speed decreases. The extracted wind power can be maintained around its rated value under the NAC even when wind speed varies, which the VC, GSPI and FLC cannot provide. The active generating power \(P_{\text{m}}\) and reactive generating power \(Q_{\text{m}}\) of the PMSG-WT are shown in Figure 5g,h, respectively.  
In the previous section, it mentions that the proposed NAC can estimate the defined perturbation terms Equations (17) and (36) via the designed observers Equations (19), (38) and (39) to compensate the real perturbation. It can be seen from Figure 6 that both the states and perturbations can be well estimated by the designed SPO.