esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The overall framework is shown in Figure 2. The methodology consists of 5 steps: pre-processing, texture analysis, principal component analysis (PCA), supervised classification, and accuracy assessment. The two sets of polarized SAR images, ALOS-2 and Sentinel-1, were applied in this framework, and their results were evaluated by comparison to truth data. Due to the lack of the real ground truth data in the study area, the Landsat 8 optical image was considered as a base map for preparing the truth data. The truth data contains the polygons of different land covers. All processes in this section were done using Environment for Visualizing Images (ENVI) 5.3.1 software (Exelis Visual Information, Boulder, CO, USA).  
Figure 2: Flowchart of land cover classification using the multi-spectral optical image and two synthetic aperture radar (SAR) datasets. GLCM: gray-level co-occurrence matrix; SVM: support vector machine; ML: Maximum Likelihood; PCT: principal components of the textures.
## 3 Results and Discussion
### Physicochemical Characteristics
In Lithuanian honey, the identified variation in EC was between 0.27 and 0.89 mS/cm, while rapeseed honey was found to have the lowest EC [46]. For chestnut honey produced in the province of Sinop in Turkiye, EC values were found to be above 0.8 mS/cm [47]. With sufficiently low water activity (a\({}_{w}\)), honey can be microbially stable. However, a critical limit of a\({}_{w}\) is around 0.6, above which yeasts can grow, and for this reason a\({}_{w}\) acts as a critical quality parameter [48]. Moreover, it was determined that a\({}_{w}\) presents a linear relationship with the moisture content [49]. In a study conducted on Slovenian honey, a statistically significant linear correlation was found between a\({}_{w}\) and water content. At the same water content, a\({}_{w}\) in sweet sap (secretion) honey is higher than in flower honey [50]. In Turkish samples, the a\({}_{w}\) and moisture values of flower honey were determined to be between 0.470 and 0.563 and between 15.0% and 20.4%, respectively. The a\({}_{w}\) and moisture values of pine honey samples were determined between 0.492 and 0.589 with 15.1% and 20.4%, respectively [20]. In the other eight honey samples originating from Mexico, a\({}_{w}\) values were determined between 0.569 and 0.613 [51]. The relevance of the water content of honey lies in the fact that it affects the physical, microbiological, and sensorial properties, as well as the commercial value of honey [52; 53]. For the present study, the moisture values of the samples were 14.6-18.4%, which is a typical pattern. It was reported that moisture higher than 20% could be problematic due to the possible initiation of fermentative processes [54], meaning that the commercial samples were acceptable. In a study conducted on Hungarian honey, it was reported that the moisture content was below 20% [6], while commercial Indian honey showed moisture of 17 to 22.6% [37].
## 2 Materials and Methods
Spatially, analysis from MODIS data and from ecosystem service valuation was then considered at the scale of the nine individual provinces of South Africa (Figure 3) in order to examine the temporal variations of changes in land-cover type and ecosystem service values at a regional level. \begin{table}
\begin{tabular}{c c c} \hline \hline
**\#** & **Land-Cover Type** & **Value (USD ha\({}^{-1}\) yr\({}^{-1}\))** \\ \hline
0 & Water Bodies & 12,119.5 \\
1 & Evergreen Needleleaf Forests & 1777.0 \\
2 & Evergreen Broadleaf Forests & 1777.0 \\
4 & Deciduous Broadleaf Forests & 1777.0 \\
5 & Mixed Forests & 1777.0 \\
6 & Closed Shrublands & 4075.5 \\
7 & Open Shrublands & 4075.5 \\
8 & Woody Savannas & 4075.5 \\
9 & Savannas & 4075.5 \\
10 & Grasslands & 4075.5 \\
11 & Permanent Wetlands & 2243.5 \\
12 & Croplands & 26,351.0 \\
13 & Urban and Built-up Lands & 2846.5 \\
14 & Cropland/Natural Vegetation Mosaics & 6661.0 \\
15 & Non-Vegetated Lands & 2846.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Economic values (USD ha\({}^{-1}\) yr\({}^{-1}\)) derived from [3], used in this study for calculation of total ecosystem service values per land-cover class.
## 2 Background
### Identity Management  
Identity management is the task of creating and maintaining user identities. This task recognizes the individuals in a company, network, or country, and controls their access to the assets [40]. Most companies use identity management to lower the burden of data storage because it is not economical to save the record of a user who visited the company only once. An identity provider also enables the option of single sign-on (SSO) for users who want to access the system only once. The current study uses Shibboleth identity management because it is easy to configure and is free to use for study purposes.  
Shibboleth is an open-source, multinational, federated identity management architecture [41]. The main concerns of identity management relate to the identity provider (IdP), service provider (SP), and the communication between them [42]. Shibboleth identity management uses Security Assertion Markup Language (SAML) for information transmission between the IdP and SP. Shibboleth identity management also permits the SP to manage their shared users' profile data. It also supports local single sign-on (SSO) and organizational level SSO, for inter-individual and inter-organizational communication, respectively. Users must follow the required procedure to receive the service from Shibboleth identity management. First, the user asks the SP for the service. This request is then forwarded to the IdP by the SP, and the IdP asks for user authentication. The IdP validates the response from the user. After validation, the IdP asks the SP to include the request of the user and provide the user with the service that it demanded [43].
## 3 Proposed System Model
### Assignment of Power Coefficients  
The allocation of power coefficients, as detailed in the literature, can be performed by using different methods based on the users' channel conditions. However, most of these methods have been applied to only two users [1]. In order to adapt to more users, the proposed system is based on [PERSON]'s triangle for power coefficient allocation, which is illustrated in Equation (3). [PERSON]'s triangle is named after the well-known French mathematician and philosopher, [PERSON] [60]. It is a triangular array based on binomial expansion coefficients. [PERSON]'s triangle can be applied in different areas in engineering and algebra [60].  
Generally, the binomial expansion is formulated as follows:  
\[(x+y)^{n}=a_{0}x^{n}+a_{1}x^{n-1}y+a_{2}x^{n-2}y^{2}+\ldots+a_{n-1}xy^{n-1}+a_ {n}y^{n}, \tag{11}\]  
where \(x+y\) is the binomial, and \(a_{i}\) is element \(i\) on row \(n\) of [PERSON]'s triangle and is equal to:  
\[a_{i}=\left(\begin{array}{c}n\\ i\end{array}\right)=\frac{n!}{i!(n-i)!}. \tag{12}\]  
In the power allocation algorithm, illustrated by Algorithm 3, the number of users is equivalent to the row number, \(n\), under the constraint illustrated by Equation (2).  
```
1:Inputs: Required number of users, N.
2:1:loop i = 1: N
3:2:\(\mathrm{p(i)}=\frac{\mathrm{N}!}{i(\mathrm{N}-i)!}\)
4:endloop
5:4:\(\mathrm{Sum}=\sum_{i=1}^{N}p(i)\)
6:loop I = 1: N
7:\(\mathrm{p(i)}=\frac{a(i)}{\mathrm{Sum}}\)
8:endloop
9:loop I = 1: N/2
10:\(\mathrm{p(i)}\) = \(\mathrm{p(i)}\times(4/5)\)
11:endloop
12:loop I = (N/2) + 1: N
13:\(\mathrm{p(i)}\) = \(\mathrm{p(i)}\times(1/5)\)
14:endloop
```  
**Outputs:** Power coefficient, \(\mathrm{p_{i}}\) for each user.  
Figure 6: Adopted long-short-term memory (LSTM) neural network construction.
# Student Behavior Recognition in Classroom Based on Deep Learning
## 5 Experiment and Analysis
### Performance Comparison  
In the comparative experiments of this paper, in order to ensure the consistency of other factors, we use the control variable method. The specific training parameters are set as follows: the initial learning rate is 0.01, training is 200 rounds, the weight decay coefficient is 0.0005, the border loss coefficient is 0.05, the classification loss coefficient is 0.5, and an adaptive anchor box adjustment strategy is adopted.  
The selection of network models for this study includes an experimental analysis of two prevalent detection models: YOLOv5 and Faster R-CNN.  
Through experimental comparisons, it was observed that all three models converged after 199 iterations. The maximum mAP value achieved by each model during the iteration process is shown in bold in Table 4. The mAP of the SBD model reached its highest value of 0.821, which was 5.2 percentage points higher than Faster R-CNN and 4.6 percentage points higher than YOLOv5. At the end of the iteration, the mAP of the SBD model was still significantly higher than that of the other two models. The comparison results of Faster R-CNN, YOLOv5, and SBD are shown in Table 4 below.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **EPOC** & **40** & **80** & **120** & **160** & **199** \\ \hline Faster R-CNN & 0.521 & 0.607 & **0.769** & 0.756 & 0.741 \\ YOLOv5 & 0.543 & 0.606 & 0.727 & **0.775** & 0.766 \\ SBD & 0.552 & 0.621 & 0.751 & **0.821** & 0.811 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of Faster R-CNN, YOLOv5, and SBD.  
After 199 iterations, all three models reached convergence and achieved their highest mAP values. Subsequently, further analysis was conducted on other evaluation metrics, including the F1 score, recall, and mAP. The results indicate that the SBD model shows advantages in the F1 score, recall, and mAP. The detailed results are presented in Table 5.  
The accuracy of YOLOv5 and SBD is shown in Figures 8 and 9 below. The accuracy results demonstrate that the SBD model exhibits superior convergence across these metrics compared to YOLOv5, and the prediction of slippage improved to a certain extent. Specifically, the accuracy increased from 31.6% with YOLOv5 to 38.4% with SBD, an improvement of 6.8 percentage points.  
The precision-recall (PR) curve of the SBD model is shown in Figure 10. The PR curve indicates that the model converges well, further confirming the improvement effect of the SBD model.  
Some of the detection results of the SBD model are shown in Figure 11. Table 3 above categorizes the various student engagement states in the classroom, with each coded number representing a specific behavior. The rectangular regions indicate the detection zones identified by the model. It can be seen that the improved SBD model can accurately detect various classroom behaviors of students. The model achieved high accuracy in classroom behavior detection across different behavior types.
## 5 Conclusions and Future Work  
This study addresses the limitations of traditional polarized 3D reconstruction methods when applied to outdoor maritime scenes. By analyzing the composition of surface-emitted light under natural outdoor light conditions and applying Fresnel's and [PERSON]'s laws, we proposed a method for solving the zenith angle using a mixed reflection model. The proposed method effectively mitigates the detail loss commonly encountered in conventional polarized 3D reconstruction methods when working in mixed natural light environments, particularly in maritime settings. The experimental results demonstrate that the proposed method is better suited for outdoor maritime scenes because it successfully preserves fine details. The proposed method offers notable improvements in terms of depth level, contour accuracy, and reflection processing, enabling the generation of more realistic and refined results in practical 3D reconstruction scenarios, especially under complex lighting conditions.  
Figure 16: Reconstruction results of the target in Figure 14. The reconstruction result obtained using (**a**) the proposed method and (**b**) the diffuse reflection theoretical model.  
Despite these advancements, our method still faces challenges. Under intense sunlight, which can lead to strong specular reflections, the reconstructed results may exhibit noticeable collapse and distortion in the affected regions, thereby negatively affecting the overall visual quality. In addition, the degree of polarization of the target can influence the reconstruction accuracy. Given long-distance imaging conditions outdoors, the polarization degree of the reflected light received by the detector is often attenuated. Furthermore, the polarization degree of seawater is often higher than that of the target, and their refractive indices are also different. If the refractive index of the target is set while ignoring the seawater, failure to properly extract the seawater prior to reconstruction can lead to the target being \"submerged\" by the seawater. In future work, we plan to address these challenges by developing techniques to improve target extraction, especially in high-polarization environments. Additionally, we aim to optimize the algorithm to reduce the runtime, enhancing the overall efficiency of the reconstruction process.  
Conceptualization, Z.Z. and S.Z.; methodology, S.Z.; software, S.Z.; validation, Z.Z. and R.M.; formal analysis, S.Z.; investigation, R.M.; resources, R.M.; data curation, S.Z. and Z.W.; writing--original draft preparation, S.Z.; writing--review and editing, Z.Z., Q.J. and S.Z.; visualization, Z.W. and R.M.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
## 3 Controller Design
### Speed Control  
For speed control, including speed control and yaw rate control in the next section, a PI control algorithm based on state feedback is used. Consider the following decoupled model of a marine craft in surge motion:  
\[(m-X_{\rm\hat{u}})\ \dot{u}+X_{u}\ u+X_{u|u|}\ u\ |\ u\ |=T_{x} \tag{26}\]  
The commanded acceleration adopts PI control with acceleration feedforward, and its formula is as follows:  
\[a_{\rm cu}=\dot{u}_{\rm d}-K_{\rm pu}(u-u_{\rm d})-K_{\rm iu}\int_{0}^{t}(u-u_{ \rm d}){\rm d}\tau \tag{27}\]  
Assuming that the commanded thrust is equal to the actual thrust, i.e., \(T_{\rm xc}=T_{x}\), from Equations (26) and (27), and making the state feedback term \(n(u)=X_{u}u+X_{u|u|}u|u|\), the following can be obtained:  
\[T_{\rm xc}-n(u)=(m-X_{\rm\hat{u}})a_{\rm cu} \tag{28}\]  
Therefore, the commanded thrust in surge motion is  
\[T_{\rm xc}=(m-X_{\rm\hat{u}})[\dot{u}_{\rm d}-K_{\rm pu}(u-u_{\rm d})-K_{\rm i u }\int_{0}^{t}(u-u_{\rm d}){\rm d}\tau]+n(u) \tag{29}\]  
Figure 3: Schematic diagram of motion control system.  
According to the pole placement method, the parameters of the PI controller can be obtained as follows:  
\[K_{\rm pu}=2\lambda_{u} \tag{30}\]  
\[K_{\rm iu}=\lambda_{u}^{2} \tag{31}\]  
At this point, the equilibrium point \(u-u_{\rm d}=0\) is globally exponentially stable [15], where \(\lambda_{u}=\zeta\omega_{\rm n}\) is the designed parameter. The damping ratio \(\zeta\) (taking \(\zeta=1\)) and natural frequency \(\omega_{\rm r}\) of the reference signal should be determined based on expected performance indicators (such as overshoot \(\sigma\%\) and adjustment time \(t_{\rm s}\)). Then, \(\omega_{\rm n}\) should be determined according to the principle that the natural frequency \(\omega_{\rm n}\) of the closed-loop control system is greater than \(\omega_{\rm r}\) to complete the design of the control parameters.  
The surge velocity reference model adopts a second-order oscillation element, and it can be expressed as  
\[\frac{u_{\rm d}}{u_{\rm c}}=\frac{\omega_{\rm nu}^{2}}{s^{2}+2\zeta_{u}\omega_{ \rm nu}+\omega_{\rm nu}^{2}} \tag{32}\]  
where \(u_{\rm d}\) is the reference surge velocity, \(\zeta_{u}\) is the damping ratio, and \(\omega_{\rm nu}\) is the natural frequency.
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 3 Single-Phase Magnetic Photocatalysts
### M-Type Hexaferrites  
Concerning the photocatalytic activity of single-phase M-type hexaferrites, their efficiency is often much lower than that of known photocatalytic materials, but they can absorb a broad spectrum of visible light, and thus their vis activity is of some interest, as summarized in Table 2. For example, [PERSON] et al. studied the photocatalytic properties of pure and Nd/Cu co-doped BaFe\({}_{12}\)O\({}_{19}\) under solar light in Pakistan, using methylene green dye as a probe [78]. Both types of ferrites could remove the target compound, with a final efficiency of 37% and 93% for pristine and doped materials, respectively, after 60 min of  
Figure 4: Bandgap energies of spinel ferrites (NHE, pH = 0 and 14). Reprinted with permission from [72]. Copyright (2021) Elsevier.  
irradiation. It has been proposed that the increased activity of a doped structure is caused by the possible formation of oxygen vacancies (due to the dopants' presence), which results in hindering of electron-hole recombination. Moreover, only a slight decrease in the efficiency from 93% to 91% was observed during five cycles, indicating high reusability of the Nd/Cu co-doped photocatalyst. Interestingly, [PERSON] et al. showed that pristine BaFe\({}_{12}\)O\({}_{19}\), prepared via the molten salt method, exhibits even higher photocatalytic activity than the well-known P25 TiO\({}_{2}\) (famous due to exceptionally high activity for various reactions and thus used as a standard for activity checking [79; 80]) under both UV and UV/vis irradiation for degradation of hexahydro-1,3,5-trinitro-1,3,5-triazine (hexogen, RDX) [81]. The highest activity of the ferrite, observed under neutral pH conditions, was explained by efficient light absorption (bandgap of 2.1 eV) and thus an increased number of generated charge carriers. The high reusability of the photocatalyst was confirmed during three cycles, with the efficiency change from 100% to 98%. [PERSON] et al. investigated the SrFe\({}_{12}\)O\({}_{19}\) structure for photocatalytic degradation of methylene blue (MB) under UV irradiation [82]. It was found that 46% of dye could be removed during 120 min of irradiation, but reusability was not determined. Finally, the unique structure of the CdFe\({}_{12}\)O\({}_{19}\) photocatalyst, both in its non-modified form (with some Fe\({}_{2}\)O\({}_{3}\) impurities) and as a composite with CdTiO\({}_{3}\) phase, was investigated by [PERSON] et al. for removal of MB, methyl orange (MO), and methyl violet (MV) dyes under UV irradiation (without recycling analysis) [83]. It was found that the composite (CdFe\({}_{12}\)O\({}_{19}\)/CdTiO\({}_{3}\)) is more active than a one-component photocatalyst for all the studied dyes, reaching the highest activity for MO--71% degradation during 70 min of irradiation.
## 2 Materials and Methods
### Electrical Characterization  
All the electric measurements were conducted with a semiconductor parameter analyzer (Agilent, Santa Clara, CA, USA, B1500A). Scanning photocurrent microscopy (SPCM)analysis system combines the electrical characterization system (Agilent B1500A) and the Raman system (Renishaw in Via, Gloucestershire, UK) using a motorized XYZ stage. The semiconductor diode laser (633 nm, Renishaw in Via, Gloucestershire, UK) was focused onto the device's channel region utilizing a long working distance objective lens with a 50\(\times\) magnification (NA = 0.5). A mechanical chopper (BET-ES from Beijing Nbet Technology Co., Ltd., Beijing, China) and an oscilloscope (Agilent 86100A) were employed to analyze the time-dependent photoresponse. The hysteretic behavior of the device transfer curve in the air environment can be effectively removed by alternating current pulse scanning (Alternating Polarity, AP) by changing the polarity of the gate voltage, which was applied to transistors of various channel materials [29; 30]. Short gate voltage (V\({}_{\rm G}\)) pulses were applied to the device to obtain the transfer characteristics with and without illumination.
# Forensic Analysis on Damage of a Cable-Stayed Bridge without Backstays Using BIM Information
## 6 Conclusions  
This paper uses the BIM model and FE analysis method to conduct the damage forensic analysis of a cable-stayed bridge without backstays. The BIM model is established based on the recorded design and construction information. Then, the FE model for different construction stages is generated based on the BIM model. After that, the causes for the severe cracking in the pylon are carefully analyzed and revealed. The major conclusions are as follows:  
1. A large difference in concrete age and shrinkage exists in the pylon due to the unreasonable process of concrete casting in construction, which results in longitudinal and vertical cracks in the pylon walls between Piers 108 and 109.
2. The Poisson effect and the component of the cable force lead to incline cracks in the anchorage zone of C10-C18 cables. Moreover, the unsuitable positions of the anchorage of the prestressed tendons in the pylon exacerbate the development of cracks in the region.
3. The combination of temperature load and shrinkage results in short vertical and longitudinal cracks in the pylon near Pier 110. The measured force of stayed cables is less than the corresponding design force, which results in a negative bending moment at the bottom of the pylon, exacerbating the cracking.
4. The cause of cracks in the bridge is closely related to the shrinkage effect of the mass concrete bridge pylon cast in stages. It is necessary to carry out the construction process simulation and crack resistance analysis during the design.
5. The combined analyses using the BIM and FE methods agree well with the field inspection and provide a better way to reveal and understand the causes and distribution of cracks.  
Conceptualization, Q.X., X.S. and H.M.; methodology, Q.X., X.S. and Z.X.; investigation, Q.X. and Z.X.; writing--original draft preparation, Q.X. and H.M.; writing--review and editing, Z.X. and X.S.; supervision, X.S. and H.M.; funding acquisition, X.S. and H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Science and Technology of Guangdong Province (Grant No. 2021B1111610002), the Fundamental Research Funds for the Central Universities (Grant No. 2023-2-YB-17) and Guangdong Transportation Group Science and Technology Project (Grant No. JT2023 YB41), which is gratefully acknowledged.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Thanks are extended to anonymous reviewers whose suggestions improved this manuscript.  
The authors declare no conflicts of interest.