esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This article proposes a Transfer OAM mode classifier for detecting distorted LG beams. By introducing transfer learning to find the connections between existing and new features, the network can quickly learn new knowledge, avoiding the process of randomly initializing weights during training from scratch and achieving high-precision detection of 15 OAM modes. Compared to traditional convolutional neural networks, the Transfer OAM mode classifier proposed in this paper not only reduces the dataset size to 1/10 of its original size but also successfully improves the OAM detection accuracy by 15.84%. We first studied the amount of data required for the model. Secondly, the model accuracy was analyzed under different turbulence intensities and transmission distances; Finally, the generalization ability of the model on training sets with different AT intensities was explored. In addition, the model obtained on the mixed training set can achieve an average detection accuracy of 97.36% for any AT intensity with turbulence intensity ranging from \(C_{n}^{2}=1\times 10^{-16}\) m\({}^{-2/3}\) to \(C_{n}^{2}=3\times 10^{-14}\) m\({}^{-2/3}\). This model exhibits good robustness under unknown atmospheric turbulence strengths, providing a new approach for the identification and detection of vortex optical OAM modes.  
Conceptualization, C.K. and Y.C.; Data curation, C.K. and Y.C.; Formal analysis, X.K.; Funding acquisition, C.K. and X.K.; Investigation, C.K.; Methodology, C.K. and Y.C.; Project administration, Y.C.; Resources, Y.C.; Software, Y.C.; Supervision, X.K.; Validation, C.K., Y.C. and X.K.; Writing--original draft, Y.C.; Writing--review and editing, C.K., Y.C. and X.K. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China [No.61377080]; Special Research Program of Shaanxi Provincial Department of Education [22]K0530]; Xi'an Science and Technology Plan Project [No.23 KGDW0018-2023]; Natural Science Basic Research Program of Shaanxi [Program No.2024 JC-YBMS-557, 2024 JC-YBMS-562]; Shaanxi Fundamental Science Research Project for Mathematics and Physics [Grant No.23 JSQ024]; Key Industrial Innovation Projects in Shaanxi Province [No.2017 ZDCXL-GY-06-01]; the Xi'an Science and Technology Plan [22 GXFW0115]; the Scientific Research Team of Xi'an University [D202309].  
Not applicable.  
Figure 9: Detection accuracy of Transfer OAM pattern classifier on specific and mixed datasets.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
###### Abstract  
One of the three main use cases of the fifth generation of mobile networks (5G) is massive machine-type communications (mMTC). The latter refers to the highly synchronized accesses to the cellular base stations from a great number of wireless devices, as a product of the automated exchange of small amounts of data. Clearly, an efficient mMTC is required to support the Internet-of-Things (IoT). Nevertheless, the method to change from idle to connected mode, known as the random access procedure (RAP), of 4G has been directly inherited by 5G, at least, until the first phase of standardization. Research has demonstrated the RAP is inefficient to support mMTC, hence, access control schemes are needed to obtain an adequate performance. In this paper, we compare the benefits of using different filtering methods to configure an access control scheme included in the 5G standards: the access class barring (ACB), according to the intensity of access requests. These filtering methods are a key component of our proposed ACB configuration scheme, which can lead to more than a three-fold increase in the probability of successfully completing the random access procedure under the most typical network configuration and mMTC scenario.  
accs class barring (ACB); adaptive algorithms; Internet-of-Things (IoT); massive machine-type communication (mMTC); recursive-least squares (RLS) algorithm Article
# Digestate Management and Processing Practices: A Review
## 5 Digestate Processing
### Treatments of the Solid Fraction  
After solid-liquid separation of the DG, each fraction can be treated to remove or extract nutrients. The solid fraction, which is partially stabilized after liquid fraction separation, can be used directly as a biofertilizer or soil conditioner. Since it still contains some biodegradable materials, microbial activity can still be active and odor emissions can also occur [98]. Therefore, a solid fraction can be chemically or thermally stabilized. Chemical stabilization is usually performed by adding an acid to decrease the pH and prevent the loss of ammonia during storage or land spreading, or by adding alkali to raise the pH, kill pathogens, and neutralize odors [91].
# Line of Sight and Image Motion Compensation for Step and Stare Imaging System
## 3 LOS and Motion Compensation Kinematics and Algorithm
### LOS Command Angle  
According to the working principle, LOS is realized by the gimbaled subsystem. To keep LOS stable, it is necessary to adjust the pointing of the inner and outer gimbals in real-time with the change of the attitude of the aircraft. Therefore, the LOS command angle refers to the pointing angle of the gimbal.  
In the geographical coordinate frame, the coordinates of the target and the aircraft are respectively \(\left(\rm x_{TIN},\rm y_{TIN},\rm z_{TIN}\right)\) and \(\left(\rm x_{bN},\rm y_{bN},\rm z_{bN}\right)\). L is defined as the photographic distance, and its expression is:  
\[\left|\sqrt{\left(\rm x_{TIN}-\rm x_{bN}\right)^{2}+\left(\rm y_{TIN}-\rm y_{ bN}\right)^{2}+\left(\rm z_{TIN}-\rm z_{bN}\right)^{2}}\right| \tag{10}\]  
Then the unit vector of the LOS pointing is:  
\[\left(\begin{array}{c}\rm u_{Nx}\\ \rm u_{Ny}\\ \rm u_{Nz}\end{array}\right)=\frac{1}{L}\left(\begin{array}{c}\rm x_{TIN}- \rm x_{bN}\\ \rm y_{TIN}-\rm y_{bN}\\ \rm z_{TIN}-\rm z_{bN}\end{array}\right) \tag{11}\]  
Converting the vector to that in the aircraft body coordinate is:  
\[\left(\begin{array}{c}\rm u_{bx}\\ \rm u_{by}\\ \rm u_{bz}\end{array}\right)=C_{N}^{b}\left(\begin{array}{c}\rm u_{Nx}\\ \rm u_{Ny}\\ \rm u_{Nz}\end{array}\right) \tag{12}\]  
Then the pointing angles of the inner gimbals\(\alpha\)and the outer gimbals\(\beta\)are:  
\[\alpha=\arctan\left(\frac{\rm u_{by}}{\rm u_{bz}}\right) \tag{13}\]  
\[\beta=-\arcsin(\rm u_{bx}) \tag{14}\]  
The functional block diagram and control sequence are shown in Figure 5.
## 6 Simulation Results and Discussion
### Scenario (1)  
#### 6.1.1 The 33-Bus Test System  
The first tested network was a 33-bus radial distribution system with 33 buses and 32 branches [25]. It operated at a base voltage of 12.66 kV, with an apparent base power of 10 MVA. Without compensation, precisely without the DSTATCOM installation in the RDS, the active power loss was 202.68 kW, costing 106,526.61 USD/year. The minimum voltage and VSI values were 0.91309 and 0.69511 p.u., respectively. Detailed system data can be found in the Appendix A.  
Table 3 presents the optimization results for three cases: the insertion of single, two, and three DSTATCOMs. The optimal locations and sizes of the DSTATCOMs were determined using the I-GWO algorithm. The table shows that active power losses decreased in all three cases. Additionally, the minimum voltage increased, and the voltage stability index improved. The results show that the case with two DSTATCOMs offered the highest TACS compared to the other two cases. This suggests that there is an optimal number of DSTATCOM installations for the modeled distribution system that maximizes cost savings. Installing either fewer or more DSTATCOMs than this optimal number results in a diminished return on investment.  
In the case of two DSTATCOMs, the proposed algorithm determined the optimal sizes to be 699.58 kVAr and 1386.89 kVAr. These were to be installed on buses 14 and 30, respectively. With this configuration, there was a total cost saving of USD 19,654.05 per year. Additionally, the total power losses were measured at 144.23 kW, equating to a significant  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Test System** & \multicolumn{3}{c}{**33-Bus**} & \multicolumn{3}{c}{**69-Bus**} & \multicolumn{3}{c}{**85-Bus**} \\ \hline Number of DSTATCOMs & Single & Two & Three & Single & Two & Three & Single & Two & Three \\ \hline Population Size & 100 & 200 & 400 & 200 & 500 & 800 & 400 & 800 & 1000 \\ Max Iterations of I-GWO & 500 & 1000 & 2000 & 500 & 1000 & 2000 & 500 & 1000 & 2000 \\ \(\overset{\rightarrow}{a}\) Range & [0, 2] & [0, 2] & [0, 2] & [0, 2] & [0, 2] & [0, 2] & [0, 2] & [0, 2] \\ Max Iteration of Load Flow & 50 & 50 & 50 & 50 & 50 & 50 & 50 & 50 & 50 \\ Tolerance of Load Flow & \(10^{-5}\) & \(10^{-5}\) & \(10^{-5}\) & \(10^{-5}\) & \(10^{-5}\) & \(10^{-5}\) & \(10^{-5}\) & \(10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters in the I-GWO method.  
reduction of 28.84% compared to the base case. It is also worth highlighting the improvement in the voltage profile of the test system, as illustrated in Figure 10. Specifically, the minimum voltage level was enhanced from 0.91309, as observed in the base case, to 0.95184 with the two DSTATCOs in place, while the maximum voltage level remained at 1.0 p.u. at the source bus.
# Dynamic Characteristics of Post-Cyclic Saturated Loess
###### Abstract  
Damage to soil structure caused by strong earthquakes is one of the main reasons for post-earthquake geohazard development. To investigate the nonlinear dynamic behaviors of the post-earthquake loess, a pre-shock reconsolidation test was designed to simulate the process of the loess undergoing earthquake and post-earthquake reconsolidation in a natural state. Furthermore, dynamic triaxial tests of the specimens before and after pre-shock action and consolidation stabilization with different over-consolidation ratios (OCR) were conducted to investigate the variety of the dynamic modulus and damping ratio of the saturated loess. The influence of pre-shock and reconsolidation on the dynamic behaviors was determined. Moreover, the mechanism of the changes after pre-shock and consolidation was discussed by combining the microstructure test results of soil samples before and after pre-shock and reconsolidation. The results suggest that the kinetic stiffness of the pre-shock saturated loess decreases significantly under the same consolidation conditions. The growth of the damping ratio-dynamic strain curve increases, and the deformation potential of the loess has a remarkable growth. With an increase in OCR, the dynamic elastic modulus after pre-shock increases continuously; however, the damping ratio decreases significantly. The dynamic stiffness increases and the deformation potential weakens significantly. The strong earthquake leads to the weakening of interparticle cementation, pore penetration, and structural reorganization in the local area, causes connecting of the macropores, and produces microfractures in the soil, which makes a significant decrease in the dynamic shear modulus ratio and an increase in the damping ratio of the loess, leading to the enhancement of soil dynamic nonlinearity and the attenuation of the dynamic strength. Moreover, the compaction effect of reconsolidation on the soil increases the interparticle friction and heals some microfractures, which leads to an increase in soil stiffness. This makes the maximum dynamic shear modulus and the maximum dynamic shear stress amplitude of the post-cyclic saturated loess perform at the same level compared with the natural loess without shock when the OCR equals three. However, the dynamic shear modulus and the damping ratio of the post-cyclic saturated loess are close to the natural loess when the OCR equals two.  
pre-shock; dynamic modulus; reconsolidation; damping ratio; post-seismic effect +
Footnote †: journal:
## 3 Derivation of a New Finite Element
### Calculation of Bending Moment Functions  
Two approaches can generally be applied to obtain the bending moment distribution functions, differing in the implemented method as well as in the degree of the resulting polynomials.  
The well-known mechanical differential relations (\(M_{z}(x)=EI_{z}\cdot v^{\prime\prime}(x)\)) between the transverse displacements, slopes, and bending moment functions (i.e., differential equation of the deflection curve, DEDC) are implemented in the formal mechanical approach. Since transverse displacement functions to the left and to the right differ for a cracked element, this consequently yields two bending moment functions inside a single cracked element. If the transverse displacement functions were ideal, i.e., accurate, this process would also lead to exact bending moment functions. However, since cubic polynomial functions are selected for the description of transverse displacements, therefore, the obtained bending moment functions are just approximations in the form of linear functions within the finite element. Consequently, the discrepancies appear in the nodes as well as within the finite element.  
In the second approach, the initially obtained bending moment and shear force nodal values from vector \(\{Q\}\) are interpolated to obtain polynomial functions. When implementing standard Hermite cubic interpolation polynomials of level 1 (H1), just first nodal derivatives of the bending moments are required. They are generally expressed as  
\[M_{z}{}^{\prime}(0)=V_{y1}+k_{G}\cdot\Phi_{1} \tag{32}\]  
and  
\[M_{z}{}^{\prime}(L)=V_{y2}+k_{G}\cdot\Phi_{2} \tag{33}\]  
for the first and the second node, respectively.  
However, the implementation of the Hermite interpolation polynomials of level 2 (H2, or quintic) is also possible where the nodal values of the bending moments second derivatives are additionally included in the interpolation:  
\[M_{z}{}^{\prime\prime}(0)=q_{L}-k\cdot Y_{1}+\frac{k_{G}}{EI_{z}}\cdot M_{z1} \tag{34}\]  
and  
\[M_{z}{}^{\prime\prime}(L)=q_{R}-k\cdot Y_{2}+\frac{k_{G}}{EI_{z}}\cdot M_{z2} \tag{35}\]  
for the first and the second node, respectively.  
In this way, not only a higher degree polynomial is being applied, but also all main mechanical parameters (\(EI_{z}\), \(k\) and \(k_{G}\)) are apparently included in the analysis. It should be further noted that by applying interpolation (either cubic H1 or quintic H2) of nodal values, a single function is obtained for the whole element where the directly evaluated nodal values of bending moments (\(M_{z1}\) and \(M_{z2}\)) and shear forces (\(V_{y1}\) and \(V_{y2}\)) from vector \(\{Q\}\) are preserved.
## 4 Discussion  
### GPPmax Dominates Annual GPP Trend  
The annual GPP of China's deciduous forest ecosystems increased significantly during 2001-2020 (11.97 gC/m\({}^{2}\)/yr), which can be attributed to climate change and human activities in China [13]. Previous studies also showed a rapid increase in the annual GPP in the areas covered by deciduous forests. For example, the annual GPP of deciduous forests in the Qin Mountains all showed the largest increasing rates (>10 gC/m\({}^{2}\)/yr) [14; 43; 44]. The increasing rate of the forest GPP in the Greater Khingan Mountains was usually below 5 gC/m\({}^{2}\)/yr, which was consistent with the estimation from the Global Land Surface Satellite (GLASS) GPP product [45]. Moreover, the annual GPP of China derived from Pemman-Monteith-Leuning Version 2 (PML-V2) and vegetation photosynthesis model (VPM) datasets increased by 8.99 g C/m\({}^{2}\)/yr and 9.68 g C/m\({}^{2}\)/yr, a little lower than the increasing rate of deciduous forest in this study [43; 46]. Though the GOSIF-GPP may have provided a biased estimate for China's deciduous forest, the standard deviation values in North China are relatively low [38].  
The changes in the annual GPP can be associated with the GSL and GPPmax, which could allow us to better understand the changes in GPP from vegetation phenology and physiology [47]. Our results show that the GPPmax trend correlated more with the variation in the GPP than the GSL trend in China's deciduous forest ecosystems. [PERSON] et al. (2016) pointed out that the number of high values in ecosystem fluxes (e.g., the GPP, NEP) were strongly correlated with their annual, and the effect of phenological dates showed less importance [48]. [PERSON] et al. (2019) also found that the GPPmax dominated the interannual variability in the global carbon sink, rather than the GSL [49]. Therefore, a slight fluctuance in the GPPmax would lead to substantial changes in the annual GPP [35]. Moreover, the extension in the GSL likely, but not necessarily, would lead to a larger annual GPP, especially in temperature-limited ecosystems with a single period of carbon uptake per growing season (e.g., deciduous forest ecosystems) [30]. However, in water-limited ecosystems,  
Figure 8: The temporal trends in the annual mean temperature (TEM) (**a**), annual sum precipitation (PRE) (**b**), and annual global radiation (RAD) (**c**) in natural forests and planted forests and different seasons during 2001–2020. Note: * refers \(p<\) 0.05.  
changes in the GSL may appreciably change the annual GPP, due to precipitation events [50]. This indicates that the limiting factors of vegetation photosynthesis should be considered when evaluating the effects of vegetation phenology and physiology on the GPP.
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 2 Background
### Phishing Techniques and Attack Vectors
#### 2.2.4 Zero-Font Phishing Attacks  
Zero-Font phishing is a sophisticated and increasingly recognized email-based attack that exploits font rendering to bypass security mechanisms. It involves inserting text styled with a font size of zero, rendering it invisible to recipients while still being parsed by email scanners. This technique allows attackers to obscure malicious content from users while manipulating filters to misclassify the message as legitimate (\"ham\") [3]. The deceptive design of such attacks makes them particularly effective for delivering malware or conducting scams.  
This tactic typically involves the use of HTML or CSS to embed invisible text. Attackers may style sections of text with zero font size to evade detection while still being flagged by email-processing systems. In addition, research has shown that malicious fonts can subtly alter the way content is displayed in a browser without modifying the underlying HTML or CSS [3, 13]. These strategies highlight how attackers exploit visual presentation to mislead users and circumvent detection.  
As reported by GBHackers [14], even advanced filters such as those in Microsoft Outlook can be fooled by Zero-Font techniques. Outlook's Natural Language Processing (NLP) engine scans for key phrases to flag phishing attempts. For example, it may look for \"(c) 2018 Microsoft Corporation. All rights reserved\" and verify the sender. However, by inserting invisible characters between words (e.g., \"2018 Microsoft Corporation\" with font size zero), attackers can alter the text enough to evade detection while preserving its visual appearance. This manipulation can trick the NLP engine into treating malicious content as legitimate as shown in Figures 1 and 2.  
These examples underscore the pressing need for more advanced filtering technologies and greater user awareness to counter Zero-Font phishing threats effectively.  
Another variation of this tactic is discussed by ISC SANS [15]. An example of this Zero-Font phishing technique is represented in Figure 3. Here, attackers attempt to make the email appear scanned and approved by inserting hidden text into the <span> HTML element (e.g., <span style=\"FONT-SIZE: 0 px\">) as shown in Figure 4. Although the font is set to zero, the content may still be visible in the email listing pane due to quirks in how Microsoft Outlook renders messages. This highlights another vulnerability that attackers can exploit. As threat actors continue to combine various evasion techniques in a single phishing campaign, it becomes imperative that both individuals and organizations remain informed and adopt layered security measures.  
Figure 1: What Microsoft filter sees in Zero-Font phishing attack.  
Figure 2: What the victim sees in a Zero-Font phishing attack.
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 4 Mathematical Model
### Numerical Method  
The in-house numerical code written in C++ and Python programming languages with the use of the SciPy numerical library [71] is utilized to calculate the results. The partial differential equations are discretized using the finite volume method. The discussion of the finite volume method is omitted here, since many excellent textbooks which describe the method in detail are available [72; 73]. The systems of linear algebraic equations, the result of the discretization procedure, are solved iteratively using the SciPy implementation of the bi-conjugate gradients stabilized method of [PERSON] [74] with the tolerance \(10^{-8}\).  
The developed FVM solver was tested using the method of manufactured solutions (MMS) [75]. The following benchmark equation has been used:  
\[\varphi_{\text{bench}}\,(x,y)=\left\{C_{1}+\dfrac{C_{2}}{2}\mathrm{ e}^{x}\,(x-1)\left[(x+1)\sin\left(L-x\right)+(x-1)\cos\left(L-x\right)\right]\right\}\\ +\left\{C_{3}+\dfrac{C_{4}}{2}\mathrm{e}^{y}\,(y-1)\left[(y+1) \sin\left(H-y\right)+(y-1)\cos\left(H-y\right)\right]\right\}. \tag{26}\]  
The values of the constants were assumed as \(C_{1}=C_{3}=10.0\), \(C_{2}=C_{4}=1.0\). It was found that the relative percent error of the numerical solution is always less than \(10^{-3}\,\%\) for the tests that mimic the simulated system. Based on the results of the MMS, the grid resolution was chosen as follows: in the \(x\)-direction, there are 80 control volumes (CVs) and, in the \(y\)-direction, there are 60 CVs for the cathode channel, 25 CVs for the cathode, five CVs for the electrolyte, 60 CVs for the anode and 30 CVs for the anode channel.The grid system is shown in Figure 9.  
The stop conditions were chosen as follows: for the species transport, the absolute change of the partial pressures between two subsequent iterations should be less than \(10^{-1}\,\mathrm{Pa}\); for the energy transport, the absolute change of the temperature between two subsequent iterations should be less than \(10^{-6}\,\mathrm{K}\) and, for the charge transport, the absolute change of the potential between two subsequent iterations should be less than \(10^{-5}\,\mathrm{V}\). In addition, the relative change of the values of the volumic current density, calculated using the Butler-Volmer model, should be less than \(10^{-4}\).  
The numerical computations made use of the Prometheus computing cluster, located at AGH University of Science and Technology, Krakow, Poland [76]. A single computational node of the cluster, HP ProLiant XL730f Gen 9 (Hewlett Packard Enterprise, Palo Alto, CA, USA), contains of two 2.5 GHz Intel Xeon E5-2680v3 processors (Santa Clara, CA, USA) with 24 cores in total and 128 GB of the RAM memory. The computations were performed on a single node and the time of the computation for a chosen operating voltage was approximately 20 h.