esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experimental Results in Urban Canyon 3  
#### 3.4.1 Results of GNSS-RTK Availability Assessment  
To perform extensive validation of the proposed method, the experiment is implemented on the urban-3 with a much longer length. The ATE of the u-blox measurement and the correspondent mean elevation angle mask of the surrounding local map is depicted in Figure 10a. The mean elevation angle masks and the ATE of the u-blox solutions are roughly correlated. When the ATE of the u-blox solution becomes large, the corresponding mean elevation angle mask would become large following and vice versa. In detail, at the 1 place seen in Figure 10b1, the overpasses over the vehicle result in poor GNSS solutions with large ATE. More than half of the corresponding sky mask of the surrounding point cloud reaches up to 60\({}^{\circ}\) calculated as the Equation (3) shown in Figure 10c1. Then the 2 place, displayed in Figure 10b2, is relatively open with low objects on both sides which leads to accurate GNSS solutions with small ATE. Conforming to the scene, most elevation angles depicted in the mask in Figure 10c2 are smaller than 30\({}^{\circ}\). Around the 3 place, which is near an open crossing, the ATE of the u-blox solutions is small due to the unblocked transmission of the signal as shown in the top panel of Figure 10a. However, the mean elevation angle mask calculated from the point cloud is particularly large as shown in the bottom panel of Figure 10a. The false evaluation is caused by the trees over the vehicle and the tall double-decker bus around detected by the LiDAR presented in Figure 10b3. Most elevation angles of the surrounding points are larger than 60 degrees as plotted in the sky mask in Figure 10c3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(el_{th}\) (\({}^{\circ}\))} & **ATE (m)** \\ \cline{3-4}  & & & **RMSE** \\ \hline \multirow{4}{*}{**Urban-2**} & Alone GNSS RTK & - & 15.75 \\ \cline{2-4}  & LIO & - & 1.80 \\ \cline{2-4}  & LIO-SAM & - & 13.64 \\ \cline{2-4}  & & 90 & 8.58 \\ \cline{2-4}  & Adaptive Integrated GNSS-RTK/LIO & 35 & 8.65 \\ \cline{2-4}  & & 20 & 4.86 \\ \cline{2-4}  & & **15** & **1.74** \\ \hline \hline \end{tabular}
\end{table}
Table 4: ATE of the trajectory estimated by different pipelines on urban-2.  
Figure 10: **(a)**. The top panel shows the ATE of the u-blox RTK solutions produced by RTKLIB [7] collected in urban-3. The bottom panel shows the mean elevation angle mask of the local point cloud map around the correspondent u-blox epoch. The mean elevation angle mask is defined as that of a fixed-size local map with 50 m (Euclidean distance) both forward and backward the rover. Thus u-blox epochs within 50 m from the start and the end have no corresponding mean elevation angle masks. 1��❝1, 2�2, and 3� denote representative cases. (b1–b3) are streetscapes of the three places denoted in (**a**) respectively. (c1–c3) are the sky masks of the three places denoted in (**a**) respectively.
###### Abstract  
The rapid development of sixth-generation mobile communication systems has brought about significant advancements in both Quality of Service (QoS) and Quality of Experience (QoE) for users, largely due to the extremely high data rates and a diverse range of service offerings. However, these advancements have also introduced challenges, especially concerning the growing demand for a wireless spectrum and the limited availability of resources. Various efforts have been made and research has attempted to tackle this issue such as the use of Cognitive Radio Networks (CRNs), which allows opportunistic spectrum access and intelligent resource management. This work demonstrate a new method in the optimization of allocation resource in CRNs based on the Snake Optimizer (SO) along with reinforcement learning (RL), which is an effective meta-heuristic algorithm that simulates snake cloning behavior. SO is tested over three different scenarios with varying numbers of secondary users (SUs), primary users (PUs), and frequency bands available. The obtained results reveal that the proposed approach is able to largely satisfy the aforementioned requirements and ensures high spectrum utilization efficiency and low collision rates, which eventually lead to the maximum possible spectral capacity. The study also demonstrates that SO is versatile and resilient and thus indicates its capability of serving as an effective method for augmenting resource management in next-generation wireless communication systems.  
Cognitive Radio Networks; Snake Optimizer; spectrum utilization; meta-heuristic algorithm; resource allocation; reinforcement learning; wireless communication
# Security Enhancement for Data Migration in the Cloud
## 2 Background Preliminaries
### Cryptographic Algorithms
#### 2.1.1 Symmetric-Key Algorithm Also Known as Secret Key Encryption  
A single key is used for both encryption and decryption. The key, in practice, represents a shared secret between two or more communicating parties for secure communication. A few of the well-known symmetric encryption algorithms include Data Encryption Standard (DES), AES, Blowfish and Skipjack.
## 4 Discussion
Although longer term kinetics of LON accumulation (>24 h) was not studied, it can be assumed that such LONs will require longer times for their endosomal escape, similarly to cholesterol-conjugated siRNAs [57]. Cytoplasmic distribution of the developed LONs may be advantageous as far as the targets of therapeutic oligonucleotides (antisense oligonucleotides, siRNAs and anti-miRNAs) are localized in the cytosol such as mRNAs and many miRNAs. Application of the developed LONs to modulate splicing of pre-mRNA requires additional investigation including optimization of the number and length of the side-chains, selection of optimal delivery conditions etc. to achieve nuclear delivery of the conjugates. We demonstrated that dodecylphenyl and hexadecyl modifications did not significantly impede gene-silencing activity of lipophilic siRNAs delivered into cells by Lipofectamine (Figure 8). However, further studies on elucidating a wider spectrum of biological activities of siRNAs and anti-sense oligonucleotides carrying such (sulfonyl)phosphoramide-based lipophilic groups are required. One potential application for lipid-oligonucleotide conjugates as described herein could be the design of therapeutic oligonucleotides for the treatment of multiple sclerosis (MS), a severe neurodegenerative disease characterized by the destruction of the myelin sheaths of neurons [58]. [PERSON] et al. [29] uncovered a link between the destruction of axonal myelin sheaths during MS and repulsive guidance molecule a (RGMa): a glycoprotein, which is a powerful inhibitor of neuronal regeneration. Inhibition of RGMa by human monoclonal antibodies stimulate re-myelination and regeneration of damaged neurons in the laboratory in vivo model of MS [36]. Thus, reducing the level of RGMa by downregulation of the corresponding gene by antisense oligonucleotide or siRNA could be a promising strategy for therapeutic intervention in MS.  
We studied RGMa inhibition in the RGMa-expressing PK-59 pancreatic cancer cell line using lipid-siRNA conjugates containing either two or four dodecylphenyl or hexadecyl chains at the 3'-end of one or both of the complementary RNA strands, respectively (Figure 1B; Table 1). It was evident that siRNA conjugates containing two adjacent lipid groups in the passenger strand were able to downregulate RGMa mRNA with a similar efficiency to that of the unmodified control RGMa siRNA at 100 nM concentration while those carrying four of either dodecylphenyl or hexadecyl groups were significantly inferior (Figure 8). We foresee that such therapeutic lipid-oligonucleotide conjugates (LONs) displaying low toxicity and improved cellular uptake may demonstrate high efficiency and minimal side effects in future experiments in vivo.
## 2 Materials and Methodology
### Sample Collection  
The GCF sample was taken using Periopaper (Oraflow, New York, NY, USA). Three strips were taken per site at the right upper first molar mesial zone for each sampling time (basal, 24 h, 48 h, and 5 days).  
The participants were requested not to brush their teeth before sample collection to prevent bleeding and sample contamination. Excess saliva, food, or dental plaque was removed using sterile gauze. The area was isolated with cotton rolls to avoid saliva contamination and the GCF was collected by inserting the Periopaper 1 mm into the gingival cricular sulcus for 30 s.  
A basal GCF sample was taken before placement of the elastic separator (ortho technology), and then, at 24 h (T1), 48 h (T2), and 5 days (T3), GCF samples were taken again. The GCF samples were stored in Eppendorf tubes at \(-80\)\({}^{\circ}\)C.  
To determine the amount of GCF obtained, a standard curve was created in duplicate where the weight in mg of the Periopaper strips was recorded by placing different amounts of distilled water. It is worth mentioning that Periopaper strips can absorb up to 2 \(\mu\)L.  
The standard curve consisted of measurements in mg of wet Periopaper strips with 0.5 \(\mu\)L, 1 \(\mu\)L, 1.5 \(\mu\)L, and 2 \(\mu\)L of distilled water and a dry Periopaper that corresponds to a value of 0 \(\mu\)L of distilled water.  
Subsequently, the r value was analyzed to confirm the quality of the curve, ensuring it was greater than r = 0.98. Subsequently, the GCF sample collected in each Periopaper strip was weighed with an analytical balance. Then, the Periopapers' weight was correlated with a standard curve to find the \(\mu\)L of GCF collected at every sample time. It is important to know that the standard curve was created every day when a GCF sample was taken.  
Since three Periopaper strips were obtained in each sampling, to determine the amount of GCF obtained, the amount of gingival cricular fluid collected from the three Periopapers strip was recorded.
# Improved Subsidence Assessment for More Reliable Excavation Activity in Tehran
## 2 Numerical Modeling
### Creating the Model and Generating the Mesh  
The two-dimensional pattern of the tunnel designed for the project is in Figure 3. According to the pattern, the height of the tunnel was 8.335 m and its width was 8.593 m. Also, the thickness of concrete applied for this tunnel was 500 mm, and the thickness of sprayed concrete (shotcrete) was 310 mm [24].  
Figure 3: The two-dimensional pattern designed in the project (I, II, shows the sequence of excavation).  
After the creation of the geometry of the model, a finite element model composed of six-node triangles can automatically be generated, based on the composition of clusters and lines in the geometry model [23]. Due to the fact that the geometry was symmetrical, only one-half of the model was created. Symmetry conditions were also adopted in the center plane (Figure 4). The Mohr-Coulomb model was accomplished because of the soil condition, and the region. Moreover, it was assumed that the soil's behavior was semi-continuous; hence, the Mohr-Coulomb model was considered for this region.
## 2 Theory of the Inverse Analysis of Structural Damage  
### The Model-Based Inverse Method for Structural Damage Identification  
Inverse problems in structural mechanics are well-known frameworks for structural damage identification. The underlying assumption can be described as being when damage occurs in a structure and its features are output responses. This can be represented as \(f_{output}=(f_{1},f_{2},\ldots,f_{m})\), in which response functions are selected for particular purposes and an optimization problem is formulated; where \(m\) is the number of possible extracted features form the structure. The optimization problem should be developed with corresponding features to reflect the changes in the structure in comparison with the original intact structure. The updated damage parameters related to \(n\) elements \(\alpha=(\alpha_{1},\alpha_{2},\ldots,\ \alpha_{n})\), which represent the damage occurred in the structural elements, are the parameters of the function of the input \(f_{output}=(\alpha_{1},\alpha_{2},\ldots,\ \alpha_{n})\). The target of the optimization problem is to confirm whether the parameter vector \(\alpha\) matches the current situation of the structure or not. So, when we update the model, the final outcomes obtained from finite element analysis are consistent with the known real \(f_{output}\) of the damaged structure. Tracking the values of \(\alpha\) requires an appropriate objective function and that its characteristics are linked to the updating parameters \(\alpha=(\alpha_{1},\alpha_{2},\ldots,\ \alpha_{n})\). Hence, the objective function is a vital factor for tracking the correct vector \(\alpha=(\alpha_{1},\alpha_{2},\ldots,\ \alpha_{n})\). As was observed in the literature review section, several features have been developed in the field of structural damage identification, and it is impossible to choose a \"one fits all\" objective function. Moreover, the inverse problem is a multi-stage mathematical problem in which the link between \(\alpha\) and \(f_{output}\) is indirect and contains a multiplication of large vectors and matrices, which makes the exact physical description difficult to comprehend. Additionally, the use of stochastics algorithms to solve the inverse problem makes the overall process a black box. In the next section, this work will introduce a novel combined objective function, which is designed to incorporate three modal features that are able to extract the most possible damage information from the structure.
## 6 Conclusions  
This study established an innovative monitoring framework integrating 3S techniques, addressing the application of 3S techniques in the comprehensive monitoring of SDAs during the construction of HSRs. This innovative approach effectively mitigates the limitations of traditional manual monitoring, which often suffers from insufficient observation accuracy, periodicity, and efficiency. The proposed framework holds considerable promise for advancing circular economy principles by enabling the accurate monitoring of dynamic changes within SDAs. This facilitates optimized resource management, minimizes the environmental impact of waste, and promotes the efficient reutilization of construction materials.  
Specifically, GF-1 satellite data, combined with unsupervised classification, expert manual verification, and spatial analysis, accurately identified the disturbance boundaries of SDAs. UAV data, coupled with visual interpretation and spatial overlay analysis, facilitated the rapid and precise extraction of water conservation measures and assessment of their maintenance status, overcoming the limitations of traditional satellite RS imagery's low resolution and slow update rates. Using Sentinel-1 satellite data, the D-InSAR method efficiently estimated dynamic changes in SDA's elevation and volume of spoil. Despite a 15.5% error in volume changes estimation of spoil, the method demonstrates significant potential for future applications.  
Challenges remain, particularly spatial data inaccuracies and resolution limitations affecting monitoring accuracy. Future research should focus on enhancing data resolution and leveraging advanced analytical techniques, such as machine learning, to improve processing efficiency and automation for better imagery classification and water conservation measure extraction. Additionally, improving D-InSAR monitoring accuracy is crucial. With technological advancements, 3S techniques are expected to be more extensively utilized in comprehensive SDA monitoring, aiming for superior data integration and optimized D-InSAR monitoring across various scenarios. These efforts will significantly contribute to the sustainable development of HSR projects.  
Conceptualization, X.H. and B.X.; methodology, X.H. and Y.G.; data curation, Y.Y.; writing--original draft preparation, X.H. and Y.G.; writing--review and editing, B.X. and H.C.; supervision, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 72171237.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Experimental Data and Estimation Algorithms
### Interacting Multiple Model (IMM)  
Most real-world systems can be defined by a set of operating modes. The IMM provides a solution for systems that have a multitude of operating modes. It makes use of several models and calculates the likelihood values for each model based on state estimates and error covariance [19], after which the likelihood values are processed to select the operating mode. The IMM algorithm is summarized below [19]:  
\((i)\)_Calculation of the Mixing Probabilities_ :  
\[\mu_{ij|k|k}=\frac{1}{\bar{c}_{j}}\ p_{ij}\mu_{i,k} \tag{20}\]  
\[\bar{c}_{j}=\sum_{i=1}^{r}p_{ij}\mu_{i,k} \tag{21}\]  
\((ii)\)_Mixing Stage_  
\[\hat{x}_{0|j,k|k}=\sum_{i=1}^{r}\hat{x}_{i,k|k}\mu_{ij|k|k} \tag{22}\]  
\[P_{0|j,k|k}=\sum_{i=1}^{r}\mu_{ij|k}\bigg{\{}P_{i,k|k}+\Big{(}\hat{x}_{i,k|k}- \hat{x}_{0j,\ k|k}\Big{)}\Big{(}\hat{x}_{i,k|k}-\hat{x}_{0j,\ k|k}\Big{)}^{T} \bigg{\}} \tag{23}\]\((iii)\) _Mode - Matched Filtering_ :  
\[\Lambda_{j,k+1}=\mathcal{N}\Big{(}z_{k+1};\hat{z}_{j,k+1|k},\ S_{j,k+1}\Big{)} \tag{24}\]  
\[\Lambda_{j,k+1}=\frac{1}{\sqrt{\Big{|}2\pi S_{j,k+1}\Big{|}_{Abs}}}\exp\left( \frac{-\frac{1}{2}\ \varepsilon_{j,z,k+1}^{T}\varepsilon_{j,z,k+1|k}}{S_{j,k+1}}\right) \tag{25}\]  
\((iv)\) _Mode Probability Update_ :  
\[\mu_{j,k}=\frac{1}{c}\Lambda_{j,k+1}\sum_{i=1}^{r}p_{ij}\mu_{i,k} \tag{26}\]  
\[c=\sum_{j=1}^{r}\Lambda_{j,k+1}\sum_{i=1}^{r}p_{ij}\mu_{i,k} \tag{27}\]  
\((v)\) _State Estimate and Covariance Combination_ :  
\[\hat{x}_{k+1|k+1}=\sum_{j=1}^{r}\mu_{j,k+1}\hat{x}_{j,k+1|k+1} \tag{28}\]  
\[P_{k+1|k+1}=\sum_{j=1}^{r}\mu_{j,k+1}\bigg{\{}P_{j,k+1|k+1}+\Big{(}\hat{x}_{j,k+1|k+1}-\hat{x}_{k+1|k+1}\Big{)}\Big{(}\hat{x}_{j,k+1|k+1}-\hat{x}_{k+1|k+1} \Big{)}^{T}\bigg{\}} \tag{29}\]
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 3 Results
### Biconical Antenna
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Code** & **Frequency (GHz)** & **RD Impedance (\%)** & **RD Memory (\%)** & **RD Time (\%)** \\ \hline \multirow{3}{*}{Wire-Grid} & 1 & 0.82 & \({}^{*}-1738\) & 10.64 \\  & 1.5 & \(-3.55\) & \(-600\) & 68.20 \\  & 2 & \(-0.67\) & \(-390\) & 79.30 \\ \hline \multirow{3}{*}{Triangle-Grid} & 1 & \(-1.07\) & \(-50\) & 96.06 \\  & 1.5 & \(-5.94\) & 0 & 83.23 \\ \cline{1-1}  & 2 & \(-6.96\) & \(-80\) & 54.64 \\ \hline \hline \end{tabular} \({}^{*}(-)\) indicates by what percent the value is bigger than the reference one (here EMPro).  
\end{table} 
Table 9: The percentage of the relative difference between the considered input impedance values of the planar bow-tie antenna in Table 8 and EMPro. field intensity was calculated in the \(E\) plane. The obtained antenna RPs were compared at the frequencies of 0.1, 0.5, and 1 GHz in Figures 27-29, respectively. As can be observed, the results for both codes illustrated a good agreement with those obtained analytically by [PERSON] [348] and [PERSON] [349]. Figure 26: Isometric view of the biconical antenna (**a**), and its constructed gridin Triangle-Grid (**b**), and in Wire-Grid (the red wire is the excitation source) (**c**). Figure 27: RPs of the biconical antenna at a frequency of 0.1 GHz in the \(E\) plane: Triangle-grid (- - -), Wire-grid (- - -), digitalized from [PERSON] 1998 (- -), analytically by [PERSON] 1951 (- -) and [PERSON] 1998 (- -). Figure 28: RPs of the biconical antenna at a frequency of 0.5 GHz in the \(E\) plane: Triangle-grid (- - -), Wire-grid (- - -), digitalized from [PERSON] 1998 (- -), analytically by [PERSON] 1951 (- -) and [PERSON] 1998 (- -). Figure 29: RPs of the biconical antenna at a frequency of 1 GHz in the \(E\) plane: Triangle-grid (- - -), Wire-grid (- - -), digitalized from [PERSON] 1998 (- -), analytically by [PERSON] 1951 (- -) and [PERSON] 1998 (- -). Table 10 summarizes the antenna input impedance calculated values, the required memory, the simulation time spent, as well as \(N\) and the cond of the SLAE matrix \(\mathbf{Z}\). Table 11 contains the percentage of the relative difference between the input impedance absolute values from Table 10 using the Wire-Grid and Triangle-Grid codes and those obtained analytically. It can be seen that Wire-Grid gives more accurate impedance results than Triangle-Grid with less computational costs. Even though, Triangle-Grid results are also acceptable with a maximum difference of 34%. Despite that, these results validate the accuracy of the codes used.
# Occurrence of Zeralenone and Enniatin B in Swiss Wheat Grains and Wheat Flours
## 4 Discussion  
The results of this study show that except for one sample, the ZEA content of the analyzed wheat grains and flours did not exceed the legal concentration of 100 ug/kg for unprocessed cereals other than maize [20], except for one sample of wheat grains (G3). However, with up to 938 ug ENB per kilogram found in wheat grains and up to 200 ug/kg found in wheat flours, the chronic exposure to the mycotoxin ENB via the consumption of wheat flour in our daily life cannot be excluded.  
As underlined by [PERSON] et al. [21], missing regulations for emerging mycotoxins drive gaps in routine analysis; the emerging mycotoxins such as ENB are neither routinely determined nor monitored and thus can go undetected. In comparison, ZEA, the levels of which are regulated, was only found in half of the samples, in levels below the current regulation for unprocessed cereals, except for one sample. Therefore, our study intends to fill the knowledge gap on emerging mycotoxins, providing information on ENB contamination of wheat grains and commercial flours found in Switzerland.  
Temperature and precipitation play a critical role in determining the mycotoxin concentration levels during the growth phase according to [PERSON] et al. (2017) in their review [11]. Global warming is bringing major changes in the weather, such as increases in temperatures and CO\({}_{2}\) levels, which are predicted to increase the risk of mycotoxin contamination of all crops [22; 23]. Combined with the global political and economic context of 2022, where cereals shortages in Europe became a reality [24], the results of this study also show a need for decontamination solutions for mycotoxins, especially when 5-10% of the world's food is wasted because of fungal spoilage [25].  
It has been shown that the contamination by microorganisms of wheat grains is located close to the surface, and that removing the outer layers of the grains (pericarp) could lead to a substantial reduction of the microbial contamination [3]. However, during this process, a large amount of nutritionally valuable components is lost, for example dietary fiber, ferulic acid, arabinoxylans, vitamin B, and phenolic compounds contained in the outer layers of the wheat grains. Moreover, other studies show that the reduction of mycotoxin contamination by applying milling technologies is toxin-dependent [26]. As shown in our study, the emerging mycotoxin ENB can also be found in several micrograms per kilogram in commercial wheat flours, the whole grain flour not being significantly more contaminated that the type 405 white wheat flour, indicating that although the _Fusarium_ contamination stays at the surface layer, the ENB contamination migrates toward the inner layers of the wheat grain.  
For all those reasons, mycotoxin decontamination strategies are needed in order to improve both food safety and food quality and are currently under investigation. The objective would be to reintroduce safe whole grains that are rich in nutritionally valuable compounds into the food value chain.