esa-sceva/satcom-chunk-collection huggingface dataset:
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Quality Measures  
GNSS data obtained from the in-service bridge contained a number of errors that were considered outliers. Through the application of the developed quality control algorithm, a number of DIA procedures were applied that affected the outliers; these procedures might have minimized coordinate errors in the final GNSS positioning solution. In this process, the validation of the algorithm and the effect of outlier removal can be explained by the quality measures described previously. This enables confirmation that we obtained an enhanced GNSS positioning solution.
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 3 False Information Detection Method
### Late Fusion  
Because the deep autoencoder changes the feature vector from high dimensional to low dimensional, it not only removes some irrelevant features but also loses some original information of text and images. Therefore, this paper does not use the joint features \(F_{co}\) extracted from the early fusion module to train classifier 2 but uses the joint features \(F_{final}\) enhanced by the original information to train it. The enhanced joint features \(F_{final}\) isobtained by concatenating the text features \(T_{i}\) and image feature \(V_{i}\) with the multimodal joint feature \(F_{co}\).  
\[F_{final}=T_{i}\oplus F_{CO}\oplus V_{i} \tag{16}\]  
\(\oplus\) is the concatenating operation, \(F_{final}=[T_{i},F_{co},V_{i}]\).  
The three classifiers, \(FC_{1}\), \(FC_{2}\) and \(FC_{3}\), are all fully connected layers with activation function \(softmax\). \(FC_{1}\) classifies the true and false information through text information, \(FC_{2}\) classifies the true and false information through the fusion information of text and images, and \(FC_{3}\) classifies the true and false information through image information. The text feature \(T_{i}\), the enhanced joint features \(F_{final}\) and the image feature \(V_{i}\) are projected to the binary target space to obtain the probability distributions \(P_{text}\), \(P_{co}\) and \(P_{image}\).  
\[P_{text}=softmax(W_{t}T_{i}+b_{t}) \tag{17}\]  
\[P_{co}=softmax(W_{c}F_{final}+b_{c}) \tag{18}\]  
\[P_{image}=softmax(W_{v}V_{i}+b_{v}) \tag{19}\]  
where \(W_{t}\), \(W_{c}\) and \(W_{v}\) represent weight parameters, and \(b_{t}\), \(b_{c}\) and \(b_{v}\) represent bias terms.  
Then, the obtained three probability distributions are fused in proportion to obtain the final predicted probability.  
\[P_{final}=w_{1}p_{co}+w_{2}p_{text}+w_{e}p_{image} \tag{20}\]  
\[w_{1}+w_{2}+w_{3}=1 \tag{21}\]  
where \(w_{1}\), \(w_{2}\) and \(w_{3}\), respectively, represent the degree of influence of different parts of the model on the final detection results.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 2 Background and Problem Statement  
### Multipath Transmission on Overlay Networks and SDSON  
Multipath transmission on overlay networks has the advantages of flexible and efficient development and deployment in bearing networks compared with other multipath implementations on other network layers, partially thanks to its no modification on network devices in bearing networks and efficient redirection on network traffic. We achieve multipath transmission on application-layer overlay networks based on relaying network traffic. We deploy application-layer relay nodes in bearing networks to redirect networks traffic into multiple concurrent end-to-end paths. In order to achieve optimal and intelligent control and management on relay nodes, we use the software-defined networking (SDN) paradigm to orchestrate relay nodes and conduct forwarding control and we name the SDN-based multipath transmission framework Software-Defined Service Overlay Network (SDSON) [17; 18; 19; 20].  
The structure of SDSON is shown in Figure 1, which is built on the application-layer overlay networks. The bottom plane is the bearing network layer, consisting of network forwarding devices; this underlay network provides the basic transmission for overlay networks. The middle plane is the forwarding plane of SDSON, and there exist two network entities of SDSON, namely Relay Server (RS) and User Agent (UA); RS works as the forwarding nodes, i.e., the relay nodes that providing relayed forwarding services, and UA provides the end host with the access to SDSON. The top plane is the control plane of SDSON, where Relay Controller (RC) conducts global control and management functions. A scenario of end-to-end multipath transmission is shown in Figure 1, and three multiple paths are generated on forwarding plane, and corresponding paths in underlay networks. Like other multipath transmission solutions on application-layer overlay networks, in SDSON, there exist two challenging problems to solve, namely, (i) the topology match between overlay networks and underlay networks, and (ii) the enhancement on multipath transmission performance.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{2}{c}{**(a) _Fagus s._**} & \multicolumn{3}{c}{**(b) _Fagus s. HT_**} \\ \hline
**Compound** & **GC-MS** & **SPME** & **Compound** & **GC-MS** & **SPME** \\ \hline
1H-Benzocyclabetpene, 2,4a,5,6,7,8- & & 1.42 & 1(2H)-Naphthalenone, & 0.22 \\ hexahydro-3,5,5,9-tetramethyl & & 7-(1,1-dimethylylethyl)-3,4-dihydro & & 0.32 \\ \hline
2,6-Disopropylnphthalene & 0.79 & & 1,2-Benzenedicarboxylic acid, bis (2-methylpropyl) ester & 0.32 \\ \hline
2-Bromo dodecane & 0.29 & & 1,2-Benzenedicarboxylic acid, butyl & 0.10 \\ \hline
2-Hepten-1-one, & & & \\
1-(2-hydroxy-5-methylphenyl) & & 2.67 & 1-Decanol, 2-hexyl & 2.25 \\ \hline
2-methylocacosane & 0.35 & & 1H-Benzimidazol-2-amine & 1.11 \\ \hline
3-Acetylphenanthrene & 1.77 & & 1H-Benzocyclabetpene, 2,4a,5,6,7,8- & \\ hexahydro-3,5,5,9-tetramethyl & & & 0.34 \\ \hline
3-Methoxyamphetamine & & 4.48 & 1H-Indole, 2-methyl & 9.76 \\ \hline
4-((1E)-3-Hydroxy-1-properyl)-2- & & & 3-Isobutyl-4,5-dimethyl-3H- & 0.35 \\ methoxyphenyl & & & isobenzofuran-1-one & 0.35 \\ \hline
4,7-Dimethoxy-2-methyl-1H-indene & & 0.37 & 4-(3,5-Dimethyl-2-benzofuranyl)-2- & 0.32 \\ \hline
4-Acetyl-1-methylcyclohex & & 0.49 & 4-Acetyl-1-methylcyclohex & 0.29 \\ \hline
7,9-Di-tert-butyl-1-oxaspiro(4,5)deca- & 1.72 & & Acetic acid & 5.10 \\
6,9-diene-2,8-dione & & & \\ \hline Acetic acid & 3.82 & Ar-tumerone & 1.48 \\ \hline Ar-tumerone & & 0.77 & Cresol & 0.22 \\ \hline Azulen-2-ol, & & & \\
1,4-dimethyl-7-(1-methylethyl) & & 1.07 & Decane,5,6-dipropyl & 4.65 \\ \hline Benzene, (1,1-dimethyl-2-propenyl) & & 0.19 & Eicosane & 2.74 \\ \hline cis-(-)2,4a,5,6,9a-Hexahydro-3,5,5,9- & & & \\ tetramethyl(1H)benzocyclabetpene & & 0.15 & Ethanone, 1-(2-methylphenyl) & 0.19 \\ \hline Decanal & & 0.16 & Ethanone, 1-(9-anthracenyl) & 1.20 \\ \hline Dodecane & 0.10 & & Ethyl Acetate & 1.21 \\ \hline Eicosane & 4.02 & & Furfural & 0.56 \\ \hline Ethanone, 1-(4-methylphenyl) & & 0.43 & Heneicosane & 1.23 \\ \hline Ethyl Acetate & 0.44 & & Henriacontane & 0.85 \\ \hline Ethlyletramethylcyclopentadiene & & 10.20 & Heptacosane & 1.00 \\ \hline Heptacosane & 1.01 & Hexacosane & 1.07 \\ \hline Heptadecane & 1.48 & Hexadecane & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Compounds found in GC-MS and SPME analysis of **(a)** unmodified and **(b)** modified (HT) been wood water extractives.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Batch** & \(\mu_{\text{max}}\) **(Log CFU/h)** & **Lag (h)** & **SE** & \(\mathbf{R^{2}}\) \\ \hline Batch 1 & -0.0019 & 1032 & 0.25 & 0.743 \\ Batch 2 & -0.0013 & 780 & 0.22 & 0.792 \\ Batch 3 & -0.0014 & 776 & 0.18 & 0.864 \\ \hline \end{tabular}
\end{table} 
Table 3: Output parameters estimated by the DMFit program for each growth curve in three batches of experimentally produced spreadable salami. Furthermore, evidences have also shown that non bacteriocin-producing strains of _L. sakei_ are able to retard spoilage and to reduce the number of pathogens by developing dominant populations in vacuum packaged fresh meats [65]. The inhibitory potential of non bacteriocin _L. sakei_ strains might be attributable to the high specialization of the ecological niche represented by meat products, enabling this microorganism to be an efficient competitor for nutrients and to have a better colonizing capability [66]. Furthermore, some _L. plantarum_ strains have been characterized as bacteriocin producers [67; 68], highlighting their potential application as bio-preservatives in meat and meat products [69]. Indeed, some studies have suggested that certain strains of _L. plantarum_ are able to produce class IIb bacterios with synergistic antibacterial effects [70]. [PERSON] et al. [69] have also highlighted the remarkable antilisterial activity of _L. plantarum_ isolated from a fermented salami and able to produce a two-peptide lantibiotic plantaric. Similarly to what was previously mentioned for _L. sakei_ and _L. plantarum_, it has been reported in literature that _P. pentosaceus_ effectively inhibits the growth of _L. monocytogenes_ in pork fermented sausage, mainly due to the production of a non-lantibiotic class IIa bacteriocin [71]. It is likely that the inhibition of _L. monocytogenes_ in the targeted spreadable salami was determined by the combination of the physicochemical parameters associated with the inhibitory effect of the lactic flora exerted mainly through the production of antimicrobial peptides, as reported by others authors [16; 39; 57]. Furthermore, it has been reported elsewhere that in presence of nitrate and nitrite additives, the fermentation process is able to limit and even suppress the growth of _L. monocytogenes_ in fermented sausages [18]. In this study, the food matrix was challenged with _Listeria monocytogenes_ during both ripening and storage by performing the contamination on meat batter at the beginning of the production process. This innovative approach enabled the thorough assessment of pathogen behavior in this peculiar meat production compared to challenge study only during shelf life [20; 39]. Finally, the results of the present study are in agreement with the hurdle technology concept [72; 73], which demonstrates that the combination and interaction of all the major preservative factors for foods are pivotal for optimal food preservation ensuring food stability and safety.
# Effects of Fractional Time Delay as a Low-Power True Time Delay Digital Beamforming Architecture
## 4 Simulation Results and Analysis  
For the proposed \(N=29\) VFD lowpass FIR filter DBF architecture, Figure 5 shows the simulated antenna pattern when steering to \(\theta_{QSLL}\). The maximum QSLL of the system was determined to be at \(-35.3\) dB, which is 2.7 dB poorer than the expected value of \(-38\) dB. This performance decrease can be explained as periodic sawtooth error sampled across the aperture of the array, which causes spatial harmonics to add in phase [25]. When compared to the ideal floating point fractional delay, the VFD lowpass FIR filter architecture is determined to be 9.6 dB worse at the location of the maximum QSLL when steering to the same angle. In both cases, a 4 GHz signal will produce the worst-case quantization error, as its period is the shortest within the desired bandwidth, which in turn requires the most fractional time steps in order to steer the array properly.  
Figure 4: Implemented architecture in SIMULINK.  
Figure 3: Block diagram of proposed architecture.  
Figure 6 performs a simulation using RF signals of 2-4 GHz. This figure shows that there is no discernible beam squinting of the array across a wide bandwidth. Additionally, it demonstrates that the max QSLL and sidelobe floor remain consistent when operating with different signal frequencies, with the 4 GHz signal producing the noisiest result due to the non-linear VFD group delay at 4 GHz.
## 6 Conclusions  
This investigation examines the application of quantum key distribution (QKD) to enhance data security within UAV-based agricultural geographic information systems (GIS). The study critically analyses the limitations of traditional encryption methods in low-orbit communication environments, highlighting challenges such as a high computational complexity, vulnerability to cyber-attacks, and adverse environmental influences. To address these issues, a novel approach based on the BB84 protocol combined with a hybrid encryption scheme is proposed. This method harnesses the strengths of both symmetric and asymmetric encryption, thereby delivering high data transfer rates alongside robust protection against unauthorized access.  
Numerical simulations validate the efficacy of the proposed method, demonstrating that its key generation speed is sufficient for secure, real-time data transmission even under conditions of limited bandwidth and adverse atmospheric disturbances. Additionally, the sensitivity of the algorithm to various system parameters and environmental factors is rigorously evaluated, and the impact of data compression on system performance is thoroughly investigated.  
The results underscore the potential of employing QKD for secure data transmission in UAV-based agrotechnical GIS, providing a promising solution for reliable data protection under low Earth orbit conditions. This advancement not only enhances agricultural monitoring capabilities but also paves the way for broader UAV applications. Future research should focus on conducting empirical experiments in real UAV operating environments and exploring the integration of the proposed algorithm with emerging technologies such as blockchain and artificial intelligence.  
**Author Contributions:** Conceptualization, M.B. and L.L.S.; methodology, K.M. and N.Z.; software, S.A.; validation, M.B., L.L.S. and S.A.; formal analysis, K.M. and N.Z.; investigation, L.L.S.; resources, M.B. and N.Z.; data curation, L.L.S. and N.Z.; writing--original draft preparation, K.M. and N.Z.; writing--review and editing, M.B.; visualization, S.A.; supervision, K.M.; project administration, K.M.; funding acquisition, L.L.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors express gratitude to the Ministry of Higher Education and Science of the Republic of Kazakhstan, which allocated program-targeted funding for 2024-2026. IRN AP23486167.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Pre-Processing of Multi-Sensor Data from the Unmanned Ship
### Unification of GPS/BDS Space-Time Reference  
In order to obtain the trajectory information of the unmanned ship collected by the GPS/BDS dual-module sensor, it is necessary to unify the time of the GPS and BDS satellite systems. Due to the jump error between GPS time (GPST) and UTC time (UTCT) [16], as time goes by, the deviation between the two gradually increases, and at present the deviation has reached 16 s, that is  
\[\text{GPST}\approx\text{UTCT}+16 \tag{8}\]  
BDS time (BDST) takes the national standard unit system s as the basic unit for time accumulation. Because there is no jump second in BDST, it is continuous time. The start time of BDST is converted into the number of weeks of GPST and the count of seconds in a week is 1356 weeks--14.000 s. Therefore, the following relationship exists between BDST and GPST  
\[\text{BDST}_{\text{week number}}=\text{GPST}_{\text{week number}}+1356 \tag{9}\]  
\[\text{BDST}_{\text{seconds of the week count}}=\text{GPST}_{\text{seconds of the week count}}+14 \tag{10}\]  
Each country has established different spatial benchmarks according to its own satellite navigation system, so it is necessary to unify the spatial benchmarks when performing combined positioning of different satellite systems. WGS-84 was used for GPS, and the 2000 National Geodetic Coordinate System (NGCS2000) was used for the BDS. The origin, scale, orientation, and the scale evolved from the orientation defined by the two coordinate systems were the same, and both are closely related to the Earth Reference System (ERS). After multiple optimizations, the difference between the two is only at the centimeter level [17]. For most non-precision positioning applications such as navigation, the WGS-84 and NGCS2000 coordinate systems do not need to be converted, so they are considered as unified coordinate systems in this paper.
# Surface Plasmon Polariton Triggered Generation of 1D-Low Spatial Frequency LIPSS on Fused Silica
## 1 Introduction
These parallel-oriented structures are attributed to radiation remnants [13; 37]. SPP-generated LSFL having an orientation perpendicular to the polarization of the laser with \(\Lambda_{LSFL}<\lambda\) on fused silica have been calculated by [PERSON] et al. [38] within the efficacy factor theory for excited high carrier concentrations. However, [PERSON] et al. never observed such oriented structures experimentally. [PERSON] et al. [39] performed numerical simulations regarding the generation of both HSFL and LSFL on fused silica. Their simulations revealed the generation of LSFL being parallel to the polarization of the laser deeper in the material due to far-field interaction without a plasmonic electromagnetic scenario (low carrier densities). The transition from HSFL to these type of LSFL is executed by the removal of the material at carrier densities below or near a critical carrier concentration. Furthermore, their calculations showed that LSFL that are oriented perpendicularly are hard to generate due to the removal of the material by ablation. For the non-scanning case, we also observed these parallel LSFL in a previous study, but never found LSFL with a perpendicular orientation [28]. [PERSON] et al. [40] found LSFL on fused silica oriented parallel to the polarization of the laser for both, scanning perpendicularly and parallel to the laser polarization. For perpendicular scanning, we also observed parallel-oriented structures in a previous publication [16]. Therefore, we assume that the scanning direction (parallel to the polarization) in combination with the ideal parameters is the key to overcome this limitation for generating LSFL with an orientation perpendicular to the laser polarization. Against this background, in this contribution, we report on a detailed evaluation of 1D-LSFL generated with a 1030-nm femtosecond laser on fused silica. The influence of the laser fluence and the scanning speed by scanning parallel to the polarization of the laser on LSFL is investigated. To the best of our knowledge, we show an unreported phenomenon on fused silica, namely a perpendicular orientation of LSFL versus laser polarization. However, the periodicity of the structures reveals values between 0.9 \(\upmu\)m and 1.0 \(\upmu\)m, i.e., being smaller than the laser wavelength.
## 2 Materials and Methods
### RBF-SVM Intelligent Grading System Design
#### 2.2.4 Establishment and Analysis of Three Classification Model Evaluation Criteria  
To verify the performance of the RBF-SVM algorithm proposed in this paper, two classification algorithms, the GBDT algorithm and BP neural network (Back Propagation), were used to compare with the RBF-SVM algorithm. The maximum error (Max error), mean absolute error (\(MAE\)), mean absolute percentage error (\(MAP\)), root mean square error (\(RMSE\)), and coefficient of determination (\(R^{2}\)) of the model were calculated. The maximum error describes the extreme case of the prediction result, and the mean absolute error and the average absolute percentage error are based on the absolute error, which can be used together to describe the model's prediction of the limit value, and the root mean square error and coefficient of determination describe the overall regression of the model. The results of the evaluation indicators are shown in Table 3.  
\[MAE(y,\hat{y}) =\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{4}\] \[MAPE(y,\hat{y}) =\frac{1}{n}\sum_{i=1}^{n}\frac{\lvert y_{i}-\hat{y}_{i}\rvert}{ \hat{y}_{i}}\] (5) \[RMSE(y,\hat{y}) =\sqrt{\frac{1}{n}(y_{i}-\hat{y}_{i})^{2}}\] (6) \[R^{2}(y,\hat{y}) =1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_ {i}-\overline{y}_{i})^{2}} \tag{7}\]  
The comparison diagram and absolute percentage absolute errors of the prediction results of the SVM, BPNN, and GBDT quality prediction models are shown in Figure 7. The bias of the BPNN prediction was large, all its regression indicators were the worst, and its comprehensive prediction accuracy was the lowest. The prediction deviations of GBDT and SVM were similar, and the average absolute error and average absolute percentage error indicators were also relatively close; however, the maximum error, root mean square error, and coefficient of determination index were different, with the GBDT model having extreme errors. The subjective evaluation results of humans are linear, and the use of non-sexual models can cause bias during the training process of the sample, resulting in a decrease in the stability of the accuracy of the prediction results.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **SVM** & **BPNN** & **GBDT** \\ \hline Max error & 7.69\% & 19.3\% & 9.68\% \\ MAE & 0.38 & 0.72 & 0.42 \\ MAPE & 5.38\% & 10.35\% & 6.04\% \\ RMSE & 0.392 & 0.82 & 0.43 \\ R\({}^{2}\) & 0.9846 & 0.9528 & 0.9786 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of three models’ regression evaluation index data.