esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Secure Aggregation of Cluster Head Algorithm and Secure Sink Authentication
#### 3.6.2 Secure Sink Authentication  
Algorithm 4 is implemented in our proposed SDAA protocol that helps in secure multiple sink deployment, with authentication, and secure data aggregation optimized method, for verifying the legitimacy of new nodes that are deployed, based upon multiple mobile sinks (MMS) nodes in the network. The process is performed with the base station (BS) and the CH in clustered-based network design network (CBDN) methods. Our proposed SDAA protocol is deployed in a similar condition in which other secure MAC protocols, such as SESFC, SDA, and EEST were deployed; however, without CBND and MMS methods in the network. Therefore, in our proposed SDAA protocol, we utilized MMS and optimized protocol [26; 27] and deployed it in the system model, including secure data aggregation and authentication in CBND. Based upon this, we reevaluate performance metrics for reliability and energy efficiency of the UVWSN network based on Algorithm 4 as below.  
```
BS: base station; \(NM_{s}\): number of Mobile sinks; \(CH\): cluster head nodes; Cert: certificate of the Mobile sink; \(MM_{i}\): mobile information message. \(MS_{q}\): query messages for \(CH\), \(B_{s}\), and \(MS_{n}\); \(A_{p}\): Approval; \(MS_{n}\): Mobile sink in the network; \(SD_{p}\): secure data packets \(D_{L}\): distance between mobile sink; \(T\): Broadcast time of Mobile nodes; \(VM_{s}\): valid mobile sink; \(LM_{s}\): legitimate mobile sink; \(Ms_{ii}\): illegitimate mobile sensor node; \(EM_{n}\): entry into the network \(1\). Input \((MM_{i}\), \(NM_{id}\), \(NM_{s}\), \(T\), \(D_{L})\) \(2\). Output \((A_{p}\), _Cert_)
3. for each member of the mobile sink in the SDAA models do
4. \(CH\) transmits a query message to all its cluster member mobile sink node \(\left(MS_{q}\right)\)
5. after receiving \(\left(MS_{q}\right)\), the mobile sink node computes \(MS_{q}(CH_{ii}\parallel MS_{n}\parallel MM_{i}\parallel T)\) \(6\). \(CH\to MS_{q}\) \(7\). If any new mobile sensor node or mobile sink joins the network \(8\). \(NM_{s}\to EM_{n}\in MS_{n}\)then
9. \(CH\) update \(B_{s}\) \(B_{s}\) recall \(NM_{s}\)
10. \(B_{s}\) investigate \(\mathit{Cert}\left(NM_{id},SD_{p},T,D_{L}\right)\) for the \(VM_{s}\)
11. set \(A_{p}\) for \(NM_{s}\); \(A_{p}=\left(B_{s},\,MM_{i},\,SD_{p},\,T,\,D_{L},\,NM_{id}\right)\)
13. \(B_{s}\) check broadcast \(A_{p}\left(MS_{q}\right)\) with \(CH\)
14. \(\textbf{If}\,NM_{s}==VM_{s}=LM_{s}(NM_{s})\)and
15. \(NM_{s}==MS_{q}\)then
16. \(B_{s}\) approval \(NM_{s}==VM_{s}\in MS_{n}\)
17. \(\textbf{Else}\)
18. \(NM_{s}\
eq VM_{ms}\) and \(NM_{s}\
eq MS_{q},\,\,MS_{i}=VM_{s}\)
19. \(B_{s}\
eq NM_{s}\) and not approval of \(NM_{s}\)
20. \(\textbf{End if}\)
21. \(\textbf{End if}\)
22. \(\textbf{End for}\)
```  
**Algorithm 4** Secure Multiple Sink Authentication
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 2 Literature Review
### Sewer Defect Detection Based on Deep Learning Techniques
#### 2.2.1 Sewer Defect Identification Based on Image Classification Techniques  
[PERSON] et al. [11] presented a method for sewer pipe defect classification based on multiple binary CNNs, with an average accuracy of 86.2%. The higher classification performance proved the feasibility of CNNs in sewer defect detection. [PERSON] et al. [12] proposed a method using Resnet18 to classify defects in CCTV images. This method is divided into two steps, first distinguishing the defective images from normal images, and then classifying the defective images individually. The hierarchical classification method significantly improves the defect detection accuracy, with an accuracy rate of up to 83.2%. [PERSON] et al. [13] designed a two-level hierarchical CNN method for sewer defect classification. This method was trained using 40,000 images and achieved an accuracy of 94.96% in classifying 6 defects such as deposition, stagger, high water level and barrier; it also effectively solved the problem of data imbalance. The method demonstrates the high accuracy of CNN in sewer defect classification tasks and has been applied to practical inspection tasks. However, this method can only detect one type of defect on a single image, whereas sewer pipes may have multiple types of defects at the same location [10].
# Optimal Disintegration Strategy in Multiplex Networks under Layer Node-Based Attack
## 3 Experimental Analysis
### The Existing Disintegration Strategies  
There have been many disintegration strategies in monoplex networks. We extend some of them to the layer node-based attack in multiplex networks. Considering the particularity of multiplex networks, we divide the strategies into two categories according to the nodes information we used: multiplex node-based strategies and layer node-based strategies. The high multiplex degree (HMD) strategy, the high multiplex degree adaptive (HMDA) and the effective multiplex degree (EMD) strategy are based on the information of the multiplex nodes in networks and are called multiplex node-based strategies. The high layer degree (HLD) strategy, the high layer degree adaptive (HLDA) strategy and the collective influence (CI) strategy are based on the information of the layer nodes in networks and name layer node-based strategies.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 6 Evaluation and Discussion of the Results
### Comparison with Other Approaches
#### 6.3.2 Variable Data Sizes  
In the last set of simulations (see Figure 6), we focus on the comparison between the synchronous approaches in a variable data size scenario. In this scenario, 500 nodes are used where the data size of each node is randomly allocated from [10,000 \(-X\), 10,000 \(+X\)] bytes, where \(X=[1000,...,8000]\). The Aloha approaches are not included in this study for presentation purposes due their very high data collection times. _Global_'s advantage is obvious due to its capability to fill the empty slots left by nodes that finish transmissions earlier than nodes with more transmissions. _Light_ schedules nodes' activities based on the first transmission without considering the amount of data per node. As a consequence, nodes that complete sooner leave empty and never again used slots. TS-LoRa provides no SF re-assignment resulting in very high frame sizes. The performance gap increases with higher data variance since _Global_ finds more options to fill empty slots. We can see that _Global_'s performance is almost independent of the data variance, while _Light_'s and TS-LoRa's ones present an increasing trend. In terms of energy consumption, the difference between TS-LoRa and the scheduling approaches is about 45%. All the approaches have the same synchronisation cost per frame but the scheduling approaches spend more energy due to the higher average SF.
## 2 Materials and Methods
### Analysis of Microbial Community by 16S rRNA and ITS Sequencing  
Employing the CTAB technique, total genomic DNA was isolated from YH or FHYH, quantified, and the purity was checked on 1% agarose gels. The DNA samples were diluted to 1 ng/uL in sterile water, and used as templates for 16S and ITS metagenomic analysis using the Illumina HiSeq platform (Beijing Novogene Genomics Technology Co., Ltd., Beijing, China, Wuhan Metawell Biotechnology Co. Ltd., Wuhan, Hubei Province, China). The 16s rRNA library was prepared by amplifying the V4 h region of the 16S rRNA gene with 515F (GTGCCAGCMGCCGGCG) and 806R (GGACTACHVGGGTWTCTAAT) primers. The ITS library was generated by amplifying the target sequences ITS1 (CTTGGTCATTAGAGAGTAA) and ITS2 (GCTGCTTCTCTCTCATGC). All PCR reactions were performed using 15 uL Phusion(r) High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, USA), 2 uM each of forward and reverse primers, and 10 ng template DNA. Using the TruSeq(r) DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA), sequencing libraries were created, and index codes were added. The Qubit@ 2.0 Fluorom-eter (Thermo Fisher Scientific, Shanghai City, China) and the Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA) were used to evaluate the library's quality. The Illumina NovaSeq platform was used to finish sequencing the collection. After that, FQtrim (V0.94) was utilized to obtain superior clean readings. With the aid of QIIME2, alpha, and beta diversity were computed in order to measure species richness and diversity. Principal component analysis (PCoA) was used to examine the changes in microbiological makeup. Linear discriminant analysis (LDA) with linear discriminant effect size (LEfSe) was used for species analysis with significant differences.
## 4 Experiments
### Analysis of Mapping Results  
Figure 10 illustrates the final mapping outcomes of the FastSLAM-PSO algorithm in scenario 1, highlighting the impact of varying particle counts \(N\) on the mapping quality. It is observable that when \(N\) equals 10, the mapping performance is suboptimal, characterized by distorted paths and a skewed representation of the terrain, deviating from  
Figure 8: Analysis of the population parameters \(N\) in the large-scale scenario scene_1.  
Figure 9: Analysis of the population parameters \(N\) in the large-scale scenario scene_1.  
the actual topography. As the particle count increases, the mapping accuracy improves, demonstrating a trend towards stabilization. Notably, at \(N=80\), \(N=90\), and \(N=100\), the mapping performance appears to plateau, indicating that the ability to refine the map does not improve significantly with further increases in particle count. This stabilization likely occurs because the algorithm has sufficient particles to adequately explore and represent the environment, making the addition of more particles redundant. It is evident that the incorporation of PSO can partially compensate for the absence of loop closure detection, thereby enhancing the mapping results. However, at \(N=100\), the mapping quality is not as optimal as at \(N=90\), which may be attributed to the increased likelihood of errors arising from a higher number of particles.  
To further elaborate, the mapping results are significantly influenced by the number of particles utilized in the FastSLAM-PSO algorithm. An inadequate particle count leads to a poor representation of the environment, with inaccuracies in both path tracing and terrain mapping. Conversely, an optimal particle count ensures a more accurate and reliable mapping outcome. The introduction of PSO serves to refine the particle distribution, reducing the degradation issues typically associated with particle filters and improving the ability to adapt to environmental changes. The decrement in performance at \(N=100\) suggests that there is an upper limit to the number of particles beyond which the benefits of increased diversity are offset by the potential for increased noise and error. This observation underscores the importance of calibrating the particle count to achieve a balance between exploration and exploitation, ensuring that the algorithm remains robust and efficient.
# UAV-Borne Profiling Radar for Forest Research
## 4 Conclusions  
An UAV-based profiling radar for forest research was depicted. Using a helicopter field test, the developed system was analysed against Riegl VQ-480-U laser scanner. Using high-quality georeferencing system, we measured radar profiles (i.e., waveform) from forest canopies and compared them with ALS data. The results showed good agreement of radar and lidar signal from forest structures. However, future research is needed to better understand the characterizing the role of canopy gaps and radar attenuation in the Ku-band in forest canopy as a function of forest-stand attributes. Such characterization could not be conducted in the early studies of profiling radars due to a lack of georeferencing technology. Thus, the objective of future research on the new system is to improve our understanding of laser and radar beam interaction in forest canopy. The following preliminary conclusions were drawn:  
* The Tomoradar can collect the profile information from tree canopy top to ground level for both co- and cross-polarizations.
* Strong cross-polarization signal, which is at the level of co-polarized return, can be detected at dense coniferous tree tops due to volume backscattering of the needles at Ku-band.
* Height of canopy is underestimated due to penetration of Ku-band signal into forests.
* The canopy return is a combination of canopy gaps and density of canopies affecting attenuation of canopies[PERSON]  
This hardware development of the study was financially supported by the Academy of Finland project \"Interaction of Lidar/Radar Beams with Forests Using Mini-UAV and Mobile Forest Tomography\". Experimental part of the work was also supported by the Academy of Finland project \"Centre of Excellence in Laser Scanning Research (CoE-LaSR) (2Z2195)\" and by the European Community's Seventh Framework Programme ([PERSON]/2007-2013) under grant agreement No. 606971. Additionally, Chinese Academy of Science (I81811 KYSB20130003), by Chinese Ministry of Science and Technology (2015 DFA70930) and the National Nature Science Foundation of China (41304004) are acknowledged.  
[PERSON] and [PERSON] designed the Tomoradar hardware system, performed the field tests, collected the data and wrote the major parts of the paper; [PERSON], [PERSON] and [PERSON] processed the collected data; [PERSON] designed the practical field test; [PERSON] and [PERSON] collected airborne LiDAR data as reference, [PERSON] also developed the mechanical support structure for the systems in field test, [PERSON] reviewed the content and offer substantial improvement for this paper. [PERSON], [PERSON] and [PERSON] planned the scientific rationale for Tomoradar and its tests, and acquired funding for the work (the named two Academy of Finland and EC projects).  
The authors declare no conflict of interest.
# Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
## 5 Conclusions  
Due to the obvious asymmetry of the raceway contact region of the high-speed BSs, a non-Hertzian elastohydrodynamic contact stress calculation method based on the minimum excess principle is proposed, and the conclusions below can be drawn:  
(1) The normal contact stress error between the Hertzian solution and the non-Hertzian solution increases as the helix angle, and the contact stress error of the screw raceway contact region is always greater than that of the nut raceway contact region.  
(2) The non-Hertzian elastohydrodynamic contact stress of the BSs show different trends in the contact regions of the screw raceway and nut raceway with the increase of the  
Figure 12: Comparison of the elastohydrodynamic stress between the Hertzian solution and non-Hertzian solution under different screw speeds. (**a**) Central elastohydrodynamic contact stress of point \(A\). (**b**) Central elastohydrodynamic contact stress of point \(B\).  
Figure 13: Comparison of the asperity contact stress between the Hertzian solution and non-Hertzian solution under different screw speeds. (**a**) Central asperity contact stress of point \(A\). (**b**) Central asperity contact stress of point \(B\).  
helix angle and screw speed, respectively. The non-Hertzian solution is smaller than the Hertzian solution of the central elastohydrodynamic contact stress at the screw raceway contact region. However, the non-Hertzian solution is greater than the Hertzian solution of the central elastohydrodynamic contact stress at the nut raceway contact region.  
(3) The positioning accuracy of the DNBSs is obviously influenced by the wear of the raceway due to the asperity contact problem. Under the helix angle of 21.71\({}^{\circ}\), the calculation accuracies of asperity contact stress for the non-Hertzian solution are 2.93% and 3.01% more accurate than that of the Hertzian solution at the screw raceway contact region and the nut raceway contact region, respectively. Therefore, the non-Hertzian elastohydrodynamic contact stress calculation method is essential to precisely analyze the wear mechanism of the high-speed BSs.  
[PERSON] proposed and simulated the calculation method, designed the experiment, and wrote the paper. [PERSON] implemented the experiment. [PERSON] guided the general idea of this paper. [PERSON] analyzed the experimental data. All authors have read and agreed to the published version of the manuscript.  
This study was sponsored by the National Natural Science Foundation of China (No. 51975020, 51575014 and 51875008), Beijing Municipal Natural Science Foundation (No. 3202005), and the International Research Cooperation Seed Fund of Beijing University of Technology (No. 2021A10).  
The authors declare no conflict of interest.
# Locomotion Efficiency Optimization of Biologically Inspired Snake Robots
## 3 Optimization of Motion
### System Input
#### 3.1.1 Motion Pattern  
Both land-based and swimming snake robots commonly use undulatory motion patterns in order to move forward. Previous studies on snake robots moving on land mostly adopt the lateral undulation motion pattern, which is considered as the fastest form of land-based snake robot locomotion [1]. Furthermore, studies on swimming snake robots often consider both lateral undulation and eel-like motion patterns as common motion modes to achieve propulsion. In [31], a general sinusoidal (undulatory) motion pattern was proposed, which makes it possible to describe different undulatory motion patterns including the lateral undulation and eel-like motion. To achieve an undulatory motion pattern, each joint \(i\in\{1,\cdots,n-1\}\) of the snake robot is commanded to follow the reference signal  
\[\phi_{i}^{*}(t)=\text{ag}(i,n)\text{sin}(\omega t+(i-1)\delta)+\gamma, \tag{13}\]  
where the proper choice of the scaling factor \(\text{g}(i,n)\) makes it possible to describe different undulatory motion patterns. For instance, it is possible to achieve lateral undulation by simply choosing \(\text{g}(i,n)=1\), while \(\text{g}(i,n)=(n-i)/(n+1)\) results in an eel-like motion pattern. The gait parameters \(\alpha\), \(\omega\) and \(\delta\) give the amplitude, frequency and the phase shift of the sinusoidal motion pattern, respectively. Furthermore, the joint offset parameter \(\gamma\) is commonly used to control the heading of the robot [1].
# AutoFL: Towards AutoML in a Federated Learning Context
## 4 Design and Implementation of the AutoFL Framework
### Conceptual Overview
It follows an approach similar to the one proposed in [42]. The component evaluates these class imbalances at both the individual node level and the federation level as a whole. This component addresses limitation 5. The components responsible for _Configuration and Deployment Constraints_ as well as _Security and Privacy Constraints_ play a crucial role in defining the criteria for valid models and imposing necessary limitations. For instance, they impose limitations on the maximum amount of memory to be used either to train the model or after putting the model in production (e.g., physical constraints of target devices) or the average time required to evaluate the model for a single test sample (e.g., to support real-time data analysis such as network traffic monitoring). The latter component may introduce security and privacy measures during the aggregation process. For instance, secure aggregation techniques are enforced to address concerns related to an untrusted coordinating server, while differential privacy methods are enforced to uphold data confidentiality on individual nodes. If certain models do not support these security and privacy tactics, they will be excluded during the process of model selection. AutoFL cannot decide on its own whether these tactics are necessary; they have to be declared by the data scientist or MLOPs engineer. These two components help address limitations 6, 7, and 8. The current implementation of AutoFL successfully addresses all challenges except limitation 4. Although basic data pre-processing techniques such as min-max normalization can be performed in a federated manner, there is currently limited support for federated dimensionality reduction and data/feature pre-processing in a privacy-preserving manner.
## 3 Validation of MANTiS-32 Sensing Performance
### Integrated Measurement Performance Evaluation of the MANTiS-6050 Sensor and MANTiS-32 System
#### 3.4.4 Assessment of Measurement Accuracy in the MANTiS-32 Wireless Module  
With all eight MANTiS-6050 sensors operating concurrently and connected to a single MANTiS-32 module, the system demonstrated stable and uninterrupted signal acquisition, with no observable time delays or communication bottlenecks. This enabled seamless  
Figure 14: Sensor response signals acquired by the MANTiS-32 module.  
Figure 13: Cantilever beam finite element model with lumped masses.  
execution of real-time modal analysis for the cantilever beam without any degradation in data quality or transmission performance. The experimentally obtained dynamic characteristics were then compared to those derived from the FE model, as illustrated in Figure 15. Both the mode shapes and corresponding natural frequencies were accurately identified. The deviation between the measured and simulated results was minimal, with the first natural frequency exhibiting a relative error of 0.462% and the second a deviation of 1.584%. These results validate the precision and robustness of the MANTiS-32 system in performing wireless modal sensing, confirming its suitability for real-time structural health monitoring applications.  
Both the first and second mode shapes were identified as bending modes, exhibiting qualitative agreement between the finite element analysis and experimental results. To quantitatively assess the level of agreement, a Modal Assurance Criterion (MAC) analysis was performed using Equation (2).  
\[MAC(i,j)=\frac{\left|\{\phi_{i}\}\right\}^{T}\{\psi_{j}\}\right|^{2} \tag{2}\]  
Here, \(\{\phi_{i}\}\) represents the \(i\)-th mode shape vector obtained from the finite element analysis, while \(\{\psi_{j}\}\) denotes the \(j\)-th mode shape vector extracted from the multi-sensor experimental measurements. As shown in Figure 16, the MAC analysis yielded a correlation exceeding 99.9%, indicating excellent agreement between the simulated and measured mode shapes. This result confirms the high accuracy and reliability of the MANTiS-32 system and the connected MANTiS-6050 sensors in capturing dynamic structural responses.  
These findings confirm that the MANTiS-32 wireless sensing module, operating in conjunction with eight simultaneously connected MANTiS-6050 sensors, is capable of conducting stable, interference-free measurements. The system demonstrated outstanding performance in capturing structural modal characteristics, thereby validating its effectiveness for high-fidelity modal sensing applications.  
Figure 15: Comparison of computed and measured eigenvalues and eigenvectors (FE vs. MANTiS-32).
## 6 Limitations and Future Work
Future research will also focus on performance improvements, which according to our studies will help to gain a wider user acceptance. We would like to investigate if changing parameters such as the number of hops in an anonymization network could have a positive influence on anonymity and usability, e.g., decreasing the number of hops could result in lower latency. This could result in broader user acceptance, leading to more users and increasing anonymity in general, but possibly leaving routes much more open to compromise. Another approach could be a performance-oriented one in which the behavior of the anonymization network is not focused on guaranteeing a certain degree of anonymity, but on a guarantee of performance. For example, one could include statistics such as number of participants and latency of nodes when calculating the route within the anonymization network. Such an approach is adopted by the I2P anonymity system, which takes the performance of the peers into account when calculating routes through the network. This is of course a compromise between performance and privacy [53].