esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spectral Scattering Imaging
### Analysis of Scattering Images  
Figure 8 shows two typical multispectral and hyperspectral scattering images acquired from apple fruit. The two types of scattering images differ in obtaining scattering profiles, which are one dimensional, but follow the same procedures for analysing these scattering profiles. Moreover, for hyperspectral images, additional procedures are needed for extracting and exploiting spectral features. Figure 9 summarizes the overall procedures for processing these scattering images to obtain useful information for fruit quality assessment. The following discussion details these procedures for image analysis of scattering images individually.
## 5 Open Research Issues, Challenges and Opportunities
### Security and Privacy Challenges  
Given the specific requirements and constraints of wearable WBAN solutions, in terms of limited resource capabilities and harsh environmental conditions, the design of new security and privacy mechanisms remains an important challenge yet to be achieved. Unlike traditional communication networks, there has been relatively little research so far on solving the security and privacy issues in Wearable WBANs [132; 133; 134]. The major security and privacy requirements for Wearable WBAN solutions can be classified in three main categories [132]:  
* The _data storage security requirements_ in terms of data confidentiality, data integrity assurance and data dependability;
* The _data access security requirements_ in terms of users' access control, users' accountability, users and/or WBANs nodes revocability and data non-repudiation;* The _data communication security requirements_ in terms of users and/or WBANs nodes authentication, WBANs systems availability, data confidentiality, data integrity, and data freshness.  
As already discussed in Section 3.6, the IEEE 802.15.6 standard [9] is already proposing three basic security levels for WBAN systems, including the _unsecured communications_ (level 0), the _authenticated but not encrypted communications_ (level 1), and the _authenticated and encrypted communications_ (level 2). In this latter security level, all the messages are transmitted within authenticated and encrypted frames, enabling thus the authenticity, integrity, confidentiality, privacy and replay defense of the exchanged messages. However, these traditional encryption and authentication mechanisms remain not perfectly suitable for WBAN systems due to their stringent requirements in terms of limited power consumption, computation capability, heterogeneous Intra/Inter/Beyond-WBANs communications, infrastructure-less deployments, and so on.  
In this context, new lightweight security and privacy paradigms are currently being developed, such as the efficient generation and management of cryptographic keys using biometric techniques [135] (_i.e.,_ based on collected physiological signals, such as ECG, _etc._) and physical channel based approaches [136] (_i.e.,_ based on radio channel impulse responses and/or RSSI values). Another promising research trend is the development of new schemes to solve WBANs' dependability issues [134], defined as the conjunction of reliability, security, and availability, from both an Intra-WBAN and Inter-WBAN communication perspective.
## 5 Deployment and Evaluation
### Experiment Setup  
We dedicated a 40 \(\times\) 20 m field illustrated in Figure 5 to track the vehicles and workers. Six anchors from 0 to 5 are placed on the perimeter of the field.  
Our evaluation falls into two main categories, worker tracking, and vehicle tracking. For each category, we conducted multiple scenarios where entities move in a predefined  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline \((x,y)\) & Location of the vehicle \\ \(\theta\) & Orientation of the vehicle \\ \(T\) & Number of tags mounted on the vehicle \\ \(size_{i}\) & Number of locations from tag \(i\) \\ \((x_{i,j},\ y_{i,j})\) & \(j^{\text{th}}\) location of tag \(i\) \\ \((p_{x,i},\ p_{y,i})\) & Position of tags relative to the center of the vehicle \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter table for pose estimation optimizer.  
Figure 4: The pose estimator estimates the boundary by aligning the positions of the mounted tags on the vehicle (circles) with the calculated locations (cross).  
Figure 5: The anchor placement around the tracking zone.  
trajectory. In some scenarios, we added a bulldozer to block and distort the transmitted signals to anchor 4. We then compared the results of vehicle tracking and worker tracking for scenarios with and without the presence of the bulldozer.
## 3 Results and Discussion
### The Impact of PEF Pretreatment and Drying Methods on FTIR Spectra  
The FTIR spectra analysis was performed to assess the changes in the chemical structure of untreated and PEF-treated yellow mealworm larvae dried by means of diverse methods (Figure 5).  
In the region of 3200-3300 cm\({}^{-1}\), weak stretching vibrations of the O-H bond related to water molecules, phenolic compounds, or amino acids [27,54] are visible, as well as weak stretching vibrations of the N-H bond of the amide A group [55]. Strong stretching vibrations of the C-H bond of the -CH\({}_{2}\) and -CH\({}_{3}\) alkyl groups are seen at around 2920 cm\({}^{-1}\) and 2850 cm\({}^{-1}\), respectively. Moreover, a strong band at 2920 cm\({}^{-1}\) may also indicate stretching vibrations of the C-H bond of the amide B group [54,56]. A medium band around 1730-1760 cm\({}^{-1}\) is related to amide I [57], while strong bands at 1400-1560 cm\({}^{-1}\) and 1250-1300 cm\({}^{-1}\) may be identified as amide II and amide III, respectively [54,56]. Bioactive compounds such as polyphenols may be related to a strong peak around 1630 cm\({}^{-1}\)[28]. Protein vitality (quality) and structure is determined mainly by amide I and amide II owning to the presence of a sensitive C=O bond [57,58]. Greater protein quality (demonstrated by a higher absorbance) was noticed for the sample dried by means of the CD method compared to the one dried by means of the IR-CD method. The PEF pretreatment affected the quality of dried yellow mealworm larvae. For the CD  
Figure 4: Effect of the PEF pretreatment and drying methods (CD—convective drying, IR-CD—infrared–convective drying) on (**a**) crustacean and (**b**) mollusk content of yellow mealworm larvae. The different letters above columns denote statistical differences between samples (\(p<0.05\)).  
method, it was found that the PEF-treated sample at 5 kJ/kg indicated the most damaged structure. The PEF-treated samples at 20 and 40 kJ/kg showed better quality, although nonetheless lower than the sample without PEF treatment (based on the absorbance of amide I C=O bond). For the IR-CD method, similar FTIR spectra were obtained when a dose of the PEF energy at 20 and 40 kJ/kg was applied, while the PEF-treated sample at 5 kJ/kg showed reduced absorbance. The untreated sample represented the highest structural damage (the lowest absorbance). Furthermore, similar FTIR spectra were obtained for samples marked as PEF20_CD, PEF40_CD, and PEF5_IR-CD. Thus, it may be concluded that the PEF pretreatment had a major impact on the protein structure and infrared light absorption.
## 5 Experiments
### Imaging System Setup  
A wearable and portable device in a hat-like shape proposed in our previous work [42] was used. The device contains an array of six flexible antennas created using thin polyethylene terephthalate. The operating frequency of these antennas are 800 MHz to 2.5 GHz. The antennas were first created in CST studio suite [43]. A vector network analyzer was used to transmit and receive the signals. The generated signals were sent to a PC for further processing. The proposed wearable device is radar-based and used planer monopole antennas due to its low cost.The use of only six antennas makes the device lightweight; however, it affects the spatial resolution of the reconstructed brain images.  
The sensors were made using A8 silicon rubber with a relative permittivity of 2.99. An SMA was attached to the sensor using epoxy. The design of the sensor is based on a rectangular planar monopole antenna structure. The substrate of the antenna is made using a flexible textile, and the conductive part of the antenna is made using a 6 mm thick material. It operates from 1 to 4.2 GHz and exhibits a radiation pattern of 6 dB front-to-back ratio at 2.5 GHz. Further information about antenna can be found in [44].  
Furthermore, a flexible, low-cost switching system was developed that consists of SMA connectors and one-pole and one-throw (1P1T) switches. The sensors and switching systems were then combined into a wearable device. The total weight of the device (components include a hat, two switching circuits and six sensors) is 291.7 g. The insertion loss of the proposed system is \(-\)3.9 dB, which is slightly larger due to the flexibility of the device. Further information about the switching systems, VNA and a PC can be found in [42].
## 3 Almost Anti-Periodic Oscillation of the Mechanical System
### Stability of the Almost Anti-Periodic Solutions
\begin{array}{ll}&\Big{\|}\sum\limits_{j<-N_{2}}^{n-2}\Big{[}\prod\limits_{i=1 }^{n-j-1}M(n-i)\Big{]}c(j)\Big{\|}\\ \leq&\sum\limits_{j<-N_{2}}\Big{\|}\Big{[}\prod\limits_{i=1}^{n-j-1}M(n-i) \Big{]}\Big{\|}\|c(j)\|\leq\sum\limits_{j<-N_{2}}\Big{[}\prod\limits_{i=1}^{n- j-1}\|M(n-i)\|\Big{]}\|c(j)\|\\ =&\sum\limits_{j<-N_{2}}\Big{[}\prod\limits_{i=1}^{n}\|M(n-i)\|\prod\limits_{ i=n+1}^{n-j-1}\|M(n)\|\Big{]}\|c(j)\|\\ =&\sum\limits_{j<-N_{2}}\Big{[}\prod\limits_{r=n-1}^{0}\|M(r)\|\prod\limits_{ i=1}^{j+1}\|M(i)\|\Big{]}\|c(j)\|\\ =&\prod\limits_{r=n-1}^{0}\|M(r)\|\sum\limits_{j<-N_{2}}\Big{[}\prod\limits_{ i=j+1}^{-1}\|M(i)\|\Big{]}\|c(j)\|.\end{array} \]  
By Equation (11), one has  
\[ \begin{array}{ll}\prod\limits_{r=n-1}^{0}\|M(r)\|\sum\limits_{j<-N_{2}} \lambda_{2}^{-j+1}\|c(j)\|\leq&\prod\limits_{r=n-1}^{0}\|M(r)\|\sum\limits_{ j<-N_{2}}\Big{[}\prod\limits_{i=j+1}^{-1}\|M(i)\|\Big{]}\|c(j)\|\\ \leq&\prod\limits_{r=n-1}^{0}\|M(r)\|\sum\limits_{j<-N_{2}}\lambda_{1}^{-j+1 }\|c(j)\|.\end{array} \]  
Hence, the series \(\prod\limits_{r=n-1}^{0}\|M(r)\|\sum\limits_{j<-N_{2}}\bigg{[}\prod\limits_{ i=j+1}^{-1}\|M(i)\|\bigg{]}\|c(j)\|\) converges, which implies that the following series converges:  
\[\sum\limits_{j=-\infty}^{n-2}\Big{[}\prod\limits_{i=1}^{n-j-1}M(n-i)\Big{]}c( j).\]  
Hence, for  
\[x_{0}(n)=\sum\limits_{j=-\infty}^{n-2}\Big{[}\prod\limits_{i=1}^{n-j-1}M(n-i) \Big{]}c(j)+c(n-1),\]  
this series converges for \(n\in\mathbb{Z}\). Therefore, we prove that it is well-defined. Step 2. We will prove that \(x_{0}(n)\) is a solution of Equation (8). Since  
\[ \begin{array}{ll}x_{0}(n+1)=\sum\limits_{j=-\infty}^{n-1}\Big{[}\prod \limits_{i=1}^{n-j}M(n+1-i)\Big{]}c(j)+c(n)\\ =\sum\limits_{j=-\infty}^{n-2}\Big{[}\prod\limits_{i=1}^{n-j}M(n+1-i) \Big{]}c(j)+c(n)+\Big{[}\prod\limits_{i=1}^{n-(n-1)}M(n+1-i)\Big{]}c(n-1)\\ =\sum\limits_{j=-\infty}^{n-2}\Big{[}M(n)\prod\limits_{i=2}^{n-j}M(n+1-i) \Big{]}c(j)+c(n)+M(n)c(n-1)\\ =M(n)\sum\limits_{j=-\infty}^{n-2}\Big{[}\prod\limits_{i=1}^{n-j-1}M(n-i) \Big{]}c(j)+M(n)c(n-1)+c(n)\\ =M(n)\Big{(}\sum\limits_{j=-\infty}^{n-2}\Big{[}\prod\limits_{i=1}^{n-j-1}M(n-i )\Big{]}c(j)+c(n-1)\Big{)}+c(n)\\ =M(n)x_{0}(n)+c(n),\end{array} \]  
which means that \(x_{0}(n)\) is a solution of Equation (8). Step 3. We will prove that the solution of Equation (8) is an almost anti-periodic discrete process. By Step 1, we have  
\[\lambda_{2}^{n}<\prod_{j=-n}^{-1}M_{*}\leq\prod_{j=-n}^{-1}\|M(j)\|\leq\prod_{j=- n}^{-1}M^{*}<\lambda_{1}^{n}\]  
for \(n>N_{2}\).
## 5 Results and Discussion
### Effects of Hyperparameter Optimization on Tree-Based Ensembles
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Classifier** & **ET** & **RF** & **Ada** & **GB** & **HGB** & **XGB** & **CAT** \\ \hline Camel 1.2 & 13 & 16 & 21 & 138 & 168 & 77 & 1842 \\ Debug & 9 & 12 & 4 & 71 & 78 & 31 & 673 \\ Eclipse 2.0 & 5 & 7 & 13 & 234 & 366 & 62 & 3915 \\ Eclipse 2.1 & 6 & 8 & 15 & 291 & 467 & 80 & 4358 \\ Eclipse 3.0 & 5 & 8 & 15 & 342 & 446 & 72 & 5136 \\ Eclipse JDT Core & 5 & 6 & 10 & 48 & 67 & 30 & 797 \\ Eclipse PDE UI & 5 & 6 & 9 & 59 & 87 & 32 & 918 \\ JM1 & 20 & 27 & 40 & 930 & 634 & 116 & 7318 \\ KC1 & 14 & 23 & 32 & 177 & 260 & 84 & 2907 \\ Lucene 2.4 & 10 & 14 & 17 & 77 & 82 & 37 & 743 \\ Mylyn & 11 & 12 & 16 & 135 & 181 & 46 & 1143 \\ PC5 & 20 & 26 & 35 & 362 & 406 & 112 & 4976 \\ Prop 1 & 6 & 8 & 13 & 414 & 321 & 883 & 4095 \\ Prop 2 & 6 & 8 & 20 & 283 & 287 & 816 & 2126 \\ Prop 3 & 5 & 7 & 13 & 204 & 251 & 750 & 2038 \\ Prop 4 & 5 & 7 & 13 & 216 & 263 & 796 & 1910 \\ Prop 43 & 5 & 7 & 12 & 196 & 231 & 574 & 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The computational cost of applying grid search hyperparameter optimization in milliseconds. Figure 2: Wilcoxon effect size for each of the studied tree-based ensembles on every dataset. **RQ1 Answer. ** RF ensembles were most impacted when performing hyperparameter optimization. Hyperparameter optimization had the lowest impact on HGB ensembles. The impact of hyperparameter optimization on RF ensemble was negative on most of the datasets. On average, the magnitude of change was considered negligible to small for the other tree-based ensembles. Hyperparameter optimization of CAT was most costly in terms of computational time compared to the other tree-based ensembles.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 7 Experimental Study
### Performance Evaluation for ART\({}^{+}\)  
We also evaluated the performance of ART\({}^{+}\) structure and compared it to its predecessor, ART [17]. Each cluster node of the ART and ART\({}^{+}\) is a BATON\({}^{*}\) and \(D^{3}\)-Tree structure respectively. BATON\({}^{*}\) was implemented and evaluated in [10], while ART was evaluated in [17], using the Distributed Java D-P2P-Sim simulator presented in [21]. The source code of the whole evaluation process, which showcases the improved performance, scalability, and robustness of ART over BATON\({}^{*}\) is publicly available ([[http://code.google.com/p/d-p2p-sim/](http://code.google.com/p/d-p2p-sim/)]([http://code.google.com/p/d-p2p-sim/](http://code.google.com/p/d-p2p-sim/))) (accessed on 5 December 2021). For the performance evaluation of ART\({}^{+}\), we used the \(D^{3}\)-Tree simulator.  
To evaluate the performance of ART and ART\({}^{+}\) for the search and load-balancing operations, we ran experiments with a different number of total nodes \(N\) from 50,000 to 500,000. As proved in [17], each cluster node stores no more than \(0.75\log^{2}N\) nodes in smooth distributions (normal, beta, uniform) and no more than \(2.5\log^{2}N\) nodes in non-smooth distributions (powlow, zipfian, weibull). Moreover, we inserted elements equal to the network size multiplied by 2000, numbers from the universe [1... 1,000,000,000]. Finally, we used the number of passing messages to measure the performance.  
Note here that, as proved in [17], ART outperforms BATON\({}^{*}\) in search operations, except for the case where \(b=2\). Moreover, ART achieves better load-balancing compared to BATON\({}^{*}\), since the cluster node overlay remains unaffected WHP through joins/de  
Figure 8: Single queries without/with node failures. (**a**) average messages without failures, (**b**) effect of massive failure, (**c**) average messages of \(D^{3}\)-Tree, (**d**) success percentage of \(D^{3}\)-Tree.  
partures of nodes, and the load-balancing performance is restricted inside a cluster node. Consequently, in this work, ART\({}^{+}\) is compared directly to ART.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 7 Conclusions and Discussion
### Recommendations for ETC Industry Development  
Based on current technological development status and future trend analysis, the following recommendations are proposed for ETC industry development:  
1. Policy formulation should strengthen top-level design and comprehensive planning. It is recommended to establish ETC data open sharing policies that promote orderly data circulation and efficient utilization while protecting user privacy. Simultaneously, targeted incentive measures should be introduced to encourage ETC technology promotion and application in urban traffic scenarios, establishing and improving industry standard systems to regulate ETC system construction and operational management.
2. Technology development roadmaps should emphasize phased and systematic approaches. Near-term priorities should focus on breakthrough bottlenecks in massive data real-time processing capabilities and enhancing system integration and standardization levels. Medium-term goals should achieve a deep fusion of ETC with emerging technologies, including 5G, artificial intelligence, and vehicle-road cooperation, expanding application scenarios and service capabilities. Long-term objectives should construct ETC-based intelligent transportation systems to achieve the comprehensive intelligent operation of transportation systems.
3. Industrial ecosystem construction should form collaborative development patterns. Industrial ecosystem systems featuring collaborative development among equipment manufacturers, system integrators, operational service providers, and application developers, promoting the coordinated development of upstream and downstream industrial chains should be established. Open technology platforms and standardized interfaces to reduce application development barriers and facilitate the rapid emergence of innovative applications should be constructed. Professional talent team cultivation should be strengthened to provide intellectual support for sustainable industrial development.
# A Regionalization Approach Based on the Comparison of Different Clustering Techniques
### Most Important Variables in the Construction of the Regions  
The regions resulting from the previously selected model, K-means++ for 5 regions with spatial contiguity as a third-degree polynomial, have been used as dependent variables in a GBM algorithm. After iterations, the best fit obtained an RMSE of 0.3661 in the training phase. The three variables with the highest significance (Table 4) were as follows: Annual Mean Temperature (Bio1), Mean Temperature of Coldest Quarter (Bio11), and Precipitation Seasonality (Coefficient of Variation) (Bio15).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Feature** & **Relative Importance** \\ \hline
**Bio1—**Annual Mean Temperature & 51.71885 \\
**Bio11—**Mean Temperature of Coldest Quarter & 17.91296 \\
**Bio15—**Precipitation Seasonality (Coefficient of Variation) & 10.6/240 \\
**Bio14—**Precipitation of Priest Month & 4.64482 \\
**Bio12—**Annual Precipitation & 2.75624 \\
**Bio18—**Precipitation of Warmest Quarter & 2.72849 \\
**Bio14—**Temperature Seasonality (standard deviation \(\times\)100) & 1.97000 \\
**Euroo**—**Potential evapotranspiration & 1.57400 \\
**Bio19—**Precipitation of Coldest Quarter & 1.56720 \\
**Bio17—**Precipitation of Drieset Quarter & 1.26907 \\
**Bio13—**Precipitation of Wotest Month & 1.08024 \\
**Bio13—**Nathermality (BIO2/BIO7) (\(\times\)100) & 0.73156 \\
**Bio6—**Min Temperature of Coldest Month & 0.35595 \\
**Bio16—**Precipitation of Wottest Quarter & 0.22083 \\
**Ph**—PH of soil & 0.17214 \\
**Bio10—**Mean Temperature of Warmest Quarter & 0.16941 \\
**Bio2—**Mean Diurnal Range (Mean of monthly (max temp—min temp)) & 0.11501 \\
**Bio8—**Mean Temperature of Wotest Quarter & 0.11230 \\
**Bio9—**Mean Temperature of Drieset Quarter & 0.10589 \\
**Bio7—**Temperature Annual Range (BIO5-BIO6) & 0.08240 \\
**Bio15—**Precipitation Seasonality (Coefficient of Variation) & 0.04013 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative importance of applying gradient boosting with the variables of the model with the highest kappa index. (K-means++ ( TSA) with 5 regions).  
Figure 11: Result of the federated heuristic optimization based on fuzzy clustering using the centroids of the K-means++ TSA model and k = 5 as centers for fuzzy clustering.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 2 Functional Architecture of Digital Twinning
### The Architecture
#### 2.2.2 Communication  
Communication aims to ensure the transmission of data between the physical and digital spaces. Various technological solutions can be incorporated to ensure secure, efficient, and real-time data transmission. Edge processing seeks to transform data created according to proprietary protocols (characteristic of commercially available solutions designed for specific environments) into formats better suited for communication, facilitating this process.  
Communication interfaces, which encompass a range of possibilities such as Wi-Fi, 4G/5G, Ethernet, and Satellite, are responsible for transmitting data from sensors to the virtual space, where they are then integrated. This phase has evolved significantly in recent years, driven by technological developments such as the Internet of Things.  
Finally, edge security aims to ensure the security of data sent through mechanisms such as firewalls, encryption keys, and device certificates [40]. As assets become more dependent on the Internet Protocol for data transmission, the development of such security measures becomes increasingly relevant for organisations.  
Nonetheless, certain data may be communicated manually, for example, after asset inspection. These data, although not transmitted automatically--and thus not contributing to the real-time synchronisation of the DT--can be aggregated later into databases and become part of decision support models. From the digital to the physical space, this phase is responsible for sending information about the decision made regarding the physical asset. If performed automatically, the transmission of information involves a coding process and then its transmission through signals to the actuators.
###### Abstract  
Vehicles driving in urban canyons are always confronted with a degraded Global Navigation Satellite System (GNSS) signal environment. The surrounding obstacles may cause signal reflections or blockages, which lead to large multipath noises and intermittent GNSS reception. Under these circumstances, it is not feasible to use conventional real-time kinematic (RTK) algorithms to maintain high-precision performance for positioning. In order to meet the special requirements of safety-critical applications under non-ideal observation conditions, a novel tightly coupled RTK/Inertial Navigation System (INS) algorithm is proposed in this paper, which can provide accurate and reliable positioning results continuously. Our integrated RTK/INS algorithm has three features. Firstly, INS measurements are used to help search for integer ambiguities in the position domain. INS solutions can provide a more accurate initial location and a more efficient search region. Secondly, the criterion for determining whether a candidate position is the correct solution is only related to the fractional value of the carrier-phase measurement. Thus, the new algorithm is immune to cycle slips as well as large pseudorange noises. Thirdly, our algorithm can provide more accurate ranging information than the pseudorange, even though it may not necessarily be fixed successfully, because it selects the weighted ambiguity solution as the result rather than the candidate point with maximum probability. The proposed algorithm is demonstrated on both simulated and real datasets. Compared with single epoch RTK and conventional tightly coupled RTK/INS integrations that search integer ambiguities in the ambiguity domain, our method attains better accuracy and stability in a simulated environment. Moreover, the real experimental results are presented to validate the performance of the new integrated navigation algorithm.  
tightly-coupled navigation system; RTK; INS; multi-constellation; sensor integration; integer ambiguity resolution; high-precision 2018; 182160; doi:10.3390/s18072160