esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of Sample Preparation
#### 3.2.4 Improvement of Sample Preparation Conditions  
During the analysis of multiple samples using the established sample pretreatment and instrument detection methods, significant interference was observed in the chromatographic peaks of IVM and MOX across various feed formulations. This interference prevented the proper integration of the drug peaks in spiked samples at the quantitative limit (QL). Additionally, the recovery rates of EPR (eprinometin) in some concentrated feed samples were notably low.  
To address issues related to matrix interference and extraneous peaks, an additional n-hexane extraction step was incorporated into the purification process for formula feed and concentrated feed samples. This step is specifically designed to remove common non-polar interfering substances, such as lipids and pigments, from the feed matrix ([PERSON] & [PERSON], 2004) [27]. The average recovery rates of the target drugs in formulated feed, with and without n-hexane treatment, are presented in Figure 6a, while the corresponding results for concentrated feed are shown in Figure 6b. The results demonstrated that the recovery efficiency for both feed types improved significantly after n-hexane treatment. This step successfully resolved the interference issues affecting the analysis of IVM and MOX in formulated feed. Furthermore, all target drugs in the concentrated feed samples exhibited satisfactory recovery rates. These findings suggest that for samples with complex matrices, where conventional methods fail to yield reliable results, the inclusion of an n-hexane extraction step during sample pretreatment can effectively purify the samples and meet analytical requirements.  
Figure 5: Recovery effects of different washing conditions on HLB purification column in feed sample ((**a**): average recovery rate (**b**): matrix effect rate). _Remarks: the X-axis represents six macrolide drugs; the Y-axis represents the recovery rate of drugs by different washing conditions on HLB purification column in feed sample (**a**); the Y-axis also represents the matrix rate of drugs by different washing conditions on HLB purification column in feed sample (**b**)._
## 4 Discussion
### Limitations and Future Work  
During this research, several limitations were identified and tracked down. The main limitation is the adoption of XR-driven technologies and the greater need to embrace familiarity with and understand their applicability in various scenarios. Another drawback during our app development was the lack of access to high-performance computing resources essential for XR applications that often require significantly high performance. The system's response time due to unstable internet speed when performing tests had some latency impacts on the quality of the designed XR environments, especially when various queued questions were assigned to the virtual educator. Finally, the challenge of efficient testing and validation of XR apps was considered one of the most important challenges since they require considerable time and valuable resources to ensure stability in performance, accuracy, and interoperability in all platforms. The mock-up created in Figure 9 presents an example of the existing proof of concepts with a static-designed UI concept. The UI depicts various future functionalities such as virtual certificates, modules for courses, progress evaluation, etc. Integrating various modules is crucial to understating the full crop growth circle in various agricultural setups. In a future scenario, researchers can simulate various variations in crop response to agricultural practices so that the entities involved in the learning process can see the simulated result of their practices.  
Figure 9: Future students’ view in virtual environment of agri education.
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
###### Abstract  
In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology involves positioning multiple UAVs at pre-specified locations using the Delaunay triangulation algorithm with performance of O (n log n). This is compared with the conventional single UAV approach at a near distance. Our findings reveal that the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, significantly improves object detection accuracy, especially when compared to a single UAV operating in close proximity. This research employs advanced image processing techniques to identify objects in the area under surveillance. Results indicate a substantial enhancement in the collective surveillance capabilities of the multi-UAV system, demonstrating its efficacy in unconstrained scenarios. This research not only contributes to the optimization of aerial surveillance operations but also underscores the potential of spatially informed UAV networks for applications demanding heightened object detection accuracy. The integration of Delaunay triangulation with multi-UAV systems emerges as a promising strategy for advancing the capabilities of aerial surveillance in scenarios ranging from security and emergency response to environmental monitoring.  
unmanned aerial vehicles (UAVs); Delaunay triangulation; image processing; precision; object detection; aerial surveillance; multiple-UAV systems +
Footnote †: journal:  
0000-0001-5000-0001-5000][PERSON]
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 1 Introduction  
The ocean area accounts for about 71% of the earth's surface area. With the increasingly prominent contradiction between the global shortage of food, resources, and energy supply and the rapid population growth, the development of marine resources is inevitable for historical development. Using wireless sensor networks (WSNs) to perceive and monitor marine environment information can improve the utilization efficiency of marine resources, coordinate the allocation of marine and land resources, and realize the maximum utilization value of marine resources.  
Due to the poor propagation of electromagnetic waves in seawater and since optical communications will be strongly affected by scattering, acoustic waves can enable communications over long-range links, so they provide the most obvious medium to enable underwater communication. Underwater wireless sensor networks are wireless communication networks based on acoustic signals, in which sensors are deployed underwater, where the environments are time-varying, called underwater acoustic networks (UANs). They use aircraft, submarines, or surface ships to randomly deploy a large number of cheap, miniature sensor nodes in the seawater. The nodes form a multi-hop self-organizing network system through underwater acoustic communication, which can cooperatively sense, collect, and process the information of the sensing objects in the network coverage area, and send it to the receiver. They are mainly used to carry out coordinated tasks, such as oceanographic data collection, pollution prediction, ocean mining, shipwreck avoidance, ocean monitoring, etc.  
With the increasing use of UANs in industry and the military, the need to transmit sensitive information on insecure channels is also increasing. It is easy for adversaries toeavesdrop, intercept, modify, and delete the information, which leads to various attacks and causes huge losses [1]. Therefore, it is essential to control access to UANs' information and services and ensure that sensitive information is securely exchanged between users and sensor nodes. At the same time, the UANs are required to be able to respond to the relevant information of the marine environment in real time, reflecting the real-time requirements of the UANs. Only by continuous and real-time monitoring of the changing state of the ocean can humans grasp the ocean data in time, develop, and use the data.  
Authentication can ascertain the user legitimacy of using the network resource and establishing the session key between the user and the sensor node to protect the confidentiality and integrity of the data from the attacker. A number of security authentication and key agreement schemes have been proposed for terrestrial wireless sensor networks (TWSNs), but most of them are not applicable to UANs, due to the energy limitations and susceptibility to capture of underwater sensors and gateways. Therefore, a security mechanism specifically for UANs is needed [2].
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 9 Future PHY Layer Security Challenges and Opportunities beyond 5G  
In many countries, the deployment of 5G is still in the initial stages, with the most employed NSA mode being in the FR1 band. However, the new SA mode-based services based on private networks are currently growing, as well as the use of the FR2 band. In the near future, the promising Frequency Range 3 (FR3) band will play its role, and 5G is also expected to move to space, with the help of the current standardization activities on Non Terrestrial Networks (NTN). With the transition from 5G to 6G, several emerging technologies will gain much importance to provide additional functionalities and improve coverage. The two most prominent examples are probably Reconfigurable Intelligent Surfaces (RIS) and Joint Communication and Sensing (JCaS). The reconfigurable surfaces (see Figure 14) promise to provide coverage in areas beyond the line of sight, or to improve the signal transitions from outdoor to indoor environments, while the JCaS aims to share spectral resources, to provide environment awareness and/or to optimize the radio link performance.  
With the new technologies, new risks and opportunities arise. With implicit environment awareness, the JCaS has the potential to increase PHY layer security [173], but on the other hand, it can also open up new ways of information and privacy leakage [174]. The possibility of attacking legitimate communication without any internal energy to generate jamming signals by sophisticated reflection of the signals from the legitimate transmitter to the legitimate receiver with the use of RIS has been documented in [85]. On the other hand, recently designed Simultaneous Transmitting and Receiving RIS (STAR-RIS) could provide the potential for defeating eavesdropping in a new field of so-called covert communications [175].  
The current and prospective axes of PHY layer security research towards 6G thus include, but are not limited to, the means to secure JCaS transmissions [174], RIS deployments [176; 177], and even to combine these technologies together [178]. In parallel, the security challenges arising from the combination of terrestrial and satellite networks [179] will also be at the center of interest with the rise of 5G NTN.  
Figure 14: Propagation mechanisms of RIS (**left**), principle of RIS deployment (**right**).
## 4 Conclusion
Moving virtual sensing algorithms generate a virtual microphone capable of tracking a virtual location that is moving through the sound field. Three moving virtual sensing algorithms have been developed; the remote moving virtual microphone technique [10], the adaptive LMS moving virtual microphone technique [11] and the Kalman filtering moving virtual sensing method [12]. When combined with an active noise control algorithm, these three moving virtual sensing algorithms were shown to achieve greater attenuation at the moving virtual location than control at a fixed physical or virtual sensor.
## 4 Experiments and Discussion
### Local Difference Analysis Module  
As mentioned in Section 3.3, the LDA module can effectively improve the accuracy of defect segmentation. It can remove falsely detected background regions by feature analysis. Figure 6 exhibits its special effect in detail, while Table 3 records the comparative quantitative results of methods with and without LDA.  
The first method directly adopts morphological operation on the residual image. Even so, there were many falsely detected regions, and low quantitative indicators were drawn. The second method applied LDA, i.e., it removed almost all of the interfering factors. This is reflected by the fact that all three indicators increased by some degree.
## 3 Evaluation
### Evaluation Protocol  
An evaluation protocol based on the definitions suggested by the ISO/IEC 19795 standard ([30; 31]) was applied to quantify the performance of the different methods described above, ensuring fair comparison between them and hopefully future research. It is composed of three parts:  
1. _Dataset Organisation._ First, each evaluation dataset was divided into validation and test subsets, which contain 70% and 30% of the samples respectively. Next, validation subset is in turn separated into enrolment and access samples (also 70% and 30%, respectively), to generate the biometric profile of the users and to simulate accesses into the system. This way, methods were validated and the acceptance threshold was adjusted. Then, new accesses into the already configured system were made using the test samples, allowing for the calculation of more realistic performance rates.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Database** & **Access** & **\#Users** & **\#Images** & **Image Size** & **Color** & **Variations** \\ \hline Ext. Yale B & Public & 28 & 16,128 & 640\(\times\)480 & gray & L, P \\ ORL & Public & 40 & 400 & 92\(\times\)112 & gray & L, T, E, A \\ BioID & Public & 23 & 1521 & 384\(\times\)286 & gray & B, L, S \\ EUCFI & Public & 395 & 7900 & 180\(\times\)200 & color & B, S, E, A \\ PrintAttack & Public & 38 & 1400 & 320\(\times\)240 & color & L \\ gb2s\(\mu\)MOD & Public & 60 & 4220 & 640\(\times\)480 & color & B, S, A \\ gb2 sTablet & Private & 60 & 16,593 & 320\(\times\)240 & color & B, S, A \\ gb2s\_Selfies & Private & 26 & 262 & - & color & B, L, T, E, A \\ gb2s\_IDCards & Private & 26 & 261 & - & gray & L, T \\ \hline \hline \end{tabular}
\end{table}
Table 4: Databases overview. A, B, E, L, P, S and T stand for Accessories, Background, Expressions, Lighting, Pose, Scale and Time, respectively.  
2. _Computation of Scores_. A list of genuine and zero-effort impostor scores was generated at this stage. To this end, the biometric template of each user was created using only one enrolment sample, and access samples were divided into genuine and impostor, corresponding to authentic and forger users, respectively. Then, genuine scores were computed by comparing the access samples against the reference template of the same user, and impostor scores were obtained by comparing each access sample against the biometric templates of the other users. The same process was repeated for each enrolment sample.
3. _Metrics calculation_. Finally, certain metrics about the performance of the system were obtained from genuine and impostor scores. Concretely, validation results are offered in terms of Equal Error Rate (EER) and test results were measured in terms of False Match Rate (FMR) and False Non-Match Rate (FNMR).The threshold associated to the EER computed in the validation stage was used in the test stage.
## 4 Numerical Simulation
### Gaussian Mixture Noise  
The measurement noise is Gaussian mixed noise. Gaussian mixed noise is a typical non-Gaussian distribution, \(r_{k}\sim 0.99N(\mathbf{0},R_{k})+0.01N(\mathbf{0},\mathbf{500}*R_{k})\). The RMSE\({}_{\text{pos}}\) and RMSE\({}_{\text{vel}}\) of the RSRCD-MCCKF algorithm at the final time instant, when the kernel bandwidth \(\sigma\) changes in the non-Gaussian noise environment, are shown in Table 2.  
As shown in Table 2, the value of \(\sigma\) that can suppress the non-Gaussian noise has changed. Compared with the Gaussian conditions, the filtering accuracy of the algorithms is reduced, but the appropriate \(\sigma\) can still suppress the non-Gaussian noise that appears in the measurement. Therefore, it is more important to adaptively select the value of \(\sigma\) according to different scenarios.  
Here, the Gaussian kernel bandwidth \(\sigma=5\) is also selected under the condition of non-Gaussian noise. The RMSE\({}_{\text{pos}}\) and RMSE\({}_{\text{vel}}\) comparison of each algorithm is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **RMSE\({}_{\text{pos}}\) [km]** & **RMSE\({}_{\text{vel}}\) [km-s\({}^{\text{-1}}\)]** \\ \hline RSRCD-MCCKF (\(\sigma=2\)) & — & 0.0349 \\ RSRCD-MCCKF (\(\sigma=3\)) & 1.0829 & 0.0043 \\ RSRCD-MCCKF (\(\sigma=5\)) & 0.4716 & 0.0014 \\ RSRCD-MCCKF (\(\sigma=7\)) & 0.4813 & 0.0014 \\ RSRCD-MCCKF (\(\sigma=10\)) & 0.5375 & 0.0016 \\ SRCD-CKF & 0.6679 & 0.0019 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm accuracy under non-Gaussian noise when kernel bandwidth is different.  
Figure 2: The RMSE comparison of each algorithm under Gaussian noise. (**a**) RMSE\({}_{\text{pos}}\) of each algorithm under Gaussian noise; (**b**) RMSE\({}_{\text{vel}}\) of each algorithm under Gaussian noise.  
It can be seen from Figure 3 that when the measurement noise is non-Gaussian noise, the errors of each algorithm increase slightly. The HRSRCD-CKF algorithm and the MRSRCD-CKF algorithm cannot effectively suppress the non-Gaussian measurement noise, and the algorithm basically fails. The VBSRCD-CKF algorithm and the RSRCD-MCCKF algorithm can suppress non-Gaussian noise to a certain extent. The filtering performance of the two algorithms is basically equivalent and finally shows relatively high convergence accuracy, which reflects their ability to deal with non-Gaussian noise problems.
## 3 Analytical Methods  
In this study, 40 whole rock samples were collected from fresh, unaltered, calcite vein-free layers of the Upper Quilitage Formation in the Kekeqigankake section, Keping area, Tarim basin (GPS: 78 deg41 deg40.8'' N; 40 deg56 deg26.3'' E). The section is an outcrop. We used PM04-0-1 as the first sample at the bottom of the profile, sampling from the old to the new stratigraphic succession until we collected sample PM04-24-1 (Table 1). Due to the length of the profile, we started numbering from PM13-0-1, sampling in chronological order from old to new until PM13-16-1. The samples are limestone and dolomite. All the samples were surface-cleaned and dried, grounded to 200 meshes in an agate bowl [14], and then sieved and packed in paper sample bags. C and O isotope analyses for 40 powder samples were performed using MAT-253 Mass Spectrometer manufactured by Finnigan MAT at the Analytical Laboratory of Beijing Research Institute of Uranium Geology (BRIUG), Beijing, China. Whole rock trace-element concentrations were also determined in BRIUG for further selected 27 powder samples, using a Perkin Elmer Elan DRC-e Induced Coupled Plasma-Mass Spectrometer against primary standard solutions and validated against certified standard rock materials. The analytical precision was generally better than 5%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample No.** & **Lithology** & \(\xi^{\text{13}}\)**CPDB** & \(\xi^{\text{18}}\)**OPDB** & \(\xi^{\text{18}}\)**OSMOW** & **Z** & **T (°C)** \\  & \(f\)\(\
u_{\infty}\) & \(f\)\(\
u_{\infty}\) & **Value** & **T (°C)** \\ \hline PM04-0-1 & Arenaceous limestone & 2.6 & \(-8.8\) & 21.9 & 128.24 & 24.79 \\ PM04-1-1 & Arenaceous limestone & 2.6 & \(-8.5\) & 22.2 & 128.39 & 23.40 \\ PM04-2-1 & Arenaceous limestone & 1.6 & \(-8.2\) & 22.4 & 126.49 & 22.04 \\ PM04-3-1 & Dolomitic limestone & 0.6 & \(-8.8\) & 21.8 & 124.15 & 24.79 \\ PM04-4-1 & Dolomitic limestone & 2.3 & \(-9\) & 21.6 & 127.53 & 25.73 \\ PM04-5-1 & Micrite limestone & 3.2 & \(-8.4\) & 22.3 & 129.67 & 22.94 \\ PM04-6-1 & Dolomitic limestone & 3.4 & \(-8.9\) & 21.7 & 129.83 & 25.26 \\ PM04-7-1 & Micrite limestone & 2.8 & \(-8.5\) & 22.2 & 128.8 & 23.40 \\ PM04-8-1 & Arenaceous limestone & 4.3 & \(-8.9\) & 21.7 & 131.67 & 25.26 \\ PM04-9-1 & Dolomitic limestone & 3.3 & \(-9.3\) & 21.4 & 129.43 & 27.15 \\ PM04-10-1 & Micrite limestone & 3.6 & \(-9.7\) & 20.9 & 129.84 & 29.09 \\ PM04-11-1 & Arenaceous limestone & 2.3 & \(-9.3\) & 21.3 & 127.38 & 27.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis results of C and O isotopic values of carbonate rocks of the Upper Quilitage Formation in Kekeqigankake section, Keping area.
## 2 Challenges in the Market Structure
### Proposed Architecture and System Layout  
Figure 6 shows the basic planned system layout. To appropriately manage the weather variability in RES generation and demand, particular care must be given while establishing data communication between weather forecasting modules and planning modules.  
A cloud computing system will be taken into consideration for this project and, if necessary, employed for modeling purposes based on the required computer resources for Energy projections. The basic architecture of the suggested web application is consisted of the following:  
1. Additionally, a similar computing engine will be created for analysis of the grid and calculation. Databases and storage accounts are necessary for data storage. The two types of computing resources that have been allocated are continuously and per computation/transaction allocation. Power grid information and any necessary market data would be kept on a dedicated, secure server that was physically located at Energo Info Group (EIG) facilities or at another partner's facilities, as agreed. However, power system analysis calculations would also be done, making the application both incredibly fast and \"light\" in terms of the storage requirements.
2. The calculation engine will be powerful enough to handle calculations for transmission, distribution and any specialized microgrid architecture.
3. The database must be organized using the standard RDBM Rational model. Given that the primary datasets are geographically oriented, a spatial geographic component must be implemented. All datasets may now be represented, aggregated, and queried more easily. The development of Web Map Services (WMS) and Web Feature Services is required to display.
4. Infrastructure security for servers is given special attention. Secure Shell (SSH) keys must be used to encrypt server access codes for the development environment. Access must also be limited to certain IP address ranges. On the user side, access to the web application must be restricted to verified accounts made by the administrator.
## 2 Materials and Methods
### Electroless Cu Plating  
Electroless Cu plating was conducted by immersing the Pd/BSA/SDBS-treated ENM in a plating solution composed of 40 mg/mL NaOH, 140 mg/mL KNaC\({}_{4}\)H\({}_{4}\)O\({}_{6}\cdot\)4H\({}_{2}\)O, and 30 mg/mL CuSO\({}_{4}\cdot\)5H\({}_{2}\)O in DI water. Formaldehyde (0.1 mg/mL) was subsequently added in a 10:1 volume ratio (plating solution/formaldehyde). The reaction proceeded via a Cannizzaro-type mechanism as represented by the following equation [27]:  
\[\mathrm{Cu}^{2+}+2\mathrm{HCHO}+4\mathrm{OH}^{-}\rightarrow\mathrm{Cu}+2 \mathrm{HCOO}^{-}+2\mathrm{H}_{2}\mathrm{O}+2\mathrm{H}_{\mathrm{ads}} \tag{1}\]  
Plating was performed at RT for 9-13 min. To ensure uniform deposition, the nanofiber web was flipped once during the process. Conductive wires were attached using silver paste. After plating, the samples were rinsed thoroughly with DI water and air-dried in a fume hood for 2 h. The final device, consisting of a Cu layer deposited on the Pd/BSA/SDBS-treated ENM, is referred to as the ENM planar heater (ENMH). A variant fabricated without the BSA step is denoted as ENMH(-BSA step).