esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Securing ITS Applications: A Practical Case Study
### Test-Bed Platform  
In order to evaluate the performance of the ETSI TC ITS security layer under realistic conditions, we have implemented and experimentally benchmarked the aforementioned security headers, certificate and security profiles. We also analyzed the security weaknesses in the existing standard. To the best knowledge of the authors, there have been very few works [95; 96] that have attempted to evaluate the ETSI ITS security architecture, especially in terms of achieved cryptographic performances.  
The ETSI ITS security layer was implemented in conformance with the latest version of ETSI TS 103 097 [24] and was integrated into an existing standard compliant V2X platform [90]. This platform implements the latest ETSI TC ITS suite of standards, as shown in Figure 9. Our implementation was tested and validated during the fourth ETSI ITS Plugtest event (March 2015) and against the online Fraunhofer FOKUS ETSI TS 103 097 web validator [97]. ETSI TS 103 097 [24] was implemented as a standalone software module and was integrated with the GeoNetworking and facilities layers through standard compliant service access points [98]. In particular, the SN-ENCAP and SN-DECAP service primitives [98] were implemented to enable the above security profiles (_cf._ Section 5.3.3). An SN-ENCAP request is sent from the networking and transport layer to the security entity to request the encapsulation of an outbound message (e.g., CAM, DENM, _etc._) into a SecuredMessage envelope according to a security profile. The outbound message is secured (signed) and encapsulated into a SecuredMessage (as already described in Section 5.3) and is sent back to the requesting layer. The secured message is then encapsulated into a GeoNetworking packet, which is transmitted over the wireless channel by the MAC layer (e.g., IEEE 802.11p). An SN-DECAP request is sent from the networking and transport layer to the security entity to decapsulate an inbound message from the SecuredMessage envelope. The message payload is extracted, verified and made available to the networking and transport layer.  
The elliptic curve digital signature algorithm was implemented using the Bouncy Castle Crypto APIs [99]. For the sake of the completeness of this study, different ECDSA schemas were implemented for signing and verifying CAM and DENM messages: (1) ecdsa_nistp256_with_sha256 in conformance with ETSI TS 103 097 [24]; (2) ecdsa_nistp224_with_sha224 as recommended by the IEEE 1609.2 standard [92]; and (3) three NIST-recommended schemes [94], _i.e._, ecdsa_nistp192_with_sha256, ecdsa_nistp224_with_sha256 and ecdsa_nistp384_with_sha384. The SN-ENCAP and SN-DECAP service primitives were benchmarked on two different CPU architectures in terms of achieved operations per second (OPS) for signing and verifying CAM messages (a payload of 100 bytes) in conformance with all of the rules of the CAM security profile [24]. The results were averaged over 1000 iterations within the 95% confidence interval.
## 5 Conclusions  
In this paper, we proposed an RIS-aided UAV secure communication scheme to protect the privacy information. In the proposed scheme, the beamforming is utilized to maximize the signal power at the RIS in motion. The passive targeted reflection of RIS provided a variable channel environment for legitimate users to acquire the light-of-sight transmission model. Therefore, the secure performance can be improved. For the proposed scheme, we formulated a secrecy rate maximization problem and solved it using the SCA and Riemann manifold methods. The simulation shows the secrecy rate improvement of the proposed scheme, which is about 20% higher than that of the eavesdropping elimination method. Moreover, only the optimization of single base station and single user scenarios is considered in this paper. Multiple base stations and multiple users imply a more complex optimization process, which will be studied in depth in the future.  
[]D.W.: Conceptualization, Methodology, Software, Writing--Original Draft Preparation, Writing--Review and Editing, Visualization, and Funding Acquisition. Y.Z.: Conceptualization, Methodology, Software, and Writing--Original Draft Preparation. Y.H.: Conceptualization, Resources, and Writing--Review and Editing. X.T.: Conceptualization, Resources, Writing--Review and Editing, and Funding Acquisition. L.L.: Conceptualization, Resources, and Funding Acquisition. R.Z.: Conceptualization, Resources, and Funding Acquisition. D.Z.: Conceptualization, Resources, and Funding Acquisition. D.Z.: Conceptualization, Resources, and Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
[]Funding: This work was supported in part by Foundation of the Science, Technology, and Innovation Commission of Shenzhen Municipality under Grant JCYJ20190806160218174, in part by the National Natural Science Foundation of China under Grant 61901379, Grant 61941119, Grant 61901378, and Grant 61901366, in part by National Key Research and Development Program of China under Grant 2020 YFB1807003, in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University under Grant 2020D04, and in part by the Program of China Scholarships Council under Grant 202006290166.  
[]Institutional Review Board Statement: Not applicable.  
[]Informed Consent Statement: Not applicable.  
[]Data Availability Statement: Not applicable.  
[]Conflicts of Interest: The authors declare no conflict of interest.  
Figure 5: Maximum average secure transmission rate with the reflective units on each RIS for different methods (P = 2.5 dbm).
## 3 Investigation Methodology and Experiment Setup
Because these errors are cyclic, their harmonic analysis helps to define what kind of signal imperfections cause them. The first harmonic of the SDE is a result of a nonzero background level of the A and B signals. The second harmonic could be caused by unequal amplitudes or a phase shift. Third and higher harmonics are the result of high-order distortions of the electric signals. Usually, they are caused by diffractive effects in optical-light modulation processes. Thus, an error decomposed into harmonics can be mathematically expressed by the following formula:  
\[\delta(x)=\sum\
olimits_{i=1}^{n}\left(A_{i}cos(\frac{2\pi}{p}ix+\varphi_{i}) \right)+\varepsilon_{n}\text{ }for\text{ }0\leq x\leq p, \tag{2}\]  
where \(\delta(x)\) denotes the SDE inside one period of the encoder grating, \(A_{i}\) and \(\varphi_{i}\) indicate the amplitude and the phase of the harmonic, and \(x\) denotes the relative position inside a period \(p\). The number of the harmonic is marked as \(n\), and \(\varepsilon\) indicates the random error.
## 2 Experimental Test Overview
### Experimental Setup  
#### 2.2.1 Specimen Installation and Setup  
Figure 4 illustrates the specimen's installation and configuration for the shaking table test. The electrical cabinet model was assembled by bolting plates and dummy masses for each floor to a frame fabricated from rectangular hollow sections. It was anchored to M10 anchor bolts pre-installed in the concrete foundation, tightened to 400 kgf-cm using a torque wrench [46]. The shaking table tests were conducted using a six-degree-of-freedom shaking table at the Seismic Research and Test Center (SESTEC), an accredited institution in Korea for seismic performance certification of nuclear power plant equipment. The specimen was mounted by rigidly anchoring the concrete foundation directly to the shaking table. Tri-axial accelerometers (PCB Piezotronics models 356A16, 356A17, and 3711B1130G) were installed at the base of the concrete foundation (A1) and on the main vertical frame members of each cabinet floor (A2-A4), as illustrated in Figure 4, to measure acceleration responses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{3}{c}{**Dimensions (mm)**} & \multicolumn{1}{c}{**Weight**} & \multicolumn{1}{c}{**Resonant Frequency**} \\  & **Length** & **Width** & **Height** & **(kg)** & **(Hz)** \\ \hline MCC & & & & & \\ (480 V) & 550 & 1140 & 2650 & 1290 & 20.25 (Side-side) \\  & & & & & 22.50 (Front–back) \\ \hline Switchgear & & & & & \\ (4.16 kV, 50 kA, 1250 A) & 2700 & 1000 & 2755 & 4312 & 8.50 (Side-side) \\  & & & & & 18.00 (Front–back) \\ \hline Inverter & & & & & \\ (40 kVA) & 2400 & 1000 & 2200 & 2900 & 9.00 (Side-side) \\  & & & & & 23.25 (Front–back) \\ \hline Battery charger & & & & & \\ (DC125 V, 600 A) & 920 & 1600 & 2215 & 1700 & 12.25 (Side-side) \\  & & & & & 29.00 (Front–back) \\ \hline Load center & & & & & \\ (480 V) & 1600 & 2000 & 2480 & 3750 & 12.25 (Side-side) \\  & & & & & 25.00 (Front–back) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of resonant frequency for safety-related equipment in Korean-type NPPs.  
Figure 4: Experimental setup: (**a**) drawings; (**b**) photos.
###### Abstract  
Polycyclic aromatic hydrocarbons (PAHs) are a matter of deep concern as they pose significant environmental and health hazards due to their persistence, bioaccumulation, and toxic effects. In soils, PAHs accumulate, contaminating ecosystems, entering food chains, and posing threats to plant and human health. This study conducted a comprehensive assessment of PAH contamination in agricultural soils (particle size \(<\) 20 \(\mu\)m) across various land-use categories and depths to determine contamination levels and associated health risks, as such comprehensive studies are very rare in Bangladesh. Soil samples were investigated using gas chromatography-mass spectrometry (GC-MS). The findings revealed that the concentrations of \(\sum\)16 PAH ranged from 299 to 1928 \(\mu\)g/kg, with the lowest levels observed in research areas and the highest in industrial soils. Concentration trends were as follows: industrial areas \(>\) coastal areas \(>\) local market areas \(>\) rural areas \(>\) research areas. Higher concentrations were noted for individual PAHs such as Nap, Phe, BBF, and BKF (313.14 \(\mu\)g/kg \(\pm\) 62.01 to 546.97 \(\mu\)g/kg \(\pm\) 146.76), while the lowest concentrations were recorded for BghiP, DBaH, and Ind (1.25 \(\mu\)g/kg \(\pm\) 1.10 to 5.74 \(\mu\)g/kg \(\pm\) 3.77). PAH levels were highest in surface soils, following a depth sequence of 0-5 cm \(>\) 5-10 cm \(>\) 10-15 cm. The results also showed that low-, intermediate-, and high-molecular-weight PAHs comprised 46.06-83.18%, 16.47-48.68%, and 0.23-6.51% of total PAHs, respectively. The source of PAHs was identified as integration of pyrogenic petrogenic. Children were found to have higher exposure through ingestion compared to adults, with lower exposure through inhalation and dermal pathways. The total incremental lifetime cancer risk (ILCR) for children ranged from 2.03 \(\times\) 10\({}^{-8}\) to 9.07 \(\times\) 10\({}^{-7}\), and for adults from 1.96 \(\times\) 10\({}^{-8}\) to 8.04 \(\times\) 10\({}^{-7}\). Both groups exhibited no carcinogenic risk, as per the USEPA threshold. These findings underscore the need for ongoing monitoring and management strategies to mitigate PAH contamination in agricultural soils, thus protecting environmental and public health concerns.  
Keywords:PAHs; soil layer; agricultural soils; land-use categories; health risk +
Footnote †: journal: applied sciences
# Laser Use in Creating Orthodontic Adhesion to Ceramic Surfaces
## 4 Discussion
### The Effect of Laser on Non-Feldspathic Ceramic  
Only few articles focus on more recent types of ceramics, as most are developed on feldspathic porcelain. Two articles, in vitro research done by the same team lead by [PERSON], focus on zirconia ceramics [12,13]. Both studies used Ti: sapphire femtosecond laser for surface preparation. In [12], 180 samples were bonded with metallic brackets, testing mainly the intensity of the femtosecond laser; the conclusion reached was that the ideal setting is 200 mW. In the other study published by [PERSON] et al., the authors focused on comparing femtosecond laser surface preparation to other types of roughening, such as air particle abrasion, silica coating and silane, and a combination of air particle abrasion and femtosecond laser. The ceramic bracket groups had higher shear bond strength values in all groups and femtosecond laser proved to be beneficial either alone or in combination with air particle abrasion for both type of brackets [13].  
An article published in 2015 [14] compared feldspathic ceramics to IPS Empress E-max when using several types of lasers for surface preparation. The two types of ceramics showed similar shear bond strength values in all groups. Ti:sapphire laser showed higher SBS than sandblasting, and even higher than hydrofluoric acid. The ER:YAG and Nd:YAG laser had similar, lower results.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
###### Abstract  
The poses of base station antennas play an important role in cellular network optimization. Existing methods of pose estimation are based on physical measurements performed either by tower climbers or using additional sensors attached to antennas. In this paper, we present a novel non-contact method of antenna pose measurement based on multi-view images of the antenna and inertial measurement unit (IMU) data captured by a mobile phone. Given a known 3D model of the antenna, we first estimate the antenna pose relative to the phone camera from the multi-view images and then employ the corresponding IMU data to transform the pose from the camera coordinate frame into the Earth coordinate frame. To enhance the resulting accuracy, we improve existing camera-IMU calibration models by introducing additional degrees of freedom between the IMU sensors and defining a new error metric based on both the downtilt and azimuth angles, instead of a unified rotational error metric, to refine the calibration. In comparison with existing camera-IMU calibration methods, our method achieves an improvement in azimuth accuracy of approximately 1.0 degree on average while maintaining the same level of downtilt accuracy. For the pose estimation in the camera coordinate frame, we propose an automatic method of initializing the optimization solver and generating bounding constraints on the resulting pose to achieve better accuracy. With this initialization, state-of-the-art visual pose estimation methods yield satisfactory results in more than 75% of cases when plugged into our pipeline, and our solution, which takes advantage of the constraints, achieves even lower estimation errors on the downtilt and azimuth angles, both on average (0.13 and 0.3 degrees lower, respectively) and in the worst case (0.15 and 7.3 degrees lower, respectively), according to an evaluation conducted on a dataset consisting of 65 groups of data. We show that both of our enhancements contribute to the performance improvement offered by the proposed estimation pipeline, which achieves downtilt and azimuth accuracies of respectively 0.47 and 5.6 degrees on average and 1.38 and 12.0 degrees in the worst case, thereby satisfying the accuracy requirements for network optimization in the telecommunication industry.  
pose estimation; sensor fusion; mobile phone 1  
[PERSON], [PERSON] and [PERSON] 1  
10.3390/s17040806
## 3 Experiment
### Durability Test
#### 3.7.2 Relative Mass Change  
Relative mass change was determined using the formula:  
\[RM=\frac{m_{t}-m_{0}}{m_{0}-M}\cdot 100, \tag{2}\]  
where \(RM\) is the relative mass change in %, \(m_{t}\) is the mass at the given time \(t\) in g, \(m_{0}\) is the original mass in g (meaning the zero reading at the age of 7 days before the specimen placement in the sulfate bath) and \(M\) is the total mass of the two measuring pins and the handling hook (the weight of the test specimen fittings). In the case of each specimen, the relative mass change of a model mortar was determined without the influence of the weight of the specimen fittings. Relative mass changes were monitored at the same time intervals as in the case of relative length changes.  
Length and mass changes were monitored for each MM (M1 to M8) on 4 test specimens placed in a sulfate bath as well as on 4 test specimens placed in the water bath. In addition to the comparison of the results of individual MMs with each other, it was also possible to compare the results of the specimens in the sulfate bath and in the water bath for each MM separately. Graphical outputs therefore distinguish between data acquired from samples in the sulfate bath (marked \"s\") and data acquired from the reference samples placed in the water bath (marked \"w\").  
The values determined from the reference (=stored in the water bath) test specimens of the model mortars have a double significance. Firstly, they were supposed to verify that the corresponding model mortars do not behave irregularly under normal conditions, i.e., do not crack due to internal stresses, do not disintegrate due to lack of water for the hydration reaction, etc. Secondly, it is evident that the set of values for the relative mass change of the test specimens placed in the water bath actually represents an output of a kind of non-standard one-year absorbency test of the individual tested model mortars. The significance of the reference samples in the water bath can therefore be seen especially in the possibility to partially verify the results measured on the MMs placed in the sulfate bath. The assumption is that samples with higher absorbency will also have a structure more susceptible to sulfate penetration and will therefore be less resistant. However, the results acquired in this manner do not consider the influence of the chemistry of a particular model mortar and its binder. Both above mentioned aspects integrally apply only if samples in a sulfate bath are monitored at the same time as well. Consequently, a test of samples placed in a water bath alone cannot substitute a comprehensive test in a sulfate bath, yet it can suitably complement it.
## 3 Experiment and Results
### Heating Properties of WPI-SNPs  
One of the most essential criteria for magnetic hyperthermia is the heating capacity of nanoparticles. In other words, magnetic nanoparticles can treat tumor cells because they can be delivered to target cells and release heat through magnetic induction. According to previous reports, nanoparticles heated to 39-48 \({}^{\circ}\)C are effective in treating tumors [27; 28; 29]. Therefore, induction heating devices were used to test the heating of the modified nanoparticles shown in Figure 5a. The original nanoparticles and the prepared bio-modified nanoparticles (WPI = NPs = 50 wt.%) were placed in a heat-resistant tube at the center of the AC electromagnetic coil and heated, exposing to AC magnetic fields by induction heating devices. Figure 5b,c show the magnetic thermal efficiency was compared with the original nanoparticles (50 mg mL\({}^{-1}\)) and the modified \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)Fe\({}_{3}\)O\({}_{4}\) (basic nanoparticle quantifies 50 mg, and then modified with WPI 50 mg) in the same high-power dissipation (6 KW) induction heating for 15 s. The WPI-modified nanoparticles performed well, and the heating temperature was higher than the original nanoparticles. Although the results may be related to the refined superparamagnetic nanoparticles, the biomaterials modification also played a role.  
After a number of experiments on concentrations of WPI-nanoparticles, which contained \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) and Fe\({}_{3}\)O\({}_{4}\) ranged from 50 wt% to 90 wt.%, under normal power dissipation conditions (3 KW), frequency (100 kHz), and rated time (15 s), we found optimal conditions that WPI-\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) with \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) (67 wt.%) and WPI-Fe\({}_{3}\)O\({}_{4}\) with Fe\({}_{3}\)O\({}_{4}\) (85 wt.%) are shown in Figure 5d-g. WPI-\(\gamma\)-Fe\({}_{2}\)O\({}_{3(67 wt.%)}\) can be an easier control of temperature, continuously heated, and slower temperature drop. WPI-Fe\({}_{3}\)O\({}_{4}\)/85 wt%) can be rapid heating to target temperature in a short time, good thermal destructive power, and faster temperature drop. Based on the above results, two different types of modified superparamagnetic nanoparticles can be selected for different tumor types or different treatment options.  
Figure 5h shows M\({}_{\circ}\) value of WPI-\(\gamma\)-Fe\({}_{2}\)O\({}_{3(67 wt.%)}\) and WPI-Fe\({}_{3}\)O\({}_{4(85 wt.%)}\) is 51 (emu/g) and 69 (emu/g). The net magnetic moment, which can be expressed alternatively in terms of \(\mu_{\rm B}\) (Bohr Magnetron) as calculated by the distribution obtained experimentally using Mossbauer spectroscopy, indicated that 3.7 (WPI-\(\gamma\)-Fe\({}_{2}\)O\({}_{3(67 wt.%)}\)) and 4.2 (WPI-Fe\({}_{3}\)O\({}_{4(85 wt.%)}\)) were clearly seen shown in Figure 5i.  
Figure 5: _Cont._Figure 5: _Cont._
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 2 Data and Methodology
### Methodology
#### 2.2.4 Evaluation  
In this paper, we applied four pixel-level quantitative evaluation metrics, namely accuracy, precision, recall and F1 [22].  
Accuracy is expressed as:  
\[Accuracy=\frac{\sum_{i=1}^{n}p_{i,i}}{\sum_{j=1}^{n}\sum_{i=1}^{n}p_{i,j}}, \tag{24}\]  
where \(p_{i,j}\) represents the total number of pixels that belong to class \(i\) and are assigned to class \(j\) and \(n\) represents the number of categories.  
True positive (TP), false positive (FP), and false negative (FN) represent the number of correctly extracted classes, incorrectly extracted classes, and missing classes, respectively. Using these counts, recall, precision and F1 are defined as:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{25}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{26}\]  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}}. \tag{27}\]
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 10 Discussion and Conclusions  
This article presents a simulation-based approach for the challenging and complex winter navigation system in the Bay of Bothnia. While simulation techniques have been applied before to this problem, the novelty of this paper's application lies in the powerful visualization and the integration of data from different sources into a unified, functional model. The model also captures varying ice conditions in detail and their impact on vessel speeds and dirways. Icebreaker assistance was modelled and the impact of adding intelligence to their decision making was studied through preliminary experiments. Furthermore, convoy situations and their related parameters were also successfully captured. The model is based on real-world historical data and inputs from field experts. The behavior of vessels and icebreakers was defined using an agent-based simulation paradigm to capture these effects. The model has been thoroughly verified and validated, both by visual inspection and by comparison with historical data. Experimental results have been presented to describe the validation process in a systematic and detailed manner. The results indicate that the model is in close agreement with the conceptual description and with the historical data. The differences with real-world data observed were as expected. The model is a successful proof of concept and provides a basis for building a simulation-based decision-support system for winter navigation. The navigation of merchant vessels in ice and their interactions with icebreakers are highly complex. Through multiple experiments, some of these  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Vessels in System** & \multicolumn{4}{c}{**Average Difference in Schedule (\%)**} \\  & **3 kn** & **4 kn** & **5 kn** & **6 kn** \\ \hline
50 & 8.38 & 8.93 & 8.93 & 8.93 \\ \hline
116 & 9.18 & 9.18 & 10.43 & 21.06 \\ \hline \end{tabular}
\end{table}
Table 8: Icebreaker assistance threshold variation.  
complexities have been identified and studied. The interdependencies of the parameters have also been highlighted at multiple instances.