esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Object Detection Algorithms
### Evaluation of Object Detection Algorithms  
The research study by [PERSON] et al. [25] makes a comparative analysis of the algorithms that contain Faster R-CNN, YOLOv3, SSD, and RetinaNet. Experiments solve vehicle detection tasks on the KITTI dataset and set the parameters for evaluation. Experimental results have demonstrated that the two-stage detector Faster R-CNN has higher accuracy of 81.09% AP, but the performance of 0.68 FPS is still lower in contrast to single-stage. On the other hand, RetinaNet is considered to have excellent accuracy, which overcomes the preceding algorithm with 89.83% accuracy and optimized performance of 3.59 FPS. In this case, YOLOv3 and SSD belong to the models, which have the worst performance and accuracy parameters. Nevertheless, SSD's performance of 14.27 FPS is suitable for devices with limited hardware. The study also gives increased attention to the variability of possible environments, which extensively affect correct detection.  
The single-stage detectors are suitable for execution on CPUs, mainly SSD and YOLO. Two-stage detectors are adapted to run on GPUs. Faster R-CNN is adapted for a wide range of variable conditions and achieves higher accuracy. However, RetinaNet and newer YOLO versions are real competitors against the two-stage detector.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 4 Examples of Field Applications
### Discussion on Limitations and Future Work Toward Full Automation  
The effectiveness of implementing automated SSI-based OMA for long-term SHM has been investigated and illustrated using the aforementioned datasets. Consequently, the proposed procedure establishes a robust foundation for effective monitoring, facilitates early issue detection, and supports data-driven maintenance strategies. Certainly, it remains limited to assumptions regarding system identification based on SSI, as well as OMA, i.e., linear models, stationary excitations, and non-severe SNR, among other conditions. The identified modes are also restricted to the number and location of the sensors. For diagnosis and prognosis, experienced engineers are still preferred to carry out comprehensive  
Figure 10: The identified frequencies of the second dataset using the proposed procedure.  
assessment and provide informed decision-making concerning maintenance. However, applications of automated SSI-based OMA are not generally restricted by structure type, indicating a good transferability to other structure types or domains once the measurement configuration is adequate. When building a fully-automated SHM system, the P-wave picker can be abridged if stationary excitation can be secured. Moreover, further study is also recommended to fully demonstrate the continuous performance of SHM systems before field deployment, in particular, a detailed investigation on damage detection.
## 2 The Experimental Setup
### Strain Gages and Strain Indicator  
Strain testing is simple in normal environments, whereas, here, the pipeline was buried directly in highly saturated soil, and the strain gauge was tested in a watery environment. To date, there have been few studies on waterproof strain gauge, and thus, there are few established products in existence. Therefore, BX120-3 AA electrical resistance strain gauges waterproofed with underwater, curable adhesive were used in this experiment. The position of the strain gauge patch on the pipe is shown in Figure 2. There were 18 strain gauges in five sections of the pipe and nine test points.  
Figure 1: The buried polyethylene (PE) pipe used in the experiment.  
Figure 2: The layout of the strain gauges and velocity sensor.  
A 16-channel dynamic strain indicator (UT3416) with a maximum sampling frequency of 128 kHz was used for strain measurement. Considering the short duration of the explosion impact process, the sampling frequency was set to 128 KHz.
## 3 Results and Discussion
### Phenolic Compound Profiles
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**N**} & **Rt** & **Mass \({}^{\text{a}}\)** & **Adduct** & **Fragment \({}^{\text{b}}\)** & **Formula** & **Identification** & **Identification Basis** & **Occurrence in Samples\({}^{\text{c}}\)** \\  & **Min** & **\(m/z\)** & & & & & & \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{3.88} & \multirow{2}{*}{153.01868} & \multirow{2}{*}{\([\text{M}-\text{H}]^{-}\)} & \multirow{2}{*}{109.02970} & \multirow{2}{*}{C\({}_{7}\)H\({}_{6}\)O\({}_{4}\)} & 3,4- & \multirow{2}{*}{Molecular ion \([\text{M}-\text{H}]^{-}\)\(m/z\) 153 and \(m/z\) 109, produced after the neutral loss of CO\({}_{2}\) (44 Da)} & \multirow{2}{*}{1B, 5B, 6, 7, 8, 9, 10} \\  & & & & & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{5.12} & \multirow{2}{*}{139.03909} & \multirow{2}{*}{\([\text{M}-\text{H}]^{-}\)} & \multirow{2}{*}{93.03460} & \multirow{2}{*}{C\({}_{7}\)H\({}_{6}\)O\({}_{3}\)} & \multirow{2}{*}{Salicylic acid} & \multirow{2}{*}{Molecular ion \([\text{M}-\text{H}]^{-}\)\(m/z\) 137, which further yielded a fragment ion at \(m/z\) 93 due to the loss of a CO\({}_{2}\) group} & \multirow{2}{*}{5A, 5B, 6, 7, 10, 11} \\  & & & & & & \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{8.72} & \multirow{2}{*}{179.03498} & \multirow{2}{*}{\([\text{M}-\text{H}]^{-}\)} & \multirow{2}{*}{135.04810} & \multirow{2}{*}{C\({}_{9}\)H\({}_{6}\)O\({}_{4}\)} & \multirow{2}{*}{Caffeic acid} & \multirow{2}{*}{Molecular ion \([\text{M}-\text{H}]^{-}\)\(m/z\) 179 and its characteristic product ion 133 due to the loss of the CO\({}_{2}\) group} & \multirow{2}{*}{1A,1B, 2, 5A, 5B, 6, 7, 10} \\  & & & & & & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{13.92} & \multirow{2}{*}{167.03498} & \multirow{2}{*}{\([\text{M}-\text{H}]^{-}\)} & \multirow{2}{*}{152.00996} & \multirow{2}{*}{C\({}_{8}\)H\({}_{6}\)O\({}_{4}\)} & \multirow{2}{*}{Vanillic acid} & \multirow{2}{*}{Molecular ion \([\text{M}-\text{H}]^{-}\)\(m/z\) 167 and its characteristic product ion 152 due to the loss of CH\({}_{4}\)} & \multirow{2}{*}{1A,1B, 2, 5B, 6, 7, 8, 10} \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{16.68} & \multirow{2}{*}{163.04007} & \multirow{2}{*}{\([\text{M}-\text{H}]^{-}\)} & \multirow{2}{*}{119.04881} & \multirow{2}{*}{C\({}_{9}\)H\({}_{6}\)O\({}_{3}\)} & \multirow{2}{*}{\(p\)-counter acid} & \multirow{2}{*}{Molecular ion \([\text{M}-\text{H}]^{-}\)\(m/z\) 163 and its characteristic product ion 119 due to the loss of the CO\({}_{2}\) group} & \multirow{2}{*}{1A,1B, 2, 5A, 5B, 6, 7, 8, 9, 11} \\  & & & & & & \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{24.56} & \multirow{2}{*}{137.02442} & \multirow{2}{*}{\([\text{M}-\text{H}]^{-}\)} & \multirow{2}{*}{93.03325} & \multirow{2}{*}{C\({}_{7}\)H\({}_{6}\)O\({}_{3}\)} & \multirow{2}{*}{4-hydroxybenzoic acid} & \multirow{2}{*}{Molecular ion \([\text{M}-\text{H}]^{-}\)\(m/z\) 137 and its characteristic product ion 93, generated by the loss of the CO\({}_{2}\) group} & \multirow{2}{*}{1A,1B, 2, 3, 4, 5A, 5B, 6, 7, 8, 9, 10, 11} \\ \cline{2-2} \cline{1-2} \cl
## 5 Experiments
### Overall Comparison  
In order to ascertain the comparative effectiveness of the various clustering algorithms, we collate the maximum adversarial and clean accuracies attained under FGSM and PGD attack scenarios. These empirical outcomes are systematically tabulated in Tables 1 and 2, and are juxtaposed with the results obtained through traditional adversarial training techniques.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Clean Accuracy \%** & **FGSM Accuracy \%** & **PGD Accuracy \%** \\ \hline Original model (clean data only) & 98.5 & 17.2 & 13.9 \\ AT baseline & 89.1 & 81.8 & 68.7 \\ K-means + AT & 93.7 & 88.3 & 83.1 \\ K-means\_ASC + AT & 94.1 & 88.9 & 83.8 \\ Agglomerative clustering + AT & 92.5 & 87.9 & 82.2 \\ Agglomerative clustering\_ASC + AT & 92.7 & 88.2 & 82.7 \\ DBSCAN + AT & 95.4 & 91.7 & 86.6 \\ DBSCAN\_ASC + AT & 96.3 & 92.1 & 87.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of model robustness under FGSM and PGD attacks on MNIST.  
From Tables 1 and 2, we see that all models that were preprocessed using clustering algorithms outperformed the baseline adversarial training model. This suggests that the process of clustering, by naturally grouping similar adversarial examples together, has a positive effect on the model's ability to generalize and defend against adversarial attacks.  
Interestingly, the model that used our proposed adversarial structural clustering (ASC) algorithm for preprocessing exhibited the highest robustness across both FGSM and PGD attacks. This indicates that ASC, which takes into account the unique properties of adversarial examples, is better equipped to enhance the model's resistance to these specific types of attacks. This substantiates our initial claim that considering the adversarial structure during clustering would yield models with improved robustness.  
In conclusion, our experiments validate that integrating clustering algorithms in the preprocessing stage of adversarial training can enhance the model's robustness against adversarial attacks. More importantly, the results show that our proposed ASC algorithm outperforms traditional clustering algorithms, demonstrating its effectiveness in this context.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 2 Methodology
### Challenges  
During data extraction and initial data exploration, we have encountered three challenges that constrain our analysis.  
1. _Compression Thresholds_--The ELSPEC PQA instruments have a compression algorithm that introduces a lower cut-off level for the harmonic components in their compression algorithm. Contributions to the overall signal below this cut-off value for each harmonic component will not be recorded in the stored data from the instrument. This threshold may vary between measuring devices, depending on the harmonic noise and the needs of the measurements at the given site. The threshold is usually set to be in a range from 0.1 to 0.2% of the base harmonic component. Values below this level will be stored as 0 values, and is referred to as such in the discussion below.
2. _Computational Tractability_--We had initially set out to load 96 harmonics and six voltages for all three nodes over the four year time period. However, the database proved uncooperative and required frequent restarts during the extraction. We therefore limited the analysis of 96 harmonics to a month.
3. _THD Calculation_--The Elspec instruments also record THD directly, although neither the aggregation interval nor function is clearly documented. We observe a median difference of 21% (ranging from 0 to 56% at the 1 and 99 percentile, respectively) between the THD calculated by our own procedure and the THD directly reportedby the Elspec instrument. We base the analysis in this paper on the above THD calculation for transparency reasons.
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 5 Implementation Strategies
### Pilot Projects in Brazil  
Some MNOs had their pilot projects implemented and tested starting in 2020. The TIM operator carrier, TIP, and INATEL Institute organized a study to test Open RAN technology. The test evaluated the state-of-the-art industry and the maturity levels of the vendors [49].  
In another pilot test in 2020, MNO Telefonica Vivo with Altiostar and Mavenir started a project to test the technology in a 4G wireless mobile network that overlays existing 3G infrastructure. This compiles past technology into new solutions, allowing 3G to be turned off in the future [50].  
Algar Telecom created a pilot test for an Open RAN environment with IBM, Flex, and FIT. The pilot project aims to simplify the network infrastructure using virtualization and clouds. It was intended to extend the 4G coverage of MNO and prepare for 5G implementation [51].  
The Claro pilot project with Huawei and SLC Agricola aimed to enable an agricultural private 5G IoT network. This project tested high-resolution cameras, drones, and sensors aggregated in machines to increase rural productivity. The study helps sustainability by reducing the farm's consumption of power, water, and pesticides using new solutions such as drones, robotics, AI, and cloud and edge computing [42].
# Comparative Tissue Responses of Marine Mollusks on Seasonal Changes in the Northern Adriatic Sea
###### Abstract  
In the shallow Northern Adriatic, marine mollusks are affected by bottom trawling and seafood disturbance. Seasonal oscillations of oceanographic factors additionally influence their physiology, stress responses and survival. Tissue responses to seasonal variations in green ormer (_Haliois tuberculata_ L) and Mediterranean scallop (_Pecten jacobaeus_ L) in the Northern Adriatic have not been reported. Hence, their biochemical and antioxidant defense properties over seasons were studied and the microanatomical structure of their tissue was correlated with function. Histological analysis of gonads revealed two peaks of gonadal maturation and spawning during the spring/summer period and winter season for scallops, and one peak during the fall for corners. The gonadal maturation of both species was correlated with their seasonal variations of metabolic demands and antioxidant capacity. The lipid vacuoles of tubuloacinir terminations in the digestive gland differed between the two species; in scallop they are several-fold larger in size and number. Low temperatures in winter contributed to a decline in enzymatic antioxidant defense in scallop tissues, having lower superoxide dismutase (SOD) and glutathione peroxidase (GPx) activity, and higher concentrations of thiobarbituric acid reactive substances (TBARS) and total antioxidant status (TAS). In ormers, winter induced lower TAS, TBARS, SOD and GPx concentrations. The significant difference of winter TAS and TBARS levels between ormers and scallops was correlated with variations in their reproductive cycles, as well as in antioxidant defense systems. The most important factor for stress-related parameters for both species in this work was found to be the season-induced temperature change.  
Adratic Sea; Istria; Haliotis tuberculata; Pecten jacobaeus; tissue biochemistry; histology +
Footnote †: journal: Applied Sciences
# Synthesis of Nitrogen-Doped Graphene Quantum Dots from Sucrose Carbonization
## 2 Materials and Methods
### Optical Characterizations  
Absorbance measurements were performed at room temperature on an Ocean Optics single-beam QE Pro spectrophotometer operating at wavelengths from 200 to 850 nm. The bandgap energies (Eg) of the GQDs were estimated from the Tauc graph [26], that is, the curve of (\(\alpha\)hv)\({}^{\mathrm{r}}\) as a function of hv, obtained from the UV-VIS absorbance spectrum, where \(\alpha\), h, and v are the absorption coefficient, Planck's constant, and optical frequency, respectively; r = 1/2 for an indirect bandgap material; and r = 2 for a direct bandgap material. Photoluminescence (PL) analyses were performed with a spectrofluorometer (CaryEclipse, Agilent, Santa Clara, CA, USA) at room temperature. The excitation wavelengths ranged between 220 and 600 nm, and the PL was evaluated in the range between 400 and 700 nm. For the optical characterizations, GQDs and N-GQDs were dispersed in DMSO and inserted into quartz cuvettes with a 10 mm optical path.
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
###### Abstract  
Defect size recognition is significant to the evaluation of optical element surface quality. Currently, it's mainly achieved by the conventional image process, such as threshold segmentation. However, as the defect size gradually approaches the diffraction limit of the imaging system, the defect gray distribution changes from bimodal to unimodal, which makes it difficult to be accurately recognized. In this paper, an electromagnetic simulation model of the microscopic scattering dark-field imaging (MSDI) system is built based on the finite-difference time-domain (FDTD) method to research the defect imaging mechanism. The point spread function (PSF) of our MSDI system is measured to revise the far-field simulation light intensity distribution, and the mean value of the distance between three groups of feature points, whose intensity is 0.75, 0.5, and 0.25 of the light intensity distribution peak value, is taken as the feature parameter of the light intensity distribution. To obtain the defect size, the decision regression tree (DRT) is proposed to get the relationship between the feature parameter and the defect size. Besides, some scratches samples are made to verify the validity of the DRT. The results show the relative error of DRT is within 10%, which is better than the threshold segmentation.  
dark-field imaging; finite-difference time-domain; decision regression tree; diffraction limit; point spread function; defect size recognition +
Footnote †: journal: Applied
## 5 Control Algorithms for UAV Networking  
### Centralized Control Algorithm  
In UAV swarm systems, centralized control is a common approach, in which a single point of control, usually a ground control station or a master UAV, plays the role of collecting and analyzing information, making decisions, and issuing commands to all member UAVs. This model helps ensure consistency in operational strategy, allowing all UAVs to coordinate with each other according to a common plan without having to make independent decisions [78]. Figure 25 illustrates an example of a centralized control system, where all UAVs in the formation receive commands directly from a single control station. This model is often applied in surveillance, search and rescue, or military operations, where a high level of precision and coordination between UAVs is required.  
Figure 24: MEC system model with U2X communication.
## 3 Materials and Methods  
In this paper, we propose an approach in which a platform with four generic 20 kg one-dimensional force sensors based on strain gauges (Figure 1a) is used instead of the 6 Dforce-torque sensor. These one-dimensional force sensors were put into an approximately rectangular configuration, as shown in Figure 1b, on top of which an aluminium plate with a robot mounted on it was placed. Note that strain gauge sensors do not need to be placed in the strictly and precisely rectangular configuration. This is due to neural networks' ability to learn from data as is. Therefore, the only strict requirement is that once sensors are in place, their placement should not be changed during the experiment. Finally, note that the platform shown in Figure 1c was for presentation purposes and was not used in the actual experiments.  
Two experiments were performed in this research, and the results of each of them are presented in a separate subsection of the paper. Both experiments were conducted on the described platform and strain gauge measurements were made available by using the HX711 24-bit analog-digital (A/D) converter and Arduino Uno (the same Arduino board was used to acquire data from all of the strain gauge sensors simultaneously). Please note that, during the study, raw A/D converter outputs (i.e., number of levels) were used as neural network inputs, without the need for any additional calibration or data processing.  
The experiments consisted of the data collection phase and the neural networks training/testing phase. All relevant data were collected at once (i.e., they were time-stamped for later software synchronisation) and were used both for training neural networks for end-effector force estimation and joint-side torque estimation. Please note that the 6D force-torque sensor had a sampling rate of 100 Hz, while the strain gauge-based subsystem had a sampling rate of 10 Hz.
# Isothiocyanates: An Overview of Their Antimicrobial Activity against Human Infections
## 4 Isothiocyanates Antimicrobial Properties against Human Pathogens
### Helicobacter Pylori  
_Helicobacter pylori (H. pylori)_ is a Gram-negative bacterium that colonizes human gastric mucosa, causing gastritis and peptic ulcer disease, a condition that may be associated with the development of gastric cancer. _H. pylori_ infection is difficult to eradicate, and therapeutic failure is due to the onset of resistant strains which persist in the gastrointestinal tract in a non-growing tolerant form or as an intracellular pathogen [69; 70; 71; 72].  
In 2002 [PERSON] et al. investigated the antibacterial properties of sulforaphene (SFN) against _H. pylori_[43]. Obtained from broccoli seeds, SFN was used to determine the minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) against three reference strains (_H. pylori_ 26695, J99 and ATCC 43504) and 45 clinical isolates of _H. pylori_ from patients with gastritis and gastric or duodenal ulcers. Regarding the 45 clinical isolates, six were resistant to both antibiotics (CLARMetR strains). The results indicated that SFN showed an interesting high inhibitory activity (median MIC = 2 ug/mL) against bacterial strains resistant to two antibiotics, clarithromycin and metronidazolezo. In addition SNF showed similar antibacterial activity against sensitive strains (median MIC = 2 ug/mL) compared to resistant strains, suggesting that SNF's antimicrobial activity was not dependent on bacteria phenotype [43]. Furthermore, SFN was able to kill the intracellular forms of _H. pylori_--responsible for _H. pylori_--refer only 24 h of treatment [43]. This result could be of great clinical value because one of the reasons for the clinical treatment failure is the onset of resistant strains which persist as intracellular pathogens, as indicated before [43].  
SFN's antibacterial effects against _H. pylori_ were also confirmed by in vivo experiments [16]. [PERSON] et al. [16] demonstrated that natural SFN induced complete eradication of _H. pylori_ from human gastric xenografts implanted in nude mice [16]. The authors report that eight of 11 xenografts treated with SFN, showed completely eradicated _H. pylori_ infections.  
The mechanism of action of SFN against _H. pylori_ was recently investigated [73]. It is well known that _H. pylori_ gastric infection is ensured by its ability to survive in the acidic stomach environment by producing a wide range of ureases [74] which convert urea into ammonia which neutralizes the acidic environment of the stomach [75]. These evidences led [PERSON] et al. to investigate a possible correlation between SFN's bactericidal activity and _H. pylori_ urease inactivation [73]. However, the experimental data did not suggest any functional relationship between those properties. Further investigations will have to be conducted to clarify the molecular mechanism involved in SFN's antimicrobial activity against _H. pylori_[73].
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 3 Review of [PERSON] et al.'s Protocol
### Registration Process  
In [PERSON] et al.'s protocol, a new \(MU\) who requests roaming services must register their identity with the \(HA\). Figure 1 indicates the user registration process of [PERSON] et al.'s protocol [16] and this process is described in detail as follows.  
**Step 1:**: A mobile user \(MU\) inputs \(1D_{MU}andPW_{MU}\) and selects a random number \(N\). Then, \(MU\) computes \(R_{1}=h(ID_{MU}||N)\) and sends a request message to the \(HA\) via a public channel.
**Step 2:**: After obtaining messages \(\{R_{1}\}\), the \(HA\) calculates \(R=(h(ID_{MU}||N)||ID_{HA}||d)\), \(a=h(d)\) and \(C_{MU}=(g^{a}\) mod \(p)\oplus h(R)\). After that, \(HA\) sets the value of the counter \(K=0\) and stores \(\{K,R\}\) in a secure database. Then, \(HA\) sends \(\{R,C_{MU},K,h(.)\}\) to \(MU\) over a secure channel.
**Step 3:**: After obtaining messages \(\{R,C_{MU},K,h(.)\}\), the \(MU\) computes \(K_{MU}=h(ID_{MU}||PW_{MU}||R)\) and stores it in a mobile device. Finally, the mobile device of the \(MU\) contains \(\{R,C_{MU},K,K_{MU},h(.)\}\).