esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 5 Dataset and Performance Metrics
### Performance Metrics  
To measure the performance of our method, we employed several commonly used performance metrics, including accuracy (Acc), precision (Prec), recall (Rec), and F1-score (F1). These metrics provide insights into the effectiveness of our defense technique in correctly classifying network flows:  
* Accuracy: This is a common metric used to evaluate the performance of ML models. It measures the proportion of correctly classified samples relative to the total number of samples. \[\text{Accuracy}:\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}\] (9)
* Precision: This metric measures the proportion of true-positive predictions to the total number of positive predictions. \[\text{Precision}:\frac{\text{TP}}{\text{TP}+\text{FP}}\] (10)
* Recall: This metric measures the proportion of true-positive predictions to the total number of positive samples in the dataset. \[\text{Recall}:\frac{\text{TP}}{\text{TP}+\text{FN}}\] (11)
* F1-score: This metric is the harmonic mean of precision and recall, providing a single score that balances both metrics. It ranges from 0 to 1, with higher values indicating better performance. F1-score : 2 \(\times\)\(\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{Recall}}\) (12)  
True positives (TPs) and true negatives (TNs) represent the number of instances that were correctly classified as positive and negative, respectively, whereas false positives (FPs) and false negatives (FNs) represent the number of instances that were incorrectly classified as positive and negative, respectively.  
For adversarial ML, additional evaluation metrics may be required to measure the robustness of the model against adversarial attacks. A commonly used metric for evaluating the robustness of ML models is adversarial accuracy:  
* Adversarial Accuracy: Adversarial accuracy measures the percentage of correct predictions made by the model on the AEs. This metric provides a measure of the model's ability to correctly classify AEs. \[\text{Adv Acc}=\frac{\text{TP}_{\text{adv}}+\text{TN}_{\text{adv}}}{\text{TP}_ {\text{adv}}+\text{FP}_{\text{adv}}+\text{TN}_{\text{adv}}+\text{FN}_{ \text{adv}}}\] (13)  
A higher value of adversarial accuracy indicates that the model is more resilient to AEs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Benign** & **GoldenEye** & **Slowloris** \\ \hline \multicolumn{4}{c}{Before preprocessing (raw dataset)} \\ \hline Class counts & 996,077 & 41,508 & 10,990 \\ Proportion of total & 94.99\% & 3.96\% & 1.05\% \\ \hline \multicolumn{4}{c}{After preprocessing (re-sampled dataset)} \\ \hline Class counts & 21,200 & 18,400 & 10,800 \\ Proportion of total & 42.06\% & 36.51\% & 21.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The breakdown of the CSE-CIC-IDS2018 dataset before and after preprocessing.
## 4 Results and Discussion
In this case, in order to maintain the controllability of the transport aggregate, the MDD front axle must be equipped with a 165 kg ballast. A similar situation is obtained with an increase in the installation angle of the lower links of the attached mechanism of the MDD power module up to \(10^{\circ}\) (Figure 8). Figure 8: Dependence of forces \(N_{a}\) and \(N_{c}\) on the installation angle \(\alpha\) of the central link of the rear-mounted mechanism of the tractor when installing lower \(\beta=10^{\circ}\) (\(\alpha>\beta\)). Figure 7: Dependence of forces \(N_{a}\) and \(N_{c}\) on the angle of installation \(\alpha\) of the central link of the rear-mounted mechanism of the tractor when installing lower \(\beta=3^{\circ}\) (\(\alpha>\beta\)). In this case, as the calculations show, at angles \(\alpha=15^{\circ}\) and \(\beta=10^{\circ}\), the controllability of the transport aggregate will be ensured when the front wheels of the PM are ballasted with a ballast weight of 50 kg. It turned out that the third variant of setting the rear hinged mechanism of the MDD power module was rather specific. If, in the previous cases, an increase in the value of angle \(\alpha\) at \(z\geq 0\) determined a certain increase in force \(N_{c}\) (Figures 5-8), then, in this variant, there is a decrease (Figure 9). In addition, the value \(N_{c}\) does not exceed the maximum load capacity of the tires of the technological module wheels, even at values of angle \(\alpha\) close to zero. The value of the vertical load on the front wheels of the MDD power module increases with a corresponding increase in the value of the angle of installation of the central link of the rear linkage of the PM. At certain values of the parameters \(\alpha\) and \(z\), this increase also reaches such values that exceed the level of the maximum load capacity of the tires of the front axle of the MDD. In particular, at \(z=-0.30\) m, this occurs when the angle is approximately \(6.5^{\circ}\). At \(z=0\), this is observed when \(\alpha\) is equal to \(7^{\circ}\). In the case of a positive value of the parameter \(z\) at the level of \(0.30\) m, this takes place at \(\alpha\) at approximately \(7.5^{\circ}\). It should be noted that, for all the variants of setting the rear-attached mechanism of the PM, the change in the vertical load on the wheels of the axle of the TM at the value of the parameter \(z=-0.30\) m is so small that it practically does not depend on the change in the value of angle \(\alpha\).
## 3 Results and Discussion
### Fluorescence Calibration
#### 3.3.2 FITC Dextran  
The average fluorescence intensity was obtained for the \(\sigma\) range of FITC dextran (Figure 9) in which the maximum number of bound FITC dextran molecules after incubation (Figure 7), was estimated to be 1.8 \(\times\) 10\({}^{11}\) molecules per cm\({}^{2}\) (based on the observed calibration for the \"dry state\"). This number was lower compared to the value obtained with Alexa 488 dextran. A possible reason for this discrepancy is the different molecular weight of the two types of dextran molecules. The hydrodynamic diameter of FITC dextran (M\({}_{\rm W}\) = 70 kDa) is larger than the Con A monomer molecule with a published dimension of 4.2 \(\times\) 4.0 \(\times\) 3.9 nm\({}^{3}\)[43]. In fact, the size of the FITC dextran is closer to the dimension of the Con A tetramer (6.3 \(\times\) 11.8 \(\times\) 16.5 nm\({}^{3}\)) [44] with four binding sites. If the labelling molecule is larger than the molecule aimed to be detected, the labelling molecule may cover several active Con A molecules and hence obstruct the binding of a second dextran molecule to an active Con A. Another reason might be the association between the long chain dextran and binding sites on multiple Con A molecules [45]. Therefore, the molecular weight of dextran may have an effect on the quantitative result. The smaller Alexa 488 dextran (M\({}_{\rm W}\) = 3 kDa) has a hydrodynamic diameter below that of Con A, which would facilitate binding to only one active Con A molecule, and with a lower tendency of obstructing the binding site of the neighbouring monomer/molecule. A similar observation was made in the paper by [PERSON] et al. [46].  
Figure 8: Calibration graph of the Alexa 488 dextran in the wet and dry state, with linear regression fitting. Error bars represent the standard error of the mean, \(n\) = 30.
## 3 Results
### Effects of the Intervention Program on Players' Health and Physical Fitness  
Table 3 shows the mean values obtained for the assessment of body composition in the esports players. Significant differences were reported when comparing the mean values for muscle mass (M\({}_{\text{pre}}\) = 51.56 \(\pm\) 7.27 vs. M\({}_{\text{post}}\) = 52.54 \(\pm\) 7.78; \(p\) < 0.05), basal metabolism (M\({}_{\text{pre}}\) = 1628.20 \(\pm\) 220.00 vs. M\({}_{\text{post}}\) = 1652.40 \(\pm\) 231.00; \(p\) < 0.05), and body water percentage (M\({}_{\text{pre}}\) = 64.50 \(\pm\) 4.70 vs. M\({}_{\text{post}}\) = 63.06 \(\pm\) 4.70; \(p\) < 0.05) before and after the intervention program. In addition, a reduction in the players' mean heart rate was reported during in-game training sessions, although with no significant differences. On the contrary, no significant differences were revealed before and after the application of the intervention program concerning the variables of motivation and stress perception (\(p\) > 0.05) (Table 4). Regarding the muscular capacity of esports players, the results showed significant differences when comparing the data before and after the application of the intervention program (\(p\) < 0.05). More specifically, pre- and post-eight-week training program variations were observed between the jump height achieved by the players in all jumps before and after the in-game training sessions (before in-game: M = +12.54% after intervention program, \(p\) < 0.05; after in-game: +16.63% after intervention program, \(p\) < 0.01). The same results were found for data of the pre- and post-eight-week training period concerning players' mobility in the shoulder (before in-game = +7.06%, \(p\) < 0.05; after in-game = +10.17%, \(p\) < 0.01) and wrist (before in-game = +17.67%, \(p\) < 0.05; after in-game = +12.11, \(p\) > 0.05) (see Table 5). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Exercise**} & \multicolumn{2}{c}{**Game 1**} & \multicolumn{2}{c}{**Game 4**} & \multicolumn{2}{c}{**Game 6**} & \multirow{2}{*}{**X2**} & \multirow{2}{*}{**_P_} & ES \\  & **M** & **SD** & **M** & **SD** & **M** & **SD** & & **Games 1 and 6** \\ \hline RPE Phy. S1 & 9.60 & 2.07 & 13.00 & 5.29 & 14.00 & 4.30 & 19.266 & 0.026 \({}^{*}\) & 0.568 \\ RPE Cog. S1 & 10.40 & 2.51 & 15.80 & 3.27 & 18.40 & 2.07 & 21.948 & 0.002 \({}^{**}\) & 0.693 \\ ROF scale S1 & 3.00 & 0.71 & 6.40 & 2.51 & 8.00 & 1.58 & 23.179 & 0.002 \({}^{**}\) & 0.829 \\ RPE Phy. S2 & 7.20 & 1.79 & 8.80 & 2.28 & 10.60 & 2.88 & 17.360 & 0.004 & 0.641 \\ RPE Cog. S2 & 9.20 & 3.03 & 11.20 & 2.59 & 12.60 & 3.36 & 22.233 & 0.004 \({}^{**}\) & 0.800 \\ ROF scale S2 & 1.80 & 1.48 & 3.80 & 1.64 & 4.80 & 2.17 & 21.899 & 0.004 \({}^{**}\) & 0.824 \\ \hline \hline \end{tabular} Note: RPE = rating of perceived exertion; Phy. = physical; Cog. = cognitive; ROF = rating of fatigue; S1 = pre-intervention program; S2 = post-intervention program; M = mean; SD = standard deviation. \({}^{**}\) = \(p\)-value < 0.01; \({}^{*}\) = \(p\)-value < 0.05.  
\end{table} 
Table 2: Changes in RPE values after each game.
# Applicability of Semi-Quantitative Evaluation of the Intercondylar Notch
###### Abstract  
The intercondylar notch (IN) can differ in morphology and size, influencing the contained ligaments. For a better understanding of the influence of the IN's anatomy on knee pathologies, a classification of different shapes was proposed. However, a detailed evaluation of the reliability of these classifications is lacking thus far. In coronal knee MRIs of 330 patients, the IN width was measured and three shapes were calculated to generate objective control results. Notch shapes were classified by two blinded investigators, first without and then with visual assistance to guide the shape classification. The distribution of the three different shapes was as follows: A-shape: n = 43, 13.0%; inverse U-shape: n = 100, 30.3%; \(\Omega\)-shape: n = 183, 56.7%. The semi-quantitative evaluation distribution was as follows: A-shape: n = 44, 13.3%; inverse U-shape: n = 37, 11.2%; \(\Omega\)-shape: n = 249, 75%; there was fair (\(\kappa\) = 0.35) agreement compared to that of the control results. The assisted semi-quantitative evaluation distribution was as follows: A-shape: n = 44, 13.3%; inverse U-shape: 103, 31.2%; \(\Omega\)-shape: n = 183, 55.3%; there was very good (\(\kappa\) = 0.92) agreement compared to that of the control results. In the shape evaluation of the IN, rigid guidelines and visual assistance must be used to ensure reliability. The utilization of visual assistance led to higher inter- and intra-rater agreements in the semi-quantitatively evaluation of the IN shape when compared to those in the classification without visual assistance.  
MRI; knee joint; intercondylar notch; intercondylar space; semi-quantitative evaluation +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Cystatin C, a 13-kDa protein, is a member of a family of competitive inhibitors of lysosomal cysteine proteases synthesized in all nucleated cells at a constant rate [1]. Because of its small free filtration in the glomerulus, its complete reabsorption in the proximal tubule, and lack of tubular secretion, it has been proposed as a more reliable marker of renal function than serum creatinine [2,3]. Cystatin C has also emerged as a marker of cardiovascular risk since high levels of circulating cystatin C have been shown to be consistently and strongly associated with cardiovascular outcomes, in different clinical scenarios [4,5]. However, the mechanisms linking increased cystatin C levels and impaired cardiovascular outcome are not completely understood, so far, although renal dysfunction is a definite plausible link. Early studies suggested that raised cystatin C levels identify a 'prelclinical' kidney dysfunction that may be associated with adverse clinical outcomes [6]. Most recently, [PERSON] et al. [7], in a large meta-analysis of 11 general-population studies and 5 studies with subjects suffering from chronic renal disease, showed that use of cystatin C for estimation of the glomerular filtration rate (eGRF) improved the role of renal function in risk classification with reference to all-cause mortality, cardiovascular mortality, and renal failure. On the other hand, since elastolytic cysteine proteases and their inhibitors are involved in the pathogenesis of atherosclerosis, it has also been suggested that cystatin C levels may be directly linked to the development and progression of atherosclerosis [8,9]. However, mendelian randomization studies [10,11] have shown that the association between cystatin C and cardiovascular disease is not causal. Nonetheless, its role as a marker of atherosclerosis (reverse causality) is still controversial. Therefore, we designed the present study enrolling patients with first non-ST-segment elevation acute coronary syndrome (NSTE-ACS) to evaluate the relationship between serum cystatin C levels and different features of atherosclerosis, namely, 1) characteristics of coronary plaque vulnerability as assessed by optical coherence tomography (OCT), 2) coronary artery calcium (CAC) by means of computed tomography (CT) scan, and 3) aortic wall inflammation as assessed by \({}^{18}\)F-Fluorodeoxyglucose (\({}^{18}\)F-FDG)-positron emission tomography (PET).
## 1 Introduction
Also, by reducing the diameter of the core in MOF, the nonlinearity can be increased and the zero-dispersion wavelength (ZDW) can be controlled over a wide range of wavelengths [21]. Indeed, in order to obtain an efficient supercontinuum source, the fiber has to be pumped close to the ZDW. One of the main advantages of MOFs is to obtain strong flexibility in dispersion modification. The intrinsic ZDWs of chalcogenide glasses are normally located beyond 5 \(\upmu\)m, where it is difficult to find available powerful laser pump sources, not to mention fiber lasers. Then, it would be beneficial to shift the ZDW to a shorter wavelength: 1.55 \(\upmu\)m for the telecom wavelength, 2 \(\upmu\)m, or at least less than 4 \(\upmu\)m.  
For example, by altering the core diameter and with the microstructured geometry, a suspended-core chalcogenide As\({}_{2}\)S\({}_{3}\) fiber with a ZDW blue-shifted to around 2 \(\upmu\)m was realized [11], whereas the ZDW of the bulk glass is around 5 \(\upmu\)m. In this study, the simulated results were confirmed by experimental measurements. Besides, in a Ge-As-Se system [22], where the ZDW of the bulk material is around 7 \(\upmu\)m, a microstructured fiber with a ZDW blue-shifted to 3.6 \(\upmu\)m was achieved by tapering the fiber core diameter from 12 to 6 \(\upmu\)m [22]. Therefore, chalcogenide MOFs combine a broad Mid-IR transmission, high nonlinearity, single-mode guiding, and dispersion tunability, which open up the way to the generation of Mid-IR supercontinua.
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
## 4 Conclusions  
In summary, a DLW approach was used to fabricate reduced graphene oxide from a polyimide-based precursor. The rGO on a CFRP composite performed as an excellent strain gauge with GF \(\sim\)40, which is much higher than that of commercial strain gauges with GF below a value of 5. Further, the rGO could be prepared on various substrates ranging from metals to polymers and showed excellent superhydrophobic properties in all cases. The superhydrophobic was also stable upon complete immersion under water for 25 h. In particular, a laser-based approach that combines flexibility with the substrate-independent one-step fabrication of stable superhydrophobic extends the scope of LIG based sensors for even extreme environments.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13084935/s1](https://www.mdpi.com/article/10.3390/app13084935/s1)]([https://www.mdpi.com/article/10.3390/app13084935/s1](https://www.mdpi.com/article/10.3390/app13084935/s1)), Figure S1: EDS spectra obtained from SH-LIG on the following substrates, (a) Sodalime glass; (b) Al 6061; (c) Silicon. Gold coating was used for the measured sample; Video S1: Water jet bounce-off from the ultrahydrophobic LIG surface.  
Conceptualization, D.M.N.M. and D.J.; validation, D.M.N.M., D.J. and M.G.; investigation, D.M.N.M., D.J. and M.G.; writing--original draft preparation, D.M.N.M.; writing--review and editing, D.M.N.M., D.J. and M.G.; visualization, D.M.N.M. and D.J.; supervision, D.J.; project administration, D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 10: (**a**) Impact and bounce of water-droplet on the SH-LIG before immersion and (**b**) after immersion in de-ionized water for 24 h.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 5 Improving a Bin-Picking Workcell through Human-Robot Collaboration
### Object and Human Detection
#### 5.2.2 Human Detection  
Because we were interested in improving bin-picking performance through human-robot collaboration using only off-the-shelf components, the presence of humans is detected using the same vision sensor used for 3D data acquisition.  
As mentioned above, the Fanuc 3D sensor is composed of a projector and two camera units. One of the cameras was also used for human detection, exploiting the pre-defined 2D iRVision tools. However, the use of Fanuc iRVision tools does not easily allow the detection of complex shapes, such as human hands. Moreover, the two camera units are monochrome cameras and, therefore, it is not possible to detect a specific color in the captured image. Taking into account these limitations, we assumed that the human operator working alongside the cobot wears a special bracelet with geometric shapes that are easy to detect (rhombuses), as can be seen from Figure 4. Geometric shapes are placed all around the bracelet, making it easy to detect the presence of the operator with the arm in different positions and orientations.  
The geometric target is recognized using the _Geometric Pattern Matching (GPM) Locator Tool_, which detects and locates a previously trained image pattern. This tool is based on 2D vision, and to make recognition even faster, the resolution of captured images was reduced. Detecting the presence of a human operator can be useful, for example, to slow down the robot if the operator enters the workcell and to avoid collisions.
###### Abstract  
As rainfall exhibits high spatiotemporal variability, accurate and real-time rainfall monitoring is vitally important in fields such as hydrometeorological research, agriculture and disaster prevention and control. Nevertheless, the current dedicated rain sensors cannot fulfill the requirement for comprehensive precipitation observation, owing to their respective limitations. Within the last two decades, the utilization of commercial microwave links (CMLs) for rainfall estimation, as an opportunistic sensing method, has generated considerable attention. Relying on CML networks deployed and maintained by mobile network operators can provide near-surface precipitation information over large areas at a low cost. Although scholars have developed several algorithms for obtaining rainfall estimates from CML data, the rainfall estimation technique based on CMLs remains challenging due to the complex effect in the microwave radiation transmission process. In this paper, we provide a comprehensive review of the technical principles, developments and workflows for this technology, alongside its application in environmental monitoring and hydrological modeling. Furthermore, this paper outlines the current challenges and future research directions, which will hopefully draw the attention of researchers and provide valuable guidance.  
commercial microwave links (CMLs); opportunistic sensing; rainfall estimation; environment monitoring; remote sensing 2023154821[[https://doi.org/10.3390/rs15194821](https://doi.org/10.3390/rs15194821)]([https://doi.org/10.3390/rs15194821](https://doi.org/10.3390/rs15194821))  
1
# An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
## 4 Optimisation Approach
### Case Studies  
We propose three case studies to test the optimisation algorithms by using simulations. They feature three, five, and 10 UAVs, each one composed of 100 different scenarios in which the UAVs begin to build the formation from different points in the simulation arena, as shown in Figure 6. Although many different initial conditions were covered, a central sphere having a 10-m radius was initially left empty to simulate the UAVs approaching the rogue drone from different distant points.  
These scenarios were modelled in ARGoS [26], a multi-physics robot simulator which can efficiently simulate large-scale swarms of robots of any kind. In our study, we have used the model of the Spiri UAVs [27] by using the range and bearing communication model provided by ARGoS (Figure 7). Each UAV is only aware of the relative distance and angles to the other robots, calculated from the received beacon signals. We have worked in an area of 30 \(\times\) 30 \(\times\) 30 metres, although the system can be easily adapted by scaling the UAVs' parameters according to the new dimensions [20]. The distance to the rogue drone (\(D_{RS}\)) used was five metres, set up according to the simulation arena dimensions.  
The controller of the UAVs was programmed to move in the direction provided by the DFA [3] with the parameterised speed (\(Spd_{i}\)). No specific collision-avoidance algorithm was needed as the same repelling force between UAVs used in the formation, prevents them from being too close to each other and colliding.  
Figure 6: UAVs’ initial positions for the three case studies.