esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Trace Elements Concentrations in Plant Species
In general, several studies demonstrated that TEs' availability is higher in acid soils containing low concentrations of organic matter, such as in LM (0.34%) [29; 30; 31]. Hence, Cu uptake increases within a pH range of 5 to 7, corresponding to LM conditions (pH 5.3). It is also interesting to compare the spatial trend of trace element contents in the selected biomonitors and the content of the same elements in atmospheric deposition, the leading mechanism of transport of pollutants from the nearby industrial sources to the soil and biota in the study area. Our group has reported enrichment of a set of relevant trace elements, including Mn, Cu, As, Cd, Pb, Sb, V, and Co, in both soluble and insoluble fractions of atmospheric deposition [32] in samples collected in the area by a novel filtrating bulk passive sampling device appropriate for on-site measurements [33]. The spatial trend of trace element levels in the atmospheric deposition was similar to the overall trend found in the selected biomonitor samples reported in this work, with higher concentrations in the vicinity of the industrial complex and decreasing concentrations at longer distances and especially upwind. A more detailed study of insoluble atmospheric deposition composition in the area by digestion + ICP-MS, focusing on Al, As, Ba, Cd, Co, Cu, Fe, K, Mn, Pb, Sb, Ti, V, and Zn, showed a similar trend. This study also allowed to identify industrial, traffic, and mineral-crustal factors by principal component analysis and soil critical loads exceedances by atmospheric deposition of Pb at all sampling locations in the area affected by the industrial emissions, more significant in La Greda, close to the industrial complex [34]. Regarding the plant toxicity levels, the case of Cu is especially remarkable, reaching concentration levels 5-25 times higher than the toxic level concentration. Indeed, _O. picensis_ (OP) showed critical trace metal concentrations (above toxicity levels) in LM for Cr, Cu, As, and V, whereas Pb and Ni levels were also found at toxic concentrations. The prevalence of critical Cu concentrations in LG suggests that the impact of the copper smelter could be the primary source of these elements in the area. Even more, both sampling locations (LG and LM) had trace element concentrations much higher than the levels found at the reference site Cachagua (CH), ranging from around 2 (Mn) to 38 (Cu) times, which highlights the impact of the industrial activities in the area. The concentration of these elements in the plants was inversely correlated to the distance of the primary emission sources, as observed in other soil studies in the same area [10].
## 5 Discussion
### Impact of Loss Functions  
The choice of loss function significantly influences the training dynamics and performance of deep learning models by shaping the optimization objective, thereby affecting convergence and generalization across various metrics. Each loss function addresses different aspects of class imbalance and model confidence. For instance, FocalLoss, designed to focus on hard-to-classify examples, slightly enhances performance for models like EfficientNet-B2, as evidenced by its higher accuracy and mAP. Conversely, CrossEntropy and LabelSmoothing yield comparable results for most models, though LabelSmoothing may improve generalization by reducing overconfidence, as seen in MobileNetV3's balanced metrics. However, models like ViT-B-16 and ConvNeXt exhibit poor performance across all loss functions. The radar graphs (Figure 9(a)-c) generated for each loss function visually illustrate these effects, comparing the performance of the best models across the evaluated metrics, and highlighting the trade-offs and effectiveness of each loss function in optimizing diverse model architectures.  
Figure 9: Radar graph comparing model performance across different learning rates for the car collision detection system while keeping schedulers, loss, and optimizer the same.
## 4 Discussion
### Limitations  
This study has several limitations that should be considered when interpreting the findings. First, the variability in jump performance poses a challenge for statistical anal ysis, as a greater variability in individual measures reduces their ability to explain jump height. Another limitation is the use of 2D kinematic analysis instead of a more precise 3D analysis, which could provide a more comprehensive understanding of joint kinematics. Furthermore, the isometric strength tests used in this study do not closely replicate the dynamic conditions of an SLCMJ. While jumping involves force production in a closed kinetic chain, our strength assessments were conducted in an open kinetic chain, potentially limiting the ecological validity of the strength measurements. Previous research suggests that the relationship between lower-limb strength and jump performance is influenced by contraction type and body positioning during testing [14; 23; 42; 49; 52]. Finally, despite standardized jump execution instructions, individual variations in technique and neuromuscular coordination may have influenced the results, leading to differing contributions from specific muscle groups among participants. Taking this into account, it should be emphasized that this is a preliminary study. Future research should be conducted using more sophisticated methods (3D kinematic analysis and dynamic strength tests) to enhance ecological validity, thereby improving the applicability of the findings to real-world training and competition settings. Incorporating advanced motion capture systems, larger and more diverse athlete samples, and longitudinal designs could offer deeper insights into performance adaptations and better inform evidence-based training practices.
# Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
## 1 Introduction
That is, domain experts are employed to determine the key hyperparameters by analyzing the detection performance of candidate solutions. This way of determining key hyperparameters relies too much on domain experts and their experience. The swarm intelligence optimization algorithms have been effectively applied in intrusion detection (ID) for many years [28; 29; 30]. For example, the Genetic Algorithm (GA) is used to optimize features and parameters of the classifier which is used to identify attacks in the ID systems [28; 31]. The Particle Swarm Optimization (PSO) algorithm is combined with some machine learning algorithms, such as k-means, to improve the performance of anomaly detection [28; 32]. The Ant Colony Optimization (ACO) algorithm is combined with Decision Tree to build a multiple level hybrid classifier for classifying attacks in the ID systems [28; 33]. Inspired by these works, in this paper, to improve the deep learning-based detection methods we propose an approach based on the Harris Hawks Optimization (HHO) algorithm [34] for the recommendation attack. The improved detection methods replace the manual optimization in the original detection methods by automatic optimization for optimizing the key hyperparameters. Major contributions of this work are described as follows. * We proposed a hyperparameter type conversion algorithm to convert the key hyperparameters of discrete type to continuous type according to the uniform distribution theory to expand the application scope of HHO algorithm. * We reduced the optimization iterations by using the detection stability as an early stop condition to improve the HHO algorithm. We proposed a hyperparameter automatic optimization algorithm based on the improved HHO algorithm to automatically optimize the key hyperparameters for the deep learning-based detection methods. We proposed a detection algorithm for the recommendation attack by training the deep learning-based detection methods with the optimized key hyperparameters to generate classifiers for the detection. * We conducted a large number of experiments on two benchmark datasets to verify the effectiveness of the proposed approach. The remainder of this research is as follows. Section 2 reviews the related work and describe the background of HHO algorithm. Section 3 details the proposed approach. Section 4 shows the experimental results and analysis. Section 5 shows the conclusion and future work.
## Appendix A Proof of Corollary 1  
Let \(X_{\Omega}\subset\mathbb{R}^{d}\times\{\omega_{1},\omega_{2},\omega_{3}\}\), \(d\in\mathbb{N}\), constitute a 3-class classification task. Furthermore, let the corresponding PSM, \(M\), for some LSM, \(\mu\in\mathcal{M}^{d}\), be defined as follows:  
\[M^{(id)}= \left(\begin{array}{ccc}\omega_{1}&\omega_{2}&\omega_{3}\\ 0&e&f\\ e&0&g\\ f&g&0\end{array}\right),\quad\text{with }e,f,g>0. \tag{1}\]  
Let two of the three values, \(e,f,g\), be equal and smaller than the remaining one. Without loss of generality, we assume it holds that  
\[(e=f)\wedge(g>e=f).\]  
As it holds that \(f<g\), it follows that \(M^{(id)}\) does not constitute an ordinal arrangement, because the last row of matrix \(M^{(id)}\) is not monotonously decreasing. Therefore, the PSM, \(M^{(id)}\), violates the properties of Equation (6). Thus, it directly follows that \(M^{(-id)}\) also violates the properties of Equation (6).  
Let us now define permutation \(\
u\in\mathcal{T}^{3}\), as \(\
u:(1,2,3)\mapsto(1,3,2)\). Therefore, the resulting PSM is equal to  
\[M^{(\
u)}= \left(\begin{array}{ccc}\omega_{1}&\omega_{3}&\omega_{2}\\ 0&f&e\\ f&0&g\\ e&g&0\end{array}\right). \tag{2}\]  
As it holds that \(e<g\), it follows that \(M^{(\
u)}\) does not constitute an ordinal arrangement, because the last row of matrix \(M^{(\
u)}\) violates the properties of Equation (6). Therefore, itdirectly follows that \(M^{(-\
u)}\) also violates the properties of Equation (6).  
Let us now define permutation \(\tau\in\mathcal{T}^{3}\), as \(\tau:(1,2,3)\mapsto(2,1,3)\), i.e., \(\tau\
eq\pm id\) and \(\tau\
eq\pm\
u\). Therefore, with \(-\tau:(1,2,3)\mapsto(3,1,2)\), the resulting PSMs are equal to  
\[M^{(\tau)}= \begin{pmatrix}\omega_{2}&\omega_{1}&\omega_{3}\\ 0&e&g\\ e&0&f\\ g&f&0\end{pmatrix},\quad M^{(-\tau)}= \begin{pmatrix}0&f&g\\ f&0&e\\ g&e&0\end{pmatrix}. \tag{10}\]  
Thus, both matrices \(M^{(\tau)}\) and \(M^{(-\tau)}\) fulfil the properties of Equation (6). Note that the number of elements in \(\mathcal{T}^{3}\) is equal to 6, i.e., \(\mathcal{T}^{3}=\{\pm id,\pm\
u,\pm\tau\}\). We showed that \(M^{(\pm\tau)}\) fulfils the properties of Equation (6) (existence), whereas \(M^{(\pm id)}\) and \(M^{(\pm\
u)}\) violate the properties of Equation (6) (uniqueness). Therefore, we showed that the task \(X_{\Omega}\) is FS-ordinal specific to \((\mu,\pm\tau)\) by Definition 3.  
Analogously, based on the Equations (10)-(10), we can observe that for the case \((e=g)\wedge(f>e=g)\), the task \(X_{\Omega}\) is FS-ordinal specific to \((\mu,\pm id)\), whereas for the case \((f=g)\wedge(e>f=g)\), the task \(X_{\Omega}\) is FS-ordinal specific to \((\mu,\pm\
u)\) by Definition 3 (Note that this proof works analogously to the proof in [21]). \(\square\)
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 4 Numerical Application
### Cycle Random Analysis
Finally, as a comparison between the experimental data with the formulation given in Section 3, the principal results are shown in \[\sigma_{a}=\frac{\sigma_{1}-\sigma_{2}}{2} \tag{28}\]5 From this table, notice that because the addressed Weibull parameters completely reproduce the maximum and minimum vibration stresses, clearly, this Weibull family efficiently lets us predict the reliability indices as it was performed in \[\sigma_{a}=\frac{\sigma_{1}-\sigma_{2}}{2} \tag{28}\]6  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vibration** & **Experimental Data** & **Formulation** & **Estimated Value** \\
**Analysis** & & & \\ \hline Profile & (See Table 1 and Figure 2) & - & - \\ Applied (PSD) & (See Table 2 and Figure 2) & - & - \\ \hline Accel Resp (\(A_{res}\)) & \(A_{res(X)}=71.0\) g & & * \(A_{res(X)}=70.98\) g \\ (See Table 2 and Figure 2) & \(A_{res(y)}=73.0\) g & Equation (4) & * \(A_{res(y)}=73.05\) g \\ Figure 5) & \(A_{res(z)}=72.0\) g & & * \(A_{res(z)}=72.02\) g \\ \hline Dynamic & & & \\ Factor & - & Equation (5) & 5.5 Psi/g \\ (\(\sigma_{dyn}\)) & & & \\ \hline Vibration Stress & \(\sigma_{1}\)= 1188 Psi & Equation (20) & \(\sigma_{1}\)= 1188 Psi \\ (\(\sigma_{vib}\)) & \(\sigma_{2}\)= 330 Psi & Equation (19) & \(\sigma_{2}\)= 330 Psi \\ (See Table 3) & \(\sigma_{\rm ave}\)= 759 Psi & Equation (25) & \(\sigma_{\rm ave}\)= 759 Psi \\ \hline Weibull & & & \\ \(\beta\) & - & Equation (12) & 1.7087 \\ Parameter & & & \\ \hline Weibull & & & \\ \(\eta\) & - & Equation (13) & 62
# Flattening the Curve of Flexible Space Robotics [PERSON]
## 3 Results
### Gain Stabilization Plus Structural Filters (Bandpass and Notch) for the First Four Flexible Modes  
Gain stabilization with the fully compensated first mode was augmented with structural filters using Equation (33), where structural resonant frequencies were used as filter natural frequencies and filter damping was adjusted to visually \"flatten the curve\" of the frequency response plot at the location of the second mode. First, a bandpass variant was designed for the second anti-resonant spike. Next, a notch variant was designed for the second resonant spike. Figure 21 displays the results. Notice once again in the step response plot in Figure 21 that the nominal performances are nearly indistinguishable, while in the \"inset\" plot distinct differences are apparent, where the quantitative results are displayed in Table 4. Figure 22 displays the four frequency response plots overlaid. The nominal frequency response (after gain stabilization) is indicated by the dashed blue line. The thin, solid red line indicates addition of full compensation of the first resonant mode. Meanwhile, the overlaid dashed gold line indicates bandpass compensation of the second anti-resonance. Finally, the \"flattened\" frequency response plot's thin, dotted black line indicates full mode compensation with the addition of a notch filter at the first three structural resonant frequencies. Effects on frequency response phase is displayed in Table 4. Different than the cases of compensating for the second and third structural modes, addition of the bandpass filter at the fourth anti-resonance restores the outstanding gain margin performance, but addition of the notch filter at the fourth resonance does not restore the phase margin performance. Compensation of the fourth resonant structure mode does not exceed the nominal performance, compensating only for the first vibrational mode and should therefore be avoided, despite an instinct to \"flatten the curve\" of the frequency response plot magnitude.
## 1 Introduction
### Paper Organization  
The remainder of this paper is organized as follows. Section 2 presents a comprehensive literature review. Section 3 presents the problem definition, related assumptions, and overviews the proposed architecture and its main components. The preliminaries are discussed in Section 3.1, and an outline of the proposed model for deep ROI and feature extraction is provided in Section 3.2. Sections 3.3-3.5 detail the proposed ROI extraction, feature representation, and matching modules, respectively. The obtained results are detailed in Section 4. Finally, Section 5 concludes the paper with some final remarks and outlines open research problems.
## 2 Structural Design of Semi-Active Vibration Damping Seat Based on Eddy Currents
### The Principle and Basic Equation of Eddy Current Damping Vibration Suppression  
The principle of eddy current damping for vibration suppression is primarily based on the relative motion between a metal conductor and a magnetic field. As the metal conductor moves, it cuts across magnetic field lines, inducing eddy currents on the conductor surface. These eddy currents create an internal magnetic field within the metal conductor, which interacts with the external magnetic field. The eddy current damper utilizes this phenomenon to generate damping forces, thereby slowing down or controlling the motion of an object. The theoretical basis for this principle is as follows.  
[PERSON]'s equations describe electromagnetic fields, and in finite element simulation, these equations need to be derived in differential form. When a conductor moves in a magnetic field or when the magnetic field changes, [PERSON]'s law tells us that an induced electromotive force (EMF) is produced on the conductor. This relationship is expressed by Equation (1):  
\[\
abla\times E=-\frac{\partial B}{\partial t} \tag{1}\]  
where **E** is the induced EMF and **B** is the magnetic flux density.  
At this point, an electric field will induce a current within a closed conductor. This current is known as an eddy current, as shown in Equation (2):  
\[J=\sigma E \tag{2}\]  
where \(\sigma\) is the electrical conductivity and **J** is the current density.  
The interaction between eddy currents and the magnetic field generates a damping force that opposes the motion of the conductor. This damping force is expressed by Equation (3):  
\[F=JB \tag{3}\]  
where **F** is the damping force.  
When a metal conductor undergoes a cutting motion within a magnetic field, it generates eddy currents. From an energy conversion perspective, this process allows the kinetic energy of the moving conductor to transform into Joule heat, resulting in the dissipation of energy and the deceleration or cessation of the moving object. The proposed  
Figure 2: Arrangement of eddy current damping module.  
vibration-damping seat incorporates an array of electromagnets and conductor plates in its damping system. By utilizing the back-and-forth motion of the electromagnets between the conductor plates, eddy currents are induced on the conductor plate, resulting in damping forces and achieving vibration suppression. The damping force of a plate-type eddy current damper is influenced by material parameters, structural characteristics, and the velocity of the components within the system [19; 24].  
It can be seen from Equations (1)-(3) that, to regulate the eddy current damping force \(\mathbf{F_{c}}\), it is necessary in this vibration damping seat system to control the magnetic induction intensity \(\mathbf{B}\). For the control of the magnetic induction intensity \(\mathbf{B}\), the relationship between the system inputs and the quantity to be controlled needs to be explored.
# Piezoelectric Transformers: An Historical Review
## 2 Space, Defense and Security Applications of Piezoelectric Transformers  
During the 2000s, NASA granted several Small Business Innovation Research (SBIR) projects to integrate piezoelectric transformers into space applications [115].  
High Voltage Piezoelectric-Based Power Supply to Drive the Main Payload of Satellite Communication Systems: The Traveling Wave Tube  
This NASA SBIR-founded research program, led by Dr. [PERSON], developed a piezoelectric-based electronic power conditioner (EPC) module to supply power to traveling wave tubes (TWTs) used for space-earth long-distance wireless communication [116; 117; 118]. The developed design eliminated conventional electromagnetic transformers with a compact and highly efficient piezoelectric transformer driver. The developed EPC unit consisted of several modules providing different levels of high voltage and power to the different circuits of a TWT. Figure 20 illustrates the concept of the project (Figure 20a) and one of the developed EPC modules used to drive a 4 kV/10 W cathode of the TWT.  
Development of New Integrated Ignition Systems for Small Satellite Thruster by Using Piezoelectric Transformer Technology  
This NASA SBIR-founded research program developed a new discharge initiation (DI) system using a piezoelectric transformer to initiate the discharge of Pulsed Plasma Thrusters used in small satellites using solid-state spark-plugs [119; 120; 121; 122]. The program took as a reference the design specifications of the PPT development for the Earth Orbit experimental satellite (EO-1) launched in November 2000. The complete prototype of the system (called IGNIT-SONER) was tested in May 2005 at NASA Glenn Research Center under high vacuum conditions (\(10^{-6}\) to \(10^{-7}\) torr) for over 50,000 accumulated cycles. Moreover, the unit was tested for over 500,000 accumulated cycles under atmospheric lab conditions under maximum firing rates of 5 Hz. The developed transformer is an input to output 3 kV-isolated PT, and able to step up the input voltage to over 2.5 kV. The physics associated with semiconductor-type spark plugs used for ignition under vacuum conditions led to the development of a piezoelectric-to-capacitive discharge topology. This topology allows the storing the high voltage energy delivery by the piezoelectric transformer in a capacitor, and then suddenly releasing it to the spark-plug. This sudden release makes it possible to achieve very high current levels of over 100 A for few microseconds, as required to generate the spark. Figure 21a shows one of the  
Figure 20: (a) Modular concept using Transoner technology applied to a traveling wave tube (TWT); (b) Detail of a 4 kV/10 W cathode power supply using piezoelectric transformer.  
IGNIT-SONER developed prototypes. Figure 21b shows the vacuum chamber at NASA Glenn where the prototype was tested.