esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method
## 2 Related Work
Their work recognized radar Doppler images of six human activities with an accuracy of 82.33%. [PERSON] et al. [34] also used the CNN-LSTM model to recognize human activities. The authors proposed an approach to collect data on volunteer activity by placing a non-invasive tri-axial accelerometer device. Their innovation lies in two aspects: they used LSTM to classify time series and they proposed a new data enhancement method. The results show that their model is more robust. [PERSON] et al. [35] used IR-UWB radar combined with CNN for binary classification to recognize falling and normal activities with an accuracy of 96.35%. [PERSON] et al. [36] applied a five-layer convolutional neural network AlexNet with fewer layers on HAR. They believed that features could be better extracted by using fewer convolution layers. Although deep learning has a strong learning ability and high accuracy in HAR, it needs a large volume of data for training purposes. Due to the particularity of the elderly, it is difficult for them to generate some high-risk activities for data collection. In order to solve this problem, we proposed a method that combines improved principal component analysis (PCA) with an improved VGG16 model (a 16-layer neural network model pre-trained by the Visual Geometry Group) for HAR. This method enhances feature dimensions with high-value information while preserving the basic features of the raw data. Moreover, it speeds up the convergence rate and reduces over-fitting.
# Investigations on Interface Shear Fatigue of Semi-Precast Slabs with Lattice Girders
## 4 Fatigue Tests on Semi-Precast Slabs with Lattice Girders
### Test Results
#### 4.5.4 Influence of Steel Strains  
Figure 19 exemplarily shows the measured steel strains of the diagonals \(\epsilon_{\text{dia}}\) and the welded node \(\epsilon_{\text{wel}}\) of the lattice girders for Specimen EG12 to evaluate the influence of the stress ranges in the lattice girders. The stress range in the diagonals of the lattice girders calculated from the measured steel strain range was about \(\Delta\sigma_{\text{dia}}=167\) N/mm\({}^{2}\) for a period of \(N>2.0\) million load cycles. This exceeds the approved stress range by 76% according to the technical approvals of the lattice girders of \(\Delta\sigma_{\text{Risk}}=92\) N/mm\({}^{2}\). The measured stress ranges of the welded nodes were determined to be only \(\Delta\sigma_{\text{wel}}=42\) N/mm\({}^{2}\). After the exposure of the lattice girders, fractures could only be determined in the diagonal and vertical bars of the lattice girders (Figure 19) but not in the welded nodes.  
Table 5 compares the largest measured stress ranges in the lattice girders \(\Delta\sigma_{\text{max,test}}\) to the calculated stress ranges \(\Delta\sigma_{\text{calc,1}}\), determined by the initially applied shear range \(\Delta V_{1}\) according to the interface fatigue regulations of the technical approvals [38,39]. The measured stress ranges in the diagonals of the lattice girders, as well as the calculated stress ranges were generally larger than the stress range limit of the technical approvals with \(\Delta\sigma_{\text{Risk}}=92\) N/mm\({}^{2}\). Despite these high stress ranges, the slabs usually resist more load cycles than expected. Structural failure of the slabs induced by fatigue failure of the lattice girders (in Table 5, tests with fractured lattice girders are labeled by * in the column of \(\Delta\sigma_{\text{max,test}}\)) could generally not be determined after the initially applied load level \(\Delta V_{1}\) but only after increasing the top load or the amplitude. Despite Specimen EG09, which can be assessed as aberration, structural failure with fractured lattice girders could only be determined for specimens with large numbers of load cycles and high stress ranges. The failure of individual bars does not generally lead to structural failure, which has already been shown in other fatigue tests on shear reinforcement [9,10].  
A more accurate design model considering the number of load cycles applied can better represent the measured stress ranges. However, due to the different crack patterns of  
Figure 19: Influence of steel strains: load history, strain measurements of lattice girder, and fractured lattice girder for Specimen EG12.  
the tests and the resulting range of measured steel stresses, a clear influence could not yet be determined. Further investigations are necessary and will be performed in the future.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
###### Abstract  
We surveyed the variation in perfluorinated compound (PFC) concentrations entering urban wastewater treatment plants and then designed an optimal PFCs treatment method based on a pilot test. The PFCs influent concentration was found to be affected by the types of industries and operating rate. The concentration of PFCs in the wastewater treatment effluent was slightly lower than that of the influent. Thus, PFCs had not been adequately removed by the existing biological treatments. The pilot test results showed that about 10% of PFCs was removed by coagulation and precipitation, and the ozone and chlorine test showed that few, if any, PFCs were removed regardless of the oxidant dose. The activated carbon adsorption test showed that the removal significantly increased with empty bed contact time, with about a 60% removal in five minutes and over 90% removal in over 15 minutes. Therefore, a more stable and higher PFCs removal would result from continuous oxidation processes, such as ozone and adsorption processes involving activated carbon, rather than a single biological treatment.  
Keywords:perfluorinated compounds; coagulation; ozone; chlorination; activated carbon +
Footnote †: journal: Applied Sciences
# A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms
## 3 Urban Rail Train Turn-Back
### Operating System and Communication Modules
#### 3.2.1 Automatic End Change (AEC)  
When the automatic train protection (ATP) at the head meets the auto-switch condition, the automatic reverse (AR) light at the head turns on and the man-machine interface (MMI) displays an icon that allows the auto-switch; the driver then presses the turn-back button at the head. The AR light then flashes, the MMI switches to the auto-switch icon, and the parking brake is activated (after pressing the button, the driver can pull out the first key). When the driver at the end presses the button, they also send a \"status during reentry\" message to the ATP at the end. After the parking brake, the head-end sends the transfer request and positioning information to the tail end, and the AR lamp at the tail end enters  
Figure 3: Schematic diagram of urban rail train interior.  
Figure 2: Train route.  
an always-on state. After the end acknowledges the request, the end remains the same, and the end registers with the zone controller (ZC) and outputs the activation and AR state of the ZC. After successfully registering with the ZC, the tail sends the activation status to the ZC to activate and the reentry status to the AR. The ZC outputs special control messages or performs mobile authorization. The ZC receives the end of the activation state, which is also successful registration of the end; the end of the first sent activation state becomes a non-activation state. At this point, the tail satisfies the centralized traffic control (CTC) upgrade condition, the tail is upgraded to CTC operational control level, and the AR lights start flashing. After the tail-end driver inserts the key, it outputs the parking brake. The tail-end driver then presses the tail-end to turn the back button. The tail-end changes into the activation mode and sends the command \"end turn-back\" to the head. After receiving the end of the command to switch to standby (STB) mode, the parking brake eases and the AR lights are turned off; then, the ZC cancellation request begins. After receiving the message, the terminal stops sending the AR state to the ZC, the parking brake begins to ease, and the tail AR lamp is turned off. This concludes the automatic end change process.
## 1 Introduction
However, the implementation complexities with the tuning of parameters are amajor hindrance of this finding. The above-mentioned algorithms' drawbacks can be handled by applying Flower Pollination (FPA) as an MPPT technique. [PERSON] et al. [22] has discussed the FPA algorithm for PV MPPT under dynamic operating conditions. This algorithm provides single-stage global searching, simpler coding, and lower tuned specification requirements with low cost implementation and has fast response compared to P&O and PSO techniques under dynamic weather conditions. Included in this work, a novel flower pollination algorithm is contemplated and associated with the hybrid ANFIS MPPT [23] algorithm. Compared to the FPA algorithm, the merits of the hybrid ANFIS-Flower Pollination Algorithm (FPA) are simple implementation, high convergence speed with tune parameters and easier code compilation. Due to presence of parasitic components, the voltage outcomes and power transform adequacy are restrained in classical switched-power converters. However, re-lift/triple lift methodology is employed by Luo converter to enhance the voltage balance and run-over the limiter issue. Equated with Zeta, single-ended primary-inductor converter (SEPIC) and Cuk converters, the Luo converter has accurate steady and dynamic system behavior. However, the Cuk converter comprises maximum transients and more settled periods with average system performance of SEPIC and Zeta compared to the Luo converter, which is applicable for upraised power utility and electrical drive operation [24; 25]. In contrast with different employed power converters, modern Luo convertershave been considered for this research approach as they deliver better power/density ratio with economical implementation. The most recent attempt, using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred and examined using dSPACE (DS1104) platform under changing weather conditions.
## 1 Introduction
### Motivation and Contributions
In recent years, the academic community has proposed various solutions to address privacy protection and security issues in FL. For example, technologies such as Differential Privacy [9] and Homomorphic Encryption [10] have been applied to protect data privacy during model updates. However, these methods may increase communication overhead in high-latency and limited-bandwidth network environments, affecting model convergence speed and performance. The main contributions of this paper are as follows:  
Adaptive Transfer Learning Knowledge Sharing Framework. We propose an adaptive knowledge sharing model for Space-Air-Ground Integrated Networks (SAGINs) that quantifies device contributions through dynamic weighting factors while matching task similarities to enable differentiated knowledge transfer across devices. This design effectively mitigates the negative impact of non-IID (non-Independent and Identically Distributed) data distribution on global model convergence and enhances collaborative efficiency among heterogeneous devices. Bi-Level Reinforcement Learning and Hierarchical Attention Mechanism. We develop a novel \"global-local\" bi-level reinforcement learning model where the global tier selects device categories while the local tier identifies specific devices using hierarchical attention mechanisms and meta-learning. This hierarchical approach reduces search space and addresses dynamic topology changes, improving convergence speed and reducing communication overhead. Dynamic Privacy Budget Allocation and Robust Aggregation. We design a reinforcement learning-based privacy budget allocation strategy that adaptively adjusts differential privacy noise intensity, balancing privacy protection with model accuracy. We incorporate clipping methods and transfer correction terms to enhance aggregation robustness, while implementing caching and redundancy strategies to ensure continuity during communication interruptions, improving overall system reliability and efficiency.
# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 3 Methods
### Datasets  
We implement our framework on the ISIC2019 challenge's Skin Lesion Classification dataset [34], which is a compilation of skin lesion images utilized for the identification and categorization of melanoma. The dataset comprises \(33,126\) images with dimensions of \(1024\times 1024\) pixels, with binary labels indicating whether each lesion is benign (0) or malignant (1). Additional metadata are also provided with other information, including the approximate ages of the participants. We performed 5-fold cross-validation on the dataset and report the mean and standard deviation. To demonstrate the transferability of MA-ADRel, we also used the PAD20 dataset for testing [35], which contains dermatoscopic images of skin lesions collected from a public health center in Brazil using smartphones. It contains 2298 images with various metadata, including age.
# Stability Analysis of GNSS Stations Affected by Samos Earthquake
###### Abstract  
An earthquake cycle can cause meters of displacement on the surface and at Global Navigation Satellite System (GNSS) stations. This study focuses on the identification of GNSS stations that have significant displacement because of a Mw 7.0 earthquake near Samos Island on 30 October 2020. The S-transformation method is used to examine 3D, 2D and 1D coordinate systems along with threshold and statistical test approaches. The highestoseismic offset among the 21 GNSS stations is displayed by SAMO, and CESM, MNTS, IZMI and IKAR also experience significant displacement. Significantly displaced stations are successfully identified in both 3D and 2D analyses. In the up component, SAMO is the only unstable station. The coordinate S-transformation method can be used in detecting unstable points in a GNSS network and provide valuable information about the effects of an earthquake on GNSS stations.  
c 2024 14 2301. [[https://doi.org/10.3390/app14062301](https://doi.org/10.3390/app14062301)]([https://doi.org/10.3390/app14062301](https://doi.org/10.3390/app14062301))
## 2 Operations of Li-Ion and LA Batteries
### Preliminary of Li-Ion Batteries  
This technology has existed for less than half a century. It is acknowledged in the electronics and transportation industries, mainly in the performance of plug-in-hybrid electric vehicles (PHEVs) and power systems implementations [21]. Its negative electrode consists of graphite, while the positive electrode consists of 'lithiated' metal oxide, such as lithium cobalt (III) oxide (LiCoO\({}_{2}\)) or lithium nickel dioxide powder (LiNiO\({}_{2}\)); the electrolyte solution consists of lithium salt such as lithium hexafluorophosphate (LiPF\({}_{6}\)) or lithium perchlorate LiClO\({}_{4}\) that is solvated in an 'organic carbonate' solution. The lithium cations move to the anode when charging and to the cathode when discharging--a form of 'intercalation' chemical reaction [22]. Equations (3) and (4) depict the reversible reactions required in the charged/discharged states at negative and positive electrodes, respectively [23].  
\[\text{C}+\text{xLi}^{+}+\text{xe}^{-}\leftrightarrow\text{Li}_{\text{x}}\text{C} \tag{3}\]  
\[\text{LiMO}_{2}\leftrightarrow\text{Li}_{1-\text{x}}\text{MO}_{2}+\text{xLi}^ {+}+\text{xe}^{-} \tag{4}\]
## 2 Sampled Apodized Grating Waveguide Structure and Design
### Apodized Sampled Waveguide Grating Design  
In previous section, the design has been optimized well to satisfy \(\Delta\lambda_{b}\) and SLSR in the single Bragg grating. The numbers of parameters of \(N\) and \(t\) should be optimized well to realize the required \(\Delta\lambda_{s}\). The reflection property of each sampled period is related to the mentioned Bragg grating in the Section 2.4 and connected waveguide with a length of \(d\)-\(P\). The mentioned Bragg grating depends the bandwidth of the reflection spectrum and the connected waveguide changes the optical phase, which affects \(\Delta\lambda_{s}\). The transfer matrix method is adapted to solve the sampled grating reflection spectrum. In the transfer matrix of sampled waveguide grating, \(G_{i}\) expresses the reflection property in the \(i\)-th sampled period, which is the product of phase-shift matrix \(F_{pi}\) for a phase shift before the mentioned factor of \(F_{grat}\)[32]. The expression of \(F_{pi}\) is shown in Equation (13) and the calculation of the reflection spectrum is expressed in Equation (14).  
\[F_{pi}\ =\ \left[\begin{array}{cc}\exp[-j\beta(d-P)]&0\\ 0&\exp[j\beta(d-P)]\end{array}\right]^{\prime}\!\!G_{i}\ =\ F_{pi}F_{grat} \tag{13}\]  
\[G_{grat}\ =\ G_{N}G_{N-1}\cdots G_{i}\cdots G_{1}\ =\ \left[\begin{array}{cc}G_{11}&G_{ 12}\\ G_{21}&G_{22}\end{array}\right](i\ =\ 1,2\cdots N)\]  
\[  
Figure 6: Reflection spectrum of the proposed apodized sampled grating related to \(N\). (**a**) \(N=1\); (**b**) \(N=3\); (**c**) \(N=5\); (**d**) \(N=10\).  
Figure 7: Reflection spectrum of the proposed apodized sampled grating related to \(t\) when \(N=5\). (**a**) \(t=0.1\); (**b**) \(t=0.3\); (**c**) \(t=0.5\); (**d**) \(t=0.7\).  
This metal-printing-defined apodized grating waveguide device can realize a blueshift of the wavelength by applying power to the electrode due to the negative polymer TO coefficient. In order to realize a blueshift of every reflection spectrum peak, probes should be placed on the leftmost and rightmost electrode plates. The value of wavelength blue shift can be expressed as  
\[2(\frac{dn}{dT}+\alpha n)\Delta T\Lambda\ =\ m\Delta\lambda \tag{15}\]  
In Equation (15), \(dn/dT\) is TO coefficient of the material and \(\alpha\) is the thermal expansion coefficient of the waveguide material. The TO coefficient and thermal conductivity of FBPA-PC EP are \(-1.85\times 10^{-4}\)\({}^{\circ}\)C\({}^{-1}\) and \(0.25\) Wm\({}^{-1}\)C\({}^{-1}\), respectively. Figure 9a shows the cross-sectional thermal field distribution of the metal-printing heater and Figure 9b shows the reflection spectrum when the temperature change (\(\Delta T\)) generated from the electrode heater are 0, 10, and 20 \({}^{\circ}\)C, respectively. The wavelengths have a shift of 1.9 and 3.7 nm when the values of \(\Delta T\) are 10 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, which are approximately equal to the calculation of Equation (15). This device has the ability to tune the wavelength with its thermo optic effect.
# Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
## 3 Material Characterization
### Potting Polyurethane (UIR5608)  
The Young's modulus of UR5608 was measured using a procedure based on ASTM D638 [26] on Type I dog done specimens. A slow crosshead speed of 0.5 mm/min was used to match the quasi-static nature of thermal loading (described in Section 1). The tests were conducted between 20 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, using an Instron 1185 with a mounted environmental chamber. Strain was measured using an extensometer with a gauge length of 50 mm. The resulting stress-strain curves are presented in Figure 3, showing that the polymer is well approximated as linearly elastic across the entire temperature range. Themodulus of UR5608 was also not observed to vary significantly with temperature, at least at a loading rate of 0.5 mm/min. A temperature-independent modulus of 33 MPa was used for the simulations. Poisson's ratio was measured previously to be 0.48 [14] and was also approximated to be constant with temperature.  
Figure 3: Tensile stress-strain curve for UR5608 at various temperatures.  
Figure 2: (a) Stress-free thermal strain output of six ferrite samples, SS400 (reference) and the lead wire, (b) calculated CTE of ferrite vs. temperature.  
Thermal expansion was measured using a dynamic mechanical analyser (DMA Q800 from T.A. Instruments, New Castle, DE, USA) in 'film tension' mode, with a temperature ramp of 1 \({}^{\circ}\)C/min. The thermal expansion of the polymer with temperature was very linear and repeatable across three samples. Based on the average slope of the thermal expansion curves, the coefficient of thermal expansion (CTE) of the polymer was determined to be 177 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C. A thermal conductivity of 0.78 W/m\(\cdot\)K was taken from the supplier datasheet [27].
# Tuning of Graphene-Based Optical Devices Operating in the Near-Infrared
## 3 Graphene-Based Optical Devices and Tuning
The first includes all procedures where graphene is in contact, at some stage, with a liquid. In the  
Figure 7: Examples of the device configurations: (**a**,**a1**) Mach–Zehnder graphene modulator (M-ZM), (**b**,**b1**) planar photonic crystal nanocavity-based modulator (CNM) and (**c**,**c1**)–micro-ring resonator (MRR). second, one face of graphene is protected from contacting any liquid, while the other is typically in contact with a polymer, eventually dissolved by solvents. A big problem is the development of dry transfer technology to preserve the ultra-high quality of graphene up to 450 mm in size [69]. Since graphene is a monolayer and, therefore, each of its atoms is a surface, graphene is highly sensitive to impurities left during production, transfer or manufacturing. In order to remove them, several methods have been developed (thermal annealing in H\({}_{2}\)/Ar is normally used to remove polymer residuals for cleaning after transfer and removal of solvents/surfactants in LPE graphene) [70]. Additionally, one of the problems remains the cracking of single layer graphene at the edges of the steps of the waveguide [8]. Another question that can be faced during development is the choice of material that can be used as an insulator in a capacitor implementation. One of the popular oxides in this matter is Al\({}_{2}\)O\({}_{3}\)[8; 9; 10; 11; 12; 13; 18; 19; 24; 25; 64], however, in addition to it, it is advisable to use Si\({}_{3}\)N\({}_{4}\)[21; 24; 26] and HfO\({}_{2}\)[20], as well as an electrolyte in the form of a mixture of LiClO\({}_{4}\) and polyethylene oxide (PEO) [72] or ion-gels [7; 19; 20]. In the Fermi levels \(\mid\mu_{c}\mid>0.5\) eV, the optimal choice of the insulating capacitor layer formed either by one GE or two GEs, is another issue since there is the problem of graphene absorption due to interband transitions. In this regard, Equation (6) reflects the shift of the Fermi level to \(\mid\mu_{c}\mid\) = 0.5 eV, which corresponds to the electric field on the capacitor insulator  
\[E_{0.5\mathrm{eV}}=\frac{qn_{s}}{\varepsilon_{ox}\varepsilon_{0}}\cong\frac{ 3.6\cdot 10^{7}}{\varepsilon_{ox}}\bigg{[}\frac{\mathrm{V}}{\mathrm{cm}} \bigg{]}, \tag{8}\]  
where \(\varepsilon_{ox}\) is the relative dielectric constant of the insulator [21]. Using Equation (8), it is possible to characterize the choice of an insulator from the point of view of performance in case of dielectric breakdown. The analysis carried out in [21] shows that silicon nitride (Si\({}_{3}\)N\({}_{4}\), \(\varepsilon_{ox}\) = 7.5) is the best option among Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) at \(E_{0.5\mathrm{eV}}\) = 5e6 V/cm at a breakdown field of about 1e7 V/cm.