esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning  
Some of the most frequent concerns about traditional ML include privacy weaknesses. In the industrial area, companies invest to protect their intellectual property. However, traditional ML and, more specifically, deep learning model optimization often go against those privacy requirements. It is indeed necessary to store potentially large quantities of data close to the large processing power to train, validate, and test models. Therefore, such data collections, when sensitive, must be communicated and centralized at a high-security level to prevent any data leakage or attacks. However, threats remain. The traditional ML approach thus has topology threats, which require alternatives to be found. As an answer to these issues, federated learning promotes a new topology to limit the data transfers and, consequently, the data footprint. However, some privacy issues are already identified and must be combated in this new setting.  
Current studies exploring privacy attacks in FL and providing existing privacy-preserving defense techniques are very limited. Thus, we define the following research questions on the privacy aspect of the FL:  
* RQ4: What are the privacy threats and attacks in the FL ecosystem?
* RQ5: What are the privacy-preserving techniques that tackle each type of the identified attacks in RQ1?
* RQ6: What new technology could enhance the general privacy-preserving feature of FL?  
In the rest of this section, we answer each research question based on the studied publications in the FL domain.
## 4 Discussion  
### Biomass Yield  
In the current study, both willow and poplar proved to be more effective in decreasing most trace elements in the soil than natural attenuation. These species belong to the so-called fast-growing woody species that are proposed for phytoextraction because, unlike most hyper-accumulator plants, they are able to produce high amounts of biomass, thus compensating for the reduced trace element uptake rate [71]. Because they also possess other specific properties (e.g., fast and easy establishment, long-lasting, easy to manage, possibility to use the biomass for energy purposes), these species have great potential for use in phytoextraction. Our trial shows that, in the establishment year, poplar is much more productive than willow, whereas after the first cutback, both species showed comparable biomass production values. This response has been observed by other authors, demonstrating that, while poplar does not necessarily need to be harvested during the establishment year [72], it is very useful for willow, as the cutback encourages the formation of vigorous, multi-stemmed stools in the following year [73]. The current study also shows that both species can be managed for a long period of time (4 years), producing a relatively high amount (i.e., 12.5 Mg ha\({}^{-1}\)) of woody biomass each year. The high biomass yield per hectare was mainly due to a rather low mortality rate of the plants, which is rarely reported in the literature regarding annual rotation cycles. In fact, many studies emphasized the decrease in the aboveground growth of tree species due to increased stump mortality [74; 75] following coppicing. For instance, biomass yield decline was observed under a one-year cutting cycle of a poplar short-rotation coppice in Italy following a severe stump mortality [76]. Similar results were also observed on willow when harvested annually [77]. In the present study, the relatively high biomass yield may be attributed to the contribution of irrigation and fertilization, which are very important for plants to be able to take full advantage of the high air temperature and the long growing season of southern latitudes. In the current study, poplar averaged slightly higher woody biomass yield compared to willow, which is in line with other studies carried out under Mediterranean climatic conditions [78].
## 3 Numerical Simulation of AE Signals
### Sensor Simulation
#### 3.3.2 Resonant Sensor  
AE sensors cannot transmit all the information of the detected signal, not only because of their sensitivities but also because they transmit the information of the average velocity over the surface in contact [56]. Sensor effect was considered with the same procedure described in [51], summarized hereafter. The resonant sensors were considered by their transfer function \(S_{sensor}\), which was experimentally determined by the reciprocity method [44; 45]. The velocity \(v_{z}\) of axial wave was calculated at nodal locations on the sensor surface. In order to account for the so-called aperture effect, the function \(k\) represented the sensitivity of a point on the sensor surface at the distance \(r\) from the center of the sensor surface. The function value was 1 (maximum sensitivity) at the center and decreased when moving from the center to the edge [51]. An average axial wave velocity \(V_{Z}\) was calculated by Equation (1):  
\[V_{z}(t)=\frac{1}{\int\ k\ dx\ dy}\iint k.v_{z}(x,y,t)\ dx\ dy \tag{1}\]  
\(\int\ k\ dx\ dy\) represents the weighted area of the sensor. The radius for the sensors nano 30 was equal to 3.2 mm. The out-of-plane velocity corresponding to the sensor surface contact was calculated and then convoluted with the sensor transfer function following Equation (2):  
\[V_{surface}(\omega)\times S_{sensor}(\omega)=V_{sensor}(\omega) \tag{2}\]  
where \(V_{sensor}\) is the velocity calculated considering the sensor, \(V_{surface}\) (\(\omega\)) represents the mean of out-of-plane velocity on the surface and \(S_{sensor}\) (\(\omega\)) is the sensor sensitivity function.  
The simulated signals were analyzed in time domain and in frequency domain. The main descriptors to be investigated were amplitude or energy, frequency centroid (FC), peak frequency (PF) and the partial powers, denoted PPi. PPi evaluate the frequency distribution in the signal. They represent the percentage of energy contained in separate frequency  
Figure 4: Variation of debonding length as a function of time. (**a**) Model A: instantaneous debonding, (**b**) Model B: linear progressive debonding (L = 100 \(\upmu\)m), (**c**) Model C: uniform progressive debonding by step (L = 100 \(\upmu\)m) and (**d**) Model D: non uniform progressive debonding by step (L = 100 \(\upmu\)m).  
intervals. For the nano 30 sensor, the intervals were the following, PP1 [0-125 kHz], PP2 [125-250 kHz], PP3 [250-450 kHz] and PP4 [450-1200 kHz]. Additionally, the roll-off frequency was also investigated. The interval [0-f\({}_{\text{roll-off}}\)] contained 95% of the energy of the complete spectrum.
# Validation and Parametric Study of a Soil-Structure Interaction Nonlinear Simplified Model
## 3 Improved Penzien Model Validation
### Validation of the Nonlinear Model of Superstructure
#### 3.1.2 Comparison of Results  
The 3-DOF bilinear model was analyzed for seismic effect using the COMBIN40 element in ANSYS software. In the MATLAB software, the seismic effect analysis was conducted using both the central difference method and the Newmark method for nonlinear systems, and compared with data from the reference literature [39] for hysteresis curves, displacements, velocities, and acceleration time-history responses. Due to space limitations, only the hysteresis curve, displacement, velocity, and acceleration time-history curves of the first story were shown, respectively, as shown in Figure 2. The comparisons of the peak responses for each story in MATLAB, ANSYS and the reference data were presented in Table 1.  
From Table 1, it can be observed that the maximum relative differences in peak displacements, velocities, and accelerations are 9.55%, 5.8%, and 5.23%, respectively. There were two main sources of these errors: Firstly, these different integration methods introduced errors after the structure entered the nonlinear phase. Secondly, the literature comparison data from the literature were obtained by extracting data points from figures using GETDATE (Transact-SQL) software, which could introduce some inaccuracies. The relative differences in peak responses for each story were within 5%. As shown in Figure 2, the hysteresis curves and the time-history curves of the first story obtained by different methods closely match, indicating the effective representation of the dynamic response characteristics of the structure by the bilinear restoring force model used for the superstructure in the simplified model. Moreover, the simplified model of the superstructure was both effective and accurate. Additionally, both the central difference method and the Newmark method for nonlinear systems were reliable and met usage requirements. The resultsobtained using the COMBIN40 element in ANSYS software to simulate the superstructure model were also dependable. The results obtained using the Newmark method and the central difference method in MATLAB software generally aligned. However, the Newmark method is more commonly employed in engineering calculations and is more reliable for MDOF structural systems. Therefore, in subsequent seismic response analyses, MATLAB software will exclusively use the Newmark method.
# Comparison of Analytical Methods for Determining Methylesterification and Acetylation of Pectin
## 2 Materials and Methods
### Determination of DM by FT-IR  
The DM of pectin was determined by FT-IR as previously described [29]. In the FT-IR spectrum, specific bands at 1740 and 1630 cm\({}^{-1}\) corresponded to the absorption of esterified and free carboxyl groups, respectively. The DM content was proportional to the ratio of the area from the band at 1740 cm\({}^{-1}\) over the sum of the areas from the bands at 1740 and 1630 cm\({}^{-1}\). To quantify the DM of different pectin samples, a calibration curve was constructed based on pectin standards of known DM content (0, 22, 44, 66 and 88%) that were prepared by mixing the appropriate quantities of CP-HG and CP-HM-HG. Each pectin sample (2 mg) was mixed with KBr (100 mg) and then pressed into a 1 mm pellet for FT-IR measurement. The FT-IR spectrum was obtained using a Spectrum Two FT-IR spectrometer average of 4 scans with a spectral resolution over the range of 4000-400 cm\({}^{-1}\) (Perkin Elmer, USA). From each standard and CP pectin, three independent samples were taken, their FT-IR spectra were recorded, and the area of interest measured.  
Results were analyzed and calculated by using OMNIC software (version 9.2, Thermo Fisher Scientific, USA). The FT-IR spectra were smoothed ('automatic smooth' function) and baseline-corrected ('automatic baseline correct' function) in OMNIC software. In absorbance mode, the peaks of 1740 and 1630 cm\({}^{-1}\) were deconvoluted using the curve-fitting method with the Levenberg-Marquardt algorithm and the Gaussian function for the peak shapes.
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 3 Results
### Measurement  
#### 3.2.1 Impedance Matching of the GAA Prototypes  
The GAA prototype alone and when covered by the radome and the MTR using a 0.9 mm thick foam slab as separator (in blue in Figure 9) has been measured and proper impedance matching in the target 24.05-24.25 GHz band is achieved. Simulations do not account for connectorization effects and manufacturing tolerances, which cause a slight shift downwards in frequency. From Figure 9, the impedance matching bandwidths obtained from measurements for the prototypes can be derived. The results are 961 MHz (4%) and 950 MHz (4%) for the GAA and MTR-GAA, respectively, while the result is 716 MHz (3%) for the GAA+radome. The radome degrades the GAA matching regarding S\({}_{11}\) level and bandwidth reduction.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 3 Mathematical Modelling and Problem Definition  
### Cost/Benefit Models  
To consider the effects of infrastructure sharing on energy and emissions costs, first consider the general model to relate a telecommunications mast 'load' to costs, as presented in Equation (1) below:  
\[C_{i}=f_{i}(l_{i}) \tag{1}\]  
where \(C_{i}\) represents costs (energy/fuel or emissions, and infrastructure itself) over a specified period of time for mast \(i,f_{i}\) () represents a loading function for mast \(i\) and \(0\leq l_{i}\leq l_{i,max}\) represents a measure of the loading of mast \(i\), including all components such as generators, batteries and RF equipment. In an infrastructure with \(N\) such masts and \(T\) transmitters, assuming one transmitter is assigned to each mast, we will have \(N\) such functions \(f_{1}(l_{1}),f_{2}(l_{2}),f_{3}(l_{3}),\,\ldots,f_{N}(l_{N})\), with each mast having a specified mast loading level \(l\) and the total cost \(C\) obtained through summation:  
\[C=\sum_{i=1}^{N}C_{i}=\sum_{i=1}^{N}f_{i}(l_{i}) \tag{2}\]  
Although arbitrary functions may be used for each of the \(f_{i}\) (), in reality they will be likely be discontinuous affine or quadratic functions of the load, for example:  
\[f_{i}=\left\{\begin{array}{cl}0&:l_{i}=0\\ a_{i}+b_{i}l_{i}&:l_{i}>0\end{array}\right\} \tag{3}\]  
In other words, with no transmitters assigned to the mast, there is effectively zero load, and the mast can be'switched off', and for positive loads with one or more assigned transmitters, the mast is'switched on' with base cost \(a_{i}\) for operating the mast and an additional linear dependence upon the loading factor through the term \(b_{i}l_{i}\) for appropriate empirical constants \(a_{i}\) and \(b_{i}\). Therefore, with mast colocation and infrastructure sharing, the opportunity arises to potentially minimize the operating costs by moving transmitter loads between masts to minimize the total cost \(C\). Consider for example the effect of shifting load from mast 2 to mast 1 in an \(N\) mast system; assuming that \(l_{1}+l_{2}\leq l_{1,\max}\), if it holds that \(f_{1}(l_{1}+l_{2})+f_{2}(0)<f_{1}(l_{1})+f_{2}(l_{2})\), then this is a feasible colocation that will reduce the total cost \(C\). One objective of the colocation problem is therefore to find a feasible minimal cost assignment of each of the \(T\) transmitters in a system to one of the \(N\) masts, with \(N\leq T\)
# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 2 Related Works  
FER has undergone a significant evolution, transitioning from handcrafted feature extraction methods to powerful deep learning architectures that increasingly approximate human-level emotional understanding [16]. This progression reflects broader trends in computer vision and affective computing, driven by the pursuit of robustness, generalizability, and real-time performance. In this section, we review key developments across three major phases of FER research: traditional handcrafted approaches, convolutional neural network (CNN)-based models, and the emerging generation of transformer-based architectures. We also highlight efforts in multimodal learning and temporal modeling, which are essential for understanding FERONet's position within the current landscape. The earliest FER systems primarily relied on handcrafted features, such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) [17], Gabor wavelets, and Scale-Invariant Feature Transform (SIFT) [18], to describe facial textures and geometric structures. These features were often used in conjunction with shallow classifiers like Support Vector Machines (SVMs) or k-Nearest Neighbors (k-NN) [19]. Although such methods performed reasonably well in controlled environments, they suffered from limited robustness to variations in lighting, pose, and facial occlusion [20]. More importantly, handcrafted descriptors lacked the representational capacity to capture subtle and dynamic variations in facial expressions, rendering them ineffective in naturalistic, in-the-wild scenarios.
## 4 Establishment of a Prediction Model for Dual-Mode Shield Tunneling Parameters
### Data Preprocessing  
#### 4.2.1 Steady-State Segment Data Extraction  
During the tunneling process, equipment monitors data on a timely basis. Interruptions in tunneling due to cutterhead tool replacement, segment assembly, and other reasons are within the scope of monitoring. This results in a substantial volume of raw data of lower quality. To extract steady-state data, it is necessary to eliminate empty thrusting data and exclude short-term unstable data [26]. In general processing, cutterhead thrust (F), cutterhead torque (T), cutterhead speed (RPM), and tunneling speed (V) are considered state-discriminant parameters. Any value of zero parameters is considered blank data recorded during shield machine operations, and the entire row of data is removed. Short-term unstable data in each tunneling cycle, typically occurring during the start-up process of the shield machine, should be selectively excluded to reduce potential errors in subsequent calculations.  
Figure 3 shows a shield tunneling process for data segment 140. Although the displayed unstable data in the initial stages of tunneling are minimal, considering that not every start-up process proceeds smoothly, it is advisable to sequentially exclude the first 10% of data from each start-up phase chronologically. This approach aims to optimize the quality of the data.
## 1 Introduction
[PERSON] [25] developed the sensitized long-scale FBG sensor and its standardized production method, studied the moving load identification method and the bridge structural damage identification method based on the long-scale strain influence line, and studied the deflection inversion method based on the long-scale strain. Despite these advancements, challenges remain in accurately identifying high-speed moving loads and minimizing computational complexity. [PERSON] et al. [26] introduced weakened mode shape functions for high-speed load identification. His method used a surrogate beam model to eliminate the singularities of the solution, and the accuracy is sensitive to the noise. Whereas [PERSON] et al. [27] and [PERSON] et al. [28] focused on strain monitoring and moving train load identification. Furthermore, [PERSON] et al. [29] and [PERSON] et al. [30] investigated structural parameter identification and rotation response analysis, respectively, under moving loads. The above research used various methods to solve the inverse problem of the dynamic, and valuable results were obtained under certain conditions. However, the inverse method for the dynamic cannot solve the sensitive problem of noise. In addition, the dynamic excitation effects on the bridge induced by the moving vehicle were discussed [31, 32, 33]. This research shows that dynamic effects will be amplified or dampened by the bridge structure itself, depending on the relative vibration frequencies of the bridge and the moving heavy vehicle. To address these challenges, this study proposes a novel bridge load identification method based on the field-based influence line, integrating data-driven techniques and advanced signal processing. By using the area of the displacement influence line, this proposed approach can eliminate the effect of noise. Also, the present method aims to improve accuracy and computational efficiency, particularly for complex loading scenarios. By leveraging the strengths of existing methods and incorporating innovative algorithms, this research contributes to the advancement of bridge load identification and SHM. Finally, the present method was verified by using the FEM and practical results. The outcomes of this study are expected to enhance the reliability of load estimation and damage detection in bridge engineering applications.
## 5 Conclusions  
In this paper, we proposed two fast algorithms based on LUT method to calculate the acoustic field distribution of a planar ultrasound transducer. The numerical calculation of the Rayleigh integral obtains the acoustic field distribution by discretizing the source surface into a collection of source points and superimposing the acoustic field of each point. In the ALUT algorithm, the sound source surface is discretized as a set of source points. The distance relationship between the point source and each sampling point in space is calculated in advance and compressed into a table. In the process of calculating the acoustic field, the distance between each source point and the sampling points is obtained by looking up table, without the need for point-to-point calculations. Thus, the calculation time is reduced. Based on the fact that some neighboring source points in the ultrasound transducer have the same amplitude and phase, the ASLUT method combines these source points into sub-regions and calculates a table of sub-region distances from the sampling points. The reduction in the number of calculations further reduce the time. Simulation and experiments are used to demonstrate the reliability of the proposed algorithm. Compared to the point source superposition method, both the ALUT and ASLUT methods take significantly less time to calculate ultrasound transducers of different shapes. The time for calculating the acoustic field by the ALUT method can be reduced to one third of that by the point source superposition method, and the ASLUT method further reduces the time to one eighteenth of that by the point source method. In addition, the digital holographic interference light path measures the internal acoustic field of a rectangular transducer-based acousto-optical device. The measured acoustic field distribution is compared with the calculated results of the proposed methods, which verify the practicality of both calculation methods. Moreover, the results measured by laser vibrometer will be considered for comparison with computational results in future work. This work achieves a fast and accurate calculation of the acoustic field generated by the ultrasonic transducer, which can be used in NDT, HIFU, and other fields requiring optimization of the acoustic field.  
Conceptualization: J.W., Z.W. and G.L; methodology, J.W.; software, K.T. and L.L.; validation, K.T., L.L, and S.W.; formal analysis, K.T.; writing--original draft preparation, J.W.; writing--review and editing, Z.W. and G.L.; funding acquisition, Z.W. and G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Engineering Research Center for Optoelectronic Crystalline Materials (OCM-2020-03) and the National Key Research and Development Program of China (2021 YFB3602500).  
Not applicable.  
The authors declare no conflict of interest.