esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
** The aim of this work is to find out whether the time series data of the LIB ageing model can be recovered by gradient inversion in a transformer-based architecture in an FL environment and how differential privacy can prevent privacy leakage. The following aspects are considered:  
* It is important to model the ageing of LIBs using degradation prediction through machine learning to enable better monitoring and predictive maintenance, which is critical for optimising electric vehicle performance and extending battery useful life. * The security of existing transformer-based models is critical in the context of the FL framework. By identifying and quantifying privacy leaks using GIAs in the training scenarios, sensitive battery time-series data can be protected. * Many FL systems incorporate differential privacy. Investigating whether these protections are effective for transformer-based models using privacy auditing is essential for improving the security of FL. **Contributions. ** The main innovation of this work can be summarised as follows:  
* The transformer-based time-series model for LIB ageing is trained, indicating its applicability in the energy sector to evaluate battery longevity, safety and remaining useful life. * Differential privacy with Gaussian mechanism and with different multiplicative noise factors is incorporated into the battery ageing model to evaluate the privacy-preserving behaviour of the model against privacy leakage. * The privacy auditing is elaborated using a model inversion approach. The reconstruction of LIB data from transformer-based model gradients is considered applicable in the case of FL. The model to be trained is inverted to extract information. **The rest of this article is organised as follows:** The following section introduces the relevant background of the theoretical foundations of time-series federated learning, gradient inversion and differential privacy, including their mathematical definitions and basic properties. Section 3 describes the methodology used to reconstruct battery time series data from transformer-based model gradient updates. Section 4 presents the experimental results obtained under the considered conditions. Section 5 discusses the experimental results and identifies future research.
## 3 Numerical Scheme
### Scheme Update
#### 3.6.1 Vertical Polarisation  
Expanding the operators in Equations (47) and (53a), and combining Equations (49) and (51), leads to a two-step recursion algorithm in vector-matrix form, to be updated at each time step \(n\):  
\[\mathbf{w}^{n+1} =\mathbf{B}\mathbf{w}^{n}+\mathbf{C}\mathbf{w}^{n-1}+A\mathbf{ \mu}_{t}\mathbf{J}_{\mathbf{w}}^{n}\mathbf{f}_{\mathbf{w}}^{n} \tag{69a}\] \[\mathbf{w}_{FB}^{n+1} =2\mathbf{w}_{FB}^{n}-\mathbf{w}_{FB}^{n-1}+k^{2}\mathbf{M}_{FB}^{ -1}\left(\mathbf{f}_{\mathbf{w}FB}^{n}+\mathbf{f}_{\mathbf{ext}w,FB}^{n}\right) \tag{69b}\]  
where \(A\) is a constant, and \(\mathbf{B}\), \(\mathbf{C}\) are matrices, written in terms of the physical parameters of the string, the time step \(k\), and the grid spacing \(h\), through the spatial difference matrices:  
\[A =\frac{2k^{2}}{\rho(2+\lambda_{1}k)} \tag{70a}\] \[\mathbf{B} =\frac{2}{2+\lambda_{1}k}\left(2+\left(\frac{Tk^{2}}{\rho}+ \lambda_{2}k\right)\mathbf{D}_{xx}-\frac{El_{0}k^{2}}{\rho}\mathbf{D}_{xx} \mathbf{D}_{xx}\right)\] (70b) \[\mathbf{C} =\frac{2}{2+\lambda_{1}k}\left(\frac{\lambda_{1}k}{2}-1-\lambda_ {2}k\mathbf{D}_{xx}\right) \tag{70c}\]  
However, the nonlinearity of the contact model doesn't allow for a simple explicit update. Combining Equations (69a) and (69b), and rewriting in terms of \(\mathbf{\Delta}^{n}\), leads to a nonlinear equation in matrix form, in terms of the unknown vector \(\mathbf{r}^{n}=\mathbf{\Delta}^{n+1}-\mathbf{\Delta}^{n-1}\):  
\[\mathbf{\Lambda}_{1}^{n}\mathbf{r}^{n}+\mathbf{\Lambda}_{2}^{n}\mathbf{f}_{ \Phi}^{n}+\mathbf{b}_{\mathbf{w}}^{n}=0 \tag{71}\]  
where the matrices \(
# Effect of Stiff Hangers on the Longitudinal Structural Behavior of Tied-Arch Bridges
## 2 Reference Bridge and Load Cases  
All the bridges shown in this paper are based on a given configuration, the so-called reference bridge, shown in Figure 3. Since the study is mainly qualitative, the reference bridge is taken as being representative of typical tied-arch bridges. This reference model is composed of a straight deck supported by a vertical planar arch. The bridge belongs to the typology known as \"tied-arch\" bridge, since the springing points of the arch are tied by a tensioned deck. The arch and the deck are linked by a set of vertical hangers attached to the deck centerline. In the reference model, the hangers are composed of cables and are pinned at both ends. This paper focuses on the effects of stiffening these hangers.  
The reference model is a 100-m span (\(L\), Figure 3) bridge. The deck spacing (\(s\)) between successive anchorages of hangers is 5.0 m. The rise of the arch (\(f\)) is 20.0 m. The loaded width of the deck, \(b\), is 8.0 m. These dimensions have been inspired by real arch bridges, and are relatively common (see for example [PERSON] [9], or [PERSON] [10]). All the bridges have been designed according to EC-3-2 [11].  
The dimensions of the structural elements of the bridge, which are also relatively normal in bridge designs, are shown in Table 1.  
Only static loads have been considered. For dynamic loads, [PERSON] [12] and [PERSON] et al. [13] can be consulted. The load cases that are listed below have been considered:  
* The self-weight (\(g\)), is evaluated for a specific weigh of 78.5 kN/m\({}^{3}\), plus the dead load (\(g_{DL}\)) on the deck (3 kN/m\({}^{2}\)). The value of the dead load corresponds to a concrete slab with 12 cm of thickness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Cross-Section** & **Dimensions** &
\begin{tabular}{c} **Young’s Modulus** \\ **E (kh/m\({}^{2}\))** \\ \end{tabular} \\ \hline Arch & Square box hollow section (SHS) & \(1000\times 1000\) mm, \(t_{y,A}=t_{m,A}=25\) mm & \(2.0\times 10^{5}\) \\ Hangers & Circular solid section & \(\approx 80\) mm & \(1.6\times 10^{5}\) \\ Deck & Rectangular hollow section (RHS) & \(4000\times 1000\) mm, \(t_{y,D}=t_{m,D}=15\) mm & \(2.0\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Materials and dimensions of the structural components of the reference arch bridge.  
Figure 3: Geometrical definition of the reference arch bridge (see Table 1).  
* Regarding the objective of this research, it is not necessary to consider all the possible live load cases than may appear in bridge design. Therefore, only pedestrian loads distributions will be considered, since they illustrate accurately enough the effect on the longitudinal structural behavior when the hangers are stiffened. Thus, three live loads distributions (\(q_{1}\), \(q_{2}\), \(q_{3}\)), shown in Figure 4, have been considered, where the shadowed area is loaded with a vertical uniformly distributed load \(q_{i}\) of 5 kN/m\({}^{2}\) acting downwards, which corresponds to the pedestrian load defined in IAP [14] and EC-1 [15].
## 5 Discussion  
Because HJ-1B has a single thermal channel, and single-channel LST retrievals may be unstable [1], we have not used the LST retrieved by HJ-1B to perform comparisons. As shown in Section 4.3, the direct use of MODIS-LST calculation results is ill-founded and causes obvious \"boxy\" anomalies in estimates of LE. In contrast, the temperature-sharpening algorithms used to estimate the GWR-LSTs and the DisTrad-LSTs are capable of decreasing the effects of the heterogeneity of the LST, which is consistent with results from previous studies [1,24]. However, the following problems require further analysis.  
(1) In this paper, LST is derived from MODIS and NDVI uses 30 m information from the VNIR bands of HJ-1B. When LST and NDVI are derived from different sensors, inter-sensor calibration should be completed; the comparison of the thermal images across sensors is complex, due to the differences in the instantaneous field of view, the swath width, the orbital geometry, the spectral response functions and the overpass times [68].  
(2) We use a one-source energy balance model when estimating LE. As discussed in Section 4.4.2, the accuracy of the estimated canopy height greatly influences the estimated turbulent heat flux. The canopy height is known a priori based on phenophase classifications and influences the accuracy of the surface roughness calculation. Multi-source remote-sensing data, such as active microwave and LiDAR data, could be used to obtain canopy heights [24], which would decrease the dependence on the accuracy of the classification. In addition, to correct the discrepancies between the remotely sensed radiative surface temperatures and the aerodynamic temperatures at the source of heat transport, a brief and well-performed parameterization scheme (under a uniform and flat plant surface) including excess resistance was used to calculate the aerodynamic resistance to heat transfer [24]. Since the surface of the HRB is very heterogeneous, multiple parameterization methods should be compared to select the optimum method.  
(3) The energy balance closure has a significant influence on the evaluation of the model-estimated heat flux values. In this paper, the EC energy balance closure ratio was greater than 0.75. Studies have shown that extension of the averaging time [69], uncaptured low-frequency eddies [70], and the lack of an accurate accounting of heat storage terms [71], among other potential causes, may lead to these energy imbalances. The residual closure technique and the conserving Bowen ratio are often used to force the energy balance. We chose the former method because the conserving Bowen ratio method yields irrational sensible heat fluxes, due to the small or negative Bowen ratios that occur in oasis and desert environments [24]. The energy balance closure is problematic at times for turbulent flux systems and tends to be associated with significant discrepancies in LE [72].
# Editorial for Special Issue \"Remote Sensing of Precipitation: Part II\"
## 2 Overview of Contributions
[25] established a spatial and temporal distribution model of precipitation in Hubei Province, China from 2006 to 2014 based on the data of 75 meteorological stations. This paper applied a geographically and temporally weighted regression kriging model to precipitation and assessed the effects of timescales and a time-weighted function on precipitation interpolation.
## 4 Experimental Results and Discussion
### Comparison with State-of-the-Art
#### 4.4.1 Qualitative Comparison  
We implemented two state-of-the-art methods and one baseline method for qualitative comparison. Figure 11 presents a part of results from the reduced test set of UP-3D by our proposed method and other methods. These results demonstrate that our proposed method can remove the self-intersection of the statistical body shape model effectively and produces more reasonable results.  
As is can seen from Figure 11, the results obtained by the baseline method without any self-intersection penalty tends to intersects with itself. The Laplacian regularization can not strictly avoid self-intersection and often leads to unnatural results. We can see that it is not suitable to employ Laplacian regularization in statistical body shape model because of the fact that this laplacian term brings negative effect to 3D pose estimation. The method of sphere approximation is very competitive in removing the self-intersection of body mesh, however method requires designing a appropriate set of spheres and we found that it is an excessive trivial procedure to set the radius and the coordinate of  
Figure 10: Spheres designed to approximate the human body are kept in the same pose with the body shape model.  
each sphere appropriately. In addition, since the body mesh can not be approximated accurately by spheres, this method may lead to fail results by simply removing the intersection between spheres.  
Compared with other approach, our proposed method can obtain more visually appealing results from two points. One is that when there is no self-intersection, our proposed SPT will have no effect on the body mesh, this means no side effects on 3D pose estimation. The other is that our proposed approach can avoid the self-intersection of body mesh strictly without taking any approximation.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.11 Sunshine  
The \"Sunshine\" project, which involves participants from nine countries, deals with energy assessment of buildings and aims at providing building managers with all the necessary information so as to reduce energy waste by heating/cooling systems. Apart from this, it is also planned to control the public illumination system based on AMR [91].
## 5 Conclusions
This finding is significant, as it indicates that even mechanical strain near the yield strength does not cause detachment in the transducers and, therefore, does not significantly influence the S21 parameter. Test Model 4 was the last test performed, a test that combines the actuation of mechanical deformation and temperature variation. With deformations up to 125 \(\upmu\)m/m and temperatures up to 140 \({}^{\circ}\)C, the ultrasonic communication is also not interrupted and has variations of 0.2 dB for just the deformation (this result is consistent with the Model 3 test) and of 1.4 dB combining the deformation with temperature. Except for UT misalignment, the ultrasonic feedback remains stable for all the interferences we studied. Whether these are temperature variation, adding multiple barriers, mechanical deformation, or combining mechanical deformation with temperature variation, the feedback remains consistent. Even with a low signal and up to 40% of intersections, ultrasonic communication persists. It is important to note that transmission interruption is rare, only happening with 12.5% and 0% of horizontal non-alignment. Conceptualization, I.G.C. and P.M.P.G. ; methodology, I.G.C., L.P.B.d.S. and P.M.P.G. ; validation, I.G.C. and L.P.B.d.S. ; formal analysis, I.G.C. ; investigation, I.G.C. ; resources, I.G.C. ; data curation, I.G.C. ; writing--original draft preparation, I.G.C. and L.P.B.d.S. ; writing--review and editing, I.G.C., L.P.B.d.S., P.M.P.G. and A.M.B.B. ; visualization, A.M.B.B. and P.M.P.G. ; supervision, A.M.B.B. ; project administration, A.M.B.B. ; funding acquisition, I.G.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest.
## 3 Methods
### Overall Network Architecture  
The network architecture of this thesis consists of three parts.  
Social Attention Module: This module focuses on pedestrian interaction information and calculates the influence weights of different pedestrians using an attention mechanism. As illustrated in the orange dashed box in Figure 2, this section is used to extract social interaction information of pedestrians. First, four types of interaction information are extracted from the pedestrian trajectories. Then, this interaction information is input into a multi-head attention mechanism to compute the influence weights of different pedestrians and output the comprehensive social interaction information of pedestrians.  
Figure 2: Network structure of SIS-GAN model. The upper blue dashed section represents the environmental information module, and the middle orange dashed section represents the interaction attention module. The bottom is a GAN-based trajectory prediction framework, where the historical trajectory information of pedestrians and the auxiliary information from the previous two sections are input into the decoder to predict pedestrian trajectories. The discriminator scores the generated trajectories, and the loss derived from these scores is returned to the generator, incentivizing it to continuously generate more realistic trajectories.  
Environmental Attention Module: As depicted in the blue dashed box in Figure 2, this module first computes the historical trajectory data of pedestrians using a Gaussian kernel function to obtain the probability density values of different regions. Next, the dataset is transformed into image format, from which feature maps are extracted using a convolutional neural network. The environmental information is then computed from these two types of data within the scene attention module.  
Pedestrian Trajectory Prediction Module: As shown in the pink and blue sections of Figure 2, this module predicts pedestrian trajectories using a GAN framework. The GAN consists of two components: the generator and the discriminator. The generator produces future pedestrian trajectory data, while the discriminator evaluates whether the generated trajectories are authentic. The overall model structure is depicted in Figure 2.
## 4 Change Detection Mechanism
### Detectability Analysis
Thus, Equation (15) can be rewritten as:  
\[\sum_{l\inchange}(1/t_{l})\geq\frac{2\epsilon\sqrt{\frac{\overline{\beta}(1- \overline{\beta})^{2}\lambda_{l}}{IRB_{slot}}}}{\left(1-\overline{\beta}^{m+1 }\right)^{5/2}(1+L)} \tag{16}\]  
As an illustrative example, Figure 3 shows the minimal detectable traffic changes for different system scenarios for a typical IEEE 802.15.4-compliant node with data rate of 250 kbps and time slot length of 80 bits. A 95% confidence level is used. It can be shown that as \(\beta\) increases, the proposed detection mechanism becomes less sensitive to traffic changes. It can also be shown from Equation (15) as well as Figure 3 that more sensitive detection can be obtained when (1) the packet length is longer; (2) when the transmission rate is higher or (3) when the update interval is longer. Note that the minimal detectable aggregate traffic is also the upper bound of the error of traffic estimation. In actual implementations, as the errors are unbiased, the errors among different update  
Figure 2: Sensitivity with respect to busy channel probability. intervals can be canceled and in the long run, the achievability of the throughput demand will not be compromised, as shown in Section 5.
# Reducing Soil Permeability Using Bacteria-Produced Biopolymer
## 2 Materials and Methods
### Production of the Xanthan Gum Biopolymer Compound  
The production of the biopolymer enriched compound, illustrated in Figure 2, consisted of the following steps [43]: (i) _Stentotrophomonas maltophilis_ strain Faro439 (UC-CCB 127), obtained from the University of Coimbra Bacterial Culture Collection ([[https://ucccb.uc.pt/](https://ucccb.uc.pt/)]([https://ucccb.uc.pt/](https://ucccb.uc.pt/)), accessed on 15 January 2020), was grown on R2A agar (Reasoner's 2A agar medium) plates for 3 days, at a temperature of 25 \({}^{\circ}\)C; (ii) the cells grown were used to inoculate the xanthan medium [44]. The cells were grown in a batch system incubated for 24 h in a shaker at 150 rpm. These cells were used as inoculum in a new batch system with xanthan medium incubated for 3 days in a shaker at 150 rpm before undergoing the centrifugation process to recover the biomass containing the biopolymer compound; (iii) the biomass was drained for 24 h to remove the excess of water and lyophilized for 24 h to obtain the powder form and to facilitate the mixing with the soil.  
The use of this xanthan-like, enriched compound is justified by its simple production process. Indeed, the compound is obtained without the use of purification steps, solvents,or procedures, or any equipment that leads to consequences from the ecological point of view. Therefore, this xanthan gum-like compound production can be considered more environmentally friendly than the commercial XG. Its availability at reasonable prices in relation to other biopolymers [21] and the ability of this biopolymer to decrease the soil's permeability are other positive aspects.
## 3 Lightweight Cryptography for Smart Objects
### Lightweight Cryptographic Hash Functions
#### 3.3.5 Keccak  
Keccak[52] is a family of sponge functions. Keccak uses a sponge construction in which message blocks are XORed into the initial bits of the state, which is then invertibly permuted. In the version used in Keccak, the state consists of a 5\(\times\)5 array of 64-bit words, 1600 bits total. Keccak produces arbitrary output length.  
Keccak has been selected by the NIST as the winner of the SHA-3 competition [53] on October 2, 2012 and is now referenced as SHA-3.
## 3 Experiment Results
### PAM Modulation
signals dramatically increases from 0.56 dBm to 2.16 dBm when the data rate rises from 100 Mbps to 167 Mbps.
## 2 The Influence of Different Factors on Oral Microbiome
### Hormonal Influences in Pregnancy and Menopause  
Pregnancy induces significant hormonal changes that affect the oral microbiome [122]. Elevated levels of hormones such as estrogen and progesterone during pregnancy can alter the microbial environment in the oral cavity, often leading to an increase in periodontal pathogens. This hormonal influence can exacerbate conditions like gingivities and periodontitis, commonly referred to as pregnancy gingivities [123]. [PERSON] et al. [124] have shown that pregnant women are more susceptible to oral infections due to these hormonal fluctuations, leading to an increase in periodontal pathogens such as _Porhyromonas gingivalis_ and _Prevolella internedia_. These bacteria exacerbate periodontal inflammation, creating a chronic inflammatory state in the oral cavity. Chronic inflammation is a recognized risk factor for OPMD and may facilitate the malignant transformation of premalignant lesions into OSCC. Additionally, the immune modulation associated with pregnancy may reduce the host's ability to control oncogenic microbial species, further increasing the risk of OPMD and OSCC [125]. Similar influences are observed during other periods of hormonal fluctuation, such as menopause, where declining estrogen levels contribute to changes in microbial diversity and an increased prevalence of oral diseases such as OPMD and OSCC [126]. Furthermore, estrogen deficiency has been linked to a decrease in salivary antimicrobial peptides, which can allow carcinogenic microbial species to proliferate, potentially contributing to oral carcinogenesis [127, 128]. The interplay between hormonal changes, dysbiosis, and inflammation highlights the importance of tailored oral care during pregnancy and other hormonally dynamic life stages. Gender-related hormonal factors, including the use of oral contraceptives, can also affect microbial composition and the host immune response, further promoting dysbiosis and carcinogenesis, potentially increasing the likelihood of OPMD and OSCC in susceptible individuals [129].