esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, a speed field inversion method is proposed to reduce the influence of speed field inaccuracy on PAI in this paper. It combines clustering with updates to the speed field. \"K-means + GMM\" is used to fit the photoacoustic pressure data. This not only provides the regional location of various tissues for the construction of the speed field, but also reduces the complexity of the sound speed field model. Based on the simplified sound speed field model, the step length of each tissue is adjusted adaptively by weight allocation to balance the updated amplitude of the sound speed of each tissue. This can ensure that the algorithm converges to the global optimal solution and obtains the inhomogeneous sound speed field closest to the real sound speed distribution. A lifelike digital breast model is applied to verify the effectiveness of the proposed method. Based on the research results, the initial sound speed field and the optimal sound speed field obtained by the proposed method are compared. The comparison results show that the proposed method significantly improves the accuracy of the inhomogeneous sound speed field by iterative optimization. In addition, the comparison results with other speed estimation methods also show that the proposed method has significant advantages in dealing with complex sound speed field inversion problems. The method proposed in this paper can assist PAI in reconstructing high-quality images of complex breast models. The PSNR value of the photoacoustic image obtained by this method reaches 26.719 db, and the SSIM value reaches 0.703. This indicates that this method has potential applications in the clinical diagnosis of breast disease and helps to improve the accuracy of disease detection. At present, the proposed method still cannot completely avoid the influence of tissue interaction on algorithm convergence. Our future research will be devoted to developing more intelligent optimization algorithms to avoid this problem more effectively.  
Conceptualization, S.C. and X.J.; methodology, S.C. and X.J.; software, S.C., X.J. and H.Y.; validation, S.C.; formal analysis, S.C.; investigation, S.C. and X.J.; resources, X.J.; data  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PSNR (db)** & **SSIM** \\ \hline Homogeneous sound speed & 13.610 & 0.435 \\ K-means method & 17.435 & 0.461 \\ Proposed method & 26.719 & 0.703 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative evaluation results of photoacoustic image.  
curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, X.J., S.L. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42174162.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 2 Methodology
### Observation Model  
K-band and L-band observations were used for SRP modeling. The BDS-3 K-band ISLs follow a concurrent spatial time division duplexing (CSTDD) working system [29]. There are multiple links between constellation members at the same timeslot. Each satellite completes two one-way measurements with a preassigned satellite within a 3 s timeslot [30]. Therefore, in order to achieve the joint processing of ISLs and L-band observations, the dual one-way ISL observations are converted to the \(t_{0}\) epoch aligned to L-band observations [16]. The derived observations are denoted as \(P_{orb}^{AB}\) and \(P_{clk}^{AB}\), with the clock offsets and orbit parameters removed, respectively. As for L-band observations, the combination of dual frequency can eliminate the influence of the first-order ionospheric effect. The observations of the B1I and B3I frequencies are used to form the ionospheric-free (IF) code observation \(P_{k,IF}^{j}\) and phase observation \(\Phi_{k,IF}^{j}\). The combined observation model for SRP modeling is expressed as  
Figure 1: Dimensions of the satellite body and its shadow on the panel (unit: m).  
\[\left\{\begin{aligned} P^{AB}_{orb}(t_{0})=\rho^{AB}(t_{0})+ \frac{\varsigma}{2}\cdot\left(\tau^{A}_{Rcv}+\tau^{A}_{Send}+\tau^{B}_{Send}+ \tau^{B}_{Send}\right)+\varepsilon_{orb}\\ P^{AB}_{clk}(t_{0})=c\cdot\left(dt^{A}(t_{0})-dt^{B}(t_{0}) \right)+\frac{\varsigma}{2}\cdot\left(\tau^{A}_{Rcv}-\tau^{A}_{Send}-\tau^{B} _{Rcv}+\tau^{B}_{Send}\right)+\varepsilon_{clk}\\ P^{i}_{k,\;IF}(t_{0})=\rho^{i}_{k}(t_{0})-c\cdot\left(dt^{j}(t_ {0})-dt_{k}(t_{0})\right)+Tr+\epsilon^{j}_{k,p}\\ \Phi^{j}_{k,IF}(t_{0})=\rho^{i}_{k}(t_{0})-c\cdot\left(dt^{j}(t_ {0})-dt_{k}(t_{0})\right)+Tr-\lambda_{IF}N^{j}_{k,IF}+\epsilon^{j}_{k,L}\end{aligned}\right. \tag{10}\]  
where \(\rho^{AB}\) represents the distance between satellites A and B; \(\rho^{i}_{k}\) represents the distance between satellite \(j\) and receiver \(k\); \(dt\) is the corresponding clock offset of satellite or receiver; and \(\tau\) is the hardware delay of the ISL when receiving and transmitting signals [31]. \(Tr\) is the tropospheric delay, considered only in L-band observations; \(c\) is the speed of light in a vacuum; \(\lambda_{IF}N_{IF}\) is ambiguous by an unknown integer number \(N_{IF}\) of wavelengths \(\lambda_{IF}\); and \(\epsilon\) represents the ranging noise and the unmodeled errors. The weights assigned to \(\Phi^{j}_{k,IF}\), \(P^{j}_{k,\;IF}\), \(P^{AB}_{orb}\), and \(P^{AB}_{clk}\) are 0.002 m, 0.2 m, 0.1 m, and 0.1 m, respectively.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 5 Privacy-Preserving Solution for EU Digital Vaccine Certificates
### Cryptographic Core
* The VER reconstructs the symmetric key \(k_{\text{enc}}=KDF(j)\), decrypts the attributes \(m_{i}=Dec_{SYM}(c_{i},k_{\text{enc}})\), and verifies the resulting proof \(\pi\) by using its challenge _nonce_ and the issuer's public key \(pk_{I}\). Figure 5 depicts the Show-Verify protocol in detail. The part of the protocol where the communication between the communicating parties is encrypted is marked in red. **User**  
\[params_{1}=(q,\mathbb{G}_{1},\mathbb{G}_{2},\mathbb{G}_{T},g_{1},g_{2},\mathbf{e}),pk_{I}\]  
\[ \begin{array}{ll}\hline&sk_{l}=(x_{0},\ldots,x_{n})\\ &(m_{1},\ldots,m_{n})\\ \hline&&&\\ &&&\sigma=g_{1}^{\frac{1}{q_{1}+m_{1}+m_{1}+m_{n}}}\\ &&&\sigma_{x_{0}}=\sigma_{x_{0}}^{x_{0}}\\ &&&\sigma_{x_{1}}=\sigma_{x_{1}}^{x_{1}}\\ &&&\vdots\\ &&&\sigma_{x_{n}}=\sigma_{x_{n}}^{x_{n}}\\ \hline\end{array} \]  
\[ \begin{array}{ll}\hline&\text{Store: }\sigma,A=(\sigma_{x_{0}},\sigma_{x_{1}}, \ldots,\sigma_{x_{n}})\\ \hline&&&\\ \hline\end{array} \]  
**User**  
\[params_{1}=(q,\mathbb{G}_{1},\mathbb{G}_{2},\mathbb{G}_{T},g_{1},g_{2},\mathbf{e }),pk_{I},pk_{V}\]  
\[ \begin{array}{ll}(m_{1},\ldots,m_{n})&pk_{I}=(h_{0},\ldots,h_{n})=(g_{2}^{x_ {0}},\ldots,g_{2}^{x_{n}})\\ \hline&&&\\ \hline&&&\\ \hline\end{array} \]  
\[ \begin{array}{ll}\hline&\text{\emph{nonce}}\\ \hline&\text{\emph{e}},\rho,\rho_{v},\rho_{m_{i,i,i}}\in_{R}\mathbb{Z}_{q}\\ \hat{\sigma}\leftarrow\sigma^{\rho}\\ t=g_{1}^{\rho_{v}}(\prod_{i\
ot\in D}\sigma_{x_{i}}^{\rho_{n}})^{\rho}\\ \\ e\leftarrow\mathcal{H}(t,\hat{\sigma},\text{\emph{nonce}})\\ \langle s_{m_{i}}\leftarrow\rho_{m_{i}}-em_{i}\rangle_{i\
ot\in D}\\ s_{0}\leftarrow\rho_{v}+\emph{e}\\ \pi\leftarrow(t,s_{m_{i,i}\
ot\in D},s_{v})\\ j\leftarrow pk_{V}^{\prime},u\gets g_{1}^{\varepsilon}\\ k_{\emph{nonce}}\gets KDF(j)\\ c_{i}\leftarrow Enc_{SYM}(m_{i},k_{\emph{nonce}})\\ \\ \hline\end{array} \]  
\[ \begin{array}{ll}\hline&\text{\emph{e}}^{\prime}\gets u^{\mathbb{St}_{V }}\\ &k_{\emph{nonce}}^{\prime}\gets KDF(j^{\prime})\\ &m_{i}^{\prime}\leftarrow Dec_{SYM}(z_{i},k_{\emph{nonce}})\\ &e^{\prime}\leftarrow\mathcal{H}(t_{\emph{verify}},\vartheta,t_{\emph{nonce}}) \\ &\alpha\gets h_{0}^{-\varepsilon^{\prime}}\cdot\prod_{i\
ot\in D}h_{i}^{ \varepsilon_{i}}\prod_{i\in D}h_{i}^{-\varepsilon^{\prime}m_{i}}\\ &\mathbf{e}(t_{\emph{verify}},g_{2})=\mathbf{e}(\vartheta,\alpha)\cdot \mathbf{e}(g_{1}^{\kappa_{v}},g_{2})\\ \hline\end{array} \]  
Figure 4: Issue protocol— Issue of Privacy-Preserving Digital Vaccine Certificate. Figure 5: Show-Verify protocol—proof of possession of the attributes from the Privacy-Preserving Digital Vaccine Certificate.
## 3 Results
### Effect of Digested Beef on SCFA Content and the Fecal Microbiome  
After adding digested beef to the fecal fermentation systems, we observed a gradual increase in the amount of SCFA produced over a 90 min period, while samples without the digested beef did not show the same increase in SCFAs (Figure S2).  
We observed a significant increase in acetic acid in all of the samples following the addition of digested beef powders to the fermentation broth (\(p<0.05\)), while propionic acid did not increase in subject 4 and butyric acid did not increase in subjects 3, 4, and 6 (Figure 2). The increase in acetic acid was the highest, while the increase in butyric acid was the lowest. In addition, these evaluations revealed that the total concentration of SCFAs increased in half of the subjects in response to digested beef powder.  
Figure 1: Microbial diversity among the donors (alpha diversity) showed varied richness (**A**) and diversity (**B**). Microbial strata among the fecal donors also found to be individualistic at the phylum (**C**) and genus level (**D**).  
We observed a slight shift in the composition of the individual microbial communities in response to fecal fermentation in the presence of digested beef powder (Figure S3), although none of these shifts appeared to be significant (AMOVA, \(p>0.05\)).  
Fecal fermentation induced minor shifts in taxonomic composition at the phylum level. The results shown in Figure 3 show that digested beef powder increased the abundance of both the Firmicutes and Actinobacteria while decreasing the abundance of Bacteroidetes. At the genus level, we observed a decrease in _Prerotella_ and an increase in _Bifidobacterium_ among all subjects except for subject 6, who did not have any _Prerotella_ at initiation. The genus-level composition comparison analysis showed varying degrees of abundance shifts in each subject.  
Differential abundance analysis between samples with and without digested beef powder showed that five genera (_Bifidobacterium_, _Blautia_, _Ruminococcus_, _Fusicatenibacter_, and _Holdemanella_) were significantly increased in more than four subjects, while another five genera (_Bacteroides_, _Faecalibacterium_, _Prerotella_, _Parabacteroides_, and _Sutterella_) were significantly decreased in more than four subjects (Figure 4). In addition, many differentially abundant genera were not common among the subjects. Each subject showed approximately 10-20 increasing genera and 5-10 decreasing genera, likely in response to the digested beef powder.  
Figure 2: Quantitative comparison of SCFA production in each subject with and without digested beef treatment, as represented by grey and black bars, respectively (**\(\boldsymbol{\text{*}};p<0.05\)**).  
Figure 3: Beef-induced shift in microbial taxonomic composition at the phylum (**A**) and genus level (**B**), where red and blue letters indicate subjects with and without the beef treatment, respectively.
# Laboratory Evaluation of Tensile Creep Behavior of Concrete at Early Ages
## 4 Test Results  
### Creep Compliance  
Figure 4 shows the time-dependent displacement of the test specimens placed under constant tensile load at the designated testing ages. It is noted that the measured deformation is the drying creep under the effect of drying shrinkage that occurred during the test period. Drying creep is known to induce a non-linear effect, particularly in long-term response; as such, it is always greater than the sum of separate measurements of creep under sealed conditions (basic creep) and drying shrinkage under load-free conditions (free shrinkage) [37; 38]. However, this study focuses on short-term creep in early-age concrete within the context of linear theory. Creep tests were accordingly conducted where the non-linear effect during the 5 h testing period is assumed negligible. With these assumptions, the measured total strain (\(\epsilon_{test}\)) can be expressed by the sum of basic creep (\(\epsilon_{crop}\)) and shrinkage (\(\epsilon_{sh}\)) as shown in Equation (12). Note that the drying shrinkage occurs in the opposite direction to the tensile creep. The basic creep (\(\epsilon_{crop}\)) is the test load-induced time-dependent deformation of the concrete that can be determined by the constitutive relations shown in Equation (13). Substituting Equation (13) into Equation (12) gives Equation (14), which was used for the estimation of the creep compliance \(D(t,t^{\prime})\) of each test specimen.  
\[\epsilon_{test}(t-t)=\epsilon_{creep}(t-t)-\epsilon_{sh}(t-tt) \tag{12}\]  
\[\epsilon_{creep}(t-t)=\int\limits_{0}^{t}D(t,t)\frac{\partial\sigma_{0}\cdot H (t)}{\partial t}dt \tag{13}\]  
\[D(t,t)=\frac{\epsilon_{test}(t-t)+\epsilon_{sh}(t-t)}{\sigma_{0}(t)} \tag{14}\]  
where \(D(t,t)\) = uniaxial creep compliance, \(\sigma_{0}\) = constant testing load in terms of stress, and \(H(t)\) = step function (0 for \(t\leq t\) and 1 for \(t>t\)).  
The magnitude of drying shrinkage (\(\epsilon_{sh}\)) of the test concrete was estimated by the BP model [39]. Estimated maximum shrinkage after the 5 h testing period at the ages of 2.7, 4.3, 6.8, and 13.3 days were 7.72, 6.55, 5.71, and 4.76 microstrains, respectively. The basic creep was then calculated by Equation (12). Figure 5 shows the three components of deformation for the respective test samples. Creep compliance curves estimated by Equation (14) for the test concrete at different ages are shown in Figure 6. As shown, there is a sharp reduction in the creep response of the concrete between the samples aged 2.7 and 4.3 days. Much stiffer responses were observed from the samples at later ages. The difference in the response is not so pronounced between the 6.8- and 13.3-day-old samples, suggesting the aging effect on the creep response may not be significant for concrete aged more than 7 days.  
Figure 4: Time-dependent displacements as the result of tensile creep tests.
## 1 Introduction
### Framework of the Approach  
Several possible assembly states may exist after the parts are assembled by multiple mating planes because the number of contact points on each plane may be multiple. In addition, each possible assembly state corresponds to a type of POD. Thus, a problem in POD computation is to determine all possible assembly states. Moreover, surface deviations of the mating planes, which act as inputs, have direct impacts on the results of POD computation. Consequently, modeling surface deviations is also a problem. The core problem of POD computation is to successively determine each contact point by continuously adjusting position and orientation. As a result, the framework of POD computation by successively determining contact points is established, as shown in Figure 3.  
**Assembly state determination**. The six-point positioning principle is used to determine cases in which the constrained DOFs correspond to each mating plane. Consequently, each assembly state is determined. Considering the situation in which each assembly state corresponds to a type of POD, the approach based on successively determining contact points should be separately employed to determine the PODs corresponding to each assembly state.  
**Surface deviation modeling**. The PODs are directly affected by the surface deviations. For consistency of the surface deviations and the actual morphologies of each mating plane, a modeling method from the perspective of manufacturing errors is employed [31; 32; 33]. The deviation functions corresponding to manufacturing errors over the process of machining each mating plane are established, and the surface deviation model of each mating plane is acquired by accumulating the deviation functions. In addition, the coefficients of these deviation functions should be determined according to the impact of each manufacturing error on the surface deviations. Consequently, the surface deviations of each mating plane can be obtained by inputting the coefficients into the surface deviation model.  
**Successive contact point determination**. Determining contact points starts from the assembly position under the ideal circumstances. Next, three contact points on the main mating plane are determined employing the one-step-adjusting-based strategy [24]. To determine the contact points on the auxiliary mating planes, the position and orientation of the assembled part are continuously adjusted according to the adjustment values acquired by computing the distances between the corresponding points on each mating plane. In addition, the recursive process is carried out to repair the broken contact states of the previously acquired points. As a result, the adjustment quantities converge progressively. After each type of adjustment value meets the contact condition, the expected PODs are acquired.
## 4 Experiment and Result Analysis
### Visualization  
In order to better represent the effect of our model, we conduct a visualization on the Conll04 dataset. Some visualizations are shown in Figure 4. As shown in Figure 4, EINET is able to accurately identify entity-pair relationships that are not identified by the baseline model (_Khrushchev_, _Live In_, _Soviet_). As shown in Figure 4, EINET recognizes the entity type of \"_DOE_\" as \"_Org_\" and the relation triple (_[PERSON]_, _Work For_, _DOE_), but the baseline model can not recognize them. The experimental results show that EINET can extract entities and relationships that cannot be recognized by the baseline model after integrating rich semantic information. The rich semantic information makes the model's semantic understanding of text more accurate.
# Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method
## 3 The Augmented Lagrangian Subset Simulation Optimization
### Convergence Criterion for the Outer Loop  
This subsection defines the convergence criterion for the outer loop. It has been suggested that if the absolute difference between \(\lambda^{k+1}\) and \(\lambda^{k}\) is less than or equal to a pre-specified tolerance \(\varepsilon\), then the optimization is considered to have converged. However, when this stopping criterion was checked for active constraints, both the authors and [PERSON] and [PERSON] [25] experienced instability on the convergence properties of the Lagrange multipliers and penalty parameters. [PERSON] and [PERSON] [25] proposed a hybrid convergence criterion by combining the absolute difference between the Lagrange multipliers with that of the objective function. In this study, we add a new convergence standard to the convergence criterion of Lagrange multipliers or the objective functions. In the new convergence standard, a feasible design is always preferable, and it possesses a higher priority than the other two criteria, i.e., the absolute difference criterion or the hybrid one. Suppose that \(\mathbf{x}^{\ast k}\) is the global minimum obtained from the \(k\)th sub-problem in Equation (6) using SSO; the process is terminated if  
\[\left\|\widetilde{g}\Big{(}\mathbf{x}^{\ast k}\Big{)}\right\|_{2}\leq\varepsilon \tag{12}\]  
where \(\varepsilon\) is the user-defined tolerance for feasibility and \(\left\|\widetilde{g}\Big{(}\mathbf{x}^{\ast k}\Big{)}\right\|_{2}\) is the feasibility norm defined by  
\[\left\|\widetilde{g}\Big{(}\mathbf{x}^{\ast k}\Big{)}\right\|_{2}=\sqrt{\sum _{i=1}^{ne}h_{i}^{2}\big{(}\mathbf{x}^{\ast k}\big{)}+\sum_{i=1}^{ni}\bigl{(} \max\bigl{\{}g_{i}\big{(}\mathbf{x}^{\ast k}\big{)},0\bigr{\}}\bigr{)}^{2}}. \tag{13}\]  
Combining the above three convergence criteria, the proposed ALSSO produces comparable optimal solutions in most cases. In some cases, however, a maximum number of iterations must be included in the convergence criterion because the instability of Lagrange multipliers is encountered even if using all three convergence criteria. If the iterative procedure does not meet the convergence criteria even at the maximum iteration number, the optimal solution obtained by the proposed ALSSO shall be considered as an approximate solution to the constrained optimization problem.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Specific Characteristics of Sensor Networks
#### 2.5.2 WSN Features  
In addition to the sensor nodes' intrinsic characteristics, there are other characteristics that are related to the network: sensor self-configuration, scalability, fault tolerance, communication capacity, network topology, bandwidth, and density.  
* _Sensor autoconfiguration._ In a WSN, sensors are deployed either in a random manner by a missile, drone, or airplane, or manually deployed by a human or a robot, inside or around an observed phenomenon such as a field of war, volcanic surface, or sick patient [46]. Thus, each sensor node has the ability, on the one hand, to self-configure in the network, and, on the other hand, to collaborate with other nodes in order to dynamically reconfigure the network in case of network-topology changes [41]. As a reminder, each sensor in a WSN has a transmitting/receiving unit that allows it to communicate with the sensors that are close to it. By exchanging information with them, the sensor can then discover its neighbors and thus know the routing method that it will adopt according to the needs of the application. Autoconfiguration appears to be a necessary feature in the case of WSNs since, on the one hand, their deployment is done randomly in the majority of applications, and, on the other hand, the number of sensors is very high.
* _Scalability._ Unlike traditional wireless networks (personal, local, or extended), a WSN consists of a very large number of sensors (hundreds or thousands) [33]. The sensor network is scalable in that it has the ability to accept a large number of sensors that work together to achieve a common goal.
* _Fault tolerance._ In case of a sensor malfunction (lack of energy, interference with observation environment) or if new sensors are added to the network, the existing sensor nodes in the WSN must continue to normally operate without interruption [33]. This explains the fact that a WSN does not adopt a fixed but rather dynamic topology.
* _Communication ability._ It can take one of two aspects, multihop or one-hop. Multihop communication is less energy-intensive, and it remains the most sought-after type of communication for WSNapplications that require low energy consumption. Finally, it should be noted that WSNs use a nonstandardized protocol stack. However, the majority of scientific work that deals with the WSN issue is based on the protocol stack proposed in 2002 by [PERSON] et al. [33], which is presented in Section 2.6.
# Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System
## 2 Thermal Model of a Single PV Module
### Models
With this approximation, the front surface of PV module saw only the sky, whereas the back surface saw only the ground. Radiative heat exchange coefficients were simplified by this assumption. * All surfaces had the same area \(A\) of the PV module: \(A\) = 1.31 m\({}^{2}\) in the case study. * Batteries did not produce any heat flow while charging or discharging (battery losses were neglected).