esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 2 Materials and Methods
### Synthesis of Microgel  
The microgel was synthesized by precipitation polymerization and it consisted of the main monomer MEO\({}_{3}\)MA, two co-monomers OEGMA and AA and EGDMA was used as a linker. All ingredients were dissolved in 60 mL of deionized water. The total concentration of monomers in the reaction mixture was set to 70 mM (81 mol% of MEO\({}_{2}\)MA, 10 mol% of OEGMA, 7 mol% of AA and 2 mol% of EGDMA), and 17 mg of APS was added later to the solution. Synthesis was carried out in a 250 mL three-neck flask equipped with a reflux condenser, inlet and outlet of inert gas and a magnetic stirrer. For 30 min, the mixture was purged with argon, and then the temperature was increased to initiate the polymerization process by thermal decomposition of the initiator (APS). The reaction was carried out under an argon blanket for 6 h, and the stirring rate was set at 250 rpm during the entire polymerization process. After synthesis, the products were dialyzed with 5 L of water for one week at room temperature, and water was changed daily.
## 4 Nowcasting of Rainfall Field
### Nowcasting of Rainfall Field Based on Deep Learning
Then, \(\mathbf{h}_{t}\) as an input is transferred to fully connected layer that aims to establish the mapping relation between \(\mathbf{h}_{t}\) and the rain intensity vector \(\mathbf{r}_{t+1}\) of target rainfall field in the next time step:  
\[\mathbf{r}_{t+1}=W\mathbf{h}_{t}+\mathbf{b} \tag{26}\]  
where W and \(\mathbf{b}\) are the weight matrix and bias vector, respectively. Here, as \(\mathbf{r}_{t+1}\) is reshaped to a two-dimension matrix, it denotes the predicted rainfall field \(R_{t+1,p}\) in \(t+1\) time step. Finally, based on the errors between predictions and measurements, the regression layer iteratively adjusts the parameters in LSTM and fully connected layers to make the below loss function (Equation (27)) meet the terminal condition. \[\textit{loss}=\frac{1}{2N}\sum_{t=1}^{N}\sum_{j=1}^{H_{R}\mathbf{W}_{R}}\Big{(} R_{t+1,m}(j)-R_{t+1,p}(j)\Big{)}^{2} \tag{27}\]  
where \(R_{t+1,m}\) and \(R_{t+1,p}\) denote the rain intensity of predicted- and measured rainfall field at \(t+1\) time step, respectively. Here, the terminal condition is loss \(<0.01\) or reaching maximum iteration.
### Organization  
This paper is organized as follows: The signal model used is described in Section 2. The proposed reception system with the incorporation of frequency dispersion is addressed in Section 3. The low complexity non-linear detection proposed is described in Section 4. The complete block structure of the transmitter and receiver is presented in Section 5. Computational complexity analysis is addressed in Section 6. The simulation results are presented in Section 7. Finally, the conclusions are stated in Section 8.
## 2 Materials and Methods
### Functional Evaluation of L. plantarum CNTA 628 in C. elegans
#### 2.10.4 RNA Extraction and qPCR Analyses  
Briefly, approximately 800-1000 L1 worms were exposed to the probiotic (3 \(\times\) 10\({}^{5}\) CFU/mL) or water (NGM control) until L4, when they were collected, washed, and treated with TRIzol(r) RNA isolation reagent (Thermo Fisher Scientific, Paisley, UK). Six replicates were used for each condition. Total RNA was extracted following the manufacturer's instructions. RNA purity was determined by measuring the absorbance at 260/280 nm in a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). Subsequently, 500 ng of RNA was treated with DNase I (DNase I-RNasefree, Invitrogen Life Technologies, Paisley, UK) according to the standard protocol and were reverse-transcribed using 200 IU of M-MLV-RT (Invitrogen Life Technologies) in the presence of 40 IU of recombinant RNAsin(r) ribonuclease inhibitor (Promega, Madison, WI, USA), as previously described [27].  
Gene expression analyses were performed by quantitative real-time PCR (qPCR) using TaqMan Universal PCR master mix and specific probes from Applied Biosystems Technologies (Thermo Fisher Scientific Inc., Waltham, MA, USA) and Integrated DNA Technologies Inc. (Coralville, IA, USA) [27]. All reactions were performed using a CFX384 TouchTM Real-Time PCR Detection System (BioRad, Hercules, CA, USA). The expression level of each gene was normalized compared to the expression of the _pmp-3_ gene from Life Technologies (TaqMan Gene Expression Assays, Carlsbad, CA, USA), which were used as housekeeping gene control [39]. Gene expression differences between treated and untreated worms were analyzed using the relative quantification 2\({}^{-\Delta\Delta\text{CI}}\) method [40].
# Implementing GDPR-Compliant Surveys Using Blockchain
###### Abstract  
The immutability of data stored in a blockchain is a crucial pillar of trust in this technology, which has led to its increasing adoption in various use cases. However, there are situations where the inability to change or delete data may be illegal. European Union's General Data Protection Regulation (GDPR)--applying to any company processing personal data from European citizens--explicitly entitles individuals to the right to rectification and the right to be forgotten. In this paper, we describe the design of a system to deploy and process survey data in a GDPR-compliant manner. It combines an Hyperledger Fabric blockchain to ensure that data cannot be tampered with and InterPlanetary File Systems (IPFS) for storage. With the proposed arrangement, we reap several security benefits enabled by blockchain's immutability without running afoul of the regulations. Furthermore, the proof-of-concept is generic and can easily be adapted to various domains.  
blockchain; GDPR; personal information; sensitive data; data protection; implementation; hyperledger fabric; IPFS +
Footnote †: journal:  
system for performing surveys with a variable number of fields involving personal data without compromising security. Our proposal segregates personal data (name, age, phone, email) from the answers to the survey and, consequently, enables its isolated processing. Anonymity is assured since records pertaining to the answers do not contain information that could identify the DS. Further, required separate consents for the two data types enable more flexible processing. The automatic deletion of the survey responses when a deadline expires further assures the correct handling of sensitive data. In summary, the proposed system:  
* Complies with GDPR's article 16 (right to rectification);
* Complies with GDPR's article 17 (right to erasure, also known as \"right to be forgotten\");
* Enforces time constraints on the preservation of the data;
* Requires separate explicit consents for processing personal and survey data;
* Ensures processing of the survey data without disclosing the identity of the respondents;
* Ensures that only registered users can answer the survey.  
The remainder of this paper is organized as follows: in the next section, we describe our research method, and in Section 3, we provide some common ground on key topics. Then, in Section 4, we address system design, followed by system implementation in Section 5 and evaluation in Section 6. We discuss related work in Section 7 before closing with conclusions that restate research contributions, main limitations, and future work.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 1 Introduction
Through these two pathways, isopentenyl pyrophosphate (IPP) and its isomer dimethyl  
Figure 1: Chemical structures of some terpenoids. lallyl pyrophosphate (DMAPP), which are the basic building blocks of terpenoids, can be synthesized. They condensed in a head-to-tail manner to form monoterpenes (C\({}_{10}\)), sesquiterpenenes (C\({}_{15}\)), diterpenenes (C\({}_{20}\)), sesterterpenes (C\({}_{20}\)), triterpenes (C\({}_{30}\)), and tetraterpenes (C\({}_{40}\)) (Figure 2b). Subsequently, the bioactive terpenoid compounds can be produced by a series of modification processes, including cyclization, redox reactions, methylation, acylation, and glycosylation (Figure 2c). Designing and constructing cell factories for high-value terpenoids by utilizing metabolic engineering and synthetic biology strategy, combined with fermentation process control, is gradually demonstrating its unique advantages and potential. Microorganisms serve as key executors in biosynthetic reactions, and their growth physiological characteristics directly influence production efficiency and commercial potential. In this review, we highlight several prokaryotic and eukaryotic microorganisms suitable as chassis for generating edible terpenoids, along with their applications. Subsequently, we present a comprehensive review of the most recent advancements in cell factories for terpenoids  
Figure 2: Biosynthetic pathway of terpenoids. (**a**) Natural and non-natural upstream pathways for the C\({}_{5}\) unit precursors isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). Mevalonate pathway (MVA pathway) in yeast and animal cells utilizes acetyl-CoA (Ac-CoA) as the starting substrate, while methylerythtitol 4-phosphate pathway (MEP pathway) in gram-negative bacteria and eukaryotic organelles uses 3-phosphoglycerate (G3P) and pyruvate as the starting substrates. In the non-natural isopentenol utilization pathway (IUP), IPP and DMAPP can be synthesized by sequential phosphorylation steps using isopentenol isomers isopenol and preenol as precursors. (**b**) Carbon-chain elongation pathway. IPP and DMAPP are condensed to form C\({}_{10}\), C\({}_{15}\), C\({}_{20}\), and other longer terpenoid precursors for monoterpenes, sesquiterpenenes, diterpenenes, sisterterpenes, triterpenes, and tetraterpenes. (**c**) The biosynthesis of stevioside is presented as an example to demonstrate terpenoid synthesis. C\({}_{5}\) building blocks condensed to form GGPP as the precursor of diterpenenes, then terpenen synthases or cyclases helped to form the terpene skeleton, followed by the modification step, such as oxidation and glycosylation. utilized in food flavor, preservation, nutritional enhancement, and healthcare. Finally, we provide insights into the prospective directions and challenges associated with the commercial production of edible terpenoids based on microbial cell factories.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 3 The Proposed Algorithm
### Illumination Map Enhancement Based on Gaussian Laplacian Pyramid Fusion
#### 3.2.4 Gaussian Laplacian Pyramid Fusion  
In order to obtain a refined illumination map, we use the Gaussian-Laplacian Pyramid method [51] to fuse two input images obtained from Equations (13) and (15) with the fusion weights from Equation (20). We decompose either input image into a Laplacian pyramid to extract image features and decompose either normalized weight \(\overline{W}_{k}\) into a Gaussian pyramid to smooth strong transitions [27]. Then, we fuse these pyramids by mixing each layer, and the final enhanced illumination map is obtained by summing up-sampling on the fused pyramids:  
\[I_{final}=\sum_{d}U_{d}\Big{(}{\sum_{k=1}^{2}G_{l}\{\overline{W}_{k}\}}\times L _{l}\{l_{k}\}\Big{)}, \tag{22}\]  
where \(U_{d}\) is the up-sampling operator with factor \(d=2^{l}-1\) (\(l\) is the number of layers of the pyramid; we select \(l=6\)), and \(L_{l}\{\cdot\}\) and \(G_{l}\{\cdot\}\) are the Laplacian decomposition and Gaussian decomposition, respectively.
# Interface Growth and Void Formation in Sn/Cu and Sn0.7 Cu/Cu Systems
## 3 Results and Discussion  
### Sn/HP Cu and Sn/EP Cu joints after Aging at 150 \({}^{\circ}\)C  
The interfacial microstructure of Sn/HP Cu and Sn/EP Cu joints after aging at 150 \({}^{\circ}\)C with different aging periods are shown in Figures 1 and 2. In all of the images, the upper, bottom, and middle materials are solder alloy, copper, and interfacial IMC layer, respectively. The chemical compositions of the IMC layers were characterized by EDS analysis.  
Figure 1: Interfacial microstructure of Sn/HP Cu joints after aging at 150 \({}^{\circ}\)C for (**a**) 0 h, (**b**) 240 h, and (**c**) 480 h.  
Figure 1a shows the interfacial microstructure of the as-reflowed Sn/HP Cu joint. A layer of scallop-shaped Cu\({}_{6}\)Sn\({}_{5}\) formed at the interface, and no voids appeared at the interface. After aging at 150 \({}^{\circ}\)C for 240 h, a Cu\({}_{3}\)Sn layer emerged at the interface of Cu\({}_{6}\)Sn\({}_{5}\)/HP Cu, but no voids were detected, as presented in Figure 1b. Even after aging at 150 \({}^{\circ}\)C for 480 h, as seen in Figure 1c, no voids were observed at the Cu\({}_{3}\)Sn/HP Cu interface. Moreover, the thickness of the Cu\({}_{3}\)Sn layer grew rapidly, but the thickness of Cu\({}_{6}\)Sn\({}_{5}\) layer seemed to remain constant.  
The interfacial microstructure of the as-reflowed Sn/EP Cu joints can be observed in Figure 2a. Similar to the Sn/HP Cu joints, a scallop-shaped Cu\({}_{6}\)Sn\({}_{5}\) layer formed at the interface and no voids were detected. After aging at 150 \({}^{\circ}\)C for 240 h, the total thickness of the IMC layers gradually increased in the Sn/EP Cu joints, and the Cu\({}_{3}\)Sn layer grew much faster than the Cu\({}_{6}\)Sn\({}_{5}\) layer. Moreover, a certain number of voids were observed at the Cu\({}_{3}\)Sn/Cu interface, as seen in Figure 2b. When the aging time was extended to 480 h, the Cu\({}_{6}\)Sn\({}_{5}\) layer thickness increased not obviously (Figure 2c). However, the thickness of the Cu\({}_{3}\)Sn layer became thicker with the rising aging time. Moreover, the size and number of voids were further increased at the Cu\({}_{3}\)Sn/Cu interface. In addition, the IMCs morphology became flattened, because Cu diffusion through the scallop valleys was faster than that through the scallop hills [21].
# Meta Learning Based Deception Detection from Speech
## 5 Evaluation
### Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline MNA-_Five-features_[55] & 55.82 & 0.2977 & 0.4122 & 0.3444 \\ MNA-_Wav2 Vec 2.0_ & 60.46 & 0.3411 & 0.4208 & 0.3745 \\ MNA-FT-_Five-futures_ & 55 & 0.3129 & 0.4922 & 0.3822 \\ MNA-FT-_Wav2 Vec 2.0_ & 54.3 & 0.3051 & 0.4803 & 0.3731 \\ Prototypical [50]-_Five-features_ & 53.66 & 0.294 & 0.4547 & 0.3548 \\ Prototypical [50]-_Wav2 Vec 2.0_ & 52.24 & 0.2765 & 0.4253 & 0.335 \\ MAML [51]-_Five-features_ & 58.8 & 0.3148 & 0.3865 & 0.3466 \\ MAML [51]-_Wav2 Vec 2.0_ & 61.28 & 0.349 & 0.424 & 0.3823 \\ CHAML-_Five-features_ & 58.59 & 0.3345 & 0.4585 & 0.3826 \\ CHAML-_Wav2 Vec 2.0_ & **61.34** & 0.3515 & 0.4307 & **0.3857** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of models performance. while having a similar computation time to the Prototypical network, but achieves much higher performance. Moreover, we find that using the Wav2 Vec 2.0 embedding Section 4.1.2 for the deception detection task on our data, seems to have better results in the main models: MNA, MAML, and CHAML. Interestingly, even in the MNA classifier, which was originally specially designed for the five-sound features embedding, the Wav2 Vec 2.0 embedding performs better. In order to confirm that CHAML uses the examples and does not ignore them, we conduct another experiment in which the support set was mixed and did not have the two true examples first and the two false examples later (as required by CHAML). In this case, the F1-score has significantly decreased from 0.3857 to 0.3707 in the Wav2 Vec 2.0 setting and from 0.3826 to 0.332 in the Five-Features setting. This indicates that CHAML relies on the examples for providing an accurate prediction.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 4 Configurations Dataset
### Dataset Generation  
The proposed algorithm (described in the following section) requires a dataset of admissible configurations. As mentioned in the previous section, a configuration can be generated by choosing \(\rho\) and finding th corresponding \(\mathbf{r}\), which finds a minimum potential energy of the structure. This can be directly replicated in an experimental study by choosing elastic elements with a given \(\rho\) (which in practice means changing cable lengths or strut lengths) and recording steady-state positions of the nodes.  
In order to generate the configuration dataset, one must establish which elements of \(\rho\) are going to change. The indexes of the elements of \(\rho\) that change are stored in a vector \(\mathbf{a}\in\mathbb{R}^{u}\), meaning that it is elements \(\rho_{a_{1}}\),..., \(\rho_{a_{w}}\) that are changing, while the rest remain static. We define a vector of changing rest lengths \(\rho_{a}\):  
\[\rho_{a}=\begin{bmatrix}\rho_{a_{1}}&...&\rho_{a_{w}}\end{bmatrix}^{\top} \tag{11}\]  
Additionally, we define projector matrices \(\mathbf{P}_{a}\) and \(\mathbf{P}_{a}^{\perp}\), such that the following holds:  
\[\begin{cases}\mathbf{P}_{a}\rho=\rho_{a}\\ \rho=\rho_{a}+\mathbf{P}_{a}^{\perp}\rho\end{cases} \tag{12}\]  
Then, given the value of \(\mathbf{P}_{a}^{\perp}\rho\) and a set of values of \(\rho_{a}\), one can find a corresponding \(\rho\), and hence the corresponding configuration vector \(\mathbf{r}^{q}\).  
Algorithm 1 performs generation of a configuration dataset based on the process described above. In the Algorithm 1, \(\rho_{0}\) is the vector of default rest length values for the elastic elements in the tensegrity structure, \(\mathcal{R}_{a}\) is the set of all admissible values of \(\rho_{a}\) (changing rest lengths of the elastic elements), \(\Pi\) is the potential energy of the structure, \(\gamma\) is a small regularization coefficient, and \(\mathbf{r}^{0}\) is the configuration vector that corresponds to the static equilibrium for the structure given by the vector of default rest lengths \(\rho_{0}\). Note that minimization in \(\mathbf{r}^{q}=\operatorname*{argmin}\left(\Pi(\mathbf{r},\rho^{q})+\gamma \big{|}\big{|}\mathbf{r}^{0}-\mathbf{r}\big{|}\big{|}\right)\) is understood as the result of applying gradient descent from the initial guess \(\mathbf{r}^{0}\).  
```
Input:\(\mathbf{r}^{0},\rho_{0}\), \(\mathcal{R}_{a}\) Output: Dataset while\(q\leq\text{length}(\mathcal{R}_{a})\)do \(\rho_{a}^{q}=\text{read}(\mathcal{R}_{a})\) \(\rho^{q}=\rho_{a}^{q}+\mathbf{P}_{a}^{\perp}\rho_{0}\) \(\mathbf{r}^{q}=\text{argmin}\big{(}\Pi(\mathbf{r},\rho^{q})+\gamma\big{|} \big{|}\mathbf{r}^{0}-\mathbf{r}\big{|}\big{|}\big{)}\)  Dataset.append(\(\mathbf{r}^{q}\)) endwhile
```  
**Algorithm 1** Configurations dataset generation algorithm
## 2 System Modeling
### HESS Modeling  
As shown in Figure 1, the hybrid energy storage system consists of a battery pack and an SC pack, connected in series with a DC/DC converter. \(R_{b}\) and \(R_{SC}\) are the internal resistance of the battery pack and SC pack, respectively. \(L_{b}\) and \(L_{SC}\) are the converter inductances, and \(C_{DC}\) is used to stabilize the bus voltage. \(I_{b}\) and \(I_{SC}\) are the battery and SC currents, and the arrow direction is the defined positive direction.  
In this paper, the battery pack is regarded as a voltage source and resistance in series. The battery SoC is calculated by the ampere-time integration method [37], given as follows:  
Figure 3: Three typical operation conditions for legged robots: general power demand, high power demand, and high feedback power demand.  
\[S\dot{o}C_{b}=-\frac{\eta}{Q_{b}}I_{b}, \tag{12}\]  
where \(\eta\) is the charging/discharging efficiency. \(Q_{b}\) is the battery pack capacity. The battery current \(I_{b}\) can be expressed as  
\[I_{b}=\frac{V_{b}-\sqrt{V_{b}^{2}-4R_{b}P_{b}}}{2R_{b}P_{b}}. \tag{13}\]  
Similar to the battery pack, the SC pack is also seen as a large capacitance with an internal resistance. The SOC of SC has a linear relationship with its OCV, so the SC SoC is estimated as the following [26]:  
\[S\dot{o}C_{SC}=\left\{\begin{aligned} &-\frac{\eta_{dit}I_{SC}}{C_{SC}V_{SC,max}},if \ I_{SC}>0\\ &-\frac{\eta_{dit}I_{SC}}{C_{SC}V_{SC,max}},if\ I_{SC}<0\end{aligned}\right. \tag{14}\]  
and the SC current \(I_{SC}\) can be expressed as  
\[I_{SC}=\frac{V_{SC}-\sqrt{V_{SC}^{2}-4R_{SC}P_{SC}}}{2R_{SC}}. \tag{15}\]  
In the context of the legged robot, certain constraints must be consistently upheld during its operation:  
\[\left\{\begin{aligned} & I_{d}=I_{b,out}+I_{SC,out},\\ & P_{d}=P_{b}+P_{SC},\end{aligned}\right. \tag{16}\]  
where \(I_{d}\) is the load current, and \(I_{b,out}\) and \(I_{SC,out}\) are the output current of the two DC-DC converters, respectively. \(P_{d}\) is the load power demand. \(P_{b}\) and \(P_{SC}\) represent the output power of the battery pack and SC pack.
# A Generalized Framework for the Estimation of Edge Infection Probabilities
## 4 Evaluation
### Acyclic Examples
#### 4.2.1 Underdetermination  
A significant challenge exists in the estimation of edge infection values, which is illustrated using the following small example. Consider the example network in Figure 1a. There are two vertices, \(A\) and \(B\), connected by a directed edge pointing from \(A\) to \(B\). \(A\) and \(B\) are infected in succeeding iterations, \(A\) in \(t=0\) and \(B\) in \(t=1\), and therefore, both have an observed binary flag of one. For the sake of simplicity, we consider the IC infection model. The best and optimal way to explain this scenario is to consider the connecting edge to have an infection probability of one. This is obviously an artifact arising from the available observations, a lack of information, since we have only one observation on a highly stochastic process. From the optimization point of view, however, this is an optimal solution to the scenario, since this is the most likely explanation of the observations. If we have real-valued observations on vertices \(A\) and \(B\), the edge infection probability is the one satisfying the equation \(p_{B}=p_{A}*w_{AB}\), where \(p_{A}\) and \(p_{B}\) denote the probability that \(A\) and \(B\) are infected. Next, consider the case shown in Figure 1b. There are now two possible sources of infection for \(B\). Vertex \(A\) and vertex \(C\) both have a directed edge pointing toward, \(B\) and both were infected in the iteration before \(B\) was infected. Suppose that \(p_{A}\) and \(p_{C}\) are independent of each other. This way, \(p_{B}=1-((1-p_{A}*w_{AB})*(1-p_{C}*w_{CB}))\). If the edge infection probabilities are real, there are many, possibly infinite ways of explaining this event, and this occurs at both binary flags and real \(p_{v}\)-s. The challenge in accurate edge weight estimation again arises from a lack of information: we cannot decide the value of one edge value without knowing the other. Similar phenomena can be observed in the other infection models.  
We will refer to the problems above as the underdetermination of the inverse infection task. Underdetermination occurs in most real-life examples, even when the infection process is completely observable.