esa-sceva/satcom-chunk-collection huggingface dataset:
# Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer
## 5 Discussion  
The hydraulic conductivities of the dam and dam foundation change with operation and loading, affecting the effectiveness and safety of the hydraulic project. An Improved Gray Wolf Optimizer for solving the problem that hydraulic conductivity is not easily determined, is introduced in this paper.  
The objective hydraulic conductivity was determined based on IGWO. The obtained hydraulic conductivities of the dam and dam foundation were applied in the finite element positive analysis. The results show the effectiveness of IGWO in determining hydraulic conductivity. It is suggested that IGWO could be used to obtain reasonable simulation results in similar inverse problems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Leakage**} & **Measured** & **Simulated** & **Absolute Error** & **Relative Error** \\  & **Values** & **Values** & **(m3/s)** & **(m3/s)** \\ \hline Dam body & 0.40 & 0.38 & \(-\)0.02 & 5.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the simulated and measured values of the dam body leakage.  
Figure 8: Distribution of seepage contours at the maximum section of the dam.  
The hydraulic conductivity of each medium in the dam body and dam foundation simulated in this paper was obtained with a stable upstream water level. In fact, the upstream water level of a reservoir varies continuously with the operating conditions and purpose. The hydraulic conductivity will also change with the monitored values. It is worth noting that IGWO is still applicable under this condition.
## 3 Results and Discussion
### Solvent Residues Analysis  
In pharmaceutical products, the solvent residue is a serious problem and is under consideration. In this work, the preparation of spiramycin particles was realized by the HAP process using the International Conference on Harmonization (ICH) class III solvent DMSO with low toxicity [43]. As shown in Figure 8, the residual DMSO content in the spiramycin (prepared using DMSO as the solvent) was determined by GC analysis, DMSO with retention time of 8.45 min, and a standard curve of \(Y=341.68X+2.02\) (\(R^{2}=0.9997\)), where \(Y\) is the peak area and \(X\) is the DMSO concentration. The residual DMSO concentration in the spiramycin microparticles was calculated to be 0.33%, according to this regression equation. The spiramycin microparticles conform to the ICH standard and are, therefore, suitable for pharmaceutical utilization.
# Model Modification of the Soil-Water Characteristic Curve of Unsaturated Weak Expansive Soil
## 6 Correction of the Soil-Water Characteristic Curve by the Mercury Intrusion Test
### Correction Result Verification  
To verify the correctness and applicability of the correction results of Equation (18), the results were verified using instruments proposed by [PERSON] et al. [23] and [PERSON] et al. [24]. [PERSON] et al. used a stress-related soil-water characteristic curve pressure plate instrument (SDSWCC-H) to analyze the soil and water characteristics of Nanyang expansion soil in a certain stress state. [PERSON] et al. studied the SWCC of weakly expansive soil on a slope of a highway in Hubei Province using the gas phase method and dialysis (liquid phase) method and predicted its relative permeability coefficient. The results are shown in Figure 10.  
The Pearson correlation coefficient analysis values for testing and predicting matric suction were 0.955 [23] and 0.999 [24], respectively. The characteristic curve of expanding soil and soil water in the full suction range can be obtained by adopting the gas phase method and dialysis method (0.01-309 MPa). The soil-water characteristic curve of typical unsaturated soil is divided into boundary effect segments, transition segments, and unsaturated residual segments based on the intake value and residual suction value. The data shown in Figure 9 indicate that the suction force gap in the expansive soil matrix is nearly five quantitative intervals. The correction curve coincides with the test curve in the transition section, and the prediction result in the boundary effect segment is slightly higher than the measured value. Because of the experimental range limitation of the filter paper method, the non-saturated residual suction section cannot be predicted. Therefore, it is inferred that the prediction model proposed in this paper can better reflect the development law of the soil-water characteristic curve of the expansive soil transition section. Considering that there are few studies on expansive soils in this region, and differencesin soil properties across regions affect the model results, the universality of the modified model needs to be further verified.
# Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)
## 2 Methods
### Wasserstein Generative Adversarial Networks  
The WGAN [12] was developed in order to alleviate the issue of the vanishing gradient problem. To measure the distance between probability distributions, rather than use the Jensen-Shannon (JS) divergence (expressed by Equation (1)) as in the GAN, [PERSON] et al proposed the Earth-Mover (EM) or Wasserstein-1 distance:  
\[W\big{(}\mathbb{P}_{r},\mathbb{P}_{g}\big{)}=\inf_{\gamma\in\Pi\big{(}\mathbb{ P}_{r},\mathbb{P}_{g}\big{)}}\mathbb{E}_{(\mathbf{x},\mathbf{y})\sim\gamma}[\|\mathbf{x}-\mathbf{y} \|], \tag{3}\]  
where \(\Pi\big{(}\mathbb{P}_{r},\mathbb{P}_{g}\big{)}\) denotes the set of all joint distributions \(\gamma(\mathbf{x},\mathbf{y})\) whose marginals are respectively \(\mathbb{P}_{r}\) (real data) and \(\mathbb{P}_{g}\) (generated data) [12]. The Wasserstein-1 distance is able to provide a similarity measure between two probability distributions, even when the two probability distributions have no overlap, making it a more sensible cost function. The discriminative model is renamed the **critic** in the WGAN, as it is not explicitly attempting to classify inputs as real or fake, but rather to determine how real an input is. The WGAN value function is constructed via the Kantorovich-Rubinstein duality as Equation (3) is computationally intractable [12]:  
\[\min_{G}\max_{D\in\mathcal{D}}\ \ \mathop{\mathbb{E}}_{\mathbf{x}\sim\mathbb{P}_{r}}[D( \mathbf{x})]-\mathop{\mathbb{E}}_{\mathbf{x}\sim\mathbb{P}_{g}}[D(\mathbf{\hat{x}}))] \tag{4}\]  
where \(\mathcal{D}\) is the set of 1-Lipschitz functions. To enforce the Lipschitz constraint, weight clipping was originally used by [PERSON] et al. [12], who stated that this method of enforcement was terrible, despite it working well for the examples shown in their paper and was, at least, simple. [PERSON] et al. [13] introduced an improvement to weight clipping, by enforcing the Lipschitz constraint with a **Gradient Penalty (GP)** method. By enforcing a soft version of the constraint with a penalty, the new loss function becomes:  
\[L=\underbrace{\mathbb{E}_{\mathbf{x}\sim\mathbb{P}_{g}}[D(\mathbf{\hat{x}})]-\mathbb{E }_{\mathbf{x}\sim\mathbb{P}_{r}}[D(\mathbf{x})]}_{\text{loss of the critic}}+\underbrace{ \mathbb{E}_{\mathbf{x}\sim\mathbb{P}_{g}}\big{[}(\lVert\
abla_{\mathbf{x}}D(\mathbf{\hat{x }})\rVert_{2}-1)^{2}\big{]}}_{\text{gradient penalty}}. \tag{5}\]  
Throughout this study we enforce the Lipschitz constraint by using the GP method in our WGAN models.
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
## 6 Conclusions  
A 77 GHz packaged transmit array antenna for automotive radar applications has been presented. The whole structure is compact and realized in planar technology, which make it compatible with standard quad flat no-lead package (QFN) technology. A planar feed and a transmit array are both integrated into a single package. The electromagnetic field radiated by a feed is focused by the transmit array antenna placed on the package cover. The unit cell of the array is composed of two pairs of stacked patches separated by a central ground plane. The primary feed is realized with a planar patch antenna surrounded by a mushroom-type EBG. The relevant parameters of the proposed TA, like directivity, gain and spillover efficiency, have been evaluated using an analytical model. The final configuration has then been optimized by means of full-wave simulations. The simulated gain is 14.2 dBi at 77 Ghz, while the half-power beamwidth is 22\({}^{\circ}\). The gain and beamwidth are in line with medium- and short-range automotive radar requirements [1; 28]. The proposed transmit array is a good candidate for highly integrated mid-gain automotive applications.  
**Author Contributions:** Conceptualization, L.B. and E.A.; software, F.G. and R.D.M.; validation, E.A.; formal analysis, F.G.; writing--original draft preparation, E.A.; writing, E.A. and F.G.; visualization, F.G.; supervision, G.A.; project administration, G.A.; funding acquisition, G.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Freq (GHz)** & **W (mm)** & **Eff (\%)** & **Feed Type** & **QFN Compatible** \\ \hline
[8] & 76.5 & 40.35 & 5 & Planar (SIW Slot) & NO \\
[26] & 60 & 50 & 14.2 & Not planar (Horn) & NO \\
[27] & 77 & 46.55 & 19.7 & Not planar (Horn) & NO \\
**This Work** & 77 & 12 & 22 & Planar (Patch) & YES \\ \hline \end{tabular}
\end{table}
Table 1: Transmit array comparison.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 4 System Design and Methodology
### Image Storage and Blockchain Recording  
After data encryption and verification, to ensure long-term storage and immutability, the encrypted image is stored in IPFS, and its hash value is recorded in the blockchain. IPFS stores and transmits data by dividing the encrypted image into \(l\) small blocks, typically 256 KB each, with each block denoted as \(B_{i}\). The image data is represented by Equation (19).  
\[Encrypt(I_{selective}(u,v))=\{B_{1},B_{2},\ldots,B_{l}\} \tag{19}\]  
Each data block generates a unique identifier \(H(B_{i})\) through a hash function. IPFS uses a Directed Acyclic Graph (DAG) to organize and manage these data blocks. The identifier of each image data block acts as a leaf node in the Merkle DAG. The hash values of higher-level nodes are computed layer by layer until the root node, whose hash value \(H_{\text{root}}\) serves as the unique identifier for the entire image file, as shown in Equation (20). The image data blocks and their corresponding hash values are then stored across multiple nodes in the IPFS network. When a retrieval request is made, it is based on the identifier \(H_{\text{root}}\). The IPFS network locates the corresponding Merkle DAG using the Content Identifier (CID), resolves it layer by layer, downloads all data blocks, and reconstructs the original file.  
\[H_{\text{root}}=\{H(B_{1})||H(B_{2})||\ldots||H(B_{l})\} \tag{20}\]  
To ensure data security and privacy, only authorized users can access and verify encrypted data stored in IPFS. IPFS creates an Access Control List (ACL) for each CIDand its stored data. Assuming that the CID is \(CID_{d}\), and the corresponding ACL is \(ACL_{d}\), it includes the public keys or identity identifiers of authorized user nodes. When a user requests access, they must provide their identity information and access proof, generating a timestamped access request as shown in Equation (21). The server receives the request, verifies the signature using the user's public key, and checks if the user is listed in the corresponding ACL, as shown in Equation (22). If verified, the user is granted access to download and decrypt the data; otherwise, the request is denied. This ensures that only authorized users can access and verify the encrypted data in IPFS, protecting data security and privacy.  
\[\text{AccessRequest}=Sign_{skuser}(CID_{d}\big{|}|Timestamp), \tag{21}\]  
\[Verify(AccessRequest,pk_{user},ACL_{d}) \tag{22}\]
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 4 Improved Ant Colony Algorithm
### Customer Selection Method
#### 4.1.2 Noninitial Customer Selection  
The noninitial customer selection method refers to the state transition rule in the traditional ant colony algorithm, as shown in Equation (13):  
\[\begin{cases}j=\operatorname*{argmax}\Bigl{\{}\bigl{[}\tau_{iy}(t)\bigr{]}^{ \alpha}[\eta_{iy}(t)]^{\beta}\Bigr{\}},&q\leq q_{0}\\ p_{ij}^{k}=\frac{[\tau_{ij}(t)]^{\alpha}[\eta_{ij}(t)]^{\beta}}{\sum\limits_{y \in allov(0)}[\tau_{y}(t)]^{\alpha}[\eta_{iy}(t)]^{\beta}},&q>q_{0}\end{cases} \tag{13}\]  
where \(p_{ij}^{k}\) denotes the probability that the next customer of vehicle \(k\) selects \(j\), \(\tau_{ij}(t)\) denotes the pheromone content on path (\(i,j\)) at time \(t\), and \(\eta_{ij}\) denotes the heuristic information of customers \(i\) and \(j\). In this paper, we set the heuristic information as the reciprocal of the distance between two customer points, which can be expressed as \(\eta_{ij}=1/d_{ij}\). \(\alpha\) is the pheromone concentration factor, and \(\beta\) is the expected heuristic factor, which reflect the relative importance of the accumulated pheromone and heuristic information in the ants' path selection, respectively. The variable \(allow(i)\) denotes the set of customer points with transportation demand when the vehicle is at customer point \(i\).  
In the early stage of the algorithm, the value of \(q_{0}\) is large, and the ants have a large probability of directly selecting the customer point through the pheromone content and heuristic information, so as to enhance the convergence speed in the early stage of the algorithm. In the middle and late stages of the algorithm, after a number of iterations, the positive feedback adjustment of pheromones makes the current best path accumulate more pheromones, and the algorithm easily falls into a local optimum. At this time, the value of \(q_{0}\) is small, and the ants are more likely to select customer points according to roulette rules, so as to enhance the global search ability of the algorithm and improve the possibility of the algorithm jumping out of a local optimum.
## 4 Discussion
### Melting Characteristics  
Differential scanning calorimetry (DSC) is a thermoanalytical technique widely used to characterize phase transitions in vegetable oils. It is an established method that has many advantages, as it does not require any chemical or complicated pretreatment or time-consuming steps before each measurement. Various vegetable oils exhibit complex thermal properties that are mainly due to the great variety of triglycerides as their main constituents, as well as their natural polymorphism [42; 48].  
Based on the results obtained in this study, it can be assumed that tomato seed oil is a rich source of unsaturated fatty acids because the endothermic peaks occurred at low temperatures. In contrast, crude coconut oil is rich in monounsaturated and saturated fatty acids due to the fact that the resulting endothermic peaks occurred at higher temperatures.  
[PERSON] et al. [49], in their work on the valorization of tomato processing byproducts using the DSC method, obtained results that dry tomato pomace showed two slight endothermic peaks at temperatures \(-22\)\({}^{\circ}\)C and \(26\)\({}^{\circ}\)C, which were related to the seed fraction. The lower temperature peak could be associated with the unsaturated fatty acid esters present in tomato seed oil, such as linoleic and oleic acid; however, the peak at \(26\)\({}^{\circ}\)C was attributed to saturated homologs (palmitic and stearic acids). This agrees with the results obtained in this study (last endothermic peak at \(-23.55\)\({}^{\circ}\)C for tomato oil sample).  
As for crude coconut oil (sample B), the obtained data are in agreement with the experimental data reported by [PERSON] et al. [37], who acquired an endothermic peak at a minimum temperature of \(23.4\)\({}^{\circ}\)C in comparison with our study in which the endothermic peak was achieved at \(24\)\({}^{\circ}\)C.  
[PERSON] et al. [50] observed in their research that along with an increase in the percentage of coconut oil in the mixture, the melting peaks shifted from a lower temperature to a higher temperature. An almost identical temperature range of endothermic peaks was observed in this study. The mixture containing a greater percentage of coconut oil (sample A25B75) than tomato seed oil showed the farthest shift in endothermic peaks toward higher temperatures. As a consequence, mixture A25B75 showed the highest melting point, while oil blend A75B25 indicated the lowest melting point.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions  
We now present the results obtained from the simulation experiments. These are presented in two parts. First, we present results indicating the comparative performance of the proposed FL model with respect to a centralized ML model in terms of traffic volume, trained model performance, and computation cost. These results demonstrate that the FL model largely reduces the communication cost and computations for achieving a comparable model performance to the centralized ML model. Next, we present an extensive  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Epochs** & **PCA Components** & **Volume of Data** & **Recall** & **Precision** & **Accuracy** \\ \hline \multirow{3}{*}{60} & 10 & 49.1 & 0.934 & 0.953 & 0.954 \\ \cline{2-6}  & 20 & 95.9 & 0.963 & 0.918 & 0.950 \\ \cline{2-6}  & 30 & 142.7 & 0.830 & 0.932 & 0.905 \\ \hline \multirow{3}{*}{80} & 10 & 49.1 & 0.866 & 0.922 & 0.915 \\ \cline{2-6}  & 20 & 95.9 & 0.871 & 0.942 & 0.925 \\ \cline{2-6}  & 30 & 142.7 & 0.805 & 0.954 & 0.904 \\ \hline \multirow{3}{*}{**100**} & 10 & 49.1 & 0.803 & 0.914 & 0.888 \\ \cline{2-6}  & **20** & **95.9** & **0.990** & **0.958** & **0.978** \\ \cline{1-1} \cline{2-6}  & 30 & 142.7 & 0.903 & 0.959 & 0.944 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic volume in kB for federated learning with PCA considered for various numbers of epochs and PCA components with 3 communication rounds.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of layers & 3 \\ \hline Layers type & Dense \\ \hline Neurons in input layer & 20 \\ \hline Neurons in hidden layer and activation & 15; ReLU \\ \hline Neurons in output layer and activation & 20; Sigmoid \\ \hline Optimizer & Adam \\ \hline Loss function & MAE \\ \hline \hline \end{tabular}
\end{table}
Table 3: Autoencoder model details.  
study of the proposed FL-LoRaMAC framework, including the impact of packet loss on the model performance, model training time, and energy consumption, and the quantitative benefits of pruning and the proposed QoS-based SF distribution scheme.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 5 Resource Allocation Algorithms for MMCDR
### Greedy Suboptimal Algorithm  
As mentioned in Section 4.2, the computational complexity in solving the MILP problem is nonnegligible. Therefore, a greedy suboptimal algorithm with low complexity is proposed to solve problem P2. To avoid CCI, the inter-beam distance threshold \(d_{th}^{sat}\) is also used in the greedy algorithm for MMCDR, and the transmission capacity of each satellite terminal in each time slot can be calculated by (28). The greedy algorithm for MMCDR is summarized in Algorithm 2. For each time slot in a BH time window, the satellite terminal or the terrestrial system with the minimum CDR will have priority in time slot allocation. At the beginning of the algorithm, the resource allocation results and the transmission capacity of the satellite and terrestrial systems are initialized (lines 1-2), respectively. For each time slot \(t\in\Gamma\), the resource allocation results can be obtained as follows. Initialize the candidate sets \(\Omega_{s}\) and \(\Omega_{t}\) (line 4). In each iteration, if \(\Omega_{s}\) is not empty and the number of served satellite terminals in time slot \(t\) is less than \(N_{b}\), the time slot \(t\) is allocated to one of the candidate satellite terminals or one of the candidate terrestrial BSs as follows:  
* If the minimum CDR of the satellite terminals is less than or equal to that of the terrestrial BSs, the time slot \(t\) is allocated to one of the candidate satellite terminals (lines 6-12). The set \(\Theta_{s}\) indicates the satellite terminals with the minimum CDR in \(\Omega_{s}\). The time slot \(t\) is allocated to the satellite terminal \(i^{*}\in\Theta_{s}\) with the maximum \(D_{i}^{sat}\). Then, \(C_{i^{*}}^{sat}\) is updated. To avoid inter-beam and inter-system CCI, \(\Omega_{s}\) and \(\Omega_{t}\) are updated based on \(d_{th}^{sat}\) and \(d_{pro}^{st}\), respectively. * Otherwise, the time slot \(t\) is allocated to one of the candidate terrestrial BSs (lines 13-20). The set \(\Theta_{t}\) indicates the terrestrial BSs with the minimum CDR in \(\Omega_{t}\). The time slot \(t\) is allocated to the terrestrial BS \(j^{*}\in\Theta_{t}\) with the maximum \(D_{j}^{ler}\). Then, \(C_{i^{*}}^{ler}\) is updated, and the terrestrial BS \(j^{*}\) is removed from \(\Omega_{t}\). To avoid inter-system CCI, \(\Omega_{s}\) is updated based on \(d_{pro}^{st}\). After determining the served satellite terminals in time slot \(t\), the time slot \(t\) is allocated to the remaining terrestrial BSs in \(\Omega_{t}\), and their transmission capacity is updated accordingly (lines 22-25). Repeat the above process until all time slots are allocated.
_Int. J. Mol. Sci._**2014**, _15_, 18267-18280; doi:10.3390/ijms151018267  
International Journal of  
Molecular Sciences  
ISSN 1422-0067  
www.mdpi.com/journal/ijms  
_Review_  
**tRNA Modification Enzymes GdA and MnmE:**  
**Potential Role in Virulence of Bacterial Pathogens**  
**[PERSON] and [PERSON] ***  
Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-608-890-2398; Fax: +1-608-262-5157.  
External Editor: [PERSON]  
_Received: 3 September 2014; in revised form: 2 October 2014 / Accepted: 8 October 2014 / Published: 10 October 2014_  
**Abstract:** Transfer RNA (tRNA) is an RNA molecule that carries amino acids to the ribosomes for protein synthesis. These tRNAs function at the peptidyl (P) and aminoacyl (A) binding sites of the ribosome during translation, with each codon being recognized by a specific tRNA. Due to this specificity, tRNA modification is essential for translational efficiency. Many enzymes have been implicated in the modification of bacterial tRNAs, and these enzymes may complex with one another or interact individually with the tRNA. Approximately, 100 tRNA modification enzymes have been identified with glucose-inhibited division (GidA) protein and MnmE being two of the enzymes studied. In _Escherichia coli_ and _Salmonella_, GidA and MnmE bind together to form a functional complex responsible for the proper biosynthesis of 5-methylaminomethyl-2-thiouridine (mnm\({}^{5}\)s\({}^{2}\)U34) of tRNAs. Studies have implicated this pathway in a major pathogenic regulatory mechanism as deletion of _gidA_ and/or _mnmE_ has attenuated several bacterial pathogens like _Salmonella enterica_ serovar Typhimurium, _Pseudomonas syringae_, _Aeromonas hydrophila_, and many others. In this review, we summarize the potential role of the GidA/MnmE tRNA modification pathway in bacterial virulence, interactions with the host, and potential therapeutic strategies resulting from a greater understanding of this regulatory mechanism.  
**Keywords:** tRNA modification; GidA; MnmE; bacterial pathogenesis