esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### High-Pass Flat Negative Group Delay Circuit
#### 2.1.2 High-Pass Negative Group Delay Circuits with Different \(L_{1}\) Values  
When the values of \(C_{1}\), \(R_{1}\), \(L_{2}\), \(R_{2}\), and \(L_{3}\) in the circuit remain unchanged, as shown in Table 1, we change the values of \(L_{1}\) to 0.9 nH, 3.9 nH, and 5.9 nH in sequence and observe the changes in the circuit transfer function and group delay. The circuit results in these three scenarios are shown in Table 3 and Figures 7 and 8. Through observation, it is found that, as the value of inductance \(L_{1}\) increases, the group delay of the high-pass circuit at 0 MHz decreases. As the frequency increases, the larger the value of capacitance \(L_{1}\), the smaller the negative group delay value. The change in inductance \(C_{1}\
## 3 Materials and Methods
### Step 5: Multi-Objective Optimization Using NSGA-II  
The MOOM-OEP problem (Maximize \(f_{1}(x)\), Minimize \(g_{2}(x)\) subject to constraints (9)-(19)) is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) (S10-S14) [32]. Key hyperparameters for the NSGA-II implementation included a population size of 100, a crossover probability of 0.9, a mutation probability of 0.1, and termination after 200 generations.  
* Initialization: Generate initial population \(P_{0}\) of size \(N\).
* Evaluation: Calculate \(f_{1}(x)\) and \(g_{2}(x)\) for all \(x\in P_{0}\).
* Non-Dominated Sorting: Rank solutions into fronts \(F_{1},F_{2},\dots\) based on Pareto dominance. \(x_{1}\) dominates \(x_{2}\) if \[(\forall l\in\{1,2\},f_{l}(x_{1})\geq f_{l}(x_{2}))\quad\wedge(\exists l^{ \prime}\in\{1,2\},f_{l^{\prime}}(x_{1})>f_{l^{\prime}}(x_{2}))\] (20) For consistency with the Pareto dominance check, which typically assumes maximization for all objectives, the environmental impact \(g_{2}(x)\) (a minimization objective) is reformulated as \(f_{2}^{\prime}(x)=-g_{2}(x)\).
* Crowding Distance Calculation: Compute distance \(d_{i}\) for solution \(x_{i}\) within its front \(F_{k}\) to maintain diversity: \[d_{i}=\sum_{l=1}^{M}\frac{f_{l}(x_{i+1})-f_{l}(x_{i-1})}{f_{l}^{\max}-f_{l}^{ \min}}\] (21) where \(M=2\) objectives, solutions \(x_{i+1},x_{i-1}\) are neighbors in the sorted list for objective \(l\), and \(f_{l}^{\max},f_{l}^{\min}\) are max/min values for objective \(l\).
* Selection, Crossover, Mutation: Use tournament selection (based on rank and \(d_{i}\)), simulated binary crossover (SBX), and polynomial mutation to create an offspring population \(Q_{t}\). \[x_{ijk}^{(c)} =\frac{x_{ijk}^{(p1)}+x_{ijk}^{(p2)}}{2}\] (22) \[x_{ijk}^{(c)} =x_{ijk}^{(c)}+\epsilon,\quad\epsilon\sim N(0,\sigma^{2})\] (23)
* Population Update: Combine \(R_{t}=P_{t}\cup Q_{t}\). Select the best \(N\) solutions from \(R_{t}\) based on non-domination rank and crowding distance to form \(P_{t+1}\).
* Termination: Stop after a fixed number of generations. The final non-dominated set is the Pareto-optimal front \(P^{*}\): \[P^{*}=\{x\in P_{\text{final}}\mid\
exists x^{\prime}\in P_{\text{final}}\text { s.t. }x^{\prime}\text{ dominates }x\}\] (24)
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Predictive Maintenance and Diagnostics
#### 4.4.2 Fault Diagnosis Support  
When equipment issues arise, timely and accurate diagnosis is essential. LLMs can assist engineers in troubleshooting by providing solutions based on historical data and technical documentation. By inputting symptoms or error messages, engineers can receive guidance on potential causes and recommended corrective actions [136; 137].  
The model can cross-reference similar past incidents and suggest effective remedies, expediting the repair process. This support reduces the reliance on individual expertise and ensures that even less experienced personnel can address technical issues competently. Furthermore, LLMs can keep engineers updated with the latest technical bulletins and manufacturer advisories relevant to the equipment in question [138].  
The applications of Large Language Models in maritime operations are diverse and hold significant promise for enhancing safety and efficiency. By improving communication, supporting training and education, aiding decision-making, and optimizing maintenance processes, LLMs can address key challenges within the maritime industry. The integration of these advanced AI technologies requires careful consideration of operational needs and a commitment to responsible implementation, but the potential benefits underscore the value of pursuing such innovations [139].
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement  
After flexible packaging, the sensors captured good pulse signals, but noise interference still remained. Therefore, to obtain accurate blood pressure values, further processing of the pulse signals is necessary, including filtering and extraction of peak and trough values. Subsequently, the preliminary blood pressure values are obtained based on arterial tonometry using the peak and trough values. Then, data preprocessing is performed by extracting other pulse wave feature parameters and normalizing the feature set. Finally, multiple machine learning algorithms are applied for regression analysis to achieve precise blood pressure measurement. The overall flowchart of the blood pressure calculation is shown in Figure 7.  
Figure 6: Pulse waveform plots: (**a**) pulse waveform collected by the sensor without flexibility; (**b**) pulse waveform acquired by the sensor after flexible packaging (1:35); (**c**) pulse waveform acquired by the sensor after flexible packaging (1:40); (**d**) pulse waveform acquired by the sensor after flexible packaging (1:45).
## 3 Background
### Variational Autoencoder  
A variational autoencoder is an autoencoder structured as a Bayesian network that learns the relationship between two random variables, latent variable \(z\) and observed variable \(x\). The latent variable \(z\) is constrained to follow a pre-defined prior distribution \(p(z)\), usually a standard normal distribution. On the other hand, the distribution of the likelihood \(p(x|z)\) can be defined as Normal or Bernoulli, depending on the task. In the Bayesian network, obtaining the posterior distribution of the latent variable \(p(z|x)\) is the main focus. However, the true posterior is intractable for a continuous latent space \(z\)[31]. Thus, the variational inference technique is used to approximate the posterior.  
Figure 3: 1D convolution operation with a multi-channel kernel on multi-channel data.  
As a variational inference algorithm, the SGVB estimator [32] can be used effectively to estimate the true posterior. The algorithm uses a variational net as \(qnet\) and a generative net as \(pnet\) to maximize the evidence lower bound ELBO for training the VAE model. The ELBO is:  
\[\begin{split}\mathcal{L}(x,\theta,\phi)&=\mathbb{E} _{q_{\theta}(z|x)}[\log p_{\theta}(x|z)]-KL[q_{\phi}(z|x)||p_{\theta}(z)]\\ &=\mathbb{E}_{q_{\theta}(z|x)}[\log p_{\theta}(x|z)+\log p_{ \theta}(z)-\log q_{\phi}(z|x)]\end{split} \tag{3}\]  
where \(\theta\) and \(\phi\) are parameters of \(pnet\) and \(qnet\), respectively. \(KL\) is the Kullback-Leibler divergence, this term is to measure the difference between the approximated posterior \(q_{\phi}(z|x)\) and the prior distribution \(p_{\theta}(z)\). The expectation with \(q_{\phi}\) is approximated by taking \(L\) samples using Monte Carlo integration [33] as Equation (4), where \(f(z^{(l)})\) are sampled from \(q_{\phi}(z|x)\).  
\[\mathbb{E}_{q_{\phi}(z|x)}[f(z)]\approx\frac{1}{L}\sum_{l=1}^{L}f(z^{(l)}) \tag{4}\]
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 2 Preliminaries  
**Notation.**_There are some notations that we will use throughout this paper. We denote \(\mathbb{Z}/q\mathbb{Z}\) as \(\mathbb{Z}_{q}\) and its elements are in the range of \((-q/2,\ q/2]\). We use bold uppercase letters (e.g., \(\mathbf{A},\mathbf{B}\)) to represent matrices, and bold lowercase letters (e.g., \(\mathbf{a},\mathbf{b}\)) to represent vectors. All vectors in this paper are default column vectors. For a vector \(\mathbf{a}=(\mathbf{a}_{1},\mathbf{a}_{2},\ldots,\mathbf{a}_{n})\in\mathbb{Z}_{q}^{n}\), \(\mathbf{a}_{i}\) denotes the \(i\)-th component scalar. For a matrix \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\), \(\mathbf{A}[i,j]\) denotes the \(i\)-th row and the \(j\)-th column element of \(\mathbf{A}\). Let denote the Euclidean norm of a vector \(\mathbf{a}\) as \(\|\mathbf{a}\|=\sqrt{\sum\mathbf{a}_{i}^{2}}\) and \(\mathbf{s}_{1}(\mathbf{R})\) represent the maximum singular value of matrix \(\mathbf{R}\). We denote \([\mathbf{A}|\mathbf{B}]\) as the concatenation of two matrices._  
Let \(n\) denote the security parameter. We define \([n]=\{1,2,\ldots,n\}\) for any positive integer \(n\). Let \(negl(n)\) denote a negligible function that grows slower than \(n^{-c}\) for any constant \(c>0\) and any sufficiently large value of \(n\). We say that an event happens with overwhelming probability if it happens with probability at least \(1-negl(n)\) for some negligible \(negl(\cdot)\). Let \(\omega(\cdot)\) denotes the degree of asymptotic when \(f(n)=\omega(g(n))\). That is \(\lim\limits_{n\to\infty}\frac{f(n)}{g(n)}=\infty\) for any positive integer \(\mathbf{c}\) and a positive integer \(d\) satisfy \(n>d\), \(0\leq\mathbf{c}\cdot\mathsf{g}(n)<f(n)\).
## 4 Partitioning Detection and Connectivity Restoration
### Pseudo Code of PCR Algorithm  
Figure 6 shows the high level pseudo code of PCR algorithm running on each actor in a distributed manner. Initially, all the actors are initialized as non-critical (line 1). A localized cut-vertex detection procedure determines whether node \(A\) is critical or not (lines 2-4). If actor \(A\) is critical then it will select and designate an appropriate backup actor among the neighbors (lines 5-7). The selection of backup is made based on the criteria specified in Section 4. The backup actor \(A\) detects failure of its primary by continuously monitoring its health through heartbeat messages. Upon detecting the failure of the primary, it initiates the recovery process. If backup actor \(A\) is non-critical, it simply moves to the location of \(F\) (lines 9-10). If node \(A\) is critical and simultaneously primary and backup of node \(F\) (_i.e._, SimPrimBackUp) then it selects another node as backup. In other words, node \(A\) and \(F\) were serving as backups for each other, Since when \(F\) fails, node \(A\) loses not only its primary but also its backup, therefore node \(A\) appoints another backup before going to replace \(F\). Node \(A\) notifies its movement to newly assigned backup and moves to the location of \(F\) (lines 11-14). Otherwise, if node \(A\) is a critical node it notifies its backup and moves to replace \(F\) (lines 15-18). If node \(A\) receives a movement notification message from \(F\), it checks whether \(F\) is serving as both primary and backup. If so then node \(A\) designates a new backup, notifies its neighbors and moves to replace \(F\) (lines 19-23), otherwise, it moves to replace \(F\) (lines 24-27).  
Figure 6: High level pseudo code for PCR algorithm.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 1 Introduction
For precise Doppler error quantification, we utilized a software-defined radio locked to a GPS-disciplined oscillator. Based on this developed model, we emulated extensive satellite passes and used a joint Doppler shift and AoA measurement likelihood function to estimate the ground IoT device location. We applied stochastic optimization to find the global maximum of the likelihood function, which corresponds to the best location estimate of the ground IoT device. The contributions of the study are outlined as follows:  
* A satellite-based localization method using joint Doppler and AoA measurements received from the ground IoT device is proposed. * The likelihood function of Doppler and AoA measurements based is derived on the Gaussian error and estimated Kent error distributions, respectively. * The Doppler measurement error was investigated using real measurements from LEO satellites. * The localization performance behavior against varying Doppler and AoA error deviations is illustrated.
## 5 Discussion
### Paleenvironment Reconstruction
#### 5.1.2 Redox Condition  
Redox conditions, a pivotal factor influencing organic matter enrichment, play a crucial role in reconstructing paleo-sedimentary environments [47]. Certain elements, sensitive to the oxygen content in the water column, serve as reliable proxies for characterizing the redox condition, with redox-sensitive trace elements such as Mo and U being noteworthy examples. Additionally, certain ratios of redox-sensitive trace elements, such as V/Cr and Ni/Co, offer insights into the redox environment in the water column [48]. The redox-sensitive trace element ratios for the Da'anzhai Member shale in the study area are illustrated in Figure 6 and Table 4. Results indicate that the Da'anzhai Member primarily deposited in an anoxic environment, with no significant differences in redox conditions observed among different lithofacies. Based on the content and ratios of redox-sensitive  
Figure 4: Various geochemical profiles of the analyzed Da’anzhai Member shale.  
Figure 5: The correlation between the Fe/Mn ratio and CIA value among different lithofacies of the analyzed Da’anzhai Member shale (ORA: organic-rich argillaceous shale; OPA: organic-poor argillaceous shale; ORM: organic-rich mixed shale; \(n=36\), \(n\)—number of experimental samples; \(p<0.05\)).  
elements, the Da'anzhai Member shale in the study area is determined to have deposited in an oxic environment. Furthermore, [PERSON] and [PERSON] (2007) proposed that under anoxic conditions, the reductive dissolution of Fe oxyhydroxoxides can release phosphorus (P) from marine sediments [49]. Therefore, a high Corg/P ratio can reflect a relatively reducing environment. Generally, Corg/P values less than 50, 50 to 100, and more than 100 reflect oxic, suboxic, and anoxic depositional environments, respectively [50]. The Corg/P ratio for the Da'anzhai Member shale in the study area varies from 7.31 to 126.17 (average = 48.6). It is noteworthy that there are differences in the Corg/P ratio among different lithofacies. The distribution ranges of the Corg/P ratio for ORA, ORM, and OPA are 28.9 to 122.2 (average = 65.2), 19.1 to 92.3 (average = 48.5), and 17.2 to 64.1 (average = 35.6), respectively. The Corg/P ratio indicates that the Da'anzhai Member shale in the study area primarily deposited in an anoxic environment, with ORA and ORM depositing in relatively reducing conditions. However, it is essential to note that the results from the Corg/P ratio and the content (ratios) of redox-sensitive elements slightly differ. Considering both the present study and previous research, we conclude that the Da'anzhai Member shale mainly deposited in an anoxic environment.
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
## 3 Analysis of the Numerical Simulation Results
### Response Surface Optimization Test and Results Analysis
#### 3.2.3 Validation of Response Surface Optimization Results  
The regression Equation (3) was solved with the maximum pile-soil stress ratio. The optimal solution of the embankment filling parameters and grid strength could be obtained. Its parameters were a cohesion of 5 kPa, an internal friction angle of 25.076\({}^{\circ}\), an embankment elastic modulus of 10 MPa, and a geogrid strength of 117.749 kN/m. In order to consider the convenience of construction, the internal friction angle was 25\({}^{\circ}\), the grid strength was 120 kN/m, and the parameters were taken as the recommended values of the response surface optimization results, as shown in Table 7. The error between the prediction results of the response surface and the calculated values of the optimized model was 6.79%, indicating that the regression equation had a good fitting degree.  
According to the parameters before and after optimization, the pile-soil stress ratio was calculated and compared in ABAQUS, as shown in Figure 11. The pile-soil stress ratio was 9.7 under the initial design, and the optimized pile-soil stress ratio was 11. Compared with the original design, the optimized pile-soil stress ratio increased by 13.4%. It can be seen that the optimized model can significantly promote the load transfer.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Cohesion [kPa]**} & \multirow{2}{*}{**Internal Friction Angle [\({}^{\circ}\)]**} & **Elastic Modulus of Angle [\({}^{\circ}\)]**} & **Geogrid** & **Pile–Soil Stress** \\  & & **Angle [\({}^{\circ}\)]** & **Embankment [MPa]** & **Strength [kN/m]** & **Ratio \(n\)** \\ \hline Original Embankment Design & 6 & 30 & 15 & 30 & 9.7 \\ Predict the Optimal Combination Optimization Model Verification & 5 & 25.076 & 10 & 117.749 & 10.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Response surface optimization results.  
Figure 10: Interaction between parameters affecting pile-soil stress ratio; (**a**) response surface of cohesion and internal friction angle; (**b**) two-dimensional diagram of interaction between cohesion and internal friction angle; (**c**) response surface of cohesion and elastic modulus of embankment; (**d**) two-dimensional diagram of interaction between cohesion and elastic modulus of embankment.
## 1 Measurement and Analysis of Local Humidity
### Relationship between Local Humidity and Local Temperature in the Bay
Statistical analysis revealed that the slope of the relational expression of the rate of change of temperature and humidity can vary from one day to another. The maximum difference between slopes can reach \(-2.0\%\) RH/\({}^{\circ}\)C, that is, when the average local temperature of a bay rises by 1 \({}^{\circ}\)C, the difference between in humidity measured on different days decreases and can reach up to 2.0% RH. However, when the temperature and humidity variations measured at the measurement point 15 (thermometer shelter) are included in the analysis, a new conclusion emerges. Regardless of date, the ratio of the slope of the relational expression of the rate of change of temperature or humidity to that of the thermometer shelter is usually a fixed value. In other words, if the slope of the relational expression of the rate of change of temperature and humidity of a bay is \(q_{i}\), and the slope of the thermometer shelter is \(q_{0}\), the value of \(q_{i}\)/\(q_{0}\) is only related to the position and structural type of the bay. Here, \(p_{i}=q_{i}/q_{0}\) is called the humidity characteristic coefficient (HCC) of the bay and \(q_{i}\) is a dimensionless quantity that reflects the responsiveness of the local humidity of the bay to temperature changes. The temperature and humidity data of 30 days were statistically processed. Average \(\overline{p_{i}}\) and variance \(\sigma^{2}\) values of \(p_{i}\) are listed in the Table 1.  
It can be seen that the average \(\overline{p_{i}}\) values of all but measurement point 15 (thermometer shelter) are less than 1. This indicates that the \(\mid q_{i}\mid\) values of all bays are smaller than \(\mid q_{0}\mid\), which means that the responsiveness of local humidity to temperature changes is smaller in all bays than in the thermometer shelter. This is mainly attributed to the closed nature of the bay. The more closed the bay, the lower the value of \(\overline{p_{i}}\). Since the sample size for calculating \(\overline{p_{i}}\) values in Table 1 is large and the variance \(\sigma^{2}\) is small, the HCC \(p_{i}\) of each bay is given a \(\overline{p_{i}}\) value, listed in Table 1.
## 3 Detailed Operators
### Subsystem Weight Considering the Failure Diagnosis Based on FRPN
#### 3.2.1 Fuzzy Production Rules  
Fuzzy production rules describe the relationship between two fuzzy propositions. \(E=\{e_{1},\,e_{2},\,\ldots,\,e_{m}\}\) denotes the set of fuzzy production rules. Confidence is the degree obtained by experiences that a phenomenon could be true. The \(j\)th fuzzy production rule \(e_{j}\) based on the corresponding \(c_{j}\) is represented as: \(e_{j}\) (\(c_{j}\)): if \(p_{i}(u_{i})\), then \(p_{k}(u_{k})\), where \(c_{j}\) is the confidence of the fuzzy production rule \(e_{j}\) and it is within (0,1). \(p_{i}\) and \(p_{k}\) denote the propositions containing fuzzy variables which are difficult to describe with accurate value. \(u_{i}\) and \(u_{k}\) are the true degree of the propositions \(p_{i}\) and \(p_{k}\), and they are also within (0,1). The truth degrees denote the fuzzy possibility of the events. A higher value indicates that the event will break down more easily. During the iteration of the truth degrees, the faults propagate in the system.
# Space-Efficient Fully Dynamic DFS
## 7 More Space-Efficient Dynamic DFS
### Fault Tolerant DFS  
We begin with the algorithm for the fault tolerant DFS problem. Following the original algorithm described in Section 3.1, the important point is that once a data structure for answering \(Q\) and \(Q^{\prime}\) is built, the reduced adjacency list \(L\) is used and the whole adjacency list of the original graph is no longer needed. Moreover, information used in the algorithm other than the data structure and the reduced adjacency list takes only \(O(n\log n)=o(m)\log n\) bits, as described in Section 5. Since we have already shown in Corollary 2 that the data structure takes \((m+o(m))\log n\) bits, we have only to consider the size of the reduced adjacency list \(L\). From Section 3.1, for any \(k(\leq n)\) graph updates, the number of edges in \(L\) is at most \(O(n)\) under the incremental case, and \(O(nk\log n)\) under the fully dynamic case. The time complexity of this algorithm can be calculated from Lemma 1 and Corollary 2. The required space of this algorithm is \((m+o(m))\log n\) bits plus the space required for \(L\), that is, \(O(m_{L}\log n)\) bits (with standard linked lists) where \(m_{L}\) is the upper bound of the number of edges in \(L\). To sum up, we can obtain the following lemma.  
**Lemma 9**.: _An undirected graph \(G\) and its DFS tree \(T\) are given. Then there exists an algorithm such that with \(O(m\sqrt{\log n})\) preprocessing time, a DFS tree for the graph obtained by applying any \(k(\leq n)\) edge insertions to \(G\) can be built in \(O(n\log n)\) time. This algorithm requires \((m+o(m))\log n\) bits once the preprocessing is finished. Similarly, there exists an algorithm such that with \(O(m\sqrt{\log n})\) preprocessing time, a DFS tree for the graph obtained by applying any \(k(\leq n)\) updates (vertex/edge insertions/deletions) to \(G\) can be built in \(O(nk\log^{2}n)\) time. This algorithm requires \((m+o(m))\log n+O(nk\log^{2}n)\) bits once the preprocessing is finished._
## 2 Materials and Methods
### Irrigation Scheduling Management
#### 2.2.3 Irrigation Scheduling Based on the Monitoring of Plant and Soil Water Content (PlaSoMan)
Finally, the system also had rain sensors that could pause irrigation when it rained heavily and resume it when the rainfall was not enough to restore field capacity on its own.