esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and [PERSON] summarized the recent progress and gas sensing applications in graphene hybrid materials, including fabrication method of graphene-based sensors, nanostructures of hybrid materials, gas sensing mechanisms and future perspectives [20]. Nevertheless, there is few reports about the gas sensing properties of SnO\({}_{2}\) nanoparticle-reduced graphene oxide (SnO\({}_{2}\)-rGO) nanocomposites for C\({}_{2}\)H\({}_{2}\) gas detection at a ppm level. Herein, we investigated the gas sensing properties (including optimum operating temperature, response to concentration, dynamic sensing transient, response and recovery time, selectivity and long-term stability) of SnO\({}_{2}\)-rGO nanocomposites with respect to C\({}_{2}\)H\({}_{2}\) gas detection. The SnO\({}_{2}\)-rGO nanomaterials were synthesized via a simple two-step hydrothermal method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), Raman spectra, X-ray photoelectron spectroscopy (XPS) and thermogravimetric analyses (TGA). Meanwhile, planar sensors were fabricated to examine the gas sensing performance with respect to C\({}_{2}\)H\({}_{2}\) gas. The results indicate that the SnO\({}_{2}\)-rGO can efficiently detect the C\({}_{2}\)H\({}_{2}\) gas under a low operating temperature, showing better sensing performances than those of rGO alone. Finally, the possible gas sensing mechanisms of the SnO\({}_{2}\)-rGO with respect to C\({}_{2}\)H\({}_{2}\) detection have been discussed.
## 5 Methodology for Flood Analysis
### Estimation of River Overflow Pattern from Water Level Data
#### 5.1.2 Backward Flow Rate from the Second Drainage Gate  
Because the second drainage gate was still open when the river water level was still rising, the river water intruded backward into the downstream end of the floodplain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Point** & **Symbol** & **Measurement Duration** & **Elapsed Time** \\ \hline Sukagawa (water level) & \(H_{50}\) & 12 th 12:00–14 th 0:00 & 0.00–36:00 \\ Sukagawa (flow rate) & \(Q_{50}\) & 12 th 12:00–14 th 0:00 & 0.00–36:00 \\ Road bridge A & \(H_{A}\) & 12 th 23:00–13 th 11:50 & 11:00–23:50 \\ Road bridge B & \(H_{B}\) & 12 th 23:00–13 th 11:50 & 11:00–23:50 \\ Discharge gate \#1 & \(H_{\text{H}}\) & 12 th 17:00–13 th 0:50 & 5:00–12:50 \\ Discharge gate \#2 & \(H_{\text{D}}\) & 12 th 17:30–13 th 2:30 & 5:30–14:30 \\ River channel (flood peak trace) & – & After flood & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Locations and durations of hydrological measurements for the 2019 Abukuma flood.  
Figure 20: Schematic view of flow field during the 2019 flood.  
through the gate, and the inundation slowly expanded upstream against topographical gradient. Until the gate was closed, the water level was recorded by a water gauge installed on the land side (see \(H_{D}\) in Figure 19). Because the water surface of the slow reverse flow was almost horizontal, the flow rate, \(q_{D}\), was calculated from the following volume conservation equation:  
\[q_{D}(t)=\frac{d}{dt}\left(\int_{z_{0}}^{H_{D}(t)}A(\zeta)d\zeta\right), \tag{2}\]  
where \(\zeta\) is the vertical coordinate taken upward from some datum level, and \(A(\zeta)\) is the area of the water surface at height \(\zeta\).  
The overflow rate (\(q_{A},\ q_{B},\ q_{D}\)) calculated based on the above assumptions is shown in Figure 21 together with the observed flow rate at Sukagawa. It can be seen that they are about 10% of the river channel flow rate at the maximum. The remaining overflow rates (\(q_{C},\ q_{H},\ q_{E}\)) were obtained in a flow simulation using the numerical model described below.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 5 Performance Evaluation  
Through the computer simulation, we compare UCMAC with the conventional MACA-U [32] and CD-MACA [12] in terms of system throughput, latency, energy consumption, and single-hop PDR. MACA-U is the non-cooperative underwater-specific MACA [37] protocol that basically uses 3-way handshaking (RTS-CTS-DATA) and optionally adds ACK at the end. In our evaluation, we consider the MACA-U with ACK to make the comparison fair. CD-MACA has a simple cooperation mechanism that allows neighboring nodes to opportunistically participate in retransmission.
# Spectral Envelope Transformation in Singing Voice for Advanced Pitch Shifting
## 3 Analysis of Parameters and Their Variation along Pitch
### Formant Analysis
#### 3.2.2. Formants 2 and 3  
Unlike the first formant, the shifting of formants \(F2\) and \(F3\) do not seem to follow an approximate linear variation with pitch. Figures 4 and 5 show that the central frequency of these formants seems to be related to the pitch in a logarithmic way.  
Thus, linear least square regression [28] is used to obtain the parameters corresponding to a first-order exponential relation between these formants and the pitch. Figures 4 and 5 also show the estimated curves for the data samples depicted.  
As has been extensively demonstrated in the literature [29], it is very important to maintain the relative distance between \(F1\) and \(F2\) in order to keep vowel intelligibility. Therefore, at the sight of the results obtained in Section 3.2.1, it seems reasonable to find a proportional variation of \(F2\) along pitch.  
In the case of \(F3\), a smaller variation with pitch is found. However, as in the case of \(F2\), this shift is relative to \(F1\), and it is important to maintain this type of relationship in order to preserve some qualitative aspects of the sound.  
Again, no meaningful relation between the bandwidths of these formants (\(B2\) and \(B3\)) and the pitch was found. As in the case of \(B1\), modifying \(B2\) and \(B3\) causes timbre variations related to the intensity of the sound, but not related to pitch.
## 3 RS Applications in Ocean
### Ship Detection (SD)
#### 3.6.2 SAR
However, false alarms were observed, and the method had a problem when two ships were side by side (they were detected as one ship).
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 2 Related Work
### Methods of Daily Activity Recognition Based on LSTM  
[PERSON] et al. investigated various deep learning models, such as LSTM, Convolutional Neural Networks (CNN), CNN-LSTM and autoencoder-CNN-LSTM, for recognizing and predicting abnormal ADL in older people [29].  
[PERSON] et al. used CNN for daily activity recognition and compared it with LSTM, recurrent neural networks and other machine learning algorithms. The results show that 1D-CNNs perform similarly to LSTMs and better than other probabilistic models [30].  
[PERSON] et al. proposed different deep learning models to classify human activities. In particular, LSTM was used to model the spatio-temporal sequences obtained by smart home sensors. It was also validated on publicly available datasets, and the results showed that the proposed LSTM model outperformed existing deep learning methods [31].  
[PERSON] et al. adapted the LSTM algorithm to a synchronous algorithm (sync-LSTM). sync-LSTM is able to input multiple parallel sequences simultaneously and produce multiple parallel synchronous output sequences. The proposed method was used for simultaneous daily activity recognition using heterogeneous sensor data in smart homes [32].  
Various methods for daily activity recognition using LSTM are presented by [PERSON] et al. These methods exploit the temporal dependencies present in sensor data to produce richer representations and further improve classification accuracy. Finally, a comparison is made with some baseline classification algorithms on real-world datasets. It is also concluded that the accuracy of the LSTM improves with more temporal information on the input [33].  
Based on the above exploration regarding the LSTM model, we further propose a fine-tuning method based on the LSTM model to transfer the laws of sensor event flow sequences in two heterogeneous smart homes.
# Robust Control of an Inverted Pendulum System Based on Policy Iteration in Reinforcement Learning
## 5 Robust Control of Nonlinear IPS
### RL Algorithm for Nonlinear IPS  
For a nonlinear IPS, we consider the optimal control problems (30) and (31). For any admissible control, the cost function corresponding to the optimal control problem can be expressed as  
\[V[x(t)] =\int_{t}^{\infty}[f_{max}^{2}(x)+x^{T}x+u^{T}u]dt\] \[=\int_{t}^{t+\phi}[f_{max}^{2}(x)+x^{T}x+u^{T}u]dt+\int_{t+\phi} ^{\infty}[f_{max}^{2}(x)+x^{T}x+u^{T}u]dt\]  
where \(\phi>0\) is an arbitrarily selected constant. We can then obtain the integral reinforcement relation satisfied by the cost function  
\[V[x(t)]=\int_{t}^{t+\phi}[f_{max}^{2}(x)+x^{T}x+u^{T}u]dt+V[x(t+\phi)] \tag{41}\]  
According to the integral-based reinforcement relations (41) and the optimal controller (35), the RL algorithm for the robust control of the nonlinear IPS is as follows.  
In Algorithm 2, by providing an initial stabilizing control law, the algorithm iterates repeatedly between steps 3 and 4 until convergence. We can then obtain the robust control gain \(u\) of system (28).  
\begin{tabular}{l l} (1) & A non-negative function \(f_{max}(x)\) is selected. \\ (2) & An initial stabilization control law \(u_{0}(x)\) is selected. \\ (3) & Policy evaluation: the \(V_{i}(x)\) from \(V_{i}[x(t)]=\int_{t}^{t+\phi}[f_{max}^{2}(x)+x^{T}x+u_{i}^{T}(x)u_{i}(x)]dt+V_{ i}[x(t+\phi)]\) is solved. \\ (4) & Policy improvement: \(u_{i+1}(x)=-\frac{1}{2}B^{T}\
abla V_{i}\). \\ (5) & \(i=i+1\) is set and steps 3 and 4 are repeated until \(\|V_{i+1}-V_{i}\|\leq\epsilon\), where \(\epsilon>0\) is a small constant. \\ \end{tabular}  
Next, we prove the convergence of Algorithm 2. The following conclusion provides an equivalent form of the integral strengthening relation in step 3.  
**Lemma 7**.: _On assuming that \(u_{i}(x)\) is the stabilization control function of the nominal system (30), solving the cost function \(V_{i}(x)\) from the equation in step 3 in Algorithm 2 can be equivalent to solving Equation (42)._  
\[f_{max}^{2}(x)+x^{T
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 3 Impact Assessment Model for Traffic Operation Status of Large Bridges  
Based on the established indicators of infrastructure, the degree of impact of each indicator item on traffic, and the weight values of each traffic impact indicator, this paper proposes a traffic state impact model that combines the Analytic Hierarchy Process (AHP), the Delphi method, and Singular Value Decomposition (SVD) to evaluate the impact of infrastructure state changes on traffic operation status. The overall process of this evaluation system is shown in Figure 2.
## 2 Materials and Methods
### Ground-Based Analyses  
#### 2.4.1 Cell Growth and Morphology  
Cells were fixed with paraformaldehyde (4% \(v/v\), final) either in flight or immediately following hardware de-integration post flight. Direct manual cell counts were performedin duplicate using a Bright-Line hemocytometer (Sigma Aldrich) quantifying total cells as well as the contribution of cells with an extended morphology (filamentation; length greater than 2\(\times\) the width of the cell) to the population. Samples were also stained with Calcofluor White (25 uM final, Molecular Probes) to stain chitin in the yeast cell wall and visualized using epifluorescence microscopy. Cell diameter and filament length were recorded using image collection on a Nikon Eclipse E-800 and interpreted using the software program Metamorph.  
Samples of non-fixed cultures were cultured post-flight on YPD agar plates as an indication of viability. Viability was calculated using colony forming units (CFU) with the actual/observed number of colonies compared to that expected based on cell counts. It is important to note that cell viability is likely to be higher than interpreted from the CFU analyses included in these studies due to the convention of our cell counting; budding cells are counted as two cells since that is how they will likely contribute to growth in planktonic cultures, whereas, these cells may remain associated during the plating process and give rise to a single colony. With this in mind, it should be understood that post-flight cell viability was quite high.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 4 Energy Replenishment Strategy
### Charging Cells Selection  
The energy states of nodes change over time in WSN. The nodes with low residual energy and high energy consumption should have a higher priority of replenishment for the integrity of the network. In the meantime, the charging efficiency of the MCV is related to the number of charging cells. A large number of charging cells allows more nodes to gain energy replenishment in time, but this will the increase the moving time of the MCV and reduce the charging efficiency. Based on the observations above, we selected the charging cells as follows.  
The MCV calculates the charging efficiency of cells based on the state information of all nodes at the beginning of each round. Taking cell \(c_{m}\) as an example, its charging efficiency is calculated as follows:  
\[ce_{c_{m}}=\min_{i\in c_{m}}(\frac{e_{i}}{E_{i}}) \tag{3}\]  
where \(E_{i}\) and \(e_{i}\) are the residual energy and the energy consumption of node \(v_{i}\) in the previous round, respectively. It is assumed that the energy consumption is zero and the residual energy is \(E_{o}\) for all nodes in the first round. The MCV can reach higher charging efficiency in the cells where the nodes with low residual energy and high energy consumption are located.  
Then, the MCV sorts the cells in a decreasing order according to their charging efficiencies and records the sorted list by \(PAS\). \(PAS(m)\) denotes the \(i\)th element in the list of \(PAS\), specifically referring to the cell with the \(i\)th largest charging efficiency. Thus, \(PAS\) is a sorted list of cells containing charging efficiency information. The MCV, starting from the first element, extracts cells to charge them one by one sequentially according to the \(PAS\) and controls the number of charging cells based on the energy dispersion. The energy dispersion is calculated as follows:  
\[\alpha=\frac{1}{ace}\sqrt{\frac{1}{|AC|}\sum_{c_{m}\in AC}(ce_{c_{m}}-ace)^{2}} \tag{4}\]  
where \(AC\) is the set of cells where all nodes are located and \(|AC|\) is the number of elements in set \(AC\). \(ace\) is the average charging efficiency of all cells in \(AC\). \(\alpha\) is the energy dispersion of cells in set \(AC\); a higher \(\alpha\) means that the energy states of cells have great differences. Using energy dispersion, the charging cells can be determined as follows:  
\[CV=\{PAS(m)\big{|}m\in[1,n],n=\lfloor(1-\alpha)|AC|\rfloor\} \tag{5}\]  
where \(CV\) contains all charging cells in one round. Based on the mechanism above, the MCV charges more cells when the network energy distribution is uniform in order to maintain the overall energy level of the network. In addition, it reduces the number of charging cells when the energy dispersion is large in order to enhance the energy replenishment for low energy nodes.
## 3 Results and Discussion  
### Mathematical Modeling  
Table 2 shows the statistical parameters obtained from the fitting of the experimental data of the drying experiments to the 11 models listed in Table 1. According to the tables, it can be seen that, in the method of hybrid \(HAI\) drying with ultrasonic pretreatment, the logistic model has the highest value of determination coefficient (0.9997) and the lowest values of chi-square (\(\chi^{2}\) = 0.0007) and root mean square error (\(RMSE\) = 0.0177) compared to other models.
## 2 Inverse Solution Algorithms
### Underdetermined Systems
#### 2.2.2 Iterative Approaches  
In the following, we apply four iterative algorithms to solve underdetermined problems. The first method is Focal Underdetermined System Solver (FOCUSS) [27], which is an iterative technique well suited for finding sparse solutions to underdetermined linear systems. The algorithm has two integral parts: a low-resolution initial estimate of the real signal and the iteration process that refines the initial solution to the final localized solution. Because the system is underdetermined, the sensors are more numerous than the sources. In this case, we assume our dictionary contains 36 sources. These sources are located at \(5^{\circ}\) intervals from the \(x\) label. Actually, this case has only three sources. Therefore, if the result is perfect, our final solution has only three nonzero solutions.  
The FOCUSS algorithm can be summarized in three steps,  
\[\mathbf{W}_{k}=[\mathrm{diag}(\mathbf{s}_{k-1})] \tag{21}\]  
\[\mathbf{q}_{k}=\left(\mathbf{A}\mathbf{W}_{k}\right)^{+}\mathbf{p} \tag{22}\]  
\[\mathbf{s}_{k}=\mathbf{W}_{k}\mathbf{q}_{k} \tag{23}\]  
In Equation (21), \([\mathrm{diag}(\mathbf{s}_{k-1})]\) converts the vector \(\mathbf{s}_{k-1}\) into a diagonal weight matrix. The TIKR solution is used as the initial condition. Similar to other fixed-point iteration methods, the algorithms converge within finite numbers of iterations to the sparse solution with appropriate initial conditions.  
The large term in the weighting reduces the 2-norm of \(\mathbf{q}\)  
\[\left\|\mathbf{W}^{+}\mathbf{s}_{p}\right\|^{2}=\left\|\mathbf{q}\right\|^{2} =\sum_{i=1,\omega_{i}\
eq 0}^{n}\left(\frac{\mathbf{s}_{pi}}{w_{i}}\right)^{2} \tag{24}\]  
The relatively large entries in \(\mathbf{W}\) reduce the contributions of the corresponding elements of \(\mathbf{s}_{p}\) to the cost, and the solution is nonzero in the source direction. The pseudoinverse in Equation (22) can also be implemented by using the TIKR method. Therefore, it can also be written as  
\[\mathbf{q}_{k}=\left(\mathbf{W}_{k-1}^{H}\mathbf{A}^{H}\mathbf{A}\mathbf{W}_{ k-1}+\beta^{2}\mathbf{I}\right)^{-1}\mathbf{W}_{k-1}^{H}\mathbf{A}^{H}\mathbf{p} \tag{25}\]  
The FOCUSS-TIKR method is robust to noise because of the regularization parameter \(\beta\). The iteration process stops when the solution converges within a prespecified tolerance window (0.0001 in our case).
## 5 Use Case of 6G-OPENSEC-Security
### Security Closed-Loop Automation Architecture  
At its core, the SCLA integrates monitoring, decision-making, and automated responses to ensure robustness against potential threats while maintaining adaptability and scalability. Unlike conventional security systems that react to known threats, the SCLA continuously monitors the environment, learns from it, and adjusts its defences in real-time based on predictive analysis, taking advantage of technologies like AI and policy-based networking. Its key objectives include:  
Figure 7: 6G-OPENSEC-SECURITY reference architecture.  
* **Proactive Security:** Shifting from reactive to proactive measures, the system anticipates and mitigates threats before they manifest by continuously monitoring and adapting its defences based on predictive analysis.
* **Real-time** Threat Response: Acting swiftly and decisively in the face of security threats, it minimises potential damage through modules like Security Decision and Security Data Analytics, which enable real-time detection of anomalies and a response.
* **Adaptive Learning:** Staying ahead of evolving threats by continuously learning from past experiences and current data.
* **Operational Efficiency:** Ensuring that security measures enhance system performance rather than hinder it.  
To fulfil these objectives, the SCLA has been proposed to contain the following software components, which are the primary line of defence in networks that implement this kind of security architecture:  
* **Security Data Collection:** The purpose of this component is to collect network data, as it is proposed to be integrated with network probes that capture packets of network traffic and are later retrieved by the component. Data collection can be carried out via APIs or through data brokers, whichever best fits the network topology.
* **Security Data Analytics:** This component is responsible for analysing the collected data and detecting any possible threats or anomalies within the surveilled network through the use of AI models. The proposed project model integrates a DDoS detection model such as LUCID [99]; however, this component is flexible enough to integrate other types of ML and DL models that can detect different types of attacks in network traffic.
* **Security Decision:** Since the deployed and surveilled services must comply with specific security policies, this component was developed to ensure that appropriate action is taken to solve the issue in case of an attack or non-compliance with the requested policies. A knowledge base is required to ensure that each violated policy has an appropriate corrective directive.
* **Security and Privacy data service:** This is the specialised module responsible for securely managing and storing security data within the closed loop. In other words, it is the SCLA's database.  
In Figure 8 it is possible to see how these components interact with each other in a high-level way.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## 4 Experiment  
The Wenchuan earthquake occurred in Sichuan Province, China on 12 May 2008 at 8.0 Richter scale, killing 69,227 people and injuring 374,643 people. Gauze, tents and water were dispatched from Xi'an and Lanzhou to Chengdu, Shifang and Jiangyou during the disaster. The disaster and supply depots are shown in Figure 4. The distances between the depots are shown in Table 6.
# Cloud Based Smart City Services for Industrial Internet of Things in Software-Defined Networking
## 3 Proposed Architecture for Smart Cities in SDN-IoT
### Application Layer  
This layer is considered as the uppermost layer of the system's architecture in which the built network's application fields are deployed. This system architecture has been proposed by the researchers, specifically for smart city applications as shown in Figure 4.  
Figure 3: Proposed Framework for SDN-IoT in Smart Cities.  
Figure 2: Clustering in IoT for SDN POX Controller.  
This layer includes a range of applications for smart cities such as smart homes, smart energy, smart cars, smart health, smart transport, etc. [30]. In addition, it involves server and cloud infrastructures that exchange content and facilitate the user with real-time services. The most important tasks of this layer are both data processing and supplying services.
## 5 Performance Evaluation
### Simulation Experiments  
Two strategies of the node deployment are considered in the experiment: regular deployment and random deployment. We assume that the nodes are uniformly deployed in the detected region. Our work focuses on anisotropy network. The letter-shaped topology is most commonly used to compare and verify the localization accuracy [4; 7; 8; 18; 21]. In order to give a fairly comparison with the classical methods, such as DV-hop, PDM and LSVR, we adopt the same network topologies in the simulation.