esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 1 Introduction  
In traditional static wireless sensor networks (WSNs), all static nodes transmit data to a sink node or base station in a multi-hop manner. Therefore, the nodes which are close to the base station or sink node usually need to take on a tremendous data forwarding task. This will lead to nodes' premature death due to the excessive energy consumption that easily forms \"energy holes\", and eventually affects the normal operation of the entire network. Thus, how to prolong the survival time of the network becomes the key problem to be studied in resource-constrained WSNs. Studies show that dynamic clustering and the introduction of Mobile Sinks (MSs) can better achieve the result of balancing the nodes' energy consumption [1; 2]. The communication patterns in WSNs can be divided into single hop transmission, multi-hop transmission and assistant cache transmission [3; 4]. In the assistant cache transmission model, the sensed data can be transmitted to auxiliary nodes in single hops or multi-hops, and the MS only needs to traverse the auxiliary nodes within a single hop instead of communicating with all nodes in the network. The model reduces multi-hop data transmission and avoids causing energy holes as much as possible. In order to better improve some aspects of the performance of WSNs, they need to take full advantage of the MS mobility. Therefore, it is important to choose the mode and plan the mobile route for MS's movement. There are several mobile modes for a sink node,including random movement, desired trajectory movement, controlled movement and choosing the mobile location by using an optimization algorithm, _etc._ The introduction of the the dynamic clustering concept and MS movement planning will have a great influence on key management of wireless sensor networks, and it extends static key exchange to dynamic key exchange.  
The proposed scheme determines the MS's traversal path by using a Genetic Algorithm (GA), and fully considers the relative shortest path and the probability of path selection based on the topological structure of dynamic clustering in the wireless sensor network. In the scenario with a MS, the cluster head and MS establish a session key by the improved Diffie-Hellman (ECDH) key agreement. Meanwhile, the member nodes with limited resources set up a session key with the cluster head by using the binary symmetric function. Compared with some existing key management schemes, the proposed scheme has better connectivity, storage effectiveness, safety and energy consumption advantages.  
The rest of this article is organized as follows: in Section 2, some background and related work on MS and key management in sensor networks are introduced. Then, in Section 3, we describe the design of the proposed scheme in detail. Section 4 presents the simulation and results analysis, and finally we conclude the paper in Section 5.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Node Distribution  
The authors in [23] proposed to choose the smallest possible number of nodes that will cover the critical targets by using a cost function called the Critical Control Factor. This function is mapped into 3 loops: (1) sensor availability check (adding the cover sets); (2) uncovered target check (populating the added cover sets with sensors); and (3) sensor applicability check (sorting the nodes and choosing the top-scoring node). The main benefits of this proposed solution are that it is flexible, has a low complexity, and has a short execution time. In the end, the authors concluded that there are two solutions for node deployment: static and dynamic. In the static method, the proposed algorithm performed better than the other approaches in terms of execution time, and exhibited near-optimal results in the generated coverage sets. The dynamic method on the other hand yielded better results but with a longer execution time.
## 2 Materials and Methods
### Experimental Design  
The 196 eggs were arranged in four treatments of 49 eggs integrated by seven replications of seven hatching eggs each. The experimental batches were established according to the sibling contact regime of the eggs in different phases of the incubation process: (a) eggs without contact during the whole incubation process; (b) eggs in contact during the incubation phase (from the beginning to day 20 of incubation); (c) eggs in contact during the hatching phase (from day 20 of incubation to hatching); (d) eggs in permanent contact throughout the incubation process (from the beginning of incubation to hatching). Such sibling contact regimes were conceived to test the hypothesis that hatching synchrony is modified when the eggs are in close contact during late stages of embryonic development. The nodal point to define the experimental treatments was chosen at day 20 of incubation because (i) the literature reports that hatching synchrony is achieved when eggs remain in close contact in the last 20% of incubation (reviewed in [PERSON] [18]), and (ii) in _A. rufa_ eggs, the whole incubation lasts 23-24 days [8, 9, 10, 11, 12] while transference from incubator to hatcher is usually carried out on day 20 of incubation [1,8,16].  
In order to keep the eggs in contact, two types of experimental containers were built. For eggs to be kept in contact until day 20 of incubation, when the eggs needed to be turned, boxes made of rigid plastic grid were built. These boxes consisted of 4 cm in height and 10 cm in diameter cylinders provided with two lids. In each box, eggs were placed small end down and immobilized, when necessary, with small spongy plastic pieces to prevent the eggs from moving and falling when the incubator turned the trays on which the boxes were fixed.  
For eggs to be kept in contact from day 20 of incubation during the hatching phase, artificial nests made of rigid plastic grids were built. These artificial nests were 15 cm in diameter and 15 in height to prevent chicks from escaping. To keep the eggs in contact, their base consisted of a 5 cm deep conic nest made of felt, which forced the eggs to remain grouped in the center of the nest. These artificial nests were loaded into the hatcher and allowed the chicks to easily hatch while the eggs remained in close contact.  
Both container models received seven eggs, and each egg directly contacted at least other three eggs, thus ensuring the potential effect of sibling contact on hatching synchrony.  
In order to keep the eggs without contact during the incubation phase, the eggs were put in incubator trays, consisting of individual cells measuring 3.5 \(\times\) 3.5 cm, which prevented the eggs from coming into contact. To prevent the eggs from coming into contact during the hatching phase, they were placed in hatching trays divided in individual 10 cm side triangular cells delimited by rigid plastic grid walls.
## 3 Results
### Data Dissection
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data Reprocessing** & **Data I** & **Data II** & **Data III** & **Data III** \\ \hline Refinement & & & & \\ \hline Resolution range (Å) & 40.98–1.30 & 45.82–1.30 & 45.94–1.30 & 45.87–1.30 \\  & (1.35–1.30) & (1.35–1.30) & (1.35–1.30) & (1.35–1.30) \\  & 0.1567 (0.2445) & 0.1570 (0.2545) & 0.1603 (0.2634) & 0.1623 (0.2838) \\  & 0.1733 (0.2104) & 0.1656 (0.2689) & 0.1712 (0.2792) & 0.1686 (0.2978) \\  & 0.969 (0.891) & 0.971 (0.876) & 0.970 (0.865) & 0.971 (0.818) \\  & 0.952 (0.941) & 0.971 (0.843) & 0.973 (0.869) & 0.966 (0.813) \\ \hline No. of non-H atoms & & & & \\ \hline Protein & 1589 & 1589 & 1589 & 1589 \\ Ligand & 34 & 34 & 34 & 34 \\ Water & 332 & 332 & 332 & 332 \\ \hline R.m.s. deviations & & & & \\ \hline Bonds (Å) & 0.011 & 0.008 & 0.007 & 0.007 \\ Angles (\({}^{\circ}\)) & 1.14 & 1.15 & 0.97 & 0.98 \\ Average \(B\) factors (Å\({}^{2}\)) & 14.25 & 15.30 & 15.73 & 16.36 \\ Protein & 11.85 & 12.89 & 13.27 & 13.92 \\ Water & 25.73 & 26.81 & 27.47 & 28.04 \\ Other & 14.01 & 15.80 & 15.82 & 17.04 \\ \hline Ramachandran plot & & & & \\ \hline Most favored (\%) & 98.54 & 99.02 & 98.54 & 99.02 \\ Allowed (\%) & 1.46 & 0.98 & 1.46 & 0.98 \\ \hline \end{tabular} Statistics for the highest-resolution shell are shown in parentheses.  
\end{table} 
Table 3: _Cont._In the mFo-DFc electron density analysis, Data I and Data II did not show any notable negative electron density maps while Data III and Data IV showed strong negative density maps around disulfide bonds C56-C66, C126-C177, C149-C158, and C159-C164. As a result, no notable radiation damage was observed in the overall data statistics and electron density map for Thaumatin\({}^{100 ms}\) (1-360\({}^{\circ}\)). However, based on the electron density map, specific radiation damage occurred for some disulfide bonds as the X-ray exposure time increased, even for Thaumatin\({}^{100 ms}\). This indicates that structural information with minimized radiation damage can be obtained by minimizing diffraction images.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 2 Green Energy Utilized in WWTPs
### Energy Recovery of WW&S
#### 2.3.1 Heat Energy and Wastewater Source Heat Pump  
It was estimated that 350 TWh of energy was lost annually through building water discharge with a notable potential for heat recovery [69]. Wastewater source heat pump (WWSHP) technology is the main way to utilize heat energy in addition to solar energy in wastewater treatment. It can supply cooling and heating to the outside through the refrigerant state cycle inside the unit [70]. The application of WWSHP is currently used in WWTPs, such as sludge drying, reactor heating and so on [71]. Figure 2 is a common structure chart of WWSHP.  
WWSHP mainly uses urban wastewater as the cold and heat source for energy extraction and storage. Wastewater temperature is 3-5 \({}^{\circ}\)C higher than that of groundwater when heating, and 10-15 \({}^{\circ}\)C lower than that of air when cooling. The coefficient of performance values of the reviewed studies ranges from 1.77 to 10.63 for heating and 2.23 to 5.35 for cooling based on the experimental and simulated values [72]. In addition to energy-saving and economic benefits, WWSHP also has environmental benefits. Recovering heat energy from wastewater can reduce the utilization of coal and other energy sources, which can reduce the generation of pollutants such as NO\({}_{\times}\), SO\({}_{\times}\) and dust.  
Although it has various advantages, WWSHP also has some problems to be solved. Firstly, wastewater will pollute heat exchangers. Scaling and corrosion will damage the equipment and decrease the coefficient of performance of the system. Secondly, most of the WWTPs and WWSHP stations are far away from the city which makes the heat pump system far away from the users, increasing the energy consumption for transportation. However, with the development of monitoring technology and control technology, WWSHP has a bright future.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Energy in WW\&S** & **Technologies** & **Applications** & **Reference** \\ \hline Heat energy & Wastewater source heat pump & Sludge drying, Heating & [65] \\ \hline \multirow{2}{*}{Biomass energy} & Microalgae culture, Anaerobic, & Fuel production, Power generation & [58; 66; 67] \\  & Microbial fuel cell & Fuel production, Power generation & [58; 66; 67] \\ \hline Chemical energy & Thermochemistry & Fuel production, Power generation & [68] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some available energy and utilization ways in WW&S.  
Figure 2: Common structure chart of the wastewater source heat pump (WWSHP).
## 4 Experiments and Analysis
### Experiment 3: Positioning Accuracy of Different FiMa-Reader System Output Rates  
In this experiment, we aimed to determine the positioning accuracy of different FiMa-Reader system output rates to verify the effectiveness of the multithread framework described in Section 3.1.3. We used the same scenario as in Experiment 2, and the mission was navigated with docking, with the navigation target and DAH pattern placement remaining the same throughout all experiments. We tested three frame rates in this experiment: 15 Hz, 30 Hz, and 60 Hz. Different output frame rates were achieved by adjusting  
Figure 7: The repeatable positioning accuracy of pure navigation and navigation with docking using the FiMa-Reader system. (**a**) The x-y distribution of the Trans position locations. (**b**)-top: The x-direction positioning accuracy distribution, unit mm. (**b**)-middle: The y- direction positioning accuracy distribution, unit mm. (**b**)-bottom: The theta position accuracy distribution, unit degrees.  
the camera capture rate. Each rate experiment was repeated 500 times, and the evaluation metric was the PGV100 position when the mission was completed.  
Figure 8 illustrates the positioning accuracy of the FiMa-Reader system at 15 Hz, 30 Hz, and 60 Hz output frame rates. The box lengths of the different rates are close to each other, indicating that the docking process relies not only on the FiMa-Reader system but also on wheel encoders and an IMU for dead reckoning, which are highly accurate at short distances. However, the data range decreases as the frame rate increases, indicating that a higher observation rate can reduce the positioning deviation. This is because the Trans can update its position at a higher rate to determine whether it has arrived at the navigation target.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### LTE-A Delay
#### 4.1.2 User Plane (Uu Interface)
For multicast/broadcast transmissions, the only difference that occurs in the DL phase is when the packet is being communicated through the Broadcast-Multicast-Service Centre (BM-SC). However, the network processing values for unicast and multicast/broadcast are the same--20 ms [27]. Figure 2 provides the total delay in the cases where BSR is used (UL \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Description** & **Time (ms)** \\ \hline Unicast & eNB processing and scheduling & 1.5 \\ \hline MBSFN & Scheduling period (MSP = **40**) & MSP/2 + 1 \\ \hline SC-PTM & Scheduling period (SSP = 1, 10) & max (SSP/ 2 + 1, 2) \\ \hline Unicast & Transmission of DL data (10\% BLER) & 1.8 \\ \hline MBSFN/SC-PTM & Transmission of DL data & 1 \\ \hline All & UE LI/L2 processing & 1.5 \\ \hline All & UE upper layer processing & 3 \\ \hline  & **Total Unicast** & **7.8** \\ \hline  & **Total MBSFN** & **26.5** \\ \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} \\ \end{tabular}
\end{table} 
Table 6: Downlink delay in LTE-A [27]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheduling Type** & **Description** & **Time (ms)** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **Centralized** \\  
DS BSR) or not used (UL DS) and for SPS. Moreover, all types of communication, including unicast, MBMS, and SC-PTM, are used. The E2E delay can be modeled as follows:  
\[\mathrm{t_{E2E}}=\mathrm{t_{UL}}+\mathrm{t_{core}}+\mathrm{t_{DL}}. \tag{1}\]  
To give an example, we can assume a unicast transmission with SPS. The \(\mathrm{t_{UL}}\) equals 8 ms (Table 5), the \(\mathrm{t_{DL}}\) for unicast mode is 7.8 ms (Table 6), and the \(\mathrm{t_{core}}\) is always 20 ms, leading to \(\mathrm{t_{E2E}}=35.8\) ms, as presented in the figure below. Note that local breakout schemes enable the exchange of packets in a confined area and avoid routing through the core network. The Selected IP Traffic Offload (SIPTO) solution is an approach that could be considered for V2X communication as it would avoid the delay when crossing the core network, but it cannot provide an uninterrupted service under all scenarios [35].
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
## 3 Experimental Results and Stability Analyses
### Instability Characteristics along Streamwise Direction
As is shown in Figure 10, with the increase in the unit Reynolds number, the initial position of disturbance growth at the same frequency gradually moves backward. Under the condition of \(Re_{\text{unit}}=2.52\times 10^{6}\) (Figure 10a), the second mode of 190 kHz in the boundary layer decreases. Moreover, the growth rate of the disturbance is higher than that in the case of a low unit Reynolds number and moves further forward.
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Deep Learning Techniques
#### 3.2.2 Gated Recurrent Unit (GRU) Model  
The Gated Recurrent Unit (GRU) is another type of recurrent neural network (RNN) architecture designed to process sequential data. It addresses the issue of vanishing gradients commonly found in traditional RNNs. The GRU cell comprises several components, including a reset gate and an update gate. These components work together to regulate the flow of information within the cell. The reset gate determines which information from the prior hidden state should be reset or forgotten, while the update gate controls the extent the new input should impact the updating process of the hidden state. By combining these gates, GRU cells can selectively update their hidden states, allowing them to capture long-term dependencies in sequential data [48]. Figure 2 presents the base structure of the GRU. In comparison to other RNN versions like the LSTM, the GRU's design is simpler due to its ability to update the hidden state quickly without the use of a dedicated memory unit (Long Short-term Memory). Since the GRU is so effective at modeling long-term  
Figure 3: Architecture of LSTM and GRU models.  
Figure 2: The base architecture of the GRU is shown on the left side of the figure, and the LSTM is shown on the right side of the figure. In this figure, the input data at time step \(t\) are denoted as \(x_{t}\), the hidden states are shown with \(h\), and the cell states are indicated by \(C\).  
dependencies, it is frequently used for tasks like NLP, speech recognition, and time series analysis. The architecture of our used GRU model is shown in Figure 3.  
For anomaly detection, we employed models based on both the LSTM and GRU, which share the same underlying architecture. We employed a straightforward design consisting of two LSTM/GRU layers, each comprising a pair of LSTM/GRUs units.
# Correlation Analysis of Macro-Micro Parameters of Sandstone Based on PFC3D
## 4 Instance Verification
### Numerical Simulation  
To ensure that the compression-tension ratio in the simulation aligned more closely with the actual rock conditions, a compression-tension ratio \(\sigma_{c}/\sigma_{t}\) of 12.1, measured in the laboratory, was directly used as the basis for calibrating the microscopic parameters. The specific calibration steps for the microscopic parameters were as follows:  
(1) The results of the aforementioned correlation analysis clearly show that \(E_{c}/\overline{E}_{c}\), \(R_{E}\), \(\mu,k\), and \(R_{k}\) have almost no impact on the macroscopic mechanical response values. Based on the value of \(\sigma_{c}/\sigma_{t}\) and using the 13 th set of data from the orthogonal numerical test results in Table 5 as a reference, the preliminary values were set as \(E_{c}/\overline{E}_{c}=0.3\), \(R_{E}=0.1\), \(\mu=0.7\), \(k=4\), \(R_{k}=2\), and \(\overline{\beta}=0.5\), \(R_{f}=0.7\).  
(2) By substituting the known experimental values (see Table 8) and the microscopic parameters values into Equations (5)-(10), the values of other microscopic parameters are obtained as \(\overline{E}_{c}\)= 194, \(k=6\), \(\overline{\sigma}_{t}=111\), \(\overline{c}/\overline{\sigma}_{t}=2\), \(\mu=1.0\), \(\overline{\phi}=50\), \(R_{\sigma}=0.11\).  
(3) We conducted preliminary PFC simulations using known microscopic parameters. We obtain the corresponding simulation values through uniaxial compression, uniaxial tension, and triaxial compression tests. We compared these values with the experimental values and analyzed the trend in the stress-strain curve. We further adjusted the micro  
Figure 6: Compressive, tensile, and shear stress–strain curves.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Macroscopic Parameters** & \(E\) **(GPa)** & \(\
u\) & \(\sigma_{c}\) **(MPa)** & \(\varphi\) **(\({}^{\circ}\))** & **c(MPa)** & \(\sigma_{cd}\)/\(\sigma_{c}\) & \(\sigma_{c}\)/\(\sigma_{t}\)** \\ \hline Experimental values & 32.2 & 0.25 & 54.5 & 43 & 3.92 & 0.53 & 12.1 \\ \hline \end{tabular}
\end{table}
Table 8: Test results of mechanical parameters of sandstone.  
scopic parameters to minimize errors. The final values of the microscopic parameters are shown in Table 9.
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 5 Correctness, Security, and Performance Analysis
### Correctness
However, we note that introducing commitment and NIZK proofs for security verification, although one-time and binding, leads to communication and computation costs that grow linearly with \(N\), as each party must broadcast verification data; this may impose a communication burden in large-scale deployments. For complex computations, our scheme supports Boolean circuits \(F\) composed of addition and multiplication gates with fan-in 2, allowing multiple ciphertexts to be jointly evaluated, thereby offering strong computational expressiveness. Nonetheless, once the circuit depth exceeds the predefined limit, ciphertext noise will surpass the decryption bound, making correct decryption unreliable. Thus, homomorphic evaluation remains constrained by the circuit depth parameter \(l\).
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 2 Related Work
### Background Literature  
In monitoring applications, the detection of assets is carried out by the nodes. Whenever the assets are detected, the node becomes the source node, and it starts transmitting packets to a sink node, indicating that the assets have been detected in its surroundings [8]. Usually, the distance between the source node and the sink node is not in the transmission range of sensor nodes. For this reason, WSNs create multi-hop communication [9]. One of the significant issues in WSNs is the security of the packets being transmitted in multi-hop touch.  
One major factor is energy, and it is primarily a limited resource in WSNs, so it needs to be carefully designed. Mobile nodes require different protocols than those that are static.
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 5 Numerical Simulations  
To demonstrate the effectiveness of the acquisition scheme and the control law to the satellite platform, we developed a nonlinear simulator with three satellite platforms including ground stations.