esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model and Background
### Message Authentication Code  
MACs are used by nodes to verify the data integrity. The data packet structure in this paper is the link layer data packet structure under TinySec authentication mode [27], which uses a MAC field to verify the whole packet data. The data stored in FH(Data) is encrypted by privacy homomorphic encryption. Let Dest, AM, Len and PNum denote the destination address, active message type, message length and packet sequence number, respectively. The packet structure of our scheme is shown in Figure 1.  
In this structure, the 4-byte MAC consists of \(T+1\) subMACs, and each of these is constructed by selecting some bits of a MAC, such that one of them is computed by an aggregator and the remaining \(T\) subMACs are computed by its \(T\) monitoring nodes. Algorithm 1 shows the generation of the subMAC which is in the similar approach with [9]. We assume that each sensor node has the same pseudo-random number generator (PRNG) [28] that generates random numbers ranging from 1 to 32. After the groups are formed and their group keys (\(K_{group}\)) are established, sensor nodes initiate their PRNGs using their \(K_{group}\) as the seed. Let \(Z\) denotes the total bits of the MAC, each random number indicates the index of a bit location in MAC. To select the bits from MAC, the monitoring node \(M\) runs its PRNG \(Z/(T+1)\) times, results in \(Z/(T+1)\) random numbers, and then forms the subMAC. The subMAC computed by \(M_{l}\) can be verified by the corresponding group member \(F_{j}\) who computes the subMAC and matches each other. Note that PRNGs can be out of synchronization due to packet loses. In this paper, PRNG synchronization is achieved using packet sequence numbers that are added to aggregated data packets [9].  
```
Assumption:\(M_{l}\) is a member of a node group, and it shares the key \(K_{group}\) with other group states. Each sensor node has the same pseudo-random sequence generator. begin  node \(M_{l}\) uses \(K_{group}\) to compute MAC(\(M_{l}\)); \(M_{l}\) runs pseudo-random sequence generator \(Z/(T+1)\) times to select \(Z/(T+1)\) bits of MAC(\(M_{l}\)), and then forms the subMAC(\(M_{l}\));  return subMAC(\(M_{l}\)); end
```  
**Algorithm 1** The generation of subMAC  
In the formation of MACs, the aggregator determines the order of subMACs in any way and informs each group members of monitoring node about its subMAC location individually. Consequently, an adversary cannot know in advance the exact location of subMAC bits for a given forward node. Therefore, in order to inject a false message, an adversary has to try all possibilities, which improves the security of the network to a large extent.  
**4. FESA: Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks**  
In this section, we present the detailed structure and design of our scheme.  
Figure 1: The packet structure of FESA scheme, where MAC is composed of \(T+1\) subMACs. The byte size of each field is enclosed in parentheses. The acronyms of the fields are destination address (Dest), active message type (AM), message length (Len), packet sequence number (PNum).
## 3 A Novel Model of the Reversible Rankine-Based Thermodynamic Cycle (RRTC)
### The Mathematical Description of RRTC in ORC Mode  
In the ORC mode of the RRTC, the heat carrier enters the HE1 with a certain mass flow rate through the pipelines. The energy balance in this device between the heat source and the working fluid inside the cycle can be described by Equations (1)-(3), as follows:  
\[\dot{Q}_{\text{in}}\ =\ \dot{Q}_{\text{HE1}}, \tag{1}\]  
\[\dot{Q}_{\text{HE1}}=\dot{m}_{\text{wf}}\Delta h_{1\to 2}, \tag{2}\]  
\[\dot{m}_{\text{in}}\Delta h_{\text{in}}=\ \dot{m}_{\text{wf}}\bigg{(}\bigg{(}\left.c _{\text{p,wf}}\right|_{1}^{T_{\text{sat}1}}\Delta T_{1\to\text{sat}1}\bigg{)}+ \Delta h_{\text{sat}1\to 2}\bigg{)}, \tag{3}\]  
where \(\dot{Q}\), \(\dot{m}\), and \(\Delta h\) describe the heat transfer rate, working fluid mass flow rate, and working fluid specific enthalpy changes, respectively. Subscripts wf, in, sat, and l represent working fluid, input (heat carrier), saturation, and liquid, respectively. In addition, other subscripts indicated in Equations (1) and (2) relate to the process in Figure 1a,c. In this case, the energy balance of the heat source and working fluid inside the cycle may be calculated as a function of enthalpy or temperature changes.  
In the following step, the expansion process starts when the working fluid reaches the saturated vapor state (\(x=1\)), which is indicated with the number 2 (red color) in Figure 1. The real expansion process in the expander can be expressed as a function of enthalpy change by introducing the expander's isentropic efficiency, described by Equations (4) and (5):  
\[P_{\text{EXR}}=\dot{m}_{\text{wf}}\Delta h_{2\to 3_{\text{w}}}\eta_{\text{ EXR,is}}, \tag{4}\]  
\[\eta_{\text{EXR,is}}=\frac{\Delta h_{2\to 3}}{\Delta h_{2\to 3_{\text{s}}}}, \tag{5}\]  
where \(P\) and \(\eta\) represent the power and the efficiency, respectively. The subscript \"is\" represents the isentropic process. The following process is the working fluid condensation (using HE2, illustrated in Figure 1a,c) to alter the phase of working fluid back into a liquid state (\(x=0\)). Then, liquified working fluid is ready to be pumped (using LP, presented in Figure 1a,c), and the cycle completes. The process in the condenser (HE2) and pump (LP) can also be expressed using a function of enthalpy changes, described by Equations (6)-(8) for the condenser and Equations (9) and (10) for the pump:  
\[\dot{Q}_{\text{out}}=\dot{Q}_{\text{HE2}}, \tag{6}\]  
\[\dot{Q}_{\text{HE2}}=\ \dot{m}_{\text{wf}}\Delta h_{3\to 4}, \tag{7}\]  
\[\dot{m}_{\text{out}}\Delta h_{\text{out}}=\ \dot{m}_{\text{wf}}\Delta h_{3\to 4}, \tag{8}\]  
\[P_{\text{LP}}=\dot{m}_{\text{wf}}\Delta h_{4\to 1_{\text{s}}}/\eta_{\text{ LP,is}}, \tag{9}\]  
\[\eta_{\text{LP,is}}=\frac{\Delta h_{4\to 1_{\text{s}}}}{\Delta h_{4\to 1}}. \tag{10}\]  
Using the Equations (1), (4), and (9), the overall efficiency of the RRTC in ORC mode can be expressed as Equation (11):  
\[\eta_{\text{RRTC,ORC}}=\frac{P_{\text{EXR}}-P_{\text{LP}}}{\dot{Q}_{\text{HE1 }}}. \tag{11}\]
## 4 Analysis of the Results
### Bearing Capacity of Dam Body with Hydraulic Fracturing
#### 4.3.2 Suggestions on Dam Crack Propagation Treatment  
The crack opening displacement (COD) is crucial to suggestions on the treatment of dam cracks. As can be seen from Figure 19, the two curves, respectively, denote the set of minimums and maximums of the COD of the dam body at different elevations. One minimum (25.7 mm) is selected from the maximum value curve as the upper limit line, one maximum (18.1 mm) is selected from the minimum value curve as the middle limit line, and one minimum (9.2 mm) is selected as the lower limit line from the minimum value curve. If the COD is less than the lower limit line (9.2 mm), the risk of crack propagation in the dam body is low, and there is no need to worry about dam damage; when the COD is between the lower limit line and the middle limit line (18.1 mm), careful observation and appropriate repair is indicated as the crack has a great risk of expansion; when the COD is between the middle limit line and the upper limit line (25.7 mm), this reveals that the risk of dam instability is increasing as a result of the continuous expansion of the cracks, with actions needed to lower the water level for reduced water pressure and to repair the cracks as soon as possible; once the COD exceeds the upper limit line, the dam body has the highest risk of instability, which urges evacuation in the downstream area.  
Figure 17: Dam crest displacement along the river with different crack heights (enlargement on the right).  
Figure 18: Overload coefficient of dam crack initiation with different crack lengths.
# Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems
## 2 Background and Related Work
### Variational Autoencoders
It is computed as presented in the following Equation (3):  
\[\text{KL Divergence Loss}=-\frac{1}{2}\sum_{i=1}^{d}\Bigl{(}1+\log(\sigma_{i}^ {2})-\mu_{i}^{2}-\sigma_{i}^{2}\Bigr{)} \tag{3}\]  
Thus, as displayed in the following Equation (4), the total VAE loss is the sum of the reconstruction loss and the KL divergence loss:  
\[\text{VAE Loss}=\text{Reconstruction Loss}+\text{KL Divergence Loss} \tag{4}\]  
Figure 1: Architecture of variational autoencoder model. By training a VAE, new data records are generated by sampling the learned latent space distribution. This makes VAEs useful for tasks such as recommender systems, where they can generate prediction ratings for all items, including those that the user has not rated yet, by sampling from the latent space. Overall, the VAE architecture combines the benefits of autoencoders, which can learn compact representations, with probabilistic modeling techniques, enabling the model to capture uncertainty and generate new data records. In recommender systems, the VAE can be applied to effectively manage multi-criteria data by the extraction of user preferences and interactions. The VAE model starts by encoding multi-criteria ratings, such as Acting, Direction, Story, and Visualization, into a latent space through an encoder network. This process generates parameters (\(\mu\) and \(\sigma\)) for a latent variable distribution, which allows the model to capture representative patterns of user preferences. The decoder network reconstructs the ratings from these latent variables, ensuring that the predicted ratings align with actual user data. The VAE model can effectively handle data sparsity in recommender systems through several mechanisms. First, VAEs map sparse user-item interaction matrices into a latent space, capturing essential features despite missing entries. By minimizing reconstruction loss, VAEs focus on existing ratings to infer missing values based on correlations identified in the latent representation. The inclusion of a KL divergence term in the loss function encourages the latent space to follow a Gaussian distribution, enabling the model to sample values for missing entries. Additionally, VAEs can impute missing ratings by sampling from the learned latent distribution, allowing for the generation of potential ratings based on similarities with other users. In multi-criteria recommendation systems, the VAE model leverages relationships across different criteria to enhance the imputation of missing data. This capability makes VAEs a robust tool in real-world applications where data sparsity is prevalent, improving the accuracy and relevance of recommendations.
## 3 Vehicle Model and Experimental Setup
### ADAMS Model  
Before validating the proposed method on the experimental setup, we develop an ADAMS model of the vehicle with fidelity higher than that of the _7-dof_ mathematical model to be used in an intermediate verification step. The finite-element model of the chassis is developed in Nastran and imported to ADAMS. The four mounting locations \(B_{i}\) are defined as interface nodes. In addition, the model has three more interface nodes denoted as D, E and F corresponding to the centre-of-mass locations of the driver, engine and battery, respectively (see Figure 5 right). Three rigid bodies with mass and inertia properties representing the driver, engine-driveline assembly and battery are attached to these interface nodes. The inertia properties of the driver are obtained from [36] and those of the engine and battery are obtained from the vehicle CAD model.  
Four spring-dashpot pairs with the equivalent properties from Table 2 are attached between points \(B_{i}\) on the car body and the unsprung masses at wheel axle \(A_{i}\). The unsprung masses are connected to ground contact points \(C_{i}\) by springs and dashpots representing the tyre properties (see Table 2).  
Figure 4: (**a**) Accelerometers mounted at \(A_{i}\) and \(B_{i}\) locations. (**b**) Data acquisition system mounted on the vehicle dashboard records axle \(\hat{y}_{i}\) and suspension-to-body point \(\hat{z}_{i}\) accelerations. (**c**) Schematic connection diagram used for data acquisition from different MEMS accelerometers. (**d**) VBox-3i IMU with data logger to record sprung mass acceleration \(\bar{z}\) and roll _phi_ and pitch \(\hat{\theta}\) rates.  
For performing ADAMS simulation, a flexible multi-body dynamics model is developed. The vehicle chassis is made of a roll-cage type structure. The effect of frame flexibility is captured by means of the FE model (Figure 5 right) of the roll cage, as shown in Figure 5 left.
## 5 Data Analysis Results and Research Findings
### Coefficient of Reliability  
This study analysed the reliability of the scale items before conducting the EFA. The Cronbach alpha coefficient reliability analysis was used to measure the internal reliability of a psychometric measurement score for the identified sample in the response, based on the recommended threshold of acceptable 0.70 and above [111; 112]. The reliability results in Table 1 indicate that [PERSON]'s alpha coefficient of reliability for all constructs ranges from 0.807 to 0.950, which is above the recommended threshold. Therefore, all measurement items in the constructs were confirmed to be reliable and acceptable for further testing the EFA for this study.
# Modeling GPR Wave Propagation in Complex Underground Structures Using Conformal ADI-FDTD Algorithm
## 3 Numerical Simulations
### Complex Underground Structures  
We used the conformal ADI-FDTD algorithm to simulate complex underground structure models. Figure 16 shows schematic diagrams of rectangular and circular void models. There was a metal pipe located 0.5 m below the surface with an outside diameter of 0.3 m and an inside diameter of 0.2 m. There was a rectangular cavity of a size of 0.1 m on the left side of the metal pipe. On the other side, there was a circular cavity of a diameter of 0.1 m. The space step and time step were set as 0.005 m and 0.01 ns, and simulation iterations were 5000. The model had two layers: the upper layer was an air layer, and the lower layer was a clay layer. Table 2 provides the relative permittivity and conductivity. The excitation source and GPR system were the same as for the previous model.  
Figure 16: Schematic diagram of complex underground cavity model.  
Figure 15: Simulation results of underground multi–pipe model with different pipe materials.  
Figure 17 shows the GPR B-scan image obtained by the underground cavity model using the conformal ADI-FDTD algorithm. By analyzing Figure 17, the metal pipe produces strong reflected and diffracted waves. It is due to the strong response of the electromagnetic waves to metals. The upper interface of the rectangular cavity is still a flat interface in the radar forward image. Meanwhile, the lower interface is similar to the upper interface but retains weak reflected wave energy. The circular cavity produces a clear reflected wave, and the diffracted wave is weaker due to its smaller size. Moreover, some mutual interference clutters and multiple reflection waves can be observed at the bottom of the image. By analyzing the GPR scans, the characteristics of the model can be inferred more comprehensively and accurately, helping to interpret the GPR information.
## 5 Conclusions  
In conclusion, we developed a machine learning-assisted system, where the LSTM network is for the first time to implemented onto the MDI-QKD system for reference-frame calibrations. Furthermore, we carry out experimental demonstrations over 100 km and 250 km transmission distances by running the present MDI-QKD system. Experimental results show that our present machine learning-assisted mode can dramatically improve the transmission efficiency of MDI-QKD systems compared with using the traditional scanning-and-transmitting approach. Meanwhile, our present system can keep quite good stability over long time running. In addition, the biased basis choice idea has been employed to reduce the redundancy of decoy pulses in Z basis, and thus diminish the influence of the finite-data-size effect. Therefore, our present work can provide valuable references for the implementation of large-scale quantum communication [30; 31] in the near future.  
[PERSON] and [PERSON] did the experiment, collected the data, and wrote the manuscript; [PERSON] assisted in designing electronic control circuit, [PERSON] did the numerical simulations; [PERSON] and [PERSON] proposed the idea, assisted in analyzing the data, and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
The National Key R&D Program of China through Grant Nos. 2017 YFA0304100, 2018 YFA-0306400, the National Natural Science Foundation of China through Grants Nos. 12074194, 11774180, the Leading-edge technology Program of Jiangsu Natural Science Foundation through Grant No. BK20192001, and the NUPTSF through Grant No. NY220122, NY220123.  
The authors appreciate [PERSON] and [PERSON] for their productive discussions during this work.  
The authors declare no conflict of interest.  
Figure 5: Comparisons between applying traditional scanning-and-transmitting program and using current LSTM-model-based QKD systems on QBER at the transmission distance of 100 km.
## 3 Results and Discussion
### Hydroxyl Radical scavenging Activity and Fe\({}^{2+}\) Chelating Activity  
Among the oxidizing agents, hydroxyl radicals have the highest toxicity. Since they are generated in the Fenton reaction between Fe\({}^{2+}\) and hydrogen peroxides, they serve as important biomarkers to assess the effects of antioxidant activity on candidate materials;  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Concentration** \\ **(\(\mathrm{\SIUnitSymbolMicro g}\)/mL)** \\ \end{tabular}  } & \multicolumn{3}{c}{**BHT \({}^{1}\)**} & \multicolumn{3}{c}{**Different Parts of _M. crystallinity_ (Mean \(\pm\) S.D.)} & \multirow{2}{*}{ \begin{tabular}{c} **F-Value** \\ **(\(\mathrm{\SIUnitSymbolMicro g}\))** \\ \end{tabular}  } \\  & **(\(\mathrm{\SIUnitSymbolMicro g}\)/mL)** & **(\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{\SIUnitSymbolMicro g}\))** & **Cotyledon** & **Stem** & **Shoot** & **(\(\mathrm{\SIUnitSymbolMicro g}\))** \\ \hline \multirow{6}{*}{\ \begin{tabular}{c} DPPH \\ (\(\mathrm{\SIUnitSymbolMicro g}\)) \\ \end{tabular}  } & 62.5 & 10.99 \(\pm\) 2.13 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 10.41 \(\pm\) 0.90 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 8.47 \(\pm\) 1.06 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 12.96 \(\pm\) 0.08 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 23.305 \\  & 125 & 23.17 \(\pm\) 3.16 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 12.95 \(\pm\) 1.54 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 14.01 \(\pm\) 1.33 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 19.32 \(\pm\) 0.86 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 21.447 \\  & 250 & 44.10 \(\pm\) 2.54 \({}^{\mathrm{\SIUnitSymbolMicro c}}\) & 22.90 \(\pm\) 2.09 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 22.03 \(\pm\) 2.83 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 30.46 \(\pm\) 1.22 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 12.168 \\  & 500 & 68.11 \(\pm\) 1.50 \({}^{\mathrm{\SIUnitSymbolMicro c}}\) & 28.49 \(\pm\) 2.14 \({}^{\mathrm{\SIUnitSymbolMicro c}}\) & 31.07 \(\pm\) 3.15 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 38.51 \(\pm\) 3.14 \({}^{\mathrm{\SISymbolMicro b}}\) & 10.007 \\  & 1000 & 85.99 \(\pm\) 0.55 \({}^{\mathrm{\SIUnitSymbolMicro e}}\) & 34.03 \(\pm\) 4.14 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 37.81 \(\pm\) 3.77 \({}^{\mathrm{\SIUnitSymbolMicro a}}\) & 51.79 \(\pm\) 4.18 \({}^{\mathrm{\SIUnitSymbolMicro b}}\) & 16.118 \\  & \multicolumn{3}{c}{F-value (\(\mathrm{\SIUnitSymbolMicro g}\))} & 606.818 (\(\mathrm{\SIUnitSymbolMicro g}\)/0.001) & 51.711 (\(\mathrm{\SIUnitSymbolMicro g}\)/0.001) & 61.704 (\(\mathrm{\SIUnitSymbolMicro 0}\)/0.000) & 120.352 (\(\mathrm{\SIUnitSymbolMicro 0}\)/0.000) & \\ \hline \multirow{6}{*}{ \begin{tabular}{c} ABTS\({}^{+}\) \\ (\(\mathrm{\SIUnitSymbolMicro g}\)) \\ \end{tabular}
###### Abstract  
This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2 Seq and LSTM networks, the method improves prediction accuracy and applicability, especially for mobile terminals such as aviation and maritime ones. Unlike traditional approaches, it does not require extensive statistical data and can be generalized to real-world systems, providing stable long-term traffic demand predictions. This study utilizes real-world flight data mapped to corresponding satellite beams, allowing the precise prediction of beam-specific traffic demands. The results show that aggregating historical demand data for beams with similar trends achieves an average predictive Mean Squared Error (MSE) of 0.0007 and a maximum MSE fluctuation of 0.014, significantly outperforming predictions based on average values in terms of stability and accuracy. This novel solution for resource management in satellite communication ensures efficient and accurate long-term traffic demand predictions.  
satellite communication; traffic demand prediction; deep learning; Seq2 Seq; long short-term memory (LSTM) +
Footnote †: journal: Applied Sciences
## 4 Results of the 2D Inversion  
The most distinct feature of the 2D inversion model is the widely distributed conductive channels along the MT profile (Figure 7), which divided the electrical model into several resistive blocks. We marked them as conductors C1-C5 and some resistive regions are marked as resistive blocks R1-R4. The 2D inversion model has significant differences between Junggar Basin and the Dulate-Yemaquan island arc.  
Sedimentary rocks in the Junggar Basin are characterized by a horizontal zone (C1 in Figure 7) of low resistivity (10 \(\Omega\)m-100 \(\Omega\)m), with a wedge thickness of about 100 m-4 km. The upper crust is imaged as >10,000 \(\Omega\)m zone (R1, R2) with a thickness of \(\sim\)20 km beneath the Junggar Basin, dipping to south. There is a strip zone (C5) between R1 and R2, characterized by resistivity values (>100 \(\Omega\)m), decreasing from the low crust to the upper crust. It reveals that the Junggar Basin is probably characterized by double basement structure. R1 may represent the Paleozoic basement and R2 is mainly composed of the crystalline basement. C5 is the detachment between the Paleozoic basement and the crystalline basement of the upper crust. A north-dipping low-resistivity belt (C4) characterized by resistivity values (50 \(\Omega\)m-200 \(\Omega\)m), is interpreted as the Wunlungunan fault, which controls the sedimentary boundary of Wulungu depression. The resistive upper crust (R3) under the Yemaquan island arc with a thickness of about 30 km, is characterized by resistivity values of \(\sim\)10,000 \(\Omega\)m, which probably represents residual Precambrian continental crust. There are some conductors and resistors in the shallow part of the Yemaquan island arc, which reveals complex faults and folds in Lower Paleozoic. The Zhaheba complexes are imaged as a block resistor with resistivity values (~1000 \(\Omega\)m). A horizontal zone (C2) of low resistivity (<100 \(\Omega\)m) in the shallow part of the Dulate arc, is interpreted as Lower Paleozoic volcanic sedimentary rocks with a thickness of about 100 m-3 km. Irregular resistors of resistivity (~3000 \(\Omega\)m) in the upper crust under the Dulate represent the Lower Carboniferous granite and grandoiorite, considering the outcrops. The north-dipping channel of low resistivity (~50 \(\Omega\)m) is interpreted as the Zhahaha-Aermantai fault, being probably a pipeline for hot material flow.  
Figure 7: Two-dimensional resistivity model and the RMS of every MT point.