esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 5 Experiment and Result Analysis  
Resisting eavesdropping attacks and link-flooding attacks are the two main functions of path hopping. An eavesdropping attack is a method of obtaining sensitive data or confidential information by listening to and stealing information during the communication process. Attackers usually collect enough packets in time and space from one or more nodes in the network, analyze them, and reorganize them to obtain key data. A link-flooding attack refers to the attacker sending a large number of fake or deceptive packets to the network link, causing link overload or resource exhaustion, thus affecting the normal function and performance of the network. This paper designs a multi-strategy path hopping active defense method considered from two angles: key nodes and path bandwidth. The weight of key nodes is used as the selection probability, combined with path similarity constraints, to enhance the unpredictability of path hopping to resist eavesdropping attacks. The path bandwidth weight is used as the selection probability to enhance the selection probability of high-bandwidth paths to resist link-flooding attacks.  
To verify the effectiveness and performance overhead of the SDADM designed in this paper, this chapter uses Mininet simulation software [21] and creates a network topology as shown in Figure 3 using the Ryu controller as the controller for the entire network. The topology consists of 1 Ryu controller and 12 OpenFlow switches. For the simplicity of the network topology, only two communication hosts, H1 and H2, are labeled in Figure 3, and each switch also connects two communication hosts and communicates, providing a certain amount of data flow for the network to simulate a real network. The configuration information of the experimental environment is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment Configuration** & **Parameters** \\ \hline
**OS** & Ubuntu 16.04.7 LTS \\
**CPU** & Intel(R) Xeon(R) E5-26700 \\
**RAM** & Samsung 16 GByte \\
**Ryu** & Ryu 4.34 \\
**Mininet** & Mininet 2.3.1b1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration parameters.
# Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
## 4 Efficient Management of Harvested Energy
### Management of the Harvested Energy
The duty cycle adaptation is also considered in this algorithm because the IWS stays on standby mode if it has not accumulated the energy necessary to transmit a data bit. The transmission range was firstly set at 1 km, and the maximum data size that can be transmitted was 1500 bits. The results obtained in terms of cumulative data over one month are shown in Figure 17.  
* Figure 17a shows the transmitted data accumulated over the month for a transmission range of 1 km. The result also highlights the frequency of transmission of the IWS. Overall, it was achieved that the IWS depletes its energy reserve during the first hours of measurement. For example,  
Figure 16: Block diagram of proposed protocol: Maximizing Data Size Protocol (MDSP). for a transmission with a maximum size of 4 kbits, the average throughput at the start of the month was 4.36 bits / s, unlike 0.5 bits / s observed towards the end of the month. * In Figure 17b, the maximum range was increased to 3 km, and it is observed that the amount of data transmitted over the whole month decreases, which was an expected result since the energy cost of the IWS has increased. * In Figure 17c, the transmission range varies from 100 to 5000 km, with a data size set to the legend's values. The amount of data transmitted over the month increases with increasing packet size. For example, for packets of size 4096 bits, it was obtained that 94.21 kbits of data were transmitted during the month under the basis of the energy harvested from the vibrations. This amount of data corresponds to an average throughput of 2.15 bits per second. * For different packet sizes (512 bits, 1024 bits, 2048 bits, and 4096 bits), the evolution of the residual energy of the IWS is shown in Figure 17d. The evolution of the residual energy agrees with the previous results, in which it was noted that the transmission frequency is higher in the first hours of the month. It was also observed that the residual energy of the IWS increased after each transmission. This was justified because the harvestable energy calculation during each measurement cycle is based on the PHEV's predicted power. This power was less than or equal to the real power, as shown in Figure 13a. This is another advantage of PHEV, which avoids overestimating the harvestable power, as is the EWMA predictor. Overall, the achieved throughput based on the harvested energy can be satisfactory for monitoring temperature data, which varies very slowly due to thermal inertia. Note that the real throughput can also be improved if the residual energy of the IWS is updated after each transmission since, in most of the results obtained, the predicted energy is below the actual energy. Figure 17: Transmitted data analysis. (**a**) transmission range of 1 km. (**b**) transmission range of 3 km. (**c**) transmission range between 0.1 and 5 km. (**d**) residual energy.
## 3 Experiments
### Experiments on Public Datasets  
#### 3.2.1 Experiment Setup  
We evaluate our knowledge distillation method on three widely used public datasets, namely MiniImageNet, CIFAR-FS, and CUB. The experiments were conducted on a workstation equipped with an NVIDIA 3090 Ti GPU and implemented using Pytorch software. To ensure a fair comparison with current few-shot image classification methods, a commonly used 4-layer convolutional neural network (CNN) and ResNet12 were adopted as the student network and teacher network, respectively. During the training phase, we used the SGD optimizer to optimize our models in all experiments, where momentum was set to 0.9 and weight decay was set to \(5\times 10^{-4}\). We trained for 100 epochs with an initial learning rate of 0.025, which was reduced by half after 60 epochs. In the testing phase, we conducted 5-way-1-shot and 5-way-5-shot tests. Specifically, we randomly performed 2000 classification subtasks on the testing dataset. In each subtask, 15 images were randomly selected from each class as query images for testing. The evaluation criterion  
Figure 2: Some images of EEI-100 dataset. They represent different electrical equipment. (**a**) wind power tower; (**b**) heavy-duty transformer; (**c**) heavy-duty distribution cabinet; (**d**) energy storage battery pack; (**e**) electrical insulator; (**f**) split-core current transformer; (**g**) three-phase motor; (**h**) heavy-duty circuit breaker; (**i**) contactor; (**j**) cooling fan; (**k**) electric energy meter; (**l**) dragline board.  
for the algorithm's classification performance was the average accuracy of all subtasks, and the standard deviation of the accuracy under a 95% confidence interval should also be provided.  
Please note that we also conducted a similar experiment on the 1080 Ti GPU and achieved comparable performance. This significantly alleviates the economic burden associated with model training. Leveraging a 4-layer CNN architecture, the student model occupies a mere 2 MB in size, which is approximately 50 times smaller than the teacher model. This lightweight model can be deployed on diverse edge processors, substantially lowering the hardware requirements for its implementation.
## 3 Results and Discussion
### Total Fat Content and Fatty Acid Composition
In both quinoa and amaranth oils, unsaturated fatty acids tended to be located in the internal position sn-2 of triacylglycerlevels (Figure 2). The proportion of saturated fatty acids in the sn-2 position was below 33.3%, which means that saturated fatty acids were mainly located in the external sn-1.3 positions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Fatty Acids** & **Quinoa** & **Amaranth** \\ \hline C14:0 & 0.14 \(\pm\) 0.08 \({}^{\text{a}}\) & 0.25 \(\pm\) 0.08 \({}^{\text{a}}\) \\ C15:0 & 0.05 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.09 \(\pm\) 0.02 \({}^{\text{a}}\) \\ C16:0 & 9.59 \(\pm\) 1.21 \({}^{\text{b}}\) & 18.92 \(\pm\) 1.15 \({}^{\text{b}}\) \\ C16:1 & 0.07 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.11 \(\pm\) 0.04 \({}^{\text{a}}\) \\ C17:0 & 0.04 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.08 \(\pm\) 0.03 \({}^{\text{a}}\) \\ C18:0 & 0.59 \(\pm\) 0.10 \({}^{\text{b}}\) & 4.50 \(\pm\) 0.59 \({}^{\text{b}}\) \\ C18:1 cis & 28.84 \(\pm\) 2.15 \({}^{\text{a}}\) & 25.03 \(\pm\) 1.87 \({}^{\text{a}}\) \\ C18:2 n-6 & 52.59 \(\pm\) 2.67 \({}^{\text{a}}\) & 48.55 \(\pm\) 2.48 \({}^{\text{a}}\) \\ C18:3 n-3 & 5.42 \(\pm\) 1.26 \({}^{\text{a}}\) & 1.06 \(\pm\) 0.36 \({}^{\text{b}}\) \\ C20:0 & 0.57 \(\pm\) 0.16 \({}^{\text{a}}\) & 1.10 \(\pm\) 0.24 \({}^{\text{b}}\) \\ C20:1 & 1.91 \(\pm\) 0.34 \({}^{\text{a}}\) & 0.32 \(\pm\) 0.11 \({}^{\text{b}}\) \\ C20:2 & 0.18 \(\pm\) 0.08 & - \\ SFA & 10.98 \(\pm\) 1.57 \({}^{\text{a}}\) & 24.94 \(\pm\) 2.11 \({}^{\text{b}}\) \\ MUFA & 30.82 \(\pm\) 2.50 \({}^{\text{a}}\) & 25.46 \(\pm\) 2.02 \({}^{\text{a}}\) \\ PUFA & 58.19 \(\pm\) 4.01 \({}^{\text{a}}\) & 49.61 \(\pm\) 2.84 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Data denoted by the same letter are not statistically different (\(\alpha\) = 0.05). SFA—saturated fatty acids, MUFA—monounsaturated fatty acids, PUFA—polyunsaturated fatty acids.  
\end{table} 
Table 2: Fatty acids composition (%) of oil extracted from quinoa and amaranth seeds. In the literature, influence of the TAG structure on the oxidative stability of oils is not always consistent. Some researchers reported that TAG with unsaturated fatty acids located at the sn-2 positions were more stable than TAG with unsaturated fatty acids in external positions [34; 35]. Saturated fatty acids coexisting in TAG with unsaturated fatty acids, did not affect the oxidative of fat, whereas short chain saturated fatty acids can enhance oxidative stability of unsaturated TAG. On the other hand, [PERSON] et al. [36] discussed that location of the fatty acids in the TAG position did not seem to be conclusive of the oxidative stability of fat.
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 3 Methodology
### Pseudolite Signal Preprocessing  
The pseudorange measurement vector is one of the pseudolite distance observation signals. It is obtained by multiplying the propagation time of the ranging code transmitted by the pseudolite from the transmitter source to the receiver antenna by the speed of light to yield the corresponding distance. The pseudorange measurement vector at time t, consisting of pseudorange measurements from N base stations, can be expressed by Equations (1) and (2):  
\[\rho(t)=[\rho^{(1)}(t),\rho^{(2)}(t),\ldots\rho^{(i)}(t),\ldots,\rho^{(n)}(t)] \tag{1}\]  
\[\rho^{(i)}(t)\ =\ R^{(i)}(t)+c\Big{(}\delta t_{u}-\delta t^{(i)}\Big{)}+\delta^{(i)}_ {trop}(t)+\varepsilon^{(i)}(t) \tag{2}\]  
\[R^{(i)}(t)=\sqrt{\left(x^{(i)}-x_{u}\right)^{2}+\left(y^{(i)}-y_{u}\right)^{2 }+\left(z^{(i)}-z_{u}\right)^{2}} \tag{3}\]  
where i is the base station index (i = 1,2,..., n), \(R^{(i)}\) is the geometric distance between the ith base station and the receiver at time t, and \(\Big{(}x^{(i)},y^{(i)},z^{(i)}\Big{)}\) and \((x_{u},y_{u},z_{u})\) represent the coordinates of the ith base station and the phase center coordinates of the receiver's antenna, respectively. c is the speed of light. \(\delta t_{u}\) is the clock error of the receiver, \(\delta t^{(i)}\) is the clock error of the ith base station, \(\delta^{(i)}_{trop}(t)\) is the tropospheric delay, and \(\varepsilon^{(i)}(t)\) represents error caused by factors such as multipath bias/NLOS, receiver noise, and antenna delay. (The multipath/NLOS error is dominant in \(\varepsilon\).)  
Figure 1: The proposed positioning model in the tunnel is based on GH-LPS. The blue parts represent the base stations, which are arranged in a straight line along the tunnel ceiling.  
The differential operation of the pseudorange measurements from two visible pseudolites can effectively eliminate the impact of the receiver clock error. Therefore, the single-differenced pseudorange vector at time t can be expressed as  
\[\Delta\rho^{(ij)}(t)\ =\ \rho^{(i)}(t)-\rho^{(j)}\ (t)\ =\ r^{(ij)}(t)-c\delta t^{( ij)}(t)+\varepsilon^{(ij)}(t) \tag{4}\]  
where i and j represent the ith and jth base stations, respectively (i,j = 1,2, \(\ldots\), \(n\), given \(i\
eq j\)), and there are n(n \(-\) 1)/2 single-differenced pseudorange measurements in \(\Delta\rho^{(ij)}(t)\). \(r^{(ij)}\) is the single-differenced geometric range, \(\delta t^{(ij)}\) is the clock difference, and \(\varepsilon^{(ij)}\) is the single-differenced multipath error and measurement noise.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Data Communication Phase  
This phase involves transmitting the data aggregated by the CHs to the BS for further processing based on the application type. Packet transmission from SNs to the CH is called intra-cluster transmission, while packet transmission from the CHs to the BS is called inter-cluster transmission. Intra-cluster communication is sub-classified into single-hop and multi-hop transmission, while inter-cluster communication is sub-classified into direct and multi-hop transmission. All the SNs in a cluster can forward their sensed data directly to the CH during single-hop transmission [35], and this form of transmission does not  
Figure 8: Process of the data aggregation phase.  
consider distance [66]. Regarding multi-hop transmission, the sensed data obtained by the SNs of a cluster are first sent to the nearest neighbor for forwarding to the CH [67]. Cluster heads directly send the aggregated data to the BS in the direct communication method [68], irrespective of the distance. Multi-hop communication allows all the CHs to forward their aggregated data to the most proximal CH to the BS. Figure 9 presents the data communication process.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 4 Results
### Forecast Analysis  
The overall experimental process is divided into three modules, which explore the effects on a single model before and after decomposition, the effects on prediction using different weight allocation methods, and testing the validity of the model through DM. Specifically, each module works as follows. First, by interacting the ARIMA, LSTM, and GRU models with SSA, EMD, and VMD, respectively, each prediction model in the proposed optimal prediction combination is generated, which proves that the use of denoising to process the input data of a single model in advance can significantly improve the prediction effect of the model. Secondly, by combining the single models in different combinations through different methods of assigning weights, the performance of the three weight assignment methods, namely, Mean, Median, and PSO-GA, is compared among the homogeneous, heterogeneous, and semi-heterogeneous combination strategies, and it is proven that the optimized semi-heterogeneous combination prediction of the PSO-GA method is the best in terms of the performance of the combination in a comprehensive way. Finally, a Diebold-Mariano test is performed to determine whether there is a significant difference between the different combinatorial prediction models optimized based on PSO-GA.
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 5 Results  
The results are presented here in four distinguished parts. First, Sections 5.1.1 and 5.1.2 contain the evaluation of the mass flow rate across the air amplifier under downstream and upstream operating conditions, defined in Section 3 as blowing and suction configurations. Subsequently, a correlation between the experimental and numerical data serves as a validation test by comparing the wake profiles produced by an isolated air amplifier obtained during the experimental campaign (Section 5.2), followed by a numerical characterization of the air amplifier performance under different input conditions. A wide range of primary flow pressure values, \(p_{c}\), and Coanda nozzle gaps, \(\delta\), are explored in Section 5.3.1. With the help of numerical simulations, this study delves into the comprehensive characterization of an air amplifier, with the aim of providing valuable information on its flow patterns, operational parameters, and sensitivity to geometric variations. Finally, the scaling effects of the air amplifier are explored numerically in Section 5.3.3, investigating the limitations and potential of these devices.  
Figure 15: Geometry comparison between 3D (**left**) and axisymmetric (**right**) model.
## 4 Error Budget of Height Estimation Accuracy  
A general approach to the development of an accuracy error budget of each height estimation procedure is to perform a propagation of the height reconstruction error. If only random errors are considered, i.e. assuming that a limited number of ground control points can be used to reduce systematic errors, for any given model:  
\[h=h\big{(}S_{1},S_{2},...,S_{n}\big{)} \tag{15}\]  
the basic propagation formula for the variance of \(h\) in terms of all the related error sources \(S_{1}\), \(S_{2}\), \(...\), \(S_{n}\) is:  
\begin{table}
\begin{tabular}{|l|l|l|} \hline Semimajor axis (km) & 6,997.9 \\ Inclination (\({}^{\circ}\)) & 97.87 \\ Eccentricity & 0.0018 \\ Argument of perigee (\({}^{\circ}\)) & 90 \\ \hline \end{tabular}
\end{table}
Table 1: Monostatic satellite orbital parameters [18, 19].  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Bistatic off-nadir** & \multirow{2}{*}{**RAAN difference**} & **Mean anomaly** & \multirow{2}{*}{**Covered latitudes**} \\
**angle** & & **difference** & \\ \hline
5\({}^{\circ}\) & 5.14\({}^{\circ}\) & 1.04\({}^{\circ}\) & [-65.7\({}^{\circ}\), 66.8\({}^{\circ}\)] \\
10\({}^{\circ}\) & 4.64\({}^{\circ}\) & 0.942\({}^{\circ}\) & [-69.3\({}^{\circ}\), 70.7\({}^{\circ}\)] \\
15\({}^{\circ}\) & 4.12\({}^{\circ}\) & 0.837\({}^{\circ}\) & [-73.8\({}^{\circ}\), 75.6\({}^{\circ}\)] \\
20\({}^{\circ}\) & 3.58\({}^{\circ}\) & 0.792\({}^{\circ}\) & [-78.1\({}^{\circ}\), 81.5\({}^{\circ}\)] \\ \hline \end{tabular}
\end{table}
Table 2: Latitude intervals where satisfactory bistatic baselines and angles are achieved during ascending phase in pendulum configurations for four off-nadir angles of the bistatic sensor.  
\[\sigma_{{}_{k}}^{2}=\sum_{i}\left(\frac{\partial h}{\partial S_{{}_{i}}}\right)^{ 2}\sigma_{{}_{S}}^{2}+\sum_{{}_{i,j}}^{i\epsilon_{j}}\left|\frac{\partial h}{ \partial S_{{}_{i}}}\frac{\partial h}{\partial S_{{}_{j}}}\right|\sigma_{{}_{S },{}_{S_{j}}} \tag{16}\]  
Table 3 resumes the functional models connected to the methods for monostatic-bistatic DEM generation reported in section 2.2. In a monostatic-bistatic approach, all the parameters can be considered uncorrelated with the exception of the monostatic and bistatic slant ranges [17], therefore:  
\[\sigma_{{}_{S_{i}},{}_{S_{j}}}\
eq 0\ \ \ \Leftrightarrow\ \ \left(S_{i}=r^{\prime},S_{{}_{j}}=r^{\star}\right)\ \ \ or\ \ \ \left(S_{i}=r^{\star},S_{{}_{j}
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 4 Experimental Validation  
The experimental validation process was performed in two sections. First, the vibration responses of the modeled structures were measured in the anechoic chamber. Afterwards, the sound absorption performance of the built structures were measured in standard reverberation room measurements.  
Figure 12: Comparison of the normalized mean surface velocity \(\overline{\frac{v}{p}}(f)\) of different sized ABH for three different radii.
## 2 Theoretical Background  
### Kesterites and the Cu-Zn Disorder  
CZTSe crystallises in the kesterite crystal structure (space group \(I\bar{4}\), no. 82), which can be derived from a doubled zincblende crystal structure by two particular pairwise cation substitutions [20]. For CZTSe, the cation planes perpendicular to the crystallographic \(c\) axis of the conventional ordered kesterite crystal structure are shown in Figure 1a. Therein, the cation positions of Cu, Zn, and Sn are highlighted and located at Wyckoff positions (2a) (0,0,0) and (2c) \(\left(0,\frac{1}{2},\frac{1}{4}\right)\), (2d) \(\left(0,\frac{1}{2},\frac{3}{4}\right)\), and (2b) \(\left(0,0,\frac{1}{2}\right)\), whereas the Se anions are located at the general Wyckoff position (8 g) (x,y,z), respectively.  
However, a further complication in CZTSe originates from the so-called Cu-Zn disorder, present in virtually all grown samples with its extent depe  
Figure 1: Cu, Zn, and Sn cation planes with \(z=0.0,0.25,0.5\) and \(0.75\) (bottom to top) in the (**a**) ordered kesterite (space group \(I\bar{4}\), no. 82) and (**b**) disordered kesterite (space group \(I\bar{4}2m\), no. 121) crystal structures, respectively. The dashed lines in the disordered kesterite structure (**b**) denote additional symmetry elements due to the total Cu-Zn disorder in the \(z=0.25\) and \(z=0.75\) planes. Also given are the colour-coded Wyckoff positions for each of the structural polymorphs. The crystal structure figures have been prepared using VESTA [21].  
growth conditions. Originating from a small Cu\({}_{\text{Zn}}\)-Zn\({}_{\text{Cu}}\) defect complex formation energy, this leads to a mixing of Cu and Zn cations within the Cu-Zn planes, introduces additional symmetry elements, as indicated by the dashed lines in Figure 1b, and increases the symmetry of the disordered kesterite to space group \(I\bar{4}2m\) (no. 121), respectively. Coincidentally, a structurally similar chalcogenide polymorph, namely the stannite crystal structure [20,22], crystallises in the same space group (\(I\bar{4}2m\)). Together with the fact that Cu\({}^{+}\) and Zn\({}^{2+}\) cations are isoelectronic and therefore indistinguishable for standard X-ray diffraction, this has hindered the identification of the correct ground state crystal structure of kesterite materials in the past and required alternative experimental investigations, e.g., neutron diffraction [11].
## 3 Case Study
### Project Specification
#### 3.2.1 Interval Estimation of the Duncan-Chang Model Parameters for High Rockfill Dams
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \(c\)**/kPa** & \(q\varphi^{0}\) & \(R_{f}\) & \(K\) & \(K_{b}\) & \(m\) & \(n\) \\ \hline \multirow{2}{*}{Mean} & Lower limit & 89.65 & 26.48 & 0.81 & 407.00 & 291.06 & 0.29 & 0.42 \\  & Upper limit & 115.92 & 28.20 & 0.83 & 442.02 & 328.05 & 0.35 & 0.47 \\ \hline \multirow{2}{*}{SD} & Lower limit & 50.62 & 3.43 & 0.05 & 65.18 & 70.67 & 0.11 & 0.10 \\  & Upper limit & 64.71 & 4.38 & 0.07 & 98.45 & 94.33 & 0.14 & 0.14 \\ \hline \multirow{2}{*}{VC} & Lower limit & 0.51 & 0.12 & 0.06 & 0.15 & 0.23 & 0.34 & 0.21 \\  & Upper limit & 0.63 & 0.16 & 0.08 & 0.22 & 0.30 & 0.44 & 0.32 \\ \hline \hline \end{tabular} Note: SD, standard deviation; VC, variation coefficient.  
\end{table} 
Table 1: Interval estimation of the Duncan–Chang E-B model parameters in the core wall. Figure 4: Relationship curves between the material parameter characteristic values and the sampling frequencies of high core rockfill dams.