esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
## 2 Materials and Methods
### Firmware Transfer  
The firmware transfer step involves the base station transferring the firmware image to the device. The packet structure of the transmitted firmware image includes the device ID, the packet number that is being transmitted, 16-bytes of the encrypted firmware image, and a 16-byte MAC to ensure authenticity. The device responds with either an acknowledgement (Ack) or negative acknowledgment (Nack) depending on whether the data arrived reliably and is authenticated via a MAC calculation. The device stores the encrypted data in external memory as it is received. The data is encrypted as a countermeasure to adversarial attacks that attempt to read the image from external memory. If an Ack is received, the base station sends the next packet of the firmware image. If a Nack is received, the base station re-sends the same packet. This process is repeated 8000 times to transfer the entire set of 16-byte encrypted data packets constituting the 128 kB firmware image. If the base station does not receive a response or gets a Nack after three consecutive packet transfer attempts, the base station will re-initiate the ADR to update the optimal communication setting. The base station can also monitor the RSSI of the incoming packets from the device. If there is a significant increase in RSSI, the base station can direct the device to a lower communication setting to speed up the data transfer process.
# Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average
## 4 Experimental Results
### Ablation Experiment  
In this section, we conducted experiments including an ablation study and an accuracy test of action recognition due to the temporal relation learning proposed in this paper. Table 1 shows the results of ablation studies for UCF101 and KTH datasets. In Table 1, \"RGB\" represents a backbone using VGG16-net and indicates the result of using the RGB image feature map of the input video clip. \"RGB+Diff\" represents a deep feature map, which is the result of combining the proposed RGB image feature map and difference image feature map to the backbone in Section 3.3. For each dataset, the performance was improved by 6.2% and 7.4% compared to the previous backbone experiment results. \"RGB+Diff+Moving avg.\" means that a weight is assigned to the action section using the bidirectional exponential moving average neural network proposed in Section 3.4 with the deep feature map.  
The performance of \"RGB+Diff+Moving avg.\" was improved by 1.4% and 0.4% compared with the result of the previous step. Lastly, \"RGB+Diff+Moving avg.+C3D\" is a structure in which all steps of the proposed method in this paper are implemented by the late fusion of C3D. Compared with the previous experiment, this structure improved accuracy by about 16.1% and 0.5%. In conclusion, the experimental results of the proposed method achieved 23.63% and 8.33% higher recognition rates than the backbone performance. Therefore, the results of the ablation study in Table 1 show that the proposed method has an improved accuracy by including spatio-temporal information and temporal relation information.  
In order to verify the effect of the bidirectional exponential moving average neural network in action recognition using the proposed deep feature map, an experiment was conducted on parameters that give weights to the main action sections. For the experiment,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{2-3}  & **UCF101** & **KTH Dataset** \\ \hline RGB & 48.40 & 65.28 \\ RGB + Diff & 54.62 & 72.69 \\ RGB+Diff+Moving avg. & 55.97 & 73.10 \\ RGB+Diff+Moving avg. + C3D & 72.03 & 73.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study of the proposed method.  
the size of the parameter \(\alpha\) of the bidirectional moving average neural network was changed based on the proposed deep feature map. As described in Section 3.4, the bidirectional exponential moving average was recursively calculated with \(\alpha\in[0,1]\). The closer to 1.0 \(\alpha\) gets, the higher the weight given to the middle frame.  
Table 2 shows the increasing accuracy as a high weight of close to 1 is given to the frame, which is the action Implementation section. Therefore, as a result of the experiment, the value of the parameter \(\alpha\) was most effective when set to 0.9.
## 2 System Model
### Prosumer PV System Modeling
#### 2.2.1 Fractional Integral Polynomial Method  
The suggested fractional integral model calculates the PV module as a function of the voltage, which is given as follows:  
\[i(u)=i^{\prime}_{sc}-i^{\prime}_{sc}(\frac{u}{u^{\prime}_{\rm{OC}}})^{\gamma+ \delta}, \tag{3}\]where \(i^{\prime}_{sc}\) and \(u^{\prime}_{oc}\) are the short circuit current and open circuit voltage parameters, respectively. Additionally, \(i\) represents the module current in the range \(0\leq i\leq i^{\prime}_{sc}\), \(u\) represents the voltage of the module in the range \(0\leq u\leq u^{\prime}_{oc}\), and \(\gamma\) represents a value given in the range \(0\leq\gamma<1\). \(\delta\) is a non-negative integer value. The power of the PV module can be determined as follows:  
\[p(u)=i(u)\cdot u=(i^{\prime}_{sc}-i^{\prime}_{sc}(\frac{u}{u^{\prime}_{oc}})^{ \gamma+\delta})u \tag{4}\]  
The above equations depend on the following major parameters: \(i^{\prime}_{sc},u^{\prime}_{oc}\), and the sum of \(\gamma+\delta\). These parameters depend on the atmospheric conditions and the manufacturer characteristics. The following equations determine the values of \(i^{\prime}_{sc},u^{\prime}_{oc}\), and the sum of \(\gamma+\delta\):  
\[u^{\prime}_{oc}=S_{m}u_{t}(\frac{G}{G_{stc}})(T-T_{stc})+u_{max}-(u_{max}-u_{ min})\cdot e^{(\frac{G}{G_{stc}})_{ln}(\frac{u_{max}-u_{oc}}{u_{max}-u_{min}}))} \tag{5}\]  
\[i^{\prime}_{sc}=P_{m}(\frac{G}{G_{stc}})(i_{sc}+i_{t}(T-T_{stc})) \tag{6}\]  
\[\gamma+\delta=\frac{i_{sc}}{i_{sc}-i_{opt}}, \tag{7}\]  
where, \(S_{m}\) and \(P_{m}\) are the series and parallel connected modules, respectively. \(G_{stc}\) and \(T_{stc}\) represent the standard test conditions (STC) parameters, \(G\) and \(T\) represent the arbitrary irradiance and temperature values, respectively, \(u_{t}\) represents the open circuit voltage temperature coefficient, \(i_{t}\) represents the short circuit current temperature coefficient, \(u_{max}\) and \(u_{min}\) represent the voltage limits for the PV module, and \(u_{oc}\) and \(i_{sc}\) are the rated PV module parameters. The validation of the Equations (3)-(7) is presented in the following subsections by varying the model parameters. Additionally, for complete mathematical details and derivation of the presented model, the readers are encouraged to go through the analysis provided in [32].
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 3 Double-Scale Adaptive Transmission Mechanism for UASNs  
### System Model  
Most UASNs can be modeled as a multi-hop clustered network as shown in Figure 2. The clustered UASN consists of a surface sink node and underwater sensor nodes. Generally, the sensor nodes can be classified into cluster headers (CH) or cluster members (CM). The CM nodes gather environmental information and forward the data to its CH. Then, each CH transmits collected data to the sink node through single-hop or multi-hop transmission. The transmission slots of all sensors are scheduled based on media access control (MAC) algorithms, such as Time Division Multiple Access (TDMA). Each node sends at the scheduled slot, and the time interval between two transmissions is assumed to be equal.  
The signal-to-noise ratio (SNR) can be considered as an indicator of the channel state, which is defined as the received SNR corresponding to a transmission power of one unit. We assume that the channel state remains stable in each slot and would change in the next slot. In each time slot, some packets arrive at the transmitter node and enter the data buffer. \(B_{max}\) denotes the buffer capacity of the transmitter. At the beginning of slot \(i\), the existing buffer length is \(B_{i}\). Let the number of arriving packets be \(g_{i}\), and the number of transmitted packets be \(l_{i}\), the new buffer length can be expressed as  
\[B_{i+1}=min\{max\{B_{i}-l_{i},0\}+g_{i},B_{max}\}. \tag{1}\]  
The amount of arrival data in the future can be predicted by recursive least squares (RLS) filter [43] and artificial neural network [44]. So, in this paper, for the sake of simplicity, it is assumed that the sensors collect sensory data at certain time intervals for a period of time so that each node can know the data arrival rate in the future period.  
The set of modulation and coding modes is defined as \(\{m_{0},m_{1},\dots,m_{M}\}\), which is sorted in ascending order of transmission rate. In this set, \(m_{0}\) means stop transmission and \(m_{M}\) yields max transmission rate. \(P_{max}\) denotes the maximum transmission power of the transmitter and \(M\) represents the number of modulation and coding modes.
## 3 Results and Discussion
### Volatile Compounds Analysis  
Volatile compounds of honey fractions were very complex and involved different classes of chemical compounds. The identified components were 55 including alcohols, acids, aldehydes, ketones, terpenes, hydrocarbons, benzene, and furan derivatives.  
Chromotograms of four representative different samples are presented in Figures 1-4. Table S3 shows the isolated compounds. Volatile compounds found mainly in fir honey samples were octane, undecane, nonanal, nonanol, decanal, methyl nonanoate, and nonanoic acid. The main compounds of the volatile fraction of thyme honey samples were benzaldehyde, 2-phenylacetaldehyde, undecane, nonanal, 2-phenylacetonitrile, 1-phenylbutane-2,3-dione, methyl nonanoate, 5-isopropyl-2-methylphenol (carvacrol), nonanoic acid, 3-hydroxy-4-phenylbutan-2-one, and 3-hydroxy-4-phenylbut-3-en-2-one. The volatile fraction of pine honey samples mainly consisted of undecane, nonanal, nonanal, decanal, methyl nonanoate, and nonanoic acid. The compounds detected in the volatile fraction in citrus honey samples were (2S,5S)-2-methyl-5-(propyl-1-en-2-yl)-2-vinyltetrahydroofuran (herboxide), (2S,5R)-2-methyl-5-(propyl-1-en-2-yl)-2-vinyltetrahydro)foruran (herboxide second isomer), 2-((2S,5R)-5-methyl-5-vinyltetathyroidismofuran-2-yl)propan-2-ol, 3,7-dimethylocta-1,6-dien-3-ol (linalool), nonanal, (R)-2-((2S,5S)-5-methyl-5-vinyltetathyroidismfuran-2-yl)propanol (illac aldehyde A), (S)-2-((2S,5S)-5-methyl-5-vinyltetathyroidismfuran-2-yl)propanol (illac aldehyde B), (S)-2-((2R,5S)-5-methyl-5-vinyltetathyroidismfuran-2-yl)propanol (illac aldehyde C), (3S,3 aS,7 aR)-3,6-dimethyl-2,3,3a,4,5,7a-hexahydrobenzofuran; decanal, 2-(4-methylcyclohex-3-en-1-yl)propanol (p-menth-1-en-9-al), and methyl 2-aminobenzoate (methyl anthranite). Many of these compounds have been reported previously to dominate the volatile profile of fir [27; 28], thyme [29; 30], pine [4; 31], and citrus [29; 32; 33] honey.
## 4 Simulation Results  
In this section, the effectiveness of the proposed STPAP beamforming algorithm based on the MVDR-CMS criterion is validated firstly. Then, the output C/N\({}_{0}\) and computational complexity performance of the proposed STPAP algorithm based on the MVDR-CMS criterion are compared with those of the PM criterion in [29] and the MVDR-CSS criterion in [30], respectively. To observe the array pattern in the whole frequency band clearly, the linear uniform DPSA is used preferentially. Thus, an eight-element linear uniform DPSA with half wavelength spacing is utilized and each dipole of the DPSA is followed by a TDL with eight taps. The desired GNSS signal is BeiDou-2 (BD2) signal at B3 band whose carrier frequency is 1268.52 MHz and mainlobe bandwidth is 20.46 MHz. The analog intermediate frequency is 46.52 MHz. Moreover, the sampling frequency and the number of samples are set as 62 MHz and 62,000, respectively. The received digital signal is firstly generated and then a quadrature down-conversion mixer is adopted to acquire baseband I/Q signals. After that, a low-pass filter is used to remove the noise that is out of the mainlobe. Finally, the proposed STPAP beamforming algorithm can be applied to process the signal.  
Three scenarios have been designed and the simulation parameters are presented in Table 2. Note that 20 Monte-Carlo simulations, in which the locations of interferences vary from one to the next as shown in Table 2, are carried out to get averages of the results in scenarios 2 and 3.  
(a) Scenario 1: Without loss of generality, two desired BD2 signals and one wideband interference with EP are involved in this scenario. To validate the effectiveness of the proposed STPAP algorithm, we can observe whether null forms towards the interference or beams form towards the two desired GNSS signals in the joint space-time-polarization domain. Moreover, the BD2 software receiver is also utilized to validate that the desired GNSS signals can be acquired.  
(b) Scenario 2: Twelve desired BD2 signals and four wideband interferences are generated. The output C/N0 performance of the proposed STPAP algorithm is compared with those of the existing STPAP algorithms based on the criterions in [29; 30; 31].  
Figure 3: Flow chart of the proposed algorithm.  
(c) Scenario 3: To verify that the proposed algorithm can be also valid when the polarization discrimination is removed, all the twelve desired BD2 signals and four wideband interferences are assumed to be RHCP. Moreover, the output C/N\({}_{0}\) performance of the proposed STPAP algorithm is also compared with those of the existing STPAP algorithms based on the criterions in [29; 30; 31].
## 4 Performance Analysis and Evaluation
### Signaling Cost Analysis of IP-WSN
Where, C\({}_{\text{sd}}\) and C\({}_{\text{bu}}\) are the sensor mobility cost and binding update cost respectively:  
\[\text{SigCostpmipv6}=\text{M}_{\text{intra-ip-wsn}}\text{.}\text{ Csd}+\text{M}_{\text{inter-ip-wsn}}\text{.}\text{(Csd}+\text{C}_{\text{bu}}\text{)} \tag{10a}\]  
where Csd and Cbu are calculated in term of PMIPv6 as follows:  
\[\text{C}_{\text{sd}}=\alpha\text{.}\left(\text{ RS}_{\text{pmipv6}}+\text{RA}_{\text{pmipv6}}\right)\text{D}_{\text{sn}} \tag{10b}\] \[\text{C}_{\text{bu}}=\beta\text{.}\left(\text{PBU}_{\text{pmipv6}}+\text{ PBA}_{\text{pmipv6}}\right)\text{D}_{\text{smag}} \tag{10c}\]  
From Equation (11) the total signaling cost (SigCost\({}_{\text{pmipv6}}\)) of the proposed IP-WSN scheme based on SPMIPv6 is calculated by summing up the individual cost of intra-IP-WSN mobility (M\({}_{\text{intra-ip-wsn}}\). C\({}_{\text{sd}}\)) \begin{table}
\begin{tabular}{l l} \hline
**Symbol** & **Description** \\ \hline BU & Binding Update Message \\ BA & Binding Acknowledgement Message \\ PBU & Proxy Binding Update Message \\ PBA & Proxy Binding Acknowledgement Message \\ D\({}_{\text{smag, BR}}\) & Distance between SMAG and BR \\ D\({}_{\text{sn-smag}}\) & Distance between SN and SMAG \\ \(\alpha\) & Unit transmission cost in a wireless link \\ \(\beta\) & Unit transmission cost in a wired link \\ RS & Router Solicitation Message \\ RA & Router Advertisement Message \\ C\({}_{\text{sd}}\) & Sensor Mobility Cost \\ C\({}_{\text{bu}}\) & Binding Update Cost \\ \hline \end{tabular}
\end{table} 
Table 1: System Parameters. and inter-IP-WSN mobility (\(\rm{M_{inter-ip-wsn.}(C_{sd}+C_{bu})}\)). Where, \(\rm{C_{sd}}\) and \(\rm{C_{bu}}\) are the sensor mobility cost and binding update cost respectively:  
\[\rm{SigCost_{spimpv6}=M_{intra-ip-wsn.}\ C_{sd}+M_{inter-ip-wsn.}(C_{sd}+C_{bu})}\] (11a) where \[\rm{C_{sd}}\] and \[\rm{C_{bu}}\] are calculated in term of SPMIPv6 as follows: \[\rm{C_{sd}=\alpha.\ (RS_{spimpv6}+RA_{spimpv6})\ D_{sn-smag}} \tag{11b}\] \[\rm{C_{bu}=\beta.\ (\mathit{PBU_{spimpv6}+PBA_{spimpv6}})\ D_{smag-BR}} \tag{11c}\]
## 3 Numerical Calculation Application and Experimental Setup  
### Case Examined  
In this study, commercial computational codes Fluent and ANSYS Mechanical were applied as the CFD and rotordynamics analysis tools, respectively. The whole process including pre-processing, post-processing, physical field coupling, and numerical solving were implemented on the ANSYS Workbench co-simulation platform [30].  
A horizontal split centrifugal pump with a six-blade double suction impeller was chosen as the research object. The pump was manufactured by Hunan M&W pump Co., Ltd., and it was designed for the clean water transportation. Its whole major structure is completely symmetric, so the axial hydraulic force on the impeller is fully balanced in theory due to the double suction structures of the suction chamber and impeller. The main structural and performance parameters of the centrifugal pump are listed in Table 1. The three-dimensional structure is shown in Figure 3 and the middle section of hydraulic model is represented in Figure 4, where the suction chamber, the impeller with six blade passages and the discharge chamber were exhibited with different colors. In addition, an X-Y orthogonal coordinate system with its origin located at the impeller center was established, and the rotating of impeller was set as counterclockwise.  
Figure 5 shows the three-dimensional structure and parameters of the main shaft, where the installation positions of bearing were marked with upward arrows. The main shaft is made of structural steel and can be divided into five sections with different diameters. Specifically, the middle section with a diameter of 0.049 m and a length of 0.664 m is designed for carrying the impeller and its accessories. Two sections next to the impeller (diameter = 0.045 m and length = 0.194 m) are designed for the mounting of ball bearings and the end sections (diameter = 0.042 m and length = 0.135 m) are used for the connecting of coupler.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Rated flow & \(Q_{\text{des}}\) & 620 m\({}^{3}\)/h \\ Rated head & \(H_{\text{des}}\) & 96 m \\ Rated speed & \(n_{\text{des}}\) & 1480 rev min\({}^{-1}\) \\ Specific speed & \(n_{\text{s}}\) & 52 \\ NPSH required & NPSHr & 3.6 m \\ Impeller inlet diameter & \(D_{1}\) & 0.18 m \\ Impeller outlet diameter & \(D_{2}\) & 0.56 m \\ Number of blades & \(Z\) & 6 \\ Impeller outlet width & \(b_{2}\) & 0.029 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the research object
## 4 Performance Evaluation
### Real Data Experiment
\begin{table}
\begin{tabular}{c c} \hline \hline
**Distribution** & **KS Distance** \\ \hline K distribution & 0.0011 \\ Pareto distribution & 0.0066 \\ CG-IG distribution & 0.0013 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Kolmogorov-Smirnov (KS) distances for each distribution fit. Figure 7: Compound-Gaussian distribution modeling results of real data. In Figure 8, we graph the PDs of the proposed detectors across various SCRs in the absence of signal mismatch. The observation reveals that the results from the real data are basically consistent with the simulation experiment. The detection performances of AE-GLRT, AE-Rao, AE-Wald, and AE-Gradient are comparable and significantly better than AE-Durbin. Specifically, AE-Rao still has the highest PD, and the performances of AE-Wald and AE-Gradient slightly surpass that of AE-GLRT. In Figure 9, utilizing the measured data from the IPIX radar, we compare and analyze the variation in the PDs of the proposed detectors in relation to the degree of signal mismatch. It can be observed that the result of the measured data is also basically consistent with the simulation experiment, indicating that AE-Durbin has the strongest robustness; when the signal mismatch angle is approximately 53 degrees, AE-Durbin can still detect the target with a PD of 0.9, while both AE-GLRT and AE-Rao have good selectivity, but AE-GLRT has stronger selectivity, indicating a better ability to suppress mismatched signals. Meanwhile, we also conduct experiments with \"19980204_224024_ANTSTEP.mat\" real data, which is well fitted by the Pareto distribution, and the results are basically consistent with the above. We can reasonably infer that the detectors proposed in this paper have a certain universality in power heterogeneous clutter environments. Figure 8: PD versus SCR without signal mismatch with IPIX radar data.
## 2 Materials and Methods
### Waveguide Characterization  
We characterized the refractive index contrast \(\Delta n\) of the poly-DVB waveguides using an interferometric imaging system [24]. Briefly, we illuminated the waveguides perpendicularly with a collimated beam from an He-Ne laser (\(\lambda\) = 632.8 nm) and imaged the waveguides on a complementary metal-oxide-semiconductor (CMOS) digital camera in a 4f imaging system incorporating a 20\(\times\), 0.42 NA objective. A plane wave reference beam was introduced and interfered with the imaging wave front at a predetermined angle to create an off-axis hologram. The phase change that the illumination laser beam undergoes passing the waveguide region reveals the refractive index contrast given the known waveguide dimensions, which was extracted from the numerically reconstructed object wave front.  
The transmission loss (TL) measurements were performed by coupling broadband light from a thermal light source into the waveguides [24]. We used a 50\(\times\), 0.8 NA objective for focusing the light at the input end of the waveguide being tested, and a 20\(\times\), 0.42 NA objective for imaging the waveguide output on a CMOS camera by means of a 4f imaging system. The TL measurements were carried out through the cut-back method, where we measured the ratio of the output power from the same waveguides cut into two different lengths, \(L_{1}\) and \(L_{2}\).This ratio indicates the optical loss due to material absorption and light scattering caused by inhomogeneous waveguide walls and polymer density fluctuation. To obtain better statistics, we measured three different isolated waveguides fabricated under identical parameters in each type of both simple and compound waveguides. The transmission loss was measured at several spectral points using a number of color filters right after the light is coupled into the waveguides. We calculated the transmission loss in dB/cm for different wavelength regions following the formula:  
\[TL=10 log_{10}(P_{ratio})\Delta L \tag{1}\]  
where \(P_{ratio}=\frac{P_{1}}{P_{2}}\), \(\Delta L=L_{1}-L_{2}\), and \(P_{1}\) and \(P_{2}\) are the measured waveguide output power at lengths \(L_{1}\) and \(L_{2}\), respectively.