esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Results  
#### 4.3.1 Different Backbone  
We tested the modules on different backbone networks. As shown in Table 1, the experimental results show that the results increase in different backbone networks by adding the modules. Figure 6 depicts increases in accuracy of 6.65% for ResNet-18, 3.60% for DenseNet121, 2.84% for VGG16, 3.59% for InceptionNet, 6.54% for MobileNet, and 2.61% for EfficientNet. Among them, the optimal result of 83.3% was reached on ResNet-18, and the increase in recall, specificity, and F1 was 2.90%, 3.11%, and 1.75%, respectively.  
The experimental results are also analyzed using the macro-ROC curves and macro-AUC. As shown in Figure 7, the ROC curves on the six popular backbone networks showed improvements in the results, and the AUC values of our method were all larger than those of the baseline.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1** \\ \hline \multirow{2}{*}{ResNet-18} & Baseline & 0.767 & 0.499 & 0.483 & 0.821 & 0.480 \\  & Ours & 0.833 & 0.488 & 0.512 & 0.852 & 0.498 \\ \hline \multirow{2}{*}{DenseNet-121} & Baseline & 0.780 & 0.419 & 0.438 & 0.780 & 0.423 \\  & Ours & 0.816 & 0.471 & 0.505 & 0.846 & 0.486 \\ \hline \multirow{2}{*}{VGG-16} & Baseline & 0.795 & 0.456 & 0.493 & 0.839 & 0.471 \\  & Ours & 0.823 & 0.477 & 0.502 & 0.846 & 0.488 \\ \hline \multirow{2}{*}{InceptionNet} & Baseline & 0.748 & 0.409 & 0.473 & 0.757 & 0.434 \\  & Ours & 0.784 & 0.454 & 0.501 & 0.834 & 0.471 \\ \hline \multirow{2}{*}{MobileNet} & Baseline & 0.742 & 0.501 & 0.414 & 0.753 & 0.405 \\  & Ours & 0.807 & 0.565 & 0.454 & 0.789 & 0.450 \\ \hline \multirow{2}{*}{EfficientNet} & Baseline & 0.761 & 0.511 & 0.466 & 0.752 & 0.439 \\  & Ours & 0.787 & 0.415 & 0.447 & 0.790 & 0.428 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on different backbone networks.  
Figure 6: Growth in accuracy.  
The In conclusion, the method proposed in this paper has been effectively validated in several popular deep networks, and the results are substantially improved. The experimental results demonstrate the effectiveness of our proposed method, which can be better targeted to the specificity of the ACL tear classification task. Additionally, its direct integration into any network demonstrates the method's generalizability and has improved the performance of numerous networks.
# Secure Image Signal Transmission Scheme Using Poly-Polarization Filtering and Orthogonal Matrix
## 4 The Principle of the PPF-OM Scheme
### Signal Processing on the Transmitter Side
\begin{array}{c}\mathbf{F}^{\mathrm{o}}\\ \mathbf{F}^{\mathrm{e}}\end{array} \right]\), which resulted in the following:  
\[ \begin{array}{l}\mathbf{T}_{\mathrm{H}}{=}\sum\limits_{a=1}^{\frac{l}{2}} \mathbf{\Gamma}^{\mathrm{o}}(:,a)\left(\cos\gamma_{a}\mathbf{x}_{a}+\cos \gamma_{a+\frac{l}{2}}\mathbf{y}_{a}\right)\\ \mathbf{T}_{\mathrm{V}}{=}\sum\limits_{a=1}^{\frac{l}{2}}\mathbf{\Gamma}^{ \mathrm{e}}(:,a)\left(\sin\gamma_{a}e^{\mathrm{j}\eta_{a}}\mathbf{x}_{a}+\sin \gamma_{a+\frac{l}{2}}e^{\mathrm{j}\eta_{a+1/2}}\mathbf{y}_{a}\right)\end{array}  \tag{13}\]  
It was found that \(\mathbf{T}_{\mathrm{H}}\) and \(\mathbf{T}_{\mathrm{V}}\) were the \(M\times\frac{2\mu}{l}\) matrix, which had to be reshaped into \(1\times\frac{2\mu M}{l}\) vectors \(\mathbf{\bar{T}}_{\mathrm{H}}\) and \(\mathbf{\bar{T}}_{\mathrm{V}}\) for transmission by two orthogonal polarized antennas, respectively.
# Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
## 3 Deep Kernel Method for Dynamic MRI Recovery
### Free-Breathing and Ungated Cardiac MRI Recovery Using Deep Kernel Representation  
With the deep kernel representation, we can use the following minimization criterion to get the parameters \(\theta,\bar{\mathbf{\varphi}}\), and \(\mathbf{\alpha}=[\mathbf{\alpha}_{i}]\) for free-breathing and ungated cardiac MRI reconstruction:  
\[\mathcal{C}(\theta,\bar{\mathbf{\varphi}},\mathbf{\alpha})=\sum_{i=1}^{M}\left( \underbrace{||\mathcal{A}_{i}\Big{[}(\Phi_{\theta}(\mathcal{A}^{H}\mathbf{b} )*\bar{\mathbf{\varphi}})\mathbf{\alpha}_{i}\Big{]}-\mathbf{b}_{i}||^{2}}_{\text{data term}}+ \underbrace{\lambda\cdot TV[(\Phi_{\theta}(\mathcal{A}^{H}\mathbf{b})*\bar{ \mathbf{\varphi}})\mathbf{\alpha}_{i}]}_{\text{image regularization}}\right). \tag{3}\]  
Here \(TV\) is the total variation regularization, and \(\lambda\) is chosen to be 0.01 in this work.  
The parameters \(\theta,\bar{\mathbf{\varphi}}\), and \(\mathbf{\alpha}\) are learned in an unsupervised fashion from only the measured k-t space data of the specific patient. We use ADAM optimization for the learning.
# Digital Twin-Based Vibration Monitoring of Plant Factory Transplanting Machine
###### Abstract  
In response to the problem of bowl seedling detachment caused by the shaking of the transplanting machine in plant factories, this paper proposes a physical entity monitoring method for the digital twin (DT) plant factory transplanting system. The method is used to analyze the vibration signals of the transplanting machine under different operating conditions and explore the optimal working conditions. Firstly, a demand analysis for the physical entity of the DT plant factory transplanting system is conducted, focusing on practical applications. Then, an optimal deployment plan is designed based on the axiomatic design (AD) theory. Subsequently, a comparative analysis of the operating conditions of the plant factory transplanting equipment is carried out using data-driven approaches. Finally, the optimal working condition parameters are determined by comparing the modal vibration power spectral density of the transplanting equipment under different operating conditions. The results show that the maximum amplitude occurs in the \(Z\)-axis, with a magnitude of 2.1 m/s\({}^{2}\). By comparing the cloud maps, it is evident that the vibration trends in the \(Z\)-axis and \(X\)-axis above the transplanting robotic arm are more pronounced compared to the \(Y\)-axis. This indicates that under the operating condition of transplanting 3000 plants per hour, a high transplanting efficiency can be maintained, and the vibration signals in the \(XYZ\)-axis above the transplanting robotic arm are relatively smooth, making them suitable for transplanting operations. This study combines digital twin technology to analyze the vibration signals of the plant factory transplanting machine under different operating conditions and explore the optimal working conditions. Compared to traditional monitoring platforms, this method facilitates the real-time visualization of different operating conditions of the transplanting machine in a virtual mapping, providing a more intuitive reflection of the equipment operation status.  
digital twin (DT); plant; transplanter; vibration analysis; axiomatic design (AD)
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Precision of the Simulations  
#### 4.4.1 FER Precision and Number of PPDUs  
As a general rule, the quality control metric for each test is the FER. The FER has been selected instead of the Bit Error Rate (BER), due to the fact that many PHY procedures are performed once per PPDU, like frame alignment, equalization, header decoding and Viterbi decoder flushing. Additionally the impact in the Medium Access Control (MAC) is directly related to the FER as a frame error will discard completely a MAC packet independently of the number of erroneous bits. In order to understand the results, it is important to analyze the user perception regarding FER values.  
* \(FER=10^{-1}\). This FER value is considered the minimum acceptable value so communication can be started. Although many losses are expected, communication is feasible in this environment. Since it is a minimum value, it is unlikely that at Medium Access Control (MAC) level one device with this PPDUs loss rate could become a repeater for others and keep a stable topology. Some narrowband PLC technologies make their sensitivity limit for \(FER=10^{-1}\), such as [20].
* \(FER=5\times 10^{-2}\). This FER value is used for some narrowband technologies as the quality threshold for good communication. Reference [21] considers this threshold for PLC communication without errors (considering that this percentage of errors will be solved by higher layers).  
Figure 8: Synchronous impulsive noise power in time.  
* \(FER=10^{-2}\). This more restrictive FER value is considered a satisfactory FER for the performance of this technology. One device operating with this amount of FER can behave thoroughly as a repeater. The PRIME Alliance uses this value as its threshold in reference [22].  
For better FER values (such as FER of \(10^{-3}\)), performance is exceptionally good and the probability of losing a PPDU will not be the limiting factor. It can be considered that in these scenarios there will be other limiting factors, such as MAC collisions, time or spatial variability, _etc._  
Obtaining low FER values with precision through simulation requires a high number of simulated PPDUs (and long simulation periods).  
The number of PPDUs required to obtain precise FER values were determined by calculating normalized standard deviation of the estimated FER, this is, the standard deviation of the estimated FER (\(\sigma(FER^{\prime})\)) compared to the real \(FER\). Results of these calculations are summarized in Figure 9.  
Considering the above criteria, and it being the case that \(FER=10^{-3}\) is of exceptionally good quality, the precision limit is considered to be sufficient when the probability of being better than \(FER=2\times 10^{-3}\) is higher than 99.9% (still five times better than \(FER=10^{-2}\)). This happens for \(\sigma(FER^{\prime})/FER\) lower than 0.32. Figure 9 shows that simulations with \(10,000\) PPDUs are precise enough.
# TMD Damping for Structures with Uncertain Modal Parameters
## 4 Results
### Assessment  
The computed normalized displacement responses \(X_{1}/X_{1,static}\) are assessed by their maximum value, which depends on the considered TMD mass ratio, TMD damping increase factor, and the ratio between actual and nominal modal parameters  
\[max(X_{1}/X_{1,static})=max\bigg{(}X_{1}/X_{1,static}\bigg{(}\mu,\gamma,\frac {f_{1}}{f_{1,nom}},\frac{m_{1}}{m_{1,nom}}\bigg{)}\bigg{)} \tag{16}\]  
The results of \(max(X_{1}/X_{1,static})\) for the TMD mass ratio \(\mu\) = 1% and for the considered combinations of \(f_{1}/f_{1,nom}\) and \(m_{1}/m_{1,nom}\) are depicted in Figure 3a for \(\gamma\) = 1 and in Figure 3b for \(\gamma\) = 1.20. It is observed that increased TMD damping mainly reduces the maximum displacement amplitude of the primary structure at large deviations between the actual modal parameters, i.e., eigenfrequency and modal mass, and their nominal values; the numbers of \(max(X_{1}/X_{1,static})\) are given at selected points on the surfaces in Figure 3a,b.
## 3 Results and Discussion
### Microstructural Characteristics
#### 3.3.3. Fourier Transform Infrared Spectroscopy (FTIR) Analysis
[101], the spectral band at 566 cm\({}^{-1}\) is attributed to the symmetric stretching of Al-O-Si. The band below 550 cm\({}^{-1}\) is an indication of the degree of amorphization of the material, since its intensity does not depend on the degree of crystallization [102; 103]. The symmetrical stretching vibrations of Si-O located between 687 cm\({}^{-1}\) and 749, as well as between 1140 cm\({}^{-1}\) and 1192 cm\({}^{-1}\), are linked to the existence of unreacted and/or partially reacted quartz [104]. The mainband from 940 cm\({}^{-1}\) to 970 cm\({}^{-1}\), ascribed to the asymmetric stretching vibration of Si-O-Si and Si-O-Al ([PERSON] et al., 2020), confirms more intensive peaks and a higher formation of the reaction products of Si-O-Si and the Si-O-Al composition [105]. This peak shifted to a higher wavenumber, revealing the substitution of Al\({}^{3+}\) with Si\({}^{4+}\) species and the growth of more stable Si-O-Si bonds. According to [PERSON] et al. [106], geopolymer strengthening is enhanced when Si tetrahedrals replace Al species in Si-rich gels.
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
## 3 Recent Development of the OEO's Application
### Wideband Tunable Frequency Generation  
The conventional OEO has a narrowband tunability, which is a few MHz due to the bandwidth limitation of the electrical bandpass filter. To improve the tunability of the OEO, tunable microwave resonators or microwave photonic filters have been proposed [61]. They can provide tunability from hundreds of MHz up to tens of GHz. In 2010, [PERSON] published the first research paper for a wideband tunable OEO [80]. In 2017, a wideband tunable frequency generator based on a dispersion compensation fiber OEO was proposed that had a tuning range of 3-42 GHz [81]. The SSB phase noise was lower than \(-110\) dBc/Hz at a 10 kHz offset from the carrier for the whole tuning range (span of about 39 GHz). A microwave photonic filter was used for frequency tuning, while the dispersion compensation fiber was used to compensate for the chromatic dispersion of the single-mode fiber. In 2019, a wideband frequency generator based on frequency division without a bandpass filter, but biasing the Mach-Zehnder Modulator (MZM) at the minimum transmission point, was introduced [82]. The wideband signal from 6 GHz to 10 GHz (or 10 GHz to 18 GHz) was obtained depending on the input signal. A phase noise improvement of 5.71 dB was achieved at the output signal [82]. A micro-ring resonator (MRR) is another possible solution to bring wideband tunability to the OEO configuration [83]. Wideband frequency generation between 0 and 20 GHz was achieved using an OEO with a MRR, wherein the SSB phase noise of \(-95\) dBc/Hz was measured at a 10 kHz offset from the 12.23 GHz carrier frequency [83]. The configuration of the OEO with the MRR is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Optical Delay Line** & **Central Frequency** & **Long-term Stability** & **Phase Noise (@10 kHz** \\  & **Length** & & & **offset from the carrier)** \\ \hline Temperature stabilization [75] & N/A & 10 GHz & 0.1 ppm/K & \(-143\) dBc/Hz \\ OEO with feedback-control & & & & \\ loop [76] & 15 km & 3 GHz & 0.05 ppm/K & \(<-130\) dBc/Hz \\ \hline Optical delay stabilization & & & & \\ system [77] & 3 km & 3 GHz & 0.02 ppm/K & \(-123\) dBc/Hz \\ \hline OEO with PLL [78] & \
\begin{tabular}{c} 500 m \\ (Dispersion deduced fiber) \\ \end{tabular} & 3 GHz &
\begin{tabular}{c} 6.98 \(\times\) 10\({}^{-14}\) \\ (average time of 1000 s) \\ \end{tabular} & \(<-100\) dBc/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the different techniques to achieve long-term stability in the OEO.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 4 OCTA Algorithms
### Classification of Present OCTA Algorithms
In a vessel, this depends on the backscattering cross-section of RBCs (which depends on orientation according to Section 2.2), and the RBC density (hematocrit). As discussed further in Section 7.1, \(|\mathrm{R}(\tau)|\) is a monotonically decreasing function under certain conditions, with the decorrelation rate, or rate of autocorrelation decay, being proportional to speed. As difference methods depend on \(\mathrm{R}(0)\) and \(\mathrm{R}(\mathrm{T})\), there are two regimes to consider in understanding Equation (16). The first is when \(\mathrm{T}\) is much longer than the intrinsic decorrelation time. In this case, \(\mathrm{R}(\mathrm{T})\ll\mathrm{R}(0)\), and \(\mathrm{D}(\mathrm{T})\) is proportional to the signal power \(\mathrm{R}(0)\), typically related to backscattering (RBC density and orientation). If \(\mathrm{T}\) is on the order of the intrinsic decorrelation time, the difference \(\mathrm{D}(\mathrm{T})\) depends on both the signal power \(\mathrm{R}(0)\) and the decorrelation rate. In this case, the interpretation of the difference \(\mathrm{D}(\mathrm{T})\) becomes more ambiguous, and it can be affected by the signal power or decorrelation rate, which can be impacted by the RBC density, orientation, and speed. With the normalization in Equation (17), \(\mathrm{r}(\mathrm{T})\) is more directly related to the rate of decorrelation, and hence, the RBC speed. However, to rigorously account for the possible presence of static scattering, measurements at several time lags [18] are required.
## 4 Contribution
### Construction of SUUIL Model for Urban Environment Ultra-Low Altitude  
At ultra-low altitudes, the distribution density of complex scatterers such as buildings and vegetation is relatively high. These scatterers cause various interferences such as reflection, scattering, and diffraction in the communication process of the UAV. In this case, the channel performance is mainly affected by shadow fading and multipath fading. In this section, lognormal shadow model is adopted to describe the path loss during signal transmission, and Rice distribution is used to describe the multipath fading, so as to establish the suitable channel model.
# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.3 Sequence Learning for ReID
A temporal stream network was built by inserting several multi-scale 3D (M3D) convolution layers into a 2D CNN network. The M3D convolution network introduces a fraction of parameters into the 2D CNN to gain the ability of multi-scale temporal feature learning. In addition, a temporal stream was included using residual attention layers to refine the temporal features further. The jointly learning of spatial-temporal attention masks in a residual manner enables the identification of the discriminative spatial regions and temporal cues. Model evaluations were performed on three widely used benchmarks datasets: the MARS, PRID2011, and iLIDS-VID datasets, with the proposed model obtaining a mAP on 0.740 on the MARS dataset. RNN in the form of Long Short-Term Memory (LSTM) of Gated Recurrent Unit (GRU) are commonly employed to capture temporal or spatial features. In ReID tasks, often the use of RNN is applied into sequences of images or video frames to capture spatial features extracted from CNN. One example of use of LSTM is presented by [6], to progressive aggregate frame-wise human region representation at each frame extracted from the Local Binary Patterns (LBPs) detector, yielding a sequence feature representation. LSTM enables remembering and propagating previously accumulated representative features while forgetting irrelevant ones. The proposed RNN acts as a feature aggregation, generating highly discriminating sequence-level object representations. The evaluations of the models were conducted using the iLIDS-VID and PRID 2011 datasets, obtaining a Rank 1 of 49.3 on iLIDS-VID. A RNN to jointly use spatial and temporal features is presented by [67], enabling exploring all relevant information useful for the person ReID task. The method encompasses a temporal attention mechanism to automatically pick the most discriminating features in a specific frame obtained from a CNN, while integrating surrounding information. Experiments were carried out using the iLIDS-VID, PRID 2011, and MARS datasets, with the proposed model achieving a Rank-1 of 70.6 on MARS. A performance evaluation of the reviewed methods for person ReID using sequence learning is given in Table 4.  
A performance comparison among the described works is presented, as depicted in Figure 7.  
While the use of RNN networks seems to e a natural choice to capture relevant features for the person ReID task, recently, authors have relied on 3D CNN to automatically capture these dependencies, obtaining models that are less complex to train and achieve superior performance when compared with traditional RNN, such as LSTM or GRU.
## 2 Battery Modeling
To implement the one-state hysteresis model to KFs, the above equations need to be discretized to the following linear discrete form [31]:  
\[U_{L,\;k}=U_{oc,k}+R_{0}I_{k}+U_{1,\;k}+h_{k} \tag{3}\]  
\[U_{1,k}=\exp\biggl{(}-\frac{T_{s}}{R_{1}C_{1}}\biggr{)}U_{1,k-1}+\biggl{[}1- \exp\biggl{(}-\frac{T_{s}}{R_{1}C_{1}}\biggr{)}\biggr{]}R_{1}I_{k-1} \tag{4}\]  
\[SOC_{k}=SOC_{k-1}+\frac{\eta T_{s}}{C_{N}}I_{k-1} \tag{5}\]  
Figure 1: The schematic diagram of the one-state hysteresis equivalent circuit model (ECM). For the hysteresis state function, let \(h(S,\ t)\) be the hysteresis voltage as the function of SOC and time, and \(\hat{S}=dS/dt\). \[\frac{dh(S,\ t)}{dS}=\gamma\text{sgn}(\hat{S})(M(S,\ \hat{S})-h(S,\ t)) \tag{6}\]  
where \(M(S,\ \hat{S})\) is the maximum hysteresis voltage as a function of SOC and SOC changing rate. \(\gamma\) is a positive constant to tune the rate of decay, and \(\text{sgn}(\hat{S})\) is to make the equation stable for charge and discharge process. Since \(dh(S,\ t)/dt=dh(S,\ t)/dS\times dS/dt\), the deformation of Equation (6) can be expressed by Equation (7):  
\[\frac{dh(S,\ t)}{dt}=\gamma\text{sgn}(\hat{S})(M(S,\ \hat{S})-h(S,\ t))\times \frac{dS}{dt} \tag{7}\]  
According to Equation (2), it is easy to conclude that \(dS/dt=\eta I/C_{N}\). Meanwhile, \(\text{sgn}(\hat{S})\times dS/dt=|dS/dt|\). Then, Equation (7) is rewritten by:  
\[\hat{h}(t)=\left|\frac{\eta I(t)\gamma}{C_{N}}\right|M(S,\hat{S})-\left|\frac {\eta I(t)\gamma}{C_{N}}\right|h(t) \tag{8}\]  
Figure 2: Open circuit voltage (OCV) discharge and charge boundary at 30 \({}^{\circ}\)C: (**a**) the discharge and charge curve; (**b**) the hysteresis level. And then, discretize Equation (8) to:  
\[h_{k}=\exp\left(-\left|\frac{\eta I(t)\gamma}{C_{N}}\right|\right)h_{k-1}+\left(1- \exp\left(-\left|\frac{\eta I(t)\gamma}{C_{N}}\right|\right)\right)M(S,\hat{S}) \tag{9}\]
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario
### Simulation Framework  
The tests are carried out in an open-source simulation framework called OpenNet [14] that runs on top of Mininet and ns-3. It offers a Python programming interface to create the topologies. Mininet provides SDN support by emulating OF switches that run Open vSwitch (OVS) and by enabling the connection with an external controller. In contrast, ns-3 offers WiFi and LTE channel models, as well as logical entities like OF-APs, eNBs, and the EPC (S/P-GW and MME). The ns-3 emulator counts on a Friss channel model to provide an accurate implementation of 802.11 and LTE outdoor wireless channels. The integration of SDN in WiFi and LTE networks is different. WiFi access points (APs) are fully SDN-based, and they can be managed from the controller, while SDN support in the LTE network lies in the interconnection between traditional EPC entities and eNBs. This means that those entities are treated as end-hosts by the SDN controller, so that it cannot install any flow rule to manage their operation.