esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Findings from the Case Study Comparison
#### 3.5.5 Christchurch/South Island New Zealand--Case Study Findings  
The historic city of Christchurch sits on the Pacific ring of fire, and experiences regular seismic events, most are minor, but the 2011 earthquake was one of the most destructive and deadly. About 70% of inner-city buildings collapsed, other parts were uninhabitable. Around 10,000 homes were destroyed, and 185 people died. The city took a holistic approach to the rebuilding of Christchurch as a \"smart city\". This approach was driven by the risk of repeat events, the scale of infrastructure damage (buildings, roads, power grid), and the desire to shift to a lower-carbon path and a more livable city. The earthquake and the geology of the local area led to severe soil liquefaction and extreme ground shaking. This contributed to the extent of the destruction but also led to generation-level outages and affected buried cables and transformers. Interviews with key stakeholders confirmed that the development of a microgrid was not considered during the initial recovery phase or the rebuild of the inner-city as the destruction of essential infrastructure was too large. Instead, diesel generators, of which many locals owned their own, were deployed. Like Odisha, the rebuild of the energy infrastructure did not involve a microgrid but focused on grid resilience. Enhanced energy efficiency and renewable energy use for buildings will reduce the city's carbon footprint [93]. A core pillar for Christchurch's target for net zero emissions by 2045 (excluding methane), is the Electric Vehicle initiative, which will decarbonise the transport sector [94]. The rebuilding of a smart city has drawn national and international attention to Christchurch as an innovation hub and a climate change leader. Table 6 shows how the case studies differ regarding their enablers and barriers, however, the demand for grid resiliency appears to be a common driver among all examples. A common barrier is the reliance on public/external funding and regulatory issues.
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 4 Experiments and Results
and UDP traffic from all the hosts in the network. This traffic includes legitimate traffic from all hosts on multiple TCP and UDP ports. We integrated sFlow [53] with our solution to monitor and evaluate the traffic passing through all four switches. This allowed us to obtain a comprehensive and statistically accurate view of the network traffic, monitor the hit count for each flow in the switch, and identify potential traffic interruptions. The results in Figure 11 depict the accuracy of the permanent customised rule and its impact on other host machines connected to the network. For each host that generated malicious traffic, we evaluated the accuracy by comparing the malicious probe traffic generated with the malicious probe traffic dropped. The overall accuracy of the permanent responses generated via the proposed solution is 97.68%. This shows that the permanent rule generated via the response solution does not affect the legitimate traffic from the affected hosts. This also shows that the network traffic for other hosts connected to the same SDN switch is not tempered after the addition of this flow. These statistics demonstrate that our method accurately responds to the timing side-channel attacks. Figure 11: Accuracy of Permanent Customized Rule and Its Impact on Host Machines. Figure 10: Response Time Trend for Previously Processed Attack Sources. Our experimental results show that our response mechanism is effective in responding to timing side-channel attacks in SDNs. Specifically, this solution is distinguished via the successful implementation of customised flow responses designed to counter these attacks. The results demonstrated that the response time to the redetected sources was notably fast. Consequently, the implementation of a customised permanent rule in such instances has minimal effects. In this experiment, attack probes were launched at random intervals. This ensured that the background traffic was distinct each time an attack was launched.
# Compression of Vehicle Trajectories with a Variational Autoencoder
## 2 Problem Formulation  
Convolutional neural networks (CNN) and LSTM Recurrent Neural Networks [27; 28] for the optimization are studied in this paper. Different tools can be applied for the maneuver classification problem, such as Support Vector Machine, Gaussian Classifier, and LSTM neural networks [10; 11; 12; 13; 14]. For more precise trajectory forecasting and analysis, one way is to use some method of dimension reduction.  
The obvious choice for sequence analysis is an RNN architecture. Still, in the case of longer sequences, the vanishing and exploding gradient problem become the main obstacle of the optimization, according to [29]. To avoid this, one can use, e.g., LSTM RNN, but its complexity (the number of parameters) can be high. Therefore, the training, as well as the inference process, can be slow. It is not an important aspect during the research work if GPUs are available for accelerating the optimization and the inference. Lately, several forecasting and classification concepts have been thoroughly proven in the literature. However, carefully choosing the neural networks' architecture to minimize the model complexity can be useful in the practice.  
A CNN can also find and learn temporal features and patterns in a one-dimensional \"image\"; in our case, this dimension is the time [16]. Every pixel is one-time step, and the number of channels is equal to the \(d\) feature number. CNN based autoencoder can yield the same or better result as a Bidirectional LSTM with a tenth of its complexity.  
The context vector can be used for maneuver detection, classification, and data visualization. This paper presents autoencoders trained to copy the trajectories while the context vector is low dimensional and carries useful information. Using the same architecture for predicting future trajectory fails because of the lack of information about the surrounding vehicles [15; 30; 31]. A hypothesis is set up predicting the future trajectory's context vector instead of the future trajectory itself. From this, it is decoded that the performance should be better. It is shown that the experimental result overturns this hypothesis, so the trajectories do not decode enough latent information about surrounding vehicle motions.
# PnV: An Efficient Parallel Consensus Protocol Integrating Proof and Voting
## 4 PnV Consensus Protocol  
This section provides a comprehensive elucidation of the three-phase process entailed in generating append-only block groups within the PnV consensus protocol. With the exception of the genesis block group, all subsequent block groups necessitate consensus from both voters and bookkeepers. To enhance consensus speed, \(N_{b}\) bookkeepers engage in parallel block generation during a consensus round, ultimately amalgamating into a block group.
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis
### Deep Neural Network
#### 8.3.1 Recurrent Neural Networks (RNN)  
RNN is a deep learning network that uses past information and updates its neural network to improve present and future inputs. The unique feature of RNN is the use of hidden states and loops between different states, see e.g., Figure 9. RNN uses the weights for learning; it retains the current input state weight and hidden state weight. In simple words, for the sequential classification problems, RNN has a hidden state loopback that retains previous states' weight and feeds them forward.  
It is well-known that, with time, information retention becomes problematic as it suffers from the so-called vanishing gradient problems. RNN uses the loss function, which is the difference between the actual value and the predicted value. This loss function is backpropagated to calculate the gradient and adjust the weights for network learning. Because the gradient adjustment is recursively applied multiple times, the gradient might either explode or vanish [45]. In both cases, the learning of the network gets worse. To mitigate this problem, LSTM [46] invented a specialized variant of RNN.
## 3 Results
### Smartphone Applications for DFU  
[PERSON] et al. [6] applied ML models to predict the healing outcomes of DFUs by integrating clinical data from electronic health records (EHRs) with imaging features extracted from smartphone photographs. Researchers collected the dataset from 2291 clinical visits involving 381 DFUs from 155 patients treated at the Michigan Medicine Podiatry and Wound Clinic. They utilized handcrafted color and texture features and deep learning-based features from ResNet50 to analyze wound images. Podiatry staff manually segmented handcrafted imaging features, such as mean and standard deviation of color intensity, from wound photographs and processed them using MATLAB. In addition, deep learning features were extracted using ResNet-50; however, handcrafted features outperformed others, achieving an AUC of up to 0.794. The researchers trained ML models using RF and SVM algorithms to predict whether ulcers were healed or not healed. Despite some limitations, such as limited sample size and data imputation challenges, this work demonstrates the promise of combining ML with smartphone devices to improve DFU prediction. [PERSON] et al. [73] evaluated the reliability of the mobile application CARES4 WOUNDS (C4W), Tetsuyu, Singapore, which is an AI-enabled tool for wound imaging and measurement. The study involved 28 patients with DFUs, and the main wound parameters were length and width. Using 547 wound images, the researchers measured parameters such as wound length, width, and area. The results demonstrated excellent intra-rater reliability (0.933-0.994) across three different devices and good inter-rater reliability when compared to manual wound measurements. Despite some limitations in wound depth detection, the study demonstrated that the C4W system is an effective tool for consistent wound monitoring. [PERSON] et al. [74] presented an automated wound localization system using the YOLOv3 model, integrated into an iOS mobile application. The model is trained on the AZH Wound Database [75]. The system isolates wounds and their surrounding tissues in 2D images, removing irrelevant regions to enhance subsequent tasks like segmentation and classification. The YOLOv3 model was compared with the Single shot multibox detector (SSD) [76]; the performance evaluation showed that YOLOv3 achieved a mean Average Precision (mAP) of 93.9% on the AZH Wound Database, outperforming the SSD's mAP of 86.4%. Retraining on the larger Wound Database further improved the mAP to 97.3%. The system's robustness was validated on the Medetec Wound Database [77], demonstrating high generalizability. A lighter version of the model Tiny-YOLOv3 enables wound detection on mobile devices, ensuring ease of access for remote healthcare. [PERSON] et al. [77] conducted a proof-of-concept clinical evaluation of an AI-based system for automated DFU detection using smartphones. The study involved 81 diabetic patients recruited from two UK hospitals, who provided a total of 203 foot photographs via an Android mobile application with varying resolutions. The system utilizes a deep
# Mapping Wild Leek through the Forest Canopy Using a UAV
## 1 Introduction
Previous methods to map wild leek using remote sensing data obtained good accuracies but relied heavily on field data, human interpretation, and expertise to separate different vegetation types [18]. The objective of the present study is to test the effectiveness of high-resolution UAV-based imagery in combination with SfM techniques for mapping wild leek in Gatineau Park. We use SfM and point cloud filtering techniques to derive orthomosaics displaying only ground features, and then apply a simple RGB-based color index threshold to discriminate wild leek from other vegetation. Finally, we discuss the prospects for using such a method for the long-term monitoring of wild leek in protected areas.
# A Low Complexity Reactive Tabu Search Based Constellation Constraints in Signal Detection
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Per-Symbol-Complexity in Number of Real Operations**} \\ \cline{2-9}  & \multicolumn{3}{c}{**and SNR Required to Achieved \(10^{-2}\) BER for QPSK**} & \multicolumn{3}{c}{**The Computational Complexity of**} \\ \cline{2-9}  & \multicolumn{2}{c}{**4 \(\times\) 4**} & \multicolumn{2}{c}{**8 \(\times\) 8**} & \multicolumn{2}{c}{**16 \(\times\) 16**} & \multicolumn{2}{c}{**24 \(\times\) 24**} & \multicolumn{2}{c}{**Complexity of**} \\ \cline{2-9}  & **PSC** & **SNR** & **PSC** & **SNR** & **PSC** & **SNR** & **PSC** & **SNR** & \\ \hline RTS & \(1320\) & 8.3 & 2600 & 7.1 & 20,520 & 6 & 46120 & 5.8 & O(\(M\)) + O(\(N_{t}^{2}\)) \\ \hline CC-RTS & \(573\) & 8.2 & \(1148\) & 7.1 & 9168 & 6 & 20662 & 5.8 & O(\(M\)) + O(\(N_{t}^{2}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Per-symbol-complexity (PSC) in number of real operations and SNR required to achieve BER = \(10^{-2}\) with QPSK for \(4\times 4\), \(8\times 8\), \(16\times 16\), \(24\times 24\) MIMO systems (Ref: Figure 5). Figure 6: Per-symbol-complexity (PSC) in number of real operations and SNR required to achieve \(10^{-2}\) BER in QPSK for \(4\times 4\), \(8\times 8\), \(16\times 16\), \(24\times 24\) MIMO system (Ref: Figure 5).
## 6 Conclusions  
An improved modal parameter identification method using reconstructed displacements was proposed in this work. The main findings are presented below:  
1. In comparison to the acceleration-based modal parameter identification method, the proposed method based on reconstructed displacements could suppress high-frequency components and reduce the non-stationary characteristics of measured data  
Figure 17: Stability diagrams obtained based on **(a)** calculated accelerations and **(b)** reconstructed displacements in the period of 0–2 s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Calculated by**} & \multicolumn{2}{c}{**Calculated by**} & \multicolumn{2}{c}{**Calculated by**} & \multicolumn{1}{c}{**By the Method**} \\  & & **Sinusoidal** & **Accelerations** & **Displacements** & **of He [1]** \\ \hline First-order & Frequency (Hz) & 4.304 & 4.013 & 4.407 & 4.535 \\  & Damping ratio & 2.10\% & 1.42\% & 2.31\% & 2.90\% \\ Second-order & Frequency (Hz) & 20.839 & 20.241 & 20.189 & 20.351 \\  & Damping ratio & 3.68\% & 2.71\% & 3.49\% & 4.20\% \\ Third-order & Frequency (Hz) & 30.518 & 30.256 & 30.188 & 30.258 \\  & Damping ratio & 2.30\% & 1.55\% & 1.50\% & 3.50\% \\ Fourth-order & Frequency (Hz) & 55.839 & 51.821 & 55.449 & 53.556 \\  & Damping ratio & 3.88\% & 4.80\% & 3.50\% & 5.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency and damping ratios of the cantilever plate.  
more effectively. Therefore, this method could provide more reliable and accurate parameter identification results for small model order selection.
* In comparison to other integration methods, the proposed method had no error accumulation and long period drift of reconstructed displacements, resulting in higher computational efficiency.
* Experimental results of the cantilever plate revealed that the improved SSI method could improve the recognition efficiency and also identify more stable results for low-frequency structures.  
Conceptualization, X.G., C.L. and D.C.; data curation, C.L.; formal analysis, C.L.; funding acquisition, X.G.; investigation, C.L.; methodology, X.G., C.L. and D.C.; project administration, X.G. and Z.L.; resources, X.G. and D.C.; software, C.L.; supervision, Z.L. and D.C.; validation, X.G. and Z.L.; visualization, C.L.; writing--original draft, C.L.; writing--review and editing, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 11772010 and 11832002, and the Key Laboratory of Vibration and Control of Aero-Propulsion System (Northeastern University), Ministry of Education, grant number VCAME 202004.  
Not applicable.  
The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 11772010 and 11832002), and the Key Laboratory of Vibration and Control of Aero-Propulsion System (Northeastern University), Ministry of Education (Grant No. VCAME 202004).  
The authors declare no conflict of interest.
# A High-Sensitivity Cesium Atomic Magnetometer Based on A Cesium Spectral Lamp
## 5 Magnetometer Testing  
The sensitivity of the Cs atomic magnetometer prototype based on a Cesium spectral lamp was tested under B\({}_{0}=50\)\(\mu\)T, generated from a uniform magnetic field generator (LC-CPBT-W7) inside a 6-layer magnetic shielding, as shown in Figure 13. The cell temperature and the feedback magnetic field were set as the optimized values as discussed above. Since the ambient magnetic noise was greater than the sensor's intrinsic noise, a parallel measurement was conducted using two separate sensors to evaluate the intrinsic sensitivity of a single magnetometer. By performing autocorrelation processing, the environmental noise can be effectively subtracted, allowing the noise level evaluation of each sensor to be tested. The two sensor heads were placed in the uniform area of the magnetic field generator, and the electronic units were positioned outside of the shielding. The base line of the two sensor heads was 10 cm.  
Figure 12: Resonance linewidth and signal amplitude dependences on feedback magnetic field.  
Figure 13: Configuration of cesium magnetometer sensitivity testing.  
The test results are shown in Figure 14, where the red and blue lines represent the square root of the power spectral density (transit to magnetometric sensitivity) of Sensor 1 and Sensor 2, respectively. The black line represents the performance of a single sensor after autocorrelation processing between two sensors. It can be shown that the sensor's working sensitivity is around 140 fT/\(\sqrt{\mathrm{Hz}}\) at 1 Hz. This is higher by a factor of \(\sim\)1.4 than the estimated performance using the measured system noise level. The reasons could be the gradient field fluctuations between the testing sensor heads, the noise of the frequency counting units, or imperfections during the autocorrelation calculation. The system-noise level determined sensitivity is only 10% higher than the shot-noise limited performance, revealing a high-level performance of the whole system, where the optical power fluctuations and other technical noise can nearly be neglected. Overall, the demonstrated sensitivity is higher than the photon-shot noise limited sensitivity by \(\sim\)1.5. The quantum limit sensitivity is too far to reach and new working schemes are under development. The sensitivity performance against various buffer gas pressures is under investigation.
## 2 Speech Recognition Scheme for Voice-Driven Multi-UAV Control
### Proposed Speech Recognition Scheme for Voice-Driven Multi-UAV Control  
To address the drawbacks of conventional speech recognition schemes, this study proposes an efficient recognition scheme for voice-driven multi-UAV control. The proposed scheme is summarized as distributed speech recognition. As shown in Figure 3, the user device and UAVs share speech recognition processes. Here, the user device processes the front-end module, thus producing acoustic features from the input speech. Once the feature is transmitted to the UAVs, a system on the UAVs performs recognition processes following the front-end process.  
There are two main reasons the front-end is assigned to the user device. First, the distribution of a sequence of recognition processes mitigates the user device's computational burden that the conventional device-embedded scheme should manage. Next, if the UAV system performs all processes (including front-end processes), the user device should send raw speech data to the UAV. However, the speech data may be degraded due to packet loss problems because speech data are much larger in size compared to feature data.  
The mechanism that the UAV system manages recognition processes without passing a recognition server allows the UAV to obtain recognition results directly. Thus, recognition errors and missing commands can be reduced, and the UAV can respond rapidly to user command. In fact, most UAV systems have insufficient computing capacity to handle continuous speech recognition that should  
Figure 2: Comparison of **(a)** device-embedded and **(b)** server-centric speech recognition schemes for voice-driven multi-UAV control.  
process a number of vocabularies. However, general messages regarding UAV control have a short sentence form comprising several word sequences. Special purpose UAVs have sufficient hardware capacity to process such connected word recognition tasks with a limited number of vocabularies.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 5 Antenna Poses Estimated from Captured Images
### Pose Refinement
#### 5.2.3 Validation of the Effectiveness of the Bounds Applied for Pose Refinement  
In Table 1, we present the statistics of the estimation error with respect to the ground truth based on the refinement results obtained using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm [62] and COBYLA as solvers for Equation (9) on an antenna dataset named AntennaL, which consists of 65 groups of data (described in detail in Section 6.1). Both solvers seek a local optimum, but the latter is a solver that can take advantage of bounding constraints, whereas the former is not. A comparison reveals that COBYLA yields far better estimates of both the downtilt error and the azimuth error, although there are cases in which the azimuth error is greater than the maximum tolerance allowed in the industry (15 degrees).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Downtilt**} & \multicolumn{2}{c}{**Azimuth**} \\ \cline{2-5}  & **BFGS** & **COBYLA** & **BFGS** & **COBYLA** \\ \hline
**Q1** & 0.5 & 0.3 & 6.6 & 3.69 \\
**Q2** & 0.9 & 0.43 & 16.1 & 5.47 \\
**Q3** & 2.14 & 0.63 & 22.97 & 7.17 \\
**Mean** & 1.46 & 0.5 & 15.82 & 6.21 \\
**Std.** & 1.3 & 0.3 & 10.5 & 4 \\
**Max** & 5.24 & 1.23 & 42.17 & 22.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lower quartile (Q1), median (Q2) and the upper quartile (Q3) of error distribution of downtilt and azimuth estimation using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm and constrained optimization by linear approximations (COBYLA) on AntennaL, together with the mean, standard deviation (Std.) and maximum error are given for comparison.