esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 7 Conclusions  
In the presented work, we proposed a relative-position-based visual servoing system employing a high-speed target recognition method and a novel controller design. Compared to previous research on visual servoing of UAVs, our system used visual position information for controlling the UAV directly with high rates, and the feasibility of the proposed control scheme was verified in a position hold flight. The advantages of the proposed method were demonstrated through real-time target tracking experiments. The experimental results showed that our proposed method can achieve better tracking performance than a low-speed target recognition setting.  
In future work, we will aim to embark on improving the performance of the proposed vision-based state estimator, including the accuracy and robustness to the environment. Then we will focus on developing a pure vision-based flight controller, which means that the visual information will be used for controlling the UAV directly. Furthermore, we will perform real-time feature extraction and feature tracking of any actual targets. By extracting features in the surrounding environment directly, we aim to realize high-speed manipulation of UAVs by direct visual servoing, thereby attaining various tasks, like high-speed dynamic avoidance and high-speed target catching in any environment.  
Conceptualization, H.-M.C. and A.N.; methodology, H.-M.C. and A.N.; software, H.-M.C.; validation, H.-M.C. and D.H.; formal analysis, H.-M.C.; writing--original draft preparation, H.-M.C.; writing--review and editing, A.N.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Data Preparation and Analysis Flow  
The dataset was prepared as indicated in Figure 3. The data was measured with a timestamp and contained latitude and longitude information as summarized in Table 3. The area of interest was marked by its latitude and longitude range. The respective timestamps were identified and the relevant data was extracted. The dataset was transformed from a two-dimensional, latitude/longitude base to a one-dimensional, virtual-odometer base to simplify the following steps and allow comparative analysis. Special care was taken to ensure that the extracted data contained exactly the same start and endpoint. Since the data were collected at various speeds resulting in various distances between the actual measurement points, the data points were resampled with a constant distance interval of one meter. Missing data points were filled in based on linear interpolation.  
Figure 3: The data preparation flow used for this study.  
Figure 2: The antenna setup of the vehicle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Range/Resolution /Format** & **Unit** \\ \hline Latitude & 0.000001 & \({}^{\circ}\) dec. \\ Longitude & 0.000001 & \({}^{\circ}\) dec. \\ Time of day & YYYY-MM-DD & - \\  & HH:MM:SS.000 & - \\ Data rate & 1 & Hz \\ RSRP & \(-120\)... 0 & dBmW \\ RSSI & \(-120\)... 0 & dBmW \\ RSRQ & \(-10\)... 0 & dB \\ Number of operators LTE (4G) & 3 & - \\ Number of operators GSM & 3 & - \\ (2G) & 3 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured parameters.  
The prepared data were analyzed based on the flow presented in Figure 4. A spatial correlation was performed for RSRQ, RSRP, and RSSI to check the degree of consistency for independent measurements of the same parameters. In the next step, an assessment for each provider was performed followed by a comparative analysis of the overall assessment. To check the quality of the overall measurements the three measured quantities were analyzed for correlations.
## 3 Background
### Conceptual Spaces  
Conceptual Spaces Theory [14] is one of the cognitive approaches that form the basis of RANs modeling. This theory views the concepts as regions within a multi-dimensional space, with the data features representing the dimensions. The similarity among the concepts can be identified based upon the geometrical distance between the objects. The conceptual spaces thus serve as a natural way or tool to capture the similarity relationships among concepts, or objects. Under this setting, one data instance corresponds to a single point in the space. Formally we can say, the quality dimensions, i.e., a set of \(D_{1}\),....,\(D_{n}\), forms the conceptual space \(S\). A point in \(S\) is represented by a vector \(v=\left\langle d_{1},....,d_{n}\right\rangle\), where _(1,...,n)_ are the indexes of the dimensions. Atomic concepts are convex regions--a convex region \(C\) having point \(x\) that falls between points \(x_{1}\in C\) and \(x_{2}\in C\) also belongs to \(C\). The quality dimension is the basic requirement for conceptual spaces [35]. An example is a color space with the dimensions Hue, Saturation, and Brightness. Each quality dimension has a geometrical structure. For example, Hue is circular, whereas brightness and saturation correspond with finite linear scales (see Figure 2).  
The theory of conceptual spaces also addresses prototype theory of categorization [37; 38; 39]. The main idea of prototype theory is that within a category of objects, like those instantiating a concept, certain members are judged to be more representative of the group than others. For example, robins are judged to be more representative of the category \"bird\" than are ravens, penguins, and emus. If convex regions of conceptual space describes concepts, then prototype effect is, indeed, expected, i.e., the most likely central position of a convex region describes an abstract concept. For example, if color concepts in a convex region identified as subsets of the color space, then the central points of these regions would be the most prototypical examples of the color.  
Clustering is a suitable way of identifying and learning atomic convex concepts in conceptual spaces. There are several clustering techniques, like hierarchical clustering, subspace clustering [40], partitioning relocation clustering, density-based clustering, grid-based clustering and many more. Many are frequently used in the statistical and scientific analysis of data [41; 42], and in machine learning for the identification of concepts/features [43]. On the other hand, the creation of a hierarchy of sub/super-concepts is a way to represent more abstract concepts and their taxonomic-like relations. Deep learning techniques [44; 45; 46; 47; 48] found in the literature can also be used to create deep hierarchical representations, but usually do not interpret data as points in conceptual spaces. In the proposed approach, the clustering techniques enable us to identify categories of concepts in a conceptual space thus laying the foundation to form a layer of abstract representation of concepts.
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 4 Experiments and Analysis
### Results  
The second part of Table 3 displays the results of ablation studies, where  
* VGG Backbone: The spatial encoding is replaced with the first 10 layers of VGG-16, and the network width has been adjusted accordingly to maintain a similar parameter count. The feature maps at each stage are up-sampled to match the original image resolution, and after channel-wise concatenation, they undergo temporal encoding.
* No Dense Connection: Remove the dense connections from the spatial encoding, where the input of each layer is only the output of the previous layer.
* No Transformer encoder: Remove the Transformer encoder and directly perform temporal mean fusion on the feature map time series obtained after spatial encoding. Then, obtain the final segmentation result through convolution.
* Single Date (August): Select one image from SITS taken in August for training, and the model produces segmentation results after spatial encoding.
* Single Date (May): Select one image from SITS taken in May for training.
## 2 Materials and Methods
### Signal Acquisition System  
Electrocardiography (ECG) and photoplethysmography (PPG) signals constituted the primary data sources for the experimental analysis. Signal acquisition was performed using the MAXREFDES104# device, Mouser Electronics, Mansfield, CT, USA, a specialized wearable bracelet designed to capture ECG and PPG signals. In addition, this device integrates a low-energy Bluetooth (BLE) module, ensuring efficient and precise wireless data transmission. Consequently, all processes related to data acquisition, visualization, and parameter modification for PPG and ECG signals were performed using MAXREFDES104 Health Sensor Platform 3.0 software, which enabled signal recording at a sampling frequency of 128 Hz. Ultimately, the data were archived in CSV files, encompassing both raw and software-filtered samples in a detailed format.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 6 Security Analysis
### Information Integrity
#### 6.1.2 Intercepted Messages  
Adversaries cannot falsify the readings during their transmission to \(CS\), as the messages are encrypted by the powerful ring-LWE crypto-system. If \(\mathcal{A}\) manages to intercept the message \((\tilde{h},\tilde{z})\) that was sent from drone \(D_{q}\) to the cluster heads of the group \(D_{x}\) and \(D_{y}\), he/she cannot modify its value because \(\mathcal{A}\) does not have the decryption key \(\tilde{r}_{2 cs}\) and consequently cannot extract the plaintext measurement \(m_{j}\) from \((\tilde{h},\tilde{z})\). Moreover, the messages contain timestamps and random nonces toprevent the replay attack. Thus, \(\mathcal{A}\) cannot interpret and modify the message's content or begin a replay attack.  
According to join/leave procedure messages, they are encrypted by the cluster heads' session keys; only the head and the connecting drone share the key to manage the drone join/leave process. The cluster heads and consequently their session keys are changing per minute, i.e., every round, so that the probability of compromising these keys is diminished. Even if certain cluster heads, and their keys, are compromised, the \(CS\) still obtains the accurate monitoring information for the area via alternative ways.
## 2 SF-PPP Methodology
### SF-PPP Observation Equations  
For the receiver \(r\) and satellite \(s\), the basic observation equations of the pseudo-range and carrier phase on frequency \(f\) can be formulated as:  
\[\left\{\begin{array}{c}P_{r,f}^{s}=\rho_{r}^{s}+cdt_{r}-cdt^{s}+T_{r}^{s}+Ion _{r,f}^{s}+c\left(d_{r,f}-d_{f}^{s}\right)+ant_{po+pc}+\varepsilon_{P}\\ \phi_{r,f}^{s}=\rho_{r}^{s}+cdt_{r}-cdt^{s}+T_{r}^{s}-Ion_{r,f}^{s}+\lambda_{ f}\left(N_{r,f}^{s}+b_{r,f}-b_{f}^{s}\right)+ant_{po+pc}+\varphi_{\phi}+ \xi_{\phi}\end{array}\right., \tag{1}\]  
where the \(\rho_{r}^{s}\) denotes the geometric distance between the satellite and receiver in meters; \(c\) denotes the speed of light; \(dt_{r}\) and \(dt^{s}\) are the clock offsets of the receiver and satellite, respectively; \(T_{r}^{s}\) is the slant tropospheric delay in the satellite sight line direction; \(Ion_{r,f}^{s}\) is the first-order slant ionospheric delay at the \(f\) frequency (higher-order ionospheric delay are often at mm/cm level and can be ignored in SF-PPP); \(d_{r,f}\) and \(d_{f}^{s}\) are the frequency-dependent code hardware delay on receiver \(r\) and satellite \(s\), respectively; \(\lambda_{f}\) is the wavelength of the frequency \(f\) and \(N_{r,f}^{s}\) is the carrier integer ambiguity; \(b_{r,f}\) and \(b_{f}^{s}\) are the frequency-dependent carrier phase hardware delay for receiver \(r\) and satellite \(s\), respectively; \(ant_{pc+pc}\) is the sum of the satellite and receiver antenna phase offsets and variations; \(|\varphi_{\phi}\) is the phase wind-up effect of phase observation; and \(\varepsilon_{P}\) and \(\xi_{\phi}\) are the sum of measurement noise and multipath error for the pseudorange and carrier phase, respectively. It should be noted that the inter-system bias (ISB) parameter should be considered when using multi-system observations.  
The variances of raw code or phase measurements are assumed to be independent. The elevation-dependent stochastic model, which has been adopted in many studies [29; 30] is applied as follows:  
\[\left\{\begin{array}{l}\sigma_{p}^{2}=a^{2}/\left(4\cdot sin^{2}e\right)\\ \sigma_{L}^{2}=b^{2}/\left(4\cdot sin^{2}e\right)\end{array}\right., \tag{2}\]  
where \(a\) and \(b\) are the standard deviations for raw code and phase measurement noise, respectively (in this study, they are set as \(a\) = 0.3 m, \(b\) = 0.003 m), and \(e\) is the elevation angle of the satellite [30].
## 4 Model-Based Calibration
### Range Impulse Response  
The aim of the residual range impulse response calibration is to measure systematic, amplitude and non-linear phase variations as illustrated in Figure 5. Such variations occur within the pulse bandwidth when internal calibration measurements fail to faithfully reproduce the transfer function of the radar instrument.  
Estimation begins with pre-processing the filtered raw data to remove constant offsets in gain and residual delay as follows:  
\[H_{pq}^{s}(az,f_{r})=\mathrm{FFT}_{rg\to f_{r}}\left[\frac{F_{pq}^{s}(az,rg)}{ \sqrt{\sum_{rg}\left|F_{pq}^{s}(az,rg)\right|^{2}}}\right]\exp\!\left(\frac{-jf_ {r}}{f_{0}}\delta\chi_{pq}^{s}(az)\right). \tag{23}\]  
The gain and phase of transfer function corrections are estimated independently. Each combination of antenna elements \(pq\) is thereby associated with corrections \(\Delta C_{pq}^{gain}(f_{r})\) and \(\Delta C_{pq}^{phase}(f_{r})\) that constitute the parameter set \(\mathcal{O}\) in the corresponding optimization problem (21). The error measures used are  
\[E_{pq}^{s,gain}(az,f_{r})=w_{pq}^{s}(az)\left(20\log_{10}\!\left(\left|H_{pq}^{ s}(az,f_{r})\right|\right)-\Delta C_{pq}^{gain}(f_{r})\right) \tag{24}\]  
and  
\[E_{pq}^{s,phase}(az,f_{r})=w_{pq}^{s}(az)\left(\arg\left(\frac{H_{pq}^{s}(az,f_ {r})}{\sum_{f_{r}}\frac{H_{pq}^{s}(az,f_{r})}{\sum_{f_{r}}H_{pq}^{s}(az,f_{r}) }}\right)-\Delta C_{pq}^{phase}(f_{r})\right), \tag{25}\]  
where the coherence \(w_{pq}^{s}(az)\) of Equation (19) is used to emphasize the influence of reliable measurements in the estimated corrections.  
The iterative solution of (22) achieves a robust fit over all pulses in each target response and involves two equations for each discrete range frequency \(f_{r}\), one related to \(\Delta C_{pq}^{gain}(f_{r})\) and one related  
Figure 5: The phase of the C-band VV-pol range IRF estimated from a data comprising the range histories of reference targets in multiple calibration acquisitions. The solid line indicates the estimated correction. It is used to compensate for the systematic variations observed as a function of radar frequency. A similar analysis is carried out for the IRF amplitude.  
to \(\Delta C_{pq}^{phase}(f_{r})\). Upon convergence, the complex valued impulse response correction for the antenna element combination \(pq\) is given by  
\[\Delta C_{pq}(f_{r})=10^{\frac{\Delta C_{pq}^{\text{gain}}(f_{r})}{2\overline{ \partial}}}e^{i\Delta C_{pq}^{\text{bias}}(f_{r})}. \tag{26}\]  
After residual range frequency response corrections have been obtained, reference target analysis is repeated, as shown in Figure 1, where these corrections are taken into account in Equation (9).
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
## 3 Anonymous Routing Using a Mix Network
### Threat Model  
The objective of the adversary is to link the sender and receiver in our OppNets system. Specifically, we assumed that the attacker could access our OppNets system. It can wiretap, forward, and delete a message. Meanwhile, the attacker can also compromise the user and mix nodes in the system. However, we assumed that at least some mixes were honest.  
Our threat model was based on [14]. There is no perfect security defender that could resist all kinds of attacks. In our assumption, we mainly considered three different threat types.  
* Traffic analysis. Attacker \(A\) can conduct a traffic analysis to probe the link between sender node \(S\) and receiver node \(D\). \(A\) can obtain access to every single message \(M\), and its goal is to speculate that \(S\) and \(D\) are communicating with each other in high probability.
* Compromised user nodes. We also assumed that attacker \(A\) could compromise user nodes. Thus, \(A\) can obtain message \(M\) and forward it. In our scenarios, we assumed that at least two users were honest.  
* Compromised mix nodes. Mix nodes can also be compromised, but at least one of them should be honest. The compromised mix nodes can conduct a link attack to deanonymize the system.
## 5 RegTech Applications in Non-Financial Sectors
### Challenges in Non-Financial Sectors  
Based on the above investigation of RegTech applications, some common challenges associated with a range of non-financial industry sectors are identified:  
* _Lack of Customization:_ Existing RegTech applications, derived from the financial industry, may not be readily applicable to the wide range of non-financial regulations to cater for the specific compliance needs of different sectors, which will create gaps in compliance coverage and hinder effective regulatory compliance in specific industries.
* _Complex and Evolving Regulatory Landscape:_ Keeping pace with the evolving regulatory landscape is challenging as regulations of various industries can be complex and rapidly changing, which leads to difficulties in staying up to date with regulatory changes, gaps in compliance coverage, and potential non-compliance risks.
* _Limited Adoption and Awareness:_ RegTech adoption in non-financial sectors is relatively low, and the limited awareness of available RegTech solutions may result in a lack of understanding about these technologies' potential benefits and limitations.
* _Integration Challenges:_ Many non-financial organizations have legacy systems that were not designed to accommodate modern RegTech solutions. Integrating new RegTech applications with existing infrastructure can be challenging. It requires additional investments in technology and staff training and may also cause disruptions during implementation. In addition, disparate systems with different data sources collected from non-financial entities are not easily integrated, which presents great challenges to the existing applications in effectively aggregating, normalizing, and analyzing data.
* _Standardization:_ The implementation of RegTech in the financial sector is comparatively advanced and well-established, with hundreds of applications providing relatively complete functional modules and practical technical applications. However, it is still in the initial application stage in other sectors, such as agriculture, the food industry, supply-chain management, etc. No matter how developed they are, on-shelf applications lack uniformity and standardization.  
To address these challenges, RegTech solution providers must recognize non-financial sectors' diverse compliance requirements and develop customized solutions. Collaboration between RegTech providers, regulators or supervisors, regulatory producers, and industry stakeholders can help foster innovation and awareness of effective compliance solutions. Additionally, solution providers should consider the development based on specific industry requirements and seek integration strategies that ensure seamless data flow and interoperability with existing systems.
## 4 Results and Discussion
### The Results of the ADI Mitigation Models
#### 4.2.3 The Results of the CNN Classifier-Based ADI Mitigation System  
The performance metrics for the CNN-based ADI mitigation models under both Scenario One and Scenario Two are summarized in Table 23. Additionally, the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) for the CNN-based mitigation systems, evaluated at the receiver side for various learning rates, are presented in Table 24.  
The evaluation of the CNN classifier-based ADI mitigation system reveals varying levels of performance across different models and scenarios. In Scenario One, the highest test accuracy (0.66) was achieved by Model 3 at a learning rate of 0.004, though it had a relatively low F1 score (0.34). Model 2 showed a balanced performance with a test accuracy of 0.64 and a higher F1 score of 0.50, suggesting a more reliable balance between precision and recall. The combination of Model 2 and 3 offered slightly improved F1 performance (0.56) with decent precision and recall, indicating its effectiveness in mitigating ADI while maintaining model robustness. In Scenario Two, Model 1 outperformed others in terms of test accuracy (0.67) and had a moderate F1 score (0.45), while the highest F1 score (0.53) was achieved by Model 3. The model combination strategies in this scenario did not significantly enhance performance metrics over individual models.  
In terms of the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR), as shown in Table 24, the CNN-based mitigation systems consistently showed better performance under the extended configuration approach, especially when datasets from multiple base stations were used. Model 3 achieved the lowest BER (0.003) and highest SNR values (\(-\)13.70 dB  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Scenario One**} & \multicolumn{5}{c}{**Scenarios Two**} \\ \hline
**Classifier** & & & & & & & & & & & & \\
**CNN** & & & & & & & & & & & & \\ \hline Model 1 & 0.012 & 0.61 & 0.74 & 0.64 & 0.37 & 0.15 & 0.004 & 0.67 & 0.72 & 0.54 & 0.45 & 0.14 \\ \hline Model 2 & 0.016 & 0.64 & 0.75 & 0.53 & 0.50 & 0.20 & 0.024 & 0.63 & 0.67 & 0.62 & 0.47 & 0.21 \\ \hline Model 3 & 0.004 & 0.66 & 0.63 & 0.64 & 0.34 & 0.21 & 0.028 & 0.61 & 0.61 & 0.64 & 0.53 & 0.21 \\ \hline Model 1 and 2 & 0.016 & 0.60 & 0.69 & 0.65 & 0.52 & 0.22 & 0.016 & 0.61 & 0.69 & 0.61 & 0.42 & 0.19 \\ \hline Model 2 and 3 & 0.028 & 0.61 & 0.66 & 0.56 & 0.56 & 0.12 & 0.008 & 0.60 & 0.64 & 0.53 & 0.43 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 23: The evaluation parameters of the CNN classifier-based ADI mitigation models.  
and \(-13.20\) dB) under these conditions, highlighting its strong capability in reducing interference effects. Similarly, Model 2 also performed well with a low BER and a high SNR under the extended setup, particularly with a learning rate of \(0.016\).