esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 4 Simulation Results and Analysis
### Simulation Experiment 2  
The purpose of this experiment is to verify the filtering performance of the proposed algorithm for targets in scenarios where the extended targets are close to each other and moving in parallel. The survival time of extended target 1 and extended target 2 is both 1-100 s. The strength function of the newborn target in this scenario is given by the following:  
\[\gamma_{t}(x)=0.1\mathcal{N}\Big{(}x;m_{\gamma,t}^{(1)},P_{\gamma,t}\Big{)}+0.1 \mathcal{N}\Big{(}x;m_{\gamma,t}^{(2)},P_{\gamma,t}\Big{)} \tag{13}\]  
where \(m_{\gamma,t}^{(1)}=[-600\) m, \(-500\) m, \(0\) m/s\(,0\) m/s\(]^{T}\), \(m_{\gamma,t}^{(2)}=[-600\) m, \(-600\) m, \(0\) m/s\(,0\) m/s\(]^{T}\).  
The real target track of the target in scenario 2 is shown in Figure 7a, which consists of two parallel moving extended targets that are close to each other. Figure 7b shows the simulation results of the proposed algorithm. It can be seen from the figure that the kinematic state of the extended targets can be estimated relatively accurately even when the distance between the extended targets is close.  
Figure 8a shows the OSPA distance comparison diagram of the three partitioning methods for scenario 2, and the OSPA distance index is calculated by the comprehensive calculation of the extended target state estimation and the target number estimation. It can be seen from the figure that the proposed algorithm has the smallest OSPA distance and the most accurate target number estimation at the same time. Scenes of parallel moving objects that are close to each other also verify the effectiveness of the algorithm. Meanwhile, the comparison figure of the target number estimation in Figure 8b also reflects the effectiveness of the algorithm, and the target number estimation of the proposed algorithm is significantly better than that of the other two algorithms.  
Figure 9a,b shows the average number of partitions and the average running time of the measurement set partition at each time, respectively. The figures show that the partition method proposed in this paper has the least number of partitions and the least running time. Since the proposed algorithm considers the relevance of the adjacent time of the extended target, the measurement set of the surviving target is divided by using the prior information, which greatly reduces the number of undivided measurements in the measurement set and the number of partitions.  
Figure 7: (**a**) Real trajectory plot of the target in Scenario 2. (**b**) Measurement values, true target trajectories, and estimated trajectories in Scenario 2.
# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 3 Proposed Method
### Feature Aggregation and DCGAN  
The feature aggregation module uses features from various attention mechanisms in a multimodal way to generate a unified representation. To further refine these representations, the module incorporates a DCGAN framework with an adversarial learning strategy. The generator is driven to synthesise feature aggregates that closely mimic the real aggregated feature distribution, while the discriminator strengthens the model's ability to distinguish between genuine and synthesised features.  
Figure 2: The internal structure of the proposed ConvCARU.  
Role of DCGAN in Feature Aggregation  
The adversarial training process can be formulated as a minimax role involving two key agents, as follows: the generator \(G\) and the discriminator \(D\). In this context, the objective of the generator is to produce realistic aggregated features from latent input features. The objective of the discriminator is to classify whether the provided features originate from the true aggregated distribution or the generator's synthesis.  
\[V(D,G)=E_{x\sim P_{data}}[\log D(x)]+E_{x-p_{z}}[\log(1-D(G(z)))] \tag{7}\]  
Here, the notation \(E_{x\sim P_{data}}\) represents the expectation, or expected value, of a function over a random variable sampled from the real data distribution \(P_{data}\). \(x\) represents features aggregated from the attention mechanisms and \(z\) is drawn from the prior distribution \(p_{z}\). However, in the early stages of training, the gradient received by \(G\) through the term \(\log[1-D(G(z))]\) may diminish, thus impeding learning from (7). To counteract this effect, a non-saturating heuristic is adopted. The loss of the discriminator objective then becomes as follows:  
\[\arg\min_{D}(L_{D})=-E_{x\sim P_{data}}[\log(1-D(x))] \tag{8}\]  
This modification ensures that the generator receives stronger gradient signals during training. It can also be modified further by the generator target (non-saturated) formula to encourage more efficient synthesis of aggregated features:  
\[\arg\max_{G}(L_{G})=-E_{x-p_{z}}[\log(D(G(z)))] \tag{9}\]
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 4 Orthogonal Experiments  
The analyses above demonstrate how variables, like base thickness, fin numbers, and fin length, will impact the thermal performance of the heat sink. Set three different variables for each factor: base thickness (factor 1, set to 20 mm, 30 mm, and 40 mm); fin numbers (factor 2, set to 4, 8, and 12); and fin length (factor 3, set to 12 mm, 24 mm, and 36 mm). Then, the significant effects of various factors on the performance of heat sinks are assessed. Following the optimization of the orthogonal experimental law, nine experimental groups are arranged in accordance with the orthogonal table L\({}_{9}\) (3\({}^{3}\)) (33) (as shown in Table 5).  
In order to analyze the degree of influence of variables on heat transfer efficiency, the average maximum-temperature value of three levers for each variable is shown in Table 6. It can be seen from the variances that the base thickness has a very small impact on the maximum temperature of the driver. The fin length has a greater impact on the maximum temperature of the driver. The fin number has a great influence on the maximum temperature of the driver. This result is consistent with the simulation results obtained from Figures 15-17.  
Figure 17: Influences of fin numbers on thermal performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**No.**} & **Base** & **Fin Length/** & **Maximum** \\  & **Thickness/mm** & **min** & **Temperature/\({}^{o}\)C** \\ \hline
1 & 20 & 4 & 12 & 54.1 \\
2 & 20 & 8 & 24 & 49.4 \\
3 & 20 & 12 & 36 & 45.9 \\
4 & 30 & 4 & 24 & 51.7 \\
5 & 30 & 8 & 36 & 47.6 \\
6 & 30 & 12 & 12 & 51.1 \\
7 & 40 & 4 & 36 & 50.6 \\
8 & 40 & 8 & 12 & 52.6 \\
9 & 40 & 12 & 24 & 47.2 \\ \hline \end{tabular}
\end{table}
Table 5: Orthogonal experimental data.
# Single Trace Analysis against HyMES by Exploitation of Joint Distributions of Leakages
## 2 Related Works
### Binary Goppa Code  
This paper only discusses basic information about the binary Goppa code over the finite field \(F_{2}\). The Goppa code consists of the Goppa polynomial \(g(z)\) and support \(L_{sec}\). The \(g(z)\) and \(L_{sec}\) are defined in Definition 3.  
**Definition 1**.: _For positive integer \(m,t\), Goppa polynomial \(g(z)\) and support \(L\) are as follows._  
\[g(z)=\sum_{i=0}^{t}g_{i}z^{i}\in F_{2^{m}}[z]\]  
\[L=\{\alpha_{0},...,\alpha_{n-1}\}\in F_{2^{m}}^{n},\ g(\alpha_{j})\
eq 0,\ \forall 0\leq j\leq n-1.\]  
The syndrome \(S_{\ell}\) required to decode the Goppa code is denoted in Equation (1). The binary \([n,k,d_{c}]\)-Goppa code is defined as the set of all \(\hat{c}\) that have the syndrome \(S_{\ell}\) at zero in Equation (2). Since the parity check matrix \(H\) spans the null space of \(C\), as shown in Definition 2, we can derive \(H\) from the syndrome \(S_{\ell}\) as in Equation (3).  
**Definition 2**.: _For the Goppa polynomial \(g(z)\) and the support \(L=\{\alpha_{0},...,\alpha_{n-1}\}\), the syndrome \(S_{\ell}\) is as follows._  
\[S_{\ell}=-\sum_{i=0}^{n-1}\frac{\hat{c_{i}}}{g(\alpha_{i})}\frac{g(z)-g(\alpha _{i})}{z-\alpha_{i}}\equiv\sum_{i=0}^{n-1}\frac{\hat{c_{i}}}{z-\alpha_{i}} \mod g(z), \tag{1}\]  
\[Goppa(L,g(z))=\{\hat{c}\in F_{2^{m}}^{n}\ |\ S_{\ell}=\sum_{i=0}^{n-1}\frac{ \hat{c_{i}}}{z-\alpha_{i}}\equiv 0\mod g(z)\}, \tag{2}\]  
\[H=\left(\begin{array}{cccc}\frac{g_{\ell}}{g(\alpha_{0})}&\frac{g_{\ell}}{ g(\alpha_{1})}&...&\frac{g_{\ell}}{g(\alpha_{n-1})}\\ \frac{g_{\ell}\alpha_{0}+g_{\ell-1}}{g(\alpha_{0})}&\frac{g_{\ell}\alpha_{0}+ g_{\ell-1}}{g(\alpha_{1})}&...&\frac{g_{\ell}\alpha_{0}+g_{\ell-1}}{g(\alpha_{n-1})} \\...&...&...\\ \frac{g_{\ell}\alpha_{0}^{\ell-1}+-+g_{2}\alpha+g_{1}}{g(\alpha_{0})}&\frac{g _{\ell}\alpha_{0}^{\ell-1}+-+g_{2}\alpha+g_{1}}{g(\alpha_{1})}&...&\frac{g_{ \ell}\alpha_{0}^{\ell-1}+-+g_{2}\alpha+g_{1}}{g(\alpha_{n-1})}\end{array} \right). \tag{3}\]  
The \(H\) in Equation (3) can be simplified as shown in Equation (4), and the \(\hat{H}\) can be used as the parity check matrix because the determinant of \(H_{g}\) is not 0.  
\[H=\left(\begin{array}{cccc}g_{t}&0&...&0\\ S_{t-1}&g_{t}&...&0\\...&...&...\\ S_{1}&g_{2}&...&g_{t}\end{array}\right)\times\left(\begin{array}{cccc}\frac{1 }{g(\alpha_{0})}&\frac{1}{g(\alpha_{1})}&...&\frac{1}{g(\alpha_{n-1})}\\ \frac{\alpha_{0}}{g(\alpha_{0})}&\frac{\alpha_{1}}{g(\alpha_{1})}&...&\frac{ \alpha_{n-1}}{g(\alpha_{n-1})}\\...&...&...\\ \frac{\alpha_{n-1}}{g(\alpha_{0})}&\frac{\alpha_{1}^{\ell-1}}{g(\alpha_{1})}&...&\frac{\alpha_{n-1}^{\ell-1}}{g(\alpha_{n-1})}\end{array}\right)=H_{g} \times\hat{H}. \tag{4}\]
## 7 Interference Mitigation Strategies
### Step 2: Utilizing an Improved Antenna with Reduced Side Lobes  
Established techniques for enhancing RF device performance include strategies for both interference protection and radiation pattern control. High-power interference mitigation often involves broadband protective components like superconducting or shielded transmission lines [23; 24]. Concurrently, side lobe suppression, crucial for minimizing off-axis interference, can be achieved using advanced materials [25; 26], optimized antenna geometries, or modified reflectors. However, given that the existing antenna is ill-suited for the target operational scenario in Lebanon, this work focuses on a fundamental antenna redesign. This approach is selected over applying mitigation techniques like advanced materials or shielding to a baseline design not inherently optimized for the required performance.  
The currently used 1.2 m parabolic antenna exhibits a first side lobe gain of approximately 30 dBi. To reduce the interference towards the GEO satellite at 35\({}^{\circ}\) west, a new antenna design is proposed.  
The antenna is designed and optimized using computer simulation technology (CST) software (Figure 6a). The new antenna has a diameter of 1.1 m. The far-field pattern of the proposed antenna is shown in Figure 6b. The plot shows that the proposed antenna achieves a main lobe magnitude of 42 dB, which is comparable to the 41.5 dB of the currently used antenna, ensuring that the signal strength in the intended coverage area (MVDS customers in Beirut) is maintained. The 3 dB angular width of the main lobe is 1.4 degrees, a slight increase from the current 1.1 degrees. The most significant improvement is observed in the side lobe levels. The proposed antenna exhibits a side lobe level of \(-\)28.7 dB relative to the main lobe. This translates to a first side lobe gain of approximately 13.9 dB, a substantial reduction of over 16 dB compared to the first side lobe gain of the currently used antenna (approximately 30.2 dB).  
Table 8 presents the updated calculated interference levels at the ES4 when P\({}_{\text{MVDS}}\) is reduced and the proposed antenna design is employed. The expected interference levels are considerably lower compared to the previous scenario, as shown in Table 6 (\(-\)10 dBW to 10 dBW). It presents a comprehensive analysis of the MVDS side lobe interference effect on the GEO satellite system in a realistic scenario. This table quantifies the impact of increasing MVDS transmitted power on key performance metrics at the satellite receiver.  
The proposed system's signal quality remains above the \"critical\" threshold up to 5 dBW, whereas the current system reaches the \"critical\" level at 10 dBW. This table demonstrates the effectiveness of the proposed two-step mitigation technique in decreasing the interference caused by MVDS side lobe radiation towards GEO satellites across a range of MVDS transmission powers.  
Figure 6: (**a**) Proposed antenna. (**b**) Far-field elevation pattern of the antenna at 14 GHz; 1.2 m parabolic antenna.
###### Abstract  
Purification of lithium carbonate, in the battery industry, is an important step in the future. In this experiment, the waste lithium-ion batteries were crushed, sieved, leached with sulfuric acid, eluted with an extractant, and finally sulphate solutions were extracted, through selective precipitation. Next, sodium carbonate was first added to the sulphate solutions, to precipitate lithium carbonate (Li\({}_{2}\)CO\({}_{3}\)). After that, lithium carbonate was put into the water to create lithium carbonate slurry and CO\({}_{2}\) was added to it. The aeration of CO\({}_{2}\) and the hydrogenation temperature were controlled, in this experiment. Subsequently, Dowex G26 resin was used to remove impurities, such as the calcium and sodium in lithium carbonate. Moreover, the adsorption isotherms, described by means of the Langmuir and Freundlich isotherms, were used to investigate the ion-exchange behaviors of impurities. After removing the impurities, the different heating rate was controlled to obtain lithium carbonate. In a nutshell, this study showed the optimum condition of CO\({}_{2}\) aeration, hydrogenation temperature, ion-exchange resin and the heating rate to get high yields and purity of lithium carbonate.  
Keywords:lithium carbonate; sulphate solutions; hydrogenation; ion-exchange; purification +
Footnote †: journal: Applied Sciences
## 4 Experimental Verification
### Rheological Properties of the Slurry  
The slurry mixed with the eccentric rotor at various speeds underwent rheometer testing (as depicted in Figure 13c), yielding the apparent viscosity curve of the slurry concerning the shear rate at different speeds, as shown in Figure 16 It is evident that corn syrup displays relatively good Newtonian properties, but it transitions into a non-Newtonian fluid when mixed with flour. The viscosity of the slurry decreases with increasing shear rate, with a more pronounced decrease in viscosity at higher shear rates. The apparent viscosity of the slurry exhibits a consistent trend across different rotational speeds. At low shear rates, corresponding to high-viscosity values, there are noticeable differences in the apparent viscosity of the slurry at different rotational speeds. The maximum apparent viscosity occurs at a rotational speed of 15 r/min, while the minimum apparent viscosity occurs at 55 r/min. However, under the same shear rate conditions, the average apparent viscosity decreases consistently as the rotational speed increases, albeit the differences are generally minimal.  
Figure 16 shows the rheological characteristics of slurry at different rotational speeds. The slurries are pseudoplastic fluids in non-Newtonian fluids, and the apparent viscosity \(\eta\) of the slurry is represented by a power law model as follows:  
\[\eta=k\dot{\gamma}^{n-1} \tag{6}\]  
where \(n\) refers to the non-Newtonian fluid index, \(k\) refers to the consistency coefficient, and refers to the shear strain rate, which is defined as follows:  
\[\dot{\gamma}=\sqrt{2(\mathbf{D}:\mathbf{D})} \tag{7}\]  
As can be seen from the above formula, when \(n<1\), the apparent viscosity decreases with the increase of shear strain rate, and the power law fluid becomes shear thinner. When  
Figure 16: Slurry viscosity curve with shear rate at different rotation speeds.  
\(n>1\), the apparent viscosity increases with the increase of shear strain rate, and the power law fluid appears to be shear thickened.  
The non-Newtonian fluid index varies little at different speeds and fluctuates in the range of 1%, while the consistency coefficient fluctuates greatly at about 5% (shown in Table 5). In general, the power law characteristics of the slurry at different speeds are similar, and the difference is not large. The fitted R-square value is greater than 0.9 except that 55 r /min is slightly smaller.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Key Generation
#### 4.1.6 Impact of the Use of QKD Technology on the Network Topology  
A typical SCADA network topology consist of two primary units: (1) Master Terminal Unit acting as the control center (2) Remote Terminal Unit acting as the field site which gather information from sensors [1; 38; 39]. Our proposed algorithm mainly focuses on securing communication between the RTU and MTU. The communication link between the MTU and the RTU in the SCADA network topology is a point-to-point link.  
When QKD is used on the network topology, the RTU and MTU are connected by two channels: Quantum Channel (fiber optic) or Classical Channel (Internet or fiber optic). If the SCADA network topology is based on hardwired communication, we can say that 2*N fiber optics are needed to deploy QKD [40]. The point-to-point link provides low latency and fast communication. One major constraint of QKD on point-to-point network topology is that if one of the communication links is broken, the entire network fails [39; 41]. However, the impact of authenticated QKD in SCADA network also includes resistance against intrusion and quantum attacks.  
For future work in practical QKD research, a quantum channel can be designed which is capable of carrying both quantum and classical information. It may lead to reduction of number of fiber optics or, additional communication channel in a network topology.
###### Abstract  
One key challenge within the domain of network science is accurately finding important nodes within a network. In recent years, researchers have proposed various node centrality indicators from different perspectives. However, many existing methods have their limitations. For instance, certain approaches lack a balance between time efficiency and accuracy, while the majority of research neglects the significance of local clustering coefficients, a crucial node property. Thus, this paper introduces a centrality metric called DNC (degree and neighborhood information centrality) that considers both node degree and local clustering coefficients. The combination of these two aspects provides DNC with the ability to create a more comprehensive measure of nodes' local centrality. In addition, in order to obtain better performance in different networks, this paper sets a tunable parameter \(\alpha\) to control the effect of neighbor information on the importance of nodes. Subsequently, the paper proceeds with a sequence of experiments, including connectivity tests, to validate the efficacy of DNC. The results of the experiments demonstrate that DNC captures more information and outperforms the other eight centrality metrics.  
key nodes; complex network; robustness; local clustering coefficient +
Footnote †: journal: Applied sciences
## 4 Results
### Data Collection  
#### 4.1.1 Crowdsourced Data Collection  
To crowdsource the collection of historic photographs and re-create these photos to collect spatial metadata, we equipped the mobile application with additional functionalities which can be used worldwide. To increase their use, we conduct local citizen contests. The contest was ran in Jena in 2022, with 4000 images collected, and will take place in the city of Schleiz in Thuringia in 2024.  
The Jena citizen contest was conducted in autumn 2022. The citizens were asked to upload private historical photos, postcards, and other historical images from the period between 1900 and 2000 (see [80]). Images could be submitted in three different ways:  
* If the photos were digitized, they could be uploaded from within the application.
* If the photos were still in analogue form, they could either be photographed directly within the application or submitted at various collection points such as the Jena City Museum and the Thuringian State and University Library (Thulb). Especially for larger quantities, the images were digitized at Thulb and then we transferred them to the application database.
* To determine the position of the historical photos, citizens were also asked to \"rephotograph\" images that were already in the database. To do this, the participants had to identify where the respective historical photo was taken and position themselves so as to take a new photo from the same viewpoint and angle. The corresponding information about geolocation, etc., was then automatically transferred from the mobile device to the database and used to project the images on the models.  
To advertise the contest, we cooperated with regional newspapers, which regularly reported on its progress. Furthermore, to address the citizens directly, four-five public stands were set up weekly in the Jena City Museum, the University Library, and the Market Square. Here, people could hand in analogue pictures personally. In addition, advertising materials were sent to schools and cultural associations and distributed in bars and pubs. To ensure maximum accessibility, we kept the threshold as low participation. Participants were required to provide their name, email address, and telephone number in the application. This also allowed the participants to choose whether to transfer the rights to the photographs.  
Prizes could be won in a total of six categories shown on the official website (Figure 1) created for the competition by an advertising agency. The winners were selected by a high-ranking jury, including the Thuringian State Secretary for Culture and the Mayor of Jena.  
In total, over 4000 historical photos and rephotographs were submitted, of which a selection was used to evaluate the 3D reconstruction methods.
## 3 Drone Routing Algorithms
### Security Algorithms Used in Drone Routing
#### 3.3.2 Blockchain-Powered Privacy-Aware Flight Compliance (PA-NOP)  
Of all the major issues in drone-based delivery service system, one of the major concerns is how to control the flights of the drones when all the drones are developed by third-party organizations. In addition, while designing drone routes, it is important to consider that the route excludes unauthorized areas to ensure privacy and security. Moreover, ensuring that the drones for the delivery purpose comply with these rules and regulations is also an important issue to consider. Therefore, in [57], the authors designed a route planning mechanism where they jointly considered the security of the citizens and contract-based policy for collision avoidance to design a secure and reliable drone-based delivery systems. A three-layered architecture is proposed. The first layer defines the flight policy and the predefined route, which are then programmed into the drone. The second layer manages the drone services following the set of guidelines set by the first layer by collecting all the related data regarding the drones and the flight path. The final layer is the blockchain layer which receives all the data from the second layer and enforces the policies required to ensure the security guidelines. Drones that do not follow the preset guidelines by the controllers receive a penalty from the blockchain controller. Thus, security and collision are ensured by following the proposed path planning mechanism. However, this study only considers enforcement of flight policies and excludes communication security.