esa-sceva/satcom-chunk-collection huggingface dataset:
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 5 Additional Use Cases  
Throughout the paper, a computer vision system has been introduced with the primary aim of reducing the manual workload for video editors during live broadcasts, while simultaneously making the point of interest visualizations more dynamic for viewers. Although this is the main application of the system, it also has potential for other uses, such as improving user engagement, automating reporting, and generating metadata.  
Next to dynamic live visualizations, the system can also be used to generate clips of POIs for sharing through various other mediums, such as social media. This could be particularly interesting for cities looking to promote tourism to a broader audience.  
Figure 14: Example visualizations generated by Vizrt based on the POI recognition and tracking data, demonstrating more dynamic POI overlays.  
These clips can also incorporate tracking information for visualization purposes and can be augmented with text overlays or voice-overs using automatically generated descriptions by a large language model. Additionally, all POI clips could be compiled into a comprehensive touristic summary video, including footage that did not make the final broadcast.  
Moreover, the race organization and the cities through which the race passes agree on the specific POIs to be showcased and the duration. However, currently, there is a lack of proof on whether the actual broadcasting time aligns with the agreed-upon duration. Therefore, this system could automatically generate reports detailing the POIs featured in the broadcast and their corresponding airtime, serving as evidence for the cities.  
Finally, the POI detection and tracking information can be stored as metadata alongside the broadcast. This facilitates the querying and retrieval of images and video clips of these POIs in the future, which can be useful for other programs or series in need of video footage of certain POIs.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Space Segment Modeling
#### 3.1.3 Visualization  
SatScope utilizes the VTK library to achieve a 3D visualization of the fifth shell, as detailed in Table 1 and illustrated in Figure 5. In the space segment, SatScope implements a data-driven, lightweight simulation framework that allows for flexible adjustments to the network topology's scale and structure, thereby accommodating a variety of research requirements.
## 3 Experimental Investigation  
As a case study, a building of a high school in Sisak (Figure 1) built in 1935 was taken. The building has four floors: basement, ground floor, and two upper floors. In the central part of the building there is a three-legged staircase with a landing. The foundations are constructed as a reinforced concrete slab. The load-bearing part of structure consists of walls made of solid bricks of 29(39) cm \(\times\) 15 cm \(\times\) 6.5 cm, floor slabs and reinforced concrete ribbed floor slabs. The thickness of the load-bearing walls in the basement is 60 cm and 75 cm, and 50 cm and 60 cm on the ground floor and upper floors. The partition walls are 15 cm, 18 cm, 25 cm or 30 cm thick and are made of hollow blocks. The floor slab is 25 cm thick and the reinforced concrete ribbed floor slab has a total height of 33 cm. The clear distance between the ribs is 60 cm, the width of the ribs is 13 cm, and the height is 23 cm. At the entrance of the building on the first and second floors, there is a load-bearing part of the structure that rests on columns with the square section of 75 cm \(\times\) 75 cm and a height of 470 cm. The columns are arranged in two rows of four or six columns at equal centre distance of 330 cm. The mezzanine structure in this part is reinforced with beams of 75 cm \(\times\) 123 cm, placed at an axial distance of 330 cm above the columns. The supporting structure of the roof is made of wood and is 418 cm high. The geometrical dimensions of the building are 37 m \(\times\) 37 m.  
Within the experimental investigation of case study determination of mechanical properties (Figure 2) of the masonry walls and operational modal analysis (OMA) were considered in addition to visual inspection (Figure 3).  
Figure 1: High school building in Sisak (**a**) entrance in school (**b**) bird view.  
Figure 3: Observed characteristic damages on (**a**) the load bearing walls (**b**) the walls between the openings.  
Figure 2: Measurement points for determination of mechanical properties of walls on the (**a**) Ground Floor (**b**) First Floor (**c**) Second Floor.
## 2 Dynamic Model of FFPMSM  
The FFPMSM model focuses solely on fundamental and third-harmonic components. Additionally, the model simplifies by ignoring cogging, magnetic saturation, and temperature effects on the flux density of the permanent magnets.
## 5 The SNR of MFIR Filters
### The Transfer Functions  
In case _MFIR_ filters are considered, the stages are indicated by \(M_{i}\left(z\right)\) or \(M_{i}(e^{j\omega_{n}})\) or in short form \(M_{i}\). The total filter transfer function is written as \(M\left(z\right)\), \(M\left(e^{j\omega_{n}}\right)\) or \(M\). A scaled stage is indicated by \(\overline{M_{i}(z)}\), \(\overline{M_{i}(e^{j\omega_{n}})}\) or \(\overline{M_{i}}\).  
The ordering where the first stage is stage \(i=0\), the second is \(i=1\) and so on, will be called the forward (sequential) ordering. Consequently, in case of MFIR structures and forward ordering:  
\[G_{i}\left(e^{j\omega_{n}}\right)=\prod_{m=i+1}^{P-1}M_{m}\left(e^{j\omega_{n} }\right), \tag{47}\]  
\[\overline{G_{i}\left(e^{j\omega_{n}}\right)}=\prod_{m=i+1}^{P-1}\left(M_{m} \left(e^{j\omega_{n}}\right)\right)=\prod_{m=i+1}^{P-1}\left(S_{m}M_{m}\left(e ^{j\omega_{n}}\right)\right)=\left(\prod_{m=i+1}^{P-1}\left(S_{m}\right) \right)G_{i}\left(e^{j\omega_{n}}\right), \tag{48}\]  
\[F_{i}\left(e^{j\omega_{n}}\right)=\prod_{m=0}^{i}M_{m}\left(e^{j\omega_{n}} \right), \tag{49}\]  
\[\overline{F_{i}\left(e^{j\omega_{n}}\right)}=\prod_{m=0}^{i}\left(M_{m}\left( e^{j\omega_{n}}\right)\right)=\prod_{m=0}^{i}\left(S_{m}M_{m}\left(e^{j\omega_{n}} \right)\right)=\left(\prod_{m=0}^{i}\left(S_{m}\right)\right)F_{i}\left(e^{j \omega_{n}}\right). \tag{50}\]  
Notice that \(F_{P-1}\left(e^{j\omega_{n}}\right)=M\left(e^{j\omega_{n}}\right)\) and \(G_{P-1}\left(e^{j\omega_{n}}\right)=1\) (see Figures 2 and 3).  
**Figure 5.** The unscaled and scaled MFIR filter cascade transfer functions for reverse ordering.  
The ordering where stage \(i=P-1\) is the first stage, \(i=P-2\) is the second stage and so on, is called the reverse (sequential) ordering. In case of reverse ordering of MFIR stages, the transfer functions are shown in Figure 5 and are defined by:  
\[G_{i}\left(e^{j\alpha_{i}}\right)=\prod_{m=0}^{i-1}M_{m}\left(e^{j \alpha_{i}}\right),\quad i>0 \tag{51}\] \[\overline{G_{i}\left(e^{j\alpha_{i}}\right)}=\prod_{m=0}^{i-1} \left(\overline{M_{m}\left(e^{j\alpha_{i}}\right)}\right)=\prod_{m=0}^{i-1} \left(S_{m}M_{m}\left(e^{j\alpha_{i}}\right)\right)=\left(\prod_{m=0}^{i-1} \left(S_{m}\right)\right)G_{i}\left(e^{j\alpha_{i}}\right),\qquad i>0\] (52) \[F_{i}\left(e^{j\alpha_{i}}\right)=\prod_{m=i}^{P-1}M_{m}\left(e^ {j\alpha_{i}}\right),\] (53) \[\overline{F_{i}\left(e^{j\alpha_{i}}\right)}=\prod_{m=i}^{P-1} \left(\overline{M_{m}\left(e^{j\alpha_{i}}\right)}\right)=\prod_{m=i}^{P-1} \left(S_{m}M_{m}\left(e^{j\alpha_{i}}\right)\right)=\left(\prod_{m=i}^{P-1} \left(S_{m}\right)\right)F_{i}\left(e^{j\alpha_{i}}\right). \tag{54}\]  
Notice that in case of reverse ordering \(F_{0}(e^{j\alpha n})=M(e^{j\alpha n})\) and \(G_{0}(e^{j\alpha n})=1\).  
Although there are \(P!\) Possible ordering combinations of the MFIR stages, experiments have shown that choosing the best option between forward and reverse sequential ordering is usually satisfactory.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 4 Adaptive Channel Estimation Scheme Based on Preamble
### Performing Channel Estimation  
In this step, both channel estimation schemes are separately performed on the channel estimate obtained in the initial step. As mentioned above, this step is described for each specific case. When switching between STA and MMSE, the methods described in Sections 3.2 and 3.4 are used as follows:  
\[H_{STA}(0,k)=\sum_{\lambda=-\beta}^{\lambda=\beta}\omega_{\lambda}H(0,k+ \lambda). \tag{19}\]  
Figure 4: Flowchart of the proposed scheme.  
\[\mathbf{H}_{MMSE}^{0}=\mathbf{R}_{HP}^{0}\left(\mathbf{R}_{PP}^{0}+\sigma_{0}^{2} \mathbf{I}\right)^{-1}\mathbf{P}_{0}, \tag{20}\]  
where parameter \(\beta\) is usually set two in order to obtain best performance [25]. For switching between STA and TRFI, the STA channel estimates are obtained using (18). In the TRFI case, assuming that all channel estimates have passed the time reliability test, the TRFI channel estimate is set to the that of the long preamble \(H(0,k)\). Hence, the TRFI channel estimate is defined by  
\[H_{TRFI}(0,k) \triangleq H_{LS}(0,k)\] \[= \frac{1}{2}\left(\frac{Y(1,k)+Y(2,k)}{2X(k)}\right). \tag{21}\]  
This channel estimate is used to calculate the adaptation algorithm, and details are covered in Section 4.4.
## 2 Formulation of the Planning Problem  
In what follows we refer to an unbalanced, three-phase LV network, connected to the medium voltage (MV) distribution grid through an MV/LV transformer. The LV network includes single-phase and three-phase loads and photovoltaic DG units connected through power converters. It is supposed that all the microgrid's distributed resources are owned by a unique entity or belong to a consortium of different owners. A microgrid operator manages the grid to provide benefits to the single or clustered owners. The resources are supposed to be coordinated by a centralized control system. Also, it is assumed that the DG units can be controlled by the network operator in terms of reactive power.  
In this framework, the proposed procedure is aimed at allocating both single-phase and three phase DESSs in the LV network. The DESSs considered in this paper are batteries connected to the grid through interfacing power converters that can be controlled in terms of active and reactive powers.  
The planning of DESSs is formulated as a mixed integer, non-linear, constrained, minimization problem [30]:  
\[\min f_{obj}(\mathbf{x},\mathbf{u}), \tag{1}\]  
s.t.  
\[\psi_{\gamma}(\mathbf{x},\mathbf{u})=0\quad\mathbf{\gamma}=1,\ldots,n_{eq}, \tag{2}\]  
\[\phi_{\gamma}(\mathbf{x},\mathbf{u})\leq 0\quad\gamma=1,\ldots,n_{\text{ineq}}, \tag{3}\]where \(\psi_{\
u}\) and \(\phi_{\gamma}\) are the vth equality and \(\gamma\)th inequality constraints, respectively, \(\mathbf{x}\) is the vector of state variables (e.g., magnitude and argument of phase voltages at all buses), and \(\mathbf{u}\) is the DESS unit vector which includes the number of single batteries installed at each phase of each bus of the system:  
\[\mathbf{u}=\Big{[}n^{1}_{DESS_{1}}n^{2}_{DESS_{1}},\ldots,n^{p}_{DESS_{i^{ \prime}}},\ \ldots,\ n^{3}_{DESS_{qg}}\Big{]}, \tag{4}\]  
being \(n^{p}_{DESS_{i}}\) the number of battery units connected at bus \(i\) with phase \(p\). If the numbers of battery units connected at the phases of the bus \(i\) are all greater than zero (i.e., \(n^{1}_{DESS_{i}}>0,n^{2}_{DESS_{i}}>0,n^{3}_{DESS_{i}}>0\)), then a three-phase converter will connect the battery to the distribution system. On the contrary, if any of \(n^{p}_{DESS_{i}}\) is equal to zero, one or two single-phase DESSs will be installed. Therefore, both the allocation and typologies of DESSs (single-phase or three-phase) are included in (4).  
The planning horizon of the optimization problem refers to a specified number of years (\(ny\)). Each year y is characterized by \(nd_{y}\) typical days that identically apply \(N_{y,d}\) times in the year. Each typical day is divided into \(nt\) time intervals of duration \(\Delta\ t\). Growth of both load demand and power productions are assumed in the planning time horizon.
## 3 The First Modal Inertial/Invariant Single Load Vectors Pushover Analysis Methods
### The Energy-Based Pushover Analysis Method  
Energy-based pushover analysis [46; 47; 48] is a method that enhances nonlinear static analysis by integrating inertial properties and kinetic energy into force or displacement patterns. This method is particularly relevant for seismic input as it utilizes the fundamental mode shape \(\Phi\), effective damping, and a velocity profile to represent a structure's dynamic behavior and to scale the response spectrum. The method is insightful for understanding kinetic energy dissipation through plastic behavior but has limitations, especially for braced structures [31; 49; 50].  
The procedure of energy-based pushover analysis can be summarized as follows:  
1. Model selection: choose a structural model based on the building's characteristics and identify the dominant mode shape (\(\Phi\)) through modal analysis.
2. Force application: apply lateral forces (\(F_{k}\)) according to the mode shape and mass (\(m_{i}\)) distribution.
3. Incremental loading: incrementally apply loads and measure displacements (\(d_{jk}\)) at each step.
4. Equivalent SDOF system: calculate both the equivalent mass (\(m^{\star}=\Sigma m_{i}\)) and the base shear (\(F^{\star}=\Sigma F_{i}\)).
5. Displacement calculation: sum incremental displacements to find total equivalent displacement (\(\delta^{\star}=\Sigma(\Delta\phi)\)).
6. Capacity curve construction: plot the capacity curve as base shear (\(F^{\star}\)) versus total equivalent displacement (\(\delta^{\star}\)).
7. Energy analysis: calculate the ductility ratio (\(\mu^{\star}=\delta^{\star}{}_{max}/\delta^{\star}y\)) and compute the energy term (\(\Pi_{x}=1/2F^{\star}_{y}\delta^{\star}_{y}(2\mu^{\star}-1)\)).  
Assessment of limitations: review the obtained results, being aware of potential constraints, particularly when dealing with braced structures. The cumulative equivalent displacement, denoted as \(\delta^{\star}\), is determined by summing up all the incremental values of \(\Delta\delta^{\star}\). Figure 5 depicts the overall concept of this method  
This method was applied on a three-level steel structure conceptualized for Los Angeles within the framework of the SAC initiative.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 4 Experimental Results and Analysis
### Calculation of Ice Thickness  
Upon detecting the edge of the ice-covered transmission line, the predicted ice thickness can be computed by analyzing the pixel difference between the upper and lower edges of the image. The ice-covered edge can be predicted by selecting four ice-covered transmission lines with varying thicknesses. Figure 8 illustrates the results of ice-covered edge prediction.  
Figure 7: Edge detection visualization diagram: (**a**) original image and (**b**) ground truth. (**c**) RCF image, (**d**) PidiNet image, and (**e**) CG-UNet image.  
Once the edge map of the ice-covered transmission line is acquired, the calculation of the ice thickness proceeds as follows:  
\[h=\frac{d_{2}-d_{1}}{2}\times\frac{D_{1}}{d_{1}}, \tag{9}\]  
where \(D_{1}\) represents the actual diameter of the transmission line, \(d_{1}\) represents the pixel width of the upper and lower boundary outlines of the ice-free transmission line image, \(d_{2}\) represents the pixel width of the upper and lower boundary outlines of the ice-covered line image. Among them, \(D_{1}\) and \(d_{1}\) are pre-known parameters, through which we can obtain the correspondence between pixel width and actual size. Then, by comparing the pixel difference between the upper and lower boundaries with and without ice cover, we can obtain the actual ice cover thickness. The experimental results are displayed in Table 4.  
As illustrated in Table 4, the ice thickness detected by the CG-UNet model through edge computation exhibited a maximum error of 7.2% and a minimum error of 3.4%. Compared to UAED and S-UNet, our model had smaller recognition errors for both thin and thick ice cover. The overall error fell within a controllable range, affirming the CG-UNet model's capability to detect ice cover thickness on transmission lines.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Thickness/mm** & **Test Method** & **Predicted Thickness/mm** & **Error** \\ \hline \multirow{4}{*}{5.0} & CG-UNet & 4.64 & 0.072 \\  & UAED & 4.53 & 0.094 \\  & S-UNet & 5.76 & 0.152 \\  & CG-UNet & 11.52 & 0.047 \\ \multirow{4}{*}{11.0} & UAED & 10.33 & 0.061 \\  & S-UNet & 11.86 & 0.078 \\ \multirow{4}{*}{17.5} & CG-UNet & 16.90 & 0.034 \\  & UAED & 18.34 & 0.048 \\ \multirow{4}{*}{20.5} & S-UNet & 16.62 & 0.050 \\  & CG-UNet & 21.96 & 0.071 \\ \multirow{4}{*}{20.5} & UAED & 22.34 & 0.089 \\  & S-UNet & 18.73 & 0.086 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ice thickness calculation results.  
Figure 8: Ice edge prediction results: (a) 35 mm image, (b) 47 mm image, (c) 60 mm image, and (d) 66 mm image.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Microbial Degradation of Plastics
#### 2.1.4 Microbial Consortia  
Microbial consortia have shown significant potential in plastic biodegradation by leveraging the synergistic effect of various microbial communities, enhancing the degradation efficiency beyond what individual strains can achieve [65; 66]. Plastics are often more susceptible to fungi and bacteria when applied in consortia [59]. For example, several PET-degrading strains such as _Sarcina aurantiaca_ (TB3), _Bacillus subtilis_ (TB8), _Aspergillus flavus_ (STF1), and _Aspergillus niger_ (STF2) were obtained from plastic waste disposal sites. The efficacy of these isolates in PET degradation was investigated on PET films for 60 days at 37 \({}^{\circ}\)C as individual organisms and as microbial consortia (TB3 + TB8 + STF1 + STF2). This study showed 28.78% (\(w/w\)) weight loss for the PET films through the microbial consortia (_S. aurantiaca + B. subtilis + A. flavus + A. niger_). Further investigations on the isolate's hydrophobicity, viability, and total protein concentration showed that there were no hazards to human health or the environment when using this microbial consortium to degrade PET-based waste [67]. Another microbial consortium of biodegradation was assessed in both mixed and individual forms. _Penicillium raperi_, _Aspergillus flavus_, _Penicillium glaucoroseum_, and other _Pseudomonas_ sp. were tested for their biodegradation ability under both unstimulated and H\({}_{2}\)O\({}_{2}\)-stimulated conditions. In the unstimulated conditions, the strains were tested under both mixed cultures over 270 days and as individual cultures over 100 days, while the H\({}_{2}\)O\({}_{2}\)-stimulated strains were tested only in the mixed culture for 30 days. Among these isolated strains, _Aspergillus_ flavus showed the highest weight loss (of 5.5% (\(w/w\))) of PE within 100 days in unstimulated culture conditions [68].
###### Abstract  
Over the past few years, life expectancy has increased significantly. However, elderly individuals living independently often require assistance due to mobility issues, symptoms of dementia, or other health-related challenges. In these situations, high-quality elderly care systems for the aging population require innovative approaches to guarantee Quality of Service (QoS) and Quality of Experience (QoE). Traditional remote elderly care methods face several challenges, including high latency and poor service quality, which affect their transparency and stability. This paper proposes an Edge Computational Intelligence (ECI)-based haptic-driven ECI-TeleCaring system for the remote caring and monitoring of elderly people. It utilizes a Software-Defined Network (SDN) and Mobile Edge Computing (MEC) to reduce latency and enhance responsiveness. Dual Long Short-Term Memory (LSTM) models are deployed at the edge to enable real-time location-aware activity prediction to ensure QoS and QoE. The results from the simulation demonstrate that the proposed system is proficient in managing the transmission of data in real time without and with an activity recognition and location-aware model by communication latency under 2.5 ms (more than 60%) and from 11\(\sim\)12 ms (60\(\sim\)95%) for 10 to 1000 data packets, respectively. The results also show that the proposed system ensures a trade-off between the transparency and stability of the system from the QoS and QoE perspectives. Moreover, the proposed system serves as a testbed for implementing, investigating, and managing elder telecaring services for QoS/QoE provisioning. It facilitates real-time monitoring of the deployed technological parameters along with network delay and packet loss, and it oversees data exchange between the master domain (human operator) and slave domain (telerobot).  
5G/6G; URLLC; relative position; IoT; robotics Article
## 3 Security and Privacy Challenges
### Classification of Edge Computing Security Threats  
According to Statista's 2017 report, approximately 159,700 cyberattacks targeted edge networks and were grouped under six distinct groups: side-channel attacks, malware injection attacks, DDoS attacks, man-in-the-middle attacks, authentication and authorization attacks, and corrupt data injection attacks. The percentage share of each class of attacks is shown in Figure 6. User privacy and data security are the most important factors from the service provider's perspective. Sensing network data can extract a lot of private and vulnerable information. For example, access to the data from the electricity and water meters can provide information about the occupancy of a house. There are still open challenges that need to be answered by the EC service providers to protect user-sensitive data. Figure 7 shows a classification of security and privacy threats, listing their types and origins across various levels and layers within EC networks. All stakeholders in EC, including service providers, system and application developers, and end users, must realize their responsibility against data security threats. Another essential data privacy and security issue is establishing the ownership of collected data at the network edge. A suggested solution is to collect and store data at the edge while leaving ownership to the user. Capable tools and technologies are needed to ensure data privacy and security while meeting EC requirements. Edge nodes are resource-constrained, making deploying advanced data security measures difficult due to their resource-intensive nature. Furthermore, the dynamic nature of the location at the network edge increases vulnerability to security attacks and illegal access to user data. Table 3 summarizes the security and privacy challenges as well  
Figure 6: Percentage share of attacks on edge network. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Strategy** & **Description** \\ \hline Edge Node Security & Uniform security levels are applied at edge nodes to ensure appropriate safety protocols. Different security levels may allow attackers to break through the nodes with weaker security algorithms. \\ \hline Full-time Monitoring & Warrants nonstop monitoring of edge nodes while offering network clarity to users through a collaborative interface. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mitigating edge network security threats [119].