esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Algorithms and Implementation
### Extended Kalman Filter (EKF)  
The need to apply the EKF is the expected non-linearity of a true physical system. Often, in engineering practice, the knee-jerk reaction to nonlinear systems is to linearize them and hope that the resulting linearization errors prove to be insignificant. If this approach is applied to KF, the result is linearized KF (LKF). Unfortunately, the KF typically demonstrates very poor performance since it linearizes both the dynamics of the system and covariance of the system, compounding the error due to linearization. The solution is the EKF, which is an extremely important variant of the regular KF. Actually, the EKF is adapted to work with nonlinear systems while requiring a minimal change to the fundamental algorithm/processes [43].  
The computing of a deterministic nonlinear equation is not easy for most users implementing state observers. In addition, the nonlinear computations are preserved and reducing linearization errors. On the other hand, nonlinear covariance and stochastic properties must be linearized since closed-form transformations of nonlinear systems are extremely challenging to solve or may not even exist. Consequently, linearization of these stochastic quantities is necessary to attempt to preserve the Gaussian of the filter, even if only an approximation. Therefore, the EKF is not an optimal observer since no guarantees of performance could be made due to the need to linearize. Finally, there exist other more advanced estimators, such as Bayesian estimator and particle filter, that could give more accurate estimation results but at a trade-off of computational expensiveness [43; 44; 45].  
Space Model:  
\[x_{k+1}=f(x_{k},u_{k})+w_{k}\]  
\[y_{k}=\text{g}(x_{k},u_{k})+v_{k}\]  
where \(w_{k}\) and \(v_{k}\) are independent, zero-mean, Gaussian of covariance matrices, respectively.  
Considering:  
\[A_{k}=\frac{\partial f(x_{k},\,u_{k})}{\partial x}\]  
\[C_{k}=\frac{\partial\text{g}(x_{k},\,u_{k})}{\partial x}\]  
For \(k=1,2,\,\dots\) compute:  
Time Update:  
\[\hat{x}_{k}^{-}=f\Big{(}\hat{x}_{k-1}^{+},u_{k-1}\Big{)}\]  
\[\hat{\sum_{\vec{x},k}}=A_{k-1}\sum_{\vec{x},k-1}^{+}A^{T}_{k-1}+\sum_{W}\]Measurements Update:  
\[L_{k}=\sum_{\overline{x},k}^{-}C_{k}^{T}\left[C_{k}\sum_{\overline{x},k}^{-}C_{k}^ {T}+\sum_{\overline{v}}\right]^{-1}\]  
\[\hat{x}_{k}^{+}=\hat{x}_{k}^{-}+L_{k}\big{[}y_{k}-\mathrm{g}\big{(}\hat{x}_{k}^ {-},u_{k}\big{)}\big{]}\]  
\[\sum_{\overline{x},k}^{+}=(I-L_{k}C_{k})\sum_{\overline{x},k}^{-}\]
## 3 The UDVRP Model
### Basic Assumptions  
We introduce a series of basic assumptions in the modeling process to simplify the problem and make it more feasible. These assumptions include:  
1. The precise whereabouts of the distribution center are known, and the distribution center has sufficient charging facilities.  
\begin{table}
\begin{tabular}{c l} \hline
**Parameters** & **Descriptions** \\ \hline \(N\) & Distribution point collection, \(N=1,2,3,\ldots,n\) \\ \(s\) & The distribution center \\ \(K\) & Collection of unmanned distribution vehicles, \(K=1,2,3,\ldots,k\) \\ \(D\) & Maximum driving distance of unmanned delivery vehicles \\ \(Q\) & The maximal load capacity of unmanned delivery vehicles \\ \(r_{i}\) & The quantity demanded at distribution point \(i\) \\ \(d_{ij}\) & The distance from distribution point \(i\) to \(j\) \\ \(c\) & Fixed costs of unmanned delivery vehicles \\ \(w\) & Unit transportation cost of unmanned delivery vehicles \\ \(e\) & Specific energy consumption of unmanned delivery vehicles \\ \(h\) & Unit energy consumption cost \\ \(T_{i}\) & Service duration at distribution point \(i\) \\ \(t_{i}\) & The actual time when the vehicle arrives at the distribution point \(i\) \\ \(t_{ij}\) & The duration of the vehicle from distribution point i to distribution \\  & point \(j\) \\ \(m_{i}\) & The loading quantity of distribution point \(i\) \\ \(n_{i}\) & The discharge quantity of distribution point \(i\) \\ \(q_{i}^{k}\) & The load of vehicle \(k\) when it leaves the distribution point \(i\) \\ \(y_{i}^{k}\) & Indicates whether the distribution point \(i\) is served by vehicle \(k\), binary variable \\ \(x_{ij}^{k}\) & Indicates whether the vehicle \(k\) travels immediately from distribution \\  & point \(i\) to \(j\), binary variable \\ \(\alpha\) & Penalty coefficient \\ \(\beta\) & Constant, equal to 1.316 \\ \hline \end{tabular}
\end{table}
Table 3: The meanings of parameters and variables.  
2. The location, demand, and best service time window of each distribution point are known.
3. All unmanned vehicles have the same attributes and have maximum load capacity and maximum driving distance limits;
4. While the autonomous vehicle is traveling, the weight of the battery does not change.
5. The unmanned vehicle maintains a consistent velocity, with a constant coefficient of battery power consumption, the power consumed has a linear relationship with the driving distance, and the unmanned vehicle has sufficient power to meet the delivery process.
6. The time spent by an autonomous vehicle at a demand point does not exceed a fixed value.
7. Only the energy consumption of autonomous vehicles under ideal conditions is considered, and the influence of factors such as weather is not considered.
8. It is assumed that each demand point can only be delivered by one unmanned vehicle, but one unmanned vehicle can serve multiple demand points.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Previous & 2.40 & 2.61 & 1.94 & & 2.31 & 0.60 & 1.53 & & 1.65 & 1.08 & 0.27 & 0.34 & 1.42 & 0.31 & 0.66 & 0.26 \\ Proposed & 1.41 & 2.55 & 0.84 & & 0.47 & 0.66 & & & & & & & & \\ \hline  & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** \\  & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **Avg.** \\ \hline Previous & 1.55 & 2.73 & 0.21 & 1.92 & & 3.04 & 2.94 & 1.61 & 0.96 & 4.01 & 1.29 & 0.99 & 1.64 \\ Proposed & 0.73 & 2.8 & 0.39 & 1.83 & & 1.04 & 1.04 & 1.61 & 0.48 & 1.82 & 0.87 & 0.76 & 1.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Position RMSE of Video 2 (m). investigation of the motion models in the various road environments such as highly curved roads in highway interchanges as well as straight roads in the future study. In Video 1, Target 2 is moving horizontally, thus most of the errors are found in the \(x\) direction as shown in Figures 18b and 19b. In Figures 18d and 19d, the error of Target 4 is mostly found in the \(y\) direction because the target moves vertically. When the target states are fused, a more accurate position estimate is obtained although the velocity estimate becomes less accurate. Considering only the association states of the two targets, the position accuracy improved by 83.6%. In Video 2, a total of 23 various targets are considered and the drone changes a direction once, which expands the surveillance coverage in two dimensions. A total of 26 valid tracks are generated but two of them are segmented tracks for one target (bicycle), thus the tracking efficiency is 92%. Only one target (bus) was responsible for two additional tracks. Except for them, no redundant track was generated. The position accuracy increased by 26%. The experimental results show the proposed method is very effective in reducing redundant tracks and increasing the position accuracy.
# Honey-Based Medicinal Formulations: A Critical Review
## 3 Honey-Based Medicinal Formulations
### Honey-Based Ointments  
Ointments help to entrap water, keep the skin moist, and provide an embilient protective film, which are all important elements for wound healing. L-Mesitran(tm) ointment, which is manufactured by Theo Manufacturing BV, Maastrich, the Netherlands, and can be used to treat a wide range of wounds, contains 48% medical-grade honey alongside other natural agents (medical grade hypoallergenic lanolin, sunflower oil, cod liver oil, vitamins C and E, and zinc oxide). Revamil Honey Balm Wound Ointment(tm) is manufactured by Oswell Penda and indicated for the prevention or treatment of infections, including those caused by multidrug-resistant bacteria. As mentioned earlier, Revamil source (RS) honey is a standardized medical-grade honey made under controlled environments in greenhouses.  
According to the manufacturer, Revamil ointment is mainly appropriate for the treatment of surface wounds, such as grazes and wounds caused by the scratching of eczema and for domestic first aid. One of the simplest honey ointments is ActibalmTM topical ointment, which is composed of only two ingredients, white pharmaceutical-grade petroleum jelly and Manuka honey. It aims to provide soothing relief for dry, cracked lips by creating a protective barrier to lock in moisture, shielding the skin to drying, and aiding in the restoration of dry skin. This product is, however, not suitable for application on broken skin or mucosal surfaces. Melcura Plus Melladerm PhytoTM is an ointment which contains 48% honey and antioxidant agents to provide a comprehensive wound care treatment option. With its honey content, the formulation aims to create an environment unfavurable for microorganism proliferation, remove devitalized tissue, reduce wound odor, and promote granulation and epithelialization. The Australian company Nature's Gold manufactures two ointments using Australian Manuka honey. Manuka Power Concentrated OintmentTM contains 30% Manuka honey and claims to protect the skin on dry, hot, or windy days, lessen the sting and itch of insect and mosquito bites, relieve the symptoms of chafing, and to be beneficial in the treatment of minor cuts, minor steam burns, and abrasions. The company also produces Manuka Power Concentrated OintmentTM for pets prepared by blending 30% Manuka honey with sunflower seed oil, bees wax, coca seed butter, shea butte, and lecithin. This ointment is recommended for treating scratches, bites, inflamed red skin, eczema, rashes, and otherwise damaged skin. The manufacturer also claims that it can be applied on skin folds and on paws to maintain a healthy pet skin.
## 4 Conclusions  
Based on this experiment, the refractive index from THz-TDS identified glycol contamination levels of 300+ ppm from uncontaminated oil. While no glycol from coolant should be present in engine oil [7], the 300 ppm contamination level was below the 500 level that was not found to cause engine damage [8]. Thus, a critical leak of engine coolant--including glycol into engine oil--could be detected by THz-TDS before engine damage and signal the need to reject the engine oil and perform cooling system repairs. The difference in mean refractive index was greatest at the lowest frequency, but still near the limit of detection for THz-TDS. While a THz-TDS system may reliably detect this difference, each THz-TDS system may result in a slightly different range of refractive index values and may require calibration to determine an engine oil reject level for glycol contamination based on refractive index.  
The absorption coefficient results from THz-TDS showed better potential for distinguishing differences in glycol contamination levels (0 ppm, 150 ppm, 300 ppm, and 500 ppm) of 5W-30 gasoline/diesel engine oil. Across the frequency range of 1.01-2.471 THz, each of the four contamination levels was significantly different from the other three levels. The mean differences between contamination levels were all over 0.1 cm\({}^{-1}\) and well within the limits of detection for absorption coefficient. Based on the results of this study, THz-TDS demonstrated sensitivity to identify glycol contamination in engine oil. While this indicates potential, a subsequent study would be required  
Figure 4: Absorption coefficient at 1.75 THz predicting glycol contamination (ppm) with a linear model.  
to determine the ability of THz-TDS to discriminate levels of coolant (glycol and water mixtures) contamination in engine oil. Further studies will require investigation of the discrimination of different contaminants at the same time. As no resonant glycol-specific features were found, a final sensor system for detecting multiple contaminants at the same time may require different methods--possibly a multi-spectral system. This paper has shown that a compactified THz sensing element could be part of such a sensor concept.  
Conceptualization, O.M.A., A.M.A.-M. and D.G.W.; methodology, O.M.A., A.M.A.-M. and M.M.A.; formal analysis, O.M.A., A.M.A.-M. and D.G.W.; resources, O.M.A. and S.P.; writing--original draft preparation, O.M.A. and A.M.A.-M.; writing--reviewing and editing, S.P. and D.G.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to extend their thanks to the physics faculty at the Technische Universitat Darmstadt (TUD), Germany, where the THz-TDS work was performed in the laboratory of [PERSON].  
The authors declare no conflict of interest.
# Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links
## 2 Materials and Methods
### GeoSOT-Based Grid Modeling
We named \(grid_{n}\)'s code as \(gcode_{n}\), and for the satellite at moment \(t\), its ground coverage \(C_{s,t\to e}\) can be encoded as \(G_{s,t\to e}\), i.e.,  
\[\left\{G_{s,t\to e}\to G_{s,t\to e}\right. \tag{2}\]  
In Equation (2), \(C_{s,t,i}\) are the covered subregions that are consistent with the spatial extent of grid \(grid_{i}\) or conform to the boundary treatment principles. The grid conversion Algorithm 1 for the coverage area is shown below:  
```
Input: Boundary Point Sequence \(CoverLine\_pt\_seq\) Output:\(\{gcode_{border}\}\) and \(\{gcode_{inner}\}\)
1: Traverse the boundary nodes of the spatial object \(O\),
2: convert each node in turn to a node code with a profile level of \(n\)-level
3: record the inclusive box grid range \(R\) of the boundary nodes
4: generate an \(m\times n\) inclusive box grid matrix, when \(j=n\), \(Vij=-1\), else, \(Vij=0\). 5: fill the node grid code \(Code_{i}\) with \(Code_{i+1}\) in turn, generate the boundary grid set \(\{gcode_{border}\}\). 6: set the value of the corresponding grid element in \(V\) to 1 according to the boundary coverage processing rules, and set \(i=1\)
7: while \(i<R\)
8: Set flag \(Flag=Flag\) and visit elements \(V_{ij}\) in matrix \(V\) one by one from left to right:
9: if \(V_{ij}=1\) and \(V_{i,j+1}=0\), then
10: Flag = true
11: end if
12: if \(V_{ij}=0\) and \(Flag=true\), then
13: Let \(V_{ij}\in\{gcode_{inner}\}\)
14: end if
15: \(i=i+1\)
16: End while
17: return \(\{gcode_{border}\}\) and \(\{gcode_{inner}\}\). ```  
**Algorithm 1** Coverage area grid conversion algorithm  
Figure 2: Grid-based modeling of satellite coverage areas. The traditional satellite and ground base station coverage-area calculation methods cannot provide a unified description of different types of satellite observations, making it difficult to judge the spatial relationship between observation targets and coverage areas. The grid modeling of satellite range by GeoSOT can effectively solve the problem of uniform characterization of the coverage grid. Through layer selection, the grid size of each layer can just cover the application requirements of the satellite orbit and user terminal. While ensuring the accuracy of representation, it avoids the costs of data storage and computation of too-fine grids.
# A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch
## 2 Related Work
We have to conduct a significant amount of development if we implement a log analysis platform from scratch with Spark. Elasticsearch has the capability of indexing logs and searching specific records from billions of log records instantly; however, it only provides basic analysis functions, e.g., monthly aggregation and total count. Meaningful hidden patterns cannot be automatically detected by the ELK stack. Furthermore, Elasticsearch is a search engine, not a parallel analysis framework. A trend is to integrate Spark and Elasticsearch to implement an efficient and scalable log management and analysis system. [PERSON] et al. proposed a streaming architecture based on ELK, Spark, and Hadoop to do anomaly detection from network connection logs in near real time [21]. [PERSON] et al. introduced a method to speed up log mining with Elasticsearch and Spark; however, Elasticsearch and Spark work independently in their framework [22]. Specifically, Spark is only used to split logs and distribute them to multiple machines prior to the log-mining process. In this research, we propose a log-mining framework integrating both Elasticsearch and Spark, and design a log partitioner approach to solve the skewed data problem. The framework aims to (1) present a platform which integrates Spark and Elasticsearch to make full use of the functionalities offered by both; (2) serve as a guide about how to speed up Elasticsearch-based data analysis with Spark. This paper introduces our research on utilizing cloud, Spark, and Elasticsearch in an interactively and optimized fashion to mine interesting patterns from log files. In addition to the introduction (Section 1) and literature review (Section 2), Section 3 introduces the logs used in the experiment. Section 4 delineates the proposed log-mining framework and the log-partitioning algorithm. Section 5 details the experiment, and Section 6 interprets and evaluates the experimental results. Section 7 reviews the framework and discusses future work.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Unmanned Arial Vehicle (UAV)

Table 4: _Cont._reliability and minimal latency are two advantages of using a B5G network for UAV operations. This implies that a UAV can quickly receive and respond to orders sent by the ground control system or pilot. B5G speeds up the process of transmitting, receiving, and responding to orders, thus lowering the error margin that may occur during the flight. This low latency is very important when UAVs are flying in areas where a global positioning system (GPS) is not available or when they are flying beyond line of sight (BLOS). UAVs cannot use GPS in this situation, so they have to use visual-inertial odometry (VIO) to navigate in places where the view of the pilot is occluded [147]. To provide the pilot with a precise view of where the UAV is, B5G will allow the UAV's camera feed to be updated in real-time on the ground control system (GCS) of the pilot [148]. Air inspection, delivery, film and entertainment, critical missions, surveillance, intelligence, and mapping are just a few of the applications that will benefit from UAVs running on a B5G network [149]. UAVs enabled-5G can safely carry medical supplies such as COVID-19 testing to impacted populations by restricting human-to-human interaction and therefore avoiding infection spread [150]. A UAV used for rescue and search can send data and images in real-time and with low latency, which increases the speed and efficiency of the search and rescue process. In general, the accuracy and low latency of B5G will enable these new use cases and boost the adoption of UAVs. This exponential growth of UAV-enabled applications in the high-speed wireless communication field B5G has resulted in a paradigm shift in the wireless communication field [151]. The main benefit of UAV-assisted wireless communication is that it is the most appropriate technique for providing wireless connection and coverage to end-users who lack infrastructure coverage. The main benefit of UAV-assisted wireless communication is that it is the most appropriate technique for providing wireless connection and coverage to end-users who lack infrastructure coverage because of mountainous terrain, densely populated areas, severe shadowing, and degradation of communication infrastructure due to natural disasters [152]. The general scenario of UAV communication is depicted in Figure 13.
## 3 Results
### AI-Driven Security and Privacy Frameworks with Edge Computing
As an added measure, blockchains have also been trialed to maintain tamper-proof records of student credentials or device legitimacy, thus thwarting  
Figure 2: Real-time personalized learning flow in a 5G + AI classroom. Student devices transmit engagement data to the AI-driven learning engine, which analyzes performance and provides adaptive feedback. Teachers can monitor dashboards in real time and intervene when needed. fraudulent modifications [56]. Security assessments prior to deployment have also been proposed, such as a semantic reasoning-based toolchain for cloud configurations to pinpoint vulnerabilities early in the design phase, which could also be applied in educational settings [57]. Alongside these security methods, edge computing can enhance real-time data processing. Instead of forwarding all camera feeds, biometric signals, or quiz responses to cloud servers, local edge nodes can filter, aggregate, and analyze data on-site [58]. This distributed approach not only reduces round-trip latency, vital for synchronous VR or collaborative laboratories, but also lowers bandwidth usage and central server loads. Critically, edge nodes can de-identify or anonymize data before forwarding summarized findings to the cloud, aligning with privacy regulations and minimizing potential large-scale data leaks. Still, implementing edge solutions requires technical expertise, reliable maintenance, and upfront costs, which many under-resourced institutions cannot easily absorb [10]. As illustrated in Figure 3, a proposed 5G-enabled school network architecture should integrate a firewall and AI-driven Intrusion Detection and Prevention System (IDPS) for real-time zero-trust security. Traffic from the Internet and 5G tower passes through the firewall and IDPS, which applies continuous authentication and threat mitigation. A core switch then routes data to distinct VLAN segments for management, guests, faculty, and students, each served by dedicated edge switches. This segmentation isolates high-bandwidth IoT devices, VR/AR headsets, and personal computers (PCs) while enabling Wi-Fi for mobile users. Logs and model updates are sent to a cloud infrastructure for advanced analytics, enhancing both security and adaptive learning features. Figure 3: AI-driven IDPS at the network’s edge applying zero-trust security.