esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of GPS Auto-Surveying Techniques
## 2 Materials and Methods
### Position Determination  
#### 2.2.1 Least Squares  
Combining the pseudorange model of Equation (1) with Equation (2) and assuming the modeled errors have been removed from the pseudorange measurement, the pseudorange model can be rewritten as [2]  
\[\rho^{i}=\sqrt{\left(x^{i}-x\right)^{2}+\left(y^{i}-y\right)^{2}+\left(z^{i}- z\right)^{2}}+c\delta t_{r} \tag{4}\]  
which is an equation with four unknowns: the receiver coordinates in an ECEF frame, \(\left(x,y,z\right)\), and the receiver clock offset, \(\delta t_{r}\). In order to solve this equation, at least four independent measurements are required, after linearizing the equations.  
Since Equation (4) is non-linear, [PERSON]'s series expansion can be applied to the satellite-receiver range, with the linearization being conducted around an initial receiver position estimate \(\mathbf{x_{0}}=\left(x_{0},y_{0},z_{0}\right)\)[14]. By applying this linearization to Equation (4), it yields [14]  
\[\rho^{i}-R_{0}^{i}=\frac{x_{0}-x^{i}}{R_{0}^{i}}\Delta x+\frac{y_{0}-y^{i}}{R_ {0}^{i}}\Delta y+\frac{z_{0}-z^{i}}{R_{0}^{i}}\Delta z+c\Delta t \tag{5}\]  
in a static, fixed position. In this model, the state vector \(\mathbf{x}\), state transition matrix \(\mathbf{\Phi}\), and the noise covariance matrix \(\mathbf{Q}\) are given by [17]  
\[\mathbf{x}=\begin{bmatrix}x\\ y\\ z\\ x_{\phi}\\ x_{f}\end{bmatrix} \tag{12}\]  
\[\mathbf{\Phi}=\begin{bmatrix}1&0&0&0&0\\ 0&1&0&0&0\\ 0&0&1&0&0\\ 0&0&0&1&\Delta t\\ 0&0&0&0&1\end{bmatrix} \tag{13}\]  
\[\mathbf{Q}=\begin{bmatrix}0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&q_{\phi}&0\\ 0&0&0&0&q_{f}\end{bmatrix} \tag{14}\]  
where \(q_{\phi}\) and \(q_{f}\) are associated with the Allan variance parameters. In the case of a low-cost temperature-compensated crystal oscillator, those parameters are approximated by [17]  
\[\begin{split} q_{\phi}&\approx\frac{h_{0}}{2}=\frac{2\cdot 10^{-19} }{2}\\ q_{f}&\approx 2\pi^{2}h_{-2}=2\pi^{2}\Big{(}2\cdot 10^{-20}\Big{)}\end{split} \tag{15}\]  
The noise covariance matrix of the discrete-time dynamics model, \(\mathbf{Q_{k}}\), can be obtained from Equations (13) and (14):  
\[\begin{split}\mathbf{Q_{k}}&\approx\mathbf{\Phi Q\Phi^{T} \Delta t}\\ &=\Delta t\begin{bmatrix}0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&0&0\\ 0&0&0&c^{2}\Big{(}q_{\phi}+\frac{q_{f}\Delta t^{2}}{3}\Big{)}&\frac{c^{2}q_{f} \Delta t}{2}\\ 0&0&0&\frac{c^{2}q_{f}\Delta t}{2}&c^{2}q_{f}\end{bmatrix}\end{split} \tag{16}\]  
with the clock variances multiplied by \(c^{2}\) to convert the clock errors to units of meters.
## 7 Prospective Aspects of WebAssembly
### Prospective WebAssembly Specifications  
Wasm, as a crucial aspect of modern web technology, continues to evolve and expand in scope and functionality. As it has been adopted in diverse fields such as IoT, the development community and browser vendors are keen to continue rolling out new features and specifications that bolster the language's power and flexibility. This section explores several of the potential future specifications and enhancements to Wasm that could have profound implications for its usage in IoT and beyond.  
* **WASI** The WASI is another promising future specification. It seeks to standardize the way in which Wasm modules interact with the underlying operating system to facilitate consistent and secure system calls. With WASI, developers can create universal applications that are platform-independent, enhancing the portability of Wasm. For IoT, this could mean that a single Wasm module could run on a myriad of devices, irrespective of the device's specific operating system or architecture.
* **Multi-Threading and SIMD Support** Future specifications of Wasm include the introduction of multi-threading and SIMD support. Multi-threading would allow Wasm applications to execute multiple threads simultaneously, significantly increasing computational speed and efficiency for multi-core processors. SIMD, on the other hand, would enable a single operation to be performed on multiple data points at once, which is crucial for heavy computational tasks such as graphics rendering, audio processing, or machine learning algorithms.
* **Enhanced Debugging Support** Another area of focus for future Wasm specifications is enhanced debugging support. At present, debugging Wasm code can be challenging, especially when compared to more traditional languages such as JavaScript. Future enhancements promise a more accessible and efficient debugging process, enabling developers to easily track and fix bugs within their Wasm code. This would make the development process more streamlined while resulting in more stable and reliable Wasm applications.
* **Module Types** The Module Types proposal is a future specification that aims to provide a robust mechanism for defining the interface of a Wasm module. This includes the functions it exports, the types of these functions, the memories it uses, and more. With this feature, developers could build more complex modular applications with clearly defined interactions between different modules.
## 3 The Bias Compensation-Based Parameter and State Estimation Algorithm
### The State Estimation Algorithm
The steps for implementing the bias compensation-based parameter and state estimation (BC-PSE) algorithm in Equations (26) and (47) for state-space systems with colored noise are listed as follows. 1. Let \(k=1\), and set the initial values \(\hat{\mathbf{\vartheta}}_{\mathrm{LS}}(0)=\hat{\mathbf{\vartheta}}_{\mathrm{C}}(0)= \mathbf{1}_{2n}/p_{0}\), \(\hat{\mathbf{\vartheta}}_{\mathrm{v}}(0)=\mathbf{1}_{n_{\varepsilon}}/p_{0}\), \(P(0)=p_{0}\mathbf{1}_{2n}\), \(J(0)=0\), \(p_{0}=10^{6}\). 2. Collect the input-output data \(u(k)\) and \(y(k)\). Construct \(\mathbf{\phi}_{y}(k)\) using Equation (44), \(\mathbf{\phi}_{\mathrm{u}}(k)\) using Equation (45) and \(\mathbf{\phi}_{\mathrm{s}}(k)\) using Equation (29). 3. Compute \(\mathbf{P}(k)\) using Equation (28) and \(J(k)\) using Equation (31). Update the parameter estimate \(\hat{\mathbf{\vartheta}}_{\mathrm{LS}}(k)\) using Equation (27). 4. Construct \(\hat{\mathbf{\phi}}_{\mathrm{n}}(k)\) using Equation (35) and \(\hat{\mathbf{\phi}}_{\mathrm{v}}(k)\) using Equation (36). Compute \(\hat{\mathbf{\varepsilon}}(k)\) using Equation (37) and \(\hat{\mathbf{\vartheta}}(k)\) using Equation (38), and compute \(\mathbf{P}_{\mathrm{v}}(k)\) using Equation (40). Update the noise parameter estimate \(\hat{\mathbf{\vartheta}}_{\mathrm{v}}(k)\) using Equation (39). 5. Compute \(\hat{\mathbf{\xi}}(k)\), \(\hat{\mathbf{Q}}(k)\) and \(\hat{R}(i)/\hat{\delta}(k-1)\) using Equations (32)-(34). Compute \(\hat{\delta}(k)\) using Equation (30) and \(\hat{\mathbf{\vartheta}}_{\mathrm{C}}(k)\) using Equation (26). 6. Read \(\hat{\mathbf{\vartheta}}_{\mathrm{g}}(k)\) and \(\hat{\mathbf{\vartheta}}_{\mathrm{h}}(k)\) using Equation (29). Acquire the parameter estimates \(\hat{g}_{i}(k)\) and \(\hat{h}_{i}(k)\) using Equations (41) and (42). 7. Construct \(\hat{\mathbf{\phi}}_{\mathrm{e}}(k)\) using Equation (46) and \(\hat{M}(k)\) using Equation (47). 8. Compute the state estimate \(\hat{\mathbf{x}}(k)\) using Equation (43). 9. Let \(\hat{\mathbf{\vartheta}}(k):=\left[ \begin{array}{c}\hat{g}(k)\\ \hat{\mathbf{h}}(k)\\ \hat{\mathbf{\vartheta}}_{\mathrm{v}}(k)\end{array} \right]\). If \(k<L\), increase \(k\) by one, and go to Step 2; otherwise, stop, and obtain the parameter estimation vector \(\hat{\mathbf{\vartheta}}(L)\).
## 4 Experimental Evaluation
### Quantitative Analysis  
The proposed UA-BFET model is compared with some existing state-of-the-art baseline models based on unaligned data sequences. The methods based on word forced alignment include early fusion LSTM (EF-LSTM), late fusion (LF-LSTM), Multimodal Factorization Model (MFM) [8], Recurrent Attended Variation Embedding Network (RAVEN) [13], Hierarchical Feature Fusion Network (HFFN) [10], Multimodal Cyclic Translation Network model (MCTN) [9], Modality-Invariant and -Specific Representations (MISA) [11], and Multimodal Split Attention Fusion (MSAF) [42]. The methods based on word unaligned include Multimodal Transformer (MuIT) [14], Low-Rank Matrix Factorization Transformer (LMT-MULT) [35], Learn Modality-Fused Representations with CB-Transformer (LMR-CBT) [15], Self-Supervised Multi-task Multimodal sentiment analysis network (Self-MM) [24], Unimodal Reinforced Transformer (UR-Transformer) [17], Progressive Modal Enhancement (PMR) [18], and Weighted Cross-Modal Attention Mechanism [36].
# Radioactivity in Soils of Kosovo and Radiological Implications
## 2 Materials and Methods
### Sample Collection and Preparation  
For a homogeneous sampling process in the whole country, the cluster sampling method was chosen [18]. The territory of Kosovo was divided into thirty equal surface units with a rectangular shape. One cell represented around 363 km\({}^{2}\), and a soil sample was taken from each. A total of 30 soil samples were taken in the year 2020. Geographical sampling locations were recorded by portable GPS during sampling time. The digital map with the sampling location was prepared using free open-source QGIS. The location distributions of the sampling are illustrated in Figure 1. The soil samples, were collected using a shovel, from 0 to 5 cm in depth. The soil samples were removed from unwanted redundancies (stones, vegetation, and roots) and were placed in clean, zip-locked bags (1-2 kg). The samples were then transferred to the laboratory. They were dried in an oven at 60 \({}^{\circ}\)C for 48 h, grained, passed through 2 mm sieves, weighed, and transferred into uncontaminated, empty Marinelli beakers of uniform size. Finally, the samples were stored for four weeks to allow the daughter products to come into radioactive secular equilibrium with their parents, \({}^{226}\)Ra and \({}^{222}\)Rn.  
Figure 1: The map of Kosovo and the location of the sampling points.
# Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
## 6 Simulation Research
### Simulation Results
#### 6.2.2 Pareto Fronts Obtained by MPC_CPDMO-NSGA-II Algorithm  
Figure 10 give the Pareto fronts obtained by MPC_CPDMO-NSGA-II in a receding horizon. All of the objectives are normalized. It can be seen that there is no conflict between TTS and TTD, but they are obviously conflicting with sum of TE and TF respectively. The conflicts are similar.  
Figure 10: Pareto fronts in a horizon of receding optimization with MPC_CPDMO-NSGA-II. **(a)** The Pareto front of total time spend (TTS) and total travel distance (TTD); **(b)** The Pareto front of TTD, and TE and TF; **(c)** The Pareto front of TTS, and TE and TF.  
Figure 9: Emissions and fuel consumption of the freeway with fixed speed limit.  
Figure 11 displays the control solution with MPC_SOO and MPC_CPDMO-NSGA-II, including VSLs for segment 1 and 2, and RM strategy. In Figure 11, the actual road network is taken into account, only VSLs are considered for segment 1 and 2, and no VSLs are implemented in Section 3. However, we supplement the flow, density and speed of segment 3 under different methods in Table 4. In terms of safety, the MPC_CPDMO-NSGA-II offers a smoother solution that can reduce the risk of driver's slow respond to changes. From 10 min to 1 h, the value of VSLs are small with proposed method. Afterwards, it provides larger VSLs most of the time. As for ramp metering rate, it allows more vehicles to enter into the mainline than MPC_SOO most of the time.
## 4 Model Selection and Development  
### Model Selection  
In the study, a linear programming (specifically DLP) model was used to achieve a cost-effective, sustainable and demand-responsive shuttle service at the Yalova Shipyard. Dynamic linear programming (DLP) is an optimization method where complex problems are broken down into simpler stages solved sequentially, and optimality is preserved throughout. It is particularly suited for problems that involve sequential decision making and clearly defined stages and states [65]. It is the most suitable optimization method for the study because it provides guaranteed optimal solutions, efficiently manages structured and linear constraints and aligns closely with the sequential decision-making nature inherent in fleet scheduling and vehicle allocation problems. DLP is particularly effective for problems involving clearly defined linear constraints, such as vehicle types, capacities, scheduling restrictions, driver availability and route feasibility. Because employee shuttle services involve sequential and interconnected decisions, such as assigning appropriate vehicles, designing schedules and routing, DLP's structured approach efficiently identifies globally optimal solutions. Moreover, DLP offers predictability and consistent results, making it  
Figure 5: Average occupancy rate of services by days.  
highly reliable for real-world operational decision making where accuracy and stability are critical [66].  
Unlike metaheuristic approaches, such as genetic algorithms (GAs), simulated annealing (SA), tabu search (TS) and ant colony optimization (ACO), which explore solutions stochastically and typically deliver approximate or near-optimal outcomes without guarantees, DLP ensures precise and globally optimal solutions. This is especially crucial in shuttle fleet management, where constraints such as vehicle type limitations, passenger capacities, route assignments and scheduling are rigid, clearly defined and linear. Furthermore, DLP's deterministic and predictable performance provides reliable and stable solutions required for operational implementation, whereas heuristic and metaheuristic methods can introduce variability and uncertainty due to their stochastic nature and complex parameter tuning. Therefore, considering the need for reliable, optimal and structured management solutions in shuttle service scenarios, DLP clearly emerges as the best choice over alternative optimization and metaheuristic methods. A performance comparison of the DLP and other optimization methods is summarized in Table 5 below.  
In conclusion, DLP stands out as the most suitable optimization method for managing an employee shuttle service under complex yet clearly defined inhomogeneous fleet constraints. Its capacity to guarantee optimal solutions, effectively manage structured constraints, support sequential decisions and provide stable, predictable results aligns precisely with the operational demands and precision required by this specific service management scenario.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 3 Experimental Results
### Watermark Detection for G-Code Programs and Voxel Models  
After testing the encoder, we conducted another experiment to evaluate the decoder: At first, we fed the G-code programs to the simulator and virtually manufacture three voxel models. These contents were then processed by the decoder to extract the hidden watermarks. The extracted watermarks are displayed in the lower row of Figure 7. At the following step, the decoder calculated the dHash values between the extracted and recorded watermarks. The dHash values were represented by 128-bit binary strings. Finally, the similarities between the extracted and recorded watermarks were computed by using the dHash values, based on Hamming distances [28]. The results are presented in Table 2.  
The test models are not the original ones but reproduced by using the G-code programs. However, the G-code programs are genuine, and thus the test models should be regarded as legitimate copies of the raw models. As the test results shown in Table 2, the similarities between the detected and recorded watermarks are high. Therefore, our decoder successfully verifies these contents. Furthermore, the genuineness of the G-code programs is also implicitly asserted in this experiment. The efficacy of our decoder on authenticating G-code programs and geometric models were proven in this experiment.  
Among the test models, the mug generates the highest similarity while the tetrapod produces the lowest score. The tetrapod is relatively complex. The G-code generation and virtual manufacturing process induces more geometric noises into its virtual model. Thus, the similarity between the extracted and recorded watermarks is decreased. On the other hand, the mug has a simple shape, such that the watermark preserves its pattern after the digital-to-analog and analog-to-digital conversions. Hence, the captured and recorded watermarks of this model are more similar.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Similarities** \\ \hline Tetrapod & 0.91504 \\ Bowl & 0.93750 \\ Mug & 0.94434 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Similarity test results.  
Figure 7: The recorded and extracted watermarks from the tetrapod (**left**), the bowl (**middle**), and the mug (**right**). The recorded and extracted watermarks are shown in the upper and lower rows, respectively.
## 1 Introduction  
### Background and Motivation  
In recent years, a demand for clean and renewable energy resources has increased significantly because of the air pollution caused by traditional fossil fuels. Wind power, which is one of the most promising renewable resources, proved to be an ideal alternative. Currently, wind energy has been successfully employed in many countries, representing approximately 10% of energy consumption in Europe and more than 15% in countries like America and Spain [1]. However, wind speed, which is one of the most essential factors in wind power generation, is difficult to forecast due to many natural factors such as pressure, temperature, and the rotation of the planet.  
Therefore, in the development of wind energy, wind speed forecasting is rather important. The accuracy of the forecasting result can influence the wind rotating equipment, operation cost,and the limitations of wind power penetration. With a precise prediction of wind speed, the dispatching department is able to make adjustments to the program efficiently, so that the influence of wind energy itself on the power station and the adverse effect of the wind farm on the power system can be minimized, making wind power more competitive in the global electricity market.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 6 Open Challenges and Future Research Directions
### Real-Time Constraints in IoT and Edge Computing  
In IoT and edge computing environments, real-time constraints require that detection mechanisms operate within strict timing and resource limitations. Sensor nodes in these networks often operate on microcontrollers with limited computational power and energy reserves, frequently entering sleep modes to conserve energy [53]. Detection algorithms that are computationally intensive or have prolonged execution times are impractical, as they could prevent timely responses to threats like IFAs and deplete the nodes' limited resources. The integration of detection mechanisms must be carefully managed to avoid interfering with the primary functions of the nodes, ensuring that critical tasks are not delayed or missed due to the overhead introduced by security processes [53].  
To address these challenges, future research should focus on developing lightweight, online algorithms optimized for microcontroller-based implementations. Lightweight solutions may involve simplifying existing detection methods or designing new algorithms with low computational complexity from the ground up. Implementing interrupt-driven detection mechanisms can provide rapid responses to potential attacks; when a node's PIT reaches a predefined threshold, an interrupt could trigger immediate mitigation actions, thereby preventing PIT overflow and maintaining network stability. Testing and designing these detection systems within Real-Time Operating Systems (RTOSs) is crucial. These RTOS platforms are designed for embedded systems, offering features that support real-time constraints and efficient resource management, making them suitable environments for deploying and evaluating IFA defense mechanisms in IoT contexts [48].
## 3 Available Photovoltaic Software
### Planning and Analysis Tools  
Planning and analysis tools are suitable to help users in planning, designing, sizing, optimizing sources, and defining proposed system to the highest standard. Some software provides database of PV components available from market to help users design their system in detail. Due to diligence review in United States, PVsyst is the most commonly used for project development [45]. The founder of PVsyst is a graduate from the University of Geneva, Switzerland. PVsyst is software that able to size, design, simulate and analyze grid connected, standalone and DC-grid connected PV system. Beside Meteo Database existed in the software, this software can also import meteorological data available from web. The software also has a PV-components database that is available in the market. This system also provides variations of parameter settings possibility in the design with high precision result. However, this software does not allow interconnection with other programs. Besides, the sizing is also restricted to collector configuration. There are two types of PVsyst products, which are Pro30 (maximum 30 kW installation) and Premium. For Pro30 (unlimited installation), the price for first licence is CHF 1000, and CHF 1300 for premium product. While, for second license, the price is CHF 700 and CHF 1000, and for third licence, the price is CHF 500 and CHF 700 for both Pro30 and premium product [3; 57; 48]. PV*SQL Expert is a 3D design software for simulates and data analysis of roof-parallel and roof-integrated PV system performances. This software is suitable to plan PV system, from small off grid to large grid connected system. The developer is Solar Design Company from Powys, UK. This software also analyses shading effect on energy performances [13]. Besides, it also gives optimization configuration of PV modules among inverters. PV*SQL objectives include quick design,financial analysis and gives suitable proposal. The outputs included yield report, system efficiency, losses and economic reports. Unfortunately, interconnection with another program is not possible [59]. Laplace System Co., Ltd. (Kyoto, Japan), develops SolarPro Japan. Same as PV*SOL Expert, SolarPro is also 3D design software, and able to calculate shading losses. However, this software can only be used to simulate grid connected photovoltaic system only. Its database subsists meteorological from more than 8000 sites and PV components. SolarPro provides output in term of I-V curve, and power generation based on latitudes, longitudes, and weather conditions. Economic analysis in term of life cycle cost is also available in the software.