esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Decomposition Results Based on Different Regression Models  
#### 3.2.1 Model Training Results for Population Density Regression on Subdistrict Scale  
As mentioned above, the population densities of all the subdistricts were taken as the dependent variables (Y), while the values of nineteen influence factors were selected as the independent variables (X). The twelve models were trained using the Grid  
From Tables 4 and 5, it can be seen that the models trained by the various algorithms presented obvious differences. When the RMSE metric is used as the only indicator to measure model accuracy, the BSR model is the best model, while the SVR model is the worst model. The results demonstrate that the BSR model can find the optimal estimator through loop traversal in theory. When the MAPE metric is used as the only indicator to measure model accuracy, the KNN model is the best model and the PCR model is the worst model. The models trained by the twelve algorithms are effective when the R\({}^{2}\) indicator is applied as the only metric for measuring the model's performance. Generally speaking, when the accuracies of several models are relatively close, simple models should be given priority over complex models. Among the nine linear regression models, the LARS model is the simplest one, with only four independent variables. The PCR model is the second
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 7 Discussion and Future Research
### Automated High-Fidelity Emulation for Complex and Custom Peripherals  
Firmware in safety-critical applications demands high-fidelity emulation for fuzzing, as even minor errors can have severe consequences. For instance, small mistakes in ADAS firmware can result in traffic accidents, and deviations in medical IoT device firmware could endanger lives. However, devices in automotive and industrial sectors often use complex or custom peripherals that current emulators cannot accurately replicate. For example, there is no existing tool that can automatically model complex peripherals like USB or WiFi with precision. As a result, firmware analysis for these devices relies heavily on statistical analysis or custom emulation models, requiring significant manual effort. Automating the accurate generation of complex peripheral emulation models without real hardware is a major technical challenge.  
Advancements in AI technology offer new opportunities for achieving high-fidelity peripheral emulation more automatically. For instance, _Conware_ converts logs of real peripheral interactions (such as interrupts, reads, and writes) into directed acyclic graphs (DAGs) and uses a novel graph-transformation technique to generate high-fidelity peripheral models. Advanced Reinforcement Learning [52] can also be applied to continually learn from recorded logs, enhancing peripheral modeling. Similarly, SEmu utilizes NLP to automatically generate peripheral models from hardware manuals, improving fidelity for complex peripherals. The development of large language models like GPT-4, which have shown greater capability than traditional NLP [53], may further enhance peripheral modeling by learning from hardware manuals. With the advancement of LLMs, we can train specialized models for firmware emulation using extensive firmware datasets, hardware manuals, and open-source MCU software stacks in future.
## 3 Atmospheric Conditions  
### Synoptic Conditions  
At 00 UTC on 12 January 2013, about the middle of the UWKA flight period, the MBM region in southern Wyoming was in a cold airmass, with a low tropopause overhead. A deep upper-level trough was present to the southwest of the MBM, with a closed circulation at all levels up to 300 hPa, and a strong southwesterly 300 hPa jet across the Great Plains (Figure 2a). The 500 hPa flow was rather weak in the cold airmass over the MBM region, with several weak vortices evident in the NAM data (Figure 2b). The low-level cold-frontal trough was well east of the MBM, extending from eastern North Dakota to eastern Colorado (Figure 2c). At the surface, the cold-frontal trough was further to the east, e.g., in southeastern Nebraska, and was followed by cold-air advection across the northern Great Plains (Figure 2d). The wind in the MBM area was generally westerly from the surface to 300 hPa, slightly backing with height (Figure 2), except for the 400 hPa level, where the flow was weak from the southeast (not shown). At \(\sim\)30 knots, the 700 mb (near MBM crest level) westerly flow in the MBM area was not exceptionally strong (Figure 2c).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline Domains: \(\Delta\)x = \(\Delta\)y & d01: 9 km & d02: 3 km & d03: 1 km \\ \hline Simulation time & \multicolumn{3}{c}{30 h starting on 11 January 2013 at 00 UTC} \\ \hline grid points & 241 \(\times\) 121 & 181 \(\times\) 181 & 181 \(\times\) 181 \\ time steps (seconds) & 30 & 10 & 3.3 \\ driver & 12 km NAM & nested & nested \\ vertical level & & 60 layers & \\ \hline PBL and Turbulence & MYJ scheme [38] and Horizontal Smagorinsky first-order closure \\ \hline SW radiation & \multicolumn{3}{c}{Dudhia scheme [41]} \\ LW radiation & \multicolumn{3}{c}{RRTM scheme [40]} \\ land surface & \multicolumn{3}{c}{Noah land-surface Model} \\ microphysics & \multicolumn{3}{c}{WSM 6-class graupel scheme [39]} \\ \hline \hline \end{tabular}
\end{table}
Table 1: WRF model configuration and domains used in this study.
# Enhancing Efficiency of Electromagnetic Simulation in Time Domain with Transformation Optics
## 2 Coordinate Transformation Applied in Simulation and Mapping Functions
Figure 2: Schematic side view (**a**) and top view (**b**) of the configuration for the finite-difference time-domain (FDTD) simulation employed with transformation optics (TO) to generate non-uniform grids. The aperture width \(w\), length \(l\), and height \(h\) are fixed to 100, 300, and 100 nm, respectively. The film extends to the entire \(xz\) plane of the physical system, and its lengths are \(L_{x}=L_{z}=8\)\(\upmu\)m. The film is illuminated by a broadband incident electromagnetic (EM) wave from the top with the electric field polarized along the \(x\) axis. the points \(\{X_{1},\,\ldots,\,X_{j},\,\ldots,\,X_{m}\}\) are defined as the end points of the regions starting from \(x=0\), where \(j\) is an integer varying from 1 to \(m\), and \(X_{m}=L_{x}/2\). For simplicity, we assume that each region has a constant cell size. Then, the mapping function can be expressed as the combination of the multiple-linear functions. For \(x>0\), we obtain:  
\[u=f(x)= \begin{cases}\frac{\Delta u}{\Delta X_{1}}x,&0<x\leq X_{1}\\ \frac{\Delta u}{\Delta X_{2}}x+U_{1}-\frac{\Delta u}{\Delta X_{2}}X_{1},&X_{1} <x\leq X_{2}\\ \vdots\\ \frac{\Delta u}{\Delta X_{j}}x+U_{j-1}-\frac{\Delta u}{\Delta X_{j}}X_{j-1},&X_ {j-1}<x\leq X_{j}\\ \vdots\\ \end{cases}  \tag{5}\]  
where \(\Delta X_{j}\) represents the cell size in the region \(j\), and:  
\[U_{j}=U_{j-1}+\frac{\Delta u}{\Delta X_{j}}(X_{j}-X_{j-1}). \tag{6}\]  
In this case, \(X_{0}=U_{0}=0\). The mapping function should be odd. For \(x=0,f(x)=0\); for \(x<0\), we let \(f(x)=-f(-x)\). In another demonstration, we could also use a nonlinear function to generate \(x\)-varying cell size in the most outside region \(X_{m-1}<x<X_{m}\) since it is considered less important to the accuracy. Since the gradual decrease of the slope is required, an inverse tangent function might be suitable. Thus, in the region, the function can be expressed as:  
\[u=\frac{1}{\alpha}\tan^{-1}(\frac{x}{\gamma}), \tag{7}\]  
where \(\gamma\) and \(\alpha\) can be determined by the end point of the previous region \(x=X_{m-1}\) and the minimum slope we design for the boundary \(x=X_{m}\). In this case, \(U_{m}\) is decided mathematically by these conditions. As a result, the \(x\)-varying cell size of the grid centered at \(x=x_{i+1/2}\) in this region is:  
\[\Delta x_{i}|_{x=x_{i+1/2}}=\frac{\alpha\gamma}{\cos(\alpha u_{i+1/2})}\Delta u, \tag{8}\]  
where \((U_{m-1}+L_{u}/2)/\Delta u-n<|\,i\,|<n\).
## 4 Discussion and Limitation
The result implies that both the frequency of clinical records and data quality are important for AKI prediction model. Our study has several limitations. First, our cohort is derived from a single hospital, and the prediction algorithm has not been externally validated with other cohorts which may limit its generalization. Second, the best feature window length and time gap length may vary depending on the availability of the clinical records. However, our work could still provide a practical data mining process for developing a prediction model in ICU.
# Compression of Vehicle Trajectories with a Variational Autoencoder
## 3 The Training Dataset  
The data set for training is \(\mathcal{X}\equiv\mathbb{R}^{d\times s}\) and every \(x\in\mathcal{X}\) data point is a vehicle trajectory with sequence length \(s\) and feature dimension \(d\). Sequence length is \(s=60\), which means \(6\,\mathrm{s}\), and the feature dimension is \(d=2\), which is the longitudinal and lateral coordinate of the vehicle in every time step. The traffic situation is a multi-lane highway road in the US, and the data collection was made by the New Generation Simulations (NGSIM).  
The NGSIM Unites States Highway 101 (US-101) (Figure 1) and Interstate 80 (I-80) (Figure 2) datasets [25; 26] are used for training and evaluating the trajectory autoencoding problem. This trajectory data has precise location, velocity, and acceleration of each vehicle within a specific area every \(0.1\,\mathrm{s}\). Furthermore, it provides relative positions to surrounding vehicles and lane position in every frame. There are \(11.8\) million rows and \(25\) columns in this dataset. Each row represents one vehicle in a specific frame with all the information. The columns in ascending order are the following: vehicle identification number, frame identification number ascending by start time, the total number of frames in which the vehicle appears, global time, lateral (\(x\)) and longitudinal (\(y\)) coordinate of the vehicle's front center concerning the left-most edge of the section in the direction of travel, global \(x,y\) coordinate, length and width of the vehicle, vehicle type (1--motorcycle, 2--auto, 3--truck), instantaneous velocity and acceleration, and lane position.
## 2 Experimental Test  
### Materials  
The experimental work implemented two main materials, Ordinary Portland cement (OPC) CN PA-L 42.5 type 1 and OWA. The OPC, which was obtained from the Sabaa plant in Tripoli, Lebanon, presented a particle size distribution adequate for proper cement hydration. The OWA obtained from Zgharta, North Lebanon, had a density of 950 kg/m\({}^{3}\) and a water absorption rate of 28%. Additionally, the morphology of OWA particles was characterized by a rough texture and angular, irregular, or partially rounded shapes. Upon the cultivation of olive trees, stems, leaves, and any fragments not used in the oil extraction mechanism were removed. The traditional oil extraction method started with washing the harvested olives with water. These cleaned olives were milled between two mats to produce a fine paste, facilitating the release of oil. After extraction, the paste underwent a centrifugation technique to separate the solid particles suspended in the olive oil, resulting in virgin olive oil and olive pomace as the solid residue. The olive pomace, which was collected from an olive press, was then burnt in a closed-system boiler at a high temperature of 500 \({}^{\circ}\)C for approximately 8 h until it was transformed into ashes. Once cooled, the resulting ashes were subjected to grinding using a Los Angeles machine to break down larger particles. The final step involved sieving the ashes through a Nb 200 sieve to obtain fine OWA particles. A brief description of the process of olive waste ash (OWA) production is clarified in Figure 1. The OWA's particle size distribution is shown in Figure 2, and its chemical composition is depicted in Table 1. The analysis indicated that the OWA was primarily composed of silicon (Si) and calcium (Ca), rendering it a highly appealing precursor for pozzolanic materials [24].  
Figure 1: Process of OWA preparation.  
Figure 2: Particle size distribution of OWA and cement.
## 4 The SMA-LRBs
### The Hysteresis Model of SMA Wires
#### 4.2.4 Efficiency of Different Wire Configurations  
An essential prerequisite for the efficient performance of SMA-LRB is for the SMA wires to stay in their super-elastic range under lateral displacements. The SMA alloy that is used in this study is FeNiCoAlTaB, with 13.5% maximum super-elastic strain. As explained above, the strain in the wire is a function of wires' configuration and LRB's dimension and is independent of the wire's radius. Here, LRBs geometry is constant; therefore, the strain is only dependent on the wires' arrangement.  
In order to find the range of shear strain that each configuration can remain applicable, the induced strain in wires due to a continuous range of shear strain (i.e., lateral displacement amplitude divided by total rubber height), \(\gamma\), ranging between 0% and 250% calculated and plotted for all arrangements of wires in Figure 8. The wire strain is calculated using Equations (7) to (14). As can be observed, shear strain in the cross configuration is lower than in the others, but the performance of double-cross and straight wires are almost the same. At the ultimate shear strain, the induced strain in straight, cross and double-cross wire is 10.44%, 3.23%, and 11.39%, respectively, which is below the super-elastic strain limit of the iron-based SMA alloy. Thus, with this LRB dimension and this SMA alloy, wires in all three configurations can perform effectively.  
It is worth mentioning that, for shear strain levels between 50% and 150%, the strain in SMA wire with cross arrangement varies between 0.13% and 1.18%, which is not high enough to create the flag-shaped hysteresis of SMA. Therefore, in lower shear strains, the cross configuration is not as effective as the other arrangements.
# Convergence and Stability of Explicit and Implicit Schemes in the Simulation of the Heat Equation
## 4 Explicit Methods: Centered, Forward and Backward in Time Differences
### Direct Finite Difference of First Order
#### 4.1.2 First-Order Centered Difference  
To obtain the centered finite difference approximation, we start from Equations (9) and (10), shown below:  
\[\phi_{i+1}=\phi_{i}+\Delta x\left.\frac{\partial\phi}{\partial x}\right|_{x_{i }}+\frac{\Delta x}{2}\left.\frac{\partial^{2}\phi}{\partial x^{2}}\right|_{x_ {i}}+\frac{\left(\Delta x\right)^{3}}{3!}\left.\frac{\partial^{3}\phi}{ \partial x^{3}}\right|_{x_{i}}+\ldots \tag{9}\]  
\[\phi_{i-1}=\phi_{i}-\Delta x\left.\frac{\partial\phi}{\partial x}\right|_{x_ {i}}+\frac{\Delta x}{2}\left.\frac{\partial^{2}\phi}{\partial x^{2}}\right|_{ x_{i}}-\frac{\left(\Delta x\right)^{3}}{3!}\left.\frac{\partial^{3}\phi}{ \partial x^{3}}\right|_{x_{i}}+\ldots \tag{10}\]  
By subtracting the results of Equation (10) from Equation (9)  
\[\frac{\partial\phi}{\partial x}\Big{|}_{x_{i}}=\frac{\phi_{i+1}-\phi_{i-1}}{2 \Delta x}+\text{O}\Big{(}\Delta x^{2}\Big{)} \tag{11}\]  
This is the centered difference approximation to \(\left(\frac{\partial\varnothing}{\partial x}\right)x_{i}\), the truncation error for approximation goes to zero much faster than the truncation error in Equation (7) or (8). There is a complication with Equation (11) because it does not include the value for \(\varnothing_{i}\). This can cause problems when the centered difference approximation is included in an approximation to a differential equation.  
Figure 3: Finite difference backward in time (BTCS). Known values are indicated with black circles and unknown values with a white circle.
# Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators
## 2 Modeling and Control
### Control  
There are different possibilities for control strategies [21] of the membrane oxygenator. One example would be the maximization of the CO\({}_{2}\) flux through the membrane. Another objective could be to set the CO\({}_{2}\) flux at the desired value. In this work, we investigated a strategy that sets the concentration of CO\({}_{2}\) in the blood at the desired value. This strategy was applied with three different algorithms--a classic PI controller, a modern non-linear model predictive controller, and a deep neural network-based reinforcement learning controller. Each controller acts both on the blood flow rate and the gas flow rate and has access to all system states.
# Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images
## 4 Discussion and Conclusions
The attention, in this scenario, shall be paid to the transition edge from dark to bright sea: if the border is almost linear in the tile, it can lead to false wake identification, as the one shown in Figure 8. This has occurred in one out of three tested trials. The analysis of scenarios increases the understanding of wake recognition performance in different conditions, showing the potentialities of a Radon-based approach for wake detection also in the cases of dark vessels. This analysis paves the way to future investigations in which the benefits, and the limitations, of Radon-based approaches must be recast in a more complete framework, i.e., the data fusion with other techniques. This includes the analysis of wind field, especially in the case of low wind speed areas, and the on-going studies on wake recognition by CNN. Indeed, it is well-known that the processing of very wide scene is a challenge for the Radon-based technique. Similarly, letting a deep network be aware of the quality of its prediction is yet an important problem [37]. Hence, combining the two approaches, e.g., in a cascade prescreening/discrimination fashion, can offer indisputable capabilities of either fast processing of wide scene (by CNN) and of keeping the quality of the classification of wake component (by Radon-based approaches), both required for an effective maritime surveillance. Methodology, M.D.G. and A.R. ; software, M.D.G. ; validation, M.D.G. and A.R. ; writing--original draft preparation, M.D.G. ; writing--review and editing, A.R. All authors have read and agreed to the published version of the manuscript. This research was funded by the University of Naples \"Federico II\", in the framework of \"PROGRAMIA PER IL FINANZIAMENTO DELLA RICERCA DI ATENEO\"--Project: A wake Revelation system for GO-fast vessels (ARGO). Not applicable. **Data Availability Statement:** SAR images listed in Table 1 were made available by European Space Agency by Copernicus Open Access Hub. AIS data were made available by NATO Science & Technology Organization Centre for Maritime Research and Experimentation (STO-CMRE).
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### General Observations
#### 4.4.2 Exponential Mobility  
In this model, vehicles follow independent random speed distributions, causing frequent changes in network topology. This creates a more dynamic and challenging environment for VANETs. With lognormal path loss, even though signal strength weakens steadily, the frequent topology changes due to varying speeds can cause link breakage, requiring more robust routing protocols. In the Rayleigh model, the already erratic signal strength is further disrupted by exponential mobility, making it difficult to maintain stable communication links. With the more general Nakagami-\(m\) model, similar to synchronised mobility, network performance depends on the \(m\) parameter. However, maintaining connections becomes more challenging due to the dynamic topology. Lastly, with Weibull path loss, network behavior is influenced by the path-loss exponent. Higher exponents provide some stability, while lower exponents exacerbate the difficulty of maintaining connections in a rapidly changing network.  
In summary, synchronised mobility provides a more predictable environment for VANETs, particularly with path-loss models like lognormal, which experience a steady decrease in signal strength, similar to a static environment. In contrast, exponential mobility poses a significant challenge due to the dynamic network topology and frequent link disruptions. Path-loss models with more severe fading (such as Rayleigh, higher Nakagami-\(m\), or lower Weibull exponents) exacerbate these challenges. Additionally, obstructions caused by big vehicles make communication in VANETs even more predictably difficult.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 4 Fault Ride-Through Control Strategies
### HVRT Strategy in Generation Condition  
Based on previous analysis, regardless of the operating conditions, high voltage faults cause DC overvoltage, GSC overmodulation, and power imbalance under all operating conditions. LVRT strategies from power generation scenarios are applicable to address HVRT issues. Overmodulation typically occurs when grid voltage surpasses the GSC's controllable range. Therefore, regardless of the unit's operating conditions, injecting a certain amount of inductive reactive power can help reduce the AC voltage of the GSC. Similar to reactive power compensation in LVRT, HVRT can also adopt the reactive power compensation strategy. The reference value of reactive current is as follows:  
\[i_{gpref}\geq K_{1}(e-1.1)i_{N},\ \ 1.1\leq e\leq 1.35 \tag{18}\]  
If the power grid experiences excessive overvoltage, transient power imbalances can significantly raise bus voltage. To keep DC voltage within safe limits, the DC crowbar circuit can dissipate excess energy using the dynamic braking resistor.
# Functionality Investigation of the UAV Arranged FMCW Solid-State Marine Radar
## 3 Experimental Setup
The payload was implemented as stand-alone equipment with its power supply (Li-Po battery), GNSS receiver dongle, and communication systems as presented in Figure 2.0 It provides simple integration of the payload into the UAV to test the proof of concept. Usually, the Simrad 4G\({}^{\text{TM}}\) radar is connected to the chart plotter via interface RI-11. Both would add unnecessary weight if used. We were able to bypass the usage of those components by using open source code software OpenCPN to control the radar and display radar measurement data. The whole radar system was arranged at the bottom of the UAV in a bottom-up position to achieve greater stability of the UAV. The Simrad 4G\({}^{\text{TM}}\) radar was powered directly from an 11.1 V Li-Po battery. The radar data was sent via Ethernet cable connected to a Wi-Fi station module with a simple network switch in between. Radar, AIS, and GNSS position data were collected by the Raspberry Pi 2 computer and transferred together with the radar data stream in real-time via an ordinary 5.8 GHz Wi-Fi communication link to the computer on the ground.