esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Co-Simulation System
### TruckSim Model
#### 2.1.3 The Driver Model  
The driver model utilizes closed-loop control, coupled with both the road and vehicle models, forming a human-vehicle-road closed-loop system. Within this system, the car and road are regarded as the controlled elements. The TruckSim driver model comprises primary control modules, including steering control, speed control, and braking control. These modules collaborate to simulate the driving behavior of actual vehicles during uphill driving processes.  
Steering control ensures that the vehicle follows the predetermined path, guaranteeing that the vehicle travels along the intended route. Speed control regulates the entry speed of the vehicle. In this simulation, the entry speed is set to 80 km/h, 100 km/h, and 120 km/h. On horizontal road segments, the vehicle travels at the entry speed. When the vehicle encounters an uphill segment, the speed naturally decreases from the entry speed due to limited power output. No braking measures are applied to ensure that the vehicle speed is not influenced by braking.  
The steering control is used to manipulate the vehicle's trajectory, ensuring it follows the predetermined route. Speed control, on the other hand, is responsible for managing the vehicle's initial velocity. In this simulation, we determined the initial velocities of experimental vehicles on roads with different design speeds based on the recommendations for initial velocities of heavy-duty vehicles in the Chinese highway standard \"Specifications for Highway Safety Audit\" [32]. Taking into account the characteristic that the speed of heavy-duty vehicles decreases faster when entering longitudinal slope sections with higher initial speeds, we set the initial velocities of experimental vehicles on roads with different  
Figure 3: Engine speed–torque curve for fuel-powered vehicles.  
design speeds as follows: for roads with a design speed of 80 km/h, the initial velocity is set at 65 km/h; for roads with a design speed of 100 km/h, the initial velocity is set at 75 km/h; and for roads with a design speed of 120 km/h, the initial velocity is set at 80 km/h. This setup not only complies with the standard requirements but also considers the speed variation characteristics of heavy-duty vehicles on longitudinal slope sections with different design speeds during actual operations. When vehicles are traveling on horizontal road sections, they will maintain the set initial velocity. However, when vehicles are traveling on uphill sections, the limited output of vehicle power will cause the speed to gradually decrease based on the set initial velocity, without the need for braking measures, to ensure that the vehicle speed is not affected by braking.
## 5 Experiments
### Experiment Setup  
In our experiments, we implemented the baselines based on GNNs with Pyg, while for DroidEvolver, we utilized the source codes provided in the original paper. Additionally, we re-implemented HinDroid. These experiments were conducted in the environment specified in Table 5. In addition, we utilized two-hop paths between nodes of different types (e.g., Permission) in methods that required manual specification of meta-paths. It is worth noting that for the generic baselines, we adopted a structure comprising two convolutional layers and one linear layer in our experiments.  
For our proposed framework, we configured the learning rate to be 0.01 and employed the mini-batch technique with a batch size of 512. The weight decay of the optimizer was set to 0.0005. Furthermore, we specified that all multi-headed attention or multi-headed messaging should utilize eight heads, and we set the hidden channels to 128. For the baselines, we set the hyperparameters according to the recommended parameters of each baseline's paper. In cases where the baseline papers did not explicitly specify certain hyperparameters, we aligned them consistently with the configuration of HertDroid.  
With regards to the training techniques, the experiments were optimized using the AdamW optimizer ([PERSON] and [PERSON], 2014). To control the number of epochs, we implemented the early stopping method based on the validation loss, with a maximum of 200 epochs and a patience threshold set to 20 epochs. Furthermore, we employed the mini-batch technique with a heterogeneous graph sampler ([PERSON] and [PERSON], 2014) and a neighbor sampler ([PERSON], 2015) for heterogeneous and homogeneous graphs, respectively. The number of input nodes for loaders was set to 128, and we sampled 512 nodes per iteration for four iterations. It is noteworthy that we used different loaders for the training, validation, and testing sets. This ensures that the application nodes in the testing set do not appear in the training or validation sets, thereby preventing potential information leakage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Environment** & **Version** & **Environment Type** \\ \hline GPU & NVIDIA RTX4090 & Hardware \\ Python & 3.8.10 & Programming language \\ Androguard ([PERSON], 2015) & 3.3.5 & Decompiled tool \\ Pytorch ([PERSON], 2015) & 1.12.1 & Python package \\ Pytorch Geometric ([PERSON], 2015) & 2.1.0.post1 & Python package \\ Networkkit ([PERSON], 2015) & 10.1 & Python package \\ Pandas ([PERSON], 2018) & 1.5.2 & Python package \\ Matplotlib ([PERSON], 2018) & 3.5.2 & Python package \\ Neo4j ([PERSON], 2015) & 4.4.17 & Database \\ \hline \hline \end{tabular}
\end{table}
Table 5: Environment of evaluation.
## 3 Method  
The aim of our work is to provide the scientific community with a system capable of recording data from smartphone sensors (and the possibly paired smartwatch) to be easily used in evaluating and training machine learning techniques. The evaluation and training of these techniques require the availability of datasets containing signals recorded by sensors while subjects are carrying out activities, such as walking, sitting, or running. The subjects range from children to the elderly, with a scarce or marked attitude toward the use of computer technologies. Signals can range from inertial sensors data (e.g., accelerometers, gyroscopes, and compasses) to any kind of physiological or environmental sensor (e.g., pressure, temperature, and heart rate). In addition, these signals may come from sensors hosted in smartphones and/or smartwatches that differ from each other in terms of characteristics (e.g., precision, acquisition rate, and value ranges) depending on their manufacturer. Such signals must be labeled with the true activities carried out by the subjects, who can autonomously choose which activity to carry out and for how long or follow a protocol (i.e., a fixed sequence of actions and relative durations) defined by the researchers.  
Thus, the definition of such datasets requires systems that are easy to use even by inexperienced users and that are easily configurable. To this aim, simple system configurations (e.g., a list of smartphone and smartwatch positions) should be possible without the applications having to be recompiled and deployed to the device. The data acquisition component must be designed in such a way that it does not depend on the physical characteristics of specific sensors. This allows a portable application that automatically adapts to the actual execution context to be obtained. The data labeling should be as consistent as possible to the activity the user is performing. Finally, the data and their labels should be accompanied by a set of additional information to be used in the technique evaluation. Such information may include, for example, the sensor type, position, and acquisition frequency.  
An acquisition and labeling system must therefore address the following requirements: _labeling accuracy:_ the data must be labeled according to the corresponding performed activity; _usability:_ the system must be easily used to avoid mistakes and must be easily configured without the presence of a programmer; _self-adaptability:_ the system must be able to be used with different smartphones, smartwatches, and sensors; _completeness:_ the system must acquire and provide additional information to improve the dataset usage in the experimentations. Such requirements result in the following guidelines for the design of the system: _data reliability, configurability, user friendliness, hardware adaptability,_ and _data meaningfulness._ Figure 1 sketches the identified requirements, the corresponding guidelines, and the design and implementation choices that fulfil them. The following subsections detail the guidelines and the identified design and implementation solutions.
## 2 Materials and Methods  
The experimental setup of the external cavity MgO:APLN-MOPO is shown in Figure 1. The pump source of the MOPO was an AO Q-switched Nd:YVO\({}_{4}\) laser working at 1.064-\(\mu\)m, the maximum output power was 40 W with the pulse width of 9.86 ns@60 kHz. The laser crystal was an adhesive-free bond a-cut YVO\({}_{4}\)/Nd:YVO\({}_{4}\) crystal with 0.25 at% Nd concentration. Its cross section was 3 mm \(\times\) 3 mm, and it consisted of a 16 mm Nd\({}^{3+}\)-doped YVO\({}_{4}\) and two 4 mm pure YVO\({}_{4}\). The output 1.064 \(\mu\)m laser was converted to linear polarization after the polarizer. An optical isolator and a half-wave plate(HWP) were used to isolate the 1.064 \(\mu\)m laser returning to the pump source, and the laser was focused into the MgO:APLN crystal by a convex lens with a focal length of 150 mm. To eliminate the walk-off effect between the beams, we used the maximum nonlinear coefficient d\({}_{33}\) of the MgO:APLN crystal. The multiple OPO cavity consisted of M1, a beam splitter (BS), and a concave mirror M2; the parameters of the cavity mirror coating are listed in Table 1.  
In a previous study, we designed an aperiodically poled MgO:LN (MgO:APLN) domain structure for two-phase mismatch compensation; the phase mismatch and the polarization structure is shown in Figure 2 and the inset, respectively. The superlattice was customized and produced by the HCP company, which provided two inverted lattice vectors for the MgO:APLN crystal [19; 20]. According to the Fourier transform, the Fourier coefficients corresponding to the two-phase mismatch compensation \(\triangle\)K were 0.41 and 0.42, respectively, which provided an effective inverse lattice vector of (1.57 \(\mu\)m, 3.30 \(\mu\)m) and (1.47 \(\mu\)m, 3.84 \(\mu\)m) pumped by 1.06 \(\mu\)m. The 5% MgO-doped APLN, as the nonlinear medium, was placed in the OV50 thermostat, and the temperature was maintained at 25 \({}^{\circ}\)C. The MgO:APLN crystal dimension was 50 mm \(\times\) 3 mm \(\times\) 3 mm, and both ends of the faces were anti-reflection coated at 1.06 \(\mu\)m, 1.4-1.7 \(\mu\)m, and 3.3-4.2 \(\mu\)m.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Small-Scale Blast Waves
#### 4.3.5 Laser-Induced Plasma  
[PERSON] et al. [160] utilised plasma induction via lasers for implementing calibration-free seedless velocimetry. The technical aspects and outcomes of the majority of this paper are beyond the scope of this literature review. However, the shock wave formation involved in the experimental work forms an area of interest. When a laser pulse of high intensity is focused at a singular point within a gas, the molecules in the \"focal volume\" will form an intense plasma, otherwise termed laser-induced plasma (LIP) [160; 161]. Temperatures and pressures within the plasma are reported to reach over 20,000 \({}^{\circ}\)K and 540 kPa, respectively [162]. This causes the formation of a strong shock wave, although at an extremely small scale.  
Laser-induced plasma could be a rapid shock wave generation method for minuscule-scaled blast experiments. Some initial challenges present themselves, including the non-uniformity of the shock wave and the scalability of this approach. At a time of 1000 ns, the approximate diameter of the shock wave in the \(x\) direction is 4.5 mm and in the \(y\) direction is 3.5 mm. Additionally, the 1000 ns shock image was the latest one captured; earlier images displayed even more non-uniformity. In regards to the scalability, it may be reasonable to assume that a higher-power laser would produce a more intense plasma or be able to induce plasma in a larger focal volume. These assumptions are speculative and require further investigation. Laser-induced plasma may also serve as a detonation method to initiate sub-gram explosives as a substitute for conventional detonators, which can significantly alter blast propagation for small explosive masses.
## 7 Simulations and Results
### Scenario 1: HEMS with Grid Only  
In this scenario, a conventional home without an ESS or PV integration was considered. The electricity consumers of such conventional homes do not have their own ESS and PV generation. Therefore, such consumers can only import energy from the grid to fulfill their daily load requirements without any provision for H2G energy export.
## 6 Four-Zero Texture Model Validation
### Valid Regions for Parameters \(A_{u},A_{d},\phi_{1}\), and \(\phi_{2}\)  
In this subsection, we present the results obtained after applying the HE-DEPSO algorithm to search for valid regions of the \(A_{u}\), \(A_{d}\), \(\phi_{1}\), and \(\phi_{2}\) parameters. This process was carried out using the most recent experimental values of the \(V_{CKM}\) matrix elements, the quark masses, and the Jarlskog invariant (see Appendix A).  
Figures 7 and 8 present the allowed regions for the parameters \(A_{u}\), \(A_{d}\), \(\phi_{1}\), and \(\phi_{2}\), scaled as \(A_{u}/m_{t},A_{d}/m_{b},\phi_{1}/\pi\), and \(\phi_{2}/\pi\), respectively, in the first case study of Table 1. These regions are classified by the solutions found using the HE-DEPSO algorithm and are represented by black, blue, and orange dots, which correspond to different levels of precision of the \(\chi^{2}(X)\) function. The orange region, the smallest, indicates the region of highest precision, where experimental data agree best with theoretical predictions. These solutions were found by iteratively executing the algorithm until a total of 5000 solutions were collected at each of the precisions (\(\chi^{2}<1\), \(\chi^{2}<1\times 10^{-1}\), and \(\chi^{2}<1\times 10^{-2}\)).  
It is worth noting that, for the rest of the case studies (see Table 1), the found regions for the parameters \(A_{u}\), \(A_{d}\), \(\phi_{1}\), and \(\phi_{2}\) were similar.  
Figure 6: Box and whisker plots of the best global fit values obtained by HE-DEPSO and the DEPSO, SHADE, CoDE, DE, and PSO algorithms in the 31 independent repetitions in the \(\chi^{2}(X)\) function. The horizontal axis shows the algorithms to be compared, and the vertical axis shows the global fit values.  
Figure 7: Allowed regions for \(A_{u}\) and \(A_{d}\) constrained based on current experimental data with different levels of precision: \(\chi^{2}<1\) (black dots), \(\chi^{2}<1\times 10^{-1}\) (blue dots), and \(\chi^{2}<1\times 10^{-2}\) (orange dots).
## 4 Results and Discussion
### The Grid-Connected Photovoltaic Systems (On Grid-PV) Design
#### 4.3.6 Inverter Sizing and MPPT (Maximum Power Point Tracking)  
The MPPT is the maximum power point tracker, a circuit employed in most modern PV inverters, and its function is to maximize the power available from the connected solar module arrays at any time of operation [78]. An inverter without an MPPT circuit would result in suboptimal operating conditions between any PV module (or string of modules) and the inverter.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{3}{c}{**Electrical Parameters AT STC (At Standard Test Condition)**} & **Unit** \\ \hline Rated Maximum Power (Pmax) & 390 & W \\ Open Circuit Voltage (Voc) & 49.35 & V \\ Maximum Power Voltage (Vmp) & 40.21 & V \\ Short Circuit Current (Isc) & 10.22 & A \\ Maximum Power Current (Imp) & 9.70 & A \\ Module Efficiency & 19.5 & \% \\ Power Tolerance & 0\(\rightarrow\)5 & 0\(\rightarrow\)5 W \\ Temperature Coefficient of Isc (\(\alpha\)\(\_\)Isc) & +0.060\%/\({}^{\circ}\)C & \%/\({}^{\circ}\)C \\ Temperature Coefficient of Voc (\(\beta\)\(\_\)Voc) & \(-\)0.300\%/\({}^{\circ}\)C & \%/\({}^{\circ}\)C \\ Temperature Coefficient of Pmax (\(\gamma\)\(\_\)Pmp) & \(-\)0.370\%/\({}^{\circ}\)C & \%/\({}^{\circ}\)C \\ STC Irradiance 1000 W/m\({}^{2}\), cell temperature 25 \({}^{\circ}\)C, AM1.5G & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the JA Solar JAM72S09-390/PR 390 Wp panels [79].  
The MPPT circuit constantly monitors the array voltage and current and attempts to bring the operating point of the inverter to the maximum power point of the array, resulting in the highest energy harvest; in most applications with two strings or more, two MPPTs are better than one [80].  
For sizing the inverter, the following data are necessary: maximum layout power (Kwp): 22 kwp divided into two inverters yields 12 kwp per inverter, maximum current (Isc): 12.77 amps, open circuit voltage (Voc): 690.9 volts, operating voltage (Vmp): 562 volts.  
It is recommended to use a Solis-3P12K-4G inverter, 3-phase 220-380 Volts, 2 MMPT with two MPPT trackers, whose characteristics are shown in Table 5.
# Cross-Correlation and Fractal Analysis in the Images Diatoms Symmetry
## 2 Materials and Methods
### Fractal Dimension Analysis  
For the images analysis, the Fractal Dimension (Nf) was calculated by the Hausdorff method [34] (Box-Counting) [35]. The Nf is an indicator of the geometry complexity from structural patterns, in this case, diatoms morphology. This statistical index is a comparison measurement of how the detail in a pattern change with the scale. This algorithm was obtained from the file exchange website on MATLAB(r) [36], and it was applied to the different diatoms images used in this study.  
The Hausdorff dimension or Hausdorff-Besicovitch dimension is a metric generalization of the concept of the dimension of a topological space, which allows defining a fractional (not integer) dimension for a fractal object.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 5 Discussion on Recommendations for Utilising Satellite Data for Integrated Emergency Management
* Nighttime light satellite data: The case study on electricity transmission faults proved the complexity and challenges of using currently available nighttime data. Analysing current satellite imagery in isolation is not as potent as it would be with further contextual and background information. Thus, we recommend combining further geospatial-enabled datasets, such as reported power outages, to better understand the impacts of a storm, such as power cuts and power restoration. To verify visible \"empty\" values in rendered nighttime light data indicating a possible power outage in the area, we suggest using the nighttime radiation values of the same VNP46A2 dataset for a known location with a working power supply.
# Joint Transmit Waveform and Receive Mismatched Filter Design to Suppress Range Sidelobe
## 4 Numerical Simulations
This demonstrates that the NLFM-CPM waveform has significant advantages in sidelobe suppression and convergence  
Figure 8: The relationship between the Modulation Index \(h\) of CPM and the LPG after mismatch filtering. Figure 9: The ISL fitness function values obtained after iterating with the ADMM algorithm for different waveforms. speed during optimization, outperforming both LFM and P3 code. The LFM performs moderately, while the P3 code exhibits the weakest performance. In Figure 10, the clutter is randomly distributed across distance units 20 to 100. Two strong clutter echoes are present at distance units 65 and 70, while a weak target exists at distance unit 55. After processing with the matched filter, the primary sidelobe of the clutter often exceeds the mainlobe of the target, rendering it challenging to discern weak targets near the cluttered region. Conversely, the EWMF algorithm can substantially diminish clutter sidelobes, thereby safeguarding the target's mainlobe from being obscured by dominant clutter. This increases the SINR between the target's mainlobe and the first sidelobe of the clutter from \(-7\) dB to 28 dB. The distance autocorrelation varies with different transmitted waveforms, with integrated waveforms experiencing greater mainlobe loss. This is attributed to communication modulation disrupting the inherent autocorrelation of the original transmitted waveform. Optimized low-range sidelobes effectively reduce clutter interference from different range cells. Especially in complex environments, low sidelobes can suppress non-target echoes and reduce responses to surrounding clutter, thereby improving target detection accuracy and positioning precision, and preventing false alarms. At the same time, low sidelobes improve range resolution, reducing interference from nearby targets, and are particularly effective in preventing mutual interference of echo signals in multi-target detection. Furthermore, low sidelobes can increase SNR, improving the ability to extract target echoes from noise and enhancing the reliability of detection.
# Improving Lossless Image Compression with Contextual Memory
## 6 Conclusions and Future Work  
This paper provides a description of the state-of-the-art compression program PAQ8 PX from the point of view of grayscale image compression. The main contribution of this paper is an application agnostic algorithm for predicting probabilities based on the contextual information available with learning done in an online fashion. The usefulness of the algorithm is demonstrated by integrating it with the PAQ8 PX algorithm and testing it on several image compression benchmarks. The results show an overall compression ratio improvement across all the datasets without special crafted features. One important difference from existing ensemble mixing algorithms is that, in our algorithm, we assume that various contexts apply together and the prediction benefits from the synergy of the side predictions, unlike the ensembles that assume model independence.  
In its current state, the algorithm was not applied to color or 16 bpp images. For future developments, we intend to extend the algorithm for three-dimensional medical image compression and support 16 bpp depth. Context modeling can be done similarly to the work described in [15].  
The architecture chosen for the algorithm does not take into account parallel optimization techniques that are suitable for hardware implementation like the CCSDS developed image compression algorithms [17]. Unlike the CCSDS standard, the focus for PAQ8 is set to provide the unconstrained liberty for exploring techniques to improve the compression ratio. However, if one wants to standardize an epoch of development, the code can be converted to the ZPAQ open standard [36] that, among other things, aims for forward and backward compatibility of archives. Although not presented in this paper, in future releases we can compare the compression ratio with the CCSDS family of algorithms.  
In [37], a context function is proposed to statistically discriminate error residual classes of full pixel prediction, useful for lossless and near-lossless compression schemes. Due to its properties, such a function could be integrated within the context mixing layer and the adaptive probability maps.  
Design space exploration needs to be done for context modeling. Moving contexts from other modeling types to the contextual memory predictor might also bring benefits. The side predictions of the algorithm can be also be passed to the context mixing network. Splitting various contexts into two or more contextual memory structures can also lead to finding better correlations.  
Conceptualization, A.D. and R.B.; methodology, A.D.; software, A.D.; validation, A.D.; formal analysis, A.D.; investigation, A.D.; resources, A.D.; writing--original draft preparation, A.D.; writing--review and editing, A.D.; supervision, R.B. This research received no external funding. We thank Assoc. Prof. [PERSON] for his helpful comments during the writing of this paper. The authors declare no conflict of interest.