esa-sceva/satcom-chunk-collection huggingface dataset:
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 6 Discussion
### Usability Analysis
#### 6.3.1 Efficiency (Including Performance)  
Analysis  
Our usability testing focused on the _efficiency_ of each user's file encryption-decryption performance. Based on the results, the total time required for the encryption or decryption processes was confirmed to be only the respective execution times shown in Table 1. That is to say, the associated ECMSDK user-session-based trigger execution and the subsequent generation of the users' list of spatial files for encryption/decryption are instant. Additionally, since all processing is carried out within the database, there is no additional overhead, regardless of the protocol used to access the spatial files.  
In terms of efficiency, the encryption/decryption times for spatial files are small and do enable us to use them for practical implementation in spatial organisations. While it takes more time to encrypt and decrypt spatial files using the DBMS procedures (e.g., Oracle DBMS_CRYPTO_TOOLKIT), it takes little time when an encryption executable such as OpenSSL is used. Furthermore, more advanced hardware solutions can further improve performance by an order of magnitude for use with greater ease. Thus, encryption time should not be an issue in enterprise implementation.  
Indeed, during normal file operations (opening, editing, and saving spatial files) stored in the DBFS, compared to traditional data storage, there will be delays for a user, as reported in previous research [51, 52]. However, these delays were found to be reasonable.
## 5 Conclusions  
The nanomechanical properties of nanostructured films made of flame-formed carbonaceous particles have been investigated for the first time by means of Atomic Force Microscopy nanoindentation. More specifically, the hardness H was obtained from the force-distance curves measured in spectroscopy modes and the images of intends acquired in semi-contact mode, while the Young's modulus E was measured by fitting the linear part of the retraction force curves using a DMT model based on Hertz theory. The experimental procedure was tested on PEN films, a medium soft material, and HOPG, a medium hard material used as a reference in the analysis of soot particle properties. Two different samples of soot particle layers were analyzed: Sample #1 films, produced in flame conditions characterized by a bimodal particle size distribution with a first particle mode of 2-4 nm and a second mode of 12-15 nm; Sample #2 films, produced in flame conditions in which >10 nm soot particles and aggregates are mainly produced and characterized by a higher graphitization degree compared to Sample #1 particles. Both Sample #1 and Sample #2 films showed lower hardness and Young's modulus compared to HOPG, suggesting that the deformation of particle films measured by indentation is likely governed by inter-grain mechanisms. Sample #2 is characterized by a higher hardness and a higher Young's modulus and the differences can be related to the different properties of the nanograins that composes the two films and to the agglomeration of particles in the nanograins during the deposition process. Future works will be devoted to improving the experimental technique with the aim to investigate the nanomechanical properties of nanostructured films made of different soot particles, as well as to understand whether the technique could be applied also to singlet soot particles.  
Conceptualization, G.D.F., M.C., P.M. and A.D.; methodology, G.D.F. and F.C.; investigation, G.D.F. and F.C.; writing--original draft preparation, G.D.F.; writing--review and editing, G.D.F., M.C., P.M. and A.D.; project administration, G.D.F., M.C., P.M. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Ministero dell'Istruzione, dell'Universita e della Ricerca\", grant number: PRIN project 2017 PJ5 XXX \"MAGIC DUST\".  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Hardness H, GPa** & **Young’s Modulus E, GPa** \\ \hline _Sample \#1_ & 0.75 \(\pm\) 0.05 & 4.2 \(\pm\) 0.3 \\ _Sample \#2_ & 0.90 \(\pm\) 0.05 & 7.2 \(\pm\) 0.4 \\ _PEN_ & 0.70 \(\pm\) 0.05 & 3.8 \(\pm\) 0.3 \\ _HOPG_ & 2.40 \(\pm\) 0.10 & 7.5 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardness and Young’s modulus measured by AFM nanoindentation on Sample #1 and Sample #2 and compared to PEN and HOPG. Errors are reported as the mean standard deviation from >20 independent measurements.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models
### GNSS Code and Phase Observations
#### 3.1.5 Melbourne-Wubbena (MW) Function  
The MW function, denoted as \(A_{MW}\), calculates the difference between the WL phase and NL code and can be expressed as  
\[A_{MW}=\Phi_{WL}-P_{NL} \tag{31}\]  
This function possesses characteristics of both IF and GF combinations. It remains independent of geometrical models, orbits, clocks, or receiver positions, relying solely on the WL ambiguity and hardware biases. Consequently, the MW function plays a crucial role in PPP ambiguity resolution and cycle slip detection [73].
## 3 Methodology: Operating System and Hardware Selection
### Realistic Use Cases for Appropriate Baseline Power Consumption  
A large issue preceding the direct comparison from the power draw of an idling device to a device that is being attacked is the unrealistic usage scenario that would create. Devices within industries are likely to always have some sort of processing duties (in between standby/low power mode cycling). A few realistic use case scenarios could be based on initially connecting to the gateway and initialising the routing for the network, and then a series (or one) request can be cycled every so often to provide an update to the user. Multiple realistic usage scenarios have been outlined within research ranging from air quality monitoring systems [34] to critical health devices [35]. With multiple ranging baselines throughout the literature, average use is likely all dependent on the frequency of packet transfer of the devices. However, some sources claim 10 years at a data transmission rate of 128 bytes per 10 s [36] and others claim 306 h [37] on one charge with frequent data transfer (as it was a vital sign monitoring system). Therefore, it is important  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Hardware** & **Arch.** & \begin{tabular}{c} **Large** \\ **Community** \\ **Support** \\ \end{tabular} & \begin{tabular}{c} **Wireless** \\ **Support** \\ \end{tabular} & \begin{tabular}{c} **Price** \\ **Avail.** \\ **Extras** \\ **for OSes** \\ **Selected** \\ \end{tabular} & \begin{tabular}{c} **Avail.** \\ **Extras** \\ **for OSes** \\ **Selected** \\ \end{tabular} & \begin{tabular}{c} **Support** \\ **for OSes** \\ **Selected** \\ \end{tabular} \\ \hline \begin{tabular}{c} Intel \\ Edison \\ \end{tabular} & x86 & Y & N & \$50 & N & Y & N \\ \hline \begin{tabular}{c} Raspberry \\ Pi Pico \\ \end{tabular} & ARM & Y & N & \(\varepsilon 5\) & Y & N & N \\ \hline \begin{tabular}{c} Raspberry \\ Pi Zero \\ \end{tabular} & ARM & Y & N & \(\varepsilon 10\) & Y & SD card & Y \\ \hline \begin{tabular}{c} Raspberry \\ Pi Zero W \\ \end{tabular} & ARM & Y & Y & \(\varepsilon 12\) & Y & SD card & Y \\ \hline \begin{tabular}{c} Raspberry \\ Pi Zero 2 W \\ \end{tabular} & ARM & Y & Y & \(\varepsilon 12\) & N & SD card & Y \\ \hline \
\begin{tabular}{c} Zolertia \\ Z1 \\ \end{tabular} & RISC & Y & Y & \$170 & N & SW & N \\ \hline
\begin{tabular}{c} Zolertia \\ Firefly \\ \end{tabular} & ARM & Y & Y & \(\varepsilon 50\) & Y & SW & Y \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware options available  
to select the best choice for an appropriate baseline power draw for the study, increasing the comparability of the study's results to real-world examples where a default operation time has already been established. Basing the impact of any possible attacks on the decrease of operational time would likely result in an unbiased proposal for ranking the effectiveness of each propositional attack.
## 4 Experiment and Results
### Results  
#### 4.3.1 Data Regeneration Performance Comparison  
Two methods were selected to verify the performance of the SSA technique used for data conversion. Short-time fourier transform (STFT) and wavelet transform. In STFT, a long signal that changes over time is divided into short time units and then Fourier transform is applied.  
The Wavelet Transform increases time-time resolution and lowers frequency resolution for signals with high frequency components. For signals with low frequency components, frequency resolution is increased, and time resolution is decreased. Unlike Fourier transforms, which use sinusoidal curves with infinite time axes as fundamental functions, wavelet transforms use time-limited wavelet functions as fundamental functions.  
Therefore, the experiment was conducted by applying STFT and wavelet transform to the same data set for data conversion and regeneration of the Singular Spectrum Analysis technique used in the proposed data classification. The experimental results are as follows.  
Similarity was demonstrated in the order of reconstruction data, wavelet transform, Fourier transform and through the SSA technique as a result of reconstruction the value of the input data as illustrated in Figure 8a. As a result of visually examining the elementary matrix in Figure 9, it can be observed that the diagonal structure of the trajectory matrix is lacking. Without examining vectors related to the elementary matrix or reconstructing the time series of each component, the \(i\)-th elementary matrix figure implies the properties of each component, such as trend, periodicity, or noise. From the beginning, it can be observed that the \(L\) and \(K\) lagged vectors show relatively slow changes throughout the matrix, indicating a trend of data, and showing a distinct check pattern, indicating periodicity. Because the subsequent matrix becomes blurred again, the subsequent matrix is highly related to the noise of the time series.  
According to the relative contribution graph in Figure 10a, the contribution changes at the time point when i = 1. In addition, in Figure 10b, the first ten component values based on the point contributed 98% to the expansion of the basic matrix. Figure 10 is the result of accumulating \(X_{i}\) components according to the contribution graph. The example output data by combining the \(X\) components can be seen in Figure 11.  
Figure 8: Results of the data conversion (**a**) gathered original data, (**b**) data extracted through Fourier transform, (**c**) data re-generated using the SSA technique, and (**d**) data extracted through wavelet transformed data.  
Figure 9: First 15 elementary matrix graphs out of 332 properties.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 7 Open Challenges
### Model Interpretability  
Despite their superior performance, deep learning based ASC models are often considered as \"black boxes\" due to their high complexity and large number of parameters. One main challenge is to develop methods that allow better interpreting the model predictions and internal feature representations. As discussed in Section 6.3, attention mechanisms allow neural networks to focus on relevant subsets of the input data. [PERSON] et al. investigated an attention based ASC model and demonstrated that only fractions of long-term scene recordings were relevant for its classification [74]. Similarly, [PERSON] et al. visualized internal attention matrices obtained for different acoustic scenes [54]. The results confirmed that either stationary and short-term signal components were most relevant for particular acoustic scenes.  
Another common strategy to investigate the class separability in intermediate feature representations are dimension reduction techniques such as t-SNE [27]. Techniques such as layer-wise relevance propagation (LRP) [89] allow interpreting neural networks by investigation the pixel-wise contributions of input features to classification decisions.
# Atmospheric Effects on OFDM Wireless Links Operating in the Millimeter Wave Regime
## 5 Technical Specifications of the Next Generation Cellular Network  
The definition of signal frame structure for the next generation cellular network was discussed in detail during the 3 GPP and in many regulation forums [18; 19; 20; 21]. Requirements throughout the RF specifications defined separately for different frequency ranges (FR). Two different frequency bands are available for the 5G technology, designated as: FR1-410 MHz to 7.125 GHz and FR2-24.25 GHz to 52.3 GHz [19; 21; 22].  
The 5G New Radio (NR) Cellular Network is designed to operate in the FR2 operating bands presented in Table 1. Both Uplink (UL) and Downlink (DL) bands operated in Time Division Duplexing (TDD).  
In addition to FR1 and FR2, the Multiple Gigabit Wireless System (MGWS) was defined to operate in frequencies around 60 GHz. The MGWS network can be used in short-range, line-of-sight, and non-line-of-sight circumstances with traditional Wireless Local Area Network (WLAN) topologies [23].  
Flexible Multi-Numerology are supported in an effort to present diverse services and user requirements of NR [24]. This numerology is characterized by different values of OFDM waveform parameters, such as SCS (Subcarrier Spacing) and CP (Cyclic Prefix) overhead. According to the 3 GPP [22], the maximum channel bandwidth per NR carrier is 400 MHz and the maximum SCS is 480 kHz. At least for single numerology cases, the maximum number of subcarriers per band is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Operating Band**} & \multirow{2}{*}{**Carrier Frequency [GHz]**} & \multicolumn{2}{c}{**Uplink (UL) and**} \\  & & **Downlink (DL)** & **Duplex Mode** \\  & & **Operating Bands [GHz]** & \\ \hline n257 & 28 & 26.5–29.5 &
\begin{tabular}{c} Time Division \\ Duplexing (TDD) \\ \end{tabular} \\ \hline n258 & 26 & 24.25–27.5 & TDD \\ \hline n260 & 39 & 37–40 & TDD \\ \hline n261 & 28 & 27.5–28.35 & TDD \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequency operating bands of New Radio (NR) in Ka-Band spectrum [21; 22].  
3300 or 6600 [25]. Frame Length is fixed to be 10 ms. The number of OFDM symbols per slot time is depending on subcarrier spacing. For subcarrier spacing less than 60 kHz, the number of OFDM symbols per slot is 7 or 14. For subcarrier spacing greater than 60 kHz, the number of OFDM symbols per slot is 14 [26]. Table 2. presents the OFDM parameters designated for the 5G technology.
# A CSAR 3D Imaging Method Suitable for Edge Computation
## 1 Introduction  
As a remote sensing method, airborne CSAR is an important part of the Internet of Things (IoT) sensing system. Making full use of edge computing power for, e.g., aircraft or small ground stations can reduce the transmission pressure of the IoT and improve the efficiency of information acquisition [1; 2]. Compared to linear SAR, CSAR imaging on UAVs requires a long observation time and a large amount of echo data, so imaging requires more computing resources. In general, the original data must transmit to the ground for processing or post-processing after the flight. It is challenging to use edge computing power for real-time imaging processing on a UAV platform. Therefore, the speed of acquiring imaging target information is poor.  
The process of CSAR observations and imaging consumes significant computing resources due to the application of the Back Projection (BP) imaging algorithm in the time domain [3]. The computational efficiency of CSAR is also not high. Taking X-band CSAR imaging carried by a small rotor unmanned aerial vehicle (SRUAV) as an example, when theflight radius is 600 m, there are 180,000 heading sampling points and 3000 range direction sampling points. The BP imaging algorithm needs to carry out phase compensation in the echo signal for each unit in the 3D imaging grid at each sampling point, which requires many calculations and has low imaging speed. The authors in [4] proposed an improved BP algorithm (IBP) by constructing a geometric interpolation kernel to transform 3D interpolation operations into 1D interpolations and range vector searching operations. Using this method, the BP 3D imaging time was compressed by two-thirds. However, the imaging speed was still not ideal. Other 3D imaging methods of the CSAR sub-aperture class based on BP imaging [5; 6; 7] showed no optimization in terms of imaging speed and were focused on sub-aperture partitioning according to the scattering characteristics of the target. Overall, the high time delay and low speed of the current BP 3D imaging algorithms severely limit the realization of CSAR 3D imaging at edge nodes.  
In this paper, a CSAR 3D imaging method suitable for edge computation is proposed. This method applies inverse Radon transform (IRT) and Hough transform to complete the 3D point cloud extraction and replaces phase compensation processing of the BP algorithm with azimuth point-by-point and grid-based one-by-one 3D imaging. This method can greatly improve the rate of CSAR 3D imaging and reduce imaging delays. The 3D imaging results of the simulation and measured CSAR echo data carried by an SRUAV demonstrate the correctness and effectiveness of the proposed method.  
This paper is organized as follows. Section 2 introduces the principle of BP 3D imaging. Then, the processes and key technologies of the CSAR 3D imaging method suitable for edge computation are described in Section 3. Section 4 presents the results and analyses of the proposed method for ideal targets and the X-band CSAR echo dataset carried by an SRUAV. Finally, in Section 5, conclusions are drawn.
## 3 Framework of Forecasting AOG Models in Aviation
### Benefits and Importance of Predictive Maintenance  
Implementing predictive maintenance offers significant benefits, including forecasting equipment issues, reducing costs, and improving asset performance and reliability. A 2022 Deloitte report highlights that it can cut facility downtime by 5-15% and increase labor productivity by 5-20%. Predictive maintenance supports sustainability by lowering energy consumption and waste while reducing unexpected breakdowns and maintenance frequency. With AI integration, forecasts become more accurate, helping organizations manage spare parts and labor more effectively, even amidst rising costs and disruptions.  
Data preprocessing is critical for machine learning in predictive maintenance. It involves cleaning raw data (removing duplicates and missing values), transforming it (scaling, encoding, and filtering), and addressing issues like class imbalance. For instance, techniques like SMOTE balance datasets by increasing minority class representation, while normalization ensures all data fit within a standard range, preventing scale-related biases. Advanced methods like semi-pivoted QR (SPQR) and simple moving averages further enhance model accuracy. These steps ensure high-quality data for effective machine learning, ultimately improving predictive maintenance outcomes. Their two-stage preprocessing approach first applied the Relief algorithm for feature selection and then used K-means clustering to remove noisy data. The MLP model stood out with the lowest Root Mean Square Error (RMSE) of 0.985, demonstrating its reliability for predicting equipment failures.  
Ref. [21] addressed the critical need for accurate algorithms to estimate the remaining useful life of aircraft engines, given their low fault tolerance and high safety requirements. They used advanced models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to predict system failures based on historical data, enabling more effective predictive maintenance.  
Additionally, ref. [22] explored machine learning in aircraft engine design, focusing on predicting thrust-specific fuel consumption (TSFC) and core sizes of turbofan engines. Using the Keras API and TensorFlow framework, the study trained predictive models based on engine design parameters, highlighting the potential of machine learning in improving engine performance and design.
merged them as a filler in c-GIC and found that the addition of the nano-HA-silica composite provided an increase of ~73% in the hardness values compared to the c-GIC [21]. Other researchers have found a better exchange of ions between tooth structure and nano-HA-GIC [22] and nano-HA-SiO\({}_{2}\)-GIC [23], advocating that higher remineralization was achievable when compared to c-GIC. Furthermore, the cytotoxicity evaluation of the nano-HA-SiO\({}_{2}\)-GIC on human dental pulp stem cells (DPSCs) demonstrated a favorable response [24]. Another study by [PERSON] et al. showed that nano-HA-SiO\({}_{2}\)-GIC exhibited good biocompatibility on DPSCs, which was comparable to c-GIC. Also, nano-HA-SiO\({}_{2}\)-GIC encouraged the attachment and spreading of DPSCs with prominent filopodia, which indicates the good activity and viability of these cells [25]. Despite many studies with promising results for the mechanical, chemical, and in vitro biological evaluations of nano-HA-SiO\({}_{2}\)-GIC, to our knowledge, no in-vivo study has been performed to test and compare the biocompatibility of nano-HA-SiO\({}_{2}\)-GIC and c-GIC in terms of the dentin-pulp complex response. Hence, this study aimed to assess the dentin-pulp complex response to nano-hydroxyapatite-silica-GIC occlusal and cervical restorations in Wistar rats' molar teeth as compared to conventional GIC in terms of pulp tissue disorganization, infiltration of inflammatory cells, bacterial detection, and deposition of tertiary dentin.