esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The high availability of medium and high spatial resolution optical satellite data, from the MSI sensor aboard the Sentinel-2A and Sentinel-2B satellites, has enabled the development of more detailed post-fire mapping. While many research studies focus on the development and comparison of spectral indices for burned area detection [2; 34; 39], there is still a lack of investigation over the development of proper procedures that take advantage of newly available optical satellite sensors. Scientific investigations concerning burned area mapping from optical satellite data commonly rely on analysis of known wildfire events using two satellite acquisitions, collecting spectrally derived information about cover conditions before and after the wildfire took place. S2 satellites provide superior advantages due to the high revisit frequency and improved spatial and spectral resolution of the MSI sensor, which effectively produces medium-high spatial resolution burned area mapping using dense multitemporal series.  
The present research study presented a novel procedure for mapping burned areas using S2 data, benefiting from all of the multitemporal observations and defining a new paradigm that goes beyond the standard method of combining one pair of pre- and post-fire satellite acquisitions. Though the use of bi-temporal difference spectral indices, based on spectral indices and arithmetic differences between pre-fire and post-fire values, have been shown to provide better results compared to the methods based on a single temporal observation [2], the availability of dense multitemporal series of satellite observations for the identification of burned areas could play a key role in improving the accuracy of wildfire-affected area identification. In fact, quick post-fire vegetation restoration has been seen to be a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spectral Index** & **Procedure Step** & **Omission Error (\%)** & **Commission Error (\%)** \\ \hline  & A & 74.23 & 21.85 \\  & B & 76.40 & 21.06 \\ NBR + dNBR & C & 40.19 & 25.48 \\  & D & **40.70** & 25.23 \\ \hline  & A & 72.94 & 23.88 \\  & B & 74.99 & 23.13 \\ BAIS2 + dBAIS2 & C & 43.53 & 24.65 \\  & D & 44.14 & **24.28** \\ \hline  & A & 74.53 & 21.76 \\ BAIS2\(\alpha\) + dBAIS2\(\alpha\) & B & 76.73 & 21.00 \\  & C & 44.61 & 24.91 \\  & D & 45.24 & 24.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Omission and commission errors calculated using a pixel-based product comparison with the Copernicus EMS rapid mapping activations. Percentages are reported for each of the four procedure steps (A–D) and the three spectral indices.  
major source of omission errors in the burned area detection [57]. The exploitation of the entire S2 time series could therefore reduce omission errors, considering that fire-induced variations in reflectance as a consequence of changes in vegetation cover can be captured for a few months, depending on the pace of vegetation regrowth [58].
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## Appendix C Proof of Theorem 3
\begin{split} h\bigg{(}\frac{\mathbf{\mathrm{g}}_{\text{ ee}}\mathbf{\mathrm{h}}_{\text{sd}}^{\mathcal{H}}}{|\mathbf{\mathrm{h}}_{\text{sd}}|}X_{1}^{ \prime}+\mathbf{\eta}_{e,1}\bigg{)}-h\Big{(}\mathbf{\eta}_{e,1}\Big{)}\overset{(\mathbf{ \mathrm{e}}_{1})}{\leq}\log\!\bigg{(}1+\frac{\left\|\mathbf{\mathrm{h}}_{\text{sd} }\mathbf{\mathrm{g}}_{\text{ee}}^{\mathcal{H}}\right\|^{2}P}{\left\|\mathbf{\mathrm{h }}_{\text{sd}}\right\|\sigma_{e}^{2}}\bigg{)},\\ h\Big{(}\mathbf{\mathrm{g}}_{\text{de}}X_{f,n}+\mathbf{\eta}_{fe,n}\Big{)} -h\Big{(}\mathbf{\eta}_{fe,n}\Big{)}\leq\log\!\bigg{(}1+\frac{\left\|\mathbf{\mathrm{g }}_{\text{de}}\right\|^{2}P_{f}}{\sigma_{fe}^{2}}\bigg{)},\end{split} \] (A52) where \((e_{1})\) is similar to (A49). Substituting (A51) into (65), we conclude that \[\Delta_{I}\geq 1-\frac{\overset{\text{Information leaked by Source at time 1}}{\log\!\bigg{(}1+\frac{\left\|\mathbf{\mathrm{h}}_{\text{sd}}\mathbf{\mathrm{g}}_{ \text{ee}}^{\mathcal{H}}\right\|^{2}P}{\left\|\mathbf{\mathrm{h}}_{\text{sd}} \right\|\sigma_{e}^{2}}\bigg{)}}}{N\mathcal{R}_{\text{FRC}}^{\text{NFR}}(N, \epsilon)}-\frac{\overset{\text{Information leaked in feedback channel at time 1}}{(N-1)\log\!\bigg{(}1+\frac{\left\|\mathbf{\mathrm{g}}_{\text{de}} \right\|^{2}P_{f}}{\sigma_{fe}^{2}}\bigg{)}}}{N\mathcal{R}_{\text{FRC}}^{ \text{NFR}}(N,\epsilon)},\] (A53) where \(\mathcal{R}_{\text{FRC}}^{\text{NFR}}(N,\epsilon)\) is given in (50). According to (I)-(II), the proof of Theorem 3 is completed.
# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 3 Results
Blue indicates that all tasks are offloaded to the backbone nodes of the network, yellow indicates that all tasks are offloaded to the edge nodes of the network, and red indicates the mixed offloading mode proposed in this paper, where some tasks are offloaded to the backbone nodes and the others are offloaded to the edge nodes. Compare the delay cost of the mixed computation offloading under a certain work request volume with that of the mode in which a particular offloading sites are selected separately. Based on the simulation results of the algorithm, the number of network switches and the task offloading mode, the DQN-based offloading decision scheme can be applied to the Space-Air-Ground Integrated Network, which has large number of tasks to be offloaded, the complex offloading environment and the limited network resources, and it can effectively reduce the time delay and energy consumption during the task offloading process.
## 4 Evaluation
### Implementation and Setup
#### 4.1.2 Benchmarks  
Our dataset is used to evaluate the performance of BlockMatch and baselines at the basic block level. We construct the dataset using six projects, i.e., GoAhead-5.2.0 [52], CoreUtils-8.30 [53], OpenSSL-1.1.11 [54], DiffUtils-3.5 [55], Linux-5.4.0 [56], and SQLite-3 [57], where SQLite-3, Linux, GoAhead, and OpenSSL are widely used in various software, and CoreUtils and DiffUtils are popularly used as benchmarks in the current BCSD research [10; 23; 41]. We compile them using two compilers (GCC and Clang) with six optimization levels (00, 01, 02, 03, and Os, Ofast) on the x86-64 architecture. The dataset includes a total of 3,367,762 basic blocks.  
To evaluate the performance of BlockMatch on previously unseen projects, we adopt a leave-one-out strategy. Specifically, we randomly select one project to be excluded from the training phase. The functions from the rest of the projects are then divided into three distinct datasets of basic blocks for training, validation, and testing based on 10-fold cross-validation. During the pre-training phase, we conduct unsupervised pre-training on the MLM and ROM tasks using all of basic blocks from the training dataset. Pre-training is considered complete when the loss stabilizes and converges. During the fine-tuning phase, we use the Block Argumentation Model to generate contrastive samples for the basic blocks in the training set and perform self-supervised fine-tuning using BSM tasks. During the evaluation process, we utilize the testing dataset to conduct two different tasks including: cross optimization levels (XO) and cross compilers (XC). By incorporating these subsets into our evaluation phase, we can comprehensively evaluate the performance of BlockMatch across various scenarios.
## 3 Results and Discussion
### Comprehensive Sensory Evaluation
#### 3.4.2 E-Tongue Analysis  
The E-tongue is converted from electrical signals to gustatory signals that distinguish the flavors of food, providing a sensitive threshold and eliminating subjectivity associated with sensory evaluation [38]. PCA was conducted on E-tongue for different heating methods as shown in Figure 2c (PC1 and PC2 explained 98.1% and 1.8% of the total variance, respectively). The E-tongue characterization revealed similarities between the baking and microwaving groups while exhibiting sharp differences from the steaming group. Furthermore, saltiness, umami, and richness displayed distinct correlations as they occupied separate quadrants: microwaving and baking yielded significantly higher umami taste than steaming (\(p<0.05\)), whereas there was no significant difference in richness among all three methods (\(p>0.05\)). Saltiness followed a pattern of microwaving > baking > steaming, which was consistent with the results of the salt content determination. The results showed that the discrimination of SEY under three heat treatments could be effectively facilitated by employing an E-tongue. A previous study also revealed that different sensors of the E-tongue can significantly differentiate between different varieties of egg yolk flavors with distinct separation distances [39].
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 3 Results and Discussion
### Self-Organizing Control of Coplanar Satellites
The control effects of coplanar satellites in the geocentric inertial system are shown in Figure 18. The exact velocity increments have no value of reference because the initial orbit conditions are dramatically bad. However, Figure 16 reveals the convergence of the proposed self-organizing control. Figure 12.0 Semi-major axis differences away from the target value. Figure 12.1 The differences in minimum bounds away from the corresponding constraints. Red line is zero-value standard. Blue lines are minimum bound differences.
# Review of Web Mapping: Eras, Trends and Directions
## 2 Background and Developments
### Characterization of Web Mapping Developments  
In the previous section, we defined web mapping through analyzing some existing definitions. In this section, we will move further to explore the characteristics of web mapping which can help: (1) distinguish web mapping from others, e.g., Web GIS, Internet GIS, etc.; (2) support the design of web mapping applications; and (3) inform the selection of web mapping technologies for developments.  
Web mapping uses the web, the world's largest multimedia platform, to deliver maps and deals primarily with technological issues, but also requires additional studies on cartographic theories, among other issues. This means that web mapping not only follows various Internet protocols but also utilizes web specific protocols. It also means that new technologies and cartographic principles are needed to effectively design maps for web mapping delivery.  
Different from desktop mapping technologies, web mapping is mostly not an off-the-shelf solution, although many of its core technologies have already been well developed. It often requires some programming and demands breadth in terms of skills, knowledge and organizational structures to deploy practical applications [13].  
Web maps and their related content presented in a web environment, i.e., web browsers, require an appropriate interface and often optional functionality for queries and reports. When designing web maps, audience, interface (e.g., size, space and color) and fast-response functions become important factors to consider. For example, the interactive characteristic of web mapping requires that some operations, if not all, be carried out quasi-instantaneously to maintain relevance, logic flow and user's attention. Map layouts may be restricted by the limited space within web browsers and on the display of mobile devices. Further, new cartographic design rules are needed for web mapping applications [14], although much has already been explored in depth.  
With the recent development of content-based and user-generated web mapping, [PERSON] et al. [15] present six ideas (i.e., characteristics) of web mapping and neo-geography: (1) individual production and user-generated content; (2) application of the power of the crowd; (3) massive data produced; (4) principle of participation; (5) the effect of network; and (6) openness.  
A number of attempts have been made to capture web mapping developments. This is mostly done concerning technology improvements at either a broad generic level or specifically linking to prominent technology initiatives. However, the above general characteristics of web mapping have become quite clear.
# A Review of Oil Spill Remote Sensing
###### Abstract  
The technical aspects of oil spill remote sensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day-night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable.  
oil spill remote sensing; oil spill detection; oil remote sensing 2018160103991; doi:10.3390/s18010091
## 3 Methodology
### Dataset  
In our experiments, we evaluated the performance of attack and defence strategies on a time-series standard benchmark dataset for testing lithium-ion battery ageing based on electrical vehicle real-driving profiles [58]. Battery data is time-series, which has temporal dependencies and patterns. These batteries, the predominant energy storage system in electric vehicles, are subject to inevitable degradation during storage and use [59]. Diagnosis of battery ageing and remaining useful life (RUL), which enables robust development and fine-tuning of battery ageing models, is critical for ensuring operational safety, maintenance planning and modelling of diagnostic methods [60].  
Key aspects of EV battery ageing dataset include real-world driving and diagnostic test data from 10 INR21700-M50T commercial lithium-ion battery cells (manufactured by Samsung SDI, Seoul, Republic of Korea) tested over a 23-month period using various constant current (CC) and constant voltage (CV) charging protocols at charge rates of C/4, C/2, 1C and 3C. The discharge ageing profiles mimic a typical electric vehicle driving pattern in the form of an Urban Dynamometer Schedule (UDDS), which brings the battery's state of charge (SOC) from 80% to 20%.  
The ageing data set contains the following values of the INR21700 M50T batteries: charge and discharge curves; capacity retention over multiple cycles; voltage profiles during cycling; current rates used for charging and discharging; temperature data during cycling, etc. More information can be found in the paper [PERSON] et al. [58].  
We focus on the transformer DL architecture for time series tasks and give as input current I(t) and voltage U(t) and as output discharge capacitance \(\Delta C\) for a defined time window W (discrete samples of all these parameters). The output of the DNN correlates  
Figure 3: Local FL transformer model for estimating battery discharge capacity and RUL.  
with the state of depreciation of the battery and the remaining utilisation time, RUL. We want to protect this actual information.  
In our experiments, the dataset is split into a training set and a validation set in a ratio of 70:30, which is a common practice for a balanced trade-off between learning and evaluation practice in transformer architectures with time-series data. In the case of signal recovery with discrete waveforms, a separate, independent matching/probing set is created from this dataset.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Environment**} & **Frequency** & **Bandwidth** & \multirow{2}{*}{**Distance (m)**} & \multirow{2}{*}{**Parameters of Study**} \\  & & **(GHz)** & **(MHz)** & & \\ \hline [PERSON] et al. [17] & Indoor & 26 & 1000 & 2–67 & Path loss, delay, and angular spreads \\ [PERSON] et al. [18] & Indoor & 28 & 250 & – & Power delay profile \\ [PERSON] et al. [19] & Indoor & 6.5-38 GHz & 1000 & 1–40 & Path loss and delay spread \\ [PERSON] et al. [20] & Outdoor & 28 & 400 & 30–500 & Path loss and power delay \\ [PERSON] et al. [21] & Outdoor & 28 and 38 & 400 & 50–200 & Path loss \\ [PERSON] et al. [22] & Outdoor & 28 and 73 & 400 & 27–190 & Path loss \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of some outdoor and indoor studies at millimeter wave bands. While propagation studies for the coexistence of 5G in the mmWave bands have been aggressively performed for some time, the characterization of the 5G channel model still requires further investigation. This is because most of the measurement campaigns are conducted using different settings, including measurement environments; morphologies; equipment like channel sounder, antennas, and clock synchronization; and even the post-processing method, which may influence the propagation characteristics. As a result, more mmWave channel measurements and characterizations are still needed in order to fully characterize and later develop a unified channel model framework for large mmWave bands. To fill the aforementioned gaps, we carried out an extensive mmWave channel measurement campaign on a particular NLOS indoor-to-outdoor (I2O) scenario, covering frequencies of 19, 28, and 38 GHz. The contributions of this paper are threefold. First, this work compares the propagation characteristics of different mmWave frequency bands, where the 19, 28, and 38 GHz channel measurements are carried out with the same configuration. In addition, the measurement campaign is conducted using a 1000 mega chips-per-second (Mcps) high-band correlation channel with a greater chip-rate than the measurement conducted in the literature [2; 16; 23; 24]. The second contribution of the paper is the study of path loss for a single frequency based on the close-in (CI) space-lost reference path model, while the path loss for the multi-frequency is based on CI and alpha-beta-gamma (ABG) models. Finally, the third contribution constitutes of the computation of the root mean square (RMS), the average excess (MN-EX), and the maximum excess delays (MAX-EX), to characterize the time dispersion parameters for all of the measured frequencies. The rest of this article is structured accordingly. The measurement method and environment are described in Sections 2 and 3, respectively. The post processing of the data is explained in Section 4. Sections 5 and 6 discuss and analyze the path-loss patterns and time dispersion parameters, and provides the outcomes and discussions, respectively. In Section 7, this work is compared with the state of art. The conclusion of the paper is drawn in Section 8.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 5 Anthropometric and Morphological Approaches for HRTF Personalisation
### Regression
#### 5.3.2 Two-Dimensional Images  
In the category of two-dimensional images, machine learning models focus on generating HRTFs using two-dimensional data, such as images or anthropometric measurements derived from pictures. These techniques leverage 2D representations to infer spatial auditory cues, providing a less complex but effective means of HRTF generation. One notable study is that of [PERSON] [164]. In their approach, HRTFs from the CIPIC [147] dataset are processed to extract the pinna reflection transfer function (PRTF). This is derived by isolating the spectral features related to pinna reflections from the measured HRTFs using a series of filtering techniques. Specifically, the model focuses on the central frequencies of spectral notches in the PRTF, which are linked to different reflection paths on the pinna surface. To identify these reflection paths, the model also incorporates geometric data extracted from 2D images of the subject's pinna, including manually traced contours representing distinct anatomical features such as the helix border, concha outer border, and antihelix. These contours are then used to map the observed notch frequencies to specific reflection surfaces on the pinna, allowing the model to predict the effects of its anatomy on HRTF variations. Years later, in 2023, [PERSON] [165] discussed the use of convolutional neural networks to predict HRTFs from images of a listener's ear. The method extracts anthropometric features from ear images to find a matching set of HRTFs. The process involves training a CNN to identify ear landmarks, computing key distances, and matching them with a database to retrieve personalised HRTFs. The approach aims to simplify HRTF individualisation with minimal user input, though further research is needed to optimise and refine the process.
# Computer Simulations of Dual-Antenna Microwave Ablation and Comparison to Experimental Measurements
## 2 Materials and Methods
### Influence of Heating Parameters and Blood Perfusion on Coagulation Zone  
In dual-antenna MWA, the heating parameters mainly refer to the MWA treatment time, the microwave input power, and the spacing between two applicators. The doctor can change the heating parameters to obtain different sizes of coagulation zones. In order to comprehensively analyze the changes in coagulation zones with heating parameters, a total of nine kinds of different simulations were carried out. The time of MWA was set to 8 min.  
Figure 3: Schematic diagram of \(R_{\text{max}}\), \(R_{\text{L}}\), and \(R_{\text{f}}\) in the fused coagulation zone.  
The simulation settings include total input powers of 40 W, 50 W, and 60 W combined with antenna spacings of 20 mm, 30 mm, and 40 mm. The heating parameters of dual-antenna MWA for application to treat liver tumors can be obtained from these simulations.  
Blood perfusion is defined as the blood flow per unit volume of tissue per unit of time in microcircutancy units such as the capillary network, small arteries, and small veins [37]. The perfusion is generally expressed as \(\omega_{\rm b}\), and the unit is usually kg/(m\({}^{3}\cdot\)s) [38]. The blood perfusion of the normal human liver is approximately 8.0 kg/(m\({}^{3}\cdot\)s), while the perfusion of tumors is between 1.5 and 8.0 kg/(m\({}^{3}\cdot\)s) [39]. Different dual-antenna spacings and heating parameters were set. The effect of blood perfusion in MWA was explored based on the temperature distribution and the coagulation zone volume to improve the accuracy and to obtain more realistic results.