esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Maneuver-Based Driving Envelope Generation Approach for Driving Safety Assessment
## 4 The Applications of the Driving Envelope
### Motion Planning
The evasive trajectory can be divided into two parts, \(\tau_{adj}\) and \(\tau_{eva}\). In the first part \(\tau_{adj}\), we assume that the vehicle moves at a constant lateral acceleration \(a_{lat,adj}\). The used time to adjust the lateral speed in the reference frame is \(t_{1}=v_{lat}/a_{lat,adj}\). In the second part, we assume that the trajectory follows the motion model in Equation (2). The \(H\) can be obtained, thus we can get the time \(t_{lat}\) using Equation (4). The total time to finish the lateral motion is \(t_{arrive}=t_{lat}+t_{1}\). Figure 16: Collision checking for an obstacle. (3) Case III, the host vehicle is preparing for an evasive maneuver and has not started lateral motion, as shown in Figure 17c and Figure 17d. We assume that the trajectory follows the evasive motion model in Equation (2). Given the starting and target lateral position, the total lateral distance \(H\) can be obtained, thus we can get the time \(t_{lat}\) to arrive at the target lateral position using Equation (4), \(t_{arrive}=t_{lat}\). After the range of parameter \(T\) is determined, the trajectories can be generated. The trajectories can be evaluated according to a cost function to obtain the suboptimal trajectory. The cost function is designed considering the smoothness of the trajectory, the distance to obstacles, the stability, etc. We have utilized the trajectory generation and evaluation method in a semi-autonomous driving system [41]. One advantage of the method is that the range of parameter \(T\) is determined, the motion planner can generate fewer trajectories. Another advantage of the method is that with the help of the driving envelope, the problem of missing collision checking with obstacles shown in Figure 16 can be solved.
# JSON Schemas with Semantic Annotations Supporting Data Translation
###### Abstract
This article is structured into nine sections. After the Introduction, the related work is presented in Section 2, followed by an introduction on the Arrowhead Framework and the original TAG-Tool (Section 3). For extending the expressiveness of legacy systems using JSON, a method for introducing semantic annotations and complement data values to JSON schemas is proposed in Section 4. A translation method for translating JSON Schemas to XML Schemas is presented in Section 5, and JSON to XML and XML to JSON translation methods are presented in Section 6. The previous version of the TAG-Tool was extended with the use of the proposed annotation mechanisms and translation methods, as presented in Section 7; the new TAG-Tool architecture was introduced, bringing data translation supported by JSON Schemas and automatic translator generation to support the translation of JSON to JSON, JSON to XML, and XML to JSON messages. The evaluation of the work is presented in Section 8, whilst the conclusions on the proposals are presented in Section 9.
## 5 Applications
### Es Distribution  
Another important application of the 3D model is its potential to characterize a large variety of properties that the units contain. Analyses of engineering properties that are heterogeneously distributed within geological units play a role in UUS evaluation [6; 9; 61].  
Geotechnical properties are adopted as one indicator for evaluation, as described before. The compression modulus is included in the Standard for Geological Assessment on Urban Underground Space Resources in Beijing City (DB11/T 1895--2021, Beijing Institute of Geological Survey, 2022) considering its influence on engineering geological conditions. Its quantification results in this work are shown in Table 3. Evaluation criteria are divided into three intervals containing 6-10, 3-6, and 0-3 representing simple, medium, and complicated geotechnical conditions, respectively. Three spatial zones divided by Es values are then calculated based on the 3D property model, and the compression modulus of two silty clay units with values larger than 15 is interpreted as simple with a score interval of 6-10, as shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c} \hline
**Stratigraphic Structures** & **Sample Images** & **Characteristics** \\ \hline single-layered & Homogeneous lithology \\ \hline double-layered & Two major layers; lenses may develop locally within major units; heterogeneous lithology and property across two units \\ \hline multilayered & Multilayers, containing interbedded layers or lenses, heterogeneous in both lithologies and properties \\ \hline \end{tabular}
\end{table}
Table 2: Stratigraphic structure is identified as one of the UUS evaluation indicators (adapted after Refs. [59; 60]).  
\begin{table}
\begin{tabular}{l l l l} \hline
**Indicator** & \multicolumn{2}{l}{**Evaluation Criteria**} \\ \cline{2-4}  & **6–10** & **3–6** & **0–3** \\ \hline Geological conditions & simple & medium & complex \\ \hline Es values (MPa) & \(\geq\)15 & 4–15 & \(\leq\)4 \\ \hline Stratigraphic structure & single-layered & double-layered & multilayered \\ \hline \end{tabular}
\end{table}
Table 3: Es values and stratigraphic structure considered as evaluation indicators for UUS suitability. The quantification result is divided into three intervals containing 6–10, 3–6, and 0–3, representing simple, medium, and complicated geological conditions respectively.
## 2 Related Work  
### Fuzzy Logic-Based Broadcasting Schemes  
Fuzzy logic has been widely employed in MANETs to cope with the uncertainties and imprecision inherent in wireless environments. Early fuzzy logic broadcasting schemes typically utilized static fuzzy rule bases and fixed membership functions for key parameters [3, 4], such as node density, Residual Energy [5, 6], and Received Signal Strength Indicators (RSSIs), to decide whether a node should forward a broadcast packet [7]. By incorporating linguistic variables and fuzzy inference, these approaches proved to be more robust than purely threshold-based methods in handling continuously varying network states.  
However, a principal drawback of the earlier fuzzy logic approaches is their static nature [8, 9]. Once the membership functions and rule sets are defined, they often remain unchanged throughout the network operation. As a result, these approaches may fail to adapt to rapidly evolving conditions, such as fluctuating node mobility or changing topology densities. The protocols that dynamically evaluated link metrics (e.g., relative mobility or link age, etc.) using a fuzzy system to decide further action proved to be efficient [10, 11, 12]. Consequently, static fuzzy schemes can cause either excessive redundant transmissions or inadequate coverage when network parameters drift beyond their initially configured ranges [13, 14]. Despite these limitations, fuzzy logic-based broadcasting remains a foundational method due to its interpretability and relative simplicity in implementation. In our previous work [2], we found this issue to be an important factor for future improvement, as dynamic membership or parameter selection and adaptability could ensure the model'scapability for adapting to new environments and network changes; this alone could enhance model performance, aside from further enhancements that could be achieved by, for example, the introduction of RL into the module.
# Framework for Developing Bio-Inspired Morphologies for Walking Robots
## 7 Discussion
However, the anchor morphology still has a greater reach in those positions. Hence, the anchor morphology with its improved _dexterity_ can better utilize its workspace and overcome obstacles than a template morphology. The anchor morphology helps to protect the robot from fatigue-inducing impulses that, over time, will damage mechanical components, such as gears, bearings and structural components, which ultimately lowers the robot's service life [23; 32]. For example, the anchor morphology has an overall lower acceleration level of 7.9% during gait, while the most noticeable difference is the 13.6% lower accelerations of the DOF2 motor. The anchor morphology's longer stride length lowers the frequency with 21.1%, which contributes to the previously mentioned reductions. The lower step frequency of the anchor morphology also minimizes the number of impulses from foot strikes per distance travelled. Mechanical shock dampeners and/or control strategies can be used to mitigate impulses from foot strikes [32], however, lowering the overall number of impacts per distance travelled lowers the robot's general exposure to impacts and contributes to the extended service life. Subsequently, impact mitigating strategies can be implemented as an addition to lower the detrimental effects even further. For example, impedance control can be implemented to mitigate the effects of impacts during movement [23]. \begin{table}
\begin{tabular}{l l|c|c|c} \hline \hline  & \multicolumn{1}{c|}{**Template**} & **Anchor** & **Difference** \\ \hline
**Movement versatility:** & & & & \\ Step length & [m] & 0.18 & 0.27 & 50.0\% \\ \hline
**Decarity:** & & & & \\ Reachable positions & [\#] & 48 & 54 & 12.5\% \\ Usable positions & [\#] & 22 & 43 & 95.5\% \\ \hline
**Durability:** & & & & \\ Maximum torque level & [Nm] & 5.88 & 4.80 & \(-\)18.37\% \\ Stepping frequency & [Hz] & 0.19 & 0.15 & \(-\)21.1\% \\ Average acceleration DOF1 & [deg/s\({}^{2}\)] & 20.20 & 18.73 & \(-\)7.3\% \\ Average acceleration DOF2 & [deg/s\({}^{2}\)] & 24.27 & 20.97 & \(-\)13.6\% \\ Average acceleration DOF3 & [deg/s\({}^{2}\)] & 19.05 & 18.79 & \(-\)1.3\% \\ Average acceleration Total & [deg/s\({}^{2}\)] & 63.52 & 58.50 & 7.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the template and anchor morphologies’ performancesCompared to the template morphology, the anchor morphology also lowers the torque output of the motors during gait (Figure 13) and better prevents motor overload. Hence, the anchor morphology provides a greater _durability_ based on the improved torque distribution (Figure 13) and less stress on mechanical components (Figures 9 and 12).
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 3 Simulation Analysis
### Snr  
In this study, simulations were conducted for both HBT and MVDR-HBT under SNR values of \(0\) dB, \(-5\) dB, and \(-10\) dB. The simulation results of the HBT and MVDR-HBT algorithms for an SNR of \(-10\) dB and the sound source location (\(5,5\)) are shown in Figure 4. HBT generated position coordinates of (\(5.9\), \(4.9\)) at the maximum value of the correlation function, giving a relative error of localization of \(12.81\%\). MVDR-HBT generated position coordinates of (\(5.2\), \(5\)), giving a relative error of localization of \(2.83\%\), indicating that MVDR-HBT produced a more accurate location at a low SNR. Subsequently, MVDR-HBT was used to perform localization at \(-5\) dB and \(0\) dB. These localization results were then compared with those of HBT, as summarized in Table 1. With decreasing SNR, the localization error and relative error of both MVDR-HBT and HBT increased. However, compared with HBT, the relative error percentage of MVDR-HBT was reduced by \(1.41\%\), \(4.59\%\), and \(9.98\%\) when the SNR was \(0\) dB, \(-5\) dB, and \(10\) dB, respectively. These results indicate that MVDR-HBT has a better localization effect than HBT at low SNR and produces higher localization accuracy.  
Figure 3: Microphone array structure.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 3 Investigation of All-Bands BadNets Attack in Remote Sensing Dataset
### Threat Model  
We consider a targeted attack where the attacker chooses a target label, and the backdoored model will predict the input as the target label regardless of the true label of the input, as long as the input contains the predefined trigger. The attacker has full access to the model during training and can modify the batch size, learning rate, and model parameters. During training, the attacker selects a target label and poisons some training samples by embedding the predefined trigger into the samples and labeling the poisoned samples with the target label. In the experiment, we chose the trigger as a square with different sizes of pixel intensity of '1', and selected the 'River' class as the target class label. We poisoned 10% of the samples from each class in the training dataset to plant the backdoor.  
These assumptions align with typical backdoor research and are appropriate, especially considering that small companies or local government agencies may not possess significant in-house computational capabilities. The victim may hire an outsourcing company to train their deep learning model and keep an in-house validation dataset to validate the trained outsourced model. The victim rejects the model if it does not perform well on the victim's validation datasets. Backdoor attacks in natural images typically embed the trigger into red, green, and blue bands of natural images. Similarly, in the all-bands BadNets backdoor attack in remote sensing imagery, we embedded the trigger into all of the bands in the data.
## 6 VLC-IoT: Existing and Proposed Approaches
### Modulation Techniques in VLC-IoT
#### 6.2.1 Existing Approaches  
Based on the review of the relevant literature, we have identified the following modulation techniques as the existing modulation schemes deployed for VLC-IoT.  
1. On-off key (OOK) modulation  
The on-off key (OOK) modulation is one of the most employed modulation schemes for VLC-aided systems. In actual fact, it is the most widely deployed modulation scheme for intensity modulation/direct modulation in low-to-moderate data rate VLC systems. In addition, OOK is widely used for dimming control in VLC-IoT systems [16; 33; 38; 41]. To achieve a dimming target, OOK adds a group of redundant 0 s and 1 s to a signal and allocates different light intensity levels to the 0 s and 1 s to achieve the dimming goal [41]. Although, by adjusting the light intensity allocated to 0 s and 1 s, the negative impact of adding redundant bits on the performance of the VLC-IoT system is eliminated. However, it leads to chromaticity shifts by under-driving the LEDs. Nonetheless, OOK is still the most deployed modulation scheme in VLC-IoT systems due to its simplicity and low power consumption [38]. Other advantages of OOK include the fact that it is not hardware and resource intensive [16; 33; 46].  
2. Pulse position modulation (PPM)  
In the pulse position modulation (PPM) scheme a single pulse is transmitted in each period of \(L\) equal time slots. This scheme is based on the pulse position wherein the position of a pulse signifies the symbol that is transmitted. The PPM is a commonly deployed modulation scheme in VLC systems due to the simplicity of its implementation [16; 33; 41]. Besides, the PPM has a greater power efficiency than OOK albeit with higher complexity and increased bandwidth requirements. Hence, several variants of the PPM, e.g., multi-pulse PPM (MPPM) and overlapping PPM (OPPM) have been advanced where multiple pulses can be transmitted during the duration of a symbol to achieve higher spectral efficiency and dimming control. Similarly, a new variant known as the variable PPM (VPPM) has been proposed specifically for VLC systems in which the pulse width of the transmitted symbol can be adjusted to achieve different dimming level requirements. To further improve spectral efficiency and improve dimming control levels other variants of the PPM which combines both MPPM and OPPM known as the overlapping multiple PPM (OMPPM) have recently been proposed. In the OMPPM, more than one pulse position is permitted in each optical pulse [6; 9; 38].
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 2 Internet Latency
### Time Measurement  
We distinguish two methods for measuring the transit time between two machines. The One Way Transit Time (OWTT) represents the time measured between the sending of a packet and the arrival to the destination. This approach attempts to capture the real-time separating two endpoints but demands a precise clock synchronization of those points and sending the timestamp along with the packet.  
The Round Trip Time (RTT) is measuring the time between the sending and the reception of a response. As this is a one-sided measure, there is no need for clock synchronization. The approximation \(OWTT=\frac{RTT}{2}\) is often made, but there is no insurance that the transit times in both directions are comparable. It is then preferable to consider RTT as a stand-alone metric rather than a way to measure OWTT.  
In this paper, we adopt the RTT metric. Using the OWTT metric constrains one of the party members of our protocol to send its timestamp data to the other party for a travel time to be computed. This has at least two clear downsides: (1) it raises the overall quantity of data to be sent, and (2) it may become a breach for an attack aiming to falsify the measures.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 3 Existing Literature Reviews on Clustering Based on Optimization
The review focused on the positives and limitations of these techniques by classifying them into block, chain, and grid-based techniques. The methods were evaluated based on their scalability, cluster stability, delivery delay, and energy efficiency. Clustering protocols were also reviewed in Ref. [13] based on their positives and shortcomings. Cluster-based routing techniques were classified and presented in three broad categories: block, grid, and chain-based clustering techniques. Existing schemes were also comparatively evaluated regarding delivery delay, energy awareness, load balancing, cluster stability, and algorithmic complexity. The classification of different WSN clustering protocols into homogeneous and heterogeneous WSNs was presented in Ref. [14] considering the node and resource capabilities of the networks. Each protocol was reviewed based on its challenges, while the comparison included but was not limited to cluster count, inter-cluster communication, number of CHs, clustering objects, and complexity. Unequal clustering techniques were surveyed in Ref. [15] based on their objectives and features. Unequal clustering techniques were categorized into probabilistic, preset, and deterministic methods and compared with respect to clustering/cluster properties and the clustering process. Some of the methods were also simulated to determine their energy usage and service life. Existing clustering techniques were reviewed in Ref. [16] based on their general classification parameters and criteria; the clustering schemes were categorized into classical, fuzzy-based, meta-heuristic and hybrid meta-heuristic-based algorithms. Cluster-based routing protocols were also broadly categorized into methodology-based parameters and clustering-based parameters. Ref. [17] presented ongoing machine learning or computational intelligence progressive methods. Computations were grouped based on their different computational intelligence uses into categories of swarm intelligence, fuzzy logic, neural network, genetic algorithm, and reinforcement learning. These computational intelligence uses were analyzed based on their scalability, data delivery rate, and data aggregation. We correspondingly noted that these methods improved the lifetime and service quality of the network. The hybrid model combinations also improved the interference of the network.
## 2 Materials and Methods
### Statistical Analysis  
Sociodemographic and baseline clinical characteristics were reported as frequencies, percent, or mean (\(\pm\)SD), depending on the features of the variables. We applied paired \(t\)-test or Wilcoxon signed rank test to compare variables, depending on their distributional characteristics. Linear mixed-effects regression models with restricted maximum likelihood algorithms were fitted in R software version 4.4.1 [36] using the \"lme4\" package [37]. The models in the prediction of MADRS and SDQ scores included sex, age, levels of neurometabolites, time (pre-, post-), and NIR doses as fixed factors and repeated measurements across participants as the random factor. The violations and robustness of the models were evaluated with residual histograms, normality test of residuals, residual vs. fitted plots, and QQ plots. A \(p\)-value of \(<\)0.05 was used to denote statistical significance. The Benjamini-Hochberg \(p\) adjustment method was used to control false discovery rate when multiple tests were performed. When a statistically significant difference was found, the effect size (Cohen's \(d\)) was calculated and presented.  
Figure 1: Magnetic resonance spectroscopy (1H-MRS) voxel placement, segmentation, and magnetic resonance (MR) spectra. (**A**) Both pre- and post- left prefrontal MRS voxel (13 cm\({}^{3}\)) overlap density maps for all participants in sagittal and axial planes. Individual MRS voxels were transformed to the MNI space and then combined to show the overlap. (**B**) Representative spectrum from the left dorsolateral prefrontal cortex, showing prominent peaks for N-acetylaspartate (NAA), creatine (Cr), choline (Cho), and myo-inositol (mlns). (**C**) Voxel composition of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in pre- and post-treatment scans.
## 2 Datasets and Methods
### Double-Difference Technique Based on Backscatter Simulations over the Global Oceans  
A backscatter calibration method based on numerical weather prediction (NWP) winds, called NOC, has been widely used for the calibration of spaceborne scatterometer measurements [20]. In this study, the double-difference technique is essentially the difference between the individual NOC results of two of the HSCAT instruments.  
For each observed backscatter measurement (\(\sigma_{\rm s}^{0}\)) over an ocean, the NWP wind can be matched. Then the simulated backscatter value (\(\sigma_{\rm s}^{0}\)) can be computed via the NSCAT-4 geophysical model function (GMF) [27]:  
\[\sigma_{\rm s}^{0}=\mathrm{GMF}(V,\varphi,\theta,p), \tag{3}\]  
where \(V\) is the sea surface wind speed, \(\varphi\) is the wind direction relative to the direction of radar antenna look, \(\theta\) is the associated incidence angle, and \(p\) is the polarization. The NOC result (\(\Delta\sigma_{\rm noc}^{0}\)) is then defined as the distance between the observed and simulated \(\sigma^{0}\) data for each polarization:  
\[\Delta\sigma_{\rm noc}^{0}=\overline{\sigma_{\rm s}^{0}}-\overline{\sigma_{ \rm o}^{0}}, \tag{4}\]  
where \(\overline{\sigma_{\rm s}^{0}}\) and \(\overline{\sigma_{\rm o}^{0}}\) are \(\sigma^{0}\) values that are first average over each wind direction in \(6^{\circ}\) bins, and then over all wind speeds with a weight proportional to the wind speed PDF over 1 m/s bins. In effect, the measured or simulated \(\sigma^{0}\) are averaged over all wind azimuths weighted in accordance with a uniform wind azimuth distribution, and then they are averaged over all wind speeds weighted in accordance with the wind speed distribution.  
The double-difference result is then defined as the difference between \(\Delta\sigma_{\rm noc}^{0}\) of two HSCAT instruments.