esa-sceva/satcom-chunk-collection huggingface dataset:
# Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAlHub
## 2 Materials and Methods
### System Modules
#### 2.3.4 Compute Module  
The Compute Module is where client-side computing capabilities are utilized. When a user enters an Individual Model Page, the Compute Module loads the config file simultaneously with the Model Management Module. After the user enters their input (if applicable), the Compute Module is initiated once the user decides to run the model. The module starts by reading the input if the config states the existence of an input. How the input is read depends on the input type, but at the end, the input is stored as a JavaScript variable. Once the input is read, the client-side computing resources are put to use. In order to do so without interfering with how the interface of EarthAlHub works, we implemented a JavaScript Web Worker. A web worker is created right after the input is read. The input, configuration of the model, and the location of the TensorFlow.js model file are communicated to the worker right after the worker is created.  
The worker first imports TensorFlow.js and the TensorFlow.js _GraphDef_ model and then runs preprocessing on the input variable depending on the specifications defined in the config file. After that, the worker feeds the preprocessed (if applicable) input to the imported model. Once the model's output is obtained, the worker then runs postprocessing over the output, again according to the specifications in the config. After the postprocessing, the output is then converted to a vanilla JavaScript variable and communicated back outside of the worker's scope. Output is specifically transferred in such a format because (a) we do not import TensorFlow.js outside of the worker's scope, and (b) sending messages to and receiving messages from a web worker does not support external object types. Finally, the Compute Module gives the output back to the Model Management Module to render it according to the config. The workflow of the Compute Module can be seen in Figure 4.  
Figure 4: Compute Module schema.
# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 4 Experiment Testbed
### Collection of Reference Points  
This section discusses the collection of reference point phase. The reference points were collected manually from several points in the tracking area of interest and stored in a database file. Two different gathering processes (the collection of reference points in the tracking area) were conducted in order to analyze the impact of the reference point density on localization accuracy.  
A total number of 68 and 126 reference points were collected from equally distributed points in the IIRC lab for gathering tasks 1 and 2, respectively. In addition, we merged the two datasets to produce a new RSS fingerprinting dataset with a total of 194 reference points. Table 4 presents general statistics on the 3 different RSS fingerprint datasets (small, medium, and large) collected. However, the difference between the RSS datasets is analyzed and compared according to the following parameters:  
1. Total number of collected reference points (rp): this refers to the total number of reference points in the tracking area, where rp involves the 2D location of a reference point in the tracking area. Figure 10 depicts the total number of reference points that were collected using 3 different experiments (small, medium and large).
2. Density of reference points: this refers to the total number of reference points over the dimension (meter square) of the tracking area (rp/m\({}^{2}\)).
3. Gathering process time: this presents the total time in minutes needed to accomplish the offline phase (the collection of reference points) for each RSS dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Transceiver & XBee series 2 \\ Communication protocol & ZigBee \\ Transmission range & 100 m \\ Test-bed size & 20.2 \(\times\) 7.6 m \\ number of reference nodes & 4 \\ number of target nodes & 1 \\ Power mode & 4: High \\ Node type & Router API \\ Minimum RSS value & 0 \\ Maximum RSS value & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment testbed parameters.  
Figure 9: The architecture of the station reference node.  
For validation purposes, the RSS datasets were divided into two subsets: 70% training subset and 30% testing subset, where the training subset is used to train the ML model, and the testing subset is employed to test the localization accuracy.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 4 Five Scenarios for Smartglass-Assisted Interactive Telementoring
### SAIT for Maintenance Scenario  
* _Mentors and Mentees of Different Levels_. This SAIT scenario involves on-site workers as mentees and remote experts as mentors. As illustrated in Figure 7, working conditions on the site may be too narrow and confined to hold multiple workers physically. In this scenario, mentors with more experience and expertise support and assist mentees to carry out maintenance or repairing tasks successfully.
* _Sharing of First-person and Eye-level View via Smartglass_. The mentees share their views captured by HMDs with remote expert mentors. Then the mentors can intervene to assess the equipment and cables visually as well as instructing complex procedures to the mentees.
* _Extended Use Cases with Bi-directional Interaction_. In this scenario, voice-based UIs are used to communicate between mentors and mentees in real-time. Furthermore, telestration UIs will enhance communication to be more clear and allow identifying problematic areas of interest. For example, the mentor can highlight parts to be examined or replaced.  
Figure 6: An illustration of SAIT for paramedics scenario.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure
### Random Communications Ranges
#### 3.2.4 Lognormal Channel Shadowing Effect
\] (23a) The expectation \[\mathbb{E}[R]\] and variance \[\mathbb{V}ar[R]\] variable \[R\], for a non-big vehicle shadowed channel using same procedure and results in [26] after some mathematical simplification yields respectively; \[\mathbb{E}[R]=\frac{\exp\big{(}\frac{\sigma^{2}}{2\alpha^{2}}\big{)}}{\Lambda ^{\frac{1}{\alpha}}},\;\text{and}\;\mathbb{V}ar[R]=\frac{\exp\big{(}\frac{ \sigma^{2}}{\alpha^{2}}\big{)}\Big{\{}\exp\big{(}\frac{\sigma^{2}}{\alpha^{2} }\big{)}-1\Big{\}}}{\Lambda^{\frac{2}{\alpha}}}.\]  
Indicated as before, for the corresponding big vehicle shadowed channel, \(\hat{\Lambda}\) substituted for \(\Lambda\) instead. We give a summary for the distribution (PDFs of R) in Table 2, in the different channels and their statistics in terms of expectations and variances (where \(\Lambda\) and \(\hat{\Lambda}\) can be used for non-bus and bus shadowing effects respectively). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel Model** & \(f_{R}(r)\) & \(\mathbb{E}[R]\) & \(\mathbb{V}ar[R]\) \\ \hline Pathloss & – & \(\Big{(}\frac{P_{1}G}{P_{0}P_{r}}\Big{)}^{1/\alpha}\) & – \\ \hline Rayleigh fading & \(\alpha\Lambda r^{\alpha-1}e^{-\Lambda r^{\alpha}}\) & \(\frac{\Gamma(\frac{1}{\varepsilon})}{\alpha\Lambda^{\frac{1}{\varepsilon}}}\) & \(\frac{\Gamma(\frac{1}{\varepsilon})\big{(}\alpha^{2}\Gamma(\frac{1}{2}+\frac{1 }{\varepsilon})-\sqrt{\pi}\Gamma(\frac{1}{\varepsilon})\big{)}}{\sqrt{\pi} \alpha^{2}\Lambda^{2/\varepsilon}}\) \\ \[PERSON]-_m_ fading & \(\frac{\alpha(m\Lambda)^{m,m-1}}{\Gamma(m)}e^{-m\Lambda r^{\alpha}}\) & \(\frac{\Gamma\big{(}m+\frac{1}{\varepsilon}\big{)}}{(m\Lambda)^{1/\alpha}\Gamma (m)}\) & \(\frac{\Gamma(m)\Gamma\big{(}m+\frac{1}{\varepsilon}\big{)}-\big{(}\Gamma(m+\frac{1 }{\varepsilon})\big{)}^{2}}{\big{(}(m\Lambda)^{1/\alpha}\Gamma(m)\big{)}^{2}}\) \\ \hline Weibull fading & \(\hat{\alpha}\Lambda_{c}r^{\hat{\alpha}-1}e^{-\Lambda_{c}r^{\hat{\alpha}}}\) & \(\frac{\Gamma\big{(}\frac{1}{\varepsilon}\big{)}}{\hat{\alpha}\Lambda^{\frac{1}{ \varepsilon}}_{\hat{\alpha}}/\hat{\alpha}}\) & \(\frac{\Gamma(\frac{1}{\varepsilon})\big{(}\hat{\alpha}2^{2/\alpha}\Gamma(\frac{1 }{2}+\frac{1}{\varepsilon})-\sqrt{\pi}\Gamma(\frac{1}{\varepsilon})\big{)}}{ \sqrt{\pi}\hat{\alpha}^{2}\Lambda^{2/\varepsilon}_{\alpha}}\) \\ \hline Lognormal shadowing & \(\frac{\alpha}{\sqrt{2\pi}\sigma r}\exp\Big{(}-\frac{(\ln(\Lambda r^{\alpha}))^{2}}{2 \sigma^{2}}\Big{)}\) & \(\frac{\exp\big{(}\frac{\sigma^{2}}{\alpha^{2}}\big{)}}{\Lambda^{1/\alpha}}\) & \(\frac{\exp\big{(}\frac{2\sigma^{2}}{\alpha^{2}}\big{)}-\exp\big{(}\frac{\sigma^{2}}{ \alpha^{2}}\big{)}}{\Lambda^{2/\alpha}}\) \\ \hline \hline \end{tabular} The constants \(\hat{\alpha}=\frac{\alpha C}{2}\) and \(\hat{\Lambda}=\big{(}\Lambda\Gamma(1+\frac{2}{\varepsilon})\big{)}^{\frac{1}{ \varepsilon}}\) are for Weibull distribution expression.  
\end{table} 
Table 2: Statistics of transmission ranges, R.
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 3 Simulation and Measurement Results
### Results of Low-Band Operation  
Figure 9 presents out simulated and measured results achieved by varying the bias voltage of the varactor diode in the \(f_{\rm L}\) band and fixing the bias voltage to 0 V in the \(f_{\rm H}\) band. Figure 9a shows the simulated and measured PSRs. The phase at \(f_{\rm L}\) band tuned, while the \(f_{\rm H}\) band was maintained constantly. Based on our experiment, we determined that the PSR of the \(f_{\rm L}\) was 114.194\({}^{\circ}\)\(\pm\) 8.2615\({}^{\circ}\) within a bandwidth of 100 MHz, a result that was achieved by varying the bias voltage from 0 V to 16 V. While varying the phase at the \(f_{\rm L}\) band, the phase of \(f_{\rm H}\) remained at 0.225\({}^{\circ}\)\(\pm\) 0.936\({}^{\circ}\) within the bandwidth. Figure 9b shows the simulated and measured IL and input/output RLs. The measured ILs at the \(f_{\rm L}\) band and the \(f_{\rm H}\) band were smaller than 1.867 dB and 1.897 dB, and the input/output RLs were higher than 19.674 dB and 16.684 dB, respectively.  
Table 4 provides the measured results after varying the bias voltage of the varactor diode at the \(f_{\rm L}\) band and fixing the bias voltage at 2.5 V and 16 V at the \(f_{\rm H}\) band. As seen from Table 4, the \(f_{\rm L}\) band achieved almost the same PSR and the phase of the \(f_{\rm H}\) was constantly maintained. The measured ILs were smaller than 2 dB, and RLs were higher than 19.6 dB.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 2 Preliminaries
### Physically Unclonable Functions  
As a new hardware security primitive, the Physically Unclonable Function (PUF) is a hardware function implementation circuit that relies on chip features, with uniqueness and randomness. By extracting process parameter deviations that are inevitably introduced during chip manufacturing, it achieves a function that uniquely corresponds to the excitation and response signals [34]. In our scheme, PUF was used to protect the information stored in the gateway and sensors.
## 3 Results and Discussion
### Real Sample Measurements
#### 3.3.2 New Psychoactive Substance on Herbal Mixture  
Another practical application is illustrated in Figure 4. It shows the ability to detect synthetic cannabinoid species directly on herbal mixtures. These herbal blends are commonly coated with so-called new psychoactive substances (NPS). The investigated compound belongs to these NPS and is a cannabinoid aminoalkyl indazole with a carboxamide-linked cumyl moiety (SF-Cumyl-PINACA).  
Figure 3: Desorption in negative CI mode of TNT from the surface of shrapnel recovered from a defused bomb of the Second World War. All shrapnel in the upper image show clear signals of TNT when investigated. A part of one of those single ion traces can be seen in the mass profile where the TNT signal at \(m/z\) 227 is plotted. The full size mass spectrum shows the first peak at about 1 min. The panel in the lower left shows the spectrum of a 10 ng TNT standard sample, measured under the same conditions. The signals look similar for every detected TNT trace and shrapnel.  
For better comparability and reliable substance identification, through fragment identification, EI, was applied as the ionization technique.  
Strong EI fragmentation signal at low \(m/z\) are typical when investigating complex natural samples. Direct measurement of the investigated herbal mixture revealed the usage of 5F-Cumyl-PINACA as an active substance at \(m/z\) 367 by comparing the molecular ion signal as well as the fragment pattern [35]. For direct measurement of the herbal mixture, a S/N ratio of 50.9 was calculated for the base peak at \(m/z\) 352 and a S/N ratio of 3.7 for the molecular ion signal at \(m/z\) 367. For the respective measurement of the emptied bag, the S/N ratio for the base peak at \(m/z\) 352 was 45.6 and for the molecular ion signal at \(m/z\) 367 the S/N ratio was 3.1. Even an emptied bag, in which the mixture was sold, showed a clear presence of 5F-Cumyl-PINACA. Therefore, a fast decision can be made whether the target substance is illicit or not simply by measuring suspect or confiscated objects. A reasonable scenario could be the rapid identification of designer drugs and related products found in conspicuous parcels in postal distribution centers.
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 3 The Definition and Scope of Space Debris Remediation  
Remediation mainly aims at removing existing pieces of orbital debris through active debris removal (ADR). Active debris removal involves the removal of intact but non-functional and/or uncontrolled objects (i.e., defunct satellites and rocket bodies). Moreover, these efforts could be supplemented by so-called on-orbit servicing of satellites (OOS). OOS aims at ameliorating the capabilities of satellites on orbit which have become non-functional through refueling and upgrading in order, first, to diminish the break-up risks and thus the creation of space debris, and second, to extend the satellite's life. As such measures relate to existing space objects, OOS can be considered partly a mitigation measure [45]. On-orbit servicing might also develop into repurposing or scavenging of valuable components from defunct satellites. Such concepts are currently being investigated by DARPA's Phoenix program [46; 47], and certainly need to overcome challenges in automation and robotics in space operations and would benefit from standard interface ports for docking and modular designs [48].  
Unlike mitigation measures, which aim at reducing the number of objects to be launched in orbit in the future, space debris remediation is designed to act against the consequences of orbital congestion with debris and aims at removing objects that are not functional anymore and thus represent a risk to space activities. So far, space debris remediation measures have been proposed but not yet applied in practice.  
The effectiveness of the different disposal methods depends strongly on the type, mass and orbital position of the satellite. Such concepts for the removal objects from orbits include tethering, tugging, beaming with an electrostatic tractor (for GEO) [49], ion-beaming through relocation and lasering, net capturing [50], docking with a nozzle (especially in GEO), harpooning, de-orbiting with a drag augmentation sail, and de-orbit kits [51]. There are, however, also passive debris removal concepts. They involve the pre-launch instalment of systems such as drag augmentation devices which can deploy sails to accelerate the natural decay of satellites [52]; electrodynamic tethers [53; 54] for de-orbiting, and thrust propulsion systems enabling de- or re-orbiting.  
Moreover, the concept of laser debris removal foresees installing plasma jets on objects in order to enable controlled re-entry [55; 56; 57].  
The focus of proposed remediation measures lies within the removal of larger objects and not of small objects as they act as triggers for the cascading effect. This has been shown through the results of the 2007 Fengyun 1C anti-satellite test by China in 2007 which \"was adding more than 3300 trackable objects to the US Space Surveillance Network catalogue, increasing its size by 25% in just one incident\" [58; 59].
## 3 Results and Discussion  
### Physicochemical Quality Parameters of Camelina Oils  
Lipid oxidation and hydrolysis are the main processes leading to deterioration of edible oils; therefore, it is very important that the degree of these changes be as low as possible. The degree of hydrolytic changes, indicating the free fatty acid content of tested camelina oils, was evaluated on the basis of the acid value. The content of primary oxidation products was determined by peroxide value (PV), and the secondary oxidation products--carbonyl compounds--were determined by anisidine value (_p_-AnV). The oxidation state of the tested oil was determined using the TOTOX indicator. UV-specific absorbance was also determined: K\({}_{232}\) (the level of primary oxidation products and conjugated dienes) and K\({}_{268}\) (the level of secondary oxidation products and conjugated trienes). The results obtained are provided in Table 1.  
The acid value (AV) of the tested oils, ranged from 0.56 to 1.62 mg KOH/g (Table 1). The analyses performed exhibited a large variation in the obtained results, and thus a differentiated degree of hydrolysis of the tested oils. Nevertheless, all tested _Camelina sativa_ oils met the quality requirements provided by Codex Alimentarius (<4 mg KOH/g) for cold-pressed oils. [PERSON] et al. [22], when testing camelina oil pressed from seeds of winter and spring conventional crops, obtained similar AV values (1.34-1.49 mg KOH/g).
# A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis
## 3 A CAV Reference Architecture: Components and Related Attack Surfaces
### CAV Reference Architecture
#### 3.1.3 Internal Communications (Virtual)  
As well as a communication system that allows a CAV to communicate with external devices (such as RSUs or 3 rd party vehicles), they also have an internal communication system such as a CAN bus. This is used to connect the multiple components that form an implementation of the functions specified in the architecture. This communication network is not explicitly specified as a component,as it is implicit due to the components interacting. Vehicles may also use a different internal interaction (such as Ethernet) in the future and by under-specifying this implementation detail, the reference architecture is more generic.  
\begin{tabular}{l} \hline \hline
**Example Attacks** \\ \hline \(\bullet\) Send crafted packets [11; 45; 46; 43] \\ \(\bullet\) DoS [45] \\ \(\bullet\) Eavesdrop [45] \\ \hline \hline \end{tabular}  
Attacking this internal communication network can be performed by a direct connection to it, for example, via an OBD-II port. Alternatively, an attacker can gain access to this internal network via vulnerabilities in the components that connect to it. Once these components are compromised an attacker will have access to eavesdrop on messages sent [45] or the ability to inject malicious messages [43; 45; 46]. With access to the internal network of a vehicle many functional aspects of the vehicle can be controlled, including: the radio, instrument panel, the vehicle's body, engine, brakes, HVAC and others [21]. A solution to these issues is to use encryption and authentication of messages [47], however, vehicles currently on the road act as if the CAN bus is a walled garden and do not attempt to encrypt or authenticate messages sent on the bus.
## 2 Materials and Methods
The generated laser was immediately divided into pump and probe beams using a polarized beam splitter. A mechanical delay stage controlled the time delay between the pump and probe pulses. To increase the signal-to-noise ratio of detection, we modulated the pump/probe at 1 MHz/200 Hz with an electro-optic modulator (EOM)/optical chopper. Due to the group velocity dispersion of the EOM, the pulse width of the pump pulse was increased to 1.2 ps. We irradiated the pump and probe beams on the transducer surface at a spot size of 6 \(\upmu\)m with incident fluences of 6 and 0.6 J m\({}^{-2}\), respectively, for pump and probe. After reflection from the transducer layer, \(\Delta R\) and \(\Delta\theta\) values of the probe were collected by a normal and balanced photodetector, respectively. The raw signal from the balanced detector showed a small contribution from \(\Delta R\). To collect the pure MOKE signal, we measured \(\Delta\theta\) with magnetic fields of + and\(-\)0.5 T, and then collected the difference between them. When we changed the direction of magnetization with magnetic fields, \(\Delta\theta\) changed its sign, whereas \(\Delta R\) did not. The schematic of the measurement setup is shown in Figure 2.  
Figure 1: (**a**) Schematics for the sputtering process. An Ar\({}^{+}\) ion bombards the CoB alloy target. Target elements are ejected from the target surface and deposited on the substrate. (**b**) The number distribution (\(f\)) of the sputtered elements from the target surface is a function of the kinetic energy (\(E\)). The maximum\(f\) occurs at \(E\) with around half the surface binding energy (\(E_{\text{sb}}\)) of the target material.
## 2 Materials and Methods
### Quantitative Measurements and Statistical Analysis  
To quantitatively measure the PDFF estimations, a region of interest (ROI) of 105 mm\({}^{2}\) was manually located at the right hepatic lobe of the slice where the liver had the greatest  
Figure 2: Evolution of training (orange) and validation (blue) losses during NLV-Net training process.  
diameter, and ROIs of 25 mm\({}^{2}\) were manually located at the parenchyma of the pancreatic head and body. The size of ROIs was selected to ensure they could be accommodated across all subjects. To maintain data consistency and quality, the placement of all ROIs was adapted to each subject's anatomy, with vessels visually avoided.  
To assess the accuracy of the neural networks, the PDFF estimates for pancreatic head and body obtained from each method (U-Net, MDWF-Net, NLV-Net with three and six echoes and three-echo graph cut) were compared against those from the reference six-echo graph-cut method. Linear regression, Spearman rank correlation coefficient (rho), Bland-Altman analysis, and a Friedman test with post hoc test were performed to determine the correlation, bias, and statistically significant differences of the results using the software Prism-GraphPad 10. A significance level of 0.05 was used for all the statistical tests.  
To investigate the relationship between pancreatic and hepatic steatosis, we compared the PDFF estimates of the liver with those of the pancreatic head and body. This comparison involved performing linear regression, computing the Spearman rank and correlation coefficient, and conducting Wilcoxon tests, using both the graph-cut method and the best-performing neural network on six-echo MR images.