esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT Reader for Wireless Passive Electromagnetic Sensors
## 2 Materials and Methods
### IoT Mini-Computer Layer
The protocol is based on four letter commands where the communication (Figure 19b) is as follows: first of all, the _SERVER_ is waiting for incoming connections; then, the connection between the _CLIENT_ and the _SERVER_ is established and the _SERVER_ sends the command _CONN_ to the _CLIENT_ confirming the connection; once the connection is confirmed, the _CLIENT_ sends the command _READ_ which means a request for reading the sensor; after that, the _SERVER_ sends the command _DATA_ which means that it will start sampling the sensor response and sending the data packets; finally, after sending all the packets, the _SERVER_ sends the command _CLOS_ which means the correct ending of the communication. The data packets are divided into three values: _time_ (clock time in ms for each sample), _sync_ (a sample of the digitized triangular signal) and _val_ (a sample of the digitized response  
Figure 18: Pinout and signal conditioning circuits for the Arduino MKR1000 board. of the sensor). 600 samples are sent to the _CLIENT_ with the current configuration, which results in six signal periods. On the other hand, if the _CLIENT_ sends an incorrect command (_XXXX_ in Figure 19c) the _SERVER_ answers with the command _ERROR_ which means that the command is unknown. Then, the _SERVER_ sends the command _CLOS_ ending the communication. Figure 19: Protocol stack model (**a**), communication protocol in a normal situation (**b**) and in a situation where an incorrect command is sent by the _CLIENT_ (**c**). Figure 20: Flow chart of the IoT reader (_SERVER_) program. The flow chart of the IoT-R _SERVER_ program is shown in Figure 20. The program works as follows: after starting the program, it waits until the reader is linked to the WiFi network and the _SERVER_ is started. Then, the _SERVER_ waits until a _CLIENT_ is connected. After the connection is established, the _SERVER_ waits for an input command. Once the _SERVER_ receives the input command _READ_ from the _CLIENT_, the samples are sent and the _CLIENT_ connection is finished, otherwise the _CLIENT_ connection is closed without sending the samples. Finally, after closing the _CLIENT_ connection the _SERVER_ waits until other _CLIENT_ establishes a connection for starting the process again.
## 2 Materials and Methods
### Statistical Analyses  
Descriptive statistics are presented as mean (\(\overline{\text{X}}\)) and standard deviation (SD). The Shapiro-Wilk test was used to assess the normality of the data distribution, confirming that the data followed a normal distribution and that the conditions for parametric tests were met. The intraclass correlation coefficient (ICC), using a two-way random effects model with absolute agreement for a single measure (ICC(2,1)), and standard error of measurement (SEM) were calculated to evaluate the test-retest reliability of the RCMJ-30 performances used in the PAPE protocol. A one-way repeated measures ANOVA was conducted to determine differences in unilateral CMJ and postural sway values at baseline, and at varying recovery intervals following the PAPE protocol. Generalized eta-squared was used to measure the standardized effect size, with values interpreted as small (0.01-0.059), medium (0.06-0.139), or large (\(\geq\)0.14). If a significant main effect (time) was found, a Bonferroni post hoc test was applied to perform pairwise comparisons between the five conditions (i.e., baseline, R2, R4, R6, and R8), and a power analysis (\(1-\beta\)) was also conducted. [PERSON]'s d calculation of the effect size was used for pairwise comparisons and interpreted as small (0.20-0.49), medium (0.50-0.79), or large (\(\geq\)0.80) [45]. Additionally, statistical differences between recovery intervals were illustrated using line charts. All analyses were performed separately for the dominant and non-dominant legs. The level of statistical significance was set at \(p\leq 0.05\). All statistical analyses were performed using SPSS statistical software (SPSS for Windows, version 21.0, SPSS Inc., Chicago, IL, USA).
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 3 BDS DCB Positioning Correction Model
### DCB Correction Based on the BDS Broadcast Satellite Clock  
The hardware delay in the BDS navigation message is referenced to the B3I signal. The satellite clock of the BDS's broadcast ephemeris is also based on the B3 signal; thus, for SPP using only the broadcast ephemeris and the observation file data, the signals of all frequencies need to take into account the hardware delay introduced by the broadcast ephemeris clock reference. The TGD parameters (TGD1/TGD2) provided by D1 and D2 navigation messages are the hardware delays of B1I and B2I signals with respect to those of the B3I reference signal [26]. Since the B1C and B2a signals contain pilot components and data components, the B-CNAV1 and B-CNAV2 navigation messages contain delay difference compensation for B1C and B2a pilot components to compensate for delay differences between the data components and pilot components of corresponding signals. The broadcast clock information of the BDS contains the hardware delay of B3I, which can be defined as follows:  
\[\overline{\delta t}_{brd}^{s}=\delta t^{s}-B_{3} \tag{2}\]  
where \(\overline{\delta t}_{brd}^{s}\) represents the satellite clock from the IGS broadcast ephemeris.  
Combined with Equation (1) and considering the definition of \(DCB\), the single-frequency pseudo-range observation quantity of the BDS can be written as follows [27]:  
\[P_{i}=\rho+c\cdot\delta t_{r}+T+k_{1i}l_{i}-c\cdot\overline{\delta t}_{brd}^{ s}+DCB_{i3} \tag{3}\]  
where \(DCB_{ij}=B_{i}-B_{j}\) (\(i\), \(j\) = 1, 2, 3, 4) is defined as the DCB and represents the difference in hardware delay from signal \(i\) to signal \(j\). For the dual-frequency ionosphere-free linear combination, the satellite hardware delay relative to the benchmark for any two signal combinations is  
\[B_{i\text{-corr}}^{brd}=\frac{1}{f_{j}^{2}-f_{i}^{2}}(f_{j}^{2}B_{j}-f_{i}^{2} \cdot B_{i})-B_{3}=\frac{1}{1-k_{ij}}DCB_{j3}+\frac{k_{ij}}{1-k_{ij}}DCB_{3i} \tag{4}\]  
The DCB correction model of the dual-frequency observation equation of different signal combinations based on Equation (4) can be written as  
\[\begin{split} PC_{12}&=\rho+c\cdot\delta t_{r}+T-c \cdot\overline{\delta t}_{brd}^{s}-\frac{k_{12}}{1-k_{12}}DCB_{13}+\frac{1}{1- k_{12}}DCB_{23},\\ PC_{13}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t}_{brd}^{s}-\frac{k_{13}}{1-k_{13}}DCB_{13},\\ PC_{14}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t}_{brd}^{s}+\frac{k_{14}}{1-k_{14}}DCB_{43}-\frac{k_{14}}{1-k_{14}}DCB_ {13},\\ PC_{23}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t}_{brd}^{s}-\frac{k_{13}}{1-k_{13}}DCB_{23},\\ PC_{24}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t}_{brd}^{s}+\frac{k_{24}}{1-k_{24}}DCB_{43}-\frac{k_{24}}{1-k_{24}}DCB_ {23},\\ PC_{34}&=\rho+c\cdot\delta t_{r}+T-c\cdot\overline{ \delta t}_{brd}^{s}+\frac{1}{1-k_{34}}DCB_{43},\end{split} \tag{5}\]
## 3 Source Rocks Geochemical Characteristics and Distribution
### Organic Matter Types  
The types of kerogen have an obvious influence on the hydrocarbon generating ability. Type I kerogen has high original hydrogen content, low oxygen content, and the most substantial oil generating ability. The hydrogen content of type II kerogen is higher but slightly lower than that of type I kerogen. Type III kerogen has low original hydrogen content and high oxygen content. Its oil generating ability is poor, and it primarily generates natural gas.  
The kerogen elemental diagram is the standard method to determine the type of organic matter. The H/C of kerogen in the Fengcheng Formation source rock is as high as 1.8, as shown in Figure 4. The main types of kerogen are type I and type II\({}_{1}\), and the proportion of type II\({}_{1}\) organic matter is relatively high, mainly humic organic matter. At the present phase of the hydrocarbon exploration no exploratory well has been drilled to the center of hydrocarbon producing sag. The source rock samples are mainly distributed in the slope area with shallow water bodies.  
Figure 4: Kerogen types of source rocks of the Permian Fengcheng Formation.  
Figure 3: Crosplot of TOC and \(S_{1}+S_{2}\) of the Permian Fengcheng Formation source rocks.
## Abbreviations  
(ACT) Active Thermography; (AD) Adaptive; (AFD/E) Automated Feature Detection/Evaluation; (AR/MR) Augmented Reality/Mixed Reality; (BIM) Building Information Modelling; (CBA) Complete Bouguer Anomaly; (CCM) Category Connection Matrix; (CLSS) Circular Laser Scanning System; (CNN) Convolutional Neural Network; (CRP) Close-Range Photogrammetry; (DIFCAM) Digital Imaging for Condition Asset Monitoring System; (DL) Deep Learning; (DSI) Dynamic Survey Interaction; (DTT) Digital Twin Tunnel; (EPI) Enhanced Photorealistic Immersive; (ERT) Electrical Resistivity Tomography; (FA) Fully Autonomous; (GPR) Ground Penetrating Radar; (GPS) Global Positioning System; (GS) Gravity Surveys; (HCE) Hidden Critical Element; (IRT) Infrared Thermography; (IST) Infnasonic Testing; (LiDAR) Light Detection And Ranging; (MTPM) Moving Tunnel Profile Measurement; (NDI/E) Non-Destructive Inspection/Evaluation; (OLE) Overhead Line Equipment; (PR) Pulsed Radar; (ROCS) Rigid Overhead Conductor Rail System; (RRV) Road-Rail Vehicle; (RS) ROBO-SPECT; (RT) Rail-Trolley; (RTI) Railway Tunnel Inspection; (RTSSI) Railway Tunnel Subsurface Inspection; (RTU) Robotic Traction Unit; (SA) Semi-Autonomous; (SFCW) Step-Frequency Continuous Wave; (SfM) Structure From Motion; (SRS) SafeRailSystem; (TLS) Terrestrial Laser Scanning; (UAV) Unmanned Aerial Vehicle; (UST) Ultrasonic Testing; (VR) Virtual Reality; (XR) Extended Reality; (ZARR) Zetica Advanced Rail Radar.
# Monte Carlo Simulations in Aviation Contrail Study: A Review
## 2 Contrails in Aviation: Generalities
### Atmospheric Fluid Dynamics and Early-Stage Contrails  
Beyond the general thermodynamic framework discussed above, the actual process that leads to contrail formation and eventual persistence is driven by a complex aerodynamic trade-off involving turbulence generated during the aircraft's passage, the engine's exhaust, and the resulting down- and upward streams of moist air. In fact, for modeling purposes, the aircraft wake-engine plume dynamics are generally divided into four phases, characterized by different time scales:  
1. (up to \(t\sim 10\) s, _jet regime_) The counter-rotating vortices initiated by the aircraft wings trap the engine plume, whose water vapour content is higher than the atmospheric counterpart, colder, and more rarefied. The water vapour from the plume tends to condensate into water droplets or ice, which are transported by the wake vortices. Condensation is eventually enhanced by the soot released by engines within the plume.
2. (up to \(t\sim 100\) s, _vortex regime_) Vortices descend downwards in the atmosphere, creating a secondary wake in the opposite direction. A part of the condensed water vapour is trapped and transported upwards.
3. (up to \(t\sim 1000\) s, _dissipation regime_) While primary and secondary vortices dissipate, the condensed phases of water are released into the atmosphere: the contrail has been formed.  
4. (a few hours after the emission, _diffusion regime_) The new condensation trail diffuses within the atmosphere, completely mixing with it within a few hours.  
In the first decade of the current millennium, several studies have been developed on this subject. Few research groups have tackled the problem of producing high-fidelity simulations that describe the details of contrail formation from an aerodynamics point of view. In [19], in particular, the coupling of large eddy simulations (LES) with a Lagrangian particle-tracking approach, a class of methods described below, allowed one of the first studies concerning the sensitivity to atmospheric parameters of contrail formation. The work has been extended in [20], with the inclusion of other parameters, notably the considered aircraft linear dimensions.
# A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
## 3 Proposed Method
### Gap Filling Algorithm  
#### 3.2.1 Frame-Wise Selection of Markers for Reconstruction Assistance  
In the previous subsection, we described the process of defining the set of auxiliary markers \(A_{i}\) of each marker \(i\). However, when a certain marker is missing at a given frame, it is possible that some of its auxiliary markers are also missing. As such, at each frame \(f\) and for each missing marker \(i\), a selection of a subset of reconstruction-assistance markers will take place. If we consider that \(A_{i}\) represents the set of auxiliary markers of marker \(i\) and that \(R_{i}(f)\) represents the set of markers that will be selected to reconstruct marker \(i\) at frame \(f\), this means that \(R_{i}(f)\subseteq A_{i}\).  
Two conditions were defined in order to obtain \(R_{i}(f)\) and ascertain whether \(R_{i}(f)\) should include missing markers or not:  
1. If \(A_{i}\) includes more than four non-missing markers in frame \(f\), \(R_{i}(f)\) shall consist of the subset of non-missing markers of \(A_{i}\) in \(f\);
2. Otherwise, \(R_{i}(f)=A_{i}\), i.e., \(R_{i}(f)\) may include missing markers.  
Table 1 illustrates this process in a hypothetical scenario where five markers--indexes 1, 2, 3, 4 and 5--are missing in a certain frame \(f\) and \(a_{thr}\)--the maximum number of auxiliary markers--is 6. The table shows the set of auxiliary markers identified during the initialization step, as well as the resulting reconstruction markers at frame \(f\).  
Auxiliary sets \(A_{1}\), \(A_{2}\) and \(A_{3}\) fulfill condition one at frame \(f\); therefore, the corresponding reconstruction sets \(R_{1}(f)\), \(R_{2}(f)\) and \(R_{3}(f)\) do not include any missing marker--although missing markers were present in \(A_{1}\) and \(A_{2}\) at frame \(f\). The auxiliary sets \(A_{4}\) and \(A_{5}\) fulfill condition two at frame \(f\), as they solely contain three and four non-missing markers, respectively. For this reason, \(R_{4}(f)\) and \(R_{5}(f)\) include missing markers.
## 3 Methodology
### Implementation Details  
The computational setup for this study utilized Google Colab Pro (version: CUDA 11.2) with an NVIDIA Tesla A100 GPU to ensure efficient training and evaluation. The model implementation and fine-tuning were carried out using the Transformer library from HuggingFace (version: 4.10.0), while PyTorch (version: 1.9.0) was employed for training and evaluation. The 'Evaluate' library was used to compute the relevant performance metrics, as detailed in Section 3.9. To manage memory effectively, gradient clipping was applied during training to prevent exploding gradients. The model was trained for a total of 100 epochs, with no early stopping criteria employed. Instead, a fixed number of epochs was used to ensure thorough fine-tuning, particularly on the low-resource language data (Twi). During training, the dynamic aggregation weights were adjusted using a heuristic approach. Specifically, higher weights were assigned to the low-resource language (Twi) to enhance its translation accuracy. These weights were dynamically adjusted based on the performance of the model in both high-resource (English) and low-resource (Twi) languages. A batch size of 8 sentences was used to balance computational efficiency with memory constraints.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Selective and Cross-Reactive Applications
#### 3.5.3 Localized Surface Plasmon Resonance (LSPR)-Based Sensors  
Non-conventional chromophores, such as MNPs, have also been investigated in optical sensors, due to their interesting optical properties. LSPR-based gas sensors [36; 40] are based on the dependence of the LSPR properties of the MNPs from the refractive index of the dielectric environment surrounding the NP [36] (i.e., coating, surrounding medium, supportive substrate [51]). Refractive index alternation of the medium changes the wavelength of the incident light [36]. The main advantage of MNPs is the fact that the extinction coefficients are several orders of magnitude higher than those of conventional dyes, in the visible spectrum, allowing higher sensitivity and lower LOD for the desired analyte [51].  
Optical detection of VOCs using various compositions and shapes of MNPs has also been reported. Au NPs, Ag NPs, and Au NSs have been used for chlorobenzene, \(m\)-xylene,  
Figure 13: Detailed color difference maps of six VOCs at three volumes (0.75, 1.5, and 3 mL saturated vapor, respectively) saturated analyte vapor at 20 \({}^{\circ}\)C, acquired by metalloporphyrin-AuNRs and dyes-based optical chemical sensor. Metalloporphyrin-AuNRs exhibited increased responsiveness and high sensitivity and selectivity due to their high affinity. Reprinted with permission from Ref. [141] Copyright © 2014 Elsevier B.V. All rights reserved.  
pentanol, toluene, and octane cross-reactive detection [195]. Hybrid materials containing MNPs have been used as well. Selective polymer-coated Au nano-islands, for instance, have effectively detected a-pinene [196].
## 3 Simulation and Experimental Results  
The optical layout of the FSBC experiment is shown in Figure 2[24]. Two beamlets emitting from the collimators with different wavelengths are combined by the edge filter. Each incident channel delivers an optical output power of about 5.5 kW, and a combined output power of over 10 kW has been achieved. For the combining element, the filter needs to transmit a laser beam at 1090 nm, and it needs to reflect the 1070 nm laser beam; thus, the combining efficiency and beam quality of the combined output beam are very relevant to the filter's reflectivity and the steepness of the rising edge.
# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 4 Main Problems of GNSS/INS Integration and Their Solutions
To solve this problem, simultaneous localization and mapping (SLAM) method using various sets of sensors, such as camera, LIDAR, INS, GNSS receiver and radar, depending on the application and desired level of accuracy, can be implemented. In [80], an integrated GNSS/INS/monocular vision camera system is proposed for autonomous land vehicle tracking. The authors of this work established the accurate dynamics model of the vehicle, proposed a prior height model for pose initialization and optimized the process of multisensory fusion by using factor graph optimization. This combination allowed the authors to achieve the absolute trajectory error of 0.03 m.
# Indoor Positioning Systems Can Revolutionise Digital Lean
## 4 Application to the Monitoring of a Flexible Manufacturing Process
### Discussion, Utilisation of the Results  
The results are shown in Figure 7, where we note that the _Queueing time_ and _Wasting time_ are almost 20% of the analysed manufacturing time. Table 2 summarises these times  
Figure 6: The Gantt diagrams show the states of the production of a given product. The comparison of the two diagrams shows the benefit of the additional information of the IPS. (**a**) Gantt diagram based on MES data. (**b**) Gantt diagram based on IPS and MES data.  
according to the stations and shows that the _AO2_ workplace has the most significant queuing time, so the continuous improvement project should focus on that first.  
The IPS serves as a non-stop monitoring system that contributes to the everyday work of Lean specialists. As a first step, an alarm system can be set up at each workstation to notify if the working or waiting times in that station exceed their predefined limits; so the line advisor can take required supportive action on time.  
The integrated application IPS and process mining supports the redesign of the layout thanks to its ability to detect hidden stations and states of the process.
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 3 Materials and Methods
### Description of the Experiment in Laboratory Conditions  
In the first step, the proposed algorithm was tested on a test rig with the use of a wheeled robot (Figure 5a) with sensors enabling, among other things, the measurement of driving speed, vibration, gyroscope, and accelerometer signals.  
As a part of the experiments, five different 18 cm wide road modules (panels) made of cardboard, bottle caps, and pens were prepared. The combinations of these modules created characteristic patterns on the longer route. The scheme of the prepared patterns is presented in Figure 6. The scheme shows the following patterns: (1) alternating caps; (2) caps evenly distributed on one side; (3) three consecutive humps at different distances, with the same height; (4) modification of pattern three with different hump heights; (5) two humps of the same height, but the second is three times as long.  
Figure 4: The concept of using the presented method in the problem of tracking mining vehicles in underground excavation: (**a**) vehicle position as calculated using method described in [39]; (**b**) selection of characteristic road patterns; (**c**) vehicle position after correction, using methods described in [29]; (**d**) recognition of patterns using DTW algorithm for further evaluation and path correction. It can be seen that during one haulage cycle, the same part of the route is covered by the vehicle in two different directions; therefore, two patterns should be saved in the catalog.  
During the experiment, 10 training drives were performed to learn every single pattern, and then three test drives (for each module) were conducted to verify the pattern recognition algorithm. The training drives were made using the following panel combinations:  
1. [label=()]
2. [label=()]
3. panels 1-5 arranged directly one after the other,
4. panels 1, 3, and 5 arranged directly one after the other,
5. isolated panels 1, 3, and 5.  
The test routes were created by placing one of the patterns at the beginning and the other panels later. Such drives were made for each of the a-c patterns.