esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 3 System Modeling
### Dynamic Model
#### 3.2.2 Tire Vertical Force Estimation  
The tire force calculation for a multi-axis vehicle is a statically indeterminate problem. Although, in assumption (4), the suspension is omitted and the rolling/pitching movements are also omitted in the dynamic model proposed in Section 3.2, to calculate the tire vertical force for tire model (9), an assumption is proposed, i.e., the vertical force is proportional to the suspension deformation.  
This assumption is only used in the tire vertical force calculation, and the suspension deformation is a virtual displacement and would not appear in the final calculating results of vertical force. The equations of force are expressed as:  
\[\left\{\begin{array}{l}F_{zf}+F_{zm}+F_{zr}=G\cos\theta\\ ma_{x}h+G\sin\theta h+G\cos\theta a-F_{zm}(a-b)-F_{zr}(a-c)=0\\ \frac{\Delta_{zr}-\Delta_{zf}}{\Delta_{zm}-\Delta_{zf}}=\frac{a-c}{a-b}\\ F_{zl}=k_{si}\Delta_{zl}\ i=f,m,r\end{array}\right. \tag{10}\]where \(F_{zf},F_{zm},F_{zr}\) denote the vertical force on the front, middle, and rear axis, respectively; \(h\) denotes the height of mass center; \(\Delta_{zi}\) denotes the suspension deformation on each axis; \(k_{si}\) denotes the suspension stiffness, which would be divided out in the calculating procedure, and therefore the value would not influence the result. The tire vertical force on the longitudinal direction is as (11):  
\[\left\{\begin{array}{l}F_{zf}=\dfrac{G\cos(\theta)\big{(}-a(b+c)+b^{2}+c^{2} \big{)}}{2(a^{2}-a(b+c)+b^{2}-bc+c^{2})}\\ \dfrac{G\cos(\theta)\big{(}a^{2}-ab+c(c-b)\big{)}}{2(a^{2}-a(b+c)+b^{2}-bc+c^{2 })}\\ F_{zm}=\dfrac{+Gh\sin(\theta)(a-2b+c)+hma_{x}(a-2b+c)}{2(a^{2}-a(b+c)+b^{2}-bc+ c^{2})}\\ G\cos(\theta)\big{(}a^{2}-ac+b(b-c)\big{)}\\ F_{zr}=\dfrac{+Gh\sin(\theta)(a+b-2c)+hma_{x}(a+b-2c)}{2(a^{2}-a(b+c)+b^{2}-bc+ c^{2})}\\ \end{array}\right. \tag{11}\]  
Taking lateral acceleration into account:  
\[\Delta F_{zr}=-\Delta F_{zl}=\dfrac{ma_{y}h}{B} \tag{12}\]  
where \(\Delta F_{zr},\Delta F_{zl}\) is the vertical force variation of the right and left side caused by lateral acceleration.  
Figure 3: Tire models.  
Then, combining (11) and (12), the vertical tire force is calculated by:  
\[F_{zij}=\left\{\begin{array}{l}\frac{F_{zi}}{2}-\frac{ma_{y}h}{3B}\quad i=f,m,r \quad j=l\\ \frac{F_{zi}}{2}+\frac{ma_{y}h}{3B}\quad i=f,m,r\quad j=r\end{array}\right. \tag{13}\]  
The simulation result in TruckSim is shown in Figure 4. From 8 s, the vehicle begins to accelerate, the load on the rear axis increases, while the load on front axis decreases. From 30 s, the vehicle turns, the load on right side increases and the load on left side decrease. The continuous line denotes the tire load simulated from TruckSim, while the dashed line denotes the load calculated by (13). The simulation result shows that the vertical force calculation method shown in (13) satisfies the accuracy requirements at different driving conditions.
## 2 Data and Methods
### Absorption Measurements  
Water samples for absorption and HPLC measurements were filtered onto a GF/F filter and stored in liquid nitrogen before laboratory measurements. Spectrophotometers were used to measure the absorbance and then to calculate the absorption coefficient of particles (\(a_{\mathrm{p}}\)) and detrital matter (\(a_{\mathrm{d}}\)). The phytoplankton absorption coefficient (\(a_{\mathrm{ph}}(\lambda)\)) were obtained by subtracting \(a_{\mathrm{d}}\) from \(a_{\mathrm{p}}\) following NASA Ocean Optics Protocols, Revision 4, Volume IV protocol [72]. These \(a_{\mathrm{ph}}(\lambda)\) spectra generally cover 400-800 nm with spectral resolution around 3 nm. They were interpolated into 1 nm resolution for studies here.  
Following [PERSON] and [PERSON] [37; 64] and [PERSON] et al. [63; 73], the phytoplankton absorption coefficients were decomposed into 13 Gaussian curves using the least square curve fitting  
Figure 1: In situ data distribution, the (**o**) are the stations for quantitative filter technique (QFT) \(a_{\mathrm{ph}}(\lambda)\) from SeaBASS, (**o**) and (**+**) are the subset_1 and subset_2 stations of matchups of \(a_{\mathrm{ph}}(\lambda)\) and HPLC from SeaBASS, (**o**) is the HPLC location for BATS (Bermuda Atlantic Time-Series Study), (**o**) are the BIOSOPE \(R_{\mathrm{rs}}(\lambda)\), \(a_{\mathrm{ph}}(\lambda)\) and HPLC locations, (**o**) are locations of the \(R_{\mathrm{rs}}(\lambda)\), \(a_{\mathrm{ph}}(\lambda)\) and HPLC from VIIRS val/cal cruises in 2014 and 2015, and (**o**) are the locations of \(R_{\mathrm{rs}}(\lambda)\), \(a_{\mathrm{ph}}(\lambda)\) and HPLC from Tara Oceans expedition.  
technique provided in MATLAB and Statistics Toolbox (Release 2016a, MathWorks, Inc. Natick, MA, USA):  
\[a_{\text{ph}}(\lambda)=\sum_{i=1}^{n}a_{\text{Gau}}(\lambda_{i})\exp\left[-0.5 \bigg{(}\frac{\lambda-\lambda_{i}}{\sigma_{i}}\bigg{)}^{2}\right] \tag{1}\]  
where \(\sigma_{i}\) and \(a_{\text{Gau}}(\lambda_{i})\) are the width and peak magnitude of the \(i\)-th Gaussian curve at peak center (\(\lambda_{i}\)) as shown in Table 2. The obtained \(a_{\text{Gau}}(\lambda)\) are used as ground truth to validate the inversion results from \(R_{\text{rs}}(\lambda)\).
## 5 Discussion
[42] also applied interval estimation techniques to semantic segmentation and reported that Monte Carlo dropout resulted in inferior classification accuracy compared to variational inference. Our study offers a plausible explanation for this lower uncertainty accuracy: the narrower intervals generated via Monte Carlo dropout likely contribute to its reduced accuracy. Therefore, for project planners seeking high uncertainty accuracy, bootstrapping would prove beneficial; for those seeking narrower intervals, Monte Carlo dropout may be preferable. Notably, integrating the complexity index into the interval estimates significantly improved both methods' predictive performance, with Monte Carlo dropout showing particularly marked improvement. These findings underscore the value of the complexity index in achieving reliable infrastructure project cost contingency estimates. In terms of practical implications, the demonstrated predictive power of ANNs and XGBoost presents a significant opportunity for industry adoption. Given their ability to capture complex, non-linear relationships between project characteristics and cost contingencies, these ML models provide a reliable alternative to traditional estimation methods. Construction firms and policymakers can leverage these models to enhance the accuracy of contingency forecasts. Second, the finding that project complexity significantly influences infrastructure cost contingency materialization is particularly relevant for project stakeholders. By incorporating the complexity index, they can better anticipate cost fluctuations and refine their budgetary strategies accordingly. Third, the introduction of interval predictions further enhances the decision-making process by offering project managers a probabilistic view of potential cost deviations. This added layer of uncertainty estimation is particularly valuable, as it facilitates proactive risk management by allowing project teams to assess worst-case and best-case scenarios. These findings suggest that bootstrapping is preferable for those prioritizing uncertainty accuracy, while Monte Carlo dropout demonstrates notable potential when integrated with the complexity index. This distinction allows practitioners to tailor their approach based on specific project characteristics and their risk tolerance.
# Influence of Active Flow Control of Synthetic Jet at
###### Abstract  
In order to investigate the effect of a synthetic jet arranged at the suction surface on the aerodynamic performance of an axial flow compressor, a high-speed subsonic axial flow compressor rotor was numerically simulated in this paper. The results showed that after being excited by the synthetic jet, the flow margin of the compressor decreased by 1.3%, but the peak efficiency increased by 0.47%. After analyzing the internal flow field, it was found that although the alternating blowing and suction effect of the synthetic jet can reduce the separation loss of the suction surface, the radial velocity of the flow is higher when stimulated and converges towards the blade tip, resulting in more severe blockage at the blade tip than in the prototype compressor, leading to a slight decrease in stability margin. To balance the stability margin of the compressor, a coupled flow control numerical simulation combining passive control of the casing treatment and active control of the suction surface synthetic jet was subsequently carried out. The results showed that the coupled flow control could exert their respective advantages, resulting in an increase of 17.84% in the compressor flow margin and a decrease of only 0.2% in peak efficiency.  
axial flow compressor; synthetic jet; aerodynamic performance; stability margin; numerical simulation +
Footnote †: journal: Applied Sciences
## 2 Principles and Technical Architecture of the Satellite Navigation Signal Authentication  
Satellite navigation signal authentication uses cryptographic methods to improve the anti-spoofing of civil GNSS signals and provides users with more credible PNT services. First of all, this section introduces the principle of satellite navigation signal authentication. Then, it describes the technical architecture of navigation signal authentication based on space segment, ground section and user segment, Finally, it analyzes the new capabilities brought by satellite navigation signal authentication, as well as the advantages and limitations in anti-spoofing.
## 3 Materials and Methods
### Mesh Cell-FTIR Analysis  
Mesh cell-FTIR analyses were carried out with the fresh VOOs, following the method proposed by [PERSON] et al. [6]. Aliquots of the fresh oils (20 \(\upmu\)L) were deposited onto the mesh and they were stored at 35 \({}^{\circ}\)C and 400 \(\upmu\)L. The spectral changes during the experiment were monitored daily during an incubation time of 576 h using a Bruker vertex 70 FTIR spectrometer (Bruker, Optics, Germany) equipped with a deuterated triglycine sulfate (DTGS) detector. The spectra were collected and manipulated with OPUS software version 7.2 (Bruker Optics, Etlingen, Germany). The band assigned to the C=O stretching of unsaturated aldehydes (1685 cm\({}^{-1}\)) was selected to track the changes during incubation. The peak heights of this band were measured relative to a selected single-point baseline at 1576 cm\({}^{-1}\) by implementing a macro programed on Omic 7.3 (Thermo Electron Inc., Madison, WI, USA).
## 4 Data and Calculation
In this case, the stability of the computed results can be ensured. Comprehensively, we briefly summarized the data processing flow to following steps:  
* First, convert the original data recorded by the borehole strainmeter into the actual deformation base on the prior calibration results. * Second, remove the responses of air pressure, tides, and borehole-curing to obtain the residual strain. Then, according to the rainfall records, select the strain data corresponding to the heavy rainfall time in the residual strain, and try to use a recursive digital filter to separate the quick and slow response caused by rainfall. * Calculate the deconvolution results of RQR and rainfall to obtain the rainfall impact function, which shows the strain-time curve of every falling rainwater unit before flowing away. * After an event finished, we went to check the next rainfall event, and repeated the steps described above. We then averaged all the results to obtain a general function and apply it to all time of the station. Since the quick response caused by the rainfall load is the compressive strain, all the strain mentioned in the text below were denoted compressive as negative in the plots.
# Far Off-Resonance Laser Frequency Stabilization Technology
## 3 Far Off-Resonance Laser Frequency Stabilization by Using the Faraday Effect
This frequency stabilization scheme requires complex calculations to obtain the temperature corresponding to the desired frequency stabilization point. It takes tens of minutes for the temperature to gradually reach  
Figure 6: Experimental setup. PBS: polarizing beam splitter; \(\lambda/2\): half wave plate; PD: photo detector; Double-pass AOM system: a system consists of acousto–optic modulator, quarter wave plate, plano-convex mirror and reflector; ECDL: an external cavity diode laser at 852 nm. (Figure reproduced from [53]). stability, and there are some problems of large hysteresis and poor repeatability. It is difficult to achieve fast and accurate adjustment of the frequency stability. Deepening of the research on far off-resonance laser frequency stabilization by using the Faraday effect has resulted in its continuous improvement. Moreover, the off-resonance Faraday effect also finds applications in high-bandwidth slow-light atomic probes [54], optical isolators [55], and so on.
## 2 Overview of SWARM IoT Constellation
### Comparison of Satellite Connectivities Across Different Locations
These remote locations, including Wager Bay, NU; Princess Catherine, NU; and Summit Camp, Greenland, have more robust satellite coverage, which is reflected in their higher scores. This chart helps establish that areas with less infrastructure or more isolated environments tend to benefit from stronger satellite connections, while urban areas with established networks show weaker connections. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Points** & **1** & **2** & **3** & **4** \\ \hline _Points_ & **Duration t [hrs]** & **Elevation [deg]** & **30** & **50** & **70** & **90** \\ \hline
1 & t \(<\) 0.5 & & 1 & 2 & 3 & 4 \\ \hline
2 & 0.5 \(<\) t \(<\) 1 & 2 & 4 & 6 & 8 \\ \hline
3 & 1 \(<\) t \(<\) 2 & 3 & 6 & 9 & 12 \\ \hline
4 & t \(>\) 2 & 4 & 8 & 12 & 16 \\ \hline \end{tabular}
\end{table} 
Table 1: Points distribution (intensity factor) to determine the best area of connection. The increase in elevation angle helps with a stronger connection to the satellite (the higher the point count, the brighter the green color - which means the best connection (point 16)) while increasing duration in hours will establish a longer connection to the said satellite. The darker the red color (worst-intensity factor of 1), the worse the points distribution and the worse the connection. Yellow color means middle (natural) connection (intensity factor 6). Figure 5: Greenland low-orbit satellite connection [35]. The left table illustrates signal transfer quality, with color and elevation angle indicating the strength of the connection. The pin on the map highlights the specific location of signal measurement for that time and day. Greenland shows the most favorable conditions for rapid message transmission with the highest connectivity.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
###### Abstract  
The digital transformation is exciting the uptake of Internet-of-Things technologies, and raises the questions surrounding our knowledge of the positions of many of these things. A review of indoor localization technologies summarized in this paper shows that with conventional RF-based techniques, a significant challenge exists in terms of achieving good accuracy with a low power consumption at the device side. We present hybrid RF-acoustic approaches as an interesting alternative: the slow propagation speed of sound allows for accurate distance measurements, while RF can easily provide synchronization, data, and power to the devices. We explain how the combination of adequate signaling realizing a late wake-up of the devices with backscattering could position energy-neutral devices. Experiments in a real-life testbed confirmed the potential 10 cm-accuracy based on RF-harvested energy. Nonetheless, these also expose open challenges to be resolved in order to achieve accurate 3D positioning.  
localization; acoustic signals; IoT; energy harvesting; backscattering; experiments +
Footnote †: journal: _Future Internet_  
This paper is further organized as follows. In the next section, the basic set-up of a positioning system and related terminology are introduced and the state of the art is summarized. Section 3 presents hybrid RF-acoustic positioning approaches, highlighting signaling and backscattering technologies that have the potential to achieve energy neutrality at the device side. Experiments in a real-life testbed are provided in Section 4. Finally, Section 5 summarizes the main conclusions and envisions the road ahead.
## 2 Materials and Methods
### Determination of Volatile Compounds
#### 2.3.1 Extraction of Volatile Compounds  
The extraction of headspace volatile compounds of citrus and nectar honey samples was carried out with the use of a divinyl benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (50/30 \(\upmu\)m) (Supelco, Bellefonte, PA, USA). Before using the fiber for the extraction of headspace volatile compounds, conditioning was mandatory according to the manufacturer's recommendations. The headspace extraction of volatile compounds was accomplished using an optimized protocol [15]. For the preparation of samples, 2 g of honey was dissolved in 2 mL of distilled water, followed by the addition of 0.20 g sodium chloride (NaCl, Merck, Darmstadt, Germany) and 20 \(\upmu\)L of benzophenone as internal standard (100 \(\upmu\)g/mL) (Sigma-Aldrich). Samples were placed in vials of 15 mL volume, with a screw-cap and equipped with septa of PTFE/silicone. A magnetic stir bar (cross-shaped PTFE-coated magnetic stir bar of 10 mm diameter ([PERSON], Ostermundigen, Bern, Switzerland) was also placed inside each vial to assist in the extraction of volatile compounds. Afterwards, the vials were placed in a water bath (45 \({}^{\circ}\)C) and stirring at 600 rpm was applied during the headspace extraction of volatile compounds. To avoid any contamination that could cause false results, the fiber was cleaned daily before use with the \"clean-program\" method [15], and blank runs were carried out during the consecutive analysis of honey samples. Each sample was run in duplicate (in total 88 runs).
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 4 Simulation Results
### DAILC Under Sensor Saturation  
In this case, we applied the DAILC method to the test AC microgrid considering sensor saturation. As shown in Figure 4, despite not accounting for sensor saturation and relying solely on input-output data, this method successfully restored the output voltage to the preset value of 311 V. In this case, the saturation threshold for the sensors was set at 315 V. In the case of sensor saturation, due to the limitations of the sensor's measurement range, there is a deviation between the measured value of the output voltage and its actual value. It can be observed from Figure 4 that during the transient processes in the AC microgrid, the peak of the output voltage was limited by the saturation threshold of 315 V. After processing with the DAILC, the output voltage eventually stabilized at the set value of 311 V, and the error converged to zero, thus verifying the effectiveness and practicality of the DAILC method.
## 2 Mission Concept
### Mission Analysis
#### 2.4.2 Access Time Analysis  
As part of mission planning, access time is crucial for satellite missions. Many analyzers rely on the access time analysis, such as the data budget calculations, power budget calculations, and attitude determination, which is mandatory in planning the concept of operation of the mission. Light-1 can communicate with three ground stations: Abu Dhabi in United Arab Emirates, Vilnius in Lithuania, and Aalborg in Denmark. The three ground stations are used for \(S-\)band downlinking, while UHF transmission uses the ground station in Abu Dhabi. Thus, access time simulations were conducted for three ground stations. The access time is calculated per day to validate that daily generated payload data can be downloaded in the worst and average case. The ground stations in Vilnius and Aalborg are situated close to each other. Therefore, due to the high speed of the CubeSat, an overlap in the access time between the two ground stations will occur, which can be seen in Figure 2. As a result, in case of an overlap, the access time of Vilnius ground station will be considered. The result shows that using the three ground stations, the average access time per day is 37.14 min/day, a minimum of 22.3 min/day, and a maximum of 64.73 min/day.  
Figure 2: Light-1 ground station coverage.
## 1 Introduction  
The evolution and diffusion of the Internet of Things (IoT) paradigm and smart devices in recent years have resulted in the development of new healthcare, lifestyle analysis, and environmental protection solutions [1, 2, 3, 4]. Wearable devices with various sensor types for gathering characteristics relevant to various quantifiable domains have drawn the interest of the scientific community and companies [5, 6, 7, 8]. These technologies have been shown to be particularly effective for the continuous monitoring of a user's health as it is impacted by behavioral, physiological, psychological, and, most importantly, environmental parameters [9, 10]. In addition to typical wearables that can only measure parameters in one domain, hybrid solutions for monitoring several parameters have evolved, such as devices that can assess both biophysical and environmental aspects. Among many other application fields, hybrid solutions are strongly revolutionizing workplace safety in various industrial sectors, including metallurgical, chemical, mining, and food industries. In these sectors, workers are frequently exposed to hazardous conditions, which can be harmful to their health. Notably, exposure to high temperatures is a common risk in the metallurgical industry, as well as exposure to dangerous gas leakages related to combustion processes or steel production additives [11]. Similarly, the risk of exposure to harmful gases related to chemical reactions is ever-present in the chemical industry, as well as in the food industry, where chemical products are commonly used to preserve or treat food products [12]. Additionally, miners constantly face the danger of exposure to gas released as a result of excavations or drilling, as well as falls or injuries caused by collapses [13]. Wearable technologies can thus increase workplace safety, provide real-time monitoring of the worker's health state during dangerous activities, and analyze the collected data for early action in emergencies [14; 15; 16]. On the other hand, wearable devices must fulfill several criteria for ease of use, sensor setup, price, and data accessibility [17; 18; 19].