esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 4 Statistical Analysis and Feature Extraction
### Feature Extraction and Classification  
The statistical analyses described above are integral to constructing comprehensive feature vectors. These vectors incorporate a range of computed features, including probability density metrics, mean, variance, skewness, kurtosis, autocorrelation coefficients, and PSD values. In addition, we integrated the raw sensor data into a neural network, thereby enhancing the model's ability to learn complex patterns.  
Feature Vector Construction: Each data sample was transformed into a feature vector that encapsulates the statistical and spectral properties of the sensor data. This vector served as the input for various classification algorithms.  
Classifier Selection and Optimization: We experimented with a diverse array of classifiers, including traditional methods (SVM, Decision Trees, and Random Forests) and advanced deep learning architectures (CNN, LSTM, Transformer, Swin Transformer, and TransUNet). Hyperparameters were systematically optimized through grid search and cross-validation, with specific configurations detailed in Section 5.2.  
Figure 6: Autocorrelation coefficients and power spectral density (PSD) of the accelerometer data along the x, y, and z axes during walking.  
Computational Efficiency Considerations: Beyond classification accuracy, we evaluated the computational demands of each model--a critical factor for IoT deployments.  
The tables below summarize the statistical features extracted from sensor data for different activities, further illustrating the distinct profiles obtained through our comprehensive analysis. In particular, Table 1 presents the statistical features of walking, which were computed from the accelerometer and gyroscope data during the walking activity, Table 2 presents the statistical features of running, which were computed from the accelerometer and gyroscope data during the running activity, and Table 3 presents the statistical features of squatting, which were computed from the accelerometer and gyroscope data during the squatting activity.  
Our innovative integration of detailed statistical analysis with modern machine learning methods forms the basis of a robust framework for human activity recognition. This contribution not only improves the accuracy of activity classification but also provides a scalable approach for real-time applications in health monitoring and preventive healthcare.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 2 GNSS Signal Model, Meta-Signals, and HO-BOC Signals
### Signal Model
In that perspective, the propagation delay \(\tau\left(t\right)\) is constant \(\tau\left(t\right)=\tau=\frac{\left\|\mathbf{p}_{T}\right\|}{c}\), and the baseband output of the receiver's Hilbert filter is given by:  
\[x\left(t\right)\approx\alpha c\left(t-\tau\right)\ +\ n\left(t\right), \tag{4}\]  
with \(n(t)\) a complex white Gaussian noise within the filter bandwidth with unknown variance \(\sigma_{n}^{2}\) and \(\alpha=\alpha_{R}e^{-j\pi/c\tau}\). The discrete vector signal model is built from \(N=N_{2}-N_{1}\ +\ 1\) samples at \(T_{s}=\frac{1}{T_{s}}\), where \(F_{s}\) is the sampling frequency:  
\[\mathbf{x} =\alpha\mathbf{c}\left(\tau\right)\ +\ \mathbf{n}, \tag{5}\] \[\mathbf{x} =\left(x\left(N_{1}T_{s}\right),\ldots,x\left(N_{2}T_{s}\right) \right)^{\top},\] \[\mathbf{n} =\left(n\left(N_{1}T_{s}\right),\ldots,n\left(N_{2}T_{s}\right) \right)^{\top},\] \[\mathbf{c}\left(\tau\right) =\left(c\left(N_{1}T_{s}-\tau\right),\ldots,c\left(N_{2}T_{s}- \tau\right)\right)^{\top},\]  
where \(\mathbf{n}\sim\mathcal{CN}\left(\mathbf{0},\sigma_{n}^{2}\mathbf{I}_{N}\right)\). Since the transmitter/receiver antenna gains and polarization vectors are in general unknown, \(\alpha\) is assumed to be an unknown complex parameter as well [18; 21; 22; 23; 24]. Thus, the unknown deterministic parameters [25] can be gathered in a vector \(\underline{\boldsymbol{\epsilon}}=\left(\sigma_{n}^{2},\tau,\alpha,\alpha^{*} \right)^{\top}\), where \(\alpha^{*}\) is the complex conjugate of \(\alpha\). The model in (5) can be reparametrized to make explicit the phase parameter,  
\[\mathbf{x}=\rho\mathbf{c}^{\prime}\left(\boldsymbol{\theta}\right)\ +\ \mathbf{n},\quad \mathbf{c}^{\prime}\left(\boldsymbol{\theta}\right)=\mathbf{c}\left(\tau \right)e^{i\varphi},\ \rho\in\mathbb{R}\ ^{+}\,\boldsymbol{\theta}^{\top}=\left( \varphi,\tau\right), \tag{6}\]  
and then, the unknown deterministic parameters are \(\underline{\boldsymbol{\epsilon}}=\left(\sigma_{n}^{2},\rho,\boldsymbol{ \theta}^{\top}\right)^{\top}\); \(\rho\) is the amplitude of the signal; and \(\theta\) gathers the unknown phase \(\varphi\) and time-delay \(\tau\).
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 4 Adaptability of the Formulation  
While the presence of the \(\alpha\) index underscores the capability of our framework to dissect the system into its constituent parts, it should not be seen as a constraint. Instead, it highlights the model's versatility:  
* For problems that emphasize the behavior of individual particles or specific interactions, the \(\alpha\) index can be maintained to take advantage of the full descriptive power of the model.
* For broader analyses where such specificity is not necessary, the index can be dropped or integrated over, simplifying the equations without compromising the theoretical integrity.
* This adaptability ensures that our extended entropy formulation remains applicable across a wide range of scenarios, from deeply microscopic investigations to more generalized thermodynamic studies.
# Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic
## 2 Materials and Methods  
The studied region represents a geographical area limited by the following coordinates: 60\({}^{\circ}\) N, 100\({}^{\circ}\) E; 77\({}^{\circ}\) N, 100\({}^{\circ}\) E; 77\({}^{\circ}\) N, 165\({}^{\circ}\) W; 57.5\({}^{\circ}\) N, 165\({}^{\circ}\) W; 57.5\({}^{\circ}\) N, 138\({}^{\circ}\) E; and 60\({}^{\circ}\) N, 138\({}^{\circ}\) E (dashed line in Figure 1). All of the following four existing earthquake catalogs for the period 1962-2020 were considered as the initial data (Tables 1 and 2):  
1. The regional catalog of Yakutia from the annual journals _Earthquakes in the USSR_ (1962-1991), _Earthquakes in Northern Eurasia_ (1992-2014), and _Earthquakes in Russia_ (2015-2019) (GS RAS) (hereinafter YAK);
2. The regional catalog of the northeast of Russia from the annual journals _Earthquakes in the USSR_ (1968-1991), _Earthquakes in Northern Eurasia_ (1992-2014), and _Earthquakes of Russia_ (2015-2019) (GS RAS) (hereinafter NER);
3. The regional catalog of earthquakes in Kamchatka of the Kamchatka Branch of the GS RAS, 1962-2019 (hereinafter KAM); and
4. The ISC 1962-2020 catalog, which is a composite catalog containing data from many world and Russian agencies. It has to be noted that the technique developed in [34] allows the pairwise merging of earthquake catalogs. Thus, any two chosen catalogs are merged in the first step. Then, another catalog is merged with them, and so on. At the same time, we emphasize that the list of events in the integrated catalog weakly depend on the sequence of pairwise merging. It was shown in [34] that the merging procedure is symmetric. In other words, when two catalogs are merged, the same events are selected as duplicates, regardless of the catalogs' merging sequence. The only difference will be which version of the earthquake record (from which input catalog) falls into the merged catalog. We believe that earthquake identification based on global network data is the most reliable. A subset of these events from the ISC catalog is the core--the main catalog to which other catalogs will be added. The core only contains information about strong and moderate earthquakes in the region because weak earthquakes are not registered by global networks. Further, it is logical to add data from local networks, which provide information about weak earthquakes in the region. In the final merge step, we use the data from the ISC catalog that was not included in the core. Thus, to merge catalogs (Tables 1 and 2), the following sequence for sources of the initial data was chosen:
## 3 Results  
### Dataset Description  
The dataset consists of well logging data from eight wells (CB32, CB82, CB85, CB89, CB323, CB327, CB832, and CBX395) in the Chengbei Operation Area of Shengli Oilfield, covering the Ed1 to Ed4 sections. Based on the logging interpretation results and following the Chinese petroleum industry's classification standard for clastic grain size [51], the lithology labels for the dataset are classified into the following five classes: (1) mudstone, (2) siltstone, (3) fine sandstone, (4) coarse sandstone, and (5) pebbled sandstone. The dataset includes seven well logging curves for lithology discrimination: 1 GR (gamma ray): Gamma ray logging curve is used to measure the intensity of gamma rays in the formation, is usually related to the content of radioactive materials in the rock, and can help identify lithologies. 2 CAL (caliper): Caliper logging curve measures the diameter of the borehole. Changes in the borehole diameter can reflect the stability of the borehole and possible changes in geological structure. 3 RD (deep investigate double lateral resistivity log): RD is a method of measuring formation resistivity, focusing on detecting strata deeper than the borehole, which helps to identify the type and content of fluids in deep layers. 4 RS (shallow investigate double lateral resistivity log): The detection depth of RS is shallower than RD. It is usually used to evaluate the resistivity of strata near the borehole and plays an important role in identifying the distribution of fluids near the borehole. 5 AC (acoustic log): Acoustic logging curve measures the propagation time of sound waves in the formation and is used to estimate the porosity and fluid properties of the formation. 6 CNL (compensated neutron log): Compensated neutron logging curve is used to measure neutron absorption in the formation and is related to the porosity and fluid type of the formation. 2 DEN (density log): Density logging curve measures the density of the formation and can reflect the porosity and mineral composition of the rock.
## 3 Results and Discussion
### Cooling Energy Reduction
Comparing the two cases, it can be seen that natural NV saves more energy for all air flow rates except for the very lowest of 0.5 ACH where they are almost equal. On this basis, it's apparent that the natural NV case provides the best energy efficiency and should be the preferred strategy if both solutions are equally feasible to implement in a building. Moreover, the total energy savings are decreasing after 3 ACH for the hybrid NV strategy (see Figure 11b). It can also be seen that it's not worthwhile to increase the airflow rates more than 2 ACH for this strategy, as the total energy savings are reaching a saturated value more quickly before starting to reduce. The results show the effect where energy usage by fans eventually catches up with the energy savings for cooling which is also found in other studies, although the air  
Figure 10: Energy reduction for local cooling in the building. (**a**) Natural NV strategy. (**b**) Hybrid NV strategy. flow rate at which the drop off occurs varies [27; 51]. An important parameter that comes into play is the specific fan power (SFP) of the ventilation system. An energy efficient system with a low SFP can delay this effect. These calculations show a limitation of hybrid NV systems compared to fully passive systems, but there are more aspects that can be analyzed with the hybrid system. It may be desirable to reduce the cooling power load for a building even if the maximum energy savings are not achieved. Higher air flow rates and more PCM was shown previously to be very effective for this measure.
## 3 Reduced Dynamic Orbit Determination of COSMIC-2
### Assessment of Orbit Accuracy
#### 3.3.1 Analysis of Carrier-Phase Residuals  
The carrier-phase residuals mainly contain the modelled and un-modelled errors, and the RMS of the residuals can, to a certain extent, reflect the set strategies and whether selected mechanics models are suitable or not, so the RMS of the residuals can be used as one of the indicators for the evaluation of the internal conformity accuracy of POD [42]. The smaller the RMS of the residual is, the higher the accuracy of the internal conformity is. Table 5 shows the statistical results of the RMS of the carrier-phase residuals for the six COSMIC-2 satellites with the RD orbit determination method for each day from 18-22 May 2021 (DOY 138-142).  
It can be seen from Table 5 that the RMS values of the carrier-phase residuals of the six COSMIC-2 satellites vary between 6.0 mm and 7.5 mm, and the mean RMS values of the residual for Sat-2 in the five days is the smallest and that of Sat-4 is the largest. The  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models/Parameters** & **Description** \\ \hline Mean earth gravity & EGM2008, SMALL \\ N-body & JPL DE405 \\ Relativity & IERS2010 XY \\ Ocean Tides & FES2004 \\ Solid-earth tides & TIDE2000 \\ GPS precise ephemeris & [[ftp://ftp.aiub.unibe.ch/CODE](ftp://ftp.aiub.unibe.ch/CODE)]([ftp://ftp.aiub.unibe.ch/CODE](ftp://ftp.aiub.unibe.ch/CODE)) (accessed on 12 March 2021) \\ GPS precise clock offset & [[ftp://ftp.aiub.unibe.ch/CODE](ftp://ftp.aiub.unibe.ch/CODE)]([ftp://ftp.aiub.unibe.ch/CODE](ftp://ftp.aiub.unibe.ch/CODE)) (accessed on 12 March 2021) \\ Antenna PCO and PCV & lgs14.atx \\ Elevation cutoff & \(5^{\circ}\) \\ Sampling interval & 10 s \\ \hline Pseudostochastic pulses & 6 min \\ \hline Arc length of orbit & 24 h \\ determination & \\ \hline \hline \end{tabular}
\end{table}
Table 4: RD orbit determination strategies of COSMIC-2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DOY** & **Sat-1** & **Sat-2** & **Sat-3** & **Sat-4** & **Sat-5** & **Sat-6** \\ \hline
138 & 6.4 & 6.0 & 6.4 & 7.0 & 6.6 & 6.6 \\
139 & 6.7 & 6.2 & 6.3 & 6.0 & 6.7 & 7.0 \\
140 & — & 6.5 & 6.6 & 6.3 & 6.8 & 6.8 \\
141 & — & 6.4 & 6.9 & 6.2 & 6.7 & 7.2 \\
142 & 6.1 & 6.6 & 6.0 & 6.2 & 6.5 & 7.5 \\ Mean & 6.4 & 6.0 & 6.4 & 7.0 & 6.6 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical results of RMS of carrier-phase residuals (mm).  
mean residual RMS values of the six COSMIC-2 satellites in DOY 138-142 vary between 6.0 mm and 7.0 mm, which indicates that the observations measured by the space-borne GPS receiver are relatively stable.
## 3 Simulation Results
### Performance of LASM(B) and LASM(P)
The communication cost and position error versus the number of nodes is tested in Figure 6. Each node just communicates with neighbors once in each calculation step of the LASM. Therefore, the number of calculation steps represents the communication cost. The ratio of anchor nodes is also 20%, with \(R\)=1.5. The number of sensor nodes goes from 40 to 800, in increments of 40, i.e., \(n=40\times i\), and \(n_{anc}=8\times i\), \(L=(10/\sqrt{5})\times\sqrt{i}\) (\(i=1,2\cdots 20\)), where \(n\) and \(n_{anc}\) are the number of total nodes and anchor nodes respectively, \(L\) is the edge length of the square where the nodes are deployed. The average number of calculation steps doesn't change much versus the number of nodes. As shown in Figures 6(a,b), it is about 100 in LASM(B) and about 150 in LASM(P). It means that the communication cost is almost the same in different sizes of sensor networks. Therefore, we don't test more experiments with the number of nodes larger than 800. It is a good performance that the cost is nearly a constant with the increase of sensor nodes. This means that the LASM localization algorithm  
Figure 5: The robustness of the LASM algorithm in 100 randomly generated examples. (a) general calculation steps in LASM(B). (b) general calculation steps in LASM(P). (c) position error in LASM(B). (d) position error in LASM(P). is very appropriate in applications of large sensor networks with large amount of sensor nodes in it. The communication cost for each node is _O_(l), and the total communication cost is _O_(_n_), compared with the MDS-MAP algorithm with _O_(_n_log_n_) communication cost. Each sensor node executes the localization algorithm in parallel model, and has a computational complexity of _O_(l), therefore the time consumption for each sensor node is also _O_(l). The position error has a small tendency to go smaller as shown in Figures 6(c,d). This shows that when the number of nodes increases, the blind nodes can use more information about other nodes, therefore decrease the position error.
# Experimental Study on Strength of Polypropylene Fiber Reinforced Cemented Silt Soil
## 4 Study on Macro and Micro Mechanism of Polypropylene Fiber Improving Cement-Soil Strength
### Failure Mode of Fiber Cement Soil under Micro-State  
The scanning electron microscope of the cement soil without fiber with cement content of 15% is shown in Figure 11a. It can be seen that the hydration products were wrapped outside the soil particles to form a dense structure, which can improve the compressive strength of the soil. Figure 11b-d shows the cement-soil mixed with fibers (cement dosage was 15% and fiber length was 9 mm). The fibers were distributed in a disorderly manner, forming a network structure, and the hydration products formed by cement were wrapped on the surface of the fibers. These products wrapped the fibers to increase their surface area and provided help for the interfacial friction between soil particles, making the reinforced soil more compact and improving the normal stress and contact area of the fibers. Dispersed fibers also formed a network structure, and soil particles and fibers were agglomerated to connect with each other to form a whole. Soil particles and fibers were mutually constrained so that fiber reinforcement was not easy to crack. When the fiber cement soil was damaged and cracks were generated, the friction force generated by the interface between the cement soil and the fiber was combined with the characteristics of the fiber itself when the fiber was forced to slide or be pulled out to improve the tension between the fiber and the cement soil. The toughness of the reinforced soil was thus improved so that the fiber cement soil still had a certain bearing capacity after the cracks were generated [20].  
Micro-tests showed that the mutual modes between soil particles and fibers were contact, bending, and interleaving. These three modes provided the interfacial shear stress between soil particles and fibers and the tensile stress of fibers for the reinforced soil. The movement of soil particles was limited by shear stress and tensile stress, and the shear strength and deformation resistance of the specimen were improved [14; 47].  
Figure 10: Failure pattern of fiber cement soil with different fiber content (1:1). (**a**) fiber content 0%; (**b**) fiber content 0.1%; (**c**) fiber content 0.2%; (**d**) fiber content 0.4%.
## 3 GNSS Data Collection with Android-Based Smartphones
### The 'Urright' and 'Lying Down' Setup Configurations  
The built-in antennas of the smartphones have been found to be sensitive to poor quality GNSS signals and the surrounding environment [27]. The GNSS signals tracked by the S20 and GP4 smartphones are likely interfered with when placed too close to other objects even inside an RF shielding box. In this contribution we therefore assess the performance of the smartphones under two different setup configurations: (_i_) when the smartphones are in an _upright_ position as depicted in Figure 2a and (_ii_) when _lying down_ in Figure 2b. As a result of the zero-baseline setup, multipath is largely eliminated. The remaining small multipath effects would mainly be due to the non-simultaneity of sampling between the receivers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site ID** & **Receiver** & **Chipset** & **Constellations** & **Frequencies** \\ \hline SGD1 & Samsung & & & GPS, Galileo, & L1 + L5, E1 + E5a, L1 \\ \hline SGD2 & Galaxy S20 & Broadcom BCM47755 & & QZSS, BDS & + L5, B1 \\ \hline GPD1 & Google Pixel 4 & Qualcomm Snapdragon & GPS, Galileo, & L1 + L5, E1 + E5a, L1 \\ \hline GPD2 & & 855 embedded & QZSS, BDS & + L5, B1 \\ \hline UBX1 & \multirow{2}{*}{\(\mu\)Blox} & \multirow{2}{*}{F9P} & GPS, Galileo, & L1 + L2, E1 + E5b, \\ UBX2 & & & & QZSS, BDS & L1 + L2, B1 + B2 \\ \hline TRD1 & \multirow{2}{*}{Trimble NetR9} & \multirow{2}{*}{MaxwellITM 6} & GPS, Galileo, & L1 + L2, E1 + E5b, L1 \\ TRD2 & & & & QZSS, BDS & + L2, B1 + B2 + B3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Site identity, receivers and the GNSS signals used in this study.  
Figure 1: General external antenna setup configuration of S20 and GP4 smartphones that will be benchmarked against survey-grade Trimble NetR9 and low-cost ublox receivers/antennas. All receivers/antennas are placed in a RF shielding box to prevent them from receiving GNSS signals that are not from the re-radiating antenna. The direct GNSS signals are collected from the active low-cost antenna on the rooftop, Swift GPS500, and re-radiated via a passive antenna. A signal amplifier is connected in between the rooftop antenna and re-radiating antenna to reduce the effect of signal attenuation. Inset (right column) depicts the location map of Dunedin, New Zealand.  
In addition to the zero-baselines in Figure 2, we assess the performance of four short-baseline setup configurations, namely with external antennas (_i_) in the upright position (cf. Figure 3a,d) and (_ii_) lying down position (Figure 3b,e). We further show results with internal smartphone antennas in the upright position (Figure 4a,b) and in a lying down position (Figure 4c,d).
# Parametric Selection of Optimized Epicyclic Gearbox Layouts for Wind Power Plant Applications
## 1 Introduction
For example, planetary drives are common in both fixed-wing and rotary-wing aircraft due to their compact size and reliability, while machine tools might use a more complex drive to provide reversing capability in cases where it is impractical to stop and reverse the main motor. Furthermore, as planetary drives may be built to be controlled using brakes and/or clutches, they may be used as shifting gearboxes in propulsion applications, especially when dealing with hybrid or electric vehicles [42; 46; 47; 48]. Additionally, when dealing with applications that require a single transmission ratio, a simpler gearbox permanently locked to a single gear ratio is employed. The purpose of this article is to conduct an analysis and optimization of a multi-stage multiplier gearbox for the operation of a windmill power turbine. The idea of the article is that a systematic approach with parametric analysis based on the application constraints will provide a new or improved version of an existing design solution. The purpose-developed software was used to generate and analyze various design solutions according to the optimization criteria of maximum efficiency, minimum weight, and minimal larger ring gear diameter. The best two- and three-carrier gearbox solutions are then selected, from which the optimal solutions are then picked. The analysis of the best two-and three-carrier gear train has resulted in recommendations for the selection of either two-carrier and three-carrier gear train solutions according to the application demands. This will result in lighter, more efficient designs with smaller radial dimensions; however, the final selection of the gearbox layout must be performed by the gearbox designer according to the technological and other design requirements.