esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 3 Materials and Methods
### Feature Extraction and Fusion  
In this study, linear (time domain and frequency domain) analysis and nonlinear analysis methods were used to extract features. We obtained three sets of features from ECG and SpO2 signals, which were RR intervals features, R-wave amplitudes features, and SpO2 features. The details of these features and fusion strategy are described below.
## 4 The Multi-Resolution Pre-Stack Migration Imaging Method
### The Correlation Stacking Method for Differential Pulse Imaging  
The requirements for imaging resolution in TEM are becoming increasingly stringent. However, previous research has primarily focused on data processing and interpretation methods, often neglecting the impact of the field source on detection capabilities. Due to the resolution limitations inherent to the field source, simply optimizing data processing and interpretation methods is insufficient for fundamentally enhancing the ability to identify electrical interfaces. In this study, we propose a multi-resolution imaging approach using differential pulses with varying pulse widths to image electrical interfaces at different depths in the subsurface, which effectively enhances the detection capabilities of the TEM method.  
For a given survey line, \(n\) differential pulses with different pulse widths are first used to excite the TEM field, resulting in \(n\) sets of diffusion field data. Next, the virtual wavefields are extracted from these \(n\) diffusion fields using the preconditioned precise integration time-sweeping algorithm. Then, the finite-difference method is applied to obtain the migration imaging of these n virtual wavefields. Finally, the migration images with different resolutions are stacked to produce a high-resolution imaging profile. Since the migration imaging is performed prior to the \"stacking\" process, this method is referred to as multi-resolution pre-stack migration imaging of TEM virtual wavefields, as illustrated in Figure 7.  
Figure 7: Flowchart of multi-resolution pre-stack migration imaging for TEM virtual wavefields.
## 4 Proposed Scheme
### Adjusting the Anonymity Level with \(T\)
#### 4.4.2 Implement the \(T\) Parameter  
To integrate \(T\) into the cryptographic scheme, adjustments can be made in several steps:  
* Message blinding: The factor \(r\) can be chosen as a function of \(T\). For example, if \(T\) is large, \(r\) must be a random number with many bits, which increases the computational complexity for an attacker to eliminate the orbit.
* Signing: The signing function \(S(M,T)\) is designed to include \(T\) as an input parameter. The formula for the orbit signature \(\sigma^{\prime}\) becomes: \[\sigma^{\prime}=(M^{\prime}\cdot T)^{K_{priv}}(mod\ n),\] (6) where \(T\) modulates the confidentiality level of the signature.
* Verification: The verification function \(V(\sigma,M,T)\) must take into account the value of \(T\) to correctly validate the signature: \[V(\sigma,M,T)=True\text{ if }\sigma^{e}(mod\ n)=M\cdot T.\] (7)
## 2 Background and Related Work
### Challenges and Opportunities in Semantic Interoperability in Industrial Digitalisation  
Despite the compelling advantages of semantic interoperability and FIWARE in the realm of industrial digitalisation, implementing these advanced frameworks is not without challenges. Key issues include, but are not limited to, the complexity of managing various data formats, ensuring data security and privacy, and the adaptability of existing systems to new open standards [31].  
However, these challenges also present opportunities for innovation and refinement. For example, the complexities in data management could catalyse the development of more robust data normalisation and translation algorithms [32]. Data security concerns could lead to advancements in secure data transmission techniques, potentially using blockchain or other cryptographic solutions [33]. Furthermore, the momentum towards standardisation via FIWARE offers a beacon for industry leaders to cohesively shape universal protocols, paving the way for more fluid and secure system interactions [34]. In actuality, preliminary triumphs are on record where entities have adeptly dismantled traditional data enclosures, realising impressive operational optimisations through the judicious use of semantic interoperability coupled with FIWARE's capabilities.  
In summary, the challenges inherent to implementing semantic interoperability and FIWARE in industrial digitalisation not only highlight areas for technological improvement but also pave the way for collaborative, industry-wide solutions that could further accelerate the digital transformation pathway.
# Optimizing Geospatial Data for ML/CV Applications:
## 6 Conclusions  
The study presented a method for optimizing geospatial datasets. The use of a Python-based script automated data retrieval, tiling, masking and image processing. Results demonstrated a significant reduction in file sizes across different resolutions and road categories, with reductions exceeding 90% of the original size in some cases. Storage efficiency and computational performance were enhanced by the effective removal of non-essential information while preserving critical data within parcel geometries. Optimized datasets address limitations in current geospatial data handling. The reduction in computational and storage demand has the potential to improve ML and CV model training time and performance. The automation and integration of publicly available libraries and resources make the method accessible and practical for widespread use. In conclusion, the proposed optimization method offers a valuable solution for managing large-scale geospatial datasets. It streamlines data processing workflows, reduces resource consumption and supports the development of efficient ML and CV models. Future research could explore the direct impact of these optimized datasets on ML/CV applications.  
Conceptualization, D.K. and M.P.; methodology, D.K.; software, D.K.; validation, D.K. and M.P.; formal analysis, M.P.; investigation, D.K.; data curation, D.K.; writing--original draft preparation, D.K. and M.P.; writing--review and editing, D.K. and M.P.; supervision, M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare that the data used within this work are fully accessible from (1) Geoportal [27] and (2) ULDK [52] without any copyright restrictions (governmental Open Data platform) and (3) the data from LP-Portal are available for non-commercial \"preview purpose\" use at [51].  
The authors declare no conflicts of interest.
## 3 Application to Synthetic Data
### Model 1  
In the first example, we invert synthetic data generated by the 3D model shown in Figure 3. Two 100 m thick resistive targets (100 ohm-m) are embedded in a conductive (1 ohm-m) host, under a 1500 m thick water layer. The first target is a T shaped body placed at 900 m below the sea bed; the second is a slab at a depth of 1900 m below the sea bed.  
The model covers a total area of 320 km\({}^{2}\). The survey configuration (Figure 4) has nine receiver lines, and seven source lines running at 50 m above the flat sea bed. The receiver lines are equally spaced, with a separation of 2000 m. Each line has 21 receivers spaced by 1 km. The source lines extend from 10 km to the left of the first receiver to 10 km to the right of the last receiver, between \(y\) coordinates \(-8000\) m and 8000 m. The transmitter positions are at 100 m intervals.  
Data were generated at three frequencies: 0.25 Hz, 0.75 Hz and 1.25 Hz.  
The 3D synthetic observations were calculated with an Integral Equation code, based on the method presented by [15].  
Figure 4: Acquisition layout for the simulated survey. The triangles represent the receivers and the black lines the towed source.  
Figure 3: Test model: 1.5 km water layer with 0.3 ohm-m, sediment background with 1 ohm-m and two 100 ohm-m targets.
# Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems
###### Abstract  
The future of mobile communication systems is evolving rapidly toward being more intelligent, while also having the ability to interconnect with everything and be aware of the current wireless environment. For complex scenarios, the asymmetry features of channel state information (CSI) will seriously restrict the performance of the communication system. With its accurate positioning technology and high-speed communication rate, ultra-wideband (UWB) has a promising future as a solution to integrate communication and positioning functions. The traditional CSI channel estimation usually requires channel training, which will greatly increase the overhead of the system. This paper proposes a location information-assisted beamforming to replace the traditional channel training process. First, we use the user location parameter information to reconstruct the channel model and derive the CSI error distribution based on the location distribution. Second, considering the uncertainty of the user positioning error, we model the robust beamforming optimization problem that minimizes the total transmit power. To solve this non-convex problem effectively, we design a new beamforming algorithm by using semidefinite relaxation (SDR) and Bernstein-type inequalities. Finally, simulation results verify the robustness of the proposed robust beamforming compared to the worst-case robust beamforming.  
UWB; beamforming; multipath radio propagation +
Footnote †: journal:  
Article
## 2 Materials and Methods
### SCO Extraction  
In laboratory scale, an accelerated solvent extraction procedure (ASE 350, DIONEX, Sunnyvale, CA, USA) was used to extract the SCO from lyophilized biomass of strain V134. The extraction was performed at 180 \({}^{\circ}\)C using a static extraction time of 5 min per cycle (6 cycles) at a pressure of 10 MPa. A flush volume of 75% of cell volume and a purge time of 90 s were applied. N-Hexane was used for the extraction with a solvent/biomass ratio of 50 mL per g DCW. The resulting extract was vacuum dried in a rotavapor (50 \({}^{\circ}\)C, water bath) and the SCO yield was measured gravimetrically.  
Since methods for the pretreatment of the biomass are required to improve lipid recovery [31], four approaches for pretreatment prior to ASE extraction were evaluated. Wet biomass was subjected to chemical pretreatment using acid hydrolysis with 8% of H\({}_{2}\)SO\({}_{4}\). The hydrolysis step was performed at 140 \({}^{\circ}\)C for 2 h. For mechanical cell disruption, a bead mill with a cylindrical vessel and a capacity of 1 L was used. The ceramic beads had a diameter of 15 mm. lyophilized biomass was placed in a beaker with grinding beads and kept under orbital rotation overnight at ambient temperature. In an attempt to further improve lipid recovery, bead milling was used in combination with autolysis [32]. To induce autolysis of cells, the bead-milled biomass was mixed with water in a ratio of 1:1 (\(w/v\)) and stirred overnight at a temperature of 50 \({}^{\circ}\)C.  
For the extraction of SCO in larger scale a miniPilot reactor (Buchi, Flawil, Switzerland) with a working volume of 5 L was used. An amount of 250 g lyophilized biomass was placed in the reactor vessel and treated with saturated steam for 5 min. Due to condensation, 1 L of water remained in the vessel. To induce cell lysis in the vessel, the mixture of lyophilized biomass and condensate was maintained at 90 \({}^{\circ}\)C for 30 min. Subsequently, the mixture was cooled down to 50 \({}^{\circ}\)C, and n-hexane was added to the reactor in a ratio of 1:1 (\(v/v\)). The mixture was stirred for another 30 min at 50 \({}^{\circ}\)C. The hexane phase was separated and the solvent evaporated until dryness under vacuum pressure.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 3 Results
### Reported NCC and Other Trackers versus This Work's NCC  
This experiment was performed to show that the proposed NCC implementation behaves as reported by other authors. We used all the 314 videos of the ALOV dataset for testing in this experiment. The tests labeled OURS use the described GPU implementation of NCC and the tests labeled CTRL are the ones reported for NCC [3; 44].  
As illustrated in Figure 7, the difference in average F-score between OURS and CTRL is small. The distance between the averages is 9.06% of the standard deviation of all the tests, not significant. These results validate that the proposed GPU implementation of NCC performs as good as expected in terms of F-score.  
Figure 8 shows survival curves for OURS, CTRL and 18 trackers reported by [PERSON] [44]. Survival curves are obtained by sorting the scores for every video and show a general overview of the video tracker performance; the closer the curve is to the perfect score (1.0), the better. The other trackers that are displayed are: Struck (STR) [45], Foreground-Background Tracker (FBT) [46], Tracking Learning and Detection (TLD) [47], Tracking by Sampling Trackers (TST) [48], Lucas-Kanade Tracker (LKT) [49], Kalman Appearance Tracker (KAT) [50], Fragments-based Robust Tracking (FRT) [51], Mean Shift Tracking (MST) [52], Locally Orderless Tracking (LOT) [53], Incremental Visual Tracking (IVT) [54], Tracking on the Affine Group (TAG) [55], Tracking by Monte Carlo (TMC) [56], Adaptive Coupled-layer Tracking (ACT) [57], \(\ell_{1}\)-minimization Tracker (L1T) [58], \(\ell_{1}\) Tracker with Occlusion detection (L1O) [59], Hough-Based Tracking (HBT) [60], Super Pixel tracking (SPT) [61], and Multiple Instance learning Tracking (MIT) [62].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average** & **F-Score \({}^{\star}\)** & **F-Score \({}^{\star}\)** \\  & **Time per Frame** & **Average** & **Deviation** \\  & **[spf]** & **[arb. unit]** & **[arb. unit]** \\ \hline CPU NO BEE & 202.1399 & 0.5453 & 0.3056 \\ GPU NO BEE & 1.1823 & 0.5051 & 0.3327 \\ Both & 101.6611 & 0.5252 & 0.3193 \\ \hline \hline \end{tabular} (\({}^{\star}\)) Only 100 random videos of the dataset taken into account.  
\end{table}
Table 3: Comparison between using GPU or CPU to compute exhaustive NCC. The average time per frame contemplates all the videos of the dataset, the units are seconds per frame. ’Both’ shows the average and standard deviation of CPU NO BEE and GPU NO BEE combined.  
Figure 7: Comparison of F-score between using this work’s NCC (OURS) and another NCC implementation (CTRL), the raw data was obtained from [44]. ’Both’ shows the average and standard deviation of OURS and CTRL combined.  
According to this experiment, our implementation of NCC is validated and can be considered a tracker of acceptable performance in terms of accuracy.
## 3 Results and Discussion
### Droplets Size Spectra Measured
#### 3.2.2 Hand-Type Nozzle  
Similarly to the cannon-type nozzle, the three studied droplet size variables in the hand-type nozzle also depend on the HP, LFR and AS (_p_ < 0.001). This fact is consistent with the air speed decrease found in the hand-type nozzle (Figure 4). General droplet size interval for every tested position can be observed in Figure 5b. Droplet size was more heterogeneous in the XP compared to that observed in the cannon spout.  
The D50 values measured for the hand-type nozzle (Figure 7a) were slightly higher than those obtained in the cannon spout. Thus, CP resulted in D50 below 150 \(\upmu\)m, with the minimum mean value of 62 \(\upmu\)m for the combination between minimum LFR and maximum AS. XP, on the other hand, resulted in D50 values above 125 \(\upmu\)m, with a maximum value of 407 \(\upmu\)m for the combination of minimum LFR and minimum AS. In this particular case, the atypical behavior already observed for the cannon was also registered in the hand-type spout, with a decrease in the droplet size with the increase in the LFR. There was also a considerable influence of the other two evaluated parameters (_p_ < 10\({}^{-3}\)) on D50. In the case of LFR, there was a positive effect on D50, whilst the opposite was found for AS, as expected from previous works [53]. The mean D50 increase that can be found between CP and AP was 42%. From AP to OP there was a mean increase of 39%. Last, OP to XP had a mean VMD increment of 75%.  
The D10 values (Figure 7b) comprised a minimum value of 23 \(\upmu\)m for the combination of CP, intermediate LFR and maximum AS, and 179 \(\upmu\)m for the combination of XP, minimum LFR and  
Figure 7: **(a)** D50 (Volume Median Diameter, VMD), **(b)** D10 and **(c)** D90 values (\(\upmu\)m) per liquid hose position (HP), liquid flow rate (LFR) and airflow speed (AS) for the hand-type nozzle. Bars show the mean \(\pm\) Standard Error. CP, conventional insertion of liquid hose position; AP, alternative position; OP, out position; XP, extreme position.  
minimum AS. The CP presented D10 values all below 50 \(\upmu\)m, while the XP presented values above 55 \(\upmu\)m. The mean increase in this parameter from CP to AP was 49%, from AP to OP it was 3% and from OP to XP it was 135%.  
The D90 values (Figure 7c) were all found below 280 \(\upmu\)m in CP and above 370 \(\upmu\)m in XP. The most extreme values were 112 \(\upmu\)m (for CP/min LFR/max AS) and 735 \(\upmu\)m (for XP/min LFR/min AS). The mean increase from CP to AP was 35%, from AP to OP it was 28% and from OP to XP it was 94%.  
In general, it can be observed that the most extreme positions generated an atypical response of the LFR on the droplet size parameters. Nevertheless, the three LFR levels did not produce great differences in comparison with the AS ones. It could be stated that LFR had a lesser impact on droplet size in the outermost positions of the liquid hose.
## 3 Results
### Meteorological and Microclimate Data
Photosynthetically active radiation (PAR) was also decreased after the treatment of covering purslane as expected: in the year 2018, covering purslane reduced the PAR by 16.9-54.4% compared with the control from the anthesis to the harvest (Figure 2b). In the year 2019, covering purslane increased the PAR in the bunch zone by 5.6% in the pea-size period. During the veraison period, covering purslane reduced the PAR in the bunch zone by about 42.3%. The reason was the insufficient irrigation of the vineyards by the local government's water control in May and June. The growth of the purslane in the early period was weak. In July, the rainfall and the irrigation increased during the veraison period, which made the purslane vigor better. So the covering purslane reduced the PAR in the fruit zone significantly at 40-62 days after anthesis. In terms of the relative humidity, the covering purslane increased the relative humidity (Figure 2c,f). It was speculated that the respiration of the grapevines increased the water content in the air of the fruit zone, which further led to an increase in the relative humidity there. On the other hand, the covering purslane decreased the water content in soil 40 cm below the ground surface, and it was speculated that purslanes could compete with the grapevines for the water absorption and utilization, resulting in a decrease in the soil water content (Table S2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Bioclimatic Indices** & **2018** & **2019** \\ \hline Growing season temperature (GST, \({}^{\circ}\)C) & 19.57 & 20.39 \\ Heliothermal index (HI, \({}^{\circ}\)C) & 2741.0 & 2966.4 \\ Growing degree-days (GDD, \({}^{\circ}\)C) & 2011.3 & 2126.7 \\ Cumulative rainfall (mm, (from 1 April to 30 September)) & 109.6 & 102.0 \\ Daily average sunshine duration (h, (from 1 April to 30 September)) & 9.92 & 9.21 \\ Daily average mean temperature of July (\({}^{\circ}\)C) & 27.33 & 27.65 \\ Daily average maximum temperature of July (\({}^{\circ}\)C) & 33.69 & 33.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Meteorological data from the vintages of 2018 and 2019.
## 2 Materials and Methods
### Handgrip Strength Test (HGS) and Synchronized sEMG Activity  
The HGS strength test was performed with a customized hand dynamometer equipped with a strain gauge (Muscle-Lab, Bosco-System 4000e, Ergotest Innovation, Stathelle, Norway). HGS was measured on the participant, who was seated with a straight back and the feet parallel and completely in contact with the floor. The shoulder was abducted, the elbow flexed to 90\({}^{\circ}\), with the forearm placed on the table, and the wrist was positioned in the midrange of pronation and supination (neutral wrist posture). The dynamometer was adjusted to fit the participant hand and standardized. The base of the HGS was placed on a table. The position of the hands, body, and sensor was marked to standardize all tests over time. The participant was instructed to \"squeeze\" with maximal isometric contraction and maintain this for approximately 3-4 s. Grip force should be applied smoothly, without rapid jerking motion [73]. Three trials were performed and the best trial of the three was selected. The muscle activity of Deltoid Anterior (DA), Biceps Brachii (BB), and Palmaris Longus (PL) was measured during the HGS, using an integrated sEMG system. The electrodes were placed in the mentioned muscles following guidelines for the non-invasive assessment of sEMG [63], and the skin was prepared, to minimize the impedance (<5 k\(\Omega\)). The electrodes and EMG cables were secured with elastic strips (Flexa Elast, Pic Solution, Pikdare S.p.A.) to prevent motion artifacts. EMG activity was recorded by using the triode electrode (T3402 M, nickel-plated brass, electrode diameter = 1 cm, interelectrode distance = 2 cm; Thought Technology Ltd., Montreal, QC, Canada). The sEMG detection technique entailed full-wave true RMS conversion of the signal from the preamplifier. The size of the averaging window was 100 ms, with a resolution of a 16-bit A/D converter. The EMG preamplifier characteristics were as follows: voltage supply, \(\pm\)5 VDC; input impedance, 2 G\(\Omega\); common mode rejection rate, 100 dB; gain at 100 Hz, 500; 3 dB low-cut frequency, 8 Hz; and 3 dB high-cut frequency, (Muscle-Lab 4000e, Bosco-System, Ergotest Innovation, Stathelle, Norway). During the HGS, the force-time relationship was recorded through the Muscle-Lab software. The trial with the highest values in each hand were used for analysis. To analyze the sEMGRMS (mV) of the upper limb's muscles, a window of 400 ms around the peak of the maximal voluntary isometric contraction (200 ms before and 200 ms after) was opened. The rate of force development (RFD) in the initial portion of the force-time relationship (the first 0.200 ms) was also determined.  
The HGS differences between the hands were calculated as the ratio of the maximal strongest and weakest HGS (Strongest HGS/Weakest HGS). The HGS asymmetries could be considered in the following way: symmetric (less than 15% difference, \(<\)1.15), mild asymmetry (15-25% difference, or 1.15-1.25), and moderate asymmetry (more than 25% difference, \(>\)1.25) [57].