esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 3 Experimental Results  
### Experimental Platform  
In this study, a CPCI chassis equipped with multiple MVB cards was used as the MVB network simulation platform, and then an MVB protocol analyzer and multi-channel oscilloscope were used to capture the data of the MVB link layer and the voltage waveform data of the physical layer. A picture of the MVB network simulation platform is shown in Figure 8.  
Here are the technical parameters of the equipment shown in Figure 8. Three CPCI MVB cards (class 4 devices according to IEC 61375-3-1 2012) are inserted in the CPCI chassis, all of which possess bus administrator (BA) capability, enabling them to independently  
Figure 8: MVB network simulation platform.  
manage MVB communication activities. The MSO44 Tektronix oscilloscope, equipped with a maximum sampling rate of 6.25 GHz and an input bandwidth of 200 MHz, achieves voltage measurements on MVB dual lines and electrical isolation of the input through two PINTECH N1008B differential probes. The bus analyzer uses the MVB protocol analyzer RB-MVB/AN02 produced by AMIT (assembled together with the differential probes). It can intercept and analyze MVB communication messages without affecting the normal communication activities. The protocol analyzer also transmits the analysis results to a PC via Ethernet.  
Since the sampling rate of the physical-layer voltage waveform is limited by both data accuracy and storage analysis cost, it is necessary to optimize the sampling rate of the physical-layer voltage waveform. The sampling rate should be reduced as much as possible without significantly affecting the data content. In the experiment, multiple sampling rates were used to capture MVB physical-layer voltage waveform data, and the ideal sampling rate was determined according to the data quality.  
The MVB network simulation platform has normal and fault conditions. The latter sequentially injects impedance mismatch, short-circuit and open-circuit faults. The structure of the MVB terminator using the EMD medium is shown in Figure 9. The role of the terminal resistor is to absorb energy at the end of the MVB network, reduce the degree of voltage signal reflection and reduce communication interference. In addition, the impedance characteristics of the terminal resistor are specific values. Changing the resistance value of the terminal resistor may cause different degrees of signal reflection, causing distortion in the MVB waveform. Based on determining the sampling rate of the MVB physical-layer voltage waveform, dual-modal data under different conditions of the MVB are collected and processed using a feature-extraction algorithm.
## Results
### Msako Survey
#### 3.2.2 Cross-Section  
The interpreted Msako subsurface resistivity cross-section (Figure 7b) had a minimum sensitivity threshold of \(-3\) applied, corresponding to a DOI of 120 m. A lowest possible water level was derived for the cross-section by interpolating the elevation profiles of the nearby flowing rivers, that were assumed to be gaining. This level was estimated at 10 m BGL for the whole cross-section.  
A fracturing-weathering profile was identified with resistivities decreasing progressively upwards from a few kOhm.m to a few 10s of Ohm.m. Within this profile, it is proposed clay-poor units (fractured basement, saprock and gravelly-sandy saprolite) were distinguished from clay-rich units (clayey saprolite, laterite and topsoil) using a threshold of 100 Ohm.m. The fracturing-weathering profile was observed to have a fairly uneven base and to vary greatly in thickness from 30 m in the west to 5 m in the east. The clay-poor units were uninterrupted laterally but thinning out from 20 m in the west to a few metres in the east. The clay-rich units were laterally discontinuous, with a maximum thickness of 20 m in the West. Within the deeper basement, three conductive anomalies were observed elongated and dipping at c. 45\({}^{\circ}\) (anomalies at 500 Ohm.m; rest of the deeper basement at 1-3 kOhm.m, hence fresh basement). These conductive anomalies were interpreted as well-developed fault zones, rich in open, water-filled fractures, fault gouge or authigenic clays. Two uplifted basement blocks were identified between the faults interpreted as being inverse faults and indicative of a compressive tectonic regime.  
Figure 7: Msako survey: (**a**) site survey line location (with individual sounding mid points marked as crosses) shown on a Google Earth satellite image (July 2020 download) of the area with accompanying landscape photograph; and, (**b**) interpreted subsurface resistivity cross-section obtain for section line AB marked on (**a**).
## 1 Introduction
(2016) suggested the formulation of an inverse problem to generate a spherical harmonic representation of acoustic fields predicted using FDTD. A binaural rendering is achieved based on the relation between spherical harmonics, plane waves and HRTFs. An implementation of a Plane Wave Expansion (PWE) is carried out in the current study as an alternative methodology to generate an interactive auralization from predicted acoustic numerical data. The methodology has been evaluated by using FE results, but can readily be implemented using other numerical methods. The approach is based on the concept that the acoustic pressure at each node of the mesh can be understood as the output of a virtual omnidirectional microphone. By using the data from the mesh, it is possible to create a virtual microphone array, which, with the implementation of an inverse method, allows for the estimation of complex amplitudes of a set of plane waves that synthesize the desired acoustic field. The use of an inverse method to generate a plane wave expansion from predicted pressure data was previously studied by [PERSON] and [PERSON] for 2D cases [PERSON] and [PERSON] (2017). An extension of their work is presented in this paper to the 3D case. Based on a plane wave representation, mathematical operators can be implemented to enable interactive features for auralization applications. The translational movement of the listener can be generated by the translation of the plane wave expansion ([PERSON], 2017; [PERSON] et al., 2010). In terms of the listener's rotation, a spherical harmonic transformation can be used to rotate the acoustic field [PERSON] et al. (2010). A rotation in the plane wave domain is achieved by the implementation of a VBAP algorithm. Nevertheless, experiments conducted by [PERSON] (2011) suggest that the spherical harmonic transformation is more accurate for this specific application. A complete framework for an interactive auralization based on a plane wave representation is presented in this article. The processing chain involves the use of an inverse method to extract spatial information from FE simulations, the use of translation and rotation operators, the combination of real-time audio processing with a visual interface and binaural rendering to reproduce the acoustic field. The proposed approach is evaluated by testing it within a real-time auralization system as a reference case. This auralization system allows us to emulate interactively wave phenomena, such as the modal behaviour of the enclosure or acoustic diffraction. The remaining parts of the paper are organized as follows: The mathematical foundations of the plane wave expansion and the derivation of the translation and rotation operators are reviewed in Section 2. The implementation of an inverse method to generate a plane wave representation of the predicted acoustic fields is presented in Section 3. In Section 4, a real-time auralization system is developed, which allows for the evaluation of the proposed approach. The discussion and evaluation of the results are considered in Section 5. Finally, conclusions of the current work are presented in Section 6.
## 4 Deep Learning Methods for HDR Reconstruction
### Transformers for HDR Reconstruction
#### 4.3.1 Alignment-Based Methods  
Some state-of-the-art methods focus on feature extraction with an emphasis on aligning the input frames, using the medium-exposure frame as a reference image for object positions in the scene. Alignment may be performed using convolutional blocks, attention mechanisms, or even entire Transformer architectures, leading to a clearly separable two-step approach (alignment, then feature fusion), as shown in Figure 8(a).  
The HFT architecture [156] uses a Shallow Feature Alignment (SFA) block for frame alignment. This block concatenates the medium-exposure frame with either the over- or underexposed frame and applies two successive convolutions to produce a single aligned frame. To refine features, the approach employs a Fusion Transformer architecture, extracting multi-scale features and merging them to reconstruct the optimal HDR image with final convolution blocks. The results were validated on the [PERSON] [15] and [PERSON] et al. [48] (IISc VAL and IISc VAL 7-1 datasets) works, as well as on images captured by a cell phone to verify the generalization quality of the model. Ablation studies highlight the relevance of the SFA model, Fusion Transformers, and channel-level attention block.  
[PERSON] et al. [157] propose a different alignment approach by performing a quadruple Bayesian filter preprocessing of the input LDR images, converting them into a single single-channel image that captures information from all LDR frames in the dataset. Subsequently, the image is split into patches with four-channel depth, from which channel-level attention maps are extracted and fused with the patches via elementwise multiplication. These images with attention are aligned using a multi-scale Transformer architecture and then  
Figure 9: Transformer-based architectures.  
refined with convolutions to reconstruct an HDR image. For validation, a synthetic dataset was introduced, along with generalization tests using the [PERSON] et al. [158] dataset.  
Another architecture using alignment-based methodology is HyHDRNet [159], aligning frames through ghost attention, carrying out alignment at the patch level by identifying misaligned elements, and performing the corresponding correction. After consecutively repeating these corrections, all frames are concatenated, including the initial reference frame. The extraction of features from the aligned images and their refinement is handled by the SwinIR [160] Transformer architecture. A final convolution plus the sum of the initial features reconstructs the HDR image from three LDR images. The results were validated with the [PERSON] and [PERSON] et al. [63] datasets and generalized using [PERSON] et al. [14] and [PERSON] et al. [65]. Ablation studies highlight the efficiency of its alignment blocks.
## 2 Materials and Methods
### Cell Culturing  
The MCF-7, MCF-7 PARP-HA, COS-1, MDA-MB-231, HuH-7 and mouse embryonic fibroblast (MEFs) cell lines were used in these studies. MCF-7 cells are ER\(\alpha\) positive luminal A subtype breast cancer cells routinely used to study ER\(\alpha\) signaling. The generation of the doxycycline (DOX)-inducible PARP-hemagglutinin (HA) overexpressing MCF-7 cell line (MCF-7 PARP-HA) has been previously described [13]. HuH-7 human hepatoma cells were used because they are ER\(\alpha\) negative and easily transfected at high efficiency. MDA-MB-231 cells are triple negative breast cancer cells that are ER\(\alpha\) negative. COS-1 cells are African green monkey kidney fibroblast-like cells that are transfected at high efficiency, and we were able to overexpress PARP7 at higher levels in these cells compared with MCF-7 or HuH-7 cells. Isolation and immortalization of _Parp7\({}^{\prime+/+}\)_ and _Parp7\({}^{\prime-/-}\)_ MEFs have been described elsewhere [17]. Generation of the _Parp7\({}^{\prime}\)F352A_ mice by CRISPR-Cas9 gene editing is described elsewhere ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], unpublished data). _Parp7\({}^{\prime}\)F1532A_ (_Tiparp7\({}^{\prime}\)F1532A_) mice were designed and created by [PERSON] (Santa Clara, CA, USA). Briefly, a gRNA sequence was designed to target the amino acid residue H532 located in exon 6 of murine _Parp7_. An oligo donor with targeting sequence, flanked by 60 bp homologous sequence containing the H532A (CAT to GCC) mutation was introduced into exon 6 by homology-directed repair. Once the mutation was confirmed, the mouse colony was expanded and maintained by breeding _Parp7\({}^{\prime+/\text{H532A}}\)_ heterozygous mice. The generation of _Parp7\({}^{\prime}\)F1532A_ MEFs isolated from these mice was done as previously described [17].  
All cell lines were cultured in DMEM (1.0 g/L glucose), supplemented with 10% \(v/v\) fetal bovine serum (FBS), 1% \(v/v\) L-glutamine and 1% \(v/v\) penicillin-streptomycin(P/S). Cells were maintained at 37 \({}^{\circ}\)C, with 100% humidity and 5% CO\({}_{2}\), and subcultured when 80% confluence was reached. For experiments involving estrogenic compounds, cells were starved in phenol red-free DMEM (1.0 g/L glucose), supplemented with 5% \(v/v\) dextran-coated charcoal (DCC)-stripped FBS, 1% \(v/v\) L-glutamine and 1% \(v/v\) P/S for at least 48 h before treatment with ligand.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 7 Conclusions  
A wake-up radio is a promising solution for energy-efficient cellular IoT applications like Smart Cities. In this paper, an overview of recent 3 GPP activities on standardizing NB-IoT and 5G wake-up signals is presented. Wake-up receiver performance tradeoffs and  
Figure 19: Test setup (**left**) and measured wake-up event (**right**).  
Figure 20: Power vs. sensitivity performance of the presented NB-IoT WUR (starred) compared to other receivers supporting coherent modulation schemes (blue dots).  
recent research trends in ultra-low-power receiver designs are discussed. A state-of-the-art fully integrated NB-IoT wake-up receiver is presented. The wake-up receiver achieves a good communication range (\(-107\) dBm sensitivity) with a total power consumption of \(2.4\) mW.  
While there have been significant advancements in ULP radios, the design of high-performance and standard-compliant ULP radios remains a big challenge. Such a challenge must be addressed in order to enable massive deployment and resilient operation of cellular IoT. Future research directions include circuit-level novelties as well as signaling-hardware co-design to alleviate the hardware requirements.  
[PERSON] was the lead author of the paper and was primarily responsible for drafting the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed, documented, and summarized key documents related to recent 3 GPP RAN1 activities on 5G WUS standardization. Similarly, [PERSON] and [PERSON] carried out such activities for NB-IoT. [PERSON] was also the lead designer of the NB-IoT WUR chip presented in this paper. [PERSON] provided project supervision and helped refine the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Simulation Analysis
Figure 1: Change in the mean of taxi offline order acceptance probability. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Order Volume**} & \multicolumn{6}{c}{**The Probability of Taxi Hailing During Cruising**} \\ \cline{2-9}  & **Mean Value 1** & **Mean Value 2** & **Mean Value 3** & **Mean Value 4** & **Mean Value 5** & **Mean Value 6** & **Mean Value 7** & **Mean Value 8** \\ \hline
5000 & 23.02\% & 23.34\% & 23.25\% & 23.41\% & 23.56\% & 23.58\% & 23.66\% & 23.72\% \\
10,000 & 33.90\% & 33.94\% & 34.53\% & 34.85\% & 34.23\% & 34.10\% & 34.22\% & 34.12\% \\
15,000 & 43.35\% & 42.83\% & 42.07\% & 42.48\% & 42.87\% & 42.74\% & 42.73\% & 42.80\% \\
20,000 & 48.32\% & 48.86\% & 48.62\% & 48.66\% & 49.13\% & 49.34\% & 49.25\% & 49.19\% \\
25,000 & 54.40\% & 54.14\% & 53.59\% & 54.14\% & 54.42\% & 54.49\% & 54.54\% & 54.63\% \\
30,000 & 58.98\% & 58.63\% & 58.60\% & 59.26\% & 59.25\% & 59.29\% & 59.28\% & 59.40\% \\
35,000 & 62.60\% & 63.84\% & 63.48\% & 63.47\% & 63.57\% & 63.83\% & 63.74\% & 63.66\% \\
400,000 & 68.15\% & 69.07\% & 69.11\% & 69.01\% & 69.02\% & 69.06\% & 69.03\% & 69.29\% \\
45,000 & 73.70\% & 74.37\% & 73.15\% & 73.47\% & 73.36\% & 73.08\% & 73.07\% & 73.00\% \\
50,000 & 75.64\% & 75.76\% & 76.53\% & 76.38\% & 76.16\% & 76.34\% & 76.37\% & 76.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Probability of taxis accepting offline orders and order fulfillment probability under different order densities. Figure 2: Taxi offline order acceptance probability under different order densities. The experimental data revealed that, as the number of regional orders increased, i.e., as order density, \(\rho\), continued to rise, the proportion of taxis choosing to accept offline orders gradually increased. This change will have an impact on the order processing speed of ride-hailing platforms. Simultaneously, the growth rate of the proportion of offline taxi orders showed a gradually slowing trend. The reason is that, despite high order density, the starting and ending points of orders are random, which means taxi drivers may not always find waiting passengers within their visual range when searching for customers. Furthermore, taxis face competitive pressure when accepting offline rides. When the number of empty taxis near passengers in a certain area exceeds passenger demand, some taxis may fail to secure rides at that location. Additionally, the order allocation mechanism of ride-hailing platforms and dynamic fluctuations in the supply-demand relationship in the ride-hailing market further complicate the process of offline ride acceptance for taxis. These factors limit the unlimited growth of the proportion of offline ride acceptance for taxis.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 3 Results
### Data Fusion Approaches  
Several studies employed a data fusion approach by combining data from multiple sensor types into an analysis. For example, [PERSON] et al. [79] used textural information from Sentinel-1 imagery and spectral information from Landsat 8 to create ten different models (including combinations of strict radar and radar/multispectral fusion) to map winter wheat with a maximum overall accuracy of 99.8%. [PERSON] et al. [80] utilized Landsat ETM+ and RADARSAT-1 data fusion to classify landcover, including coffee plantations and an agroforestry class. These methods produced a moderately low overall accuracy, however, with the most successful model achieving an accuracy of 71.0%. [PERSON] et al. [46] performed an analysis of both the comparison and data fusion of Landsat TM imagery, SPOT imagery, and textural images derived from SPOT imagery calculated using eight texture measures and nine sizes of moving windows. These models also performed relatively poorly, with a maximum overall accuracy of 61.8%. [PERSON] et al. [81] used Sentinel-1, Sentinel-2, Landsat 8, and a digital elevation model (DEM) to map forest type and obtained a maximum accuracy of 82.8%. [PERSON] et al. [82] used textural information from Sentinel-1 imagery and spectral information from RapidEye to create eight models (multispectral, radar, and sensor fusion) to map cacao, with an accuracy ranging from 78.8-88.1%. [PERSON] et al. [83] used a multispectral and hyperspectral data fusion process with Landsat and 2005 Hymap, respectively, to map forest type and tree plantation by species composition in Costa Rica. Utilizing both multispectral and hyperspectral data increased the overall accuracy of the model compared to hyperspectral data-only models, with an improvement from 83.5% to 88.5%.
## 2 Materials and Methods  
### Plant Material and Growing Area Characteristics  
The study was conducted over two consecutive years in an olive grove on the island Dugi otok, near the location of Zman (43\({}^{\circ}\)57\({}^{\prime}\)42.95\" N, 15\({}^{\circ}\)7\({}^{\prime}\)21.53\" E). The average age of olive trees sampled was 10 years. The trees were planted as part of an expanded extensive olive orchard and the polyconic vase training system, up to 4 primary branches, was in use.  
The local climate is Mediterranean, characterised by mild, rainy and moderately windy winters, with hot and dry summers [22]. Based on the data collected by the PinovaMeteo meteorological station, located inside the olive orchard grove, total rainfall in 2019 was 1037.9 mm, and 804 mm in 2020 [23].  
Figure 1 shows data on rainfall and reference evapotranspiration (ETo) during the two-year study period.  
In 2019 there was more precipitation at the beginning of the vegetation and during the flowering period, while in 2020, rainfall was higher during the ripening period [23]. The average daily ETo, calculated based on the data obtained from the PinovaMeteo meteorological station, was 3.79 mm in 2020 during the irrigation season (April-October), while it was somewhat lower in 2019 (3.66 mm) [23].  
The water used for irrigation in all three treatments was desalinated water from the producer's water well situated in the olive grove. The water was analysed in the middle of the irrigation cycle during both study years (21 August 2019 and 12 August 2020). The main measured values were: pH (at 25 \({}^{\circ}\)C) 7.0 in 2019 and 7.9 in 2020; electroconductivity of 346 \(\upmu\)S/cm in 2019 and 394 \(\upmu\)S/cm in 2020; and total chlorides of 71.3 mg Cl/1 in 2019 and 149 mg Cl/1 in 2020. The values were higher during dry months, but the water was found to be adequate for irrigation throughout the irrigation cycle in both observed years [24; 25].  
The soil in the olive grove is characterized as ameliorate karst soils, with an anthropogenic horizon up to 40 cm in depth. The top layer of the soil profile is a silly clay texture with a skeletal content of 35.6%, while the deeper layer consists of clay and loam texture with a skeletal content of 61.8%. Average bulk soil density is 1.23 g/cm\({}^{3}\). The average field water capacity, after correction for the skeletal content, was very low and estimated at 17.8%, while the wilting point was 10.2%. Soil pH was basic (7.9), rich in organic matter, well saturated with nitrogen (0.18 N), rich in potassium (31.3 mg K\({}_{2}\)O/100 g), and poor in phosphorus (5.84 mg P\({}_{2}\)O\({}_{5}\)/100 g) [26]. The grove is grass covered and regularly trimmed with cultivation of the surface area under the olive tree canopy.  
The two-year experiment was conducted on 12 own-rooted trees of the Italian cultivar 'Coratina' highly adaptable to various environmental and cultivation conditions and characterised by a high and regular production. The variety bears large fruits of high variability in size and the oil yield has a very fruity flavour and high concentration of polyphenics [27; 28; 29].