esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Structured Data Analysis  
Figure 11 is a visualization of the IPC/data matrix constructed from the patent set selected in the previous step. The analysis excluded IPC symbols that were related to only one patent during these years and used contingency. For example, the description of A63H is toys, e.g., tops, dolls, hoops, or building blocks; B63H is marine propulsion or steering; G08G is traffic control system; and H04R is loudspeakers, which are far away from UAVs. The matrix shows that the range of technology concerned in the patents DJI applied for became more extensive from 2010 to 2020. Between 2010 and 2013, it mainly focused on three technologies, since DJI applied for few patents: (1) measuring distances, levels, or bearings (G01C); (2) aeroplanes; helicopters (B64C); (3) radio direction-finding; radio navigation; determining distance or velocity by use of radio waves; locating or presence-detecting by use of the reflection or reradiation of radio waves; analogous arrangements  
Figure 9: DJI’s unmanned aerial vehicle series products.  
using other waves (G01S). These are for the inertia measuring model (navigation system), the overall system, and remote control equipment of a UAV. In the next three years, DJI applied for many patents and also became involved in more technical domains, such as apparatus for taking photographs or viewing them (G03B), dynamo-electric machines (H01M and H02K), and wireless communication networks (H04N and H04W). These are shown in Figure 11 as patents with larger dots.  
A technology change map was then built using the rules discussed in Section 3.2.3, to decide the value of technological novelty of each IPC symbol, according to Equation (1); the results are shown in Table 2.
## 2 Applications for VANETs
### Vehicular Cloud  
The vehicles can connect together forming a vehicular cloud that will offer storage space, computing resources, sensor readings, _etc._ as an on-demand service to clients. The clients could be individuals, corporations, or government departments such as the meteorology department, police, or highway authorities. Moreover, with cameras onboard vehicles and high-speed connectivity, the police or emergency services can even request images from vehicles for an area that they want to monitor such as the site of an accident. Ericsson has already developed its own Connected Vehicle Cloud (CVC) for vehicle telematics and is behind Volvo Cars Sensus, which is an infotainment system for Volvo vehicles [10]. The CVC is designed to open new business opportunities by providing the vehicle data to companies, allowing developers to develop new applications by giving them an open platform, and facilitating the drivers by connecting them to the manufacturers and other service providers.
## 3 Proposed Approach
### Data and Pre-Processing
#### 3.1.3 Normalization (Min-Max Scaling)  
Each feature \(x_{i}\) is scaled to the range \([0,1]\):  
\[x_{i}^{\text{norm}}=\frac{x_{i}-\text{min}(x)}{\text{max}(x)-\text{min}(x)}\]
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 2 Partial Discharge Test and Signal Preprocessing  
Four discharge models are developed to simulate common transformer insulation defects: tip discharge, surface discharge, bubble discharge, and floating discharge [20]. The cardboard used for the tip, surface, and floating discharge models has a thickness of 2 mm, while the bubble discharge model consists of three layers of cardboard with thicknesses of 0.5 mm, 2 mm, and 0.5 mm. The dimensions and structures of the discharge models are illustrated in Figure 1[21].  
The partial discharge pulses were measured using the high-frequency current method [22], and the experimental circuit is shown in Figure 2. The experiments were conducted in an electromagnetic shielding room, and high-frequency partial discharge signals were collected from the grounding wire using the high-frequency current method. A synchronized acquisition card was employed to ensure the synchronization between the collected partial discharge signals and the reference voltage signals. After applying the target voltage, sampling began once the discharge signals stabilized. A triggered sampling method was used, where samples were collected when the discharge amplitude exceeded the trigger threshold. The sampling bandwidth was 100 MHz, and discharge signals over 100 power frequency cycles were continuously collected as long-term signal samples. In order to further improve the quality of the PD test signal, the research group proposes an adaptive weighted-frame fast sparse-representation denoising method [23], which has smaller amplitude error and waveform distortion and higher efficiency. This method is also used to denoise the partial discharge test samples. The single-pulse partial discharge was extracted using an adaptive double-threshold extraction method [24]. The time-domain waveforms of the four types of single-pulse partial discharge are shown in Figure 3[21].  
To replicate the characteristics of limited samples in the real PD signal dataset, the PD single-pulse samples for each discharge type were proportionally reduced to create  
Figure 1: Partial discharge models [21].  
Figure 3: Four different waveforms of PD single pulse [21].  
Figure 2: Experimental circuit wiring diagram.  
a training sample library. The distribution of each pulse type in the library is shown in Table 1.
## 3 Results and Discussion
### Single-Notch Filter  
The overall design was as shown in Figure 2a; however, resistive terminations were used, and ports were floated using paired capacitors. To allow frequency tuneability, all capacitors included varicaps. To confine magnetic fields and ensure nearest-neighbour coupling, all inductors used low-loss ferrimagnetic toroidal cores with inner and outer diameters of 13.2 and 21 mm (Fair-Rite 5967000601, based on \"67\" Ni-Zn ferrite material with cutoff at 50 MHz). The initial relative permeability was \(\approx\)40, implying that the magnetic flux was almost completely contained in the core. Two windings (with inductances \(L_{\text{C}}\) and \(L_{\text{T}}\), such that \(L_{\text{C}}+L_{\text{T}}=L\)) were used for the waveguide elements, and three (with inductances \(L_{\text{C}}\), \(L_{\text{C}}\), and \(L_{\text{S}}\), such that \(2L_{\text{C}}+L_{\text{S}}=L\)) for the coupling resonators. Adjacent windings were reversed to obtain negative coupling coefficients. A method was used to allow the tuning of mutual inductance using components constructed by 3D printing with a polycarbonate filament. Figure 6a shows this mechanism, allowing control over the relative position of two copper windings. The coupling coefficient was a nonlinear function of the winding separation with minimum separation yielding maximum coupling.  
Figure 6b shows a photograph of the completed filter constructed as a copper-clad printed circuit board using low-loss mica capacitors. Mechanical tuning was provided for all mutual inductances. The value of \(M\) needed for impedance matching to a 50 \(\Omega\) load at resonance was first identified. inductors \(L_{\text{s}}\) were then chosen to equalise the self-inductance of all resonators, and capacitances were tuned to achieve resonance at 13.56 MHz. Table 1 shows the values of the most important parameters after tuning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Range of Adjustment** \\ \hline \(L_{T}\) & \(1.16\)\(\mu\)H & N/A (not applicable) \\ \(L_{c}\) & \(460\)\(\,\)nH & N/A \\ \(L_{\text{s}}\) & \(710\)\(\,\)nH & N/A \\ \(\kappa\) & \(0.725\) & \(0.51\)–\(0.92\) \\ \(\kappa_{c}\) & \(0.28\) & \(0.19\)–\(0.34\) \\ \(\omega_{0}\) & \(13.56\)\(\,\)MHz & DC to 50\(\,\)MHz \\ \(Q\) & \(200\pm 10\%\) & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameter values.  
Figure 6: MI directional filter: (**a**) mechanism for tuning of magnetic coupling (LH–maximum coupling; RH–minimum coupling); (**b**) complete PCB with labelled port numbers.  
Four-port scattering parameters were measured using an electronic network analyser (Agilent E5061B). To begin with, \(\lambda\) was set to unity so that responses were independent of the port used as input. Figure 7a compares the frequency dependence of S-parameters with the predictions of the theoretical model for \(\kappa_{c}=0.28\). Excellent agreement with experimental results can be seen. Larger values of \(\mu\) were investigated and found to reduce losses and increase the passband of the bandpass channel and the notch depth.
## 4 Analyses
### Analysis on Effect of Position of Fixed Base Plate  
In the case of FSW of dissimilar materials, most researchers have suggested that harder material should be kept at the AS to achieve an excellent weld joint surface. From the experimental analyses, it could be observed that an excellent weld quality was obtained when the Cu base was fixed at the AS (Figure 8). On the other hand, a longitudinal crack was observed in the weld surface when the Cu base was fixed at the RS (Figure 9). During welding, due to the frictional heat, a solid material at the RS is transformed into a semi-solid one and moves towards AS where it cools and is fused with the other material. Thus, when the Cu base plate (high melting point) was fixed at the retreating side, the frictional heat generated by the tool was insufficient to make the Cu soft enough to fuse with the Al base plate placed on the AS. As a result, a longitudinal crack developed due to improper fusion of the two materials [16].  
Figure 7: (a–c) Scatter diagrams of output responses—tensile properties (YS, UTS, % E).
## 2 Materials and Methods  
### Site Description  
The present study area is located at the lower reach of HRB region, northwest China. The climate in this region is hyper-arid [31], with the mean annual precipitation (\(P\)) being approximately 37.5 mm and the pan evaporation (\(E_{p}\)) being approximately 2240.5 mm according to data collected for the period 1957-2016 from the Ejin Meteorological station provided by China Meteorological Data Sharing Services. The aridity index is less than 0.05 in this region. Mean annual temperature is estimated to be 8.9 \({}^{\circ}\)C, with a mean monthly temperature ranging from \(-\)11.5 \({}^{\circ}\)C in January to 27.0 \({}^{\circ}\)C in July. The \(P\) shows an obvious seasonal pattern (with approximately 75% of the rainfall falling during June-October period) with significant inter-annual variations (of approximately 7-103 mm for the present study period 1957-2016) (Table S1).  
The observation stand is situated at the Qidaoqiao _P. euphratica_ Forest National Natural Refuge (Figure 1, 41\({}^{\circ}\)59\({}^{\prime}\) N, 101\({}^{\circ}\)10\({}^{\prime}\) E, 920.46 m elevation) [31; 32]. The study site contains one of the best-grown riparian forests in the lower HRB region. The riparian forest in the study area is composed of _Populus euphratica_ Oliv. (_P. euphratica_) and _Tamarix ramossissima_ Ledeb. (_T. ramossissima_) [32]. The _P. euphratica_ is the dominant plant species with a density of 320 stems ha\({}^{-1}\), and contributes approximately 75% of the total basal area in the study region [32]. The characteristics of _P. euphratica_ including tree height (10.1 \(\pm\) 1.7 m), canopy height (5 m), diameter at breast height (DHB, 22.9 \(\pm\) 4.8 cm), mean age (40 years) and canopy coverage (52%) were measured in a 100 m \(\times\) 100 m plot. Moreover, the soils in the _P. euphratica_ stand consist of a sandy loam approximately 2 m deep and which has a saturated volumetric water content of 0.35 m\({}^{3}\) m\({}^{-3}\)[31]. At the study site, the mean groundwater depth (GWD) was 1.4 m. In order to acquire the data, the eddy covariance instruments were installed at a relatively flat and homogenous ground, and the dominant plants extended for several kilometers in all directions. The site's roughness length, zero-displacement height and slope were 1.38 m, 7.9 m and 1/5000, respectively. The prevailing wind was from southeast and northwest directions and the measured fluxes originated between 109 and 1520 m from the tower (i.e., peak at 228 m) in the prevailing wind direction. The peak of about 228 m represented the area with the highest carbon dioxide flux contribution in the footprint.
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 3 Proposed Methodology  
### Stage 1: Deep K-SVD Algorithm with Household Appliance Data  
In the first stage, the deep non-negative K-SVD algorithm was implemented for load profile compression and DUBPs extraction. DUBPs were fed into the second stage as input to enhance the efficiency and stability of load forecasting.  
The construction of a suitable initialization dictionary \(D_{s}\) is a kernel point in the sparse coding problem. Current studies have generally used a part of the original signal or classical bases (e.g., Fourier and Gaussian bases) as the initial dictionary matrix. The signature of electrical devices was first introduced to load forecasting in [23], and the experiment showed that a combination of historical load and household appliance data can considerably enhance the forecasting of individual consumer loads. The sparse coding process with appliance load is illustrated in Figure 3. This study selected the load profiles of several representative appliances related to the lifestyle of residents as the initial dictionary matrix to satisfy the diversity of actual loads.  
Figure 2: Schematic diagram of deep equipment behavior patterns extraction.  
Houses equipped with sub-meters can obtain noiseless household appliance consumption profiles. Load disaggregation or non-intrusive load monitoring (NILM) technology is applied to recognize the consumption of each appliance when only the overall current or load reading is available [30]. Unlike the submetering, appliance consumption gathered by NILM is noisy. This study hypothesized that when the load of appliances has been gathered, the initialization dictionary matrix can be defined as:  
\[D_{s}=\left(\begin{array}{cccccccccccc}a_{11}&\cdots&a_{1n}&b_{11}&\cdots&b_{ 1n}&c_{11}&\cdots&c_{1n}&d_{11}&\cdots&d_{1n}\\ \vdots&\ddots&\vdots&\vdots&\ddots&\vdots&\vdots&\ddots&\vdots&\vdots&\ddots& \vdots\\ a_{m1}&\cdots&a_{mn}&b_{m1}&\cdots&b_{mn}&c_{m1}&\cdots&c_{mn}&d_{m1}&\cdots&d_ {mn}\end{array}\right) \tag{5}\]  
where \(a_{11}\cdots a_{mn},b_{11}\cdots b_{mn},c_{11}\cdots c_{mn},\text{and}\ d_{11} \cdots d_{mn}\) denote the load of four electrical appliances. The values of m and n are determined by the dimension of the original signal and the size of the dictionary matrix, respectively.  
The load of the electrical devices is added to the deep non-negative K-SVD sparse coding to obtain an over-completed dictionary, where DUBPs must be consistent with actual customer behavior as much as possible, and the coefficients must be optimized to simultaneously ensure sparsity and reduce the reconstruction error.
## 3 Fecal VOC in Gastrointestinal DISEASES
### Other Gastrointestinal Diseases  
#### 3.5.1 Celiac Disease  
Celiac disease is a chronic autoimmune disorder mainly affecting the small intestine, resulting in villous atrophy and related malabsorption. The immune system is triggered by ingestion of gluten from wheat, rye and barley in genetically susceptible individuals [53]. Approximately 1.4% of people worldwide are affected by celiac disease [54]. Nowadays, gluten-free diet (GFD) is the only effective treatment currently available, however, compliance to this dietary therapy is complex. In search for a potential role of probiotics in addition to current therapy, two studies have been conducted using fecal VOC analyses [55; 56].  
Firstly, [PERSON] et al. aimed to study the effect of celiac disease pathology on the fecal microbiats of children, by comparing microbiota and VOC patterns between seven treated and seven untreated children with celiac disease and seven healthy children, by means of GC-MS [56]. There was a significantly higher concentration of one VOC in treated celiac disease and healthy controls vs. untreated celiac disease. It is described that these results are linked to absence of _Lactobacillus_ and _Bifidobacterium_ in untreated celiac disease, whereas these two bacterial strains play a major role in the metabolism of SCFAs. The same research group conducted a follow-up study aiming at studying differences in fecal VOC profiles between 19 treated children with celiac disease (two year treatment with CFD) and 15 children without celiac disease and other known food intolerances whom underwent upper endoscopy for symptoms related to functional dysppesia [55]. Higher concentrations of one type of VOC in healthy controls were found compared to treated celiac disease (Table 3). Furthermore, higher concentrations of three types of VOCS were found in treated celiac disease compared to healthy controls (Table 3). These results might indicate that, after a period of two years GFD treatment, microbiota and volatile metabolome composition of children with celiac disease is still not fully restored.  
In summary, studies mentioned above showed the potential of fecal VOCs analysis to assess disease activity and therapeutic efficacy in celiac disease. However, since sensitivity and specificity of anti-transglutaminase type 2 and anti-endomysium antibodies are very high, the need for novel biomarkers such as VOC analysis to assess therapeutic efficacy in clinical practice could be questioned.
# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
## 3 Pipeline Leakage Detection
### Leakage Detection with Five States  
In this experiment, five leakages at different section of pipeline are chosen as the five states in ergodic HMM model (Figure 9). Leakage signals are collected by sensor \(P_{1}\). As shown in Figure 7, leakage at Section 1 (from \(P_{1}\) to \(L_{1}\) in Figure 5) of the pipeline as State 1, leakage at Section 2 (from \(L_{1}\) to \(L_{2}\)) of the pipeline as State 2, leakage at Section 3 (from \(L_{2}\) to \(L_{3}\) ) of the pipeline as State 3, leakage at Section 4 (from \(L_{3}\) to \(L_{4}\)) of the pipeline as State 4 and leakage at Section 5 (from \(L_{4}\) to \(L_{5}\)) of the pipeline as State 5 are considered.  
Figure 8: Comparison between GMM-HMM and DNN-HMM with three states.  
Figure 7: Schematic diagram of three pipeline states.  
For each location, the leakage experiment is repeated for 20 times. Each experiment generated 100 data points which contain the damage indices (\(DI_{1}\) and \(DI_{2}\)). In total, 600 data points are extracted for the hybrid model, as shown in Figure 10. Among all these extracted damage indices, 80% of the data points are used for training, while the rest are used for testing the performance of hybrid model.  
The testing performance between detected state sequence and actual state sequence of DNN-HMM is calculated by micro \(F_{1}\) score, which approaches 0.96, while the micro \(F_{1}\) score of GMM-HMM is about 0.69, as shown in Figure 11. Compared with the performance of GMM-HMM and DNN-HMM model initialize with three states, the increase of states reduces the accuracy of GMM-HMM model. However, in both experiments, DNN-HMM performed better than GMM-HMM.  
Figure 10: Damage Indices.  
Figure 9: Schematic diagram of 5 states pipeline status.
## 4 Different Application Models of Wireless Power Transfer Technologies  
This section discusses the different models of WPT technologies. In this section, we present some of the application scenarios of WPT technology. WPT has emerged with strong capability to address the energy problems of a WSN with potentials that can permanently provide energy-source solutions to future generation WSNs. WPT-based WSN is a network in which sensors are wirelessly energized through dedicated energy sources such as inductive coupling, magnetic resonance coupling, and RF-WPT technologies [5]. Here, the power transferred from the source to the target is not subject to weather or seasonal constraints compared to the traditional EH techniques.
###### Abstract  
In modern implantable medical devices (IMDs), wireless power transmission (WPT) between inside and outside of the animal body is essential to power the IMD. Unlike conventional WPT, which transmits the wireless power only between fixed Tx and Rx coils, the wirelessly-powered cage system can wirelessly power the IMD implanted in a small animal subject while the animal freely moves inside the cage during the experiment. A few wirelessly-powered cage systems have been developed to either directly power the IMD or recharge batteries during the experiment. Since these systems adapted different power carrier frequencies, coil configurations, subject tracking techniques, and wireless powered area, it is important for designers to select suitable wirelessly-powered cage designs, considering the practical limitations in wirelessly powering the IMD, such as power transfer efficiency (PTE), power delivered to load (PDL), closed-loop power control (CLPC), scalability, spatial/angular misalignment, near-field data telemetry, and safety issues against various perturbations during the longitudinal animal experiment. In this article, we review the trend of state-of-the-art wirelessly-powered cage designs and practical considerations of relevant technologies for various IMD applications.  
wirelessly-powered cage; inductive power transmission; implantable medical device; animal experiment +
Footnote †: journal: Electronics Letters  
1
## 4 Experimental Results
### Comparison of the Measured Results  
The main measured figures of merit are compared against recent state-of-the-art CMOS Ka-band power amplifiers in Table 3. In order to present a complete view, we have included several single-ended and differential stacked power amplifier structures and even one differential common source structure using different modern CMOS technologies (45 nm CMOS SOI, 22 nm CMOS FDSOI and 28 nm bulk CMOS). It can be seen that the proposed PA showed very competitive results against the state-of-the-art. For example, the proposed PA showed the highest 1 dB compression point. In addition, the proposed PA had the highest gain and was the smallest in size compared with other three and four-stacked 45 nm SOI PAs.  
Compared with [22], we used a lower VDD (smaller available signal swing and thus lower output power) but had a slightly better gain and smaller size with the same PAE. Note also that the proposed four-stack in [22] was inherently nonlinear. A high PAE was achieved in [10] with a lower VDD, which means that the Psat was relatively low. In addition, the size was large partly due to the PAE enhancing stack resonators. A differential 3-stack with 22 nm CMOS SOI [13] provides very good output power with a comparable PAE but smaller gain, whereas a differential 2-stack using a 22 nm CMOS SOI [23] shows a very good efficiency, gain and 1 dB compression point, but due to the low VDD, the output power is not comparable to 3 or 4-stack PAs. A differential 2-stack [24,25] implemented using a 28 nm bulk CMOS also shows very high efficiency with a comparable gain and linearity but smaller output power and larger size. Two-stack power amplifiers based on pMOS transistors using 45 nm CMOS SOI [26] also show a very high PAE and  
Figure 12: (**a**) EVM, PAE and (**b**) ACLR as a function of average output channel power at three different bias currents at 24 GHz using 100 MHz 16-QAM 3 GPP/NR2 OFDM signal.  
small size. However, the gain and output power are smaller when compared against the proposed four-stack PA. In addition, a pMOS two-stack at a high power mode is inherently nonlinear. Finally, the differential common source (CS) PA [27] show very good gain, which is due to the fact that the driver stage is included in the structure. The size is very small but, due to the low VDD, the achieved output power and P1 dB are not very high compared to a four-stack PA.