esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods and Means of Eddy Current Testing of Soldered Lap Joints of Electrical Machines
## 3 Results
### On-Site Testing  
As a result of the on-site testing of soldered joints in the stator winding of a TVF-60-2 turbine generator, the performance of the developed ECP was demonstrated to assess the quality of the stator winding joints by measuring the actual value of the soldering integrity of the soldered lap joints. The statistics of the measurements are shown in Figure 13. The screening threshold is established in the technological instructions for the eddy current testing of soldered joints in the stator windings of turbine and hydro-generator stators. The value for the internal defects of the soldered joints in the form of discontinuities should not exceed 40%. Consequently, the quality of soldering is characterized by the value of soldering integrity and should be no less than 60%. Thus, based on the measurement results, the value for soldering internal defects in stator winding joints does not exceed the screening threshold; hence, the quality of soldering in stator winding busbar joints complies with the requirements of the technological instructions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **PP-37** & **PP-42** & **PP-58** \\ \hline Minimum TO thickness, mm & 12.5 & 14 & 20 \\ Maximum TO thickness, mm & 31 & 31.5 & 44.5 \\ Minimum TO length, mm & 30 & 30 & 30 \\ Maximum length of TO tested zone, mm & 96 & 105 & 105 \\ Minimum TO width, mm & 30 & 31 & 38 \\ Maximum width of TO tested zone, mm & 57 & 62 & 78 \\ Range for setting the distance between & 22.5-32 & 24–33 & 30–45.5 \\ the probe planks, mm & 120 & 120 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of ECP versions.  
The on-site testing results display the applicability of the designed ECP to evaluate the soldering integrity of soldered lap joints in current-carrying busbars for stator winding electrical machines. Compared to the existing approach for unsoldered lap joint detection based on thermal inspection, the developed method provides a more safe and precise technique, allowing for not only the detection of but also the evaluation of the soldering quality of soldered lap joints. The developed technical method provides the following:  
* Suppression of the influence of electrical conductivity, temperature, and technological variations in the geometry of the tested zone for both the TO and RBs;
* The required reliability in the measurement results without strict requirements regarding the deviation in the electromagnetic and geometrical parameters of the TO and RB from their nominal values;
* The detection of defects and the measurement of the degree of soldering integrity in the soldered lap joints with a relative error of measurement of no more than 5%.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results  
### Feature Importance Results  
The first stage was to understand all the input variables and their impact. Because MCD19A2 and daily and weekly MERRA-2 were not part of the initial testing, TROPOMI NO\({}_{2}\), monthly average wind speed, monthly average temperature, monthly averaged precipitation, monthly averaged cloud percentage, day of the year, day of the week, and day of the month were all assessed without it. Their importance is shown in Figure 7. The static features that did not change for one site, such as latitude, longitude, and site ID, reported no significance. The highest impactful features were TROPOMI NO\({}_{2}\), day of the year, and the monthly averaged cloud fraction, ordered from high to low importance. Figure 7 was generated without preprocessing, scaling, or reshaping applied to the features.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 2 Materials and Methods
### Apple Feature Extraction
#### 2.4.3 Apple Color Feature Extraction  
The color features of apples are key indicators in grading tasks. Based on the segmented apple images, this paper sequentially converts the RGB color space of the front and rear views into the HSV color space. Using the threshold range in the HSV color space that represents a color similar to apple red, the red regions are extracted. The HSV range for extracting apple color characteristics is 0-12 or 170-180 for H, 30-255 for S, and 50-200 for V. Subsequently, the red coloring ratio of the apple is calculated, with the specific calculation process shown in Equation (9).  
\[ratio=\frac{pixel_{red}}{pixel_{all}} \tag{9}\]  
In this context, \(pixel_{red}\) represents the number of red pixels in the apple, while \(pixel_{all}\) denotes the total number of pixels in the apple.
# Three-Level Hybrid Envelope Tracking Supply Modulator with High-Bandwidth Wide-Output-Swing
###### Abstract  
Envelope tracking is a dynamic supply modulation technique, which is mainly used to improve the efficiency of radio frequency power amplifier. For 5G NR mobile devices, this paper presents an envelope tracking supply modulator which is composed of a three-level switching converter and linear amplifier (LA) in parallel. The hysteresis control method is adopted in the supply modulator to improve the bandwidth of the switching converter and the loop response speed. At the same time, a rail-to-rail input-output class AB linear amplifier is designed to achieve a wide output swing and a super source follower is proposed to reduce the output impedance of LA. Designed with the 180 nm CMOS technology, the supply modulator operated at 5G NR 100 MHz envelope signal. The output power of the modulator reaches 1.5 W for a 6 \(\Omega\) load resistor and the output swing is 4.3 V at 5.0 V supply, and the maximum efficiency reaches 85%.  
envelope tracking; supply modulator; three-level switching regulator; 5G NR signal; hysteresis control +
Footnote †: journal:  
traditional control method of the 3L-ET modulator is pulse width modulation (PWM) control [23]. In this structure, the flying capacitor voltage (V\({}_{\text{CF}}\)) needs to be stabilized at a certain value, and the PWM control requires an additional calibration loop. In [24], a hysteresis control loop was proposed to solve the problem.  
Based on the above analysis, this paper presents a hysteresis-controlled 3L-ET modulator and employs a high-speed hysteresis comparator to calibrate the V\({}_{\text{CF}}\). At the same time, a wideband LA is designed. The structure of this paper is as follows. Section 2 presents the overall structure of the system and makes a brief analysis. Detailed circuit design of each circuit module in Sections 3 and 4 describes the simulation results of the overall circuit and the final summary is concluded in Section 5.
## 4 Experiments and Analyses
### Result and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Model** & **Modality** & **FUNSD (F1\%)** & **XFUND\({}_{\text{zh}}\) (F1\%)** \\ \hline \multirow{8}{*}{SER} & BERT [20] & Text & 60.26 & - \\  & LayoutLM [12] & Text + Layout & 79.27 & - \\  & XLM-RoBERTa [49] & Text\({}^{\star}\) & 66.70 & 87.74 \\  & InfoXLM [27] & Text\({}^{\star}\) & 68.52 & 88.68 \\  & BROS [21] & Text + Layout & 83.05 & - \\  & LiLT [50] & Text\({}^{\star}\) + Layout & 88.41 & 89.38 \\  & StrucText [25] & Text + Layout + Image & 83.09 & - \\  & LayoutLMyz [22] & Text + Layout + Image & 82.76 & 85.44 \\  & LayoutLM [23] & Text\({}^{\star}\) + Layout + Image & 89.50 & 89.24 \\  & XYLayoutLMM [28] & Text + Layout + Image & 83.35 & 91.76 \\  & VI-LayoutXLM [29] & Text\({}^{\star}\) + Layout & 87.79 & 90.46 \\  & **RVI-LayoutXLM (Ours)** & **Text\({}^{\star}\) + Layout** & **89.57** & **91.92** \\ \hline \multirow{8}{*}{RE} & BERT [20] & Text & 27.65 & - \\  & LayoutLM [12] & Text + Layout & 45.86 & - \\  & XLM-RoBERTa [49] & Text\({}^{\star}\) & 26.59 & 51.05 \\  & InfoXLM [27] & Text\({}^{\star}\) & 29.20 & 52.14 \\  & SERA [51] & Text + Layout & 65.96 & - \\  & BROS [21] & Text + Layout & 71.46 & - \\  & LiLT [50] & Text\({}^{\star}\) + Layout & 62.76 & 72.97 \\  & LayoutLMv2 [22] & Text + Layout + Image & 42.91 & 67.77 \\  & LayoutKLM [23] & Text\({}^{\star}\) + Layout + Image & 54.83 & 70.73 \\  & XYLayoutLM [28] & Text + Layout + Image & - & 74.45 \\  & VI-LayoutXLM [29] & Text\({}^{\star}\) + Layout & 74.87 & 83.92 \\  & **RVI-LayoutLM (Ours)** & **Text\({}^{\star}\) + Layout** & **76.35** & **85.01** \\ \hline \hline \end{tabular} The winner is in bold. Text\({}^{\star}\) indicates that the pre-trained model was obtained by training on the multilingual dataset.  
\end{table} 
Table 4: Comparison with existing published models on the FUNSD and XFUND\({}_{\text{zh}}\) datasets. The data reported in this table represent the mean values calculated from several experimental runs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Backbone** & **mAP@.5** & **mAP@.5-95** & **mAP@.75** \\ \hline Faster-RCNN [32] & Swim & 97.31\% & 70.82\% & 77.62\% \\ YOLOv3 [34] & Darknet-53 & 87.30\% & 60.87\% & 68.43\% \\ YOLOv4 [35] & CSPDarknet-53 & 87.64\% & 65.19\% & 70.52\% \\ YOLOv5-s [36] & CSPDarknet-53 & 92.35\% & 67.94\% & 73.28\% \\ YOLOX-s [37] & CSPDarknet & 96.21\% & 72.23\% & 78.94\% \\ PPYOLOE-s [38] & CSPResnet & 95.21\% & 63.07\% & 75.93\% \\
**RIFD-Net (Ours)** & **CSPDarknet + IFAM** & **98.26\%** & **74.00\%** & **81.10\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of evaluation results of different object detection models on CMERD. Figure 16: RE task visualization results for the three datasets. Figure 17: Detailed results of the associated entity detection stage on the CMERD dataset. Figure 18: Result of the integration of the CMERD table body coordinate.
## 4 Design Architecture
### Distance Comparison Unit  
Every DCU contains a generic amount of processing elements, working in a systolic array. The results calculated by the PE can be filtered by a threshold checker and are then passed on to the result collector unit.  
In the used streaming-based architecture, both signatures are only connected to the first PE of every DCU, shown as \(PE_{1}\) in Figure 2. However, static and dynamic signatures are treated differently  
Figure 1: Overview of the system architecture.  
during further processing. Static signatures are passed via shift register to the next PE until one signature is assigned to each PE and stored there locally. The dynamic signatures are also streamed to the first PE and sent from there to the next PE. Each time a dynamic signature is valid, the distance between it and the static signature of the PE is calculated. With this data flow concept, a new incoming dynamic signature can be processed by the design in each clock cycle. To avoid delays, a FIFO for the dynamic signatures is used to buffer the inputs.  
To accommodate lots of PE instances on the FPGA, it was necessary to constrain the maximum fanout of the reset signal; otherwise, the routing of the design was failing quickly due to timing violations. Furthermore, the input clock for the PE is gated by a BUFGCE (BUFG with clock enable) from the AXI clock, to further improve the fanout. The enable for the BUFGCE is generated by the result collector unit and disabled when no further results can be handled and the design needs to stop producing new results.  
Since the space for PE on the FPGA is limited it is likely that not all static signatures can be processed in one iteration. Therefore, the PEs have to be reinitialized when all calculations have finished and dynamic signatures have to be streamed again. This way the static signatures are partially processed, while the dynamic signatures are streamed multiple times. Despite the very limited local storage, the streaming-based design results in high-throughput, which directly depends on the number of parallel used PEs.  
Using this technique it is possible to calculate all distances between a large number of signatures on one FPGA. Furthermore, multiple FPGAs can be used to work in parallel on the sequences. In such a setup, every FPGA would process different static signatures while the dynamic signatures are streamed between the FPGAs. This minimizes how often the dynamic signatures have to be streamed from the DDR. The DCU-based design also enables the partitioning of the design, e.g., for applying one DCU to every available super logic region (SLR) on an FPGA.
## 3 The Proposed IAVMD Method
### Teager Energy Operator  
For a given continuous-time signal with modulated information \(x(t)=a(t)\cos(\phi(t))\), TEO is defined as:  
\[\psi[x(t)]=\big{[}\dot{x}(t)\big{]}^{2}-x(t)\ddot{x}(t) \tag{15}\]  
where \(\dot{x}(t)\) and \(\ddot{x}(t)\) mean the first and second order derivative functions of \(x(t)\), respectively. For a discrete signal \(x(n)\), TEO is modified into \(\psi[x(n)]=x^{2}(n)-x(n-1)x(n+1)\), where \(n\) means the length of \(x(n)\). The instantaneous amplitudes IA and IF of \(x(n)\) are capable of being acquired using the discrete energy separation method (see the DESA-2 function in MATLAB), as shown in Equations (16) and (17):  
\[a(n)=\frac{2\psi[x(n)]}{\sqrt{\psi[x(n+1)-x(n-1)]}} \tag{16}\]  
\[f(n)=\frac{1}{4\pi}\arccos\!\left(1-\frac{\psi[x(n+1)-x(n-1)]}{2\psi[x(n)]}\right) \tag{17}\]  
where \(a(n)\) represents the instantaneous amplitude, and \(f(n)\) represents the instantaneous frequency. One major advantage of TEO compared with HT is that the calculated IA and IF have fewer errors.
## 3 Model Formulation
### Operating Cost (\(C_{o}\))
#### 3.2.2 Feeder Bus Personnel Cost (\(C_{pF}\))  
The feeder bus personnel cost (\(C_{pF}\)), including the drivers and administrative costs, is dependent on the fleet size, hourly pay and insurance rate. Since the time for boarding and alighting (_i.e._, dwell time), as well as bus slack time have important roles in spending time for personnel, in this study, an effort has been made to represent the improved concept for the determination of these costs. Hence,in order to increase the accuracy of the cost function (objective function), adding slack time (\(S_{kj}\)) into the schedule of bus route \(k\) at station \(j\) and average rest time were considered for each bus at stations.  
Moreover, on mathematical formulation, the dwell times were added into the calculation of personnel costs with respect to the interrelationship among cost terms. The derivation of the \(C_{pF}\) is given in Appendix A. Therefore, \(C_{pF}\) for feeder bus route \(k\) can be formulated as follows:  
\[C_{pF}=\lambda_{p}\left[\left(\frac{2F_{k}}{V_{k}}\times L_{K}\right)+\left(Q _{k}\times t_{dF}\right)+\left(F_{k}\times S_{kj}\right)\right] \tag{11}\]  
The first and second terms in Equation (11) rely on the feeder running time and the dwell time in route \(k\), respectively. Accordingly, the third term denotes the personnel cost when drivers are in the rest time or queue.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 4 Performance Evaluation
### Evaluation of the Metering Application Reliability (Comparision between CCN and IP Protocols)  
In wireless multi-hop LMN environment, each meter can relay received metering packet. If the communication reliability is not stable between LMN group meters and DUC, multi-hop routing path can be utilized to improve its metering data reliability. Figure 8 shows the cumulative packet delivery ratio of the network. In this figure, the network density represents the number of devices in the specified network region. As seen in the figure, the performance of TOP-CCN with Flooding Control (FC) outperforms not only RPL but also CCN schemes, because the flooding control scheme reduce the CCN packet flooding in the wireless network. Furthermore, the selected PMPR AMI devices in the TOP-CCN can cache contents such as remote firmware update and peak price false alert from the SG server, which allows SG devices to receive these contents from intermediate devices instead of the server. This contributes to lesser hop distance that data has to traverse, which in turn reduces chance of collision and interference. The comparison of average hop distance between content location and device is shown in Figure 9. As aforementioned, the hop distance of the CCN schemes show outstanding performance compared to address based RPL protocol because SG devices can temporary cache various SG data and respond to content request from nearby devices in place of the server.  
When packets are lost due to collision, protocols such as IEEE 802.15.4 low rate MAC/PHY provides acknowledgement and retransmission algorithm to improve the transmission reliability between 1-hop neighbor devices. However, it is inefficient for the retransmission to frequently occur as  
Figure 8: Cumulative end to end packet delivery ratio.  
Figure 9: Average hop distance between content location and device.  
it may also induce excessive overhead. Figure 10 shows the average MAC retransmission count of the device per packet transmission. In this result, the number of average retransmission counts is slightly increased depending on network density because the device is influenced by adjacent LMN devices. TOP-CCN with FC shows the least number of MAC retransmission because the number of hops and control packet transmission are reduced using MPR and PMPR devices. In overall, we can conclude that the proposed TOP-CCN can greatly increase reliability of SG network as well as reduce potential delay in end-to-end communication. This allows better servicing of various 1:M SG applications that can utilize CCN-based technique, as well as better services for existing IP-based monitoring data as the bandwidth usage and collision can be drastically reduced through TOP-CCN.
## 3 State of the Art
### Energy Harvesting  
The topic of energy harvesting and its importance in WuR devices should not be dismissed. WuR facilitates low standby energy consumption while retaining communication capabilities, so this technology is the most compatible with energy harvesting.  
Energy-harvesting devices use local energy production to complement their battery charge or to power themselves completely. Many kinds of energy can be harvested [32]: one of the most used in WuR devices is directed RF energy [33], as only an additional step is required beyond using the energy from the WuS to power the passive receiver. Another  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Implementation** & **Date** & **WuR Range** & **Frequency Band** & **Receiver Consumption** \\ \hline \hline \multicolumn{1}{c}{MagoNode++ [26]} & 2016 & 21 m [29] & 433/868 MHz & 1.3 μW \\ \hline BEH [9] & 2019 & 3 m & 915 MHz & 1.69 mW \\ \hline \hline \multicolumn{1}{c}{WuLoRa [23]} & 2017 & 55 m & 868 MHz & 4.6 μW \\ \hline \multicolumn{1}{c}{ICARUS [24]} & 2021 & 1-2 m \({}^{(1)}\) & 868 MHz & 1.91 μW \({}^{(2)}\) \\ \hline \hline \multicolumn{1}{c}{BLE + AS3930 [28]} & 2020 & 3 m & 125 kHz & 27 μW \\ \hline \multicolumn{1}{c}{LDWuR [27]} & 2015 & 1 km & 2.4 GHz & 27 mW \\ \hline \multicolumn{1}{c}{Long-Range LoRa [25]} & 2019 & 8.74 km & 433 MHz & 6 mW \\ \hline \hline \end{tabular} \({}^{(1)}\) Estimated from –70 dBm [30]. \({}^{(2)}\) Calculated from the indicated 3.3 V supply and 580 nA consumption.  
\end{table}
Table 1: Nonexhaustive review of nodes proposed for WuR testing.  
energy source that easily enables zero-standby solutions is the optical energy collected from a laser [34]. Those two methods are efficient on the receiver side, but they need dedicated transmitters in the vicinity of the nodes and even a line of sight for laser-based solutions. Alternatively, some solutions use ambient energy through photo-voltaic cells or ambient RF energy, providing the advantage of not requiring any additional transceiver. Even indoor harvesting is possible, depending on the existing ceiling light, which has very low illuminance requirements [35].  
In some scenarios, specific energy sources may be employed. In wireless body area networks (WBANs), the body's thermal [36] and kinetic [37] energies can be harnessed. Acoustic energy can be harvested in urban or industrial settings [38], whereas for outdoor or agricultural environments, both soil [39] and plant [40] energy can serve as viable energy sources.  
Many implementations and protocols operate under the assumption of energy harvesting, using dynamic energy distribution in the network [41] that routes data through the largest number of devices that have the best energy capabilities.  
The topic of combining energy harvesting and WuR was previously addressed, mainly for the development of passive WuR [21], which uses RF energy. However, the combination of low-energy WuR communication and an efficient low-footprint energy harvester led to the development of devices with an extended lifetime and energy autonomy [23], some of them being battery-free [9].
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
###### Abstract  
Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (_DMY_) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season _DMY_ using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash-Sutcliffe model efficiency (_NSE_) values (average \(\pm\) standard deviation) for the calibration, validation and evaluation of 0.92 \(\pm\) 0.01, 0.55 \(\pm\) 0.22 and 0.86 \(\pm\) 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance.  
forage; dry matter yield; machine learning regression; Sentinel-2; high latitudes Article