esa-sceva/satcom-chunk-collection huggingface dataset:
# A Three-Port Power Electronic Transformer Based on Magnetic Integration
## 6 Economic Benefits Analysis
### Device Cost Comparison  
The submodule device cost comparison of proposed PET with MMC + distributed DABs PET was shown in Table 3 and Figure 13. It mainly contains the cost of IGBTs, IGBT drivers, capacitors, and other auxiliary components (including fiber communication, auxiliary power supply, sampling, voltage transducer, controller ICs and copper busbar). For the cost of IGBTs, all the MMC and DAB IGBTs are selected from Infineon Technology according to device current stress. Since the MMC current stress of the proposed PET (275A) is slightly larger than that of the MMC + distributed DABs PET (199A), it leads to higher MMC IGBT cost (an increase of $4140). However, the cost of DAB IGBTs in the proposed PET (only six half-bridge modules are needed) is much lower (a reduction of $32,310) than that of MMC + distributed DABs PET (720 half-bridge modules). Finally, the total IGBTs cost of proposed PET results in a saving of $28,170 compare to MMC + distributed DABs PET.  
Figure 13: Devices cost comparison of proposed PET with existing typical PETs.  
For the cost of IGBT drivers, it is directly proportional to the number of IGBTs, thus the total cost of IGBT drivers of MMC + distributed DABs PET ($78,300) is significantly higher than proposed PET ($16,182).  
For the cost of capacitors, all the SMs capacitors in each PET are the same (5.5 mF), thus the cost of MMC capacitors is equal ($154,800). Although the total capacity of DAB capacitors is the same (under the same voltage ripple of 2%), the cost of DAB capacitors of MMC + distributed DABs PET ($990) is higher than that of the proposed PET ($1932), since too many capacitors need to be installed. So, the total cost of capacitors in the proposed PET results in a saving of about $7968 compared to the MMC + distributed DABs PET. For the cost of auxiliary components, due to the considerable number of DABs (6N) in the MMC + distributed DABs PET, there are too many extra fiber communication, auxiliary power supply, A/D sampling, voltage transducer, controller ICs and copper busbar used for the DABs, which significantly increases the cost of PET. Thus, as shown in Table 3 and
# Characteristics and Processing of Hydrogen-Treated Copper Powders for EB-PBF Additive Manufacturing
###### Abstract  
The fabrication of high purity copper using additive manufacturing has proven difficult because of oxidation of the powder feedstock. Here, we present work on the hydrogen heat treatment of copper powders for electron beam powder bed fusion (EB-PBF), in order to enable the fabrication of high purity copper components for applications such as accelerator components and vacuum electronic devices. Copper powder with varying initial oxygen contents were hydrogen heat-treated and characterized for their chemistry, morphology, and microstructure. Higher initial oxygen content powders were found to not only reduce surface oxides, but also reduce oxides along the grain boundaries and form trapped H\({}_{2}\)O vapor inside the particles. The trapped H\({}_{2}\)O vapor was verified by thermogravimetric analysis (TGA) and residual gas analysis (RGA) while melting. The mechanism of the H\({}_{2}\)O vapor escaping the particles was determined by in-situ SEM heated stage experiments, where the particles were observed to crack along the grain boundaries. To determine the effect of the EB-PBF processing on the H\({}_{2}\)O vapor, the thermal simulation and the validation of single melt track width wafers were conducted along with melting single layer discs for chemistry analysis. A high speed video of the EB-PBF melting was performed in order to determine the effect of the trapped H\({}_{2}\)O vapor on the melt pool. Finally, solid samples were fabricated from hydrogen-treated copper powder, where the final oxygen content measured \(\sim\)50 wt. ppm, with a minimal residue hydrogen content, indicating the complete removal of trapped H\({}_{2}\)O vapor from the solid parts.  
electron beam melting; copper; hydrogen treatment; high purity copper +
Footnote †: journal: Applied Physics Letters
## 3 Results
### Aggregation Router Assignment  
Gathered QoE assessment data may be applied to estimate the degradation level of the geographical area.  
STBs are connected to the aggregation terminals through the ONTs. The aggregation router names have been replaced at the operator's request. The assignment of unique devices and generated events to aggregation routers shows that many devices reach the highest possible grade (Table 8). The heatmap of devices with the highest assessment shows that most of them are connected to the single aggregation router located in the center of Poznan, the fifth-largest city in Poland (Figure 13). The common feature of all aggregation routers is that the highest viewership is noted between 7 PM and 9 PM. The higher number of devices with grade 5, the better the TV system performance.  
However, to improve the quality of provided services, it is necessary to localize the source of problems with the devices that reports the Bad QoE level (Figure 14). Naturally, the highest degradation can be connected to the region with the highest population in the number of unique devices. Nonetheless, the interesting might be Aggregation-23, Aggregation-27, or Aggregation-33. In these locations, the unique devices with Bad grades were increased in reference to the unique devices with Excellent quality. The percentage share of negatively (Bad) assessed devices to positively (Excellent) was 1.76%, 1.56%, and 1.32%, respectively. Aggregation-35 has 1.06% negatively assessed devices. The ratio for the rest of the aggregation routers was less than 1%.  
The number of unique devices and events have a linear relation (Figure 15). The devices are working correctly. Hence the number of events is minimal. On the other hand, the small number of malfunctioning devices can generate as many events as all devices located within a particular area that works correctly. A total of 212 devices with degradation generated almost 50,000 events. In the comparison, a similar number of events (50,198) were generated by 15,895 devices that reported Excellent quality in a given time.  
Figure 12: Clustering results for a standard scenario #2. _X_-axis–hour and minute timestamp, _Y_-axis–cluster and features used during clustering.  
Devices with impairment are mainly located within Greater Poland voivodeship (Figure 16). Over 20 devices are working in Poznan or suburban rings. A device placement map can be used to send the fitters assigned to specific areas to deploy proactive monitoring solutions. The main goal ought to be to eradicate problematic devices and increase customer satisfaction as a result.  
Figure 14: Heatmap with the unique count of STBs with the assigned Bad (1) cluster. _X_-axis-aggregation router, _Y_-axis-time span (hours), _Z_-axis-count of unique devices connected to the aggregation router.  
Figure 15: The number of events assigned to grade 1 (orange) or grade 5 (blue).
## 2 Study Area and Data  
### Overview of the Study Area  
The Yellow River Delta is located in Dongying City, Shandong Province (37\({}^{\circ}\)40\({}^{\prime}\) N-38\({}^{\circ}\) 10\({}^{\prime}\) N, 118\({}^{\circ}\)41\({}^{\prime}\) E-119\({}^{\circ}\)16\({}^{\prime}\) E), and bordered by the Bohai Sea in the north and Jiaozhou Bay in the south, encompassing a total area of about 5400 km\({}^{2}\). The region has an average elevation of less than 15 m and falls under the classification of a temperate continental monsoon climate [18]. The region experiences well-defined seasons with an average annual temperature of 12.2 \({}^{\circ}\)C and annual precipitation ranging from 540 to 620 mm, primarily concentrated in July and August. The average annual evaporation is 2200 mm [19]. The soils of the region are dominated by tidal and saline soils, which were formed by the Yellow River sediment deposition [20].  
The study area (Figure 1) is situated in the eastern part of the modern Yellow River Delta. It primarily comprises coastal wetlands and contains mudflats, grassy swamps, pits and ponds, etc. The predominant land type in this area is characterized by temperate deciduous broadleaf forest, and the native vegetation is dominated by _Phragmites, Suaeda glauca, Tamarik_, and a small amount of _Typha orientalis Presl_. Additionally, there is an invasive vegetation species, _Spartina alterniflora_[21]. This paper focuses on the above five vegetation types (Table 1).
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 7 IoT and Blockchain Integration
### Application Areas of IoT and BCN Integration
#### 7.1.2 Industrial IoT Devices and Identity Management  
One of the challenges of IoT is the huge number of connected devices. Blockchain network has promising features such as decentralization and tamper-proofing of data, which could solve the issue of managing user and device identities. [PERSON] et al. [67] demonstrated that identity and access management could be better managed on a private blockchain for enterprise IoT devices. [PERSON] et al. [68] proposed distributed identity and access management for a group of industries sharing many IoT devices. They used an architecture that utilizes a blockchain network as a communication layer. [PERSON] [69] utilized blockchain technology for identity and authentication management for mobile users and telecommunication companies. Several business startups such as ShoCard ([[https://www.shocard.com/en.html](https://www.shocard.com/en.html)]([https://www.shocard.com/en.html](https://www.shocard.com/en.html)) (accessed on 3 August 2022)), UniquID ([[https://uniquid.com/](https://uniquid.com/)]([https://uniquid.com/](https://uniquid.com/)) (accessed on 3 August 2022)), Chronicled ([[https://chronicled.com](https://chronicled.com)]([https://chronicled.com](https://chronicled.com)) (accessed on 3 August 2022)), Riddle and Code ([[https://www.ridleandcode.com/](https://www.ridleandcode.com/)]([https://www.ridleandcode.com/](https://www.ridleandcode.com/)) (accessed on 3 August 2022)), among others are developing blockchain-based identity verification and management platforms based on blockchain for IoT devices and users.
# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
However, the energy consumption of drilling pumps is affected by various nonlinear factors, such as pressure fluctuations, flow rate variations, and mechanical wear, which are difficult to capture accurately using conventional methods. LSTMs, with their multi-layered network structure, can automatically learn and extract complex patterns and nonlinear relationships from data without human intervention. This capability enables LSTMs to better understand and model the complex interactions between energy consumption and various operational parameters in drilling pump energy prediction, thereby improving predictive accuracy. Moreover, drilling pump energy consumption data may not always be collected at regular time intervals, or different time periods may involve different critical variables. Traditional predictive models often assume uniformly distributed data and can only handle a limited number of input variables, which constrains their applicability in real-world scenarios. LSTM models are capable of flexibly handling irregular time intervals and multivariate inputs [18]. By dynamically adjusting the time steps, LSTM can adapt to data collected at varying time intervals and simultaneously process multidimensional input data, capturing complex interactions between these variables. This feature enhances LSTM's adaptability and predictive capability in dealing with the complexity of real-world data. To address these issues, this paper proposes an LSTM-based approach for predicting the energy consumption of drilling pumps, with an attention mechanism integrated to enhance the model's ability to capture key features, thereby improving prediction accuracy and interpretability. Byincorporating the attention mechanism [19] into the LSTM model, the proposed approach can automatically focus on the most critical time steps and features for energy consumption prediction. This not only enhances predictive performance but also provides deeper insights into the impact of different time points and conditions on drilling pump energy consumption. This study offers a novel technical solution to achieve accurate energy consumption prediction for drilling pumps and provides valuable references for energy optimization and efficiency improvement in production. This paper proposes an LSTM algorithm based on the attention mechanism to handle complex temporal relationships for predicting drilling pump energy consumption. The method first introduces the Random Sample Consensus (RANSAC) algorithm [20] to select valid data based on the correlations between pump pressure, flow rate, and pump power. Then, it combines LSTM and the attention mechanism for intelligent prediction of drilling pump energy consumption. Leveraging the model's capability to capture long-term sequence data features, the proposed approach demonstrates excellent performance in predicting pump power, thereby extending the application of artificial intelligence in the performance prediction of drilling equipment.
## 3 Results and Discussion
### Blackcurrant Firmness Reduction During Storage  
The ANOVA test (Table 1) showed that storage time is the only factor that significantly influences blackcurrant firmness. Over the storage period, the firmness of blackcurrants decreased from an average of 170 g/mm to 110 g/mm (Figure 3).  
UVC pretreatment has been reported to maintain fruit firmness in other postharvest storage systems. It has been mentioned that a higher dose (8 kg/m\({}^{2}\)) of UVC had a better effect on maintaining tomato firmness than the lower dose of UVC (3 kg/m\({}^{2}\)) [44]. One reason for maintaining firmness through UVC pretreatment might be related to inhibiting cell wall degradation enzyme activities, such as xylase and \(\beta\)-galactosidase. The multi-step UVC treatment (multiple times of UVC irradiations) on strawberries was the most effective method for maintaining berry firmness [45]. Therefore, it is worth trying the repetitive UVC treatment on blackcurrants in future investigations.  
Figure 3: Blackcurrant berry firmness changes during storage. Each treatment contained three replicates. Bars indicate the mean \(\pm\) standard deviations of the replicates. The letters indicate significant differences across storage.
## 6 Results
### Measurements Analysis  
All measurement errors were found to be very consistent throughout all the scenarios, with higher noise during dynamic scenarios compared to the static ones. As mentioned before, measurements were filtered to avoid biases in statistics due to outliers. Yet, a more thorough analysis of these outliers would be interesting to better understand the resilience of devices in different environments. Moreover, Android provides a certain amount of flags on code and phase tracking quality, as well as multipath flags, directly within the API. These flags have not been used in this analysis but should be checked for more advanced positioning computations, as assessed in previous studies [48; 49].  
Pseudoranges errors from certain devices (i.e., ON2 and X11) appeared to have a much smaller dispersion. A possible explanation could be pseudorange smoothing using Doppler or phase measurements, as this technique is often used to reduce pseudorange noise [9].  
Regarding Doppler measurements, a relatively small standard deviation was observed for GP7 and GPW, compared to the other devices. Both Doppler and phase measurements showed a tendency for a small positive, regardless of the devices. While the exact reason is not clear, it is believed to be due to the time-differencing operations performed to obtain the error measurements, as described in Section 5.3. Future research should review the effects on measurements, possibly using other techniques such as the Code-Minus-Carrier combination (see Section 5.3).  
C/N0 comparison showed a distinct drop in their values between L1 and L5 tracking in all devices. Yet, L5 signals are designed with a higher minimum received power [50], which shows that smart device hardware is not optimized yet for the reception of this additional frequency. Further comparison between frequencies should be performed to further assess the impact on the measurement quality.
# Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition
## 3 Method  
### Discrete Temporal Sequence and Empirical Models  
Vision behavior occurs with change in RGB information; for example, a hand wave causes a change in the associated screen area within a specified time. If binary data (0 meaning no change, 1 meaning a change) is used to represent the change in a certain area of the screen, as shown in the top half of Figure 1, passing vehicles through the region is represented as \"1\", but different driving situations cannot be distinguished. By subdividing the screen into four regions as illustrated in the bottom half of Figure 1, data from each of the four regions can be encoded separately with the same screen space, allowing for the differentiation of different vehicle situations in the same scene. The four regions can be coded in chronological order, and the four coding sequences of \"0000\", \"0011\", \"1100\", and \"0110\" can be obtained to represent changes of the four regions over time. This way, by finely dividing and covering the entire visual image area, visual information can be encoded as a DTS, and feature information extraction can be achieved. For similar intersections or other complex dynamic scenarios, the structure shown in Figure 1 may not be sufficient, so additional refinement is necessary.  
Following the principle of associative memory, our designed agents serially model the input discrete temporal sequence. In addition, the model is a discrete sequence. The model is used to match the new input DTS using the model to achieve the clustering capability of the discrete temporal sequence. IDs of selected models from multiple agents are merged at a higher-layer agent to form a new DTS. As shown in Figure 2, data such as \"a\", \"b\", \"c\", and \"d\", which are input from different frames of an agent, are gradually combined into the DTS \"abcd...\" The agent selects the most suitable model by matching it with the input DTS and outputs the model number as the clustering result using multiple models that are saved locally. When \"1\", \"2\", \"2\", and \"1\" are output at different frames, they are combined into a new DTS \"1221...\" at the next agent, which represents the activity of the agent. As for the model built and used by the agents using the memory-like method, we call it the empirical model (EM). EM is the result of clustering the input DTS by an agent. By matching the input DTS with the EM, the merging operation can execute the features of the DTS and EM that matched, which updates the learning model and enables online learning.  
Figure 1: Encoding the DTS.
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
###### Abstract  
We present a comprehensive review on the geometric-phase based metasurfaces for orbital angular momentum (OAM) generation and detection. These metasurfaces manipulate the electromagnetic (EM) wave by introducing abrupt phase change, which is strongly dependent on the polarization state of incident EM wave and can be interpreted by geometric phase. Hence, the conventional bulk devices that based on the accumulated phase change along the optical path can be avoided.  
metasurfaces; geometric phase; orbital angular momentum (OAM) 2018
## 2 Method Analysis  
The DMVDR beamforming method uses a local average consensus algorithm to update the beamforming value in each node by only communicating with neighbor nodes. Each node can obtain the same estimated beamforming result as centralized MVDR [10; 14; 15]. Based on this method, through improving both the local average consensus algorithm and noise estimation algorithm, the IDMVDR beamforming algorithm was proposed to enhance the bird audios which are collected through the WASN in nature. The process flow chart of the proposed algorithm is shown in Figure 1. The audio enhancement was performed in the time-frequency domain, so short-time discrete Fourier transform (STDFT) was first performed to the audio signal. After the estimations of noise PSD and acoustic transfer function (ATF), two initial beamforming values, \(\widetilde{Y}(0)\) and \(\widetilde{N}(0)\), were calculated. The consistency weight matrix was used to update the beamforming value by iteration. Lastly, the enhanced audio signal could be obtained after convergence. In order to express the contribution of this paper more easily, in this section, the distributed beamformer was interpreted in detail after the centralized beamformer was introduced.
## 2 Materials and Methods
### ATR-FTIR Measurements  
The infrared spectra of the potato samples were recorded on a PerkinElmer Spectrum Two(tm) (PerkinElmer, Waltham, MA, USA) FTIR spectrometer consisting of a deuterated triglycine sulfate (DTGS) detector and a PerkinElmer Universal Attenuated Total Reflectance (uATR) accessory equipped with a single bounce diamond crystal. Each spectrum was registered from 4000 cm\({}^{-1}\) to 400 cm\({}^{-1}\) with a 4 cm\({}^{-1}\) instrumental resolution and ten scans were averaged per spectral replicate. The background was collected with the crystal exposed to the air. Before each measurement, the ATR crystal was cleaned with methanol and air dried. ATR-FTIR spectra were collected on different sections of each potato tuber obtained by cutting the tubers in thick slices and by contacting the central part of the slice with the ATR crystal. A consistent force was applied using the pressure monitoring system integrated with the instrument to maximize the spectrum intensity. The spectra were collected from eight to nine tubers of each accession and three to five spectra were recorded from each tuber at different depths. The tubers analyzed using ATR-FTIR were randomly extracted from those with size \(>\)60 mm (descriptor N4 in Table A1, Appendix A) collected during morpho-agronomic analysis (ranging between about 30 and 180, depending on the accession) and stored in the dark in a dry and fresh room. Acquisition of the spectra of the various accessions was carried out in a random order and was completed in one week in December 2018 to avoid variations caused by differences in aging.