esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Enabling Technologies for Ku-Band Airborne SATCOM Phased-Arrays
## 5 Test Campaign Results  
The activity has been finalized with an exhaustive test campaign dedicated to evaluate first, the performance of the single radiating element in active configuration (Figure 21), second, considering it as a demonstrator of a linear sub-array of 5 active radiating elements embedded within the complete tile composed by 36 dummy radiators in triangular lattice (Figure 22).  
Figure 21: Active radiating element in free space configuration.  
Figure 20: DVM chip return loss performance: (**a**) port 1; (**b**) port 2.  
The radiation pattern of the active radiating element in free space configuration have been measured in several cases where the impinging RF wave in linear polarization was characterized by a defined alignment angle with respect to the radiating element axes. Setting the proper amplitude and phase coefficients, the active radiating element was able to align correctly the received polarization providing a very good discrimination between the two orthogonal polarizations. Figure 23a reports the received cross polar component over the complete operating band, when the attenuator and the phase shifter controls are correctly set for a copolar signal with the polarization rotated by 155 degrees; while Figure 23b shows the same behavior for the transmit channel over the entire frequency band.  
Figure 24a shows the measured radiation pattern in receive when a misalignment of 30 degrees has been corrected by the Tx/Rx Active Module.  
The same performance has been provided for the active radiating element operating in transmit mode: Figure 24b shows an example of radiation pattern for a transmitted polarization rotated of 15 degrees with respect to the nominal axes.  
Figure 22 shows the manufactured antenna demonstrator which implements the triangular array geometry in the real dimensions and representing the tile of the hemi-spherical phased array shown in Figure 2. A subset composed of five radiating elements is connected to the Tx/Rx active modules and the radiation pattern has been measured for different beam steering positions and polarization alignments.  
Figure 25a shows the measured radiation pattern when the excitation coefficients have been applied to steer the beam at 10 degrees without the recovery of a polarization misalignment (30 degrees); while in Figure 25b it reported the same measurement but with the recovery of the polarization angle.  
Figure 23: Example of cross polarization discrimination over the frequency band: (**a**) Rx; (**b**) Tx.  
Figure 22: Active antenna demonstrator.  
Figure 26 shows the measured radiation pattern in transmit mode when the excitation coefficients are applied to provide the beam steering of \(-10\) degrees and a polarization rotation of 30 degrees.
## 4 Results
### Contour Profile Inspection
#### 4.1.3 Form Deviation Analysis on Slope Profile  
The slope profile analysis, as depicted in Figure 29, outlines the trends in form deviation, showing the correlation with \(M_{\Delta}(k)\) values obtained from the \(l_{d,S}(x,y)^{SG}\). The materials involved in this analysis include aluminum, nylon, delrin, and steel.  
Beginning with aluminum, which has \(M_{\Delta,S}(k)=\ 0.3771\), the slope profile analysis reveals moderate form deviations. The results show that the slope profile of aluminum exhibits noticeable variations from the ideal form, emphasizing the importance of understanding these deviations in applications where slope precision is critical.  
Nylon, with \(M_{\Delta,S}(k)=\ 0.8373\), exhibits higher-form deviations in its slope profile than aluminum. The results indicate that nylon is out of tolerance for essential applications where slope accuracy is paramount. The nylon deviations may impact suitability for certain use cases requiring precise slope control.  
With \(M_{\Delta,S}(k)=\ 0.5769\), delrin presents a moderate range of form deviations in its slope profile. Understanding these deviations is crucial for applications that demand a  
Figure 28: Form deviation: Line probability plot.  
Figure 29: Form deviation: Slope probability plot.  
specific slope precision, as delrin may display variations that must be considered in the design and manufacturing processes.  
Steel, with \(M_{\Delta,S}(k)=0.5521\), demonstrates a relatively low level of form deviations in its slope profile. The results show that the slope profile of Steel is closer to the ideal form, making it potentially well-suited for applications requiring precise slope control.  
Similar to the previous analyses, the correlation between varying machining conditions and distinct levels of profile form deviations is highlighted in the slope profile analysis. The results demonstrate a correlation and underscore the importance of considering the influence of machining parameters, such as cutting speeds, feeds, and tool geometries, on the slope precision of these materials. Adjustments in machining conditions may be explored in future research to minimize slope deviations and enhance the overall accuracy of slope profiles.
## 3 Results and Discussion
### Impact of Solution pH  
The effect of the initial solution pH on the adsorption of DY50 on A21, A23 and A24 was investigated by shaking 0.5 g of the resin with 50 mL of a solution containing 500 mg/L of dye. The pH of the solution is a very important parameter controlling the sorption processes [77]. The ion exchange process depends on the acidity or alkalinity of the solution. A21, A23 and A24 are weakly basic anion exchangers for which the most effective working environment is the range of pH = 1-7. A slight decrease in sorption capacity with an increase in solution pH was noted in all cases, which can be explained by the protonation of functional groups at pH below 8-9 (Figure 11). This indicates that the mechanism of DY50 adsorption on the weakly basic anion exchangers is not only the result of interaction between resins functional groups and sulphonic groups of the dye.
## 4 Experimental Results and Discussion
### The Platform of Automatic Coal Petrographic Analysis  
The proposed maceral analysis method, MISC, based on image segmentation and classification makes it possible to identify the maceral composition automatically and intelligently. In order to facilitate the usage by petrologists, we developed a standalone software implemented in Matlab. We integrate the two-level K-means and various classification algorithms into the software for intelligent identification of maceral composition. Figure 8 is the screen snapshot of MISC software. Users can submit a microscopic image of coal with the degree of coalification R0 \(<\) 1.0%. The segmentation results are presented as four subfigures, corresponding to the binders and three maceral groups. The classification result for each object detected by image segmentation is shown with different colors for visualization. The MISC is freely available for users at the following website: [[https://github.com/[PERSON]/MISC-Master](https://github.com/[PERSON]/MISC-Master)]([https://github.com/[PERSON]/MISC-Master](https://github.com/[PERSON]/MISC-Master)). It can be used to support the decisions of petrologists in classifying maceral components. To the best of our knowledge, it is the first non-commercial software for identification of maceral components. It is an efficient and effective tool for the complete analysis of maceral composition.  
Figure 7: The identification accuracies with different number of trees in random forest.
## 4 Results and Discussion
### Comparison of Voice Detection Performance  
Figure 4 shows the average performances of singing voice detection for the _Main_, \(Main(AUX)\), \(JDC_{A}\), and \(JDC_{S}\) networks evaluated on the four test sets. \(JDC_{S}(O_{mv})\) achieved the best voice detection performance, leading to improved melody extraction performance. The F1 score of _Main_ was 91.0%, and that of \(JDC_{S}(O_{mv})\) was 93.3% (\(p\)-value \(<\) 0.05). F1 scores of other JDC networks were higher than _Main_, but there were no significant differences. For \(Main(AUX)\), \(Main(SVD)\), voice detection performance was significantly lower (the F1 scores were 87.5% and 88.9%, respectively). This seems to be due to the fact that the used training set had a higher percentage of voice segments than non-voice segments. If enough data can be used for model training, there is a possibility that the performance of SVD may be further improved.  
Figure 5 displays the performances of the proposed networks evaluated on the Jamendo dataset, which is dedicated to singing voice detection and unseen in training the models. As observed in the melody extraction results, the voice detection output of the main network was more conservative. This led to a low VR and VFA. On the other hand, the \(JDC\) networks that had the separate singing voice detector became more responsive, having higher VR and VFA. When comparing the two families of JDC networks, \(JDC_{S}\) was more conservative than \(JDC_{A}\) as the voice loss function contained the voice output from the main network. A similar result was found among the voice detection outputs. That is, JDC with \(o_{mv}\) had lower VR and VFA than JDC with \(o_{v}\) or \(o_{sv}\). While the JDC networks returned comparable results, the best performance in terms of accuracy was obtained by \(JDC_{S}(o_{sv})\). The average of VR of \(JDC_{S}(o_{sv})\) was 18.3% higher than that of _Main_, maintaining a low VFA of 22.6%.  
In Table 2, we compare the voice detection result with other state-of-the-art algorithms. [PERSON] et al. (2019) reproduced each algorithm using the Jamendo dataset as the training data under the same conditions, and we used the results for comparison. The performance of \(JDC_{S}(o_{sv})\) was lower; however, considering that the compared models were in fact trained with the same Jamendo dataset (by using different splits for training and testing), the result from our proposed model was highly encouraging, showing that it generalized to some extent.  
Figure 4: Performance of singing voice detection for different evaluation metrics. “ACC” indicates the overall accuracy of voice detection. “PR” and “F1” indicate precision and F1 score, respectively.
# The New CAP Theorem on Blockchain Consensus Systems
## 5 Discussion
### Security and Finality Implications
#### 5.4.3 Practical Security Implications of the New CAP Theorem Tradeoffs  
In Bitcoin, maximizing Consensus by requiring all nodes to validate transactions strengthens the resistance against Sybil and 51% attacks but significantly increases energy consumption. Ethereum 2.0 balances Autonomy and Consensus, reducing the risks of delayed transaction finality and improving the energy efficiency compared to systems like Bitcoin, while Hyperledger Fabric employs limited Consensus participation, which enhances Autonomy and reduces energy consumption but introduces potential vulnerabilities to Byzantine failures.  
The intrinsic tradeoffs between Autonomy (A), Consensus achievement (C), and Entropic Performance (P) significantly affect Blockchain system security, as presented in Table 2. Summarizing the above, we have the following:  
* High Autonomy/Low Consensus: Increases the risk of Sybil attacks and 51% attacks. Fewer nodes validate each event, making it easier for malicious attackers to dominate the system. For example, systems with minimal node consensus, such as space and lightweight IoT Blockchains, can easily become targets of Sybil and 51% attacks. A malicious attacker may deploy multiple compromised nodes without being noticed.
* High Consensus/Low Autonomy: Enhances the resilience against Sybil and Byzantine attacks. The requirement of broad validation, though, increases the system vulnerability to denial-of-service (DoS) attacks. As nodes become highly interdependent, the attacker can infuse congestion to the network more easily. For example, Bitcoin requires all nodes to validate all transactions, providing strong security against fraudulent consensus but also making the network more susceptible to congestion attacks.
* Balanced Consensus and Autonomy: Partially sacrificing absolute consensus and autonomy offers better scalability and resistance to DoS attacks while still maintaining the desired resilience levels against Sybil and Byzantine threats. As a practical example,Ethereum 2.0 uses a set number of validators for consensus. This approach improves resilience against DoS balancing the hazard of Sybil attacks as well. Still, it increases the vulnerability to long-range attacks through the potential historical key compromise in certain nodes. These tradeoffs highlight once more the practical necessity of designing scalable Blockchain systems that adjust \(W\) dynamically to mitigate security risks while maintaining the desired traits in terms of entropic Performance every time.
## 2 Materials and Methods
### Solar Radiation Measurements  
As is indicated in Table 2, the actinometric station is equipped with pyranometers for measuring global, diffuse, and reflected solar radiation, a pyheliometer for measuring direct normal radiation as well as the sunshine duration and photosynthetic photon flux density for measuring global and diffuse PAR radiation components. All instruments are obtained from Kipp & Zonen Company except the Quantum sensors of PAR which were obtained from Light Company. All instruments are factory calibrated. The shortwave components (Global (\(G\)), Diffuse (\(D\)), Direct Normal (\(B_{n}\)) and Diffuse PAR (\(DPPFD\))) are installed on a 2 AP solar tracker system. Diffuse radiation (\(D\)) and \(DPPFD\) are protected from the direct sunlight with a shading ball arm. PAR is expressed either in terms of Photosynthetic Photon Flux density (\(PPFD\)) in units of \(\upmu\)mol /s/m\({}^{2}\), since ph toosynthesis is a quantum process, or in terms of energy (PAR, Photosynthetically Active Radiation) in units of W/m\({}^{2}\), which is more suitable for energy balance studies. \(PPFD\) is recorded in the database, and is converted into energy units according to the [PERSON] (1972) [PERSON] (1972) [PERSON] (1972) conversion factor of 4.57 \(\upmu\)mol/J.  
All the instruments are checked on a daily basis by the meteorological staff of the Radiosonde station. Routine checks consist of cleaning the domes, inspection of cable connections and instrument leveling, checking the proper functioning of the solar tracker system including the shading of the pyranometer which measures the diffuse radiation, as well as the proper orientation of the pyheliometer.  
The period of measurements is June 2020 to May 2021. A Campbell Scientific Instrument data-logger (model CR10) located at the site, monitors and stores the data at 10 min intervals. The sensors are scanned every 10 s and average, maximum and minimum values at the 10 min intervals are calculated and stored. The stored data are downloaded to a desktop computer periodically. The data refer to GMT time (LST = GMT + 2 h) in winter and LST = GMT + 3 h in summer. A database was developed in order to manage the large volume of data. The database system is also used for checking the real-time measurements, becoming therefore, a comprehensive quality control system through graphics and tables or transferring the raw data to a special statistical package or spreadsheet program. The data management includes daily or monthly reports which can be used effectively for quality control of the data and detects any missing data. Details about the method of estimation of various solar variables and the quality control procedure which was followed are given in the next sections.
# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 3 Applications of Self-Powered Triboelectric Nanogenerator in Gait Monitoring
### Basic Gait Information Monitoring
Even if there is a partition wall or an obstacle between the human body and the TENG, the sensor can still track the human body's movement trajectory based on the signal differences generated by different movement paths (Figure 3(eiv)). In summary, the self-powered gait monitoring sensors based on TENGs have been widely applied in the field of sports monitoring. The durability of the device, stability of signal output, and the detection sensitivity are crucial for long-term and high-precision gait monitoring.
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 5 Objective Function
### Constraints  
At any given interval, the total production capacity of the hybrid production system should be able to meet the total demand for the load, taking into account the terms and conditions of reliability, which is calculated by the equation  
\[P_{Gen\_inv}(t)+P_{FC\_inv}(t)=(P_{Load}(t)+P_{sale}(t))/\eta_{inv} \tag{25}\]  
Accordingly, \(P_{Load}(t)\), \(P_{sale}(t)\), \(P_{Gen\_inv}(t)\), \(P_{FC\_inv}(t)\) represent the total load demand (disconnect-able and non-interruptible), high power upstream network, the power transmitted from DGs to battery and the transient power from fuel cell stacks to the DC/AC converter, respectively. Since 10% of the total demand per hour is considered as a removable load and the remaining 90% is assumed as an irremovable load, Therefore, when (_Loss\_power_ (\(t\))/\(P_{Load}\) (\(t\)) \(<\) 0.1), load interruptions are not included in the reliability calculations of the system. Consequently, when (_Loss\_power_ (\(t\))/\(P_{Load}\) (\(t\)) \(>\) 0.1), the \(ELF\) index in the reliability calculations of the system is computed. Thus, until the Equation (33) is correct, the load procurement can be continued.  
\[E[ELF]\leq ELF_{max} \tag{26}\]  
and the capacity to be sold to the upstream network should not exceed a certain limit, which is determined by prior agreements  
\[P_{sale}\leq P_{sale}^{\max} \tag{27}\]  
The energy stored in the hydrogen tank and the battery should be within the following limits:  
\[E_{battmin}(t)\leq E_{batt}(t)\leq E_{batt\_Max}(t) \tag{28}\]  
\[0\leq E_{tank}(t)\leq E_{tank\_Max}(t) \tag{29}\]  
\[E_{batt}(0)\leq E_{batt}(8760) \tag{30}\]  
\[E_{tank}(0)\leq E_{tank}(8760) \tag{31}\]  
The last two constraints imply that the energy stored in the tank and the battery at the end of the year should not be less than the energy stored at the beginning of the year. This ensures that the reliability calculations are performed for the worst possible situation.  
The proposed formulation is optimized using particle swarm optimization (PSO) algorithm. The capacity of DGs is constant while the number of DGs is variable. In the other words our variables should be optimized are the number of DGs using PSO. By optimizing the variable, the objective function is concluded which is the total cost of VPP. The interested readers are referred to [31] for more studying about PSO.
## 2 Materials and Methods  
### Experimental Setup  
Micro-EDM drilling experiments are carried out on a SARIX(r) SX-100-HPM machine tool (Sarix SA, Sant'Antonino, Switzerland) in order to obtain the required datasets for testing the data-driven approaches for tool wear prediction. The machine tool is equipped with a PULSAR power supply from SARIX(r) and a process monitoring system, which is embedded in the control unit of the machine tool. This system allows for monitoring and recording of several quantities during the drilling process, such as the amount of discharge pulses and the average gap voltage. Regarding the architecture and operation of this process monitoring system, further details can be found in the published previous work [17].  
Small plates of Ti6 Al4V alloy of 2 mm thickness, 6 mm width and 70 mm length are used as workpiece. Ti6 Al4V is selected as workpiece material since it is widely used, especially in the aerospace and biomedical sectors [5; 18; 19]. Brass tubes provided by SARIX(r) (outer diameter: 350 \(\upmu\)m, inner diameter: 130 \(\upmu\)m) are used as tool electrode, which means that the aspect ratio of the micro holes is approximately 6. Hydrocarbon oil (HEDMA(r) 111) is applied as dielectric liquid. In particular, side flushing and internal flushing through the tubular tool are applied simultaneously.  
Brass tubular tools of small diameter are rather flexible. For this reason, a tool guiding system is used to reduce the run out of the tool electrode when approaching the workpiece. This guiding system consists of a ceramic tool guide and a guide holder, as shown in the magnified view in Figure 1. During the experiments, the guide holder is positioned such that the ceramic guide is above the top surface of the workpiece at a distance of approximately 2 mm.  
In order to drill holes of constant depth, the micro-EDM drilling process is continued until the breakthrough of the tool electrode through the workpiece is observed. The Ti6 Al4V plates are clamped as cantilevers to facilitate observation of this breakthrough. A Dino-Lite(r) Edge AM4115 ZT digital microscope (AnMo Electronics Corporation, New Taipei, Taiwan) is used to monitor the drilling process, which is interrupted as soon as sparks are visually observed at the bottom surface of the workpiece. This allows to interrupt the drilling process before the tool has fully broken through, avoiding the instabilities in the discharging process occurring during the breakthrough stage [20; 21], as these are not the subject of this research.  
Figure 1: Micro-EDM drilling setup.
## 3 Results and Discussion
### Change in Amount of Volatile Compounds Associated with Desirable Arona Descriptors
#### 3.4.2 Esters  
Esters, synthesized during the fermentation process, play a pivotal role in shaping the aromatic profile of chocolates, imparting them with characteristic fruity and floral aromas. These compounds are recognized as significant markers of aroma quality [43]. Throughout fermentation, the amounts of esters generally increase, indicative of aroma development, before experiencing a subsequent decrease, a trend corroborated by various studies [10; 12; 20]. Specifically, the esters 1-methoxy-propyl acetate and 2-phenethyl acetate, pertinent to chocolate batches from cacao production clusters C1, C3, C4, C5, and C7, followed this pattern, as shown in Figure S5. Notably, these esters reached the peak amount across most agroclimatic regions after 96 h of fermentation, only to diminish as fermentation continued, as shown in Figure S5.  
Furthermore, the fruity sensory attributes identified in chocolate samples from 96 and 120 h of fermentation have been directly linked to the presence of these esters during those specific fermentation intervals [10; 12]. It has been observed that extending fermentation beyond 96 h leads to a decline in ester production, adversely affecting the aroma quality of chocolate and beans, particularly diminishing the intensity of fruity notes.