esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Review of Microgrid Control and Design
### Primary Control Level
#### 4.3.1 Grid following Control  
DG voltage and frequency are controlled by the main grid while operating in a grid-connected mode. In this case, the generation systems typically operate in current control mode to extract the maximum possible energy, for example, maximum power point tracking (MPPT) for solar and wind systems [72]. Furthermore, if the active and reactive power references are established by superior control levels, this kind of control may function at a non-optimal point beyond the maximum power band [82]. In this context, the work of [PERSON] et al. [83] reviews the grid following control schemes and covers grid synchronization techniques, including phase-locked loop, zero crossing, and grid voltage filtering. These techniques are made up of two nested loops: an external voltage loop and a fast-dynamic internal current loop [84]. Following this work, a categorization of primary control methods was recently discussed in the work of [PERSON], [PERSON] et al. [85], namely, the dq reference frame, the alpha beta reference frame, and the abc reference frame. Similarly, some of the most relevant control techniques for the integration of MGs into the electrical grid are detailed in the works of [PERSON] et al. [86] and [PERSON] et al. [87].
# Radiation-Induced Structural Changes of _Miscanthus_ Biomass
## 3 Results and Discussion
### Influence of Irradiation Treatment on Structural Properties of Miscanthus Biomass
#### 3.3.3 XRD Analysis  
XRD measurement was conducted to assess the crystalline phase of _Miscanthus_ biomass under different absorbed doses. As observed in Figure 3, the \(CrI\) peaks at \(\sim\)16\({}^{\circ}\) and 22\({}^{\circ}\) lattices remained stable without new lattice generation after irradiation treatment. As described in Table 3, the \(CrI\) values showed an absorbed dose-dependent decrease, indicating the crystalline area of cellulose was destroyed to some extent. The \(CrI\) values of irradiated _Miscanthus_ declined slightly from 37.86% to 28.43% under the absorbed doses from 0 to 1200 kGy (Table 3). The effect of irradiation treatment on cellulose crystalline phase is dependent on absorbed dosage and the biomass species [16,26]. [PERSON] et al. [26] demonstrated that the crystalline phase cleavage by irradiation treatment is contributed by the enzymatic hydrolysis of biomass.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
###### Abstract  
Quasi-static strain influence lines (ILs) based on the Brillouin optical time domain analysis (BOTDA) technique have been proposed to effectively locate damage in beam bridges. Using measurement points with a high spatial resolution, the BOTDA technique supplies enough strain ILs to help detect damage in bridges. Unlike quasi-static strain ILs based on the BOTDA technique, quasi-static displacement ILs are relatively easy to implement in actual bridges; furthermore, only a few quasi-static displacement ILs are necessary for actual bridges. On this basis, an improved method is proposed to determine the existence of damage in beam bridges by using only a few quasi-static displacement ILs. First, the Hankel matrix of the damage feature, established based on the number of strain ILs, is reconstructed to generate the damage feature using only a few quasi-static displacement ILs. Second, the method used to obtain the metric for evaluating the damage feature is improved, thereby greatly increasing the efficiency of damage detection using quasi-static ILs. Finally, the effectiveness of the proposed method is demonstrated through both numerical analysis and experimentally measured data obtained during a quasi-static load test of a model bridge.  
keywords: beam bridges; damage detection; Quasi-static displacement influence line; null space; novel detection +
Footnote †: journal: Applied Sciences
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
###### Abstract  
The transmission of satellite payload data is critical for services provided by aerospace ground networks. To ensure the correctness of data transmission, the TCP data transmission protocol has been used typically. However, the standard TCP congestion control algorithm is incompatible with networks with a long time delay and a large bandwidth, resulting in low throughput and resource waste. This article compares recent studies on TCP-based acceleration algorithms and proposes an acceleration algorithm based on the learning of historical characteristics, such as end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment's deep data inspection, and random packet loss caused by various factors. The proposed algorithm is evaluated and compared with the TCP congestion control algorithms under both laboratory and ground network conditions. Experimental results indicate that the proposed acceleration algorithm is efficient and can significantly increase throughput. Therefore, it has a promising application prospect in high-speed data transmission in aerospace-ground service networks.  
network delay; packet loss rate; aerospace-ground service network; BoostTCP acceleration algorithm; bottleneck bandwidth and round-trip propagation time congestion control algorithm; cubic congestion control algorithm +
Footnote †: journal: Sensors  
Sensors2022229187  
22 September 2022  
23 November 2022  
26 November 2022
## 5 Energy Harvesting Evaluation  
In order to evaluate the energy consumption of the monitoring system it is essential to determine the consumption of each separate system component. Table 5 shows the consumption measured in various operating modes of an Arduino node using the class library JeeLib for the \"deep sleep\" mode and an Xbee 868 Mhz radio transmitter.  
Some conclusions can be drawn from the results. On the one hand we see that the major source of consumption is the radio transmitter, especially when it is transmitting data. We can obtain a  
Figure 5: Short distance test of the ultrasonic distance sensors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Operating Mode**} & **Consumption (mA)** \\ \hline Transmitting maximum power & Awake mode Radio & (150–750 mA) \\ (400 mW) & transmitter on Sensor off & \\ Transmitting (300 mW) & Awake mode Radio & \\  & transmitter on Sensor off & \\ Receiving & Awake mode Radio & \\  & transmitter on Sensor off & \\ Sensing & Awake mode Radio & \\  & transmitter off Sensor on & \\  & “Deep sleep” mode Radio & \\ Sleeping & transmitter off Sensor off & 39 mA \\ \hline \hline \end{tabular}
\end{table}
Table 5: Energy consumption in different operating modes.  
significant energy saving if the transmission power is not too high, but this reduces the transmission range. On the other hand, the consumption when the radio is turned on but not transmitting (only receiving) is not negligible and therefore it is essential to turn the radio off when it is not in use.  
Regarding the data gathering process, we have observed that the ADXL345 sensor has an almost negligible consumption (in the order of uA). In the \"sensing\" mode most of the consumption comes from the Arduino board itself, not from the sensor.  
Finally, and contrary to what was expected, in the \"deep sleep\" mode the energy consumption of Arduino is relatively high, although the microprocessor is off. This is due to the voltage regulator incorporated inside the Arduino board. This regulator is responsible for transforming the voltage input (7-12 V) to the voltage actually needed by the board (5 V). This voltage regulator runs constantly even when the processor is off and produces high power consumption. Bearing in mind that the board will be in the \"deep sleep\" mode most of the time, it is essential to optimize the energy consumption in this mode. A possible solution to this problem would be to cancel the voltage regulator in the Arduino board and to manage this issue through an external device or battery more efficiently.  
Anyway, due to the high energy consumption of the board, some mechanisms are needed to increase the life time of the battery that supplies energy to the board. For this reason, different energy harvesting devices were selected to determine the best suited to our requirements. The following subsections discuss them in the context of railway infrastructures.
# The Use of Radio and Telemedicine by TMAS Centers in
## 3 Results
### Study Characteristics
#### 3.4.2 Telemedicine Technologies
As these studies do not align with the research objectives, they were therefore excluded from the final survey. Telemedicine is becoming an increasingly valuable tool for medical assistance at sea as technology advances. Adopted works in this review have highlighted the implementation of ship-to-shore video conferencing, which has revolutionized the provision of medical assistance at sea [23; 24]. Through this technology, medical officers onboard can communicate with onshore doctors in real time, allowing a more accurate diagnosis and treatment. Medical personnel on board can consult with experienced professionals onshore through video streaming and reliable communication channels. In this way, ships without qualified health professionals can still obtain high-level medical care and transfer at-risk patients to ashore medical facilities. Ship-to-shore video conferencing has greatly improved the  
Figure 3: On-board telemedicine case containing medical devices courtesy of TelePharmaTec, the spin-off of Camerino University, Italy. lives of mariners, ensuring timely and effective medical treatment and reducing the risks associated with remote treatment.
## 3 Results and Discussion
### Length of Nails
#### 3.1.2 Footing Settlement  
The foundation settlements were measured at three points on the footing and then the average value was presented. The footing settlements were measured by means of three LVDTs. The footing settlements were measured at each surcharge load increment. Figure 7 presents the footing settlement in terms of the fraction of the slope height (S/H), i.e., vertical settlement/slope height, for different nail lengths at different footing pressures. It is clear from the figure that the footing settlement decreases from 12% to 51% with the  
Figure 4: Experimental models with different nail parameters. H—Excavation height; B—Footing width; Sv—Nail vertical spacing; Sh—Nail horizontal spacing; Ln—Nail length; q—Surcharge; 5—Inclination of the nail; X—Distance of footing from crest; 0—Slope angle; RD—Relative density of the sand.  
increase in the nail length from half of the slope height to a nail length of more than the slope height, with respect to the footing settlement on the slope without nailing. The decrease in footing settlement is not large under a lower footing pressure range, but a large decrease in the footing settlement is observed with a higher footing pressure. This may be due to the fact that the lateral displacement of the facing slope decreases with the increase in nail length, and this will also result in a lower footing settlement. It can be inferred that the soil nailing system can sustain more surcharge load without failure.  
Figure 5: Lateral movement of the slope at different nail lengths.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 2 Problem Formulation
### Optimal Allocation of ESSs  
The optimal placements and rated power of ESSs are formulated as a constrained nonlinear integer optimization problem where both the locations and sizes of the storage devices are discrete. The objective function encompasses the expected system costs, the emissions, and the voltage fluctuation under the consideration of multiple equality and inequality constraints.
## 4 Discussion  
Although the suitability of GW-S co-composting has been already demonstrated, in previous works, restrictions had been observed for the agricultural application of co-composts, especially those with a high S amount [14; 17; 18] ([PERSON] et al., 2016; [PERSON] et al., 2020; [PERSON] et al., 2023). The biological assessment (microbial biomass and abundance, respiration rate, eco-enzyme stoichiometry, plant cultivation) allowed for the extensive investigation of the dynamic of S-GW composting and the stability of final compost to better valorize their application for agricultural purposes. The initial chemical properties and the monitoring of co-composting confirmed the suitability of the S and GW to be co-composed, as previously seen in [PERSON] et al. [17] (2023). In fact, the pH was in an optimal range (5.5-8) for the microbial development and activities [1] ([PERSON] et al., 2018). The increase in microbial biomass and respiration rate during co-composting suggests the optimal conditions for biological activities and the absence of possible osmotic stress, despite the initial high EC in all piles [45; 46; 47] ([PERSON] and [PERSON], 2019; [PERSON] et al., 2012; [PERSON] 2023). The relative amount of TOC and TN was an important indicator of nutrient contents and organic matter degradation fate [48] ([PERSON] et al., 2017). The three piles showed a lower initial TOC/TN than the recommended values (25-30), suggesting a fast organic matter degradation process and the possible ammonia volatilization [1; 48; 49] ([PERSON] et al., 2014; [PERSON] et al., 2017; [PERSON] 2018). However, the feasibility of composting at lower TOC/TN ratios had been previously demonstrated ([PERSON] et al., 2010), including for S-GW co-composting [17] ([PERSON] et al., 2023). During the co-composting, the TOC reduction is expected due to the conversion of organic carbon \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Dry Weight (kg)**} \\ \hline
**Substrate** & **Height (cm)** & **Aboveground** & **Belowground** & **Total** & **Shoot/ Root** & **DW/ FW** & **Leaf Area (m\({}^{2}\))** \\ \hline Control & \(45.3\pm 5.77\) ns & \(0.25\pm 0.01\) ns & \(0.05\pm 0.02\) ns & \(0.30\pm 0.03\) ns & \(5.13\pm 1.58\) ns & \(0.42\pm 0.03\) ns & \(0.63\pm 0.08\) ns \\ A & \(48.7\pm 5.77\) ns & \(0.23\pm 0.05\) ns & \(0.07\pm 0.01\) ns & \(0.30\pm 0.07\) ns & \(3.25\pm 0.35\) ns & \(0.43\pm 0.01\) ns & \(0.61\pm 0.17\) ns \\ B & \(46.7\pm 8.15\) ns & \(0.22\pm 0.05\) ns & \(0.06\pm 0.01\) ns & \(0.28\pm 0.05\) ns & \(3.94\pm 1.03\) ns & \(0.43\pm 0.03\) ns & \(0.56\pm 0.11\) ns \\ C & \(44.7\pm 2.08\) ns & \(0.20\pm 0.04\) ns & \(0.05\pm 0.01\) ns & \(0.25\pm 0.05\) ns & \(3.58\pm 0.13\) ns & \(0.44\pm 0.02\) ns & \(0.54\pm 0.05\) ns \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average (\(\pm\)standard deviation) growth measures in _Viburuum titus_ plants (n = 4), growing on 3S:1 GW (A), 1S:1 GW (B), and 1S:3 GW (C)-based growing media. Different letters indicate statistically different values amongst substrates, according to the one-way ANOVA followed by HSD Tukey’s test (\(p\leq 0.05\)). ns = not significant.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Characteristics of LIS Systems  
A LIS is a promising near-field wireless communications, remote sensing, and positioning technology. A LIS is composed of a variety of tiny panels, each of which generates a number of baseband outputs and may be turned on or off. The proposed LIS system is efficient for urban environments, reducing computational complexity and power consumption by selectively activating antennas. LIS systems differ from traditional wireless communication setups in several fundamental ways [16; 17]:  
* Large surface for signal reflection and transmission: unlike conventional MIMO systems with separate antennas, a LIS is a Large Intelligent Surface with many small antennas that control radio waves.
* More flexibility in selecting the signal path: a LIS improves signal quality and reduces interference, leading to improving spectral efficiency and coverage.
* Reconfigurable electromagnetic properties: unlike traditional base stations, a LIS can change wave properties for better transmission and lower energy consumption.
* Reduced hardware complexity: instead of complex and powerful equipment, a LIS uses simple and low-energy components for sending and receiving signals [14].
* Compatible with 6G networks: a LIS can match with 6G systems easily, providing ultra-reliable, low-latency communication (URLLC) and massive machine-type communication (m-MTC) through intelligent signal manipulation [18].
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
###### Abstract  
Parameters often take key roles in determining the accuracy of algorithms, logics, and models for practical applications. Previously, we have proposed a general-purpose _parameter optimization algorithm_, and studied its applications in various practical problems. This algorithm optimizes the parameter values by repeating small changes of them based on a local search method with hill-climbing capabilities. In this paper, we present three diverse applications of this algorithm to show the versatility and effectiveness. The first application is the _fingerprint-based indoor localization system using IEEE802.15.4 devices_ called _FILS15.4_ that can detect the location of a user in an indoor environment. It is shown that the number of fingerprints for each detection point, the fingerprint values, and the detection interval are optimized together, and the average detection accuracy exceeds 99%. The second application is the _human face contour approximation model_ that is described by a combination of half circles, line segments, and a quadratic curve. It is shown that the simple functions can well approximate the face contour of various persons by optimizing the center coordinates, radii, and coefficients. The third application is the _computational fluid dynamic (CFD) simulation_ to estimate temperature changes in a room. It is shown that the thermal conductivity is optimized to make the average temperature difference between the estimated and measured 0.22 \({}^{\circ}\)C.  
parameter optimization; application; indoor localization; face contour model; computational fluid dynamics
## 3 Dynamic Analysis and Design for General VSM with Energy Recovery Control
### Parameter Design Considering Stability Requirement  
Overall system stability requirement should also be met when designing the VSM parameters such as the virtual inertia and damping terms. Eigenvalue analysis is adopted to determine the critical parameters, as exemplified in Figure 11 where VSM based on the hybridization of SAESS and SAESS is considered. The effect of virtual governor gain in SAESS on root locus is examined, with and without FAESS adopting virtual inertia and damping control in the system. As observed, the VSM control from FAESS will enhance system stability. More comprehensive stability analysis can be found in the literature and the authors' previous work [34]. Notice a small leading term (time constant \(T_{h}\)) may be added to alleviate the synchronous and sub-synchronous oscillation modes.  
For multiple VSMs in the power system, parameter coherency, namely matched products of inertia and the connecting inductance, is expected to reduce the inter-oscillation among multiple VSMs or conventional synchronous generators.
## 3 Results and Discussion  
After analyzing the dataset, the ML models were trained on the data to generate prediction models and quality metrics were used to assess the different models. The other objective of this work is to test the generalization of the ML to be used regardless of flow conditions. For that purpose, the datasets were split again into three datasets for the different turbulence classes. The approach introduced here is to train the model on a single dataset and then use that model to predict the outputs in the other two datasets. Before testing this generalization, 10 different ML models were trained and tested for each turbulence class dataset to determine the highest accuracy model. The highest accuracy model was then chosen to proceed with the generalization test. A summary of the models' quality metrics, namely \(R^{2}\), _RMSE_, and training time (TT) in seconds, is shown in Table 2. The four outputs that are used for predictions are the blade tip deflections in the flapwise and edgewise directions denoted by _FW Def_ and _EW Def_, respectively, and the blade root shear forces in the flapwise and edgewise directions denoted by _FW F_ and _EW F_, respectively. The linear models show almost identical results, with prediction accuracies ranging between 40 and 60%. Although the training time is the least among all other ML models, the prediction accuracy is unsatisfactory and cannot be trusted for reliable predictions of the output measures. Adding some nonlinearity to train the data using a second-order polynomial model has slightly improved the prediction accuracies to be within the range of 56 to 77%, yet these accuracies are not sufficient. More improvements should be made to produce a reliable regression model. The neural network model, which utilizes the deep neural multilayer perceptron (MLP) method using the Scikit-learn library in Python, gives an initial judgment on the deep learning capabilities on the dataset before further investigating it in more detail. The preliminary results are not promising except for the edgewise shear force predictions. In addition, the training time for the blade root shear force outputs is much higher compared to the other ML models. Finally, the ensemble models show the highest accuracy among all other models for most of the outputs and turbulence classes. For all outputs, decision trees and random forests give the best performance and the highest prediction accuracy. The decision tree model's accuracy is slightly lower than the random forest, however, the training time is much shorter. Since the models are further investigated for possible generalization, prediction accuracy is a more critical factor to consider than the computational cost. To emphasize the quality of the models before generalization, the coefficient of determination aggregate mean value with all turbulence classes and outputs are visually shown in Figures 5 and 6, respectively.