esa-sceva/satcom-chunk-collection huggingface dataset:
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 11 Transmission Performance of HCFs
### Fiber Ratings  
Can a single figure of merit for the use of HCF in networks be defined?  
In our opinion, no, primarily due to different line codes and bit rates of transponders in use and in development. For example, dispersion is critical at high bit rates (\(\geq\)200 Gb/s), but not at low/medium ones, e.g., 10 Gb/s. The quality of HCF is best presented as a latency-attenuation-dispersion triangle (Figure 23a), but weights given to each parameter vary. In DWDM networks, low-loss bandwidth and nonlinearity are two additional criteria for fiber evaluation (Figure 23b).  
NANF or DNANF is best in terms of transmission properties, while the advantages of HC-PBGF over SMF are limited to low latency and a superior radiation resistance. HC-PBGFs can replace graded-index multimode fibers in short links for medium bit rates (10-100 Gb/s) if low latency or radiation hardness is required. FEC and dispersion compensation can increase the bit rate and reach [111], but adds an unacceptable latency (Section 12.1.2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Channel** \\ **Data Rate** \\ **[Gb/s]** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Channels** \\ \end{tabular} & \begin{tabular}{c} **Aggregate Data** \\ **Rate [Tb/s]** \\ \end{tabular} & \begin{tabular}{c} **Line** \\ **Code** \\ \end{tabular} & \
\begin{tabular}{c} **Channel** \\ **Spacing [GHz]** \\ \end{tabular} &
\begin{tabular}{c} **Minimum Reach** \\ **[km]** \\ \end{tabular} & **Year** & **Ref.** \\ \hline
25 & 61 & 1.525 & PM-QPSK & 50 & 341 & 2019 & [112] \\
25 & 61 & 1.525 & PM-16 QAM & 50 & 125 & 2019 & [112] \\
25 & 61 & 1.525 & PM-QPSK & 50 & 618 & 2020 & [113] \\
25 & 41 & 1.025 & PM-QPSK & 50 & 2760 & 2021 & [114] \\
400 & 38 & 15.2 & DP-16 QAM 1 & 100 & 10.25 2 & 2021 & [115] \\
400 & 63 & 25.6 & DP-16 QAM 3 & 75 & 1128 & 2021 & [54] \\
800 & 47 & 38.4 & No data 4 & 100 & 126 & 2021 & [54] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of DWDM transmission experiments with NANF fibers in the C band.
## 2 Comparative Evaluation of UCS and TCP Estimation Methods
### Data Recording Process  
To ensure accurate and repeatable measurements, a structured multi-step procedure was followed for data acquisition and alignment. Each step was designed to progressively refine the robot and measurement system configuration to enable reliable coordinate system estimation. The data recording process consisted of steps performed internally in the robot system (0-2) and using external Polyworks Inspector(r) 2023 probing software for coordinate system alignment and data sampling. Figure 3 presents a graphical representation of the gathered data and geometrically estimated (Method C) position of the World Coordinate System (WCS) of each robot. The steps of the procedure are outlined below:  
Step 0: Payload Configuration  
The robot's payload mass and center of gravity were measured and entered into the controller.  
Step 1: TCP Measurement  
The Tool Center Point (TCP) was measured using dedicated conical fixtures and probing routines.  
Step 2: Robot Program Preparation  
Robot programs were created to reach multiple angular poses distributed across the robot's workspace.  
Step 3: UCS Alignment in PolyWorks Inspector(r)  
Figure 2: Experimental stand with two ABB IRB-6640 robots and a Leica measuring system.
## 4 Methodology and Prototype Implementation  
In this section we outline the methodology involved in the presented research. The core component for answering the research questions in a practical context is a prototype integrating an automated, decentralized EMS, which controls DERs according to a smart grid traffic light based DSM, into the SMGW infrastructure. In particular, we chose to integrate an electric vehicle charging station and EVs into such a local EMS, as they offer a high potential for increasing grid stability. Since our primary goal was the demonstration of the integration into the SMGW infrastructure, we abstained from adding further DERs. Nevertheless, more DERs could easily be added and managed by the local EMS due to the versatility of the presented architecture described in Section 4.8. Please see the Supplementary Materials provided with this paper for the source code of the implemented prototype, which is described in detail in this section. The key functionality of SMGWs used by this prototype is the provision of a transparent data tunnel for secure and bidirectional communication.
## 4 Proposed MCA-IoT Design
### Hardware Component Selection with Justification
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Hardware Component** & **Function** & **Why Selected** \\ \hline RPi 3B+ controller & Processing, storage, hosting other hardware peripherals; serves as main controller for MNs, CHs, and BS & Has onboard Wi-Fi and BLE 4.2 radio modules, as well as a PoE port, for easy remote programming; is cheap, ubiquitous, and able to withstand adverse weather conditions \\ \hline LoRaWAN gateway (BS) with USB-LoRa end nodes & For communication between the edge RCHs and BS, and then the BS and the TTN cloud & Offers cheap and flexible communication options for different MCA-IoT applications \\ \hline BLE 4.2 Module & For intra-cluster and inter-cluster/multihop communication & Supports the cluster-based architecture without fixed infrastructure requirements; is ubiquitous, simple to implement, and cheap \\ \hline STEMMA soil moisture sensor (Adafruit) & For sampling soil moisture and temperature data & Is hardy, accurate with a wider/suitable measurement range, and has freely available libraries at the Adafruit Library \\ \hline Solar-powered 60,000 mAH (5v/3A) LICORNE and ANYRONG power banks & Powers MNs, CHs, RCHs, and BS & Suits network devices’ power requirements \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MCA-IoT hardware components list. 3. **Energy Efficiency:** Low-power components and energy-saving techniques, such as duty-cycled sampling, role rotation, and control of redundant data transmission, were implemented to ensure the network's efficiency throughout the crop season, especially given the lack of reliable electricity in many SSA farms. 4. **Environmental Robustness:** Hardware was tested under extreme climatic conditions (40 \({}^{\circ}\)C to 51 \({}^{\circ}\)C), similar to those in the Sahel Region, with the RPi 3B+ performing stably compared to other controllers that failed even under indoor conditions. 5. **Standardized Manufacturing:** The selection of components ensured compatibility and smooth integration, streamlining production and assembly, and maintaining system coherence. In summary, the effectiveness of the MCA-IoT network is closely linked to the choice of components and the architecture of event routing, ensuring that the desired performance outcomes are achieved.
## 3 Results and Discussion
### Impact on Antioxidant Activity  
To test whether Qt decomposition could impact its antioxidant properties to a significant extent, a Qt solution of 100 \(\upmu\)mol L\({}^{-1}\) was assayed before and after treatment at pH 7.5 and 90 \({}^{\circ}\)C, for 50 min. As can be seen in Figure 8, both A\({}_{\text{AR}}\) and P\({}_{\text{R}}\) were significantly reduced after treatment (\(p\) < 0.05). This outcome clearly pointed to a detrimental effect of Qt thermal break down on its antioxidant potency. The examination of solutions with 200 \(\upmu\)mol L\({}^{-1}\) of either AsA, CySH and sulfite showed that A\({}_{\text{AR}}\) may be less affected, although the reduction was still significant compared to non-treated Qt solution. Regarding P\({}_{\text{R}}\), the reduction was significant irrespective of the antioxidant added.  
Figure 8: Antiradical activity (**upper plot**) and ferric-reducing power (**lower plot**) of a Qt solution (100 \(\upmu\)mol L\({}^{-1}\)), at pH 7.5, prior to thermal treatment (Qt(i)), after 50 min at 90 \({}^{\circ}\)C (Qt(f)) and after 50 min at 90 \({}^{\circ}\)C in the presence of 200 \(\upmu\)mol L\({}^{-1}\) AsA, CySH and sulfite. Bars designated with different letters indicate statistically different value (\(p\) < 0.05).  
Qt is considered a powerful antioxidant, due to unique structural features, which are not encountered in other flavonoids, such as the combination of the o-diphenylon in B-ring, the C2-C3 double bond, the 4-oxo group and the 5-hydroxyl group [39]. Once the skeleton is cleaved, these features are abrogated and thus it would be reasonable to anticipate reduced antioxidant activity. Furthermore, the two major degradation products, PCA and PGCA, have been proven to be of considerably lower efficiency [22]. AsA, CySH, and sulfite may also degrade and/or oxidize to some extent and apparently could not compensate for the antioxidant activity lost.
# Damage Identification in Warren Truss Bridges by Two Different Time-Frequency Algorithms
## 3 Numerical Algorithms for Signal Processing
### Continuous Wavelet Transform (CWT)  
The first procedure that is used for damage detection uses the Continuous Wavelet Transform (CWT) to extract useful indexes from the acceleration on the bogie frame.  
The CWT [38] can exploit different waveforms to extract time-frequency information from the processed signals, as recalled in the following equation:  
\[C_{s,\tau}=\frac{1}{\sqrt{s}}\int_{-\infty}^{\infty}x(t)\cdot\psi_{s,\tau}^{*} \left(\frac{t-\tau}{s}\right)d\tau \tag{1}\]  
where \(\tau\) represents the \(s\) time shifting value and \(s\) is defined as a scaling factor. Therefore, once the two parameters are set, \(\psi_{s,\tau}^{*}\left(\frac{t-\tau}{s}\right)\) represents a copy of the original mother wavelet scaled by \(s\) and centered around time \(\tau\). The wavelet coefficients indicate, along the time axis, if the shape associated to the defect is present in the time history of the acceleration on the bogie. The choice of a specific mother wavelet among those available in the literature can be made according to the features to be captured from the signal under analysis, which makes a CWT-based method versatile and flexible as a mean to extract a given signature in a time series. According to previous studies and to the oscillating pattern of the acceleration signals we dealt with in this work (see for example Figure 7, the choice fell on a real-valued Morlet Wavelet.  
The scheme of the algorithm flow reported in Figure 5 shows that the CWT is applied separately to the accelerations measured in the case of the healthy and damaged bridge, respectively. Every time a train runs along a specific bridge, the wavelet coefficients of the current acquisition are evaluated and subtracted to the wavelet coefficient of the reference acquisition, corresponding to the healthy state of the bridge. The absolute value of the difference between the two series of CWT coefficients is then calculated: any peak pointing out a relevant difference between the current and the reference signal would indicate the presence of a defect and its position along the bridge span. The possibility to analyze, in a wayside server, data from different trains of an instrumented fleet would enhance the capability of the diagnostic system to filter outliers or false positives.  
The comparison between the coefficients of the CWT calculated separately on the damaged and healthy time series, instead of calculating the CWT of the difference between the two time series, is expected to give some advantages in terms of lower sensitivity to extraneous effects not related to the damage.  
This aspect will be investigated when considering the impact of different track irregularity in the damaged and healthy conditions.
# Economic Feasibility of Underground Pumped Storage Hydropower Plants Providing Ancillary Services
## 3 Results and Discussion
### Estimation of Income and Expenses
The electricity generated is reduced from 226.46 to 203.00 GWh year\({}^{-1}\) in HPS 1, from 391.19 to 350.16 GWh year\({}^{-1}\) in HPS 2 and from 789.73 to 701.32 GWh year\({}^{-1}\) in HPS 3 when the DTC increases from 4 to 10 h. The maximum theoretical income reaches 49.06 MeV year\({}^{-1}\) and is obtained in HPS 3 when the DTC is 4 h. The maximum income is reduced by 16.70% and the minimum costs are increased by 8.56% in HPS 3 when the DTC is increased to 10 h. Consequently, the spread between the income from selling electricity and the cost for purchasing electricity is also reduced from 31.59 to 21.91 Mc year\({}^{-1}\) in HPS 3 when the DTC increases to 10 h. The maximum incomes reach 13.91 and 24.41 Mc year\({}^{-1}\) in HPS 1 and HPS 2 when the DTC is 4 h. In addition,  
Figure 5: Maximum income from selling energy and minimum cost for purchasing energy, considering DTCs between 4 and 10 h: (**a**) HPS 1; (**b**) HPS 2; (**c**) HPS 3. (**d**) Amount of energy generated and consumed for HPS 1, HPS 2 and HPS 3.  
the spreads between the income and expenses are reduced by 45.86% and 31.12% in HPS 1 and HPS 2, respectively, when the DTC increases from 4 to 10 h day\({}^{-1}\). As a presumable guideline, Table 2 shows the maximum theoretical income and the expenses of the three HPSs when considering a DTC of 8 h. In this scenario, the maximum output powers (turbine mode) for HPS 1, HPS 2 and HPS 3 are 62, 109 and 219 MW, respectively, while the maximum input powers (pumping mode) are 53, 92 and 185 MW for HPS 1, HPS 2 and HPS 3, respectively. The maximum income and the minimum cost for purchasing energy are obtained participating in the day-ahead and the ancillary services markets. The costs for purchasing energy represent 84%, 85% and 87% of the total costs in HPS 1, HPS 2 and HPS 3, respectively. In addition, O&M costs, start-up costs, grid access tariff and hydraulic cannon have been considered. As established in Spanish electrical regulation, a cost of 0.5 \(\upxi\) MWh\({}^{-1}\) has been considered as grid access tariff. O&M costs include personnel, insurance, spare parts and external service costs. Finally, the operating margins (income from selling electricity minus the cost for purchasing electricity and operation costs) have been estimated, reaching 6.07, 10.99 and 22.71 Mc year\({}^{-1}\) in HPS 1, HPS 2 and HPS 3, respectively. Repeating all these considerations when a DTC of 4 h is designed, the operating margins increase to 7.83, 14.11 and 28.81 Mc year\({}^{-1}\) in HPS 1, HPS 2 and HPS 3, respectively.
## 3 Deep Learning Meta-Model
### ChebNet GCNN  
After testing several architectures of GCNN, we ascertain that GCNN with the Chebyshev convolutional layer (ChebNet GCNN) [35] provides the best results in our case. It pertains to spectral GCNNs, which represent the graph convolution in terms of polynomials of a graph Laplacian matrix \(L\).  
For a brief general description, let \(*_{G}\) be the graph convolutional operator, \(\mathbf{x}\in\mathbb{R}^{|\mathcal{V}|}\) be a vector of input values associated with graph vertices, and \(\mathbf{g}_{\theta}\in\mathbb{R}^{K\times Ch}\) be the convolutional kernel. The variable \(K\) stands for the maximal polynomial degree, and \(Ch\) represents the number of channels of the kernel [33] (ch. 9). Then, the graph convolution of \(\mathbf{x}\) and \(\mathbf{g}_{\theta}\) is:  
\[\mathbf{x}*_{G}\mathbf{g}_{\theta}=p_{\theta}^{K}(L)\mathbf{x}, \tag{4}\]  
where \(p\) is a polynomial of eigenvalues of \(L\) and has maximum degree \(K\). When using ChebNet GCNN, \(p^{K}(L)\) is defined via Chebyshev polynomials \(T_{k}\):  
\[\mathbf{x}*_{G}\mathbf{g}_{\theta}=(\sum_{k=0}^{K-1}\theta_{k}T_{k}(L))\mathbf{x}, \tag{5}\]  
where \(\theta_{k}\in R^{Ch}\) represents the learnable parameters of the kernel that are adjusted during the neural network learning procedure. The kernel is K-localized, i.e., the filtered signal at  
Figure 2: An illustrative example of a random field on an unstructured mesh with the corresponding graph representation.  
a particular vertex depends on information from vertices in its K-hop neighborhood. For a comprehensive explanation of GCNN, see [31] (p. 81).
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 4 Finite Element Analysis  
### General  
The pull-out tests of conventional and notched perfobond shear connectors were simulated by using the finite element method, as shown in Figure 7. The aim of this numerical analysis was to study the failure mechanism and the pull-out resistance of perfobond shear connectors. The general analysis package ABAQUS [25] was adopted to generate validated finite element models instead of expensive and time-consuming pull-out tests. The material and geometric nonlinearities were considered by using the dynamic explicit method. The loading rate of the pull-out tests was slow enough to be taken as a quasi-static loading procedure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Pull-out Resistance**} & \multicolumn{2}{c}{**Pull-out Stiffness**} & \multicolumn{2}{c}{**Separation Deformation**} \\ \cline{2-7}  & \(T_{u,i}\) (kN) & \(T_{u,avg}\) (kN) & \(k_{t,j}\) (kN) & \(k_{t,eng}\) (kN) & \(s_{p,i}\) (kN) & \(s_{p,eng}\) (kN) \\ \hline CPT–1 & 279.8 & & 631.3 & & 1.00 & \\ CPT–2 & 245.3 & 262.6 & 609.5 & 621.9 & 0.98 & 1.14 \\ CPT–3 & 262.7 & & 625.0 & & 1.43 & \\ \hline NPT–1 & 294.2 & & 662.5 & & 1.15 & \\ NPT–2 & 279.8 & 282.7 & 674.0 & 658.0 & 1.06 & 1.19 \\ NPT–3 & 274.1 & & 637.5 & & 1.35 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pull-out test results.  
Figure 6: Pull-out mechanism. (**a**) Conventional perfobond shear connector; (**b**) notched perfobond shear connector.
# Dynamic Correction of the Influence of Long Measuring Path Irregularity in Antenna Tests
###### Abstract  
This article investigates the influence of random microwave discontinuities on the characteristics of long transmission paths. This is most important for dynamic measuring stands, accompanied by multiple space movement of long transmission paths with their bending or twisting during the measurement process. In modern active electronically scanned arrays this issue also becomes relevant, due to increased requirements for the accuracy of beam shaping. The aim of this study is to develop a theoretical background and perform experimental verification for taking into account the effect of random microwave discontinuities on the transmission path characteristics. A method for correcting the effect of such irregularities is considered based on electrical length control by measuring the input reflection coefficient. Relations for the magnitude and phase of the path's input reflection coefficient depending on the S-parameters of a long four-terminal network terminated with mismatched load are obtained and plotted. Using theory of sensitivity, the mathematical expressions of conditions were obtained to achieve maximum accuracy of measuring the electrical length of a long microwave path. The possibility of dynamic error correction in antenna measurements with a long test path caused by random microwave irregularities along it has been experimentally proved.  
long transmission paths; dynamic measuring; random microwave discontinuities; dynamic error correction +
Footnote †: 2021: 11, 8183. [[https://doi.org/10.3390/app11178183](https://doi.org/10.3390/app11178183)]([https://doi.org/10.3390/app11178183](https://doi.org/10.3390/app11178183))
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Hypothesis-Driven Studies in Identification of mTOR Signaling Components
#### 4.4.2 Hypothesis-Driven Discovery of Rheb as a GTPase in Activating mTORC1  
Discovery of a critical role of TSC2/TSC1 as a GAP (GTPase activating protein) to mTORC1 suggests that TSC2/TSC1 might function through an unknown GTPase to regulate mTORC1 activity. [PERSON] et al. thus examined binding of a Flag-tagged C-terminal truncated version of TSC2 with a panel of GST-tagged Ras proteins including Rheb, Ha-Ras, R-Ras, Rap1A, and RalA loaded with GppNHp (nonhydrolyzable GTP analog) in the presence of MgCl\({}_{2}\) in HEK293T cell lysates, and found that only Rheb displayed a strong binding ability. Following experiments further demonstrated that TSC2 was a Rheb GAP in cells and Rheb expression efficiently triggered S6K and S6 phosphorylation in cells [84].
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Analysis of Proposed Architecture
#### 3.6.2 Comparison of Restoration Performance for Various Threshold Values  
In this section, we quantitatively compare the restoration results for each A value determined by each threshold in the image restoration process. Table 5 demonstrates the quantitative measurement results using PSNR and SSIM scores for each threshold value on the test dataset.  
As mentioned in Section 2.4, a haze removal method using the image degradation model through transmission maps needs the global atmospheric light to calculate the clean image. In (11), the atmospheric value is determined by selecting average of the 0.1% darkest pixel in the transmission map.  
As shown in Table 5, the best results on PSNR and SSIM values are obtained when choosing average of the 0.1% darkest pixel in the transmission map.  
Rain and haze are significant obstacles to operating many camera-based vision systems because of the degraded input image. In real-world road conditions, not only good weather but also hazy and rainy weather exist, which can cause serious visual problems. Many studies have been conducted on removing rain and haze from images, but none have studied removing rain and haze simultaneously