esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Turkiye has relatively sparse observations from the eddy covariance of surface-atmosphere carbon dioxide exchange from which GPP can be inferred [34; 35; 36], emphasizing the importance of satellite remote sensing in interpreting carbon cycling dynamics [37]. It also has a relatively large and growing population and a changing climate with increases in temperature and atmospheric heat content [38]. Precipitation has been increasing in parts of the north and east and decreasing in the west [39; 40], with an expansion of semi-arid regions [41] and predicted future declines in precipitation throughout the country [42; 43]. Carbon cycle processes have changed in response to changes in aridity across different parts of Turkiye [44; 45], further emphasizing the importance of understanding trends in GPP. Here, we analyze multi-decadal time series from six different GPP products across the seven different regions of Turkiye, employing various trend analysis methods, both standard and novel, to interpret patterns. In this study, alongside the conventional trend method (Mann-Kendall), the simultaneous interpretation of two graphical trend methods (ITA and EMD) aims to develop an original methodology in ecological and carbon cycle studies. This approach is designed to prevent the over-simplification of trend analyses, thereby identifying trends with higher temporal resolution. In addition, we aim to provide insight into how key components of the carbon cycle of Turkiye have been changing and to provide a framework for multi-method approaches for understanding these changes.
## 3 Results and Discussion  
### Hybrid Coupler  
Table 4 presents the calculated and optimized dimensions of the BLC, where different parameters were varied during the optimization process. The optimized s-parameters and phase difference in the BLC are depicted in Figures 10 and 11, respectively. The results indicate that the S-parameters, specifically S\({}_{11}\) and S\({}_{14}\), exhibit minimal reflection at ports 1 and 4, respectively, which indicates good impedance matching and minimal signal loss at these ports. While similar behaviors are reported in the literature by [PERSON] et al. [75], this study extends these findings by demonstrating the optimized performance of the Butler matrix components specifically within an underground mining environment. The S\({}_{12}\) and S\({}_{13}\) represent the isolation of port 1 from port 2 and 3, respectively. Achieving good isolation between these ports is essential for reducing crosstalk and improving overall system performance. Similar findings are observed by [PERSON] et al. [76] in coupled line-based millimeter-wave Complementary Metal-Oxide-Semiconductor (CMOS) power dividers, where the isolation (S\({}_{32}\) and S\({}_{23}\)) between output ports significantly improves system performance by minimizing interference between channels. Furthermore, Figure 10 demonstrates a phase difference of 87.27\({}^{\circ}\) between port 2 and port 3, which is crucial for applications requiring phase-shifted signals, such as phased-array antennas and power dividers. This phase difference is consistent with findings by [PERSON] et al. [77], which emphasize that phase accuracy is crucial for maintaining signal coherence and beamforming performance in multi-port systems.  
The hybrid coupler dimensions determined in this study align with those reported previously. Building on this foundation, the work introduces a novel application by integrating the coupler into an antenna system tailored for underground mining communication. The design features a Butler matrix coupled with a circularly polarized square patch array, enhancing both signal reliability and coverage. The system achieves a bandwidth of 400 MHz and a gain of 9.69 dBi, outperforming existing designs in addressing the challenges of underground environments. Application-specific testing further validates the system's effectiveness, establishing its contribution to advancing communication technologies in mining.
## 3 Hierarchical Trajectory Clustering Algorithm
### DBSCAN Algorithm with Automated Parameter Tuning  
#### 3.2.1 Definition of DBSCAN Algorithm  
DBSCAN (density-based spatial clustering of applications with noise) is a clustering algorithm proposed by [PERSON] et al. [41]; this differs from traditional clustering methods, which rely on measuring the distance between data points to form clusters.  
[PERSON] et al. [42] have highlighted the versatility of the DBSCAN algorithm in handling datasets with varying densities, shapes, and sizes. They emphasize its advantages compared with other clustering algorithms, which may struggle with uneven data distributions or require a predefined number of clusters. They note the ease with which DBSCAN can distinguish noise from actual data points.  
[PERSON] et al. [43] compared the performance of density-based clustering algorithms such as DBSCAN, OPTICS, and Mean-shift, while [PERSON] et al. [44] compared DBSCAN, K-means, and Quadratic Variation Utility. The authors of both these studies concluded that there is no universally superior algorithm for all scenarios and that the choice of algorithm depends on the data characteristics and the application's requirements.  
The results of clustering the data set that went through the preprocessing process proposed in this study using DBSCAN, OPTICS, K-means, and Mean-shift methods are shown in Figure 4. Figure 4 presents the DBSCAN clustering result, showing the noise cluster and five cluster findings. Figure 4 presents the OPTICS clustering result, similar to the DBSCAN result. However, it can be seen that only the data classified as noise are slightly different. Figure 4 presents the K-means clustering result, and in the K-means algorithm, the number of clusters must be set in advance. For direct comparison with the results of DBSCAN and OPTICS, the number of clusters was set to 6. Figure 4 presents the mean-shift clustering result.  
Two key parameters must be defined for the effective functioning of the DBSCAN algorithm.  
1. [label=(0)]
2. Epsilon: The neighborhood radius around the data point is defined; this essentially sets the spatial extent over which the algorithm searches for adjacent data points to form clusters.
3. MinPts: Determines the minimum number of data points required for the Epsilon neighborhood to be considered a cluster. A larger MinPts value may reduce the number of clusters containing denser data points, whereas a smaller value may result in more clusters containing fewer data points.  
In DBSCAN, the parameters are crucial as they directly affect the algorithm's ability to identify clusters of varying densities and shapes, making DBSCAN a versatile tool for exploratory data analysis across different domains. However, there is still no clear method for setting these parameters; many researchers have proposed various approaches to determine the optimal parameter settings.
# Routing Protocols Performance on 6 LoWPAN IoT Networks
## 4 Results and Discussions
RPL tends to operate at lower power levels, which may indicate better energy efficiency. The relationships among the power metrics are largely positive, indicating that power consumption in one area is typically associated with increased power consumption in others. This analysis suggests that the choice of routing protocol has a significant impact on power consumption patterns in different areas, with RPL being more energy-efficient, and AODV and LOADng requiring more power. The investigation presents a systematic breakdown of power consumptions into components i.e., CPU Power, Listen Power, CPU Power, and Total Power consumption related to each protocol. This detailed examination reveals that power consumption increases as the number of motes rises, providing valuable insights to guide future protocol improvements focused on energy efficiency of 6 LoWPAN.
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
## 3 Results and Discussion
### Power Throughput Optimisation
Improved scintillation may be favoured over a reduction in power throughput, though the overall power distribution in fibre is ultimately the most important metric when considering the operation of fibre-based FSO components such as detectors or amplifiers. The throughput distributions are presented in Figure 4, with the inclusion of the upper and lower quartile of the total power throughput from each simulation. Predictably, as link distance and turbulence strength increase, so too does the interquartile range of the throughput distribution. For a given link, as the aperture size approaches the Fried parameter from Table 1, the interquartile range also increases as the wavefront of the received beam becomes increasingly aberrated. The effect of TT compensation is better highlighted in Figure 4, showing a significant reduction in the power throughput distribution's interquartile range for all turbulence strengths over the 750 m and 2400 m links, with a maximum reduction of 7 dB for the most turbulent 2400 m link. With TT compensation, the interquartile range does not exceed 3 dB. As discussed previously, the 10,600 m link causes predominantly non-TT wavefront perturbations, so TT compensation does little to the power throughput distribution, except at sufficiently low \(C_{n}^{2}\) values. Most importantly, Figure 4 shows that the TT-compensated interquartile ranges are not strongly dependent on \(D\). This confirms that choosing \(D\) to optimise \(\eta_{net}\) does not come at the cost of a sub-optimal fibre power distribution. Figure 4 also reinforces that proper operation of the TT compensation system is critical in achieving optimal fibre power distributions. These analyses can be used to evaluate the possible improvements of FSO demonstrations in the literature. [PERSON] et al. [14] demonstrated time and frequency transfer over a 14 km link with an estimated turbulence strength of \(4\times 10^{-14}\) m\({}^{-2/3}\). The authors use transmit/receive aperture diameters of 40 mm. A net throughput of \(-46\) dB is observed, with an interquartile range of 30 dB. Using Figure 3, we find that using a 140 mm aperture would result in a 25 dB throughput improvement. Instead, for the same 40 mm aperture size, the authors could see a 15 dB improvement in net throughput by following the design rules outlined in this paper. Additionally, the authors could see a 10 dB improvement in power fluctuations. In general, many FSO demonstrations appropriately select a \(\sim\)50 mm aperture diameter for 1-2 km link distances [1; 13], yet maintain this aperture size for longer links [10; 15]. Optimal design of the transmit/receive optics can greatly improve the net throughput and power fluctuations before having to implement costly, higher-order adaptive optics.
## 2 Overview of Supplementary Damping Systems
### Brief History and Development of Damping Technologies
#### 2.3.4 Advanced Systems and Integration (2000s-Present)  
2000s: Development of high-performance viscous dampers with large force capacities (e.g., [30]). In recent years, there has been a notable increase in research across various industrial sectors focused on the development of semi-active vibration control devices. Notably, devices that utilize magneto-rheological (MR) fluid have garnered significant attention due to their ability to function as capacity-variable dampers with high performance. MR fluids are unique in that they can be manipulated by external magnetic fields, which induce a dramatic change in their viscosity, transforming them from a liquid state to a semi-solid state.  
2010s: Integration of damping systems with structural health monitoring technologies [31; 32; 33]. A previous article [33] outlined the development and findings from a three-year project that implemented an automated vibration-based structural health monitoring system. This system was aimed at modal tracking of the dynamic characteristics of a concrete-masonry Civic Tower in Rieti, Italy, which features a non-conventional tuned mass damper for passive vibration control. The system relies on data obtained from a small array of high-sensitivity accelerometers, which are utilized for remote automated tracking of modal parameters.  
2020s: Exploration of bio-inspired damping systems and smart materials [34; 35; 36]. Shape memory alloy (SMA) exemplifies a prominent category of smart materials that are employed to enhance operational efficiency and mitigate various shortcomings associated with traditional base isolation systems. These shortcomings include inadequate recentering ability and significant isolator displacement, which can result in the bounding effect. SMA is notable for its unique property of tolerating exceptionally high strain during the loading phase while demonstrating no permanent deformation upon unloading. Nitinol (Ni-Ti) is predominantly utilized as the SMA material in most studies and applications.
## 2 Background Study
### Effective Rainfall Rate  
An inconsistency was found between the rainfall rate and link length reduction factor in [42]. As a result, in Equation (13), \"path length reduction factor\" was defined as:  
\[r_{p}=\frac{A_{p}}{kR_{p}^{a}\cdot d} \tag{13}\]  
where \(A_{p}\) is the rain attenuation, \(R_{p}\) is the point rainfall rate exceeded at \(p\%\) of the time, k and \(a\) are the specific attenuation coefficients, and d is the path length. Based on the developed idea, the effective rainfall rate concept was applied to the rain attenuation model. Proceeding with the same approach as per Equation (13), effective rainfall rate was derived as in Equation (14):  
\[R_{eff}=R_{eff}\Big{(}\cos\theta+a_{1}\cdot R^{a_{2}+a_{3}/L_{s}\cos\theta} \cdot L_{S}^{a_{4}}\cdot\sin\theta\Big{)} \tag{14}\]  
where \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\) are constants that can be obtained through the curve fitting technique, \(L_{S}=(H_{R}-H_{S})/sin\theta\) is the slant path in the precipitation (km), \(\theta\) is the elevation angle (see Figure 2). This Figure 2 presents a general Earth-space link scenario that will be used as the reference, from now and on-wards. With the SST technique, rainfall time series can be converted into rain attenuation time series, which is a useful tool in case of the absence of beacon measured attenuation data regardless of polarization and range of frequency bands but restricted to within elevations above 10\({}^{\circ}\). It requires the advection velocity v (m/s) of rain, the length of the satellite path \(\zeta\) (km), and the location of the ground station, which is indicated by \(x_{0}\). In the next section, the meaning of these symbols will be that \(L_{G}\) is the length of horizontal projection, \(H_{S}\) is height above the mean sea level of the Earth station (km), \(H_{R}\) is rain height, \(H_{R}^{\prime}\) is the melting layer top limit, and \(\varphi\) is the latitude of the Earth station (degrees), if not mentioned otherwise.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Monoaxial Rain Cell** & **Biaxial Rain Cell** \\ \hline Peak rainfall rate (\(R_{M}\)) & & \\ Cell radius/radii & \(\rho_{0}\) & \(\rho_{x},\rho_{y}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of EXCELL model.
# Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction
## 4 Experiments
### Ablation Studies
#### 4.3.5 Influence of Sequence Pooling Methods  
In Table 8, we compare the performance of the proposed model with different sequence pooling methods. \"Target Pooling\" represents the proposed model with target pooing in Section 3.4, and \"Mean Pooling\" is the proposed model with mean pooling in Section 3.4. The proposed model uses the attention-based weighted sum pooling method by default.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Datasets** & **Evaluation** & **FESeq** & **FESeq w/o Pos** & **FESeq w/o Time** \\ \hline \multirow{2}{*}{Ele.me} & AUC & **0.6127** & 0.5757 & 0.6040 \\  & Logloss & **0.0918** & 0.0928 & 0.0921 \\ \multirow{2}{*}{Bundle} & AUC & **0.8912** & 0.8866 & 0.8902 \\  & Logloss & 0.0117 & **0.0111** & 0.0123 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation studies of models without positional embedding or linear time interval embedding in sequence-refiner layer. The bold number denotes the best value in the column.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **Evaluation** & **FESeq** & **FESeq w/o Causal** \\ \hline \multirow{2}{*}{Ele.me} & AUC & **0.6127** & 0.6124 \\  & Logloss & **0.0918** & 0.0920 \\ \multirow{2}{*}{Bundle} & AUC & **0.8912** & 0.8905 \\  & Logloss & 0.0117 & **0.0108** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation studies of models with and without causal masks in sequence-refiner layer. The bold number denotes the best value in the column.  
The results show that attention-based weighted sum pooling is superior to simple target pooling and mean pooling in the proposed model.
# Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors
### State of the Art  
To this point, previous rotor optimization approaches, discussed in the technical literature, focus on single objective problems and typically optimize the stored energy density (SED) or the energy to material cost ratio. In [12] an optimization approach is presented that optimizes the cost to energy ratio based only on material costs. Since flywheel rotors are scalable, finding the solution with the highest energy to cost ratio is sufficient to create each cost- and energy combination required. However, FESS technology is also expanding into applications where the construction volume is limited, such as rail and automotive transportation. Scaling of cost to energy ratio-optimized designs is therefore not possible and the ability to choose between different optimal designs on a Pareto-front becomes more useful. Furthermore, the manufacturing process has not been modeled in previous studies and therefore manufacturing time and cost have been neglected. Also, the material selection and the number of rims were externally given, therefore the optimization in [12; 13] was limited to optimal rim thicknesses.
# Multi-Timescale Control of Variable-Speed Wind Turbine for Inertia Provision
## 2 Principle of Inertia Emulation
### DFIG System Control
#### 3.2.1 GSC Control Scheme  
Under the synchronous reference frame of the GSC, the relationship between the output voltage and output current is given by:  
\[v_{d}=R_{g}i_{d}+L_{g}\frac{di_{d}}{dt}-\omega_{0}L_{g}i_{q}+v_{\rm gd} \tag{9}\]  
\[v_{q}=R_{g}i_{q}+L_{g}\frac{di_{q}}{dt}+\omega_{0}L_{g}i_{d}+v_{\rm gq} \tag{10}\]  
Figure 8: Simulation results for RoCoF acquisition.  
where \(v_{d}\) and \(v_{q}\) are the GSC output voltage and current, respectively; \(i_{d}\) and \(i_{q}\) are the GSC output and current, respectively; \(v_{\text{gd}}\) and \(v_{\text{gq}}\) are the grid voltages; and \(R_{g}\) and \(L_{g}\) are the grid resistance and inductance, respectively. The active and reactive power injected to the grid can be expressed as:  
\[P_{g}=3(v_{d}i_{d}+v_{q}i_{q}) \tag{11}\]  
\[Q_{g}=3(v_{q}i_{d}-v_{d}i_{q}) \tag{12}\]  
A phase-locked loop (PLL) is usually implemented to obtain the phase angle information for the abc/dq transformation. The PLL dynamically regulates the phase angle such that \(v_{q}=0\). In this regard, the active power, \(P_{g}\), is determined by the \(d\)-axis current, \(i_{d}\), whereas the reactive power, \(Q_{g}\), is determined by the \(q\)-axis current, \(i_{q}\). On this basis, the current references for the GSC are given by:  
\[i_{\text{dref}}=k_{\text{p1}}(v_{\text{dc}}-v_{\text{dc\_ref}})+k_{\text{i1}} \int{(v_{\text{dc}}-v_{\text{dc\_ref}})dt} \tag{13}\]  
\[i_{\text{qref}}=k_{\text{p2}}(Q_{\text{ref}}-Q_{g})+k_{\text{i2}}\int{(Q_{ \text{ref}}-Q_{g})dt} \tag{14}\]  
where \(k_{\text{p1,2}}\) and \(k_{\text{i1,2}}\) are the proportional and integral control gains, respectively; \(v_{\text{dc\_ref}}\) is the reference DC-link voltage; and \(Q_{\text{ref}}\) is the reference reactive power. Figure 9 shows the control block diagram of the GSC, where the cross-coupling terms in Equations (8) and (9) have been decoupled through the current feedforward control, and \(k_{\text{p3}}\) and \(k_{\text{i3}}\) are the proportional and integral control gains, respectively, for the inner-loop current control.
## 5 Experiment
### Feature Extraction Experiment
The basic idea of the LSTM-SAE model is to encode the input sequence through the LSTM layer and then decode and reconstruct it through the reverse LSTM layer. The stacking of multiple LSTM layers helps to learn richer and more advanced sequence features. The Time-VAE model is a variational autoencoder model for time series data modeling. It is an extension of the standard VAE model specifically designed to handle the features and structures of time series data. By training the Time-VAE model, we can achieve meaningful low-dimensional representation learning of time series data and at the same time have the ability to reconstruct and generate new samples. This latent representation learning can be used in various applications such as data visualization, dimensionality reduction, and anomaly detection.
## 3. Thrice Subtraction Method  
By analyzing previous research ideas [13; 19], a thrice subtraction method was proposed to obtain a PI feature for reducing the effect of permeability change. The thrice subtraction method included three subtraction steps and the operating process is shown in Figure 3. The first subtraction was implemented to eliminate the effect of PEC signal from air. A difference PEC signal was obtained after the subtraction process. Then, the normalization signal of the difference PEC signal was obtained and the second subtraction was performed between the difference PEC signal and its normalization signal, which was used to amplify the variation of the difference PEC signal. Next, the standard deviation of the difference normalization signal was calculated and the third subtraction was implemented, which was used to reduce the effect of permeability. Finally, the PI feature could be extracted from thrice subtraction signals when permeability changed. The detailed steps are described below.  
First, the subtraction between PEC signals of sample and air is implemented to obtain the difference PEC signal. The subtraction process can be shown as  
\[\Delta U(t)=\left|U(t)_{plate}-U(t)_{air}\right| \tag{7}\]  
where, \(U(t)_{plate}\) is the PEC signal of sample, \(U(t)_{air}\) is the PEC signal of air.  
Second, the subtraction between the difference PEC signal and its normalization signal is performed to obtain the difference normalization signal. The difference normalization signal can be formulated as  
\[U_{d}^{norm}=\Delta U(t)-\frac{\Delta U(t)}{\max(\Delta U(t))} \tag{8}\]  
where, \(\frac{\Delta U(t)}{\max(\Delta U(t))}\) denotes the normalization signal of the difference PEC signal, \(\max(\Delta U(t))\) denotes the maximum value of the difference PEC signal.  
Then, the third subtraction between the difference normalization signal and its standard deviation is calculated to obtain the thrice subtraction signal. The thrice subtraction signal can be expressed as  
\[U_{std}=U_{d}^{norm}-std(U_{d}^{norm}) \tag{9}\]  
where, \(std(U_{d}^{norm})\) denotes the standard deviation of the difference normalization signal.  
By changing the permeability of samples, the different thrice subtraction signals can be obtained and the PI feature can be extracted by mutual subtraction between them as  
\[U_{std-1}-U_{std-2}=0. \tag{10}\]  
Finally, by solving Equation (10), the amplitude and time of the PI feature can be obtained. It is easy to think that the common point of both different signals, called the PI feature point, is unaffected by the change in permeability. In other words, the PI feature is immune to the permeability effect.