esa-sceva/satcom-chunk-collection huggingface dataset:
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 2 Introduction: The Radar System
Arrays with electronic beam steering capability are called phased-arrays antennas. A linear array is costly and complicated to design if compared to a circular dish antenna. However, the inherent flexibility of phased-array antennas to steer and control the beam shape represents an attractive feature for multi-function radar system manufacturing [10; 11; 12; 13; 14]. In Figure 4, the radiation pattern for the linear array is illustrated. The grating lobes, evident in the plot of Figure 4, get closer to the main beam when the distance \(d\) between the elements increases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Normalized Intensity Pattern Expression:**\(|E(\beta)|=|\frac{2\pi r^{2}J(kr\sin\beta)}{kr\sin\beta}|\) & \\ \hline \# & **Symbol** & **Description** & **Numerical Values for Input** & **Unit** \\ \hline \(1\) & \(\lambda\) & Wavelength & \(0.1\) & \(m\) \\ \(2\) & \(d\) & Aperture diameter & \(0.7\) & \(m\) \\ \(3\) & \(r\) & radius = \(\frac{d}{2}\) & \(0.35\) & \(m\) \\ \(4\) & \(k\) & Propagation constant \(k=\frac{2\pi}{\lambda}\) & \(62.8320\) & \(m^{-1}\) \\ \(5\) & \(\beta\) & steering angle & \(-\pi:\frac{\pi}{200}:\pi\) & \(rad\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Circular dish antenna [5]. Figure 3: Radiation pattern for the circular aperture antenna of Table 1 computed using Matlab code. The electromagnetic wave radiated by the antenna is a mixture of electric and magnetic fields. Polarization may be classified as linear, circular, or elliptical. Radar antennas are designed to transmit and receive signals at specific polarizations. The two most common forms of polarization employed in radar technology are horizontal linear polarization (or H), and vertical linear polarization (or V) [15]; by convention, the \"polarization\" of electromagnetic waves refers to the direction of the electric field. Circular polarization is also used in some important radar applications, such as in weather radar applications [16; 17]. Some antennas for radar systems are designed to receive and transmit signal on the same polarization (e.g., HH for horizontal receive and horizontal transmit and VV for vertical receive and vertical transmit), while some complex radar system antennas may be designed transmitting and receiving components working on different polarizations (e.g., HV for horizontal transmit and vertical receive and VH for vertical transmit and horizontal receive). Antennas for radar systems can present various levels of polarization combination, e.g., single-level polarization (HH, VV, VH, or HV), double-level polarization (HH, VV; HH, HV; or VV, VH), triple-level polarization (HH, VV, HV or HH, VV, VH), and fourfold-level polarization (HH, VV, VH, HV) [18; 19; 20; 21; 22; 23; 24; 25; 26; 27].
## 4 Evolution of Cadmium Transporters and Their Functions in Cadmium Accumulation
### Function of Cd Transporters
#### 4.2.5 OPTs  
The OPT family, which contains YSL transporters, is involved in transporting metal-nicotianamine (NA) complexes through the plant cell membrane. Thus, when Cd\({}^{2+}\) is chelated, it can be taken up through the OPT or YSL proteins [135]. In order to enhance the availability of metal ions in the rhizosphere, plant roots secrete LMW organic acids, such as mugineic acids (Mas) and phytosiderophores (PS), to form metal-ligand complexes, which are then transported by YSL transporters [135]. This strategy is very efficient, as it facilitates the uptake of Fe from Fe-deficient soils by some Poaceae species [136]. In addition, YSLs also play an important role in Cd transport. In _Zen mays_, ZmYS1 has been suggested to transport the Cd-PS and Cd-NA complexes at a low rate [66]. Two _ZmYS1_ orthologs isolated from rice and Cd hyperaccumulator _Solanum nigrum_, _OsYSL2_ and _SnYSL3_, have been found to transport Cd-NA complexes when expressed heterologously in yeast [137; 138].
# Active Electromagnetic Clutch for Crankshaft Decoupling from a Belt Drive System
## 4 Clutch State Estimation
### Back-EMF Disturbance Method  
To open the clutch, a coil current between 5 to 6 A is needed to overcome friction and inertial contributions to open the toothed profile and pull the mover. This current is applied for 0.2 s. Then, a holding current of 2 A is used to maintain the clutch open. Due to iron saturation, the previously presented inductance estimation method is not a suitable candidate to estimate the state of the clutch. Note from Figure 5 that the variation of the inductance with the clutch air gap is not indicative when the coil current is larger than 0.6 A. However, the back-EMF voltage is not negligible when the clutch is opening. This disturbance is here exploited to estimate the clutch state.  
In fact, there is an increase in the back-EMF voltage (\(e_{c}\)) when the clutch is opening/closing. By converse, \(e_{c}\) becomes zero when the clutch hub is hardly stopped due to mechanical contact at both stroke ends (minimum/maximum air gap). This sudden change in back-EMF generates a strong variation of the coil current at the instant of the impact. Thus, monitoring both \(e_{c}\) and \(i_{c}\) in real time could be useful to identify the effectiveness of the clutch operation. The proper estimate of the back-EMF is challenging to obtain because it presents highly nonlinear behavior at the instant of the impact and it is not a persistent disturbance.
## 2 Method
### Gamma Process Model  
In reliability modeling, the gamma process represents the degradation process as a form of cumulative damage through which deterioration occurs gradually over time, and the small increments are always monotonic [22]. For a non-negative-value process \(\{y(t)\), \(t\geq 0\}\), \(y(t)\) is the measured degradation, such as the capacity loss, for a test battery at time \(t\). Assuming that the degradation process maintains a steady state, the gamma process has the following properties [23]:  
* \(y(0)=0\)
* the increments \(\Delta y(t)=y(t+\tau)-y(t)\) are independent
* \(\Delta y(t)\) has a gamma distribution G(\(\alpha\tau\), \(\beta\)), with the probability density function (PDF) defined by  
\[\text{f}(y)=\frac{\beta^{-\alpha\tau}}{\Gamma(\alpha\tau)}y^{\alpha\tau-1}e^{- y/\beta} \tag{5}\]Here, the shape parameter is \(\alpha>0\), the scale parameter is \(\beta>0\), and the lifetime \(t_{Z}\) is defined as the time when the degradation reaches the failure threshold \(Z_{0}\). The cumulative density function of \(t_{Z}\) is defined by  
\[F_{t_{Z}}(t)=\frac{\Gamma(\alpha t,Z_{0}/\beta)}{\Gamma(\alpha t)} \tag{6}\]
## 3 Space Matching Process Using Multi-Resolution Cube Grid
### Software Verification  
An experiment for securing installation space for a virtual equipment in a space of a simple shape was conducted before applying the developed algorithm to an actual site. Virtual equipment with the size of 1(m) \(\times\) 1(m) \(\times\) 4(m) were applied, which simplified the unit node value to 1 (m) (Grid Box Size 1 (m)) for the convenience of calculation in order to check the difference from the actual calculation. Furthermore, the unit node value of 1 (m) was also applied to the target space as described above. The calculation and algorithm applications were carried out without the rotation of equipment because the present calculation was a process of merely comparing two resulting values, rather than requiring the number of all equipment arrangement spaces.  
Next, the corresponding space data was applied to the developed algorithm and the results were checked. Examples of these results are shown below in Table 5.  
Table 5 shows the coordinates (x, y, z) of the grid box where the virtual equipment is located within the 3D grids of the entire site enabling to obtain coordinate values in which the virtual equipment can be arranged within the site. The node size represents the grid box size. If point cloud was found within the box of node size defined by the algorithm, the xyz coordinate value was used to store the location.  
The sample results obtained from the algorithm and the calculation results for the number of spaces that can be actually arranged were compared and outlined in Table 6. To compare and organize the number of spaces available where equipment can be arranged, the resulting sample value through algorithm calculation which was made based on the box volume that equipment occupies, was compared with the computed number of space where true arrangement of equipment is feasible.  
In experiments 1 and 3, the two results are identical, but they are different in experiment 2. This problem appears because the starting point for applying the algorithm is different from the starting point of the calculation process. If the two starting points are identical or the reference node value is lowered, the same results are obtained.
###### Abstract  
The present study investigates the extent and spatial distribution of metal concentration in stream sediments that flow into Keban Dam Lake, Turkey. Sediment samples were analysed for trace and potentially toxic elements (PTEs), including V, Cr, Co, Ni, Cu, Zn, Pb, Tl, Th, and U. Enrichment Factor (EF), Contamination Factor (CF), Geo-accumulation Index (Igeo), and Pollution Load Index (PLI) were employed to assess contamination levels. Results reveal that Cr exhibited very high enrichment (EF = 15.95) in downstream urban samples, while Cu and Zn showed high enrichment in samples collected from the middle to lower reaches of the stream, probably indicating anthropogenic contributions. Most other elements, such as Pb, Tl, Th, and U, were within natural background levels. Sediment Quality Guidelines (SQGs) indicate that Cr, Ni, and Cu may pose potential ecological risks, especially in samples from urban-influenced and downstream areas where concentrations exceed the Probable Effect Levels (PEL; Cr: 160 mg/kg, Ni: 42.8 mg/kg, Cu: 108 mg/kg). Multivariate statistical analyses, including Pearson correlation and hierarchical clustering, reveal three distinct geochemical groupings. Among these, the most contaminated cluster--corresponding to midstream and downstream regions--is characterized by elevated Cu and Zn concentrations. Strong correlations among Cu-Zn, Ni-Cu, and Th-U suggest there is a combination of anthropogenic and lithogenic sources for most metals. While most sites showed low to moderate pollution, urban downstream locations exhibited significant metal accumulation, necessitating the region's continued environmental monitoring and management strategies.  
urban pollution; environmental risk assessment; anthropogenic pollution; Keban Dam Lake catchment Article  
1  
[PERSON], [PERSON] Geochemical Assessment of Potentially Toxic Elements in Urban Stream Sediments Daining into the Keban Dam Lake, Turkey. _Appl. Sci._**2025**, 15, 7565.  
[[https://doi.org/10.3390/app15137565](https://doi.org/10.3390/app15137565)]([https://doi.org/10.3390/app15137565](https://doi.org/10.3390/app15137565))
# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison
### Countermeasures for GPS-Denied Environment--Alternate Positioning
#### 6.3.3 Star Tracker-Based Approaches  
A star tracker is a sensor that recognizes star patterns in the sky and can be used for navigation purposes. Star tracker-based navigation approaches have existed for thousands of years, particularly in nautical applications. Modern star tracker systems are mature technology. Star tracker systems are commonly used in space-based and aviation-based navigation applications; however, star trackers by themselves do not typically provide position estimates. Rather, they are used to determine orientation and can be used in conjunction with other sensors to determine position. Star tracker methods can achieve high accuracy in orientation determination. These systems perform well in low-light and clear-sky scenarios; however, their performance suffers in daylight hours due to interference from the sun. Performance also suffers in cloudy conditions due to the occlusion of the observable stars.  
Researchers have recently attempted to leverage star tracker techniques and improve performance, although this is not an active research area. There are examples of researchers attempting to fuse star tracker data with IMU data in a multi-sensor fusion approach [152; 153]. There are also examples of ongoing work to attempt to improve star tracker performance during daytime operations [154; 155]. The authors of [155] proposed an approach where they characterized ambient polarized skylight and then fused skylight, starlight, and IMU data through a Kalman filter approach. The authors of [154] focused on characterizing sky background radiation and stellar radiation, which can be used to calibrate and configure the star tracker hardware and software for optimal detection. The results from these papers are promising; however, star tracker performance in the daytime continues to be a significant limitation and an unsolved problem. Star tracker systems are often expensive, limiting their applicability to many unmanned systems. This research area is unlikely to experience near-term technical breakthroughs since active research is limited.
# Transparent Attribution of Contributions to Research: Aligning Guidelines to Real-Life Practices
## 1 Introduction  
Openness in research publishing is valued by readers, beneficial for authors and other contributors, and useful to researchers, research evaluators and funders. Critical questions that academic researchers and professionals in research publication are raising about traditional editorial and publishing practices (see, for example, other articles in _Publications_ and [1]) are a clear sign that stakeholders in research publishing increasingly value openness, especially open peer review, open access and open data [2]. Now that research dissemination is digital and global, how research is performed, written and shared has changed in important ways since pre-internet times, and further progress aimed at optimizing rigor, transparency and openness can be anticipated.  
Calls for more openness have stimulated awareness that many research publication policies are based on tradition, habit and widely held but unverified assumptions, rather than on knowledge about current incentives, motivations, processes and outcomes [3]. In the context of continuing efforts to challenge assumptions that inform publication policies and ethics, much attention has focused on authorship criteria, and many recommendations have been made to guide the attribution of authorship and the acknowledgment of non-author contributors (e.g., [4, 5]). A relatively recent attempt to systematize how the many different contributions to published research are recognized is the Contributor Roles Taxonomy (CRediT; [[https://cascrai.org/credit/](https://cascrai.org/credit/)]([https://cascrai.org/credit/](https://cascrai.org/credit/))). CRediT was devised to permit,in a machine-readable manner, the specification of specialized capacities that different persons bring to a study, in any scientific discipline, irrespective of whether a given contribution merits authorship or not [6].  
But our analysis suggests that CRediT, launched in 2014, is essentially a detailed authorship classification in conflict with how contributors was initially conceived [7], and that its implementation risks depriving non-author contributors of due credit. Here, we briefly review key concepts of authorship and contributors, examine the range of non-author contributions that may (or may not) be acknowledged in research reports, and describe the types of non-author editorial contributions to research that we are most familiar with through our own work as professional authors' editors [8; 9; 10; 11; 12; 13] and translators. (Readers should note here that we are writing from our individual standpoint and combined experience of several decades.) We then scrutinize CRediT and identify some of its potential pitfalls. To close, we offer solutions to these shortcomings along with recommendations for making credit attribution more transparent, accurate and open. Our suggestions are intended as a starting point for further conversations about ways to make all contributions to research transparent.
## 5 Application Results and Analysis  
### Simulation PD Models and Denoising Performance Evaluation  
#### 5.1.1 Simulated PD Pulses  
Simulated PD signals are indispensable in verifying the denoising performance of the proposed method and offer comparisons with previous methods. Generally, damped exponential pulse (DEP)  
Figure 3: Schematic diagram of PD on-line measurement system.  
and damped oscillatory pulse (DOP) are adopted as the simulated PD models [11; 16], expressed as Equations (17) and (18), respectively:  
\[s_{1}=A\big{(}e^{-t/\tau_{1}}-e^{-t/\tau_{2}}\big{)} \tag{17}\]  
\[s_{2}=A\big{(}e^{-t/\tau_{1}}-e^{-t/\tau_{2}}\big{)}\sin(2\pi f_{c}t) \tag{18}\]  
In the PD models, A is the pulse peak value, time constants \(\tau_{1}\) and \(\tau_{2}\) determine the typical PD parameters such as pulse risetime, pulse width and pulse decay time. \(f_{c}\) is the oscillatory frequency of the DOP type pulse. The corresponding pulse waveforms are shown in Figure 4.  
When an electrical method is used to detect PD activities, the waveforms of the measured PD pulse depend on the PD source, the frequency response of the PD sensors, and stator winding propagation characteristics between them [25]. Multi-source PDs are commonly present in insulation systems simultaneously during operation due to imperfect insulation. PD pulses of different types present different features in waveforms based on PD mechanisms. The PD pulses in the measured signal are diverse in time and frequency waveforms. In order to well simulate the measured signal, the simulated signals are obtained by combining various pulses with controlling parameters of (17) and (18).
## 2 Principle of the Conventional and Improved FOC Strategy
### Principle of the Improved FOC Strategy
#### 2.2.2 Amelioration of FOC Strategy Using 3L_NPC Inverter  
To enhance the transient and steady state performances of the FOC strategy for an asynchronous machine, and to decrease voltage stress in semi conductors, particularly for high power applications, with a suitable SPWM control strategy, a 3L_NPC inverter can replace a 2 L inverter. As Figure 8 shows, all the other blocks of the FOC strategy scheme are still the same [23].
## 3 Results and Discussion  
### Construction of Engineered Bacterial Strain BL21-7  
_Pseudomonas_ K-62 is known for its high tolerance to Hg [7; 16]. It can transport Hg into the cell by MerT-MerP [16]. The carbon-Hg linkages in organic Hg can be split by MerB1 and MerB2, releasing Hg\({}^{2+}\) intracellularly [17]. An artificial operon, _P16S-g10-merT-merP-merB1-merB2-ppk-rpsT_, was constructed in this study (Figure 1 and Supplementary Materials S2). _P16S_ is one of the strongest constitutive promoters. It is derived from the 5\({}^{\prime}\) UTR of the plastid 16S ribosomal RNA gene [12; 13; 14]. The Hg tolerance of bacteria in which the transcription of _mt-1_ is promoted by _P16S_ was at least six times greater than that of bacteria in which _P16S_ was not included [6]. _G10_ is a well-known translation enhancer. When placed upstream of a synthetic ribosome-binding site (RBS), there was a 110-fold increase in the translation efficiency of the downstream _lacZ_ gene [11]. As a strong terminator, _rpsT_ can enhance transcript stability by forming a secondary structure at the 3\({}^{\prime}\) end of the mRNA [15]. The _ppk_ gene encodes a key enzyme responsible for synthesizing polyphosphate, which can respond to mercuric ions by chelating reactions and decrease their toxicity to cells [18; 19]. The mercuric ions in cells were transferred from the extracellular space by MerT-MerP [16] or from organic Hg by MerB1 and MerB2 [17]. The _E. coli_ BL21 (DE3) strain transformed with plasmid pET28a harboring the artificial operon _P16S-g10-merT-merP-merB1-merB2-ppk-rpsT_ was named BL21-7.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 3 Data Analysis and Model Construction
### Learning Model Description
#### 3.2.2 Model Construction
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},\ x_{t}]+b_{r})\] (6)
* Current memory content (\(h^{\prime}{}_{t}\)): calculates the new memory content based on the reset gate. \[h^{\prime}{}_{t}=tanh(W_{r}\cdot[r_{t}\cdot h_{t-1},\ x_{t}]+b_{h})\] (7)
* Final memory at time t: combines the previous hidden state and the current memory content. \[h_{t}=(1-z_{t})\cdot h_{t-1}+z_{t}\cdot h^{\prime}{}_{t}\] (8)  
GRU's simplified architecture, with fewer gates compared to LSTM, allows for lower computational overhead while still capturing the necessary temporal dependencies for AoA estimation. In addition, Transformer-based models were also explored due to their self-attention mechanism, which enables effective long-range dependency modeling. However, AoA estimation primarily requires capturing short-range temporal dependencies in phase drift signals. The self-attention mechanism in Transformers increases computational cost while not necessarily improving accuracy for this task. Our comparative analysis confirmed that Transformers are less efficient than GRU in terms of both computational complexity and estimation accuracy. GRU layers were therefore employed to effectively learn sequential patterns in time series data, enabling the model to make accurate predictions based on historical phase drift values. Following the GRU layers, several fully connected (dense) layers were added to transform the learned temporal features into a suitable form for regression. The dense layers play a role in extracting angle characteristics from the processed data, identifying the complex relationships between input features that are necessary for accurate angle estimation. The mathematical operation in a dense layer is as follows:  
\[y=W\cdot x+b. \tag{9}\]  
Each dense layer was followed by a ReLU activation function, introducing nonlinearity into the model and enhancing its capacity to learn complex patterns from the data. The final layer of the model was a dense layer with a single unit, corresponding to the predicted angle. Since this was a regression task requiring continuous output values, no activation function was applied to this layer. Despite the data being collected at 10-degree intervals, the regression approach allowed the model to make precise predictions within these intervals, further demonstrating the model's robustness and accuracy in angle estimation.
## 5 Experimental Results and Discussion
### Confidence Intervals and Statistical Testing  
Confidence intervals for accuracy and F1 scores were calculated as shown in Table 8. Paired \(t\)-tests demonstrated statistically significant performance differences between CNN and AraBERT (\(p<0.01\)).  
Figure 12: Precision–recall (PR) curve for a repetition of the experiment with different deep learning models for the second layer (AraBERT pretrained embeddings).  
Figure 13: Precision–recall (PR) curve for a repetition of the experiment with different deep learning models for the second layer (AraBERT pretrained embeddings).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Metric** & **Mean (\%)** & **95\% CI (\%)** \\ \hline CNN & Accuracy & 92.0 & [91.5, 92.5] \\ CNN & F1 Score & 93.0 & [92.4, 93.6] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confidence intervals for accuracy and F1 scores for CNN.