esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This research investigates resonator width optimization for simultaneously enhancing electrical performance and mechanical reliability in wideband RF MEMS filters through systematic evaluation of three configurations: 0% (L1), 60% (L2), and 100% (L3) matching ratios between cap and bottom wafers using Au-Au thermocompression bonding. The study demonstrates that resonator width alignment significantly influences both electromagnetic field coupling and bonding interface integrity. The L3 configuration with complete width matching achieved optimal RF performance, demonstrating 3.34 dB insertion loss across 4.5 GHz bandwidth (25% fractional bandwidth), outperforming L2 (3.56 dB) and L1 (3.10 dB), while providing enhanced electromagnetic wave coupling and minimized contact resistance. Mechanical reliability testing revealed superior bonding strength for the L3 configuration, withstanding up to 7.14 Kgf in shear pull tests, significantly exceeding L1 (4.22 Kgf) and L2 (2.24 Kgf). SEM analysis confirmed uniform bonding interfaces with minimal void formation (-180 nm), while Q-factor measurements showed L3 achieved optimal loaded Q-factor (Q\({}_{\text{L}}\) = 3.31) suitable for wideband operation. Comprehensive environmental testing, including thermal cycling (-50 \({}^{\circ}\)C to +145 \({}^{\circ}\)C) and humidity exposure per MIL-STD-810E standards, validated long-term stability across all configurations. This investigation establishes that complete resonator width matching between cap and bottom wafers optimizes both electromagnetic performance and mechanical bonding reliability, providing a validated framework for developing high-performance, reliable RF MEMS devices for next-generation communication, radar, and sensing applications.  
M 202516878  
1  
MSOTECH, 1005, 1006, Dongtan Biz Tower 63-12, Dongtan Cheomdan Saneuop 1-Ro, Hwaseong-si 18469, Republic of Korea; [EMAIL_ADDRESS] (M.J.); [EMAIL_ADDRESS] (S.L.); rvan, [EMAIL_ADDRESS] (Y.J.)
## 3 Proposed SLM PAPR Scheme
### Proposed Low-Complexity Conversion Vectors
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Nonzero Elements** & **Indices of Nonzero Elements** & **Values of Nonzero Elements** \\ \hline \(K~{}=~{}3\) & \(\mathbf{T}~{}=~{}\{0,3,6\}\), \(\mathbf{T}~{}=~{}\{0,4,5\}\), & \(a^{(0)}~{}=~{}1,a^{(1)},a^{(2)}~{}=~{}\{\pm 1,\pm j\}\) and \\  & and \(\mathbf{T}~{}=~{}\{0,2,7\}\) & \(a^{(0)}~{}=~{}1-ja^{(1)},a^{(2)}~{}=~{}\{\pm 1,\pm j\}\) \\ \(K~{}=~{}4\) & \(\mathbf{T}~{}=~{}\{0,5,6,7\}\) and \(\mathbf{T}~{}=~{}\{0,3,7,8\}\) & \(a^{(0)}~{}=~{}1,a^{(1)},a^{(2)},a^{(3)}~{}=~{}\{\pm 1,\pm j\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters chosen for generating candidate vectors. Figure 3: Basic architecture of the SLM scheme using conversion vectors. of the elements of each selected phase rotation vector is larger than that of the other vectors. The corresponding vectors of the selected \(U\)-1 phase rotation vectors in \(\Re\) results in the final set \(\Re^{\prime}=\{\mathbf{p}^{(0)},\ldots,\mathbf{p}^{(i)},\mathbf{p}^{(U-1)}\big{|} \mathbf{p}^{(i)}\in\Re\}\), where \(\mathbf{p}^{(0)}=\left[1,0,\ldots,0\right]^{\mathrm{T}}\) represents the vector corresponding to the original signal. Then, with the final set \(\Re^{\prime}\), the candidate signals can be generated by using Equation (11). Since the equivalent channel can be expressed by Equation (10), it is easy to see that the length of the equivalent channel is \(L^{\prime}=L_{u}+L-1\). Due to the small values of \(L_{u}\), \(L^{\prime}\) can also be smaller than the number of pilot subcarriers used for channel estimation in the OFDM system. Therefore, the equivalent channel in the proposed SLM scheme can be estimated in the same way as that in the conventional OFDM system, and the effect of the applied phase rotation vector can be removed in the equalization procedure. In addition, the coherent bandwidth of the equivalent may be slightly smaller than that of the conventional OFDM system. It is noted that, in many practical OFDM systems, the spacing between two consecutive pilot sub-channels is sufficiently small as compared to the coherent bandwidth of the wireless channel. In such systems, the channel frequency responses can be obtained simply by linear interpolation between the channel frequency responses at two consecutive pilot sub-channels. **Remark 1**. : _In the proposed scheme, by making full use of the equivalent channel, the proposed SLM scheme can easily equalize the received data in the frequency domain as the conventional OFDM systems without SI detection and decoding. This does not increase the computational load at the receiver._  
**Remark 2**. : _It is pointed out in [16] that only the simplified SLM scheme [17] can be used in the point-to-multipoint scenarios. However, by using the equivalent channel, the proposed SLM scheme can be easily extended to the multiple-input multiple-output (MIMO) systems without special signal processing algorithms at both the transmitter and receiver._
## 3 Performance Evaluation
### Outage Probability
\begin{array}{ll}\mathrm{A_{1}}&=1-\sum\limits_{t=0}^{K-1}\sum\limits_{m=0}^{M -1}(-1)^{m}\frac{2C_{M-1}^{m}M}{t!}(m+1)^{\frac{t-1}{2}}\left(\frac{\lambda_{ \mathrm{BS}}\lambda_{\mathrm{SR}}\rho}{\kappa\Delta}\right)^{\frac{t+1}{2}}K_{ 1-t}\left(2\sqrt{\frac{(m+1)\lambda_{\mathrm{BS}}\lambda_{\mathrm{SR}}\rho}{ \kappa\Delta}}\right)\\ &-\sum\limits_{t=0}^{K-1}\sum\limits_{n=1}^{N}\sum\limits_{m=0}^{M-1}(-1)^{n+ m}C_{M-1}^{m}C_{N}^{n}\frac{2M\lambda_{\mathrm{SR}}}{t!}\Big{(}\frac{\rho}{n \lambda_{\mathrm{SR}}\rho}\Big{)}^{\frac{1-t}{2}}\left(\frac{\lambda_{\mathrm{ SR}}\rho}{\kappa\Delta}\right)^{\frac{t+1}{2}}\left(\frac{\lambda_{ \mathrm{SR}}\rho}{\kappa\Delta}\right)^{\frac{t+1}{2}}\\ &\times K_{1-t}\left(2\sqrt{\frac{\lambda_{\mathrm{BS}}}{\kappa\Delta}\left(n \lambda_{\mathrm{SP}}\delta\Delta+\left(m+1\right)\lambda_{\mathrm{SR}}\rho \right)}\right),\end{array}  \tag{46}\]  
where \(K_{1-t}\left(.\right)\) is modified Bessel function of the second kind ([53], Equation (8.407.1)). Next, with the same manner as deriving \(\mathrm{A_{1}}\), we can obtain an exact closed-form expression for \(\mathrm{A_{2}}\) as  
\[ \begin{array}{ll}&\mathrm{A_{2}}=1-\sum\limits_{t=0}^{K-1}\frac{2}{t!}\Big{(} \frac{\lambda_{\mathrm{BR}}\lambda_{\mathrm{RD}}\rho}{\kappa\Delta}\Big{)}^{ \frac{t+1}{2}}K_{1-t}\left(2\sqrt{\frac{\lambda_{\mathrm{BR}}\lambda_{\mathrm{ RD}}\rho}{\kappa\Delta}}\right)-\\ &\sum\limits_{t=0}^{K-1}\sum\limits_{n=1}^{N}(-1)^{n}C_{N}^{n}\frac{2\lambda_{ \mathrm{BD}}}{t!}\Big{(}\frac{\rho}{n\lambda_{\mathrm{RP}}\delta\Delta+ \lambda_{\mathrm{RD}}\rho}\Big{)}^{\frac{1-t}{2}}\Big{(}\frac{\lambda_{ \mathrm{BR}}\rho}{\kappa\Delta}\Big{)}^{\frac{t+1}{2}}K_{1-t}\bigg{(}2\sqrt{ \frac{\lambda_{\mathrm{BR}}}{\kappa\Delta}\left(n\lambda_{\mathrm{RP}}\delta \Delta+\lambda_{\mathrm{RD}}\rho\right)}\bigg{)}.\end{array}  \tag{47}\]  
Substituting (46) and (47) into (36), we obtain (34). Next, by replacing \(\lambda_{\mathrm{SR}}\), \(\lambda_{\mathrm{RD}}\), \(\lambda_{\mathrm{BS}}\), \(\lambda_{\mathrm{BR}}\), \(\lambda_{\mathrm{SP}}\) and \(\lambda_{\mathrm{RP}}\) in (34) by \(\lambda_{\mathrm{RD}}\), \(\lambda_{\mathrm{SR}}\), \(\lambda_{\mathrm{BR}}\), \(\lambda_{\mathrm{BS}}\), \(\lambda_{\mathrm{RP}}\) and \(\lambda_{\mathrm{SP}}\), respectively, we can obtain (35). Next, we provide an exact closed-form expression of the e2e OP for the B-ORS protocol as presented in Lemma 2. **Lemma 2**: _When \(1-\tau_{\mathrm{D}}^{2}\theta>0\), \(\mathrm{OP_{B-ORS}}\) can be computed by_  
\[
## 5 Discussion
As, Cr, Cu, Ni, Pb, Sb, Ti, V and Zn registered a moderate enrichment in all black crust samples (\(3<\) EF \(<33.6\)), which could be associated with different anthropogenic sources such as industrial activities [64; 65; 66], pavement wear [67], or vehicular traffic. Previous studies carried out on atmospheric particulate matter and dust sampled from the soil in the Naples area [68; 69] highlighted the presence of two different pollutant sources: mobile and fixed. In particular, the latter one is related to the ILVA steel mill (working until 1993) located in the industrial area of Bagnoli-Coroglio and to the oil refineries (i.e., Q8, Esso, Tamoil) in the Eastern area of Naples. These industrial emissions have certainly affected enrichment in some elements such as Ti, Zn, Pb, Cu, Ni and V. Furthermore, the presence of high Sb values in these samples could be attributed to the incinerator plants [70] located 30 km north of the city [71; 72]. Additionally, by comparing the lower contents of As, Ni and V determined in this study with respect to previous research [23; 26; 27; 30; 32; 33; 73], it is possible to assert that these values may be dependent on the moderate degree of use of domestic heating resulting from the more temperate climate of Naples compared to that of other Italian and European cities. In fact, the above-mentioned elements may be generally associated with the different fuels used in heating, such as coal (in the past) or fuel oils [74; 75]. Considering the data obtained, it is possible to assert that the greatest enrichment in heavy metals of the black crusts from the church of _San Domenico Maggiore_ (samples SD2 and SD3) is certainly due to the use of Pb-based gasoline, as well as tire and brake wear and other artefacts of machine wear. This aspect is also evident in Figure 7, which shows the location of the two case studies in the center of Naples. In fact, although the church of _San Domenico Maggiore_ is nowadays located in a pedestrian area (i.e., _Piazza San Domenico Maggiore_ and _Vico San Domenico Maggiore_), it was surrounded by a high volume of vehicular traffic until a few decades ago. With respect to the samples from the cloister of _San Marcellino e Festo_ (SM-S1, SM-A1 and SM-P1), data show an enrichment in various heavy metals (As, Cr, Cu, Ni, Pb, Sb, Ti, V and Zn) that can be traced to different polluting sources (industrial activities and vehicular traffic). The sampled artifacts are located inside a closed cloister and not exposed to direct sources of emissions, but they still showed weathering phenomena related to stone decay and air pollution. The results obtained indicate that all monuments, even those not directly exposed to sources of direct emissions, can undergo decay processes. This demonstrates how the conservation of built cultural heritage is strictly connected to environmental protection issues and how it is necessary to intensify the related intervention policies. Figure 6: Correlation between the Zn vs. Pb, Cu vs. Zn e Sb vs. Cu of the two different sites: (**a–c**) Complex of _San Domenico Maggiore_, (**d–f**) Cloister of _San Marcellino e Festo_.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 3 Results
### Prediction Model
#### 3.3.1 Development of the Regression Model
In most cases, the constant and coefficient of the explanatory variable, obtained for each developed axle torque prediction regression model, showed significant differences (\(p\) < 0.01), except for the constant (\(p\) = 0.115) in Model A, engine speed (\(p\) = 0.018) in Model D, and travel speed (\(p\) = 0.077) in Model F. Nevertheless, Table 7 shows that the \(p\)-values of the regression Models A, D, and F were significantly different (\(p\) < 0.01). Thus, Models A, D, and F are considered to be usable. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Variable** & **S.E** & \(\beta\) & **t** & \(p\)**-Value** & **Tolerance** & **VIF** \\ \hline \multirow{4}{*}{A} & Constant & 159.556 & & 1.581 & 0.115 & & \\ \cline{2-6}  & \(T_{e}\) & 0.209 & 0.806 & 7.794 & 0.000 & 0.182 & 5.485 \\ \cline{2-6}  & \(S_{e}\) & 0.043 & 0.333 & 3.218 & 0.001 & 0.182 & 5.485 \\ \hline \multirow{4}{*}{B} & Constant & 7.992 & & 86.274 & 0.000 & & \\ \cline{2-6}  & \(D_{t}\) & 0.472 & 0.931 & 49.123 & 0.000 & 1.000 & 1.000 \\ \hline \multirow{4}{*}{C} & Constant & 32.052 & & 28.678 & 0.000 & & \\ \cline{2-6}  & \(s\) & 0.751 & 0.807 & 26.111 & 0.000 & 0.524 & 1.910 \\ \cline{2-6}  & \(S_{t}\) & 3.998 & 0.130 & \(-\)4.218 & 0.000 & 0.524 & 1.910 \\ \hline \multirow{4}{*}{D} & Constant & 65.721 & & 7.373 & 0.000 & & \\ \cline{2-6}  & \(D_{t}\) & 0.508 & 0.874 & 42.856 & 0.000 & 0.789 & 1.268 \\ \cline{2-6}  & \(T_{e}\) & 0.090 & 0.208 & 4.649 & 0.000 & 0.165 & 6.078 \\ \cline{2-6}  & \(S_{e}\) & 0.018 & 0.102 & 2.383 & 0.018 & 0.179 & 5.573 \\ \hline \multirow{4}{*}{E} & Constant & 51.057 & & 4.646 & 0.000 & & \\ \cline{2-6}  & \(T_{e}\) & 0.069 & 1.271 & 37.306 & 0.000 & 0.172 & 5.817 \\ \cline{2-6}  & \(S_{t}\) & 1.882 & 0.831 & \(-\)57.115 & 0.000 & 0.942 & 1.062 \\ \cline{2-6}  & \(S_{e}\) & 0.014 & 0.726 & 21.478 & 0.000 & 0.175 & 5.722 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the coefficient analysis for each prediction model.
## 2 Materials and Methods
### Proposed Model
#### 2.3.1. Created Tagged Speech  
First, the voice data obtained in this study were subjected to a Wiener filter as a preprocessing step to eliminate noises from environmental factors and microphone noises [38]. The Wiener filter is a method performed in frequency space, where \(y(n)\) is a noisy speech signal that is expressed using Equation (5):  
\[y(n)=x(n)+v(n), \tag{5}\]  
where \(x(n)\) is the clean signal, \(v(n)\) is the white Gaussian noise, and \(n\) is the discrete time variable.  
The error signal (\(e_{x}(n)\)) between the clean speech sample at time \(n\) and its estimate can be defined using Equation (6):  
\[e_{x}(n)\triangleq x_{n}-\mathbf{h}^{T}\mathbf{y}(n), \tag{6}\]  
where the superscript \({}^{T}\) denotes the transpose of a vector or a matrix. \(\mathbf{h}\) is a Finite Impulse Response (FIR) filter of length \(L\) and its transpose is given in Equation (7).  
\[\mathbf{h}^{T}=[h_{0},h_{1},\dots,h_{L-1},] \tag{7}\]  
\(\mathbf{y}\) is a vector containing the most recent samples of the observation signal \(y(n)\) and its transpose is given in Equation (8).  
\[\mathbf{y}^{T}=[y(n),\dots,y(n-1),\ y(n-L+1)] \tag{8}\]  
The Wiener filter coefficients are obtained by minimizing the average squared error expressed by \(e_{x}^{2}(n)\).  
To create a dataset and assign an appropriate speech class (C/V/S) to each speech signal, every speech sentence should have an associated phoneme-level segmentation.  
In this work, to create a reference dataset, manual segmentation methods were used. For this purpose, the \"Pratt\" package was used as a major tool. Every speech sentence was carefully examined and labeled manually using the selected tool. After labeling was completed, a tagged speech label file was created.  
The manual segmentation method is considered to exhibit more accurate performance in comparison with automatic segmentation [39; 40; 41]. Therefore, manual segmentation was employed to create the unique labeled Kurdish dataset to be used in other studies in the future.  
The phoneme segmentation is represented in Figure 3. In this figure, a sample annotated speech waveform and its spectrogram representing the Kurdish sentence \"Bivir u hinceken din\" spoken by an adult female are shown.  
Figure 3. Speech waveform (**a**); spectrogram (**b**) and phonemes and words segmentation (**c**) of the Kurdish sentence.
# Performance Aspects of Retrodirective RFID Tags +
###### Abstract  
Although RFID(radio frequency identification) tags do not require a direct line of sight, their operational range is often characterized as being limited. Indeed, in the case of passive RFID tags, the interrogating signal from the transmitter needs to reach the tag's radio transponder and trigger a nearly omnidirectional scattered signal to be harvested by the receiver. This two-way (from Tx to the tag and back to Rx) channel exhibits increased attenuation not only due to the doubled distance (in case Tx and Rx are collocated) but also to the uncontrolled (i.e., unfocused) backscattering. In the work at hand, we propose a way to control the backscattered radiation and focus the produced beam towards the direction of the reader (the Tx-Rx device). Indeed, one can utilize the concept of retrodirective arrays to immediately control the direction of departure of the backscatter link, maximizing the scattered power towards the reader and thus delivering an increase in the operational range of the tag. This of course means that in this case, the tag should be equipped with a minimum of two element radiators. Thus, retrodirective RFID array tags are introduced in the current work to increase the operating range with minimal costs and levels of complexity since 90\({}^{\circ}\) hybrids are used to achieve proper backscattering. To evaluate the proposed passive tag array, performance aspects are addressed. Specifically, we examine the Bit Error Rate with respect to the Signal to Noise Ratio for the retrodirective tag, the one antenna, the broadside, and the spatial diversity array. The results prove that the proposed tag allows for a significant increase in the operational range.  
RFID array tags; retrodirective antenna array; retrodirective signaling matrix; pinhole channel model for RF tags +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Materials and Methods  
Field work and sample selection was performed during the autumn months of 2020. A total of 59 tufa samples were collected at two locations--in the GRB and in the SRB. Tufa samples were collected from: active waterfalls, barrages, pools (recent tufa), inactive waterfalls, and fossil calcareous tufa. Facies are mainly distinguished based on field observations and confirmed afterward by detailed analyzes in thin sections. All samples were cleaned of weathered surfaces, before preparing thin sections, and then optically analyzed using a petrographic polarized microscope for transmitted light (Leica DMLSP), which was connected to a Leica DFC290 HD camera via the LAS V4.1 application. The mineralogical compositions of the twelve samples were determined by X-ray powder diffraction (XRD). Fifteen representative tufa samples were selected and examined using a JEOL JSM-6610 LV scanning electron microscope connected to an X-Max energy dispersion spectrometer. Samples were covered with carbon using a BALTEC-SCD-005 Sputter coating device. Results were recorded under high vacuum conditions with an accelerating voltage of 20 kV and a beam current of 0.5-1.8 nA. All the above analyses were performed at the Department of Mineralogy, Crystallography, Petrology and Geochemistry, Faculty of Mining and Geology, University of Belgrade.  
Stable isotope (oxygen and carbon) signatures in tufa have been used to clarify important climatic and environmental conditions in their formation [3; 12; 36; 37; 38; 39; 40; 41; 42]. In general, oxygen isotopes are indicators for paleotemperature, while carbon isotopes can point to a source of CO\({}_{2}\). A set of eight samples (four samples from SRB, as well as four samples from GRB) was selected for stable isotope analyzes. Stable oxygen and carbon isotope values (\(\delta^{13}\)C and \(\delta^{18}\)O) of the carbonates were measured at the Jozef Stefan Institute in Ljubljana, Slovenia. The measurements were calibrated on the VPDB scale with the reference materials IAEA-CO8 and NBS-19. The measurements' precision was better than 0.1% for both the O and C values.  
Figure 1: Geographical position of the studied sites.
# Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations
## 2 Related Work
### Optimization Methods for HRSs  
Many optimization methods have been proposed for HRSs to meet the growing demand. In [16], multi-energy systems were proposed by integrating fast charging and HRSs under the uncertainties in renewable energy sources and vehicle demand. [PERSON] et al. [17] investigated the optimization approach for electric vehicle charging stations supported by hydrogen energy systems, highlighting the importance of charging demand modeling and problem formulation. [PERSON] et al. [18] proposed the robust optimization approach to design a solar-powered charging station supporting electric and hydrogen vehicles under uncertainties in power generation and demand. In [19], the integrated optimization model was developed with the entire hydrogen supply chain network, which considers feedstock supply, plant installation, transportation, and carbon capture systems. [PERSON] et al. [20] proposed an optimization model with reinforcement learning to determine the optimal placement of HRSs. [PERSON] et al. [21] developed a decision support system by incorporating the scenario management, optimization model, visualization, and data management components to optimize the locations of HRSs and hydrogen supply chains. [PERSON] et al. [22] proposed a modeling approach for an on-site electrolysis-based HRS considering factors such as layout design, operational strategy, and renewable energy integration. [PERSON] et al. [23] introduced an advanced optimization model incorporating uncertainty in hydrogen demand to minimize costs and ensure customer satisfaction using stochastic programming. In [24], real-world operational data from HRSs provide insight into energy performance, showing variations in efficiency and energy consumption over time.  
These studies have demonstrated the importance of adaptive data modeling in optimizing HRS operations. However, data variables can vary depending on the environmental conditions and equipment in HRS. Thus, the performance of the data model optimized for a specific infrastructure may not be guaranteed for a new one. For stability, the data model should consider different external or internal factors, which change continuously when facilities are added or removed. For robustness to these changes, we propose an imputation model with the attention mechanism called the AHRI model. The attention mechanism is used to determine primary features, which improve the predictive performance and help quickly adapt new equipment.
# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 4 Results and Discussion
### Results  
After data preparation, Artificial Neural Networks with optimal parameters were applied. The training process was evaluated using training and evaluation loss, as well as the accuracy. Before dealing with imbalance, the model gives an accuracy of 90% but a very low precision of 68% which calls for questions about the model's ability to classify. The Area Under Receiver Characteristic Curve (AUC-ROC) for the original data was 94%. After dealing with the imbalance, accuracy was 92% and precision goes to 85%. The AUC-ROC after dealing with the data imbalance was 95%. The oversampling method made the model much more accurate and precise.  
From the learning curves above in Figure 2, it can be seen that the training loss kept on dropping throughout the process, which is the same for the validation loss. On the contrary, the training and testing accuracy look to have reached the maximum level mid way through training. From the learning curves, the results are promising, and next is testing the model to verify if it does not over-fit.