esa-sceva/satcom-chunk-collection huggingface dataset:
# Picoammeters Based on Gradiometric Superconducting Quantum Interference Devices
## 2 Design, Fabrication and Experimental Setup
A high intrinsic responsivity of SQUID (V\({}_{\phi}\) = \(\partial\)V/\(\partial\Phi\)\(\propto\) I\({}_{0}\)R\({}_{\rm s}\)) was obtained by increasing the critical current density of the Josephson junctions rather than the shunt resistor value to limit the increase in McCumber parameter:  
\[\beta_{C}=\frac{2\pi I_{0}C_{j}R_{s}^{2}}{\Phi_{0}}\]  
which has a quadratic dependence on the resistance, which can give rise to hysteresis phenomena in the current-voltage characteristics [16]. Here, \(C_{j}\) is the junction capacitance, 2 \(I_{0}\) is the SQUID critical current, \(R_{s}\) is the shunt resistor and \(\Phi_{0}\) the magnetic flux quantum. To avoid performance degradation due to a nonoptimal inductance parameter \(\beta_{\rm L}\) = 2 LI\({}_{0}\)/\(\Phi_{0}\), a damping resistor equal to the junction shunt resistor across each SQUID was inserted (Figure 1) [17]. The fabrication process was extensively described elsewhere [18] and is only briefly summarized below. The process is based on so-called _trilayer technique_, in which in an ultrahigh-vacuum system, a multilayer structure consisting of Nb/Al-AlOx/Nb is deposited by dc sputtering without vacuum break on a silicon substrate patterned by optical photolithography to define the sample geometry. At this step, the tunnel barrier is obtained by filling the chamber by dry oxygen after the aluminum deposition. A lift-off process completes this first stage. The Josephson junctions, having a square shape with an area of 4 \(\times\) 4 \(\mu\)m\({}^{2}\) each one, are patterned again by optical photolithography and achieved via selective niobium anodization process (SNAP) of top niobium. A further insulation layer of silicon dioxide deposited by rf sputtering enhances the insulation, especially on the junction edges. The shunt and damping resistors of the SQUID are made by depositing a layer of Au-Pd alloy by dc sputtering in a second UHV system. In the same system equipped with an ion gun, after a last photolithography step to define the geometry of the device wiring, a thicker layer (500 nm) of Nb film is deposited once again by dc sputtering aftera preliminary cleaning of the surface oxide using the ion gun. The complete characterization of the sensors, including measurements of the voltage (V)-magnetic flux (F), the input current-magnetic flux transfer factor and the spectral density of magnetic flux noise, were performed in a liquid helium bath at T = 4.2 K by using a cryogenic insert equipped with a coaxial lead-cryoperm double cylinder for high shielding to magnetic disturbances. A copper box for radiofrequency shielding, held at room temperature, contains the readout electronics, which due to intrinsic high-voltage swing of the sensors, can be directly coupled to an ultralow-noise amplifier. The voltage noise spectral density of the amplifier used for the following SQUID characterization is 0.7 nV/Hz\({}^{1/2}\).
## 4 Discussion
### Limitations and Future Directions  
This study has several limitations. First, the sampling method was non-probabilistic and based on convenience and voluntary participation, which limits the generalizability of the findings to broader populations. The results may not apply to women from different age groups, ethnic backgrounds, or with health conditions not represented in our sample. Furthermore, potential confounding variables--such as hormonal status, baseline physical activity, or individual neuromuscular characteristics--were not controlled and could have influenced the outcomes. Future research should aim to include more diverse and randomly selected samples to enhance external validity and better understand the applicability of AHT in various populations. On the other hand, while the randomized design of this study as well as the assessor and data scientist blinding were a strength, the absence of an active comparator group (e.g., PFMT) limits the generalizability of the findings. Also, we based our sample size estimations on detecting a 20% difference, which may not be considered clinically relevant; however, when we started our study, evidence was not sufficient to base sample size on an even larger effect. Additionally, the absence of follow-up period precludes conclusions about the long-term efficacy of AHT. Long-term studies are neededto evaluate the sustainability of these benefits and to establish standardized maintenance protocols. Another important limitation of this study is the absence of formal data on test-retest reliability or measurement error for the specific model of the Phenix biofeedback Kit used. While this device is commonly employed in clinical and research settings to assess PFM strength, its psychometric properties have not been systematically established in scientific literature. Consequently, the potential impact of measurement variability cannot be fully ruled out, and future studies are encouraged to evaluate the reliability and validity of this instrument in controlled conditions. Furthermore, the feedback provided by the Phenix device during measurements may facilitate learning how to contract and localize the PFM, which, in itself, could improve MVC independently of the intervention. Future research, both cross sectional and longitudinal, should explore the integration of AHT with other therapeutic modalities, as well as its applicability across diverse populations, including postpartum women and those with specific pelvic floor disorders. Finally, another limitation of this study is the absence of specific data on the measurement error or test-retest reliability of the Phenix biofeedback Kit model used. Although the device is commonly used in clinical practice and has been applied in previous studies to assess PFM strength, further validation studies are needed to determine its accuracy, reliability, and sensitivity to change.
# Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization +
## 3 Proposed Algorithm
### Union of Orthogonal Sparse Coding in DCT Domain  
To improve performance, we expanded the number of orthogonal dictionaries. Since this leads to an increase in computation time, it significantly impaired the strength of orthogonal sparse coding. To prevent this, we used the double sparsity model in Section 2.3 with the DCT matrix. We constructed our dictionary by using the product two-dimensional DCT matrix as a fixed base dictionary and another dictionary.  
In a mathematical formulation:  
\[D=TH, \tag{6}\]  
where \(T\) is a DCT matrix in \(\mathbb{R}^{n\times n}\) and \(H\) is an orthonormal matrix in \(\mathbb{R}^{n\times n}\).  
As mentioned above, the objective of using the DCT matrix as the base dictionary was to reduce the convergence time of the algorithm. Since the DCT matrix achieves remarkable sparsity in advance, as is well known, it accelerates the algorithm with fewer iterations than the case, which constructs a dictionary with only a dictionary. Based on Equations (4) and (5), the proposed method can be formulated in detail:  
For input data \(X=[X_{1}|X_{2}|\ldots|X_{L}]\in\mathbb{R}^{n\times N}\), \(X_{i}\) is a subdataset with the patches. It has a direction between \((90^{\circ}/L)(i-1)\) and \((90^{\circ}/L)i\), the dictionary is \(D=T[H_{1}|H_{2}|\ldots|H_{L}]\in\mathbb{R}^{n\times Ln}\), and the sparse coefficient matrix is \(A=[A_{1}^{\mathsf{T}}|A_{2}^{\mathsf{T}}|\ldots|A_{L}^{\mathsf{T}}]^{\mathsf{ T}}\in\mathbb{R}^{Ln\times N}\).  
\[\begin{array}{rl}\min\limits_{H_{i},\lambda}\sum\limits_{i=1}^{L}\left\{\|X_ {i}-TH_{i}A_{i}\|_{F}^{2}+\lambda\|A_{i}\|_{0}\right\}\\ s.t.\quad H_{i}^{\mathsf{T}}H_{i}=H_{i}H_{i}^{\mathsf{T}}=I_{n},\end{array} \tag{7}\]  
where \(i=1,\ldots,L\).  
For efficient implementation, all data were processed in the DCT domain during all procedures. First, the input image patches were transformed in the DCT domain by a product with a two-dimensional DCT matrix. Second, the patches were classified by using Equation (4). Since a DCT matrix is an orthonormal matrix, the Frobenius norm of the DCT matrix, \(\|T\|_{F}\), is 1.  
\[\begin{array}{rl}\|X_{i}-TH_{i}A_{i}\|_{F}^{2}=\left\|T^{T}X_{i}-T^{T}TH_{i}A _{i}\right\|_{F}^{2}\\ =\left\|T^{T}X_{i}-H_{i}A_{i}\right\|_{F}^{2}\end{array} \tag{8}\]  
Then, Equation (7) is transformed as follows:  
\[\begin{array}{rl}\min\limits_{H_{i},\lambda}\sum\limits_{i=1}^{L}\left\{\left\| \widehat{X}_{i}-H_{i}A_{i}\right\|_{F}^{2}+\lambda\|A_{i}\|_{0}\right\}\\ s.t.\quad H_{i}^{\mathsf{T}}H_{i}=H_{i}H_{i}^{\mathsf{T}}=I_{n},\end{array} \tag{9}\]  
Here, \(\widehat{X}_{We computed the variables with iterations of the two equations. Since each equation in the summation is independent, the optimization can be processed in parallel processing for multi-core hardware.
## 8 Wall Pressure Prediction Results  
Wall pressure is of great significance in the evaluation of aerostatic performance and can also be used to extract third-component force coefficients. Figure 11 shows the comparison of the wall pressure prediction results and the CFD calculation results. Due to space limitations, only four typical shapes and two for each shape are shown. Since the shapes used in this paper are all symmetrical up and down, only the wall pressure in the upper part is shown.  
Figure 11: _Cont._  
In general, the analysis of the wall pressure prediction results shows that the basic trend of the wall pressure distribution can be simulated based on the model proposed in this paper, but there are certain errors. The prediction results of the positive pressure area are generally better than those of the negative pressure area. Among them, the area with the largest error is mainly concentrated in the negative pressure area generated by flow separation and the negative pressure area near the wake. The absolute error value does not exceed 0.2 at the most, and the range is small. Meanwhile, the prediction errors of the rectangle and rhombus are generally larger than those of the ellipse and hexagon. The author believes that this is mainly caused by the uncomplicated UNet model. The original UNet model is mainly used to deal with classification problems. Although this paper has modified it, the modified UNet model still has a lot of room for improvement when dealing with regression problems.
## 3 Results and Discussion  
### Formation Energy of Doped TiZrCoIn  
The formation energy of the doped systems were analyzed with respect to the ordered case and estimated by using formula  
\[E_{f}(dop)=E(dop)-E(TiZrCoIn)-\sum m_{i}\mu_{i}(bulk) \tag{1}\]  
where \(E(dop)\) is the total energy of the doped case, and \(E(TiZrCoIn)\) is the total energy of the ordered TiZrCoIn structure in the same supercell size. The last term \(\sum\ m_{i}\mu_{i}(bulk)\) can be derived from the number of atoms added or removed from the ideal alloy. The number of altered atoms relative to that in the ideal bulk is represented by \(m_{i}\), which is either positive, when atoms are added to the original system, or negative, when atoms are removed from system. The symbol \(\mu_{i}(bulk)\) stands for the chemical potential of the corresponding atom in its bulk phase of ground state. Experimentalpreparation conditions and annealing environment can affect the formation of these doped systems. Thus, the formation energy of a doped structure is influenced by the chemical potential of the host atoms, which are affected by the environment. To obtain the chemical potentials from their bulk phase, we assumed that Ti, Zr, Co, In, and Ge are in the thermodynamic equilibrium with their bulk solid phase in the host rich condition. The hcp, hcp, hcp, fcc, and fcc structures for the bulk phases of Ti, Zr, Co, In, and Ge, respectively, were adopted.  
The formation energy of each doped system was calculated with the formula above. The results are shown in the first column of Table 1. All the doped systems had negative formation energies. Thus, In atoms in the ordered phase were easily replaced by Ge atoms. The ideal bulk of TiZrCoIn alloy was affected by the impurity Ge during growth. The variations among these values indicated differences in stability. Lower formation energy indicates that the doped one is easier to be formed spontaneously during the growth. It is shown in table 1 the formation energy gets smaller with the doping concentration, which manifests the doped system tends to be formed at a high doping concentration. The highest formation energy was obtained at a doping concentration of 3.125%. However, it can still form spontaneously during growth owing to its negative formation energy (\(-\)9.256 eV). The lowest formation energy was obtained at 100% doping concentration. Hence, the doped compound (TiZrCoGe) is most likely to be fabricated experimentally compared to the other Ge doped compounds. Meanwhile, Ge doping at Z-site may further stabilize the compound.
# Value Assessment of UGC Short Videos through Element Mining and Data Analysis
## 2 Literature Review
### Study on Value Assessment of UGC Short Videos through the Construction of an Index System  
The value assessment method based on constructing an index system refers to the process of developing a UGC value assessment index system. This method involves enhancing the existing index system through qualitative or quantitative methods and subsequently utilizing the index system for assessing the value of UGC short videos. It serves as a crucial foundation for evaluating the value of UGC short videos. For instance, [PERSON] et al. [12] developed a comprehensive six-dimensional UGC short video value assessment index system that encompasses attributes of publishers, viewer behavior, video content, and more. They conducted a questionnaire survey and identified video quality, title, and viewer interest as the primary factors influencing the value of UGC short videos. [PERSON] et al. [13] applied rooting theory to identify the fundamental value elements of rural landscape short videos. Additionally, [PERSON] et al. [14] employed data analysis and gathered short video value factors using a randomly administered online questionnaire. Their findings revealed that enhancing user stickiness and increasing user scale can substantially boost the value of short UGC videos. [PERSON] et al. [15] developed an online brand-based UGC value assessment system and employed partial least squares-structural equation modeling (PLS-SEM) to analyze empirical data. They revealed that content and technology are the primary elements that influence the value of UGC short videos. [PERSON] et al. [16] analyzed the value elements of short video communication using the all-information emotion theory and the super IP theory. They employed the fuzzy set theory-DEMATEL model to identify the key influencing factors on the secondary indicators and identified \(11\) value elements, which include public expression and popularity demand, among others.
## 4 Evaluation
### Transmission Overhead and Average Energy Consumption under Changing Data Traffic  
Under low data traffic, buffer occupancy is low and message loss is mostly limited to TTL exhaustion. Messages therefore stay longer in node buffers and due to fewer messages, they are successfully forwarded each time a more suitable relay node is encountered. Hence, high transmission overhead is incurred due to more successful transmissions. However, contrary to expected results, Figure 24 shows that less transmission overhead is incurred as data traffic increases. This is because node buffers become more occupied and more messages are lost from frequent buffer overflows. Increased buffer occupancy also leads to failed transmissions due to insufficient time to complete  
Figure 27: Routing fairness and throughput under homogeneous and heterogeneous buffer capacities.  
forwarding transactions. Interestingly, failed transmissions consume energy instead of incurring transmission overhead. This explains why Figure 24 shows more energy consumption as transmission overhead decreases.  
PRoPHET records the highest transmission overhead and average energy consumption since messages are forwarded whenever a relay node presents a higher forwarding utility. CCAF, on the other hand, records the lowest transmission overhead and average energy consumption by forwarding messages only when a relay node presents a much higher forwarding utility. FLARoute records a higher transmission overhead and average energy consumption as compared with CCAF by allowing more number of less popular nodes to contribute in improving throughput. In this case, the main concern is whether the energy consumed on the most popular nodes (or the maximum energy consumption) is also increased in the process. To investigate this, Figure 25 shows the energy consumed by the 5 most popular nodes when running on each routing technique. CCAF records less energy consumption on popular nodes under low data traffic. However, as the average energy consumption increases under higher data traffic, FLARoute distributes the burden among a wider set of nodes thereby consuming less energy on popular nodes.
# Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System
## 4 Modeling a Stochastic Mrp-Based Production System
### Mrp Procedure  
During the execution of the MRP logic, the netting, lot-sizing, backward scheduling, and BOM explosion steps are subsequently performed for the specified planning horizon. For the netting step, the outputs are the items to produce, which are not yet organized as production lots. For the subsequent lot sizing step, the required information is a lot sizing policy, i.e., the fixed order quantity (FOQ) and the associated parameter value representing the amounts for the production lot. One of the targets is to reduce setup times by selecting efficient lot sizes. The process of machine setup plays an important role in the context of lot sizing. The planned lead time is the required input for the step of backward scheduling. With backward scheduling, the start date of a production order is computed. The outputs of the last step (BOM explosion) are the required quantities for the subsequent BOM levels. The items of BOM level 0 are processed, and followed by the process of higher BOM levels. Notice that level 0 provides the gross requirements for subsequent levels: the required quantities for BOM level \(n\) are passed to BOM level \(n+1\). Our actual research activities are focused around MRP and it is, therefore, the selected production planning approach for this publication. MRP is still widely applied in industry and the target of current research activities. Therefore, this article will contribute to MRP-related research activities and provides also input for practitioners of systematically setting MRP planning parameters with respect to minimizing inventory and backorder costs.  
Figure 1: A 3-level BOM applied during stochastic MRP simulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Material**} \\ \hline Parameter & 10 & 11 & 20 & 21 \\ \hline FOQ: LB; UB; Mode & 400; 800; 600 & 600; 800; 700 & 400; 500; 450 & 400; 500; 450 \\ \hline SS: LB; UB; Mode & 1; 5; 3 & 1; 5; 3 & 1; 5; 3 & 1; 5; 3 \\ \hline PL: LB; UB; Mode & 0; 4; 2 & 0; 4; 2 & 0; 4; 2 & 0; 4; 2 \\ \hline Processing time in h & 0.17328 & 0.17328 & 0.17328 & 0.17328 \\ \hline Holding costs per day & 1 & 1 & 0.5 & 0.5 \\ \hline Tadiness costs per day & 19 & 19 & & \\ \hline Avg. demand per day & 47 & 70 & 47 & 47 \\ \hline FOQ = Fixed Order Quantity; LB = Lower Bound; UB = Upper Bound; SS = Safety stock; PL = Planned lead time \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of the simulation parameters employed.
# Single-Baseline RTK Positioning Using Dual-Frequency GNSS Receivers Inside Smartphones
## 2 Smartphones Dual Frequency GNSS Receivers  
As previous stated, until 2018, smartphones multi-constellation GNSS chipsets were able to acquire only one frequency signal from each satellite of the different constellations (GPS L1, Galileo E1, BeiDou B1, and GLONASS L1). In June 2018 the Xiaomi MI 8 smartphone was introduced to the market: this smartphone is the first device equipped with the Broadcom BCM47755 GNSS chipset, the first dual-frequency mass-market receiver designed by the Broadcom Limited company (San Jose, CA, USA). In addition to the previously described frequencies, this chipset uses also the GPS L5 and the Galileo E5a frequencies (Figure 1). This feature is an important step forward in the positioning field and in particular for real-time smartphone-based navigation.  
Acquiring observations on more than one frequency allows us to perform the data combination procedure which uses GNSS observations acquired by the same receiver at the same station in order to estimate GNSS biases. The new dual-frequency receiver is able to acquire both code and carrier-phase measurements, which means that the combination can be made on both these observables.  
Figure 1: Global Navigation Satellite System (GNSS) signal structures considering only bands around GPS L1 and L5 frequencies, which were used in these tests.  
In the state of art, the first and most important application of dual-frequency combination methods is the estimation and elimination of the ionospheric effects due to their dependency on the signal frequency. The ionospheric effect is an important error source in GNSS positioning as it generates a delay in the order of tens of meters. Combining code and carrier-phase observations linearly, it is possible to eliminate the ionospheric effect in the measurements. This procedure is called Iono-free combination. Another combination method is the geometry-free code-code or phase-phase combination, which removes all the errors in the observation equations except for the ionospheric term and the ambiguity parameters. Other possible linear combinations of carrier-phase measurements are the so called wide-line (difference) and narrow-line (sum) combinations.  
Finally, frequency diversity could increase signal robustness to interference and jamming together with increasing the capability to distinguish between the real line of sight signals from the reflected ones.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 4 Full-Wave Electromagnetic Simulation Results
### Multibeam Beamforming
#### 4.1.4 Same Signal Transmission in Three DODs  
As an example of three-direction beamforming, consider the case where \(\theta_{1}=-45^{\circ}\), \(\theta_{2}=0^{\circ}\), and \(\theta_{3}=45^{\circ}\), with corresponding \(\alpha_{1}=-35.26^{\circ}\), \(\alpha_{2}=0^{\circ}\), and \(\alpha_{3}=35.26^{\circ}\), respectively. The magnitude frequency response \(|H(e^{i\omega_{x}},e^{i\omega_{ct}})|\) of the 2-D FIR filters of order \(14\times 64\), with a nonsparse impulse response and a sparse impulse response (only with 161 nonzero elements), is shown in Figure 8c and Figure 8d, respectively. Values for the other parameters are \(\mu=0.02\) and \(\epsilon_{th}=0.010455\). The wideband transmit beam patterns produced by the nonsparse and sparse filters are shown in Figure 8g and Figure 8h, respectively. Moreover, Table 2 provides a comparison of wideband beam pattern and DSP complexity between the proposed 2D sparse and nonsparse FIR filter-based transmit beamformers. The results clearly indicate the minimal effect of sparsification of the filter impulse response towards the performance of the filter with regard to beamforming, while saving more than 83% in DSP complexity.