esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Induction Coils
### Coil Structure Parameters  
After researching coil arrays, coil structure parameters, shown in Figure 18, are also essential to be determined. In Figure 18, A, B, and C are, respectively, the width of the air gap, inner coil, and outer coil. Different turns of a coil or different wire diameters can be studied by controlling for a single variable A, B, or C. Moreover, it can be considered from the following aspects.  
The first one is the reasonable wire diameter. Small wire diameter means the small volume of the sensor. In this way, the number of turns and the length of the wire could be added to improve the sensor's sensitivity. Nevertheless, the coil resistance increases as the diameter get smaller, increasing the signal loss and the complexity of the welding process.  
The next is to choose suitable inductance because the more inductance, the more output voltage. To improve the inductance, the number of turns and cross-section area of wire should be increased. Whether increasing the inductance or reducing the resistance value, suitable coil parameters can improve the quality factor of the coil.
# Single-Baseline RTK Positioning Using Dual-Frequency GNSS Receivers Inside Smartphones
## 5 Experimental Results
The RMS values in the second case are slightly better than in case of FIX+FLOAT solutions, even if these values are still around 0.2-0.4 m and 0.5 m for North, East, and up components, respectively. Figure 5: Positioning results considering case 1, with GPS-only constellation, L1 frequency, “continuous” method for the ambiguity resolution and ratio = 3. Figure 6: Positioning results considering case 1, with GPS-only constellation, L1+L5 frequencies, “continuous” method for the ambiguity resolution and ratio = 3. If the second frequency (L5) is considered, the results are definitely better: the percentage of FIX solutions decreases by up to 0.6% (Figure 6) and an improvement in terms of precision and accuracy is observed. As shown in Table 5, the use of L5 frequency allows the increasing of the precision (std) with an order of magnitude, going from about 14 cm and 30 cm for planimetry and up components in case of L1-only solution up to 1.5 cm and 3.8 cm in case of L1+L5 approach. These results can be still improved if the threshold value of the ratio-test is increased up to 30 (Figure 7): Table 6 shows that the std values are closed to 0.1 cm and 0.4 cm for planimetry and up components in the case of FLOAT-only solutions, while they are about 1.5 cm and 3.8 cm in the case of the FIX+FLOAT approach. Also RMS values are decreased, even if the difference is not so high: in this case the main improvement is for the up component, where the RMS value is decreased by about 12 cm, from 59.6 cm to 47.6 cm. The previous results, shown in Tables 3 and 4;; are representative of those obtained in many other sessions that are not presented in this paper: thus, both mean and standard deviation values can be considered as reference values for this positioning technique, using these sensors.
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 3 Results and Discussion
### Experiments in the Measurement of River Velocity and Discharge  
The texture angle of the STI is detected by image classification, and the surface velocity of each velocity point is calculated, and then the total discharge and mean velocity of the river are obtained. The results obtained from the current meter are usually considered as the true value and treated as a criterion for comparison with other methods in the measurement of river velocity and discharge. We have chosen vertical mean velocity, total discharge, and mean velocity as the key measurement indicators in this research. The river images are captured at a resolution of 1920 \(\times\) 1080 in all experiments. During the same time period that the current meter is operating, comparative measurement experiments are carried out in an artificially repaired and two natural rivers, respectively, by using GTM [9], FFT [11], FD-DIS-G [31], and the proposed method. FD-DIS-G proposed by [PERSON] et al. [31] combines the frame difference with fast optical flow estimation using Dense Inverse Search (DIS) [32], and generates a motion significance graph to capture the water surface motion features by calculating the difference between image frames, then uses the DIS algorithm to calculate the dense optical flow, and finally performs singular value processing on the obtained data by means of groupings. The errors associated with each method are then compared and analyzed to assess their accuracy and reliability.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 6 Advantages and Disadvantages
It also offers efficient and effective communication because its bandwidth is massive. Unregulated LiFi offers high data transmission speeds for indoor applications with high-speed communication. With a frequency of over 1 MHz, the LiFi operation is very high quality [21; 23; 88], subsequently making the system very effective [23; 183]. In reality, if anyone goes beyond the LED source, the signal is lost because of barriers. LiFi also does not work when fully obstructed (when there are no LOS nor NLOS paths) [183; 184]. Given that light cannot go through objects, the signal is immediately cut off; this phenomenon is called a blocking issue [23; 57; 106; 115; 183; 184]. The reliability and coverage of the network are major challenges [23] because of the reflection of light [88].
## 4 Results
### Forest Aboveground Biomass Update
#### 4.3.3 Updated Forest Aboveground Biomass  
The predicted forest AGB of 2012 (Figure 5) was obtained based on the annual ABIs ranging from 2009 to 2012 and the baseline forest AGB of 2008. The forest AGB values ranging from 5 to 9 kg/m\({}^{2}\) accounted for 81.89% of this study area.  
The cross-comparison between the predicted forest AGB and the ALS-based forest AGB of 2012 (Figure 6a,b) found that the consistent spatial distribution pattern could be observed in both forest AGB maps. The areas with high or low values of forest AGB from the BEPS-based AGB map could also be observed in the ALS-based estimates. However, the ALS-based AGB tended to over- and under-estimate the results obtained using the BEPS-based method in the high- and low-AGB areas. From the AGB differences map (Figure 6c), we found that most of the differences were between \(-\)3 kg/m\({}^{2}\) and 3 kg/m\({}^{2}\) except in areas where tree height was taller than 2 m.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Forest Type**} & **GPP** & **NPP** & **AGB** & **Increment of** & **Increment of** & **Increment of AGB** \\  & **(gC/m\({}^{2}\))** & **(gC/m\({}^{2}\))** & **(kg/m\({}^{2}\))** & **GPP (gC/m\({}^{2}\)/yr)** & **NPP (gC/m\({}^{2}\)/yr)** & **(ABI) (kg/m\({}^{2}\)/yr)** \\ \hline Needle leaf & 1322 & 672 & 6.29 & 42.39 & 13.82 & 0.16 \\ Broadleaf & 1319 & 863 & 8.01 & 37.87 & 14.81 & 0.14 \\ Mixed & 1613 & 679 & 7.88 & 40.47 & 14.97 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Annual mean GPP, NPP and AGB values and their annual mean increment of different forest types from 2008 to 2012.  
Figure 5: The BEPS-based forest AGB for 2012.  
Figure 6: The spatial distribution maps of BEPS-based (**a**), ALS-based (**b**) forest AGB for 2012 and the differences map between BEPS- and ALS-based maps (**c**).  
Although the BEPS model tended to overestimate forest AGB which could have resulted from the overestimated NPP simulated by BEPS model, this BEPS model-based AGB map accounted for 31% of variation in the field-based forest AGB (R\({}^{2}\) = 0.31, \(n\) = 35, \(p\) < 0.05) (Figure 7a). Also, it could effectively capture the overall variation in the ALS-based forest AGB (R\({}^{2}\) = 0.85, \(n\) = 100, \(p\) < 0.01) (Figure 7b).
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 1 Introduction
Most previous studies have ignored this problem. Autoencoder is used to discover more abstract, high-level hidden features. By using sparse features, the dimension of the original data is reduced, and the representative information is retained to improve the robustness of the algorithm and the accuracy of the prediction. Based on the above considerations, this paper presents a deep learning day-ahead wind power prediction framework based on discrete wavelet transform, sparse feature extraction and bidirectional deep learning. The main contributions of this paper are as follows:  
1. According to the characteristics of wind power forecasting, a deep learning framework \(\text{DWT\_AE\_BiLSTM}\) is first proposed. 2. Through the discrete wavelet transform technology, the nonstationary original data is decomposed into several subsequences, and the original data is filtered and denoised. 3. An autoencoder is employed to extract highly nonlinear feature data, and then the extracted hidden feature data is input into the BiLSTM framework to predict power generation. This paper is organized as follows: Section 2 describes some preliminaries. Section 3 illustrates the overall framework of our proposed short-term wind power prediction model based on an autoencoder and BiLSTM. In Section 4, we delineate three case studies, setup the experimental initialization parameters and show the results, and Section 5 presents the conclusions.
# Placental Transcriptome Adapations to Maternal Nutrient Restriction in Sheep
## 4 Materials and Methods  
### Animal Study and Tissue Collection  
Mature Hampshire ewes of similar parity, frame size, and initial body condition were fed to meet 100% of their National Research Council (NRC) [48] nutritional requirements, and served as embryo transfer recipients [5; 13]. Ewes were synchronized into estrus, and a single embryo from a superovulated Hampshire donor ewe of normal body condition was transferred into the uterus of a recipient ewe on day 6 post-estrus [5]. Pregnancy was diagnosed by ultrasound on day 28 of gestation. All ewes were individually housed in pens with concrete flooring from days 28 to 135 of gestation, and fed once daily. Beginning on day 28 of gestation, body weight was measured weekly, and feed intake was adjusted based on changes in body weight. On day 35 of pregnancy, ewes were assigned randomly to either a control-fed group (100% NRC; \(n=8\)) or a nutrient-restricted (NR) group (50% NRC; \(n=28\)); composition of their respective diets has been published previously [49]. NR ewes were provided 50% of the total weight of feed that the control-fed group received, in order to induce a total caloric restriction equally across macromolecule groups. Vitamins and minerals were provided as recommended or in excess for all ewes.  
On day 70 of pregnancy, a single placentome was surgically removed, as previously described [9]. Briefly, care was taken to remove a placentome from near the antimesometrial greater curvature of the gravid uterus and proximal to the anterior end of the amniotic membrane. The placentome was finely minced and thoroughly mixed in order to ensure representation of all cell types, before being snap-frozen in liquid nitrogen for RNA analyses. Necropsies were performed on day 135 of gestation. At this time, placentomes were dissected, weighed, and then processed, as on day 70.  
Fetuses from ewes fed 100% NRC were the control group (\(n=8\)). Fetuses within the NR group (\(n=28\)) were segregated into quartiles based on their fetal weights. The highest (NR NonSGA; \(n=7\)) and lowest (NR SGA; \(n=7\)) quartiles were selected for further investigation [10; 12].
# Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo
## 2 Simultaneous Quantile Regression
### Simultaneous Quantile Regression  
Many approaches have been proposed to overcome the crossing problem. See, among others, in the frequentist context [30; 31; 32]. In the simultaneous Bayesian context, on the other hand, [PERSON]. [33] suggested a two-stage semi-parametric regression method. [PERSON] and [PERSON] [34] have also proposed a novel treatment to nonlinear simultaneous quantile regression. They have achieved this by specifying a Bayesian nonparametric model for the conditional distribution. In a similar vein, [PERSON] and [PERSON] [35] represent the quantile process by articulating it as a weighted sum of B-spline basis functions corresponding to the quantile level. In this study, the method proposed in [27] is considered, which assumes the error term has an ALD. It is noteworthy that imposing a given distribution on \(u_{t}\) is restrictive but provides a fully Bayesian approach to handling simultaneous regression quantiles of different orders. This is performed first by partitioning the entire sample into \(s\) sub-samples, where \(s\) is the number of quantiles of interest; second, by using the relationship between any two ALD quantiles; and third, by rewriting the entire likelihood into \(s\) terms where the \(j\)-th term depends only on \(q_{\gamma j}\), where \(q_{\gamma j}\) is the \(j\)-th term of the vector of quantiles. Let us explain how it works:  
1. Consider partitioning the entire sample into the sub-samples below: \[(\mathbf{x}_{l_{j}},Y_{l_{j}})=\{(\mathbf{x}_{i},Y_{i})_{i\in I_{j}}\},\forall j\in\{ 1,\ldots,s\},\] where \(I_{j}=\{(j-1)r+1,\ldots,jr\}\) and with \(I_{j}\bigcap~{}I_{k}=\emptyset~{}\forall j\
eq k\) and \(j,k\in\{1,\ldots,s\}\); assume that \(r=\dfrac{n}{s}\) is an integer without loss of generality.
2. In order to characterize the likelihood through all quantiles of interest, we use the relationship that connects any \(\tau\)-th quantile to the \(p\)-th quantile of the \(\text{ALD}(q_{p},\sigma,p)\): \[q_{\tau}(x)=q_{p}(x)+\sigma g(\tau,p)\] (7) where \(g(\tau,p)=\dfrac{1}{1-p}\log\biggl{(}\dfrac{\tau}{p}\biggr{)}\mathds{1}_{0<\tau <p}+\dfrac{1}{p}\log\biggl{(}\dfrac{1-\tau}{1-p}\biggr{)}\mathds{1}_{p<\tau <1}\).
3. We rewrite the model given by (5) from Equation (7) as follows: \[Y_{l_{j}}=q_{\gamma j},I_{j}-\sigma g(\tau_{j},p)+\epsilon_{l_{j}},\forall j=1,\ldots,s\] where \[q_{\gamma j,l_{j}}=(q_{\gamma}(\mathbf{x}_{i_{j}}))_{i_{j}\in I_{j}},\] \[g(\gamma,p)=\Bigl{(}g(\gamma,p)\mathds{1}_{i_{j}\in I_{j}}\Bigr{)} _{i\in I_{j}},\] \[\epsilon_{l_{j}}=(\epsilon_{i_{j}})_{i_{j}\in I_{j}}.\] The likelihood connected with the model provided by Equation (5) is then rewritten as the product of \(s\) likelihoods, each depending on only one sub-sample: \[L(q_{\tau_{1},I_{1}},\ldots,q_{\tau_{s},I_{s}},\sigma,p;\mathbf{x}_{l_{1}},y_{l_{1} },\ldots,y_{l_{s}})=\prod_{j=1}^{s}\prod_{i_{j}\in I_{j}}\dfrac{p(1-p)}{\sigma }\exp\{-\dfrac{\rho_{p}(y_{i_{j}}-(q_{\tau_{j}}(x_{i_{j}})-\sigma g(\tau_{j},p )))}{\sigma}\}\]
# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 4 Evaluation
### Overhead  
For practical use in real-time scenarios, it is essential to evaluate the overhead of our scheduling scheme. To analyze this, we measured the time required to schedule 10,000 jobs with a fixed check interval of 20 s under different maximum IOPS settings. With a maximum IOPS of 234 GB, the scheduling time was 362.33 s, while at 468 GB it was 306.31 s. As the maximum IOPS increased to 937 GB, the scheduling time decreased significantly to 161.63 s. Further increasing the maximum IOPS to 1875 GB reduced the scheduling time even more to 84.27 s. The variation in scheduling times across different maximum IOPS settings is due to the increased need for resource checks and allocations when the IOPS capacity is lower. With lower maximum IOPS, the system must perform more frequent checks and allocation operations over a longer period to efficiently manage all jobs. Most HPC systems provide more than 1000 GB of IOPS, enabling them to schedule at least 63 jobs per second, which is sufficient for real-time scheduling.  
Figure 7: Graph showing the average I/O throughput at each check time for two scenarios measured at different maximum IOPS values. The x-axis represents the configured maximum IOPS, while the y-axis indicates the percentage of resource utilization.
# Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method
## 3 Selection, Processing, and Correlation Analysis of Training Samples
### Model Parameter and Evaluation Metric Settings  
The LSTM-BP model in this study is constructed by integrating the LSTM and BP models. For the LSTM-BP model, the input sample sequence length is defined as 3, indicating that it is trained to predict the formation pressure loss at a specific point using the data from the preceding 15 m. Further details regarding the model's parameter settings are provided in Table 1. The evaluation metrics employed include MSE (mean squared error), MAE (mean absolute error), and R\({}^{2}\) (coefficient of determination). The relevant formulas for computing these metrics are presented in Equations (4)-(6).
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 3 Electronic Packaging  
For telecom and datacom applications, photonic devices are required to operate at high speeds and with very high bandwidths. In many cases, this involves the (de-)multiplexing of several 25 Gbps electrical-channels to/from a single \(N\times 25\) Gbps optical-channel, with \(N\) = 4, 6, 12, etc. The efficient, low-reflection, artifact-free, routing of these high-speed electrical signals from a centimeter-scale SMA (SubMiniature, version A) /SMK (SubMiniature, version K) connector to the microscopic structures on the PIC can present a design challenge. If a very high number of electrical connections to the PIC are needed, or if precise (sub-ns) control of switching is needed on multiple channels, then vertical integration of a custom electronic integrated circuit (EIC) driver-chip may also be needed.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 6 Results and Analysis
### Noise Handling  
Initially, a selected number of spans is chosen to enrich the original dataset with values of EWMA and EWMS. In [12], an automated approach to selecting feasible values for the span \(s\) is investigated, which is a purely data-driven method to establish the system dynamics. Herein we look at results in Table 1 to determine feasible span values, since they are available from the battery ECM parameter optimization.  
We then, as in [12], explore only four possible values for the span in EWMA and EWMS calculation simultaneously in order to limit memory demand.  
Ranging from a very short dynamic response to a longer time span, considering the battery ECM full charge or discharge (equivalent to a value approximately between 5\(\tau\)'s to 6\(\tau\)'s), the resulting selected span values are \(s=\{10,50,100,1000\}\).  
Figure 5 shows a uniform distribution of absolute attributions across the time spans, and the most impactful features are consistently among the time, capacity, and energy quantities, variables naturally needed for the SOC estimation.
## 6 Experimental Studies
### Vision-Based Tracking
#### 6.2.1 Validation of Visual Feedback  
In order to validate the visually determined joint angles \(\alpha_{\text{vis}}\), \(\beta_{\text{vis}}\), incremental encoder signals \(\alpha_{\text{vis,enc}}\), \(\beta_{\text{vis,enc}}\) are recorded in parallel, that is, measured by encoders while the control loop is closed with visual feedback, and taken as reference to compare with. The differences between visual and incremental measurements  
\[\Delta\alpha=\alpha_{\text{vis}}-\alpha_{\text{vis,enc}}\,\qquad\Delta\beta= \beta_{\text{vis}}-\beta_{\text{vis,enc}} \tag{35}\]  
are plotted in Figure 15. The absolute deviation \(|\Delta\beta|\) is smaller than \(1.6^{\circ}\) and for \(|\Delta\alpha|\) the maximum value is even smaller than \(0.6^{\circ}\), which confirms the very good agreement of the visual measurement with the incremental reference measurement. Some reasons for the small remaining deviations are mentioned below:  
* Image resolution: For the chosen resolution, one pixel in the image corresponds up to 4 mm in the planar workspace. This discretization induces an inaccuracy in position estimation.
* Marker detection: Because of the discretization of the image, the edge of a marker is not a sharp line, but spread across several pixels. Those pixels might pass the filter or not, depending on the color filter settings, which results in an inaccuracy for the marker's computed center point.  
Figure 13: Desired end-effector trajectory for the motion in the second scenario.  
* Time offset between sensor signals: There is a time offset of up to 20 ms between the measurement signals that is not represented in Figure 14. Taking such an offset into account, the signals match even better and the deviations shown in Figure 15 are even smaller.  
Figure 14: Visually measured generalized coordinates \(\mathbf{q}_{\text{vis}}=\left[\alpha_{\text{vis}},\beta_{\text{vis}}\right]^{\text{T}}\) and velocities \(\dot{\mathbf{q}}_{\text{vis}}=\left[\dot{\alpha}_{\text{vis}},\dot{\beta}_{\text{ vis}}\right]^{\text{T}}\) in case of visual feedback in comparison to the generalized coordinates \(\mathbf{q}_{\text{enc}}=\left[\alpha_{\text{enc}},\beta_{\text{enc}}\right]^{ \text{T}}\) and velocities \(\dot{\mathbf{q}}_{\text{enc}}=\left[\dot{\alpha}_{\text{enc}},\dot{\beta}_{\text{ enc}}\right]^{\text{T}}\) measured by incremental encoders in case of encoder feedback and the desired trajectories \(\mathbf{q}_{\text{des}}=\left[\alpha_{\text{des}},\beta_{\text{des}}\right]^{ \text{T}}\), \(\dot{\mathbf{q}}_{\text{des}}=\left[\dot{\alpha}_{\text{des}},\dot{\beta}_{\text{ des}}\right]^{\text{T}}\) in the second study with a sampling interval of 200 ms. Furthermore, the applied input in case of encoder feedback \(\mathbf{u}_{\text{enc}}=\left[I_{1,\text{enc}},I_{2,\text{enc}}\right]^{\text{T}}\) and visual feedback \(\mathbf{u}_{\text{vis}}=\left[I_{1,\text{vis}},I_{2,\text{vis}}\right]^{\text{T}}\), respectively, determined by the MPC in comparison with the feed-forward controller \(\mathbf{u}_{\text{ff}}=\left[I_{1,\text{ff}},I_{2,\text{ff}}\right]^{\text{T}}\).
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 8 Conclusions  
In the wake of 5G interference with legacy radar altimeters, the article deliberates on the feasibility of employing mmWave FMCW automotive radars for UAS altimetry. A brief history of contemporary systems and the rationale for device selection is briefly touched upon in the context of frequency regulations, opportunities, and drawbacks. Theoretical and mathematical basis, coupled with tutorial-themed discussions, for understanding the intertwined performance metrics are furnished to address a broad range of readers. A ground clutter estimation using the hardware specifications of a mmWave automotive radar for a pure look-down case is provided. Owing to the dearth of MOPSs for UAS radar altimeters, the existing standards for commercial aviation are appraised for the requisite adaptation. A systems engineering methodology for deriving waveform and radar specifications from operational requirements is offered. Subsequently, the authors' perspective on the optimization of performance metrics and potential improvements through alternative methods was penned down. It is argued that the scope of this work is poised to propel the discussion towards mmWave altimeters. Lastly, the scale of future work and associated challenges in realizing the full potential of the proposed approach are put forth.  
Investigation, resources, visualization, and writing--original draft preparation, M.A.A.; conceptualization, M.A.A., Y.D. and A.K.; validation, supervision, and writing--review and editing, Y.D., M.D. and A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.