esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 3 Description of the Tensor Sensing Problem
### Alt-Min Algorithm  
The goal of tensor sensing is to recover the loss field tensor \(\mathcal{X}\) from the linear map matrix \(\mathbf{A}\) and the measurement vector \(\mathbf{y}\), which is formulated as follows:  
\[\widehat{\mathbf{X}}=\operatorname*{arg\,min}_{\mathbf{X}}\|\mathbf{y}- \mathbf{A}\mathbf{X}\|_{\text{F}}^{2},\text{s.t.rank}(\mathbf{X})\leq r. \tag{1}\]  
This method uses the Alt-Min algorithm (Algorithm 1) to estimate two low rank matrices iteratively whose matrix product is the squeezed matrix of the object tensor \(\mathcal{X}\).  
Next, the least squares minimization problem is solved to estimate \(\mathbf{V}\) of the next iteration (Algorithm 2, Line 3 and Line 4). The least minimization problem is formulated as follows:  
\[\mathrm{vec}(\mathbf{V})=\underset{\mathbf{V}}{\arg\min}\,\|\mathbf{y}-\mathbf{ A}\mathbf{U}_{b}\mathbf{V}\|_{F}^{2} \tag{3}\]  
As the estimated \(\mathbf{V}\) is in vector form \(\mathrm{vec}(\mathbf{V})\), \(\mathrm{vec}(\mathbf{V})\) is transformed back to matrix form \(\mathbf{V}\) (Algorithm 2, Line 5). To estimate \(\mathbf{U}\), the least squares minimization problem in Equation (3) should be transposed:  
\[\mathrm{vec}(\mathbf{U}_{t})=\underset{\mathbf{U}_{t}}{\arg\min}\,\|\mathbf{y }-\mathbf{A}_{t}\mathbf{V}_{t}\mathbf{U}_{t}\|_{F}^{2} \tag{4}\]  
The process of estimating \(\mathbf{U}\) is similar to estimating \(\mathbf{V}\), which is implemented with the corresponding transposed matrix (Algorithm 2 Lines 6-12). Next, the above process is repeated until the number of iterations reaches the set value. Lastly, the two estimated matrices are multiplied to get the final result matrix (Algorithm 2, Line 14).
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 5 Numerical Results  
In this section, we provide some numerical results obtained through simulations performed with Matlab Software version 2024a, aimed to investigate the performance of the most widespread techniques and protocols exploited in cooperative UWSNs and discussed in the previous sections. Specifically, performance analysis regards physical layer cooperative schemes, link layer ARQ protocols and network layer routing strategies. For the sake of clarity, we would highlight that the goal of such analysis is not to introduce and evaluate the effectiveness of novel solutions, but to support our literature review with some numerical references.
## 4 Experimental Details of Atomization Rate  
In this study, the process of making an ultrasonic atomizer can be described as follows. First, there were about 400 micro-tapered apertures processed on the middle bulge of an atomizing film via laser technology. Second, a circular piezoelectric ceramic was attached to the disperser. Finally, a conductor was welded on the position between the piezoelectric ceramic ring and disperser.  
Figure 7a shows the atomizing film structure used in this study, which consisted of a piezoelectric ceramic ring, a disperser with a micro-tapered aperture, and the micro-tapered aperture group on the substrate in the middle tapered blank part. Figure 7b,c show the front view and back view of the atomizing film, respectively. During atomization progress, the surface in Figure 7c comes into contact with the liquid chamber, while the surface, as shown in Figure 7b, makes contact with the external environment. The description for the specific parameters of the atomizing film is provided as follows: piezoelectric ceramic ring with an outer diameter of 15.96 mm; inner diameter of 7.69 mm; thickness of 0.63 mm; disperser with a diameter of 15.96 mm; and thickness of 0.05 mm. The measurements of the outlet diameter and inlet diameter of the micro-tapered aperture were performed on the electron microscope, and the specific parameters are given in the Results and Discussion.  
Figure 8 shows the equipment of the atomization rate measurement. During the experimental progress, a signal generator and power amplifier were utilized to supply power to the atomizer, and an oscilloscope was used to control the driving signal. The atomizer was placed on a high-precision analytical balance to measure the liquid's decrease in the liquid chamber of the atomizer after 1 min.  
Figure 6: Vibration modes of resonance points [29].  
The decrease in the amount of liquid is the atomization volume in an atomizer per minute, that is, the definition of the atomization rate.  
In this study, the atomization rate represents the weight of the liquid atomized during one minute. During the test of the atomization rate, the atomizer is placed on a high-precision analytical balance, and the stopwatch is used for timing the power supply of the voltage to the atomizer. After one minute, the supplying voltage to the atomizer and the atomization rate stopped being collected. Therefore, the atomization rate was obtained by measuring the reduction of the liquid amount in the liquid chamber per minute using a high-precision analytical balance.
## 4 PSI-CA Protocol Constructed Based on DH Key Exchange Mechanism
### Protocol Process  
1. **Recover Data Encryption**: The receiver encrypts \(H(y_{j})\) with its private key \(\beta\), obtaining \(H(y_{j})^{\beta}\), and sends it to the sender.
2. **Sender Computation**: Upon receiving \(H(y_{j})^{\beta}\), the sender applies their private key \(\alpha\) to compute \((H(y_{j})^{\beta})^{\alpha}\) and shuffles it before sending it back to the receiver.
3. **Sender Data Encryption**: The sender encrypts \(H(x_{i})\) with its private key \(\alpha\), resulting in \(H(x_{i})^{\alpha}\), and sends it to the receiver to facilitate the computation of the intersection cardinality.
## 4 Experiment and Performance Evaluation of the New Screen Sand Production
### Experimental Methods  
On the basis of the previous methods of evaluating the fluidity of the sand screen, the author recommends that the productivity of the down hole reservoir converted from the meter production index to the length of the down hole reservoirs be used to evaluate and analyze the bottom hole fluidity of the sand screen. The specific experimental processes are as follows:  
1. Filling the formation sand or simulated sand sample outside the screen, and studying the test flow rate and pressure relationship under different pressures, and calculating the ratio of flow rate to pressure under the sand stabilization condition, and converting it into the daily output of each meter of the sand screen under a pressure difference of 1 MPa, that is, the meter production index. Considering the compatibility between the formation sand and the sand screen can better simulate the down hole production condition and reflect the flow performance truly and intuitively.
2. Testing the amount of formation sand and simulated sand, collecting the sand produced and weighing it, and then judging the sand control effect according to the sand control requirements.
3. Evaluating the applicability of the screen in combination with the capabilities and test plan of the artificial lifting and surface test equipment.
4. Testing the flow resistance of the screen tube itself with clear water.  
The formation sand and simulated sand sample experiments are used to convert the meter production index, which can better reflect the actual situation of the well site.  
Figure 3: Schematic diagram of the design structure of Class B sand screen.  
Meanwhile, it can intuitively reflect the conditions and requirements of meeting the target productivity of gas hydrate testing production, and check the flow performance of different types of sand screens.
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
## 2 Data and Method  
In this section, the wind measurements from multiple satellites and the approach for assembling winds from multiple satellites are described. A description and settings of WW3 are introduced. Additionally, ECMWFs reanalysis (ERA-5) of winds, waves, and altimeter-measured significant wave heights (SWHs) are presented. Lastly, the metric variables for the error analysis are shown.
## 8 Conclusions  
With the experimental results obtained with an emulated multi-site satellite network, it was shown that an SDN/VNF approach could dynamically switch transmissions among the feeder links of an HTS system to alleviate the performance degradation brought by the adverse atmospheric conditions, which can temporarily affect one or more of the gateway sites and cause network congestion. The use of reinforcement learning-based algorithms was explored for this task to reduce both packet losses and the average latency. Since reinforcement learning does not need prior training, the proposed mechanisms help to improve the autonomy of HTS systems.  
Conceptualization, R.L. and G.V.; methodology, R.L. and G.V.; software, R.L. and G.V.; validation, G.V.; formal analysis, R.L. and G.V.; investigation, R.L. and G.V.; resources, R.L. and G.V.; data curation, R.L. and G.V.; writing--original draft preparation, R.L. and G.V.; writing--review and editing, R.L. and G.V.; visualization, R.L. and G.V.; supervision, R.L.; project administration, R.L.; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Aeronautics and Space Administration (NASA) grant #80 NSSC17K0525.  
The authors declare no conflict of interest.
## Appendix A Variable Retrieval  
### Soil Moisture Retrieval  
The algorithm developed to estimate SM was conceived to combine a multi-temporal approach with a good computational efficiency. The latter characteristic is particularly suitable for an operational application, such as the use of SM maps in NWP models. A multi-temporal approach to estimate SM from SAR assumes that the temporal scale of variation of soil roughness is considerably slower than that of SM. Hence, if a dense time-series of SAR data is available, as expected using Sentinel-1, short term changes in the backscattering coefficient \(\sigma^{0}\) (that represents the SAR measurement) are basically related to SM variations [52; 53; 54]. The algorithm is based on a multi-temporal maximum likelihood approach, for example that in Reference [53], used to invert a direct scattering model of backscattering from a bare soil, namely that proposed by [PERSON] [55]. The Oh Model relates the state of the soil, described by the pair (SM, \(s\)), where \(s\) denotes the height standard deviation of the rough surface, to the \(\sigma^{0}\) measured at an incidence angle \(\theta\). This relationship is represented in form of lookup table that was generated by applying the Oh model as described in Reference [56]. The retrieval algorithm assumes that a time series of \(M+1\) measurements (at the current time \(t\) and at \(M\) previous times \(t-1\),..., \(t-M\)) is available. It performs a least square search minimizing the square difference between measured and modelled values of \(\sigma^{0}\):  
\[d[\text{SM}(t),\text{SM}(t-1),...,\text{SM}(t-M),s,\theta]=\sum_{j=0}^{M}\{ \sigma^{0}_{VV,soil}(t-j)|_{dB}-\sigma^{0}_{VV,model}[\text{SM}(t-j),s,\theta ]|_{dB}\}^{2}, \tag{1}\]where \(d\) is the cost function that has to be minimized and the symbol \(|_{dB}\) indicates that the backscatter values are expressed in logarithmic units. In (2), \(\sigma^{0}_{VV,soil}\) refers to the soil contribution to the Sentinel-1 measurements, so that, if vegetation is present, its effects on the Sentinel-1 backscattering measurements must be corrected before determining the cost function \(d\). The choice of \(M\) is a compromise between SM estimation accuracy and computational time: \(M=4\) was chosen, as done by Reference [54]. The Water Cloud Model [57], [WCM], with the parameters proposed by Reference [58] for all land uses, was used for the correction of the vegetation effects on \(\sigma^{0}\). More details on the SM retrieval algorithm can be found in Reference [56].
# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 1 Introduction
[23] developed an online health prediction framework combining a deep convolutional neural network (DCNN) and a dual-layer LSTM, leveraging DCNN for spatial feature extraction and LSTM for time-series analysis to ensure high computational precision. Based on previous studies, it can be observed that most researchers have primarily focused on using single models for lithium battery SOH prediction, with relatively few comparisons and evaluations across multiple models. Therefore, to comprehensively assess the applicability and performance of different methods in lithium battery SOH prediction, this study selects LSTM, CNN, and SVR for comparative analysis. By comparing the prediction performance of these models, this study aims to reveal the strengths and limitations of different methods under various conditions, providing more accurate and reliable decision support for lithium battery health state estimation. Furthermore, to improve prediction accuracy and reduce manual intervention during model training, this research employs the PSO algorithm to optimize the key parameters of the three models, further enhancing prediction performance and ensuring the accuracy and reliability of the results.
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 1 Introduction
We could use resonant cavity enhanced (RCE) structure [33] employing distributed Bragg reflector (DBR) [34], subwavelength grating (SWG) mirrors [35] as bottom reflectors and  
Figure 1: Features of microwave, millimeter–wave, and terahertz spectrum [8; 9; 10; 11]. dielectric layers as top reflectors to increase O/E conversion efficiency, while using high conductivity AlN [36; 37], diamond [38] or SiC [39] substrates to reduce heat dissipation. Moreover, the slow light effect resulting from Bragg grating structures [40; 41] with a remarkably low group velocity might offer a possible and promising solution to successfully compress optical signals and enhance light-matter interactions, and the enhanced O/E conversion efficiency in PDs could be possible while the joule heat problem at a higher bias voltage, device footprint reduction, and low power consumption could also be solved in the future. This review work is structured as follows. Section 2 presents the overall design considerations for photodiodes (PDs), including the principles of PIN-PD and UTC-PD, 3-dB bandwidth analysis and discussion, equivalent circuit models, and saturation current and RF output power. Section 3 shows the solutions of bandwidth-responsivity trade-off, which are integrated or quasi-integrated with high-reflected mirrors for III-V and group-IV photodiodes, and microstructure photodiodes with microholes for enhancing light-matter interactions. Section 4 narrates the photodiode photonic-integrated applications, which relate to the photodiodes integrated with short stubs for output power improvement as well as the integrations with planar antennas for transmitters/emitters from MMW to THz bands. The discussion and conclusions are given in Sections 5 and 6.
# Performance Comparison of Machine Learning Disruption Predictors at JET
## 2 Materials and Methods
### Performance Metrics  
In binary classification, a true positive (TP) is counted if a positive instance is predicted as positive, whereas it is counted as a false negative (FN) if it is predicted as negative. A negative instance predicted as negative is defined as a true negative (TN), whereas it is counted as a false positive (FP) when predicted as positive. These four values can be summarized in a \(2\times 2\) confusion matrix, where each row contains the instances in the actual class whereas each column contains the instances in the predicted class.  
Note that such definitions do not take into account the warning time \(\Delta t_{warning}\) provided by the predictor to act on the plasma. However, they can be adapted to the disruption prediction definitions, introduced in Section 2.1, including tardy detections (TD) and missed alarms (MA) in the counting of a FN, and when evaluated, premature detections (PRD) in a FP. Thus, a direct correspondence between the two approaches for the performance evaluation can be found when the instance is the discharge. Note that a TP corresponds to an SP, and TNs are evaluated as the difference between negative instances N (number of non-disrupted discharges in the test set) and those counted as an FA. The positive instances are indicated as P (number of disrupted discharges in the test set).  
Therefore, some performance indices can be used, valid for both the previous definitions:  
* \(PRECISION=\frac{TP}{TP+FP}\)
* \(RECALL=\frac{TP}{TP+FN}=\frac{TP}{P}\)
* \(SPECIFICITY=\frac{TN}{TN+TP}=\frac{TN}{N}\)
* \(ACCURACY=\frac{TP+TN}{P+FN}\)  
In addition, the F-score indicators, which encompass the information of \(PRECISION\) and \(RECALL\), can be defined as:  
\[F_{\alpha}=\left(1+\alpha^{2}\right)\cdot\frac{PRECISION\cdot RECALL}{\alpha ^{2}\cdot RECISION+RECALL}\]\(F_{1}\) score is the harmonic mean between \(PRECISION\) and \(RECALL\), whereas \(F_{2}\) assigns a higher cost to the disrupted misclassifications.  
For a binary classifier parametrized by a threshold, as in our case, the relative trade-off between benefits and costs can be displayed by the ROC curve, which draws the true positive rate \(TPR=\frac{TP}{P}\) as a function of the false negative rate \(FNR=\frac{FN}{P}\) by varying the threshold. Moreover, the area under the ROC curve (AUC) can also be used to assess the ability of the model to distinguish between the two classes.  
However, in the disruption prediction literature, the most informative figure of merit is defined by the accumulated fraction of detected disruptions as a function of \(\Delta t_{warming}\). It allows one to read, in a unique graph, the successful prediction and the tardy detections, as well as a general overview of the premature detections and the alarm anticipation times.  
All these metrics will be presented in the following to compare the performance of the three DP models.
# Additive Manufacturing for Sensors: Piezoresistive Strain Gauge with Temperature Compensation
## 2 Materials and Methods
### Experimental Set-Up  
The experimental set-up is shown in Figure 2. It consists of:  
1. An RV-2F-D industrial robot (Mitsubishi MELFA), with a spatial resolution of 0.01 mm along each axis and a repeatability of 0.02 mm. The robot is moved vertically to impress known force or displacement on the sensors and to allow repetitive experimental tests.
2. Two FFF sensors, fabricated by using the same production parameters: one sensor under test (\(S_{a}\)), which is pushed by the robot, and another dummy sensor (\(S_{d}\)), subject only to temperature variations and environmental factors. In this way, it is possible to evaluate the effect of temperature changes separately.
3. A load cell (500 g measurement range) to measure the force applied by the robot. A two-point calibration is performed on the load cell by employing calibrated weights before its use.
4. A thermocouple, placed near \(S_{a}\); and two digital temperature sensors (Dallas DS18B20) with a resolution of 0.06 \({}^{\circ}\)C: one is placed near the dummy sensor (\(S_{d}\)), the other near the load cell.
5. Three GDM-8351 digital multimeters to measure the resistance of the two FFF sensors and thermocouple voltage.
6. A control unit was developed in LabVIEW to control the robot and the instruments and for data processing.  
Figure 1: (a) 3D model and (b) realization of the proposed strain sensor.  
The robot end-effector holds a rounded tip/head to touch one extremity of \(S_{a}\) and to apply a bending deformation. \(S_{a}\), on the other extremity, is fixed on the load cell (through a metal structure), which measures the force applied on \(S_{a}\). The variation of resistance of the load cell is converted using the XFW-HX711 module, which employs a high-precision 24-bit analog-to-digital converter (ADC), and the converted signal is acquired by an Arduino UNO board, which also acquires the temperature measured by the two digital temperature sensors. Each measured force value is the mean of 40 acquired load cell measurements. The number of repeated measurements has been chosen as a good compromise between noise reduction and data rate. The resistance of the FFF sensors is measured using the multimeters with a 2-wire configuration since the resistance of the FFF sensors is higher than 20 k\(\Omega\).
## 3 Materials and Methods
### Extraction Protocol  
All extractions were performed in 96-well plates using plate-compatible SH10 Heater-Shaker (Ingenieurburo CAT, M. Zipperer GmbH, Ballrechten-Dottingen, Germany). The extraction protocol included: 1 st preconditioning (1.5 mL methanol/water (50/50, \(v/v\)), 1 h, 850 rpm agitation); 2 nd preconditioning (1 mL methanol/water (50/50, \(v/v\)), 1 h, 850 rpm agitation); 1 st rinse (1.5 mL water, 5 s, no agitation); extraction (1 mL of testing mixture (50 ng/mL in PBS), 2.5 h, 850 rpm); 2 nd rinse (1.5 mL water, 5 s, no agitation); and desorption (1 mL of desorption solvent, 2 h, 850 rpm). The temperature during the entire protocol was monitored and kept at 18.8 \({}^{\circ}\)C. Nine different variants of desorption solvents were used, and their compositions are shown in Table 4. Each variant was spiked with a mixture of deuterium-labelled reference standards at a concentration of 3 ng/mL.  
Additionally, the reference samples were transferred to the unoccupied wells of the 96-well plates prior to the desorption step. Therefore, the samples and the reference samples  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Desorption Solvent** & **Composition** \\ \hline DS1a & IPA/W/FA (80/19.9/0.1, \(v/v\)) \\ DS1n & IPA/W (80/20, \(v/v\)) \\ DS1b & IPA/W/AH (80/19.9/0.1, \(v/v\)) \\ DS2a & ACN/W/FA (80/19.9/0.1, \(v/v\)) \\ DS2n & ACN/W (80/20, \(v/v\)) \\ DS2b & ACN/W/AH (80/19.9/0.1, \(v/v\)) \\ DS3a & MeOH/W/FA (80/19.9/0.1, \(v/v\)) \\ DS3n & MeOH/W/(80/20, \(v/v\)) \\ DS3b & MeOH/W/AH (80/19.9/0.1, \(v/v\)) \\ \hline \hline \end{tabular} Chemicals used: ACN = acetonitrile (LC-MS grade; CHROMASOLY(tm), Honeywell International Inc., Charlotte, NC, USA); AH = ammonium hydroxide (LC-MS grade; Fluka(tm), Honeywell International Inc., Charlotte, NC, USA); FA = formic acid (LC-MS grade; Optima(tm), Fisher Chemical, Thermo Fisher Scientific Inc., Waltham, MA, USA); IPA = isopropanol (LC-MS grade; CHROMASOLY(tm), Honeywell International Inc., Charlotte, NC, USA); MeOH = methanol (LC-MS grade; CHROMASOLY(tm), Honeywell International Inc., Charlotte, NC, USA, W = water (LC-MS grade; LiChrosoly(tm), Merck KGaA, Darmstadt, Germany).  
\end{table}
Table 4: Composition of desorption solvents.  
were affected equally by the evaporation of the desorption solvents. Such an approach excluded the impact of the evaporation on the results.