esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Darkfield and Fluorescence Images Monitor  
Darkfield and fluorescence images were obtained using the 10\(\times\) objective lens of the CytoViva microscope. The A549 cells were seeded at a density of 12 \(\times\) 10\({}^{3}\) cells per well in 8-well chamber slides and incubated for 24 h. PEGylated- and TBO-PEGylated nanopeanuts were tested with and without a laser in addition to two controls, viz., the cell alone and the cell exposed to the laser. Three hundred microliters of 10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL nanopeanuts in the medium were prepared and added for the corresponding treatment. After 4 h of incubation at 37 \({}^{\circ}\)C, the cells were washed with PBS buffer twice and then replaced with the medium, followed by 3 min of exposure to a 633 nm diode laser (200 mW/cm\({}^{2}\) beam with a 1-mm\({}^{2}\) spot area) in the dark. Then, after 16 h of incubation at 37 \({}^{\circ}\)C, each well was washed twice with PBS. The cells were then fixed using 4% paraformaldehyde/PBS for 30 min at room temperature. The paraformaldehyde/PBS mixture was removed and washed twice with PBS. Three hundred microliters of Triton X-100 (0.2%) were added per well and incubated for 5 min, followed by washing as described above. Finally, the nuclei were stained with Hoechst (blue color), and the cytoskeleton was stained with Alexa 594 (red color) for CytoViva microscope analysis.  
Singlet Oxygen Detection from PEGylated Nanopoeanuts and TBO-PEGylated Nanopoeanuts Excited by an Infrared Femtosecond Cr: Forsterite 1230 nm Laser  
We prepared 100 \(\mathrm{\SIUnitSymbolMicro L}\) of 20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL TBO-PEGylated nanopeanuts, added 10 \(\mathrm{\SIUnitSymbolMicro L}\) of 1 mM Singlet Oxygen Sensor Green Reagent (Invitrogen) (Ex/Em: 505/525 nm) per well in a 96-well culture plate, and then exposed the plate to an infrared femtosecond Cr:forsterite 1230 nm laser (104 mW/cm\({}^{2}\)) in the dark. The singlet oxygen production was measured with the luminance intensity of probes.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 6 Conclusions  
In our proposed algorithms, it has been seen that it is an energy-efficient clustering approach that supports an optimized way to elect CH for safety-critical WSN applications, with a particular emphasis on a forest fire. Energy efficiency has been identified as a critical concern for IoT networks. It was proposed to address this difficulty by using an enhanced CH exercise approach with a unique fitness function that included factors for energy efficiency, cluster building, and other parameters. We tested the effectiveness of the suggested strategy in a forest fire scenario, comparing it to previous studies and taking into account varied sink node locations. The results were promising. Various performance parameters, such as overall residuary energy, the count of live nodes, and network building, were considered in simulation evaluations. These are the following conclusions that we reached: (1) our proposed EE-SS performs significantly better in a disaster area than the current best practices; (2) the sink node's location (the distance between the nodes) affects the execution of the algorithm. (3) In a catastrophic scenario, probabilistic algorithms such as LEACH and LEACH-C do not function as expected. Compared with the conventional algorithms, the simulation results proved the effectiveness of the proposed EE-SS clustering algorithm.
# A Smart Rehabilitation Glove Based on Shape-Memory Alloys for Stroke Recovery
## 2 Design
### Structural Design  
The overall components of the smart rehabilitation glove are shown in Figure 1a. The glove mainly consists of SMA wires, carbon wires, an amplifier, an aluminum shell, and an outer shell. Within the aluminum shell, each carbon wire is connected with a corresponding SMA wire to pull each finger for recovery movements. To complete rehabilitation training, a contraction displacement of 54 mm is required for each finger. The SMA can provide, at most, 8% recoverable strain; thus, a total of at least 680 mm of SMA wires is needed, which takes up a large volume in the glove [31]. To minimize the size of glove, an amplifier structure was designed, as shown in Figure 1b, taking up a small volume and providing an amplified contraction. The lines represent the SMA wires assembled on insulated shafts, and the pulley wheel represents the fixed pillar to keep the carbon wires and SMA tight and stable. Furthermore, due to the heat generated by the SMA wires when powered on, the carbon wires are connected to the SMA wires in the finger region for user safety. One side of the SMA is fixed, while the other side is continuously winded around the pulley wheel to achieve reciprocating coiling, resulting in an N-fold amplification of the contraction (N is the number of coiling times), effectively reducing the space occupied by the SMA wires and the volume of product. In the glove, a single finger employs a value of N = 6, corresponding to a 6-fold amplification, which enables the accommodation of an SMA with a length of 160 \(\times\) 6 = 960 mm, thus fulfilling the stroke requirements for each finger. Using the amplification system, multiple SMA wires and shafts are combined to form the force-driving module that is connected to the finger joints to become the prototype of a smart rehabilitation glove, enabling the recovery movement of fingers from (I) a natural state to (III) a fully extended state, as shown in Figure 1c.
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 3 Result
### Comparison of FRIB-Based and Depth-Based Response Strategies
Compared to the conventional depth-based model, this FRIB-based methodology offers finer distinctions in risk levels, ultimately playing a crucial role in strengthening urban disaster resilience and minimizing overall damage.
###### Abstract  
Understanding the water and carbon cycles within terrestrial ecosystems is crucial for effective monitoring and management of regional water resources and the ecological environment. However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our study, we leveraged multiple variables (vegetation growth, surface moisture, radiative energy, and other relative variables) as inputs for various regression algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN), to estimate water (ET) and carbon fluxes (NEE) in the Haihe River Basin, and compared the estimated results with the observations from six eddy covariance flux towers. We aimed to (1) assess the impacts of different input variables on the accuracy of ET and NEE estimations, (2) compare the accuracy of the three regression methods, including three machine learning algorithms and Multiple Linear Regression, and (3) evaluate the performance of ET and NEE estimation models across various regions. The key findings include: (1) Increasing the number of input variables typically improved the accuracy of ET and NEE estimations. (2) RFR proved to be the most accurate for both ET and NEE estimations among the three regression algorithms. Of these, the four types of variables used together with RFR resulted in the best accuracy for ET (R\({}^{2}\) of 0.81 and an RMSE of 1.13 mm) and NEE (R\({}^{2}\) of 0.83 and an RMSE of 2.83 gC/m\({}^{2}\)) estimations. (3) Vegetation growth variables (i.e., VIs) are the most important inputs for ET and NEE estimation. (4) The proposed ET and NEE estimation models exhibited some variation in accuracy across different validation sites. Despite these variations, the accuracy levels across all six validation sites remained relatively high. Overall, this study lays the groundwork for an efficient approach to agricultural water resources and ecosystem monitoring and management.  
Google earth engine; evapotranspiration; net ecosystem exchange; Landsat; machine learning 2024 163280. [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))
# Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems
## 5 Conclusions  
By means of the auxiliary model identification idea, this paper employs the coupling identification concept to propose a novel recursive identification method for multivariate output-error systems. The proposed methods have the following properties:  
* The C-S-AM-RLS algorithm and the C-AM-RLS algorithm are presented by forming a coupled relationship between the parameter estimation vectors of the subsystems, and they avoid computing the matrix inversion in the multivariable AM-RLS algorithm so they require lower computational load and achieve highly accurate parameter estimates.
* With the noise-to-signal ratios decreasing, the parameter estimation errors given by the proposed algorithms become smaller.  
The basic idea of the proposed algorithms in this paper can be extended and applied to other fields [29; 30; 31].  
**Acknowledgments:** This work was supported in part by the National Natural Science Foundation of China (No. 61293194) and and Natural Science Research of Colleges and Universities in Jiangsu Province (No. 16 KJB120007, China).  
Figure 6: The AM-RLS, C-S-AM-RLS and C-AM-RLS estimation errors \(\delta\) versus \(t\) for Example 2 (\(\sigma^{2}=0.50^{2}\)).  
Figure 7: The AM-RLS, C-S-AM-RLS and C-AM-RLS estimation errors \(\delta\) versus \(t\) for Example 2 (\(\sigma^{2}=0.20^{2}\)).  
**Author Contributions:** [PERSON] conceived the whole paper and supervised his student [PERSON] to write the paper and [PERSON] designed and performed the simulation experiments. Both authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
It is important to remark that the data collection can have some delay, but the algorithms proposed in this paper can work in real-time. This paper is organized as follows: Section 2 discusses the related work about human activity recognition in the environment of mobile phones and current research works based on the analysis of CDR. Section 3 details the case study that is proposed in this research. Section 4 explains our proposal for clustering calling behaviours and detecting outlier calls and callers. Finally, Section 5 concludes the paper.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 3 Calibration of Magnetic Flux Sensors
### Estimation of Magnetometer Biases  
The calibration of the magnetic flux requires measurements with _rotational dynamics_ of multiple epochs to enable a _separation_ of \(m^{8}\) and \(b_{m^{8}}\). The magnetic flux \(m^{8}\) and bias \(b_{m^{8}}\) can be assumed constant over a few epochs. Therefore, we stack the magnetic flux measurement of Equation (1) of \(n\) epochs in a column vector and express them in terms of the unknowns:  
\[\left(\begin{array}{c}m^{8}(t_{1})\\ \vdots\\ m^{8}(t_{n})\end{array}\right)=A\left(\begin{array}{c}m^{8}\\ b_{m^{8}}\end{array}\right)+\left(\begin{array}{c}\eta_{m^{8}}(t_{1})\\ \vdots\\ \eta_{m^{8}}(t_{n})\end{array}\right)\quad\mbox{with}\quad A=\left(\begin{array} []{cc}R_{8}^{8}(t_{1})&1\\ \vdots&\vdots\\ R_{8}^{8}(t_{n})&1\end{array}\right). \tag{6}\]  
The magnetic flux and bias are determined such that the sum of squared measurement residuals is minimized, i.e.,  
\[\left(\begin{array}{c}\hat{m}^{8}\\ \hat{b}_{m^{8}}\end{array}\right) = \arg\min_{m^{8},b_{m^{8}}}\left\|\left(\begin{array}{c}m^{8}(t _{1})\\ \vdots\\ m^{8}(t_{n})\end{array}\right)-A\left(\begin{array}{c}m^{8}\\ b_{m^{8}}\end{array}\right)\right\|_{\Sigma^{-1}}^{2} \tag{7}\] \[= (A^{\mathrm{T}}\Sigma^{-1}A)^{-1}A^{\mathrm{T}}\Sigma^{-1}\left( \begin{array}{c}m^{8}(t_{1})\\ \vdots\\ m^{8}(t_{n})\end{array}\right).\]
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 3 Methods
### Modality Feature Extraction Module  
This article employs three main tools and techniques to extract feature vectors from different modalities: the BERT pre-processing model, COVAREP, and OpenFace, used for extracting features from text, audio, and visual modalities, respectively.  
Firstly, the model utilized the BERT pre-processing model for word embedding. The BERT (bidirectional encoder representations from transformers) model is a leading pre-trained language-representation model, involving multiple key steps in the word-embedding process. After processing through the BERT structure, we obtained feature vectors for the entire text and for each word. These feature vectors encapsulated contextual information within the text and were directly used as inputs for subsequent downstream tasks. Additionally, the pre-trained BERT model itself possesses powerful capabilities for learning contextual information, which were embedded into our model as an upstream structure and to dynamically adjust word embeddings during model training.  
Next, for audio modality data, this model utilized COVAREP [10] for feature extraction. COVAREP is a tool used to extract speech features, primarily including fundamental frequency, voice intensity, and spectral envelope, among other basic acoustic parameters. In the experiments with this model, the audio signal was framed in frames of 20 milliseconds, and acoustic parameters were extracted, including fundamental frequency in the range of 50 Hz to 400 Hz and voice intensity between 40 and 90 dB. Additionally, spectral analysis was conducted to capture the energy distribution of the audio signal at different frequencies.  
Finally, for visual modality data, this model employed OpenFace [11] for feature extraction. OpenFace is a tool used for facial expression recognition and facial feature extraction, capable of extracting facial expressions, actions, geometric features, and other  
Figure 5: ACAM architecture diagram.  
information from images or videos. In the processing of visual modality, the images were first processed and face detection was performed, then OpenFace was used to extract facial features. OpenFace utilizes deep learning techniques such as convolutional neural networks (CNNs), residual networks (ResNet), etc. Through these deep learning models, OpenFace efficiently identified facial regions and extracted rich facial features. These features included facial expressions, facial actions, facial geometric features, etc., providing important visual information for the multimodal model.
# Tracking of Stiffness Variation in Structural Members Using Input Error Function Observers
## 1 Introduction
[39] have applied Single-Walled Carbon Nanotube Composite Coatings to a test specimen and measured spectral shifts of the nanotube near-infrared fluorescence peaks using a circularized-beam diode laser. As the technology is improving, video cameras with higher frame rates and resolutions are becoming available at a reduced cost and finding their application in structural health monitoring. [PERSON] and [PERSON] [40] have identified natural frequencies and mode shapes of a simple supported beam structure from measurements using one video camera and show good correlation between the data obtained using an array of accelerometers. In another study, the authors of [41] have evaluated the performance of the proposed vision sensor for dynamic displacement measurements via shaking table experiments on a scaled three-story frame structure and those measured by high-performance laser displacement sensors. [PERSON] et al. [42] have identified vibration frequencies and mode shapes of the World War I Memorial Bridge in Portsmouth by measuring displacements with a developed video processing technique. Deep learning technologies can be implemented for vibration-based structural monitoring, but it is essential to have algorithms that can correlate the measured signal to the present status of the structure [43]. The current study is an attempt to provide one of such algorithms for real-time structural health monitoring and condition-based maintenance. Using the proposed algorithm, the stiffness of individual structural members can be estimated, and an updated analytical model can be made available for further evaluation. A unique error function corresponding to individual structural members is established using an interaction matrix formulation and enables implementing optimization techniques by minimizing the input error function signal. The entire process can be automated for real-time stiffness tracking of structural members, and the proposed algorithm manages system uncertainties very well. The statistical approach is easy to implement in order to address uncertainties in estimating structural stiffness from noise in input-output measurements. This paper is organized as follows. We will present the input error function formulation in Section 2. Observers based on the input error function are discussed in Section 3 to track the stiffness variation in all three springs for a three spring-mass-damper system. Optimization of the input error function is discussed in Section 4 where the robustness of the proposed input error functions for estimating the severity of damage in structural members is verified. Finally, the proposed optimization of the input error function technique is used to track stiffness variation in the structural member for a three-bay two-dimensional truss structure (as in long span bridge trusses) in Section 5.
## 2 Navigation Sensors and Techniques for Drones
### Barometer  
The barometer is a reliable sensor for altitude determination. Although conceptually it is a relative positioning device that uses the measured atmospheric pressure to determine the altitude, it is often used as an absolute positioning sensor. When it is used as a standalone sensor with no corrections, the hypsometric equation can be used with the standard atmospheric pressure as the reference to compute the estimated altitude [9]. However, as the atmospheric pressure is dependent on the weather, geolocation, and time of day, it needs to be corrected. [PERSON] investigated the accuracy and variations of barometric altitude around the United States and in Boulder, Colorado, and concluded that without corrections, the barometric altitude accuracy has a standard deviation of 11 m [10]. Furthermore, he looked at how the local barometric corrections could aid in improving the accuracy of the altitude estimates up to a standard deviation of 2.3 m. However, the problem remains that without corrections and in the presence of pressure gradients due to a storm, the altitude accuracy is very limited. Furthermore, barometers are limited to altitude measurements and cannot provide horizontal PVT information.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 3 Materials and Methods
### Numerically Controlled Oscillator  
The NCO is used to determine the position of the optimal sampling instant for the next symbol, i.e., \(P_{n+1}\). Figure 7 shows the flowchart of NCO in the CB-Gardner algorithm.  
In the CB-Gardner, we modified the expression of the control word in the NCO. Since the NCO only needs to obtain the position of the next symbol and does not require knowledge of the midpoint position between two symbols, the NCO only needs to overflow once per symbol period, i.e., the NCO overflows with a period of \(T_{i}=T\). Therefore, the initial value of the control word is  
\[W_{0}=T_{s}/T_{i}=\frac{1}{F_{s}}/T=\frac{1}{F_{s}}/\frac{1}{F_{b}}==1/(\frac {F_{s}}{F_{b}})=1/sps \tag{14}\]  
Figure 6: The positions of three actual detection points (\(y_{1},y_{2},y_{3}\)), when \(\lambda<1\), \(\varphi_{y_{1}}=\pi/2\).  
where \(T_{s}\) is the sampling period, and \(sps=\frac{F_{s}}{K}\) denotes the average number of samples per symbol. For the \(n\)th symbol, the control word within the NCO is  
\[W_{n}=W_{0}-E_{LP,n}=1/sps-E_{LF,n} \tag{15}\]  
Below, we provide a brief derivation of the register values in the NCO after each overflow. We define the location of each NCO overflow as the best sampling instant for the symbols. For the \(n\)th symbol, we assume that the NCO overflows at the position of \(P_{n}=m_{n}+u_{n}\), where \(m_{n}\) is the integer part of \(P_{n}\) and \(u_{n}\) is the fractional part. The register value after the overflow is \(\eta_{n}\), which is  
\[\eta_{n}=(m_{k}+u_{k})W_{n} \tag{16}\]  
where \(m_{k}\) is an integer and \(u_{k}\) is a fractional number. From \(P_{n}\), \(\eta_{n}\) and \(W_{n}\), we can directly obtain the position and register value of the next NCO overflow, as given by  
\[P_{n+1}=m_{n}+1+m_{k}+u_{k}=\text{int}(P_{n})+1+\frac{\eta_{n}}{W_{n}} \tag{17}\]  
\[\eta_{n+1}=\eta_{n}-(m_{k}+1)W_{n}+1=\eta_{n}-[\text{int}(\frac{\eta_{n}}{W_{n }})+1]W_{n}+1 \tag{18}\]  
Then, from the \(n\)th symbol to the (\(n+1\))th symbol, the NCO estimates the signal expansion factor as  
\[\lambda_{n}=sps/(P_{n+1}-P_{n})=sps/[\text{int}(P_{n})+1+\frac{\eta_{n}}{W_{n }}-P_{n}] \tag{19}\]  
In this way, we estimate the magnitude of the signal expansion factor in real time. Subsequently, we again search for the nearest three error detection points after the (\(n+1\))th symbol to form a feedback loop.
## 4 Experiment and Results  
### Experimental Setup  
The setup of the bimodal information collection system is shown in Figure 1. The MS600 multispectral module containing those six specific bands and bio-radar module are carried by the M100 Quad-rotor UAV system for target detection and recognition. The flight mission was carried out in a wheat field in Hu County, Xi'an City, Shaanxi Province on 24 March 2021. The weather is sunny, cloudless with downwards third-class winds, which is suitable for the UAV flight. The flight remained at an altitude of 120 m, 5.3 m/s, 5 routes with 80% course overlap rate and 75% side overlap rate.  
Two kinds of typical experiments were carried out in this study, namely the suspected target detection experiment based on multispectral feature information only and the human subject reconfirmation experiment with auxiliary human vital sign information. More specifically, the first kind of experiment was also conducted in two typical scenarios illustrated by Figure 10. _Scenario 1_ holds a relatively homogeneous environmental background with a few kinds of components (green grassland and, a small piece of land). Twelve sets of green camouflage clothes were laid on the field randomly to disguise as human subjects. _Scenario 2_ is purposely selected as an outdoor bush environment in autumn, which is more complex and closer to practical applications as shrubs, green lawns, trees, dark yellow, and other vegetation randomly scattered. Here, three colors of camouflage (11 pieces of camouflage and one real person in desert camouflage) were placed in different vegetation areas of similar colors. Further, in the complex _Scenario 2_ described above, the subsequent human subject reconfirmation experiment aims to distinguish between fake targets and true human targets from multiple suspected targets with additional respiration information detected by bio-radar.  
Figure 9: Block diagram of ALE.