esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Potential Future Technological Developments
### Development of New Materials and Composite Structures  
On the one hand, there has been a continuous development of modified bentonite materials that are more resistant to pollution. By incorporating specific nano-additives or organic polymers, the adsorption capacity of bentonite for heavy metal ions and its ability to immobilize them have been enhanced. Simultaneously, the chemical stability and durability of the material have been improved. On the other hand, efforts have been made to explore the composite structural design of cut-off walls. For instance, based on bentonite vertical cut-off walls, new combinations with biochar material layers or photocatalytic material layers have been proposed [152]. Biochar can adsorb some organic pollutants and create a synergistic effect with bentonite. Under light conditions, photocatalytic materials are able to decompose part of the toxic and hazardous substances. These advancements further enhance the cut-off wall's environmental adaptability and pollution prevention capabilities, thereby broadening its application in complex pollution sites.
## 3 Materials and Methods
### GC-MS Analysis of Volatile Compounds in Samples  
For volatile compound analysis in _Camellia sinensis_ tea samples, solid-phase microextraction (SPME) was introduced, frozen samples were ground to a fine powder in liquid nitrogen, and transferred to a 20 mL headspace bottle. Two microliters of ethyl caprate (58.84 \(\upmu\)L/mL in water) were used as an internal standard to the sample. Samples were incubated at 65 \({}^{\circ}\)C for 50 min. Metabolite analysis was performed by gas chromatography (Agilent 7890A)/mass spectrometry (Agilent 5975C; GC/MS), with a DB-WAX capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent) to separate metabolites. Pure helium was used as the carrier gas, with a flow rate of 1 mL/min. The GC oven was maintained at 40 \({}^{\circ}\)C for 5 min and a ramp of 5 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C for 5 min under splitless full-scan mode. The energy was -70 eV in electron impact mode. The mass spectrometry data were acquired in full-scan mode with an m/z range of 20-400 after a solvent delay of 0 s. All metabolite peaks were recorded and stored in the format of ChemiStation data files. To estimate the retention index values, a mixture of even-numbered chain n-alkenes (C10-C40, Restek, FL, USA, catalog no. 31266) as a standard was used to deconvolute the metabolite peaks. At least six biological repetitions were performed for each sample.
## 3 Results  
### Performance Parametric  
The detection performance of the model is primarily assessed by evaluating accuracy (Ac). However, due to the class imbalance in the dataset, particularly with more subjects diagnosed with severe apnea (n = 49) compared to non-severe apnea (n = 17), relying solely on accuracy can be misleading. This class imbalance can introduce bias into the performance results. For instance, if the model were to classify all subjects as having severe apnea, the accuracy would be calculated as 0.74 (49/(49 + 17)), which might appear acceptable. However, this would fail to reflect the model's inability to correctly predict non-severe apnea cases. Therefore, additional performance metrics are necessary to provide a more comprehensive evaluation.  
To address this, we included three additional metrics: sensitivity (Sn), specificity (Sp), and precision (Pr). Sensitivity (Sn) measures the probability of correctly detecting subjects with severe apnea, providing insight into the model's ability to identify true positive cases. Specificity (Sp) reflects the probability of correctly identifying subjects with non-severe apnea, highlighting the model's effectiveness in recognizing true negative cases. Precision (Pr) indicates the likelihood that subjects predicted to have severe apnea are correctly classified, thus focusing on the accuracy of positive predictions. The performance of the model is evaluated using the following parameters: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). These parameters are defined as follows: TP represents the count of correctly predicted severe apnea cases, TN represents the count of correctly predicted non-severe apnea cases, FP indicates the count of incorrectly predicted severe apnea cases, and FN indicates the count of incorrectly predicted non-severe apnea cases.  
The formulas used to calculate Sn, Sp, Pr, and Ac are as follows:  
\[\text{Sn}=\text{TP}/(\text{TP}+\text{FN}) \tag{1}\]  
\[\text{Sp}=\text{TN}/(\text{TN}+\text{FP}) \tag{2}\]  
\[\text{Pr}=\text{TP}/(\text{TP}+\text{FP}) \tag{3}\]  
\[\text{Ac}=(\text{TP}+\text{TN})/(\text{TP}+\text{FN}+\text{FP}+\text{TN}) \tag{4}\]  
By utilizing these metrics, we gain a clearer understanding of the model's performance across different aspects of prediction. Sensitivity ensures that the model is proficient at detecting severe apnea cases, specificity ensures that non-severe apnea cases are not overlooked, and precision ensures that the predictions of severe apnea are reliable. This multifaceted approach to performance evaluation provides a more robust and accurate assessment of the model's capabilities, especially in the presence of class imbalance.
## 3 Selected Results of Optical Metrology Techniques
### Multiple Simultaneous Viewing Directions Using Four Digital Holographic Microscopes (MuVie)  
The first example significantly extends the range of applicability of a measurement system based on the principle setup shown in Figure 2 with respect to the shape of the object under investigation. In order to achieve the goals of a fast measurement (\(<\)1 s/part), a measurement uncertainty \(<\) 5 \(\upmu\)m, robustness against mechanical disturbances an and a 360\({}^{\circ}\) view of a complex geometry with a non-destructive, non-contact system, four key elements have been combined: (i) the method of digital holography, (ii) four directions of observation, (iii) two-wavelength contouring and (iv) the use of coherence gating.  
The resulting setup of the Multiple-View holographic system (MuVie) is shown in Figure 3. It has been developed within the frame of project \(B5\) of the DFG funded collaborative research center _SFB 747 Micro Cold Forming_[22]. The system consists of four digital holographic units to provide sufficient observation directions to the same object to cover a 360\({}^{\circ}\) view. Each of the microscopes operates with two wavelengths \(\lambda_{1}=636.55\) nm and \(\lambda_{2}=642.10\) nm to yield a synthetic wavelength of \(\Lambda=73.64\)\(\upmu\)m. The telecentric long distance microscope objectives have 10 times magnification and a numerical aperture of \(NA=0.21\). The setup allows recording of multiple holograms at the same time through coherence gating. For this purpose, the system utilizes four laser diodes with a coherence length of approximately 1 mm. Using a system of optical fibers with appropriate lengths, interference is obtained between object and reference wave of the same holographic unit but impeded for light from different holographic units. The technique allows the holograms to be separated in Fourier space. For more details on this approach, see Reference [22].  
The object is a micro cold formed micro cup made from aluminum with diameter and height of approximately 1 mm. The region of interest is the curved area around the top of the cup. As seen from the shape measurement in Figure 3b, the system can be used to quantify imperfections, such as dents, scratches and deformations. In the current configuration, it provides a lateral resolution of 2 um and a measurement uncertainty of 5 um. In the example shown here, the overall recording time is 50 ms. However, processing of the holograms requires approximately 15 s, due to refocusing and stitching requirements. To extend the depth of focus, we calculated the wave field across 10 object planes using Equation (4), with \(\Delta z=65\,\upmu\)m.
# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## Abbreviations  
The following abbreviations are used in this manuscript:  
RES & Renewable Energy Systems  
CNN & Convolution Neural Network  
LSTM & Long Short Term Memory  
AR & Autoregressive  
ARX & Autoregressive with Exogenous Variables  
ARMA & Autoregressive Moving Average  
ANN & Artificial Neural Network  
RNN & Recurrent Neural Network  
VSTLF & Very Short Term Load Forecasting  
STLF & Short Term Load Forecasting  
MTLF & Medium Term Load Forecasting  
LTLF & Long Term Load Forecasting  
DER & Distributed Energy Resources  
DSO & Distribution System Operator  
LV & Low Voltage  
HV & High Voltage  
PCA & Principal Component Analysis  
MLR & Multiple Linear Regression  
HEMS & Home Energy Management Systems  
LEM & Local Energy Markets  
THI & Temperature-Humidity Index  
PJM & Pennsylvania-New Jersey-Maryland  
AMI & Advanced Metering Infrastructure  
PI & Prediction Intervals  
LUBE & Lower Upper Bound Estimation  
SSA & Singular Spectrum Analysis  
PC & Principal Components  
SVM & Support Vector Machine  
WNN & Wavelet Neural Network  
SRWNN & Self-Recurrent Wavelet Neural Network  
CRBM & Conditional Restricted Boltzmann Machine  
FCRBM & Factored Conditional Restricted Boltzmann Machine  
CKD & Conditional Kernel Density  
MLP & Multilayer Perceptron  
SIANN & Space Invariant Artificial Neural Networks  
ReLU & Rectified Linear Unit  
ADF & Augmented Dickey Fuller  
STD & standard deviation  
ACF & Auto-Correlation Function  
MAE & Mean Absolute Error  
RMSE & Root Mean Square Error  
MAPE & Mean Absolute Percentage Error
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT  
This section describes the NB-IoT key technologies to achieve low energy consumption and extended coverage, including baseband characteristics, data transmission scheme, extended discontinuous reception (eDRX), power saving mode (PSM), and control-plane cellular IoT (CP CloT) EPS optimization.
## 4 Methodology
### PROMETHEE Method and Its Application  
The PROMETHEE method was selected due to its robust ability to handle multi-criteria decision-making, offering a structured yet flexible way to rank alternatives based on preferences across a range of criteria. PROMETHEE has been used in multiple domains, such as transport policy, renewable energy planning, and infrastructure projects, where multiple stakeholders and criteria are involved [29; 31]. The PROMETHEE method includes the following key steps:  
1. Alternatives represent different strategies for integrating EVs into transportation and energy systems. For this research, the alternatives could include various EV charging strategies, renewable energy integration models (such as wind and solar), and smart grid interaction frameworks [30]. Criteria are selected based on factors like cost, environmental impact, energy efficiency, user convenience, scalability, and grid resilience [29].
2. In PROMETHEE, a preference function is defined for each criterion, which describes how the performance of one alternative compares to another. Common preference functions include usual, U-shape, V-shape, level, linear, and Gaussian functions. These preference functions allow for flexibility in modeling both qualitative and quantitative criteria [34].
3. For each pair of alternatives, a preference index \(\pi(a,b)\) is calculated, indicating the extent to which alternative \(a\) is preferred over \(b\). This index reflects the strength of the preference, which is then used in subsequent steps to calculate the positive and negative flows [28].  
4. The positive flow \(\phi^{+}(a)\) represents how much alternative \(a\) is preferred over other alternatives, while the negative flow \(\phi^{-}(a)\) shows how much it is dominated by other alternatives. These flows are computed as follows: \[\phi^{+}(a)=\sum_{b\
eq a}\pi(a,b),\quad\phi^{-}(a)=\sum_{b\
eq a}\pi(b,a)\] (4)
5. The net flow \(\phi(a)\), calculated as the difference between positive and negative flows, provides a comprehensive measure of an alternative's overall performance: \[\phi(a)=\phi^{+}(a)-\phi^{-}(a)\] (5) A higher net flow indicates a more preferred alternative, which is then used to rank the alternatives [29].
6. Based on the net flow values, the alternatives are ranked, providing a clear order of preference. This ranking helps decision-makers choose the best strategy for integrating EVs and renewable energy into the grid, accounting for multiple conflicting objectives [31].
## 1 Introduction
The circular landform in Dinosaur Valley, situated in the mountains of the Yunnan Plateau, has garnered considerable attention due to its distinctive circular features captured in remote sensing images. Hence, its topographic characterization holds crucial theoretical significance in the realm of remote sensing geology [16]. However, current studies predominantly concentrate on small-area topographic characterization using UAV data [17; 18; 19], often neglecting comprehensive consideration of the overall topographic features. In addition, various terrain feature parameters depict terrain features from distinct perspectives. Determining the optimal combination of these parameters holds specific research significance for identifying and quantitatively analyzing terrain features across multiple dimensions. This also presents a novel research concept and foundation for exploring terrain features. This study focuses on the annular landform in Dinosaur Valley, located in Lufeng City, Chuxiong, Yunnan Province, as the designated study area. Utilizing ALOS DEM data, terrain feature parameters are extracted and optimal combinations are explored for multi-dimensional, qualitative, and quantitative identification, measurement, and analysis of the terrain features. This is achieved through visual interpretation, DEM visualization, and GIS terrain feature extraction and analysis. The study showcases the potential application of ALOS DEM data in characterizing terrain in the intricate mountains of the Yunnan Plateau. It offers precise and efficient technical methods for exploring terrain features,presenting fresh research perspectives and geomorphological foundations for resource and environmental management; sustainable development; and erosion prevention and control in the circumferential landform of Lufeng Dinosaur Valley.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 2 FH Dual One-Way Ranging in ISL
### FH-DOWR Signal Mode
However, the ranging performance is inevitably affected by discontinuous signals due to the utilization of the time division duplex (TDD) mode for transmission and reception. To enhance the anti-interference capabilities, we propose the utilization of frequency hopping (FH) signals. FH signals are known for their ability to mitigate interference by rapidly changing their carrier frequencies. Building upon the foundation of Figure 2, the integration of FH signals into our system significantly improves its resilience against interference. The structure of the FH DOWR is depicted in Figure 3, and the transmitting signal is represented in (9). \[s(t)=C(t)D(t)\cos[2\pi(f_{0}\pm nf_{\Delta})t]\quad n=1,2,\ldots,m \tag{9}\]  
Here, \(f_{0}\) is the FH reference frequency and \(f_{\Delta}\) is the FH frequency interval. In this mode, carrier phase measurements can be guaranteed as long as two frequency point signals can be properly received. Frequency hopping signal is a multicarrier transmission technology that can switch between multiple frequencies to achieve signal transmission. Its working principle is that when a signal is received it will switch from the received frequency to another frequency and transmit the signal between the different frequencies. Frequency hopping signals can provide faster transmission rates and higher efficiency bandwidth. In Figure 3, we show the hopping sequence of an FH signal. The advantages of this method are as follows:  
1. When multiple frequency signals exist simultaneously, the satellite can obtain unambiguous carrier phase measurements via integer ambiguity resolving, providing a DOWR accuracy of one tenth of a wavelength;
2. If there exists two frequency point signals without interference, then the carrier phase measurement accuracy of the signal can be guaranteed;
3. The addition of signal frequency points effectively improves the anti-jamming and anti-intercept performance of the signal. Figure 3: FH DOWR.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 3 Elaboration of Sensor Materials
### Conductive Materials  
The conductive layer is the core part of a flexible piezoresistive sensor, which not only needs to provide conductivity for the sensor but also has the elasticity to adapt to applied external force. The conductive layer usually refers to the conductive polymer composite formed by mixing conductive fillers and insulative elastomer or conductive nanomaterials coated on insulative elastomer substrates. Materials of the conductive layer are divided into three categories: metallic materials, carbon-type nanomaterials and hybrid materials.  
In accordance with the dimensions of the conductive fillers, conductive materials are divided into 0/1/2/3D materials. 0D materials mainly include nanometallic particles and CB; 1D materials mainly contain metal nanowires and CNTs; 2D materials refer to graphene flakes; and 3D materials refer to conductive polymers [46]. Amongst these materials, 1D materials have a high aspect ratio, which is helpful for forming conductive paths. Therefore, most flexible conductive composites have been prepared using 1D metal nanowires or CNTs.
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 3 MADDPG-Based Resource Allocation
### MDP Formulation  
As depicted in Figure 2, each sensor functions as an individual agent. At time step \(k\), agent \(n\) receives an observation \(\mathbf{o_{n}^{k}}\) from the environment and takes an action \(\mathbf{a_{n}^{k}}\). At time step \(k\), given the set of states \(\mathbf{s}^{k}=\{\mathbf{o_{1}^{k}},\mathbf{o_{2}^{k}},\dots,\mathbf{o_{N}^{k}}\}\) and the set of joint actions \(\mathbf{a_{k}}=\{\mathbf{a_{1}^{k}},\mathbf{a_{2}^{k}},\dots,\mathbf{a_{N}^{k} }\}\), the agent receives an immediate reward \(r_{n}^{k}\) and the subsequent observation \(o_{n}^{k+1}\). For sensor \(n\), the observable information at time \(k\) comprises both the channel quality state based on its current location and the remote state estimation (RSE) error covariance. This can be represented as \(\mathbf{o_{n}^{k}}=\{\mathbf{h}_{n}^{k},\mathbf{p}_{n}^{k}\}\). The system state at time \(k\) is the collection of all sensor observations, expressed as \(\mathbf{s_{n}^{k}}=\{\mathbf{o_{n}^{k}}\mid n\in\mathcal{N}\}\). At each time step, each sensor selects its action, which consists of the power allocation \(p_{n}^{k}\), sensor grouping \(b_{n,g}^{k}\), and beamforming selection for the RIS, denoted as \(\mathbf{\Theta}^{k}\). Thus, the action \(\mathbf{a_{n}^{k}}\) can be written as \(\mathbf{a_{n}^{k}}=\{\mathbf{\Theta}^{k},p_{n}^{k},b_{n,g}^{k}\}\). The reward function assesses the agent's performance and guides its decision-making. The objective of this letter is to minimize the total RSE error of all sensors. MADDPG is typically used to solve maximization problems, where each agent aims to maximize its reward by selecting actions [18]. However, Problem 2 is a minimization problem, so we need to invert the objective value by defining the reward as the negative of the cost, i.e., \(r_{n}^{k}=-D_{n}^{k}=\gamma_{n}^{k}-\alpha\ln\left[u_{n}\left(\mathbf{p}_{n}^{k -1}\right)-\mathbf{\tilde{p}}_{n}\right]\).
# ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management
## 3 Proposed Zone-Based MEC Architecture for User Device Mobility Management  
To provide efficient service to the mobile user, we propose a mobility management scheme that manages MECS as Zones. To this end, the proposed architecture has an Edge Management Orchestra (EMO) that manages access MECS by creating a specific Zone, and a Zone Management Orchestra (ZMO) that manages Zones. In other words, the proposed structure is able to provide efficient service even in the presence of moving mobile users by hierarchically managing access MECS as the concept of Zone. It is assumed that all access MECS transmit their own information to a pre-established EMO by an operator such as a carrier in the initial connection establishment process. The proposed scheme has four stages. The detailed procedure is as follows.
## 1 Introduction
However, the repetition-based broadcasting still suffers from high probability of errors and long delay when the channel is experiencing the deep fading, because the simple retransmission by the source vehicle is ineffective in mitigating the wireless channel impairments caused by the deep fading. In addition, excessive repetitions may waste network resources and cause network congestions. On the other hand, cooperative retransmission has been proved a promising way to realize the spatial diversity by forming a virtual antenna array [11]. Due to the broadcast nature of wireless transmissions, the neighboring nodes of the source node can help forward the packet to the destination when necessary. Then, the destination node receives more than one copy of the packet through multiple independent links. By combining these copies, a diversity gain can be achieved. In VANETs, the coherence time of wireless channels is up to tens of milliseconds [12; 13]. If the channel is undergoing the deep fading, it may last as long as the duration of multiple frames [14], and the packet may experience a long delay. During this duration, the cooperative retransmission can be done. Therefore, we propose a repetition-based cooperative broadcasting (RBCB) scheme for VANETs in order to improve the transmission reliability of safety messages and reduce the number of rebroadcasting. The main contributions of this paper are three-fold. First, we propose a repetition-based cooperative broadcasting scheme for safety messages, which enables the selected helper vehicles to perform the cooperative rebroadcasting along with the source node. Second, we analyze the performance of RBCB scheme under the Rayleigh fading channel in terms of packet delivery probability and average packet delay. Third, simulations are conducted to verify the mathematical analysis and evaluate the performance improvement. The remainder of this paper is organized as follows. Section 2 reviews the related work on repetition-based broadcasting schemes and cooperative MAC protocols. Section 3 describes the system model we consider. Section 4 presents the proposed RBCB scheme. The performance analysis of RBCB scheme in terms of packet delivery probability and average packet delay is discussed in Section 5. Section 6 presents the numerical results. This paper concludes with Section 7.