esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Prediction Using Machine Learning with Measurements Analysis
## 1 Introduction
Thus, the noise frequencies most harmful to humans were identified and machine learning was used to target these frequency bands for prediction. Through this method, we confirmed the noise map of the examined field, attempted to add meaningful time and historical features from the previous analysis, and predicted the likelihood of harmful noise [15; 16; 17; 18] at future time points in the operating environment. The results in this paper can be used to prevent noise pollution in advance to create better working conditions. The main process in this paper is divided into three parts, as shown in Figure 1:  
1. According to the data provided by the National Synchrotron Radiation Research Center (NSRRC), we performed daily and monthly statistical analyses on the noise data of 12 sensors at different frequencies. Once collected, the data were cleaned to derive useful information and analyze the data distribution. 2. We derived and extracted the features from the data analysis. We identified the frequency, time, and eight sensors from related history features, and then input a harmful frequency and the noisiest dBA sensor as extracted features. 3. We extracted the Leq historical features and time-related features from 80% of the data inputted to the machine learning model for training; the data for the remaining 20% was used for testing.
# A Notched Long-Period Fiber Grating Magnetic Field Sensor Based on Nanoparticle Magnetic Fluid
## 3 Results and Discussion  
The NLPFG is a loss tunable filter based on periodic refractive index modulation caused by external loadings. When the NLPFG is placed under axial loading, it generates a periodic strain field owing to the expanded cross section area, which subsequently affects the refractive index periodic distribution of the optical fiber and produces an attenuated loss dip in the spectrum. Before we applied the external magnetic field to the NLPFG magnetic field sensor, we first gave the sensor some fixed loadings on the platforms, which were used to firmly hold the fibers straight, in order to have the center wavelength of the attenuation band be 1540.78 nm and the dip of the transmission loss be \(-\)19.41 dB. The optical transmission characteristics, included absorption and scattering effect, of MF under the application of magnetic fields have been demonstrated [29; 30]. The behavior of induced loss of the NLPFG magnetic sensor within the ferrofluid is related to magnetic field stimulus. The spectra between the magnetic flux density and the resonant wavelength of the attenuation band loss are shown in Figure 5. As the applied magnetic flux density was gradually increased from 0 mT to 27.74 mT, the transmission loss was increased from \(-\)19.41 to \(-\)15.93 dB and the variations of loss in the spectra were about 3.48 dB. Figure 6 presents the relationship between the magnetic flux density and the dip of the transmission loss. We can see that the dip of the transmission loss was increased as the magnetic flux density was increased, which can be fitted well by a Langevin function with a R\({}^{2}\) value of 0.9983. The curve can be used to calibrate the nonlinearity between the transmission loss and magnetic flux density during the modulation. The sensitivity was 0.125 dB/mT. That sensitivity was compared, in turn, with the magnetic field sensitivity of the magnetic field sensors developed by [PERSON] _et al._[14] and [PERSON] _et al._[17], which were reported to be 0.0382 dB/mT and \(-\)0.1233 dB/mT, respectively. The magnetic field sensitivity of the NLPFG based on the ferromagnetic fluid presented in this paper was higher than the sensitivities of those other sensors.  
Figure 4: Schematic diagram of the experimental setup of the NLPFG magnetic field sensor with MF based on Fe\({}_{3}\)O\({}_{4}\) nanoparticles.
## 4 Results and Discussions
### Key Findings
#### 4.2.2 Improvement for Each Setup
Immediately after the precomputation of clusters, DProSA-AP performs better than DProSA-AG, maintaining this advantage throughout the five loops. By the end of the fifth loop, although DProSA-AG sees some improvements compared to the no-DProSA setup with a 10 s difference in dwell time between the two, the affinity propagation variant is able to achieve a 40 s improvement to the average dwell time of grocery shoppers. The affinity propagation model's ability to work with sparse matrices elevates its performance even when there are multiple pairwise connections not yet formed. In summary, the ML-DProSA variants, specifically agglomerative and affinity propagation clustering, have demonstrated a significant reduction in average dwell time and improved optimization of shopping paths compared to scenarios without product sorting implementation, as evidenced by the simulation results. These outcomes underscore the potential effectiveness of the proposed method for real-time applications. The dynamic nature of these methods adapts well to diverse store layouts and shopper behavior, consistently enhancing navigation efficiency across varied scenarios. However, to further validate these promising results, future research should include validation against real-world data and ground truth observations. Figure 12: Configuration 1c. _This scenario depicts the performance of the three setups for large-sized lists. The average time gap for every 50 shoppers is also shown._
## 3 Methodology
### Conventional Control (PID)
#### 3.2.1 PID Control  
The block diagram shown in Figure 4 shows the multiloop configuration of the system where a PID controller is used to control the Pitch displacement (\(\theta\)), Roll displacement (\(\varphi\)) and the Yaw rate (r).  
The controllers are tuned using MATLAB's Control System tuner, whereby a tracking reference of 20% is defined along with a maximum damping frequency of 10 Hz to prevent excessive oscillation in the time domain in the response and gain and phase margins of 5 dB and 40 deg, respectively. These stability margins were added to ensure robustness to process variations when applied to a real system. The gain margin was set to avoid changes in rotor dynamics/inertia, while the phase margin was introduced to avoid problems introduced with time sensitive slow computational hardware.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 2 Materials and Methods  
### Field Measurements  
The overall workflow of this study is shown in Figure 1. The field samplings were conducted from 2019 to 2020 in northern Sweden (63.0-65.5\({}^{\circ}\)N, Figure 2) at four locations: As, Lannas, Ojebyn and Robacksdalen. The fields in different locations were mixes of different species, including timothy (_Phleum pratense_), red clover (_Trifolium pratense_) and weeds (e.g., _Elymus repens_).  
One-way ANOVA tests based on daily climate parameters were conducted to check the seasonal and spatial meteorological difference. The residual normality and variance homogeneity were checked, and if the conditions were not fulfilled, non-parametric Kruskal-Wallis rank sum test was used alternatively. Based on the analyses, there were no significant seasonal differences (\(p>0.05\)) among the four locations in terms of the climate conditions for the field season (approximately from 1 May to 30 September). For example, mean temperature ranged from 11-13 \({}^{\circ}\)C in two years and four places (Table 1). The exception was Ojebyn, where the precipitation in 2019 was significantly higher than 2020.  
Figure 1: Overall workflow of this study.  
Figure 2: Locations of the study sites across northern Sweden. The green dots in the left figure show the experimental sites and the blue dots in the right figures denote the field sampling sites. The scale in the left figure is for whole Sweden, and the scale at the corner in the right figures is for the right 8 figures showing different fields.  
The spatial variations in each season depended on climate parameters. In each season, the spatial variations of temperature among different locations were not significant (\(p>0.05\)); however, the spatial differences of precipitation and radiation among different locations were significant (\(p<0.05\)). For example, the average precipitation in Ojebyn was 355.1 in 2019, which was more than for other sites (Table 1). The exception occurred in 2020 for precipitation, in which there was a significant difference in precipitation among different locations.  
In each sampling site and year, a quadrat with 50 cm sides was used to take samples from May to September. Each sample consisted of three subsamples with a spacing of 1-2 m between quadrats. GPS coordinates were recorded and samples were taken 8 cm above the ground to follow the typical farming practice (Table 2). Subsamples were hand-separated into three groups (grass, clover and weeds) for botanical composition (BC) measurement. Subsamples were stored at 4 \({}^{\circ}\)C before fresh weight was measured. Subsamples were then oven-dried at 60 \({}^{\circ}\)C for 48 h and weighed again for _DMY_ determination for different species [3]. Total _DMY_ was calculated as the sum of _DMY_ from each group. BC was expressed as the proportion of _DMY_ from each species relative to the total _DMY_. Sample (i.e., related to one Sentinel-2 pixel) _DMY_ was obtained by averaging the three subsample values.
## 2 Mathematical Preliminaries
### Control Objective  
The adaptive output feedback tracking controllers are constructed for ship heading systems (3) with stochastic noise and dead-zone input (4) such that a desired signal \(y_{d}(t)\) can be followed by the output under the ADT \(\tau_{a}\), while the boundedness in the probability of the resulting systems is guaranteed.  
**Assumption 1**.: _The continuous desirable heading signal \(y_{d}(t)\) is bounded, and likewise for \(\dot{y}_{d}(t)\)._  
**Remark 3**.: _The related adaptive backstepping control results require the knowledge of \(y_{d}^{i}(t)\)\((i=0,1,\cdots,j-1)\), where \(j\) is the order of the controlled system. Assumption 1 only needs \(y_{d}(t)\) and \(y_{d}(t)\), which is less stringent, and the resulting heading control scheme is more suitable for actual ship control._  
**Lemma 1**.: _Reference [22]: Consider the unknown function \(f(\cdot)\in\Omega\). FLS \(W^{\mathrm{T}}S(\cdot)\) is utilized such that \(W^{\mathrm{T}}S(\cdot)+\delta(\cdot)\), \(|\delta(\cdot)|\leq\varepsilon\) with \(\varepsilon>0\) an approximate error; \(W=(w_{1},\cdots,w_{\mathrm{P}})^{\mathrm{T}}\) and \(S(\cdot)=\frac{(\varepsilon_{1}(\cdot),\cdots,\varepsilon_{p}(\cdot))^{ \mathrm{T}}}{\sum_{i=1}^{p}s_{i}(\cdot)}\) denote the ideal constant weight vector and the basis function vector; \(P>1\) is the fuzzy rules' number, and \(s_{i}(\cdot)=\exp(\frac{-(-\mu_{i})^{\mathrm{T}}(-\mu_{i})}{\eta_{i}^{2}})\); in the Gaussian function, \(\mu_{i}\) and \(\eta_{i}\) are the center vector and width, respectively; \(i=1,\cdots,P\)._  
**Lemma 2**.: _Reference [22]: For a stochastic system with the hypothesis that there exist constants \(p_{1},p_{2}\), \(\mathcal{K}_{\infty}\) functions \(\bar{\sigma}\), \(\underline{\varrho}\), a function \(V\in C^{2}:R^{n}\to R^{+}\), for \(\forall x\in R^{n},t\geq 0\), such that if_  
\[\left\{\begin{array}{l}\partial(|x|)\leq V(x)\leq\underline{\varrho}(|x|),\\ \mathcal{L}V(x)\leq p_{1}-p_{2}V(x),\end{array}\right. \tag{9}\]  
_hold, then the boundedness in the probability holds._  
**Definition 1**.: _Reference [32]: For \(N_{0}\geq 0,\tau_{a}>0\) and the switchings' number occurring in \([T_{1},T_{2})\) (\(N_{\sigma}(T_{2},T_{1})\)) satisfying:_  
\[N_{\sigma}(T_{2},T_{1})\leq N_{0}+\frac{T_{2}-T_{1}}{\tau_{a}}, \tag{10}\]  
_then the switching signal \(\sigma(t)\) of the considered switched system has an \(ADT\)\(\tau_{a}\)._
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 3 UAV Formation Control Technologies
### Velocity Alignment  
Velocity alignment is achieved by introducing a velocity term based on the difference in velocity vectors among nearby agents. The objective is to synchronize motions and reduce self-induced oscillations due to delays or noise. A smooth velocity decay function, \(D(\cdot)\), based on an ideal braking curve is adopted, with constant acceleration at high speed and exponential convergence at low speed.  
\[D(r,a,p)=\begin{cases}0,r<0\\ rp,0\leq rp\leq\frac{a}{p}\\ \sqrt{2 ar-\frac{a^{2}}{p^{2}}},\text{otherwise}\end{cases}, \tag{7}\]  
where \(r\) is the distance to the target stopping point, \(a\) is the preferred acceleration, and \(p\) is a linear gain that determines the crossover between two deceleration phases.  
The fundamental principle of the velocity alignment term is to constrain the velocity difference between any two agents such that it does not exceed the maximum allowed by the ideal braking curve at a given distance. As such, this alignment mechanism can be treated as a motion planning term within the force-based motion model  
\[\mathbf{V}_{ij}^{frict}=\begin{cases}C^{frict}\cdot\left(v_{ij}-v_{ij}^{frict max}\right)\cdot\frac{v_{ij}-v_{ij}}{v_{ij}},v_{ij}>v_{ij}^{frictmax}\\ 0,\text{otherwise}\end{cases}, \tag{8}\]  
where \(v_{ij}^{frictmax}=\max\left(v^{frict},D\left(r_{ij}-r_{0}^{frict},a^{frict},p^ {frict}\right)\right)\), \(C^{frict}\) denotes the linear coefficient for reducing velocity alignment error, \(v^{frict}\) is the velocity relaxation term that allows a certain degree of velocity difference to exist independently of the inter-agent distance. The variable \(r_{0}^{frict}\) refers to the distance between agent \(i\) and the predicted stopping point ahead of agent \(j\). Parameters \(p^{frict}\) and \(a^{frict}\) represent the pairwise linear gain and acceleration parameters, respectively. The amplitude of the speed difference between agent \(i\) and \(j\) is denoted by \(v_{ij}=\left|v_{i}-v_{j}\right|\). The total velocity alignment term for agent \(i\) with respect to all other agents is given by  
\[\mathbf{V}_{i}^{frict}=\sum_{j\
eq i}\mathbf{V}_{ij}^{frict}. \tag{9}\]  
In addition, the locality condition of the velocity alignment mechanism is implicitly ensured by defining a maximum interaction range \(D(\cdot)=2v^{max}\). On the other hand, this formulation allows for flexible scalability in the velocity domain. Specifically, for swarms operating at higher speeds, it is beneficial to begin reducing velocity differences at larger distances to prevent potential collisions and improve formation coherence.
# Depth Image Super Resolution Based on Edge-Guided Method
## 1 Introduction and Related Works
The method considers not only the weight of distance, but also the weight of pixel value and gradient. (4) Example-based SR: This method learns the transformation between LR and HR image from example database, and then an HR depth image can be reconstructed through the learned transformation when an LR depth image is inputted. [PERSON] [21] uses a sparse coding method to grasp the transformation. Therefore, HR image patches can be represented by a sparse linear combination of HR dictionary atoms. [PERSON] [6] modifies the above sparse coding method, and uses the K-SVD [22] and the orthogonal matching pursuit (OMP) [23] to train an LR and HR dictionary pair. [PERSON] [24] proposes a pairwise dictionary training method with local coordinate constraints for depth image SR. [PERSON] [7] clusters the dictionary atoms into sub-dictionaries using K-NN algorithm, and then the HR patches can be represented by the most suited sub-dictionary. [PERSON] [25] presents an accurate color image SR method based on VGG-NET [26], which can also be applied to solve the depth image SR problem. Although the above methods can effectively reconstruct SR depth image from LR input, some existing problems cannot be ignored as well. The methods of the first category can cause discontinuous regions jagged and blurred. The methods of the second category need to be subject to the rigorous assumption that adjacent images only have slight movements on the plane parallel to the focal plane of camera. This assumption is usually difficult to satisfy in practical scenarios. Depth image SR based on fusing depth image and HR color image needs first to obtain an HR color image which register fully with the depth image. The example-based method has a strong dependence on training databases. That is, the difference of training databases may have a great effect on experiment results. To address these problems, in this study, an edge-guided method for depth image SR is presented. We first train a pair of sparse dictionaries to recover high-quality edge information, and then an HR depth image is interpolated with the guidance of these high-quality edges. This method is a mixture of the example-based method and the interpolated method. We make full use of the advantage of the two methods. In this way, the proposed method can achieve improved results that are comparable to current state-of-the-art methods. Our approach needs neither strict assumptions nor the assistance of HR color image, so it can be used to improve depth image resolution conveniently. At the same time,our approach not only can achieve the goal of preserving sharp edge in depth image SR, but also can get a better color image SR. The remainder of this paper is organized as follows. A detailed overview of the proposed method is presented in Section 2. Section 3 reports and discusses the results of the experiments. Finally, Section 4 concludes the paper.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 5 Lessons Learned and Recommendation
A CH might not have sufficient transmission windows to transfer all the data. However, if the sink is aware of the data to be transmitted and adjusts its moving speed accordingly, this limitation can be reduced. However, this also increases the volume of control packet consumption, which increases the packet delivery ratio. It is a serious problem to consider if all the data have equal priorities. * CH Position-Based Data Collection and m-Mobility Adjustment  
The CH selection mechanism depends on various parameters such as position, energy level, node degree, inter-cluster distance, and intra-cluster distance. If the sink adjusts its positions based on the position of the elected CHs, the transmission distance will be reduced, and the CHs will have a larger transmission window. As a result, the energy consumption will be significantly reduced compared to the energy consumption in a static infrastructure-based solution. * Optimal Number of Clusters  
From the simulations, we observed that in the case of energy consumption, CPDC performed the worst. One of the primary reasons behind this phenomenon is the smaller number of clusters with a high number of CMs. The CHs die early owing to their (receiving) energy consumption. If the number of clusters is too high and single-member clusters increase, the energy consumption due to CH-sink long-distance data transmission increases. It can be seen that these two contradictory situations create an optimization problem from which the optimal number of clusters can be derived. * Multiple CHs or Introducing Different Role  
An alternative solution is to have more than one CH in a cluster. A CH failure may occur at any time before the next round of data collection. This may result in the loss of a large volume of data. However, data duplication to a node with the role of an assistant CH may provide a solution. * Data Propagation via Multi-hop CH Approach to the Sink  
In the case of a large ROI and static sink, the CHs can propagate data in a multi-hop fashion. This helps energy consumption in two ways. First, the transmission distance is shorter; second, the data can be aggregated by propagating to the sink. * Multi-Sink-Based Solution  
Multi-sinks can be used for time-constrained data collection and to reduce energy consumption. In this way, a CH can deliver data to the nearest sink. As the sinks are not energy-constrained and the transmission range is considerably higher than that of sensor nodes, they can transmit the data to the desired destination. * Priority Based Transmission  
Prioritizing data has many benefits. In some cases, if a sensor node is about to die, it can only transmit prioritized data. For the mobile sink scenario, the less prioritized data can be transmitted at a later stage, when the sink gets closer to the CH.
# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 3 Prediction Models
### Physics-Based Model  
SHAKE2000 [6; 37], a widely adopted software for site-specific seismic response analysis [5; 38; 39; 40], is used as the physics-based model for predicting the ground response in this study. SHAKE2000 employs a 1D wave propagation model, assuming that seismic waves travel vertically through horizontally layered soil deposits. The model inputs include a soil profile, which encompasses the thickness of layers and soil properties such as density and dynamic properties, and a bedrock motion, which can be acceleration, velocity, or displacement time histories, serving as the seismic excitation at the base of the 1D soil column.  
SHAKE2000 utilizes an equivalent-linear analysis model [5; 41] to account for nonlinear soil behavior under seismic loading. The bedrock time history motion (\(a_{in}(t)\) in Equation (1)) is first transformed from the time domain to the frequency domain using a Fast Fourier Transform (FFT in Equation (1)). The model then calculates the wave propagation through the soil layers using transfer functions (\(f(\omega)\) in Equations (1) and (2)) based on the layers' dynamic properties. In Equation (2), \(u_{out}(\omega)\) is the ground surface displacement and \(u_{in}(\omega)\) is the bedrock displacement. The equivalent-linear procedure iteratively adjusts the shear modulus and damping ratio of each layer according to the induced shear strains based on shear modulus reduction and damping curves. This process repeats until the iteration of the shear modulus and damping ratio converges. Upon convergence, the model calculates the final response in the frequency domain and converts it back to the time domain using an inverse FFT (IFFT in Equation (1)), returning the ground surface time history motion (\(a_{out}(t)\) in Equation (1)).  
\[a_{out}(t)=IFFT[f(\omega)\times FFT[a_{in}(t)]] \tag{1}\]  
\[f(\omega)=\frac{u_{out}(\omega)}{u_{in}(\omega)} \tag{2}\]  
In this study, in order to determine the dynamic soil properties (i.e., shear modulus reduction and damping curves) for the site, we classify the site layer types as rock, gravel, and sand based on the layer material information provided by the KiK-Net borehole data. Detailed layer information for KiK-Net sites can be found in [PERSON] et al. [32]. Then, the mean values of the dynamic properties from the literature [42] are taken based on the classified layer types. Figure 3 shows the shear modulus reduction and damping curves used in this study. Another important input data for SHAKE2000 are the shear wave velocity and density profile. We use the data provided by KiK-Net as described in Figure 2. Based on the selected properties, the equivalent-linear analysis is conducted to estimate the ground surface response spectra.