esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Investigation of As Removal Mechanism  
To investigate the role of nWTRs in As retention in biosolids-treated soil, a Fourier Transmission Infrared Spectroscopy (FTIR) analysis was performed (Table 2).  
The FTIR spectrum of nWTRs/biosolids-treated soil before and after As saturation are presented in Table 2. Table 2 showed the shifting and disappeared of some functional groups revealed that changes in sorbent properties occurred after As sorption. These shifts are a strong evidence for chemical complexation between As and functional groups of nWTRs and biosolids. Before As saturation (Table 2), most changes between 1700 and 500 cm\({}^{-1}\) refers to complexation of As ions with COOH and O-H groups present in phenolic and carboxylic compounds present in biosolids and nWTRs [24; 37]. In conclusion, the FTIR spectra of the biosolids-amended soil that was treated with different levels of nWTRs during a 60-day incubation period showed initial HOH stretching and bending, as well as a bending vibration of hydroxyl groups on metal oxides, and then some peaks completely disappeared or shifted, suggesting that those OH groups on the WTR particle surface reacted with As.  
Figure 2: Arsenic sorption efficiency in the calcareous soil amended with biosolids (3%, w/w) and treated with different levels of nWTRs of 0%, 0.25%, 0.5%, and 1% (w/w). Data shown are means and standard deviation (n = 3). The error bars were too small to be visible in the figure.
## 1 Introduction
Moreover, disturbance rejection approaches considering robust controllers combined with disturbance observer can be found in [18,19]. The weak points of the aforementioned methods lie in the following:  
1. For the LESO to increase the estimation accuracy, the bandwidth of the LESO has to be increased, which tolerates noise and leads to hardware difficulties. Additionally, the LESO suffers from a peaking phenomenon due to large gain values. 2. For the nonlinear ESO, the performance will abruptly deteriorate when the amplitude or derivative of the generalized disturbance goes large to a certain degree [20]. Moreover, stability analysis and performance analysis are very complicated for the nonlinear ESO. 3. For other classes of observers like the AESO, the parameter tuning process becomes more time-consuming as the observer order goes higher. In this paper, we offer a novel simple structure based on LESO, namely, the nested LESO, which combines the advantages of both linear and nonlinear ESOs. It consists of two LESOs connected in parallel sharing the same plant output. The proposed observer efficiently estimates the generalized disturbance without increasing the observer bandwidth and requires fewer computations for parameter tuning since it is built from LESOs which needs a single parameter to be tuned, i.e., the LESO bandwidth. Moreover, due to its linear structure, the proposed nested LESO inherits the simplicity of the LESO stability analysis, while the performance evaluation of the closed-loop system of an uncertain nonlinear signle-input-single-output (SISO) system is achieved very easily with the proposed nested LESO. An outline of this paper's contents and organisation follows. Section 2 presents the problem statement and contribution of this work. Section 3 briefly presents the concepts behind Active Disturbance Rejection Control (ADRC). A description of the proposed nested LESO and the relevant stability tests are included in Section 4. The numerical simulations verifying the validity of the proposed configuration are provided in Section 5. Finally, the conclusion is given in Section 6, along with recommendations for future work.
## 4 Results and Discussion  
The rainfall observation network built with multiple ESLs and HMLs enables measuring sparsely distributed rain rates in the area. Then, the OK algorithm and RBF neural network are used to reconstruct the sparsely distributed rain rates to obtain a continuous distribution of rainfall fields in the area. To investigate the performance of the link network in retrieving rainfall field, we used the HYCELL model and real rainfall fields for relevant validation. The root mean square error (RMSE), mean bias (MB) and Pearson correlation coefficient (CC) are used to give a quantitative evaluation of the retrieval results. For clarification, RMSE is mainly used to give the error between the retrieved rainfall field \(R_{retrieve}\) and the real rainfall field \(R_{real}\), MB gives the unbiasedness between \(R_{retrieve}\) and \(R_{real}\), and CC is used to evaluate the degree of similarity between \(R_{retrieve}\) and \(R_{real}\). Additionally, they are defined by  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(R_{retrieve,i}-R_{real,i})^{2}}, \tag{25}\]  
\[\text{MB}=\frac{1}{n}\sum_{i=1}^{n}(R_{retrieve,i}-R_{real,i}), \tag{26}\]  
and  
\[\text{CC}=\frac{cov(R_{retrieve,i}\,R_{real,i})}{\sigma_{R_{retrieve}}\cdot \sigma_{R_{real}}}, \tag{27}\]  
where \(cov(R_{retrieve},R_{real})\) denotes the covariance between \(R_{retrieve}\) and \(R_{real}\), and \(\sigma_{Retrieve}\) and \(\sigma_{Retrieve}\) are the standard deviations of \(R_{retrieve}\) and \(R_{real}\), respectively.  
Figure 5: Network distribution of ESLs and HMLs in Xiamen: (**a**) Distribution of the original link network; (**b**) Distribution of the link network in the gridded area. The blue lines represent the ESLs, the blue markers “o” indicate the location of the satellite antennas, the purple lines represent the HMLs, and the red markers “**” indicate the midpoint of the links.
# Modeling and Analysis of a Generic Internal Cargo Airdrop System for a Tandem Helicopter
## 5 Stability Analysis  
The present model can also simulate and analyze the stabilities of the tandem rotor helicopter in the process of airdrop using the analysis method of stability [44]. The open-loop dynamics of the CH-47 series tandem rotor helicopter mainly include two longitudinal aperiodic modes (the longitudinal velocity and pitch mode, and the heave-pitch mode) and one longitudinal periodic mode (the phugoid mode), and two lateral aperiodic modes (the roll mode, and the spiral mode) and one lateral periodic mode (the dutch-roll mode) [46]. Simulation results of these modes at helicopter flight velocities of 0, 40, 80, 120, and 150 knots are shown in Figures 12-14, in which the effects of airdrop on the stability of the tandem rotor helicopter are clearly depicted.  
Figure 12: Simulation data of **(a)** the heave-pitch mode and **(b)** the longitudinal velocity and pitch mode.  
In general, the effects of airdrop on the stability of the tandem rotor helicopter can be classified as two phases, both the phase of cargo moving backward relative to the helicopter and the phase of cargo beginning to rotate around the point of tipoff. Because the airdrop operation is mainly conducted in the longitudinal channel and has little effect on the vertical channel, but some of the effect on the lateral channel, and the backward moving cargo will increase the upward pitching moment of the cargo acting on the helicopter, thus making the pitching attitude angle have a positive increasing trend, some interesting analysis can be drawn as follows:  
1. The airdrop operation will not reduce the stability of the heave-pitch mode; on the contrary, it will make the stability root of the mode increase negatively evidently when the helicopter flies at a high velocity of greater than 120 knots. This might be attributed to the little effect of the airdrop operation on the vertical channel and the large minus pitching attitude angle at the high flight velocity. For the same reason, the airdrop operation will reduce the stabilities of the longitudinal velocity and pitch mode, especially when the helicopter flies at a high velocity of greater than 120 knots.
2. The airdrop operation will evidently reduce the stabilities of both the roll mode and the spiral mode, especially as the flight velocity increases, except the spiral mode at a helicopter velocity of 0 knots. This might be contributed to the changing lateral force and the changing rolling and yawing moment of the cargo acting on the helicopter during the airdrop.
3. The stabilities of both the longitudinal and lateral periodic modes (the phugoid mode and the clutch-roll mode) are continuously decreased during the backward movement of the cargo. In particular, when the airdrop is conducted at hovering, the stability of the clutch-roll mode will continue to decrease in the process of the rotation of the cargo around the point of tipoff.  
Figure 14: Simulation data of (**a**) the phugoid mode and (**b**) the clutch-roll mode.  
Figure 13: Simulation data of (**a**) the roll mode and (**b**) the spiral mode.
### Article
We propose an efficient deep neural network model by applying the tensor decomposition method to Binary Neural Networks (BNNs). 2. We introduce an algorithm for selecting the rank of the tensor to decompose the models based on the sensitivity of the layer for decomposition. 3. We compare three methods for selecting the rank for decomposition, namely the random method; Variational Bayes Matrix Factorization (VBMF); and our method, which selects ranks based on layer sensitivity. 4. We demonstrate the effectiveness of our method using six different models on four different datasets, namely LeNet-5 on MNIST; Network in Network, Alexnet, ResNet-20, and ResNet-32 on CIFAR-10; ResNet-20 and ResNet-32 on CIFAR-100; and, finally, Alexnet and Resnet-18 on ImageNet. 5. We use a crowd-counting application as a case study for our method, using two different models (MCNN and CSRNet) and four different datasets (UCF-QNRF, ShanghaiTech B, UCF_CC_50, and WorldEXPO10). 6. We conduct an ablation study on improving the accuracy of decomposed binary models using different optimizers, activation functions, and training methods. We show that decomposed binary models yield deeper models that take more time to converge, but applying orthogonal initialization can help the model converge to a better minimum. Our work presents a novel idea in which the use of tensor rank creates a trade-off between compression and model performance accuracy, making our method dynamically applicable to many different applications that require deep learning on the edge. This approach not only advances the state of the art in model compression but also bridges the gap between complex deep learning models and resource-constrained edge devices. By enabling flexible deployment of powerful neural networks in edge computing scenarios, we pave the way for more intelligent, responsive, and energy-efficient IoT systems, mobile applications, and autonomous platforms. The dynamic nature of our method allows for real-time adjustments based on the specific requirements of each application, balancing computational efficiency with model accuracy as needed. The remainder of this paper is organized as follows. Section Notation shows a summary of the mathematical notations, and Section 2 provides an overview of the related work. Section 3 describes our proposed ultimate compression method in detail. Section 4 presents our experimental results for various datasets and models. Section 5 discusses our findings and presents an ablation study. Section 6 presents a real-world case study on crowd counting. Section 7 presents the limitations of our method. Finally, Section 8 concludes the paper and suggests directions for future work.
## 2 Place- and Person-Based Accessibility Measures
### Place-Based Measures  
Place-based measures calculate how easy it is for people departing from a place to reach opportunities located in another [9; 16]. Following this definition, three main types of place-based measures have been developed: _cumulative-opportunity_, _gravity-based_ and _adapted gravity-based_ measures ([9; 17]; Table 1). All of these are a function of two core elements: (a) an attraction factor given by the amount, spatial distribution and quality of opportunities to access; and (b) an impedance factor given by the effort needed to reach these opportunities [18]. These are combined in different ways. The _cumulative-opportunity measure_ (e.g., refs. [19; 20; 21]) counts the number of opportunities reachable from an origin location within a predetermined threshold usually defined in terms of travel time or cost. It is a very straightforward approach, but it shows some limits since counted opportunities have the same importance regardless of the effort needed to reach them. The _gravity-based measure_ (e.g., refs. [22; 23; 24]) addresses these limits since it calculates the accessibility of an origin as a function of the number and importance of opportunities at the destination, weighed against the travel effort needed to reach them. However, it neglects the competition between the demand for and supply of opportunities. The _adapted gravity-based measure_ (e.g., refs. [25; 26; 27]) incorporates them through a double constrained spatial interaction model with two mutually-dependent balancing factors [17].  
These three measures have two main limitations that person-based measures aim to overcome [13; 28]. First, they assume that all individuals who depart from the same origin location experience the same level of accessibility regardless of their different spatial, temporal and modal constraints. Second, they calculate accessibility for a single reference location (typically home place) overlooking the fact that people generally perform a sequence of daily activities that are differently located in space and time, and this sequence affects their accessibility.
## 1 Introduction  
Wireless sensor networks (WSNs) are an emerging technology in existing research and have the potential to transform the way of human life (_i.e.,_ make life more comfortable). A wireless sensor is the smallest unit of a network that has unique features, such as, it supports large scale deployment, mobility, reliability, _etc._ WSNs are not limited to science and engineering, but they are also included in other popular applications such as the military, water monitoring, infrastructure monitoring, government security policy, habitat monitoring, environment monitoring, and earthquake monitoring, are few examples. A sensor network consists of a discrete group of independent nodes with low cost, low power, less memory, and limited computational power that communicate wirelessly over limited frequencies at low bandwidth [1]. The main goals of WSNs are to deploy a number of sensor devices over an unattended area, and collect the environmental data and transmit it to the base station or remote location. Later, the raw data is processed online or offline for detailed analysis at the remote server according to the application requirements.
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 2 System Model  
We consider a radar-assisted mmWave V2I network as depicted in Figure 1, where each RSU serves \(K\) high mobility vehicles. Each RSU has deployed a downlink DFRC system which is equipped with a mmWave massive MIMO uniform linear array (ULA) consisting of \(N_{t}\) transmit antennas and \(N_{r}\) receive antennas, while each vehicle is also equipped with a ULA array consisting of \(M\) antennas. As the computing center of multi-RSU cooperation, ES can receive reports from RSUs and issue cooperation results to each RSU. At a certain moment, a vehicle can only be served by one RSU, while other RSUs only track vehicle movement trajectory by radar echoes. For simplicity, we assume that the Line-of-Sight (LoS) path always exists between RSU and the vehicle, and the radar echoes are reflected through the LoS path. Figure 1 shows the linear mobility scenario where the vehicle trajectory is parallel to the antenna array of the RSU, thus the AOD for beamforming and the AOA for beam combining are equal. For the curvilinear mobility scenario, the case is different and will be illustrated later.  
To establish dependable connections for communication, each RSU needs to acquire the accurate azimuth angle of the vehicles, and similarly, each vehicle also needs to acquire the RSU's relative angle. After acquiring angle information, the RSU and the vehicle could both utilize their antenna arrays to create narrow beams, enabling precise alignment toward each other. During initial access, a conventional beam searching scheme could be employed to acquire initial estimation, such as angle, distance, speed, etc. For the linear mobility scenario, prediction is divided into two steps. In step I, the estimate of the AOD at the \((n-1)\)-th slot, denoted as \(\hat{\theta}_{n-1}\), is used to predict the AOD at the \(n\)-th slot, denoted as \(\hat{\theta}_{n|n-1}\). In step II, \(\hat{\theta}_{n|n-1}\) is used for predicting AOD at the \((n+1)\)-th slot, denoted as \(\hat{\theta}_{n+1|n-1}\). At time slot \(n\), the RSU formulates directional transmit beams aimed at vehicles by using \(\hat{\theta}_{n|n-1}\), and the DFRC signal transmitted by RSU contains the step II prediction \(\hat{\theta}_{n+1|n-1}\), which is used to formulate receive beams at the vehicle at the \((n+1)\)-th slot. Itshould be noted that \(\hat{\theta}_{n+1|n-1}\) is not suitable for beam combining on the vehicle side for curvilinear mobility scenarios since AOD is not equal to AOA. Moreover, the partial DFRC signal reflected by the vehicle is used to calibrate predicted parameters at time slot \(n\). The kinematic parameters that have been calibrated are subsequently utilized as inputs for the Extended Kalman Filter (EKF) in the \((n+1)\)-th and \((n+2)\)-th slots. In what follows, we will introduce the vehicle mobility model and signal processing model.
## 4 Experiments and Results
### Experiment 3--Performance of Personalized Recommendation
As seen in Figure 5, MPUSMs have the best accuracy in the Music dataset, and MPUSMs-IMFECOA also has the least degradation in the performance of \(HR\) and \(AP\) metrics in the Music dataset, as reflected in the Growth of the three datasets in Table 5. The other may be caused by the introduction of low-rated individuals into the pre-recommended list resulting in an insufficient number of competent individuals. In other words, the distribution of evolved population will be somewhat different from the distribution of actual excellent items. For example, Figure 5 indicates that MPUSMs perform better than DRBM-Social on \(RMSE\), but proposed algorithm performance on \(HR\) and \(AP\) metrics is worse. From the Growth rate of \(SSD\) in Table 5, it can be seen that the proposed algorithm shows a slight improvement in each dataset, which further demonstrates that MPUSMs' interactive dynamic update improves its ability to avoid homogeneous recommendations. Similarly, the improvement in \(Sur\) indicates that MPUSMs-IMFECOA is more likely to uncover items that are not similar to users' historical preferences but that they are satisfied with. It not only confirms the accuracy of MPUSMs, but also reveals the algorithm's ability to explore unknown items at the same time. Overall, it can be seen from the Total values in Table 5 that, at the expense of 5% accuracy, MPUSMs-IMFECOA simultaneously improves the diversity and novelty of the recommended results. In addition, the reduction of \(HR\) and \(AP\) is limited and does not actually have a negative impact on the user experience. Hence, MPUSMs-IMFECOA is capable of guaranteeing the accuracy of recommendations, exploiting knowledge among preferences to improve diversity and novelty and ultimately bringing the user a more satisfying personalized recommendation experience.
## 4 Experimental Setup and Results
### Experimental Data
#### 4.4.5 English Sentence Boundary Disambiguation Based on BERT and Multi-BERT  
In the previous study, the Tibetan SBD was verified on the Tibetan BERT model trained in this study and the publicly available Tibetan BERT model, and from the results, it can be seen that the BERT (BPE) trained in this study has the best performance, followed by the publicly available pre-trained language model for Tibetan, TiBERT, and the BERT (SP) trained in this study. In contrast, the SBD for Tibetan based on Multi-BERT has the worst experimental results in the previous study. To verify the generalizability of the pre-trained language models on the task of sentence boundary disambiguation, this study experiments with the English SBD based on the BERT and Multi-BERT, and the window sizes were set to 3 and 5. The experimental results are shown in Tables 11 and 12.  
From Tables 11 and 12, we can see that when BERT and Multi BERT are introduced for English sentence boundary disambiguation, they achieve more than 98% disambiguation effect, respectively. Meanwhile, we find that the F1 score is slightly higher than BERT when Multi BERT is used for sentence boundary disambiguation, and the indexes are slightly improved when the window is increased from 3 to 5. Under the BERT pre-trained language model, all the sentence boundary disambiguation methods based on the improved BERT model (i.e., BERT-CNN, BERT-RNN) have slightly improved than the BERT model, in which the F1 value of the BERT-CNN is the highest. Under the Multi BERT pre-trained language model, the performance of SBD based on the BERT model is higher than that based on the improved BERT model (i.e., BERT-CNN and BERT-RNN), so from the results of the English SBD experiments, it can be seen that the English SBD performance is higher than Tibetan SBD performance because there is ambiguous punctuation in English as \".\", from the previous introduction, it is known that for the English punctuation \".\", 54.7-92.8% of them appear at the end of the sentence, about 90% indicate the end of the sentence, 10% indicate an abbreviation, and 0.5% both, so it is easier to distinguish whether it is an end-of-sentence marker or not while in Tibetan, the structure of Tibetan characters is complex, the function of a shad (\"[\"]) is more diverse, and there are marking phrases, fragments, and end-of-sentence markers in many cases, so the problem of SBD in Tibetan has been a matter of concern and deserves more investigation.
## 5 Benchmark Example Applications
### Application to Compressible Elastic Plate
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Linear Triangle (Regular)**}} & \multicolumn{4}{c}{**Quadratic Triangle (Regular)**} \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**Stress Recovery (ZZ)**}} & \multicolumn{4}{c}{**Displ. Recovery**} & \multicolumn{4}{c}{**Stress Recovery (ZZ)**} & \multicolumn{4}{c}{**Displ. Recovery**} \\ \hline
**Mesh** & **FEM** & **Error** & \multirow{2}{*}{**Error** (\%)**} & **Error** & **Mesh** & **FEM** & **Error** & \multirow{2}{*}{**Error** (\%)**} & \multirow{2}{*}{**Error** (\%)**} & \multirow{2}{*}{**Error** (\%)**} \\
**Size** & **Error** & **(\(\times\)10\({}^{-3}\))** & & **(\(\times\)10\({}^{-3}\))** & & **(\(\times\)10\({}^{-3}\))** & & **(1/h)** & **(\(\times\)10\({}^{-3}\))** & & **(\(\times\)10\({}^{-3}\))** & & **(\(\times\)10\({}^{-3}\))** & \\ \hline \(\frac{1}{1}\) & 93.75 & 144.2486 & 1.4313 & 58.48 & 0.9300 & \(\frac{1}{2}\) & 13.17 & 4.93 & 0.95024 & 12.26 & 1.2556 \\ \(1/16\) & 24.44 & 22.6413 & 1.2449 & 4.46 & 0.9896 & \(1/12\) & 1.52 & 0.30 & 0.95295 & 0.53 & 1.0405 \\ \(1/32\) & 12.25 & 8.29942 & 1.1451 & 1.16 & 0.9970 & \(1/24\) & 0.38 & 0.06 & 0.95729 & 0.07 & 1.0127 \\ \hline Conv. & 0.9786 & 1.3731 & & 1.8860 & & & 1.9785 & 2.5062 & & 2.8845 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error in Stress (Energy Norm) and global effectivity (\(\theta\)) in compressible elastic square plate example considering _ZZ_ stress and displacement recovery techniques [20,24]. Figure 4: Adaptive mesh required for 3% target error (L\({}_{2}\)-Norm) in compressible elastic square plate example solution variables considering displacement and stress recovery techniques (Initial regular linear triangular mesh: N = 580, DOF = 652). Figure 5: Adaptive mesh required for 1% target error (L\({}_{2}\)-Norm) in compressible elastic square plate example solution variables considering displacement and stress recovery techniques (Initial regular quadratic triangular mesh: N = 28, DOF = 146).