esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Despite several known beneficial attributes, biochar is suspected to cause harm to soil organisms when present in relatively high quantities in the soil. To determine the potential detrimental effects of biochar, for 96 h, we exposed the earthworm _Eisenia fetida_ to 0, 2, 4 and 8 mg glyphosate (GLY) per kg in non-amended and biochar-amended soil at rates of 5, 10 and 15%. The results indicated that in non-amended soil, survival was significantly decreased in the highest GLY concentration. Although no median lethal concentration (LC\({}_{50}\)) could be computed due to the lack of sufficient mortality, in the absence of biochar, a lethal concentration 10% (LC\({}_{10}\)) of 5.540 mg/kg and a lethal concentration 20% (LC\({}_{20}\)) of 7.067 mg/kg were calculated. In the biochar-amended soil, no mortality occurred in the control and GLY treatments for all three biochar amendment rates. Biomass results showed significant biomass loss in the highest GLY treatment in the absence of biochar, with an effective concentration of 10% (EC\({}_{10}\)) of 5.23 mg/kg and an effective concentration of 20% (EC\({}_{20}\)) of 6.848 mg/kg. In the amended soil, overall, slight non-significant increases in biomass were recorded and no effective concentrations could be calculated due to the lack of significant biomass loss. The assessment of neurotoxicity via the activity of acetylcholine esterase (AChE) showed no change in AchE due to GLY in all the non-amended treatments. However, in the biochar-amended treatments, statistically high levels of AchE occurred (\(p<0.05\)) even in the control (in the absence of GLY). The assessment of oxidative stress through catalase (CAT) activity, showed similar results with no significant effects of GLY alone on CAT activity, but rather dramatic increases in activity in the control and GLY treatments in the biochar-amended soil, with one significant increase in the 10% amended in 8 mg GLY/Kg (\(p<0.05\)). Such significant increases in both AChE and CAT were only observed in soil amended with 10 and 15% biochar. Our findings show that although seemingly beneficial for whole body endpoints, biomarker responses indicate that a biochar amendment higher than 5% adds considerable additional stress to earthworms and should be avoided.  
biochar effects; ecotoxicological effects; mechanistic biomarkers; herbicide toxicity; earthworm ecotoxicology 2022
# Research on On-Line Detection Method and System for Nitrate in Groundwater
###### Abstract  
In order to monitor the concentration of nitrate in groundwater and evaluate water quality, a specialized sensor has been developed to solve the problems of the passivation effect, short life and poor repeatability of electrodes in conventional electrochemical detection, a novel preparation method of copper-sensitive film was proposed, and the method was combined with a microelectrochemical sensing electrode to detect nitrate in water. Microfabrication technology was used to prepare the micro sensor electrode. A total of 0.05 mol/L CuSO\({}_{4}\) was added into the supporting electrolyte, and the dissolution and deposition of copper on the surface of the working electrode and the catalytic reduction reaction of nitrate ions were performed. On-line cleaning of microelectrode, in situ deposition of copper-sensitive film and detection of nitrate concentration in water were realized, respectively. An on-line modified electrode was used to detect the nitrate standard sample, achieving a high detection sensitivity (0.031 uA/[umol L L\({}^{-1}\)]) in the concentration range of 0-3500 umol/L. The relative standard deviation of the same nitrate standard sample repeated for 15 times was only 2.48%, which improved the long-term repeatability of nitrate detection, and, at the same time, an on-line system was adopted that included an on-line detection system (DS) and a wireless transmission system (WS).  
on-line modified electrode; copper-sensitive film; nitrate detection; repeatability; on-line monitoring
# New Advances and Challenges of Fall Detection Systems: A Survey
## 4 Challenges and Potentials
### How to Define the Fall Based on Universal Features  
Before solving a problem, the predominant and antecedent step is to define the problem. The definition of the fall determines in which way the researcher detects the fall, and then impacts which sensors are deployed and used, which algorithms are employed, and how the system is built. This problem is difficult to address due to the lack of knowledge on the complexed dynamics model of the fall. Some of researchers have attempted to define human's fall action based on thresholds, shapes or rules. Naturally, they proposed these conventional methods: threshold-based, shape-based or rule-based algorithms. To distinguish a fall from other actions and process complex information, researchers use methods based on machine learning and extract some features of the actions which performed well on classifying the fall and other actions. However, it merely reflects one aspect of the fall rather than the essence of the fall, which will make the system sensitive to situations. Once the experiment condition is changed, the algorithm and system cannot be reused any more. Therefore, it is necessary to build the model and analyze fall in a comprehensive way to reflect the essence of fall. In our work, we regarded the fall as a kind of imbalance action from the view of biomechanics. In this way, the fall does not require to be categorized into specific actions, but the mathematical model is built to define the fall. Based on this idea, the tentative study has already worked in progress [32; 33].
## 4 Quantum and Classical Image-Processing Algorithms
### Plenoptic Tomography  
Novel reconstruction algorithm with the advantage of real 3D image lack of artifacts is based on the idea of recasting plenoptic imaging as an absorption tomography. The classical refocusing algorithm is based on superimposing images of the 3D scene from different viewpoints, which necessarily results in the contribution of out-of-focus parts of the scene, the effect responsible for the existence of the blurred part of 3D image reconstruction. The tomography approach is based on a different principle and provides sufficient axial and transversal resolution without artifacts. For this purpose, the object space is divided into voxels and the goal is to reconstruct absorption coefficients of each voxel. The measured correlation coefficient of a pair of points from the correlated planes is transformed to be linearly proportional to the attenuation along the ray path connecting those two points. Classical inverse Radon transform can be used in this scenario to obtain a 3D image, but using the Maximum Likelihood absorption tomography algorithm [54] further enhances the quality of the tomography reconstruction and performs well even for a small range of projections angels. In fact, advanced tomographic reconstruction algorithms based on the Maximum Likelihood principle are more resistant to noise and require fewer acquisitions, for a given precision, in comparison to standard tomographic protocols. We are investigating special tools to deal with informationally incomplete detection schemes for very high resolution and optimal methods for data analysis based on convex programming tools.
## 5 MTMD Applications on Taizhou Bridge
### Parametric Analysis on the MTMD Application
#### 5.4.2 Mass Ratio  
The control efficiency of the lateral and vertical MTMD systems is also investigated with different mass ratios, as shown in Figure 16. During the process, the damping ratio and frequency bandwidth ratio are kept the same as the parametric analysis on the number of TMDs above, while the number of TMDs is updated with 13 for the lateral MTMD and 11 for the vertical MTMD.  
Figure 15: Influence of the number of TMDs on the control efficiency of MTMD: (**a**) Lateral case; (**b**) Vertical case.  
As shown in Figure 16, the control efficiency of the lateral MTMD increases with the augment of the mass ratio, but the ascending gradient is gradually decreasing. For example, the control efficiency goes up by 8.99% when the mass ratio varies from 0.01 to 0.03, however, it increases by only 7.18% when the mass ratio changes from 0.03 to 0.10. It is indicated that the improvement of the control efficiency is rather limited when the mass ratio is larger than 0.03. Hence, the mass ratio is updated as 0.03 for the lateral MTMD.  
For the vertical MTMD, there is also a rising trend between the control efficiency and the mass ratio. But the control efficiency gradually decreases when the mass ratio is larger than 0.07. This is mainly because the mass distribution of the bridge structure will be changed when the mass ratio of the MTMD is too large. Then, the natural frequency of the vertical dominant mode will have a big shift from the design value, which accounts for the decreasing of the attenuation factor of the MTMD. This phenomenon is not captured in the lateral case, which means the lateral mode is not obviously influenced by the investigated mass ratios. Since the control efficiency is improved the most dramatically from 0.01 to 0.02 and the value when mass ratio is 0.02 has reached 30.33%, the preferred mass ratio of the vertical MTMD is determined as 0.02 in this round.
## 3 Meta-PPO Anti-Jamming Intelligent Decision Algorithm
### Meta-PPO Intelligent Anti-Jamming Algorithm  
The Meta-PPO intelligent anti-jamming algorithm designed in this paper is shown in Algorithm 1. After iterating \(n\) times, the meta-learner will send the final initial model parameter \(\theta\) to the basic learner, and then learn and test new tasks that are not in the training set \(D_{i}^{train}\).  
```
1:initialize: Number of initial tasks \(T_{i}\); Number of tracks to be extracted in each task \(k\); Number of training rounds \(n\); Inner loop gradient update hyperparameter \(\alpha\); Outer loop gradient update hyperparameter \(\beta\); Random initialization Actor network parameters \(\theta\); Critic network parameters \(\mu\).
2:for\(t=1,2,...,T\) do
3: Extract \(k\) tracks to form a training set \(D_{i}^{train}\) and use PPO neural network algorithm for training
4: Calculate the temporal difference error \(\delta_{i}\) according to Equation (7)
5: Update Critic network parameters by minimizing Critic network loss function according to Equation (9) to obtain the updated parameters \(\widetilde{\mu}\)
6: Update the Actor network parameters by minimizing the Actor network loss function according to Equation (10), and obtain the updated parameters \(\widetilde{\theta}\)
7: Calculate the gradient as the difference between the model parameters before and after training. Then use this gradient to update the Actor network's parameters \(\theta\), as specified in Equation (12).
8:\(t=t+1\);
9:endfor
10: When the training round is over, the meta-learner will send the initial model parameters \(\theta\) and \(\mu\) obtained in the current round and the random initial model parameters for the next round to the base learner and test the new tasks outside the training set.
```  
**Algorithm 1** Meta-PPO
## 3 The Next Video Frame Prediction Techniques Using SA-ConvLSTM and Encoder-Decoder Structures
### Loss Function  
In this study, the aim was not only to generate realistic frames but also to ensure that these frames are continuous and accurately capture the motion of the object. For this purpose, we adopted the mean squared error (MSE) as the loss function. The MSE is defined as follows:  
\[\mathrm{L}=\frac{1}{N}\sum_{i=1}^{N}[y(i)-\hat{y}(i)]^{2} \tag{9}\]  
where \(N\) is the total number of pixels; \(y(i)\) is the value of the \(i\)th pixel in the actual frame; and \(\hat{y}(i)\) is the value of the \(i\)th pixel in the frame predicted by the model.  
There are several reasons for using the MSE as the loss function:  
* Ensured continuity: MSE calculates the difference between the predicted and actual frames in pixels. This prevents abrupt changes between consecutive frames and promotes smooth transitions.
* Motion accuracy: Subtle differences between frames must be recognized to accurately capture the motion of an object. MSE is sensitive to these subtle differences, allowing the model to learn motion more accurately.
* Improved sharpness: MSE is effective in minimizing blurring effects and producing sharp images. This is because the model is trained to predict the exact value of each pixel.
* Computational efficiency: MSE is computationally simple and efficient, and is advantageous for training on large datasets.
* Differentiability: MSE is a continuous and differentiable function, which makes it suitable for optimization using gradient descent.  
Experiments have shown that models trained using the MSE loss can produce sharp and consistent frames. This is because MSE can capture detailed features well while preserving the overall structure of the frame.
## 4 Implementation and Performance Analysis of CAES-Based Image Encryption
### Histogram Analysis and Mean Absolute Deviation  
For an image, the histogram is an important statistical feature. A good cryptosystem can encrypt an image, and the histogram of the cipher image is evenly distributed, such that attackers can not analyze the statistical features of the original image from its cipher image. Figure 10(a)-d presents the visual effect of the plain and cipher images, respectively. Figure 10(e)-h shows the histograms of the plain and cipher images, respectively, demonstrating that the encryption can safely obscure the histogram information.  
Figure 11: _Cont._To evaluate the uniform distribution of histograms, the mean absolute deviation (MAD) [27] was calculated using the following equation:  
\[MAD=\frac{1}{256}{\sum\limits_{i=0}^{255}{|x_{i}-m(X)|}}, \tag{10}\]  
where \(x_{i}\) is the number of pixel values (i), \(X=\{x_{0},x_{1},\cdots,x_{255}\}\) and \(m(X)=\frac{1}{256}{\sum\limits_{i=0}^{255}{x_{i}}}\) is the mean of \(X\). Obviously, for images with a size of \(256\times 256\), \(m(X)=256\).  
From Table 2, it is revealed that not all of the histograms of the encrypted images from AES-EBC had a uniform distribution. However, AES-EBC and CAES-CBC always had good mean absolute deviations. Furthermore, CAES-CBC had the smallest average MAD. This means that the uniform distribution of CAES-CBC was slightly better than AES-CBC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{3}{c}{**Histogram MAD Uniform Distribution Test**} \\ \cline{2-4}  & **AES-ECB** & **AES-CBC** & **CAES-CBC** \\ \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{12.3} & \multirow{2}{*}{12.67} & \multirow{2}{*}{12.67} \\  & & & \\ \end{tabular}
\end{table}
Table 2: Mean absolute deviation (MAD) uniform distribution test.  
Figure 11: The visual effect and histograms of the plain and cipher images.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 2 Related Work  
Edge-server location selection is a combinatorial optimization problem, the computational complexity of which grows exponentially with the problem size [7]. To tackle this challenge, previous studies have employed various optimization algorithms, including spatial clustering [8; 9], integer linear programming [10; 11], game theory [12], bionic intelligence [13; 14], and heuristic [3] algorithms.  
In terms of static placement, a deep reinforcement learning-based multi-objective edge server placement strategy was previously proposed for the performance of a vehicular networking task [15]; this approach was designed to improve the coverage and workload balance and reduce the average latency. Additionally, [PERSON] et al. [16] formulated the edge-server placement problem as a constrained optimization problem and decreased the network latency and energy consumption via a particle swarm optimization (PSO) algorithm. As a result, the total edge-server cost was reduced. In another work, the server deployment and resource allocation were optimized to achieve low latency and load balancing. This was achieved through subregion partitioning, the whale optimization algorithm, neural networks, and game theory [17]. Finally, [PERSON] et al. [18] employed a fault tolerance mechanism and a binary-based gray-wolf genetic policy algorithm for edge-server deployment; this approach ensured normal system operation in case of failures.  
In terms of dynamic placement, a non-dominated sequential genetic algorithm was previously employed to reconstruct existing locations [19]. In that work, the minimum reconstruction cost was computed based on the Kuhn-Munkres two-part graph matching algorithm. Further, [PERSON] et al. [20] employed proximity policy optimization to reduce the total server placement cost, and used heat and grey-scale maps to convert the network states into network inputs.  
In the mentioned dynamic placement methods, some have poor scalability, requiring consideration of all edge servers each time deployment is adjusted [19]. Some have high deployment costs [20]. In this paper, by categorizing edge servers into static servers and dynamic servers, only the operations such as addition and movement of dynamic servers need to be considered during each adjustment. This approach reduces deployment costs and improves scalability.
## 4 Development of ViPER+ for Construction Vehicles and Equipment Pose Estimation
### Data Collection Subsystem
#### 4.1.1 Time Synchronization  
Besides having anchors and tags, time synchronization is another requirement that is crucial for the operation of TDOA systems. Since anchors are reporting the timestamp of the received signals, all anchors must implement a time synchronization technique to correct the received signal timestamp before using them for localization. A group of TDOA systems uses wired infrastructure to transmit time synchronization messages to anchors. However, wiring all anchors are not plausible in all environments, including construction sites.  
Another group of solutions uses wireless radios to provide synchronization which is more feasible in a construction site environment. In this approach, the system assigns an anchor (an external anchor or one of the localization anchors) to be the time sync anchor. This anchor periodically starts a global two-way ranging (TWR) session with other anchors to estimate its distance with anchors and perform time synchronization. The ranging session starts with a time-sync anchor broadcasting a TWR poll (TWR-POLL) message to all anchors. Upon reception of the TWR-POLL message, each anchor replies with a TWR response (TWR-RESP) message. Finally, the time-sync anchor broadcasts the TWR final (TWR-FINAL) message, containing the received timestamp of all TWR-RESP messages, and indicating the end of the ranging session.  
Figure 1: Overview of ViPER+.  
Since anchors run on different clock speeds, the time sync anchor needs to perform synchronization periodically throughout the tracking session. In our design, when anchors receive the TWR-FINAL message, they send a report packet containing the content of the TWR-FINAL message along with the transmission timestamp of the TWR-RESP and received timestamp of TWR-POLL and TWR-FINAL to the server.
## 2 Materials and Methods
### Analytical Approach
#### 2.2.3 Complex Wavenumber Derivation  
Generally, we use the smallest non-zero imaginary wavenumber to evaluate the attenuation. The smallest imaginary part of the wavenumber is defined as the attenuation constant. Therefore, to predict the attenuation performance inside the stopband, the numerical formulations in the form of \(k(\omega)\) need to be derived.  
It is acknowledged that the electrical induction equation and the elastodynamic equation of the piezoelectric material can be described with the following [36]:  
\[\rho\frac{\partial^{2}u_{i}}{\partial t^{2}}=\frac{\partial T_{ij}}{\partial x _{j}},\frac{\partial D_{j}}{\partial x_{j}}=0, \tag{28}\]  
where \(\rho\) is the mass density and the stress charge type constitutive equation is reformulated:  
\[\begin{split} T_{ij}&=C_{ijl}\frac{\partial u_{l}} {\partial x_{k}}+e_{kij}\frac{\partial\varphi}{\partial x_{k}}\\ D_{j}&=e_{jkl}\frac{\partial u_{l}}{\partial x_{k} }-\varepsilon_{jk}\frac{\partial\varphi}{\partial x_{k}}\end{split}. \tag{29}\]  
In Equation (29), \((i,j,k,l)\in\{1;2;3\}\) are indexes for the three directions in space, \(x_{1}=x\), \(x_{2}=y\), \(x_{3}=z\). For a harmonic solution, \(u_{x}\), \(u_{y}\), \(u_{z}\), and \(\varphi\) are:  
\[\begin{split} u_{x}&=U_{x}(x,y,z)e^{i(k_{x}x+k_{y}y -\omega t)}\\ u_{y}&=U_{y}(x,y,z)e^{i(k_{x}x+k_{y}y-\omega t)}\\ u_{z}&=U_{z}(x,y,z)e^{i(k_{x}x+k_{y}y-\omega t)} \\ \varphi&=\Phi(x,y,z)e^{i(k_{x}x+k_{y}y-\omega t)} \end{split}, \tag{30}\]  
where \(k\) and \(\omega\) are the wave vector and the angular frequency, respectively. Due to the Bloch Floquet conditions:  
\[\begin{split} U_{x}(x+l,y+l)&=U_{x}(x,y)\\ U_{y}(x+l,y+l)&=U_{y}(x,y)\\ U_{z}(x+l,y+l)&=U_{z}(x,y)\\ \Phi(x+l,y+l)&=\Phi(x,y)\end{split}. \tag{31}\]  
The PDE module of the software Comsol Multiphysics is employed to establish the following polynomial equation, and the angular frequency \(\omega\) is given to resolve the eigenvalue of the equation:  
\[\begin{split}\Lambda^{2}\mathbf{e}_{a}\mathbf{U}+\
abla\cdot(- \mathbf{C}\
abla\mathbf{U}-\mathbf{\alpha}\mathbf{U})+\mathbf{\beta}\cdot \
abla\mathbf{U}+\mathbf{A}\mathbf{U}=0\\ \mathbf{U}&=[U_{x},U_{y},U_{z},\Phi]^{T}\\ \
abla&=[\frac{\partial}{\partial x},\frac{\partial} {\partial y},\frac{\partial}{\partial z}]\end{split}, \tag{32}\]where the matrices **A**, **C**, **e\({}_{a}\)**, \(\beta\), and \(\alpha\) can be found in Appendix A. Furthermore, \(\lambda\) and shear modulus \(\mu\) are defined as follows:  
\[\mu=\frac{E_{m}}{2(1+v)}, \tag{33}\]  
\[\lambda=\frac{vE_{m}}{(1+v)(1-2v)}. \tag{34}\]  
In Equations (33) and (34), \(E_{m}\) is [PERSON]'s modulus and \(v\) is the [PERSON]'s ratio of the material.