esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Performance Metrics  
We considered the metrics of lifetime and total consumed energy to evaluate the schemes and methods proposed by this research comparatively. However, using the total consumed energy per round to measure energy efficiency may not be an accurate measure for lifetime evaluation. This is because the total energy might be maintained by a small percentage of nodes, while all other nodes were depleted or the total maintained energy might be less, but is distributed over a larger percentage of nodes. In order to judge the energy efficiency of the WSN clustering technique accurately, we propose a way to measure how much the clustering technique can maintain of the remaining total energy distributed equally through the nodes. If it manages to keep the total remaining energy distributed more equally, then more nodes will be live for further rounds. To the best of our knowledge, this metric of equality for the distribution of remaining energy through the nodes is used here for the first time. Furthermore, we use the well-known Gini index as a means to measure this property.  
The Gini index is used in the context of measuring the extent of the inequality of income among the population or to measure how unequally the resources are distributed among population samples. The Gini index value is between zero and one; a value of zero means the inequality of the measured resource among all samples is zero, i.e., the resource is distributed equally among population samples, which happens when all samples have the same amount of resource; while a Gini index value of one means that the inequality of resources among population samples is at the maximum, when all of the resources are possessed by one sample only. In general, the larger the value of the Gini index, the more unequal the resources among the population samples, and vice versa. Mathematically, the Gini coefficient is defined as the ratio of area confined between the wealth distribution curve, Lorenz curve, and the line of equality, area \(\mathbf{A}\), to the area under the line of equality, area \(\mathbf{A}+\mathbf{B}\), in Figure 7.  
The Lorenz curve is the accumulative percentage of people against the accumulative share of wealth [62]. However, in the context of this research, the population is the sensor nodes, and the remaining energy is used in place of wealth. We adopt it to measure the extent to which the clustering algorithm keeps the remaining energy distributed equally among the nodes. Thus, the smaller the Gini index, the more equal the distribution of the remaining energy.  
We also measure the network lifetime in terms of the First Node Dead (FND), 10% of Nodes Dead (10 PND), Quarter of Nodes Dead (QND), Half of Nodes Dead (HND), and 75% of nodes dead.
## 7 Discussion
Therefore, other wind and current data sources with a high spatial resolution derived from numerical models will be considered for future studies. Apart from met-ocean conditions and oil size, factors such as oil type (e.g., soybean or mineral oil) and thickness could potentially influence variations in oil-drift velocity and direction, and their dissipation over hours or days, as shown in [31]. However, accurate information on the detected oil types in our cases is unavailable, preventing a detailed analysis in this context. Likewise, the differences in the spatial resolution of satellite sensors may impact the evaluation of changes in the shape of oil slicks, especially between Sentinel-1/2 and Sentinel-3 and Landsat-8. Additionally, the difference in the observation of oil spills between SAR and optical sensors may contribute to the oil size and shape variations. Since images acquired by both SAR and optical sensors with comparable spatial resolution and simultaneous acquisition time and area are unavailable, definitive conclusions on this matter cannot be drawn in this paper. This aspect should be thoroughly investigated in future studies. The oil-drift variables--such as direction, velocity, and changes in oil size and shape--obtained by combining multi-source satellite data--are highly practical for rescue operations aimed at protecting marine biodiversity. This is particularly crucial off the coast of Mauritius, where the ecosystem is vulnerable, and recovery is prolonged. This approach provides accurate and timely information, which is essential for short-term emergency responses like cleanup and contaminated area zoning, as well as for long-term solutions such as depollution and recovery.
# On Finding the Right Sampling Line Height through a Parametric Study of Gas Dispersion in a NVB
## 2 Materials and Methods
### CFD Validation for Gas Dispersion
#### 2.2.4 Turbulent Schmidt Number Definition and Dispersion Rate  
The turbulent Schmidt number for the RANS turbulence model was defined following the Fick's law analogy [14], after applying the RANS decomposition to the gas transport equation (if x is a variable, x = X + x', X = <x> is the mean part, and x' is the fluctuation part of x):  
\[Q_{t,i}=-D_{t}\frac{\partial C}{\partial x_{i}} \tag{1}\]  
Here, \(Q_{t,i}\) is the turbulent mass flux in the \(x_{i}\) direction (\(i\) = 1, 2, 3) of a Cartesian coordinate system (in kg m\({}^{-2}\) s\({}^{-1}\)), \(C\) is the mean gas concentration (in kg m\({}^{-3}\)), and \(D_{t}~{}=~{}v_{t}/\text{Sc}_{\text{t}}\) is the turbulent diffusivity (in m\({}^{2}\) s\({}^{-1}\)). The turbulent viscosity \(v_{t}\) was computed by the solver from the flow characteristic, and the introduced turbulent Schmidt number had to be given by the user. The standard value of the Sc\({}_{\text{t}}\)-number in Ansys Fluent is 0.7. However, as already discussed in the Introduction, numerous previous researchers showed that the right Sc\({}_{\text{t}}\)-number value for their specific application can diverge from the standard value.  
The method used for finding the Sc\({}_{\text{t}}\)-number corresponding to this specific study was the comparison of the dispersion rates obtained from the measurement to the ones from the numerical simulation, for different values of the Sc\({}_{\text{t}}\)-number varying from 0.1 to 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Domain Cell Number** & **All Points’ Mean Concentration Rel. Diff. in \%** \\ \hline
1,158,870 & \\
2,728,878 & 26.6\% \\
3,319,102 & 5.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid convergence.  
We defined the dispersion rate in our study by dividing the gas concentration at one point by the concentration at the point at the beginning of the line, for example in Line 2:  
\[\text{dispersion rate}=\frac{\text{concentration at P05 or P08}}{\text{ concentration at P02}} \tag{2}\]
## 1 Introduction
A laboratory study about the behavior of Brasilia subgrade lateritic soil showed that the pore size distribution was a bimodal curve and changed with the compression, which also indicated that the microstructure was strongly related to the mechanical behavior [23]. The impact of nanosilica on the microstructure and mechanical characteristics of cemented sandy soil was investigated by atomic force microscopy (AFM), SEM, and XRD tests. The results indicated that the addition of nanosilica with the optimum percentage to cement stabilized sandy soil could enhance its mechanical and microstructure properties [24]. The microstructural behavior of Brasilia clay was studied by the bimodal pore size distribution (PSD) analyses, and the bimodal van Genuchten curve was used to perform the model of the PSD curves. The loading and the compacting had no influence on the micro-pores of soil, and the changes only occurred on the macro-pores [25]. The content of the structural unit with a larger equivalent diameter of cement modified soil increased with the cement content increased, while the porosity and the fractal dimension decreased [26]. The pore size distribution and pore shape coefficient of cement reinforced clay with different cement content differed before and after loading, but the orientation of the pores was remarkable no matter what the cement content was [27]. The microscopic parameters are related to cohesion, internal friction angle, and shear strength [28]. The cement content is an important factor affected the shearing form of the modified soil. When the cement content was greater than 10%, the macroscopic failure form was dilatation damage after loading, and the pore distribution was oriented [29]. In summary, comprehensive analysis of the microstructure and the macroscopic mechanical properties under F-T cycles is necessary. The promoted microstructure can enhance the mechanical properties of subgrade silty soil to solve construction problems. Therefore, the macroscopic mechanical properties of unmodified and cement modified silty soil under F-T were studied by a static triaxial test; meanwhile, the microstructure images of those under the same conditions were obtained by SEM. The microscopic mechanism of the macroscopic mechanical properties of the cement modified silty soil was revealed with the grey relation analysis (GRA), which would provide reliable reference data for cement modified subgrade silty soil (CMSS) in seasonally frozen areas.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 6 Discussion  
The authors have developed two visible-light CDMA experimental systems so far [28; 29]. Figure 8 shows an experimental system reported in [29]. This system is implemented with an FPGA board (ALTERA Cyclone III 3C120) and five white LEDs (OSW4 XME1C1S-100). The developed system adopts the EWO scheme as an MUI cancellation scheme. It can multiplex five data channels on each of which \(31.25\) kHz MIDI data is transmitted. MIDI data are coded with a non-inverted or an inverted MPSC. The spread sequence length is \(16\) and the chip rate for each channel is \(500\) k chips per second. It was confirmed that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{3}{c}{**Parameter**} \\ \hline Chip duration & \(T_{c}\) & \(10\) (ns) & Noise-equivalent bandwidth & \(B\) & \(50\) (MHz) \\ Background light power & \(P_{b}\) & \(-45\) (dBm) & Receiver noise temperature & \(T_{r}\) & \(320\) (K) \\ Received load resistance & \(R_{L}\) & \(820\) (\(\Omega\)) & Quantum efficiency & \(\eta\) & \(0.6\) \\ Wavelength & \(1/f\) & \(660\) (nm) & Modulation extinction ratio & \(M_{e}\) & \(100\) \\ Dark current & \(I_{d}\) & \(0.1\) (nA) & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Nominal parameters in system model.  
Figure 7: Comparisons of \(P_{w}\) versus BER performance. (**a**) \(q=4\), \(N_{\mathrm{L}}=8\), and \(N=8\), (**b**) \(q=8\), \(N_{\mathrm{L}}=32\), and \(N=32\).  
system can correctly decode every MIDI datum transmitted simultaneously. Experimental results validated that the capability of channel multiplexing can be maintained even if the inverted sequences are used.  
Although the total data rate of 156.25 kbps and the communication distance of 60 cm were achieved with our experimental system, the potential working bandwidth and communication distance would be higher than these values. Recently, many devices have been studied and developed for VLC [2; 3; 4; 5; 6; 7; 8; 9]. It is reported that even phosphorescent LEDs and simple OOK modulation enable 100-230 Mbps data rates [3]. By using high-speed and high-power devices, we think that our scheme will achieve a total data rate more than 100 Mbps and a transmission distance of several meters in the near future.  
Recently, VLC with a dimming control function has been gaining attention for indoor and outdoor lighting systems [11; 30]. Our future work will develop such a VLC system using the technique proposed in the present study.
## 3 Bearing State Space Model Establishment and Fault Identification Process  
### Bearing State Space Model Establishment  
The real state of bearing vibration is \(\mathrm{x}(\mathrm{k}),\mathrm{k}=1,2,\cdots,\mathrm{N}\), and the state vector is formed by the real state sequence \(\{\mathrm{x}(\mathrm{k})\}\). Let the augmented state vector be \(\mathrm{X}(\mathrm{k})\) as  
\[\mathrm{X}(\mathrm{k})=[\mathrm{x}(\mathrm{k})\;\mathrm{x}(\mathrm{k}-1)\cdots \;\mathrm{x}(\mathrm{k}-\mathrm{n}+1)\;]^{\mathrm{T}} \tag{26}\]  
According to the relationship of the AR model (1), the state equation is obtained:  
\[\begin{bmatrix}x(\mathrm{k})\\ x(\mathrm{k}-1)\\ \vdots\\ x(\mathrm{k}-\mathrm{n}+2)\\ x(\mathrm{k}-\mathrm{n}+1)\end{bmatrix}=\begin{bmatrix}\varphi_{1}&\varphi_{2}& \cdots&\varphi_{\mathrm{n}-1}&\varphi_{\mathrm{n}}\\ 1&0&\cdots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 0&0&\vdots&0&0\\ 0&0&\vdots&1&0\end{bmatrix}\begin{bmatrix}x(\mathrm{k}-1)\\ x(\mathrm{k}-2)\\ \vdots\\ x(\mathrm{k}-\mathrm{n}+1)\\ x(\mathrm{k}-\mathrm{n})\end{bmatrix}+\begin{bmatrix}1\\ 0\\ 0\\ 0\\ 0\end{bmatrix}\mathrm{w}_{\mathrm{k}-1} \tag{27}\]  
The one-step transition matrix is obtained by formula (27):  
\[\Phi=\begin{bmatrix}\varphi_{1}&\varphi_{2}&\cdots&\varphi_{\mathrm{n}-1}& \varphi_{\mathrm{n}}\\ 1&0&\cdots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 0&0&\vdots&0&0\\ 0&0&\vdots&1&0\end{bmatrix} \tag{28}\]  
System noise driving matrix:  
\[\Gamma=\begin{bmatrix}1\\ 0\\ \vdots\\ 0\\ 0\end{bmatrix} \tag{29}\]  
The model error sequence \(\mathrm{a}(\mathrm{k})\) is the system noise \(\mathrm{w}(\mathrm{k})\), so the system noise variance matrix is \(\mathrm{Q}=\sigma_{\mathrm{a}}^{2}\).  
From the equation of state in formula (27), it can be seen that the measured value of the noise-containing bearing in the observation equation is \(\{\mathrm{y}(\mathrm{k})\}\). Let \(\mathrm{Y}(\mathrm{k})=\mathrm{y}(\mathrm{k})\), and compute the measurement equation as:  
\[\mathrm{Y}(\mathrm{k})=[1\;0\cdots 0\;0]\mathrm{X}(\mathrm{k})+\;\mathrm{v}( \mathrm{k}) \tag{30}\]  
The measurement matrix is:  
\[\mathrm{H}=[1\;0\cdots 0\;0]_{1\times\mathrm{n}} \tag{31}\]  
where the observation noise \(\mathrm{v}(\mathrm{k})\) is zero mean white noise, and the variance matrix is R.
## 2 Methods  
The architecture of NR-UNet is depicted in Figure 2. This symmetrical architecture comprises encoders and decoders, denoted as \(En_{i}\) and \(De_{i}\), respectively, where \(i\in[1,2,3,4]\) indicates the level of encoders and decoders. The transfer of feature maps is conducted between each level of \(En\) and \(De\), following the approach proposed by [1]. The CNN-based en/decoders consist of two-layer CNNs and batch normalization (BN) with up- or downsampling. The ViT-based encoders are designed to extract hidden high-level feature information in \(En_{4}\) and use a bottleneck structure to further enhance feature learning, and 2-layer ViT is designed in each block. The ViT layer includes layer normalization (LN) [34], multihead self-attention (MSA), and multilayer perceptron (MLP), as described in [21]. The proposed ADL is briefly sketched in Figure 3, where a certain number of noisy labels are able to be detected and removed. We provide details on the segmentation network and adaptive denoising learning strategy in the sections below.  
Figure 2: The framework of the proposed NR-UNet. (**a**) The U-shaped encoder–decoder segmentation network through transformer-improved encoder and convolution decoder. ViT-based blocks and CNN-based blocks are green and yellow, respectively. (**b**) The green CNN-based block consists of two successive CNN layers. (**c**) The yellow ViT-based block consists of two successive ViT layers.
# A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem
## 4 5-Approximation Algorithm for \(P\big{|}s_{j},p-batch,K_{i}\big{|}C_{\max}\)
We use the binary search method to find the makespan of a feasible solution in the range of the \([LB,UB]\) interval. Firstly, set \(T=\left\lceil\frac{LB+UB}{2}\right\rceil\), and then classify both the jobs and batches into long, short, and median. A job \(J_{j}\) is long if \(p_{j}>T/2\), median if \(T/4<p_{j}\leq T/2\), or short if \(p_{j}\leq T/4\). Similarly, a batch \(B_{i,g}\) is long if \(p(B_{i,g})>T/2\), median if \(T/4<p(B_{i,g})\leq T/2\), or short if \(p(B_{i,g})\leq T/4\). Certainly, long batches may contain median and short jobs, and median batches may contain short jobs. After classification, we use the following SCMF-LPTJF (smallest capacity machine first processed and longest processing time job first processed) procedure to search for a schedule with a makespan at most \(9T/4\), which permits one-job-overfull batches. If our above operation fails, we will continue searching for the upper half of the interval and set \(LB=T\); otherwise, we will continue searching for the lower half of the interval, record \(OPT=T\), and set \(UB=T\). The binary search method is then repeated in the new range of the \([LB,UB]\) interval until \(LB\geq UB\). **Lemma 2**. : _If \(OPT\leq T\), then the SCMF-LPTJF algorithm will generates an optimal schedule for \(P\big{|}s_{j},p-batch,K_{i}\big{|}C_{\max}\) with one-job-overfull batches whose makespan is at most \(9T/4\)._  
Let \(\Sigma\) be an optimal schedule whose makespan is \(OPT\). Let \(H\) be the set of long jobs and median jobs. ```
1:Input: \(\mathcal{J}=\cup_{i=1}^{m}\mathcal{J}_{i}\), \(T\)
2:Output:C\({}_{\max}\)--best found solution, \(RT\)--running time
3:\(Q_{0}=\phi\), \(\textit{Assigned}S=\phi\)// denote the jobs have been assigned to batch as \(\textit{Assigned}S\)
4:for\(i=1\) to \(m\)do
5: Sort \(\mathcal{J}_{i}\) according to the rule that processing time of jobs is not increased, denote \(\mathcal{J}_{i}^{\prime}\)
6:end for
7:for\(i=1\) to \(m\)do
8:\(Q_{i}=Q_{i-1}\cup\mathcal{J}_{i}^{\prime}\) - \(\textit{Assigned}S\)
9: Apply the MBLPT rule to \(Q_{i}\) and \(M_{i}\), get \(B_{i}=\left[B_{i,g}:g=1,2,\ldots,h_{i}\right]\)
10: Sort \(\mathcal{B}_{i}\) according to the rule that processing time of batches is not increased, denote \(\mathcal{B}_{i}^{\prime}\)
11: Denote long batches set, median batches set, and short batched set as \(LongBS\), \(\textit{MedianBS}\), and \(\textit{ShortBS}\)
12:\(LongBS=\phi\), \(\textit{MedianBS}=\phi\), \(\textit{ShortBS}=\phi\)
13:for batch \(b\) in \(\mathcal{B}_{i}^{\prime}\) do// classify batches into long, median and short.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Specific Characteristics of Remote Monitoring in Urban VANETs
#### 2.2.2 Data Rate Requirement  
As highlighted in Section 2.1, the type of data monitored for remote monitoring applications includes images, various signals from physiological sensors, audio, video, etc. To enable remote monitoring of these data through communication networks, different data rates are required. Table 1 highlights the data rate requirements of various sensors used by remote monitoring applications.
## 2 IVF Culture, Embryo Development, and Selection
### Embryo Selection and Implantation  
In order to achieve successful pregnancy after IVF, the selection of embryos for uterus implantation has been crucially investigated from the earliest stage of development to distinguish the quality of the fertilized embryos [9; 16], particularly at the cleavage and blastocyst stages [25]. However, in common practice, blastocysts at day 5 are preferred among embryos, since this development stage was assessed for evidence of compaction and there was a good prognosis for blastocyst development [26; 27; 28]. Although there is consensus about the type of embryo transferred, i.e., embryos in which more than 50% of the blastomeres have large vacuoles should not be transferred or frozen as their implantation rate is practically zero [29], the selection method of the developing embryo in either invasive or non-invasive IVF is significantly related to the embryos' quality. Non-invasive techniques such as time-lapse microscopy represent new methodologies to analyze embryos in stable culture conditions without the risk of sample contamination or sample error [30]. Additionally, non-invasive selection methods appear to be a better strategy for the identification of potential embryos without the risk of possible impacts due to the investigation itself [31].  
While not all embryos can develop to the level of a blastocyst prior to insemination, the embryos tend to have a higher success rate if the blastocyst grade is good quality [32]. In fact, the ability of the embryo to become a blastocyst is determined by the blastulation rate, in which a higher rate is found for the blastocyst stage than the cleavage stage [17] and is significantly lower for the pronuclear stage [21]. Therefore, if an embryo's blastulation rate is low and it is progressing slowly, it may be an early indication that something is amiss with the environment in which the embryo is growing. Hence, the embryologist may require a postmortem or \"troubleshoot\" to identify the cause.
# MLPs Are All You Need for Human Activity Recognition
## 1 Introduction  
The last two decades have witnessed the rapid growth of wearable devices, which are increasingly being used for ubiquitous health monitoring. Human activity recognition (HAR) aims at detecting simple behaviours, such as walking or gestures; more complex behaviours, like cooking or opening a door, with various use-cases that continue to grow as the field expands; and assistive technology, such as identifying odd behaviours in the elderly, including falls [1], skill assessment [2], helping with rehabilitation [3], sports injury detection, and ambient assisted living [4; 5; 6]. Accurately predicting human activities from sensor data is difficult due to the complexity of human behaviour and the noise in the sensor data [7].  
With larger datasets and more computational power, deep learning has evolved, removing the need for manually created features and inductive biases from models and increasing the reliance on automatically learning features from raw labelled data [8]. Complex deep learning techniques, such as convolutions and attention-based mechanisms, are used increasingly with growing computational capacity. These techniques perform well with larger models, resulting in processes that are generally more expensive computationally and memory-wise than previous techniques. Although wearable devices and smartphones have rapidly increased in computation efficiency over the past two decades, they are still limited in power and storage; this prevents them from using state-of-the-art deep learning techniques in HAR.  
MLP-Mixers, recently created by Google Brain [8], are simplistic and less computationally expensive models, yet they produce near state-of-the-art results in computer vision tasks. Wearable devices could produce competitive results in HAR without the significantcomputational demands that current state-of-the-art models impose if MLP-Mixers performed similarly in HAR, which would help advance HAR toward low-powered devices. The main contributions of this paper are as follows:  
* We investigate the performance of the MLP-Mixer in multi-sensor HAR, achieving competitive, and in some cases, state-of-the-art performance in HAR without convolution, recurrent, or attention-based mechanisms in the model. The accompanied code can be found here [[https://github.com/KMC07/MLPMixerHAR](https://github.com/KMC07/MLPMixerHAR)]([https://github.com/KMC07/MLPMixerHAR](https://github.com/KMC07/MLPMixerHAR)) (accessed on 6 October 2023).
* We analyse the impact of each layer in the Mixer for HAR.
* We analyse the effect of the sliding windows on the Mixer's performance in HAR.
* We perform a visual analysis of the Mixer's weights to validate that the Mixer is successfully recognising different human activities.
## 4 Energy Management Strategies
### Integration with Wireless Sensor Networks
#### 4.4.1 System Design Considerations  
* **Compatibility:** Ensuring that energy harvesting components are compatible with the sensor node and network architecture [84].
* **Scalability:** Designing energy management systems that can scale with the network size and application demands [85, 86].