esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Here a numerical model is used to verify the anti-drift performance of the method. In this study, we aim to estimate a dynamic load in the time domain by introducing an excitation identification step in Kalman filter. This step combines the time update and the measurement update steps to recover the external force in real time. The proposed method works with a finite as well as an infinite number of degrees of freedom using the modal transform method. The method is validated using experimental results as well as by comparison with a standard method. The source code of the developed algorithm is implemented in LabVIEW (National Instruments Ltd.) and made available in the paper Appendix A. The implementation is used to verify the algorithm experimentally. The main objective of our work is to provide an online dynamic load identification approach by significantly reducing the CPU time needed to solve the inverse problem. The presented results show the significant reduction in the CPU time with the proposed method when compared to the standard method. The rest of this paper is structured as follows. In Section 2 the proposed algorithm is presented and the implementation to a finite and infinite number of degrees of freedom is explained. To verify the accuracy of the proposed algorithm, numerical simulations are performed in Section 3. Furthermore, lab experiments are also used to study the performance of the method which helps to establish the reliability of the proposed algorithm. To this end in Section 4 a prototype software is developed in LabVIEW (National Instruments Ltd.) and used for the lab experiments in two tests where experimental and numerical results are compared to illustrate the efficiency of the proposed algorithm. We finish the paper with some concluding remarks.
## 3 Materials and Methods
### Multi-Objective Optimization Encoders
#### 3.3.2 Joint Loss Optimization  
To achieve effective optimization, we introduce a joint optimization strategy of clustering loss and graph reconstruction loss, which aims to find a model configuration that achieves efficient graph structure reconstruction and accurate node clustering simultaneously.  
Clustering loss: Calculated based on the output of spectral clustering, where each node is represented by a pseudo-label indicating its cluster center. Using the K-means algorithm as the final step of spectral clustering, the clustering loss is defined as follows:  
\[L_{cluster}\ =\sum_{i=1}^{n}||z_{i}-\mu_{ci}||^{2} \tag{13}\]  
where \(z_{i}\) represents the embedding of node \(i\), \(\mu_{ci}\) is the center of the cluster to which the node belongs, and n is the total number of nodes.  
Graph reconstruction loss: Defined by comparing the difference between the adjacency matrix of the original graph and that of the reconstructed graph. Mean Squared Error (MSE) is used to quantify this difference:  
\[L_{recon}=\frac{1}{n^{2}}{\sum_{i=1}^{n}\sum_{j=1}^{n}\left(A_{ij},\ \hat{A}_{ij} \right)^{2}} \tag{14}\]  
where \(A_{ij}\) and \(\hat{A}_{ij}\) represent the elements of the original and reconstructed graph's adjacency matrices, respectively.  
Joint Optimization: Joint loss is minimized by the following equation:  
\[L_{total}\ =L_{cluster}\ +\lambda L_{recon} \tag{15}\]  
where \(\lambda\) is used to balance the impact of reconstruction loss.  
Parameters are updated through the gradient descent based on \(L_{total}\). This involves calculating the gradients of the joint loss and using these gradients to update the parameters of the encoder and the decoder.
## 5 Discussion on Engineering Application Effect
### Comprehensive Benefits  
1. Safety benefits During the rapid crossing of a major fault locally, most of the gangue in the fault-affected area was excavated in advance using the method of PDRs. In addition, the rock pillars between the PDRs were found to have deformed plastically in their entirety under the front abutment pressure, allowing the shearer to directly cut the narrow rock pillars. This greatly mitigated the wear and tear on the mining equipment (the cost incurred by wear and tear was approximately CNY 0.82 million) and improve coalface safety. Targeted initial support and reinforced support schemes were proposed for the PDRs, ensuring that the deformation of the surrounding rock of the roadway was within a controllable range and reducing the risk of accidents during the rapid crossing of the fault at the coalface and subsequent engineering operations.
2. Economic benefits After implementation of the aforementioned method, the coalface was able to maintain stable coal production (over 3000 t per day) during passage through the fault. A total of 8.5 \(\times\) 10\({}^{4}\) t of coal was extracted over this period, creating direct economic returns of about CNY 34 million. As the majority of the gangue in the fault-affected region had been dug out in advance, the coal produced from the coalface during its passage through the fault showed significantly improved quality, which created an economic benefit increase of CNY  
Figure 14: Field measurement results of surrounding rock deformation in #4 PDR.  
41 million. The cost of the entire PDRs was low, at about CNY 4.72 million. This method was thus estimated to create CNY 71.1 million of economic benefits in total.
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 4 Experiments and Result Analysis
### Evaluating the Impact of Frame Aggregation Strategy on Model Accuracy  
In this section, 3D-SE-Net models with and without frame aggregation strategy are compared. To better reflect the role of the frame aggregation strategy, we use images of driving scenes in dark environments as our test set and the images in the test set are all derived from the FDF dataset. We use the same dataset to train both models and guarantee the same training epoch.  
As can be seen from Figure 11, under dark conditions, the recognition accuracy of the group with frame aggregation strategy is significantly higher than that of the group without. The purpose of the frame aggregation strategy is to prevent the loss of video frames, which is difficult to avoid in the dark driving environment with poor lighting conditions. Judging from the recognition accuracy of each driving behavior, the recognition accuracy of each driving behavior is increased by 4-7 percentage points, which shows that the frame aggregation strategy can well improve the problem of poor lighting conditions.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 4 Photonic Implementations in Fog Computing Domains  
Integrated photonics has begun to shape the deployment of Fog computing across various domains where real-time processing, energy-efficiency, and scalability are essential. This section reorganizes key application areas by domain to emphasize real-world deployment contexts and highlight how photonic technologies enable these systems.
## 3 Experiment and Results Discussion
### COD Detection Compensation Based on Fusion of UV-Vis Spectroscopy and Environmental Factors
#### 3.2.2 Data Processing of UV-Vis Spectroscopy and Environmental Factors
Firstly, the max-min normalization (\(\overline{x}=\frac{x-x_{min}}{x_{max}-x_{min}}\)) is used to normalize environmental factors, and then the normalized data are used as the 15 th-dimensional spectral absorbance data. In this way, data alignment is completed, and each type of data in data fusion is within a unified reference framework, and then feature-level data fusion modeling is performed.
# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 5 Conclusions  
We have demonstrated how to efficiently predict the power output of a wind turbine using SCADA data and then to leverage these predictions to detect possible performance deterioration. This analysis can be extended to any machine and helps to proactively maintain such equipment in a healthy state, to run them optimally and to get the best performance while maximizing profit for the business/company.  
Our presented results showed that we were able to predict deterioration for all wind turbines where the classification F1-score was between 0.634 and 0.892. We achieved this with an automatized workflow that can create a prediction model based on training data, predict outputs for a validation dataset to calculate performance cutoff limits, and then label newly observed test data as good or bad performing. Although we did some initial trial and error to achieve a stable prediction performance over all wind turbines, we refrained from fine-tuning/specializing prediction models to the needs of specific wind turbines or do anything that might make application in a real-world business scenario difficult. Thus, the our approach demonstrates to be a valuable asset in wind farm maintenance, offering additional insights to enhance the current maintenance strategy, which relies on scheduled inspections and alarms triggered by exceeding static threshold values.  
For future work, we aim to extend the scope of our research by exploring additional deep neural networks and time series prediction approaches to further enhance our approach. Extending our analysis to encompass broader class of machinery will allow us to generalize our findings and contribute to a more comprehensive understanding of predictive maintenance. Additionally, incorporating a Functional Basis Neural Network with Fourier or wavelet functions holds promise for improving modeling results, warranting further investigation. We plan to incorporate our work to more Atria Power Wind Farms as next steps. Another important avenue is understanding the important features and their impact. Finally, expanding our time scope beyond six months will enable us to capture and account for all seasonalities, other kinds of bad states, providing a more comprehensive assessment of performance over time.  
Conceptualization, J.B., A.R.R., C.V. and C.G.; Methodology, J.B., A.R.R. and C.V.; Validation, J.B., A.R.R., A.P. and A.V.K.; Formal analysis, J.B. and A.R.R.; Investigation, J.B. and A.R.R.; Writing--original draft, J.B. and A.R.R.; Writing--review & editing, J.B., A.R.R., C.V., A.P., A.V.K. and C.G.; Supervision, C.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 4 Discussion
Apart from their single action, it has been well documented that NPVs can be perfectly combined with various chemical insecticides, like azadirachtin [50; 51; 52; 53], emamectin [54; 52], chlorantraniliprole [50; 55], spinetoram [54], thiamethoxam, diflubenzuron [56], endosulfan [57; 58], and metaflumizone [52]. Most of the time, this synergy presented an additive effect in contrast with their separate application, providing successful control. Although many cases of moth pests that have been successfully controlled by their own NPVs have been reported, others isolated from different species have failed to cause high mortality. HaNPV treatment failed to control _Plutella xylostella_ (L.) (Lepidoptera: Plutellidae) [59]. On the contrary, we showed that viruses originally isolated from other hosts significantly decreased the numbers of _T. pityocampa_ and _H. armigera_ larvae under laboratory conditions. Similarly, viruses from the alfalfa looper _Autographa californica_ ([PERSON]) (AcMNPV) and the celery looper _Anagrapha califerica_ ([PERSON]) (AfMNPV) have been very potent against codling moth _Cydia pomonella_ (L.) (Lepidoptera: Tortricidae) [60], _Mamestra brassicae_ NPV (MbNPV) demonstrated high virulence against _P. xylostella_[61], _S. litura_ NPV (SliMNPV) also killed _Arna pseudoscopera_ (Strand) (Lepidoptera: Erebidae) [44], and _Mythimnu separata_ NPV (Ms-NPV) caused higher mortality in _S. exigua_ than its own virus (_Se_-NPV) [38]. Moreover, an NPV isolate from the greater wax moth _Galeria mellonella_ L. (Lepidoptera: Pyralidae) caused severe infection in several other moth pests, like _P. xylostella_, _Crocidolomia binotalis_ Zeller (Lepidoptera: Cramidae), the tobacco budworm _Heliothis virescens_ (Fabricius), and the cabbage moth _Mamestra brassicae_ ([PERSON]) (Lepidoptera: Notuidae) [62]. All these examples highlight the existence of certain viral entomogens with relatively broad host ranges, a theory that has been verified by this study as well.
## 1 Introduction
In the existing literature, there is a lack of information regarding the nutritional and non-nutritive value of the microgreens of rare oilseed plants (such as nigella, camelina and safflower). The seeds of these plants are better explored. They contain many compounds with a wide range of pro-health effects. Since microgreens are consumed in the earliest stages of epidermis development, their mineral content will be higher than that of mature vegetables because of their better bioavailability. Therefore, microgreens possess the potential to serve as an exceptional functional food, particularly for populations worldwide that are deficient in minerals, although they can also be a source of contaminants such as heavy metals or nitrites [15]. The aim of this study was to measure the levels of selected heavy metals (i.e., cadmium, arsenic, lead, chromium, aluminium, zinc, copper, cobalt, molybdenum, manganese, vanadium, boron, antimony, thallium, titanium and strontium), as well as nitrates and nitrites, in the microgreens, at various stages of vegetation, of uncommon oilseed plants like nigella--_Nigella sativa_ L., safflower--_Carthamus tinctorius_ L., and amelina--_Camelina sativu_ L. Plants were harvested twice: the first harvest with the cotyledons and the first true leaf and second harvest with 1-2 true leaves. Microgreens are a new source of functional foods with great potential for the sustainable diversification of global food systems, the promotion of human health and the facilitation of access to fresh plants (microgreens) for ever-increasing populations, especially urban ones. Hence, there is a need to broaden our knowledge on their impact on health, including the presence of absolute or relatively harmful heavy metals, and monitor their levels. So, an important reason for starting this research is, on the one hand, the high potential nutritional value of the young shoots of rare oilseed plants and, on the other, the risks associated with the amount of toxic contaminants they contain and the lack of available literature on the subject. A remarkable result of this study will be the knowledge, gained for the first time, on the content of nitrates and nitrites, as well as selected heavy metals, present in the microgreens of safflower (_Carthamus tinctorius_ L.), nigella (_Nigella sativa_ L.) and amelina (_Camelina sativa_ L.) in various plant vegetation stages, including their so far unexplored immature stages.
# A Low Complexity Reactive Tabu Search Based Constellation Constraints in Signal Detection
## 2 System Model  
The system model we considered in this paper can be expressed as the following  
\[\mathbf{r}=\mathbf{H}\mathbf{s}+\mathbf{n}, \tag{1}\]  
where \(\mathbf{r}=[r_{1},r_{2},\ldots,r_{N_{r}}]^{T}\) is an \(N_{r}\times 1\) received signal vector, and \(r_{j}\) represents the received signal at the \(j\)th received antenna. Vector \(\mathbf{s}=[s_{1},s_{2},\ldots,s_{N_{t}}]^{T}\) is the \(N_{t}\times 1\) transmitted signal vector, and \(\mathbf{s}_{i}\in\mathbf{A}\) represents the transmitted signal at the \(i_{th}\) transmitting antenna. The signal is modulated by quadrature phase shift keying (QPSK) modulation, and \(\mathbf{A}\) can be expressed as a constellation set of \(\mathbf{A}=\frac{1}{\sqrt{2}}\{-1-j,-1+j,1-j,1+j\}\). Noise \(\mathbf{n}\) is a \(N_{t}\times 1\) vector that obeys Gaussian distribution with the mean value of 0 and variance \(\sigma^{2}\). In addition, the matrix \(\mathbf{H}\) is with the size of \(N_{r}\times N_{t}\) and its elements follow Rayleigh distribution.
## 5 Approximation Algorithms for \(\mathsf{SVP}^{(p)}\) and \(\mathsf{CVP}^{(p)}\)
### Algorithm for Approximate \(\mathsf{CVP}^{(p)}\)  
Given a lattice \(\mathcal{L}\) and a target vector \(\mathbf{t}\), let \(d\) denote the distance of the closest vector in \(\mathcal{L}\) to \(\mathbf{t}\). Just as in Section 3.2, we assume that we know the value of \(d\) within a factor of \(1+1/n\). We can get rid of this assumption by using [PERSON]'s [68] algorithm to guess the value of \(d\) within a factor of \(2^{n}\) and then run our algorithm for polynomially many values of \(d\). For \(\tau>0\), define the following \((n+1)-\)dimensional lattice \(\mathcal{L}^{\prime}\)  
\[\mathcal{L}^{\prime}=\mathcal{L}\Big{(}\{(\mathbf{v},0):\mathbf{v}\in \mathcal{L}\}\cup\{(\mathbf{t},\tau d/2)\}\Big{)}\.\]  
Let \(\mathbf{z}^{*}\in\mathcal{L}\) be the lattice vector closest to \(\mathbf{t}\). Then \(\mathbf{u}=(\mathbf{z}^{*}-\mathbf{t},-\tau d/2)\in\mathcal{L}^{\prime}\setminus (\mathcal{L}-k^{\prime}\mathbf{t},0)\) for some \(k^{\prime}\in\mathbb{Z}\). We sample \(N\) vector pairs \((\mathbf{e},\mathbf{y})\in B_{n}^{(p)}(\xi d)\times\mathcal{P}(\mathbf{B}^{ \prime})\) (8-12 of Algorithm 3), where \(\mathbf{B}^{\prime}=[(\mathbf{b}_{1},0),\dots,(\mathbf{b}_{n},0),(\mathbf{t}, \tau d/2)]\) is a basis for \(\mathcal{L}^{\prime}\). Next, we run a number of iterations of the sieving Algorithm 2 to obtain a number of vector pairs such that \(\|\mathbf{y}\|_{p}\leq R=\frac{\xi d}{1-\gamma}+o(1)\). Further details can be found in Algorithm 3. Note that in the algorithm, \(\mathbf{v}|_{|n|}\) is the \(n-\)dimensional vector \(\mathbf{v}^{\prime}\) obtained by restricting \(\mathbf{v}\) to the first \(n\) co-ordinates (with respect to the computational basis). From Lemma 7, we have seen that after \(\lceil\log_{\gamma}\left(\frac{\xi}{nR_{0}(1-\gamma)}\right)\rceil\) iterations (where \(R_{0}=n\cdot\max_{i}\|\mathbf{b}_{i}\|_{p}\)), \(R\leq\frac{\xi\gamma}{n(1-\gamma)}+\frac{\xi d}{1-\gamma}\Big{[}1-\frac{\xi} {nR_{0}(1-\gamma)}\Big{]}\). Thus, after the sieving iterations, the set \(S^{\prime}\) consists of vector pairs such that the corresponding lattice vector \(\mathbf{v}\) has \(\|\mathbf{v}\|_{p}\leq\frac{\xi d}{1-\gamma}+\xi d+c=\frac{\xi(2-\gamma)d}{1- \gamma}+o(1)\). Selecting \(\xi<\frac{(1-\gamma)\gamma}{2-\gamma}-o(1)\) ensures that our sieving algorithm does not return vectors from \((\mathcal{L},0)-(k^{\prime}\mathbf{t},k^{\prime}\tau d/2)\) for some \(k^{\prime}\) such that \(|k^{\prime}|\geq 2\). Then, every vector has \(\|\mathbf{v}\|_{p}<\tau d\), and so either \(\mathbf{v}=\pm(\mathbf{z}^{\prime}-\mathbf{t},0)\) or \(\mathbf{v}=\pm(\mathbf{z}-\mathbf{t},-\tau d/2)\) for some lattice vector \(\mathbf{z},\mathbf{z}^{\prime}\in\mathcal{L}\). With similar arguments as in [49] (using the tossing argument outlined in Section 3.2), we can conclude that with some non-zero probability we have at least one vector in \(\mathcal{L}^{\prime}\setminus(\mathcal{L}\pm\mathbf{t},0)\) after the sieving iterations. Thus, we obtain the following result.
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Software Design of the System
#### 3.6.1 Heuristic Description for Leaf and Router Nodes  
The heuristic of the node depends on its designation. A single node designated as leaf features short-range communication defined by its function and energy requirements. The standard function of the leaf node is to collect information coming from the sensors and deliver the data to the router node and go to sleep. The operation of the leaf node focuses on energy saving by following the steps of waking up, collecting data from sensors, pre-processing, packing data, transmitting information, and sleeping. These sets of actions ensure low power consumption. Figure 11 illustrates a flowchart of the sequence of procedures performed by the leaf node.  
A single node designated as a router features long-range communication defined by its function and energy requirements. In this case, the router does not sleep and consumes more power to keep active links to adjacent tower sites. Most of the time, the router node forwards data from the previous tower router node to the next tower router node. The ultimate destination of the collected sensor information is the central server behind the border node gateway.  
The main idea of the current framework design considers the nodes' arrangement in a mesh network topology operating in a multi-hop packet routing, according to the topology seen in Figure 4. Then, the router's primary function is to concentrate the collected sensor data from leaf nodes and transfer information to the next tower's router node, having the border node at the sub-station as an end point. In this case, it is better to understand a router node having a dual function. First, it acts by passing information from other nodes and, second, passing its information by working as a leaf node. Thus, in router node mode, it waits for leaf nodes to send data from sensors. Then, it arranges data from leaf nodes, determines the destination address, and sends all leaf nodes' data to the router node of the next tower. Furthermore, when the node acts as a leaf, it aggregates the received data and the information from the current site leaf nodes and passes it forward.
## 2 Background
### MRAM Technology
#### 2.3.3 Sot MRAM  
SOT MRAM is a promising technology that can potentially replace STT MRAM. In SOT MRAM, programming cycles involve applying electric currents parallel to the magnetic tunnel junction [27]. This programming scheme is different from STT MRAM and is considered gentle, which improves the reliability of read cycles and reduces the likelihood of \"read disturbs\" occurring.  
Additionally, SOT switching in SOT MRAM holds promise for enhancing the speed and endurance of MRAM technology while lowering power [28]. However, it is important to note that SOT MRAM is still in the developmental stage and not yet ready for large-scale production. Intensive research programs are currently in progress to address challenges such as reducing bit error rates and decreasing the size of the memory cell. These efforts aim to make SOT MRAM a viable and commercially available memory technology in the future.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results
### FTP Download Response Time  
The download response time, which is the time to download the file over FTP client, is verified in two ways. First, the duration is calculated based on the theoretical throughput (see Equation (11)). The expected response time for the scenario \(p_{drop}=0\%\) is the size of the transferred file \(s\) divided by the theoretical throughput \(T_{TCP}\).  
\[t_{download}=\frac{s}{T_{TCP}} \tag{12}\]  
\[t_{download}=\frac{10,000,000}{219,000}=45.66\;\mathrm{s} \tag{13}\]  
This does not correlate very well with the 60.21 s obtained with OPNET. Furthermore, it also does not correlate well with the graphs in Figures 3, 5 and 8. They all suggest that traffic gets sent from around \(t=10\) to \(t=60\), which would mean roughly 50 s for the scenario \(p_{drop}=0\%\).  
Second, a small, virtualized network was setup to verify the download response time with an experiment. The setup consists of two Linux hosts and a Linux router. The Linux router runs netem (part of the Linux kernel for network emulation) to introduce a 20 milli-seconds delay in each direction and a certain drop probability \(p_{drop}\). The Linux router was configured with the commands as shown in Figure 9.  
Figure 9: Linux router configuration commands.  
\begin{table}
\begin{tabular}{c c c} \hline
**Scenario** & **Response Time (s)** & **Difference from \(p_{drop}\) = 0\%** \\ \hline \(p_{drop}=0.00\%\) & 50.25 & 0\% \\ \hline \(p_{drop}=0.01\%\) & 50.25 & 0\% \\ \hline \(p_{drop}=0.10\%\) & 51.97 & 3.42\% \\ \hline \(p_{drop}=1.00\%\) & 75.84 & 50.65\% \\ \hline \(p_{drop}=10.00\%\) & 259.665 & 416.75 \\ \hline \end{tabular}
\end{table}
Table 8: Download Response Time in Virtualized Network Environment.