esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Datasets  
We evaluate the proposed approach on four public available datasets: YouTube Instrumental ([[https://www.di.ens.fr/willow/research/instructionvideos](https://www.di.ens.fr/willow/research/instructionvideos)]([https://www.di.ens.fr/willow/research/instructionvideos](https://www.di.ens.fr/willow/research/instructionvideos)), accessed on 12 January 2021), YouCook2 ([[http://youcook2.eecs.umich.edu](http://youcook2.eecs.umich.edu)]([http://youcook2.eecs.umich.edu](http://youcook2.eecs.umich.edu)), accessed on 12 January 2021), Breakfast ([[https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset](https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset)]([https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset](https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset)), accessed on 12 January 2021), and 50 Salads ([[http://cvip.computing.dundee.ac.uk/datasets/foodpreparation/50](http://cvip.computing.dundee.ac.uk/datasets/foodpreparation/50) salads]([http://cvip.computing.dundee.ac.uk/datasets/foodpreparation/50](http://cvip.computing.dundee.ac.uk/datasets/foodpreparation/50) salads), accessed on 12 January 2021).  
YouTube Instructions [24] contains 150 samples from YouTube on five tasks: making coffee, changing a car tire, CPR, jumping a car, and potting a plant, with approximately two minutes per sample.  
YouCook2 [25] contains about \(2k\) samples from YouTube on 90 cooking recipes, with approximately 3\(\sim\)15 steps per recipe class, where each step is a temporally aligned narration collected from paid human workers.  
Breakfast[38] contains about \(2k\) samples on ten common kitchen tasks with approximately eight steps per task. The average length of each task varies from 30 s to 5 min.  
50 Salads[18] contains 4.5 h of 25 people preparing 2 mixed salads each, with approximately 10k frames per sample. Each sub-action corresponds to two levels of granularity, and each low-level granularity is further divided into pre-, core-, and post-phase.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 5 Conclusions  
To address the shortcomings of traditional remote sensing image retrieval algorithms such as insufficient semantic information acquisition and low retrieval accuracy, this research proposed a one-shot object detection algorithm for remote sensing images based on Siamese networks. Therefore, a scene-graph-based remote sensing image retrieval algorithm was developed. We created the RSSG dataset, comprising 929 remote sensing images, and employed a variety of scene graph construction strategies to create a corresponding scene graph for each image. Experimental evaluations performed on the DIOR dataset and our custom RSSG dataset demonstrated significant improvements in precision and recall compared to other algorithms, verifying the effectiveness of the developed approach.  
Our method developed scene graphs using the node attributes and positional information extracted from object detection outputs, which were then used to represent the similarity among the original remote sensing images based on scene graph similarity. Fundamentally, this approach constituted a semantic-based image retrieval method. However, this process might overlook certain image details of individual targets, such as their color and shape. Therefore, our future work aims to integrate both the semantic information of individual target objects and the global positional information of remote sensing images,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Recall@1** & **Recall@5** & **Recall@10** \\ \hline AMFMN & 6.9 & 12.8 & 25.7 \\ \hline MiLaN & 5.1 & 10.3 & 22.4 \\ \hline AHCL & 8.1 & 14.4 & 28.5 \\ \hline SGSRSIIR & 9.4 & 16.5 & 30.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the comparison experiment on Recall (%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Precision@1** & **Precision@5** & **Precision@10** \\ \hline AMFMN & 27.6 & 10.2 & 10.3 \\ \hline MiLaN & 20.4 & 8.2 & 9 \\ \hline AHCL & 32.4 & 11.5 & 11.4 \\ \hline SGSRSIIR & 37.6 & 13.2 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the comparison experiment on Precision (%).  
while reintroducing image features specific to individual targets. This integration will facilitate similarity calculations based on fused semantic and image features.  
Conceptualization, Y.R. and Z.Z.; methodology, J.J. and Y.J.; software, Y.Y.; validation, Y.R., D.L. and K.C.; formal analysis, Z.Z., K.C., G.Y. and Y.R.; investigation, Y.J.; resources, Y.Y.; data curation, Y.J. and D.L.; writing--original draft preparation, Y.R.; writing--review and editing, Z.Z. and J.J.; visualization, D.L.; supervision, Y.Y.; project administration, G.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant numbers 62137001 and 62272093.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 3 Data Processing Schemes
### Target Positioning
Compared to DCTTP (a one-dimensional data processing method), the bitmap process in CTCI is a two-dimensional data processing method. Thus, the sampling points after the bitmap process will have several combinations the for \(x\) and \(y\) coordinates. From Figure 10a, we observe three basic combinations of the sampling points: (1) (0,0) means both the values of the \(x\) and \(y\) coordinates are approximated to the average value at the same time; (2) (1,0) or (0,1) means a value of the \(x\) or \(y\) coordinate is approximated to the average value, but the other one is not; (3) (1,1) means both values are not represented by the average value. The minus \"\(-\)\" means that the value of the \(x\) or \(y\) coordinate is smaller than the average value. Thus, the sample region is divided into nine parts by the bitmap process. In Figure 10b,c, the distributions of the original and compressed sampling points show the distortion of the \(x\) and \(y\) coordinates, respectively. The data within the compressing bound are represented to the average value, which causes the distortion. Thus, there is a trade-off between the energy consumption and the extent of distortion. Therefore, each sub-cluster member transmits the compressed data, including (1) the average value and the standard deviation of the original sample points and (2) the differences between the sample points and the average value, to the leader node. Afterwards, the leader may transmit the position estimate to the base station through clusterheads and gateways in a multi-hop manner or directly disseminate the data from the clusterhead to the base station. In the leader node, the processed data are decompressed, and the external contour of the sample region is depicted by recovering the original sample points associated with (1,1), (\(-\)1,1), (\(-\)1,\(-\)1), (\(-\)1,\(-\)1) representations as the results for data processing. The meaning of the result is shown in Figure 11, which means that the result of the target positioning is not only just a position estimation, but also a similar area of the original region for sampling. After obtaining the distribution of the position estimate, each tasking sensor sends the mean and variance of the distribution to the tasking leader.
# Chinese Named Entity Recognition Model Based on Multi-Task Learning
###### Abstract  
Compared to English, Chinese named entity recognition has lower performance due to the greater ambiguity in entity boundaries in Chinese text, making boundary prediction more difficult. While traditional models have attempted to enhance the definition of Chinese entity boundaries by incorporating external features such as lexicons or glyphs, they have rarely disentangled the entity boundary prediction problem for separate study. In order to leverage entity boundary information, the named entity recognition task has been decomposed into two subtasks: boundary annotation and type annotation, and a multi-task learning network (MTL-BERT) has been proposed that combines a bidirectional encoder (BERT) model. This network performs joint encoding and specific decoding of the subtasks, enhancing the model's feature extraction abilities by reinforcing the feature associations between subtasks. Multiple sets of experiments conducted on Weibo NER, MSRA, and OntoNote4.0 public datasets show that the F1 values of MTL-BERT reach 73.8%, 96.5%, and 86.7%, respectively, effectively improving the performance and efficiency of Chinese named entity recognition tasks.  
multi-task learning; Chinese named entity recognition; joint learning; feature interaction; bi-directional encoder (BERT) +
Footnote †: journal: Applied Sciences
## 4 Experiment Simulation and Discussion
### Algorithm Performance Comparison and Discussion  
Simulations have been carried out in three different size scenarios. Genetic algorithm (GA) and stochastic algorithm are used as a comparison to compare algorithm stability and efficiency. The population size of the genetic algorithm is set to 20, the maximum number of iterations is set to 100, and the crossover probability and mutation probability are adaptive.The crossover operator uses two-point crossover, and the mutation operator uses single-point mutation. The three methods performed 20 experiments on each experimental case.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Size** & **Number of GS** & **Number of Satellite** & **Days** \\ \hline Small & 5 & 10 & 10 \\ Medium & 10 & 15 & 10 \\ Large & 15 & 20 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Original benchmark.  
In the small scenario experimental case, it can be seen from Figure 6 that GA has the strongest stability, and stable results can be obtained in all 15 experimental cases. The stochastic algorithm has the worst stability, with a profit difference of more than 100 in multiple experimental cases (S4, S8, S9, S11), and the average number of outliers is twice that of SATLD. In these experimental cases, the difference in SATLD profit is within 80. The average profit of SATLD is comparable to GA on multiple experimental cases (S2, S3, S5, S6, S10, S12, S13, S14), and it is better than GA on S11. In each experimental case in Figure 7, the average time consumption of GA and SATLD differs by an order of magnitude.  
In the medium scenario experimental case (Figures 8 and 9), the stability of SATLD is improved compared to the small scenario experimental case, and it is only worse than GA in one-third of the cases (S3, S4, S5, S11, S12). In the large scenario experimental case (Figure 10), SATLD is almost identical to GA. As the problem size increases, from the average time consumption (Figures 7, 9 and 11), the time consumption of GA increases in second order, while the SATLD growth rate is an order of magnitude lower than GA, from the profit and stability point of view SATLD gradually approaching GA.  
The experimental results show that the SATLD algorithm proposed in this paper is a method of balancing profit and efficiency. It achieves the same level of profit as GA with an average time consumption that is an order of magnitude lower than GA, and even outperforms GA in some experimental cases. This efficiency improvement makes it more suitable for running on satellites where hardware performance is insufficient. Compared with the random algorithm with less time consumption, SATLD has stronger stability, which can ensure that there is no large difference between the pros and cons of the planning results of the random algorithm during the running process.  
Figure 6: Distribution in small scenario.  
Figure 8: Distribution in medium scenario.  
Figure 7: Average time consumption in small scenario.  
Figure 10: Distribution in large scenario.  
Figure 9: Average time consumption in medium scenario.
## 1 Introduction  
From consumer gadgets to electric vehicles (EVs), lithium-ion batteries have evolved into a pillar of modern energy storage technology, supporting a broad spectrum of uses. Among them, the very long cycle life, high energy density, and great dependability of the nickel-manganese-cobalt (NMC) and lithium cobalt oxide (LCO) chemistries of 18650 cylindrical batteries make them especially important for EV uses. Like other batteries, lithium-ion cells fade with time, so accurate prediction of their RUL becomes quite important. Forecasting the RUL of lithium-ion batteries guarantees effective use, improves safety, and lowers running and maintenance costs, all of which help to sustainably deploy electric vehicles. Factors including charge/discharge mechanisms, temperature, and voltage interact complexly to affect the degradation process of lithium-ion batteries. Conventional approaches to RUL prediction can depend on empirical models that find it difficult to fit several operating environments. Characterized by calendar aging and cycle aging, the aging phenomena raise operating expenses, compromise equipment lifetime and safety, and compromise operating expenses. Techniques like machine learning (ML) present a good path to solving these problems. ML helps to build predictive models that go beyond the constraints of conventional methods by using data-driven approaches, therefore avoiding the requirement for intrusive disassembly of battery cells. Moreover, ML models can make accurate and flexible RUL projections by using big datasets. Still, the application of ML in real-time for user-friendly EV battery monitoring is restricted. The goals of this research are as follows:  
To build and evaluate machine learning models for predicting the RUL of lithium-ion batteries.  
To highlight key factors influencing battery degradation and incorporate them into the predictive models.  
To design a web-based application enabling users to input battery data and obtain RUL predictions.  
To provide actionable insights into optimizing battery usage based on predicted RUL.  
This study focuses on NMC-LCO 18650 cells and leverages controlled laboratory datasets for model training and validation. While the web-based application is presented as a prototype, it provides a foundation for future advancements in commercial deployment. By addressing the challenges of RUL forecasting of advanced ML techniques and practical algorithm design, this research promotes the broader goals of energy efficiency, environmental conservation, and sustainable EV adoption.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 2 Principle and Requirements  
### Principle of Accelerometer  
A common accelerometer comprises a spring, a damper, a seismic mass, and a displacement sensor arranged within a housing attached to a base [8], as shown in Figure 1. In operation, the base is mounted on the vibrating structure to be measured, and the relative displacement between the seismic mass and the base is recorded by the displacement sensor.  
Following [PERSON]'s second law, the force acting on the seismic mass m can be expressed as  
\[ma=-k(x_{m}-x_{b})-c(\dot{x}_{m}-\dot{x}_{b}), \tag{1}\]  
where \(x_{m}\) is the displacement of the seismic mass, \(x_{b}\) is the displacement of the base, \(a\) is the acceleration to be detected, \(k\) is the elastic coefficient of spring, and \(c\) is the damper coefficient of the accelerometer. The relative displacement \(z\) between the seismic mass and the base can be expressed as  
\[z(t)=x_{m}(t)-x_{b}(t). \tag{2}\]  
Assuming the detected vibration is a simple harmonic vibration, we have \(x_{b}(t)=X_{b}\) cos(\(\omega_{b}t\)), where \(\omega_{b}\) is the frequency of vibration. Bring \(x_{b}(t)\) and \(z(t)\) into Equation (1):  
\[m\ddot{z}+c\dot{z}+kz=m\omega_{b}^{2}X_{b}\cos\omega_{b}t. \tag{3}\]  
Solve Equation (3) and obtain:  
\[z(t)=\bigg{[}1\bigg{/}\bigg{(}\sqrt{\left(\omega_{n}^{2}-\omega_{b}^{2}\right) ^{2}+\left(2\xi\omega_{n}\omega_{b}\right)^{2}}\bigg{)}\bigg{]}*\big{[}\omega_ {b}^{2}X_{b}\cos(\omega_{b}t-\varphi)\bigg{]}, \tag{4}\]  
where \(\omega_{n}=(k/m)^{1/2}\) is the resonant frequency of the accelerometer, \(\xi=c/2(m\omega_{n})\) is the damping ratio of the accelerometer, and \(\varphi\) can be expressed as  
\[\varphi=\tan^{-1}\bigg{[}\left(2\xi(\omega_{b}/\omega_{n})\right)\bigg{/} \Big{(}1-\left(\omega_{b}/\omega_{n}\right)^{2}\Big{)}\bigg{]}. \tag{5}\]  
Figure 1: Typical accelerometer structure diagram.  
Additionally, the acceleration caused by vibration is  
\[\ddot{x}_{b}(t)=-\omega_{b}^{2}X_{b}\cos(\omega_{b}t). \tag{6}\]  
Equation (4) can be rewritten as  
\[\omega_{n}^{2}z(t)=\left[1\middle/\left(\sqrt{\left(1-\left(\omega_{b}^{2}/ \omega_{n}^{2}\right)^{2}+\left(2\xi\omega_{b}/\omega_{n}\right)^{2}\right)} \right]\right.\ast\left[\omega_{b}^{2}X_{b}\cos(\omega_{b}t-\varphi)\right] \tag{7}\]  
Order  
\[H(\omega_{b}/\omega_{n})=1\middle/\left(\sqrt{\left(1-\left(\omega_{b}^{2}/ \omega_{n}^{2}\right)^{2}+\left(2\xi\omega_{b}/\omega_{n}\right)^{2}}\right). \tag{8}\]  
When \(\omega_{b}\ll\omega_{n}\), the \(H(\omega_{b}/\omega_{n})\approx 1\) and \(\varphi\approx 0\). Then the acceleration value \(\ddot{x}_{b}\) can be calculated as  
\[\ddot{x}_{b}\approx\omega_{n}^{2}z(t). \tag{9}\]  
As can be seen from Equation (9), the mechanical sensitivity of the accelerometer can be approximated as the inverse of the square of its resonant frequency, so the higher the resonant frequency, the lower the mechanical sensitivity. Therefore, the low frequency accelerometer needs to have a lower resonant frequency. Additionally, the use of high-sensitivity displacement sensors can also improve the sensitivity of accelerometers.
# Fault Tolerant DHT-Based Routing in MANET
## 4 Fault Tolerant DHT Network (FTDN)
### CASE-II: Partition Detection  
If two adjacent critical nodes u and v in the network (i.e., logical tree T(r) rooted at a node r with the lowest LID in the network) do not hear each other for a specific time interval (i.e., Partition_Timer), a Partition_Event is triggered. In this case, the leaving neighboring node must be k-hop critical (i.e., k-hop neighbors of the leaving node u remains unreachable to node v and its neighbors N(v) if node u moves/fails) and causes network partition, i.e., there exist two distinct instances of the logical tree. The leaving node u and its siblings share a common prefix and form a connected sub-tree rooted at u, i.e., T(u) by key invariant property. Node v remains in the tree rooted T(r), where nodes in T(u) invoke the root election routine and select an appropriate root node, i.e., with the lowest LID. Thus, two valid instances of the logical tree (LS) with different roots exist. Algorithm 4 timely detect the partition event and ensures the existence of valid disjoint sub-trees. (line: 1-3): if two critical nodes with similar roots do not hear each other and the partition timer expires the partition event triggers. (lines: 4-5: nodes recover and reuse the LS by changing the roots).  
Figure 4: Physical vs. logical network.  
```
1:for each (u,v) \(\in\) E do
2:if (root[u]==root[v]) then
3:if ((N[u] \(\cap\) (N[v]\(\setminus\)u,v) == {}) \(\wedge\)Partiti_Timer_Expire == true) then
4: root[u]=u
5: v.purge[u]
6:endif
7:endif
8:endfor
```  
**Algorithm 4** Partition Detection
## 4 Simulation and Evaluation
### Choice of \(E_{b}/N_{0}\) Values  
Figure 4 shows the BER performance of the KDCD over the AWGN and downlink Rayleigh fading channel after it was trained using each of the aforementioned scenarios. The simulation findings show that BER performances vary under different training scenarios. The reasons for this are as follows.  
* S1: The chosen \(E_{b}/N_{0}\) values for training are very low, resulting in excessive noise interference in signal transmission, and thereby reducing the effectiveness of training.
* S2: The network model is overfitted as a result of the \(E_{b}/N_{0}\) values being chosen as excessively high, which decreases the BER performance of the KDCD at low \(E_{b}/N_{0}\) values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type/Stride** & **Filter Shape** & **Input Size** \\ \hline Conv/s2 & \(3\times 3\times 1\times 8\) & \(56\times 56\times 1\) \\ \hline Conv1 dw/s1 & \(3\times 3\times 8\) dw & \(28\times 28\times 8\) \\ \hline Conv1/s1 & \(1\times 1\times 8\times 8\) & \(28\times 28\times 8\) \\ \hline Conv2 dw/s2 & \(3\times 3\times 8\) dw & \(28\times 28\times 8\) \\ \hline Conv2/s1 & \(1\times 1\times 8\times 16\) & \(14\times 14\times 8\) \\ \hline Conv3 dw/s1 & \(3\times 3\times 16\) dw & \(14\times 14\times 16\) \\ \hline Conv3/s1 & \(1\times 1\times 16\times 16\) & \(14\times 14\times 16\) \\ \hline Conv4 dw/s2 & \(3\times 3\times 16\) dw & \(14\times 14\times 16\) \\ \hline Conv4/s1 & \(1\times 1\times 16\times 56\) & \(7\times 7\times 16\) \\ \hline Avg Pool/s1 & Pool \(7\times 7\) & \(7\times 7\times 56\) \\ \hline FC/s1 & \(56\times 4096\) & \(1\times 1\times 56\) \\ \hline Softmax/s1 & Classifier & \(1\times 1\times 4096\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure of modified version of the MobileNet model.  
S3: It is not able to prevent the detrimental effects of very low and excessively high \(E_{b}/N_{0}\) values on training, which leads to a suboptimal BER performance, even though a wide range of \(E_{b}/N_{0}\) values are chosen for training.
* S4: The best BER performance is achieved by choosing the appropriate range of \(E_{b}/N_{0}\) values that prevents overfitting in the network model and enhances training efficacy.  
When compared with the other scenarios, S4 is the optimum training strategy. Therefore, the noise \(\mathbf{n}\) for training is randomly generated with different values of \(E_{b}/N_{0}\): 8, 10, 12, 14, and 16 in the rest of this work.
## 2 Materials and Methods
### Characterization of MNPs  
The Synthesized unmodified and modified MNPs were characterized by using SEM and energy-dispersive X-Ray Spectroscopy-SEM (EDX)The different samples of MNPs were attached to the stub and subjected to SEM imaging. Images of samples were made on a MIRA 3 SEM from the TESCAN company (TESCAN Ltd., Brno, Czech Republic, EU). The used detectors have In-Beam secondary electrons (SE) with an accelerating voltage of 15 kV. The working distance was set to 3 mm and ultra-high resolution was used as a scan mode. The measurement was performed in a high vacuum. Elemental analysis was done on the energy-dispersive x-ray spectroscopy (EDX) detector X-MAX 50 (Oxford instruments plc, Abingdon, UK) with high vacuum conditions and an accelerating voltage of 15 kV. A silicon composite stub was used for the elemental analysis of UN-MNPs and MNPs-CH, whereas an aluminum composite stub was used for the MNPs-TEOS. The working distance was 15 mm and an external SE detector was used. The input energy for the UN-MNPs was set to 20,000-25,000 cts, the output energy was set to about 10,000-11,000 cts, and dead time fluctuated between 50-54%. The input energy for the modified MNPs was set to 11,000-13,000 cts, the output energy was set to about 7700-8500 cts, and dead time fluctuated between 30-36%. The time for each analysis was 20 min, whereas the spot size was approximately 75 nm. The concentration of Fe in the synthesized MNPs was determined by atomic absorption spectroscopy (AAS) 280Z AA (Agilent Technologies, Santa Clara, CA, USA) with electrothermal atomization, using Zeeman background correction. Moreover, inductively coupled plasma mass spectroscopy (ICP-MS) (Agilent Technologies, Tokyo, Japan), was also applied for the determination of concentrations.