esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
It would be possible to obtain a somewhat generalized solution trained for different scenarios starting with a common base that considers this feature differentiation type [52]. These data-handling strategies could make it possible to obtain a real-time prediction which is the next big challenge for this research area. The high-dimensionality problem may be solved using data processing techniques to derive relevant features through methods, such as clustering, chi-square, Minimum-Redundancy-Maximum-Relevance (mRMR), and predictor importance, among others. Some authors, such as [28], have worked on this strategy for dimensionality reduction using clustering, but other pre-processing techniques could also be tested. Regarding algorithms, we were able to identify two stages for which machine learning algorithms were assigned. The pre-processing stage includes the tasks of ranking and selecting features, while the classification stage includes the selection of the model. For pre-processing, the most common algorithm is Random Forest; and, for classification, the most common algorithms are some variants of Neural Networks (Long Short-Term Memory NN, Convolutional NN, Deep NN, and Feed Forward NN). This algorithm selection is consistent with the fact that deep learning models applied in the area of Traffic Accident Prediction are becoming more popular. Most authors use shallow learning algorithms as baseline algorithms to compare the performance of their models based on neural networks. This tendency marks a path for research in learning-based accident prediction. The metrics more commonly used for classification problems are accuracy, sensitivity, and F1 Score; meanwhile, for regression problems, Mean Absolute Error and Root Mean-Square Error. However, there is such a diversity of experimental design, data volume, and structure used in the various studies that it is difficult to compare results using simply evaluation metrics. Not to mention that some proposals present non-normalized values for their evaluation metrics. The datasets are typically unbalanced, and performance must be understood in a contextualized way. Therefore, to compare models to find the one with the best performance is not necessarily real because the results are not completely comparable among the studies. Although there is no precise rule to split data for training, validation, and testing, a tacit agreement establishes an approximate data split configuration. From the analysis, we could establish that a higher percentage (more than 50%) of data are used for training and a lower percentage (less than 50%) for testing. The most common data split configuration among proposals is 80% for training and 20% for testing. Some models even establish a low percentage of data for validation. It was noted that there is no evidence or justification for splitting data in one way or the other or whether such a data split configuration could improve the performance of the models. Because of this drawback, we could suggest using data splitting methods (e.g., SPlit [53]) instead of splitting randomly to obtain the optimal configuration.
## 4 Proposed Methodology: Dynamic Context-Aware Transformer (DCAT)
### Operational Workflow  
DCAT is an innovative model designed to address the complexities of image captioning, particularly for low-resource languages like Tamil. It integrates state-of-the-art components in an architecture that facilitates efficient and contextually aware image captioning. DCAT's workflow is streamlined into several key phases, each contributing to the end goal of generating an image caption:  
1. **Image Processing:** The input image is processed by the ViT encoder, where it is split into patches and transformed into a sequence of embeddings.
2. **Context Embedding:** The encoded features from ViT are passed to the Context Embedding Layer, where they are transformed into a context-rich format suitable for text generation.
3. **Caption Generation:** These context-enriched features are then fed into the GPT-3 decoder, which generates the final caption. GPT-3 utilizes its language modeling capabilities to ensure that the caption is relevant and coherent.
4. **Iterative Refinement:** The model may employ an iterative process, where partial captions are incrementally refined to improve coherence and contextual alignment.  
Through this sophisticated architecture, DCAT not only processes visual data efficiently but also ensures that the generated captions are infused with a deep understanding of cultural and contextual nuances. This makes DCAT a groundbreaking model in the field of image captioning, especially for Tamil and other low-resource languages, setting new standards for accuracy and contextual relevance.
## 1 Introduction
### The Motivation of Hybrid Spatiotemporal Encoding  
The second challenge lies in the simultaneous encoding of low-level fine-grained spatiotemporal features together with high-level long-range temporal patterns. Appearance information, which can be regarded as visual features presented by static composing frames without taking temporal order into account, also serves as vital to the start and finish boundary indicators of actions (e.g., 'cutting a tomato' is often only subtly different from 'peeling a cucumber' spatially, such as the food types 'tomatoes' or 'cucumbers' and food states'sliced', 'diced', 'peeled', and so on).  
We experiment with three deep neural network models for encoding. In each case the encoder consists of two modules (or sub-networks): a spatiotemporal visual module Table 2 encoding per frame into one feature vector, and a temporal module Table 3 encoding the sequence of per-frame feature vectors into a sequence of sub-action labels. The visual module is common across the three models; they only differ in the temporal module (Figure 2).
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
###### Abstract  
Privacy must be preserved when working with client data in machine learning. Federated learning (FL) provides a way to preserve user data privacy by aggregating locally trained models without sharing the user data. Still, the privacy of user identity is not preserved. Secure aggregation is a popular technique in FL for aggregating gradients without disclosing individual data. However, it is costly and inaccurate. Therefore, we propose a novel, scalable, cost-effective group signature-based secure aggregation algorithm in FL, called GSFedSec, where secure aggregation helps conceal the user's update while the group signature helps conceal their identity. Our algorithm preserves the data and their source. Our simulation results show that the proposed algorithm does not suffer from a loss of accuracy, handles increases in network size competently, offers computational and communication efficiency, and is secure against various security attacks. We have compared the results of efficiency and security against existing algorithms in FL. Also, the security of the algorithm is verified using Burrows-Abadi-Needham (BAN) logic and simulated via the Automated Validation of Internet Security Protocols and Applications (AVISPA) protocol.  
Keywords:data privacy; user privacy; federated learning; secure aggregation; efficiency; group signature; AVISPA +
Footnote †: journal: Applied sciences
# Comprehensive Evaluation of Conveyor Belt Impact Resistance Using Canonical Correlation Analysis
## 2 Materials and Methods
### Result of Experimental Research  
The result of the experiment is described by several parameters: rebound height, impact force, tension force, relative amount of absorbed energy, and degree of damage to the conveyor belt after the impact of the hammer. During the test itself, the rebound height, the amount of the tension, and the impact forces are measured using two strain gauge sensors. The measured values are recorded by PP065 electronics, which provide control of the test rig and recording of measurement results.  
The rebound height (m) is the height to which the hammer with the impactor bounces after each rebound from the belt until this movement is dampened.  
Impact and tension force  
The impact force (kN) is the force exerted by the hammer impactor on the conveyor belt. The tension force (kN) is adjusted at the beginning of the measurement based on the strength of the belt sample, according to the manufacturer's recommendations (1/10 of the belt strength per mm width). The amount of tension force s changes during the measurement when the hammer impactor comes into contact with the belt.  
Based on the rebound height after the first rebound, we observed the parameter relative amount of absorbed energy. This is determined as the relative amount of energy absorbed by the conveyor belt after the first impact of the material on the belt sample (Figure 4).  
The relative amount of energy absorbed after the first impact is defined as the following ratio:  
\[E_{\text{{{\it{realt}}}}}=\frac{{{E_{p1}}-{E_{p2}}}}{{{E_{p1}}}}=\frac{{{mgh_{1 }}-{mgh_{2}}}}{{mgh_{1}}}=\frac{{h_{1}}-{h_{2}}}{{h_{1}}}, \tag{1}\]  
where \(E_{p1}\) is the potential energy of the hammer before impact (at impact height \(h_{1}\)) and \(E_{p2}\) is the potential energy of the hammer after rebounding from the conveyor belt (at the rebound height \(h_{2}\)). \(m\) is the weight of the hammer and \(g\) is gravitational acceleration.  
Determining the degree of damage consists of the visual inspection of the conveyor belt after the test. The variable damage represents the severity of damage to the composite at the point of impact of the material and describes two main categories of damage: non-serious damage and serious damage (Table 1). A detailed classification of damage is in article [37].  
Figure 4: The relative amount of energy absorbed [36].  
Damage 0 and Damage 1 define the degree of damage that, in the case of the practical use of the conveyor belt, would not cause it to be immediately taken out of service. On the other hand, Damage 2 and Damage 3 (serious damage) are defined as damage that would cause a conveyor belt to be taken out of service. Examples of possible damage to the test specimens are shown in Figure 5.
# Dynamic Monitoring of the Water Flowing Fractured Zone during the Mining Process under a River
## 2 Survey Area
### Failure Characteristics of the Overburden Strata  
The three zones of the overlying strata are shown in Figure 3. The caved zone is located in the lowest part of the overlying strata. There is ample space between the rocks in this zone, which leads to strong connectivity. Water and sand can burst into the longwall panel through this tunnel. The fractured zone is located above the caving zone. Although the lithology changes little compared  
Figure 1: Location of the survey area.  
Figure 2: Geological log profile.  
to the original strata, the fractures that generally exist in this zone promote the development of the water-conducting channel through which water can move downward to the goat. The WFFZ is composed of the caved zone and the fractured zone. The bending zone refers to the strata between the top of the fractured zone and the surface. There are few fractures in this zone, and the water conductivity remains unchanged. Sometimes, the fractured zone can develop up to the surface when mining the shallow-buried longwall face. In this case, the overlying strata can be divided into two zones: the caved zone and the fractured zone.  
During underground longwall mining, when the gob reaches a certain size, strata movement will reach the surface and form the subsidence basin. The scope of the subsidence area is larger than that of the gob. In other words, surface subsidence occurs in front of the longwall face under the influence of mining. The rock fractures of the overlying strata are developed in advance. This phenomenon is known as the advanced influence. Zone A is the fractured zone developed ahead of the working face, as illustrated in Figure 3. The advanced distance (\(l\)) is defined as the horizontal distance between the working face and the point where the surface begins to move (with a surface subsidence of 10 mm). The angle of advanced influence (\(\delta\), also known as the major influence angle) is related to the advanced distance and mining depth (\(h\)), and their relationship is as follows [18]:  
\[\delta=\tan^{-1}\frac{h}{l} \tag{1}\]  
For the longwall face beneath the aquifer, the key to safe mining lies in the determination of whether the WFFZ can connect to the aquifer. Because the unconfined aquifer under the river has strong lateral recharge capacity, and the water conductivity of WFFZ is greatly enhanced compared to the original strata, the connection of the WFFZ and aquifer will lead to the downward flow of water. This will greatly increase the risk of water intrusion accidents. Therefore, it has great significance to accurately determine the height of the WFFZ to ensure mining safety of 8101 longwall face.
# Multi-Location Assortment Optimization with Drone and Human Courier Joint Delivery
## 5 Numerical Study
### Effectiveness of MILP and Conic + MC  
In this subsection, we test the effectiveness of MILP and Conic + MC. First, we set \(d_{ik}=d_{ki},i\in M,k\in M\) from a uniform U[1, 20] distribution and \(d_{ii}=0,i\in M\). The \(D_{max}\) and \(D_{min}\) are 6 and 3, the weight of product \(w_{j},j\in N\) and marginal revenue \(r^{j}_{i}=r_{j},i\in M,j\in N\) from a uniform U[1, 5] distribution, respectively. The load capacity of a drone \(W\) is 3. To produce the arrival rate \(\theta_{i}\) of online store \(i,i\in M\), we generate \(\alpha_{i}\) from the uniform distribution over \([0,1]\) for all \(i=1,\ldots,m\). Then, we set \(\theta_{i}=\alpha_{i}/\sum_{i=1}^{m}(\alpha_{i})\).  
In Table 3, we vary five different parameters, which are the number of stores \(M\), varying from one of these possible values: \(\{10,15,20,25,30\}\); and the number of products \(N\), varying from one of these possible values: \(\{100,120,140,160,180,200\}\). We set the cardinality constraints \(c_{i},i\in M\) of the online stores from the uniform distribution whose ranges are from \(\{[4,6],[6,8],[8,10],[10,12],[12,14],[14,16]\}\); customers' no-purchase preference on product \(u^{0}_{i}\) vary from these values: \(\{10,20,30,40,50\}\); and purchase preference \(u^{j}_{ik}\) vary from the uniform distribution whose ranges are from \(\{[0,1],[1,2],[2,3],[3,4],[4,5]\}\). Therefore, we have a total of 27 scenarios. We use MILP (19)-(36) and Conic + MC (38)-(64) to obtain the optimal valve and running 20 times, respectively. The gap equal to \((Value^{Conic+MC}-Value^{MILP})/Value^{Conic+MC}\). We set the time limit as 600s and show the effectiveness of MILP and Conic + MC in Table 3. From Table 3, we could safely conclude that Conic + MC performs better than MILP in these parameter settings. MILP can hardly finish computing in 600 s, even though the problem is easy to solve in Table 3 and Conic + MC solved the problem within 300 s. We can find that the optimal values between these two methods are roughly the same, but the Conic + MC that we propose for this MADH problem can obtain the optimal value much faster. The effectiveness of Conic + MC also shows that when the \(M\), \(N\), \(c_{i}\) is bigger, the calculating time of Conic + MC is longer, and the calculation time is more sensitive to \(c_{i}\) and \(M\) than \(N\). When we change the \(u_{i}^{0}\) and \(u_{ik}^{j}\), the calculating time of Conic + MC is shorter when the \(u_{i}^{0}\) becomes larger, but the calculating time increases first and then decreases with the increase of \(u_{ik}^{j}\).
## 3 Experiment & Results
Finally, we improve the two-photon transition by adjusting the repetition frequency to corresponding S-TPT resonance frequency. The PMT signal is delivered into a lock-in amplifier to generate an error signal. The error signal is fed back to tune the PZT and pump source to lock center scanning frequency of \(f_{rep}\) to corresponding 5S\({}_{1/2}\)(F = 2)-5P\({}_{3/2}\)(F = 3)-5D\({}_{5/2}\)(F = 4) S-TPT resonance frequency. Next, we fine scan \(f_{rep}\) to get Rb TPT signals. Direct frequency comb spectroscopy of cold Rb TPTs is shown in Figure 6. The peak fluorescence intensity detected through PMT at the optimization spectrum in Figure 6 is about 1189 photons per millisecond when we tune frep with PZT and pump power. If we only optimize DFCS with controlling PZT, the peak intensity is 903 per millisecond. As a comparison, the peak intensity of most DFCS (about 75% DFCS) without frep optimization is around 400 photons per millisecond. The final TPT intensity can increase by up to three times before optimization. The experimental results are consistent with the simulation in Figure 4 which shows that the peak intensity (transition intensity after optimization) is 850 photons per millisecond and the bottom intensity (transition intensity without optimization) is 320 photons per millisecond. Due to weak Doppler Effect in MOT, the linewidth of the cooled Rb TPT resonance is improved. We achieve better linewidth in cooled atoms (about 900 kHz) compared with linewidth in thermal atoms in our previous work [6] (about 2 MHz). The residual linewidth is mainly due to the 600-kHz natural linewidth and the 300-kHz transit time broadening. The reduced linewidth is approximately equal to the Doppler broadening of the thermal atoms (about 1.2 MHz). This shows that we can ignore Doppler broadening in cold atoms. Figure 5: The small \(f_{rep}\) change caused by the pump power change.
## 2 Materials and Methods
### Data
#### 2.2.3 Sea Level Data  
To understand how tidal range might influence Landsat-derived coastlines, hourly sea-level data was used to identify the sea level at Landsat acquisition times. The University of Hawaii Sea Level Center (UHSLC) provides global sea level data measured by tide gauge since 1846 [59]. The sea level data can be accessed as Fast Delivery (FD) data or Research Quality data (RQD). For the present analysis, RQD from gauge station Qui Nhon in Central Vietnam was used from 1994 to 2018.
## 5 Experiments and Performance Evaluation  
### Perception Experiment  
#### 5.1.1 Experimental System  
We built a SOP perception system based on the USRP platform. The hardware uses B210 USRP produced by Ettus and a DELL notebook. The device connection relationship is shown in the Figure 24, USRP's radio port is connected to a standard 2.4 GHz omnidirectional antenna, and data port is connected to the laptop through the USB3.0 interface.  
USRP B210 integrates a AD9361 RFIC direct conversion transceiver, providing up to 56 MHz real-time bandwidth, and the radio frequency range is from 70 MHz to 6 GHz. The onboard signal processing and control of the AD9361 is performed by a Spartan6 XC6 SLX150 FPGA, which is connected to the host PC using USB3.0, and the PC performs further processing on the collected data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Learning Rate** & **Batch Size** & **Training Iteration Number** \\ \hline Negative learning 1 & 0.000002 & 30 & 10 \\ Positive learning & 0.0003 & 30 & 30 \\ Negative learning 2 & 0.00001 & 30 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter setting for the three training processes.  
Figure 23: Training curves: (**a**) loss curve; (**b**) accuracy curve.  
The notebook model is DELL's P74G, with i7-8550U dual-core CPU, 8 GB RAM, Windows 10 operating system, the installed software includes USRP Universal Hardware Driver (UHD) driver, LabVIEW 2020, python 3.8. Remove the internal wireless network card during the test to avoid interference.  
The structure and data flow of the entire perception system is shown in the Figure 25.  
The USRP completes the sampling of the wireless signal and sends the data to the PC via UHD and USB3.0. LabVIEW software completes the subsequent signal processing and interactive interface. Signal processing includes time-frequency image representation, image preprocessing, perception model management, signal classification, etc. The signal classification uses the python node to run the pre-trained CNN model through LabVIEW. We use python to implement the CNN model based on the pytorch library. The system software interface is shown in Figure 26. The functions include system settings, spectrum monitoring, real-time preview, classification results display, data collection and storage, etc.
## 5 Results and Discussion
### Wastewater Treatment  
#### 5.2.1 Ipomoea Aquatic  
Figure 3 shows readings for the Al content accumulated by _I. aquatica_ for 10 days of treatment. Readings recorded on day 0, 2, 4, 6, 8, and 10 were \(\sim\)0.30, 0.13, 0.12, 0.10, 0.08, and 0.04 mg/L, respectively. On the final day of the treatment, the heavy-metal concentration of the wastewater decreased up to 87%. A significant drop was recorded on the second day of treatment, with \(\sim\)57%. Figure 3 also shows readings for the Fe content for 10 days of treatment. Readings recorded on day 0, 2, 4, 6, 8, and 10 were \(\sim\)1.09, 0.87, 0.86, 0.8, 0.81, and 0.68 mg/L respectively. The heavy-metal concentration decreased by \(\sim\)38% on the final day of treatment. A significant drop was recorded on the second day of treatment, which was \(\sim\)20%.
# Topology Optimisation Using MPBILs and Multi-Grid Ground Element
## 3 Performance Enhancements of Multi-Objective, Population-Based Incremental Learning
### The Performance Index and Non-Parametric Statistical Test  
MPBIL and its enhanced versions are classified as MOEAs, while the obtained results are classified as approximate Pareto optimal frontiers. In comparing the searching performance of MOEAs, the methods are employed to solve design optimization problems with equivalent total number of function of evaluations for number of attempts. The approximate Pareto frontiers obtained from various MOEAs are then compared using a performance indicator, which is called a hyper-volume (HV) [29] indicator. This indicator represents the hyper-area above a Pareto frontier for bi-objective optimization problem as shown in Figure 2, whereas it is called hyper-volume for three objective functions and more. Therefore, HV sums up all discrete areas \(v_{\text{i}}\) or volumes of hyper-areas or hyper-volumes with respect to a given referent point, respectively.  
A technique for comparing the performance of each MOEA in this research is a non-parametric statistical test, which is called the Friedman test. This technique has been used by [PERSON] and [PERSON] [30] for studying the performance of meta-heuristics (MHs) in solving the four-bar linkage path generation problems. The Friedman test is suitable for comparing more classifiers over multiple data sets.  
Figure 2: Hyper volume sums up all areas covered by the non-dominated solutions and a reference point.