esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Discussion
### Model Interpretation  
To interpret the model predictions, we analyze how the localizations change qualitatively as we perform Grad-CAM with respect to different features maps in our model. As we can see from Figure 7, in the first few layers, CNN pays more attention to the edge and color information which are important for the next layers. Then, later convolutional layers start to detect the texture associated with the tooth marks. The Grad-CAM highlights the discriminative regions, which are usually some indentations along the lateral borders, and the color and brightness of them differ from the normal regions. It is interesting to see how our recognition method can serve as a tool to understand the network better by providing a localized high-resolution visualization of the tooth-marked regions. It shows precise localization to support the model's prediction. We think it helps to judge which model is more effective, as stated in Section 4.4, and also helps doctors to analyze the region of the tooth-marked tongue, rather than simply giving the classification results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Kernel Size** & **Receptive Field** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **F2 Score** \\ \hline
**3 \(\times\) 3** & **94** & **78.6\%** & **75.9\%** & **78.1\%** & **0.77** & **0.78** \\
5 \(\times\) 5 & 156 & 76.0\% & 74.1\% & 74.1\% & 0.74 & 0.74 \\
7 \(\times\) 7 & 218 & 73.6\% & 71.1\% & 72.9\% & 0.72 & 0.73 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison between different kernel sizes with the same convolutional layers, and the 3 \(\times\) 3 kernel size is the most effective. (Conv Layer: 5-Conv layer).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Conv Layer** & **Receptive Field** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **F2 Score** \\ \hline
3-Conv & 22 & 71.3\% & 70.4\%. & 62.1\% & 0.66 & 0.64 \\
4-Conv & 46 & 73.6\% & 77.3\%. & 56.1\% & 0.65 & 0.59 \\
**5-Conv** & **94** & **78.6\%** & **75.9\%** & **78.1\%** & **0.77** & **0.78** \\
6-Conv & 190 & 72.1\% & 72.5\%. & 67.7\% & 0.70 & 0.69 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison between different convolutional layers with the same kernel size, and the 5-Conv layer is the most effective. (Kernel Size: 3 \(\times\) 3).
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.3.3 Machine Learning Models  
Various machine learning models are employed to process data collected from IoT devices and enhance decision-making. Common models include convolutional neural networks (as seen in [65; 66; 68; 78]) especially for image recognition. Furthermore, LLMs such as GTP-4 are also employed to interpret sensor data and generate scene descriptions for AR experiences as seen in [74]. Support vector machines were also used in papers for classification tasks (e.g., [66; 72]). Other conventional ML algorithms such as decision trees for predictive analytics were used in [67], as well as logistic regression, Naive Bayes and k-means appeared in [71], but they were significantly less prominent in bibliography compared to DL algorithms and neural networks, which also tend to achieve much higher accuracies compared to more conventional algorithms especially in more advanced tasks such as object recognitions etc. These models are crucial in applications such as healthcare diagnostics, industrial maintenance, and educational assessments. Last but not least, another prominent algorithm was YOLO (You Only Look Once) which was used in several papers for real-time object detection and it appeared to perform commendably well in various applications.
## 4 Discussion
### Limitations  
The model's limitations are influenced by the data access restrictions and data completeness and detail. For instance, as noted in [9], the Basque Country and Navarre maintain their own cadastres and cadastre INSPIRE datasets, which are excluded from this model, limiting geographic comprehensiveness. Additionally, the lack of detailed data on building systems and energy carriers is a significant source of bias; including such data would substantially enhance model accuracy. Another challenge is the presence of mislabeled or erroneous entries in available databases, especially in the energy performance certificates (EPCs). These issues necessitate extensive data cleaning, introducing potential errors into the algorithm and affecting precision.  
Although the model's validation is adequate, individual building predictions may not be fully reliable due to missing data on building systems, a low percentage of buildings with EPCs, and inconsistencies in EPC data. Therefore, while the model is a robust tool for analyzing patterns and trends among groups of buildings within specific areas or typologies, it is less suited for precise, single-building analysis.
## 5 Results and Discussion
### Estimation of the Residence Time of Solid Particles in the Overlayer Space
The calculations show that for the \"weighted layer\" mode with a gas flow velocity of 2.4 m/s in a free cross-section, the velocity of particles with average diameter of 2 mm is 10-12 m/s, the flow mass concentration of 3 kg/kg, the volume concentration of solid phase in the weighted layer 0.34 m\({}^{3}\)/m\({}^{3}\) and the particle velocity on the surface of the shelf is 0.1 m/s, the residence time of the particle for the first zone is \(\tau_{1}\) = 5.73-5.97 s. For the second zone, at a pulsating velocity of particles in the layer of 0.14 m/s, the residence time is \(\tau_{2}\) = 2 s. The total estimated residence time of the particles in the weighted layer \(\tau_{\Sigma}\) = 7.73-7.97 s. The experimental residence time of the particles in the layer (calculated as the ratio of the material amount in the weighted layer to its mass flow rate) is \(\tau_{ex}\) = 7.72 s. For the gravitationally falling layer mode, the estimated time at a particle velocity of 0.25 m/s and a volume concentration of the solid phase in the layer above the shelf of 0.15 m\({}^{3}\)/m\({}^{3}\) is \(\tau_{1}\) = 1.12-1.15 s, and experimental \(\tau_{ex}\) = 1.16 s. The calculation results (Table 1) show that the residence time of the particles in the layer increases with the increase of the solid phase volume concentration in the layer and the number of shelves.
# Improving Transaction Speed and Scalability of Blockchain Systems via Parallel Proof of Work +
## 3 Method of Proposed Solution
### Distribution of Data  
At the outset, as depicted in Figure 1, the manager will create a transaction hash with the unconfirmed transactions and, at the same time, will generate several groups of nonces. Each group will contain a range of nonce values; no same nonce value should be in multiple groups. If m numbers of miners are active in the network, the manager must initially generate and register at least m number of groups. The manager will then distribute the transaction hash and groups of nonces to each active miner. The system will ensure that no two miners have the same group. With the exception of the manager, all miners will now try to find a solution for the next block with the available transaction data and the range of nonces allocated to each of them.  
At the same time, the manager will generate and register more groups of nonces. Once a miner has used all of the nonce values of the allocated range, the miner will ask the manager for a new nonce range. The manager will then provide an unused range to that miner. Again, if a new miner enters into the network and asks the manager for required data, the manager will provide him/her with the same transaction data and a new group of nonces. For this reason, the manager should generate as many groups of nonces as possible. The process will continue until a designated solution for the current nonce is found.
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 1 Introduction  
The tuna fishing industry occupies an extremely important position in global offshore fisheries due to its high efficiency and productivity characteristics. With the establishment of regional fisheries management organizations and the decline of tuna resources, the exploitation of high seas fisheries resources is subject to increasingly stringent regulation and control [1]. Therefore, the orderly and efficient development of tuna fisheries resources has become a focal issue of concern for participants from various countries. Accurate capture data statistics aid fisheries managers in comprehensively understanding fishing activities, enabling the formulation of scientifically sound management measures, thus achieving sustainable development of the fisheries industry. However, human observers struggle to maintain the accuracy of statistical data during sustained, high-intensity work. Additionally, tuna longline fishing vessels typically operate continuously at sea for over a year, leading to significant deficiencies in the timeliness of fishing logs [2]. Therefore, to enhance the accuracy and timeliness of statistical data, this paper employs deep learning-based object detection techniques along with target tracking and counting methods to achieve real-time monitoring of fishing data.  
In this paper, a target detection algorithm based on improved YOLOv7 is proposed. Firstly, to address the temporal characteristics of the dataset in this study, we introduce the temporal dynamic convolution DySnakeConv into the backbone network. This enables better extraction of temporal features from the data, thereby enhancing detection performance. Secondly, integrating CoordConv into the head network incorporates coordinate information into the convolutional neural network, enabling the network to better perceive the spatial information of the input data and enhancing the model's generalization ability. Furthermore, to address the issue of complex backgrounds in tuna detection, the dynamic detection head, DyHead, is introduced, enabling the model to focus attention and suppress background interference. As a result, YOLOv7-Tuna achieves improved detection accuracy.  
Additionally, improvements were made to the ByteTrack algorithm by adding designated counting areas, which enhanced the accuracy of tuna counting.  
The structure of this paper is as follows. Section 2 discusses the current research status of fish identification and target tracking counting. Section 3 provides a comprehensive introduction to the improved YOLOv7 algorithm and the enhanced ByteTrack algorithm. Section 4 describes the tuna dataset, experimental setup, and analysis of experimental results. Section 5 provides a summary of the research findings of this study.
## 2 Materials and Method
### PMCAMx-Trj Model  
We used the one-dimensional Lagrangian chemical transport model PMCAMx-Trj with the 2D-VBS module [18; 19] simulating air parcels that arrived at Hyytiala. The model solves the atmospheric general dynamic equation taking into account the relevant atmospheric processes: gas and aqueous-phase chemistry, atmospheric transport and turbulent dispersion, aerosol dynamics, dry and wet removal of gases and particles, inorganic and organic aerosol formation, and finally, area and point emissions from both anthropogenic and biogenic sources. Ten computational cells were used in this application with heights varying from 60 m near the ground to almost 3 km. An extended version of the SAPRC [27] chemical mechanism is used in this work for the simulation of gas-phase chemistry. The meteorological parameters (temperature, pressure, horizontal winds, clouds, water vapor, rainfall, vertical dispersion coefficients and land use) used as inputs in the model are provided by the Weather Research and Forecasting (WRF) model. The WRF simulation was periodically re-initialized (every 3 days) to ensure the accuracy of the inputs to the CTM. Area and point emissions were also provided by the inputs of the regular PMCAMx simulation for the European domain. This domain contains all of continental Europe, all the Mediterranean and parts of north Africa, Turkey and parts of Asia, Iceland and the North Sea as well as the western Atlantic. The approaches used for the estimation of the species' emission rates are the same as those described in [PERSON] et al. [20]. The details of the emission inventory can be found in [PERSON] et al. [28] and implemented in the 2D-VBS framework with the [PERSON] et al. [18] method. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) provides the emissions from terrestrial ecosystems. These are different for each simulation and vary with space and time [20]. Vertically resolved initial conditions and the top boundary conditions for PMCAMx-Trj were obtained from the corresponding output of the three-dimensional PMCAMx simulation for the same period of spring-early summer 2013.
## 3 Results and Discussion
### Effect of H\({}_{2}\)O\({}_{2}\) Dose  
Hydrogen peroxide plays a fundamental role as an oxidizing agent in the Fenton oxidation process. For this reason, the effect of H\({}_{2}\)O\({}_{2}\) concentration on DAP degradation was optimized using 100 mg/L of DAP, a constant [H\({}_{2}\)O\({}_{2}\)]/[Fe\({}^{2+}\)] molar ratio of 20 at pH 3.2 and different H\({}_{2}\)O\({}_{2}\) concentrations ranging from 0 to 3000 mg/L. The results are illustrated in Figure 5. It was noticeably observed that the DAP degradation yield depended largely on the H\({}_{2}\)O\({}_{2}\) dose. The increase in H\({}_{2}\)O\({}_{2}\) concentration from 0 to 1000 mg/L increased the DAP degradation yield from 5% to 100% after 120 min of treatment. This can be due to a greater generation of \({}^{\bullet}\)OH radical in the solution by catalytic decomposition of hydrogen peroxide. The highest DAP degradation yield was obtained when 1000 mg/L H\({}_{2}\)O\({}_{2}\) was added to 100 mg/L DAP. A further increase in H\({}_{2}\)O\({}_{2}\) concentration beyond 1000 mg/L had no effect on the degradation yield. This can be explained by the acceleration of competitive reactions between hydroxyl radicals \({}^{\bullet}\)OH with pollutants and their scavenging by the excess in H\({}_{2}\)O\({}_{2}\) ((Equation (3)) and (Equation (4))) [26; 32]. Additionally, the auto-decomposition of H\({}_{2}\)O\({}_{2}\) into O\({}_{2}\) and H\({}_{2}\)O (Equation (5)) consumes a significant amount of H\({}_{2}\)O\({}_{2}\) for higher H\({}_{2}\)O\({}_{2}\) doses than 1000 mg/L. Accordingly, a H\({}_{2}\)O\({}_{2}\) dose of 1000 mg/L is optimal to reach the highest DAP degradation yield by Fenton oxidation, which corresponds to [H\({}_{2}\)O\({}_{2}\)]/[DAP] mass ratio equal to 10.  
\[\text{HO}^{\bullet}+\text{Organic Pollutant}\rightarrow\text{Products} \tag{3}\]  
\[\text{HO}^{\bullet}+\text{H}_{2}\text{O}_{2}\rightarrow\text{H}_{2}\text{O} +\text{HO}_{2}{{}^{\bullet}} \tag{4}\]  
\[\text{2H}_{2}\text{O}_{2}\rightarrow\text{O}_{2}+\text{2H}_{2}\text{O} \tag{5}\]  
Figure 4: Effect of the iron source on the degradation of DAP kinetics during the Fenton process (H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\)). Experimental conditions: [DAP]\({}_{0}\) = 100 mg/L; [H\({}_{2}\)O\({}_{2}\)] = 1000 mg/L; [Fe\({}^{2+}\)] = 50 mg/L; pH = 3.2.
## 5 Proposed Edge-Cloud Framework for Automated Fault Detection and Localization
### Time Performance Indicator  
In this subsection, we provide the results related to the elapsed time of the entire fault localization procedure in a real environment. According to Figure 7, the time from the fault inception moment until the moment when the FLA results are received by the DSO can be expressed as:  
\[t=t_{stop}-t_{start}=t_{1}+t_{2}+t_{3}+t_{4}. \tag{1}\]  
Figure 7: Time delay of the fault localization algorithm (FLA) components.  
In Equation (1), the t\({}_{1}\) represents the time elapsed from the moment when the PMU in the primary substation reports the phasors containing the fault until the moment when the _Event detector_ recognizes the state of a network as faulted. This time includes the time required for saving the PMU measurements to a co-located ePC and the running time of a fault detection script, which compares the phasors to the predefined thresholds. Once the fault is detected, a trigger is sent from the primary substation to the _Hosted application_, which is run in the cloud. This communication-induced delay is labelled as t\({}_{2}\). Next, the processing time of the _Hosted application_ is labelled as t\({}_{3}\) and consists of two parts, i.e., the time required to transfer data from the _Database_ of PQMs and the _Time-series databases_ of PMUs to the FLA and the processing time of the FLA script itself. Finally, the report of a fault takes some time (t\({}_{4}\)) to be transferred from the cloud-based _Hosted application_ to the _Control center_ of the DSO, where it is displayed to the operator.  
In order to evaluate the elapsed time from the event inception moment until the moment that the DSO receives the corresponding report, we manually triggered 100 artificial events. This was done by tweaking the thresholds of the _Event detector_ application and observing the value of each individual time component. The minimum, maximum and average values for each of them are presented in Table 1. The values of t\({}_{1}\) indicate that the _Event detector_ application is indeed running in real-time (the 30 ms threshold is not surpassed). On the other hand, the average elapsed time of approximately 2 s places the combined fault detection and localization procedure in the category of near real-time applications.
# Impact of Sentence Representation Matching in Neural Machine Translation
## 4 Experiment Setting
### Setting for Robustness Analysis
BE is extracted from a pre-trained multilingual BERT [31] model ([[https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip)]([https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip)), accessed on 18 January 2022) consisting of an embedder and a number of encoders. RE, WV, and BE are fixed embedded vectors in every experiment. To compare the effects on widely used NMT architecture, we selected LSTM [10] with a unidirectional encoder and transformer [16]. In the transformer, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Element Activation**} \\
**Embedding** & **Unit** & **Language** & **Min** & **Max** & **Mean** & **Std** \\ \hline WV & token & source & \(-\)0.8674 & 0.8900 & \(-\)0.0003 & 0.0641 \\ WV & token & target & \(-\)0.8819 & 0.8824 & 0.0018 & 0.0609 \\ \hline RE & token & source & \(-\)0.1000 & 0.1000 & 0.0000 & 0.0577 \\ RE & token & target & \(-\)0.1000 & 0.1000 & 0.0000 & 0.0577 \\ \hline BE & word & source + target & \(-\)0.8911 & 0.4352 & \(-\)0.0073 & 0.0463 \\ BE & position & source + target & \(-\)0.8643 & 0.4100 & 0.0000 & 0.0150 \\ BE & token & source + target & \(-\)0.3017 & 0.2326 & 0.0004 & 0.0178 \\ \hline \hline \multicolumn{5}{c}{**Euclidean Distance**} \\
**Embedding** & **Unit** & **Language** & **Min** & **Max** & **Mean** & **Std** \\ \hline WV & token & source & 0.0248 & 5.9327 & 1.0426 & 1.0090 \\ WV & token & target & 0.0243 & 6.3314 & 0.8861 & 1.0553 \\ \hline RE & token & source & 1.1810 & 1.4202 & 1.3061 & 0.0259 \\ RE & token & target & 1.1971 & 1.4040 & 1.3061 & 0.0257 \\ \hline BE & word & source + target & 0.6753 & 1.8641 & 1.2880 & 0.1630 \\ BE & position & source + target & 0.3509 & 1.3160 & 0.4126 & 0.0465 \\ BE & token & source + target & 0.4846 & 0.5031 & 0.4939 & 0.0092 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of embedding vectors for robustness analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Corpus** & **Sentence (Fr-En)** & **Token (Fr)** & **Token (En)** \\ \hline Train & 1,737,355 & 44,201,334 & 40,094,199 \\ Valid & 22,960 & 746,023 & 650,469 \\ Test1 & 3,003 & 81,191 & 71,114 \\ Test2 & 2,007 & 58,682 & 49,690 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data Statistics for robustness analysis in French to English translation (train: Europarl-v7, validation: commontest, test1: newstest-2014, test2: newscommentary-2007). receives the final output vector of the encoder-transformer as its input and calculates its distance to the sum of word-embedding vectors of the decoder-transformer. Detailed model parameters are shown in Table 7. The setting of the matching layer is equal to the other experiments using source-side-2-layers, 250 hidden dimensions, and normalized hamming distance.
# Simulation Analysis and Experiment of Piezoelectric Pump with Tapered Cross-Section Vibrator
## 8 Conclusions  
The above research showed that for the complexity of the flow field in the pump chamber, the simulation based on multi-physical-fields coupling can accurately analyze the flow pattern and fluid velocity distribution in the pump chamber. Tests further verified that the pump had good fluid delivery performance. And the structure of the piezoelectric pump with a tapered cross-sectional vibrator integrates the vibrator base and the tail fin part, which effectively reduced the number of core components and simplified the processing technology. The conclusions of this study are summarized as follows:  
1. According to the bionic principle of fish swimming, using the inverse piezoelectric effect of piezoelectric ceramics, the design of a valve-less piezoelectric pump with the tapered cross-section vibrator was proposed. The corresponding relationship between the piezoelectric vibrator and the fish-shaped body was given. And the approximate analytical expression of pumping flow rate was derived through theoretical analysis.
2. The flow field of the pump is simulated and analyzed based on the multi-physical-fields coupling with the software of COMSOL Multiphysics 6.1, and two vortices generated in the flow field have been discussed, which was consistent with the swimming characteristics of fish; for the obvious fluid disturbance area of the flow field around the vibrator tail region, the velocity change of the flow field was analyzed at each typical time point in a vibration period, and the simulated flow rate of the pump has been obtained after post-processing.
3. A prototype of the pump was made, and the performance tests were carried out. The results showed that the tested flow rate changes in the same trend as the simulated flow rate under different driving frequencies, and the overall error of the simulated flow rate relative to the tested flow rate was 9%. And the reasons for the error have been analyzed.  
Conceptualization, C.H. and X.H.; methodology, X.H.; software, W.J. and Q.Z.; validation, J.J. and X.H.; formal analysis, S.L.; investigation, W.J.; resources, Q.Z.; data curation, Q.Z.; writing--original draft preparation, C.H.; writing--review and editing, W.J.; visualization, S.L.; supervision, X.H.; project administration, J.J.; funding acquisition, C.H. 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 (No: 31971801) and the Natural Science Foundation of Shandong Province of China (No: ZR2020 ME252, ZR2020 ME250).  
Not applicable.  
The data presented in this study are available on request from the first author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.