esa-sceva/satcom-chunk-collection huggingface dataset:
# ChatGPT and Open-AI Models: A Preliminary Review
## 2 ChatGPT Training Process  
ChatGPT is a sophisticated large-scale, pre-trained language model developed by OpenAI. It has performed exceptionally on various natural language processing tasks, from language modeling and classification to text generation [12]. The success of ChatGPT stems from its unique training process, which involves using a large amount of unlabeled text data and an innovative training algorithm strategically designed to optimize the model's capacity to generate coherent and contextually suitable responses to natural language input.  
ChatGPT was introduced in November 2022, and its primary purpose is to provide accurate responses to users' questions. As mentioned, it consists of different deep learning and reinforcement algorithms trained in the content of over 150 billion human-generated items, such as books, articles, blog posts, conversations, and reviews [16]. The platform has one million users and counting in just the first week, and it came out as an emerging technology in AI and natural language processing [17].  
The foundation of ChatGPT goes back to the development of GPT, an AI language model developed by OpenAI in 2018. GPT was designed to guess the next word or complete a sentence in a human-generated text, and an immense number of human-generated texts trained its model. The technology was considered a successful and handy tool for several applications, including machine learning, language generation, text prediction in smartphone typing, and many more.  
The OpenAI API utilizes various models with distinct capabilities. Among these models, GPT-3.5 is an upgraded version of GPT-3 and can comprehend and produce natural language and code. Meanwhile, DALL'E is a model that generates and modifies images based on a natural language input [18]. On the other hand, Whisper is a model that converts audio to text [19]. Embedding is a model group that transforms text into a numerical representation [20]. Codex is a collection of models that can interpret and produce code, including translating natural language into code [21]. Additionally, Moderation is a fine-tuned model that identifies potentially sensitive or unsafe text [22]. Lastly, GPT-3 is a set of models that can both comprehend and produce natural language [23].  
OpenAI's models have applications in both research and production for developers. The GPT-3.5 series comprises a suite of models trained on a heterogeneous amalgam of text and code data predating Q4 2021. The code-DaVinci-002 model is primarily suitable for tasks that require pure code completion. Meanwhile, the text-DaVinci-002 model is an InstructGPT model that builds upon the code-DaVinci-002 model. Finally, the text-DaVinci-003 model advances upon the text-DaVinci-002 model [24].  
This chapter presents an extensive exposition of the ChatGPT training process. The discussion entails the essential constituents of the training process, encompassing the model's architecture, text data pre-processing, and training algorithm.
# Geochemical Analysis of a Multi-Layer Hydrocarbon Reservoir in the Wuerhe Area, Junggar Basin
## 4 Results and Discussion  
### Group Composition  
The composition of crude oil changes greatly from bottom to top (Figure 5). In Permian source rocks, saturated hydrocarbons make up 75.6-90.4%, while non-hydrocarbons and asphaltenes make up 6.1-13.7%. In Permian-Triassic normal oil, 0.8400-0.8809 g/cm\({}^{3}\) for oil density (Table 2), saturated hydrocarbons make up 63.8-85.2% of its contents while non-hydrocarbons and asphaltenes make up 6.2-20.4%. In Jurassic heavy oil, 0.9270-0.9564 g/cm\({}^{3}\) for oil density, saturated hydrocarbons make up 31.8%-57.1% of its contents while non-hydrocarbons and asphaltenes make up 27.1-40.1%. Cretaceous solid bitumen density is greater than 1 g/cm\({}^{3}\), average 1.1245 g/cm\({}^{3}\). In the solid bitumen, saturated hydrocarbons make up on average 13.5% while non-hydrocarbon and asphaltenes make up on average 80.1%. From Permian to Cretaceous, the saturated hydrocarbon content of crude oil decreases, while the content of non-hydrocarbons and asphaltenes increases. Thus, it may be concluded that the group composition of crude oil is altered by secondary actions such as biodegradation, water washing, and oxidation. The content of each component in the source rock is similar to that in the normal oil, indicating that the Permian-Triassic oil is from the Permian source rock. The decrease in the content of light components (such as saturated hydrocarbons) and the increase in the contents of heavy components (such as non-hydrocarbons and asphaltene) increases the density and viscosity of crude oil.
## 2 Geopolymer Concrete (GPC)
### Mix Design
#### 2.2.4 Binder to Sand Ratio-Based Mix Design  
Researchers also determined the mix design of GPC based on the binder to sand ratio because of its considerable influence on the mechanical, rheological, and microstructural properties of GPC. [PERSON] et al. [84] determined the mix design based on the precursor to sand ratio and found the factors influencing the mix design are the concentration of activators, activator to binder ratio, water to binder ratio, silicates to hydroxide ratio, and temperature characteristics. In this mix design, the optimum binder to sand ratio is obtained. Then, the hydroxide concentration and optimum silicate to hydroxide ratio is determined. After establishing the activator molarity and ratios, the activator to binder and water to binder ratios are considered as dependent parameters. The flow chart of the respective mix design is illustrated in Figure 6. Moreover, different key factors considered in mix design by various researchers is provided in Table 3.  
Based on the literature review, it is concluded that while determining mix design of GPC, all key factors contributing to strength and durability properties such as molarity of activator, aluminosilicate type, silica/alumina ratio, silicates/hydroxides ratio, activator/binder ratio, cur  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & \begin{tabular}{c} **Water to** \\ **Binder Ratio** \\ \end{tabular} & \begin{tabular}{c} **Activator to** \\ **Binder Ratio** \\ \end{tabular} & \begin{tabular}{c} **Silicate to** \\ **Hydrooxide Ratio** \\ \end{tabular} & \begin{tabular}{c} **Binder to** \\ **Sand Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Aggregate Content** \\ **and Grading** \\ \end{tabular} &
\begin{tabular}{c} **Superplasticizer** \\ **Consideration** \\ \end{tabular} \\ \hline
[85] & 0.27 & - & - & 1 & - & 0.8\% of binder \\
[86] & - & 0.42–0.49 & - & - & - & - \\
[87] & 0.16–0.24 & 0.3–0.45 & 2.0–2.5 & - & \(\checkmarkmark\) & 1.5–4\% of binder \\
[80] & 0.23–0.29 & 0.37–0.5 & 2.5 & - & \(\checkmark\) & - \\
[88] & - & 0.5 & 2.5 & - & - & 2.6\% of binder \\
[81] & 0.25 & 0.5 & 3 & - & \(\checkmark\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factors to be considered in mix design of different studies.  
Figure 6: Mix design of GPC based on binder to sand ratio [84].  
ratio, aggregate behavior, and addition of admixtures should be addressed. The summary of mix designs of GPC is presented in Table 4.
## 1 Introduction
The remainder of this paper is organized in the following manner. The most recent geocast routing methods used in VANETs are reviewed in Section 2. The proposed EGR approach for a highway traffic environment is presented in Section 3. Section 4 presents the simulation's results. The conclusion and future work are explained in Section 5.
## 6 Simulation Results  
### Theoretical Definition of Performance  
As addressed in [13], when routing a packet in any multihop network, energy resources are consumed not only on the source node but also on every node the packet moves through. Every node along any message path is affected when vampire attacks take effect. Therefore, the performance is evaluated in terms of average end to end transmission cost, which is defined by the following:  
\[E_{avg}=\frac{E_{total}}{Num_{tr}} \tag{22}\]  
where \(E_{total}\) is the total energy cost of all the transmissions in the simulation of this scenario, and \(Num_{tr}\) is the number of the transmissions exercised. Note that this metric does not only represent energy efficiency performance but also is an indicator of safety and latency performance; reasons are later provided in Sections 6.6.2 and 6.6.3. As an effort that is trying to imitate the realistic scenario, in every simulation scenario, a large number of data packet transmissions are carried out, and each transmission originates from a randomly picked source node to another randomly picked destination node.  
Later in this section, the performances of the RCPED routing protocol together with its selected competitors are analyzed. The first competing protocol for comparison is PLGPa, which is keen on mitigating the negative effect of vampire attacks employing a cryptographic approach. The other one for comparison is AODV-EHA, an energy-efficient routing protocol considering energy harvesting [30].
## 5 Case Study and Analysis  
### Data  
This section presents the data of customers seen in Table 1 where ID means the identifier number of customers (0 stands for the depot), and X and Y stand for the longitude coordinates and latitude coordinates, respectively. Priority represents the priority of customers (1 means that the customer has to be delivered to first on the tour, customers with priority 0 are delivered to any time). Demand means the demand quantity, and types represents the good property (3 types in total, and B and C are incompatible and cannot be loaded into the same vehicle; others can be combined in the same vehicle). In addition, two kinds of vehicles are used: one with capacity 120 and the other with capacity 176.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**ID** & **X** & **Y** & **Priority** & **Demand** & **Types** & **ID** & **X** & **Y** & **Priority** & **Demand** & **Types** \\ \hline
0 & 118.64611 & 31.94249 & 0 & 0 & 0 & 24 & 118.84352 & 31.86329 & 0 & 33 & A \\
1 & 118.72178 & 32.20923 & 1 & 3 & A & 25 & 118.98820 & 32.10
## 4 Experiments
### Model Comparisons
#### 4.2.2 Syntactic-Based Models  
**LSTM + SynATT**[40]: They proposed a method that can better capture the semantic meaning of aspects, proposing an attention model to integrate syntactic information into the attention mechanism.  
**CDT**: They propose to use BiLSTM to obtain the feature representation of the sentence, and to further enhance the embedding by directly performing convolution operations on the dependency tree.  
**ASGCN**: It is proposed to learn feature representations of specific aspects through GCN and dependency trees to solve the long-distance multi-word dependency problem.  
**BiGCN**[41]: They built a concept hierarchy on both the syntactic and lexical graphs for differentiating various types of dependency relations or lexical word pairs, designing a bi-level interactive graph convolution network to fully exploit these two graphs.
## 3 Results and Discussion
### Self-Piercing Riveted Joint of Vibration-Damping Aluminum and GA590 DP
The failure modes of the tensile tests were investigated, and the results are shown in Figure 11. The figure shows that for the configuration where vibration-damping Al panels were on top, all of the joint failures showed \"upper panel tearing\" ((a), (b), and (c)). The results also indicate that since the failure was due to the vibration-damping Al tearing, the tensile shear strength does not depend on the interlock width (during the failure, the riveted joints were intact). When the vibration-damping Al panels were positioned on the bottom, all of the joint failures showed \"rivet pull-out\" and \"bottom sheet upper skin shear-out\" ((d), (e), and (f)). In this case, the tensile shear loads were irrelevant to the interlock width since the failure of the joints was due to the bottom panel tearing. It is worthwhile to discuss the reason behind the higher shear strength associated with the steel panel positioned on top. The analysis of the failure mode shows that when the vibration-damping Al panel is on top, the tear-out begins at the top panel where the rivet pierces the material. Such a result indicates that the force at that location is mostly uniaxial. When the vibration-damping Al panel is on the bottom, however, the failure begins at the upper skin, which tears the Al panel due to the shear forces and eventually leads to \"rivet pull-out\" failure. This type of failure reflects that the force at the interface between the rivet and the Al panel is biaxial. The distribution of the force in two directions could be the reason behind the difference in shear load strength for the different panel placement configurations.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 5 Evaluation  
Simulations and tests were performed on a Mininet with a P4 switch simulator (BMV2 [36]) to evaluate the performance of the proposed strategy. A comparison was carried out between its performance and existing schemes to validate its feasibility.
## 5 Discussion  
This research aims to study the contribution of changing hyperparameters and changing bandpass filter parameters of Chebyshev type 2 to a CNN method and a Transformer method in brain-computer interface motor imagery. Regarding the CNN method, EEG-TCNet is a novel study for research. The increase in the number of filters and kernel sizes in EEG-TCNet architecture provides better performance. However, the bandpass filter Chebyshev type 2 in data preprocessing is inefficient for training EEG-TCNet. In terms of the Transformer method, EEG-Conformer is chosen for this research. The average accuracy improves when the number of filters is between 20 and 40. The Chebyshev type 2 bandpass filter was found to contribute significantly to the accuracy of each subject, producing an average accuracy of 83.41%. However, EEG-TCNet still has other hyperparameters, such as dropout, depth of TCN blocks, depth in EEGNet module, or EEG-Conformer, still having dropout and head number in multi-head that cannot be examined in this study because of the limit of training resource and time.  
In this research, a new model called EEG-TCNTransformer is created, which combines the convolution parts of EEG-TCNet and the sequence of self-attention blocks. The average accuracy of EEG-TCNTransformer improves when the number of TCN blocks increases, which learns better the low-level temporal features in temporal and spatial features. The number of filters also contributes to the performance, but it gets overfitting at specific filters. Moreover, the bandpass filter is inefficient for EEG-TCNTransformer, so the raw EEG data with standardisation are used in training and testing. Therefore, the optimised hyperparameters for EEG-TCNTransformer generate better accuracy for each subject compared with baseline EEG-TCNet and baseline EEG-Conformer without a bandpass filter in preprocessing, and the average accuracy of EEG-TCNTransformer is 83.41%.
## 5 Conclusions  
In this study, a method based on Taguchi experimental design was analyzed and demonstrated. The influences of various process parameters on material removal of robotic belt grinding were studied. In the Taguchi experiment, five-level values of force, speed, and mesh for three factors were applied. According to the analysis and discussion of the experimental results, the following general conclusions can be drawn:  
1. Adaptive Neuro-Fuzzy Inference System (ANFIS) was applied to predict the removal depth of robot belt grinding material. Based on ANFIS, a MISO system with different grinding parameters affecting MRD was established and verified by practical data. The prediction outcomes demonstrated the high accuracy of the ANFIS robot abrasive belt grinding material removal prediction model, with a \(MAPE\) of only 3.976%.  
2. Based on the main effect analysis diagram in Figure 7, it can be concluded that speed: 550 m/min, force: 40 N, and mesh: 120 were the parameters corresponding to the maximum MRD.  
3. The same data set was applied to three models, ANN, SVR, and RF, and their \(MAPE\) values compared with ANFIS. The \(MAPE\) values of ANFIS, ANN, SVR, and RF were 3.976%, 5.713%, 12.717%, and 16.635%, respectively. As can be observed, ANFIS significantly outperformed the other models, while RF performed poorly.  
4. ANFIS is flexible and can be used as an alternative to traditional modeling techniques after a large amount of data training. After a suitable fuzzy inference model is established, the depth of material removal can be analyzed without actual experiments. Therefore, the model may be used for actual grinding, and the quantity of grinding can be monitored online.  
[PERSON], [PERSON] and [PERSON] have generated the new research idea, done investigations and conducted experiments; [PERSON], [PERSON], [PERSON] and [PERSON] have conducted the experiments and writing original draft; [PERSON] supported the material and equipment. [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Zhejiang Province's \"Leading Goose\" R&D Program: Key Technology and System Engineering R&D Project of Surface Grinding and Polishing Robot, China, [NO. 2022C01096].
## 4 Materials and Methodology
### Estimating GHG Emission Reduction  
The utilization of biogas and biomethane as an energy source can achieve emissions reduction by replacing conventional fossil fuels and avoiding fugitive CH\({}_{4}\) and CO\({}_{2}\) emissions, resulting from anaerobic decomposition of sludge in landfills/storage facilities. The annual emission reductions were calculated for Kuwait as a reference case, due the availability of sludge data and composition. Fuel displacements were based on the total heat content of potential biomethane, such that the annual expected heat energy from biomethane was converted into equivalent volumes of natural gas, crude oil, gas oil, and heavy fuel oil (HFO). The emissions and fuel volumes were based on the emission factors and heat content reported by the US Environmental Protection Agency [49]. Emissions from sludge disposal were based on the assumption that dry sludge undergoes anaerobic decomposition in a landfill, with similar performance to an anaerobic digestor. Consequently, CH\({}_{4}\) and CO\({}_{2}\) emissions from dry sludge disposal in a landfill are similar to those estimated by the AD model. This study compared two emissions scenarios; a base scenario where electricity is produced from conventional fuels and wastewater sludge is stored or disposed of in a landfill. The alternative scenario is where sludge is anaerobically converted into biogas and utilized as an energy source to produce electricity.
# Assessment of Affordable Real-Time PPP Solutions for Transportation Applications
## 4 Discussion
RT-PPP solutions, and both BKG and WHU demonstrate similar performance for the GPS/GLONASS RT-PPP solution. * The GPS-based RT-PPP solutions from the three BKG, CNE, and WHU SSR products require a long time to converge compared with the other solutions, which could be a limitation of our suggested solutions. For this reason, the RT-PPP ambiguity resolution solution will be considered in our future work to improve the convergence time. * Based on the attained positioning accuracy and compared with the common PPP, RTK, and PPP-RTK solutions, our proposed RT-PPP approach is a cost-effective solution, particularly when compared with the geodetic-grade PPP solution; in addition, its configuration is less complicated than the RTK and PPP-RTK solutions. * Finally, our processing scenario is based on a suburban context, and continuous SSR corrections are necessary for a successful solution.
# Efficient Algorithms for the Maximum Sum Problems
## 2 Parallel Algorithms for the MSA Problem
### Sequential Algorithm  
The computation in Algorithm 1[8] proceeds with the strip of the array from position \(k\) to position \(i\). See Figure 3. The variable \(column[i][j]\) is the sum of array elements in the \(j\)-th column from position \(k\) to position \(i\) in array \(a\). The variable \(sum[i][j]\), called a prefix-sum, is the sum of the strip from Position 1 to position \(j\). Within this strip, variable \(j\) sweeps to compute \(column[i][j]\) by adding \(a[i][j]\) to \(column[i-1][j]\). Then, the prefix sum of this strip from Position 1 to position \(j\) is computed  
Figure 2: Two-dimensional architecture.  
by adding \(column[i][j]\) to \(sum[i][j-1]\). The variable \(min\_sum[i][j]\) is the minimum prefix sum of this strip from Position 1 to position \(j\). If the current \(sum\) is smaller than \(min\_sum[i][j]\), \(min\_sum[i][j]\) is replaced by it. \(sol[i][j]\) is the maximum sum in this strip so far found from Position 1 to position \(j\). It is computed by taking the maximum of \(sol[i][j-1]\) and \(sum[i][j]-min\_sum[i][j]\), expressed by \(max\_sum\) in the figure. After the computation for this strip is over, the global solution, \(S\), is updated by \(sol[i][n]\). This computation is done for all possible \(i\) and \(k\), taking \(O(n^{3})\) time.  
```
1:for\(1\leq i\leq n\)do
2:\(min\_sum[i][0]\leftarrow\infty;sum[i][0]\gets 0;sol[i][0]\leftarrow-\infty\);
3:endfor
4:\(S\leftarrow-\infty\);
5:for\(k\gets 1\) to \(n\)do
6:for\(j\gets 1\) to \(n\)do
7:\(column[k-1][j]\gets 0\);
8:endfor
9:for\(i\gets k\) to \(n\)do
10:for\(j\gets 1\) to \(n\)do
11:\(column[i][j]\gets column[i-1][j]+a[i][j]\);
12:\(sum[i][j]\gets sum[i][j-1]+column[i][j]\);
13:\(min\_sum[i][j]\leftarrow\)\(\operatorname{MIN}\{sum[i][j],min\_sum[i][j-1]\) };
14:\(max\_sum[i][j]\gets sum[i][j]-min\_sum[i][j]\);
15:\(sol[i][j]\leftarrow\)\(\operatorname{MAX}\{max\_sum[i][j],sol[i][j-1]\}\);
16:endfor
17:if\(sol[i][n]>S\)then
18:\(S\gets sol[i][n]\);
19:endif
20:endfor
21:endfor
```  
**Algorithm 1** MSA: sequential.
# Orthogonal Neural Network: An Analytical Model for Deep Learning
## 1 Introduction
Likewise, the good properties of this orthogonality of frequencies have inspired the progress in communication technology and the development of frequency division multiplexing. The information can be modulated to different frequencies in the same transmission channel to achieve multiplexed information transmission. Inspired by this theory, we consider whether we can study the deep learning model as a communication system, treating each input feature as a signal, and investigate the deep learning model by analyzing the relationship between the signal and the communication system. Based on the above reflection, we propose an analytical model for deep learning named orthogonal neural network model in this paper. The contributions are listed as follows. * The orthogonal neural network model we propose is an interpretable deep learning approach. * Modulation and demodulation are introduced in communication theory to extend the definition of the deep learning model into frequency space. The property of orthogonality between frequencies is used to ensure that the individual initial features of the input orthogonal neural network model are both independent of each other and correspond to meaning in the deep learning model. * The model proposed in this paper can be used to analyze a type of relationship between the computed results of a deep learning model and the initial features in the form of weights based on an interpretable deep learning approach. From the size of the weights, it is possible to determine the primary and secondary features on which the deep learning model will focus. An additional note is that the most used activation function in current deep learning models is the ReLU [15] function, which is in Equation (1). In this paper, discussion of the activation functions of deep learning models is restricted to the ReLU function. \[ReLU(x)=\left\{ \begin{array}{ll}x&if\ x\geq 0\\ 0&if\ x<0\end{array} \right. \tag{1}\]