esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experiments in Cost Prediction of WMS
This indicates that by exploring the correlation knowledge among different subentry costs, the prediction accuracy of WSM cost can be effectively improved. The scatter plot in Figure 6 depicts the fitting results of the sample within dataset 4, with the horizontal and vertical coordinates of the points representing its actual total cost \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Index** & **Real Value** & **mSVR** & **BPNN** & **MTR-MLS** & **Index** & **Real value** & **mSVR** & **BPNN** & **MTR-MLS** \\ \hline
01 & 5009 & 5146 & 4946 & 5080 & 13 & 5321 & 4986 & 4996 & 5348 \\
02 & 4933 & 4523 & 5166 & 4856 & 14 & 5035 & 4460 & 5232 & 5175 \\
03 & 5686 & 5528 & 5523 & 5547 & 15 & 5072 & 5417 & 5000 & 5057 \\
04 & 5686 & 5874 & 5451 & 5607 & 16 & 4822 & 4734 & 4871 & 4595 \\
05 & 5484 & 5390 & 5169 & 5327 & 17 & 5393 & 5003 & 4978 & 5469 \\
06 & 5034 & 4436 & 4913 & 4993 & 18 & 5688 & 6030 & 5407 & 5575 \\
07 & 5072 & 5168 & 4933 & 5130 & 19 & 4944 & 4749 & 5104 & 5046 \\
08 & 4932 & 5022 & 4977 & 4941 & 20 & 4822 & 5086 & 5263 & 4788 \\
09 & 4960 & 5065 & 4907 & 4966 & 21 & 5402 & 5629 & 5336 & 5551 \\
10 & 5393 & 5543 & 5515 & 5477 & 22 & 5007 & 5393 & 5065 & 5110 \\
11 & 5114 & 5240 & 4936 & 5030 & 23 & 5601 & 5439 & 5939 & 5707 \\
12 & 5007 & 5229 & 5183 & 4974 & 24 & 5392 & 5601 & 5776 & 5292 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of total cost prediction results of three different algorithms in Dataset 4 (unit: million yuan). Figure 5: The results of three methods for predicting the total cost of WSM. and the predicted total cost value obtained from the model prediction, respectively. It is evident that compared with other multi-output approaches, the prediction errors of the proposed method in this paper are less volatile, which indicates that the prediction results of the method are more stable and less susceptible to the interference of outliers.
## 5 Simulation Results
### DoS Attack Results
The emulated inertia still works to damp frequency oscillations even during these DoS attacks. However, as shown in Figure 6, for the DoS attacks that occur at \(t=12\,\mathrm{s}\), there would be a larger SSFD in the LFC of the system with AC/HVDC transmission lines and virtual inertia. This is because the ESS is mainly used for a short period of time (\(2\,\mathrm{s}\) to \(5\,\mathrm{s}\)) to emulate virtual inertia (recall Section 2.3), while the load step event starts from \(t=5\,\mathrm{s}\). To conclude, the frequency dynamics of the LFC system under DoS attacks would become worse comparing with the scenario where there is no DoS attack, while the effects of DoS attacks (quantified by attack impact index, e.g., SSFD) on the three LFC system models of this article depend to the time that the DoS attack occurs.
## 4 Radiation Metrics
### Power Density  
Power density is the primary metric of radiation used by international standards and guidelines governing mmWave frequencies. The power density can be defined as follows:  
\[PD=\frac{P_{1}}{S_{a}}=\left|\overset{\rightarrow}{E}\times\overset{ \rightarrow}{H}\right| \tag{10}\]  
where \(S_{a}\) is the area exposed to the incident wave, and \(\overset{\rightarrow}{E}\) and \(\overset{\rightarrow}{H}\) are the vectors of the electric and magnetic fields.
## 2 Results and Discussion
### Operational Stability of the Biocatalyst  
Figure 4 shows the conversion in ethyl esters produced after different cycles of reuse using Novozym(r) 435, under optimal conditions of reactions. It is possible to verify that at the end of 10 consecutive cycles, the enzyme maintained 91% of catalytic activity.  
The results obtained are in agreement with those presented by other authors. [PERSON] et al. (2019) [42], investigated the reuse of Novozym(r) 435, lipase from _Rhizomucor miehei_ (RML) and lipase from _Thermomyces lanuginosus_ (TLL), with different enzymes concentrations, in the transesterification of residual fish oil for the production of biodiesel. Novozym(r) 435 lost only 16% of its initial activity after the second re-use and, after that, the ethyl esters production was kept constant for 10 cycles of reuse, regardless of the mass of enzyme used. On the other hand, RML and TLL lost more than 75% of their activity after the first reuse and almost 90% after the second cycle, to 12.5 and 25% in mass of enzyme. However, both lipases showed the same behavior after reuse, they showed a rapid deactivation after the first cycle, probably caused due to alcohol inhibition and glycerol adsorption (the co-product of transesterification reaction) by lipase support [90]. As glycerol is insoluble in hexane, it is probably not able to remove the glycerol layer in the case of more hydrophilic supports [91; 92].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytical** & **Technical Norm** & **Biodiesel** & **Reference** & **ASTM D6751** & **EN 14,214 [60]** \\
**Parameter** & **Adopted** & **Babassu** & **[58]** & **[83]** & **EN 14,214 [60]** \\ \hline \hline \begin{tabular}{c} Acidity Level \\ (mgKOH/g) \\ \end{tabular} & \begin{tabular}{c} [PERSON] \\ Institute [59] \\ EN 14,103 [72] \\ \end{tabular} & 0.41 & 0.425 & 0.5 maximum & 0.5 maximum \\ \begin{tabular}{c} Ester content (\%) \\ Kinematic \\ Viscosity at 40 \({}^{\circ}\)C \\ (mm\({}^{2}\)/s) \\ \end{tabular} & 96.8 & nr\({}^{\text{a}}\) & nr\({}^{\text{a}}\) & 96.5 \\ \begin{tabular}{c} Density at 20 \({}^{\circ}\)C \\ (kg/m\({}^{3}\)) \\ \end{tabular} & \begin{tabular}{c} ASTM D45-18 [61] \\ ASTM D1480 [84] \\ \end{tabular} & 1.6 & 4.2 & 1.9–6.0 & 3.5–5.0 \\ \
\begin{tabular}{c} Density at 20 \({}^{\circ}\)C \\ (kg/m\({}^{3}\)) \\ \end{tabular} &
\begin{tabular}{c} ASTM D1480 [84] \\ \end{tabular} & 820 & 872 & \(<\)880 & 860–900 \\ \hline \hline \end{tabular}  
* nr = none reported.  
\end{table}
Table 6: Biodiesel properties of residual oil from babassu obtained enzymatically.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 3 Channel Estimation
### Channel Prediction  
DMD/PiDMD has demonstrated its efficacy in extracting intrinsic time and space modes from empirical measurements. In this section, we explore the effectiveness of DMD/PiDMD within the context of CF-MMIMO systems. Specifically, DMD methods prove to be an exceptionally powerful tool for channel prediction. This section will address the major challenges associated with channel prediction and discuss the utility of DMD/PiDMD/mpeDMD in overcoming these challenges.
## 5 Experimental Results
### Case Study of VLSP Benchmark  
This benchmark utilised is the VLSP-LLM 2023 [56], which mirrors HuggingFace's Open-LLM Leaderboard [57]. However, it is customised for the Vietnamese language. It consists of four unique evaluations: ARC Challenge, HellaSwag, MMLU, and TruthfulQA, which are fine-tuned for the Vietnamese language. This extensive suite of benchmarks facilitates a thorough assessment of the language models' abilities to comprehend Vietnamese text across diverse domains and levels of complexity. Within this benchmark, we have conducted an analysis among our model, Expert-B, and the subsequent models as depicted in Table 5 as follows:  
* Bkai-foundation-models/vietnamese-llama2-7b-40 GB: This model is a LLaMA-2 variant, which has an extended vocabulary size in Vietnamese and has been pretrained on a Vietnamese corpus.
* SeaLLMs/SeaLLM-7B-v1 [58]: Multilingual LLM supported language in South-East Asia Countries, which achieved SOTA on a multi-SEA benchmark.
* Vinai/PhoGPT-7B5-Instruct [59]: A monolingual model that has been developed for an instruction following ability for chatbots operating in Vietnamese.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**English**} & \multicolumn{2}{c}{**Vietnamese**} \\ \hline
**Categorical** & **Phoenix** & **Expert-B** & **Total** & **Phoenix** & **Expert-B** \\ \hline coding & **5** & 2 & 7 & 6 & 1 \\ common-sense & 2 & **8** & 10 & 4 & **6** \\ counter-factual & 4 & **6** & **6** & 4 & \\ fermi & **9** & 1 & 10 & **8** & 2 \\ generic & 2 & **8** & 10 & **5** & **5** \\ knowledge & 1 & **9** & 10 & 3 & **7** \\ math & 1 & **2** & 3 & **3** & 0 \\ roleplay & 4 & **6** & 10 & 4 & **6** \\ writing & 1 & **9** & 10 & **5** & **5** \\ \hline Total wins & 29 & **51** & 80 & **44** & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of the number of wins for each model over the categories in both English and Vietnamese. The bold numbers indicate the model that won in each category.  
Experimental results show that Expert-B has demonstrated superior performance, surpassing all other models with impressive results and significantly outstripping its counterparts. In the realm of supervised fine-tuning, our findings consistently demonstrate the exceptional performance of our model, Expert-B, surpassing other models in the comparison. This notable accomplishment underscores the effectiveness of the fine-tuning methodology and the model's adeptness at capitalizing on its training data to achieve outstanding results, even when pitted against larger counterparts. Furthermore, it is noteworthy that Expert-B does not undergo continued pretraining in Vietnamese, yet it still outperforms the aforementioned models, achieving remarkable performance levels. The success of Expert-B emphasises the substantial potential of well-executed fine-tuning approaches with synthetic data in enhancing the capabilities of large language models, particularly in contexts necessitating specialised language processing.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Reference Benchmarking Scenario
* _Real-world traces, indoor environment_: We use the Haggle traces [23], a dataset on a human mobility experiment lasting four days at Infocom 2006. A total of 78 volunteers joined the experiment carrying an iMote device with a Bluetooth radio (about 30 m of range) used to log contacts between users. * _Real-world traces, outdoor environment_: We use the Rome Taxi traces [48], a dataset on a set of 320 taxi cabs monitored for 30 days, from 1 February-2 March of 2014. Each taxi driver has a tablet equipped with a GPS device that periodically (7 s) retrieves the GPS position and sends it to a central server. The settings used to run our simulations are summarized in Table 2.  
The metrics used to compare the performance of the protocols are described in the following:  
* _Delivery ratio_ is the ratio between the delivered messages over the generated ones. * _Overhead_ is the ratio between the total number of transmissions over the number of delivered messages. * _Average delay_ is the average time needed for a message to reach its destination. Those metrics have been used not only to evaluate the performance of the DTN protocols, but also to evaluate to what extent the proposed techniques, namely QM, FW and R, impact performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Indoor Environment** & **Outdoor Environment** \\ \hline Simulation time & 43,200 s (12 h) & 30 days \\ Transmission speed & 25 kBps & 25 kBps \\ Transmission range & 10 m, 30 m & 100 m \\ Node speed & 0.8 m/s... 2.0 m/s & 5 km/h... 50 km/h \\ Message size & 100 B-200 B & 100 B-200 B \\ Message creation interval & [60–180] s & [60–180] s \\ Buffer size & 10 kB, 100 kB & 100 kB, 1 MB \\ Number of nodes & 10, 20, 40, 70 & 320 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation settings. The results are obtained varying the above parameters for each protocol and averaging the results over fifteen runs. Each experiment was repeated as many times as was deemed appropriate in order for the standard deviation of the results to lie within the 95% confidence interval. We note that the existing version of the ONE simulator implements only one of the above mobility models (namely, random walk). We extended ONE by implementing the two other synthetic mobility models (Levy Walk [16] and Community [6; 22]) and a mechanism for reproducing the motion of nodes based on real traces of mobility [23; 24]. Our implementation of the two models and the extensions to reproduce real-world mobility traces are open-source and available online, to be used freely by other researchers [27].
## 3 LSTM Network
### Back Pass of LSTM Network with a Hybrid Loss Function  
The BPTT (back propagation through time) is an inverse process of the forward pass of LSTM, which is aimed to determine the proper weight matrices and biases of the network. As presented in Figure 8, the aeroelastic force output of the current time step was used as the input of the Newmark-\(\beta\) algorithm to calculate the corresponding bridge deck response, which became the input of the next time step. During such a process, any small error will be  
Figure 6: Schematic of LSTM cell.  
accumulated and augmented through time steps, and finally may cause inaccurate results or divergence.  
To improve the training efficiency and stability, a hybrid loss function was designed as:  
\[L_{h}=L_{f}+L_{r} \tag{11}\]  
where \(L_{f}\) and \(L_{r}\) respectively represent the loss functions related to the aeroelastic forces and bridge deck responses. These losses can be expressed as:  
\[L_{f}=\frac{1}{2N}{\sum_{j=1}^{N}}\bigg{(}F_{j}-\widetilde{F}_{j})^{2} \tag{12}\]  
\[L_{r}=\frac{1}{2N}{\sum_{j=1}^{N}}\bigg{(}R_{j}-\widetilde{R}_{j})^{2} \tag{13}\]  
where \(N\) represents the number of training samples; \(F_{j}\) is the target aeroelastic force (based on CFD simulations); \(\widetilde{F}_{j}\) is the corresponding results generated by the LSTM network; \(R_{j}\) is the target bridge deck response (based on the CFD simulations); and \(\widetilde{R}_{j}\) is the corresponding results generated by the Newmark-\(\beta\) algorithm. For each batch of training datasets, as shown in Figure 9, the network first generated the aeroelastic force time history (and the corresponding \(L_{f}\)) that were used as the input for the Newmark-\(\beta\) algorithm to calculate the bridge deck responses (and the corresponding \(L_{r}\)). The Adam method was adopted to minimize the final hybrid loss function \(L_{h}\) through updating the weight matrix and biases  
Figure 8: The LSTM network prediction procedure.  
Figure 7: The structure of a multiple-layer LSTM network.  
of the network. The LSTM network in this study contained three hidden layers, and the hidden size of each LSTM layer was set to be 32. Other hyperparameters of the LSTM network, such as the batch size of datasets (200) and the learning rate, were determined by trial and error.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
### Contributions of This Study  
1. We proposed a novel Position-Monitor-based Hybrid Routing Protocol (PMHRP), which possesses topology and geographic routing protocol features. Furthermore, the PMHRP establishes the shortest path based on the locations of UAVs.
2. The proposed scheme introduces the PHC-UAV which limits the message exchange and avoids message flooding across the network. Furthermore, we have considered the position of UAV as the main parameter based on three different approaches: (a) centroid approach, (b) static approach, and (c) random approach based on linear congruential generator (LCG) according to the network environment.
3. The PHC-UAV only shares the geographic information of all UAVs in the network and is not responsible for calculating the shortest path for each UAV; therefore, the packet overhead and load on the PHC-UAV decrease. Unlike other clustering routing schemes where the cluster head forwards the data across the network as result, the load on cluster head increases, which results in the degradation of the whole network's performance.
4. Moreover, for the centroid approach of PHC-UAV selection, centrality and degree of betweenness are considered to have a stable and efficient path from source to destination.
5. To justify the efficiency of the proposed scheme in comparison with benchmark schemes under different network scenarios and various mobility speeds considered. The results show the efficiency of our proposed scheme in complex network conditions.
## 6 Results and Discussion
### The Comparative Analysis of Localization Performance  
Figure 8 presents a comparative analysis of AE source localization performance between two approaches: (a) a hybrid deep learning model incorporating GAN-based data augmentation and an Inception network, and (b) a model using only the Inception network without GAN-based augmentation. In Figure 8a, the hybrid deep learning approach demonstrates remarkable accuracy. The predicted locations (stars) closely align with the actual source locations (squares) across the grid, with minimal deviation. This high accuracy is consistent across the entire plate, suggesting robust performance regardless of the source's position relative to the sensor. In contrast, Figure 8b shows results without GAN-based augmentation. Here, the discrepancies between actual and predicted locations are significantly more pronounced. Many predicted points (stars) are substantially displaced from their corresponding actual locations (squares). Notably, the errors appear to increase towards the edges and corners of the plate, indicating degraded performance for sources further from the sensor. The marked difference in accuracy between these approaches underscores the significant impact of GAN-based data augmentation. By generating additional synthetic training data, the hybrid model appears to have developed a more robust and generalized representation of AE signal characteristics across the entire plate. This results in more accurate predictions, even for locations that might be underrepresented in the original dataset.
## 1 Introduction
Prior studies have attempted fine-grained rewards, aiming to obtain an optimal policy of action for continuous control. Meanwhile, Model Predictive Control (MPC) [26; 27] optimizes vehicle control under constraints using objective functions. However, despite yielding optimal solutions, the computational overhead for real-time optimization in high-speed driving scenarios remains a challenge. Complex state space models and longer horizon calculations escalate computational demands significantly. This paper proposes a framework for achieving stable driving maneuver training with reinforcement learning. To further promote efficient learning, we devise a two-stage hierarchical structure for driving tasks. This structure separates decision-making and trajectory generation modules, enabling online sub-goal generation conditioned for optimal trajectory planning. Leveraging both CER and the hierarchical structure, the RL agent exhibits enhanced training stability and adaptivity to evolving sub-goals. Moreover, we develop an MPC-synchronized reward structure that eliminates the need for extensive reward shaping. This hybrid structure allows concurrent computation of rewards alongside MPC control signals, facilitating training by utilizing optimized references with reduced reliance on manual crafting. Importantly, both the reward function and MPC are bypassed during the evaluation phases, ensuring real-time performance without their computational overhead. To evaluate our approach, agents are trained and tested in a complex gym highway environment [28], showcasing the efficacy of the proposed RL-MPC hybrid approach in navigating challenging highway scenarios efficiently. By demonstrating the adaptability and efficiency of our framework in this robust testing ground, we pave the way for the successful deployment of RL-based autonomous driving systems in dynamic highway settings. Our contributions in this paper are as follows:We address imbalanced goal distributions in driving environments through a novel off-policy training method called CER, enabling the RL agent to dynamically adapt to changing sub-goals during driving scenarios. 2. We propose a two-stage hierarchical structure for driving tasks, separating decision making and trajectory generation. This approach fosters efficient learning by facilitating online sub-goal generation tailored for optimal trajectory planning. 3. We introduce an MPC-synchronized reward structure that eliminates the need for extensive reward shaping. This hybrid framework allows concurrent reward computation alongside control signals, utilizing optimized references and minimizing manual tuning. The remainder of the paper is organized as follows: Section 2 presents an overview of related works on the proposed method. Section 3 demonstrates the CER and the framework for a highway driving task, while Section 4 presents the experimental environment and the training results. Subsequently, we demonstrate the driving performance of the proposed method. Our framework is then evaluated qualitatively and quantitatively compared with other methods. Finally, Section 5 closes the paper with the conclusions.
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion
### Geographical Distribution  
Figure 5 shows the location of the trough minimum on global maps separately for the northern and southern hemispheres. The trough minimum location is distributed around the magnetic pole (marked by a magenta star sign), forming an approximately elliptical shape.  
Figure 4: Schematic diagram of the mid-latitude trough characteristic points.
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 4 Experimental Results  
### Training Environment  
In terms of the training equipment, tests in the paper are conducted through a PC equipped with AMD Ryzen 7 3700X CPU @3.60 GHz with eight cores, 16 Threads, an NVIDIA GeForce RTX 3090 GPU with 24 GB GDDR6X VRAM, and 32 GB DDR4 2400 MHz RAM with a Windows 10 Education operating system. The source codes of the CNN models are developed in MATLAB.  
In this experiment, pre-trained models such as MobileNetv2, VGG16, AlexNet, ResNet, DenseNet201, and GoogleNet have their hyperparameters tweaked, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Type** \\ \hline Image Size & \(160\times 160,224\times 224,227\times 227\) \\ \hline Cross Validation & Holdout \\ \hline Training/Testing & \(80/20\) \\ \hline Optimizer & SGDM, ADAM, RMSprop \\ \hline Max Epochs & \(30\) \\ \hline Minibatch Size & \(64\) \\ \hline Shuffle & Every epoch \\ \hline Initial Learn Rate & \(10^{-3}\) no decay \\ \hline \hline \end{tabular}
\end{table}
Table 3: HyperParameters for All CNN Models.  
Figure 7: Image samples for color QR codes (see Table 2 for printer brands).  
The only variable in this test is the restricted learning rate. Since all the CNN models were pre-trained at a learning rate of 0.001 on an ImageNet database with over 1 million images and 1000 different categories, the research keeps the learning rate at 0.001 for consistency. Moreover, setting a higher value than recommended, such as 0.01, might result in unstable models with lower validation rates and/or overfitting.  
Five image augmentations are used to increase the generalization of the pretrained model during training and to determine whether they could identify each printer's intrinsic signatures and banding correctly when images are rotated, mirrored, and scaled. These augmentations are Xreflection, Xtranslation, Ytranslation, Xscale, and Yscale. The augmentation techniques applied to the training dataset are summarized in Table 4.