esa-sceva/satcom-chunk-collection huggingface dataset:
# Recycling Red Ceramic Waste as a Raw Material for Lightweight Aggregates
## 2 Materials and Methods
### Materials
#### 2.1.3 Mineralogical Composition of Raw Materials  
The mineralogical characterisation of RC and RCW was conducted through the utilisation of X-ray diffraction. The XRD was carried out using Shimadzu XRD-7000 equipment, Shimadzu Corporation, Kyoto, Japan, with Cu K\(\alpha\) radiation at 40 mA and 40 kV in the 10 to 80\({}^{\circ}\) (20) range. The scanning speed adopted was 1.20\({}^{\circ}\)/min, and the angular step was 0.02\({}^{\circ}\). The XRD analysis of the red clay (Figure 2a) revealed a crystalline composition of biotite, illite and quartz. In turn, the main phases found in the RCW were haematite, illite, muscovite and quartz (Figure 2b). The results found are consistent with the data obtained in the XRF test. However, the characteristics of the RCW were not absolutely identical to those presented in other studies [14; 41; 42]. According to [PERSON] et al. [43], this variability in the mineralogical composition of RCW is mainly linked to the origin of the raw material and the manufacturing method of the ceramic element. The findings of this study lend further support to the hypothesis that RC and RCW possess considerable potential for the fabrication of LWAs, as the minerals identified in these materials are often attributed to controlling the viscosity of the liquid phase and the generation of gases during the sintering of light aggregate granules [11; 44].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Sample** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **Others** & **LOI (\%)** \\ \hline RC & 41.45 & 21.02 & 27.99 & 1.17 & 2.09 & 0.00 & 2.96 & 3.32 & 8.6\% \\ RCW & 46.23 & 5.98 & 26.31 & 5.19 & 0.00 & 0.00 & 8.56 & 7.73 & 1.09\% \\ \hline \end{tabular} \({}_{\text{LOI}}=\text{loss}\) on ignition.  
\end{table}
Table 1: Chemical composition of raw materials.
## 2 Enhanced YOLO Algorithm  
### Overview of the Enhanced YOLO Algorithm  
YOLOv5 (You Only Look Once version 5) represents the fifth iteration in the YOLO series of real-time object detection network models. Since the inception of YOLOv1, the YOLO series has undergone several enhancements, with YOLOv5 showcasing significant improvements in performance and speed over its predecessors like YOLOv4. It is capable of real-time object detection with minimal latency, making it exceptionally well-suited for applications that demand rapid responses, such as autonomous driving and security surveillance. The model employs an end-to-end training approach, allowing it to accomplish the task of object detection in a single pass without the need for multi-stage processing. Meanwhile, complex engineering scenarios such as rockfall containment require ensuring the stability and maturity of the model. The YOLOv5 model, with its sufficient development time and history of applications, boasts a more robust user base and community support, offering developers a more stable performance compared to newer models like YOLOv8.  
As depicted in Figure 1, the architectural framework of YOLOv5 is intricately composed of convolutional layers, pooling layers, residual blocks, and upsampling layers. Diverging from other object detection algorithms, YOLOv5 harnesses the technology of cross-stage feature aggregation (PANet) [17], amalgamating feature maps from various hierarchical levels to enhance the precision and recall of detections. Concurrently, YOLOv5 incorporates residual connections and convolutional modules akin to those found in ResNet [18] alongside attention mechanisms, endowing the model with robust feature representation capabilities. Moreover, YOLOv5 integrates methodologies such as data augmentation and label smoothing, significantly augmenting the model's generalization capacity and robustness. In this section, we compare the performance of mainstream activation functions to ensure the generalization ability of the training model and select the most suitable activation function and data processing configuration for specific scenarios.
## 3 Results and Discussion
### Compressive Strength
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mixture** & \(f_{cm,2}\)**/\(f_{cm,28}\) Ratio** & **Compressive Strength Development Rate at 20 \({}^{\circ}\)C** \\ \hline C55\_SFA15\_SF3 & 0.09 & very slow \\ C100\_SFA15\_SF3 & 0.16 & slow \\ C100\_SFA30\_SF3 & 0.19 & slow \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Rate of compressive strength development for HPC mixtures according to EN 206.  
where \(f_{c}(t)\) is the compressive strength of concrete at an age of \(t\) days in MPa, \(f_{c28}\) is the compressive strength of concrete at an age of 28 days, and \(d,\beta_{cc}\) are coefficients, the values of which depend on cement type and strength class. As the above-mentioned models were created to predict compressive strength development of concretes containing mainly OPC, the strength development of the designed HPC was predicted using coefficients suggested by [PERSON] et al. (2018) and [PERSON] (2019) for concretes containing GGBFS, as reported in Table 5. A comparison of the test results with the models is presented in Figure 5 and Table 6.  
Analyzing the results obtained in an early curing period, it can be stated that the predicted 2-day compressive strength values were vastly greater than the experimental values for each of the time-dependent models. The most significant overestimation of early compressive strength was observed for the Eurocode 2 and MC 2010 standards. Significant differences between early strength test results and values predicted using this model were also observed for High-Strength Concretes with slag cement CEM III/A 32.5 N in the article of [PERSON] [29]. Starting from the 7 th day of curing for the C100_SFA15_SF3 and C100_SFA30_SF3 mixtures and the 14 th day for the C55_SFA15_SF3 mixture, a gradual decrease in the difference between the tested and calculated values was observed. This observation confirms the conclusion presented in Table 4 regarding the slow and very slow compressive strength development of HPCs with a low Portland cement clinker content. Based on the predicted strength values after 28 days of curing, it can be confirmed that HPC containing slag and mineral additives can achieve compressive strength comparable to concretes with OPC in the long curing period. However, the commonly used time-dependent strength development models do not describe the rate of low-clinker HPC strength gain sufficiently, especially in an early curing period.
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 2 Design of Antenna Structure
### The Compact Multiband Monopole Antenna
The current flows similarly to Figure 4b and still radiates electromagnetic power effectively. Figure 4e exhibits a current distribution similar to Figure 4d but with the distribution along the right arm of the ground at 6.32 GHz, where the current flows similarly to Figure 4b and radiates electromagnetic power effectively. Figure 4f shows a different current distribution. The current along the half-wavelength rectangular slot radiates electromagnetic power. This rectangular slot is formed by the middle arm and the parallel arm. The current flows similarly to an inductive slot resonator, with zero current occurring in the middle of the longer side of the rectangular slot and maximum current appearing at the shorter sides of the rectangular slot. Figure 4: The current distributions and radiation patterns of the compact multiband monopole antenna. (**a**) 2.46 GHz. (**b**) 3.47 GHz. (**c**) 4.41 GHz. (**d**) 5.45 GHz. (**e**) 6.32 GHz. (**f**) 7.06 GHz. Figure 5 shows that the simulation of radiation efficiency is mostly above 70% within the required bands, with peak values of 86.7%, 83.3%, 97.5%, and 95.1% occurring at 2.3 GHz, 3.3 GHz, 5 GHz, and 6.7 GHz, respectively. The simulated radiated power mostly matches the radiation efficiency, with peak values of 0.8 W, 0.75 W, 0.97 W, and 0.93 W occurring at 2.4 GHz, 3.45 GHz, 4.4 GHz, and 6.4 GHz, respectively. The gain is consistently positive within the required bands, with peak values of 1.43 dBi, 1.44 dBi, 3.8 dBi, and 4.7 dBi occurring at 2.46 GHz, 3.47 GHz, 5.7 GHz, and 7 GHz, respectively. Due to the superposition of currents, the radiation pattern exhibits higher gain at higher frequencies, but it also creates an unbalanced omnidirectional radiation pattern.
# Instability Study of Magnetic Journal Bearing under S-CO\({}_{2}\) Condition
## 3 Experiments with Magnetic Bearings Under S-CO2 Conditions  
### Magnetic Bearing Experiment  
The purposes of the magnetic bearing experiment were obtaining the shaft trajectory data and finding the correlation with the lubrication model prediction. After analyzing the correlation, the required AMB performance to levitate the shaft for prescribed operating conditions can be suggested.  
From the analysis, it was shown that the fluid force is sensitive to the temperature and pressure. Therefore, the experiment was performed while considering those properties under control. The pre-existing S-CO2 pressurizing experiment (S-CO2 PE) facility in KAIST was utilized to achieve appropriate experimental conditions for studying the magnetic bearing. S-CO2 PE consists of an S-CO2 pump, a printed circuit heat exchanger (PCHE)-type pre-cooler and a water-cooling loop to control the temperature and the pressure. This facility is shown in Figure 10.  
The AMB test rig was constructed as shown in Figure 11. The test rig was a compressor mockup consisting of an AMB and its controller. The AMB has a displacement sensor for active control; therefore, this test rig had the advantage of obtaining shaft position data in real time. By removing the impeller from the compressor mockup, only the effect of the thin film surrounding the rotating shaft was analyzed. The main test data were the shaft position data from the displacement sensor in the AMB and the electric current into the electromagnets.  
Figure 11: Design drawing of the test compressor with the AMB.  
Figure 10: S-CO2 power cycle experimental facility (S-CO2 PE) in KAIST.  
The relationship between the operation speed and the shaft balance with AMB is shown in Figure 12, which is the compressor's Campbell diagram. From this figure, the test speed 30,000 rpm is far from the critical speed, which is around 90,000 rpm.  
To reach appropriate test conditions, the AMB test rig was connected to the S-CO\({}_{2}\)PE, as shown in Figures 13 and 14. The booster pump was used to increase the inlet pressure of the S-CO2 PE. The pump generated flow to the AMB test rig and compensated the pressure drop. The generated heat was dissipated by the water-cooling system.  
Figure 14: Layout of the magnetic bearing instability experiment.  
Figure 12: Campbell diagram of test compressor.  
Figure 13: AMB and the compressor system for S-CO\({}_{2}\).  
The displacement sensor used in the magnetic bearing was an inductance type which used an AC coil and its inductance corresponded with the distance between the coil probe and the target. The output signal of the sensor had a sensitivity of \(\pm 400\)\(\upmu\)m/2 V with 16 bit. From the sensor test, the noise level was observed to be less than 1 \(\upmu\)m of the RMS (Root Mean Square) and 3 \(\upmu\)m of the peak. While the eddy current sensor's performance was sensitive to the sheet resistance change with different temperature, the inductance sensor was stable.
# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 4 Experimental
### Data Generation and Model Training  
We tested our method on a commonly used benchmark for 3D point cloud completion, i.e., ShapeNet. We used 13 and 16 categories of different objects in the benchmark dataset ShapeNet-Part [27]. Since a more diverse dataset can comprehensively test the capabilities of the model, we also conducted experiments using all 55 categories in ShapeNet. Similar to the dataset of PoinTr, we randomly sampled 80% of the objects from each category of ShapeNet-55 to form the training set, and 20% for evaluation, resulting in 41,952 models for training and 10,518 models for testing. Complete point clouds were generated by uniformly sampling 2048 points from 5 randomly distributed viewpoints. During training and testing, we sampled 512 points as the ground truth \(\mathrm{F_{gt}}\) of the incomplete point cloud, that is, 25% of the original data were missing. We applied iterative farthest point sampling (IFPS) [28] to extract ground truths \(\mathrm{F_{gt}^{\prime}}\) and \(\mathrm{F_{gt}^{\prime\prime}}\) with 128 and 64 points, respectively.  
We implemented all of our models with the PyTorch deep learning framework, and used the Adam optimizer to update the parameters of the network during training. The learning rate was initially set to 0.0001, the model was trained for 200 epochs, and the continuous learning rate decreased by 0.2 every 40 epochs. We defined the batch size to be 24. The models were trained and tested on a NVIDIA GeForce RTX 3090 GPU with CUDA 11.7. The mixed attention module can be used flexibly in convolutional neural networks (CNNs) to capture feature correlations effectively, and the bidirectional point pyramid attention network (BiPPAN) can be used as a decoder in the network.
###### Abstract  
Particularly in Internet of Things (IoT) scenarios, the rapid growth and diversity of network traffic pose a growing challenge to network intrusion detection systems (NIDs). In this work, we perform a comparative analysis of lightweight machine learning models, such as logistic regression (LR) and k-nearest neighbors (KNNs), alongside other machine learning models, such as decision trees (DTs), support vector machines (SVMs), multilayer perceptron (MLP), and random forests (RFs) with deep learning architectures, specifically a convolutional neural network (CNN) coupled with bidirectional long short-term memory (BiLSTM), for intrusion detection. We assess these models' scalability, performance, and robustness using the NSL-KDD and UNSW-NB15 benchmark datasets. We evaluate important metrics, such as accuracy, precision, recall, F1-score, and false alarm rate, to offer insights into the effectiveness of each model in securing network systems within IoT deployments. Notably, the study emphasizes the utilization of lightweight machine learning models, highlighting their efficiency in achieving high detection accuracy while maintaining lower computational costs. Furthermore, standard deviation metrics have been incorporated into the accuracy evaluations, enhancing the reliability and comprehensiveness of our results. Using the CNN-BiLSTM model, we achieved noteworthy accuracies of 99.89% and 98.95% on the NSL-KDD and UNSW-NB15 datasets, respectively. However, the CNN-BiLSTM model outperforms lightweight traditional machine learning methods by a margin ranging from 1.5% to 3.5%. This study contributes to the ongoing efforts to enhance network security in IoT scenarios by exploring a trade-off between traditional machine learning and deep learning techniques.  
deep learning: intrusion detection; Internet of Things; machine learning; network security +
Footnote †: journal: Applied Sciences
## 4 Applications  
### Dual Frequency Actuation  
As an application, we propose a dual-frequency actuation in a single surface transducer, with a high-frequency wave for data transmission (above 18 kHz) and low-frequency wave (up to 250 Hz) for haptic effects. To validate this approach, we measured output signals with additional low-frequency vibrations, as described in Figure 6. Both of the signals are generated on the basis of rectangular waves. The amplitude of the data signals was set to 0.5 V, and that for the haptic effects was set to 0.1 V. Power consumption was measured as approximately 280 mW (a 40% increment compared to the data-only transmission) when low-frequency haptic signals are actuated together with the data signals; an input voltage of 5 V was used for the audio amplifier.  
Figure 7 shows that the intended high frequency signals (19 kHz) were successfully captured. Although additional noises are observed, they do not dominate over the target frequencies, meaning that the actuation of two signals does not affect the system performance. In summary, haptic effects can be designed into an application to give users the sense of actual information flow.  
Figure 6: Input signals with additional low frequency vibrations for haptic effects.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation
### Experimental Results
#### 4.3.2 BLEU Score Analysis  
Figure 10 shows the comparison of BLEU scores between the Fine-Tune and RAG phases for the four models: mT5, GPT-2, mBART, and GPT-Neo. The graph clearly indicates that all four models perform better in the RAG phase compared to the Fine-Tune phase, with improvements in BLEU scores observed across the board. From the graph, it is evident that mT5 achieved the greatest improvement in BLEU score when switching from the Fine-Tune to the RAG phase. The increase in BLEU score for mT5 was substantial, suggesting that the RAG approach significantly enhanced the model's ability to generate more accurate translations [53]. GPT-2 also showed an improvement in BLEU score, though the change was smaller compared to mT5. This indicates that while the RAG technique improved GPT-2's performance, the effect was less pronounced than in mT5. Similarly, both mBART and GPT-Neo showed steady but smaller improvements in BLEU scores under the RAG phase. While the improvements were not as large as those seen in mT5, the RAG approach still resulted in a noticeable enhancement in translation quality for these models. The results presented in Figure 10 suggest that the RAG approach consistently led to better performance across all the models tested. This improvement in BLEU scores indicates that RAG helps the models generate more fluent and accurate translations. The ability of RAG to incorporate relevant external information likely contributed to this increase in translation quality. These findings demonstrate that RAG is an effective method for improving the performance of models in Swahili NLP tasks [54].  
Figure 9: Layer-freezing performance comparison for Swahili data across models and techniques (fine-tuned and RAG).
## 5 Performance Comparison Before and After Optimization
### Analysis of Anti-Demagnetization Performance  
The comparison of the maximum demagnetization rate of the permanent magnets for the basic and optimized models with different input currents at a temperature setting of 200 \({}^{\circ}\)C is shown in Figure 17. As can be seen in Figure 17, the maximum demagnetization rates of both the second and third layers of permanent magnets of the optimized model are far lower than those of the basic model at 8 times the rated current. Figure 18 presents the distribution of the permanent magnet demagnetization rate for the optimized model at 200 \({}^{\circ}\)C and 8 times the rated current. The anti-demagnetization performance of both the second and third layers of permanent magnets are improved compared to the basic model. Table 4 illustrates the maximum demagnetization rate of permanent magnets for the basic and optimized models at 200 \({}^{\circ}\)C and 8 times the rated current. Compared with the basic model, the maximum demagnetization rate of the second layer permanent magnet of the optimized model is reduced by 81.44%, and the anti-demagnetization performance is significantly improved. This greatly reduces the risk of demagnetization of permanent magnets at high temperatures and overload conditions.  
Figure 16: Comparison of torque waveforms of basic and optimized models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Unit** & **Basic Model** & **Opti. Model** \\ \hline Average torque & N\(\cdot\)m & 10.55 & 10.30 \\ Torque ripple & \% & 20.39 & 11.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of torque performance between basic and optimized models.  
Figure 17: Comparison of the maximum demagnetization rate of permanent magnets between the basic and optimized models.  
Table 5 shows data from several studies on the optimization of demagnetization of PMA-SynRMs. There are some differences in the optimization due to the use of different types of permanent magnets and different temperature settings. The optimized demagnetization rate in the literature [26, 27] has a significant reduction compared to the pre-optimization demagnetization rate, which is consistent with the study in this paper.
## 3 Results
### Effect of Induction Temperature on Tricylene Production  
The formation of inclusion bodies due to protein misfolding may lead to inefficient catalysis and is often determined by the induction temperature [35; 36]. Thus, different temperatures of 25 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 37 \({}^{\circ}\)C were set to compare tricylene titers (Figure 1F). As a result, no detectable tricyclene was produced at 37 \({}^{\circ}\)C, while tricyclene titers of 0.06 mg/L were achieved at 25 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, indicating that a lowered temperature could enhance tricyclene titers. Given that the optimum growth temperature of the _E. coli_ strain BS1101 derived from strain MG1655 was 30 \({}^{\circ}\)C, 30 \({}^{\circ}\)C was selected as the induction temperature.  
Figure 1: GC-MS analysis of tricyclene. (**A**) _E. coli_ provides acetyl-CoA for the production of IPP and DMAPP by the MVA pathway (green box). Tricyclene was synthesized from IPP and DMAPP through a two-step conversion using GPPS and TS (blue box). (**B,C**) Total ion chromatogram (TIC) of the ZMJ01 strain, harboring one copy of the 1411-coding gene (**B**), and the tricyclene standard (**C**). (**D,E**) Mass spectrum of the tricyclene peak of the ZMJ01 strain (**D**) and the tricyclene standard (**E**). The biosynthesis of tricyclene was determined by comparing the retention time and mass spectrum with tricyclene standard. (**F**) Effects of different induction temperatures and 1411 copy number on tricyclene titers. The expression of tricyclene-producing synthases was induced by IPTG at different temperatures of 25 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 37 \({}^{\circ}\)C for 48 h. Corresponding tricyclene titers are shown. Double copies of 1411-coding gene at the induction temperature of 30 \({}^{\circ}\)C resulted in a significantly higher tricyclene titer. N.D., not detected. The error bars represent one standard deviation for three replicates. Significance (_p_-value) was evaluated using a two-tailed t-test (* \(p\) < 0.05). N.S., statistically no significance.