esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
###### Abstract  
A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses. Building such systems for low-resource languages like Swahili presents significant challenges due to the limited availability of large-scale training datasets. This paper proposes a Retrieval-Augmented Generation-based system to address these challenges and improve the quality of Swahili conversational AI. The system leverages fine-tuning, where models are trained on available Swahili data, combined with external knowledge retrieval to enhance response accuracy and fluency. Four models--mT5, GPT-2, mBART, and GPT-Neo--were evaluated using metrics such as BLEU, METEOR, Query Performance, and inference time. Results show that Retrieval-Augmented Generation consistently outperforms fine-tuning alone, particularly in generating detailed and contextually appropriate responses. Among the tested models, mT5 with Retrieval-Augmented Generation demonstrated the best performance, achieving a BLEU score of 56.88%, a METEOR score of 72.72%, and a Query Performance score of 84.34%, while maintaining relevance and fluency. Although Retrieval-Augmented Generation introduces slightly longer response times, its ability to significantly improve response quality makes it an effective approach for Swahili conversational systems. This study highlights the potential of Retrieval-Augmented Generation to advance conversational AI for Swahili and other low-resource languages, with future work focusing on optimizing efficiency and exploring multilingual applications.  
Keywords:retrieval-augmented generation; Swahili NLP; language model optimization; low-resource languages; model performance
## 3 Results and Discussion
### Validation of Thermal Plumes  
#### 3.1.1 Particle Image Velocimetry  
In this paragraph, we propose a comparison between the experimental velocity magnitude distributions obtained by PIV in the sagittal and coronal planes of the thermal manikin and the same distributions obtained from the CFD model. Although we performed the same type of comparison for each thermal dummy, all data are not presented for the sake of brevity. The comparison between the experimental and numerical results represents a situation where the thermal plume develops naturally, without interaction with the ventilation system.  
Figure 9 presents the velocity magnitude distributions from PIV and CFD in the coronal plane, above the head of the thermal manikin. Figure 10 presents the velocity distributions in the sagittal plane of the same manikin. Numerical results and experimental measurements for the velocity field in the sagittal plane have the same ranges at the same height. A higher value of the velocity magnitude in the back part of the manikin can be seen in the numerical model, and this is due to a better capture of the natural convective  
Figure 8: Temperature-boundary conditions on the surface of the virtual manikins.  
boundary layer that forms from the lower part of the manikin, which tends to grow in thickness and velocity.
## 4 Discussion
### Innovation and Limitation  
The innovation of this study lies in coupling station observation data and satellite data with the optimized MLR model. This approach reduces the errors that would occur from their independent applications. Moreover, based on this coupled model, we inverted the GPP in arid regions, revealing the spatiotemporal characteristics, driving factors, and expression patterns of GPP in these regions. Currently, such studies lack systematic answers, especially in validating the use of the MLR model for GPP inversion in arid regions. Therefore, this study improves the shortcomings of the MLR model for inverting GPP in arid regions, and provides a theoretical reference for the sustainable development in arid regions.  
The limitations of this study include the following aspects. Xinjiang has complex comprehensive characteristics, and there may be slight differences in individual factors. These differences may affect the application and evaluation of models and satellites. In addition, due to factors like power outages and equipment faults, there are some discontinuous data in the station measurement data. Although interpolation and quality control have been carried out, there may still be extremely minor deviations. Finally, the resolution of remote sensing products may influence the comprehensive inversion results. In the future, it is advisable to consider using higher-resolution satellite products.
# Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors
## 2 ViT Model and Fault Resilience Evaluation Platform
### Fault Resilience Evaluation Platform
#### 2.2.3 Fault Injection Method  
In this paper, we mainly focus on bit-flip errors in memory, which are pertinent to ViT due to the large storage requirement for weights and intermediate states. In prior work, Ares by [PERSON] et al. [40] offers a fault resilience evaluation platform for DNN models based on PyTorch. Ares targets custom hardware implementations and establishes a complete resilience evaluation flow from training to fault injection. Our fault resilience evaluation platform has several extensions compared to Ares, and the differences are summarized in Table 2.  
The overall fault injection workflow consists of three main steps, namely preparation, fault injection, and inference, as shown in Figure 3:  
1. Given the original 32-bit floating-point weights \(\mathrm{W_{FP32}}\), we first apply post-training quantization to obtain the 8-bit integer representation \(\mathrm{W_{Int8}}\): \[\mathrm{W_{Int8}}=\mathrm{Quantize}(\mathrm{W_{FP32}})\] (11) where the \(\mathrm{Quantize}(\cdot)\) function is defined at (8).
2. Bit-flip faults are injected directly into the quantized integer weights: \[\mathrm{W_{Int8}^{Fault}}=\mathrm{BitFlip}(\mathrm{W_{Int8}},\mathcal{F})\] (12) where \(\mathcal{F}\) represents the injected fault mask.
3. The corrupted integer weights are then dequantized back to floating-point format: \[\mathrm{W_{FP32}^{Fault}}=\mathrm{Dequantize}(\mathrm{W_{Int8}^{Fault}})\] (13)  
\[\hat{\mathrm{y}}=\mathcal{M}(\mathbf{x};\mathrm{W_{FP32}^{Fault}}) \tag{14}\]  
where the \(\mathrm{Dequantize}(\cdot)\) function is defined at (10), \(\mathbf{x}\) is the model input, \(\mathcal{M}\) is the target ViT model, and \(\hat{\mathrm{y}}\) is the model output prediction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ares** & **Our Extensions** \\ \hline Quantization Method & Fixed-Point Quantization & Integer Quantization \\  & & (Int16, Int8, Int4) \\ Support Models & Classical CNNs (e.g., NLP, LeNet, AlexNet, VGG, etc.) & Different kinds of ViTs (e.g., original ViTs, Swin \\ Different Architecture & & Transformer, and DeepViT) \\ Granularity & Model-wise, Layer-wise & Type-wise, Head-wise \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between Ares and our improved approach.
# Efficient Filtering for Edge Extraction under Perspective Effect
## 3 Lane Marking Detection
### Scale and Orientation Assignment for Lane Markings  
Once we have the parametric function of the lane markings in the frame buffer, the local orientation at the boundary of the lane markings is the tangent direction of the parametric function at that location. This can be formulated as the derivative of c to r at any pixel location,  
\[O(c,r)=\frac{\partial c}{\partial r}|_{(c,r)}. \tag{24}\]  
Substituting this into the parametric function in Equation (23), we can determine the parametric form of the local orientation at the boundary of the lane markings, according to the arc length parameter \(t\),  
\[O(c,r)=\frac{r_{f}\big{(}t^{2}\big{(}-c_{3}h_{p}\phi_{p}+2c_{0}c_{3}\phi_{p}+c _{2}\big{)}-c_{0}\big{)}}{c_{f}h_{p}\big{(}c_{3}t^{2}\phi_{p}-1\big{)}}+\frac{ r_{f}\big{(}6c_{2}c_{3}t^{4}\phi_{p}+t^{3}\big{(}2c_{3}-2c_{2}^{2}\phi_{p} \big{)}\big{)}}{c_{f}h_{p}\big{(}c_{3}t^{2}\phi_{p}-1\big{)}}. \tag{25}\]  
According to this parametric function, we can calculate all the possible local orientations of the lane markings, varying the parameters in these equations. In practice, the lateral position parameter \(c_{0}\) is the most important indicator of the local orientation of the lane markings. By varying \(c_{0}\) and \(t\) and leaving the others constant, we can create a map showing the local orientation at every location that may be a pixel from the boundary of the lane markings. We call this the orientation map, as illustrated in Figure 8. Some of the other parameters, such as the intrinsic and extrinsic parameters, can also reasonably be set as constant. This is because all of the cameras in DAS have been mounted carefully and can be carefully calibrated before application. The other parameters are either almost constant and have little influence on the appearance of the lane markings in the frame buffer or can be determined using the tracking strategy.  
According to our definition, the localized target size is directly related to the width of the lane markings, which are in proportion to the derivative of \(c\) to \(c_{0}\) along the normal direction of the local orientation at that location, which can be determined by the following:  
\[S(c,r)=\frac{\partial c}{\partial c_{0}}|_{(c,r)}sin(O(c,r)). \tag{26}\]  
Similarly, substituting the localized target size into parametric function in Equation (23), we can determine the parametric form of the size of the localized lane markings at the boundary of the lane markings, according to the arc length parameter \(t\),  
\[S(c,r)=\frac{2c_{3}t^{2}\phi_{p}+2c_{2}t\phi_{p}+1}{tc_{f}}sin(O(c,r)). \tag{27}\]  
Combining this information with the parametric function in Equation (25), we can create a map showing the size of the localized lane markings at every location that may be a pixel from the boundary of the lane markings. We call this the orientation map, as illustrated in Figure 8.  
With the orientation map and the scale map, we can steer the local edge extractor for the lane markings to the expected orientation and scale at every location in the frame buffer.
# A Study on the Application of Laser Cleaning on the Weld of the Gas Fuel Supply Pipe for DF Engine
## 2 Materials and Methods
### Experimental Methods  
In this study, experiments were conducted according to laser cleaning process parameters such as energy density (\(D_{\text{e}}\)), defocused distance (\(f_{\text{d}}\)), and an angle of a laser head (\(\theta\)) for butt and fillet welded specimens. A portable optical head was fixed and a stage was used to derive quantitative results according to process parameters. As manual cleaning based on a portable optical head was applied to prototypes, a fundamental experiment was performed to identify a range of conditions of process parameters for facilitating laser cleaning.  
Figure 2 illustrates the laser cleaning experiments conducted according to the shapes of weldment. A butt weld specimen was placed on top of the stage, and a fillet welded specimen was fixed at a jig, which was also placed at the stage and utilized to adjust the rotation angle. Laser beams were irradiated on the specimens in the x direction by a scanner, and the stage was moved to the y direction to scan the 30 mm \(\times\) 30 mm areas of the specimens. To derive uniform cleaning results, laser beams with Gaussian distribution were overlapped by 50%, as illustrated in the schematic in Figure 2a. In the processing process using a pulse laser, the laser beam overlap rate \(R\) is obtained through Equation (1) [17,18]. Here, \(v\) is the processing speed (mm/s), \(d\) is the beam diameter (mm), and \(f\) is the pulse frequency (Hz).  
\[R=[1-(\frac{\
u}{d\times f})]\times 100 \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Q-Switching Fiber Laser** & **Value** \\ \hline Beam mode & Single mode \\ Input beam diameter (\(D\)) & 7 mm \\ Focal length (\(f\)) & 250 mm \\ Wave length (\(\lambda\)) & 1064 nm \\ Average power (\(P_{\text{ave}}\)) & 100 W \\ Peak power (\(P_{\text{P}}\)) & 6.7 kW \\ Pulse duration time (\(\tau_{P}\)) & 150 ns \\ Beam size & 97 \(\upmu\)m \\ M\({}^{2}\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of laser cleaning equipment.  
Figure 1: Experimental equipment and setup for laser cleaning.
# Assessment of Air-Pollution Control Policy's Impact on China's PV Power: A System Dynamics Analysis
## 3 Methodology
### Flow Diagram
#### 3.2.3 Installed PV Capacity Subsystem  
To stimulate the development situation of China's PV power, we built the installed PV capacity subsystem, as shown in Figure 6. At present, China has price subsidy for full generation of distributed PV power, which can be feed back to grid after self-consumption at coal-fired benchmark price. PV power plants can sale all electricity to grid at PV benchmark price. These electricity price and price subsidies determine the revenue of PV power industry, which will be partly used to make investment in new installed capacity. The investment is also influenced by the gap between installed PV capacity target and actual value. Therefore, the government can control installed PV capacity by adjusting capacity target. Newly-added installed PV power capacity is determined by its investment and construction cost per capacity. We assume that the utilization hours of distributed PV power and PV plant are equal. Thus, the composition of installed PV capacity decided the composition of PV power generation.  
\[\mathrm{RPD}=\mathrm{GPD}\times\mathrm{SPD}+(1-\mathrm{PS})\times\mathrm{GPD} \times\mathrm{PD} \tag{7}\]  
\[\mathrm{RPP}=\mathrm{GPP}\times\mathrm{PB} \tag{8}\]  
\[\mathrm{IPD}=\mathrm{RPD}\times\mathrm{IPPD}\times\mathrm{PFD}/\mathrm{ISP} \tag{9}\]  
\[\mathrm{IPP}=\mathrm{RPP}\times\mathrm{IPPP}\times\mathrm{PFP}/\mathrm{ISP} \tag{10}\]  
\[\mathrm{CPD(t)}=\mathrm{CPD(t-dt)}+(\mathrm{CPDN})\mathrm{dt}=\mathrm{CPD(t-dt) }+(\mathrm{IPD}\times 100/\mathrm{CPD})\mathrm{dt} \tag{11}\]  
\[\mathrm{CPP(t)}=\mathrm{CPP(t-dt)}+(\mathrm{CPPN})\mathrm{dt}=\mathrm{CPP(t-dt) }+(\mathrm{IPP}\times 100/\mathrm{CPP})\mathrm{dt} \tag{12}\]  
Figure 6: Flow diagram for installed PV capacity subsystem.  
We can calculate the revenue of distributed PV power (RPD) as in Equation (7), and calculate the revenue of PV power plant (RPP) as Equation (8). Because distributed PV power has electricity price subsidy (PS) for the whole generation, and its excessive generation can be fed back to grid after self-consumption at desulfurating electricity price (PS). PV power plants can only get revenue (RPP) from selling all their generation (GPP) to grid at PV benchmark price (PB). Investment of distributed PV power (IPD) depends on RPD, discretionary investment proportion (IPDP), stimulating factor of policy (PFD) and proportion of investment subsidy (ISP) as in Equation (9). In addition, investment of PV power plant (IPP) is very similar as in Equation (10). Distributed PV installed capacity (CPD) at time t depends on CPD at time t-dt and newly-added capacity (CPDN), which determined by IPD and building costs per unit (CPD). The capacity of PV power plant (CPP) has very similar function as in Equation (12).
# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments
### Data Analysis
#### 4.1.4 Implementation Details  
For the implementation phase of our study, we've adopted a rigorous and replicable approach to ensure the reliability of our results. Specifically, we've carried out each experiment a total of five times, each with a unique seed, to ensure the robustness of our findings. The average of these runs is then reported as the final performance metric of our model, providing a solid foundation for the evaluation of our spatial GNN's effectiveness in predicting product demand.  
In the detailed setup of each experiment, our model is meticulously trained over a span of 50 epochs. This extensive training period is carefully monitored with an early stopping mechanism in place, a strategic decision aimed at curbing the potential for overfitting and ensuring the model's generalizability to unseen data. Furthermore, we have calibrated the architecture of our spatial GNN to consist of three layers. This specific configuration is chosen to strike a balance between capturing the intricate spatial relationships within the data and avoiding the pitfall of over-smoothing, where the model's output becomes indistinct and loses valuable detail.  
The technical execution of our method leverages the powerful capabilities of PyTorch v2.3.0, a choice that facilitates the efficient and flexible development of deep learning models. To harness the computational intensity of training a spatial GNN, our experiments are conducted on a state-of-the-art NVIDIA RTX 4090 GPU manufactured by TSMC in the Hsinchu, Taiwan, equipped with an ample 24 GB of memory. This hardware setup not only accelerates the training process but also enables the handling of complex models and large datasets with ease, ensuring that our implementation is both fast and effective.
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
## 2 Materials and Methods
### Laser Cleaning Robot Trajectory Planning
#### 2.3.3 Execute Point Fitting to Generate Cleaning Robot Trajectory
Hierarchical Smooth Transition of Motion Trajectories  
During laser cleaning processes, it is crucial for the robot to achieve smooth transitions between different cleaning levels. By employing B-spline curve fitting, discrete cleaning execution points have been successfully transformed into continuous path planning. However, it is necessary to ensure smooth and appropriate transitions between these paths at different levels, reducing trajectory discontinuities when switching between levels, thus ensuring stability and efficiency in the cleaning process. Between the endpoints of each level and the starting points of the next level, the adjustment of control points in B-spline curves enables achieving consistent first-order derivatives at the connecting points of two different levels. This optimization of the connection path ensures smoothness, as shown in Equation (22). When the first-order derivatives at the connecting points of two different-level curves are equal, it indicates continuity in the tangent direction at those points. Figure 11 provides a comparative illustration, demonstrating the difference between connecting points of two different-level curves with and without the implementation of the first-order derivative consistency constraint. This approach helps avoid abrupt changes in angles at the path connections, ensuring smoother and more natural robot motion. \[C^{\prime}(d_{1})=C^{\prime}(d_{2}) \tag{22}\]  
In the equations, \(C^{\prime}(d)\) represents the first derivatives of the curve at point d. \(d_{1}\) and \(d_{2}\) are the parameter values at the connection points of adjacent level paths. During the robot's transition between levels, it is essential to reduce its motion speed appropriately to avoid mechanical arm vibrations and posture deviations caused by rapid movements. Moreover, adjustments to the robot's posture are necessary to maintain an appropriate distance and angle between the laser cleaning head and the workpiece surface. This helps decrease mechanical wear and the potential risks of malfunctions resulting from improper motion.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 3 Hybrid Complementary Energy Storage System
### Predictive Converter Control Method
#### 3.3.1. Current Prediction Model of Converter  
In Boost mode, V\({}_{2}\) and the anti-parallel freewheeling diode D\({}_{1}\) are in working state. During the turn-on stage of switch V\({}_{2}\) (Sc\({}_{2}\) = 1), the energy storage unit charges the inductor \(L\) through V\({}_{2}\), as shown in Figure 5a. Equation (4) can be obtained from its equivalent circuit, which is further discretized to obtain the predicted current value at the \(k+1\)th moment, shown in Equation (5).  
\[L\mathrm{d}I_{\mathrm{sto}}/\mathrm{d}t=U_{\mathrm{sto}}, \tag{4}\]  
\[i_{\mathrm{sto}}(k+1)=T_{\mathrm{s}}u_{\mathrm{sto}}(k)/L+i_{\mathrm{sto}}(k), \tag{5}\]  
where \(u_{\mathrm{sto}}(k)\) denotes the voltage detected at both ends of the energy storage unit (the battery or the supercapacitor) at the \(k\)th moment; \(i_{\mathrm{sto}}(k)\) represents the current of the energy storage unit, which flowing through inductance \(L\) at the \(k\)th moment and \(T_{\mathrm{s}}\) is the sampling period.  
During the switching off stage of V\({}_{2}\) (Sc\({}_{2}\) = 0), the electromagnetic energy stored in inductance \(L\) is released to the DC side through the anti-parallel freewheeling diode D\({}_{1}\). The following formula can be obtained from the circuit shown in Figure 5b.  
\[L\mathrm{d}I_{\mathrm{sto}}/\mathrm{d}t=U_{\mathrm{sto}}-U_{\mathrm{dc\_sto}}. \tag{6}\]  
Equation (6) is discretized and the predicted current is as follows.  
\[i_{\mathrm{sto}}(k+1)=T_{\mathrm{s}}[u_{\mathrm{sto}}(k)-u_{\mathrm{dc\_sto} }(k)]/L+i_{\mathrm{sto}}(k), \tag{7}\]  
where \(u_{\mathrm{dc\_sto}}(k)\) denotes the DC side voltage of the converter at the \(k\)th moment.  
Similarly, when the bidirectional DC/DC converter operates in Buck mode, its prediction model of current is established as follows.  
\[\left\{\begin{array}{ll}i_{\mathrm{sto}}(k+1)=T_{\mathrm{s}}[-u_{\mathrm{sto }}(k)+u_{\mathrm{dc\_sto}}(k)]/L+i_{\mathrm{sto}}(k),&(\mathrm{S}_{\mathrm{C1 }}=1)\\ i_{\mathrm{sto}}(k+1)=-T_{\mathrm{s}}u_{\mathrm{sto}}(k)/L+i_{\mathrm{sto}}(k),&(\mathrm{S}_{\mathrm{C1}}=0)\end{array}\right., \tag{8}\]  
where S\({}_{\mathrm{C1}}\) = 1 represents the conducting state of switch V\({}_{1}\).