esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Algorithm
### Iterative Optimization  
The iterative optimization process is crucial for refining the candidate movements and achieving an accurate estimation of the vehicle's ego-motion. The above steps are repeated iteratively until the cost function converges to a predefined threshold th or a maximum number of iterations \(MAX_{\text{iter}}\) is reached.  
\[\text{Terminate if cost}(I_{1},I_{2,k})<\text{th}\quad\text{or}\quad\text{if iterations}\geq MAX_{\text{iter}} \tag{14}\]  
This ensures that the optimization process does not continue indefinitely and allows for a controlled exploration of the search space. The iterative nature of the algorithm allows for continual refinement of the candidate movements, as adjustments are made based on the results of the cost function evaluations.  
Although individual steps of seed initialization, ego-motion application, and mutation can be executed in parallel, the overall iteration process remains sequential due to dependencies on the results of the previous iterations. Specifically, the frequency of the GPU will limit the rate at which these steps can be executed. As such, careful consideration is given to the number of iterations to balance computational efficiency with the quality of the ego-motion estimation.  
The general algorithm can be summarized as shown in Algorithm 5.  
```
1:Input: Initial candidate movements \(M\), predefined threshold th, maximum iterations \(MAX_{\text{iter}}\)
2:Initialize iteration count \(iter\gets 0\)
3:while true do
4: Seed Initialization: Randomly generate \(n\) candidate movements.
5:Ego-motion Application: Apply ego-motion transformations to the second image and compare it with the first image.
6:Selection and Mutation: Retain the top \(n/2\) best candidate movements based on a cost function, and generate \(n/2\) new candidates by slightly mutating the retained movements.
7:Calculate cost \(\text{cost}(I_{1},I_{2,k})\)
8:if \(\text{cost}(I_{1},I_{2,k})<\text{th}\) or \(iter\geq MAX_{\text{iter}}\)then
9:Terminate the optimization process
10:endif
11: Increment \(iter\gets iter+1\)
12:endwhile
13:return Best candidate movement
```  
**Algorithm 5**Iterative Optimization for Ego-Motion Estimation
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 6 Testbed and Experimental Results
Regarding the occupancy of the _mpls_forward_ table, this value depends on the physical location of the node in the topology. Bmv2 devices such as sw05, sw06, sw07, sw09, and sw10 are more likely to be included in mpls tunnels used to implement the virtual paths than others such as sw01 and sw04. The relation between the physical location of devices and the number of entries is shown in Figure 10. The mean number of entries in the _mpls_forward_ table of the experiments with optimal P4-KBR system is shown next to each device. Figure 12 compares the occupancy of the tables required by the P4-KBR routing system compared to the OpenFlow-based solution (OFC-KBR) [4]. In this case, trying to optimize even more the table occupation of the nodes, the implementation of P4-KBR and OFC-KBR systems merge the wildcarded ranges with the same forwarding action when possible. That is, if the action associated to more than one wildcarded range defines the forwarding to the same node, and the ranges can be merged using the adequate bitmask, just one table entry will be required. This merging process will be more likely in small topologies than in large topologies. Figure 11: P4-KBR system. Occupacy of the device tables: number of entries in the _key_routing_ and _mpls_forwarding_ tables. As can be seen, considering the Abilene topology, the number of entries in the _key_routing_ table is reduced to an average value of 4.78 in the case of the optimal P4-KBR system implementation (using RANGE matching) and 12.17 in the case of \"translation of ranges solution\" P4-KBR (using LPM matching). The number of entries in the _mpls_forward_ table is also reduced in both cases. As previously commented, the occupancy of the _mpls_forward_ table also depends on the location of the device in the topology (see the values in green in Figure 10). In any case, it can be observed that P4-KBR system improves the occupation of tables substantially compared with the OpenFlow-based solution OFC-KBR. The obtained values indicate an average saving in the number of entries of 88.15% by the optimal P4-KBR (using RANGE matching). This saving is lightly reduced to 79.5% when LPM matching is used in the implementation.
# Improving Response Time for Crowd Management in Hajj
## 2 Related Work
The geolocation-based protocols are dynamic and select the next hop based on demand. Their geolocation-based algorithm operated over the traditional IP layer protocol. The geolocation is added to the sender node's header, and the node selects the closest possible neighbor based on the location in its header. This mechanism appears proactive and efficient. [PERSON] et al. [21] monitored a crowded area using multiple UAVs and presented a centralized data-oriented communication architecture for crowd surveillance. The data-oriented approach prioritizes the message before sending it. The ground station of the UAV network served as a central coordinator to manage the bandwidth of UAVs. The bandwidth was optimized by prioritizing the data as critical and vital. The central coordinator manages the UAVs according to the priority of information that UAVs want to send. This approach saves the energy and bandwidth of the UAV network.
# Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantization** & **Presenter/** & **Characteristics** & **Limitations** \\ \hline \multirow{3}{*}{MIN-MAX} & \multirow{3}{*}{Tengine} & The principle is simple, the operation is fast and efficient, and it is suitable for uniform distribution. & Non-uniform distribution of data results in a significant loss of accuracy. \\ \hline \multirow{3}{*}{KLD} & \multirow{3}{*}{NVIDIA} & Measure the degree of difference in the distributions before and after the quantization to find the optimal threshold value. & Unbounded, asymmetric, and the results obtained are meaningless if the two distributions do not overlap. \\ \hline \multirow{3}{*}{Easy Quant} & \multirow{3}{*}{/} & Achieves close to Int8 and Float32 performance even at Int7 or lower bits. & The accuracy of int8 seems to be similar to KLD. \\ \hline \multirow{3}{*}{AVG-MAX} & \multirow{3}{*}{/} & Taking into account the principle of simplicity and balance. & Same as MIN-MAX. \\ \hline \multirow{3}{*}{EMA} & \multirow{3}{*}{TensorFlow} & Calculates the bounds of activations on the fly. & Frequently modifying boundaries is not suitable for quantization parameter learning. \\ \hline \multirow{3}{*}{Ours} & \multirow{3}{*}{/} & It is a general quantization method that can use many of the quantization strategies described earlier. & Offline quantization training is complex and time-consuming. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison table of key characteristics and limitations of quantization strategies. Therefore, aiming at the neural network model fused with LSTM layers based on sequential data, it is necessary to design a quantization method for neural network models that integrates the LSTM layer. That is, on the one hand, the LSTM layer is split from the software level into multiple basic operators supported by the neural network accelerator, so that the neural network accelerator can support the LSTM layer on the embedded platform, and it can be combined with the traditional model quantization algorithm to broaden the scope of application of the quantization algorithm. On the other hand, the combination strategy using quantization calibration sets takes into account the sequential characteristics of the LSTM layer. The combined images are designed for the quantization calibration of regular layers. When encountering the LSTM layer, according to the time step of LSTM, the diverse image groups are gradually sent to each time step, where each diverse image group has sequential characteristics. It solves the hardware deployment problem of the sequential forecasting model and improves the accuracy of the quantization algorithm. This not only ensures the diversity and scientificity of quantization but also improves the quantization accuracy of models with LSTM layers.
## 3 Basics for Implementation of the Machine-Learning Algorithm
### Damage-Identification Results
#### 3.3.2 Damage Localization  
The dataset in Table 6 was used for localization ANN training; it was composed of 276 observation samples, including 81 features each. Also in this context, the PCA could be used to reduce the dataset dimensionality. It can be noticed in Figure 21 that more than 95% of the information within the signals was captured by considering the first 20 principal components of the dataset only.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\# Observations** & **Damage Radial Position** & **Units of Measure** \\ \hline
92 & 12 & mm \\ \hline
92 & 24 & mm \\ \hline
92 & 36 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training-set composition for damage localization.  
Figure 20: Detection ANN performances. Results are provided for the training, validation, and test subsets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equivalent Pitted Area** & **\# Errors** & **Type** \\ \hline
0 & 16/280 & False alarm \\ \hline
0.2 & 20/56 & Missed detection \\ \hline
0.35 & 13/56 & Missed detection \\ \hline
0.48 & 4/56 & Missed detection \\ \hline
0.63 & 0/56 & Missed detection \\ \hline
1 & 0/56 & Missed detection \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detailed detection ANN performance.  
Thus, the first 20 principal components of the dataset were used to train the regression ANN for damage localization. The network architecture included 20 input nodes, one output node describing the radial position of the damage as a continuous variable, and 10 hidden nodes, again defined in a way to avoid overfitting. The ANN performances are presented in Figure 22, in which the training, validation, and test regression plots are shown. As the increasing trend was correctly captured by the ANN in all the subsets, it was possible to conclude that the algorithm could generalize on new data, and no overfitting occurred. It is worth noticing that the entire dataset was processed by the algorithm in Figure 22a-c, including small damages with reduced sensitivity.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 1 Introduction
In addition, data from two contrasting surveying periods--spring and summer--were also compared in order to assess the effect of the composition of different layers (i.e., no living grass in the summer period) on the classification accuracy. In order to increase the processing quality, we tested a methodology based on the combination of spectral and semantic information to improve the classification procedure through the combined use of three informative layers: R-G-NIR mosaics, NDVI (normalized difference vegetation index), and DSM (digital surface model). The paper is organized as follows. Section 2 provides details about the study area and the dataset, and gives a general description of OTB. Section 3 deals with methodological issues by explaining preprocessing, segmentation, classification methods and tools, and the performed accuracy analyses. In Section 4, the obtained results are shown, and they are discussed in Section 5. Section 6 summarizes the results and highlights the limitations of the present research, open questions, and suggested future research directions.
## 1 Introduction
Within the educational sphere, this idea has great applicability, as it permits to use a model discovered on a previous course (source) to an ongoing course (target) that does not have a model for any reason whatsoever, and to apply these models with certain guarantees to this new course [11]. Most of the previous works related with model portability use a Transfer Learning (TL) approach in which there is a tune-up process, usually based on deep learning approaches, so that the updated model is transferred from one course to another, as shown in [12; 13]. Other different works use a Generalization approach that tries to discover one single general model that fit to all the exited courses [14; 15]. This is the reason why, in this paper, we have used the term \"portability\" instead of the related terms \"transferability\" and \"generalization\", since we think that it better describes the direct application of a model obtained with one dataset to a different dataset. In this regard, the goal of this research is to study the portability of predictive models between courses taught via blended-learning (b-learning) in formal university education. These predictive models try to predict whether a student will succeed or not in a certain course (pass or fail) starting to the log data generated from the student interactions with Moodle LMS. Specifically, the problem we want to resolve is: if we have available data for different university courses, could we use or apply the performance prediction model obtained in one specific course in other different course (in which we do not have enough data or we do not have a prediction model) without losing much accuracy. However, due to that the number of courses in a University can be large, and thus, the number of combinations will be huge, and it seems logical to think that good model portability only occurs between similar courses. This is why, in this paper, we propose to group courses in two different ways; our main objective in this paper is to answer the following two research questions:  
Can the models obtained in one (source) course be used in other different similar (target) courses of the same degree, while maintaining an acceptable predictive quality?Can the models obtained in one (source) course be used in other different (target) courses that make a similar level of usage of Moodle activities/resources? The rest of the document is arranged in the following order: Section 2 reviews the literature related to this research. Section 3 describes the data and experiments. The results are shown in Section 4. Section 5 discusses the results obtained. Finally, Section 6 presents the conclusions and suggests future lines of research.
# Research on Process Quality Prediction and Control of Spindle Housings in Flexible Production Lines
## 1 Introduction
At present, quality control research in multi-variety and variable batch production models can be broadly divided into non-SPC methods and SPC methods. The former is a knowledge-based systems approach and an artificial neural network approach, using the experience of field workers and expert systems to analyze some complex and fuzzy process laws. Conversely, the latter method uses a large amount of sample data and does not require complex mathematical models to analyze production line quality control problems. However, traditional SPC applications are inadequate in multi-variety and variable batch manufacturing models. In summary, research on quality control and forecasting has yielded many results. However, there is a need for more research on quality control and prediction techniques in the production environment and on process quality control in flexible production lines. There are no studies on the identification of critical control points in the process quality control target and no effective optimization of predictive control using a model with a small amount of data, and therefore further research is needed. The thesis of this study takes the spindle housing of a high-grade CNC machine tool in a flexible production line as the research object. We investigate quality control and prediction of the critical processes of a flexible production line, focusing on the analysis and corresponding control of the quality influencing factors of the spindle housing in a flexible production line, the identification of critical quality control, and the prediction optimization and control of the quality characteristic values of the critical processes. Potential problems reflected in the quality data are accurately predicted, and an implementation plan for process quality prediction of the spindle housing in a flexible production line is finally determined. The main work to study the process quality prediction and control of the spindle housing in a flexible production line is as follows:  
1. A fuzzy hierarchical analysis model is established for spindle housing. The index value of each scheme is analyzed and calculated, and a comprehensive analysis of various factors affecting the production line is conducted to determine the most excellent scheme for quality control. 2. A dynamic prediction model through the GM(1,1) principle is established to obtain sample prediction values, to train the residuals of the prediction values through a BP neural network for the network model, and to verify the validity and feasibility of the combined prediction scheme by analyzing and comparing the prediction value errors of the process quality data. 3. SPC control of the critical processes of a spindle box is used to achieve quality prediction and monitoring of each process in the production line, to complete the process quality prediction and control scheme for the flexible production line of a spindle box.
## 4 Conclusions  
The TIMED/SABER-observed NO 5.3 \(\upmu\)m infrared radiation is studied during two consecutive auroral events, with the Dst index reaching a minimum value of \(-422\) nT in 18-23 November 2003. Then, this period was chosen for the analysis of the spatiotemporal variations in NO 5.3 \(\upmu\)m radiance. The time series of NO 5.3 \(\upmu\)m radiance was analyzed at 55\({}^{\circ}\)N 160\({}^{\circ}\)W considering different altitudes. The altitude range was then divided into four groups for statistical analysis. The radiance value within the altitude range of 100-150 km showed the most significant response to auroral disturbances, reaching a maximum value of 3.38 \(\times\) 10\({}^{-3}\) W/m\({}^{2}\)/sr at 123 km of altitude. This value was approximately 10 times higher than the average radiance value during quiet periods. Moreover, the radiance value remained consistently high for almost 40 h primarily at an altitude of approximately 120-130 km. As the first disturbance recovered, the radiation value entered a recovery phase. However, at 18:00 UT on 22 November, a sudden increase in NO 5.3 \(\upmu\)m radiance occurred around 120 km altitude. This increase expanded over a range of approximately \(\pm\)20 km due to the second aurora. Although not as intense as the first one, the radiance value reached 9.85 \(\times\) 10\({}^{-4}\) W/m\({}^{2}\)/sr by 13:00 UT on 23 November, which was 7 times higher than the average value during quiet periods. Subsequently, the time series of radiance at the tangent point of 120 km between the latitude 51\({}^{\circ}\)S-83\({}^{\circ}\)N and longitude 60\({}^{\circ}\)W-160\({}^{\circ}\)W was analyzed. The differences between the northern and southern hemispheres during the \"recovery process\" of the NO 5.3 \(\upmu\)m radiance value were observed, with the recovery process starting earlier in the northern hemisphere. In the subsequent statistical analysis, the minimum radiation value remained relatively constant throughout the entire disturbance process, reflecting spatial and temperature limitations in the auroral excitation process. However, the maximum value and degree of variation altered significantly. Finally, the correlation between the NO 5.3 \(\upmu\)m radiance value at 120 km and temperature, solar wind speed, AE index, and Dst index were analyzed. Only the correlation with the Dst index showed significance, with an R\({}^{2}_{\text{adj}}\) value of 0.42. Subsequently, the correlation between the Dst index and the NO 5.3 \(\upmu\)m radiance (with a 10 km interval) within 50-200 km was analyzed, indicating the highest correlation with the radiance at 170 km (R\({}^{2}_{\text{adj}}=0.88\)).
## 3 Results  
### Flow System  
The channel quality in maintaining the integrity of the cells during the treatment with ultrasound was evaluated in cell detachment experiments. The cell detachment was found to increase with the increased flow speed of the solution inside the channel, as shown in Figure S2. The mean number of cells detached from the channel with different flow speeds varied from ~100 cells for the lower speed of 2.5 mm/s, to ~1100 cells for the highest speed of 20 mm/s. The average number of cells in each channel were ~200,000, resulting in the percentages of detached cells as 0.04%, 0.22%, 0.40% and 0.53% for 2.5, 5, 10, and 20 mm/s flow speed, respectively.
# Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
## 5 Discussion  
The reason for the phenomenon that the forecast of China's road freight volume is higher than China's actual road freight volume is due to the outbreak of the new coronavirus epidemic in 2019; the mobility of personnel is reduced, people voluntarily reduce travel, reduce online shopping, or reduce the type of goods purchased to reduce the indirect infection caused by freight transportation. National policies during the epidemic affected road freight volume. During the epidemic, domestic economic activities have been seriously affected. Domestic production capacity has recovered slowly, and investment and consumption have been in a downturn. Many enterprises have experienced problems such as supply chain disruption and employee shortages during the epidemic, and they cannot maintain a normal production rhythm. This has directly reduced the number of goods that need to be transported. Enterprises are on the verge of bankruptcy due to the serious impact of the epidemic, which has affected the volume of road freight transportation. At the same time, transportation cost is also a factor worth considering. In recent years, due to the increase in fuel price and labor cost, the proportion of road freight transportation has decreased.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **R\({}^{2}\)** & **MAE** & **MBE** & **RMSE** \\ \hline training sets & 0.99424 & 52,617.2825 & \(-\)20,266.4305 & 78,755.4264 \\ testing set & 0.99776 & 32,936.508 & 2942.7318 & 35,812.9792 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test results of training set and prediction set.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **2019** & **2020** & **2021** \\ \hline Forecast (ten thousand tons) & 3,837,847.547 & 3,827,709.298 & 3,968,614.472 \\ Actual (ten thousand tons) & 3,435,480 & 3,426,413 & 3,913,889 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Forecast results of China’s freight volume from 2019 to 2021.  
In addition, road freight has certain limitations. Compared with other modes of transportation, road freight has the disadvantages of less cargo capacity and high transportation costs and is not suitable for long-distance transportation. Compared with other freight modes that are substitutes for each other, when the substitutes for road freight are more cost-effective, out of the choice of maximum value, shippers often choose the substitutes for road freight, which is also a reason for the decline in road freight volume. Finally, at the national level, the state strongly advocates the development of multimodal transport in 'road to rail' and 'road to water', resulting in a slow downward trend in the proportion of road freight volume.