esa-sceva/satcom-chunk-collection huggingface dataset:
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 3 Methods
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMD)
#### 3.3.3 Complete Ensemble Empirical Mode Decomposition (CEEMD)  
EEMD is a further step to do the data analysis, but it is found that the number of experiments \(n\) needed to get a satisfactory decomposition is large [25]. To address this problem, Ref. [25] proposed CEEMD. In EEMD, white Gaussian noise has been added to \(S(t)\) compared to EMD. In CEEMD, white Gaussian noises are added for every mode. In other words, each IMF can be regarded as the original definition plus a Gaussian noise. Overall, we get the following steps:  
1. \(S(t)=P_{0}^{i}(t)+\sigma_{0}^{i}(t),i=1,2,\cdots,n\), where \(\sigma_{0}^{i}(t)\) is a white noise series. Decompose \(P_{0}^{i}(t)\) by EMD to get \(IMF_{0}^{i}(t)\). \(IMF_{0}(t)=\frac{1}{n}\sum_{i=1}^{n}IMF_{0}^{i}(t)\)
2. \(r_{1}(t)=S(t)-IMF_{0}(t)\).
3. Let \(r_{1}(t)=P_{1}^{i}(t)+\sigma_{1}^{i}(t),i=1,2,\cdots,n\). Decompose \(P_{1}^{i}(t)\) by EMD to get \(IMF_{1}^{i}(t)\), then \(IMF_{1}(t)=\frac{1}{n}\sum_{i=1}^{n}IMF_{1}^{i}(t)\)
4. Repeat the above process K steps. For each \(k=2,\cdots,K\), \(r_{k}(t)=r_{k-1}(t)-IMF_{k}(t)\), \(r_{k}(t)=P_{k}(t)+\sigma_{k}(t)\), where \(\sigma_{k}(t),k=0,1,2,\cdots,K\) is a bunch of noise series, with the variance decreasing in general.
5. \(S(t)=\sum_{i=0}^{K}IMF_{i}(t)+res(t)\), where K is determined by a pre-defined stopping criteria.  
With the proposed CEEMD, it is easier to capture the signals of the series of prices. The original price series change subtly, which makes it difficult for the neural network to learn how to extract informative information to do the forecasting. After CEEMD, the trend component provides the trend information and 3 mode components indicate the explicit fluctuation of stock price.  
Note that after the EEMD is applied several IMFs are generated by the decomposition. Selection of suitable IMFs is always a challenge, Ref. [48] has discussed the selection criteria by calculating the cross-correlation for all the modes considered. However, for RNNs, as the fixed number of input features is required, for simplicity, the price series is decomposed to a certain number of IMFs and all will be chosen.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 2 Methods
### U-Net
Secondly, bilinear interpolation is devoid of any parameters that require learning, thereby simplifying the model and reducing the potential for overfitting. Lastly, it avoids the so-called \"checkerboard effect\" that can result from transposed convolution, thereby ensuring a smoother output image [31]. Thus, through employing bilinear interpolation for image enlargement in our research, we have managed to preserve, to a certain extent, image smoothness and detail information. This has had the effect of enhancing the network's resolution, thus augmenting the model's accuracy and stability.
# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 4 Discussion  
### Analyzing Minimap2's Throughput and Profile  
The Minimap2's software execution presented different throughput and profile measurements for each dataset tested, as shown in Section 3.1. The highest throughput occurred for the mapping of the simulated dataset, with 40,336 kbases/s, about double that of the real dataset. This can be explained by the fact the simulated dataset was derived from the reference and might present a greater similarity. All dataset cases make it evident that the total execution time is very high, reinforcing the importance of hardware acceleration in genome assembly.  
In profiling, the main identified bottlenecks are the chaining and the extending steps, representing together more than 57% of the execution time on all three datasets. Previous works reinforce this conclusion [26; 28] or reveal that these are also the same bottlenecks of Minimap2 on short-read execution [45]. The extending step had a higher contribution running on the two PacBio datasets, whereas the chaining step had a higher contribution running on the ONT dataset.  
These differences in profile could have been influenced by input data characteristics, such as read length distribution and similarities between references and reads. They could also have been affected by the mapping rate since the alignment step is skipped when a read fails to be mapped to the reference. However, the only clear direct correlation observed on the three datasets was between the throughput and the extending step time. The simulated case shows little chaining overhead, probably due to the high similarity to the reference, which eases the identification of correlated anchors.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 4 Proposed Deep Learning Model
### Dataset and Annotation  
The dataset used for training consists of 1500 medical vial images, categorized into 11 classes, and collected under varied environmental conditions, such as different lighting conditions, different levels of background clutter, and varying angles. To ensure a well-balanced model, the dataset was divided into 70% training, 15% validation, and 15% testing. Each vial was manually annotated with bounding boxes and class labels.  
To enhance generalization, data augmentation techniques were applied, including random rotation (\(\pm\)20\({}^{\circ}\)), brightness variation (\(\pm\)30%), horizontal flipping, contrast adjustment, and random cropping (up to 15% per image). These augmentations ensure that the model can effectively detect vials under diverse real-world conditions. Figure 3 presents a sample of the dataset, illustrating the challenge posed by visually similar vials.  
Figure 2: Deployment pipeline of the MR-assisted vial detection system. The YOLO-based deep learning model is first trained and converted to the ONNX format for optimized inference. The ONNX model is then imported into Unity and integrated into the MR environment using Visual Studio. The compiled application is deployed on HoloLens 2, allowing for real-time object detection and interaction within the Mixed Reality workspace.
# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
###### Abstract  
In the light of the \"Biological Diversity\" concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.  
forest habitat; habitat classification; habitat mapping; habitat monitoring; Random Forests; supervised machine learning modelling; Sentinel-2; Copernicus 2021
## 5 Quantitative Results
### Geography  
After analyzing the collected data, we observed that the distance most participants had to walk to use public transportation was within 2 km. Thus, we defined this as our division length between pedestrian transport and motorized transport; this distance coincide with studies, such as [25], where they indicate that around 80 percent of trips shorter than one mile (1.6 km) are made on foot, as well as that this drops significantly for trips more than two miles (3.2 km). Of our original 382 routes, 142 (38%) had a distance shorter than 2 km. When the distance was shorter than 2 km, the participants'  
Figure 6: Scatter plot of net travel time gained or lost (Suggested route travel time minus real route travel time).  
Figure 7: Graph of relation between total divergences, real route distance, and suggested route distance.  
route was shorter 71.62% (106/148) of the time than the one suggested. In contrast, our dataset of routes longer than 2 km indicates that only 23.07% (54/234) of the time the route taken by participants was shorter than the suggested route.  
Among the calculated geodesics, the largest one was over 20 square kilometers, while the smallest had an area of 0.00017 square kilometers. 17% of geodesics had an area above 758,775 square meters. This means that, most of the time, the route that is suggested by Google Maps is slightly different from the participants' actual route. The knowledge that participants had about the environmental conditions of the neighborhood (such as hills, unpaved roads, unmarked paths, and others) influenced the routes they took, as will be discussed in Section 6.  
It could be expected that, when walking, people would take a shorter (or equally long) route than the one suggested. However, from our data analysis, we can see that people do not always take the shortest route, as other factors may influence their mobility decisions. One of these factors is the geography of where they are traveling. For example, in Figure 8, we can see a case where the participant (green-yellow route) walked a longer route than the one that was suggested by Google Maps; in this case, the length difference between both routes was 0.39 km. On the right side of Figure 8, we can see that there is a much smaller inclination in the route that the participant took, as opposed to a steeper hill in the route that was suggested by Google Maps.
## 2 Mobility data
### Testing Scenarios
#### 5.3.1 Queries Supported  
In this subsection, we describe the queries supported by our EMDI system on the unified views generated for pollution and mobility data.  
1. Top-N query  
To measure the potential of our EMDI system in handling complex combined analytics of pollution, meteorological and mobility data, we tested it on a top-N kind of geospatial queries, such that we retrieve sorted counts of mobility data with predicates on location and pollution data dimensions. Specifically, we tested with the following query: \"what are the top-N neighbourhoods in Bologna in Italy in terms of the amount of human mobility density where the PM\({}_{10}\) concentrations are greater than or equal to a threshold, e.g., 24?\". We computationally stress the system with this kind of queries. This is an aggregation type of query that involves grouping and sorting by city regions in addition to the cross-domain spatial join and count upstream.  
2. Average query  
Specifically, we tested the following query: \"what is the average value of PM\({}_{10}\) concentration per neighbourhood in regions affected by PM\({}_{10}\) value that exceeds a threshold, e.g., 24?\". With this query, we test the EMDI system's ability to answer single geo-statistical queries such as'mean'.
## 4 Proposed Deep Learning Model for Malaria Detection
The value of pixel \((x,y)\) is computed as  
\[\bar{I}(x,y)=\frac{1}{w}\sum_{-d\leq i,j\leq d}\Big{[}I(x+i,y+j)g_{r}(\|I(x+i,y +j)-I(x,y)\|)g_{s}(\|i\|,\|j\|)\Big{]} \tag{1}\]  
where \(\bar{I}\) is the filtered image, \(w\) is the normalizing factor, and \(g_{r}\) is the range kernel computed as  
\[g_{r}(i)=\exp^{-\frac{i^{2}}{2\sigma_{r}^{2}}}, \tag{2}\]  
The \(g_{s}\) contains the distance of pixel \((i,j)\) from the center pixel \((x,y)\). It is calculated as  
\[g_{s}(l,m)=\exp^{-\frac{(l^{2}+m^{2})}{2\sigma_{s}^{2}}}, \tag{3}\]  
where \(\sigma_{r}\) and \(\sigma_{s}\) are variance parameters. All images are filtered using Equation (1) and are resized to equal dimensions (\(125\times 125\)), as the deep learning models require the same input shape for all images of the dataset. Figure 3 shows the results of applying bilateral filtering on sample infected and uninfected cell images. Figure 2: Block diagram of the proposed malaria detector. We propose a customized CNN model for efficient malaria detection by the classification of infected and uninfected RBC images. The input images with \(125\times 125\times 3\) dimensions are fed toa model with 5 convolutional layers, 5 max-pooling layers, and 2 fully-connected layers. The proposed model is shown in Figure 4. The convolutional layers of model use \(3\times 3\) kernel size, ReLU activation function, and 32, 64, 128, 256, 300 filters for all five layers, respectively. By visualizing the convolutional layers of the customized CNN model in Figure 5, it can be seen that the initial layer extracts low-level features that are more recognizable to humans and the final convolutional layer extracts high-level features that are more model-recognizable. Figure 4: Proposed customized model architecture for malaria detection. Figure 3: Bilateral filtering results: (**a**) input infected and uninfected images, (**b**) results of applying bilateral filtering on (**a**). All max-pooling layers of the model following the convolutional layers use \(2\times 2\) pool size with the stride of 2 pixels that crisps the outputs of feature maps produced by the convolutional layers. The output of the fourth pooling layer is fed to the fully connected (FC) layers with 0.5 dropouts after each FC layer, and the output of the final FC layer is fed to the sigmoid classifier. The model is trained with hyper-parameters, 64 batch size, 25 epochs, ADAM optimizer, binary cross-entropy loss, and 0.5 dropout ratio for regularization, as many pieces of research recommended this ratio of dropout [72]. The dropout layer is used to reduce the over-fitting of the model and to generalize the results of the model.
# An Integrated Decision Support Model for Evaluating Public Transport Quality
###### Abstract  
Big cities suffer from serious complex problems such as air pollution, congestion, and traffic accidents. Developing public transport quality in such cities is considered an efficient remedy to obviate these critical issues. This paper aims to determine the significant supply quality criteria of public transportation. As a methodology, a hybrid Analytic Hierarchy Process (AHP) combined with the Best Worst Method (BWM) is applied. The proposed model is basically a hierarchy structure with at least a \(5\times 5\) pairwise comparison matrix or larger. A real-world complex problem was examined to validate the created model (public transport quality improvement). An urban bus transport system in the Jordanian capital city, Amman, was used as a case study; three stakeholder groups (passengers, nonpassengers, and representatives of the local government) participated in the evaluation process. The conventional Analytic Hierarchy Process (AHP) leads to weak consistency in the case of existing \(5\times 5\) pairwise comparison matrices or larger, particularly in estimating complex problems. To avoid this critical issue in AHP, we used Best Worst Method (BWM) comparisons, which make the evaluation process easier for decision makers; moreover, it saves survey time and provides more consistency when compared to AHP pairwise comparisons. The model adopted highlighted the most significant service quality criteria that influence urban bus transport systems. Furthermore, the sensitivity analysis conducted detected the stability of the criteria ranking in the three levels of the hierarchical structure. Since the proposed AHP-BWM model (which is the sole example of this sort of combination) is independent from the decision attributes, it can be applied to arbitrary hierarchically structured decision problems with a relatively large number of pairwise comparisons.  
analytic hierarchy process; best worst method; decision making; public transport; transport planning +
Footnote †: journal: Applied Statistics and Applications
## 3 Theoretical Analysis
### Advantages of the Method
#### 3.6.2 More Accurate Swapping Strategy  
The CAPCOM method uses the delay-to-power ratio as the cell weight, leading to the optimal solution if the optimization object has only one path. Considering that the object is the overall timing (all paths), the CAPCOM method multiplies the delay-to-power ratio by the number of critical paths to represent the impact of the cell on the overall timing.  
The advantage of the algorithm in this paper is that, compared with previous algorithms, the impact of cells on timing is considered more precisely by using specific path constraints rather than simply dividing them into critical or non-critical paths. The traversal is divided into two stages. The first stage obtains the path with the largest weight. The larger the path weight, the more significant the proportion of LVTs needed within the path, and the greater the probability that cells within this path will be replaced with LVTs. Prioritizing the cells with higher replacement probability can effectively avoid redundant replacements. The second stage is to obtain the cell with the largest weight within the path. The cell weight depends on the path weight and the delay-to-power ratio, which reflects the cell's ability to influence the overall timing in more detail.
# Towards Sustainable Regional Planning: Potential of Commuter Rail in the Madrid Urban Region
## 2 Materials and Methods
### Research Methods
We also classified the former based on the number of commuter rail trips (in and out) per population--using data from 2018, which were the most recent available--and investigated possible relations between the use of the commuter rail and the population and employment densities. Lastly, to study the potential for urban growth around railway stations, we first analysed the evolution of artificial area from 1990 to 2018 using CORINE Land Cover data. Then, we analysed with greater detail the current amounts of each type of land from an urban development perspective. To do so, we distinguished following classifications according to the current legislation in Spain. Based on this, the land of a given municipality can be: (a) consolidated urban; (b) non-consolidated urban; (c) sectorized developable; (d) non-sectorized developable; and (e) non-developable land. The last item includes land that is protected and should not be developed for various reasons, e.g., environmental reasons or flooding risk, as well as land the current legal status of which does not allow the city to grow over it but could be developed in the future. Analysis of the areas around the stations based on classifications according to current municipal urban planning provides a more refined estimate of how much land is likely to be built, on land already parcelled out, or undergo a new process of plot delimitation, urbanization and building around the stations. Based on this analysis of the types of land, we classified the stations and mapped the potential urban growth in the short, medium and long term around the stations. The short and medium terms were assimilated with the category of developable land, while the long term was assimilated with potentially developable land, i.e., the category of non-developable land, excluding from it what was protected. We analysed the areas within 800, 1000, 1200 and 1600 m around each station, corresponding to walking trips of 10 to 20 min. The limits of the catchment area around a commuter rail station are not clear and vary across cultures and geographies. We set the limit as 1600 m (1 mile), as this is one of the maxima considered in previous studies [1; 44; 45; 46] and considering that the travel times for this first or last mile could be much shorter for people using a bicycle, a personal mobility vehicle or other modes of public transportation to travel to/from the station.
## 4 Analysis of Anti-Jamming Performance
Here, the bit error rate for the \(l\)th symbol is used to represent the system bit error rate. \[BER_{RSST-SR-DCSK}==\frac{1}{4}\biggl{[}erfc\biggl{[}\frac{E[y_{l}|(b_{l}=+1)]}{ \sqrt{2 Var[y_{l}|(b_{l}=+1)]}}\biggr{]}\biggr{]}+erfc\biggl{[}\frac{E[y_{l}|(b_ {l}=-1)]}{\sqrt{2 Var[y_{l}|(b_{l}=-1)]}}\biggr{]} \tag{27}\]  
Calculate the mean and variance of Equation (21) separately, and we obtain  
\[E[y_{l}|(\alpha_{l}=+1)]=PRP_{s}+PRP_{jam}cos(\pi F)-\frac{P_{jam}sin(\pi F)}{ sin\Bigl{(}\frac{\pi F}{PR}\Bigr{)}}cos\biggl{(}4l\pi F-2\pi F+\frac{\pi F}{PR}+2 \theta\biggr{)} \tag{28}\]  
\[E[y_{l}|(\alpha_{l}=-1)]=-PRP_{s}+PRP_{jam}cos(\pi F)-\frac{P_{jam}sin(\pi F)}{ sin\Bigl{(}\frac{\pi F}{PR}\Bigr{)}}cos\biggl{(}4l\pi F-2\pi F+\frac{\pi F}{PR}+2 \theta\biggr{)} \tag{29}\]  
\[Var[y_{l}|(\alpha_{l}=+1)]\approx P^{2}R\rho+4P^{2}RP_{s}P_{jam}\biggl{[}cos \biggl{(}\frac{\pi F}{2}\biggr{)}\biggr{]}^{2}+PRP_{s}\frac{N_{0}}{2}+P^{2} RP_{s}\frac{N_{0}}{2}+PRP_{jam}N_{0}+PR\frac{N_{0}^{2}}{4} \tag{30}\]  
\[Var[y_{l}|(\alpha_{l}=-1)]\approx P^{2}R\rho+4P^{2}RP_{s}P_{jam}\biggl{[}sin \biggl{(}\frac{\pi F}{2}\biggr{)}\biggr{]}^{2}+PRP_{s}\frac{N_{0}}{2}+P^{2} RP_{s}\frac{N_{0}}{2}+PRP_{jam}N_{0}++PR\frac{N_{0}^{2}}{4} \tag{31}\]Substituting Equations (28)-(31) into Equation (27), we find  
\[BER^{(l)}_{RSSST-SR-DCSK}=\frac{1}{4}\bigg{[}erfc\bigg{[}\frac{E[y |(\left(\alpha=+1\right)]]}{\sqrt{2\
u^{2}ar[y](\left(\alpha=+1\right)]}}\bigg{]}+ erfc\bigg{[}\frac{-E[y|(\left(\alpha=-1\right)]]}{\sqrt{2\
u^{2}ar[y](\left(\alpha=-1 \right)]}}\bigg{]}\bigg{]} \tag{32}\] \[=\bigg{[}\frac{E[y|(\alpha=+1)]=PRB_{y}+PRB_{y}\alpha\cos(\pi R)- \frac{P_{y}\alpha\sin(\pi R)}{\sin(\frac{\pi R}{R})}\cos(4\pi F-2\pi F+\frac{ \pi R}{R}+2\theta)}{\sqrt{2\
u^{2}R+8\
u^{2}R}P_{y}\alpha\cos(4\pi F-2\pi F+ \frac{\pi R}{R}+2\theta)}\bigg{]}\] \[+erfc\bigg{[}\frac{PRB_{y}-PRB_{y}\alpha\cos(\pi R)+\frac{P_{y} \alpha\sin(\pi R)}{\sin(\frac{\pi R}{R})}\cos(4\pi F-2\pi F+\frac{\pi R}{R}+2 \theta)}{\sqrt{2\
u^{2}R}P_{y}\alpha\cos(4\pi F-2\pi F+\frac{\pi R}{R}+2 \theta)}\bigg{]}\bigg{]}\]  
As can be seen from the above formula, when the interference and noise power spectral density is \(N_{0}/2\), the bit error rate can be reduced by reducing the sequence variance and increasing the signal power.