esa-sceva/satcom-chunk-collection huggingface dataset:
# SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
## 3 Method
### Backbone and Neck  
We adopted an excellent combination, Residual-Network [42] and Feature Pyramid Network [43], as the backbone and neck. This combination takes advantage of ResNet's deep residual connections and FPN's multiscale feature fusion. It can result in a robust architecture that effectively utilizes both high-level and low-level features, leading to improved performance in object detection and segmentation tasks. The combination is, therefore, well suited to the detection of sunspots, which have large differences in size and characteristics on high-resolution solar images.  
The backbone is a network for extracting features from the input images as the first step in a detector model. Some excellent backbones have been proposed in recent years, such as VGG [44], Hourglass [45], MobileNet [46], ResNet, and so on. The shortcut connection of ResNet increases its information flow and alleviates the disappearance of the gradient caused by too great a depth. ResNet has several versions with different layers. Among them, ResNet-50 [42] has a total of 50 layers, which lowers its complexity, leading to a good balance of accuracy and speed. The construction consists of five stages for extracting five-level feature maps with different sizes and channels, e.g., stages 1 to 5 separately generate the feature maps whose size is from 1/4 to 1/32 of the input image, such as C1 to C5 in Figure 2. Stage 1 is a normal convolution operator consisting of a \(7\times 7\) convolution and \(7\times 7\) max pooling. Other stages consist of the residual blocks, which range in number from 3 to 6. The residual block is the key component of ResNet, which includes three common convolution layers and an identity link that builds from input to output to overcome the degradation problem of deep networks. The final layer of ResNet-50 is a fully connected layer.  
Figure 2: The model of SIPNet consists of four main components: backbone, neck, prediction head, and prototype mask branch.  
The FPN combines the low-level detail information and the high-level semantic information from the different level feature maps outputted from the backbone. This allows each level to gain more contextual information; the low-level layer plays a particularly crucial role in detecting small targets. Due to the large size of C1 and C2, only feature maps from C3 to C5 are selected for fusion by FPN. This step generates objects of different sizes in feature maps at different levels, named P3 to P7. P6 is produced by convolving C5 with a kernel size of 3 x 3 and a step size of 2. P7 follows by convolving P6 with the same kernel size and the same step size. P5 is created by convolving C5 with a kernel size of 3 x 3. P4 is created by merging upsampled P5 with C4 and then P3. As a result of the neck, the fused feature maps from P3 to P7 are fed into the prediction head and the maps from P3 to P5 are fed into the prototype mask branch.
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 4 Tests Performed
### Test Scenario  
The performance tests were carried out in the Catalonia region (Spain), where we established a link between two NVIS nodes. The transmitter node was located at La Salle University-URL in Barcelona, and the receiver node was located at a remote location in Cambrils (Tarragona). In Figure 3, we can see the receiver node with two perpendicular inverted Vee antennas. The link between both locations is about 96 km, ensuring an NVIS communication as we can see in Figure 4. The receiver was not set in Barcelona due to the high electromagnetic noise and interference at the HF band. For that reason, it was preferable to set the receiver in Cambrils. The tests performed were taken for 15 days from 7 December to 22 December 2019. The frames were transmitted every minute during these days.  
Figure 2: Diagram of the phasing network.
## 4 Results and Discussion
The largest improvements can be noted for the classes _anger_, _fear_ and _disgust_. In fact, for both the RNN and the CNN models the _anger_ class gained around 25% in accuracy, with the biggest increase of 27% being _disgust_ for the CNN model. Similarly, the misclassification values were generally diminished with the biggest being misclassifying _disgust_ as _fear_, in both of the models. The RNN lip model also diminished the misclassification of _neutral_ as _sad_ by 22%. The exception is misclassifying _surprise_ as _happines_ that increased for both of the models. Figure 11 shows some frames from RAVDESS of Actor 18 expressing _fear_ and pronouncing the phrase _\"The kids are talking by the door.\"_ The video sequence was run through the 60 frame models and Figure 12 shows the attribution of probability to the classes _fear_ and _sadness_, respectively, for different initial frames provided in input. The CNN lip model and the RNN lip model respectively assign a very high probability of the correct emotion to the sequence regardless of the number of frames in input. However, both the CNN and the RNN models assigned a much higher probability to _sadness_ irrespective of the starting frame, aside from the CNN model at initial frame 5, where it shows indecisiveness among the two emotions. The difference in performance among the lip models and their respective face models can be explained by mouth configurations of the actor's performance influencing the face models to classify the expressed emotion incorrectly. In this sequence especially, both the expression of emotion and the mouth movements are particularly exaggerated by the actor, making it more likely to confuse the face models. This, however, occurs to a far lesser extent with the lip models due to them being able to compensate somewhat for the mouth movements related to articulation. From the above considerations, we can claim that the inclusion of lip-related features to a deep learning model classifying basic emotions in speaking subjects improves recognition accuracy, but that the extent of the improvement is highly dependent on the number of frames provided in input. However, while the RAVDESS dataset is high by definition, the pose of the speakers stays the same, the utterances are the same accross speakers, there are not occlusions and the illumination is consistent; datasets in-the-wild and videos of spontaneous interactions will most likely not satisfy most of the conditions listed above. Furthermore, while usual AFER models are known to display lesser accuracy when provided with such input, the lip model introduces an additional component sensitive to all of the factors above, the model extracting lip features. If any of the factors above influence lip reading performance--among which we suspect head pose variations to be the most deleterious to accuracy--the same will propagate to our model. This gives way to the expectation that in cases of datasets in-the-wild and videos of spontaneous interactions, the increases in accuracy will be lower than the ones reported in our experiment.
# A Comprehensive Review on Music Transcription
## 3 Transcription Techniques: Insights and Advances
### Vocal Melody Extraction  
Vocal melody extraction is the sub-task of frame-level transcription. It is the process of identifying the sung or spoken pitch in a vocal performance [36; 41; 42]. The encoder/decoder network described in Section 3.1 for general melody extraction [39] is also used for vocal melody transcription. The work in [41] uses a high-resolution network (HRNet) to separate vocals from polyphonic music and an encoder-decoder network to estimate vocal F0 values. The experimental result shows that the HRNet-based singing voice separation method effectively reduces the accompaniment interference, which outperforms other state-of-the-art algorithms in most cases. Similarly, the work in [42] introduces a novel melody extraction system that treats the problem as semantic segmentation on a time-frequency image. It distinguishes melody from non-melody elements by using a deep convolutional neural network (DCNN) with dilated convolution. The utilization of an adaptive progressive neural network which transfers the knowledge from symbolic to audio data demonstrate the system's accuracy across different datasets. Similar to general melody extraction, vocal melody involves F0 estimation for each frame, but with a focus on capturing the melody carried by the human voice [43; 44; 45]. Inspired by the object detection model in image processing, the research work in [43] introduced a patch-based CNN model for extracting vocal melodies. This patch-based CNN allows for efficient training with limited labeled data. Similarly, the work in [44] utilized the power of semi-supervised learning using a teacher-student network, which outperforms supervised learning in terms of accuracy. The work in [45] uses multi-task learning to train the classification and detection network for singing voice detection and pitch estimation at the same time. The vocalmelody extraction is essential for applications like lyrics-to-audio synchronization, karaoke generation, and vocal synthesis.
## 6 Results
### Data Association
#### 6.3.3 Branch and Bound  
The experimental results of the sensitivity analyses for the Branch and Bound are shown in Figure 12. Figure 11(a),b show the results of the particle-based sensitivity analysis with \(N_{l}=256\) and \(N_{l}=1024\) respectively, while Figure 10(b) shows the results for the landmark-based analysis (\(N_{p}=1024\)). The fully parallelized implementation (\(MBNT\)) demonstrates the best performance among all test cases. It is worth pointing out that for a low number of landmarks, the block-based (\(MB1T\)) parallelization performs better than the thread-based parallelization (\(1 BNT\)), which, in turn, is more efficient for a higher number of landmarks.  
The most efficient implementation reduces the processing time by a factor of around 2500 for the highest numbers of particles (\(N_{l}=1024\)), a factor of almost 220 for the highest values of \(N_{l}\), with \(N_{p}=1024\).
# ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries
## 4 Research Methodology  
### Measures of ICT Adoption and Stock Market Development  
This study focused on the impact of ICT adoption and stock market development in Africa. A panel of eleven stock exchanges for the period from 2008 to 2017 was employed in this study. The ICT and stock market development data were sourced from the International Telecommunications Union and the World Bank Global Financial Development databases, respectively, whilst the data for the control variables of GDP and financial freedom were sourced from the World Bank Global Financial Development and Heritage Foundation databases, respectively. The variables employed in this study are described in Table 3. These are the ICT adoption variables, the stock market development variables as well as the control variables. Panel data techniques were used to analyse the data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Country** & **Name** & **Abbreviation** & \
\begin{tabular}{c} **Number of Listed** \\ **Companies** \\ \end{tabular} &
\begin{tabular}{c} **Market Capitalisation** \\ \end{tabular} \\ \hline Botswana & Botswana Stock Exchange & BSE & 33 & USD 37,505,515,000 \\ \hline Côte d’Ivoire & BRVM Stock Exchange & BRVM & 45 & USD 6,960,322,592 \\ \hline Egypt & Egypt Exchange & EGX & 223 & USD 45,628,544,290 \\ \hline Ghana & Ghana Stock Exchange & GSE & 42 & USD 9,951,00,000 \\ \hline Kenya & Nairobi Securities Exchange & NSE & 63 & USD 22,007,506,496 \\ \hline Mauritius & Stock Exchange of Mauritius & SEM & 97 & USD 8,980,000,000 \\ \hline Morocco & Casablanca Stock Exchange & Casa SE & 75 & USD 60,576,297,847 \\ \hline Namibia & Namibia Stock Exchange & NSX & 40 & USD 141,128,581,560 \\ \hline Nigeria & Nigeria Stock Exchange & NSE & 147 & USD 35,925,467,734 \\ \hline South Africa & Johannesburg Stock Exchange & JSE & 353 & USD 898,990,000,000 \\ \hline Tunisia & Bourse de Tunis & BVMT & 88 & USD 8,922,590,000 \\ \hline \hline \end{tabular} Source: Authors’ construction based on data obtained from World Bank Global Financial Development database.  
\end{table}
Table 2: Selected African stock exchanges.
## 2 Scanning OCT versus Full-Field OCT and Technological Description of the Setups Description
To enhance the photon collection, we used higher NA objectives, either 40X, 0.8 NA in water, or 30X, 1.05 NA in silicone oil, achieving transverse resolution below 500 nm, and depth of focus between 1 and 2 \(\upmu\)m. Interestingly, in this system, the optical sectioning ability comes from the lateral spatial coherence [43,44] controlled by the depth of focus, and therefore the objective NA rather than the temporal coherence, meaning that simple LEDs can be used without sacrificing the axial resolution. Both systems are illuminated by spatially incoherent sources, which eliminate the cross talks [45], and preserve the transverse resolution from degradation due to low order aberrations [46]. The illumination sources are mounted in a Kohler illumination to provide an illumination as homogeneous as possible. Figure 1: FF-OCT systems layouts. (**A**) Standard layout, as in the LLTech commercial system. It is based on a Linnik interferometer, with identical microscope objectives in both arms. The system can be illuminated by a simple thermal source, or a standard LED, and the image is recorded with a CMOS camera; (**B**) Principle of 3D acquisition in FF-OCT. A 2D plane can be recorded in a single camera acquisition, then the field is scanned in the axial direction; (**C**) Another FF-OCT layout developed in the lab, offering the possibility to work with higher NA objectives, and to add a fluorescence channel simultaneously to the FF-OCT image. Both systems are now operated with a new CMOS camera developed by Adimec during an ERC project (CAReIOCA) led by LLTech [47]. This camera can handle a Full Well Capacity of about 2 Me\({}^{-}\) on 2 million pixels at frame rates up to 700 Hz, which typically enhanced our signal-to-noise ratio by a three-fold factor, and acquisition speed by a five-fold factor, with respect to previously used cameras (PhotonFocus, MV1-D1024E-160 CL, Lachen, Switzerland).
## 5 Results
### Dataset Preparation  
We removed all personally identifiable information (see Figure 2) and image lines with a naming protocol (e.g., image0.png) (see Figure 3). A summary of collected data is presented in Table 9.  
Furthermore, we prepared the Ground Truth (GT) Transcription file, which is single-line plain text corresponding to the line images. We saved GT files with 8-bit encoding and named them with the identical name as the line image. We saved with the extension of _.gt.txt_,(e.g., image0.gt.txt), see Figure 4.  
We used Notepad++ text editor for the preparation of the single-line plain text and created 522 image lines and 522 GT files, in total 1044 files.  
The other preparation activities were performed as follows. Tesseract engine performed _binarization_. Basically, the process aims at converting color images into black and white equivalents. In our case, the documents were already in black and white. However, Tesseract still performed the process. _Dewrapping_ is another pre-process activity in data preparation. However, it is required when the images have been taken using different kinds of input devices, and therefore they might have distortions, skews, and other attributes that could negatively affect the result of the recognition. In our case, we scanned the documents manually and all with the same settings and a single scanner. Therefore, we did not find it necessary to apply _dewarping_. Because of a small set of data, we also applied _segmentation_, _cropping_, and image _rotation_ manually.  
In the first phase of training/fine-tuning the Arabic Trained data, the language model was not able to handle ZWNJ properly, and therefore, we replaced all \"_s_\" without ZWNJ character to \"_s_\" with ZWNJ character, to unbind wrong concatenated characters. After that, other replacement methods will be applied subsequently to correct common wrongly concatenated characters, see Algorithm 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Page segmentation & Set to 13 default for RTL languages \\ OCR engine mode & 1 (LSTM mode only) \\ Debug-Interval & \(-1\) \\ Max-Iteration & 10,000 \\ Lang-Type & RTL \\ Norm-Mode & 1 \\ Random-Seed & 0 \\ OMP-Thread-Limited & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Training Parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \# & **Type of Data** & **Amount** & **Rate \%** & **Total** \\ \hline
1 & Data utilized for training & 99 printed & 90 \% & 110 printed \\  & documents & & documents \\ \hline
2 & Data utilized for evaluation & 11 printed & 10 \% & \\  & documents & & documents \\ \hline
3 & Image lines prepared & & \\  & from the documents & 522 files & 1044 files & \\ \hline
4 & Ground Truth transcriptions & & \\  & prepared from documents & 522 files & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of data utilized for the training and evaluation process.
## 2 Materials and Methods
### Data Analysis  
For calculating the leg stiffness (\(K_{L}\)), the lower limb was modeled as a non-linear spring, connecting the center of pressure (COP) of the GRF and the hip joint center (Figure 1). During walking, the varying \(K_{L}\) was calculated as the gradients of the force-deformation curve [15]. The total leg stiffness (\(K_{T}\)) was then obtained as the sum of the leg stiffness values of both sides, and the proportional share of the total leg stiffness (\(K_{S}\)) by a lower limb over time was calculated during the double-limb support phases of gait (i.e., loading response, LR, and pre-swing, PS) as follows (Figures 1 and 2):  
\[K_{s}(t)=\frac{K_{L}(t)}{K_{T}(t)}\times 100\% \tag{1}\]  
Figure 1: Definitions of the leg stiffness (\(K_{L}\)) and total leg stiffness (\(K_{T}\)). \(F_{e}(t)\) is effective GRF, \(L_{e}(t)\) is effective leg length and \(\varphi(t)\) is the angle between the longitudinal axis of the shank and the line joining the COP to the center of the hip joint. \(K_{T}\) is defined as the sum of \(K_{L}\) of the left leg (\(K_{LL}\)) and \(K_{L}\) of the right leg (\(K_{RL}\)) (Adapted from [14]).  
The proportional share, \(K_{S}(t)\), was then time-averaged over the double-limb support phases of gait (i.e., LR and PS). The symmetry index (SI (%)) of bilateral \(K_{L}\) was calculated for each gait phase as follows:  
\[\text{SI}=2\times\left|\frac{\int K_{LL}(t)dt-\int K_{RL}(t)dt}{\int K_{LL}(t)dt+ \int K_{RL}(t)dt}\right|\times 100\% \tag{2}\]  
where \(K_{LL}\) was the leg stiffness of the left leg, \(K_{RL}\) was the leg stiffness of the right leg and the integrations were over the subphases of loading response (LR or initial DLS), mid-stance (MS), terminal stance (TS) and pre-swing (PS or terminal DLS) (Figure 2). While an SI of zero indicated perfect bilateral symmetry, the greater the SI value, the greater the bilateral asymmetry in leg stiffness. Temporal-spatial parameters were also obtained, namely gait speed (normalized to leg length), cadence, stride time, stride length (normalized to leg length) and step width.
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 3 Proposed RL-ChoA
### Tent Chaos Sequence  
The Tent chaos sequence has the characteristics of randomness, ergodicity, and regularity [67]. Using these characteristics to optimize the search can effectively maintain the diversity of the population, suppress the algorithm from falling into the local optimum, and improve the global search ability. The original chimp optimization algorithm adopts The rand function and randomly initializes the population, and the resulting population distribution is not broad enough. This paper studies Logistic chaotic map [50] and Tent chaotic map; the results demonstrate that the Tent chaotic map has better population distribution uniformity and faster search speed than the Logistic chaotic map. Figures 3 and 4 represent the histograms of the Tent chaotic sequence and the Logistics chaotic sequence, respectively. From these two figures, it can be found that the value probability of the Logistic chaotic map between \([0,0.05]\) and \([0.95,1]\) is higher than that of other segments, while the value probability of the Tent chaotic map in each segment of the feasible region is relatively uniform. Therefore, Tent chaotic map is better than the Logistic chaotic map in population initialization. In this paper, the ergodicity of the Tent chaotic map is used to generate a uniformly distributed chaotic sequence, which reduces the influence of the initial population distribution on the algorithm optimization.  
Figure 2: Population distribution observed at various stages in RL-ChoA.  
The expression of the Tent chaotic map is as follows:  
\[x_{i+1}=\left\{\begin{matrix}2x_{i},0\leq x\leq 0.5\\ 2(1-x_{i}),0.5<x\leq 1\end{matrix}\right. \tag{9}\]  
After the Bernoulli shift transformation, the expression is as follows:  
\[x_{i+1}=(2x_{i})mod1 \tag{10}\]  
By analyzing the Tent chaotic iterative sequence, it can be found that there are short periods and unstable period points in the sequence. To avoid the Tent chaotic sequence falling into small period points and unstable period points during iteration, a random variable \(rand(0,1)\times\frac{1}{N}\) is introduced into the Tent chaotic map expression. Then, the improved Tent chaotic map expression is as follows:  
\[x_{i+1}=\left\{\begin{matrix}2x_{i}+rand(0,1)\times\frac{1}{N},0\leq x\leq 0.5 \\ 2(1-x_{i})+rand(0,1)\times\frac{1}{N},0.5<x\leq 1\end{matrix}\right. \tag{11}\]  
The transformed expression is as follows:  
\[x_{i+1}=(2x_{i})mod1+rand(0,1)\times\frac{1}{N} \tag{12}\]  
Figure 4: Logistic chaotic sequence distribution histogram.  
Figure 3: Tent chaotic sequence distribution histogram.  
where \(N\) is the number of particles in the sequence. \(rand(0,1)\) is a random number in the range \([0,1]\). The introduction of the random variable \(rand(0,1)\times\frac{1}{N}\) not only maintains the randomness, ergodicity, and regularity of the Tent chaotic map but can also effectively avoid iterative falling into small periodic points and unstable periodic points.