esa-sceva/satcom-chunk-collection huggingface dataset:
# Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
## 3 Methodology
### Feature Extraction Layer
Each output feature map at location (\(p\), \(q\)) is computed using appropriate kernels (\(W^{L}\) and \(W^{H}\)), applying regular convolution for intra-frequency update and removing the need of explicitly computing and sorting on up/down sampling for inter-frequency communication as follows:  
\[Y^{H}_{p,q}\ =\ \sum_{i,j\in\mathbb{N}_{k}}\biggl{(}W^{H\to H}_{i+\frac{k-1}{2},j+ \frac{k-1}{2}}\biggr{)}^{\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
where \(z_{t}\), \(r_{t}\), \(h_{t}\), and \(h_{t}\) are update gate, reset gate, current memory, and final memory at the current time step, respectively. \(W\), \(U\), and \(b\) are parameter matrices and vector; \(\sigma\) and \(\tanh\) stand for sigmoid and hyperbolic tangent function, respectively.  
Using the embedding matrix \(W_{emb}\) the hidden vector \(h_{t}\) is converted to embedding matrix \(b_{t}\) as follows:  
\[b_{t}\ =\ W_{emb}h_{t},\ t\ =\ u-\tau,\ldots,u+\tau \tag{9}\]  
By applying a linear projection on the embedded vector \(b_{t}\) query (\(q_{t}\)), values (\(v_{t}\)), and keys (\(k_{t}\)) vectors are computed as follows:  
\[q_{t}\ =\ Qb_{t},t\ =\ u \tag{10}\]  
\[k_{t}\ =\ Kb_{t},t\ =\ u-\tau,\ldots,u+\tau \tag{11}\]  
\[v_{t}\ =\ Vb_{t},t\ =\ u-\tau,\ldots,u+\tau \tag{12}\]  
where \(Q\), \(K\), and \(V\) are query, key and value matrices, respectively. Based on these results, attention weight \(a_{u}\) and attention result \(c_{u}\) are derived as follows:  
\[e_{ut}\ =\ \frac{q_{u}^{T}k_{t}}{\sqrt{d_{k}^{\prime}}} \tag{13}\]  
\[a_{ut}\ =\ \frac{\exp(e_{ut})}{\sum_{t^{\prime}\ =\ 1}^{u+\tau}\exp (e_{ut^{\prime}})}, \tag{14}\]  
\[c_{u}\ =\ \sum_{t\ =\ u-\tau}^{u+\tau}a_{ut}h_{t} \tag{15}\]  
To address the conditional independence assumption in CTC, an attention layer is placed before the CTC projection layer \(ph_{u}\) and transforms it to a particular dimension representing the number of CTC output labels. Then, the attention layer output that carries context information is served as the input of CTC projection layer at the current time \(u\). \[ph_{u}\ =\ W_{proj}c_{u}+b \tag{16}\]  
where \(W_{proj}\) and \(b\) are the weight matrix and bias of the CTC projection layer, respectively. Finally, the projected output is optimized as follows:  
\[X^{L}\ =\ X^{ac\times\frac{1}{2}\times\frac{w}{2}} \tag{2}\]0  
where \(y\) denotes the output label sequence. A many-to-one mapping \(B\) is defined to determine the correspondence between a set of paths and the output label sequences. The self-attention layer links all positions with a constant number of operations that are performed in sequence.
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 3 Methodological Framework
### Theoretical Foundations  
At the core of our thoughtfully created methodological framework lies a complex synthesis that seamlessly combines numerous established principles from both traditional machine learning methods and modern deep learning advancements. The strategic use of the eICU Collaborative Research Database provides a comprehensive repository of clinical data, including diverse patient demographics, vital signs, laboratory results, and diagnostic codes, all carefully processed through extensive data cleaning, normalization, and feature engineering to guarantee their integrity and usability. This well-organized dataset forms the essential foundation for the application of advanced gradient-boosting methods, including XGBoost [49] and CatBoost [50], which have reliably exhibited an exceptional performance in managing high-dimensional, heterogeneous datasets typical of real-world clinical situations.  
Moreover, it is essential to emphasize that a key element of our theoretical framework is the significant focus on the complexities of probability calibration and threshold optimization. Given the dataset's imbalance, with stroke cases constituting a minority, we utilize specialized loss functions and sophisticated calibration techniques to ensure that the predicted probabilities accurately reflect the actual clinical risks observed in practice. This solid theoretical foundation is firmly rooted in the established literature on statistical methodologies and machine learning principles, and is specifically designed to tackle the intricate diagnostic challenges associated with the occurrence and identification of stroke cases.
# Enhancing Absorption Bandwidth through Vertically Oriented Metamaterials
## 7 Discussion of Parameter Optimization  
Maximum coupling between individual SRRs occurs when their peak absorption lies at the same frequency. For this reason, the parameter space of each single-SRR unit cell was investigated. This section provides a brief discussion of the sensitivity of each SRR to some of its geometric parameters. Since the spectral location of the peak PSRR absorption resonance was made to match that of the VSRR, the parameter space of the single-PSRR unit cell was more thoroughly explored, covering the \(w_{s}\), \(w_{g}\), and \(h_{s}\) parameters. For brevity, only the height of the VSRR is discussed here. In both cases, the thickness of the SRRs were constrained by fabrication tolerances to 0.05 \(\upmu\)m.  
Figure 7: The DSRR geometry is shown with: (**top**) a VSRR rotated 180\({}^{\circ}\); (**middle**) a PSRR rotated 180\({}^{\circ}\); and (**bottom**) both VSRR and PSRR rotated 180\({}^{\circ}\).  
The height of the VSRR was swept from 1.7 \(\upmu\)m to 2.8 \(\upmu\)m. As the height of the VSRR increases, peak location moves towards lower frequencies (ranging from about 30 THz to about 40 THz), and the absorption peak broadens. Peak absorption amplitude remains \(>\)90%.  
Values for the height of the PSRR were swept from 1.3 \(\upmu\)m to 2.1 \(\upmu\)m. As the height of the PSRR increases, the amplitude of the absorption peak increases and the FWHM broadens, shifting to lower frequencies (ranging from about 33 THz to about 40 THz).  
Values for the width of the PSRR gap (\(w_{\rm g}\)) were swept from 0.03 \(\upmu\)m to 0.55 \(\upmu\)m. At low gap width values, the absorption peak maintains an amplitude of \(A>0.95\), and shifts to lower frequencies (ranging from about 32 THz to about 34 THz). As the PSRR gap widens, the absorption peak decreases in amplitude, undergoing a decrease in FWHM.  
Values for the width of the PSRR (\(w_{\rm s}\)) were swept from 0.5 \(\upmu\)m, to 1.2 \(\upmu\)m. A very weak absorption peak is present even for low values of \(w_{\rm s}\). As the PSRR widens, the absorption peak increases in amplitude, reaching a maximum, and then decreasing again for larger values of \(w_{\rm s}\). The spectral location of the absorption peak shifts to lower frequencies as \(w_{\rm s}\) is increased.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 4 Discussion
### Limitations in NPP Validation  
Site-level validation of MODIS NPP has been more limited because of the logistical constraints of measuring NPP and scaling it to the size of a MODIS grid cell [43; 51]. The BigFoot project was designed to provide ground validation data for MODIS land products, including NPP. Thus, the BigFoot data used in this paper to validate GLASS NPP results, varies widely, according site characteristics, i.e., different climate and vegetation types. As BigFoot data are only generated from 2000 to 2004 (Table 1), the problem of time-inconsistency exists when validating the GLASS NPP estimates. To reduce the bias, mean BigFoot NPP in different years (Table 1) were used to validate the estimated mean NPP in 2004, 2008 and 2012. In conclusion, BigFoot NPP could be only used to validate the GLASS NPP estimates preliminarily.
## 3 Results  
The assessments took place in a timely manner, with no issues to report. All students were successfully examined, so there is no possibility of skewed results. The descriptive results of the study can be seen in Table 3. It shows the averages and standard deviation of the three courses for both cohorts, as well as breaking down the results by gender and academic discipline. Table 3 reveals significant differences in the grades of both cohorts, which are systematically better for the online evaluation. There are also slight differences between gender. Finally, the students of the Biology and Geology degrees systematically fared better than the rest. Table 4 below shows the frequency distribution of the different answers possible in the self-assessment questions designed as a Likert scale, which were filled by the 517 participants of the online exam cohort of 2020-2021. The results observed in Table 4 indicate that student stress perception declines rapidly once an exam has begun. Ultimately, stress levels follow a similar pattern to that of on-site evaluation; 54.2% of respondents indicate it is the same. Regarding grades, students do not perceive much difference between online and on-site assessment, considering both to be fair. Most students are satisfied with the remote exam experience and would like to repeat it in the second semester (63.1%). Finally, most students believe that online evaluation will become common in the future (54.4%). In order to respond to the research objectives, these perceptions revealed in the descriptive analysis of results need to be contrasted using correlational and inferenti \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Assessment Used** & **AyDP (M/SD)** & **EyESyF (M/SD)** & **PyCE (M/SD)** \\ \hline On-site (year 2019–2020) & 7.03 (1.76) & 7.54 (1.75) & 6.60 (1.31) \\ Online (year 2020–2021) & 8.27 (1.52) & 9.02 (1.14) & 7.95 (1.22) \\ \hline
**Gender** & **AyDP (M/SD)** & **EyESyF (M/SD)** & **PyCE (M/SD)** \\ \hline Female & 7.82 (1.69) & 8.44 (1.62) & 7.44 (1.41) \\ Male & 7.61 (1.80) & 8.29 (1.62) & 7.26 (1.44) \\ \hline
**Academic Discipline** & **AyDP (M/SD)** & **EyESyF (M/SD)** & **PyCE (M/SD)** \\ \hline Biology and Geology & 8.24 (1.59) & 8.77 (1.40) & 7.67 (1.35) \\ Economy & 7.42 (1.76) & 7.93 (1.83) & 7.17 (1.45) \\ Technology & 7.66 (1.72) & 8.56 (1.40) & 7.40 (1.37) \\ Physics and Chemistry & 7.91 (1.64) & 8.42 (1.58) & 7.37 (1.34) \\ Geography and History & 7.27 (1.79) & 7.83 (1.70) & 7.03 (1.51) \\ English & 7.95 (1.69) & 8.23 (1.83) & 7.35 (1.51) \\ Spanish Language and Literature & 7.59 (1.99) & 7.80 (1.92) & 6.76 (1.67) \\ Mathematics & 7.60 (1.88) & 8.64 (1.55) & 7.46 (1.47) \\ \hline
**Sample Total** & **7.73 (1.74)** & **8.37 (1.62)** & **7.36 (1.43)** \\ \hline \hline \end{tabular}  
* AyDP = Learning and development of personality; EyESyF = Education and Social and Family EnvironmentPyCE = _Educational Processes and Contexts_.  
\end{table} 
Table 3: Student grade average by course and standard deviation according to different variables.
# Person Search via Deep Integrated Networks
## 3 Overview of the Deep Integrated Networks for Person Search  
Figure 1 shows an overview of the proposed deep networks by integrating person detection and identification (IPnetwork) for person search. The IPnetwork was composed of two modules: one is the detection network, and the other one is the identification module. Two images captured by non-overlapped cameras were input to the detection network, which was based on the Faster R-CNN network [35]. The regions of a person were then detected and cropped. Each cropped region was further input to the identification module, which consists of a convolutional neural network (CNN) to extract the discriminative features. Note that, rather than casting person ReID as a two-class classification problem [13; 15; 20], the identification module was trained as the multi-class classification in which each person was given one ID, and the convolution layers aimed to capture discriminative features. Then, the similarity of each pair of feature vectors was calculated by XQDA [11]. In the test process, the matching results were obtained by sorting the similarities between the probe person image and gallery images in descending order, and the top-\(k\) results are shown. Note that the \(k\) value is user-defined, and if \(k\) = 1, only one gallery image with the highest similarity is shown. The detection network, which is composed of sharing convolutional layers, region proposal network (RPN) [35] and region of interest (ROI) pooling and classification, is introduced in Section 3.1. The identification module--composed of a multi-class CNN network--and the metric learning for the similarity measurement are introduced in Sections 3.2 and 3.3, respectively.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 2 Signal Model and Problem Statement
### Convolutional Neural Networks
#### 3.1.2 Convolutional Layer  
The convolutional layer is also called the up-sampling layer as it extracts features from the input data. The features extraction is performed by several convolutional filters, as different kernels represent different features of the input data. The number of extracted features is directly related to the number of convolutional kernels in the up-sampling layer. One can always learn better hidden representation of input data by increasing the depth of the convolutional layer. A convolutional operation can be expressed as:  
\[h_{k}^{m}=f(x^{m}*w_{k}{}^{m}+b_{k}^{m}) \tag{6}\]  
\[x^{m}*w_{k}^{m}(i)=\sum\
olimits_{l=-\infty}^{+\infty}x(l)w_{k}^{m}(i-l) \tag{7}\]  
where, \(x\in\mathbb{R}\) is the set of inputs and \(w\) represents the kernel. To achieve the nonlinear mapping of the kernel outputs, an activation function \(f(.)\) is employed in Equation 6. There are many activation functions such as Sigmoid, tanh and Rectified Linear Unit (ReLU). In this study, ReLU is used as the activation function with the convolutional Layer.  
\[f(x)=\left\{\begin{array}{ll}x&\quad\quad if\ x>0\\ 0&\quad\quad if\ x\leq 0\end{array}\right. \tag{8}\]
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 3 Dataset and Methodology
### Model Establishment  
#### 3.3.1 Single Intelligence Models  
In this study, we developed single intelligent prediction models with pytorch based on BP, LSTM, and 1 DCNN. In order to make the models comparable and keep the model complexity consistent, all network parameters are set similarly. A sample input to the BP neural network is a one-dimensional vector, including 130 feature variables, and 13 features under each time step. A sample input of the LSTM network is a \(10\times 13\) matrix, which represents a time window of 10 and each time step contains 13 features. The 1 DCNN network input sample is a \(10\times 13\) two-dimensional vector. The network contains three one-dimensional convolution layers (Conv1D) and one Flatten layer (Flatten), where the convolution kernel is 3, 3, and 2 in sequence, and the dropout probability is 0.2. The structure parameters of each network model are shown in Table 4.
A different alternative to the embedded antenna booster is the so-called flexible printed circuit antenna (FPC antenna) (Figure 0(b)). It is based on a conductive element printed on a very thin substrate attached to the device, usually on the plastic cover above the PCB. These antennas come with a 50 \(\Omega\) micro-coaxial transmission line, so the designer freely places the FPC antenna within the device.  
This paper compares antenna boosters and FPC antennas to design IoT devices for cellular frequencies from 698-960 MHz and 1710-2690 MHz in terms of total efficiency \(\eta_{\mathrm{t}}\). Total efficiency considers both radiation efficiency (\(\eta_{\mathrm{r}}\)) and mismatching as follows [19]:  
\[\eta_{\mathrm{t}}=\eta_{\mathrm{r}}\cdot(1-\mid S_{\mathrm{11}}\mid^{2}) \tag{1}\]  
From a power perspective, total efficiency is the ratio between power radiated to space and the available power from the source, such as a transceiver. For example, if the power emitted by a transceiver is 23 dBm and the total efficiency is 50%, the radiated power to space is 20 dBm.  
This paper is structured as follows: antenna technologies are presented in Section 2. Results in terms of total efficiency are discussed in Section 3, considering an FPC antenna at several positions and heights for three different PCB sizes: a big one of 131 mm \(\times\) 60 mm, a medium one of 95 mm \(\times\) 42 mm, and a small one of 65 mm \(\times\) 42 mm. The dependence of the performance as a function of the FPC antenna location is shown in Section 4. Results for the three different PCBs are discussed in Section 5. Finally, conclusions are drawn in Section 6.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 2 Related Work
Reference [23] provided a framework with an alternating-direction multiplier method to optimize the decision-making process of task offloading in a way that optimizes resource utilization and system performance. By synchronously offloading computing tasks at three levels, terminal devices, edge servers, and cloud servers, this framework minimizes the computational burden on the network, reduces network latency, and improves energy efficiency to the greatest extent possible.
## 4 Conclusions  
In this paper, we carried out an evaluation of the suitability of predicting the long-term environmental acoustic pattern of a position based on information collected in a short-term interval using artificial neural networks. For this, we used a dataset with sound pressure level values from the city of Barcelona, Spain, captured with a wireless acoustic sensor network. Using several performance metrics, we performed a comparison between 192 models designed with eight different architectures and trained using hourly sound pressure level datasets.  
In general, the results show that artificial neural networks can classify short-term acoustic data into one of several recognized long-term environmental acoustic patterns. From a global perspective, models with higher quantity of hidden layers have better performance, even though this performance is not affected by the amount of neurons, and the performance increases if the data are gathered in an hourly time slot included in the interval from 14:00 to 22:00. Regarding particular environmental acoustic patterns, those with lower sound pressure level variability are easier to estimate using hourly sound pressure level measurements.  
The provided insights are crucial to define the data collection methodology in order to assure the most accurate pattern category prediction and avoid bias created by stable routines with temporal stations. Moreover, it is recommended to capture data at the same time slot in different locations, as this improves recognition of the specific environmental acoustic behavior of a place.  
Conceptualization, A.P. and J.M.N.; data curation, A.P.; investigation, A.P. and J.M.N.; methodology, A.P.; software, A.P.; modeling A.P.; supervision, J.M.N.; validation, J.M.N. and A.P.; writing--original draft, A.P. and J.M.N.; writing--review and editing, A.P. and J.M.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by MCIN/ AEI/10.13039/501100011033 under grant PID2020-112827 GB-I00.  
Not applicable.  
The data source described in this paper has been shared by the Departament d'Avaluacio i Gestio Ambiental in Ajuntament de Barcelona, which owns and has rights over all data.  
The authors acknowledge [PERSON] and [PERSON] for sharing the data.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANN & Artificial Neural Networks \\ BCN & Barcelona City (Spain) \\ IoT & Internet of Things \\ GPS & Global Positioning System \\ ReLU & Rectified Linear Unit \\ SNN & Strategic Noise Map \\ WASN & Wireless Acoustic Sensor Network \\ \end{tabular}
## 2 System Model Network
### Optimal Power-Splitting (PS) Factor  
In this section, we can calculate the optimal value \(\rho^{*}\) by solving the equation \(\frac{d\tau(p)}{d\rho}=0\), using the AT expression in Equations (23) and (34). Here, we use the Golden section search algorithm as in [25; 28; 29; 30], which is popularly used in many global optimization problems in communications.
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 4 Implementation
### Address Translation and Routing  
By definition, edge switches in eTorii are responsible of translating the MAC addresses into HLMAC, and vice versa. To this purpose, the controller installs a set of rules at edge switches to modify the addresses in the Ethernet frames arriving at it.  
Unicast routing in eTorii has been implemented in a reactive fashion, calculating the path switch by switch based on the destination HLMAC address contained in the frame and according to the eTorii logic. Broadcast routing is performed using the traffic distribution tree previously mentioned.
## 2 Related Works
### Transformer Models in Computer Vision
The authors of [6] provided additional details on the application of ViT for the multilabel classification of satellite imagery and suggested ForestViT. ViT was used by [PERSON] et al. in [7] to classify femur fractures. With regard to detection, [PERSON] et al. [8] were the first to use a supervised pretrained ViT in conjunction with a faster region-based convolutional neural network detector for object detection. The authors of [9] suggested an unsupervised learning-based method for identifying manipulation in satellite photos by the use of ViT. A bridged transformer (BrT) was suggested by the authors in [10] for 3D object detection. The model was used for 3D object detection in point clouds and vision. Transformers can also be used to segment images. Medical image segmentation was accomplished in [11] using a combination of ViT and U-Net. The transformer was used by the authors in place of the encoder in the traditional U-Net. The authors of [12] presented a brand-new image segmentation technique called \"language-aware ViT\" (LAVT). In a similar vein, high-resolution ViT for semantic segmentation was proposed in another work [13]. MaskFormer was proposed by [PERSON] et al. in [14] for image segmentation. As to image super-resolution, Eformer was suggested by [PERSON] et al. in [15] for medical image denoising. The authors proposed SUNet for image denoising in [16] by way of combining UNet and the Swin transformer [17]. The authors of [18] suggested DenSformer as a method for image denoising. The three modules that made up the DenSformer were preprocessing, feature extraction, and reconstruction. Anomaly detection is another potential use. In [19], a unique ViT network for image anomaly detection and localization was created. The authors of this study used the BTAD real-world dataset. In a similar vein, the authors of [20] suggested AnoViT for anomaly detection and localization. TransAnomaly is a video ViT and U-Net-based framework that [PERSON] et al. proposed in [21] for the detection of anomalies in the videos. The results show that the main applications of ViTs are as follows: 50% are for image classification, 40% are for object detection, 1% are for segmentation, 1% are for compression, 2% are for super-resolution, 3% are for denoising, and 3% are for anomaly detection [22].