esa-sceva/satcom-chunk-collection huggingface dataset:
# OntoIMM: An Ontology for Product Intelligent Master Model
## 2 Related Works
### Ontology  
Originally, the term ontology derives from philosophy. It is the description of the existence of beings in the world. In computer science, artificial intelligence (AI) researchers adopted the term ontology to describe what can be represented of the world in a program [38]. It is usually defined as an \"explicit, formal specification of a shared conceptualization\" [39]. Because of advantages such as implementing interoperability, integrating different applications, sharing information, and reusing knowledge [40], ontologies have been widely used to serve as generic information models in many systems or domains and facilitate semantic interoperability [41]. [PERSON] et al. [32] presented an ontology-based multilevel product modeling framework to address the need of semantic richness from different stakeholders across the product life cycle. [PERSON] et al. [42] reported an approach based on ontology for facilitating applications interoperability in a manufacturing environment. Based on an ontology, [PERSON] et al. [14] created OntoSTEP which can transform digital models with geometric information into semantically rich models. [PERSON] et al. [43] presented an ontology called PRoduct ONTOlogy (PRONTO) for modeling product data, which intends to provide a consensual knowledge model in product modeling domain. In order to enable the semantic interoperability across different application domains, [PERSON] et al. [27] proposed an ontology-based framework by using product semantic representation language (PSRL) to represent product information related. The PSRL is a basis for a formal representation of product information [10]. In addition, many ontologies have been created, such as domain ontologies, generic ontologies [44], application ontologies [45], representation ontologies [46], and method and task ontologies [47].  
The two most widely-used ontology modeling paradigms are OWL and Frame [48]. The Frame Ontology [46], which allows expressing knowledge in a frame-based or object-oriented way, defines concepts such as frames, slots, and slot constraints. The ontology is suitable for modeling IMM because that it is based on a closed-world assumption where everything is prohibited until it is permitted.
## 3 Method
### Autoregressive Conversion Model  
We adopt an encoder-decoder recurrent network for the conversion model. The encoder we use is based on the Tacotron model [43]. Firstly, a sequence of features including the combined SI-PPGs and speaker embeddings is taken as input. The input vectors are then passed through a Pre-Net, a non-linear transformation, which includes a bottleneck layer with dropout to facilitate convergence. The pre-net outputs are further processed using a CBHG module [43] to produce the final encoder representation. The effectiveness of CBHG stems from its ability to learn context-dependent representations at a high level. CBHG's architecture consists of a 1-D convolutional bank, highway networks [44], and Bidirectional Gated Recurrent Unit (Bi-GRU) [45] layers.  
We define a sequence of linguistic features \(P=(p_{1},\ldots,p_{2},p_{N})\) of length \(N\) as the encoder input, where \(P\in\mathbb{R}^{N\times D_{p}}\). Here, \(N\) represents the number of frames and \(D_{p}\) is the dimension of the features sequence. The Pre-Net layer produces a set of output vectors denoted as \(R=[r_{1},r_{2},\ldots,r_{N}]\), where \(R\in\mathbb{R}^{N\times D_{r}}\).  
The encoder outputs sequence \(H=[h_{1},h_{2},\ldots,h_{N}]\) (i.e., the hidden representations) are obtained, where \(H\in\mathbb{R}^{N\times D_{h}}\), \(D_{h}\) is the dimension of the encoder output vectors.  
For the decoder, we use an attention-based decoder, which is an autoregressive RNN model. This means that it predicts a sequence of acoustic features using the encoder outputs. The architecture consists of an attention layer, an LSTM layer, and a Pre-Net layer. The attention layer employs a \"Bahdanau\" attention mechanism, which compresses the encoder output into a fixed-length context vector. This context vector is then combined with the output of the attention layer and used as input for the LSTM layer.  
We define the sequence of acoustic features representing the decoder output as \(O=(o_{1},o_{2},\ldots,o_{N})\), where \(O\in\mathbb{R}^{N\times D_{o}}\). First, at each step \(t\), the attention layer generates a fused representation, denoted as \(f_{t}\in\mathbb{R}^{1\times D}\), which is computed by concatenating the previous acoustic feature \(o_{t-1}\in\mathbb{R}^{1\times D_{o}}\) with the encoder output \(h_{t}\in\mathbb{R}^{1\times D_{h}}\) using the following formulas:  
\[f_{t}=a_{att}^{T}C_{t} \tag{2}\]  
where \(C_{t}\in\mathbb{R}^{2\times D}\) is the concatenated representations as  
\[C_{t}=[o_{t-1}W_{o};h_{t}W_{h}] \tag{3}\]  
Secondly, the LSTM layer is initialized using two inputs: the generated fusion representation denoted \(f_{t}\), and the decoder hidden state from the previous step denoted \(h_{t-1}^{d}\). The decoder LSTM output \(o_{t}^{d}\) is produced as  
\[o_{t}^{d}=LSTM(f_{t},h_{t-1}) \tag{4}\]  
The LSTM output \(o_{t}^{d}\) is then fed into the pre-net layer to generate the decoder output. The Pre-Net serves as bottleneck information needed to learn the autoregressive decoder.  
The acoustic feature vector \(o_{t}\in\mathbb{R}^{1\times D_{o}}\) representing the decoder output is finally generated.
# Image Quality Improvement and Memory-Saving in a Permanent-Magnet-Array-Based MRI System
###### Abstract  
Point-of-care magnetic resonance imaging (MRI) requires clear images within a short scanning time, a small footprint of the scanner, and relatively low memory required for image reconstruction. A permanent magnet array (PMA)-based MRI system is a good candidate to supply a magnetic field due to its compactness and low power consumption. However, it has relatively inhomogeneous magnetic field and thus non-linear gradients, which results in location-dependent k-spaces (so called local k-spaces) and uneven signal point populations in the local k-spaces, compromising the image quality. Moreover, owing to the non-linearity, imaging reconstruction using Fourier transform does not work, which leads to an increase in the required computation memory. In this study, in order to improve the image quality, the approaches of compensating the uneven signal point population by increasing the numbers of sampling points or rotation angles are investigated in terms of their impacts on image quality improvement, acquisition time, image reconstruction time, and memory consumption. Both methods give a significant improvement on image image quality although they result in a large and dense encoding matrix and thus a large memory consumption. To lower the memory consumption, it is further proposed to transform such a matrix to frequency domain where the matrix could be sparse. Moreover, a row-wise truncation to the transformed encoding matrix is applied to further reduce the memory consumption. Through the results of numerical experiments, it is shown that the required memory for calculation can effectively be reduced by 71.6% while the image becomes clearer by increasing the number of sampling point and/or the number of rotation angles. With the successful demonstration where improved image quality and a lowered memory required can be obtained simultaneously, the proposed study is one step forward for a PMA-based MRI system towards its targeted point-of-care application scenario.  
low-field MRI; portable MRI; permanent magnet array (PMA); magnetic resonance imaging; MRI; spatial encoding magnetic field; SEM +
Footnote †: journal: Applied  
Article
## 2 The Problem Statement
### Explainable AI  
Explainable AI (XAI) has developed as a research area dedicated to mitigating the opaque qualities of many AI models. XAI endeavors to clarify which features influence the decision-making process of an AI model, both on a global scale over numerous data instances and locally on individual instances. XAI approaches can be model-agnostic, suitable for any AI model, or model-specific, designed for particular AI models. Techniques involve constructing surrogate models or producing lists of important features for the AI model [50; 51].  
**Main Methodologies of SHAP and LIME:** One widely used explainable artificial intelligence (XAI) tool is SHAP [50], which leverages the game theory concept of Shapley values to elucidate an AI model's decisions. SHAP computes Shapley values for each feature by evaluating the prediction using all other features except the one under scrutiny. It then quantifies the impact of each feature's contribution by subtracting the results, thereby determining its relative importance to the prediction. This iterative process assigns a \"fair\" contribution to each feature using Shapley values, enabling a comprehensive analysis of feature contributions. SHAP facilitates both global and local model analyses and is generally model-agnostic, albeit with some model-specific techniques.  
Another prominent XAI tool is Local Interpretable Model-agnostic Explanations (LIME) [51]. LIME enhances interpretability by approximating the AI model locally with an interpretable surrogate model. It provides insights into the model's predictions by creating surrogate models that approximate the behavior of the original model within a specified locality surrounding the analyzed sample. By discerning the features that exert the most influence on the model's output for each prediction, LIME highlights the key contributing features, enhancing the understanding of the model's decision-making process.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 5 Discussion
### Locality and Clipping Limits  
The two control parameters strongly affect the quality of the resulting images. However, finding the right balance can strongly depend on the visualization goal. Higher exponents mean stronger local contrast enhancement, but can also enhance noise. The contrast limit works against the distortions, but also limits the achievable feature amplification. Two image grids in Figures 7 and 8 demonstrate these issues.  
The parameter sensitivity of the algorithm is an important question. The TMQI structural similarity score is depicted in Figure 10 as the function of the exponent and the clip limit. There is a central region where the TMQI score is relatively stable and any parameter pair from this range is a reasonably good choice, e.g., \(a=1.0\), \(clip\_limit=5\) seems to be a good starting point. As will be discussed in the Optimization section, the calculation can be sped up without sacrificing the image quality too much. With lower precision calculations, the parameter space is quickly discoverable.  
Figure 9: Structural similarity map for the head CT example. Brighter shades belong to higher local structural similarity (white = 1.0, black = 0.0).
# Calculation of the Relaxation Modulus in the Andrade Model by Using the Laplace Transform
## 4 Asymptotic Behaviour via Tauberian Theorem  
Next, we will obtain the asymptotic behaviour of the relaxation modulus \(G_{\mathrm{a}}(t)\) as \(t\to 0^{+}\) and as \(t\to+\infty\) from its Laplace transform \(\bar{G}_{\mathrm{a}}(s)\) by using the following version of the Tauberian theorem, (for other version of the Tauberian theorem, see [16]).  
Theorem 4.1: _Consider that the Laplace transform of a function \(f(t)\) is given by \(\tilde{f}(s)=\mathcal{L}[f(t);s]\). The asymptotic behaviour of \(\tilde{f}(s)\) as \(s\to+\infty\) is given by_  
\[\tilde{f}(s)\approx\mathcal{L}[g(t);s],\quad s\to+\infty, \tag{57}\]_where \(g(t)\) is the asymptotic behaviour of \(f(t)\) as \(t\to 0^{+}\). Furthermore, the asymptotic behaviour of \(\tilde{f}(s)\) as \(s\to 0^{+}\) is given by_  
\[\tilde{f}(s)\approx\mathcal{L}[h(t);s],\quad s\to 0^{+}, \tag{58}\]  
_where \(h(t)\) is the asymptotic behaviour of \(f(t)\) as \(t\to+\infty\)._  
**Proof.** On the one hand, consider that the asymptotic behaviour of \(f(t)\) as \(t\to 0^{+}\) is given by  
\[f(t)\approx g(t)=\sum_{k=1}^{N}a_{k}\,t^{b_{k}},\quad t\to 0^{+}, \tag{59}\]  
with \(N=0,1,2,\ldots\), and \(0\leq b_{1}<b_{2}<\ldots<b_{N}\). Apply the Laplace transform to (59) in order to obtain  
\[\tilde{f}(s)=\mathcal{L}[f(t);s]\approx\mathcal{L}[g(t);s]=\sum_{k=1}^{N}a_{k} \,\mathcal{L}\Big{[}t^{b_{k}};s\Big{]}=\sum_{k=1}^{N}\frac{a_{k}\,\Gamma(b_{k} +1)}{s^{b_{k}+1}}.\]  
Therefore, we obtain the asymptotic behaviour of \(\tilde{f}(s)\) as \(s\to+\infty\),  
\[\tilde{f}(s)\approx\sum_{k=0}^{N}\frac{a_{k}\,\Gamma(b_{k}+1)}{s^{b_{k}+1}}, \quad s\to+\infty,\]  
as we wanted to prove.  
On the other hand, consider that the asymptotic behaviour of \(\tilde{f}(s)\) as \(s\to 0^{+}\) is given by  
\[\tilde{f}(s)\approx\sum_{k=1}^{N}c_{k}\,s^{d_{k}},\quad s\to 0^{+}, \tag{60}\]  
with \(N=0,1,2,\ldots\), and \(0\leq d_{1}<d_{2}<\ldots<d_{N}\). Apply the inverse Laplace transform to (60) in order to obtain  
\[f(t)=\mathcal{L}^{-1}[\tilde{f}(s);t]=\sum_{k=1}^{N}c_{k}\,\mathcal{L}^{-1} \Big{[}s^{d_{k}};t\Big{]}=\sum_{k=1}^{N}\frac{c_{k}}{\Gamma(-d_{k})\,t^{d_{k} +1}}\,,\]  
Therefore, we obtain the asymptotic behaviour of \(f(t)\) as \(t\to+\infty\),  
\[f(t)\approx h(t)=\sum_{k=1}^{N}\frac{c_{k}}{\Gamma(-d_{k})\,t^{d_{k}+1}}, \quad t\to+\infty,\]  
as we wanted to prove.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 2 Motivation  
In this work is aimed at the investigation of the speech activity present in the EEG signals. The main focusing on the cooperation of several areas of the brain, especially speech activity that is associated with visual stimuli. The stimuli used in the experiment were images and written text. According to the study in Reference [10], the visual stimulus can generate inner speech. In speech communication, a person uses the visual area of the brain which accelerates the speech generation. The human brain often works with the visual or textual form of objects, to identify the correct grammatical, audio, and articulatory form of the word. Such cooperation of several areas of the brain improves the communication process.  
As mentioned we expected increased activity of the two main areas of the brain during EEG signal acquisition which are the speech area, and the visual area. During EEG recording the subjects were presented with both images with showing specific colours and the text forms of individual colours. By presenting the subjects with these images their visual and imaginary parts of the brain became activated. The important fact to realised is that the brain areas which control what we see and what we can imagine are the same. When communicating, we often involve these visual areas in addition to speech areas. The relationship between imagination and vision is very close and hence these two actions elicit similar signals. The main difference between visual and mental images lies in the communication of the cerebral pathway, which started from the eye, leads to the primary visual cortex [11; 12]. The study in Reference [13] assumes that image recognition and image naming involves the activity of occipitotemporal and prefrontal areas.  
Speech production is created by a so-called conversational loop that moves the information signal to different parts of the brain tin order to compose a word pattern before it is pronounced. Such a phonological loop involves the processing of the linguistic-auditory signal which is in the first instance formed in the auditory cortex followed by the transfer of the audio form of the word to the Wernicke's area. In the Wernicke's area the sound pattern is processed and from there it travels to the Broca's area where syntax, morphology, and articulation instructions are generated. This information is sent to the motor cortex finally where the movements of the vocal cords, tongue, diaphragm, and other voicing muscles are controlled [14].
# Evolutionary Statistical System Based on Novelty Search:
## 3 Novelty-Based Approach for the Optimization Stage in a Wildfire Prediction System
### New Framework: Ess-Ns
\(\mathbf{RFL_{i}}\): real fire line of instant \(t_{i}\); **OS-Master**: Optimization Stage in Master; **OS-Worker\(\{\mathbf{1.\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{1}}}}}}}}}}\}\)**: Optimization Stage in Workers 1 to \(n\); _PEA_: Parallel Evolutionary Algorithm; **NS-based GA**: Novelty Search-based Genetic Algorithm; \(\rho(\mathbf{x})\): novelty score function from Equation (1); \(\mathbf{PV_{(1.\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{1}}}}}}}}}}})}}\): parameter vectors (scenarios); **FS**: fire simulator; \(\mathbf{PEA_{F}}\): Parallel Evolutionary Algorithm (fitness evaluation); **CS**: Calibration Stage; **SS**: Statistical Stage; **FF**: fitness function; \(\mathbf{FFL_{i}}\): predicted fire line of instant \(t_{i}\); \(\mathbf{K_{ign}}\): _Key Ignition Value_ for \(t_{i}\); \(\mathbf{SK_{ign}}\): _Key Ignition Value_ search; **PS**: Prediction Stage. discussed in Section 2.2, fitness-based evolutionary algorithms converge to a population of similar individuals, which are redundant, and the evolved population will almost inevitably include some random or not-so-fit individuals (that were generated and selected during the last iteration) that do not contribute to the solution. Since this collection is used for the purposes of reducing uncertainty in the **SS**, we considered that the advantages of this new design could be an appropriate match for said stage. It is important to note that ESS-NS differs from the most recent approaches in that it uses the simpler model of Master/Worker (with no islands and only one population). Although the novelty-based strategy performs more steps than the original ESS, the Master process only delegates the simulation and evaluation of individuals to the Workers since this is the most demanding part of the prediction process. Even so, this has not been a problem in our experimentation since the additional steps, such as novelty score computation and updating of sets, do not add significant delays to the execution. The simplification of the parallel hierarchy is motivated by the need to have a baseline algorithm for future comparisons and to be able to analyze the impact of NS alone on the quality of results. Considering that NS uses a strategy that was designed not only to keep diversity and emphasize exploration but to actively seek them, it serves as an alternative route to solve the problem that originally made it necessary to resort to mechanisms such as the island model. Additionally, such a design would require additional mechanisms, for example, for handling the migrations, and these can directly affect both the quality and the efficiency of the method, making it harder to assess the performance of the method. At the moment, these considerations and possible variants are left as future work.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 1 Introduction
### ImageCLEF Caption Detection Task
#### 1.8.1 ImageCLEF Caption Detection Task 2017  
The ImageCLEF 2017 Caption Detection task was divided into two subtasks: concept detection and caption prediction. Participants first identified relevant UMLS concepts in biomedical images and then generated intelligible captions based on these concepts and image features. Capturing concept interactions was crucial to reconstruct the original captions beyond simply detecting individual visual elements.  
The training set contained 164,614 biomedical images extracted from PubMed Central, with corresponding UMLS concepts for concept detection and image-caption pairs for caption prediction. Validation and test sets included 10,000 images each. Evaluationmetrics were the F1-score for concept detection and the BLEU score for caption prediction. Performance summaries are shown in Tables 3-6:  
[PERSON] et al. [19] achieved the best results in the caption prediction task using an encoder-decoder architecture combining CNNs for feature extraction and RNNs with attention mechanisms for caption generation. They fine-tuned VGG19 on the ImageCLEF dataset and trained the model using stochastic gradient descent with the Adam optimizer and dropout regularization.  
For concept detection, [PERSON] et al. [20] employed multilabel classification with CNNs and Binary Relevance-Decision Trees (BR-DTs), using GoogleNet for feature extraction. They also explored various visual descriptors such as CEDD and FCTH [17], finding that CEDD yielded the best performance in their experiments.  
[PERSON] and [PERSON] [21] proposed a probabilistic k-nearest neighbor (PKNN) approach, combining bag-of-visual-words (BoVW) and Quantized Bag of Colors (QBoC) models, achieving second-best performance without external resources. Dense SIFT features and late fusion techniques were used to enhance retrieval and prediction performance.  
Overall, the 2017 task demonstrated the effectiveness of deep learning, information retrieval, and ensemble methods for biomedical image captioning.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 2 Experimental Section
### Set of Antennas  
There is no standard antenna type to use in each situation for underwater radiocommunications. This is why in this work various antennas have been tested, in order to find out which one is appropriate for our sensor network. Basically, we tested electric dipoles and magnetic dipoles and four configurations (Figure 4):  
* Insulated dipoles;
* Insulated dipoles with uninsulated tips;
* Magnetic loops of 22 and 40 cm.  
Dipole antennas have been designed as a body with two screwed elements surrounded by a tight PVC pipe screwed to the body as well. Each element was 59 cm long and was made to be easily unmounted to transport or be replaced with diverse kinds of terminations. This enables the possibility of using two types of dipoles: completely insulated bars [14] and insulated with uninsulated terminations [6]. The body of the dipole was built with high pressure pipe parts leaving a cavity inside for the connection with coaxials and a balun circuit. The reason for using dipole antennas, apart from being widely used in literature, is that they allow an easy deployment, and crossed dipoles could permit a comfortable orientation. A conduction phenomenon was found when using uninsulated dipoles in sea water, producing high losses. Therefore, these antennas were not used in harbour's experiments [13]. In addition, loop antennas were built to be able to compare their performances and because some authors consider these as the most suitable antennas for underwater communications [7]. Two versions were designed and tested, a pair with a radius of 22 cm and another with 40 cm to assess the effect of size increase. Loop antennas were built with 10 turns of enamelled copper wire (coating insulation). All copper turns were packed together and covered with self-vulcanizing tape for protection, and a balun was used for a balanced excitation.  
Figure 4: Antenna examples: on the **left** 22 cm magnetic loop and insulated crossed-dipole on the **right**.  
After several tests in the field, dipole antennas showed very poor performance (Section 3.2) compared to magnetic loops, in line with some literature [15]. Magnetic loop antennas were selected, and all the experimental results in this paper were obtained with them.