esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 4 Methods
### Advanced Models
#### 4.2.1 Dr-Caml  
**DR-CAML** is a CNN-based model adopted for ICD coding [23]. When an ICD code is defined by the WHO, it is accompanied by a label definition expressed in natural language to guide the model towards learning the appropriate parameter values of the model. For this purpose, the model employs a per-label attention mechanism enabling it to learn distinct document representations for each label. It has been shown that for labels for which there are very few training instances available, this approach is advantageous. The idea is that the description of a target code is itself a very good training example for the corresponding code. Similarity between the representation of a given test sample and the representation of the description of a target code gives extra confidence in assigning this label.  
In general, after the convolutional layer, DR-CAML employs a per-label attention mechanism to attend to the relevant parts of text for each predicted label. An additional advantage is that the per-label attention mechanism provides the model with the ability of explaining why it decided to assign each code by showing the spans of text relevant for the ICD code.  
DR-CAML consists of two modules: one for the representation of the input text, and the other for the embedding of the label's description as is visualized in Figure 5. The CAML module has a CNN at the base layer which takes a sequence of the embeddings of the text tokens as input and consequently represents the document as the matrix \(H\). Then, the per-label attention mechanism applies. Attention in this context means learning which parts of some context (the label description vectors) are relevant for a given input vector.  
After calculating the attention vector \(\alpha\) using a softmax activation function, it is applied as a product with \(H\). With \(h_{l}\), the vector parameter for label \(l\), the vector representation for each label is computed as  
\[v_{l}=\sum_{n=1}^{N}\alpha_{l,n}h_{n} \tag{13}\]  
Given the vector representation of a document and the probability for label \(l\), \(\hat{g}_{l}\) can be obtained as shown in Figure 5.  
The CNN modules on the left hand side try to minimize the binary cross entropy loss. The second module is a max-pooling CNN model which produces a max-pooled vector, \(z_{l}\), by getting the description of code \(l\). Assuming \(n_{y}\) is the number of true labels in train data, the final loss is computed by adding a regularization term to the base loss function. The loss function is explained in more detail in Section 4.2.3.
## 5 Experimental Analysis and Verification
### Comparison with Other PQD Diagnosis Methods  
In order to further verify the validity of the proposed method, we compare the RDCA with some recent research results are compared, as listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Structure** & **Parameters** & **Accuracy (\%)** & **Test Time (s)** \\ \hline AE-1 & 32768-2000 & 131 106 k & 94.02 & 2.12 \\ AE-3 & 32768-5000-1000-512 & 338 749 k & 96.32 & 2.39 \\ CAE-1 & L2 regularization & 31 k & 97.85 & 5.54 \\ CAE-3 [17] & L2 regularization & 136 k & 98.07 & 6.35 \\ DCNN [33] & AlexNet: five layers & 3807 k & 97.81 & 8.48 \\ RDCA-1 & Classifier: SCNN & 31 k & 98.5 & 5.24 \\ RDCA-3 & Classifier: Softmax & 137 k & 98.06 & 7.19 \\
**RDCA-3** & **Classifier: SCNN** & **137 k** & **99.34** & **6.31** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different feature extraction methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Diagnosis Methods** & \begin{tabular}{c} **Num. of** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **Num. of** \\ **PQDs** \\ \end{tabular} & \
\begin{tabular}{c} **SNR (dB)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline FDST and FDT [34] & 5 & 13 & 30 & 97.95 \\ ST, TT and PFS [9] & 62 & 11 & 20 & 95.2 \\ WT and PNN [30] & 9 & 16 & 20 & 93.6 \\ DRST, DAG-SVM [16] & 9 & 9 & 20 & 97.77 \\ Hybrid ST and DT [35] & 5 & 11 & 20 & 94.36 \\ VMD and DT [36] & 4 & 14 & 30 & 96.73 \\ ADLINE and FFNN [7] & 2 & 12 & 20 & 90.58 \\ DWT and FFT [37] & 10 & 12 & 20 & 99.04 \\
**RDCA and SCNN** & **Automatic** & **16** & **20** & **99.34** \\ \hline \hline \end{tabular}
\end{table}
Table 3: PQD classification performance comparison.  
It is observed that different methods have different types and quantities of manually selected features. For instance, the method proposed in [9] has 62 kinds of features, while the proposal in [36] has 4 features. However, the accuracy of manually selected feature extraction methods is still lower than the automatic feature extraction. Moreover, sixteen types of disturbances are reported in [30], it is obviously difficult to distinguish all the PQDs by nine features. Particularly, only 30 dB noise conditions of the PQDs are reported in [34,36]. It can be seen from Table 3 that the proposed method performs with the highest classification accuracy under the condition of 20 dB noise level.
# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 2 Method
### Noise2 Noise  
The N2N [37] methodology addresses the challenge of image restoration without the need for clean reference data, utilizing a self-supervised approach. This methodology introduces the idea of training a noise removal model using only pairs of noisy images. These are generated from the same object or scene but with different noise realizations. The authors argued that although noisy images do not contain direct information about the object or scene without noise, they can still provide valuable information to teach a model to remove noise.  
Traditionally, the learning process for determining the correlation between a clean image and a noisy image involves solving the following optimization problem:  
\[\operatorname*{argmin}_{\theta}\mathbb{E}_{(x,y)}\{\mathcal{L}(f_{\theta}(x), y)\}, \tag{2}\]  
where during training, we attempt to learn the parameters \(\theta\) that minimize the value of the cost function \(\mathcal{L}\) between the noisy input image after processing through the neural network given by the function \(f_{\theta}(x)\) and the clean output image \(y\).  
Employing the \(L2\) cost function in this optimization problem leads to the derivation of parameters that guide us toward an expected value. This expected value effectively represents the mean of the observed values, assuming a normal distribution similar to a Gaussian distribution.  
In the N2N paradigm, rather than targeting a clean image as the output \(y\), several noisy images are used as the training target. The network then learns to reconstruct the average of these noisy images. Assuming that the noise present within these images adheres to a distribution centered on a zero mean and that a substantial variety of noise realizations are provided, the derived network parameters exhibit consistency across diverse sets of noisy inputs.  
In essence, through the \(L2\) framework, the network is steered toward learning parameters that converge on the expected value: the mean of the noisy image instances. This expected value, if the noise indeed averages zero and sufficient noise variations are presented, aligns with the clean image. This mechanism underscores the ability of the \(L2\) loss to extract the signal (or clean image) from a noisy environment by leveraging the statistical properties of the noise.  
Therefore, for this methodology, the optimization problem to be solved as follows:  
\[\operatorname*{argmin}_{\theta}\sum_{i}\mathcal{L}(f_{\theta}(\widehat{x}_{i} ),\widehat{x}_{j}), \tag{3}\]  
where both the noisy input \(\widehat{x}_{i}\) and unobserved clean target \(\widehat{x}_{j}\) are drawn from a corrupted distribution.
## 2 Materials and Methods
### Antioxidant Activity Analysis  
#### 2.6.1 DPPH (1,1-diphenyl-2-picrylhydrazyl) Radical Scavenging Assay  
The DPPH radical scavenging ability was determined following the instructions provided with the assay kit. Briefly, 0.4 mL of the oil sample and 0.6 mL of DPPH radical solution were combined in a tube and thoroughly mixed. After storing in the dark at room temperature for 30 min, the mixture was centrifuged at 12,000 rpm for 5 min at room temperature. Subsequently, 800 \(\upmu\)L of the supernatant was transferred into a glass cuvette, and the absorbance value was measured at 517 nm, with 80% methanol serving as a control. The standard curve was constructed using concentrations of 0, 5, 10, 15, 20, and 25 \(\upmu\)g/mLof Trolox (Y = 1.2639X \(-\) 0.7915; R\({}^{2}\) = 0.9895). The DPPH radical scavenging abilities of the oil samples were calculated using the following formulas:  
\[\text{DPPH radical scavenging ability}(\%)=[1-(\text{A}_{1}-\text{A}_{2})/\text{A}_{3 }]\times 100 \tag{1}\]  
\[\text{DPPH radical scavenging ability}(\text{$\mu$g Trolox}/\text{mL})=(\text{Y}+0.7915) /1.2639\times\text{A}_{4} \tag{2}\]  
In the formulas, A\({}_{1}\), A\({}_{2}\), and A\({}_{3}\) represent the absorbance values of the oil sample, control, and blank, respectively; Y denotes the DPPH radical scavenging ability (%); A\({}_{4}\) signifies the dilution factor.
## 4 Discussion
However, the present analysis indicated that there was suitable consistency among the four consecutive slope values for the FBF and NIRS-derived variables (O\({}_{2}\)Hb, HHb, tHb, and HbDiff). This agreed well with the previous work of [PERSON] et al. [46], but these investigators only examined tissue oxygenation in a mixed-sex sample of 13 CVD patients. Here, the current study extended these findings to a healthy cohort and examined sex differences. Specifically, our results demonstrated acceptable consistency for FBF (ICC = 0.919) and for NIRS-derived variables (ICCs = 0.758-0.877) except for HbDiff (ICC = 0.377) (Table 5). Additionally, when separated by sex, there were generally higher ICC values for the women (ICCs = 0.777-0.947) compared to men (ICCs = 0.732-0.873). Notably, HbDiff remained much lower than the other ICCs, women: ICC = 0.577 and men: ICC = 0.115. [PERSON] et al. [3] also reported ICC values; however, their analysis aimed to determine match between tissue saturation index, O\({}_{2}\)Hb, and HHb to FBF, not consistency across four consecutive measures. These authors reported poor ICCs (i.e., match) for all comparisons (ICCs 0.00-0.07) at rest. Taken together, the current results, in conjunction with those of [PERSON] et al. [3], strongly suggest that FBF and NIRS variables should not be interchanged, as well as that a single measure of the slope value may be sufficient given the high consistency among four consecutive measures. Experimental considerations. Readers are encouraged to consider the following aspects when evaluating the presented interpretations of the current data. This study was conducted using an apparently healthy, young adult population, and therefore, this limits the results' generalizability to additional populations. Although the participants were advised to maintain a regular diet and other habits, as well as complete a standardized rest phase, it is possible that other external factors beyond our current ability to control may have influenced resting blood flow. In addition, it is possible that the currently used sample size may be perceived as a limitation and lessen the enthusiasm related to the conclusions. Additionally, it has previously been suggested that NIRS variables should only be assessed across the first cardiac cycle during venous occlusion [63], but the current study assessed slopes across multiple cardiac cycles. The present study did not include a time-aligned electrocardiogram (ECG), so the ability to only assess the first cycle was not possible. It is our recommendation that future studies include time-aligned ECG tracings to determine if FBF and NIRS-derived outcomes match better than the currently reported results. We also did not apply a unique correction factor for NIRS signals as used by [PERSON] et al. [5]. Finally, we acknowledge that our decision not to control for the menstrual cycle in the female participants may be viewed as a limitation. While acknowledging this, based on available evidence [34], it remains unlikely that natural interindividual variability in menstrual cycle phase substantially affected the current results.
## 3 Results and Discussion
### Inhibition of the Maillard Reaction by Phenolic Compounds
#### 3.3.2 Trapping or Elimination of Intermediates of Maillard Reactions  
Phenolic compounds can be used as trapping agents for \(\alpha\)-dicarbonyls, such as glyoxal, methylglyoxal, and deoxyosones, which are produced during the fragmentation and dehydration of Amadori or Heyns products [5]. In particular, flavonoids and phenylpropanoids found in tea, cinnamon, rosemary, and other herbaceous plants have been studied [19]. It has been observed that even low-molecular-weight phenolic acids such as gallic acid can trap \(\alpha\)-dicarbonyls [5]. Trapping is possible due to the presence of hydroxyl groups that react with the reactive carbonyl compounds, forming adducts through electrophilic aromatic-substitution reactions; this allows the progression of the Maillard reaction to be inhibited. The effectiveness of trapping depends on the number and position of hydroxyl groups present in the phenolic compound [20].
## 3 Materials and Method
### Modeling Process  
Bid risk prediction modeling through integrated analysis of unstructured text data and structured numeric data is conducted in three stages. First, input data is refined to analyze the integrated data analysis through data collection and preprocessing. In the case of text data, unlike numeric data, a preprocessing process of segmenting sentences into tokens and converting them into vectors is additionally required. Therefore, text data and numeric data were analyzed separately during the preprocessing. The preprocessing process, which converts textual data into a structured numeric vector, is usually followed by tokenization, stopping, stemming, normalization, and vector generation [20].  
After preprocessing, the text data and numeric data are integrated and ready for data analysis. Integrated data were randomly separated into a training data set and a testing data set for the learning of risk patterns. Eighty percent of the total data were used as training data and 20% as testing data in this study. The first 260 data sets collected of infrastructure projects were reduced to 243 data sets by preprocessing; 195 projects were therefore randomly allocated to the training data, and 48 projects were randomly allocated to the testing data. The three different risk group projects were allocated to the training data set and testing data set at an equal rate. Training data is learned by classification algorithms to identify risk patterns present in the data. This study applied four representative classification algorithms: ANN (Artificial Neural Network), SVM (Support Vector Machine), KNN (k-nearest neighbors), and NB (Naive Bayes).  
The classification model for each algorithm learned from training data verifies the performance of the classification models through testing data, which are not involved in the training process. Classification performances of the four different classification algorithms were compared in order to determine the classifier that has the best classification power. Moreover, classification performance with integrated data was compared to the classification performance with numeric data only to verify that the performance of the integrated prediction model is superior to the prediction model using only numeric data. Figure 3 shows the process of the prediction modeling in this study.
## 5 Experimental Results
### Results
#### 5.4.2 LMVSegRNN Outperforms Conventional Geometric Approaches  
Our proposed method outperforms the Graph Cut-based framework, specifically tailored for tooth mesh segmentation. As evident in Table 2, the performance is almost on par with our solution (1.5% worse on _W-IoU_ metric then our performance). However, in complex cases, the performance is worse by 2.6% on _W-IoU_. This suggests the effectiveness of our learning-based method, which better generalizes to atypical cases compared to the rule-based algorithm, even though explicitly tweaked for dental mesh segmentation.  
Figure 4: Qualitative assessment of the method without (**left**) and with (**right**) the region voting. In the former, predictions are back-projected to mesh sequentially in canonical order, for example, from R8 to L8. Blue and green region cells with misclassified labels are effectively reassigned based on a higher certainty value.
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
## 2 High-Throughput Satellite Communication System Model
### High-Throughput Satellite Communication System Signal Model  
The forward link of high-throughput satellite system transmits independent information streams to multiple fixed terminals through multiple beams. The scenario of a single gateway station with a single feed per beam is studied in this paper, in which a gateway station manages adjacent beams with full spectrum reuse by controlling feeds on the satellite. However, full spectrum reuse will bring serious co-frequency interference; that is, at the same time, users in each beam will receive interference from users in other beams when receiving their own signals. The system adopts a time division multiplexing scheme, in which each beam provides service for only one user in each time slot. The schematic diagram of the forward link of a high-throughput satellite communication system is shown in Figure 2.  
Suppose that the \(i\)-th user signal sent by the ground gateway can be expressed as \(s_{i}\). For any user signal \(s_{i}\) sent to \(K\) beams, its average power is 1; that is, \(E[s_{i}^{2}]=1,\forall i=1,2\cdots K\). Suppose that the precoding vector is \(\mathbf{t}_{i}=\sqrt{p_{i}}\mathbf{w}_{i}\), where \(p_{i}=\left\|\mathbf{t}_{i}^{2}\right\|\) is the signal power of the \(i\)-th user and \(\mathbf{w}_{i}=\frac{\mathbf{t}_{i}}{\left\|\mathbf{t}_{i}\right\|}\) is the normalized precoding vector. The onboard transmitted signal is the superposition of all beam signals, i.e., \(\sum\limits_{i=1}^{K}\mathbf{t}_{i}s_{i}\). The received signal of user \(i\) can be expressed as:  
\[y_{i}=\mathbf{h}_{i}^{H}\mathbf{t}_{i}\mathbf{s}_{i}+\mathbf{h}_{i}^{H}\sum \limits_{j=1,j\
eq i}^{K}\mathbf{t}_{j}s_{j}+n_{i} \tag{1}\]  
where the first term is the useful signal, the second term is the interference signal, and the third term \(n_{i}\) is zero-mean Gaussian random noise with independent identical distribution.  
Figure 1: Forward link and reverse link of high-throughput satellite system.  
Figure 2: Schematic diagram of the forward link of a high-throughput satellite communication system.  
\(\mathbf{h}_{i}=\mathbf{b}_{i}\mathbf{d}_{i}\) is \(N\times 1\) dimension channel matrix, where \(\mathbf{b}_{i}\) is multi-beam gain, and \(\mathbf{d}_{i}\) is logarithmic random attenuation.  
The SINR at the receiving end of user \(i\) can be expressed as:  
\[SINR_{i}=\frac{\left|\mathbf{h}_{i}^{H}\mathbf{t}_{i}\right|^{2}}{\sum\limits_ {j\
eq i}\left|\mathbf{h}_{i}^{H}\mathbf{t}_{j}\right|^{2}+N_{0}W}=\frac{p_{i }\left|\mathbf{h}_{i}^{H}\mathbf{w}_{i}\right|^{2}}{\sum\limits_{j\
eq i}p_{j }\left|\mathbf{h}_{i}^{H}\mathbf{w}_{j}\right|^{2}+N_{0}W} \tag{2}\]  
where \(N_{0}\) is the noise power spectral density and \(W\) is the total bandwidth. Therefore, the maximum achievable Shannon rate of user \(i\) is:  
\[r_{i}=W\log_{2}(1+SINR_{i}),\forall i=1,2,\ldots,K \tag{3}\]
###### Abstract  
Stability deficit is one of the most common and disabling signs of multiple sclerosis; therefore, balance training is essential for most patients. Intrinsic foot muscles are a key element in stability, but their influence in multiple sclerosis patients has not been assessed. **Objective:** The aim of the study was to assess the efficacy of intrinsic foot muscle training on stability in patients with multiple sclerosis. **Methodology:** A randomized single-blind clinical trial was conducted using a sample of multiple sclerosis patients divided into a control group (CG) and an intervention group (IG). Subjects in the intervention group completed an eight-week intrinsic foot muscles training programme. Static and dynamic stability were measured using the Activities-specific Balance Confidence questionnaire (AsBC), the Four Square Step Test (FSST), the Frailty and Injuries: Cooperative Studies of Intervention Techniques (FICSIT), and the Six Spot Step Test (SSST). Pre- and post-intervention outcomes and differences between groups were calculated. **Results:** The amount of change comparing pre- and post-intervention results was \(-0.94\) (CG) and \(5.59\) (IG) in the AsBC questionnaire (\(p\) 0.17); \(-1.0\) (CG) and \(-1.5\) (IG) in the FSST (\(p\) 0.72); 0.0 for both groups in FICSIT (\(p\) 0.629); and \(-1.5\) (CG) and \(-2.0\) (IG) in SSST (\(p\) 0.692). **Conclusions:** Intrinsic foot muscle training produces positive changes in dynamic stability and self-perceived confidence in multiple sclerosis patients.  
multiple sclerosis; stability; balance; intrinsic foot muscles; foot core
## 4 PFAC Calibration Results  
This section presents the bias and uncertainty of the radar altimeters in Sentinel-3A, Sentinel-3B and Jason-3. First, absolute Cal/Val results are determined for each pass of the altimeters using the sea-surface and/or the transponder techniques. Then, relative calibration is carried out for the tandem phase of Sentinel-3A and Sentinel-3B, followed by crossover analysis for Sentinel-3A/Jason-3.