esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 7 Conclusions  
In this paper, we have surveyed the current methods used for classification of clinical reports based on ICD codes using neural networks. We have combined the techniques already present in the literature and assessed the relative importance of all present components. Combining a convolutional framework with self-attention as well as regularizing, the loss function with attention on the long descriptions of target ICD codes proved to be valuable. Furthermore, a hierarchical objective was integrated in all presented models. Its added value lies especially in a setting with low amounts of available training data. Last, extending the dataset with the information present in other medical documents introduced too much noise into the data, hindering the performance of the tested models.  
Concerning future research directions, it would be valuable to test the techniques on a ICD-10 or ICD-11 dataset of larger size. This would give better insights into which performance these models could achieve in current hospital settings. On a similar note, tackling the problem of lack of data by finding a way to combine the available training data from different datasets (e.g., MIMIC-III and CodiEsp) and different ontologies (e.g., ICD-9, ICD-10, and MeSH) could further improve the classification performance of all models. Last, it would be interesting to investigate the use of hierarchical descriptions as an addition to the loss function, giving another use for the information inherently present in the ICD taxonomy.  
Conceptualization, E.M. and M.-F.M.; Methodology, E.M. and M.-F.M.; Software, E.M., A.A. and A.K.; Validation, E.M., A.A. and A.K.; Formal Analysis, E.M. and M.-F.M.; Investigation, E.M.; Resources, A.K.; Data Curation, A.K.; Writing-Original Draft Preparation, E.M. and M.-F.M.;Writing-Review Editing, E.M., A.A., A.K. and M.-F.M.; Visualization, E.M.; Supervision, M.-F.M.; Project Administration, A.A. and M.-F.M.; Funding Acquisition, M.-F.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the VLAIO SBO project, grant number 150056. M.-F.M. is co-funded by the ERC Advanced Grant CALCULUS H2020-ERC-2017-ADG 788506.  
Authors declare no conflict of interest.
## 3 Finite Element Modeling
### Geometric Models  
Based on ADINA finite element analysis software, a realistic three-dimensional transient thermal conductivity double-tunnel temperature field model was established for the connection aisle to dynamically simulate the evolution of the permafrost curtain. The distance between the centers of the two tunnels was 10 m, and the tunnel radius was 2.75 m along the tunnel boring direction (longitudinal Z). The dimension was determined as 23 m along the tunnel section lateral, and the distance between the central axes of the twin tunnels was 10 m. The lateral (X-direction) dimension was determined as 16 m, and along the vertical direction (Y-direction), the dimension was 10 m. In this way, the whole model dimension was set in the form of X \(\times\) Y \(\times\) Z as a 16 m \(\times\) 10 m \(\times\) 23 m freezing tube. In this study, the frozen tube radius was set at 0.045 m. The geometry size and meshing of the model used in this study are shown in Figure 2. In order to ensure the accuracy of the calculation and reduce the amount of calculation, a 4-node meshing method was chosen to mesh the model. When the model was encrypted, the freezing time was set to 40 d, the calculation time step was 40, and each step was for 24 h. The freezing tube arrangement and the grid model are shown in Figure 3.  
Figure 2: Geometric model and meshes of soil and tunnel (unit: mm).
## 2 Comparison of BRI with Other Global and Regional Infrastructure Initiatives
### Infrastructure Objectives
(2024) found that hard physical infrastructure has minimal influence on trade, whereas ICT and institutional efficiency have significant positive effects, particularly on supply chain trade. [PERSON] et al. (2005) found that soft infrastructure, i.e., the quality of market institutions, plays a more significant role in attracting FDI inflows relative to hard infrastructure. In this regard, PGII, GG, and FOIP may hold an advantage. The investments under PGII are oriented towards soft aspects of development, namely climate security, health security, digital technology and connectivity, and gender equity and equality ([PERSON] & [PERSON], 2022). The investments under GG also emphasise these soft aspects (but less explicitly gender equality) with the addition of food security, transport, education, and research ([PERSON], 2023). Comparatively speaking, the BRI primarily promotes the hard aspects of development, i.e., the building of infrastructure overseas by leveraging China's comparative advantage in manufacturing value chains. Likewise, even though FOIP promotes a rule-based regional order that comes with soft infrastructure components, its infrastructure implementation pillar PQI primarily focuses on quality physical infrastructure in the areas of transportation, water management and energy in Asia ([PERSON], 2020; [PERSON] & Sinkkonen, 2023). Similarly, 3 SI primarily focuses on both physical and digital infrastructure in Central and Eastern Europe. The quality of infrastructure--including electricity supply, air transport, roads, ports, and railways--in 3 SI countries (which are primarily new EU member states) trails behind that of their non-3 SI counterparts (older, more developed EU member states), underscoring the need to invest in critical infrastructure development ([PERSON], 2019). Ultimately, the dichotomy between hard and soft infrastructure may be misleading. They are not substitutes but complements. Without roads and electricity, digital services cannot operate; without institutional quality and digital literacy, physical infrastructure underperforms.
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 3 Results
For FND, the value obtained was more than twice that of LEACH or CRT2 FLACO, while it was 30% greater than with CHEF and significantly greater than with ICUU. In this case, the difference between HND and LND was greater than in the other scenarios. Considering HND and LND, CFC3 PSO achieved the best results in the three cases because of the location of the BS in the center of the area. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithms** & **FND** & **HND** & **LND** \\ \hline LEACH & 754.2 & 905.3 & 1002.1 \\ CHEF & 1414.3 & 1766.3 & 1827 \\ CRT2 FLACO & 671.3 & 1596.8 & 2148.4 \\ ICUU & 1397 & 1900.7 & 2115.9 \\ CFC3 PSO & 1827.3 & 2117.7 & 2222.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results with the BS located in the corner of the deployment area. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **FND** & **HND** & **LND** \\ \hline LEACH & 713.1 & 841.03 & 994.46 \\ CHEF & 1379.4 & 1753.1 & 1785.5 \\ CRT2 FLACO & 604.1 & 1479.4 & 1837.4 \\ ICUU & 1231.9 & 1769.6 & 2040.6 \\ CFC3 PSO & 1868.5 & 1972.7 & 2067.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results with the BS located at coordinates (150, 50) m.  
In conclusion, we could establish that with the proposed clustering method CFC3 PSO, a WSN that uses a fuzzy controller with an optimized KB for the three stages can achieve better metrics than the other approaches, even those with more complex methods like ICUU or CRT2 FLACO, which use a Type-2 fuzzy system (our approach only needs a Type-1 fuzzy system, which requires less computational resources) and an ACO. In the following subsection, we present the results of applying the previous clustering methods and the proposed CFC3 PSO to two new WSN designs, to test the adaptability of the latter method to BS locations other than those used in the optimization process.
## 6 Conclusions  
The main findings of this work can be summarized by the following two points:  
1. We introduce a new method to estimate the parameters of the tapered Gutenberg-Richter distribution and their uncertainties in the case of catalogs with a variable through-time magnitude of completeness;
2. We apply this method to the Atlantic ridge seismicity, finding a clear distinct behavior both for the parameters \(\beta\) and corner magnitude, depending on the faulting mechanism: larger \(\beta\) and smaller corner magnitude for normal events, smaller \(\beta\) and larger corner magnitude for strike-slip events.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app112412166/s1](https://www.mdpi.com/article/10.3390/app112412166/s1)]([https://www.mdpi.com/article/10.3390/app112412166/s1](https://www.mdpi.com/article/10.3390/app112412166/s1)). Figure S1: 95% confidence region estimation (curves) and MLE (dots) for the whole catalog (black), strike-slip events (red), and normal events (green) (completeness thresholds +0.1); Figure S2: 95% confidence region estimation (curves) and MLE (dots) for the whole catalog (black), strike-slip events (red), and normal events (green) (completeness thresholds +0.2).  
**Author Contributions:** [PERSON] and [PERSON] conceived the method; [PERSON] and [PERSON] defined the application; [PERSON] performed the data analysis and created the figures; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work benefited of the agreement between Istituto Nazionale di Geofisica e Vulcanologia and the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile (DPC), and of the project \"Assessment of Cascading Events triggered by the Interaction of Natural Hazards and Technological Scenarios involving the release of Hazardous Substances\", funded by the Italian Ministry MIUR PRIN (Progetti di Ricerca di Rilevante Interesse Nazionale) 2017--Grant 2017 CEYP88.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data and code used in this paper are available at: [[https://github.com/[PERSON]/TaperedGR_Estimation_Catalogs_with_variable_completeness](https://github.com/[PERSON]/TaperedGR_Estimation_Catalogs_with_variable_completeness)]([https://github.com/[PERSON]/TaperedGR_Estimation_Catalogs_with_variable_completeness](https://github.com/[PERSON]/TaperedGR_Estimation_Catalogs_with_variable_completeness)) (accessed on 8 July 2021).  
**Acknowledgments:** The authors thank the two anonymous reviewers for their comments which greatly improved the paper.  
**Conflicts of Interest:** Authors declare no conflict of interest.  
**Abbreviations**  
**Abbreviation**  
**Meaning**  
MmaxmaxMaximum magnitude  
MLEMaximum likelihood estimation  
MminMagnitude of completeness  
CMTCentroid moment tensor catalog  
C\({}_{M}\)Corner magnitude
# SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship
## 4 Experimental Evaluation
### Datasets and Statistical Analysis  
To evaluate the effectiveness and efficiency of SR-GNN, we conducted experiments on four benchmark datasets: Epinions, Slashdot, WikiRFA, and Bitcoin-OTC. These publicly accessible signed social network datasets originate from Stanford University's SNAP project (available online: [[https://snap.stanford.edu/data/index.html](https://snap.stanford.edu/data/index.html)]([https://snap.stanford.edu/data/index.html](https://snap.stanford.edu/data/index.html)), accessed on 20 November 2024). Table 1 presents the statistics of these datasets.  
1. Epinions: A customer review website that allows users to mark \"who to trust, not whom\" relationships.  
Slashdot: A news website that allows users to tag other users as friends/foes based on their own views.
* WikiRfa: A voting network, used for electing administrators of different sections. If a user is agreed upon by the majority, they can be chosen as an administrator of a given subject, responsible for the maintenance of entries; otherwise, the election fails.
* Bitcoin-OTC: A cryptocurrency trading network where users remark on whether their trading competitors are reliable.  
Based on the analysis of the four datasets, it is evident that the proportion of positive edges is considerably greater than that of negative edges. This poses a challenge for prediction tasks due to the imbalance in the data. The proportion of reciprocal edges varies. However, there was a significant majority of positive reciprocal edges compared to negative ones in the four datasets. A high percentage of non-triadic closure (e.g., 45.3% in WikiRfa) suggests that structural balance theory-based models may struggle in these networks. In contrast, status theory remains effective, as it first gives features of pairwise nodes, and then, by applying transitivity, it establishes triadic closure among two-hop neighbors.
## 3 Flume Experiments  
### Experimental Setup  
Figure 2 shows the experimental setups and main parameters of the flume experiments performed in this study. Here, \(H\) is the total drop between the initial occurrence and final deposition points of the debris flow, \(L\) is the total travel distance. The setup for the flume experiments was composed of a sample box, a channel, and a deposition plane. The sample box was 0.2 m long, 0.15 m wide, and 0.3 m high, and installed at the top of the channel. The sediment-water mixture was supplied to the channel by manually lifting the gate of the sample box. The channel was made of steel in consideration of the strong erosion that accompanies debris flow, and had a length of 1.3-1.9 m, a width of 0.15 m, and a height of 0.3 m (Table 2). The channel was fabricated in separate upper and lower slope segments so that a 0.6-m long berm could be installed between them. At the outlet of the channel, a 1.5-m long, 1.0-m wide deposition plane was installed to analyze the debris flow deposition characteristics. The deposition plane was composed of 0.05-m grids in the longitudinal and lateral directions.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Flow Type** & **Equation** & **Reference** \\ \hline Newtonian laminar flow & \(\mathbf{v}\ =\ \frac{\mathbf{e}\mathbf{g}\mathbf{h}^{2}\mathbf{x}}{\mathbf{k}\mathbf{n}}\) (2) & [15] \\ Dilatant grain shearing flow & \(\mathbf{v}\ =\ \frac{2}{3}\zeta\mathbf{h}^{3/2}\mathbf{a}^{1/2}\) (3) & [15; 22] \\ Newtonian turbulent flow & \(\mathbf{v}\ =\ \frac{1}{3}\mathbf{h}^{2/3}\mathbf{a}^{1/2}\) (4) & [15; 27] \\ Voellmy flow & \(\mathbf{v}\ =\ \mathbf{C}_{1}\mathbf{h}^{1/2}\mathbf{a}^{1/2}\) (5) & [16; 26; 29] \\ Empirical equation & \(\mathbf{v}\ =\ \mathbf{C}_{2}\mathbf{h}^{0.22}\mathbf{a}^{0.33}\) (6) & [26] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equations used to estimate the mean velocity of debris flow according to flow type.  
To observe the flow and deposition characteristics of the debris flow, cameras capable of capturing video at 60 fps were installed at the front and on a side of the flume. The front camera was used to measure the mean velocity of the head of the debris flow from the top of the channel to the outlet, and the side camera was used to measure the maximum depth of the debris flow at a point 0.1 m upstream of the channel outlet. The debris flow mobility ratio was calculated upon the completion of sediment-water mixture deposition by observing the total drop \(H\) and total travel distance \(L\), as indicated in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Type** & **Length (m)** & **Width (m)** & **Height (m)** \\ \hline Straight channel test & 1.3 & 0.15 & 0.3 \\ Single-berm channel test & 1.9 & 0.15 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of the channels for each test type.  
Figure 2: \(Cont.\)
## 2 Materials and Methods
### Correlation Analysis  
Correlation analysis is a statistical technique used to investigate relationships between multiple random variables of equal significance. In this study, this approach was utilized to examine the correlation between corn NPP and climatic parameters. The analysis focused on establishing time-series relationships between monthly corn NPP and temperature, precipitation, and solar radiation throughout the corn growth season and subsequently computing correlation coefficients [39]. This analysis focused on the spatial distribution characteristics of corn NPP and their correlation with climatic parameters. This correlation was determined by computing the correlation coefficients using the ArcGIS raster cell values. The following equation was applied to calculate correlation coefficients:\[R_{xy}=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{ \sqrt{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}\sum\limits_{i=1}^{n}{(y_{ i}-\overline{y})^{2}}}}} \tag{4}\]  
where \(R_{xy}\) is the correlation coefficient, which is calculated using the values of variables \(x\) and \(y\) in the \(i\)th period, where \(x_{i}\) and \(y_{i}\) represent these values. The mean values of variables \(x\) and \(y\) are denoted by \(\overline{x}\) and \(\overline{y}\), respectively. The sample capacity is denoted as \(n\). The correlation coefficient is a numerical value that can vary from \(-1\) to \(1\). Positive values imply a direct relationship between variables, whereas negative values indicate an inverse relationship. The magnitude of the value indicates the intensity of the correlation between the variables.  
Using significance levels \(\alpha\) = 0.05 and \(\alpha\) = 0.01 and referring to the significance test table of correlation coefficients, the study established threshold values as f\({}_{\alpha}\)(n \(-\) 2) = f\({}_{0.05}\)(n \(-\) 2) = 0.7067, f\({}_{\alpha}\)(n \(-\) 2) = f\({}_{0.01}\)(n \(-\) 2) = 0.8343, facilitating the classification of correlations into six distinct levels: significant negative correlation (R \(<\)\(-\)0.8343), moderate negative correlation (\(-\)0.8343 \(<\) R \(<\)\(-\)0.7067), mild negative correlation (\(-\)0.7067 \(<\) R \(<\) 0), mild positive correlation (0 \(<\) R \(<\) 0.7067), moderate positive correlation (0.7067 \(<\) R \(<\) 0.8343), and a significant positive correlation (R \(>\) 0.8343).
## 3 Motion and Power Transmission  
### Particle Elliptical Motion of the Stator  
The research to date on the TRUM motion mechanism is relatively mature. Applying two-phase excitation signals that differ in time phase by \(\pi\)/2 to the two sets of piezoelectric ceramics polarized in a specific manner can excite B\({}_{0n}\) bending modes in the stator elastic body that differ in space phase by \(\pi\)/2. The two orthogonal modes with the same frequency and shape are superimposed to form a traveling wave that propagates in a certain direction in the stator elastic body, thereby causing any particle on the surface of the stator to move elliptically [17; 18]. At this time, the rotor with some friction material is in contact with the stator under a certain preload, and the elliptical motion of the stator drives the rotation of the rotor, thereby determining the motor output speed and torque (shown in Figure 2).  
According to the literature [19], the displacement of point P in the directions of \(z\) and \(x\) will be  
\[\left\{\begin{array}{l}\xi_{p}=w_{0}\sin(kx-\omega_{n}t)\\ \zeta_{p}=-w_{0}hk\cos(kx-\omega_{n}t)\end{array}\right. \tag{1}\]  
Then, the elliptical motion trajectory of any particle P on the stator surface can be expressed by  
\[\left(\frac{\xi_{p}}{w_{0}}\right)^{2}+\left(\frac{\zeta_{p}}{nhw_{0}/r_{c}} \right)^{2}=1 \tag{2}\]  
where \(\xi_{p}\) is the normal displacement of point P, \(\zeta_{p}\) is the tangential displacement, \(w_{0}\) is the normal amplitude of the stator, \(n\) is the working mode order, \(h\) is the distance from the stator tooth surface to the neutral surface, \(r_{c}\) is the outer radius of the stator, and \(k=2\pi/\lambda=n/r_{c}\).
## 3 Results
### Effects of Image Enhancement Methods  
Next, we discuss the effectiveness of using different color correction methods. The experiment uses three different correction methods: CLAHE, MSRCR, and MSRCP. All methods were trained by 300 epochs; the results of the RSSCN7 image dataset that were obtained are shown in Table 4. The table shows the test accuracy and the correct number of test images. It can be seen from Table 4 that the best test accuracy can be obtained by using the CLAHE correction method. When compared with the training results of the original image dataset, the total number is 1400. When considering the test image prediction, the number of correctly predicted images increased by 12.  
Furthermore, we compare the three methods utilizing the WHU-RS19 image dataset. It can be seen from Table 5 that the best accuracy can be obtained with the CLAHE correction method, and when compared with the original image dataset, the number of correct images also improved.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 2 Related Work  
Anomaly detection is one of several common mining tasks for extracting meaningful knowledge from time series data [9]. However, non-stationary time series pose significant challenges for anomaly detection as it is rather more difficult to detect and interpret anomalous changes from a moving series than a stationary one, especially in real time [6; 7; 8]. Most existing methods can be categorised as identifying anomalies via model-based classification or statistical anomaly detection methods.  
Model-based anomaly detection approaches can be further divided into two groups: estimation models and prediction models. Estimation models detect anomalies with the best-fit models, while prediction models focus on the prediction of the sequential states of the time series and can detect anomalies in an autoregressive way [9; 17; 18]. All these model-based methods [1; 3; 4; 5] rely heavily on accurately labelled training data to generate clusters of 'anomalous' and 'normal' data to which subsequent data are assigned. However,such labelled data are often expensive and hard to obtain in real time. Compared to our approach, these techniques retain little information on the structure of anomalies which would admit further analysis, such as clustering [6; 7; 8].  
Statistical anomaly detection methods mainly use density-based approaches or histogram-based approaches. Density-based anomaly detection methods are based on the concept of the neighbourhood, which is more complex for ordered data. Therefore, these methods are rarely used for temporal data. Histogram-based anomaly approaches create histogram representations of the original time series and remove anomalies to achieve lower error in these representations [9; 10]. Statistical anomaly detection methods work in an unsupervised manner using, for instance, distribution-based clustering [2; 19; 20]. However, these methods are limited by the assumption that the 'normal' behaviour is stationary or has a fixed periodic pattern which may be uncommon in practice [6; 7; 8].  
Our method carries the advantages of both approaches by first detrending the data and then applying anomaly detection on the (stationary) residual series, without the need for labelled 'training' anomalies. It has the additional advantage of being able to cluster multiple anomalies based on the distribution of the residuals within. We proceed to describe the constituent parts of our approach in more detail.