esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Transfer Learning  
Traditionally, the goal of transfer learning is to solve the problem of domain gap when the knowledge gained on training data (source) needs to be re-used on different test (target) data. This aids in the problem of insufficient training data [32]. The primary layers of a DCNN (Deep Convolutional Neural Network), trained on a large dataset, can extract general features, as shown by [33; 34]. Therefore, by recruiting deep transfer learning on a big dataset, we can fine-tune a pre-trained model on our small-size dataset and solve a binary classification task to PD/HC much more efficiently.  
1. We used the VGG-16 architecture [35] trained on the ImageNet dataset as our pre-trained model, and we used only weights of convolutional layers of the pre-trained model.
2. To better fit our classification task, we modified the base model of VGG-16 in this order: (A) Implementing a fully connected layer (1 \(\times\) 1 \(\times\) 512) after the last max-pooling layer (7 \(\times\) 7 \(\times\) 512). (B) Developing a fully connected layer (1 \(\times\) 1 \(\times\) 64) with activation function of \"Relu\" to better map reduced features to the last layer. (C) Applying the fully connected layer (1 \(\times\) 1 \(\times\) 2) with the activation function of \"Softmax\" for the classification task consists of two categories, [PERSON]'s disease and healthy controls.
3. We tried two techniques of fine-tuning: The network, which we call the Totally trained model, is represented in Figure 3, while the other alternative is represented in the Appendix A just for further information.
4. To implement the first technique, we used weights of convolutional layers as initial weights and updated all layers' weights with our data. The network architecture is shown in Figure 3. Tables 2 and 3 represent the details of this network. In addition, Figure 11 shows the best performance of the proposed model over the epochs.  
We compared the best performance of both techniques (see the Appendix A for the alternative one) to choose the best fine-tuning strategy. As it is shown in Table 4, The totally-trained model surpassed the limited-trained model (expanded upon in the Appendix A). Our implementation was performed in Python using Keras with Tensorflow Backend and underwent 43 s of training on Google Colab. The test data set was 25% of data that had been randomly selected.  
Figure 3: Totally trained network architecture.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 3 Mathematical Model  
The aim of this paper is the introduction of a novel optimum mathematical method to deliver data (sub)flows across a network considering the fairness-efficiency trade-off that was already mentioned in Section 2. High efficiency means that the aggregate throughput of all (sub)flows should be maximized by utilizing the available links to the maximum possible extent. Fairness means that no (sub)flow should starve by assigning only a small amount of link capacity. Both goals cannot be achieved at the same time; a compromise has to be identified as discussed in Section 2.4.  
The resource allocation of (MP)TCP flows to bandwidth-limited links leads to a mixed-integer nonlinear programming (MINLP) problem. In general, an (N)LP problem is defined by an objective function which takes a number of input arguments that are limited by constraints. The task of the (N)LP solver is to find values for the input arguments which yield the maximum value for the objective function while keeping the constraints. The MINLP model is developed in two steps. In Section 3.1, the basic model is shown in case throughput is the only value to be optimized. In Section 3.2, the objective function of the model is extended for the different fairness approaches.
## 6 Related Work  
### Dependency Recommendation and Generation  
We can see the substantial research dependency in library recommendation and generation. Previous studies to make recommendations on dependency libraries used already working libraries to generate the others. LibRec [14] adopts association rule mining and collaborative filtering to recommend libraries. LibFinder [5] improves the performance of LibRec by linking the usage of libraries and the semantic similarity of the identifiers of the source code. DSCRec [25] conducted an empirical study that proposes domain-specific categories and demonstrates the practicality of maintaining libraries through domain-specific categories.  
In the last few years, automatic library migration has become a research hotspot. [PERSON] et al. [26] conducted a descriptive mixed methods study on how and why library migrations happen in Java software projects. DeepLib [2] accepted a set of library versions and forecast the subsequent versions of related libraries using deep neural networks. EvoPlan [27] made recommendations of different upgrade plans given a pair of library versions as input using migration graphs. As we all have realized the significance of usage patterns in dependency library, [PERSON] [13] proposed a pattern mining tool via static code differencing technique for automatic library migration. [PERSON] et al. [28] have formulated the library migration problem as a mining and ranking problem, developed four advanced metrics for ranking, and achieved the most favorable results in the problem domain.  
Research on library recommendation and generation has received less attention, and more studies focus on library migration with various deep learning algorithms. Meanwhile, these studies confirm the prioritization of library usage patterns, employing various static mining methods to extract them. We believe that introducing domain information and using the neural language model can better learn usage patterns and fit the language features of the dependency code.
## 3 Experimental Study
### Evaluation Metrics  
To evaluate the prediction performance of the compared models, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Symmetric Mean Absolute Percentage Error (SMAPE), and Mean Absolute Percentage Error (MAPE) metrics are used.  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert\hat{y}_{i}-y_{i}\rvert \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{5}\]  
\[\text{SMAPE}=\frac{1}{n}\Bigg{(}\sum_{i=1}^{n}\frac{\lvert y_{i}-\hat{y}_{i} \rvert}{(y_{i}+\hat{y}_{i})/2}\Bigg{)}\times 100 \tag{6}\]  
\[\text{MAPE}=\frac{1}{n}\Bigg{(}\sum_{i=1}^{n}\left\lvert\frac{\hat{y}_{i}-y_{i }}{y_{i}}\right\rvert\Bigg{)}\times 100 \tag{7}\]  
where \(n\) is number of the sample, \(\hat{y}_{i}\) is the \(i\)-th predicted value, and \(y_{i}\) is the \(i\)-th actual value.
# Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series
## 4 Motivation and Contributions  
In the previous sections, we discussed issues regarding employing features based on persistence landscapes for clustering time series. Persistence landscapes constitute representations of topological features in a Hilbert space: hence, using landscapes of all orders as features may be the most informative approach in statistical learning [17]; however, constructing landscapes of all orders may be computationally expensive. In general, lower-order persistence landscapes contain information about important topological features of the data, while higher-order persistence landscapes are closer to zero, and generally represent noise.  
If \(\tilde{K}\) denotes the number of local minima in the smoothed periodogram \(\tilde{I}(\omega_{j})\), then in computing its persistence landscapes \(PL_{\
u}(1),\ldots,PL_{\
u}(L)\) of all orders \(\
u\), a large fraction of the computing time is used for sorting the \(\tilde{K}\) topologies for each value of \(\ell\). When the time series \(X_{t}\) has complex signals (perhaps consisting of a complicated mixture of several patterns), or \(X_{t}\) is very noisy (perhaps with a small signal-to-noise ratio, and/or time-varying variances), then \(\tilde{K}\) is likely to be large, and it may be important to construct landscapes of large order, to pull out information useful for learning the time series. However, for time series with simpler stochastic patterns, the order of landscapes need not be very high, and computing them may be costly, but wasted effort. In summary, while the use of persistence landscapes for time series clustering is a useful and novel idea, the method can be time-consuming if landscapes of all orders are used, and inaccurate if too few (say, one) landscapes are used as features.  
In particular, when we cluster or classify a large number \(N\) of time series, an algorithm to decide the order of useful persistence landscapes is useful. There is no approach in the existing literature, to our knowledge, for determining the optimal number of persistence landscapes as features for clustering time series: this motivated our work to explore this question and contribute to the literature in this regard.  
Specifically, the contribution of this research is the development of an algorithm to select the smallest order of persistence landscapes to be employed as features for clustering a large number of time series, in order to speed up the computing without sacrificing accuracy. The implementation is described in the next section.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
###### Abstract  
During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.  
large wireless sensor networks; clustering; metaheuristic; computational intelligence; machine learning +
Footnote †: journal: Sensors
## 7 Discussion  
### Devices  
The proposed framework has chosen smartphones as the target devices due to their ubiquity and the mature AR development frameworks included in the major operating systems. The prototype was implemented with Apple Inc.'s ARKit framework on iOS. Thus, it can be operated on a wide range of iPhones and iPads. Google LLC also provides Android and iOS developers with ARCore [39], which has similar functionality. Therefore, the framework can be implemented in Android smartphones and tablets in a manner similar to the demonstrated prototype.  
One disadvantage of image-tracking-based AR navigation is that users need to keep the camera in a certain range of angles and distances to achieve successful recognition of the reference markers. Users unfamiliar with AR applications may find it challenging to consistently maintain this requirement. In addition, when implemented with smartphones, after recognizing the reference marker, users need to keep holding the device so the camera has enough view to track positions and align displays. This can be tiresome and detrimental to the device's battery life. Meanwhile, holding a smartphone while walking is typically considered unsafe, even if the user can perceive the front view from the screen. Further studies should explore solutions incorporating other indoor positioning technologies, enabling users to put down their devices while moving without losing track of the position.  
Although still expensive and not widely available at present, wearable devices such as head-mount displays (HMDs) will be an important vehicle for AR applications in the future. It is posited that the proposed framework can be modified to accommodate HMDs as long as the device supports VIO because the underlying principles remain unchanged.
## 4 A Modified Chaotic Based PSO with Uniform Initialization
### Uniform Initialization  
Random initialization, chaotic initialization, and opposition-based initialization experience challenges in avoiding the aggregation of initial particles. To solve this problem, a uniform initialization strategy [23] is employed. As shown in Algorithm 2, the first line of code initializes a particle randomly as the base point, and \(X_{1}=[X_{11},X_{12},\ldots,X_{1D}]\) is the position of the base point. Lines 2-4 generate a \(D*\ (n-1)\) random matrix \(R=[R_{1},R_{2},\ldots,R_{D}]\) to ensure the randomness of the initial particles. Lines 5-12 divide the length of each dimension by \(n\) to obtain the minimum distance between particles in the corresponding dimension. Lines 5-12 distribute particles in each dimension uniformly and avoid the aggregation of particles. If the position of a particles exceeds the allowed range of a dimension, it will subtract the range of the corresponding dimension.  
Uniform initialization ensures that the distance between particles is greater than a certain value. It can avoid the aggregation of particles and distribute the initial particle uniformly to recognize more areas at the beginning.  
```
(1) Initialize \(X_{1}\) in the search area randomly (2) for\(d=1\) to \(D\)do (3) randomly rearrange [1, 2,..., (\(n-1\))] to get \(R_{d}=[R_{1d},R_{2d},\ldots,R_{(n-1)d}]\) (4) end for (5) for\(i=1\) to \(n\)do (6) for\(d=1\) to \(D\)do (7) \(X_{id}=X_{1d}+R_{(i-1)d}\div n*(X_{dmax}-X_{dmin})\) (8) if\(X_{id}>X_{dmax}\)then (9) \(X_{id}=X_{id}-(X_{dmax}-X_{dmin})\) (10) end if (11) end for
```  
**Algorithm 2** Uniform particle initialization algorithm.
# Analysing and Computing the Impact of Geometric Asymmetric Coils on Transformer Stray Losses
## 3 Assessment and Model of the Stray Magnetic Field
### Assessment of the Stray Magnetic Field Density on Tank Walls  
The magnitudes of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) on the tank wall can be examined in Figure 2a, excluding the influence of magnetic derivations. For clarity, wall dimensions (Lw and Tw) have been omitted for better visualisation. In particular, the maximum values of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\), which reach 0.9 T, are concentrated in the centre of the Rw side wall. This concentration is attributed to its proximity to the coil. Walls Fw and Bw are shown with a low value of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) = 0.4 T at the centre of the wall. Figure 2b shows the characterised curve of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) on the tank walls. Figure 3a shows the values of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\), considering vertically placed magnetic leads. In this configuration, \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) on the tank walls can be reduced to 0.2 T, while the walls of the magnetic shunts have higher values of 0.9 T.  
Figure 3: With magnetic shunts: (**a**) stray magnetic flux density \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) norm at tank walls; (**b**) characterisation of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\).  
Figure 2: Without magnetic shunts: (**a**) stray magnetic flux density \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) norm at tank walls; (**b**) characterisation of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\).  
The characterisation of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) on the tank walls protected by magnetic shunts is shown in Figure 3b; magnetic shunts thickness and gap separation with the tank were the same 9 mm (3/8\({}^{\prime\prime}\)). In particular, wall Lw keeps \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) at elevated values, since it remains unprotected by derivations. On the contrary, the values of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) on the wall Rw decreases to 0.4 T. This reduction depends on the type of material and the thickness of the shunts. In this case, magnetic shunts of the same core material, specifically GOES Steel, were used and simulated. The root mean square (rms) value of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) in the absence of shunts was 0.68 T. After adding shunts, the rms values of \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) decreased to levels as low as 0.4 T, representing a substantial 41.1% reduction in \(\mid\)B\({}_{\mathrm{d}}\)\(\mid\) on the walls of the tank.
# Nutritional Support for Liver Diseases
## 2 Nutritional Support for Liver Diseases
### Cirrhosis
#### 2.3.2 Supplementation  
In addition, supplementation with branched-chain amino acids BCAA (Grade B, consensus 89%) has a positive effect on muscle mass [(127; 128)]. In patients with cirrhosis, the supply of amino acids to the muscles is disturbed. BCAAs taken together or as single ingredients prevent lipolysis and proteolysis, stimulate albumin synthesis, improve nitrogen balance, have a positive effect on muscle mass, and also show improvements in hepatic encephalopathy, their general condition, and the quality of life in this group of patients [(129; 130)]. BCAA, specifically leucine, lowers insulin resistance and is important in controlling glucose oxidation in skeletal muscle by stimulating the glucose-alanine cycle [(122)]. Plant protein can positively affect the composition of the intestinal microbiome [(131)].  
The adequate supply of vitamins and minerals (Grade GPP, strong consensus 100%) should also be taken care of because their deficiencies are common due to the malabsorption of fats, the use of diuretics, and insufficient intake [(132; 133)]. In addition, liver dysfunction itself can lead to changes in micronutrient metabolism. Reduced concentrations of zinc, selenium, iron, and magnesium are observed [(134; 135)], while copper and manganese may be elevated [(132; 136)]. Zinc supplementation may improve encephalopathy [(5)]. Deficiencies in fat-soluble vitamins are also common in cirrhosis due to the insufficient supply of bile acids [(137)]. Interestingly, prolonged vitamin D deficiency is associated with a reduction in type 2 muscle fibers [(138)]. On the other hand, vitamin D deficiency is also associated with an increased risk of NAFLD [(139; 140)]. In addition, the level of water-soluble vitamins, especially vitamins C, B1, B2, B6, and folic acid, is lowered, whereas the level of vitamin B12 may be falsely elevated due to the release of vitamin B12 from damaged liver cells into the circulation [(93)].  
In addition, supplementation with probiotics may, on the one hand, promote the growth of beneficial bacteria and, on the other hand, limit the growth of undesirable bacteria and the production of their harmful metabolites while supporting intestinal transport [(141; 142)]. Based on the results of a meta-analysis and a network meta-analysis, [PERSON] et al. [(119)] concluded that EN+ gut probiotics may be the most effective intervention, relatively speaking. In patients with cirrhosis, antibiotic therapy is often necessary, which leads to an imbalance of the intestinal flora, which results in the generation of enterotoxins and the metabolites of pathogenic microflora [(119)].
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 2 Data  
In order to generate coastline extraction results with higher resolution, Landsat and SAR images were used to identify the boundary between land (including glacier terminals and ice shelves) and open water. The quality of the image sources remains an important factor for coastline extraction, since optical sensors cannot be used during the period of polar nights [15]. This required us to focus on satellite-derived optical data for the austral summer (January to early March).  
Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images and Landsat-8 Operational Land Imager (OLI) images were used in this study. The data were downloaded in Geo-tiff format from the U.S. Geological Survey (USGS) website (USGS; [[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/))). The spatial resolution was 30 m for bands 1, 2, 3 of Landsat-7 and bands 2, 3, 4 of Landsat-8. The spectral bands of the OLI sensor provided a significant enhancement over the corresponding bands when compared to earlier Landsat instruments. For this study we selected scenes with less than 20% cloud cover. The current algorithm was applied to band 1 (Landsat-7) and band 2 (Landsat-8) data to identify the coastline. Color composite images (R, G, B) were also generated from Landsat-7 (bands 1, 2, and 3) and Landsat-8 OLI (bands 2, 3, and 4). These images clearly show the boundary between water and ice, and therefore, they were used to validate the output coastline as explained in Section 4.2.  
Data from two SAR systems, onboard the Environmental Satellite (ENVISAT) and Sentinel-1 (both launched by the European Space Agency--ESA), were used in this study when atmospheric haze or cloud cover hindered the use of Landsat data (i.e., to fill gaps of Landsat-7 data under cloudy conditions). The Advanced SAR (ASAR) onboard Environmental Satellite (ENVISAT) operates in the C-band wavelength (5.3 cm) in different modes. This satellite was launched in 2002 and ceased operation in April 2012. We used the Wide Swath Mode (WSM) images at medium resolution (75 m) and a swath width of 400 km. Sentinel-1A and 1B are two satellites in a constellation, launched by ESA in April 2014 and 2016, respectively, carrying another C-band SAR. The data mode used in this study was the Interferometric Wide Swath (IW) with spatial resolution of \(5\times 20\) m. In our study, the polarization of both SAR systems was HH-polarization (horizontal transmit and horizontal receive). As for co-polarizations, HH-polarization is preferred for operational coastline delineation, because the clutter of ocean is more suppressed at HH-polarization than at VV-polarization (vertical transmit and vertical receive). Hence, imagery of the former is less noisy, and well suited for coastline extraction [16]. ASAR and Sentinel-1 data can be freely downloaded from the European Space Agency (ESA) website ([[https://www.esa.int/ESA](https://www.esa.int/ESA)]([https://www.esa.int/ESA](https://www.esa.int/ESA))). Table 1 presents information about the four sensors used in the study.
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 3 SIMO DLCSK System Model  
For the general architecture of the SIMO DLCSK system, refer to Figure 1. The proposed design involves two phases. In the first phase, the chaotic sequences are transmitted through an offline training phase under different channel conditions, and then the deployment/test phase initializes the modulated data transmission.