esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Hence, the main contributions of the manuscript are as follows:  
* An algorithm, which is supported by the S-transform and Wigner distribution function to provide protection to the transmission line and which is effective at eliminating the requirement of GPS and the communication system, is introduced in this paper. * The developed algorithm is less complex and effective at identifying and classifying faults on the transmission line within the duration of a quarter cycle. * This algorithm accurately recognizes the faults using the measurement of the current signal at a single end of the transmission line. This paper is divided into nine sections. Section 2 presents the modified test system of the double end transmission line. Section 3 details the proposed fault identification and classifying technique. Implementation steps of the technique to detect faults are illustrated in Section 4. The implementation steps of the algorithm for the classification of faults are illustrated in Section 5. Different event studies are presented in Section 6. Section 7 describes the results pertaining to switching events, which help to discriminate these events from faults. A study for the comparison of the performance of the proposed protection algorithm with the work already reported in literature is presented in Section 8, followed by the conclusion in Section 9.
## 6 Experimental Settings
### Visual Encoders  
To obtain the image representations, we experimented with one transformer-based model and one CNN-based architecture.  
* The visual transformer (ViT) [53] is a transformer encoder that is adapted for image classification. Since transformers are geared primarily towards sequence processing, the authors proposed to convert the input images into sequences by splitting them into a sequence patches of 16 \(\times\) 16 pixels. The authors note that the use of transformers, a model with fewer \"inductive biases\" than CNNs (e.g., no assumptions about locality), yields models that scale better with larger pretraining datasets. We used the vit-base variant (12 layers, 768 hidden size) ([[https://huggingface.co/google/vit-base-patch1-6-224](https://huggingface.co/google/vit-base-patch1-6-224)]([https://huggingface.co/google/vit-base-patch1-6-224](https://huggingface.co/google/vit-base-patch1-6-224)), accessed on 31 May 2022). ViT was pretrained in a supervised fashion, for image classification on Imagenet-21k dataset [95], which consists of 14M images with labels across 21,841 classes. The model was trained on Google Cloud TPUv3 hardware for 90 epochs, on 3-channel images of \(224\times 224\) pixels, using 4096 batch size, Adam optimizer and linear learning-rate decay. Training took 230 TPUv3 core days (i.e., the number of TPUv3 cores used for training times the training time in days).
* The residual network (ResNet) architecture was proposed in [49]. ResNets comprise of multiple layers of \(2D\) convolutions, with residual (i.e., shortcut) connections jumping every 2 layers. The authors demonstrated that residual connections facilitate the training of very deep networks, combating the degradation problem of deep architectures. We used the ResNet18 variant ([[https://pytorch.org/vision/stable/generated/torchvision.models.resnet18.html](https://pytorch.org/vision/stable/generated/torchvision.models.resnet18.html)]([https://pytorch.org/vision/stable/generated/torchvision.models.resnet18.html](https://pytorch.org/vision/stable/generated/torchvision.models.resnet18.html)), accessed on 31 May 2022), which is a 18-layer convolutional architecture. ResNet18 was pretrained for supervised image classification on Imagenet-1k [47] dataset, which is a subset of Imagenet-21k that contains 1.2M images labeled across 1000 classes. ResNet18 was pretrained on 3-channel \(224\times 224\) patches, using random crop, image flip and scale data augmentations using stochastic gradient descent with batch size 256 for a maximum of \(60\times 10^{4}\). Learning rate started at 0.1 and was divided by 10 every time an error plateau was reached. Batch normalization [96] was employed after each convolutional layer. Training needed \(1.8\times 10^{9}\) floating point operations (FLOPs).
# The Effect of Cu (II) on Swelling and Shrinkage Characteristics of Sodium Bentonite in Landfills
###### Abstract  
Wastes in municipal landfills will release heavy metal cations over a long period of time. Therefore, the objective of this paper was to investigate the effect of copper (Cu) in the leachate from landfill on the swell-shrinking potential of bentonite liner. Copper sulfate solution with 4 different groups of concentrations (0 g/L, 2.5 g/L, 5 g/L, 10 g/L) were added to bentonite for conducting a series of swelling and shrinkage experiments. Then the Does Response model was used to describe the swelling and shrinkage process of bentonite in different copper sulfate solutions and the applicability of the model was evaluated. At the same time, clay mineral analysis experiments (XRD and XRF) were carried out to analyze the variation of interlayer space and element content of montmorillonite. The results show that the swell volume of bentonite decreases with the increase of the concentration of Cu (II). The rate of swelling was high and inversely proportional to the concentration of Cu (II). The shrinkage curve of bentonite could be divided into uniform velocity stage, variable velocity stage, and stable stage. The shrinkage rate at the uniform velocity stage and shrinkage at the stable stage decreased with the increase of the concentration of Cu (II). The model was suitable for describing swelling (or shrinkage) curves with smaller expansibility (or shrinkage). Results of XRD and XRF show that the erosion of Cu (II) led to the decrease of Na\({}^{+}\) content in sodium bentonite, and then narrowed interlayer space of montmorillonite. When the solution concentration increases, both values of interlayer space of montmorillonite and Na\({}^{+}\) content in sodium bentonite become lower, and that led to swelling and shrinkage of bentonite liner was getting smaller and smaller.  
b
## 2 Challenges in Underwater Communications
### Short Network Lifetime  
The limited battery power of the sensor nodes in UWSNs results in a short network lifetime [14]. As described before, battery replacement is not an efficient solution. As soon as the sensor nodes start depleting their battery power, energy holes (dead nodes) start to be created. This, in effect, starts the degradation of the network performance and hinders the delivery of data packets to the surface of the water. This limitation demands for efficient and effective use of the electrical power of sensor nodes in UWSNs.  
Routing protocols for UWSNs need to optimally select the routing paths with respect to the energy consumption. This prolongs the network lifetime. If nodes drain their battery power at an early stage of network operation, they become dead and are called energy holes. Such energy holes then interrupt packet delivery to the destination.
## 4 Results and Discussion
### Effect of Streak
Notably, small increases in specificity and precision and a decrease in recall are observed when the logistic regression (LR) models are compared with those without Streak, implying that'streak' plays a slightly more significant role in predicting victories than in predicting losses. These findings contradict existing qualitative research, which suggests that players attribute more significance to losing streaks [26], but the observed discrepancies are minor. We can reasonably conclude that, due to the relative unimportance of streak as a feature overall, the imputation method is unlikely to have introduced significant bias, as shown by the sensitivity analysis in Figure 12a. The finding further corroborates the result that streak (as computed in this study) is unlikely to be a significant performance indicator at scale. The construct validity of streak as a feature needs to be carefully evaluated when attempting to correlate these findings with offline studies, as the'streak' variable serves as a surrogate measure for the psychological states of confidence or tilt, which are influenced by consecutive wins or losses, respectively [76], but also additional factors [16]. In summary, the investigation revealed a negligible effect of'streak' on the outcomes of matches, but further study on larger datasets is needed without the need to impute streaks for missing data. There seems to be a difference in how the feature is treated in linear models versus tree-based models as well, which should be explored in the future. Figure 11: Feature importance (based on impurity reduction) from the XGBoost model. Figure 12: (a) Sensitivity analysis of mean vs. median imputed streak. (b) Comparison of the models with the streak removed.
# Community Detection Framework Using Deep Learning in Social Media Analysis
## 2 Related Work
### The Evolution of Social Media Analysis  
Ref. [14] provide an overview of the challenges and future directions in social media analysis. The authors discuss the unique features of social media data, the limitations of existing techniques, and the need for new methods to address issues such as privacy and scalability. They also discuss the importance of interdisciplinary collaboration in social media analysis, as it involves a range of fields, such as computer science, law, and psychology. They suggest that future research should involve collaboration across these fields to develop comprehensive solutions for social media analysis. Ref. [15] presents a method for detecting and tracking political abuse in social media. The authors note that social media has become a powerful tool for political communication, but it is also vulnerable to abuse, such as the spread of false information and the use of aggressive or inflammatory language. Detecting and tracking this kind of abuse is essential for maintaining the integrity of political discourse and protecting the public from harm. The authors propose a machine learning approach to identifying and tracking political abuse in social media. They use a variety of features, such as user profile information, linguistic features, and temporal features, to train a classifier that can detect abusive content. They also develop a clustering algorithm that can group similar instances of abusive content together, allowing for the tracking of how this content spreads over time. Ref. [16] provides a comprehensive survey of the use of deep learning techniques for text-based sarcasm detection in social media data. Sarcasm is a form of verbal irony that is often used in social media to express humor, criticism, or other emotions. However, detecting sarcasm in social media data can be challenging,as it often relies on context, tone, and other subtle cues that can be difficult for machines to identify. The authors of this paper review various techniques for detecting sarcasm in social media data, including traditional machine learning algorithms, deep learning models, and hybrid approaches that combine multiple techniques. They also discuss various types of features that can be used to train these models, such as lexical features, syntactic features, and semantic features. Compared with the previous research, our approach specifically integrates social network topology with communication content and provides a specialized, context-aware solution for community detection that is particularly relevant for addressing cybercrime in social media.
## 1 Introduction
The geographical detector model (GDM) is based on spatial variance analysis and is a new statistical method used to investigate spatial differentiation and reveal the driving factors behind it with no linear assumptions [25; 26]. Its research area ranges from the national scale [27] to the regional scale [28]. It has been widely used to analyse vegetation cover variation [29; 30; 31; 32; 33; 34], and the spatial differentiation of vegetation can be ascertained effectively. For the CI, GDM not only can detect the influence of the spatial distribution characteristics of numerical data such as the average annual temperature and of qualitative data such as soil type on CI, but it can also determine the interactions among different driving factors on the spatial differentiation of the CI. The Sanjiang Plain is the largest concentrated area of freshwater marshes in China and is a vital base for grain production [35]. It ensures regional ecological security and food security in China. The vegetation coverage in this area can indicate the overall condition of the ecological system. Although some researchers have studied the variation of the vegetation normalized difference vegetation index (NDVI) in the Sanjiang Plain, the effects of land use type and climatic factors on vegetation coverage are usually the focus [36; 37]. However, vegetation growth is affected by climate, terrain, land use type, soil type, and other factors [38]. Moreover, NDVI can easily become saturated during the flourishing period of vegetation growth, and the disturbance of background soil on NDVI cannot be ignored [39]. The main aims of this study were to (1) investigate the spatial and temporal variation characteristics of foliage CI in the Sanjiang Plain from 2001 to 2015; (2) explore the correlation between CI and LAI; and (3) quantify the impacts of natural and anthropogenic factors on the spatial distribution of CI. This study determines reliable clumping effects for the Sanjiang Plain, improving the accuracies of the retrieval of vegetation structures and the simulation of land surface models. In addition, it provides a research foundation for analysing the driving mechanism of CI.
###### Abstract  
The distributed point source method, or DPSM, developed in the last decade has been used for solving various engineering problems--such as elastic and electromagnetic wave propagation, electrostatic, and fluid flow problems. Based on a semi-analytical formulation, the DPSM solution is generally built by superimposing the point source solutions or Green's functions. However, the DPSM solution can be also obtained by superimposing elemental solutions of volume sources having some source density called the equivalent source density (ESD). In earlier works mostly point sources were used. In this paper the DPSM formulation is modified to introduce a new kind of ESD, replacing the classical single point source by a family of point sources that are referred to as quantum sources. The proposed formulation with these quantum sources do not change the dimension of the global matrix to be inverted to solve the problem when compared with the classical point source-based DPSM formulation. To assess the performance of this new formulation, the ultrasonic field generated by a circular planer transducer was compared with the classical DPSM formulation and analytical solution. The results show a significant improvement in the near field computation.  
acoustic source modeling; numerical method; distributed point source method +
Footnote †: journal: Applied
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 4 Discussion  
The transferability of the SAM model to instance segmentation of volumetric XXL-CT data-sets requires careful consideration. The presented results indicate that its two-dimensional image-based segmentation quality is insufficient for this specific problem domain. This limitation becomes particularly evident when dealing with the concatenation of numerous intertwined cross-sectional images in the volumetric case. The low contrast and high noise in these images pose challenges in accurately delineating individual segments. Additionally, using domain specific fine-tuning and improving slice-wise predictions did not yield substantial improvements for volumetric predictions.  
One potential source of error in the presented method might be the limited computational resources allocated for both fine-tuning and subsequent hyperparameter search. A more thorough optimization process could potentially improve the results. Furthermore, the availability of labelled training data-sets of sufficient quality in this problem domain was relatively limited for training the vision transformers included in SAM. Specifically, the absence of neighbouring voxels when adding the 512 voxel wide border around the data-set for the Me 163 data-set may have possibly contributed to a decrease in segmentation quality.  
Additionally, considering improved algorithms for merging the slice-wise predictions could be an initial step in the further development process. Previous studies [26; 27; 28] have demonstrated ample opportunities for the development of more sophisticated algorithms in this area. Implementing and embedding such algorithms into the processing pipeline has the potential to significantly enhance the segmentation quality.  
Figure 20: Slices obtained using the fine-tuned vit_bCVB model and tile size of \(48\times 48\times 48\) voxels. Due to under-segmentation, the predicted segment erroneously intersects and merges multiple reference segments.
## 3 Performance Analysis and Simulations
### System Efficiency
#### 3.2.3 System Cost  
To evaluate and compare the cost efficiency of the proposed CPV system with an additional low-cost solar cell to that of the conventional CPV system without the additional low-cost solar cell, the following \"merit function\" \(R\) is used as the capital cost of annual electrical energy delivered. Since \(R\) is expressed as cost per unit of power, low values of \(R\) are more meritorious [29]:  
\[R=\frac{system\ cost\ (\$)}{annual\ nominal\ generated\ electrical\ energy\ (kWh)}. \tag{18}\]  
The merit function \(R\) and energy output \(E\) of the conventional CPV system are determined as follows [29]:  
\[R_{\text{C}}=\frac{C_{0}+C_{\text{CPV}}}{E_{\text{C}}}+C_{\text{non-cell}}, \tag{19}\]  
\[E_{\text{C}}=G_{\text{B}}\times\eta_{\text{cell\_CPV}}\eta_{\text{opt\_CPV}}, \tag{20}\]  
Figure 13: Improvement factor vs. diffuse-to-global ratio.  
where \(R_{\rm C}\) and \(E_{\rm C}\) is the capital cost of annual generated electrical energy and the nominal annual DC electrical energy density of the conventional CPV system, respectively. \(C_{0}\) and \(C_{\rm non-cell}\) are the costs that are proportional to system entry aperture area (the module cover, frame, tracking system, the structural supporting beams, land use, etc.), and system power (inverter, etc.), respectively. \(C_{0}\) and \(C_{\rm non-cell}\) are common to the respective CPV systems; and \(C_{\rm CPV}\) is the cost specific to the use of the high efficiency MJ cells (MJ cells, heat sink, etc.).  
The merit function and energy output of the proposed CPV system with the additional low-cost solar cell are calculated as follows [29]:  
\[R_{\rm D}=\frac{C_{0}+C_{\rm CPV}+C_{\rm PV}}{E_{\rm D}}+C_{\rm non-cell}, \tag{21}\]  
\[E_{\rm D}=G_{\rm B}\times\eta_{\rm cell\_CPV}\eta_{\rm opt\_CPV}+G_{D}\times \eta_{\rm cell\_PV}\eta_{\rm opt\_PV}, \tag{22}\]  
where \(R_{\rm D}\) and \(E_{\rm D}\) is the capital cost of annual generated electrical energy and the nominal annual DC electrical energy density of the proposed CPV system, respectively; and \(C_{\rm PV}\) is the cost specific to the use of the additional low-cost solar cell.  
Some experimental results [28; 29; 30] showed that the CPV system with the additional low-cost solar cell outperformed the conventional CPV without the additional solar cell, with \(E_{\rm D}/E_{\rm C}=1.15\). The addition of the low-cost solar cell to the CPV system resulted in 15% more electricity generated per dollar.
## 2 Materials and Methods
### Diet Formulation and Experimental Design  
Powder biomass of the autotrophic cultured microalgae _Nannochloropsis oceanic_ was supplied by Allmicroalgae, Natural Products S.A., Pataias, Portugal. A supplemented diet for hens was prepared by adding spray-dried _Nannochloropsis oceanica_, in a proportion of 3% (_w/w_), to a standard feed provided by Alimave, S.A, Leiria, Portugal. The composition of standard and supplemented feeds as well as of the microalgae biomass was characterized concerning fatty acid profile, total protein, ash, and lipid content.  
During 28 days (4 weeks), hens (about 3000 birds) from an outdoor farming system (Gameirovo, Caldas da Rainha, Portugal) were fed ad libitum with diet supplemented with _N. oceanica_. A group with the same size (3000 hens), fed with a standard diet, was used as control. The zootechnical performance of hens was monitored based on feed intake and eggs production. In each week (identified as W1, W2, W3, and W4) eggs were randomly collected and analyzed. In the harvest day, eggs (N = 12) were evaluated for (i) shell thickness and colour; (ii) total weight; (iii) egg quality and (iv) yolk colour. The eggs (N = 5) for chemical analysis (protein, lipids, ash, moisture, and fatty acid profile) were shelled, freeze-dried, ground to powder, and preserved at 4 \({}^{\circ}\)C until use. The weight ratio between fresh and freeze-dried eggs was used to express the parameters values as fresh weight. Eggs collected at day zero were used as control.  
To evaluate the shelf life during 28 days of storage (legal expiration time), control eggs and eggs from hens fed with supplemented diet for 4 weeks (W4), were stored in paper boxes, protected from heat and light. Every week, eggs were evaluated for the parameters previously mentioned.
# A Compression-Based Toolkit for Modelling and Processing Natural Language Text
## 4 Sample Applications Provided by the Toolkit
### Calculating Compression Codelengths without Coding  
The method CodelengthText in Algorithm 4 calculates the compression codelength for a text object without performing any coding operation. It is similar to the encoding and decoding methods above, except that it uses the routine TLM_update_context to update the model (line 5) instead of either TLM_encode_symbol or TLM_decode_symbol as above. The argument \(\mathit{Model}\) specifiesthe language model being used. The text _SourceText_ passed as an argument would usually have the sentinel symbol marking the end of the text so therefore this does not need to be explicitly processed at the end of the stream (as was the case with Algorithm 2). The variable _Codelength_ (line 2) is used to accumulate the totals of the compression codelengths as each symbol in the text is processed. It is returned by the method at the end (line 9). The codelength values that are the cost of encoding each symbol are obtained by accessing the variable TLM_codelength (see line 6) and reflect the theoretically optimum encoding of each symbol for which the actual (physical) encoding to disk using an arithmetic coder is a very close approximation [27; 29]. This method can be used to speed up processing as it does not perform any physical coding and has wide applications. A variant of the algorithm has been used to perform the automatic cryptanalysis of transposition ciphers, for example [30].  
```
1 methodCodelengthText(SourceText,Model)
2\(Codelength\gets 0.0\);
3\(Context\leftarrow\) TLM_create_context(Model);
4 foreachSymbolinSourceTextdo
5 TLM_update_context(Model,Context,Symbol);
6\(Codelength\) += TLM_Codelength;
7
8 end for
9 TLM_release_context(Context);
10 returnCodelength;
11
12 end for
```  
**Algorithm 4**A context-based method for computing compression codelength without coding