esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of coronary atherosclerosis. Computational modeling with lipidomics analysis can be used for prediction of coronary atherosclerotic plaque progression. Methods: 187 patients (480 vessels) with stable coronary artery disease (CAD) undergoing CCTA scan at baseline and after 6.2 \(\pm\) 1.4 years were selected from the SMARTool clinical study cohort (Clinicaltrial.gov Identifiers NCT0448691) according to a computed tomography (CT) scan image quality suitable for three-dimensional (3D) reconstruction of coronary arteries and the absence of implanted coronary stents. Clinical and biohumoral data were collected, and plasma lipidomics analysis was performed. Blood flow and low-density lipoprotein (LDL) transport were modeled using patient-specific data to estimate endothelial shear stress (ESS) and LDL accumulation based on a previously developed methodology. Additionally, non-invasive Fractional Flow Reserve (FFR) was calculated (SmartFFR). Plaque progression was defined as significant change of at least two of the morphological metrics: lumen area, plaque area, plaque burden. Results: a multi-parametric predictive model, including traditional risk factors, plasma lipids, 3D imaging parameters, and computational data demonstrated 88% accuracy to predict site-specific plaque progression, outperforming current computational models. Conclusions: Low ESS and LDL accumulation, estimated by computational modeling of CCTA imaging, can be used to predict site-specific progression of coronary atherosclerotic plaques.  
prediction of plaque progression; computational modeling; endothelial shear stress; LDL transport; non-invasive FFR
## 3 Methodology
### Spaces Division and Extraction  
#### 3.3.1 Spaces Partition  
An RGB-D mapping system uses short range distance measurements and high-frequency data collection, which facilitates the semantic labeling for the RGB-D frames during scene scanning. RGB-D datasets consist of a series of RGB-D frames and each frame is localized by the camera pose and timestamp. Therefore, rather than distinguishing room spaces based on the detected wall planes, we interactively define the start-stop position for each functional space (e.g., room, corridor, kitchen, etc.). Thus, the RGB-D sequences are divided into several subsections associated with the camera trajectories. And the corresponding point cloud for each functional spaces can be obtained through merging all frames in each start-stop section. Similarly, the frames on the start-stop positions and their adjacent frames of each functional space are generally recognized as the frames containing door components. The point cloud containing doors can be extracted through merging the point clouds of all adjacent frames. Figure 4 shows sample data of the labeled RGB-D sequences. Each functional space is described by a series of RGB-D frames, a subsection of camera trajectory. Different colors in the camera trajectory belong to different functional spaces. Based on the camera pose and the RGB-D frames, a colorized point cloud of each space can be recovered.
# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
## 2 Materials and Methods
### Image Preprocessing  
The quality of the acquired images is a fundamental factor for the correct detection and classification of possible pathologies present in the concrete structure to be evaluated. Poor-quality images may result in non-detection or even misclassification of the detected pathology. Possible noise needs to be removed from the images.  
Although every care is required for good image acquisition, the conditions may not be favorable [21], resulting in images that need to be enhanced for analysis. Some techniques can be employed for image enhancement, such as correction of illumination, sharpening, removing noise, and correcting any imperfections the image may have.  
The method proposed in this paper suggests image enhancement so that the poor quality of an image does not interfere with the pathology identification result. Still, the experiments did not use these techniques since the images were from public datasets with images already ready for pathology identification processing. Several techniques can be employed for image enhancement; the choice depends on the problem the image contains, which is related to the conditions under which the image will be taken, as well as the conditions of the environment where the structure to be analyzed is located.  
One possibility is the use of filters for image enhancement. It is possible to soften the image with a filter that works with the transition of intensities in the image. Enhancement filters can increase the sharpness of the image, emphasizing edges and details of the image, and can work not only on sharpness but also on contrast and detail enhancement. Image intensity adjustment can also be worked on to improve brightness and contrast to make the image sharper and more detailed.  
Filters can also be used to remove or reduce noise. The filter to be used will depend on the type of noise in the image, and tests need to be performed to see whether the processing results in the desired image. Color correction can be worked on to correct the distribution of colors in the image. This technique can improve image quality and result in a more realistic image with vibrant colors.  
It is important to note that each image produced is a unique artifact, depending on many variants, the way it was made, the equipment used, and the environmental conditions at the time it was made, among many others. For this reason, therefore, each case needs to be analyzed and a strategy adopted for the need of each specific case.
# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 2 Bacterotherapy in CRC Treatment
### Mechanisms Used in Bacterotherapy in CRC
#### 2.1.6 Maintaining Microbial Equilibrium
brevis_ 8803 that has been demonstrated to promote apoptosis in SW620 cells by activating the ERK pathway, whereby it has been proposed to act as a less toxic anti-tumor agent compared to standard cancer medicines [131]. Another species of _Lactobacillus_, showing anti-cancer properties is represented by _Lactobacillus casei_ BL23. In a study model of colon cancer triggered by azoxymethane and dextran sodium sulfate, _Lactobacillus casei_ was able to modify the immune response and thereby to reduce adenoma formation. Together the study's findings revealed that _L. casei_ BL23 was able to protect mice from CRC by suppressing tumor formation and proliferation as well as showing a great immunomodulatory impact, highlighted by the downregulation of IL-22 and overexpression of Caspase-7, Caspase-9 and Bik [132]. Besides _Lactobacillus_, _Bifidobacteria_ as another probiotic family, in particular _Bifidobacterium longum_, _Bifidobacterium infantis_, _Bifidobacterium abdescentis_ and _Bifidobacterium breve_ have been recognized to act as CRC suppressors, since they are able to fight CRC by modulating the immune response, binding and degrading potential carcinogens as well as maintaining a healthy balance in the intestinal microflora, producing anti-tumorigenic or anti-mutagenic agents in the colon and altering metabolic activities of the intestinal microflora [133; 134]. For instance, in previous research, it has been shown that CRC cell lines such as Caco-2, HT-29 and SW480 were inhibited by butanol extract from _B. adolescentis_ SPM0212. Furthermore, _B. adolescentis_ SPM0212 was demonstrated to activate macrophages and dramatically enhance the production of TNF and nitric oxide, boosting the immune response activity to control immunological modulation and tumor cell death [135]. Overall, on the one hand probiotics have been shown to help prevent CRC development and to keep the intestinal micro-ecology in balance. On the other hand, they have been shown to be effective as CRC proliferation suppressors and anti-cancer immune modulators.
# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
## 3 Experimental Results
### Metrics and Axioms  
Table 2 reports the three quantitative quality indicators (see Section 2.2.3) for the different explanation techniques. Regarding the _local fidelity_, the two explanation techniques were on par; both showed full fidelity. Since the integrated gradients method uses the original model, its fidelity is by default 100%. For LIME, the _local_ fidelity on all instances in the test set was also 100%. The local surrogate models that LIME built to explain the predictions of the test instances predicted in all 2003 cases (i.e., all observations in the test set), the same class out of the seven skin lesion classes as the original model. Note that this also means that the local surrogate model predicted the wrong class if the original model predicted the wrong class (see Figure 2 for the test set predictions).  
Regarding the _stability_ and _robustness_, the integrated gradient method clearly outperformed LIME. Although integrated gradients always gave the same results (feature attributions) when the explanation was repeated for the same instance and same settings (100% stability on the test set), LIME always gave a different result (0% stability on the test set).  
Figure 5: Attribution maps/visual explanation of the explanation techniques for those images in the test set that the classifier misclassified with the highest probability. From left to right: original preprocessed image of the class that the classifier misclassified with the highest probability, integrated gradient explanation, integrated gradient explanation overlayed on the misclassified image, LIME explanation, LIME explanation overlayed on the misclassified image.  
Similarly, the integrated gradient method proved to be more robust than LIME. For all classes, the Lipschitz robustness indicator [59] was smaller (i.e., better) for the integrated gradients explanation technique than for LIME. To visualize this difference, Figure 6 shows the Lipschitz robustness indicator for the two explanation techniques, as an example, for the test instances of the basal cell carcinoma (bcc) class.  
In sum, the integrated gradients explanation technique seems better with regard to the quantitative evaluation metrics. However, one should keep in mind that LIME is model-agnostic, while the integrated gradients method can only be applied if the original model is differentiable. Because of this, the LIME explainer is also more portable and can be used even if the original model would be changed.
## 2 Background
### Operational Power System Use Cases
#### 2.4.2 Use Case #2: Loose Connection  
A loose bus connection can cause arcing, Figure 3b. This can be a loose screw or poor contact between the lines and the bus. The loose connection increases the dielectric constant within the connection and increases power draw, wasting energy. Additionally, arcing can lead to a fire if timely corrective actions are not taken. Figure 3b shows an arcing event around 2.5 ms. After the event, each line phase is rotated \(-90^{\circ}\), which is evident as the post-event phase of Phase \(A\) is equal to the pre-event phase of Phase \(C\).
# Communication-Efficient Vertical Federated Learning
## 4 Experimental Setup
### Training Autoencoder  
Local data of each of the clients is split into 3 sets; training set (70%), validation set (15%), and testing set (15%). The train, validation, and test splits are performed to prevent the autoencoder model from overfitting and to accurately evaluate it. The hyperparameters used for the training of autoencoders were:  
* **Encoding dimension:** This refers to the number of nodes at the bottleneck and is the dimensionality of the encoded representation of the data. For our experiments, a reduction percentage (25%, 50%, 75%) was provided during training.
* **Learning rate:** The learning rate was varied between 0.1 to 0.001.
* **Batch size:** The batch size is the number of samples per gradient update. The following values were tested: 10, 100, and 1000.
* **Epoch:** Experiments were tested with 1000 epochs
* **Optimizer:** This is the optimization algorithm that is used to minimize the loss. The \"Adam\" optimizer was used for the experiments.
* **Loss Function:** This is the error function which is evaluated during the neural network training stage and the training strives to minimize the losses. The \"mean_squared_error\" loss function was used.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **Instances** & **Features** \\ \hline Adult Income Dataset & 22,404 & 14 \\ Heart Disease Prediction Dataset & 302 & 13 \\ Wine Quality Dataset & 5329 & 12 \\ Rice MSC Dataset & 75,000 & 106 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of Datasets.
# Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
###### Abstract
Differently from other unsupervised techniques such as Variational Autoencoders [20] that require both an encoder and a decoder, the considered method only needs and encoder, almost halving the complexity. Variational techniques powered by neural networks for mutual information estimation, and in general divergences between probabilities, have been widely used in the literature [22; 23; 24; 25; 26]. The complete pipeline we propose consists of firstly compressing the input time series into smaller cardinality vectors, and then feed these vectors to a binary classifier for scintillation detection. During training, the unsupervised compression is performed for all datapoints, whereas, obviously, the training of the classifier is performed only for the labeled datapoints. Figure 1: Semi-supervised learning. Feature extraction is performed in an unsupervised fashion, while classification is based on a supervised approach (labels). The aim of this paper is then to develop a new scintillation detection strategy based on Convolutional Neural Networks and semi-supervised learning. In more detail, we extend the results and approaches presented in [11], in which classification of scintillation events is performed using supervised decision trees. While showing good performance, this method has two fundamental problems. First, it lacks the capability of considering as a whole a single scintillation event sampled at different time instants, as the time feature is not taken into account. Second, it requires to have a fully labeled dataset, where the process of annotation had to be done manually by a team of experts in the field. We instead propose a solution that mitigates both problems by using a system that naturally:  
* handles subsequent samples in time, by applying an overlapping time window to all features, thus including the information about the temporal correlation;
* requires only a smaller portion of the dataset to be labeled. We show that to reach the same level of accuracy, just about 20% of datapoints must be manually labeled. For a faithful comparison, we used the same dataset as in [11] and we performed statistical measurements of testing classification performance using the same splitting of datasets for training and testing. Moreover, when the amount of labeled datapoints is augmented, the proposed method reaches extremely high performance with a top accuracy on the test set of 0.9993. The overview of the paper is then the following: in Section 2, we discuss the need for unsupervised compression; in Section 3, we give a brief overview of the DeepInfomax approach that we further expand in Appendix A; in Section 4, we describe the considered datasets and their properties; in Section 5, we give an overview of the system architecture and training and testing procedure; and, in Section 6, we show the results of the proposed classification method. Finally, in Section 8, we discuss on the results and suggest possible future work directions. We also include a complete Appendix B to briefly describe the neural network architectures and training technicalities.
## 3 Materials and Methods
### Building Energy Model in EnergyPlus  
We used the OpenStudio v. 2.7 tool and EnergyPlus v. 9.1 software for the simulations. As a first step, we modeled the baseline of the building, i.e., its actual situation in terms of energy (Figure 4). For this, we considered all internal loads from Table 2, and people occupancy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Construction** & **Density** & **Thermal** & **Specific Heat** & **U-Value** \\  & **[kg/m\({}^{3}\)]** & **Conductivity** & **[W/m-K]** & **[J/kg-K]** & **[W/m\({}^{2}\)-K]** \\ \hline Roof & & & \\ Steel sheet (1 mm) & 7800 & 50 & 450 & \\ Heavy weight concrete & 2240 & 1.31 & 837 & \\ (30 cm) & & & \\ \hline Wall & & & \\ Hollow concrete block & 1600 & 0.47 & 1000 & \\ (9 cm) & 800 & 0.37 & 340 & \\ \hline Floor & & & & \\ Heavy weight concrete & 2240 & 1.31 & 837 & \\ Acoustic tile (2 cm) & 368 & 0.06 & 59 & \\ \hline Windows & & & & \\ Clear glass (6 mm) & & & & 5.78 \\ Metal frame & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and thermal properties of envelope building materials (adapted from [14]).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Description** & **Installed Power [W]** \\ \hline Lighting & LED and saving lighting & 11,974 \\ Electric equipment & Appliances & 25,716 \\ Air-conditioning & Direct expansion mini and floor/ceiling splits & 170,567 (581,999 BTU/h) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Building internal loads: installed power by end-use (from [49]).  
We established the schedules of the building based on the information provided by the users. We fixed the infiltration rates in 0.54 air changes per hour (ACH) [18]. Air-conditioners were modeled with a standard COP of 3, considering that the systems installed in the case study are obsolete.
## 4 Training Efficiency Evaluation and Statistical Analysis
### Evaluation Conditions and Questionnaire  
#### 4.1.1 Evaluation Conditions  
The targets of evaluation were 60 college students majoring in the railway vehicle field. To exclude the effect of prior experience, the participants were college students who did not have experience in railway vehicle maintenance. Table 5 shows the overall information of the participants. The average age of the participants was 25.1, and most participants (52) were men.  
According to the central limit theorem [25], the evaluation was conducted for 12 days (from 15 to 26 March), dividing 60 participants into two groups (A and B). Group A, with 30 participants, received a 30 min collective education about the light maintenance procedure of the axle-mounted disc brake system through the booklet-type manual used in the operating organization. Group B, with the remaining 30 participants, was trained regarding the light maintenance procedure of the axle-mounted disc brake system through AR using mobile devices for 30 min. Subsequently, the participants in each group responded to the evaluation questions. Figure 15 shows the method of teaching the maintenance process in two groups (A and B).
## 3 Momentum-Based Motion Detection
### Motion Detection Methodology  
Based on the MRSS method, we can detect an MN's behavior by two thresholds, the positive threshold \((TH_{P})\) and the negative threshold \((TH_{N})\). The state of an MN's motion is then defined by the following rules:  
* Approaching state: when MRSS is positive and rising above \((TH_{P})\), the MN is considered to be approaching the BS.
* Stationary state: when MRSS is close to zero and stays between \((TH_{N})\) and \((TH_{P})\), the MN is considered to be staying at the same place.
* Leaving state: when MRSS is negative and declines below \((TH_{N})\), the MN is considered to be leaving the BS.  
Figure 2 shows the detected user motion state obtained by the proposed MRSS scheme. Thus, by using the MRSS-based motion detection method, a motion detection based handoff algorithm can trigger a network discovery process at correct timing.  
Figure 1: The MRSS calculation.  
Figure 2: The detected user motion state of the MRSS method.
## 2 Data and Methods
### Data
#### 2.1.3 Climate Change Impacts
It is a joint product from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR)). Both sets of simulations extend from 1970 to 2010. We investigated the influences of changes in mean environmental conditions and in the occurrence of prolonged adverse conditions (bad harvests, droughts) on population redistribution. The resulting variables are called MeanYield and MeanFlow and represent the abundance or scarcity level relative to the long-term average. We then calculate the maximum number of consecutive years below the median crop yield/river flow for each decade. The resulting variables are called ConsecLowYield and ConsecLowFlow and measure the successive shocks to crop production and water availability systems in each decade. Of course, extreme wet and dry events may significantly affect the water and agricultural systems [24]. For example, [PERSON] et al. analyzed the influence of extreme weather disasters on global crop production and found that production losses due to droughts were associated with a reduction in both harvested area and yields, whereas extreme heat mainly decreased cereal yields [48]. The societal infrastructure is becoming more sensitive to weather and climate extremes, which would be exacerbated by climate change [49]. However, on the one hand, a majority of climate change models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality [38]; on the other hand, environmental change is expected to increase the likelihood and impacts of extreme weather events, and the scientific argument of climate-related disasters is increasingly confident, but the impacts on human population patterns are unclear and unpredictable. [50]. Therefore, the effects of extreme weather events on population redistribution are not considered in this paper.
## 5 Adaptive Data Transmission Algorithm Based on Reinforcement Learning
### RL-ADTA Implementation  
RL-ADTA is implemented, as shown in Algorithm 2. The agents in underwater wireless sensor networks can obtain the status information of neighbor nodes and transmission channels by monitoring the packet information transmitted in the network. When a new packet is generated or received, the Q value is calculated according to the environment state of the current node where the packet is located, and the best policy is selected to send the packet. Once the packet is sent, the agent will record whether the packet is successfully sent, and update the agent state.  
```
Initialize the positions of the nodes  While (true)  If (the new data to send)  While (times\(<\) times\({}_{\text{p}}\))  Create the virtual routing pipe using Formula (9)  Obtain the status of neighbors, channels, and the current node from the environment status base  Obtain the V-value, threshold, and super-parameters from the transport policy base  Update the S and A using Formulas (10)-(12)  Calculate Q function using Formulas (13)-(20)  Update the V-value with the max \(Q\) value using Formula (21)  Determine the number of correction data sent \(n_{\text{new}}\) by the max Q value  Determine the relay node using the max Q value  Form the packet by (p, d, q), \(\{n_{\text{new}},i,\;\text{correct}\}\) and V-value  Forward the packet to the relay node  If (It is detected that the packet has been forwarded)  Break  Else  times\({}_{\text{send}}\)++  End If  End While  End if  End While
```  
**Algorithm 2** Adaptive Data Transmission Algorithm Based on Reinforcement Learning (RL-ADTA)