esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods for Landslide Identification  
### Semi-Supervised Learning  
Semi-supervised learning lies between supervised and unsupervised learning, with the goal of achieving excellent performance using only a small number of labeled samples. The basic idea of semi-supervised learning is to use the distribution information of a large amount of easily accessible unlabeled data for model learning, and use a small amount of labeled data for auxiliary training, so as to achieve a model with strong generalization ability and prediction performance [33]. Common semi-supervised learning methods include self-training algorithms, co-training algorithms, and generative models. A self-training algorithm is a simple semi-supervised learning method. It trains the initial model with labeled samples, then predicts unlabeled samples with the model, adds the samples with high prediction confidence to the labeled samples, and iterates training repeatedly until the model converges [34; 35]. This algorithm is easy to understand but is sensitive to label noise and errors, and is easily affected by noise and outliers in unlabeled data. A co-training algorithm uses multiple models to cross-train each other. It divides the feature space into two non-overlapping subspaces and uses one model to correct the predictions of the other model. However, it relies on the selection of classifiers and requires consistency in the distribution of labeled data; otherwise, the learning results may be unstable [36]. Generative models are a class of models that can learn data distributions and generate new data through distribution features. Due to their data generation capability, they are widely used in tasks such as sample interpolation, image generation, and text generation [37]. Generative adversarial networks (GANs) have become the most typical generative models in recent years and have become a focus of many studies [38]. Compared with traditional generative models, such as probabilistic graph models and variational autoencoders, GANs do not require an explicit definition of the likelihood function or assumptions about the distribution. They complete learning solely from the data perspective, demonstrating more sufficient applicability for semi-supervised learning [39]. Therefore, this study uses a GAN to implement semi-supervised learning for landslide identification tasks.
# A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
## 4 Data Preprocessing and Models Architecture  
As already explained in Section 2, the data are averaged on an hourly basis, so that every channel counts one value per hour. A preliminary statistical analysis of the data led to not considering some measurements when constructing the model; in particular, the different models consider the 15 tiltmeters, the hydrometer level and the two temperatures Temp1 and Temp2. The other variables are not taken into account since they are not related to the bridge's behaviour.  
To bring the data in the format required for their processing with neural networks, some manipulations are performed according to the method described in [27]. Firstly, the data are arranged in a matrix, whose dimensions are \(N_{\text{samples}}\times 18\), with 18 being the number of measurements used by the model. Secondly, each column of the matrix for the training set is normalised by adopting a standardisation technique [28]:  
\[z_{ij}=\frac{x_{ij}-\mu_{j}}{\sigma_{j}}\quad\text{for}\quad j=1,\cdots,17\quad \text{and}\quad i=1,\cdots,N_{\text{samples}} \tag{6}\]  
where \(\mu_{j}\) and \(\sigma_{j}\), respectively, represent the mean value and the standard deviation for the measure of the \(j^{th}\) sensor in the training set. In this way, the distribution of samples for each variable will be centred on zero with a standard deviation of 1.  
For the iterative models, the dimensions of the input-output tensors, respectively, become \(N_{\text{samples}}\times 17\) and \(N_{\text{samples}}\times 1\). The architecture of the network (represented in Figure 6) is made of two dense layers with the ReLU activation function, a dropout layer to avoid overfitting [29] and a final dense output with a linear activation function.  
While for the AU model, the input and output of the network are the same, including all the measurements available, i.e., a matrix with the dimensions \(N_{\text{samples}}\times 18\). The architecture, in this case, is represented in Figure 7.  
For the case study of Candia Bridge, the model is trained with data from December 2021 to December 2022, during which the bridge is considered in a new, stable equilibrium position. The testing phase is then performed on data from December 2020 to November 2021 to verify the hypothesis that the bridge was in a \"transitory\" phase due to the flood event. The objective is first to assess if the bridge is in stable con  
Figure 6: Iterative model’s architecture.  
find the moment at which it arrived to this condition; in this way, it is possible to assess the bridge's safety after an extraordinary event.
## 3 Experimental Setting and Results
### Datasets  
The comparative experiments are conducted on three large-scale RS image datasets, including UC Merced land use dataset (UCMD) [37], SAT-4 dataset [38] and SAT-6 dataset [38]. The details of these three RS image datasets are introduced below.  
1. UCMD [37] stores aerial image scenes with a human label. There are 21 land cover categories, and each category includes 100 images with the normalized size of 256 \(\times\) 256 pixels. The spatial resolution of each pixel is 0.3 m. We randomly choose 420 images as query samples and the remaining 1680 images are utilized as training samples.
2. The total number of images in SAT-4 [38] is 500k and it includes four broad land cover classes: barren land, grass land, trees and other. The size of images is normalized to 28 \(\times\) 28 pixels and the spatial resolution of each pixel is 1 m. We randomly select 400k images to train the network and the other 100k images to test the ANN search performance.
3. The SAT-6 [38] dataset contains 405k images covering barren land, buildings, grassland, roads, trees and water bodies. These images are normalized to 28 \(\times\) 28 pixels size and the spatial resolution of each pixel is 1 m. We randomly select 81k images as query set and the other 324k images as training set.  
Some sample images of the above three datasets are shown in Figures 4-6, and the statistics are summarized in Table 2.
## 2 Materials and Methods  
### Reagents and Solutions  
The reagent used in this study were of analytical grade and were applied without purification. HPLC-grade water was prepared by reverse osmosis, followed by double distillation. A stock solution of formaldehyde (400 \(\upmu\)g mL\({}^{-1}\)) was done by diluting 37%(\(v/v\)) formaldehyde (Merck, Darmstadt, Germany). Glacial acetic acid (Reagent Plus, \(\geq\)99%, Sigma-Aldrich, Poland), ammonium acetate (purity \(\geq\) 98.0%, Avantor Performance Materials, Poland) and acetylacetone (purity \(\geq\) 99.0%, Merck) were applied for formaldehyde derivatization in the Hantzsch reaction. Benzyldimethylhexadecylammonium chloride (purity \(\geq\) 98.0%), trihexyl(tetradecyl)phosphonium chloride (purity \(\geq\) 95.0%), lauric acid (purity \(\geq\) 98.0%) were purchased from Sigma-Aldrich and 1-docosanol (purity \(\geq\) 98.0%) was bought from Merck. Sodium chloride (purity 99.99% Suprapur(r), Merck) was used to check the ionic strength effect. Sodium hydroxide solution (30%, Suprapur(r), Merck) and hydrochloric acid (30%, Suprapur(r), Merck) after proper dilution were applied for adjusting the pH of the samples. Acetonitrile (gradient grade for LC, LiChroSolv(r)) and methanol (gradient grade for LC, LiChroSolv(r)) were bought from Merck and applied for both desorption of the derivatized formaldehyde and its chromatographic determination.
## 4 Experiments and Results
### Results on Bound57
This indicates that learning the underlying shapes is more effective than learning to predict moving vectors. Furthermore, the use of projected neighborhood grouping in 2D combined with 3D features enables better completion of low-density regions, which geometric methods often fail to restore due to their reliance on local consistency alone. Figure 12 shows some of the boundary point clouds output by those methods. We chose some simple and some complex shapes to compare the smoothing effects of different methods on boundaries, and demonstrate objects with more curves, because our proposed method may deal with a large number of flexible and deformable targets. It can be seen from the visual comparison that the smoothed boundary of PcBD has the best consistency, while the outputs from other methods are scattered and sparse. In parts of the input point cloud with lower density (such as a corner of a pillow, a motorcycle handle, and a rifle muzzle), the comparison methods predicted poorly and had fractures. On the other hand, PcBD can better identify these low-density parts and make up for them during the smoothing process. This also proves the superiority of our proposed Bound57 dataset, which generates more errors in complex parts to challenge the performance of a method. To sum up, PcBD's performance on the Bound57 dataset exceeds that of most existing point cloud processing methods. This is under the circumstance that we introduced ground truth labels for other methods as a reference. The results support the conclusion that PcBD's combined processing flow of outlier removal, boundary detection, and smoothing forms a coherent beneficial pipeline. The continuous feature across modules contributes not only to improved accuracy but also robustness across object types. The advantages of PcBD as a point cloud processing flow are once again reflected: if the best-performing models except PcBD in the above three tests are selected and combined into one processing flow, the final output smoothed boundary results will only be worse (as in Figure 13), while PcBD applies the point cloud features extracted from each part of the task to subsequent operations, ensuring the ability to complete all tasks.
# Influence of Climate Conditions on Deficiencies of Building Roofs
## 5 Conclusions  
A direct relation was shown to exist between deficiencies in building roofs and their corresponding localization (latitude, situation, type of climate, precipitation, heating degree days, cooling degree days, and average wind speed).  
In the general distribution by type of deficiency, there is a high presence of 'localized infiltrations' (TCD1 = 47%), followed by'moisture by infiltration' (TCD2 = 34%). The results by building typology show that, most of the time, problems occurred in 'apartment blocks' (BT1 = 56.23%), with this typology being predominant in all types of deficiencies studied, except for 'fissures in gable-walls and slab faces' (TCD5), which are far more common in 'detached houses' (BT2): TCD5 BT2 = 53.33%.  
Four areas of concentration of anomalies were defined, according to the ranks by normalized frequencies (1.00; 0.59; 0.35 and 0.29). This allows us to evaluate the different combinations possible (sorted from higher to lower), for the climate-geographic conditions; based on this, different climate-geographical areas were created and named R, O, Y and W. In this manner, it was found that buildings localized in the 'Coastal-Mediterranean' and 'Coastal-North' climate segments accumulate over 40% of the percentage of presence of the construction deficiencies.  
It was found that the deficiency most likely to occur in a building roof is TCD1 (localized infiltration), having the greatest importance if localized in a city close to the edge of the Cantabrian sea (Coastal-North-Oceanic: %RD >15), being more likely to result in a complaint by the users or owners. On the other hand, a building roof located inland, close to the southern Mediterranean area, has low chances of experiencing fissures in TCD5 (gable-walls and slab faces).  
It was found, through techniques based on inferential statistics, that an average annual wind speed greater than 3 m/s increases the likelihood of deficiency in roofs. It was also found that there is a higher presence of deficiencies in those geographical zones where there is a thermal demand of between 1800 and 2800 heating degree days or between 450 and 700 cooling degree days.  
Finally, significant differences were observed between the variable 'deficiencies per 1000 inhabitants' and the number of degree days and the wind speed of the building location, which confirms what was found in the trend analysis.  
Conceptualization, M.J.C.-A. and A.M.-C.; Data curation, M.J.C.-A.; Formal analysis, J.G.-S.-C.; Investigation, M.J.C.-A., A.M.-C. and J.G.-S.-C.; Methodology, A.M.-C.; Validation, [PERSON]; Writing--original draft, [PERSON]; Writing--review & editing, [PERSON]. This research received no external funding. This research took place within the Action Plan of the Musaat Foundation, in which research on construction anomalies was approved to take place in Spain, titled 'National statistical analysis on building anomalies'. The authors declare no conflict of interest.
# A Brief Review of Multipath TCP for Vehicular Networks
## 7 Open Research Problem
A better solution to improve energy efficiency while ensuring other QoS requirements should be discussed;
* Context-aware MPTCP vehicular system: The design of MPTCP vehicular networks should fully consider the vehicular environments, such as vehicle density, vehicle mobility, vehicle capability, and vehicle applications. Since the vehicle connection states change frequently, it is also a promising way to adjust the interface type to transmission type by considering the vehicular environment, thus maximizing the overall network performance. Vehicular environment information could also be utilized to optimize MPTCP scheduling;
* Collaboration of vehicular network with UAVs: Due to the constant movement of vehicles and limited communication links, the connection time between vehicles varies and sometimes can be very short. The introduction of UAVs to vehicular network systems will be able to alleviate this problem to some extent. Since, connection time of connected vehicles could be extended by deploying UAVs appropriately;
* MPTCP with SDN in vehicular networks: Due to the characteristic of centralized control of SDN technology, it has been utilized in vehicular networks to improve network performance. For example, [PERSON] et al. in [98] conducted a study on network handover based on SDN technology in a vehicular network. Likewise, it can also be expected to improve the network performance by introducing SDN technology in a MPTCP vehicular network;
* Security issues: Since multipath TCP has multiple paths to transmit data, therefore the possibility to be attacked by malicious attackers also increase compared to conventional TCP. Security issues are discussed in [30]. They mainly put their efforts on the security issues of connection initiation stage. More security issues should be studied in future work.
###### Abstract
[19] found that diffusion is the main migration mode of trichloroethylene (TCE) vapor through a detailed, field-scale analysis of the transport behavior of solvent vapors within the unsaturated zone. The unsaturated zone at the site is approximately 3.5 m thick and comprises the upper portion of the sequence of glaciolacustrine sands and silts of the Borden aquifer. [PERSON] et al. [20] found that although the transient advective flux can be greater than the diffusive flux; under most of the field conditions the net contribution of the advective flux is one to three orders of magnitude less than the diffusive flux. The advective flux contributes comparably with the diffusive flux only when the gas-filled porosity is less than 0.05. The advective transport of VOCs can be induced by the discrepancy in density between gas phase VOCs and clean air [21; 22; 23], water table fluctuation at  
Figure 1: Problems of compacted clay cover desiccation and possible migration of VOCs [6; 7; 8; 9; 10; 11; 12; 13; 14]. coastal sites [21; 23; 24; 25], and atmospheric pressure fluctuation [26; 27]. Therefore, in most non-coastal sites and unsaturated zones with low permeability, the VOCs' migration mode in the organic contaminated sites is mainly diffusion [28; 29; 30]. [PERSON] [31] found that the gas barrier should not only prevent water migration, but also the organic contaminants transmission. Therefore, the development of amended CCC with excellent moisture retention and VOCs/SVOCs diffusion barrier performance is significant. A systematic study of the moisture retention, hydraulic conductivity, and gas barrier properties of amended CCC was investigated through laboratory experiments. In this paper, the optimal dosage and ratio of dual-additives of the amended CCC were studied through the laboratory test. The hydraulic conductivity and gas barrier properties of amended CCC with optimal dosage and ratio were evaluated. Then, the mechanism behind the phenomenon was demonstrated through SEM, BET, MIP, and TGA test analyses.
## 1 Introduction
The objective of this study was to optimize the process model using parameter sensitivity analysis methods and parameter optimization methods based on observation data to estimate the long-term carbon flux variability in the study area and explore its responses to meteorological factors. The study used the extended Fourier amplitude sensitivity test (eFAST) method to explore the most sensitive parameters for the carbon flux outputs of Biome-BGC model, and then used the model-independent parameter estimation (PEST) method and the observation data of the eddy covariance system to optimize the model parameters. The 11-year net ecosystem exchange (NEE) and gross primary production (GPP) in the study area were simulated, and their relationships with air temperature and rainfall were analyzed. The study attempted to discuss the following questions: (1) What are the most sensitive ecophysiological parameters for the carbon flux of the Biome-BGC model in rubber plantations? (2) Is Biome-BGC suitable for carbon flux simulation of the rubber plantation in the study area? (3) How does the carbon flux of the rubber plantation in the study area respond to long-term changes in meteorological factors? To answer these questions, the study designed the following numerical experiments: (1) to identify the most sensitive parameters for the model output, as well as their variability for different outputs in different simulation years; (2) to analyze the differences in parameters' sensitivity to different parameter value ranges; (3) to compare the differences in the model simulation results by choosing different parameters for optimization; and (4) to simulate carbon flux in the study area with optimal parameters and analyze its response to long-term changes in meteorological factors.
###### Abstract  
Background: Brain connectivity has shown to be a key characteristic in the study of both Parkinson's Disease (PD) and the response of the patients to the dopaminergic medication. Time series analysis has been used here for the first time to study brain connectivity changes during motor activation in PD. Methods: A 64-channel EEG signal was registered during unilateral motor activation and resting-state in 6 non-demented PD patients before and after the administration of levodopa and in 6 matched healthy controls. Spectral entropy correlation, coherence, and interhemispheric divergence differences among PD patients and controls were analyzed under the assumption of stationarity of the time series. Results: During the motor activation test, PD patients showed an increased correlation coefficient (both hands \(p<0.001\)) and a remarkable increase in coherence in all frequency range compared to the generalized reduction observed in controls (both hands \(p<0.001\)). The Kullback-Leibler Divergence (KLD) of the Spectral Entropy between brain hemispheres was observed to increase in controls (right hand \(p=0.01\); left hand \(p=0.015\)) and to decrease in PD patients (right hand \(p=0.02\); left hand \(p=0.002\)) with motor activation. Conclusions: Our results suggest that the oscillatory activity of the different cortex areas within healthy brains is relatively independent of the rest. PD brains exhibit a stronger connectivity which grows during motor activation. The levodopa mitigates this anomalous performance.  
Parkinson's Disease; electroencephalography; entropy; connectivity; levodopa +
Footnote †: journal: Medical Sciences  
Article
## 2 Background
### The Model-Based Development Approach  
The model-based development approach is a recent technique that enables rapid software development and validation. The greatest advantages are that the technique can detect errors in software development early based on analysis of customer requirements and it uses simulation techniques to merge software complexities. The model is a tool for customer specification analysis, so it can be useful for early verification via prototyping, and serves as an oracle when generating test cases using a formal model language or a specific tool-based model such as Simulink. The test cases are created by analyzing the activity and state diagrams of the various models [5; 6]. In the UML context, many attempts have been made to generate test cases using state diagrams. [PERSON] et al. developed a predictive AVM that afforded path-based coverage using fewer test cases than previous methods [7]. However, given the limitations of an AVM, no globally optimal solution was found and coverage was not 100 percent. [PERSON] presented a path-based test case generation method; a parent solution was used to create a path-based child solution employing a genetic algorithm [8], but the method was not compared to other algorithms. Furthermore, these methods were not tested in real industrial situations. As a different approach, [PERSON] and [PERSON] generated test suites for fault localization from UML models [9]. Several efforts have been made to create test cases based on requirements manifested in use-case tables or diagrams [10]; these are advanced test scenarios reflecting customer demands. However, it is difficult to use a testing tool to create runnable test cases directly. Here, to study test case optimization employing the model-based development approach, we use a software based on the UML 2.1 standard. The Eclipse-based Papyrus open-source tool is a well-known UML-based tool [11]. Enterprise Architect [12] and IBM Rational Rhapsody are representative commercial UML tools. In this paper, Rhapsody is used as a UML model-based tool. Rhapsody uses its own target simulation framework, called the Object Execution Framework (OXF) to run code simulating the behavioral diagrams of the developed software model. However, Rhapsody is incompatible with fUML [13] [the executable UML standard of the Object Management Group (OMG)]. In fUML, action language is used to implement model behavior; a behavioral diagram corresponding to a platform-independent model is derived. The action language may be directly used for software simulation, or translated into platform-specific languages [14]. We use Rhapsody principally to create class, object, and state diagrams. C is employed to implement specific events, transitions, guards, and actions in state diagrams; we do not use an action language. To generate test cases automatically, we employ a concolic execution method in a custom parser-based simulation environment, instead of the OXF framework.