esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Obtained Data  
The process of designing the database to store the data monitored by BIPapp was critical. For this purpose, a MySQL relational database hosted on a cloud server was used; this was due to the relationship between the tables, enabled by means of the unique identifier assigned to each user. The different tables created to store the collected data were as follows.  
* Apps. Stores specific events performed in certain applications. (e.g., sending an email, opening a WhatsApp conversation, liking a Facebook post, etc.).
* AppsInstalled. These are records of the apps installed on each user's device.
* Lock. This stores the locking and unlocking events of the mobile device.
* Buttons. This stores events related to pressing physical buttons, such as those to increase and decrease the volume.
* ActivityDetect. This records the different physical activities performed by the user and related to the event performed with the device at that moment.
* Calls. The numbers and dates of incoming, outgoing, and missed calls are recorded.
* FirstPlane. This stores the use of a given application along with the event produced in that application.
* Keyboard. This logs events performed with the keyboard, specifically metrics that measure the typing of letters, symbols, and emoticons (it does not store conversations).
* Users. These are the distinct encrypted identifiers for users.  
At the end of the experiment, there were approximately 4,500,000 records in the database. The criterion for inclusion in the experiments was that a full, uninterrupted week of data was available, so a subset of users had to be disregarded. In the end, 29 users were included in the dataset, with a total of 3,805,000 data records. The following section describes how the data were processed to obtain the final dataset.  
Figure 3: Overview of BIPapp: configuration, services, and architecture.
# Pushing Mechatronic Applications to the Limits via Smart Motion Control
## 4 Building Blocks and Methodology
### Real-Time Wireless Sensors (BB2)  
BB2 can be considered as an I/O expansion of BB1. It implements fast wireless network ensuring secure, synchronized, and reliable data transfer from sensors to the master node (or central computing module) in an energy efficient way [44]. Fast update rates and low latency allow complex control methods utilizing auxiliary load-side sensing to be implemented. Moreover, the measurement precision is improved. In general, the purpose of the BB2 real-time wireless sensor is to acquire the data from sensors (e.g., accelerometer, force, etc.) with as low as possible latency (\(<\)500 us), while ensuring reliable data transmission and low power consumption. BB2 real-time wireless sensor consists of seven key elements: microcontroller (MCU) for data management, a wireless communication module for wireless data transmission, accelerometer (Acc) (+ interfaces to external sensors) for data acquisition, USB/Serial Port for configuration and data collection, and Li-Ion Battery to operate autonomously, as well as long distance (\(>\)20 cm) wireless charging module (Figure 5).
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 4 System Design and Simulation Configuration  
### Environment and System Description  
We consider a practical AmBC system in which the cellular network acts as a source of ambient signal, and several BDs and readers are located in an outdoor and indoor urban city environment. While the cellular network communicates with its actual users e.g., smartphones, laptops, TV, etc., both BD and the reader may also receive the signal from the ambient source. For simulations, we have considered a three-dimensional building data of the downtown area from Helsinki city. The area under consideration has buildings and structures with irregular shapes and heights, and roads and streets with varying width. The buildings are of multiple stories ranging from two to eight floors. The target area is covered with ten three-sectored macro sites, where the location of macro sites were selected with careful planning and consideration. The transmit antenna of each sector is mounted at a height of 30 m above the ground, and it is above the average height of buildings' rooftop. Figure 4 shows the two dimensional map of the area under consideration, and it highlights the location of each site and the azimuth direction of each sector.  
Our target is to investigate the performance of bi-static dislocated mode of BC in a real world urban city environment i.e., Helsinki, a capital city of Finland. For this purpose, 750 static BDs with 1.5 m height are homogeneously and randomly distributed in a target area, as shown in Figure 5. Out of those 750 static backscatter devices, 113 BDs are placed  
Figure 4: Area under consideration with site locations and cell azimuths.  
in an outdoor environment, as marked with blue circles, and 667 BDs are positioned in an indoor environment at different floors as marked with orange circles in Figure 5. As mentioned earlier, unlike regular Manhattan type building grid with equal building heights and street spacing, we have considered a real world city environment with variable building heights, and the indoor BDs are distributed at four different floors i.e., ground floor (1.5 m), second floor (7.5 m), fifth floor (16.5 m), and seventh floor (22.5 m), depending upon the height of the building. Table 1 presents the distribution of the BDs at different floors.
# Reducing Soil Permeability Using Bacteria-Produced Biopolymer
## 3 Results and Discussion
### Effect of Curing Time  
Figure 5 highlights the effect of the curing time on the evolution of the average permeability coefficient over the curing time, for the mixtures with a grout content of 0.33% and subjected to a hydraulic gradient of 6.9. Independently of the process of production of the XG, a sharp decrease in the soil's permeability during the first 3 days of curing is observed, followed by a slight decrease over time to a minimum permeability coefficient for a curing time of 14 days. Although these results indicate that the hydration of the hydrogels tends to occur fundamentally over the short term (\(<\)3 days), they also suggest that there is still some hydration of the hydrogels during the first 14 days of curing time. In fact, as the hydration starts from the outside of the hydrogels, this seems to hinder the water penetrating to the inner part of the hydrogels, which leads to a slow and gradual hydration of this inner zone. After 14 days of curing, a slight increase in the soil's permeability is observed with the use of both types of XG, which is probably due to some biodegradation of the XG [14] and/or the dehydration of the hydrogels with the consequent shrinkage of the XG. These two factors increase the pore spaces in the soil over time, which favours the flow of water: consequently, an increase in the soil's permeability is observed. The decrease in the soil's permeability over the short term (fewer than 14 days) is in line with the results of [PERSON] et al. [33] for a similar range of curing times, while the increase in the permeability coefficient over the long term (more than 14 days) matches the findings of [PERSON] et al. [34] and [PERSON] et al. [25].  
As expected, the specimens chemically stabilised with cement show a reduction in the permeability coefficient with curing time, which is consistent with the development of the pozzolanic reactions that occur in the long term. Thus, for a curing time of 28 days, the coefficient of permeability of the soil treated with cement and the commercial XG with the same content (0.33%) show similar effectiveness, while in the short term, the commercial XG induces a greater reduction in the soil's permeability. Figure 5 also emphasizes the better effectiveness of the commercial XG in relation to the LabXLG, which can be fundamentally attributed to its higher level of purity.
# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 2 Adversarial Examples and Deep Learning Models
### Deep Neural Networks  
Deep learning models allow the characteristics of the input data to be learned at various abstraction layers, which in turn allows systems to learn the most complex functions to identify characteristics of the study domain. Feedforward, Convolutional Neural Network (CNN), and Recurrent/Recursive Neural Network (RNN) models and their variants have been the most widely implemented models for sentiment analysis tasks due to their natural ability to handle sequences and understand the relations between different elements. In particular, Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN) models are able to learn about sequences both locally and in the long term, preserving the most important and complex features to help the model understand the complete relationships. Recently, two major advances in deep learning have been popularized to address text-related tasks: (1) _sequence-to-sequence learning_[28] and (2) _attention mechanisms_[29].  
* Recurrent Neural Networks. RNN models can handle input sequences with variable lengths. RNNs create and process arbitrary memory sequences of input patterns. Unlike traditional methods for automatic sequence synthesis, RNN models can process sequential and parallel information naturally and efficiently [30].
* Long Short-Term Memory Networks. LSTM models are a particular type of RNN composed of units of the same type. Conventional RNN models can encounter problems during training, as the gradients tend to grow enormously or fade over time due to their dependence on present and past errors. The accumulation of errors can cause difficulties when memorizing dependencies in long texts. LSTMs tackle these problems by incorporating decisions about which information will be stored and which will be discarded [31].  
* Convolutional Neural Networks. CNN models consist of multiple layers of convolution filters of one or more dimensions. After each layer, a function is added to perform nonlinear causal mapping. At the beginning of the CNN, the feature extraction phase is composed of convolutional and downsampling neurons; as more data are processed, its dimensionality decreases, with the neurons in distant layers being much less sensitive to data perturbations while being activated by increasingly complex features ([[https://www.juanbarrios.com/redes-neurales-convolutionales/](https://www.juanbarrios.com/redes-neurales-convolutionales/)]([https://www.juanbarrios.com/redes-neurales-convolutionales/](https://www.juanbarrios.com/redes-neurales-convolutionales/))) (accessed on 12 April 2024). At the end of the network, perceptron neurons perform the final classification on the extracted features.
## 3 Results and Discussion
### TPCB's Effect on the Performance of Asphalt  
#### 3.2.1 TPCB's Effect on the Physical Performance of Asphalt  
The physical performance of GF-70 and TPCB/GF-70 asphalt binders is shown in Table 4. The penetration, softening point, ductility, and segregation test results of TPCB/GF-70 all meet the requirements of AH-70 B grade in China [31].  
Penetration, softening point, and T\({}_{800}\) are able to reflect the high-temperature performance of an asphalt binder: the lower the penetration, and the higher the T\({}_{800}\) and softening point, the better the high-temperature performance [32]. The penetration of GF-70 decreases by 15.5% with the addition of a 15% dosage of TPCB, while the softening point and T\({}_{800}\) increase by 2.9 \({}^{\circ}\)C and 2.8 \({}^{\circ}\)C, respectively, which shows that TPCB can improve the high-temperature performance of asphalt.  
The ductility and T\({}_{1.2}\) belong to the indexes for evaluating the low-temperature performance of an asphalt binder. After adding TPCB, the ductility decreases by 58.4% and the T\({}_{1.2}\) increases by 5.7 \({}^{\circ}\)C, which shows that the 15% dosage of TPCB can obviously decrease the low-temperature performance of asphalt.  
The PI can reflect the temperature sensitivity of an asphalt binder: the higher the PI, the lower the temperature sensitivity. In general, the PI of a sol-gel asphalt binder should be in the range of \(-2\)-\(2\). It can be seen from Table 4 that the PI of both GF-70 and TPCB/GF-70 belong to the sol-gel asphalt binders. The PI of TPCB/GF-70 is bigger than that of GF-70; therefore, TPCB can decrease the temperature sensitivity of asphalt.  
The softening point difference of segregation test is 0.7 \({}^{\circ}\)C, which shows that a 15% dosage of TPCB-modified asphalt has good storage stability, and TPCB has good compatibility with asphalt.
# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Ethical Implications of Transparent Communication  
Transparent communication in data management carries several ethical implications:  
* Trust building: Transparent communication helps build trust between organizations and their stakeholders, including data subjects [62].
* Informed consent: Clear communication is crucial for ensuring that data subjects can give truly informed consent for the use of their data [63].
* Accountability: Transparency in communication promotes accountability in data management practices [64].
* Empowerment: By providing clear information about data practices, organizations empower individuals to make informed decisions about their personal data [65].  
These ethical implications highlight the importance of prioritizing transparent communication in all aspects of data management.
# CommuSpotter: Scene Text Spotting with Multi-Task Communication
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Methods** & **Backbone** & **Proposals** & **Customized** & **Seg** & **Rec** \\ \hline CNN & Qin et al. [5] & CNN & RPN & RoI Masking & Instance & Att \\  & Mask TextSpotter v2 [19] & CNN & RPN & Box Detection & Instance & Att \\  & Mask TextSpotter v3 [6] & CNN & - & Seg Proposals & Instance & Att \\  & ABCNet [8] & CNN & - & Bezier Curve & Instance & CTC \\  & MANGO [7] & CNN & - & Mask Attention & Instance & Att \\ \hline Trans & SwinTextSpotter [14] & CNN + Trans & Query Box & Recognition Conversion & Instance & Att \\  & TextTransSpotter [15] & CNN + Trans & Query Box & Hungarian Match & Instance & Att \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between typical models. There are mainly two categories of methods divided by Transformer architecture. “CNN” is the traditional ResNet backbone. “Trans” stands for Transformer architecture. “Seg” is equal to “segmentation” for short. “Att” is the attention-based loss, while “CTC” is the CTC loss. Specific module techniques and details can be found in each method. Figure 1: An illustration of typical text spotters (**a**); frameworks compared with our proposed spotter with communication mechanisms in multi-tasks (**b**). from all tasks complementing and assisting each individual task. Drawing inspiration from the image retrieval task [11], we develop a Conversation Mechanism (CM) to embed expertise vectors for multi-task interaction. Thus, this communication of expertise facilitates information exchanges among text detection and recognition modules, as shown in Figure 1b, breaking the unidirectional pipeline and reducing accumulated errors from the beginning. Additionally, we introduce text semantic segmentation for text recognition without requiring further annotations. This is achieved by adopting the independently pre-trained module weights as priors, replacing complex customized modules and their extra annotations. Finally, CommuSpotter constructs a concise framework and achieves fast convergence. The contributions of this paper are threefold: (1) Instead of independently and sequentially performing text detection (or customized modules) and text recognition multi-tasks, we developed CommuSpotter, which facilitates explicit communication through the designed Conversation Mechanism (CM). This mechanism embeds task-specific expertise concurrently across all tasks, compensating for task-specific information or expertise and reducing error propagation throughout the entire text-spotting process. (2) We employ text semantic segmentation expertise for text recognition tasks, reducing the need for complex custom module designs and their associated costly annotations. (3) We conduct comprehensive experiments on multiple text datasets. The comparisons between existing approaches demonstrate the advantages of our proposed method.
## 4 Discussion
### Effects of the Environment Barriers' Characteristics  
This study proposes a method for classifying pedestrians' gaits (normal and abnormal) and identifying environmental barriers based on a cascaded unidirectional and bidirectional LSTM-based DRNN. For this purpose, data generated from walking was used. In the analysis, there are normal gaits even on an environmental barrier. Depending on the size and location of the environmental barrier, it may affect walkability or gait pattern. Figure 7 is an example showing how the response can change depending on the size and location of the environment barrier. In this study, the authors compare the effects by paying attention to the characteristics (location, width, and depth) of three types of environmental barriers.  
Figure 9 compares environmental barriers with different widths. When the depth of the two environmental barriers is less than the step length, the representative action that pedestrians can take is crossing or avoiding the environmental barrier. When crossing the environmental barrier, it appears similarly regardless of the width in both cases in Figure 9. However, when avoiding the environmental barrier, when the environmental barrier is wide, abnormal gaits may appear higher than when the width of the environment barrier is good due to insufficient width of the pathway.  
Figure 9b compares the location of an environmental barrier of the same size. Even an environmental barrier of the same size can affect the gait depending on the location. A sufficient walking path can be secured if the environment barrier is located on one side (Figure 9b left). As a result, the rate of abnormal gait can be measured relatively low. On the other hand, a relatively high rate of abnormal gaits may appear when the environment barrier is located in the middle of the pedestrian path and insufficient width of the gait on both sides is secured (Figure 9b right).  
Figure 9c compares environmental barriers with different depths. If the depth of the environmental barrier is less than the stride length, a pedestrian can easily jump over it, but otherwise (if the depth exceeds the stride length), you have to jump over it. This difference in behavior results in the different ratio of abnormal gaits in a specific location.  
Even similar environmental barriers can cause different reactions depending on their size and location. Therefore, there is a need to perform built environment monitoring based on the reaction between environmental barriers and pedestrians, beyond finding the presence or absence of environmental barriers.
# Investigation of Cavitation Noise in Cavitating Flows around an NACA0015 Hydrofoil
## 2 Mathematical Model
### Zwart Cavitation Model  
The mass transport equation for vapor can be written as follows:  
\[\frac{\partial\rho_{\mathrm{v}}\alpha_{\mathrm{v}}}{\partial t}+\frac{ \partial\rho_{\mathrm{v}}\alpha_{\mathrm{v}}u_{\mathrm{i}}}{\partial x_{ \mathrm{i}}}=\dot{m}^{+}+\dot{m}^{-}, \tag{5}\]  
where the \(\rho_{\mathrm{v}}\) represent the vapor density and \(\alpha_{\mathrm{v}}\) is the vapor volume fraction.  
The Zwart Model is derived from the simplified Rayleigh-Plesset Equation, which describes the bubble dynamics. This model's evaporation source terms and condensation source terms are as follows:  
\[\dot{m}^{-}=-F_{\mathrm{e}}\frac{3r_{\mathrm{nuc}}(1-a)\rho_{\mathrm{v}}}{R} \sqrt{\frac{2}{3}\frac{P_{\mathrm{v}}-p}{\rho_{\mathrm{I}}}}, \tag{6}\]  
\[\dot{m}^{+}=F_{\mathrm{c}}\frac{3\alpha\alpha_{\mathrm{v}}}{R}\sqrt{\frac{2}{ 3}\frac{p-P_{\mathrm{v}}}{\rho_{\mathrm{I}}}}, \tag{7}\]  
where \(R\) represents bubble diameter, \(r_{\mathrm{nuc}}\) represents nucleation site volume fraction, \(F_{\mathrm{e}}\) represents evaporation coefficient, \(F_{\mathrm{c}}\) represents condensation coefficient, and \(P_{\mathrm{v}}\) represents vaporization pressure. This article set these coefficients as follows: \(R=1.0\times 10^{-6}\) m, \(r_{\mathrm{nuc}}=5.0\times 10^{-4}\) m, \(F_{\mathrm{e}}=50\), \(F_{\mathrm{c}}=0.01\).
## Biomedical Applications
### Antiviral Activity  
Viruses are obligate intracellular parasites; they require host cells for replication. Antiviral drugs can inhibit viral entry or exit or inhibit viral processes in cells. If the inhibitor is non-selective, host cell function can be disrupted and toxicity can occur. Ulvan has been tested against several viruses (Table 1) [98; 99; 100].  
Further mechanical research has revealed that the sulfated polysaccharides from ulvan can block the absorption of the virus and therefore make the virus unable to enter the cell. Sulfated polysaccharides also effectively reduce pro-inflammatory cytokine production caused by JEV mixes that primarily infected glia cells. In animal studies, JEV-infected mice appear to have neurobehavioral abnormalities on day 5 and die on day 7 post-infection. However, treatment with sulfated polysaccharides can delay the onset of paralytic hind limbs and thus prevent mice from dying [98]. Ulvan mainly consists of heteroglycurona, which has antiviral effects against influenza A/PR/8/34 (H1N1) [99]. In the polysaccharide delivery system, _U. lactuca_ is made up as comprising monogalactosyldiacylglycerol (MGDG) tubular immunostimulating complexes (TI-complexes) because they are considered to be effective as lipid matrix for protein subunits in antigens [100; 101].  
Ulvan in some studies is considered to have antiviral activity because of its metabolic content in ulcers and polysaccharides. _U. lactuca_ is considered to be a good lipid matrix in the transmission of anti-genes [98]. However, the mechanism of _U. lactuca_ as an antiviral source is yet to be clarified.