esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Back-Trajectory Footprints
#### 3.4.1 Cumulative Footprints
Our wind analysis shows that there may be significant offset between the modeled and the observed wind direction at any particular point, even if the model's performance overall is deemed to be good. With enough resources, one way to address this would be to run an ensemble of simulations and generate the corresponding footprints to find the most probable mean footprint of the region for conducting this coverage assessment. Additionally, the uncertainty in the distribution of bottom-up emissions, which can be large, may affect the coverage analysis. A similar approach involving increasing the number of estimates and creating an ensemble analysis would be ideal; however, it is often difficult to find multiple emissions estimates for the same region. Even with these sources of uncertainty, we can conclude from our analysis that the current network is sufficient to constrain a significant fraction of the regional fossil fuel emissions.
## 6 Results  
In this article, we show that it is possible to implement low-cost and low-energy consumption systems, even for domestic consumption, based on LoRa networks and an ESP32 microprocessor. Nevertheless, there are some considerations that concern us, and these need to be solved so that the system's effectiveness can be measured, namely:  
* Statistical analysis of RSSI in remote areas, rural areas, and rugged terrains.
* Shadow zones with several urban and natural obstacles, such as buildings and hills, among others, that cause disturbances in communication, reducing coverage.
* The poor network of gateways installed in Portugal, which does not allow for minimum acceptable coverage for the implementation of a generalized system.
* The tests carried out, despite being very limited and taking into account the fact that the gateway is installed indoors, did not allow communications beyond 1.2 km, which, although not negligible, is below the expected value.
* The one-channel experimental gateway does not allow for application stress tests, nor system overload and robustness, so it only served as a proof of concept; however, the recent installation in the multi-channel gateway region can easily extend the range of the test and its robustness.  
To analyze the performance in terms of the received signal power, several locations were previously selected at 10 distances (in meters), as presented in the following table (Table 3), with average values obtained from 100 measurements of RSSI power (LoRa gateway):  
Figure 11: Real-time display of data sent by the DHT22 sensor.  
Figure 12 shows a graph of the average data obtained, as presented in the previous table.  
Higher RSSI values represent greater signal quality, while lower values represent poorer signal quality. According to [36], the RSSI values for LoRa networks are:  
* -30 dBm -> excellent quality;
* -120 dBm -> very poor quality.  
The results obtained in previously defined hybrid rural and urban areas were different from what was expected, and it was found that any obstacle, wall, or housing could interfere with the signal. It was also found that, with the used equipment, it is not possible to communicate beyond 450 m. Nevertheless, several restrictions must be considered, namely the fact that the test gateway is only one channel, with an indoor antenna housed at the bottom, as shown in Figure 5. During data collection, the device operator remained in the same place for some time in order to collect 10 samples for each distance, moving only the LoRa node (random movement inside one circle with a bias of no more than 5 m) to check if there were failures, which was confirmed.
# Numerical and Experimental Investigation of Recycled Brick Coarse Aggregate Concrete
## 3 Results and Discussion
### Effect of Interfacial Transition Zone Thickness  
The interface transition zone is an equal-thickness thin layer between the aggregate and mortar matrix; its performance significantly impacts the mechanical properties of concrete [44]. As the incorporation of RBCA complicates the internal interface of concrete, it is necessary to analyze the thickness of the concrete interface transition zone and study the failure mechanism of RBCA concrete. This can provide a basis for studying changes in the macromechanical properties of the concrete.  
The thickness of the interface transition zone is minimal; simulations performed by previous researchers with different selected interface thicknesses have considered the calculation limitation. Here, the thickness of the interface transition zone was set as 0.5 mm, 0.75 mm, and 1.0 mm, respectively [34,39]. Figure 14 shows the stress-strain relationship of RBCA concrete with different thicknesses of the interfacial transition zone in RF-100. The load-displacement curves of each group of specimens almost wholly overlap before the strength reaches 17 MPa. After the strength reaches 17 MPa, the specimens with largerinterface thickness first enter the strengthening stage, while the other specimens with smaller interface thickness remain in the elastic deformation stage. It can be observed that the slopes of the load-displacement curves of the specimens with different interface thicknesses in the elastic stage are similar. However, the elastic stage lasts longer for the specimens with smaller interface thickness. The main reason for this is that the porosity of the interface transition zone in RBCA is relatively large and its material properties are relatively weak. Therefore, the greater the thickness of the interface transition zone, the smaller the overall compressive strength of the recycled concrete specimen is, and vice versa.  
When the interface thickness increases from 0.5 mm to 0.75 mm, the compressive strength decreases by 8.47% and the peak strain decreases by 4.5%. When the interface thickness increases from 0.5 mm to 1 mm, the compressive strength decreases to 15.2% and the peak strain decreases by 9.5%. The concrete's peak strain and compressive strength gradually decrease as the interfacial transition zone thickness increases. Thus, the thickness of the interfacial transition zone significantly influences the compressive strength of RBCA concrete. Figure 14 shows the stress-strain relationship of RBCA concrete with different thicknesses of the interfacial transition zone in RF-100.  
Figure 13: The effect of RCA replacement rate: (**a**) compressive strength, (**b**) elastic modulus, (**c**) peak strain, (**d**) ratio of ultimate strain to peak strain.
## 1 Introduction
### Background of PEPT Measurements  
PEPT can track the motion of tracer particles that have been radiolabelled with a positron emitting radionuclide and that share physical properties with the solid species in the system that is to be tracked [13]. Positrons from the tracer radionuclide annihilate with electrons in the surrounding medium within the experimental system and produce pairs of almost colinear gamma ray photons. Two gamma rays that are detected within a short time window are labeled as being coincident, and the path of the gamma ray pair forms a virtual line of response (LOR) between two detectors in the scanner. The LOR traces a path indicating the potential source of the annihilation event. In any finite density medium, each point on the line has equal probability of being the location of the annihilation event, given that the LOR is a true coincidence. With multiple LORs recorded in a short time interval, the tracer particle can be located in the region of closest intersection of the LORs using a location algorithm. Time-series data of the tracer location can be compiled by selecting a sample of LORs and then applying a location algorithm to derive the tracer \"location\" in three dimensions at discrete time points \((X_{i}(t_{i}),Y_{i}(t_{i}),Z_{i}(t_{i}))\).  
Different scanners specialised for PEPT applications have been adapted from their original medical imaging purposes to measure particle behaviour in systems of flow. These include the ADAC Forte [14], Modular [15], MicroPEPT [16], and SuperPEPT [17] cameras at the University of Birmingham, UK, where the PEPT technique was first developed [18], and the Siemens ECAT EXACT3D HR++ camera at PEPT Cape Town at the University of Cape Town, South Africa [19] (see Figure 1; referred to as \"HR++\" henceforth). Other facilities specialising in PEPT measurements are the University of Tennessee, Knoxville in the US [20] and the University of Bergen in Norway [21].
## 3 Results and Discussion
### Electropolishing  
The electropolishing can basically be described as a controlled electrochemical dissolution process of a surface in an attempt to construct it less roughly at the macroscale (levelling) \(>\)1 \(\upmu\)m and microscale (brightening) \(<\)1 \(\upmu\)m [35]. The basic principles of this process are film formation and a mass transport limited current plateau from the polarisation response. So as to achieve the conditions for macrosmoothing, the ohmic control or the mass transport control has to be conducted, while for microsmoothing, the mass transport mechanism is sufficient [35].  
In the present work, metallic copper was electropolished in choline chloride-based IL at 20 \({}^{\circ}\)C at a potential of +1.2 V, as exhibited in Figure 9. This is the first time that a DES has been shown to be useful electropolishing electrolyte for a single metal. Figure 9 exhibits a metallic copper surface which is brighter but there are obvious signs of pitting on the surface. It could actually be questioned whether this is truly electropolished or just brightened alone.
## 2 Materials and Methods
### Fluorescence Spectroscopy and Fluorescence Region Integration (FRI)  
An aliquot of 5 mg-C/L for each of the four SHS solutions was measured by a fluorescence spectrophotometer (Hitachi, F-7000). The three-dimensional fluorescence excitation/emission matrix (EEM) was recorded. Before the measurement, the SHS solutions were adjusted to pH 7 with 0.3 N H\({}_{2}\)SO\({}_{4}\). Fluorescent scanning used an excitation wavelength of 200-450 nm with 5 nm increments and an emission wavelength of 250-550 nm with 2 nm increments. The scan rate was 2400 nm/min, the slit width was 5 nm, and the voltage amplification was 700 V. The spectra were obtained by subtracting an ultrapure water blank spectrum, recorded in the same conditions, to eliminate the Raman scatter peaks. The HIX was calculated when the excitation wavelength was 254 nm. The sum of the emission wavelength at 435-480 nm was divided by the sum of the emission wavelength at 300-345 nm ([PERSON] et al., 2017) as in Equation (1). The other fluorescence indexes (biological index (BIX) and fluorescence index (FI)) were also analyzed in the present study. These indicators were not significantly different between the AD and FD samples; they are not discussed in the study and the BIX and FI data are listed in the Supplementary Information (Table S1).  
Fluorescence region integration (FRI) is a quantitative analysis tool that divides the EEM into five regions and utilizes the fluorescence intensity at all excitation-emission wavelength pairs for sample characterization (Hitachi, U-2900, 22; [PERSON] et al., 2017). The percentage of fluorescence response (P\({}_{i,n}\) = \(\phi_{i,n}\)/\(\phi_{\text{T,n}}\)) was calculated according to [PERSON] et al. (2017) and [PERSON] et al. (2018),where \(\Phi_{\rm in}\) and \(\Phi_{\rm T,n}\) were the normalized excitation-emission area volumes referring to the value of region i and the entire region, respectively.  
\[\rm{HIX}=\frac{E_{m}=435-480\;nm}{E_{m}=300-345\;nm}\;\;at\;E_{x}=254\;nm \tag{1}\]
## 3 Theory and Methods
### Neural Network Methods  
#### 3.2.1 LSTM Neural Network  
An LSTM neural network is derived from enhancing an RNN model. In comparison to the RNN model, LSTM integrates three gating units including an input gate, a forget gate, and an output gate. The input gate is responsible for reading data, while the forget gate filters and transmits data, and the output gate manages backward passes and data outputs. These units address the gradient-vanishing issue encountered in RNN training, leading to a significant enhancement in learning accuracy. Additionally, the memory unit within an LSTM effectively handles the intricate patterns within time series data. Figure 8 illustrates the model structure of the LSTM, where \(f_{t}\) denotes the output value of the forget gate, \(X_{t}\) is the input value at moment \(t\), \(h_{t-1}\) denotes the state value of the hidden layer at moment \(t-1\), \(i_{t}\) denotes the state value of the input gate, \(C_{t}\) denotes the internal memory unit, \(O_{t}\) denotes the output value of the output gate, and \(\sigma\) and tanh denote the activation function and hyperbolic tangent function, respectively.  
Figure 7: VMD decomposition results for DO data: (**a**) IMF1-IMF4 sequences, (**b**) IMF1–IMF4 spectrum diagrams, (**c**) IMF5–IMF8 sequences, and (**d**) IMF5–IMF8 spectrum diagrams.  
The forget gate determines whether to retain the memory information of the previous moment and the amount of previous moment information that needs to be retained at the current moment. It determines what information is important and what information can be ignored through weights. The specific formula of the forgetting gate is the following:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},X_{t}]+b_{f}\Big{)} \tag{7}\]  
where \(W_{f}\) is the weight matrix of \(f_{t}\) at moment \(t\) and \(b_{f}\) is the bias.  
The input gate determines how much new information is added to the memory unit at the current time. The calculation formulas of the input gate and memory unit candidate state \(\widetilde{C}_{t}\) are the following:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},X_{t}]+b_{i}) \tag{8}\]  
\[\widetilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},X_{t}]+b_{c}) \tag{9}\]  
where \(W_{i}\) is the weight matrix of \(i_{t}\) at moment \(t\), \(W_{c}\) is the weight matrix of \(\widetilde{C}_{t}\) at moment \(t\), and \(b_{f}\) and \(b_{c}\) are the biases. \"[]\" denotes the connection of two matrices.  
The function of the output gate is to filter the contents of the memory unit so that only the part that passes through the output gate will be output to the hidden state. The calculation formula of the output gate is the following:  
\[O_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o})h_{t} \tag{10}\]  
where \(W_{o}\) is the weight matrix of \(O_{t}\) at moment \(t\) and \(b_{o}\) is the bias.
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
###### Abstract  
In this work, the evaluation of electromagnetic effect of Ultra High Frequency Radio Frequency Identification (UHF-RFID) passive tags used in the healthcare environment is presented. In order to evaluate exposure levels caused by EM field (865-868 MHz) of UHF-RFID readers, EM measurements in an anechoic chamber and in a real medical environment (Hospital Universitario de Canarias), as well as simulations by 3D Ray Launching algorithm, and of biophysical exposure effects in human models are presented. The results obtained show that the EM exposure is localized, in close vicinity of RFID reader and inversely proportional to its reading range. The EM exposure levels detected are sufficient to cause EM immunity effects in electronic devices (malfunctions in medical equipment or implants). Moreover, more than negligible direct effects in humans (exceeding relevant SAR values) were found only next to the reader, up to approximately 30% of the reading range. As a consequence, the EM risk could be firstly evaluated based on RFID parameters, but should include an in situ exposure assessment. It requires attention and additional studies, as increased applications of monitoring systems are observed in the healthcare sector--specifically when any system is located close to the workplace that is permanently occupied.  
UHF-RFID; occupational exposure; public health; SAR; 3D-RL; electromagnetic risk; hospitals; 2D contour maps +
Footnote †: journal: Applied Sciences
# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
## 3 Domain Analysis and Definition of Conceptual Model  
### Data  
Waterway depth is the vertical distance from the water surface to the bottom (seafloor) of a waterway. At present, the waterway depth data can be extracted from the electronic charts visualized cartographically, as shown in Figure 1a. In addition to the ECDIS charts, the berth map also presents the waterway depth data. Unlike an electronic chart, the berth map divides the watercourse according to the geographical position, displaying waterway depth information from berths, as shown in Figure 1b. Figure 1a,b illustrates two waterway depth data acquisition methods. The waterway depth data can be obtained from ECDIS or from berth map. On the other hand, the prediction of unknown depth data from the available depth-related data is the main research goal of this paper.  
The AIS data represent the static data and the dynamic data of the ships. The dynamic data have information such as Maritime Mobile Communication Service Identification (MMSI) number, latitude and longitude, speed/course over ground, steering rate, and headings [36]. The static data include inherent information about a ship, such as the length, the breadth, the maximum draft, and the type. Table 1 shows an example of the AIS data.  
Since there are missing points in AIS data, the longitude and latitude data must be imputed by the data interpolation method, making the water depth information more complete. The cubic spline interpolation method obtains waterway depth data corresponding to different latitude and longitude points in the waterway, and the obtained waterway depth data are integrated according to the longitude and latitude values and the AIS data of the waterway, so that the depth data and the AIS dynamic data are combined with static data.
## 5 Modelling the Communication Solutions
### Model Performance
Figure 7: ROC curves including AUC value when determining the rank position for each solution for each evaluation metric. A Receiver Operating Characteristic (ROC) graph is used to determine how well the physical features used in the modelling process are predisposed to distinguishing the rank of each solution for each evaluation metric within the training data set. Figure 7 shows the curves and Area Under Curve (AUC) for each. Each curve gives a value for a binomial class distinction. It shows how well the predictor variables can determine if a solution performs in a given rank, or not in this rank. For example, a single curve will specify how well the predictor variables distinguish between experiments where ETX performed in 1 st place and those where ETX did not perform in 1 st place. The AUC gives a single value metric between 0 and 1 with a higher value denoting a better predictor variable set for such a class distinction. The models return an AUC value between 0.63 and 0.97. The physical features used as predictor variables used in the models prove more adept at distinguishing ranked places for the ETT solution with near perfect classification achieved for reliability (1 st and 3 rd place) and efficiency (3 rd place) although somewhat surprisingly does not show as high AUC values for latency given the stark delineation of rank. The predictor variables perform well classifying reliability, efficiency (1 st place) and to a lesser extent stability (1 st place), however, are less well predisposed in classifying the placed ranks for latency.
# ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies
## 4 Experiments
### Dataset  
There is no labeled dataset of security policies for network access control. Therefore, based on the role of each part of the security policy in the network, this research constructed a dataset of network access control policies [28]. A security policy primarily filters, allows, or discards data packets on interfaces by setting specific conditions. Therefore, we annotated the definition statements and conditions within the security policy to accurately express its semantics. In this experiment, we used two types of data, ACL data and firewall data.  
ACL data contains seven datasets, among which Data_mix for training is composed of actual data used by three vendors: Cisco, Huawei, and Ruijie. The six datasets used in the experiment were simulated based on the actual usage frequency of network configurations for standard ACLs and extended ACLs from three different vendors, with each dataset containing 3000 policy rules. Firewall data included two datasets, among which Firewall_train for training is the actual data used by TOPSEC, and Firewall_test for experiments is the simulated data based on the actual configuration frequency.  
Our dataset was manually annotated. The volume of various data is shown in Table 1, and the number of labels of each dataset is also shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ACL**} & \multicolumn{4}{c}{**Firewall**} \\ \hline  & \multicolumn{2}{c}{**Standard\_ACL**} & \multicolumn{2}{c}{**Extend\_ACL**} & \multicolumn{2}{c}{**Data\_Mix**} & \multicolumn{2}{c}{**Firewall\_Test**} & \multicolumn{2}{c}{**Firewall\_Train**} \\ \cline{2-9}  & **Num** & **Labels** & **Num** & **Labels** & **Num** & **Labels** & **Num** & **Labels** \\ \hline Huawei & 3000 & 6 & 3000 & 9 & & & & & \\ \hline Cisco & 3000 & 4 & 3000 & 10 & 3437 & 13 & 2000 & 10 & 668 & 10 \\ \hline Ruijie & 3000 & 4 & 3000 & 7 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data used in the experiments.  
In Table 1, the number of labels corresponding to each dataset represents the number of distinct label categories that appeared in that dataset. Taking Cisco's extended ACL dataset as an example, this dataset includes two types of extended ACLs: one that contains port information and one that does not. The total number of distinct label categories for these two types of security policies is 10.  
The example datasets used in this experiment are shown in Table 2, and due to the large number of firewall definition statements, only two types are shown here.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 1 Introduction
The connection between the ECG patch and the backend system is enabled by integrating the latest Internet of Things (IoT) communication protocols (i.e., Bluetooth Low Energy (BLE) and MQTT (Message Queueing Telemetry Transport)). Moreover, the ECG patch introduces interactive modes of operations that are dynamically configured based on the patients' health conditions or the healthcare provider's recommendations. The remainder of the paper is organized as follows: Section 2 reviews related work in this area. The proposed platform architecture is described in Section 3, and we demonstrate the end-to-end solution for real-time ECG monitoring. Section 4 describes the developed prototype of the ECG patch for evaluating the proposed platform. Section 5 contains performance evaluation, and experimental results are given in Section 5. Lastly, Section 6 provides concluding remarks and draws future directions.