esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement Quality Analysis
### MQA4: Pseudorange Residual Analysis  
The ephemeris data provide accurate satellite position estimation and satellite clock correction. In order to further analyze the satellite orbit and clock errors in the pseudorange, the pseudorange residual was used. First, the range between the known position and the satellite position calculated from the ephemeris was obtained. The definition of the pseudorange residual is the pseudorange that removed all the errors we calculate before (ionospheric, tropospheric, and user clock bias) and minus the range between the known user position and the estimated satellite position. The errors in the pseudorange residuals are the satellite orbit, satellite clock errors, multipath errors, and measurement noise. Figure 22 shows all GPS satellite pseudorange residuals (different colors stand for different satellites). The variation trend for each satellite is removing due to the error source which change with time has been removed. 95% of the GPS satellite orbit and clock errors is less than 2.53 m and the maximum error value is around 5 m. The statistical results are shown in Table 11.  
The GPS precise ephemeris from the International GNSS Service (IGS) was applied to confirm our analysis results. However, no open source data supports precise ephemeris for BDS. Thus, only GPS satellite performance is analyzed in this section. Different from broadcast navigation data, the precise orbits are 3D satellite positions at the ECEF coordinate. The precise satellite position was recorded at intervals of 15 min. Thus, the user needs to interpolate the orbits between each precise satellite position. The interpolation method is from [39]. Figure 23 shows the corresponding satellite orbit error for Figure 22. The main error in the pseudorange residual is the satellite orbit error.  
The BDS satellite pseudorange residuals are plotted in Figure 24. By verifying with the precise orbit information from [40], the satellite orbit error is shown in Figure 25. The BDS PRN3, PRN11, PRN12 is lack of precise orbit information. The results show that 95% of the BDS satellite errors is below about 10 m. The maximum value is over 25 m. The BDS ephemeris error is thus larger than that for GPS. This result indicates that the satellite control and prediction of BDS faces some issues. For real-time positioning, the pseudorange residual is proportional to the positioning error. As a result,  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **GPS** & & **BDS** & \\ \hline
**Orbit** & MEO & GEO & IGSO & MEO \\ \hline
**Mean** & 0.98 & 4.84 & 4.06 & 2.45 \\
**Std** & 0.78 & 3.53 & 3.11 & 2.04 \\ \hline \end{tabular}
\end{table}
Table 11: MQA4 statistical results (unit. m).  
Figure 23: GPS satellite orbit error (compared with precise orbit correction).  
the satellite orbit and clock errors are the main reason that BDS could not provide the service as good as that of GPS.
## 3 Solution Methodology
### Flow Characteristics  
The axial velocity deserves to provide the main features of flow behavior in the microchannel for microfluidic applications. Figure 3a-c is portrayed to inspect the evolutions in the velocity profile across the microchannel for various values of Deborah number \(\Gamma\), electroosmotic parameter \(m_{e}\) and maximum electroosmotic velocity \(U_{ls}\). Figure 3a shows the variation of axial velocity for various values of \(\Gamma\). It is demonstrated that the magnitude of velocity \(u\) decreases as \(\Gamma\) increases at \(y~{}=~{}0\). The reason behind this trend is EDL (electric double layer). It means flow of fluid resists in the presence of EDL. But opposite behavior is witnessed near the channel wall \(y~{}=~{}h(x)\). Figure 3b predicts that \(m_{e}\) boosts the magnitude of \(u\) i.e., magnitude of \(u\) increases as \(m_{e}\) increases at the central region of the channel and reduces near the walls of the channel. Since \(m_{e}\) is the ratio of the channel height to the Debye length \(\lambda_{D}\), it specifies that \(\lambda_{D}\) is inversely proportional to EDL. Hence, more fluid flows in the central region \(y~{}=~{}0\). Figure 3c depicts that \(u\) decent as \(U_{ls}\) ascents in \(y~{}=~{}0\), whereas opposite movement is noticed at \(y~{}=~{}h(x)\). As \(U_{ls}\) physically concludes that velocity of fluid decreases when thickness of EDL increases. Therefore, fluid flow decreases in EDL presence.
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation  
Lingbo Wang  
Caleum sulfoaluminate cement (CSA) is a type of low-CO2 binder which has been widely applied in the production of concrete. To investigate the protection capability offered by CSA to keep steel from corrosion, the pore solution chemistry of CSA on steel passivation was investigated in this study. The pore solution of CSA pastes, extracted by an ex situ leaching method, was studied and compared with ordinary Portland cement (OPC). The results show that the alkalinity of the CSA pore solution is not only much lower than that of OPC, but also that a new type of ion, Al(OH)4\({}^{-}\), and high concentration of SO4\({}^{2-}\) were detected in the liquid phase of CSA. Based on pore solution chemistry analysis, a simulated pore solution (SPS) system was designed to assess the comprehensive impact of alkalinity and ion composition, featured as properties of CSA, on steel passivation. The results of the corrosion potential evolution highlight the importance of alkalinity in passivation. SO4\({}^{2-}\) can cause degassivation when there is not enough hydroxyl, but Al(OH)4\({}^{-}\) is able to maintain the alkalinity of the system, enhancing the stability of the passive film.
# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
###### Abstract  
This article presents UaVirBASE, a publicly available dataset for the sound source localization (SSL) of unmanned aerial vehicles (UAVs). The dataset contains synchronized multi-microphone recordings captured under controlled conditions, featuring variations in UAV distances, altitudes, azimuths, and orientations relative to a fixed microphone array. UAV orientations include front, back, left, and right-facing configurations. UaVirBASE addresses the growing need for standardized SSL datasets tailored for UAV applications, filling a gap left behind by existing databases that often lack such specific variations. Additionally, we describe the software and hardware employed for data acquisition and annotation alongside an analysis of the dataset's structure. With its well-annotated and diverse data, UaVirBASE is ideally suited for applications in artificial intelligence, particularly in developing and benchmarking machine learning and deep learning models for SSL. Controlling the dataset's variations enables the training of AI systems capable of adapting to complex UAV-based scenarios. We also demonstrate the architecture and results of the deep neural network (DNN) trained on this dataset, evaluating model performance across different features. Our results show an average Mean Absolute Error (MAE) of 0.5 m for distance and height, an average azimuth error of around 1 degree, and side errors under 10 degrees. UaVirBASE serves as a valuable resource to support reproducible research and foster innovation in UAV-based acoustic signal processing by addressing the need for a standardized and versatile UAV SSL dataset.  
acoustics; sound source localization; audio dataset; microphone arrays; unmanned aerial vehicle Article
## 2 Methodology
### Visibility Analysis  
The visibility analysis algorithm generates a binary raster of visible (coded as \"1\") and invisible areas (coded as \"0\") based on an input observation point [16]. Such outputs can merely determine the landscape visibility, without quantitatively depicting tourists' viewing experiences. This section optimizes the parameters in the visibility algorithm, specifically the vertical angle and relative slope, to quantify how topographic attributes like gradient, distance and elevation influence sightseeing and visual perception.
## 4 Verification and Validation
### Validation of the Applicability of the Proposed Model Using Real Project Data
#### 4.3.4 Overview of Projects  
The interviewed experts were asked to provide the details of two projects of their company with different characteristics. The projects chosen by the experts were as follows:  
**Project 1--High-rise building:** This project was delivered by the experts' company in Dubai, UAE. The project included constructing a complex high-rise commercial building equipped with IoT devices. It was a design-bid-build (DBB) project. Therefore, the construction company was not responsible for the design development. Their scope only included the construction phase. The project utilized BIM technologies, and project stakeholders collaborated via a CDE.  
**Project 2--Theme park:** The second project provided by the interviewees was a horizontal construction project for a theme park in Abu Dhabi. The project utilized advanced construction technologies, such as virtual reality for collaborating over building information models, augmented reality onsite for visualizing building elements using tablet PCs, and robots to collect data for assessing site conditions. The theme park included various state-of-the-art IoT devices for automation during the operation and maintenance phase.  
Moreover, the experts mentioned that this project's client had strict measures regarding the confidentiality of project information.  
The summary of the project-related information, including the security level of the projects, is presented in Table 9. The project phase for both examples is construction since the company was only responsible for construction works in both cases. Moreover, the experts indicated the project security level as \"medium\" and mentioned that they had similar cybersecurity measures in both projects.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 2 Background  
In the indoor situation, there are several alternative localization methods. Perhaps the most frequently reported upon is based on signal strength (RSSI). Since signal strength depends on many factors other than distance, it is not possible to unambiguously recover the distance of a smartphone from an access point. To overcome this problem, so-called finger-printing methods have been developed which require the measurement of signal strengths of multiple APs at each point on a grid. The accuracy achievable depends in part on how fine a grid one is willing to survey. Spatial fluctuations in the electromagnetic field due to interference of signals traveling along different paths have a \"texture\" on the order of half a wavelength [37] (which is 60 mm in the 5 GHz band). So, ideally, one should sample the field at that scale--something that is not practical, particularly if carried out for \"all\" locations on a 3D grid rather than just on a single horizontal plane.  
Further, modern enterprise Wi-Fi systems optimize their performance and minimize interference by periodically reassigning AP channel frequencies and by changing AP transmit power. Such adjustments make pre-determined fingerprinting data much less useful. Also, fingerprinting needs to be redone when there are changes in the radio frequency environment, such as caused by the movement of absorbers or reflectors.  
Another approach to indoor localization is based on scattering a large number of low-power beacons (typically Bluetooth) over the area of interest. The location reported for the device is then the (known) location of the beacon with the strongest signal (perhaps also taking into account the locations of the beacons with the second and third largest signal). The accuracy of this approach depends on how many beacons one is willing to spread around. This approach requires additional investment in hardware, installation, and maintainence--unlike methods exploiting pre-existing Wi-Fi infrastructure.  
Since the accuracy of time-of-flight methods is inversely proportional to the bandwidth of the signal, there is an advantage to very wide bandwidths. So-called ultra wide-band (UWB) systems (\(>\)500 MHz bandwidth) can make accurate distance measurements. However, the Federal Communication Commission (FCC) limits the RF power of such systems(\(-41.3\) dBm/MHz) to the point where they are only useful for short distances--the order of a few meters, as opposed to a few tens of meters for Wi-Fi signals.  
None of these methods have found wide-spread use for indoor localization--and certainly not for outdoor localization.  
Meantime, IEEE Wi-Fi standards are continuing to increase the available channel bandwidth, with IEEE 802.11 ac (2013) supporting 80 MHz, IEEE 802.11 ax (2021) supporting 160 MHz, and the next standard, IEEE 802.11 be (2024), supporting 320 MHz bandwidth. Thus, FTM RTT is approaching the ranging accuracy possible with UWB, but without the stringent power limitations, thus making it useful for distances of interest here.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 6 Performance Evaluation
### Simulation Results
#### 6.2.2 Impact of Increasing the Number of TP Levels  
We evaluate EE-LoRa in terms of overall energy efficiency and active energy efficiency as defined by Equation (27) with 4000 nodes in two different scenarios using two TP sets of TP \(\mathcal{T}_{1}\) and \(\mathcal{T}_{2}\), which differ by the TP step, 1 dB and 3 dB, respectively. The maximum allowed TP level is constant and equals 14 dBm, and we change the minimum TP. For example, when the minimum TP is 8 dBm, we will have 3 TP levels (14, 11, and 8 dBm) for \(\mathcal{T}_{1}\) and 7 TP levels (14, 13, 12, 11, 10, 9, and 8 dBm) when using \(\mathcal{T}_{2}\) at the power-control step.  
Figure 13 shows that the energy efficiency improvement increases when we decrease the minimum TP used, where we have lower TP levels that can be used in power control. The energy efficiency becomes approximately stable when the minimum TP is below 8 dBm. This can be explained by the fact that the current consumption of the transceiver in transmit mode increases only by 1 mA when the TP increases from 2 to 8 dBm. Moreover, the energy efficiency and the active energy efficiency are better when we decrease the TP step to 1 dB because we will have more discrete TP levels. For example, when the minimum TP is 2 dBm, the improvements in energy efficiency and active energy efficiency using the 1 dB step become 5.9% and 6.3%, respectively, compared to those using the 3 dB step.  
Figure 11: Energy efficiency comparison.  
Figure 12: Computation time to solve our optimization problem when \(N_{t}\) = 4000 nodes.
# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 3 AgriEnt: Architecture
### Presentation Layer  
In many cases, farmers make their decisions solely on advice received by experts. However, in rural areas, there is a lack of agricultural experts that provide the support required. To address this issue, the AgriEnt platform provides a Web application that farmers can use to make a decision regarding insect pest control and management by providing the crop's symptoms as input and obtain a diagnosis of the insect pest affecting their crops. One of the main advantages of Web applications is that they can be used in every device with internet access and a Web browser installed. Furthermore, the Web application follows a responsive design, i.e., all Web pages render and work well across desktops and mobile devices such as tablets and smartphones.  
Figure 3 depicts the main user interface of the AgriEnt Web application which contains a menu with the available options such as diagnosis, prevention, and login. The diagnosis Web page shows the six different crops for which AgriEnt provides support for insect pest management. Once users select the crop, the application asks them for all symptoms they perceived in their crop. Then, based on all symptoms specified by the farmer, the Web application shows the diagnosed insect pest, the reason why it was selected, and a set of recommendations for controlling and managing it. On the other hand, the Web application is also valid for prevention purposes i.e. it also provides users with a set of expert recommendations for preventing insect pests.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Treatment Suggestions** \\ \hline
1 & Collect and destroy the egg masses. \\
2 & Release Isotima Javensis at 100 pairs/ha \\
3 & Spray insecticides such as Carborufan 3\% G 33.3 kg/ha, Bronate 10\% G 30 kg/ha, or \\  & Chlorantraniprole 18.5\% SC 375 mL/ha \\ \hline \hline \end{tabular}
\end{table}
Table 4: Treatment suggestions for _Scripophaga excerptalis_.
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 3 Description of the Proposed SDN-Based Service for Rail and Road Coexistence
### Objectives of the Implemented Service  
The implementation of an SDN-based solution relies on the implementation of an SDN application capable of managing the entire road-rail emergency service. The SDN application that we developed can be decomposed into two parts: (1) Differentiating the emergency messages from common messages to distribute them to both rail and road emergency servers, and (2) detecting DoS/DDoS attack scenarios that could disrupt system operation:  
1. **Managing emergency messages:** Cars and trains can only communicate with their own servers and with devices/nodes that belong to the same VLAN of the same physical network (cars/trains). However, during emergency situations, the application must have the capability to distribute emergency data messages to every node in the network, including assigned emergency servers. This means that regardless of whether it is a car or a train, all nodes will receive the emergency message. Detailed information about common emergency service implementation is presented in Sections 4.1 and 4.2. The application addresses two specific cases:  
* **Case 1: Sending emergency messages to assigned servers** When an emergency message is sent from cars to the car emergency server, the message is also forwarded to the rail emergency server. Similarly, if an emergency message is sent from trains to the rail emergency server, it is also transmitted to the car emergency server. In this case, an emergency message is first sent to the centralized SDN controller and the controller distributes the message to assigned servers. The servers, upon receiving these emergency messages, disseminate them to the cars and trains located in the affected area. This mechanism ensures the comprehensive distribution of emergency messages among the relevant vehicles.
* **Case 2: Sending emergency messages directly to cars, trains, and to assigned servers** In this case, our SDN application enables direct communication of emergency messages between cars and trains. It allows cars and trains to send and receive only emergency messages to each other, without the need for routing through controller and intermediate servers. To enable this mechanism, all the nodes (cars and trains) are defined with two wireless interfaces, where one interface is reserved only for emergency data communication.
2. **Handling DoS/DDoS attacks:** To avoid DoS, a mechanism is developed based on monitoring the sudden change in the messaging behavior during the transmission of the emergency messages. More information about the mechanism to handle the DoS/DDoS situation is presented in Section 4.3. A mechanism to address the DDoS situation has also been evaluated.
## 5 Considered System  
We numerically test the effectiveness of the introduced equations. The test-used exemplary model corresponds to a planar beam connected with a double pendulum that oscillates and slides on this beam (Figure 1a). Since we set the principal focus on the constraint equations, the beam is modeled with the use of 50 finite elements, only. The number of the used elements is low, but we have to remember that the high precision of modeling of the beam behaviour is not the critical point of the present investigation. In the prepared numerical model, both the abovementioned elements are introduced. We introduce the abovementioned constraint equations in the same form as we present them in the above parts of the paper, as well as employing the above-presented method of coordinate partitioning and elimination. Finally, obtained dynamic equations are integrated numerically.  
The investigated multibody system is planar and composed of four bodies. The bodies construct a single serial kinematical chain. If one starts counting from the reference body, the two first joints are translational, and the two last joints are rotational (each being a single degree of freedom). The first two bodies are fictitious massless elements. The first arm (body #3) is 1.5 m long. Its mass equals 20 kg, its mass centre is in the geometrical centre of its length, its moment of inertia is 2 kg.m\({}^{2}\) (with respect to the mass centre), and its inertia products equal zero. The second arm is 1 m long. Its mass equals 20 kg, its mass centre is in the geometrical centre of its length, the moment of inertia is 2 kg.m\({}^{2}\), and its inertia products equal zero. The beam's length equals 5 m, and 50 finite elements are used to model it (we intend to validate the behaviour of the numerical model; the detailed investigation of beam vibrations is not in the scope of the present tests). The surface of its cross sections equals 1.5 \(\times\) 10\({}^{-4}\) m\({}^{2}\). The beam's geometrical moment of inertia equals 2.81 \(\times\) 10\({}^{-9}\) m\({}^{4}\), and the density of its material is 7.86 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\). Its Young modulus equals 2.1 \(\times\) 10\({}^{11}\) Pa.