esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Performance Analysis of BDS-3 Integrity Concept
## 1 Introduction
This benefits from the reasonable system design and effective risk monitoring and control measures. However, this does not mean that BDS-3 has completely eliminated the integrity hazards, especially considering that the system was built in such a short period of time (2012-2020). To ensure the safety and reliability of the service, it is necessary and valuable to carry out more in-depth research and evaluation. This study focuses on the design and the performance analysis of BDS-3's integrity concept. In order to improve the reliability and safety of the service, BDS-3 upgraded and improved the conceptual design and function of integrity compared to BDS-2 and carried out standard development work in ICAO SARPs at the same time. The satellite service failure and constellation service failure concepts are clearly defined, and the integrity risk probability calculation method is designed for the three OS signals of B1I/B1C/B2a. The integrity fault tree model is studied and established, and the corresponding monitoring measures for weakness in the space segment and the ground segment of the system is proposed. In terms of alarms, BDS-3 is designed with a hybrid alarm mechanism of the ground alarm approach and the satellite alarm approach, to make up for the limitations for BDS's failure to deploy ground monitoring stations globally. Finally, the actual operation data of 2019 was used to verify the integrity performance of the system service. The results show that the actual operation of the system is consistent with the conceptual design, which proves the safety and reliability of the BDS-3 OS service. The remainder of this paper is structured as follows. Section 2 defines the integrity of the BDS OS signals (B1I/B1C/B2a), introduces the alarm approach, and gives the calculation method of the satellite service failure probability (P\({}_{\text{sat}}\)) and the constellation service failure probability (P\({}_{\text{const}}\)). In Section 3, a fault tree model for the BDS satellite signal-in-space (SIS) is established, the bottom events from the space segment and the ground segment are decomposed, and the system design values of \(\mathrm{P_{sat}}\) and \(\mathrm{P_{const}}\) are derived. In Section 4, the integrity prevention and control measures taken by BDS-3 in the space segment and the ground segment are introduced, respectively. Finally, Section 5 presents the integrity risk verification work on BDS-3 and the analyses of the results.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 4 Forecast Experiment Design  
In this study, we conducted a performance evaluation of five tree-based models (DT, RF, GBT, hybrid DT-GBT, and hybrid RF-GBT) in forecasting soil temperature at three depths (10 cm, 30 cm, and 50 cm) in Bangladesh. Our aim was to determine the optimal tree-based forecast model for soil temperatures in Bangladesh by inputting different meteorological factors. Since different lead times, time window sizes, and input scenarios play key roles in the accuracy of soil temperature forecasting in Bangladesh, we considered the following cases:  
Three types of lead times: one day, three days, and five days.  
Five sizes of time window: one day, two days, three days, four days, and five days.  
Eight combinations of daily minimum temperature (MIN), i.e., daily maximum temperature (MAX), daily mean temperature (MEAN), daily sunshine duration (SUN), daily rainfall (RAIN), and daily humidity (HUM), were used as model inputs. In particular, the combinations were as follows: (1) MEAN, (2) MEAN-SUN, (3) MEAN-MAX-MIN, (4) MEAN-HUM-RAIN, (5) MEAN-MAX-MIN-SUN, (6) MEAN-MAX-MIN-SUN-RAIN, (7) MEAN-MAX-MIN-SUN-HUM, and (8) MEAN-MAX-MIN-SUN-HUM-RAIN.  
In almost all machine learning studies, the partition of training and testing datasets follows a 70-30 split rule ([11]), so the soil temperature dataset in Bangladesh was divided as follows: the data covering the period from 1 January 2001 to 31 December 2015 were used for the training dataset, and the data covering the period from 1 January 2016 to 31 December 2022 were used for the testing dataset.
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 7 Conclusions  
The article discusses enhancing a clothoid-based lateral controller for autonomous vehicle navigation, focusing on path-planning and -control accuracy. The controller utilizes  
Figure 17: Comparative results with an existing MPC controller. (**a**) Tracked trajectory. (**b**) CTE vs. distance plot.  
Figure 18: (**a**) Tracked speed vs. arc length. (**b**) Steering angle vs. time plot for the tracked trajectory.  
an adaptive Frechet distance-driven lookahead strategy for precise trajectory tracking and maneuvers. The incorporation of a speed controller to complement the lateral control simulations added realistic constraints to the vehicle's dynamics, particularly in handling lateral accelerations preventing the semi-tractor trailer rollover. The findings indicate that the smart velocity waypoint generation method, in tandem with the clothoid-based approach, substantially improves path tracking. The results obtained from the implementation of the controller are promising. The controller consistently demonstrated good lateral tracking capabilities, at speeds of 30 kmph showcasing its ability to maintain precise control over challenging curves and maneuvers, such as figure-8 and dual-lane-change maneuvers, with minimal cross-track error. Additionally, the controller's well-behaved steering wheel angle output indicates its suitability for driver's comfort and overall vehicle stability, reinforcing its relevance for real-world applications. That said, the current configuration of the lead filter, tailored to the estimated lag transfer function for a specific speed (i.e., 30 km/h), presents suboptimal performance for high-speed tracking scenarios. Future work will extend the controller's capabilities to higher speeds and refine the closed-loop control to further minimize cross-track errors. The groundwork laid by this study paves the way for advanced autonomous vehicle systems capable of precise navigation and increased safety in complex driving scenarios.  
Conceptualization: A.S., S.S. and M.A.; methodology: A.S. and S.S.; software: A.S.; validation, A.S.; formal analysis: A.S., S.S. and M.A.; investigation: A.S. and S.S.; resources: M.A.; data curation, A.S.; writing--original draft: A.S.; preparation, A.S.; writing--review and editing, A.S. and M.A.; visualization, A.S. and S.S.; supervision: S.S. and M.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Hydration, Setting and Crack-Resistance Properties of Activated HVFACs at Early Stages
## 5 Conclusions  
In this study, the influences of several main variables on the hydration heat, setting time, microstructure and crack-resistance properties of FACs/HVFAC are investigated. The main results and proposals can be drawn as follows:  
(1) Fly ash can effectively decrease the release of hydration heat, and the hydration heat decreases gradually as the mass of fly ash increases. Meanwhile, the decrease in the magnitude gets smaller when the curing age increases.  
(2) The combined activator CaO and Na\({}_{2}\)SO\({}_{4}\) can effectively stimulate the activity of fly ash and decrease the setting time of FACs with the increase of the dosage of the activator. However, if the used dosage of the activator (Na\({}_{2}\)SO\({}_{4}\)) is too high, the hydration heat of the FA-PC system and the crack development of FACs both will increase with the activator.  
(3) With the addition of fly ash, the initial and final setting times of FACs both increase distinctly. On the other hand, when activator CaO + Na\({}_{2}\)SO\({}_{4}\) is used, the initial and final setting time of FACs both decrease as the dosages of activator increase. Based on this, this study considered that the initial and final setting times of FAC activated by CaO (10%) and Na\({}_{2}\)SO\({}_{4}\) (1.5%) can be 80% of the predictions of [PERSON] model and 1.3-times the proposed initial setting time, respectively.  
(4) Fly ash activated by CaO + Na\({}_{2}\)SO\({}_{4}\) can postpone the occurrence of initial crack, decrease the total cracked area of and maximum crack width of FACs/HVFACs. With the increase of curing time, the total cracked areas increase in FAC specimens with and without activator. When the dosage of activator Na\({}_{2}\)SO\({}_{4}\) increases, the total cracked areas of and maximum crack widths of FAC both increase.  
(5) Synthetically, comparing to other chemical activators in the paper, 10% of CaO and 1.5% of Na\({}_{2}\)SO\({}_{4}\) can be suggested as the most effective activator for obtaining appropriate hydration heat, setting time and the crack development rate of FACs/HVFACs using low calcium FA at early stages when FA replaces about 50% of PC.  
Figure 15: Mechanism of the crack-resistance of FAC activated by Na\({}_{2}\)SO\({}_{4}\).  
**Acknowledgments:** The authors are grateful to the fund support from the Natural Nature Science Foundation of China (No. 51202222).  
**Author Contributions:** [PERSON] conceived of and designed the experiments. [PERSON] analyzed the data, developed the models and wrote this paper. [PERSON] analyzed the test data and wrote this paper. [PERSON] and [PERSON] performed and completed the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Method
### Algorithms
#### 3.4.1 SDN Gateway Matching Functions  
The SDN gateway pre-processes vehicular data using two OpenFlow tables _Tv(0)_ and _Tv(1)_, with corresponding matching rule functions _Rv(0)_ (Algorithm 1) and _Rv(1)_ (Algorithm 2). The input ports are defined as _Rv(in) = [1, 2,...,10]_, and the output port is _Rv(out) = 13_. The sensor data mappings are Camera (Ports 1-5), LiDAR (Ports 6-8), and RADAR (Ports 9 and 10).  
All SDVs were configured to communicate directly with the gNB using VXLAN Port 13. Upon the receipt of vehicular data on SDN gateway's table 0, _Tv(0)_, a packet inspection is performed to extract the Network ID of the received vehicular data using the SDN gateway's matching rule function 0 _Rv(0)_. The retrieved Network ID is used to verify the integrity of incoming vehicular data before forwarding it to SDN gateway's table 1, _Tv(1)_. On _Tv(1)_, matching rule function 1 _Rv(1)_ is implemented to match the input port _Rv(in)_ and data type (_d_) of received vehicular data (_D_) before a unique Slice ID is assigned.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **TEST 1 (PPS)** & **TEST 2 (PPS)** & **TEST 3 (PPS)** \\ \hline CAMERA & 2.3 & 4.6 & 6.6 \\ LIDAR & 1.4 & 2.8 & 4.0 \\ RADAR & 0.6 & 1.3 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Arrival and service rates at the SDN gateway and VXLAN link to gNB (PPS).
## 3 Data Collection and Preparation  
### Experimental Measurement of UCS of Cement-stabilized Soil  
In this study, a database containing 216 soil samples were collected at Tran Nguyen Han street, Thanh Binh district, Hai Duong city, Vietnam (Figure 1). Soil samples were collected at two \(-2.0\) m and \(-4.0\) m depth, in three boreholes. Those boreholes were made with 10 m distance from each other. Soil samples were collected by pressing a cylinder into the soil block at the boreholes, and then covered with a moisture-proof bag to keep original water content (Figure 2). Collected soil were stored and transported by boxcar from the collecting site to the laboratory.  
Soil parameters was analyzed, evaluated and processed through specific stages: moisture content tests; wet density tests; mix soil cement; forming and curing of specimen and then unconfined compression tests were performed using specialized equipment (Figure 3). Experimental results obtained the relationship of axial stress-strain curves (Figure 4) and the UCS of the samples were defined as destructive compressive axial stress. An example of axial stress-strain curves are given in Appendix B (Figure A1).  
It is important to note that three kinds of general cement in the north of Vietnam were used for the cement mixing test, including Vissai cement, Chinfon Hai Phong cement and Nghi Son cement. A cement mixing ratio (i.e., the amount of cement in q m\({}^{3}\) of the mixture) was applied to 100 kg/m\({}^{3}\), 150 kg/m\({}^{3}\) and 200 kg/m\({}^{3}\). The specimens were cured during 7 days and 28 days at the indoor and outdoor condition. With the indoor curing condition, the specimens were covered with a sealant to prevent water evaporation from the specimen and the specimen was placed in the curing container. On the other hand, outdoor curing of specimens were placed in a room without any controlled intern of temperature and humidity, and without the sealant.  
Figure 1: Experiment location.  
Figure 2: Undisturbed samples.
## 2 Materials and Methods
### Flux Data and Meteorological Observation  
An open-path eddy covariance (OPEC) system was installed on the platform at a height of 36 m on a flux tower to measure CO\({}_{2}\) flux [43; 44]. The CO\({}_{2}\) exchange rate through turbulence between canopy and the atmosphere was measured by the system. The whole system was made up of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) and a fast response infrared gas analyzer (IRGA, Model LI-7500A, Li-Cor Inc., Lincoln, NE, USA). The raw EC data were collected at a frequency of 10 Hz and stored in a datalogger (Li-7550, Li-Cor Inc., Lincoln, NE, USA). Then the raw data were processed into 30 min timescale to acquire the net ecosystem exchange (NEE) of CO\({}_{2}\). GPP can be calculated from the NEE using the equation  
\[\text{NEE}=\text{Re}-\text{GPP} \tag{1}\]  
where Re is the respiration of the ecosystem during the day, calculated as an exponential function of air temperature (Ta) using the relationship developed from NEE at night, and measured by the flux tower and the corresponding temperature observations [45]. Therefore, GPP can be partitioned from the daytime NEE [46].  
The meteorological gradient observation system included a 7-layer anemometer (Model WindSonic, Gill Inc., Hampshire, UK and a 7-layer HMP155 temperature and humidity sensor (Vaisala, Vantaa, Finland) (installation heights are 5, 8, 11, 14, 18, 26 and 32 m, respectively). In addition, at a height of 17 m, a CNR-1 net pyranometer (Kipp and Zonen, Delft, The Netherlands), and a Li-190 SB point light quantum meter (Li-Cor Inc., Lincoln, NE, USA), and a rain gauge (Model TE525 MM, Campbell Scientific Inc., Logan, UT, USA)) were installed. The soil temperature and moisture probe (METER Group, Inc., Pullman, WA, USA) was installed at a soil depth of 5, 10 and 20 cm. The above instrument was connected to a CR1000 dataloger (Campbell Sci., Logan, UT, USA), the raw data was collected every 15 s and mean or accumulated data were output every half hour [42].  
The vapor pressure deficit (VPD) is a parameter that characterizes the degree of air dryness and was calculated according to Equation (2)  
\[VPD=0.611\times e\frac{17.27\times Ta}{237.3+Ta}\times\left(1-\frac{RH}{100}\right) \tag{2}\]
## 4 Simulation Setup, Result Analysis, and Discussion
### Average Data Reliability Ratio  
In the simulation result, we determined the average data reliability ratio of the proposed SDAA protocol. We compared it with the other standard secure MAC protocols,  
Figure 4: Average end-to-end delay with a varying number of attackers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Parameters Value** \\ \hline Number of nodes & 250 \\ \hline Simulation time & 600 s \\ \hline Pause time & 5 s \\ \hline MAC protocols compared & SFSC, SDA, EEST, and SDAA \\ \hline Traffic rate generated & 60 Kb/s \\ \hline Attackers generated & 1 to 18 \\ \hline Propagation method used & Two ray ground \\ \hline Antenna used & Omni antenna \\ \hline The initial energy of each node & 50 J \\ \hline Received power & 0.8 W \\ \hline Transmission power & 2.5 W \\ \hline Mobile sink location & \((300,400)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.  
including SFSC, SDA, and EEST. The average data reliability ratio in the network is the ratio of data packets received successfully to the total number of packets transmitted in the network. The average data reliability ratio reflects the efficiency and reliability of the network. Figure 5 depicts the results of the data reliability ratio for all the approaches. From Figure 5, as attackers increased from 1 to 18, the average delivery ratio of the SDAA protocol decreased from 100% to 98.4%, and the delivery ratio of EEST decreased from 100% to 74.3%. The delivery ratio of SEFSC decreases from 100% to 80%, and the delivery ratio of SDA drops from 100% to 88.2%. Hence, the proposed SDAA protocol gets a 10.2% to 24.1% higher delivery ratio than other contending approaches.
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 3 Background  
### Pana  
PANA (Protocol for Carrying Authentication for Network Access) [21] is an EAP lower layer protocol standardized by the IETF to enable authentication for network access and key establishment between devices and a network infrastructure. Following a client-server approach, PANA is a UDP-based protocol that runs between an IoT device acting as a PANA Client (PaC) and an IoT controller acting as a PANA Agent (PAA). PANA has its own re-transmission technique for the reliable delivery of EAP messages. The main payload of PANA are IP messages that enable authentication and key establishment.  
For the case of PANA, we show in Figure 1, an exchange using a generic EAP method (EAP-X). At first glance, we can appreciate that it contains more message exchanges than LO-CoAP-EAP. This is because PANA was not designed with the constraints of IoT in mind. PANA uses a first message to trigger the PANA exchange (PANA Client Initiation message); afterwards, the PANA Client (PaC) and the PANA Agent (PAA) start exchanging nonces, the typical EAP request, and response identity messages, prior to the EAP method exchange. After the EAP method is finished and the PaC is authenticated, the PAA receives the EAP success message along with the MSK to derive key material and further secure communications. Details and specifics of the PANA exchange can be reviewed in work by [PERSON] et al. [21].  
For constrained scenarios, [PERSON] et al. [22] developed an open-source lightweight version of a PANA client (PaC) for the Contiki Operation System (OS) [23], the so-called \"PANATIKI.\" PANATIKI ([[https://sourceforge.net/projects/panatiki/](https://sourceforge.net/projects/panatiki/)]([https://sourceforge.net/projects/panatiki/](https://sourceforge.net/projects/panatiki/))) is a PANA adaptation for wireless IoT devices with limitations in terms of computing and networking. PANATIKI follows the PANA standard, although reducing the original state machine of the PaC client to create a suitable version for limited devices. This lightweight PANA version is an optimized PANA implementation and is ideal for the comparison and evaluation of the LO-CoAP-EAP proposal for constrained devices with NB-IoT wireless technology.
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 2 Measurements  
### The Instrumented Train  
An Irish Rail Hyundai Rotem InterCity fleet car (Figure 1a) was instrumented while using inertial sensors that were installed on the trailer (non-powered) bogie in December 2015. The data was measured on the leading car (22337) of Set 37, a five-car train set. The train was in operation in the Dublin-Belfast Enterprise service line and the measurement equipment was installed while the train was stabled for a routine examination. The sensors were installed on the trailer bogie to minimize the noise contamination from the power train. A tri-axial accelerometer (Disynet-DA3802-015g with a range of \(\pm 15\) g) and tri-axial gyrometer (Crossbow VG400 CC-200 with a range of \(\pm 200^{\circ}\)/s) (Figure 1b) were installed on the bogie as close as possible to its center of mass. The data were measured at a sampling frequency of 500 Hz. The train location was also recorded while using a GPS antenna. The approximate forward speed of the train was obtained from the GPS data and referenced to its location. The GPS system recorded the train position at a frequency of 5 Hz. The sensors and GPS system were connected to a HBM Somat eDAQ-lite data logger with 32 GB storage capacity to collect and store the data. The data logger was installed at an optimal location on the train which needed minimum cabling. Power was taken from a spare circuit breaker on the underside of the carriage, which meant that the system required no connection into the carriage, satisfying the requirements of Irish Rail. The data were collected on the Dublin-Belfast line from 13 January to 3 February 2016. In total, 57 return journeys were taken on the line during this time. There was insufficient bandwidth to allow for the transfer of the data over a GSM network. Instead, an operator used a secure Wi-Fi connection to download the data from the data logger once every 7-10 days. The Wi-Fi connection avoided the need for physical access to the data logger, which was located underneath the carriage.