esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Preparation and Properties of Sintered Brick from Multi-Source Solid Waste
## 2 Materials and Methods
### Testing Methods
#### 2.3.2 Weathering Resistance Test  
Water absorption is a key factor affecting the performance of sintered brick, and is greatly influenced by the sintering method. In this paper, the water absorption rate at room temperature, and the 3 h boiling water absorption rate of the sample bricks were studied experimentally, and double specimens were prepared before the test, with 5 sintered bricks in each specimen. According to GB/T 2542-2012, \"Masonry Wall Brick Test Method\" [26], a quarter of the regular size of the sample brick was taken as the test specimen, and the specimen was firstly cleaned on the surface and dried in a blast drying oven at 105 \(\pm\) 5 \({}^{\circ}\)C to a constant weight, and the dry weight was weighed as m\({}_{\text{c0}}\) and m\({}_{\text{z0}}\), respectively, after cooling. The wet weight was placed on the electronic scale after weighing m\({}_{\text{c1}}\) and m\({}_{\text{z1}}\), respectively, and the mz series specimens were placed into the boiling box; the water level injected into the box was 50 mm higher than the surface of the specimen. After 3 h, heating was stopped and the specimens were cooled to room temperature, with the boiling wet mass being recorded as m\({}_{\text{z2}}\). The water absorption rate of the sample brick at room temperature and the 3 h boiling water absorption rate was calculated according to Formulas (4) and (5).  
\[\text{W}_{1}=\frac{\text{m}_{\text{c1}}-\text{m}_{\text{c0}}}{\text{m}_{\text{ c0}}}\times 100\% \tag{4}\]  
W\({}_{1}\)--Water absorption of the sample brick immersed in water at room temperature for 24 h, %.  
m\({}_{\text{c0}}\)--Sample brick drying 24 h mass, unit kg.  
m\({}_{\text{c1}}\)--Sample brick soaking 24 h mass, unit kg.  
\[\text{W}_{2}=\frac{\text{m}_{\text{z2}}-\text{m}_{\text{z0}}}{\text{m}_{\text{ z0}}}\times 100\% \tag{5}\]  
W\({}_{2}\)--Water absorption of the sample brick by boiling for 3 h, %.  
m\({}_{\text{z0}}\)--Sample brick drying mass for 24 h, unit kg.  
m\({}_{\text{z2}}\)--Sample brick boiling 3 h mass, unit kg.
# Study on SPH Viscosity Term Formulations
## 3 Materials and Methods II--SPH Viscosity Term Formulations
### Direct Form Viscosity Force Model-02 (VISC-02)  
Originally aiming to improve the accuracy of Laplacian approximation, [PERSON] et al. (2004) derived a new form of the equations by directly taking the divergence of a SPH gradient model. This was actually founded on the twice differentiation of the SPH interpolant, and therefore it intrinsically contains the second-derivative of a kernel. By adopting a similar approach of the derivation, the viscosity term can be modeled as  
\[\
abla^{2}\mathbf{u}^{\alpha}=\sum_{j=1}^{N}\frac{m_{j}}{\rho_{j}}\mathbf{u}_ {ij}^{\alpha}(\frac{\partial^{2}W_{ij}}{\partial\tau_{ij}^{2}}-\frac{1}{ \mathbf{r}_{ij}}\frac{\partial W_{ij}}{\partial\tau_{ij}}) \tag{16}\]  
However, as pointed out by [PERSON] et al. (2004), this formulation is very sensitive to the particle disorder, especially when the order of the kernel function is low.
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
## 4 Implementation and Evaluation
### BGP Hijacking Protection Evaluation
#### 4.3.3 Experiment: Indirect Affection  
For investigating what would have happened if the attack had been focused on downstreams of SDX members, an indirect connect scene (see Figure 14) was conducted for evaluation. In this scene, traffic destined for the hijacking route (prefix 208.65.153.0/24) in SDX was forwarding to Global Crossing AS3549 router. The reason is that traffic still matched the intent of the valid route from YouTube (prefix 208.65.152.0/22). The Global Crossing router received both routes, 208.65.152.0/22 from YouTube and 208.65.153.0/24 from the malicious AS Pakistan Telecom. Therefore, when traffic destined for YouTube (prefix 208.65.153.0/24) was sending to the Global Crossing Router, it tried to forward back the traffic to the Pakistan Telecom through SDX because of the longer prefix matches route. Figure 15 indicates the BGP routing table on Global Crossing Router. In this case, the traffic was dropped because there is no datapath in the SDX for such an invalid route in validation, while it also interrupted the ping test made by Client Network (see Figure 16). Hence, under these circumstances, the SDX is still available to stop half-way direction of the traffic affected by poisoning route. For further investigation and enhancement, To improve the protection scope of SDX in this situation, more developments are planned in future work.  
Figure 14: Hijacking in SDX with indirect connection.  
Figure 13: Ping test result in first scene.  
GlobalCrossing show ip bgp  
BGP table version is 0, local router ID is 192.168.50.152  
Status codes: s suppressed, d damped, h history, * valid, > best, = multipath,  
i internal, r RIB-failure, S Stale, R Removed  
Origin codes: i - IGP, e - EGP,? - incomplete  
Network Next Hop Metric LocPrf Weight Path
*> 80.83.176.0/20 192.168.50.52 @ 65000 34868 i
*> 208.65.152.0/22 10.153.199.29 @ @ 36561 i
*> 208.65.153.0 @ 192.168.50.52 @ 65000 3491 17557 i  
Total number of prefixes 3  
Root@clientrouter:+#ping -I 80.83.176.1 208.65.153.101 -c 5  
PING 208.65.153.101 (208.65.153.101) from 80.83.176.1 : 56(84) bytes of data.  
--- 208.65.153.101 ping statistics ---  
5 packets transmitted, 0 received, 100% packet loss, time 4088 ms
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
## 5 Conclusions  
In a traditional droppable detection system, the receiving antenna, the droppable release device, and the sounders are usually placed separately in the airship. However, the utilization of space was improved in this study by employing a four-armed helical antenna as the receiving antenna and as the inner wall of the droppable release device at the same time. However, when the antenna is directly used as the inner wall, the airflow at high altitude causes the sounder to be in direct contact with the inner wall, which may damage the structure and stability of the antenna. Therefore, direct contact with the sounder is avoided by printing the four-armed helical antenna on top of the medium as the outer wall using flexible materials, allowing the weight of the device to be further reduced. The designed antenna was modeled with HFSS, and the modeled four-armed helical antenna was set up for frequency sweeping and simulation. The resonance frequency of the antenna is around 403 MHz and reaches \(-17.9\) dB at 403 MHz. The voltage VSWR is 1.29, which is less than 1.5. The maximum gain of the antenna reaches 6.3 dB, and it can achieve a gain effect in all directions. The antenna meets the requirement of the cardioid directional graph and can reach the performance index set in this study after researching antennas commonly used and available on the market.  
Software, Y.L.; Writing--review & editing, Z.L. and T.L.; Supervision, T.H. All authors have read and agreed to the published version of the manuscript.  
The work by [PERSON] is supported by the National Natural Science Foundation of China under Grant Nos. 61827901 and 3F72F5D9.  
**References**
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
###### Abstract  
As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.  
d  
d _arxiv_:10.3390/a12010022
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Tracking Techniques  
The tracking techniques can be characterised as continuous localization techniques, thus tracking an object's position for a specific period of time. They usually rely on specific hardware, which is exposed to the environment; therefore, the system's surroundings are of vital importance for tracing a position accurately. The hardware for outdoors applications includes satellites and cell towers, whereas for indoors, sensors and cameras are commonly used. The use of a camera can enable the tracking an object in a 3D space; the Accuware [62] and the commercial car parking guidance system [63] are good examples of camera-based tracking systems.  
Moreover, a comprehensive overview of the tracking systems participating in the Indoor Positioning and Indoor Navigation (IPIN) competition in 2019 and 2020 is provided in [58, 59], respectively. The competitions mainly consisted of smartphone-based systems with sensor fusion, which could accurately identify and track their position. A variety of different systems and techniques were utilised for the competitions, including PDR systems mounted on the foot of the pedestrian, positioning systems based on augmented reality (AR) and mixed reality, localization algorithms using particle filters, and multi-sensor fusion systems combining wireless signals, such as Wi-Fi, with information from various sensors, such as magnetometers, accelerometers, gyroscopes, and light sensors (luxmeters). Some systems were designed for discriminating the indoor and outdoor environment, i.e., by utilizing the luxmeter sensor. In particular, the competition area included an outdoor scenario, with a satellite view and an indoor scenario without satellite view, and the competitors had no prior knowledge of the test route; therefore, they could only rely on their smartphone to calculate the vehicle position. In such scenarios, a variety of tracking techniques and wireless sensors can be utilised, such as the fusion of GNSS and inertial navigation system (INS), together with filters, such as the extended Kalman filter (EKF), or neural networks.  
In this section, three of the most popular tracking algorithms are discussed: the dead reckoning, filters, and SLAM.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods
### Watermark Insertion Algorithm  
The input is a glb file, which may contain a single model, a scene, or even several scenes consisting of multiple models. Therefore, it is necessary to first find at least one model with a sufficient number of vertices to embed the HMAC. The presented algorithm modifies the model with the highest number of vertices in the case of a scene containing multiple models. In this way, the probability of making less noticeable changes has been increased.  
The algorithm requires a steganographic key, which is provided externally. The steganographic key determines in which attribute of a vertex (e.g., position, normals, texture coordinates) the HMAC should be placed and how many least significant bits of each attribute component should be allocated for its embedding. Based on this information, the corresponding bits (starting with LSB) are zeroed, which will eventually be replaced by the bits of the HMAC.  
Each attribute of a vertex is a 2D or 3D vector. The coordinates of the vectors are of the type IEEE-754 binary32 (float). To alter LSBs, first, a float number (a block of 32 bits in the memory) is interpreted as an integer number, and then bitwise operations are performed.  
To prevent the embedded HMAC from being easily located and to avoid the accumulation of changes in a small part of the model, the HMAC bits are scattered throughout the model. The information about the vertices used is stored in the seed of a pseudorandom number generator. This approach makes it possible to identify the relevant vertices during the watermark extraction process.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 3 TOSM-Based Autonomous Navigation Framework
### Semantic Autonomous Navigation
#### 3.3.4 Mental Simulation  
Mental simulation is one of the integral cognitive skills, which has been long studied by cognitive science and neuropsychology research [99]. Nonetheless, just a few works tried to implement such concepts into robotic platforms [72]. Some usages of the mental simulation are re-enacting memories, reasoning about different possible outcomes, and learning by imagination. In this work, the mental simulation module is used to perform reinforcement learning aiming for autonomous navigation. The simulation building algorithm was originally proposed on [100].  
One of the main issues of performing mental simulation on robots is the need of domain experts to model the simulated world based on the robot working environment. Modeling the environment is not feasible when robots navigate through large-scale dynamic environments. To overcome such limitation, we built a middle-ware capable of generating a simulated world, including the robot itself, using only the information stored on the on-demand database. By doing so, whenever the robot updates its internal knowledge about the environment, the simulation is also updated in the same manner. Whenever the simulated environment is needed, the middle-ware queries the needed data from the on-demand database and generates Universal Robot Description Format (URDF) and Simulation Description Format (SDF) files, which are used to create the virtual world on the Gazebo Simulator. Figure 8 shows an example output of the simulation building algorithm. It uses the data stored on the on-demand database to generate the simulation environment. To ensure this simulation is robust while also maintaining its consistency with the real world, we use a library containing 3D  
Figure 7: A Finite State Machine diagram showing a simplified version of the _Enter Elevator_ behavior stored on the Behavior Database.  
meshes for common objects such as doors, chairs, and tables. If the robot encounters a real-world object in which there is no 3D model available, a placeholder is automatically generated using the perceived shape and color of the object. Such point can be seen on Figure 8, where the simulated door uses a 3D mesh, while the vending-machine and the beverage storage are represented by automatically generated shapes.
## 2 Methodology
### Ensemble Model (Benefits)
#### 2.4.10 Robustness to Concept Drift  
Ensemble models have greater resilience to idea drift, which refers to the alteration of the statistical characteristics of the target variable over time. Ensemble approaches can effectively handle changes in data distributions by incorporating models trained on different time periods or utilizing alternative algorithms. Unlike fixed models that require periodic tuning, ensemble models can be updated dynamically. They can also be weightedto prioritize the most essential models based on incoming data, making them robust in non-stationary situations [51; 55].
## 4 Technical Viability  
### Case Study  
Nowadays, the analysis and design of tunnels in urban environments require the use of numerical simulations to simulate with accuracy complex geometries of the tunnel and ground layering. For simplicity's sake, the case study selected is a deep tunnel in a rock massif (hydrostatic stress field of 5 MPa), with a 3 m radius circular cross-section, where the analytical method proposed by [PERSON] and [PERSON] [11] is valid and is briefly described in Annex 1. In this example, stress relief factor \(\lambda\) is taken equal to 85% and lining thickness, \(\mathrm{t_{c}}\), is equal to 200 mm for CNA shotcrete.  
For the lining of the tunnel, shotcrete with different CRCA incorporation ratios (IR) was considered, whose mechanical properties were determined experimentally by [PERSON] et al. [25]. Table 5 shows the values of the design compressive strength and modulus of elasticity obtained, including the percentage of reduction of the mechanical properties in relation to the shotcrete mix without incorporation of CRCA (IR0).  
Table 5 shows that incorporating CRCA in the mix results in lower mechanical properties, ranging from about \(-3\%\) for IR20 to \(-31\%\) for IR100. For tunnels, the reduction of strength and stiffness in the shotcrete with recycled aggregates can be balanced by an increase of the lining's thickness. The lining's equivalent thickness for different CRCA incorporation ratios was computed for two criteria: (i) lining with similar stiffness and (ii) lining with similar yield stress.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **IR0** & **IR20** & **IR50** & **IR100** \\ \hline \(\mathrm{\ell_{cd}}\) [MPa] & 22.1 & 19.9 & 17.6 & 15.5 \\ (\(\mathrm{\Delta f_{cd,IR0}}\) [\%]) & (-) & (\(-10\%\)) & (\(-21\%\)) & (\(-30\%\)) \\ \hline \(\mathrm{E_{c}}\) [GPa] & 26.1 & 25.4 & 24.3 & 18.1 \\ (\(\mathrm{\Delta E_{c,IR0}}\) [\%]) & (-) & (\(-3\%\)) & (\(-7\%\)) & (\(-31\%\)) \\ \hline \end{tabular}
\end{table}
Table 5: Design compressive strength and modulus of elasticity of each composition.
# A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles
## 3 Model Frame Construction
### POI List Candidate Areas  
Shared bicycles provide the flexibility to park near actual destinations, unlike buses and BRT, which require fixed stations [27]. However, strengthened urban road management has led to regulations mandating riders to park only in designated areas to prevent issues like congestion and environmental pollution. These restrictions might obscure real travel purposes [30]. In analyzing the impact of urban spatial structure on shared bicycle usage and the influence of policy on travel intentions, this study uses an empirical walking radius of 200 m, established by [PERSON] [31], as a key parameter to define the area around the rider's destination, as shown in Figure 2.  
Figure 1: Framework and workflow of the travel purpose inference method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity Type** & **POI Name** & **Mapped Category** \\ \hline Transfer & Subway stations; passenger and high-speed train stations; bus stations, light rail stations, ports & 1 \\ \hline Medical & General hospitals; specialty hospitals; emergency centers, disease prevention institutions; medical and healthcare service locations, clinics, healthcare stores & 2 \\ \hline Education & Higher education institutions; middle schools, primary schools, kindergartens; vocational and technical schools; training institutions; research institutions & 3 \\ \hline Entertainment & Parks and squares; entertainment and leisure venues; sports and leisure venues; scenic spots; art galleries, science and technology museums, planetariums, culture palaces, archives, literary and art groups; museums, exhibition halls, convention centers; cinemas, tea houses, coffee shops & 4 \\ \hline Home & Commercial and residential buildings & 5 \\ \hline Dining & Chinese cuisine; Western cuisine; desserts, cold drinks; fast food & 6 \\ \hline Life Services & Post offices, logistics express; ATMs, banks, baby service locations, photo printing shops, laundries, intermediary agencies, repair stations, beauty salons, travel agencies & 7 \\ \hline Shopping & Commercial streets, general markets, shopping centers; home building material markets, home appliance markets; flower, bird, fish, and insect markets; supermarkets; personal care, clothing, shoes, leather goods stores; stationery, sports goods stores & 8 \\ \hline Work & Financial and insurance institutions, financial companies, insurance companies, media organizations, security companies; general enterprises, famous enterprises, industrial and commercial tax authorities; industrial parks; factories; government organizations and social groups & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mapping relationship between travel purposes and related POI categories.
## 4 Discussion
### General Discussion
#### 4.2.4 Are Threshold Levels Required?
Other co-injected tracers or intrinsic tracers [91] may also be monitored in order to strengthen the conclusions drawn from monitoring of the \(\delta^{13}\)C\({}_{\rm CO2}\). Noble gases originating from the CO\({}_{2}\)-production process and injected along with CO\({}_{2}\) at the Lacq-Rousse pilot represent one of these types of tracers that are important to monitor [67]. The second option is to adopt another strategy for defining threshold levels, if necessary. Such levels are not systematically required for ensuring the viability of storage containment [59; 86], but are often desired by the authorities. The interpretation of soil-gas and soil-flux data obtained in geothermal and volcanic areas is commonly based on the use of graphical statistical analysis (GSA) and of log probability plots [93], or on the use of more complex conditional sequential Gaussian simulations (sGs; [94]). These methods can be adapted in case the dataset is skewed and does not fall under normal or lognormal distribution, which is the case for our soil-gas data. Figure 8 presents some insights using the GSA approach. The calculation refers to winter and summer data acquired during the baseline period and to winter data acquired during the injection period. This corresponds to the time periods used as references to define the different threshold levels during the pilot activity. As a consequence of high soil CO\({}_{2}\) concentrations in the Lacq-Rousse area, the thresholds that maybe defined using the GSA approach have little scatter (4.5 to 5.2%). The situation is different with CO\({}_{2}\)-fluxes because of great variations between summer and winter times. The GSA-defined CO\({}_{2}\) thresholds are also in good agreement with the \"vigilance\" threshold used at Lacq-Rousse suggesting that even basic statistics may have some soundness. The CO\({}_{2}\)-flux threshold that may be postulated from injection data--or from the mean of the summer and winter baseline data--is less in agreement with the \"vigilance\" threshold that was used because it includes larger variability related to meteorological forcing. Another threshold can be defined for isotope data; it ranges in the lower part of the biological pool (\(-25\) to \(-27\%\); see Figure 6). As it is mainly defined by referring to carbon isotope data of CO\({}_{2}\) that existed prior to any CO\({}_{2}\) injection in the reservoir, it highlights that the first threshold that was suggested to be used during the post-injection phase (\(-26\%\)) does not represent an anomalous value by itself and only a biological end-member.