esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Practical Application of the Results of the BMP Tests
Nevertheless, if those production lines becomes firmly established, they could add to the potential biogas production of exhausted grape marc, complementing it and providing a non-seasonal source of substrate for biogas production. Using only brewers' spent grains and grape marc, both from wine production and exhausted from _orujo_ distillation, the produced energy and CO\({}_{2}\) reduction potential for these wastes can be calculated following the methodology in Ozer (using an electricity conversion efficiency factor, n=40%) [57]. Other wastes and by-products have not been evaluated due to the difficulties in their production assessment. The results are:  
Total methane potential: TMP = 57,951 m\({}^{3}\) CH4/year  
Amount of possible electricity generation from methane: e\({}_{\text{bio}}\) = 231,804 kWh/year  
Emission reduction from electricity generation: ER\({}_{\text{e}}\)1= 516,993 kg CO\({}_{2}\)/year  
Total emission reduction: TER = 1,476,578 kg CO\({}_{2}\)/year  
As a final note, the adoption and development of anaerobic treatment solutions should consider several factors for their successful implementation. Ideally, the technologies should require installations of a limited size [58] as the premises of the industries are usually small. This would also be influential on the economic aspects of the process. Another question that must be taken into account is that of the possible benefits of other agri-food industries in the region, making new possibilities available for treatment of their wastes. Fish preserving is another important industrial field in the region, with a number of small factories gathered in locations not far from some beverage production areas (as the municipalities of Santona and Castro Urdiales, very close or included in the wine-making areas of the \"Costa de Cantabria\") as well as a number of small cheese factories scattered by the region (normally with no management plans for whey and other by-products). Even if specific waste management plans are generally out of the reach of these small factories, their wastes could add to the substrates used for biogas production. Further research should be necessary about the feasibility of the biological processes with such co-substrates due to their peculiarities (high content of protein and/or some other elements as sodium). The economic feasibility of anaerobic digestion technologies could depend on the capability of the industries to join forces and create consortiums, among industries of the same alcoholic beverage production field or adding other agri-food industries or agricultural exploitations to benefit from scale economy advantages in order to afford installations of an adequate size, which could have a centralized management and operation, reducing costs and payback time (ideally 15 years or less) for the needed investments [59; 60; 61].
## 2 Materials and Methods
### Statistical Analysis  
All data from RSPTM and RST40S were confirmed to be distributed by the Shapiro-Wilk test; therefore, no further transformation was required. Data are presented as mean values (mean) and SD. A 2-Way (group \(\times\) trial) analysis of variance (ANOVA) was performed for resisted sprint performance (time) and mechanical variables (peak speed, peak power, peak velocity, step length, and step frequency), all of which were calculated for each sprint distance (0-20, 0-5, 5-10, 10-15, and 15-20 m). An independent \(t\)-test was conducted to compare the differences of pre- and post-intervention in CMJ variables between RST2M and RST40S. To assess fatigue level, the fatigue index (FI) and Sdec (%) were used, and a paired \(t\)-test was conducted to analyze the differences between the groups. The FI (%) was derived using the best value as a reference and the last attempt, because the first attempt may not always be the best. The equation is as follows: (maximum\(-\)minimum value)/maximum value \(\times\) 100. The Sdec (%) was calculated by dividing the total sprint time (TT) by the product of the peak sprint time (PT) and the number of sprints, subtracting one from the quotient, and multiplying the result by 100. The effect size (ES) was calculated using Hedges' g and partial eta squared (\(\eta^{2}\)) for the two-way ANOVA data to provide a comprehensive assessment of the magnitude of observed effects, considering both the study design and the analysis methods. Hedges' g was used for its suitability in adjusting for small sample biases in standardized mean differences, while partial eta squared (\(\eta^{2}\)) offers insight into the proportion of variance explained by our model in the context of ANOVA. Additionally, Cohen's d was employed for the CMJ variables, FI (%) and Sdec (%), facilitating comparisons between conditions within our study and enabling integration into broader meta-analyses. This varied approach allows for a more sophisticated interpretation of effect sizes that align with both intra-individual and between-subjects designs, enhancing the practical significance and cumulative scientific value of our findings. Statistical significance was set at \(p<0.05\). Statistical analyses were performed using the IBM SPSS Statistics 25 (IBM Corp., Armonk, NY, USA).
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis  
In recent years, the growth of Information Technologies (IT), including robotics, signal and image processing, computing, sensing, and communications, has coexisted with intelligent transportation systems' widespread use. It is anticipated that current junctions will operate more effectively by utilizing AV and communication technologies, such as Vehicle-to-Vehicle (V2V), Vehicle to Infrastructure (V2I), and Infrastructure-to-Vehicle (I2V.s) [23]. Using The Global Positioning System (GPS), laser, Light Detection and Ranging (LiDAR), camera, ultrasonic sensors, and laser, AVs may learn more about their surroundings. As a result, AVs are anticipated to improve the transportation system's safety, effectiveness, environmental sustainability, and passenger comfort.  
This section examines alternative methods for controlling traffic made up of connected and autonomous vehicles. Each main research article was carefully read and reviewed, and relevant qualitative and quantitative data were evaluated and reported in detail, as seen in Table 3. Research Questions, as outlined in Table 1, were taken into consideration.  
Figure 3: Publications over time.
## Abbreviations  
TES: thermal energy storage; PCMs, phase change materials; mPCMs, microencapsulated phase change materials; SHTES, sensible heat thermal energy storage; LHTES, latent heat thermal energy storage; TCS, thermochemical; Cp, specific heat; SA, stearic acid; PA, palmitic acid; MA, myistic acid; LA, lauric acid; CA, capric acid; MF, melamine-formaldehyde resin, UF urea-formaldehyde resin, PU polyuria; PS, polystyrene; St, styrene; MMA, methyl methacrylate; PMMA polymethyl methacrylate; SMA, styrene-maleic copolymer; MMF, methanol-modified MF; MMF, methylated melamine-formaldehyde; BMF, butylated melamine-formaldehyde; Fe\({}_{2}\)O\({}_{3}\), iron(III) oxide; ZnO, zinc oxide; TiO\({}_{2}\), titanium dioxide, CNFs, cellulose nanofibers; MDI, methylene diphenyl diisocyanate; TDI, toluene diisocyanate, TPDI, isophoronedisocyanate; TEOS, tetraethylorthosilicate; SiO\({}_{2}\), silicon dioxide; PLA, polylactic acid; PCL, polycaprolactone; MTES, methyltriethoxysilane; TNET, titanium (IV) butoxide; CaCO\({}_{3}\), calcium carbonate; CdS, cadmium sulfide; SnO\({}_{2}\), stannic oxide, GO, graphene oxide; Cu\({}_{2}\)O, copper (I) oxide; ZrO\({}_{2}\), zirconium oxide.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### The Yaw Angle Differences and the Duration of Eclipse
#### 3.2.2 GLONASS-M Satellites  
Different from the noon maneuver of GPS satellites, the GLONASS-M satellites do not start adjusting their attitude when the yaw rates exceed the maximum hardware yaw rates (\(0.25^{\circ}\)/s), but start at a certain time before the noon maneuver, rotating with the maximum hardware yaw rates. The attitude is symmetrical distribution centered around the noon. The orbital angle of the start time (\(\mu_{s}\)) and the end time (\(\mu_{e}\)) of maneuver satisfy \(\mu_{s}+\mu_{e}=2\pi\). To calculate the start time and the end time, an iterative algorithm is needed [9]. As shown in Figure 5, the maximum yaw angle differences of the noon maneuver are smaller than that of the GPS satellites for the same beta angle. When the sun angle is approximately \(0.05^{\circ}\) in Figure 5c, the difference can be up to around \(71.38^{\circ}\) and the duration can be up to around \(12\) min.  
Figure 4: The yaw angle of G06 (IIF) during shadow crossing for different sun angles: (**a**) beta = \(4.19^{\circ}\); (**b**) beta = \(1.58^{\circ}\); and (**c**) beta = \(-0.17^{\circ}\). The nominal yaw angle is shown in green, while the modeled one is in red.  
Figure 5: The yaw angle of R10 (M) during noon maneuver for different sun angles: (**a**) beta = \(-0.70^{\circ}\); (**b**) beta = \(-0.33^{\circ}\); and (**c**) beta = \(0.05^{\circ}\). The nominal yaw angle is shown in green, while the modeled one is in red.  
Figure 3: The duration and the yaw angle difference of G06 (IIF) during noon maneuver for different sun angles (\(-0.38^{\circ}\)–\(3.97^{\circ}\)): (**a**) the duration; and (**b**) the difference between the nominal yaw angle and the modeled yaw angle.  
As soon as the GLONASS-M satellites enter into the shadow crossing regime, they will rotate with the maximum hardware yaw rates so that the nominal attitude can be reached before the exit of the shadow crossing. The length of this period also depends on the value of the sun angle. Then, the attitude remains unchanged until out of the shadow crossing. As shown in Figure 6, the maximum yaw angle differences of the shadow crossing are significant bigger than those of the GPS IIF satellites for the same beta angle. Even the GLONASS-M satellite is at the shadow entry, as shown in Figure 6c, the maximum difference can be up to more than 20\({}^{\circ}\). When the sun angle is small (0.24\({}^{\circ}\)), as shown in Figure 6a, the maximum difference can be up to about 180\({}^{\circ}\) and the duration can be up to about 53 min. Therefore, the influence of the shadow crossing for GLONASS-M satellites is apparently greater than that of the GPS satellites (including both the IIR and IIF satellites).
###### Abstract  
In this paper, we investigate a novel power beacon (PB)-aided wireless sensor-powered non-orthogonal multiple-access (NOMA) Internet-of-Things (IoT) network under imperfect channel state information (CSI). Furthermore, the exact expression outage probability (OP) of two IoT users is derived to analyze the performance of the considered network. To give further insight, the expression asymptotic OP and diversity order are also expressed when the transmit power at the PB goes to infinity. Furthermore, a deep neural network (DNN) framework is proposed to concurrently forecast IoT users' OP in relation to real-time setups for IoT users. Additionally, when compared to the traditional analysis, our created DNN shows the shortest run-time prediction, and the outcomes predicted by the DNN model almost match those of the simulation. In addition, numerical results validate our analysis, simulation, and prediction through a Monte Carlo Simulation. Furthermore, the results show the impact of the main parameter on our proposed system. Finally, these findings show that NOMA performs better than the conventional orthogonal multiple-access (OMA) techniques.  
IoT; non-orthogonal multiple access (NOMA); imperfect CSI; deep neural network (DNN) +
Footnote †: journal: Applied Sciences  
1 Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam; [EMAIL_ADDRESS]  
2 Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic; [EMAIL_ADDRESS]  
3 Faculty of Fundamental 21, Pages and Telecommunications Institute of Technology,  
Ho Chi Minh City 70000, Vietnam; [EMAIL_ADDRESS]  
4 Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City 754000, Vietnam  
5 Faculty of Electrical Engineering and Informatics, University of Pardubice, 53210 Pardubice, Czech Republic; [EMAIL_ADDRESS]
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 3 Games Theory for WSNs Security
### Non-Cooperative Games
#### 3.2.4 Jamming Game  
The jamming game represents a scenario between the WSN defender player against the jamming attack. The attack aims to disrupt the transmitted data stream [58; 59]. The jamming game is fundamentally inspired from the zero-sum game framework in which the two players aim at maximizing their conflicting utilities. Consequently, the resultant of the two utilities are zero. Interestingly, the jamming game is used to treat the complexity constraints of implanted biomedical sensors as in [60; 61]. Furthermore, underwater sensor network is one of the booming applications that utilize the concept of jamming game [59].
## 5 Numerical Simulation on the Basis of NDT Results
### Mechanical Behavior under Various External Loads
#### 5.2.1 The Effect of Water Pressure  
In order to analyze the mechanical response of a damaged structure to the variation of water pressure, the control variates method was employed in this study [26]. The damage position remains the same, and different water pressure loads are calculated according to Equation (3) and applied to the numerical model. The numerical results under water pressures of 427 KPa, 457 KPa, and 487 KPa were obtained, as displayed in Figure 12a, in which \(C\) describes circumferential stress and \(L\) is used to describe longitudinal stress. The characteristics of stress distribution under different water pressures are described in the same coordinate system. Numbering the nodes clockwise from the  
Figure 11: Numerical modeling for the study site: **(a)** geological conditions; and **(b)** numerical model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground** & **Horizontal Pressure** & **Ground Resistance** & **Unit Weight** \\
**Types** & **Coefficient** & **(MPa/m)** & **(KN/m3)** \\ \hline Silt & 0.43 & 5 & 19.4 \\ Fine sand-1 & 0.40 & 50 & 19.3 \\ Fine sand-3 & 0.37 & 35 & 20.2 \\ Silt clay & 0.65 & 12 & 18.6 \\ Gravel & 0.25 & 80 & 20.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical parameters of surrounding rocks in the site.  
top of the hole, the curves of stress under different water pressures were calculated based on the numerical results, as displayed in Figure 12b. These figures indicated that the local damage decreases the stress circumferentially, but increases the stress longitudinally. These figures indicate that local damage causes an increase in the circumferential stress, and the longitudinal stress is greater than the circumferential stress. The effect of local damage diminishes with distance until it can be ignored. Furthermore, the stress response to the variation of water pressure changes in different directions. In detail, the longitudinal stress expresses a positive correlation with the increase of water pressure, but the contrary occurs in the circumferential direction. The maximum response value of stress longitudinally was 45 KPa in the case of water level changes per meter.
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 5 State of the Art of Multipath Congestion Control  
In recent years, significant strides have been made in optimizing MPTCP performance through the development of packet scheduling and congestion control mechanisms [21; 22; 23; 24]. Noteworthy contributions include [PERSON] et al.'s adaptive approach for multipath live stream ing [25] and the shared bottleneck multipath detection mechanism proposed by [26]. Multi-path congestion control has been addressed in works such as [27; 28; 29; 30], while advancements in packet scheduling are discussed in [31; 32; 33]. The MPTCP scheduler, employing the \"min-RTT\" policy [34], allocates packets to the path with the smallest RTT and has been widely studied [15; 16; 28; 35; 36; 37; 38].  
Recent machine learning advances have influenced MPTCP redesign, with notable contributions focusing on control strategies [15; 16; 17; 39; 40]. [PERSON] et al. [17] applied a deterministic policy gradient for efficient control strategies, while [PERSON] et al. [39] proposed multipath scheduling based on Deep Reinforcement Learning (DRL) on heterogeneous paths. [PERSON] et al. [40] introduced an adaptive virtual reality with content-aware prioritization to enhance MPTCP's performance.  
Three new multipath congestion control designs [15; 16] focus on congestion control but do not address packet scheduling. Notably, all the mentioned MPTCP designs [15; 16; 17; 39; 40] consider congestion control or packet scheduling separately instead of jointly.  
In the realm of MPTCP controllers,[16] demonstrates advantages when applied to a group of sources, diverging from the more common one-controller-per-source scenario. However, despite stability being a key goal in the original MPTCP design philosophy [6], the stability of the proposed algorithms [15; 16; 17; 39; 40] remains unaddressed.
## 2 Materials and Methods
### Functional and Non-Functional Requirements  
This section explains user requirements and key findings for functional and non-functional requirements. The initial requirements were derived from our previous study [3] as described in items 1 to 6 in Table 2, and then combined with the key findings (items 7 and 8) from our present study of the technology acceptance model.  
In addition, Thailand's Department of Highways' inspection procedure flow chart for bridge substructure was modified by adding a mobile application with assisted technology for the visual inspection process. (Figure 2) [25].  
Next, the system is designed to automatically synchronize data with the cloud server system via a mobile phone network (Figure 3).  
\begin{table}
\begin{tabular}{l} \hline \hline
**User Requirements and Key Findings** \\ \hline
1. The application can communicate in the same way as instant messaging. \\
2. The application can report to project managers in real-time. \\
3. The application can upload multimedia files, such as images or videos. \\
4. The application contains a progress status for each subtask. \\
5. The application is able to identify the responsible person for each task. \\
6. The application can send the report via email. \\
7. The application can be trialed for free before committing to use. \\
8. The application trial convinces the user that it has the potential to improve the user’s work \\ performance. This suggests that product features such as image acquisition to capture the bridge (VIS1), identify defects (VIS2), environment setup assistance (VIS3), and bridge defect information and bridge history information (VIS4), will support user collaboration and inspection issues. \\ \hline \hline \end{tabular}
\end{table}
Table 2: User requirements and key findings for bridge inspection mobile application.  
Figure 2: Modified inspection process of reinforced concrete bridge substructure by using mobile application with visual defect inspection assisted technology.
# Effect of Slag Particle Size on Fracture Toughness of Concrete
## 3 Experimental Details
### Specimen Fabrication  
Cylinder specimens (\(\Downarrow\)100 \(\times\) 200 mm) were cast from each mixture for compression testing; three cylinders each for testing at 4 ages (7, 14, 28, 56 days). Prism specimens with diminution of 50 \(\times\) 50 \(\times\) 650 mm and 80 \(\times\) 150 \(\times\) 700 mm were cast for the determination of fracture energy (G\({}_{\rm F}\)) and the critical stress intensity factor (K\({}^{\rm S}\)\({}_{\rm IC}\)), respectively. Four specimens were fabricated for each  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Chemical Analysis (\%)**} & \multicolumn{1}{c}{**SiO\({}_{2}\)**} & \multicolumn{1}{c}{**Al\({}_{2}\)O\({}_{3}\)**} & \multicolumn{1}{c}{**Fe\({}_{2}\)O\({}_{3}\)**} & \multicolumn{1}{c}{** CaO**} & \multicolumn{1}{c}{**MgO**} & \multicolumn{1}{c}{**SO\({}_{3}\)**} & \multicolumn{1}{c}{**Na\({}_{2}\)O**} & \multicolumn{1}{c}{**K\({}_{2}\)O**} & \multicolumn{1}{c}{**LOOI**} \\ \hline Cement & 20.9 & 5.62 & 3.21 & 63.6 & 2.52 & 2.16 & 0.27 & 0.52 & 0.92 \\ GGBS & 33.1 & 15.6 & 0.33 & 40.7 & 7.7 & 0.1 & - & - & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical analysis of cement and ground granulated blast furnace slag (GGBS).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Mixture No.** & **w/cm** & **Water** & **Cement** & **Slag** & **Sand** & **Aggregate** & **SP.** & **Slump** & **Slump Flow** \\ \hline R0 & & & & **kg** & & & **mm** & **mm** \\ S4R2 & & & 416 & 104 & 850 & 803 & 7.5 & 230 & 625 \\ S4R4 & & & 312 & 208 & 870 & 773 & 6.8 & 265 & 580 \\ S5R2 & 0.35 & 182 & 416 & 104 & 850 & 803 & 7.8 & 250 & 620 \\ S5R4 & & & 312 & 208 & 860 & 783 & 6.8 & 255 & 610 \\ S6R2 & & & 416 & 104 & 850 & 803 & 7.5 & 255 & 585 \\ S6R4 & & & 312 & 208 & 840 & 793 & 7.0 & 235 & 580 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mixture proportions and properties of fresh concrete.  
mixture that tested the two specimens at 2 ages (14 and 56 days). The specimens and concrete mixtures were prepared in accordance with ASTM C192. After casting, test specimens were covered with plastic sheets and left in the casting room for 24 h. They were then demolded and put into a 100% RH moist-curing room at about 23 \({}^{\circ}\)C until time for testing. One day before testing, the fracture energy specimen and the critical stress intensity factor specimen, as shown in Figures 1 and 2, were prepared by cutting a notches 25 mm deep by 2 mm width and 50 mm deep by 2 mm width, respectively, at the middle of the beam specimens.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 2 Credible Kalman Filter Model
### GNSS Module  
GNSS receivers generally use the WGS-84 ellipsoidal coordinate system [27] for coordinate representation, while the auxiliary sensor system generally uses the navigation coordinate system. In order to analyze the terminal position, the auxiliary sensor system in the mobile terminal and the GNSS location information must be put together in the same coordinate system. Therefore, it is necessary to transform the latitude, longitude and height information obtained from the GNSS in the geodetic coordinate system to the navigation coordinate system.  
Assuming the latitude, longitude and height information of the starting position \(\rho_{0}\) is \((b_{0},l_{0},h_{0})\), where \(b_{0}\) is the latitude, \(l_{0}\) is the longitude, and \(h_{0}\) is the height. Then, the position of \(\rho_{0}\) transformed into the WGS-84 coordinate system is \((X_{0},Y_{0},Z_{0})\). In the navigation coordinate system, the moving position \(\rho\) of the mobile terminal in the geodetic coordinate system is \((b,l,h)\), and the position \(\rho\) transformed into the navigation coordinate system is \((e^{\rm G},n^{\rm G},u^{\rm G})\). The major axis of the ellipsoid model is \(a=6,378,137\) km, and the oblateness is \(e=1/298.257\). Then, the state parameter \(x^{\rm G}\) of the GNSS model in the navigation coordinate system is  
\[x^{\rm G}=\left[\begin{array}{c}e^{\rm G}\\ n^{\rm G}\\ u^{\rm G}\end{array}\right]=\left[\begin{array}{ccc}-\sin l_{0}&\cos l_{0}& 0\\ -\sin b_{0}\cdot\cos l_{0}&-\sin b_{0}\cdot\sin l_{0}&\cos b_{0}\\ cosb_{0}\cdot\cos l_{0}&\cos b_{0}\cdot\sin l_{0}&\sin b_{0}\end{array}\right] \left[\begin{array}{c}(a+h)\cdot\cos b\cdot\cos l-X_{0}\\ (a+h)\cdot\cos b\cdot\sin l-Y_{0}\\ (a\cdot(1-e^{2})+h)\cdot\sin b-Z_{0}\end{array}\right]. \tag{1}\]