esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Generation of Collision Advoidance Course
#### 3.3.3 Optimal Collision Free Trajectory
\tag{44}\]  
Using (43), (30), (41) and (42), the velocities of the new collision avoidance trajectory and the solvable matrices \(\mathbf{c}^{new}\) and \(\mathbf{s}^{new}\) can be formulated and calculated, respectively, as:  
\[v_{i}^{x}=3a_{4i}\varpi^{2}+2a_{3i}\varpi+a_{2i} \tag{45}\]  
\[v_{i}^{y}=3d_{4i}\varpi^{2}+2d_{3i}\varpi+d_{2i} \tag{46}\]  
\[\mathbf{c}^{new}=\mathbf{A}^{-1}\mathbf{g}^{new} \tag{47}\]  
\[\mathbf{s}^{new}=\mathbf{A}^{-1}\mathbf{h}^{new} \tag{48}\]  
To allow the controlled USV sail on ocean with a reference velocity \(U_{ref}\), we reformulated (22) as the following:  
\[\dot{\varpi}(t)=\frac{U_{d}(t)}{\sqrt{\mathbf{x^{\prime}}_{d}(\varpi)^{2}+y^{ \prime}_{d}(\varpi)^{2}}},\varpi(t_{k})=k \tag{49}\]  
where \(\varpi\) (\(t_{k}\)) = \(k\) is the initial condition of the differential Equation (49). Let the reference velocity, \(U_{ref}\) be the input of the first-order system below,  
\[T\dot{U}_{d}(t)+U_{d}(t)=U_{ref},T>0 \tag{50}\]  
where \(T\) is a time constant, and the initial condition is \(U_{d}=0\). It is easy to get the result \(\lim\limits_{t\rightarrow\infty}(U_{ref}-U_{d}(t))=0\) from (50). The desired heading angle (\(\psi\)) of the controlled USV can be presented as:  
\[\psi_{d}=\lim\limits_{\Delta\to 0}(\text{atan2}(y_{d}(\varpi+\Delta)-y_{d}( \varpi),x_{d}(\varpi+\Delta)-x_{d}(\varpi))) \tag{51}\]  
where \(\Delta\) ideally approaches 0. From the above mathematical derivations, a new collision avoidance trajectory with a designated velocity can generated by using (45), (46), (47), (48), (49), and (51).
## 5 Performance Analysis
### Packet-Received Ratio  
Figure 21 shows the number of packets received by the BS while the network status is stable. The network throughput of D-LEACH increased relative to those of LEACH and MH-LEACH. This is because the D-LEACH algorithm adopts the direction of data transmission to reduce unnecessary energy consumption. Thus, the nodes have a longer survival time. The time that the nodes survive in the network is longer than that of other algorithms, and the number of packets received by the BS also increases.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 4 Design of PCEB-MS Protocol
### UCEBA Algorithm  
The UCEBA algorithm is mainly used in the selection of the cluster head, calculation of the unequal campaign radius, and generation of the final cluster head. Each sensor node joins the cluster in one hop. For the overlapping region with two sinks, the sensor node will obtain a two sink ID; these sensor nodes select the closer sink and transmit the data to that sink directly. To guarantee the energy balance and to prolong the network lifetime, the cluster that is furthest from the sink has a larger radius and more cluster members than the cluster that is closer to the sink. Figure 8 shows the specific clustering mechanism involved. Accordingly, the cluster near sink 1 has a small radius, whereas that which is furthest from sink 1 has a large radius. The communication process is described as follows. First, the sinks are set in the middle of the coalmine tunnel at random intervals (i.e., not more than the maximum communication radius of each sink). Second, the transmission power between the sinks is controlled to ensure network coverage dynamically based on the DPCA algorithm. Third, the clusters are built within the coverage of each sink as individual regions using the UCEBA algorithm; the transmission power of the sensor nodes can then be controlled to guarantee minimum energy consumption. Finally, the sensor nodes use one hop or two hops to transmit data to the sink directly or via the cluster head.  
Figure 7: Procedure of DPCA algorithm.
## 5 Results and Discussion
### Stiffness  
The bending stiffness was determined by a factor calculated using Equation (9). Due to the stiffness degradation of the real and numerical elements for the elastic-plastic model, the coefficient was estimated for a load range of 0.1 to 0.4 \(F_{max}\). The estimated stiffness comparison is presented in Table 7. As demonstrated by the findings of [5; 7; 27; 28; 29], a high degree of accuracy was observed between the experimental and theoretical stiffness values, with maximum differences of 6%. A comparable degree of accuracy was also achieved in numerical models in which the LVL is represented as an elastic-plastic material. As in the work [28], the most significant discrepancies were observed in the case of CFRP UHM sheet reinforcement. In the case of numerical models with elastic modeling, the results obtained in each case analyzed indicated that the gain was more significant than the experimental value. At most, the difference between the two values was several per cent.
## 6 Numerical Examples
### Multiple Intersecting Spherical Pressure Hulls  
In practical applications, a pressure hull requires a certain intermediate blank area to be reserved for storing equipment and liquids [42]. Therefore, in this example, the optimal design of the axisymmetric model of the pressure hull was carried out. The initial design domain, initial pressure constraints, and boundary conditions are shown in Figure 23. To improve the calculation efficiency, the 1/4 model was used for the calculation. The design area was a rectangular area with dimensions of 2.5 m \(\times\) 1 m. The value of the hydrostatic pressure was 2 \(\times\) 10\({}^{-2}\). The area was divided into a 90 \(\times\) 36 grid. The design material volume fraction was 0.3 of the design area.  
First, the topology optimization design for the compliance minimization problem subjected to volume constraints was carried out. The final optimization result is shown in Figure 24. The first-order buckling factor of the optimized structure was 0.80. The optimization results subjected to different buckling constraints are shown in Figure 21.  
The optimization results with different constraints are shown in Figure 25. When the buckling constraint was 0.85, the material moved to the constraint at the bottom. As the buckling constraint increased, the material gathered at the left constraint and generated holes or support rods. When the buckling constraint was increased to 1.00, two support rods were created, and a hole and a support rod were created at the bottom-right constraint, which was similar to the three previous examples.  
The above final optimization structures were solved with 90 \(\times\) 36 mesh grids, and the optimization results with large-scale mesh grids (270 \(\times\) 108) subjected to different buckling constraints are shown in Figure 26. Because of the high concentrations of stress in the corners of the structures, with the increase in the scale of the mesh grid division, the stress in the corners increases. This results in an increase in the value of the determinant of the geometric stiffness matrix, so the values of the buckling factor become smaller. For a certain pressure structure, the critical buckling loads were constant and equal to the buckling factor, timing the hydrostatic pressure. Therefore, to highlight the clear change in the structures with increasing buckling factor values, hydrostatic pressure was set to 2 \(\times\) 10\({}^{-4}\) in this numerical example, and the buckling factor was set to 25-29.5.  
Figure 23: (**a**) Design domain, (**b**) 1/4 boundary conditions of the axisymmetric model.  
Figure 24: Compliance minimized result.  
Figure 25: Optimization results with different constraints.  
Figure 26: Optimization results with different constraints.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 6 oupPLUS: The Quality Assurance View on Smart Cities

Table 1: SAPs and examples for their implementation by protocols. The above-mentioned abstract open interfaces are constituted by the Service Access Points (SAPs) in Figure 5. The term SAP is borrowed from the Internet/telecommunication domain (especially TCP/IP and ISO/OSI). The SAPs of oupPLUS are abstract interconnections between various layers with their belonging capabilities and instantiating components in a real urban ICT instantiation of oupPLUS. Thereby, the SAPs normally connect between modules, components, or services. A particular instantiation of an SAP is given by a whole communication stack instance including a physical-, link-, network- and service-layer protocol, such as Ethernet/IPv6/TCP/HTTP/DCAT-AP to give an example. More detailed descriptions on the single SAPs are provided in [6]. However, in Table 1 we provide a brief introduction of the SAPs from Figure 5 and list relevant protocols stacks and standards which can play a role within a smart city ICT backbone. Furthermore, the information in Table 1 already hints on how oupPLUS can be used as a concept for Quality Assurance, namely by simply \"testing the SAPs\" which automatically means the testing of the relevant protocol stacks. In this setup, all possible types of test examinations--conformance-, interoperability-, security/penetration-, load- and performance-testing--can be executed  
\begin{table}
\begin{tabular}{p{34.1 pt} p{284.5 pt}} \hline \hline
**SAP-Name: Description** & \multicolumn{1}{c}{**Possible Protocols (Protocol Stacks) and Additional Standards for the SAP Implementation**} \\ \hline \multirow{3}{*}{RtCD-SAP: Routing Coordination and Discovery SAP} & Network Layer Protocols: (1) Internet Control Message Protocol (ICMP) for IPv4/v6), (2) IPv4/v6, (3) Internet Protocol \\ \cline{2-3}  & Security (IPSec), (4) Multiprotocol Label Switching (MPLS) \\ \cline{2-3}  & Routing Protocols: (1) Routing Information Protocol (RIP) or RIP next generation (RIP-ng), (2) Open Shortest Path First \\ \cline{2-3}  & (OSPF v3), (3) Border Gateway Protocol (BGP v4), (4) Intermediate System to Intermediate System routing protocol (IS-IS) \\ \cline{2-3}  & Other network coordination protocols: (1) Network Time Protocol, (2) DHCPv6), (3) Neighbor Discovery Protocol (ND), (4) Address Resolution Protocol (ARP), (5) Domain Name System (DNS) \\ \hline \multirow{3}{*}{ATH-SAP: Authentication-/Authorization  
leading to the systematic approach to high quality ICT components and complex systems. The aspect of Quality Assurance is further detailed in Section 7.2.
## 3 Materials and Methods
### ECG Signal Preprocessing  
The performance of machine learning algorithms can be brought down due to errors that may appear due to noise interruption or other input data corruption leading to improper feature values. Thus, a signal preprocessing is required to remove unwanted data corruption of the ECG signals: breathing, skin interference, baseline wander, powerline interference, motion artifact due to electrodes, muscle artifact, white Gaussian noise, etc. [43]. Since this work proposes the full data flow analysis from acquisition to classification as in a real scenario, we add this data preprocessing step, too. The steps used in the preprocessing stage to prepare signals for later processes are:  
* Reduction of the baseline wandering, aiming to provide better quality and definition of the temporal signal, which will later result in better feature extraction. This stage involves the introduction of an 8 th order infinite impulse response filter (IIR) with a Butterworth bandpass type ranging from 1 Hz to 45 Hz [44,45]. Figure 6 shows the effect of applying this bandpass filter, resulting in a reduction of the baseline.
* Later, a Window Reference Marks (WRM) and a time window (_tw_) are obtained. The mark indicates the start of each time window from which the features will be extracted. Consecutive time windows are obtained to analyze all ECG data. As the values between 50 and 120 beats per minute (bpm) can be considered a normal heart rate range [46], the minimum and maximum distance values between any two consecutive WRMs were established in 0.5 s and 1.2 s, respectively. Next, an algorithm already developed by the authors in [47] was used to obtain the calculation of WRM reference marks. A time window _tw_ of 1.2 s (150 samples) in length was obtained, starting at each WRM reference mark, as the following equation shows, with \(\{j=1,2,\ldots,NLMC\}\), where \(NLMC\) is the number of local maxima LM marks existing in the ECG signal: \[tw_{i}=[WRM_{j},WRM_{j}+1.2s]\]  
Figure 6: Bandpass filter application to a data segment from the _Normal_ ECG class, and resulting baseline reduction.  
* For each time window, the Taken's Delay Embedding Theorem is applied to convert the ECG data (a time series) to a Representation of Point Cloud data (RPC), a Persistence Diagram Representation (PDR), a Persistence Landscape (LR) and a weighted Silhouette Representation (SR).
# Metabolomic Insights into the Potential of Chestnut Biochar as a Functional Feed Ingredient
## 2 Materials and Methods
### QTOF HPLC MS/MS Metabolomic Characterization of the Chestnut Agro-Residue Biochar Extract  
Qualitative metabolomic analysis of CB extracts was performed using Q-TOF HPLC MS/MS (Sciex, Framingham, MA, USA). The separation column used was a Synergy Hydro- RP 80A, LC column 250 mm, 4.6 mm, 4 \(\upmu\)m (Phenomenex, LaneCove, Australia) set at a temperature of 25 \({}^{\circ}\)C with a binary mobile phase: A (H\({}_{2}\)O with 1% formic acid) and B (acetonitrile with 0.1% of formic acid). The gradient elution program was a 0.4 mL min\({}^{-1}\) flow rate with a linear gradient: from 0 to 5 min 0.5% of A and 95.5% of B; from 5 to 25 min 75% of A and 25% of B; from 25 to 31 min 5% of A and 95% of B; and from 31 to 35 min 95% of A and 5% of B. The injection volume was 10 \(\upmu\)L. MS/MS analysis was performed in both positive and negative modes. The raw data were analyzed using two libraries, metabolites, and natural compounds (Sciex OS version 3.1, Framingham, MA, USA)
## 4 Numerical Verification for Design
### Results Analysis
#### 4.4.2 Acceleration Response  
The acceleration response and peak values obtained through time history analysis (based on the input motion for each location under seismic loading) are presented in Figure 10. The maximum acceleration response occurred at the location corresponding to the footing concrete (A1) of the target structure. It can be observed that the acceleration response decreases as it moves towards the upper part of the hydrogen storage vessel (A4). Accordingly, the amplification generated at the lower part by seismic motion is predicted to decrease as it is transferred towards the point where the center of gravity of the target structure is located, resulting in a relatively smaller impact on acceleration. However, in the case of support columns composed of flexible materials such as steel, acceleration amplification may be induced. This is anticipated to have a significant impact on the anchor system connecting the hydrogen storage vessel and concrete.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode No.** & **Eigenvalue** & \
\begin{tabular}{c} **Natural Frequency** \\ **(cycle/sec)** \\ \end{tabular} &
\begin{tabular}{c} **Effective Mass * \\ **(X-Component)** \\ \end{tabular} \\ \hline
1 & 8071.7 & 14.299 & 1.05935 \\
2 & 8072.1 & 14.299 & 0.000339 \\
3 & 34,610 & 29.609 & 1.24 \(\times\) 10\({}^{-9}\) \\
4 & 57,194 & 38.062 & 6.28 \(\times\) 10\({}^{-8}\) \\
5 & 57,194 & 38.062 & 6.01 \(\times\) 10\({}^{-8}\) \\
6 & 57,194 & 38.062 & 6.42 \(\times\) 10\({}^{-8}\) \\
7 & 57,195 & 38.063 & 6.12 \(\times\) 10\({}^{-8}\) \\
8 & 59,922 & 38.959 & 1.88 \(\times\) 10\({}^{-6}\) \\
9 & 59,923 & 38.960 & 1.66 \(\times\) 10\({}^{-6}\) \\
10 & 59,923 & 38.960 & 2.09 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}  
* The total effective mass at axis-X is 1.05955.  
\end{table}
Table 3: Eigenvalue analysis results.  
Figure 9: View of the first mode shape (scale: 1:200): (**a**) isometric and (**b**) axis X-Y: side.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Predictive** & \multicolumn{2}{c}{**Input**} & \multicolumn{1}{c}{**Stages**} & **RMSE** & **MAE** & **LMI** & **CC** & **WI** & **SRMSE** \\
**Models** & **Combination** & **Stages** & **RMSE** & **MAE** & **LMI** & **CC** & **WI** & **SRMSE** \\ \hline ANFIS-DE & Model-8 & Train & 0.376 & 0.284 & 0.533 & 0.865 & 0.925 & 21.474 \\ ANFIS-DE & Model-8 & Test & 0.406 & 0.299 & 0.520 & 0.863 & 0.917 & 25.314 \\ ANFIS-DE & Model-9 & Train & 0.416 & 0.308 & 0.494 & 0.831 & 0.902 & 23.745 \\ ANFIS-DE & Model-9 & Test & 0.470 & 0.358 & 0.426 & 0.811 & 0.876 & 29.340 \\ ANFIS-DE & Model-10 & Train & 0.379 & 0.280 & 0.540 & 0.862 & 0.921 & 21.659 \\ ANFIS-DE & Model-10 & Test & 0.404 & 0.293 & 0.530 & 0.866 & 0.916 & 25.229 \\ ANFIS-DE & Model-11 & Train & 0.383 & 0.285 & 0.531 & 0.859 & 0.919 & 21.876 \\ ANFIS-DE & Model-11 & Test & 0.408 & 0.293 & 0.529 & 0.862 & 0.914 & 25.466 \\ ANFIS-DE & Model-12 & Train & 0.559 & 0.450 & 0.260 & 0.674 & 0.780 & 31.958 \\ ANFIS-DE & Model-12 & Test & 0.583 & 0.471 & 0.243 & 0.692 & 0.797 & 36.364 \\
**ANFIS-DE** & **Model-13** & **Train** & **0.375** & **0.281** & **0.538** & **0.865** & **0.922** & **21.417** \\
**ANFIS-DE** & **Model-13** & **Test** & **0.398** & **0.291** & **0.533** & **0.870** & **0.919** & **24.838** \\ ANFIS-DE & Model-14 & Train & 0.404 & 0.307 & 0.496 & 0.845 & 0.909 & 23.073 \\ ANFIS-DE & Model-14 & Test & 0.411 & 0.308 & 0.506 & 0.858 & 0.915 & 25.631 \\ ANFIS-DE & Model-15 & Train & 0.402 & 0.295 & 0.515 & 0.844 & 0.913 & 22.941 \\ ANFIS-DE & Model-15 & Test & 0.462 & 0.345 & 0.445 & 0.815 & 0.889 & 28.833 \\ ANFIS-DE & Model-16 & Train & 0.377 & 0.281 & 0.538 & 0.863 & 0.922 & 21.542 \\ ANFIS-DE & Model-16 & Test & 0.399 & 0.289 & 0.536 & 0.870 & 0.918 & 24.912 \\ ANFIS-DE & Model-17 & Train & 0.381 & 0.283 & 0.534 & 0.861 & 0.919 & 21.745 \\ ANFIS-DE & Model-17 & Test & 0.399 & 0.287 & 0.540 & 0.870 & 0.917 & 24.893 \\ ANFIS-DE & Model-18 & Train & 0.580 & 0.477 & 0.216 & 0.631 & 0.740 & 33.116 \\ ANFIS-DE & Model-18 & Test & 0.599 & 0.477 & 0.235 & 0.659 & 0.773 & 37.391 \\ \hline \hline \end{tabular}
\end{table}
## 5 Research Methodology and Data Analysis  
### Research Methodology  
This study combines quantitative and qualitative research methods to identify effective ways to improve AOG processes at the research base and evaluate their impact on efficiency. Using this mixed-methods approach, this study provides a comprehensive understanding of AOG challenges and solutions, offering insights that are relevant not only to the research base but also to other airlines. The quantitative analysis focuses on key trends such as AOG incidents, spare parts usage, and maintenance patterns. These data are used to develop accurate forecasting models for spare parts demand and maintenance schedules, helping to reduce AOG events. The quantitative aspect also involves gathering feedback from industry experts, engineers, and supply chain managers through structured questionnaires, allowing for measurable insights into logistical and operational challenges. The qualitative approach complements this by capturing experiences and practical difficulties that cannot be fully expressed through numbers. This includes insights into spare parts management and logistical coordination, offering a deeper perspective on day-to-day challenges. Together, these methods provide a well-rounded analysis of AOG management at SmartLynx, offering actionable recommendations to minimize downtime and improve  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Total AOG**} & \multicolumn{2}{c}{**Total Downtime**} & \multirow{2}{*}{**Total Cost (EUR)**} \\  & **Incidents** & **(Hours)** & \\ \hline
2020 & 147 & 630 & 1,470,000 \\ \hline
2021 & 131 & 580 & 1,310,000 \\ \hline
2022 & 106 & 560 & 1,060,000 \\ \hline
2023 & 90 & 430 & 900,000 \\ \hline
2024 & 63 & 320 & 630,000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical overview of AOG incidents in SmartLynx. Source: SmartLynx Quarterly Technical Reliability Reports (2020–2024).  
operational performance. Moreover, the authors conducted a detailed survey and expert interviews to dig deeply and research the airline, aiming to address the findings.
## 2 Underwater Glider Modeling  
### The Petrel-L Underwater Glider  
Petrel-L, depicted in Figure 1, is an advanced underwater glider developed by Tianjin University in China. It features a cylindrical pressure hull that accommodates several components, including the emergency device, fixed wings, two fairings, and a trailing antenna. Within this pressure hull, the glider is equipped with numerous subsystems such as buoyancy-regulating, pitch-regulating, roll-regulating, and control units, as well as communication, navigation units, and a battery pack.  
Figure 1: Petrel-L underwater glider.  
As illustrated in Figure 2, UGs performing ocean observation operations are generally remotely controlled via satellite communication from a shore-based control center. Upon receiving instructions, the glider undertakes profile observation maneuvers, while onboard sensors simultaneously gather data on the marine environment. The conventional workflow comprises three phases: the surface communication, diving, and surfacing phases. Due to technological limitations, current technologies do not allow for stable communication during deep water operations; the glider is, thus, in a no-communication state while diving.  
Surface communication phase: The buoyancy drive unit of the underwater glider adjusts the total displacement volume to ensure its net buoyancy exceeds gravity, allowing it to float on the surface. According to a predetermined optimal communication angle, the attitude adjustment unit configures the glider in a nose-down posture, exposing the stern antenna above the water surface for satellite link communication with the shore-based control center. During this phase, the glider transmits its location and attitude information and receives control commands.  
Diving phase: Following the reception of a \"start gliding\" command from the control center, the buoyancy drive unit adjusts the displacement volume to render the glider's net buoyancy less than gravity. The attitude adjustment unit continuously modifies the pitch and heading angles to maintain a pre-set pitch angle for the diving maneuver. The onboard control unit concurrently activates the task sensors to begin data collection.  
Climbing phase: Once the depth sensor detects that the actual depth has reached a pre-set threshold, the buoyancy drive unit adjusts the buoyancy of the glider to exceed gravity, initiating upward movement. Simultaneously, the attitude adjustment unit transitions the glider from a nose-down to a nose-up posture, maintaining the pre-set pitch angle during the ascent. The task sensors operate similarly to the diving phase until the depth sensor indicates that the actual depth has reached the surface level, after which the various modules of the underwater glider enter the next profile surface communication phase.