esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment of a Grouting Pipeline Blockage Model
### Fluent Software Settings  
FLUENT 2020R2 is a commonly used CFD [20; 21; 22] software for simulating fluid flows, heat transfer, and chemical reactions. It can provide results that closely approximate experimental findings and reflect the overall characteristics of fluid motion, such as flow velocity, pressure, and other dynamic parameters. This study investigates the flow behavior of slurry in injection pipelines, where the pipeline temperature is approximately 20 \({}^{\circ}\)C. Since the filling slurry is a multiphase complex fluid containing gas, liquid, and solid phases, and it behaves as a non-Newtonian Bingham plastic [23; 24], the slurry maintains its homogeneity and uniformity during pipeline transport without segregation or layering. As a simplification, the slurry flow is assumed to be single-phase. The semi-implicit method for pressure-coupling equations is selected. The inlet is set as a velocity inlet with an inflow velocity of 2 m/s, and the outlet is set as a pressure outlet with a static pressure of 10,100 Pa (relative pressure under atmospheric conditions). To verify the accuracy of the simulation model and computational precision, a residual monitoring window is set, and the residual curve is shown in Figure 8. After fluid initialization, the iteration step is set to 600 steps for computation. During the iterations, the residual curves for each condition converge, and the flux conservation at the inlet and outlet approaches zero, as shown in Figure 9. This indicates that the simulation model closely represents the actual slurry blockage scenarios. The slurry is modeled as a non-Newtonian Bingham fluid, specifically using the H-B model. The slurry density is set to 1700 kg/m\({}^{3}\), and the H-B model parameters are configured as follows: the exponent is set to 1, the critical shear stress is 100 r/s, the apparent viscosity is 165.6 Pa\(\cdot\)s, and the slurry yield stress is 8.277 Pa.  
The rheological model for the slurry is recommended to be the Herschel-Bulkley model, abbreviated as H-B model, with the general equation as follows:  
\[\tau=\tau_{0}+\mu\gamma^{n}, \tag{7}\]  
where  
\(\tau\) is the shear stress, in Pa;  
\(\gamma\) is the shear rate, in s\({}^{-1}\);  
\(\mu\) is the apparent viscosity, in Pa\(\cdot\)s;  
\(\tau_{0}\) is the initial yield stress, in Pa;  
\(n\) is the flow behavior index. When \(n=1\) and \(\tau_{0}=0\), it is a Newtonian fluid;  
Figure 8: Residual curves for simulated operating conditions.  
Figure 9: Inflow and outflow diagram for simulated operating conditions.  
when \(n=1\) and, \(\tau_{0}>0\), it is a Bingham plastic; when \(n>1\), it is a dilatant fluid; when \(n<1\), it is a pseudoplastic fluid.
## 4 Results and Discussion
### Variation Characteristics of Wavelength of Compression Waves  
Figure 12 presents the pressure waveforms of the initial compression waves at the measurement points of 1 km, 4 km, 7 km, 10 km, 13 km, 16 km, and 19 km when propagating in a 20 km tunnel without a tunnel entrance hood. Figure 13 represents the variation characteristics of the wavelength with propagation distance. Figure 14 depicts the variation pattern of the pressure gradient with the wavelength of the compression waves during its propagation in the tunnel. As the propagation distance of the compression waves increases in the tunnel, its amplitude decreases while the wavelength shortens and the waveform steepens. During the propagation of the compression waves, its wavelength exhibits a trend of initially decreasing and then gradually increasing. The wavelength reaches its minimum value of 23.88 m at a distance of 5 km. During the propagation of the compression waves, the pressure gradient increases as the wavelength of the compression waves decreases. The smaller the wavelength of the compression waves, the more pronounced the variation in the pressure gradient.  
Figure 12: The pressure waveforms at different measurement points during the propagation of compression waves.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 5 Discussion
### Traffic Efficiency and Flow  
The findings from this study support the growing body of literature indicating that AVs can enhance traffic flow efficiency, particularly under specific driving behaviors and penetration rates. Aggressive and platooning AVs significantly reduced queue lengths, travel times, and delays compared to human-driven vehicles. For instance, at a 60 s cycle time, aggressive AVs reduced queue lengths by up to 56.62%, travel times by up to 21.15%, and vehicle delays by up to 27.59%. Similarly, platooning AVs at 100% penetration achieved comparable reductions, highlighting their efficiency in streamlining traffic flow.  
However, the aggressive AV behavior introduced instability at higher signal cycle times, leading to increased vehicle oscillations and traffic disturbances, particularly at a 204 s cycle time where the reductions in queue lengths and travel times dropped to about 23.14% and 6.63%, respectively. This phenomenon aligns with the findings of [11], who highlighted trade-offs between aggressive AV driving patterns and traffic flow stability, especially in dense urban settings. To mitigate these issues, adaptive traffic signal control systems that dynamically adjust to real-time traffic conditions and AV behaviors could be implemented. Moreover, integrating cooperative AV algorithms that enable vehicle-to-vehicle and vehicle-to-infrastructure communications could further reduce traffic oscillations.  
On the contrary, cautious AVs, while enhancing safety and promoting smoother traffic flow, resulted in longer queue lengths and higher delays, particularly at higher penetration rates. At 100% penetration and a 60 s cycle time, cautious AVs increased queue lengths by up to 82.30% and delays by up to 59.03%. This supports research by [4; 15; 38], which suggests that risk-averse AVs may induce traffic inefficiencies due to larger gaps and slower acceleration rates, especially in interactions with human drivers.  
Normal AVs provided a more balanced performance, mitigating extremes in traffic behavior by reducing queue lengths by up to 33.91% and delays by up to 15.26% at 60 s. This balance suggests that normal AVs may offer an optimal compromise between efficiency and safety in mixed-traffic environments as they navigate between the conservative and aggressive extremes.  
In mixed AV environments, vehicle delays and queue lengths were more stable, generally falling between the extremes presented by cautious and aggressive behaviors. This mixed strategy led to reductions ranging from about 14.34% to 38.82% in queue lengths and from 0.85% to 16.98% in vehicle delays at 60 s. This demonstrates that a diversified approach to AV behavior can provide smoother and more predictable traffic flow, ensuring broader benefits across different traffic scenarios.
# Guided Self-Accelerating Airy Beams--A Mini-Review
## 2 Results and Discussion
### Nonlinear Guidance
#### 2.1.2 Saturable Medium  
In the saturable NL medium, the nonlinearity is of the form \(\delta n=|\psi|^{2}/(1+|\psi|^{2})\). The behavior of interacting Airy beams is quite similar to the case of the Kerr medium, but the interactions also become \"plastic\". As a rule, the in-phase case can generate individual solitons that are positioned centrally. For small amplitudes \(A_{1}\) and \(A_{2}\), the individual solitons or soliton pairs cannot be formed in the interaction. Importantly, the repulsion between soliton pairs formed in the saturable NL medium is stronger than that in the Kerr medium. Different from the Kerr case, the propagation in saturable NL medium is stable for arbitrary \(A_{1}\) and \(A_{2}\).
# Study on Fire Smoke Distribution and Safety Evacuation of Subway Station Based on BIM
## 4 Fire Smoke Analysis of Subway Station
### Fire Simulation Analysis at the End of the Platform
#### 4.2.3 CO Concentration  
The smoke concentrations at 60 s, 120 s, 180 s, 240 s, 300 s and 360 s after the fire at the end of the platform is shown in Figure 7. With the continuous combustion of the ignition source, the CO concentration gradually increases. When t = 60 s, the CO concentration is low; when t = 120 s, the CO concentration at the top of the fire source at the end of the platform rises to 500 ppm; when t = 180 s, with the spread of smoke, the CO concentration at the top of the first group of escalators rises to 700 ppm; when t = 240 s, the CO concentration at the top of the fire source reaches 1100 ppm. Meanwhile, the CO concentration at the ceiling in the middle of the platform reaches 500 ppm. The CO concentration at the two groups of stairs and escalators on the right side of the platform is low; when t = 300 s, the CO concentration at the ceiling at the top of the fire source reaches 1700 ppm, and the CO concentration at the two groups of stairs and escalators on the right side of the platform reaches 500 ppm; when t = 360 s, the CO concentration at the top of the fire source reaches the dangerous critical value, and the CO concentration at other locations is lower than 1900 ppm.  
Within 360 s, CO accumulates only near the fire source in the whole subway platform, leading to the critical value of CO concentration in the ceiling near the fire source. The CO concentration in other locations is low and does not reach the critical value, which does not affect the safety evacuation of the subway platform personnel.
## 3 Results
### Flexural Strength  
UHPC's flexural strength is directly proportional to the silica fume (SF) content employed in this investigation. At the 5-day age of UHPC, using 15% and 20% SF resulted in increases of 28.4% and 42.3% compared to 10% SF. At 28 days, these increases were 17.5% and 43.9%. When replacing SF with RHA at 5% and 10% of the binder's mass, the flexural performance of UHPC improved both at 5 days and 28 days. The increase in samples using 10% RHA compared to those using only SF (SF20) was 8% at 28 days. It is in line with research findings from [46], who concluded that replacing SF with RHA can enhance UHPC's flexural strength by 10.5% at 28 days of age. The flexural strength of UHPC is enhanced as a result of the improved bonding of high-strength steel fibers regarding the matrix of concrete, which is facilitated by the increase in compressive strength. Figure 6 shows the flexural strength of UHPC at 5 days and 28 days of age when using different ratios of SF and RHA.  
The outcome can be elucidated primarily through improvements in the packing of granular mixtures. Insights gleaned from the study outlined highlight that in mixtures of a 0.165 water-to-cement ratio, a significant portion of up to 50% of the cement fails the approach of substituting cement with alternative materials. Furthermore, the incorporation of finer materials emerges as a strategic avenue capable of yielding a more favorable packing density. It contributes to an amelioration in the fundamental properties of the concrete, signifying a notable enhancement in its overall structural performance.
# Adaptive Trajectory Tracking Control for
## 2 Problem Formulation and Preliminaries
### Problem Formulation  
The corresponding relationship between the body-fixed frame and the earth-fixed inertial frame is shown in Figure 1.  
\(o-x_{0}y_{0}z_{0}\) is the body-fixed frame and \(O-X_{0}Y_{0}Z_{0}\) is the earth-fixed inertial frame. As shown in Figure 1, USV has six degrees of freedoms (DOFs): surge velocity \(u\), sway velocity \(v\), yaw rate \(r\), heave velocity \(w\), rolling rate \(p\) and pitching rate \(q\). However, in order to reduce the complexity of the model, heave velocity, rolling rate and pitching rate are often ignored in the actual study of its motion control. To be more exact, \(w~{}=~{}p~{}=~{}q~{}=~{}0\). According to [28], the three DOFs kinematic and dynamic models of underactuated USV can be expressed as Equations (1) and (2), respectively.  
\[\left\{\begin{array}{rcl}\dot{x}&=&u\cos(\psi)-v\sin(\psi)\\ \dot{y}&=&u\sin(\psi)+v\cos(\psi)\\ \dot{\psi}&=&r\end{array}\right. \tag{1}\]  
\[\left\{\begin{array}{rcl}\dot{u}&=&f_{u}-\frac{1}{m_{11}}\Delta_{u}+\frac{1} {m_{11}}\tau_{u}+\frac{1}{m_{11}}b_{u}\\ \dot{v}&=&f_{v}-\frac{1}{m_{12}}\Delta_{v}+\frac{1}{m_{12}}b_{v}\\ \dot{r}&=&f_{r}-\frac{1}{m_{33}}\Delta_{r}+\frac{1}{m_{33}}\tau_{r}+\frac{1} {m_{33}}b_{r}\end{array}\right. \tag{2}\]  
with \(f_{u}~{}=~{}\frac{m_{22}}{m_{11}}vr-\frac{d_{11}}{m_{11}}u\), \(f_{v}~{}=~{}-\frac{m_{11}}{m_{22}}ur-\frac{d_{22}}{m_{22}}v\), \(f_{r}~{}=~{}\frac{m_{11}-m_{22}}{m_{33}}uv-\frac{d_{33}}{m_{33}}r\), where \((x,y)\) represents the position of the USV; \(\psi\) is the heading angle; \(m_{11}\), \(m_{22}\), \(m_{33}\), \(d_{11}\), \(d_{22}\) and \(d_{33}\) are the corresponding model parameters; \(\Delta_{u}\), \(\Delta_{v}\) and \(\Delta_{r}\) represent unknown dynamics in each direction, respectively; \(\tau_{u}\) and \(\tau_{r}\) are the control inputs: the surge force and yaw moment; \(b_{u}\), \(b_{v}\) and \(b_{r}\) are used to describe the non measurable and time-varying external disturbances caused by wind, waves, and currents.  
**Assumption 1**.: _Assume that the unknown dynamics and external disturbances satisfy \(|\Delta_{u}|\leq\Delta_{u\,\max}\), \(|\Delta_{v}|\leq\Delta_{v\,\max}\), \(|\Delta_{r}|\leq\Delta_{r}\max\), \(|b_{u}|\leq b_{u\,\max}\), \(|b_{v}|\leq b_{v\,\max}\), \(|b_{r}|\leq b_{r\,\max}\), where \(\Delta_{u\,\max}\), \(\Delta_{v\,\max}\), \(\Delta_{r\,\max}\), \(b_{u\,\max}\), \(b_{v\,\max}\) and \(b_{r\,\max}\) are unknown positive constants._  
Control objective: Note, the practical conditions considered in this paper include unknown dynamics and time-varing external disturbances. The control objective is to propose a practical adaptive trajectory tracking control strategy (the surge force \(\tau_{u}\) and the yaw moment \(\tau_{r}\)) to cope with the above considered conditions, so that the underactuated USV can track the reference trajectory \((x_{d},y_{d})\). All state variables of the underactuated USV are uniform ultimate bounded (UUB).  
Figure 1: The earth-fixed inertial and body-fixed frame.
# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 1 Introduction
Then, by assessing the maximum available IOPS suitable for scheduling, the user sets the maximum IOPS for their system and defines an interval within which to check for executable applications. Finally, our scheme schedules those applications with the highest IOPS from the set of currently available applications while ensuring that the maximum IOPS limit is not exceeded. We implemented and evaluated our scheme using large-scale log data obtained from real HPC systems, allowing us to capture diverse and unpredictable job patterns to demonstrate the robustness of our scheme. Moreover, our scheme is designed to be highly customizable, allowing for configuration adjustments based on the system characteristics and specific scheduling requirements of applications. We evaluated the performance of our proposed scheme in real-world scenarios while considering the various factors that different applications may encounter. The experimental results show that our scheme reduces the total execution time by 305 h, decreases costs by USD 53, and increases I/O utilization by 8% when scheduling 10,000 jobs utilizing public cloud resources. Our contributions are as follows:  
* We develop a scheme based on large-scale log data obtained from a real HPC system, ensuring robustness even for unpredictable and diverse job patterns. * We design our scheme to be customizable, allowing it to be applied to different scheduling systems to meet varying requirements. * We evaluate our proposed scheme in real-world scenarios, demonstrating its robustness and reliability for practical applications.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.2 Character Encoding  
Character encoding is the process of converting characters into a format that computers can easily access and manipulate. In this study, we addressed the complexity of the Arabic language--rich in morphological structures--by implementing character-level encoding. After removing special characters and spaces from the dataset, a vocabulary was created based on unique characters [26]. Each character was then assigned an index to form a mapping dictionary. This mapping translates every text input into a sequence of numerical indices, thus preserving distinct characters. The maximum sequence length was derived from the longest text in the dataset to ensure consistency in input size through sequence padding. This encoding method is particularly effective for managing spelling variations and noisy text, as it retains character-level patterns. The code snippet for character encoding is depicted in Figure 7.
# Android Malware Detection Based on Hypergraph Neural Networks
## 5 Evaluation
### Evaluation Results
As seen in Table 4, the optimal accuracy of 97.1% was obtained with HGNNP, while the optimal precision, recall, and F1 scores obtained with HGNN were 97.2%, 96.8%, and 0.9699, respectively. Compared to GCN, the hypergraph-based HGNN and HGNNP improved in accuracy by 3.2% and 3.3%, respectively, and in F1-Score by 0.41 and 0.31, respectively. This suggests that for spectral-based convolutional networks, the use of hypergraph modeling is able to extract richer higher-order information to help classify malicious applications than the GNN approach that directly utilizes FCGs. For UniGNN, we still chose UniGIN as the hypergraph method being evaluated. Compared to GIN, UniGIN does not show an advantage in accuracy but gains 0.004 on the F1-score. As is well known, GraphSAGE generalizes better due to the inclusion of sampling of neighboring vertices. It is noteworthy that the GraphSAGE method yields a high-level F1-Score of 0.9639, outperforming HyperGCN and UniGIN, which may be due to the fact that the aggregation of K-order neighbor information by GraphSAGE is comparable to the direct construction of K-order neighbor hyperedges. However, compared to GraphSAGE, the hypergraph-based HGNNP and HGNN obtained superior results in terms of accuracy and F1-Score with an improvement of 0.6% and 0.006, respectively, which illustrates the benefits of introducing hypergraphs into android malware detection.
# Provision of Energy- and Wavelength-Efficient Traffic
## 2 Scenario and Models of Traffic Grooming in the DSCNs  
As shown in Figure 1, the DSCNs including a group of U satellites provide service for users within its coverage. Through optical ISLs, large-capacity optical networks can be constructed to support long-distance transmission for large regions [19; 20; 21]. In this paper, we neglect the impact of the specific terrestrial users and assume that satellites in the DSCNs serve as the source and destination nodes. It is equivalent to the result that satellites have collected the traffic requests of users within their coverage. As depicted in Figure 1, data flow 1, 2, and 3 originate from satellites 2, U, and 3, and then terminate at satellites 3, 2, and 4, respectively.
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 4 Carrier Sense Non-Orthogonal Multiple Access (CSNOMA)
### Signaling Design  
In this section, we provide the practical signaling process and the control packet frame structure. The proposed signaling scheme is compatible with IEEE 802.11 DCF mode. Figure 5 shows the proposed signaling process of CSNOMA. We try to modify the signaling process of IEEE 802.11 DCF minimally. Thus, it may be possible to colocate the node applying CSNOMA and the node applying IEEE 802.11 DCF.  
Figure 6 shows the signaling process for protecting the receiver of additional transmitting nodes as explained in the previous section. The CTS packet that is sent by overheard receiver (rx_othr1) to the silent transmitter (tx_othr1) contains the original form of CTS packet in CSMA. It should be noted that the term CTS means clear-to-send; however, in this case, the term CTS means cease-to-send. That is, the transmitter (tx_othr1) should shutdown the transmission.  
CTS Frame Format  
To enable CSNOMA maintaining the same signaling structure of IEEE 802.11, we apply the hierarchical modulation [42] on the CTS control message as shown in Figure 7. If the node that received the CTS frame can decode the hiearchical modulation, additional information can be sent, such as maximum payload size, signal strength related information, and geolocation information, as shown  
Figure 5: The signaling process of Carrier Sense Non-Orthogonal Multiple Access (CSNOMA).  
Figure 6: The signaling process for protecting the receiver of additional transmitting nodes.  
in Figure 6(b). This node decides whether to transmit or not based on the additional information. Otherwise, if the node cannot decode the higher modulation part, the remaining operation is the same as the conventional signaling using the frame structure as shown in Figure 6(a).
# Domain Model Based Design of Business Process Architectures
## 5 The dBPA Method
### Procedure
** Given the business entities \(b^{\prime}\) and \(b^{\prime\prime}\) derived from the classes \(c^{\prime}\) and \(c^{\prime\prime}\), which are related by the association \(a_{k}\) with direction \(dir(a_{k})=c^{\prime}c^{\prime\prime}\) and that involves an existential dependency (\(exist(a_{k})=true\)), and a transition with (_proc,signal_) = (\(\varphi_{i}\),\(\varphi_{j}\)) in \(l^{\prime}\) related to a transition with \(proc=\varphi_{j}\) in \(l^{\prime\prime}\). Then,processes \(\varphi_{i}\) and \(\varphi_{j}\) are related by a trigger relation in which the former is the trigger source and the latter is the trigger target. * **Resource flow relation. ** Relations between sub-processes of the same high-level processes are usually considered to be of type resource flow. These relations are derived by analyzing the respective OLC \(l\). For each state \(s\in l\), the _proc_ part of the label of its incoming transition is the source of the resource flow relation, while the _proc_ part of the label of its outgoing transition is the target of the resource flow relation. In case of self-transitions, they are considered only as outgoing transitions because they do not influence a state change. The second case considers the business entities \(b^{\prime}\) and \(b^{\prime\prime}\) derived from the classes \(c^{\prime}\) and \(c^{\prime\prime}\), which are related by the association \(a_{k}\) with direction is \(dir(a_{k})=c^{\prime}c^{\prime\prime}\) and that does not involve an existential dependency (_exist(\(a_{k}\)) = false_), and a transition with (_proc_,_signal_) = \((\varphi_{i},\varphi_{j})\) in \(l^{\prime}\) related to a transition with \(proc=\varphi_{j}\) in \(l^{\prime\prime}\). Given the aforementioned, then processes \(\varphi_{i}\) and \(\varphi_{j}\) are related by a resource flow relation in which the former is the resource source and the latter is the resource target. * **Specialization relation. ** A specialization relation is identified whenever two different transitions within the same OLC are labeled with the same _proc_ but have a different _guard_. Domain-expert knowledge is used for identifying which of them corresponds to the general and specialized processes. _In the example._ The resulting BPA model for the retail company is shown in Figure 6. The model shows five high-level processes, namely _customer management_, _courier management_, _invoice management_, _purchase management_, and _purchase item management_. Each of these high-level processes is composed of a number of sub-processes, e.g., _invoice management_ is composed by _issue_ and _collect_. Figure 6 also shows the remaining business process relations: trigger (e.g., _close_ triggers _cancel_), resource flow (e.g., resource flow from _place_ to _billing_), and specialization (e.g., _deliver_ and _international deliver_).