esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation
### Casting Blasting Simulation
#### 4.2.2 Blast Effect Analysis
The results indicate that when the stemming length is between 2.5 and 3.0 m, the coal and rock blasting effect meets the mining requirements, with no significant coal-rock interaction at the coal-rock interface and no coal casting phenomenon. Figure 16 illustrates the damage contour maps of the coal seam bench under various charge structures. In Scheme S-1, the bench damage contour map (Figure 16a) reveals that the rock mass near the stratified interface exhibits significant tensile crack propagation and severe damage due to the combined effects of blasting stress waves and reflected tensile stress waves. Scheme S-2 (Figure 16b), compared to Scheme S-1, demonstrates that the increase in stemming length results in a longer duration of the stress wave within the rock mass, which is fully utilized internally. This causes the intensity of the stress wave reaching the stratification interface to decrease, resulting in weakened damage to the rock at the interface; however, the damage degree remains relatively sufficient. The degree of damage to the bench on the free face is consistent with Scheme S-1. Figure 16c shows the damage to the coal seam bench under Scheme S-3, with a stemming length of 3.0 m. The damage to the stratification interface rock mass is further weakened, but the damage cracks can still penetrate successively. However, the damaged rock in the middle of the free face of the bench is more significant compared to the previous two schemes. This is primarily due to the shortening of the explosive interval, which concentrates the explosive energy, increasing the rock damage at the middle of the bench. For Scheme S-4 (Figure 16d), the stemming length is 3.5 m, and the interval length is shortened to 0.35 m. The explosive energy is concentrated and fully utilized in the bench's middle and lower rock mass, significantly reducing rock damage at the coal-rock interface. The tensile cracks fail to propagate effectively, and the surrounding rock damage at the free surface is concentrated in the lower part of the borehole. However, the overall damage characteristics do not achieve the required homogeneous fragmentation of the rock mass. These results indicate that when the interval length at the rock bottom is 2.0 m, and the stemming length in the upper layer is between 2.5 and 3.0 m, the damaged rock is evenly distributed, with moderate fragmentation, and there is no bulging or coal-rock interaction (explosion mixing) phenomenon. When the stemming length is \(\leq\)2.0 m, transitional damage near the coal-rock interface with severe fragmentation is unfavorable for achieving coal-rock mixed blasting separation. However, when the stemming length is \(\geq\)3.5 m, the damage to the rock mass near the coal bench stratification interface is minimal, and the fragmentation is insufficient. Although this facilitates mixed blasting separation, the increase in large rock fragments raises mining and transportation costs. Figure 16: Cloud map of coal bench damage.
## 4 Discussion
### Constitutive Model Rationality  
Figure 6 shows the comparison between the experimental curve and the model curve. The model curve has a relatively high degree of fit with the experimental curve. This model can not only accurately characterize the nonlinear hardening characteristics of the rock sample during the compaction stage under different strain rates, but it can also respond well to the strength and deformation laws of the rock sample.  
To quantitatively evaluate the rationality of the model, the coefficient of determination (\(R^{2}\)) and the root mean square error (\(RMSE\)) are introduced for judgment ([PERSON], 1997).  
\[R^{2}=1-\frac{(n-1)\sum_{t=1}^{n}(\sigma_{t}-\sigma_{t}^{\prime})^{2}}{(n-2) \sum_{t=1}^{n}(\sigma_{t}-\overline{\sigma})^{2}} \tag{18}\]  
\[RMSE=\sqrt{\frac{\sum_{t=1}^{n}(\sigma_{t}-\sigma_{t}^{\prime})^{2}}{n}} \tag{19}\]  
Figure 6: Comparison of experimental curve with model curve: (**a**) 1 mm/min; (**b**) 3 mm/min; (**c**) 5 mm/min; (**d**) 7 mm/min.  
where \(n\) is the total amount of data; \(\sigma_{t}\) and \(\sigma_{t}^{\prime}\) are, respectively, the measured and theoretical values of the stress corresponding to the \(t\)-th data point; \(\overline{\sigma}\) is the average measured stress of the total data quantity.  
Figure 7 shows the rationality analysis of the model. With the increase in the strain rate, the fitting degree among rock samples with different strain rates gradually increases, and all are greater than 0.908. Furthermore, as the strain rate increases, the standard deviations of the rock samples with different strain rates first increase and then decrease. The standard deviations among the rock samples fluctuated greatly but were all less than 4. This indicates that the model constructed in this paper is reasonable. This model establishes a cross-scale correlation equation between the macroscopic mechanical response and the mesoscopic damage evolution, providing a quantifiable assessment basis for the prevention and control of engineering dynamic disasters in deep rock masses.
## 4 Results of the Study
### Numerical Simulation Analysis of the Amount of Consolidated Surface Settlement
#### 4.4.3 Different Grouting Pressures in Reinforced Soils  
In order to investigate the effect of different shield tunnel grouting pressure sizes on roadbed settlement after soil reinforcement, corresponding models were established with PLAXIS 3D software, and the parameters of each model are shown in Table 9. The plotted changes in surface settlement are shown in Figure 13.  
Through Figure 13, when the grouting pressure was gradually increased in the range of 350 kPa to 450 kPa, the surface settlement or uplift of the soil above the grouting ring showed a slight increasing trend. However, the surface settlement of the shield tunnel was strictly controlled within the variation range of \(-1\) mm to 1.5 mm, which reflects the effective control of the settlement amount during the grouting process. The magnitude of the grouting pressure has less influence on the surface settlement value, which indicates that the grouting reinforcement of the soil above the shield plays a very important role.
## 3 Modeling Methods and Results
Then, gas is throttled in the valve up to Point 3, by an isenthalpic process. The area under the curve from Point 1 to 1\({}^{\prime}\) represents the heat \(q^{\prime}\) that has to be provided to the gas. Usually, a 2-7 K increase of the gas temperature per 1 MPa of the gas pressure is necessary. In the present study, we propose the utilization of a rolling piston expander as an engine, instead of using a throttling valve, allowing the recovery of power that is lost in a pressure reduction station. However in this case, the gas has to be heated to a higher temperature at Point 2, to be able to expand it politropically to the temperature \(T_{min}\) at Point 3. This process is represented by successive points 1-2-3 in Figure 14. The area under the curve from Point 1 to 2 represents heat \(q\) that has to be provided to the gas during isobaric heating, when a rolling piston is used. Figure 14: Isobaric heating, isenthalpic throttling and polytropic expansion of natural gas depicted in the T-s diagram. Figure 6:0 Electric power versus \(\sigma=p_{s}/p_{d}\) for different polytropic exponents (\(m\)), \(m=1.333\) (blue line), \(m=1.4973\) (red line), \(m=1.6615\) (orange line), \(m=1.8258\) (violet line), \(m=1.99\) (green line). This means that in order to obtain work, one has to input more heat, compared to the case of throttling valve usage. Thus, the natural question to ask is when such a process (heating the working fluid and then expanding it in the rolling piston machine) would be profitable. To assess this issue, a novel coefficient was formulated and called the Power Recovery Factor (\(PRF\)), which is defined as the ratio of work output \(l_{l2-3}\) to heat input, and is expressed as follows  
Figure 6:1  
A plot of the \(PRF\) versus \(m\) for different expansion ratios \(\sigma\) is presented in Figure 6:2 For the fixed outlet pressure \(p_{d}\), with increasing inlet pressure \(p_{s}\), \(PRF\) decreases. This means, that for higher inlet pressures, temperature at Point 2 strongly increases, and thus heat input is higher. For very high pressures (\(\sigma\geq 8\)), usage of the rolling piston is justified only for \(m\leq 1.4\). For processes close to adiabatic conditions (\(m\to k\)) \(PRF\) increases. One can see that for low expansion ratios (\(\sigma=2\)) and a small polytropic exponent (\(m<1.4\)), almost 60% of work can be recovered.
# Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings
## 1 Introduction
The authors in [34] considered an energy-efficient no-wait permutation FSSP with makespan and energy consumption minimization. To achieve a better energy utilization, the processing speeds of machines can be dynamically adjusted for different jobs, altering both processing times and energy consumption. To solve the multi-criteria optimization problem, an adaptive multi-objective Variable Neighborhood Search algorithm was proposed, outperforming several state-of-art evolutionary algorithms. A modification of a classic no-wait, a limited wait constraint, in the context of a hybrid FSSP, was considered in [35]. A limited wait occurs, when some types of products (jobs) can wait for being processed at the next machine, and some cannot. The problem was solved by a discrete Particle Swarm Optimization algorithm. In [36], a two-machine FSSP with both no-idle and no-wait constraints considered simultaneously was researched. Authors proposed a metaheuristic algorithm, utilizing Integer Linear Programming formulation. The method was able to obtain competitive results for instances consisting of up to 500 jobs. Another example of time constraints is minimum-maximum time allowed between consecutive operations of a single job. A two-machine FSSP with such constraints was researched in [20], where some theoretical proprieties of the problem were proven. Nevertheless, none of the mentioned problems can precisely model the aforementioned concrete process. An adequate model was shown in [37], along with B&B and TS solving methods. However, the authors considered only the permutation variant of FSSP-TC and did not propose any elimination properties. We aim to rectify those drawbacks. Thus, our contributions can be summarized as:  
1. Formulation of the mathematical model for the concreting process. 2. Identification of several problem properties, including elimination blocks. 3. Proposal of three solving algorithms for the considered problem: B&B, TS, and GA, out of which TS utilizes the proposed block properties. The remainder of the paper is organized as follows: in Section 2, a formal mathematical model of the problem is formulated, along with a graph representation. Next, in Section 3, several problem properties are proven, including the elimination (block) property. Then, solving algorithms are presented in Section 4. The algorithms are empirically evaluated in Section 5. The paper is concluded in Section 6.
# FRAMUX-EV: A Framework for Evaluating User Experience in Agile Software Development
## 6 Validating the Framework
### Quantitative Results
#### 6.1.3 Quantitative Results: UX Events  
Table 8 summarizes the survey results for dimensions D1 and D2 of the UX events proposed in the first version of the FRAMUX-EV. A brief analysis of the descriptive statistics is provided below.  
**(D1) Usefulness.** The UX event that obtained the best rating from the practitioners was \"weekly UX meeting\", with a score of 4.41 out of 5.00, followed by \"pre-planning UX meeting\", with a score of 4.09 out of 5.00. On the other hand, the UX events with the worst usefulness perceived by the participants were \"pre-review UX meeting\" and \"weekly user meeting\", with a score of 3.85 out of 5.00. However, although these UX events were the worst evaluated, none were rated as \"not very useful\" (score 2) or \"very unhelpful\" (score 1).  
**(D2) Ease of integration.** The UX event considered the easiest to integrate by the practitioners was \"pre-review UX meeting\", with a score of 3.97 out of 5.00, followed by \"pre-planning UX meeting\", with a score of 3.94 out of 5.00. On the other hand, the UX event with the worst rating assigned by the participants was \"weekly user meeting\", with a score of 2.35 out of 5.00. Surprisingly, none of the UX events were classified as \"ease to integrate\" (score 4).  
Initially, all UX events were perceived as useful, and none should be discarded from this initial version of FRAMUX-EV, as each received a usefulness rating above 3. However, it is important to analyze the feedback and suggestions provided by the experts to understand the reasons behind the lower score given to \"weekly user meeting\". The decision to discard UX events or modify them to improve their ease of integration will be revisited after analyzing the qualitative feedback from the experiment.
# Prediction of Intraday Electricity Supply Curves
## 2 State of the Art for Supply Curve Prediction
This approach has the disadvantage that it increases the number of nodes to approximate the curves. The authors of [18] followed the approach of using a grid of points to generate stepwise functions for the supply curves at the Spanish secondary market, which is one of the reserve markets managed by Red Electrica de Espana. The authors consider random forest, gradient bootstrap, dense neural networks, and long short-term memory models to predict supply curves assumed to be observed on a fixed grid with 50 nodes. In [19], a distance learning procedure is proposed to forecast the day-ahead curves for the daily market. This approach does not need to approximate the curves because it relies on the distances between them. It proposes the prediction of the distances between the curves on day D+1 and the curves in the training set based on the distances between the curves on day D and those curves in the training set. The procedure is highly computationally intensive and it is not clear how other scalar covariates will be incorporated. All the papers mentioned focus on daily markets, with the exception of [18], which studies a secondary reserve market. None of them have studied the intraday market with its respective sessions. It should be noted that this structure of the electricity market, which is subdivided into daily markets, reserve markets, and intraday markets, is common in many European countries (see [20; 21]). Prediction in the intraday market has the added difficulty of several sessions being carried out on the same day and different prediction horizons, as shown in Table 1. In this project, we turn this difficulty into an advantage because we can use the information from the previous session to predict the session that follows. As we can see in Table 1, the publication times of the session results make this approach feasible.
## 6 Open Issues  
As we envision the future of trust management in 6G networks, several promising avenues for further research and development emerge. One such direction involves exploring hybrid trust evaluation models that amalgamate fuzzy logic algorithms with machine learning techniques. This integration holds the potential to bolster the accuracy and efficiency of trust assessment processes significantly. Additionally, investigating the incorporation of emerging technologies like artificial intelligence and blockchain into trust management frameworks could yield innovative solutions for fortifying the security and dependability of 6G networks.  
Scalability and dynamicity: Future research endeavors should focus on devising trust assessment systems capable of accommodating the scalability and dynamic nature of evolving network architectures. By adapting to fluctuating network conditions and escalating complexities, research efforts can ensure the continued effectiveness of trust management systems.  
Privacy-aware technologies: Addressing the paramount concern of safeguarding sensitive user data and mitigating the risk of privacy breaches necessitates the development of advanced encryption techniques, secure data-sharing protocols, and privacy-preserving mechanisms. Despite its critical importance, this area remains relatively underexplored and warrants dedicated research efforts to enhance the confidentiality and integrity of user information in 6G networks.  
Context-aware trust models: Dynamic adjustments in behavior based on environmental and situational cues are imperative for enhancing the adaptability, intelligence, and energy efficiency of 6G networks. Future research endeavors should explore the seamless integration of context-awareness into trust evaluation frameworks, thereby facilitating real-time decision making and augmenting the trustworthiness of network interactions.  
Newcomer nodes solution: Another critical aspect that demands attention is the evaluation of trust for newcomer nodes. As newcomers join the network, determining their initial trust value becomes challenging due to the lack of availability of relevant information. Future work should address this challenge by devising methodologies to evaluate trust for new nodes effectively.  
Overall, the future trajectory of trust management for 6G networks hinges on leveraging cutting-edge technologies, enhancing the robustness of trust evaluation models, and confronting the evolving challenges posed by next-generation network environments. By pursuing these avenues of research, we can pave the way for the realization of secure, reliable, and resilient 6G networks.
## 3 On-Site Measurements
### Embankment Section
#### 3.2.2 Immission Measurements  
The geophones were re-installed at the same locations 4 weeks later to capture 56 high-speed long-distance trains and 18 freight trains (note that the latter operated on track no. 6 and no. 8, while all long-distance passenger trains ran on track no. 3 and no. 4, as shown in Figure 12).  
Figure 14 summarizes the vibration velocity spectra at a distance of 12 m from track no. 3 for the most frequent train categories. The solid lines indicate the mean commission spectra, and the surrounding hatched area represents the range between the minimum and maximum immissions (percentiles were not used due to the considerably smaller  
Figure 12: Measurement layout along the embankment track including an insert showing the impact tester.  
Figure 13: Measured point transfer admittances at MP1 (dashed lines) and computed line transfer admittances when integrating over all measured \(Y_{point}\) and when using the simplified method, which only requires measurement of \(Y_{point,max}\).  
number of available train records compared to the tunnel section). As the recorded train velocities were uniform throughout the day of the survey, Figure 14 could not be split into different velocity classes. The amplitudes of the freight trains were corrected for the extra distance between the sensor and track no. 6 and no. 8. Vibration amplitudes at 16 Hz are considerable at this track section compared to the tunnel immissions in Section 3.1.2, where vibrations below 31.5 Hz are negligible. Overall, the trends observed for each train category are the same as on the tunnel track: Both Westbahn and ICE repeatedly produce very similar spectra with a small divergence, while freight trains and Railjets show a considerable spread between the minimum and maximum amplitude in each one-third octave band. One reason for this is the disposition of vehicles along the investigated line: While only a limited number of different Westbahn and ICE trains pass the site, the Railjet vehicle pool is much bigger, and the types and compositions of freight trains are much more varied than for passenger trains. Another reason could be that both ICE and Westbahn are articulated multiple-unit trains, while Railjets are locomotive-hauled push-pull high-speed trains, leading to more varied carriage behavior. The unsprung masses of the different train types and the stiffnesses of the primary suspensions or the under sleeper pads were unknown and could thus not be investigated as possible further causes for the observed differences.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 3 Methodology  
### Multi-Agent Modeling Approach  
To utilize the multi-agent modeling approach for analyzing the effect of movable jammers on WSN reliability, it is crucial to initially model the various agents that exist in the scenario. This paper presents a multi-agent modeling method that includes three distinct agent types: sensor agent, base station agent, and jammer agent.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 2 Background
### Unmanned Aerial Vehicles (UAVs)  
UAVs are flying devices (aircraft) that do not require a human pilot on board and are equipped with radio communication modules. These devices can be controlled manually by a human at a base station on the ground or autonomously through flight programs. The advance in miniaturization technology and the development of integrated systems have achieved a considerable reduction in the physical size of the UAV, and likewise lowering its cost [15].  
Using UAVs as Aerial Base Stations (ASBs) is an alternative technology that can provide efficient wireless connectivity to users on the ground. Due to its mobility and flexibility, an ASB can help with increased coverage capacity on demand. Service providers can use this solution for dynamic network densification, rapid network deployment in an emergency, or temporary coverage of an area [3]. Other applications of UAVs are in the military area, as well as in the civil area, whether in surveillance, location, 3D aerial mapping for earthmoving projects, disaster scenarios, and ad hoc FANET (ad hoc FANET networks are a kind of self-organized decentralized wireless network carried by a group of UAVs (nodes), so that they can communicate with each other independently of the fixed infrastructure [16]) for search and rescue missions among other application scenarios [16].
# Ambiguity-Resolved Model Tests for Carrier-Phase GNSS
## 2 The Mixed-Integer GNSS Model  
In this section, we describe the hypotheses of carrier-phase GNSS and introduce its class of mixed-integer model tests.