esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Construction and Solving Algorithm
### Solving Algorithm
#### 3.2.2 Application of NSGA-II-TOPSIS-GCA Algorithm for MOODPR Problem
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Scheme Sets**} & \multicolumn{1}{c}{**Positive Ideal Solutions**} & \multicolumn{1}{c}{**Negative Ideal Solutions**} \\ \hline A & \(R+=\) (0.0262, 0.0326, 0.0233, 0.0157) & \(R^{-}=\) (0.0243, 0.0211, 0.0265, 0.0358) \\ B & \(R+=\) (0.0272, 0.0294, 0.0240, 0.0210) & \(R^{-}=\) (0.0225, 0.0218, 0.0259, 0.0282) \\ C & \(R+=\) (0.0264, 0.0285, 0.0227, 0.0215) & \(R^{-}=\) (0.0241, 0.0212, 0.0273, 0.0279) \\ D & \(R+=\) (0.0320, 0.0378, 0.0222, 0.0192) & \(R^{-}=\) (0.0152, 0.0108, 0.0276, 0.0294) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Positive and negative ideal solutions for four scheme sets. ### Decision Schemes Analysis

Table 6 shows the objective values and characteristic indexes of the final decision schemes for four scheme sets. For the comprehensive water supply, from the wet year to the dry year, the comprehensive water supply gradually increased with the increase in the comprehensive water demand. It increased from 4.577 \(\times\) 10\({}^{8}\) m\({}^{3}\) under a 25% inflow frequency to 5.563 \(\times\) 10\({}^{8}\) m\({}^{3}\) under a 75% inflow frequency, with an increase of 22.1%. In the extraordinarily dry year, the comprehensive water supply was the smallest due to the least inflow. With the decrease in inflow of parallel reservoirs, the comprehensive water shortage gradually increased with the decrease in inflow, and the guaranteed rate in the flood season was obviously higher than that in the non-flood season. The water shortage depth increased from 5% in the wet year to 37% in the extraordinarily dry year, and the number of maximum continuous water shortage periods increased from one ten-day period in the wet year to four ten-day periods in the extraordinarily dry year. For the ecological water supply, with the decrease in the inflow of parallel reservoirs, the ecological water demand under different inflow frequencies remained unchanged, so the ecological water supply decreased from 9.06 \(\times\) 10\({}^{7}\) m\({}^{3}\) under a 25% inflow frequency to 7.35 \(\times\) 10\({}^{7}\) m\({}^{3}\) under a 95% inflow frequency, with a decrease of 18.9%. The ecological AAPFD value increased by 3.062. The guaranteed rate decreased from the wet year to the dry year, and the guaranteed rate in the flood season was obviously higher than that in the non-flood season. The ecological water shortage increased gradually from the wet year to the dry year. The water shortage depth increased from 3% to 21%, and the number of maximum continuous water shortage periods increased from two ten-day periods in the extraordinarily dry year.
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 4 The Megalos Framework
### Relationship with ETSI NFV Architecture  
It is worth noticing that the Megalos architecture is partially compliant with the European Telecommunications Standards Institute (ETSI) Network Function Virtualization (NFV) architecture [27]. In particular, using the ETSI terminology, Megalos implements the Network Function Virtualization Infrastructure (NFVI) Layer, the Virtual Network Function (VNF) Layer and part of the NFV Management and Orchestration (MANO) component, as shown in Figure 1.  
The Docker container engine provides the Virtual Computing module and the VNF Layer (yellow colour in Figure 1), while Kubernetes implements the Virtualized Infrastructure Manager (VIM) component. Megalos relies on Kubernetes also for some of the VNF Manager (VNFM) functionalities, such as VNF failure handling, scaling or updating. This is represented with the blue colour in Figure 1. The Virtual Storage module, marked in green, is provided by Kubernetes through a multitude of supported drivers [28].  
The custom network plug-in described in Section 5 (in red in Figure 1) implements the Virtual Network module of the NFVI layer.  
The VNFM component of the NFV MANO is provided by the Megalos Controller (in red in Figure 1). Moreover, the Megalos Controller also implements some features of the NFV Orchestrator (NFVO). On the other hand, observe that Megalos cannot be classified  
Figure 1: The ETSI NFV Architecture and its relationship with Megalos. In yellow the parts implemented using Docker, in green the Kubernetes volume drivers, in blue Kubernetes itself, and in red the Megalos Controller and CNI.  
as an ETSI NFV orchestrator, but rather as a network scenarios emulator. As described extensively in [29], there are many NFV orchestrators such as: OpenStack's Tacker, OSM, ONAP, CloudNFV, OPNFV, or CloudBand. Among these NFV orchestrators, OPNFV is attracting an increasing attention. It is worth to remark, however, that Megalos and OPNFV have different purposes. In fact, while Megalos is a distributed network emulation system that embodies some orchestrator functions, OPNFV is a general purpose orchestrator for the development of virtual network functions.
## 3 Energy Management Strategy
### Previous EMSs
#### 3.1.2 Equivalent Consumption Minimization Strategy (ECMS)  
Based on the powertrain configuration in [44], a simplified ECMS was employed to define the optimal PEMFC power by finding an optimal solution \(\mathrm{x}=(\mathrm{P_{FC}},\mathrm{\alpha},\mathrm{P_{BAT}})\) with the cost function as follows:  
\[\mathrm{F}=(\mathrm{P_{FC}}+\mathrm{\alpha}\mathrm{P_{BAT}})\mathrm{\Delta T} \tag{15}\]  
where F is the cost function, \(\mathrm{\alpha}\) is the penalty coefficient, \(\mathrm{\Delta T}\) is the sampling time (s), \(\mathrm{P_{FC}}\) and \(\mathrm{P_{BAT}}\) are the PEMFC power and BAT power (W), respectively.  
Equality constraints were given by:  
\[\mathrm{P_{load}}=\mathrm{P_{FC}}+\mathrm{P_{BAT}} \tag{16}\]  
\[\mathrm{\alpha}=1-2\mathrm{\mu}\frac{(\mathrm{SOC}-0.5(\mathrm{SOC_{BAT,max} }+\mathrm{SOC_{BAT,min}}))}{\mathrm{SOC_{BAT,max}}+\mathrm{SOC_{BAT,min}}} \tag{17}\]  
with boundary conditions as  
\[\mathrm{P_{FC,min}}\leq\mathrm{P_{FC}}\leq\mathrm{P_{FC,max}}\] \[\mathrm{P_{BAT,min}}\leq\mathrm{P_{BAT}}\leq\mathrm{P_{BAT,max}}\] \[0\leq\mathrm{\alpha}\leq 100\]  
where \(\mathrm{P_{load}}\) was the load power demand (W), \(\mathrm{\mu}\) was a constant (<0.6) to handle the BAT SOC range, \(\mathrm{SOC_{BAT,min}}\) and \(\mathrm{SOC_{BAT,max}}\) were the minimum and maximum BAT SOC level (%), respectively; \(\mathrm{P_{FC,min}}\) and \(\mathrm{P_{FC,max}}\) were the minimum and maximum PEMFC power (W), respectively; \(\mathrm{P_{BAT,min}}\) and \(\mathrm{P_{BAT,max}}\) were the minimum and maximum BAT power (W), respectively.  
Figure 5: Driving cycle of the hybrid tramway [19; 20; 33; 45].  
This approach illustrates high energy management efficiency during the overall driving cycle and minimizes hydrogen consumption. However, the unknown operation points of the PEMFC and BAT lead to challenges in determining the equivalent values of fuel consumption as well as consuming more computing time, resulting in an unstable system under abrupt changes in operating modes.
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 4 Discussion
### Practical Implications  
The findings of this review suggest that smartwatches can serve as effective tools for early stress detection and mental health support in everyday settings. This has meaningful implications for preventive healthcare, particularly in environments where access to mental health professionals is limited. Smartwatches offer a low-cost, non-invasive means to continuously monitor psychological well-being and could be integrated into digital health platforms to alert users--or clinicians--when early signs of stress emerge.  
From a design perspective, the review emphasizes the importance of personal comfort, perceived usefulness, and trust in data privacy. Developers and manufacturers must prioritize ergonomic form factors, intuitive interfaces, and transparent data handling policies to maximize adoption. Furthermore, machine learning systems for stress detection should allow for personalized calibration based on individual baseline data, which has been shown to improve predictive accuracy.  
Healthcare providers and digital health startups can use these insights to design interventions that combine passive monitoring with active guidance, such as delivering personalized coping strategies when stress is detected. Finally, policymakers must address ethical and legal concerns, particularly around data ownership, informed consent, and the medical validity of algorithmic diagnoses, to ensure safe and responsible use of smartwatch-based health technologies.
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
## 3 Model Formulation
### Berth Allocation Agent
This ensures that unloading will finish before loading, leaving more time for the truck-pickup-related windows. Conversely, when there are more TEUs to be unloaded than loaded, the vessel will first be processed in single mode. This ensures loading does not start earlier than needed, thus allowing more time for deliveries to arrive later. The processing mode also impacts the time-windows \(w^{D}_{v}\) and \(w^{p}_{v}\) for each vessel, which take values \(i\in\mathcal{I}\). A time-period \(i\) represents a set of time-units within the examined working week (e.g., all minutes in a single hour). The time-unit indicating the start of cargo loading is associated with the last possible time-period for delivery jobs, establishing the upper bound of \(w^{D}_{v}\). In contrast, the time-unit marking the completion of cargo unloading designates the first possible period of \(w^{p}_{v}\). Deliveries are assumed to occur at any time before the upper bound of \(w^{D}_{v}\), and pickups after the lower bound of \(w^{p}_{v}\), although they are generally expected to take place within 12 h of the respective bounds. Overall, the decision-making agent responsible for berth allocation aims to minimize the waiting and processing times for all vessels. Its objective function (termed the vessel_process function), subject to the requirements outlined earlier, is defined as:  
\[\text{vessel\_process}(x,y)=\sum_{v\in\mathcal{V}}(m_{v}-A_{v}+p_{v}) \tag{7}\]
## 3 Inverse Kinematics
### Closed-Form Solution for Single-GSJ Section  
The kinematic parameters \(\alpha_{i}\), \(\beta_{i}\) and \(\gamma_{i}\) for a single-GSJ section shown in Figure 2 can be determined for the given orientation \(\mathbf{R}_{i}^{i-1}\) of Frame \(i\) with respect to Frame \(i-1\). Moreover, the solution of these kinematic parameters is in closed-form expression. Considering the actual mechanical structure of GSJ, the range of the three joint angles \(\alpha_{i}\), \(\beta_{i}\) and \(\gamma_{i}\) can be given by \(\alpha_{i}\in[-\pi/2,\pi/2]\), \(\beta_{i}\in(-\pi/2,\pi/2)\) and \(\gamma_{i}\in(-\pi/2,\pi/2)\), respectively. According to (1), \(\alpha_{i}\), \(\beta_{i}\) and \(\gamma_{i}\) can be given by  
\[\alpha_{i}=\left\{\begin{array}{cc}\tan^{-1}\frac{n_{i-1}^{i-1}}{s_{i,y}^{i -1}},&if\;s_{i,y}^{i-1}\
eq 0\\ sgn\left(n_{i,y}^{i-1}\right)\cdot\pi/2,if\;s_{i,y}^{i-1}=0\end{array}\right. \tag{5}\]  
and  
\[\beta_{i}=\tan^{-1}\frac{a_{i}^{i-1}}{a_{i}^{i-1}}\] \[\gamma_{i}=\tan^{-1}\frac{-a_{i}^{i-1}}{\sqrt{\left(n_{i,y}^{i-1 }\right)^{2}+\left(n_{i,y}^{i-1}\right)^{2}}}\;, \tag{6}\]  
respectively, where \(c_{\gamma_{i}}>0\) is exploited in Equation (5) and the first equation of Equation (6), and \(sgn(\cdot)\) denotes the signum function.  
For actual manipulator, the value of angle \(\alpha_{i}\) is constant. Hence, for any given desired orientation \(\mathbf{R}_{i}^{i-1}\) within the workspace of the single-GSJ section, the computed value of \(\alpha_{i}\) by using Equation (5) should be equal to the actual value. From Equation (6), the value of \(\beta_{i}\) and \(\gamma_{i}\) only depend on the component \(\mathbf{a}_{i}^{i-1}\) of the given rotation matrix. This is another important fact for the following inverse kinematics of the whole manipulator.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 4 Suitability of Legacy Techniques and Standards for 5G and Upcoming Networks  
We assess the most widely (commonly) used legacy techniques and standards based on the parameters (adaptability, dependability, extensibility, and energy saving) as mentioned in Table 2. The aim of this analysis is not to compare the legacy techniques with themselves but to evaluate them for their applicability in 5G and future networks, in the light of mentioned parameters in Table 2. The role of each technique and standard is provided as follows.
# Design and Optimization of Modular Solid Rocket Grain Matching Multi-Thrust Performance Curve
###### Abstract  
Multi-thrust solid rocket motors are extensively used in tactical missiles. To effectively achieve the desired multi-thrust performance curve, firstly, the concept of modular grain is introduced. Star grain, slot grain, and end-burning grain are chosen as the fundamental templates, which can be flexibly combined to form an arbitrary multi-thrust performance curve. Secondly, a quadric approximation of the burning perimeter is derived, leading to the establishment of a governing equation for modular grain design. This equation ensures a close match between the resulting performance curve and the target one. Thirdly, the Nelder-Mead optimization algorithm is employed to maximize the propellant loading fraction and reduce the combustion chamber size. Finally, the method successfully produces single-thrust, dual-thrust, and triple-thrust grains. The results show that the relative maximum deviation between the designed and target pressure curves is less than 6.1%. Additionally, the best grain configuration is identified, which maximizes the propellant loading fraction while adhering to the throat-to-port ratio constraints. Consequently, the concept of modular grain offers a valuable approach for creating complex internal ballistic characteristics by combining simpler grain templates. This approach allows for fast, responsive motor conceptual design, prototyping, testing, and even production, thereby advancing the development of solid rocket motors in a more efficient and effective manner.  
s +
Footnote †: journal: applied sciences  
olid rocket motor; multi-thrust motor; performance matching; modular grain; burning surface regression; star grain; slot grain; end-burning grain
# A Structure for Accurately Determining the Mass and Center of Gravity of Rigid Bodies
## 2 Background and Related Work
### Traditional Structure and the Improved Struture  
Figure 3 shows some types of structure. (a) Cone joint with conical pan, which is a traditional mounting and it is used for individual load cells, (b) and (c) are types of rocker bearings. The distortion of the load cell's reading is practically absent for rocker bearings, because in this case only slight rolling friction is present instead of a bending stress. However, the horizontal restraint of the rocker bearing is significantly less than fixed bearings. Rocker bearings are recommended if the position of the superstructure only changes horizontally [15].  
We have researched CG determination for many years, and one traditional method is placing three load cells on the same horizontal flat, as shown in Figure 4. In particular, each load cell has a ball groove and each groove holds a ball, making the center of the ball groove on the designed axis of the load cell. The upper platform has a similar construction. The ball can stay at the center of the ball groove because of the gravity.  
Figure 3: The construction of the traditional structure. (**a**) Cone joint with conical pan; (**b**) Ball joint with groove; (**c**) Ball joint with fillet.  
Figure 2: Correct loading on a load cell and some examples of incorrect loading. (**a**) Central load application; (**b**) Non-central load application; (**c**) Non-axial load application or side forces; (**d**) Moment-loading (torsion) load application.  
However, due to manufacturing tolerance and imperfect assembly, axis 2 is not the same as axis 1 shown in Figure 5, making it hard to guarantee \(F\) pass through each designed axis of the load cell. Moreover, reducing these errors will increase the costs significantly.  
Regarding the above issue, an improved structure is proposed, as shown in Figure 6. Similar to Figure 4, three load cells rest on the base platform, each of them has a ball groove and each groove holds a ball, ensuring the center of the ball groove goes through the designed axis of the load cell. The base platform should be horizontal and this structure should minimize the friction.  
Specifically, the upper platform has one ball groove, one cylindrical groove and one planar surface. Because of the characteristic of the cylindrical groove and the plane, the upper platform can adjust its position and ensure F passes through the designed axis of the load cell without side force, even though these grooves may have position errors. A mathematical analysis of the structure will be performed in the next part.  
Figure 4: The construction of the traditional structure.  
Figure 5: Axes of the ball grooves.  
Figure 6: The construction of the new structure.
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
## 4 Simulation Results
The proposed multi-user D&F Cop-PD-NOMA outperforms the single-user D&F Cop-PD-NOMA. As we can observe in Figures 9 and 10, cooperation by multiple users can improve the BER performance of \(R_{4}\) by 4 dB for the same BER threshold \(10^{-3}\), while cooperation by a single user can only improve it by 1 dB or less. Figure 8: BER performance analysis for the registered and unregistered users under the proposed secure PD-NOMA technique. Figure 7: BER performance analysis of 4 users uplink PD-NOMA system. The performance of the proposed user clustering strategy is tested using outage probability analysis. As discussed in Section 3.4, better performance can be experienced in such clusters that have a maximum distance separation among their users. Two cluster types, cluster A and cluster B, are given with two users, \(R_{1}\) and \(R_{2}\). In cluster A, user \(R_{1}\) has a distance (\(d_{1}\)) of 100 m and user \(R_{2}\) has a distance (\(d_{2}\)) of 500 m. Similarly, in cluster B, user \(R_{1}\) has \(d_{1}=100\) m and user \(R_{2}\) has \(d_{2}=150\) m. The distance separation between the users of cluster A is 400 meters, while it is only 50 meters in the case of cluster B. The outage probabilities for both the clusters are plotted vs different SNRs, as shown in Figure 11. Cluster A has a low outage probability because users are as far apart as possible and do not interfere with each other. Cluster B, on the other hand, has a high outage probability because users are close together and interfere with each other at their maximum. Figure 10: BER performance analysis of single-user D&F Cop-PD-NOMA communication [23]. Figure 9: BER performance analysis of the proposed four users D&F Cop-PD-NOMA communication.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 2 Related Work
### Hybrid MAC  
Hybrid mechanisms typically support additional features. Based on the advantages of both the CSMA and TDMA approaches, CSMA-STDMA is a self-organized MAC protocol, based on hybrid mechanisms, that provides reliable and real-time wireless network communications [32]. CSMA-STDMA utilizes the CSMA mechanism to provide synchronization among nodes, and then transmits data in a self-organized TDMA manner. CSMA-STDMA suffers from frequent initialization and energy consumption during idle listening.  
To meet different packet requirements, the E-hybrid protocol operates in the contention period and priority schedule period based on CSMA and TDMA, respectively [33]. When the data traffic is low, the E-hybrid protocol increases the contention access period. Otherwise, E-hybrid extends the reserved slots to the scheduling mode in high-traffic conditions, or according to urgent data, implements an emergent contention-free period at the beginning of the super frame. According to industrial process-controlling applications, WirelessHART is the preferred wireless communication standard based on the IEEE802.15.4 protocol [34]. Utilizing the advantages of CSMA and TDMA, WirelessHART is a hybrid scheme for strict real-time and high-security industrial applications. However, it is difficult for the two protocols described above to deal with rapid changes in traffic, such as in disaster-monitoring applications.  
One of the goals of hybrid schemes is to satisfy throughput and delay requirements. Z-MAC is a hybrid scheme of CSMA and TDMA [35]. CSMA channel access under low traffic conditions improves channel utilization and reduces latency, and the TDMA channel mode under high traffic conditions can reduce collisions and interference. In Z-MAC, centralized scheduling allocation can only slot for nodes at the initial stage of the protocol and cannot be rerun periodically. Based on the control information between the nodes, a token cycle scheduling method called TCS was proposed to improve the throughput and delay performance of the TDMA approach [36]. This scheme is adapted for low-delay applications; however, it requires an additional control channel.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Smart Healthcare Context Layer  
Smart healthcare contexts are configured using the components in the first layer. The broker configures and maintains the contexts of the patient, for example, home, hospital, or workplace. A smart healthcare network must support patient mobility and switch between different location contexts [59]. Details of this layer are explained in Section 4.