esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Design of a Novel Vibration Energy Absorbing Mechanism for Cables
###### Abstract  
A novel design of a vibration energy absorbing mechanism (VEAM) that is based on multi-physics (magnetic spring, hydraulic system, structural dynamics, etc.) for cable vibration is proposed. The minimum working force of the hydraulic cylinder has been exploited in this design in order to combine a non-linear stiffness vibration isolation module that is composed of permanent magnetic springs with hydraulic viscous vibration damping modules. In response to different environmental vibration impacts, VEAM can automatically switch the vibration control modes without an electronic mechanism. Additionally, the non-contact design effectively reduces the wear that is induced by the reciprocating motion of the small amplitude of the hydraulic viscous dampers. The proposed mechanism is explained and a theoretical model is established. The transmissibility of the two modules at a single degree of freedom is derived using the harmonic balance method. After that, a series of variable control numerical simulations were performed for each important parameter. Empirical rules for designing the system were created by comparing the influence of each parameter on the vibration isolation performance of the entire system.  
vibration energy control; multi-physics mechanism theory; non-linear stiffness; isolation; hydraulic system; magnetic spring Article
# Integration of ERT, IP and SP Methods in Hard Rock Engineering
## 2 Study Area and Hydrogeology  
The projected area is located in Huangbu, a coastal area of the South China Sea in Guangdong province of China (Figure 1). This investigation is a part of the project conducted by the Institute of Geology and Geophysics, Chinese Academy of Sciences to assess the hard-rock site for the development of engineering structures, such as facilities, laboratories and accelerator-driven systems. The investigated site is located in the Huidong Mountain Ranges including a tributary of Kaozhouyang River in northeast. The simplified geological settings of Huangbu consist of several localized faults, an uncomformable boundary between the volcanic acidic rocks of Upper Jurassic and the magmatic/granite rocks of Lower Jurassic, and dynamo metamorphic zones [33]. Tuff rocks are dominant throughout the study area. The project area has a tropical climate system with the monsoon (rainy) season between May and September. The annual mean precipitation is 1950 mm [33].  
Huangbu faces natural hazards, such as frequent typhoons, earthquakes and landslides, etc., [10]. It has a long summer with a maximum temperature of 40 \({}^{\circ}\)C and a short winter with a minimum temperature of 5 \({}^{\circ}\)C. The geological processes, such as weathering, tectonic and hydrothermal processes mainly cause fracturing in the study area [3]. The excess rainwater enters the fractures/discontinuities and makes the aquifer system. Groundwater resources are found within the weathered rock lying between the topsoil layer and the unweathered rock. Therefore, the delineation of weathered and unweathered rock, clay, and faults is the main challenge prior to the construction of engineered structures.
## 4 Framework Dimensions and Measurement
### Learning Evolution Index (L)  
The Learning Evolution Index (L) quantifies a system's capacity to enhance its security posture through iterative learning from past incidents and environmental changes. It focuses on the adaptive improvement phase, measuring how effectively a system transforms experiential data into actionable defensive capabilities by modeling the interplay of knowledge retention, temporal relevance, and organizational adaptability for evolutionary progress.  
Theoretically, \(L(t)\) captures knowledge accumulation and decay:  
\[L(t)=L_{0}+\lambda\sum_{i=1}^{n}e^{-\gamma(t-t_{i})}\cdot\left[1-\frac{1}{1+ \eta\cdot\Delta E_{i}}\right] \tag{7}\]  
\(L_{0}\) is the initial learning capacity. The summation reflects time-weighted learning from discrete events. The temporal decay \(e^{-\gamma(t-t_{i})}\) (decay rate \(\gamma\), e.g., 0.01/hour, from \(\frac{dK_{i}}{dt}=-\gamma K_{i}\) where \(K_{i}\) is retained knowledge) models knowledge obsolescence, balancing historical lessons with recent insights. The experiential gain \(1-\frac{1}{1+\eta\cdot\Delta E_{i}}\) uses a sigmoidal function for bounded learning benefits from event experience increment \(\Delta E_{i}\), with sensitivity \(\eta\); this approximates logistic growth (\(\frac{dG_{i}}{d\Delta E_{i}}=\eta G_{i}(1-G_{i})\)), reflecting diminishing returns. The learning efficiency \(\lambda\in(0,1)\) scales accumulated knowledge based on organizational factors. This captures the dynamic tension between knowledge acquisition, retention, and obsolescence.  
Practically, \(L(t)\) is operationalized via indicators for system self-healing, policy adaptation, and knowledge integration, using existing enterprise tools. System self-healing is measured by the success rate of automated recovery actions (from logs of Ansible, Puppet, Nagios; or incident dashboards). Policy adaptation is assessed by metrics like update frequency of security configurations or credentials post-incident (from IAM audit trails or configuration management tools), reflecting adaptive responsiveness (e.g., time to update ACLs). Knowledge integration measures the translation of post-incident reviews (from ServiceNow or manual reports) into actionable system updates (frequency and impact of after-action reports leading to changes).  
Implementation involves automated data collection (from SIEMs for incidents, CMDBs for changes, ticketing systems for reviews), normalization of raw metrics (e.g., self-healing success as a percentage, policy update cycles in days), and aggregation into a unified \(L(t)\) score with customizable weighting. This adaptable framework, allowing tool substitution (e.g., Splunk for SIEM), ensures \(L(t)\) is actionable and relevant by grounding measurements in mature technologies and existing workflows.
## 3 Analytical Non-Stationary S2A Channel Model
### Time Variant Transfer Function  
The time variant transfer function \(H\left(t,f\right)\) can be obtained by Fourier transform of impulse response \(h\left(t,\tau\right)\) with respect to the variable \(\tau\), as shown in Equation (25). With \(H\left(t,f\right)\), time variant random spectrum of the channel can be observed.  
\[H\left(t,f\right)=H_{\text{LOS}}\left(t,f\right)+H_{\text{SPE}}\left(t,f \right)+H_{\text{DIF}}\left(t,f\right) \tag{25}\]  
The terms \(H_{\text{LOS}}\left(t,f\right)\), \(H_{\text{SPE}}\left(t,f\right)\), and \(H_{\text{DIF}}\left(t,f\right)\) denote the time-variant transfer functions of the LOS component, specular reflection component, and scattering components, respectively. Thus Equations (26)-(28) can be obtained.  
\[\begin{split} H_{\text{LOS}}\left(t,f\right)&=a_{ \text{LOS}}\left(t\right)\cdot\exp\left(-\text{j}2\pi f\tau_{\text{LOS}} \left(t\right)\right)\\ &\cdot\exp\left(\text{j}k_{0}d_{\text{LOS}}\left(t\right)\right) \cdot\exp\left(\text{j}\frac{2\pi}{\lambda}\int_{0}^{t}\mathbf{n}_{\text{RT} }^{\text{T}}\left(t\right)\cdot\mathbf{v_{RT}}\left(t\right)\text{d}t\right) \end{split} \tag{26}\]  
\[\begin{split} H_{\text{SPE}}\left(t,f\right)&= \boldsymbol{\Gamma}\left(t\right)a_{\text{SPE}}\left(t\right)\cdot\exp\left(- \text{j}2\pi f\tau_{\text{SPE}}\left(t\right)\right)\cdot\exp\left(\text{j}k _{0}d_{\text{SPE}}\left(t\right)\right)\\ &\cdot\exp\left(\text{j}\frac{2\pi}{\lambda}\int_{0}^{t}\left( \mathbf{n}_{\text{TO}}^{\text{T}}\left(t\right)\cdot\mathbf{v_{T}}\left(t \right)+\mathbf{n}_{\text{RO}}^{\text{T}}\left(t\right)\cdot\mathbf{v_{R}} \left(t\right)\right)\text{d}t\right)\end{split} \tag{27}\]  
\[\begin{split} H_{\text{DIF}}\left(t,f\right)&=\sum_{l= 1}^{L}\alpha_{\text{DIF},l}\left(t\right)\exp\left(-\text{j}2\pi f\tau_{ \text{DIF},l}\left(t\right)\right)\cdot\exp\left(\text{j}\varphi_{l}\left(t \right)\right)\\ &\cdot\exp\left(\text{j}k_{0}d_{\text{DIF},l}\left(t\right)\right) \cdot\exp\left(\text{j}\frac{2\pi}{\lambda}\int_{0}^{t}\left(\mathbf{n}_{ \text{TD},l}^{\text{T}}\left(t\right)\cdot\mathbf{v_{T}}\left(t\right)+ \mathbf{n}_{\text{RD},l}^{\text{T}}\left(t\right)\cdot\mathbf{v_{R}}\left(t \right)\right)\text{d}t\right)\end{split} \tag{28}\]  
Equations (26)-(28) can be used to derive time-frequency auto-correlation functions as in Section 3.3.
## 3 Analytical Model of the HIL Equipment
### Pneumatic Cylinder  
Each chamber of the double acting pneumatic cylinder is modeled as a variable volume \(V\), filled with air with density \(\rho\), according to Equation (5):  
\[G_{\rm in}-G_{\rm out}=\frac{d(\rho V)}{dt}=\rho\frac{dV}{dt}+V\frac{d\rho}{dt} \tag{5}\]  
In Equation (5), \(G_{\rm in}\) is the flow at the inlet, whereas \(G_{\rm out}\) is the flow at the outlet. Solving Equation (5) for each chamber, assuming that the variation of volume only depends on the stroke of the piston, yields:  
\[\frac{dP_{1}}{dt}=\frac{G_{1}nRT_{1\rm i}}{A_{1}(x_{0}+x_{\rm m1}+x_{3})\left( \frac{P_{1}}{P_{\rm i1}}\right)^{\frac{1}{n}-1}}-\frac{P_{1}n}{(x_{0}+x_{\rm m 1}+x_{3})}\frac{dx_{3}}{dt} \tag{6}\]  
\[\frac{dP_{2}}{dt}=\frac{G_{2}nRT_{2\rm i}}{A_{2}(x_{0}+x_{\rm m2}-x_{3})\left( \frac{P_{2}}{P_{\rm i2}}\right)^{\frac{1}{n}-1}}+\frac{P_{2}n}{(x_{0}+x_{\rm m 1}-x_{3})}\frac{dx_{3}}{dt} \tag{7}\]  
The subscript 1 and 2 refer, respectively, to the rear and front chamber of the cylinder, while \(x_{3}\) is the relative position of the piston rod with respect to the cylinder barrel, starting from the central position.  
The dynamics of the piston rod is described by Equation (8), which refers to Figure 5:  
\[m\left(\frac{d^{2}x_{3}}{dt^{2}}+\frac{d^{2}x_{1}}{dt^{2}}\right)+\gamma\frac {dx_{3}}{dt}+F_{\rm e}+P_{2}A_{2}-P_{1}A_{1}+F_{\rm fr}\cdot\text{sgn}\left( \frac{dx_{3}}{dt}\right)=0 \tag{8}\]  
In Equation (8), \(x_{1}\) is the absolute displacement of the cylinder body; thus, \(x_{1}+x_{3}\) is equal to the absolute position of the piston rod. \(F_{\rm e}\) is the external force acting on the piston rod, given by thecontact between the perturbator and the subject. The dynamics of the cylinder (Figure 6) is expressed by the following equation:  
\[m\left(\frac{d^{2}x_{3}}{dt^{2}}+\frac{d^{2}x_{1}}{dt^{2}}\right)+M_{1}\frac{d^{2 }x_{1}}{dt^{2}}+F_{\mathrm{e}}+K_{1}x_{1}+\beta_{1}\frac{dx_{1}}{dt}=0 \tag{9}\]  
The external force acting on the piston rod is related to the compression of the interface (buffer), described by the Kelvin-Voigt model, during the impact phase. The equilibrium of the end striker at the interface (Figure 7) is expressed as:  
\[F_{\mathrm{e}}=K_{3}(x_{3}+x_{1}-dx-x_{2})+\beta_{3}\left(\frac{dx_{3}}{dt}+ \frac{dx_{1}}{dt}-\frac{dx_{2}}{dt}\right) \tag{10}\]  
in which \(dx\) is the initial distance between the rod and the stricken body, whose displacement is \(x_{2}\).
## 4 Particle Energy Dissipation Mechanism
### Analysis of Energy Dissipation Path and Its Proportion  
Different modes of energy dissipation play distinct roles in the changes in void ratio during vibratory compaction. Moreover, their relative contributions to the total dissipated energy will evolve as void ratio variations occur. Figure 10 illustrates the ratio of each dissipation component to the total energy over load cycles. As specific dissipation values exhibit considerable randomness within individual cycles, average trendlines over 10-cycle intervals were presented to showcase clearer tendencies over time.  
Figure 10a shows dashpot energy accounting for 6-14% initially, before rapidly decreasing in the rapid void ratio change zone and slightly rising to 3-4% during stabilization. This implied dashpot did not wholly facilitate void ratio reductions since its proportion increased again once void ratio became approximately constant.  
The slip energy proportion in Figure 10b ranged from 30 to 45% initially but quickly declined with void ratio changes and later stabilized between 5 and 12%. This agrees with past findings relating particle sliding and rotations to energy dissipation during compaction [34]. A certain magnitude continued even after stabilization, indicating sliding does not completely promote densification.  
Figure 10c demonstrates rolling slip energy following a similar decreasing trend from initial 12-22% portions to near 0% at stability. In contrast with sliding dissipation, rolling slip energy primarily existed during void ratio variations and played an absolute role in enabling compaction.  
As shown in Figure 10d, strain energy accounted for 30-40% initially before rapidly increasing with load cycles during void ratio reductions and maintaining a high 70-80% proportion at stability. Its evolution closely matched void ratio changes but did not directly facilitate compaction.  
Figure 10: The proportion of energy consumption to total energy dissipation during a single load period: (**a**) dashpot energy; (**b**) slip energy; (**c**) rolling slip energy; and (**d**) strain energy.  
In summary, dashpot energy does not directly cause void ratio changes, sliding provides mixed densification effects, rolling predominantly enables compaction during void ratio variations, and strain energy passively reflects void ratio trends.
# Brainware Computing: Concepts, Scopes and Challenges
## 2 Need for Brainware Computing
### Limitations of Related Systems
#### 2.1.2. Inefficiency  
Recent advancements of the 5G internet, edge, and cloud computing allow high-speed data transmission and processing toward a very large scale [26; 27]. Though existing service-oriented architectures such as IaaS, SaaS, and RaaS utilize the data processing advantages of cloud computing, they overlook the possibilities of edge computing and 5G internet [13; 14; 16]. For example, RaaS provides services based on cloud computing and external control by the developers without utilizing edge computing advancement and high data transmission opportunity [16]. Let us consider a robot that is designed to recognize human action using a deep learning model in videos. If 3D-Resnet101 [28] is selected as the backbone network for this task, then the trained model size will be 365.1 MB considering 64 frames per clip [29]. 5G internet allows a data transmission rate up to 1+ Gbps in peak and 100+ Mbps on average with very low latency [30]. So, on average, recent high-speed 5G internet requires approximately 3 to 4 s to deploy the brain image of action recognition tasks in the robot. Moreover, considering task processing and data sharing capacity in edge computing, this latency can be reduced with improved reliability [31]. Existing robotics technology including service-oriented structures is not capable of grabbing these benefits yet. This scope of high data transmission and computational capacity in edge and cloud computing allows deploying and enabling different service-oriented brains at a different time into the robot to open up a new era in robotics technology. These brains are stored in core and edge clouds.
# Scheduling Algorithms for a Hybrid Flow Shop under Uncertainty
## 3 Related Work
Classification of products according to their future demands is more demanding. [PERSON] et al. [26] have examined an application case of order classification in production planning, but they use k-means clustering in order to group customers into segments. The main difference to the problem analyzed in this paper is in the position in the supply chain. Customers are the first layer in a supply chain because they are causing the demand for orders. The demand becomes more distorted and volatile when customers' order quantities are planned through the different stages of the supply chain [27]. We consider a problem which is more at the end of a supply chain, so the so-called bullwhip effect can be intense. To forecast demands, regression or time series can be applied. There is no one method with an optimal parameter set that is best to predict demand in different settings; instead, algorithms are developed to select good parameters [PERSON] and [PERSON] [28] or to even select the optimal prediction method together with the parameters, as carried out by [PERSON] et al. [29]. According to our results and in order to generate a robust schedule, it is sufficient to oversupply the demand for high-risk jobs, which are identified by clustering algorithms. Although we do not develop new optimization techniques, the originality of our study is that, to the best of our knowledge, it combines the statistical analysis of production data with heuristic and metaheuristic optimization methods to the specific problem of this paper and the subsequent detailed simulation of a real production problem. The experiments give insights into the behavior of different local search metaheuristics and indicate as expected that local search metaheuristics are able to improve the makespan significantly compared to schedules resulting from simple heuristics like SPT. Moreover, local search and tabu search metaheuristics to the best of our knowledge have not been adapted to the specific problem of this paper in the literature before.
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 4 Discussion
In the 2018 Lombbok earthquake testing, the _Mw_ estimation generated based on the single observation point was 6.898 which was close to the referenced _Mw_, 6.9. Unlike the estimation results in previous studies, where the magnitude generally started with a low value [5; 7; 9; 11; 12; 13], the first generated magnitude value that was estimated in this study was already close to the reference value and then increased or decreased slowly until it stabilized. According to the data shown in Figures 6 and 8, the first _Mw_ estimation generated in elapsed time \(t=173.81\) s was generated from the observation data of a single GPS station. The estimation value was relatively stable compared with the _Mw_ estimations at the following elapsed times generated from multiple GPS stations. In the case of the 2018 Lombbok earthquake, due to the limited observation data, the _Mw_ estimation was also generated from a single GPS station. However, the estimation result was close to the referenced value of _Mw_. It meant that in the proposed method, the number of GPS stations used as observation points did not significantly affect the values of the estimated parameter results. The two main processes in the proposed model were permanent displacement estimation based on GPS time series data and the inversion of [PERSON]'s model via GA to convert the displacement data into estimated source parameters. Hence, the proposed model has the potential to be applied to almost any earthquake case, as long as the earthquake causes surface deformation and significant permanent displacement that can be estimated using GPS time series data. However, the proposed model still requires some predetermined source parameters to perform the inversion, such as strike and dip parameters. Therefore, for different earthquake types such as an outer rise event, there may be constraints that need to be studied further in order for the model to estimate properly.
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 4 Dataset Used
### Gait Types  
The publicly accessible foot pressure and 3D skeleton datasets used in this study include normal, antalgic, lurch, steppage, and stiff-legged gait types. In a normal gait, the spine and pelvis play crucial roles. Antalgic gait is characterized by pain resulting from a specific disease or leg injury. Steppage gait occurs when the toes of the foot avoid contact with the ground due to muscle and motor nerve abnormalities in the front shin. Lurch gait is induced by hip area abnormalities, such as weakness or paralysis of the gluteus maximus. Stiff-legged gait, also known as stiff-knee gait, is often due to quadriceps weakness, caused by joint abnormalities in the knee region. In contrast, Trendelenburg gait results from weakness or paralysis of the blunt middle force, often due to a weak gluteus medius muscle, causing the torso to tilt in the direction of the symptoms during walking [32].
## 4 Validation of Clustering Results  
### Algorithm Validation  
To ensure the stability and rationality of the clustering results and feature importance analysis, four machine learning algorithms--Random Forest, decision tree, Gradient Boosting decision tree (GBDT), and logistic regression--were employed for validation. The evaluation metrics were divided into male and female groups and presented in tabular form (Tables 5 and 6) to provide detailed numerical insights.  
The clustering performance for male and female student groups was validated using four machine learning algorithms. The results demonstrated that all models effectively confirmed the stability and rationality of the clustering outcomes. Specifically, for the male group, the logistic regression model exhibited the highest performance, achieving superior values across all four evaluation metrics (accuracy, precision, recall, and F1 score). This indicates exceptional accuracy and stability. Gradient Boosting Trees and Random Forestmodels followed closely, delivering excellent performance, whereas the decision tree model showed slightly lower results, yet still maintained a high level of validation efficacy.  
In the female group, the logistic regression model also performed exceptionally well, with all evaluation metrics approaching unity, thereby substantiating the reliability of the clustering results. Gradient Boosting Trees and Random Forest models demonstrated performance marginally lower than those observed in the male group but remained consistently stable and effective. The decision tree model, while exhibiting relatively weaker performance, still maintained a reasonable degree of accuracy.  
Overall, the consistent validation results across the four machine learning algorithms affirm the reasonableness and stability of the clustering analysis. Notably, the logistic regression model achieved the best performance in both male and female groups, providing robust data support for subsequent physical fitness interventions.