esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Simulation and Techno-Economic Analysis
For each technique or technology included in the process scenarios, a mass and energy balance were carried out, in addition to the evaluation of the necessary size of each equipment. The energy consumption by scenario was also evaluated. The cost of production, as well as the energy consumption, were made using the software SuperPro Designer v10(r). It was considered 330 days of continuous operation mode. The cost of production includes the sum of costs related to raw materials, labor, services and additional operating costs related to the use of the facility. The costs considered for the services were US $0.1/kWh for energy, US $12/tons for steam (heat) and US $0.05/tons for cooling water. For the costs associated with raw materials, those reported per ton in 2018 were considered. The raw materials are formed by the nutrients necessary for the culture (as reported by [PERSON] et al. [48]) and the three flocculants considered in the secondary harvest. Regarding labor, only operators were considered to keep the technologies of each stage of the process in operation, with a cost of US $0.37/h. Additional costs depending on the use of the facility are calculated as the sum of the costs associated with the maintenance of the equipment, the depreciation of the fixed capital cost and some other expenses such as insurance, local (property) taxes and others. All of these are determined as a percentage of the direct fixed capital cost (DFC), which in turn includes the sum of costs that are estimated as percentages of the total equipment purchase cost. For this study, the base cost proposed by the simulator was considered. The simulator has a database of design parameters, which are used in the equipment model to obtain the purchase cost of the equipment, and the software SuperPro Designer v10(r) uses the Chemical Engineering Cost Index to account for inflation and thus adjust the cost to different years (Intelligen, Inc., Scotch Plains, NJ, USA). Computer simulation has been used successfully in many industrial processes to model and predict production costs. They provide the ability to estimate the effect of rising raw material and utility costs, variations in material composition, and the incorporation of new technologies [57]. It always starts with a base case scenario and the design of the model to effectively simulate the conditions that are desired; this allows the user to estimate results of the process alternatives with confidence.
## 4 Simulation Results and Discussion
It can be determined with certainty that the best product is obtained when the fixed-DG: DA configuration with rRD selected as 400 ps/nm is applied to the dispersion-managed link. Through analyzing the results of Figure 7, it can be judged that the linear-DG: AD is the excellent dispersion map at rRD = 1280 ps/nm, but it cannot be recognized as the effective dispersion map that satisfies the purpose of this paper, since there is no significant difference from the product in the conventional dispersion map designed with the same rRD. Analyzing from the viewpoint of improving the product characteristics compared to the conventional dispersion map, it can be confirmed that the degree of improvement increases as the rRD applied to the dispersion map increases to 400 ps/nm, but decreases when the rRD exceeds 400 ps/nm. An interesting result from Figure 7 is that the dispersion map configuration that can lead to the good product is different for each rRD, but the dispersion map results in the minimum product for all rRDs is only flatted-DG: DA of Figure 2g. This result suggests that the maximum cumulative dispersion of the link must be variable, not constant, regardless of the detailed scheme of the dispersion map, in order to effectively compensate for the distorted WDM channels.
## 3 Results and Discussion  
### Response of the Linear Structure Coupled with the Shunt Circuit  
In this section, we investigate the linear vibration of the beam where geometric and inertia nonlinearities are assumed to be negligible. Nevertheless, the quadratic nonlinearity of the piezoelectric capacitance is preserved to maintain the 2:1 internal resonance between the structure and the circuit active. The cantilever beam is subjected to a resonant harmonic lateral excitation that is tuned to the first natural frequency of the beam. The circuit parameters are \(L~{}=~{}3470\) H, \(R~{}=~{}3500\)\(\Omega\), and \(N~{}=~{}10^{10}\). These parameters are selected such that a 2:1 internal resonance between the structure's first natural frequency, \(\omega_{1}\), and the electrical frequency, \(\omega_{e}\), is maintained. By tuning the excitation frequency tot the first natural frequency of the beam and increasing the excitation amplitude, the amplitude-force plots for the modal displacements \(x_{1}\) and \(x_{2}\) are shown in Figure 2. The figure shows the saturation phenomenon as the force amplitude reaches a threshold of 0.16 N. Beyond this level of excitation; the input energy is transferred to the modal displacement \(x_{1}\) while the amplitude of the modal displacement \(x_{2}\) remains constant. The time history of the modal displacements \(x_{1}\) and \(x_{2}\) before and after saturation is shown in Figure 3. As the figures show, there is a significant reduction in the vibration amplitude of modal coordinate \(x_{2}\), which contributes most to the beam's actual vibration coordinate. According to Equations (10) and (A7), the beam's deflection is defined as  
\[u(\vec{z},t)=\phi(\vec{z})q(t)=\phi(\vec{z})(-\theta x_{1}+x_{2}). \tag{34}\]  
The numerical value for the constant \(\theta\) is 0.042 for the set of parameters used in this study. This means that the actual vibration amplitude is mostly contributed by the modal coordinate \(x_{2}\). The saturation amplitude and the forcing threshold depend on the circuit's parameters, the modal damping of the structure, and the piezoelectric patch size and configuration. The significance of these parameters will be further investigated in the next section. The effectiveness of the shunt circuit in attenuating the beam's vibration amplitude is identified by presenting the beam's response without the absorber, which is called the uncoupled system, and the beam's response with the absorber, which is called the coupled system, as shown in Figure 4. As the figure shows, there is a significant reduction in the resulting vibration amplitude due to the coupling between the beam and the shunt circuit, where the energy transfer is directed to the circuit after the force has reached its threshold. Figure 4b also shows the linear behavior of the beam since the inertia and geometric nonlinearities have not been considered in this simulation.  
Figure 2: Force-response plots for the modal coordinates \(x_{1}\) and \(x_{2}\).  
Figure 4: _Cont_.  
Figure 3: Time response for the modal coordinates \(x_{1}\) and \(x_{2}\) at an excitation amplitude of 0.5 N.
## 5 Information Processing Module (IPM) Design
### Exploratory Data Analysis (EDA)
Subsequently, the remaining features were selected based on inter-feature correlations, as well as their MI and FS with the target variable:  
* From the feature set {\(\rm{i_{inDC}}\), \(\rm{i_{outDC}}\), \(\rm{P_{inDC}}\), and \(\rm{P_{outDC}}\)}, only \(\rm{P_{inDC}}\) was retained due to having the highest FS value;
* From the feature set {\(\rm{P_{inAC}}\), \(\rm{i_{nAC}}\)}, \(\rm{i_{nAC}}\) was retained based on its superior MI and FS values;
* From the feature set {\(\rm{v_{outDC}}\), DC}, \(\rm{v_{outDC}}\) was chosen due to having the highest MI and FS values. To summarize, the final input features of the ML model include the following:  
* \(\rm{i_{outAC}}\)--represents the AC component of \(\rm{i_{out}}\);
* \(\rm{v_{outAC}}\)--represents the AC component of \(\rm{v_{out}}\);
* \(\rm{P_{outAC}}\)--represents the AC component of \(\rm{p_{out}}\);
* R--represents the load resistance;
* \(\rm{P_{inDC}}\)--represents the DC component of \(\rm{p_{in}}\);
* \(\rm{i_{inAC}}\)--represents the AC component of \(\rm{i_{in}}\);
* \(\rm{v_{outDC}}\)--represents the DC component of \(\rm{v_{out}}\). Figure 12 presents the final dataset after feature normalization to ensure that all features contribute equally to the model. A min-max scaling transformation was applied to achieve feature normalization (4):  
\[X_{normalized}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{4}\]  
where \(X\) represents the actual feature value, \(X_{min}\) the minimum value of \(X\) in the dataset, and \(X_{max}\) the maximum value of \(X\) in the dataset.
# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
## 2 Basic Theory and Algorithm
### Feature Selection  
To reduce the computational burden and improve the prediction accuracy, it is necessary to select the sensitive features of the bearing degradation indicators that clearly represent the bearing degradation state information, and eliminate the irrelevant or redundant features that are useless or even affect the prediction accuracy of bearing RUL [35]. In this paper, we propose a three-stage feature selection method based on FCBF-AMB and MIC, which reduces feature redundancy and reduces feature data dimension based on the bearing degradation indicator subsets fusion method.
## 3 Materials, Methods, and Equipment
### Classical Bearing Capacity Methods
#### 3.6.5 [PERSON] (1976) Method  
The [PERSON] method (1976) offers a sophisticated approach to calculating pile bearing capacity in sandy soils, where load-bearing capacity primarily comes from frictional resistance. The methodology integrates empirical data with theoretical principles to address the complex relationships between soil properties, pile geometry, and loading conditions [32]. Central to this approach are the bearing capacity factors \(N_{c}\) and \(N_{q}\), which represent contributions from cohesion and surcharge, respectively. These factors vary with the angle of internal friction (\(\phi\)), soil density, and other parameters, as illustrated in the Supplementary Material S1. In [PERSON]'s formulation, both \(N_{c}\) and \(N_{q}\) increase with higher friction angles, reflecting enhanced soil resistance as \(\phi\) rises and emphasizing the importance of accurately determining soil frictional properties. [PERSON]'s method also incorporates shape and depth factors to refine calculations. Shape factors adjust for the pile's cross-sectional geometry, while depth factors account for increased resistance from deeper embedment [33]. When applying this method, engineers must determine the soil's angle of internal friction and sand density, and then use [PERSON]'s equations or graphs to establish appropriate \(N_{c}\) and \(N_{q}\) values. The ultimate bearing capacity is then calculated using Equation (12), which defines the end-bearing capacity component, providing a comprehensive assessment of pile performance in sandy conditions.  
\[Q_{b}=A_{b}\times\sigma_{\
u}^{\prime}\times N_{q}\times s_{q}\times d_{q} \leq q_{lim}A_{b} \tag{12}\]Depth factors: \(d_{q}=1+0.1\sqrt{k}\left(\frac{D}{B}\right)\). Shape factors: \(s_{q}=1+0.1k\frac{B}{L}\) The bearing capacity factor is determined as shown in Equation (13).  
\[N_{q}=e^{\pi tand\psi}tan^{\psi}tan^{3}\left(45+\frac{\psi^{\prime}}{3}\right) \tag{13}\]  
The total shaft resistance of a pile can be expressed in segments 1 and 2, as in the Terzaghi case. This equation accounts for both the soil above and below the water table, considering the change in effective stress due to buoyancy effects.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
###### Abstract  
In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system.  
WSN; pipeline leak detection; routing; multi-hop; energy efficiency Article
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 3 Testbed Design and Test Cases
### Testbed Design
#### 3.1.1 End Devices  
The two EDs used in our testbed are:  
* A device built around the LoRaWAN module RHF PS01509 [30] acting as an authorized user (i.e., the \"victim\") has been used in our experiments. Note that this device can be replaced by any other LoRaWAN-compatible transducer or commercial product.
* The device built around a Murata [31] transceiver working in conjunction with I-CUBE-LRWAN [32] implementing the control interface and connected to a computer has been used to emulate an attack (i.e., \"attacker\").
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Social RS-Based Proxies
#### 4.3.1 Services and Infrastructures
However, often buildings are in a mixed use, i.e., half commercial and half residential, limiting the application of this proxy. In this case, using UAV/oblique images, which contain information about the facades of the buildings, can be useful. For example, one can extract signs/banners of shops located on buildings' facades to identify a functional use of these buildings. Number of inhabitants per settlement indicates the recovery processes, serving as a proxy for a contentment level of new settlements. Low occupancy numbers in new developments demonstrate the dissatisfaction in new settlements, which is usually due to lack of facilities, infrastructure, and job opportunities [135]. RS data is used to detect new settlements and demographic data to extract number of inhabitants [131]. Transportation facilities are a vital infrastructure. Hence, their availability define the better life condition for the people living in the region and they form the basis for assessing social vulnerability of an urban area [39]. In addition, a distance between buildings and lifelines (e.g., hospitals) is important in emergency cases: the longer this distance the higher is the social vulnerability of urban areas [33; 93; 136; 137]. Potential to evacuate based on the density of available roads (road/km\({}^{2}\)) has been used as a proxy to assess social resilience of an urban area during floods [31]. Although the availability of evacuation points is important, distance to them and early warning systems are also crucial. As another disaster resilience proxy, the presence of open spaces, including free and green areas and the street networks, has been used for providing services, gathering and social interactions after/during a disaster (e.g., an earthquake [138]). In addition, open spaces, such as hills and street networks, are significant for evacuation purposes [138; 139].
## 2 Vibration Characteristics of Idling Vehicles
### Summary of Relevant Results and Analysis  
Engine vibrations can be transmitted to the vehicle body through three primary paths: the engine mount, the drivetrain, and the tailpipe. Among these, the engine mount pathway has the most significant impact and was the focus of a study [16]. The engine block vibrations are primarily attributed to the reaction forces generated by the movement of the piston and crankshaft. By dividing the vehicle into four main components--body, frame, powertrain, and suspension--it was observed that the body and frame of the target vehicle exhibit a natural frequency of approximately 24 Hz during idling vibrations.  
For engine-induced vibrations transmitted to the chassis through the powertrain mounts, the disturbances primarily occur in the low-frequency range. These vibrations originate from two main sources: the combustion of fuel in the cylinder and the rotational motion of various engine components [17].  
Idle vibrations in automotive systems originate primarily from the powertrain. Combustion plays a significant role in influencing engine vibrations, particularly at low speeds. At higher engine speeds, inertial forces and fluctuating torque generation cause piston slap and bearing impact forces, which manifest as noise or vibrations transmitted through various engine components [18]. For a vehicle with a two-cylinder power train, the fundamental frequency of the idle vibrations under no load conditions (850 rpm) and full-load conditions (950 rpm) peaked at 14.2 Hz and 15.8 Hz, respectively.  
During idling, low-frequency vibrations were observed in the floor, seats, and steering wheel. Up-and-down trembling vibrations typically occur in the frequency ranges of 20-35 Hz for four-cylinder vehicles and 30-50 Hz for six-cylinder vehicles. Additionally, swaying up-and-down vibrations, caused by uneven combustion, were observed at 5-10 Hz. The primary source of these swaying vibrations was postulated to be engine roll vibration [19].  
The feasibility of using micro-electromechanical system (MEMS) accelerometers to measure vibration parameters in a sedan was explored, focusing on automotive dynamic functions. Accelerometers were installed at various locations, including the hood above the engine, the hood above the radiator fan, the exhaust pipe, and the dashboard. During vehicle idling, the vibration frequencies recorded on the hood above the engine and the radiator fan were approximately 44 Hz and 38 Hz, respectively [20].  
Vibration measurements were conducted on a small passenger car with a monocoque body structure powered by a diesel engine. In this type of structure, individual body components are welded together to form a unified shell. Diesel engines are known to generate higher vibrations compared to other engine types due to the nature of diesel combustion. Using a vibrometer with a connected probe, the vibration spectrum at 800 rpm during idling revealed a peak at 26.25 Hz. Additionally, the vibration spectrum during free vibration excitation, triggered by slamming the car door while the vehicle was off, showed the highest response at 3.75 Hz [21].
### Main Contribution  
Based on the above works, this paper aims at solving an energy efficient joint power control and user association optimization problem while considering the load constraint of each BS in massive MIMO enabled HetNets. The main contributions of this paper are summarized as follows:  
* In this paper, we consider the characteristic of channel hardening in the massive MIMO system and calculate ergodic rate of each user with perfect channel state information (CSI). In addition, we establish the energy consumption model in massive MIMO enabled HetNets and then define the network EE in consideration of user fairness. Finally, a joint optimization problem of power control and user association is formulated to maximize the network EE with the constraint of each BS's maximum load.
* Giving priority to the EE, an energy efficient joint optimization algorithm of power control and user association is proposed in massive MIMO enabled HetNets. We note that the joint optimization problem is a fractional and mixed integer nonlinear programming (FMINLP) problem. Firstly, the objective function of the problem is transformed into an integral expression with weight coefficient based on the Dinkelbach's theorem and an optimal EE iterative algorithm is proposed correspondingly. Then, the joint optimization problem is decomposed into power control and user association optimization subproblem. Both subproblems can be transformed into convex optimization problems with the help of the approximately iterative method and [PERSON]'s dual method, respectively. Finally, the joint optimization algorithm is proposed through the interior point method and the sub-gradient method. In addition, the convergence of the proposed algorithm is analyzed and we discuss the gap between the solution obtained from the proposed algorithm and the optimal one.
* This paper analyzes the performance and its influence factors of the proposed algorithm. First, this paper proves the validity of all the proposed algorithms through convergence results. In addition,the paper analyzes the EE of the network and its functional relationship with other factors, such as spectrum efficiency, weight coefficient, the number of antennas, and so on. Finally, comparing with other algorithms, this paper analyzes superiority of the proposed algorithm in terms of the EE.  
The paper is organized as follows. Section 2 introduces the system model and the joint optimization problem formulation. Section 3 presents the joint optimization problem reformulation and then joint optimization algorithms for power control and user association are proposed. Section 4 simulates and analyzes the performance of EE and its influence factors. Finally, the conclusion and future work of the paper are obtained.
## 4 Results and Discussion
### Underlying Hindrance Groupings
#### 4.2.1 Lack of Collaboration and Model Integration
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Hindrances to BIM Implementation**} & \multirow{2}{*}{**Communality**} & \multicolumn{3}{c}{**Hindrance Grouping**} \\ \cline{3-6}  & & **1** & **2** & **3** \\ \hline \hline \multicolumn{6}{c}{_Groping 3: Lack of Executive Vision and Training (LEVT)_} \\ \hline H01 & Exectives failing to recognize the value of BIM-based processes & 0.758 & — & — & 0.814 \\ \hline H04 & Lack of skilled employees and need for training them on BIM and & 0.725 & — & — & 0.770 \\ \hline H02 & Concerns over or uninterested in sharing liabilities and financial & 0.537 & — & — & 0.654 \\ \hline H05 & Industry’s conservativeness, fear of the unknown, and resistance to & 0.699 & — & — & 0.620 \\ \hline H47 & Firms cannot make most use of IFC and use proprietary formats & 0.596 & — & — & 0.554 \\ \hline \hline \multicolumn{6}{c}{Eigenvalue} & 11.142 & 1.256 & 1.057 \\ \hline \multicolumn{6}{c}{Variance (\%)} & 53.059 & 5.979 & 5.033 \\ \hline \hline \multicolumn{6}{c}{CPV (\%)} & 53.059 & 59.038 & 64.070 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. To implement BIM along with OSM, the digital models should be fixed early and fit for off-site fabrication and on-site assembly (H36), because any changes would be costly after the fabrication commences [25]. However, design environments were difficult for multi-disciplinary integration at the construction level (H45) [7] due to \"interoperability issues such as software selection and insufficient standards\" (H43) [37]. In addition, the multi-discipline model integration also requires \"technical expertise, protocols, and infrastructure for multiuser model access\" (H46) [9; 22]. The post-survey interviewees also suggested that in Singapore different parties tended to use various software or software versions, creating a difficulty in integrating digital models across disciplines.