esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Workflow of the Study  
The main aim of the study is to estimate the influence of packaging overhanging at the pallet on the box compression strength of the EFCO F201 packaging. To achieve this goal, the finite element method models of the box compression test were built for different packaging geometries and using several types of corrugated boards. Moreover, to quantify the influence of the size of overhang comprehensively, several magnitudes of overhanging were considered for three directions, i.e., along the short side, long side and in the diagonal direction of the box. Reference models were prepared, in which the uniform upper and lower boundary conditions were assumed, i.e., without overhanging (for more details, see Section 2.2). Additionally, before the main computations, the validation of the model was performed using the experimental data presented by [PERSON] et al. [35]. Details regarding validation are presented in Section 3.1. In Section 3.2, the main results of the computations are presented; the target models are also described in Section 2.2 with all numerical and modeling details, such as material modeling, both constitutive approach and material parameters, boundary conditions and finite elements used.  
To investigate the topic comprehensively, various dimensions of the box were analyzed. The following dimensions of length \(L\), width \(B\) and height \(H\) were considered: 150, 300, 450 and 600 mm. The box designs with all dimensional combinations without repetitions were modeled, resulting in 64 designs (\(4\times 4\times 4\)), which were finally reduced to 40 cases. All geometries are given in the tables in Appendix A. Later in the article, each packaging is labeled with its dimensions, e.g., a box with a length of 450 mm, a width of 300 mm and a height of 600 mm is marked as 450 \(\times\) 300 \(\times\) 600.  
Several corrugated board materials were adopted, which also allowed the board type space to be explored. After successful validation of corrugated materials assumed based on the paper by [PERSON] et al. (2015), three- and five-layer boards were used in this study. Namely, the boards with fluting types B, C, EB and BC were assumed. The thicknesses of the boards were 2.95, 4.01, 4.15 and 6.65 mm, respectively. The material samples were conditioned in accordance with the TAPPI T402 laboratory standard ([PERSON] et al., 2015), which specifies a standard atmosphere for conditioning and testing papers, cardboards, etc. Therefore, the relative humidity in the pre-conditioning chamber was set at 50% and the temperature at 23 \({}^{\circ}\)C.  
The main topic of this study is to quantify the reduction in box compression strength due to packaging overhanging from the pallet. Therefore, nine overhang magnitudes were assumed, namely 1%, 3%, 5%, 10%, 15%, 20%, 25%, 35% and 50% of the shorter wall, longer wall and along the diagonal direction. The BCT results of these cases, labeled as \(BCT^{oh}\), were confronted with reference cases, labeled as \(BCT^{ref}\), where no overhanging was assumed. The schematic workflow of the study is presented in Figure 1.
# A Weighted and Distributed Algorithm for Range-Based Multi-Hop Localization Using a Newton Method
## 1 Introduction
The last reasons imply that the accuracy and rate of convergence of range-based iterative algorithms for multi-hop network localization are highly dependent of topologies; therefore, robust approaches must be applied in order to minimize errors in position estimates. Moreover, it is well known that the mathematical formulation of the localization scheme typically requires solving a nonlinear and non-convex optimization problem [21; 22; 23; 24; 25]. In this research, a robust and weighted localization algorithm is proposed based on the Newton method approach (RWNM). The algorithm presents the flexibility that any unknown sensor, in a distributed way, can use a dynamic scaling parameter that weights neighboring sensors with the goal of reducing errors in position estimates. Such a scaling parameter is dependent on distance errors with neighboring sensors and the hop-proximity of them towards anchor nodes. Results demonstrated that our approach has a good performance in multi-hop networks with irregular distributions on sensor nodes. It overcomes similar approaches in accuracy on position estimates and requires fewer iterations than others, which is essential in distributed schemes to save energy. Summarizing, the main contributions of the work are as follows:  
* A novel localization method is introduced that is robust to outliers. To remove or mitigate the influence of possible atypical measurements, the proposed method uses two kind of weights: one is based on the average hop-proximity to the anchors, and the other is based on the determination of the degree of outlierness degree in the noisy distances. * The proposed method is based on the Newton method, which improves the unknown sensors' positions in a few iterations, even though when rough initial estimates are given. * The method demonstrates good performance under both isotropic and anisotropic topologies. The rest of this paper is structured as follows. Section 2 describes the effect of large errors and outliers that could be present in the position estimation process. Section 3 details the problem formulation for multi-hop network scenarios. Section 4 describes the proposedalgorithm. Section 5 determines the computational complexity of the algorithm. Section 6 shows some experimental results using isotropic and anisotropic networks, and finally Section 7 draws the main conclusions.
## 2 Data and Methodology
### Methodological Framework
#### 2.2.1 Assessment of Net Ecosystem Productivity  
Net Ecosystem Productivity (NEP), a fundamental metric in ecological research, encapsulates the net balance between the Net Primary Productivity (NPP) of vegetation and the carbon emissions attributable to soil microbial respiration (RH) within a particular ecosystem. This metric is instrumental in elucidating the role of ecosystems in the global carbon cycle. A positive NEP indicates that an ecosystem is functioning as a carbon sink, actively sequestering carbon dioxide from the atmosphere, while a negative NEP suggests that it operates as a carbon source, releasing more carbon than it absorbs [29; 30].  
This distinction holds critical significance in the realms of ecological health and carbon-balance management. Ecosystems demonstrating a sustained positive NEP are vital in the context of climate change, as they contribute to reducing atmospheric CO\({}_{2}\) levels. On the other hand, ecosystems with negative NEP values signify regions where carbon release exceeds sequestration, underscoring the need for focused conservation and management interventions. An accurate assessment and comprehension of NEP enable researchers and policymakers to formulate more effective strategies aimed at preserving or augmenting the carbon-sequestration capacities of ecosystems. By focusing on NEP, we gain valuable insights into the ecological processes governing carbon dynamics, which is crucial for informed decision-making in environmental stewardship and climate change mitigation efforts.  
The formula is as follows [31]:  
\[NEP=NPP-R_{H}, \tag{1}\]  
\(NPP\) signifies the net primary productivity, while \(R_{H}\) refers to heterotrophic respiration.  
In this study, soil respiration (\(R_{H}\)) was calculated using an empirical formula, and the calculation formula is as follows [32; 33]:  
\[R_{H}=0.22\times(\exp(0.0913~{}T)+\ln(0.3145R+1))\times 30\times 46.5\%, \tag{2}\]  
\(T\) represents air temperature, \(R\) stands for precipitation, 30 corresponds to the number of days since monthly meteorological data are employed, and 46.5% represents the proportion of soil carbon emissions released through microbial respiration. This formula has been validated through on-site sampling in previous studies and can be applied for monitoring soil respiration in regions such as Northwestern China [31]. To adapt the proposed formula for global application, especially the \"46.5% representing the proportion of soil carbon emissions released through microbial respiration\", these parameter values need to be adjusted based on specific regions. This percentage, derived from data in Northwestern China, may vary due to soil characteristics and environmental conditions in different regions. Researchers can obtain specific local values through field measurements, literature reviews, or model estimations. It is recommended to calibrate the necessary parameters for regional adaptability before applying the formula, to ensure the research's accuracy and universality.
###### Abstract  
This research deals with the problem regarding the optimal siting and sizing of distribution static compensators (D-STATCOMs) via the application of a master-slave optimization technique. The master stage determines the nodes where the D-STATCOMs must be located and their nominal rates by applying the generalized normal distribution optimizer (GNDO) with a discrete-continuous codification. In the slave stage, the successive approximations power flow method is implemented in order to establish the technical feasibility of the solution provided by the master stage, i.e., voltage regulation and device capabilities, among other features. The main goal of the proposed master-slave optimizer is to minimize the expected annual operating costs of the distribution grid, which includes the energy loss and investment costs of the D-STATCOMs. With the purpose of improving the effectiveness of reactive power compensation during the daily operation of the distribution grid, an optimal reactive power flow (ORPF) approach is used that considers the nodes where D-STATCOMs are located as inputs in order to obtain their daily expected dynamical behavior with regard to reactive power injection to obtain additional net profits. The GNDO approach and the power flow method are implemented in the MATLAB programming environment, and the ORPF approach is implemented in the GAMS software using a test feeder comprising 33 nodes with both radial and meshed configurations. A complete comparative analysis with the Salp Swarm Algorithm is presented in order to demonstrate the effectiveness of the proposed two-stage optimization approach in the fixed operation scenario regarding the final objective function values. In addition, different tests considering the possibility of hourly power injection using D-STATCOMs through the ORPF solution demonstrate that additional gains can be obtained in the expected annual operative costs of the grid.  
generalized normal distribution optimizer; optimal reactive power flow; distribution static compensators; radial and meshed distribution networks; annual operating cost minimization  
22 December 2022  
29 December 2022  
2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Intelligent Anomaly Identification Model of Uplift Pressure  
### Principles of the DBSCAN  
As a machine learning algorithm, the DBSCAN is an unsupervised intelligent clustering algorithm [31]. The DBSCAN can divide data with high density and irregular shape into clusters, which is widely used in tracks [32], medical treatment [33], teaching [34], modal identification [35], and other fields of anomaly identification and early diagnosis. Compared with conventional clustering methods such as K-means, DBSCAN has many advantages, such as no need to specify the number of clusters in advance, being unaffected by initial values, finding clusters with arbitrary shapes, and identifying noise points [36].  
The DBSCAN algorithm starts from a selected core point and continuously expands to the area with attainable density, to obtain a maximized area containing core points and boundary points. The calculation flow is shown in Figure 1, and more detailed calculation procedures can be found in the literature [31].
###### Abstract  
This work evaluates the feasibility of utilizing an organic Rankine cycle (ORC) for waste heat recovery in internal combustion engines to meet the stringent regulations for reducing emissions resulting from the combustion of fossil fuels. The turbine is the most crucial component of the ORC cycle since it is responsible for power production. In this study, a variable geometry radial inflow turbine is designed to cope with variable exhaust conditions. A variable geometry turbine is simply a radial turbine with different throat openings: 30, 60, and 100%. The exhaust gases of a heavy-duty diesel engine are utilized as a heat source for the ORC system. Different engine operating points are explored, in which each point has a different exhaust temperature and mass flow rate. The results showed that the maximum improvements in engine power and brake specific fuel consumption (BSFC) were 5.5% and 5.3% when coupled to the ORC system with a variable geometry turbine. Moreover, the variable geometry turbine increased the thermal efficiency of the cycle by at least 20% compared to the system with a fixed geometry turbine. Therefore, variable geometry turbines are considered a promising technology in the field and should be further investigated by scholars.  
variable geometry; radial inflow turbine; organic Rankine cycle; waste heat recovery Article
# Adsorption of Arsenic and Heavy Metals from Solutions by Unmodified Iron-Ore Sludge
## 3 Results and Discussion
### Desorption of As and Heavy Metals at Different pH Values  
In both the single- and mixed-metal desorption experiments the desorption rates of the metals were in the following order: Pb < As < Zn < Cd < Mn (Figure 8). The removal rate of Pb was 0.9%-3.0%, whereas that of Mn was 16.4%-27.8%. The desorption efficiency of metal ions decreased with increasing pH\({}_{\text{solution}}\), with the exception of As. Ion exchange and complexation could be involved in metal desorption from the adsorbent. This result is in agreement with previous studies [32; 59].  
The desorption efficiencies of the metal ions in this study are lower than those reported previously [32; 44], possibly due to the low pH in the previous studies. The low desorption rates of SBC at pH values of 4.0-9.0 indicate a low possibility of leaching of metal ions from this adsorbent into the environment. However, the low desorption efficiency also suggests that the regeneration  
\begin{table}
\begin{tabular}{c c c c} \hline
**Heavy Metals** & **Adsorbent** & **Q\({}_{max}^{o}\) (mg/g)** & **Authors** \\ \hline \multirow{6}{*}{As} & SBC & 1.113 & This study \\  & Laterite (Tam Duong) & 0.756 & [34] \\  & Laterite (OBY) & 0.702 & [51] \\  & Laterite soil & 1.384 & [33] \\  & Modified red mud & 1.08 & [52] \\  & 8-FeOOH & 37.3 & [53] \\ \hline \multirow{6}{*}{Pb} & SBC & 1.305 & This study \\  & Laterite (Tam Duong) & 1.553 & [34] \\  & Laterite (OBY) & 0.658 & [51] \\  & Activated carbon & 20.7 & [57] \\  & Kaolinite & 4.730 & [36] \\  & Montmorillonite & 31.1 & [55] \\  & Glucose AC & 28.2 & [56] \\ \hline \multirow{6}{*}{Cd} & SBC & 0.771 & This study \\  & Laterite (Tam Duong) & 0.397 & [34] \\  & & 4.700 & [54] \\  & Montmorillonite & 30.7 & [55] \\  & Activated carbon & 17.8 & [57] \\  & Modified biosorbents & \textgreater{} 45.4 & [32] \\ \hline \multirow{6}{*}{Zn} & SBC & 0.745 & This study \\  & Laterite (Tam Duong) & 0.281 & [34] \\  & Activated carbon & 19.9 & [57] \\  & ICZ (Iron-coated & 6.22 & [44] \\  & zeolite & & \\ \hline \multirow{2}{*}{Mn} & SBC & 0.710 & This study \\  & Laterite (Tam Duong) & 0.143 & [34] \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of maximum adsorption capacity (Q\({}_{max}^{o}\)) of some adsorbents  
Figure 8: Desorption efficiency (%) of metal ions at various pH values from (**a**) single-metal experiments and (**b**) mixed-metal experiments.  
capacity of the adsorbent is weak. Therefore, the unmodified SBC should not be used directly for water treatment, given the fine grain size. This raw material should be modified to make proper grain size granular and increase the adsorption capacity for As and heavy metals.
## 5 Conclusions and Suggestions  
### Conclusions  
The electrical resistivity profile varies with strata, so this study built seven common geological models, including the single horizontal layer stratum, horizontal interlayer stratum, single vertical layer stratum, vertical interlayer stratum, composite stratum, tilted layer stratum, and debris mixed stratum, and used a Wenner array to arrange the electrodes. The findings show that besides the depth, the resolution capability of 2D electrical resistivity profiles may be influenced by the resistivity ratio, layer depth, covering depth, interlayer thickness, tilt angle, medium size, and noise intensity in different strata. In a single horizontal stratum, as the current flowed in the lower resistivity layer, the relatively low resistance stratum usually had good resolution capability, whereas the relatively high resistance stratum was influenced by the relatively low resistance stratum; if it was in the lower layer of relatively low resistance, the resolution capability began to decline. In the horizontal interlayer stratum, the resistivity of relatively low resistance stratum was close to the set value. However, if the relatively high resistance stratum was in the interlayer and lower layer, under the effect of relatively low resistance stratum, the resistivity was lower than the set value, and it was difficult to identify the layer. In the tilted layer, the tilted layer was more obvious when the tilt angle decreased. However, when the relatively low resistance was in the upper layer, the tilted layer was blurred. On the contrary, when the relatively high resistance was in the upper layer, the tilted layer could be identified clearly. If there was homogeneous material in the strata, no matter whether the material in the strata had relatively high or low resistance, it could be identified, but the material identification depth was influenced by the resistivity ratio. If there were multiple materials in the strata, e.g., debris mixed stratum, the material of Layer 1 could be identified clearly, but the material after Layer 2 was influenced by the resistivity of the background soil layer, fuzzified and displayed in zones on the profile. If there was noise interference in the course of measurement, the resistivity profile had a random jump, and the jump worsened as the noise increased. The boundary effect and 3D effect had similar influential distances, which were  
Figure 19: Influential distances of the boundary effect.  
6-8 m. The above situation should be paid special attention to when interpreting electrical resistivity profile images so as to avoid misrecognition.
# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 5 Field Experimental Validation  
To validate the general consistency between the simulation results obtained via SPACNet and the practical behavior of the physical UACNs, a field test was performed in Xiamen harbor sea area.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 2 A Complete Paradigm Shift
### Cloud-Based Design
In fact, the next-generation Leading Edge Aviation Propulsion (LEAP) jet engine developed by CFM International, which contains 19 of these 3D printed fuel nozzles, has already undergone several flight tests. Such 3D printed fuel nozzles are also being developed by GE Aviation for the huge new GE9X jet engine [12]. The fact that companies such as GE Aviation are already making use of 3D printing to manufacture aircraft parts is concrete testimony to the sheer disruptive possibilities that 3D printing can offer, but also stresses the urgency of developing the required cyber-security measures in the industrial manufacturing sector. However, in a combined effort by researchers of the Ben-Gurion University of the Negev, the University of South Alabama and the Singapore University of Technology [13], an experiment was performed where the propeller blades for a remote-controlled drone were designed and 3D printed with structural defects at critical points in the propeller blade construction that would reduce the fatigue life of the propeller blades. After less than 2 min of flying time, the defective propeller blade failed catastrophically during mid-flight tests, causing the drone to crash and effectively be destroyed. Regardless of the fact that the propeller blade design was compromised, the research team performed the design compromise by means of a full-cycle simulated cyber-physical attack that made use of security vulnerabilities that had been in the public domain for a number of years already. In particular, the WinRAR ZIP file name spoofing vulnerability played a key role in delivering a malicious file to trigger other exploits utilized in the attack [14]. Needless to say, the research team succeeded in illustrating how a relatively simple cyber-physical attack could lead to catastrophic and potentially deadly consequences. This notion becomes extremely serious when it is viewed in the context of aircraft parts already being manufactured by means of 3D printing. With design companies increasingly outsourcing manufacturing tasks and sharing information via the cloud, it is therefore clear that proper security measures are urgently required to make use of cloud-based design platforms safely.