esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
## 5 Conclusions  
The purpose of this review is to provide a summary and analyse the main mechanisms involved in the Laser Powder Directed Energy Deposition process (LP-DED). From the literature review, three mechanisms are identified: laser irradiation and material addition, the generation of the melt pool, and the solidification. For each of these mechanisms, the most significant parameters and their effect on the deposition characteristic have been discussed. Compared to the currently available literature, which focuses on specific aspects of the process, this paper provides a comprehensive overview of the main mechanisms of the LP-DED process.  
The process analysis highlighted the complexity of the phenomena occurring during the deposition process and suggested future studies are needed to improve the process. Some possible activities should focus on the development and testing of new deposition heads that can maximize capture efficiency, the analysis of the effect of a wider range of process parameters and a wider range of materials, the investigation of new deposition strategies and process parameters for residual stress mitigation, and, finally, the structured analysis on the dimensional and geometrical capabilities of the LP-DED process.  
Conceptualization, G.P., [PERSON] ([PERSON]), A.S. ([PERSON]) and E.A.; writing--original draft preparation, G.P., A.S. ([PERSON]), A.S. ([PERSON]) and E.A.; writing--review and editing, G.P., A.S. ([PERSON]), A.S. ([PERSON]) and E.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Microstructure** & **Residual Stress** & **Surface Quality** \\ \hline  & Control the morphology and & Influence the internal & Influence the surface \\  & the dimension of & stress state and the & roughness and the \\  & the grains & part/substrate distortions & dimensional accuracy \\ \hline Material properties & [130, 131, 132, 137] & [143, 144, 148] & \\ Thermal gradient and & [124, 125, 126, 127] & [150, 151, 152, 153, 154] & \\ solidification rate & [128, 93, 134] & [143, 145, 168] & [171, 176] \\ Laser power & [134, 135, 136] & [143, 163] & [171] \\ Travel speed & [133, 134, 135] & [143, 163] & [171] \\ Powder flow rate & [133] & & [174, 175] \\ Deposition strategy & [128] & [137, 144, 161, 162] & [168, 181] \\ Substrate temperature & & [158, 159, 160] & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the factors influencing the solidification mechanisms.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 6 Results
### Research Gaps and Challenges
#### 6.1.3 Methods and Techniques  
Most of the studies herein proposed techniques based on employing artificial intelligence (AI). AI is considered as an interesting approach in detecting IoT botnets because it can accelerate the process of making decisions, and these approaches and techniques could be integrated with different trendy technologies to form more powerful techniques, such as SDN [97; 98; 99] or blockchain [72]. Hence, more studies are required in this area. At the same time, the proposed methods tend to be concentrated on defensive techniques, whereas a proactive approach could help to understand the techniques of IoT botnet and therefore prevent the damage that may be caused by a variety of malicious activities by IoT botnets.
## 2 Experimental Setup  
The experimental setup of the fiber amplifier used in this work is shown in Figure 1. The seed laser is a low-noise, single-frequency, diode-pumped solid-state laser (DPSSL) (Cobolt, a Hubner Company, Stockholm, Sweden), described in detail elsewhere [5]. Light from the seed source was free-space-coupled with a coupling efficiency into a single-cladding polarization-maintaining (PM) fiber greater than 85%. This fiber was spliced to a high-power PM isolator (Advanced Fiber Resources, Beijing, China), which was itself spliced to a \((2+1)\times 1\) PM combiner (ITF Technologies, Quebec, CA, USA).  
A high pump-power stability was achieved by using a wavelength-stabilized pump-diode module (BWT Beijing, China) operating at 976 nm. It delivered up to 60 W of CW power at the maximum current rating of 12 A. A high-power, compact laser driver (DAE-20050, Monocorm, Barcelona, Spain) was used to control the pump diodes in either the CW or QCW mode. The active fiber used is the commercially available PM Yb-doped fiber with the highest Yb concentration (Liekki Yb1200-10/125 DC-PM). It has a core/cladding diameter of \(10/125\)\(\upmu\)m, and a core numerical aperture of 0.08, making it single-moded at 1064 nm. It has a cladding absorption of 6.5 dB/m at 976 nm. The fiber was coiled to a diameter of 8 cm. The unabsorbed pump power was stripped by a single-cladding PM passive fiber with the same core/cladding dimensions that was spliced to the end of the active fiber, with the possibility of replacing it with an anti-reflective end-cap in the future. The SBS signal power was monitored in the backward direction by a photodiode (see Figure 1). The onset of SBS was defined as the signal power at which the backward-propagating pulses started to deform and exhibit narrow spikes.  
For single-pass external cavity nonlinear frequency conversion, we evaluated the performance of a 10-mm periodically poled MgO-doped LiNbO\({}_{3}\) (MgO:PPLN) crystal (Covesion, Southampton, UK) that was kept at constant temperature in a temperature-controlled oven. The crystal temperature was adjusted to achieve quasi-phase matching  
Figure 1: The schematic of the QCW fiber amplifier divided into two parts: the upper block depicts the QCW single-stage fiber amplifier. All components use the same \(10/125\)-\(\upmu\)m polarization-maintaining fiber; the lower block is a schematic representation of the single-pass frequency conversion to 532 nm.  
and thereby maximize the conversion efficiency. No interface material such as thermal paste or copper foil was used to facilitate heat exchange between the crystal and the oven atmosphere. Three dichroic mirrors were placed after the non-linear crystal to remove the residual 1064 nm light from the frequency-converted 532-nm light. The power transmission value of the mirrors was nearly 98-99%, and consequently more than one mirror was needed. The maximum residual fundamental power was, therefore, at most \(\sim\)0.001% (or less than 0.4 mW).
## 6 Model Analysis
### CTMC Steady-State Probability Computation  
For the system security attributes, we have described the system's dynamic behavior by a CTMC model with the state space \(X_{s}=\{R,G,T,C\}\) and the transitions between these states. In order to carry out the security quantification analysis, we need to determine the stationary distribution of the CTMC model.  
The steady-state probabilities \(\{\pi_{i},i\in X_{s}\}\) of the CTMC can be computed by solving the system of linear equations [31]:  
\[\boldsymbol{\pi}\mathbf{Q}=0, \tag{5}\]  
where \(\boldsymbol{\pi}=[\pi_{R},\pi_{G},\pi_{T},\pi_{C}]\) and \(\mathbf{Q}\) is the infinitesimal generator (or transition-rate matrix), which can be written as:  
\[\mathbf{Q}=\begin{array}{cccc}R&G&T&C\\ G&\left(\begin{array}{cccc}-\lambda_{4}&\lambda_{4}&0&0\\ \lambda_{1}&-\lambda_{1}-\lambda_{2}&\lambda_{2}&0\\ \lambda_{1}&\lambda_{6}&-\lambda_{1}-\lambda_{3}-\lambda_{6}&\lambda_{3}\\ \lambda_{5}&0&0&-\lambda_{5}\end{array}\right)\end{array} \tag{6}\]  
In addition, we have the total probability relationship:  
\[\sum_{i}\pi_{i}=1\quad i\in X_{s}. \tag{7}\]  
The transition-rate matrix \(\mathbf{Q}\) describes the dynamic behavior of the security model, as shown in Figure 2. The first step towards quantitatively evaluating security attributes is to find the steady-state probability vector \(\pi\) of the CTMC states by solving Equations (5) and (7). We obtain:  
\[\pi_{R} =\frac{[(\lambda_{1}+\lambda_{2})(\lambda_{1}+\lambda_{3})+ \lambda_{1}\lambda_{6}]\lambda_{5}}{\phi}, \tag{8}\] \[\pi_{G} =\frac{(\lambda_{1}+\lambda_{3}+\lambda_{6})\lambda_{4}\lambda_{ 5}}{\phi},\] \[\pi_{T} =\frac{\lambda_{2}\lambda_{4}\lambda_{5}}{\phi},\] \[\pi_{C} =\frac{\lambda_{2}\lambda_{3}\lambda_{4}}{\phi}\,\]  
for the sake of brevity, where \(\phi=(\lambda_{1}+\lambda_{4})(\lambda_{1}+\lambda_{3}+\lambda_{6})\lambda_{ 5}+[(\lambda_{1}+\lambda_{4})\lambda_{5}+(\lambda_{4}+\lambda_{5})\lambda_{3} ]\lambda_{2}\).  
Given the steady-state probabilities of CTMC model, the confidentiality measure \(\Lambda\) and \(\Theta\) measures can be computed via Equations (1) and (2):  
\[\Theta=w\frac{[(\lambda_{1}+\lambda_{2})(\lambda_{1}+\lambda_{3})+\lambda_{1} \lambda_{6}]\lambda_{5}}{\phi}+(1-w)\frac{\lambda_{2}\lambda_{3}\lambda_{4}}{ \phi}\, \tag{9}\]  
\[\Lambda=1-\frac{\lambda_{2}\lambda_{3}\lambda_{4}}{\phi}. \tag{10}\]
## 2 Preliminaries
### Sensor Network and Database
A straightforward implementation of a join query in WSNs is to perform the _external join_: Every sensor node that participates in the query transmits all the sensor tuples satisfying the selection predicates to the base station where the join is computed. Since the external join transmits non-joinable tuples as well, it would not be energy-efficient when the join is highly selective, _i.e._, join selectivity is very low (for a join \(R\bowtie S\), join selectivity (or join selectivity factor) is defined to be \(\frac{|R\bowtie S|}{|R|\cdot|S|}\)[9]. It would incur much more communication than necessary, depleting the battery of sensor nodes severely. In-network join processing could be a more efficient implementation. In in-network query processing, query operators are pushed into the network. Their executions are distributed across the sensor nodes of the network, consuming the sensor tuples and producing intermediate query results. The sensor tuples are filtered, aggregated, or cross-referenced inside the network instead of being transmitted to the base station. With in-network join processing, the amount of data transmission could be considerably reduced compared to the external join.
# Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong
## 4 Case Study
### Computational Results  
The proposed model, implemented in Visual Studio 2022 using C#, was solved using Gurobi 11.0.3. Figure 1a illustrates the total transportation cost and the cost proportions for booking barges in advance and renting barges temporarily. The optimal transportation cost of modules solved by [M1] is HKD 148,951. The total transportation cost comprises 21% from temporary rentals and 79% from advance bookings, as shown in Figure 1a. The cost of booking barges in advance constitutes the main portion of the total transportation cost, so effective decision-making in the first stage substantially helped to reduce transportation costs.  
The optimal booked barge plan for InnoCell in one scenario is shown in Table 5 and Figure 2 (results derived by solving [M1]). The optimal value of \(\alpha_{k}\) by solving [M1] is denoted as \(\alpha_{k}^{*}\). Table 5 demonstrates the round trips finished by booked barges. Figure 2 illustrates the demand for modules and the number of trips finished by temporarily rented ships in one of the total 50 scenarios tested in our computational results. The results suggest that the contractor should book a 60-TEU barge for one round trip every week in advance and rent additional barges temporarily to meet excess demand each week. Most of the week's demand is below 30 units, and it is evident that booking a 60-TEU barge for one round trip in advance can meet the demand for most weeks. For the few weeks with higher module demand, additional capacity can be managed by temporarily renting an additional round (Figure 2).  
Figure 1: The composition and value of transportation costs.
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 3 Results and Discussion
### The Effect of Speed on the PSD  
To determine the effect of speed on the vibration levels, PSD spectra were collected and analyzed for the three forklifts (Figure 7). A comparison of the speed effect was conducted based on the forklift and road type. The highest intensity vibration was observed at the fork line tips, therefore data analysis was conducted using vibration levels obtained at the tips of the fork lines, while carrying a 680 kg payload.  
For all three types of forklifts, it was observed that increasing the speed of the forklift increases the vibration intensity by around 2 dB. This change was consistent for both investigated road types.
## 3 Results and Discussion
### Soil Leaching by Inorganic Acids
#### 3.1.2 Effect of HCl Leaching Conditions in Orthogonal Experiment  
The effects of HCl concentration, solid-to-liquid ratio, and stirring time on the remediation efficiency were investigated by an orthogonal experiment according to Table S1. The results and analysis are shown in Table S3. The influence of each factor on the remediation efficiency of contaminated soil was determined by the range (\(R\)), as shown in Equation (11):  
\[R_{i}=k_{i\text{ (max)}}-k_{i\text{ (min)}} \tag{11}\]  
where \(k_{i}\) is the average value of \(K_{i}\), and \(K\) values (\(K_{1}\), \(K_{2}\), \(K_{3}\), \(K_{4}\)) are the sum of the results of each level and represent different levels of each factor between the minimum and the maximum in the orthogonal experiments.  
According to the \(R\) value of various factors, the order of factors on remediation efficiency was as follows: acid concentration > solid-to-liquid ratio \(\approx\) stirring time, and their \(R\) values are 71.9%, 19.8%, and 14.5%, respectively. The HCl concentration is the key factor, and its \(k\) value changed noticeably from 0.314% to 72.2%. The larger the concentration of HCl, the higher the removal efficiency of Pb from soil, with the highest remediation efficiency of 74.0%. The concentration of lead in the soil was reduced from 8043 to 2091.2 mg\(\cdot\)kg\({}^{-1}\). However, excessively high HCl concentration could cause severe soil acidification and damage the soil structure. It is necessary to further find an optimal HCl concentration with a higher remediation efficiency. The \(k\) value of the solid-to-liquid ratio increased from 17.7% to 37.5% when it was decreased from 1:10 to 1:25. When the solid-liquid ratio is relatively large, reducing the solid-to-liquid ratio can significantly improve the repair efficiency. When the solid-to-liquid ratio decreased from 1:10 to 1:15, the \(k\) value increased from 17.7% to 31.5%. However, there were no significant changes in the remediation efficiency when the solid-to-liquid ratio exceeded 1:20, which may mean that the total amount of HCl had reached relative saturation and that continuously decreasing the solid-to-liquid ratio will increase the cost of soil remediation. Based on the average remediation efficiency and remediation cost, the optimal solid-to-liquid ratio is selected as 1:20. It can be seen from Table S3 that with the increase in stirring time from 8 to 48 h, the \(k\) value decreased from 34.5% to 30.7%. This shows that the stirring time also has a certain influence on the remediation efficiency. Although the change is not obvious, it is worth further exploring the optimal stirring time to shorten the experimental process.
## 2 Materials and Methods
To retrieve the signatures of the ionospheric irregularities, we analyzed the rate of change of the total electron content along the GOES GPS-GPS transmitter links, using a method similar to one applied for ionospheric irregularity detection with the ground-based GNSS observations (more details in [28]). To confirm the presence of ionospheric plasma gradients and irregularities within an area of GOES GEO RO ionospheric soundings, we examined observations from the Global Navigation Satellite System (GNSS) networks, which allowed us to detect such ionospheric density gradients and irregularities at high, middle, and low latitudes. For the overall representation of the spatial evolution of plasma irregularities, we analyzed the daily ROTI polar maps. Such maps are based on multi-station GNSS observations and include the following: the calculation of the rate of TEC (ROT) values for every selected ground station-to-GNSS transmitter link, calculation of the ROT index (ROTI) [28], and mapping of all the ROTI values in the magnetic latitudes-magnetic local time coordinate system. The ROTI mapping technique was described in detail in [PERSON] et al. [43; 44]. Such ROTI maps can be used to monitor ionospheric irregularities with a daily overview, which clearly show the evolution of the irregularity oval as a geomagnetic storm develops. For detailed investigations of ionospheric irregularity dynamics and the ionospheric response to space weather events, we constructed and analyzed the global GNSS ROTI maps with a high spatio-temporal resolution. To construct these maps, we applied the same ROT/ROTI calculation technique but analyzed a significantly larger number of stations to maximize the number of observations in the areas of interest, and the resulting values were mapped in the geographical coordinate domain. Such maps can be constructed with up to 1-5 min in temporal resolution and 0.5-1\({}^{\circ}\) in spatial resolution [67; 68]. Because most ground-based stations track both GPS and GLONASS signals, we combined these GNSS signals together to obtain better spatio-temporal coverage. For this particular study, we constructed GNSS ROTI maps for every 5 min interval with a latitudinal/longitudinal resolution of 1 \(\times\) 1 degree. Figure 1: The sketch view of the GOES–GPS and LEO–GPS radio occultation experiment configurations.
## 4 Numerical Simulation Analysis of Tunnel Stability  
### Modeling  
In this paper, the ZK13 + 090 \(\sim\) ZK13 + 130 section with Grade V surrounding rock is analyzed, and ABAQUS software (2020) for finite elements is used for simulation analysis. The model is 120 m \(\times\) 40 m \(\times\) 100 m (length \(\times\) width \(\times\) height), and the tunnel buried depth is 50 m, as in Figure 9a. The model is surrounded by displacement constraints, with a fixed boundary at the bottom and a free boundary at the upper surface [14; 15]. The simulation of the surrounding rock and support elements is performed by solid elements, whereas the grid element type is a three-dimensional eight-node hexahedral linear element (C3D8). The anchor is a rod element with a cross-sectional area of 3.8 \(\times\) 10\({}^{-4}\) m\({}^{2}\), and the grid element type is a three-dimensional two-node linear element (T3D2) [27; 28; 29]. The surrounding rock of the tunnel is a Mohr-Coulomb plastic model, and the support is a linear elastic model [30]. The physical and the mechanical parameters for the surrounding rock and the support types are presented in Table 2.  
Because the tunnel is surrounded by Grade V, the method of reserving core soil with upper and lower steps (as shown in Figure 9b) is utilized for construction, being divided into 20 sections along the longitudinal direction during numerical simulation, with an excavation footage of 2 m. The arch of the upper step is 4 m ahead of the core soil, and the core soil is 4 m ahead of the lower step. The tunnel excavation steps are as follows: the first and second excavation steps for arch excavation of the upper step, the third and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & **Bulk Density** & **Elastic Modulus** & **Poisson’s Ratio** & **Cohesion** & **Internal Friction** \\  & _/_(kN\(\cdot\)m\({}^{-3}\))_ & _/_GPa_ & _/_MPa_ & _Angle/Degrees_ \\ \hline Surrounding rock & 22.5 & 0.08 & 0.30 & 0.1 & 22 \\ Primary supports & 22.0 & 26 & 0.25 & — & — \\ Secondary lining & 25.0 & 31.5 & 0.20 & — & — \\ Anchor & 78.5 & 210 & 0.20 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical parameters for surrounding rock and tunnel supports.  
Figure 9: Numerical model: (**a**) three-dimensional model and (**b**) excavation sequence.  
fourth excavation steps for core soil excavation, and the fifth excavation step for lower step excavation, which circulate in turn. There are 24 excavation steps in the construction of the tunnel. The primary supports need to be performed in time after each excavation, and the secondary lining shall be completed in the 9 th, 14 th, 19 th, and 24 th excavation steps.
## 2 FSO Vertical Channel Model
### Beam Divergence Angle Optimization
Therefore, when pointing errors occur frequently, wider beams are proposed to mitigate the scintillation effect in an FSO vertical uplink channel. However, the transmitter gain is insufficient when a wide beam is constantly utilized to reduce the effect of scintillation. Therefore, the beam must be adjusted adaptively according to the pointing error.
In the context of MSK rehabilitation using MSK modelling and simulation tools, the precise load applied to MSK tissues during functional activities can be estimated and compared to loading guidelines. Physical therapy or exercise regimes can then be modified to provide optimal, patient-specific MSK loading to deliver the optimal regenerative response (see Section 4). In addition to the investigation and optimisation of MSK loading, MSK modelling and simulations can be employed in the design and optimisation of active prosthetics to improve motor function. Likewise, critical parameters for pre-surgical planning procedures, implant and assistive device design can be evaluated and their impact on post-intervention functionality evaluated _in silico_ (see Section 5). The applications introduced in this review have a strong emphasis on lower-limb joint contact loading due to the high prevalence in literature and its widespread applications to various MSK conditions. The third section of this review presents a discussion on modelling and simulation considerations and a roadmap for the future integration of MSK modelling and simulations in clinical practice. Indeed, despite clear integration pathways and advantages for clinical applications, the use of  
Figure 1: Total numbers of papers (blue) using musculoskeletal (MSK) modelling (Source: PubMed [[https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)]([https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/))), and number of clinical science papers (orange) using MSK modelling (Source: Dimensions [[https://app.dimensions.ai/](https://app.dimensions.ai/)]([https://app.dimensions.ai/](https://app.dimensions.ai/))). MSK modelling and simulations remains scarce in standard clinical practice. Poor integration is likely multifactorial with barriers, including lack of easy point-of-care integration, specialist knowledge for use and interpretation and, in some cases, lack of knowledge of the added benefits of MSK modelling and simulations (see Section 8). Additionally, technical limitations exist regarding the accuracy of simulation results, simplifications made within MSK models, and the often cost- and time-intensive frameworks (see Section 7).