esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Discussion of Results and Implications on Smart Port Management  
The results of our analysis provide significant insights into smart port performance by highlighting key dimensions that influence operational effectiveness and long-term strategic sustainability. Using NCMs, our methodology effectively modeled the interdependent factors influencing smart port operations, capturing the inherent uncertainty in expert decision-making processes.  
Our findings indicate that technological advancement (TA) is the most important of the six performance variables studied. This shows that investments in digitalization, automation, and smart infrastructure are critical for improving port operations. The ranking's emphasis on TA reinforces current research that highlights the revolutionary influence of emerging technologies such as Blockchain, Internet of Things (IoT), and AI-driven decision-making tools on port efficiency.  
Operational efficiency (OE) emerged as the second most significant factor, highlighting its importance in optimizing logistics and minimizing vessel turnaround times. Ports that use smart technology in their operations may dramatically increase berth utilization and cargo processing times, decreasing congestion and increasing service delivery.  
The results obtained also indicate that the financial (F) dimension, while important, is significantly influenced by the operational and technological dimensions. This demonstrates that financial sustainability in smart ports is more than just a revenue function, it is also about how effectively resources are allocated and operational costs are handled using smart solutions.  
Environmental sustainability (ES), safety and security (SS), and customer-centric (CC) orientation, on the other hand, had relatively lower total centrality scores, indicating that, while important, these factors are more dependent on the implementation of technological and operational improvements than on serving as primary drivers of port performance.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 2 Mathematical Model of the Mobile Photovoltaic-Diesel-Storage Microgrid System
### Battery Pack  
The energy storage system consists of primary and secondary batteries. Its presence enhances the flexibility of the mobile photovoltaic-storage-diesel microgrid system. When the sunlight is adequate and the load demand is satisfied, excess electricity generated by the photovoltaic will be stored in the energy storage system. When the photovoltaic is unavailable, it can continue to supply power to the load, maximizing energy utilization efficiency.  
The battery pack mainly includes non-rechargeable primary batteries and rechargeable secondary batteries. According to the literature, most battery models consider the variable state of charge. The state of charge of a battery pack is based on the relationship between the power generation and load consumption. It should be maintained within the minimum and maximum range given by the battery manufacturer to ensure the safety of the battery pack during use.  
When the power generation in the system exceeds the demand of the task load, the excess produced energy can be stored in the battery pack to minimize the waste of power generation. The charging process of the battery can be described by the following formula:  
Figure 1: Mobile microgrid structure.  
\[SOC_{t+1}=SOC_{t}+\frac{Energy\;Input}{Battery\;Capacity}\times 100\% \tag{2}\]  
In some cases, such as when the task requires silence and the generator with loud noise must not be turned on, or when the weather conditions are poor and the photovoltaic panel power generation is low, and the power generation cannot meet the task load, the battery pack discharges to meet the load and ensure the completion of the task. The discharging process of the battery can be described by the following equation:  
\[SOC_{t+1}=SOC_{t}-\frac{Energy\;Output}{Battery\;Capacity}\times 100\% \tag{3}\]  
where \(SOC_{t}\) is the state of charge at t, and the state of charge of a battery pack is based on the relationship between power generation and load consumption, and the state of charge should be maintained within the minimum and maximum range given by the battery manufacturer to ensure the safety of the battery pack during use. _Energy Input_ is the amount of energy fed into the battery, and _Energy Output_ is the amount of energy powered to the load, and _Battery Capacity_ is the total energy capacity of the battery.
## 4 Experimental Validation  
### Method  
The validation of the results was carried out using the NFP method through multiple time-domain tests. Validating the GFM properties as a whole requires the analysis and evaluation of a wide range of operating points. The NFP plot can be used as a tool to identify the characteristic behavior of a GFM inverter system, and thereby distinguish it from other GFM controls or even from GFL systems. Furthermore, the NFP Bode-like plot provides a visual way to observe the GFM characteristics at a glance and synthesize dozens of simulation results into a single graph [32,33].  
The objective of this work is not only to demonstrate that a POD algorithm can be implemented in GFM control and tested in a working case. The main goal is to prove that the theoretical analysis and constraints described in the previous sections are valid. To this end, instead of testing a single case, the GFM inverter control algorithm was modified and evaluated for the different configurations presented. NFP Bode plots were obtained for each parameter set.  
First, Bode plots were generated for different values of \(m_{p}\), synthesizing the same inertia (H = 2.5 s and H = 5 s), for both the \(\Delta P/\Delta\omega_{g}\) and \(\Delta P/\Delta P^{*}\) cases. The former is obtained by exciting the system frequency with a sinusoidal signal, and the latter by  
Figure 12: \(\Delta P/\Delta\omega_{g}\) NFP Bode plot for the base case (without POD, H = 2.5 s, \(m_{p}=0.015\), and \(T_{f}=0.075\)) compared to the proposed POD.  
exciting the active power setpoint of the inverter. In both cases, the output active power of the inverter was recorded.  
The Bode plots were validated by obtaining the same frequency response on both the test bench and the theoretical model. The amplitude of the excitation signal was compared to the output signal, and their ratio in [dB] was used to draw the magnitude of the Bode plot. Regarding phase, the phase shift between the excitation and output signals was calculated from the measurements. For each Bode plot, 16 operating points were measured--that is, 16 different excitation frequencies were applied, and the system's response was recorded. After validating the base cases, the proposed POD-P control was also validated using the same procedure.
## 3 System Design and Development
### Office Manager System  
The OMS is a web program consisting of the Field Operator Information Management module and the OMS Communication module. The Field Operator Information Management module was developed to prevent disconnection even if the FOS device IP address changes. For this purpose, the OMS was designed to manage its own ID and the ID and IP address of the FOS device through the Application Server and Relay Server of the Communication Server. The OMS runs on a desktop or tablet PC equipped with any web browser.  
The OMS communication module shows the MRC video sent by the FOS Communication module in real-time on a web browser, as shown in Figure 6. Office managers can monitor the site by watching this video. Furthermore, the manager can verbally communicate with a field operator through the OMS Communication module. Data are directly transferred between the OMS and FOS through the dynamic IP address of the FOS and the OMS devices.
## 2 Methodology
### Theoretical Background and Modeling
Figure 5: Fluid domain: (**a**) the dimpled corrugated channel (light blue) along with the inlet and outlet vessels (gray); (**b**) cross-sectional view highlighting the channel volume details. The stress tensor term \(\overline{\tau}\) has been computed with a two-equation turbulence model. [PERSON] et al. [13] reported the existence of a channel entrance region of unknown length, where the flow is not fully turbulent. Nevertheless, the Reynolds numbers inspected consistently exceeded 200, which is a threshold commonly applied [5; 26] for turbulence modeling in CFD simulations of BPHEs. Both \(k-\varepsilon\) and \(k-\omega\) schemes were initially tested to determine the most suitable option for the studied BPHEs. For the former, the realizable variant with enhanced wall treatment (EWT) was chosen due to its ability of capturing flow features such as strong streamline curvature, vortices, and recirculation [27]; for the latter, the Shear-Stress Transport (SST) was selected, because it is reliable across a broader range of flow types [28]. Two distinct definitions for the hydraulic diameter, appearing in the Reynolds number and friction factor, were introduced. By taking a cross-section between two rows of brazed points as reference, it can be approximated as a rectangular surface, allowing for a simplified expression of the hydraulic diameter:  
\[D_{h}=\frac{4A}{\Gamma}\approx 2h \tag{3}\]  
where \(A\) is the area and \(\Gamma\) the wetted perimeter, assuming \(W\gg h\). As discussed in the following sections, the HP wall waviness, along with the brazed regions, causes a continuous and periodic change in \(A\) and \(\Gamma\). Hence, an improved formulation could be applied, as suggested by [PERSON] et al. [29]:  
\[\widetilde{D}_{h}=\frac{4V_{u}}{A_{w,u}} \tag{4}\]  
with \(V_{u}\) and \(A_{w,u}\) that are the volume and the wetted area of the characteristic unit volume, respectively.
# Advances in Chronic Kidney Disease in Africa
###### Abstract  
Africa, particularly sub-Sharan Africa (SSA), faces major challenges in respect to chronic kidney disease (CKD). There is a rising prevalence due to the combined effects of hypertension, diabetes, and human immunodeficiency virus (HIV) (and the interaction between them) and the effect of apolipoprotein L1 (APOL1) variants on the susceptibility to CKD. Epidemiological data on the prevalence of CKD are of low-to-medium quality, and reliable data are urgently needed for health planning. Furthermore, there are important deficiencies in creatinine-based equations in underestimating the prevalence of CKD in Africa, and evidence suggests that cystatin C based equations are more reliable. There is a changing spectrum of HIV related CKD with the greater availability of antiretroviral treatment. Major clinical trials using SGLT2 inhibitors have signalled a major advance in the treatment of CKD, especially in relation to type 2 diabetes, but the affordability, availability, and relevance to the African population is not established. The importance of the effects of hypertension in pregnancy and pregnancy related acute kidney injury on CKD and the newer concept of CKD of unknown cause (CKDu) are highlighted. Hypertension remains a dominant cause of CKD in Africa, and newer information suggests that the most appropriate treatment to control blood pressure and thus prevent CKD is the combination of either amlodipine plus a thiazole diuretic or angiotensin converting enzyme (ACE) inhibitor.  
chronic kidney disease; Africa; advances +
Footnote †: journal: Medical  
The regional prevalence of CKD in SSA ranges from 12.2 to 16.5%, based on a recent systematic review, but these estimates are based on mainly low-quality studies [4]. Nevertheless, this is considerably higher than the global prevalence of 9.1%. It is, therefore, timely to discuss the challenges and advances in CKD in SSA. This review will focus on advances related to estimating glomerular filtration rate (GFR) in Africans, HIV related CKD, diabetic kidney disease, hypertension, CKD after hypertension in pregnancy, the interaction of HIV with diabetes/hypertension, and other topical issues.
# Predicting Behavior of Grounded Dowel Connections Using Interfacial Cohesive Elements
## 5 Model Validation
### Validation Examples
#### 5.2.2. Grounded Ducts for Segmental Bridges  
The bond-slip model was implemented in an FE analysis to model the specimens presented in [PERSON] et al. [11]. In their study, the authors performed a series of monotonic pull-out tests on grouted connections to explore the bond-slip response and anchorage of stainless energy dissipating bars in segmental bridge columns. The specimen was in the form of a concrete beam with embedded corrugated ducts. The concrete beams were 810 \(\times\) 240 \(\times\) 900 mm (\(L\times W\times H\)). The tests were performed on imperial No. 8 (25.4 mm) and No. 11 (36 mm) bars. The average 28-day compressive strengths of the concrete and grout were 44 and 57.2 MPa, respectively. The FE model of the tests is shown in Figure 10a. The results of the analyses for two No. 8 bars embedded at 7.87 and 3.94 \(d_{b}\) are illustrated in Figure 10b,c, respectively. The selected specimens were chosen so that they allowed the verification of the model predictions under elastic and plastic bar conditions. Figure 10b shows the specimens with an embedment length of 7.87 \(d_{b}\), where the bars yielded at a stress corresponding to \(\sim\)400 MPa. The yielding of the bars was accompanied by additional slip, which is evident from the sudden dropping of the load curve at \(\sim\)400 MPa. The overall trend of the numerical curve at 400 MPa shows a post-yield increase in slip. The small differences between the experimental and numerical envelopes observed in the post-yield region is attributed to the absence of a yield plateau in the assumed steel model. Overall, the model predictions are in good agreement with the experimental results in the ascending branch, plateau, and descending branch of the curve (based on the assumed model). Figure 10c shows the bar stress vs. displacements at the loaded end when the bars were embedded at 3.94 \(d_{b}\). Acceptable correlation was observed between the model predictions and corresponding experimental results as reflected by the error calculated (19-20%) in Table 1.  
Considering the relative error as calculated from Equation (18), it should be noted that the specific results were extracted by digitization of the experimental curves. Since most of the results were superimposed on single figures, the accuracy of the digitization decreased. Also, the MAPE was in the range of 8-10%, which reflects good agreement with experimental results, further highlighting the stringency of the used validation metric. Additional analysis was conducted using a hypothetical bond-slip law based on Equations (4) and (5). The model captured the trend of the experimental curve in terms of initial stiffness and plateau regions. Some discrepancies were observed in the descending branch. This is due to the assumption of residual bond stress of 50% of \(\tau_{max}\). While this is a crude approximation of a rather complex phenomenon, the results are deemed sufficiently acceptable if no experimental results can be utilized to calibrate the model.
## 2 Literature Review
### Industry 4.0 Technologies  
In the scientific literature, there is a growing effort to classify and assess the impact of advanced digital technologies that shape the foundations of Industry 4.0. One of the main directions of research is to explore the potential of solutions such as blockchain, the IoT, Big Data, and cyber-physical systems in the context of industrial transformation and enhanced production system efficiency.  
[PERSON], [PERSON], and [PERSON] [4] analyse the potential of integrating blockchain technology with energy management platforms in sustainable business models for the energy sector. [PERSON] et al. [5] systematically review the impact of eleven key Industry 4.0 technologies on supply chain performance, identifying AI, the IoT, and Big Data as critical success factors.  
[PERSON] [6] offers a detailed discussion the IIoT architecture as a foundation for industrial digitalization, paying attention to security and interoperability issues. More broadly, [PERSON] et al. [7] propose a research agenda for advancing of the Industry 4.0 concept, defining its main technologies and application areas.  
[PERSON] et al. [8] and [PERSON], [PERSON], and [PERSON] [9] emphasize the role of data-driven solutions, such as predictive analytics, in agriculture and intelligent manufacturing systems. In a similar vein, [PERSON] et al. [10] analyse the potential of resource virtualization and service selection in the context of cloud-based logistics, and [PERSON] et al. [11] underline the necessity of integrating technological innovations with strategic and organizational processes.  
[PERSON] et al. [12]'s focus on the role of the IoT in creating smart factories aligns with the Industry 4.0 paradigm, while [PERSON] et al. [13] present methods for organizing technologies in a multi-criteria approach. [PERSON] [14] provides a cross-sectional analysis of the adoption of Industry 4.0 solutions in supply chain operations.  
The advancement of virtual technologies is the subject of the work of [PERSON] et al. [15], who investigate the potential of simulation environments and digital twins. [PERSON] and [PERSON] [16] examine the impact of digital technologies on the efficiency of the wood sector, especially in terms of raw material tracking and process optimization.  
[PERSON] et al. [17] discuss the application of Industry 4.0 technologies in energy-intensive industries, emphasizing their significance in the context of clean production and emission reduction. [PERSON] et al. [18] analyse the conceptual foundations of Industry 4.0, pointing out the challenges and future opportunities of technological transformation.  
In summary, the literature affirms the strategic importance of Industry 4.0 technologies in modern industry. However, as numerous analyses have shown, there is still no unified approach to their prioritization and assessment, taking into account different decision-making perspectives, which justifies the application of multi-criteria methods, such as DEMATEL and PROMETHEE II, in the subsequent section of this study.
# Multi-Means Comprehensive Forecasting Technology for Water Inrush in Long Tunnels
\[\mathbf{J}=\sigma\mathbf{E}+L(-\
abla p+\omega^{2}\rho_{f}\mathbf{u}) \tag{1}\]  
\[-\mathrm{i}\omega\mathbf{w}=L\mathbf{E}+(-\
abla p+\omega^{2}\rho_{f}\mathbf{u})\kappa/\eta \tag{2}\]  
In this formulation, \(\omega\) represents angular frequency, \(\mathbf{J}\) and \(\mathbf{E}\) denote current density and electric field intensity, respectively, \(\mathbf{u}\) signifies solid-phase displacement, \(\mathbf{w}\) corresponds to relative fluid-solid displacement (seepage displacement), \(p\) indicates pore fluid pressure, \(\sigma\) characterizes medium conductivity, \(\eta\) and \(\rho_{f}\) represent fluid viscosity and density, while \(\kappa\) denotes frequency-dependent dynamic permeability as defined by [PERSON] et al. [34]. The critical parameter \(L\) embodies the seismoelectric coupling coefficient--a frequency-dependent complex variable whose magnitude governs the elastic-electromagnetic energy conversion ratio in homogeneous media, reflecting rock's electromechanical transduction capacity. The phase component dictates the elastic-electromagnetic wave phase differential. Notably, when \(L\) = 0, the system decouples into [PERSON]'s poroelastic equations [35] and [PERSON]'s electromagnetic formulations. Practical implementation requires method optimization based on project-specific geological configurations and hydrogeological parameters. This research develops an integrated detection framework combining electromagnetic wave analysis, seismic reflection techniques, transient electromagnetic detection, and seismoelectric coupling theory. Field validation through Fuzhou tunnel engineering applications demonstrates the system's efficacy in achieving precise water-bearing structure identification and inrush risk assessment. Technical integration enables comprehensive geological forecasting ahead of workfaces, significantly enhancing construction safety through multi-parameter verification and spatial correlation analysis.
## 3 Results Analysis
### Error Analysis  
According to the comparison between the theory and measurement results, the difference analysis is shown below. Figure 16 shows the RCS of the Bessel beam on the metal plate for the experimental measurement and the PO numerical calculation. Compared with the theoretical calculation, the measured RCS curves presented asymmetry due to the excitation of the non-ideal Bessel wave source. The main lobe and the secondary lobe were roughly consistent, and the difference mainly appeared in the rapid drop in the measurement sidelobe level, which was about 6 dB below the theoretical value. The differences in sidelobe distribution may have been due to the fact that the ideal Bessel beam had a better half-cone angle and OAM spectrum purity than the actual beam.
## 1 Introduction
Widely used today for industrial lighting applications as a result of significant improvements in luminous power output and efficiency, LEDs have shown good performance with respect to high-speed operation as well [17]. In this work, we investigated the characteristics and performance of a long-distance microscope imaging system optimized for quantification of droplets and particle fields in high-speed flows. We thoroughly characterized illumination systems based on light-emitting diode (LED) and a laser technology newly introduced for imaging applications: the vertical-cavity surface-emitting laser (VCSEL). The illumination systems were designed to generate short pulses of uniform light over the small probe volume created by the camera and associated long-distance microscope objective. Relevant characteristics of the high-resolution, high-throughput machine-vision camera were also measured, to ensure optimal image quality and signal integrity. The energy transmission characteristics of the illumination systems and their relationship to the image acquired by the camera were measured and analyzed, providing estimates of the energy delivered by the illumination systems to the object and image planes. Such information is necessary to understand how much energy is needed to optimize dynamic range and maximize signal-to-noise ratio of a digital imaging system. Laboratory testing was undertaken to characterize the imaging system with both illuminations devices, focusing on parameters relevant to microscopy measurements of droplet and particles in high-speed flows. We finally performed droplet size measurements on a droplet field produced by a commercial air-operated spray system, eventually demonstrating the high performance of the system in that regard. The present manuscript follows the organization given hereafter. The experimental apparatus is presented in Section 2, including a description of the various components of the imaging and illumination systems. Section 3 aims at characterizing the LED and VCSEL illumination systems and evaluate their performance in the context of line-of-sight microscopy for high-speed operation. In Section 4, the imaging system is characterized, again comparing the two illumination devices, and including measurement-based correlations between photonic energy and digital levels. This section also reports the sample droplet sizing experiments with the two illumination systems. Section 5 summarizes the findings and concludes this work.