esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 1 Introduction  
Over the first three decades of this century, global urban land cover is projected to expand at a rate exceeding all prior urban growth in human history. Cities with populations ranging from 500,000 to 10 million residents will continue to evolve, intensifying land demand in high-density areas [1]. In response, underground construction emerges as a viable solution, offering new opportunities for sustainable urban development. The economic, environmental, and social effects of underground infrastructure are substantial, with well-planned and strategically developed urban underground space (UUS) potentially improving quality of life by fostering compact, efficient, and sustainable urban environments. In civil, architectural, and hydraulic engineering, the development and utilization of UUS rank among the top 10 engineering development fronts [2].  
This trend is particularly evident in the last two decades, where rapid economic growth and urbanization have driven the need for increased infrastructure, including underground space excavation for metro, road, and railway tunnels, as well as a diverse array of utility lines. This has created a mounting demand for the construction of shafts. The global shaft sinking equipment market size was USD 213.4 million in 2023 and is expected to reach USD 324.1 million by 2030, exhibiting a compound annual growth rate (CAGR) of 6.5% during the forecast period [3], as illustrated in Figure 1. This growth reflects the increasing demand for efficient underground construction technologies, driven by the need for optimized urban development. As cities expand, achieving effective urban development becomes essential for promoting new infrastructure and digital and intelligent construction technologies, which play a vital role in the development and utilization of underground space.  
Where there is initially no access to the bottom, a shaft is defined as a vertical or near-vertical tunnel extending from the top downward [4]. The application of vertical shaft technology in soft soil conditions presents significant engineering challenges due to the complex geotechnical characteristics of these soils, including low shear strength, high compressibility, and susceptibility to instability during construction and long-term use. Factors such as restricted space and groundwater make shaft excavation or sinking one of the most challenging development methods, with various measures complicating the process [5].
## 3 Methods
### Theoretical Background of the GPP Model  
In this research, we used three LUE-based GPP models, namely, VPM [66], MOD17 [10; 67], and Wu [63], to model the GPP according to the following formula:  
\[\text{GPP}=\text{FPAR}\times\text{PAR}\times\text{LUEmax}\times\text{f(T)} \times\text{g(VPD)} \tag{2}\]  
In Equation (2), PAR is the photosynthetically active radiation, and FPAR is the fraction of the absorbed PAR, which is calculated as:  
\[FPAR=1-e^{-k\times LAI} \tag{3}\]  
where \(k\) is the extinction coefficient, and LAI is the leaf area index in the selected area at a specific canopy height. LUEmax is the maximum LUE, which is scaled by temperature and the VPD response under diverse situations. Therefore, LUE can be expressed as:  
\[\text{LUE}=\text{LUEmax}\times\text{f(T)}\times\text{g(VPD)} \tag{4}\]  
Detailed information on the three GPP models can be found in the Supplementary Material. The models have different parameterizations for f(T) and g(VPD).
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 6 Conclusions  
In this paper, we investigated how a realistic power amplifier operating condition can impact on the performance degradation of a GaN HEMT microwave devices. We compared the effects of a saturated class-AB condition and a high-efficiency class-E condition by means of stress experiments carried out using a low-frequency load-pull system. The thorough comparison of the experimental results clearly shows that:  
1. The level of degradation reached by the device under test depends on the actual RF operating condition adopted for the stress experiment. This confirms the need for realistic stress tests to understand what specific degradation mechanisms can impact on the active device when used in actual applications and estimate its realistic lifetime.
2. Thermal degradation effects, magnified by the saturated class-AB condition, are more critical than the effects due to high fields, and dominant when the active device was operated under class-E condition.
3. The developed characterization methodology and the achieved experimental findings can be used to accurately and deeply investigate the degradation and failure mechanisms affecting microwave technology to enhance its performance and robustness for realistic operation in practical microwave applications.  
Conceptualization, G.B. and A.R.; methodology, G.B. and A.R.; software, G.B.; validation, G.B., A.R. and V.V.; formal analysis, G.B., A.R. and V.V.; investigation, G.B. and A.R.; resources, A.R. and R.G.; data curation, G.B.; writing--original draft preparation, G.B. and A.R.; writing--review and editing, V.V., R.G., G.C. and G.V.; supervision, A.R. and G.V.; project administration, A.R.; funding acquisition, A.R. and G.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Italian Ministry of University and Research (MUR) through the PRIN 2017 Project under Grant 2017 FL8C9N.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 4 Robustness Measure for JSP with Preventive Maintenance  
Since the high complexity status and the cost of intensive computation for large problem exists, the exact methods are not suitable for practical application [2]. The Monte Carlo simulation can get accurate results under the condition that the number of simulation times is sufficient, but suffers from a considerable computation burden [3]. Therefore, we propose an analytical approximation method for fast and accurate computing schedule robustness in this section.
# A High Dynamic Weak Spread Spectrum Signal Acquisition Strategy Based on Iterative Local Search
## 3 ILS Acquisition Strategy
### ILS Acquisition Strategy Computation  
We used the ILS acquisition strategy to improve the parallel code phase acquisition algorithm as an example to expand the analysis. As shown in Figure 5, in parallel code phase searching, the received signal is first mixed with the local carrier signal to remove the carrier, and an FFT is then performed. Simultaneously, the local pseudo-code sequence is Fourier transformed and conjugated. After the two signals are processed, they are multiplied, and an IFFT is performed. The peak value detection is then used to determine whether the signal is captured.  
Completing a frequency band search requires two dot multiplications of real numbers, a complex multiplication, an FFT, and an IFFT. Completing a complex multiplication requires four real multiplications and two real additions; completing an n-point FFT or IFFT requires \(3n\log_{2}n\) real additions and \(2n\log_{2}n\) real multiplications. The computation required to complete a parallel code phase search is as follows [18]:  
Real number addition operands: \((6+2)n\log_{2}n+3n\);  
Real number multiplication operands: \((4+1)n\log_{2}n+6n\), where \(n=f_{s}T_{c}\) is the number of points required for one parallel code phase search, where \(f_{s}\) is the sampling rate, and \(T_{c}\) is the coherent integration time of the parallel code phase search.  
For the process of the \(n_{\text{cell}}\) band parallel code phase search, the required amount of calculation is as follows:  
Real number addition operands: \((6n_{\text{cell}}\,+2)f_{s}T_{c}\log_{2}(f_{s}T_{c})+3f_{s}T_{c}n_{\text{cell}}\) ;  
Real number multiplication operands: \((4n_{\text{cell}}\,+1)f_{s}T_{c}\log_{2}(f_{s}T_{c})+6f_{s}T_{c}n_{\text{cell}}\).  
After using ILS to improve the parallel code phase search algorithm, the acquisition process is shown in Figure 6. The Doppler, code phase, and acquisition time are obtained by the parallel code phase search of the received signal, and then, the above signal parameters are transferred to the next search by the transfer model. In the iteration process, the coherent integration time, the number of non-coherent integrations, and the Doppler frequency search step of the parallel code phase search are changed by the search parameter control module. After completing the preset number of iterations, the final Doppler frequency and code phase are transmitted to the tracking loop.  
The computation amount of the ILS strategy is the sum of the computation amount of multiple rounds of parallel code phase searches. The parallel code phase algorithm based on the ILS strategy for \(N\) iterative searches' calculation can be represented as follows:  
Real number addition operands: \(\sum_{i=1}^{N}((6n_{i}+2)f_{s}T_{c}\log_{2}(f_{s}T_{c})+3f_{s}T_{c}n_{i})\);  
Real number multiplication operands: \(\sum_{i=1}^{N}((4n_{i}+1)f_{s}T_{c}\log_{2}(f_{s}T_{c})+6f_{s}T_{c}n_{i})\), where \(n_{i}\) is the number of frequency bands for the \(i\)th parallel code phase search.
# Second-Order Text Matching Algorithm for Agricultural Text
## 4 Proposed Method
### Complexity Analysis  
The aim of this paper is to find a faster and more accurate text matching algorithm. Therefore, it is crucial to analyze the complexity of the algorithm. The time complexity of BM25 algorithm mainly depends on the size of the document collection \(|D|\) and the number of query \(|Q|\). Overall, the time complexity of BM25 algorithm can be considered as \(O(|D|\times|Q|)\), which is relatively low complexity and has an excellent efficiency.  
The complexity analysis of BERT model includes several factors, the computation of BERT mainly comes from the encoder layers. In the multi-head self-attention sub-layer, the complexity of self-attention operation is \(O(n^{2}d)\) and the complexity of the multi-head projection operation is \(O(nd^{2})\). In the FFN sub-layer, the complexity is \(O(nd^{2})\) where \(n\) is the length of the text sequence, and \(d\) is the dimension of the hidden layer [40]. Assuming the number of encoder layers is \(L\), the number of independent texts to be matched is \(|Q|\), the candidate set size generated by BM25 is \(s\), and the number of texts in the database available for matching is \(k\). The complexity of BERT model can be simplified as: \(O(|Q|\times k\times L\times(n^{2}d+nd^{2}))\). On the other hand, the complexity of the SOTM algorithm can be approximately considered as: \(O(|Q|\times(|D|+|D|\times log|D|+s\times L\times(n^{2}d+nd^{2})))\). Since \((|D|+|D|\times log|D|)\) is very low, it can be ignored. Moreover, as \(s\ll k\), compared to the complexity of BERT model, there is a significant reduction, especially when dealing with large-scale texts.
###### Abstract  
Standalone renewable energy systems usually incorporate batteries to get a steady energy supply. Currently, Li-ion batteries are gradually displacing lead-acid ones. In practice, the choice is made without previous comparison of its profitability in each case. This work compares the economic performance of both types of battery, in five real case studies with different demand profiles. For each case, two sets of simulations are carried out. In one of the sets, the energy demand is supplied by a standalone photovoltaic system and, in the other one, by a standalone hybrid photovoltaic-diesel system. Through optimization processes, the economic optimum solutions are obtained. In addition, sensitivity analyses on various parameters have been carried out, seeking the influence in favor of one or another type of battery. The results show that if the type of battery is changed, to achieve the economic optimum the entire system must be resized. In some cases, the economic optimum is reached with Li-ion and in others with lead-acid batteries, depending on the demand profiles. Thus, both types of batteries can be profitable options in standalone energy systems, with a greater tendency to lead-acid in fully photovoltaic systems and to Li-ion in hybrids. The price reductions that would make Li-ion the only choice is quantified.  
Li-ion batteries; lead-acid batteries; electric energy storage; standalone photovoltaic energy systems; hybrid energy systems; off-grid storage; microgrids +
Footnote †: journal: Applied
## 4 Results and Discussion
### Qualitative Results  
The data for which we cannot quantize needs qualitative analysis to get a deep perception of the trends hidden inside the structure of the problem and its association with the prediction algorithms related to its parametric variations leading to conclusive results. Below are the results of applying our SCT on the test set of the MOT 16 dataset. These are the images taken from video sequences of the test set. Each person is assigned a bounding box for tracking with an identity number (ID-Verification). The bounding box moves along with the person showing the same ID number. The qualitative results have been shown in Figures 4-9. From these, we can conclude that in two adjacent frames of Figures 4 and 5 from the test set of the MOT16 dataset, we can see the movement of bounding boxes along with two ladies. Rectangular bounding boxes are drawn around every person, and a numeric identity is assigned to every person. It has been illustrated in Figure 4 that two ladies have
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
## 1 Introduction
The case of the LC filter was studied in [18] in detail; the limitation was that the equilibrium points' computation was based on a single-VSC system and could not be utilized in a system with multiple VSCs. Time delay was also not considered in either of the studies [17; 18], so the dynamics of the high frequency phenomena could not be analyzed. Systems with multiple VSCs have been studied in [19; 20; 21] too; however, these studies mainly focused on the Phase-Locked Loop (PLL)'s dynamic impact, and the outer-loop control was overlooked. In this paper, therefore, the internal interactions of a nonlinear state-space model of a power system with multiple grid-connected VSCs were studied, covering the abovementioned research gaps. For that purpose, the model entailed an LC filter and outer-loop control, and the VSC sub-models corresponded to the wind turbines of a wind farm. A novel generalized methodology, based on the number of VSCs in the system, was utilized in order to estimate the equilibrium points of each state variable of the nonlinear model; thus, the model could be linearized around them for several system and control parameters. The dynamics within the wind farm were then highlighted, first by applying the eigenvalue-based stability analysis and then the controller gains were swept for each converter. In this way, the equilibrium points of the whole model were recalculated and led to another equilibrium state and its corresponding eigenvalue analysis. Hence, the interactions of both the inner and outer control loops were identified by observing the eigenvalues trace for each controller, as well as by implementing participation factor analysis for the critically unstable system modes. Simulation results in the time domain and the frequency domain were carried out in order to validate the eigenvalue-based stability analysis results.
## 5 Simulation Setup and Result Analysis  
We have generated and simulated a disaster recovery scenario that covers indoor and outdoor activities using the ns-3.10 network simulator on the Ubuntu 14.04 operating system. In this scenario, different activities are performed that involve monitoring indoor patients and hospital staff. In addition, the movements of rescue team members are also monitored. The network scenario is divided into three regions. Each region involves three events. All monitored events are reported to the control room. The obtained simulation results are quite convincing and nearly identical to realistic experimental results. The main goal of the simulation is to accelerate the disaster recovery process by consuming the less energy and improving the QoS provisioning. We deploy the PT model with O-MAC to evaluate the performance and compare it with the following hybrid protocols: the enhanced cross-layer protocol (ECLP) [29], cross-layer MAC (CL-MAC) [30], pipeline-featured MAC (P-MAC) [31], routing-enhanced-duty-cycle MAC (RMAC) [32], MAC cross-layer (MAC-CROSS) [33], the unified cross-layer protocol (XLM) [34], and the energy optimization protocol (EOA) [35]. Similar parameters have been used to simulate competing hybrid protocols.  
The network contains 450 sensor nodes that are randomly deployed over a 1200 \(\times\) 1200 m\({}^{2}\) field. The network is divided into three regions and each region is handled and controlled by a DON. We initially let the DON remain at a corner of each region and distribute the sensor nodes in the three regions. When the simulation begins, the mobile sensor nodes move back and forth in the network regions and initiate their assigned responsibilities. Each simulation run lasts for 45 min. Furthermore, five routing protocols are deployed: PT, the multi-speed routing protocol (MMSPEED) [36], multi-constrained QoS multi-path routing (MCMP) [37], the energy aware-routing protocol (EAP) [38], and sensor protocols for information via negotiation (SPIN) [39].  
We use a sensor application module with a constant bit-rate source that maintains QoS requirements [40]. The simulation parameters are presented in Table 7. We obtained several results but use the following metrics to demonstrate the performance of the PT model and O-MAC with PT:  
* Predicted pheromone rates at each link with respect to the number of delivered packets by deploying MCMP, MMSPEED, SPIN, EAP and PT.  
Figure 6: Routing process of the pheromone termite model.  
* Throughputs of MCMP, MMSPEED, SPIN, EAP, and PT with respect to the different mobility rates.
* The end-to-end delays of packets using MCMP, MMSPEED, SPIN, EAP and PT.
* The successful packet delivery rates and energy utilization efficiencies using the O-MAC and hybrid protocols.
* Throughput performance of the O-MAC and hybrid MAC protocols.
* Latency of the O-MAC and hybrid MAC protocols.
## 4 Materials and Methods
### Parameter Extraction
#### 4.3.5 Singular Value Decomposition Entropy (SVDE)  
The SVDE is a complexity measure based on the singular value decomposition of the data matrix (signal). The value of SVDE corresponds to the orderliness or disorderliness of a signal, which is elucidated by a single eigenvector. Mathematically, SVDE is defined using Equation (7) [55]. The complexity of a signal is a function of its attributes. A higher value of the SVDE of a signal suggests that there is more complexity and the involvement of more attributes [56].  
\[\text{SVDE}=-\sum_{i=1}^{M}\overline{\sigma_{i}}\cdot\log_{2}\left(\overline{ \sigma_{i}}\right) \tag{7}\]  
where \(M\) represents the number of values and \(\overline{\sigma_{i}}\) represents the normalized singular value.
## 4 Results
### Comparison to Other MCDM Methods  
A comparison of the proposed model with other existing MCDM models based on FCMs is provided in Table 13. Two existing MCDM models that utilize FCM analysis are used for comparison based on the ranking of dimensions/factors, the capture of indeterminacy present in the real world, the analysis of the influence of one dimension/factor on others and its capacity to capture dynamic behavior efficiently. It is worth noting that we did not find any research study that applies the NCM approach in the context of smart port performance, adding novelty to our proposed method.  
_Method 1_[78]. The study uses fuzzy ANP (analytic network process) to choose the optimum container port while taking into account the interdependence of various port performance indicators. Fuzzy logic is used to deal with ambiguity and imprecision in expert judgement, as well as to evaluate the relevance of various factors.  
_Method 2_[79]. This study uses fuzzy DEA (data envelopment analysis) to assess the effectiveness of cargo ports. The approach evaluates the relative efficiency of ports using input-output models and fuzzy logic to handle unclear or ambiguous data.  
The above table demonstrates that the suggested approach is better suited to handle real-world data with human experts using multi-criteria decision-making as it offers a more flexible, dynamic and customizable approach to smart port performance evaluation. It addresses the key limitations of existing FCM-based methods while introducing dynamic, uncertain, and stakeholder-driven perspectives. This flexibility makes it better suited to address the complex and interdependent nature of smart port performance factors.  
While this study presents an illustrative situation, further research might further validate the method through real-world port decision-making. Collaborations with port authorities or logistics businesses could provide empirical data to test and enhance the model under realistic settings.
## 4 Concept
### Data Processing
#### 4.3.1 Data Acquisition  
During the operation of the CPPS, the connected data sources or devices (sensors, IIoT devices, PLCs, etc.) deliver raw data into the pipeline to the data store. The incoming raw data includes the context-related effects as well as superimposed extraneous effects and errors, for example, performance limits, network failures may result in patchy data histories, or data sources may fail completely. Maintenance work on non-CPPS data sources such as ERP systems can also result in outages. In this task, the macroscopic availability of the data is checked. This means checking whether the files and data streams are available and whether the data sets are complete. The raw data includes metadata, whose availability and completeness are also checked. Missing data or data gaps are to be documented in the dataset report, and if necessary indicated if the trustworthiness and analysis accuracy of the ML application and thus the functionality of the CPPS are affected.  
From a data quality perspective, missing and incomplete data pose a high risk of making the ML application impossible to use or highly error-prone, which can lead to functional limitations or even failure of the CPPS. Missing or incomplete metadata at least limits the interpretability of the data and thus the explainability/reproducibility of the analysis. During data acquisition, the completeness of the data is checked.