esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 4 Factors Affecting Abrasion Resistance
### Influence of Material Properties
#### 4.1.4 Water-Cement Ratio  
The strength and porosity of concrete are notably influenced by the w/c ratio. Higher abrasive strength is typically the consequence of a lower w/c ratio. This is because a denser concrete matrix with less porosity and permeability results from a lower w/c, which increases the concrete's resistance to abrasive particle penetration [47].  
The findings indicate that, with the same w/c ratio, the abrasion loss only drops by 8% when the amount of cement is increased, but it drops by 50% when the water dose is reduced. Increasing the mortar volume component can result in a 60% reduction in abrasion loss when the w/c ratio is quite low. At relatively low ratios of coarse to fine aggregate, abrasion loss rises by 56%. When the air concentration is more than 4%, it rises by more than 30% [48]. A study on the comparison of abrasion resistance between Ordinary Portland cement-based concrete (OPPC) and geopolymer concrete (GPC) depicted that since GPC has fewer pores and more refined microstructure, a reduction in water content was crucial for increasing abrasion resistance. The GPC's resistance to abrasion was improved by the reduction in holes within the structure and the availability of free water for evaporation and moisture loss [49]. A study carried out using concrete mixtures with varying w/c ratios and surface hardener admixtures (SHAs) showed that the addition of surface hardener admixtures increased the concrete mixture's resistance to abrasion in all the concrete series [50].  
Another factor that affects abrasion resistance is the concrete surface's microstructure. Concrete with a dense and well-hydrated microstructure will withstand abrasion better than concrete with a porous and poorly hydrated microstructure [51].
## Materials and Methods
### Clinical Procedure  
Dental implants (\"OsseoSpeed TX\", Astra Tech Implant System--Dentsply Implants; Molndal, Sweden) were placed following a two-stage protocol according to the manufacturer's instructions. These kinds of implant have two main features: an exclusive implant surface with a fluoride-treated nanostructure that stimulates early bone formation and provides a firmer bone-implant connection; micro-threads on the neck of the implant that ensure optimal load distribution and optimal stress values. Implants were placed exclusively in the upper jaw. For definition purposes, implants placed in the \"anterior\" maxilla were meant to replace central and lateral incisors and canines; whereas in the \"posterior\" maxilla implants were placed to replace premolars and molars. Implants were usually positioned with the implant shoulder at the level of the alveolar bone crest and then covered with the mucosal flap. All the implants were placed in native bone and without bone regeneration. The torque was measured through the implant motor. The implants, placed with handpiece, had all torque up to 35 ncm. The second-phase surgery was carried out at 3 months. Different implant lengths (9, 11 and 13 mm) and diameters (3.5 and 4 mm) were used. The diameter of the last tool used was based on the diameter of the implants, it was 2.7 for diameter 3.5 and 3.2 for diameter 4. A beta-lactam antibiotic (Amoxicillin) was given to all patients for 5 days post-surgically. The postoperative therapy required good oral hygiene, rinsing with mouth wash containing 0.2% chlorheximide solution twice a day for four post-operative weeks from the surgery. Sutures were removed at seven days at the surgery. All implants were evaluated with peri-apical x-rays immediately after insertion and after 3 months. Definitive crowns were delivered at 4-6 months post-surgery. All prosthesis were manufactured in order to facilitate oral hygiene procedures. No implant failures were recorded. The bone quality was clinically evaluated using the index of Lekholm and Zarb, in agreement with the radiographic evaluations and the drilling resistance perceived by the clinician operator [23]. Implants distribution according to bone quality is showed in Table 1. Implant stability measurements through resonance frequency analysis (RFA) were performed by a single operator immediately after implant placement in terms of the implant stability quotient (ISQ1) and after 3 months (ISQ2). The ISQ was obtained installing a \"Smartpeg\" transducer (Integration Diagnostics AB, Goteborg, Sweden) into the fixture and approaching it perpendicularly with the handpiece probe of the Osstell (Integration Diagnostics AB, Goteborg, Sweden) device.
## 1 Introduction
For DSS-Fix, we first search the orders that can be shared, and insert these shared orders into the shareability network of DSR-Fix. Compared to the DSR-Fix variant without considering order sharing, DSS-Fix can improve the system throughput. Finally, we combine the flexible time windows spitting mechanism (proposed for DSR-Fle) and shareability network updating mechanism (proposed for DSS-Fix) to design an efficient algorithm for DSS-Fle. Besides the time efficiency, we systemically guarantee that order sharing is beneficial on maximizing system throughput, i.e., DSS-Fix \(\succ\) DSR-Fix, and DSS-Fle \(\succ\) DSR-Fle; the symbol \(\succ\) indicates the prior relation on maximizing system throughput. More specifically, this paper advances the state of the art in the following ways. Compared with the most related DSR-Fle studies, which employ the local search heuristic, we propose the approximate algorithm with system throughput guarantee. Compared with most related DSS-Fix studies, which do not take the flexible time windows into account, we propose a time-efficient algorithm for DSS-Fle and systematically analyze the advantage of sharing on maximizing system throughput. Finally, we conduct a series of experiments on synthetic datasets to validate the proposed algorithms. The remainder of this paper is organized as follows. In Section 2, we provide a review of the related literature on DSR with fixed/flexible time windows, and with/without sharing. In Section 3, we formulate the DSS-Fle problem and propose the algorithm framework. In Section 4, we propose four algorithms for the DSR-Fix, DSR-Fle, DSS-Fix, and DSS-Fle four variants, respectively. In Section 5, we conduct a series of experiments on synthetic datasets to validate the proposed algorithms efficiency and effectiveness. Finally, we conclude our paper in Section 6.
## 4 Digitalization of MGs
### Cloud Computing
The CC can also improve the resiliency of MGs by providing backup power and redundancy, and by enabling remote monitoring and control of microgrid components. This can help to minimize the impact of power outages and other disruptions, and can ensure that critical infrastructure and services remain operational during emergencies. It can enhance the flexibility of MGs by enabling real-time monitoring of energy usage and automatic adjustment of energy production and consumption based on changing conditions. This can help to reduce energy waste and to optimize the use of RESs such as solar and wind power. The CC can also enable more advanced data analytics in MGs, by providing the computing resources needed to analyze large amounts of data in real time [186]. This can help to identify patterns and trends in energy usage, and to make more accurate predictions about future energy needs. This method can also enable greater collaboration between microgrid operators, energy providers, and other stakeholders. Cloud-based platforms can provide a secure and transparent marketplace for energy trading, and can enable peer-to-peer energy trading between consumers. This can help to promote sustainability, and to build more resilient and efficient energy systems. Overall, the integration of the CC in MGs has the potential to revolutionize the way we generate, store, and consume energy. By providing the computing resources needed to optimize energy production, storage, and consumption, the CC can help to reduce energy costs, improve reliability, and promote sustainability. As the demand for RESs continues to increase, and as the need for more resilient and efficient energy systems becomes more pressing, the CC is likely to play an increasingly important role in the management and optimization of microgrid operations. However, to fully realize the potential of the CC in MGs, it will be important to address the challenges associated with its use and to ensure that the computing infrastructure is reliable, resilient, and secure.
## 7 Analysis  
In the realm of business security enhancement, the LiSEA framework, as articulated within this paper, encapsulates a spectrum of notable attributes as follows:  
1. Inherently Geared Towards Business-Centric Security: LiSEA is intrinsically tailored to the exigencies of business scenarios, engendering a more congruent alignment with the semantic intricacies inherent to business operations. This focus safeguards against undue semantic distortion, fostering a harmonious fusion of security and business imperatives.
2. Precision-Driven Intervention Points via APIs: The architectural underpinning of LiSEA enables the establishment of discerningly positioned intervention points through APIs, thereby furnishing an elevated degree of control granularity. This fine-tuned control mechanism augments the efficacy of security measures, ensuring a judicious allocation of security resources.
3. Multifaceted SDK for Development Streamlining: LiSEA capitalizes on a multifaceted Software Development Kit (SDK), which effectively mitigates the developmental overhead. This adaptive SDK harmoniously bridges the divide between business demands and security considerations, thereby obviating undue developmental burdens.
4. Real-Time Conduit for Proactive Illicit Access Interception: An inherent attribute of LiSEA is the real-time interception of illicit access attempts. This proactive mechanism acts as a sentinel, promptly detecting and curtailing unauthorized access endeavors, thereby precluding their escalation into potential breaches.  
It is, however, imperative to underscore certain limitations inherent to this architectural paradigm. A symbiotic collaboration between application developers and security stakeholders is requisite, necessitating a cohesive alliance to optimize the synergy between LiSEA's capabilities and the broader operational landscape. Moreover, the comprehensive integration of all business interfaces with LiSEA poses a noteworthy challenge, requiring meticulous orchestration and coordination.  
When harnessing the LiSEA framework to extend security capabilities, an array of strategic modalities emerges. Business models and ACLs are instrumental in affording precise control over well-acknowledged risks. Concurrently, the realm of uncharted risks is effectively addressed through dynamic risk models, which proactively evaluate and judge emerging security threats, thereby fortifying the resilience of business operations.
# A New Flow Control Method of Slat-Grid Channel-Coupled Configuration on High-Lift Device
## 3 Verification
### Grid Independence Verification  
In order to ensure the accuracy of numerical calculation and reduce the calculation cost, the multi-section airfoil 30P30N is taken as an example to verify the independence of the computational grid. Three-grid generation schemes are used for simulation calculation. Table 2 shows the grid convergence verification results at an attack of \(8^{\circ}\). The lift coefficient \(Cl\) converges with the increase in the number of grid cells. The drag coefficients, shown in Figure 6a, are basically the same using 2- and 3-grid generation schemes at different angles of attack. As shown in Figure 6c,d, the surface pressure coefficient distribution of the main wing and flap coincide very well. Using 2- and 3-grid generation schemes, the calculation results are similar, which meets the requirements of grid independence. Therefore, the grid division scheme of Case2 is adopted for subsequent calculations of cost.  
Figure 4: Computational mesh.  
Figure 3: Geometry profile of 30P30N.  
Figure 5: Verification results of 30P30N airfoil. (**a**) Lift Coefficient; (**b**) Slat Pressure Distribution [18,19]; (**c**) Main Pressure Distribution; (**d**) Flap Pressure Distribution.  
Figure 6: Grid independence verification. (**a**) Drag Coefficient; (**b**) Slat Pressure Distribution; (**c**) Main Pressure Distribution; (**d**) Flap Pressure Distribution.
# Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk
## 4 Simulation Results
### The Power Output of the PVs/WTs in the Summer/Non-Summer  
The weather data used are from 1 July 2018 to 21 July 2018 and 1 December 2018 to 31 December 2018. The time horizon chosen was one day of 24 h. By using LSSVM, the wind speed and global radiation were forecast simultaneously, where the power output of WT/PV was calculated using Equations (6) and (7). The number of data samples for CVAR was 50. The emulation results of WT/PV power output are shown in Figures 7 and 8. Figure 7 shows the power output of PV in the summer and non-summer seasons, and Figure 8 shows the power output of WT in the summer and non-summer seasons.  
Figure 10 shows the load curtailment in the summer and non-summer seasons. On a summer day, the control center of the VPP must curtail more load to satisfy the objectives. On the contrary, the VPP needs to curtail less of the load to obtain a better profit in the non-summer season.  
Figure 11 shows the CVAR and the summer daily profit of the VPP with various confidence levels (\(\beta=100\%\), \(\beta=95\%\), \(\beta=90\%\)). The CVAR in Figure 11 shows that a higher confidence level leads to a lower CVAR of maximal profit, i.e., with a lower risk.  
Figure 11: The summer daily profit of the VPP with various confidence levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Summer**} & \multicolumn{2}{c}{**Non-Summer**} \\ \hline \multirow{2}{*}{**Algorithm**} & **Maximal** & **Average** & **Maximal** & **Average** \\  & **Converged** & **Execution** & **Converged** & **Execution** \\  & **Profit (NTS)** & **Times (s)** & **Profit (NTS)** & **Times (s)** \\ \hline EP & 8,174,430 & 238 & 7,623,943 & 245 \\ PSO & 8,139,733 & 227 & 7,639,699 & 234 \\ GA & 8,190,471 & 237 & 7,639,155 & 244 \\ BFA & 8,259,395 & 213 & 7,654,015 & 219 \\ EBFA & 8,283,248 & 227 & 7,654,580 & 234 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Robustness test for EP, GA, PSO, BFA, and EBFA algorithms.  
Figure 12: The convergence characteristics of EP, GA, PSO, BFA, and EBFA in summer.
## 4 Results and Analysis  
The AST model is chosen as the baseline model in our manuscript, with the input features being the Fbank features of the original audio. During training, the cross-entropy loss function is used to train the model. The model employs the AdamW optimizer, with a base learning rate of \(1\times 10^{-5}\) for the ESC-50 and UrbanSound8K datasets. Additionally, the training process is optimized using exponential learning rate decay with a factor of 0.98 and a warm-up period of 10 epochs. The batch size is set to 16. The pre-trained AST model is fine-tuned for 50 epochs.  
The experimental environment includes an Intel Core i5-13490F processor (Intel Corporation, Santa Clara, CA, USA) and an RTX 4060 Ti (16 GB) graphics card (NVIDIA, Santa Clara, CA, USA). All experiments are conducted in a Python 3.11.5 and Pytorch 2.1.2 platform.
# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 4 Experimental Verification and Analysis
### Optimization Path Planning for Multi-View Recognition
#### 4.3.1 Path Planning with Different Perspectives  
The viewing angle interval is divided into 36 parts in the latitude direction and nine parts in the longitude direction. The number of divided viewing angle intervals of the target observation model is 324. The images of the medicine box are collected, the data set corresponding to the viewing angle interval \(\Phi_{jl}\)(\(i\) = 1, 2, \(\ldots\), 36; \(j\) = 1, 2, \(\ldots\), 9) is reorganized, obtaining 324 groups of data. There are 15 original images in each group of data, and any two groups of data can generate 15 \(\times\) 15 image pairs, which are used for classification training and verification of basic classifiers. Set the number of basic classifiers \(\text{C}_{l}\) = 3. The input image size is 128 \(\times\) 128. The sampling block size is 5 \(\times\) 5. The size of histogram block is 8 \(\times\) 8. The block overlap rate is 0.5. Gaussian kernel function is selected. In adjacency matrix \(\text{D}_{i}\), \(d^{-1}\) = 0.3, \(d^{-2}\) = 0.6, \(d^{-3}\) = 1, \(\omega\) = 0.5, \(\varepsilon\) = 1 \(\times\) 10\({}^{-6}\). Firstly, the starting point is set to interval \(\Phi_{11}\), and the number of viewing angles k = 3, 4, 5 are taken respectively. The observed viewpoint optimization path is shown in Figure 10.  
Figure 9: Structure of MoveIt! package.  
Figure 10 lists the top view, front view and camera recognition view that can characterize the position relationship with the target object at different viewpoints in path planning. The first line is the starting viewpoint. When the number of viewing angles is selected as three, the planned camera will reach the position of lines 2, 3 and 4 from the position of line  
Figure 10: Viewpoint optimization path planning.  
1 one by one. Similarly, increasing the number of viewing angles, the manipulator adds the planning to the position of line 5 and line 6. According to the image recognition results, when the number of viewing angles is three, the recognition accuracy has reached 94.63%. Increasing the number of viewing angles, although the recognition accuracy has increased, it has little change. When the number of viewing angles is 5, the planned path is shown in Figure 11. It can be seen that the initial path is mainly the elevation in the Z-axis direction, and then move in the X-axis and Y-axis. The target is observed in multiple directions to improve the recognition accuracy.
## 2 Materials and Methods  
### Sample Collection and Fungal Isolation  
Maize leaf samples were aseptically collected from the North-West University Agricultural Research Farm in Molekwang on March 2022 (25\({}^{\circ}\)47\({}^{\prime}\)26.4\({}^{\prime\prime}\) S 25\({}^{\circ}\)37\({}^{\prime}\)01.6\({}^{\prime\prime}\) E). From the two maize fields sampled at the research farm, leaf samples were collected from maize plants showing leaf blight diseases at three replicates per sample. The samples were separately packed in zip-lock bags and carried in an ice box to the laboratory where the leaves were rinsed in running tap water, cut into manageable pieces, sterilized in 5% NaOCl, and rinsed in three exchanges of sterile distilled water. The leaves were allowed to dry under a lamina flow hood and then plated in potato dextrose agar (PDA) poured plates. At 28 \({}^{\circ}\)C, the plates were incubated for a period of seven days under 12 h/12 h cycles of light and dark and observed for fungal growth. Isolates with similar morphological characteristics such as the growth rate, growth patterns, colour and morphological appearance were grouped together. The isolates were sub-cultured to obtain a pure culture of each fungal isolate, and the stock inoculums were prepared and stored at \(-4\)\({}^{\circ}\)C. Other chemical reagents used for the synthesis of the nanoparticles were para-tolualdehyde (CAS-4702-76-5), 4-ethylaniline (CAS-589-16-2), ethanol (CAS-64-17-5), methanol (CAS-67-56-1), dichloromethane (CAS-75-09-2), sodium hydroxide (CAS-1310-73-2), carbon disulphide (CAS-75-15-0), ammonium hydroxide (1336-21-6), bismuth (III) nitrate pentahydrate (CAS-10035-06-0), melamine (108-78-1) and dimethyl sulfoxide (DMSO) (CAS-67-68-5). They were used in pure form as sourced from Sigma Aldrich.
## 2 Related Work
### Position Encoding  
Transformers are invariant to the order of the input sequence, which means disrupting the order does not affect the outputs of transformers. As a result, the positional information is ignored in transformers. Thus, position encodings are necessary for transformers to add important spatial information back. The most commonly used methods of position encodings are absolute positional encoding (APE) [4; 5; 35; 36] and relative positional encoding (RPE) [21; 23; 24; 37]. The absolute positional encoding can be computed by sine and cosine functions with different frequencies or predefined by the learnable parameters. The relative position encoding takes the relative coordinates of queries and keys as input and is often implemented by predefined trainable parameters. Both APE and RPE are independent of the input feature maps and are usually designed for a fixed input size. Different from existing APE and RPE, we meticulously design a dynamical contextual positional encoding (DCPE). DCPE uses a lightweight network to generate the position encodings, thus it can be applied to any input resolution. In addition, a global average pooling is utilized to incorporate contextual information.
# Review of Propulsion System Design Strategies for Unmanned Aerial Vehicles
## 2 Materials and Methods
#### Data Analysis  
The 123 papers were categorized according to the first taxonomy, which focused on the main purpose of the work. This classification involved the following categories:  
* functional design, sizing;
* optimization methods;
* controls, identification, modeling;
* detailed design.  
The first category collected the papers depicting design methods for the identification of the functional characteristics of a drone or sizing procedures for the propulsion system from a methodological perspective. Papers describing the code and strategies for the definition of optimal solutions for specific viewpoints were gathered in the optimization methods group. The third category collected papers concerning control systems, parameter identification methods, or the modeling of already selected or existing devices. Papers focusing on the study of specific aspects or components of the drone were finally included in the detailed design category.  
For the current purposes, the works classified within the functional design and sizing group were analyzed specifically to capture which kinds of requirements were evaluated along the design process. Those requirements were arranged into three main sets:  
* technical constraints;
* normative framework-related limits;
* custom requirements.  
For the technical group, a further distinction was made among constraints related to drone Geometry (G), Dynamics (D), Power Supply (PS), and Mission Requirements (MRs).  
All 123 papers were then assessed according to the second taxonomy, which analyzed the content of the works. Four main aspects were investigated:  
1. UAV type;
2. actuation type;
3. design level;
4. topics.  
The first class evaluated the type of UAVs explicitly considered by the authors as the main target of their work. In particular, four sub-categories were identified: Fixed-Wing vehicles (FxWs), Flapping-Wing drones (FIWs), Multirotors (Mrs), and Other kinds of UAVs (O). The class actuation type focused on the propulsion strategy that the UAV presented. More in detail, the following sub-categories were considered: Fuel (F), Electric (E), Hybrid (H), and other. The third class evaluated the level of detail considered by the described design framework. Three main sub-categories were defined: Vehicle (Ve), Propulsion System (PrS), and Propulsion Component (PrC). For the latter, the distinction among components was also performed, considering the entries: Motor (Mo), Propeller (Pl), Batteries (B), and other. Finally, the class topics investigated the macro-areas involved in the design framework proposed by the specific work. For this analysis, the following sub-categories were defined: kinematics, dynamics (also meant as aero- and fluid-dynamics), Mechanics (Me), power supply, Mission Planning (MP), Optimization (Op), Validation (Va), Fault Management (FM), and Control (C).