esa-sceva/satcom-chunk-collection huggingface dataset:
# Logistics Space: A Literature Review from the Sustainability Perspective
## 2 Logistics Space
### The Influencing Factors of the Evolution of Logistics Space
#### 2.2.4. Agglomeration Advantage  
Enterprises in the site selection, in order to obtain a better environment for development, often choose to locate in the industrial agglomeration area. [PERSON] illustrates the agglomeration advantages of logistics enterprises based on three sources of Marshall's agglomeration economies: namely labor market (specialized labor); inputs sharing (combining transport flows, reducing transportation costs and promoting the development of multimodal transport services) and knowledge spillovers (learning and cooperation between enterprises) [38]. [PERSON] divides the benefits of logistics agglomeration into: the sharing of physical assets, the sharing of production factors and the strategic alliances of existing facilities and value-added services, and concludes that the logistics cluster improves the efficiency of global supply chain and reduces the cost, improve the basic transportation network [3; 37]. [PERSON]'s \"Diamond Model\": Industrial agglomeration is the result of competition, which is conducive to industrial competitiveness and national competitiveness. Based on [PERSON]'s diamond model, [PERSON] evaluated the competitiveness of major Asian logistics clusters [41]. Transportation costs, service and other advantages attract logistics enterprises to join the cluster [42]. In addition, as a producer service industry, the logistics industry needs to be the target of service around its agglomeration area. Logistics industry is demand-oriented, that is, the logistics industry is in spatial connection with other industries. Logistics enterprises in order to obtain a larger market share, will take the initiative to focus on the concentration of related industries. The coordinated development of manufacturing and logistics is an objective requirement for reducing manufacturing costs and accelerating the development of logistics enterprises. The negative externalities of agglomeration, such as the rising prices of land and labor and the congestion of transport roads, will also reduce the agglomeration advantages and thus remove the agglomeration area [37]. For logistics enterprises, the agglomeration area provides sufficient professional labor force, transportation advantages, resource sharing, value-added services and perfect infrastructure for logistics enterprises. In addition, the government promotes logistics agglomeration through various financial incentives and tax incentives.
# Analysis and Optimization of the Robotic Mobile Fulfillment Systems Considering Congestion
## 4 Simulation and Result Analysis
### Result Analysis  
#### 4.2.1 The Optimal Number of AGVs Configuration  
The number of AGVs configuration needs to consider two aspects, one is the engineering requirements, the other is the task arrival rate. The utilization rate of AGVs is generally required to be kept within a certain range, most of which are 60-90%. When the number of vehicles is low, the vehicle utilization will become too high and the waiting time will be longer. When the number of vehicles is high, the idle rate of vehicles will increase, resulting in a waste of resources. In order to optimize the number of AGVs, we set the length-width ratio of the warehouse as 0.5, the number of storage spaces as 2690, and the number of vehicles as 3, 5, and 10, respectively. When the task arrival rate is 40-120, the throughput time of the task and the utilization rate of the vehicles are obtained through the experiment, and the result charts are as shown below.  
From Figure 8, it can be seen that the utilization rate of the vehicles increases significantly with the increase of the task arrival rate, and the fewer the number of vehicles, the higher the utilization rate. As can be seen from Figure 9, although the number of AGVs is different, the task throughput time shows an upward trend with the increase of task arrival rate. This is because task waiting time and vehicle congestion will increase as the increase of task arrival rate, and then task throughput time will increase. Through the experiment, we can conclude that the analytical model can be used to plan the minimum number of vehicles to meet the task requirements under the premise of known engineering requirements and task arrival rate.  
Figure 8: The curve of the vehicle utilization rate with the task arrival rate.
## 3 Results
### Docking Analysis Predicts That Suforaphane Interacts with NAMPT  
The crystallized structure of NAMPT, resolved at a resolution of 2.00 A, provided the foundation for this investigation. On this structural basis, a meticulous docking analysis was performed to examine the interaction dynamics between NAMPT and sulforaphane.  
Figure 5: Protein–protein interaction network functional enrichment analysis for sulforaphane-underexpressed genes.  
Interestingly, like NAT (Figure 4A,B), a known activating compound of this enzyme, sulforaphane exhibited positioning at one end of the channel responsible for substrate entry, strategically distanced from the active site (Figure 6C,D).  
Figure 6: **Sulforaphane (SFN) interacts with NAMPT.** The three-dimensional model shows how NAT interacts with NAMPT (**A**,**B**) and the similar manner in which sulforaphane does (**C**,**D**). Comparative molecular dynamics analysis (**E**,**F**) of NAMPT without ligand (blue lines) and in complex with NAT (green lines) or sulforaphane (red lines). RMSD: root mean square deviations, RMSF: root mean square fluctuation.  
This strategic placement ensures that the presence of sulforaphane does not disrupt either the substrates or the enzyme product. Notably, sulforaphane binding did not induce any significant architectural changes in the enzyme, underscoring the non-disruptive nature of this interaction. Sulforaphane was predicted to establish hydrogen bonds between its sulfinyl group, a potent electron-extracting entity, and the electron-donating amino groups of residues K189 and R349 of NAMPT. In addition, it is possible for it to form a hydrogen bond with a water molecule which, in turn, is hydrogen bonded to the hydroxyl group of residue S275 of NAMPT.  
The \(\Delta\)GBinding calculated for the sulforaphane-NAMPT complex (\(-29.96\) kcal/mol) was found to be higher than that for the NAT-NAMPT complex (\(-53.69\) kcal/mol), indicating a lower affinity for NAMPT in the case of sulforaphane. Molecular dynamics simulations were used to evaluate the interaction between sulforaphane and NAMPT over a period of 100 ns. The stability of both NAT and sulforaphane within the NAMPT channel was evident throughout the simulation period (Figure 6E). The RMSD plot shows mean deviations ranging from 1.0 to 1.5 A, with the sulforaphane complex showing slightly higher values. Although the NAT complex reached equilibrium earlier, settling in the initial 50 ns, as is typical for this type of analysis, both complexes demonstrated stability. RMSF analysis revealed comparable fluctuations in both complexes, although more pronounced differences were observed in the sulforaphane-NAMPT complex compared to NAT-NAMPT and ligand-free NAMPT, suggesting greater flexibility (Figure 6F). These observations collectively propose the potential of sulforaphane to act as an allosteric activator of NAMPT, warranting experimental validation of these results.
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 5 Methodology  
Although the proposed system is designed for different movable bridges all over the world, a case study analysis for Fort Madison Swing Bridge is demonstrated in this section. The software application is also developed for the case study, which can later be modified for any other bridges. Redundant components have also been considered for a more generalized analysis of movable bridges, which can be narrowed down to singular components for simpler bridges. The step-by-step analysis for the chosen case study is demonstrated in the following subsections.
## 3 System Profile and Overall Scheme
### Adaptive Routing Algorithm  
In the aggregation model described in Section 3.3, the aggregated data of each sensor node are sent to the optimal next-hop node, and the next-hop node selection is determined by the Q-learning adaptive algorithm proposed in this paper (see Section 4). The adaptive routing algorithm consists of three parts: (1) The sending node selects the node with the highest priority in the Q routing table to send the data packet; (2) The receiving node feeds back the reward value information to the sending node according to the received data packet; (3) The sending node accepts the reward value information and updates its Q routing table.
## 2 Pretreatment Methods for Solids Reduction
### Chemical Pretreatment
#### 2.3.3 Fenton's Reagent Pretreatment  
The Fenton's Reagent process is an oxidation process that uses hydrogen peroxide (H2O2) in the presence of iron ions (Fe2+) to produce hydroxyl radicals which are a very strong oxidizers that can disintegrate recalcitrant organic pollutants to more soluble species. The addition of iron (Fe, via scrap iron) also enhances the biodegradation of complex substrates and diversifies acetoclastic bacteria useful in acetogenesis reaction with AD systems. Both H2O2 and iron ionic solutions are unstable chemicals that can easily lose activity which can lead to wastage of reagents which is a drawback of this method. Also, waste sludge contains non-targeted chemical species (peroxidases and catalases) that can serve as parent oxidizers and radical scavengers which can increase costs and delay treatment time. The extent of sludge disintegration depends on the applied concentrations of H2O2 and Fe2+ (both in amount and respective dose-ratio), treatment time, and system pH. Due to iron catalyst deactivation and radical (OH) scavenging, the organic reactants present in the sludges cannot be treated effectively at both low and high pH; therefore, studies report that the optimal pH range to be 2-4 for best results [47]. [PERSON]'s reagent oxidation using 50 g H2O2/kg DS and 0.07 g Fe2+/g H2O2 at pH 3 showed a 75% increase in biogas production compared to the AD system using the untreated input sludge. A five times increase in sCOD with oxidation over the untreated control was reported by [PERSON] et al. [48]. Increasing the H2O2 dosage slightly increased biogas production which suggests that [PERSON]'s reagent treatment breaks the sludge into smaller molecules that are easier to degrade. Additionally, the oxidation step appears to degrade the refractory COD into a more biodegradable by-products making up the COD. Negative aspects of the process include the low pH values required to minimize precipitation of Fe2+ (pH < 4), when using higher pH values, the production of iron (precipitation) sludge product [47], hydroxyl radical scavenging by the parent oxidizer (H2O2) or radical initiator (Fe2+), higher chemicals costs (due to the needed quantities), and the potential need for waste iron sludge storage and transportation [25]. To summarize, Fenton's reagent pretreatment methods require a narrow pH range for an effective outcome, results in relatively higher chemical costs, and has the potential for producing an iron sludge which needs to be dewatered and disposed [47].
# Blockchain Applications in Forestry: A Systematic Literature Review
## 4 Blockchain Applications in the Forestry Sector
### Forest Management
The SVM algorithm has 90% accuracy in classifying the type of trees [4]. Then, the detailed information (such as cut down trees) is stored in smart contracts. The details of cutdown trees were stored in the blockchain using smart contracts. The smart contracts are stored in the blockchain network module. This blockchain module in this application is created using Reactlys, Ganache, Truffle, and Internet3 framework. All stakeholders (such as environmentalists and authorities) can see the number of cutdown trees and the information of trees' type, age, dimensions, locations, date, and time of the extraction [4]. [PERSON] et al. [48] proposed participatory forest management with blockchain technology. This participatory blockchain-based governance approach in forest management aims to reduce shareholders' inequalities. This proposed approach is to store the forest inventory data into the blockchain [48]. [PERSON] et al. [44] introduced a blockchain-based web application for forestry nurseries management, implementing a traceability system using Radio Frequency Identification (RFID) and Near Field Communication (NFC) technologies. This application can trace individual forest plants in forest nurseries, monitor the plant in the nursery in real-time, and provide the final user (customers) the origin of the plant [44]. The workflow of using this application has two stages. The first stage is to record information and associate with NFC tags. The information includes the date of planting, species of plants, the origin of seeds, etc. Then, the second stage is to add and synchronize data with a remote database. The architecture of this blockchain-based web application was developed to integrate with the blockchain. The data manipulation software layer was added to communicate with the blockchain through the Gateway service API [44]. This blockchain-based application permits maintenance of this evolutive management and safeguards the production quality and select seedlings, trees, and timbers. [PERSON] et al. [47] analyzed the effects of applying blockchain technology on forestry carbon sink trading. This study [47] performed a game analysis on the forest farmers' incomes before and after applying blockchain technology.
# Sentinel-1 GRD Preprocessing Workflow +
###### Abstract  
The Copernicus Programme has become the world's largest space data provider, providing complete, free and open access to satellite data, mainly acquired by Sentinel satellites. Sentinel-1 Synthetic Aperture Radar (SAR) data have improved spatial resolution and high revisit frequency, making them useful for a wide range of applications. While few research applications need Sentinel-1 Ground Range Detected (GRD) data with few corrections applied, a wider range of users needs products with a standard set of corrections applied. In order to facilitate the exploitation of Sentinel-1 GRD products, there is the need to standardise procedures to preprocess SAR data to a higher processing level. A standard generic workflow to preprocess Copernicus Sentinel-1 GRD data is presented here. The workflow aims to apply a series of standard corrections, and to apply a precise orbit of acquisition, remove thermal and image border noise, perform radiometric calibration, and apply range Doppler and terrain correction. Additionally, the workflow allows spatially snapping of Sentinel-1 GRD products to Sentinel-2 MSI data grids, in order to promote the use of satellite virtual constellations by means of data fusion techniques. The presented workflow allows the production of a set of preprocessed Sentinel-1 GRD data, offering a benchmark for the development of new products and operational down-streaming services based on consistent Copernicus Sentinel-1 GRD datasets, with the aim of providing reliable information of interest to a wide range of communities.  
Sentinel-1; Synthetic Aperture Radar; Ground Range Detected; GRD; preprocessing; radiometric calibration; terrain correction +
Footnote †: _Proceedings_  
[PERSON]
###### Abstract
Furthermore, the simultaneous transmission of multiple data streams imposes demanding bandwidth and computing power requirements on the \"cloud processors\". Compared to the \"cloud computing\" processing approach, edge computing distributes the data processing mission to the data acquisition endpoints, reducing the latency during data transmission and alleviating the burden on the \"cloud processors\". Therefore, it is better suited for real-time data analysis and intelligent processing. However, edge computing devices typically have limited computational power and may struggle to hold out complicated deep neural networks. To enable real-time processing capabilities on edge computing devices, it is necessary to compress and optimize the corresponding algorithms, reducing network model complexity and minimizing computational load. Furthermore, as high-speed conveyor belts continue to evolve, they demand greater frame capture rates from cameras. Cameras with higher frame rates will transmit high-quality images to edge computing devices. If the network's foreign object recognition and detection speed is not increased accordingly, it can lead to asynchronous image input and output signals, resulting in information delay to the upper computer system and cloud operation platform. This delay can impact subsequent decision-making and operations, thereby increasing the risk of conveyor belt damage. More precisely, the term 'hardware-friendly' algorithm pertains to network models characterized by minimal parameter counts and reduced computational demands. Employing these network models on edge devices within coal mines, which often have constrained computational capabilities, not only aligns with resource constraints in such scenarios, including limited computing power and storage space, but also aligns with the requirements of advancing high-speed conveyor belt technologies. Based on the above situation, this article proposes a \"hardware friendly\" coal mine conveyor belt foreign body identification and detection method, which uses an improved YOLOv8 network to \"slim down\" the conveyor belt foreign object detection network. Compress the parameter amount and storage space occupation of the network model, and make the network reduce the calculation amount and improve the reasoning detection speed without losing the detection accuracy so as to enhance its potential for utilization in edge computing devices. The remainder of this paper is structured as follows: Section 2 outlines the data preparation procedure, Section 3 introduces the enhancements made to the algorithm, Section 4 evaluates the experimental outcomes and discusses associated observations, and finally, Section 5 summarizes the research outcomes and considers future work.
# A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things +
###### Abstract  
The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. A Beta probabilistic distribution is used to combine different trust evidences and direct trust has been calculated. The theory of ARMA/GARCH has been used to combine the recommendation trust evidences and predict the resultant trust value of each node in multi-step ahead. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models.  
MANET; security; trust; ARMA; GARCH; clustering; IoT; predictive +
Footnote †: This is an extended version of the conference paper, [PERSON]; [PERSON]; [PERSON] A trusted framework for secure routing in wireless ad hoc networks. In Proceedings of the 2015 International Conference on Collaboration Technologies and Systems (CTS), Atlanta, GA, USA, 1-5 June 2015.
# Study on Compressive Strength and Water Permeability of Steel Slag-Fly Ash Mixed Permeable Brick
## 2 Materials and Methods
### Research Method  
#### 2.4.1 Study Method of Compressive Strength  
The compressive strength of the samples in this test was measured according to ASTM C109/C109M-12, Standard Test Methods for Compressive Strength of Hydraulic Cement Mortars. Therefore, the sizes of the permeable brick samples were 50.8 mm \(\times\) 50.8 mm \(\times\) 50.8 mm, and the samples were continuously and uniformly pressurized by a hydraulic universal press. The loading speed was controlled to be 0.5 MPa/s. Compressive strength was calculated according to the following formula [14]:  
\[f_{c}=\frac{P}{A} \tag{2}\]  
In the above formula, \(f_{c}\) is the compressive strength (MPa) of the sample, \(P\) is the breaking load (N) when the sample is broken, and \(A\) is the pressure receiving area (mm\({}^{2}\)).  
At the beginning of the test, the strength of the samples of each group was tested for seven days and cured for 28 days to ensure that the various parameters adopted in the subsequent test met the relevant standards of permeable bricks [15; 16]. Secondly, the water-cement ratio, steel slag content, fly ash content, and porosity were set as variables, and the proportion design schemes of different groups were determined according to the characteristics of different variables. Finally, based on the test results, the effects of different variables on the compressive strength of permeable brick were discussed.
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 5 Results and Discussions  
The presented system in this paper involves two main steps: (a) stabilization and (b) gas leak detection. In this section, the acquired data from the experiments explained in Section 4 are used to evaluate the two main steps and the general performance of the system. In the following, a comparative analysis of candidate methods is provided in Section 5.1. In addition, the proposed gas leak detection method is evaluated in Section 5.2. It is worth mentioning that all the analyses were carried on a Ubuntu 18.04 Linux operating on a workstation with Intel Core i7-9750H CPU, 16 GB RAM, and Nvidia Quadro T1000 Graphic card.
# Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism
## 2 Materials and Methods  
### Datasets  
In this study, the dataset used is the IU-Xray dataset, which is publicly provided by Indiana University and comes from actual medical records of two large hospitals in Indiana. This dataset integrates detailed narrative chest X-ray reports and corresponding chest X-ray images, providing rich practical application background and data support for deep learning models. The average report contained 77 words, while the average number of words in reports marked as abnormal increased to 84, reflecting that abnormal reports are relatively more detailed in content. Each report in the dataset consists of three main parts: X-ray, impression, and finding. Specifically, each set of X-rays contains two images, a front view and a side view, to fully display the patient's chest condition; the impression part is a comprehensive diagnosis given by the radiologist based on the X-ray images and the patient's clinical history, which is usually concise in one sentence; the finding part is a detailed description of the X-ray image, which is developed in paragraphs according to the standard requirements of imaging examinations. During the process of screening and preprocessing the dataset, we excluded some data with poor quality or incomplete information, and finally retained 8121 high-quality X-ray images and 3996 corresponding reports for research. In this research task, we focus on leveraging the text data of X-ray images and the finding part, which provides a detailed description of the X-ray images.