esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
DFT keeps the roots of the crops fully submerged in nutrient solution for continuous nourishment, while NFT delivers nutrient solution in a thin film to the lower roots, offering the advantage of providing ample oxygen to the root zone. These methods involve simple installation and minimize the impact on crops during technical issues. However, they may cause physiological problems in some crops [26], and the water used can become increasingly contaminated over time, necessitating waste solution management. This increase in waste solution accelerates environmental pollution, undermines the economic viability of smart farms, and requires the installation of costly water purification systems to meet stringent agricultural environmental regulations [27; 28]. Additionally, these methods often require expensive iron frameworks to store large volumes of water, significantly increasing the overall system costs. On the other hand, the aeroponic method uses comparatively less water, which significantly reduces the volume of wastewater produced. This technique achieves high productivity through efficient oxygen and nutrient supply [29; 30; 31], making it ideal for cultivating root crops such as ginseng sprouts, seed potatoes, and carrots [32; 33], as well as high-value medicinal and functional plants like burdock, nettle, yerba mansa, lavender, thymme, and St. John's wort [34; 35]. However, the technical complexities of aeroponics, such as the extensive use of spray nozzles, maintaining spray pressure, ensuring uniformity of spray, high initial installation costs, and the potential negative impact of residual pesticides and fertilizers in the nutrient solution on surface and groundwater, must be considered [36; 37; 38; 39]. This paper proposes a new recirculating aeroponic system that addresses these issues by minimizing water use and wastewater production, effectively meets environmental regulations, and provides an economically viable model for cultivating high-value crops. While numerous studies have explored various smart farming technologies, our research uniquely focuses on the development of an inflatable and portable aeroponics system designed for high-value crops. Unlike traditional fixed structures, our system offers unparalleled portability and ease of installation, making it particularly suited for urban environments and areas with limited space. Therefore, this study develops a new smart farm structure that integrates enclosed smart farming with aeroponic hydroponic methods while addressing the major limitations of current systems, such as production and installation costs, indoor space utilization efficiency, energy loss, wastewater generation, and water use efficiency. This work aims to present a new direction for sustainable smart farm technology. The structure of this paper is as follows: Section 2 provides a detailed description of the proposed smart farm structure; Section 3 presents the experimental results using this structure; Section 4 discusses the implications of the research findings and future topics for discussion; and Section 5 summarizes the conclusions.
## 4 Discussion
### Effect of Varying Effluent Concentrations on Proximate Composition of S. limacinum Dried Biomass  
The elevated concentrations of the effluent resulted in reduced protein contents. In fact, the protein content was negatively correlated (Pearson correlation 0.988, \(p<0.01\)) to the biomass productivity, indicating that the assimilated nitrogen in the elevated concentrations of effluent was more directed towards growth rather than deposition, with 16% and 32% levels, resulting in the highest growth and the lowest protein contents [72]. Furthermore, the increasing lipid content followed by reduced crude carbohydrate and total organic carbon content indicates that the influx of crude glycerol is utilized for lipogenesis. Gross energy values recorded at 16% and 32% effluent concentrations were the lowest, reflecting a utilization trend towards biomass production as revealed by the higher yield in these treatments. On the other hand, at low effluent concentrations (0% and 8%) _S. limacinum_ exhibited the highest gross energy values that arise primarily from an increased accumulation of carbohydrates and to a lesser extent of proteins. The ash content was significantly higher at moderate and elevated effluent concentrations compared to earlier studies, which reported values ranging from 2.1-15.3% [73]. This can be probably attributed due to the inorganic salts NH\({}_{4}\)Cl and KH\({}_{2}\)PO\({}_{3}\) used in order to regulate nitrogen and phosphorus concentration in the culture media. This is in accordance with previous research, where the addition of ammonium sulfate led to an increased accumulation of ash [40]. Moreover, most microalgae species have the ability to uptake more phosphorus than necessary for cell growth and development when it becomes available. This ability is defined as luxury uptake where the surplus phosphorus is stored in the form of inorganic polyphosphates [74].  
Carbon Assimilation by S. limacinum on Media with Crude Glycerol and Varying Effluent Concentrations  
The fact that a 40% reduction of crude glycerol content was achieved at elevated effluent concentrations suggests that heterotrophic cultivation of _S. limacinum_ can successfully remediate waste products from the biofuel industry. However, the crude glycerol consumption observed in this study is lower compared to earlier studies, where crude glycerol was assimilated up to 70% [9].  
This could be attributed to the higher crude glycerine concentration used in the batch fermentation of the present study that, in turn, induced substrate inhibition, which is often the case at high glycerol concentrations [36; 75].
# New Remote Care Models in Patients with Spinal Cord Injury: A Systematic Review of the Literature
###### Abstract  
Background: Spinal cord injury is a multisystem disease which compromises independence and quality of life; remote care models represent an opportunity for long-term management of complications. The aim of this study was to explore remote care models for chronic spinal cord injury patients. Methods: A systematic review of the literature was carried out. Five databases (PubMed, CINAHL, Web of Science, Cochrane Library, Google Scholar) were systematically explored with a time limit of five years. Included studies were assessed using Jadad Score and PEDro Scale. Results: Four RCTs were included in this systematic review. In all studies, multidisciplinary home care supported by technology were compared with in-person models. Remote care models were effective in managing pressure injury, infection, and muscle atrophy and improve quality of life. Conclusions: Remote care models can be a key tool for improving self-efficacy, decreasing hospitalizations and preventing long-term mortality.  
spinal cord injury; TeleSCI; telehealth; E-health; remote care system; remote consultation; platform; App; RCT; systematic review
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Private Blockchains
#### 3.2.2 Raft
Performance optimization approaches focus on improving the real-time processing efficiency and fault tolerance by reducing consensus delay and balancing load pressure through dynamic leader reconfiguration, streamlined log replication protocols, and weighted election mechanisms. Scenario-specific adaptation designs are tailored to meet the specific requirements of various vertical domains. Examples include multi-channel transaction tracking in supply chain scenarios, privacy and compliance optimization in medical healthcare data management, and lightweight Byzantine fault tolerance frameworks for IoT edge networks, highlighting the customizability of Raft in cross-industry applications. Network adaptability improvements focus on wireless communication environments (such as 6G non-ideal channels and node mobility). By employing probabilistic reliability modeling and quantifying channel loss, these enhancements strengthen the protocol's robustness in dynamic topologies and high-interference scenarios. These improvements indicate that Raft's evolutionary path is centered on modular expansion, adjusting election logic, log management strategies, and network interaction rules to balance algorithm simplicity with the need for an adaptation to complex scenarios. Future research may need to further explore hybrid consensus models (such as incorporating the fault tolerance mechanism of PBFT) and intelligent decision-making technologies (such as machine learning-driven dynamic parameter tuning) to address the challenges of decentralized systems in ultra-large-scale, highly heterogeneous environments.
# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 3 Discussion
### Evaluation Metrics  
Cluster validity is the evaluation of clustering quality. A cluster of good quality minimizes the distance within clusters (intra-cluster distance) and maximizes the distance between clusters (inter-cluster distance). The definition of clusters highly depends on the user and the domain where clustering is applied [10]. The two major categories of evaluation metrics in clustering are internal criteria and external criteria [71]. They are evaluated based on the presence of data labels. Table 3 shows the equations of each evaluation metrics. Although the equations for _accuracy_ and _rand index_ look the same, they treat the class label differently. _Accuracy_ compares the true labels (ground truth) with the predicted labels. As there are no true labels in clustering, the _rand index_ will effectively check the similarity between the two data points. Internal criteria measure the validity of the clusters by applying similarity measure techniques. Internal criteria evaluate the clustering result by using only feature and information inherent in a dataset [71]. The metrics are usually used if the ground truth is unknown. It can only make an assumption about the cluster quality. Table 4 shows the evaluation metric with its associated range and clustering quality. Note that the increase or decrease of the value in different evaluation metric indicates a better performance quality of the clustering technique. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Similarity Measure**} & \multicolumn{2}{c}{**Partition-Based**} & \multicolumn{2}{c}{**Hierarchical**} & \multicolumn{2}{c}{**Density-Based**} & \multicolumn{1}{c}{**Fuzzy**} \\ \cline{2-7}  & _k-Means_ & _k-Medoids_ & _CURE_ & _Bisecting _k-Means_ & _DBSCAN_ & _FCM_ \\ \hline Cosine similarity & 2 & 1 & 1 & 1 & 0 & 1 \\ \hline Euclidean distance & 4 & 4 & 2 & 0 & 1 & 5 \\ \hline Fuzzy similarity & 0 & 0 & 1 & 0 & 0 & 0 \\ \hline Hamming distance & 0 & 1 & 0 & 0 & 0 & 0 \\ \hline Jaccard similarity & 0 & 0 & 1 & 0 & 0 & 0 \\ \hline Manhattan distance & 0 & 0 & 1 & 0 & 0 & 1 \\ \hline (New) SSM & 0 & 0 & 0 & 0 & 1 & 0 \\ \hline (New) VLVD & 1 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Frequency heat map of similarity measure with respect to the clustering algorithms.
# Application of Workflow Technology for Big Data Analysis Service
## 2 Big Data Analysis Oriented Cloud Workflow System  
This section describes a big data analysis oriented cloud workflow system that manages and investigates business tasks and supports shared business workflows. Figure 2 shows the system topology that involves three parts--namely, a web server that provides friendly user interfaces (UIs) for users to design their applications; a workflow engine that mainly focuses on workflow parsing, execution, scheduling, and management that can be deployed on a high-performance server or reused in a parallel computing cluster; and a distributed cloud platform that provides real-time analysis capacity, offline data mining capacity, and distributed storage resources.  
The parallel cloud platform not only implements big data analysis algorithms, but must also consider how to communicate with workflows. Figure 3 shows the work process of workflows, and the detailed procedures are as follows:  
Step 1: Users drag and drop components to create their big data analysis applications as workflows via web UI. The web marks the submitted workflows with normal business (e.g., data exchange, result visualization, and reports) and cloud business (e.g., MapReduce, Hive, and Storm). The normal business can be performed by the web server, whereas the cloud business may be submitted to the distributed cloud platform. The marked workflows will be sent to the workflow parsing engine and stored in the workflow definition database. The workflow will be instantiated according to user's configuration parameters, and the corresponding parsed XML description documents and DAGs will be stored in the workflow instance database.  
Figure 2: System topology.  
Step 2: The workflow execution engine will respond to workflow execution requests after task information is submitted. The cloud platform starts the daemon process and calls the workflow XML definition documents and DAGs. The daemon carries out the tasks in order with control and data streams.  
Step 3: A distributed computing platform is constructed using Hadoop, the open source of MapReduce and Google File System, and Storm. Our system combines the advantages of MapReduce, Storm, Hadoop Distributed File System (HDFS), and HBase to analyze big data. The analysis results will be sent to users and the results or analysis data will be stored according to the user's setting.  
Step 4: The workflow execution engine executes the entire process according to the workflow definition, and users can obtain the result on the web UI.  
The workflow engine mainly involves three parts: workflow parsing engine, workflow execution engine, and workflow scheduling. They are elucidated below.
## 1 Introduction  
It is no longer possible to consider business and government services without the use of the Internet. The impressive growth rate of the Internet and its technologies allows many companies and government agencies to provide their services online, bringing more practicality to users. This growth,however, also results in a more frequent occurrence of cyberattacks that impact users and business, such as to shut down victims' servers or steal critical information from attacked networks.  
For most applications, especially those considered critical (e.g., Internet Banking), having its assets compromised or being forcibly off for a period is inadmissible because it represents revenue loss. It has become clear that, not only when attacked, one must have early detection and mitigation techniques to minimize damages and recover normal operation as fast as possible. This early detection can be achieved with the use of advanced network tools and analysis in conjunction with skilled people within the cybersecurity domain.  
One approach to investigate network attacks is the use of Deep Packet Inspection (DPI). Basically, this technique consists of a thorough examination of the fields contained in the packets that flow within the investigated network. It allows for detecting anomalies into the network flow, along with the other important information that is useful when dealing with incident response, such as the IP address involved, type, time and duration of the attack, along with other data that helps security professionals to mitigate incidents.  
Network forensics is used to identify and generate evidence against attacks that can be related to the commitment of crime such as the stealing of information that is not publicly available. Although it may not always be the case, it is important to gather and examine evidence as if they were to be used in court. Having such assumptions in mind, it is important to follow Chain of Custody (CoC) procedures when dealing with incident response and cyberattack mitigation.  
This paper provides DPI and analysis of network traffic data obtained from a Honeynet maintained for research purposes in a network laboratory at the University of Brasilia. This data was analyzed to evaluate the behavior of attackers, the content and other characteristics of some attacks directed to the environment.  
The main contribution of this paper is to provide a characterization of real attacks using DPI to evaluate the modus operandi of some bots (i.e., W32.IRC), botnets (i.e., Mirai), evaluation of brute-force attacks' behavior conducted either by humans or bots providing distribution statistics of the source of the attacks, protocols used and link analysis of the data.  
The remainder of this paper is organized as follows. Section 2 provides a review of the DPI technique and researches within the traffic analysis and network forensics domain. Section 3 describes techniques used to detect network anomalies using DPI. Section 4 shows the results obtained against real attacks traffic data using DPI and network forensics procedures. Section 5 concludes this paper.
# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 1 Introduction  
The motivation of this paper is to further elaborate on a conference paper [1], the aim of which was to explore how business decision making in complex manufacturing ecosystems can be supported by a systematic combination of digital twins. In contrast to digital models focusing on individual equipment, the digital twin models discussed in this paper need to incorporate a multitude of objects and resources, as well as business processes for services and operations. This is required to manage the performance of equipment and their operations. The decision problems generally relate to business processes in the entire lifecycle _and in this paper, they specifically relate to questions of maintenance and performance optimization of specific equipment in conjunction with the other machines in the installed base. It was elaborated in [1] that for objects and resources, their past, current, and predicted performance condition make up the set of relevant system variables driving the decisions. For the operations and service processes, this means taking into account service resources with limited availability. These are typically humanresources for maintenance or spare equipment, for instance. While managing equipment based on its specific digital twin is adequate for the performance optimization of this individual equipment, we extend this view to a systems perspective by integrating the equipment into a system in conjunction with other relevant actors in the ecosystem. This multitude of objects impacting business decisions requires that a systematic combination of digital twins is designed that is tailored to the decision problems in the operations of manufacturing ecosystems.  
By extending the conference paper [1], this paper further elaborates the model-based systems engineering perspective by developing the semantics modeling to the level of ontology classes, objects, and languages and then to AnyLogic [2] models for system verification. A special focus in the extension of [1] is put on the implementation of the system of digital twins in a simulation model in the case of an archetypal SME (small and medium-sized enterprise). In order to model the different individual twins of the entire system, a mixed model simulation approach is implemented and linked back to the semantic model.  
The aim is to design and integrate an architecture of digital twins that is derived from the actors' needs and that leverages the potential of the synergies in the ecosystem and to demonstrate the feasibility of the implementation of this architecture in a technical simulation model. Therefore, the research questions are as follows:  
1. How can multiple types of digital twins be systematically combined to create value for decision support in complex industrial service ecosystems?
2. How can digital twins be integrated to promote data interoperability in complex industrial service ecosystems?
## 4 Discussion  
### Frequency Ratio Accuracy Analysis  
It can be seen from Table 2 that the FR values of the very high susceptibility levels of the PSO-MLP, MLP-only, BPNN, and IV models were 4.546, 4.344, 4.229, and 4.184, respectively, suggesting that a small area with a very high susceptibility level can interpret many landslide locations. Furthermore, the FR values of the landslide susceptibility levels of all four models increased rapidly from the very low to the very high levels, suggesting that the landslide susceptibility maps generated by the four models were all good and reliable. In addition, the FR accuracy of the prediction model was defined as the ratio of the sum of the high and very high susceptibility levels to the sum of all the susceptibility levels [13]. A higher FR accuracy value indicated the greater prediction performance of the model. Table 2 shows that the FR accuracies of the PSO-MLP, MLP-only, BPNN, and IV models were 0.856,0.829, 0.840, and 0.824, respectively. Hence, the prediction performance of the PSO-MLP model was higher compared with those of the other three models.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 5 Performance Comparison
### Experimental Setup  
In order to calculate the computation overhead of the proposed and related competing protocols, hash function and point multiplication are only considered as cryptographic operations. These cryptographic operations are implemented separately for user \(\mathcal{U}_{k}\), fog-node \(\mathcal{FN}_{f}\) and cloud service provider \(\mathcal{CSP}\), according to their respective devices in a controlled fog-based IoT environment, to determine their time complexity. The average values of these cryptographic operations are obtained by implementing them several times under same assumptions. The cryptographic operations that have been used at the \(\mathcal{FN}_{f}\) side are implemented on Arduino, while the cryptographic operations used at the \(\mathcal{CSP}\) and \(\mathcal{U}_{k}\) end are implemented on a cloud server and mobile device, respectively. The specifications of Arduino, the cloud server and the mobile device are listed down in Table 3.