esa-sceva/satcom-chunk-collection huggingface dataset:
# A Query Understanding Framework for Earth Data Discovery
###### Abstract  
One longstanding complication with Earth data discovery involves understanding a user's search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user's search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user's query from multiple perspectives, with the goal of understanding the consumer's quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user's intent.  
geospatial cyberinfrastructure; GeoAI; CyberGIS; semantics; big spatiotemporal data analytics +
Footnote †: journal: Applied Sciences
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 2 Error Correction Scheme (ECS)
### Hybrid Automatic Repeat Request (HARQ)  
The performance of FEC is better during poor channel condition, whereas redundant bits that are introduced in FEC become an unwanted cost during good channel condition. The ARQ maintained reliable communication using retransmission that is very costly during poor channel due to frequent retransmission. However, the performance of ARQ is good during the good channel condition. Hybrid Automatic Repeat Request (HARQ) [28] scheme is developed by exploiting the advantages of both ARQ and FEC. In HARQ, the receiver tries to correct the detected errors and ask for retransmission request if error correction is not possible.  
There are two types of HRAQ, called HARQ-I and HARQ-II. In the HARQ-I scheme, transmitter transmits coded packet and the coded packet retransmits if the receiver is unable to correct all errors during the decoding. HARQ-II is more complex than the HARQ-I. In HARQ-II scheme, the transmitter transmits coded packet with limited parity bits initially. If the receiver is unable to correct all errors, the transmitter transmits extra parity than the previously transmitted. Subsequently, the decoder attempts to decode by combining the previously received coded packet and extra parity bits at the receiver. This scheme is more bandwidth efficient than HARQ-I, but the HARQ-II scheme required storing a previously transmitted packet, but HARQ-I does not. As we discussed earlier, the HARQ scheme consists of both error detection and re-transmission, and thus involves both PHY and MAC layers of the OSI model.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 2 Review Objectives and Methodology
### Study Selection and Analysis  
Literature retrieval and study selection was completed in four stages in accordance with the PRISMA protocol (Figure 1). The search was conducted in three different databases with the following filters:  
* Studies conducted within last 10 years, from 2013 onwards, due to the focus on Industry 4.0;
* Only peer-reviewed journal articles and conference proceedings, i.e., books, editorials, patents, and theses were filtered out;
* Articles available in English.  
With those filters on, using the pre-defined search strings, a total of 907 publications were retrieved, 419 from Scopus, 320 from IEEE Xplore, and 168 from MDPI. Once duplicates were removed, a total of 739 articles were screened based on the title, abstract, and conclusion. Papers that were not fully accessible were also removed at this stage. Screening resulted in the removal of 670 papers, leaving us with 69 studies to be assessed for the eligibility criteria. All 69 articles were retrieved, read, and assessed based on the inclusion  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Databases** & **Search Strings** \\ \hline \multirow{6}{*}{
\begin{tabular}{l} Scopus, \\ IEEE Xplore, \\ MDPI \\ \end{tabular} } & \"Industry 4.0 definition\", 3D Packing Strategies, Optimal 3D Packing Techniques, \\  & Efficient 3D Bin Packing Solutions, Three-Dimensional Bin Packing Challenges, \\  & \"Packing Efficiency\", 3D Bin Packing OR “Three-Dimensional Packing”, 3D Bin Packing Problem” OR “Three-Dimensional Packing Problem”, 3D Packing Algorithm” OR “Three-Dimensional Packing Algorithm”, “Container Packing”, “Bin Packing Optimisation” OR “Bin Packing Optimization”, “Packing Heuristics”, “Three-Dimensional Container Loading”, (“3D Bin Packing” OR “Three-Dimensional Packing”) AND (“Industry 4.0” OR “Fourth Industrial Revolution”) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Databases and search strings used.  
and exclusion criteria, as well as on rigour and relevance of the research. At the end of this phase, only 60 papers were selected for the final review.  
In alignment with our research questions, we carried out an in-depth analysis of the selected studies with the focus on specific techniques, algorithmic approaches, and technological solutions used, as well as evaluating the impact of Industry-4.0-led solutions on the real-world applications.
## 4 Major Components of Network Slicing System
### SDN Controller FLEXRAN for RAN Slicing  
FLEXRAN is an open-source and highly programmable Software-defined RAN (SD-RAN) platform that separates the RAN control plane to the data plane. It provides programmability support at two levels: the first one is to develop control and management applications over the FlexRAN controller and the second is to develop applications within the controller for performing the automatic deployment of control functions. It can also control the multiple distributed base station and their coordination among each other.  
The FlexRAN consists of two main components, namely, FlexRAN control plane and FlexRAN agent API. The first component FlexRAN control plane composed of a FlexRAN master controller which is further connected to FlexRAN agents. Although, one FlexRAN agent is for each eNodeB and also acts as a local controller while communicating with the master controller and other agents. The second component FlexRAN agent API is a southbound API which separates the data plane to control plane such as the separation of FlexRAN control plane to the eNB data plane. On the other hand, the FlexRAN- protocol is responsible for the two communication between agents and master-controller of FlexRAN. The master controller can control and manages the operation of each eNB with the help of FlexRAN-protocol. The abstract view of FlexRAN controller and its communication with eNodeB data plane is shown in Figure 6. On the top, some monitoring and control applications communicate with the master controller by using the northbound-API interface. The responsibility of these applications are to modify the RAN resources by checking the statistics and monitoring logs of the eNB in the FlexRAN control plane [42].  
The role of the FlexRAN controller in our network slicing system is to slice the RAN by getting the network slice template from the IBN component RAN slicing configurator. The FLEXRAN accepts the policies in the form of a JSON file. Thus, our RAN slicing configurator converts the slice request which is generated through the IBN system. Finally, after getting the slice configurations from the RAN policy configurator the FlexRAN controller deploys those slices configurations to eNB. Furthermore, FlexRAN controller can be able to share the RAN resources with the help of the master controller and the agents. Although the controller can also monitor and control the RAN resources, these monitoring logs can be further used for deep learning models for the slice admission decision.  
Figure 6: FlexRAN controller abstract view.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 3 System Models and Problem Analysis
### Problem Analysis  
With the example of a typical time synchronization algorithm, i.e., flooding time synchronization protocol (FTSP) [23], in this section, we perform a security analysis of the FTSP in layered IWSNs. From Equation (2), it can be observed that node \(i\) could estimate the compensation parameters related to its reference node \(j\) with the linear relationship among two clocks. In the FTSP, a linear regression table of sending timestamp \(\tau_{j}(t)\) and receiving timestamp \(\tau_{i}(t)\) is established; the estimation of \(\alpha_{ij}\) and \(\beta_{ij}\) are as  
\[\left\{\begin{array}{l}\hat{\alpha}_{ij}=\frac{\sum_{k=1}^{n}\left(\tau_{j} (k)-\overline{\tau}_{i}\right)(\tau_{i}(k)-\overline{\tau}_{i})}{\sum_{k=1}^{ n}(\tau(k)-\overline{\tau}_{i})^{2}}\\ \hat{\beta}_{ij}=\overline{\tau}_{j}-\hat{\alpha}_{ij}\overline{\tau}_{i} \end{array}\right., \tag{4}\]  
where \(n\) is the size of the regression table and \(\overline{\tau}\) is the mean. Normally, the reliability of message pairs \(<\!\tau_{i}(k)\), \(\tau_{j}(k)\!\!>\), \(k=1,2,\cdots,n\) is a prerequisite for accurately estimating \(\alpha_{ij}\) and \(\beta_{ij}\). If an attacker exists, the anomalous sending timestamp \(\tau_{A}^{j}(t)=\tau_{A}(t)+\omega_{A}(t)\) hidden in the regression table would interfere with the estimation process. For message manipulation, the attacker may only affect the synchronization accuracy. Specifically, the Sybil attacker will mislead the node with more serious destructiveness.  
To clearly describe the performance of FTSP under various attacks, we perform a simple simulation analysis on a chain IWSN with 5 nodes, and node 0 is the root. The hops between the normal nodes and the reference node are 1, 2, 3, and 4. The hop number and node identifier are the same. We assume that node 2 is a manipulation attacker or disguised by a Sybil attacker. Figure 2 shows the performance of safe nodes in FTSP under attacks. It can be clearly observed that the synchronization accuracy seriously decreases under manipulation attacks, and the clock offset of nodes behind the attackers would diverge under Sybil attacks.  
Figure 2: Performance of FTSP under various attacks. (**a**) No attacks; (**b**) manipulation attacks; and (**c**) Sybil attacks.
# Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
## 2 Background
### Low-Cost Digital System Design Challenges  
Low-cost digital systems differ in their design and implementation compared to conventional digital systems. Developers are faced with a number of challenges when designing such systems. Common design challenges identified in the literature revolve around extendability, standardised interfaces and open-source technologies. In particular, low-cost digital systems _should be_ constructed as a sequence of _reusable_ objects that combine both data and functions [35]. Moreover, selected information and communication _technologies need to be inexpensive_ and _should easily integrate with external systems_ by adopting standardised data formats and an open system architecture [36]. External systems include legacy devices and an existing IT infrastructure [37]. As part of several non-technical challenges, digital systems _should be easily accessible to non-expert users_[13], for example, by reducing the number of functions. Additionally, _hardware and software components should be open-source_ while developers need to _achieve acceptable levels of reliability and safety_ in an industrial setting [38]. For manufacturing, low-cost digital systems are likely to be created by _combining disparate components_ through standardised interfaces, and such systems are likely to be _built successively_ instead of as an integrated whole [16; 39].
## 3 Dynamic Analysis  
### Three-Dimensional Numerical Model  
#### 3.1.1 Track Geometry and Materials  
The geometry and subgrade profile of the section DK65 + 629 on the Baotou-Shenmu HAL railway line are shown in Figure 1b, and the 3D FE dynamic model, consisting of 190,078 elements and 201,812 nodes, is shown in Figure 5. The model dimensions were 18, 41.5, and 12.6 m in the longitudinal, horizontal, and vertical directions, respectively. All the track components (i.e., sleeper, ballast, top layer, bottom layer, embankment, and foundation) were modeled using 3D solid elements. To represent model geometry, 25 sleepers were placed along the longitudinal direction at spacing intervals of 0.6 m. The interaction between sleepers and ballast is set to be \"hard contact\" together with a friction of \"rough\". The sleepers, ballast, and foundation soils were considered as linear elastic materials, whereas the top layer, bottom layer, and embankment were modeled using user-defined materials, as expressed in Equation (1). The material constants were obtained based on the LFWD inverse analysis described previously. The properties of all the materials considered for this model are summarized in Table 1.  
Figure 5: Track model for dynamic analysis: (**a**) complete model and (**b**) 1-1 cross-section.  
In a dynamic analysis, the model boundaries must be selected carefully to ensure the accuracy of the results. Furthermore, as described in Section 2.2.1, it is necessary to simulate the effect of an unbounded domain for the case of long-term periodic or moving loads. Hence, the FE meshes were surrounded by the infinite elements at the bottom and side boundaries to absorb the stress waves at the boundary surfaces (see Figure 5b).
## 3 Research Methodology
### CLP Predictive Modeling  
According to the literature review on past CLP modeling techniques, ANN and ANFIS have been found to perform well and thus, were chosen for this study. ANN is a suitable model for complex relationships between CLP and the factors that influence it, as these relationships cannot be obtained in a precise manner [12; 24]. ANFIS models have been widely used in past CLP studies because of their superiority in being less reliant on expert knowledge and having a systematic data-driven process [36]. In order to optimize ANFIS parameters, the integration of ANFIS and GA was also developed. Another algorithm that shows accurate performance in a number of studies in other disciplines is RF, which was developed and compared with the other techniques in this study. Results from past studies show that RF is highly capable of solving non-linear classification problems, compared to other ML models [35]. As most crucial factors related to CLP do not follow a normal distribution, RF is a common ML technique in modeling construction productivity [57]. The following sections discuss the structure and components of these four widely used ML modeling techniques developed in this study.
# Blockchain-Based Loyalty Management System
## 2 Literature Review
### Background
#### 2.1.2 Blockchain  
Blockchain is a recent technology that has introduced a new paradigm for the secure storage and sharing of information. This technology essentially consists of a distributed database of a chain of blocks, where each block stores a transaction. To guarantee security, blockchain utilizes several mechanisms, such as cryptography, consensus algorithms, and digital signatures [1]. All transactions are performed in a decentralized way, removing the need for intermediaries, and once inside a block, the transaction cannot be deleted. This modern technology is based on the following core principles [8]:  
* Decentralization--one of the main goals of blockchain is to remove an entity's ability to have absolute control of all data. Hence, this technology is based on a network in which every user has equal power and the users themselves validate the transactions [3];
* Immutability--every node in the network holds a copy of the digital ledger. To add a new transaction, all nodes must check the transaction, and most of them must validate it. Validated records become irreversible and cannot be changed. In a scenario of a sudden change in one node, all nodes check the validity of the new state. When allnodes finish the verification, a voting process is started, and if the majority rejects the change, the new state is rejected. Then, a copy of a trusted digital ledger is sent to that node [8];
* Transparency--any user can access any record by accessing the node and consulting the timestamp [1];
* Security--when interacting with the blockchain, it is mandatory for a user to enter the key every time s/he wants to create a transaction. Each record is individually encrypted. The information inside the blockchain is hashed cryptographically, which means that each piece of data has its own unique identifier [8];
* Disintermediation--in order to avoid the use of third-party entities, blockchain takes advantage of smart contracts. These contracts are used to facilitate the negotiation process but still provide security and reliability to the process.  
Although blockchain is a relatively new technology, there is growing interest in and research into its applicability across a range of sectors. In [1], the authors describe what distinguishes blockchain from currently known paradigms, and characterize their specifications from fundamentals to architecture. After specifying the capabilities of the technology, the authors present recent studies on the incorporation of blockchain in certain areas. The domains involved in these studies are financial activities, health, information systems, wireless networks, IoT (Internet of Things), smart grids, government and military services, and defense. For each specified domain, this survey presents some examples of applicability and their respective benefits, limits, and challenges.
## 2 Combined Seal Structure and Material Parameters  
### Structure Parameters  
As shown in Figure 1, the combined sealing structure in this study comprises four components: a rubber O-ring, a gasket, a piston, and a steel shell. To protect the O-ring from potential damage during installation and ensure optimal sealing performance, an introductory angle \(\theta\) is incorporated at the bottom end of the steel shell. The pertinent structural parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Dimensions and Data** \\ \hline \(Z\) & introductory length & 1.14 mm \\ \(\theta\) & introductory angle & 20\({}^{\circ}\) \\ R\({}_{1}\) & inner fillet & 0.8 mm \\ R\({}_{2}\) & outside fillet & 0.3 mm \\ R\({}_{0}\) & fillet of gasket & 0.2 mm \\ R\({}_{3}\) & fillet of piston & 0.3 mm \\ R & O–ring radius & 0.76 mm \\ L & groove depth & 1.2 mm \\ H & groove length & 3 mm \\ h & thickness of gasket & 0.8 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of combined sealing structure.  
Figure 1: Combined sealing structure section view.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 2 Related Work
### GIFT Block Cipher
#### 2.2.1 Sbox of GIFT Block Cipher  
The \(n\)-bit block (\(n=64,128\)) is split into 4 bits and becomes the input value of the 4-bit Sbox. The Sbox of GIFT block cipher is given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \(i\) & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\ \(P_{64}(i)\) & 0 & 16 & 32 & 48 & 1 & 17 & 33 & 49 & 2 & 18 & 34 & 50 & 3 & 19 & 35 & 51 \\ \hline \(i\) &
## 2 Methodology
### Statistical Methods
If the matrix \(\mathbf{B}\) is not interpretable, rotational techniques are utilized, keeping the fit of the data. However, current research suggests losing a bit of fit to gain interpretation, using the technique of disjoint principal component analysis (DPCA) [45; 46]. When using disjoint components, each original variable belongs to just one component. Furthermore, each component must take at least one of the variables. This facilitates the interpretation of the matrix \(\mathbf{B}\) and allows an easy characterization of the components. In [45], an alternate least squares algorithm for calculating disjoint components is stated. In [46], an algorithm is proposed, based on particle swarm optimization, to calculate disjoint components and can be applied to big matrices with good execution times. In this work, we utilize both algorithms obtaining the same results. In [38], an efficient text document clustering approach using a multi-search arithmetic optimization algorithm was introduced. From [47], we have the following definition. **Definition 1** (Disjoint matrix). : _Let \(\mathbf{B}=(b_{jl})\) be a \(p\times r\) matrix, where \(p>r\). Then, \(\mathbf{B}\) is disjoint if and only if, for any \(j\), there exists only one \(l\) such that \(b_{jl}\
eq 0\), and for any \(l\), there exists \(j\) such that \(b_{jl}\
eq 0\). If \(\mathbf{B}\) also satisfies \(\mathbf{B}^{\top}\mathbf{B}=\mathbf{I}_{r}\), where \(\mathbf{I}_{r}\) is the \(r\times r\) identity matrix; then, \(\mathbf{B}\) is a disjoint orthogonal matrix._  
To compute disjoint components, the optimization problem expressed as  
\[\min_{\mathbf{A},\mathbf{B}}\|\mathbf{X}-\mathbf{A}\mathbf{B}^{\top}\|^{2},\] subject to: \[\mathbf{B}^{\top}\mathbf{B}=\mathbf{I}_{r},\]  
with \(\mathbf{B}\) being a disjoint matrix that must be solved. We can view an orthogonal disjoint matrix as a special type of a sparse matrix. When disjoint components are used, each component is written as a linear combination of a subset of the original variables. The algorithm that computes the disjoint components decides which original variables disappear from the linear combination. From the expression given in (3) and without loss of generality, assume that the \(h\)-th component, with \(l=h\), groups the \(k\) first original variables \(X_{1},\ldots,X_{k}\). Consider the linear combination  
\[\mathbf{X}=\mathbf{F}\mathbf{\Lambda}^{\top}+\mathbf{E}, \tag{2}\]0  
Note that in the equation given in (5), the last \(p-k\) scalars are equal to zero, that is, \(b_{(k+1)h}=\cdots=b_{ph}=0\). Thus, the component \(c_{h}\) is a linear combination of the original variables \(X_{1},\ldots,X_{k}\). This facilitates the interpretation of the loading matrix \(\mathbf{B}\) and, therefore, the characterization of the \(r\) principal components.
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 4 Results and Discussion
### Tomato Case Study  
In this study, the performance of the proposed algorithms was analyzed, assuming tomato as a case study. It is worth emphasizing that the method proposed in this study can also be used in different agricultural terrain. Table 3 presents an example of the growth cycle of tomatoes through to the harvest process. After the fruit is tough, the fruit ripens over 45-70 days, depending upon the cultivar, climate, and growth conditions. The fruit continues growing until the stage of green ripeness. The stages of the tomatoes ripening are categorized into three steps, as shown in Table 3. These stages are valid from the beginning to the end of the first harvest.  
In addition to the information above, also inspired by the information in [68; 69], we try to propose a more realistic and accurate metaheuristic-based method by considering the tomato characteristics such as irrigation mechanism, growth pattern, and relations between seedling growth and the prevailing environment. In addition, we used all this information to impact the behavior of autonomous robots.