esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
Moreover, setting a threshold for normalized relative Shannon entropy to output results requires the threshold to be relatively high to avoid false positives, which undoubtedly reduces detection sensitivity. Replacing relative Shannon entropy analysis with EWT or using SVM to process FFT outputs and generate detection results are reasonable alternatives. SVM can balance detection sensitivity and false positives better in two-class problems. Like EWT, SVM has the potential to capture time-frequency features, providing more information for fault classification [56]. Furthermore, SVM's functions can all be implemented through MAC operations, making it well-suited for FPGA-based hardware acceleration and pipeline operations to enhance data throughput. We only extracted features characterizing fault modes from IMFs 6 and 7. Attempts to fuse the features extracted from these two IMFs for result output showed no significant improvement. In experiments, IMF7 demonstrated better performance in fault detection compared to IMF6, leading to the use of IMF7 alone for fault detection. We believe that further optimizing the logic resource consumption for IMFs extraction and communication to within 2 kLEs can further reduce costs. Efforts are being made in this direction, potentially involving changes to FPGA memory scheduling and management. However, these changes will undoubtedly impact latency, the maximum clock frequency of the FPGA, and the maximum input sampling signal frequency, requiring further improvements and validation. Conceptualization, methodology, supervision, data curation, and investigation, C.L.K. ; investigation, visualization, and funding acquisition, Y.D. ; methodology, data curation, and software, Y.Y.K. ; methodology, resources, and software, M.X. ; project administration, visualization, and formal analysis, T.H.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data are unavailable due to privacy concerns. The authors would like to extend their appreciation to The University of Newcastle, Australia, for financing the project and making it possible. The authors declare no conflicts of interest. \begin{table}
\begin{tabular}{c c c c c c} \hline  & [53] & [54] & [55] & **This Work w/o Reuse** & **This Work with Reuse** \\ \hline Clock Rate (Mhz) & 12.5 & 50 & 46.44 & 200 & 200 \\ \hline Throughput (Mbps) & 140 & 58.5 & 620 & 200 & 100 \\ \hline Interpolation Method & STI & STI/CSI/HA & STI/CSI & STI & STI \\ \hline Architecture & Pipeline & Pipeline & Pipeline & Pipeline & Pipeline \\ \hline Data Format & Integer & Integer & Fixed Point & Fixed Point & Fixed Point \\ \hline \end{tabular}
\end{table} 
Table 3: Abnormal recognition accuracy.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 2 Supporting Technologies in the IoT-Edge-Cloud Continuum
### Blockchain Technology  
The ever-increasing number of interconnected devices on the Internet has raised many concerns regarding security and privacy preservation, as was previously mentioned. For example, in domestic or e-health IoT scenarios, multiple attacks may take place due to the diverse nature of the involved communication protocols [47; 48]. To this end, blockchain technology is a credible way to ensure security and privacy in heterogeneous infrastructures. A blockchain is a distributed ledger technology with growing lists of records (blocks) that are securely linked together via cryptographic hashes. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. These blocks are interconnected to form a chain. Therefore, for a particular block (i.e., \(n\)th block), its hash value is calculated by hashing the whole part of the \(n\) 1 block, which in turn includes the hash of the \(n\) 2 block, etc. [49; 50].  
A key novelty of blockchain technology is that it does not require a central authority for node identification and verification, but transactions are made on a peer-to-peer (P2P) basis. In general, blockchain is a decentralized security mechanism, where multiple copies of blocks are held in different nodes. Therefore, a tampering attempt would have to alter all blocks in all participating nodes. Moreover, since timestamps are inserted in all related blocks, it is not possible to alter the encrypted content after it has been published to the ledger, making it more trustworthy and secure as a result. In addition, timestamps are also helpful for the tracking of the generated blocks and for statistical analysis.  
The integration of blockchain technology in IoT networks faces many technical challenges since the encryption and decryption process of the blocks requires computational resources that cannot always be supported by lightweight IoT devices. Recent advances in the development of \"light clients\" for blockchain platforms have enabled nodes to issue transactions in the blockchain network without downloading the entire blockchain [51]. Therefore, by combining blockchain with FL, IoT sensing devices can offload a portion of their data to an edge server for local model training. However, there are still open issues to be addressed, such as a common blockchain framework that can be adopted by all involved entities, which is a key concept towards scalability in large-scale networks. Blockchain is usually combined with smart contracts, stored on a blockchain, and run only when predetermined conditions are met [52; 53]. Therefore, human intervention is minimized. Smart contracts do not contain legal language, terms, or agreements--only code that executes actions. Hence, the need for trusted intermediators is reduced, while at the same time, malicious and accidental exceptions are minimized.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 5 Controversies in Thermal Conductivity Measurements
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**NPs** & **BF** & **Size [nm]** & **Concentration** & **Keff Increase [\%]** & **Method** & **Ref.** \\ \hline \multirow{9}{*}{Water} & 33 & 1 and 2 vol.\% & 5.4 & Theory & [104] \\  & 30–60 & 0.5, 1.0, 2, 3 and & 1.96 & THW & [105] \\  & 10, 20–30 and 150 & Up to 1.5 vol.\% & 23 & Theory & [106] \\  & 13 & 0.1, 0.15, 0.20 & & & \\  & 13 & and 0.25 vol.\% & 6.40 for 0.25 vol.\% NF & THW & [107] \\  & 41 & 18 vol.\% & 31 & THW & [54] \\  & 36 & 10 vol.\% & 30 & SSCB & [108] \\  & 36 & 6 vol.\% & 28 & SSCB & [109] \\  & 20 & 14.6 vol.\% & 22 & TSHW & [110] \\  & 282 & 4 vol.\% & 17.7 & THW & [111] \\  & 40 & 4 vol.\% & 14.4 & Flash & [112] \\  & 15–50 & 4 vol.\% & 10.1 & TPS & [61] \\  & 43 & 3 vol.\% & 9.7 & THW & [113] \\  & 11 & 1 vol.\% & 9 & THW & [53] \\  & 38 & 3 vol.\% & 8 & THW & [114] \\  & 43 & 2 vol.\% & 7.52 & THW & [113] \\  & 10 & 0.08 vol.\% & 7.1 & THW & [115] \\  & 12 & 4 vol.\% & 5.4 & THW & [116] \\  & 10 & 0.05 vol.\% & 4.7 & THW & [115] \\  & 43 & 0.75 vol.\% & 3.28 & THW & [113] \\  & 10 & 0.04 vol.\% & 3.1 & THW & [115] \\  & 43 & 0.33 vol.\% & 1.64 & THW & [113] \\  & 10 & 0.08 vol.\% & 22 & THW & [115] \\  & 10 & 0.06 vol.\% & 17.3 & THW & [115] \\  & 282 & 3 vol.\% & 16.3 & THW & [111] \\  & 12 & 4 vol.\% & 14.3 & THW & [116] \\  & 38 & 3 vol.\% & 10.6 & THW & [114] \\  & 45 & 4 vol.\% & 9.7 & 3\(\omega\) & [117] \\  & 13 & 2 vol.\% & 12.6 & THW & [118] \\  & 10 & 3 vol.\% & 11.3 & THW & [116] \\ EG/Water & 50 & 3 vol.\% & 10.4 & THW & [116] \\  & 13 & 2 vol.\% & 8.4 & THW & [118] \\  & 13 & 2 vol.\% & 16.2 & THW & [118] \\  & 45 & 4 vol.\% & 13.3 & 3\(\omega\) & [117] \\ DI & 48 & 1 vol.\% & 4 & THW & [28] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thermal conductivity enhancement measurements for different nanofluids (NFs).
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
## 3 Discussion And Results  
Nanosatellites constellations could be more easily deployed in the Moon or Mars compared with bigger satellite constellations. In addition, those will be cheaper to replace in the less protective atmospheres of the Moon and Mars. Based on analysis performed in [11; 12], we can estimate the required number of nanosatellites to provide ubiquitous coverage (from a few of kbps until a few Mbps) in the Moon and Mars with the ratio of surface areas of the Moon and Mars to the Earth. This calculation results in a number of about 400 and 200 for Mars and the Moon, respectively. Nonetheless, current necessities on the Moon and Mars do not include ubiquitous communication coverage with several Mbps, and smaller constellations will work well for exploration. A Walker constellation design with configuration \(8/2/90\), as indicated in Section 2.1, is shown in Table 2. Similar nanosatellites constellation designs could be implemented both in the Moon and Mars at different altitudes and inclinations, resulting in a quicker and focused surface-area-sweep in the case of inclinations close to the local equator.  
The suitability of a communication payload and retrieval of data from sensors on the Moon [6] and Mars can be evaluated with a the link budget in the absence of a channel model as the ones that have been developed for Earth. Figure 3 shows a link budget evaluation for the received power at frequencies in the range of 500  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Orbital Plane** & **RAAN (deg)** & **True Anomaly (deg)** \\ \hline
2 & 1 & 0 & 90 \\
3 & 1 & 0 & 180 \\
4 & 1 & 0 & 270 \\
5 & 2 & 180 & 90 \\
6 & 2 & 180 & 180 \\
7 & 2 & 180 & 270 \\
8 & 2 & 180 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: [PERSON] satellite constellation design for two orbital planes.  
altitudes from 50 km to 1000 km. The results show a much greater receiver power than in Earth, mainly caused by the negligible local atmospheres and suitability of orbits starting from a few tens of kilometers.
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 2 Problem Definition
### Author Profile Mis-Configuration  
Let \(P^{a}\) denote the author profile of scholar \(a\), containing the introduction, interested research fields, a claimed co-author list and a claimed paper list. Some evaluation metrics such as h-index and total number of citations are also a part of the author profile. The claimed co-author list only contains co-authors the scholar wants to display. Denoting the paper list of scholar \(a\) as \(l^{a}\), which consists of \(M^{a}\) paper entries; each paper entry \(l^{a}_{i}\) in the paper list \(l^{a}\) has its title and an author list with name abbreviations of its authors. Some of the paper entries also have the information of their publication venues, publication years and the numbers of citations. We regard \(P^{a}\) as a mis-configured author profile if there exists at least one paper entry in \(l^{a}\) that does not belong to scholar \(a\). Otherwise, the author profile is treated as a reliable one.  
In this paper, given the author profiles of a set of scholars \(A\), we try to judge whether each of them to be mis-configured. We formulate the problem of mis-configuration detection as a binary classification problem.  
\[\forall a\in A,f(P^{a})\rightarrow\{0,1\} \tag{1}\]  
where \(f(P^{a})=1\) means \(P^{a}\) is reliable, while \(f(P^{a})=0\) represents that \(P^{a}\) is a mis-configured author profile. An undirected co-author graph \(G=(V,E)\) can be constructed based on the paper lists of scholars \(A\) to assist the mis-configuration detection, where each node \(v\in V\) denotes a scholar and each edge \(e\in E\) represents the collaboration between two scholars. Due to the large scale of the dataset, the detection system must be automatic and scalable. Note that, in this paper, we do not attempt to explicitly find the papers which are wrongly claimed in the paper list and we leave this for the future work.
## 4 Profiling
### Basic Application Profiles for the Examined Use Case  
For the described use case, we consider that the equipment under test is a new smart meter (new consumer) and its integration to the rest of the system needs to be tested. It is also assumed that the LNAP is integrated within the smart meter and that the functions over interface M and H2 (Figures 7 and 8) remain the same. Therefore, the interaction link of interest is interface C between the LNAP and NNAP.  
The rest of the actors are considered to comprise the test bed and to be known. The interoperability layers of interest are the component, the communication and the information, since we are interested in the technological part of the interoperability test. The business layer is mostly linked to market issues, which is outside the scope of this work. Without loss of generality we assume that the rest of the actors are characterized by the BAPs shown in Table 7, thus that they operate with the equivalent standards/options shown in Table 7.  
Given that the test bed is known, it makes sense that the standards/ options for information exchange are already defined. For the interoperability test, one link of interaction is the one to be examined, which depends on the equipment under test and its configuration. In this example, the interaction link of interest is between the LNAP and the NNAP (smart meter to data concentrator) and the possible BAPs vary depending on the supporting technology by the smart meter. Table 8 gives an overview of the possible BAPs for the examining interaction link.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BAP ID** & **Use Case ID** & **Standard** & \
\begin{tabular}{c} **Interaction Link** \\ **between Actors** \\ \end{tabular} &
\begin{tabular}{c} **Interoperability** \\ **Layer** \\ \end{tabular} \\ \hline I1 & DSM/DR1 & EN 62056 & NNAP-HES & Information \\ I2 & DSM/DR1 & EN 61968 & HES-MDMS & Information \\ I3 & DSM/DR1 & EN 61968 & MDMS-Actor B & Information \\ I4 & DSM/DR1 & EN 62325 & Actor B-Actor A & Information \\ I5 & DSM/DR1 & EN 50090 & Actor A-CEMS & Information \\ I6 & DSM/DR1 & EN 62056 & Smart meter-LNAP & Information \\ I7 & DSM/DR1 & EN 50090 & LNAP-CEMS & Information \\ C1 & DSM/DR1 & ETSI TS 121 101 & NNAP-HES & Communication \\ C2 & DSM/DR1 & EN 60870-5 & HES-MDMS & Communication \\ C3 & DSM/DR1 & EN 62746 & MDMS-Actor B & Communication \\ C4 & DSM/DR1 & EN 62325 & Actor B-Actor A & Communication \\ C5 & DSM/DR1 & EN 50090 & Actor A-CEMS & Communication \\ C6 & DSM/DR1 & EN 50090 & Smart meter-LNAP & Communication \\ C7 & DSM/DR1 & EN 50090 & LNAP-CEMS & Communication \\ \hline \hline \end{tabular}
\end{table}
Table 7: BAPs for the equipment comprising the test bed.  
Once the equipment under test is specified, the standard/option used will be known and the BAP can be defined. It should be reminded at this point that each BAP doesn't contain several options. Therefore, attention should be paid so as to make sure that the specific option of a standard (if any) is specified.
## 3 Results
### Eye Tracking Experimental Data
#### 3.1.2 Results of Eye Tracking Experimental Data  
In this study, the results of the data we collected through eye-tracking technology reveal the effects of different barricade designs on drivers' visual attention. The data results show that the R-barricade design excels in several key metrics as follows:  
* Number of points of interest: The R-barricade design elicits more points of interest from the driver, which suggests that the driver's attention is more focused on the roadblock, thus helping to recognize it earlier and take appropriate avoidance measures;
* Gaze duration: drivers' longer average gaze duration at the R-barricade reflects more in-depth cognitive processing of the roadblock, which contributes to an increased awareness of the importance of the roadblock and alertness to potential hazards;
* Gaze time variance: The R-barricade's smaller gaze time variance means that the driver's visual attention to the roadblock is more stable, reducing the potential for delayed reactions due to distraction;
* Visual Search Efficiency: The new design of the roadblock has improved visual search efficiency, allowing drivers to locate the roadblock in the visual scene more quickly, which is especially important in emergency situations;
* Purity and proportionality: eye-tracking data shows that the R-barricade's design effectively directs the driver's attention allocation, enabling them to quickly notice and react to roadblocks when necessary.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Planned Number** & **1** & **2** \\ \hline Gaze Points & 170 & 188 \\ Variance of gaze point duration & 0.22 & 0.31 \\ Average duration of gaze points (s) & 6.51 & 6.78 \\ Purity (\%) & \(1/(170/10/1)\times 100\%=5.88\%\) & \(1/(188/10/1)\times 100\%=5.32\%\) \\ Ratio (s) & \(1/0.22=4.55\) & \(1/0.31=3.23\) \\ Contrast (s) & \(170\times 0.22/10=3.74\) & \(188\times 0.31/10=5.83\) \\ C/T & \(3.74/6.51=0.57\) & \(5.83/6.78=0.86\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Purity, proportion, contrast, C/T calculation case.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Planned Number** &
\begin{tabular}{c} **Average** \\ **Duration of** \\ **Gaze Point T (s)** \\ \end{tabular} & **Gaze Points** & **Purity (\%)** & **Variance** \\
**(Statistics)** & **Proportion (\%)** & **Contrast (s)** & **C/T** \\ \hline
1 & 6.51 & 170 & 5.88\% & 0.22 & 4.55 & 3.74 & 0.57 \\
2 & 6.78 & 188 & 5.32\% & 0.31 & 3.23 & 5.83 & 0.86 \\
3 & 6.62 & 143 & 6.99\% & 0.27 & 3.70 & 3.86 & 0.58 \\
4 & 6.71 & 184 & 5.43\% & 0.25 &In summary, the results of the optimized data show that the design improvement of the R-barricade has practical application value in improving the warning effect of the barricade and reducing the risk of accidents, which provides effective support for traffic safety during highway emergency repair.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 4 Two Case Studies of the Proposed Framework
### Traffic Light System for Smart Transportation  
#### 4.1.1 Motivation and Approach  
Traditional schedule-driven traffic light systems often struggle to adapt to dynamic traffic conditions, leading to unnecessary delays and congestion. To address these limitations, we propose an event-driven architecture (EDA) for adaptive traffic light control within the context of smart transportation. This innovative approach leverages real-time data from diverse sources to dynamically adjust signal timings, potentially minimizing delays and promoting smoother traffic flow compared to fixed-schedule systems [30].  
Our proposed system builds upon existing advanced traffic control systems (ATCSs) by incorporating an event-driven paradigm. This enables seamless communication and response between various system components acting as both event producers and consumers. Real-time events such as pedestrian crossings, accidents, and congestion can be efficiently communicated and acted upon, empowering the system with rapid adaptability to changing traffic conditions. Elixir's inherent scalability and concurrency, as discussed in previous sections, further contribute to the system's responsiveness and effectiveness in handling dynamic traffic patterns.  
In addressing the specific scheduling challenges of traffic light signals, our approach incorporates the Oldest Arrival First (OAF) algorithm [31]. The OAF algorithm, known for its efficiency in vehicular traffic scheduling, utilizes real-time position and speed data of individual vehicles. By focusing on isolated traffic intersections, the OAF algorithm aims to minimize delays, enhancing the overall fluidity of traffic movement. By integrating real-time data received from sensors into this algorithm, we can dynamically adjust the scheduling of traffic lights based on real-time analysis. The output of the OAF algorithm, combined with our EDA's responsive policies, allows us to effectively address constantly changing traffic events.  
The OAF algorithm's unique capability to process per-vehicle data enables us to dynamically adjust traffic signals in response to immediate traffic conditions. The synergy between the OAF algorithm and our EDA forms the foundation of our proposed smart transportation system.
# A Process Decision-Making Method for Planar Machining of Box-Type Components
## 3 Constructing a Hierarchical Information Model for Process Decision-Making
### Hierarchical Information Representation Model for Machining Process Decision-Making
#### 3.2.2 Design Features Layer  
The \"Design Features Layer\" represents the second tier of the hierarchical model for the planar machining process decision-making of box-type parts. The primary function of this layer is to extract design feature information related to process decision-making for the box-type parts and its planar surfaces from the part model. Additionally, it establishes the subordinate relationships between the part, its planar surfaces, and their corresponding design feature information. Design features relevant to planar machining process decision-making for box-type parts include both part design features and machined surface design features, as illustrated in Figure 5.  
Figure 4: Subordination of case parts to the plane to be machined.  
Figure 3: Hierarchical model for decision-making in planar machining process of box-type components. Note: Part—box-type components; \(\text{f}_{\text{m}}\)—plane being machined (\(\text{m}=1,2,3,\cdots\)); \(\text{FDP}_{\text{m}}\)—related design features of the plane being machined; PDF—related design features of the part; \(\text{FMP}_{\text{m}}\)—flat machining program; \(\text{FPP}_{\text{m}}\)—process parameters of the machined plane; PPP—process parameters of the blank.  
The design features of the box-type parts encompass several key aspects, including production type (_PT_), heat treatment (_HT_), casting method (CM), and material composition (_PM_). The conceptual set representing these design features is formulated as shown in Equation (1) [32].  
\[PDF=\{\;PT,\;HT,\;CM,\;PM\;\}. \tag{1}\]  
* Production types (_PT_) can be classified into small-batch production, medium-batch production, and large-batch production.
* Common materials (_PM_) for box-type parts include cast steel, gray cast iron, and ductile iron.
* Casting methods (_CM_) typically used for box-type parts involve sand casting (manual molding and machine molding), shell molding, metal mold casting, and investment casting.
* Heat treatment methods (_HT_) commonly applied to box-type parts include annealing, normalizing, aging treatment, and quenching.  
The design features of the machined surface primarily include basic dimensions (_BD_), length (_Len_), width of machined surfaces (_Wid_), surface roughness (_Ra_), economic accuracy class (_IT_), nominal dimensional tolerance (_NDT_), geometrical tolerance constraints (_GT_), geometrical tolerance class (_GT_), and positioning reference (_PR_). The conceptual set representing these design features is defined in Equation (2).  
\[FDF=\{\;BD,\;Len,\;Wid,\;Ra,\;IT,\;NDT,\;GT,\;GTC,\;PR\;\}. \tag{2}\]  
For planar machining, positioning references can be categorized into coarse datum and fine datum. As described in Reference [32], geometrical tolerance constraints can be classified into straightness, flatness, parallelism, and perpendicularity.