esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
## 3 The Problem with Planar Graphs and Planarization  
GG and RNG are representative planar graphs constructed using only local information and do not divide a network. They are built by removing what might be intersecting edges among the edges between a message transfer node and its neighbors. Figure 1 shows the process of building a local GG: Figure 1a represents the local network graph of node \(u\) and Figure 1b depicts the planarization process that eliminates edges that do not fit the GG edge condition. An edge is removed if there is a node other than node \(u\) and a neighbor in the circle whose diameter is their edge. To illustrate this, in Figure 1b, node \(a\) exists in the circle within the edge diameter between nodes \(u\) and \(x\), so \(edge(u,x)\) is removed. Figure 1c shows the planar graph generated after planarization. For the edge removal process according to the GG edge condition, long edges are removed and only short edges are left. Since the constructed planar graph consists of short edges, it causes a problem when forwarding a message sequentially along multiple nodes in the face in spite of being able to send it directly when in radio range.  
In Figure 2a, if nodes \(u,v,w,x,y\), and \(d\) are on the routing path that make up the face, a problem with efficiency arises in that a message should have been transferred to node \(y\) after exploring nodes \(v,w\), and \(x\). Although node \(y\) is within radio range, the message cannot be forwarded from node \(u\) directly to node \(y\) because \(edge(u,y)\) was removed during planarization. Consequently, in a high density WSN, the inefficiency caused by this becomes even more exacerbated, as shown in Figure 2b; a message is transmitted through 15 hops (\(u\to v_{1}\rightarrow...\to v_{13}\to y\to d\)) even though it could be transferred in only 2 hops (\(u\to y\to d\)). As the density of the nodes increases, the number of nodes within radio range increases, and, as this happens, the edges of the planar graph become shorter during planarization. The shortened edges intensify the inefficiency because they increase the number of nodes that need to be traversed when forwarding a message.  
Figure 1: Planarization process: (**a**) local full network graph (\(r\): radio range); (**b**) planarization; and (**c**) local Gabriel graph (GG).
# Public Perception of the Introduction of Autonomous Vehicles
## 1 Introduction
Smart cities are metropolitan areas that use cutting-edge technology to enhance quality of life among citizens and economic growth. They use the Internet of Things (IoT), which generates big data, ICT, and AI to process data. As they have the potential to revolutionise transport, decrease traffic and pollution, and enable new mobility services, autonomous cars are seen as a crucial part of smart cities. The futuristic city of NEOM, which is being constructed in Saudi Arabia, is one example of a smart city that is being developed [12; 13]. The 26,500 square mile metropolis of NEOM is built to be a global hub for cutting-edge technologies [14]. Autonomous vehicles will be used for transport services and to link the city to the surrounding areas. The deployment of autonomous vehicles in NEOM is anticipated to provide several advantages, including greater mobility for citizens, increased safety, and decreased congestion and pollution. AV technology is ready for deployment when the following four stages of the technology acceptance model (TAM) are completed: engineering, legal, moral, and ethical. They are shown in Figure 2. The engineering stage has bidirectional links, because we have feedback involved in the continuous engineering improvements based on regular evaluations, technology progress, and changes in customer requirements. Figure 1: (a) Electrical vehicle designed by RMIT University students. (b) Autonomous vehicle with control software designed by RMIT University students. Autonomous vehicles, at all levels of autonomy, play a significant role in the development of modern transport systems [15]. Clarifying issues, especially from stage 3 to stage 5, as shown in Figure 2, is the most significant obstacle to AV adoption, not the technical aspect [16]. Despite the amount of potential effective solutions from the adoption of self-driving vehicles in improving road safety, we need to highlight benefits associated with self-driving vehicles. According to previous studies, there is notable concern among drivers about self-driving vehicles and many of them are hesitant to use them [17]; however, drivers welcomed active safety through driver warning systems [18]. Therefore, the basic benefits of AVs, such as increased safety and fuel efficiency, may not be enough [19]. We believe that increased public awareness of AVs may contribute to promoting this technology and advance its adoption. While some efforts were made to understand why people would use AVs or to what extent social and personal factors may influence this acceptance, few of them have measured the extent of familiarity with AVs. The community's attitudes and readiness to purchase AVs have an impact on when and how autonomous technologies will be adopted. The presented study aims to fill in this research gap and obtain stakeholders' views. We investigated public awareness of AVs and measured consumers' readiness for the transition to new technologies in Saudi Arabia. Up to now, there has been a lack of studies on public opinion about autonomous vehicles in the Middle East.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### The Multiple Profiles Test Series
The Non-PEAR results show that 25% of the mid-cluster devices do not respect the energy profile with an average overconsumption per field device of 52.6 mWh/day compared with the 97.5% success rate and average overconsumption of 7.3 mWh/day for the PEAR variant. The 7.2 times less average overconsumption argues the PEAR capacity to balance the energy not only between clusters but also within a cluster profile even for the overconsuming devices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Profile Cluster** & **Number of Devices** & **Energy Profile** & **PEAR Profile** & **Threshold** \\  & & **[months]** & **[DPDUs/min]** \\ \hline Low cluster & 20 & 48 & 80 \\ Mid cluster & 40 & 24 & 160 \\ High cluster & 10 & 12 & 325 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The three profile cluster scenario parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Profile Cluster** & **Energy Balancing** & **Lifetime** & **Average Energy** & **Cluster** & **Success Rate** \\  & **(Months)** & **Consumption** & **Overconsumption** & **(\%)** \\  & & **[mWh/day]** & **[mWh/day]** & **[mWh/day]** & **[\%)** \\ \hline Low & PEAR & 54.2 & 23.25 & 0 & 100\% \\  & Non-PEAR & 54.2 & 24.68 & 0 & 100\% \\ \hline Mid & PEAR & 22.2 & 34.97 & 7.30 & 97.5\% \\  & Non-PEAR & 12.4 & 49.07 & 526.03 & 75\% \\ \hline High & PEAR & 12.1 & 91.34 & 0 & 100\% \\  & Non-PEAR & 9.8 & 94.92 & 136.72 & 70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparison between the PEAR and Non-PEAR for the three profile cluster scenario.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.4 NanoLCARUS Mote  
The nanoLCARUS mote [35] was designed for IoT applications. Concerning the processor and memory specifications, the mote incorporates the ultra-low power ARM Cortex-M4 32-bit RISC core MCU (STM32L476 RG) that can operate at up to 80 MHz. It contains 1 MB flash memory and static RAM of 128 kB. Also, an off-chip FRAM (MR45V256A) is installed on the board for optional memory. Pertaining to the communication capabilities, the authors utilized an XBee footprint socket for accommodating radio modules such as LoRa, ZigBee, BLE, etc. In addition, the temperature and humidity sensor (SHT21), light intensity sensor (VEML6030) and the battery gauge (MAX17048G+) are integrated in the prototype node. An option for additional sensing modules is available through the I2C and I/O ports. The sensor node can be powered by voltages ranging roughly from 1.5 V to 3.7 V. A current of around 33 nA is consumed during the sleep state due to the presence of an off-chip RTC and power switch. The mote consumes around 14 mA in the active state.
# Numerical Study on Hydrodynamic Performance of a New Semi-Submersible Aquaculture Platform
## 3 Numerical Model
### Net System  
Empirical formulas are usually employed for the rapid prediction of hydrodynamic forces on the nets to calculate the loads. The Morison equation is used to compute the hydrodynamic loads on each unit, and the loads on the nets are obtained by summing up the calculated forces on all units. The equation used for this calculation is presented below:  
\[F_{Net}=\rho C_{M}V\cdot i\dot{u}+\frac{1}{2}\rho C_{D}A\cdot u|u| \tag{13}\]  
where \(C_{M}\) is the inertia coefficient; \(V\) is the volume of water displacement; \(C_{D}\) represents the drag coefficient; \(u\) is the flow velocity; \(\dot{u}\) is the flow acceleration; and \(A\) represents the cross-sectional area of the structure perpendicular to the flow direction.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 3 Materials and Methods
### Materials
#### 3.1.1 Hyperledger Fabric Components and Features
_Privacy and confidentiality of transactions_: Channels allow a subset of nodes, through the anchor node, to link different organizations that compose the consortium. The ledger of a channel can be accessed only by those organizations that are part of the  
Figure 3: Hyperledger Fabric components and transaction flow. channel. Therefore, participants can view only the network features and data relevant to their specific channel. 3. _Highly modular and configurable architecture:_ Hyperledger Fabric enables plug-and-play ordering, membership, endorsement, and validation services. A pluggable consensus algorithm also improves the platform. Ledger supports LevelDB and CouchDB databases [33]. 4. _Efficient data query:_ By using CouchDB, it could execute queries more efficiently compared to other relational databases with less latency and with simpler queries. 5. _High transaction throughput performance:_ Hyperledger Fabric is scalable. Peer nodes are liberated from ordering (consensus) responsibilities, while transaction execution is independent of ordering and commitment. The division of labor relieves the ordering nodes of transaction execution and ledger maintenance. 6. _Low latency of transaction confirmation:_ Hyperledger Fabric is considered the fastest amongst all the permissioned blockchains, generated within only a few organizations, thus contributing to reducing latency. Furthermore, it does not have a mining process like blockchain, and it makes the system fast in verifying and committing transactions [31]. 7. _Offering multiple languages to write smart contracts:_ One can write smart contracts in different programming languages, such as Java, Go, or Node.js [20; 33]. 8. _No cryptocurrency:_ Unlike a public blockchain and several other technologies, Hyperledger Fabric does not involve any cryptocurrency ([33]). 9. _The ability to monitor the network performance:_ Hyperledger Caliper is a network benchmarking tool that measures performance using indicators like transactions per second, latency and throughput utilization of resources, and more [47]. It provides the flexibility to specify and configure parameters for conducting tests on the platform, aiming for optimal results. In light of the above, Hyperledger Fabric is a prominent blockchain framework that effectively addresses security, scalability, and privacy. The versatility and robustness of this solution are attributed to its Docker container-based architecture, private data collections, and diverse language support for chaincode development. This system's enhanced security features, systematic approach to transactions, and adaptability via APIs and SDKs highlight its potential.
## 4 Methodology
### Multi-Perspective Hypergraph Construction
#### 4.1.1 Collective Preference Hypergraph  
In order to effectively capture the high-order interactions between users and locations, we construct a collective preference hypergraph, \(\mathcal{G}_{\mathbb{C}}=(\mathcal{V}_{\mathbb{C}},\mathcal{E}_{\mathbb{C}}, \mathbf{H}_{\mathbb{C}},\mathbf{W}_{\mathbb{C}})\), where \(\mathcal{V}_{\mathbb{C}}=\{l_{1},l_{2},\ldots,l_{|L|}\}\) denotes the set of locations, and \(\mathcal{E}_{\mathbb{C}}=\{e_{u}\mid u\in U\}\) represents the collection of user-specific trajectories encoded as hyperedges. Each hyperedge, \(e_{u}\subseteq\mathcal{E}_{\mathbb{C}}\), comprises the locations visited by user \(u\) and is weighted by a diagonal matrix \(\mathbf{W}_{\mathbb{C}}=\text{diag}(w_{1},\ldots,w_{|U|})\), where each hyperedge weight, \(w_{u}=1/|e_{u}|\), provides trajectory length normalization to counterbalance varying visitation frequencies. Hyperedges are formed based on the check-in trajectories of individual users. Specifically, each user's visit history is represented as a hyperedge, where each hyperedge connects the locations that the user has visited. These hyperedges are weighted by the length of the user's trajectory to normalize the visitation frequencies, ensuring that users with more frequent visits do not dominate the learning process. The user-location visit pattern is encoded by the incidence matrix \(\mathbf{H}_{\mathbb{C}}\in\{0,1\}^{|L|\times|U|}\), with elements defined as follows:  
\[H_{\mathbb{C}}(l,e_{u})=\begin{cases}1,&\text{if }l\in e_{u}\\ 0,&\text{otherwise}\end{cases} \tag{1}\]  
The vertex degrees are computed as \(d(v)=\sum_{e_{u}\in\mathcal{E}_{\mathbb{C}}}\mathbf{H}_{\mathbb{C}}(v,e_{u}) \cdot w_{u}\), while the hyperedge degrees are given by \(\delta(e_{u})=\sum_{v\in\mathcal{V}_{\mathbb{C}}}\mathbf{H}_{\mathbb{C}}(v,e_{ u})\), forming diagonal degree matrices \(\mathbf{D}_{v}\in\mathbb{R}^{|L|\times|L|}\) and \(\mathbf{D}_{e}\in\mathbb{R}^{|U|\times|U|}\).  
The intra-sequence co-visitation structure is captured by the weighted adjacency matrix \(\mathbf{H}_{\mathbb{C}}\mathbf{W}_{\mathbb{C}}\mathbf{H}_{\mathbb{C}}^{\top}\), which encodes the relational strength between locations. To capture inter-sequence user dependencies based on shared location visitation, we define a hyperedge similarity metric using cosine similarity as follows:  
\[\text{Sim}(u_{i},u_{j})=\frac{|\mathbf{H}_{\mathbb{C}}(:,e_{u_{i}})^{\top} \mathbf{H}_{\mathbb{C}}(:,e_{u_{j}})|}{\|\mathbf{H}_{\mathbb{C}}(:,e_{u_{i}}) \|\cdot\|\mathbf{H}_{\mathbb{C}}(:,e_{u_{j}})\|}, \tag{2}\]  
where \(\mathbf{H}_{\mathbb{C}}(:,e_{u})\) denotes the column of \(\mathbf{H}_{\mathbb{C}}\) corresponding to location space. The proposed collective preference hypergraph \(\mathcal{G}_{C}\) facilitates the modeling of both local user trajectory structures and global co-visitation patterns.
# DFA-UNet: Efficient Railroad Image Segmentation
## 6 Conclusions  
In this paper, we propose a new encoder-decoder architecture for image segmentation: DFA-UNet. The proposed model consists of the GAMP module and the DFA module. The GAMP module can effectively extract the low-level features of the image and reduce the number of parameters of the model, and the DFA module can realize the fusion of different scale feature maps. We evaluated our model on the Remrail19 dataset, and the results showed that our model has higher mIoU evaluation metrics compared to other models. Our model can perform better for complex image segmentation tasks. In the future, we will focus on optimizing its performance so that it can segment images more accurately in real time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **FLOPs** & **Params** & **FPS** \\ \hline U-Net & 31.1191G & 13.3953M & 64.67 \\ Attention UNet & 66.6318G & 34.8785M & 34.47 \\ LevitUNet & 33.2142G & 52.1438M & 43.33 \\ Resunet++ & 70.9938G & 14.4825M & 36.61 \\ SwinUNet & 10.7228G & 27.1458M & 51.14 \\ UNet+SE & 31.1229G & 13.4711M & 48.69 \\ DFA-UNet & **24.9564G** & **8.7517M** & 49.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of other parameters of different models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Keral Size** & **mIoU** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \(3\times 3\) & 0.8463 & 0.9909 & 0.8766 & 0.8952 & 0.8805 \\ \(5\times 5\) & 0.8562 & 0.9912 & 0.8872 & **0.9089** & 0.8935 \\ \(7\times 7\) & **0.8662** & **0.9924** & **0.9037** & 0.9057 & **0.8997** \\ \(9\times 9\) & 0.8455 & 0.9907 & 0.8780 & 0.8950 & 0.8813 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Study of different kernel sizes in DFA modules of the DFA-UNet architecture on railway datasets.  
**Author Contributions:** Methodology, Y.Z.; software, K.L.; validation, G.Z., Z.Z. and P.W.; formal analysis, Y.Z. and G.Z.; investigation, Y.Z.; resources, Y.Z. and G.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., K.L. and G.Z.; visualization, K.L. and G.Z.; supervision, Z.Z. and P.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Shandong Province (ZR2021 MF064) and the China Postdoctoral Science Foundation (2021M702030).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset source: [[https://wilddash.cc/railsem19](https://wilddash.cc/railsem19)]([https://wilddash.cc/railsem19](https://wilddash.cc/railsem19)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussions
### Robustness Analysis
It is concluded from Table 5 that the channel R-CNN achieves stable and consistent distribution for segmentation in each scoring index, the results of video sequence v3 and v4 on _SMA_ and _DC_ reach over 90%, reaching 91.2% and 90.9%, respectively. Therefore, the generalization ability of our system is proved by performance evaluation using the video sequences, by \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Events [\%]**} & \multicolumn{1}{c}{**Frames**} & \multicolumn{1}{c}{**Time [\(s\)]**} & \multicolumn{1}{c}{**t/Frame (ms)**} \\ \cline{2-7}  & **AO** & **TO** & **PO** & **CV** & & \\ \hline v1 & 35.09 & 23.11 & 13.28 & 28.52 & 634 & 26.41 & 41.6 \\ v2 & 25.04 & 42.93 & 26.56 & 5.47 & 515 & 21.45 & 41.7 \\ v3 & 31.89 & 24.23 & 19.49 & 24.39 & 798 & 33.25 & 41.7 \\ v4 & 32.78 & 18.17 & 16.92 & 32.13 & 606 & 25.25 & 41.7 \\ v5 & 19.09 & 39.45 & 21.73 & 19.73 & 1320 & 55.00 & 41.7 \\ v6 & 26.37 & 42.76 & 14.61 & 16.26 & 493 & 20.54 & 41.6 \\ v7 & 22.29 & 38.06 & 15.57 & 24.08 & 508 & 21.16 & 41.7 \\ v8 & 16.54 & 48.48 & 9.27 & 25.71 & 284 & 11.83 & 41.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptions of the duration, frame count, and the proportion of frames showcasing primary states within the cutting curve dataset for video sequences. Figure 11: Visual cutting curves of the TNH under different conditions from a real surgery video. (**a**) Appliance occlusion (AO). (**b**) Tissue occlusion (TO). (**c**) Partial occlusion (PO). (**d**) Complete visible (CV). which the thoracoscopic surgery is indicated using the clinical information from target regions and cutting curves.
# Photocatalytic Degradation of Selected Non-Opioid Analgesics Driven by Solar Light Exposure
## 2 Materials and Methods  
Paracetamol (PAR), diclofenac (DFC), ibuprofen (IBU), ketoprofen (KET), and naproxen (NAX), were purchased from Sigma Aldrich. Methanol was obtained from Merck (Darmstadt, Germany) and formic acid from Sigma-Aldrich. All solvents were HPLC-grade. Water was purified by a Milli-Q system (Millipore).  
Individual stock solutions (1 mg/mL) of these compounds were prepared separately using methanol-water (50/50, \(v/v\)) as a solvent. All the stock solutions were stored at 4 \({}^{\circ}\)C. The standard solutions were obtained by appropriate dilution of stock solutions with ultrapure water before analysis.  
Chromatographic measurements were carried out using an Azura Analytical HPLC system (Knauer, Germany) equipped with an AZURA P 6.1L gradient pump and an AZURA 2.1L diode array detector (190-700 nm). The wavelengths 200 nm and 230 nm for naproxen, optimal for UV detection, were chosen. The determination of drugs was accomplished with an X-Bridge Shield C18 (4.6 \(\times\) 100 mm; 3.5 \(\upmu\)m) column. The flow rate was set to 1 mL/min, and the injection volume was 10 \(\upmu\)L. Measurements were carried out in isocratic elution, optimized for each analyte.  
All analytes exhibit weak acidic properties. Paracetamol with a pK\({}_{\text{a}}\) of 9.38 is neutral over a wide pH range; however, in the case of NSAIDs (p\(K_{\text{a}}\) = 4-5), a low pH of the mobile phase was required. For paracetamol, 10 mM formic acid at pH 2.8 and MeOH in a ratio of 80:20 \(v/v\); for ketoprofen and naproxen, formic acid at pH 2.8 and MeOH (30:70 \(v/v\)); and for diclofenac, ibuprofen formic acid at pH 2.8 and MeOH (20:80 \(v/v\)) were used. Photodegradation of pharmaceuticals was controlled for model solutions of a single drug with a concentration of 10 \(\upmu\)g/mL (for ibuprofen 20 \(\upmu\)g/mL) to observe the degradation products. The ClarityChrom chromatography software analyzed the data obtained.  
In the UV photolysis processes, a UV lamp, Honle VG UVAHAND, emitting radiation with a wavelength of 254 nm and a light intensity of 250 mW/cm\({}^{2}\), was used. In the photolysis processes, an Oriel 150 W solar simulator equipped with an AM 1.5 G filter was utilized, which was a photoelectrode-light source space that provided an irradiation intensity on the surface photoelectrode of 100 mW/cm\({}^{2}\) (determined by a light meter).
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 4 LoRa Network Performance Evaluation
### ED Power Consumption Model
#### 4.2.2 ED Operating State Classification  
After the ED is powered on for the first time, it enters the initialization phase, completing ED initialization, sending a network join request, receiving a join response, collecting data, sending data, receiving an ACK, and then entering sleep mode. Class A EDs use the pure ALOHA protocol, switching from sleep mode to active mode only when data need to be uploaded. The state classification in normal operation mode is shown in Figure 5. After the ED sends data to the GW, the GW will send an acknowledgment frame (ACK) within two receive windows. The ED should open the first receive window (RX1) within a specified time _RECEIVE_DELAY1_ after sending the data, using the same communication parameters as when sending the data packet. If no data are received in the first window, the ED should open the second receive window (RX2) within a specified time _RECEIVE_DELAY2_, using the slowest data rate to receive the acknowledgment frame. If the data frame is not successfully transmitted due to a collision, the ED will not receive an ACK within the two receive windows and will then enter sleep mode. After a while, the ED will wake up again and repeat the normal operating state. A diagram of the ED's failed data transmission state is shown in Figure 6.
## 2 UAV in Wireless Communication Networks
### Use Cases of UAV Wireless Networks
#### 2.2.3 UAV Aggregator  
UAV aggregator flies around the sky to disseminate (collect) information to (from) the distributed wireless devices such as sensors. Data should be delay-tolerant, which allows the traveling of data within a given latency. Monitoring for public safety or agriculture is an example [51]. The minimization of power consumption is a key challenge to prolong the lifetime of UAV-enabled networks for IoT applications and M2M communications [48].