esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we proposed a range-free localization scheme for UASN with a mobile beacon that provides the potential locations with weighting factors according to residuals and estimates the location of a sensor node through the weighted mean of the potential locations. Because LoMoB localizes a sensor node based on the weights of the potential locations, it improves the localization accuracy and is more tolerant to errors in the estimation of the distance between projected beacon points and sensor nodes. Simulation results show that LoMoB significantly improves the localization accuracy of LDB, especially in underwater environments that cause irregularities in the radius of the circle formed by the transceiver's beam and the location error of a mobile beacon. Our simulations demonstrated that LoMoB is more robust with respect to errors in distance estimation than LDB.
## 3 Results and Discussion
### Mechanisms of Phosphate Removal
The precipitated products from different initial pH values  
Figure 8: SEM images of phosphate precipitation in aluminum–air batteries (**a**) and EDS results (**b**). Figure 7: SEM results of the Al electrode in this study, (**a**) the original electrode, and (**b**) the electrode after the operation. were examined by XRD to reveal the crystalline structure (Figure 9). Three diffraction peaks were observed in the flocs. The second peak was in the range of 23-33\({}^{\circ}\), and the third peak was at approximately 41\({}^{\circ}\), which was likely within the spectrum of Al(OH)\({}_{3}\). The crystal plane of AlPO\({}_{4}\) was also found in the second peak range [45; 46; 47]. However, the absence of sharp peaks in the XRD results indicated that the flocs had a poor crystalline structure, and that many of the precipitates were in an amorphous state, which was similar to a previous report on aluminum coagulants [47]. Combining the precipitates, XRD results, and the aluminum species constructions with different pH values, it can be concluded aluminum hydroxide adsorption was the main mechanism for removing phosphate. A schematic diagram of the phosphate removal process is summarized in Figure 10.
## 3 Numerical Examples
### Example 2  
Figure 9 shows a model with four static bubbles in liquid nitrogen. The radii of these four bubbles from bottom to top are 1.3 mm, 1.5 mm, 1 mm, 1.7 mm. The electric potential results of bubbles in liquid nitrogen are obtained by XFEM and CFEM and shown in Figure 10.  
In Figure 10, \(x\) and \(y\) represent the coordinate value of the node, and _level_ represents the electric potential at the node. The difference of electric potential between CFEM and XFEM at the same node is very small. Statistically, the maximum relative error is 0.29%, and the average relative error is 0.03%.
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 4 Deterministic Transaction Execution
### Pipeline Optimization  
Firstly, requesting the read set from the remote shard may incur a complete RTT (round-trip time). Secondly, synchronous blocking is required to wait for the receipt of \(f+1\) read sets from the corresponding shard to ensure the validity of the read sets. At this synchronization point, it is necessary to wait for communication between nodes, thereby introducing delays in the overall progress. Therefore, NeuChain+ uses pipeline technology to overlap communication with execution, reducing the communication latency of 0.5 RTT while avoiding blocking waiting.  
Algorithm 1 describes the optimized read sets transmission process. During the read sets transmission process, we define the shard that generates a read set as the reading shard and the shard that requires a read set to generate a write set as the writing shard. When executing cross-shard transactions with remote read-write dependencies, the reading shard directly sends the read set to the writing shard (Lines 1-3). Note that epoch \(n\) must be attached to the read set when sending to ensure that both the reading and writing shards are synchronized. In the writing shard, a hash table \(RR\) (Line 5) is initialized to cache the read sets sent by other shards in the structure of \(<\)\(account,balance>\). After receiving \(f+1\) of the same read set, the writing shard stores it into \(RR\) and wakes up the pending transaction (Lines 6-8). When the writing shard executes the corresponding transaction, it can directly read from the hash table according to the required \(account\) (Lines 9-11). When the corresponding \(account\) in \(RR\) is empty, it proves that no verifiable read set has been received yet, and the transaction will be pending and other transactions will continue to be processed (Lines 12-13).  
```
1:The reading shard state database snapshot \(DB[n-1]\), the cross-shard transaction \(T\) with remote read-write dependencies of epoch \(n\)
2:The read-write set \(\{T.RS,T.WS\}\)\(\triangleright\) In reading shard
3:\([T.RS,T.WS]\)\(\leftarrow\) execute operation of \(T\) base on \(DB[n-1]\);
4:Send \(T.RS\) with epoch \(n\) to all Block Servers in writing shard;
5:functionReadSetReceiver(\(T\))\(\triangleright\) In writing shard
6:Initialize reading result \(RR\);
7:if receive the same \(T.RS\) over \(f+1\)then
8:\(RR[T.RS.key]=T.RS.value\);
9:\(T\_\)notify();
10:functionExecute(\(T\))\(\triangleright\) In writing shard
11:if\(RR[account_{req}]\)!= NULL then\(\triangleright\) if \(T.RS\) received
12:\(T.WS\leftarrow\)execute operation of \(T\) base on \(DB[n-1]\) and \(RR[account_{req}]\);
13:else
14:\(T.\)wait();
```  
**Algorithm 1** Pipelining read sets transmission.
# Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
## 3 Preliminaries on GAN for Anomaly Detection
### GAN-Based Unsupervised Normal Distribution Learning  
Generative Adversarial Networks (GANs), developed and introduced in 2014, are a powerful class of neural networks that are used for learning the latent distribution of high-dimensional data. In GANs, there are two core components: generator \(G\) and discriminator \(D\), both of which is a deep neural network as multilayer perceptron, convolutional neural network, etc. In the model, the generator \(G\) attempts to learn the latent distribution \(P_{data}(x)\) of the real data \(x\), and generates a fake sample \(G(z)\) that satisfies the latent distribution based on a random noise \(z\). The discriminator \(D\) takes the generated data \(G(z)\) and the real data \(x\) as input, and is responsible for binary classification to identify whether a sample is \(G(z)\) or \(x\). In short, the goal of \(G\) is to confuse the fake with the real, and the goal of \(D\) is to distinguish between the real and fake. Therefore, the generator \(G\) and discriminator \(D\) constitute a min-max game. After multiple alternating trainings, the generator \(G\) and discriminator \(D\) gradually converge, and then the generator \(G\) has learned the latent distribution of the real data \(x\).  
The objective function of GAN model is as follows:  
\[\min_{G}\max_{D}V(D,G)=\underset{x\sim P_{data}(x)}{E}[\log(D(x))]+\underset{z \sim P_{Z}(z)}{E}[\log(1-D(G(z)))] \tag{1}\]  
where the \(G\) tries to minimize the distance between \(G(z)\) and \(x\), and the \(D\) tries to maximize the distance between \(G(z)\) and \(x\).
# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 6 Conclusions  
We presented TraLDA, a distributed framework aimed primarily at improving fairness in forwarding among nodes in mobile social networks. In TraLDA, we introduce a novel calculation of node popularity, a function of inherent and social-relations popularity. We have demonstrated that TraLDA achieves this fairness, reducing the GINI index of BubbleRap and SimBet, but at the expense of a slight increase of delivery delay of these routing schemes. Given that mobile social networks are assumed to be delay-tolerant, the increased delivery latency is a reasonable trade-off given the enhanced network traffic fairness and lower resource use in the most popular nodes.  
For future work, we believe that TraLDA can be incorporated with buffer congestion control to further improve traffic load balancing across network nodes and simultaneously avoid congestion mainly in the most popular nodes.  
Figure 13: Performance evaluation of SimBet and SimBet-TLDA (\(\xi\) = 0.8) for the Sassy mobility scenario. Similar with the case in Reality, here SimBet-TLDA also improves the traffic fairness in the network (indicated by the reduced GINI index), while keeping other delivery performances as high as those of SimBet.  
**Author Contributions:** Conceptualization, B.S.; methodology, B.S.; software, B.S. and V.A.; validation, B.S.; formal analysis, B.S.; investigation, B.S.; resources, B.S. and V.A.; data curation, B.S.; writing--original draft preparation, B.S.; writing--review and editing, B.S. and V.A.; visualization, B.S.; supervision, B.S.; project administration, B.S.; funding acquisition, B.S. and V.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by the Sanata Dharma University Research Grants 2021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The Java implementation of TraLDA is available online at: [[https://github.com/soelistijanto/TraLDA](https://github.com/soelistijanto/TraLDA)]([https://github.com/soelistijanto/TraLDA](https://github.com/soelistijanto/TraLDA)) (accessed on 14 June 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Modeling
### Ideal Dynamics of the DRC Antenna  
The DRC antenna uses stepping motors and harmonic drive gears as actuators. The dynamics of the DRC antenna can be expressed by following equation:  
\[\begin{split}&\left(i_{a2}\text{sin}^{2}\phi_{2}+i_{a3}\text{ cos}^{2}\phi_{2}+\mu\text{d}_{2}^{2}\text{cos}^{2}\phi_{2}\right)\ddot{\mathbf{ \phi}}_{1}=-k_{1}\left(\phi_{1}-\psi_{1}/n_{1}~{}\right)-c_{1}\phi_{1}=\tau_{ \phi_{1}}\\ &\left(i_{a_{1}}+\mu\text{d}_{2}^{2}\right)\ddot{\mathbf{\phi}}_{2}=- k_{2}\left(\phi_{2}-\psi_{2}/n_{2}~{}\right)-c_{2}\phi_{2}=\tau_{\phi_{2}} \end{split} \tag{12}\]  
where \(k_{i},c_{i}\) and \(n_{i}(i=\phi_{1},\phi_{2})\) represents the rigidity, damping coefficient and reduction ratio of the harmonic drive gears. \(\psi_{1},\psi_{2}\) represents the rotation angle of the stepping motor which corresponds to the antenna joint angles \(\phi_{1},\phi_{2}\). \(\mathbf{\tau}_{\phi}=\left[\tau_{\phi_{1}}~{}~{}\tau_{\phi_{2}}\right]^{T}\) represents the torque acting at the antenna joints.
## 1 Introduction
To solve the above problems, in regions with lots of information, such as Lake Van, it is necessary to determine the boundaries of the particular lake area and the surface size of the lake by accurately computing the appropriate threshold value by automated means. The advent of the GEE has revolutionized the conventional method of processing remote sensing data. GEE is a cloud-based platform designed for the analysis of large geographical data [47]. This platform offers customers access to high-resolution satellite imagery data from satellitessuch as the Sentinel and Landsat. Additionally, it executes image preprocessing procedures on a cloud-based platform. In light of all these studies, the following knowledge can be gained:  
* Since Lake Van is the world's largest soda lake and the largest lake in Turkey, it is very important to protect its unique habitat. In order to protect this situation, it is very important to know the boundaries and water surface area of the lake in order to take the necessary measures. When the literature on this subject was analyzed, no study on the determination of the boundaries and water surface area of Lake Van was found. * The validity of the Otsu thresholding method was investigated in order to eliminate the problem of double thresholding in the Canny edge detection algorithm in large water bodies such as Lake Van and to determine the appropriate threshold automatically. * In large water bodies such as Lake Van, the performances of the SVM, RF, and CART machine learning algorithms used in the study were compared by determining the boundaries and water surface areas of the lake on the GEE platform. This is the first study in which Canny edge detection and Otsu thresholding methods and AWEI, NDWI, and MNDWI indices are included in the analyses to determine the water boundaries of Lake Van using CART, SVM, and RF methods (all analyses are performed in GEE). In order to determine the correct lake area boundaries, each image was preprocessed at the same level on the GEE platform (Section 2.2) and spectral indices were calculated after cloud, scale, and date filtering (Section 2.5). The Canny edge detection algorithm was calculated with the optimal threshold selected by the Otsu method (Sections 2.3 and 2.4). Finally, the accuracy assessment and performance of the model in extracting the surface boundaries of Lake Van were compared with the machine learning algorithms: CART, SVM, and RF methods (Sections 2.6 and 2.7). The results of all the methods used in the study show that this model shows that the boundaries of Lake Van can be extracted quite accurately (Section 3).
However, in the existing research, the majority of scholars have concentrated on the peeling of the nodules from the seabed, with less focus given to the transportation process of the nodules. During the transportation process, the nodules may not be delivered to the desired location as expected, thus resulting in a significant decline in the hydraulic collector's performance. Therefore, it is more accurate and comprehensive to evaluate the performance of the hydraulic collector as a whole by considering the two functions of collection and transportation at the same time. In addition, the performance of a hydraulic collector is influenced by numerous factors, including structural and working parameters. Therefore, a thorough and encompassing analysis is needed. Particularly, it is critical to determine which parameters deserve priority in the design and development process. The development of a new type of hydraulic collector with good collection performance is also of great research value. In this paper, a hydraulic polymetallic nodule collector with two acquisition nozzles and one transmission nozzle is studied. Firstly, the structural composition and operating principle of the hydraulic collector are analyzed and introduced. Subsequently, based on the solid-liquid two-phase flow method, a numerical model of the hydraulic collector is established, and the correctness of the established model is verified by experimental tests. Based on the established numerical model, the acquisition and transportation processes of this hydraulic collector are analyzed and their working mechanisms are explored. Finally, the influence of various structural parameters and working parameters on the collection efficiency of the hydraulic collector is investigated, and a parameter importance analysis is conducted. The work conducted in this study can provide a theoretical basis and design reference for the development of the hydraulic collector.
## 2 Numerical Approach
### Fluid Properties and Two-Phase Modeling
#### 2.3.3 Mixture Model  
Several approaches exist to model two-phase flows, such as the volume of fluid (VOF) method, the mixture model, the Eulerian model or the discrete phase model (DPM) among other models. [PERSON] et al. [19] already demonstrated the superiority of two-phase models over the single-phase one. The Eulerian, VOF and mixture models giving very similar results in their case, only the mixture model will be considered here due to its simplicity, stability and lowest computational costs required.  
The mixture model treats the nanofluid as a single fluid consisting of two strongly coupled phases. It defines the concept of phase volume fractions, which are continuous functions and their sum equals one. Each phase has its own velocity. The governing equations of the two-phase model are:  
* Conservation of mass: \[\
abla\cdot(\rho_{m}\vec{V}_{m})=0\] (15)
* Conservation of momentum: \[\rho_{m}\vec{V}_{m}\cdot\
abla\vec{V}_{m}=-\
abla P_{m}+\
abla\cdot(\mu_{m} \
abla\vec{V}_{m})+\rho_{m}g+\
abla\cdot(\sum_{k=1}^{n}\varphi_{k}\rho_{k} \vec{V}_{dr,k}\vec{V}_{dr,k})\] (16) where the mixture velocity, density and viscosity are respectively: \[\vec{V}_{m}=\frac{\sum_{k=1}^{n}\varphi_{k}\rho_{k}\vec{V}_{k}}{\rho_{m}}\] (17) \[\rho_{m}=\sum_{k=1}^{n}\varphi_{k}\rho_{k}\] (18) \[\mu_{m}=\sum_{k=1}^{n}\varphi_{k}\mu_{k}\] (19)
* The drift velocity of the \(k\)th phase writes: \[\vec{V}_{dr,k}=\vec{V}_{k}-\vec{V}_{m}\] (20)
* Conservation of energy: \[\
abla\cdot(\sum_{k=1}^{n}\varphi_{k}\rho_{k}\vec{V}_{k}H_{k})=-\
abla\cdot q _{m}-\tau_{m}:\
abla\vec{V}_{m}\] (21)
* Conservation of the volume fraction in nanoparticles: \[\
abla\cdot(\varphi_{np}\rho_{np}\vec{V}_{m})=-\
abla\cdot(\varphi_{np}\rho_ {np}\vec{V}_{dr,np})\] (22)* The slip velocity is defined as the velocity of a second phase (np: nanoparticles) relative to the primary phase (bf: base fluid): \[\vec{V}_{pf}=\vec{V}_{np}-\vec{V}_{bf}\] (23)
* The drift velocity is related to the relative velocity by: \[\vec{V}_{dr,np}=\vec{V}_{pf}-\sum_{k=1}^{n}\frac{\varphi_{k}\rho_{k}}{\rho_{ eff}}\vec{V}_{fg}\] (24)
* The relative velocity is evaluated through the following equation proposed by [PERSON] et al. [38]: \[\vec{V}_{pf}=\frac{\tau_{p}d_{np}^{2}}{18\mu_{bf}f_{drag}}\frac{(\rho_{np}- \rho_{eff})}{\rho_{np}}\vec{a}\] (25) where \(f_{drag}\) is the drag function calculated from [PERSON] and [PERSON] [39]: \[f_{drag}=\begin{cases}1+0.15 Re_{np}^{0.687}&Re_{np}\leq 1000\\ 0.0183 Re_{np}&Re_{np}>1000\end{cases}\] (26) with \(Re_{np}=(V_{m}d_{np})/\ v_{eff}\) and \(\vec{a}=\vec{g}-(\vec{v}_{m}\cdot\
abla)\vec{v}_{m}\).
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 5 Conclusions  
In this study, a lightweight temperature downscaling model with a two-layer CNN structure is used to achieve accurate prediction of the observed temperature at a single station in Macau from ERA5 reanalysis data.  
Figure 7: Comparison of loss values under different prediction time limits (**a**) for the current moment, (**b**) for the next 1 h, (**c**) for the next 2 h, and (**d**) for the next 3 h.  
The experimental results show that our model is far better than the traditional interpolation method in the instantaneous prediction (0 h); the \(RMSE\) of the model is 21% lower than that of Nearest interpolation and 31% lower than that of Linear and Cubic interpolation. Our model's stability is especially outstanding in multi-temporal prediction, and the RMSE of the next 3 h (+3 h) prediction is still better than the instantaneous results of all the interpolation methods, with the correlation coefficients of all the time periods higher than 0.97. Moreover, the error increases with the increase in prediction step, which is in line with the law of meteorological prediction.  
The excellent performance of this model provides a more reliable and accurate prediction tool for climate research, ecological environment monitoring, and other fields, and strongly promotes the research progress and practical application of related fields in a wider scope. In view of its excellent results in the region around Macau and the universality of the principle of spatial and temporal variations in meteorological elements, the model is expected to be extended to other regions, providing solid data support and technical guarantee for cross-regional decision-making and environmental management, and has a broad application prospect in future meteorological and geo-environmental studies.  
Conceptualization, H.K. and C.W.; data curation, H.K.; formal analysis, N.P. and C.W.; methodology, N.P., H.K., C.W., Y.D. and J.C.-H.L.; resources, H.K. and C.W.; software, N.P., H.K. and C.W.; validation, N.P., H.K. and Z.L.; writing--original draft, N.P., H.K. and J.C.-H.L.; writing--review and editing, N.P., H.K., Z.L., Y.D. and J.C.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities
### Feature Analysis
#### 2.3.8 Compatibility  
IPv6 is fully compatible with the existing Internet infrastructure, allowing seamless interoperation between a wide range of standard nodes. In contrast, BPv7 is specifically designed for delay-tolerant networking, and disruption management requires that every node involved in an intermittent link in the communication path implement its DTN-specific functionalities for the protocol to work.  
Another beneficial aspect of using the IP stack is the existence of network management protocols that, with proper profiling, can work for DTN and non-DTN nodes [22; 23]. Such a solution for the BP is still in development and will require a separate management process from the rest of the existing internet nodes.
## 5 Discussion and Research Findings
### Heatmap Analysis of Benefits and Challenges  
To provide a visual summary of the benefits and challenges discussed, a heatmap (Figure 6) is presented below. This heatmap illustrates the intensity and distribution of the various benefits and challenges associated with blockchain technology in higher education. It shows that while blockchain technology offers substantial benefits in areas like credential management, education administration, e-learning, financial support, and future applications, it also presents significant challenges. These include data immutability, implementation complexity, security and privacy issues, environmental impact, regulatory compliance, and scalability concerns. This balanced view underscores the multifaceted nature of blockchain's potential and the importance of addressing its drawbacks for successful integration into educational systems.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Description** & **Key References** \\ \hline Data Immutability & Difficulty in altering incorrect data & [7; 14; 21] \\ \hline Implementation Complexity & Requires thorough testing, infrastructure, and resources & [24; 43; 51] \\ \hline Security and Privacy & Risk of data exposure and system compromise & [7; 24; 53] \\ \hline Environmental Impact & High energy consumption and carbon footprint & [11; 40] \\ \hline Regulatory Compliance & Lack of consistent global regulations & [11; 35; 49] \\ \hline Scalability & Performance issues with increased transactions & [6; 40; 44] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of key challenges of blockchain in higher education.
# Key Issues on Integrating 5G into Industrial Systems
## 3 Typical Applications in Industrial Systems
### PLC Automated Production
#### 3.1.1 PLC's Working Principle  
PLC refers to a microcomputer system specifically used for industrial control, so its working principle is nowadays basically the same as computer systems. The CPU of PLC adopts the principle of time-sharing operation--executing one operation at a time and executing operations in chronological order. Programs in PLC are divided into two types: operating system programs and user programs. The CPU in PLC executes user programs and performs control functions in a cyclic scanning manner. Figure 2 shows the flowchart of the cyclic scanning working mode of PLC [34; 35].