esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The coarse registration method of point cloud based on SIFT feature points and the fine registration method based on the K-D tree improved ICP algorithm were proposed. The results showed that they were superior to other algorithms (such as SURF feature points, Harris feature points, and LM improved ICP algorithm) when taking the Stanford Bunny as an example, and have high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, where research results showed that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall, and artificial excavation.  
Figure 8: Registration results.  
Figure 9: Slope deformation measurement results.  
**Author Contributions:** [PERSON] and [PERSON] initiated and developed the concepts related to this research work; [PERSON] and [PERSON] proposed the point cloud registration method; [PERSON] and [PERSON] proposed the improved point cloud comparison method; [PERSON] wrote the paper draft under [PERSON]'s guidance. All authors discussed the results and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Acknowledgments:** This research was supported by the National Natural Science Foundation of China (Grant No. 51808327) and the Natural Science Foundation of Shandong Province (Grant No. ZR2019 PEE016).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Protocol  
### Problem Statement  
VANETs use pre-existing proactive and reactive routing protocols like ad hoc on-demand distance vector (AODV), destination-sequenced distance-vector (DSDV), dynamic source routing (DSR), and optimized link state routing (OLSR) [6]. Path selection in these protocols is based on various parameters such as hop count, end-to-end reliability, energy, etc. The major problem in data routing among vehicles is the link failure as a result of node mobility, particularly with high speeds. It is desired to propose a protocol that considers this mobility problem and also to avoid the network congestion to alleviate the traffic load in the network and therefore enhance its performance.
###### Abstract
Subsequently, statistical analysis and spectral analysis methods were employed in an attempt to decipher the complex signals embedded in the data. However, these methods often fell short of effectively separating valuable MS from the sea of data that includes numerous irrelevant noise signals [15]. The complexities of the monitoring environment and the dynamic nature of the collected signals present significant challenges. These traditional approaches require operators to possess a deep understanding of geophysics and signal processing to establish precise identification criteria. However, due to the inherent variability in signals, achieving consistent and accurate identification has proven to be a formidable task. Additionally, traditional methods of identifying microseismic events have become time-consuming and inefficient as monitoring datasets continue to grow [16]. Nonetheless, the rise of machine learning, particularly deep learning approaches, has shown remarkable accuracy in recognizing and classifying microseismic occurrences. Deep learning algorithms, when compared to standard methods, have overcome constraints and offer intriguing possibilities for complicated monitoring situations [17]. While various studies have examined the use of machine learning in MMS, there has yet to be a comprehensive study of the combination of deep learning and image recognition in this context. We undertook this study to fill this void and conduct targeted research. Our goal is to explore the complexities of using deep learning to microseismic event waveform picture detection and classification, providing insights into its potential and limitations. The significance of this work is that it focuses on the investigation of cutting-edge deep learning applications in microseismic monitoring. We provide a complete overview of the available methods and a comparative study of their strengths and limitations through an in-depth examination of picture recognition. Furthermore, this study serves as a vital reference for researchers, practitioners, and stakeholders in geological engineering and earth sciences. It lays the groundwork for future advancements in geological hazard prediction and monitoring, ultimately enhancing safety in mining operations. As deep learning continues to advance, its seamless integration into microseismic analysis promises valuable insights and practical applications, bolstering geological safety in mining activities. The rest of the article is organized as follows: Section 2 presents the materials and methods and provides detailed descriptions of the microseismic monitoring system, microseismic monitoring data, and waveform characteristics. We also summarize the representative literature on microseismic event identification and outline various identification methods. Section 3 focuses on the analysis and discussion of the results. We classify existing methods, conduct a SWOT analysis of deep learning methods, compare existing models, and explore future development opportunities and challenges. Finally, we conclude this study by summarizing the findings and proposing possible directions for future research.
## 2 Dataset Description  
### Study Site  
Our study site is the \"Domaine du Merle\", an experimental farm of 450 hectares located in southeastern France (center: 43.64\({}^{\circ}\)N, 5.01\({}^{\circ}\)E, Figure 1). Within this farm, 150 hectares (52 parcels) are irrigated grasslands for hay production. The produced hay is certified (with the French label \"AOP\") due to specific environmental factors and irrigation practices that ensure a high-quality floristic composition [69].  
The study site is characterized by a Mediterranean climate, with a rainy season between September and November and an average cumulative rainfall between 350 mm and 800 mm [15]. The evaporation rate can reach 10 mm/day during the summer season due to high temperatures associated with dry and windy conditions. Hourly temperature and precipitation data acquired by a meteorological station installed at the study area were available. Figure 2 shows the mean daily air temperature recorded in 2013 during the remote sensing acquisitions (\(\mathrm{Tmean}=14.4\ ^{\circ}\mathrm{C}\), and \(\mathrm{Tmax}=35.4\ ^{\circ}\mathrm{C}\)).  
The soil has a mean retention capacity with concentrated vegetation roots in the upper 30 cm [70]. Moreover, the top soil is characterized by an absence or low presence of pebble (15%-20% of pebble stone at most) [69]. The top soil texture of the irrigated plots is a loam with a depth varying between 30 cm and 80 cm, depending on the plot age (between 10 years and three centuries) [69, 71].  
Figure 1: Location of the study site in France (Domaine du Merle). Black polygons delineate the sampled plots. **(a)** TerraSAR-X image was acquired on 24 April 2013 in RGB colors (R: HH, G: HV, B, HH-HV). **(b)** SPOT-4 image acquired on 22 April 2013 in RGB colors (R: NIR, G: R, B: G).  
The plots were leveled with a very gentle slope that allows for surface irrigation by gravity (border irrigation). The total flow rate available for the farm (between 150 and 300 L/s) allows for the irrigation of one or two parcels simultaneously, the largest parcels being split into two or more subplots. Water is applied between March and September via canals, which bring water at the highest extremity of the subplots over a few hours. Water flows by gravity down to the lowest extremity of the plots. When the waterfront reaches 90% of the plot length, the water supply is stopped. The waterfront continues to flow and infiltrate until the tail end of the plot is reached. At the lowest side, excess water is evacuated through a drainage channel.  
A rotation is applied so that all the parcels can be irrigated when necessary, approximately every 10 days on average. The plots are harvested three times a year, in May, June and September.
# A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
## 4 Evaluation
### Evaluation of the B-REP Mechanism  
The B-REP algorithm implementation uses the SPF algorithm in conjunction with any type of LS routing protocol, although we used OSPF for the pilot implementation. The B-REP SPF algorithm is applied to calculate the alternative shortest path used in the event of a failure. The main advantage of the B-REP FRR mechanism is that the algorithm implements an efficient and standardized way to mark an alternative path using the B-S field.  
In the B-REP we use the bit-string to exactly define hop-by-hop routing behavior, where due to B-S we can precisely define the whole alternative path of routers chain. This feature might be used for an administrator to manually configure the alternative route in the event of a need.  
Figure 16: Output from the OMNeT++ console.  
Figure 15: Processing of B-REP packet (R5).  
Possibility of the explicitly defined alternative path can not only define the backup path close to a failed element in the network but also across the whole area which statistically can also be damaged.  
Compared to other existing FRR mechanisms, the B-REP mechanism brings the new approach of defining alternative path (bit-string) into the FRR area and provides advantages in comparison with other mechanisms such as custom alternative path, easy implementation into existing architecture because of bit-string and 100% repair coverage. A summary of the advantages and disadvantages of the B-REP mechanism is given in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Source/Destination** & **Name** & **Destination Address** \\ \hline  
3 & UDPBasicAppData-185 & 192.168.3.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Output from the OMNeT++ simulation.  
A more exact comparison of the selected features with other existing FRR solutions is presented in Table 8 below. In this table we compare possibility of repairing all failures within the network, precomputing, modification of packets and support of custom alternative path.  
The biggest time-consuming operation during the FRR process is the detection of link or node failure. For these purposes, the bidirectional forwarding detection (BFD) protocol is used. The BFD protocol is standardized by IETF in RFC 5880. Usually, the detection of the failure by BFD protocol is less than 30 ms depending on the timer settings. Another part of rerouting is switching to an alternate FRR path.  
Existing proactive FRR solutions calculate alternative path in advance. Therefore, the rerouting time of the specific FRR mechanism is minimal because the alternative path is prepared and switchovers to that path immediately. B-REP algorithm calculates alternative path in advance, therefore its speed of recovery after link or node failure depends only on the time of failure detection, as is characteristic of proactive mechanisms.
## 3 Simulated Results and Error Analysis
### Reconstruction Error Comparison Principle  
In this paper, the average relative error (ARE) and average root-mean-square error (ARMSE) are used to evaluate the quality of the temperature and concentration field reconstruction. The specific mathematical formulae are expressed using Equations (13) and (14) [33; 34].  
\[\text{ARE}=\frac{1}{n}\underset{r=1}{\sum}^{n}\left(\frac{1}{N}\underset{j=1 }{\sum}^{N}\left|\frac{x_{rec,j}-x_{sim,j}}{x_{sim,j}}\right|\right) \tag{13}\]  
\[\text{ARMSE}=\frac{1}{n}\underset{r=1}{\sum}^{n}\left(\sqrt{\underset{i=1}{ \sum}^{N}\left(x_{rec,j}-x_{sim,j}\right)^{2}}/\sqrt{\underset{i=1}{\sum}^{N} \left(x_{sim,j}\right)^{2}}\right) \tag{14}\]  
where \(n\) is the number of simulations conducted per group, and \(N\) is the number of reconstruction parameters to be solved. \(x_{rec,i}\) and \(x_{sim,j}\) denote the reconstruction value and the true value (temperature or concentration), respectively. In the simulation, \(n\) was 100.
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 3 Results and Discussion
### Grain Size Analysis  
Table 2 compares the grain sizes of the as-cast Mg/Mg-20 GMB hybrid composite at the shell, interface, and core regions. The grain size at the shell, core, and interface regions were similar, considering the standard deviation. At the shell region, the grains are well-formed and equiaxed. At the interface region, the grains are generally smaller in size, and some are distorted and elongated in shape. At the core region, the grains are usually equiaxed in shape.  
Grain refinement is critical for the strengthening of engineering alloys. Normally, the yield strength of a material is contrarily corresponding to the square root of grain size, as stated by the Hall-Petch equation:  
\[6\mathrm{=6_{o}+Kd^{(-1/2)}} \tag{1}\]  
where 6 is the yield stress, 6o is the yield stress of a single crystal, K is a constant, and d is the grain size [29]. The value of K depends upon the number of slip systems and is larger for HCP metals as compared to face-centered cubic (FCC) and body-centered cubic (BCC) metals [30]. Therefore, HCP metals possess higher strength sensitivity to the grain size. In a metal matrix composite, the secondary reinforcing phase can have a remarkable impact on the grain size of the matrix. The grain refinement is caused by the various nucleations of the Mg phase on secondary particles, and that will restrict the development of the magnesium crystals due to the presence of rigid secondary phases.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 3 Bacteria-Encoded STKs in Phage-Bacteria Interactions
### Streptomyces PglW: Component of Pgl/BREX Defense [32]  
_Streptomyces coelicolor_ STK PglW is linked to the phage growth limitation (Pgl) and BREX anti-phage systems, which block phage \(\Phi\)C31 DNA replication. In Pgl, PglW's kinase activity is essential for signaling during phage infection, likely phosphorylating downstream targets like PglZ (a phosphatase-like antitoxin) and PglX (a toxin DNA methyltransferase) to execute phage restriction. This system uniquely combines toxin-antitoxin regulation with DNA modification: PglX methylates phage DNA during the first-round infection, marking progeny phages for attenuation in subsequent infections of Pgl+ host cells. Unlike classical Restriction-Modification systems, Pgl tolerates transient modification failures but restricts phage spread through phase variation [48; 49; 50; 51], which is regulated by a hypervariable G-tract in _pglX_ gene to balance defense costs and evade phage counteradaptation.
## 4 Trust Model
### Indirect/Recommended Trust
#### 4.3.2 Discounting Combination  
Assume two vehicles x and z where x trusts z, denoted by \(\mathbf{w}_{z}^{x}=[b_{z}^{x},\ d_{z}^{x},\ n_{z}^{x},\ e_{z}^{x}]\), for the purpose of judging the trustworthiness of y. In addition z has trust in y, denoted by \(\mathbf{w}_{y}^{z}=[b_{y}^{z},\ d_{y}^{z},\ n_{y}^{z},\ e_{y}^{z}]\). Vehicle x can then derive its trust in y by discounting z's trust in y with x's trust in z, denoted by \(\mathbf{w}_{y}^{xz}=\mathbf{w}_{z}^{x}\bigtriangleup\mathbf{w}_{y}^{z}=[b_{ y}^{y,z},\ d_{y}^{xz},\ n_{y}^{y,z},\ e_{y}^{yz}]\). Four elements of \(\mathbf{w}_{y}^{xz}\) are as follows:  
\[\begin{split}& b_{y}^{xz}=b_{z}^{x}b_{y}^{z},\\ & d_{y}^{xz}=b_{z}^{x}d_{y}^{z},\\ & n_{y}^{xz}=1-b_{z}^{z}b_{y}^{z}-b_{z}^{x}d_{y}^{z}-e_{y}^{z}, \\ & e_{y}^{xz}=e_{y}^{z}.\end{split} \tag{5}\]The discounting operation \(\triangle\) increases uncertainty as it comes from building one vehicle subjective opinion on other vehicle's objective assessment.
## 3 Geophysical Survey
### Magnetic Survey  
In this study, field work was carried out with a GEM GSMP-35G v8.0 portable potassium magnetometer (GEM SYSTEMS, Markham, ON, Canada). This potassium magnetometer obtains high data quality due to its high sensitivity (0.0003 nT), minimum reading error, resolution (0.0001 nT) and high precision (+/\(-\)0.05 nT). It has the capacity to take up to 20 readings per second, which provides a remarkable sampling density while walking [37]. Nineteen continuous profiles in the NW-SE direction were made in walking mode, with a length between 250-350 m and a separation between profiles of 10 m. Given the stability of the equipment used, measurements were taken every 50 ms. The georeferencing was carried out with a NovAtel GPS device mounted on the top of the mobile device.  
To calculate the main magnetic field, the International Geomagnetic Reference Field (IGRF, 13 th) calculator of the British Geological Survey ([[https://geomag.bgs.ac.uk/data_service/home.html](https://geomag.bgs.ac.uk/data_service/home.html)]([https://geomag.bgs.ac.uk/data_service/home.html](https://geomag.bgs.ac.uk/data_service/home.html)), accessed on 24 March 2023) was used. Regarding the effect of external fields, diurnal correction was carried out. For this, data from the San Pablo Observatory of the National Geographic Institute ([[https://www.ign.es/web/ign/portal/gmt-area-geomagnetismo](https://www.ign.es/web/ign/portal/gmt-area-geomagnetismo)]([https://www.ign.es/web/ign/portal/gmt-area-geomagnetismo](https://www.ign.es/web/ign/portal/gmt-area-geomagnetismo)), accessed on 24 March 2023) were used.  
To obtain the total field magnetic anomaly, the value of the main magnetic field (obtained with the IGRF model) was subtracted from the contributions of the external field (diurnal correction) and the values measured with the magnetometer at each point. Daytime correction and despiking were carried out using the software GEMLink version 5.4. The software Surfer was used for data interpolation and 2D representation. The data were analysed with different interpolation methods (kriging, cokriging, natural neighbour, nearest neighbour, etc.). Ultimately, kriging was selected as the interpolation method. The corresponding variogram showed the best fit with the maximum distance.
# An IVTIFN-TOPSIS Based Computational Approach for Pipe Materials Selection
###### Abstract  
This paper proposes a multicriteria decision-making (MCDM) approach, coupling intervaled trapezoidal intuitionistic fuzzy number (IVTIFN) with the technique for order preference by similarity to ideal solution (TOPSIS) to facilitate the selection of pipe materials. Their integration can maximize the advantage in better expressing decision maker's preference on the proposed evaluation criteria by using a bounded limit instead of an exact value, to rank material alternatives based upon their functional, economic and environmental attributes. To reduce possible information overlapping resulted from the criteria, Mahalanobis distance is incorporated into IVTIFN-TOPSIS to improve the selection results. An illustrative example is provided to verify the proposed approach and demonstrate its practical application, in which four common alternative materials, including carbon steel, galvanized steel, polyvinyl chloride (PVC) and high-density polyethylenes (HDPE), are subject to precise selection to determine their adaptability in waste-water piping. The selection result indicates that the plastic materials are superior to the metal materials. In particular, HDPE is the optimal material alternative for waste-water collection and transport.  
materials selection; pipe material; multicriteria decision making; IVTIFN; TOPSIS; Mahalanobis distance
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 3 The Proposed Method
### Cluster Construction and Maintenance
#### 3.1.2 CM Joining/Leaving  
Every vehicle is equipped with a CM table designed to handle CM information in case it becomes a CH later. The CM table comprises two essential components for each CM entry: (i) a list of content with names and timestamps that denote the reception time and (ii) a cluster-keep-alive timer. This timer is refreshed upon receiving beacons from the CM and triggers the removal of the CM entry if it expires.  
Each vehicle initially assumes the role of a CH. A CH without any CMs in its cluster is identified as an Orphan Vehicle (OV). When an OV receives a beacon from another CH, it transmits a beacon claiming its joining into the CH's cluster as a CM if specific conditions are satisfied as follows:  
* Both the beacon sender and receiver move in the same direction.
* The distance between the two vehicles is less than or equal to \(d_{cluster}\).
* The relative velocity between the two vehicles is less than or equal to \(v_{cluster}\).  
When a CH receives a beacon from a CM for the first time, it adds the CM's information to its CM table. When the CH receives a beacon from the CM again, it resets the CM's cluster-keep-alive timer. When a CM receives a beacon from its CH, it resets its cluster-keep-alive timer.  
Figure 1: The overview of the proposed method.  
When one of the following conditions is satisfied, a CM leaves its cluster, and its CH considers that the CM has left its cluster.  
* The cluster-keep-alive timer expires at either a CM or a CH.
* The distance between a CH and a CM is more than \(d_{cluster}\).
## 4 The Scheduling Algorithm Based on GA
### Carrier Aircraft Dispatch  
In the process of optimizing the objective function, when the maximum time is optimal, other related targets can also obtain the optimal value at the same time.  
\[Min(max(T_{n}^{j})) \tag{24}\]  
In the formula, \(T_{n}^{j}\) represents the takeoff completion time of aircraft \(j\) in the \(n\)-th dispatch. The corresponding constraints are as follows. The symbolic variable description is shown in Table 2.  
(1) For any aircraft, only one takeoff position can be selected for the ejection takeoff operation. A\({}_{j}\) is \(j\) process set for the aircraft.  
\[\sum_{i=1}^{M_{k}}b_{i,j}^{k}=1\forall j\in N,k\in A_{j} \tag{25}\]  
(2) For a takeoff position in any state, one aircraft at most can take off.  
\[\sum_{j\in\mathrm{N}}b_{i,j}^{k}\leq 1\ \forall i,\forall k \tag{26}\]  
(3) The completion and departure time constraints of any aircraft after processing on a machine at any takeoff position.  
\[\left\{\begin{array}{l}C_{i,j}^{k}(t)=S_{i,j}^{k}(t)+P_{i,j}^{k}\\ D_{i,j}^{k}(t)\geq S_{i,j}^{k}(t)+P_{i,j}^{k}\end{array}\right. \tag{27}\](4) Priority constraints on the same aircraft before and after the operation process. For an aircraft, only after the completion of the previous operation can the next operation be carried out.  
\[\left\{\begin{array}{l}S^{k}_{i,j}(t)\geq\mathcal{C}^{k-1}_{i,j}(t)\\ S^{k}_{i,j}(t)=D^{k-1}_{i,j}(t)\end{array}\right.k>1,i\in M_{k},\forall j\in N \tag{28}\]  
(5) Takeoff safety constraints. During the ejection takeoff process, all takeoff positions can take off at most one aircraft at a time.  
\[\sum_{j=1}^{n}\sum_{i=1}^{M_{k}}b^{k}_{i,j}\leq 1 \tag{29}\]  
(6) Takeoff safety wake interval constraint. The takeoff interval between any two aircraft \(j_{p}\), \(j_{q}\) from the same catapult during the takeoff phase must not be less than the minimum interval \(\Delta T_{1}\).  
\[\left\{\begin{array}{l}S^{k}_{i,j_{q}}(t)+M(1-a^{k,i}_{j_{q},j_{p}})\geq C^ {k}_{i,j_{p}}(t)+\Delta T_{1}\\ S^{k}_{i,j_{p}}(t)+Ma^{k,i}_{j_{q},j_{p}}\geq C^{k}_{i,j_{p}}(t)+\Delta T_{1} \end{array}\right. \tag{30}\]  
(7) The takeoff interval between any two aircraft \(j_{p}\) and \(j_{q}\) on different catapults \(i\) and \(i^{\prime}\) must not be less than the minimum interval \(\Delta T_{2}\).  
\[\left\{\begin{array}{l}S^{k}_{i^{\prime},j_{q}}(t)+M(1-a^{k,i^{\prime}}_{j_ {q},j_{p}})\geq C^{k}_{i,j_{p}}(t)+\Delta T_{2}\\ S^{k}_{i,j_{p}}(t)+Ma^{k,i}_{j_{q},j_{p}}\geq C^{k}_{i^{\prime},j_{p}}(t)+ \Delta T_{2}\end{array}\right. \tag{31}\]