esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we proposed a new, neural network-based WBM coloring scheme called Bin2 Vec to preserve the relationship between different bin codes to better understand WBMs and find significant bad wafer patterns. Bin2 Vec maps a scalar bin code onto a three-dimensional continuous vector in order to assign a unique set of RGB values to the bin code. In addition, we also built a CNN-based WBM classification model to automate the bad wafer classification process, which is done manually by engineers. The experimental results showed that it is easier to discover significant WBM patterns with Bin2 Vec than with the random RGB method. In addition, the proposed CNN model with Bin2 Vec yielded high classification accuracy regardless of the product type.  
Despite the favorable experimental results, there are some limitations in the current study, which lead us to future research directions. First, although it was confirmed that Bin2 Vec helped identify meaningful bad wafer patterns, it can be more efficient that the significant bad WBM patterns are automatically detected and grouped. This, in turn, raises the following questions: (1) Is this WBM good or bad? (2) If it is bad, to what type of WBM pattern does it belong? It is possible for WBM patterns to change over time. Therefore, it can be more helpful if a continuous WBM monitoring system is constructed.  
[PERSON] initiated the research idea and carried out the experiment. He also wrote the draft of the paper. [PERSON], [PERSON] and [PERSON] support the experiment. [PERSON] wrote and finalized the paper.  
This research was supported by Basic Science Research Pro-gram through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03930729) and Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2017-0-00349, Development of Media Streaming system with Machine Learning using QoE (Quality of Experience)). This work was also supported by Korea Electric Power Corporation (Grant number: R18 XA05).  
The authors declare no conflict of interest.  
Figure 17: Visualizing bad wafers based on t-SNE and random RGB.
## 3 Results
### Corrosion Rate  
The corrosion rates for TSA specimens with one damage (damage area 4.6-18.3%) showed scatter in the early stages of immersion with values in the range of 0.05-0.15 mm/year in the initial stages (Figure 4a). However, the scatter lessened with time of exposure and the corrosion rate decreased reaching values <0.02 mm/year after a month. The scatter in the data was reduced significantly after a few months of exposure and the specimens reached calculated corrosion rates <0.005 mm/year after testing for 250 days. The corrosion rate values obtained in the first few days clearly indicate that the damage area has some effect, primarily due to the dependence of the corrosion kinetics on the relative areas of anode and cathode. Unsurprisingly, the specimen with larger damage area (18.3%) shows a higher corrosion rate of aluminium compared to the specimen with smaller damage area (4.6%).  
The calculated corrosion rate for specimens with different number and size of damage (with same estimated total area \(\sim\)9-10%) showed similar trends (Figure 4b). The disparity in the calculated corrosion rate was not as substantial as with the specimens with different damage areas. However, the calculated corrosion rates were not too different and the values reduce to <0.01 mm/y within 3 months of testing. The corrosion processes stabilised with time and this was reflected in decreased corrosion rates. The values reached \(\sim\)0.005 mm/year after 250 days of testing (Figure 4b). The initial reduction in the corrosion rate at the start was expected to be due to the presence of a passive air-formed oxide film. This film subsequently dissolves in CI- solution, thus increasing the corrosion rate. A further reduction in corrosion rate occurred when aluminium oxide/hydroxide forms on the TSA coating and calcareous deposits predominantly form on the damage area.  
Figure 3: Measured open circuit potential (OCP) of TSA specimens showing the effect of damage size (**a**), and distribution (**b**). The measurements were carried out in synthetic seawater (ASTM D1141) at 25 \({}^{\circ}\)C.  
The change in the trend observed at a couple of time points was due to failure in the heater. However, the laboratory where the test was carried out was at 21 \({}^{\circ}\)C. The change in the corrosion rate to values below 0.01 mm/y after a few months and below 0.005 mm/y after a year of exposure shows that the thermally sprayed coating was being consumed at a very low rate after the initial period, and the reduction of the corrosion rate was probably due to the formation of protective aluminium oxide/hydroxide and associated calcareous deposits (predominantly in the damage area).
## 2 Materials and Methods
### Methods
#### 2.3.2 Pearson Correlation  
Pearson correlation is considered a standard method to analyze the relationship between two variables. It can be expressed as follows:  
\[r=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(y_{i}-\overline{y} \right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}}\sqrt{\sum_ {j=1}^{n}\left(y_{j}-\overline{y}\right)^{2}}} \tag{2}\]  
where \(x_{i}\) and \(y_{i}\) (\(i=1\), \(2\),......, \(n\)) are the time series for two variables; \(\overline{x}\) and \(\overline{y}\) are the multi-year average values of two variables; and, \(r\) is the correlation coefficient. \(r>0\) indicates a positive correlation between two variables, whereas \(r<0\) suggests a negative correlation. The absolute value of \(r\) can represent the closeness of two variables; the larger the \(r\), the closer the two variables are and vice versa.  
Several factors affect the distribution and variation of GPP in both temporal and spatial dimensions. Meteorological elements and phenological parameters are widely considered as influencing factors and verified in many regions [40; 57; 58; 59]. Apart from meteorological and phenological factors, we also considered FPAR, representing the structure and the adaptive capacity of vegetation, as a factor. Pearson Correlation analysis was conducted between the above factors and GPP during the period of 1982-2015 at a pixel resolution to assess the influence of individual environment drivers on vegetation dynamics.
# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 5 Results and Comparisons
### C2C Distance Comparison
The comparison of the point clouds made with images acquired from the smartphone and the model generated from the NIKON data shows that the variations are minimal and that the models are almost the same. The only exception results from the model created through the SCANN3D app; in this case, the incompleteness of the model is not due to the performances of the smartphone camera but, rather, to the algorithms exploited by the application itself and to the computational performances of the smartphone. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Software/Dataset** & **Min [m]** & **Max [m]** & **Mean Distance [m]** & **Std Deviation [m]** \\ \hline AMP/50 images & 0.000 & 0.005 & 0.0012 & 0.0011 \\ AMP/25 images & 0.000 & 0.005 & 0.0009 & 0.0010 \\ AMP/12 images & 0.000 & 0.005 & 0.0007 & 0.0008 \\ VSFM/50 images & 0.000 & 0.005 & 0.0009 & 0.0010 \\ VSFM/25 images & 0.000 & 0.005 & 0.0014 & 0.0009 \\ SCANN3D/25 images & 0.000 & 0.005 & 0.0027 & 0.0016 \\ SCANN3D/12 images & 0.000 & 0.005 & 0.0017 & 0.0016 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The statistical results from the distance comparison between the smartphone point clouds and the reference point cloud generated with the NIKON D800E images.
## 5 ESEDG Comparison with Existing Routing Protocol
### Performing Assessment
#### 5.3.2 Impact of Path and Transmission Loss Analysis  
During the the AUV journey, a common underwater scenario might be that the transmission and path are disturbed. During the data collection process, the AUV is monitoring the GNs and MNs as data is loaded. The AUV is surrounded by MNs, so the path between the AUV-AUV communication may be disturbed. To evaluate such a situation, we compare our proposed technique with the base scheme AEDG (AUV path are used), SEDG (saleable path are used), ALP (path location indicator is present), AEEDCO, and AEEDCO-A. Path planning and trajectory are analyzed, and the DSG-DGA (sensor and AUV data gathering mechanism are used) are compared. The path loss is measured in dB, and it is only 50 dB for 50 to 60 nodes, while for 300 plus nodes, it decreases further by 10 dB and reaches only 4 dB for ESEDG. Meanwhile, for other techniques, such asAEDG, it reaches 58 dB, and for SEDG it further increases to 65 dB. SEDG has a very long trajectory to complete the path; therefore, it can gradually increase. As for the concern about the location indicator, ALP goes to a maximum value of 72 dB which is slightly increased for AEEDCO-A to 2 dB, and DSG-DGA has 11 dB.. Path loss comparison between ESEDG and other techniques is shown in Figure 6.  
In Figure 7, the transmission loss analysis has the same value with respect to path loss analysis as described earlier. Transmission loss was as follows: for DSG-DGA 68 dB, for AEEDCO 71 dB, for AEEDCO-A 74 dB, and for ALP 70 dB. These values lie between DSG-DGA and AEEDCO, respectively. Similarly, SEDG and AEDG have 60 dB to 65 dB for transmission loss analysis, while the proposed technique outperformed all with only 46 dB.  
Figure 5: Analysis of E2E delay with baseline schemes.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### Field Test
As shown in Figure 27, a total station was used to synchronously measure the deformation of the same measured point in the tunnel every 1 h, while the system conducted real-time monitoring during construction as shown in Figure 28.  
In the field test, a large number of data were obtained, and part of the system measurement results corresponding with the station measurement results are presented in Table 7.  
Figure 28: System measurement. Figure 26: (a) Measured points; (b) image acquisition module mounted on measured points. Figure 27: Total station measurement. According to Table 6, the monitoring system and total station measurement results are close, which indicates that the monitoring system has good accuracy, stability, and repeatability in a field application.
# Supporting Heterogenous Traffic on Top of Point-to-Multipoint Light-Trees +
## 2 Optical Layer Supporting P2P and P2 MP Traffic
CAPEX reduction comes as a result of decreasing the number of required transceivers. Moreover, OCSs and SCs can be created, modified, and eliminated dynamically, which provides the needed flexibility of the connections as per traffic demands.
###### Abstract  
The assessment of forest carbon storage plays a crucial role in forest management and ecosystem exploration, enabling the evaluation of forest quality, resources, carbon cycle and management. The Global Ecosystem Dynamics Investigation (GEDI) satellite provides a means to accurately measure these various forest vertical structure parameters by penetrating the forest canopy. However, the distribution of the footprint along the orbit track is heterogeneous and discontinuous, preventing the acquisition of spatially distributed carbon storage formation at the county level. Consequently, this study integrated GEDI and Landsat 9 data to estimate _Quercus aquifolioides_ carbon storage in Shangri-La. By applying the Kriging interpolation to previously pretreated footprints, surface information from the GEDI L2B footprints was obtained. At the same time, Landsat 9 vegetation indices and band reflectance were extracted to analyze the correlation with the carbon storage of _Quercus aquifolioides_ samples. Then, three methods (support vector machine, bagging, and random forest) were used to create a carbon storage estimation model for Shangri-La. The research results showed that (1) among the models for the selection of GEDI footprint parameters based on semi-variance, the optimal model of the digital_elevation_model was the spherical model, while the best model of percentage tree cover from the MODIS data (modis_treecover) and the foliage height diversity index (fhd_normal) was the exponential model. (2) Analyzing the thirty-three extracted independent variable factors correlated with the carbon storage of _Quercus aquifolioides_ showed that the top five variables with the highest correlation were digital_elevation_model, modis_treecover, fhd_normal, DEM, and band 1 (B1). (3) After variable selection, the R\({}^{2}\) = 0.82 and RMSE = 11.92 t/hm\({}^{2}\) values of the _Quercus aquifolioides_ carbon storage estimation model established via random forest were obtained, and its evaluation precision was superior to that of the support vector machine method and bagging regression. The carbon storage of _Quercus aquifolioides_ was primarily in the range of 8.22-94.63 t/hm\({}^{2}\), and the mean value was 42.44 t/hm\({}^{2}\), while the total carbon storage was about 5,374,137.62 t. The findings from this paper illustrated the feasibility of obtaining carbon storage data on a county scale by combining GEDI LiDAR data with Landsat 9 optical data. The results also suggested a new perspective for combining GEDI L2B data with other remote sensing images to estimate other forest structure parameters.  
GEDI; LiDAR; Landsat 9; carbon storage; inversion +
Footnote †: journal: _Sustainability_
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 1 Introduction
### Contribution and Organization  
In this work, we employ the Double DQN algorithm to predict service content for LEO satellites. The proposed method aims to improve the cache hit rate of LEO satellites. Moreover, as the cache hit rate increases, the frequency of content requests within the network decreases, thereby reducing the likelihood of communication congestion in the entire network. Additionally, it can decrease the computational resources consumed via the system for content re-encoding and decoding. Within this framework, the main contributions of this paper are as follows:  
* We propose a three-layer architecture integrating cloud, fog, and edge computing. The fog layer can provide services to edge users through its own content or content from neighboring nodes while also accessing content from the cloud, thereby achieving synergistic effects among cloud, fog, and edge computing.
* We utilize a reinforcement learning-based approach to train Double DQN agents and conduct simulation experiments based on synthetic data, providing an optimal solution for satellite caching schemes.
* We established a simulation experiment verification scheme and compare it with multiple methods to validate the effectiveness of the proposed approach in satellite edge caching strategies.  
The rest of this paper is organized as follows: in Section 2, we present the network model of the cloud-fog-edge three-layer architecture and introduce the caching mode. Section 3 introduces the Double DQN method adopted in this study. Section 4 describes conducted simulation experiments and analyzes the results based on the proposed architecture and method. In Section 5, we summarize the entire work and provide future research prospects.
## 2 Materials and Methods
### Experimental Setup  
In October 2023, an experiment with the vehicle-mounted emergency communication system using BeiDou RSMC for firefighting in forest areas without a public network was carried out at the Experimental Forestry Farm of Maover Mountain, Harbin City, Heilongjiang Province. The experiment simulated the whole process of a fire from onset to extinguishing, and a firetruck equipped with the vehicle-mounted emergency communication system completed the firefighting operation. At the end of the experiment, the reliability of the system was analyzed.  
The LF1352 JP tracked firefighting vehicle was used for the experiment. The vehicle was manufactured by Songjiang Tractor Co. in Harbin, China, and consists of a power system, a crawler chassis, a control system, an obstacle removal device, a hydraulic winch, a water cannon and water tank, and other equipment [41]. The LF1352 JP has the functions of firefighting, transporting feed, winching for self-rescue and other rescues, etc. It is one of most commonly used firetrucks for forest fires in northeastern and northern China [42].  
Figure 3: Signal processing flow of satellite communication network terminal.  
The experiment uses the BeiDou-2 two-way civil smart card. Different user categories of this smart card have different definitions for registering service frequency ranges and communication levels [43]. In the practical application scenario of this system, the personnel at the remote command center observe the data trends and determine further actions of the firefighting operation based on the transmitted data information, and this process accounts for more than 90% of a complete work cycle. Therefore, the BeiDou-2 two-way civil smart card, with a service frequency of 60 s/time and a message length of 848 bit/article, was selected for testing in this experiment.  
The smart card was installed in the card slot of the satellite communication network terminal, and the vehicle-mounted emergency communication system using BeiDou RSMC for fighting fires in forest areas without a public network was installed on each part of the LF1352 JP firetuck, and its assembly structure is shown in Figure 4a.  
Role A is classified as the firefighting site operator and Role B is the remote command center operator. The LF1352 JP crawler-type firetuck is equipped with a local self-organizing network terminal and a satellite communication network terminal; the nodes of the local terminal are mounted to each part of the firetuck (Figure 4b-d), and the satellite terminal is mounted to the top of the driver's cab (Figure 4e).
## 2 Materials and Methods
### Validation Plan  
The study was carried out at the Food Microbiology Laboratory of the Istituto Zooproff-lattico Sperimentale of Sicily, Palermo (Italy), according to the validation process explained by ISO 16140-2:2016 [15]. According to ISO 16140, we demonstrated that the results obtained with the alternative method (LAMP kit) were comparable (at least equivalent) to the results obtained with the reference method. Following the validation protocol described by ISO 16140-2:2016 [15], we did a comparative study of the alternative method with the corresponding reference method, conducted by the Food Microbiology Laboratory of the Istituto Zooprofilattico Sperimentale of Sicily, Palermo (Italy). Validation determined the following parameters: relative limit of detection (RLOD), inclusivity, exclusivity and a method comparison study including relative accuracy (AC), relative specificity (SP), and sensitivity (SE). Examining the samples both with the alternative (LAMP kit) method and the reference method, the parameters of AC, SP, and SE were calculated as follows:  
\[\text{AC}=(\text{PA}+\text{NA})/\text{N}\times 100\% \tag{1}\]  
\[\text{SP}=(\text{NA})/\text{N}_{-}\times 100\% \tag{2}\]  
\[\text{SE}=(\text{PA})/\text{N}_{+}\times 100\% \tag{3}\]  
where PA = positive agreement; NA = negative agreement; N = total number of samples; N\({}_{-}\) = number of negative samples; and N\({}_{+}\) = number of positive samples.  
The RLOD was calculated as follows:  
\[\text{RLOD}=\text{LOD50}\text{ alternative method}/\text{LOD50}\text{ reference method} \tag{4}\]  
where LOD50 = the limit of detection (LOD) of 50% = the smallest amount of analyte that can be detected but not quantified with a 50% probability. Therefore, LOD50 is the level of detection for which 50% of tests give a positive result [19].  
RLOD, SP, and SE were performed against the reference method ISO 6579-1:2017 [6] \"Microbiology of the food chain-Horizontal method for the detection, enumeration and serotyping of _Salmonella_-Part 1: Detection of _Salmonella_ spp.\"
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 7 Case Study
### Full Deployment  
This platform can be used in several aquatic environments. Table 2, maps the sensors to the use in these several environments. The system was designed to retrieve the frequently measured parameters, described in Table 2, that aquaculture operators collect with manual probes or in fixed points. Other sensors could be used and integrated in this platform. However, this solution was developed with earth ponds or earth tanks in mind, where water circulation is forced (one in and one out), and therefore, does not require a water flow sensor. Moreover, there is no system, that continuously measure toxins and pollutants, available on the market. The normal process to measure those parameters includes the collection of a water sample, followed by the analysis in a lab to screen for toxins and or pollutants.  
The biggest advantage of this system is the ability to dynamically map the area instead of depending on fixed measuring points that can mislead the technicians in charge and the alarm systems. However, this solution has limitations in indoor environments: as a result of the use of a GPS sensor in the navigation system, the mobile nodes can only be used in outdoor environments. Moreover, due to the size of the USVs, this platform is not suitable for open water problems.  
There are three types of platform's setups:  
1. Static deployment with a Buoy;
2. Team deployment **without** missions' data update;
3. Team deployment **with** missions' data update.  
For the setup number one, we need to deploy a LoRa DTS connected to the server, along with the Buoy. For the other setups, the platform has to have a LoRa DTS and a MC and can have a Wi-Fi DTS. If we only have a LoRa DTS, then the missions' data cannot be updated, entering the setup number two. If we have at least one Wi-Fi DTS reachable to the team, then the platform is in setup number three and can receive new missions' descriptions.  
Figure 14: Mobile app.  
\begin{table}
\begin{tabular}{c c c c} \hline  
In scenarios where the missions' descriptions do not change often and the data being measured has a low periodicity, the inclusion of a Wi-Fi DTS may not be the most efficient compared to only having a LoRa DTS. For example, in AlgaPlus ([[https://www.algaplus.pt/](https://www.algaplus.pt/)]([https://www.algaplus.pt/](https://www.algaplus.pt/)))' earth ponds (with large dimensions), the most suitable platform setup is a team of USVs with a LoRa DTS connected to the server and a MC to initiate the mission on a specific time. Moreover, the Buoy, when connected to the battery, it immediately starts to measure the variables periodically. However, the USVs only start to move and measure after receiving a mission, through a request to update missions' data, or another mission.
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 3 Results
### Product Validation  
The validation results indicate robust performance for the classification of most habitat categories, particularly for distinguishing _Cystoseira_ s.l. from rocky reef habitats without coverage by such canopy algae. The Overall Accuracy of the classification was 95%, confirming the reliability of the mapping framework. Accuracy metrics for the supervised  
Figure 11: Zoomed map of the central/northern part of the region.  
Figure 12: Fraction cover map of _Cystoseira_ s.l.  
classification are presented in Tables 4 and 5, and the fractional cover analysis metrics in Table 6.