esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 5 Results and Analysis
### Image Data
#### 5.2.1 Storage  
With different amounts of remote sensing image data inserted into MongoDB and PostgreSQL, the average storage time with the two databases can be computed through multiple experiments with Formula (1), which is chosen for the comparison in this experiment. The experimental result is shown in Figure 7.  
As can be observed from Figure 7, the time required to store the image data of both MongoDB and PostgreSQL increases along with the increasing volume of the remote sensing image data, while the storage performance of the MongoDB database is relatively more stable. In addition, when storing the same image data file, PostgreSQL consumes more time than MongoDB. The time difference between the two databases is obvious, especially when the inserted image data amount is large. For example, when inserting the data \"S3A_OL_1_EFR_2016*\", the time that PostgreSQL takes is one point six times longer than that of MongoDB. To sum up, MongoDB performs better than PostgreSQL in storing large remote sensing image data.
# uDMA: An Efficient User-Level DMA for NVMe SSDs +
## 4 Design of uDMA
### Overview of uDMA  
Figure 2 shows the framework of uDMA. We adopted the statically pinning memory strategy for small I/O requests equal to or less than 4 KB. Thus, we allocate and pin three memory blocks during the library initialization process. Users can set the size of memory blocks according to their experience. These memory blocks remain pinned throughout the life cycle of the application. Because of NVMe SSDs' fast reading and slow writing characteristics, we use one piece of memory for reading requests and the remaining two blocks for writing requests. Our design is because small data I/O requests do not need to take up too much memory. If we maintain a pinned memory state throughout the application's life cycle, we do not waste memory, and we only need to spend one-time pinning memory.  
We designed a pinned memory pool for medium data I/O requests and proposed allocation and release algorithms. Unlike small I/O requests and big I/O requests, the copy and pinning memory costs for medium data are relatively high. The typical way to process these is to pin the memory of the corresponding size for user-level DMA operation for each I/O request. In the face of I/O-intensive applications, the pinned memory area cannot be reused, and then the processed I/O requests need to be pinned again, which introduces a lot of pinned memory overhead.  
For a big data's I/O request, which is greater than or equal to 4 MB, we adopted the strategy of dynamically pinned memory. This strategy dynamically allocates and pins memory blocks when the application reads from and writes to NVMe SSDs through user-level DMA. We unpin and release the pinned memory block immediately after the user-level DMA operation. We cannot use the static pinned memory strategy for I/O requests for big data because these requests take up more memory. If we occupy memory for a long time, it can have a destructive impact on other processes. Another reason is that the pinned memory time is less than the big data's copy time.  
Figure 2: The framework of uDMA.
# Path Algorithms for Contact Sequence Temporal Graphs +
## 3 Results
### Results for Cyclic TRG Algorithms  
In this section, we present the results of running our algorithms for cyclic TRGs. We measured the average time using 100 different randomly selected start vertices as conducted in the experiments of [8].  
The speedup obtained by TRG_Ours is computed using the formula ((time taken by TRG_Wu)/time taken by TRG_Ours). These speedups are shown visually in Figure 2. The speedups were computed using the non-rounded run times and then rounded to decimal points.  
As can be seen, TRG_Ours is faster than TRG_Wu on 11 of the 12 datasets for the fastest paths, the min-hop paths, and the foremost paths problems. TRG_Ours is faster than TRG_Wu on all 12 datasets for the shortest paths problem. The speedup obtained by TRG_Ours on the fastest paths problem ranges from 0.63 to 6.81 and the speedup obtained for the minhop problem ranges from 0.76 to 7.76. The speedup ranges for the shortest paths and foremost paths problems are \([1.46,9.15]\) and \([0.60,6.52]\), respectively. On average, the speedup obtained by TRG_Ours over TRG_Wu is 3.63 on the fastest paths problem, 2.86 on the minhop problem, 2.95 for the shortest paths problem, and 2.85 for the foremost paths problem, across the 12 datasets.  
We do not compare our cyclic TRG algorithms with the OSE algorithms of [8] as the latter are limited to contact sequence temporal graphs in which no edge weights 0, and so these OSE algorithms work only on instances whose TRGs are acyclic. Hence, the OSE algorithms are more appropriately compared to the algorithms for acyclic TRGs. While [PERSON] et al. [8], have stated that their OSE algorithms could be extended to handle the case when edges weigh 0, such an extension would most likely increase the OSE algorithms' run time.
## 3 Results
### Result of Typhoon Fanapi from an S-band Radar of Taiwan  
Typhoon Fanapi formed as a tropical storm on 14 September 2010, southeast of Taiwan. It intensified into a typhoon on 16 September and started moving northwest. By 18 September, Fanapi had strengthened into a category three typhoon heading toward Taiwan with maximum sustained wind speeds of 54 m/s. Fanapi made landfall early on 19 September, at which time it changed direction and started moving southwest. Later that day, it weakened to a tropical storm that lasted 9 h, before becoming a tropical depression on 20 September and totally dissipating on 21 September.  
The dealiasing of typhoon observations is always a challenge for any algorithm because of the high wind speed of the typhoon vortex. The phenomena of a large area and multiple aliasing may happen easily. The dealiasing algorithms often fail especially when the typhoon eyes is close to the radar station. The radial velocity field will exhibit asymmetry on both sides of the \"zero\" velocity line. Figure 3 shows the Fanapi Typhoon case observation by the Hualien radar at 23:53 (UTC), 18 September 2010, just before the typhoon made landfall. Figure 3a,c display the raw radial velocity field observed at the elevation angle of 3.3\({}^{\circ}\) at a 14.6\({}^{\circ}\), respectively. The new algorithm can correctly change the aliased velocity in both angles (Figure 3b,d). As mentioned in module 2, the GVAD  
Figure 3: Radial velocity fields observed by the Hualian radar station in the Taiwan radar network at 23:53 (UTC), 18 Sep 2010. (**a**) Original radial velocity at an elevation angle of 3.3\({}^{\circ}\), (**b**) dealiasing result at an elevation angle of 3.3\({}^{\circ}\), (**c**) original radial velocity at an elevation angle of 14.6\({}^{\circ}\), and (**d**) dealiasing result at an elevation angle of 14.6\({}^{\circ}\). The spatial scales of the pairs of images at different elevations are different.  
technique found out the first reference radial near 330\({}^{\circ}\) on a 14.6\({}^{\circ}\) elevation angle. The correct first reference makes sure the rest of the modules were executed successfully.
## 3 The Detection and Isolation Method of Data Anomalies
### The Indicators
#### 3.2.1 The Proportion of Error Points  
The proportion of error point \(r\) refers to the ratio of points whose detection function exceeds the threshold to the total sampling points in a single diagnosis period. The calculation formula of \(r\) is defined as follows:  
\[t(i)=\left\{\begin{array}{rl}1&,\ F_{\rm Di}-T_{\rm D}>0\\ 0&,\ F_{\rm Di}-T_{\rm D}\leq 0\end{array}\right. \tag{27}\]  
\[r=\frac{\sum\limits_{i=1}^{N}t(i)}{N} \tag{28}\]  
Figure 5: (**a**) Detection function of different anomalies; (**b**) detection function of different anomalies in a single diagnosis period.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 2 Background
### Range-Doppler Algorithm  
The RDA, an algorithm originally created for radar signal processing, was developed with a primary focus on maritime surveillance. An RDA can process signals received from radar to obtain high-quality images, furnishing detailed and precise information about the detected objects. Consequently, the RDA is an important part of radar technology, facilitating reliable and efficient object detection and tracking in various applications, and has been widely studied [10; 11; 12; 13; 14].  
Figure 2 shows the operation flow of the RDA for SAR image generation. The RDA consists of range compression, RCMC, and azimuth compression, and it is efficient by performing calculations in the frequency domain for both range and azimuth. Range compression proceeds with FFT, followed by range-matched filtering and IFFT in the range direction. Matched filtering involves multiplying the received signal by the reference signal, enhancing the desired signal, and minimizing the noise. Azimuth compression follows a process similar to range compression but includes an additional RCMC operation after azimuth FFT. Range cell migration (RCM) refers to the continuous change in the range between the radar sensor and the target during target detection using SAR. As these changes contribute to low-quality SAR images, the RDA corrects the RCM through RCMC using sinc interpolation to obtain high-quality SAR images.  
The RDA is characterized by the inclusion of multiple FFT and IFFT operations on 2D images in both the range and azimuth directions. The radix-2 FFT algorithm, first introduced in 1963, is widely utilized in signal processing as a foundational method [32]. In this study, the radix-2 algorithm was selected for its fundamental yet highly flexible approach to FFT computation. Additionally, it is more suitable than radix-4 or radix-8 for supporting variable FFT lengths, as it allows lengths that are powers of 2, ensuring both versatility and efficiency. The butterfly structure at the radix-2 FFT algorithm receives two inputs and performs arithmetic operations, such as addition, subtraction, and multiplication to generate two outputs. The number of butterfly operations increases proportionally as the length \(N\) of the FFT. Because of the substantial volume of raw input data required for the RDA in SAR image generation, performing FFT operations with extensive lengths, denoted as \(N\), becomes imperative. This translates into numerous butterfly operations during the FFT process. However, FFTs with numerous butterfly operations require a significant number of memory requests, resulting in extended execution times. Hence, the acceleration of FFT operations is paramount for enhancing the overall performance of the RDA.
## 2 Materials and Methods
### Fifth Phase: Statistical Analysis and Design of Experiments  
Kolmogorov-Smirnov (KS) tests were performed to check normality (\(p>0.05\)) and the Levene analysis (LA) was used to evaluate the equality of variances (\(p>0.05\)) of the experimental data. The EC and EO processes were optimized for maximum COD removal using a fractional orthogonal design L\({}_{9}\) (3\({}^{4}\)) to minimize the number of runs and standard error of the model (nine experiments with three replicates) [52] with response surface methodology (RSM).  
RSM was used to evaluate the effects of the variables of the processes and thus determine the conditions of greater removal of COD [53]. The statistical program Minitab 14 (Minitab LLC.(r), State College, PA, USA) was used for the analysis of experimental data, including fractional orthogonal design, response surface methodology, and analysis of variance (ANOVA). The values of the independent variables in the EC and EO systems were established based on data from the literature and preliminary experimental tests. The experimental conditions are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Electro-Coagulation**} \\ \hline
**Experiment** & **I (A)** & **Stirring (RPM)** & **pH** \\ \hline E1 & 2.5 & 0 & 6.0 \\ E2 & 2.5 & 100 & 7.0 \\ E3 & 2.5 & 200 & 8.0 \\ E4 & 4.0 & 0 & 7.0 \\ E5 & 4.0 & 100 & 8.0 \\ E6 & 4.0 & 200 & 6.0 \\ E7 & 5.5 & 0 & 8.0 \\ E8 & 5.5 & 100 & 6.0 \\ E9 & 5.5 & 200 & 7.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental conditions of electro-coagulation and electro-oxidation treatments.  
Figure 3: Experimental setup for electro-coagulation and electro-oxidation processes.
# Architectural Patterns for Blockchain Systems and Application Design
## 4 Pattern Description
### Transactional View Patterns
#### 4.3.1 On-Chain Pipe-Filter Pattern  
**Summary:** This pattern addresses concerns regarding how data are transferred from a client to a shared ledger while retaining data integrity and confidentiality.  
**Context:** A software application utilizes blockchain services as a distributed ledger.  
**Solution:** Data in the blockchain are processed in the form of transactions. A transaction is a single instruction constructed and cryptographically signed by a client external to the scope of the blockchain. The process of adding new transactions to the ledger is a Pipe-Filter architecture, as shown in Figure 7. A transaction passes several filters (validators/miners) from the client (source) to the ledger (sink). The number and types of filters differ among blockchain systems. Initially, a transaction must be cryptographically signed at the client application using the user's private key. The signed transactions then enter a pending pool. They are then filtered by validators/miners. Subsequently, a certain number of valid transactions are bundled into a block. The block size determines the number of transactions included within a block. The block size also differs between the blockchain systems. Finally, a valid block is added to the ledger and synchronized with all participants.  
**Known uses:** Transactions of Ethereum-based applications are submitted to Ethereum's pending transaction pool and then validated by miners, who are rewarded for generating new blocks. Transactions of Hyperledger Fabric-based applications are validated by endorsing, ordering, and committing peers. Transactions of Steem-based applications are submitted to Steem's pending transaction pool and then validated by witnesses, who are rewarded for producing new blocks.  
**Related patterns:** The generated blocks can be linked as in the chain-of-blocks pattern.
## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
In wrapper methods, an attribute subset is employed in the training of a system prototype that will use those attributes. Once the predecessor model has obtained a result, further decisions are made regarding the inclusion or exclusion of attributes from the candidate subset. BFS, LFS and GSS are the three popular wrapper methods used in this analysis. These methods have been combined with WrapperSubsetEval which acts as a predefined search algorithm in WEKA. The combination of these three components generates a reduced attribute set. Once the reduced attribute set is generated through the application of the filter and wrapper approach, the next phase is the classification process where the reduced chronic disease datasets are subjected to classification using the Decision Tree algorithm. The Decision Tree algorithm has been tested on diabetes, breast cancer, and heart disease datasets. The authors have analyzed the impact of the filter wrapper approach on the attribute selection process and its impact on the efficiency of chronic disease classification. Three filter methods have been used in this study, including the Information gain method, the Correlation feature selection method and the Chi-square method. The ranker algorithm was the predefined search algorithm in this approach. In the case of the wrapper approach, WrapperSubsetEval was the used attribute evaluator while the best first search, the linear forward selection method and the greedy step wise search method were used as search algorithms. This section presents the results obtained after implementing the proposed model in the WEKA software. This section is divided into two subsections. Firstly, the attribute significance graph has been presented to highlight the importance of individual symptoms upon the application of both filters and wrappers. The attributes are ranked on the basis of their relevance and priority. On the basis of the importance of the symptoms, the attribute significance graphs have been developed to identify the relevant and irrelevant symptoms in the dataset. Every attribute is assigned a score level out of 10 on the attribute significance graph. The \(X\)-axis represents the attributes of a chronic disease dataset while the \(Y\)-axis denotes the score level of the individual attributes that have  
Figure 5: The Work-flow Model of Wrapper-Filter Methodology. been obtained after the application of an attribute evaluator. According to the results shown in the graphs, the less relevant symptoms have been eliminated from the dataset. The obtained results are discussed throughout this section.
# Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
## 5 Decision Support Systems for Smart Beehives
### Generic Architecture for Smart Beehives  
Our solution is inspired by a standard API (Application Programming Interface) for managing contextual information (NCSI-LD API) [48]. This standard enables near real-time access to information from many different sources (not only IoT data source). NCSI-LD open specification released by ETSI defines the context information model and the API to produce, consume, and subscribe to context information. In our solution, different sensors are used to measure and quantify the indicators of the beehives. The most used are weight and temperature. The weight can be used to see the increase or decrease in the weight of the hives. It makes it possible to monitor the production of honey and to trigger alerts if the variation in weight is significant. Weight can also indicate the best feeding and processing strategy applied to a hive, particularly comparing weight over time and between different hives/apiary. Outdoor weather conditions change over time, and the temperature, humidity, and wind speed must be taken into consideration. All these different types of data are used to optimize bekeeping decisions. One of the major problems in beekeeping is the varroa destructor mite. It is the most destructive enemy of the Western honeybee (Apis mellifera). The varroa is a serious threat to bee health. A bee colony infested with mites will typically die off in these regions within three years. Until now, it has been difficult to deal with this problem. Indeed, counting varroa mites is interesting when quantifying how many bees are infected per hive. However this task remains very complicated because it is often carried out manually. Our platform offers an automatic counting service based on photos of frame bees. This service is based on deep learning methods to process images and detect objects. In fact, two neural networks for object detection and image segmentation are used. Faster RCNN is used for detecting bee locations; for each bee in the image it predicts a bounding box surrounding the object. Figure 5 illustrates the network outputs. To locate varroas, the U-NET takes as input an image containing one bee and segments it into two regions: varroa and background. Figure 6 illustrates the U-NET outputs. Estimating the infestation level is performed by combining these two detections; first the frame image is  
Figure 4: Global view of the decision support systems for smart beehives.  
given to the system, then the bees are identified, and after that the presence of varroa is predicted for each bee. The infestation percentage is calculated by dividing the number of infected bees by the number of bees. This system needs data for learning; in fact, bee images were retrieved from project partners, from camera photos, and from the Internet. Thanks to annotation tools, the images were annotated by bounding boxes for bees and ellipses for varroas.
## 3 Research Findings and Discussion
### Tensile Test Results
#### 3.1.3 AZ91-Carbon Fiber; Bonded, Bolted and Bolted-Bonded Single Lap Plates
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Maximum Tensile** \\ **Stress (MPa)** \\ \end{tabular} & \begin{tabular}{c} **Maximum Tensile** \\ **Force (N)** \\ \end{tabular} & \begin{tabular}{c} **Maximum Tensile** \\ **Elongation (\%)** \\ \end{tabular} & \begin{tabular}{c} **Maximum Tensile** \\ **Elongation (mm)** \\ \end{tabular} & \begin{tabular}{c} **Damage Type** \\ \end{tabular} & \begin{tabular}{c} **Standard Deviation** \\ \end{tabular} \\ \hline AZ91-CF-bonded & 34.41 & 5162.25 & 3.33 & 0.83 & Local rupture & 1.23 \\  & & & & in adhesive & & \\ \hline AZ91-CF-bolted & 34.69 & 5203.81 & 10.33 & 2.58 & \
\begin{tabular}{c} AZ91 Crushing in \\ the hole, \\ Dealamination \\ Breaking in the bolt \\ \end{tabular} & 1.08 \\  & & & & &
\begin{tabular}{c} AZ91 Crushing in \\ the hole, breaking \\ in the bolt, local \\ rupture in adhesive \\ \end{tabular} & 1.03 \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Damage type, standard deviation and tensile test values of AZ91-CF bonded, bolted, and bolted- bonded single lap plate joints. 3.1.4. AZ91-AZ91, AZ91-Carbon Fiber and Carbon Fiber-Carbon Fiber; Bonded, Bolted and Bolted-Bonded Single Lap Plates  
When Figure 9 and Table 6 are examined, in Figure 9a it can be seen that the maximum tensile force is 5323.75 N and the tensile elongation in AZ91-AZ91 plates is 0.80 mm in the CF-CF bonded samples plate. In Figure 9b, it can be seen that the maximum tensile force is 5266.73 N in CF-CF bonded plates and the tensile elongation is 2.58 mm in the AZ91-CF bonded plate. In Figure 9c, it can also be seen that the maximum tensile force is 5786.65 N in CF-CF bonded plates and the tensile elongation is 3.19 mm in bolted adhesive samples. Figure 9: Force-elongation diagram of AZ91-AZ91, AZ91-Carbon fiber and carbon fiber-carbon fiber (**a**) bonded (**b**) bolted and (**c**) bolted-bonded single lap plate joints. In line with these data, it was determined that maximum tensile stress and maximum tensile elongation were seen in bolted- bonded samples, and the highest tensile stress value occurred in AZ91-CF bolted- bonded samples while the maximum elongation value occurred in CF-CF bolted- bonded samples. When considering the tensile force and elongation of the adhesive joints under tensile test, it can be seen that it receives values in the bonded < bolted < bolted- bonded order. No peeling effect or plastic deformation was observed on the bonded surfaces. Plastic deformation was observed in bolted and bolted bonded samples and delamination damage was observed in the holes in the carbon fiber materials. It can be seen that bolt holes are deformed along the loading direction in all samples and fiber extraction and delamination occur around the hole in carbon fiber samples.