esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 2 Materials and Methods
### Cybersecurity Vulnerability Evaluation
#### 2.2.5 Management Consoles
The use of User Sync and Tag Sync is instrumental to fine-grained AC and facilitates ABAC-style security. Apache Atlas provides a framework for managing metadata associated with data and processes. Software program hooks are installed on components of the ecosystem, e.g., the HDFS Name Node and YARN Resource Manager, to communicate information on the data to Atlas so that the metadata can be organized and stored based on the given relationships and types. These metadata can then be used by Ranger to define and enforce tag-based security policies. The definition and management of the metadata are necessary for tracking data provenance. Atlas is designed to be extensible. Thus, if a hook does not exist for a new component of the Hadoop ecosystem, it can be developed. Hooks are lightweight programs (e.g., script cURL commands) written to interface the tracked object types (e.g., data, databases, and programs) to the repository of Atlas metadata through a REST API. In addition to using pre-defined types, new metadata types can be defined, including complex types that inherit attributes from other types of metadata. The metadata can be classified with attributes to indicate their sensitivity, e.g., PII, and the classification can be propagated from metadata on the process input to those on the output. The lineage of an entity, an instance of a type of metadata, can be tracked by using this feature of propagation as the data are processed through a lifecycle. When we install Apache Atlas, we select H-Base to store the metadata and Solar to store the index, where this is the default configuration. Other core components include the graph engine that supports the relationships between entities. For example, the relations of columns and databases and files and folders are managed by the graph engine. The ingest/export features capture messages from the sources of metadata, create entities, and update events. A challenge in Apache Atlas is that nothing is performed automatically. One needs to launch the hooks, i.e., cURL commands, in scripts that run regularly on the component systems to crawl through the data store. Some hooks are available from the Apache Atlas repository for installation and configuration on the core components. For other data sources, one has to write hooks by using the cURL calls to register and tag the entities. In Figure 8, we show a simple entity (instance) of an HDFS file type (class) created in our experiment with a small number of attributes used as a basis for policies. Additional more complex examples of JSON files used to create entities and types in Atlas are
## 2 Results
### HS-SPME/GC-MS Analysis
#### 2.1.2 PDMS/DVB Fiber
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **Compound** & **CD 1** & **RI 2** & **S. romana** & **S. romana** & **S. romana** & **S. romana** & **S. montana** & **S. montana** \\  & & & & **K** & **B** & **M** & **P** & **J** & **M** \\ \hline
1 & Pentanal & & \textless{}900 & 0.02 & - & - & 0.03 & - & - \\
2 & Hexanal & & \textless{}900 & 0.02 & 0.03 & 0.12 & 0.10 & 0.01 & 0.02 \\
3 & (_E_)-Hex-2-enal & & & \textless{}900 & 0.02 & 0.03 & 0.10 & 0.06 & 0.03 & 0.05 \\
4 & \(\alpha\)-Thujene & & 936 & 0.55 & 0.41 & 0.79 & 0.58 & 0.07 & 0.04 \\
5 & \(\alpha\)-Pinene & C1\({}^{\prime}\) & 944 & 2.09 & 2.31 & 3.91 & 2.68 & 1.69 & 1.14 \\
6 & Camphene & & 960 & 0.05 & 0.04 & 0.10 & 0.06 & 0.02 & - \\
7 & Benzaldehyde & & 969 & 0.06 & 0.08 & 0.40 & 0.15 & 0.04 & 0.10 \\
8 & Sabinene & & 981 & 0.56 & 0.35 & 0.46 & 0.43 & 0.12 & 0.10 \\
9 & \(\beta\)-Pinene & C2\({}^{\prime}\) & 985 & 6.78 & 8.01 & 13.26 & 10.35 & 1.80 & 1.46 \\
10 & \(\beta\)-Myrcene & & 995 & 0.55 & 0.40 & 0.99 & 0.80 & 0.28 & 0.20 \\
11 & (_E_,_E_)-Hepta-2,4- & & & 1001 & - & 0.05 & 0.12 & 0.07 & 0.02 & 0.03 \\  & & dienal & & 1011 & 0.15 & 0.83 & 0.27 & 0.22 & 0.02 & - \\
12 & \(\alpha\)-Treplandrene & C3\({}^{\prime}\) & 1023 & 0.53 & - & 1.15 & 1.01 & - & - \\
14 & \(p\)-Cymene & C4\({}^{\prime}\) & 1032 & 0.81 & 0.16 & 3.10 & 1.57 & - & - \\
15 & Limonene & C5\({}^{\prime}\) & 1036 & 3.86 & 1.97 & 3.31 & 2.27 & 3.74 & 2.91 \\
16 & Benzyl alcohol & C6\({}^{\prime}\) & 1040 & 0.63 & 0.96 & 2.55 & 1.12 & 0.16 & 0.55 \\
17 & (_Z_)-\(\beta\)-coyrene & & 1043 & 0.03 & - & - & - & - & - \\
18 & Phenyleneacetaldehyde & & 1051 & 0.03 & - & - & - & - & 0.02 \\
19 & (_E_)-\(\beta\)-coyrene & & 1054 & 0.02 & - & 0.11 & - & - & - \\
20 & \(\gamma\)-Terpinene & C7\({}^{\prime}\) & 1066 & 2.53 & 0.11 & 8.96 & 4.49 & - & - \\
21 & Octan-1-ol & C8\({}^{\prime}\) & 1075 & 1.17 & 1.33 & 2.46 & 2.24 & 0.12 & 0.16 \\
22 & \(\alpha\)-Terpinolene & & 1093 & 0.11 & 0.07 & 0.19 & 0.14 & 0.01 & - \\
23 & Linalool & & 1103 & - & 0.86 & 0.25 & 0.22 & - & - \\
24 & Nonanal & & 1107 & 0.03 & - & - & - & - & - \\
25 & Benzeenethanol & & 1117 & 0.02 & - & - & - & - & - \\
26 & _trans_-Pinocarveol 3 & & 1145 & - & 0.05 & 0.35 & - & - & - \\
27 & Pinocarvone & & 1169 & 0.10 & 0.04 & 0.26 & - & - & - \\
28 & 4-Terpineol & & 1182 & 0.06 & 0.03 & 0.35 & 0.15 & - & - \\
29 & \(\alpha\)-Terpineol & & 1195 & 0.22 & 0.58 & 0.41 & - & - & - \\
30 & Myrtrenal & & 1199 & 0.15 & 0.07 & 0.70 & 0.11 & 0.02 & - \\
31 & Decanal & & 1208 & - & - & 0.19 & 0.11 & - & - \\
32 & Verberone & & 1212 & 0.04 & - & 0.10 & - & - & - \\
33 & 2-Phenoxyethanol & & 1223 & 0.01 & - & - & - & - & - \\
34 & Bicyclolelemene & C9\({}^{\prime}\) & 1342 & 2.18 & 2.40 & 0.43 & 1.73 & 0.34 & 0.68 \\
35 & \(\alpha\)-Cubebene & & 1353 & - & - & - & - & 0.04 & 0.05 \\
36 & \(\alpha\)-Copaene & C10\({}^{\prime}\) & 1375 & 0.07 & - & - & - & 1.33 & 1.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Volatile organic compounds (VOCs) composition (%) of _S. romana_ and _S. montana_ obtained by HS-SPME/GC-MS with PDMS/DVB fiber.
## 3 Results and Discussion
### Volatile Compounds  
Table 4 lists the volatile compounds (area units, AU; \(\times 10^{7}\)) isolated from the cheeses by SPME-GC-MS. We detected 86 compounds: 19 acids, 10 ketones, 14 alcohols, 20 esters, 7 aliphatic hydrocarbons, 6 aromatic compounds, 4 lactones, 2 aldehydes, 3 furanoids, and 1 sulphur compound. Although the concentration of volatile compounds in cheeses made of vegetable rennet was lower, the variability of compounds (particularly esters, aliphatic hydrocarbons, and aldehydes) was higher in the vegetable rennet cheeses. Differences in the enzymatic activity of each type of coagulant may explain this phenomenon. Vegetable coagulant cheeses present slower lipolysis and faster proteolysis rates than those prepared with animal rennet ([PERSON], 2017; [PERSON], 2017). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LRI 1** & **Volatile Compound** & **AU 2** & **A\(\times\)C** & **A\(\times\)DOP** & **V\(\times\)C** & **V\(\times\)DOP 3** \\ \hline
**Acids** & & & 2475.37 \(\pm\) 1342.30 & & & \\  
entadecanoic acid & 36.57 \(\pm\) 35.49 & & & & \(\times\) \\
2680 & Hexadecanoic acid & 170.52 \(\pm\) 425.29 & & \(\times\) & \(\times\) & \(\times\) \\
2714 & 9-Hexadecenoic acid & 35.88 \(\pm\) 42.40 & & & & \(\times\) \\
**Ketones** & & & 223.65 \(\pm\) 166.11 & & & \\  
*Esters** & & & & 110.44 \(\pm\) 43.95 & & & \\
903 & Ethyl acetate & 13.17 \(\pm\) 6.52 & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\
1044 & Ethyl btanoate & 36.68 \(\pm\) 16.01 & \(\Short-chain fatty acids were the most abundant volatile compounds of all the identified fractions. Butanoic, hexanoic, and acetic acids had the highest percentages in the volatile fraction of Payoyo cheese (the cheese from Payoya goats milk), in decreasing order. Acids also play a predominant role in the aroma of many other goat cheeses, such as Ibores, Majorero, Palmero, Sepet, Xinotyri, and Sainte-Maure [42; 43; 44; 45; 46; 47]. Free fatty acids containing two or more carbon atoms may originate from lipolysis, proteolysis, or the degradation of lactose. The source of these enzymatic activities can be starter cultures, moulds, or indigenous milk enzymes [48]. The amount of total acids increased during ripening owing to the fat hydrolysis process. However, shorter fatty acids can also be produced by the oxidation of ketones, esters, and aldehydes. The absence of starter cultures during manufacturing, together with the low amount of aldehydes present in the cheees (qualitatively and quantitatively), suggests that acetic or propionic acids may have been derived from the oxidation of the corresponding aldehydes. Methylketones were the most abundant type within this fraction of volatile compounds, as in other goat varieties and surface-ripened cheeses [42; 48]. Methylketones are precursors of secondary alcohols in the \(\beta\)-oxidation of free fatty acids [49], and they have low perception thresholds. Two ketones were especially abundant: 2-nonanone and 2-heptanone. Similar results were also found in
## 3 Deep Learning-Based Target Recognition Techniques
### Vision-Based 3D Reconstruction Technology of Fruit
#### 3.3.2 Three-Dimensional Reconstruction Technology Based on RGB-D Vision Sensor  
RGB-D vision sensors can acquire the sensor-to-object distance information directly through structured light or time-of-flight, offering high measurement accuracy without being easily affected by lighting conditions. RGB-D vision sensors are widely used in the vision recognition system of picking robots. To improve the system's ability to perceive the environment, [PERSON] et al. [90] proposed a point cloud processing-based apple recognition method. Firstly, the point cloud data of the orchard was acquired by using an RGB-D vision sensor, and the color features were fused with the 3D features in the point cloud data to further classify the fruits, branches, and leaves in the image, and then a genetic algorithm and support vector machine were used to classify the data into three categories. According to the test, the accuracy rates of apple, branch and leaf recognition were 92.3%, 88.03% and 80.34%, respectively. As fruit growth in a natural environment is irregular, the branches of fruit trees not only block the fruit but also obstruct the end-effector of the picking robot. For this reason, [PERSON] et al. [91] proposed a method to identify and locate guava fruit using RGB-D vision sensors and successfully reconstructed the fruit and branches in three dimensions. First, Mask R-CNN was used to segment the fruit from the image. Second, a sphere was used to describe the fruit and a cylinder to describe the  
Figure 10: Stereo matching principle based on binocular vision.  
branch. Finally, a fitting algorithm was used to process the point cloud to reconstruct the 3D position. The system was tested in an orchard with an F1 score of 83.3% for the 3D reconstruction of fruits and 41.5% for the 3D reconstruction of branches. Because the point cloud collected by the RGB-D vision sensor is sparse for slender branches, which affects the 3D reconstruction effect of branches. For the low recognition accuracy in occluded fruits, [PERSON] et al. [92] proposed an apple recognition system based on an RGB-D vision sensor. The system segments the apple fruit in the image by using the YOLACT++ model and then estimates the depth information of the apple by the 3D vision cone before constructing the 3D information of the apple. Through practical experiments in orchards, the apple-picking robot was able to adjust the picking posture of the robotic arm according to the fruit position and size output from this system.
## 2 Materials and Methods
### Precipitation Products
#### 2.3.3 Persiann-Cdr  
The PERSIANN applies an artificial neural network model to measure rainfall rate using IR brightness temperature data from geostationary satellites and updating its parameter using PMW observations from low-orbital satellites [40]. The PERSIANN-CDR was developed from the PERSIANN algorithm using Gridded Satellite Infrared Data (GridSat-B1) and adjusted by the GPCP monthly products [23]. The PERSIANN-CDR daily grids were estimated at 00:00 UTC [23]. The PERSIANN-CDR used in this study was acquired from NOAA's National Climatic Data Center and this CDR was originally developed by [PERSON] and colleagues [41] for NOAA's CDR Program.
## 2 Modular System-Level Modeling Approach
### Modular System-Level Model  
The modular system-level model is made up of mutually independent modules. Thus, to build the modular system-level model, the electromagnetic characteristics of the whole system should be considered.  
According to researches and on-site experience, the disturbance source could be classified into three forms: conducted, induced, and radiated form which are mutually independent. Therefore, three source modules are built for BTM system: radiation disturbance, conducted disturbance and induction disturbance modules. The devices in the BTM system, which are sensitive to interference, are encapsulated as sensitive equipment module. The disturbances need paths to \"go\" into BTM system like ground loops, radiation and the cable crosstalk coupling [22]. Thus, radiation, conduction, and induction coupling modules could be built according to the coupling path. All the modules are linked by the input and output port of them.  
Each module is the encapsulation of objects, which are the devices or components with the same electromagnetic characteristics. For example, the radiation coupling modular model contains antenna, aperture, and wires object models. One object may have lots of properties. Table 2 lists a few object properties, which is utilized to build BTM EMS prediction model. The character of modules will be changed only by changing these electromagnetic properties of the object model.  
Then, all the modules are put into three layers and the whole modular system-level model is completed. The three layers are divided based on three elements of EMC, i.e., electromagnetic disturbance source, EMI coupling, and sensitive equipment layers, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Object**} & \multicolumn{2}{c}{**Properties**} \\ \hline \multirow{3}{*}{Cable} & \(\bullet\) & Cable type and location \\  & \(\bullet\) & Wiring mode \\  & \(\bullet\) & Grounding method \\  & \(\bullet\) & \(\cdots\) \\ \hline \multirow{3}{*}{Antenna} & \(\bullet\) & Antenna type \\  & \(\bullet\) & Antenna location \\  & \(\bullet\) & Distance from the EMI source \\  & \(\bullet\) & \(\cdots\) \\ \hline \multirow{3}{*}{Aperture} & \(\bullet\) & location of Aperture \\  & \(\bullet\) & Aperture size \\  & \(\bullet\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Objects and their properties.  
Figure 3: Modular System-level Model.
## 3 Results and Discussion
### Definition of Control Strategy  
The HMCVT is simulated and modeled according to the dynamic model in Section 2. The main parameters of the tractor are shown in Table 1. The HMCVT shift control strategy is different from other gearboxes (shifting only by the clutch) [22]. The HMCVT shift control strategy requires joint control of the pump-motor hydraulic system and the solenoid valve-clutch system. Its primary function is to control the displacement ratio of the pump-motor hydraulic system and the change sequence in the oil pressure of the clutch system. The control strategy is defined as follows:  
\[H=(x,y,N) \tag{30}\]  
where \(x\) represents the time difference between the maximum displacement ratio and the separation oil pressure of the clutch, and \(y\) represents the time difference between the clutch oil flushing and the oil draining. \(N\) = 0 means no control, \(N\) = 1 means in control state.  
Figure 14: Speed and Torque control effect diagram at variable displacement ratio. (**a**) motor output speed; (**b**) motor output torque.
# Globally Scheduling Volunteer Computing
## 4 The global Volunteer Computing Scheduling Problem
### Preventing Device Starvation  
Can a project \(P\) supply jobs that use a processing resource \(R\) on a device \(D\)? This is central to the global scheduling problem, but it is not a simple question. We define:  
_Usable\((P,D,R)\)_: this predicate is true if \(P\) has an app version for \(D\)'s platform that uses \(R\), and \(D\) has virtualization software if needed by the app version. This may change over time as \(P\) adds and removes app versions.  
If _Usable\((P,\,D,\,R)\)_ holds, it means that \(P\) can potentially supply jobs to \(D\) that use \(R\). However, it does not mean that \(P\) can supply such jobs right now. \(P\)'s server may be temporarily down, or it may not have jobs that use \(R\), or its jobs that use \(R\) may have keywords disallowed by the user's preferences, or its jobs may require a different GPU driver version than the one on \(D\), and so on.  
If these conditions hold, and \(P\) is the only attached project, \(R\) is \"starved\", i.e., the client has no jobs that use it. To maximize throughput, we want to avoid this situation.  
Science United does not know about these factors, so it cannot directly know whether \(P\) can currently supply the needed jobs. Instead, it learns this from the BOINC client. The client keeps track of (a) projects for which the last scheduler RPC failed; and (b) (_project, resource_) pairs for which the last scheduler RPC requested work for the given resource, but none was returned. This information is included in the AM RPC request.  
We then define:  
_Usable_now(\(P\), \(D\), \(R\)): this predicate is true if (a) _Usable(\(P\), \(D\), \(R\))_ is true, and (b) the most recent scheduler RPC from \(D\) to \(P\) succeeded and returned jobs using \(R\).  
Science United tries to attach \(D\) to projects that can use all its processing resources immediately, i.e., for which _Usable_now(\(P\), \(D\), \(R\)) holds. However, it also must attach projects for which _Usable(\(P\), \(D\), \(R\))_ holds but _Usable_now(\(P\), \(D\), \(R\))_ does not, because the conditions that caused \(R\) to be unusable may be temporary. \(P\)'s server may come back up, or new jobs may be submitted. The only way for Science United to detect this is to attach \(D\) to \(P\) and let it request work for \(R\). This will typically happen at least once per day: when a scheduler RPC fails, or fails to return jobs, the client uses exponential backoff with a maximum delay of one day.
## 4 Topological Arrangements of QS Genes in Bacterial Genomes
The L1 type QS circuits contain an overlap between transiently transcribed _luxR_ and _rsaL_ gene. The length of the overlap varies in different species. The L1 type QS circuit of _P. aeruginosa_ contains an overlap of 10 bp and the same overlap is 20 bp long in _P. fuscovaginae_. In contrast, _P. putida_ has an L1 circuit where the R and L are close (4 bp apart) but not overlapping [48]. The R2 topology contains an overlap of 2 to 79 bp between the convergently transcribed _luxR_ and _luxI_ genes. Previously it has been suggested that the expression of one member of a convergent or overlapping gene pair might antagonize the expression of the second member resulting in activation or repression of different functions or phenotypes [34]. This may happen by either failed recognition by the second RNA polymerase molecule, or hybridization of the two complimentary mRNAs, although this is yet to be confirmed. Even though overlapping genes are \begin{table}
\begin{tabular}{c c c c} \hline
**Gene-types** & **No. found in complete bacterial genomes** & **Potential role** & **Example of genomes** \\ \hline _rsaL_ & 11 & Negative regulator & _P. putida_, _P. fuscovaginae_, _P. aeruginosa_ \\  & & & LESB58, _P. aeruginosa_ PAO1 \\ _rsaM_ & 29 & Negative regulator & _B. pseudomallei_ K96243 \\ _mupX_ & 1 & Negative regulator & _P. fluorescens NCIMB_ 10586 \\ Integases/transposases & 2 & DNA mobilization & _B. vietnamiensis_ G4 \\  & & & _Methylobacterium nodulans_ ORS 2060 \\ LuxR like regulator & 1 &? & _Gluconacetobacter diazotrophic_ PA1 5 \\ Unknown function & 5 &? & _B. mallei_ NCTC 10247, \\  & & & _Saccharophagus degradans_ 2-40 \\ \hline \end{tabular}
\end{table} 
Table 2: Intervening genes in short, conserved topological patternsnot uncommon in tightly co-regulated gene circuits of bacteria [50], e.g., restriction modification systems [36], the overlaps of convergently transcribed genes are less common in the literature. However, a few examples of convergent antisense RNA regulation have been reported also in bacteria, including S-box transcripts that are convergent to the _ubiG-mccBA_ operon in _C. acetobutylicum_[51]. In contrast to simple topologies, the group of longer, complex topological patterns shows greater variety although the number of occurrences we found was considerably less. The groups of _Agrobacteriumium_ and various _Rhizobia_ often contain many genes between the _luxR_ and _luxI_ type genes. An interesting example in this group is the MI pattern of _B. ambifaria_, where two well distinguishable QS genes (_rsaM_ and _luxI_) appear in their usual tandem topology, but without an annotated or computationally identifiable _luxR_ homolog in their vicinity.
## 4 Results and Discussion
### Thermal Annealing with Constant Electric Currentand Current Density (Uniform Cross-Sectional Areas)
If the flux of vacancies cannot be absorbed by the IMC structure, the vacancies reach saturation near the cathode side and the nucleation of voids occurs. The growth of the void corresponding to the flux of vacancies will eventually end up as an open interconnected circuit and cause failure [22; 23]. However, it appeared that with the cross-section increases on the sample anode ends from 1 mm and 3 mm up to 7 mm and 9 mm, the density of the electrons decreased. Given the circumstances of the samples tested in this study, it can be assumed that the widened cross-section prevented additional atoms from moving toward the anode side and electromigration effects were reduced as a result. Moreover, according to [PERSON] and [PERSON] [10; 13], an electromigration back current stress gradient exists where the highest compressive current stress can be found near the anode side due to the accumulation of atoms. The results demonstrate that the electromigration compressive current stress could be annihilated. In summary, regardless of the temperature or the current density, it was found that the IMC formation was always thicker at the anode end than at the cathode end, even after 4 days of heating and being subjected to the current. This indicates that the high electronic current density causes electromigration and atoms to accumulate at the anode end, resulting in greater thermal energy than that at the cathode end. Moreover, the movement causes the atoms in the metal to gain more heat energy, increasing their driving force and resulting in a thicker IMC layer at the anode end and voids at the cathode end.