esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 3 Deep-Learning-Based Framework
### Methodology Framework
#### 3.2.1 Multi-Hazard Disaster-Scene Database
When assessing the images from the 2011 Christchurch Earthquake, images with ground water-related scenes (due to liquefaction) were removed. Damage-level recognition and labeling bear more uncertainties. First, no standard or rule can guide damage-level scaling agnostic to hazard types. The most commonly used guide for field-based visual damage scaling is found in [39], which defines five levels of identifiable damage based on visual features of a low-rise building. However, it is for earthquake-induced damage only. In this effort, with five hazard types and considering indefinite variations in building materials and types, three levels of damage are enacted to describe damage-level, which is assumed to be invariant to hazard types and building properties. Consequentially, the authors propose to use the following three damage levels. (1) The 'Minor' level stands for slight damage (usually the structure stands and appears to have a few instances of cracks, cluttered objects, or debris); (2) The 'Moderate' level describes globally moderate to locally severe damage (visually, the object stands but appears to have many cluttered artifacts, severe cracks, or distorted elements); (3) The 'Major' level covers both globally severe damage and full collapse (visually the structure shows partial to full collapse). A numeric tag is uniquely assigned to simplify the labeling, which differentiates both the hazard type and the damage level. After the manual labeling and bounding-box creation, all these visually obtained manual tagging and annotation results become the critical metadata as an integral part of the resulting disaster-scene database for the ensuing machine-learning framework. Furthermore, the labels and bounding-box information (including the coordinates of the bounding-box corners in images) for each image is written in an XML file. The number of the resulting XML files is the same as the number of images in the database. Figure 3 illustrates the distribution of the resulting labels. It is noted that the instances of class labels are not well-balanced, where the Earthquake type takes 43.5% of all hazard-type class labels, and the Moderate type owns 44.5% of all damage-level labels. This imbalance needs to be heeded when evaluating model performance. Figure 3: Distribution of class instances: (**a**) Hazard-type; (**b**) Damage-level.
## 5 Results and Discussion
### End of Life (EOL) Prediction  
The accuracy of the EOL model was tested using the model and equations explained in Section 3.2. The value for \(\eta_{C}=0.9987\) was chosen. The number of particles used was 400, which results in a good accuracy, sustaining a sensible computer workload. The particles were normalized using random numbers through a normal distribution with mean \(0.5,3\times 10^{5}\) and \(1.8\) for \(\beta_{1},\beta_{2}\) and \(C\) respectively. The algorithm tracks the state of the system by modifying these parameters. The state parameters evolve with time by means of the noise added in the state transition model by the terms \(\varphi_{1,k},\varphi_{1,k}\) and \(\varphi_{k}\). The following paragraphs and figures show the different tests of the framework for EOL prediction.
## 4 System Design and Goals
### Message Sequence
#### 4.2.1 DB_SELF State  
This sequence occurs if:  
* The router starts up for the first time. It records all its interface addresses in the Local Interface Set [46]. MANET/D2D or OLSR addresses are marked with a 1, while non-OLSR addresses are marked with a 0.
* The address of the router changes, and it records the information in the Removed Interface Address Set [46]. Nodes in the DB_SELF state can immediately move to the DB_NEIGHBORS_RCVD state if a HELLO broadcast is received on their OLSR interface, and if they are finished with updating the content of their Local or Removed Interface Sets. This state is shown in Figure 3.
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 2 Bus-Based Energy Management System
### The Battery SoC and Intelligent Alternator Operation Mode  
The battery charge level is divided into three modes: deficient, working, and recovery [8]. When the battery SoC is in the deficient mode, the battery should be charged quickly. When the battery SoC is in the working or recovery modes, the charging and discharging of the battery should be controlled according to driving situations and the total power demand of the electrical loads. The recovery mode is considered to provide sufficient capacity for braking energy recovery. All battery modes are shown in Figure 2.  
The intelligent alternator has four operation modes, as shown in Figure 3. These modes are ready, pre-excitation, voltage regulation, and excitation off. The ready mode is activated only when the sleep command is received from the energy management controller, and the rotational speed of the alternator \(n_{G}\) does not exceed the rotational speed threshold \(n_{0}\). The alternator switches into pre-excitation mode if a valid set voltage U\({}_{S}\) is more than the voltage threshold U\({}_{0}\) and \(n_{G}\) < \(n_{0}\). For the pre-excitation mode, if \(n_{G}\) exceeds \(n_{0}\), the voltage regulation state is entered, and the output voltage of alternator can be regulated. Without an activation command, and if the rotational speed of the alternator \(n_{G}\) exceeds the rotational speed \(n_{1}\) (\(n_{1}\) > \(n_{0}\)), the alternator switches into the voltage regulation state directly. When the command of excitation off from the energy management controller is received, the alternator stops the excitation until a valid new set voltage U\({}_{S}\) is received.  
When more electrical loads are switched on, and the SoC of battery is in the recovery mode, the energy management controller reduces the alternator voltage or closes its excitation and makes the battery work for the electrical loads to maintain a low level of engine fuel consumption. When the battery SoC is in the working or recovery modes, the higher voltage of the alternator is regulated by the energy management controller to recover the braking energy. When the battery SoC is in the  
Figure 3: Intelligent alternator operation mode.  
Figure 2: Modes of battery SoC.  
deficient mode, the energy management controller raises the charging voltage of the battery, so the battery SoC can return to the working mode quickly.
## 2 Literature Review
### Ontology Instantiation: Populating Knowledge Graphs
In general, three main approaches can be distinguished when dealing with OP for extracting domain-specific terms [55]: (i) rule-based ontology population systems [15; 58], (ii) ontology population systems using ML or NLP [54; 59], and (iii) ontology population systems that use statistical approaches [57]. In rule-based systems (i), the rule group is designed and developed for the location, classification, and extraction of information in predefined categories such as people, organizations, time expressions, places, etc., known as named entities. NER are tools and techniques used for these tasks' automatization. In [15], the authors describe the use of NER, a set of predefined predicates and a lexical database (WordNet) to generate the instance classification rules that will be used to populate the ontology. Another strategy for OP (ii) makes use of a predefined ontological schema for the term's comparison, along with learning algorithms using ML and NLP tasks for information extraction from unstructured data [54]. The main disadvantage is the supervision in the selection of the initial concepts and the schema construction. In the same line, a blend of techniques is described in [59] using association rules, clustering, classification based on associations through advanced analytics employing NLP and data mining techniques. The system uses federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and KG representation. The processing process integrates data from multiple sources virtually by creating virtual databases. Finally, in statistics-based systems (iii) is the measurement of the semantic similarity between the terms extracted from the unstructured data and the ontology instances. However, this approach is not appropriate for all situations. For example, it cannot treat terms not covered by synonym dictionaries. Similarly, this approach is not suitable for understanding abbreviations. In [57], a k-means algorithm is proposed for topic identification and data classification. The algorithm compares and obtains named entities from unstructured data to later transform them into Linked Open Data (LOD) enriched with DBpedia URIs. Our approach employs a classification model to identify candidate instances through a semi-supervised process using NLP and our own pattern learning algorithm of political marketing concepts. The algorithm filters the candidate terms and NLP tools facilitate the extraction of instances from unstructured text to populate the PMont. Our work presents improvements in the ontology and KG population process, implementing source connection and data collection, data enrichment with sentiment analysis, and redundancies elimination, producing as a result a populated political-marketing-related KG.
# Evaluation of a New-Concept Secure File Server Solution
## 4 Results  
Initially, the transfer time of different client-server platforms (defined in Section 3.2.1) was tested using a 1 Gbit/s point-to-point network connection. This was important partly because it can be regarded as typical for today's non-profit organizations and SMEs, and partly because it provided transfer time characteristics for each system, thereby serving as a basis of comparison for evaluating the results of subsequent tests.  
This was followed by a 10 Gbit/s point-to-point network connection data transmission, which provided a good point of reference for \"enterprise class\" network infrastructures. The Microsoft Windows 7 \(\times\) 64 operating system was not tested here, as it does not support the Intel X520-DA2 network card, and it would have been unrealistic to use an EOL (End of Life) operating system operating system while providing a modern network infrastructure for the measurements.  
It was also important to increase the throughput of the network following the tests at 1 Gbit/s connection speeds because tests at 1 Gbit/s were, in most cases, close to the upper limit of the theoretical maximum throughput (125 MiB/s), making performance differences less detectable. At that bandwidth, it was not possible to model workstations and servers being placed under significant workload.  
When evaluating the measurement results, we found that the results of the first measurements differed from the results of the subsequent nine measurements in terms of transfer time, especially for smaller files (500 KiB, 4 MiB), typically in the case of Windows operating systems. However, since the standard deviations calculated from the results of the subsequent nine measurements did not show significant differences anywhere, the results could be considered representative based on the average of the measurements. In the case of the 1 Gbit/s network connection, the average of the transfer speeds of the ten measurements was 846 Mbit/s (100.85 MiB/s), while for the 10 Gbit/s connection, the average transfer speed was 9.38 Gbit/s (1118.18 MiB/s).  
During the measurements, neither client nor server CPU usage exceeded 47%, and system memory utilization did not exceed 52%. However, the SSD drives repeatedly showed 100% utilization in the faster, 10 Gbit/s bandwidth tests.  
The detailed results of the tests are presented in the following subsections.
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 4 Experimental Results and Analysis  
### 500 MB PageRank Experiments  
#### 4.1.1 Results with Changing JVM Heap Configurations  
Figure 3 shows the experimental results of each stage in the PageRank workload by changing the JVM heap sizes. In the _Distinct_ stage, Spark reads the input data and distinguishes the URL and links. As we can see from the results of the _Distinct0_ stage,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Option** & **Caching Policy** & **Shuffle** & **Storage** \\ \hline N\_1 & No Caching & 60\%: 1.62 GB & 30\%: 0.81 GB \\ N\_2 & No Caching & 50\%: 1.35 GB & 40\%: 1.08 GB \\ N\_3 & No Caching & 20\%: 0.54 GB & 60\%: 1.62 GB \\ M\_1 & Memory only & 60\%: 1.62 GB & 30\%: 0.81 GB \\ M\_2 & Memory only & 5  
the overall execution time decreases by changing the JVM heap sizes from _1 and _2 to \(3\) options, mainly due to the _gartage collection_ (GC). For example, the GC time takes 25 s, 24 s, and 16 s in M&S_1, M&S_2, and M&S_3, respectively. Therefore, in the _Distinct0_ stage, as we increase the amount of storage space, we can improve the overall performance by reducing the GC time. On the other hand, in the _Distinct1_ stage, the overall execution time increases as we change the options from _1 and _2 to \(3\). This is mainly because of the _shuffle spill_. When we checked the Spark web UI, the shuffle data were spilled onto the disk because of the lack of shuffle memory space. For example, the sizes of shuffle spill data on disk in M&S_1, M&S_2, and M&S_3 are 0, 220 MB, and 376 MB respectively. When the shuffle spill occurs, the CPU overheads for spilling the data onto the disk increase because the data need to be serialized.  
After the _Distinct_ stages, there are _iterative flatMap_ stages to obtain ranks. Basically, _flatMap_ stages generate a lot of shuffle data, which can make our cluster lack the necessary shuffle memory space. Therefore, as the available amount of shuffle space decreases (in order from options _1, _2, and _3_), the more shuffle spill can occur, which can potentially affect the overall job execution time (e.g., M&S option _flatMap2_ stage _1: 37 s, _2: 40 s, _3_: 49 s). However, when the data are cached on memory only (i.e., M_1, M_2, and M_3), they show another pattern. The main reason for this behavior is that the Spark scheduler schedules the tasks unevenly because there is a lack of memory storage space for caching the RDD on options _1 and _2. If a worker does not have RDDs, it is excluded from the scheduling pool. Therefore, the other workers have to handle additional tasks with GC overheads which can affect the whole job execution time.  
Figure 3: The PageRank job execution time for 500 MB dataset per stage (s). Each shade represents each stage in a Spark job. Overall, M&S_1 case shows the best performance.
###### Abstract  
Global Navigation Satellite System Reflectometry (GNSS-R) is of great significance for the extraction and research of precise information of sea surface topography. Improving measurement accuracy is necessary for realizing spaceborne GNSS-R sea surface altimetry application. The main error source of GNSS-R distance measurement is the error of the specular reflection point positioning, which directly affects the sea surface altimetry accuracy on the reference datum. There is an elevation error of several tens of meters between the reflection reference surface used by the existing specular reflection point geometric positioning methods and the sea surface elevation, which is importantly influenced by the earth's gravity field. Therefore, the gravity field reflection reference surface correction is the key to improving the specular reflection point positioning accuracy. In this study, based on the correction of the GNSS-R reflection reference surface, research on improving the positioning accuracy of the specular reflection point is carried out. Firstly, in order to reduce the positioning error caused by the elevation difference between the reflection reference surface and the sea surface, the gravity field reflection reference surface correction method (GFRRSCM) which corrects the reflection reference surface from the WGS-84 ellipsoid to geoid is proposed, and the positioning accuracy is improved by 25.15 m. Secondly, the normal projection reflection reference surface correction method (NPRRSCM) is proposed to correct the specular reflection point determined by the GFRRSCM from the reflection reference plane of the radial to that of the normal. Additionally, in the process of solving the spatial geometric relationship of the reflection path, the approximate substitution error is reduced by directly solving the normal projection on the plane, and the positioning accuracy is further improved by 13.05 m towards the normal. Thirdly, based on the gravity field normal projection reflection reference surface combination correction method (GF-NPRRSCM), the specular reflection point positioning accuracy is synthetically improved by 28.66 m.  
Global Navigation Satellite System Reflectometry; specular reflection point position; the earth gravity; geoid; sea surface altimetry; TechDemoSat-1 2019/11, 33 doi:10.3390/rs11010033
## 1 Introduction
[PERSON] [11] described the importance of comparative analysis of national and European standards and familiarization of local specialists with the European approach  
Figure 1: Distribution of design codes around the world (modified from [8]). and its application in national practice. The major difference between the considered codes of practice lies in the different mechanisms of economic management and freedom in their application. EC applies the creative approach and provides the requirements for the result of the construction work, whereas the Kazakhstan building regulation provides strict directions in terms of design requirements. That is why the Kazakhstan approach prevents constructors from selecting the most appropriate design solution and introducing new construction technologies [12]. A direct transition from the Kazakhstan building regulations to EC in Kazakhstan is impossible, as the local construction industry is focused on the application of national regulations. The introduction of the European approach must be performed based on a complex programmed approach that gradually considers the particularities of Kazakhstan. The territory of Kazakhstan is distinguished by large variety in nature and climate, which are significantly different from the European ones. The main difficulty in the application of EC in Kazakhstan is the need to consider the specifics of the territory of Kazakhstan and the development of the National Annex (NA) with nationally determined parameters for the application of the European approach. Raft and pile foundations are two common foundation types for the realization of geotechnical projects in Kazakhstan. Because the rapid urbanization rate in the country requires geotechnical engineers to overcome technical and economic questions associated with the implementation of large-scale construction projects, piled raft foundations are an alternative solution for the support of high-rise buildings, which would be constructed in the near future [13]. The objective of this paper is to (1) perform a comparative analysis of the European and Kazakhstan approaches for the design of raft, pile, and piled raft foundations; (2) identify the differences in the design procedures and the obtained results; and (3) promote familiarization of local specialists with EC. The transition from the Kazakhstan building regulations to Eurocode represents a complex and gradual process demanding a high level of qualification of designers and civil engineers. Thus, the study of the differences between the Kazakhstan and European approaches is crucial for the development of the country's construction industry.
## 3 Results
### Reliability and Sensitivity
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Kinematic Variables** & **Mean \(\pm\) SD** & **Min–Max** & **CV\%\({}_{\text{(985)}}\)** & **CV\%\({}_{\text{(985)}}\)** & SWC\({}_{\text{(985)}}\)** & **SWC\({}_{\text{(6.9)}}\)** & **TE** & **Sensitivity** & **ICC (CI95\%)** \\ \hline Velocity [m/s] & 8.51 \(\pm\) 0.33 & 7.53 & **9.21** & **3.86** & **0.73** & **0.06** & **0.16** & **0.05** & **GOOD** & **0.97** (0.94–0.99) \\ Trial 1 & 8.50 \(\pm\) 0.33 & 7.42 & 8.99 & & & & & & & \\ Trial 2 & 8.52 \(\pm\) 0.33 & 7.53 & 9.21 & & & & & & & \\
**Glide time [ms]** & **402 \(\pm\) 22** & **345** & **469** & **5.63** & **2.76** & **4** & **11** & **8** & **GOOD** & **0.82** (0.64–0.91) \\ Trial 1 & 404 \(\pm\) 21 & 364 & 456 & & & & & & & \\
**Trail 2** & **40 \(\pm\) 27** & **344** & **469** & & & & & & & \\
**Double support time [ms]** & **72 \(\pm\) 9** & **49** & **98** & **12.90** & **7.10** & **2** & 5 & **5** & **SATISFACTORY** & **0.73** (0.45–0.87) \\ Trial 1 & 73 \(\pm\) 10 & 54 & 106 & & & & & & & & \\ Trial 2 & **71 \(\pm\) 11** & 49 & 98 & & & & & & & & \\
**Glide length [m]** & **3.47 \(\pm\) 0.21** & **2.96** & **3.87** & **6.04** & **2.85** & **0.04** & **0.10** & **0.07** & **GOOD** & **0.83** (0.66–0.92) \\ Trial 1 & 3.48 \(\pm\) 0.21 & 2.94 & 3.88 & & & & & & & & \\ Trial 2 & 3.46 \(\pm\) 0.24 & 2.96 & 3.87 & & & & & & & & \\
**Double support length [m]** & **0.63 \(\pm\) 0.09** & **0.43** & **0.81** & **13.74** & **7.64** & **0.02** & **0.04** & **0.04** & **SATISFACTORY** & **0.74** (0.47–0.88) \\ Trial 1 & 0.64 \(\pm\) 0.09 & 0.46 \(\pm\) 0.90 & & & & & & & & & \\ Trial 2 & 0.62 \(\pm\) 0.11 & 0.43 & 0.81 & & & & & & & & \\
**Step length [m]** & **2.83 \(\pm\) 0.16** & **2.46** & **3.17** & **5.69** & **2.01** & **0.03** & **0.08** & **0.04** & **GOOD** & **0.90** (0.78–0.95) \\ Trial 1 & 2.84 \(\pm\) 0.16 & 2.46 \(\pm\) 3.12 & & & & & & & & & \\ Trial 2 & 2.83 \(\pm\) 0.18 & 2.46 & 3.174 & & & & & & & & \\
**Step frequency [step/s]** & **3.07 \(\pm\) 0.17** & **2.71** & **3.51** & **5.50** & **2.32** & **0.03** & **0.08** & **0.06** & **GOOD** & **0.85** (0.69–0.93) \\ Trial 1 & 3.05 \(\pm\) 0.16 & 2.69 \(\pm\) 3.34 & & & & & & & & & \\ Trial 2 & 3.09 \(\pm\) 0.20 & 2.71 & 3.51 & & & & & & & & \\ \hline \end{tabular} Legend: ms = milliseconds; SD = standard deviation; Min = minimum value; Max = maximum value; CV%\({}_{\text{(985)}}\) = between-subject coefficient of variation; CV%\({}_{\text{(985)}}\) = within-subject coefficient of variation; ICC = intra-class correlation coefficient; SWC = smallest worthwhile change; TE = typical error of measurement; CI95% = 95% confidence interval.  
\end{table} 
Table 1: Descriptive and reliability parameters for the kinematic variables, on average over the total 80 m distance.