esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatio-Temporal Variability of Water Temperature of Arctic Rivers in Russia over the Past 60 Years
## 4 Discussion
The report analyzed the trend in average annual water temperatures. Our study found that, between 1961 and 2021, the trend in water temperature was generally similar to the average annual trend, except in May and June, when the monthly trends exceeded the average annual value. Significant differences were observed, especially in the Asian part of the Russian Arctic.
## 7 Application Layer in WMSN
### Multimedia Source Coding Techniques
It is also shown that this technique is only useful with sparse images that have limited number of events (less than 9,000 events) otherwise it will become less efficient than other scanning image techniques. In [76], eight popular image compression algorithms are reviewed and compared with each other to find the most suitable image compression algorithm for implementation in WMSNs and can meet their requirements including a fast and efficient image processing capability, low memory requirement, high compression quality, less complex system, and low computational load. Image compression process, removing the highly correlated redundant information within the image, contains basically two stages: image transformation stage and entropy coding stage. According to [76], image coding process are categorized into: first generation, which focuses more on how the information contained in the transformed image can be efficiently encoded (such as Discrete cosine transform (DCT), Embedded zerotree wavelet (EZW), Set-partitioning in hierarchical trees (SPIHT), Embedded block coding with optimized truncation EBCOT), and second generation, which put more importance on how the useful information can be extracted and exploited (such as pyramidal coding, Directional decomposition, segmentation based coding, vector quantization). The second generation image compression requires more complex and extensive image processing compared to the first generation image coding as shown in [76]. Among the first generation algorithms, it is shown that SPIHT wavelet-based image compression is the most suitable hardware implemented image compression algorithm for WMSN because it is less complex and needs less computational load and memory allocation.
## 3 Results and Discussion
### Impact of pH
[PERSON] [34] reported enhanced 1,3-PD production at pH 8.0 and attributed this observation to the expression level of enzymes, especially glycerol dehydatase and 1,3-PD oxidoreductase. [PERSON] et al. [35] determined that at pH 7.0 the activities of glycerol dehydrogenase, 1,3-PD oxidoreductase, and glycerol dehydatase were at their maximal levels in _K. pneumoniae_. [PERSON] et al. [34] reported that the optimum pH for 1,3-PD production with _Enterobacter_ sp. is 8.0, while the reports for _K. pneumoniae_ indicate that the optimal value is between 6.0 and 7.5 [36, 37, 38, 39]. Not all fermentation processes are performed with a constant pH setpoint. [PERSON] et al. [37] developed a fermentation strategy which included a forced fluctuation of pH between 6.3 and 7.3. This resulted in reduced amounts of by-products produced and an increased 1,3-PD production [37]. A similar approach was also employed by [PERSON] and [PERSON] [5] who reported a 10% increase of the maximal concentration of 1,3-PD obtained. Fermentation with a pH shift was thus planned by us. The initial pH setpoint was 7.5, as the production rate of 1,3-PD at this pH was higher than at 7.0 (Figure 5). After 24 h of the process, the pH was set to 7.0. As no significant changes in final 1,3-PD concentration were observed (Figure 7), a constant pH value of 7.0 was selected for use in further research.
# Mechanical Stress Prediction of an Aircraft Torque Tube Based on the Neural Network Application
## 3 ATT-Mechanical-Problem Identification--Method-Application Object  
Study presented in Figure 1 is applied on an Airbus A320 aircraft-brake part, which is aircraft torque tube (ATT). The brake and, thus, the ATT are parts of the retractable landing gear. The ATT is part of the brake, which is heavily stressed by fatigue load. Although it is made of solid material, it is exposed to a wide range of temperatures during its operation  
Figure 2: ATT cracks: (**a**) 25,000 cycles; (**b**) 17,000 cycles; (**c**) 36,000 cycles; (**d**) 17,000 cycles; (**e,f**) ATT cracks in the rib area visualized using a non-destructive technique.  
and, at the same time, it is subjected to pressure and shock during braking. The average number of life cycles for this component is around 30,000 from the operating data; however, the life of ATT varies depending on its operating conditions. Therefore, it would be more efficient to change ATT operation time to its limit. It is essential to estimate the correct time to change the ATT, as some ATTs can withstand 17,000 cycles, while others can withstand up to 36,000 cycles (Figure 2). As mentioned above, the number of cycles is affected by the loads during the operation of the ATT; therefore, the main goal of this article is to create an algorithm that is able to calculate the maximum stress on the critical part of ATT during each landing of an aircraft under different conditions (outside temperature, brake temperature, landing speed). The stress calculated by the novel method can be used for the life-cycle-counting algorithm for the ATT part, which can be used maximally but within safe limits.  
In Figure 2, the area of interest for the ATT can be seen, which is the area of the ATT ribs where the loads due to the brake plates' impact are concentrated. Various cracks were observed during the ATT maintenance and the non-destructive method was used for better representation (Figure 2e,f). Using the maintenance results in Figure 2, it is possible to compare the correlation between the real conditions and the FEM analysis, whether the stress have the same location in calculation and, thus, whether the results are relevant.
## 4 Results
### Analysis of Heating Influence  
After thermal inertia, further analysis examined the heating influence on the thermal behaviour, by comparing the temperatures evolution between October (without heating) and November (with heating). Figure 7 shows the average temperatures for October (blue curve) and November (red curve), highlighting the following observations:  
* In October (blue curve), the building temperatures proportionally decrease with the weather pattern (dashed blue curve), as analysed in Section 4.1. However, in November, the building remains within a uniform range between 19.5 and 22.5 \({}^{\circ}\)C (dashed red lines) because of heating. Furthermore, as discussed in Section 4.6, this range complies with recommended directives: in winter, indoor temperatures should be between 17 \({}^{\circ}\)C and 21 \({}^{\circ}\)C, considering thermal comfort to temperatures around 19 \({}^{\circ}\)C;
* In November (red curve), as indicated in Section 4.2, heating results in a uniform rise-and-fall pattern throughout a week. In October (blue curve), this pattern is less recognisable because weather conditions become the dominant influencing factor. This leads to an analysis of morphology of thermal behaviour, detailed in Section 4.4.  
To further analyse the uniform pattern with heating, Figure 8a shows the detailed temperature evolution for November (red curve), including the timeslots when the heating is on (orange vertical stripes). These timeslots (on/off switching) follow criteria from Green Office [53], according to a threshold algorithm (by comparing the weather in previous days), and fulfilling the mentioned directives RD 486/1997 [49], RDL 14/2022 [50], and EN 16798-1:2019 [51]. As Figure 8a shows, temperatures remain within a uniform range (green lines), which varies by week: between 20 and 22 \({}^{\circ}\)C (weeks 1 and 4), 21.5 and 23 \({}^{\circ}\)C (week 2), and 21 and 22.5 \({}^{\circ}\)C (week 3). This demonstrates that the heating effectively regulates and stabilises temperatures, maintaining consistency from day to day, even when weather conditions vary. Figure 8b, adding outdoor temperatures (blue curve) to Figure 8a,  
Figure 6: Analysis of thermal inertia (holyday period 2): (a) Weather; (b) Comparison by zones.  
Figure 7: Analysis of heating influence.  
shows that indoor temperatures remain uniform although weather conditions fluctuate. Note that the outdoor temperatures (blue curve) are decreasing (in week 2 and 3) while heating maintains the indoor temperatures (red curve) within a uniform range (green lines).
## 2 Materials and Methods
### Intervention Study Design
#### 2.2.1 Questionnaires  
Nutritional assessment was evaluated using a semi-quantitative food frequency questionnaire (FFQ) [35], modified to include additional natural functional foods, without alterations to the type of questions [36]. Modified categories included dairy products (low-fat, enriched-fortified, etc.), meat (semi-dough, etc.), superfoods, sweet (low-fat, sugar-free), fruit juices, fruit and vegetables, cereals (vegetable fiber, etc.) beverages (stew, without alcohol, with sweeteners), oil (olive oil, dried fruits, fish oil). Participants were informed of the suggested portion sizes for each food included on the list and were asked to record their frequency of consumption. Frequency of consumption was recorded as \"everyday\", \"3-6 times per week\", \"2 times per week\", \"once a week\", \"1-2 times per month\" and \"seldom/never\". Moreover, participants were asked to complete a questionnaire assessing adherence to a Mediterranean-style diet (MedDietScore) [31; 32; 33]. The final score provides three levels of compliance categorized as: low 0-20, moderate 21-35 and high 36-55 adherence. Higher values of this diet score indicate greater adherence to the Mediterranean diet.  
Self-reported questionnaires were used, as a reaction to COVID-19 restrictions and changes. More specifically, a general self-reported sociodemographic questionnaire was used to obtain data on age, gender, occupation, years of education, income, etc. Participants also self-reported their medical history answering a standard questionnaire on several diseases in the form of Yes-or-No questions. In addition, self-reported data were collected for their weight and height [37; 38; 39].  
Physical activity levels were evaluated using a 3-day questionnaire via online personal interview. Women were asked to report the time spent, alone or with a companion, in various physical activities on two weekdays and one weekend day. The questionnaire used classifies all activities during work, at the gym, and during leisure time into four categories related to the average intensity of each activity and its effects on the cardiovascular and musculoskeletal systems [40]. Questionnaires were designed to assess the frequency of physical activity (months/year, weeks/month, days/week), duration (hours) and intensity (moderate to high). Based on these data, the total time spent in organized (including all activities regularly performed each week, usually in the gym under the guidance of a trainer) and unorganized activities that promote bone mass were calculated. The total weekly hours found to be spent in such activities were defined as moderate-intensity and intense physical activity. Physical activity was assessed only at the beginning of the study.
## 4 Evaluation of Model Developments
### Evaluation of Updated Snow
#### 4.4.2 Global Offline Evaluation of the Snow Depth  
The synoptic observations of snow depth on the Global Telecommunication System (GTS), which are routinely assimilated in the ECMWF snow analysis ([PERSON], 2017), are used to evaluate global offline simulations with the multi-layer (ML5) and single-layer (SL) snow schemes. The evaluation period covers the months between fall to spring (September to May) from 2014 to 2018. Figure 17 shows a general reduction of the root-mean-square error (RMSE) of snow depth when using the multi-layer snow scheme, with the largest improvements over North America and complex terrain regions. Analysis of the error distribution shows a reduction of the median value of the bias and RMSE, and generally an improvement at stations characterized by the largest errors (see Figure 17b).
## 3 Materials and Methodology
### Normalized Difference Snow Index  
In 1999 and 2002, the MODIS model was launched in the Earth Observation System for snow detection. The normalized snow index method mainly uses the Terra and Aqua platforms to accurately determine snow [53]. Daily snow cover data from MODIS Terra and Aqua (MOD10A1 and MYD10A1 version 61) are available from the National Snow and Ice Data Center Distributed Data Archive from 2000 to the present ([[https://nsidc.org/data/MOD10A1/versions/61](https://nsidc.org/data/MOD10A1/versions/61)]([https://nsidc.org/data/MOD10A1/versions/61](https://nsidc.org/data/MOD10A1/versions/61)), accessed on 15 January 2023). For the entire Varzob River Basin, we have extracted the MODIS datasets from 2001 to 2022. To create MOD10A1 landscapes in the Terra platform spanning the morning time (approximately 11:00 at the equator) and Aqua platform spanning the afternoon time (approximately 14:00 at the equator) the MODIS data from those two platforms were applied. The normalized snow index method is used to classify snow and is computed, applying reflected solar radiation in the observable (band 4, 0.545-0.565 m) and shortwave infrared (band 6, 1.628-1.672 m) for the MODIS Terra and (band 7, 2.105-2.155 m) for MODIS Aqua bands. In order to increase the contrast between snow-covered and snow-free surfaces, band 7 is used instead of band 6 in the MODIS Aqua model [54].
# Exploratory Study on Kali NetHunter Lite: A Digital Forensics Approach
## 4 Analysis and Findings
### NetHunter Lite Service Analysis  
In this section, we analyze and compare the events that happened on the OnePlus 7T smartphone before and after the NetHunter Lite services were used. Given that the previous phase of the analysis has shown the main folders and paths of the artifacts stored, this section will look at any modified files inside those locations but it will not be limited to them. For the second go-around examination, only folders related to the NetHunter Lite application, and not the Store or Terminal, are examined.  
When comparing the last physical image of the experiment with the image taken before using the services, the \data\data folder had created new files. The \data\data\com.offsec.NetHunter\databases folder had three additionally modified/created databases, 'CustomCommandsFragment', 'SearchSploit', and 'USBArmoryFragment'. In a different folder \data\data\com.offsec.NetHunter\shared_prefs, additional files were created. These two new files were named 'com.offsec.NetHunter.xml' and 'embryo.xml'.  
Figure 2: Partial folder hierarchy of Kali NetHunter Lite. (**a**) Local folder. (**b**) Media folder.  
The two files show a change of preferences on the chroot, language index, selected interface, and many more variables. Other files in other folders such as \data\data\com.offset.NetHunter\scripts were not modified or changed.  
In the folder data\local\
hsystem\kali-arm64, five different databases were created. These databases relate to the Kismet service that is part of the 'Wardriving' application within the NetHunter Lite Application. Out of the five databases, 'Kismet-20211211-16-11-13-1.Kismet' (see Figure 3) showed all of the scanned networks during the attack as well as the parameters such as source MAC address, destination MAC address, frequency, packet length, signal strength, etc.  
Moreover, the data\local\
hsystem\kali-arm64\root\.Kismet\Kismet\Kismet_httpd.conf file revealed the password and the username (see Figure 4) previously set for the webserver during initialization of the 'Wardriving' application.
# Testing the Effectiveness of Voxels for Structural Analysis
###### Abstract  
To assess the condition of cultural heritage assets for conservation, reality-based 3D models can be analyzed using FEA (finite element analysis) software, yielding valuable insights into their structural integrity. Three-dimensional point clouds obtained through photogrammetric and laser scanning techniques can be transformed into volumetric data suitable for FEA by utilizing voxels. When directly using the point cloud data in this process, it is crucial to employ the highest level of accuracy. The fidelity of r point clouds can be compromised by various factors, including uncooperative materials or surfaces, poor lighting conditions, reflections, intricate geometries, and limitations in the precision of the instruments. This data not only skews the inherent structure of the point cloud but also introduces extraneous information. Hence, the geometric accuracy of the resulting model may be diminished, ultimately impacting the reliability of any analyses conducted upon it. The removal of noise from point clouds, a crucial aspect of 3D data processing, known as point cloud denoising, is gaining significant attention due to its ability to reveal the true underlying point cloud structure. This paper focuses on evaluating the geometric precision of the voxelization process, which transforms denoised 3D point clouds into volumetric models suitable for structural analyses.  
denoising; point clouds; voxel; retopology; photogrammetry; non-uniform rational B-splines (NURBS); finite element analysis (FEA)
# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
## 2 Image Data  
In the context of carrying out research in the geographical division of Peloponnese to which the University of Patras belongs, scans were acquired in the following geographical areas by means of the GSSI Utility SIR-3000 Scan LT, 400 MHz GPR system. With this system, clear information is obtained from a 5 to 6 m depth with a resolution of about 5 cm. The first subsurface scan was carried out in the lowland settlement at the foot of Mount Didyma, which is part of the prefecture of Argolis (Figure 1). Two huge crater-shaped caves and dolines are the main ornaments of the area, which appear in carbonate limestone rocks and have been caused by subsidence of the soil.  
Panagopoula is a coastal area in northern Peloponnese. It is a narrow strip of land between the foothills of Panachaicon Mountain and the Gulf of Corinth. The area of Panagopoula (Figure 2) is prone to slope instability, and in the past, there have been serious landslides that created problems in the road network. For this reason, our visit aimed to study the influence of landslides on the subsoil of the area with ground-penetrating radar.  
From both regions, a series of scans were acquired in pairs, forward and backward directions, in order to check the subsoil as the two images illustrated in Figure 3.  
Figure 1: The two craters at Didyma in the Argolis region. Map Data: Google Earth.  
Figure 3: (**a**) The forward direction image scan at Didyma’s region. (**b**) The backward direction image scan at Didyma’s region.  
Figure 2: Panagopoula area in the Achaia region. Map Data: Google Earth.
# RT-GAN: GAN Based Architecture for Precise Segmentation of Railway Tracks
## 2 Related Work  
### Vision-Based Railway Target Segmentation  
Image detection and segmentation are widely used in the field of railway safety monitoring. Some studies have used traditional image methods such as morphological operator [15], visual saliency [16], and Markov random field [2; 17] to extract the location and shape of rail surface defects in short-range rail images from an inspection car.  
In recent years, deep convolutional neural network (DCNN)-based methods for track target detection have become popular. Most of these studies use various general-purpose image segmentation networks to classify track targets and then design dedicated evaluation metrics to determine whether the targets are defective or not. [PERSON] et al. [4] used U-Net, a full convolutional encoder-decoder type segmentation network, to segment the track regions in UAV images. [PERSON] et al. [5] proposed a two-stage detection and classification system based on GoogLeNet. The first stage detects the presence of tracks, while the second stage further classifies objects, such as lines, ballast, anchors, sleepers and fasteners, in UAV-based images. [PERSON] et al. [18] identified the location of track targets such as fasteners and insulators in the images by Faster RCNN. [PERSON] et al. [19] also finely segmented the fastener region using Yolact++ to detect the presence of looseness. [PERSON] et al. [20] designed a cascaded depth segmentation network (CDSNets) based on Deeplabv3 to extract insulators from suspended chainline images. A GAN-based defective sample generation network was proposed for solving the problem of the scarcity of defective samples.  
Although vision-based rail target segmentation and recognition have been widely studied, there is still a lack of systematic methods for rail detection on UAV platforms. Most of the traditional methods are only applicable to fixed-view cameras and cannot handle railways in complex scenes (e.g., lighting changes and background noise interference ), and existing deep neural networks ignore the intrinsic features of railway tracks (e.g., line shape and unique structure). A dedicated and fast railway track segmentation network needs to be designed to meet the demand of UAV-based track inspection.
## 2 Materials and Methods
### Determination of the Minimum Inhibitory Concentrations  
The working solutions of each antibiotic were prepared by making two-fold dilutions of the stock solutions in Nutrient broth (Himedia, Mumbai, India). _M. smegmatis_ cultures of all studied strains were cultivated overnight under the conditions described above (refer to \"Bacterial strains, media, and growth conditions\" section) until they reached the mid-log phase of growth. The bacterial cultures were adjusted to optical density OD\({}_{600}\) = 0.1 with the use of a spectrophotometer DiluPhotometer (Implen, Munchen, Germany). A total of 100 \(\mu\)L of each antibiotic working solution was dispensed into separate wells of the 96-well microtiter plate (Corning Inc., Corning, NY, USA), starting from the highest concentration towards the lowest concentration. Then, 100 \(\mu\)L of the diluted bacterial cultures were added to each well, ensuring that the final volume was 200 \(\mu\)L per well and including positive and negative controls in the plate. The positive control contained only the bacterial culture without any antibiotic, whereas the negative control contained only the antibiotic-working solution without any bacteria. The plates were incubated at 37 \({}^{\circ}\)C for 72 h. After the incubation period, the wells were visually inspected for growth. The minimum inhibitory concentration (MIC) was defined as the lowest concentration of antibiotic that completely inhibited the visible growth of the bacteria. The experiment was repeated three times to ensure reproducibility.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 4 Results
### Diagenetic Processes
#### 4.2.2.2 Cementation
In the early stage of diagenesis, calcite cement occupied in sandstone is generally formed from dissolved calcium components in pore water in open environmental conditions during the early diagenetic stage (burial depth < 1000 m) [53; 54], whereas in the later stages, calcite cement is mainly occupied by cementation resulting from the pressure dissolution of carbonate particles [53]. The occurrence of calcite cement in the Enping Formation reservoir in Huizhou Sag is attributed to early pore water.