esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Delivery
### Packing  
The CubeSat has to be taken from the laboratory's clean environment to another clean environment at the launch service provider's site without being exposed to the dust. Hence, packing the satellite properly is crucial. A sealable antistatic bag to cover the entire satellite is usually sufficient. Vacuuming the bag may be preferred, although small amount of air inside the bag could act as cushion and protect the CubeSat against hard impacts.  
In general, solar panels are very delicate to scratches, so ideally nothing should touch them during their entire lifetime. A common measure to avoid any accidental contact with the solar panels during ground operations are methacrylate covers. They are quite effective to protect solar panels, but they usually need designated points in the structure to attach to with the accompanying fasteners.  
Figure 16: Temperature profile during thermal-cycling test.  
To transport the satellite safely a shock-absorbent suitcase is required, such as those used for transporting delicate instruments like optics and filming equipment. These suitcases usually come with shock absorbing foams inside, which protects the item from any impact. However, foam materials can be easily charged electrically, and they can also leave residues after trimming them to size, both of which may be detrimental to clean room operations. Additionally, tamper-evident seals, such as a simple signed zip tie, should be used to prevent the suitcase from being opened during transportation.
## 5 Discussion
### The Niche Role of Low-Cost UAS Platforms for Snow Depth Mapping in the Arctic  
The proposed snow mapping method is applicable for all the surveyed scenarios with the tested equipment. The average difference between measured and estimated snow depth ranged between 0.015 m and 0.16 m, with the RMSE between 0.05 to 0.18 m. Although these statistics are based on a limited validation power (see validation caveats section), these values are comparable to LiDAR estimates, with reported RMSE of single DEMs as great as 0.1 m and accuracies of snow depth estimates of 0.15 m [21]. The spatial resolutions for our method ranged from 0.06 to 0.09 m, whereas for LiDAR airborne datasets are on the order of 1 m point spacing [21].  
While the technical set-ups investigated in this study are theoretically capable of mapping considerably larger areal extents, this method is, however, not intended as a substitute for airborne LiDAR, which will always be more efficient at covering catchment-scale areas. This low-cost UAS method is instead intended as an easily applicable economic alternative to track snow depth in relatively small areas that require high spatial resolution and ease of repeatability.  
This low-cost and high-resolution methodology for mapping snow depth is applicable to many research and civil applications in the vast and sparsely populated Arctic, where observing power is limited [1]. Minimal set-ups (e.g., DJI Phantom(r) UASs and GoPro(r)) are nowadays extremely common and affordable. Beyond Arctic researchers requiring snow depth estimates across multiple disciplines, the demonstrated workflow can also assist local environmental and municipal entities. This use of  
Figure 6: Snow depth transects across two exemplar cases of diverse underlying topographies are shown for both the advanced set-up on area Green1 (**a**) and for the minimal set-up on area Svall (**b**). The blue line represents the snow cover, the brown line the underlying topography. Panel (**c**) displays an example of topography induced shadow which introduces a Structure from Motion (SfM) error in the reconstruction. The arrow at the marked position in panel (**b**) indicates the location of this error. The arrow in panel (**a**) indicates instead the position of the reconstruction error associated with the land-based surveys’ poor camera positions. Overall, highly detailed features such as snow deposition on top of the boulders and small-scale footsteps are correctly represented.  
consumer-grade equipment for scientific applications is also beneficial in terms of the cost, ease of handling, and ability to perform in extreme conditions. Other opportunities are also foreseen through engagement in citizen science as individual use of these easily affordable solutions grows [40; 45].
# Augmented Reality in Precision Farming: Concepts and Applications
###### Abstract  
The amount of arable land is limited, yet the demand for agricultural food products is increasing. This issue has led to the notion of precision farming, where smart city-based technologies (e.g., Internet of Things, digital twins, artificial intelligence) are employed in combination to cater for increased production with fewer resources. Widely used in manufacturing, augmented reality has demonstrated impactful solutions for information communication, remote monitoring and increased interaction. Yet, the technology has only recently begun to find a footing alongside precision farming solutions, despite the many benefits possible to farmers through augmenting the physical world with digital objects. Therefore, this article reflects on literature discussing current applied solutions within agriculture, where augmented reality has demonstrated a significant impact for monitoring and production. The findings discuss that augmented reality must be coupled with other technologies (e.g., simultaneous localization and mapping algorithms, global positioning systems, and sensors), specifically 9 are identified across 2 application domains (livestock and crop farming) to be beneficial. Attention is also provided on how augmented reality should be employed within agriculture, where related-work examples are drawn from in order to discuss suitable hardware approaches and constraints (e.g., mobility).  
augmented reality; agriculture; precision farming; smart cities 10.3390/smartcities4040077  
10 November 2021  
1
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 4 A Multi-Server SSE Scheme
#### Security Analysis
Given the above leakage, we now define the game and how to construct a simulator, \(\mathcal{S}\). In a game for non-adaptive adversary, the adversary submits its sequence of queries to the challenger all at once. The sequence must begin with input for setup, and subsequent queries are search query, add document query or remove document query. For any adversary \(\mathcal{A}\), consider the simulator \(\mathcal{S}\) defined below. From \(\mathcal{L}^{setup}=(|B|,b)\), \(\mathcal{S}\) generates \(|B|\) random binary strings with length \(b\). These random strings becomes \(\mathcal{S}\)'s set of unnamed blocks. Then, by referring to search queries, \(\mathcal{S}\) is given \(\mathcal{L}^{query}=|B_{q}|\). \(\mathcal{S}\) checks whether the queried block identifiers exists in its set of labeled blocks. If the identifiers have not been used, \(\mathcal{S}\) chooses \(|B_{q}|\) unnamed blocks and labels them with the block identifiers submitted by the SDV controller. Similarly, \(\mathcal{S}\) refers to file removal queries and use \(\mathcal{L}^{update}=|B_{q}|\). If there is no encrypted block labeled with the list of block identifiers to be deleted from storage, \(\mathcal{S}\) chooses \(|B_{q}|\) unnamed encrypted blocks and labels them accordingly. For file addition queries with given \(\mathcal{L}^{update}=|B_{q}|\), \(\mathcal{S}\) generates \(|B_{q}|\) binary strings of length \(b\) and labels them with the submitted block identifiers. Finally, \(\mathcal{S}\) labels all other unnamed encrypted blocks with random binary string of length identical to the length of the block identifiers. Since the encryption scheme \(\mathcal{E}\) used in the SDV is IND-CPA, the random binary strings generated by \(\mathcal{S}\) are indistinguishable from the encrypted blocks generated by the SDV scheme. Thus, using the set of labeled blocks, the transcript for the query sequence produced by \(\mathcal{S}\) is indistinguishable from the transcript produced by the SDV scheme. We note that the above security analysis is based on the assumption that the SDV controller resides on a trusted query gateway (proxy server). This means the index table \(I\) is stored at the gateway and thus leakage due to index is not factored in. In the case where the query gateway is assumed to be semi-honest (honest-but-curious), then the leakage profile of the index table must be considered. It means leakage of \(\mathcal{L}^{query}=|B_{q}|\) even _before_ queries, compared to the above analysis where this leakage happens only _after_ queries. We further remark that, if an additional query module with more expressive search is deployed, the leakage due to keywords, instead of just leakage due to filename, on the overall SDV system will be at least the leakage profile of the deployed query module.
## 4 Methodology
### Measurement Model Assessment
#### 4.3.1 Outliers  
The Mahalanobis distance is a widely known measure for pinpointing outliers in a multivariate data set [64]. Potential outliers can be detected by checking for high Mahalanobis distance, where both p1 and p2 are zero. Although outlier removal was performed accordingly for 14 cases, the omission of these outliers had non-significant influence on the SEM results. On this basis, further analyses in the study were conducted with the outliers included.
# Numerical-Experimental Analysis to Optimize the Geometry of a Prosthetic Abtment
## 5 Experimental Tests
### Type of Sensors and Their Arrangement  
In order to acquire the data of the experimental tests, n.5 displacement sensors were positioned according to the diagram of Figure 23. It was thus possible to determine the displacements of the crown in the vestibule-lingual direction (1-2), the vertical displacement of the test machine crosshead with respect to the basement (3), and the related displacements between crown and abutment (4-5). The used sensors are HBM-WA/2 mm L-type inductive displacement transducers (LVDTs), with range 0-2 mm, and \(\pm\)0.2% linearity deviation. Figure 24a,b show the positions of the sensors during the experimental tests.  
To experimentally detect the pressure distribution during the compression test, a pressure-sensitive film was interposed between the internal surface of the crown and the upper part of the abutment. Fuji pre-scale films were used since they are recommended for small-scale applications. In particular, starting from FEA, according to which stress values around 10 MPa were expected, two different films were adopted: low-pressure LW (2.5-10 MPa) and medium-pressure MW (10-50 MPa). Stress state was determined through an accurate analysis of the colors intensity shown on the film once loads were applied. This analysis was performed by a suitable color-processing toolbox (ColorLab 1.0) implemented in a MatLab(r) environment [47].  
Figure 23: Load diagram and arrangement of LVDT sensors in a typical compression test.  
Figure 22: Separate elements of the prototype.  
First, the MW pre-scale film was used. Since measured stress values were found to be less than 10 MPa, which is the minimum threshold of the measuring range, it was necessary to use the LW pre-scale films for the tests. The obtained values are shown in Figure 25.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 4 Simulation and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Frequency (MHz)** & \(\mathbf{\theta_{12}}\) & \(\mathbf{\theta_{23}}\) & \(\mathbf{\theta_{34}}\) & \(\mathbf{\theta_{45}}\) & \(\mathbf{\theta_{51}}\) & **Azimuth (\({}^{\circ}\))** \\ \hline \(151\) & \(-81.397\) & \(-131.6\) & \(-0.24426\) & \(131.66\) & \(81.587\) & \(0\) \\ \(151\) & \(-71.317\) & \(-135.07\) & \(-12.096\) & \(127.76\) & \(90.726\) & \(5\) \\ \(151\) & \(-60.364\) & \(-137.2\) & \(-24.359\) & \(122.38\) & \(99.546\) & \(10\) \\ \(151\) & \(-49.47\) & \(-138.45\) & \(-35.756\) & \(116.07\) & \(107.61\) & \(15\) \\ \hline
**Frequency (MHz)** & \(\mathbf{\theta_{13}}\) & \(\mathbf{\theta_{24}}\) & \(\mathbf{\theta_{35}}\) & \(\mathbf{\theta_{14}}\) & \(\mathbf{\theta_{25}}\) & **Azimuth (\({}^{\circ}\))** \\ \hline \(151\) & \(147\) & \(-131.85\) & \(131.41\) & \(-146.75\) & \(0.19037\) & \(0\) \\ \(151\) & \(153.61\) & \(-147.17\) & \(115.66\) & \(-141.51\) & \(19.409\) & \(5\) \\ \(151\) & \(162.43  
* **Datasets. ** Training Dataset \(T_{1}\): 72,000 ideal simulation signal samples are generated under ideal conditions, with a frequency range of 100 MHz-200 MHz. Training Dataset \(T_{2}\): 1800 actual signal samples are collected in the actual environment, with a frequency range of 150 MHz-200 MHz. Test Dataset \(D\): 1800 actual signal samples, which are different with \(T_{2}\), with a frequency range of 150 MHz-200 MHz. Angular values of all signal samples are replaced by sine and cosine values, which are the labels.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 5 Future Directions
### Cooperative Communication  
Hybrid cooperative schemes combining amplify-and-forward (AF) and decode-and-forward (DF) relaying could be explored to optimize system performance. Scenarios with diverse channel conditions should be taken into account. Intelligent reflecting surface (IRS)-assisted cooperative communication is another potential way to improve the secrecy capacity while minimizing interference and improving energy efficiency. AI-based cooperative schemes can be used to optimize performance in real time by adapting to dynamic channel conditions and eavesdropping attacks.  
A cooperative scheme with adaptive resource allocation is another potential way to improve secrecy capacity and energy efficiency. Such techniques can be made resilient to channel estimation errors, interference, and uncertainties. Secure cooperative jamming could be explored to degrade the eavesdropper's channel and improve the security of the legitimate channel. Secure edge computing considering data privacy and confidentiality can be investigated for complex ISTNs. Furthermore, a trade-off between different requirements based on diverse and dynamic channel conditions can be considered.
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 2 Types of Wastewater Treatment Plants Where _Thiothrix_ Morphotype Can Occur
### Membrane bioreactors  
Membrane bioreactors (MBRs) are modern, high-intensity biological treatment facilities. In membrane bioreactors, the separation of activated sludge flocks from treated wastewater is achieved by filtering the sludge mixture through an ultrafiltration or micro-filtration membrane, after which microorganisms are removed from the membrane surface by an aeration system [62].  
Challenges in operating membrane bioreactors are related to frequent membrane clogging, including the growth of filamentous bacteria [63]. _Thiothrix eikeloomii_ (= _Thiolineaeikelboomii)_ was reported to decrease the sludge sedimentability, increase the apparent viscosity in aerobic environments and cause dense biofilm formation on the membrane surface [12]. Membrane contamination due to an increase in the number of bacteria of the _Thiodrix_ morphotype is more active at low temperatures [41].  
Several methods have been proposed to offset the negative effects of bacteria of the _Thiodrix_ morphotype in MBR operation. The overgrowth of _Thiodrix_-like organisms was suppressed by using micellian granules to granulate activated sludge [64], by the use of high sludge concentrations in the MBR [63] and by backwashing the membrane with NaClIO [65].
## 3 Method and Data
### Data and Variable  
The calculation of carbon emissions includes two parts: fossil energy and electricity. The original provincial data of 2005-2017 are from the China Energy Statistical Yearbook (2006-2018). The standard coal coefficient, carbon emissions coefficient and average CO2 emissions factor of the regional power grid are derived from the IPCC guidelines for national greenhouse gas inventories and the general principles for computing comprehensive energy consumption (GB/T2589-2008). The control variables for evaluating carbon emission effect include population factors, economic factors, technological factors, environmental regulations, and innovation driving factors. Here, population size and the urbanization rate are population factors, per capita GDP and the industrialization rate are economic factors, the carbon intensity and the energy structure are technological factors, the intensity of the environmental pollution control is an environmental regulation factor, and the research structure and research intensity are innovation driving factors. The selected control variables for GDP are the capital stock, the labor force, and energy consumption. Environmental effect indicators and economic effect indicators constitute an indicator system for testing the \"Porter effect\" of China's carbon trading pilot. The original data of population size, urbanization rate, per capita GDP, industrialization rate, capital stock, and labor force are obtained from the China Statistical Yearbook (2006-2018). The original data of carbon intensity, energy structure and energy consumption are obtained from China Energy Statistical Yearbook (2006-2018). The intensity of environmental pollution control, research structure and research intensity are obtained from the China Environmental Yearbook (2006-2018) and the China Scientific and the Statistical Yearbook (2006-2018). The specific meaning and calculation method of each index are shown in Table 1 below.
# Conductive Textiles for Signal Sensing and Technical Applications
## 5 Application of Conductive Textiles  
Electrically conductive textiles are used in various fields, such as the medical, sports, soft robotics, security, and military industries, and as wearable displays [287]. Textile-based electrical components include electrodes, sensors, wearable antennas, wearable displays, actuators, and heating devices [288].
## 3 Results and Discussion
### Study of Adaptive Properties of Fungi
#### 3.3.1 Ligninolytic Enzymes Production
Different studies have shown that extracellular ligninolytic enzymes of these fungi are responsible for the initial oxidation of different pollutants, including PAHs. Ligninolytic fungi were found to polymerize alkylphenols as a result of their initial attack by extracellular lignin-modifying enzymes, and to degrade the primary structure of these compounds [14; 24; 37; 38]. In our experiments, the study of the activity of the main enzymes of the ligninolytic complex in the cultivation medium with or without pollutants revealed the production of lacase, Mn-peroxidase, and lignin peroxidase. Differences in the activity and composition of the ligninolytic complexes of individual species were noted: _P. ostreatus_ strains, _T. versicolor_ strains, and _S. commune_ produced lacase and Mn-peroxidase; _S. rugososanulata_ produced only lacase under the experimental conditions, and _B. adusta_ produced lignin peroxidase and Mn-peroxidase. Finally, the main active producers of ligninolytic enzymes were fungi belonging to _Pleurotus_. The degradation of pollutants was accompanied by the production of lacases and Mn-peroxidases in basidiomycetes, whereas the ascomycetes produced only peroxididases. Three from studied basidiomycetes (_Sch. commune_, _Str. rugososanulata_, and _T. versicolor_) can produce a third ligninolytic enzyme--lignin peroxidase--as response to the presence of neonol AF9-12 or isononylphenol. Thus, among the studied fungi, lacase and Mn-peroxidase are the most common ligninolytic enzymes. Earlier, we showed that the presence of these enzymes is a necessary condition for the complete degradation of PAH [76; 77; 78]. The production of ligninolytic lacases and peroxididases in the presence of pollutants can be seen as one of the mechanisms of adaptation of fungi.
## 3 Results and Discussion
### Thermomechanical Properties
#### 3.3.3 Damping Factor (tan\(\delta\))  
Figure 5c presents the ratio of the loss modulus (E\") and the storage modulus (E\") which is the mechanical loss factor or tan\(\delta\). It can be related to the impact resistance of a material. The polyester (control) showed the highest tan\(\delta\) value indicative of maximum chain mobility due to poor thermal resistance of polyester resin, unlike phenolic resin, which showed a higher thermal resistance than its natural fibre reinforced composite [83]. However, introduction of CPH fibre shifts the curve to the left showing that fibre incorporation affects damping which could be due to the inhibition of chain mobility due to filler addition. Another reason being that the type and distribution of fibres incorporated, as well as the filler/matrix interaction and void content, all have an impact on damping  
Figure 5: (**a**) Average storage modulus curve (E”) for CPH reinforced composite. (**b**) Average loss modulus curve (E”) for CPH reinforced composite. (**c**) Average damping factor tan \(\delta\) for CPH reinforced composite. (**d**) Average storage modulus curve (E”) for hybrid composite. (**e**) Average loss modulus curve (E”) for hybrid composite. (**f**) Average damping factor tan \(\delta\) for hybrid composite.  
factor [84]. As the temperature rises, the damping values reach a peak in the transition area before falling in the rubbery region.  
It was observed that with the addition of clay particles, a lowering of tan\(\delta\) peaks with increase in fibre loading having sample E2 with 18% fibre content with the lowest peak. This is due to improvement in interfacial bonding as shown in Figure 5f. The position and height of the tan\(\delta\) peak are indicators of a composite material's structure and properties [77]. Stronger interfacial adhesion means low molecular chain mobility hence less energy dissipation [83; 85] observed by the lowering of tan\(\delta\). Below 100 \({}^{\circ}\)C the fabricated composites displayed higher damping than the polyester suggesting that addition of CPH/clay to composites can improve their energy accumulation capabilities therefore they could be used as dampers at such temperatures.
## 3 System Modeling
### Evaluation Metrics  
To objectively and comprehensively evaluate in situ data management schemes driven by different algorithms, this study simulates the in situ data access management environment under the InS system. To comprehensively assess the proposed in situ data management mechanism, three metrics are selected: Average Waiting Delay (AWD), Average Residual Requirements (ARR), and Average Response Latency (ARL). These metrics respectively quantify system performance from the perspectives of response capability, queue congestion, and the processing efficiency of completed requests, thereby providing a comprehensive reflection of the data management strategy in terms of real-time performance and load balancing.