esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Framework Architecture
### Proposed Method  
* The system is initiated by the administrator, who also establishes the system's hierarchy of roles and tasks. Channel 1 makes it easier to upload the system's created parameters for roles and tasks to the cloud;
* When consumers want cloud data access rights, they first submit an access request through Channel 2 based on their tasks and roles;
* If the consumer request is accepted, Channel 5 is used by the role entity to transfer the request to the task's entity. The cloud responds by providing consumers with a cryptographic task-role based access control (T-RBAC) plan;
* Encryption and uploading of data performed through Channel 3 can only be given by the owner if they feel the role or task can be trusted. The owner also informs the feedback collector of the consumers' identities during this stage;
* When an owner finds leakage of their data due to an untrustworthy consumer, they should report this role or task to the feedback collector through Channel 14;
* When an approved owner provides new feedback, the feedback collector sends it to the centralized database through Channel 11 to archive each confidence report and interaction record produced when the roles and tasks interact;
* The interaction logs are then stored in a centralized database, which is used by the trust decision engine to determine the trust value of roles and tasks using Channel 10;
* At any point, the roles' entity may approve trust assessments for the roles from the TMS, whereupon the roles' entity responds to the trust management system through Channel 13 to receive feedback from the trust decision engine;
* The role entity reviews the role parameters that decide a consumer's cloud role membership until the feedback is obtained from the trust decision engine, and any malicious consumer membership is terminated;
* When an owner provides negative feedback regarding a role as a result of data through Channel 4;
* At any point, the tasks entity may approve trust assessments for the tasks from the TMS, whereupon the tasks entity responds to the trust management system through Channel 12 to receive feedback from the trust decision engine;
* The task entity reviews the parameters of the task that decide a consumer's cloud task membership until the feedback is obtained from the trust decision engine, and any malicious consumer membership is terminated;
* When an owner provides negative feedback regarding a task as a result of data leakage, the task entity transfers the leakage details to the task behavior analyzer through Channel 7;
* The analyzers then use Channels 6 and 8 to keep updating the trust details for the roles and tasks in the centralized database;
* If an owner requests that their data be uploaded and encrypted in the cloud, the TMS performs a trust assessment. Once the TMS receives the request, it follows up with the owners through Channel 9;
* The trust decision engine provides the owners with details of the trust assessment for their roles and tasks. The data owners then determine whether or not to allow consumers access to their services based on the results.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 3 Motivation for OWL vs. XML
### eXtensible Markup Language (XML)  
The second approach is to use the XML-based technology, i.e., use XSD as a base for expressing types and then use XML to describe instance XML data about the environment and the resources. There are two main approaches to the storage of XML documents--as an XML enabled database or as a native XML database. The former is a relational database that transfers data between XML documents and tuples, whereas the latter stores XML data directly [40]. Device descriptions can be represented in XML. They can be queried, transformed, exported, and returned to application requests expressed in XQuery--the \"native\" query language for XML. Application requests represented as XQuery queries are processed by the XQuery processor which matches XML data against the query and returns the matching results.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 2 Materials and Methods
### Thermal Imagery
#### 2.2.1 The Correction of Thermal Imagery  
The temperatures of four ground features (tile, polyvinyl chloride board, wood, and asphalt road) with known emissivity (Table 2), were measured by an infrared thermal imager (T-420, FLIR Systems, Inc., Wilsonville, OR, USA). The coordinates of the features were measured using an RTK global positioning satellite device. Then, the linear regression relationship between the temperatures and DN values of the four features in the TIR images was established; consequently, the gray TIR image was converted into brightness temperature (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Mean** & **Standard Deviation** & **Maximum** & **Minimum** \\ \hline DBH (m) & 8.14 & 3.31 & 25 & 1 \\ H (m) & 4.5 & 1.6 & 9.8 & 1.2 \\ CD (m) & 2.2 & 1.0 & 7.3 & 0.5 \\ SD (ha\({}^{-1}\)) & 1206 & 660 & 2560 & 868 \\ LAI (m\({}^{2}\)-m\({}^{-2}\)) & 0.89 & 0.47 & 2 & 0.4 \\ SDR (\%) & 6.5 & 15.11 & 100 & 0 \\ SDR\({}_{\text{plot}}\) (\%) & 14.96 & 6.22 & 34 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics for each tree variables of two plots (\(n=409\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Emissivity** & **Area (m\({}^{2}\))** & **Temperature (\({}^{\circ}\)C)** & **Object Distance (m)** \\ \hline PVC board & 0.93 & 0.8 \(\times\) 1.2 & 24.5 & 1.0 \\ Tile & 0.94 & 0.8 \(\times\) 0.8 & 29.9 & 1.0 \\ Wood & 0.83 & 1.2 \(\times\) 1.2 & 27.5 & 1.0 \\ Asphalt road & 0.97 & 1.5 \(\times\) 1.5 & 39.1 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The characteristic parameters and temperatures of four ground features.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 3 A 2-Port MIMO System Using Two Flipped Parallel 1 \(\times\) 2 Arrays Antenna
### Design of a 2-Port MIMO System Using Two Flipped Parallel 1 \(\times\) 2 Arrays Arrangement  
Modern communication systems necessitate the use of MIMO antennas to enhance network capacity. A two-element MIMO combined from two flipped (1 \(\times\) 2) arrays is designed, simulated, and fabricated. Spatial diversity of antennas sufficiently controls MIMO efficiency to increase channel capacity. The design of the MIMO array antenna incorporates redundant operations with high gain, leading to improved error rates and enhanced efficiency in wireless systems. To create a 2-port MIMO system using two flipped parallel 1 \(\times\) 2 arrays, two antenna elements are initially placed at a distance of half a wavelength apart. A 0.1 mm-thin copper grounding line connects the two opposing 1 \(\times\) 2 arrays. This grounded connection helps reduce mutual coupling between the patches. Figure 13 displays both the simulated design and the fabricated prototype of the design. Figure 13e illustrates that the maximum radiation efficiency reaches 95% at 3.5 GHz and 94.7% at 5.8 GHz. Table 4 provides the actual design dimensions, which have an overall size of 132 \(\times\) 66 \(\times\) 1.57 mm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & **Substrate Width** & **Substrate Length** & **W1** & **W2** & **Wg** & **Ws** & **Lg** \\ \hline (mm) & 132 & 66 & 30.73 & 96.69 & 66 & 0.1 & 20.89 \\ \hline \end{tabular}
\end{table}
Table 4: Dimensions of two 1 \(\times\) 2 arrays MIMO.  
Figure 12: The E-plane: (**a**) simulated at 3.5 GHz; (**b**) at 5.8 GHz, of 1 \(\times\) 2 design.
## 4 GPU Architecture  
The architecture of GPUs has evolved over the last two decades from fixed pipelines to programmable processors which has extended their use to scientific applications or in fact any application that exhibits a high level of data parallelism. In this paper, the GPU is used to speed up the SSSP algorithm and find optimal paths between the DPs much faster. The GPU used is an NVIDIA RTX 2080 Ti [18]. A simplified diagram of the architecture of NVIDIA GPUs is shown in Figure 1. The GPU is composed of a large number of cores grouped into multiprocessors (MP). Each core has its own registers, but share the control logic and the shared memory with the other cores in the same MP. The GPU also has a global memory which is shared by all the cores across all the MPs. The RTX 2080 Ti has 4352 cores, 68 MPs, 64 K registers, and 64 KB of shared memory per MP. The global memory is 11 GB in size and has a bandwidth of 616 GB/s which is much faster than that of a typical CPU. The cores of the RTX 2080 Ti operates at a base frequency of 1350 MHz and a boost frequency of 1545 MHz producing 13.45 TFlops of computing power.  
NVIDIA GPUs can be programmed using the CUDA language which is an extension to the C and C++ languages. It allows the programming of kernel functions which are parallel functions executed on the GPU. When launching a kernel, the threads are divided into thread blocks. Each block is mapped to a SM. Threads in the same thread block can share data using the shared memory, but threads from different thread blocks cannot communicate between themselves. The only way to synchronize or share data across the thread block is to launch a new kernel. Although there are a large number of cores per SM, there is only a limited number of control logic units. This means that maximum performance is achieved when all the threads within a same SM execute the same instruction at the same time. If thread takes different branches of an if statement, referred to as thread divergence, branches are executed sequentially.
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
### Contribution  
The contribution of this work is the development and design of an outer loop regressor-based adaptive controller for a quadrotor equipped with an inner loop controller to achieve trajectory tracking. In this regard, the procedure is performed in two major steps: First, similar to previous works [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25], we assume the presence of an inaccessible and unmodifiable inner loop controller that not only guarantees internal stability but also allows one to transform the inputs from forces and torques to kinematic commands and represents the dynamics of the quadrotor as a general second-order system. Second, we design an outer loop controller directly on the already-obtained second-order system, thus achieving trajectory tracking and simplifying the analysis. As the outer loop is a regressor-based adaptive controller, the uncertainties coming from the inner loop are compensated in real time. In addition, the proposed outer loop scheme includes an integral action that increases the robustness of the controller and helps to cope with constant external disturbances. The proposed outer loop controller aims to provide a simpler adaptive solution in comparison with [24; 25], which are more elaborated adaptive neural network-based controllers. Furthermore, the proposed scheme's regression matrix is simpler in contrast to the proposed schemes in [19; 21].
# A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
## 2 Related Work
### Extraction and Presentation of Tourism Information  
In recent years, various studies have been conducted on the provision and analysis of tourism-related information on the Web.  
[PERSON] et al. [14], proposed an automatic score generation method in favor of the least reviewed local restaurants by analyzing the reviews posted on the Internet. They proposed a decision model using a convolution neural network with two hidden layers under a back propagation algorithm.  
[PERSON] et al. [15] proposed a geo-social event detection system by monitoring crowd behaviors indirectly through Twitter. Their proposed method focuses on temporal features within the target spot as an important factor for extracting geo-social events.  
On the other hand, [PERSON] et al. [16] proposed a method of predicting a user's location by focusing on the content of the tweet. Their method relies on the approach of the three key features which are (a) reliance purely on tweet content; (b) classification of words in tweets with a strong local geo-scope; and (c) a lattice-based neighborhood smoothing model.  
[PERSON] et al. [17] studied event detection from Twitter data, by applying Kalman filtering and particle filtering, which are widely used for location estimation in pervasive computing.  
In summary, these studies show that User-Generated Content has become a popular medium for expressing opinions and sharing knowledge about items such as products and travel entities while on the other hand, an essential tool for researchers to extract information.  
Tourist Information Recommendation  
Several studies propose recommendations of POI by suggesting suitable locations based on user preferences.  
[PERSON] et al. [18] proposed a method of mapping geotagged tweets to sightspots based on the substantial activity regions of the spots. Their method learns from One-Class Support Vector Machines-based classifier which first extracts temporal and phrasal features of the pattern sentences for classification and further maps the tweets into respective regions. Location-based SNS such as Foursquare were useful in this study by providing geotagged post data.  
[PERSON] et al. [6] suggested a method that identifies on-site likelihood adequateness of posted tweets with a two-stage method, which includes rule-based filtering, and a machine learning (ML) technique. In their method, a previous and next tweet was taken into consideration as a potential target defining context information. The analysis of the experimental results shows the effectiveness of the combined applied techniques.  
Overall, as discussed above, there have been some studies attempting to extract characteristics of the target regions based on geotagged contents.  
However, while many of the above-mentioned studies, focus on the extraction of information using either rule-based approaches or simple ML classifiers (e.g., SVM), we focus on extraction of online opinions and assigning scores by adopting a state-of-the-art neural network-based architecture (BERT).
## 2 Methodology  
In order to investigate our hypothesis, measurements were performed with a dodecahedron speaker and with common directional loudspeakers for the same source and microphone positions. The measurements were carried out in three spaces of the Technological Educational Institute of Crete, Department of Music Technology and Acoustics (Figure 1) with volumes of 88 m\({}^{3}\), 192.4 m\({}^{3}\) and 1088 m\({}^{3}\) (referred to as small, midsized and large in the manuscript respectively). The first space is a small classroom mainly used as an acoustic laboratory with many absorbent and diffusing surfaces, the second one is a typical classroom with minimal absorbent surfaces and the last one is an amphitheater with highly absorbent seats. The microphone and source positions, for which the measurements will be presented in the results section, are shown in Figure 1.  
Three different directional speakers were used that belong to different price categories (Figure 2). A Philips PCA220 SD/EU (Philips, Amsterdam, The Netherlands) is a typical multimedia speaker (or computer speaker) with an internal amplifier belonging in the low-price range. A Behringer Truth B2031A Active 2-Way Reference Studio Monitor (Behringer, Willich, Germany) [40] is a speaker in the medium/low price range. Finally, a Genelec 8030B Studio Monitor (Genelec, Lislami, Finland) [41] was used which is of the medium to high price range. The Behringer and Genelec are two-way speakers, comprised from a tweeter and a driver speaker, while the Philips is a one-way design comprised of a single driver speaker. A dodecahedron loudspeaker, 01 dB-Stell, Type DO12 (01 dB-Stell, Limonest, France) was employed in order to compare the measurements.  
The pair of Philips speaker costs around 50 euros, the pair of Behringer speakers costs around 400 euros and the pair of Genelec speakers costs around 1000 euros. The model of the dodecahedron speaker that we have used (Type DO12) has been discontinued by the company (01 dB), but the price for the newer equivalent models (LS01, LS02) is around 6000 euros.  
For all the measurement positions an omnidirectional microphone, Earthworks M30, Type 4190 (Earthworks, Milford, CT, USA) with a flat frequency response was used [42].  
Measurements were performed with different placements of the directional speaker (twelve positions similar to the twelve positions of the faces of a dodecahedron speaker, different rotations of the loudspeakers for a total sum of twenty six and fourteen positions). The twelve, twenty six and fourteen impulse responses obtained were added up creating a single impulse response for each case.
## 4 Results and Discussions
### Simplified Gearbox Analysis  
A simple gearbox operating at steady-state was studied with imbalance caused in the rotor i.e., slow shaft, and the high speed shaft rotating at 750 rpm. The characteristic frequencies of the gearbox are summarised in Table 2.  
Figure 7: Cyclic Spectral Coherence of the second bearing with roller defect; top figure shows the 2 \(\times\) BSF frequency (80.0 Hz) and the FT (4.4 Hz) frequency and the bottom figure is the integration of the top figure along frequency axis.  
The Cyclic Spectral Coherence was calculated using 4 s vibration signals. The calculated results are presented in Figure 8. As the induced fault was imbalance, modulations and cyclic behaviour at the gear-mesh (GM) frequency of the first stage of the gearbox (10.5 Hz) were caused.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Frequency (Hz)** \\ \hline Output Shaft & 12.5 \\
3 rd Stage Gear-mesh & 337.5 \\ Interim Shaft & 3.55 \\
2 nd Stage Gear-mesh & 35.5 \\ Slow Shaft & 0.83 \\
1 st Stage Gear-mesh & 11.6 \\
1 st GAPF & 5.8 \\ Input Shaft & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The characteristic frequencies of the used gearbox at 740 rpm speed. Here the GAPF stands for Gear Assembly Phase Frequency.  
Figure 8: Cyclic Spectral Coherence of unbalanced condition. From left to right the top row shows the original signal and filtered signal using Wavelet Daubechies-3 (db3) filter. The middle row is the first and second IMFs and the bottom row is 6 th and 8 th IMFs.  
As it can be seen in Figure 8a, the result from the analysis of the original raw signal is less clear in comparison with the bearing test. For comparison purposes, the analysis was also applied to the de-noised signal using the Wavelet de-noising technique. This was carried out using Daubechies-3 wavelet. However, de-noising the signal worsened the Cyclic Spectral Coherence result as it is shown in Figure 8b.  
Using the EEMD technique, the signal was decomposed into 8 IMFs and the Cyclic Spectral Coherence of each IMF was calculated. The result of the first two IMFs is presented in Figure 8c,d respectively. As it can be seen, the result is slightly less contaminated compared with the raw and de-noised signal. However, more interestingly, in Figure 8e,f which shows the result on the 6 th and 8 th IMFs two other frequencies are visible. These frequencies are the first stage GAPF frequency (5.3 Hz) and the second stage rotational frequency (3.5 Hz).  
The presence of both first stage gear mesh and GAPF cyclic behaviour clearly indicates the imbalance applied to the first stage. The existence of the second stage rotational frequency could as well indicate the imbalance. Nonetheless, it can also show the normal vibration contaminating the signal.
# Cyanobacteria as Chassis for Industrial Biotechnology: Progress and Prospects
## 2 Cyanobacterial \"Omics\"
The metabolomic profile suggested that the limiting factor of isoprene production under stress might be due to an insufficient precursor level, while the transcriptome revealed an upregulation of RNA characteristic of acclimation to stress [64]. Tolerance of _Synechocystis_ 6803 towards other products, like butanol, was investigated using transcriptomics and it was revealed that the gene encoding HspA, a small heat shock protein, was induced when _Synechocystis_ 6803 was grown in the presence of butanol. By overexpressing this gene, _Synechocystis_ was more tolerant to butanol [65]. Proteomics analysis identified genes in _Synechocystis_ 6803 that are induced in response to hexane, butanol and ethanol [66; 67; 68]. The information gained can be used to engineer tolerance into _Synechocystis_ 6803 in the hope of optimizing the production of biofuels and other toxic products in this model organism. Such studies can later be expanded to include other cyanobacterial strains. Over the past decade, _Synechocystis_ 6803 metabolism has been modeled [69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79] to understand and improve cyanobacterial biofuel production. In 2009, the first genome-scale model of _Synechocystis_ 6803 to predict the effect of biofuel production was produced [80]. The in silico predictions introducing an ethanol-producing pathway into _Synechocystis_ 6803 matched the experimental data [80]. This represents a good example of how transcriptomics and proteomics can assist in a more directed research approach that can immensely help in saving time. Recently, a visual representation of the metabolic network of _Synechocystis_ 6803 called FAME was produced ([[http://f-a-m-e.org/synechocystis/](http://f-a-m-e.org/synechocystis/)]([http://f-a-m-e.org/synechocystis/](http://f-a-m-e.org/synechocystis/))), which can incorporate experimental and computational data. FAME can be used as an aid to engineer the metabolic pathway of _Synechocystis_ 6803 for the production of a range of high value products and biofuels [81]. Thus far, the majority of the transcriptomic, proteomic, metabolomic and systems biology data available has focused on _Synechocystis_ 6803 with little work performed on other cyanobacterial strains. Although a genome-scale model has been produced for _Synechococcus_ 7942, [82] modeling the nitrogen-fixing _Cyanothece_ has already been attempted [83; 84]. However, for cyanobacterial strains without a well-annotated genome, producing a genome-scale model will be challenging.
## 5 Limitations and Future Works  
The proposed method has two main limitations. Firstly, although the surface detail decoder enhances surface information, some reconstructed object shapes, such as the speaker in Figure 6, lack prominent surface detail. This limitation may be attributed to the introduction of redundant local features during the implicit field deformation procedure. To address this, future studies should explore adaptive neglect of unnecessary local features as an attractive direction for improvement. Secondly, while ED\({}^{2}\)IF\({}^{2}\)-Net outperforms similar methods in terms of inference speed and performance, it is not specifically designed for real-time 3D reconstruction. This may pose challenges for systems that require real-time reconstruction. To tackle this issue, we plan to leverage a sparse sphere rendering algorithm [33; 66] to accelerate inference speed. Additionally, we aim to explore more advanced transformers, such as Swin Transformer V2 [67], to enhance the feature extraction capability of ED\({}^{2}\)IF\({}^{2}\)-Net.  
In future work, we will optimize the proposed framework for embedded platforms, considering the following aspects: (1) reducing model parameters and computational complexity by minimizing the number of layers in the Pyramid Vision Transformer or reducing the number of channels in the deformation decoder's convolutional layer while maintaining performance; (2) improving the readout speed of implicit fields and displacement fields by utilizing more efficient data structures, such as hash tables, for data storage; (3) optimizing the training and prediction process through techniques such as distillation [68]; (4) deploying the framework on native embedded platforms to reduce communication and latency.