esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 4 Robust DOA Estimation with Array Sensor Failure
### Analysis on Computational Complexity  
In this subsection, we provide theoretical analysis on the computational complexity of the proposed algorithm. The proposed algorithm mainly involves the Hankel tensor completion and the completed covariance tensor decomposition, whose computational complexities are \(\mathcal{O}\big{(}(M+N+T)(M-\tau_{1})(N-\tau_{2})(T-\tau_{3})\tau_{1}\tau_{2} \tau_{3}\mathcal{Q}_{\rm TC}\big{)}\) and \(\mathcal{O}\big{(}M^{2}N^{2}T+(K^{3}+M^{2}N^{2})K\mathcal{Q}_{\rm CPD}\big{)}\). Here, \(Q_{\rm TC}\) and \(Q_{\rm CPD}\) are, respectively, the numbers of iterations for implementing the tensor completion and CPD. The computational complexity of the proposed algorithm can then be measured by \(\mathcal{O}\big{(}(M+N+T)(M-\tau_{1})(N-\tau_{2})\)\((T-\tau_{3})\tau_{1}\tau_{2}\tau_{3}\mathcal{Q}_{\rm TC}+M^{2}N^{2}(Q_{\rm CPD}+T) \big{)}\). Due to the globally convergent ADMM for tensor completion, the proposed [PERSON] tensor completion approach presents satisfactory convergence behavior. It normally converges within dozens of iterations, making \(Q_{\rm TC}\) relatively small.  
The computational complexity of the matrix completion (MC) method [23] is \(\mathcal{Q}\big{(}T+\log(\frac{1}{\eta})(rMNT+r^{2}(MN+T))+T(M^{2}N^{2}+MN)+ MNQ_{\rm SG}\big{)}\), where \(\eta\) and \(Q_{\rm SG}\), respectively, denote the convergence threshold and number of spectral searching grids for implementing the matrix completion. In addition, the computational complexity of the direct CPD method [37] is \(\mathcal{O}\big{(}M^{2}N^{2}T+(K^{3}+M^{2}N^{2})K\mathcal{Q}_{\rm CPD}\big{)}\). The proposed algorithm is more computational costly compared to the direct CPD method due to the Hankel tensor completion. Also, the high-order tensor formulation and multi-linear tensor completion would cause increased complexities than the matrix completion and spectral searching procedures. However, the satisfactory convergence of the ADMM and closed-form solution of CPD enables comparable efficiency. Thus, though incorporating the high-order tensor formulation and optimization, the computational complexity of this proposed algorithm can be moderate compared with existing methods.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 3 Microbial Diversity and Ecology
### Microbial Degradation Mechanism  
The most complete and fastest degradation of organic pollutants occurs under aerobic conditions [21]. The primary intracellular attack of pollutants is activation, and an oxidative process, as well as the integration of oxygen, is the enzymatic instrumental reaction catalyzed by peroxididases and oxygenases [30]. Organic pollutants gradually convert peripheral degradation pathways into intermediate stages of the central intermediate metabolism, e.g., the tricarboxylic acid cycle [21]. The biosynthesis of biomass cells occurs on the central metabolites, e.g., pyruvate, succinate, and acetyl-CoA. The saccharides needed for diverse growth and biosynthesis are synthesized via gluconeogenesis. Specific enzyme systems could also be used to mediate the degradation of PHs. Other microbial degradation techniques utilized are the development of biosurfactants and the microbial attachment of cells to the substrates [31]. PHS can be selectively metabolized by a microbial consortium of strains or a single strain of microorganisms belonging to different or the same genera [32]. The consortium showed a way to degrade or metabolize PHs more than the  
Figure 4: Schematic representation of microbial community analysis of crude oil-polluted soil using high throughput sequencing methods. Source: [PERSON] & [PERSON] [21].  
individual cultures [9; 33]. Table 2 shows some microorganisms used in the biodegradation of petroleum products.
## 2 Materials and Methods
### Experimental Evaluation
#### 2.2.3 NIRS Channel Selection  
We used the Contrast-to-Noise Ratio (CNR) of [Hb] as an indicator of task-induced hemodynamic response. Thus, each channel had two CNR values, one corresponding to [HbO] and the other to [HbR]. The CNR compares the amplitude contrast between hand-closing and hand-opening states. A task-induced response has a CNR value greater than zero for [HbO] and lower for [HbR] as in (1). A larger absolute value means a greater task-induced response. CNRs have been used for selecting NIRS channels containing task-evoked hemodynamic responses [15; 16].  
\[\text{CNR}=\sum\
olimits_{\text{i}=1}^{15}\frac{\text{mean}\Big{(}\left[ \text{Hb}\right]_{\text{hand-close, }\text{i}^{\text{th}}}\Big{)}-\text{mean}\Big{(}\left[ \text{Hb}\right]_{\text{hand-open, }\text{i}^{\text{th}}}\Big{)}}{\sqrt{\text{var}\Big{(}\left[ \text{Hb}\right]_{\text{hand-close, }\text{i}^{\text{th}}}\Big{)}-\text{var}\Big{(}\left[ \text{Hb}\right]_{\text{hand-open, }\text{i}^{\text{th}}}\Big{)}}}, \tag{1}\]  
The entire hand-closing period (15 s) and the final 15 s of the hand-opening period were used for calculating the CNRs for the respective periods. Here, the CNR values for a channel are the mean CNRs of 15 trials after bandpass filtering.
## 2 Proposed Method  
Most image thresholding methods use the grayscale histogram information of the image to find the threshold value for image segmentation. The statistical approach [31] and the functional histogram approximation approach [34] are two common approaches that are used to develop image thresholding algorithms based on the grayscale histogram information. In this section, we are describing a new functional histogram approximation-based infrared image thresholding algorithm. First, the one-dimensional histogram information such as the global maximum grayscale frequency and its corresponding intensity level are extracted to split the histogram into two parts. The reason behind extracting the global maximum and its related information is to reduce the search space region for getting an optimal threshold value for image segmentation. In the proposed method, the grayscale intensity space above the grayscale intensity corresponding to the global maximum pixel frequency was used as the search space region in order to obtain an optimal threshold value.  
An image can be represented in the three-dimensional coordinate system. Let \(f(x,y)\) be a grayscale image where \(x\) and \(y\) represent the spatial position of the pixels and \(f(x,y)\) represent the intensity of the pixel located at the point \((x,y)\) on the image. The histogram of an image represents the number of pixels associated with each grayscale intensity in that image. The pixel distribution on the grayscale histogram can be represented by \(L\) discrete gray-levels, \([0,1,2\dots\ L-1]\). The one dimensional (1D) histogram of a digital image can be defined as [35]  
\[h_{i}=n_{i};\hskip 14.226378 pti\epsilon[0,L-1] \tag{1}\]  
where \(n_{i}\) represents the gray level frequency at gray-level \(i\).  
In the proposed method, the 1D-histogram of the image is partitioned into two parts based on the maximum gray-level frequency \(h_{m}\). The first part of the histogram is represented with \(h_{1i}\) and the second part of the histogram is represented with \(h_{2i}\) as shown in Figure 1b. The mathematical expression of the grayscale histogram of an image \(h_{i}\) can be written as  
\[h_{i}=\left\{\begin{array}{l}h_{1i}\text{ for }\hskip 8.535827 pt0\leq i=f(x,y) \leq i_{m}\\ h_{2i}\text{ for }i_{m}\leq f(x,y)\leq L-1\end{array}\right. \tag{2}\]  
here, \(i_{m}\) is the grayscale intensity corresponding to the grayscale frequency \(h_{m}\).  
This process is explained more explicitly in Figure 1. Figure 1a shows an infrared image of the person walking and Figure 1b shows the histogram of the corresponding infrared image. In Figure 1b, we can see that the whole histogram \(h_{i}\) is portioned into two parts based on the value of \(i_{m}\). The first part of the histogram is labeled by \(h_{1i}\) and the second part of the histogram is labeled by \(h_{2i}\).  
Figure 1: (**a**) Infrared image of a person; (**b**) grayscale histogram of the image.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 4 Tools and Methodology
### Testbed Deployments  
In addition to the network simulator tools to assess the network performance, LoRaWAN testbeds are another way to assess different network parameters and their impact on performance. Until now, several small scale deployments already have been deployed [18; 24; 32; 33; 34; 35; 36; 37; 38; 39; 54; 55; 56] that include a small number of end nodes. For large scale and massive deployments of test-beds, frameworks and methodologies for easy deployment, control and management are required.  
In [57; 58], a framework for cloud management of different LPWAN network devices has been presented, where LoRaWAN gateways and network servers are considered too. The control APIs of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Environment** & \multicolumn{8}{c}{**Included Features**} \\ \hline  & & **Multi** & **OWL** & **DBLBL** & **DBLBL** & **Conflirmed** & **MAC** & **Phy** & **Model** \\  & & **GW** & **Conflirmed** & **Traffic** & **Confirmed** & **TARffic** & **Commons** & **Model** \\ \hline
[40] & NS3 & Yes & Yes & Yes & Yes & No & All interference based \\ \hline
[42] & NS3 & Yes & Yes & Yes & Yes & Yes & Power difference based \\ \hline
[41] & Python & Yes & No & No & No & No & Power difference based \\ \hline
[50] & Python & Yes & No & Yes & No & No & Power difference based \\ \hline
[51] & Python & Yes & No & No & No & No &
\begin{tabular}{c} SIR based with \\ log-normal channel fading \\ \end{tabular} \\ \hline
[48] & NS3 & No & No & No & No & Yes & Received power based \\ \hline
[53] & NS3 & \multicolumn{8}{c}{Extend the LoRaWAN module in [48] with p-CSMA based MAC protocol} \\ \hline
[24] & C++ & No & Yes & No & No & No & SNR based \\ \hline \hline \end{tabular}
\end{table}
Table 4: LoRaWAN simulators.  
different network equipment are unified in Lightweight Machine to Machine (LwM2M) APIs using a modular virtual device adapter. The unified APIs make it easy for managing of different network devices from a central cloud-based network operator. Such an approach can be applied to any device type and vendor by only modifying the adapter module that makes the translation of APIs. In addition, the framework can be easily deployed in multiple devices by using docker images.  
The study in [59] went a step further in their framework by controlling and managing not only network devices but also end devices. Different parameters of the end devices can be controlled, such as SF used, transmit power, channel bandwidth and FEC code rates. This will make it possible to have controllable and reproducible experiments in LoRaWAN testbeds. Moreover, the network can be controlled by controlling each node parameter individually.
## 2 Space Segment
### Satellite Antennas Technologies

Table 4: Evolution of broadband satellite antenna systems. geometries. There is also a trend to use larger reflector apertures to produce higher spectrum reuse over the field of view. With solid reflector technology typically limited to diameters up to about 3.5 m due to fairing constraints, mesh reflectors are being considered as candidate technologies for future missions. Large deployable reflectors typically used in space for missions at lower frequencies, e.g., S- and L-bands, are now being developed for Ku- and Ka-bands, with products available in the 5 m diameter range providing performance compatible with Ka-band operation [89], and much larger diameters are being considered. Besides these developments focusing on the user link, there has also been a number of dedicated activities aiming at providing feeder-link antenna systems at Q/V-bands and above [90]. These can still rely on solid reflector technology but require improved tracking systems adapted to the much narrower beamwidths.
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 6 Conclusions  
This paper is the first step towards direct comparison of existing domain adaptation methods in the literature on Arabic sentiment classification to get a clear understanding of the most effective methods. The findings are limited to the methods covered in this study, and the aim is to investigate more approaches in future work to reach more comprehensive conclusions. Among the methods tested, we found AraBERT finetuning to be the best performing method on Arabic, beating even the methods specially designed for domain adaptation. In general, our experiments show the effectiveness of using pretrained LM, which is demonstrated by the gap in performance between TF/IDF-DANN and AraBERT-DANN. In addition to accuracy, we utilized transfer loss as a metric to compare overall performance on MSA vs. DA and on Arabic vs. English. The results showed a drop in performance of the methods that perform well on English, which can be attributed to the complexity of Arabic morphology and other issues related to the scarcity and quality of the available resources. Moreover, we found that all methods with TF/IDF representation performed better on MSA, which means that dialects impose additional challenges for domain adaptation on Arabic. These gaps encourage future research in two directions: Arabic resources and domain adaptation methods. A considerable amount of work must be undertaken to collect more DA datasets and create larger Arabic LMs to overcome the challenges associated with the Arabic language. The best adaptations achieved in our experiments were those utilizing AraBERT LM, although they were still much smaller than other LMs trained on other languages. Thus, training more Arabic LMs on vast amounts of data with a diverse set of Arabic dialects has the potential to further enhance domain adaption outcomes as well as other NLP tasks. Furthermore, we call for future research to develop novel Arabic-specific domain adaptation methods that take feature sparsity and dialect variability into account.  
Conceptualization, Y.A., N.A.-T. and A.A.; data curation, Y.A.; formal analysis, Y.A.; funding acquisition, A.A.; methodology, Y.A. and N.A.-T.; software, Y.A.; supervision, N.A.-T. and A.A.; validation, Y.A.; writing--original draft, Y.A.; writing--review and editing, N.A.-T. and A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research, King Saud University.  
Figure 4: Average transfer loss of DANN and mSDA on Arabic and English datasets.
## 2 Materials and Methods
### DE-CTA Image Reconstruction  
Transverse low- and high-kVp DE-CTA imaging data were reconstructed using a medium sharp convolution kernel (Qr40). A specific dual-energy post-processing workstation with a patented algorithm (Syngo MMWP version VA 20; Siemens Healthcare, Forchheim, Germany) was used for image analysis, generating VNC images from DE-CTA images.  
The algorithm is able to quantify the iodine amount of every single pixel generating an iodine distribution map. VNC images were then reconstructed to 3-mm thick axial images.  
\begin{table}
\begin{tabular}{c c} \hline
**Demographic** & **n (Range)** \\ \hline \(\bullet\) & Patients (n) & 40 \\ \(\bullet\) & Age (yrs) & 69 (41–93) \\ \(\bullet\) & Weight (kg) & 77 (53–126) \\ \(\bullet\) & Height (cm) & 171 (158–193) \\ \(\bullet\) & BMI (kg/m\({}^{2}\)) & 25.7 (17–33.5) \\ \hline \end{tabular}
\end{table}
Table 1: Demographic and clinical characteristics of the study population.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Single-Energy CT** & **DE-CT** \\ \hline Pitch & 0.6 & 0.4 \\ Slice collimation & 3 mm (acquisition 192 \(\times\) 0.6 mm\({}^{2}\)) & 1 mm (acquisition 192 \(\times\) 0.6 mm\({}^{2}\)) \\ Rotation time & 0.5 & 0.5 \\ Field of view (FOV) & 300 mm & 300 mm \\ Reconstruction slice thickness & 3 mm & 1 mm \\ \hline \end{tabular}
\end{table}
Table 2: Single energy computed tomography (CT) and dual-energy computed tomography (DE-CT) scanning parameters.  
Multi-planar reformats (MPR; thickness, 1.5 mm; increment, 1.0 mm) and maximum-intensity projections (MIP; thickness, 10.0 mm; increment, 1.0 mm) in transverse, oblique coronal, and sagittal orientation were also obtained.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 5 L-Index
### Maintenance  
The maintenance of L-index includes two aspects: one is to synchronously split the corresponding L-index when a tRegion is split, to ensure one-to-one correspondence between tRegion and L-index.  
The second is to synchronously migrate the corresponding L-index when a tRegion is assigned to another RegionServer, to ensure that both always coreside on same node. The maintenance of L-index is also performed by Observer coprocessor.  
* TRegion split: After a tRegion is split, Observer captures the postCompleteSplit event to trigger the split of the corresponding L-index [16]. During split processing, the existing grid structure of G-index remains unchanged, each SE-List is directly divided into two sublists by the rowkey of split point, and then each sublist is linked to the corresponding G-index grid cell of new tRegion. The above split operation of L-index avoids the heavy overhead caused by quad-tree splitting, thereby ensuring splitting efficiency. Its time complexity is O(\(n\)), where \(n\) is the total number of SE in the L-index.
* TRegion assignment: The tRegion assignment usually occurs during load balancing, Observer triggers the migration of L-index by capturing postBalance event [16]. First, the information of assigned Regions, source RegionServers and destination RegionServers are obtained by reading Region Plan parameter [16], and then according to these information, each corresponding L-index is migrated to the same destination RegionServer.
## 2 Theory
### Network Architecture
(**b**) The deep neural network architecture of the GAN. It is composed of three infrastructures, called the encoding block, residual block, and decoding block. where \(F_{2}\) and \(D_{2}\) represent the generator and discriminator of GAN2. To make use of the knowledge contained in the forward mapping, the cycle-consistency structure is added by us to further optimize the inverse mapping, and it can be formulated as follows:  
\[\mathcal{L}_{\text{cyc}}(F_{1},F_{2})= \mathbb{E}_{\mathbf{X}_{2}\sim P_{data}}(\mathbf{x}_{2})[\|F_{1} (F_{2}(\mathbf{X}_{2}))-\mathbf{X}_{2}\|_{1}]\] \[+ \mathbb{E}_{\mathbf{X}_{1}\sim P_{data}}(\mathbf{x}_{1})[\|F_{2} (F_{1}(\mathbf{X}_{1}))-\mathbf{X}_{1}\|_{1}], \tag{3}\]  
where \(\|\cdot\|_{1}\) represents the \(\ell_{1}\) norm of the matrix. The input low-resolution trace \(\mathbf{X}_{1}\) is first fed to the \(F_{1}\) in the inverse process and then outputs the estimated high-resolution result. The estimated result is fed to the \(F_{2}\) to reconstruct the original low-resolution data. The cycle-consistency loss is calculated by comparing \(\mathbf{X}_{1}\) and the reconstructed low-resolution result. Similarly, the forward process uses the high-resolution trace \(\mathbf{X}_{2}\) to calculate the cycle-consistency loss. Compared with the one-way learning model (1), the cycle-consistency structure adds more loss items (3) and utilizes forward mapping \(F_{2}\) to further optimize the inverse mapping \(F_{1}\). Finally, the complete loss function is defined as  
\[\mathcal{L}_{\text{all}}(F_{1},F_{2},D_{1},D_{2})\] \[=\lambda_{1}\mathcal{L}_{\text{GAN}_{1}}(F_{1},D_{1})+\lambda_{2 }\mathcal{L}_{\text{GAN}_{2}}(F_{2},D_{2})+\lambda_{3}\mathcal{L}_{\text{ cyc}}(F_{1},F_{2}), \tag{4}\]  
where \(\lambda_{1}\) = 0.6, \(\lambda_{2}\) = 0.2, and \(\lambda_{3}\) = 0.2 are weighting parameters for controlling the training process.
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 3 Results and Discussion  
### Nutrient Content  
The levels of P, K and Ca (Figure 4) fall within the ranges considered adequate for full crop development [44], with values ranging from 2.5 to 5.0 g.kg\({}^{-1}\) for phosphorus, 8.0 to 50.0 g.kg\({}^{-1}\) for potassium and 8.0 to 30.0 g.kg\({}^{-1}\) for calcium. These ranges are an indication of the nutritional status of the plant, where values less than those recommended represent a nutritional deficiency with a consequent need for correction, while greater values indicate toxicity [44; 45].  
During the cycle of _Vigna unquiculata_ (L.) Walp, the levels of P, K and Zn in the leaves decrease, while the Ca content remains steady or increases (Figure 4). This can partly be explained by the differential translocation of these nutrients, where calcium is hardly translocated by the phloem [46] and tends to accumulate in the leaves during plant development. A reduction in the other nutrients under evaluation may be a consequence of their translocation from the leaves to the reproductive parts of the plant.  
As a nutrient, P is necessary during each phenological stage of the crop, especially the early stages, since its limitation can restrict plant development [47]. The nutrient acts by increasing cells [46] and is mostly absorbed during the pre-flowering stage; however, according to [48], as the crop develops, the P content is reduced.  
There is an antagonistic relationship between K and Ca, in which one element affects the absorption of the other [49]. The greatest requirement for Ca at phenological stage R9 (Figure 4c) is related to pod fixation, where, due to the antagonism between the two nutrients, the K content is reduced (Figure 4b).  
Zinc (Zn) is an essential micronutrient for plant nutrition [50]. It influences plant and root growth, is involved in many metabolic pathways and allows the photosynthetic area to expand [51]. As such, it is mainly required during the early stages of the plant and decreases as the crop cycle progresses (Figure 4d).
## 4 Discussion
### Frequency and Rate of Movement and Capture  
We studied marker movement at a fixed frequency of 96 bpm. Our study demonstrated the clear ability to detect and resolve markers during movement at that rate. We chose this rate as it encompasses and exceeds the rate of typical human intentional movements that may be encountered in flexibility and diagnostic assessment. Over the operating range of 1-12 ft tested, an optimal range for reduced error was noted within, between 2 ft and 10 ft. These findings are consistent with observations of others indicating the importance of considering not only the image capture rate as it relates to movement frequency or speed, but also the operating distance for the markers being measured [47]. Future studies will aim at determining the accuracy of the system over a range of motion rates exceeding the rate studied herein. Demonstrating the efficacy of the system at even faster rates will expand the potential utility of this method for use in comparative assessment of muscle function and fatigue rates.