esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Radiotherapy for Rectal Cancer
### Total Neoadjuvant Approach  
Distant metastases (DM), now the primary cause of treatment failure, have prompted interest in total neoadjuvant treatment (TNT), combining preoperative RT, and systemic chemotherapy. TNT offers timely chemotherapy, potentially higher pCR rates, and better tolerance. Key trials demonstrated TNT's benefits. The RAPIDO trial [36] found TNT reduced disease-related treatment failure and DM, with a higher pCR rate (28% vs. 14%) and good tolerability. Similarly, the PRODIGE-23 trial [37] showed improved 3-year DFS and higher pCR rates with TNT, mainly through reduced DM, although LR rates remained unchanged.  
Despite its promise, TNT raises concerns about overtreatment, non-responders, and optimal regimens. Current evidence supports 6-9 cycles of neoadjuvant FOLFOX [30; 36] or equivalent chemotherapy. Consolidation chemotherapy may enhance response and prolong surgery intervals, but sequencing remains debated. Both SCRT- and LCCRT-based TNT approaches appear effective, with institutional preference guiding choice.
# An Overview of Machine Learning and 5G for People with Disabilities
## 2 Machine Learning for People with Disabilities
### Physically Impaired
#### 2.3.1 Human-Machine Interfaces  
A _human-machine interface_ is proposed, which allows people with spinal cord injuries to use a computer [31]. The designed human-machine interface uses head movements and eye blinking for mouse control. The head mouse control is based on facial recognition. The head direction, as well as eye states (open, closed), are detected from the computer's camera; a convolutional neural network transforms the head movements to the actual coordinates of the mouse; the mouse pointer is controlled with head movements and the mouse buttons with eye blinks.  
_A Brain Computer Interface (BCI)_[32] integrates eye movement information into electroencephalographic (EEG) signals. EEG recordings are classified into six categories using a random forest algorithm. This way, it is possible to _move an electromechanical wheelchair_ left, right, forward, and backward.  
_A hybrid brain-computer interface system for home automation control_ is proposed [33]. It utilizes steady state visually evoked potentials (SSVEP) and eye blinks to provide 38 commands (i.e., 6 \(\times\) 6 SSVEP commands and two eye blink commands) for controlling daily activities through a single bipolar channel. SSVEP signals provide selection functions, and single eye blinks provide the functionality to confirm selections. Resetting a selection requires one to perform a simple double eye blink. The short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The proposed system provides 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel.  
An _online brain-computer interface video game_ (MindGomoku) for entertaining people with disabilities is introduced [34]. It uses a simplified Bayesian convolutional neural network (SBCNN) for P300 signal classification and character recognition with good accuracy, even on a small training data set.
## 4 Evaluation Methodology
### Evaluation Method  
#### 4.3.1 Detection Rate  
The detection rate is the value calculated for evaluation focused on detection performance only by Formula (1). In general, for anomaly detection with a very large normal data class, this value cannot measure performance in the case of class imbalance.
## 2 Materials and Methods
### De Novo Assembly and Functional Annotation  
In the present study, we used SOAPnuke v 2.10 to control the raw read quality ([[https://github.com/BGI-flexlab/SOAPnuke/releases/tag/SOAPnuke2.1.0](https://github.com/BGI-flexlab/SOAPnuke/releases/tag/SOAPnuke2.1.0)]([https://github.com/BGI-flexlab/SOAPnuke/releases/tag/SOAPnuke2.1.0](https://github.com/BGI-flexlab/SOAPnuke/releases/tag/SOAPnuke2.1.0)), accessed on 24 July 2019). Clean data were obtained after raw read trimming by using Trimmomatic v0.35 [28]. The transcriptome was _de novo_ assembled and combined using the clean data based on the Trinity software package (version: 2.0.6) [29]. Then, completeness of the assembly was assessed using BUSCO v 5.0.0 (Benchmarking Universal Single-Copy Orthologs) base on the Actinopterygii reference set [30]. All unigenes were annotated through comparison with databases, including NR (version: Release-20190312) and NT (version: Release-20190312) database of NCBI, Gene Ontology (GO) database (version: Release-20191101), Eukaryotic Orthologous Group (KOG) database (version: Release 201407), Kyoto Encyclopedia of Genes and Genomes (KEGG) database (version: Release 89.1) and Swiss-Prot protein database (version: Release 201902) (BLASTn 2.6.0 and BLASTx 2.5.0 with an E-value threshold of \(1\times 10^{-5}\)).
_applied_  
_sciences_  
_Article_  
Three-Dimensional Numerical Investigations of the Flow  
Pattern and Evolution of the Horseshoe Vortex at a Circular  
Pier during the Development of a Scour Hole  
[PERSON] *\(\copyright\) and [PERSON]  
Irrigation and Hydraulics Department, Faculty of Engineering, Cairo University, Giza 12316, Egypt;  
[EMAIL_ADDRESS]  
* Correspondence: [PERSON]ESS]  
**Featurered Application: The (\(k\)-\(\omega\)) turbulence model showed good results in capturing the flow structure at the wake zone downstream of the pier, with continuous oscillation matching the pier downstream turbulence pattern. Personal computer computational and storage resources were sufficient to run the computational fluid dynamics (_CFD_) simulations for the case in hand with the selected turbulence model. The required computational resources are cheap and affordable compared to those required for the Large Eddy Simulation (_LES_) and Detached Eddy Simulation (_DES_). This will lead to significant savings in time and resources in future _CFD_ modeling for similar cases.  
In the current study, a three-dimensional _CFD_ model is utilized to investigate the variation of the flow structure and bed shear stress at a single cylindrical pier during scour development. The scour development is presented by seven solidified geometries of the scour hole, collected during previous experimental work at different scour stages. Different turbulence models are evaluated and the (\(k\)-\(\omega\)) model is chosen due to its relative accuracy in capturing the flow oscillation and vortex shedding at the pier downstream side with personal computer computational and storage resources. The numerical results are verified against dimensionless parameters from different previous experimental works. This research describes in detail the flow structure and bed shear stress variations through seven stages of the scour hole development. The dimensionless area-averaged circulation coefficient (\(\
abla_{t}\)) is developed to evaluate the changes in the vortex strength through the scouring process by eliminating the calculation area effect. It was concluded that the circulation in the (\(\gamma\)) direction is the main driving factor in the development of the scour hole more than the circulation in the (\(X\)) direction. The ratio between the horseshoe vortex (\(HV\)) mean size and the scouring depth (\(D_{V}/d_{S}\)) in addition to the location of the maximum bed shear stress are investigated during different stages of the scour development.  
**Keywords:** bridge piers; _CFD_; circulation; horseshoe vortex; scour; vorticity
## 1 Introduction  
Beekeeping in the tropics and subtropics is challenged by shortage of rich nectar floral resources with variations in the dates of honeyflow occurrence [1]. _Ziziphus nummularia_ (Burm. f.) is among the major melliferous plant species prevailing in the ecosystems of central Arabia, including Saudi Arabia [2, 3]. This plant is well adapted to harsh conditions, such as drought, high temperatures, and high salinity [4, 5], and is abundant in the desert oases of Saudi Arabia. It is important for the preservation of wild fauna, as it represents a high-potential honey source and plays roles in facilitating insect diversity and pollination [2, 6, 7].  
Approximately 170 native species of the _Ziziphus_ genus have been reported in tropical and subtropical regions of the world [8]. These species provide multipurpose benefits,such as conserving soil, stabilizing dunes, favoring the presence of perennial grasses, and serving as forage for livestock [5; 9]. Plants of this genus are also used as fuel and construction materials; their edible fruits are eaten by humans; and the plants are used as medicine for some human and animal diseases due to their potent antimicrobial and analgesic properties [5; 10; 11; 12; 13; 14; 15]. These trees have the ability to tolerate and withstand harsh conditions such as heat, drought, and salinity [4; 13].  
The Rawdat-Khuraim oasis is located in central Saudi Arabia and represents a unique ecosystem within an arid desert. This oasis is a model nature preserve, providing an excellent and unique habitat for biodiverse animal and plant species. The floral diversity of this ecosystem harbors as many as 153 plant species of 32 families [16], and the region is an optimal area for potential studies of pollinator communities, including honey bees, due to its rich plant flora [17; 18; 16]. Pollinator diversity is crucial for reproduction processes of plants [19; 20].  
The foraging and production activities of honey bees are greatly influenced by the climatic conditions, the nature of flowering plants (the forage materials of bees), and the quantity of secreted nectar [7; 21; 22; 23]. Nectar secretion is directly related to visitation by pollininators and weather conditions [24; 25; 26; 27]. Honey bees show seasonal foraging activity patterns depending on the prevailing weather conditions [28]. A hot-dry environment exhibits differential effects on various honey bee species and subspecies [29; 30].  
The main objectives of this study were to investigate the foraging activities (outgoing and pollen-gathering activities) of two honey bee subspecies in the hot-dry environment of central Saudi Arabia during the abundant presence of \(Z\). _numnumularia_ flora and to compare the activity patterns of the studied honey bee subspecies in relation to the weather conditions. The results of this study enhance our understanding of the flowering dynamics of bee plants in desert ecosystems and provide data for further studies aiming to improve beekeeping and honey production in arid environments.
# Seismic Response Mitigation of Base-Isolated Buildings
## 5 Numerical Study
### Performance of TMDs in Response Mitigation  
Figure 4 illustrates the time history and Fourier Amplitude Spectra (FAS) of top floor acceleration and bearing displacement of BI and BI equipped by different TMD schemes under LA04 earthquake ground motion. It is observed that the BI building has relatively low acceleration at the top of building, but very large bearing displacement. The TMDs effectively reduce this large displacement demand. The peak bearing displacement of BI, BI + STMD, BI + MTMDs, and d-MTMDs are, respectively, 1.86 m, 1.5 m, 1.18, and 1.12 m. It is observed that the STMD could reduce the displacement by about 20%, whereas, MTMDs and d-MTMDs could reduce displacement by up to 40%. It is also evident that this reduction in bearing displacement is not at the cost of amplified acceleration.  
Figure 4: Time history and Fourier Amplitude Spectra (FAS) of top floor acceleration and bearing displacement of 5 story BI and BI equipped by TMD schemes under LA04 earthquake ground motions.  
Figure 5: Reduction in average peak bearing displacement of BI and BI equipped by TMD schemes under DBE earthquake ground motions.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description
### System Model  
In the relaying network structure with binary data transmission, the transmitter sends only one of two possible signals during each bit interval \(T_{b}\). The encoder structure for a system employing BICM-ID is realized by a serial concatenation of the convolutional encoder, pseudo-random interleaver and mapper [19], as depicted in Figure 2. However, the mapper can be considered as a code due to \(M\)-bits being associated with each symbol, where \(M\) is defined as the constellation size of the utilized modulation scheme.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
###### Abstract  
In the modern computing environment, smart cards are being used extensively, which are intended to authenticate a user with the system or server. Owing to the constrictions of computational resources, smart card-based systems require an effective design and efficient security scheme. In this paper, a smart card authentication protocol based on the concept of elliptic curve signcryption has been proposed and developed, which provides security attributes, including confidentiality of messages, non-repudiation, the integrity of messages, mutual authentication, anonymity, availability, and forward security. Moreover, the analysis of security functionalities shows that the protocol developed and explained in this paper is secure from password guessing attacks, user and server impersonation, replay attacks, de-synchronization attacks, insider attacks, known key attacks, and man-in-the-middle attacks. The results have demonstrated that the proposed smart card security protocol reduces the computational overhead on a smart card by 33.3% and the communication cost of a smart card by 34.5%, in comparison to the existing efficient protocols. It can, thus, be inferred from the results that using elliptic curve signcryption in the authentication mechanism reduces the computational cost and communication overhead by a significant amount.  
s 2020 10 8291; doi:10.3390/app10228291
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 3 Related Work
The study utilized five classifiers--CART, Random Forest (RF), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM)--alongside feature fusion techniques like concatenation and Principal Component Analysis (PCA). The experiments revealed that the RF classifier, initially achieving an accuracy of 86.64%, improved significantly with different blending modes, reaching approximately 95% with RF and CART. In particular, the Gradient Boosting Machine (GBM) stack late fusion mode further enhanced accuracy. The best feature selection technique identified was Recursive Feature Elimination (RFE). When comparing the results with other studies using the same dataset, the methods applied in this study demonstrated the highest performance. [PERSON] et al. [32] developed a wearable device with multiple sensors to collect muscle activity and movement data simultaneously. Instead of using EMG electrodes, they utilized air pressure sensors [33, 34] to measure muscle activity more practically and cost-effectively. The prototype includes a control module, an IMU, an air pressure module, and a power module. Researchers collected data from seven men and one woman performing 11 activities over three hours. The extracted features were used as input for five classification algorithms: k-nearest neighbors (kNN), decision trees (DT), naive Bayes (NB), Support Vector Machines (SVM), and Random Forest (RF). They conducted three classification experiments: Activity Pattern Classification. (APC), Movement Classification (MC), and Combined Classification (CC). The results indicate that DT, NB, and RF performed best, with CC achieving the highest accuracy, precision, recall, and F-measure averages. This highlights the performance improvement resulting from integrating air pressure capabilities into the device. [PERSON] et al. [35] created a model that combines LiDAR signals and inertial sensors to estimate human 3D poses in real time. This approach addresses the limitations of vision-based, IMU-based, or heterogeneous sensor methods. LiDAR data track body position, while IMU data estimate the orientation and position of joints. The study used an Ouster OS-0 LiDAR sensor and Xsens IMU sensors to monitor and reconstruct the pose on a 3D avatar. Subjects were asked to walk, sit, and move toward a table to test the system. The system's accuracy was measured, demonstrating an error margin of 2.82 cm for the pelvis and 2.42 cm for the foot. This confirmed the enhanced accuracy of tracking human movement using multiple heterogeneous sensors.
## 4 Discussion
A moderate recall score (**70.08%**) indicates false negative classification at a higher rate, which translates in some instances to argumentative behaviors not being identified. Moreover, low Negative Predictive Values scores (**75.53%**) indicate that the model has a lower predictive ability of non-argumentative behaviors and potentially misclassifies true negatives, as shown in Figure 15. Additionally, the model exhibits a performance drop from training to testing as shown in Figures 4 and 13, correspondingly. This could be due to data discrepancies in the dataset used during training and testing, which attests to the need for further testing in real-world scenarios. The current dataset contains frames from verbal arguments and physical conversations. The controlled nature of the dataset could affect the model's ability to perform well in real-world scenarios, and the use of clinical data may improve the model's performance and its applicability in realistic conditions. The choice of Random Forest, Gradient Boosting and Ridge Classifier was based on their computational efficiency for real-time clinical application, their robustness, efficiency and interpretability in handling complex characteristics, and their balancing accuracy, clinical relevance and resource efficiency. Nonetheless, models such as SVM or deep learning models were deemed less suitable due to difficulties with interpretability, their requirement for bigger datasets to work efficiently and their greater computational requirements. More specifically, Support Vector Machines rely on hyperplane separation and weight features, which conflict with the nature of coordinate-based data since all coordinates are needed to perform accurate detections and not weighted features. Also, Support Vector Machines have less interpretability, which is not suitable for clinical data. Deep learning also provides limited interpretability with difficulty in extracting meaningful patterns directly. Althoughthey can theoretically capture complex dependencies, they require larger datasets in order to work correctly while needing high computational power [48; 49].
# Data Exchange in Cluster Structure for Longevity of IoT
## 3 Proposed Property-Based Data Transfer
### System Operation Process
#### 3.3.1 Advertisement Phase  
In the \"advertisement phase\", each thing(node) broadcasts its remaining energy and participates in the header election. Figure 2 shows the sequence diagram of \"advertisement phase\" with four nodes. In Figure 1, it is regarded that node 1 has the most remaining energy. The steps of this phase are described as follows.  
1) When the advertisement phase starts, all the nodes are initialized to the general node, but not the header node or member node. Each node broadcasts information of its remaining energy and receives the information of remaining energies from the surrounding nodes.  
2) After exchanging the information of remaining energies, each node attempts to identify the node with the most remaining energy from the surrounding nodes. If the node itself has the most remaining energy, it assumes the role of a header node. However, if there are other nodes with more remaining energy than its own, it becomes a member node and is ready to participate in the other clusters. The node elected as the header broadcasts a message with its ID. This node not only becomes the header of a cluster, but also is ready to recruit the member nodes of the cluster.  
3) The member nodes save the \"advertisement\" messages received from the surrounding header nodes and are ready to choose their cluster.  
In Figure 2, node 1 becomes the header node because it has the most remaining energy. As the header node, node 1 broadcasts its information to the surrounding nodes. The other nodes become the member nodes and are included in the cluster. The sequence diagram in Figure 2 shows the process of configuration of a cluster.
## 4 Measurements and Characterization  
### Test Environment Setup  
Figure 9a shows the small-signal test setup. The test is performed using a vector network analyzer, with the drain and gate voltage supplies set to 28 V and \(-2.2\) V, respectively. A 30 dB attenuator is added to protect the instrument but is removed during the S\({}_{22}\) measurement. Figure 9b illustrates the large-signal test environment. To ensure sufficient input power for measuring the saturation power of the DUT, a 6-18 GHz solid-state power amplifier is used to amplify the signal from the signal generator. This amplifier provides up to 50 W of output power and is able to measure both the forward and reflected power, meaning that the power gain can be calculated accordingly. A power meter with a continuous wave power probe measures the output power of DUT, while a 30 dB attenuator is used to protect the instrument. The DC power supply can display the DC power consumption, meaning that the PAE can be calculated accordingly.
## 2 Related Work
However, they did not perform well in the presence of other types of noise, such as impulse noise, which caused blurring around edges and introduced halo-like artifacts in the denoised image [52]. The blurring and halo artifacts can distort the fine details of objects and degrade the overall visual quality of the image. Gaussian noise often distorts digital images, which is an essential problem in image processing. In [53], the impulse and Gaussian noise in the CT image were removed based on the edge-preserving median filter algorithm. The sparse, non-local regularization algorithm weighted coding was used to remove the impulse and Gaussian noise in the mixed noise, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated to evaluate the quality of the denoised CT image. The performance of the median filter for Gaussian noise removal could have been more effective due to its discrete nature, acceptable detail loss, and edge preservation. Authors in [23; 24; 25; 26; 27; 27; 28; 29; 30; 31; 32; 33] discussed the challenges of image denoising based on the state-of-the-art medical image denoising techniques, such as bioinspired optimization-based filters and spatial filters using CNN, which included the preservation of image details, trade-off between noise removal and detail preservation, noise characteristics, computational complexity, and spatial and temporal coherence. However, these conventional image denoising techniques do not remove additive Gaussian noise from CT scan images because these spatial filters and denoising techniques [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33] may excel at reducing noise but need help to maintain the integrity of intricate anatomical information. Conversely, filters that prioritize preserving details might not adequately reduce noise, making images challenging to interpret. To this end, we propose an ensemble approach that uses DnCNN, anisotropic Gaussian filters, and Haar wavelet transform. By leveraging the strengths of each technique, the proposed ensemble approach achieves superior denoising performance, preserving image details while effectively suppressing the noise.