esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 3 Simulation Setup and Performance Metrics
### Performance Metrics
#### 3.2.2 Delay (s)  
Delay is the average time taken for data packets to be transmitted from the source flying node to the destination flying node across the network. Delay is an important design factor and performance characteristic of a communications network. End-to-end delays contain the processing, queuing and transmission delay of the link in a network. The average end-to-end delay can be shown mathematically, as in Equation (2):  
\[\text{D}_{\text{end-to-end}}\,=\,\sum_{\text{t=1}}^{\text{N}}(\text{T}_{\text{t }}+\text{R}_{\text{t}}+\text{B}_{\text{t}}+\text{P}_{\text{rt}}) \tag{2}\]  
where,  
\(\text{T}_{\text{t}}=\text{Transmission time}\).  
\(\text{R}_{\text{t}}=\text{Retransmission time}\).  
\(\text{B}_{\text{t}}=\text{Buffer time}\).  
\(\text{P}_{\text{rt}}=\)Processing time.
## 2 Materials and Methods
### [99 mTc]Tc-Sestamibi-High Resolution SPECT  
BSGI with [99 mTc]Tc-Sestamibi investigations were performed as described in a previous study [17]. Briefly, a BSGI scan was performed in 10-15 min following an intravenous administration of 740 MBq [99 mTc]Tc-Sestamibi through an antecubital vein contralateral to the suspicious breast side to avoid potential false-positive uptake in the axillary lymph nodes. The patients remained seated during the procedure. Craniad and mediolateral oblique (MLO) images were obtained in both breasts using high-resolution BSGI.  
All 40 patients had biopsy. BSGI was performed before biopsy in 25 patients and after biopsy in 15 patients. When BSGI was performed after biopsy, the minimum interval between biopsy and imaging was 7 days in an attempt to avoid the effects of post-biopsy inflammation as much as possible.  
For the qualitative analysis of the BSGI, two investigators classified positive and negative findings. Lesions with no demonstrable uptake and those with diffuse heterogeneous or minimal patchy uptake were considered negative, whereas lesions with scattered patchy uptake, partially focal uptake, or any other focal uptake were regarded as positive. Irregular-shaped regions of interest (ROIs) were used to encase the lesions. The evaluation of the lesion to nonlesion ratio (L/N) was estimated according to the study of [PERSON] et al. [18]. For patients who underwent BSGI with [99 mTc]Tc-Sestamibi before biopsy (n = 25), the BSGI-guided biopsy procedure was performed as previously described [19].
# A Widely Tunable Three-Section DBR Lasers for Multi-Species Gas Detection
## 3 Results and Discussion
### Characterization of the DBR Laser
(**b**) Lasing wavelength dependence on Phase current [23]. Figure 5: (**a**) Lasing wavelength varies with DBR current and work temperature. (**b**) The SMSR (side mode suppression ratio) of DBR laser over the entire wavelength range [23]. laser in a 60 mA gain current at room temperature. The horizontal and vertical divergence angles are 14\({}^{\circ}\) and 36\({}^{\circ}\), respectively, which is comparable with the traditional DFB lasers for communication. Furthermore, we also tested the device's wavelength stability for seven hours when the temperature of the laser was controlled to 20 \({}^{\circ}\)C, as shown in Figure 6. It can be seen from the figure that the wavelength change of the laser is less than 0.05 nm which is the minimum resolution of the spectrometer. It can be seen from the above test results that the DBR laser can achieve continuous wavelength tuning, single-mode output, and good wavelength stability, which meet the requirements of light source for gas detection.
## 3 Materials and Methods
### Multi-Scale Feature Fusion Architecture  
To tackle segmentation tasks for small targets like kidneys, a common approach is to use image pyramids, enhancing the detection capability by transforming the target at multiple scales. However, this method results in significant computational overhead and struggles to effectively fuse semantic information across different scales. In this study, we adopted the concept of feature pyramid networks (FPN) [27]. The FPN not only overcomes the computational complexity of image pyramid methods but also extracts features at different scales. This allows us to generate multi-scale feature representations, with each scale possessing robust semantic information, thereby improving the accurate segmentation efficiency for the kidney region.  
This module employs the ResNeXt50 network structure for feature extraction, enhancing network expressive capability with a multi-branch structure distinct from the ResNet50 network. Residual connections and multi-layer processing are applied to the  
Figure 1: The structure of the MRFA-Net network model. RFAC module stands for residual full-attention convolutional module, while FLA represents the full-attentional mechanism used in this paper.  
feature maps of each branch after grouped convolution, extracting higher-level kidney feature representations. We divide the module (as shown in Figure 2) into five different stages based on the slice size requirements, and the last feature layer in each stage is selected as the corresponding level feature in the network hierarchy. During the upsampling process, we use the output of the deepest layer (Layer 4) of the feature-extraction network as input without further \(1\times 1\) convolution to change channel numbers. The size of the upsampled feature map is adjusted to match the size of the corresponding feature map in the previous layer, and they are added together for feature fusion output. The shallow feature information from lateral connections is added together to obtain multi-scale kidney features. In this process, bilinear interpolation was used for two-fold upsampling because it produces smooth transitions, is computationally simple and efficient, and largely overcomes the discontinuities associated with nearest-neighbor interpolation. The quality of the scaled image is high, preserving the details of the image. It is worth noting that compared to using deconvolution for upsampling, if the parameters are not properly configured, the output feature maps may exhibit significant instability in the form of checkerboard artifacts [28]. Therefore, bilinear interpolation is preferred. To prevent aliasing, a 3 \(\times\) 3 convolution is applied to the output, ensuring feature stability. Finally, the four feature maps are concatenated, resulting in a multi-scale feature map that integrates spatial and channel information. This feature map has the same size as the processed Layer 4 and a channel count equal to the sum of the four feature maps.
## 2 Materials and Methods
### MAO Activity Assay  
Cells were treated with 0, 1, 5, and 10 \(\upmu\)M \(\beta\)-lactolin or 10 \(\upmu\)M pargyline hydrochloride (Sigma-Aldrich), a selective MAO-B inhibitor, for 5 h. The cells were washed with PBS and collected by scraping the culture dishes. After centrifugation, mitochondria were isolated using the Mitochondria Isolation Kit for culture cells (Thermo Fisher Scientific, Waltham, MA, USA) as per the manufacturer's protocol. Mitochondrial fractions were diluted in PBS and used to determine the MAO-B activity. Total protein concentration of each mitochondrial fraction was measured using the Pierce\({}^{\upmu\text{M}}\) bicinchoninic acid (BCA) Protein Assay Kit (Thermo Fisher Scientific). Pierce\({}^{\upmu\text{M}}\) Bovine Serum Albumin Standard Ampules (Thermo Fisher Scientific) were used as standards. MAO-B activity was measured using an MAO Assay Kit (Cell Biolabs, San Diego, CA, USA) as per the manufacturer's protocol. The substrate reacts with MAO-B isolated from the cells and generates H\({}_{2}\)O\({}_{2}\), which reacts with the colorimetric probe and produces a red/pink product. The color change was quantified by measuring absorbance at 570 nm using a BioTek Eon microplate spectrophotometer (BioTek, Winooski, VT, USA) and expressed as MAO-B activity.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 3 Problem Definition  
In WSNs, an application often has a specific requirement for packet latency. For example, packets may be required to reach the sink within a certain delay limitation with a given probability. Let random variable \(Z\) denote the E2E delay. Then, \(Z\) can be considered as the sum of the one-hop delays on the path from the source to the sink. Let \(Y\) denote a vector in which each element is the one-hop delay of a path from the source to the sink. If \(h\) is the hop count, \(Y=\left\{Y_{1},Y_{2},...,Y_{h}\right\}\) with \(Y_{q}\) as the one-hop delay at the \(q\)-th hop (\(1\leq q\leq h\)). Therefore, \(Z\) can be expressed as a function of \(Y\):  
\[Z=\sum_{q=1}^{h}(Y_{q}) \tag{1}\]  
If we apply duty cycling in WSNs, a one-hop delay will be affected by sleep scheduling (the duty cycle interval and active time) and network parameters. \(Y_{q}\) can be represented as a function of the duty cycle interval and other parameters:  
\[Y_{q}=f(T,\phi_{q}) \tag{2}\]  
where \(\phi_{q}\) denotes the set of network parameters, such as the number of neighboring nodes, the total area and the communication range of a node.  
Then, the optimization problem in this paper can be formulated as follows. Maximize duty cycle interval \(T\) with given network parameters and the E2E delay constraint that a packet should reach the sink by delay bound \(\xi\) with given success ratio \(p_{s}\). That is,  
\[\begin{split}&\text{maximize }T\\ &\text{subject to: }P(Z<\xi)\geq p_{s}\end{split} \tag{3}\]  
where \(p_{s}\) is the required DCSR.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Feature Extraction and Preprocessing  
#### 3.2.1 Overview of Processing Pipelines  
Feature extraction and preprocessing are foundational steps in MMLA, serving to transform raw data into structured, analysable inputs suitable for modelling. These processes vary according to the type and source of data but often involve a combination of cleaning, synchronisation, segmentation, and dimensionality reduction.  
Several studies implement structured pipelines that begin with noise reduction and data transformation. For example, raw physiological signals such as EDA or EEG are frequently filtered and normalised to improve data quality prior to analysis [39; 64]. This step is often followed by resampling or interpolation to handle missing values and align data streams temporally [65]. The integration of heterogeneous data sources, such as video, audio, and interaction logs, necessitates careful synchronisation and time alignment across modalities [66].  
Many frameworks employ segmentation methods to divide continuous data into analysis-ready units. These units may be defined by fixed time windows [47], event-based triggers [45], or learning phases such as planning and reflection [67]. Some approaches leverage automated tools to streamline this process, such as CoTrack for multimodal annotation and segmentation [68].  
Dimensionality reduction techniques like principal component analysis (PCA) and factor analysis are often employed to simplify high-dimensional feature spaces while retaining relevant variance [69]. Feature selection algorithms, such as RELIEF-F, have also been applied to optimise model inputs [49]. In certain cases, attention mechanisms orhierarchical representations are used to refine feature representations and reduce the need for manual engineering [8; 69; 70; 71].  
Finally, various frameworks stress the ethical and privacy implications of preprocessing, particularly when sensitive learner data are involved. Strategies include anonymisation, localised processing, or limiting raw data storage [58; 66]. These pipelines not only enhance model performance but also ensure data integrity and user trust throughout the analytic process.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 4 Discussion
### Beneficial Impact of Mangrove Forests in Flood Mitigation  
Throughout eastern Africa, both Madagascar and Mozambique show the largest proportion of highly exposed coastline to threats such as sea level rise, flooding, storms, particularly as a consequence of rising populations in urban coastal areas, due to better economic opportunities than further inland [37]. The areas of mangrove forest comprised in Sofala and Zambezia cover 50% of the total mangrove cover in Mozambique [17]. Despite a scarcity of studies in central Mozambique, it was estimated in 2017 that Sofala and Zambezia provinces had a high exposure to coastal climate hazards and erosion, through the calculation of an exposure index (EI) due to high tides, lower elevation, erosion, and high infrastructure development [13].  
Globally, cyclones have been responsible for 46% of mangrove mortality. This was emphasized by cyclone Idai in 2019 which has considerably impacted Mozambique and the exposed region of Sofala. However, due to limited impact studies of cyclone Idai, little is known about the long-lasting impact of cyclone Idai on mangrove communities. In attempting to map out degradation in one cyclone season after Ana, it was found that there was no considerable degradation in mangrove forests following the cyclone in Mozambique, despite increased patchiness in areas close to already-barren areas which may have amplified mangrove vulnerability to storms [8]. As can be seen in Figure 6, most flooding in Sofala, Mozambique, occurred further inland along the Zambezi River, an area unprotected by mangrove forests. Overall, mangrove forests in both Madagascar and Mozambique displayed very low degradation levels compared to before the cyclone season in 2021, suggesting that healthy mangrove populations are present for 2022 at least. In light of this positive aspect, it was found that, despite a high population density in coastal areas in Mozambique, the majority of flooding occurred further inland, thus supporting the hypothesis that mangrove forests have successfully mitigated the impact of Ana on coastal communities [13]. However, the negligible NDVI change could also be a consequence of strong changes following cyclone Idai, which have been repercussed from 2019-2022 and cannot be appreciated through a one-year assessment.
# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 2 YOLOv5-Sewer Network Design
### YOLOv5-Sewer Network Architecture
#### 2.2.4 Coordinate Attention  
To overcome the limitations of CBAM, particularly in neglecting long-range dependencies, channel attention (CA) is integrated into the model. In contrast to CBAM, CA not only considers channel information but also incorporates position information related to the direction, providing a more comprehensive understanding of the relationships between defects at different locations. This holistic consideration enables the model to more accurately locate and identify sewer defects, taking into account not only the features within channels but also their spatial relationships.  
Additionally, the lightweight design of CA allows it to be easily integrated into existing network structures without significantly increasing the computational complexity. The structure of CA is illustrated in Figure 6. This integration represents a thoughtful enhancement, addressing the limitations of CBAM and improving the model's capability to capture both channel-wise and spatial dependencies for more effective sewer defect detection.  
The CA module is designed to address the limitations of CBAM in handling long-range dependencies. The module follows a series of operations to effectively capture spatial and positional information, contributing to the accurate localization of targets. The process is as follows.  
1. Decomposition of Avg pool: To preserve spatial information and obtain positional information for long-range channel dependencies, CA decomposes the Avg pool by pooling separately in the X and Y directions. This generates feature maps of dimensions \(C\times H\times 1\) and \(C\times 1\times W\), allowing the attention module to capture long-range dependencies along one spatial direction while preserving the positional information along the other spatial direction.
2. Feature map fusion: The obtained feature maps are fused through a concatenate operation.
3. Processing through convolution and activation: Through a \(1\times 1\) convolutional kernel and activation operation, the fused feature maps are processed.
4. Spatial split: A split operation is applied along the spatial dimension, dividing the feature maps into two parts, \(C/r\times H\times 1\) and \(C/r\times 1\times W\).  
5. Up-sampling and final attention vector: An up-sampling operation is performed through a \(1\times 1\) convolutional kernel, combined with the sigmoid activation function, to obtain the final attention vector.
6. Element-wise multiplication: The final attention vector is used to perform element-wise multiplication on the original feature maps, resulting in the final feature maps with attention weights in the X and Y directions.  
The CA attention module, with its ability to effectively handle long-range dependencies, proves to be a valuable enhancement over CBAM. Additionally, its lightweight design makes it a practical and efficient tool that can be seamlessly integrated into sewer defect detection models based on YOLOv5s.
# An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs
## 1 Introduction
Simulation-based models are characterized by the near realistic simulated behavior [12]. The coordination between WSN and UAV nodes is characterized by the erratic and dynamic behavior of the networks. Vehicular models like  
Figure 1: UAV application scenarios. synthetic, survey and simulation-based approaches don't suffice as the inherent inconsistencies of the erratic network behavior hinder the overall mathematical formulation of the scenario as well as the survey and simulation of every single scenario is not feasible. Trace-based models don't suffice under disaster conditions, military applications, and unforeseen events. In order to entertain the collaborative network formation, a mobility model is required that understands the overall dynamic nature of the network and can react to ever-changing topological conditions. In this article, a novel mobility model for multi-UAV enabled Wireless Sensor networks is proposed which takes into account the attraction factor for setting up the way-points for UAV movements. The way-points are configured in order to increase the coverage without over application of the UAV nodes and minimize the number of hops. The efficiency of the approach comes from its simplicity and easily implementable nature. The proposed approach is compared against the entity mobility models i.e., 3D Random Way Point, 3D Random Walk and the Gauss-Markov Mobility Model [13]. The proposed mobility scheme is also compared against traditional techniques of fixed UAV maneuvers. The rest of the paper is organized as follows: Section 2 gives the detailed view of the problem and our contribution. Section 3 consists of related works. Section 4 presents the system model and the proposed approach. Section 5 evaluates the performance and finally Section 6 concludes the paper.
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 4 Simulation and Results
### Comparison of SR and SPs' Basic Bidirectional Trust Evaluation Results  
In this part of the experiment, the positive evaluation rate is set to 50%, and the task similarity is 40%. The experiment runs 500 ticks, and one service/task is executed in each tick. In addition, 10 SPs and 1 SR were randomly selected for observation. Figure 5a,b shows the trust evaluation results of 10 SPs and SR. As shown in Figure 5a, compared with the direct trust evaluation results of each SP, the compositive trust evaluation results for SR have less difference and more comprehensive opinions, which reflects that the evaluation method based on subjective logic can better integrate the recommendations from different sources so that most SPs can have a more consistent evaluation for SR. Similarly, as shown in Figure 5b, after the SR obtains the recommendations of other intermediate nodes in the network, it obtains the integrated evaluation results of each SP's trust. It can be seen that the recommendations of other intermediate nodes will facilitate the SR to make a more accurate evaluation on the trustworthiness of SPs.
## 4 Performance Analysis of Islanding Detection Methods
### Sensitivity to Cyber-Attacks  
In islanding detection, a cyber-attack can occur due to the injection of false data into the system. The injection of false data manipulates the threshold setting of the relay to another value, which leads to false islanding detection, causing needless generation rescheduling and load shedding [55]. Therefore, the islanding detection method has to be capable to handle cyber threats.  
As mentioned above, various criteria can be considered during the selection of islanding detection methods. Based on the type of application and end-user, more criteria can be taken into account or some criteria can be neglected.
## 4 Discussion
### Limits  
We acknowledge several limits in the present work. Firstly, the quality of a literature review is only scientifically as good as the quality of reviewed research, which is valid for every'secondary' study. The level of evidence considered here being mostly level III (with the exception of a single level II study [41]), with level IV and V studies as well, our systematic review can be considered level V. However, given the research available on recent innovations in WBCT concerning imaging, most investigations can be performed ex vivo, rendering level II and level I research mostly superfluous. Furthermore, being at the diagnostic level for most (with the exception of patient-specific applications), the impact on treatment outcomes is still very difficult to assess, being a multifactorial problem with implications that spread out through a timescale that far exceeds the development of innovations that we chose to define as recent. Secondly, not all available research databases were assessed, as we limited the scope of this review to the records found on a single scientific database (PubMed). However, this method was chosen after a preliminary search of the most used other databases (Embase, Cochrane, Scopus, Web of Science) which did not lead to find any additional reference related to the development of WBCT. Thirdly, our classification of advanced techniques could be deemed arbitrary. However, we would like to emphasize that, to the best of our knowledge, no author so far has proposed any specific classification system in this area, so the present is intended as a proposal for a flexible benchmark to work from, not the immutable ground truth. The authors would be happy to revise or replace the proposed classification as progress is made, new methods arise and the technical landscape evolves in the future. Finally, we have only considered the foot and ankle field in the present work, but we fully acknowledge that WBCT devices already encompass the knee [20,65,78], with devices already available to image the pelvis and the upper limbs. There is no doubt that soon, devices able to image the full weight-bearing skeleton will be available. In this context, there is no doubt that, pending necessary adaptations locally, the recent innovations reported here will be disseminated to other joints, including the spine and the shoulders. One well-reported example is distance or 3D-JSW mapping, which has been reported on in the knees as well as the feet and ankles and should in the future become the gold standard for the evaluation of degenerative joint disease.