esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results  
The statistical graph experiment involved varying the sentence lengths in the BERT model, specifically using shorter (128 tokens) and longer (280 tokens) lengths. Although the default length for BertGCN is 128 tokens [19], we studied whether there would be a performance change with increased sentence length. The subsequent section will discuss the effect in detail.  
The presence of direct keywords within a document is a significant factor for classification performance. As such, the experiment was performed under two different conditions: an abstract containing critical keywords, such as 'in vivo' or 'in vitro' as clues (clued), and an abstract with no such keywords (un-clued). Out of the 1407 papers, 161 papers contained the critical keywords(11.4%), while the un-clued experiment involved removing these keywords from the dataset. Of the 161 un-clued papers, 28 were from the in vivo class, 93 were in vitro, and the remaining 40 were 'other'. The performance was measured under various \(\lambda\) conditions according to the model configuration in Section 3 (\(\lambda\) = 0.0 to 1.0 with 0.1 intervals) in both clued and un-clued conditions. Notably, the \(\lambda\) value of zero represents a BERT-only model and served as the baseline.  
The study involved multiple trials to evaluate the expected performance. A trial represents one round of training with validation accuracy measurements. The entire dataset was partitioned into disjoint training and testing sets with ratios of 0.75 and 0.25, respectively. Subsequently, in accordance with the experimental design of the transductive BertGCN model [19], a validation set was randomly selected from the training dataset with a ratio of 0.10. For each experimental condition (length, clued/un-clued, and \(\lambda\) condition), there were 40 trials for the statistical GCN and 20 trials for the citation-based GCN. The average validation accuracy was calculated across all trials to select the best model. Subsequently, the performance of the best model was evaluated on the test dataset.  
The initial graph network was constructed by setting the word embedding dimension to 768, corresponding to the BERT embedding. Word co-occurrence was calculated during edge construction for the statistical graph using a sliding window of 20. The model configuration of the BertGCN consisted of a batch size of 25 for a length of 128 and a batch size of 40 for a length of 280, with early stopping applied during 50 epochs. The model had one hidden layer with a default of 200 dimensions, a dropout of 0.5, a learning rate of \(1\times 10^{-5}\), and employed an Adam optimizer. We chose these values empirically while keeping within the memory limit of the hardware. For fine-tuning the BERT model, the 'RoBERTa-base' was imported as a pre-trained model with a maximum input length of 128 tokens and 280 tokens, a learning rate of \(1\times 10^{-5}\), and an Adam optimizer. The experiment was conducted using CUDA version 10.1 and Nvidia Titan RTX (24 GB).
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
## 3 Preliminaries
### Network Model  
In Figure 2, the network model of the proposed scheme is shown, which includes three entities: onboard unit (OBU), roadside unit (RSU), and department of transportation (DoT). The following are the explanatory steps:  
* OBU: It is a 5G-enabled communication device fixed on a vehicle that can communicate with RSU and other OBUs. It is responsible for registering itself with the DoT by sending its identity in an encrypted form. The DoT first decrypts the received encrypted identity, generates a partial private key for this identity, and returns it to the OBU in an encrypted format using an insecure channel. Then, the OBU generates a private key and a public key, generates a signature on data, and sends it to the RSU via an open network.
* RSU: It is a 5G-enabled base station responsible for managing and conducting V-I communication. It is responsible for registering itself with the DoT by sending its identity in an encrypted form. The DoT generates a partial private key for this identity and returns it to the RSU in an encrypted format using an insecure channel. Then, the RSU can produce a complete private key and public key. When the RSU receives signed data from the OBU, it verifies the signature and either accepts the message or generates an error message depending on the results. RSU also works as a signature aggregator.
* DoT: The DoT is a reliable third party (TA) with significant processing power and storage capability. When the DoT is provided with the identities of OBU and RSU, it produces a partial private key pair and sends it back to the OBU and RSU in two packages in an encrypted form using an insecure channel. Then, both OBU and RSU create their remaining private and public keys for themselves.
## 4 Performance Comparison to State-of-the-Art of Other Studies on Atrial Fibrillation Detection  
Table 8 illustrates a comparison of existing research results that focus on the detection of atrial fibrillation using machine learning algorithms with our research. As shown in Table 8, all previous studies used as a comparison with this research have considered the morphological features of the ECG signal, i.e., DWT and energy entropy, such as in [PERSON], et al. [18], [PERSON], et al. [31], [PERSON], et al. [57], [PERSON], et al. [58], [PERSON], et al. [59]. Table 8 also shows that the performance results are measured using either a single metric [57; 58; 59] or several metrics [18; 31]. In general, our research has the best AF detection sensitivity compared to the existing studies. Sensitivity is the most important metric in health research that can compute the probability of getting a positive test result in subjects with AF [60]. For accuracy and specificity, our results only differ by about 2% and 1%, respectively, from Asgari  
different ranges when the order of DBWF changes from DB\({}_{1}\) to DB\({}_{10}\); however, the pattern of the three metrics tends to be constant. This is due to the nature of discrete wavelet transform, which has an orthogonal transformation (shifting and scaling) of wavelet basis function [61]. In addition, the almost similar waveforms for DB\({}_{1}\) to DB\({}_{10}\) also contribute to the results. These two facts imply that false-positives and false-negatives in detecting atrial fibrillation fluctuate at nearly constant values.  
The different condition occurs when the decomposition level changes from DL\({}_{1}\) to DL\({}_{10}\). All three metrics decrease with different rates. In general, this decrease is triggered by the fact that an increase in the decomposition level (from 1 to 10) causes the WCI signal to deviate from the original ECG signal. Furthermore, the sensitivity metric has a slower decrease than the other two metrics, which is due to higher false-positives found, compared to false-negatives, in the experiments. This finding is in line with the results of research by [PERSON], et al. [62], who explored the effect of WBF and the decomposition level in electroencephalography (EEG) signals. [PERSON], et al. [62] concluded that the decomposition level was sensitive to the performance of EEG signal detection, and wavelet basis functions have insignificant effects.  
This research also compared the experimental results with other state-of-the-art research on the detection of AF. As described in Section 4, our study has the best sensitivity in detecting AF compared to other previous studies. In addition, the accuracy and specificity that we obtained were almost the same as the previous studies' results. More importantly, our study shows the importance of both wavelet basis function and decomposition level to get the best results, which has not been explored before in other studies.
## 3 Results and Discussion
### Mechanical Properties
#### 3.2.2 Flexural Properties  
Figure 3c illustrates the flexural behaviour of the cowpea husk reinforced composites. Flexural strength and flexural modulus peaked at 10 wt.% reinforcement with a value of 25.36 MPa and 2.686 GPa respectively. It can be seen that initial 10% inclusion of cowpea husk to polyester resin reduced the flexural strength of composites by 32.49%, while increasing its flexural modulus by 16.27%. This behaviour was also observed by [PERSON] et al. [59] in addition of seawater-neutralized bauxite residue in unsaturated polyester and [PERSON] et al. [64] with sugar palm yarn fibre reinforced unsaturated polyester composite. CPH addition seems to encourage crack propagation thereby reducing composite toughness which results in lower values of flexural strength [65]. In addition, the increase in stiffness compared to the unreinforced polyester was observed, and it is due to the presence of strong interfacial adhesion between the cowpea husk  
Figure 3: (**a**) Tensile properties (**b**) Tensile properties hybrid composite (**c**) Flexural properties (**d**) Flexural properties of hybrid composite.  
particles and UPT matrix [66]. Sufficient amount of cowpea husk in the UPT matrix facilitates effective stress transfer within the matrix due to intrinsic properties of cowpea husk [67].  
Beyond a 16% addition of the cowpea husk, there is a reduction in interfacial distance leading to a decrease in chain mobility and total surface for fibre matrix interaction. Hence a decline in flexural property of the UPT composite probably due to formation of agglomerates at higher filler concentration causing insufficient stress transfer between fibres and the matrix as observed by [PERSON] et al. [46] for Bambara nut shell polyester composite, for chicken feather [68] and for coconut and snail shell polyester composites [36]. The incorporation of clay (Figure 3d) improved the flexural modulus property of the hybrid composite with sample D2 containing 16% fibre and 83% UPT recording the maximum flexural modulus value of 2.912 GPa and sample E2 with 18% fibre having the minimum at 1.99 GPa.  
The enhancement of flexural modulus can be explained by the relatively higher aspect ratio of the clay, which can fill the holes between the reinforcing fibre matrix, [38; 66; 69] thus, decreasing the void content as the weak point of the composite material. [PERSON] et al. [38] observed a similar trend in increase in flexural property by adding 1% nanoclay in a hybrid composite. However, there was a decrease in flexural strength upon addition of clay and this can be linked to the formation of agglomerates which creates stress concentration points leading to early failure of the composites. Similar results have been observed by [PERSON] et al. [57] and [PERSON] et al. [60].
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 1 Introduction
The segmentation accuracy of traditional deep convolutional networks for high-resolution remote sensing images of buildings needs to be further improved, and the details of the segmentation results are not sufficient. To address this, we designed a small-sample deep convolutional network tailored for high-resolution remote sensing imagery, achieving the precise extraction of buildings from high-resolution remote sensing images from different sources. To validate our model's adaptability to various high-resolution remote sensing images, we conducted tests on a self-built Jilin-1 dataset and two publicly available high-resolution remote sensing datasets. The main contributions of this paper are as follows: we introduced semantic segmentation models related to remote sensing applications in the Related Work section. Based on the advantages of these models, we designed a multi-feature fusion net (MFFNet) and tested it against traditional convolutional networks for assessing three different datasets. Whether in terms of MIoU, accuracy, or F1 score, MFFNet outperformed other models. In the discussion section, we elaborated on the advantages and disadvantages of MFFNet compared to other traditional models and contrasted it with ViT (visual transformer) models. Through comparisons between different models, we validated the superiority of MFFNet.
# Coded-GFDM for Reliable Communication in Underwater Acoustic Channels
## 3 System Architecture  
This section details the GFDM transmitter and receiver design, FEC codes, and the shallow underwater channel that we used for performing simulations.
## 2 Materials and Methods
### Discrepancy Model  
In engineering problems, the analysis of physical systems is often performed by means of computational models which are commonly based on analytical equations governing the system or numerical methods. A computational forward model \(\mathcal{M}\) is usually defined as a function that maps a set of model input parameters, \(x\), governing the system to predict certain output quantities of interest (QoI) \(y\). However, forward models are only a mathematical representation of the real system. As a consequence, a discrepancy term shall be introduced to connect model predictions \(\mathcal{M}(x)\) to the experimental observations \(y\)[6]:  
\[y=\mathcal{M}(x)+\varepsilon. \tag{2}\]  
In this discrepancy term, the effects of measurement error on \(y\in Y\) and model inaccuracy are gathered. The assumption of additive Gaussian discrepancy with zero mean and residual covariance matrix \(\Sigma\) has been made in this work:  
\[\varepsilon\sim N(\varepsilon|0,\Sigma). \tag{3}\]  
\(\Sigma=\Sigma(x_{\varepsilon})\), where its parameters \(x_{\varepsilon}\) are additional unknowns to infer jointly with the input parameters. \(x_{\mathcal{M}}\) of \(\mathcal{M}(x)\)[6] is a quantity not known a priori, this issue being overcome by parametrizing the residual covariance matrix. A diagonal covariance matrix with unknown residual variances \(\sigma^{2}\) is assumed, specifically \(\Sigma=\sigma^{2}I\). This results in reducing the discrepancy parameter vector in a single scalar (\(x_{\varepsilon}\equiv\sigma^{2}\)) and in setting the parameter vector as \(x=\big{(}x_{\mathcal{M}},\sigma^{2}\big{)}\). Assuming then a prior distribution \(\pi\big{(}\sigma^{2}\big{)}\) for the unknown variance of \(\varepsilon\), as well as independency on the priors of the uncertain model and the discrepancy, the joint prior distribution can be drawn out:  
\[\pi(x)=\pi(x_{\mathcal{M}})\ \pi\big{(}\sigma^{2}\big{)}. \tag{4}\]  
With these assumptions, a particular measurement point \(y_{\text{i}}\in Y\) is a realization of a Gaussian distribution with mean value \(\mathcal{M}(x)\) and variance \(\sigma^{2}\). In this way the likelihood function reads:  
\[\mathcal{L}\big{(}x_{\mathcal{M}},\sigma^{2};Y\big{)}=N\Big{(}y|\mathcal{M}(x ),\sigma^{2}\big{)}. \tag{5}\]  
Specifically:  
\[\mathcal{L}\big{(}x_{\mathcal{M}},\sigma^{2};Y\big{)}=\prod_{i=1}^{N}\frac{1 }{\sqrt{\big{(}2\pi\sigma^{2}\big{)}^{N}}}e^{-\frac{1}{2\sigma^{2}}(y_{\text{ i}}-\mathcal{M}(x_{\mathcal{M}}))^{T}(y_{\text{i}}-\mathcal{M}(x_{\mathcal{M}}))}, \tag{6}\]  
where \(N\) is the number of measurement points. Hence, the posterior distribution can be computed as:  
\[\pi\big{(}x_{\mathcal{M}},\sigma^{2}\big{|}Y\big{)}=\frac{1}{Z}\pi(x_{ \mathcal{M}})\pi\big{(}\sigma^{2}\big{)}\mathcal{L}\big{(}x_{\mathcal{M}}, \sigma^{2};Y\big{)}, \tag{7}\]which summarizes the updated information about the unknowns as \(\mathbf{x}=\left(\mathbf{x}_{\mathbf{M}},\sigma^{2}\right)\) after conditioning on the observation \(\mathbf{y}\).
## 5 Experiments
### Performance Metrics
#### 5.2.2 Vehicle Travel Efficiency  
For the evaluation of the fleet efficiency we propose a measure derived from the _total vehicle travel distance_. A few relevant metrics are presented in Beamon [60] already, e.g., total amounts or ratios calculated from empty and loaded vehicle travel. We found convincing arguments in the criticism of these measures. Therefore, a larger vehicle utilisation or consequently larger amounts of (loaded) vehicle travel do not necessarily correspond to better system performance. Thus, these metrics alone (utilisation and travel distance) could hardly be considered consistent with the actual goals of a production facility.  
Therefore, we propose a measure that presents a figure of the invested effort relative to the actual requested task. Similarly to how the _Value Added Efficiency_[60] measures the ratio of _productive time_ divided by _spent time_, for the purpose of our evaluation, we define the _Vehicle Travel Efficiency_\(\eta_{travel}\) as the ratio of the minimum distance loads were requested to be moved \(d_{requested}\) divided by the total amount of loaded \(d_{loaded}\) and empty \(d_{empty}\) distance travelled to satisfy the requirements.  
\[\eta_{travel}=\frac{d_{requested}}{d_{loaded}+d_{empty}} \tag{4}\]  
The requested transport distance we define as the euclidean distance between origin and destination of the move request. We calculate this statistic over the whole fleet and all requested transports.
## 3 Results  
### Expression Analysis of ADGRG3 Gene in Various Cancers  
#### 3.1.1 Pan-Cancer Analysis  
Previous studies have highlighted the role of aGPCRs, including ADGRG3 (GPR97), in various cancers, particularly in hematological malignancies such as AML. The high expression of certain aGPCRs, including ADGRG3, has been linked to poor prognosis in AML, particularly through pathways such as interleukin-8 (IL8) signaling [22]. However, compared to other GPCR family members, ADGRG3 remains less studied, especially across a broader range of cancers. To expand on this, this study analyzed ADGRG3 expression using TCGA data across various cancer types to understand its potential involvement in tumorigenesis and immune modulation.  
This TCGA analysis revealed the significant differential expression of ADGRG3 between tumor and normal tissues across multiple cancers (Figure 1A,B). Notably, ADGRG3 was upregulated in certain cancers, with the highest increase in expression in acute myeloid leukemia (LAML, +2.46); elevated levels were also seen in cholangiocarcinoma (CHOL, +0.61) and pancreatic adenocarcinoma (PAAD, +0.58). Conversely, ADGRG3 expression was markedly downregulated in other cancers, including thymoma (THYM, -7.52) and diffuse large B-cell lymphoma (DLBC, -7.35). These results underscore ADGRG3's complex, context-dependent role in cancer biology. The high expression in leukemia and other immune-involved cancers suggests its potential as a therapeutic target. In contrast, its downregulation in certain cancers may indicate a different or even protective role in tumor progression.  
To further examine ADGRG3 alterations across various cancers, analyzed mutation data using the cBioPortal platform. The oncoprint visualization (Figure S1A,B) illustrates the distribution of ADGRG3 mutations across different cancer types, with the most frequent alterations observed as missense mutations, amplifications, and deep deletions. This pattern suggests that ADGRG3 undergoes diverse genetic changes, which may impact its function across different malignancies.  
In addition, amino acid mutations in ADGRG3 were mapped to specific protein regions (Figure S2). The analysis identified important mutation sites within functional domains, such as the GPS (GPCR proteolysis site) and 7 tm_2 (7-transmembrane domain) regions. The fact that these mutations were predicted to be damaging by both the GPS and 7 tm_2 algorithms suggests that these sites may have critical structural and functional roles in ADGRG3's activity, potentially influencing its impact on cancer progression. The top four cancers with differentially upregulated expression of ADGRG3, namely, LAML, CHOL, HNSC, and PAAD, were further analyzed based on their clinical attributes, as described below.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Training Process
#### 3.1.5 Cloud Model Aggregation  
Considering the powerful communication capabilities of both the edge server and the cloud server, the communication delay during parameter uploading from the former to the latter is negligible. Therefore, upon receiving the uploaded parameters, the cloud server promptly performs a round of cloud aggregation. Similarly, it is crucial to incorporate an obsolescence function to mitigate the impact of outdated model parameters on the global model. The obsolescence function is associated with the model parameters uploaded by each edge server; it is linked to the overall obsolescence of the clients who participated in the last round of aggregation and is defined as follows:\[H_{j}^{(t)}=\frac{\sum_{i\in S_{j}}H_{i}^{(t)}}{\left|S_{j}\right|}. \tag{5}\]  
Then, the update of model parameters on the cloud server is given as  
\[\mathbf{\omega}_{0}^{(t)}=\frac{\left|D_{S}^{(t-1)}\middle|\mathbf{\omega}^{(t-1)}+H_{j} ^{(t)}\middle|D_{S_{j}}^{(t)}\middle|\mathbf{\omega}_{j}^{(t)}\right.}{\left|D_{S}^ {(t-1)}\middle|+H_{j}^{(t)}\middle|D_{S_{j}}^{(t)}\right|}, \tag{6}\]  
where \(D_{S_{j}}^{(t)}=\bigcup_{i\in S_{j}^{(t)}}D_{i}\) is the total dataset of the clients associated with edge server \(j\) in round \(t\), and \(D_{S_{0}}^{(t)}=\bigcup_{t^{\prime}=1}^{t}D_{S_{j}^{(t^{\prime})}}^{(t^{ \prime})}\). After the cloud model aggregation, the cloud server starts the next round of training via choosing a set of clients and distributing the latest global model parameters to them.
# Nutritional and Functional Properties of Gluten-Free Flours
## 3 Results
### Gelatinization and Retrogradation Properties  
Gelatinization temperatures and enthalpies associated with starch gelatinization varied among the GF flows (Table 6). The variation in the thermal properties among the different flows are influenced by some factors such as: size of the starch granule, molecular structure of the amylopectin (branch, length and weight), starch, protein and dietary fiber content, as well as the presence of other compounds [63]. During starch gelatinization, the helix structure and crystallinity of the starch is lost and the granule is disrupted. Among all the samples under investigation, T\({}_{\text{p}}\) was highest for the tiger nut flour (_p_ < 0.05), which can be explained by its highest fat and fiber content. The swelling of the starch granule is disturbed by the presence of non-starch compounds such as fat, which leads to higher gelatinization temperatures [64]. [PERSON] et al. [36] also found higher gelatinization temperatures in tiger nut flour than in rice flour because of its higher amount of oil, fiber, and sugar. On the other hand, the lowest T\({}_{\text{p}}\) was for the oat flour (_p_ < 0.05). Lower gelatinization temperature is proof of shorter amylopectin chains as lower temperatures are needed for their completely dissociation [65]. The enthalpy of gelatinization reflects the amount of energy needed to break the molecular interactions in the starch during the gelatinization. Gelatinization enthalpy was significantly higher (_p_ < 0.05) in plantain and both types of rice flours than in the other samples, because of the high crystallinity of the starch granules in rice and plantain [66; 67]. In addition, the higher starch content in rice flours and plantain flour needs more energy to open the double helical structure of starch. A less thermal energy was needed for starch gelatinization in the tiger nut and oat flours, followed by chickpea and gram samples. Retrogradation temperature and enthalpy were considerably lower than the gelatinization parameters, which denotes weaker starch crystallinity (Table 6). As described by [PERSON] et al. [68], the lower values for the thermal transitions during retrogradation were attributed to improper realignment of amylose and amylopectin molecules that forms a less stable crystalline structure.