esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 2 Related Works  
Currently, application of XAI is in the early stage; the methods were introduced in the mid-2010s. Most implementations on deepfake detection methods are focused on image recognition. Although there has been attempt to joint both visual and audio data to detect the deepfake, the interpretation on the model has not been tried [17]. A recent study implemented LIME and LRP to provide interpretability only for visual deepfake detection [2]. For deepfake facial images, XAI methods provide a heatmap for the trained model to suggest the regions to focus on.  
Implementation of XAI on speech recognition rather than both visual and audio deepfake detection is scant, but it exists; the same XAI method, LRP, is commonly used to provide interpretability. A study by [PERSON] suggested a bidirectional-GRU-based model built for the implementation of XAI methods and finding the relevance score for each input [11]. Through perturbation comparison, they suggested the superiority of bi-GRU over uni-GRU, LSTM, and bi-LSTM. [PERSON] et al. compared the implementation of LRP on speech recognition with two tasks: digit classification and gender classification [18]. Each task was trained with AlexNet and compared using the LRP attribution score. Along with the comparison, the authors proved the meaningfulness of LRP on speech recognition through data perturbation.  
These studies focused on quantitative evaluation of the XAI methods on spoken language processing. Though the studies shared some ideas, our study focuses on qualitative evaluation.  
The XAI methods applied to various tasks, yet focused on image related tasks, such as layer-wise relevance propagation or Deep Taylor decomposition, provide intuitive model interpretations with a form of a heatmap. The interpretation can be effortlessly obtained without modifying the existing trained model. The generic idea of such Taylor decomposition-based XAI methods is to trace back the contribution of every input for each trained node to the final prediction layer-by-layer. By iteratively calculating the contribution of every node, the model can be interpreted pixel-by-pixel, which is a heatmap of the input image.  
Although interpretability adoptable to the existing trained model is significant, as [PERSON] et al. mentioned, the methods show limitations: high noise of heatmap output and lack of class-discriminativeness [19]. Considering such limitations, we found it necessity to interpret at the human cognitive level. By focusing on qualitative evaluation of the interpretation, noise of the heatmap can be overcame through the human cognitive level of contextual interpretation. Hence, based on the interpretation to the human-in-loop system as a guideline, lack of class-discriminativeness is also expected to be efficiently resolved.
## 4 Digital Twin Modelling Methodology
### Model Performance Predictors  
At the last stage of model development, we ran evaluations to predict TBM penetration and advance rate. This section compares the models mentioned above to choose the most efficient one. The results obtained from these models were examined according to performance indices based on the statistical parameters, including RMSE, R\({}^{2}\), and VAF. The performance of the model was evaluated using RMSE, R\({}^{2}\), and the VAF in percent (%), using Equations (6)-(8).  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}(\text{y}-\text{y}^{ \prime})^{2}} \tag{6}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{N}}(\text{y}-\text{y}^{\prime})^{2}}{ \sum_{i=1}^{\text{N}}(\text{y}-\text{\hat{y}})^{2}} \tag{7}\]  
\[\text{VAF}=\left[1-\frac{\text{var}(\text{y}-\text{y}^{\prime})}{\text{var}( \text{y})}\right]\times 100\% \tag{8}\]  
y and y\({}^{\prime}\) are the measured and predicted values of output, respectively, \(\text{\hat{y}}\) stands for the mean of the variable y, and N is the total number of datasets. Based on the created datasets, ANN models were developed to predict TBM PR, and their evaluations were performed based on R\({}^{2}\), RMSE, and VAF. To obtain a theoretically perfect network model, RMSE, R\({}^{2}\), and VAF should be 0, 1, and 100, respectively.
## 4 Chemical Composition and Structure of _Astragalus_ Polysaccharides
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Name** & \begin{tabular}{l} **Molecular** \\ **Mass (Da)** \\ \end{tabular} & \begin{tabular}{l} **Monosaccharide Composition** \\ \end{tabular} & \begin{tabular}{l} **Molar Ratio** \\ \end{tabular} & \
\begin{tabular}{l} **Method** \\ \end{tabular} &
\begin{tabular}{l} **Reference** \\ \end{tabular} \\ \hline APS & \(3.0\times 10^{6}\) & L-Rhammoe, D-Xylose, D-Glucose, D-Glucose & 1:45:1.5 & GC; H1-C,-13 NMR & [PERSON] et al. [30] \\ LMw-APS & \(5.6\times 10^{5}\) & Glucose, Galactose, Arabinone, Galactoside acid, Xylose & 10:13.1/7.095.1 & HPLC; GC & Qu [28] \\ APS & \(2.1\times 10^{4}\) & 1.4 Glucose backbone, \(L\) Glucose branch & NMR & [PERSON] et al. [31] \\ Rap-APS & \(1.3\times 10^{6}\) & Rhammoe, Arabinone, Glucose, galactose, Galactoside acid & 0.031:00.2/7.36/0.30 & GC-MS; H1, 3C NMR & [PERSON] et al. [32] \\ APS & \(7.6\times 10^{6}\) & L-Azhniose, D-Calactose, D-Calactose aromatic acid, D-Glucuronic acid & 18:18:1.1 & Electrophoresis; GC & [PERSON] et al. [33] \\ APS & \(3.6\times 10^{6}\) & \(\text{-D}(1-\text{d})\) glucose & GC-MS; UCNMR & [PERSON] et al. [34] \\  & \(1.1\times 10^{4}\) & Rhammoe, Glucose, Galactose, Arabinone & 1.197:2/01.58:20.95 & HPLC; I-R, IHNMR & [PERSON] et al. [35] \\ MAPS-5 & \(1.3\times 10^{4}\) & \(\text{a-D}(1-\text{d})\) glucose & GC-MS; IR; NMR & [PERSON] et al. [36] \\ APS1 & \(4.8\times 10^{6}\) & Arabinone, Xylose, Glucose & 0.54:10/0.18:14 & HPLC; GC; IR; NMR & [PERSON] [37] \\ APS II & \(8.7\times 10^{4}\) & Arabinone, Xylose, Glucose & 0.231:00.29/3.9 & HPLC; GC; IR, NMR & [PERSON] et al. [37] \\ APS & \(3.8\times 10^{4}\) & Glucose, Galactose, Arabinone & 1.00:09.05/0.70 & HPLC; & [PERSON] et al. [38] \\ APS & \(3.6\times 10^{4}\) & \(\text{a-D}\) glucose & FTIR, AMLLC; GC-MS, NMR & [PERSON] et al. [39] \\ HAPS & Rhammoe, Arabinone, Xylose, Mannsoe, Galactose & Glucose & 1.002:26.02/01.742.49/19.47 & HPLC; GC & [PERSON] et al. [40] \\ APS & Rhammoe, Arabinone, Xylose, Mannsoe, Galactose, Glucose & 1.004:34.92/1.951.41/41.02/8.25 HPLC; GC & [PERSON] et al. [40] \\  & \(1.7\times 10^{6}\) & Mannsoe, Glucose, Xylose, Arabinone, Glacturonic acid, Rhammoe & 0.062:84.30/58.240.30/2.94 & UPLC; GC-MS; IT-IR & [PERSON] et al. [41] \\ APS & \(2.0\times 10^{6}\) & [PERSON], Glucose, Xylose, Arabinone, Glacuronic acid, Rhammoe & 0.072:12.83/1.63/0.71/0.40.56 & & [PERSON] et al. [41] \\  & \(2.3\times 10^{6}\) & Rhammoe, Galactose, Arabinone, Glucose & 1.002:13.22/26.18 & FTIR; GC; NMR & [PERSON] et al. [42] \\ \hline \hline \end{tabular} Note: APS: _Astrophis polysaccharides_; GC: Gas chromatography; NMR: Nuclear magnetic resonance; HPLC: High-performance liquid chromatography; MS: Mass spectrometry; IR: Infrared spectroscopy.  
\end{table} 
Table 1: The chemical composition of _Astrophis_ Polysaccharides.
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 3 Results and Discussion
### Freeze-Thaw Cyclic Test  
The 28 d (five cycle) compressive strength loss (BDR) of slag/fly-ash-based geopolymer-stabilized macadam are shown in Table 5. It can be seen that the BDR value of geopolymer-stabilized macadam is greater than that of the cement-fly-ash-stabilized macadam under the same mix proportion after freeze-thaw cyclic tests. This suggests that the geopolymer  
Figure 1: Mechanical properties change with age. (**a**) Compressive strength. (**b**) Compressive elastic modulus.  
Figure 2: Effect of different geopolymer dosage on mechanical properties of specimens. (**a**) Compressive strength. (**b**) Compressive elastic modulus. (**c**) Tensile strength.  
cementitious material have a higher cementing property, which improves the compression resistance and freezing resistance of the stabilized macadam of base course.  
The calculated loss rate of compressive strength is shown in Figure 3. The strength loss after freeze-thaw cyclic tests decreased as the content of geopolymer continuously increased. The strength loss reduced from 14.2% to 2.5% when the dosage increased from 2% to 5%.
## 5 Evaluation Metrics
### Bilingual Evaluation Understudy (BLEU)  
BLEU (Bilingual Evaluation Understudy) [87] is a metric used to assess machine-translated text. It measures the machine translation's similarity to a group of expert reference translations ranging from 0 to 1 (a value of 1 denotes high quality). It is the evaluation criterion most often used. It examines the n-gram correlation between the reference translation and evaluated sentences.
# Opal-Like Photonic Structuring of Perovskite Solar Cells Using a Genetic Algorithm Approach
## 5 Conclusions  
The present study numerically investigates photonic structuring of the photo-active layer within PSCs. A genetic algorithm approach constrains the \(1.4\times 10^{9}\) potential parameter combinations down to an optimal configuration, giving an internal quantum efficiency of 98.1% over the considered wavelength range (310-800 nm). The presence of a hybrid uniform-structured photo-active layer shows an iQE value 2% higher than an equivalent unstructured photo-active layer. The structuring consists of perovskite spheres arranged in an _fcc_ closed-packed structure with \(ABC\) stacking. This allows the excitation of the quasi-guided mode in order to provide higher light trapping within the photo-active layer. The proposed structure is robust against experimental deviations that may occur in the thicknesses of the perovskite layers or the radius of the perovskite spheres. It also presents stable angular dependency. Nevertheless, as the present study only focuses on photonic aspects, further work  
Figure 6: Integrated quantum efficiency (iQE) as a function of the incidence angle for unpolarized light for the optimal combination.  
should take into consideration the electronic effects before considering any realistic implementation within a working PSC.  
Conceptualization, M.L., A.M. ([PERSON]) P.P., J.D., A.M. ([PERSON]), C.H., and J.L.; methodology, M.L., A.M. ([PERSON]), P.P., J.D., A.M. ([PERSON]), C.H., and J.L.; software, M.L. and A.M. ([PERSON]); formal analysis and investigation, M.L. and A.M. ([PERSON]); optimization, A.M. ([PERSON]); supervision, J.L.; project administration, J.L. and C.H.; funding acquisition, J.D., C.H., and J.L.; original draft preparation, M.L.; writing--review and editing, A.M. ([PERSON]), A.M. ([PERSON]), P.P., J.D., C.H., and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Federation Wallonie-Bruxelles (grant for Concerted Research Actions). Computational resources were provided by the Consortium des Equipements de Calcul Intensif (CECI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11 and by the Walloon Region. [PERSON] is funded by the Fund for Scientific Research (F.R.S.-FNRS) of Belgium. He is a member of NaXys, Namur Institute for Complex Systems, University of Namur, Belgium.  
[PERSON], director of GREENMAT, [PERSON], director of LPS, and fellow team members.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Handovers and Mobility Management  
When users move out of the coverage area of the original connection, a handover is used to transfer the connection between cells. Handovers occur more frequently in an HSTN due to the orbital motion of MEO and LEO satellites and the mobility of mobile users. Thus, efficient mobility and handover management is critical to ensure service continuity and achievement of QoS requirements. A standard handover procedures in each network can be used to execute handover when it only happens between terrestrial or satellite networks. However, changeover across various networks is complex and more challenging.  
In cooperative HSTNs, where the satellite network and the terrestrial network work together to deliver improved communication services for ground users, more complicated handovers may take place. Since users are serviced by two cooperative connections in such an architecture, the cooperation must be transferred to the new connection when conducting a handover for one of the connections. The complexity of handovers is increased by the possibility of simultaneous handovers of the two connections under more unique circumstances.
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 4 Methods
### Modeling Hierarchical Dependencies
#### 4.3.2 Hierarchical Loss Function  
To capture the hierarchical relationships in a given model, the loss function of the above models can be extended with an additional term. This leads to the definition of a **Hierarchical loss function** (\(loss_{H}\)). This loss function penalizes classifications that contradict the inherent ICD hierarchy. More specifically, when a parent category is not predicted to be true, none of its child categories should be predicted to be true. The hierarchical loss between a child and its parent in the tree is then defined as the difference between their computed probability scores, with 0 as a lower bound. More formally, for the entire loss function \(loss_{H\_Model}\) for a category of layer \(X\), combining the regular training loss \(loss_{Model}\) described above and the hierarchical loss \(loss_{H}\), is calculated as follows,  
\[P(X) = Probability(X==True) \tag{20}\] \[Par(X) = Probability(Parent(X)==True)\] (21) \[L(X) = True\,label\,of\,\,\,X(0\,or\,\,1)\] (22) \[loss_{H}(X) = Clip(P(X)-Par(X),0,1)\] (23) \[loss_{H\_Model}(X) = (1-\lambda)loss_{Model}(X)+\lambda loss_{H}(X) \tag{24}\]  
which leaves a parameter \(\lambda\) to optimize the loss function (parameter \(\lambda\) is optimized over the training set).
# SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship
## 4 Experimental Evaluation
### Experiment Results
#### 4.2.4 Node Ranking  
Besides sign prediction, SR-GNN facilitates node ranking by computing status scores. In order to evaluate the effectiveness of node ranking, the top 10 list of status scores generated by SR-GNN, BESIDE, and S-GNN with the statistical results of the number of times users were elected as administrators for a subject in the dataset is compared, since only BESIDE and S-GNN are based on status theory.  
Validating the performance of node ranking is fairly challenging; most models focus solely on the sign prediction task because real-world datasets often lack ranking attributes. However, the WikiRfA and WikiElec datasets provide an ideal evaluation setup, as the nodes correspond to Wikipedia users whose election results depend on who received more votes (positive edges), making the frequency of being successfully elected as an administrator a quantifiable ranking metric.  
The ranking lists given by three models partially overlap. Results indicate that each algorithm has learned a part of the topological features of the network, but not complete features (Table 4).  
Table 4 shows that SR-GNN outperformed the two models, demonstrating more accurate node ranking results in the WikiRfA and WikiElec datasets. SR-GNN successfully identified users who were elected as administrators multiple times. A notable case is user \"[PERSON]\", who was picked out by SR-GNN while being overlooked by other models.  
\"[PERSON]\" is an active user, engaging in a high level of positive interactions (positive reciprocal edges) with certain neighbors while simultaneously \"attacking\" others. This behavioral pattern results in a relatively high status score, eventually leading to \"[PERSON]\" being successfully elected as an administrator. The same kind of behavioral pattern can also be observed in real-world scenarios. For example, a less famous rapper may significantly increase their public exposure by verbally \"attacking\" other popular rappers [25]. This can suggest that mutual interactions play a significant role in status formation. This pattern highlights a key advantage of SR-GNN, which explicitly encodes reciprocity, allowing it to capture status dynamics that models such as S-GNN, which do not account for reciprocity, fail to recognize.
## 5 TB Vaccine Candidates in Clinical Trials
### Subunit Vaccines  
#### 5.2.1 Subunit Viral Vectored Vaccines  
There are three subunit vaccines that use attenuated viral vectors by different routes of administration.  
Ad5 HuAg85A vaccine, developed by the University of McMaster in Canada, consists of a human adenovirus serotype 5 vector that expresses Ag85A and is administered intramuscularly (Figure 3). AdHu5 Ag85A has been evaluated in a Phase 1 safety and immunogenicity study in BCG-naive and BCG-immunized healthy adults by the intramuscular route showing adequate safety and tolerability [47; 66] (Figure 2).  
ChAdOx85A/MVA85A vaccine strategy, developed by the University of Oxford, like the recombinant vox vaccine MVA85A (mentioned above), ChAdOx85A is a simian adenovirus which also expresses Ag85A (Figure 3). Both vaccine candidates are tested together in a joint heterologous prime-boost regimen delivered through both systemic and mucosal routes in BCG-vaccinated individuals. A Phase I trial of intramuscular administration of ChAdOx85A in BCG vaccinated adults in the United Kingdom, tested alone and as part of a prime-boost strategy with MVA85A, has been completed. Aerosol administration of ChAdOx185A is currently evaluated in a Phase I trial among BCG-vaccinated adults in Switzerland (Figure 2). Safety and immunogenicity of aerosol administration of MVA85A in BCG-vaccinated individuals and in people with a latent TB infection have been evaluated. In 2019, plans for a Phase IIa safety and immunogenicity trial among adults and adolescents in Uganda were underway with the aim of evaluating intramuscular administration of ChAdOx185A and MVA85A [2].  
TB/FLU-04L is based on an attenuated replication-deficient influenza virus vector expressing antigens Ag85A and ESAT-6 (Figure 3). It was designed as a preventive booster vaccine in BCG-vaccinated infants, adolescents and adults. It is about to start a Phase IIa study in individuals with latent TB [2] (Figure 2).
## 6 Conclusions  
The development of national and global cloud radar networks is inhibited by the high cost of these instruments. This work presents a Ka-band FMCW cloud radar design, with an estimated cost under $20,000 USD. Although the current design was developed with recycled components from a radio telescope, many of the components have a commercial substitute that gives the cost estimation. The radar is capable of detecting clouds and estimating their range, up to 500 m of altitude. This range enables the retrieval of fog properties from the base to the top, at least in certain types of topographies such as those found in the coastal range of Chile, at a much lower cost when compared to available cloud radars.  
We also present the methods used to calibrate and evaluate the radar. The cloud sampled in the validation campaign with the Lidar and Radar indicates that the Radar prototype operates correctly and is able to detect clouds of approximately \(-60\) dBZ of reflectivity at 400 m of distance. This experiment also indicates that the UAV distance calibration was a key step to have correct range measurements with this radar architecture.  
The radar is extremely portable and low power compared to commercial clouds radars, facilitating its operation in remote sites. This design is meant to serve as a complement to the ceilometer measurement as well as to work as a distributed radar network to study the dynamics of the boundary layer together with the formation and dissolution of clouds and fog at a mesoscale.  
Conceptualization, R.R. ([PERSON]) and M.D.; Supervision, M.D. and R.R. ([PERSON]); Investigation, R.A., F.T., and R.R. ([PERSON]) and M.D.; methodology, R.A., F.T. and R.R. ([PERSON]); software, R.A. and F.T.; validation R.A. and N.R.; formal analysis, R.A., F.T., M.D., and R.R. ([PERSON]); resources, N.R., R.R. ([PERSON]), and M.D.; data curation, R.A., F.T., and M.D.; writing--original draft preparation, R.A. and M.D.; writing--review and editing, F.T., R.A., and R.R. ([PERSON]); funding acquisition, M.D. and R.R. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Fondecyt grants 1151476, 1151125, and 1161356. It was also partially supported by Conicyt Grant AFB180004 and BASAL Centre CATA PFB-06/2007. F.F. In addition, this material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-18-1-0249.  
We would like to thank the strong support of [PERSON] from Space and Planetary Exploration Laboratory and [PERSON] from Department of Geophysics at University of Chile.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the recollection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### Discussion on Parameters Selection
#### 4.3.2 Optimized Circular Tiers Partition  
In this section, when assuming the outermost layer width \(d_{k}=\delta r\), how to partition the circular tiers under the constraint of transmission range of \(r\) to achieve the optimization of the minimization between actual second tier width and the width in theory is discussed.  
It can be seen from the previous sections that considering the constraint that the tier width could meet the goal that when collecting nodes in the outer layer finish transmission to the parent collecting nodes in the inner layer and the collecting nodes in the inner layer just finish data aggregation, we have the equation that is \(\tau_{k-1\_n}=\tau_{k}\). It is \(\xi_{k-1}=\xi_{k}+1\) after simplification. Therefore, the tier widthcan be obtained. Thus, the second tier width \({d_{2}}^{\mathit{the}}\) can be theoretically calculated. However, the actual second tier width \(d_{2}\) is obtained by:  
\[d_{2}=\frac{\pi R}{2}-\sum\limits_{m=2}^{k}d_{m}-d_{1} \tag{21}\]  
where, \(d_{1}\) represents the width of hotspots and \(d_{1}=r\). Therefore, the optimization is to obtain the minimization of difference between the theoretically value \({d_{2}}^{\mathit{the}}\) and the actual value \(d_{2}\), which is the following formula:  
\[\min(\varepsilon)=\min(\left|d_{2}-d_{2}^{\mathit{the}}\right|) \tag{22}\]  
The minimization of difference between the theoretically value \({d_{2}}^{\mathit{the}}\) and the actual value \(d_{2}\) can be achieved by optimizing the circular tiers partition under the constraint of transmission range of \(r\) and \(d_{k}=\delta r\). The detailed procedure to select the optimizing parameters of the number of circular tiers is described in Figure 7. Figure 7 shows how to select parameters to optimize the proposed TSR-DARDA scheme.