esa-sceva/satcom-chunk-collection huggingface dataset:
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Methodology
#### 1.4.1 Defining Measures in Terms of the Information Leakages  
The privacy, interpretability, and transferability measures are defined in terms of the information leakages:  
* Privacy leakage is measured as the amount of information about private/sensitive variables leaked by the shared variables;
* Interpretability is measured as the amount of information about interpretable parameters leaked by the model;
* Transferability is measured as the amount of information about the source domain model output leaked by the target domain model output.
## 3 Introduced Concepts and Methodologies  
### Convolutional Neural Network (CNN)  
A convolutional neural network (CNN) [18] is a class of deep learning networks that has achieved state-of-the-art performance in many computer vision areas, such as image classification, pattern recognition, object detection, etc. Typically, CNN consists of three main types of components: convolutional layer, pooling layer, and fully-connected layer, as illustrated in Figure 1.  
The convolutional layer [20] uses multiple convolution filters (or convolution kernels) to extract the higher-level features from the low-level information, such as detecting the edges, corners, connection points, and other features from input images. Since multiple convolution filters will dramatically increase the size of the feature map and accompanied by tedious calcula  
Figure 1: Schematic diagram of CNN, using the Vgg19 architecture for example [19].  
layer [21] to reduce the feature map size and thus leads to a faster convergence rate for networks. Finally, all of the multi-dimensional feature maps are converted into a one-dimensional feature vector and input to the fully-connected layer. The fully-connected layer is basically a regular multilayer perceptron (MLP) and is used to generate class predictions for the further classification task. In this paper, we use the VGG19 architecture [19] and Inception V3 architecture [22] in our experiments, since both architectures are well-designed and have shown their great ability in ImageNet competition [23].
## 3 Proposed Approaches
### Proposed Method
#### 3.3.3 Post-Processing  
Standardization was used to prevent divergence of the training and test data in the pre-processing stage, and the standardized training and test data are replaced with the original values using Equations (13) and (14), respectively:  
\[Train_{pred}=Train_{sig}\times Train_{pred}+Train_{mean}, \tag{13}\]  
\[Test_{pred}=Test_{sig}\times Test_{pred}+Test_{mean}. \tag{14}\]  
Figure 7 shows the solar power forecast results using a proposed LSTM for January 2019. Figure 7 compares between observed and predicted values and Figure 7b predicted error between observed and predicted values. As shown in Figure 7, the proposed method is more accurate than the traditional LSTM used in the experiment. The proposed LSTM can adapt to fluctuations in the observed data, which improves its accuracy and performance. Additionally, the prediction error of the traditional LSTM is 6.68, while the prediction error of the proposed LSTM is 2.07. Therefore, the proposed LSTM method is more effective and adaptive for solar power forecasting.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Factors** & **No.** & **Factors** \\ \hline
1 & SP & 12 & SP, SR, WS \\
2 & SP, SR & 13 & SP, SR, Sunlight, Humidity \\
3 & SP, Sunlight & 14 & SP, SR, Sunlight, Temperature \\
4 & SP, Humidity & 15 & SP, SR, Sunlight, CC \\
5 & SP, Temperature & 16 & SP, SR, Sunlight, WS \\
6 & SP, CC & 17 & SP, SR, Sunlight, Humidity, Temperature \\
7 & SP, WS & 18 & SP, SR, Sunlight, Humidity, CC \\
8 & SP, SR, Sunlight & 19 & SP, SR, Sunlight, Humidity, WS \\
9 & SP, SR, Humidity & 20 & SP, SR, Sunlight, Humidity, Temperature, CC \\
10 & SP, SR, Temperature & 21 & SP, SR, Sunlight, Humidity, Temperature, WS \\
11 & SP, SR, CC & 22 & SP, SR, Sunlight, Humidity, Temperature, CC, WS \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multivariate numerical models.
## 5 Discussion  
Historically, drones have predominantly relied on manual intervention for specific tasks. However, with the rapid advancements in artificial intelligence technology, drones can now autonomously execute tasks such as object recognition and flight. While various object detection algorithms have made substantial improvements in performance, striking the right balance between accuracy and speed remains a challenge due to variations in object scale, the presence of numerous small objects, and the complexity of scenes within drone images.  
The PDWT-YOLO algorithm showcased exceptional performance on the VisDrone-2019 dataset when compared with state-of-the-art algorithms, including Faster R-CNN, Cascade R-CNN, CenterNet, and the YOLO series, as demonstrated in Table 1. It achieved the highest [EMAIL_ADDRESS] with the second-lowest number of required parameters. Notably, PDWT-YOLO outperformed YOLOv7-tiny, which had fewer parameters but a lower [EMAIL_ADDRESS] score. PDWT-YOLO particularly excelled in enhancing the detection accuracy for small objects, as is evident in Tables 2 and 3 and Figure 6. Furthermore, it exhibited superior accuracy on the HIT-UAV drone dataset, which encompasses multiscale objects.  
In the ablation experiment (Table 3), the addition of the decoupled head to PDWT-YOLO resulted in improvements across all performance indicators, with the exception of APL, which was reduced by 2.3%. Most notably, [EMAIL_ADDRESS] increased by 1%, indicating an enhanced ability to extract object position and category information from the feature map. This shows that the decoupled head achieved better performance by separating the classification and location regression of the model. Subsequent incorporation of small-object layers further improved detection indicators for objects of all sizes, with APS increasing by 3.4%, APM by 4.2%, and APL by 3.8%. It can be concluded that the P2 (the added small target layer based on FPN) makes the model more focused on small targets, and improves the detection performance of the model for multiscale targets. The addition of WIoU further heightened accuracy, resulting in a 1% increase in [EMAIL_ADDRESS] without adding computational complexity, thus ensuring both high accuracy and faster convergence. In comparison with the baseline algorithm, the PDWT-YOLO algorithm significantly boosted performance metrics, elevating [EMAIL_ADDRESS] by 6.7%, [EMAIL_ADDRESS]:0.95 by 5%, APS by 4.7%, and APM by 5.3%. In summary, these three innovations in the PDWT-YOLO model have demonstrated the ability to enhance detection performance, whether implemented individually or in combination, and offer a substantial improvement in object detection for UAV applications.
## 4 Self-Tuning Fuzzy Control Strategy
### Process Architecture of Self-Tuning Control Strategy  
The self-tuning control strategy adopts a hierarchic structure that consists of a supervisory controller and a sub-level controller. Taking into account that the pulse-type near-fault earthquake has large acceleration amplitudes and velocity pulses, the supervisory controller is employed to evaluate the nature of the earthquake according to the current acceleration and velocity level. A sub-level controller is designed to determine the command voltage of the PFD. Base displacement and velocity are employed as two input variables, and the command voltage is defined as a single output for the sub-level controller. Since the amplitudes of base displacement and velocity differ greatly from near-fault and far-field earthquakes, the decision on the normalization factor and the scaling factor is evaluated by the supervisory controller. The block diagram of the proposed self-tuning fuzzy control strategy is shown in Figure 4.
## 4 Results
### Regional Preferences' Analysis at the Regional Level  
The second step consists of the spatial analysis of tourists' preferences to explore spatial patterns of positive judgments at the regional level.  
The application of spatial analytical techniques aims at exploring the spatial patterns of tourists' perceptions and their relationships with other territorial variables. For each TLS, the database built from TripAdvisor.com and Booking.com includes a score record, which is the average of six attributes:  
1. location (the TLS's geographic position);
2. services (presence of retail shops, bus stops, cafeterias, dining rooms and so on);
3. price/quality ratio;
4. staff (kindness);
5. room cleanliness (cleaning); and
6. the TLS's perceived comfort.  
The first two attributes are related to the quality of the TLS's location, while the others concern the qualitative assessment of the TLS. Thus, through the available data we can investigate the spatial patterns of preferences across the whole region.  
Data from Tripadvisor.com and Booking.com are normalized and ranked on the same scale. The six attributes are divided into two main categories which represent: i. location and ii. service quality. The analysis of the categories leads to discovering the level of satisfaction with the destination and its services, which may provide useful clues for choosing the most sustainable tourist options in case of other destinations.  
Figure 2 shows the distribution of the tourists' positive preferences incidence (TPPI). The TPPI shows a high spatial concentration in North-eastern Sardinia. The Costa Smeralda district seems to be the only area where the global tourists' preferences fulfill overall tourists' expectations. The analysis at the municipal level shows that Alghero reveals the highest TPPI rate. The other two municipalities with a high TPPI are Cagliari and Olbia.  
The map reported in Figure 2 not only detects locations already affected by the phenomenon, but also identifies areas suitable for future tourist developments. It is notable that most of the municipalities located along the coastal area attract tourists, while the inner areas represent a cold spot. Moreover, the presence of three major clusters representing the spatial distribution of TPPI within Sardinia is important as well. The first cluster is located in the Nurra district and includes the Sassari, Stintino, and Alghero municipalities. The presence of this cluster is probably due to the attractive power of the municipality of Alghero; that is, the only municipality which preserves the Catalan tradition. The second cluster is located in the Cagliari metropolitan area, which entails that Cagliari is strategic in terms of tourism attraction.  
Finally, the last cluster covers a large part of the Gallura district, which represents a historical tourism landmark for the presence of the Costa Smeralda, which has been recognized as such since the 1960s due to Prince [PERSON]'s massive investment.
## 3 Preliminaries
### Security Requirements for Bio-2 FA-IoD
* **Scalability**: The authentication mechanism should be able to support a potentially large number of drones and users without significant performance degradation or unmanageable key distribution overhead. * **Flexibility** and usability: The protocol should be adaptable to various IoD operational scenarios and be user-friendly for the operator during the authentication process. This includes considerations for password/PIN changes and device revocation. * **Dynamic** operations support: The protocol should ideally support dynamic drone addition to the network and handle drone revocation or temporary disconnections and re-authentication seamlessly. Handover authentication [6] is also critical.
# Shear Strength of Trapezoidal Corrugated Steel Webs for Horizontally Curved Girder Bridges
###### Abstract  
Curved composite girder bridges with corrugated steel webs (CSWs) have already been constructed around the world. However, limited work has been done on their shear behavior. In this paper, the corrugated steel web (CSW) in horizontally curved girders (HCGs) is treated as an orthotropic cylindrical shallow shell, and the analytical formula for the elastic global shear buckling stress is deduced by the Galerkin method. Calculation tables for the global shear buckling coefficient for a four-edge simple support, for a four-edge fixed support, and for the two edges constrained by flanges fixed and the other two edges simply supported are given. Then, a parametric study based on a linear buckling analysis is performed to analyze the effect of the curvature radius and girder span on the shear buckling stress. Analytical and numerical results show that the difference of shear buckling stress of CSWs between curved girders and straight girders is small, so the shear design formulas for straight girders can be applied for curved girders. Finally, a series of tests were performed on three curved box girders with CSWs. Similar to CSWs in straight girders, the shear strain distributions of CSWs in HCGs are almost uniform along the direction of the web height and the principal strain direction angles are close to 45\({}^{\circ}\). For the three specimens, CSWs carry about 76% of the shear force. In the destructive test, shear buckling after yielding occurred in all specimens which is in good agreement with the theoretical prediction, which means that the analytical formulas provide good predictions for the shear buckling stress of CSWs in HCGs and can be recommended for design purposes.  
horizontally curved girder; corrugated steel webs; shear buckling stress; [PERSON] method; finite element analysis; experimental work
# High-Functioning Autism and Virtual Reality Applications: A Scoping Review
## 3 Results
### Investigated Dimensions
#### 3.2.3 Motor Learning  
Motor difficulties are often associated with ASD. To measure the atypical postural reactivity of ASD individuals, [PERSON] and colleagues simulated different types of oscillations in a 3D environment [49]. Using fully immersive VR technology, a virtual tunnel was designed to sway at different frequencies using the \"virtual tunnel paradigm\" [68]. Posture adaptation was studied by altering visual stimuli: a tunnel with an inner checked pattern was presented in a static or dynamic setting; the latter also provided motion oscillation at three frequencies (0.125, 0.25, or 0.5 Hz). To assess body adaptation, the head's movements were tracked. Sixteen HF ASD individuals, divided into two groups according to age group (12-15 or 16-33), and thirty-four typically developing (TD) subjects were included in the study in order to assess whether postural reactivity can be considered a function of development. From the results, it emerges that ASD users are able to transfer sensory information to a motor pattern unless fast visual stimuli integration is required.  
[PERSON] and colleagues evaluated gait patterns and motor performance in children with HF ASD compared with TD peers [54]. The gaits participants were assessed in semi-immersive VR with a treadmill. Then, subjects underwent a perturbation phase consisting of 20 trials with one single-belt acceleration. Finally, a post-perturbation gait analysis was performed. The results showed an altered gait pattern in children with ASD but a moderate adaptation to perturbation in both groups.  
In another study by [PERSON] and colleagues [61], matched HF ASD and TD participants were asked to perform sinusoidal arm movements while observing a virtual reality agent in two forms (human and cobot). The agent performed a gesture at a biological motion or at a constant velocity, and participants were asked to follow the actor's movement in a congruent or incongruent way. The experiment took place in an immersive CAVE and involved 22 participants, including 10 HF ASD individuals. Results for the interference effect, caused by observing an incongruent action made by another agent, showed that individuals with ASD, in contrast to what was observed in the TD group, did not exhibit any interference effect.
# Plant Disease Diagnosis in the Visible Spectrum
## 4 Results  
Some of the test images and the results generated by the methods are presented in Figures 7-9 in order to visually appreciate the results.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Class** & **SZ** & **T** & **1\%** & **2\%** & **5\%** & **20\%** & **MAX** & **BM** \\ \hline Apple cdar & 275 & 5.6 & 98.2\% & 93.5\% & 68.4\% & 20.4\% & 98.2\% & 99\% \\ \hline Apple healthy & 287 & 4.9 & 30.3\% & 46.7\% & 65.2\% & 89.9\% & 89.9\% & 95\% \\ \hline Corn common rust & 251 & 2.8 & 95.2\% & 85.7\% & 64.5\% & 44.6\% & 95.2\% & 99\% \\ \hline Corn healthy & 259 & 6.3 & 99.6\% & 99.6\% & 99.6\% & 100.0\% & 100.0\% & 100\% \\ \hline Grape black measles & 429 & 5.8 & 100.0\% & 98.1\% & 84.6\% & 15.2\% & 100.0\% & 97\% \\ \hline Grape healthy & 423 & 6.1 & 41.6\% & 63.8\% & 91.3\% & 100.0\% & 100.0\% & 99\% \\ \hline Peach bacterial spot & 150 & 5.6 & 96.0\% & 89.3\% & 72.7\% & 43.3\% & 96.0\% & 99\% \\ \hline Peach healthy & 115 & 3.9 & 29.6\% & 51.3\% & 82.6\% & 98.3\% & 98.3\% & 98\% \\ \hline Pepper b. bacterial s. & 257 & 5.7 & 93.8\% & 86.0\% & 21.8\% & 21.8\% & 93.8\% & 81\% \\ \hline Pepper b. healthy & 247 & 6.1 & 78.5\% & 85.4\% & 93.9\% & 99.6\% & 99.6\% & 98\% \\ \hline Potato healthy & 152 & 4.9 & 71.7\% & 89.5\% & 94.1\% & 99.3\% & 99.3\% & 98\% \\ \hline Potato early blight & 183 & 4.8 & 100.0\% & 97.8\% & 78.1\% & 26.2\% & 100.0\% & 100\% \\ \hline Strawberry healthy & 456 & 6.2 & 78.3\% & 92.1\% & 99.3\% & 100.0\% & 100.0\% & 98\% \\ \hline Strawberry l. scorch & 442 & 6.0 & 100.0\% & 100.0\% & 95.5\% & 57.0\% & 100.0\% & 99\% \\ \hline Tomato early blight & 250 & 5.3 & 95.2\% & 84.4\% & 74.0\% & 54.8\% & 95.2\% & 93\% \\ \hline Tomato healthy & 257 & 5.5 & 60.7\% & 65.8\% & 82.1\% & 100.0\% & 100.0\% & 93\% \\ \hline Overall & 4433 & 5.3 & 79.3\% & 83.1\% & 79.2\% & 66.9\% & 97.8\% & 96.6\% \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation of some results for the PlantVillage dataset and comparison with benchmark.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 2 Related Works  
### Conventional Audio Inpainting Based on Mathematics  
Existing mathematical-based audio inpainting methods can be traced back to the last century. Early attempts at audio inpainting utilised spectral subtraction techniques for speech signal denoising, laying the foundation for subsequent audio restoration methods [34]. [PERSON] proposed a linear prediction method to model the spectral envelope of speech signals, which can be used to restore missing gaps through linear prediction parameters; however, it cannot handle complex audio structures and non-stationary signals [35].  
Considering the combination of statistical models, several studies have proposed the use of auto-regressive models to capture the correlation and structure within audio signals. Subsequently, the missing samples were inpainted and filled into corresponding positions through interpolation [2; 3; 4]. Furthermore, [PERSON] et al. proposed an algorithm for sinusoidal component interpolation in the context of sinusoidal modelling, which can achieve a more realistic insertion of lost audio samples, especially in the case of musical modulations such as vibrato or tremolo [5; 6]. To extend the duration of the restored audio samples, a longer audio extrapolation method was proposed in [7; 8]. Considering the possibility of restoring lost audio signals in the time-frequency domain, [PERSON] et al. proposed a method to estimate missing spectrograms in the time-frequency domain of audio signals, which can process real-world polyphonic signals through imputation [10; 11]. A more popular method is the sparsity-based approach [1; 17; 18; 19; 20; 21], which aims to find the sparse representation of the missing part of the signal that best fits the surroundings [36].  
In addition, there is another concept called Packet Loss Concealment (PLC) [15; 16], which is similar to audio inpainting but specifically focuses on mitigating the impact of lost or corrupted packets in digital audio coding and communication systems. Both PLC and audio inpainting restore the loss of information, but they operate in different contexts and are based on different methodological foundations.  
It is worth mentioning that the methods discussed above can only perform well in restoring millisecond-level gap lengths, roughly in the range of 10 ms-100 ms. For larger gaps, the restoration task tends to fail at producing plausible reconstructions, as the stationarity condition does not hold [36].
## 2 Methodology
### Cluster Analysis
#### 2.2.2 Applied Clustering Technique  
Figure 1 illustrates the step-by-step procedure performed to obtain the most suitable metric and cut-off criterion for the optimum number of clusters, thus calculating the final proximity matrix and displaying the obtained clusters through dendrograms. Accordingly, a final table with all objects (listed PUH) clustered by inconsistency coefficient and weighted match of columns has been generated. The following distance measures were included in this selection algorithm as depicted in Figure 1: basic, squared and standardized _Euclidean distances_, the _City Block-, Minkowski-, Chebychev-, Mahalanobis-, Cosine-, Correlation-, Spearman-, Hamming-_ and _Jaccard distances_.
# An Acoustic Way to Support Japanese Children's Effective English Learning in School Classrooms
## 2 Methods
### Statistical Analyses  
The English test results were analysed after the following pre-processing steps. Firstly, data of children who were absent in any lessons were removed. Secondly, weekly numbers for the data, obtained in week two of the study from children who were absent in the week one, were replaced by those from week one in order to separately examine the impact of children getting used to the type of tests after week one.  
To evaluate the discrimination difficulty of the word pairs for Japanese native children, the correct answer rate \(p_{\text{c}}\) of each word pair was estimated as the percentage of the correct answers. The question difficulty levels were defined as shown in Table 2 and were employed for adjustment in the following analyses.  
Both univariate and multivariate logistic regression analyses [53] were used to evaluate the associations between the correct answer rate \(p_{\text{c}}\) and independent factors, such as question difficulty level, habituation effects (repetition of tasks), type of the sound amplification system and seat group (Table 3). In the multivariate logistic regression analysis, the Akaike Information Criteria (AIC) was used for variable selection. To estimate the bias due to overfitting (called \"optimism\"), we also carried out bootstrap validation of the multivariate models using 200 repetitions of bootstrapping from the original data [54]. As a performance metric, we used C-statistic, defined as the area under the receiver operating characteristic curve for the prediction of the correct answer of a task. The optimism for the C-statistic was estimated as the difference between the mean C-statistic of the bootstrap sample based models and that of the original model. The optimism-corrected C-statistic was given by subtracting the optimism from the original C-statistic.  
The relationship between seat positions and acoustic characteristics realised by the two sound amplification systems (System A and System B) was explored using yet another sub data set using difficulty level 1 to 3 words. In this analysis, English test score (expressedin percentages) of each participant who attended both lessons were used. Seat groups were categorised into two groups for simplicity: Front (Seat Group 1-3) and Back (similarly, 4-5). Normality of the English test scores in each sub set was assessed using the Shapiro-Wilk test. Because the test score distributions showed non-normality, the Wilcoxon signed-rank test was applied for paired comparison of medians, and the Mann-Whitney U test for unpaired comparison. As a multiple comparison correction method, Bonferroni-corrected \(p\)-values were employed.  
\(p<0.05\) was considered to be statistically significant. All statistical analyses were performed by using R 3.6.0 [55].