esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 4 Experiments
### Analysis of Results
#### 4.3.5 Experiment on Different Dataset Selection Methods  
In this part of the experiment, we compare the effects of different dataset selection methods on the experimental results, bold numbers indicate optimal results on a dataset.  
Since the samples have been mapped into the dissimilarity space, the dataset selection method based on extracting the feature vectors is no longer applicable. Thus, we compare the EMD with another method. In each dissimilarity subspace, we select the value of the smallest attribute in each sample and take the mean value to measure the similarity between the source domain data set and the prototype set. Then, the values are sorted in descending order and the first \(\alpha\) datasets are selected as the training data. This approach is named as Method 1 [7]. The calculation is as follows:  
\[D_{XR}=\frac{1}{n}\sum_{i=1}^{n}\underset{r}{mink}\big{(}x_{i},p_{j}\big{)} \quad 1\leq i\leq n,1\leq j\leq r \tag{17}\]  
The experimental results are shown in Table 10. The results show that the performance of dataset selection method based on EMD is superior to Method1 in F-measure, AUC, and cost-effectiveness. The reason for this phenomenon is that when comparing the similarity between datasets, Method 1 has lost more information by selecting the minimum attribute and averaging these attribute values, which leads to inaccurate measurement results. However, the EMD method takes into account each attribute of the sample, so the performance of the prediction model is better.  
Figure 5: Box plot of different prototype selection methods. (**a**) is the situation of each method on F-measure; (**b**) is the situation of each method on AUC; (**c**) is the situation of each method on cost-effectiveness. Box plots represent maximum, upper quartile, median, lower quartile, and minimum values from top to bottom. In addition, circles represent outliers.
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 4 Case Study: Santa Maria Delle Grazie
The global level involved widespread masonry consolidation in terms of increasing in-plane and out-of-plane strength [37; 38] by first reconstructing mortar joints and then plating with the FRCM technique performed with basalt meshes and inorganic matrix [39]. Box-like behaviour has been improved by connecting masonry hammers and angles and inserting cross-link chains. The local level synthetically involved the reconstruction of the timber roof, the application of strips of PBO fibres in an inorganic matrix on the intrados of arches and vaults [40], including the south and east arcades, and rebuilding collapsed floors and flatbeds. The experimental campaign's design focused on the monument's south facade, particularly the arcade that defines a little logging. The south arcade is made of five arches, of which the first is round and the other four are three-centred. As shown in Figure 3, the arches have different spans; the size of the round one is 1.65 m, that of the second and third is 4.30 m, and that of the others is 3.55 and 3.25 m. The mount is 1.25 m for all arches (Figure 3). Among the five arches, the third one--characterised by a three-centred geometry and a span of 4.30 m--was selected for instrumentation and monitoring during the experimental campaign. The in situ tests performed by the executing company did not detect any filling in the upper part of the arches, highlighting that the entire arcade is made up of a single material. The restoration project provides two main interventions for this part of the structure. At first, an FRCM-type intrados reinforcement was applied to the arches with unidirec  
Figure 3: Scheme of south arcade. Figure 2: Santa Maria delle Grazie before the restoration. **Picture 1**: Main facade; **Picture 2**: North corner; **Picture 3**: Church altar; **Picture 4**: East facade. Reproduced from [31], published by Elsevier, ©2024, with permission of the authors. tional PBO strips. PBO (Polyparaphenylenebenzobisoxazole) is a synthetic fibre used in structural reinforcement systems with a cementitious matrix (FRCM). Compared to the most commonly used fibres, it has notable mechanical characteristics and has high chemical compatibility with inorganic compounds. It is often used for reinforcement interventions on masonry buildings (even those of historical value) thanks to the high compatibility of the matrix with which it is applied to the wall support. For the south arcade, three bands of 25 cm width and weight equal to 105 g/m\({}^{2}\) were placed with additional bands set orthogonally. Joint elements were placed at the intersection between longitudinal and transversal bands to prevent debonding from the support. The FRCM system was completed with PBO-MESH 105-type mortar (Ruregold, LATERLITE S.p.A., Solignano, Italy). The second intervention concerned the connection between the arcade and the back facade with 3 m stainless steel bars injected at a low pressure with grout to provide the substance and fill any voids in the masonry.
## 3 Workflow and Preliminaries
### Model of Architecture (MoA)  
Our hardware model is shown in Figure 2a. The purple parts show the components of a tile-based platform described in this section. The green components of the platform show two measurement infrastructures used to characterize our system. They are described in Section 3.3.  
We adopt a tile-based platform organization where each tile consists of a processing element (PE) with private instruction and data memories, and a communication interface. A tile can thus execute software without interfering with other tiles as long as the software does not explicitly access shared resources. Processing elements of different tiles can come with different architectures. In our evaluation platform (Figure 2a), we have three different kinds of PEs. Tile _T0_ comes with a minimal MicroBlaze processor without any hardware accelerators. _T1_, _T2_, _T3_ are equipped with a hardware multiplier (MUL) that allows integer multiplication using a single instruction. Tiles _T4_, _T5_, _T6_ provide a floating point unit (FPU) that extends the MicroBlaze architecture with floating point instructions. An execution platform consists of a finite set of tiles (Tx), a finite set of shared memories (SM) and a shared communication bus (BUS) that implements the ARM AXI4 Lite standard. In the scope of our work, we consider a first-come-first-served (FCFS) bus arbitration protocol.  
We assume that there are no caches and all components are connected to the same clock. Furthermore, all memories have a static timing behavior where each read and write access always takes the same amount of clock cycles.
## 2 Materials and Methods
### Neural Networks--Autoencoders
Autoencoders (AEs) are a distinct class of neural networks that are commonly trained to perform input reconstruction [12; 13]. Various architectures of autoencoders exist and are comprised of two distinct components, namely, the encoder and the decoder. The process of encoding involves the mapping of the input information into a fixed-point representation in a latent space. If the dimensionality of the latent space is lower than that of the input data, the encoder will acquire a more parsimonious representation of the input data, resulting in an incomplete autoencoder, namely, the number of neurons in the bottleneck layer will be smaller than the number of neurons in the input and output layers. On the other hand, if the latent dimension exceeds the input dimension, the autoencoder acquires a superfluous depiction of the input data, resulting in an overcomplete autoencoder, namely, the number of neurons in the bottleneck layer will be larger than the number of neurons in the input and output layers. The utilization of autoencoders can serve two distinct purposes. Firstly, in one scenario, autoencoders can be employed to reduce noise in a given dataset. Secondly, in another scenario, autoencoders can be utilized to identify intricate patterns within the input data, which can subsequently enhance the accuracy of their reconstruction. The decoder component receives the encoder output as its input and performs forward propagation until it gets to the output layer. At this stage, the error is computed in relation to the chosen loss function. The decoder possesses identical dimensions to those of the encoder, albeit in a mirrored orientation [12; 13]. Autoencoders undergo training via forward and backward propagation, similar to other neural networks. Throughout the training process, the autoencoder endeavors to minimize the error in reconstruction that exists between the input and the rebuilt output. In neural networks, loss functions are used to quantify the difference between the predicted outputs of the neural network and the actual target values. In this study, the convergence of the algorithm was tested with respect to the value of the loss function, which was computed at the end of each iteration (epoch). This means that the value of the loss function at the last epoch was stabilized.
## 2 Results and Discussion
### Antioxidant Activity
#### 2.3.1 Radical Scavenging Activity
Furthermore, antioxidants can revert the blue/green color of ABTS\({}^{+}\bullet\) solution back to its colorless neutral form, which follows the reduction of absorption at 734 nm [5,12]. As shown in Figure 3D, eight isolated collagen peptides (F7-P1 to F7-P8) exhibited powerful scavenging capability on ABTS\({}^{+}\bullet\) in a dose-effect fashion. F7-P3 and F7-P8 with EC\({}_{50}\) values of 0.86 \(\pm\) 0.05 mg/mL and 0.82 \(\pm\) 0.04 mg/mL had the most powerful scavenging capacity on ABTS\({}^{+}\bullet\)  
Figure 3: DPPH (A), HO\(\bullet\) (B), \(\text{O}_{2}^{-}\bullet\) (C), and 2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulphonic acid) cation radical (ABTS\({}^{+}\bullet\)) (D) scavenging activities of eight isolated peptides (F7-P1–F7-P8) from collagen hydrolysate fraction (F7) of Spanish mackerel (_S. niphonius_) skin. All data are presented as the mean \(\pm\) SD of triplicate results. among eight isolated collagen peptides (Table 2), but they were still weaker than the positive control of GSH at the tested concentrations. EC\({}_{50}\) values of F7-P3 and F7-P8 were significantly less than those of APs from protein hydrolys of food resources, including salmon muscle (FLNEFLHV: 1.548 mg/mL) [26], corn gluten meal (LLPF: 1.031 mg/mL; LPF: 1.013 mg/mL; FLPF: 1.497 mg/mL) [27], cartilage of _Raja porosa_ (FIMGPY: 1.04 mg/mL; IVAGPQ: 1.29 mg/mL) [5], and blueinf leatherjacket heads (GVPLT: 3.124 mg/mL; GPP: 2.472 mg/mL; WEGPK: 5.407 mg/mL) [12]. These findings indicate that eight isolated collagen peptides (F7-P1 to F7-P8), especially F7-P3 and F7-P8 possess a high capacity to turn ABTS\({}^{+}\bullet\) into its neutral form and inhibit free radical chain reactions.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 6 Conclusions  
In this work, we propose a novel method for CSS that integrates BEISTA-Net and BSWSS-Net into a joint network. BEISTA-Net is employed for compressed signal reconstruction, while BSWSS-Net is employed for WSS. BEISTA-Net replaces the proximal mapping in ISTA with multiple convolution operations, incorporates the BSF extractor to obtain the BSF of wideband spectrum signals, and incorporates CA to enhance BSF during the iterative process. BSWSS-Net leverages neural networks to uncover hidden information in the frequency domain of wideband spectrum signals, thus obtaining channel state information. In evaluations across two datasets, the proposed method demonstrates robust performance across different levels of sparsity and SNR conditions compared to  
Figure 19: ROC curves when \(\text{SNR}=0\) dB in NR dataset, \(\mu_{NR},\in(0,0.5]\), \(P=8\), and \(L=16\).  
Figure 18: NR dataset with \(\text{SNR}\in[-10,10]\) dB, \(P=8\), \(L=16\). (**a**) \(P_{\text{d}}\) versus SNR (dB) of BEISTA-Net+BSWSS-Net, ISTA-Net+DeepSense, CSS-Net, and R-ADMM+ParallelCNN when \(P_{\text{f}}~{}=~{}0.1\). (**b**) \(P_{\text{f}}\) versus SNR (dB) of BEISTA-Net+BSWSS-Net, ISTA-Net+DeepSense, CSS-Net, and R-ADMM+ParallelCNN when \(P_{\text{d}}=0.9\).  
several baselines. However, the improvement in performance comes at the cost of increased computational complexity. While the proposed BEISTA-Net does not exhibit a significant change in running time compared to existing methods, it requires significantly higher FLOPs and video memory usage. This poses challenges for deployment on embedded devices. In other words, when actual hardware resources are limited, the real-time performance of the proposed method may be adversely affected. Future research can be advanced in three key directions: (1) further exploring the integration of traditional reconstruction algorithms with deep learning; (2) investigating the advanced application of deep attention mechanisms in CSS; and (3) reducing the complexity of the proposed CSS algorithm to lower deployment costs on practical hardware devices.  
Conceptualization, H.Z., Y.W. and Y.Y.; methodology, G.L. and H.Z.; software, H.Z.; validation, H.Z. and Y.Y.; formal analysis, G.L.; investigation, H.Z.; resources, Y.Y. and G.L.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., Y.Y. and G.L.; project administration, Y.Y.; funding acquisition, Y.W. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2022 Industrial Foundation Reconstruction and High-Quality Development of the Manufacturing Industry Special Project Grant No. TC220H07A.  
Not applicable.  
Data availability StatementData are contained within the article.  
The authors declare no conflicts of interest.
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 6 Performance Evaluation
### 6 TiSCH Network Power Characterization  
A wireless industrial network such as 6 TiSCH exploits the concept of TSCH, where time is divided in time slots grouped in a structure referred as Slotframe. For each time slot a node can use any of the \(N\) available frequencies leading to \(N\) cells per time slot. A network schedule determines the communication actions to be taken by the device at each these cells in the Slotframe. A node, in a particular cell, can transmit (TX), receive (RX) or transmit and if nothing to send, receive (TX/RX). The cell can be dedicated by a device or shared (contention) with other devices in the network.  
TSCH has proven to enable ultra-low-power communications [18] thanks to tight synchronization, minimization of guard times, and the fact that nodes can be in sleep mode in the inactive cells. In this section we look into the energy consumed by the I3 Mote when running a simple 6 TiSCH network.  
In a TSCH network, Slotframes repeat over time. The energy characterization is based on profiling the energy consumption on each of those slots and computing the overall energy consumption for each Slotframe. The average power can be obtained by dividing the total energy by the Slotframe period. Then, applying the model is a matter of counting the number of slots of each type and calculating the energy consumed by every one of the slots (see [24] for further details).  
Several experiments have been conducted in the simple network configuration depicted in Figure 9a. Table 2 presents the charge drained by the nodes when executing different types of slots. From this values, in Table 3 we have derived a simple model were we present the average energy consumption as a function of the active cells in the schedule for the different type of cells.  
We consider different periodicity of actions, i.e., sending Enhanced Beacons happens once every 4 Slotframes while data traffic is scheduled to happen in every Slotframe. The Slotframe size of 127 timeslots and a default timeslot duration of 10 ms, with 10 s data duty cycle. As presented in Table 3, the average current consumption of this static setting is estimated by Equation (1):  
\[I_{NET}=27.9\,n+57.4\,\,\,[\mathrm{\mu A}] \tag{1}\]  
with \(n\) the number of child nodes (Figure 9 shows this equation graphically). For example, the energy consumption of Node-2 in Figure 9a, with 1 shared slot dedicated for join traffic and enhanced beacons [25] and with 4 additional active slots to send its own data and to relay traffic from 3 child nodes, is approximately 169 \(\mathrm{\mu A}\).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Action** & **Charge [\(\mathrm{\mu C}\)]** \\ \hline Beacon RX (84 bytes Broadcast Packet—no ACK) & 34.62 \\ Beacon TX (84 bytes Broadcast Packet—no ACK) & 32.92 \\ TX (102 bytes Unicast Packet, 25 bytes ACK ) & 48.67 \\ TX (no packet transmission) & 2.26 \\ RX (102 bytes Unicast Packet, 25 bytes ACK ) & 50.6 \\ RX (no packet reception) & 23.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CC2650 6 TiSCH consumption.
# Design of WPT System Based on Interleaved Boost Converter
###### Abstract  
With the improvement of technology, the demand for electrical power continues to deepen. Wireless Power Transfer (WPT) technology can transmit power without using physical media such as cables, and it has the advantages of electrical isolation, convenience, and safety. At present, the miniaturization of the secondary side is an emerging trend of WPT systems, which is analyzed in this paper. By introducing an interleaved boost converter in the front stage, the DC bus voltage of the primary side is increased, the loss of the primary side is reduced, and the system efficiency is improved. At the same time, the self-inductance of the primary and secondary sides of the loosely coupled transformer is designed to meet the stress requirements of passive devices and the realization of a closed-loop system. Finally, a WPT system with an input voltage of 100 V, an output voltage of 100 V and a transmission power of 500 W is built. The transmission distance of this system is 170 mm, with a lateral offset of 200 mm and a vertical offset of 100 mm. After the offset of the primary and secondary sides, the output voltage can be stabilized at 100 V, and the system efficiency can reach 90.1%, which proves the feasibility of the system efficiency improvement strategy and the effectiveness of the closed-loop control.  
interleaved boost converter; miniaturization of secondary side; efficiency improvement; closed-loop control +
Footnote †: journal: Applied Sciences
# Streamlining Visual UI Design: Mining UI Design Patterns for Top App Bars
## 5 Evaluation
### RQ3: How Does the Clustering Outcome Compare to Human Judgment?  
To delve deeper into the disparities in clustering performance between these models and human perceptions, we present the outcomes of these models across external indicators, as shown in Table 4. Metrics such as JC, FMI, RI, F-1, and Purity are computed using Formulas (11), (12), (13), (14), and (15), respectively. Overall, the K-means algorithm demonstrates superior performance. It outperforms other models across all indicators except Purity. This suggests that the K-means algorithm effectively balances inter-class dissimilarity and intra-class similarity in clustering TAB sequences, yielding results consistent with real labels. It is noteworthy that the GMM model, while achieving success in Purity, falls short in other metrics.  
Figure 7 showcases the TAB sequence patterns derived from the method introduced in this paper. Each cluster upholds a consistent sequence arrangement and semantic roles. For instance, cluster 0 encompasses attributes pertinent to content management and operations, featuring options like _Add_, _Edit_, _Microphone_, and _Volume_. Clusters 1, 2, 5, 6, 7, and 9 display diverse structural compositions in their sequences. Cluster 3 predominantly houses location-based functions associated with geographic services and navigation. Social interaction-centric semantics, such as _Share_, _Facebook_, and _Favorite_, dominate Cluster 4. Similarly, Cluster 8 encompasses a range of features like _Cart_, _Chat_, and _Facebook_, highlighting data exchange among users. These findings underscore the efficacy of clustering methods in discerning and categorizing ten distinct design patterns.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **JC \(\uparrow\)** & **FMI \(\uparrow\)** & **Purity \(\uparrow\)** & **RI \(\uparrow\)** & **F-1 \(\uparrow\)** \\ \hline K-means & **0.596189** & **0.615253** & 0.592848 & **0.557367** & **0.716875** \\ GMM & 0.0372244 & 0.570767 & **0.607771** & 0.505694 & 0.0606872 \\ BIRCH & 0.159146 & 0.498117 & 0.532139 & 0.403558 & 0.236254 \\ Spectral & 0.0380405 & 0.547349 & 0.578134 & 0.471077 & 0.0709708 \\ \hline \hline \end{tabular} \(\uparrow\) denotes that higher values are preferable.  
\end{table}
Table 4: Comparison of clustering algorithms on different external metrics  
Figure 7: The clustered patterns of TABs.
# Neural Graph Similarity Computation with Contrastive Learning
## 4 Experiments and Results
### Experimental Results
#### 4.2.1 Main Results  
The main experimental results are presented in Table 3. As the classical A\({}^{*}\) algorithm can calculate the exact GED on smaller datasets, i.e., AIDS and LINUX, it attains perfect results on these two datasets. However, it cannot scale to the IMDB dataset. The classical algorithms that compute the approximate GED values, i.e., Beam, Hungarian, VJ and HED, attain relatively poorer results compared with methods involving neural networks. Nevertheless, it should be noted that these classical methods can explicitly predict the edit distance, while the pure neural methods cannot. Additionally, the solutions generated by these methods are upper bounds of the optimal GED values, while neural models predict the values at both sides of the optimal ones.  
As for the GNN-based regression methods, i.e., SimGNN, GMMN, GraphSim, GGTSim, SGNN, NGMN, MGMN and H2 MN, they achieve better results than the traditional ones. Among them, H2 MN is the best performing solution, validating the usefulness of modeling substructure interactions. After combining the classical and neural models, the ranking performance of Noah and GENN-A\({}^{*}\) increases, while the mse drops slightly. Additionally, both Noah and GENN-A\({}^{*}\) cannot effectively handle the IMDB dataset.  
Our proposed method, Conga, attains the best overall performance, especially on the larger dataset IMDB. On smaller datasets, it also attains leading results. Its ranking results on AIDS is lower than GENN-A\({}^{*}\) and GOTSim, which can be partially attributed to the fact that it focuses more on estimating the correct GED distance. Additionally, its mse results is slightly inferior to H\({}^{2}\)MNNE on the LINUX dataset. That being said, its performance is more robust and consistent across all datasets/metrics.