esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cell Proliferation on the PolyHIPE Scaffolds  
Cell proliferation is an important prerequisite to evaluate the cytotoxicity of cigarette smoke. Herein, the polyHIPE material with a high porosity and large voids connected with windows was used as a scaffold for 3D cell proliferation. A549 cells were cultured on the polyHIPE scaffolds and determined the 3D cell proliferation ability using the CKK-8 assay.  
As shown in Figure 8, a significant increase in optical density (OD) value was observed with the increase of culturing time, indicating that cells proliferated steadily on the surface and/or inside the polyHIPE scaffolds with the increase of culturing time. Furthermore, a larger optical density (OD) value was observed for the polyHIPE scaffold prepared with a higher W/O ratio or a larger emulsifier amount (E/M ratio), indicating that a promoted cell proliferation could be achieved for the polyHIPE scaffolds with a higher W/O ratio and emulsifier amount. These results were likely to be associated with the difference in the porous structure of the polyHIPE scaffolds. Herein, polyHIPE scaffolds served as ECMs for cell adhesion, proliferation and apoptosis, helping the cells to maintain a more representative structure, gene expression and function. The sizes of the void and window are essential for the cell proliferation since they could affect the nutrient and waste transfer for cell growing. Larger void and connected windows, as well as the high porosity, would benefit the nutrient and waste transfer and therefore resulted in a promoted cell proliferation. Therefore, the polyHIPE scaffold prepared with a higher W/O ratio and emulsifier amount, which has a higher porosity and larger void and connected window size, could greatly promote the cell proliferation.  
Figure 8: Cell Proliferation curves of A549 cells on different scaffolds. (**a**) PolyHIPE scaffolds fabricated with different W/O ratio. (**b**) PolyHIPE scaffolds fabricated with different emulsifier amount. * Significantly difference \(p<0.05\) (equal variance \(t\)-test).  
Figure 7: N\({}_{2}\) adsorption-desorption curves of the polyHIPE scaffolds fabricated with different emulsifier amount.
# Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design
## 4 Experiment  
### Experiment Setup  
We present detailed experiments and results to demonstrate the performance and efficiency of the proposed multi-dimensional Vim-like attention module (MDVA). We evaluate MDVA on the challenging vision tasks, including classification tasks on the CIFAR-100 dataset as well as object detection tasks on MS COCO datasets. To thoroughly assess the effectiveness of the final modules, we conducted extensive ablation experiments to analyze the contribution of each component to the overall performance of the proposed multidimensional attention mechanism. CIFAR-100 is a dataset used in machine learning and computer vision, containing 60,000 color images of 32 \(\times\) 32 pixels. It has 100 classes, each with 600 images, split into 50,000 training and 10,000 test images. The 100 classes are grouped into 20 superclasses, each with 5 classes. For example, the superclass \"vehicles\", includes bicycle, bus, motorcycle, pickup truck, and train. The MS COCO dataset is a large-scale dataset used in computer vision, containing over 330,000 images with 80 object categories. It includes detailed annotations like object segmentation masks, bounding boxes, key points, and captions. MS COCO is widely used for tasks such as object detection, segmentation, key point detection, and image captioning. Results can be found in Figure 4.  
Figure 4: Performance of different attention methods: (**a**) MobileNetV2; (**b**) ShuffleNetV2; (**c**) YOLOv5s. The y-axis labels from left to right are top-1 accuracy. Clearly, our approach not only achieves almost the best result in Cifar100 classification against the SE block and CBAM but performs even better in COCO object detection.  
Figure 3: Multi-dimensional Vim-like attention module.  
We conducted the experiments using PyTorch. During training, we utilized an adaptive moment estimation with weight decay (AdamW) optimizer with a learning rate of \(1\times 10^{-3}\) and a weight decay set to \(1\times 10^{-2}\). The learning rate followed a cosine schedule, starting with an initial value of \(1\times 10^{-3}\). The models were trained on an NVIDIA RTX 2080 ti GPU with a batch size of 32. We used MobileNetV2 [46] and ShuffleNetV2 [47] as the baseline model, training all models for 100 epochs. Figure 5 shows how to integrate the MDVA attention mechanism into MobileNetV2. For data augmentation, we applied RandomResizedCrop, RandomHorizontalFlip, and Normalize, using a mean of [0.485, 0.456, 0.406] and a standard deviation of [0.229, 0.224, 0.225].
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 3 Materials and Methods  
The aquaponics system comprises the dynamic interaction of fish, plants, bacteria, and their aqueous environment [1]. The main intention of an aquaponic system is to reuse nutrients being pulled out of fishes which are necessary for growing plants [1]. Fish are grown in a fishpond, and they are regularly fed. Along their vital cycle, they produce decompositions that are rich in ammonium (\(NH_{4}^{+}\)). Such decompositions will travel along the pipeline system into the growing bed with plants and bacteria in the soil, as shown in Figure 1 below, which depicts the basic principles of an aquaponic system. The bacteria in the soil transforms ammonium ions (\(NH_{4}^{+}\)) firstly into nitrites (\(NO_{2}^{-}\)) then nitrates (\(NO_{3}^{-}\)). Nitrates (\(NO_{3}^{-}\)) are absorbed by the plants as part of their vital cycle, then water is oxygenated (\(H_{2}O\)) and filtered off. To close the loop, the oxygenated water can be returned to the fishponds, and depending on the size of the fishpond and the growing beds, one or two additional filters may need to be inserted along the cycle. First, a biofilter could be in the segment between fish decomposition and plants. This filter is not habitable by bacteria that facilitate the transformation from ammonium (\(NH_{4}^{+}\)) to nitrates (\(NO_{3}^{-}\)); hence, it could be inserted to help control the population of bacteria in the system. Second, a Swirl filter could be placed in the segment between the plant evacuation and fishes' intake of water. The purpose of this filter is to purify the water by eliminating solid materials, thereby making it suitable for fish life. This solid material is usually present when the plants cannot provide sufficient filtering (i.e., due to different parameters, e.g., size of the bed, number of fish, etc.).  
Figure 1: Aquaponics cycle.
## 3 Mathematical Background
### Graphs  
A _graph_ is a pair \((V,E)\), where \(V=\{v_{1},\ldots,v_{n}\}\) is a set of \(n\) elements--the _vertices_; and \(E\) is a subset of \(\binom{V}{2}=\{e\subset V|\ \#e=2\}\), the _edges_. The _order_ and _size_ of \(G\) are the cardinalities of the sets \(V\) and \(E\), respectively. Two different vertices \(u_{1},u_{2}\in V\) are _adjacent_ if they are connected by an edge. Analogously, two different edges \(e_{1},e_{2}\in E\) are _adjacent_ if they share one and only one vertex. The graph \(\overline{G}=(V,\overline{E})\) defined by \(\overline{E}=\{v_{i}v_{j}\in\binom{V}{2}\ |\ v_{i}v_{j}\
ot\in E\}\) is the _complementary graph_ of \(G\). This consists of pairs of non-adjacent vertices.  
If two disjoint subsets \(V_{1},V_{2}\subset V\) exist such that \(V_{1}\bigcup V_{2}=V\) and such that every edge has vertices in both sets \(V_{1}\) and \(V_{2}\), then the graph is said to be _bipartite_. Furthermore, \(G\) is _complete bipartite_ provided that every vertex in \(V_{1}\) is connected to every vertex in \(V_{2}\) and vice versa.  
Now, consider two graphs \(G=(U,D)\) and \(H=(V,E)\). Consider a bijections of sets \(\phi:U\to V\) that preserves edges; i.e., if \(\{u,v\}\in D\) implies \(\{\phi(u),\phi(v)\}\in E\). The \(\phi\) is an _isomorphism_ between \(G\) and \(H\), and \(G\) and \(H\) are said to be _isomorphic_, denoted \(G\approx H\). The graph isomorphism problem is defined as the task of finding an isomorphism between \(G\) and \(H\), or deciding that they are not isomorphic. Formally, GI can be defined as follows.  
**Decision problem**  
**Instance:** Two graphs \(G=(U,D),H=(V,E)\).  
**Solution:**\(\left\{\begin{array}{ll}1&\text{If there is an isomorphism }\phi:G\to H\\ 0&\text{Otherwise.}\end{array}\right.\)  
**Search problem**  
**Instance:** Two graphs \(G=(U,D),H=(V,E)\).  
**Solution:** Either a proof that \(H\) and \(G\) are not isomorphic or the isomorphism \(\phi:G\to H\).  
Finally, a _matching_ in a graph \(G\) is a subset \(M\subseteq E\) with the property that no to edges \(e_{1},e_{2}\in M\) are adjacent. The matching is _perfect_ if, in addition, every vertex of \(G\) is an paired by an edge of \(M\).
## 3 Results and Discussion
### Ecotoxicity Data  
The inhibition effects on the bioluminescence of _A. fischeri_ and on the growth of _R. subcapitata_ were measured in this section for characterizing the acute and chronic toxicity of DPs at concentrations of 5 mg/L. Figure 4 shows the comparison of the effects of DPs.  
Only in three DPs (DP14, DP20, and DP22) in _R. subcapitata_ and two DPs in _A. fischeri_ (DP14 and DP20) the stimulation was observed, showing no toxic effects. However, in 75% of the DPs investigated, the negative effects increased compared to the parent compound (OLM). In particular, the bioluminescence inhibition of the _A. fischeri_ in DP16 exceeded 70%. The acute toxicity in each DPs followed the order of DP14 > DP20 > DP21 > DP15 > DP22 > DP11 > DP10 > DP12 > DP18 > DP19 > DP13 > DP16, while the chronic toxicity in each DPs followed the order of DP22 > DP20 > DP14 > DP13 > DP18 > DP15 > DP19 > DP12 > DP21 > DP11 > DP10 > DP16. Considering the overall toxicity, these results demonstrate that higher inhibitory values, especially for _A. fischeri_, were achieved for DP16, while lower inhibitory values were achieved for DP20. Thus, as a strong oxidant, sodium hypochlorite could decompose non-toxic OLM into small molecules, increasing the toxicity of the compound. Although the concentrations investigated and capable of exerting toxic effects may be higher than the environmental concentrations, the possible problems due to continued exposure, which can occur through contact with water and through the food chain, should not be underestimated.
## 2 Literature Review
### Standards for the BIM-Enabled Asset Information Delivery Process  
The most prominent initiatives toward the standardization of this process are COBie and ISO 19650, which will be discussed next.  
COBie is an open data transfer specification developed by the U.S. Army Corps of Engineers that facilitates the delivery of managed asset information by using low-level formats such as Excel spreadsheets [56; 57]. COBie is built upon the concept of model view definitions (MVD), which are predefined subsets of information contained in an Industry Foundation Class (IFC) file to facilitate BIM data exchanges between specific domains [58]. Although COBie was created as a data transfer specification, the term COBie became often used to describe the whole underlying process of asset information delivery using BIM. Consequently, it is often hard to draw the line between limitations specific to COBie as a data specification and the challenges related to the whole process of using BIM for the delivery of asset information. For this reason, and considering that many asset owners are opting for ad-hoc solutions (including the case studies in this research), the discussion of the asset information delivery process will not be framed in COBie terms.  
The ISO 19650 standard is an international standard published in 2018 for managing information over the whole life cycle of a built asset using BIM. This standard contains all of the same principles and high-level requirements as the UK BIM Framework and is closely aligned with the previous BS 1192/PAS 1192-2 standards. It provides an internationally accepted framework for digital information management processes that intend to facilitate communication between stakeholders and reduce or control costs and time and improve the quality of a project [42]. However, there are limited data from case studies that could provide empirical evidence about the proposed framework in ISO 19650. So far, the main role of ISO 19650 has been to provide support for owners interested in developing and fulfilling their information requirements. Although ISO 19650 was not officially stated in the requirements of the projects discussed in this paper, its concepts were adopted to discuss the asset information delivery process in an effort to converge the findings towards a common understanding among researchers and practitioners.  
ISO 19650-1 breaks down the information requirements in four documents (Figure 3) and emphasizes that a clear definition of Organizational Information Requirements (OIR) and Asset Information Requirements (AIR) is the most important factor in achieving successful asset information delivery. However, when defining information requirements for the BIM process, many owners tend to request just COBie or IFC files without carefully considering what they will need in the FM phase [59]. A critical issue here is the difficulty in identifying relevant information for BIM FM integration [60].  
Figure 3: Hierarchy of information requirements (source: ISO 19650-1).
## 2 Materials and Methods
### Maintenance Operations Detection
#### 2.4.2 Training, Validation and Test Error Estimation  
The error estimation for the detection methodology was done using the training set, validation set and a test set. For the training, a cross-validation technique was applied. Then, the validation dataset was classified, and relative error, recall, precision and F1-score relativity to both classes, detection and no detection metrics were computed, to assess detection efficiency. Finally, the ANN is applied to the test dataset, evaluating the error on a real case scenario and to assess the robustness to avoid detecting incomplete operations.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 2 Synthesis Mechanism Used to Create a Mesoporous Material  
The same general synthesis mechanism is used in all meso-structures. Surfactants play the basic roles in the syntheses of porous silica nanoparticles [55]. These structures have hydrophobic and hydrophilic components with different characteristics. The hydrophobic part is dissolved in the nonpolar segment, and the hydrophilic part is soluble in water or polar materials [56; 57]. Structures that include hydrophobic and hydrophilic sites are called amphiphilic substances [58]. Many strategies have been introduced to understand mesoporous syntheses. Liquid crystal templating (LCT) is one of the most well-known methods. Surfactants play a crucial role in LCT [59]. In very low concentrations, these materials take the form of soluble monomers. As their concentrations are enhanced, the molecules accumulate in aqueous solutions, and isotropic spherical and tubular micelles form, reducing the free energy of the system [60]. At even greater concentrations, hexagonal compositions are created from the surfactants. These structures act as templates, and when inorganic precursors are added, they are placed on these templates [61]. Most inorganic compounds have an area with a negative charge,allowing them to interact electrostatically with the positive heads of the surfactants. When these materials are placed on the surface, the compression stage is reached, and the integrated framework of the inorganic component is established on the surface of the surfactants. Finally, when the surfactants are removed, a mesopore is created [59].  
It should be pointed out that the morphology of a silica mesopore can be affected by various factors, including the surfactant and co-surfactant used, source of the silica, temperature, alcohol used, and pH.
## 3 Results
### Test on Benchmark Datasets  
We test the proposed models on the GTSRD, TT100K, CCTSDB2021, and GTSDB datasets. Figure 10 shows some detection results on the public datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **All Class** & **Occluded** & **Displaced** & **Perforated** & **Faded** & **Rusted** & **Defaced** & **Good** \\ \hline Precision & 0.867 & 0.931 & 0.913 & 0.927 & 0.941 & 0.807 & 0.846 & 0.707 \\ Recall & 0.762 & 0.619 & 0.657 & 0.803 & 0.697 & 0.95 & 0.727 & 0.881 \\ mAP@50 & 0.847 & 0.806 & 0.804 & 0.909 & 0.808 & 0.9 & 0.81 & 0.891 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of TTA across the classes.  
Figure 7: Precision–recall curve of the mean ensemble.  
The German Traffic Sign Recognition Benchmark (GTSRD) [31] contains a large number of images of German Traffic Signs captured under various conditions. It is commonly used for training and evaluating traffic sign recognition systems.  
Tsinghua-Tencent (TT100K) [32] includes 100,000 images of traffic signs from China, which provides a diverse set of traffic sign types and conditions. It is widely used for training models to recognize a wide variety of traffic signs.  
Chinese Traffic Sign Detection Benchmark 2021 (CCTSDB2021) [33] is an updated version of the CCTSDB dataset. It consists of over 4000 real traffic scene images with detailed annotations.  
The German Traffic Sign Detection Benchmark (GTSDB) [34] is similar to GTSRD but focuses on detection rather than recognition of traffic signs. The images are annotated with bounding boxes.  
In Figure 10c, Yolov5m correctly detects good traffic signs but misclassifies an object as a rusted traffic sign, while Figure 10c is a misdetection recorded by Yolov5s.  
Figure 8: F1 score of the TTA model.  
Figure 9: Graph showing mAP@50 of the proposed models.
# Application of Generalized S-Transform in the Measurement of Dynamic Elastic Modulus
## 1 Introduction
[PERSON] introduced a full-waveform inversion workflow for precisely determining p-wave and s-wave velocities in homogeneous materials to determine elastic modulus [19]. It is difficult to directly evaluate the durability of concrete buildings through on-site measurements. To monitor the safety of the old aqueduct, [PERSON] applied a BP neural network to assess the dynamic elastic modulus using the primary modal frequencies of the structure as inputs [20]. Frequency sweeping and fast Fourier transform (FFT) are commonly employed techniques for determining the resonant frequency of rigid materials. In the operation of frequency sweeping, the vibration waves with constant amplitude from low-frequency (50 Hz) to high-frequency (20 kHz) are successively emitted by the impulse and applied to the surface of the specimen. With the action of external force, non-stationary and transient resonance signals appear on the surface of the test piece, transducer collects the feedback waveform. By comparing the amplitude value of the feedback waveform caused by the excitation of different frequencies, the resonance frequency points are finally scanned [21]. In contrast, FFT performs spectrum conversion during the frequency sweeping process, the digital processor calculates the power spectrum from the collected feedback waveform and then finds all resonance frequency points. The advantage of this algorithm is that it can eliminate the influence of amplitude output error due to the transfer function of the impulser at different frequencies. However, the FFT algorithm is difficult to guarantee its low-frequency accuracy (\(\pm\)20 Hz or more in the spectral range of 20 KHz) under the condition of limited sampling points (usually no more than 1024 points and a single conversion takes about 2 ms). For the FFT measurement process, the system needs to continuously output excitation signals to meet the same sampling time at different frequencies, which will result in a long measurement time. If the frequency change of the sweep signal is accelerated, it will cause serious spectrum aliasing [22]. As a time-frequency analysis tool, wavelet transform has the characteristic of multi-resolution, but it cannot directly correspond to the actual frequency spectral value [23; 24]. In this paper, the S-transform with multi-resolution time-frequency characteristics is introduced for measuring the dynamic elastic modulus. Based on the existing sensor hardware, the high-precision spectrum calculation method is achieved through the generalized S-transform (GST) on a DSP platform, which accelerates the conversion of frequency sweeping signals. The system observes the changing relationship between the frequency domain and the time domain by time-frequency transformation [25; 26], which can eliminate errors caused by sensor inconsistency, improve the measurement accuracy of resonant frequency, and finally improve the accuracy of the dynamic elastic modulus [27].
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 5 Simulation Results
### Performance under SC Decoders  
First, we investigate the performance of the proposed scheme, DSSCC schemes, and the DJSCC scheme with classic SPCs under the SC decoder. Figure 7 shows the BER performance versus the SNR of transmission channels. In this figure, lines labeled by \"Proposed...\" represent BERs for the proposed scheme, lines labeled by \"DSSCC...\" represent BERs for DSSCC schemes with different values of \(R_{si}\), \(R_{ci}\) shown in Table 1, and lines labeled by \"Classic SPC...\" represent the DJSCC scheme with classic SPCs. From these simulation results, we can see that the proposed scheme outperforms DSSCC schemes and the DJSCC scheme with classic SPCs by approximately 0.2\(\sim\)2 dB. It can be observed that an \"error-floor\" of \"DSSCC 1\" starts from 3.5 dB, which is completely caused by the source joint decoder (channel decoders get almost error-free codewords from 3.5 dB to 5 dB). To avoid this error, the higher compressed rate \(R_{si}\) is required. Therefore, we do not observe the \"error-floor\" for \"DSSCC 2\" and \"DSSCC 3\".  
Figure 6: Bit selection of distributed source coding (DSC) based on polar codes.  
Figure 7: The bit error rate (BER) performance under the SC decoder.