esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
It is essential to investigate the early damage caused by water accumulation in traditional concrete pavements. Environmental factors play a crucial role in the performance of pavements. The temperature and humidity variations in seasonally frozen or wet-freeze regions during different seasons can significantly affect the performance and response of PCC pavements [36]. Temperature fluctuations may cause concrete to expand or contract, thereby causing cracks [37]. High humidity levels can slow down the drying process of concrete, affecting its hardening and strength development. Additionally, humidity influences the freeze-thaw cycles, accelerating corrosion in reinforced concrete [38; 39]. [PERSON] et al. [40] conducted a study on a section of highway in a wet-freeze climate region that experienced premature damage and discovered that the alkali-silica reaction was one of the reasons for the early deterioration of the concrete pavement. PCC pavements in seasonally frozen or wet-freeze regions are more susceptible to premature failure. Limited research has been conducted specifically addressing accelerated damage caused by urban cement concrete roads. [PERSON] et al. [41] and [PERSON] [42] conducted field surveys to assess road damage caused by inadequate drainage. They reviewed the condition of road and ground drainage infrastructure and explored the reasons for poor drainage. They attributed drainage problems to the irrational design of drainage systems and a lack of proper maintenance. Few studies focus on the impact of sewer pipelines under the pavement on the cracking of urban cement concrete pavements. This study examines the service conditions of PCC pavements in seasonal frost regions. In seasonal frost regions, premature failure of cement concrete pavements often occurs due to the lack of systematic quality control methods. Research specifically addressing the accelerated damage to urban cement concrete roads is limited, prompting local governments to investigate the causes of early failures. Consequently, experimental tests were conducted at sites exhibiting early failures to examine the influence of foundation materials, bearing capacity, and drainage performance. By comparing sites with varying extents of cracking and surface damage, this research aims to assess how enhanced bearing capacity and drainage capabilities affect pavement service life, to provide insights for the design and maintenance of urban cement concrete pavements. To achieve these objectives, field visual inspections, Ground Penetrating Radar (GPR) surveys, Dynamic Cone Penetrometer (DCP) tests, Core-Hole Permeameter (CHP) tests, laboratory particle size analysis, liquid limit (LL), and plastic limit (PL) tests were performed at six test sites.
## 3 Proposed Key Exchange Protocol
### Protocol Proposal
#### 3.3.1 Level 2: ECQV Asymmetric Key  
The establishment of a secure communication channel starts with the ECQV handshake modified with the PUF function. An implicit certificate is issued by a node device as follows:  
* Step 1--Certificate request: A node generates a random number \(k_{n}\in\{0,q\}\) and calculates the corresponding elliptic curve point using \(R_{n}=k_{n}G\). The random number  
Figure 2: 2-level security key establishment protocol. The computations are grouped into six steps, each indicating sequential operations at the sender and receiver.  
\(k_{n}\) must be securely stored at the node side; then, the point \(R_{n}\), the node's identity \(ID_{N}\), and a randomly chosen challenge \(C^{t}\) are sent to the server as a certificate request.
* Step 2--Certificate generation: After receiving the request, the server generates another random number and its corresponding point \((k_{s},R_{s})\). Then, the implicit certificate is generated through hashing an elliptic curve point \(R=R_{n}+R_{s}\). Upon storing the challenge-response pair, the server computes the implicit signature \(r\) with the corresponding response \(\mathcal{R}^{t}\) of the received challenge \(\mathcal{C}^{t}\). \[r=\mathcal{H}_{1}(Cert_{N},\mathcal{R}^{t})k_{s}+k_{S}(mod\,q)\] (1)
* Step 3--Key extraction: After receiving the implicit certificate, the node generates the response \(\mathcal{R}^{t}\) from the previously selected challenge \(\mathcal{C}^{t}\). Afterward, it extracts its designated key pair \((k_{N},K_{N})\) using \(\mathcal{R}^{t}\). \[e =\mathcal{H}_{1}(Cert_{N},\mathcal{R}^{t})\] (2) \[k_{N} =ek_{n}+r(mod\,q)\] (3)
* Step 4--The server can compute the public key of the node with Equations (2) and (3) by itself, thus reducing the cost of a message from the node to share its public key.  
As mentioned earlier, depending on the security requirement of IoT applications, the ECQV handshake will be executed to provide the level 2 security channel. Otherwise, the node only executes the SKEM handshake to exchange data with the lowest cost.
## 4 Discussion
### Improve Method Knowledge Availability  
Knowledge of existing and suitable methods is essential for successfully transforming the product development process from virtual to Digital Engineering. As already mentioned above, SMEs lack this knowledge. Therefore, the third aspect of the presented research question is improved knowledge availability. The process of method integration requires great expert knowledge, primarily available in an unstructured manner. Possible sources are scientific publications, best practice reports or web-based tool(box) documentation. Therefore, the initial aim was to consider a formal model of the Digital Engineering context with its subconcepts, especially methods and applications. This goal is achievable, as shown in Section 2, where the interconnections are described. For further usage of AI4 PD, a (semi)automated integration and annotation of knowledge, available in the already mentioned unstructured sources, will be crucial. The manual workload for integrating method knowledge in this state of the ontology is high, making efficient usage difficult. One possible solution could be using text-mining techniques, especially for analysing and integrating use cases, as presented by [PERSON] et al. [32]. Another challenge is to keep the knowledge up to date, as the area of methods and toolboxes is currently undergoing very rapid development. In particular, expansion, revision and contraction methods are needed to ensure that only correct and up-to-date information is presented [41]. As mentioned by [PERSON] et al. [28], OWL ontologies enable flexible extensions due to their open world assumption (OWA), especially when compared to common databases with the closed world assumption (CWA). The essence of OWA is to allow all assumptions until explicitly disproved. In contrast, CWA prohibits everything until it is proven true. Therefore, OWA gives an excellent representation of incomplete or rapidly changing information, as is the case in the domain of Digital Engineering. Removing or including concepts is crucial for AI4 PD, especially since AI4 PD does not claim completeness, particularly for data types, methods and toolboxes. After deciding on a particular method or tool, further knowledge is required since the usage of a certain method can have further prerequisites. Possible implications are insufficient computing power or inappropriate data for a given method. The ontology allows the evaluation of the methods according to the mentioned aspectssince simple yes or no questions are possible as well. For example, the question can be asked whether the existing hardware is sufficient to use a particular tool.  
To summarise, AI4 PD provides sufficient linking and knowledge management regarding data-driven methods for product development. Furthermore, support for integrating data-driven methods in SMEs is given. Therefore, the given research question has been successfully answered.
## 3 Results and Discussion
### Evaluation of Insulin Microparticle Formulations
#### 3.2.2 Particle Size Analysis  
The insulin microparticles consisting of Eudragiti(r) L100 and _A. vera_ polysaccharides prepared by the emulsion-solvent evaporation method (E + AVP emulsion) exhibited an average particle size (D [4.3]) of 86.47 \(\upmu\)m. The span value of 2.36 is indicative of a relatively wide particle size distribution, which occurred because of the presence of a fairly large portion of smaller than the average size particles (histogram skewed/tailed to the left). Span is a parameter that indicates the width of particle size distribution. A direct correlation exists between the width of particle size distribution and the span value, which means that the smaller the span value is, the narrower the particle size distribution is [39]. The span values were compared between the microparticle formulations to indicate relative particle size distribution widths. The insulin microparticles prepared by the emulsion-solvent evaporation method consisting of _A. vera_ polysaccharides only (AVP emulsion) exhibited an average particle size (D [4.3]) of 43.84 \(\upmu\)m with a span value of 1.49, which is indicative of a relatively narrow particle size distribution.  
The insulin microparticles prepared by ionic gelation with _A. vera_ polysaccharides only (AVP ionic gelation) exhibited an average size (D [4.3]) of 151.05 \(\upmu\)m with a span value of 2.61. The larger particle size of this microparticle formulation may be explained by the formation of microparticles with weaker ionic bonds between the polysaccharide molecules and the cross-linker ions, which resulted in more diffuse, larger microparticles.  
Confocal laser scanning microscopy micrographs of each of the microparticle formulations are shown in Figure 1. A microparticle in the sample of the E + AVP emulsion microparticle formulation measured 61.86 \(\upmu\)m (Figure 1A), while a microparticle from the AVP emulsion formulation measured 43.60 \(\upmu\)m. A microparticle in the AVP ionic gelation formulation sample measured 105.65 \(\upmu\)m. In general, the sizes of the microparticles measured using the confocal laser scanning microscope were in line with the sizes obtained by measurement with the Mastersizer[80].
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Detection Learning Modes
#### 3.4.2 Semi-Supervised Anomaly Detection  
Semi-supervised anomaly detection is an approach where only standard data models and other data are classified as measuring anomalies [18].  
In the healthcare system, Bayesian network-based methods have been used in [27] to propose a long-term, semi-supervised monitoring system based on IoT to assess the quality of maternal sleep. Similarly, a reinforcement-learning technique was suggested in intelligent transportation by [PERSON] et al. [51] to detect the motor outlier over the temperature data stream of the unmanned aerial vehicle. Temporal and spatial-temporal techniques have also been suggested in the smart object domain by [PERSON] et al. [52] to detect anomalous behavior within the environmental datasets.
# Synthesis of High-Precision Sub-Micron CaCO\({}_{3}\) Anticancer Drug Carriers from Coral Remains
## 3 Result and Discussion
### Exploration of Optimal Parameters for the Synthesis of Vaterite  
#### 3.3.1 Adding Order of the Salts  
This step explored the effects of the different adding orders of the salts on the synthesis of CaCO\({}_{3}\), with operational parameters detailed in Table 3. The SEM results depicted in Figure 3 reveal that adding CaCl\({}_{2}\) before Na\({}_{2}\)CO\({}_{3}\) significantly affects the particle size, resulting in a more uniform distribution compared to adding Na\({}_{2}\)CO\({}_{3}\) before CaCl\({}_{2}\). The average particle size for CaCl\({}_{2}\)-first was found to be 423 nm, compared to 869 nm for Na\({}_{2}\)CO\({}_{3}\)-first. Consequently, CaCl\({}_{2}\)-first was chosen as the optimal parameter for subsequent reactions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ingredient** & **Proportion** \\ \hline CaO & 92.95123\% \\ MgO & 3.67358\% \\ MnO\({}_{2}\) & 0.00300\% \\ Fe\({}_{2}\)O\({}_{3}\) & 0.47249\% \\ SiO\({}_{2}\) & 2.13416\% \\ Al\({}_{2}\)O\({}_{3}\) & 0.77554\% \\ PbO & N/A \\ CdO & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of raw coral remains.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ingredient** & **Proportion** \\ \hline CaO & 99.96482\% \\ MgO & 0.00158\% \\ MnO\({}_{2}\) & 0.00006\% \\ Fe\({}_{2}\)O\({}_{3}\) & 0.00035\% \\ SiO\({}_{2}\) & 0.01910\% \\ Al\({}_{2}\)O\({}_{3}\) & 0.01409\% \\ PbO & N/A \\ CdO & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of purified coral remains.  
Figure 3: Particle size distribution of CaCO\({}_{3}\) synthesized with two different adding orders of the salts: (**a**) CaCl\({}_{2}\)-first (**b**) Na\({}_{2}\)CO\({}_{3}\)-first.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Parameters** \\ \hline Adding Order & CaCl\({}_{2}\)-first, Na\({}_{2}\)CO\({}_{3}\)-first \\ Stirring Speed (rpm) & 1000 rpm \\ Concentration of EG & 85\% \\ Reaction Time (min) & 60 \\ CaCl\({}_{2}\)/Na\({}_{2}\)CO\({}_{3}\) (M) & 1 M/0.1 M \\ Reaction Volume (mL) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for reaction base experiment.  
Figure 2: The decomposition rate under different atmospheres: (**a**) air; (**b**) N\({}_{2}\); (**c**) CO\({}_{2}\).
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 3 Experiments  
### Datasets  
We conducted experiments on three open-source hyperspectral datasets (Pavia, Botswana, and Chikusei), and since there is no ideal fusion result for labeling, this paper produces datasets according to the Wald protocol [35]. The original HS is used as the ideal fusion image, the down-sampled and then up-sampled HS is used as the HS of the network input, and the down-sampled PAN is used as the PAN of the network input, so that the relationship between the network input HS, PAN, and the ideal fusion image is consistent with the real fusion relationship. The dataset is described in detail as follows:  
1. Pavia dataset [36]: The Pavia University data are part of the hyperspectral data from the German airborne reflectance optical spectral imager image of the city of Pavia, Italy, in 2003. The spectral imager continuously images 115 bands in the wavelength range of 0.43-0.86 \(\upmu\)m with a spatial resolution of 1.3 m. In total, 12 of these bands are removed due to noise, so the remaining 103 spectral bands are generally used. The size of the data is 610 \(\times\) 340, containing a total of 2,207,400 pixels, but they contain a large number of background pixels, and only 42,776 pixels in total containing features. Nine types of features are included in these pixels, including trees, asphalt roads, bricks, meadows, etc. We partition HS into a size of 40 \(\times\) 40 \(\times\) 103 and Pan into a size of 160 \(\times\) 160 \(\times\) 1 size to make training and testing sets for training the network model.
2. Botswana dataset [37]: The Botswana dataset was acquired using the NASA EO-1 satellite in the Okavango Delta, Botswana, in May 2001, with an image size of 1476 \(\times\) 256. The wavelength range of the sensor on EO-1 is 400-2500 nm, with a spatial resolution of about 20 m. Removing 107 noise bands, the actual bands used for training are 145. We partition HS into a size of 30 \(\times\) 30 \(\times\) 145 and Pan into a size of 120 \(\times\) 120 \(\times\) 1 to make training and testing sets for training the network model.
3. Chikusei dataset [38]: The Chikusei dataset was captured using the Headwall Hyperspec-VNIR-C sensor in Tsukishi, Japan. These data contain 128 bands in the range of 343-1018 nm, with a size of 2517 \(\times\) 2335 and a spatial resolution of 2.5 m. These data were made public by Dr. [PERSON] and Prof. [PERSON] of the University of Tokyo. We partition HS into a size of 64 \(\times\) 64 \(\times\) 128 and Pan into a size of 256 \(\times\) 256 \(\times\) 1 to make training and testing sets for training the network model.
## 3 Methodology
### Bayesian Hyperparameters Optimization
In the actual problem, namely the prediction of mechanical and volumetric properties of bituminous mixtures for road pavement, \(f(\cdot)\) is defined as \(f:X_{L}\times X_{N}\times X_{act}\times X_{\alpha}\times X_{\beta}\times X_{E} \rightarrow[-1,1]\) and has to be maximized. Therefore, given the \(P=6\) hyperparameters \(L,N,act,\alpha,\beta,E,f(\cdot)\) constructs an ANN with L layers, N neurons per layer and performs a 5-fold CV experiment in which the ANN is trained for E iterations with \(\alpha\) and \(\beta\) as the learning rate and weight decay parameter, respectively. \(f(\cdot)\) returns a scalar value that expresses the average Pearson coefficient (R) obtained by the ANN on the 5 validation folds. The BO algorithm performs 500 iterations: at each iteration, \(f(\cdot)\) runs an experiment based on the hyperparameter combination sampled by the UCB algorithm on the posterior distribution; the result is used to update such probabilistic model and improve sampling of next points. The step-by-step procedure is described in Figure 7: the modeling procedure begins with the mixtures design and the execution of laboratory tests for the definition of features and targets variables; these latter are assumed to be representative of the physical problem treated; the input-target fitting is performed by a neural network, whose structure and algorithmic functioning is not known a priori; the search for the topology and the values of the training process parameters that minimize the prediction error of the ANN is handled by the Bayesian optimizer, by comparing network outputs and experimental targets; once the optimal hyperparameters combination has been identified, the model can be put into service for the designed application, by training it on the entire data set. In the current study setup, the hyperparameters to be optimized by the Bayesian approach can range in the following integer or logarithmic intervals:  
* \(X_{L}=\{1,\ldots,5\}\), for the number of hidden network layers L;
* \(X_{N}=\{4,\ldots,64\}\), for the number of neurons \(N\) for each hidden layer;
* \(X_{act}=\{tanh,ReLU,ELU\}\) for the set of activation functions to be applied after each hidden layer;
* \(X_{\alpha}=\left[10^{-6},10^{-2}\right]\) for the learning rate \(\alpha\);
* \(X_{\beta}=\left[10^{-6},10^{-2}\right]\) for the weight decay parameter \(\beta\);
* \(X_{E}=\{500,\ldots,5000\}\) for the number of learning process iterations. Figure 7: Step-by-step procedure followed in this study: it starts with the mix design (left side) and testing processes (bottom side); these tests define the set of target variables (lower right side); the input-target fitting is performed by a neural network, whose structure and algorithmic functioning are searched by the Bayesian optimizer (upper side) comparing network outputs and experimental targets (right side). These ranges were defined in the optimizer implementation [67] and have been left unchanged.
## 3 Results and Discussion
### Comparison of SPME Fibers Using TgTPS2-Kunzeaol Synthase  
To compare hydrocarbon and hydroxylated germacrenes using HS-SPME we included kunzeaol. The main product of _Tg_TPS2 when expressed in _S. cerevisiae_ has previously been shown to be kunzeaol (**3**), with the two minor products being **7** and **1**[7]. In that study, _Tg_TPS2 expressing _S. cerevisiae_ was grown in un-buffered media, resulting in a pH drop to 3 after 84 h [7]. Under these conditions, **3** was found to undergo acid-induced rearrangement to **5**, \(\alpha\)-cadinol and \(\tau\)-muurolol, with \(\delta\)-cadinene being the major product [39].  
_Tg_TPS2 was expressed in _N. benthamiana_. With an injection port temperature of 110 \({}^{\circ}\)C no products were observed for the PDMS/DVB, PDMS/CAR, and PDMS/CAR/DVB fibers (Figure 6b, Supplementary Figures 7 and 8). A few minor products could be observed with the PDMS fiber (Figure 6a). The hydroxyl group at C6 of **3** reduces the volatility compared to hydrocarbons with the same number of carbons. This can explain why a temperature of 110 \({}^{\circ}\)C was not sufficient to desorb **3** from the fiber. **3** was generally not detected at 250 \({}^{\circ}\)C using PDMS/DVB, PDMS/CAR, and PDMS/CAR/DVB fibers; however, up to 30 other sesquiterpene rearrangement products could be detected (Figure 6b, Supplementary Figures 7 and 8). At 160 \({}^{\circ}\)C PDMS/DVB and PDMS/CAR fibers were still not suited for analysis of **3** with only a very low signal detected. **3** could be detected when PDMS/CAR/DVB fibers were used at 250 \({}^{\circ}\)C and 160 \({}^{\circ}\)C, but **5** and other sesquiterpenoids (Figure 6) were present in high amounts. The majority of the observed rearrangement products are acid-induced and are similar to what was previously described [7].  
PDMS fibers yielded **3**, at 250 \({}^{\circ}\)C; however, the major products were found to be two hydroxylated sesquiterpenoids (Figure 6a). These two sesquiterpenoids might be heat-induced Cope rearrangement products of **3**, but the structure could not be verified by GC-MS. At 160 \({}^{\circ}\)C **3** was the major product; **1** and **7** were detected as the two other biosynthetic products. At 160 \({}^{\circ}\)C the hydroxylated products had decreased to become very minor products.  
These findings suggest that heat-induced Cope rearrangements of **3** might be the predominant rearrangement products using the PDMS fibers, whereas the acid-induced rearrangements that lead to **5** are predominant on the other three fibers. This result is consistent with the finding that **3** is not detected  
Figure 5: _Tp_GAS, injection port temperatures: 110 \({}^{\circ}\)C (lower), 160 \({}^{\circ}\)C (middle), and 250 \({}^{\circ}\)C (upper). **(a)** PDMS; **(b)** PDMS/DVB/CAR. Unknown sesquiterpene products are marked with (*).  
in hydro-distillated oil samples from _Kunzea_ leaves, but can be detected when gentle solvent extraction is used [39].