esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Multivariate Statistical Analysis of VOCs  
#### 3.2.1 Common and Characteristic VOCs  
Through the upset map analysis (Figure 2), it was determined that ten pear syrups shared a total of nine VOCs, which were 1-Hexanol, 2-ethyl-, Furfurral, 3-Pentanone, Butanoic acid, methyl ester, Butanoic acid, 2-methyl-, methyl ester, Hexanoic acid ethyl ester, o-Xylene, and Undecane. The flavor profile is predominantly characterized by sweet, fruity, and flowery [28; 29; 30; 31; 32]. Additionally, there were a total of 46 characteristic VOCs found in 10 pear syrups (Table 3). They included ten alcohols, four aldehydes, eleven ketones, seven esters, five aromatic hydrocarbons, two alkanes, three sulfur-containing compounds, three terpen, and one acid. LG01 contained five species, LG02 and LG04 had three species each, LG03, LG07, and LG10 contained six species each, LG05 had the highest with ten species, LG06 had four species, and both LG08 and LG09 had only one species, respectively. The outcomes indicated that the kinds of volatile organic compounds (VOCs) in various pear syrups varied significantly, with each syrup containing a specific proportion of characteristic VOCs. As a result, each syrup had a distinct aroma, making them unique.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Characteristic VOCs** \\ \hline LG01 & 3-Pentanol, 2-Furanmethanol, Octan-2-one, 3,6-dimethyl-, 2-Dodecanone, Butylated Hydroxytoluene \\ LG02 & 10-Undecen-1-ol, Hexadecan, n-propyl acetate \\ LG03 & 3-Hexanone, 1,2-Cyclopentanedione,3-methyl, 2(5H)-Furanone,5-methyl-, trans-Furfuryllieneacetone, 4-Hydroxy-2-butanone, Acetic acid \\ LG04 & Cyclohexanol,5-methyl-2-(1-methyl)-(15,2R,5S)-, Methional, Disulfide, dimethyl \\ LG05 & 1-Octan-3-ol, Linalool, 1-Octanol,2,7-dimethyl-, 2-Heptnal,(E)-, 2-Octanel,(E)-, Ionone, p-Cymene, Naphthalene,2-methyl-, Dimethyl sulfide, Cyclohexene, 3-methyl-6-(1-methylylidene)- \\ \hline \hline \end{tabular}
\end{table}
Table 3: Differential characteristic VOCs of 10 pear syrups.
## 4 Discussion
### Requirements for the Technologies in a War Conflict Setting  
Before the war, the COVID-19 pandemic had already highlighted the necessity of improving patient access to essential healthcare services for their well-being. The pandemic expedited the swift growth of telemedicine on a global scale, transcending various cultures and societies [16].  
Concerning the military situation in Ukraine, the use of telemedicine has the potential to provide cost-effective and accessible healthcare for a vulnerable population spread across different locations having difficulties to reach rehabilitation facilities [17].  
In light of the conflict and crisis, numerous additional factors must be considered to ensure the delivery of high-quality telerehabilitation care:  
1. The equipment must be lightweight and not restrict the children's movements, as it is crucial for them to reach shelter quickly in the event of an air alarm.
2. The hardware and software should be energy-efficient, considering the possible shortages caused by attacks on the infrastructure. Every family needs to use energy prudently.
3. Technologies must be tailored for children with disabilities, and the context must be considered, as many children are sensitive to loud, unexpected noises and bright lights commonly found in commercial video games and services.
4. The hardware and software should be affordable for most families in a low- or middle-income country affected by the war.
5. Technologies should not be overly complex and should not require extensive clinical and technical support, given the shortage of staff.
###### Abstract  
In this study, quasi-unidirectional continuous fiber reinforced thermoplastics (CFRTs) are joined with metal sheets via cold formed cylindrical, elliptical and polygonal pin structures which are directly pressed into the CFRT component after local infrared heating. In comparison to already available studies, the unique novelty is the use of non-rotational symmetric pin structures for the CFRT/metal hybrid joining. Thus, a variation in the fiber orientation in the CFRT component as well as a variation in the non-rotational symmetric pins' orientation in relation to the sample orientation is conducted. The created samples are consequently mechanically tested via single lap shear experiments in a quasi-static state. Finally, the failure behavior of the single lap shear samples is investigated with the help of microscopic images and detailed photographs. In the single lap shear tests, it could be shown that non-rotational symmetric pin structures lead to an increase in maximum testing forces of up to 74% when compared to cylindrical pins. However, when normalized to the pin foot print related joint strength, only one polygonal pin variation showed increased joint strength in comparison to cylindrical pin structures. The investigation of the failure behavior showed two distinct failure modes. The first failure mode was failure of the CFRT component due to an exceedance of the maximum bearing strength of the pin-hole leading to significant damage in the CFRT component. The second failure mode was pin-deflection due to the applied testing load and a subsequent pin extraction from the CFRT component resulting in significantly less visible damage in the CFRT component. Generally, CFRT failure is more likely with a fiber orientation of 0\({}^{\circ}\) in relation to the load direction while pin extraction typically occurs with a fiber orientation of 90\({}^{\circ}\). It is assumed that for future investigations, pin structures with an undercutting shape that creates an interlocking joint could counteract the tendency for pin-extraction and consequently lead to increased maximum joint strengths.  
Keywords:joining of CFRT/metal; hybrid technology; mechanical properties; pin geometry; continuous fiber reinforced thermoplastics +
Footnote †: journal: Applied Sciences
## 3 Feature Selection Algorithms
### Basic Operations of the Feature Selection Algorithms  
The basic principle of the feature selection algorithm is presented as follows:  
Let us assume that the original set of features is \(Y\) with the cardinalities \(n\) and \(d\) representing the required features in the subset \(X\), \(X\subseteq Y\); Assuming \(J(X)\) is the feature selection criterion function for the features set \(X\), then the higher the value of the \(J\) the better the feature subset without any loss of generality. The feature selection problem aims to find the subset \(X\subseteq Y\) such that \(|X|=d\) and \(J(X)=\frac{\max}{Z\subseteq Y,|Z|=d}J(Z)\)[26].
# Lightweight and Robust Malware Detection Using Dictionaries of API Calls
## 7 The Experimental Outcomes of the Statistical Model STC
### Experiment 3: Drift of STC Model  
In the field of machine learning, the usual practice is to train models using a specific dataset that represents the target problem domain. However, over time, the distribution of data may change due to various factors such as new trends, behavior shifts, or novel attack techniques in the case of security-related applications. These changes can lead to a mismatch between the training data and the real-world data that the model encounters in action, which leads to performance degradation. The STC model is exposed to the same phenomenon because it depends entirely on the API calls which may change over time.  
This test focuses on examining the effect of model drift by training the STC model with dataset1 and evaluating its performance using dataset3, which consists exclusively of malware without any samples of goodware. To the best of our knowledge, both datasets were collected at different times in 2018 and 2019; therefore, the collection time of these datasets provides reasonable information about the trend of attackers during these years.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**SCT** & \\ \hline Precision & 0.97 \\ \hline Recall & 1.0 \\ \hline F1 score & 0.98 \\ \hline Accuracy & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: STC performance results.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**SCT** & \\ \hline Precision & 0.72 \\ \hline Recall & 0.97 \\ \hline F1 score & 0.80 \\ \hline Accuracy & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: STC performance results.  
As dataset3 comprises malware only, evaluation scales of true positive (TP) and false negative (FN) are used to accurately assess the ability of the model to detect malware. Using dataset1, the full-length API call sequences are extracted to create the dictionaries of goodware and malware.  
The results of this analysis are particularly interesting. The STC shows an impressive performance by achieving a TP rate of 7106, which indicates that almost all malware samples were correctly classified. Only one sample was misclassified as goodware, resulting in an impressively low FN rate. These findings highlight the model's robustness and effectiveness in detecting malware which is reinforced by the fact that attackers often rely on similar techniques with minor modifications, resulting in a relatively limited dictionary of malware characteristics. Overall, the results of this experiment show the substantial capability of the model against the drift of the model. This knowledge enables the development of robust and adaptive models that can accurately classify malware while limiting false negatives. The results of the study contribute to enhancing cybersecurity strategies to create time-independent, real-time models in the field of malware detection.
## 2 Materials and Methods
### Multisource Driving Datasets  
The multisource remote sensing datasets used to drive the FGM in this study included the 500 m \(\times\) 500 m yearly land use and land cover data from the MOD12Q1 C6 product (2001-2016) [32], the 500 m \(\times\) 500 m 8-day LAI data from the GLASS V5 product (2001-2016) [33; 34; 35], the 500 m \(\times\) 500 m 8-day clumping index data in 2006 derived from MODIS bidirectional reflectance distribution function (BRDF) data [36], the 500 m \(\times\) 500 m 8-day photosynthetic capacity (V\({}_{\text{cmax}}\)) data derived from leaf chlorophyll content (2001-2016) [37; 38], and the 5 km \(\times\) 5 km daily downwards shortwave radiation (DSR) data from the GLASS V5 product (2001-2016) [39]. We obtained the 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) 6-h climate data (including air temperature and relative humidity) from the Climatic Research Unit-NCEP (CRUNCEP) V7 (2001-2016) [40]. The vapour pressure deficit (VPD) was calculated from atmospheric pressure, the minimum and maximum air temperature, and relative humidity. Meteorological data used to drive the FGM included the mean air temperature and VPD. In addition, site-level daily ambient CO\({}_{2}\) concentrations observed at the Mauna Loa Observatory (MLO) site (2001 to 2016) were used to drive the FGM. DSR data were resampled to a spatial resolution of 500 m \(\times\) 500 m with bilinear interpolation. Climate data were aggregated to a targeted temporal resolution of 8 days and downscaled to a spatial resolution of 500 m \(\times\) 500 m with bilinear interpolation. For the 40 EuroFLUX sites used in this study, 8-day LAI time series from 2001 to 2016 were extracted from the GLASS LAI product [33] and interpolated to daily temporal resolution using the linear interpolation method.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 2 RVoG Model
### Estimation of Pure Volume Coherence from the Complex Interferometric Coherence Observations
Furthermore, the estimation of \(\mathbf{G}_{i}^{\mathrm{T}}\mathbf{X}\) is reliable due to the small variance. However, the estimation of \(\mathbf{G}_{i}^{\mathrm{T}}\mathbf{X}\), which corresponds to a small singular value, is unreliable due to the large variance. Generally, if the standard deviation of \(\mathbf{G}_{i}^{\mathrm{T}}\mathbf{\hat{X}}\) is less than \(3\hat{\sigma}_{0}\), the estimation is considered to be reliable. Therefore, the reliable estimations with a standard deviation of less than \(3\hat{\sigma}_{0}\) can be given by \(\mathbf{J}=\left[\mathbf{\hat{X}}^{\mathrm{T}}\mathbf{G}_{1}\mathbf{G}_{1}^{ \mathrm{T}}\mathbf{\hat{X}},\mathbf{\hat{X}}^{\mathrm{T}}\mathbf{G}_{2}\mathbf{ G}_{2}^{\mathrm{T}}\mathbf{\hat{X}},\cdots,\mathbf{\hat{X}}^{\mathrm{T}}\mathbf{G}_{j} \mathbf{G}_{j}^{\mathrm{T}}\mathbf{\hat{X}}\right]\). Since the values of the reduced variances \(\hat{\sigma}_{0}^{2}/\lambda_{i}^{2}\) change as \(\hat{\sigma}_{0}^{2}/\lambda_{i}^{2}<\hat{\sigma}_{0}^{2}/\lambda_{i}^{2}< \cdots<\hat{\sigma}_{0}^{2}/\lambda_{n}^{2}\), if \(\hat{\sigma}_{0}^{2}/\lambda_{i}^{2}\) is bigger than \(90\%\) of the values of \(\mathbf{J}\), it can be considered that \(\sigma_{0}^{2}/\lambda_{i}^{2}>\mathbf{X}^{\mathrm{T}}\mathbf{G}_{i}\mathbf{ G}_{i}^{\mathrm{T}}\mathbf{X}\), and singular value \(\lambda_{i}\) needs to be truncated. The other small singular values \(\lambda_{r}\) that are smaller than \(\lambda_{i}\) can also be denoted as \(\sigma_{0}^{2}/\lambda_{r}^{2}>\mathbf{X}^{\mathrm{T}}\mathbf{G}_{r}\mathbf{ G}_{r}^{\mathrm{T}}\mathbf{X}\) and need to be truncated. The small singular values which should be truncated in the TSVD-based method are thus determined. The ill-posed problem can be well solved by TSVD with the proposed truncation method. Finally, the pure volume coherence can be estimated by the proposed TSVD-based method.
###### Abstract  
A project needs to be able to anticipate potential threats, respond effectively to adverse events, and adapt to environmental changes. This overall capability is known as project resilience. In order to make efficient project decisions when the project is subjected to disruption, such as adjusting the project budget, reformulating the work plan, and rationalizing the allocation of resources, it is necessary to quantitatively understand the level of project resilience. Therefore, this paper develops a novel approach for forecasting project performance, illustrating the changes in performance levels during the disruption and recovery phases of a project and thus quantitatively assessing project resilience. While there are several methods for assessing project resilience in existing research, the majority of assessment approaches originate from within projects and are highly subjective, which makes it difficult to objectively reflect the level of project resilience. Moreover, the availability of project samples is limited, which makes it difficult to forecast the level of project performance. In view of the fact that the Reference Class Forecasting (RCF) technique avoids subjectivity and the Radial Basis Function (RBF) neural network is known to be better at forecasting small sample datasets, this paper therefore combines the RCF technique and the RBF neural network to construct a model that forecasts the project performance of the current project after experiencing a disruption, further assessing the level of the project resilience. Specifically, this paper first presents a conceptual model of project resilience assessment; subsequently, an RBF neural network model that takes into account project budget, duration, risk level of disruption, and performance before disruption based on the RCF technique is developed to forecast project performance after experiencing disruption; and finally, the level of project resilience is assessed through calculating the ratio of recovery to loss of project performance. The model is trained and validated using 64 completed construction projects with disruptions as the datasets. The results show that the average relative errors between the forecast results of schedule performance index (SPI) and the real values are less than 5%, and the R\({}^{2}\) of the training set and the testing set is 0.991 and 0.964, respectively, and the discrepancy between the forecasted and real values of project resilience is less than 10%. These illustrate that the model performs well and is feasible for quantifying the level of project resilience, clarifying its impact on project disruption and recovery situations, and facilitating the decision-makers of the project to make reasonable decisions.  
project disruption; project resilience; resilience assessment; reference class forecasting; RBF neural network +
Footnote †: journal: applied sciences
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 2 Theoretical Background
### The Evolution of Agglomeration-Phenomenon Hypernetwork  
In a hypernetwork, when two or more nodes are present in the same hyperedge, it can be suggested that they are adjacent to each other. The presence of a hyperedge between neighbors of a node is called the clustering phenomenon. Social hypernetworks often exhibit the phenomenon of agglomeration. Based on the characteristics of such hypernetworks, we propose a hypernetwork model that considers the aggregation phenomenon. The construction steps of the model are as follows:  
1. Initialization: a hyperedge in the hypernetwork contains \(m_{0}\) nodes.
2. At each time step \(t\), \(m_{1}\) new nodes form a new hyperedge with \(m_{2}\) existing nodes, and \(m\) nonrepeating hyperedges are constructed at each time step.
3. Hyperdegree preference attachment: The probability \(\prod(k_{iu}^{h}(t))\) that the \(m_{1}\) new nodes connect to the \(u_{th}\) old node of the \(i_{th}\) batch is proportional to a function of the hyperdegree \(k_{iu}^{h}(t)\), such that: \[\prod(k_{iu}^{h}(t))=\frac{k_{iu}^{h}(t)}{\sum_{w}k_{iu}^{h}(t)}\] (1) The hyperdegree of the \(u_{th}\) node in the \(i_{th}\) batch at time \(t\) can be represented by \(k_{iu}^{h}(t)\), while the total number of hyperedgeres of all nodes at time \(t\) can be represented by \(\sum_{w}k_{w}^{h}(t)\). This process is repeated \(m_{2}\) times to select \(m_{2}\) old nodes.
4. Clustering attachment with probability \(p\): If two old nodes \(u,v\) are selected in the previous preferential attachment step to form a new hyperedge with new nodes, the new nodes randomly select \(m_{2}\) neighbors of \(u\) or \(v\) to form a new hyperedge. If all neighbors of \(u\) or \(v\) have been selected and no new nonrepeating hyperedge can be formed, then the preferential attachment step is executed.  
As shown in the schematic diagram in Figure 1, the hypernetwork model based on the agglomeration phenomenon is constructed.
# Elasto-Plastic Short Exoskeleton to Improve the Dynamic and Seismic Performance of Frame Structures
## 2 Motivation of the Study  
Many researchers used low-dimensional mechanical models to capture the main dynamic and seismic behaviour of real structures. It is a common practice in structural mechanics in the field of Civil Engineering.  
In this paper, a 2-DOF model represents a general, sufficiently regular, N-storey frame structure, whereas a 1-DOF model represents a yielding, elasto-plastic exoskeleton. The dynamics of such a reduced model is studied via the numerical integration of the governing equations of motion and provide preliminary information on the possibility of improving the seismic response of real frame structures by rigidly coupling them with an external structure. Neither technological nor building aspects are studied in this paper and, in general, they would require further studies and experimental tests.  
Some studies already focused on the coupling between a structure and an exoskeleton. For instance, in [24], adaptative exoskeletons were used to improve the performances of existing buildings or, in [25], a special type of steel exoskeletons, named diagrid structures, were analysed. Some papers studied the optimal connection between parallel structures, where one of these can be an external exoskeleton [26; 27].  
The majority of papers propose the use of exoskeletons that have the same height as the structure. In this case, the height of the exoskeleton may raise issues related to both the realisation process and a possible strong aesthetic impact of the exoskeleton. This paper considers an exoskeleton shorter than the structure to be protected (see Figure 1). Moreover, the exoskeleton is considered to be rigidly connected to the first or second storey of the structure. In such a way, the aesthetic impact could be negligible if, for instance, the existing building had an underground level so that the exoskeleton could be connected at the ground level. It is worth observing that a rigid connection to an elasto-plastic external structure is conceptually similar to the use of braced and/or knee-braced frames [28; 29; 30; 31; 32; 33; 34; 35],since the coupling adds a higher dissipation energy capability to the structure. However, contrarily to the use of braced and/or knee-braced frames, which are usually distributed in the whole structure, the coupling with the external structure affects only a small part of the structure to be protected (the first storey or first two storeys).  
This paper focuses of understanding whether it is possible to improve the dynamic and seismic performances of both the part of the structure below the connection level, namely the sub-structure, and the part above the connection, namely the super-structure. Additionally, since the exoskeleton is considered as a yielding structure with elasto-plastic constitutive behaviour, a parametric analysis is performed to find the values of the constitutive parameters of the exoskeleton that minimise the displacements and drifts of the structure.
## 5 Results and Discussion
### Discussion of Results
#### 5.3.3 Operational Features  
Wireless AIoT sensor systems provide significant advantages in the installation and test preparation compared to conventional wired sensors. Their wireless nature simplifies and accelerates the installation process, eliminating the need for space to accommodate connectors and route cables through the specimen. This allows for greater flexibility in sensor positioning and eliminates the complexity of cable management. The simplified installation process minimizes material consumption, particularly for cables and connectors, resulting in lower costs.  
Furthermore, the absence of cables eliminates potential wiring errors and the need for subsequent repairs or adjustments. This feature significantly reduces preparation and installation time while enhancing the scalability of the system. Wireless sensors can also be easily deployed in hard-to-access areas without the constraints of cabling. Additionally, the reduction in cable weight minimizes its impact on test specimens, eliminating the need for counterbalancing to ensure accurate test executions.  
The flexibility in the positioning of AIoT sensors facilitates their integration into a wide variety of specimen geometries, not only in the aerospace sector but also in other industries, such as automotive, railway, and energy. These features collectively demonstrate the superior operational efficiency and versatility of wireless AIoT systems.  
A limiting feature of the data transfer technology chosen in this study, WiFi and MQTT, is the maximum allowable transfer frequency. For the test under study, and other tests with measurement frequencies not exceeding 1 Hz, this characteristic does not pose any restriction to its application. If the number of nodes in the edge layer were to increasesignificantly, the data transfer capacity could be enhanced with a greater number of brokers in the fog nodes. However, for tests with higher sampling frequency requirements, other technologies such as WirelessHart or ZigBee would need to be employed.  
Another feature to be taken into account in long-duration tests is the power management of the wireless nodes. It must be considered that many fatigue tests can last several months or even several years. In these cases, a system must be developed to control the battery charge level of the sensors so that it does not affect the normal course of the test and no data are lost. The solution proposed in this case study is based on the sizing of the batteries with wide safety margins and the use of several batteries feeding the sensors in parallel to allow their replacement without stopping the measurement.