esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Digital Model of the Area of Interest
### Polygon of the Area of Interest  
As stated in the work [17], it is not possible to algorithmically determine a polygon based on significant points of the limited area. Therefore, it is necessary to enter which significant points and in what order will define the polygon that bounds the area of interest. In the mentioned work, it is also stated that a simple polygon can be divided into an upper and a lower part, as we can see in Figure 3. This is the way to obtain the points \(r_{0}\), \(r_{1},\cdots,r_{n}=r_{0}\) which define the polygon of the bounded area.  
Now we need to determine the length of the polygon to be able to estimate the number of sensory units of the network according to relation (2). To calculate the length of the polygon, we need to define the term _spatial curve_.  
**Definition 1**.: _Let \(\langle a,b\rangle\subset\mathbb{R}\) a closed interval. Representation \(r:\langle a,b\rangle\rightarrow\mathbb{R}^{3}\) is a spatial curve, \(r(a)\) is the starting point, \(r(b)\) is the endpoint of the curve. If \(r(a)=r(b)\) then the curve is closed. The curve \(r\) is simple, when \(r:\langle a,b\rangle\rightarrow\mathbb{R}^{3}\) is a one-to-one correspondence._  
The correspondence is one-to-one if each element of the first set is made to correspond with exactly one element of the second set, and vice versa.  
**Definition 2**.: _Let \(r:\langle a,b\rangle\rightarrow\mathbb{R}^{3}\) be a spatial curve and \(F=\{t_{0}=a<t_{1}<\cdots<t_{n}=b\}\) is the partition of the interval \(\langle a,b\rangle\). The spatial polygon \(P\), which has vertices \(r_{i}=r(t_{i})\), \(i=0,1,\cdots,n.\) is called an inscribed polygon belonging to partition \(F\). The length of the polygon is the sum of the lengths of the edges:_\[L(P)=\sum_{i=1}^{n}|r_{i}-r_{i-1}| \tag{9}\]  
A polygon of an area of interest is a simple, closed spatial curve, which is given by the division of the interval \(\langle a,b\rangle\). In three-dimensional space, a polygon is given by these points  
\[r_{i}=[x_{i},y_{i},z_{i}] \tag{10}\]  
for \(i=0,1,\cdots,n\). Therefore, we can calculate the distances between the points \(r_{i}\) and \(r_{i-1}\) (see [9; 10]) as  
\[|r_{i}-r_{i-1}|=\sqrt{(x_{i}-x_{i-1})^{2}+(y_{i}-y_{i-1})^{2}+(z_{i}-z_{i-1})^{ 2}} \tag{11}\]  
The points \(r_{i}=[x(t_{i}),y(t_{i}),z(t_{i})]\), \(i=0,1,\cdots,n\) from relation (10) define three functions \(x(t),y(t),z(t)\). These functions for our purposes at the moment are not needed, only their functional values in points \(t_{i}\), so we do not examine the properties of the functions.
## 8 Summary and Future work
### Decision Making  
When advanced diagnostics and prognostics provide vital information, a decision-making strategy is used to effectively utilize the available information and to help decision makers choose the proper action. Many well-established decision making tools are currently widely applied in business, economics and politics, but not yet in engineering. Decision making methods should have a wide range of potential applications and significant impacts in structural engineering, especially in structure assessment and maintenance.  
1. Decision making theories are of little practice to most structural managers and they often make decisions based on their experience or on common sense. It is highly desirable to evaluate the value of information and assess the impact on decision making, which promotes decision-making methods being accepted by the structural managers in practice.
2. Due to the uncertainties of damage detection in civil structures, there is a risk of making an inappropriate decision. The uncertainty quantification plays a major role in decision making and requires further research.
3. The output from the decision-making system should make it apparent to the decision maker, i.e., what the recommendation is and why, including information about the expected cost for the decision alternatives [248].
4. It is necessary to develop methods to evaluate the decision-making performance such as detection/localization rate, false alarm rate, and the optimal trade-off.
5. For the application to real-life infrastructure systems, additional work is needed to develop more efficient approaches to improve the feasibility of near-real-time applications involving large infrastructure systems [235].  
In conclusion, the damage identification methods still have limited applications in the practice application [9]. Only damage detection in Level 1 and maybe Level 2 are practically achieved for full-scale structures, while the damage quantification in Level 3 and prognosis in Level 4 are still a great challenge due to the scalability and other issues. Much research work is needed in the area of damage quantification, prognosis, and decision making for full-scale real structures in practice.  
[PERSON] and [PERSON] conceived the idea and syllabus; [PERSON] collected and analyzed the references; [PERSON] and [PERSON] summarized the references; [PERSON] wrote the paper, [PERSON] and [PERSON] revised the paper.  
The authors declare no conflict of interest.
## 4 Results and Discussion  
### Data Preprocessing Results  
#### 4.1.1 Outlier Sample Rejection Results  
The presence of outlier samples can have a significant impact on the effect of modeling; therefore, outlier sample rejection must be performed prior to modeling. The spectra and reference values were imported into the TQ software to establish the full-wavelength PLRS model for larch, hemlock, and Mongolia, respectively, and as shown in Figure 6, there are a few samples with abnormally high leverage values and studentized residuals. After removing the outlier samples, 179 samples of larch, 183 samples of hemlock, and 176 samples of Mongolia were obtained. Figure 7 shows the spectral wavelengths before and after the removal of the outlier samples, and the spectral distribution was more uniform after the removal. Figure 8 shows the distribution of the reference values of the three confer samples after the removal of the outlier samples. The
## 4 Results
### Performance Evaluation of 2L-DBMM in Fusing SER and SA
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **(g) 1D-CNN** & & & & **(h) DBMM (BERT + SVM + LR)** \\ \hline
**Sentiment** & **PREC** & **REC** & **F1** & **Sentiment** & **PREC** & **REC** & **F1** \\ \hline Negative & 0.36 & 0.67 & 0.47 & Negative & 0.95 & 0.95 & 0.95 \\ \hline Neutral & 0.67 & 0.15 & 0.25 & Neutral & 0.88 & 0.84 & 0.86 \\ \hline Positive & 0.27 & 0.27 & 0.27 & Positive & 0.72 & 0.76 & 0.74 \\ \hline Average: & 0.43 & 0.36 & 0.33 & Average: & 0.85 & 0.85 & 0.85 \\
**ACC:** & & & **0.36** & **ACC:** & & & **0.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: _Cont._lows the 2L-DBMM to effectively leverage the complementary information from both modalities, resulting in a more comprehensive and accurate understanding of the speaker's emotional state. In addition to the soft late fusion approach employed in our 2L-DBMM model, other fusion strategies can be explored in the future for multimodal emotion recognition. Early fusion, also known as feature-level fusion, involves concatenating or combining features from different modalities before feeding them into a classifier. This approach can capture low-level interactions between modalities but may suffer from the curse of dimensionality if the feature vectors are too large. To mitigate this, techniques like dimensionality reduction (e.g., PCA, LDA) or feature selection could be applied before concatenation. Hard late fusion, or decision-level fusion, involves making independent decisions for each modality and then combining them using techniques like majority voting or weighted averaging. This approach is simpler but may not fully exploit the complementary information between modalities. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Complex Emotion** & **ACC** & **PREC** & **REC** & **F1** \\ \hline CE\({}_{1}\): wistful/bittersweet & \(\bigcirc\) & 0.94 & 0.94 & 0.94 \\ \hline CE\({}_{2}\): hopeless/despairing & \(\bigcirc\) & 0.93 & 0.92 & 0.94 & 0.93 \\ \hline CE\({}_{3}\): melancholic/pensive & \(\bigcirc\) & 0.94 & 0.93 & 0.96 & 0.94 \\ \hline CE\({}_{4}\): jofult/elated & \(\bigcirc\) & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline CE\({}_{5}\): disingenuous/fake & \(\bigcirc\) & 0.97 & 0.97 & 0.97 & 0.97 \\ \hline CE\({}_{6}\): content/serene & \(\bigcirc\) & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline CE\({}_{7}\): hopeful/optimistic & \(\bigcirc\) & 0.93 & 0.92 & 0.94 & 0.93 \\ \hline CE\({}_{8}\): concerned/worried & \(\bigcirc\) & 0.98 & 0.97 & 0.99 & 0.98 \\ \hline CE\({}_{9}\): unsure/ambivalent & \(\bigcirc\) & 0.98 & 0.97 & 0.99 & 0.98 \\ \hline CE\({}_{10}\): frustrated/irritated & \(\bigcirc\) & 0.98 & 0.97 & 0.99 & 0.98 \\ \hline CE\({}_{12}\): annoyed/displeased & \(\bigcirc\) & 0.98 & 0.97 & 0.99 & 0.98 \\ \hline \hline \multicolumn{5}{c}{**Metrics Average:**} & **0.96** & **0.96** & **0.97** & **0.96** \\ \hline \end{tabular}
\end{table} 
Table 16: Results using 2L-DBMM (SER + SA) on EmoUERJ.
###### Abstract  
Geopolymer concrete offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide (CO\({}_{2}\)) content. Engineering properties of geopolymer concrete, such as compressive strength, are commonly characterised based on experimental practices requiring large volumes of raw materials, time for sample preparation, and costly equipment. To help address this inefficiency, this study proposes machine learning-assisted numerical methods to predict compressive strength of fly ash-based geopolymer (FAGP) concrete. Methods assessed included artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet), based on experimentally collected data. Performance of the proposed approaches were evaluated using various statistical measures including R-squared (R\({}^{2}\)), root mean square error (RMSE), and mean absolute percentage error (MAPE). Sensitivity analysis was carried out to identify effects of the following six input variables on the compressive strength of FAGP concrete: sodium hydroxide/sodium silicate ratio, fly ash/aggregate ratio, alkali activator/fly ash ratio, concentration of sodium hydroxide, curing time, and temperature. Fly ash/aggregate ratio was found to significantly affect compressive strength of FAGP concrete. Results obtained indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Of note was ResNet, which demonstrated the highest R\({}^{2}\) and lowest RMSE and MAPE values.  
geopolymer concrete; artificial neural network; machine learning; deep neural network; ResNet; compressive strength; fly ash +
Footnote †: journal: Applied Sciences  
1
# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 3 Background Theoretical and Conceptual Framework
### Multilayer Neural Network  
Multilayer networks are more powerful than single layer networks. In [7], it is shown that a two-layer network can be trained to approximate any arbitrary function. Figure 2 represents a multilayer neural network. This network transforms an input vector into hidden layers, where each hidden layer is composed of hidden units that compute using linear and then nonlinear transformation. A multilayer neural network can have any number of hidden layers. The total number of hidden layers is represented by (_L_), while the superscript (_k_) represents the kth hidden layer. The pre-activation layer can be computed as shown in equation (5), where b represents the bias term, \(W\) is the weights matrix, h is the hidden layer activation, and \(X\) is the input vector.  
\[a^{(k)}(X)=b^{(k)}+W^{(k)}h^{(k-1)}(X) \tag{5}\]  
The hidden layer activation is computed as:  
\[h^{(k)}(X)=g\Big{[}a^{(k)}(X)\Big{]}, \tag{6}\]  
and the output layer activation is computed as:  
\[f(x)=h^{(L+1)}(X)=o\Big{[}a^{(L+1)}\ (X)\Big{]}. \tag{7}\]  
The correct magnitudes of these hidden units are not known initially at a given input. A neural network is configured to determine these values by tuning the model's parameters in such a way that the estimated values of the hidden layers eventually converge.
# Cationic Moieties in Polystyrene Gels Swollen with D-Limonene Improved Transdermal Delivery System
###### Abstract  
D-limonene, a terpene and natural compound, has been found to be an excellent penetration enhancer for transdermal drug delivery (TDD). It hence has been incorporated within various transdermal formulations. Herein, we report the application of polystyrene gel swollen with d-limonene and its derivatives for TDD. Poly(styrene-_co_-divinylbenzene) (PS gel), poly(styrene-_co_-divinylbenzene_-_co_-4-vinylpyridine) (PS-4 VP) gel and poly(styrene-_co_-divinylbenzene_-_co_-(vinylbenzyl) trimethylammonium chloride) (PS-VBAC gel) were employed as chemical gels to improve the stability of the TDD substrates. The drug permeation properties from the PS gels swollen in limonene were examined, regarding the effect of its network density as well as their rheological properties. The lowest density of the network showed the highest steady flux of the permeation at 43.7 \(\pm\) 0.3 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{/}\mathrm{c}\mathrm{m}^{2}\). FT-IR spectra were confirmed for PS-4 VP and PS-VBAC, bearing cationic moieties and they could control the release of ibuprofen by the electrostatic interaction at the interface of organogel and skin. The steady state flux of skin permeation got low values from 55.2 \(\pm\) 0.8 to 11.6 \(\pm\) 2.0 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{/}\mathrm{c}\mathrm{m}^{2}\), when the cationic moieties were increased. Moreover, the chemical network of PS gel swollen in limonene showed high mechanical stability illustrated by elastic modulus (G') of about 98 kPa for 10% cross-linked PS gel. The developed PS gels swollen in limonene show highly promising results, suggesting their possible application in TDD.  
organogel; d-limonene; chemical enhancer; transdermal drug delivery Article
## 2 Materials and Methods
### Study Sites and Measurements
#### 2.1.2 The Observation of Tower-Based SIF  
We used the tower-based automatic spectral observation system AutoSIF-1 (Bergsun Inc., Beijing, China) to observe SIF in Chinese cork oak and poplar plantations. At both sites, the system utilized QE65 Pro and QE Pro spectrophotometers (Ocean Optics Inc., Dunedin, FL, USA) with a spectral resolution of 0.31 nm, a sampling interval of 0.155 nm,and a spectral range from 650 to 800 nm. The SIF in the arborviate plantation was collected by the vegetation canopy fluorescence observation system D-Fox (Agri-SIF Inc., Nanjing, China), which utilized a QE Pro spectrophotometer (Ocean Optics Inc., Dunedin, FL, USA). The resolution and spectral range of all three observation systems can both meet the accuracy requirements for spectral observation during fluorescence retrieval. The instruments were, respectively, set up at the top of the canopy of Chinese cork oak, poplar, and arborviate plantations at 10 m, 20 m, and 5 m, with the fiber optics located on the south side of the tower to avoid the effects of shadow and obstruction. The \"sandwich\" method was employed to reduce the observation errors caused by instantaneous fluctuations in radiation [34]. Depending on the radiation conditions, each spectral measurement interval is approximately 3-5 min. We used the Spectral Fitting Method (SFM) algorithm, which is based on the Fraunhofer Line, to retrieve SIF from reflectance for the band around O\({}_{2}\)-A (760 nm) absorption [35].
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 3 Proposed SIEM Framework
### Machine Learning Module
#### 3.3.2 Class Imbalance  
With a sufficiently large training set, the filtering problem can be solved by any classification algorithm that can categorize incoming alerts as critical or noncritical. However, class imbalance poses a challenge. An imbalanced dataset refers to a situation where there is a significant disproportion in the number of instances for each class [53]. This problem is prevalent in security alert data, where the frequency of critical alerts is much lower than that of noncritical alerts [54,55]. As a result, conventional classifiers may be biased toward noncritical alerts, thereby hindering the detection of crucial alerts associated with critical incidents. Thus, a method is required to address class imbalance in security alert data to ensure rapid responses to incidents in SOCs.  
Class imbalance is commonly addressed by resampling the training data through subsampling, oversampling, or hybrid methods. Subsampling can reduce performance due to information loss in the reduced sample size, and oversampling can be effective through data augmentation algorithms, e.g., the synthetic minority oversampling technique (SMOTE) [56], adaptive synthetic sampling [57], and SVMSMOTE [58]. However, conventional oversampling methods may be ineffective for datasets with a large number of categorical attributes. A previous study [59] demonstrated that incorporating multiple oversampling methods can synthesize samples with more diversity and higher quality. Another approach to address class imbalance is to adopt cost-sensitive approaches, e.g., WSVMs [60; 61]. A weighted support vector machine (WSVM) assigns distinct weights to positive and negative samples, which scales the margin in proportion to the class significance. This makes it more efficient in managing imbalanced datasets. In this paper, we propose an improved WSVM, which considers the different weights assigned to samples from different classes and reduces the sample size by summarizing the replicated counts into an instance weight. This results in improved training and prediction performance. We refer to this approach as the IWSVM, which is described in detail in Section 4.  
To determine the best approach to mitigate class imbalance in security alert data with a large number of categorical attributes, we compare the proposed method with popular oversampling techniques and the hybrid approach proposed by [PERSON] et al. [59]. The experimental results (Section 5) demonstrate the effectiveness of the proposed IWSVM over conventional oversampling methods and the hybrid approach.
## 5 Free Space Extraction
Thus, there is no guarantee about the duration of searching time. On the other hand, the computational time for search-based methods is pretty high if the environment is cluttered. However, JPS has a lower searching time compared to A* [32]. Let \(p_{c}=\mathbf{p}_{0}.\mathbf{p}_{1},\ldots,\mathbf{p}_{n}\) be the intermediate waypoints from start to goal pose and \(l_{i}=<\mathbf{p}_{i}.\mathbf{p}_{i+1}>\) be the \(i\)th line segment, where \(i=1,\ldots,n-1\). Each line segment constitutes convex polyhedra, namely, \(E_{i}\). Along with that, SFC can be expressed as \(SFC(P)=\{E_{i}\mid i=0,\ldots,n-1\}\). SFC has two steps: finding \(E_{i}\) that fits the \(l_{i}\) and seeking a set of linear inequalities that are tangent to \(E_{i}\). Let \(E_{i}\) be \(\varepsilon_{i}(C_{i},\mathbf{p}_{i}^{0})=\{\mathbf{p}=C_{i}\tilde{\mathbf{p} }+\mathbf{p}_{i}^{0}\mid\|\tilde{\mathbf{p}}\|_{2}\leq 1\}\). In \(\mathbb{R}^{3}\), \(C_{i}\) can be decomposed as \(R^{T}SR\), where \(R\) gives the axis of rotation between considered line segment in between \(\mathbf{p}_{i}\) and \(\mathbf{p}_{i+1}\)). The semi-axis of \(E_{i}\) is given by \(S=diag(a,b,c)\) as a diagonal matrix. \(\mathbf{p}_{i}^{0}\) is the center of \(l_{i}\). The objective of SFC is to find each pair \(E_{i}\) and \(\mathbf{p}_{0}^{i}\), given the \(l_{i}\) and obstacles set (\(Obs_{i}\)), which touches the \(E_{i}\). Initially, ellipsoids are spheres whose center poses are located as the midpoints of \(l_{i},\ i=1,\ldots,n-1\). Afterwards, semi-axes, except for the axis along \(\mathbf{p}_{i+1}-\mathbf{p}_{i}\), are shrunk until the corresponding ellipsoid contains no obstacles. Let \(\varepsilon_{i}^{*}(C_{i},\mathbf{p}_{i}^{0})\) be the \(i\)th ellipsoid after applying the shrinking process. \(\mathbf{p}_{j}\) denotes the closest point that touches the \(\varepsilon_{i}^{*}(C_{i},\mathbf{p}_{i}^{0})\), where \(j=1,\ldots,m\) and \(m\) is the number of obstacles. Hence, corresponding half-space \(H_{j}=\{\mathbf{p}_{j}\mid a_{j}^{T}\mathbf{p}_{j}<\mathbf{b}_{j}\}\) is defined as a plane that is tangential to \(\varepsilon_{i}^{*}(C,\mathbf{p}_{0})\), where \(a_{j}\) and \(\mathbf{b}_{j}\) are determined by:  
\[\mathbf{a}_{j}=\frac{d\varepsilon_{i}}{dp}_{\mathbf{p}=\mathbf{p}_{j}}=2C_{i} ^{-1}C_{i}^{-T}(\mathbf{p}_{j}-\mathbf{p}_{i}^{0}),\quad\mathbf{b}_{j}= \mathbf{a}_{j}^{T}\mathbf{p}_{j}. \tag{40}\]  
Hence, the intersections of these m half spaces create a convex polyhedron \(C=\cup_{j=0}^{m}H_{j}=\{\mathbf{p}\mid A^{T}\mathbf{p}<\mathbf{b}\}\). The same approach is applied to each line segment, \(l_{i}\) in which we can generate each \(C_{i}\). All in all, \(SFC(P)=\{C\mid i=0,\ldots,n-1\}\) can be constructed. A more descriptive formulation is given in [32] (Algorithm 1). Figure 7: Free space extraction using SFC. Once intermediate initial waypoints are defined, SFC calculates free space along the path, which is constructed from the initial waypoints.
## 4 Discussion
### Determination of the Optimal Distance Scale Factor of the New Algorithm  
In the ADLSC algorithm, a key issue is determining the optimal distance scale factor according to the monitoring data. Based on the principle of \(R\ll 1\) (Section 2.2), we constantly try to give different \(R\) values and calculate the corresponding RMS of the fitting and checking results. Detailed statistical results are shown in Figure 7, which shows that the fitting RMS decreases as the magnitude of the distance scale factor decreases. This may be caused by a high signal weight due to the decrease of the distance scale factor value, which will lead excessive noise absorption as the signal and the fitting residual are gradually reduced. However, the checking RMS presents a decreasing trend, reaches the lowest value, and then increases again. Therefore, for the ADLSC algorithm, the distance scale factor corresponding to the minimum checking RMS point in each direction is selected as the optimal value. Finally, the optimal scale factor of the eastward distance is \(10^{-25}\) and that of the northward distance is \(10^{-45}\).