esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data +
## 4 Principles of Context Reasoning
### Context Reasoning Methods
#### 4.3.2 Similarity Based on Cosine Similarity  
Dissimilar to the Euclidean distance, which values the distance between the end points of two vectors, cosine similarity verifies the appropriateness of the angle between two vectors. The \(\mathbf{sim}_{cos}(S_{c},C_{\tau})\) is calculated as follows:  
\[\mathbf{sim}_{cos}(S_{c},C_{\tau})=\frac{S_{c}\cdot C_{\tau}}{\|S_{c}\|\cdot\|C_{ \tau}\|}=\frac{\sum_{i=1}^{\omega}s_{i}^{S_{c}}\cdot s_{i}^{C_{\tau}}}{\sqrt {\sum_{i=1}^{\omega}\left(s_{i}^{S_{c}}\right)^{2}}.\sqrt{\sum_{i=1}^{\omega} \left(s_{i}^{C_{\tau}}\right)^{2}}} \tag{13}\]  
As the cosine of two vectors increases to 1, the vectors are considered more similar. All of \(C_{\tau}\) in \(\mathbf{sim}_{cos}(S_{c},C_{\tau})\) must be within the threshold \(\theta_{a}^{C_{\tau}}\) for the condition of \(C_{\tau}\) to be considered the possible next context. Failure to find such \(C_{\tau}\) delays the transition to any possible next context.  
\(\mathbf{sim}_{dist}(S_{c},C_{\tau})\) fits when finding the similarity of state spaces with Boolean-stated sensors, such as sensors generating two statuses of 0/1 or On/Off. In other words, when the change in sensor status is monotonous and, thus, the magnitude of vectors \(S_{c}\) and \(C_{\tau}\) is stable, the Euclidean distance can be applied. However, this is not applicable when the status in the sensors is non-Boolean and generates values within certain ranges since the magnitude of vectors can significantly change. For the state spaces containing those sensors, \(\mathbf{sim}_{cos}(S_{c},C_{\tau})\) is used.  
In the reasoning methods above, each vectorized element is individually calculated. As the given space is extended and the state space is scaled with more sensors, the computational complexity greatly increases and affects the performance in reasoning contexts. Since the number of sensors matters, it is critical to reduce the size of the sensors. To achieve this goal, we utilize PCA and reduce the dimension of the dataset for faster computation when calculating similarity. Note that the PCA is applied regardless of the similarity method.
## 2 Related Research  
### ACL Injuries and Prevention  
ACL injuries frequently occur in sports involving jumping and rapid direction changes, such as volleyball and skiing [1]. Most of these injuries are non-contact [6], resulting from internal risk factors (e.g., physical characteristics, athletic abilities) interacting with external risk factors (e.g., sport-specific movements, environmental factors). Reducing these risk factors can mitigate ACL injury risks. However, modifying sport-specific movements and environmental factors, such as ground conditions influenced by weather, is challenging. Therefore, reducing internal risk factors is crucial for ACL injury prevention.  
Several studies have examined internal risk factors for ACL injuries. [PERSON] et al. reported a significant gender-based discrepancy in ACL injury rates among athletes, observing that female players in soccer, rugby, and basketball experience ACL injuries at a rate approximately four times higher than their male counterparts [9]. Differences in knee anatomy, such as a smaller femoral notch in females, can predispose individuals to ACL injuries [10]. Many factors are involved in ACL injury, including hormone levels [11].  
Figure 1: Knee flexion and valgus angles.  
Among modifiable internal risk factors, the knee valgus angle has garnered attention. [PERSON] et al. reported that athletes exhibiting knee valgus during jump landings are at higher risk for ACL injuries [8]. [PERSON] et al. found that knee flexion with valgus significantly strains the ACL [12]. Thus, correcting knee valgus is vital for reducing ACL injury risk.  
Training programs to prevent ACL injuries by avoiding knee valgus during jump landings are effective. [PERSON] et al. summarized various prevention programs, including jump, running, flexibility exercises, balance training, and strength training, which reduce ACL injury risks [13]. [PERSON] et al. reported that jump and weight training decreased knee valgus angles during landings [14]. Early screening and prevention training are effective for young athletes [15], making early detection crucial. Although the knee valgus angle measurement is used for screening, conventional high-accuracy methods are costly and time-consuming, hindering accessibility for the youth.
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 2 Related Works
### Dynamic Window and Cross-Scale Attention Mechanisms  
Dynamic window and cross-scale attention mechanisms are emerging techniques in computer vision, aimed at improving the capture of multi-scale features. Dynamic window mechanisms enable models to adjust window sizes based on input content adaptively, providing flexibility in focusing on regions of interest at varying scales. Cross-scale attention mechanisms enhance feature representation by facilitating interactions between features at different scales, improving the model's ability to handle complex patterns.  
In general image classification, [PERSON] et al. [33] proposed a Dynamic Window Attention Network that dynamically adjusts window sizes to improve classification accuracy, demonstrating its effectiveness in capturing context-aware features. [PERSON] et al. [34] developed a cross-scale attention module to enhance information exchange between feature maps at different scales, boosting representational power for tasks like object recognition.  
In medical image analysis, these mechanisms are still being explored but show significant potential. [PERSON] et al. [35] applied dynamic window mechanisms to medical image segmentation, achieving improved precision by adaptively adjusting window sizes to focus on relevant anatomical structures. [PERSON] et al. [36] proposed a cross-scale attention network for medical image classification and segmentation, demonstrating its ability to integrate multi-scale features for better performance. While these methods highlight the benefits of dynamic and cross-scale attention, their application to breast cancer histopathology image classification under limited annotations remains underexplored. Our Dynamic Cross-Scale Swin Transformer (DCS-ST) addresses this gap by combining a Dynamic Window Predictor and a Cross-Scale Attention Module, enabling robust feature extraction and classification of histopathology images with minimal labeled data.
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 2 Macroscopic Saturated Porous Media Model
### Effective Stresses and Permeability Formulation  
According to the principle of effective stresses, the total stress state at any material point of the homogenized material consists of an 'extra' or 'effective' stress, denoted by the subscript \(\left(\ \boldsymbol{\cdot}\ \right)_{E}\), and a weighted pore-fluid pressure term; see, e.g., [47; 48; 49] for a review and references. Thus, \(\mathbf{T}^{\alpha}\) and \(\mathbf{\hat{p}}^{\alpha}\) with \(\alpha=\{S,F\}\) in Equation (7) can be expressed as  
\[\mathbf{T}^{S}=\mathbf{T}^{S}_{E}-n^{S}\,p\,\mathbf{I}\,,\quad\mathbf{T}^{F}= \mathbf{T}^{F}_{E}-n^{F}\,p\,\mathbf{I}\,,\quad\mathbf{\hat{p}}^{F}=\mathbf{ \hat{p}}^{F}_{E}+p\,\mathrm{grad}\,n^{F}\,. \tag{9}\]  
For a linear-elastic solid phase, the effective stress tensor \(\mathbf{T}^{S}_{E}\) can be expressed as  
\[\mathbf{T}^{S}_{E}\ =\ \kappa^{S}\,\operatorname{tr}(\boldsymbol{\varepsilon}_{S} )\,\mathbf{I}\,+\,2\,\mu^{S}\,\boldsymbol{\varepsilon}^{D}_{S} \tag{10}\]  
with \(\kappa^{S}:=\lambda^{S}+\frac{2}{3}\mu^{S}\) being the bulk modulus of the porous solid matrix, which is defined in terms of the macroscopic _Lame_ constants \(\mu^{S}\) and \(\lambda^{S}\). Moreover, \(\operatorname{tr}(\boldsymbol{\varepsilon}_{S}):=\boldsymbol{\varepsilon}_{S }\cdot\mathbf{I}=\operatorname{div}\mathbf{u}_{S}\) defines the scalar-valued trace of the strain tensor \(\boldsymbol{\varepsilon}_{S}\) with \(\boldsymbol{\varepsilon}^{D}_{S}:=\boldsymbol{\varepsilon}_{S}-\frac{1}{3} \operatorname{tr}(\boldsymbol{\varepsilon}_{S})\mathbf{I}\) as the deviatoric strain tensor.  
In defining the fluid effective stress, we proceed with the assumptions of Newtonian fluid and negligible average normal viscous stress (i.e., [PERSON] hypothesis). Thus, the effective or frictional fluid stress can be expressed as  
\[\mathbf{T}^{F}_{E}=\mathbf{T}^{F}_{\text{fric}}=\,\mu^{F}(\mathrm{grad}\, \mathbf{v}_{F}+\mathrm{grad}^{T}\mathbf{v}_{F})\,, \tag{11}\]  
with \(\mu^{F}\ :=\ n^{F}\,\mu^{\text{f}R}>0\) being the partial shear or dynamic fluid viscosity and \(\mu^{\text{f}R}\) is the effective dynamic fluid viscosity. Having possibly an anisotropic and deformation-dependent 2 nd-order intrinsic permeability tensor \(\mathbf{K}^{S}(\mathbf{u}_{S})\), the constitutive equation of the effective or frictional momentum production \(\mathbf{\hat{p}}^{F}_{E}\) can be expressed as  
\[\mathbf{\hat{p}}^{F}_{E}=\mathbf{\hat{p}}^{F}_{\text{fric}}=-\left(n^{F}\right) \mu^{F}(\mathbf{K}^{S})^{-1}\,\mathbf{w}_{F}\,. \tag{12}\]  
A detailed derivation and discussion of the above constitutive relations based on the 2 nd-law of thermodynamics can be found in [1; 31].
## 2 Preliminaries  
In this section, we review some related work of the MPR and hesitant multiplicative set (HMS). [PERSON] [15] first introduced the concept of MPR, which is a useful tool to express evaluation information. For convenience, let \(X=\{x_{1},x_{2},\cdots,x_{n}\}\) be a finite set of alternatives and \(N=\{1,2,\cdots,n\}\).  
Definition 1: [15] An MPR A on \(X\) is represented by a matrix \(A=\left(a_{ij}\right)_{n\times n}\subset X\times X\) with \(a_{ii}=1,a_{ij}\cdot a_{ji}=1,i,j\in N\), where \(a_{ij}\) denotes the ratio of preferred degree of alternative \(x_{i}\) with respect to \(x_{j}\).  
In particular, as [PERSON] [29] showed the 1-9 scale, \(a_{ij}=\{1/9,1/8,\cdots,1/2,1,2,\cdots,8,9\}\), and \(a_{ij}=1\) denotes that there is no difference between \(x_{i}\) and \(x_{j}\), \(a_{ij}=9\) denotes \(x_{i}\) is absolutely preferred to \(x_{j}\), and \(a_{ij}=\{2,3,\cdots,8\}\) (or \(a_{ij}=\{1/2,1/3,\cdots,1/8\}\)) indicates the intermediate evaluations [30].  
Definition 2: Let \(A=\left(a_{ij}\right)_{n\times n}\) be a MPR, then \(A\) is consistent, if it satisfies the transitivity, i.e., \(a_{ij}=a_{ik}\cdot a_{kj},\forall i,j,k\in N\).  
For a MPR \(A=\left(a_{ij}\right)_{n\times n}\), if there exists a crisp priority weight vector \(w=\left(w_{1},w_{2},\cdots,w_{n}\right)^{\mathsf{T}}\), such that \(a_{ij}=w_{i}/w_{j},\forall i,j\in N\), then \(A\) is consistent [15], where \(w_{i}>0,i\in N\), and \(\sum_{i=1}^{n}w_{i}=1\).  
Because of the complexity and uncertainty involved in practical GDM problems, it may be difficult for DMs to express their preference information with only one crisp number, but they can be represented by a few different crisp values.  
Definition 3: An HMS on \(X\) is defined as \(H=\{\langle x_{i},b_{\mathrm{H}}(x_{i})\rangle|i\in N\}\), where \(b_{\mathrm{H}}(x_{i})=\{\gamma_{k}\in[1/9,9]\)\(|k=1,2,\cdots,|b_{H}(x_{i})|\}\) is a hesitant multiplicative element (HME), which denotes all of the possible membership degrees of the element \(x\in X\) for the set \(H\), \(|b_{H}(x_{i})|\) is the cardinal of \(b_{H}(x_{i})\).  
Definition 4: [28] Let \(b=\{\gamma_{k}|k=1,2,\cdots,|b|\}\) be a HME, \(|b|\) be the cardinal of \(b\), the score function of \(b\) is defined by \(f(b)=\left(\prod_{k=1}^{|b|}\gamma_{k}\right)^{\
icefrac{{1}}{{b_{1}}}}\). Suppose that \(b_{1}\) and \(b_{2}\) are two HMEs, if \(f(b_{1})\geq f(b_{2})\), then \(b_{1}\geq b_{2}\).
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 2 Methods for Generating Cryptographic S-Boxes
### Random S-Boxes  
The easiest way to generate an S-box is to choose a candidate randomly. It is obvious that such a randomly generated S-box will not have the strongest cryptographic properties. On the other hand, a randomly generated Boolean function will not be too weak, either. Pseudo-random S-box generation is based on a known pseudo-random generator initialized with a known seed. If the seed is not controlled by the cipher designers, the randomness of the S-box generation can be verifiable by third parties.  
A (pseudo-)random S-box generation can be improved by examining a larger set of S-boxes and taking either the best candidate or the first candidate that fulfills the required design restrictions. An example is the basis of the algorithm used to generate S-boxes for the AES candidate MARS [12]. The authors tested roughly \(2^{26}\) candidates on five differential and four linear criteria. Note that this algorithm combines random generation with an extra \"early abort\", that is, when a special criterion (with a low probability of occurrence) is encountered, a candidate is modified in such a way that the criterion is satisfied.
# Characterizing the Tensile Behaviour of Woven and Composite Fabrics under UV Exposure
## 4 Results and Discussions
### Tensile Properties Degradation
The formation of acetic acids was further supported by the absorption peaks formed due to the presence of the CO bands (1300-1000 cm\({}^{-1}\)) which characterized the C-O stretching vibration of the acetic acids. Unlike woven fabrics, the hydroxyl and carbonyl groups belonged to the composite samples (see Figure 8b), which appeared to remain unchanged after several months of UV exposure. No peaks were observed after the final month of outdoor exposure. This result alone suggested that composite samples had not undergone any photo-oxidation degradation. Figure 8: (**a**) FTIR spectra of PP120S fabric under various UV exposure levels; (**b**) FTIR spectra of TC120 fabric under various UV exposure levels. The abovementioned results clearly describe the behaviour of engineered fabrics under photo-oxidation degradation through their chemical structure changes. Studies between these engineered fabrics indicated that woven samples were subjected to an accelerated photo-oxidation degradation compared to composite samples. Despite the significant microcracks that were observed in the SEM micrographs of composite fabrics, the FTIR spectra showed no evidence of chemical structural change. These micro-scale findings were aligned with the tensile test results that were well supported by the lowest tensile strength and retained strain percentages in woven fabrics, as well as composite fabrics that exhibited minimum to no degradation after five months of outdoor UV weathering tests. As a result, the additional layer of non-woven fabrics found in composite fabrics served as a sacrificial layer for the woven fabric to protect it from rapid degradation.
# Quantum Coding via Quasi-Cyclic Block Matrix
## 4 Long-Length Quantum Coding via Quasi-Cyclic Jacket Matrix
### The Construction Method of Jacket Matrix
Apparently, both a jacket matrix \(J\) and a \(K^{i}\) matrix have row weight \(1\) with any positive integer \(1\leq i\leq l\), so that the row weight of \(G\) is exactly \(n-1\), for a given prime \(n>2\), where the block length of the constructed code is \(N=n(n-1)\)._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Jacket Matrix** & **Decompositions of Prime Number** \\ \hline \(J_{5}\) & \(J_{2}\oplus J_{3}\) \\ \hline \(J_{7}\) & \(J_{2}^{\otimes 2}\oplus J_{3}\) \\ \hline \(J_{29}\) & \(J_{3}^{\otimes 3}\oplus J_{2}=J_{2}^{\otimes 4}\oplus J_{13}\) \\ \hline \(J_{31}\) & \(J_{3}^{\otimes 3}\oplus J_{2}^{\otimes 2}=J_{2}^{\otimes 3}\oplus J_{23}\) \\ \hline \(J_{37}\) & \(J_{2}^{\otimes 5}\oplus J_{5}=J_{2}^{\otimes 4}\oplus J_{3}\otimes J_{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The construction of jacket matrices.
## 3 Results
### Test in a Real Setting
#### 3.2.1 Tests with the 3D-Printed 3D Model  
This work is in its initial stages, and it has been developed utilizing just three synchronized cameras. However, since the object was static in these tests, the lack of camera synchronization did not impact the outcome. Figure 13 displays the 27 images captured during this test. The signalized points in the background were only used to extract scale bars to scale the model.  
Two distinct models were produced: the first scaled using a single distance scale, and the second scaled applying a scale factor estimated through ten known distances (Figure 14).  
Figure 12: Three synchronized cameras. The up row’s three cameras in frontal shooting position.  
Figure 13: The 27 images acquired on the 3D model.  
Figure 15 shows the histogram of the reconstruction errors within the range from \(-5\) mm to \(5\) mm. The blue line plot shows the error of the model scaled with a single distance of approximately \(60\) cm in length, whereas the orange line plot describes the error of the model scaled with the scale factor estimate using \(10\) known distances, each with a mean length of \(60\) cm. The standard deviation of the errors for the model scaled with a single distance is \(1.4\) mm, whereas it is \(0.9\) mm for the model scaled using \(10\) distances. Furthermore, \(80\%\) of the points in the model scaled with \(10\) distances have a distance from the reference model within \(\pm 1\) mm, compared to \(65\%\) in the model scaled with a single distance.
## 3 Results
### Effect of AGE and ASA on the Growth and Development of Pepper
#### 3.1.4 SOD, POD, and Lipid Peroxidation of Pepper Plants Applied with AGE and ASA  
The antioxidants (SOD, POD) and the lipid peroxidation (MDA content) of the pepper plants is depicted in Figure 4. It can be observed that the foliar application of AGE and ASA significantly influenced the SOD activity. AGE foliar application was substantially different to both the fertigated and control treatments, whereas the fertigation of ASA was not significantly different to control plants. The peroxidase (POD) activity also showed alterations in regard to the applied treatments. Fertigation of AGE resulted in maximum POD activity followed by foliar application of AGE. ASA root application also increased the POD content; however, there was no significant difference observed in the plants. The lipid peroxidation of the plants showed that there was no significant influence on the plants that might be attributed to the low concentration level of the treatments applied. However, an increased level in the lipid peroxidation was observed in the plants treated with the fertigation of AGE, although the effect was not statistically significant.  
Figure 4: _Cont_.  
Figure 3: Root activity of pepper seedling effected by foliar and fertigation of AGE and ASA. Bars represent means and standard errors of triplicates. Means followed by same letters are not significantly different from each other. (GS) AGE foliar, (GR) AGE fertigation, (SS) ASA foliar, (SR) ASA fertigation, (CK) distilled water foliar, and (CKR) distilled water fertigation.
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 6 Complexity Analysis  
Computational complexity is one of the most critical challenges in the design of the transmitter and receiver algorithms in wireless communication systems:  
* Transmitter complexity: As mentioned previously, in the OFDM-IM scheme, the indices of the active subcarriers are determined based on the lexicographical order of the corresponding decimal value of the \(p_{0}\) index bits. In order for the index selector to obtain the k-size vector of indexes out of \(2^{length(p_{0})}\) possibles, the combinadic unranking algorithm [29] is used at the transmitter. The time and space complexities of this algorithm are \(\mathcal{O}(n)\) and \(\mathcal{O}(nk)\), respectively [29; 36; 37]. However, in the proposed NSC-OFDM-IM scheme, there is no need to determine the locations of active subcarriers since the index selector starts by assuming all subcarriers as if they are active in the subgroup, and hence, it targets the deactivation of one of the active subcarriers depending on the decimal value of the incoming \(p_{0}\) bit substream;
* Receiver complexity: Both waveforms, OFDM-IM and NSC-OFDM-IM, have the same LLR processing, which is applied to the frequency-domain-converted signal. In the OFDM-IM scheme, the coefficients in each LLR subgroup are first sorted in order to extract the most possible occurrence of the active subcarriers. There are different types of sorting algorithms, and each has a different computational complexity. The most commonly used sorting algorithms with their worst- and best-case complexities [38; 39] are shown in Table 4. From the output of the sorting algorithm, the number of the first \(k\) indexes are chosen as the active carrier indexes. Then, the next step is to apply the combinadic ranking algorithm [29] to this index vector so that the corresponding integer value of the index sequence is obtained based on the lexicographic order. The complexity of this combinadic algorithm is given as \(\mathcal{O}(k^{2})\)[29; 36; 37]. However, in the proposed scheme, we do not need to apply the sorting and combinadic ranking algorithms at the receiver. Instead, the index detector just finds the index of the minimum value of the coefficients in each LLR subgroup. Then, by taking one less than this index number, the corresponding decimal value of the index bits is calculated. The complexity of finding the minimum value is \(\mathcal{O}(n)\). Table 5 compares the computational complexity of the proposed NSC-OFDM-IM with several existing OFDM-IM schemes.