esa-sceva/satcom-chunk-collection huggingface dataset:
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 2 Literature Review
### Emotion Recognition in Architectural Spaces  
The influence of architectural environments on human emotions has been extensively studied in environmental psychology and human-centred design [38]. Architectural factors such as lighting, spatial configuration, acoustics, and materiality significantly impact emotional and cognitive responses [39]. However, conventional architectural assessment methodologies continue to rely on qualitative methods, such as post-occupancy evaluations, behavioural observations, and psychometric assessments, rather than incorporating objective physiological data [12].  
Recent research has explored biometric data integration into architectural design. Studies in neuroarchitecture have examined EEG responses to spatial configurations, demonstrating the potential of EEG-based emotion recognition in architectural applications [14]. Additionally, biometric signals such as heart rate variability (HRV) and galvanic skin response (GSR) have been incorporated into smart environments to regulate lighting and climate systems [15]. However, these approaches remain largely experimental, and practical implementations of AI-driven affective computing in adaptive architecture are still in their early stages [13].  
The concept of sensing architecture, where built environments autonomously adjust based on real-time biometric feedback, has gained increasing attention [19]. While AI-driven affective computing has been explored in smart buildings and urban planning, its practical application in real-time adaptive architecture remains underdeveloped [7]. Current frameworks typically rely on predefined settings, lacking the ability to dynamically adjust spatial configurations based on real-time EEG-derived emotional feedback [40].
## 2 Related Work
Profiling allows researchers to study the specific part of a subject by taking its descriptive elements and characterizing a profile with them. For example, in [22], the behavioral profile of an application takes place in the form of UML stereotypes that allow researchers to trace the patterns of interactions among applications. Similar approaches include the one proposed by [PERSON] et al. [26], where the authors propose UML profiles to describe related energy requirements and KPI metrics to application design and deployment elements. Similarly to [PERSON] et al., The Object Management Group, responsible for overseeing UML, has created an extension adding support for Real-Time and Embedded Systems in UML, called the UML profile for MARTE [29]. For instance, [PERSON] et al. [27] proposed an extension of UML models that get transformed into a stochastic Petri net for their stochastic energy consumption analysis, including MARTE. Other approaches aim to model software's and services' behavior with their language, specifically behavioral relationships among software units. For instance, in [16], the authors propose a software behavior modeling language that differentiates itself by supporting business analysts and software architects from the initial phases of the SDLC. To our knowledge, most existing approaches do not describe diachronic behavior (how behavior and even its changes, change over time) that includes elements of uncertainty such as Human-Provided Services (HPSs) [30], where people are actors that affect and constrain a system's behavior [15; 31], reaching into the field of HCI [32] to study how the factors surrounding the user affect the consumption of a unit of software. This is a fundamental issue for our research and contribution, as a secondary goal is to be able to provide energy ratings based on these features. To conclude, we found that (1) most of the existing works employ profiling, (2) some of the approaches target software behavior modeling from the initial stages of the SDLC, and (3) none of the existing approaches factor software's or user's behavioral change into the profiles of software behavior they produce. Therefore, to tackle these shortcomings, our proposal employs a holistic profiling approach that allows software designers and architects to include the uncertainty of HCI, the constraints of the business, and the architectural constraints of software in profiles of the software's behavior. The resulting profiles can be later analyzed by our custom interpreter, which creates a prospective, changing software usage, which is turned into energy consumption, all from the analysis and design stages of the SDLC.
# Variation in Volatile Flavor Compounds of Cooked Mutton Meathalls during Storage
## 3 Results  
### Comparison of SPME and SAFE Extraction Methods  
The Solid-Phase Microextraction (SPME) method combined with Gas Chromatography-Ofracometry-Mass Spectrometry (GC-O-MS) was used to extract and detect volatile flavor substances from cooked mutton meatballs (freshly cooked, not frozen). Eighty-nine different kinds of volatile compounds were identified, as shown in Table 1, including 12 alcohols, 6 sulfur-containing compounds, 25 olefins, 15 aromatic compounds, 7 aldehydes, 6 esters, 3 ketones and 15 terpenes. During storage, there were 43 kinds of volatile compounds, including 7 alcohols, 3 sulfur-containing compounds, 11 olefin compounds, 5 aromatic compounds, 3 aldehydes, 4 esters, 1 ketone and 9 terpenids. Among all the volatile compounds detected, 36 odor compounds could be smelled, including 4 alcohols, 5 sulfur-containing compounds, 11 olefin compounds, 6 aromatic compounds, 3 aldehydes, 1 ketone and 7 terpenes. Similar to Solid-Phase Microextraction (SPME), the SAFE method was also applied combined with Gas Chromatography-Ofracometry-Mass Spectrometry (GC-O-MS) for the extraction and identification of the volatile flavor substances of cooked mutton balls, and 58 kinds of odor-active compounds were identified. The results are shown in Table 2, including 9 alcohols, 3 sulfur compounds, 6 olefins, 10 aromatic compounds, 3 esters, 5 aldehydes, 1 ketone, 12 terpenids and 5 phenols. During storage, there were 27 different kinds of volatile compounds identified, including 6 alcohols, 2 sulfur-containing compounds, 2 olefin compounds, 5 aromatic compounds, 2 aldehydes, 2 esters, 1 ketone, 3 terpenes and 4 phenols. Among them, 23 odor compounds could be smelled, including 5 alcohols, 2 sulfur-containing compounds, 2 olefin compounds, 3 aromatic compounds, 2 aldehydes, 1 ketone, 7 terpenes and 1 phenol. The changes of various volatile compounds in cooked mutton meatballs during storage were analyzed by SPME/GC-O-MS, as shown in Figure 1. At 0 days, the relative contents of alcohols, sulfur-containing compounds, olefin, aromatic compounds, aldehydes, esters, ketones and terpenes are the highest, and at the later stage of storage, the concentrations of all volatile compounds decreased significantly. SAFE/GC-O-MS was used to analyze the changes of various volatile compounds during the storage period of cooked mutton meatballs. As shown in Figure 2, most volatile compounds had a higher concentration in the late storage period, and the overall change of aromatic compounds was little during the storage period.
## 4 Experimental Results and Discussion  
### Simulations on Synthetic Images  
In this section, we analyze the performance of the proposed approach on the synthetic Shepp-Logan phantom test image [55] (Figure 4a) with a speckle noise variance of \(\sigma\) = 10 (Figure 4b) of a 256 \(\times\) 256 pixel size. This result helps us to understand the effectiveness of the simulated image, clearly determine the distinctive features of the image, and optimize the algorithm before testing on the clinical datasets. We compared the proposed algorithm with some standard speckle reduction  
Figure 3: Proposed despeckle model for an ultrasound image. KSVD: K-singular value decomposition.  
filters for ultrasound liver images [4]. The compared algorithms were local statistical filters such as the Frost filter [10], [PERSON] filter [11], 3 \(\times\) 3 Weiner filter [13], Kuan filter [14], 3 \(\times\) 3 median filter [15], and speckle reducing anisotropic diffusion (SRAD) filter [39]. In addition, multiscale filters such as wavelets [40] were evaluated. The despeckled images in Figure 4e-g show that the Frost, wavelet, and Kuan filters do not effectively reduce noise. In contrast, Figure 4h-j show that the median, Weiner, and SRAD filters, reduce most noise; however, the edges are not preserved and artificial noises can be introduced to a certain extent. This result verifies that the proposed SR technique reduces noise and preserves the edges better than the conventional methods on synthetic images. Table 1 shows the PSNR value and MSSIM value. The proposed algorithm reconstructs the original image with a PSNR value of 36.86 dB with Dictionary 1 and 37.04 dB with Dictionary 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **PSNR (dB)** & **MSSIM** \\ \hline Noise image & 32.113 & 0.727 \\ Frost & 32.466 & 0.768 \\ Wavelet & 33.214 & 0.801 \\ Kuan & 32.895 & 0.794 \\ Median & 34.597 & 0.839 \\ SRAD & 33.434 & 0.827 \\ Weiner & 33.782 & 0.834 \\ Proposed: Dictionary 1 & 36.862 & 0.953 \\ Proposed: Dictionary 2 & 37.044 & 0.967 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM) for the synthetic images for \(\sigma\) = 10.  
Figure 4: (**a**) Original image; (**b**) noisy image. Results of the proposed method with (**c**) Dictionary 1 and (**d**) Dictionary 2; Results of the (**e**) Frost; (**f**) wavelet; (**g**) Kuan; (**h**) median; (**i**) Weiner; and (**j**) speckle reducing anisotropic diffusion (SRAD) filters.
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 1 Introduction
Namely, images' pixel/voxels at the input are explicitly used instead of the representative (extracted or selected) features. This in turns reduces errors caused by incorrect segmentation and/or subsequent extraction of hand-crafted features. Literature research has demonstrated that machine learning (ML) and DL provide quick, automated, efficient strategies for detecting abnormalities and extracting key features of altered lung parenchyma; this may be connected to the unique signatures of the COVID-19 virus. However, the available datasets of COVID-19 are insufficient for the development of deep neural networks [11]. In short, early, accurate and rapid COVID-19 diagnosis plays a key role in timely quarantine and medical care. This is also of great significance for the prognosis of patients, the prevention of this epidemic and the protection of public health [12]. In this work, we propose a DL approach for the detection of COVID-19 from CXR images. The proposed pipeline is based on TL by employing the Xception pre-trained model for the feature extraction stage. Then, we used the global average pooling (GAP) layer to solve the vanishing gradient problem when using DL networks and to reduce overfitting probability; the activation layer was used for reducing the losses. We used a dataset which consisted of COVID-19, pneumonia and normal CXR images. We investigated different activation layers with different activation functions and optimization algorithms and compared between them to select the one that provides the best performance with minimum losses. We compared our approach with traditional pre-trained models and also with other literature studies. Our design achieves the highest performance compared to the previous studies and the pre-trained models. The rest of the paper is partitioned into the following sections. Section 2 provides an overview of the related work for recent COVID-19 detection studies using different AI algorithms. Details of the methods and the processing stages of the developed framework are fully described in Section 3. Then, Section 4 describes the performance evaluation and validation methods/metrics for evaluating classification accuracy. The experimental results showing the potential of the proposed pipeline are given in Section 5, and Section 6 presents the results discussion. Finally, Section 7 contains the conclusions.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 5 Future Aspects of IoSB
### Network Topology Control  
To improve the coverage and energy efficiency of the whole network, dynamic clustering and routing protocols are required. The author in [97] proposed a two phase dynamic method for cluster head selection in WSN. Their technique is suitable for stationary sensors. In contrast, our work focus on IoSB composed of mobile nodes. In [98], Optimal Clustering Algorithm (OCA) is expected to increase network lifespan and dynamic. The authors in [99] design and implement a new dynamic topology for mobile sensor networks, called Dynamic Broadcast Clustering Topology (DBCT). In their approach, the network topology has better feasibility and mobility. Differing from the above DBCT schemes, Ref. [100] exploited a topological control method for Chain-type Wireless Sensor Networks (CWSNs) which can evidently improve the coverage of the whole network. The literature [101] exploited a new robustness enhancing algorithm for scale-free WSNs, which can improve the robustness of the network.  
Different from the design of mobile sensor networks, such as IoV, we consider the IoSB has its own features, like low speed, limited range of movement, etc. Based on the above considerations, we conceive a hierarchical clustering heterogeneous network, which is shown in Figure 10a. Moreover, considering the ever-changing network topology of IoSB owing to the movement of nodes, we put forward a topology structure of hierarchical clustering heterogeneous network in Figure 10b. Three parts are included in this structure which are static sensor, mobile sensor and bus station. In our vision, the bus station is utilized as a fixed node and carries higher precision sensors compared to mobile node, which can be used for a data transmission relay or location beacon.  
Figure 10: Network deployment and work schematic of IoSB, (**a**) hierarchical clustering heterogeneous network; (**b**) topology structure of hierarchical clustering heterogeneous network.  
Figure 9: An AIC-based system structure of the sensing node in IoSB.
## 2 Methods
### Detection of Wall Segments
#### 2.1.2 Non-Maximum Suppression  
The idea of non-maximum suppression is similar to the famous Canny edge detector in the field of image processing [28]. We suppose the point density of the target wall has the local maximum value after projecting to the 2D horizontal surface, and the points along the normal direction need to be suppressed. The aim of such processing is to decrease the influence of windows or other building attachments and avoid spurious or repeatedly detected results. Since non-maximum suppression is often adopted in local areas and requires the estimation of an extremum point and suppression orientation, a raster-based approach is proposed and the main steps are described below:  
Figure 2: The workflow of wall segment detection and adjustments. (**a**) the input point clouds of rural buildings, (**b**) the projected wall points after density-based clustering and the enlarged region to be processed via non-maximum suppression, (**c**) the line segments detected by the multiple cues RANSAC algorithm, (**d**) the global adjustments of edge lines in post processing and (**e**) the output edge lines.  
(1) Rasterization  
The main task of this section is to divide the wall points into predefined grids and form a density image, in which the image grays represent the number of points within the grid. The image boundary is determined based on the minimal and maximal X/Y coordinate values, and the grid size is set as twice the average point density. For each pixel \(p\) in the density image, three elements are estimated, marked as \(p(pos,nv,weight)\), where \(pos\) is the gravity center of all of the projected 2D points within the grid, \(nv\) is the median value of these points' direction angles (\(-\pi/2\) to \(\pi/2\)) based on the point normal vectors, and \(weight\) is simply the point number.  
(2) Non-Maximum Suppression  
In the classic Canny algorithm, the suppression approach is realized based on the idea that the best edge pixels have the maximum gray gradient compared to their neighbors. Thus, the method deletes those non-maximum pixels and ensures that the width of the edges is just one pixel. In our work, the wall points have the maximum point density compared to their neighbors, and we can use a similar operation to remove points from windows and other wall attachments. The main issue here is to decide the suppression orientation and range. For the building wall, it is obvious that the orientation should be consistent with the wall's normal vectors. Under such consideration, the \(nv\) described in the above section is used, and we search for a region of 10 pixels. Moreover, we did not remove all of the non-maximum grids, but maintained those pixels that have a density greater than eighty percent of the maximum value.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 3 Preliminaries
### Elliptic Curve Discrete Logarithm Problem  
Let G be an elliptic curve group defined by a generator \(P\) and a prime number \(p\). Then, the two central mathematical problems in elliptic curve cryptography (ECC), namely, the discrete logarithm problem and the computational Diffie-Hellman assumption, can be defined as follows [19].  
Definition 1: Elliptic curve discrete logarithm (ECDL) problem. Let \(Q=aP\), where \(Q,P\in G\) and \(a{\in}_{R}Z_{p}^{*}\). The objective of the elliptic curve discrete logarithm problem is to find a when given two points \(Q,P\in G\).  
Definition 2: Elliptic curve computational Diffie-Hellman (ECCDH) assumption. Let \(G\) denote a representative group of order \(p\) and \(A\) denote an adversary. Consider the following experiment:  
\[\begin{array}{l}ExperimentExp_{G}^{\text{ECCDH}}(G,P,p),\\ Q_{1}=r_{1}P,Q_{2}=r_{2}P,r_{1},r_{2}{\in}_{R}Z_{p}^{*},\\ Q=A_{\text{ECCDH}}(Q_{1},Q_{2}),\\ ifQ=r_{1}\cdot r_{2}\cdot P,b=1,else\ b=0,\\ return\ b.\end{array}\]  
The advantage of \(A\) in solving the ECCDH problem is defined as follows:  
\[Adv_{G}^{\text{ECCDH}}(A)=\Pr[Exp_{G}^{\text{ECCDH}}(G,P,p)=1],\]  
\[Adv_{G}^{\text{ECCDH}}(t)=\max\left\{Adv_{G}^{\text{ECCDH}}(A)\right\},\]  
where the maximum is taken over all \(A\) with time-complexity at most \(t\).
# Xanthone Derivatives in the Fight against Glioblastoma and Other Cancers
## 2 Antineoplastic Activity of Xanthone Derivatives
### Norswertianin  
Norswertianin (NOR, **16**, Figure 8A) and other similar xanthones with a 1, 3, 7, 8-oxygenation pattern, as well as O-glicosilated forms of these compounds, were found in _Gentiana dinarica Beck. G. dinarica_ plant. This endemic plant was genetically modified in order to obtain higher xanthones accumulation and faster root growth. Norswertianin and Norswertianin \(O\)-glicoside (NOR-O-P, **17**, Figure 8B) were investigated for antiglioma activity. The results of the crystal violet assay indicated that NOR and NOR-1-O-P inhibit **U25 glioblastoma cells** growth in a dose-dependent manner (**IC\({}_{50}\) = 31.2 \(\upmu\)M** for NOR and **48.3 \(\upmu\)M** for NOR-1-O-P, respectively) [69].
# On Non-Completeness and G-Equivariance
## 3 Non-Completeness Implies 1 st-Order DPA Security?
For example, consider the masking scheme by [PERSON] described in Figure 1. First, it can be proved that all the intermediate results of the scheme, say, \(x^{0}y^{0}\), \(x^{1}y^{0}\), \(x^{0}y^{1}\), \(x^{1}y^{1}\), \(r\oplus x^{0}y^{0}\), \((r\oplus x^{0}y^{0})\oplus x^{1}y^{0}\), \(((r\oplus x^{0}y^{0})\oplus x^{1}y^{0})\oplus x^{0}y^{1}\), \(((r\oplus x^{0}y^{0})\oplus x^{1}y^{0})\oplus x^{1}y^{1}\) are statistically independent of the original input and output. More precisely, for any \(\alpha,\ \beta\in\mathrm{GF}(2)\), if \(\tau\in\big{\{}x^{0}y^{0},\ x^{1}y^{0},\ x^{0}y^{1},\ x^{1}y^{1}\big{\}}\), then \(\Pr(\tau=0)=\Pr(\tau=0\big{|}(x,y)=(\alpha,\ \beta)\big{)}=\frac{3}{4}\), while if \(\tau\in\big{\{}r\oplus x^{0}y^{0},\ (r\oplus x^{0}y^{0})\oplus x^{1}y^{0},\ ((r\oplus x^{0}y^{0})\oplus x^{1}y^{0})\oplus x^{0}y^{1},\)\((((r\oplus x^{0}y^{0})\oplus x^{1}y^{0})\oplus x^{0}y^{1})\), then \(\Pr(\tau=0)=\Pr(\tau=0\big{|}(x,y)=(\alpha,\ \beta))=\frac{1}{2}\). Similarly, we can prove that \(\Pr(\tau=0)=\Pr(\tau=0\big{|}xy=\alpha\beta))\) for any \(\alpha,\ \beta\) and \(\tau\in\big{\{}x^{0}y^{0},x^{1}y^{0},x^{0}y^{1},\)\(x^{1}y^{1},\)\(r\oplus x^{0}y^{0},\ (r\oplus x^{0}y^{0})\oplus x^{1}y^{0},\ ((r\oplus x^{0}y^{0})\oplus x^{1}y^{0}) \oplus x^{0}y^{1}\big{\}}\). Thus, the presupposition of Corollary 1 is satisfied for the [PERSON]'s scheme; however, the scheme is also known to be susceptible to the glitch attack [7; 8; 9], which contradicts the conclusion of Corollary 1. **Remark 1**. : _We note that the security guarantee of TI given in [11] is against the 1 st-order univariate DPA. Thus, the attackers are assumed to be able to utilize only the mean value of power traces gathered at a specific time moment. And, the attackers who rely on the variance value of gathered power traces or power traces gathered at several time moments are called the higher-order DPA attackers, and [11] and this paper do not consider such attackers._  
At this point, it is emphasized that we do not claim that the non-complete TI with uniform inputs has some power attack weaknesses. Actually, the non-complete TI is shown to be very secure in various leakage detection tests [10; 11; 12; 13]. Thus, it is plausible that TI gives a lot of resistance to DPA, even in the presence of glitches. However, as the previous argument shows, it is not clear why it gives such security from a theoretical viewpoint. After all, the problem is that [11] does not contain any theoretical explanation of how we can mitigate the glitch effect. And, as will be explained in the next section, the G-equivariance [14] gives a useful instrument for how to mitigate such an effect. However, G-equivariance also has its own drawbacks. Most notably, there are no (1 st-order) G-equivariant gates, the XOR sum in which the output gives rise to the ordinary 2-input AND gate evaluation, and that is why we should use the extended concept of G-equivariance to the higher-order setting in [16]. More details can be found in Section 4.
# Overview of the Development of 3D-Printing Concrete: A Review
## 3 Performance Requirements of 3D-printing Concrete Materials
### The Durable Performance of 3D-Printing Concrete  
There are fewer reports on the durability of 3D-printing concrete materials, but this is a very important performance indicator, which directly affects the safety of 3D-printing concrete buildings and building structures. Once a problem occurs, it will not only waste a lot of manpower and material resources, but also threaten life safety. Thus, it is necessary to study the durability of 3D-printing concrete materials.  
In 3D-printing buildings, there are many factors that affect the durability of materials, such as temperature, external forces, and chemical effects. Developing materials with good anti-seepage and anti-freezing properties can ensure the durability of the structure. [PERSON] et al. [49] used peeling test, four-point bending test, and compressive strength test to study the fire resistance and high temperature resistance of 3D-printing fiber-reinforced cement-based composites, and found that PVA fiber can effectively prevent cement-based materials from flaking and bursting when exposed to high temperatures. The bending and compressive strength of 3 DPFRCC at different temperatures are higher than those of 3D printed plain concrete, as shown in Figure 10. [PERSON] et al. [50] tested the freeze-thaw resistance and carbonization resistance of 3D printed cement-based materials formed by the powder bonding process. Results showed that the freeze-thaw resistance properties of 3D printed cement-based materials meet the standard requirements. With normal or increasing CO\({}_{2}\) concentration, the specimen did not undergo carbonization.