esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Stability of the Fractions  
Differential scanning calorimetry (DSC) analysis revealed that the flour protein fractions displayed endothermic events at denaturation temperatures (\(T_{d}\)) and \(T_{g}\) (glass transition temperature) that were greater than those of the LPC fractions (Figure 4 and Table 3). The observed behaviour was attributed to differences in protein content between the flour and LPC fractions and possibly residual co-products that were not completely removed during the fractionation process of flour. Notably, proteins extracted from plant materials rich in cellulose, hemicellulose, and dietary fibre often significantly discard these components [22]. Nevertheless, these findings suggest that these materials were partially eliminated during flour fractionation. Consequently, the presence of cellulose (200 to 220 \({}^{\circ}\)C), hemicellulose (40 \({}^{\circ}\)C), lignin (50 to 100 \({}^{\circ}\)C), or pectin (16.8-24.6 \({}^{\circ}\)C) in the protein fractions of flour could increase the \(T_{g}\)[34]. In that sense, the globulin (flour and LPC) and prolamin (flour) fractions had the highest \(T_{g}\) and \(T_{d}\) values compared to other fractions, indicating significant co-products in these fractions. Additionally, variations in protein structure and the interactions of proteins with residual salts contribute to the high \(T_{g}\) and \(T_{d}\)[35]. Particularly in the case of globulins extracted in NaCl solution, remnants of salts could also explain this behaviour, as well as the lack of detection of amino acids and poor functional properties. Furthermore, the albumin, prolamin, and glutelin fractions of LPC displayed higher \(T_{g}\) values than those reported for LPC (130.8 \({}^{\circ}\)C) prior to fractionation, indicating enhanced thermal stability in protein fractions and \(T_{g}\) values comparable to those reported for their protein hydrolyses (134.0-142 \({}^{\circ}\)C) [18]. All flour fractions exhibited two endothermic events at denaturation temperatures higher than those of the LPC fractions (Table 3), which were attributable to the thermal transition of proteins. In contrast, albumin and globulin isolated from LPC exhibited a  
Figure 4: Differential scanning calorimetry (DSC) analysis of protein fractions of jackfruit flour and LPC (leaf protein concentrate). (**A**) Albumin, (**B**) globulin, (**C**) prolamin, and (**D**) glutelin. \(T_{g}\): transition glass temperature.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 4 Experiment
### Datasets
#### 4.1.1 DZGSet Dataset
Examples from the DZGSet dataset are depicted in Figure 5.  
Data analysis: The DZGSet dataset consists of 4240 frames of synchronized point cloud and image data, meticulously organized to facilitate reproducibility. It is divided into training, validation, and test sets in a 60%, 20%, and 20% ratio, respectively. The dataset features a total of 24,676 annotated objects, averaging six objects per frame. Data collection occurred during daytime under two weather conditions, sunny and cloudy, with 64% of the frames captured in sunny conditions and 36% in cloudy conditions. In terms of object distribution, the frames contain 12,920 pedestrians, 7708 cars, and 4048 cyclists. Pedestrians constitute the majority at 52%, while cars and cyclists represent 31% and 17%, respectively. In contrast to other cooperative perception datasets that focus solely on vehicle categories, DZGSet enriches categories by including pedestrians and cyclists, which are often considered smaller objects in the context of autonomous driving. This enhanced diversity significantly increases the dataset's value for developing robust perception systems in complex urban environments. Figure 5: Examples from the DZGSet dataset. (**a**) Infrastructure image with 2D annotation. (**b**) Vehicle image with 2D annotation. (**c**) Infrastructure point cloud with 3D annotation. (**d**) Vehicle point cloud with 3D annotation.
## 2 Overview
### Sensing Parameters and Sensors  
The operating principle of sensors is to respond to changes in their environment by producing an electrical signal in the form of voltage, current, or frequency [31]. Sensors can commonly be divided into physical sensors and chemical sensors. In a marine monitoring system, physical sensors are used to measure some physical parameters, such as temperature, humidity, pressure, wind speed and wind direction, and chemical sensors are used to sense various chemical parameters (salinity, turbidity, pH, nitrate, chlorophyll, dissolved oxygen (DO), _etc._) as shown in Table 1.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  
The right choice of marine environment monitoring sensors depends on the user requirements of deployment area, measurement range, accuracy, resolution, power consumption, and intended deployment time.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### COBIT 5 for Cloud Security  
ISACA (Information Systems Audit and Control Association) developed COBIT 5 is a worldwide recognized methodology for enterprise IT governance and management [27]. While COBIT 5 is not created expressly for cloud security, it does present a complete set of principles and practices that can be used to successfully regulate and safeguard cloud computing systems [28]. It provides governance, risk management, and control objectives that allow organizations to successfully manage and secure cloud services.
# Multifactor Uncertainty Analysis of Construction Risk for Deep Foundation Pits
###### Abstract  
As it is affected by many uncertain factors, the construction risk of deep foundation subway station pits involves fuzzy and random uncertainties. Considering the fuzzy and random uncertainties involved in risk evaluation, an improved fuzzy comprehensive evaluation method combining a triangular cloud model and the probability density function (PDF) is proposed in this study. First, with reference to the actual situation of deep foundation pit construction, the sources of construction risk are identified, and a construction risk evaluation index system is established. Second, the Delphi method is used to analyse the importance of each index of the evaluation object in order to obtain the evaluation data. The fuzzy best worst method (FBWM) is used to calculate the weight of the evaluation indices. Then, the triangular cloud model is used to represent the risk grade membership function. In addition, the fuzzy comprehensive evaluation method is used to comprehensively evaluate the construction risk of deep foundation pits. The fuzzy comprehensive evaluation vector is obtained for the indices possibility (\(P\)) and loss (\(C\)), and the weighted average value of the vector's risk grade is calculated. Finally, probability analysis is carried out using PDF to determine the risk grade of \(P\) and C, and thus, to determine the risk grade of deep foundation pit construction. This method optimises the risk evaluation process of deep foundation pit construction and realises the visualisation of the comprehensive evaluation results, making the risk evaluation process transparent and convenient for use by risk analysts. This method is applied to predict the construction risk grade of a deep foundation pit project in Nanning, China, and the prediction results are consistent with the actual situation.  
foundation pit engineering; uncertainty analysis; construction risks; fuzzy evaluation; cloud model +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
The experimental results presented in this paper investigate the effect of several parameters on the behavior of anchors embedded in solid brick masonry and subjected to tension. The parameters that are investigated are the following: (i) compressive strength of mortar; (ii) anchor location (middle of brick or mortar joint); (iii) absence or presence of crack close to or crossing the anchor, as well as the value of the crack width; (iv) the type of loading protocol, monotonic vs. repeated. It is noted that although the conclusions related to the effect of the investigated parameters are of general validity, the arithmetic values expressing this effect are dependent on the specific characteristics of the materials used in this research, as well as on the values of the investigated parameters. The main conclusions of this investigation are as follows:  
1. The mortar quality, and more specifically, its compressive strength, affects the behavior of anchors in tension. Indeed, anchors installed in masonry walls made with weak mortar exhibited reduced capacity compared to the ones installed in walls constructed with strong mortar. This reduction is not proportional to the compressive strength of the mortar; it is more likely proportional to its square root, i.e., proportional to the tensile strength of the mortar. On the other hand, the effect of masonry mortar quality depends on the anchor location (brick or mortar) and on the occurring failure modes. More specifically, the reduction of the tension resistance is more pronounced in the case of anchors installed in mortar joints than for anchors installed in bricks (average reduction factor equal to 0.50 and 0.70 for mortar joint and brick location, respectively). It was observed, however, that the difference between the two examined locations, in terms of the effect of the mortar quality seems to be reduced, when there is a crack either crossing the anchor or at its vicinity (reduction factor equal to 0.75 and 0.70 for mortar joint and brick location respectively). This shows that the effect of cracks governs the behavior of the anchors in tension. 2. The presence of a crack, 0.50 mm wide, has led to a substantial reduction of the tension capacity of anchors for both tested locations (between 30% and 50%, depending on the anchor location and on the compressive strength of the masonry mortar), accompanied by an increase in the displacement corresponding to the attainment of the resistance. In parallel, the scatter of both resistance and corresponding displacement values is increased when the masonry is cracked. 3. Before making the decision to limit the crack width to 0.50 mm, based on an assessment of the relevant literature, the effects of larger crack values were also investigated. As expected, regardless of the anchor location, the increase in the crack width leads to a
# Attention-Aware Adversarial Network for Person Re-Identification
## 4 Experimental Results
### Evaluation with the State-of-the-Art Algorithms  
We evaluated the proposed method with the state-of-the-art methods on two large-scale benchmarks. On the Market1501 dataset, 10 recent works, BOW [29], WARCA [32], SCSP [33], DNS [34], gated [20], PS [35], CCAFA [36], CA [37], spindle [17], and GAN [30], were evaluated with our baseline and the proposed AAA-Net. The evaluation results are shown in Table 6. On the DukeMTMC-reID dataset, five state-of-the-art methods, GAN [30], OIM [38], ACRN [39], PAN [40], and APR [41], were compared with our baseline and the proposed network. The performances are demonstrated in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Rank-1 (E)** & **Rank-1 (X)** & **Rank-1 (K)** \\ \hline baseline & 77.9 & 78.0 & 78.9 \\ without fine-tuning & 79.5 & 78.6 & 80.7 \\ \hline
**with fine-tuning** & **80.3** & **80.5** & **81.5** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of training the attention-aware adversarial network with or without fine-tuning.  
Figure 4: Occlusion examples generated by the attention map in the attention-aware adversarial network with the fine-tuning operation. The black mask represents the occluded area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Rank-1 (E)** & **Rank-1 (X)** & **Rank-1 (K)** \\ \hline baseline & 77.9 & 78.0 & 78.9 \\ random erasing & 79.1 & 79.3 & 80.2 \\ \hline
**attention-aware adversarial** & **80.3** & **80.5** & **81.5** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the attention-aware adversarial network and random erasing on the baseline network.  
According to the quantitative results, our AAA-Net improved the Rank-1 by 5.9% and the mAP by 5.4% on the DukeMTMC-reID dataset, respectively. On the Market1501 dataset, the AAA-Net improved the Rank-1 by 2.6% and the mAP by 3.2%, respectively. Furthermore, our proposed method outperformed other state-of-the-art algorithms by a large margin.
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 2 Object Detection  
Object detection plays a central and crucial role in the context of quality control processes enhanced by mixed reality. It serves as the foundation for seamlessly integrating real-world and digital environments, enabling meticulous quality assessment and assurance.  
In the realm of mixed reality, which combines elements from both physical reality and virtual environments, there is a need for a robust mechanism to identify, localize, and analyze objects within this merged context. Object detection technologies, often powered by advanced computer vision techniques, play a key role in precisely recognizing and categorizing relevant items of interest.  
The effectiveness of quality control processes relies heavily on the accurate and timely identification of specific items, defects, or anomalies. Object detection, positioned at the core of the framework, empowers the system to carry out these functions with precision. Through the real-time detection of objects and the overlay of pertinent digital information, such as annotations or diagnostic insights, the quality control process achieves a heightened level of thoroughness and accuracy.  
The evolution of object detection algorithms has progressed significantly over the years, spurred by advancements in deep learning and the demand for more accurate and efficient solutions. A pivotal distinction in this evolution is the categorization of algorithms into two-stage and one-stage approaches [20,21].
## 1 Introduction
### Estimation of Peak Inter-Story Drift Ratios  
In this study, based on a previous study [26; 28], the peak inter-story drift ratio \(\theta_{i}^{P}\) of the \(i\)th story of steel frames with steel hysteretic dampers is evaluated as follows:  
\[\theta_{i}^{P}=\sqrt{\left(PF_{1,i}^{I}\cdot Sd_{1}^{I}\right)^{2}+\sum\limits_{ j=2}^{n}\left(PF_{j,i}^{E}\cdot Sd_{j}^{E}\right)^{2}} \tag{1}\]  
where \(Sd_{j}^{E}\) is a \(j\)th modal elastic spectral displacement that is obtained from response spectrum analysis using a \(j\)th modal period and a \(j\)th modal damping ratio obtained from EVA. Additionally, \(PF_{j,i}^{E}\) denotes the participation function of an inter-story drift ratio for an elastic \(j\)th mode that is defined as follows:  
\[PF_{j,i}^{E}=\frac{\Gamma_{j}^{E}\cdot\left(s\phi_{j,i}^{E}-s\phi_{j,i-1}^{E} \right)}{h_{i}} \tag{2}\]  
where \(h_{i}\) and \(s\phi_{j,i}^{E}\) signify the height of the \(i\)th story and the elastic \(j\)th mode vector obtained from EVA of a steel frame with dampers, respectively. In this paper, the subscripts '\(s\)' and '\(f\)' before the symbols refer to the entire system and bare frame that is a multi-story frame without dampers, respectively. Furthermore, \(\Gamma_{j}^{E}\) denotes the participation factor for the elastic \(j\)th mode that is defined as follows:  
\[\Gamma_{j}^{E}=\frac{\sum\limits_{i=1}^{n}m_{i}\cdot s\phi_{j,i}^{E}}{\sum \limits_{i=1}^{n}m_{i}\cdot\left(s\phi_{j,i}^{E}\right)^{2}} \tag{3}\]  
where \(m_{i}\) denotes the mass at the \(i\)th story.  
In Equation (1), \(Sd_{1}^{I}\) implies an inelastic spectral displacement, estimated using the equivalent inelastic SDOF system with multi-springs that is described in Section 2.2. Moreover, \(PF_{1,i}^{I}\) denotes an inelastic participation function of the inter-story drift ratio evaluated via Equations (2) and (3) with \(s\phi_{j,i}^{E}\) replaced by \(\phi_{i}^{I}\), which is estimated using the pattern of story drifts obtained from NSPA at the \(N\)th step, corresponding to the \(Sd_{1}^{I}\) value obtained from NTHA using the equivalent inelastic SDOF system with multi-springs. The procedure for estimating the inelastic spectral displacement \(Sd_{1}^{I}\) and inelastic mode vector \(\phi_{i}^{I}\) is explained in Section 2.3.
# Aerospace
## 5 Single Small Satellite to Venus Orbit as Secondary Payload
### Tradespace Exploration and Baseline Mission Concept  
Figure 16 shows the drag-modulation aerocapture feasibility chart for Venus using a vehicle with \(\beta_{1}\) = 20 kg/m\({}^{2}\), \(R_{N}\) = 0.235 m, and target apoapsis = 2000 km. The drag modulation vehicle design presented in Section 3 with \(\beta_{2}\)/\(\beta_{1}\) = 7.5 is shown to provide the required TCW of 0.4 deg. for arrival \(V_{\infty}\) in the range of 3 to 5 km/s. The interplanetary trajectory used by the Akatsuki mission, with a launch date of 10 May 2010 and Venus arrival date of 6 December 2010, is used as a representative Earth-Venus trajectory [44]. The \(V_{\infty}\) vector in the ICRF is [\(-\)3.269, 0.676, \(-\)1.069] km/s, and the \(V_{\infty}\) magnitude is 3.51 km/s. The selected vehicle \(\beta_{2}\)/\(\beta_{1}\) and arrival \(V_{\infty}\) is indicated by the black star in Figure 16, which offers TCW \(>\) 0.4 deg. and keeps the peak heat rate at approximately 200 W/cm\({}^{2}\).  
Figure 15: Mars small satellite in 200 \(\times\) 2000 km near-equatorial orbit post aerocapture (green), and the host orbiter in 250 \(\times\) 70,000 km polar orbit after propulsive orbit insertion.
## 3 Results and Discussion
### TGA Analysis  
Figure 3 reveals the TGA analysis of the fluorinated silica materials. The decomposition temperature (T\({}_{\text{max}}\)) and the weight loss (Wt. loss %) of these materials are presented in Table 1. From this figure, it can be noticed that the thermal decomposition curves of the fluorinated silica materials through the TGA are divided into three thermal events. The first zone, occurring at a temperature of 30-100 \({}^{\circ}\)C, is attributed to dehydration of the water and of residual organic solvent and exhibits a weight loss of about 1-4%. The second zone is illustrated at the 100-240 \({}^{\circ}\)C range and displays a weight loss of approximately 3-8%. The intermediate temperature peak is frequently assigned to the condensation of non-hydrolyzed groups (\(\equiv\)Si-OH, \(\equiv\)Si-OCH\({}_{2}\)CH\({}_{3}\)) arising from the used silane precursors and from species derived from molecules physically entrapped inside the silica matrix [44]. The third zone, occurring at the 240-750 \({}^{\circ}\)C, range exhibits a weight loss of about 4-27%. The result shows that the sample synthesized with TEOS/MTES/FS (F1) has slightly lower slopes than the other sample prepared with TEOS/MTES/HDTMES/FS (F2), which means that the long alkyl chains slightly increased the stability of the prepared material. In previous data published in the literature [45], it was indicated that the values of the initial degradation temperature of the fluorinated composites without and with silica were 307 \({}^{\circ}\)C and 315 \({}^{\circ}\)C, respectively. This change was attributed to the presence of silica in the composites, and to formation of the Si-O-Si bonds. It was also shown that the oxidation of alkyl groups (responsible for the hydrophobic behavior of silica material) can occur at high temperatures [46].
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 1 Introduction
They used the C-MIMO scheme principle in NOMA to achieve this aim. Furthermore, while a low-complexity decoding technique for C-NOMA, which combined partial maximum-likelihood sequence estimation and the popular NOMA decoding method of SIC, was proposed in [11], an effective chaos-based NOMA (CB-NOMA) system for secure wireless communication was proposed in [12] over a Rayleigh-fading environment. Equal power allocation for linked users with a chaotic code domain is used in an integrated CB-NOMA design. Various chaotic-code-formation schemes with varying levels of security, implementation, and complexity have been studied for signaling via chaos shift keying (CSK). The aforementioned studies indicated that different coherent modulation techniques, such as chaos modulation [6; 11], CSK [6], and chaos-based code-domain MA [12], have been used over NOMA. To improve the security of the DL-NOMA scheme, a chaotic downlink NOMA (C-DL-NOMA) system is proposed in this study. The proposed system uses a coherent analog modulation technique that combines various chaotic maps for chaotic masking (CM) of encrypted data and SIC decoding for complexity reduction over additive white Gaussian noise (AWGN) and Rayleigh-fading single-input-single-output (SISO) DL power-domain NOMA channels. This study's main contributions are summarized below. * The effects of various hybrid chaotic maps on DL-NOMA performance are investigated using security analysis represented by statistical analyses, which include histogram and correlation analyses. * The proposed C-DL-NOMA system provides robust PLS and a low bit error ratio (BER) with fixed power-allocation-level control, depending on the distance from the base station (BS). * The proposed C-DL-NOMA system is compared with traditional unencrypted NOMA in terms of BER and achievable data rate. The rest of this paper is organized as follows: Section 2 explains the DL-NOMA system model; Section 3 presents a C-DL-NOMA system model and different chaotic maps; Section 4 explains and discusses the simulation results; and finally, Section 5 concludes this study.