esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images
## 3 Methodology
### Joint Clustering  
However, data distribution is not well distributed in the original data space because of huge intra-variance, as shown in Figure 5. The traditional clustering algorithm performs poorly because it cannot provide the nonlinear transformation from the original data space to the latent feature space.  
To resolve this problem. We utilize the deep-stacked auto-encoder, a kind of deep neural network to map original data space to embedding, which is suitable for clustering tasks. In the FIDC network, the encoder is a nonlinear mapping function, and the decoder control reconstruction error and demands high accuracy. We perform this task iteratively, which ensures that the nonlinear mapping function is effective and stable for latent space data representation. The illustration is shown in Figure 5.  
The clustering layer in the proposed method transforms the original input feature x into soft labels represented by \(q_{ij}\). There are k clusters, n data points, and initial cluster centers \(\mu_{j}\) calculated using K-means. The clustering is performed on the compressed feature representation \(Y\) which is of much lower dimension than the original input \(x\). The probability between cluster centers \(\mu_{j}\) and embedded points \(y_{j}\) i.e., corresponding to \(x_{i}\), is measured using Student's t-distribution [16; 17; 30]. This results in a soft assignment \(q_{ij}\). The clusters are refined iteratively by learning from high-confidence assignments through the use of an auxiliary target distribution, as illustrated in Figure 6.  
\[q_{ij}=\frac{\left(1+\left\|y_{i}-\mu_{j}\right\|^{2}\right)^{-1}}{\sum_{j} \left(1+\left\|y_{i}-\mu_{j}\right\|^{2}\right)^{-1}} \tag{6}\]  
Figure 5: An instance of the clustering layer. The left box shows the original data space where we can see the different positions of digits. Due to huge intra-variance, it seems complicated to perform a clustering task. These digits are mapped to some latent feature spaces. The oval in the upper right corner displays a linear mapping, and the one in the lower right corner shows a nonlinear mapping.  
Additionally, \(p_{ij}\) target distribution is defined as,  
\[p_{ij}=\frac{q_{ij}^{2}/\sum_{i}q_{ij}}{\sum_{j}q_{ij}^{2}/\sum_{i}q_{ij}} \tag{7}\]  
We describe our goal as a loss of KL divergence between \(p_{ij}\) target distribution and soft assignments \(q_{ij}\). The encoder is fine-tuned by optimizing an objective, resulting in the prediction of the label for \(x_{i}\) is \(\text{argmax}_{i}q_{ij}\).  
\[L=KL(P|Q)=\sum_{i}\sum_{j}p_{ij}log\frac{p_{ij}}{q_{ij}} \tag{8}\]  
where \(KL\) denotes Kullback-Leibler divergence loss measures the non-symmetrical differences between, soft assignment \(q_{i}\) and target distributions \(p_{i}\).
# Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code
###### Abstract  
The image-based data hiding method is a technology used to transmit confidential information secretly. Since images (e.g., grayscale images) usually have sufficient redundancy information, they are a very suitable medium for hiding data. Absolute Moment Block Truncation Coding (AMBTC) is one of several compression methods and is appropriate for embedding data due to its very low complexity and acceptable distortion. However, since there is not enough redundant data compared to grayscale images, the research to embed data in the compressed image is a very challenging topic. That is the motivation and challenge of this research. Meanwhile, the Hamming codes are used to embed secret bits, as well as a block code that can detect up to two simultaneous bit errors and correct single bit errors. In this paper, we propose an effective data hiding method for two quantization levels of each block of AMBTC using Hamming codes. [PERSON] and [PERSON] introduced a method of applying Hamming (7,4) to two quantization levels; however, the scheme is ineffective, and the image distortion error is relatively large. To solve the problem with the image distortion errors, this paper introduces a way of optimizing codewords and reducing pixel distortion by utilizing Hamming (7,4) and lookup tables. In the experiments, when concealing 150,000 bits in the Lena image, the averages of the Normalized Cross-Correlation (NCC) and Mean-Squared Error (MSE) of our proposed method were 0.9952 and 37.9460, respectively, which were the highest. The sufficient experiments confirmed that the performance of the proposed method is satisfactory in terms of image embedding capacity and quality.  
Keywords:data hiding; AMBTC; BTC; Hamming code; LSB +
Footnote †: journal: Applied Physics Letters
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 3 Vulnerability of Deep Learning-Based Medical Image Analysis
### Adversarial Attacks against Medical Images
#### 3.1.1 FGSM (Fast Gradient Sign Method)  
The first proposed adversarial attack was the FGSM (fast gradient sign method) [74]. FGSM is a white-box attack that provides computer vision systems with hostile examples. In order to improve the loss function, this approach extracts the adversarial gradient and changes the values of the pixels. For a one-step update along the gradient descending direction, it perturbs a clean sample. The attack is designed as follows:  
\[x^{\prime}=x+\varepsilon\times\text{sign }(\
abla_{x}\operatorname{J} \left(\theta,x,y\right)) \tag{1}\]where \(x\) is the input image, \(y\) is the label and \(\theta\) represents the weights of the model. Moreover, \(\epsilon\) is the magnitude of perturbation, J (\(\theta,x,y\)) is the gradient loss, sign (\(\cdot\)) is the sign function and \(\
abla_{x}\) (\(\cdot\)) is the gradient w.r.t. \(x\).
## 3 Discussion
Limonene has been previously found in a lower percentage in EOs of other _Iris_ species, such as _I. bulleyana_ and _I. nigricans_ rhizome EOs (1.65% and 2.02%) [23; 29], whereas 1,8-cineole was found in a smaller percentage (2.0%) in _I. persica_ rhizome EO [21]. Conversely, compared to _I. adriatica_, which was examined in our study, _I. persica_ EOs from the flowers, leaves, rhizomes, and bulbs contained more furfural (13.8-39.0%) [21]. Furfural is a heterocyclic aldehyde that is probably formed during the hydrolysis of hemicellulose (depending on the type of biomass), and it can be used as a flavoring agent in the food industry, among other things [63]. Limonene and 1,8-cineole, with their lemon-like and eucalyptus aromas, are also recognized as flavoring compounds [64; 65]. [PERSON] et al. reported on the composition of Chinese EO obtained by hydrodistillation from rhizores of _I. bulleyana_ in which aristolone and cuparene were found to be the most abundant components [23]. In a study of the EO isolated from aerial fresh blooms and rhizomes of _I. nigricans_, at the pre-flowering stage, aliphatic hydrocarbons and their derivatives and oxygenated monoterpenes were the dominant compounds in the EO obtained from aerial parts, while at the post-flowering stage, as determined by GC-MS,aliphatic hydrocarbons and their derivatives prevailed. On the other hand, the EOs of rhizomes harvested after flowering were characterized by monoterpene hydrocarbons and oxygenated monoterepenes [29]. Since terpen isolated by HD accounted for less than 10% of the total identified VOCs in five out of six of the studied samples, the HS-SPME method may generally be considered more suitable for their extraction from _Iris_ samples. However, several oxygenated monoterepenes and monoterepne hydrocarbons, including terpinen-4-ol, \(\alpha\)-terpineol, myrtenol, and \(\alpha\)-pinene, were in fact detected in higher percentages in one sample of _I. pseudopallida_ EO. Considering the large number of VOCs that have been identified for the first time in this study, we believe that _I. pseudopallida, I. illlyrica_, and _I. adriatica_ show potential for additional research, which could provide evidence for their potential pharmaceutical and cosmetic applications, especially considering their observed irone and/or long-chain fatty acid contents.
## 2 Theoretical Background
### Convolutional Neural Networks  
CNNs have become one of the most common supervised learning techniques [34]. They can learn complex patterns from unstructured data (i.e., text or images) with limited domain knowledge. By leveraging the convolution operation, CNNs can perform their task on two- or higher-dimensional inputs. They can consider the neighboring region around a pixel, making them well-suited for image applications [35].  
They have found success in Medical Image Analysis (MIA) applications, namely in cancer-related problems [25]. In the prostate region, deep CNNs achieved better performance when compared with non-deep CNNs [36] for PCa classification tasks. For this task, CNNs have also been used to extract discriminative features from T1w and DCE sequences [37], from 3D features extracted either from MRI sequences [38] or Gleason Score (GS) prediction based on Transrectal Ultrasound (TRUS)-guided biopsy results [39; 40]. CNNs have also been used with U-Net inspired architectures [41; 42] for PCa segmentation.  
At the most general level, CNNs have been used in almost every anatomic area of the human body (e.g., brain, eyes, torso, knees) for various tasks (disease location, tissue segmentation or survival probability calculation) with a high degree of success [25].  
For instance, XmasNet was developed by [PERSON] et al. [33] specifically for the PROSTATEx Challenge 2017 [43], inspired by the Visual Geometry Group (VGG) net [44]. Despite its relative simplicity, it achieved state-of-the-art results, outperforming 69 methods of 33 groups and having the second highest Area Under the Receiver Operating Characteristics Curve (AUROC) on the unseen test set. XmasNet is a relatively traditional architecture: four convolutional layers and two FC layers. The architecture also makes use of Batch Normalization, Rectified Linear Unit (ReLU) activation functions, and Max Pooling.  
Other architectures were compared in this work, namely AlexNet [34], VGG16 [44] and ResNet [45]. AlexNet was the winner of the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) [46], and its success revived the interest in CNNs in Computer Vision and brought about several novel implementations: ReLU non-linear activation functions, multiple GPU training, Local Response Normalization, and Overlapping Pooling, that are still used in current architectures. VGG16 is based on AlexNet and was the first truly deep CNN with 16 convolutional layers, made possible by the use of small convolutional filters and ReLU activation functions whenever possible [34]. It achieved first and second place in ILSVRC 2014 and serves as the inspiration of XmasNet. Lastly, ResNet, can be considered the deepest network among the architectures investigated in this study, with 50 layers. Its complexity is enabled by the use of residual connections between layers, which learn a reference residual mapping, making the training of arbitrarily deep CNNs theoretically possible.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 7 Application Control Policy
### Local Policy: Reinforcement Learning Based Policy  
To design the RL policy for the OM, we follow a similar approach to the one presented in Section 6.2. In this case, the RL policy learns the optimal strategy for scaling a DSP operator at run-time through experience and direct interaction with the system. The RL agent consider the state of a single DSP operator and performs scaling actions, aiming to minimize a long-term cost.  
We define the state of an operator based on the number of running replicas, and the measured average tuple arrival rate at the operator. For each state, we have the following reconfiguration actions: add a new operator replica (i.e., scale-out decision), remove an operator replica (i.e., scale-in decision), and a _do nothing_ decision. We also consider that not all of these actions are available in all the states; indeed, we guarantee that at least one replica should be executed and that the operator has a maximum allowed replication degree.  
As for the ICS, the RL agent is driven by a long-term cost, that depends on the immediate cost function. For the DSP operator, the immediate cost function captures three different terms:  
* The reconfiguration cost \(c_{rcf}(a)\): Whenever the system carries out scale-out or a scale-in operation, the operator suffers a downtime period during which no tuple is processed.
* The performance penalty \(c_{perf}(s,a,s^{\prime})\): This is paid whenever the operator response time exceeds a per-operator bound \(R_{max,op}\).
* The resource cost \(c_{res}(s,a)\): This accounts for the cost of the computing resources used to run the operator replicas. For simplicity, we assume that we have a constant cost per operator replica.  
We combine the different costs into a single cost function as the weighted sum of the costs:  
\[w_{rcf}c_{rcf}(a)+w_{perf}c_{perf}(s,a,s^{\prime})+w_{res}c_{res}(s,a) \tag{6}\]  
where \(s\) and \(s^{\prime}\) represent the operator state before and after applying the reconfiguration action \(a\), respectively; and \(w_{rcf}\), \(w_{perf}\), and \(w_{res}\), with \(w_{rcf}+w_{perf}+w_{res}=1\) are non-negative weights for the different costs. As the OM aims to minimize the incurred cost, the cost function suggests: (i) reducing the number of requested reconfigurations; (ii) keeping the response time within the given bound; and (iii) limiting the resource usage, with the different weights expressing the relative importance of each term.  
As regards the definition of the bounds \(R_{max,op}\), we observe that they grant a share of the global bound \(R_{max}\) to each operator according to their computational weight. They could be set either statically after preliminary profiling, or dynamically estimated and adapted at run-time by the AM.
## 5 Performance Evaluations and Discussion  
### M-QAM 4-SSB Uncoded System Using Shadow Equalizer Evaluation and Complexity Analysis  
#### 5.1.1 Complexity Analysis  
The equalization problem can be seen as an iterative MMSE algorithm based on the shadow area principle. As \(N\) is the frame length, the first iteration includes the computation of a matrix inversion with the complexity of order \(N^{3}\). For the other iterations, the complexity order is about \((N-n_{i})N^{2}\) where \(n_{i}\) is the number of symbols considered as reliability over the iteration \(i\). Therefore, the total complexity is about \(\mathcal{O}\!\left(N^{3}\right)\) dominated by the complexity of the first iteration.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 5 Improved Scheme
### Password Change  
When a user \(U_{i}\) wants to update his password, the optional password change procedure will be invoked. The steps of this procedure are described as follows:  
1. \(ID_{i}\) and \(PW_{i}\) are input to \(OBU_{i}\) by \(U_{i}\).
2. First, \(OBU_{i}\) retrieves \(A_{i}=h(PW_{i})\oplus B_{i}\). Then, it checks whether \(D_{i}=h(ID_{i}||PW_{i}||A_{i})\) holds. If so, \(U_{i}\) will be requested to input his new password \(PW_{i}^{*}\). \(OBU_{i}\) computes \(B_{i}^{*}=B_{i}\oplus h(PW_{i})\oplus h(PW_{i}^{*})\) and \(D_{i}^{*}=h(ID_{i}||PW_{i}^{*}||A_{i})\) and replaces \(B_{i}\) and \(D_{i}\) with \(B_{i}^{*}\) and \(D_{i}^{*}\). Otherwise, the request will be rejected.
## Materials and Methods
### Working Standard Solution for TSNAs  
For the calibration analysis of TSNAs, primary standard (PS) solutions containing 1000 ug mL\({}^{-1}\) (\(\pm\))-N\({}^{\prime}\)-nitrosonomicotine (NNN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), (s)-\(N\)-nitroosanabasine (NAB) dissolved in methanol, and 5 mg of \(N\)-nitroosanatabine (NAT) powder were purchased (Certified Reference Materials for NNN, NNK, and NAB, Sigma-Aldrich Co., St. Louis, USA). First, NAT powder was dissolved in 99.8% methanol (Sigma-Aldrich Co.) to prepare a 5 ug mL\({}^{-1}\) solution (NAT-LS). Liquid working standards (L-WS) were then prepared by diluting three PS (NNN, NNK, and NAB) and NAT-LS with 99.9% acetonitrile (ACN, Sigma-Aldrich Co.) at seven concentrations (0.5, 1, 2, 5, 10, 50, and 100 ng mL\({}^{-1}\)) in 2 mL vials. The basic physicochemical information of the four target TSNAs to be analyzed is shown in Supplementary Table S4 online. For a background sample (blank EC solution), a mixture of propylene glycol (PG, USP, Sigma-Aldrich Co.) and glycerin (VG, USP, Sigma-Aldrich Co.), which are the main components of EC, was prepared at a weight ratio of 1:1.
## 1 Introduction
However, these studies often focus on single dimensions, such as digital technologies [18], corporate management [19; 20], or environmental policies [21; 22]. For the construction industry, which operates within the broader framework of urban economic activities, its carbon reduction effects are influenced not only by technological advancements and organizational factors but also by macro-level drivers such as urban socioeconomic development and environmental regulations [23]. These macro-level factors are particularly significant against the backdrop of \"new quality productivity\" (NQP) emerging as a critical driver of digital and green transformations across industries. Nevertheless, research on the synergistic effects of green technological innovation of the construction industry remains insufficient. Specifically, green technological innovation not only directly promotes carbon reduction but also achieves synergistic effects through continuous technological iteration and adaptive development. Such synergies are particularly pronounced from the perspective of NQP. Furthermore, existing studies have highlighted significant regional disparities in the effects of green technological innovation on carbon emissions in the construction industry [24]. In the vast context of China, regions exhibit considerable variation in resource endowments, innovation capacity, and productivity levels. This regional heterogeneity exacerbates the spatiotemporal differentiation observed in the adoption of green technologies and the transition to low-carbon construction [23]. Thus, exploring the coupling and coordination relationships between regional green technological innovation and carbon reduction in the construction industry from the perspective of NQP is a critical issue that warrants further investigation. Based on this premise, this study employs dynamic panel data from 30 Chinese provinces spanning 2013-2021. Combining econometric models, coupling coordination models, kernel density estimation, and the Dagum Gini coefficient, the study systematically analyzes the synergistic evolution and differentiated characteristics of NQP, regional green technological innovation, and carbon reduction in the construction industry. The remainder of the paper is structured as follows: Section 2 provides a literature review; Section 3 explains the research methods and data sources; Section 4 presents the empirical results; Section 5 offers a systematic discussion based on the findings; and Section 6 concludes the study, highlighting its limitations and proposing directions for future research.
## 4 Evaluation
### Method for Evaluating Simulation Results
The eyeball is not a perfect sphere; therefore, the shape of the transformed plane shown in Figure 6 is not a perfect rectangle. The stimulated area is indicated by yellow to orange color in the plane, while the unstimulated area is indicated by blue color in the plane. If the stimulated area is below the half-line on the vertical axis, this means that the lower part of the eyeball surface is being stimulated correctly. Moreover, the range from 0 to \(\pi\) on the horizontal axis is the anterior side of the eyeball surface. Therefore, by dividing the range from 0 to \(\pi\) into three areas, it is possible to evaluate whether the temporal, central or nasal region of the anterior eyeball surface is being stimulated.
# Antioxidant Production in _Dunaliella_
## 7 Challenges and Future Opportunities  
Despite the potential of _Dunaliella_ as a source for high-value compounds including antioxidants, the main challenges are as follows:  
1. Low growth rate and productivity of _Dunaliella_ strain [4; 28; 174] relative to other sources (such as fungus, yeast) [178; 179; 180; 181] and selection of a suitable strain;
2. Transferring lab-scale optimised abiotic stress strategies to a commercial scale;* Seasonal and environmental variation in biomass production, and the control of competitors in open ponds cultivation systems;
* Engineering developments of sustainable large-scale technologies (open raceways ponds and PBR) for _Dunaliella_ and microalgae in general for efficient biomass and enzymatic antioxidant production;
* Developing sustainable downstream technologies (harvesting, extraction, and purification) for antioxidant enzymes production at commercial scale;
* Establishing tools for proteomics, genomics, and metabolomics, and implement them to develop strains for the accumulation of intracellular antioxidant enzymes;
* Stabilisation of antioxidant enzymes in food ingredients and extension of their shelf life;
* Algae extracts can be used as taste enhances in foods; however, some strains are associated with a bitter or stringent taste;
* Introducing a general platform to solve regulatory and labelling issues, as this varies from country to country.  
Based on the reviewed literature, emphasis has been on carotenoid production from _Dunaliella_, limiting the exploration of _Dunaliella_ biomass to produce other high-value compounds such as antioxidant enzymes. The tolerance of _Dunaliella_ to extreme environments (such as high salinity and high light levels) makes these strains versatile. It enables them to survive in stressful environments, providing opportunities to explore these strains as natural antioxidant enzyme sources either as a main product or by-products. In addition, there is an opportunity to improve technological developments in harvesting biomass by employing cost-effective processes (such as flocculation or microbubbles techniques). Due to the lack of a rigid cell wall, the extraction process is cheaper relative to other algae cells or plants, which will reduce downstream processing costs. As antioxidant enzymes in wet _Dunaliella_ biomass can be retained for one month when stored at \(-20\)\({}^{\circ}\)C [175]. The development of an extraction procedure for antioxidant enzymes from wet biomass can save the energy costs required for drying the biomass.  
Techno-economic assessment can be conducted to assure the profitability of developed cultivation and downstream processing systems to produce specific antioxidants enzymes or biorefinery strategies. There is also an opportunity to explore novel applications of _Dunaliella_-derived antioxidant enzymes in the pharmaceuticals, nutraceuticalals, agricultural and cosmetic industries. Growing end-user applications for antioxidant enzymes and increasing end-user awareness may enhance the demand in the global market.