esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Investigating the Modulation of Skin Volatile Profiles after Fragrance Application  
Comparison of total ion chromatograms obtained after fragrance application (Figure 1) revealed distinct temporal differences as fragrance evaporation took place on participants' skin. There were 51 compounds identified in skin volatile profiles comprising 19 endogenous and 32 fragrance-derived compounds, as outlined in Table 2. The suppression of several endogenous compounds was apparent, where 5 endogenous VOCs (2-decenal, 2-undecenal, 1-dodecanol, pentadecanal, and octadecanoic acid) present in baseline skin volatile profiles were not detected in the presence of the eau fraiche. Fragrances can modulate skin volatile profiles by reducing the formation of compounds arising from air oxidation and microbial transformation of skin gland secretions [2]. The observed suppression of endogenous VOCs was most prominent immediately after fragrance application where a number of oxidation products of skin gland secretions were not detected including aldehydes, acids, ketones, and hydrocarbons. There were seven compounds common to the volatile profile of all participants across all times investigated including two endogenous compounds (2,6-dimethyl-2,6-octadiene and _n_-hexadecanoic acid) and five fragrance-derived compounds (linalool acetate, \(\gamma\)-decalactone, methyl dihydrojasmonate, isopropyl tetradecanoate, and galaxolide). Other volatiles displaying a high frequency of occurrence within the group of participants included hexanal, \(\beta\)-myrce, _cis_-linalool oxide, \(\beta\)-linalool, undecanal, geranyl acetone, 5-cyclohexadecen-1-one, and 2-ethylhexyl4-methoxycinamate. The data shown in Table 2 reflects the ongoing evaporation of fragrance components on skin, where several species were no longer present in skin HS after 1 h including \(\beta\)-pinene, \(\alpha\)-pinene, \(\beta\)-terpineol, _trans_-2-pinanol, longfolene, and cedrene. Several other volatiles were no longer present in the majority of participants after 2 h, including \(\alpha\)-cital, \(\beta\)-cital, isosafrole, nerol acetate, and dihydro-\(\beta\)-ionone.
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
## 3 Proposed Two-Stage Cooperative Routing  
In this section, we firstly design a TSC transmission model and propose a two-stage link cost in which the core helper plays a significant role for each link. Then, the selection of core helper is detailed and a TSCR algorithm based on the optimized two-stage link cost is proposed to search the optimal cooperative shortest path. It is noted that the proposed TSCR is also designed based on the general routing model as Equation (1), in contrast with the existing schemes, the cooperative transmission model is redesigned and the link cost is reformulated to achieve a better cooperative routing.
## 2 Preliminaries
### Autoencoder  
Among current techniques for dimensionality reduction and information retrieval, an autoencoder (AE) is widely used not only as a nonlinear decomposition to replace traditional linear inner product but also as a representation learning mechanism [31]. It eliminates the information redundancy by mapping data from high feature space to lower one, generating more precise and efficient data representation.  
Figure 1 illustrates the structure of a simple feedforward 1-layer AE. Generally, an AE is constituted by two main parts:  
* An encoder \(\phi:\mathcal{X}\rightarrow\mathcal{C}\) that maps the input features \(\mathcal{X}\) into the code.
* A decoder \(\psi:\mathcal{C}\rightarrow\mathcal{X}\) that reconstructs the original features from the code.  
The code \(\mathcal{C}\) itself is the hidden layer output that is usually used to characterize the input. The objective of AE is to minimize the difference between the input and the output by reconstructing \(\mathcal{X}\) from the reduced encoding \(\mathcal{C}\) as follows:  
\[\begin{split}\phi,\psi&=\operatorname*{arg\,min}_{ \phi,\psi}\|\mathcal{X}-(\psi\circ\phi)\mathcal{X}\|^{2}\\ &=\operatorname*{arg\,min}_{\phi,\psi}\|\mathcal{X}-(\psi(\phi( \mathcal{X}))\|^{2}\end{split} \tag{5}\]  
In practice, only the code \(\mathcal{C}\) is extracted to create a compressed representation of the input data that preserves the most crucial information for further analysis.  
Several modifications on the AE architecture have been introduced recently to improve the effectiveness of representation learning and dimensionality reduction. To prevent the autoencoder from learning the identity function, Denoising Autoencoder (DAE) first corrupts the input by adding noise, then learns to predict the initial data point as the output [32]. Meanwhile, despite sharing a similar architecture with a vanilla AE, Variational Autoencoder (VAE) [33] encodes the input into a multivariate latent distribution rather than a vector.  
There have been several studies successfully applying AE and its variants into RS. Collaborative Deep Learning (CDL) [34] was proposed for joint learning side information of items by a Stacked Denoising Autoencoder. Additionally, an AE-based model for rating prediction is the item-based AutoRec (I-AutoRec) [35] which fills missing ratings by applying an AE to reconstruct its input containing known ratings. In contrary to CDL, I-AutoRec directly handles explicit data to make reliable predictions without the side information. Other variations of AE [36; 37] can also optimize the network input, capturing high-order latent factors from users and items.
## 4 Conclusions  
This study used ECC as an overlay of an orthotropic steel bridge deck and evaluated the feasibility of using an epoxy adhesive to achieve wet-bonding between the steel deck and cast-in-place ECC. Single shear test and pull-off test were conducted in order to evaluate the bonding properties after the specimens were subjected to freeze-thaw cycles and hot and humid environment. Based on the results obtained in this study, the following conclusions can be drawn:  
1. Wet-bonding technology using epoxy adhesive can provide a convenient alternative to the traditional mechanical shear key bonding method and introduce opportunities to develop new overlay structures for orthotropic steel deck bridges.
2. The interfacial shear and pull-off strength decrease with an increase in the freeze-thaw cycles and with the duration of immersion in hot and humid environment. The strength is approximately linear with the number of freeze-thaw cycles. Perfect fitting can be obtained while using a  
Figure 16: Scanning electron microscopy (SEM) images: (**a**,**b**) Original interface, (**c**,**d**) 100 freeze-thaw cycles, (**e**,**f**) 28 days’ immersion in hot water.  
Figure 15: Nonuniform distribution of colloid thickness.  
quadratic polynomial to fit the relationship between strength and duration of immersion in hot water.
3. The SEM images show the presence of microvoids or microcracks at the interface. Exposure to freeze-thaw cycles and hot and humid environment increased interfacial defects. The presence of water reduces the effective bearing area, worsens the interface bonding ability, and degrades the macroperformance.
4. The interfacial shear stress is linear with the normal stress. The failure criterion under normal stress for the steel-ECC bonding interface can be obtained as \(\tau_{\text{max}}\)\(=1.3458+0.898\sigma_{\text{C}}\), which is similar to Mohr-Coulomb criterion.  
Conceptualization, S.W. and J.Y.; methodology, R.Y.; formal analysis, J.Z.; data curation, X.S.; writing--original draft preparation, S.W.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 51778140, 51578290). And it was also supported by the Fundamental Research Funds for the Central Universities and Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX18_0137).  
The authors declare no conflict of interest.
## 2 Theory  
### Intuitionistic Fuzzy Set  
At first, [PERSON] [15] introduced the concept of intuitionistic fuzzy sets (IFS) as a generalization of fuzzy sets. The IFS is the extension of fuzzy sets and gives both the membership degree and the non-membership degree of an element belonging to a set. So, the IFS has the characteristics of comprehensive delicacy and flexible description of the uncertainty system and overcomes the defect of the fuzzy set, which can only describe the \"this is also another\" and cannot describe the \"not one is not the other\".  
**Definition 1** ([12; 15]): _Let \(\mathbf{X}\) be a conclusive domain. If \(x\) corresponds to two mappings, \(u\rightarrow[0,1]\) and \(v\rightarrow[0,1]\), with the condition \(0\leq u(x)+v(x)\leq 1,\ \forall x\in\mathbf{X}\), an IFS \(\mathbf{A}\) in \(\mathbf{X}\) is defined as:_  
\[\mathbf{A}=\{\left.(x,u(x),v(x))\right|x\in\mathbf{X}\}, \tag{1}\]  
\[\pi(x)=1-u(x)-v(x), \tag{2}\]  
where \(u(x)\), \(v(x)\), and \(\pi(x)\) denote the membership function, non-membership function, and hesitancy function of element \(x\) belonging to the intuitionistic fuzzy set \(\mathbf{A}\), respectively. \(\pi(x)\) represents the hesitancy degree for the object \(x\) to \(\mathbf{A}\).  
Unlike the traditional fuzzy set, which his only characterized by a membership function, the non-membership function and hesitancy function in the IFS are used to depict uncertain information; that is, the intuitionistic fuzzy sets possess the capability of dealing with uncertain information through membership degrees, non-membership degrees, and hesitation degrees. Hence, it is more reasonable and stable in simultaneously depicting the degree of support, opposition, and neutrality of the object \(x\) for a particular state through \(u(x)\), \(v(x)\), and \(\pi(x)\), respectively. So, IFSs can provide additional options for describing the properties of things and have been widely applied in various fields. This also provides a new concept for solving the problem that the connection cloud cannot describe the hesitant characteristics of fuzzy random concepts. Here, the information is coupled with IFS to make up for the above defect in the connection cloud model.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
###### Abstract  
Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an \(F1\)-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems.  
deep learning; flood detection; SAR imaging; U-Net; transformer models 2025  
1 2025 15 7770
# Improvements of the xAAL Home Automation System
## 1 Introduction
This is a distributed system based on \"the best effort\" principle. Each component does its best according to its capacity for things to go well. There is no warranty. There is no quality requirement to expect nor to provide from or to others. For testing purposes, the proposal has been implemented in different programming languages (mainly in Python and C, even if stacks in Java and Go exist but are not actively maintained anymore) [9]. Experiments on the long-term lead us to evolve xAAL. xAAL has a little story. Version 0.4 and earlier introduced the general definition of the distributed architecture, the roles of the main functional components, and the principles of communications with text messages on an IP multicast bus. Version 0.5 brought security with message ciphering, and is considered as the former stable version. Version 0.6 has been a first attempt to optimize communications by moving from text to binary messages, without any other change. Version 0.7, presented in this paper, is the last version, and revisits all previous concepts by several improvements regarding the architecture, the message formats and schemas used to describe devices formally. It is considered to be the latest stable version, and ready-to-use on the shelf. * The architecture is consolidated and remain almost the same as in the previous version, except that Schema Repositories are withdrawn. * Schemas (description of devices) are still written in JSON, except that now data types are specified with the Concise Data Definition Language (CDDL, RFC 8610) [10] and no more with the JSON Schema dialect [11]. Moreover, data type definitions are gathered in a _data model_ section of xAAL schema documents for compactness. * The new version proposes to use the Concise Binary Object Representation (CBOR, RFC 7049) [12] in place of JavaScript Object Notation (JSON, RFC 7159) [13] for messages. * Messages are still ciphered with the Poly1305/Chacha20 algorithm [14]. * Several schemas describing common devices are revisited. The resulting proposal is an efficient, robust, and consistent open home automation system. The paper is organized as follows. Section 2 describes the architecture of the xAAL system. Section 3 details schemas, the way to document devices. Section 4 gives the normative basic schema, which is on the basis of all others. Section 5 explicitly presents the communication protocol and the security mechanisms in use. Explanations provide all required details to make feasible compatible alternative implementations. Alternatives and choices are also deeply discussed.
## 2 Materials and Methods  
### Materials  
The monomer DMDAAC (\(60\%,w/w\)) was obtained from the Aladdin Reagent Company (Shanghai, China). PAC was obtained from Lanjie Tap Water (Chongqing, China). The composite flocculant in the experiment was obtained by mixing PAC with PDMDAAC. Disperse violet H-FRL (purity \(100\%\)) was obtained from Shanyu Dyestuff Chemical Company (Taizhou, China). The other reagents used in the experiments, including ammonium persulfate, silane coupling agent (KH570), sodium hydroxide, acetone and ethanol, were of analytical grade. All aqueous and standard solutions were prepared with deionized water. The purity of nitrogen gas was higher than \(99.999\%\).
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 4 System Model
### System Model  
The overall system architecture is depicted in Figure 1. Three layers are designed in this work, including layer 1: Intra-WBAN communications, layer 2: inter-WBAN communications, and layer 3: beyond WBAN. Our proposed work consists of entities such as:  
* _Body sensors_: The body sensors \((B_{s}=B_{s1},B_{s2},\ldots B_{sn})\) are deployed in patients' bodies to monitor the patient health conditions which monitor such as heartbeat rate, BP rate, etc.
* _Environmental sensors_: The environmental sensors \((E_{s}=E_{s1},E_{s2},\ldots E_{sn})\) are located in the environment to monitor the environmental changes based on the patient's health  
Figure 1: System architecture.  
condition. These sensors monitor the air quality, humidity, etc., in the surroundings of patients' environment.
* _Patient block agent (PBA)_: The patient block agent is responsible for the transmission of data and also provides authenticity to the users in which three modules are involved namely classifier and queue manager, security manager, and channel selector for classifying and securing the data.
* _Emergency sink and periodic sink_: These sinks are responsible for storing the data on the internet and cloud server. The emergency sink (\(S_{E}\)) is used to share the critical data from the PBA while the periodic sink (\(S_{P}\)) is used to share the normal data from the PBA which helps to transmit the health information with low delay and congestion.
* _Blockchain(key management server--KMS)_: Blockchain is used for the security and privacy of the WBAN IoT networks. The _KMS_ in the blockchain is responsible for managing the key from the users during registration and authentication to verify the legitimacy of the users.
# Secure Localization in the Presence of Colluders in WSNs
## 1 Introduction
Next, let us understand other limitations of existing algorithms for handling colluders. Typically, these algorithms assume the existence of trusted third parties (e.g., a trusted base station, such as in [20]). This is problematic for several reasons. First, the trusted third parties must periodically broadcast positioning validation data to all the system nodes. Second, the only element of the system that can detect colluders is, in fact, the trusted third party. Third, these solutions require secure communication to the third party, which does not allow for decentralized processing (though this is an key requirement for WSNs). In turn, this imposes an important overhead and complexity on the final system. Also, heavy traffic to and from the base station is likely to compromise the lifespan of neighbour nodes. Ultimately, relying on a trusted third party (which, in effect, disables true distributed processing) and failing to handle attacks such as the evil ring one, completely jeopardizes such solutions. Instead, what is required is a distributed algorithm enabling the detection of colluders (including evil ring attackers) by the nodes of a WSN in a totally decentralized way [7; 21]. In this paper, we present such an algorithm, which proceeds from our previous work on the evil ring attack. Most importantly, like other studies addressing localization in the presence of colluders, such colluders are allowed to alter not only coordinates but also other messages used for localization [22]. In our approach, any location-unknown node is able to identify in a decentralized way colluders within its communication range. The rest of the paper is organized as follows. A review of the literature is conducted in Section 2, with a brief overview of our work on the evil ring attack and how it is different from the algorithm we present here. Section 3 provides the background, model and assumptions of the detection algorithm originally published in [19] for the case of a single evil ring attacker [19]. It also introduces a colluding attack model that includes three categories of colluding attackers. Section 4 presents our solution for detecting the presence of any of these three categories of colluding attackers and Section 5 provides a detailed correctness analysis of the proposed algorithm. In Section 6 we discuss our comprehensive set of simulation results. Section 7 concludes the paper.
# Computationally Efficient PM Power Loss Mapping for
## 2 Motor Design Specification  
The motor design specification is shown in Table 1. The finite element model is shown in Figure 1.  
However, the phase current of stator windings contains rich higher-order time harmonics under PWM input. The PM not only includes sinusoidal PM loss caused by the slot effect and armature reaction of the fundamental current but also includes the eddy current loss caused by the armature reaction of the harmonic current of stator windings, that is, additional PM loss.  
In this paper, additional PM losses are analyzed under the entire torque-speed envelope.
## 3 Experimental Results
### Data Decoding Latency  
Data decoding latency is another key factor for the performance of the LED-DCC system, especially for real-time data reception. In the above experiments, the data decoding latency of each frame for the data reception of the proposed LED-DCC system and the previous LED-DCC system were measured. As shown in Table 2, the proposed LED-DCC  
Figure 7: The BER performance of different LED-DCC systems versus data transmission distance.  
system decodes a single video frame with an average data decoding latency of only about 13.26 ms, which still supports real-time data reception at a capture frame rate of 30 fps. Therefore, the proposed LED-DCC system can support real-time data reception.  
Note that although the proposed LED-DCC system has a higher processing latency than the previous LED-DCC system due to a larger light-emitting area in the images and more precise image processing, the data decoding latency does not increase significantly and becomes a bottleneck for real-time data reception, which verifies the efficiency of the proposed image processing algorithm.
## 4 Experimental Setup
### Datasets
#### 4.2.3 Data Sampling  
The collected data showed significant class imbalance across all projects. Such data imbalance can negatively impact the classification process by ignoring the characteristics of the minority class and biasing towards the majority class [41; 42]. To address this issue, we can consider upsampling and downsampling. Downsampling reduces the quantity of data in the majority category to match the number of samples in the minority category. Downsampling offers advantages such as (1) freedom from overfitting compared to upsampling and (2) reduced computation time. Additionally, there is a high probability that users will not re-report issue reports related to outdated data. Therefore, (3) the loss of information due to data removal is not significant. Therefore, to mitigate the data imbalance problem, we apply downsampling. We retain all the data from the 'feature' class, while for the 'bug' class, we use a subset of the most recent data, matching the number of samples in the 'feature' class. The resulting dataset used for each experiment is as shown in the fourth  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project** & **Label** & **Original Data** & **Down-Sampled Data** \\ \hline \multirow{3}{*}{VS Code} & Bug & 1351 (58\%) & 980 (50\%) \\  & Feature & 980 (42\%) & 980 (50\%) \\  & Total & 2331 & 1960 \\ \hline \multirow{3}{*}{Kubernetes} & Bug & 866 (85\%) & 148 (50\%) \\  & Feature & 148 (15\%) & 148 (50\%) \\  & Total & 1014 & 296 \\ \hline \multirow{3}{*}{Flutter} & Bug & 2061 (73\%) & 759 (50\%) \\  & Feature & 759 (27\%) & 759 (50\%) \\  & Total & 2820 & 1518 \\ \hline \multirow{3}{*}{Roslyn} & Bug & 1341 (84\%) & 263 (50\%) \\  & Feature & 263 (16\%) & 263 (50\%) \\ \cline{1-1}  & Total & 1604 & 526 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sampled Data Distribution per Label.  
column of Table 2, with an equal number of samples for each label. We used an 80:20 training/test split on this dataset. For example, out of 1960 issue reports, 1568 were used as a training set, and 392 were used as a testing set for the VS Code project.