esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
###### Abstract  
With the increasing requirements for the security of medical data, military data, and other data transmission, data hiding technology has gradually developed from only protecting the security of secret data to all transmission data. As a necessary technical means, reversible data hiding in encrypted images (RDH-EIs) provides superior performance in terms of security. To simultaneously improve the effectiveness of RDH-EIs, this work proposes a mixed multi-bit layer embedding strategy in encrypted images. The cover image is processed into two categories: available hidden blocks (AHBs) and unavailable hidden blocks (UHBs) at the sender. Then, all data are embedded in the multi-bit layer of the encrypted pixels in AHBs through two embedding strategies to obtain the transmission image. At the receiver, the user can extract the needed data separably according to different keys to achieve error-free extraction of the secret data and lossless recovery of the cover image. The experimental results show that the proposed scheme has the advantages of superior embedding capacity and high decryption quality over the current state-of-the-art works.  
reversible data hiding in encrypted images; high capacity; separability; data transfer safety; adaptive multi-bit layer embedding strategy; fixed multi-bit layer embedding strategy [PERSON], [PERSON]-[PERSON].  
2023
# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 4 Metrics with Environmental Implications
### Assessing the Carbon Footprint or CO2e  
Understanding the environmental impact of AutoML experiments requires a multifaceted approach, considering both energy consumption and its broader implications. While directly measuring energy expenditure does not provide a perfect evaluation of specific algorithms, it serves as a valuable indicator of the overall environmental footprint. Quantifying energy consumption, despite its limitations in assessing internal efficiency, provides a commendable starting point for understanding the experiment-specific environmental impact. Hardware efficiency plays a crucial role in this assessment, as it significantly influences how much energy AutoML algorithms consume during execution.  
Accurately quantifying the carbon footprint (\(CF\)), also known as \(CO_{2}e\), associated with AI practices presents additional challenges beyond merely measuring energy consumption. Factors such as geographical location, execution duration, and various other parameters contribute to the complexity of this metric. However, despite these inherent difficulties, measuring \(CO_{2}e\) remains essential for gaining a comprehensive understanding of the environmental impact of AI operations. This comprehensive approach to calculating the carbon footprint involves considering energy consumption alongside other critical factors such as emission intensity (\(\gamma\)), which could be defined as kg CO\({}_{2}\) equivalent per kWh, showing how much CO\({}_{2}\) is emitted per unit of energy consumed, and power usage effectiveness (_PUE_), a metric used to determine the energy efficiency of a data center by comparing the total facility energy to the energy consumed by its IT equipment. Employing this comprehensive methodology, as outlined in Equation (11), fosters a deeper understanding of the sustainability and eco-friendliness of AI practices.  
\[CF=\gamma\cdot PUE\cdot E_{Total} \tag{11}\]  
By precisely measuring energy use and considering hardware efficiency and execution conditions, we can confidently approximate \(CO_{2}e\) emissions generated during AutoML processes, thus offering a more comprehensive assessment of AutoML's environmental implications. This comprehensive approach considers not only the total energy consumption but also the key parameters mentioned above. Including these factors with the total energy consumption of the AI components, the equivalent \(CO_{2}e\) rate is obtained in Equation (12), providing a measure of the carbon emissions associated with the AI operation, enabling a deeper understanding of their sustainability and eco-friendliness.  
\[CO_{2}e=\gamma\cdot PUE\cdot E_{Total} \tag{12}\]  
These calculations provide an integral view of both the direct energy consumption and its resultant environmental impact through carbon emissions. Understanding these metrics helps evaluate the sustainability of computational processes effectively.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design
### Algorithm Complexity Analysis  
For the analysis of expandability and applicability, Algorithm 1 is reasonably developed to analyze the complexity of the proposed DTCM algorithm. From Algorithm 1, during the process of information  
Figure 3: Data routing and forwarding process in two-hop feedback mechanism.  
collecting and updating, the nodes transmit state information queue to the peers they encounter, so the time complexity of this process is \(O(n)\). Additionally, each node adopts two-factor fuzzy inputs to determine transmission evaluation value in the second loop, therefore, the time complexity of this process is \(O(n)\). Eventually, the time complexity of two-hop feedback mechanism is \(O(\log_{2}n)\). To this end, the overall time complexity of DTCM algorithm is \(O(n+n+\log_{2}n)=O(n)\). Compared with the time complexity of the DTCM algorithm, the time complexity of FCNS algorithm is \(O(n)\), and those from Spray and Wait [47] and Epidemic algorithms [48] are \(O(n\log n)\) and \(O(n)\), respectively.  
```
1 Begin
2 For(\(int\)\(i=1;\)\(i\leq n;\)\(i\)\(++\))do
3\(node_{0}\) collects state queue information \(SQ_{(node_{i})}\) from \(node_{i}\);
4\(node_{i}\) collects state queue information \(SQ_{(node_{i+1})}\) from \(node_{i+1}\);
5 Endfor
6 For(\(int\)\(i=1;\)\(i\leq n;\)\(i\)\(++\))do
7 computing their own two-factor fuzzy input \(L^{c}_{(node_{i})}\) and \(L^{re}_{(node_{i})}\);
8 determining the fuzzy evaluation subset \(FM(x^{node_{i}}_{j})\) and membership degree \(\mu^{x^{node_{i}}_{j}}\);
9 assessing the transmission evaluation value \(FC^{node_{i}}\);
10 output \(FC^{node_{i}}\), \(FC^{node_{i}}\).... \(FC^{node_{n}}\);
11 Endfor
12 If(\(node_{0}\),isMessageCarrier() \(\wedge\)\(node_{i}\),isNeighbor(\(node_{0}\))) then
13 If(\(FC^{node_{0}}>FC^{node_{i}}\wedge FC^{node_{i}}>FC^{node_{i}}\)) then
14 the source \(node_{0}\) forwards messages to its neighbor \(node_{i}\);
15 the relay node \(node_{i}\) transmits messages to its neighbor \(node_{j}\);
16 Endif
17 Endif
18 End
```  
**Algorithm 1**Data routing and forwarding in a delay-tolerant routing (DTCM) algorithm based on the (curve-trapezoid Mamani fuzzy inference) CMFI system.
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### General Applications
#### 6.1.2 Monitoring and Guidance
First, the authors developed a self-loading teaching system which fetched a phone user's everyday activity data and utilized it to train a Long Short-Term Memory (LSTM) model, which is then employed to predict the phone user's future activity. The tagging system is enabled to have reference to the history of the caller ID upon the prediction, and if the call time is inappropriate, the caller ID will be automatically marked as malicious, but user's ability to adjust the tag will be sustained. Second, a local blacklist is created, and each device classifies the blacklist into two: 1. A group of caller IDs (that shares the same) whose area code match that of the prefix of call recipient. 2. A group of caller IDs whose area code differ from that of the caller recipient. The two classes of blacklist are shared by each device to the edge server and utilized to develop a regular list of caller IDs in two categories (edge local and foreign blacklist) which is intermittently disseminated to user devices at the back-end to effectively improve their tagging system. Evaluation conducted through simulation and implementation verified that the proposed system is feasible. [PERSON] [75] developed an open-source tool called IoT inspector for harvesting labelled network traffic from smart home appliance from within real-life home network based on crowdsourcing. Unlike Netalyzer, [76] and HomeNet Profiler [77], IoT inspector is designed to build datasets of smart home network traffic on a large scale. It has the ability to collect labelled network traffic from different categories of device and vendors. The aim is to make available to the public a large dataset that will drive new research into smart homes. Furthermore, the authors conducted a study on security and privacy of smart home device using labelled network traffic generated by IoT inspector from 44,956 smart home devices, 4322 users covering 13 groups and 53 vendors. The result of their study revealed that the majority of device vendors use out-of-date Transport Layer Security (TLS) versions as well as advertise weak ciphers. It also revealed different third party advertisers as well as  
Figure 29: System architecture of EasyFind [73]. tracking domains on smart TVs. IoT Inspector's data can also drive research in areas such as health care and network management.
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
## 2 Materials and Methods
### CAD Design  
Based on the studied geometries for the ED and RED reactors, the components of the devices were designed, including gaskets, flow distributors (FDs), end plates, and compression plates. The arrangement of the gaskets and FDs forms the compartments for the saline solutions and electrode rinse solutions. The gaskets primarily function as seals to prevent external leaks and cross-contamination. Material choice for the gaskets is crucial, as it impacts the reliability of the seal and fluid distribution. For optimal performance,materials with an appropriate Shore hardness are selected to ensure flexibility and resilience; a material that is too rigid may not seal properly, whereas a material that is overly soft or thin could deform or tear under pressure, compromising the seal. Therefore, choosing an adequate Shore hardness is essential to balance flexibility and strength according to the specific requirements of the gasket component. The FD, designed to ensure uniform fluid distribution within the compartments, is constructed to provide structural stability while minimizing stagnation zones. The material for the FD must withstand constant fluid flow without excessive deformation, which could impede fluid distribution and lead to pressure imbalances within the reactor.  
End plates and compression plates, which house fluid inlets and outlets and secure the stack alignment, require materials that can tolerate high-compression forces. Excessively thick materials in these components may increase mechanical resistance and compromise the overall pressure distribution. The compression plates apply even pressure throughout the reactor stack, preventing leaks and maintaining alignment. The 3D design of the reactor components was created using the open-source CAD platform Fusion 360 (Autodesk). Figure 2a,b show the design of the gaskets and the meshed FD, with a geometric area of 30 cm\({}^{2}\) and a thickness of 1 mm. Figure 3 presents a CAD model of the reactor, including all previously described components. The use of a 3D CAD model in the design process enables precise visualization of component assembly, helping identify alignment or fitting issues early, which allows for necessary adjustments before manufacturing and ultimately saves time and resources.  
Figure 3: CAD model of the assembled reactor.  
Figure 2: Design gaskets and FD: (**a**) design of gaskets and FD for the ERS; (**b**) design of gaskets and FD for saline solutions.
## 4 Performance Evaluation
Figure 6: Packet transmission success ratio with varying QL using Gaussian distribution with \(N(0,\sigma_{2})\). From the above experimental results, we can conclude that the data delivery mechanism using our method improves PTSR without the burden of large overhead occurred during training our method.
###### Abstract  
Since data are the gold of modern business, companies put a huge effort into collecting internal and external information, such as process, supply chain, or customer data. To leverage the full potential of gathered information, data have to be free of errors and corruptions. Thus, the impacts of data quality and data validation approaches become more and more relevant. At the same time, the impact of information and communication technologies has been increasing for several years. This leads to increasing energy consumption and the associated emission of climate-damaging gases such as carbon dioxide (CO\({}_{2}\)). Since these gases cause serious problems (e.g., climate change) and lead to climate targets not being met, it is a major goal for companies to become climate neutral. Our work focuses on quality aspects in smart manufacturing lines and presents a finite automaton to validate an incoming stream of manufacturing data. Through this process, we aim to achieve a sustainable use of manufacturing resources. In the course of this work, we aim to investigate possibilities to implement data validation in resource-saving ways. Our automaton enables the detection of errors in a continuous data stream and reports discrepancies directly. By making inconsistencies visible and annotating affected data sets, we are able to increase the overall data quality. Further, we build up a fast feedback loop, allowing us to quickly intervene and remove sources of interference. Through this fast feedback, we expect a lower consumption of material resources on the one hand because we can intervene in case of error and optimize our processes. On the other hand, our automaton decreases the immaterial resources needed, such as the required energy consumption for data validation, due to more efficient validation steps. We achieve the more efficient validation steps by the already-mentioned automaton structure. Furthermore, we reduce the response time through additional recognition of overtaking data records. In addition, we implement an improved check for complex inconsistencies. Our experimental results show that we are able to significantly reduce memory usage and thus decrease the energy consumption for our data validation task.  
Keywords:consistency checking; sustainable IT; green computing; big data streams; smart manufacturing +
Footnote †: journal:
# Human Activity Recognition Using CSI Information with Nexmon
## 2 Problem Statement
### Scope  
The paper's primary work scope was to evaluate possibilities for using cost-effective and less complicated hardware (Raspberry Pi) for human activity recognition using CSI information of the Wi-Fi signal. Following is the primary outline of the work.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Exp.** & **Hardware** & **Algorithm(s)** & **Preprocessing** & **Activities** \\ \hline
1 & Raspberry Pi 3B+, 4B & SVM, LSTM & noise, outlier removal & \{EMPTY\} \(\cup\)\(A_{i}\) := \{SIT, STAND, WALK\} \\
2 & Asus RT-AC86U & LSTM & normalizing raw data & \(A_{2}\) := \(A_{1}\) \(\cup\) \{SIT-DOWN, STAND-UP, FALL\} \\
3 & Asus RT-AC86U & LSTM & scaling raw data & \(A_{3}\) := \(A_{1}\) \(\cup\) \{EMPTY, LYING\} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiments performed.  
We patched the Wi-Fi driver of the Raspberry Pi 3 B+, Pi 4B, and Asus RT-AC86U with the stable version of open-source Nexmon firmware.
* We extracted the indoor environment's CSI information for the following activities on frequency bandwidth 20 MHz, 40 MHz, and 80 MHz (note, that not all activities were performed for all experiments, see Section 9)
* We made a quantitative features dataset by denoise the CSI data for possible outliers, performed principal component analysis (PCA), and extracted a feature matrix.
* We used the feature matrix for machine learning algorithms such as SVM classifier.
* We denoised raw data and preprocessed CSI data matrix for LSTM.
* The data was divided into a random 80 to 20 ratio for training and testing. The final results were mapped into confusion matrices for comparison to evaluate the feasibility of Raspberry Pi and Asus RT-AC86U hardware for a cost-effective and less complicated alternative with acceptable accuracy.
* We compared the obtained results by one-to-one comparison for the above-mentioned machine learning algorithms, and a relative comparison of the obtained results with related prior work performed using IWL 5300 NIC [12].
* CSI data were classified using machine learning algorithms and recurrent neural networks (RNN).
# Learning a Hierarchical Global Attention for Image Classification
## 4 Implementation and Discussion  
As an efficient component, HGA can be flexibly applied in different network designs. ResNet [18] and InceptionNet [14] are two classical network design patterns from which most recent networks are derived. From this point of view, we provide the applications of HGA on the two patterns, which are demonstrated in Figure 2. Figure 2a,c denotes the vanilla block designs in ResNet and Inception Net. Figure 2b,d denotes the applications of HGA. In ResNet, HGA is applied in the main path after the ResBlock processing. On one hand, this pattern could find the inherent correlations after feature exploration. On the other hand, applying HGA on main path could preserve the identical information and gradient transmission on the shortcut. Different from ResNet, in Inception Net, the HGA is applied after the Inception Block. In Inception Block, the identical information transmission is utilized by \(1\times 1\) convolutional layer. The HGA does not omit the identical transmission in Inception Net. It is because the \(1\times 1\) convolution will change the distribution of information from different channels.  
From the design, HGA is an efficient component with restricted parameters and computation complexity. Suppose the input \(\textbf{F}_{in}\) is with size \(H\times W\times C\), then the FLOPs cost on MFE module is  
\[FLOP_{MFE}=2 HWC(1^{2}+3^{2}+5^{2}+7^{2}). \tag{8}\]  
After concatenation, there are three GAP layers for global information measurement. The FLOPs cost on HAP module is  
\[FLOP_{HAP}=3 HWC. \tag{9}\]  
Finally, there are two full connection layers and two activation functions in EE module. The FLOPs cost on EE module is  
\[FLOP_{EE}=\frac{85}{2}C^{2}. \tag{10}\]  
The entire FLOP cost of HGA is,  
\[FLOP_{HGA}=FLOP_{MFE}+FLOP_{HAP}+FLOP_{EE}. \tag{11}\]  
Notice that there is no bias in both group convolutions and the full connection layers. On one hand, it will save the parameters and computation cost. On the other hand, the bias setting will modify the distribution of feature maps.  
The parameters of HGA are  
\[Param_{HGA}=(1^{2}+3^{2}+5^{2}+7^{2})C+\frac{11}{2}C^{2}. \tag{12}\]  
The left-hand side of Equation (12) denotes the parameters of MFE module and the right-hand side denotes the EE module. There is no parameter in HAP module, where only the GAP layers exist.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 3 and 5: Network Diffusion Algorithms and Performance Evaluation
#### 4.2.4 Threshold Models  
Threshold models, such as the Linear Threshold Model (LTM), operate on the principle that a node becomes active when the influence from its neighbors exceeds a pre-set threshold. These models are suitable for IoT configurations that require coordinated behavior from clusters of devices, such as in smart homes or industrial IoT applications.  
Strengths:
* Community Dynamics: The LTM is effective at modeling diffusion in clustered networks, where devices in a particular area act together.
* Efficient Activation: Once a small number of key nodes are activated, the diffusion process spreads quickly across the network. Weaknesses:
* Binary Activation: The model simplifies device states by assuming nodes are either active or inactive, which may not capture the varying states of IoT devices.
* Constant Thresholds: The assumption that thresholds remain fixed over time does not account for the dynamic nature of IoT environments. Performance Metrics:
* Diffusion Reachability: The LTM can be limited in its reachability if the initial active nodes are not sufficient to trigger widespread activation.
* Robustness: The LTM performs well in networks with resilient community structures but may struggle in highly dynamic environments.  
Example: In IoT networks, the LTM has been used to simulate the spread of firmware updates across device clusters, showing quick diffusion once critical nodes receive updates. However, its binary nature limits flexibility in dynamic networks.
## 1 Introduction
In the past, the evaluation of the transformation effect on fracture networks was achieved by characterizing the complexity of the fracture network post-hydraulic fracturing. In the early stages, a method involved adding tracers to the fracturing fluid and sectioning specimens to observe the morphological distribution and connectivity of hydraulic fractures [35]. However, this method could cause damage to rock samples, and the morphology of the fracture surfaces is relatively rough, allowing only for qualitative analysis. Moreover, obtaining a three-dimensional fracture network is particularly challenging. Acoustic emission serves as a non-destructive means to acquire information about fracture networks, but connecting discrete acoustic emission points to form accurate fractures is difficult [36; 37; 38]. Nowadays, many researchers use CT scanning technology to obtain spatial geometric shapes of fractures and porosity in samples [39; 40; 41]. However, due to limitations in sample size and the resolution of CT scanning, obtaining satisfactory results for large samples remains a challenge. Therefore, it is necessary to introduce a new quantitative characterization method to study the complexity of three-dimensional fracture networks. Therefore, this paper proposes a research method for studying the differential expansion of hydraulic fracturing fractures in marine and continental shale based on optical scanning technology (Figure 1). Using typical marine shale (outcrop of the Longmaxi Formation, Lower Silurian, Sichuan Basin, China) and continental shale (outcrop of the Dongyue Temple Formation, Late Jurassic, Sichuan Basin, China) as research subjects, three-dimensional laser scanning was performed on the samples after hydraulic fracturingexperiments. The introduction of fractal dimension was employed for the quantitative characterization of fracture complexity, allowing for an intuitive and quantitative description of the spatial morphology of fractures. This approach facilitates further detailed analysis and aims to provide a more precise model foundation for the quantitative characterization of hydraulic fracturing fracture surface geometry and numerical simulation of fracture surface permeability characteristics.