esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 4 System Design  
### System Overview  
We propose IoT-Portrait, a system to automatically identify IoT devices using transformer network without manual labeling. In IoT-Portrait, we gather information on IoT devices using active scanning and passive traffic collection approaches and then automatically label training data with the collected device information. Furthermore, we leverage the transformer network to identify device characteristics from network traffic and generate a multi-classifier. Overall, we provide an end-to-end identification scheme based on the network traffic of devices. The system architecture is shown in Figure 3, with three modules, namely the device information collection module, deep learning module, and real-time identification module. The first two modules are used for collecting device data and training multi-classifier. The last module primarily works during the device identification phase.  
Figure 1: The packet rate statistics of SmartThings Hub.  
Figure 2: The packet rate statistics of NetGear Station.  
The device information collection module runs in the early stage of the process. This module is responsible for passively collecting network traffic from various devices. For the IP information in traffic collected, we designed a comprehensive active scanning scheme to scan it. In this scheme, this module automatically obtains basic device information, such as the type of operating system and underlying hardware platform. Meanwhile, it can gather service data by scanning and then extract more detailed device information. The obtained information is used as the traffic's label.  
The deep learning module first performs traffic preprocessing, including traffic splitting, data cutting, and flow cleaning. Then, the preprocessed data are automatically matched with different labels using the information that was previously collected. Furthermore, this module leverages the transformer network to learn device features in network traffic and generates a multi-classifier. After training, the exemplars of training data will be saved in memory, and the generated classifier will be used to identify IoT devices. When new devices join the network, the old classifier will be retrained with the class incremental method using past samples and new devices' data.  
The real-time identification module collects the traffic data of devices from a central router in real time, performs preprocessing on the traffic data, and then sends the data to a classification model for device identification. The identification result is mapped to different devices, and then the system can obtain a detailed list of devices and their information in the domain. The list will be updated according to the identification result.
## 3 Simulation and Experiment
### Mechanical Simulation  
The model shown in Figure 2 was simplified in Workbench to analyze the transient stress-strain characteristics of the components during the load-lifting process. The transient-structural module in Workbench was used for analysis, and the boundary conditions were as follows: (1) the rotation speed of the lower link was \(1^{\circ}\)/s, and the rotation time was 47.6 s; (2) the driving force of the hydraulic cylinder was applied to the device according to the Equation (5); and (3) the analysis process was divided into 100 substeps. 45# steel was used as the component material. The transient analysis results of the critical components were shown in Figure 7.  
It could be seen from the analysis results that the equivalent stress of the components changed significantly during the end stage of the lifting and loading process. In particular, the equivalent stresses of the connecting pin, bearing rod, steering rod, and lever connecting pin were close to the tensile strength of 45# steel. It was necessary to adopt the materials with better performance than 45# steel to make the key components of the device. To improve the use safety factor of the lifting and loading device, 40 Cr was used as the material of key components in the device, such as the connecting pin, lever connecting pin, and steering rod. The tensile strength of 40 Cr with a hardness of 207 HB is 980 MPa [18].  
Figure 6: Kinematics analysis of the load-lifting device.
# Multi-Level Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
## 2 Background
### Value Decomposition Network  
Since the individual learner treats other agents as part of the environment in the fully decentralized methods, the transition dynamics for each agent become highly stochastic. The fully decentralized approach, such as IQL, faces the instability problem. In order to ease this instability, a centralized training and decentralized execution (CTDE) diagram is proposed. As its name reflects, CTDE allows the agents to communicate and share information in the training phase and stay isolated in the execution phase. CTDE eases the learning processes for MARL algorithms greatly and is the dominating framework currently. In addition, CTDE provides a possible way to achieve reasonable multi-agent credit assignments in the training phase.  
To encourage the cooperation among agents in the multi-agent systems, Value Decomposition Network (VDN) that follows the CTDE diagram is proposed. VDN aims to use a value decomposition network to learn a linear decomposition scheme and divide the global reward into each agent's individual reward based on its contribution to success. In VDN, each agent obtains its own value network and its output are added to obtain the total Q-value, which can be defined as  
\[Q_{tot}(s,a)=\sum_{n=1}^{N}Q_{i}(o^{n},a^{n}) \tag{1}\]  
where \(s\) is the global state, \(o^{n}\) denotes the n-th agent's observation, and \(a^{n}\) denotes the n-th agent's action and the gradient is backpropagated to update all agents' value networks. VDN manages to avoid the spurious reward signals that emerge in a fully centralized approach and ameliorates the coordination problem.
## 3 Experimental Results
### Experimental Results
#### 3.2.2 Experiment 2: Quantitative Analysis and Comparison on Sinusoidal Loads
(**c**) Comparisons of the estimated sinusoidal loads using the proposed method with respect to the ground truth values.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 2 Research Methodology
### Phase 2: Conducting a Systematic Literature Review
#### 2.2.1. Selection of Studies  
Many repositories have been used for selecting the research publications in Phase 2, such as ACM Digital Library, ProQuest, Google Scholar, IEEE explorer, Web of Science, Science Direct, Scopus, and Springer, as are presented in Figure 4. We selected highly indexed research papers from these databases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Keywords, Key Terms, and Key Phrases** \\ \hline
1. & (“Short Text” OR “ST”) AND (“STSS” OR QAS) \\
2. & (“Short Questions” OR “Short Answers”) AND (“Short Text Semantic” OR “Semantics in Sentences”) \\
3. & (“DL” OR “STSS” OR “QA”) AND (“Sparsity” OR “Ambiguity” OR “Polysemy”) \\
4. & (“STSS similarity” OR “STSS semantics”) AND (“Keywords” OR “Short Sentences” OR “Similarity”) \\
5. & (“Similarity Barriers” OR “Barriers in Sentences”) AND (“Sentence Ambiguity” OR “Word order”) \\
6. & (“ASAG” OR “short answer grading”) AND (“Answer similarity” OR “Question similarity”) AND (“QAS” OR “ASAQS” OR “STSS”) \\
7. & (“Short text classification”) AND (“Text classification”) AND (“Short Sentence length” OR “Sentence Length”) \\
8. & (“Context-Embeddings” Or “Sense information”) AND (“Context independent” Or Context-Dependent) \\
9. & (“Short Answer Dataset” OR “QAS dataset”) AND (“Multi-Lingual QAS” OR “Domain-independent QAS dataset”) \\
10. & (“QAS dataset” OR “Short sentence Dataset”) AND (“ASAGs dataset” OR “Scoring Rubric dataset”) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Keywords, key terms, and key phrases were used for this study.  
Figure 4: The number of research papers from various repositories.  
The selection criteria were further divided with inclusion and exclusion criteria presented in Tables 3 and 4 and a final sample of candidate articles was determined.
###### Abstract
Moreover, the goal of task offloading is minimizing long-term costs in terms of latency and energy consumption. * By predicting the characteristics of tasks and edge server loads, tasks are dynamically offloaded to the optimal edge server. In the decision model, the prediction is combined with task decision to dynamically allocate resources for different tasks to further reduce latency and improve service quality. * The proposed model and method are extensively evaluated with real-world datasets. The results reveal that the model developed in this paper can effectively reduce the cost using the DRL algorithm with Deep Q Network (DQN) and its variants. The OPO algorithm can maintain low task latency and task discard rate when facing large and complex scenarios. The rest of the paper is organized as follows. Section 2 reviews the related work. Section 3 gives the system model and the formulation of the problem, Section 4 gives the framework of the model in the training and offloading phases and designs the reinforcement learning offloading algorithm for the given objective. Section 5 presents the experimental evaluation of the proposed method. Section 6 concludes the work of this paper.
## 2 The Home Network as The Universal Center of Reference
### The Home as an Authority  
In our scheme, we thus introduce a Certification Authority (CA) that is resident inside the home network, as depicted in Figure 1. This _Home CA_ issues certificates to entities within the home that a) identify the entity as a member of this home and b) bind the identity to the corresponding public key, which is (in general) also issued by the Home CA. The Home CA can be implemented as a piece of software running on a home network server, but we will explain later how this design should be hardened.  
The identifier of an entity consists of two parts: the identifier of the home network plus an identifier for the entity that remains strictly local to this home network. We let the identifiers be self-certifying. The identifier of a home network thus consists of the hashed public key of the Home CA:  
\[\texttt{HomeID}\ =\ H(PubKey_{HomeCA})\]  
The identifier of an entity is then generated by hashing the public key of the Home CA and concatenating it with the hashed public key of the individual entity:  
\[\texttt{EntityID}\ =\ H(PubKey_{HomeCA}).H(PubKey_{Entity})\]  
This design fulfills R1-R5. As for user-friendliness, we have shown in [5] how an easy-to-use assistance system can be created that helps the owner of the home network and owner of the new identifier in the certification process.  
We have so far not elaborated on how the Home CA can identify itself to other entities. In the following, we describe two flavors of our approach. They can be used alone or together.
# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Current Status of SSIM Research in Medical Imaging
#### 4.6.4 Image Quality Assessment  
Most of the SSIM implementation is for IQA/IQM since the initial objective of SSIM is to substitute the traditional measures, such as MSE and PSNR. Thus, in this subsubsection, we reviewed one article allocating SSIM as an IQA to complete the comprehensive insight regarding the SSIM prospect for medical image analysis.  
The image compression and quality enhancement in medical image processing are substantial factors in reducing the content redundancy to attain the image optimal form when storing and transferring the images. Thus, [PERSON] and [PERSON] in 2021 demonstrated two novel algorithms, namely lossless and lossy compression algorithms [44]. After they compressed the images, they applied several enhancement techniques to acquire the leverage image quality from the compressed images. To assess the algorithms' performance, they analyzed them with MSE, PSNR, and SSIM. Moreover, lossy compression was used to execute DCT and DWT. Likewise, the lossless method with run-length encoding (RLE) and block truncation coding (BTC) was operated for the study. To achieve a satisfactory medical image quality, two spatial domain enhancement techniques have been used, such as adaptive histogram equalization (AHE) and morphological operations (MO).  
Their study employed the medical images from MedPix(r), which is a free and open access medical images, teaching cases, and clinical topics database. This database has approximately \(59,000\) medical images with \(12,000\) patients case scenarios and \(9000\) topics. Their findings indicated that neither AHE nor MO is not suitable for image quality enhancement because the SSIM and PSNR score is decreased after processing the image quality enhancement algorithms. However, their image compression methods are outstanding compared to the other studies, such as the joint photographic experts group (JPEG) and JPEG2000. The best compression can be achieved using DCT with an SSIM of 0.9761.
# Measuring Efficiency and Effectiveness of Highway Management in Sustainability
## 1 Introduction  
While the current literature on highway management has contributed to understanding infrastructure budget and finance, the relationship between efficiency and effectiveness measurements has not been sufficiently discussed in the context of sustainability. The purpose of this study was to empirically examine the relationship between efficiency and effectiveness of highway management in the field of sustainability.  
Highway management has great consequences because public infrastructure management is closely connected to economic and social well-being and sustainability. Despite the significance, the topic, highway management, has not been spotlighted in the field of public budgeting and finance, compared with other budgeting issues [1]. So far, previous literature on infrastructure investment in the field of public affairs can be classified into two groups. One group deals with early studies that are about the normative approach of how to manage or improve capital assets [2; 3; 4]. For example, [PERSON] [3] criticized that prior literature on capital budgeting is not useful for local governments to set capital project priorities. Rather, it was suggested that technical issues, organizational issues, and feasibility issues should form the criteria for setting priorities for capital projects. The other group is includes determinant studies of capital or infrastructure spending [5; 6] and performance [7; 8].  
This study contributes to the discussion on sustainability management by focusing on highways as the target of empirical analyses. The study deals with efficiency and effectiveness that were discussed as performance proxies in prior literature and examines the empirical investigation between them. The importance of this study is summarized in its following contributions. First, the relationship between them has not been adequately discussed, though efficiency and effectiveness are the most important values in the public sector. Second, even though some studies deal with the relation betweenthe two values [9; 10; 11], they do not provide empirical evidence on the relationship. Third, in terms of measuring efficiency, many studies for the public sector used efficiency as a dependent variable but employed just a fragmentary concept, such as a ratio of one input factor to one output factor. The concept of efficiency needs to be developed with the combination of multiple input and output factors. Fourth, the effectiveness of infrastructure management has been discussed mostly in the engineering field rather than in the field of sustainability management. Thus, the study aims to build up the concept of efficiency and effectiveness for infrastructure spending, to suggest measurement strategies, and to find empirical evidence on their relationship.
## 6 Discussion
In addition, the so-called balance comfort model (BCM) applies [PERSON]'s global heat balance equation to individual body parts and uses the set of equations of [PERSON] to predict local and overall thermal sensation and comfort of individuals. Furthermore, the researcher included thermal conduction in the energy balance equation to be able to consider the influence of contacting surfaces on a person's thermal comfort. However, as all the local comfort models mentioned before, BCM requires further experimental data to make the model more robust with respect to its prediction results. Consequently, the use of personalized climatization systems requires thermal comfort models that are able to predict an individual's thermal comfort. Here, information on global ambient conditions as well as information regarding local micro-climatic situations close to the human body are required. Such a comfort model combined with personalized climatization would be capable to ensure person-specific thermal comfort in various thermal environments. In Figures 11 and 12 the different existing heating and cooling methods are compared with respect to their tested ambient conditions, their possible impact on thermal comfort and energy demand. The Figures 11 and 12 shows the maximum range of values for the different methods, based on the published data of the single studies. It becomes clear that only a few studies contain comprehensive data about the investigated aspects. It is obvious that the majority of the studies that deal with personalized cooling use personal ventilation systems. This method has a high energy efficiency with an average energy demand of 2 W to 23 W. Although less studies focus on radiant cooling, this method is also capable to provide thermal comfort at ambient temperatures of up to 32 \({}^{\circ}\)C. Above all, radiant panels are simultaneously efficient at heating the environment and can save about 500 W per person, as it is shown in Figures 11 and 12. In comparison, only a few studies investigate the concurrent application of multiple cooling or heating methods and do not consider extreme ambient conditions. Personalized climatization is basically suitable for both, ensuring a person-specific optimized thermal comfort and for ensuring a healthy environment. It is obvious that for a broader application of personalized climatization systems, such systems have to be further investigated.
# Implementation of MS Circle Map in Digital Image Encryption
## 3 Results and Discussion
### Scalability and Computational Performance  
Despite its advantages, the MS Circle Map-based encryption algorithm has some potential vulnerabilities. First, the encryption process is computationally intensive for large images, limiting its application in real-time encryption scenarios. Future work should explore algorithmic optimizations such as parallel computing or GPU acceleration to enhance efficiency.  
Second, although the algorithm demonstrates resistance to brute force, statistical, and differential attacks, further analysis is needed to evaluate its security against known-plaintext and chosen-plaintext attacks. These types of cryptanalysis could potentially reveal patterns in the keystream generation process, reducing the algorithm's overall security.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **(a)** & **(b)** & **(c)** & **(d)** & **MSCM** & [36] & [37] & [38] & [38] \\ \hline Horizontal & \(-\)0.0030 & 0.0014 & 0.0013 & \(-\)0.0030 & \(-\)0.0008 & 0.0002 & \(<\)0.0019 & \(<\)0.0036 & \(<\)0.0023 \\ \hline Vertical & 0.0021 & \(-\)0.0036 & \(-\)0.0052 & 0.0022 & \(-\)0.0011 & 0.0009 & \(<\)0.0019 & \(<\)0.0029 & \(<\)0.0020 \\ \hline Diagonal & \(-\)0.0048 & \(-\)0.0025 & \(-\)0.0097 & 0.0048 & \(-\)0.0030 & 0.0091 & \(<\)0.0015 & \(<\)0.0029 & \(<\)0.0018 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparative analysis of correlation coefficient values.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **MSCM** & [36] & [37] & [38] & [38] & [39] & [40] \\ \hline Key sensitivity & \(10^{-15}\) & \(10^{-16}\) & \(10^{-15}\) & \(10^{-15}\) & \(10^{-15}\) & \(10^{-15}\) & - \\ \hline Key space & \(8.7\times 10^{1267}\) & \(10^{208}\) & \(9.8\times 10^{123}\) & \(3.3\times 10^{65}\) & \(8.1\times 10^{61}\) & \(10^{240}\) & \(10^{64}\) \\ \hline Complexity & O(\(n^{2}\)) & \(O(14n^{2})\) & - & - & - & - & - & \(O(78n^{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 17: Comparative analysis of key sensitivity, key space, and complexity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **(a)** & **(b)** & **(c)** & **(d)** & **MSCM** & [37] & [38] & [38] & [36] \\ \hline NPCR & 99.6203 & 99.6033 & 99.6041 & 99.6107 & 99.6096 & 99.6096 & 99.6091 & 99.6095 & 99.6078 \\ \hline UACI & 33.4678 & 33.4483 & 33.4525 & 33.4865 & 33.4638 & 33.4641 & 33.4683 & 33.4633 & 33.4547 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparative analysis of NPCR and UACI values.  
Third, key sensitivity, while beneficial for encryption robustness, also introduces a potential challenge in key synchronization, particularly in noisy transmission channels. If small errors occur during key transmission, the decryption process may fail, leading to irrecoverable data loss. Implementing robust key management strategies could mitigate this issue.  
Addressing these potential vulnerabilities through further cryptanalysis and performance optimizations will help refine the MS Circle Map encryption algorithm for broader real-world applications.
## 6 Experimental Results
### Use Cases
#### 6.2.1 Face Recognition  
The first experiment we report considers the use case of face recognition described in Section 1.1: Our real dataset consists of the YouTube Faces Dataset (YTFD) [15], including 3425 videos of 1595 different people, with videos having different resolutions (480 \(\times\) 360 pixels is the most common size) and a total of 621,126 frames containing at least a face (on average, 181.3 frames/video). The average dimension for an image is 22.3 KB. Results are included in Table 2. As in following experiments, the table reports (for each framework) the obtained throughput with 16 workers in terms of images/MB processed per second.
## 2 Preliminary Knowledge
### Interference Analysis
Furthermore, by reducing the number of sub-carriers used for PAPR reduction, the error performance can be improved.