esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Visible light communications (VLCs) utilizing multi-color light-emitting diodes (LEDs) can achieve a high modulation bandwidth and high-quality illumination compared with phosphor-converted LEDs, which have attracted much attention. However, the spectrum overlapping of different colors may cause the crosstalk problem, which should be considered in the practical multi-color LED-based VLC systems. Due to the ever-increasing energy consumption, the interest in an energy-saving communication technique has further increased. In this paper, in order to maximize energy efficiency, an optimization problem of the optical power allocation scheme is formulated for the multi-color LED-based VLC systems under the necessary communication requirements and illumination constraints with luminance, chromaticity, and signal to interference plus noise ratio (SINR) constraints. Simulation results indicate that the proposed optimal power allocation scheme can reduce energy consumption while satisfying the illumination and communication requirements.  
visible light communication (VLC); light-emitting diode (LED); multi-color crosstalk; energy-efficient optimization design; illumination constraints +
Footnote †: journal: Applied Sciences
###### Abstract  
This study proposes a novel detection model for the detection of cyber-attacks using remote sensing data on water distribution systems (i.e., pipe flow sensor, nodal pressure sensor, tank water level sensor, and programmable logic controllers) by machine learning approaches. The most commonly used and well-known machine learning algorithms (i.e., k-nearest neighbor, support vector machine, artificial neural network, and extreme learning machine) were compared to determine the one with the best detection performance. After identifying the best algorithm, several improved versions of the algorithm are compared and analyzed according to their characteristics. Their quantitative performances and abilities to correctly classify the state of the urban water system under cyber-attack were measured using various performance indices. Among the algorithms tested, the extreme learning machine (ELM) was found to exhibit the best performance. Moreover, this study not only has identified excellent algorithm among the compared algorithms but also has considered an improved version of the outstanding algorithm. Furthermore, the comparison was performed using various representative performance indices to quantitatively measure the prediction accuracy and select the most appropriate model. Therefore, this study provides a new perspective on the characteristics of various versions of machine learning algorithms and their application to different problems, and this study may be referenced as a case study for future cyber-attack detection fields.  
water distribution systems; cyber-attack detection; remote sensing data and controller; extreme learning machine; machine learning algorithms +
Footnote †: journal: Applied Sciences
## 3 Architecture
### IPCRSA Overview
#### 3.1.3 Computing Resource Node  
In IPCRSA, CRNs can be classified into three types based on their purposes: Universal-CRN, Super-CRN, and Intelligent-CRN, as shown in Table 4. These nodes can take the form of physical machines, virtual machines, or containers. Specifically, Universal-CRNs are primarily used for routine computing tasks in consumer and industry internet domains. Super-CRNs are mainly employed in the scientific field to handle large-scale scientific and engineering computing tasks. Intelligent-CRNs are mainly dedicated to processing  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Field** & **Meaning** \\ \hline CRS\_ID\_T\({}_{x}\) & Provide computing resource service with Type T\({}_{x}\) \\ Source\_IP & IP Address of the SN corresponding to the CRN \\ Next\_Hop & Source IP of the status announcement information \\ Hierarchy\_Information & Scheduling level of the SN corresponding to the CRN \\ Sequence\_Number & Sequence number of the CRN \\ Available\_CPU\_Cores (Core) & Available CPU cores of the CRN \\ Available\_Memory (GB) & Available memory of the CRN \\ Available\_Storage (GB) & Available storage of the CRN \\ Available\_Bandwidth (bps) & Available Bandwidth of the CRN \\ CPU\_Usage (\%) & CPU usage of the CRN \\ Memory\_Usage (\%) & Memory usage of the CRN \\ Storage\_Usage (\%) & Storage usage of the CRN \\ Bandwidth\_Usage (\%) & Bandwidth usage of the CRN \\ Communication\_Delay (ms) & Communication Delay between SNs \\ PUE & Power usage effectiveness of the CRN \\ Price & Price for the use of this CRN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fields of the resource status information table and their meanings.  
tasks related to artificial intelligence. The same type of CRNs have the same computing resource service identifiers, CRS_ID_T\({}_{x}\), indicating that the node can provide corresponding computing resource services with type T\({}_{x}\), as shown in Table 2.  
It is important to note that IPCRSA does not impose any restrictions on the classification methods for CRNs. The classification methods mentioned above are just examples, and IPCRSA supports other classification methods and custom computing resource service identifiers for various scenarios.
## 3 Proposed Method
### Ensuring Authorization Security
#### 3.2.3 Assigning Truth for Clauses  
To satisfy all hard clauses in \(HC\), it is necessary to search for truth assignments and make sure all clauses are true. The recursive process is described in Algorithm 6.  
```
Input:\(len(c)\) Output: true or false
1. Randomly choose clause \(c\) from \(HC\) and use \(len(c)\) as the length of \(c\);
2.if\(len(c)=1\)then
3.\(c=\)1;
4.\(HC\)=\(HC\backslash\{c\}\);
5.for each clause, including \(c^{\prime}\)v\(c\) in \(HC\), do
6.\(HC\)=\(HC\backslash\{c^{\prime}\)v\(c\}\);
7.endfor
8.for each clause, including \(c^{\prime\prime}\)v\(\
eg c\) in \(HC\), do
9.\(HC\)=\((HC\backslash\{c^{\prime\prime}\)v\(\
eg c\})\cup\{c^{\prime\prime}\}\);
10.endfor
11.return true;
12.else
13. Assigning truth for hard clauses (\(len\) (\(c\))-1);
14.return false;
15.endif
```  
**Algorithm 6** Assigning truth for hard clauses.  
In Algorithm 6, lines 2-11 indicate that if chosen clause \(c\) consists of only one literal logic, we simplify \(HC\) according to the two different cases in each iteration: when a clause including \(c^{\prime}\)v\(c\) exists, we remove it from \(HC\); when a clause including \(c^{\prime\prime}\)v\(\
eg c\) exists, we remove it from \(HC\) and insert \(c^{\prime\prime}\) into \(HC\). Next, if \(len(c)\)\(>\)1, line 13 recursively calls Algorithm 6.  
**Theorem 2**.: _For each given clause \(c\), there is a truth assignment for \(c\) using Algorithm 6._  
Proof.: On the basis of Sections 3.2.1 and 3.2.2, we can search for the truth assignment from line 2 of the algorithm using the method of mathematical induction, and detailed descriptions are omitted owing to limited space.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 5 Discussion--Open Issues  
The aforementioned works are also summarized in Table 2. To this end, the key considerations along with the adopted methodology per case are presented, along with open issues and limitations. As it became apparent from the previous analysis, the studied works consider specific use case scenarios, while at the same time addressing a subset of the IEC integration challenges, as described in Section 1. In the same context, additional key points can be outlined. \begin{table}
\begin{tabular}{c c c c c} \hline
**Survey Paper** & **Year** & **Contributions** & **Methodology** & **Limitations—Open Issues** \\ \hline
[75] & 2019 & Decentralized hybrid cloud MEC architecture & AI, security and privacy, serverless computing & Evaluation in real world scenarios \\ \hline
[76] & 2023 & Localization of IoT devices & DNS naming, IPv6 & Secure communication and multi-user management \\ \hline
[77] & 2022 & Resources integration via virtualization & ML for resource optimization, agnostic application development & Security mechanisms, efficient task offloading \\ \hline
[78] & 2023 & Parallel data ingestion & ML for resource optimization, ensemble learning & Security mechanisms \\ \hline
[79] & 2021 & Data reduction prior to cloud processing & Feature learning & Extension of the proposed framework in additional applications \\ \hline
[80] & 2022 & Multiple edge/cloud providers & Resource allocation and sharing optimization model & Evaluation in real world scenarios \\ \hline
[81] & 2021 & Node utilization in IoT environments & Security via blockchain technology & Evaluation in real world scenarios \\ \hline
[82] & 2022 & Task failure minimization & Task offloading according to network, security, and latency requirements & Optimum task offloading for complex applications \\ \hline
[83] & 2021 & Efficient task offloading via security mechanisms & Advanced encryption standard cryptographic technique & ML for efficient computational offloading \\  & & & Low complexity load balancing and computation offloading & \\ \hline
[84] & 2021 & IoT microservice deployment problem & Deep reinforcement learning & Load balancing in multi-cloud environment deployment strategies \\  & & & Deep reinforcement learning & Decentralized training approaches for privacy enhancement \\ \hline
[86] & 2023 & Delay, job failure, computational overhead, energy consumption improvement & Deep Post-Decision State (PDS) for learning efficiency & ML in large-scale MEC orientations \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the presented studies—key considerations, limitations, and open issues.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 1 Introduction
### Notation  
In our method description below, we denote vectors as bold lowercase letters, e.g., \(\mathbf{v}\), matrices as bold capital letters, e.g., \(\mathbf{M}\), and scalars as lowercase italic letters, e.g., \(s\). We use the notation \(\|\cdot\|\) as a shorthand for \(\|\cdot\|_{2}\), i.e., the Euclidean norm. We represent images as functions, \(\mathcal{I}:\Omega\rightarrow\mathbb{R}^{3}\) for 3-channel color images, and \(\mathcal{I}:\Omega\rightarrow\mathbb{R}\) for gray-scale images where \(\Omega\subset\mathbb{R}^{2}\) is the image domain. Sets are denoted by capital script letters, e.g., \(\mathcal{A}\), and the number of elements in a set is given by \(|\mathcal{A}|\). See Appendix A for an overview of Lie groups and rigid transformations used in this paper.
# A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics
## 1 Introduction
This paper proposes a new method for design of validation experiments in computational mechanics called response-adaptive experiment design (RAED). RAED is devised to reduce type II errors. When the number of experimental data is limited, RAED allocates available resources in a systematic manner within the validation domain so that the possibility of rejecting an invalid computational model is increased. It should be noted that the terms and concepts of validation are numerous and complex. In different research communities such as machine learning (ML), cross validation is a generally-accepted model validation technique. It assesses how the results of data-driven models such as multi-class support vector machines (MC-SVM) will generalize to an independent data set [13]. However, cross validation techniques do not provide the user with decisions (i.e., accept or reject) whether the data-driven models after training should be accepted for intended use or not. It is the user's responsibility to accept trained data-driven models for intended use or not. To address this challenge, the RAED method in the paper is proposed for the physics-based models in computational mechanics that are typically governed by [PERSON]'s laws of motion, and laws of thermodynamics and fluid dynamics. The remainder of this paper is organized as follows. Section 2 provides an overview of hypothesis testing for the validity check. Section 3 explains the methodology of the proposed RAED method. In Section 4, the effectiveness of RAED for reducing type II error is demonstrated through a numerical example. Finally, Section 5 offers conclusions and discusses future work.
# Radiofrequency Performance Analysis of Metal-Insulator-Graphene Diodes
## 3 Model-Based Performance Projection of MIG Rectifiers  
### Rectifying Metrics from DC Characteristics  
To evaluate the performance characteristics of the MIG diode, three key figures of merit (FoMs) were considered: the DC responsivity \(f_{\text{RES}}\), the nonlinearity \(f_{\text{NL}}\), and the asymmetry factor \(f_{\text{ASYM}}\). As for \(f_{\text{RES}}\), it was defined as the ratio of the second derivative of the current with respect to the differential conductance, as shown in Equation (1). This FoM provided a first approximation of the output current increase with respect to the input power under square-law operation [22].  
\[f_{\text{RES}}=\frac{1}{2}\cdot\frac{d^{2}I}{dV^{2}}\bigg{(}\frac{dI}{dV}\bigg{)} ^{-1} \tag{1}\]  
Figure 3: Scattering parameters. Magnitude in dB: (**a**) \(S_{11}\) and \(S_{22}\), (**b**) \(S_{12}\) and \(S_{21}\), (**c**) Phase of the scattering parameters.  
Figure 2: Large-signal equivalent circuit of the MIG diode [16]. Intrinsic elements are within the dashed box. The extrinsic (outside the dashed box) component values are \(C_{\text{fp}}=0.1\) fF, \(C_{\text{f}}=7.3\) fF, \(C_{\text{pa}}=C_{\text{pc}}=5.5\) fF, \(L_{\text{a}}=L_{\text{c}}=13\) pH, \(R_{\text{a}}=R_{\text{c}}=4.7\,\Omega\), and \(R_{\text{contact}}=150\,\Omega\).  
Moving on to \(f_{\text{NL}}\), this accounted for the diode nonlinearity and is expressed as the ratio of the differential conductance with respect to the conductance, as described in Equation (2),  
\[f_{\text{NL}}=\frac{dI}{dV}\bigg{(}\frac{I}{V}\bigg{)}^{-1} \tag{2}\]  
which represents the nonlinear response of the current with respect to the applied voltage.  
Lastly, \(f_{\text{ASYM}}\) is defined as the absolute ratio of forward \(I_{\text{F}}\) to reverse \(I_{\text{R}}\) current, as shown in Equation (3), reflecting the balance between the positive and negative bias conditions.  
\[f_{\text{ASYM}}=\bigg{|}\frac{I_{\text{F}}}{I_{\text{R}}}\bigg{|} \tag{3}\]  
The resulting values of these FoMs as a function of bias are presented in Figure 4 for the MIG diode under study (cf. Figure 1), at room temperature (295 K) and with a voltage range between \(-1\) V and 1 V. In Figure 4a, it can be observed that the highest responsivity value (14.65 V\({}^{-1}\)) was achieved at 0 V, demonstrating that at this voltage, the diode was more sensitive to voltage changes and therefore suitable for zero-bias power detection. On the other hand, the nonlinearity showed an abrupt change near zero volts, as a consequence of the transition in bias. The highest nonlinearity value was reached under a large forward bias, i.e., 6.64 at 0.7 V. The asymmetry of the diode, depicted in Figure 4b, points to a significantly higher conduction under forward bias compared to reverse bias, as a result of the different injection mechanisms between the graphene and anode contact, with a value of 806.6 at 1 V.
# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 6 Conclusions and Future Work  
In this study, a novel person re-identification method based on dual pyramids of a scale pyramid and a part pyramid was proposed for more accurate person re-identification results by extracting the visual feature elements of various sizes and shapes appearing in different regions of a person image. The scale pyramid applied to the proposed model enables more diverse and accurate feature extraction by allowing different sizes that include the rectangular-shaped feature receptive fields as well as the square-shaped feature receptive fields used in the previous studies with neural networks. The part pyramid allows various regions of an image to form innate feature embedding, thereby creating  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Rank-1**} & \multicolumn{1}{c}{**Additional**} \\ \cline{2-4}  & **DukeMTMC [18]** & **Market-1501 [19]** & **Meta-Information** \\ \hline DL multi-scale Representations [5] & 79.2\% & 88.9\% & Not used \\ PCB [10] & 83.3\% & 92.3\% & Not used \\ Horizontal Pyramid Matching [12] & 86.6\% & 94.2\% & Not used \\ OSNet [8] & 88.6\% & 94.8\% & Not used \\ Pyramidal Person Re-Id [11] & 89.0\% & 95.7\% & Not used \\ Viewpoint-Aware Loss [4] & 91.61\% & 96.79\% & View-point info. \\ St-reID [3] & 94.00\% & 98.00\% & Spatio-temporal info. \\
**Ours** & **94.79\%** & **99.25\%** & Not used \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison with recent studies [3; 4; 5; 6; 7; 8; 9; 10; 11; 12].  
feature embedding that reflects detailed visual feature elements that exist in each region for more accurate person re-identification. In addition, since the proposed method shows superior Rank-1 accuracy to the method using only the square-shaped features or the method using only global feature embedding, the multi-scaled regional features used by the dual pyramid structure are shown to be valuable in person re-identification.  
In the future, in order to obtain more accurate re-identification results, research is planned to select relatively more important areas for each region using the attention mechanism during the extraction of regional multi-scale features.  
Conceptualization, K.S.; methodology, K.S.; software, K.S.; validation, K.S. and Y.-S.K.; investigation, K.S.; resources, K.S. and Y.-S.K.; writing, original draft preparation, K.S.; writing, review and editing, K.S. and Y.-S.K.; visualization, K.S.; supervision, Y.-S.K.; project administration, Y.-S.K. All authors read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00203, Development of 5G-based Predictive Visual Security Technology for Preemptive Threat Response).  
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
# Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model
## 1 Introduction
Both approaches provide valuable information that helps cybersecurity professionals defend against the ever-evolving threat landscape. Malware analysis is an indispensable component of modern cybersecurity practices. Its contributions to understanding the intricacies of malicious software, developing countermeasures, and enhancing overall security defenses make it a vital area of research and a key factor in safeguarding the digital ecosystem against emerging threats. In previous research, dynamic and static API approaches have primarily been employed independently, with only a few instances of their combined application. This study introduces a novel hybrid model that relies on dynamic API call sequences for malware detection. The model incorporates machine learning logistic regression as an initial weight input for the neural network. This approach offers several advantages, including enhanced malware detection accuracy and mitigation of vanishing and exploding gradients in the neural network. Our hybrid model's innovation lies in its weight initialization process for neural networks, an area that continues to attract research interest. In this paper, we propose a hybrid model that utilizes weights derived from a machine learning model, specifically logistic regression, and integrates them as initial weights in a recurrent neural network model. This unique combination effectively addresses the challenges associated with weight initialization in neural networks, while maximizing the detection capabilities of both dynamic and static API methods. The rest of the paper is organized as follows. In Section 2, we summarize some previous studies and literature reviews that addressed malware analysis and detection using API calls, machine learning, and deep learning approaches. In Section 3, we elaborate on our proposed hybrid model. In Section 4, we present the results and discussion. Lastly, in Section 5, we conclude our paper and suggest future areas of work.
## 3 Technological Advancements in Bee-Based Biosensors/Biomonitors
### Acoustic Detection of Colony Pests, Diseases, and Chemicals  
On a parallel track with our military-sponsored development of Smart Hives, we developed the use of recordings of hive sounds, first for early detection of airborne threats from toxic chemicals under a SBIR (Small Business Innovation Research) contract from the US Army Center for Environmental Health Research, then for detection of the presence of bee-pests such as mite and bee diseases under SBIR awards from the US Department of Agriculture, with additional funding from the Montana Department of Commerce Board of Research. The resultant methods and scanners are described by US and Canadian patents [49; 94; 95].  
Recently, we have just completed another USDA SBIR I project focused on the detection of acute and chronic exposures of bee colonies to pesticides, especially the neonicotinoid pesticides. Preliminary data reveals that while honey bee colonies tend to become noisier when exposed to many of the older organic insecticides, colonies become quieter when exposed to neonicotinoids [96]. Our most significant finding across all of these studies is that not only are the sounds that bee colonies produce altered when the bees are exposed to different stressors, but the colony sounds change in ways which can delineate the type of chemical, pest, and disease (Figures 9a,b and 10a,b) [94, 95, 96].  
Figure 9: (**a**) Instrument specifications for hand-held acoustic scanner (HAS) for detection of bee pests, diseases, and exposures to harmful chemicals; Bee Alert Technology, Inc., (Missoula, Montana, USA); (**b**) HAS acoustic scanner. A probe microphone is slid into the colony entrance and a 60 s recording taken. The device reads out the detections and probability of detection (e.g., queenless, 95% probability; varroa mites, 28% probability; Africanized bees, 97%, and so forth).  
Figure 10: **(a)** Principal Component Analysis (PCA) of honey bee sonograms in response to exposure to a variety of chemicals, published in the patent disclosure [94]; **(b)** PCA of honey bee sonograms relative to infestation rates of varroa mites [95].  
Other investigators are looking at bee and colony sounds. A research initiative in the UK is currently funding acoustic research. Arnia uses hive sounds in their hive electronics as a generalized indicator of hive activity and for detection of swarming [97, 98], as evidenced by a warble in the sonogram of a bee colony (Figure 11). In the US, [PERSON] recently patented [99] an external acoustic sensor to monitor bee flight activity.