esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Malware Detection with Conventional Machine Learning Based Methods  
Machine learning plays an important role to capture helpful properties in malware to advance security measures. This whole process of knowledge extraction and learning of patterns helped the researchers to pave their steps into machine learning-based malware analysis and detection. Machine learning has been extensively used not only in malware detection but also for detecting malicious activity through network traffic [26]. Ref. [27] worked on Belief propagation with the file system but could not do well for new samples. Ref. [28] conducted malicious graph matching and extracted APIs/System calls but they used a small dataset. Ref. [29] used a Rule-based classifier and SVM and performed detection based on byte sequences but made use of only specific malware classes for evaluating their model. They built datasets from Windows system files and the Anti-Virus Platform. Ref. [30] also used a Rule-Based Classifier and extracted APIs/System calls but this APIs/System calls categorization was not up to the mark. They conducted their tests on features of the Windows XP system and Program Files folders. Authors of [31,32] used Random Forest and used network and API system calls, Registry, and File system but the dataset was small. Ref. [33] used Decision Trees in their research work and [34] used Naive Bayes, Random Forest, and SVM and worked on byte sequences, APIs/system calls, file systems, and Windows registry. Ref. [35] used KNN for detecting malicious PEs. Malware code causes damage to the resources, and with a little code change, malware developers can easily beat the protection layer. A lot of research was done for the detection of these variants. Ref. [36] explored the Decision Tree and Random Forest and made use of Opcodes. They used small datasets of Windows XP system and Program Files folders and generated code of malware for making part of the dataset. Ref. [37] performed  
Figure 3: Performance Metrics Used in Literature Proposing Primitive Methods for Malware Detection. Clustering with locality-sensitive hashing Byte sequences but the used dataset was very small. Ref. [38] worked on a Rule-based classifier, they worked on APIs/System calls, and Windows Registry. Ref. [39] used the clustering technique which was being used for variants detection by past researchers also. The authors chose DBSCAN but their approach was not coping with malware evasion techniques. Ref. [40] worked on Logistic Regression and Neural Networks and operated on Byte sequences and APIs/system calls. Table 2 shows the datasets and performance metrics used by the researchers in the surveyed papers that apply conventional machine learning algorithms.
# Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data
## 3 Algorithm for 3D Geometric Representation
### Data Refinement
#### 3.2.3 Structural Member Segmentation  
The candidates of the main structural members generated through Sections 3.2.2 and 3.2.3 are categorized into three groups: left and right walls, front and back walls, and ceilings and floors. First, the normal vector of the candidates is rounded up. Based on this, the candidates are divided as follows: candidates with a normal vector (\(\pm\)1, 0, 0) into the left (\(N_{left}\)) and right walls (\(N_{right}\)); candidates with a normal vector (0, \(\pm\)1, 0) into the front (\(N_{front}\)) and back walls (\(N_{back}\)); and candidates with a normal vector (0, 0, \(\pm\)1) into the ceiling (\(N_{ceiling}\)) and the floor(\(N_{floor}\)). Redundant candidates that have low qualifications to the three main structural members are deleted to minimize the errors of these processes to generate the 3D indoor representation. The raw data refined through Section 3.2 become 9D data by combining the normal vector and main structural member index data into the existing 5D data. Then, based on the refined results, data processing is performed to generate a 3D indoor representation of each room from 9D data.
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 3 Materials and Methods
### PBI-EdgeNeXt
#### 3.3.3 Loss Function  
When training deep neural networks, cross-entropy loss [28] and focal loss [29] are the most common choices. Most research works deal with class balanced datasets, which can achieve ideal results by using the traditional loss function for model training. This paper aims to improve the recognition accuracy of the model in practical applications. However, in real tasks, the collection of a fine-tuning dataset is inevitably unbalanced between classes. EdgeNeXt [25] uses cross-entropy loss, which may not be a good loss function against the problems caused by unbalanced categories of datasets [30]. A loss function commonly used to deal with unbalanced data tasks is focal loss. However, the loss function needs to fine-tune the super parameters and carry out experimental tests [29]. However, in practical applications, engineers often hope to find a simpler and more effective solution. Therefore, this paper introduces the PolyLoss [31] function to train the PBI-EdgeNeXt model, and its formula is shown in Formula (4):  
\[Loss_{focal}(p,y)=-\alpha(1-p_{y})^{\gamma}log(p_{y}) \tag{3}\]  
\[Loss_{Poly}(p,y)=-log(p_{y})+\sum_{j=1}^{N}\epsilon_{j}(1-p_{y})^{j} \tag{4}\]  
where \(p_{y}\) is the prediction probability of the model for the correct category \(y\), \(\alpha\) is the weight used to balance positive and negative samples, \(\gamma\) is the focus parameter for adjusting the weights of easy-to-classify samples, and \(\epsilon\) is the polynomial coefficient.  
As shown in Formula (3), there are many hyperparameters that need to be adjusted when using the focal loss function. PolyLoss provides a flexible framework that can adjust polynomial bases based on different tasks and data to improve performance. For imbalanced datasets, the loss function uses an adaptive penalty mechanism to effectivelyincrease the model's focus on the minority class. Specifically, we use the Poly-1 loss function, which is expressed as follows:  
\[Loss_{Poly-1}(p,y)=-log(p_{y})+\epsilon(1-p_{y}) \tag{5}\]  
We introduce the Poly-1 loss function, which allows us to adapt it to new tasks and datasets by adjusting only one hyperparameter, that is, the coefficient of the first-order polynomial, \(\epsilon\).
# Low Delay Inter-Packet Coding in Vehicular Networks
## 3 Packet Recovering Codes
### Binary Wyner-Ash codes
#### Encoding  
From Equation (7) follows a simple implementation of the systematic encoder. This encoder contains in total five delay elements, whereas for the minimal non-systematic encoder only two delay elements are required (see [18] for details). However, for the considered application, it is not important since we do not use a trellis representation of the code for ML-decoding over the BEC. The decoding complexity is at most \(\
u^{3}\), where \(\
u\) is the number of corrected erasures.  
Encoding delay is equal to the encoder block length \(n=2^{m}\) since each parity-check bit is computed immediately after receiving a new block of \(k=n-1\) latest information bits.
## 3 Results and Discussion  
### Friction Angle  
Normal force and cutting force were recorded using a tribometer and the results are presented in Figure 4. Values of \(F_{n}\)/\(F_{c}\) before and after data processing are compared and shown in Figure 4a, while the measurements of \(F_{n}\)/\(F_{c}\) at various back rake angles are presented in Figure 4b. Based on these results, it can be concluded that \(F_{n}\)/\(F_{c}\) is reduced as the back rake angle is increased.  
The friction angles determined from the experimental measurements at different back rake angles are presented in Figure 5. In both dry conditions and the drilling fluid environment, the friction angle is determined to be negatively related to back rake angle in general. Variations of friction angles with back rake angles have also been reported by other researchers, although it is commonly regarded as a constant for rock [4; 7; 21]. In this paper, an explanation and a model is proposed by analyzing the rock flow regime ahead of the cutter [21], which is schematically shown in Figure 6. It can clearly be seen that rock material ahead of the cutter has two flow directions, namely forward to the cutter top and backward to the cutter bottom. Obviously, the portion of rock material moving forward depends on the inclination of the cutter, which, in this study, equates to the back rake angle. More rock material  
Figure 4: Variation of \(F_{n}\)/\(F_{c}\) with time. (**a**) Original data and treated data after averaging (at back rake angle of 65\({}^{\circ}\)), (**b**) at different back rake angle.  
flows backward when the back rake angle increases. The ideal friction angle, which is a constant for a specific rock, is determined with the assumption that all rock material moves forward during the cutting process. As a result, the dependence of rock volume flowing forward on the back rake angle leads to changes in the friction angle.  
An approximately linear decrease of the friction angle with an increasing back rake angle was observed in both dry conditions and in drilling fluid media. This result is in accordance with previous studies by [PERSON] et al. and [PERSON] et al. [7; 21]. Linear fitting was performed for both cases. The resulting lines are shown in Figure 5, where the slopes of the two fitted lines are closely aligned. The friction angle in the dry condition was approximately 8\({}^{\circ}\) lower than that in the drilling fluid media. One possible explanation is that the infiltration of drilling fluid into rock changes the properties of the rock. All natural rocks have a measure of porosity which allows for liquid to permeate the rock, leading to a change in the mechanical properties of the rock and friction angle.
## 2 IoT in the Maritime Industry
### Evolution and Growth of IoT in the Maritime Industry  
The use of IoT in the maritime sector has been a gradual yet transformative process, evolving in tandem with technological advancements and the industry's growing need for optimization [43].  
In the early stages, IoT was employed primarily for simple tracking and monitoring purposes. The use of Automatic Identification Systems (AIS) and Radio Frequency Identification (RFID) systems, for example, improved the tracking of ships and cargo, providing real-time updates about location, speed, and direction.  
The advent of more advanced technologies saw the role of IoT expand significantly. Today, IoT is integral to'smart shipping'--a concept where vessels are equipped with myriad sensors and devices that monitor various parameters, from engine performance and fuel consumption to weather conditions and crew health. This data is processed and analyzed to optimize operations, enhance safety, improve efficiency, and reduce environmental impact [23; 25; 44; 45; 46].  
On the other hand,'smart ports' use IoT technologies to streamline operations, automate processes, and improve logistics. IoT devices installed at ports can monitor and control cargo handling, track vehicles and equipment, and automate gate operations and container movement, among other factors. These improvements have resulted in decreased waiting times, enhanced cargo turnover, and minimized human errors [47; 48; 49].  
Furthermore, IoT has found applications in maritime safety and security, environmental protection, and energy efficiency. For instance, IoT devices can detect illegal activities such as piracy or smuggling, monitor emissions to ensure compliance with environmental regulations, and optimize energy use in various operations [49; 50; 51].  
The growth of IoT in the maritime industry is expected to continue, driven by the industry's need for digital transformation, the rise of autonomous shipping, increasing environmental concerns, and advancements in technology. The global maritime IoT market, valued at $4.15 billion in 2020, is projected to reach $21.36 billion by 2027, reflecting the burgeoning importance of IoT in this sector [52; 53].  
Despite this growth, the adoption of IoT in the maritime industry is not without its challenges. Issues such as data security, privacy, interoperability, and the high cost of implementation need to be addressed to fully realize IoT's potential in the maritime industry. The following sections delve deeper into these aspects, providing a comprehensive overview of the state of IoT in the maritime sector [54].
## 4 Durability Properties of GPC  
Durability of any structural component is its ability to resist weathering action, chemical attack, abrasion, or any process of deterioration. It is considered as an important component for structural performance in aggressive environments. The durability of GPC can be determined by the properties like sorptivity, immersed absorption, water absorption, apparent volume of permeable voids (AVPV), chloride ingress, sulphate, or other acid attack [153]. In general, GPC has significant higher durability compared to ordinary OPC [154; 155; 156; 157]. The summary of properties that are used to determine the durability of GPC is presented in Table 8. The impact of each properties is analyzed and scaled from'very strong' to'strong' and 'average'. [PERSON] et al. [158] compared GPC prepared with FA and slag and found that slag-based GPC exhibit better durability performance than FA-based GPC due to stable geopolymeric structure. However, FA-based GPC showed better performance than ordinary concrete that might be due to the presence of chemical activity of Na+ and K+ ions when exposed to acidic environment. Likewise, inclusion of both slag and black rise husk ash (BRHA) improves the microstructural properties. [PERSON] et al. [159] found that the increase in BRHA content improved the durability of GPC because of reduced chloride ingress and sorptivity. The durability performance of GPC replaced with recycled coarse aggregate were studied by [PERSON] et al. and [PERSON] [61; 160]. It was reported that ordinary concrete deteriorates more when exposed to an aggressive acidic environment than recycled aggregate GPC. It was found that with an increase in silicate/hydroxide ratio, the microstructural and durability performance of MK-based GPC increases because of strong interfacial transition zone (ITZ) between recycled aggregates and binder [61]. Hence, it is concluded that durability performance of GPC compared to OPC concrete is higher due to the presence of a complex geopolymeric structure that enables the GPC to resist the acid attacks.
## 1 Introduction
Current envelopes are formed by locating and connecting the local maxima at each cycle of device operation. The time window length of these envelopes is shorter than that of original current signatures, as the envelopes initiate from the moment when a device is turned on and end at a stable point of operation. From these envelopes, time-series shapelets are determined, which provide feature components to identify devices. In COOLL public NILM dataset, there are 42 devices of different brands and power ratings. Each device has 20 instances of data sets; hence there are 840 datasets in the entire database. Current and voltage data of 6 s are recorded with a sampling frequency of 100 kHz. This work focuses on the current signatures of the devices, since the variations in current data are more prominent than those in voltage signatures. However, the devices are of major residential load types. The device types and number of each type are as follows--Drill (6), Fan (2), Grinder (2), Hair Dryer (4), Hedge Trimmer (3), Lamp (4), Paint Stripper (1), Planer (1), Router (1), Sander (3), Saw (8) and Vacuum Cleaner (7). The developed classification model has 12 classes, which correspond to the types of device instances in COOLL database. The model comprises five supervised learning algorithms--ensemble, binary decision tree (DT), discriminant analysis, naive Bayes and \(k\)-nearest neighbors (\(k\)-NN). 80% of the database is employed as the training data, whereas 20% is employed as the testing data. The proposed system is tested in MATLAB(r). A comparative study on the classification performance of the applied algorithms is presented in this paper. The most accurate classification results of the proposed multi-class system are obtained for ensemble, in where the training accuracy is 100% and the testing accuracy is more than 95%. The major contributions of this work are as follows. 1. Determines current envelopes from the original normalized signatures. Introduces a concept of features extraction by determining time-series shapelets from the envelopes based on a unique searching and matching technique. This technique depends on an implicit analysis of the fundamental behavior of device current. Develops a multi-class classification model trained and tested by these current shapelets. The remainder of the manuscript is organized as follows. Section 2 explains the proposed method, in where time-series shapelets are briefly explained and the implemented shapelets extraction method is described. Section 3 subsumes the experiments and results, in where the supervised learning methods are studied and different casework analyses are reported to present the performance evaluations of the developed device classification model. Section 4 briefly presents the specific contributions and future prospects of the proposed work. Finally, Section 5 concludes the article.
## 2 Experimental Investigation  
### Implementation of Test Equipment  
The experiments in this study were conducted on a self-developed large-scale model test platform designed to simulate gravity and strong seismic surface rupture. The core components of the platform included a soil box for the slope and overlying soil layers (dimensions: 4.96 m \(\times\) 1.85 m \(\times\) 1.4 m), a simulated bedrock fault dislocation system, and a hydraulic synchronous drive system, as shown in Figure 1. The simulated bedrock fault dislocation system comprised high-strength ribbed double-layer steel plates to simulate the relative movement of the fault blocks, a reaction support base, and a portable angle adjustment module, enabling the simulation of fault slip between the footwall and hanging wall (active and passive blocks). A hydraulic synchronous drive system was used, comprising a PLC control console with displacement loading software, four sets of hydraulic actuators, displacement lines, and hydraulic pipes connecting the control console and actuators, allowing a precise control of the bedrock plate movement and its rate. The DHDAS V1.11 software (provided by Jiangsu Donghua Testing Technology Co., Ltd., Taizhou, Jiangsu, China) was employed as the data acquisition system.  
Figure 1: Implementation diagram of physical model test equipment for cross-bedrock fault slope.  
The portable angle adjustment module of the test platform could help to adjust the fault slip angle and type, enabling multi-condition and simulation experiments. In this experiment, the fault slip type was a normal fault, with a slip angle of 70\({}^{\circ}\). During the experiment, the actuators were controlled to simulate bedrock dislocation at an empirically set rate of 1 mm/s. At the start of the experiment, the active block steel plate began to slip. Data were recorded each time the actuators lifted by 10 mm. High-definition video recordings of the front, top, and side views of the experimental phenomena were also taken to capture a complete set of test conditions.
## 5 AI-Based Cyber Threat Solutions in X
### Ensemble and Hybrid Learning
An ensemble of Deep Belief Network (DBN), Bidirectional Gated Recurrent Unit (BiGRU), and Autoencoder (AE) models is then employed to identify cyber threats. To further refine the model, a Modified Gray Wolf Optimizer (MGWO) is integrated for hyperparameter tuning, maximizing the ensemble's performance. Rigorous simulations on a benchmark database demonstrate IRSO-EDLCS's superior performance compared to existing methods, highlighting its potential to significantly advance IIoT cybersecurity, Table 1 presents the summary of the related work. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Year** & **Focus Area** & **Techniques/Models** & **Results** \\ \hline
[120] & 2021 & X bot detection & \
\begin{tabular}{c} Weight of Evidence encoding, \\ Extra Trees (feature selection), \\ Random Forest (blending) \\ \end{tabular} &
\begin{tabular}{c} 93\% AUC; rapid threat \\ detection with static profile \\ data but less effective than \\ behavioral analysis methods \\ \end{tabular} \\ \hline
[121] & 2022 & \begin{tabular}{c} IoT network attack \\ detection \\ \end{tabular} & \
\begin{tabular}{c} Unsupervised ensemble \\ learning, Deep Belief \\ Network (DBN) \\ \end{tabular} &
\begin{tabular}{c} 97.5\% detection accuracy, \\ 2.3\% false alarm rate \\ \end{tabular} \\ \hline
[122] & 2022 & \begin{tabular}{c} Hate speech detection \\ during COVID-19 \\ \end{tabular} & \
\begin{tabular}{c} Decision Tree, Stochastic \\ Gradient Boosting, TF-IDF, \\ Bag of Words, tweet length \\ \end{tabular} &
\begin{tabular}{c} Stochastic Gradient Boosting: \\ 99\% precision, 97\% recall, \\ 98\% F1-score, \\ 98.04\% accuracy \\ \end{tabular} \\ \hline
[123] & 2022 & Cyber threat detection & \
\begin{tabular}{c} Ensemble of deep \\ learning classifiers \\ \end{tabular} &
\begin{tabular}{c} Effective detection of novel \\ cyber threats, adaptable \\ Intrusion Detection \\ System (IDS) \\ \end{tabular} \\ \hline
[124] & 2023 & \begin{tabular}{c} Cyberbullying detection \\ on X \\ \end{tabular} & \
\begin{tabular}{c} Ensemble stacking, deep \\ neural networks (DNNs), \\ BERT-M, word2 vec, CBOW \\ \end{tabular} &
\begin{tabular}{c} 97.4\% accuracy on X dataset, \\ 90.97\% on combined X \\ andFacebook dataset, \\ F1-score: 0.964, precision: \\ 0.950, recall: 0.92 \\ \end{tabular} \\ \hline
[125] & 2023 & \begin{tabular}{c} Crime-related tweet \\ classification \\ \end{tabular} & \
\begin{tabular}{c} Logistic Regression, Support \\ Vector Machine, k-Nearest \\ Neighbors, Decision Tree, \\ Random Forest, TF-IDF \\ \end{tabular} &
\begin{tabular}{c} 96.2\% accuracy using soft \\ weighted Voting classifier \\ \end{tabular} \\ \hline
[126] & 2024 & Spam URL detection on X & \
\begin{tabular}{c} k-Nearest Neighbors, \\ bagging, Random Forest, \\ URL content, user profile, \\ hybrid features \\ \end{tabular} &
\begin{tabular}{c} High accuracy (>90\%) when \\ using combined feature sets \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of related work.