esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Finite State Markov Channel Modeling  
The channel coefficients between source-to-relay and relay-to-destination links are assumed to be modeled with the Markovian process with three states representing a transition between Rayleigh, Rician, and Nakagami fading environments as shown in Figure 2.  
State-A represents Rayleigh fading environment, state-B represents Rician fading environment and State-C represents Nakagami fading environment with \(P_{AB}\) being transition probability from Rayleigh to Rician fading, \(P_{BC}\) being transition probability from Rician to Nakagami fading and \(P_{CA}\) represents transition probability with Nakagami to Rayleigh fading environment and vice versa. These transition probabilities can be given by transition probability matrix P:  
\[P=\left[\begin{array}{ccc}P_{AA}&P_{AB}&P_{AC}\\ P_{BA}&P_{BB}&P_{BC}\\ P_{CA}&P_{CB}&P_{CC}\\ \end{array}\right] \tag{13}\]  
and corresponding steady state vector for three state Markov chain is given by:  
\[\Pi_{3 states}=[\pi_{1}\pi_{2}\pi_{3}] \tag{14}\]  
where \(\pi\) is the steady state probability vector and \(\pi_{1},~{}~{}\pi_{2},~{}~{}\pi_{3}\) are steady state probabilities for state A, state B, and state C respectively.  
Based on the values defined in Equations (13) and (14) value of \(p_{\gamma_{l}}\) can be determined, which follow Rayleigh, Rician, or Nakagami fading environment.
## 3 Scheduling Model of Underground Trackless Transportation
### Description of Mathematical Model of Ramp Transportation
#### 3.3.2 Cost Calculation of the Scheduling Model  
The model operation cost is calculated in three aspects: traffic priority cost calculation, waiting time cost calculation, and travel distance cost calculation.  
(1) Priority cost calculation: According to the existing scheduling priority principle of the Sanshan Island mining area, the trackless transport vehicle traffic on the slope is the first mover, and only vehicles running in one direction are allowed between the two passing bays. Vehicles on the ramp road pass first and then go down, and they first enter the contact road and then exit the contact road. Only vehicles running in one direction are allowed between the two links. The mine vehicle has highest priority; the forklift has second highest priority; and the other, lowest-priority vehicles are the different transport vehicles, giving different traffic priority coefficients. The priority coefficient \(\delta\) is introduced as follows:  
\[\min\ C\ (x)=\sum t_{i}\delta \tag{1}\]  
\[\delta=\left\{\begin{aligned} 0.2,\text{ Vehicles transporting equipment or materials}\\ 0.5,\text{Utility vehicles or cargo carriers designed for human transportation}\\ 1,\text{Ore haulage trucks.}\end{aligned}\right.\]  
At this time, when the trackless transport vehicle is given the priority coefficient \(\delta\), the system compares the loss of the two vehicles according to the principle of the peer priority principle when the two cars meet C\({}_{1}\)(x), C\({}_{2}\)(x). Because the waiting time of the trackless transport vehicles is generally roughly the same, the loss of the two cars is positively correlated with the peer priority coefficient of the two cars. If and only if the trackless transport vehicles in a certain direction are greater than a certain number,  
\[\text{C}_{\text{overall}}(x)=\sum C_{n}(x) \tag{2}\]  
(2) Waiting time cost calculation: the waiting time of trackless transport vehicles at the passing bay and the scheduling time of the transportation task interval are included in the total waiting time, which belongs to the time cost of scheduling.  
The waiting time at the passing bays is  
\[\mathrm{T_{1}(x)=\sum_{a=1}^{N}t_{a}} \tag{3}\]  
The scheduling time of the transportation task interval is  
\[\mathrm{T_{2}(x)=\sum_{a=1}^{N}t_{a}^{\prime}} \tag{4}\]  
So, the total time cost is  
\[\mathrm{min\ T(x)=\sum_{a=1}^{N}t_{a}+\sum_{a=1}^{N}t_{a}^{\prime}} \tag{5}\]  
(3) The shortest travel distance principle: suppose that the transportation task running distance of the transport vehicle with serial number A is \(\mathrm{S_{a}}\), the task interval scheduling distance is  
\[\mathrm{min\ S\ (x)=\sum_{a=1}^{A}(S_{a}+S_{a}^{\prime})} \tag{6}\]
# An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level
###### Abstract  
Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art systems also face tremendous challenges to satisfy real-time analysis requirements due to the major delay of the flow-based data preprocessing, i.e., requiring time for accumulating the packets into particular flows and then extracting features. If detecting malicious traffic can be done at the packet level, detecting time will be significantly reduced, which makes the online real-time malicious traffic detection based on deep learning technologies become very promising. With the goal of accelerating the whole detection process by considering a packet level classification, which has not been studied in the literature, in this research, we propose a novel approach in building the malicious classification system with the primary support of word embedding and the LSTM model. Specifically, we propose a novel word embedding mechanism to extract packet semantic meanings and adopt LSTM to learn the temporal relation among fields in the packet header and for further classifying whether an incoming packet is normal or a part of malicious traffic. The evaluation results on ISCX2012, USTC-TFC2016, IoT dataset from Robert Gordon University and IoT dataset collected on our Mirai Botnet show that our approach is competitive to the prior literature which detects malicious traffic at the flow level. While the network traffic is booming year by year, our first attempt can inspire the research community to exploit the advantages of deep learning to build effective IDSs without suffering significant detection delay.  
deep learning for network security; long short-term memory; malicious traffic classification Article  
An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level
###### Abstract
In contrast, cascading controllers, which typically involve multiple layers of control strategies, provide enhanced responsiveness and robustness against disturbances. Nevertheless, these cascading systems can become complex, making parameter tuning more challenging [29; 30]. Both controller types may also encounter difficulties under extreme operating conditions, where saturation effects can lead to inadequate compensation and system instability. Thus, while PI, FOPI, and cascading controllers are valuable for many applications, their limitations highlight the need for meticulous design and tuning to optimize performance in real-world scenarios. This paper elaborates on the application of cascading Proportional-Integral (PI-PI) and cascading Fractional-Order PI (FOPI-FOPI) controllers for DSTATCOMs in hybrid microgrids that incorporate photovoltaic (PV) systems and fuel cells. A novel hybrid optimization algorithm has been developed to accurately tune the proposed controller and enhance power quality. Table 1 presents a comparative analysis of this work alongside existing studies. The principal novel contributions of this research are outlined as follows:  
* A new hybrid optimization algorithm (WSO-WOA) is introduced. It integrates the White Shark Optimization (WSO) and Whale Optimization Algorithm (WOA) strengths. This hybrid approach leverages the distinct advantages of both algorithms to enhance optimization performance and accuracy. * The paper provides a detailed comparative analysis of two cascading control strategies: the PI-PI controller and the FOPI-FOPI controller. This evaluation focuses on their application in a DSTATCOM integrated into a hybrid MG, highlighting their effectiveness in different operational scenarios. * A thorough performance analysis of the proposed hybrid algorithm and the cascading controllers was conducted under a range of power conditioning issues, including conditions such as voltage sags, swells, and faults. This provided a comprehensive understanding of their behavior and effectiveness in managing power quality disturbances. * The paper also includes a comparative analysis of the proposed hybrid algorithm against other well-known optimization algorithms. This comparison evaluates the relative performance of the WSO-WOA hybrid approach in terms of accuracy, convergence speed, and overall effectiveness in optimizing the control strategies. The remainder of this paper is organized as follows: Section 2 introduces the mathematical model of the hybrid MG, establishing a foundational understanding of its components and their interactions. Section 3 delves into the control mechanisms used for the various elements within the MG. Section 4 details the cascading control strategy implemented for the DSTATCOM. Section 5 describes the operational mechanism of the proposed WSO-WOA algorithm and its role in enhancing optimization performance. Section 6 thoroughly analyzes the results from various case studies, highlighting the practical impact of the proposed methods. Finally, Section 7 concludes the paper, summarizing the key findings and contributions of the research.
## 2 Problem Formulation  
### System Overview  
The ADCS of the micro/nano satellite studied in this paper is composed of three parts: attitude sensors, attitude actuators, and an on-board computer (OBC) on which the ADCS control algorithm runs.  
The attitude actuator of the ADCS consists of a 3-axis MTQs (here, three MTQs installed orthogonally were treated as one device). The attitude sensors of the ADCS consist of a TAM and a sun sensor. The ADCS control algorithm is responsible for generating the control command and recording all the necessary data of the ADCS. As shown in Figure 1, attitude sensors, attitude actuators, and OBC are connected via the controller area network (CAN) bus, which is an international standardized serial communication protocol.
## 5 Result and Analysis  
### Generated Dynamic Load along the Traveling Direction  
The generated dynamic load from the quarter-car model along the driving direction is assessed for different pavement roughness levels, with vehicle speeds of 30 km/h, 60 km/h, and 100 km/h, and axle weights of 10 tons, 20 tons, and 30 tons, respectively, as shown in Figure 8. It can be seen that the dynamic load generated from the moving quarter-car fluctuates around its static load, and the fluctuation becomes larger with the deterioration of the pavement roughness level, reaching 2.81 times the static load in the case of \"100 km/h-10 tons-D\". Heavy axle load causes greater dynamic load and affects a greater slab area. When the axle weight is equal to 10 tons or 20 tons, the dynamic load may sometimes be less than 0 kN in Level D pavement, which is considered a \"jumping\" phenomenon [30]. In this case, the wheel loses contact with the pavement surface, causing an axle load of below 0, and then drops after a short period of time. As shown in Figure 8a,d,e,g,h, the jumping might increase as the vehicle speed increases. For stress calculation, it should be noted that because of the randomness of the generated roughness and consequently the dynamic load, for conservation, the dynamic load is applied in such a way that it reaches its maximum when the moving axle travels near the critical loading position of the mid-slab edge of Slab2.
###### Abstract  
The sixth-generation (6G) mobile networks are expected to operate at a higher frequency to achieve a wider bandwidth and to enhance the frequency reuse efficiency for improved spectrum utilization. In this regard, three-dimensional (3D) spatial reuse of millimeter-wave (mmWave) spectra by in-building small cells is considered an effective technique. In contrast to previous works exploiting microwave spectra, in this paper, we present a technique for the 3D spatial reuse of 28 and 60 GHz mmWave spectra by in-building small cells, each enabled with dual transceivers operating at 28 and 60 GHz bands, to enhance frequency reuse efficiency and achieve the expected spectral efficiency (SE) and energy efficiency (EE) requirements for 6G mobile networks. In doing so, we first present an analytical model for the 28 GHz mmWave spectrum to characterize co-channel interference (CCI) and deduce a minimum distance between co-channel small cells at both intra- and inter-floor levels in a multistory building. Using minimum distances at both intra- and inter-floor levels, we find the optimal 3D cluster size for small cells and define the corresponding 3D spatial reuse factor, such that the entire 28 and 60 GHz spectra can be reused by each 3D cluster in each building. Considering a system architecture where outdoor macrocells and picocells operate in the 2 GHz microwave spectrum, we derive system-level average capacity, SE, and EE values, as well as develop an algorithm for the proposed technique. With extensive numerical and simulation results, we show the impacts of 3D spatial reuse of multi-mmWave spectra by small cells in each building and the number of buildings per macrocell on the average SE and EE performances. Finally, it is shown that the proposed technique can satisfy the expected average SE and EE requirements for 6G mobile networks.  
6G; spectrum reuse; millimeter-wave; small cells; in-building; modeling; 28 GHz; 60 GHz +
Footnote †: journal: Accepted: 1 April 2020; Published: 6 April 2020
# Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning
## 3 Methodology
### Clothing Type Category Classification Methods
#### 3.1.1 Dataset  
As previously mentioned, at an early stage, the goal was to identify the dataset that could fit the project needs. All images taken by blind people are placed in a controlled environment and with one item of clothing at a time [13].  
Figure 1: Workflow methodology for the classification of clothing categories.  
The previous work carried out by the authors [12] allowed us to understand that more data are needed for training and to obtain more accurate results. Nevertheless, the results obtained with a fine-tuning process have shown that this could be a good approach for a small quantity of data. In this sense, a research survey was performed to look for available datasets; see Table 2.  
Based on the characteristics of each dataset, it was decided to use the Fashion Product Images Dataset, namely the low-resolution version, as illustrated in Figure 1. It provides different attributes that meet the project's current and further needs, such as category, color, and season of wear, among others. In addition, annotations with bounding boxes or clothing landmarks are avoided (since they do not fall within the scope of this work) and allow us to feed the dataset with more images without time-consuming annotations. However, despite the number of annotated features included in the dataset, it presents unbalanced data between article types, which were assumed as \"categories\" in this study. Figure 2 illustrates the clothes categories whose distribution had 500 records, i.e., each entry of the xx axis represents a specific category for which, at least, 500 images were considered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Year** & **\# of Photos** & **Description** \\ \hline DeepFashion-C [32] & 2016 & 289,222 & Annotated with clothing bounding box, pose variation type, landmark visibility, clothing type, category, and attributes. \\ Fashion Landmark & 2016 & 123,016 & Annotated with clothing type, pose variation type, landmark visibility, clothing bounding box and human body joint. \\ FashionMinist [48] & 2019 & 70,000 & Grayscale image dataset associated with a label from 10 classes. \\ DeepFashion2 [49] & 2019 & 491,000 & A versatile benchmark of four tasks including clothes detection, pose estimation, segmentation, and retrieval. \\ Fashion Product & 2019 & 44,400 & Annotated with gender, master category, subcategory, article type, base color, season, year, usage and product description. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of available datasets for fashion category classification (adapted from [30]).  
Figure 2: Distribution of the number of records for each article type/category (Fashion Product Images Dataset).  
To ensure a fair comparison between categories and to avoid imbalanced data, from the initial complete range, exactly 500 records were considered for each category depicted in Figure 1. An example of an item from each category is illustrated in Figure 3.
###### Abstract  
The presence of heavy metals in groundwater and wastewater has been a concern for health organizations. This study investigated the effectiveness of activated carbon derived from various natural precursors, including acorns from red oak trees (Quercus rubra), date seeds, and peach seeds, employing the thermal activation method for the removal of heavy metals from aqueous solutions. Batch adsorption tests investigated the effects of sorbent quantity, pH levels, disinfectant presence, and dissolved organic matter (DOM) on the removal efficiency of Pb and Cu. Characterization of the prepared activated carbon was conducted using scanning electron microscopy (SEM). Lead removal efficiency diminished at pH 7 relative to pH 3 and 5, but copper exhibited superior removal efficiencies at pH 7 compared to pH 5. The addition of monochloramine at 4 parts per million (ppm) effectively eliminated lead from the solution. A rise in free chlorine concentration from 2 to 4 mg/L led to a reduction in metal removal from water by 20 to 60%. DOM at concentrations of 1 and 6 mg/L reduced metal removal efficacy relative to DOM at 3 mg/L. Date seed-activated carbons underscore their distinctive potential, offering useful insights for the enhancement of water and wastewater treatment systems.  
Keywords: wastewater treatment; heavy metal removal; adsorption; activated carbon; date seeds; environmental remediation 2020
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Filter Bank Multi-Carrier (FBMC)  
FBMC is a multi-carrier waveform similar to OFDM that has been proposed in ITU-R M.2320 [16] as a promising waveform format for 5G. As the main feature, this waveform format does not include a CP. Therefore, its associated spectral efficiency is higher than that of OFDM. In addition, the half-Nyquist prototype filters mitigate ISI and the offset quadrature amplitude modulation (OQAM) removes ICI.  
FBMC uses subcarrier filtering. Thanks to this feature, the filter length is long (high latency), the OOB emission are reduced, and the isolation between the subcarriers increases (high robustness to time-selective channels). However, a complex method is needed to estimate and compensate the channel. Therefore, MIMO integration with FBMC is more difficult. It is worth noting that FBMC can also employ scalable numerology.
## 4 Pilot Plan and Validation Test Steps  
The proposed use case allows testing and evaluating cybersecurity and privacy risks, as well as for the CUREX platform potential impact on data exchange in healthcare services, focused on modelling and analyzing POC systems. First, a pilot plan has been defined in Table 4, in order to define the overall specificities involved with the use case. Notice that the defined pilot plan can be used as the base model to define other use cases in the current context as well as in other use case models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Description** \\ \hline Involved Partners & The use case pilot is executed involving participants at the hospital facilities. It is convenient for the execution of the pilot because the participants will use computers enabled to run the steps of the defined validations on the develop infrastructure. Furthermore, the preamble presentations and interview/focus groups are hosted as well at the piloting premises with the involvement of the participants and use case managers. \\ \hline Participants & IT staff. It is the group composed of professionals belonging to the IT department of the hospital. They have experience with the technical aspects of the HIS, medical devices and cybersecurity. Assistance staff. It is the group composed of medical doctors and nurses, who may belong to different medical specialties. These professionals have knowledge and experience in consulting clinical information in the HIS. Administrative outpatient staff. It is the group composed of professionals belonging to the hospital outpatient administrative staff. These professionals have knowledge and regularly plan the appointments for the patients of the hospital. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Use case pilot plan definition. Furthermore, following the use case diagram defined in Section 2, the different validation actions of the three different scenarios have been detailed step-by-step in Table 5. Generally, the three scenarios have a common \"start point\" step. In this so-called STEP 0, the system is analyzed by CUREX tools providing some recommendations about cybersecurity and privacy risks. Once they are implemented, it is continued with each on-demand scenario. Lastly, the execution of the different scenarios provides cyber hygiene recommendations.
## 1 Introduction
By contrast, the common technique for sentiment classification focuses on polarity shifting, which requires an analysis of a phrase's structure and semantics [28; 29; 30; 31]. [PERSON] et al. [5] used four classifiers (two baseline machine learning classifiers and two statistical classifiers) with four sub-datasets, which were comprised of various valence shifting structures. [PERSON] et al. [32] proposed an ensemble of sentiment classifiers, where several baseline classifiers trained with different types of features were combined. The authors adopted deep learning to produce features for the classifiers automatically. This paper adopts ensemble learning to classify sentiment at the document level inspired by [5; 32]. We extract various different features from datasets for base learners by identifying the various structures that cause polarity shifting in the text; we call these'surface features'. We also use word embedding and deep learning to extract other features, which are called 'deep features'. The proposed system was built and experiments were conducted using datasets to check the system's performance in Vietnamese and English. A comparison with other machine learning approaches showed that the results of the proposed system were better than even state-of-the-art deep learning models and other ensemble learning systems. The experimental results also show that taking 'deep features' into consideration for base learners improves the effectiveness of the system. The following are the contributions of this research:  
* We propose an effective ensemble learning system using datasets of base learners, which comprise features that result from exploring language characteristics and applying a deep learning model. * We adopt word embedding and develop a deep learning model for base learners that helps improve the system's effectiveness. * The proposed model proved appropriate for the Vietnamese language, and also yielded adequate results for the various English datasets. The remainder of the paper is organized as follows: Section 2 presents related existing work, Section 3 presents the proposal of our model, Section 4 describes the experiments and valuations, and Section 5 presents the conclusion and introduces directions for future research.