esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Dual-Path Speech Enhancement Model
## 3 Proposed Multi-Branch SE Model  
### Baseline Model  
Figure 1 shows the schemas of our baseline model. The baseline model is based on the Nested U-Net (U2-Net) architecture [19, 21, 22], which extends the standard U-Net by incorporating nested sub-U-Net structures within each layer. This design facilitates multi-scale feature extraction by efficiently capturing both global contextual information and local spectral details [21]. We call the sub-U-Net structure Complex Feature Extraction Units (CFEUs).  
As shown in Figure 1, each encoder and decoder comprises a stack of six CFEUs, which are responsible for progressively extracting and refining spectral features. Each CFEU block consists of an INCONV layer, followed by a variable number of convolutional layers for the encoding path (E-CONV) and the decoding path (D-CONV). The number of convolutional layers \(N_{i}\) at each depth is defined as \(N_{i}=[6,5,4,4,4,3]\). The INCONV layer serves as the initial transformation step and consists of a \((1\times 1)\) convolution, layer normalization, and PReLU activation. The E-CONV layer is a variation in the INCONV layer, where the convolution kernel is changed to \((2\times 3)\), with the dimensions corresponding to the time and frequency axes. The D-CONV layer mirrors the structure of the E-CONV layer but employs sub-pixel convolution for upsampling, facilitating high-resolution reconstruction. By stacking multiple CFEU blocks in both the encoder and decoder paths, the model is able to extract and process features at multiple scales, effectively handling complex speech signals.  
Figure 1: Schemas of (**a**) the baseline model, (**b**) CDDense block, and (**c**) CFEU-\(i\) block.  
In addition to these convolutional operations, the model incorporates CDDense and CCTFA layers [19], which are complex-valued extensions of DDense [23] and CTFA layers [22; 24]. These specialized layers extract features in the complex domain, allowing for more effective SE in the time-frequency representation.  
The detailed structure of CDDense can be found in Figure 1c. CDDense consists of an input convolution layer, an output convolution layer, and six Depthwise-Separable Convolution (DSConv) layers. Each DSConv layer is composed of a depthwise convolution, a separable convolution, layer normalization, and a PReLU activation function. This configuration ensures efficient and effective feature extraction with long-range dependencies.  
The CCTFA module, illustrated in Figure 1b, consists of time attention (TA) and frequency attention (FA), each computed by averaging along the opposite axis and passing the result through two pairs of one-dimensional convolution layers and nonlinear activation functions (ReLU and Sigmoid). The final time-frequency attention map is obtained by element-wise multiplication of the TA and FA maps in the complex domain [19].
## 1 Introduction  
Satellite communication has been used as: 1. Satellite and terrestrial integrated networks (STINs) to extend terrestrial network infrastructure for providing global broadband communication; 2. Space-based information networks--satellite networks used to collect Earth observation data and planetary exploration mission data.
## 1 Introduction
The FDD adopts a two-step detection scheme: (1) it first applies a fuzzy-based feature extraction methodology to determine the most valuable links for the DDoS link/server attacker, and then (2) it applies a heuristic approach to detect the network behaviors generated from the SEA and LFA. The FDD observes two common characteristics of the SEA and LFA: (1) they must traverse through the target links, and (2) their source IP address or destination IP address must be the servers located in the target area. When hybrid attacks occur, the number of DetNet flows that pass through the servers in the target area increases, while the number of flows that depart from the servers in the target area remains almost fixed. The FDD is also implemented in OpenDayLight, which is a typical Software-Defined Networking (SDN) controller to observe the performance of the FDD in detecting hybrid attacks in an SDN environment. The contributions of the FDD are threefold: (1) to implement a hybrid attack in OpenDayLight, which is a typical Software-Defined Networking (SDN) controller, to observe and ensure the existence of hybrid attacks and synergy in an SDN environment; (2) to provide a conceptual solution to detect hybrid attacks in the DetNet; and (3) to evaluate the performance of current solutions and discuss a possible development path to defend against hybrid attacks. This study is organized as follows. Section 2 reviews related work, including SEA detection and LFA detection. The system model and proposed method, the FDD, are described in Sections 3 and 4, respectively. The experimental environment and results are discussed in Section 5. The conclusions are given in Section 6.
## 2 Related Works
The method based on combination of CNN and GRU was proposed in [45]. The IDS18 dataset was used to analyze the problem of intrusion detection in IoT [46] and Industrial IoT (IIoT) [47]. Paper [48] presents a method that combines signature-based and AI-powered deep analysis, called PAID, controllable by a flow sensing strategy for accelerating intrusion detection in large-scale networks. In [49], a Deep Feature Fusion Convolutional Neural Network (DFFCNN) model combined with data preprocessing to execute deep DDoS attack detection is proposed. Paper [50] focuses in bio-inspired optimization techniques fo NIDS. For survey of deep learning frameworks used in network intrusion detection based on this set, see [51]. The ToN dataset was used to find, e.g., anomalies [41] and intruders in IoT [52], as well as to validate (along with, among others, NB15) novel deep-learning models [53]. In another study, where blockchain was utilized to prevent poisoning-type attacks, this dataset was used to test how the proposed system performs in recognizing patterns of network attacks [54]. ToN can be found in a study on privacy in IIoT [55] and in a research paper on the standardization of features and types of attacks in IoT [56]. In addition, the dataset was used in a study on security in medical systems: medical IoT [44] and industrial healthcare system [57]. The BoT dataset was applied in a study focused on the detection of less popular and new previously unknown aspects of the IoT: attacks [38], intrusion detection system [41], and detection of botnets IoT [58]. In many papers, multiple datasets are used to validate ML models for NIDS. In [59], a hybrid machine learning model for intelligent cyber threat identification in smart city environments is proposed and tested on BoT and ToN datasets. In [60], both sets are used to test the lightweight model for DDoS attack detection. In [61], the NB15 and ToN datasets are used to verify the method based on convolutional backbone neural networks. The paper [62] proposes the evolutionary approach for enhanced attack detection and classification that is verified using, among others, NB15, BoT, and IDS18 datasets. In [63], the concept of spatial-temporal fusion gating a multilayer perceptron for network intrusion detection is proposed and tested on NB15, BoT, and ToN. Paper [64] employs NB15, IDS18, and three other datasets to validate two deep generative models designed to enhance learning latent features for detecting network anomalies.
# Gold Nanoparticles for Drug Delivery and Cancer Therapy
## 4 Gold Nanoparticle-Based Drug Delivery
### Small Interfering Ribonucleic Acids (siRNAAs) Delivery
CSCs are inert to traditional chemotherapeutic agents due to their resistance mechanisms such as multidrug efflux transporters. CSCs are sensitive to drugs that can target survival signals in RNA. A therapy model was introduced where glucose installed sub-50 nm unimer polymer complex-assembled Au NPs were used to deliver siRNA to a CSC-rich breast cancer model. A tumor-targeting ligand was added for more efficient binding to the target cells based on ligand-receptor interactions, and this can decrease the side effects on normal tissue. The results showed the high cellular uptake of siRNA in a spheroid breast cancer (MBA-MB-231) cell culture, which was able to efficiently and significantly silence the target gene in CSC-rich orthotopic MDA-MB-231 tumor tissue. Therefore, this can be a viable method in the future for CSC-targeted cancer treatment [94]. Another study on the gene expression profiling of cancer cells shows that breast adenocarcinomas express high levels of ROR1 antigen. ROR1 is a type one orphan receptor tyrosine-kinase. ROR1 signaling pathways are involved in cell proliferation and migration. Treatment with ROR1 siRNA can potentially mitigate the invasive properties of cancerous cells. A delivery system for siRNA based on HIV-1 TAT peptide-capped Au NPs was developed to treat breast cancer. Au NPs have a high affinity to sulfur, so peptide can be conjugated to Au NPs through Au-s bonds, and positively charged NPs show high cellular uptake. Therefore, a low concentration of NPs can be used with highly efficient gene transfection and low cytotoxicity. This study shows that it can be a viable treatment method for invasive breast cancer [95]. For the unique properties of Au NPs that can contribute to cancer therapy, numerous studies on Au NP-based drug delivery are carried out due to the multivalence and functional versatility of the Au NP conjugates. Drug delivery using Au NPs as carriers has the advantages of longer circulatory halftime, better biocompatibility, enhanced binding affinity, and internalization within cancer cells. Future studies will focus more on the Au NP-induced cytotoxicity and the NP interaction with the healthy cells.
## 5 Attained Results and Discussion  
### Whole Building Thermal Characterization: Reference Model (Step 1)  
The reference building was modelled using an ideal air system for heating, activated at 20 \({}^{\circ}\)C using an ACR (air change rate) of 0.6 h\({}^{-1}\). The thermal performance of the building was assessed in terms of calculating the accumulated overheating hours over the whole year (when the indoor temperature rises above 26 \({}^{\circ}\)C). In the case of cooling, a constant airflow from the outdoor air at a rate of 0.6 h\({}^{-1}\) was considered. This ventilation system has no capacity for air-conditioning.  
The attained results were 14.32 kWh m\({}^{-2}\) a\({}^{-1}\) for the total heating demand and 24.39% of overheating rate considering the limit of 26 \({}^{\circ}\)C on an annual basis.  
Focusing on the overheating issue, TZ7 and TZ8 (thermal zones at the elevated floor with south orientation presented in Figure 3) were chosen due to the fairly high indoor temperatures registered during the summer season. Thus, for these two thermal zones, the annual results for the total heating demand and overheating rate were attained--6.36 kWh m\({}^{-2}\) a\({}^{-1}\) and 35.20%, respectively. As expected,  
Figure 4: Coefficient of determination for the best model calibrated.  
the heating demand decreased, and the overheating rate increased due to the higher heat gains through the southwest-oriented glazing.
## 6 Conclusions  
We developed a systematic method to map paddy rice called the PODS algorithm using MODIS and Sentinel-1/2 time series data. The method consists of three procedures: (i) object extraction from multi-temporal 10-m Sentinel-2 data; (ii) specifying the transplanting stage from the time-series VI data using the PhenoRice algorithm; and (iii) identifying rice paddies through flood signal detection from time-series optical and microwave signals (VH backscatter). The PODS algorithm alleviates the pepper-and-salt effect by the object-oriented classification method, accurately recognizes the start of the rice growing season by the PhenoRice method, and solves the low availability of optical remotely sensed images brought by the monsoon climate by optical and radar data fusion. The results were validated against VHR imagery from Google Earth, rice paddy related POIs, geo-referenced field photos, VI time series, and other data sources. The PODS approach is useful when mapping rice paddies in the HJH plain in China. We believe that PODS has potential applicability to other regions where cloud coverage is prevalent, croplands are scattered and fragmented, or the cropping system is complex. In the future, we will test the applicability of this method in a wider range of regions such as south and southeast Asia.  
Conceptualization, S.X. and W.X.; methodology, S.X.; software, T.H.; validation, S.X. and T.H.; formal analysis, S.X.; investigation, S.X.; resources, S.X.; data curation, T.H.; writing--original draft preparation, S.X.; writing--review and editing, S.X.; visualization, S.X.; supervision, T.H.; project administration, W.X.; funding acquisition, W.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the joint funds of the Humanities and Social Sciences of Ministry of Education Foundation of China (20C10335010), as well as by the National Nature Science Foundation of China (No. 42071250).  
The cadastral data used for validation are provided by the Bureau of Land Management in Jiashan County.  
The authors declare no conflict of interest.
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 3 Proposed Mechanism
### Generation of User Pseudonyms and Private Key Groups
#### 3.3.4 Anonymous Authentication Process with the KGC  
To verify identity validity with CKGC, the user signs a fixed bit string message using the pseudonym \(ID_{A}\) and private key \(ds_{A}\). CKGC authenticates the message by combining the user's pseudonym with the public information \(\left(ID_{A},P_{pub-a}\right)\) released by VKGC. The process is as follows:  
Figure 3: Generation of user communication identifiers and private key.  
Figure 2: Generation of multi-level identity pseudonym.  
**Sign**
* The message to be signed is a bit string _message_.
* User \(U\) first calculates \(g_{1}=e\Big{(}P_{1},P_{pub-a}+P_{pub-v}\Big{)}\).
* The user generates a random number \(r_{1}\in[1,N-1]\) and calculates \(w_{1}=\ g_{1}{}^{r_{1}}\).
* Then, \(d=H_{2}(\textit{message}||w_{1},\ N)\) is calculated.
* The user computes \(l_{1}=(r_{1}-d)\textit{ mod }N\).
* Finally, \(S_{1}=[l_{1}]ds_{A}\) is calculated, and the signature for the _message_. is \((d,S_{1})\).  
**Verification**
* Upon receiving the signature \(\big{(}d^{\prime},S_{1}{}^{\prime}\big{)}\), CKGC verifies its validity as follows:
* CKGC retrieves \(P_{pub-a}\) using \(ID_{a}\) and calculates the element \(g_{1}=e\Big{(}P_{1},\ P_{pub-a}+P_{pub-v}\Big{)}\) in group \(G_{T}\).
* CKGC then computes \(t_{1}=g_{1}{}^{\prime}\).
* It calculates \(d_{1}=H_{1}(ID_{a}||hid,\ N)\).
* Then, CKGC computes \(T=[d_{1}]P_{2}+P_{pub-v}\) and the element \(u_{1}=e\big{(}S_{1}{}^{\prime},\ T\big{)}\) in group \(G_{T}\).
* CKGC calculates \(w_{1}{}^{\prime}=u_{1}\cdot t_{1}\) and then \(d_{2}=H_{2}(\textit{message}||w_{1}{}^{\prime},\ N)\).
* If \(d_{2}=d_{1}{}^{\prime}\), the verification is successful; otherwise, it fails.  
When generating identity pseudonyms, the anonymous authentication process with VKGC follows the same method as the above identity verification: each level's pseudonym \(ID\) and corresponding private key \(ds\) are used to sign a fixed bit string, with VKGC verifying the signature. For the first-level VKGC, real identity verification is used.
# Structural Topology Design Optimization Using the Binary Bat Algorithm
## 1 Introduction
Although a great advantage over gradient-based topology optimization algorithms, in addition to the advantages mentioned above, metaheuristic algorithms are generally less efficient than mathematical programming methods [4; 5; 11]. GA has been used to solve topology optimization problems for more than three decades [5; 11; 12; 13; 14]. [PERSON] et al. [15] used the Ant Colony methodology for the topology optimization of structures to find the stiffest structures based on the element's contribution to the strain energy. [PERSON] and [PERSON] [3] modified the binary differential evolution to solve structural topology optimization problems. The finest finite element mesh size used was \(32\times 20\) (640 elements). [PERSON] et al. [16] developed binary particle swarm optimization (BPSO) to solve topology optimization problems when finite element mesh size was \(20\times 10\) (200 elements). [PERSON] and [PERSON] [25] proposed a topology optimization scheme based on HS to investigate static stiffness topology optimization problems. A finite element mesh size of \(32\times 20\) (640 elements) was used. This study is conducted because most of the previous work in implementing metaheuristic algorithms to solve topology optimization problems was limited to a few hundred design variables. Many real-life structural topology optimization problems require discretizing the design domain into a fine mesh to make sure the finite element method yields accurate results. The present study is an attempt to improve the quality of the solution of the topology optimization method using a large number of design variables (i.e., fine finite element mesh) by incorporating a new filtering algorithm and penalty function to suit the nature of the bit-array topology optimization. In this study, the numerical results of the proposed algorithm used to solve the benchmark structural topology optimization problems obtained are compared with those found using the SIMP method. The comparison is made based on minimum compliances and final design configurations to demonstrate the performance and viability of the proposed algorithm. The rest of this paper is organized as follows: Section 2 describes the problem formulation for structural topology optimization. In Section 3, the BA and the BBA are reviewed briefly. Structural topology optimization using the BBA is discussed in Section 4. The numerical and design results are presented in Section 5. Finally, the conclusions of the study are described in Section 6.
## 1 Introduction  
Reliability data modeling has been an important topic in many different fields such as engineering, biology, and medical sciences. For data sets collected in real life, the hazard rates can show different shapes: monotone increasing, monotone decreasing, bathtub-shaped and upside-down bathtub (UBT)-shaped. While the data sets with monotone or bathtub hazard rates were deeply investigated with different types of parametric models, the data sets with UBT shapes were not as well explored as the others.  
In fact, data with UBT-shaped hazard rates appeared frequently in the existing literature. For instance, for the Veteran's Administration lung cancer trial data [1; 2; 3], the mortality rate was very low at the beginning, quickly rose to a high peak, and then slowly decreased. This suggests that the data are associated with a UBT-shaped hazard rate. This was also observed in several other data sets from medical and engineering fields [4; 5; 6].  
To model data with UBT-shaped hazard rates, many parametric models have been proposed in the past. For example, the transmuted Rayleigh distribution [6] was proposed and demonstrated good performance when fitted to a reliability data set with UBT-shaped hazard rates. For more models proposed for this type of data, readers are referred to [7; 8; 9; 10; 11; 12].  
The concept of the generalized composite distributions was recently proposed to provide better options for fitting right skewed data with a high peak. It was demonstrated that special members from this family, such as exponentiated exponential-Pareto (EEP) distribution and exponentiated inverse-gamma Pareto (EIGP) distribution, can provide satisfactory performances to multiple insurance data sets [13; 14]. Moreover, these data sets have very similar features to the ones with UBT-shaped hazard rates. Thus, utilizing these distributions to fit the data sets with UBT-shaped hazard rates seems to be appropriate.  
The rest of the paper is organized as follows. Section 2 introduces the concept of the composite distribution. Section 2.2 provides the concept and some properties of the generalized exponentiated composite distribution (GEC). In Section 3, a special model named exponentiated exponential-Pareto (EEP) model is discussed. The parameter estimation of the EEP distribution is introduced in Section 4. In Section 5, by utilizing the goodness-of-fit (GoF) test, a limited simulation study is given to demonstrate the performance of the model associated with the exponent parameter. A real data analysis is presented in Section 6. Finally, the concluding remark and future directions are provided in Section 7.
## 3 Methods
### Prediction Model Construction Based on the Transformer Framework  
Multidimensional feature data contains rich spatiotemporal information. The data are fed into the deep neural network for complex temporal correlation of multi-dimensional data. The data includes the historical metro flow from highly correlated stations, historical metro flow of examined stations from two temporal perspectives, and external factor data.  
After calculating the mutual information, the selected flow data of the two sites most related to the examined site, the historical flow data of the examined site, the periodic flow data, and the external factor data are combined to obtain the model input data \(D^{i}=(d^{i}_{1},\cdots,d^{i}_{h})\), where \(D^{i}\in R^{h\times l}\). The \(l\) includes the most related station's \(S^{1}\) and \(S^{2}\)the historical flow data of the current site, the periodic flow data, the temperature, and the precipitation, and the periodic flow data contains \(X_{t}^{T}\), \(X_{t}^{D}\), and \(X_{t}^{W}\).  
The combined multi-dimensional data are then input into the transformer network. The network adopts an encoder-decoder structure, and the encoder consists of a multi-head self-attention layer and a feed-forward layer. A normalization layer is used after each layer to increase the speed of network training. The encoder is trained to generate a hidden layer vector and pass it to the decoder. The decoder consists of two multi-head self-attention layers and a feed-forward layer, each sub-layer is followed by a normalization layer, and the first multi-head self-attention layer uses a mask mechanism.  
The core of encoding-decoding is the multi-head attention mechanism, which preliminarily encodes \(D^{i}\) to obtain matrix \(A=(a_{1},\cdots,a_{h})\), where \(a_{t}\) is the vector obtained by \(d_{t}^{i}\) after encoding and \(t\in(1,2,\cdots,h)\). The initialized feature matrices \(W_{Q}\), \(W_{K}\), and \(W_{V}\) are used to linearly transform \(A\) to obtain the query vector, \(Q=(q_{1},\cdots,q_{h})\), the key vector, \(K=(k_{1},\cdots,k_{h})\), and the value vector, \(V=(v_{1},\cdots,v_{h})\), i.e.,  
\[Q=W_{Q}A,K=W_{K}A,V=W_{V}A \tag{4}\]  
In the self-attention mechanism, the scaled dot product is usually used as the attention scoring function. Firstly, the attention score is obtained through the dot product of query vector \(Q\) and key vector \(K\), then it passes the softmax and product value vector, \(V\), to obtain the whole weight and output vector. Its equation is represented as:  
\[Z(Q,K,V)=softmax\bigg{(}\frac{QK^{T}}{\sqrt{q_{k}}}\bigg{)}V \tag{5}\]  
Then, the multiple groups of \(W_{Q}\), \(W_{K}\), and \(W_{V}\) are multiplied with the encoded input vectors to obtain multiple groups of \(Q\), \(K\), and \(V\). The multiple group matrix \(b_{1},\cdots,b_{r}\) obtained from groups \(W_{Q}\), \(W_{K}\), and \(W_{V}\) are stitched together to obtain the final output, \(b\). The transformer network trains with the multi-head attention mechanism as its core, and the potential temporal correlation in the data can be effectively captured.