esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 3 Alamouti MIMO Link in the Presence of Different Interferer Techniques
### General Interferer
 \tag{11}\]  
The receiver equations are  
\[ \begin{split} r_{1}(1)&=h_{11}s_{1}+h_{12}s_{2}+g_{11}j_{1}+g_{ 12}j_{2}+n_{1}(1)\\ r_{1}^{*}(2)&=h_{12}^{*}s_{1}-h_{11}^{*}s_{2}+g_{12}^{*}j_{1}-g_{11}^{*}j_{ 1}+n_{1}^{*}(2)\\ r_{2}(1)&=h_{21}s_{1}+h_{22}s_{2}+g_{21}j_{1}+g_{22}j_{2}+n_{2}(1)\\ r_{2}^{*}(2)&=h_{22}^{*}s_{1}-h_{21}^{*}s_{2}+g_{22}^{*}j_{1}-g_{21}^{*}j_{ 2}+n_{2}^{*}(2)\end{split}  \tag{12}\]  
Substituting (12) into the combining Equation (2) yields  
\[\tilde{s}_{1} = \left(\sum_{i=1}^{2}\sum_{j=1}^{2}\left|h_{ij}\right|^{2}\right) s_{1}+h_{11}^{*}n_{1}(1)+h_{12}n_{1}^{*}(2)+h_{21}^{*}n_{2}(1)+h_{22}n_{2}^{*}(2)+\] \[+j_{1}(h_{11}^{*}g_{11}+h_{12}g_{12}^{*}+h_{21}^{*}g_{21}+h_{22}g _{22}^{*})+j_{2}(h_{11}^{*}g_{12}-h_{12}g_{11}^{*}+h_{21}^{*}g_{22}-h_{22}g_{21 }^{*})\] \[\qquad\text{and}\] \[\tilde{s}_{2} = \left(\sum_{i=1}^{2}\sum_{j=1}^{2}\left|h_{ij}\right|^{2}\right) s_{2}+h_{12}^{*}n_{1}(1)-h_{11}n_{1}^{*}(2)+h_{22}^{*}n_{2}(1)-h_{21}n_{2}^{*}(2)+\] \[+j_{1}(h_{12}^{*}g_{11}-h_{11}g_{12}^{*}+h_{22}^{*}g_{21}-h_{21}g _{22}^{*})+j_{2}(h_{12}^{*}g_{12}+h_{11}g_{11}^{*}+h_{22}^{*}g_{22}+h_{21}g_{21 }^{*})\]  
It can be seen that, compared to Equation (3), we received, in addition to the symbols, expressions containing combinations of the interferer with the channel components. As a result, errors in detecting the symbols and a decrease in performance will be obtained. In Section 4, we will present simulation results for representative cases of this phenomenon. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{t}\) & \(\mathbf{t+T_{s}}\) \\ \hline \(JX_{1}\) & \(j_{1}\) & \(-j_{2}^{*}\) \\ \(JX_{2}\) & \(j_{2}\) & \(j_{1}^{*}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transmission Alamouti coding operation for the jammer.
Data packet privacy: no node can see the information inside in a payload of the data packet except the source and the destination. Existing privacy schemes such as [1; 3; 4; 5; 6; 7], that have specifically been proposed for WSNs only provide partial network level privacy. Providing a full network level privacy is a critical and challenging issue due to the constraints imposed by the sensor nodes (e.g., energy, memory and computation power), sensor network (e.g., mobility and topology) and QoS issues (e.g., packet reach-ability and trustworthiness). Thus, an energy-efficient privacy solution is needed to address these issues. In order to achieve this goal, we incorporate basic design features from related research fields such as geographic routing and cryptographic systems. To our knowledge, we propose the first full network level privacy solution for WSNs. Our contribution lies in following features. * A new Identity, Route and Location (IRL) privacy algorithm is proposed that ensures the anonymity of source node's identity and location. It also assures that the packets will reach their destination by passing through only trusted intermediate nodes. * A new reliable Identity, Route and Location (r-IRL) privacy algorithm is proposed, which is the extension of our proposed IRL algorithm. This algorithm has the ability to forward packets from multiple secure paths to increase the packet reach-ability. * A new data privacy mechanism is proposed, which is unique in the sense that it provides data secrecy and packet authentication _in the presence of identity anonymity_. Our solutions collectively provide protection against various privacy disclosure attacks such as eavesdropping and hop-by-hop trace-back attacks. Also, our solutions are lightweight, hence consume modest memory and energy. The rest of this paper is organized as follows: Section 2. contains related work, Section 3. articulates the network model, assumptions and adversary model. Section 4. describes the proposed privacy schemes, Section 5. consists of analysis and evaluation, and Section 6. concludes the paper.
## 3 Proposed Scheme for Guaranteeing Network Lifetime  
The objective of the network lifetime guaranteeing problem is to ensure the network lifetime at least as long as the user or application demands. Since the network lifetime comes to an end when any node collapses its energy, the best way to satisfy the WSN's user or application demands for a pre-configured network lifetime is to distribute energy consumption adequately among sensor nodes. In this article, we propose a guaranteed network lifetime mechanism to achieve user- or application-specified network lifetime through a novel algorithm based on a fuzzy logic controller. We assume that the user- or application-specified network lifetime is a parameter known or given at the beginning of network deployment.  
We primarily focus on how to guarantee the previously-determined or pre-configured wireless sensor network's lifetime. In order to achieve this, we endorse a strategy where each node consumes energy approximately at the optimal energy consumption rate \(\frac{\beta}{\alpha}\), where \(\alpha\) denotes the pre-configured network lifetime and \(\beta\) represents the sensor node initial energy. With this assumption, the pre-configured lifetime \(\alpha\) is guaranteed if each node consumes energy at the proposed optimal rate \(\frac{\beta}{\alpha}\). In order to enforce each sensor node to consume its energy approximately at the optimal consumption rate, we adopted a centralized scheme to schedule sensor nodes in active/sleep mode in each round. We are also aiming to achieve real-time service while guaranteeing the pre-configured lifetime; therefore, we employ a power-aware extending transmission range mechanism. The proposed scheme involves round-based operations where the tasks of each round are completed in two different phases. A detailed description of the mentioned phases is available in our previous research [1].
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 3 Results and Discussion
### Biodegradation of DR23 Dye
#### 3.3.3 The Mechanism of DR23 Biodegradation  
HPLC analysis was used to confirm the biodegradation of DR23 using CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase, as shown in Figure 5. The HPLC analysis of the control solution containing DR23 dye showed the peak appearance at a retention time of 0.96 min, as shown in Figure 5a. The HPLC analysis of DR23 dye using CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase showed a reduction in the DR23 peak that appeared at the same retention time (0.96 min), with the appearance of an additional peak at a retention time of 1.73 min, as shown in Figure 5b. The appearance of the new peak indicated the biodegradation of DR23 and the new peak was related to the degradation products. The other  
Figure 4: The effect of dye concentration (**a**) and the effect of nano-biosorbent dosage (**b**) for DR23 degradation using Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase.  
products of degradation may not appear in the HPLC analysis at the used wavelength [41]. The results indicated the successful biodegradation of DR23 dye using immobilized lactose on the surface of CuFe\({}_{2}\)O\({}_{4}\) nanoparticles.  
The biodegradation of DR23 dye was reached through oxidation using the laccase to produce a phenolic ring that then oxidized to phenoxy radicals, and the radicals oxidized again to form carbonium ions. Then, unstable compounds were produced by the reaction of H\({}_{2}\)O with the phenolic carbon [41; 42]. In the presence of oxygen, unstable compounds are removed. This biodegradation of DR23 was achieved due to the presence of copper sites in the laccase enzyme, which is responsible for the oxidation of the substrate and the reduction in oxygen to water by the type 1 site, and type 2 and 3, respectively. Hence, the presence of copper ions in the support enhanced the catalytic performance of the laccase enzyme. The biodegradation of DR23 produces four radicals from four substrate molecules and two H\({}_{2}\)O molecules from one oxygen molecule that represent the oxidation/reduction reaction [43]. The schematic representation of the azo dyes biodegradation mechanism using laccase enzyme is presented in the Supplementary Material (Scheme S1).
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 1 Introduction
Paper [18] proposed a novel method to match different subbands of the left channel and the right channel based on PCA, through which the redundancy of two channels can be reduced further. Paper [19] mainly focused on the multichannel process and the application of PCA in the subband, and it discussed several details of PCA, such as the energy of each eigenvector and the signal waveform after PCA. This paper introduced the rotation angle with Karhunen-Loeve Transform (KLT) instead of the rotation matrix and the reduced-dimensional matrix compared to our paper. The paper [20] mainly focused on the localization of multichannel based on PCA, with which the original audio is separated into primary and ambient components. Then, these different components are used to analyze spatial perception, respectively, in order to improve the robustness of multichannel audio coding. In this paper, a multi-frame, PCA-based coding method for audio compression is proposed, which makes use of the properties of the orthogonal transformation and explores the feasibility of increasing the compression rate further after time-frequency transition. Compared to the previous work, this paper proposes a different method of applying PCA in audio coding. The main contributions of this paper include a new matrix construction method, a matrix quantization method based on PVQ, a combination method of PCA and parametric stereo, and a multi frame technique combined with PCA. In this method, the encoders transfer the matrices generated by PCA instead of the coefficients of the frequency spectrum. The proposed PCA-based coding method can hold both a mono signal and a stereo signal combined with parametric stereo. With the application of the multi-frame technique, the bitrate can be further reduced with a small impact on quality. To reduce the bitrate of the matrices, a method of matrix quantization based on PVQ [21] is put forward in this paper. The rest of the paper is organized as follows: Section 2 describes the multi-frame, PCA-based coding method for mono signals. Section 3 presents the proposed design of the matrix quantization. In Section 4, the PCA-based coding method for the mono signal is extended to stereo signals combined with improved parametric stereo. The experimental results, discussion, and conclusion are presented in Sections 5-7, respectively.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 2 Overview of AD Process  
### Feedstock for AD  
A wide variety of feedstock can be processed through AD. However, lignocellulosic biomass, as it has a high potential for biogas production, represents a promising source for alternative energy [8]. This type of feedstock is abundantly available and can be found in different wastes, such as agricultural residues (crop residues), wood, and grass. About 181.5 billion tons of lignocellulosic biomass are produced annually worldwide [12]. Nevertheless, process inhibition is commonly noticed during the digestion of such materials caused by the complexity of their structure, which contains about 10-25% lignin [13]. Accordingly, pretreatment is often needed before use in AD for biogas production, or alternatively it can be codigested with other feedstock [14]. Solid-state AD (ss-AD) digesters are ideally suited for such feedstock [15]. Consequently, more attention is being directed to this process, as it provides promising solutions either for waste management or for introducing a renewable energy source.
# Multiagent Hierarchical Cognition Difference Policy for Multiagent Cooperation
## 5 Simulation Results and Analysis  
### Simulation Settings  
In this section, the performance of MA-HCDP is evaluated in two tasks, including cooperative navigation and group containment. The cooperative navigation task is designed to evaluate the effectiveness of MA-HCDP in handling states of differnt groups of agents. The group containment tasks is designed to verify the performance of MA-HCDP in reducing redundant communication information.  
For all the tasks, the only way to obtain more information from the other agents is through limited communication. The map side length is 2 m, the detection range is 0.5 m, and the communication range is 1 m. The radius of an agent is 0.05 m and the radius of an obstacle is 0.1 m. The action space is discrete and each agent is able to control unit acceleration or deceleration in \(X\) and \(Y\) directions. The boundary condition for the environment is the four sides of the map. Each agent obtains \(R_{cross}\) for crossing the boundary.  
\[R_{cross}=\left\{\begin{array}{ll}0&if\ x<1.8\\ 10(x-1.8)&if\ 1.8\leq x<2\\ \min\left(e^{2x-18},10\right)&if\ x\geq 2\end{array}\right. \tag{25}\]  
where \(x\) is the abscissa or ordinate of the agent.  
These tasks are implemented based on [9], where the agents can move around with a double integrator dynamics model. The details of the tasks will be presented in the following sections.  
As baseline algorithms for comparing the performance, MADDPG [9] and TRANSFER [15] are taken into consideration to compare with our method MA-HCDP. MADDPG needs the state of all the agents during training to construct its critic network. It neither considers the influence of different groups nor the influence of redundant communication contents. TRANSFER adopts GCN and the soft attention mechanism to deal with the different influence of agents, but it ignores the influence of redundant communication contents.  
A workstation is utilized for training and testing throughout the simulations, in which the processor is Intel(R) Xeon 8280L(2.6 GHz), the graphics card is Nvidia TITANRTX GPU(24 G), and the RAM size is 128 GB. These simulations are implemented in the multiagent particle environment (MAPE) [9], and these algorithms are trained with PyTorch 1.0. The parameters of the training process, the network, and MA-HCDP are given in Table 1. Specifically, the learning rate is a hyperparameter used in the training of neural networks. The max gradient normalization is used to clip the gradient to avoid exploding gradients. The episode, batch size, PPO epoch, the coefficient of value loss, policy loss, entropy, and VAE are hyperparameters used in the training of PPO. The number of attention heads in HGN is a hyperparameter used for the multihead attention mechanism of HGN.
## 3 Materials and Proposed Method
### Vgg16  
The VGG16 model is characterized by containing input layers, convolutional layers, pooling layers, and FC layers as well. In general, the VGG16 transfer learning algorithm is 16 layers deep that comprises 13 convolutional layers and 3 FC layers as seen in Figure 4. It was developed into six blocks which are five convolutional blocks and one classification block. The network was modified by adding Batch Normalization and Dropout layers. The input size used was 300, and 300 and the image channel were 3.  
**Block 1.**  
* Convolutional layer 1: 64 filters, 3 kernel, activation = relu, padding = same, image_input as defined earlier.
* MaxPooling2D layer 1: Pooling size = 3, strides = 2.  
**Block 2.**  
* Convolutional layer 2: 128 filters, 3 kernel, activation = relu, padding = same, image_input as defined earlier.
* MaxPooling layer2D 2: Pooling size = 3, strides = 2.  
**Block 3.**  
* Convolutional layer 3: 256 filters, 3 kernel, activation = relu, padding = same, image_input as defined earlier.
* MaxPooling2D layer 3: Pooling size 3, strides = 2.  
**Block 4.**  
* Convolutional layer 4: 512 filters, 3 kernel, activation = relu, padding = same, image_input as defined earlier.
* MaxPooling2D layer 4: Pooling size = 3, strides = 2.  
**Block 5.**  
* Convolutional layer 5: 512 filters, 3 kernel, activation = relu, padding = same, image_input as defined earlier.  
Figure 4: VGG16 Architecture for Human Posture Image Classification.  
* Convolutional layer 5: 512 filters, 3 kernel, activation = relu, padding = same, image_input as defined earlier.
* MaxPooling2D layer 5: Pooling size = 3, strides = 2.
* **Block 6 (Classification Block)**
* Flatten.
* FC layer 1: Dense = 4096 nodes, activation = relu.
* Dropout = 0.4.
* FC layer 2: Dense = 4096 nodes, activation = relu.
* Dropout = 0.4.
* Output layer: classes, activation = softmax.
## 2 Materials and Methods
### RNA Sequence Analysis  
Publicly available sequencing data from the Gene Expression Omnibus (GEO) database [31], specifically GEO accession number GSE141740 [14], were used. The data were from WT 9-12 cells subjected to treatment with 0.1% DMSO or 10 uM sulforapahane for 24 h, followed by sequencing on the Illumina NovaSeq 6000 platform. WT 9-12 cells are epithelial cell lines that have been immortalized with SV40 large T antigen. The quality of the raw reads was assessed using the FastQC toolkit, and subsequent removal of low-quality reads and adapters was performed with Trimmomatic [32]. To align the raw reads to the GRCh38.p5 reference genome (Ensembl version 84), HISAT2 [33] was employed. Expression levels of each gene were quantified using featureCounts [34] based on Ensembl annotation version 84. Poorly expressed genes, defined as those with no more than one count per million reads (1 CPM) in at least two samples within each dataset, were excluded from further analysis. The raw counts were then normalized and used to perform differential expression analysis with Limma [35]. In addition, edgeR [36] was used to validate the results obtained with Limma. Only genes that showed consensus of overexpression or underexpression across both software packages were considered. Genes with a log2 fold change (log2 fc) greater than 1.0 and an adjusted \(p\)-value (padj) less than 0.05 were classified as upregulated, whereas those with a log2 fc less than 1.0 and a padj less than 0.05 were considered downregulated.
## 2 Materials and Methods
### Dataset
#### 2.2.2 Assessment of Model Performance  
The accuracy of wind speed simulation was evaluated in this paper using metrics such as the correlation coefficient, bias, root-mean-square error (RMSE), and mean absolute percentage error (MAPE). RMSE serves as a reliable indicator of the precision of the predicted data sequence. At the same time, MAPE was employed to describe the extent of error dispersion and circumvent the issue of offsetting biases in sequential data. Among them were the following:  
\[\text{Correlation coefficient}=r(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}} \tag{1}\]  
where the covariance between variables \(X\) and \(Y\) is represented by \(Cov(X,Y)\), while \(Var[X]\) and \(Var[Y]\) represent the variances of variables \(X\) and \(Y\), respectively. In probability theory and statistics, covariance is employed to quantify the overall error between the two variables. Variance, as a particular case of covariance, describes the situation where the two variables are identical. The formulas for their calculations are as follows:  
\[Cov(X,Y)=E[(X-E(X))(Y-E(Y))] \tag{2}\]  
\[\text{Var}[X]=\frac{\sum(X-X_{1})^{2}}{n-1} \tag{3}\]  
where \(X\) is the variable, \(X_{1}\) is the sample mean, \(n\) is the sample size, and \(E\) represents the expectation.  
\[\text{Bias}=\sum_{i=1}^{n}\frac{\left|P_{i}^{\prime}-P_{i}\right|}{n} \tag{4}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(P_{i}^{\prime}-P_{i})^{2}} \tag{5}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|P_{i}^{\prime}-P_{i}\right|}{P_{i}} \tag{6}\]  
where the sample size is denoted by \(n\), the predicted value of the ith sample by the model is represented by \(\text{P}_{i}^{\prime}\), and the measured value of the ith sample is indicated by \(\text{P}_{i}\).  
The wind direction agreement rate (WDAR) is commonly used to evaluate the accuracy of wind direction predictions. It is an indicator in meteorology that measures the similarity between two wind direction datasets. The calculation involves counting the matching wind direction values in both datasets and dividing that count by the total number of deals to obtain the agreement rate. For example, if both datasets represent wind direction values in the 0 to 360 degrees range, discounts with a difference of less than or equal to 22.5 degrees would agree. The wind direction agreement rate is typically presented as a percentage and helps assess the similarity between the two wind direction datasets. In meteorology, the wind direction agreement rate is frequently employed to evaluate the forecast accuracy of meteorological models. Usually, there exists a certain amount of error between the wind direction predicted by the model and the observed wind direction. The wind direction agreement rate can be used to quantify the magnitude of this error. A higher wind direction agreement rate indicates a higher forecast accuracy of the model.
## 4 Recognition Infrastructure
### Integration in Video Portals  
There is not only one single way how the proposed framework can be integrated into a video portal, but several integration levels might be reasonable that fit to the available resources and software architectures of different video portals. In the following, we describe four different integration levels. The first, minimal integration only consists of a manual object annotation that uses the presented keyframe clustering algorithm and the database schema. Thereby, it is sufficient to store one keyframe (or its timepoint) for an annotated object to answer video queries that include the object label as keyword. The second integration level presents an interactive annotation where a video portal has to store all keyframes that a user has selected for an object with the help of the keyframe clustering and keyframe suggestion algorithms. The region detection approach of Section 3.2 can be optionally used to support the annotation process. In this integration level, video retrieval became possible on a scene level, which means that users can navigate between different video scenes of an annotated object. The third integration level additionally uses the recognition approach selection of Algorithm 3 to annotate the object-of-interest automatically in the first few keyframes that are returned by the selected approach. In this case, it is recommended to store the information if an object instance has been annotated by the user or not and to prefer user-approved instances to answer video queries. The fourth and last integration approach includes an user feedback step to classify the automatically annotated object instances as true or false annotations. The benefit of the later integration levels is a more complete instance-list of the object-of-interest and the possibility to (semi-)automatically annotate this object in newly uploaded videos.  
Video portals have to store the annotated objects-of-interest and their instances (compare Figure 2) in order to improve the retrieval process. The computation of clustered keyframes and the feature extraction can be done at the client side (user of the video portal) before video upload or at the video portal severs after a video was uploaded. In the case of client side analysis, video portals do not have to bother about resources and scalability issues. However, we suppose that a stand-alone application is needed in this case instead of a single website. The storage of visual features at the server side is only required if object annotation should be performed in several videos at once.