esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method: Massive MIMO Network Design  
In this section, we discuss the different optimization metrics and the algorithm proposed for the design of 5G massive MIMO based network. Furthermore, we describe and formulate the optimization problem investigated while defining the objective fitness functions used, in terms of the power consumption, the downlink exposure and the uplink exposure. Depending on the way the DL and UL EMF exposure are assessed, we consider two types of objective fitness functions:  
* A tri-objective fitness function with the downlink exposure and uplink exposure considered as two separate metrics.
* A bi-objective fitness function in which the downlink and uplink exposure are combined into a single exposure metric (the total dose).
## 3 Current State of Object Detection Algorithms
### Yolo
#### 3.2.2 YOLOv4  
YOLOv4 [40] brings several significant improvements over its predecessor, YOLOv3. The most notable change is the integration of the Cross-Stage Partial Network (CSP-Net) into the Darknet architecture, creating a new backbone feature extractor known as CSP-Darknet53. The convolutional architecture used in YOLOv4 is based on a modified version of the DenseNet model, which has several advantages over traditional models. These advantages include improved gradient stability, facilitated back-propagation, reduced computational requirements, and enhanced learning. The Neck layer in YOLOv4 is a combination of SPP [44] and Path Aggregation Network (PANet) [45], which enhances the detection process by increasing the receptive field and filtering out essential features. This results in improved accuracy and reduces computational time. YOLOv4 also utilizes \"Bag of Freebies\" and \"Bag of Specialties,\" which have been proven to significantly improve the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Type** & **Backbone** & **Neck** & **Head** & **Loss Function** & **Framework** \\ \hline YOLOv3 & Fully convolution & Darknet-53 & FPN & YOLOv3 head & \begin{tabular}{c} Binary cross \\ entropy \\ \end{tabular} & Darknet \\ \hline YOLOv4 & Fully convolution & CSPDarknet53 & SPP and PANet & YOLOv3 head & \begin{tabular}{c} Binary cross \\ entropy \\ \end{tabular} & Darknet \\ \hline YOLOv5 & \begin{tabular}{c} Fully convolution \\ \end{tabular} & CSPDarknet53 & PANet & YOLOv3 head & \begin{tabular}{c} Binary cross \\ entropy and \\ logits loss \\ \end{tabular} & Pytorch \\ \hline YOLOv6 & \begin{tabular}{c} Fully convolution \\ \end{tabular} & EfficientRep & Rep-PANet & Decoupled head & \begin{tabular}{c} Varifocal Loss \\ and Distribution \\ Focal Loss \\ \end{tabular} & Pytorch \\ \hline YOLOv7 & \
\begin{tabular}{c} Fully convolution \\ \end{tabular} & E-ELAN & FPN and PANet & Multiple heads &
\begin{tabular}{c} Binary cross \\ entropy with \\ Focal loss \\ \end{tabular} & Pytorch \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of inner structure of YOLO architectures.  
performance of the algorithm. The \"Bag of Specialties\" includes features, such as the Mish activation function and modified PANet, which are not available in previous versions.
## 2 Results
### Genetic Variability of SLC10A1 in Human Liver  
In order to investigate the influence of genetic variation on _SLC10A1_/NTCP expression, we analyzed variants in the _SLC10A1_ locus including the 2 kb promoter region, all exonic and flanking intronic regions, as well as the 5\({}^{\prime}\) promotor and 3\({}^{\prime}\) untranslated region (UTR) for all 143 liver samples. Frequency distribution of all variants that have been investigated through genotyping (i.e., matrix-assisted laser desorption ionization time-of-flight mass spectrometry [MALDI-TOF MS], microarray technologies) and/or sequencing (i.e., Sanger sequencing, next-generation sequencing [NGS]) are listed in Supplementary Table S2. A total number of 34 variants were detected with common minor allele frequencies (MAF \(\geq\) 5%; \(n\) = 17), rare frequencies (MAF < 5%; \(n\) = 5), or only in single individuals (\(n\) = 12). The genotype distribution of identified variants fitted to the Hardy-Weinberg equilibrium (Supplementary Table S2). Our results are in line with data of the large Exome-Genome-NGS-cohort of Non-Finnish Europeans (\(n\) = 56,000) from the genome aggregation database (gnomAD_v2.1, [[http://gnomad.broadinstitute.org](http://gnomad.broadinstitute.org)]([http://gnomad.broadinstitute.org](http://gnomad.broadinstitute.org)), accessed on 3 May 2019) (Figure 1C). Two of the identified exonic variants in our cohort were missense SNPs (rs200149939, p.R185C; rs200746820, p.S213R). Overall, of the 34 detected genetic variants, one variant has not been described before in dbSNP. This novel variant, which is located in an intronic region, was found by NGS and was confirmed by Sanger re-sequencing (Supplementary Figure S2). A complex linkage pattern was found for several variants in the gene locus (Figure 1D) and apart from the reference haplotype we observed 7 further haplotypes with a frequency \(\geq\)1%, when including 17 common variants with MAF \(\geq\) 5% in the haplotype block analysis (Supplementary Table S3).
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 3 Research Design
### Overview of the Approach  
The running flow of the APT attack detection scheme proposed in this paper based on Cuckoo (CK) sketch and DNS traffic is shown in Figure 3. The first operation of the original traffic is data preprocessing. Since the number of original datasets is very large, this paper reduced the dataset from the three perspectives of request type filtering, white list filtering, and access frequency filtering. Furthermore, we extracted the second-level domain name to facilitate the subsequent feature extraction.  
Next, we selected the domain names in the DNS traffic as the key, and for the communication control phase of the APT attack, we selected eight features as parameters to outline the network state statistics, the specific types and characteristics of these features  
Figure 3: Overview of the approach.  
will be described in Section 3.3. The DNS traffic is counted and compressed in the form of a sketch structure, where each hash bucket in the sketch will output a data sample of dimension 8, and these hash buckets are themselves linked to a DNS domain name. Thus, the abnormal network state and traffic caused by the abnormal source DNS domain name can be demonstrated by the variety of features. The above features become the prototype of the training and testing feature set of the machine learning model in the subsequent steps.  
After the original dataset has been counted and compressed by sketch structure, several types of preprocessing operations are required, mainly including outlier removal, missing value filling, feature scaling, etc., to improve the dataset and enhance the classification effect and efficiency of the machine learning model.  
Finally, the dataset after the above processing is the dataset to be classified. In order to build the machine learning model and test its attack detection effect, we divided the dataset into a training set and test set in the ratio of three to one. The training set is marked as normal and abnormal according to the mapping relationship between the DNS domain name and grid serial number in the sketch in advance. To solve the dichotomous classification problem proposed in the last step of the algorithm, we chose eight typical classifiers for attack detection, trained and tested these eight models, recorded the results, evaluated the respective characteristics and learning effects, and selected the optimal detection model.
# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 3 Method
### Basic Trajectory Optimization Algorithm
Based on the principle of the raster diagram method, Pygame was used to build the UAV area coverage simulation platform, and the effect is shown in Figure 3. Light green indicates uncovered areas, dark green indicates covered areas, red in the upper left corner indicates the starting point, and blue means the drone's current location. The following diagrams have the same meaning as the current ones. Based on the above ResNet50 model, the drone's trajectory direction selection at each step can be gradually derived. The whole optimized trajectory algorithm can be described as the following Algorithm 1. ```
Input: number of iterations \(T\), state set \(S\), action set \(A\), step size \(\alpha\), discount factor \(\gamma\), exploration rate \(\varepsilon\), \(Q\)-network, batch size \(m\) in batch gradient descent. Output:\(Q-\)network parameters \(\omega\)
1. Randomly initialize the \(Q-\)network parameters \(\omega\), initialize the value \(Q\) for all state-action pairs based on \(\omega\), and clear the experience replay memory \(D\)
2. For \(i\gets 1\) to \(T\) do Initialize state \(S\) and extract its feature vector \(\varnothing(s)\). Repeat Input \(\varnothing(s)\) into the \(Q-\) network, output the values for each action, and select action \(a\) using an \(\varepsilon-greedy\) strategy. Execute action \(a\) to update the environment, obtaining the feature vector \(\varnothing(s^{\prime})\) of the new state \(s^{\prime}\) and the immediate reward \(\gamma\). Store the tuple \((\varnothing(s),a,\gamma,\varnothing(s^{\prime}))\), in the experience replay memory \(D\). \[s\gets s^{\prime}\] Randomly sample \(m\) examples \(\big{(}\varnothing(s_{j}),a_{j},\gamma_{j},\varnothing(s^{\prime})\big{)}\) from the experience replay memory \(D\), where \(y=1,\cdots,m\), and \(\text{ compute the target }Q\text{ values }y_{j}\). \[y_{j}=\left\{ \begin{array}{cc}\gamma_{j}&\text{ if }s=s_{-}\text{terminal}\\ \gamma_{j}+\gamma\max Q\big{(}\varnothing\big{(}s^{\prime}_{j}\big{)},a_{j}, \omega\big{)}&\text{ else}\end{array} \right.\] Use loss function \(\frac{1}{m}\sum_{j=1}^{m}\ \big{(}y_{j}-Q\big{(}\varnothing(s_{j}),\alpha_{j}, \omega\big{)}\big{)}^{2}\) and backpropagation to update the \(Q-\)network parameters \(\omega\). Until \(s=s\_terminal\)
```  
**Algorithm 1**Pseudocode of Trajectory Optimization Algorithm
## 4 Discussion
Additionally, through Markov clustering (Figure 8F), we show that tyrosol treatment results in the upregulation of two tightly clustered networks, one involving Interferon Regulatory Factors (IRFs) and interferon-induced IFIT proteins and the NF\(\kappa\)B network. Within the former network, IFIT proteins are expressed in response to a stimulus and gene expression is dependent on both pathogen-associated molecular patterns (PAMPs), as well as the JAK-STAT pathway. This is in line with our results which confirm theupregulation of both IRF7 and STAT2. [PERSON] et al., 1997 have also reported the activation of STAT2 in myeloid cell differentiation. They showed that for NB4 and U937 cells treated with ATRA, increased levels of STAT1 and STAT2 were recorded, resulting in these differentiated cells being more responsive to IFNs [44]. Induction of the JAK-STAT pathway following treatment of HL-60 cells by PMA and DMSO has been recorded [45]. Moreover, previous studies have reported the upregulation of IRF1 gene expression by ATRA on the myeloid cell lines NB4 and HL-60, and hence the results presented in this study are compatible with these findings [46; 47]. IRF1 is a transcriptional activator of both IFN and IFN inducible genes, and its level is lowest during cell proliferation and highest at cell growth arrest [48]. This upregulation is consistent with the findings outlined in Figure 7 confirming the apoptotic effect of tyrosol. Within the same network, the upregulation of PARP9 and PARP14 was also observed, with PARP having previously been found to regulate cell differentiation [49]. Our results are again consistent with those obtained by [PERSON], [PERSON], and [PERSON] (1995), who observed the upregulation of PARP for both NB4 and HL-60 cells differentiated by PMA and 1,25-D\({}_{3}\) to the monocyte/macrophage lineage [50]. Specifically, [PERSON] et al., 2016 reported the importance of PARP9 and PARP14 in activating macrophages [51]. With regards to the NFxB pathway, two members of the transcription factor family, NFkB2 and RELB, were found to be upregulated following tyrosol treatment. The upregulation of three inhibitors NFxBIA, NFxBIE, NFxBIZ show that tyrosol inhibits the NFxB pathway [52; 53]. Inhibition of this pathway has been linked to the treatment of cancer and inflammation. A study by [PERSON] and [PERSON] (2000) has shown that the naturally occurring flavonoid resveratrol exerts its effects by inhibiting the NFxB pathway, thus explaining the benefits of red wine in reducing coronary heart disease and cancer mortality [54]. Its suppressive effects have been attributed to a decline in the activities of both p65, as well as IxB [55]. This compound has also been found to downregulate this pathway in activated macrophages, leading to the inhibition of NO generation [56]. In summary, our results indicate that tyrosol is an effective differentiating agent driving the HL-60 cells to the monocyte stage, followed by apoptosis as cells regain senescence.
###### Abstract  
The long-term effectiveness of osseointegrated implants is heavily dependent on the short-term stability, primarily achieved immediately after surgery through a mechanical connection between the bone and the implant. The most common implant designs nowadays are straight and rely on screw or press-fit fixtures. Despite the promising results achieved by current transfemoral implants, the incidence of early failures and complications is still high. Starting from the hypothesis that a patient-specific approach could lead to better primary stability immediately post-surgery, this study aims to investigate the effect of implant design on primary stability. This was performed by analyzing two patient-specific implants, customized according to the medullary canal morphology, and a simple straight implant as the reference standard. To quantitatively assess the primary stability, a comparative computational analysis was conducted to examine the effective contact area, the relative micromotion, and the stress distribution at the interface between the bone and the implant stem during a static load-bearing exercise. The results showed that implants that follow the curvature of the residual femur provide lower micromotion values and a wider contact area, with a reduction of up to 30.4% and an increase of 10.8%, respectively, compared to the straight design, leading to a more homogeneous load distribution. Patient-specific prosthetic implants allow a more homogenous contact distribution that could lead to higher primary stability by reducing micromotion at the bone-implant interface concerning the straight profile, lowering the risk of loosening related to the short-term stability.  
finite element analysis; patient-specific design; transfemoral implant; osseointegration; load-bearing exercise; primary stability
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 2 Photocatalytic Mechanism and Influencing Factors  
### Reaction Mechanism  
Photocatalytic reaction is a chemical reaction that takes place under the joint action of light and the photocatalyst. This technology possesses several advantages, including environmental protection, the complete degradation of pollutants, and no secondary pollution. Figure 1 shows the basic principle of a photocatalytic reaction [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40].  
From the point of semiconductor photochemistry, photocatalytic action is the light-induced redox reaction of semiconductors. A semiconductor has an energy band structure composed of a low energy valence band (VB) and a high energy conduction band (CB), and the band gap between a conduction  
Figure 1: Photocatalytic reaction mechanism.  
band and a valence band is called a forbidden band. When the energy of the incident light is larger than the band gap of the semiconductor, the electrons in the VB of the semiconductor are excited to the CB by photons, and the corresponding holes are generated in the VB. Photogenerated electrons and holes are separated by an electric field and move to the surface of semiconductor particles. The photogenerated pores have strong oxidizing properties and can oxidize substances adsorbed on the surface or solution of the semiconductor.  
The detailed process of a photocatalytic reaction is as follows. Under a certain energy of light, electrons on a VB are excited and jump to a CB, and the holes stay on the VB. The electrons on the CB move to the surface of the catalyst and participate in a reduction reaction, and the holes on the VB diffuse to the photocatalyst surface and are involved in an oxidation reaction. In the course of the reaction, the electrons can form H\({}_{2}\)O\({}_{2}\) or a superoxide radical O\({}_{2}\)\({}^{-}\) with H\({}^{+}\) and dissolved O\({}_{2}\) in the aqueous solution, and the holes can oxidize OH\({}^{-}\) to produce hydroxyl radicals -OH and, thus, achieve the effect of degrading pollutants [41; 42; 43; 44].
## 3 Risk Assessment-Based Dynamic Access Model
### Policy Rule Checking  
In the dynamic access control model, the static processing of access requests is a set of access control based on policy rules. The rules use attributes for rule detection. Attributes are physical quantities used to describe the inherent characteristics of entities. Attributes can be divided into subject attributes and objects. There are three types of attributes and environmental attributes. The operation of attributes is also a part of rule detection. The specific definitions are as follows:  
1. Four elements \(V,X,Y,Z\), representing the subject set, the object set, the environment set, and the operation set, respectively.
2. Subject attribute \(VA\), object attribute \(XA\), resource attribute \(YA\) and operational attribute \(ZA\), where \[VA_{k}(1\leq k\leq K)\] \[XA_{m}(1\leq m\leq M)\] \[YA_{j}(1\leq j\leq J)\] \[ZA_{n}(1\leq n\leq N)\]
3. The attribute assignment relationship of the corresponding instances \(V\), \(X\), \(Y\), and \(Z\): \[Attr(V) \subseteq\{VA_{1}\times VA_{2}\times...\times VA_{k}\}\] \[Attr(X) \subseteq\{XA_{1}\times XA_{2}\times...\times XA_{M}\}\] \[Attr(Y) \subseteq\{YA_{1}\timesYA_{2}\times...\timesYA_{J}\}\] \[Attr(Z) \subseteq\{ZA_{1}\times ZA_{2}\times...\times ZA_{N}\}\]
4. In policy rule ABAC, policy \(z\) is specified as \(\{sign\leftarrow(Attr(V),Attr(X),Attr(Y),Attr(Z))\}\), sign indicates authorization (0 or 1). There could be several policies that a request matches. Equation (9) illustrates how the security policy library's policy rules impact the outcome of the present request \(req\). \[M(z_{j})=z_{1}\bigcap z_{2}\bigcap z_{3}\bigcap...\] (9)
## 2 Materials and Methods
### AE Measurement in the Compression Stress Field  
Under the load, energy stored in cracks is released as elastic waves which can be captured and monitored by AE piezoelectric sensors. The collected AE characteristics provide exact details on the processes by which damage develops inside the material. In this study, concrete specimens' degree of damage is examined using AE energy and its accumulation under loading. A configuration for performing a compression test with AE measurement is shown in Figure 4.  
The use of six piezoelectric AE sensors allowed the detection of AE activity. The AE sensor was an R15 with a 150 kHz resonance frequency. Two strain gauges installed on the specimen surface were used to gather strain data in both axial and lateral directions. An  
Figure 4: Compression test setup with AE measurement system.  
AE measuring system (SAMOS made by PAC, New Jersey, US) was employed to extract the AE characteristics. All AE parameters were synchronized with the load values obtained from the compression testing machine. Table 2 demonstrates the AE test settings.  
Because the storage, dissipation, transformation and release of energy accompany the fracture processes in concrete under load, a series of indices have been proposed to assess concrete based on energy evolution. Strain energy is defined as the energy stored in a body due to applied load and can be calculated as the area under the stress-strain curve. This stored strain energy is transformed into the surface energy of the new crack faces, leading to microcracks development in the material's internal structure.  
According to rate process theory [34], the AE energy release characteristics are evaluated by the cumulative AE energy occurrence frequency ratio [34] (Equation (2)),  
\[f_{e}(U)dU= \frac{dE_{AE}}{E_{AE}}, \tag{2}\]  
where \(f_{e}(U)\) is the AE energy at the strain energy level \(U\) (%), \(dE_{AE}\) is AE energy per unit strain energy level \(U\) and \(E_{AE}\) is the total number of AE energy. The increment of strain energy level \(dU\) is set to 0.5 N-m.  
The high AE energy release events at the beginning loading stage can reflect the damage degree in concrete samples; thus, the initial AE energy release rate \(\gamma\) is used to evaluate the damage of samples [35]. In this research, the analysis of the initial AE energy release rate is conducted at strain level in the range from 0 to 200 \(\times\) 10\({}^{-6}\) (Equation (3)):  
\[\gamma= \frac{E_{AE0\sim 200}}{E_{AE}}\times 100, \tag{3}\]  
where \(E_{AE0\sim 200}\) is cumulative AE energy in the strain levels 0-200 \(\times\) 10\({}^{-6}\) and \(E_{AE}\) is total AE energy in a series of the compression loading process.  
To find the correlation between pre-existing defects and AE events location, the AE source location analysis is conducted to determine AE events' position within the concrete sample internal structure [36; 37]. The corresponding amplitude and frequency of each AE event are also calculated.
# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 3 Experiments
### Results and Analysis  
#### 3.2.1 Experimental Setup  
The experiments were conducted using the PyTorch 2.1.2 deep learning framework for training, based on Linux and utilizing an NVIDIA GeForce RTX 3090 GPU. The CUDA version was 11.8, providing robust computing capabilities and efficient handling of large-scale data training tasks. The entire model training process was set to 300 epochs. The initial learning rate was set to 0.001, and the batch size was set to 64. The optimizerused Stochastic Gradient Descent (SGD), which helped a model converge stably during training. Input images were uniformly resized to 640 \(\times\) 640 resolution and normalized to the range \([0,\ 1]\) by dividing pixel values by 255 to ensure consistent input format and improve training stability.
## 3 Analysis of Rotor Parameter Influence on Electromagnetic and Mechanical Stress Performance
### Influence of Magnetic Bridge and Stiffeners Thickness  
Designing a magnetic bridge is a challenge for high-speed IPM motors. As shown in Figure 6, the electromagnetic performance and rotor stress are compared for different bridge thicknesses. When the bridge thickness increases from 1 mm to 3 mm, the line back-EMF decreases from 382.6 V to 348 V. The maximum rotor stress decreases to 820 MPa, a 41% reduction compared to 1 mm. Figure 6c summarizes the variation trend of no-load line back-EMF and rotor stress at different bridge thicknesses.  
Unfortunately, when the bridge thickness is 3 mm, the rotor stress is still greater than the yield strength of the electrical steel. Therefore, it is necessary to take measures to strengthen the rotor's reliability. PM materials can be divided into three segments by adding stiffeners. As shown in Figure 7a, when the thickness of the stiffener is 2.5 mm, the maximum stress of the rotor structure is 425 MPa. Figure 7b summarizes the influence of the stiffener thickness on the no-load back-EMF.  
Figure 6: The influence of the bridge thickness on back-EMF and stress. (**a**) Line back-EMF; (**b**) rotor stress; (**c**) variation trend.  
Figure 7: The influence of the stiffener thickness on line back-EMF and rotor stress. (**a**) Rotor stress; (**b**) line back-EMF.