esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
## 2 Materials and Methods
### Models
#### 2.5.2 ViT Models: UNETR and Swin-UNET  
After the CNN model, we tried to outperform the results obtained through two different models based on self-attention mechanisms, i.e., two vision transformers.  
On the one hand, we trained a model called UNETR, which is a transformer-based architecture developed for 3D medical image segmentation [30]. The main contribution of this model is to include skip connections between the transformer encoder and the convolutional decoder. We used a feature size of 64 and a dropout rate of 0.2 in this architecture, reaching a total of 104M trainable parameters.  
For the training process, we conducted a total of 1000 epochs using an initial learning rate of 0.002, Adam optimizer and a batch of 1 image. We found that the best hyperparameters for the loss function are \(\theta=1\) and \(\alpha=0.7\).  
On the other hand, we have also trained a model called Swin-Unet [31], which is a U-Net-like pure Transformer for medical image segmentation. The main difference between the UNETR and the Swin-Unet models is that the Swin-Unet uses a transformer-like architecture not only for the encoder but also for the decoder while the UNETR uses a convolutional decoder. It also includes a novel multi-head self-attention module based on shifted window named the \"swin transformer block\". In this case, we used the original Swin-Unet architecture which has 27M trainable weights.  
We have trained it for 3000 epochs using an initial learning rate of 0.002, Adam optimizer, and a batch size of 4 images. We found that the best hyperparameters for the lost function are \(\theta=2\) and \(\alpha=0.75\).
## 3 Results  
Positive results were obtained for the CSF sample in both the pan-_Flavivirus_ conventional PCR, and the MVE-specific real-time PCR. The Cycle threshold (Ct) for the MVE-specific PCR was 41, consistent with a low virus titre. The internal control virus Ct was within normal limits indicating there was no inhibition of the PCR.  
Despite the likely low titre of virus, isolation of MVEV in Vero cells was achieved. CPE was observed on day 5 with morphology consistent with flaviviruses. Confirmation was performed by Real Time PCR which indicated a continuing decrease in Ct (from 21 to 17 over 5 days). This permitted extraction of sufficient RNA for whole genome sequencing.  
A consensus genome sequence for VIDRL-MVE (Genbank Accession ID: MG452954) was prepared using approximately 10 million virus-specific sequencing reads mapped against the 10,760 bp 611W-WA-08 Genotype-1 reference genome. VIDRL-MVE differed from the reference genome by 772 Single Nucleotide Polymorphisms (SNPs) distributed throughout the genome, including the 5\({}^{\prime}\) and 3\({}^{\prime}\) UTRs (Figure S1A,B).  
A phylogenetic analysis of the VIDRL-MVE genome in comparison to the five other published full genome sequences, representing isolates 611W-WA-08, MVE-1-51, MK6684, NG156 and OR156, was performed (Figure 1). This analysis, which used all 1730 variable nucleotides positions across the viral genome, showed that VIDRL-MVE was most closely related to MVE-OR156, a Genotype 2 virus isolated from mosquitoes in the Kimberley region of Australia in 1973. VIDRL-MVE was more distantly related to the Genotype 1, 3 and 4 viruses, having an estimated 1030 SNPs in comparison to the prototype MVEV isolate MVE-1-51. Therefore, approximately 10% of the VIDRL-MVE genome is divergent relative to the Genotype 1 virus MVE-1-51. Additionally, a phylogenetic tree was constructed from all available MVEV sequences corresponding to the prM-E gene(s) (\(\sim\)2000 bp region) to further expand Figure 1 and confirm our isolate as genotype 2 (Figure 2).  
A more detailed comparison of MVE-OR156 and VIDRL-MVE identified 288 variable nucleotide positions between VIDRL-MVE and MVE-OR156 resulting in 31 predicted amino acid changes distributed throughout the viral genome (Table 1). The biological significance of these changes remains unknown.  
Figure 1: Maximum Likelihood tree (mid-point rooted) showing the phylogenetic relatedness of publicly available Murray Valley Encephalitis virus (MVEV) full genome sequences in comparison to VIDRL-MVEV. Isolate 611-WA-08 was used as the reference strain. Viral genotype (G1–G4) is also shown next to each taxon. OR156 is the G2 ancestral reference strain. All nodes had FastTree support values \(>\)0.95.  
Figure 2: Neighbor joined tree of MVEV prM-E sequences confirm VIDRL-MVEV as Genotype 2. Viral genotype (G1–G4) is indicated. Scale bar indicates 0.06 nucleotide substitutions per site.
## 2 Network Model
As a consequence, the achievable rates of the \(m\)-th user and \(M\)-th user are given by  
\[R_{m}(n,\varepsilon)=\log(1+\gamma_{m})-\sqrt{\frac{V_{m}}{n}}Q^{-1}( \varepsilon), \tag{4}\]  
and  
\[R_{M}(n,\varepsilon)=\log(1+\gamma_{M})-\sqrt{\frac{V_{M}}{n}}Q^{-1}( \varepsilon), \tag{5}\]  
respectively, where \(V_{m}=\bigg{[}1-\frac{1}{(1+\gamma_{m})^{2}}\bigg{]}(\log\varepsilon)^{2}\) and \(V_{M}=\bigg{[}1-\frac{1}{(1+\gamma_{M})^{2}}\bigg{]}(\log\varepsilon)^{2}\) are the characteristics of the channel, referred to as the channel dispersion for the \(m\)-th user and \(M\)-th user, respectively. \(Q(x)=\int_{x}^{\infty}\frac{1}{\sqrt{2\pi}}e^{-\frac{t^{2}}{2}}dt\) stands for the Gaussian function. \(\varepsilon\) denotes the transmission error probability, which has a relationship of \(\varepsilon\in[0,1]\). Effective capacity is an approach to characterize the quality of service of the link layer for wireless channels. The main features of the effective capacity, i.e., \(C_{e}\), is to characterize the wireless channel with the purpose of supporting quality of service [22], which is defined as a maximum rate while maintaining a certain latency range in the delay outage probability and the maximum delay bound \(D_{\max}\). It is worth noting that \(P_{delay}\approx e^{-\theta C_{e}D_{\max}}\) is an approximated delay outage probability, where \(\theta\) is the delay exponent to reflect the system tolerance for a long latency. As pointed out by [23], the effective capacities are formulated by  
\[C_{m,\varepsilon}=-\frac{1}{n\theta_{m}}\ln\Big{\{}\mathbb{E}\Big{[} \varepsilon+(1-\varepsilon)e^{-n\theta_{m}R_{m}}\Big{]}\Big{\}}, \tag{6}\]  
and  
\[C_{M,\varepsilon}=-\frac{1}{n\theta_{M}}\ln\Big{\{}\mathbb{E}\Big{[} \varepsilon+(1-\varepsilon)e^{-n\theta_{M}R_{M}}\Big{]}\Big{\}}, \tag{7}\]  
respectively, where \(\theta_{m}\) is a delay exponent of user the \(m\), and \(\theta_{M}\) is a delay exponent of the \(M\)-th user. (6) is effective capacity function, which stands for the maximum achievable rate [20]. Equation (1) of [24] is ergodic capacity, which is independent of the blocklength. It can be seen from Jenson inequality \(E[log(1+x)]\leq log(E(1+x)]\) that there is a difference between the inside expectation operator and the outside expectation operator in the logarithmic functions.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### Evaluation of the System without Optimization
#### 3.4.1 Wireless Communications Assumptions  
We consider two users (i.e., \(UE_{g}\) and \(UE_{d}\)), two APs (i.e., \(AP_{g}\) and \(AP_{d}\)) and an MEH, as in Figure 1. \(UE_{g}\), \(AP_{g}\), \(UE_{d}\) and \(AP_{d}\) are placed at \([5,0]\), \([5,20]\), \([8,0]\) and \([8,20]\), respectively. Both UEs are equipped with a single antenna, and both APs employ a uniform linear array of \(M_{g}=M_{d}=8\) elements. Maximal ratio combining is employed for the receive filters at both \(AP_{g}\) and \(AP_{d}\). Channel coefficients are generated as in (6), with path loss \(\beta_{ij}\) with exponent 4, while Rice fading with factor \(K=3\) is considered. A different channel realization is extracted for each experiment from the described distribution. To evaluate the performance, we assume the transmit power of \(UE_{g}\) to be fixed to \(P_{g}=100\) mW, while we vary the transmit power of \(UE_{d}\), choosing it in \([0,P_{c,\text{max}}]\) W, with 500 evenly spaced points, and \(P_{c,\text{max}}=200\) mW, in order to explore the performance as a function of \(UE_{d}\) data rate (in each simulation, we will specify the selected value whenever needed). The carrier frequency is \(f_{c}=28\) GHz ([[https://www.bmwk.de/Redaktion/EN/Publikationen/Digitale-Welt/](https://www.bmwk.de/Redaktion/EN/Publikationen/Digitale-Welt/)]([https://www.bmwk.de/Redaktion/EN/Publikationen/Digitale-Welt/](https://www.bmwk.de/Redaktion/EN/Publikationen/Digitale-Welt/))guidelines-for-5g-campus-networks-orientation-for-small-and-medium-sized-businesses. pdf accessed on 20 July 2023), with total bandwidth \(W=1\) GHz, fully shared among \(UE_{g}\) and \(UE_{d}\), while the noise power spectral density is fixed to \(N_{0}=-174\) dBm/Hz, with a 3 dB noise figure at the receiver. The transmission of \(UE_{g}\) is organized in packets, each of equal size of 32 Bytes. The PER is chosen from the vector \(\gamma=[10^{-7},10^{-6},10^{-5},10^{-4},2\times 10^{-4},4\times 10^{-4},8\times 10^{ -4},10^{-3},2\times 10^{-3},4\times 10^{-3},8\times 10^{-3},10^{-2},2\times 10^{-2}]\) to explore performance in terms of communication reliability. Therefore, at time \(t\), given a PER \(\gamma_{t}\), the transmitted packets are considered to be incorrectly received with probability \(\gamma_{t}\) chosen from the vector (and specified for each simulation whenever needed). When a packet is not correctly received, no retransmission is requested, and the bits within the packet payload are randomly chosen.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Second-Order Stokes Wave Based on Brillouin Scattering  
The method is based on the principle of inducing second-order Stokes light in Brillouin fiber ring lasers and can also be used for linewidth measurements in other lasers [53; 54]. The second-order Stokes light is output as a reference light beat with the pumping test beam, and the spectrum is almost identical to the spectral line shape of the test laser [55; 56]. The structure is shown in Figure 1f.  
The laser under test enters the resonant cavity of the fiber ring through an isolator, and the pump light propagates counterclockwise within the fiber ring, producing first-order Stokes light that propagates clockwise within the ring. The power of the first-order Stokes light reaches the stimulated Brillouin scattering (SBS) threshold when the pump light power is sufficiently high, producing a second-order Stokes light with the same direction as the pump light. The second-order Stokes light is simultaneously output and mixed with the pump light [57; 58]. The linewidth of the first-order Stokes light can be narrowed to the Hz level for a sub-MHz pump light, and the linewidth of the second-order Stokes light can be further narrowed to the sub-Hz level [59; 60]. The linewidth of the second-order Stokes light is several orders of magnitude smaller than that of the tested laser, and the FWHM of the beat spectrum is equivalent to the linewidth of the tested laser, as shown in Figure 2b.  
The method has a simple experimental setup, does not require a long delay fiber, and has high measurement accuracy and lower linewidth measurement limits. However, the laser to be tested needs to maintain single longitudinal mode operation, and there are extensive requirements for the stability of the testing environment.
# Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound
## Materials and Methods
### Investigation of Hematological Parameters  
Certified nurses collected blood from lung disease patients, and the blood was placed into 3 mL Vacuette K2E K2 EDTAA 13 \(\times\) 75 tubes with lavelander caps and black rings and equally divided into 7 parts, so that each part (\(\sim\)0.43 mL) was exposed to ultrasound (US). The Swelab Alfa system (Boule Medical AB, Stockholm, Sweden) [21] used in this research is an automated hematology analyzer for in vitro diagnostics under laboratory conditions. The Swelab Alfa analyzer was used to determine red blood cell (RBC) counts; the mean cell volume of red cells (MCV); the red cell distribution relative and absolute volumes (RDW%, RDWa); hematocrits (HTCs); the platelet count (PLT); the mean platelet volume (MPV); the platelet distribution width (PDW); platelets (PCTs); the platelet large cell ratio (LPCR); white blood cells (WBCs); hemoglobin (HGB); mean corpuscular hemoglobin (MCH); the mean corpuscular hemoglobin concentration (MCHC); lymphocytes (LYMs); granulocytes (GRANs); the minimum inhibitory dilution (MID); the lymphocyte percentage (LYM%); the granulocyte percentage (GRA%); and the mid-sized white cell percentage (MID%).  
Figure 1: Schematic diagram of the blood sample sonication setup.  
All experiments with patient blood were carried out within the first hour after blood collection. The characteristics of the low-frequency ultrasound used on the blood samples are presented in Table 1.
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
###### Abstract  
In response to the challenges posed by complex background textures and limited hardware resources in fabric defect detection, this study proposes a lightweight fabric defect detection algorithm based on an improved GSL-YOLOv8n model. Firstly, to reduce the parameter count and complexity of the YOLOv8n network, the GhostNet concept is used to construct the C2 Ghost module, replacing the conventional convolution layers in the YOLOv8n structure with Ghost convolutions. Secondly, the SimAM parameter-free attention mechanism is embedded at the end of the backbone network to eliminate redundant background, enhance semantic information for small targets, and improve the network's feature extraction capability. Lastly, a lightweight shared convolution detection head is designed, employing the scale layer to adjust features, ensuring the lightweight nature of the model while minimizing precision loss. Compared to the original YOLOv8n model, the improved GSL-YOLOv8n algorithm increases the [EMAIL_ADDRESS] by 0.60% to 98.29% and reduces model size, computational load, and parameter count by 66.7%, 58.0%, and 67.4%, respectively, meeting the application requirements for fabric defect detection in textile industry production.  
fabric defects; YOLOv8; GhostNet; attention mechanism; lightweight; object detection Article
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 4 MI-Assisted Adaptive Modulation
### Atmospheric Imperfections
```
0: data points \(x\in\{1,2,..,N\}\) and value of K
0: K\(>\)1
1:for\(k\gets 1\) to \(K\)do
2:\(\mu_{k}\leftarrow\) random cluster head location
3:endfor
4:functionLoop(until converged)
5:for\(n\gets 1\) to \(N\)do
6:\(r_{mk}\gets argmin_{k}||\mathbf{x}_{n}-\mathbf{\mu}_{k}||^{2}\)
7:endfor
8:for\(k\gets 1\) to \(K\)do
9:\(\mu_{k}\leftarrow\) mean(\(r_{mk}\), \(x_{n}\))
10:endfor
11:return cluster head locations, \(CH_{i}\), \(i\in\{1,2,..,K\}\)
12:endfunction
13:\(d_{i}\leftarrow\) distance of \(CH_{i}\) from threshold
14:choose min (\(d_{i}\)) and corresponding modulation
```  
**Algorithm 2** ML based AM  
Figure 2: Adaptive modulation using the k-means algorithm.
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method  
In this section, we describe the proposed positioning method that combines the fingerprinting technique with the double deep Q-Network. The key components of the proposed method are the construction of an RSS database using fingerprinting and the training of the DDQN model. Additionally, a block diagram of the proposed method is presented at the end of this section.  
Initially, the fingerprinting technique is employed to construct an RSS database specific to the indoor environment. Subsequently, the double deep Q-Network model is trained. To facilitate this, the elements of reinforcement learning--the agent, state, action, and reward--are defined. Once the model training is complete, the performance of the positioning is evaluated using the trained model.  
Figure 3: Overall RSS distribution.  
Figure 2: Indoor VLC scenario.
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 2 Soft X-ray Contact Microscopy
### A Note on Spatial Resolution  
It is not straightforward to assign the limit on spatial resolution in contact microscopy; however, several factors influence the spatial resolution, as seen in the following equation. This is an approximate relation, assuming all contributions have Gaussian profiles [22].  
\[\delta_{CM}=\sqrt{\delta_{s}^{2}+\delta_{plr}^{2}+\delta_{tip}^{2}} \tag{1}\]  
The \(\delta_{s}\) is a factor related to shadowing (both wavelength-related diffraction and geometrically related to variable sample-resist distances due to non-zero thickness of the sample), \(\delta_{plr}\) is a factor related to the spatial resolution of the photoresist, e.g., for HSQ/PMMA combinations below 10 nm [24], PMMA \(\sim\)10 nm [53], and \(\delta_{tip}\) is a factor related to the diameter of the AFM probe used to perform photoresist scanning after the exposure, typically 20 nm in diameter, but can be as sharp as \(\sim\)2 nm for carbon nanotube probes [26]. Taking into account all the above factors, the spatial resolution in SXCM is routinely much better than 100 nm [28; 54], allowing a practical limit of \(\sim\)10 nm [49; 55].  
More detailed numerical studies were also performed [43], in which the effects of sample and resist absorption and diffraction were considered, as was the process of isotropic development of the photoresist. The SXCM image resolution heavily depends on the exposure and the sample-to-resist distance. The contrast of small features imprinted in the photoresist depends significantly on the development procedure. If researchers are not careful, the information on the smallest features may be destroyed by excessively aggressive or long development. This is why these issues must be kept in mind during the postprocessing and interpretation of the SXCM images of high-resolution and low-contrast objects, such as biological structures.
## 7 Open Questions
### The Effect of Layer-Wise Fine-Tuning Instead of Full Fine-Tuning on DR Image Classification  
One of the main questions of applying transfer learning to DR is how deep to fine-tune the network, taking into consideration the size of the DR dataset and the architecture used. This question still needs further studies to understand the effect of each layer on the network's performance and to determine how deep to fine-tune a CNN. Full fine-tuning can be very computationally expensive, as it requires a lot of time, and it may not always guarantee to converge better than top-layer fine-tuning.
## 2 Materials and Methods
\[\frac{\partial}{\partial x_{i}}\big{(}\rho u_{i}u_{j}\big{)}=-\frac{\partial p}{ \partial x_{i}}+\frac{\partial}{\partial x_{i}}\bigg{[}(\mu+\mu_{t})\frac{ \partial u_{j}}{\partial x_{i}}\bigg{]}+\rho_{ion}E_{j}+S_{c} \tag{2}\]5  
\[\frac{\partial}{\partial x_{i}}\big{(}\rho u_{i}u_{j}\big{)}=-\frac{\partial p}{ \partial x_{i}}+\frac{\partial}{\partial x_{i}}\bigg{[}(\mu+\mu_{t})\frac{ \partial u_{j}}{\partial x_{i}}\bigg{]}+\rho_{ion}E_{j}+S_{c} \tag{2}\]6  
Particles in the contaminated air are charged by means of ions generated by corona discharge. A number of researchers have developed the particle charging theory. The field-modified diffusion model (FMD) presented by Ref. [32] has been widely employed to determine particle charge. This model considers both diffusion and field charging. It is derived as a linear differential equation of first-order, which can be solved with respect to time. Therefore, the FDM model proposed by Ref. [32] is employed in the present numerical model. \[\frac{dv}{dt_{p}}= \begin{cases}\begin{array}{cc}f(w)\frac{v-3w}{\exp(v-3w)-1},&v>3w\\ \frac{3w}{4}(1-\frac{v}{3w})^{2}+f(w),&-3w\leq v\leq 3w\\ -v+f(w)\frac{-v-3w}{\exp(-v-3w)-1},&v<-3w\end{array}\end{cases}  \tag{18}\]  
\[f(w)= \begin{cases}\frac{1}{(w+0.475)^{0.375}}&w\geq 0.525\\ 1&w<0.525\end{cases}  \tag{19}\]  
\[v=\frac{q_{p}e}{2\pi\epsilon_{0}d_{p}k_{B}T} \tag{20}\]  
\[w=\frac{\epsilon_{p}}{\epsilon_{p}+2\,2k_{B}T} \tag{21}\]  
\[t_{p}=\frac{\rho_{ion}b_{ion}t}{\epsilon_{0}} \tag{22}\]  
In the Equations (18)-(22), \(v\), \(w\), and \(t_{p}\) are dimensionless particle charge, dimensionless electric field, and dimensionless particle charging time, respectively. \(\epsilon_{p}\) is the relative permittivity of the particle. The particle trajectory equations Equation (8), and other complementary equations describing heat or mass transfer with the particle, are solved by employing the discrete phase module (DPM) of Fluent [33]. DPM applies stepwise integration over discrete time steps at each point along with the particle path to obtain the particle's velocity. Details of the DPM theory and usage can be found in the Ansys Fluent 19.0 Theory Guide [29]. Electrostatic field and particle charging modelling have been implemented to the Fluent by using the user-defined functions (UDF) incorporating with user-defined scalar (UDS) and user-defined memory (UDM) functions. Details of the UDF usage can be found in the Ansys Fluent 19.0 Theory Guide [29]. The commercial software ANSYS Fluent 19.2 is used to solve all governing equations. The pressure-velocity coupling is handled using the SIMPLE algorithm. The second-order upwind technique is used to discretize all convection terms. When the normalized residuals are less than \(10^{-5}\), the computation is assumed convergent.