esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 5 Simulation Results and Discussion
Figure 7: Link-level performance under pilot-aided perfect and DM-RS-based practical channel estimation at 500 km/h for TDL-A (NLOS): (**a**) BER, (**b**) throughput, (**c**) spectral efficiency; and for TDL-E (LOS): (**d**) BER, (**e**) throughput, (**f**) spectral efficiency. The practical channel estimation uses the PDSCH DM-RS to estimate the channel conditions. It uses noise averaging and interpolation to obtain an estimate for all resource elements in the slot. This observation is because the DM-RS symbols are specified per layer, representing the channel conditions between the transmit layers and the receive antenna, including the effect of the MIMO precoding operation. Hence, the impact of various MIMO schemes is analyzed, as shown in Figure 9.  
The various MIMO schemes are simulated for 64-QAM and 120 kHz SCS at 500 km/h under the TDL-A (LOS) channel model and practical channel estimation, as shown in Figure 9. A large number of transmit and receive antennae, such as (8 \(\times\) 8) MIMO, have shown the highest link reliability. The maximum achievable throughput of 368.832 Mbps and spectral efficiency of 2.705 bps/Hz is achieved by the MIMO 8 \(\times\) 8 scheme. The results in Figure 9 also show that utilizing the precoding-based MIMO beamforming is beneficial to support 500 km/h UE mobility in NTN. Therefore, we propose utilizing precoding-based MIMO (8 \(\times\) 8) schemes to support high mobility in NTN. The link-level performance of 5G-NR PDSCH is analyzed for NTN under the front-loaded DM-RS symbols with additional DM-RS symbols 0, 1, 2, and 3 in Figure 10. The simulation results show the improved link reliability with the three additional DM-RS symbols utilized in practical channel estimation, which is the denser symbol pattern of DM-RS symbols. High link reliability can be achieved despite sacrificing the throughput and spectral efficiency at UE mobility of 500 km/h. The front-loaded DM-RS enables fast decoding, which helps to improve link reliability. Therefore, this paper proposes 5G-NR physical layer elements, such as the SCS of 120 kHz, high MCS levels, such as 64-QAM or 256-QAM, and dense DM-RS symbol pattern-based channel estimation under MIMO schemes to support high mobility up to 500 km/h in NTN. Figure 8: Link-level performance under various 5G-NR numerologies at 500 km/h: (**a**) BER, (**b**) throughput, (**c**) spectral efficiency. Figure 9: Link-level performance under various MIMO schemes at 500 km/h: (**a**) BER, (**b**) throughput, (**c**) spectral efficiency.
# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
## 4 Results
### Experiment Parameters  
Three CNN architectures were fine-tuned block-wise. The batch size used was 64, and three learning rates, namely \(10^{-3}\), \(10^{-4}\), and \(10^{-5}\), were applied. The Adam optimizer [41] was used in all of the experiments. All of the images were kept at the original dimensions of \(96\times 96\)_pixels_. The original fully connected layers of the architectures were removed and replaced by a dropout layer with a probability of 50% to increase the network accuracy, and a new fully connected layer was used as a classifier. The training dataset was divided into two partitions: 80% and 20% for training and validation, respectively. To increase the size of the training dataset and to overcome the translation of the images, image augmentation was applied with the following parameters: horizontal and vertical flipping, 180 rotation, width and height shifting, and shearing and zooming. It is worth noting that image augmentation was applied to the training dataset only and not the validation dataset. The best model will be saved in every epoch, and the early stopping criteria of 10 epochs were applied.  
Each CNN architecture was divided into blocks based on their design. For VGG networks, the blocks were divided based on the max-pooling layers, and for the InceptionV3 network, the blocks were defined based on the inception module, meaning that every inception module represents a block. VGG architectures have a total of 5 blocks, and the InceptionV3 network has 13 blocks. Each network was fine-tuned backward, meaning that the first block to be fine-tuned in the VGG networks was the 5 th, and the first block to be fine-tuned in the InceptionV3 network was the 13 th.
###### Abstract  
Elastic moduli were theoretically computed using the Makishima-Mackenzie model for SiO\({}_{2}\)-Na\({}_{2}\)O-CaO glasses doped with Sb\({}_{2}\)O\({}_{3}\) contents. The calculated elastic moduli (Young's, bulk, shear, and longitudinal modulus) were observed to increase with an increase in the Sb\({}_{2}\)O\({}_{3}\) contents. The microhardness showed an increase, while Poisson's ratio decreased with the rise of the Sb\({}_{2}\)O\({}_{3}\) contents. In addition, gamma-ray and neutron shielding parameters were evaluated for the investigated glasses. The linear attenuation coefficient (LAC) was simulated using the Monte Carlo N-particle transport code (MCNP-5). Other parameters, such as the mass attenuation coefficient (MAC), transmission factor (TF), and half-value layer, were calculated based on the simulated LAC. The addition of Sb\({}_{2}\)O\({}_{3}\) content was observed to enhance the investigated glasses' shielding parameters, where the highest LAC was achieved for the SCNSb10 glass with 100\(\%\) Sb\({}_{2}\)O\({}_{3}\) and decreased from 0.441 to 0.154 cm\({}^{-1}\) at gamma energies between 0.248 and 1.406 MeV. Furthermore, the fast neutron effective removal cross-section (\(\sum_{\rm R}\)) was computed theoretically. The calculated results showed that the highest \(\sum_{\rm R}\) was equal to 0.0341 cm\({}^{2}\)g\({}^{-1}\) and was obtained for the SCNSb0 glass, which had no Sb\({}_{2}\)O\({}_{3}\) content, while the lowest \(\sum_{\rm R}\) was equal to 0.0286 cm\({}^{2}\) g\({}^{-1}\) for the SCNSb10 glass sample. The present work was carried out to examine the advantages of the soda-lime glasses with different Sb\({}_{2}\)O\({}_{3}\) contents in several photon shielding applications, especially for radiation safety in nuclear installations.  
Keywords:glass-based hybrid material; mechanical properties; softening temperature; gamma-ray shielding features; Monte Carlo simulation +
Footnote †: journal: Applied Sciences
# MZM Optimization of PAM-4 Transmission in Data Center Interconnect
## 4 Lab Measurements  
### Lab Measurement Setup  
A full optical link experiment was performed over standard single mode fiber (SSMF) and is presented in Figure 3.  
Figure 2: Eye diagrams at the optical transmitter output: (**a**) L/V curve of MZM; (**b**) even PAM at quadrature bias point of 5 V; (**c**) uneven PAM at bias point of 4.1 V.  
A 53 Gbaud PAM-4 signal is transmitted by a DAC with 3 dB cutoff frequency at 30 GHz and electrical output swing of 1 Vpp differential and the DAC output level is equally spaced. The DAC output is converted to the optical domain by an external MZM (Fujitsu FTM7937 EZ) with Vpi DC and Vpi RF of 5.5 V and 3 V, respectively. The MZM is driven by integrated transmitter laser assembly (ITLA). The MZM optical signal output is amplified by a booster erbium-doped fiber amplifier (EDFA) configured to a fixed output power of 6 dBm, passes through optical fiber of 2 km length, representing a residual dispersion, and is accompanied by amplified spontaneous emission (ASE) noise using a 3-dB optical coupler and ASE source, in order to generate controlled OSNR conditions. Following, the optical signal passes through a 75 GHz (full width) 3 dB optical band pass filter and is converted to the electrical domain by a receiver optical sub assembly (ROSA-Semtech GN3289). The electrical signal is sampled and quantized by a 53 GSample/sec ADC with a 3-dB cutoff frequency at 19 GHz and ENOB of 6. The OSNR is measured at the optical coupler output using optical spectrum analyzer, and it is varied by controlling the ASE source while the average optical input power at the receiver is kept at \(-1\) dBm. The bandwidth of the overall optoelectronic channel is 12 GHz (3 dB point).  
Consequently, substantial ISI is introduced and advanced real time processing is applied. The received samples are equalized and processed by a MLSE DSP [3], which includes a feed forward equalizer with 15 taps followed by an MLSE equalizer with a memory depth of 3. The resulting measured BER curves are presented in Figure 4a,b.  
Figure 4: BER vs. OSNR: (**a**) BER vs. OSNR for different MZM bias values. (**b**) Even power levels spacing at bias of 5 V, (blue curve ‘+’) compared to uneven power levels spacing with bias of 4.08 V, (green curve ‘**’), while keeping identical OMAouter using 1000 mV and 800 mV MZM input voltage swing.  
Figure 3: Lab measurement setup.
# Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia
## 4 Experimental Results
### Results of Using Optimized K* Model  
During the early stages and the preliminary experiments, many powerful regression models, such as support vector machine, linear regression, artificial neural networks, and random tree, were tested against the K* model to corroborate the model's performance. These experiments further proved the potential the K* model had in tackling the problems presented in this study. The experiments show that the K* model had the best results in electricity consumption prediction, as shown in Table 3.  
After that, the effects of tuning the hyperparameters on the electricity consumption dataset for the K* algorithm were inspected. Global blend parameter tuning was implemented via an increment of ten until the results started to worsen. The results started to decline when the global blend value increased, and the best result was when the value was at 10. Table 4 shows the experimental results of the CC, MAPE, RMSPE, RMSE, and MAE for different global blend values.  
Results from tuning the global blend on the K* model for the electricity consumption dataset show that the optimal values for all metrics were obtained through setting the parameter to 10.  
Moreover, Figure 2 illustrates the effects of the global blend of the K* model on the CC, MAPE, RMSE, RMSPE, and MAE for electricity consumption prediction.  
Figure 2 shows that the values continue to enhance for CC, MAPE, RMSPE, MAE, and RMSE as the global blend increases until it reaches around 10 marks, where the results stop improving beyond that point.  
The optimized value of the global blend parameter on the electricity consumption dataset is 10, which is used to build the optimized K* model.  
Table 5 shows the results of applying the K* model with the optimal value on both the training and testing datasets, where values for CC, MAPE, RMSPE, MAE, and RMSE are presented.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Global Blend** & **CC** & **MAPE** & **RMSPE** & **MAE** & **RMSE** \\ \hline
1 & 0.9674 & 0.2203 & 0.525 & 0.0154 & 0.03 \\
10 & 0.9779 & 0.1668 & 0.4394 & 0.0118 & 0.025 \\
20 & 0.9782 & 0.1847 & 0.4133 & 0.0125 & 0.0267 \\
30 & 0.9775 & 0.2363 & 0.4689 & 0.0148 & 0.0307 \\
40 & 0.9765 & 0.325 & 0.6325 & 0.0184 & 0.0365 \\
50 & 0.9752 & 0.4642 & 0.9306 & 0.0231 & 0.0438 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the K* algorithm with different global blend values on the electricity consumption dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **CC** & **MAPE** & **RMSPE** & **MAE** \\ \hline \multirow{4}{*}{Electricity consumption} & K* & 0.9782 & 0.1847 & 0.4133 & 0.0125 \\  & ANN & 0.9762 & 0.4973 & 1.0247 & 0.0157 \\  & Linear regression & 0.9603 & 0.6906 & 1.4609 & 0.0203 \\  & Support vector regression & 0.9572 & 0.3009 & 0.5757 & 0.0179 \\  & Random tree & 0.9663 & 0.2328 & 0.4212 & 0.0159 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Preliminary results of K* against popular machine learning models.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 8 Ultrasound
### Detection  
[PERSON] et al. [75] compared a directly learned CNN with a transfer-learned CNN (TCNN) for object detection in ultrasound videos, such as the skull, abdomen, and heart. The accuracy of the TCNN was proven to be better than the directly learned CNN. The detection of fetal standard planes provides fetal development information during pregnancy. [PERSON] et al. [76] proposed using a multitask CNN to extract features from videos. In this case, the CNN classifier was explored to extract a region of interest (ROI), which is fetal planes. LSTM was applied for scoring frames based on between-planes knowledge. A classified frame would be labeled as a fetal plane if the inferred score was more significant than the predefined threshold score. The proposed system was unsuitable for real-time feedback due to the long processing time, which is around one minute for a video containing 40 frames. Another limitation of the model was that all ultrasound images were taken from healthy mothers and babies, so the researchers could not anticipate how the system would respond when applied to a fetus with abnormalities. Another work for fetal plane detection is proposed by [77]. The proposed model is used to identify four fetal planes and is based on a CNN and an RNN. The model extracts the spatial and temporal features from US videos.  
The challenges faced by detection and segmentation tasks are low accuracy, dependency on radiologist decisions, and high processing time. One of the reasons behind these shortcomings is, firstly, that when an ultrasound test is performed, the technician tries to capture as many details as possible. This causes the ultrasound video to have many uninformative frames. Secondly, the ultrasound videos are noisy in nature, contain speckles, and are usually grayscale. Lastly, ultrasounds may generate blurry results if the patient is obese, resulting in difficulty in analysis.
## 6 Experiment and Result
### Monitoring Service  
In the experiments with the monitoring service, we analyzed the performance of message broker clusters (EMQ cluster and Kafka cluster) as they are critical components of the messaging subsystem. The performance and scalability analysis of each of the message broker clusters was carried out in isolation from other system components. Through various workload tests, we tried to establish the data flow they can support and their differences. The metrics considered were the average throughput and the average response time.
## 3 Proposed Methodology
### Transfer Learning Process
#### 3.2.2 Fully Connected (FC) Layer  
In CNN, the convolutional and pooling layers can be followed by a set of FC layers that perform like any ANN, such as MLP. The purpose of the FC layers is to combine all the features (local information) learned by the previous layers to recognize the larger patterns. For classification problems, the number of neurons at the last FC layer is equal to the number of classes [34]. In image classification problems, the standard method is to use a stack of FC layers, followed by a Soft-Max activation function [11]. The output of Soft-Max is a set of probability distributions of different classes, where the neuron with the maximum probability is considered as the classification result [35]:  
\[P_{label}=\frac{exp(former\quad layer\quad output)}{\sum_{i=1}^{k}(former\quad layer \quad output)} \tag{5}\]  
where \(P_{label}\) presents the predicted label, the former layer output refers to the last fully connected layer, and \(k\) represents the number of fully connected layers. The fundamental of the training phase is like MLP--that after defining the CNN layers, the training phase is started by determining the optimization technique first. There are two well-known optimizers to minimize the loss function Equation (6), such as adaptive moment estimation and Stochastic Gradient Descent (SGD) [36]. In this research, the loss function is the cross-entropy, which is mathematically defined as:  
\[loss=\sum_{i=1}^{N}\sum_{j=1}^{M}t_{ij}\ln P_{label_{ij}} \tag{6}\]where, \(N\) and \(M\) refer to number of samples and classes respectively. \(t_{ij}\) is an indicator that \(i\)th sample belongs to \(j\)th class [36].
## Materials and Methods
### System Portfolio Optimization  
As discussed above, the paper tries to solve the system portfolio problem considering system values and risks. The corresponding notation is as follows. Let \(S=\{s_{1},\ldots,s_{m}\}\) denotes the set of alternative systems, indicating \(m\) systems can be selected for a system portfolio. \(x_{i}=(x_{i1},\ldots,x_{im}),x_{i}\in\{0,1\}^{m}\) is one of system portfolio schemes, where \(x_{ij}=0\backslash 1\), with \(x_{ij}=0\) denoting the \(j\)th system in \(S\) is not selected in scheme \(x_{i}\), and \(x_{ij}=1\) denoting the \(j\)th system in \(S\) is selected in scheme \(x_{i}\). All possible schemes compose the solution space \(X\). Let \(E=(e_{1},\ldots,e_{m})\) represents the cost of systems, where \(e_{i}\) is the cost of system \(s_{i}\), and \(B\) denotes the total budget.  
Then, the system portfolio optimization model can be formulated as follows.  
\[\begin{array}{ll}max&V\left(x_{j}\right)=\sum\limits_{i=1}^{k}w_{i}\left(1- G\left(e_{i}\sigma_{i}\right)\right)\\ min&R\left(x_{j}\right)=1-\prod\limits_{i=1}^{k}\left[\left(1-\frac{1}{RUL_{ i}}\right)^{x_{j}}\right]\\ s.t.\left\{\begin{array}{l}\sum\limits_{i=1}^{m}x_{ij}\times e_{i}\leq B\\ x_{j}=\left(x_{j1},\ldots,x_{jm}\right),x_{j}\in\{0,1\}^{m}\end{array}\right. \end{array}\right.,\end{array} \tag{14}\]  
where \(V(x_{j})\) and \(R(x_{j})\) are the value and risk of the system portfolio scheme \(x_{j}\). \(G(c_{i},c\sigma_{i})\) represents the capability gap on the \(i\)th capability. In addition, the cost of scheme \(x_{j}\) must within the budget limitation.  
It can be seen that the optimization is a multi-objective problem and the 2 objectives are conflict with each other. Therefore, an optimal system portfolio with the best performance in both 2 objectives does not exist. The target is to obtain the Pareto optimal solutions of 2 objectives, also known as non-dominated solutions.
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 4 Roof Segmentation Dataset  
We use the Shuijing micro-image software ([[http://www.rivermap.cn/down.html](http://www.rivermap.cn/down.html)]([http://www.rivermap.cn/down.html](http://www.rivermap.cn/down.html))) (accessed on 15 May 2024) to obtain satellite images and then use the labelme ([[https://github.com/mpitid/pylabelme](https://github.com/mpitid/pylabelme)]([https://github.com/mpitid/pylabelme](https://github.com/mpitid/pylabelme))) (accessed on 15 May 2024) software to annotate satellite images. In the satellite imagery view of the Shuijing Micromap software, we select the target area through the rectangular box, then set the zoom level and the size of a single tile, and then we can download all tiles in the area. In this study, we choose Shenzhen, Guangdong, China as the target area, with a zoom level of 19. Each tile is an RGB image with a size of \(256\times 256\). It is worth pointing out that the zoom level of each satellite image should be recorded, as it will be used to calculate the roof area based on the roof mask map.  
We selected 2188 images containing buildings from the downloaded tiles and used the labelme software to obtain the roof outline annotation file. It should be noted that, in order to accurately predict the roof area, we use the roof boundaries instead of the building boundaries as the annotation target. Afterward, a program based on Opencv was written to generate roof masks from roof contour annotation files. Finally, we obtained a total of 15,468 roof masks. Some examples of the new roof segmentation dataset are presented in Figure 5. To facilitate the measurement of the performance of the model to be tested, we selected 200 images from 2188 and used these 200 images and their corresponding masks as the test set, while other images and corresponding masks were used as the training set.
###### Abstract  
(1) Background: The purpose of this study was to evaluate the morphology and linear dimensions of sella turcica in Romanian participants from all three skeletal classes to see whether there were any differences. (2) Method: We examined 90 lateral cephalometric radiographs of patients aged 12 and older and divided them into skeletal classes I, II, and III (30 participants in each). Sella turcica linear measurements such as length, depth, and anteroposterior diameter were measured and studied. To see the nature of our data, Q-Q plots tests were performed. By examining these tests performed for each variable belonging to a particular class, it can be noted that the points are fairly well distributed along some lines, meaning that the data are normally distributed. An Anova test with Bonferroni correction was used to compare the mean values of the examined parameters between the classes. Also, to observe the correlation between our experimental data, the Pearson correlation coefficient was calculated. (3) Results: In all three skeletal classes, the average length of the sella was 8.98 mm \(\pm\) 1.470, the average depth was 7.99 mm \(\pm\) 1.081, and the average diameter was 10.29 mm \(\pm\) 1.267. For all examined linear dimensions, there was a statistically significant difference between class I and class III subjects and between class II and class III subjects (_p_ < 0.001). The morphology of sella turcica was found to be normal in 51.1% of instances, representing the majority across all skeletal classes. In the Romanian population, sella turcica has shown a significant amount of variation. Class III subjects had larger sella dimensions, whereas class II subjects had smaller values. (4) Conclusions: The measurements and morphology of the sella analysed in the present research can serve as standards for subsequent research concerning the sella turcica region in individuals from Romania.  
sella turcica; skeletal class; cephalometric analysis; digital cephalometric analysis; sella size; sella morphology; orthodontics; dentistry
## 2 Methodology
### Mathematical Formulation
#### 2.2.2 Harmonic Model  
As was mentioned above, the loads in the smart home caused harmonics and the relevant harmonic model is given below. The instantaneous values for voltage and current are given in Equation (22) and (23), respectively. \(v_{n}\) and \(i_{n}\) are the instantaneous voltage and current of the \(n\). harmonic. Furthermore, \(w_{1}\) represents the angular frequency for the fundamental component. Lastly, \(\theta_{n}\) and \(\delta_{n}\) are phase angles of \(n\) voltage and current harmonics.  
\[v(t)=\sum\limits_{n=1}^{\infty}v_{n}(t)=\sum\limits_{n=1}^{\infty}\sqrt{2V_{n }\sin(n\omega_{1}t+\theta_{n})} \tag{22}\]  
\[i(t)=\sum\limits_{n=1}^{\infty}i_{n}(t)=\sum\limits_{n=1}^{\infty}\sqrt{2I_{n }\sin(n\omega_{1}t+\delta_{n})} \tag{23}\]  
The instantaneous active power expression is given in Equation (24). Herein, the active power is obtained by multiplying the current and voltage with the same frequency. Similarly, the reactive power expression is also expressed in Equation (25).  
\[P=\sum\limits_{n=1}^{\infty}V_{n}I_{n}\cos(\theta_{n}-\delta_{n}) \tag{24}\]  
\[Q=\sum\limits_{n=1}^{\infty}V_{n}I_{n}\sin(\theta_{n}-\delta_{n}) \tag{25}\]  
The effective values of current and voltage are given in Equations (26) and (27), respectively. Herein, the effective value is obtained by taking the square root of the sum of the squares of the fundamental component and other harmonic components.  
\[I=\sqrt{\sum\limits_{n=1}^{\infty}I_{n}^{2}} \tag{26}\]  
\[V=\sqrt{\sum\limits_{n=1}^{\infty}V_{n}^{2}} \tag{27}\]  
Equations (26) and (27) results in calculating the apparent power by indicating Equation (28).  
\[S=V.\Gamma \tag{28}\]  
The apparent power can be obtained from active, reactive, and distortion power types as follows:  
\[S^{2}=P^{2}+Q^{2}+D^{2} \tag{29}\]  
where \(D\) distortion power is used to define inactive power for current and voltage at different frequencies.