esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Predictive Performance Analysis of the Evaluated Models  
Table 4 gives the results of the predictive performance analysis of MLR and machine-learning algorithms to predict the GMFM-66 using gait analysis data from the validation data set. The XGB model has the best predictive performance, with a CCC of 0.49 (95%CI 0.31; 0.63) (Table 4).  
Figures 2-6 show the results of the Bland-Altman analysis. Here, too, it can be seen that the accuracy of the GMFM-66 prediction is somewhat higher for the XGB than for the other statistical models (narrower limits of agreement in the Bland-Altman diagram, \(\pm 1.96\times\) SD).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **CCC** & **MAE** & **RMSE** & **PCC** \\ \hline MLR & 0.28 (0.11; 0.44) & 6.60 (5.41; 7.78) & 8.38 (7.24; 9.95) & 0.34 (0.13; 0.53) \\ RF & 0.36 (0.20; 0.51) & 6.23 (5.18; 7.27) & 7.73 (6.68; 9.17) & 0.44 (0.24; 0.60) \\ FNN & 0.33 (0.17; 0.48) & 6.19 (5.05; 7.33) & 7.95 (6.87; 9.44) & 0.42 (0.21; 0.59) \\ SVM & 0.31 (0.13; 0.46) & 6.33 (5.20; 7.47) & 8.05 (6.95; 9.55) & 0.37 (0.16; 0.55) \\ XGBoost & 0.49 (0.31; 0.63) & 6.32 (5.35; 7.28) & 7.59 (6.56; 9.02) & 0.52 (0.33; 0.67) \\ \hline \end{tabular} Data of the 95% confidence interval are given in parentheses. CCC—concordance correlation coefficient (Lin); FNN—feed forward neural net; MAE—mean absolute error; PCC—Pearson correlation coefficient; RF—random forest; RMSE—root mean square error; SVM—support vector machine; XGBoost—Extreme Gradient Boosting model.  
\end{table}
Table 4: Results of the predictive performance analysis of machine learning algorithms to predict gross motor function using instrumental gait analysis data.  
Figure 2: Bland–Altman diagrams of the multiple linear regression model. The results of the Bland–Altman analysis are presented. The dashed lines represent the mean difference and the limits of agreement (1.96 \(\times\) standard deviation). Each circle corresponds to a prediction of the GMFM-66.  
Figure 4: Bland–Altman diagrams of the feed forward neural net model. The results of the Bland–Altman analysis are presented. The dashed lines represent the mean difference and the limits of agreement (1.96 \(\times\) standard deviation). Each circle corresponds to a prediction of the GMFM-66.  
Figure 5: Bland–Altman diagrams of the support vector machine model. The results of the Bland–Altman analysis are presented. The dashed lines represent the mean difference and the limits of agreement (1.96 \(\times\) standard deviation). Each circle corresponds to a prediction of the GMFM-66.  
Figure 3: Bland–Altman diagrams of the random forest model. The results of the Bland–Altman analysis are presented. The dashed lines represent the mean difference and the limits of agreement (1.96 \(\times\) standard deviation). Each circle corresponds to a prediction of the GMFM-66.  
SHAP values were calculated for the XGB model, as it demonstrated the highest prediction accuracy (Figure 7).
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 3 Communication System Model  
### Theory  
The composition of the Z-OFDM system, which belongs to single input-single output (SISO) communication system, is shown in Figure 2[35]. The input binary data are mapped into a symbol constellation point \(X_{p}\) by Quadrature Phase Shift Keying (QPSK) [37] and transferred to the OFDM time domain signal \(x_{p}(t)\) by the inverse fast Fourier transform (IFFT) algorithm. Up-sampling the OFDM signal \(x_{p}(t)\)[38], and then the low-pass filter \(h_{l}(t)\) is added to the system to reduce the aliasing effect. Furthermore, the cyclic prefix (CP) is inserted at the head of the transmitting sequence to minimize the impact of inter-symbol interference (ISI). The signal is then up-converted to the carrier frequency. Subsequently, the signal is transmitted through the underwater acoustic channel, and the received signal \(y(t)\) is obtained at the receiver. Then, the CP is deleted and the time-domain signal is down-converted, passed through a low-pass filter, and then down-sampled by decimation. Finally, passing the down-sampled signal through FFT and performing channel equalization to reduce channel effects, then demodulating it by QPSK to get the message sequence [39]. The following will introduce the components of the Z-OFDM system from the perspectives of the transmitter model and the receiver model.
# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 6 Experimental Results
### Baseline Results
This supports our decision to report metrics only on the annotated objects. Furthermore, since only ENCs suitable for analysis are labeled in our dataset, it is crucial that these are successfully identified. As such, at this stage, we prioritize the match rate over other metrics. The greatest difference in the number of segmented instances may be seen in the 4C animal set, where 26.56% additional ENCs were labeled and 95.77% of their tagged cells had a respective match. Similar to the results presented in Table 2, the C subjects presented a higher match rate (average of 93.95%) and mDICE (average 0.6904) when compared to WT (87.25% and 0.6317, respectively), further supporting our statement on the impact of the noises found in those images. From these results, we can also conclude that the resulting segmentation objects are not optimal. Such a fact becomes clearer when checking that, although 90.60% of the ENCs were matched, we only achieved a final mDICE of 0.6611. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Prompt Type**} & \multirow{2}{*}{**Metric**} & \multicolumn{8}{c}{**Animal Tag**} & \multirow{2}{*}{**Mean**} \\ \cline{3-3} \cline{5-10}  & & **2C** & **4C** & **5C** & **22 WT** & **23 WT** & **28 WT** \\ \hline \multirow{2}{*}{Point (Random)} & mIoU & 0.6409 & 0.6146 & **0.6816** & 0.4620 & 0.4294 & 0.4178 & 0.5411 \\  & mDICE & 0.7321 & 0.7016 & **0.7704** & 0.5376 & 0.5009 & 0.4861 & 0.6215 \\ \hline \multirow{2}{*}{Bound Box (Oracle)} & mIoU & 0.8046 & 0.8012 & **0.8070** & 0.7716 & 0.7652 & 0.7658 & 0.7859 \\  & mDICE & 0.8885 & 0.8872 & **0.8906** & 0.8677 & 0.8630 & 0.8629 & 0.8766 \\ \hline \multirow{2}{*}{Difference} & mIoU & 0.1637 & 0.1865 & 0.1254 & 0.3095 & 0.3357 & **0.3479** & 0.2448 \\  & mDICE & 0.1564 & 0.1855 & 0.1201 & 0.3300 & 0.3620 & **0.3767** & 0.2551 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metric values obtained from SAM-b, by using point (random sampling) and bounding box (oracle sample) prompts. Such a value is 0.2155 smaller than the metric obtained by SAM-b and box prompts (oracle evaluation), showing that we can still improve the masks for the segmented cells. From the presented results, we are able to verify that the final mDICE and mIoU are equivalent to 0.6611 \(\pm\) 0.0373 and 0.5393 \(\pm\) 0.0304 for YOLOv8-m. As for SAM-b (random click prompt), the same metrics are equivalent to 0.6215 \(\pm\) 0.1270 and 0.5411 \(\pm\) 0.1174. Therefore, the point-prompt segmentation model presented an mDICE 5.99% smaller and mIoU 0.33% higher when compared to the fully automatic instance segmentation method. Although there is a difference in those metrics, by applying a Wilcoxon statistical test on the mDICE values for each fold, considering both models, we obtain a \(p\)-value of 0.3125. Since this value is not smaller than 0.05, we can consider that the difference is not statistically significant.
# Communication-Efficient Federated Learning with Adaptive Consensus ADMM
## 3 FedACADMM: Adaptive Consensus ADMM for Federated Learning
### Classical ADMM  
ADMM is a powerful algorithm used for solving convex optimization problems in statistics and machine learning. This powerful technique decomposes the original problem into smaller subproblems that can be solved efficiently using dual ascent methods. After solving these subproblems, ADMM combines the obtained solutions using a multiplier method to achieve a high convergence speed. This algorithm solves the equality-constrained convex optimization problem,  
\[\min_{x\in\mathbb{R}^{d},z\in\mathbb{R}^{n}}f(x)+g(z),\,s.t.\,Ax+Bz=c \tag{4}\]  
where \(f:\mathbb{R}^{n}\to\mathbb{R}\) and \(g:\mathbb{R}^{m}\to\mathbb{R}\) are convex functions, \(A\in\mathbb{R}^{p\times n}\), \(B\in\mathbb{R}^{p\times m}\), and \(c\in\mathbb{R}^{p}\). The Lagrangian for problem (4) is  
\[\begin{split}\mathcal{L}(w,z,\pi)=f(w)+g(z)&+<Aw+Bz -c,\pi>\\ &+\frac{\sigma}{2}\big{\|}Aw+Bz-c\big{\|},\end{split} \tag{5}\]  
where \(\sigma>0\) is called the penalty parameter. Augmented Lagrangian methods can bring robustness to the dual ascent method, especially in the absence of assumptions such as strict convexity or finiteness of \(f\).  
The iteration of ADMM can be written in the form  
\[\begin{cases}x^{k+1}:=\operatorname*{argmin}_{x}\mathcal{L}_{p}(x,z^{k},y^{k })\\ z^{k+1}:=\operatorname*{argmin}_{z}\mathcal{L}_{p}(x^{k+1},z,y^{k})\\ y^{k+1}:=y^{k}+\rho(Ax^{k+1}+Bz^{k+1}-c)\end{cases} \tag{6}\]  
Here, \(\rho>0\). By choosing an appropriate value for \(\rho\), it can balance the rate of convergence and the feasibility of the iterates. The method, which consists of an x-minimization step, a z-minimization step, and a dual variable update, is very similar to dual ascent and the method of multipliers. The use of \(\rho\) as a step size in dual updates ensures that the iterates \((x^{k+1},z^{k+1})\) remain dual feasible and leads to optimality as \(Ax^{k+1}+Bz^{k+1}-c\) gradually converges to zero.  
ADMM combines the decomposability of dual ascent with the superior convergence properties of multiplier methods, making it an efficient and widely used optimization tool.
# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 2 Materials and Methods
### Training Parameters  
As presented in the previous sections, the modified algorithm requires three distinct algorithms: two for classification and one for segmentation. For the first classification algorithm, YOLOv8 was used, as in the original paper. For the second classification task, the GCViT network was employed due to its demonstrated success in achieving excellent results in classification tasks. YOLOv8 was again utilized for the segmentation task, as in the original paper.  
For the training of neural networks, defining certain hyperparameters is necessary. Initially, the batch size for all training executions was set to 16, as this was the largest value that did not cause memory issues.  
Additionally, to reduce the possibility of overfitting during training, learning rate scaling was implemented using the CosineAnnealingLR strategy [42], as defined by the following equation:  
\[\eta_{t}=\eta_{\min}+\frac{1}{2}(\eta_{\max}-\eta_{\min})\bigg{(}1+\cos\bigg{(} \frac{T_{\text{cur}}}{T_{\max}}\tau\bigg{)}\bigg{)} \tag{1}\]  
where \(\eta_{t}\) represents the current learning rate at epoch \(T_{\text{cur}}\), and \(T_{\max}\) represents the maximum number of training epochs. The parameters \(\eta_{\min}\) and \(\eta_{\max}\) define the minimum and maximum learning rates, which are set before the start of the training.  
We searched for the best hyperparameter values. \(\eta_{\min}\) was searched within the range from \(10^{-6}\) to \(10^{-8}\) in 20 equally spaced intervals. Similarly, \(\eta_{\max}\) was searched in the range from \(10^{-3}\) to \(10^{-5}\) in 20 equally spaced intervals. Additionally, we searched for the best optimizer for training, selecting between Adam and Stochastic Gradient Descent (SGD) [43].  
For hyperparameter tuning, the recall metric was used for classification training. For segmentation training, the metric used was Intersection Over Union (IoU). IoU is calculated by the ratio of the number of pixels in the intersection between the predicted and ground truth segments to the union of these segments [44].  
For YOLOv8, the loss function chosen is the standard one defined by [12] for the network. For GCViT, binary cross-entropy [45] was chosen due to its extensive use in image classification tasks.  
To reduce overfitting during training, data augmentation was applied to the selected images [46]. The selected operations included horizontal and vertical flips, translation limited to 20% of the image dimensions, rotation by a random angle between 0 and 90\({}^{\circ}\) counterclockwise, and removal of a rectangle with dimensions corresponding to 15% of the image at a random position. These operations are not applied simultaneously to the images. During each training epoch, each image undergoes a random combination of the aforementioned operations, with each operation having a 50% probability of being applied to an image in a given epoch.
# HTN: Hybrid Transformer Network for Curvature of Cervical Spine Estimation
## 3 Experiments
### Comparison with State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation** & **PT** & **MT** & **TL** \\ \hline SMAPE (\({}^{\circ}\)) & 4.73\(\%\) & 16.22\(\%\) & 18.51\(\%\) \\ MAD (\({}^{\circ}\)) & 3.19\({}^{\circ}\) & 3.81\({}^{\circ}\) & 3.91\({}^{\circ}\) \\ Confidence (\({}^{\circ}\)) & 8.77\({}^{\circ}\) & 11.95\({}^{\circ}\) & 11.02\({}^{\circ}\) \\ R (\(p\)-Value) & 0.9643 (\(<\)10\({}^{-6}\)) & 0.8944 (\(<\)10\({}^{-6}\)) & 0.9301 (\(<\)10\({}^{-6}\)) \\ Paired \(t\) test (p-Value) & 0.075 (\(<\)10\({}^{-6}\)) & 0.613 (\(<\)10\({}^{-6}\)) & 0.635 (\(<\)10\({}^{-6}\)) \\ MSE & & 44.87 (All landmarks) & \\ Overall SMAPE (\%) & & 8.29\(\%\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical comparison of the manual ground truth and our automatic method on the public AASCE MICCAI 2019 challenge dataset. Table 4 presents a fair comparison between state-of-the-art methods and our proposed methods on the public AASCE MICCAI 2019 challenge dataset. AEC-Net [8] designed two networks to detect landmarks and estimate Cobb angles separately. However, we used only one network to complete two tasks simultaneously. AEC-Net achieved 23.59% SMAPE for all angles, while our model obtained a lower SMAPE of 8.29% on the same dataset. [PERSON] et al. [11] employed a cascaded CNN to segment the spine's centerline and compute the Cobb angle. Their SMAPE was 22.96%. Seg4 Reg first segments the vertebrae of the spine and then directly regresses the predictions of angles. Seg4 Reg [12] obtained a SMAPE of 21.71%. The results of the above methods are substantially higher than ours. Seg4 Reg+ [13] is an improved version of Seg4 Reg, which considers ResNet18 [29] as the backbone and adds the dilated convolution in the pyramid pooling module. To our knowledge, Seg4 Reg+ presently reports a SMAPE of 8.47% on the same dataset using ResNet18. Our model achieved a comparable SMAPE of 8.29% using a similar backbone (ResNet10).
## 2 Background
### Situation Awareness Models  
Situation awareness models refer to systems or models designed to provide an understanding of the current situation, including the state of the environment, current objectives, and actions taken. These models aim to provide a comprehensive, real-time picture of a situation to ensure that individuals or systems can make informed decisions. According to [PERSON]'s well-accepted situation awareness model [76; 77], there are three levels of situation awareness perception, integration and comprehension, and prediction. In the first level, situation awareness encompasses the critical information observation concept. The second level involves the integration and interpretation of essential information. In the third level, the situation awareness model is concerned with the awareness to predict possible environmental events [78].  
Several situation awareness models exist, including cognitive, decision-making, and information fusion models. Cognitive models focus on the mental processes of situation awareness, such as perception, attention, and memory. On the other hand, decision-making models focus on the processes involved in using situation awareness information to make decisions. Information fusion models combine information from multiple sourcesto provide a complete situation picture. The situation awareness models are used in various fields, including the military, aviation, healthcare, and transportation [77; 79].  
Section 3 discusses the research methodology implemented to carry out the research for discussion and analysis.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 5 Discussion  
Compared to previous privacy-preserving schemes for visual SLAM, the proposed method effectively removes sensitive information from image data, thereby ensuring security in visual SLAM without compromising pose estimation accuracy. Furthermore, it also protects 3D point cloud maps. This method has real-world applicability because, in autonomous driving scenarios or augmented reality applications, cameras inevitably capture privacy-sensitive information. Protecting this information can make SLAM applications more acceptable to users. Additionally, the models used in this paper have small model sizes and fewer parameters, which makes them well suited for deployment in resource-constrained environments.  
However, this approach has certain limitations. For instance, in environments with less sensitive information, such as low-texture or unmanned indoor spaces, the frequent detection of privacy-sensitive objects in incoming images may unnecessarily increase the running time of the SLAM system.  
In future work, we will focus on adapting our method for computationally constrained environments, such as mobile and wearable devices. Additionally, we aim to make the related models lightweight to enhance efficiency. As our experimental resources improve, we plan to evaluate these methods in real-world application scenarios, including autonomous driving and drone navigation, to comprehensively assess their practical effectiveness and limitations.
## 2 Materials and Methods
### Theoretical Background
#### 2.1.3 Forward and Inverse Modelling  
Forward modelling refers to the computing of the field anomalies with respect to the physical parameters. Measurements \(m_{i}\) and parameters \(p_{j}\) are related through functions of the form \(m_{i}=f(p_{j})\ \forall\ i=1,\cdots,N;j=1,\cdots,M\), where \(N\) and \(M\) are the total number of observations and parameters, respectively. Considering the linearized form of \(f\), which has shown to provide acceptable results [31, 45], the problem takes the form  
\[\Delta m=\mathfrak{K}\Delta\mathbf{p}\, \tag{9}\]  
where \(\mathfrak{K}\) is the Jacobian matrix and it is also known as sensitivity kernel, and \(\Delta m=\mathbf{m}^{\rm(C)}-\mathbf{m}^{\rm(O)}\) gives the distance between the observed and the computed values of the physical property \(\mathbf{m}\) in terms of the distance between the measured and the computational model parameters \(\Delta\mathbf{p}=\mathbf{p}^{\rm(C)}-\mathbf{p}^{\rm(O)}\).  
On the other hand, the inverse problem consists in finding the values of the \(M\) parameters \(\mathbf{p}\) of the model that provides the observed values of the field measurements \(\mathbf{m}\) in the \(N\) nodes of the observations mesh. In practice, a good approximation is obtained by minimizing an error, cost, objective or misfit function \(e\). In this work, we used \(L_{2}\) as \(e\) because it provides a similar weight to all data as well as stability [31, 45],  
\[e=\left|\Delta m\right|=\left[\sum\text{abs}\left(\mathbf{m}^{\rm(O)}-\mathbf{m}^{\rm( C)}\right)\right]^{\frac{1}{2}}. \tag{10}\]  
A suitable inversion procedure ought to be stable (convergent) and it is generally constructed following the steps in [28].
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 2 Materials for a Flexible Antenna
### Substrate Materials
A circularly polarized wearable antenna that operates at 5.8 GHz is proposed on a polydimethylsiloxane (PDMS) substrate [39]. Highly conformal characteristics, lightweight, and a good mechanical stability of the substrate make it a good choice for a wearable antenna design. The comparison of various flexible materials and their properties are given in Table 3[27]. The properties of any substrate are characterized by the permittivity and the loss tangent. The permittivity of the substrate is given in Equation (1) [40]. \[\varepsilon=\varepsilon_{o}\varepsilon_{r} \tag{1}\]  
where \(\varepsilon_{r}\) denotes the substrate's relative permittivity, \(\varepsilon_{o}\) is the free space's permittivity. The relative permittivity of the substrate can be represented in Equation (2) [40]. \[\varepsilon_{r}=\varepsilon_{r}^{r}+je_{r}^{i} \tag{2}\]  
where \(\varepsilon_{r}^{r}\) is the real part of the relative permittivity which is popularly known as the substrate's dielectric constant and \(je_{r}^{i}\) is the imaginary part of the relative permittivity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **Polymer** & **Textile** & **Paper** & **Fluidic** \\ \hline Dielectric Loss/Loss Tangent & Low Loss & Low Loss & Medium Loss & High Loss \\ \hline Tensile Strength & High & Low & Low & Low \\ \hline Deformability & Low & High & High & High \\ \hline Thermal Stability & High & Low & Low & Low \\ \hline Fabrication Complexity & Simple & Complex & Simple & Complex \\ \hline Robustness to Wetness & High & Medium & Low & Low \\ \hline Cost of Fabrication & Medium & Low & Low & High \\ \hline Weight & Medium & Low & Low & Medium \\ \hline Overall Size & Small & Large & Large & Small \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different flexible materials and their properties. Reprinted from ref. [27]. The loss tangent (\(tan\delta\)) of the dielectric material is as in Equation (3) [40]. \[tan\delta=\frac{\epsilon_{r}^{i}}{\epsilon_{r}^{r}} \tag{3}\]  
The quality (Q) factor of the flexible antenna depends upon the dielectric constant of the substrate material. The choice of materials with a low dielectric constant decreases the quality factor and thereby increases the bandwidth. The resonant frequency of the antenna is also related to its dielectric constant. The choice of the substrate with a low dielectric constant can increase the antenna's resonant frequency and vice versa [40]. Thus, the choice of the flexible substrate with a low loss tangent and low permittivity is the best in the design of flexible antennas. Table 4 shows the various substrate materials used in the design of flexible antennas with their advantages and disadvantages.