esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Impact of Image Augmentation Techniques  
Each augmentation strategy applied (Rotation, Translation, Scaling, Shearing and Brightness adjustment) led to an improvement in accuracy compared to the \"Raw\" baseline, where no augmentation was applied. This highlights the importance of data augmentation to improve model generalisation and reduce overfitting.  
Among the models evaluated, VGG16 performed best across all conditions. Its \"Raw\" performance started at 74% and reached a peak accuracy of 80% with brightness augmentation, surpassing the best results of the other models. VGG19, while outperforming MobileNetV2 overall, did not achieve the same level of performance as VGG16.  
When looking at the individual impact of each augmentation, Rotation and Scale proved to be particularly effective geometric transformations. Both techniques yielded a substantial improvement of +5% for MobileNetV2 (reaching 72%) and +5% for VGG16 (reaching 79% for Rotation and +3% for Scale), while VGG19 also saw notable gains of +4% for both. This indicates that teaching models invariance to object orientation and size variations within images is crucial. Furthermore, the photometric augmentation, Brightness adjustment, also showed good effectiveness, especially for VGG16, providing its largest individual increase of +6% to reach 80%. MobileNetV2 and VGG19 also benefited significantly from Brightness adjustment, with increases of +4%. Translation and Shear augmentations, while beneficial, offered more modest improvements overall, typically in the range of +2% to +4%.
## 2 Materials and Methods
### Model Architecture
#### 2.2.1 Encoder  
The encoder part of our network is a modified ResNet [24] convolutional neural network. It was designed with four sequentially connected blocks. The single block was made of 3 functional units:  
Figure 3: Diagram of the model.  
1. Convolutional unit--this unit consists of a one-dimensional convolutional layer followed by batch normalization [25];
2. Squeeze-exciting unit--is a cell designed to improve the representational power of a network by enabling it to perform channel-wise feature calibration. The inputs to this block are the feature maps generated by the convolutional unit. Each of the channels is being \"squeezed\" into a single numeric value using global average pooling. This value will be passed through two feed-forward layers activated by the ReLU and sigmoid functions to add nonlinearity and give each channel a smooth gating function. Then, the output of the sigmoid function is weighted by the input feature maps to get the excitation [26];
3. Residual unit--this unit stacks two convolutional units, and on top of them, the squeeze-exciting unit is being stacked. The output is being added to the input feature maps.  
The encoder consists of four of these blocks as a single convolutional unit followed by four consecutive residual blocks. The outputs of the second and third block are being concatenated with the averaged pooled input data.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 2 Antenna and Optimization Environment Description  
In this section, the analyzed optimization problem will be deeply described. In particular, the antenna is analyzed and then, accordingly to the specific features of the analyzed problem, the optimization environment is properly designed and described.
## 3 Principal Component Analysis  
Principal component analysis is a dimensionality-reduction technique, and its goal is to minimize the data's dimensionality while preserving as much variety as possible from the original dataset.  
Steps to perform PCA are:  
Data standardization  
The matrix \(Y\) is standardized using the following calculation Formula (10):  
\[x_{ij}=\frac{y_{ij}-\overline{y_{j}}}{\sqrt{var\big{(}y_{j}\big{)}}} \tag{10}\]  
where, \(Y=\{Y_{ii}\;\}\), \(X=\{X_{ij}\}\), \(i=1\),\(2\), \(3\,\dots\,\,n\), \(j=1\),\(2\),\(3\,\dots\,\,p\).  
\[\overline{y}=\frac{1}{n}\sum_{i=1}^{n}y_{ij}\text{ and }Var\big{(}y_{j}\big{)} =\frac{1}{n-1}\sum_{i=1}^{n}(y_{ij}-\overline{y_{j}})^{2} \tag{11}\]  
Calculating covariance matrix  
The formula for covariance concerning variance is (12) and (13).  
\[Var(X)=\frac{\sum_{i=1}^{n}(X_{i}-X^{\prime})(X_{i}-X^{\prime})}{(n-1)} \tag{12}\]  
\[Cov(X,Y)=\frac{\sum_{i=1}^{n}(X_{i}-X^{\prime})(Y_{i}-Y^{\prime})}{(n-1)} \tag{13}\]  
where  
\(X^{\prime}=\) Arithmetic mean of data \(X\).  
\(Y^{\prime}=\) Arithmetic mean of data \(Y\).  
\(n=\) Number of observations.  
The Covariance matrix can be obtained as (14):  
\[\sum=\begin{bmatrix}Var(X)&Cov(x,y)\\ Cov(Y,X)&Var(Y)\end{bmatrix} \tag{14}\]  
The correlation coefficient matrix \(R\) is solved as follows (15):  
\[R=\frac{X^{T}X}{n-1} \tag{15}\]Eigenvalues are determined by solving  
\[|(S-\lambda I)|=0 \tag{16}\]  
Eigenvectors of \(\lambda_{1}\) are given below  
\[(S-\lambda I)U_{1}=0\]  
The eigenvalue and eigenvector of the coefficient matrix are calculated as follows:  
\[|R-\lambda I_{n}|=0 \tag{17}\]  
The calculated eigenvector is \(a_{i}=(a_{i1},a_{i2},a_{i3},\ldots\ldots a_{in}),for\)\(i=1,2,3\ldots,n,\) and the eigenvalue is \(\lambda_{i}\). Eigenvalues are sorted in descending order to obtain a set of principal components \(F_{i}\), as in (18):  
\[F_{i}=a_{i1}X_{1}+a_{i2}X_{2}+a_{i3}Y_{3}+\ldots\ldots+a_{in}X_{n} \tag{18}\]  
Choosing new components  
The principal components are determined as follows: the rate of contribution of the \(k\)th principal component is given as (19):  
\[\lambda_{k}\left(\sum_{j=1}^{n}\lambda_{j}\right) \tag{19}\]  
The rate of the accumulating contribution of the first \(k\) principal component is given as (20):  
\[\sum_{j=1}^{k}\lambda_{j}\left(\sum_{j=1}^{n}\lambda_{j}\right) \tag{20}\]
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
## 4 Results and Discussion
Figure 7: Coronal slices from the reconstructed images of the phantom dataset. (**a**) Three-dimensional image, where all projections belonging to all breathing phases were used for reconstruction, (**b**) projections belonging to phase 1 only, used for reconstruction considering the ground-truth breathing signal, (**c**) projections belonging to phase 1 only, used for reconstruction considering the breathing signal extracted using the proposed method, and (**d**) an image resulting from subtracting (**c**) from (**b**). The yellow vertical line in (**a**) represents the profile considered to evaluate the sharpness of the edges in the compared images. As is shown in Figure 7, when all phases are used for reconstruction, as in (a), the organ edges appear blurry, while they become significantly more distinct when reconstructed with a single phase obtained after binning, as in (b) and (c). In this figure, phase 1 was considered. In (b), the projections were sorted considering the ground-truth breathing signal, while in (c) the projections were sorted considering the extracted breathing signal, using the proposed method. The image in (d) is the result of subtracting the images in (b) and (c). As can be seen in (d), the difference is mainly in demonstrating noise and streaking artifacts in the image, and is unrelated to structural or anatomical differences in the images. To further evaluate the reconstructed images, edge profiles extracted from each of the reconstructed images were compared. The edge profile helps to evaluate the sharpness of organ edges in reconstructed images. The profile considered is extracted from the same slice and from the same location in each of the reconstructed images. It can be demonstrated by a yellow vertical line, shown in Figure 7a. Figure 8 shows the edge profiles extracted from each of the reconstructed images, namely the 3D reconstructed image using all projections, as shown in Figure 7a, the one reconstructed from phase 1, which only considers the ground-truth breathing signal, as shown in Figure 7b, and the one reconstructed from phase 1, which only considers the breathing signal extracted using the proposed method, as shown in Figure 7c. The results in Figure 8 are in accordance with the visual results provided in Figure 7, where the organ edges appear blurry when all phases are used for reconstruction, while they become significantly sharper when reconstructed with a single phase. Furthermore, the mean square error for the image reconstructed considering the breathing signal extracted using the proposed method was observed to be \(2.65\times 10^{-5}\), while the PSNR was observed to be \(45.75\).
## 4 Truck-UAV Collaborative Delivery Planning Algorithm Based on Improved Evolutionary Algorithm
### Algorithm Design
#### 4.2.1 Construction of the Road Network  
The road network serves as the fundamental framework for the execution of algorithms and must be initialized and established prior to the commencement of calculations. Initially, a comprehensive assessment of the density of demand points and the volume of deliveries is conducted across all nodes within the road network graph G to identify suitable distribution centers, referred to as depots. Nodes situated in proximity to areas with a high concentration of demand points are prioritized to minimize both delivery distance and time. Concurrently, a random simulation of node and road section failures is performed, and information pertaining to the distribution centers and failed nodes is incorporated into the institutional data of the graph. Furthermore, the operational parameters of the optimization algorithm are defined, including population size and the number of optimization iterations, alongside critical parameters related to the road network, such as the load capacity and speed of trucks and drones.
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### Open Mobile Alliance Device Management (OMA DM)  
OMA DM [84] has evolved from its mobile device management roots to address IoT device management needs. The protocol's standardized management objects and session-based communication provide a robust framework for remote device administration. OMA DM's strong security features and extensible framework enable comprehensive device configuration management, software updates, and security policy enforcement in IoT environments. The protocol flow, as shown in Figure 3, begins with mutual authentication between the server and the mobile device, ensuring a secure connection is established. After authentication, the device transmits its capabilities and authentication details to the server, allowing the server to determine the appropriate management operations required for the device. The server then issues management commands, which the device processes and executes. These commands may include updates to network configurations, the implementation of security policy changes, or software and firmware updates. Following the execution of these tasks, the device reports the results back to the server. Finally, the session concludes with the proper cleanup of resources to maintain operational efficiency.  
OMA DM plays a vital role in IoT network management by enabling centralized remote administration, significantly reducing the need for on-site interventions. Through its efficient device configuration capabilities, the protocol ensures consistent network operations by remotely pushing updates and enforcing security policies. One of its key features  
Figure 3: OMA DM protocol.  
is the ability to deliver firmware updates over the air, keeping connected devices secure and ensuring they operate with the latest software. Additionally, OMA DM facilitates task monitoring, where devices report execution results back to the server, allowing network administrators to track performance, identify potential issues, and streamline troubleshooting. This remote management capability enhances operational efficiency while ensuring the security and reliability of IoT networks.  
In the healthcare sector, OMA DM is widely used for the remote management of wearable healthcare devices, such as glucose monitors and heart rate sensors, ensuring they function optimally and securely. The protocol also enables the secure configuration and monitoring of critical medical equipment, including infusion pumps and patient monitors, which require continuous oversight to maintain accuracy and reliability. Furthermore, OMA DM supports remote diagnostics for advanced medical equipment, such as MRI machines and ventilators, allowing healthcare providers to detect and resolve potential issues before they impact patient care. By ensuring seamless device management and security enforcement, OMA DM enhances the efficiency and reliability of healthcare IoT networks.
###### Abstract  
The purpose of this computational study was to analyze the effects of different mobile-bearing (MB) total knee replacement (TKR) designs on knee joint biomechanics. A validated musculoskeletal model of the lower right extremity implanted with a cruciate-retaining fixed-bearing TKR undergoing a squat motion was adapted for three different MB TKR design variants: (I) a commercially available TKR design allowing for tibial insert rotation about the tibial tray with end stops to limit the range of rotation, (II) the same design without end stops, and (III) a multidirectional design with an additional translational degree-of-freedom (DoF) and end stops. When modeling the MB interface, two modeling strategies of different joint topologies were deployed: (I) a six DoF joint as a baseline and (2) a combined revolute-prismatic joint (two DoF joint) with end stops in both DoF. Altered knee joint kinematics for the three MB design variants were observed. The commercially available TKR design variant I yielded a deviation in internal-external rotation of the tibial insert relative to the tray up to 5\({}^{\circ}\) during knee flexion. Compared to the multidirectional design variant III, the other two variants revealed less femoral anterior-posterior translation by as much as 5 mm. Concerning the modeling strategies, the two DoF joint showed less computation time by 68%, 80%, and 82% for design variants I, II, and III, respectively. However, only slight differences in the knee joint kinematics of the two modeling strategies were recorded. In conclusion, knee joint biomechanics during a squat motion differed for each of the simulated MB design variants. Specific implant design elements, such as the presence of end stops, can impact the postoperative range of knee motion with regard to modeling strategy, and the two DoF joint option tested accurately replicated the results for the simulated designs with a considerably lower computation time than the six DoF joint. The proposed musculoskeletal multibody simulation framework is capable of virtually characterizing the knee joint dynamics for different TKR designs.  
Keywords:total knee replacement; joint topological variations; musculoskeletal multibody simulation; tibofernoral dynamics; computation time +
Footnote †: journal: Applied Sciences
## 1 Introduction
[49], upon analyzing the solar radiation data bases of 35 sites along the Mediterranean region, found optimum tilt angles with southern orientations for Irakleio, Athens, and Mikra of \(35.1^{\circ}\), \(36.8^{\circ}\), and \(38.7^{\circ}\), respectively. [PERSON] and [PERSON] [50] gave a review of the optimum tilt angles for solar applications around the world by using the PVWatts algorithm [51]; for Athens, they estimated it to be \(29^{\circ}\). [PERSON] et al. [52] found an optimum tilt angle of \(30^{\circ}\) S in Athens. Lastly, the work by [PERSON] [53] must be mentioned here as it gave a new definition for the optimal tilt angle of solar flat-plate convectors for energy policy makers. The present work provides two innovations. First, none of the studies, either in Greece or internationally, have examined the importance of the deficiency (or surplus) of the energy received between a solar flat-plate collector having a fixed tilt angle equal to the geographical latitude of its location and one with an optimum angle of inclination for maximum solar energy reception. This is served in this work for the first time. Another innovation of this work is the use of the recently derived typical meteorological years (TMYs) in Greece for PV applications at 33 sites [54]. \"A TMY is a set of meteorological and solar radiation parameters usually consisting of hourly values in a year for a given geographical location\" [54; 55]. \"More specifically, a TMY consists of a set of months selected from individual years integrated into a complete year\" [54]. Therefore, \"a TMY reflects all the climatic information of the location for a period as long as the mean life of the system\" [54]. That study derived for 33 locations in Greece TMYs for 5 different applications: TMY-meteorology-climatology, TMY-bio-meteorology, TMY-agro-meteorology-hydrology, TMY-PV, and the TMY energy design of buildings. TMYs-PV include hourly solar radiation values on a horizontal surface. These data can be transposed to solar radiation data on various tilts with southern orientations. The latter data can be used for the investigation of the positive (surplus) or negative (deficiency) difference of the energy received by solar panels having a south-facing tilt angle equal to the geographical latitude of each of the 33 locations, in respect to the energy received by solar panels tilted at angles of \(25^{\circ}\) or \(30^{\circ}\) to the south, as these angles are found to be optimal inclinations for solar flat-plate convectorsin the country. In addition, the energy received by a horizontal plane (classical reference case) is included. The structure of the paper is as follows. Section 2 describes the data collection and data analysis. Section 3 deploys the results of the study, while Section 4 discusses them from a practical point of view. Section 5 presents the conclusions and main achievements of the work, and the acknowledgements and references follow.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 4 Simulations  
In this section, the simulation results for three cases are provided to clarify the insights into the influence of the phase error. In cases 1 and 2, simulations were conducted to demonstrate the validity of the derived convergence time constant performance and steady state MSE expressions. The primary and secondary paths were FIR filters created with the MATLAB function (fir1) with cutoff frequencies of 0.4\(\pi\) and 0.5\(\pi\), respectively. The disturbance signal and command signal were defined by Equations (27) and (28), and the amplitudes of the disturbance and command signals were \(D=2\) and \(C=\)1, respectively. In case 3, the influence of phase error was demonstrated with an application of the algorithm of nonstationary noise profiling in reference [15]. The primary path P(z) and secondary path S(z) were extracted from a real acoustic duct system, which was shown in Figure 4 in [15], and their impulse response and frequency response were shown in Figure 4 in [15]. The simulation noise was the same as the case C1 in [15], in which the chirp rate is 10.
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 3 Looping Materials in Chemical Looping Gasification of Biomass
### Looping Materials
There are a limited number of oxygen carrier materials, which have the property of releasing oxygen, that can meet the requirement for multiple cycles of oxygen uncoupling processes. They must be reversible in the reactions of releasing and oxidizing oxygen. In comparison with oxygen carrier for the normal BCLG process, the metal oxides used in OU-BCLG have a suitable equilibrium partial pressure of gas-phase oxygen at a temperature range of 800-1200 \({}^{\circ}\)C. Thus, there are three metal oxide system could be used in the OU-BCLG system such as CuO/Cu\({}_{2}\)O, Mn\({}_{2}\)O\({}_{3}\)/Mn\({}_{3}\)O\({}_{4}\), and Co\({}_{3}\)O\({}_{4}\)/CoO. Their reversible reactions are as follows [11]:  
\[4\text{CuO}\rightleftharpoons 2\text{Cu}_{2}\text{O}+\text{O}_{2}\text{ }\Delta\text{H}_{850}=263.2\text{ kJ}/\text{mol O}_{2} \tag{15}\]  
\[6\text{Mn}_{2}\text{O}_{3}\rightleftharpoons 4\text{Mn}_{3}\text{O}_{4}+\text{O}_{2} \text{ }\Delta\text{H}_{850}=193.9\text{ kJ}/\text{mol O}_{2} \tag{16}\]  
\[\text{Biomass}\rightarrow\text{Volatiles + Char (mainly C) + H}_{2}\text{O} \tag{6}\]0 Main reactions in the fuel reactor: (**a**) BCLG and (**b**) OU-BCLG. \[\text{Biomass}\rightarrow\text{Volatiles + Char (mainly C) + H}_{2}\text{O} \tag{6}\]1  
Unlike the chemical looping combustion process, the BCLG concept is to produce useful combustible gas (CO and \(\text{H}_{2}\), syngas); therefore, the partial oxidation of biomass can be achieved by using oxygen carrier materials suitable for OU-BCLG. Due to the very high reactivity of oxygen uncoupling materials, less metal oxide material used is needed in the system which could reduce the reactor size and associated costs. OU-BCLG is a new concept and a few studies related to OU-CLG have been performed with biomass [57; 58; 59].
# Biomechanical Task-Based Gait Analysis Suggests ReWalk Gait Resembles Crutch Gait
## 2 Materials and Methods  
### Experimental Setup  
Six healthy male volunteers (age: 28.7 \(\pm\) 1.2 years, height: 176.2 \(\pm\) 3.3 cm, weight: 77.8 \(\pm\) 8.9 kg) participated in this study. Each subject performed four gait conditions: with ReWalk (ReWalk Robotics Ltd., Yoknean Illit, Israel), with elbow crutches (ErgoBaum Comfortable Crutches, Ergoatives LLC, Miami, FL, USA), with ankle foot orthoses (AFO; Ottobock, Duderstadt, Germany) to immobilize the ankle, and unassisted natural overground gait. Participants were each asked to walk once in a straight line along a 20 m overground walkway for all conditions at a self-selected comfortable speed except for ReWalk sessions, where the default speed was selected, which is approximately 1.6 km/h. In order to investigate muscle recruitment patterns, EMG signals were obtained from the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and soleus (SL) of the right leg (Figure S1a) using wireless EMG sensors (TrignoTM wireless system, Delsys Inc.,Boston, MA, USA). In addition, inertial measurement unit (IMU) sensors placed on the waist and thigh, shank, and foot of both limbs were used to estimate hip, knee, and ankle joint angles along with foot contact (FC) and lift-off (LO) events (Figure 1). The EMG, acceleration, angular velocity, and orientation signals were sampled at approximately 1777, 148, 148, and 74 Hz, respectively.  
All participants signed a written consent form, and the research ethics of human experiments were ensured by the approval of the gait sessions by the Institutional Review Board of Korea University (IRB No. 2021-0120-01).