esa-sceva/satcom-chunk-collection huggingface dataset:
# BNS: A Framework for Wireless Body Area Network Realistic Simulations
## 3 Proposed BNS Framework
### Healthcare Application Layer
That is why a plug-in to mediate the communication is needed. Figure 6 depicts how Antidote receives messages from the BNS framework. The arriving messages are copied to an external variable in the BNS framework. The external variable is visible to both BNS framework and BNS plug-in. Since the Communication module in Antidote owns pointers to the BNS plug-in functions, it can retrieve the messages using callback functions. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Name** & **Value** \\ \hline ApplicationName & “Manager”, “Agent” \\ hubNode & double value \\  & “pulseoximeter”, “thermometer”, “bloodpressure”, \\ application\_type & “basicECG”, “glucometer” \\ measurements\_per\_second & double value \\ confirmed\_event & true, false \\ retransmitPacket & true, false \\ maxNumOfRetransmission & integer value \\ timeOutToRetransmitPacket & double value \\ managerInitiated & true, false \\ managerInitiateMode & “singleMode”, “timePeriodMode”, “noTimePeriodMode” \\ managerInitiatedTime & double value. Used in “timePeriodMode” \\ numberOfReceived, & integer value. Used in “noTimePeriodMode” \\ MeasurementsToSendStop & integer value. Used in “noTimePeriodMode” \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BNS Healthcare Application Layer Configurable Simulation Parameters. In the simulation environment, the Communication Module needs to deal with all nodes to control their transmissions. To ensure the proper operation of the Communication Module, the _node id_ is required in each function call. This change in the Communication module is performed in our proposal. This way we know which node we are working with and apply the action for that node, without affecting other nodes. For example, if a node transits from disassociated state to associating state, without the proposed modification, this action would affect all nodes even those that should not change their state machines. Figure 7 illustrates the system modification. In this figure each arrow represents a function being called from the Communication Module. In every function in the Communication module, the _node id_ new parameter is inserted. This way the Communication module always knows the corresponding node. Other modules such as Agent, Manager and Encoders were modified to smooth operation of Antidote functions in the BNS framework. We can highlight the function created in the encoder module to convert absolute values (measurements) into scaled non-negative values as recommended by [48]. It is useful when we need to send absolute values such as \(-0.060\) which would require at least 4 bytes in X73-PHD floating point format. When converted, the number \(-0.060\) becomes 388, which requires just two bytes. Within the Manager, the conversion from scaled to absolute values is made [1].
## 3 The 6G Technology
### Sixth-Generation Key Enabling Technologies
#### 3.4.6 Unmanned Aerial Vehicles (UAV)  
UAVs are critical for the future 6G network in providing uninterrupted global coverage of wireless networks. UAVs are integral in providing integrated air-ground network coverage. It is a type of mobile air network that serves as a flying BS [106]. UAV facilitates wireless connectivity such as eMBB, URLLC, and mMTC [107]. They are becoming frequently deployed owing to their versatility and high mobility. For the future 6G network usefulness, UAVs can serve as aerial mobile relays to enhance communication services to densely populated environments, and as an aerial node to facilitate surveillance and delivery purposes [107]. While UAVs enable optimization services for improved performance of different technologies, integration with ML has further expanded its applications [15].
## 2 Development of an Advanced SR-JOINT (SR-II)
### Optimal SR-Joint Shape Design
#### 2.2.3 Optimal Design through Statistical Regression Analysis  
The response surface method, which is a design of experiment method, was used to compare the accuracy of regression analysis between the conventional optimal design method and the method using the SVM. The response surface method was devised by [PERSON] in 1951 and is still being used currently [23]. The response surface function can be expressed in the form of a quadratic polynomial as shown in Equation (3), where \(\beta\) denotes the coefficient, and \(x\) denotes the value of each design variable. Optimization was performed with an objective function that minimized the load, and the resulting equation is shown in Equation (4). The load was estimated to be 153.1 kN when the curling length was 5.7 mm, the distance was 6 mm, and the jig R was 5.0 mm.  
\[Z=\beta_{0}+\sum_{i=1}^{n}\beta_{i}x_{i}+\sum_{i=1}^{n}\beta_{i}x_{i}^{2}+ \sum_{i<j}^{n}\beta_{ij}x_{i}x_{j} \tag{3}\]  
\[\begin{array}{ll}Y_{predict}&=(-0.2236)x_{1}^{2}+(-0.0009)x_{2}^{2}+(0.8675)x _{3}^{2}+(-0.0059)x_{1}x_{2}\\ &+(0.2731)x_{1}x_{3}+(0.0045)x_{2}x_{3}+(1.2408)x_{1}+(0.0532)x_{2}+(-9.9395)x _{3}+21.0150\end{array} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Curling Length** & **Distance** & **Jig R** \\ \hline \multirow{4}{*}{Curling length} & 5.4 & & \\  & 5.5 & & \\  & 5.6 & & \\  & 5.7 & & \\ \hline \multirow{4}{*}{Distance} & \multirow{4}{*}{5.6} & 7 & \\  & & 9 & \\ \cline{1-1}  & 5.6 & 11 & 4.9 \\ \cline{1-1}  & & 13 & \\ \cline{1-1}  & & 15 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test dataset.  
Figure 11: Concept of support vector machine.
## 2 Operating Principle  
The analysis of the converter and its different operating modes was done under the following assumptions: semiconductor devices are ideal, single-core ferrite coupled inductors (flyback transformer) have unitary coupling coefficient and unity turns ratio (\(N_{P,n}:N_{S}=1\), where \(n\) refers to the n-th primary windings); the resistances are neglected, and the leakage  
Figure 1: Topology proposed, with \(N=3\) PV panels.  
inductance is \(L_{l_{N}}\); the converter operates in current discontinuous conduction mode (DCM) and steady state. Two duty cycles are defined: \(d_{1}\) for main switches (\(S_{MAIN}\)) and \(d_{2}\) for the snubber switches (\(S_{snb}\)). The magnetizing inductance is \(L_{m}\), the switching frequency is \(f_{s}\), and the commutation period is \(T_{s}\). The general aspects of the operation of this topology were based on extracting the energy from the PV panels connected to the converter in a single switching period \(T_{s}\). The flyback transformer was designed to manage the power from a single PV panel and not from all the panels together; this made it possible to reduce the size of the magnetic core. The switching period was divided in segments depending on the number of primary windings; hence, in this work, \(T_{s}\) was divided in three sections. The purpose of the snubber network was to recover the energy stored in the flyback transformer leakage inductance and limit the main switches \(dv/dt\). The leakage inductance current charged the snubber capacitor, and then that current was returned to the input capacitor \(C_{in}\) through the antiparallel diode of the main switch. This process improved the efficiency of the topology. The operating principle consisted in six stages for each PV panel used. Since in this paper \(N=3\) is considered, the total number of stages per switching period was 18. Figures 2-7 shows the first six stages per period, for the first PV panel. The main equation will be described for each state.  
Figure 4: Stage 3, time: \(t_{1}<t<t_{2}\).  
Figure 5: Stage 4, time: \(t_{2}<t<t_{3}\).  
Figure 6: Stage 5, time: \(t_{3}<t<t_{4}\).
## 2 Materials and Methods
### Signal Model of the Dual Channel Cancellation  
From the study in [32], for an arbitrary point target, we assume that the echoes of channel 1 and channel 2 received during the slow time \(\eta\) are written as follows:  
\[s_{\text{pt1}}(t)=A_{\text{pt}}(t)\exp\bigl{[}j\phi_{\text{pt}}(t)\bigr{]} \tag{1}\]  
\[s_{\text{pt2}}(t)=A_{\text{pt}}\bigl{(}t-\Delta t_{\text{pt}}\bigr{)}\exp \bigl{[}j\phi_{\text{pt}}(t)+j\Delta\phi_{\text{pt}}(\eta)\bigr{]} \tag{2}\]  
where \(t\) is quick time, \(A_{\text{pt}}(t)\) and \(\phi_{\text{pt}}(t)\) are the amplitude and phase of the echo that channel 1 received, \(\Delta t_{\text{pt}}\) is the time lag of the echoes between the two channels, and \(\Delta\phi_{\text{pt}}(\eta)\) is the phase shift of the echoes between the two channels.  
Let a source of radiation be deployed in the imaging area, transmitting signals of the same frequency band toward the dual-channel SAR. The jamming signals received by channel 1 and channel 2 can be written as  
\[s_{\text{j1}}(t)=A_{\text{j}}(t)\exp[j\phi_{\text{j}}(t)] \tag{3}\]  
\[s_{\text{j2}}(t)=A_{\text{j}}(t-\Delta t_{\text{j}})\exp[j\phi_{\text{j}}(t)+ j\Delta\phi_{\text{j}}(\eta)] \tag{4}\]  
where \(A_{\text{j}}(t)\) and \(\phi_{\text{j}}(t)\) are the amplitude and phase of the jamming signals, respectively, and \(\Delta\phi_{\text{j}}(t)\) is the IPSR. Note that \(\Delta\phi_{\text{j}}(\eta)\) is the slow time variant. Usually, if the two channels are not too far away, the time lag \(\Delta t_{\text{j}}\) is neglected, and \(A_{\text{j}}\bigl{(}t-\Delta t_{\text{j}}\bigr{)}\) in (4) becomes \(A_{\text{j}}(t)\). The signals that the two channels receive are the summation of the ground echoes and the jamming signals, which are  
\[s_{1}(t)=A_{\text{pt}}(t)\exp[j\phi_{\text{pt}}(t)]+A_{\text{j}}(t)\exp[j\phi_{ \text{j}}(t)] \tag{5}\]  
\[s_{2}(t)=A_{\text{pt}}(t)\exp[j\phi_{\text{pt}}(t)+j\Delta\phi_{\text{pt}}(\eta )]+A_{\text{j}}(t)\exp[j\phi_{\text{j}}(t)+j\Delta\phi_{\text{j}}(\eta)] \tag{6}\]Compensate (5) with \(\exp\left[\Delta\phi_{\text{j}}(\eta)\right]\), and form the difference with (6). This results in the following difference signal:  
\[\begin{array}{ll}s_{\text{d}}(t)&=s_{2}(t)-s_{1}(t)\exp\left[j\Delta\phi_{ \text{j}}(\eta)\right]\\ &=A_{\text{pt}}(t)\exp[j\phi_{\text{pt}}(t)+j\Delta\phi_{\text{pt}}(\eta)]-A_{ \text{pt}}(t)\exp[j\phi_{\text{pt}}(t)+j\Delta\phi_{\text{j}}(\eta)]\end{array} \tag{7}\]  
From (7), we can see that the jamming signals are eliminated. Meanwhile, the phase of the echoes is affected by \(\Delta\phi_{\text{j}}(\eta)\), wherein if \(\Delta\phi_{\text{pt}}(\eta)\) is close to \(\Delta\phi_{\text{j}}(\eta)\), and \(s_{\text{d}}(t)\) is close to 0, it means that some of the echoes would also be cancelled.
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 3 Research Methodology
### Image Segmentation  
Image segmentation was performed to extract the lettuce foliage from the background for further processing. This study uses the HSV segmentation model to segment the image [16]. There are two stages to the image segmentation process, which are detailed in the next two subsections.
## 5 Conclusions  
In this paper, we investigated whether targets and clutters can be distinguished with a multilayer algorithm in various environments. The proposed algorithm was applied to all data above the noise level without a preset threshold and the performance was analyzed and presented according to weak, strong, and all data. The evaluation results confirmed that a target can be detected and separated from clutters by the proposed algorithm in real underwater environments, even for low SNRs. Thus, the proposed algorithm is very useful and effective for underwater target classification at long ranges. Furthermore, this conclusion is based on experimental data acquired over several years via the application of a real active sonar system. Note that the experimental data may not represent all the characteristics of underwater environments, but we think that the data are sufficient to verify the possibility of applying our proposed algorithm to sonar systems; this is because the data were collected under a wide variety of field conditions, including different seasons, varied target motion, and wide sea areas.  
This study shows that the spatial characteristics in multilayered areas can be used as feature vectors that lead to a high classification rate with a low false alarm rate. Ping history results also indicated that the proposed approach is better than the conventional one. The ability to classify targets without the use of thresholds is a huge advantage in anti-submarine operations. Note that the proposed algorithm can be applied to all the signals without a threshold so that it attempts detection for signals that would be ignored by a conventional threshold exclusion process. Thus, the proposed algorithm can greatly improve the performance of classification and tracking, and a sonar operator's detection capability. In addition, we believe the algorithm is practical because it is the result of using real data from an active sonar operating on a naval ship in real underwater environments. For further study, the cepstrum may be a good candidate for the feature vector component due to its scale invariance property, and the fusion of different approaches may also be a useful approach for active sonar systems.  
Conceptualization and algorithmology, I.S.; software, S.K.; validation, I.S. and Y.R.; formal analysis, S.K. and Y.R.; resources, S.K.; data curation, I.S. and Y.R.; writing--original draft preparation, I.S and J.P.; writing--review and editing, I.S. and D.S.H.; visualization, J.P.; supervision, D.S.H. This research received no external funding. The authors declare no conflict of interest. The authors declare no conflict of interest.
# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 3 Methods
### Modified Model Architecture  
This section outlines our proposed method, which integrates a novel HA mechanism into the YOLOv8 algorithm to enhance fracture detection performance. Initially, we employed the YOLOv8 algorithm to train models with different size parameters on the FracAtlas dataset. FracAtlas is a robust and well-annotated dataset of X-ray images that will aid in developing and benchmarking advanced machine-learning models for fracture diagnosis. After evaluating these models, we introduce our HA mechanism to refine the detection capabilities further.
## 3 Data Preparations for Fault Localization
### Data-Preparation Process  
Figure 3 summarizes the preparation process of the fault localization using the training of the memory-updated pattern. To acquire and analyze data periodically and use it for fault localization,  
the process is divided into four vertical steps: (1) Data acquisition, (2) updated information analysis, (3) updated pattern detection, and (4) memory transformation for imaging and annotation.  
Furthermore, two horizontal phases are necessary for class annotation based on ground truth for training: (1) The unit test phase and (2) the integration test phase. First, the common steps periodically collect memory during the HiL test, analyze the memory data, and generate memory-updated information. From the next steps, there are differences according to test phase. At the unit test step, which collects normal operation information, the memory-updated pattern is found by applying the sequential pattern-mining algorithm into memory-updated information on each frame. CNNs facilitate automatic feature extraction in unit tests, where the training is performed, which enables an exploitation of the ability to identify and distinguish the features of each frame in the integration test, which is an inference phase. Thus, for convenience of use as the input data of CNN, the memory-updated pattern is transformed into an image. In other words, the image presents the total memory in matrix form by mapping a pixel of the matrix to an address and defining it as a frame. As shown in the figure, updated (U) is determined by whether the value stored in each address is changed for each frame acquired in each collection period. This is then transformed into image files, which represent whether the address of each frame has been updated as a marked pixel (updated) or a blank pixel (not updated). The transformed image file is labeled by class, which is the updated feature, as it meets the sequence of the generated updated patterns. Likewise, the integration test, which is the target that is finding the fault, transforms the memory-updated information of the frame into the image file.
## 4 Simulated Profile Retrievals Using Radiosonde Data
### Comparative Retrievals for CrIS
The dispersion using OEM is more than \(\pm\)3 K, whereas the same using RTLS is only \(\pm\)0.2 K. Interesting results are found when inaccurate a priori errors are used: RMSE values are 1, 0.7 and 0.95 kelvins for a priori errors of 20%, 50% and 100%, respectively. This is counter-intuitive and cannot be explained only by simple regularization strength. Figure 8b shows the results for information content in terms of DFR and DFS at the last iteration of retrievals. The DFS (blue plus in Figure 8b) for the OEM solution when stochastically exact a priori error is used for all profiles are \(\sim\)50% lower than the DFR using RTLS. It is also found that the DFS are highly dependent on the shape of the profiles as seen from scatter in Figure 10b, which is not the case for RTLS solution where DFR are consistent and close to 3. Out of three, one piece of information is observed for surface temperature and the other two are used for O\({}_{3}\) profile retrieval. It is worth mentioning that an exact solution is impossible using only these two piecesof information for a profile with more than 10-15 unknowns. Therefore, inevitably there will be some error in retrievals regardless of any method employed and it is required to find a balance between reduction of error in state space and maximizing the information extraction from the measurements. On the contrary, for OEM, the values of DFS are increasing with decreasing regularization strength (i.e., increasing a priori error covariances) and can reach values greater than 5 when large a priori error of 100% is used. The high DFS solution can extract information from weak space of solution, but solutions are degraded by large random errors due to low regularization as shown in Figure 7f. For comparison, getting back to Figure 7a,b, RTLS regularizes the problem optimally and weak space information is smoothed using LFDO and LSDO. Figure 7: Bivariate density scatter plots between the individual points of simulated CrIS retrievals and 277 true ozonesonde profiles. (**a**) RTLS1 employing LFDO; (**b**) RTLS2 employing LSDO; (**c–f**) OEM with four different a priori covariances (6, 20, 50, 100 percent, respectively).
## 1 Introduction  
Reinforced soil is widely used in the field of geotechnical engineering [1, 2, 3, 4]. As a type of discontinuous granular material, the tensile strength of soil is almost negligible. Through the introduction of fiber materials, which provide tensile resistance, the mechanical properties of soil can be improved significantly. On one hand, fibers can increase the strength of soil; on the other hand, they can decrease the deformation of geotechnical structures. Compared with continuous reinforced materials such as geogrids and geotextiles, discrete fiber materials such as fiber strips may be more flexible and convenient for application in some situations. In addition, some fiber strips can be made using reclaimed materials, which is environmentally friendly [5, 6].  
Fiber content and orientation are important factors that influence the engineering performance of reinforced soil. [7, 8] found that the reinforcement effects of fibers increased with fiber content within a certain range. Many researchers conducted experimental and theoretical studies regarding the mechanical properties of reinforced soil with different main directions of fiber distribution [9, 10, 11], generally reporting that the influence of fiber orientation on the reinforcement effects on soil depends on the stress mobilization in fibers. In addition, other factors related to fibers, such as aspect ratio, stiffness, and surface friction properties, can also affect the reinforcement efficiency [7, 12].  
In this study, a series of direct shear tests of sand specimens reinforced with cotton fiber strips were conducted. A method to strictly control the initial orientation of flexible fiber strips was developed in the process of specimen preparation. Based on the test results, the influences of fiber content and orientation on the mechanical properties of sand specimens, including strength, initial stiffness, and ductility, were discussed. This study with controlled variables can be helpful to understand the strengthening mechanisms of fibers in reinforced sand, although the situations in practice are much more complicated than the tests.
## Materials and Methods
### Two-Dimensional Fluid Model
#### 2.2.2 Boundary Conditions  
To obtain a unique solution for the system of coupled equations with the geometry presented in Figure 3, the boundary conditions (Dirichlet and Neumann boundary conditions) must be imposed. The boundary conditions applied for the AC plasma reactor corresponded to those found in the existing literature [32]. The following boundary condition was used to account for the particle flux in walls:  
\[\mathbf{\Gamma}_{i}\cdot\mathbf{n}=\bigg{(}a_{i}\,sgn(q_{i})\mu_{i}E\cdot \mathbf{n}+\frac{1}{4}v_{th(i)}\bigg{)}n_{i} \tag{17}\]  
where \(n\) is the normal vector pointing toward the tube wall and, \(v_{th(i)}\) is the thermal velocity of particles [32]:  
\[v_{th(i)}=\sqrt{\frac{8k_{B}}{\pi}\frac{T_{i}}{m_{i}}} \tag{18}\]  
and the number \(a_{i}\) is defined by:  
\[a_{i}=\left\{\begin{array}{l}1\,\,sgn(q_{i})\mu_{i}E\cdot\mathbf{n}\geq 0 \\ 0\,\,sgn(q_{i})\mu_{i}E\cdot\mathbf{n}\leq 0\end{array}\right. \tag{19}\]  
For electrons, as a special case, the particle flux due to secondary electron emission (SEE) was added to the system and is defined as follows [32]:  
\[\mathbf{\Gamma}_{e}\cdot\mathbf{n}=\bigg{(}-a_{e}\mu_{e}E\cdot\mathbf{n}+ \frac{1}{4}v_{th,e}\bigg{)}n_{e}-\sum_{p}\gamma_{p}\mathbf{\Gamma}_{p}\cdot \mathbf{n} \tag{20}\]  
where \(\gamma_{p}\) are the SEE coefficients, which defines the average number of electrons emitted per impact of ions \(p\) on the tube wall.  
Similarly, a boundary condition for electron energy was [32]:  
\[\mathbf{\Gamma}_{\omega}\cdot\mathbf{n}=-\frac{5}{6}v_{th,e}\omega_{e}-\sum_{ p}\gamma_{p}\varepsilon_{p}\big{(}\mathbf{\Gamma}_{p}\cdot\mathbf{n}\big{)} \tag{21}\]  
Here, the second term is the SEE energy flux, being \(\varepsilon_{p}\) the mean energy of the secondary electrons.  
The discharge was driven by a sinusoidal electric potential applied to an electrode and the other electrode was grounded. Then the boundary condition of electric potential in the grounded electrode was:  
\[\varphi=0 \tag{22}\]  
and the electric potential in the powered electrode was given by:  
\[\varphi=V_{0}sin(2\pi ft) \tag{23}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline Neutrals & \(\text{CO},\text{CO}_{2},\text{O}_{2},\text{O},\text{C}\) \\ \hline Pos. ions & \(\text{CO}_{2}^{+},\text{C}^{+},\text{O}^{+},\text{O}_{2}^{+}\) \\ \hline Neg. ions & \(\text{O}^{-}\) \\ \hline Elec. excited & \(\text{CO}_{2}^{*}\) \\ \hline Vib. excited & \(\text{CO}_{2}(\text{V}_{a.d.}),\text{CO}_{2}(\text{V}_{1})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Species considered in the model.  
with \(f\) = 50 Hz and \(V_{0}\) = 22 kV.