esa-sceva/satcom-chunk-collection huggingface dataset:
# Balance Control in Obese Subjects during Quiet Stance: A State-of-the Art
## 3 Results
### Gender Effects
#### 3.2.3 Males vs. Females  
If instability was generally observed both in obese males and female, it is not clear whether gender could influence balance. [PERSON] et al. [34] assessed the influence of BMI group (lean/overweight/obese) and gender on the postural sway of adolescents and adults during quiet upright stance in four conditions (eyes open/closed on hard/soft surface). The postural stability of obese subjects decreases with eyes closed, if compared to normal weight individuals, with no influence of the gender. Thus, they found no interactions between the BMI group and the gender, independently of the age of the subjects. On the contrary, in another study, [PERSON] et al. [32] identified a gender-specific effect in obese individuals: both genders displayed instability in AP direction, while only males presented ML destabilization. The difference in male and female body tissue distribution could potentially offer two related explanations to these outcomes. While males typically store more fatty tissue around their abdomen, (android shape), females usually carry fat around the hips and the upper portion of their legs (gynoid shape) [45]. AP instability is exacerbated in both populations by greater overall mass which leads to higher ankle torque. The male distribution of mass, however, results in increased loading of the pelvic girdle which, in turn may contribute to greater ML CoP excursion. Similar, yet alternatively, the anatomically lower position of fat storage associated with the female gynoid shape has a consequently lower CoM than the male android shape.
## 5 Results
### Backbone Network Comparison Experiment  
To select a suitable model for detecting typical satellite components in space scenes, this paper improves upon the backbone part of YOLOv8 by replacing it with some of the current mainstream feature extraction networks. The results of the backbone network comparison experiment on the test set are shown in Table 3.  
The backbone network, as the foundational part of the model, directly influences the overall model's performance and resource consumption, making it crucial for achieving model lightweighting. In selecting the backbone network, this paper considers multiple factors, including the model's parameter size, computational load, and performance metrics. As seen from Table 3, this paper compares the performance of various advanced convolutional neural networks and Vision Transformer architectures in the task of recognizing typical satellite components. Compared to the Vision Transformer architectures in the table, EfficientNet_B0 achieved the best performance (precision: 93.5, recall: 87.5, mAP50: 92.8) with the smallest parameter count and computation load. Despite the Vision Transformers' excellent performance in many computer vision tasks, their substantial computational requirements and slow detection speed make them less suitable for use in resource-constrained space environments. Compared to the convolutional neural networks in the table, EfficientNet_B0 also achieved the best performance (precision: 93.5, recall: 87.5, mAP50: 92.8) with relatively smaller parameter count and computation load. Compared to the baseline model, when EfficientNet_B0 was used as the backbone network, the parameter count decreased by 41.38%, the computational load decreased by 39.93%, and the precision increased by 0.65%. Despite the significant reductions in parameter count and computation load, EfficientNet_B0 still achieved high detection speed and accuracy. The results indicate that EfficientNet_B0, while maintaining high accuracy, demonstrates significant advantages in model size and computational efficiency, making it an ideal choice in resource-constrained environments.
# Ordered-Bipartite Consensus of Multi-Agent Systems under Finite Time Control
## 4 Simulation
### Simulation Examples for Reaching \(\mathrm{GC}\) in Finite Time  
This example considers a digraph of 18 nodes divided into four groups, represented by Figure 1. Obviously, there are edges with positive and negative weights connecting the four groups and making Figure 1 connected. Moreover, these edges between groups are directed and no-loop, i.e., there is an acyclic partition of Figure 1. The first simulation example in this section is about the finite time \(GC\) agreement of 18 agents in a networked topology relating to Figure 1. To solve for the state volume of the simulated \(MAS\), the initial state volume is now taken to be \(x(0)=(11,\,-2,\,3,\,1.5,\,-15,\,11,\,-3.5,\,4,\,-10.9,\,-21,\,17.5,\,6,\,-18,\,2,\,-27,\)36, -45, 35). Three different values for parameter \(\alpha\) are chosen below to fully outline the reliability of the simulation example.  
First, the evolution of the states \(x(t)\) for 18 agents at \(\alpha=0.25\) is given by Figure 3. From Figure 3, three aspects of messages can be obtained.  
One is that the state volume of each agent is finally converged, which means that the _MAS_ reaches the stable state. Secondly, the state volumes of all the agents converge to four different values, i.e., the _MAS_ reaches stability in four groups. Thirdly, the state volumes of four groups converge to each convergence value in partition order, which means that the _MAS_ reaches the groups' stability in order, and the order is related to the partition.  
Second, when \(\alpha=0.50\), Figure 4 gives the evolution of the state volumes of the agents in this _MAS_.  
Figure 4: \(\alpha=0.50\).  
Figure 3: \(\alpha=0.25\).  
In this case, the convergence process is consistent with the case of \(\alpha=0.25\). In addition, it is necessary to further compare the results of Figure 3 with those of Figure 4, despite the convergence values of each group of state volumes in Figures 3 and 4 being equal. However, it is worth noting that each convergence time given in Figure 4 is less than that in Figure 3.  
Finally, considering the above case, this example further considers the case of the control parameter \(\alpha=0.75\). Correspondingly, Figure 5 represents the evolution process with regard to the state volumes of all the agents. It can be found from these three figures that the change of the finite time control parameter \(\alpha\) does not change the final convergence value of the system state volumes. However, by comparing Figure 4 with Figure 5, it can be seen that the convergence time of each group given in Figure 5 is less than that of each group in Figure 4. With these two sets of comparison results, it can be inferred that as the value of parameter \(\alpha\) converges to 1, the convergence values of the state volumes remain constant but the convergence time decreases.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
###### Abstract  
In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless Sensor Networks (WSN), Mobile Ad-Hoc Networks (MANET), and DRONET Networks into one fully functional multilayered network. The proposed multilayered network model also presents the resilient way to deal with infrastructure disruption due to different reasons, such as disaster scenarios or malicious actions. In the near future, new network technologies of 5G networks and the phenomenon known as the Internet of Things (IoT) will empower the functionality of different types of networks and interconnects them into one complex network. The proposed concept is oriented on resilient, smart city applications such as public safety and health and it is able to provide critical communication when fixed network infrastructure is destroyed by deploying smart sensors and unmanned aerial vehicles. The provided simulations shows that the proposed multilayered network concept is able to perform better than traditional WSN network in term of delivery time, average number of hops and data rate speed, when disruption scenario occurs.  
WSN; MANET; DRONET; multilayered network model; 5G; IoT; smart sensors +
Footnote †: journal: Sensors  
_Article_  
Multilayered Network Model for Mobile Network Infrastructure Disruption
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## 3 Results and Discussion
### BER Performance  
The last analysis is BER study. If GFDM MIMO is given a nonlinear distortion, the performance of BER will decrease, as shown in Figure 14. The proposed method has been compared with OFDM. The effects of nonlinear distortion in OFDM and GFDM have been investigated by [33]. The effect of nonlinear distortion in OFDM will increase PAPR, increase OOB, and decrease SER. Meanwhile, the effect of nonlinear distortion on the GFDM system increases the OOB signal and decreases the SER significantly [13]. Then, nonlinear distortion on the MIMO-GFDM system caused the spread of the input signal diagram and affected the decision point area on the receiving side. As a result, the received signal was harder to detect correctly. So that the system performance decreases because there are errors in the information received. The simulation results show the performance of the MIMO GFDM with predistorter was better than the MIMO GFDM without predistorter. In the next work, Alamouti space-time coding will be added to the MIMO-GFDM system in order to decrease the needed signal-to-noise ratio.  
Theoretical BER was calculated by the Euclidean distance between the two closest signal points in the constellation diagram, which reduces the probability of errors. The probability of BER increased with the modulation size. Figure 15 shows the result of an investigation of the influence of IBO on the GFDM MIMO system if given a nonlinear distortion. It can be seen in the graph that if the IBO increases, the BER value in the GFDM MIMO system gets smaller. This is because at a larger IBO, the nonlinear distortion is smaller.  
Figure 13: GFDM constellation with predistorter.  
The comparison between BER and IBO for the GFDM system with HPA with and without predistorters is shown in Figure 16. The negative sign on IBO is used to simplify the analysis of simulation results. Looking at the IBO 6 dB, the GFDM with HPA system has the largest BER of 0.1668, while the system GFDM with predistorter has a lesser BER, i.e., 0.0001. Therefore, it can be concluded that the predistorters linearize nonlinear distortions successfully due to the HPA for larger IBO values. Future studies should focus on small IBO values to have a smaller BER value.  
Figure 14: Comparison of GFDM with and without predistorter.  
Figure 15: Bit error rate (BER) of GFDM+predistorter with IBO variations.
## 4 Model Formulation  
Figure 4 shows the signal control and CAV trajectory optimization model framework based on a variable virtual waiting zone with a dedicated CAV lane proposed in this paper. How CAVs and HVs enter the intersection at future time T is used as the input to the control model. Each CAV can transmit the following information to the control unit through the V2I communication channel: the time of entering the adjustment zone and the direction of travel at the intersection. The control system can also obtain the average arrival rate of each HV in a timely fashion by using the information obtained using roadside sensing equipment.  
The model framework includes three layers, which achieve the following: signal timing optimization, variable virtual waiting zone switching time optimization, and CAV trajectory optimization. In order to reduce the time that cars take to cross the intersection, the upper layer of the model dynamically optimizes the barrier duration according to the arrival of vehicles. Using the output from the higher layers, the phase duration of the signal light and the switching time of the variable virtual waiting zone are optimized in the middle layer, and the vehicle delay time calculated by the middle-layer model is fed back to the upper-layer model. Based on the time information transmitted by the middle-layer model, the lower-layer model accurately adjusts a CAV in the passing zone and the variable virtual waiting zone track and simultaneously transfers the CAV travel time to the middle  
Figure 3: Variable virtual waiting zone and associated signal phase. (**a**). Passage trajectory and virtual variable waiting zone. (**b**). Intersection signal phase. (**c**). Passage trajectory and virtual. (**d**). Intersection signal phase.  
layer. The overall output of the model includes a cross-optimal signal control scheme and CAV trajectory planning.
## 3 Antenna Characterization
### Antenna Polarization and Efficiency  
Since the fundamental radiating element of the proposed SIW LWA is a linearly polarized microstrip, one can also expect the SIW LWA to be linearly polarized. This was experimentally confirmed from the measured H-plane cross polarization discrimination (XPD), which is 24.83 dB. The measured normalized radiation patterns of the E-plane for co-polarization and cross-polarization at 60 GHz are shown in Figure 9. As to be expected, the E-plane did not provide a high directivity, as there were no arrayed radiating elements in this plane. The measured E-plane 3-dB beam width is \(\varphi_{\mathrm{3 dB}}=75^{\circ}\). The radiation efficiency \(\eta_{\mathrm{rad}}\) can be calculated as the quotient of gain \(g\) and directivity \(d\). Therefore, by calculating the directivity from the 3-dB beam widths of the E-plane and H-plane radiation patterns, using Equation (3), the radiation efficiency can be estimated [22]. This results in an estimated broadside radiation efficiency of 39.2% at 61 GHz and a maximum efficiency of 45.0% at 50 GHz.  
\[\eta_{\mathrm{rad}}=g/d\approx g/(32,400/\theta_{\mathrm{3 dB}}/\varphi_{ \mathrm{3 dB}}). \tag{3}\]  
In Figure 9, it can be further observed that the cross-polarization is higher than the co-polarization for beam angles lower than \(-45^{\circ}\) and larger than \(55^{\circ}\). This causes a reduced XPD in the E-plane of only 5.66 dB. A potential approach to increase directivity and XPD in the E-plane could be to use multiple SIW LWA arrays and feed them from the same input employing a substrate-integrated power divider.
## 5 Results and Discussion
### Seismic Response of the Bridge
#### 5.2.2 Traveling Wave Effects  
In this section, the traveling-wave effect, also known as the wave passage effect, is investigated for the 400 m long bridge under consideration. The wave passage effect represents a significant consideration in bridge seismic response, as seismic waves travel across the structure's supports at different times. To simulate this phenomenon, the seismic input at each pier support was implemented with appropriate time delays, calculated based on the wave propagation velocity in type C soil (VS = 300 m/s) and the distance between supports. Specifically, considering the left abtment of the bridge as the reference point, the seismic excitation at the two central piers was delayed by \(\Delta\)t = d/VS, where d represents the distance from the left abtment (100 m or 300 m), resulting in time lags of 0.33 s and 1.00 s for the adjacent and second piers, respectively. This approach provides a more realistic representation of seismic loading by accounting for the spatial variability in ground motion along the bridge length.  
Thus, Figure 13 shows the horizontal displacement at the midspan of the bridge deck and the base shear force for the five near-fault seismic excitations under consideration. The black line represents the response when the traveling wave effect is ignored, while the red line includes this phenomenon. The results indicate that consideration of the traveling wave effect can either amplify or attenuate the combined response, depending on the relationship between the seismic wave velocity, the local soil conditions, the geometry of the bridge, and the frequency content of the ground motion recordings. This highlights the importance of properly accounting for these complex soil-structure interactions and wave propagation effects in the analysis and design of such bridges in seismically active regions. The results also show that the impact of the traveling wave effect is not consistently beneficial or detrimental, as it can lead to either more or less severe structural demands depending on the specific scenario. For example, examining the case of the Northridge earthquake (17 January 1994, Station: 24088 Pacoima Kagel), the traveling wave effect leads to an increase in the maximum horizontal displacement from 412 mm (uniform ground motion) to 521 mm (non-uniform ground motion), i.e., an increase of 26%. On the other hand, for the case of the Cape Mendocino earthquake (25 April 1992, Station: 89156 Petrolia), the traveling waves effect resulted in an elastic response from the bridge in contrast to the assumption of uniform seismic excitation. This is a particularly noteworthy finding, as it demonstrates how this phenomenon can potentially mitigate or exacerbate seismic damage.
## 3 Method
### Source of the Dataset  
The video footage for this study was captured at Daseong Farm, located in Namwon-si, Republic of Korea, using an IPC675 LFW-AX4 DUPKC-VG camera. Weekly recordings were conducted to document the progression of chicken growth under diverse environmental conditions, such as high-density flock arrangements and varying lighting levels.  
For the training dataset to the classifier, positive samples were extracted by cropping chickens from the frames, ensuring a wide variety of conditions were represented. These samples included chickens across all growth stages, from small chicks to fully grown birds, and covered different flock densities, ranging from tightly packed groups to more dispersed arrangements. Additionally, the dataset accounted for various lighting conditions and dim or shadowed regions, simulating the challenges typically encountered in real-world farm environments.  
Negative samples were derived from frames that did not contain chickens, captured from footage of the empty farm. Non-chicken objects in the farm environment, such as equipment and background elements, were also included to enhance the classifier's ability to distinguish chickens from other objects. These negative samples helped reduce the likelihood of false positives and improved the classifier's generalization capabilities.  
The dataset comprises 7062 positive samples (chickens) and 7000 negative samples (non-chickens). It was divided into training (70%), validation (15%), and test (15%) sets, ensuring a balanced representation of various growth stages, flock densities, and environmental conditions across each split. This division ensured the model would generalize well to unseen data, leading to robust performance in practical farm environments.
###### Abstract  
We determined an antibiotic resistance mechanism in the eastern region, KSA, and the genetic factor clonal relatedness within Gram-negative bacteria. During our retrospective study, a total number of 29 _E. coli_ ESBL producer strains were isolated for patients visiting King Fahad Hospital, Al-Ahsa, KSA. The _bla_ genes were detected via PCR and identified via sequencing. Associated plasmid-mediated quinolone resistance genes, as well as _int1_ and _int2_ genes, were also studied. Phylogenetic groups, the ST131 clone, virulence factors, and PFGE were also checked. The _blaCTX-M-9_ (3.7%), _blaCTX-M-27_ (22.2%), and _blaCTX-M-15_ (77.8%) genes were identified; however, the _blaCNT-42_ (7.4%) gene was recorded for the first time in KSA. The _qnr51_ gene was found in 44.4% of strains, and among them, 50% concomitantly harbored the _anc(6')lb-cr_. The _int1_ gene was detected in 25.9% strains; nonetheless, the _int1_ gene was found in 7.4% of isolates. The strains belonged mainly to the B2 and D phylogroups. PFGE showed unrelated patterns. Some isolates belonged to the pandemic clone ST131. We describe a large dissemination of antibiotic resistance to third-generation cephalospororus in the eastern region, KSA, with the occurrence of the _blaCNT-42_ gene. The clone ST131 seems to be the principal contributor for _blaCTX-M-15_ gene spread.  
Keywords:antibiotic resistance; ESBL; _E. coli_; CTX-M-15; CMV-42; ST131; KSA +
Footnote †: journal: Applied
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Study Focus and Applications
#### 3.2.2 Change Detection  
As one of the leading research topics in earth systems, land cover CD in terrestrial surfaces has profound implications for human society and ecosystems (especially in wetland ecosystem functioning) [57]. Due to their vulnerability and sensitivity to variations in the environment, wetlands have become key regions for monitoring land cover changes over time. Wetland ecosystems interact with the physical environment, including hydrology, meteorology, and topography, in a variety of ways. Land cover CD in wetland sites provides an understanding of the patterns of the spatial distribution of wetland surface cover and the complexity of its spatiotemporal variations [38].  
As the most comprehensive and longest time-series RS image available, Landsat imagery is one of the most suitable data sources that provide suitable spatial and spectral resolutions as well as free-of-charge imagery. Moreover, in many developing countries, the limited data resources make Landsat imagery an important data source for filling the knowledge gaps in investigating local small-scale wetland dynamics.  
Using satellite imagery for wetland CD involves a variety of methods, which can broadly be divided into two categories: (1) change enhancement approaches, including direct comparison, vegetation indices differentiating, spectral mixture analysis, and fuzzy CD, and (2) 'from-to' change information extraction approaches [58; 59; 60]. The former methods (e.g., image differencing) do not indicate what types of land cover have been changed; they only provide information on whether there has been a change or not, and in some cases, measure the relative magnitude of that change. By contrast, the latter methods, such as the post-classification comparison approaches, are the most commonly used and effective CD techniques [61]. Post-classification comparison methods use separate classifications for images acquired at different times to produce different maps from which 'from-to' land-cover change trajectories can be detected [62]. In addition, 'from-to' quantitative information about the type of LULC changes can be gained from a cross-tabulated change matrix [63].  
Among all wetland CD methods in our meta-analysis, the post-classification approach is the most-employed technique, with a share of \(n=46\) out of 49. Taking a time interval perspective, CD studies can be categorized into two categories: (1) long-term CD and (2) short-term CD. As depicted in Figure 10, short-term CD (i.e., CD studies within a period of (1) 1-5 years and (2) 5-10 years) has a share of 20%, and long-term CD (i.e., CD studies within a period of (1) 10-20 years, (2) 20-30 years, and (3) 30-40 years) has a share of 80% among all post-classification change analysis papers. Long-term CD provides a better understanding of wetland trends and sudden changes; therefore, it is helpful to protect and analyze the dynamics of wetlands.