esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Limitations of the Study  
This work entails several limitations. One such limitation is that the gender distribution was not equal. Overall, the study included more female participants (77%) than male participants (23%). It is possible that results could have been different if there were more male participants. Some of the previous research has shown gender differences in spatial ability skills [99], where males significantly outperformed females in overall spatial ability. However, the gender distribution within both groups was fairly equal and thus reducing gender bias. The control group had two male and nine female participants, whereas the experiment group had three male and eight female participants. Therefore, the analysis was not affected due to overall unequal gender distribution. Moreover, there were no differences in the navigation task and how the two mobile navigation apps were used. Moreover, the spatial ability skills of the two groups were relatively similar.  
Another limitation of this study was its size. The small sample size makes it harder to detect statistically significant findings and even difficult to generalize the resulting findings. No statistically significant difference was found in wayfinding performance and workload of the control and experiment groups which could be potentially due to the small sample size. One of the concerns with a small sample size is more chances of producing type II errors (beta errors) and failing to reject the null hypothesis (H0) when the null is false. Therefore, these initial findings and the outlined effects need to be replicated with additional testing using a larger and more diverse sample of participants and with different navigation apps. However, we also collected qualitative data from participants to triangulate the findings, thus increasing the validity.  
The results reported in this paper should apply to other mobile navigation apps and different venues and scenarios requiring combined indoor and outdoor navigation. However, we acknowledge that the results might not be transferable to any navigation app, especially for different platforms and other indoor-outdoor wayfinding scenarios, such as hospitals or large organizations with multiple buildings. The results presented in this study may be only relevant in the context of the use of university campus wayfinding and navigation systems that provide navigation features like Google Maps and MazeMap. The average age of participants in this study is 30.8, and it is possible that similar analyses with other user groups (elderly or children) might produce different results due to age differences.
# Simultaneous Prevention of Rotational and Translational Slip for a Humanoid Robot
## 2 Dynamics Model of Slip Prevention  
### Slip Prevention Scheme Overview  
Slip usually appears quite suddenly and urgently. Humans manage to overcome slip in daily walking in a natural way through coordinated body movements. The arm swing and waist rotation play important roles in compensating the yaw moment in the vertical direction, and then prevent the possibility of potential rotational slip due to a reduction of the inertial moments. Therefore, we propose a bionic walking pattern generator to prevent rotational slipping by mimicking the yaw moment compensation mechanism of human.  
Although the generated walking pattern can prevent the rotational slip, the translational slip still exists during biped walking. As described in the introduction, the translational slip is mainly caused by the fact that the ratio of tangential force to normal reaction force is greater than the coefficient of the ground friction. Therefore, we propose a novel reaction force ratio reduction controller to prevent translational slipping by quickly compensating the CoM acceleration.  
Figure 1 shows the overview of the control architecture for translational and rotational slip prevention. First, according to the human walking mechanism, the rotational prevention pattern generator produce the desired walking pattern, which eliminates the rotational slip. The reference CoM acceleration, \((\ddot{x}_{c},\ddot{z}_{c})\), is obtained. However, this walking pattern cannot make humanoid robots walk stably because of the translational slip. The translational slip prevention controller thus works based on the current robot state, which computes the compensation of CoM acceleration \((\Delta\ddot{x}_{c},\Delta\ddot{z}_{c})\). If the ratio of the tangential force to normal reaction force is within the designated range, the translational slip may not happen. The CoM acceleration compensation should be \(\Delta\ddot{x}_{c}=0\) and \(\Delta z_{c}=0\). Otherwise, at least one of the value of the CoM acceleration compensation is not equal to 0. This CoM acceleration compensation is added to the reference CoM acceleration. The compensated CoM acceleration affects the Zero Moment Point (ZMP), and the modified ZMP then adjusts the joint angles. Finally, the humanoid robot can walk stably without rotational or translational slip.
## 3 Interference Situational Aware Beam Pointing Optimization
### Beam Pointing Optimization
The inertia weight will affect the performance and convergence speed of the algorithm [36]. In this paper, the linear inertia factor \(w(n,m)\) is adopted as follows [33]:  
\[w(n,m)=w_{\text{min}}+(w_{\text{max}}-w_{\text{min}})\frac{M-m}{M} \tag{22}\]  
where \(w_{\text{max}}\) and \(w_{\text{min}}\) are the upper and lower bounds of the inertia weight. The above process will be repeated at the time index \(n\) until the maximum iteration \(M\) is reached. The corresponding optimized result \(\hat{\boldsymbol{\vartheta}}_{r}^{C_{U}}(n)=[\hat{\theta}_{r}^{C_{U}}(n),\hat {\varphi}_{r}^{C_{U}}(n)]=p_{\text{best}}(n,M)\). Thus, the optimal result of the whole observation time \(N\) can be obtained by applying the above optimization process at each time slot, and it can be expressed as follows: \(\boldsymbol{\Psi}_{r}^{C_{U}}=\{\hat{\boldsymbol{\vartheta}}_{r}^{C_{U}}(1), \hat{\boldsymbol{\vartheta}}_{r}^{C_{U}}(2),\ldots,\hat{\boldsymbol{\vartheta }}_{r}^{C_{U}}(N)\}\). The detailed process for the PSO optimization process can be found in Algorithm 1. ```
0: Maximum iteration \(M\), number of the particle swarm \(J\), particle position \(l_{j}\), particle velocity \(v_{j}\), AoD and AoA of communication link \([\theta_{r}^{S},\varphi_{r}^{S}]\) and \([\theta_{r}^{S},\varphi_{r}^{S}]\), data set of AoD and AoA of interfering links \(\mathcal{D}_{l}^{I}(n)\) and \(\mathcal{D}_{r}^{I}(n)\), search space boundary \(L_{\theta,\text{min}}\), \(L_{\theta,\text{max}}\), \(L_{\varphi,\text{min}}\) and \(L_{\varphi,\text{max}}\), velocity limitation \(v_{\text{min}}\) and \(v_{\text{max}}\). 0: Optimal beam center pointing \(\hat{\boldsymbol{\vartheta}}_{r}^{C_{U}}(n)\);
1: Initialize \(m=1\);
2: Randomly generate particle position \(l_{j}(n,m)\) and velocity \(v_{j}(n,m)\);
3: Calculate the particle fitness \(f_{j}(n,m)\);
4: Find \(f_{a}(n,m)=\max\{f_{1}(n,m),\cdots,f_{j}(n,m)\}\), \(a\in\{1,2,\cdots,J\}\);
5: Set \(p_{\text{best}}(n,m)=l_{a}(n,m)\);
6: Set \(g_{\text{best}}(n,m)=\{l_{1}(n,m),\cdots,l_{j}(n,m)\}\);
7:repeat\(m=m+1\)
8: Update the particle position and velocity \(v_{j}(n,m+1)\) and \(l_{j}(n,m+1)\) via (20) and (22);
9: Calculate the particle fitness \(f_{j}(n,m+1)\);
10: Find \(f_{a}(n,m+1)=\max\{f_{1}(n,m+1),\cdots,f_{j}(n,m+1)\}\), \(a\in\{1,2,\cdots,J\}\);
11:if\(f_{a}(n,m+1)>f_{a}(n,m)\)then
12:\(p_{\text{best}}(n,m+1)=l_{a}(n,m+1)\);
13:else
14:\(p_{\text{best}}(n,m+1)=p_{\text{best}}(n,m)\);
15:endif
16:if\(f_{j}(n,m+1)>f_{a}(n,m)\)then
17:\(g_{\text{best}_{j}}(n,m+1)=l_{j}(n,m+1)\);
18:else
19:\(g_{\text{best}_{j}}(n,m+1)=l_{j}(n,m)\);
20:endif
21:until (\(m>\) Maximum iteration \(M\))
22: Return \(\hat{\boldsymbol{\vartheta}}_{r}^{C_{U}}(n)=p_{\text{best}}(n,M)\);
```  
**Algorithm 1** Beam pointing optimization based on PSO
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 3 Experiments
### Experiments on Spaceborne SAR Images
#### 3.1.2 Experimental Results  
(1) TerraSAR-X Images  
Different configurations, wherein different GCPs and CPs from the reference points were selected, were designed for accuracy validation. The GCP numbers selected were zero, one (labeled as 01), three (from 01 to 03), six (from 01 to 06), nine (from 01 to 09), twelve (from 01 to 12), and twenty (LOOCV). When GCP zero was used, it means that the original data set was utilized for R-D positioning. In this case, the five models were identical. Positioning tests were performed using the ICRD models with one, three, four, and six parameters, along with the classical R-D refinement model. Table 1's values represent the image geolocation accuracy in meters. In Table 1, the \"/\" signifies that there is no value at that position.  
Figure 3: Distribution of the reference points (**a**) and the GF3-C image section (**b**). The numbers in Figure (**a**) are serial numbers of the reference points.  
Figure 2: Layout of corner reflectors in the image (**a**) and a part of the TerraSAR-X image (**b**). The numbers in Figure (**a**) are serial numbers of the corner reflectors, and the blue square in Figure (**a**) is the corresponding part to Figure (**b**).  
Under the condition of not having any GCPs with which to refine the R-D model, the mean square errors of orientation accuracy in the range/azimuth direction were 0.62/0.39 pixels, and the average error absolute values for all CPs were 0.45/0.37 pixels. For the three-parameter model with six GCPs, the accuracies in the range/azimuth directions were 0.09/0.09 pixels for GCPs and 0.11/0.12 pixels for CPs.  
(2) GF3-C Image  
In this experiment, accuracy was assessed using different GCPs and CPs. The quantities of GCPs chosen were zero, one (labeled A01), three (A01 to A03), six (A01 to A06), nine (A01 to A09), and eleven (LOOCV). Various models, namely, one-, three-, four-, and six-parameter ICRD models, along with the classical R-D refinement model, were employed to calculate the positioning accuracy. The results are summarized in Table 2. Different GCPs and models were utilized to compute positioning errors, and the geolocation accuracy for CPs is presented in Table 2.  
In the absence of GCPs, the orientation accuracies in the range/azimuth directions were recorded as 16.84/2.48 pixels, while the mean error absolute values of all CPs were 16.82/2.26 pixels. Under the refinement of a three-parameter model with six GCPs, the precision values for GCPs in the range/azimuth directions were 0.55/0.68 pixels, while those for CPs were 0.63/0.65 pixels.
###### Abstract
Our study intends to fill this gap by taking a more experimental approach to testing algorithms in various settings, evaluating their results, and determining whether their performance is consistent with their design characteristics. By doing so, we can provide a nuanced comparison of the algorithms to determine which, if any, have an advantage over others in specific scenarios. The objectives of this study are fourfold:  
* To conduct edge computing experiments using the dedicated EdgeSimPy simulation tool, which offers a thorough and controlled environment for examining resource management strategies. * To systematically investigate the challenges of resource management in edge cloud infrastructures. * To present and evaluate well-known resource management algorithms in a simulated setting, each exploiting distinct characteristics to maximize resource utilization and enhance system efficiency. * To assess the effectiveness of these algorithms using key metrics such as CPU, memory, disk usage, power consumption, and latency. The results are analyzed to extract actionable insights that inform future research and practical applications. The remainder of this paper is organized as follows. Section 2 presents an overview of the edge simulation landscape, resource management strategies, and their contributions. Section 3 reviews the concepts of cloud computing, edge computing, and the resource management algorithms used in this study. It also introduces the EdgeSimPy tool and highlights the core contributions of this paper. Section 4 outlines the experimental setup, simulation configurations, and performance evaluation results. Section 5 discusses the limitations and challenges encountered, and Section 6 provides the conclusions and potential future research directions.
## 4 Discussion
The presence of the _pap_C gene in _E. coli_ isolates was not detected. The virulence genes _fim_ (type 1 fimbiae) and _pap_ (P-fimbriae) are colonization factors in extra-intestinal infections, and they contribute to biofilm formation. The _iut_A gene encodes an aerobactin receptor, which captures iron to support bacterial growth and development in iron-deficient conditions. _Cou_C represents a colonization-facilitating factor, and _kpsMT_ II is a protective factor against phagocytosis, as well as being a spreading factor [70]. One of the pathogenic _E. coli_ strains (D2) originating from chicken thigh muscle contained _kpsMT_ II, _fim_A, _cou_C, and _iut_A genes, as well as _tet_A, _qnr_A, and _qnr_B genes, had no MGEs present, and had weak biofilm formation. While it did not produce a biofilm, one of the pathogenic _E. coli_ strains (B2\({}_{2}\)) obtained from chicken cloacal swab samples did contain the genes _kpsMT_ II, _fim_A, and _iut_A in addition to _tet_A, _tet_B, _qnr_S, integrin 1, and transposon 3. The _kpsMT_ II, _fim_A, _tet_A, and _Tn3_ genes were also found in the _E. coli_commensal strain (A\({}_{1}\)), which was isolated from chicken cloacal swab samples and had a high biofilm formation potential. [PERSON] et al. (2018) discovered six genes associated with virulence factors in _E. coli_ isolates from broiler chicken gut microbiota. The genes are _kpsMT_ II (30.4%), _iss_ (29.6%), _cvaC_ (21.7%), _tsh_ (9.6%), _papC_ (4.3%), and _ibe_A (3.5%). In a study conducted by [PERSON] et al. (2018), they also identified genes encoding virulence factors in _E. coli_ isolated from poultry and swine slaughterhouses in Germany. They found that almost all isolates carried _finH_ (encoding fimbiae type 1), which is also in agreement with our results. A high percentage of the isolates studied had _astA_, _intA_, _iroN_, and _fruA_ genes. In contrast to us, they also detected the presence of _pap_CEFG genes. One isolate from a pig abattoir had the _hly_D and _cnf_1 genes present. The prevalence of _kpsMT_ II genes was higher in isolates from poultry abattoirs compared to isolates from pig abattoirs (22.9% versus 5.6%). Several researchers suggest that the presence of _E. coli_ with genes encoding virulence factors that facilitate the development of human ExPEC infections in foods of animal origin, especially poultry, is now an important food safety issue that requires continuous monitoring (2018).
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 3 Design of Miniaturized Dual-Band Broadband Printed Antennas
### The Results of Antenna Simulation  
After optimization, the final parameters of the antenna are shown in Table 1. The overall size of the antenna is 16 mm \(\times\) 29 mm, the structure is compact, and the horizontal direction of the T-shaped element is folded down to reduce the antenna size. The miniaturization of the antenna can be applied to the wireless network card and other small devices.  
According to the optimized parameters of the antenna, the \(S_{11}\) parameter curve of the antenna is shown in Figure 9. The antenna works in the two frequency bands of 4.5 GHz-7.5 GHz and 2.4 GHz-2.52 GHz, the impedance bandwidth reaches 3 GHz and 120 MHz, respectively, covering the 2.4 GHz, 5.2 GHz and 5.8 GHz three operating frequency bands of WLAN. In addition, the working broadband is realized. Figure 10 shows the Voltage Standing Wave Ratio (VSER) of antenna, and it can be inferred that the values of the standing wave ratio are less than two and the antenna has good transmission characteristics. Figure 11 depicts the input impedance of the antenna. The input impedance in the passband is about 50 \(\Omega\), and the antenna can be well matched with the microstrip feeder.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(Ws\) & \(Ls\) & \(Lf\) & \(Wf\) & \(Lg\) & \(L1\) \\ \hline
16 & 29 & 14 & 3 & 13 & 14 \\ \hline \(L2\) & \(L3\) & \(L4\) & \(L5\) & \(L6\) & \(H\) \\ \hline
7 & 3 & 12 & 12 & 3 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the optimized antenna (unit: mm). (the bold words in the first and the third row represent the parameters, the second and the fourth row represent the value of the corresponding parameters).  
Figure 8: Parameter \(S_{11}\) varies with L6.  
Figure 12 shows the gain of the antenna. As shown in the chart, the gain of the antenna in the 2.4-2.5 GHz band can reach 4-5 dB, and the gain in the 5 GHz band can reach more than 4 dB. The antenna realizes the miniaturization and achieves a higher gain. Figure 13 illustrates the E plane and H plane antenna patterns in the 2.45 GHz, 5.2 GHz and 5.8 GHz. The E plane graph is \"\(\infty\)\" shaped, and the H plane graph is circular, which indicates that the antenna has outstanding omnidirectional characteristics.  
Figure 11: Input impedance curve of the antenna.  
Figure 10: VSWR curve of the antenna.  
Figure 9: Parameter \(S_{11}\) of the antenna.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 4 Results
### Potentially Hydrocarbon Supporting Faults (F7-F10)
#### 4.2.3 Fault Sealing Capacity  
To explore potential hydrocarbon migration pathways and identify prospective areas for hydrocarbon accumulation in the study region, it is imperative to initially assess the lateral sealing capacity of faults F7-F10. This evaluation aims to ascertain which faults could serve as conduits for hydrocarbon fluids to cross and access fault array traps. Subsequently, for faults exhibiting robust lateral sealing, an assessment of their vertical sealing capacity is essential to determine the likelihood of hydrocarbon accumulation in the hanging wall traps.  
In pursuit of this goal, we conducted SGR calculations for faults F7-F10. It is noteworthy that due to the complexity of seismic reflections, identifying the T61 seismic reflection interface on F7-F10 faults presents challenges. The outcomes of our analysis reveal significant heterogeneity in the SGR distribution along the F7 fault. Specifically, the upper fault segment displays a pattern of either non-sealing (SGR \(<\) 15) or moderate (15 \(<\) SGR \(<\) 25) lateral sealing, whereas the lower fault segment exhibits robust (SGR \(>\) 25) lateral sealing (Figure 10a). This observation suggests that the upper segment of F7 could function as a lateral conduit, while the lower segment is likely to act as a barrier to hydrocarbon flow. Conversely, F8 generally exhibits non-sealing behavior along its entire fault surface, except for a moderate lateral sealing capacity in its middle portion (Figure 10b). Consequently, it may be broadly considered a lateral conduit. On the other hand, both F9 and F10 demonstrate a consistent strong lateral sealing capacity along their fault surfaces (Figure 10c,d). This finding implies that both F9 and F10 are poised to act as formidable barriers, preventing the passage of hydrocarbon fluids across these fault zones.  
Figure 10: SGR value distribution along faults F7 (**a**), F8 (**b**), F9 (**c**), and F10 (**d**). View from the side of hanging wall. See the fault locations in Figure 3.
## 3 Results
### Pathogen Identification in Cocultures  
To investigate the capability of RS to discriminate between bacteria in complex samples, we cocultured \(L\). _monocytogenes_ (Gram-positive) with \(S\). _enterica_ (Gram-negative) or \(S\). _aureus_ (Gram-positive). According to Figure 4, _Listeria_ coculture with other strains did not contribute as much to the PCA signal intensity as \(S\). _aureus_ or \(S\). _enterica_. This could be associated with the slower growth rate of \(L\). _monocytogenes_ compared to the other bacterial strains in coculture due to nutrient competition. This would explain why the coculture principal component was mixed with the neat clustering of monocultures in the plot of the three principal components in Figure 5B. The monocultures showed good clustering with no overlap between groups (Figure 5A). However, with the introduction of cocultures, the groupings were not so distinct and did not align with constituent monocultures. The source of this confounding effect was unknown, but it is hypothesised that the lower growth rate of \(L\). _monocytogenes_ means that the other pathogen in the co-culture dominates the signal. This is potentially supported by the clustering of the \(S\). _enterica_ + \(L\). _monocytogenes_ (brown) co-culture around the \(S\). _enterica_ (red) cluster and the \(S\). _aureus_ + \(L\). _monocytogenes_ (purple) around the \(S\). _aureus_ (green) cluster. We might expect that if the \(L\). _monocytogenes_ concentrations are equal to their partner culture, the resultant point would project into the space between the constituent mono-culture clusters.  
Figure 4: Comparison of control-subtracted Raman spectra (RS) of coculture mixtures and their corresponding monoculture RS components at 24 h: (**A**); \(L\). _monocytogenes_ and \(S\). _enterica_ coculture (top blue line), \(L\). _monocytogenes_ (orange line), and \(S\). _enterica_ (green line) and (**B**) \(L\). _monocytogenes_ + \(S\). _aureus_ coculture (top blue line), \(L\). _monocytogenes_ (orange line), and \(S\). _aureus_ (green line).  
Figure 5: The three principal components explain \(\sim\)60% of the variance for 24 h samples in (**A**) monocultures and (**B**) monocultures + cocultures. Code: _E. coli_ (blue), \(L\). _monocytogenes_ (orange), \(S\). _aureus_ (green), \(S\). _enterica_ (red), \(L\). _monocytogenes_ + \(S\). _aureus_ (purple), \(L\). _monocytogenes_ + \(S\). _enterica_ (brown).