esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A miniaturized size and wide-band CP monopole antenna was suggested, fabricated, and tested. The suggested antenna was 30 mm \(\times\) 32 mm (0.4 \(\times\) 0.42 \(\lambda_{0}\) at 4 GHz).  
A U-shaped radiator on the front side, a partial ground plane with two rectangle slots, and a quadrilateral-shaped parasitic strip on the back side were utilized to produce the CP feature. The CP was achieved when we excited two modes with the same amplitude and a 90\({}^{\circ}\) phase difference. This could be generated by regulating the slot's dimensions and adding a quadrilateral-shaped parasitic strip to the ground plane. The antenna's different parameters were discussed and investigated. The tested outcomes had a bandwidth of \(S_{11}\leq-10\) dB 81.25% (5.2 GHz, 3.8-9 GHz) and an AR bandwidth of 30.7% (1.63 GHz, 4.48-6.11 GHz). Based on the achieved outcomes, it can be suggested that the antenna is considered a good choice for several wireless systems, such as UWB, sub 6 GHz, and WLAN applications.  
Conceptualization, B.M.Y., A.M.A. and A.A.I.; methodology, B.M.Y. and A.A.I.; software, B.M.Y., A.M.A., A.A.I. and M.D.A.; validation, A.A.I. and M.R.; investigation, B.M.Y., A.M.A. and M.D.A.; software, fabrication, and measurements, A.M.A.; writing--original draft preparation, B.M.Y., A.M.A. and A.A.I.; writing--review and editing, B.M.Y., A.M.A., A.A.I., M.D.A. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data will be made available at a reasonable request to the corresponding author.  
The authors would like to thank Jouf University, KSA for supporting this research.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Refs.**} & \multicolumn{2}{c}{**Size**} & \multirow{2}{*}{\(\varepsilon\)**r/h (mm)**} & \multirow{2}{*}{\(f_{0}\)**/BW (\%)**} & \multirow{2}{*}{**AR (\%)**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Complexity**} \\ \hline  
& 2.86/96.5 & 63.3 & 3.5 & Simple \\ This work & 30 \(\times\) 32 & 4.4/1.6 & 6.4/81.25 & 30.7 & 3.8 & Simple \\ \hline \hline \end{tabular}
\end{table}
Table 2: The suggested design in comparison with other works.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 2 Study Area and Datasets
### Field Survey  
In order to collect land cover information, a field survey was conducted on 17 May 2016. According to the field survey, the land cover types in the study area are winter wheat, winter rapeseed, forest, water body, and urban. The land covers, which are easily identified by visual interpretation, are identified mainly from the GF-1 image, including urban, forest, and water body. GPS coordinates and land cover information for each survey site were recorded and their spatial extent was transferred to a geographic information system (GIS). Fifty percent of the reference data were selected as the training samples by stratified random sampling, and the remaining 50% were used to perform the accuracy assessment (Table 2). All the classifications were conducted at per-pixel basis.  
In this paper, a confusion matrix was used to calculate the overall accuracy, and the kappa coefficient and the F1 measure (Equation (1)) were used to evaluate the classification accuracy. The overall accuracy is computed by dividing all correctly-classified pixels by the entire validation dataset [40; 41]. The kappa coefficient is computed to determine whether the values in an error matrix are significantly better than the values in a random assignment [3]. Additionally, the F1 measure, as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acquisition Date** & **Product** & **Imaging Mode** & **Polarization** & **Incidence Angle** \\ \hline
22 November 2015 & SLC & IW & VV/VH & 33.8 \\
9 January 2016 & SLC & IW & VV/VH & 33.9 \\
26 February 2016 & SLC & IW & VV/VH & 39.0 \\
21 March 2016 & SLC & IW & VV/VH & 33.8 \\
14 April 2016 & SLC & IW & VV/VH & 33.8 \\
8 May 2016 & SLC & IW & VV/VH & 33.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of Sentinel-1A images used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Training Pixels** & **Number of Validation Pixels** \\ \hline Winter wheat & 816 & 928 \\ Rapeseed & 807 & 930 \\ Forest & 1186 & 1095 \\ Water body & 1074 & 1192 \\ Urban & 1253 & 1325 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numbers of pixels per class for the training and validation data.  
the harmonic mean of the producer's and user's accuracy [42; 43], is considered to be more meaningful than the kappa coefficient and the overall accuracy [40]. The F1 measure ranges from 0 to 1; a large F1 measure indicates good results, while a small F1 measure indicates poor results.  
\[\text{F1 measure }=2\times\frac{\text{{producer}}^{\prime}\text{s }accuracy\times\text{{user}}^{\prime}\text{s }accuracy}{\text{{user}}^{\prime}\text{s }accuracy+\text{{producer}}^{\prime}\text{s }accuracy} \tag{1}\]
## 3 How to Calculate Accurate Airtime at the AP Side
### Overview  
Traditional airtime fairness only considers the direct airtime for transmitting data. In this paper, we firstly extend the definition of airtime fairness and provide a more precise analysis, as illustrated in Figure 3. Our extended definition includes both the direct airtime for transmitting effective data and the indirect airtime associated with the wireless DCF function.  
Direct airtime, as in traditional methods, represents the wireless time spent transmitting effective data. Indirect airtime, on the other hand, encompasses fixed-length segments such as SIFS, Slot, and wireless ACK, as well as backoff time, which has a random and uncertain length. Although backoff time is somewhat random, it helps prevent wireless interference and packet loss caused by multiple stations transmitting data frames simultaneously. By using AP-side algorithms, we can derive the expected value of the backoff time for a single data transmission from a theoretical perspective, allowing us to accurately calculate the airtime for each transmission.  
Furthermore, our concept of airtime fairness considers the type of flow, whether TCP or UDP. TCP and UDP are both transport layer protocols, with TCP focusing on reliability and orderliness of data transmission and UDP emphasizing transmission efficiency and lower latency. From a wireless airtime fairness perspective, TCP flows include an additional component: the airtime required for transmitting TCP ACKs over the wireless channel. TCP ACKs ensure reliable transmission of TCP flows but do not carry effective transmission content. Therefore, the wireless airtime of TCP flows should also include the airtime for TCP ACKs, which is part of the indirect airtime.  
This paper analyzes the composition of airtime and models it in Sections 3 and 4. The symbols and their meanings involved are shown in Table 1. Additionally, some parameters (e.g., SIFS, Slot, etc.) used in the following analysis are related to the network scenarios. Similar to the 802.11 MCS rate table, AP can reference a preset data table to find the specific parameter values. In different network conditions, the calculation method of airtime in our algorithm remains the same but with different values of parameters. This allows our approach to maintain consistent performance across different network scenarios.
## 3 The Mexican Case
### Discussion  
After applying a grounded study strategy for approaching datasets about women in STEM in general and then focusing on computer science, we observed that datasets are missing. The history of female contribution in STEM is often silent about women. We concluded that currently available data make it questionable to apply DM, ML and AI methods for picturing women's history in STEM and discovering patterns to understand their apparent absence. With these observations, we approximated conjectural answers for research questions RQ1 and RQ2. This first study could not provide elements to answer RQ3 because the studied datasets do not provide variables or perspectives about the conditions in which reported contributions were produced, only scientists' year, institution, discipline, and administrative nationality (nationality of birth and age). RQ3 questions about intersectionality, and therefore we chose a concrete case, that of Mexico, for which we observed the contribution statistics that show representative scientific production and excellence in the country. We enumerated the criteria used to evaluate scientific contribution in the National System of Researchers (SNI). The question was whether these criteria could provide elements to build an intersectional perspective and a quantitative study to approach an intersectional gender gap index.  
_[RQ3] Do missing intersectional perspectives prevent performing representative quantitative analysis about women's activity and contribution in STEM?_  
Existing datasets in Mexico providing data about women in STEM are sparse, partial and reduced to a tiny group of scientists (both female and male). For example, scientists working in private universities were excluded from the possibility of obtaining a fellowship related to SNI recognition. This exclusion hides an essential group of scientists from statistics. Universities, polytechnic centres, and other institutions that develop science provide different facilities to scientists to do their research. This difference depends on their location, type, and the disciplines they support. Female scientists and their conditions almost disappear in this complex context. Building intersectional datasets combining all these elements is a project to run. In a country such as Mexico, these socio-economic and political factors produce clusters of scientific production with different characteristics and conditions. Being a female scientist at the CINVESTAV (the most important research centre in Mexico) in Mexico City is entirely different than being a scientist in Huajuapan de Leon in Oaxaca (the poorest state in Mexico). The studies about women must address these aspects that call for thorough data collection strategies (by region and by the institution) and then running analytics at a small scale before scaling to the whole country. To answer RQ3, it is true that without intersectional datasets built with methodological data collection strategies, women will continue to be hidden and difficult to locate in the scientific cartography.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 4 Discussion  
### Advantages of This Method  
In this paper, we present a method for accurate parcel extraction by combining multiple resolution images. Our approach capitalizes on the powerful generalization capability of SAM to achieve precise extraction of parcels without the need for ground samples or training. Table 4 illustrates the prediction time of different models as well as their requirements for sample sets and pre-training. It can be observed that while existing deep learning methods achieve faster inference speeds (0.0659-0.1902 s per image), they rely heavily on large annotated sample sets and lengthy training processes. In contrast, although our method has a longer prediction time (1.3645 s per image), it is entirely independent of annotated data. Moreover, in large-scale applications, this computational cost remains acceptable, particularly when acquiring training data, and adapting models poses greater challenges.  
Furthermore, during the study, we found that while larger parcels can be accurately depicted using 10 m resolution images, smaller parcels remain undetectable. This indicates the need for higher-resolution images to effectively extract parcels in complex farmland landscapes. Considering this, we designed a parcel extraction method combining Sentinel-2 and GF-7 images, and used different images to extract parcels for different farmland landscapes so as to realize accurate parcel mapping in the study area. This method provides more accurate results than existing methods that use one resolution for parcel extraction, and enables large-scale parcels mapping while solving the problem of parcel extraction for complex farmland landscapes.  
Some studies have applied SAM to parcel identification. For example, [PERSON] [31] used SAM with Sentinel-2 data to extract farmland parcels in northern Serbia. Their results indicate that the performance of parcel extraction in complex cropland areas is mediocre, which aligns with the findings of this study. [PERSON] [30] also designed a parcel extraction process based on SAM, similarly emphasizing the importance of sampling points for segmentation outcomes. They incorporated supplementary segmentation during full-scale inference, which improved accuracy. However, their experiments were conducted solely in plain regions, lacking a broader diversity of farmland landscapes. This study complements this aspect.
## 2 Materials and Methods
### Impedance Sensing  
Diced IDE devices were inserted into a custom two-piece 3D printed chambers containing an inlet and outlet. The parts were designed in SolidWorks (Dassault Systemes, Velizy-Villacoublay, France) and printed from MED610 with an Objet30 Pro (Stratasys, Eden Prairie, MN, USA). Fluid was injected with syringe pumps at 20 \(\upmu\)L/min through tygon tubing (Cole-Parmer, Vernon Hills, IL, USA) into the chamber inlet, then exited through the outlet into a waste beaker. Sensor response was recorded with each medium until signal saturation, beginning with air, PBS (negative control), then PBS with enzymes, described above. The chamber was incubated at \(\sim\)37 \({}^{\circ}\)C for every experiment to simulate physiological temperature. IDE contact pads were connected to a CHI 660D Electrochemical workstation (CH Instruments, Bee Cave, TX, USA), one pad connected to the working terminal and the other pad to shorted reference and counter terminals. The AC current was swept from 10 Hz--1 MHz with potential amplitude of 50 mV every two minutes sequentially per sensor. Sensor substrates were weighed with a microbalance and profiled before deposition, after deposition once films were dehydrated, and after experimentation to determine absolute and loss of film mass.
# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 4 Experiments
### 3D Implementation  
To further explore the performance of CCFNet, we implement a 3D implementation of CCFNet. The performance of 3D CCFNet is evaluated on the Synapse dataset and compared to a variety of state-of-the-art 3D models, and the final experimental results are shown in Table 6. Among them, nnFormer [52] is a Transformer model based on a cross structure that is mainly used to deal with the 3D image segmentation problem in medical image analysis. According to 3D implementation, the image input size is set to \(64\times 128\times 128\) in the nnFormer. The 3D CCFNet shows a 0.31% improvement over nnFormer on average DSC, and 3D CCFNet achieves 8.78 mm in HD performance, meaning that 3D CCFNet can better delineate object boundaries.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Avg. DSC (\%)** & **Avg. HD (mm)** \\ \hline Baseline & 78.91 & 24.08 \\ Baseline + SEConv & 80.20 & 19.33 \\ Baseline + SEConv + CCFM & 81.59 & 14.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation studies on effects of different components of CCFNet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DFE** & **CSF** & **SFI** & **Avg. DSC (\%)** & **Avg. HD (mm)** \\ \hline ✓ & & & 80.20 & 19.33 \\  & ✓ & & 79.93 & 18.44 \\  & ✓ & ✓ & 81.11 & 18.26 \\ ✓ & & ✓ & 79.57 & 17.65 \\ ✓ & ✓ & & 80.37 & 22.40 \\ ✓ & ✓ & ✓ & 81.59 & 14.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation studies of effects of different fusion methods on CCFM. The presence of a checkmark (✓) in the corresponding column indicates the utilization of the module, while the absence of such a mark signifies its non-utilization.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 4 Design of Cooperative Tracking Guidance Laws
### Modification of the Guidance Law for Moving Targets Tracking
After the state estimation, we get the estimation of the current state  
The position vector \(\left[x,y\right]^{T}\) is used for the basic tracking of the static target while the velocity vector \(v_{t}=\left[v_{x},v_{y}\right]^{T}\) is utilized to modify the guidance law of UAVs when tracking a moving target.
## 4 Methodology
### Multilevel Trip Volume Threshold  
Due to the issue in which some indicators exhibit poor statistical stability and are prone to misjudging functional characteristics when the trip volume in grids is low, this study establishes a multi-level trip volume threshold based on the principle of effective sample size, excluding grids below the threshold from subsequent modeling. This screening method not only ensures the validity of indicators and improves model reliability but also aligns with the research objective of focusing on core freight areas. It represents a necessary data preprocessing step to guarantee the robustness of subsequent grid clustering and model construction.  
In determining the multi-level threshold, we employ a cutting-edge nonparametric iterative approach. First, all grids are sorted in descending order of related trip volume to plot a Lorenz curve of the cumulative proportion of grid numbers versus (y-axis) the cumulative proportion of trip volumes (x-axis). A larger curvature of the Lorenz curve indicates a more uneven data distribution. A tangent line is drawn at (1, 1), and the intersection of the tangent with the x-axis is calculated to derive the first-level threshold for trip volume (Figure 4a). Only grids with trip volumes below this threshold are retained for the next iteration (Figure 4b-h). This process is repeated for the filtered sub-samples until the data exhibit a uniform distribution (Figure 4h). Ultimately, eight levels of thresholds are determined, and combining the temporal span of the data with the cumulative proportion of trip volumes, a threshold of 16 is selected to filter out grids with low trip volumes. By adaptively capturing the multi-scale hierarchical features of trip volume distributions, this method avoids the arbitrariness of subjective threshold setting and provides a sample screening basis that aligns with the actual hierarchical structure of freight activities for subsequent modeling.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.6 Resisting Cloud Service Provider Masquerading Attacks  
In the proposed protocol, \(\mathcal{CSP}\) transmits the challenge message towards \(\mathcal{U}_{k}\) through \(\mathcal{FN}_{f}\). Suppose an adversary intercepts the challenge message; even then, he cannot impersonate the valid \(\mathcal{CSP}\). Although \(\mathcal{A}_{att}\) can compute all the other values of challenge message, he cannot compute a valid \(auth_{csp1}\) and \(auth_{csp2}\). The computation of \(auth_{csp1}\) and \(auth_{csp2}\) needs the calculation of valid \(X_{k}\) and \(X_{f}\) which is only possible with secret key \(x\) of \(\mathcal{CSP}\). As, \(x\) is not known to \(\mathcal{A}_{att}\), so \(\mathcal{A}_{att}\) cannot impersonate valid \(\mathcal{CSP}\). This is how our protocol resists a \(\mathcal{CSP}\) masquerading attack.
# Temperature Comparison of Looped and Vertical Carbon Nanotube Fibers during Field Emission
## 3 Results and Discussion  
### Field Emission Measurement  
Figure 4 shows the FE current data from the single vertical CNT fiber and the looped CNT fiber. For both emitters, the current emission level on the order of mA can be easily achieved from a single CNT fiber, confirming the excellent properties of the CNT fibers as field emitters [3,5,6]. To reach the same current emission level, the looped CNT fiber emitter requires a larger applied electric field as compared to the single vertical fiber emitter. For example, at a current level of 2 mA, the applied electric fields for the looped and vertical fibers are 0.53 V/\(\upmu\)m and 0.32 V/\(\upmu\)m, corresponding to an applied voltage of 800 V and 650 V, respectively. This is due to the lower effective field enhancement factor (\(\beta_{eff}\) in Equation (1) below) of the looped fiber, because of its smaller aspect ratio and the minimized sharp edges as compared to those of the vertical fiber (Figure 2).  
Figure 5 shows the Fowler-Nordheim (FN) plot of the measured I-V curves in Figure 4 for both the looped and the single vertical CNT fibers. For FN FE, the current-gap voltage characteristics could be fitted by the FN equation [6,31,32],  
\[I(\mathrm{A})=S_{eff}A\left(\beta_{eff}E\right)^{2}e^{-\frac{B}{\beta_{eff}E}} \tag{1}\]  
Figure 4: Current-voltage (I-V) curves for both the single vertical CNT fiber and the looped CNT fiber emitters.  
Figure 3: (**a**) Schematic and (**b**) photograph of the experimental setup for the field emission measurements and the infrared (IR) (or optical) imaging.  
where \(A=1.54\times 10^{-6}/W\), \(B=6.83\times 10^{9}W^{3/2}\), \(S_{eff}\) is the effective emission area (m\({}^{2}\)) of the fiber, \(W\) is the work function (in eV) of the emitting surface, \(\beta_{eff}\) is the effective field enhancement factor, \(E=V_{g}/D\) (in V/m) is the gap electric field, \(D\) is the gap distance between the anode and the base of the CNT fiber cathode, and \(V_{g}\) is the gap voltage. In the FN plot, Equation (1) yields a straight line when plotted as \(\ln\left(I/V_{g}^{2}\right)\) vs. \(1/E\), with a slope of \(-B/\beta_{eff}\). By assuming the gap voltage is the same as the external applied voltage, \(V_{g}=V_{ext}\) (i.e., no series resistance to the emitter is assumed) [6; 33], the measured FN data can be fitted by a straight line when the applied voltage is small, as shown in Figure 5. From the slope of these linear fittings, the effective field enhancement factor is extracted to be \(\beta_{eff}=15\),\(820\) and \(21\),\(651\) for the looped fiber and the vertical fiber, respectively. In the curve fitting, a constant work function of \(W=4.8\) eV is assumed for both CNT fiber emitters.  
The slope drop in the high electric field regime is usually attributed to space charge effects [34; 35; 36], outgassing of the field emitter [2], series bulk resistance or contact resistance of the emitter [6; 33; 37]. Further study is needed to identify the origin of the slope drop in Figure 5.