esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Human Pose Estimation with Spatial-Temporal Interaction Enhancement Transformer
## 4 Experiments
### Ablation Study
#### 4.4.2 Structure Analysis  
Our proposed STFormer is a two-branch structure with constant spatio-temporal communication. To illustrate the validity of our structure, we propose some structural variants and compared them with our approach. We can abstract our proposed structure as shown in Figure 5a. Under different information exchange mode, the CDI module can implement it in a summation way (as in Figure 5b). As seen in Table 4, the summation approach is inferior to our proposed method (difference of 0.9 mm). The explanation may be that our suggested strategy is able to utilize MCA adaptively across domains to obtain the required information. Figure 5b shows an undifferentiated summation process on the received information.  
Figure 5: Structure analysis. (**a**) The abstracted model proposed in this paper. (**b**) A special case of replacing the MCA with a summation method in the CDI. (**c**) The structure of PoseFormer. (**d**) The structure of TNT. \(L\), \(L_{1}\), and \(L_{2}\) are the number of layers of the corresponding module.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(f\) & **Param (M)** & **FLOPs (M)** & **MPJPE** \\ \hline STFormer & 3 & 7.210262 & 45.9 & 50.6 \\ STFormer & 5 & 7.211286 & 76.6 & 49.5 \\ STFormer & 7 & 7.212310 & 107.2 & 48.6 \\ STFormer & 9 & 7.213334 & 137.8 & 47.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments with STFormer at different input frames.  
Furthermore, we explored the effect of temporal information, and we only transferred the spatial structure information to the temporal domain, as in Figure 5c,d. The structural design of PoseFormer [8] is used in Figure 5c, and the structural design of TNT [45] is used in Figure 5d. In the absence of injecting temporal features into the spatial domain, the results obtained in Table 4c,d are far below ours. This is a strong indication that temporal dependencies can help the model determine the state of motion in which the pose is in the current moment and, thus, adjust the structural relationships between joints, which is essential for accurate 3D human pose prediction. By comparing Table 4c,d, it is evident that frequent injection of unadjusted spatial features into the temporal domain is counterproductive and leads to performance degradation. This demonstrated that relying on Spatial Feature Extraction alone does not accurately model the spatial structure relationships of single-frame poses in the absence of temporal dependence information.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 1 Introduction
### Research Contributions  
In designing efficient routing protocol for multihop networks, MAC protocols have a large influence in finding optimal routes to make it efficient and self-operating [11; 12]. The proposed VSCL-MAC is designed to be the first to consider tactical network as a group of subnetworks and embeds route discovery as part of the MAC layer rather than the network layer. The design utilizes major contributions to the research field and provides  
1. Intelligent distributed dynamic routing and data slot scheduling for multihop simultaneous data transmissions over non-overlapping frequency sub-bands.
2. Close interaction with the MAC layer to empower efficient multihop route discovery, allowing radios to have updated neighbor information without incorporating route discovery overheads.
3. Self-forming and self-regulatory virtual subnetworks for efficient utilization of resources without having any centralized control.
4. Reliable and interference-free channel access for control and data transmissions with maximization of network throughput, minimum call setup, and access delays.
# Stability Numbers for Unsupported Conical Excavations in Multi-Layered Cohesive Soils
## 3 Results and Discussion
The variation of stability number was larger as the depth ratio (\(D/H\)) was smaller. Figure 13b shows the stability numbers at \(\beta=30^{\circ}\). Figure 13c shows the stability numbers at \(\beta=45^{\circ}\). The stability number was the same with \(c_{ul1}/c_{ul2}=1\) when the depth ratio (\(D/H\)) \(>1.5\). Figure 13d shows the stability numbers at \(\beta=60^{\circ}\). The stability number was the same with \(c_{ul1}/c_{ul2}=1\) when the depth ratio (\(D/H\)) \(>1.25\). As the angle of inclination increases, the variation of stability number gets small. Figure 14 shows the effect of the strength ratio (\(c_{ul1}/c_{ul2}\)) which is strength difference between the two layers with the relative top layer thickness to excavation depth. Numerical analyzes were conducted for various slope angle which is \(\beta=15^{\circ}\), \(\beta=30^{\circ}\), \(\beta=45^{\circ}\), and \(\beta=60^{\circ}\) with \(T/H=2\), \(H/B=0.5\). The strength ratio was defined as \(c_{ul1}/c_{ul2}=0.25\), \(c_{ul1}/c_{ul2}=0.50\), \(c_{ul1}/c_{ul2}=0.75\), \(c_{ul1}/c_{ul2}=1.00\), \(c_{ul1}/c_{ul2}=1.25\), \(c_{ul1}/c_{ul2}=1.50\), and \(c_{ul1}/c_{ul2}=2.0\). Figure 14a shows the stability numbers at \(\beta=15^{\circ}\). When \(c_{ul}/c_{ul2}<1\), the stability number when \(D/H<1\) was larger than the stability number when \(D/H=1\), but the stability number when \(D/H>1\) was smaller than the stability number when \(D/H=1\). When \(c_{ul}/c_{ul2}>1\), the stability number when \(D/H<1\) was smaller than the stability number when \(D/H=1\), but the stability number when \(D/H>1\) was larger than the stability number when \(D/H=1\), which is the top layer thickness is equal with excavation depth. Figure 14b shows the stability numbers at \(\beta=30^{\circ}\). Figure 14c shows the stability numbers at \(\beta=45^{\circ}\), and Figure 14d shows the stability numbers at \(\beta=60^{\circ}\). As the angle of slope increases (\(\beta=15^{\circ}\) to \(60^{\circ}\)), the influence of strength ratio (\(c_{ul1}/c_{ul2}\)) was decreased.
# On the Privacy-Utility Trade-Off in Differentially Private Hierarchical Text Classification
## 2 Preliminaries
### Membership Inference  
Membership Inference attacks strive for identifying the presence or absence of individual records in the training data of a machine learning model. Throughout this paper, we refer to the trained machine learning model as a _target model_ and the data owner's training data as \(\mathcal{D}_{target}^{train}\). We solely consider ANNs as target models in this paper. ANNs are structured in layers of neurons that are connected by weights. We denote the weights between a layer \(l\) and its preceding layer \(l-1\) as \(w^{(l)}\). The output of the \(l\)-th layer is denoted as \(o^{(l)}\). The ANN's final output is the output of the last layer.  
This paper builds upon the white-box MI attack against ANNs proposed by [PERSON] et al. [8]. Essentially, the white-box MI attack assumes an honest-but-curious adversary with access to the target model weight matrix \(w^{(l)}\). The white-box MI adversary leverages this knowledge to calculate attack features for each record \((x,y)\) in the form of layer outputs \(o^{(l)}(x;W)\), losses \(L(o(x;w),y)\), and gradients \(\frac{\partial L}{\partial w^{(l)}}\).  
With the aforementioned data, the white-box MI adversary trains a binary classifier, the _attack model_. The attack model allows one to classify records into members and non-members with respect to the target model and training dataset. The adversary is assumed to know a portion of the training and test data \(\mathcal{D}_{target}^{train}\) and \(\mathcal{D}_{target}^{test}\), and it generates features for training the attack model by passing the known records repeatedly through the trained target model. [PERSON] et al. [8] assumed the portion of known records at 50%, and we follow this assumption to allow comparison. The performance metrics of an MI attack model are typically evaluated on a balanced dataset including members (target model training data)and an equal number of non-members (target model test data). An illustration of the data preparation for the white-box MI attack and its evaluation is shown in Figure 1.
# Accessibility in Native Mobile Applications for Users with Disabilities: A Scoping Review
## 4 Results
### Review of the Literature to Map the Studies
#### 4.2.5 RQ5  
What Tools Are Used to Test Accessibility in Mobile Applications?  
To answer this question, we reviewed the papers by reviewing the most used tools according to disability in the full papers, as shown in Table 9. In reviewing the papers, we applied the following definitions:  
1. Accessibility Scanner: this is an automatic review tool from Google Play; it evaluates accessibility in Android applications without advanced technical knowledge.
2. Not specified: it does not specify the use of any tool to assess the accessibility of mobile applications; they are based on manual methods with guidelines defined according to some standards or defined by the authors performing the evaluation.  
This review found (1) seven studies that use the tool Accessibility Scanner to assess sensory disability, representing 31.8% of the total, and (2) fifteen studies that do not specify any type of tool used corresponding to 68.2%. One study focuses on cognitive disability, representing 4.5%; another study is related to motor disability, representing 4.5%; the most significant number of studies focuses on sensory disability, with eight studies representing 91% of the total.  
Figure 5: Type of applied research according to disability for accessible mobile applications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool** & **Cognitive** & **Motor Coordination** & **Sensory (Visually Impaired, Hearing)** \\ \hline Accessibility & & & \\ Scanner & & AS20a, AS20b, ASe20, AZ19, BK16, KK18, RZ17. \\ (seven studies) & & & \\ \hline Not specified (15 studies) & NH16. & Ro18. & AS20c, BB10, BJ18, CD18, CP16, KK16, PH19, SC15, CF16, MS20, ZZ17, CB19. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Documents according to the tool used to evaluate accessibility in mobile applications.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 2 The Principles of Operation of Miniature Fiber-Optic Pressure Sensors
### Demodulation Methods of EFPI Sensors
#### 2.2.2 Phase demodulation  
Phase demodulation, as shown in Figure 3, has high demodulation accuracy [35]. In phase demodulation, a broadband light source or a tunable laser is generally used to obtain the whole spectrum. Compared to intensity demodulation, there is no strict limitation on the cavity length, and the intensity fluctuation has little effect on the demodulation performance, so the phase demodulation can be universally applied. Many schemes, such as the fringe detection method [36], spectrum transform method [37], cross-correlation cavity length matching algorithm [38], path-matched interferometry method [39; 40], and so on, are proposed to implement phase demodulation. But most of these methods' demodulation speed is low, which makes them more suitable for static pressure measurement.  
Figure 2: Schematic of intensity demodulation system.
## 2 Methods
### The Design of the Data Compression Algorithm  
After the Argo buoy floats to the surface, the buoy sends the ocean profile data detected during the profile detection process to the shore base through the BTTT. The terminal receives the data transmitted from the main control board of the buoy and judges that it is profile detection data. First, the data are compressed, and then the compressed data are converted into message content, added with other information, and sent to the shore base.  
The Argo buoy carries a CTD sensor to detect the conductivity, temperature, and depth information of the ocean profile. The CTD data are represented in ASCII code format, including numbers, punctuation marks, spaces, and carriage returns. Environmental factors such as temperature, salinity, and depth have a certain numerical range and change with increase and decrease. The data composition is shown in Table 1. According to the characteristics of the data, differential encoding algorithms, Huffman algorithms, LZW algorithms, and mixed algorithms can be used to achieve data compression, reduce data volume, reduce the number of profile data transmission times, shorten the communication time of the terminal, and reduce power consumption.
## 3 Data Acquisition
### Cortes de Pallas Test Site
However, prior to being accepted as part of the combined 3D numeric models, the consistency of the points cloud obtained with the Kaarta system has to prove that it is consistent with the data provided by the EDM and CRP techniques. Since EDM and CRP are compatible at the 1-3 cm level (see Table 2), the point cloud obtained with the Kaarta system can be compared with the CRP solution in two ways to facilitate the analysis: first, using the dense CRP point cloud; second, employing manually measured natural photogrammetric check points. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Target**} & \multicolumn{3}{c}{**Differences CRP—EDM (mm)**} \\ \cline{2-5}  & \(\Delta\mathbf{x}\) & \(\Delta\mathbf{y}\) & \(\Delta\mathbf{z}\) & **Total** \\ \hline
1001 & 13.1 & 23.9 & 21.9 & 35.0 \\
1002 & 9.7 & 21.2 & 8.3 & 24.7 \\
1003 & 4.9 & 18.7 & 16.2 & 25.2 \\
1004 & \(-\)0.6 & 6.5 & 12.3 & 13.9 \\
1005 & 5.0 & 11.3 & 1.5 & 12.5 \\
1006 & 3.2 & \(-\)9.4 & 7.9 & 12.7 \\
1007 & \(-\)11.8 & \(-\)11.8 & 2.1 & 16.8 \\
1008 & 11.3 & 23.0 & \(-\)3.9 & 25.9 \\
1009 & 12.9 & 13.7 & 3.8 & 19.2 \\
1010 & 9.8 & \(-\)7.7 & 10.5 & 16.3 \\
1011 & 16.8 & \(-\)13.2 & 12.5 & 24.7 \\
1012 & 0.5 & \(-\)19.0 & 19.9 & 27.5 \\
1013 & 3.5 & \(-\)17.8 & \(-\)0.1 & 18.1 \\
1014 & 12.4 & \(-\)10.5 & 3.3 & 16.6 \\
1015 & 13.7 & 14.9 & 9.4 & 22.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Differences between the close-range photogrammetry (CRP) and the EDM coordinates obtained for the check points. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Pillar**} & \multicolumn{3}{c}{**Displacements and Their Corresponding Errors**} & \multicolumn{2}{c}{**Deformation**} \\ \hline
**No. ** & **Dx (mm)** & **Dy (mm)** & **Dz (mm)** & **T** & **Conclusion** \\ \hline
8001 & \(1.23\pm 0.70\) & \(0.84\pm 0.48\) & \(-2.45\pm 1.74\) & \(1.253\) & No \\
8002 & \(0.42\pm 0.37\) & \(0.32\pm 0.34\) & \(3.31\pm 1.80\) & \(3.340\) & No \\
8003 & \(0.87\pm 0.27\) & \(0.72\pm 0.37\) & \(0.48\pm 1.23\) & \(3.457\) & No \\
8004 & \(0.28\pm 0.70\) & \(-1.11\
# Unsteady-State Creep of an Asphalt Concrete
## 4 Results and Discussion
### Unsteady-State Creep Rate  
The strain rate is an important characteristic for deformation of viscoelastic materials. It is included in the governing equations as a physical characteristic of a material in the fundamental theories of viscous flow and plastic deformation of viscous liquids and solids [52; 53]. In our case, the strain rate will show how quickly the strain value of the asphalt concrete is varied in time on the unsteady-state creep site.  
Having differentiated Equation (5) for the unsteady-state creep strain, we will have the following expression for the unsteady-state creep rate of the asphalt concrete:  
\[\dot{\epsilon}(t)=\frac{\varepsilon_{0}\cdot\delta}{t^{\alpha}},\ (0<t\leq t_{1}). \tag{13}\]  
It is seen from Expression (13) that the strain rate (theoretically) is infinite at the initial time moment (\(t=0\)); it decreases with time according to the power dependence, and it has a value equal to the steady-state creep rate on the second site of the creep curve in the end of the unsteady-state creep site (\(t=t_{1}\)), i.e.,: \(\dot{\varepsilon}(0)=\infty,\dot{\epsilon}(t_{1})=\dot{\varepsilon}_{II}=const\).  
These characteristic peculiarities for variation of the unsteady-state creep rate for the asphalt concrete in time are visually demonstrated in Figure 9, where it can be seen that the strain rate is varied sharply in the initial time moments from \(t\approx 0\) to 500-600 s (theoretically from \(\infty\) at \(t=0\) to \(\approx\)1 \(-\) 2 \(\times\) 10\({}^{-3}\)%/s at \(t=\) 500-600 s); then it decreases monotonously in the following time moments, approximately according to a straight-line dependence.  
Figure 9: Graphs for the unsteady-state creep rate variation of the asphalt concrete: (a)—T = 24 \({}^{\circ}\)C, (b)—T = 36 \({}^{\circ}\)C, (c)—T = 48 \({}^{\circ}\)C, (d)—T = 60 \({}^{\circ}\)C.  
From Expression (13) it is also seen that the multiplication of the parameters \(\epsilon_{0}\cdot\delta\) is of physical nature: its numerical value is equal to the strain rate at the load duration of 1 s.  
Having taken the logarithm of Expression (13), we have:  
\[\mathrm{lg}\dot{\varepsilon}(t)=\mathrm{lg}(\epsilon_{0}\cdot\delta)-\alpha \cdot\mathrm{lg}t. \tag{14}\]  
As it is seen from Expression (14), the parameter \(\alpha\) characterizes the rate of variation (decrease) of the strain rate, i.e., it is the strain acceleration. As in Expression (14), the parameter \(\alpha\) has a negative sign, and it shows by what order the strain rate decreases at the time increase by one order. It can be called \"the logarithmic strain deceleration\".
###### Abstract  
This research aimed to assay the impact of convective drying (CD) or infrared-convective (IR-CD) drying methods on the physical and techno-functional properties, FTIR spectra, and mathematical modeling of adsorption kinetics of black soldier fly larvae powders. By using convective drying, insect powder exhibited higher water content and water activity but lower hygroscopicity than powder dried with the infrared-convective method. After drying with the convective method, the powder exhibited a significantly lower loose and tapped bulk density and oil holding capacity (OHC). Furthermore, this powder was lighter and more yellow. The FTIR spectrum of the CD-dried powder showed lower absorption at key wavenumbers for the protein (1625 and 1350-1200 cm\({}^{-1}\)), indicating lower denaturation and less ability to bind water and water vapor. The mathematical modeling of the water vapor adsorption kinetics of insect powders via the second Fick's law for transient diffusion showed that this equation is suitable for adjusting the experimental data based on the high coefficient of determination (0.997-0.999) and the low root mean square (2.50-3.34%). This study revealed that the drying method influences insect powder properties, and the IR-CD method seems better in terms of obtaining better techno-functional properties.  
Keywords:insect powders; drying; physical properties; techno-functional properties; color; bulk density; flowability; mathematical modeling 2020
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 5 Results and Discussion
### Comparative Analysis of the Models Implemented  
Table 8 provides a comparison and evaluation between the proposed models and those featured in the existing studies, focusing on their accuracy metrics. The proposed logistic regression model stands out with an exceptional accuracy of 98.28%, clearly surpassing the accuracy reported in the logistic regression models implemented in [15]. Furthermore, the SVM, decision tree, and Random Forest models introduced in [18] all yielded lower accuracy scores when compared to the proposed model, reaffirming its superior performance across these algorithms. The stack model, as detailed in [19], demonstrated a commendable accuracy of 96.24%. However, the proposed model outshines even this, registering a slightly higher accuracy. Finally, in the context of [21], where an SVM model was implemented, the reported accuracy of 98.92% was less than the proposed model, which achieved an outstanding accuracy of 99.16%. This comprehensive analysis highlights the consistent excellence of the proposed model.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FAR** \\ \hline Logistic Regression & 0.9893 & 0.9914 & 0.9963 & 0.9938 & 0.0576 \\ SVM & 0.9879 & 0.9867 & 0.9995 & 0.9930 & 0.0848 \\ Decision Tree & 0.9904 & 0.9913 & 0.9977 & 0.9945 & 0.0575 \\ Random Forest & 0.9942 & 0.9971 & 0.9963 & 0.9967 & 0.0204 \\ Decision Tree—XGB Classifier & 0.9941 & 0.9966 & 0.9966 & 0.9966 & 0.0233 \\ Decision Tree—with Features & 0.9903 & 0.9913 & 0.9976 & 0.9944 & 0.0576 \\ Random Forest—with Feature & 0.9945 & 0.9972 & 0.9965 & 0.9965 & 0.0194 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance Evaluation for all ML models.
# A Highly Integrated Millimeter-Wave Circularly Polarized Wide-Angle Scanning Antenna Unit
###### Abstract  
This paper introduces a novel, small-sized, highly integrated, circularly polarized wide-angle scanning antenna using substrate-integrated waveguide (SIW) technology at millimeter-wave frequencies. The antenna unit addresses requirements for high data transmission rates, wide spatial coverage, and strong interference resistance in communication systems. By integrating radiating square waveguides, circular polarizers, filters, and matching loads, the antenna enhances out-of-band suppression, eliminates cross-polarization, and reduces manufacturing complexity and costs. Utilizing this antenna unit as a component, a \(4\times 4\) phased array antenna with a two-dimensional \(\pm 60^{\circ}\) scanning capability is designed and simulated. The simulation and measurement results confirm that the phased array antenna achieves the desired scan range with a gain reduction of less than 3.9 dB.  
circular polarization; wide-angle scanning; phased array; millimeter-wave +
Footnote †: journal: Electronics Letters
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 4 Results and Discussion
### Development of the Generative Adversarial Network Model
For instance, the correlation between density and water/cement ratio is \(-0.391\) in the real data but only \(-0.201\) in the synthetic data, despite a correlation similarity score of 0.905. This indicates that although the general trend is captured, the synthetic data do not fully replicate the strength of the relationship. Overall, the synthetic data generation process effectively replicates the correlation structure found in the real data, though there are some deviations in the exact correlation values. These discrepancies suggest that while the synthetic data are generally reliable, some relationships might not be fully captured, particularly where the real data show weaker or more nuanced correlations. Further refinement of the synthetic data generation process could help better match these subtle relationships.