esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Glcm  
The Gray-Level Co-occurrence Matrix (GLCM) is a technique used to analyze the spatial relationships between pixels in an image, represented as a matrix, and is applied for the texture analysis of images. This matrix reflects the frequency of the occurrence of gray-level values between two adjacent pixels at angles of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\), allowingus to understand the structural arrangement of the image. By utilizing the generated matrix, 14 texture features can be calculated, with key features including the Inverse Difference Moment (IDM), contrast, Angular Second Moment (ASM), and correlation. The GLCM is effective in quantifying texture by analyzing the spatial relationships between pixels in an image, allowing it to detect subtle changes in text images. This makes it useful for identifying visual differences between English and Korean.
* Inverse Difference Moment (IDM): The IDM is a measure of the smoothness or homogeneity of a texture, with the value increasing as the texture becomes more uniformly distributed. The formula for its calculation is shown in Equation (2). \[\text{IDM}=\sum_{i}\sum_{j}\frac{1}{1+(i-j)^{2}}p(i,j)\] (2)
* Contrast: Contrast is a measure that reflects the gray-level difference between pixels in an image. It is used to assess the irregularity of the texture, and a higher contrast indicates greater differences between pixels in the image. The formula for its calculation is shown in Equation (3). \[\text{contrast}=\sum_{n=0}^{N_{g-1}}n^{2}\Big{\{}\sum_{i=0}^{N_{g}}\sum_{j=0}^{N _{g}}p(i,j)\Big{\}},\text{ where }n=|i-j|\] (3)
* Angular Second Moment (ASM): The ASM refers to the sum of the squares of the values in the matrix, indicating how uniform the texture of an image is. The value increases when the texture is repetitive, and the formula for its calculation is shown in Equation (4). \[\text{ASM}=\sum_{i}\sum_{j}p(i,j)^{2}\] (4)
* Correlation: Correlation is a measure that indicates how linearly related the gray-level values of neighboring pixels are. A higher value means that the gray-level values of neighboring pixels are more linearly dependent. The formula for its calculation is shown in Equation (5). \[\text{Correlation}=\frac{\sum_{i}\sum_{j}(i,j)p(i,j)-\mu_{x}\mu_{y}}{\sigma_{x} \sigma_{y}}\] (5)  
In a normalized gray-tone spatial dependence matrix, \(p(i,j)\) represents the entry at the (\(i,j\)) position, and it is calculated as \(p(i,j)=P(i,j)/R\). \(N_{g}\) denotes the number of distinct gray levels in the quantized image. \(\mu_{x},\mu_{y},\ \sigma_{x}\), and \(\sigma_{y}\) represent the average values and the standard deviations of \(p_{x}\) and \(p_{y}\), respectively [24].
## 4 Experimental Results
### Results of Iris Measure  
With the eye regions extracted, an accuracy of 100% was achieved in detecting the iris measure. The range of values that defines the minimum and maximum radius size for Hough transform was empirically identified to be between 28 and 45 pixels, for our database. Due to our strategy for choosing the radius with the largest frequency in the interval, the radius of the iris could be accurately obtained even if there were individual differences or errors. An example of the iris measure in 10 consecutive frames is shown in Figure 8. As can be seen from the figure, in an interval of 10 frames, there were 8 frames detected with a radius of \"33\" and 2 frames detected with a radius of \"34\", so the radius of the iris was determined to be 33.
# Differences in Nurses' Upper-Body Posture in Manual Patient Handling--A Qualitative Case Study
## 2 Materials and Methods
### Three-Dimensional Motion Analysis  
The 3D motion analysis was performed via Xsens MVN 2018, a three-dimensional kinematic motion measurement system using a constant frame rate of 60 Hz. The nurses' body movements were analyzed using 17 inertial sensors on various body segments such as the head, sternum, both sides of the shoulder, upper arm, forearm, hand, thigh, lower leg, feet, and pelvis. This setup allowed a three-dimensional analysis of the body segments in a Cartesian coordinate system [43]. The system showed strong correlations compared to the gold standard \"vicon motion analysis\" (Vicon Motion Systems, INC, Vicon, Oxford, UK) for measuring kinematics [44]. Based on its biomechanical model, the trunk flexion (forward lean angle of the trunk including pelvis tilt with respect to the vertical axis), the lateral flexion of the trunk (side lean angle of the trunk with respect to the pelvis), and the trunk's rotation (offset of the shoulder girdle with respect to the position of the pelvis) were used for upper body kinematics. The data from inertial sensors on their hands were used to analyze the acceleration of the repositioning process. This attachment allowed the acceleration of the repositioning to be recorded at the points of application (shoulder and hip) of the bed-bound patient participant (Figure 2) in X (transverse), Y (sagittal), and Z (longitudinal) directions. In the left-sided 30\({}^{\circ}\) inclined positioning, the right hand mainly guided the shoulder, while the left hand guided the hip area. In the right-sided 30\({}^{\circ}\) inclined position, the right hand guided the hip area and the left hand guided the shoulder area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nurse (n)** & **Age (y)** & **B\({}_{\mathbf{M}}\) (kg)** & **B\({}_{\mathbf{H}}\) (cm)** \\ \hline Total & 29.0 \(\pm\) 0.0 & 69.0 \(\pm\) 2.8 & 174.5 \(\pm\) 5.0 \\
1 (female) & 29 & 67 & 170 \\
2 (female) & 29 & 71 & 177 \\ \hline \hline \end{tabular}  
* BM = body mass; BH = body height. \(\dagger\) Values are mean \(\pm\) SD.  
\end{table}
Table 1: Nurses’ age and anthropometric data (N = 2) *\(\dagger\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Positioning Side** & **N** & **Age (y)** & **B\({}_{\mathbf{M}}\) (kg)** & **B\({}_{\mathbf{H}}\) (cm)** \\ \hline \multirow{4}{*}{Right} & 7 & 35.6 \(\pm\) 15.0 & 72.7 \(\pm\) 11.4 & 172.6 \(\pm\) 7.7 \\  & 2 males & 29.0 \(\pm\) 2.0 & 80.5 \(\pm\) 0.5 & 183.0 \(\pm\) 4.0 \\  & 5 females & 38.2 \(\pm\) 17.0 & 69.6 \(\pm\) 12.2 & 168.4 \(\pm\) 3.9 \\ \hline \multirow{4}{*}{Left} & 12 & 31.8 \(\pm\) 16.5 & 73.1 \(\pm\) 15.1 & 173.2 \(\pm\) 9.6 \\  & 6 males & 32.8 \(\pm\) 16.0 & 83.3 \(\pm\) 11.9 & 181.5 \(\pm\) 4.5 \\ \cline{1-1}  & 6 females & 30.8 \(\pm\) 16.9 & 62.8 \(\pm\) 10.2 & 164.8 \(\pm\) 5.1 \\ \hline \hline \end{tabular}  
* BM = body mass; BH = body height. \(\dagger\) Values are mean \(\pm\) SD.  
\end{table}
Table 2: Patient subjects’ anthropometric data (N = 15) *\(\dagger\).  
Figure 2: Xsens sensor coordinates placed on nurses’ hands.
## 2 Position Domain Control for Contour Tracking  
The dynamic model of an n-DOF robotic manipulator can be expressed as:  
\[M(q)\ddot{q}(t)+C(q,\dot{q})\dot{q}+G(q)+F(t,q,\dot{q})=\tau(t) \tag{1}\]  
where \(M(q)\) is the inertial matrix, \(C(q,\dot{q})\) is the matrix of coriolis and centrifugal forces, \(G(q)\) is the vector of gravitational terms, \(F(t,q,\dot{q})\) is the vector of friction forces, \(\tau(t)\) is the vector of joint torques, and \(q(t),\dot{q}(t)\), and \(\ddot{q}(t)\) are the joint position, velocity, and acceleration vectors, respectively [12; 13; 14].  
In the position domain control approach, the robotic manipulator is viewed as a combination of a master motion and a number of slave motions [9; 10; 11], and the dynamic model in Equation (1) can be discretized in a master motion and slave motions by taking the following form:  
\[\begin{bmatrix}m_{mm}&M_{ms}\\ M_{sm}&M_{ss}\end{bmatrix}\begin{bmatrix}\ddot{q}_{m}\\ \ddot{q}_{s}\end{bmatrix}+\begin{bmatrix}c_{mm}&C_{ms}\\ C_{sm}&C_{ss}\end{bmatrix}\begin{bmatrix}\dot{q}_{m}\\ \dot{q}_{s}\end{bmatrix}+\begin{bmatrix}G_{m}\\ G_{s}\end{bmatrix}+\begin{bmatrix}F_{m}\\ F_{s}\end{bmatrix}=\begin{bmatrix}\tau_{m}\\ \tau_{s}\end{bmatrix} \tag{2}\]  
with subscripts \(m\) and \(s\) indicating the master and the slave motions, respectively.  
The position of the master motion replaces time as the main reference for the slave motions, something that requires the reinterpretation of the slave section of Equation (2) as functions of the master motion. This reinterpretation takes the form of a one-to-one mapping from time domain to position domain as described in the following subsection.
# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 4 Experiment
### Comparison with State-of-the-Art Methods
While PSHNet is not designed with lightweight deployment as a primary goal, these results indicate that near-real-time deployment is still feasible, though future work may further optimize the architecture for size and speed if required. The results are summarized in Table 2, which compares model parameters, computational cost, and inference latency across platforms. To assess robustness across varying object scales, a scale-wise analysis was conducted on the IRSTD-1k dataset by grouping targets into small (area \(\leq\) 10 pixels), medium (10 \(<\) area \(\leq\) 40), and large (area \(>\) 40). Table 3 reports detection results for each group. PSHNet outperforms DNANet and ALCNet across all scales, particularly in the small-target regime, confirming the benefits of our ELFM module and size-aware localization branches. To complement the quantitative analysis, Figure 5 presents qualitative comparisons of detection results across multiple test cases from the IRSTD-1k dataset. PSHNet consistently achieves cleaner suppression of background clutter and more accurate localization of faint or small-scale targets. Red circles indicate true positives, green circles indicate missed detections, and yellow circles denote false alarms. These results underscore PSHNet's adaptability, making it suitable for real-world deployment in environments with diverse target characteristics and complex infrared scenes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Parameters (M)** & **GFLOPS** & **Latency (RTX 3090, ms)** & **Latency (Orin Nano, ms)** \\ \hline ALCNet & 1.50 & 4.2 & 6.63 & 17.8 \\ MDvsFA & 3.13 & 7.5 & 9.66 & 22.4 \\ DNANet & 4.69 & 10.8 & 24.05 & 35.6 \\
**Ours** & 5.7 & 12.3 & 13.4 & 28.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of model complexity and inference latency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Target Size** & **IoU (\%) \(\uparrow\)** & **Pd (\%) \(\uparrow\)** & **Fa (\(\times\)10\({}^{-6}\)) \(\downarrow\)** \\ \hline ALCNet & & 47.26 & 91.27 & 22.53 \\ DNANet & (0, 10] (small) & 49.51 & 95.24 & 20.68 \\ Ours & & **52.08** & **96.12** & **17.42** \\ \hline ALCNet & (10, 40] (medium) & 63.14 & 91.85 & 13.25 \\ DNANet & (medium) & 64.79 & 91.85 & 8.03 \\ Ours & & **66.37** & **93.16** & **6.51** \\ \hline ALCNet & & 78.46 & 93.94 & 6.78 \\ DNANet & (40, \(\infty\)) (large) & 79.20 & 96.97 & 11.02 \\ Ours & & **80.91** & **97.34** & **9.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Scale-wise detection performance comparison on IRSTD-1k (target area in pixels). \(\uparrow\) indicates higher values are better; \(\downarrow\) indicates lower values are better.
# A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack
###### Abstract  
Palmprint recognition (PPR) has recently garnered attention due to its robustness and accuracy. Many PPR methods rely on preprocessing the region of interest (ROI). However, the emergence of ROI attacks capable of generating synthetic ROI images poses a significant threat to PPR systems. Despite this, ROI attacks are less practical since PPR systems typically take hand images as input rather than just the ROI. Therefore, there is a pressing need for a method that specifically targets the system by composing hand images. The intuitive approach involves embedding an ROI into a hand image, a comparatively simpler process requiring less data than generating entirely synthetic images. However, embedding faces challenges, as the composited hand image must maintain a consistent color and texture. To overcome these challenges, we propose a training-free, end-to-end hand image composition method incorporating ROI harmonization and palm blending. The ROI harmonization process iteratively adjusts the ROI to seamlessly integrate with the hand using a modified style transfer method. Simultaneously, palm blending employs a pretrained inpainting model to composite a hand image with a continuous transition. Our results demonstrate that the proposed method achieves a high attack performance on the ITD and Tongji datasets, with the composited hand images exhibiting realistic visual quality.  
palmprint recognition; palmprint ROI extraction; hand composition; hand attack; ROI harmonization +
Footnote †: journal: Applied  
sciences
# Analysis of the Impact of Small Group Behavior on Cruise Ship Emergency Evacuation
## 4 Cruise Ship Evacuation Simulation Model Construction
### Congestion Area and Duration of Congestion for Two Evacuation Methods with Groups  
The same as the evacuation of the congested area of the cruise ship personnel without group, the change in the personnel density of a specific area with the evacuation time is shown in Figure 9, where the congestion duration in Area I-a ranges from 408 to 801 s, in Area I-b ranges from 508 to 798 s, in Area I-c ranges from 491 to 1050 s, in Area II-a ranges from 510 to 756 s, and in Area II-b ranges from 495 to 830 s.  
Figure 8: Frequency distribution of evacuation time with groups.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Evacuation** & **Evacuation Method I** & **Evacuation Method II** \\ \hline Average value/s & 1021.27 & 765.53 \\ Minimum value/s & 825 & 628 \\ Maximum value/s & 1358 & 984 \\ Standard deviation/s & 71.12 & 44.70 \\ Median/s & 1018 & 763 \\ \(P_{5}\) & 962 & 634 \\ \(P_{50}\) & 1023 & 771 \\ \(P_{95}\) & 1184 & 862 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Important parameters of evacuation time with groups.  
Figure 9: Variation in congestion density with time in the congested area with group.  
The percentage of the total evacuation time occupied by the continuous congestion time in the affected region is shown in Table 9. It can be seen that the continuous congestion time of the five selected congestion areas accounts for more than 10% of the total evacuation time, and even some areas exceeding 40%. Among them, the largest proportion is found in region B of evacuation mode II, with a value of 43.76%, while the smallest proportion is observed in region B of evacuation mode I. Furthermore, all five regions have a maximum congestion density exceeding four persons per square meter. These data indicate the significant impact of congested areas on the overall evacuation process. Therefore, it is recommended to assign personnel to these congested areas for guidance and to alleviate congestion based on the different evacuation modes.
## 4 Some Possible Reasons to Explain the Strength Recovery upon Reactivation
### Ageing and Creep  
Ageing may involve some creep deformation of the soil fabric, which enables the soil itself to stiffen and strengthen [40].  
Time-dependent deformations under constant mean stress are referred to as volumetric creep, whereas those related to a constant deviatoric stress are referred to as deviatoric creep [41].  
The secondary compression of a clay, mainly induced by volumetric creep under constant load in an oedometer, can delineate the role of the loading rate: the quasi-preconsolidation effect is an example of how the soil fabric can react more rigidly if reloaded rapidly after a period of ageing [42].  
Referring instead to what could happen in a quiescent landslide, it seems reasonable that compressive creep may have developed along the shear surface during ageing, giving rise to recovered strengths proportional to both the normal stress and the ageing time (Figure 9). However, as already anticipated, the strength recovery can only be evidenced by a certain level of reactivation speed: if reactivation occurs at a very low speed, it is likely that no strength higher than \(\tau_{0}\) could be seen (Figure 7).  
Deviatoric creep refers to a viscous displacement induced by a constant shear stress. If deviatoric stress is low, the creep displacement evolves at a decreasing rate (primary creep). When the deviatoric stress exceeds certain levels, the creep displacement may occur at a constant rate, even for a long time (secondary creep), or it may accelerate until failure (tertiary creep).  
The important role in the kinematics of deviatoric creep has been highlighted by some fundamental studies [43; 44], whereas experimental investigations have been performed by using both the ring shear and the direct shear devices [36; 37; 38; 39]. The evolution of creep into accelerated motion has been approached through consideration of a rate-dependent sliding-block model [45; 46].  
In this investigation, the ageing cycles were performed at the reference stress, \(\tau_{0}\), for which the creep displacement would have ended within the primary phase; therefore, no influence of deviatoric creep on reactivated strength could be outlined.
## 2 Synchrophasor Sequence Modal Models and Extraction of Modal Parameters
### Extracting the Parameters of the Modal Model  
Extracting the angular frequency parameters of SSOs from massive synchrophasor data is a typical problem when identifying modal parameters. The ERA serves as a prototypical algorithm for the extraction of modal parameters. Constructing the Hankel matrix and system matrix reformulates the process of modal parameter extraction into a matrix decomposition problem. This method can directly determine the angular frequency of the fundamental wave component and oscillation component with a low calculation cost and high solution efficiency.  
The ERA skillfully uses the temporal continuity of the synchrophasor sequence to extract information by constructing two Hankel matrices with specific shifts. By leveraging the correspondence between two translated Hankel matrices, the system's matrix can be acquired. Then, the parameters of the SSOs are further identified based on the system matrix.  
In the field of SSO parameter identification, there are many methods, but in contrast, the ERA method stands out because of its simple solution process and high computational efficiency. Therefore, we chose the ERA to propose a parameter identification method for sub-synchronous oscillation of a power grid.
## 4 Time-Varying Convolution
### Test Signals  
To illustrate some characteristics of time-varying convolution, we look at some cases of time-varying convolution using test signals as inputs. In the first example, we use a pulse train with a frequency of \(f_{s}/1024\) Hz and a sine wave with frequency of 100 Hz using a filter size equal to 1024 samples. This is shown in Figure 5, where we can see that with a pulse at the start of each new filter, the waveform is reconstructed perfectly. This is of course equivalent to having a fixed IR consisting of a unit sample at the start.  
In the second example (Figure 6), we decrease the pulse train frequency to \(f_{s}/1124\) Hz and now the impulses are spaced by 100 more samples than one filter length. The output then contains zeros at these samples, and the sine wave is shifted in time by 100 samples, as the impulse localises the start of the sinusoid at its corresponding time. We can see how the result is that a gap is inserted in the signal.  
The final example in Figure 7 shows the converse of this. If we increase the frequency to \(f_{s}/924\) Hz, now a new sinusoid is added to the signal before the previous one has completed its \(N\) samples. The effect is to distort the waveform shape at the points where there is an overlap. The distortion appears because the impulses do not coincide with the beginning of the waveform and the waveform itself does not complete full cycles in one filter length.  
Note that these examples are somewhat contrived. They are used to illustrate the process of time-varying convolution in a simple way, and are unlikely to arise in practical musical applications of the process. Thus, we need not infer that the quality of cross-synthesis results will be impaired due to the obvious discontinuities seen in the some of the outputs in these examples. However, they are indicative of the fact that the result of the process is very much dependent on the types of inputs as well as the filter lengths employed.  
Figure 5: Time-varying convolution using a pulse train with frequency \(f_{\rm s}/1024\) Hz and a sine wave of \(100\) Hz as inputs, with filter size \(N=1024\) and sampling rate \(f_{\rm s}=44\),100.  
Figure 6: Time-varying convolution using a pulse train with frequency \(f_{\rm s}/1124\) Hz and a sine wave of \(100\) Hz as inputs, with filter size \(N=1024\) and sampling rate \(f_{\rm s}=44\),100.
## 5 Green Emerging Extraction Techniques
### Static Headspace (HS) Extraction  
The static HS technique consists of the extraction of volatile and semi-volatile compounds directly from the gaseous phase above the solid or liquid matrix. HS is a simple, rapid, solvent-free technique, with no sample preparation needed; it is automated and easily coupled to GC. This method allows for the analysis of small amounts of volatile compounds [176]. Recent reports suggested that HS is the most efficient extraction method used to recover terpenoids from various food matrices, compared to hydrodistillation and PLE, since this method allows for the direct measurement of plant materials without any kind of sample preparation other than grinding, which may lead to alterations in the content of terpenoids. Additionally, when compared to liquid injections, static HS reduces the contamination of the MS detector, liners, and columns [184]. [PERSON] et al. [176] used hydrodistillation as the reference method to compare the efficiency of extraction of the PLE and HS methods for the recovery of terpen and terpenoids from various food matrices (cinnamon, thyme, cumin, fennel, clove, nutmeg, and orange). HS was the most efficient extraction method, yielding the most concentrated extracts. For instance, the eugenol LOD via HS was 0.0022 ug/g compared to 0.03 ug/g via liquid injection. [PERSON] et al. [184] quantified 93 terpenoids in cannabis air-dried infloresences and extracts. The method consisted of the full evaporation of samples via a static HS sampler followed by GC-MS/MS analysis. The authors also compared the developed method with decarboxylation, evaporation, and extraction methods. The results showed that sample preparation methods significantly impacted the chemical fingerprint of the samples when compared to non-treated cannabis, which emphasizes the importance of performing static HS extraction to study the natural terpenoid contents of chemovars. [PERSON] et al. [185] established the volatile profile of citrus leaves from 42 different cultivars. This study also evaluated the influence of four HS heating temperatures (40, 60, 80, and 100 \({}^{\circ}\)C) and two HS equilibration times (15 and 30 min). It was found that the extraction efficiency increased with rising temperature due to the volatilization of compounds from the matrix to the overlying HS. Moreover, at low heating temperatures, such as 40 and 60 \({}^{\circ}\)C, prolonging the incubation time from 15 to 30 min increased the amounts of extracted compounds. Based on these observations, the authors established that the optimal HS extraction conditions were a 15 min incubation at 100 \({}^{\circ}\)C. A total of 83 volatile metabolites were identified, namely, monoter hydrocarbons (68.23-95.08%, 21 compounds), alcohols (0.69-26.0%, 8 compounds), sesquiterpene hydrocarbons (0.47-5.04%, 26 compounds), aldehydes (0.12-11.26%, 10 compounds), monoterpends (0-0.36%, 7 compounds), esters 0-0.18%, 5 compounds), ketones (0-0.02%, 2 compounds), and miscellaneous compounds (0-1.11%, 4 compounds).