esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 2 Data and Methods
### Methods  
Theoretically speaking, the satellite radar altimeter data and NDBC buoy data will overlap in time and space when compared for verification purposes. However, it is seldom possible to achieve this ideal situation. Therefore, it is necessary to select radar altimeter data with small temporal and spatial differences when adopting buoy data for calculation purposes. When evaluating SWH and SSWS, the spatio-temporal windows selected in the current study were 50 km and 30 min. In the other words, the spatial separation between the satellite altimeter data and the buoy data in the matched data was no more than 50 km, and the time interval was no more than 30 min. These spatio-temporal matching criteria have been widely used in many previous studies [12; 13; 14].  
The cross-calibration with other missions detected any instrument drifts or inter-mission bias, which was essential to obtain consistent multi-satellite datasets [15]. For a single satellite or multiple satellites with the same trajectory, the intersection point of the ground trajectory is the only intersection point of the ascending and descending track, known as the self-crossover point. The intersection point of the ground trajectories of two satellites with different trajectories may be either the crossover point of ascending and descending track, or the crossover point of both ascending and descending track, known as the dual crossover point. The determination of satellite intersection points is divided into two steps: determining the approximate position and determining the precise position based on the approximate position. Preliminary screening of two trajectories with intersections, using the least squares method to perform quadratic polynomial fitting on satellite trajectories, calculating undetermined coefficients, and then, establishing a system of equations to obtain the approximate location of the intersection (\(\varphi_{i},\lambda_{i}\)) [16; 17; 18; 19; 20; 21; 22] (Figure 1).  
Using the approximate latitude obtained as a reference, select the 10 closest points to the reference latitude in the ascending segment to form a new ascending arc segment. In the descending segment, select the 10 closest points to the reference latitude to form a new descending arc segment. The intersection point of the two arc segments is the required accurate position.  
The findings of a previous study were referenced [23], and the radar altimeter performances were evaluated using the following four parameters: root-mean-squared error (RMSE); correlation coefficient (R); mean deviation (Bias); and standard of deviation (STD).
## 3 Methodology
### Datasets and Data Preparation  
We utilized two widely recognized datasets--the DeepFake Detection Challenge (DFDC) [12] and FaceForensics++ [11]. DFDC provides more than 100,000 videos (real and fake) with varying manipulation techniques (e.g., face swaps) and diverse lighting conditions. FaceForensics++ contains 1000 manipulated videos generated by methods such as FaceSwap, DeepFakes, and NeuralTextures, allowing us to gauge cross-method generalization.  
Frame Extraction and Labeling. We focused on frame-level analysis to capture intra-video variations without processing every frame. Specifically, each video was sampled at 30 fps, and up to 50 frames were extracted. For a video with \(N\) frames, we selected one frame every \(\left\lfloor\frac{N}{50}\right\rfloor\) frames, ensuring a uniform temporal spread. Each extracted frame inherits the real or fake label of its source video. Videos with fewer than 50 frames contributed all available frames. This procedure balances computational efficiency with coverage of temporal variations.  
Face Detection and Cropping. Faces were detected and cropped using a pre-trained Multi-task Cascaded Convolutional Network (MTCNN) with a confidence threshold of 0.9. Only the region around the detected face was retained, ensuring minimal background distraction and focusing the model on potential manipulation cues.  
Normalization and Resizing. Cropped images were resized to \(299\times 299\) pixels for the XCeption model and \(224\times 224\) pixels for VGG16 and ResNet variants, which were followed by pixel-value normalization to \([0,1]\). These dimensions align with the pre-trained ImageNet configurations for each respective architecture.  
Data Augmentation. To mitigate overfitting, we applied random horizontal flips, small rotations (\(\pm 15^{\circ}\)), brightness shifts (\(\pm 0.2\)), and up to 20% zoom. Augmentation was performed online (i.e., in real time) using Keras, ensuring diverse synthetic variations of training samples.  
Dataset Composition. After frame extraction, the final DFDC set contained approximately 150K frames for training, 30K for validation, and 30K for testing, maintaining a 1:1 balance of real and fake labels. FaceForensics++ yielded about 40K training frames, 8K validation frames, and 8K test frames, which were similarly balanced. We also generated additional distorted versions (e.g., H.264 compression, blur, noise) solely in the test partition to assess robustness. All reported performance metrics refer to these held-out test sets unless explicitly stated otherwise.
## 2 Materials and Methods
### Characterization  
Prior to conducting the adsorption experiment, the surface morphology of the three different adsorbent samples was analyzed to identify the sample exhibiting the highest porosity and surface area. The selected sample was employed in adsorption experiments, with its morphology characterized using field emission scanning electron microscopy (FESEM) on a Thermo Scientific Apreo system (Hillsboro, OR, USA). Nitrogen adsorption-desorption isotherms were utilized to determine the surface area, total pore volume, and average pore diameter of the activated carbon samples, employing a Micromeritics TriStar II 3020 analyzer (Norcross, GA, USA). The specific surface area of the prepared samples was calculated using the Brunauer-Emmett-Teller (BET) theory. The functional groups in the sample were identified using Fourier-transform infrared spectroscopy (FTIR) with a Perkin Elmer Spectrum GX instrument (Waltham, MA, USA). All instrumental analyses, including FESEM, BET, and FTIR, were performed at the Science Laboratory Center, Faculty of Science, Naresuan University, Phitsanulok, Thailand. Additionally, the heavy metal ion content in the adsorbent sample was analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES) following EPA Method 3052, with the analysis performed at Central Lab Thai, Chiang Mai, Thailand.
## 4 Results and Discussion  
### Research Assumptions  
The performed experiments are described by the input data sets depicted in Table 2. For CSCF servers, RACE C1, RACE C2 and links, M/M/1 queuing models were applied. For each data set, elements of two selected input variables (vectors) corresponding to domain 1 were widely modified according to Table 2, while the remaining elements of the selected vectors (corresponding to domain 2) as well as the elements of other vectors were left unchanged. Such an approach allowed investigating how modifications of particular input variables in domain 1 would affect CPP parameters in both domains. The obtained results can be used to determine changes in all output parameters when more than one input variable in domain 1 or input variables in both domains are manipulated.
### Vision and Insight  
To meet the latency requirements of various AR and VR applications, a comprehensive discussion and quantitative analysis of the specifications are required. Novel communication designs with ultra-high speed, low power, and ultra-low latency specifications should be envisioned and developed. This paper discusses existing technologies, as well as several breakthrough solutions and advancements in ultra-low-latency communications in the 5G and Beyond era. Technologies such as WiFi 7, mobile edge computing (MEC), and the Tactile Internet will unlock new possibilities in remote control, mobility, automation, and industrial control, motivating communications service providers to further upgrade users' experiences in real-time use-cases such as cloud gaming and AR/VR applications. Using such wireless technologies for network design, low-latency connectivity can also be achieved by focusing on finding an optimal end-to-end packet transmission path from transmitter's Packet Data Convergence Protocol (PDCP) to the receiver's PDCP from the device all the way to the application, wherever it is located (e.g., in a data center or cloud provider), and back. This leads to a necessity to meet all the requirements of an optimal message transmission [20], technologies, and the topology of the overall network architecture. Additionally, these technologies require an intelligent wireless network design by utilizing the mmWave-based technologies to support their requirements of an ultra-reliable and low-latency connection for delivering a realistic and seamless spatially aware experience to the user. Several technological challenges, such as video delivery through the mobile network and balancing the trade-offs with the gesture/head movement of AR/VR headsets, contribute to the delay in VR/AR technologies. Based on these requirements, this visionary paper focuses on providing an insight into the usage of several hardware and  
Figure 1: The block diagram shows the main categories and various types of errors associated with MTP latency in AR and VR HMDs.  
software requirements with the integration of 5G technology towards the upcoming 5 GB networks. We illustrate a learning-based framework for enabling an evolved experience of using AR/VR with a focus on ultra-low-latency communications.
# Dynamic Pressure Analysis of Hemispherical Shell Vibrating in Unbounded Compressible Fluid
## 2 Control Equation and Solution
### The Analysis of the Pressure  
By the characters of the Bessel function, it also can be written that:  
\[\left\{\begin{array}{l}\int j_{1}(r)dr=-j_{0}(r)\\ \int y_{1}(r)dr=-y_{0}(r)\end{array}\right.,\left\{\begin{array}{l}\lim_{r \rightarrow\infty}j_{1}(r)=0\\ \lim_{r\rightarrow\infty}j_{1}(r)=0\end{array}\right., \tag{23}\]  
Thus, the formula about pressure can be obtained:  
\[\Delta p=\rho_{o}c\cdot D\frac{\cos(\omega t+\varphi+\frac{\pi}{2})j_{0}(s)+ \sin(\omega t+\varphi+\frac{\pi}{2})y_{0}(s)}{\sqrt{\hat{f}_{1}^{2}(s_{r})+y_ {1}^{2}(s_{r})}}, \tag{24}\]  
where \(s\), \(\varphi\), \(D\) are the same as the Section 2.3. if \(s\) is large, there are:  
\[j_{0}(s)=\cos(s-\frac{\pi}{2})/s,\;\;y_{0}(s)=\sin(s-\frac{\pi}{2})/s \tag{25}\]  
Finally, the solution of the pressure can be induced:  
\[\Delta p=\frac{\rho_{o}c^{2}}{\omega}\frac{|u_{s}|}{\sqrt{\hat{f}_{1}^{2}(s_{ r})+y_{1}^{2}(s_{r})}}\frac{\cos[\omega(t-\frac{r}{c})+\varphi-\pi]}{r}=\rho_{o}c \cdot u. \tag{26}\]  
It can be seen from Equation (26) that the pressure is proportional to the velocity of vibration and the ratio is the product of the density and the sound speed. Furthermore, according to Equation (22), the convective acceleration is \(\frac{1}{2}\rho_{o}u^{2}\), which is the commonly used pressure formula derived by Bernoulli law [25]. Besides, compared to Equation (26), the value of the convective acceleration is very small. This means that neglecting the value of convective acceleration cannot cause a large error.
## 4 Proposed Protocol
### Receiving a Notification
By focusing on these aspects, we aim to elevate the discussion of user experience in cryptographic applications, highlighting that effective security measures can coexist with user satisfaction and ease of use. **Lemma 2**. : _The concatenation of \(T_{key}^{\prime}\) and \(NT_{key}^{\prime}\) for sending a notification is equivalent to the decryption of the ciphertext \(C_{3}\) in Algorithm 4._

Proof. : Assume identical parameters \(a,b,and\ p\) for \(Curve25519\), identical DH, ENC, and \(DEC\) algorithms, and that \(KDF/HKDF\) are employed in both Algorithms 1 and 2. We know that:

\[DH_{1}^{\prime}:=DH(EPK_{Alice}^{1}ESK_{Bob}^{2}):=DH(ESK_{[PERSON]}^{1}EPK_{[PERSON]}^ {2}):=DH_{1} \tag{1}\]

since in Lemma 1, we proved that \(RK_{1}^{\prime}=RK_{1}\). This implies that

\[(RK_{2}^{\prime},CK_{3}^{\prime}):=HKDF\Big{(}RK_{1}^{\prime},DH_{1}\Big{)}:= HKDF(RK_{1},DH_{1}):=(RK_{2},CK_{3}) \tag{2}\]

The above equation implies that

\[(K_{2},CK_{4}):=(K_{2}^{\prime},CK_{4}^{\prime}) \tag{3}\]

Thus, the decryption

\[DEC(K_{2},C_{3}):=DEC\Big{(}K_{2},ENC(K_{2}^{\prime},T_{key}^{\prime}||NT_{key }^{\prime})\Big{)}=T_{key}^{\prime}||NT_{key}^{\prime} \tag{4}\]

### Key Updating

When [PERSON] receives the output of Algorithm 4 from [PERSON], she deletes her old session key and adopts the new trusted key. The generation of the new trusted key follows the steps outlined in Algorithm 5. ```
Input:\((EPK_{[PERSON]}^{3},C_{5})\) Output:\((T_{key}^{\prime},NT_{key}^{\prime})\)
```
1:\(DH_{1}^{\prime}\leftarrow(EPK_{[PERSON]}^{3},ESK_{Bob}^{2})\)
2:\((RK_{3}^{\prime},CK_{5}^{\prime})\gets HKDF(RK_{2}^{\prime},DH)\)
3:\((K_{3}^{\prime},CK_{5}^{\prime})\gets HKDF(CK_{5}^{\prime}\_constant value^{\prime})\)
4:\(T_{key}\gets DEC(K_{3}^{\prime},C_{5})\)
5:\(NT_{key}=\bot\)
```  
**Algorithm 5** Key Updating  
The algorithm uses the public ephemeral key \(EPK_{Alice}^{3}\) of [PERSON] as input for the asymmetric ratchet and generates the root key \(RK_{3}^{\prime}\) and chain key \(CK_{5}^{\prime}\). It uses the chain key \(CK_{5}^{\prime}\) with a constant value such as text \"message-key send\" to obtain the key for decrypting the ciphertext \(C_{5}\). The plaintext corresponding to the ciphertext \(C_{5}\) is the new key that replaces the old key.
# A Sea Horse Optimization-Based Approach for PEM Fuel Cell Model Parameter Estimation
## 4 Simulation Study and Results
### Parametric Analysis of SHO
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \(I\) & **Time (s)** & **SSE** & \(R^{2}\) & **Explanation** \\ \hline
0.01 & 1.67 & 0.000036 & 0.999220 & Small steps, faster but limited exploration. Good solution quality, but not optimal. \\ \hline
0.03 & 1.65 & 0.000015 & 0.999688 & Optimal exploration–exploitation balance. Excellent performance in both time and SSE. \\ \hline
0.05 & 1.74 & 0.000010 & 0.999791 & Lowest SSE and highest \(R^{2}\). Ideal \(l\) value for the original SHO. \\ \hline
0.07 & 1.92 & 0.000017 & 0.999629 & Increased exploration, but also longer duration. Slight SSE increase, stability maintained. \\ \hline
0.10 & 1.97 & 0.000027 & 0.999425 & Over-exploration led to delayed convergence. SSE increased. Local stability may have weakened. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of Brownian step coefficient \(l\) on the SHO algorithm’s performance. that increasing exploration provides a more stable result despite the longer time requirements. * \(r_{1}\sim\text{Beta}(5,2)\): When exploitation bias is dominant, higher accuracy is achieved in a shorter time; however, because exploration is limited, the algorithm's ability to provide diversity in a large solution space is reduced. The findings are presented in Table 5, and each distribution case is summerized. Parametric analysis of \(r_{2}\) is conducted to evaluate the impact. This parameter governs the effects of the predation behavior of the Sea Horse Optimization (SHO) algorithm on convergence stability and solution quality. The \(r_{2}\) parameter plays a critical role in determining whether the solution update will be exploration-exploitation oriented. Although the literature commonly adopts \(r_{2}\sim U(0,1)\) (uniform distribution), this study aims to systematically investigate the effect of different distribution types on algorithm performance. The findings are reported in Table 6. Four different distributions were applied in the experimental evaluation:  
* Fixed value: \(r_{2}=0.5\)
* Exploration-biased distribution: \(r_{2}\sim\text{Beta}(2,5)\)
* Exploitation-biased distribution: \(r_{2}\sim\text{Beta}(5,2)\)
* Uniform distribution: \(r_{2}\sim U(0,1)\)  
The obtained results show that the performance of the SHO algorithm is directly affected by the distribution of \(r_{2}\). When a uniform distribution is used, the algorithm achieves a very low SSE = 0.000010 in 1.74 s, and the highest \(R^{2}=0.999791\) is observed. These results indicate that the natural balance between exploration and exploitation behaviors in the original design of SHO is positively reflected in the algorithm's performance. The algorithm performed a more comprehensive exploration in the solution space for the Beta(2,5). In this case, the SSE value was recorded as approximately 0.000029, but the algorithm's convergence time extended to approximately 4.28 s. Nevertheless, a high \(R^{2}=0.999375\) was obtained. This indicates that broad search capability has a positive effect on accuracy,
## 3 Results and Analysis
### Maximum Dry Density and Optimized Water Content
The optimum water content of graded crushed stone cannot increase infinitely with the increase in repeated compaction times. The rules of Equations (5) and (6) are accurate for instances where the repeated compaction time is small. Correlation between the Fractal Dimension of Particle Size Distribution and Repeated Compaction Times  
When the graded crushed stone was compacted under different water content conditions, the crushed stone particles in each size range were broken to different degrees, thereby changing the gradation of the graded crushed stone. After repeated compaction, the graded crushed stone samples were sieved to obtain the gradation distribution curve corresponding to each compaction state, and the fractal dimension of particle size distribution under each condition was fitted using Equation (3). Figure 8 shows the distribution of the fractal dimension of the particle size distribution against repeated compaction times under different water contents. It is observed that at a constant water content, the fractal dimension of graded crushed stone has a good linear relationship with repeated compaction time. The fractal dimension of particle size distribution increases with the increase in the repeated compaction time. The particle breakage caused by repeated compaction can make the graded crushed stone more compact. Moreover, when the water content of the graded crushed stone is close to the optimal water content, the linear relationship between the fractal dimension and repeated compaction time will be more stable. For example, as repeated compaction time increased, the increment of fractal dimension was in the range of 0.0165-0.0239 at the water content of 5%, and the increment of fractal dimensions was between 0.0074-0.0261 at the water content of 2.0%.
## 3 Materials and Methods
### Field and Laboratory Program  
A comprehensive series of both field and laboratory tests was executed in this study. The testing program was composed of the following data: (1) 531.26 m of SCPTu tests, (2) 317 seismic tests, and (3) characterization and triaxial tests on 16 samples collected from trenches and using a Shelby sampler. For the survey campaign of Structure 1, 2 SCPTu tests, 5 seismic tests, and 6 samples were collected. As for Structure 2, 7 SCPTu tests were carried out along with 168 seismic tests, and 7 samples were collected. Structure 3 underwent 11 SCPTu tests, 144 seismic tests, and sampling for 3 tailing samples. The seismic tests were executed at intervals of either 1 or 2 m.  
To complement the field tests, laboratory investigations were conducted to gain further insights into the characteristics of the iron ore tailings studied. Across all structures, a series of laboratory tests was performed, encompassing gradation analysis, void ratio determination, Atterberg limits assessment, specific gravity measurement, and triaxial tests. More detailed information regarding these tests are provided in Table 1.  
Field and laboratory data were collected by well-trained professionals from the mining sector. The best preparation and arrangement were adhered to ensuring the optimal setup and calibration of field equipment. This included precise calibration of the cones, saturation of their porous elements, and the adjustment of the standard CPTu velocity (2 mm/s) throughout the entire testing process. Similar attention to detail was devoted to the laboratory tests, encompassing thorough sample treatment and careful preparation. Triaxial tests were performed using undisturbed samples collected directly from the tailing dams. These tests facilitated the determination of cohesion and friction angle, with the latter parameter subsequently employed in conjunction with the Senneset, Sandven and
## 5 Performance Evaluation
### Simulation and Analysis
Consequently CHEF further prolongs the lifetime of the networks than LEACH. In PEGASIS, the distance of the neighbor nodes is less than that of the cluster members and the cluster heads in LEACH, and its head only receives the data of two nodes. Consequently, PEGASIS achieves higher performance than LEACH. The residual energy of the network also provides an estimate of the network lifetime. Figure 13 illustrates the mean residual energy of the network. It can be observed that the mean residual energy of the node in the case of REEMR is higher than that of the other protocols. Hence, the network lifetime under REEMR protocol is enhanced compared to LEACH, MOECS, CHEF and PEGASIS. These results corroborate the results presented in Figure 12. Figure 14 illustrates the received data at the base station. Under the first node death criterion, the received data using REEMR is approximately 11% higher than CHEF, 45% higher than PEGASIS, 68% higher than MOECS and 153% higher than LEACH.
# Perceptions and Resistance to Accept Smart Clothing:
## 2 Literature Review
### Factors Affecting the Resistance to Innovation  
#### 2.3.1 Perceived Compatibility  
Compatibility is a variable indicating how much the innovation is consistent with the value, past experience, and consumer needs [14; 22]. This means not only the consumer's existing values, but also the traditional or cultural values, and the degree to which they are consistent with the consumer's current lifestyle. [PERSON] [23] noted that the uncertainty of potential inmates is reduced, especially when an idea or product is highly compatible because people give more meaning to and think more intimately about new ideas or products that they think are highly compatible.  
According to [PERSON] and [PERSON] [24], factors affecting the acceptance of smart clothing include design and consistency with wearer images. Meanwhile, [PERSON] et al. [25] revealed that smart clothing preferences also differ depending on lifestyle types. [PERSON] [26] argues that consumers positively evaluated smart clothing when they perceived that the function of smart clothing was suitable for clothing, such as design flexibility. On the other hand, consumers resist smart clothing if they perceive the function of smart clothing is not suitable for clothing or that there is a limit when it is implemented as clothing. For example, Consumers welcomed useful features that were implemented in clothing, such as changes in patterns and colors. However, consumer's responses were negative for clothes when they were equipped with functions that can be measured more accurately with smartphones or smartwatches, such as heart rate checks and activity tracking.  
In the study of product extensions, similarity-related studies are divided into two categories: attribute similarity and concept similarity. Similarity of product propertiesmeans similarity in the function, physical characteristics, use conditions, technologies required to manufacture the product, etc. [27; 28]. [PERSON], [PERSON], and [PERSON] [29] said that for sports wearable devices, efforts to consider functional fit and design fit with conventional sportswear during the manufacturing and planning stages would be an important way to reduce consumer innovation resistance. Thus:  
**Hypothesis 1.**_Perceived compatibility of smart clothing negatively influences innovation resistance._