esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Illustration for IDS on UNSW-NB15 Dataset
Table 10 summarizes the comparative performance of the proposed MRCD-based \(T^{2}\) chart with existing algorithms. While the proposed method exhibits the highest accuracy, it also generates a relatively high false positive rate. This suggests a potential trade-off between detection accuracy and the number of non-anomalous events flagged as anomalies. Further investigation is warranted to explore methods for reducing the false positive rate while maintaining high detection accuracy. Additionally, the computational cost of the proposed method should be evaluated to assess its suitability for real-time applications. The empirical results presented in this study collectively underscore the effectiveness of the proposed approach in accurately identifying network intrusions. This capability positions the methodology as a promising asset for bolstering overall cybersecurity defenses. A key advantage of the proposed framework is its computational efficiency, a direct consequence of the underlying multivariate chart structure. This efficiency enables the model to adapt swiftly to the evolving nature of network traffic, a critical attribute for contemporary intrusion detection systems operating in dynamic and complex digital landscapes.
## 3 _Pbc_ Analysis Method and Result
### Results of PBC Effect Analysis in FCW Installation  
#### 3.5.1 Effect on Cognitive Reaction Time  
Without FCW installation, in the rear-end crash scenario, the cognitive reaction time from the start of the scenario and deceleration was 1.66 s during daytime, as shown in Figure 5, but in the case with FCW installation, the reaction time decreased to 1.46 s. In the case of nighttime, the reaction time also decreased from 1.95 s without FCW installation to 1.66 s with FCW installation. This confirms that FCW sends the stimuli to the driver in advance in case of a dangerous situation, which leads to a faster cognitive reaction time. As for comparisons between drivers aged under 60 and drivers aged 60 or above, the default (without FCW installation) cognitive reaction time of drivers aged under 60 was shorter than that of drivers aged 60 or above. With FCW installation, the cognitive reaction time decreased by 14.5% during daytime and by 17.5% during nighttime for drivers aged 60 or above, showing a larger decrease than that of drivers aged under 60, whose cognitive reaction time was reduced by 10.4% during daytime and by 13.3% during nighttime. The sample size was 70, and the variances were analyzed as 0.35, 0.27, 0.33, and 0.25 for each scenario.  
In terms of the statistical test, as shown in Table 5, for both types of normality verification (Shapiro-Wilk and Kolmogorov-Smirnov tests), the \(p\)-values (significance probability), the test statistic, were below 0.05. The \(p\)-value of the corresponding samples for the difference between with and without FCW installation was 0.00, indicating that the effect of reduced cognitive reaction time was achieved owing to FCW installation for both daytime and nighttime.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Age Range of Participants**} & \multicolumn{2}{c}{**Daytime Drivers**} & \multicolumn{2}{c}{**Nighttime Drivers**} \\ \cline{2-5}  & **No.** & **Percentage (\%)** & **No.** & **Percentage (\%)** \\ \hline Thirties & 2 & 5.4 & 4 & 12.1 \\ Forties & 5 & 13.5 & 9 & 27.3 \\ Fifties & 12 & 32.4 & 8 & 24.2 \\ Sixties & 18 & 48.6 & 12 & 36.4 \\ Sum & 37 & 100.0 & 33 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of study participants’ age and working hours.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 2 Background  
### The Popov Method  
The Popov Method models the ship-ice interaction process as an equivalent one-dimensional collision with all motions taking place along the normal to the shell at the point of impact [23]. The model does not consider sliding friction and buoyancy forces. A single hydrodynamic effect, namely the added mass of the surrounding water, is considered.  
To simplify the ship-ice interaction process to an equivalent one-dimensional collision, [PERSON] et al. [9] introduce the so-called \"reduced mass\" concept [23]. Accordingly, the total reduced mass \(M_{red},[t]\) of the ship-ice system is derived by coupling 6-degree of freedom (DOF) equations describing the motions of a ship with 3-DOF equations describing the motions of an ice floe [9]. By assuming that ice floes are of an ellipsoid shape, the 6-DOF and 3-DOF models are converted into a model with a single DOF. As a result, the contact force \(F,[MN]\) can be determined as an integral of contact pressure over the nominal contact area \(A,\left[m^{2}\right]\) per  
\[M_{red}\frac{\mathrm{d}^{2}}{\mathrm{d}t^{2}}\zeta_{tt}(t)\cdot 10^{-3}=F= \int p\Big{(}\zeta_{tt},\frac{\mathrm{d}}{\mathrm{d}t}\zeta_{tt}(t)\Big{)}dA, \left[MN\right] \tag{1}\]where \(\zeta_{n},[m]\) is indentation depth, \(t,[sec]\) is time, and \(p\big{(}\zeta_{n},\dot{\zeta_{n}}\big{)},[MPa]\) is \"contact pressure\" [9]. The initial impact conditions at \(t=0\) are as follows: \(\zeta_{n}(0)=0\) and \(\dot{\zeta_{n}}(0)=v_{1}\), where \(v_{1}\) is the reduced speed for the ship \([m/sec]\). At the end of the interaction at \(t=t_{max}\) the following conditions apply: \(\zeta_{n}(t_{max})=\zeta_{max}\) and \(\dot{\zeta_{n}}(0)=0\).  
The ship-ice contact area depends on the hull form, assumed shape of the ice edge and the assumed indentation of the ship into the ice.  
\[A=G\zeta_{n}^{a},\big{[}m^{2}\big{]} \tag{2}\]  
where \(G\) is a coefficient depending on the geometric parameters of the ship and the ice, and \(a\) is an exponent depending on the assumed shape of the ice edge at the point of impact [9]. This means that the relationship between the normal indentation and the nominal contact area can be determined for any contact geometry.
## 3 Engineering Applications
### Rock Mechanics Parameters  
Determining the macroscopic mechanical parameters of the rock mass is crucial before performing numerical simulations. Therefore, the collected rock samples were processed into standard specimens, and their quasi-static properties were measured using the ROCK-MAN 207 Hard Rock Triaxial Testing Machine at the Deep Metal Mine Laboratory of Northeast University, China. The obtained parameters for the rock specimens include density, elastic modulus, Poisson's ratio, internal friction angle, cohesion, and tensile strength (Table 5).
## 3 Datasets
### Gridded Precipitation Products (GPPs)
#### 3.1.2 Climate Hazard Group Infrared Precipitation with Stations (CHIRPS)  
The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) provides quasi-global (50\({}^{\circ}\)S-50\({}^{\circ}\)N) daily precipitation estimates at a 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) latitude/longitude spatial resolution [70]. The CHIRPS algorithm integrates three different sources of data, including (1) the Climate Hazards group Precipitation climatology (CHPLclim), (2) thermal infrared-based precipitation estimates from geostationary satellite observations, and (3) in situ rainfall observations generally from the Global Historical Climate Network (GHCN; [71,72]), the Global Summary of the Day (GSD), and Global Telecommunication System (GTS) datasets. With a latency of about three weeks, the final CHIRPS precipitation values are initially calculated at pentad and then converted to daily and monthly scales [70,73]. Further and detailed information about CHIRPS precipitation products can be found in [PERSON] et al. [70]. In this study, CHIRPS precipitation data from 2000 to the end of 2020 were employed.  
1.3 Precipitation Estimation from Remotely-Sensed Information Using Artificial Neural Networks (PERSIANN)  
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN)--the Climate Data Record (CDR) is a version of PERSIANN GPPs that provides daily quasi-global (60\({}^{\circ}\)S-60\({}^{\circ}\)N) observations at a 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution [16]. Hourly precipitation values are initially estimated by applying the PERSIANN algorithm [74] to high spatial and temporal cloud infrared images of the GridSat-B1, compiled by the International Satellite Cloud Climatology Project (ISCCP; [75]). Afterward, the hourly estimates are accumulated to generate monthly precipitation estimates at a 2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) spatial resolution and then are enhanced through bias adjustment using the monthly Global Precipitation Climatology Project (GPCP) data. Finally, the bias-adjusted values are downscaled and converted to three hourly precipitation estimates. Additional information about the PERSIANN series and also PERSIANN-CDR can be found in [PERSON] et al. [16]. In this study, PERSIANN-CDR (hereafter PERSIANN) precipitation data from 2000 to the end of 2020 were utilized for evaluation.
## 4 Theoretical Calculation and Analysis
### Finite Element Calculation Results and Analysis  
#### 4.3.1 Comparative Analysis of Bearing Capacity  
Using specimens BD4-40-1, BD4-40-2, BD4-40-3, and PD4-40-3 as examples, the calculated and measured values of the ultimate load of each specimen are shown in Table 6. \(F_{\text{u}}^{\text{c}}\) is the calculated value of the ultimate load in the table, and \(F_{\text{u}}^{\text{e}}\) is the measured value of ultimate load. \(\Delta F\) is the relative error between the calculated value of ultimate load and the measured value.  
From Table 5, the relative error between the calculated value of the limit load and measured value was between 3.69% and 9.91%, and the calculated results were slightly larger than the test results. Generally, the calculated results were in good agreement with the measured results.
## 3 Prototype Design
### Hardware Components
For our purposes, the software-defined radio (SDR) is the preferred technology over other solutions like FPGA or ASIC-based platforms, thanks to its flexibility, re-configurability and reduced development time in the prototype integration. The DSP stage was required to support a memory of at least some tens of GBytes, e.g., in a secure digital (SD) card or an embedded multimedia card (eMMC) card, for fast storage of the raw data produced during a flight mission. A number of I/O USB ports was also necessary to handle digitalized data streams and to give access to the sensor configuration parameters. The chosen platform is the Open-Android (ODROID)-X2 [26]. It is an open development 1.7 GHz ARM Cortex-A9 Quad Core platform with 2 GB RAM memory and PC-like performance. The ODROID-X2 was one of the most powerful boards available on the market at the time the activity began. It provides 2 GB RAM memory and a number of peripherals, like a high-definition multimedia interface (HDMI) monitor connector and six USB ports. It is able to support the input from the two FEs streams, thanks to the real-time management of two USB ports and the fast memory storage. This board hosts an Ubuntu Linaro Operative System (OS) distribution, booting from a 64 GB eMMC. In the current version, the sensor serves as data grabber: data are received, sampled and stored in the memory. Further developments will address the implementation of some more advanced processing directly on-board. As indicated in Figure 2, a power supply of \(5\) V is employed for the ODROID-X2 and all of the antennas, while \(12\) V is used for the FEs in order to guarantee a proper functioning of the device, in particular the stability of the master clock. The bias-tees (BTs) allow a stable power supply to the antennas, while decoupling the DC from the RF signal entering the FE. A summary of the fundamental hardware components of the radar prototype is reported in Table 1.
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.3 Chroma CENS  
The Chroma CENS (Chroma Energy Normalized Statistic) is an enhancement over the traditional chroma feature, aiming to provide a more robust representation of harmonic content in audio signals. The primary distinction of the Chroma CENS is its focus on short-time statistics over energy distributions within chroma bands rather than just the energy itself.  
Mathematically, the Chroma CENS for a given chroma vector \(C\) can be represented as  
\[\text{CENS}(C)=\frac{C-\mu(C)}{\sigma(C)} \tag{2}\]  
where  
* \(C\) is the chroma vector, typically obtained from the Chroma STFT or a similar method.
* \(\mu(C)\) is the mean energy of the chroma vector over a short time window.
* \(\sigma(C)\) is the standard deviation of the energy of the chroma vector over the same window.  
Through normalizing the chroma vector in this manner, the Chroma CENS effectively reduces the influence of dynamics (loudness variations) and timbral characteristics, focusing primarily on the harmonic content. This normalization process ensures that the resulting feature is less sensitive to variations in dynamics, recording conditions, or specific instrument timbres, making it particularly robust for tasks like music similarity and retrieval.  
The low temporal resolution of the Chroma CENS is intentional. By averaging out the chroma values over longer time frames, it captures the essence of the harmonic progression while being less affected by short-term fluctuations or transients. This makes the Chroma CENS efficient for extracting stable harmonic features from the audio, providing a summarized yet informative representation of the audio's tonal content.
## 5 Result and Discussion
### Performance Evaluation with Ishara-Lipi Dataset  
Table 9 shows the performance accuracy of the proposed model with the Ishara-Lipi dataset. The proposed system achieved 99.10% accuracy for non-segmented images and 99.60% for the segmented image.  
Table 10 shows the comparison performance accuracy of the proposed model with the state-of-the-art method using the Ishara-Lipi dataset. The result shows that the proposed model achieved 99.60% accuracy,which is better than the state-of-the-art model. In [13], the authors used four convolution layers, two max-pooling layers, one dense layer and one dropout layer. Also, an ADM optimizer was used with a learning rate of 0.001 and achieved 94.74% accuracy.  
The authors in [30], they proposed two-step classifiers for classifying the BSL dataset. As the first step they used 2 phase classifiers: Normalised NOBV and WGV. Initially, they tried to apply NOBV to classify the hand sign, but if the classification score does not satisfy the threshold value, they applied a WGV classifier. They also used the CNN-based Bengali Language Modeling (BLMA) algorithm, achieving 95.83% accuracy.  
Figure 11: Class wise Performance Accuracy of the Proposed Model with KU-BdSL Dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Name** & **Gesture** & **Sample** & **Segmen Tation** & **Pixel** & **Model** & **Vectorize** & **Accuracy (\%)** \\ \hline [PERSON] et al. [64] & 38 & 7600 & Yes & 128 \(\times\) 128 & CNN & FC \({}^{a}\) & 90.63 \\ \hline [PERSON] et al. [28] & 10 & 100 & No & N/A & R-CNN & FC & 98.20 \\ \hline Proposed model & 31 & 3000 & yes & 124 \(\times\) 124 & Ben SignNet & GAP \({}^{b}\) & **99.60** \\ \hline \hline \end{tabular} \({}^{a}\) FC- Fully connected layer; \({}^{b}\) GAP-Global average pooling layer.  
\end{table}
Table 8: State of the Art Comparison for KU-BdSL Dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model Name** & **Segmented** & **Test Set** \\ \hline Ishara-Lipi & CNN & No & 99.10 \\ \hline Ishara-Lipi & CNN & Yes & 99.60 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance Accuracy the Proposed Model with Ishara-Lipi Dataset.  
In [31], authors proposed a CNN for recognizing BSL 36 alphabets based on the Ishara-Lipi dataset, and they achieved 99.22% accuracy. They firstly augmented the dataset then produced 1000 images for each class and 36,000 images for 36 classes. Then they applied CNN with six convolutional layers, three pooling layers, fully connected layers. They employed translation scaling variance in the pooling layer to decrease the feature volume. Figure 12 shows the class-wise classification accuracy comparison between the proposed and state-of-the-art methods. Which shows, the proposed system produced more than 96% accuracy for all classes excluded two classes
## 2 Materials and Methods
### Quantifying ES Demand  
The ES demand represents the human demand and preference for ecosystem products and services within a specific time period. In this study, we used a research method [26; 65] to quantify ESD by considering land development intensity, population density, and gross domestic product (GDP) per area. This helped us to understand the coordination between the development and preservation of essential natural processes that sustain human well-being and environmental quality [29; 66; 67]. Specifically, land development intensity was measured as the percentage of construction land in the total land area. It reflects the intensity of the human consumption of ES. A higher percentage of construction land indicates a greater intensity of human land development in a given area, and consequently, a higher demand for ES. Population density serves as an indicator of the amount of ES demand. A higher population density corresponds to a greater ESD. GDP per area reflects the economic development of the region and indirectly indicates how much humans wish to consume or utilize ESs. Logarithmic methods were employed to remove fluctuations in the data. The ESD index is calculated using the following formula:  
\[\mathrm{ESDI_{i}}=\mathrm{D_{i}}\times\mathrm{lg(P_{i})}\times\mathrm{lg(G_{i})} \tag{3}\]  
where \(\mathrm{ESDI_{i}}\) is the ES demand index of each county i; Di, Pi, and Gi are the land development intensity (%), population density (person/km\({}^{2}\)), and GDP per area (yuan/km\({}^{2}\)) of county i, respectively.
## 4 Performance Analysis
### Simulation Results
#### 4.3.4 Energy Standard Deviation  
We used the energy standard deviation to evaluate the balanced consumption of energy. More specifically, an energy-efficient routing algorithm should maximise the average remaining energy levels of nodes, but with a small standard deviation to prolong the network lifetime.  
In Figure 5, FSACO demonstrates the significant performance improvement by 70.3%, 61.3% and 52.9% over EEABR, IACO and SensorAnt in terms of energy standard deviation, respectively. This result was due to the introduction of the crowd factor in the route discovery phase, which avoided the premature stagnation phenomenon in EEABR, IACO and SensorAnt. As a result, the proposed hybrid routing algorithm in Section 3.4 helped to balance the consumption of energy in the network and avoid the rapid deterioration of the optimal path.
## 2 Materials and Methods
### Data Recordings  
Two systems for kinematic analysis were employed: the gold standard optoelectronic system (Opto) and an IMU system, specifically for predicting variables related to RNLE tasks. The optoelectronic system (SMART-DX 6000 System, BTS, Milan, Italy) comprises 10 infrared cameras that, acquiring data at a frequency of 340 Hz, track the movements of markers positioned on the subject's body, and it was used to detect the motion of 6 spherical markers (15 mm in diameter) covered with reflective aluminum powder material placedbilaterally over the cutaneous projections of the spinous processes of the radial processes, the head of the third metacarpal bone, and the malleoli [23; 24].  
The Xsens MVN Link system (Xsens, Enschede, The Netherlands), which operates at a sampling rate of 60 Hz, was also used to record the whole-body kinematics of participants. The MVN motion analysis system includes a protocol for whole-body kinematic measurement, employing 17 IMUs positioned across various body segments [18]. These segments include the bilateral scapulae, upper arms, forearms, hands, thighs, shanks, and feet; one on the head; one on the sternum; and one on the pelvis (at the L5/S1 level). Xsens MVN whole-body Lycra suits in sizes ranging from M to XXL were utilized to ensure accurate sensor placement. Synchronization of the systems during the concurrent collection of data was achieved using a trigger signal generated by a synching device (BTS Trigger Box, BTS, Milan, Italy).
## 2 Materials and Methods
### Methods
#### 2.3.1 Deep Learning Model  
Figure 1 shows a schematic flowchart of the CBH retrieval algorithm. First, the collocated and preprocessed AHI-MODIS subsets from 2016 and 2018 are fed into a CNN model for training and validation, with the infrared brightness temperature (BT) and SAZ derived from AHI as inputs and CTH and CWP derived from Terra MODIS as labels, which is shown in Figure 1a,b. The CNN model, composed of an encoding-decoding architecture, has shown powerful ability in the cloud remote sensing community [10]. In the encoding part, convolutional filters extract key features from the input data. In the decoding stage, features from the shallow and deep layers are integrated by skip connections, and the final output is generated by a 1 \(\times\) 1 convolutional layer. The number of features in the encoder path progresses through 32, 64, 128, and finally 256 at the bottleneck, while the decoder path mirrors this structure symmetrically, with the channel count progressively halving from 256 back down to 32. In order to find an optimal retrieval model, we determined the final values through an iterative tuning process, where different configurations were evaluated based on the performance of the validation set, including loss function (e.g., mean square error, mean absolute error), learning rate (e.g., from 0.0001 to 0.001), activation function (e.g., ReLU, LeakyReLU, and sigmoid), and convolutional kernel size (e.g., 3, 5, and 7). Specifically, the final model employs the mean square error as a loss function, a learning rate of 0.001, the LeakyReLU activation function, and a convolutional kernel size of 3 \(\times\) 3. Additionally, to mitigate overfitting, we employed several regularization techniques. Specifically, we incorporated dropout layers and L2 regularization with a weight decay of 0.001. Furthermore, we utilized an early-stopping strategy, which terminated the training process when the validation loss failed to improve for 14 consecutive epochs [29]. Various optimizers were evaluated, with the adaptive moment estimation (Adam) optimizer selected because of its superior performance [30]. Finally, we obtained  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Count** & **Day/Nighttime** & **Period** \\ \hline Cloud pixel dataset & 143,019 and 40,320 & Daytime & 2017 \& 2022 \\ (CloudSat-CALIPSO) & 1771 & Daytime and Nighttime & 14–31 March 2025 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._an optimized model for retrieving cloud optical and microphysical properties (i.e., CTH and CWP) from AHI infrared observations.