esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 4 Results
### Evaluation Indicators  
In this paper, we use accuracy (Accuracy) as the main evaluation metric to verify the merits of the newly proposed BGF model against the benchmark model. Accuracy is the proportion of correctly predicted classifications by the model in the whole dataset, which is a simple and intuitive metric that reflects the overall performance of the model on the test set. By training and testing the model on different datasets, we can verify the effectiveness of the BGF model in text categorization tasks.  
Accuracy is calculated as the number of samples that the model predicts to be categorized correctly divided by the number of all samples in the corpus. In text categorization tasks, high accuracy of the model is usually pursued by improving the model so that it can accurately assign different texts to the correct categories. The results of the text categorization model trained on the text corpus datasets and the post-prediction statistics on the datasets are shown in Table 4.  
The formula for the accuracy of the dataset samples in category C after training the classification model is shown below.  
\[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{14}\]  
The specific computational steps are as follows:  
1. Data preparation: Initially, separate the training set and test set from the dataset. The training set is utilized to train the model, while the test set evaluates the model's performance;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Predicted Value**} & \multicolumn{2}{c}{**Predicted Value**} \\ \cline{2-4}  & **Real Value** & **Positive P** & **Negative N** \\ \hline Real Value & Positive P & TP & FN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Neighborhood table of classification results.  
Model training: Train the model using the training set to enable it to learn the features necessary for text classification;  
Model prediction: Apply the trained model to predict categories for the test set, generating predicted categories for each sample;  
Result analysis: Calculate the number of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) from the prediction results;  
Calculate accuracy: Based on the statistical results, compute the accuracy of the model on the test set using the following formula.  
In a multi-category classification task, accuracy is calculated similarly to binary classification. For instance, if there are three categories (A, B, and C), and TP, TN, FP, and FN are determined for each category, the overall accuracy for multi-category categorization is the average of the accuracies of all the categories as shown in the following equation:  
\[\text{Multicategory Accuracy}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\frac{ \text{TP}_{i}+\text{TN}_{i}}{\text{TP}_{i}+\text{TN}_{i}+\text{FP}_{i}+\text {FN}_{i}} \tag{15}\]  
where N is the total number of categories.
## 4 Ongoing Trends
### Resource Availability
#### 4.2.2 Natural Gas  
According to 2011 statistics, Malaysia had proven natural gas reserves of 46.8 Tcf, which are the 5 th largest natural gas reserves in the Asia-Pacific area ranking after Australia, China, Indonesia and India, and 21 st in the world [25]. The natural gas reserves might actually be greater than that value, as the data provided by Malaysia Energy Commission indicated the amount was 90.0 Tcf [30]. In 2011, the reserves and productions ratio of natural gas was 34 years. The production trend of natural gas [30] is shown in Figure 22.  
LNG was the main form for natural gas exports in Malaysia in 2011. In 2011, 97% of natural gas was exported through liquefaction, whereas the remainder was transferred via pipelines. The ongoing LNG export trends [30] is given in Figure 23. In 2011, Malaysia was the third-largest LNG exporter, ranking only behind Qatar and Indonesia. In 2011, 36.0 Mtoe natural gas was liquefied. Most of this LNG was exported to Japan, South Korea, Taiwan and China, which contributed 61%, 21%, 12% and 5.5% of LNG export volume [26].  
Figure 21: Density and sulfur content of selected crude oils.  
Figure 22: Natural gas production in Malaysia.  
Although Malaysia is a natural gas exporter, it also imports natural gas (mainly as LNG) for commercial and strategic purposes. Malaysia currently experiences a geographic disparity whereby the the power and industrial sectors are in Peninsular Malaysia, while the gas supply is in East Malaysia which is separated by the South China Sea. In order to meet the needs in Peninsular Malaysia, various regasification terminals are going to set up to secure supply from the global market [31].
## 8 Reliability Analysis
### Packet Loss Retransmission  
If there is a data stream communication between the two communicating parties through the MHC method, the data stream \(P=p_{1},p_{2},\ldots,p_{n}\) sent by A to B, each packet is \(p_{i}=(m_{i},seq_{i},HC_{i})\), where \(m_{i}=(hdr_{i},payload_{i})\). The message hash chain constructed by the sender is \(HC\), and the chain constructed by the receiver is \(HC^{\prime}\). Under the condition that the network has the possibility of packet loss, the following two situations must occur:  
1. At least, there is a possibility that it is greater than \(\varepsilon/2\), and the \(P\) received by B arrives in order, then the message hash chain \(HC^{\prime}_{i}=h(h(m_{i}||HC^{\prime}_{i-1}))\) constructed by B through \(P\) satisfies \(HC=HC^{\prime}\).
2. At least, there is a possibility that it is greater than \(\varepsilon/2\), and the data stream received by B may arrive out of sequence or lose packets. Assume that at a certain time \(t_{0}\), the sequence number corresponding to the sender's tail node is \(seq_{j}\), and the sequence number corresponding to the tail node used by the receiver for verification is \(seq_{k}\), \(k<j\). At this time, if the sender sends a new message \(p_{\delta}\) to reach B, and its corresponding sequence number \(seq_{\delta}>seq_{k}+1\), then set the message retention time \(t_{s}\) for \(p_{\delta}\). Subsequently, at time \(t_{1}\), where \(t_{0}<t_{1}<t_{0}+t_{s}\), if the message hash chain of the \(p_{\delta}\) has not been successfully verified, the \(p_{\delta}\) will be discarded, and the sender will request the following message corresponding to the sequence number of the current tail node of the chain. In contrast, if the chain of the \(p_{\delta}\) can be successfully verified and the corresponding message hash chain is constructed at the receiver, the verification of the message corresponding to the last sequence number is continued.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
## 2 Vibration Experiments with a Vibro-Ripper
### Striking Force Measurement  
To assess the impact loads transmitted to the housing of the vibro-ripper through the link modules, we need experimental data on the shank and tooth striking force, which can be used to validate the TPA calculation results. Therefore, a load measurement method using a load cell was adopted in deriving the impact load generated from the shank and tooth.  
A test device including a load cell was constructed as shown in Figure 4b to measure the striking force of the vibro-ripper. A schematic diagram of the in situ test device is shown in Figure 4b. Test conditions for measurements of the impact load of the vibro-ripper and the main specifications of the load cell sensor are summarized in Table 3. Results  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Specifications** & **Values** \\ \hline BR-55 & Applicable excavator & 42–52 ton \\  & Frequency & Max. 28 Hz \\  & Working pressure & 250 kg/cm\({}^{2}\) \\  & Oil flow rate & 290–310 lpm \\  & Weight & 5200 kg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the vibro-ripper.  
Figure 5: Measured impact vibration signal from the operating vibro-ripper.  
of measurements of the impact load of the vibrating ripper are shown in Figure 6. The maximum, minimum, and average values were 260, 180, and 220 kN, respectively.
## 2 Method
### LEO Satellite Clock Prediction in Ultra-Short-Term  
As described in [19; 37], different LEO satellite clock prediction algorithms do not lead to significant differences in ultra-short-term prediction, i.e., a few minutes or shorter. In this contribution, the LEO satellite clocks are predicted with periodic terms and polynomials, expressed as [18; 19]:  
\[\hat{C}lk\big{(}t_{p}-t_{0}\big{)}=\hat{a}_{0}+\hat{a}_{1}\big{(}t_{p}-t_{0} \big{)}+\cdots+\hat{a}_{m}(t_{p}-t_{0})^{m}+\sum_{j=1}^{k}\hat{A}_{j}\text{sin} \Bigg{(}\frac{2\pi}{\hat{T}_{j}}\big{(}t_{p}-t_{0}\big{)}+\hat{\phi}_{j}\Bigg{)} \tag{9}\]  
where \(\hat{C}lk\) denotes the predicted clocks, \(t_{p}\) denotes prediction epoch, \(t_{0}\) is the initial epoch, \(\hat{a}_{i}(i=1,\ldots,m)\) is the polynomial fitting coefficient, \(k\) denotes the number of periodic terms, \(\hat{T}_{j}\), \(\hat{A}_{j}\) and \(\hat{\phi}_{j}\) denotes the period, the amplitude and the phase of the periodic terms, respectively. The determination of the coefficients can be referred to [19].
## 1 Introduction
The FrFT parameters are considered as the additional keys for encryption achieving reliable cybersecurity for robust image communication. In [49], multiple fractional order chaotic systems are used in the proposed color image encrypting technique, since using multiple fractional order for image encryption considerably increases the key space and the key sensitivity. A generalization of the FrFT is the multi-parameter fractional Fourier transform (MP-FrFT). Due to the widespread use of MPFrFT in both cryptosystems [50; 51; 52; 53; 54; 55], more and more academics are becoming interested in it. The authors in [56] introduce the MP-WFRFT and chaotic scrambling-assisted directional modulation technology for improving physical layer security. To realize the power-efficient and security-enhanced wireless transmissions, the directional modulation (DM) technology with multiple parameters weighted-type fractional Fourier transform (MP-WFRFT) and chaotic scrambling (CS) was developed in [56]. In 2023, a new physical layer authentication in wireless networks-based machine learning approaches is introduced in [57]. The purpose of the work given in [57] is to identify and thoroughly compare prior research on physical layer authentication. In addition to demonstrating the most recent PLA techniques, this study examined whether machine learning techniques improved wireless network security performance in physical layer authentication models. Additionally, it pointed out problems and offered lines of inquiry for further study. Researchers and security model creators interested in employing machine learning techniques, such as the _Fron  
learning (ML) and deep learning (DL) methodologies for PLA in wireless communication systems in future research and designs will find this work to be useful. In addition, an application of machine learning techniques in medical data processing based on distributed computing and the IoT is suggested in [58]. Also, in [59], the CNN learning and offloading is used as a hybrid approach for latency and battery lifetime optimization in IoT devices. The main contributions of this research follow:  
1. New robust MCC-MF sine map is designed and analyzed. 2. New dynamic chaotic biometric (Digital Fingerprint) signature (DCBS) generator based on the combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. 3. New physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform--Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested. This paper is organized as follows. An introduction is presented in Section 1; Section 2 presents a related preliminary basics. Section 3 presents the proposed MCC-MF sine map, Section 4 presents the proposed DCBS-MP-FrFT-OFDM cryptosystem. Section 4 presents the performance analysis and simulation results discussions of the proposed DCBS-MP-FrFT-OFDM cryptosystem. The following section is the comparison results analysis. Finally, the conclusions and future works are drawn.
# Cephalometrics in Obstructive Sleep Apnea Patients with Mixed Dentition
## 3 Results  
### Cephalometric Parameters  
Table 2 shows the cephalometric analysis of 39 linear and angular variables. All parameters were statistically significant (\(p<0.001\)).  
\begin{table}
\begin{tabular}{l c c c} \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{
\begin{tabular}{c} **Mean \(\pm\) Standard** \\ **Deviation** \\ \end{tabular} } & \multicolumn{2}{c}{**One-Sample \(t\)-Test**} \\ \cline{3-4}  & & \(t\) & **Significance (2-Sided)** \\ \hline SNA & \(87.5\pm 1.4\) & 382.480 & 0.000 \\ \hline SNB & \(74.07\pm 2.07\) & 223.717 & 0.000 \\ \hline SNPg & \(78.6\pm 4.07\) & 120.796 & 0.000 \\ \hline ANB & \(9.2\pm 1.7\) & 33.953 & 0.000 \\ \hline MnPl & \(58.7\pm 5.8\) & 62.869 & 0.000 \\ \hline MxPl & \(49.3\pm 4.5\) & 68.303 & 0.000 \\ \hline MnPLSN & \(41.4\pm 2.2\) & 118.866 & 0.000 \\ \hline SNPP & \(15.6\pm 3.2\) & 30.613 & 0.000 \\ \hline MxPlMnPl & \(25.9\pm 6.3\) & 25.519 & 0.000 \\ \hline SGoNMe & \(66.3\pm 4.5\) & 92.257 & 0.000 \\ \hline ANSMe & \(56.1\pm 4.1\) & 84.838 & 0.000 \\ \hline ArGoGn & \(129.2\pm 7.6\) & 106.207 & 0.000 \\ \hline BaSN & \(137.05\pm 1.6\) & 550.002 & 0.000 \\ \hline U1 NAmm & \(3.1\pm 3.99\) & 4.837 & 0.000 \\ \hline L1 NBmm & \(4.06\pm 3.2\) & 8.020 & 0.000 \\ \hline U1 NAdegree & \(18.2\pm 7.5\) & 15.172 & 0.000 \\ \hline L1 NBdegree & \(21.7\pm 7.3\) & 18.673 & 0.000 \\ \hline Iternincisial & \(133.4\pm 10.6\) & 78.703 & 0.000 \\ \hline Overjet & \(3.9\pm 1.8\) & 13.343 & 0.000 \\ \hline overbite & \(2.5\pm 1.7\) & 9.298 & 0.000 \\ \hline MSPT & \(8.3\pm 1.9\) & 26.876 & 0.000 \\ \hline ANSPNSSPT & \(132.2\pm 8.9\) & 93.244 & 0.000 \\ \hline PNSSPT & \(28.3\pm 2.7\) & 65.573 & 0.000 \\ \hline SPTSPPp & \(28.5\pm 2.3\) & 77.559 & 0.000 \\ \hline BaSPNS & \(54.6\pm 4.9\) & 69.775 & 0.000 \\ \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of cephalometric analysis.  
According to the data obtained, the SNA parameter, which characterizes the position of the upper jaw relative to the anterior cranial base, was 87.5\({}^{\circ}\)\(\pm\) 0.23; the ANB angle, on average, and was in the range of 9.2\({}^{\circ}\)\(\pm\) 0.27. The SNB was 74.1\({}^{\circ}\)\(\pm\) 0.33. The MnPl value was 1.24 times less and amounted to 58.65 \(\pm\) 0.93, while the MxPl value was practically within the normal range -49.26 \(\pm\) 0.72. The SNPP angle (palatal plane) exceeded the norm twice and when averaged was 15.59\({}^{\circ}\)\(\pm\) 0.51. The angle of the lower jaw relative to the anterior cranial base (MnPLSN) was 41.4\({}^{\circ}\)\(\pm\) 0.35, exceeding the norm by 1.3 times.  
The L1 Nb angle averaged 21.7\({}^{\circ}\)\(\pm\) 1.16 and was indicative of Angle's class II subclass 2. The U1 NA indicator was calculated in mm and in degrees: in mm the indicator was within the minimum normal range and amounted to 3.09 mm \(\pm\) 0.64; when calculated in degrees it was less than the norm -18.22\({}^{\circ}\)\(\pm\) 1.2.
## 5 Platform Testing and Validation
### CLAP Instance
#### 5.3.3 Sensor 3: Network Traffic Sensor and Analyzer (NTSA)  
The NTSA (IP address 192.168.66.181) uses unsupervised algorithms to create a model of the normal behavior of the system e.g., by modeling the number of packets transferred during a given period of time, the volume of packets sent/received, the IP sources/destinations used in the communications, the port sources/destinations required for communications, the protocols used, etc., therefore, any traffic data falling outside the model will be considered to be suspicious, and the tool will alert the systems accordingly.  
NTSA is also a sensor of the CLAP for cyber security data of the network traffic. The main output of this tool is a list of Warnings and/or Errors that indicate unknown detected IP, or any kind of abnormal observation, as well as detected IP that has not been modeled. More details about the NTSA suite can be found in [40,41].
# Chinese Cabbage Changes Its Release of Volatiles to Defend against _Spodoptera litura_
## 3 Results
### Quantities of Total and Six Classes of Volatiles Released  
The quantity of volatiles collected and identified was highest for plants infested with _S. litura_ for 48 h was lowest for undamaged plants, mechanically damaged plants, and regurgitate-treated plants; and was intermediate for plants infested with _S. litura_ for 12 h and 24 h (\(p<0.05\); Figure 3).  
Figure 1: Percentage of _M. similis_ females attracted to treatments in a four-arm olfactometer. Preference of _M. similis_ for (**a**) Mechanically damaged vs. healthy plants and blanks (_M. similis_ olfactometer experiment (1); (**b**) RTP-treated plants vs. healthy plants and blanks (_M. similis_ olfactometer experiment (2); (**c**) Plants infested with _S. litura_ for 12 h vs. healthy plants and blanks (_M. similis_ olfactometer experiment (3), and (**d**) Mechanically damaged, RTP-treated, _S. litura_-infested, and healthy plants (_M. similis_ olfactometer experiment (4). Blank: empty odor source (no plants); HP: healthy plants; MDP: mechanically damaged plants; RTP: “regurgitate”-treated plants; SIP: _S. litura_-infested plants. Values are means + SE. Bars labeled with different letters are significantly different by [PERSON]’s HSD test (\(p<0.05\); one-way ANOVA, \(\alpha=0.05\)).  
Figure 3: Total quantities of volatile organic compounds (VOCs) released by Chinese cabbage plants in response to six treatments. Total quantities of six classes of volatiles released by Chinese cabbage plants in response to six treatments. HP: healthy plants; MDP: mechanically damaged plants; RTP: regurgitate-treated plants; SIP: plants infested with _S. litura_ for 12, 24, or 48 h. Values are means + SE. Bars labeled with different letters are significantly different by [PERSON]’s HSD test (\(p<0.05\); one-way ANOVA, \(\alpha=0.05\)).  
Figure 2: Percentage of _S. litura_ attracted to no plants (blank), healthy Chinese cabbage plants (HP), plants infested with _S. litura_ (SIP; for 12, 24, or 48 h) in _S. litura_ Petri dish experiments 1 (**a**) and 2 (**b**) and in _S. litura_ olfactometer experiments 1 (**c**) and 2 (**d**). *, ***, and ns indicate \(p<0.05\), \(<\)0.01, \(<\)0.001, and not significant, respectively, according to a \(\chi^{2}\) test.  
The volatiles were assigned to the following six main classes of HIPVs in Brassicaceae [31]: green leaf volatiles, ester, aldehydes, terpenoids, ketones, and alkanes (Figure 3). The release of esters, aldehydes, and ketones by Chinese cabbage did not significantly differ between healthy plants vs. mechanically damaged plants or plants infested with _S. litura_ for 12 h. There was no significant difference in the release of any class of volatiles between healthy and mechanically damaged plants. The regurgitate treatment increased the release of alkanes but not of other volatiles. The emission of terpenoids, ketones, and alkanes tended to increase, but the emission of green leaf volatiles decreased with the increase in _S. litura_ infestation time.
## 2 Materials and Methods
### Analysis
#### 2.4.4 GC/FID Analysis of Major Volatile Compounds  
To analyse acetaldehyde, ethyl acetate, methanol, and major higher alcohols, gas chromatography with flame ionization detection (GC/FID) was employed after direct injection. A Nexis GC-2030 GC system equipped with an AOC-20 autosampler (Shimadzu, Kyoto, Japan) was fitted with an Rtx-WAX capillary column (60 m \(\times\) 0.25 mm internal diameter \(\times\) 0.25 um film thickness) from Restek (Bellefonte, PA, USA). A split ratio of 1:20 was used, and prior to quantification via calibration curves (\(n=3\), \(r^{2}>0.995\) for all the compounds), 1-pentanol was added as an internal standard for normalization.
###### Abstract  
As a core task in remote sensing image processing, change detection plays a vital role in dynamic surface monitoring for environmental management, urban planning, and agricultural supervision. However, existing methods often suffer from missed detection of small targets and pseudo-change interference, stemming from insufficient modeling of multi-scale feature coupling and spatio-temporal differences due to factors such as background complexity and appearance variations. To this end, we propose a Differential-Perception-Enhanced Multi-Scale Attention Network for Remote Sensing Image Change Detection (MDNet), an optimized framework integrating multi-scale feature extraction, cross-scale aggregation, difference enhancement, and context modeling. Through the parallel collaborative mechanism of the designed Multi-Scale Feature Extraction Module (EMF) and Cross-Scale Adjacent Semantic Information Aggregation Module (CASAM), multi-scale semantic learning is strengthened, enabling fine-grained modeling of change targets of different sizes and improving small-target-detection capability. Meanwhile, the Differential-Perception-Enhanced Module (DPEM) and Transformer structure are introduced for global-local coupled modeling of spatio-temporal differences. They enhance spectral-structural differences to form discriminative features, use self-attention to capture long-range dependencies, and construct multi-level features from local differences to global associations, significantly suppressing pseudo-change interference. Experimental results show that, on three public datasets (LEVIR-CD, WHU-CD, and CLCD), the proposed model exhibits superior detection performance and robustness in terms of quantitative metrics and qualitative analysis compared with existing advanced methods.  
change detection (CD); multi-scale feature aggregation; differential enhancement; remote sensing images; convolutional neural network (CNN)