esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 2 Distributed Decomposition Model
### Autoregressive-Integrated Moving Average (ARIMA) Model: Prediction Model for Trend Component  
In this paper, the ARIMA \((p,d,q)\) model was used and applied to model the trend component \(T_{t}\), by which the future value of \(T_{t}\) is to be predicted based on the linear modeling function of the past trend value. As a consequence, the time-series data that are fed to ARIMA should be linear and stationary. It returns the dependent variable only to its lag value and the present and lag values of the random error term [33]. In terms of modeling, it is mainly divided into four steps as follows:  
Step 1: Import the raw data sequence to be predicted;  
Step 2: Use the augmented Dickey-Fuller (ADF) unit root test to determine whether the sequence is stable or not. If it is not stable, use differential tools to make it smooth, and record the difference order as \"\(d\)\"; if the sequence is stable, then d is set as \"1\".  
Step 3: Obtain the value of \(p\) and of the ARIMA \((p,d,q)\) model by the graphical properties of the autocorrelation function and the partial autocorrelation function after differencing was analyzed for preliminary determination. In order to select the best fitting model, the order of the model was determined according to the minimum Akaike information criterion (AIC), and the calculation formula of AIC is as follows [34]:  
\[AIC=2m-2\log(L) \tag{6}\]  
where \(m\) is the number of parameters in the model, and is the sum of \(p\) and \(q\), and \(L\) is the maximum value of likelihood function for the ARIMA \((p,d,q)\) model.  
The AIC is a measure of the likelihood and the number of parameters. In theory, the order \((p,q)\) generally does not exceed one tenth of the data length. In this study, the order was set within (0, 10) first, and then the value of AIC was sequentially calculated by traversing to form an AIC matrix. The input to the model is the training dataset, that is, the first 75% of the trend component. Finally, find the order \((p,q)\) corresponding to the minimum AIC value from the AIC matrix.  
Step 4: Build the ARIMA \((p,d,q)\) model with the parameter \(\phi_{i}\) and \(\theta_{i}\) for a variable \(X_{t}\) as  
\[\Phi(B)(1-B)^{d}\cdot X_{t}=\Theta(B)\cdot\varepsilon_{t} \tag{7}\]  
where \(\Phi(B)=(1-\sum\limits_{i=1}^{p}\phi_{i}B^{i})\) and \(\Theta(B)=(1-\sum\limits_{i=1}^{q}\theta_{i}B^{i})\) are polynomials in the lag operator \(B\); \(\varepsilon_{t}\) is white noise; \(p\) is the number of autoregressive terms; \(q\) is the number of moving average terms; and \(d\) denote the differencing step. In this study, \(X_{t}\) was set as the trend component \(T_{t}\) from the STL decomposition described in Section 2.2.
## 5 Parameter Analysis
### Effect of Distance between Capsule and Pile  
The effect of distance between the pile and the capsule \(d\) on the lateral displacement and bending moment of the pile is presented in Figures 11 and 12. The distance between the pile and the capsule is selected as 1.0 m, 1.5 m, 2.0 m, 2.5 m, and 3.0 m, respectively. Figure 11 depicts that both the lateral displacement and bending moment of the pile decrease with an increase in the distance between the pile and the capsule. For instance,  
Figure 10: Relationship between maximum lateral displacement, bending moment, and square of capsule radius.  
Figure 9: Variation in lateral displacement and bending moment with capsule expansion radius. (**a**) Lateral displacement, (**b**) bending moment.  
when the distance is 1 m, the maximum lateral displacement of the pile is about 6.3 mm, and the maximum bending moment is 711 kN\(\cdot\)m. Conversely, when the distance increases to 3 m, the maximum lateral displacement of the pile is about 2.1 mm, and the maximum bending moment is around 163 kN\(\cdot\)m. Figure 12 demonstrates that the maximum lateral displacement and bending moment for the distance of 1.0 m are 3 times and 4.4 times, respectively, compared to a distance of 3.0 m. Moreover, the bending moment decays at a faster rate compared to the lateral displacement as the distance increases.  
Figure 11: Effect of the distance between the pile and the capsule on lateral displacement and bending moment. (**a**) Lateral displacement, (**b**) bending moment.  
Figure 12: Relationship between maximum lateral displacement, bending moment of the pile, and distance between the pile and the capsule.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
###### Abstract  
This paper introduces a novel weighted fusion methodology for grid-level short-term load forecasting that addresses the critical limitations of direct aggregation methods currently used by regional dispatch centers. Traditional approaches accumulate provincial forecasts without considering regional heterogeneity in load characteristics, data quality, and forecasting capabilities. Our methodology implements a comprehensive evaluation index system that quantifies forecast trustworthiness through three key dimensions: forecast reliability, provincial impact, and forecasting complexity. The core innovation lies in our principal component analysis (PCA)-based weighted aggregation mechanism that dynamically adjusts provincial weights according to their evaluated reliability, further enhancing through time-varying weights that adapt to changing load patterns throughout the day. Experimental validation across three representative seasonal periods (moderate temperature, high temperature, and winter conditions) substantiates that our weighted fusion approach consistently outperforms direct aggregation, achieving a 24.67% improvement in overall MAPE (from 3.09% to 2.33%). Performance gains are particularly significant during critical peak periods, with up to 62.6% error reduction under high-temperature conditions. The methodology verifies remarkable adaptability across different temporal scales, seasonal variations, and regional characteristics, consistently maintaining superior performance from ultra-short-term (1 h) to medium-term (168 h) forecasting horizons. Analysis of provincial weight dynamics reveals intelligent redistribution of weights across seasons, with summer months characterized by Jiangsu dominance (0.30-0.35) shifting to increased Anhui contribution (0.30-0.35) during winter. Our approach provides grid dispatch centers with a computationally efficient solution for enhancing the integration of heterogeneous forecasts from diverse regions, leveraging the complementary strengths of individual provincial systems while supporting safer and more economical power system operations without requiring modifications to existing forecasting infrastructure.  
short-term load forecasting; data fusion; principal component analysis; weighted aggregation; multi-regional integration 2025 15 4820
# A Systematic Literature Review on Military Software Defined Networks
## 4 Identified Significant Research Contributions
### Architectures
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article** & **Application Domain** & **Contribution** & **Scope** & **Layer** & **Security** \\ \hline
[45] & Naval networks & Control systems & Monitoring and control & Physical & Mentioned \\
[15] & Tactical networks & Architectures & Monitoring and control & Control & Yes \\
[46] & Tactical networks & Tools & Basic evaluation & Control & Yes \\
[47] & Military networks & Control systems & Policy based management & Control & Yes \\
[48] & Military networks & Tools & Security & Physical & Yes \\
[49] & Tactical networks & Tools & Monitoring and control & Control & No \\
[50] & Tactical networks & Control systems & Monitoring and control & Control & Mentioned \\
[51] & Tactical networks & Control systems & Monitoring and control & Control & No \\
[52] & Tactical networks & Control systems & Basic evaluation & Control & No \\
[53] & Tactical networks & Tools & Simulations & Application & Yes \\
[54] & Military networks & Services & Design and management & Application & No \\
[55] & Mobile ad hoc networks & Control systems & Monitoring and control & Control & Yes \\
[56] & Naval networks & Control systems & Monitoring and control & Control & No \\
[57] & Coaliton networks & Control systems & Basic evaluation & Control & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Finally, investigating design and management systems, [PERSON] et al. [PERSON] et al. (2018) propose a three-tiered SDN architecture of heterogeneous and highly dense low-power femtocells at the tactical edge, aiming to satisfy the increasing capacity demands while simplifying the management overhead and increasing scalability. The proposed architecture consists of (i) the physical layer, based on dense low-power femtocells, (ii) the control layer which acts as a local controller, and (iii) a management layer which acts as a global controller. Routing and Quality of Service (QoS) decisions are made at the control layer in coordination with the management layer, the latter also being responsible for handovers. Furthermore, the authors focus on mobility and session management, describing in detail the developed sequential processes and required subsystems. The authors use the Mininet simulator in order to validate their results and evaluate the suggested architecture. The results suggest that within the utilized scenario, the proposed three-tier architecture reduces the delay, while increasing the throughput. Furthermore, the performance of the suggested architecture is evaluated in different mobility scenarios, suggesting that in low speeds a smooth flow rate among femtocells is achievable, while in speeds over 30 km/h the use of hotspots or macrocells is required in order to enhance connectivity. The utilized simulation scenarios are aligned with the high level architectural requirements of tactical networks, although limitations in terms of the deployed number of nodes and mobility models are visible.
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 5 Simulation Results and Analysis  
### Experimental Platform and Parameters Setting  
In order to verify the performance of proposed method and explore the relationship between parameters and the MAT, Monte Carlo simulations are utilized for the evaluation. The CNFS positioning signal is transmitted by FSBS positioning signal generator, which is shown in Figure 10a. Different FSBS positioning signal generators realize the synchronization using atomic clock. MATLAB software is used to analyze the detection performance and the acquisition time based on the data collected by signal collector as shown in Figure 10b. The effect of SSR method changes according to the different network topology. According to the analysis in Section 3.1, the number of signals received and the network topology influence the number of PRN code ID in the ATI that the positioning receiver obtained. The network topology, especially the distance between FSBSs, influences the CPPI. In order to give comprehensive evaluation, The PRN code ID and the relative delay of the transmitted signal are adjusted to simulate the actual scenarios.  
The values of parameters are shown in Table 1. Equations (4) and (5) indicates that the tolerance of frequency error is influenced by CIT. Therefore, the frequency search step and CIT are negatively correlated. Because of the existence of data bit transition, the most common choice of CIT is half the data bit duration. The frequency error \(\mathrm{f_{e}}\) is set to the maximum possible, which is the half of frequency search step. The parameters stay unchanged unless there are specific instructions for performance study corresponding to different value of parameters. Match filter method is used here to generate the CAF. As described in Section 2.2.1, different CAF evaluation methods differ in terms of the attenuation of integration and hardware implementation. If the difference in hardware implementation such as the hardware resource consumption is not considered, the impact of different CAF evaluation methods on the performance of the proposed method is reflected in the impact on SNR of processed signal and the block size. Therefore, the proposed method can be analyzed effectively and comprehensively without testing all CAF evaluation methods as long as different SNR and block size are taken into consideration. The methods for comparison also focus on the search space reduction and the improvement of detection strategy. The comparisons are effective as long as the same CAF evaluation method is used in these methods.  
Figure 10: (**a**) FSBS positioning signal generator; (**b**) Signal collector.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 2 Methodology
### Proposed Prediction Framework  
The framework of the proposed prediction algorithm, shown in Figure 3, includes three parts. They are Part A (Data processing), Part B (Hyperparameters optimization), and Part C (Forecasting).  
In Part A, 11 types of meteorological data (feature data in Part A), such as temperature, humidity, air pressure, and wind level, are dimensionally reduced by PCA. The data processed by the PCA algorithm and historical wind speed data together form the input of Part B. Part B uses the hyperparameters selected by DE to obtain a new LSTM model. Part C first divides the processed data in part A into a training set and a test set, and then applies the LSTM model in part B to forecast wind speed.
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### RNA m\({}^{1}\)A Modification in GBM  
N\({}^{1}\)-methyladenosine (m\({}^{1}\)A) is a reversible modification in tRNA, mRNA and lncRNAs, even in mitochondrial transcripts, and is critical for tRNA stability, and translation of mitochondrial DNA-encoded proteins [134]. In nucleus, m\({}^{1}\)A modifications in pre-tRNA and pre-mRNA are catalyzed by tRNA methyltransferase 6/61A (TRMT6-TRMT61A) catalytic complex and are erased by ALKBH1 and ALKBH3, respectively [135; 136]. Whereas in mitochondrion, m\({}^{1}\)A methylation in mt-tRNA and mt-mRNA are installed by tRNA methyltransferase 61 homolog B (TRMT61B) and tRNA methyltransferase 10C, mitochondrial RNase P subunit (TRMT10C) [137]. Recently, some RMPs of m\({}^{1}\)A, both writers and erasers, seem to involve in the progression of GBM (Figure 2, m\({}^{1}\)A part). TRMT6 and TRMT61A expression is significantly upregulated in highly aggressive GBM compared with grade II/III gliomas [79]. TRMT61A is a target of HIF1A and is downregulated after c-Myc inhibition in GBM under hypoxia [138]. The eraser ALKBH1 also seems to be an important regulator in GBM, since targeting ALKBH1 in patient-derived GBM models induces cell proliferation inhibition and extends the survival of tumor-bearing mice. However, this effect may be caused by ALKBH1-induced DNA demethylation on hypoxia response genes, while not ALKBH1-controlled tRNA stability [80].
## 3 Materials
### In Situ Rain Gauge Data  
The in-situ rainfall measurements from a network of tipping bucket rain gauges for the period from 2015 to 2018 have been provided by the Egyptian Meteorological Authority (EMA). The rain gauge data are daily totals. A preliminary analysis of the rain records was carried out in order to select the rainfall events to be included in this study. Bearing in mind the small number of rain gauges installed in the region of El-Qaa Plain in Sinai Peninsula, the precipitation events selected for investigation in the present study are comprised of rainy days for which all the rain gauges have recorded precipitation. On the basis of these in-situ data, the rainiest days in this period were deduced. The large majority of records from this arid region exhibit either no precipitation at all (i.e., 0 mm) or very small precipitation amounts (i.e., 1 mm) at some stations. The preliminary analysis of the data has revealed that, in this four-year period, there was just a single significant event in each year that is worth studying. In general, one major day-long event in each year was recorded by all existing rain gauges. These four significant events were subsequently used to evaluate the performance of the satellite-based datasets discussed above in Sections 3.1 and 3.2.  
The selected events occurred on 25 October 2015, 27 October 2016, 12 April 2017, and 28 April 2018. Based on [PERSON]'s [26] rainfall intensity classifications, these four events were classified as moderate, heavy, light, and light events, respectively.  
In all the events studied herein, light precipitation started at around 7:00 a.m., which was followed by a gradual increase in intensity reaching the peak at 1:00 pm or 2:00 p.m. and then started to decrease gradually until 7:00 p.m. or 8:00 p.m. It is also worth noting  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Product** & **Temporal Resolution** & **Spatial Resolution** & **Time of Coverage** & **Official Image** & **Product Main Data Sources** \\ \hline  & & & & & & Geostationary IR (Infra Red), TMI (TRMM Microwave Imager), TCI (Temperature Condition Index), SSMI (Special Sensor Microwave Imager),AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System), AMSU (Advanced Microwave Sounding Unit), SSMI/S (The Special Sensor Microwave Humager), MTHS (Microwave Humidity Sounder) \\ \hline  & & & & & Geostationary IR, GMI (Global Monitoring Mode Image), GCI (Ground Controlled Interception), TMLSSMI/S, AMSR2 (Advanced Microwave Scanning Radiometer 2), MHS, GPCC (Global Precipitation Climatology Centre) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the differences between TMPA and IMERG [17].  
that the 2015 and 2016 events characterized as moderate and heavy intensity caused destructive flash floods that damaged properties and caused loss of lives.
## 3 Results
### Aspect Extraction
#### 3.1.2 Implications for Real-World Applications  
The importance of this analysis lies in its ability to identify the extent to which the model is reliable when applied in real-world scenarios. In this context, overfitting can cause the model to fail to provide accurate results on diverse and evolving student review data [30]. Therefore, solutions to address this overfitting can include adjusting hyperparameters, using higher dropout techniques, or even increasing the size of the training dataset. These steps will help improve the model's generalizability and make it more ready for application in the dynamic environment of student review analysis.  
From the data listed in Table 12, the results of the aspect extraction trial with the fine-tuning method and hyper-parameter settings in the form of epoch (5), batch size (64), and dropout (0.1) showed an accuracy of 0.973 and an F1-Score of 0.952, which is the best achievement. It has been proven that batch size 64 produces better accuracy than batch size 32. A larger batch size produces higher accuracy and extends computing time [31]. In addition, increasing the epoch value also contributes to longer training time. The aspect extraction test results show that the use of dropout with a lower value produces more optimal performance. This low dropout allows the model to focus more on recognizing patterns without too many units being deactivated [32], which increases accuracy in identifying specific aspects of student reviews. We use the Adam optimizer and adjust the dropout value based on previously used optimization [32]. In addition, a learning rate of 2 \(\times\) 10\({}^{-5}\) was applied to develop ABSA because it can overcome common problems in the BERT model, such as overfitting and underfitting [33]. Fine-tuning with the correct hyper-parameter settings shows how the ABSA training process can be optimized to produce a more accurate and reliable model. With a batch size of 64, the model has more access to data in each iteration, allowing it to capture more comprehensive patterns. However, this setting must be considered with a longer computation time, so an appropriate strategy is needed, mainly if the model will be applied to large-scale data. In addition, using a learning rate of 2 \(\times\) 10\({}^{-5}\) has proven effective in helping the model achieve a more stable convergence rate. This learning rate value reduces the risk of overfitting and allows the model to better adapt to the complexity of the Indonesian language used in student review data. By maintaining a balance between training speed and model accuracy, this optimal learning rate helps ABSA achieve maximum performance. Overall, the selected hyper-parameter settings produce a model that excels in aspect extraction. This shows the importance of experimentation in finding the most appropriate combination of parameters to produce a model ready to be applied in real scenarios. With this optimal setting, the developed ABSA model can provide faster and more precise ABSA, supporting educational institutions in understanding student perceptions more comprehensively and data-oriented.
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 1 Introduction
However, as in the x-axis shown in Figure 1, if yaw motion occurs, the position repeatability of the platform varies depending on the magnitude and direction of the driving force (F) and the bearing friction between the platform and the rails. Since the friction is a result of complex tribological interaction between the balls and the rails subjected to statistical uncertainties [19; 20; 21], platform yaw is most unpredictable. In this case, it is extremely difficult to enhance the position accuracy even with compensation. In this work, a rigid body dynamics model is proposed to analyze the contributions of various factors to platform yaw motion subjected to a single offset driving force. Further, a method for minimizing the yaw motion is presented along with experimental verifications of its effectiveness. This effort led to the development of a low-cost precision stage, satisfy  
Figure 1: Scheme of offset-driven XY stage. ing the platform acceleration of 1 g and the position repeatability of \(\pm\)1.0 \(\upmu\)m required for advanced electrical test equipment. In Section 2, a rigid body dynamics model of the moving platform subjected to an offset driving force is described. In Section 3, contributions from the various factors on platform yaw motion are analyzed. The position error due to platform yaw is predicted and improved by a new XY stage design in Section 4. The improved platform position repeatability with optimized centers of mass and stiffness is verified with a test bed in Section 5.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 4 AIS to Secure the IoT: Literature Review and Analysis
### AIS in Malware Detection in the IoT  
This section highlights the work conducted in malware detection using AIS in the IoT. The original negative selection algorithm uses Binary Encoding to represent self and non-self-datasets; later on, real-valued methods were proposed, and some researchers adopted different types of malware detection techniques such as variable-sized detectors [46], hypercube detectors [47], hyper-ellipsoid detectors [48], and multi-shaped detectors [49]. Deeper investigations have been conducted using a Hypersphere detector because it has simple mathematic calculations compared to the other types. These different data representation methods have not been applied to securing the IoT since they are not sufficiently lightweight to meet the IoT system requirements.