esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The study compared LSTM and 1-D CNN models to see how well they classify sequences. The results showed that each model has strengths and limitations depending on the specific task and data characteristics. LSTM models performed well in tasks where understanding long-term patterns is important, while 1-D CNNs excelled in tasks requiring the identification of important features.  
These findings highlight the importance of choosing the right model based on the task and data. The study provides insights into the strengths and weaknesses of LSTM and 1-D CNN models and emphasizes the need for a tailored approach in model selection and optimization for sequence classification tasks.  
Future research could explore combining LSTM and 1-D CNN models to take advantage of their strengths, leading to more robust solutions for complex classification tasks. Additionally, further research into advanced regularization techniques, data augmentation methods, and hyperparameter tuning might improve model performance and applicability.  
In summary, this research contributes to our understanding of neural network applications in sequence classification and provides a nuanced perspective on selecting and optimizing models for specific classification challenges.  
Conceptualization, J.K.; methodology, J.K. and E.S.; software, J.K. and E.S.; validation, J.K., E.S. and K.S.; formal analysis, J.K.; investigation, J.K., E.S. and K.S.; resources, J.K.; data curation, K.S.; writing--original draft preparation, J.K.; writing--review and editing, J.K., E.S. and K.S.; visualization, E.S.; supervision, J.K.; project administration, J.K. All authors have read and agreed to the published version of the manuscript.
## 4 Parametric Study  
### Design Procedure  
To maximise the benefit from a design, the best design was selected among the available trial designs. An iterative method was performed to assess the response of the unit cell. The length, width, the distance between the SSR, and the size of the slits were changed based on trial-and-error. Figure 2 represents some of the trial designs. Design-1 of Figure 2 contains a single slit square ring with a uniform width of 0.50 mm. This is an SSR with a dimension of 8 \(\times\) 8 mm\({}^{2}\) and a thickness of 0.035 mm with a split gap of 0.40 mm. This SSR demonstrated two major responses at 3 GHz and 10 GHz. Design-2 illustrates a hexagonal loop with a width of 1 mm that was added to Design-1 (Figure 2b). Following the simulation, the resonance frequencies recorded were at 3 GHz, 10 GHz, and 17 GHz. Meanwhile, the modification to Design-2 gave rise to Design-3, which had an addition of two metal strips that were 6 mm long and a split gap of 1 mm. This modified design recorded resonances at 3 GHz and 10 GHz. Finally, the placement of two more metal strips with the same split gap in Design-3 in a horizontal orientation on the hexagon arms was represented as the proposed design (Figure 2d). The proposed design measured three major frequencies at 2.89 GHz, 9.42 GHz, and 15.16 GHz. Figure 3 indicates the transmission coefficient (S21) for the four different designs.  
The proposed design has a substrate with a dimension of 9 \(\times\) 9 \(\times\) 1.6 mm\({}^{3}\); the length and wide of the SSR unit cell metal strips were W1 = 8 mm and W2 = 8 mm, with a split width (g) of 0.20 mm. Moreover, G, the split gap between the placed metal bar strips of the hexagon was measured at 1 mm with every metal strip at 0.5 mm width. The proposed structure demonstrated a maximum number of resonance (frequency bands) with negative permittivity and negative index. The dimensions of the suggested metamaterial unit cell, including all designs tranmission coefficients results values, are listed in Table 2.  
Figure 2: Trial design: (**a**) Design-1, (**b**) Design-2, (**c**) Design-3, (**d**) proposed design.
## 2 Tuning of PDA Controllers for IPDT Plant
### Implementation and Noise Attenuation Low-Pass Filters  
To get proper controllers and at the same time achieve appropriate damping of the measurement noise, the ideal PDA transfer function has to be combined with binomial low-pass filter \(Q_{n}(s)\) with the relative degree \(n\geq 2\)  
\[Q_{n}(s)=Y_{f}(s)/Y_{m}(s)=1/\left(T_{f}s+1\right)^{n}=1/P_{n}(s) \tag{16}\]  
Fixed with already specified \(n\) and \(T_{f}\), \(Q_{n}(s)\) expressing the necessary filtration of the measured output \(y_{m}(t)\) can be taken into account and included into the system's step response [13]. Then, the index \"\(f\)\" of its output \(y_{f}(t)\) can be omitted. This means that the output of the filter is considered directly as the output value of the controlled system.  
If you need to search the filter parameters interactively and adapt the PIDA parameter proposal to the selected filter settings for a system, given by the IPDT model transfer function (2), the additional filter delay can be considered by a chosen delay equivalence [41], when the total loop dead time \(T_{d}\) consists of an estimate of the process delay \(T_{dp}\) (representing the sum of the process, actuator and measurement sensor delays with a communication and computation delay) and an intentionally introduced equivalent filter delay estimate \(T_{e}\)  
\[T_{d}=T_{dp}+T_{e} \tag{17}\]  
approximated as (see, e.g., [15; 41])  
\[T_{e}=nNT_{f} \tag{18}\]  
The coefficient \(N\) can be specified by values ranging from \(N=0.5\) (equivalence based on \"half rule\") to \(N=1\) (equivalence based on \"average residence time\").
## 3 Materials and Methods
### NMR Spectroscopy  
All the NMR experiments were performed at 298 K in DMSO-d\({}_{6}\) on an AVANCE III spectrometer (Bruker Biospin GmbH, Rheinstetten, Germany) operating at 500.13 MHz for \({}^{1}\)H, 125.77 MHz for \({}^{13}\)C and 202.46 MHz for \({}^{31}\)P. The \({}^{13}\)C-NMR were measured with a spectral width of 35,714 Hz, 2 s acquisition and a 10 s relaxation delay with the [PERSON] pulseprogram _zigig_. HSQC experiments used the [PERSON]'s pulse program \"hsqcedetgpsisp2.3 for multiplicity edited with a spectral width of 8012 Hz (from 0-16 ppm) and 30,182 Hz (from 0-220 ppm) for the \({}^{1}\)H- and \({}^{13}\)C-dimensions. A semi quantitative method was used for calculating the amounts of lignin linkages by using the C\({}_{2}\)-H integral as internal standard (IS). A common standard protocol was used for \({}^{31}\)P NMR sample preparation [40]. To a solution of 20 mg lignin in a 0.4 mL mixture of pyridine and CDCl\({}_{3}\) (1.6:1, _v/v_) 0.100 mL of a IS solution (0.12 M) was added. After thorough stirring, 0.1 mL of phosphitylation reagent [2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane (TMDP)] and 0.050 mL of a Cr(acac)\({}_{3}\) solution (11.4 mg/mL) was added and the sample was stirred at room temperature before transferred to a NMR tube. The \({}^{31}\)P NMR measurements were collected with a 2.0 s acquisition time and a 5.0 s relaxation delay. The spectra were calibrated using the signal of the water-derivatized signal at 132.2 ppm. For the MWLs and BLN lignin cyclohexanol was used as internal standard. For the MTBE soluble and IPA fractionated lignin \(N\)-hydroxy-5-norbornene-2,3-dicarboxylic acid imide (e-HNDI) was used to avoid overlapping of impurities with the IS.
## 3 Periodic Signal Packing
As shown in Equation (6), the conditions of the package show that the maximum payload length will be got if only the same repetition signal rates are packaged into the same message. As shown in Equation (7), we can obtain the minimum length of the payload if all the signals are in one frame in each node. \[l^{L}=\max_{1\leq i\leq n}b_{s_{i}} \tag{6}\]  
\[l^{H}=\max_{1\leq i\leq n}\sum_{r_{s_{i}}=1}^{m}b_{s_{i}} \tag{7}\]  
It will be seen that the length of the payload of the frame is in the range of \(\left[l^{L},l^{H}\right]\). Hence, the optimal frame payload length is:  
\[l^{L}\leq l^{best}\leq l^{H} \tag{8}\]  
Therefore, the static segment network utilization can be expressed by Equation (9), where \(K\) is the number of packing messages. \[\forall m_{i,j}\in M,s_{i}\in S\qquad U=\frac{\sum_{i=1}^{n}s_{i}}{K\times\max \left(m_{i,j}\right)} \tag{9}\]  
The cycle communication mode is adopted of FlexRay. So, in the communication cycle, the signal of all super-periodic is retransmitted. \[T_{sp}=LCM\left(p_{m_{1,1}},p_{m_{1,2}},p_{m_{1,3}},\cdots,p_{m_{1,i}},p_{m_{ 2,1}},p_{m_{2,2}},p_{m_{2,3}},\cdots,\ p_{m_{2,j}},\cdots,\ p_{m_{i,j}}\right) \tag{10}\]  
When the number of nodes and the number of signals in the FlexRay network are increased multiply, the traditional linear programming model will result in more complicated and difficult processes, as well as longer solving times. In this study, we will use the Next Fit Decreasing (NFD) algorithm to optimize bus utilization. First in the NFD algorithm, the signal is sorted from large to small according to the data of length, assuming that the length of the signal \(s_{1},s_{2},s_{3},\cdots,s_{i}\) is \(L_{1},L_{2},L_{3},\cdots,L_{i}\) respectively, the signal is packaged into a message in a given order. The signal \(s_{1}\) is putted into slot \(sl_{1}\) firstly, if \(L_{1}+L_{2}\leq l^{cur}\), then \(s_{2}\) will put in \(sl_{1}\), otherwise it will put into \(sl_{2}\). Continually, testing whether \(s_{2}\) could be putted into \(sl_{2}\). If can, put into the slot \(sl_{2}\), otherwise open the slot \(sl_{3}\). Figure 3 is a signal package flow chart, the main operation flow is as follows:  
1. Obtain the signal information corresponding to each node. 2. The signal of each node is arranged in ascending order of the repetition rate, and the signal of the same repetition rate in each node is arranged in descending order of the size of the data bits. 3. Calculate the exact value of the payload length of the frame, which means calculate the values of \(l^{L}\) and \(l^{H}\), and initialize \(l_{cur}=l^{L},U=0\). 4. According to the NFD algorithm, the nodes are packaged by sequentially. Finally, a message set is obtained. 5. Calculate a super-periodic network utilization in the case of the payload length of the current frame, that expressed as \(U_{cur}\), if \(U<U_{cur}\), update the current message set. 6. Set \(l_{cur}=l_{cur}+1\) and repeat steps (4) and (5), loop until \(l_{cur}=l^{H}\). 7. Take the maximum value of \(U\) when the corresponding message set and its corresponding frame payload length \(l^{best}\).
## 4 QoS-Aware Multimetric Routing Protocol for Video-Streaming Services over MANETs  
In this section we summarize the operation of our proposal called _multipath multimedia dynamic source routing_ (MMDSR) previously presented in [3; 4]. MMDSR is a QoS-aware multimetric routing protocol specially designed to transmit video-streaming over IEEE 802.11e [22] MANETs. MMDSR considers the specific characteristics of the video while establishing the forwarding path to transmit packets from source to destination, aiming to provide a good QoS.
###### Abstract
We verify the feasibility of the proposed multi-user VLCP system through both simulation and experimentation and investigate the system performances, including bit-error rate (BER) and positioning error. The results show that continuous VLC transmission with accurate VLP function can be provided for multiple users simultaneously. Therefore, the proposed scheme can effectively integrate the VLC and VLP into one system, and the supporting number of users will not be limited by the number of LEDs but can be designed according to the used number of spreading codes.
## 4 Discussion  
### Enhancing Decision-Making Systems: Overcoming the Garbage In, Garbage Out (GIGO) Problem  
It is worth mentioning the fact that the effectiveness of a decision-making process heavily relies on having up-to-date and manageable (actionable) data/information. Otherwise, experts are not able to form accurate assessments and they are led to erroneous decisions due to a lack of crucial information.  
Thus, the success of a CMSS greatly depends on the usefulness and operational value of the input data/information. Multiple operational issues like outdated data, information gaps, temporal and spatial organization complexity, inaccuracies, dynamic data, and incomplete (i.e., with gaps) information exacerbate these challenges. As a result, CMSSs are frequently vulnerable to the \"Garbage In, Garbage Out\" (GIGO) phenomenon, in the sense that the quality of the output is directly dependent on the quality of the input and, thus, databases are filled with poor-quality, inaccurate, and irrelevant (or non-useful) data. This situation poses a significant challenge to experts, preventing the use of methodologies related to improving the decision-making process. An exemplary well-known solution, the Military Decision-Making Process (MDMP) that achieves desired outcomes/results, cannot be applied.
## 5 PET Bottle Degradation Management Strategies
### Improving the Efficiency of Waste Management
#### 5.2.1 Biodegradation and Biological Recycling
Although enzymes such as FAST-PETase, modified LCC, and IsPETase variants exhibit high activity under laboratory conditions, particularly toward amorphous PET, their effectiveness in more complex environments (e.g., highly crystalline PET, laminates, or in the presence of contaminants) remains limited and requires further verification. Thus far, studies on these enzymes have mainly looked at processed samples that are less crystalline, which helps evaluate their potential but does not truly represent real industrial situations. To accelerate PET biodegradation, it is essential to use enzymatic synergy, where different enzymes work together in the degradation process. In their study, [PERSON] et al. [131] suggested using two different enzymes that work well together: leaf-branch compost cut-nase and thermostable carboxylase (Thermus thermophilus carboxylase). The first enzyme degrades the PET surface, while the second hydrolyzes intermediate degradation products. This approach enables the complete breakdown of PET products into terephthalatic acid (TPA) and ethylene glycol (EG). Combining the action of enzymes and microorganisms can lead to more effective degradation of PET microplastics during high-temperature composite [132]. The presence of microbiota, such as _Acinetobacter_ and _Bacillus_, without the addition of enzymes, contributes to a 26% reduction in PET. However, introducing PET hydrolase (WCCG, a quadruple variant of LC-cutinase) into the compost triggers a chemical reaction with PET, reducing the population of plastic-degrading bacteria, leading to a 35% degradation of PET. Effective degradation of used PET bottles can be achieved by integrating chemical and enzymatic methods [38; 133]. Studies have shown that mild alkaline pre-treatment at 25 \({}^{\circ}\)C. modifies the PET surface, reduces its crystallinity, and increases PETase accessibility to hydrolyzed bonds, significantly improving degradation efficiency [38]. Additionally, pre-treatment of PET bottles using microwave irradiation supports enzymatic plastic recycling [133]. This process effectively shortens PET polymer chains and induces favorable conformations, increasing their availability for enzymatic hydrolysis.
## 4 Verification of High-Performance Measurements
### Virtual Measurement Using Simulated DAQ Devices
Abnormal signals, such as AEs, can be automatically detected by activating the alarm feature in the program. Various configuration parameters are available for alarm supervision, such as peak, mean, and root mean square (RMS) values. However, as the measurements were conducted using simulated devices in this study, the signal waveforms were almost sinusoidal. The signals included noise but were insignificant, as shown in Figure 10. No trends or abnormalities were observed for these signals. Nevertheless, the alarm feature was deliberately activated, serving only to add additional workload to the testing program. Despite this challenge, the program successfully performed the tests. 4. **Scheduled measurement. ** For long-term condition-monitoring applications, storing sensor data over an extended period can result in a significant amount of required storage space, particularly in AE-monitoring cases. To address this issue, the sensor reading values can be stored at specific times and durations. For instance, data were stored for 1 min for every 5 min. This approach saves storage space without sacrificing critical information. Additionally, it is possible to identify trends, patterns, and anomalies in the signals. These tests successfully demonstrated the ability of the monitoring program with various configurations to conduct high-performance measurements. Although some of the configurations were superfluous for the simulated signals, they could still serve as valuable tips for conducting actual measurements and sharing experiences for developing new condition-monitoring applications.
## Materials and Methods
### Prediction Models
#### 2.3.5 Artificial Neural Network (ANN) Model  
The ANN algorithm simulates the human learning processes by establishing and reinforcing the linkages between the input and output data [66]. It is based on the data processing in biological nervous systems because numerous cells exist for the reception of information, others for forwarding and storage, and another group for the outward release of information [60]. The A neural network (NN model is selected for several reasons. First, the approach has been successful in estimating the SOC content using ancillary data [44]. Second, ANN provides excellent modeling capabilities for complex, noisy environmental datasets where the relationship between input and output parameters is not well understood. Finally, NNs are frequently considered \"black box\" models because extracting the information they develop is easier on the modeled system than with other approaches [71]. Thus, the approach is convenient to use. [PERSON] et al. [44] concluded that ANNs are the best method to predict the SOC content compared with different data mining methods and algorithms. Such modeling methods and techniques can potentially explore nonlinear relationships between various complicated variables and are thus proven to be highly powerful for digital SOC mapping. In the present study, NDVI time series are used as input parameters in the ANN model for prediction. The technical principles and processes can be found in Basheer and Hajmeer [72] and are shown in Figure 4.  
Figure 3: Support vector machine (SVM) schematic.  
A highly common type of ANN is the multilayer perceptron. The network contains interconnecting nodes called neurons that are connected to one another through weighted synapses. First, the random initial raw values of weights are placed into the synapses to make an ANN model. Thereafter, these data are increasingly corrected during training phase. Subsequently, the computed outputs of the network are contrasted with the actual values. Finally, the errors are backpropagated to adjust the weight values for minimizing errors [44; 73].
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 3 Existing Security Incidents and Limitations of Security Products
### Cases of Security Threats in Korea and Abroad
#### 3.1.3 Cyber Incidents Related to ICS/OT  
Ransomware, which was primarily recognized as a form of hacking occurring in IT environments, has recently been adopted in OT (Operational Technology) environments as well [12].  
Moreover, ransomware attacks have evolved beyond merely encrypting data and demanding payment; attackers now infiltrate deeper into internal systems and networks of victim companies, demanding additional ransom payments [13]. Table 1 below outlines recent ICS/OT cyber incidents both domestically and internationally [14].
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
###### Abstract  
The conformal antenna is a precision device installed on the wing of an aircraft, and its components are distributed on a curved surface. Quality detection is required after assembly. In solving the path planning problem for conformal antenna surface detection, the traditional genetic algorithm faces problems such as slow convergence and easily falling into a local optimal solution. To solve this problem, an improved genetic algorithm combining the historical optimal population (CHOP-IGA) is proposed. First, the algorithm uses the probability-based four-nearest-neighbor method to construct an initial population. Subsequently, the probabilities of the crossover and mutation operators are dynamically adjusted. Next, the algorithm applies the crossover and mutation operators to the population and performs mutation operations on each individual of the historical optimal population. Then, the fitness value is calculated and the next generation of individuals is selected from the parent, offspring, and historical optimal populations according to the elite selection strategy. Finally, the current best fitness is checked to determine whether updating the historical optimal population is necessary. When the termination condition is satisfied, the algorithm outputs the optimal result. Experiments showed that the algorithm satisfactorily solved the path planning problem for conformal antenna surface detection, with a 48.44% improvement in detection efficiency.  
t  
traveling salesman problem; improved genetic algorithm; probability-based four-nearest-neighbor method; historical optimal population; conformal antenna