esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Acrylic Emulsion  
The results obtained on acrylic samples employing ATR-FTIR spectroscopy (Figure 7d) provided important indications of possible changes that may have occurred because of accelerated aging. The spectrum obtained in the violet area is very similar to that of the unaged sample, while the spectrum of the grey region shows some variations. As for the previous binders, the acrylic emulsion sample shows an increase in intensity in the region between 3600 and 2600 cm\({}^{-1}\). This trend may indicate that hydrolytic degradation has occurred, leading to a gradual opening of the polymeric network [22; 24]. Water absorption is also favored by the presence of the surfactant PEO, which is hygroscopic and therefore tends to migrate to the surface when exposed to high RH values, weakening the physical and mechanical properties of the polymer, forming efflorescence on the surface and stickier areas [23; 24]. As a result of the hydrolysis, an increase in the intensity of the PEO (1341 and 1114 cm\({}^{-1}\)) and of the main binder bands (2956, 2872, 1727, and 1436 cm\({}^{-1}\)) is observed from the semi-quantitative evaluation (Supplementary Table S12). A confirmation of oxidation reactions is provided by the formation of a shoulder at 1637 cm\({}^{-1}\), probably related to the C=O _stretching_ of the ketones formed as oxidation products [76]. Finally, a new band at 2346 cm\({}^{-1}\) is visible. This band could be related to the _stretching_ of the O-H bond of the sulfonic acid group (-SO\({}_{3}\)H) [69]. Sulfuric acid, which is assumed to have formed within the weathering chamber during accelerated aging, is a good sulfonating agent that can easily insert HSO\({}_{3}\)- groups into the polymer chain [84]. The band at 1138 cm\({}^{-1}\), which is probably related to the asymmetric _stretching_ of the sulfonate group [84; 85], and the band at 853 cm\({}^{-1}\), which could be due to the S-O _stretching_ of sulfinic acid [69], may provide further indications of sulfonation.  
The results obtained with Py-GC/MS (Supplementary Table S13) confirm the hypothesis assumed with ATR-FTIR spectroscopy. An intense decrease in the area of the dimeric and trimeric fractions is visible, associated with the oxidation and hydrolysis reactions that lead to a scission of the polymer chain [24]. This hypothesis would also explain the consequent strong increase in the area of _n_-butyl acrylate (_n_BA), its pyrolysis products (1-butene, _n_-butyl acetate, and _n_-butyl tglate), and, in general, the peaks of low-molecular-weight compounds (e.g., butyl alcohol).
## 3 Results and Discussion
### Pectinase Enzyme Activity  
The APPB and solvent-treated kenar retting niches were subjected to tab on their pectinase activity after being steeped for 24 h up to 6 days. The activity was counted based on the galacturonic acid contents of the pectic substances, using the retting effluents of each retting treatment in 24 h intervals. The result of each treatment is presented in  
Figure 3: Physical characteristics of colony morphology, Gram staining (under stereo and light microscope), and SEM view of the APPB isolates on nutrient agar: (**I**) APPB1 and (**II**) APPB2.  
Figure 5. A gradual increase in the pectinase activity was observed from days one to four. It plateaued on day five, and a slightly declining trend was observed irrespective of all treatments. Among the treatments, the degree of the activity varied, and APPB1 with seawater showed the highest (14.67 U/mL) pectinase activity. The determination of the pectinase enzymatic activity in the degumming cycle indicated the state of pectinase substance degradation, which was positively correlated with retting achievement [53; 54]. However, this pectinase activity varied on several factors, including the occurrence of specific bacteria, the maceration effects of the solvents, and their biotic and abiotic factors.  
The pectinase activity of the APPB inoculated retting niches was recorded to be comparatively higher than those of the uninoculated retting niches. This phenomenon might be due to the inoculation and proliferation of pectinolytic bacteria. Even though multiple enzymes are required to degrade kenaf heterogeneous NCGs, pectinase is the main retting enzyme determining the retting dynamics and applications of high-pectinase-potential bacterial strains were advantageous to yielding quality fiber. Previous studies revealed that the inoculation of pectinolytic bacteria significantly reduced the retting duration and achieved an optimal degree of retting in half of the time when compared with traditional retting without inoculum [23; 55]. This pectinase activity was consistent with the visual degumming of kenaf plant materials. In addition, xylan is the major hemicellulose component of kenaf blast fibers, having 1,4 linked xylose units. Pectinase activities are also associated with hemicellulose and cellulase activities [45; 56; 57].  
APPB with SW treatments showed the highest pectinase activity at the 4 th day of retting. The first steps of retting were initiated by the maceration of solvents, where plants' cell walls turn soft and soluble proteins make appropriate environments for the microbes. Previous studies have suggested that alkaliophiles favor Na+ for their optimum growth [58]. Other retting influencing factors that might have affected the degumming process include microbial growth characteristics and the maceration effects of the retting solvents. In the bio-retting process, endospore-forming species are highly efficient inoculants because of their higher survival rate in harsh retting conditions and, additionally, facultatively anaerobic strains benefit both aerobic and anaerobic phases [59, 60].
# Energy Harvesting over Rician Fading Channel:
## 4 Numerical Results and Discussion
### Optimal Time-Switching Factor  
As mentioned in Section 3, the optimal time-switching factor to maximize the achievable throughput of the considered system can be found numerically by using an iterative algorithm such as the golden section search method [36]. Figure 11 plots the optimal value \(\alpha^{*}\) for various values of the ratio \(P/N_{0}\) at different hardware impairment levels.  
Figure 10: Effect of \(\alpha\) and modulation scheme on SER.  
Figure 9: Effect of \(\kappa\) and modulation scheme on SER.  
It can be observed that the optimal time-switching factor decreases as the ratio \(P/N_{0}\) increases. This is because, for large \(P/N_{0}\), the outage probability tends to reduce, so it is not necessary to use a large amount of energy to transmit data. Reversely, we need to spend more time resources to increase the throughput of the system.  
On the other hand, we can learn from this simulation that the optimal \(\alpha\) does not change much for different hardware impairment levels. Especially, for small \(\kappa\), the value of \(\alpha^{*}\) is nearly the same as the one for a perfect hardware case.
## 4 Methodology
### Model Construction  
Finding the best model configuration for a custom data set is a demanding task. This study has developed a general model using some fine-tuned parameters to find the best model for the custom data set. Subsequently, this study was able to generate 81 different custom models for the developed CNN method. Figure 2 illustrates the architecture of the CNN model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Data Set** & \multirow{2}{*}{**Types**} & **Number of** & **Volume** & **Energy** \\  & **Name** & & **Instances** & **(Gram)** & **(Kilocalorie)** \\ \hline \multirow{4}{*}{Kaggle} & \multirow{2}{*}{FOOD-101} & Apple & 1000 & 133 & 72 \\ \cline{3-6}  & & Banana & 1000 & 118 & 105 \\ \cline{3-6}  & & Donut & 1000 & 64 & 269 \\ \cline{3-6}  & & Cupcake & 1000 & 72 & 262 \\ \cline{3-6}  & & Mango & 1000 & 133 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data selection from two different data set with a typical nutrition table [4].  
This study uses several fine tuned parameters such as filter size, filter number, pool size, and dense node to generate the CNN model. Conv2D layer, relu, and other activation functions are also used in this process. Among 81 CNN models, model 44 has achieved the most accuracy which has been discussed in the following section.  
The execution time along with various parameters of the best 10 models is illustrated in Section 5. It is important to note that there is no machine to measure the exact amount of calories contained within any food item and no pre-labeled food calorie image dataset is available that can train any model.
# Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures
## 2 Kohonen Neural Networks for Clustering Problem
### Proposed Algorithms
Figure 2: (**a**) Coefficient of variation of the objective function value for two-batch mixed lot; (**b**) span factor of the objective function value for two-batch mixed lot; (**c**) coefficient of variation of the objective function value for three-batch mixed lot; (**d**) span factor of the objective function value for three-batch mixed lot; (**e**) coefficient of variation of the objective function value for four-batch mixed lot; (**f**) span factor of the objective function value for four-batch mixed lot. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **SCL** & **SCL-GREEDY(2)** & **SCL-GREEDY(3)** \\ \hline \hline  & \multicolumn{3}{c}{Two-batch mixed lot (\(p<0.00001\))} \\  & \(K1=2\) & \(KI=4\) & \(KI=6\) \\ Min & 415.627 & 371.1297 & 301.2941 \\ Max & 415.627 & 371.1322 & 337.3007 \\ Mean & 415.627 & 371.1301 & **329.28111**1  \\ \(\sigma\) & 6.38E-12 & 0.000772 & 14.67288 \\ V & 1.53E-12 & 0.000208 & 4.456033 \\ R & 2.80E-11 & 0.002558 & 36.00667 \\  & \multicolumn{3}{c}{Three-batch mixed lot (\(p<0.00001\))} \\  & \(KI=3\) & \(KI=6\) & \(KI=9\) \\ Min & 406.912 & 397.815 & 397.787 \\ Max & 438.101 & 417.747 & 407.933 \\ Mean & 431.002 & 415.140 & **406.966** \\ \(\sigma\) & 8.395 & 4.783 & 2.167 \\ V & 1.948 & 1.152 & 0.533 \\ R & 31.189 & 19.933 & 10.146 \\  & \multicolumn{3}{c}{Four-batch mixed lot (\(p=0.61708\))} \\  & \(KI=4\) & \(KI=8\) & \(KI=12\) \\ Min & 606.056 & 596.008 & 594.863 \\ Max & 614.033 & 892.644 & 903.672 \\ Mean & 609.644 & 689.886 & 704.368 \\ \(\sigma\) & 2.167 & 125.611 & 136.492 \\ V & 0.355 & 182.008 & 19.378 \\ R & 7.977 & 296.636 & 308.810 \\ \hline \end{tabular}
\end{table} 
Table 1: Objective function value summarized after 30 attempts (Chebyshev distance). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **SCL** & **SCL-GREEDY(2)** & **SCL-GREEDY(3)** \\ \hline  & \multicolumn{3}{c}{Two-batch mixed lot (\(p<0.00001\))} \\  & \(KI=2\) & \(KI=4\) & \(KI=6\) \\ Min & 198.486 & 193.825 & 191.610 \\ Max & 441.215 & 395.174 & 393.348 \\ Mean & 296.329 & 251.285 & **206.390 1**  \\ \(\sigma\) & 104.214 & 84.992 & 50.003 \\ V & 35.168 & 33.823 & 24.228 \\ R & 198.486 & 193.825 & 191.610 \\  & \multicolumn{3}{c}{Three-batch mixed lot (\(p=0.00214\))} \\  & \(KI=3\) & \(KI=6\) & \(KI=9\) \\ Min & 365.927 & 274.081 & 365.719 \\ Max & 372.730 & 370.565 & 369.705 \\ Mean & 371.892 & **366.467** & 367.961 \\ \(\sigma\) & 2.059 & 17.518 & 1.459 \\ V & 0.554 & 4.780 & 0.396 \\ R & 6.803 & 96.484 & 3.986 \\  & \multicolumn{3}{c}{Four-batch mixed lot (\(p=0.0012\))} \\  & \(KI=4\) & \(KI=8\) & \(KI=12\) \\ Min & 952.563 & 939.212 & 914.629 \\ Max & 1142.812 & 1133.992 & 1108.637 \\ Mean & 1113.352 & 1084.122 & **1071.603** \\ \(\sigma\) & 55.095 & 74.012 & 63.228 \\ V & 4.949 & 6.827 & 5.900 \\ R & 952.563 & 939.212 & 914.629 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Objective function value summarized after 30 attempts (Euclidean distance).
# Orbit Determination of Korean GEO Satellite Using Single SLR Sensor
## 3 GK-2B Orbit Determination Using SLR Data  
### Orbit Determination Strategy  
OD as well as SLR observation data generation is performed by ODTK, which utilizes a forward-running optimal sequential filter (like a Kalman filter) followed by the backward-running fixed-interval smoother process [12]. Details of mathematical theory for optimal filter and smoother are found in the literature [13]. Table 6 summarizes dynamic/observation model and its associated estimation parameters. The Global Gravity Model (GGM)03C model with degree and order of 30 is utilized for the Earth's gravity. International Earth Rotation and Reference Systems Service (IERS) 1996 conventions accommodate the solid Earth tides, ocean tides, and general relativity correction. The Sun, Moon, and all planets in the Solar system are included for the third body gravity, and the orbital ephemeris comes from DE405 of Jet Propulsion Laboratory (JPL). The air drag model is not considered in the dynamic model because of the high altitude of GEO. The cross-sectional area for solar radiation pressure is set by spherical model, along with the eclipse occurring with the Earth and the Moon. The solar radiation coefficient is estimated in the filter by Gauss-Markov process. Empirical forces are set as one-cycle-per-rev model in three axes.  
Figure 8: Simulated SLR observations for GK-2B from Geochang (**upper**: Scenario 1, **lower**: Scenario 2).
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 5 Numerical Results and Analysis
### The Effect of Communication Distance on System Performance  
This subsection presents a simulation of the convergence performance of the RIS-TD3 algorithm. The simulation conditions are as follows: the number of RIS reflection units is set to 49, the hidden layer dimensions of the Actor and Critic networks are set to 256, the number of exploration episode is 50, the agent-begins-to-learn episode is 100, and the total training episode is set to 200. We observe the cumulative reward of the episode obtained by the algorithm in each training episode, denoted as all_episode_reward, and apply a sliding average to it. Moreover, it is compared with the random RIS phase shift method, denoted as all_episode_reward_rnd. The simulation results are shown in Figure 4.  
From Figure 4, it can be seen that the algorithm is in the random exploration and data accumulation phase before it starts learning (the first 100 rounds), and the accumulated episode reward obtained at this time is small and comparable to the average of the accu  
Figure 4: Convergence performance of the proposed algorithms with \(N=49\), Hidden_dim = 256.  
Figure 3: Median GRCD versus the number of antenna \(N\) at the reader with \(K=4\), Hidden_dim = 128.  
mulated episode reward of the random RIS phase shift, which is within 200; when the algorithm starts to learn, the episode gain is able to increase rapidly, rising to a higher level of about 560 within about 25 episodes, and after that until the end of learning phase, the accumulated episode reward fluctuates in a smaller range, with the total gain above 500, which is much higher than that of the random RIS phase shifts. This demonstrates that the algorithm is able to be converged quickly and the rewards after convergence are greater, resulting in better convergence performance.
## 2 State of the Art in Optical Wireless Communications
### The Contribution of Communications Based on Visible Light in the Transmission of Information  
These visible light communications (VLCs) represent the use of the visible light spectrum (VL), between 380 nm and 780 nm, to obtain wireless data communications. Therefore, the data transfer part is performed as an additional functionality; in addition to the main purpose of lighting, both transmission and reception can be performed [56]. As for VLC, the data are modulated on the instantaneous power of light, in its simplest form known as on-off keying (OOK). In terms of data reception, they are extracted with the help of light detectable elements, photodetectors and cameras. In the case of the photodetector, it uses a reverse-bias photodiode, which is included in a transimpedance circuit, then provides an electrical signal directly proportional to the power of the incident light. In some cases, the photodetector element is replaced with systems that include cameras [57]. In this case, the extracted data are obtained by complex image processing techniques, and the performance of the system is directly proportional to the speed and quality of the camera. Regarding the IEEE 802.15.7 standard dedicated to short-range wireless optical communications using the visible light spectrum, it was officially launched at the end of 2011. The current version of the standard can cover the physical layer (PHY) but also access control (MAC) [58]. In accordance with the standard, data transfer is performed by modulating the light intensity of optical devices. Therefore, the issues associated with high-resolution flashing and grading are becoming a priority direction for studies. Following the standard also takes into account mobility issues, damage to connections due to noise and interference with other light sources. Consistent with the developed applications and the required transfer rates, the IEEE 802.15.7 standard comes with approximately three types of PHY layers [59]. The first PHY is dedicated and designed for concepts of external applications with low transfer speeds and OOK use. The modulation was performed for the variable pulse position (VPPM), the data transfer rates being between 11.67 kb/s and 267 kb/s. PHY II and PHY III were designed and proposed for indoor applications with moderate data transfer speeds, with speeds between 1.25 Mb/s and 96 Mb/s [60]. PHY II uses OOK and VPPM; in the case of PHY III, it is used in CSK (color-shift keying) applications. The CSK process can be performed with the help of multi-colored LEDs and photodetectors of selective type depending on the color or with optical filters. Under the
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 3 Transistor Level Design of the PLL
### Charge Pump and Phase/Frequency Detector
#### 3.1.2 Phase/Frequency Detector  
Since the PFD is essentially a digital block, the easiest and most effective way of hardening it against radiation is the TMR [13]. The designed architecture is shown in Figure 13a. The block-labeled PFDs in Figure 13a consist of the conventional PFD architecture shown in Figure 13b, and every PFD has its own reset voter that decides between all the output resets. In this way, the only possible loss of lock for the PLL happens when an SEE occurs on the UP and/or DOWN voters. However, this error  
Figure 11: Enhanced CP’s Gate Switching architecture with differential inputs.  
Figure 12: Transient behavior of the CP of Figure 11. The source current is in red and the sink current is in black for two different values of ON time of the input signals (UP_P-UP_N and DW_P-DW_N): (a) 3.2 ns, (b) 500 ps.  
does not lead the PFD into a wrong state causing cycle slipping, i.e., one of the input's loss (or gain) of one cycle with respect to the other input. This architecture has been developed in both CMOS logic and CML (Current Mode Logic), in order to be compatible with the designed CPs.
## 2 State of the Art
### Contention-based Protocols  
One of the first MAC protocols based on contention to reduce energy consumption is PAMAS (Power Aware Multi-Access protocol with Signalling) [2]. Its goal is to eliminate overhearing by using a separate channel to exchange RTS/CTS. S-MAC (Sensor MAC) [3] avoids overhearing, but it uses only one channel. Moreover, S-MAC reduces the idle listening of nodes by allowing them to switch their network interfaces off periodically during predefined time intervals. T-MAC (Timeout MAC) [4] is an improvement of S-MAC. It dynamically modifies the time intervals during which the sensors sleep. D-MAC (Data gathering MAC) [5] has been proposed to reduce the latency: all the nodes along the path from the source to the destination are awaken to forward the packets. All these protocols use the RTS/CTS exchange to allow one-hop neighbors of the sender and the one-hop neighbors of the receiver to sleep for the duration of the communication. IEEE 802.15.4 [6] proposed a slotted CSMA/CA algorithm that is more adapted to the energy constraints of LP-WPAN (Low Power Wireless Personal Area Network) than IEEE 802.11 CSMA/CA algorithm.
#### 3.1.2 Variation of Temperature \(\vartheta\)  
Figure 3 shows the influence of the gasification temperature \(\vartheta\) on the permanent gases for a constant \(n_{\rm O2}\). With increasing temperature, the gas yield increased due to higher carbon conversion to permanent gases. Also the H\({}_{2}\) and CO concentration increased and the H\({}_{2}\)O concentration decreased with increasing temperature, which is also related to an increased fuel conversion by steam gasification. No influence was seen on CH\({}_{4}\) and C\({}_{x}\)H\({}_{y}\) concentrations. Similar trends were found for steam-air gasification of corn straw [51] and sewage sludge [32] also, with regard to the N\({}_{2}\)-free basis, and similar gas concentrations of all permanent gases were reported. Also, the temperature influence in steam-oxygen gasification of wood is reported in literature [22] and shows similar trends compared to this study concerning H\({}_{2}\), CO\({}_{2}\), and gas yield. However, differences were reported in the trend of the CO, CH\({}_{4}\), and C\({}_{x}\)H\({}_{y}\) concentrations, which decreased slightly with temperature in the reference whereas in this study they slightly increased (CO) or stayed constant (CH\({}_{4}\), C\({}_{x}\)H\({}_{y}\)). Similar trends to this study were also obtained by steam gasification of sewage sludge in the same gasifier operated in DFB mode [52].  
Figure 2: Permanent gas concentrations in dry N\({}_{2}\)-free basis, for H\({}_{2}\)O wet N\({}_{2}\)-free basis and dry N\({}_{2}\)-free gas yield per dry ash free fuel at different oxygen ratios \(n_{\rm O2}\) and constant \(\vartheta\).
## 1 Introduction
Unlike unidirectional LSTMs, which process input sequences in a single direction from past to future, bidirectional LSTMs consider the entire context of the data, thereby improving the detection of subtle patterns and the prediction accuracy. Numerous studies have demonstrated that bidirectional LSTMs often outperform unidirectional LSTMs in tasks requiring temporal feature extraction due to their ability to capture more comprehensive temporal dependencies [30; 31; 32]. By processing sequence information in both directions simultaneously, the bidirectional LSTM enables the model to utilize richer contextual information. Additionally, a dot-product-based attention mechanism [33] applied to the LSTM in each direction helps the model focus on important patterns in the input from both directions, effectively extracting fused sequence features in the time and frequency domains. Subsequently, the outputs of these attention layers are concatenated into a single layer. Finally, a classifier is added to this combined layer to extract domain-specific features and make the final prediction. This approach allows the model to achieve more accurate predictions by leveraging attended features extracted from both directions of the input. This paper is organized as follows: In Section 1, the research background is discussed, emphasizing the significance of addressing leak issues in aging pipes. Section 2 outlinesthe process of feature extraction in the RMS and frequency domains, along with the data preprocessing techniques applied. Section 3 introduces the proposed feature fusion leak detection model, which integrates TL and bidirectional attention mechanisms. Section 4 describes the data collection setup and assesses the performance of the model. Finally, Section 5 provides a conclusion and suggests potential avenues for future research.