esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chlorination Reaction  
#### 2.2.1 Apparatus and Equipment  
Column chromatography (CC) was carried out with Kieselgel 60 (230-400 mesh, Merck, Darmstadt, Germany). HPLC was performed on a Shimadzu LC-8A system using a Shimadzu SPD-10A VP UV-VIS detector (Shimadzu, Milan, Italy). Semipreparative HPLCwas performed using an RP Gemini C18-110A preparative column (10 \(\upmu\)m particle size, 250 mm \(\times\) 21.2 mm i.d., Phenomenex, Bologna, Italy) with a flow rate of 7 mL min\({}^{-1}\). The \({}^{1}\)H- and \({}^{13}\)C NMR spectra were recorded with a NMR spectrometer, operated at 400 MHz and at 25 \({}^{\circ}\)C (Bruker DRX, Bruker Avance) and referenced in ppm to the residual solvent signals (CDCl\({}_{3}\), at \(\mathrm{s_{H}}\) 7.27 and \(\mathrm{s_{C}}\) 77.0). The proton-detected heteronuclear correlations were measured using a gradient heteronuclear single-quantum coherence (HSQC) experiment, optimized for \({}^{1}\)J\({}_{\mathrm{HC}}\) = 155 Hz, and a gradient heteronuclear multiple bond coherence (HMBC) experiment, optimized for \({}^{\eta}\)J\({}_{\mathrm{HC}}\) = 8 Hz. The MALDI-TOF mass spectrometric analyses were performed on a Voyager-De Pro MALDI mass spectrometer (PerSeptive Biosystems, Framingham, MA, USA). The UV/Vis spectra were recorded with a Perkin Elmer Lambda 7 spectrophotometer. The IR spectra were recorded with a Jasco FT/IR-430 instrument equipped with a single reflection ATR accessory.
# Improving a Manufacturing Process Using the 8 Ds Method. A Case Study in a Manufacturing Company
## 4 Results
### Describe the Problem (D2)  
As previously mentioned, 67 cable assemblies were returned to the company by customers, who complained about either the product's poor performance or regarding unacceptable features. The main problem was that the assembly did not work; however, that can be due to several types of defects. Table 4 lists the six different types of defects that were found in the cable assemblies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Defect** & **Frequency** & **Percentage** & **Cumulative Percentage** \\ \hline Inverted cables & 35 & 52\% & 52\% \\ \hline Disfigured motor & 10 & 15\% & 67\% \\ \hline Noisy motor & 9 & 13\% & 81\% \\ \hline Motor does not work & 7 & 10\% & 91\% \\ \hline Lack of ID tag & 4 & 6\% & 97\% \\ \hline Wrong cable length & 2 & 3\% & 100\% \\ \hline Total & 67 & 100\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Defects found in the rejected cable assemblies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The 8 Ds Methodology** & **PDCA Cycle** & **Teamwork Member** \\ \hline Develop the teamwork (D1) & Plan & Maintenance engineer \\ \hline Develop the problem (D2) & & \\ \hline Develop an interim containment action (D3) & & \\ \hline Define and verify root causes (D4) & Do & Production line manager \\ \hline Develop permanent corrective actions (D5) & & Intern engineer \\ \hline Implement and validate corrective actions (D6): & Check & Quality inspector 1 \\ \hline Prevent recurrences (D7): & & Quality inspector 2 \\ \hline Recognize and congratulate the teamwork as well & Act & All involved employees \\ as individual contributions (D8): & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Task assignment.  
Specifically, the data in Table 4 were used to create a Pareto diagram, as shown in Figure 5. The diagram helped define which problems or defects had to be prioritized, according to their frequencies. In this sense, the most frequent defect was inverted cables, followed by a disfigured motor. Even though both wrong cable length and the lack of an ID tag were less frequent problems, they had to be solved from the root cause as well.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 2 Multi-Objective Optimization Applied to Underwater Glider Path-Planning
### Problem Definition  
For this work, the problem of a glider that needs to navigate from a starting point to a final destination was tackled; along the path, several obstacles need to be avoided. The goal is to propose the most convenient planning taking into account the vehicle characteristics, the 3D ocean currents forecasts and the bathymetric and operational restrictions. Since ocean models produce predictions generally up to 3 days, the planning horizon has been fixed for that period too.  
Two objective functions of interest have been defined, to be optimized by NSGA-II algorithm:  
\[\begin{array}{l}f(1)=Distance(Target,LastPoint)\\ f(2)=DistanceToObstacle(Trajectory)\end{array}\]  
\(f(1)\) function is computed as the geodesic distance between the latitude-longitude coordinates of the desired target point (_Target_) and the final point of the glider trajectory (_LastPoint_). It is a minimization objective.  
\(f(2)\) function is computed as the minimal geodesic distance between the latitude-longitude coordinates of any point of the glider trajectory (_Trajectory_) and any of the defined obstacles. It is a maximization objective.
## 6 Discussion
In the future, the ideal scenario could involve the integration of satellite communication channels of all types (broad- and narrowband) to establish a global virtual IoT constellation, which would require addressing not only various technical challenges, but also various notable challenges posed by business competition and data security issues.
## 5 Discussion
### Theoretical and Practical Implications  
The theoretical implications of the results of this study, which will be interesting especially for researchers and practitioners, are in the record of challenges and good practices that still need to be documented in this way in the existing literature. The study results will provide researchers and practitioners with one place to look at challenges and good practices, based on which they can eliminate various factors in the planning phase when planning their user evaluation studies, which can negatively impact the successful implementation of user-based testing with users with disabilities.  
The results of this study have several practical Implications for different stakeholders: (1) developers who design and develop interactive solutions, (2) testers who evaluate the usability and user experience of the product, and (3) end users with disabilities who use the solutions.  
The first practical implication is related to planning and developing user-friendly, useful, and accessible solutions, where user testing is essential for enabling quality solutions. Developers will learn to improve the design and development of accessible solutions for all users--including those with disabilities. Especially for the user-based testing of the developed products, the results of this study will improve awareness about the challenges related to the inclusion of users with disabilities in end-user testing procedures. The results of this study can also help improve the testing procedures, as the knowledge provided will make practitioners better aware of good practices of active involvement in such studies. Next, the results of user testing with actively involved users with disabilities will give a better insight into all product problems related to the use of and interaction with the product. Consequently, the practical outcome will be better solutions for all users.  
Another significant practical implication of the results of this study is enabling better opportunities for users with disabilities to actively participate also in user-based testing processes, which in the past, maybe also due to a lack of knowledge about how such studies need to be correctly planned and carried out, were not included. Consequently, this will also improve the possibility for users with disabilities for inclusion in all activities related to the user testing.
## 3 Results and Discussion  
### Contamination Behavior  
#### 3.1.1 Distribution of Contamination on the Coupling Glass  
Figure 6 shows the contaminated coupling glass and contamination index distribution after W10 under the reference parameter set (D0_F0.0) in Table 1. After welding, the laser transmission region on the coupling glass turned an opaque gray color, and its surrounding area became a brown color (Figure 6a). The contamination index for all measured points ranged from 13.4% to 42.6%. Domain 1 showed the highest contamination with 41.2% of the median contamination index. Domain 2 showed 39.5%, and domain 3 showed 28.7% of the median contamination index (Figure 6b).  
During the welding trial, welding fumes adhered to the coupling glass, and some fume particles with weak adhesion detached from the coupling glass during the break between the welding trials. For the reference condition, shielding gas was not supplied, and the distribution of contamination on the coupling glass was similar to the velocity distribution of fume particles normalized to the center, as shown in Figure 6b. The contamination accumulated over the welding trial due to the successive adhesion of fume particles. As this occurred, the contamination on the coupling glass absorbed laser energy and raised the temperature of the adhered particles, which accelerated the contamination by further enhancing adhesion.  
Figure 5: Contamination analysis of the coupling glass: (a) measuring region, (b) transmittance measuring points, and (c) analysis domains.
# DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index
## 3 Method
### Deep Ensemble Recurrent Network
After some trials and errors, the optimal architecture of RNN, LSTM, and GRU are described in Table 2. We utilized two stacked of recurrent layers, such as two-layer stacked of RNN, LSTM, and GRU layer in RNN, LSTM, and GRU model, respectively. Each layer consist of 500 hidden unites with 20% dropout units and tanh as gate activation. We set the output of the recurrent layers to return a sequence. The length of the sequence corresponds to the number of steps of prediction. For instance, if the task is a five-steps-ahead prediction, the sequence length is five. The sequence will be wrapped by one TimeDistributed layer with the number of hidden units correspond to the sequence length, and will be activated by Sigmoid function. Each model is trained independently until converge by using Mean Square Error (MSE) as a loss function and Adam as an optimizer. In the ensemble layer,  
Figure 3: (**a**) Short-term (one-step-ahead) prediction; (**b**) Long-term (multi-steps-ahead) prediction. the output of each model is trained by a standard neural network with one dense layer, Sigmoid activation function, and Adam optimizer is employed to decide the final prediction. To assess how well our prediction predicted short- and long-term BDI, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Error (MAPE), denoted in Equations (13)-(15), respectively, were employed, where \(n\) corresponds to the number of data, \(y_{t}\) is the actual value of BDI at time \(t\), and \(\hat{y}\) corresponds to the predicted BDI at time \(t\). \[RMSE=\sqrt{\frac{1}{n}\sum_{t=1}^{n}(y_{t}-\hat{y_{t}})^{2}} \tag{13}\]  
\[MAE=\frac{1}{n}\sum_{t=1}^{n}(y_{t}-\hat{y_{t}}) \tag{14}\]  
\[MAPE=\frac{100\%}{n}\sum_{t=1}^{n}\left|\frac{(y_{t}-\hat{y_{t}})}{y_{t}}\right| \tag{15}\]
## Nomenclature  
AADAT Azimuth-altitude dual-axis tracker  
a-Si Amorphous silicon  
CdTe Cadmin tell  
CIGS Copper-indium-gallium-selenide  
C-Si Crystalline silicon  
DSSP Dye-sensitized photovoltaic panel  
HSAT Horizontal single-axis tracker  
Mono-Si Mono-crystalline silicon  
OPV Organic photovoltaic panel  
PASAT Polar aligned single-axis tracker  
Poly-Si Poly- crystalline silicon  
PV Photovoltaic  
SPSB Smart photovoltaic system blind  
TTDAT Tip-tilt dual-axis tracker  
TSAT Tilted single-axis tracker  
VSAT Vertical single-axis tracker
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 3 Adaptive Techniques in DSKF
### \(C/n_{0}\)-Based DSKF  
Figure 8 shows the non-linear model diagram of the \(C/N_{0}\)-based DSKF. This technique adapts the measurement noise covariance matrix \(\mathbf{R}\) while fixing the process covariance matrix \(\mathbf{Q}\). In the case of a Costas PLL, the measurement noise covariance \(R\) depends on the \(C/N_{0}\) and the integration time \(\tau_{\text{int}}\) (see Equation (30)).  
There are several methods to calculate the \(C/N_{0}\). The [PERSON]'s method is used due to its estimation accuracy and simplicity to estimate the \(C/N_{0}\)[41; 42]. This method is sub-optimal if the carrier phase lock is not achieved. However, it is assumed that the carrier phase is in lock.  
\[C/N_{0}=\frac{1}{\tau_{\text{int}}}\Bigg{(}\frac{1}{N}\sum_{v=1}^{N}\frac{P_{ n,v}}{P_{d,v}}\Bigg{)}^{-1} \tag{39}\]  
where \(N\) is the number of observed correlation samples, \(P_{n,v}\) is the estimated noise power in the correlation, and \(P_{d,v}\) is the signal-plus-noise power. \(P_{n,v}\) is represented as:  
\[P_{n,v}=\big{(}|I_{p}[v]|-|I_{p}[v-1]|\big{)}^{2} \tag{40}\]  
\(I_{p}\) is the prompt correlation value of the in-phase component. \(P_{d,v}\) is defined as:  
\[P_{d,v}=\frac{1}{2}\Big{(}I_{p}[v]^{2}+I_{p}[v-1]^{2}\Big{)} \tag{41}\]  
If the noise contribution is small, the instantaneous signal-plus-noise power \(P_{d,v}\) approximates to the signal's power.  
Figure 7: Linear model of lookup table (LUT)-DSKF.
## 2 Materials and Methods
### Extraction Procedure
#### 2.2.2 Solvent Extractions  
First, conventional extraction (Control) was performed on one set of samples. The solvents dimethyl sulfoxide (DMSO) or ethanol (EtOH) (1:1, \(v/v\)) in deionised water were added up to a final volume of 400 mL to the dry spirulina samples for nutrient extraction. Once the solvents were added, the samples were shaken at 400 rpm for 5, 15, 30, 60, 60, 120, or 180 min at room temperature to test the effect of shaking time on the extraction of compounds from the processed biomasses. Subsequently, the samples were centrifuged for 10 min at 4000 rpm using a 5810R centrifuge (Eppendorf Iberica, Madrid, Spain). The extract obtained was kept at \(-20\)\({}^{\circ}\)C for further analysis.  
Next, the remaining samples were pre-treated with PEF under the conditions described above, and then an extraction process was carried out following the same methodology used for the conventional method.  
Figure 2 shows the extracts obtained after an extraction using (a) DMSO/H2O or (b) EtOH/H2O at different extraction times (5-180 min) and the extracts obtained after pre-treatment with PEF using (c) DMSO/H2O and (d) EtOH/H2O.
# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 5 Experiments and Results Analysis
### Comparative Experiments with Common Models on CHMD2  
To further validate the performance of MSPyraNet on a more complex dataset, in this study, experiments were conducted on the CHMD2 dataset, with comparisons made against the CSA encoder [9], ConvNeXt v2 [19] and some other common deep learning models [17; 29; 30]. The experiments aimed to demonstrate the effectiveness of the MSPyraNet in more complex Chinese herbal medicine classification tasks. The results, presented in Table 3, show that MSPyraNet maintains commendable performance on the more complex CHMD2 dataset.  
On the CHMD2 dataset, MSPyraNet demonstrates outstanding performance, with an accuracy of 89.22%, precision of 88.85%, recall of 87.87%, and an F1 score of 88.16%. Compared to the best SOTA model, EfficientNetv2_m, MSPyraNet improves accuracy, precision, recall, and F1-score by 4.54%, 5.65%, 5.20%, and 5.65%, respectively. The improvements  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline ConvNeXtv2\_b & 76.70\% & 76.57\% & 74.37\% & 74.67\% \\ Resnet\_50 & 81.11\% & 80.54\% & 78.27\% & 78.86\% \\ EfficientNet\_b2 & 77.13\% & 76.89\% & 75.64\% & 75.74\% \\ EfficientNetv2\_s & 81.53\% & 80.37\% & 79.50\% & 79.59\% \\ EfficientNetv2\_m & 84.68\% & 83.20\% & 82.67\% & 82.51\% \\ CSA & 77.24\% & 76.91\% & 74.25\% & 74.87\% \\
**MSPyraNet (ours)** & **89.22\%** & **88.85\%** & **87.87\%** & **88.16\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experimental results of MSPyraNet on CHMD2.  
Figure 6: Comparative experimental results of MSPyraNet on CHMD1.  
over the CSA model are even more significant, with gains of 11.98% in accuracy, 11.94% in precision, 13.62% in recall, and 13.29% in F1-score. MSPyraNet also outperforms Resnet_50 by 8.11%, 8.31%, 9.60%, and 9.30% on the four metrics. Compared to ConvNeXtv_b, the performance gains are 12.52%, 12.28%, 13.50%, and 13.49%, respectively. These results highlight the robustness and effectiveness of MSPyraNet on this classification task. Figure 7 illustrates the performance comparison of MSPyraNet and SOTA models on the CHMD2 dataset across four evaluation metrics.
# Detection of Geomagnetic Signals as Precursors to Some Earthquakes in China
## 3 Polarization Results and Earthquakes
### Polarization Results of Haixi Earthquake
Figure 4 takes the \(Y_{\rm ZH}\) as an example to show the synchronized changes in the polarization values at CDP, GLM, and CNH stations during 2013. Obviously, some significant enhancements of \(Y_{\rm ZH}\) occurred at the CDP station in phase with that of the GLM station. Moreover, the polarization ratio value of \(Y_{\rm ZH}\) at CDP station was very high and also exceeded the threshold line. According to [PERSON] et al., the synchronized enhancements of GLM and CDP stations are related to normal geomagnetic field variations [25]. The variation of the horizontal component (\(S_{\rm H}\)) increases with increasing latitude (near the equator). If the variation of the vertical component is small throughout different latitudes, then stations in low latitudes are more likely to have high polarization values. As shown in Figure 4, the enhancements appeared in CDP and GLM stations, but not in CNH station. However, the CDP station was 1036 km away from the epicenter of the earthquake, so these high polarization values had nothing to do with the earthquake.
## 5 Discussion
While Llama-70B effectively identified errors, its corrections were often frequency-based rather than meaning-driven, leading to instances where it selected plausible but incorrect substitutions. In contrast, GPT-4o showed stronger grammatical and structural correction capabilities, particularly in handling pronoun agreement and infinitive verb structures, though it occasionally exhibited biases toward phonetic similarity over semantic accuracy. Llama models, particularly Llama-70B, provide a reliable, open-source alternative to GPT-4o, offering strong performance in grammatical error detection while allowing for greater flexibility in customization. Its accessibility as an open-source model on platforms like Hugging Face makes it an attractive option for budget-constrained applications, where fine-tuning on domain-specific text could help to mitigate some of its correction limitations. Meanwhile, GPT-4o's ability to apply grammatical rules more effectively makes it the most suitable choice for applications requiring precise and context-aware error correction. The implications of these findings are significant for digital archiving, language preservation, and educational applications. Effective GEC is essential for enhancing the readability and accessibility of digitized children's literature, which often suffers from unique OCR-induced errors. The zero-shot performance of GPT-4o and Llama models suggests that institutions and researchers can rely on these models to process OCR-scanned texts with high accuracy, even without fine-tuning on extensive domain-specific data. However, this study also highlights important limitations. The small dataset of German children's literature restricted the potential for fine-tuning encoder-based models like GBERT and GELECTRA, limiting their ability to compete with the generalization power of decoder-based models in zero-shot scenarios. This limitation underscores the need for larger, genre-specific datasets in children's literature to facilitate more effective fine-tuning for language-specific models. Beyond technical considerations, using AI for children's literature comes with ethical concerns. While AI can improve accessibility and text quality, its corrections may introduce subtle biases, altering tone, style, or cultural nuances, especially important in books for young readers. Ensuring linguistic diversity and avoiding homogenization is key to preserving the richness of children's literature. Transparency is also crucial so that educators, parents, and publishers understand the role of AI in content modification. Future research should address these challenges, ensuring AI-assisted text refinement respects pedagogical and cultural sensitivities while supporting creative storytelling.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attacks on CC
#### 5.2.2 UDP Floods  
In this attack, the network bandwidth of CC is fully exhausted, although no user exists. Adversary injects a huge number of UDP packets into the network [12], as illustrated in Figure 5.  
Figure 4: SYN flood attack.  
Figure 5: UDP flood attack.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**DDoS Attack** & **Attack Aim** & **Source IP Address** & **Attack Method** & **OSI Layer** & **Impacts of Attack** \\ \hline SYN Flood & Protocol exploiting resource depletion & Spoofed source depletion & Not using malformed packets. Exploits TCP’s three-way handshaking & L4 & Attack consumes VM’s resources, leading to EDoS attacks \\ UDP & Protocol exploited bandwidth depletion attack & Spoofed source depletion & Not using malformed packets. Sends an oversize stream of UDP packets to a specific or random port of a target & L4 & Attacks consume network bandwidth, leading to EDoS attacks \\ Ping of Death & Resource depletion using malformed packets & Spoofed packet that exceeds maximum packet size & L3 & Attacks cause a buffer overflow and system crash, leading to EDoS attacks \\ ICMP & Protocol exploited Floods & Spoofed bandwidth depletion attack & Not using malformed packets. Exploits ICMP’s ECHO REQUEST packet & L3 & Attacks saturate network bandwidth, leading to EDoS attacks \\ HTTP & Protocol exploiting resource depletion & Real (Not Spoofed) & Not using malformed packets. Exploits HTTP GET and POST request & L7 & Attacks consume all VM’s resources, leading to EDoS attacks \\ \hline \hline \end{tabular}
\end{table}
Table 5: DDoS attacks on CC.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.3 Energy-Efficient Multi-Path Grid-Based Geographic Routing Protocol (E2 MG2 RP)  
An energy-efficient multi-route based on the grid geographic routing protocol (E2 MG2 RP) for UWSNs was presented by the author in [149]. A multi-route routing technique based on grid is utilized by the E2 MG2 RP. Every grid will use a network selection weight to determine with gateway node to use depending on the network's energy level and distance from the center of every grid cell. Similar to a repeater node, a gateway node's main objective is to transfer communicating data packets from one network grid to the next network grid. In the network the gateway node will be changed, if the presented gateway node remaining energy is low from the network threshold level. Furthermore, if a Tx anchor node has communicating data packets to transmit, it will send those communicating data packets to its neighbor gateway node. Therefore, the neighbor gateway nodes will select a right way to sensor Rx node by using the process of Round-Robin (RR) technique. The communicating data packets will be returned to the neighbor gateway node if there is no reliable route, and the neighbor gateway node will look for another suitable route to transmit these communicating data packets.