esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Dimensional Fusion Feature-Enhanced Prediction Method  
The MDFFE prediction method proposed in this study is shown in Figure 1. Its model architecture consists of two parts: The data enhancement framework of multi-dimensional feature engineering and the deep fusion model. In the data enhancement framework of multi-dimensional feature engineering, starting from the original data, technical indicators, volatility indicators, time characteristics, and cross-variety linkage characteristics are added, respectively. These characteristics can reflect market dynamics, capture market risks, adapt to price volatility cycles, and enrich model information. After adding features, standardization, data processing, and lagging features are added to prevent data leakage and form integrated features. The deep fusion model consists of three parts. The first is a three-layer CNN structure, including convolution blocks 1, 2, and 3. Each layer is composed of convolution, batch normalization, and maximum pooling operations, and gradually extracts multi-scale features from local to global, from short-term to long-term. Then the fully connected layer, including fully connected layers 1 and 2, integrates and maps the features and uses the dropout mechanism to prevent overfitting. Finally, the CNN with strong temporal feature extraction ability and the XGBoost with excellent nonlinear mapping ability are fused by the adaptive weight fusion strategy to achieve efficient prediction. Table 1 lists the construction process of the MDFFE prediction method in detail,covering the key stages of data preprocessing, feature engineering, and model construction, and shows the core steps and methods of each stage and their corresponding descriptions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  
Figure 1: MDFFE model architecture diagram (the construction process of MDFFE method is listed in detail, covering the key stages of data preprocessing, feature engineering, and model construction).
## 2 Project Overview and Geotechnical Conditions  
Suzhou Metro Tunnel-Line S1, constructed in Kunshan, Suzhou, is located in the east of Suzhou and borders with Shanghai, and is a system of 41.36 km long twin tunnels and a total of 28 stations spaced at an average interval of 1.523 km along the line. Each tunnel is 6.6 m in outer diameter and 5.9 m in inner diameter. The tunnels with an axis depth of 9-25 m are constructed with a precast concrete lining, and the length of each lining section is 1.2 m.  
According to the site investigation results, most of the land area in Suzhou is quaternary sediments with a total thickness of more than 150 m, and the quaternary deposit within 70 m construction depth along Line S1 primarily consists of silty clay, mucky-silty clay, and sandy soil. Figure 1 shows the detailed stratigraphic profile between Zhanlanzhongxin station and Xihuanlu station. In this interval, the shield was mainly excavated in the soil layer of the mucky-silty clay 2\({}_{\text{Y}}\), which is widely distributed in the east (the top 460 rings) and characterized by high water content and compressibility coupled with low permeability and shear strength.  
Earth pressure balance machines fabricated by China Railway Construction Heavy Industry Corporation Limited were used to the tunnel between Zhanlanzhongxin station and Xihuanlu station. The EPB shields with a total weight of 530 t were 9100 mm long, with a cutter head diameter of 6860 mm. In addition, the diameter and skin thickness of the front, middle, and rear shields were 6810 and 50 mm, 6800 and 40 mm, and 6790 and 40 mm, respectively.  
The section of the 240 th ring of the left line tunnel was set as the monitoring section LR240 as shown in Figure 1, and the surface settlement and tunnel displacement of the monitoring section LR240 were monitored during and after the construction.
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 3 Results
### Classification Modeling
#### 3.2.1 Estimation Results of the Classification Model  
Each classification model was based on a support vector machine trained by adjusting the kernel and hyperparameters. Hyperparameters were searched based on a high F1 score. The adjusted hyperparameters were kernels, regulatory terms C, and gamma controlling the bias of the Gaussian distribution for the Gaussian kernels. Table 5 shows the parameters of the optimized classification model. In learning, cross-validation was performed to prevent overfitting (5-fold).
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 5 Experiments and Performance Measures  
### Experiments  
In general, our work is divided into two parts. In the first part of our research, we have performed the experimental setup by using the ArduPilot Mega (APM) 2.8 microcontroller and Mission Planner, which is the suitable GCS application for this microcontroller. The APM 2.8 microcontroller uses the micro air vehicle link (MAVLink) protocol [36] to maintain a connection between the APM 2.8 and GCS. During our experiments, the APM 2.8 board was tested instead of drone in order to obtain the flight data that were used to analyze the SC-FDM modulation for a UAV communication system. As we mentioned in Section 3, the UAV has a data link that utilizes the frequency range from 150 MHz to  
Figure 6: Block diagram of the SC-FDM modulation process.  
1.5 GHz. In our work, two 3 DR 915 MHz telemetry radios were used to provide this data link. To set up a connection between the APM 2.8 and Mission Planner GCS, one of the 3 DR 915 MHz telemetry radios was plugged into the APM 2.8, as shown in Figure 7, while a second telemetry radio was connected to a PC using a USB cable. Since we have the communication link between two telemetry radios, we can check the link status in terms of the number of transmitted packets, frequency range, number of channels, and \(\mathrm{T_{x}}\) power, as shown in Figure 8a. We configured the connection settings using Mission Planner. After establishing a successful connection, the telemetry data were obtained from the GCS. It should be noted that these data can be represented as signals when they are carried by the MAVLink through a wireless link between the APM 2.8 and GCS. All relevant information about APM 2.8 such as altitude, yaw, pitch, roll, attitude, vertical speed, ground speed, etc., are shown in the \"Flight Data\" screen of the Mission Planner, see Figure 8b. When we move the APM 2.8 microcontroller from one place to another, the \"Flight Data\" screen will display the telemetry data according to the new position of APM 2.8. Afterwards, the telemetry data collected from the GCS were imported into the MATLAB for the modulation process.  
Figure 8: _Cont._  
Figure 7: Layout of the experimental setup.  
In the second part of our work, we have used the data that were gathered from the GCS and static simulation parameters shown in Table 1 for the modulation process. For this part, MATLAB was used to perform the modulation process.
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 6 Conclusions
To ensure the provision of robust and reliable 5G and 6G services while avoiding interference and maintaining the service quality of the incumbent satellite systems, exploring alternative spectrum bands becomes crucial. The provision of the NTN services within existing satellite systems in the n255 and n256 bands also presents problems mentioned above. As a more feasible solution for future NTN systems, sharing the spectrum with terrestrial cellular systems is recommended. This approach eliminates the need for complex coordination procedures and reduces restrictions on the NTN systems, facilitating better integration and improved service provision. However, this approach requires further studies and evaluation. Conceptualization, A.P. and V.T. ; methodology, A.P. and O.V. ; software, A.P. ; validation, A.P. ; formal analysis, V.T. and O.V. ; investigation, V.T. and S.D. ; resources, S.D. and O.V. ; data curation, A.P. ; writing--original draft preparation, A.P. and S.D. ; writing--review and editing, S.D. and O.V. ; visualization, A.P. ; supervision, O.V. ; project administration, S.D. ; funding acquisition, V.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors would like to thank the Radio Research and Development Institute, Moscow, Russia for providing valuable technical resources to conduct this study and Geyser-Telecom, Ltd., Moscow, Russia, for extensive consultation and important suggestions regarding the assumptions used in this study. The authors declare no conflict of interest.
## 3 Phytotoxic Effects of Phenolic Compounds
### Dihydrohalcones--The Most Abundant Apple Phenolic Compounds  
Dihydrochalcones (DHCs) represent a distinct group of chalcones characterized by two aromatic rings linked by a saturated three-carbon bridge, where the double bond between \(\alpha\) and \(\beta\) C atoms, which is typical of chalcones, is reduced (Figure 4). All DHCs are glycosylated derivatives of phloretin aglycon, differing in their sugar groups and/or binding sites. Due to its typical DHC structure, and a lack of a carbohydrate ligand, phloretin (Figure 4) is a highly flexible molecule capable of efficiently interacting with biological macromolecules, possessing a broad spectrum of biological activities [209; 210; 211; 201].  
A specific natural source of DHCs is the genus _Malus_, including the domesticated apple (_Malus_\(\times\)_domestic_ Borkh.), where DHCs account for 96-97% of the total phenolic content in leaves [214; 215]. Phlorizin (phloretin \(2^{\prime}\)-O-glucoside) is the earliest identified and most abundant DHC in the genus _Malus_, making up 4-18% of the dry mass of apple leaves [216]. Substantial amounts of phlorizin are also found in the roots, stems, and bark of apple trees, from which it was first isolated in 1835 [217]. The key step in phlorizin biosynthesis is the glycosylation of the aglycone phloretin (3-(4-hydroxyphenyl)-1-(2,4,6-trihydroxyphenyl)-1-propanone) at the \(2^{\prime}\) position, catalyzed by a specific uridine diphosphate-glycosyltransferase that directly determines the concentration of phlorizin in apple tissues [218]. Due to its high prevalence, phlorizin serves as a key parameter for classifying _Malus_ species, determining phylogenetic and phytogeographic relationships within the subfamily Maloideae [219], and as a marker in the food industry for detecting the presence of apple in fruit juices [220].  
Advancements in analytical techniques have enabled the detection of phlorizin in trace amounts in other species within the Rosaceae family, such as strawberry (_Fragaria_\(\times\)_ananassa_ Duch.) [221] and dog rose (_Rosa canina_ L.) [222], as well as species from unrelated families such as Fabaceae and Ericaceae [223; 224; 225].
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 3 Methodology
### Protocol 1.1: No Partition Handling (NPH)  
We define Protocol 1.1, a generic routing protocol for underwater acoustic sensor networks. It works in two stages: Beacon dissemination and data transmission stages. During the beacon dissemination stage, the sink node initiates the process by transmitting a beacon which is then forwarded by every node that receives the beacon. Based on the neighborhood information acquired during the beacon dissemination stage, every node determines its next hop node. The next hop selection criteria is based hop count and residual energy. Protocol 1.1 differs from the proposed protocol in the following respects:  
1. Unlike the proposed protocol, protocol 1.1 does not incorporate any partition handling mechanism. Therefore, in case of network partitioning, the partitioned part will not be able to receive beacons and therefore will not be able to access the main body of the network. The drop in the packet delivery ration will depend upon the number of partitioned nodes besides other factors such as connectivity hole and erroneous reception.
2. Unlike the proposed protocol, protocol 1.1 does not implement any mechanism for diverting the traffic load to less burdened, high residual energy downstream nodes.
3. Protocol 1.1 does not incorporate a loop resolution strategy.
4. Protocol 1.1 does not enforce any minimum residual energy threshold and therefore does not follow the actions taken by the proposed scheme when a node reaches its residual energy threshold. Protocol 1.1 has the following similarities with the proposed scheme:  
1. Protocol 1.1 assumes untethered free mobility of sensor nodes with water currents. It, therefore, assumes the same network architecture as that of the proposed scheme.
2. Next hop selection criteria is the similar to that of the proposed scheme. The candidate nodes are first evaluated based on the link condition. The nodes with acceptable expected SNR are shortlisted. Among the shortlisted nodes, the nodes with minimum hop count and maximum residual energy are selected as next hop.  
The reason for comparing our scheme with protocol 1.1 is lack of available protocols that follow similar network architecture and/or assume free untethered mobility of sensor nodes. To the best of our knowledge, this area still remains to be explored.
## 3 Results and Discussion
### Antioxidant Activity
Undoubtedly, the presence of methoxyl groups in the analogs B ring favored the antioxidant activity, the chalcones **2e** and **2f** exhibit 99% of antioxidant activity, while the flavones **3e**, **3f**, and **3g** present results of 82, 99 and 84% respectively, all of them better than \(\alpha\)-tocopherol (60%). This allows for determining that the presence of electron-donor groups in the B ring benefits this activity [46; 47]. From all the synthesized analogs, it is important to highlight the results obtained for **2j**, **2k**, **3j**, and **3k**, which have anthracene and furan substitutions, while they presented antiradical activity at very low concentrations in the DPPH assay, with EC\({}_{50}\) of 1.7 and 5.4 \(\times\) 10\({}^{-3}\) ug/mL for **2j** and **2k**, and 1\(\times\) 10\({}^{-8}\) and 0.1 ug/mL for **3j** and **3k**, being far better than the control quercetin (3 ug/mL). These same four molecules showed a high antioxidant activity in the \(\beta\)-carotene technique, with results of 98% and 88% for the **2j** and **2k** chalcones, while the **3j** and **3k** flavones had 99% and 85%, respectively, surpassing the 60% antioxidant activity of \(\alpha\)-tocopherol. It is known that the oxidation of polycyclic aromatic hydrocarbons, such as anthracene generates anthraquinone type products, with 9,10-anthraquinone and 9-hydroxyanthrone being the more abundant [48], the basic skeleton for anthraquinones and anthrone, which provides them the capacity to act as electron acceptors (electrophiles); furthermore, their high conjugation gives them great stability (by resonance means), potentiating the antioxidant activity [49; 50]. On other hand, the oxidation of furans gives as a result oxyfuranones in the case of monosubstitutions, or diketones in the case of 2,5-disubstituted furans [51]; [PERSON] et al. reported the importance of diketonic systems in the antioxidant activity, both in their keto or enol form [52].
# SEA and GATOR 10 Years Later
## 8 GATOR in Human Diseases
### Cardiovascular Diseases--NPRL3  
In striking contrast to other GATOR1 components, and especially to its paralogue NPRL2, NPRL3 seems to be less important for epilepsy and cancer. Rather it appears as a crucial gene, necessary for the normal development of the cardiovascular system [41]. Mice with the deletion of NPRL3 promoter often have severe embryonal cardiac defects and die in late gestations. A single nucleotide polymorphism of NPRL3 was reported in sickle cell anemia [211], a disease characterized by various hemoglobin abnormalities. These defects are explained by the fact that the introns of NPRL3 contain super-enhancers required for high level expression of the genes encoding the \(\alpha\)-globin subunits of hemoglobin in humans and mice [212; 213]. These regulation elements appeared to be deeply preserved during evolution. A recent genomic study revealed that the NPRL3 gene carrying a strong regulatory element became linked to at least two different globin genes in ancestral vertebrate, just before the divergence between jawless and jawed vertebrates [214]. Each of these ancestral globin genes evolved in the modern hemoglobin genes, but kept their enhancers in NPRL3, which provide an explanation to a long-standing enigma of how globin genes linked to the same adjacent gene undergo convergent evolution in different species.  
Therefore, the pathologies associated with NPRL3 mutations are related with the disturbances of the transcriptional elements in the Nprl3 gene rather than with the function of the protein product in the mTORC1 pathway. Similarly, the higher recurrence of NPRL2 mutations in cancers and DEPDC5 mutations in epilepsies could be related with the specific moonlighting functions of these GATOR1 members beyond the regulation of the mTORC1 pathway.
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 2 Materials and Methods
### Optimising Propeller Geometry  
The two main geometric parameters of the propeller, the chord and twist distribution, have now been calculated, however these parameters are not optimised. The following section details how the displacement velocity ratio is optimised to produce optimal propeller geometry in an iterative process. Firstly, the non-dimensional design power coefficient, \(P_{c}\), has to be calculated using Equation (30), where \(P\) is the known shaft power supplied to the propeller.  
\[P_{c}=\frac{2P}{\rho V^{3}\pi R^{2}} \tag{30}\]  
The key equation in the iterative process is Equation (31), which after simple integration results in Equation (32).  
\[\frac{dP_{c}}{d\xi}=\frac{dJ_{1}}{d\xi}\cdot\zeta+\frac{dJ_{2}}{d\xi}\cdot\zeta ^{2} \tag{31}\]  
\[P_{c}=J_{1}\cdot\zeta+J_{2}\cdot\zeta^{2} \tag{32}\]  
\(J_{1}\) and \(J_{2}\) are simply variables used in the iteration process and have no physical meaning. 'Design of Propellers for Motorsoarers', a paper by [PERSON] (1979) [22], contains a full derivation of Equations (31)-(34).  
\[\frac{dJ_{1}}{d\xi}=4\xi G\left(1+\frac{\epsilon}{\tan\phi}\right) \tag{33}\]  
\[\frac{dJ_{2}}{d\xi}=\frac{1}{2}\cdot\frac{dJ_{1}}{d\xi}\cdot(1-\epsilon\tan \phi)\cdot\cos^{2}\phi \tag{34}\]  
After evaluating the derivatives of \(J_{1}\) and \(J_{2}\) at every radial co-ordinate, integrating with respect to the non-dimensional radius will result in values for \(J_{1}\) and \(J_{2}\). Re-arrangingEquation (32), results in Equation (35). Using calculated values of \(P_{c}\), \(J_{1}\) and \(J_{2}\), a new value of the displacement velocity ratio can be found.  
\[\zeta=-\frac{J_{1}}{2J_{2}}+\sqrt{\left(\frac{J_{1}}{2J_{2}}\right)^{2}+\frac{P_ {c}}{J_{2}}} \tag{35}\]  
The process is then repeated using the new value for the displacement velocity ratio, and is iterated until the displacement velocity ratio converges. Initially, an estimate for the displacement velocity ratio is used, which has to be small and is often zero. If the thrust, T is known then a slightly different Equation (36) (the non-dimensional Design Thrust Coefficient, _Tc_) is used with derivatives of \(I_{1}\) and \(I_{2}\). in the above procedure [30; 31; 32].  
\[T_{c}=\frac{2T}{\rho V^{2}\pi R^{2}} \tag{36}\]
# Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning
## 3 Numerical Example
### Results
#### 3.3.2 Results of Dataset 2  
Using Dataset 2, Table 5 shows the results of the parameter estimation of the software reliability model assuming NHPP and the structure of the software reliability model using deep learning. Similar to Dataset 1, Dataset 2 was divided into 80% and 90%, and parameter estimation and model fitting were performed on the divided dataset. The structure of the software reliability model using deep learning consists of three hidden layers, as in Dataset 1, and the number of nodes in each layer was set according to the size of the training dataset. The learning rates were set to 0.000001 for DNN and 0.00001 for RNN, LSTM, and GRU. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**No.** & **Model** & **MSE** & **MAE** & **PRR** & **PP** & **R2** & **PRV** & **RMSPE** & **MEOP** & **TS** & **PC** & **preSSE** \\ \hline
1 & GO & 22.6581 & 4.3155 & 4.7797 & 13.4021 & 0.9407 & 4.4490 & 4.8071 & 4.2196 & 14.1477 & 69.6287 & 321.655 \\
2 & DS & 3.9569 & 1.6600 & 1636.27 & 3.7881 & 0.9896 & 1.9677 & 2.0112 & 1.6231 & 5.9122 & 31.2376 & 6.537 \\
3 & YID & 3.5588 & 1.5488 & 58.9754 & 2.7164 & 0.9907 & 1.9001 & 1.9081 & 1.5144 & 5.6069 & 28.9044 & 11.837 \\
4 & PZ & 3.1010 & 1.4570 & 31.2349 & 2.4008 & 0.9919 & 1.7750 & 1.7812 & 1.4246 & 5.2339 & 25.8753 & 2395.537 \\
5 & PNZ & 3.1296 & 1.4638 & 27.1085 & 2.3403 & 0.9918 & 1.7798 & 1.7893 & 1.4313 & 5.2580 & 26.0772 & 40.741 \\
6 & TP & 3.2108 & 1.4898 & 26.6591 & 2.3619 & 0.9916 & 1.8069 & 1.8125 & 1.4567 & 5.3257 & 26.6407 & 4948.058 \\
7 & TC & 3.6277 & 1.5687 & 26.6646 & 3.1326 & 0.9905 & 1.9117 & 1.9263 & 1.5338 & 5.6609 & 29.3263 & 6.367 \\
8 & Vtub & 3.4308 & 1.5331 & 82.7369 & 2.6383 & 0.9910 & 1.8662 & 1.8735 & 1.4990 & 5.5051 & 28.0984 & 7.340 \\
9 & DPF & 3.0682 & 1.3714 & 0.8756 & 1.7908 & 0.9920 & 1.7676 & 1.7718 & 1.3409 & 5.2061 & 25.6412 & 65.275 \\
10 & UDPF & 2.9984 & 1.3889 & 0.6386 & 0.9113 & 0.9922 & 1.7515 & 1.7516 & 1.3581 & 5.1465 & 25.1349 & 1709.197 \\
11 & DNN & 2.5868 & 1.5030 & 0.7327 & 1.3150 & 0.9932 & 0.5792 & 1.6107 & 1.4696 & 4.7803 & 21.8866 & 13.471 \\
12 & RNN & 2.0731 & 1.4332 & 0.8317 & 1.5417 & 0.9946 & 0.1402 & 1.4400 & 1.4013 & 4.2794 & 17.0167 & 11.397 \\
13 & LSTM & 2.0257 & 1.4179 & 0.7584 & 1.3192 & 0.9947 & 0.1251 & 1.4234 & 1.3864 & 4.2302 & 16.5077 & 11.755 \\
14 & GRU & 1.9961 & 1.4061 & 0.7593 & 1.331 & 0.9948 & 0.1392 & 1.413 & 1.3749 & 4.1991 & 16.1831 & 12.103 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of all criteria using 90% of Dataset 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.
## 7 Discussion
### Context Correlation Detection and Fuse Mechanism  
This paper proposes a lightweight large language model specifically designed for the agricultural field, supporting both Chinese and Uyghur. The model is based on the Transformer architecture and optimized for specific challenges, including the collection and processing of agricultural Uyghur corpus, Chinese-Uyghur mixed word segmentation, lightweight modeling in the agricultural field, context correlation detection, and text circuit interruption mechanism. With a size of only 1.5 GB, the model is suitable for running on low VRAM GPUs, aiming to improve semantic coherence while reducing memory usage and computational requirements.  
The existing method effectively addresses the issue of non-relevant text fusion during small-scale corpus training through deep learning techniques, context detection mechanisms, and text fuse mechanisms. This approach enhances the model's robustness and potential advantages in handling complex problems.  
The existing method demonstrates significant advantages in terms of model size, hardware requirements, specific domain optimization, context correlation detection, text circuit interruption mechanisms, model transparency, and environmental impact. However, there are also some limitations. For instance, data in certain agricultural sub-fields may not be comprehensive, affecting the model's generalization ability. When handling large-scale or complex tasks, computational resource limitations can become a bottleneck. Additionally, the model's adaptability to multilingual and cross-cultural scenarios still needs further exploration and optimization. Future work will focus on expanding datasets, optimizing model structures, enhancing multilingual support, improving context detection mechanisms, increasing interpretability, and optimizing real-time performance.