esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Machine Learning (ML) Methods
#### 4.2.3 K-Nearest Neighbors  
First introduced by [PERSON] & Hart [44], KNN is a machine learning method used for regression. In this application, KNN considers each data record as a vector in an m-dimensional space (where m is the number of features), and predicts the value of each new sample based on the values of K records that are closest to that point in that space [45]. In this method, the closeness of the new point \(x\) and the training point \(x_{i}\) is measured by a Euclidean distance function in the form of Equation (9) as following [45]  
\[d(x,x_{i})=\sqrt{\sum_{j=1}^{m}\Bigl{(}x_{i}^{j}-x^{j}\Bigr{)}^{2}},\ \ \ i=1,2,\ldots,\ n\ and\ j=1,2,\ldots,m \tag{9}\]  
In this equation, \(n\) is the number of training samples and m is the number of input samples.  
The steps of KNN can be summarized as follows.  
Step 1: Specifying the parameter K (the number of nearest neighbors).  
Step 2: Computing the distance of the new input from all training samples.  
Step 3: Sorting the computed distances in ascending order and selecting the K samples with the shortest distance.  
Step 4: Forming the set of values of the selected K samples.  
Step 5: Computing the predicted value of the new sample by weighted averaging of the values in the set of Step 4.  
The first step in this method is to choose the value of K (the number of neighbors). After selecting K, the Euclidean distance between the new sample and the existing samples, i.e., \(d(x,x_{i})\) for \(i=1\), \(2\),..., \(n\) must be computed. The values of the K samples with the lowest d (shortest distance from the new sample) must then be put in Equation (10) to compute the predicted value of the new input [46]  
\[\hat{y}=\sum_{i=1}^{k}w_{i}y_{i} \tag{10}\]  
Here, \(w_{i}\) is the weight of each training sample, which depends on its distance from the new sample and is given by Equation (11)  
\[w_{i}=\frac{\frac{1}{d(x,x_{i})}}{\sum_{i=1}^{k}\frac{1}{d(x,x_{i})}} \tag{11}\]  
As this equation shows, samples that are closer to the new sample will have a greater impact on the prediction. In KNN, the new values are produced based on the previous data regardless of how much noise they have, which makes KNN sensitive to noise and irrelevant features [46].
## 5 Discussion
### Generalizability of the Research  
The student overall course performance prediction method proposed in this study demonstrates good generalizability and can be applied to different grading systems. Although different institutions may adopt varying grading standards, for example, many schools use traditional test scores as the main assessment criteria, usually categorized as midterm exams, final exams, and classroom quizzes. A student's final grade may be based on a weighted average of these exams. Some institutions use a grading scale based on sustained student performance, with assessments including class work, midterm exams, extracurricular reading reports, and group activities. Instead of numerical scores, some schools use grading systems (e.g., A, B, C, D, etc.) or descriptive grading systems (e.g., excellent, good, passing, failing), and these grades may be closely related to a student's particular skill level and stage of development. The core of the model is based on predicting students' historical performance. To apply it to different grading systems, adjustments in data preprocessing and feature engineering are required. For instance, some institutions may place more emphasis on project assessments or class participation rather than traditional exam scores, which would necessitate adaptation of the input data. Furthermore, adjustments should be made according to the local education system, student demographics, and course structure to ensure the accuracy and applicability of the model.
# Quorum Sensing and Quorum Quenching in _Agrobacteriumum_: A \"Go/No Go System\"?
## 1 Introduction  
Members of the _Agrobacteriumum_ genus are \(\alpha\)-proteobacteria that belong to the family Rhizobiaceae. They are plant pathogens, and may induce a disease known as crown gall on a wide range of dicotyledonous plants. The gall formation results from a genetic transformation process that relies upon the transfer of a piece of DNA, the transferred DNA (T-DNA), from the bacteria to the plant cell. In the bacteria, the T-DNA is located on the Ti (tumor-inducing) plasmid that carries most of the virulence determinants. The T-DNA transfer occurs via the activation of virulence (_vir_) genes. These genes encode a type IV secretion system (T4 SS) and they are transcribed under moderately acidic conditions, mostly in response to the presence of phenolics such as acetosyringone or sinapinic acid, produced by wounded plant tissues as part of the defense reaction mechanisms (for reviews on the disease induction and genetic transformation formation process, see [1; 2; 3; 4; 5; 6; 7]).  
Once in the plant cell, the T-DNA is transferred to the nucleus, and it integrates into the nuclear genome. The T-DNA genes are then expressed. They encode two major functions: (i) the production of two plant hormones, i.e., auxin and cytokinins, the concomitant production of which induces the cell proliferation and the formation of the tumor [8; 9; 10]; and, (ii) the synthesis of low molecular weight molecules called opines, that are characteristic of _Agrobacteriumum_-induced overgrowths (for reviews: [4; 5; 6; 7; 8; 9; 10; 11]). Opines play critical roles in _Agrobacteriumum_ ecology. First, they are used by the inciting Agrobacterium as specific growth substrates, the genetic determinants involved in the degradation of opines being borne on the tumour inducing (Ti) plasmid (e.g., [12; 13]). Second, some opines areinducers of the conjugative transfer of the Ti plasmid which also depends on a second T4 SS encoded by the Ti plasmid. Opines in tumors therefore contribute both to the multiplication of the pathogen and the dissemination of the pathogenic traits amongst the agrobacterial population, which in nature mostly consists in Ti-plasmid free cells (for reviews: [11; 14; 15]).
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.5 Low-Altitude Semantic Communication  
In UAV networks, low-altitude semantic communication is employed to extract the core semantics of information, significantly reducing the transmission load and latency with the help of the deep semantic parsing capabilities of LLMs. By utilizing multimodal data processing capabilities, LLMs can accurately capture environmental changes and update semantic information for collaborative decision-making between UAVs in real time, ensuring more efficient command and control and resource allocation in complex and variable low-altitude airspace. In 2025, [PERSON] et al. proposed a UAV-based SC frame work incorporating a DM, balancing between the performance and sampling steps and, thus, ensuring efficient computation and high-quality image enhancement [235]. [PERSON] et al. investigated an intelligent resource allocation method for multi-UAV-assisted semantic sensing in UAV image sensing task scenarios. This approach involves jointly optimizing UAV trajectories, spectrum bandwidth, transmit power, and the number of semantic symbols transmitted to maximize the quality of experience while minimizing transmission costs [236]. By integrating blockchain technology, it is also possible to provide privacy protection for data access within UAV systems, thus reducing the risk of exposure of LLM training data [237].
Sensors_**2009**, \(9\), 6028-6045; doi:10.3390/s90806028  
_Article_  
**MCBT: Multi-Hop Cluster Based Stable Backbone Trees for Data Collection and Dissemination in WSNs**  
**[PERSON]**\({}^{1}\)**, **[PERSON]**\({}^{2,\star}\)**, **[PERSON]**\({}^{2}\)**, **[PERSON]**\({}^{3}\) **and [PERSON]**\({}^{3}\)  
\({}^{1}\) Convergence Lab. Digital Media & Communications R&D Center, Samsung Electronics, Korea;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Computer Science and Engineering, Michigan State University, USA;  
E-Mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
\({}^{3}\) School of Information and Communication Engineering, Sungkyunkwan University, Korea;  
E-Mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-31-290-7145/7226; Fax: +82-31-299-4134  
_Received: 26 June 2009; in revised form: 9 July 2009 / Accepted: 17 July 2009/ Published: 29 July 2009_  
**Abstract:** We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs). The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD) and Multicluster, Mobile, Multimedia radio network (MMM), consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes) of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime.  
keywords:multi-hop cluster; backbone; load balancing; energy efficient routing; network lifetime
## 3 Experimental Test-Rig and Data Acquisition
### Data Collection  
The training sample data are provided for the study of the pattern recognition and quantitative diagnosis method based on the system characteristic. Three different severity leakage faults of discharge valve are designed in this paper. A circular hole with diameters of 2 mm, 6 mm, and 8 mm is machined on the normal valve to simulate different severity leakage faults of the valve, as shown in Figure 7. Data are collected when the RC pressure reaches 1.0 MPa, and the vibration signal and the current signal are simultaneously collected, to improve the reliability of the data. In this experiment, three groups of discharge valve leakage experiments and a group of normal valve test were carried out. The sampling frequency was set to 51.2 kHz and the sampling time was 30 s.  
Figure 6: Experimental test-rig for RC valve failure.
# Effect of Controlled Defoliant Application on Cotton Fiber Quality
## 3 Results and Discussion
### Fiber Quality Analyze by Advance Fiber Information System (AFIS)  
The AFIS generates data on 20 different variables which are categorized on different categories and discussed below.  
A. Information on Neps Parameter  
Neps are the entangled and knotted fibers that are formed during cotton harvesting or the ginning process. Approximately 5-20 fibers found to be knotted together to form a single Neps. Harvesting methods have a significant role in Neps; manually harvesting cotton results in fewer neps than mechanically harvested cotton. The existence of Neps in a cotton bale is unavoidable. A cotton bale with about 100 to 200 fiber Neps per gram is considered the best-case scenario, whereas 200 to 350 Neps/gm considers the range of a manageable level [57]. The cotton in each field was manually harvested. Nep size in field 1, and Nep/gm in field 2 has an insignificant result (Table 5). Similarly, the control has statistically higher Nep/gm in field 1, and higher Nep size in field 2. In both field, Nep/gm is within the best to manageable range. Similarly, AFIS also provides the information on Seed Coat Neps (SCN). The seed coats that remain with the fiber are seed coat neps. In both field the SCN is statistically insignificant.
# Electronic Properties of Electron-Deficient Zn(II) Porphyrins for HBr Splitting
## 2 Experimental
### Syntheses
#### 2.2.10 ZnTPPF\({}_{19}\)-click-CHO  
In a dry Schlenk tube, 0.0960 mmol (101.9 mg) of ZnTPPF\({}_{19}\)-N\({}_{3}\) and 0.115 mmol (14.9 mg, 1.2 eq.) of 4-ethynylbenzaldehyde were dissolved in a THF:CH\({}_{3}\)CN = 1:1 mixture (7 mL), under N\({}_{2}\) flow. The solution was degassed through 3 cycles of freeze-pump-thaw at -78 \({}^{\circ}\)C, then 0.096 mmol (17 \(\mu\)L)of N,N-disopropilethylenammine and 0.0096 mmol (2 mg, 0.1 eq.) of CuI were added and the mixture was kept under stirring overnight at RT. The solvents were evaporated in vacuo and the crude residue was purified by gravimetric chromatography (CH\({}_{2}\)Cl\({}_{2}\):n-hexane = 7:3, to remove the impurities; CH\({}_{2}\)Cl\({}_{2}\):CH\({}_{3}\)OH = 97:3, to recover the desired product), affording 68.2 mg of ZnTPPF\({}_{19}\)-click-CHO (60% yield) as a purple powder.  
\({}^{1}\)H-NMR (400 MHz, CDCl\({}_{3}\), 25 \({}^{\circ}\)C) \(\delta\), ppm: 9.74 (1H, s), 9.08 (8H, m), 8.48 (1H, s), 7.94 (2H, d), 7.76 (2H, d).  
\({}^{19}\)F-NMR (400 MHz, CDCl\({}_{3}\), 25 \({}^{\circ}\)C) \(\delta\), ppm: \(-\)134.40 (2F, dd), \(-\)136.82 (6F, t), \(-\)146.57 (2F, m), \(-\)151.83 (3F, td), \(-\)161.67 (6F, td).
## 3 Results
### Comparative Assessment With MSP  
#### 3.3.1 Me Deviation Frequency  
Table 5 summarizes the analysis of the deviation in the Me and the differences with respect to different FH plane orientations, as calculated with the linear-by-linear association test. We observed 100% case concordance to the subject selection criteria, with FH1 (\(0.56\pm 0.27\) mm) and FH2 (\(0.55\pm 0.28\) mm), thus showing a Me deviation below 2 mm. However, we recorded a distinct redistribution in the FH3 (\(1.37\pm 1.23\) mm) and FH4 (\(1.33\pm 1.16\) mm) reference planes. A statistically substantial deviation measurement \(>\)2 mm (\(p<0.001\)) was recorded in more than 18% of cases for a similar group of samples (Figure 4A).  
Figure 3: Comparison of the different spatial orientation levels (SOLs) of an increasing magnitude from column (**A–C**). An increase in the deviation in the Frankfort Horizontal (FH) plane results in a corresponding difference in the midsagittal plane (MSP), defined by nasion (N) and sella points. FH and MSP are color-coded to improve visualization of the differences (FH1: yellow; FH2: green; FH3: purple; FH4: pink). All numerical values are in degree angles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **FH2** & **FH3** & **FH4** & **\(p\)-Value** \\ \hline Frankfort Horizontal Plane & 0.602 \(\pm\) 0.503 A & 0.639 \(\pm\) 0.539 A,B & 0.944 \(\pm\) 0.778 B & 0.034 \\ Midsagittal Plane & 0.015 \(\pm\) 0.023 A & 0.644 \(\pm\) 0.546 B & 0.627 \(\pm\) 0.516 B & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the spatial orientation level (SOL) between the FH planes with FH1.
## 3 Results
### Interaction Analysis of LTAs with Cell Surface Receptors  
SPR analysis was used to examine the associations between LTAs and their receptors. The binding reaction between one molecule of analyte and one molecule of receptor can be confirmed through analysis of the association and dissociation sections between LTAs and receptors such as TLR 2, TLR4, and CD14. The binding force between LTA and the receptor is determined by the association rate constant (Ka) and the dissociation rate constant (Kd). A lower Ka value indicates that the binding is stronger and faster, while a lower Kd value indicates fast dissociation. Lastly, KD is an indicator of affinity, and the lower index value suggests that the affinity is strong. Affinity to TLR2 of LPS and LTAs isolated from the four \(L\). _plantarum_ strains was analyzed (Table 1 and Figure S2). The Ka value was highest in K8 at 9.50 \(\times\) 10\({}^{3}\) and decreased in the order of K88, K5-5, and K55-5. On the other hand, the Kd values were in the range of 5.02 \(\times\) 10\({}^{-3}\)-5.9 \(\times\) 10\({}^{-4}\), showing similar values. As a result, based on KD values, the binding affinity of LTA isolated from K5-5 and K55-5 to TLR2 was higher than that of LTA isolated from K8 and K88. The KD to TLR4 was highest in LPS and LTA isolated from K55-5. On the other hand, LTA isolated from K8 showed the lowest binding affinity to TLR4. The binding affinity to CD14 was highest with LPS and lowestwith LTA isolated from K8. This indicates a strong association between TLR4 and CD14 in LPS. Similarly, LTAs isolated from K5-5 and K55-5 appeared to induce stronger signaling than LTAs isolated from K8 and K88 through association with TLR2 and CD14.
## 3 Results  
### Classification Results and Accuracy Assessment  
#### 3.1.1 Classification Results  
This study selected high-quality images of the specific phenological period of winter wheat growth. Furthermore, the winter wheat, vegetation, urban, water, and others' class were labeled and mapped in GEE (Figure 4). The detail codes are accessible from the Code link (see Supplementary Materials).  
Figure 4: _Cont._  
Figure 4: The classification results of the study area in 2019. (**a–c**) The classification results of support vector machine (SVM), random forest (RF), and classification and regression tree (CART), respectively. (**d**) Winter wheat areas of the three machine learning algorithms. WHE = Wheat, VEG = Vegetation, URB = Urban, WAT = Water, OTH = Other.  
To compare the differences in areas of each class identified by SVM, RF, and CART, these areas were calculated (Figure 4). Meanwhile, in order to validate the accuracy of the three algorithms in identifying winter wheat pixels, the statistics of the official yearbook of winter wheat area were compared.  
The area of winter wheat identified by the three algorithms was 56,340 km\({}^{2}\) for SVM (34% of the study area), 54,469 km\({}^{2}\) for RF (33% of the study area), and 52,547 km\({}^{2}\) for CART (31% of the study area). Meanwhile, the official statistics of Henan province in 2019 were 57,066 km\({}^{2}\)[59]. By calculating winter wheat area residuals of the three algorithms, the following pattern was observed: CART (4519 km\({}^{2}\) residuals) \(>\) RF (2597 km\({}^{2}\) residuals) \(>\) SVM (726 km\({}^{2}\) residuals). The most accurate identification of the winter wheat was given by SVM, which has the lowest residual error of 726 km\({}^{2}\), followed by RF, for which the residual error compared to the actual area is 2597 km\({}^{2}\). The largest residual error was for CART with 3862.40 km\({}^{2}\). Moreover, we found that CART was able to identify the most urban and others' class pixels, while SVM and RF identified relatively few.
## 5 Experiments and Results
### SMOTE-Based Data Balancing  
Figure 4(a) illustrates the unequal distribution of samples among the various categories. We ensured balanced representation by applying SMOTE to both the primary classes and their corresponding subcategories to rectify this imbalance. We standardized the number of samples in the attack categories to equal that of the benign class while maintaining dataset balance. Additionally, we made sure that every attack class subclass was suitably balanced to avoid prejudice. This approach effectively reduces the risk of the model favoring the majority classes, thereby enhancing overall performance and accuracy.  
The results of the balancing exercise are illustrated in Figure 4(b), which confirms the improved class distribution. Initially, the benign class, which consisted of 2376 samples (as shown in Table 2), was standardized to 2100 samples to facilitate better comparability with other classes while preserving robustness and manageability. Following the application of SMOTE, each  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sl. Num** & **Class Categories** & **Instances** & **Overall Count** \\ \hline
1 & DDoS & 72,776 & \\ \hline
2 & DOS & 17,392 & \\ \hline
3 & Mirai & 5525 & 97,624 \\ \hline
4 & Spoofing & 1034 & \\ \hline
5 & Recon & 817 & \\ \hline
6 & Web & 52 & \\ \hline
7 & Brute force & 28 & \\ \hline
8 & Benign & 2376 & 2376 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Categorization of dataset.  
attack subcategory was adjusted to 300 samples, achieving a total of 2100 samples across all attack classes. This balanced distribution ensures fairness across classes and enhances the model's ability to accurately detect and classify various types of traffic.  
To distinguish between malicious (attack) and benign (non-malicious) network traffic, the problem was also restated as a binary classification task. This simplification focuses on two primary categories, streamlining the model's training and evaluation process, while improving accuracy and efficiency in identifying security threats. Figure 6 further illustrates the benign (0) and attack (1) classes' distribution.
# New Method to Estimate Rock Mass Deformation Modulus Based on BQ System
## 2 The BQ Rock Mass Quality Classification Method
### Correlation between the BQ Value and Rock Mass Deformation Modulus  
The BQ system suggests deformation modulus values for rock masses of different BQ grades on the basis of data from in situ measurements. The base data were gathered from 897 samples in 65 projects and cover 21 rock types, including granite, dolomitic limestone, limestone, sandstone, tuff, marble, shale, and mudstone. A total of 89, 184, 262, 184 and 178 of these samples are classified as having a basic quality rating of I, II, III, IV, and V, respectively. The maximum measured value is 72.2 GPa (fresh and intact plagioclase granite), and the minimum is 0.003 GPa (cataclastic fault rock). The statistical results for the deformation modulus of rock masses of each rating are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rock Mass Rating** & **I** & **II** & **III** & **IV** & **V** \\ \hline \hline \multicolumn{1}{c}{BQ value} & \multicolumn{1}{c}{\(>\)550} & 451–550 & 351–450 & 251–350 & \(\leq\)250 \\ \hline \hline \multicolumn{1}{c}{Rock mass quality} & Excellent & Good & Fair & Poor & Very poor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rock mass classification by BQ method.  
Figure 1: Empirical equations for predicting K\({}_{v}\).  
The deformation moduli recommended by the BQ system, according to Table 3, are shown in Table 4.  
As can be seen from Table 4, an interval, rather than a single value, is suggested for the deformation modulus of each BQ class. The system does not provide any further information on how to determine the modulus of deformation for a specific BQ value. This makes it necessary to establish a quantitative formula by which it can be estimated, as with the correlations between RMR, Q-system, etc. and the rock mass deformation modulus. With this aim in mind, this paper collects more than 60 in situ measurements from the Laxiwa hydropower station, Zhen'an pumped storage power station, and Maerdang hydropower station in China, including six rock types (breccias, monzonite, metasandstone, granite, marble, and granodiorite). The BQ value at each test point is also quantitatively calculated. A new relationship between the deformation modulus and the BQ value is then proposed on the basis of the results. Further, the paper develops indirect estimation formulas based on the existing empirical formula and the relationship between BQ and other rock mass classification schemes.