esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Determination Method of Optimal Measurement Speed
### Steps for Determining the Optimal Measurement Speed  
The steps for determining the optimal measurement speed of transmission errors in this paper are provided, as follows:  
1. First of all, the friction torque of the precision reducer is measured under different speeds. The selected measurement speed should range from the low speed to high speed, so that it can fully reflect the Stribeck friction effect of the precision reducer.
2. According to the measured data of friction torque and speed of a precision reducer, the Stribeck curve is drawn and the parameters of the Stribeck model are obtained by curve fitting.
3. The optimal measurement speed of transmission errors is calculated by substituting the parameter value of the Sribbeck model into Equation (5).
4. The transmission error of a precision reducer is measured and evaluated under the conditions of zero-load and optimal measurement speed.
## 5 Discussion
### Limitations of Current Models  
Despite the observed successes, several limitations persist. Firstly, although contextual models demonstrated better performance, they remain susceptible to conversational nuances such as irony, indirect references, or implicit bias, which are common in longer online dialogues. Secondly, the class imbalance in the annotated dataset necessitated oversampling, potentially introducing biases or reducing the generalizability of the models to naturally balanced datasets. Additionally, current models demonstrated a strong dependency on explicit lexical features, highlighting a critical gap in handling implicit bias effectively.  
Moreover, the application of general-purpose models to the Mediavida corpus revealed the limitations of transfer learning in hate speech detection across different domains and conversational contexts. Models trained predominantly on news comments or short social media texts exhibited insufficient adaptability to forum-based dialogues, underscoring the importance of domain-specific fine-tuning.
_Forests_**2015**, \(6\), 360-379; doi:10.3390/f6020360  
_forests_  
**ISSN 1999-4907**  
www.mdpi.com/journal/forests  
_Article_  
**A Process-Based Approach to Estimate Chinese Fir**  
_(Cunningham lanecolata)_ **Distribution and Productivity in Southern China under Climate Change**  
**[PERSON] 1,*, [PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 3,\({}^{\dagger}\)**  
* Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver V6T 1Z4, BC, Canada; E-Mail: [EMAIL_ADDRESS]
* Department of Forest and Conservation Sciences, University of British Columbia, 2424 Main Mall, Vancouver V6T 1Z4, BC, Canada; E-Mail: [EMAIL_ADDRESS]
* Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver V6T 1Z4, BC, Canada; E-Mail: [EMAIL_ADDRESS]
* These authors contributed equally to this work.
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-604-822-6452; Fax: +1-604-822-9106.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 14 October 2014 / Accepted: 22 January 2015 / Published: 2 February 2015_  
**Abstract:** Understanding the distribution and productivity of Chinese fir (_Cunningham lanecolata_) under climate change is critical given the ecological and economic importance of the species. Recently, process-based growth models have grown in their popularity given their simplicity and data availability, and they are increasingly being used to map the distribution and productivity of tree species. In this paper, we study the extent of variation of the current range shift and the productivity of the species under a changing climate. We used the Physiological Principles in Predicting Growth (3-PG) model, which calculates the extent to which climatic variables affect photosynthesis and growth of a species. These variables were then used in a decision-tree model to develop rules to provide a basis for predicting the distribution of the species under current climatic conditions. Once the distribution model was developed the productivity of the species was then assessed. Using climate projections we then simulated the growth and distribution into the future. Results indicate a northward shift from the current range. The growth model also indicates minor increases in productivity in some of the existing distribution areas, principally in central China with limited productivity predicted in newly emerged stands. We conclude that this dual modeling approach has potential to quantify impacts of climate change on selected species and examining differences in climate projections on range and productivity estimation.  
Chinese fir; climate change; modeling; GIS; distribution; productivity; NPP; 3-PG Model
## 1 Introduction
To overcome the second challenge, we design a lightweight coding algorithm grounded in a common communication protocol. Considering that the EPC Gen2 protocol is widely used in backscatter systems, we designed our algorithm within the framework of this protocol to enhance the compatibility of our system. This is to ensure that the communication process between the reader and tags within the system operates within a standard protocol framework. Figure 1: Illustration of harmonic interference generated by frequency-shifting tags. The two adjacent readers are functioning within distinct frequency bands. With all the challenges addressed, we realize the harmonic interference resilient backscatter system. Furthermore, we evaluate and implement the system on off-the-shelf backscatter devices. The results demonstrate that our system effectively compresses harmonic interference, achieving a 70% reduction in bit error rate in scenarios with multiple tag deployments. The main contributions are summarized as follows:  
* We design the first harmonic interference resilient backscatter system, which effectively reduces the generation of harmonic interference while performing frequency shifting. This innovation significantly enhances the potential for the practical large-scale deployment of backscatter systems. * We implement and evaluate the system using off-the-shelf backscatter devices. Our experiments validated the feasibility and compatibility of the harmonic interference resilient backscatter system. The structure of the paper is organized as follows. In Section 2, we describe the research methodology of the harmonic interference resilient backscatter system. In Section 3, we present the specific implementation of the system. In Section 4, the performance of the system is evaluated. Subsequently, we review the related work in Section 5. In Section 6, we discuss some directions for further research. Finally, we conclude in Section 7.
## 2 Experimental Set-Up and Procedures
(v) A Tektronix P6015A probe (Beaverton, OR, USA) equipped with a high voltage probe and a current probe was used to measure the applied voltage U (kV) and discharge current I (mA) of the jet. (v) The dose rate of the APPJ plume irradiance per unit area was measured using an 818-RAD irradiance and dosage sensor (MKS/Newport, Model No. : 818-RAD, Newport Beach, CA, USA), compatible with a 1919-R optical power meter with the following characteristics: 8 mm aperture, 200-850 nm spectral wavelength range, 100-250 mW/cm\({}^{2}\) irradiance range, and 30 W/cm\({}^{2}\) maximum power density. Different gas admixtures were used: a mixture of oxygen with argon at different ratios, where the mass flow rate of each gas was controlled and the gases flowed into a ceramic tube. Waveform signals of APPJ discharge were recorded using a 350 MHz digital oscilloscope, for which the applied voltage was measured using a Tektronix P6015A high-voltage probe; the discharge current was observed by measuring the voltage over a resistance equivalent to 33 k\(\Omega\). The average discharge power can be calculated using the voltage-current waveforms of the discharges and integrating the product of the discharge voltage \(U\) (kV) and discharge current \(I\) (mA) over one cycle as in Equation (1):  
\[P=f\ \int_{t_{0}}^{t_{0}+T}U(t)\ I(t)\ dt. \tag{1}\]  
\(P\) is the average power of the discharge, and \(f=\frac{1}{\gamma}\); \(f\) and \(T\) are the frequency and period of the discharge, respectively. In the case of a sinusoidal waveform, Equation (1) can be written [19]:  
\[P=I_{\text{rms}}\ U_{\text{rms}}\ \cos(\varphi), \tag{2}\]  
where \(I_{\text{rms}}=\frac{t_{0}}{\sqrt{2}}\) and \(U_{\text{rms}}=\frac{t_{0}}{\sqrt{2}}\) are the root mean square (rms) current and rms voltage, respectively. \(I_{0}\) and \(U_{0}\) are the peak values of current and voltage, respectively, and \(\varphi\) is the phase angle between the discharge voltage and current. The recent study in this article represents the control parameters of the plume generated by the APPJ using cold plasma. The measured jet parameters included length and width, power, applied voltage, pure argon concentration, and different admixtures of oxygen and argon plasma discharge. The 3 rd electrode with the narrow diameter, the powering characteristics, and gas flow rate contribute to:  
1. Control in the jet dimensions such as width, length, and covered area;
2. Control in the lifetime of the discharge process mode, whether laminar flow mode or turbulent flow mode;
3. Control in the heat impact emerging from the nozzle;
4. Control in the antibacterial effect factors of culture media down the nozzle such as exposure time, optical emission spectra, and irradiance.
## 4 Algorithm Analysis
### Message Flow between Nodes
#### 4.1.1 Message Flow during Uneven Clustering  
There is message flow in the several stages of the uneven clustering, as shown in Figure 6.  
In Figure 6a, the nodes are in the initialization phase of cluster head node competition. At this time, every provisional cluster head node broadcasts within \(R_{0}\); the process of message flow between nodes is shown in Figure 7a. Then, entering into the phrase of the cluster head competition is shown in Figure 6b. \(p_{2}\) firstly becomes a final cluster head node and broadcasts \(M_{CH}\) within \(R_{0}\), and \(p_{3}\), as well as \(p_{4}\) exit the competition due to receiving \(M_{CH}\) from \(p_{2}\) and broadcast \(M_{GC}\), respectively, which is shown in Figure 7b. Afterwards, \(p_{5}\) and \(p_{6}\) respectively remove \(p_{4}\). At the same time, \(p_{6}\) becomes a final cluster head node, and \(p_{5}\) exits the competition; the message flow is shown in Figure 7c. After the cluster head node competition, shown in Figure 6c, the final cluster head nodes broadcast their ID, and the non-cluster head nodes reply with their ID to the corresponding cluster head node, the message flow of which is shown in Figure 7d.  
Figure 7: **(a)** Message flow in the initialization phase of cluster head node competition; **(b,c)** message flow at the stage of cluster head node competition; **(d)** message flow after cluster head node competition.  
Figure 6: Several stage statuses of uneven clustering. **(a)** Initialization phase of cluster head node competition; **(b)** Phase of cluster head node competition; **(c)** End of cluster head node competition. (Assume that \(p_{2},p_{3}\) and \(p_{4}\) can communicate with each other within \(R_{0}\) and that they are the competition neighbor nodes for each other; so do \(p_{4}\), \(p_{5}\) and \(p_{6}\). In addition, \(p_{2}\) is the next-hop node of \(p_{6}\), and the distance between \(p_{1}\) and the sink node is greater than \(R_{c}\_Max\)).
## 2 Modeling and Problem Statement
### Static Load Model  
The static load model expresses the active and reactive powers at any instant of time as functions of the bus voltage magnitude and frequency at the same instant. The relationship between power and voltage magnitude of the model can be expressed as:  
\[P=P_{0}\left[a_{P}\left(\frac{V}{V_{0}}\right)^{2}+b_{P}\left(\frac{V}{V_{0}} \right)^{1}+c_{P}\right] \tag{1}\]  
\[Q=Q_{0}\left[a_{Q}\left(\frac{V}{V_{0}}\right)^{2}+b_{Q}\left(\frac{V}{V_{0}} \right)^{1}+c_{Q}\right] \tag{2}\]  
where \(P_{0}\) and \(Q_{0}\) are respectively the active and reactive powers consumed at the reference voltage \(V_{0}\). In (1) and (2), the sum of coefficients for real power is 1, and that for reactive power is 1 as well.
# LLM-Based Cyberattack Detection Using Network Flow Statistics
## 2 Materials and Methods
### Designing the Cyberattack Detection System  
Our proposal for the construction of a cyberattack detection system is the use of an existing pre-trained LLM. We applied a fine-tuning process to the selected model, T5, to make the complete adjustment of all their weights. Therefore, we changed its purpose from NLP tasks to cyberattack detection.  
The T5 model has a transformer-based architecture. Developed by Google AI, it was published in 2019. It is an encoder-decoder model, i.e., it uses recurrent neural networks for sequence-by-sequence problem prediction, and it is prepared to solve text-to-text tasks. This type of architecture is relatively new and, in fact, was included in Google's translation service in 2016 [48].  
Figure 1 shows the scheme of our proposed LLM-based cyberattack detection system. The system comprises distinct stages, designed to adapt a pre-trained LLM to this specific cybersecurity task. First, network traffic is transformed by the Extract, Transform, and Load (ETL) module, which performs the following tasks:  
* It transforms network packets into network flow statistics. This module is implemented using CICFlowMeter [36]. CICFlowMeter is a network traffic flow generator that produces 84 network traffic characteristics [35].
* As the LLM (in our case, the T5 model) is inherently designed for text-based input, the next stage converts the numerical features to strings.
* Finally, it sequentially concatenates these strings, building an input string that represents an abstract artificial language.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Dataset** & **Year** & **Attack Types** & **Features** & **Records** \\ \hline KDD Cup 1999 & 1999 & 4 & 41 & 4,898,431 \\ NSL-KDD & 2009 & 22 & 41 & 125,973 \\ UNSW-NB15 & 2015 & 9 & 49 & 2,540,047 \\ CIC-IDS-2017 & 2017 & 14 & 84 & 2,830,743 \\ CSE-CIC-IDS2018 & 2018 & 14 & 84 & 16,232,943 \\ CIC-DDoS2019 & 2019 & 18 & 84 & 12,794,627 \\ AWID2 & 2016 & 3 & 155 & 42,388,298 \\ AWID3 & 2021 & 13 & 253 & 15,574,911 \\ BCCC-CIC-IDS-2017 & 2025 & 13 & 122 & 2,438,052 \\ \hline \end{tabular}
\end{table}
Table 1: Datasets for detection of cyberattacks.  
The core of the system is the fine-tuned LLM. This pre-trained LLM carries out a fine-tuning process using the prepared textual representations of network flows. During fine-tuning, the model learns to recognise patterns within this abstract language that distinguish between normal and malicious network behaviors.  
Finally, the detection module uses the fine-tuned LLM to produce an alarm if the incoming network flow is classified as malignant.
# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 2 Materials and Methods
### Algorithms for Volume Calculation
#### 2.5.3 Volume Calculation with the CHC Algorithm  
The volume calculation algorithm called convex hull cluster (CHC) is the result of the integration of an algorithm from PCL (Point Cloud Library) [52] implemented into our software. It uses the method of voxel cloud connectivity segmentation (VCCS) [51], which generates volumetric over-segmentations of 3D point cloud data, known as supervoxels. These elements are searched as variant regions of k-means clusters through the point cloud (considered a voxel octree structure). They are evenly distributed across 3D space and are spatially connected to each other (Figure 9). Thus, each supervoxel maintains 26 adjacency relations (6 faces, 12 edges, and 8 vertices) in voxelated 3D space with its adjacent neighbors.  
The process starts from a set of seed points distributed evenly in space on a 3D grid with an established resolution (R\({}_{\text{seed}}\)) where the point cloud is located. The voxel resolution (R\({}_{\text{voxel}}\)) is the established size of the voxel's edge. The seed voxels begin to grow into supervoxels until they reach the minimum distance from the occupied voxels. If there are no occupied voxels near any point of the cloud among the grown supervoxels, and there are no connected voxels among their neighbors, the isolated seed voxel is deleted.  
The seed points are expanded by a distance measure calculated in a feature space consisting of spatial extent (normalized by the seeding resolution), color (the Euclidean distance in normalized RGB space), and normals (the angle between surface normal vectors).  
The supervoxels' growth is an iterative process that uses local k-means clustering.  
In this process, confirmed voxels are ignored. In this way, processing is sped up, and the amount of information that needs to be taken into account is reduced. The iterations end when all supervoxels have been confirmed or rejected, and, therefore, all points in the cloud belong to a specific cluster. The editable parameters of the supervoxel clustering are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Possible Values** \\ \hline Class to use & LiDAR class [53] to use & \(-1\) to 31 [1] \\ Compute free voxels & Compute free voxels & True, False \\ Voxel resolution & Voxel linear resolution in meters & 0.0010 to 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of the editable parameters of OctoMap.  
Figure 9: (a) VSAO; (b) example of a clustering TLS point cloud with the convex hull cluster algorithm.
## 3 Adsorption of Model Compounds by Eggshell Biocomposite Adsorbents
### Temperature Effects and Thermodynamic Parameters  
Temperature effects influence the equilibrium adsorption constant for adsorption processes, along with its role on the rate of adsorption in the kinetics of adsorption. From the isothermal data, it is possible to estimate the thermodynamic parameters, such as standard difference in enthalpy (\(\Delta\)H\({}^{\circ}\)), entropy change (\(\Delta\)S\({}^{\circ}\)), and the change in Gibbs energy (\(\Delta\)G\({}^{\circ}\)) of adsorption for the system of interest. The thermodynamic parameters can be used to determine the driving force of the adsorption process. Thus, the spontaneity or feasibility of a reaction can be ascertained [103; 104]. Table 6 provides a summative list of the thermodynamic adsorption parameters typically encountered for ESP and its bicocomposites. Positive values of \(\Delta\)S\({}^{\circ}\) values indicate a greater randomness or disorder of the adsorbate at the solid adsorbent-liquid interface. Negative \(\Delta\)S\({}^{\circ}\) indicates decrease level of freedom of the system and reduced driving force for the spontaneous adsorption of the adsorbate onto the adsorbent [105]. In summary, Tables 3-5 provide a list of equilibrium and kinetic adsorption parameters, according to the models described by Equations S1-S22 in the Supporting Material for various adsorbate-adsorbent systems. Table 6 summarizes the thermodynamic parameters of adsorption. The adsorbent materials range from ESP to ES-based composites, whereas the adsorbates range from inorganic to organic species (e.g., metal ions, organic pollutants, oxyanions, anionic pollutants, and dyes). In Table 3, the parameters for model organic pollutants are listed, while Tables 4 and 5 contain parameters for cationic and anionic pollutants, respectively. ES-composites have variable dye adsorption capacity (15.13-303 mg/g) [7; 20; 35; 49; 63; 64; 71], while ES materials (treated or untreated) also display a range values (1.03-600 mg/g) [20; 46; 48; 50; 51; 52; 53; 59; 62]. The metal-ion uptake by ES-composites (5.5-727 mg/g) [72; 74] and anion uptake (72.8-231 mg/g), while for ES materials the adsorption values were (0.07-387 mg/g) and (0.1-270 mg/g) for cationic and anionic pollutants, respectively. The adsorption capacity range revealed variable trends in uptake for the ES and ES-based composites depending on the pollutants and type of modification on the composites. Table 7 is a list of some conventional adsorbents used to remove selected pollutants from wastewater and their respective adsorption capacities. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Adsorbate** & **Adsorbent System** & **Adsorption Parameters** & \(q_{e}\) **(mg/g) or Removal (\%)** & **Equilibrium and Kinetic Models** & **Remarks A\({}^{\text{H}}\) = X\({}_{2}\) AS\({}^{\text{S}}\) = Y\({}_{3}\) and \(\Delta G^{\text{S}}\) = Z** & **Ref. ** \\ \hline Acid dye red & Calicined eggshell decorated with 100 mg/L; 200 rpm; 200 rpm; 20.05 g/10 mL; 208-328 K. & 220.2 & Langmuir and PSO & Excellent desorption with 0.5 M HNO\({}_{3}\) = -, AS\({}^{\text{S}}\) = -.
# Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce
## 2 Artificial Intelligence Techniques  
### Sentimental Analysis and Opinion Mining  
Communication between a business organization and customers occurs through user-generated content on websites and social media. The views of clients expressed on Twitter, Instagram, and Facebook can impact the service providers' image and reputation. The sentimental analysis involves methodologies that help companies evaluate the meaning of online content and develop strategies that can improve customer loyalty [7]. Thus, sentimental analysis refers to an automation process that helps customers to extract emotions from customers' online content by processing unstructured data and creating models to extract information from it [11; 12]. It helps managers or decision-makers to understand customers' responses towards particular topics, and it helps to determine if the event is neutral, positive, or negative. There is a minor difference between sentimental analysis and opinion planning. Opinion planning focuses on extraction and analyzing customers' opinions while searching for clients' expressions or words and analyzing them. Opinion mining involves using predefined rules to establish rules based on automated systems that analyze data [13]. It helps in the classification and investigation of customers' behavior and attitudes towards an event, a brand, company services, and products. Twitter is one platform that contains relevant information and hashtags that have been followed by large numbers of people [14].  
Sentimental analysis or opinion mining classifies customer emotions into three levels: the sentence level, document level, and entity level.  
Natural language processing (NLP) is a fundamental technique for promoting sentimental analysis. It is a sub-domain in AI that focuses on understanding the unstructured content found on social media platforms and organizing it to make it easier for sentimental analysis [13]. It helps the computer-aided system to assess and understand the various languages spoken by customers. NLP is also a key component in opinion mining [13]. It helps people process a large amount of information from unstructured data by analyzing the sentence structures and computing it into the sentence or document level using linguistic databases such as treebanks and WordNet [14]. Besides, sentiment polarity from user-generated texts can be classified using various approaches: a semantic approach, machine learning, lexical-based analysis, and a statistical approach [14]. These approaches help to eliminate unnecessary noise or information such as URLs, slang, and abbreviations, thus, they decrease the size of the dataset. Therefore, through sentimental analysis and opinion mining, customers can understand the type of shopping experience they are likely to have in a particular enterprise based on customers' views or opinions on social media platforms.