esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Real-World Dataset Advantages  
To remove the limitations of synthetic data, numeric parameter values were substituted with real-world parameters for the device layer in this simulation. However, the parameter values for all other devices--from level 0, which represent sensors, actuators, and smart IoT devices; level 1, which represents communication, Fog nodes, routing, and network infrastructure; and level 2, which represents Cloud-based applications, services, and software--were derived from the existing literature in line with current Fog and Cloud computing research trends. Furthermore, mobility-aware application migration, microservice orchestration, and dynamic distributed cluster formation were embedded in iFogSim2. Using real-world datasets for the smart building was adopted with other integrated components within iFogSim2, mimicking the state-of-the-art capabilities of Cloud computing, router/proxy servers, gateways, and Fog computing nodes [16]. From Table 1 in Section 2.4, a review of the current literature related to this research area demonstrates that a limited number of researchers use real-world datasets generated from IoT smart devices within a smart building. Most researchers use simulator default data settings within their infrastructure to evaluate real-world environments. Therefore, the main advantage of using real-world datasets in this study is to simulate real-world situations within a smart building infrastructure, including assessing application, software, and service requirements, efficient latency, energy management from data generation, and network congestion.
## 3 Proposed Mathematical Model of a Switched Beam Antenna with Beam Steering Abilities
Hardware Limitations: Limitations in the hardware components, such as processing delays or latency, can also contribute to synchronization errors. Delays introduced during signal processing stages can affect the alignment between the Tx and Rx ends, impacting the accuracy of beamforming operations. These synchronization errors can degrade the performance of V2V communication systems, leading to suboptimal beamforming, a reduced signal quality, and a decreased communication efficiency. Mitigation techniques such as time synchronization protocols, advanced channel estimation algorithms, and hardware improvements are essential to address and minimize these synchronization errors, ensuring accurate and precise synchronization between the transmitting and receiving ends. To address synchronization errors in V2V communication systems, several techniques can be employed. Firstly, implementing robust time synchronization protocols, such as the Precision Time Protocol (PTP) or Network Time Protocol (NTP), ensures accurate time synchronization between transmitting and receiving devices. This helps minimize propagation delays and clock drift, improving synchronization accuracy. Secondly, advanced channel estimation algorithms can be utilized to accurately estimate channel characteristics, including gains and phases. By compensating for channel estimation errors, misalignment between the intended beamforming direction and the received signals can be reduced, enhancing synchronization. Additionally, hardware improvements play a crucial role in mitigating synchronization errors. Upgrading hardware components involved in the V2V communication system, such as high-precision clocks and low-latency signal processing units, minimizes synchronization errors arising from hardware limitations. Lower processing delays and an improved precision contribute to more accurate synchronization. Furthermore, adaptive synchronization mechanisms can be employed, which dynamically adjust the synchronization parameters based on real-time feedback and system conditions. This adaptive approach helps mitigate synchronization errors caused by varying propagation delays and clock drift, maintaining accurate synchronization under changing environments. By implementing these techniques, synchronization errors in V2V communication systems can be effectively addressed, leading to improved beamforming performance, enhanced \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value (s)** \\ \hline Number of Nodes & 500 \\ HPBW Antenna Angles & 0, 30, 45, 60, 75, 90, 120, 150, 180, \\  & 210, 235, 270, 310, 360 degrees \\ Transmitting Interval & 100 ms \\ Message Size & 1 KB \\ Distance between Vehicles & 500 m \\ Average Speed & 100 km/h \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings. signal quality, and increased communication efficiency. However, it should be noted that the analysis of synchronization errors in real-time scenarios falls outside the scope of this study, which primarily focuses on simulative analysis.
###### Abstract  
The cooperative lane change among several connected and automated vehicles (CAVs) provides ideas for enhancing the traffic safety and efficiency issues caused by lane changes. However, most of the existing studies mainly focus on the independent analysis of the lateral and longitudinal movements of the lane change without considering the impact of the lateral motion on the longitudinal motion. These works usually assume that the target tracking-preceding vehicles are determined for both the lane change vehicle and the following cooperative vehicle in the target lane. This work proposes a model predictive control (MPC)-based cooperative lane change (CLC) control strategy by considering the preceding vehicle switching and the correlation between the lateral and longitudinal motions. It builds the lateral movement based on the appropriate function curve and integrates this lateral movement in the construction of the coordinated longitudinal motion control strategy by using a set of linear piecewise functions in the design of constraints and objective function of the optimization model to provide smooth preceding vehicles switch. An advanced optimization solver is used to solve the optimization control problem step by step. The proposed strategy is validated based on numerical comparative experiments with two typical lane-changing scenarios. The results show that the proposed control strategy can smoothly complete the preceding vehicle switching during the lane change and quickly realize the stable tracking of the target lane vehicles after changing lanes.  
Keywords:cooperative lane change; connected and automated vehicles; model predictive control; preceding vehicle switching +
Footnote †: journal: Applied
## Materials and Methods
### Data Processing  
In this paper, groundwater depth data were used as the host variable, and NDVI, precipitation, and hydrogeological conditions were used as covariates. The groundwater depth data within the study area were obtained using cokriging interpolation. At the same time, MHRU was divided according to DEM and land cover. Using MHRU as the carrier, through spatial autocorrelation analysis of three types of land with obvious spatial aggregation characteristics: forest land, cultivated land, and bare land. Then, the corresponding remote sensing inversion band index NDVI, net relatedness index (NRI), and normalized difference drought index (NDDI) were selected for the geographically weighted regression (see Figure 3). This model is mainly suitable for bedrock and loess hilly areas. The accuracy is higher in areas where the aquifer types are loose rock pore water, bedrock fissure water, and fracture zone vein water. Fine-grained lithological combinations and arid and semi-arid areas with poor diving are more suitable.  
The originality of this paper is to construct a spatial simulation method of groundwater depth and to present groundwater information in space through interpolation analysis. Compared with traditional kriging interpolation, cokriging interpolation combines hydrogeological conditions, precipitation and vegetation coverage, and other influencing factors, highlighting anisotropy in the interpolation model, making the simulation results more consistent with the actual groundwater cycle characteristics, and at the same time, also solving the unrealistic limitation of the traditional interpolation model due to single variable interpolation. At the same time, in subsequent research, a regression model between groundwater depth and remote sensing bands of different land types was constructed through geographically weighted regression. Considering a situation where researchers lack groundwater data, groundwater depth information can be quantitatively simulated; however, because the construction of the groundwater spatial interpolation model uses local hydrogeological information, this model is more suitable for arid and semi-arid farming-pastoral interlaced areas, especially in areas with finer lithology and poor diving.  
Figure 2: Seasonal changes in groundwater depth.
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 2 Materials and Methods
### Datasets  
#### 2.2.1 Land Cover Datasets  
Moderate-resolution Imaging Spectroradiometer land cover (MCD12Q1) [34], the Climate Change Initiative Land Cover(CCI-LC) [35], the 30 m Global Land Cover (Globaland30) [36], the Multi-Period Land Use/Land Cover Remote Sensing Monitoring Database in China (CNLUCC) ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 28 January 2021) and the China land cover dataset (CLCD) [37] were chosen to assess grassland changes on the TP (Table 1). The spatial resolutions of the above five land cover products range from 30 to 1000 m. The land cover classification system includes land cover types and land cover fractions. For example, Globeland30 divides land cover types into ten categories, and CCI-LC divides land cover types into 22 categories according to a land cover fraction [38].
## 3 Experiments
### Image Collection  
All apple leaf images used in this study were provided by the Apple Research Institute in our country. The apple leaf images were categorized into three groups, according to two types of leaf diseases and normal leaf. Figure 6 shows examples of apple leaf images. The first row shows normal leaf images, and the second and third rows show diseased leaf images. Particularly, for the diseased leaf images with marssonina blotch, as shown in the second row, the blotch colors are similar to the normal leaf colors in the background. Therefore, the color-based clustering algorithm [39] might fail to extract the blotch colors from the normal leaves. This reveals that the leaf areas, background, and spot area must be divided. In addition, a real environment was considered to some degree, in that the leaf images were more complicated in the background than those tested in [17; 21; 39], where the background colors were nearly solid. In our database, the total numbers of normal leaf images, diseased leaf images with marssonina blotch, and diseased leaf images with alternaria leaf spot were 558, 2281, and 896, respectively.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.2 Low-Level Laser Therapy  
Low-level laser therapy (LLLT), also known as cold laser therapy or photobiomodulation therapy has been explored as a potential treatment for DH. Various laser wavelengths have been investigated for this purpose, with some of the commonly used wavelengths falling within the red (630-690 nm) and near-infrared (810-980 nm) spectrum. LLLT can engage with the dental pulp, resulting in increased odontoblastic activity, which leads to the production of tertiary dentin and a reduction in the lumen size of dentin tubules. Such lasers can also induce cell activity, trigger anti-inflammatory responses, and generate analgesic effects [103; 104; 105]. LLLT is dose-dependent; therefore, it is generally used in sequential appointments with a time interval. There are various clinical protocols that dental practitioners can follow. Most of the studies found that pain levels were significantly reduced 3 months after the therapies [106].  
LLLT is believed to reduce pain during DH through various biological mechanisms. While the exact processes are still being studied, here are some proposed ways LLLT might work to alleviate pain in this context [107; 108; 109]:  
1. Increased blood flow and microcirculation. LLLT has been shown to promote vasodilation and improve microcirculation in the treated area. This enhanced blood flow can facilitate the delivery of nutrients and oxygen to the tissues, aiding in their healing and reducing inflammation. Improved circulation can also help remove waste products and inflammatory mediators that contribute to pain.
2. Stimulation of cellular activity: LLLT is thought to stimulate cellular activity, including the activation of mitochondria, the cell's energy-producing organelles. This increased cellular energy can accelerate tissue repair and regeneration, promoting healing and reducing pain.
3. Anti-inflammatory effects: LLLT is known to have anti-inflammatory effects by modulating immune responses and reducing the release of inflammatory cytokines. This can lead to a decrease in inflammation, which often accompanies pain in conditions like DH.
4. Neurological modulation: LLLT may influence nerve function and sensitivity. It could modulate nerve conduction and reduce the transmission of pain signals. Additionally, LLLT might affect nerve endings and receptors, altering their response to stimuli.
5. Endorphin release: LLLT might trigger the release of endorphins, which are natural pain-relieving chemicals produced by the body. Endorphins can help block pain signals and promote a sense of well-being.
6. Stimulation of dentin remineralization: LLLT might encourage the remineralization of dentin, potentially sealing or occluding the tubules and reducing their sensitivity. It is important to note that the effectiveness of LLLT for DH can vary among individuals and might depend on factors such as the specific laser parameters used, the duration and frequency of treatment, and the underlying causes of hypersensitivity.  
**b. Therapeutic agents that occlude the dentin tubules**
## 4 Discussion
### Radiometric Consistency
The obtained coefficients, which are close to 0.7, prove a coherence between the data collected from the different platforms and show similar spatial variability values of NDVI maps, which are to be interpreted as the same behavior in terms of crop vigor [61]. Therefore, the compatibility and integration of NDVI maps obtained by Sequoia and Pix4D systems should be feasible along with the Sentinel-2 products.
## 3 Novel Framework for Advanced Synchrophasor Analysis in Modern Control Centres
### Framework Architecture and Analysis Modules
Similar to the _disturbance detection module_, this is done online by analyzing the current phasor data stream. Compared to traditional SCADA or protection systems, a wide range of possible disturbances can be detected within short time spans (\(<1\) s) including outages from generators, lines, or renewable energy sources as well as short circuits or load trips. Precise information about the origin and type of the disturbance as well as additional time and frequency analysis information (e.g., oscillation modes, frequency drops) enable the initialization of precise countermeasures (e.g., curative actions) to restore a stable system state. These actions can be precomputed for different disturbances and updated with each new steady state condition. Today's DSA systems provide sufficient computation options to simulate different contingencies and to select and assess suitable countermeasures [13]. Further descriptions are given in Section 3.6. For a better understanding, Figure 2 shows the simplified workflow and interaction between the different synchrophasor based online and offline analysis modules. The phasor data concentrator collects the phasor measurements and continuously updates a database. At the same time, the online data stream is forwarded to the online analysis modules. The offline analysis modules can be activated periodically to process a certain amount of historical measurement records. The measurement and analysis results can be visualized with a web-based user interface (see Section 5) and can be transferred to subsequent EMS applications.
## 2 Literature Review
### Audio-Based Classification  
Educational tools for analyzing the classroom academic discourse are scarce and the few existing ones mostly use the audio signal of the recordings with the aim to distinguish the teacher lecturing from the student participation in class. LENA (Language ENvironment Analysis) is a system that records and analyzes classroom discourse to provide teachers a timely feedback that improves their skills in classroom discourse management [10; 11]. LENA is particularly oriented to child language development. Teachers using LENA wear a proprietary wearable audio recorder while teaching a regular math lesson to small children to collect speech data. LENA implements a speech recognition system aimed to identify three common discourse activities: teacher lecturing, whole class discussion and student group work [10], and it has primarily been used to assess small children's language environment [12]. No transcription of the words in the recordings is provided by LENA, instead it produces a diarization of who is talking and when, according to the predetermined categories [13].  
Another interesting audio-based classification system is the project Decibel Analysis for Research in Teaching (DART) that analyzes the volume and variance of audio recordings of science technology engineering mathematics (STEM) courses to predict how much time is spent on single-voice (e.g., lecture), multiple-voice (e.g., pair discussion), and no-voice (e.g., clicker question thinking) activities [14]. DART aims to identify the types of activities that are going on in a classroom based exclusively on sound waveforms.  
Audio-based classification has also been used for assessing time devoted to lecturing and student discussion, specifically in a flipped classroom setting [15]. Similar to DART, in the latter cited work, the authors use multiple audio recorders to detect segments of lecture that are primarily the lecturer's speech, while segments of discussion comprise students' speech, silence and noise.  
All the aforementioned systems apply speech recognition and methods from the field of audio segmentation with the aim of analyzing signal intensity (volume) and variations in the sound of a classroom. This analysis is used to identify the speaker or classify the sound into categories accordingly to detected voices. This way, a single voice is associated to teacher talk, multiple voices to students talk, no voice to silence, and other indistinguishable voices to murmuring or overlapping speech. Additionally, both LENA and DART are supervised learning systems that require human annotations of the recorded speech. LENA uses a random forest algorithm while DART uses support vector machine techniques.
## 4 Waste Conversion for Fuel Gas Production
### Pyrolysis and Gasification of Coal-Water Slurries with Industrial Waste Additives  
Increasing the efficiency of pyrolysis and gasification due to the use of oil waste in the composition of CWS and varying the ratio of components is relevant to research. For example, the characteristics of gaseous and solid pyrolysis products of slurry fuels were considered by [PERSON] et al. (2016). The conventional CWS and the slurry with an admixture of waste lubricating oil were studied. The used lubricating oil was obtained from a local vehicle maintenance plant (Hubei, China). The shares of coal (55-65 wt%), water (35-45 wt%), and waste lubricating oil (10-20 wt%) varied. The experiments were carried out using a tube electric furnace at a temperature of 800-1000 \({}^{\circ}\)C. It was found that an increase in the proportion of water in slurries promoted an increase in the gas yield. At fast heating, water reacted with volatiles or coal, contributing to the formation of additional gaseous products. As the water content in the CWS increased, the concentrations of CO\({}_{2}\) and H\({}_{2}\) increased, while CO and CH\({}_{4}\), on the contrary, decreased. The water promoted the reforming of the CH\({}_{4}\) and water gas shift reaction, which intensified with an increase in the water proportion. Similar tendencies were also observed by [PERSON] et al. (2019). The addition of waste oil to the CWS did not have a sufficient effect on the amount of pyrolysis gas. However, it was found that the ratio of the main gas components (CO\({}_{2}\), CO, CH\({}_{4}\), H\({}_{2}\)) changed when using oil. The concentrations of hydrogen and methane increased during pyrolysis of coal-oil-water slurry, while the emission of carbon oxides decreased. The decomposition of aliphatic compounds during pyrolysis of waste lubricating oil promoted the release of CH\({}_{4}\), while the formation of H\({}_{2}\) was the direct decomposition and re-condensation reaction. The study of the characteristics of the char obtained during pyrolysis showed that higher pyrolysis temperatures led to a decrease in the reactivity of char (the delay of its ignition increased). Similar trends were also noticed with an increase in the proportion of water and used turbine oil (2016). Four types of wastewater were used for the preparation of coal wastewater slurry and its gasification (2018). [PERSON] et al. (2018) examined wastewater generated from CWS gasification: washing wastewater (GWW), sulfur wastewater (SW), and carbonized wastewater (CW), and also industrial wastewater (IW) from an external source. The experiments were carried out in a commercial gasification plant for coal water slurry to produce syngas