esa-sceva/satcom-chunk-collection huggingface dataset:
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 2 Methodology
### Comparison Algorithm Model  
To validate the efficacy of the SVM-RF fusion model, we conducted a comparative analysis of the distance weighting method, the two-factor weighting method, and the latitude difference weighting method. The mathematical model for the distance weighting method is presented as follows [17]:  
\[d_{i}=\sqrt{\left(x-x_{i}\right)^{2}-\left(y-y_{i}\right)^{2}} \tag{10}\]  
\[T(x_{o},y_{o})=\sum\limits_{i=1}^{m}\frac{f(d_{i})T_{i}}{d_{i}} \tag{11}\]  
\[f(d_{i})=\frac{1}{\left(d_{i}^{2}+\varepsilon\right)^{4}} \tag{12}\]  
In Equations (10)-(12), where \(d_{i}\) represents the distance between the training station and the master station, \(T(x_{o},y_{o})\) denotes the daily variation at point \((x_{o},y_{o})\), and \(\varepsilon\) is a minute value introduced to avoid a zero denominator.  
The mathematical model for the two-factor weighting method is outlined as follows:  
\[M=\frac{\sum\limits_{i=1}^{n}\left[\left(\frac{1}{\left|B_{i}\right|}+\frac{1 }{\left|L_{i}\right|}\right)^{k}\times T_{i}\right]}{\sum\limits_{i=1}^{n} \left(\frac{1}{\left|B_{i}\right|}+\frac{1}{\left|L_{i}\right|}\right)^{k}}, \;k\geq 0 \tag{13}\]  
The mathematical model of the latitude difference weighted average method is as follows:  
\[M=\frac{\sum\limits_{i=1}^{n}\left[\left(\frac{1}{\left|B_{i}\right|}\right) \times T_{i}\right]}{\sum\limits_{i=1}^{n}\left(\frac{1}{\left|B_{i}\right|^{ 2}}\right)},\;k\geq 0 \tag{14}\]  
In Equations (13) and (14), \(T_{i}\) represents the observation data of known geomagnetic stations, \(n\) represents the total number of stations, \(M\) denotes the calculated value, \(B_{i}\) represents the difference in latitude, and \(L_{i}\) represents the difference in longitude.
## 3 Coupling Phytoremediation and Bioenergy Production
### Thermochemical Conversion of Contaminated Biomass: Combustion, Gasification, and Pyrolysis
Increasing experimental conditions can help to determine the necessary biomass pretreatments to increase the product yield and reduce toxic by-products. Ancona et al. [90] proposed that it is necessary to assess the biomass quality to determine the gasification capabilities. To verify this hypothesis, the authors evaluated specific treatments of poplar biomass from phytoremediation processes of soils polluted with metals (Be, V, Cr, Co, Ni, As, Se, Cd, Sn, Sb, Tl, and Pb under detection limits; Cu and Zn above the limits) and polychlorinated biphenyl (PCB). This research demonstrated that contaminated biomass gasification from phytoremediation produces syngas similar to that from non-contaminated biomass. This conclusion is also supported by the fact that contaminant residues were concentrated mainly in roots and undetected in shots. Similarly, the study reported that the concentration of contaminants in the pruning was negligible; the amount of metals ranged from 0.01 to 0.70 mg/kg, except for Cu and Zn (\(\sim\)20 mg/kg). It was remarked that a specific treatment for pollutant capture is necessary when the roots are sampled and used for the gasification process. The above-reviewed examples indicate that the concept of linking phytoremediation and thermochemical biofuel production is a potential and promising pathway toward sustainability, valorizing the polluted biomass into metal-free biofuels and by-products, while allowing the recovery of HMs. Besides the benefits of producing bioenergy from polluted biomass, the generation of potentially toxic streams must be minimized [91; 92], which has focused the investigation of sorbents, demineralization, and leaching techniques in order to remove or immobilize HMs. Table 3 summarizes experiments related to the analysis of phytoremediation biomass as feedstock in thermochemical processes, including determination of calorimetric values of biomass, the effectiveness of pretreatments to improve the quality and yield of products, and improvement of conversion technologies.
# A Study on Data Profiling: Focusing on Attribute Value Quality Index
## Materials and Methods
### Unstructured Data Quality Factors  
Quality standards for unstructured data should be applied somewhat differently from quality standards for structured data [3]. The types of unstructured target data refer to metadata, texts, images, sounds, videos, three-dimensional (3D) data, geographic information system (GIS) data, aerial photographs, weather satellite images, and cartographic satellite images, and the cases for classifying the contents in terms of establishing a database are shown in Table 1[3].  
The quality measurement for unstructured text was firstly considered for the target and scope of quality measurement, and the selection of measurement criteria for quality measurement, as well as the corresponding measurement items and contents, was arranged using a checklist [3; 12]. It was used directly for quality measurement by checking the measurement contents recorded in the checklist of quality measurement [3]. The definition of quality criteria for unstructured text can be defined and used according to the purpose of use by the organization, company, or institution [3]. The scope of selection of quality measurement standards can be different based on the quality control policy and direction, and a clear direction should be defined according to the long-term vision and purpose when establishing the quality diagnosis plan, allowing the measurement criteria to be selected [3]. The method of defining the weights among the measurement criteria by calculating the importance of the unstructured text quality measurements may be done using a predefined method or an ad hoc method. The predefined method selects importance based on the matching measurement standard considering the object, the purpose of use, and the purpose of diagnosis to be diagnosed. The ad hoc method quantitatively calculates a weight between measurement standards through a statistical analysis method, so as to calculate a separate importance factor as it is not predefined.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme
### Packet Queue Management  
Admission control is responsible for preventing network overload to guarantee QoS for already admitted sessions and is deployed where a new session is either admitted when resources are available or rejected. Packet queue management is responsible for anticipating signal congestion before occurrence using scheduling means to determine the order of flow of different packets. The packet scheduler is responsible for providing the actual memory used for storing packets and providing a firewall used against malicious nodes whose intent is to selfishly use up network resources. The packet scheduler is responsible for determining the order in which packets are processed at the node level and transmitted over a link. This framework utilizes absolute priority queuing to assist in packet queue management. In absolute priority queuing, the packet scheduler scans and rates queues according to high, medium, and low priority. High priority is most preferred during the forwarding of packets from queues, and this means that packets with lower priority obtain processes as soon as higher priority queues are empty. In terms of resources, packets in high-priority queues will receive the best service, and queues with a lower priority will have to be satisfied with the remaining resources. Once the packet is sent, the scheduler restarts the process again with high priority first. This mechanism is very important in terms of delay of critical data and bandwidth usage for video streaming. The packets are forwarded with minimal delay if the high-priority packets do not exceed the outgoing traffic.
# Lemongrass Essential Oil Components with Antimicrobial and Anticancer Activities
## 3 LEO Therapeutics
### Antimicrobial Potential
#### 3.1.1 Antibacterial Activity  
The antibacterial characteristic of LEO is well established [18; 40; 49; 50; 51; 52; 53]. It has been suggested that LEO induces the destruction of bacterial biofilms and hinders further bacterial growth and development [54]. Furthermore, LEO components can destabilize the bonds between the lipid bilayer and neutralize the bacteria through membrane disintegration [55]. LEO can confer structural changes, as well, in different bacteria. It was reported to cause complete disfiguration and distortion in the _Pseudomonas_ spp. [56]. The MIC values for LEO and citral against _P. aeruginosa_ were calculated as >40% and 10%, respectively. Furthermore, LEO blocks biofilm formation in bacterial colonies [54], for example, 0.125% (\(\
u/v\)) of LEO can restrict biofilm formation in methicillin-resistant _Staphylococcus aureus_ strains [57]. It can disrupt the cell membrane and inhibit cytoplasmic metabolism, making LEO effective against both Gram-negative and Gram-positive bacteria [55; 34; 49]. _Cymbopogon khasianus_ essential oil inhibited the growth of _Escherichia coli_ with the MIC and MBC (Minimum Bactericidal Concentration) values of 20 ug/mL each. It can also retard the growth of _Bacillus subtilis_, _Salmonella enterica typhimurium_, _Staphylococcus aureus_, _Klebsiella pneumoniae_ with a MIC range of 25-50 ug/mL [47]. Multiple recent studies against MDR (multidrug-resistant) bacteria [58; 59] show that, while a low concentration of LEO retards growth and biofilm formation, a higher concentration can confer complete elimination of _Salmonella_ Heidelberg. The bacteriostatic and bactericidal characteristics of LEO primarily depend on the bacteria and oil concentration [60; 61]. However, several other factors, such as oil composition, extraction method, plant developmental stage, and environmental variables including temperature can influence the oil's effectiveness. Therefore, lemongrass oils from different species might exhibit effects of different nature and intensity. Nevertheless, the host organism can also decide oil effectiveness to a certain extent depending on its morpho-physiological attributes [62]. Therefore, EOs react differently with Gram-positive and Gram-negative bacteria, owing to their dissimilar cell-wall structures [60; 63]. [PERSON] et al. [64] examined _Cymbopogon flexusosus_ EO (\(\mu\)L mL\({}_{-1}\)) against _Listeria monocytogenes_, _Staphylococcus aureus_, and _Salmonella typhimurium_ and determined their MICs and MBCs as of 3.9 \(\mu\)L mL\({}_{-1}\) each. The MICs of citral against _Cronobacter sakazakii_ strains ranged from 0.27 to 0.54 mg/mL. Scanning electron microscopy analysis further confirmed that _C. sakazakii_ cell membranes were damaged by citral [65]. Table 2 traces the efficacies of lemongrass essential oils against various gram positive and gram negative bacteria.
## 4 Discussion
### Implant Length and Stability Dip  
Implant stability consists of primary and secondary stability [PERSON] and [PERSON] (2018). Primary stability is obtained by mechanical engagement in the bone upon implant insertion, whereas secondary stability is related to the biological response to bone healing [PERSON] et al. (2018). Primary stability is an important factor for implant success, especially when immediate loading is planned, because transmission of micromotion to the implant body can cause peri-implantal bone loss or osseointegration failure [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018).  
During the early healing period, primary stability constitutes most proportion of total stability, but thereafter biologic stability becomes dominant with new bone apposition [PERSON] and [PERSON] (2018). Total stability is reported to decrease at the initial healing stage and rebound as healing progresses, showing a transient dip in total stability curve [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018). This study monitored the ISQ values longitudinally for a year to evaluate the course of stability and healing pattern. Short implant group showed excellent ISQ value high enough for required stability, without statistically significant differences to long implant group except during 3 weeks post-surgery. Additionally, both groups showed values far above the minimum ISQ value of 65, and an increasing tendency throughout 1-year monitoring period without a distinct stability dip. This result runs counter to previous studies reporting a transient dip in total stability during initial stage of healing [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018).
## 3 Methodology  
### Preliminaries  
#### 3.1.1 Formal Definition  
The compression problem for the real-time transmission of ship data via the BeiDou Navigation System can be described as follows:  
Periodically, the sender generates a set of ship status data, denoted as \(\mathbf{x}_{t}\in\mathbb{R}^{n}\), representing variables such as the ship's speed, engine torque, and fuel consumption, at the current time \(t\). Both the sender and receiver maintain a record of all historical data sequences, \(\mathbf{x}_{<t}\), which have been previously transmitted. These historical sequences are integral to the compression algorithm.  
The sender employs an encoding function, denoted as \(E\), to encode \(\mathbf{x}_{t}\) into a binary string \(c=E(\mathbf{x}_{t}|\mathbf{x}_{<t})\), which is then transmitted via the BeiDou short message communication system. Upon receiving this message \(c\), the receiver performs decoding, resulting in an approximation of the original message, \(\mathbf{x}_{t}^{\prime}=D(c|\mathbf{x}_{<t})\).  
Additionally, predetermined upper and lower limits exist, denoted as \(\mathbf{L}_{u},\mathbf{L}_{d}\in\mathbb{R}^{n}\), as well as transmission decimal places, denoted as \(\mathbf{a}\in\mathbb{Z}^{n}\). These limits and decimal places are determined by the monitoring system's administrator according to their specific requirements. Our compression algorithm must encompass all potential values of \(\mathbf{x}_{t}\), ensuring that the decoded approximation, \(\mathbf{x}_{t}^{\prime}\), meets the required level of accuracy. Moreover, the algorithm must strive to minimize the average coding length of the message \(c\).  
Formally, the compression algorithm must guarantee that:\[\forall\mathbf{x}_{t}\in\mathbb{R}^{n},\mathbf{x}_{t,i}\in[\mathbf{L}_{t,i}, \mathbf{L}_{u,i}],c=E(\mathbf{x}_{t}|\mathbf{x}_{<t}),\mathbf{x}_{t}^{\prime}=D (c|\mathbf{x}_{<t}),|\mathbf{x}_{t,i}^{\prime}-\mathbf{x}_{t,j}|\leq 10^{- \mathbf{x}_{t}},\quad 0\leq i<n \tag{1}\]  
and minimize the average coding length of the message \(c\). When applying this problem in practical scenarios, we make the following key assumptions:  
* The interpretation of each dimension within the data vector, denoted as \(\mathbf{x}\), remains constant, and each dimension represents a parameter associated with the ship's internal state.
* Both the sender and receiver utilize computers, as opposed to circuit boards, to process the data. They possess sufficient computing power and time (several seconds) to execute intricate compression algorithms.
* With the help of appropriate channel coding [8; 9], the communication process is reliable.
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 3 Implementation of Gimbal Control through PV Image Analysis
### Implementation of BLDC Control Algorithm  
The control method for the gimbal system's stabilized driving device is a proportional integral derivative (PID) controller method. The PID has a small number of parameters and a simple structure. In particular, it is intuitive to change the response characteristics in response to changes in proportional gain, integration time, and derivative time [19]. Some studies examined intelligent PIDs to automatically determine the parameters [20; 21]. However, in this work, the classical PID algorithm with simple structure is applied.  
From the calculated horizontal angle and the vertical position, we can obtain the PWM values seen in Equations (3)-(5).  
\[e(t)=y_{target}(t)-y_{cur}(t-1) \tag{3}\]\[y_{cur}(t)=K_{p}e(t)+K_{d}\frac{d\epsilon(t)}{dt}+K_{i}\int_{0}^{t}e(t)dt \tag{4}\]  
\[out2_{PWM}=w\times y_{cur}(t)+b \tag{5}\]  
where \(t\) is the unit time and \(y_{target}(t)\) is the target value obtained by image processing at \(t\) time, which can be a horizontal angle and a vertical position, \(y_{cur}(t-1)\) and \(y_{cur}(t)\) are the tilt or roll motor control value at \(t-1\) time and \(t\) time, respectively, \(e(t)\) is the position error value, \(K_{p}\), \(K_{d}\), and \(K_{i}\) are proportional, differential, and integral coefficients, respectively, \(out2_{PWM}\) is the PWM value for tilt motor control of the main controller, \(w\) is the slope of the PWM value and position value, and \(b\) is the minimum PWM value.  
The calculated horizontal and vertical positions are encoded along with the signal received from the remote control device. The relationship between the camera focus and the PV module is always fixed regardless of the tilt. Only the part cut by the image plane changes as the tilt changes. In this development, the camera tilt is adjusted around the horizontal axis of the module seen from above the PV module.
## 5 Parameters Influencing the Propagation of UWSN  
The process of converting acoustic energy into heat becomes more efficient as distance and frequency rise. Its applications include scattering, reverberation, refraction, and dispersion. In contrast to horizontal channels, which can have extremely long multi-path spreads, vertical channels have restricted time dispersion, which is limited by the water depth, high delay, and high variance in delay.  
* The throughput of the framework is severely decreased by the underwater acoustic channels' engendering speed, which is five significant degrees slower than that of the radio channel.
* It degrades the exhibition of advanced correspondences due to the Doppler spread. Correspondences with high information rates make different neighboring images medal at the recipient, which requires modern signs; it degrades the exhibition of standard correspondence conventions.
* To ensure trustworthy data delivery from sensor nodes to sink, two-hop provides a dynamic security paradigm. Which determines the ideal data packet size for effective data transport in the two-hop paradigm. Two-hop routing to boost wireless sensor network communication performance are tabulated in the Table 4.  
\(\rm\bigcirc\) Data Transmission Dynamic security  
\(\rm\bigcirc\) Bandwidth Aggregation  
\(\rm\bigcirc\) Load balance transmission  
\(\rm\bigcirc\) Congestion-free transmission  
\(\rm\bigcirc\) Low latency transmission
## 4 Application of Record Selection Model
The same discussions can be also made for both directions considering the target and mean spectral shape of selected GMs. It is noteworthy that the proposed model enforces a single-scale value for all orientations of GMs (x, y, and z), without specifying a range or limits, to ensure the spectral shape compatibility of both the target and period-dependent variability.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 6 Implementations and Use Cases
### New Mobile Edge Applications  
Corporate investment in 5G has risen rapidly because the new use cases (and revenue streams) opened by the next-generation technology will reduce expenditures and maintain flat rates for users. In their report naming mobile edge computing a key enabling technology for 5G networks,the European 5 GPPP identified multiple verticals that would be empowered by mobile edge computing: Internet of Things (IoT), caching, video streaming, augmented reality, healthcare, and connected vehicles. With the building of large-scale 5G networks, researchers are focusing on the myriad application spaces that could benefit from the low latency, proximity, high bandwidth, location-awareness, and real-time insight provided by mobile edge computing [2]. The growth of mobile edge computing will interrupt the current cloud-computing paradigm in preference to localized computing near the user.  
Below we discuss a few of the new application spaces for emerging ML-enabled mobile edge computing systems.
## 2 Experiments and Methods
### Composition and Structure Characterization  
The chitosan/CaCO\({}_{3}\) hydrogel samples prepared above were dried and subjected to compositional and structural characterization. This characterization primarily included observing the surface morphology and internal microstructure of the hydrogel at different mineralization times using scanning electron microscopy (SEM, Japanese electronics JSM-7600F, Tokyo, Japan) and transmission electron microscopy (TEM, Japan Electronics Corporation JEM2100, Tokyo, Japan). The effect of mineralization time on the size, distribution, and morphology of the CaCO\({}_{3}\) nanocrystals was analyzed. Additionally, the crystalline nature and composition of the CaCO\({}_{3}\) nanoparticles within the hydrogel were analyzed using X-ray diffraction (XRD, Shimadzu Corporation, Kyoto, Japan, XRD-7000), Raman spectroscopy (Horiba JobinYvon Company, Kyoto, Japan, Laser Confocal Raman Spectrometer LabRAM HR800), Fourier transform infrared spectrometer (FTIR, Bruker V70, Rheinstetten, Germany), and wide-angle X-ray diffraction (WAXD, Bruker lus microfocuslight tube, Germany) techniques. These techniques were employed to determine whether the desired mineral crystals (CaCO\({}_{3}\)) were present in the hydrogel following biomimetic mineralization. Thermogravimetric analysis (TGA, Platinum-Elmer Instruments Shanghai Co., Pyris1 TGA, Shanghai, China) was also used to analyze the content of CaCO\({}_{3}\) in the hydrogel. Through the compositional and structural characterizations described above, the analysis and understanding of the mineralization mechanism of CaCO\({}_{3}\) in the chitosan polymer network would contribute to a deeper understanding of the mechanisms and processes of biomineralization in nature.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Data Lakehouse  
This module offers a unified architectural framework that merges the flexibility of data lakes with the performance and consistency of traditional data warehouses. It enables ViMindXAI to manage and analyze large-scale, heterogeneous video and sensor data across multiple formats and modalities. This hybrid architecture enables both streaming and batch analytics, supporting structured metadata processing, semantic enrichment, and long-term data archival. Organizing data into logical zones such as raw, staging, and curated layers supports reproducible ETL workflows, governance, and traceability.  
Representative technologies such as Delta Lake and Apache Iceberg are leveraged to implement this architecture. Delta Lake, optimized for Spark-based workloads, provides ACID transactions, time-travel, and schema enforcement ideal for structured processing and model training pipelines. In contrast, Apache Iceberg offers native support for multiple query engines such as Spark, Flink, and Trino and is well-suited for federated environments with complex partitioning, schema evolution, and snapshot isolation. Together, these technologies provide ViMindXAI with a scalable, interoperable, and regulation-compliant data infrastructure designed for both real-time and historical video analytics.