esa-sceva/satcom-chunk-collection huggingface dataset:
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 3 PLA Unique Features for Biomedical Applications  
Over the years, PLA has attracted growing attention as a base material for various biomedical applications [28]. Its medical application was firstly reported for the repair of mandibular fractures in dogs [5]. PLA has been applied in surgery as suture materials and bone fixation devices for about three decades. For the first time in 1973, LA and glycolic acid were offered as degradable materials for the sustained delivery of bioactive agents. PLA has been proved to be a proper bioabsorbable material for medical implantable devices, such as resorbable plates and screws. Likewise, synthetic bioabsorbable polymers, such as PLA can arouse cells to regenerate tissues and release drugs, which has recently been attended as tissue engineering scaffolds [30].  
Figure 2: Stereoisomers of the polymer start from different lactides. Reproduced with permission from [28] (License number: 5235811357024).  
There are several advantages offered by PLA-based materials for biomedical applications. PLA is a biopolymer obtained from renewable resources at relatively low costs. PLA-based materials have been approved by the FDA for direct contact with biological fluids. PLA is biocompatible, biodegradable, and bioabsorbable; it is not toxic both in solid form and when degraded. Compared to other similar polymers, PLA is easily thermal processable and needs low energy consumption during the procedure, typically in processes, such as film casting, foaming, extrusion and fiber spinning. The use of PLA in biomedical applications is also based on its thermomechanical features, likely its shape memory effect. Considering these characteristics, PLA can efficiently fulfill the requirements for medical implantable devices; hence, different medical matrices have been designed from different PLA-based structures, such as degradable sutures, drug delivery devices, nanoparticles, and scaffolds for tissue regeneration [4; 11; 28; 30; 31; 33; 34].
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 3 SPinDP: Stream Purifier in Distributed Platform
### Overall Architecture of SPinDP  
This section describes the overall structure of SPinDP and the inter-relationships among its internal components. Figure 4 shows the overall architecture of SPinDP. As  
Figure 3: Taxonomy of sampling algorithms by domain.  
shown in the figure, SPinDP can be divided into four major components: Data Stream Processing Engine, Purification Library, Plan Manager, and Shared Storage. In SPinDP, Apache Kafka is used as the pipeline for the data stream, and the input-processing-output stages are executed continuously. All the information required for each stage is input through a Web-based user interface, and this input information is transmitted to each component through Plan Manager. Metadata that need to be shared in addition to the actual data stream enter Shared Storage. The Data Stream Processing Engine obtains input/output information and purification algorithm parameters through Plan Manager. It loads the algorithm in RDMA-Storm from Purification Library and then executes it, outputting the results in the specified format.  
Based on the overall architecture shown in Figure 4, each component developed in a modular way can be connected to form a unified platform through a user interface. This design allows users to selectively adopt each component according to their needs or easily add new features to the platform. In this paper, we focus on designing the entire platform for data stream purification. However, if we rebuild an algorithm library for other purposes instead of the main system's Purification Library, the rebuilt platform can be utilized for different purposes. In addition, additional features can be easily added and integrated through Plan Manager and Shared Storage.
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 4 Discussion
Our results are at the higher end of this accuracy range along with the results of [22], likely because both studies integrated manual refinement into our respective classification workflows.
# Automatic Detection and Unsupervised Clustering-Based Classification of Cetacean Vocal Signals +
### Feature Extraction  
Feature extraction transforms vocal signals into a set of feature vectors that represent the distinct characteristics of the vocal signals. The new acoustic signals from the previous section are transformed into MFCC (mel-frequency cepstral coefficients) features. After receiving the feature vector, the label information is saved to verify the unsupervised classification. However, the duration of different vocal signals is not the same, which makes it difficult to build a formal feature vector. Our method is to use a Hamming window to enframe the signal and specify the window length as one frame per row. The length of a segment depends on the window length, while the frame size refers to the beginning position of the next segment. Here, a 75% overlapped Hamming window with length \(M=2000\) is used by periodic sampling.  
Furthermore, to improve the quality of the feature vector, each frame of the signal filtered by the Hamming window is transformed into an MFCC feature. It generates the coefficients corresponding to each data window for the input audio signal and converts the signal to a frequency representation at a certain sampling rate. The \"audioread\" function is used to read the audio file and get the sampling rate. The sampling rate here depends on the characteristics of the original cetacean vocal signal. The processes of the MFCC method include the following: 1. Framing; 2. Fast Fourier transform; 3. Calculate the periodogram estimate of the power spectrum; 4. Sum the energy passing through the mel filterbank; 5. Take the logarithm of all filterbank energies and the DCT of the log filterbank energies; 6. Obtain the MFCC coefficients. More detailed descriptions of the MFCC method are available at [34].  
We only process the underwater acoustic data of the same cetacean species at a time and obtain its feature vector. Meanwhile, the corresponding label information is saved for later verification experiments. The vocal features of the same species are attached to the same label number and arranged into a column vector. After reviewing all the audio files, all species' features are grouped into different combinations to create two to five classification datasets. Afterward, the unsupervised clustering-based classification is conducted on these mixture feature vectors. Eight datasets were formulated according to our methods. Overall, we analyze five cetacean species' acoustic data during the detection process and count the file size before and after detection to sum up the extraction rate. Since the number and the size of each audio file are not the same, it is difficult to calculate the statistical extraction rate based on the time information, so the total size of the audio files are calculated. We also analyze the features of different species with mean value and standard deviation in Table 1.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 3 Voucher-Based Addressing
### Design Goals and Overview
For example, EUI-64 is a long-established address assignment methodology that employs the direct implantation of an LLID in assigned addresses. This would create an easily verifiable LL2 IP binding for each interface, but it would not incorporate a focus on privacy because the LLID of the interface is publicly exposed and the address remains fixed. Additionally, EUI-64 is a one-to-one LL2 IP binding, whereas VBA seeks to derive many IP addresses for each LLID. To meet the need for privacy, simplicity, and flexibility in generating new, obscure IP addresses during the SLAAC (Stateless Address Autoconfiguration; see RFC 4862 [24]) self-addressing process, VBA employs a trivial hashing process. Using techniques based on hash functions ensures that any LLID of an arbitrary length can be reliably bound to an irreversible address suffix that is a desirable and fixed-length. However, simply hashing an LLID will still only manifest a one-to-one binding. Many formalized IPv6 address generation schemes already offer ways to derive many privacy-focused addresses on a single interface (e.g., Section 5 and Appendix A.3 of RFC 7217 [25]). The addresses generated through these schemes are intentionally obfuscated to preserve the privacy of the node unless the reversing parties are aware of all the input parameters used by the deterministic address generation function. VBA solves this one-to-one addressing problem by employing a variant of salted hashing and key derivation functions. VBA strikes a careful balance of (1) keeping off-link actors unaware of local voucher information, and (2) ensuring on-link nodes are aware of all the parameters used to generate any neighbor's IP address(es). Off-link parties cannot derive a target's bound LLID because they cannot receive NDP messages from the target's broadcast domain, nor can they determine the binding from any patterns within the target's address itself. VBAs will always appear random as a consequence of utilizing the outputs of deterministic hash functions. Finally, using key derivation techniques as the deterministic hashing functions introduces an adjustable work factor that modifies VBA generation requirements and mitigates brute-force attacks.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### BA and BS Estimates
#### 4.1.2 Active RS Sensors
While using airborne LiDAR data provides good potential for assessing BA and BS in forestry with high accuracy, nowadays, the application of these data over large areas is limited by its cost associated with data acquisition. Another drawback to the incorporation of LiDAR measurements for the purpose of BA estimations is the lack of up-to-date pre-fire data for most forestry areas. Nevertheless, the use of LiDAR in wildfire assessments is expected to increase based on the capability to provide high spatial resolution data.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 2 Principle of Operation  
### Distance Measurement Principle  
The spherical triangle circle formula is a commonly used distance calculation method [15]. As shown in Figure 2, N is the north pole of the earth, P and Q are two points on the earth, whose latitudes are \(lat1\) and \(lat2\), respectively, and the longitudes are \(lon1\) and \(lon2\); \(\alpha_{1}\) and \(\alpha_{2}\) are spherical angles (where the latitude and longitude are, respectively, radian), and the distance between PQ points is D. Using the spherical triangle formula  
\[\cos(lat2)\cdot\cos\alpha_{2}\ =\ \sin(lat1)\cdot\sin D-\cos(lat1)\cdot\cos \alpha_{1}\cdot\cos D \tag{1}\]  
It can be solved that the spherical distance between PQ points is  
\[D\ =\ R\cdot\arccos[\sin(lat1)\cdot\sin(lat2)+\cos(lat1)\cdot\cos(lat2)\cdot \cos(lon1-lon2)] \tag{2}\]  
where \(R\) is the radius of the earth, in meters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research Team** & **Hardware Scheme** & **Software Scheme** & **Measurement Area** & **Measurement Accuracy** & **Signal Adaptability** \\ \hline [PERSON] et al. (2017) [6] & S720 handheld GIS & fixed memory + data point screening & woodland & 4.55\% & moderate occlusion \\ [PERSON] et al. (2022) [10] & MC120M BeiDou & static memory + triangulation algorithm & farmland & 0.7\% & open area \\ [PERSON] et al. (2025) [11] & Arm processor + dual-frequency & \begin{tabular}{c} BeiDou/inertial \\ navigation integrated \\ positioning system \\ \end{tabular} & woodland & 3\% & moderate occlusion \\ This design & ATGM332 D & \begin{tabular}{c} FreeRTOS dynamic \\ memory + Kalman \\ prediction \\ \end{tabular} & multi-terrain & \
\begin{tabular}{c} 0.37\% (open) \\ 2.72\% (weak) \\ \end{tabular} &
\begin{tabular}{c} open/semi- \\ closed/urban \\ canyon \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of area measurement technologies.
## 4 Results and Discussions
### Depth and Temperature Calibrations of Samples
In other words, the reducing frozen volume under the detection cone (NA) of the fiber array due to the shrinking ice-hemisphere emulates a progressive reduction in the frozen tissue thickness. During thawing, both signals remain constant, as the maximum detectible optical transmission in frozen tissue is about 1.5 mm (based on the transmission calibration procedure, Section 4.1), as light emanating from lower depths cannot be detected. Therefore, irrespective of the frozen skin depth, the backscattered signal that can be detected by array-A, comes from a maximum depth of 1.5 mm or less, while for the same reason the transmitted signal remains low until the frozen tissue also retreats to 1.5 mm or less. Figure 4: (**a**) Typical backscattering (black) and transmission intensities (red) and temperature (blue) timeline during freeze/thaw cycle for the illumination fiber near the surface of tissue depth \(x=3.0\) mm. (**b**) Sideview showing the fiber illumination cone and detection depth in the “ice hemisphere” of the tissue. Hence, the freezing depth can be determined as a function of backscattering intensity and used to measure primarily the thawing process for a maximum 1.5 mm to 0 mm (tissue surface) with an accuracy of about \(\pm\)0.1 mm, which was based on the transmission calibration (Section 4.1) and are shown in Figure 5. The detectable depth of freezing is thus dependent on the optical diffusion in frozen and unfrozen tissue and the sensitivity of the data acquisition system.
## 5 Working Operation of Blockchain in Smart Grid (SG)
### Exacerbation of Voltage Instability Issues in SGs by Volatile Energy Demands of EV Market  
Along with the advanced control and automation in the electrical grid provided by SG comes the potential for instability, which can cause serious problems. The intermittent nature of various RESs relying on renewable energy (solar and wind) necessitates the criticality of overcoming instability. Voltage instability problems are also compounded by a sudden increase or decrease in electrical load, such as one due to EV consumers. In SG, voltage instability can occur due to an incorrect configuration of the automated systems or faulty programming. The possible reasons for voltage instability in SG include:  
* A sudden increase or decrease in the electrical load due to a spike in consumer demand or a disruption in the electric supply;
* Incorrect configuration of the automated systems and controls, such as the use of inverters and storage systems;
* Faulty programming of the automated systems;
* Poor placement of devices within the grid, such as transformers and capacitors.  
Voltage instability can have a severe effect on the power supply in the SG. The most noticeable effect is a sudden drop in voltage, which can cause power outages and disruption. In addition, some devices can be permanently damaged due to sudden changes in voltage levels.  
To reduce the risk of voltage instability in SGs, a number of solutions have been proposed, including ensuring the correct configuration of the automated systems and controls, reducing the load on the grid when demand is high, e.g., from EV consumers, through structured tariffs or when disruption is expected, implementing proper programming of the automated systems, and properly placing devices in the grid, to ensure an even distribution of voltage [106].
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Intelligent Content Creation
#### 3.3.3 Examples of Applications  
Several applications demonstrate the use of MLLMs in intelligent content creation. _Automated textbook generation_ utilizes AI systems to summarize and organize information from various sources to generate textbooks and study materials. Models like GPT-3 can produce explanatory text on a given topic, compiled into educational resources.  
_Quiz and assessment generation_ involves MLLMs creating assessment items such as multiple-choice questions, short answers, and problem-solving exercises tailored to specific learning objectives [58]. This automation supports educators in developing formative and summative assessments efficiently.  
_Interactive simulations and virtual labs_ are created by integrating textual descriptions with visual and interactive elements. MLLMs help create virtual laboratory environments and simulations, allowing students to explore concepts hands-on [59].  
_Multimedia content creation_ is enhanced by MLLMs enabling the creation of educational videos, animations, and presentations by generating scripts, visual content, and even voice-overs [60].  
_Language translation and localization_ are facilitated by AI models that translate educational content into multiple languages, ensuring accessibility for non-native speakers and supporting multilingual education [61].
Numerous studies utilizing transcriptome sequencing on lentil have investigated topics such as lentil's response to biotic and abiotic stresses by studying its defense mechanisms against pathogens, drought and heat stress [23; 24; 25; 26; 27; 28]. Transcriptome analysis offers several advantages, including the discovery of stress-resilient genes and the identification of transcription factors and stress-responsive pathways [26]. In another transcriptomic analysis conducted on both small and large-seeded genotypes, the study examined the involvement of pathways related to hormone biosynthesis and crucial genes encoding kinases, transcription factors (TF) and enzymes responsible for cell wall formation. These findings suggest that the control of seed size in lentils operates through the regulation of cell division, involving both cell expansion and the overall seed growth [29]. In a recent transcriptomic analysis of lentil embryos, seed coats and whole seeds, specific genes and co-expressed gene sets were found to be prevalent in distinct tissues and developmental stages, distinguishing the expression patterns of the embryo from those of the seed coat [30].The availability of the current lentil's genome assemblies that include the cultivated lentil (_Lens culinaris_) and the wild relative (_Lens ervoides_) has facilitated the potential to gain knowledge regarding mechanisms regulating even complex traits [22]. Evidence supporting the significance of seed coat hardness in the quality of lentil products and, consequently, their market value [31] underscores the critical need to explore the factors governing tannin accumulation in lentil seeds, as tannins are the primary determinants of seed coat hardness [9; 32]. As hard seed coat appears to be determined both by genetic and environmental factors [7], to gain a better understanding of this phenomenon, it is crucial to examine the lentil's innate molecular mechanisms that control tannin production, such as the seed maturity stage, but also the effect of environmental features such as P, a principal nutrient involved in many metabolic processes. This study examined the impact of P and the seed maturity stage on both the overall gene expression patterns and those of the phenylpropanol genes in lentil, the pathway responsible for tannin biosynthesis. Insights derived from this study may have the potential to provide a plausible explanation for differences in seed coat hardness within the same lentil variety stemming from differences in P fertilization levels. These findings can inform innovative agricultural strategies designed to enhance lentil product quality.
## 2 Modified Steering Kernel and Image Matching  
When taking an image using a camera and a sensor, noise may be introduced due to a problem in the system or environment. Additive white Gaussian noise (AWGN) [8], a typical noise found in images, is an additive noise evenly distributed throughout the image. The equation of the image in which the AWGN is generated may be expressed as \(I_{ij}=Z_{i,j}+N\). Here, \(Z_{i,j}\) denotes an original image not damaged by noise, and \(N\) refers to an AWGN with a mean value of 0 and a standard deviation \(\sigma\). \(i\), \(j\) are the internal coordinates of an image with horizontal and vertical dimensions \(M\times N\), respectively. The proposed algorithm sets the center window on the image and proceeds with the noise removal process. The center window \(W^{C}_{i,j}(k,\ l)\) is fixed as a square around the pixel coordinates \((i,j)\). The size of \((2s+1)\times(2s+1)\) is set according to parameter \(s\), representing the size of the center window. The internal coordinates are set as \(k,l\).  
The window size used for filtering is a key factor affecting the filtered results. Here, a larger window size reduces noises but introduces a blurring effect that ultimately removes important information such as the edge regions and text components of the image. In contrast, a smaller window size does not reduce noise well. To resolve these drawbacks, an adaptive window size was used to properly size the mask depending on the level of noise.  
The adaptive window size \(s\) proposed in this study is defined as:  
\[s=max[\hat{s},1],\ \hat{s}=round[\alpha\cdot\sigma_{est}], \tag{1}\]  
where \(\alpha\) is the window size parameter, and \(\sigma_{est}\) is a noise estimate obtained using noise estimation [11]. The \(round[\ ]\) function means rounding and if the noise estimate is low and \(\hat{s}=0\), the window size becomes the lowest value 1.
# Security Performance Analysis of Full-Duplex UAV Assisted Relay System Based on SWIPT Technology
## 1 Introduction
Aiming at the scenario of a relay UAV with limited energy and active attack by full-duplex eavesdroppers, in the literature [18], the authors propose a model of UAV wireless power supply technology based on SWIPT technology and analyze the impact of factors such as the base station's transmit power, the energy harvesting time factor, and the environment on the security performance of the UAV communication system. In the existing literature, the physical layer security research of UAV relay systems by combining SWIPT technology and full-duplex technology is limited, and the research for the airborne UAV eavesdropping scenario still needs to be explored. Therefore, to address the above problems, firstly, this paper establishes a UAV downlink communication system model under an airborne eavesdropping scenario; then, it proposes a secure transmission protocol for a full-duplex UAV relay system based on SWIPT technology; then, it analyzes and derives the connection probability and secrecy outage probability of the downlink transmission; and finally, it explores the influence of system parameters such as environmental parameters, UAV altitude, energy allocation efficiency, power allocation factor, and other system parameters on the safety performance of the UAV system.