esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Scale Factor Calibrated Using Different AG Values
#### 4.2.2 Correlation Analysis of GD Mutual Differences
However, for the LCR-G gravimeters, because the correlation has reduced obviously, the GD residuals have decreased. Consideration of the histograms of the mutual differences (Figure 7c,d) reveals that the performance of the MBGA method is slightly better than that of the BGA method, especially for the LCR-G gravimeters. Therefore, from the perspective of the correlation between the mutual difference and the GD, the SFs calibrated using the MBGA method are considered relatively accurate (under the condition of a sufficient number of AG values participating in the calibration). Figure 7: Mutual differences of gravity difference (GD) calculated using the BGA method and the MBGA method: (**a,b**) mutual difference of the GD from two CG-5 relative gravimeters and two LCR-G gravimeters, respectively, in which the blue box and purple circle represent the mutual difference obtained by BGA method and MBGA method respectively, and the blue and purple lines are the corresponding fitting line, (**c,d**) histograms of mutual difference of the GD from CG-5 and LCR-G gravimeters, respectively.
## 2 Preliminaries
### Performance Metrics  
It is possible to provide a numerical value for the degree to which the cover image and the watermarked image differ from each other. The quality of the original cover image is diminished when it is watermarked or otherwise altered. As a result, evaluating its quality is essential [51]. When a cover image is watermarked or altered in another way, its quality is diminished. Therefore, assessing its calibre is imperative. By utilising the peak signal-to-noise ratio (PSNR) formula as shown in Equation (8), it becomes feasible to evaluate the degree of resemblance between the watermarked image and the original image. The PSNR scale is expressed in decibels (dB). A greater PSNR indicates a higher degree of similarity between the unaltered and watermarked versions of the image.  
\[\text{PSNR}=10\times\log_{10}\left(\frac{255^{2}}{MSE}\right) \tag{8}\]  
The error can also be quantified by comparing the original and watermarked versions of an image using a metric called mean squared error (MSE). The MSE can be computed using Equation (9), where \(C\) represents the original, unmodified image, and \(CW\) represents the modified, watermarked version.  
\[\text{MSE}=\frac{1}{M\times N}\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}(C(x,y)-CW(x,y)) ^{2} \tag{9}\]  
A comprehensive evaluation of the watermark retrieval quality is necessary to effectively evaluate the efficacy of the watermarking system. When evaluating the reliability of a watermarking system, academics typically use both the normalized correlation (NC) and the structural similarity index measure (SSIM) [52]. While SSIM analyses the structural similarities between the two images, the degree of similarity within the original and watermarked copies is assessed by NC. The NC and SSIM can be obtained by solving their respective equations, denoted as (10) and (11).  
\[\text{NC}=\frac{\sum_{x=1}^{M}\sum_{y=1}^{N}w(x,y)\times w^{\prime}(x,y)}{\sum _{x=1}^{M}\sum_{y=1}^{N}w^{2}(x,y)} \tag{10}\]To distinguish between the original watermarked image (\(w\)) and the recovered watermarked image (\(iw^{\prime}\)), we use the symbols \(i\) and \(w^{\prime}\), respectively.  
\[\text{SSIM}=\frac{(2\mu_{w}\mu_{w^{\prime}}+C_{1})(2\sigma_{ww^{\prime}}+C_{2})} {(\mu_{w}^{2}+\mu_{w^{\prime}}^{2}+C_{1})(\sigma_{w}^{2}+\sigma_{w^{\prime}}^ {2}+C_{1})} \tag{11}\]
# Edge and Cloud Computing in Smart Cities
## 2 Architectural Models for Edge and Cloud Computing in Smart Cities
### Hybrid Digital Twin-Enabled Edge-Cloud Architecture
The total workload across the architecture is represented by  
\[\sum_{j=1}^{K}a_{j}C_{E_{j}}+(1-a_{j})C_{C}=C_{total}, \tag{31}\]  
where \(a_{j}\) is a binary variable indicating whether task \(\tau\) is processed at the edge (\(a_{j}=1\)) or offloaded to the cloud (\(a_{j}=0\)), \(C_{E_{j}}\) denotes the computational capacity of the edge node, \(C_{C}\) represents the cloud's processing capability, and \(C_{total}\) is the overall system workload [69; 70]. In addition to computational efficiency, energy consumption remains a critical factor in determining the viability of digital twin-enabled architectures. The total energy consumption is given by:  
\[E_{total}=\sum_{j=1}^{K}\left(P_{E_{j}}T_{E_{j}}+P_{sync}T_{sync}+P_{C}T_{C} \right), \tag{32}\]  
where \(P_{E_{j}}\) represents the power consumption of edge node \(E_{j}\), \(P_{sync}\) accounts for the energy required to maintain synchronization, and \(P_{C}\) denotes the power cost of cloud-based processing. Efficient task scheduling algorithms, such as RL-based optimizations, can be incorporated to minimize \(E_{total}\) while maintaining system performance [71; 72]. The hybrid edge-cloud model offers several advantages over conventional architectures by integrating real-time simulation, predictive analytics, and adaptive resource allocation. However, maintaining consistency between the digital twin models and their physical counterparts introduces computational overhead, particularly in high-frequency synchronization scenarios. To address this challenge, adaptive synchronization strategies dynamically adjust \(f_{sync}\) based on task urgency and network conditions, ensuring efficient data transmission while preventing excessive update cycles [73; 74; 75]. From a fault-tolerance perspective, system resilience is achieved through distributed redundancy mechanisms. In the event of an edge node failure, the system dynamically redistributes computational tasks and synchronization responsibilities to adjacent nodes, mitigating service disruptions. The probability of failure in this architecture is expressed as  
\[P_{f}=1-\prod_{j=1}^{K}(1-p_{E_{j}}), \tag{33}\]  
where \(p_{E_{j}}\) denotes the failure probability of an individual edge node, and \(K\) represents the total number of edge nodes supporting redundancy. By increasing the number of participating nodes, the likelihood of service continuity is enhanced [76; 77].
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 3 LoRa/LoRaWAN Standard
### LoRaWAN Architecture  
Figure 1 illustrates a typical architecture of a LoRaWAN. It consists of four basic components [51; 52; 53]:  
* EN (end nodes);
* GW (gateways--base stations and routers);
* NS (network server);
* AS (application server).  
End nodes (ENs, terminal devices) are distributed network elements that implement specific sensor and measurement functions. They have very limited computing power but are equipped with a wireless communication module that they use to send data to gateways. The node-to-gateway transmission is called Uplink, while transmission in the opposite direction is called Downlink. Depending on the volume of transmitted data and power consumption, nodes are classified into classes A, B, and C.  
Gateways (GWs--modems and access points) receive data sent by end nodes (EN) via LoRaWAN. By design, they are transparent and have limited computing power. Their primary role is converting the transmission medium and packets to enable onward transmission via a traditional IP network.  
Figure 1: Typical LoRaWAN architecture. (Source: authors’ image based on [51; 52; 53]).  
A network server (NS) is the recipient of all data sent by the nodes via the gateways. It has significant computational power and performs complex data processing operations. The network server can be based on a direct physical infrastructure or be located in a computing cloud. It is often integrated with a database center.  
An application server (AS) supports the base application, which implements specific tasks and provides a particular service. It should be noted that there may be more than one application server, and the applications and services can vary, although they can be based on the same network and share some or all of the same data.
# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 3 Experimental Results and Performance Analysis
### Visualization and Analysis  
The detection performance of RT-DETR-R18 and LE-DETR is compared in Figure 8 under various highway environments. The figure demonstrates that LE-DETR outperforms RT-DETR-R18 in terms of detection performance. It is evident from the first row of images that LE-DETR exhibits a higher level of detection confidence than RT-DETR-R18 in the presence of external disturbances. The second row demonstrates that LE-DETR accurately identifies abandoned objects, even when they are obscured by vehicles and in extreme weather conditions, whereas RT-DETR-R18 cannot detect them. In the third row, due to the limited feature extraction capability of RT-DETR-R18, its prediction boxes overlap in sparse regions, whereas LE-DETR is able to more accurately identify sparse areas. In the fourth row, the objectives are not recognized by RT-DETR-R18 for smaller-sized abandoned objects, whereas LE-DETR successfully identifies them. Overall, LE-DETR demonstrates exceptional detection capabilities for abandoned objects of different scales in various complex environments, making it highly suitable for highway abandoned object detection tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Epochs** & \begin{tabular}{c} **Params** \\ **(M)** \\ \end{tabular} & \
\begin{tabular}{c} **FLOPs** \\ **(G)** \\ \end{tabular} &
\begin{tabular}{c} **FPS** \\ **(bs = 8)** \\ \end{tabular} & **AP5095** & **AP50** & **AP75** & **AP_s** & **AP_m** & **AP_1** \\ \hline YOLOv5-M & 112 & 21.2 & 49.0 & 133.3 & 37.5 & 80.3 & 28.2 & 25.5 & 41.0 & 33.9 \\ YOLOv7 & 200 & 36.9 & 104.7 & 75.8 & 39.2 & 81.6 & 30.5 & 15.7 & 42.1 & 37.4 \\ YOLOv8-S & 188 & 11.2 & 28.6 & 169.5 & 38.5 & 79.1 & 34.5 & 15.0 & 41.2 & 35.3 \\ YOLOv8-M & 152 & 25.9 & 78.9 & 101 & 37.5 & 77.6 & 31.4 & 15.9 & 40.7 & 34.1 \\ YOLOv9-C & 200 & 25.3 & 102.1 & 37.7 & 38.9 & 78.7 & 29.4 & 18.2 & 42.1 & 35.4 \\ \hline LE-DETR & 150 & 14.5 & 45.0 & 79.3 & 42.6 & 84.3 & 37.4 & 21.3 & 45.7 & 38.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
Figure 8: Visualization of detection results compared to the baseline model. In the figure, psw denotes abandoned objects on the highway. (**a**) Detection in the presence of external disturbances; (**b**) Detection of abandoned objects obscured by vehicles and in extreme weather conditions; (**c**) Detection in sparse regions; (**d**) Detection of smaller-sized abandoned objects.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
###### Abstract  
Remote photoplethysmography (rPPG) is a non-contact technology that monitors heart activity by detecting subtle color changes within the facial blood vessels. It provides an unconstrained and unconscious approach that can be widely applied to health monitoring systems. In recent years, research has been actively conducted to improve rPPG signals and to extract significant information from facial videos. However, rPPG can be vulnerable to degradation due to changes in the illumination and motion of a subject, and overcoming these challenges remains difficult. In this study, we propose a machine learning-based filter bank (MLFB) noise reduction algorithm to improve the quality of rPPG signals. The MLFB algorithm determines the optimal spectral band for extracting information on cardiovascular activity and reconstructing an rPPG signal using a support vector machine. The proposed approach was validated with an open dataset, achieving a 35.5% (i.e., resulting in a mean absolute error of 2.5 beats per minute) higher accuracy than those of conventional methods. The proposed algorithm can be integrated into various rPPG algorithms for the pre-processing of RGB signals. Moreover, its computational efficiency is expected to enable straightforward implementation in system development, making it broadly applicable across the healthcare field.  
remote photoplethysmography; heart rate; machine learning; filter bank; support vector machine +
Footnote †: journal: Applied sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON].  
Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank.  
**Appl. Sci. 2024, 14, 11107. [[https://doi.org/10.3390/app14231107](https://doi.org/10.3390/app14231107)]([https://doi.org/10.3390/app14231107](https://doi.org/10.3390/app14231107))**
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### Fault Distance Calculation  
A fault branch is defined according to the above principle by putting the line's beginning at the fault point so that it forms the most double-ended branch endpoint, calculating the initial distance from the fault point to the other nodes in the double-ended branch, and finally calculating the average distance between these faults.  
Using the average fault distance as the final result helps to cancel out positive and negative errors when multiple fault distances are calculated with both positive and negative errors, resulting in a more accurate positioning result. At the same time, taking the average value can also produce a compromise positioning result without a large error when the error of the calculated fault distance has the same sign.
## 2 Related Work
### Few-Shot Relation Extraction  
FSRE endeavors to extract relationships among entities using a limited amount of labeled data. [PERSON] et al. [42] played a pioneering role in creating FewRel 1.0, a comprehensive large-scale dataset for FSRE. This dataset provides various evaluation metrics for comparing different FSRE models. However, both the training and validation sets originate from Wikidata, leading to a lack of domain adaptability and affecting its generalization performance. In response to this issue, [PERSON] et al. [43] re-annotated a cross-domain test set utilizing the FewRel 1.0 dataset as the foundation, making it more challenging for models to transfer knowledge across different domains. [PERSON] et al. [44] consider the information about the matching of each query and support instances at local and instance levels, facilitating instance classification in the query set based on relationships.  
However, in FSRE, issues like dataset noise and feature sparsity can result in diminished model performance. To tackle these issues, [PERSON] et al. [45] introduced a hybrid attention mechanism to enhance the handling of instance tasks and address the issue of feature sparsity, thereby adapting to the challenges of FSRE tasks. [PERSON] et al. [17] investigated the impact of task difficulty on model performance in few-shot tasks. Their work introduced a combination of prototype networks and relationship prototype contrastive learning, providing an effective solution to address the difficulty of FSRE tasks. [PERSON] et al. [46] introduced a rule-based discriminative knowledge method to address the prediction confusion issue in FSRE. This approach utilizes a logical perception reasoning module and a distinctiveness discovery module to enhance prediction accuracy.  
Furthermore, some researchers have attempted to introduce external knowledge to enhance the accuracy of RE. [PERSON] et al. [47] leveraged external knowledge bases to extract inherent concepts of entities. They used a concept attention module to select the entity concepts that are most semantically similar to the sentence and employed a self-attention fusion module to combine them with entity-embedding vectors, enhancing entity representations and improving the accuracy of RE. [PERSON] et al. [48] introduced an innovative method known as the Entity-Masked RE Contrastive Pre-training Framework. This approach aims to leverage contextual information and entity type information for RE by studying and analyzing the impact of contextual context and entity mentions on RE performance. [PERSON] et al. [49] improved the performance of RE by utilizing context information and label-agnostic and label-aware knowledge provided by relation labels. Although the previous model performed well, its ability to handle outlier samples in FSRE tasks is limited. This paper adopts a contrastive-center loss function to improve the model's capability in managing outlier samples by enhancing both the clustering of similar samples and the separation of dissimilar ones.
## 2 Materials and Methods  
### Study Sites  
This research was carried out in two protected areas, i.e., Chwaka Bay and Menai Bay in Unguja Island in Zanzibar, Tanzania. Out of 125,000 ha of mangrove found in Tanzania, 18,000 ha are located in the Zanzibar Islands, and the two study areas composed of about 4400 ha [40]. Unguja, which is, c., 1660 km\({}^{2}\), is one of the two main islands in Tanzania's semi-autonomous archipelago of Zanzibar (Figure 1) [41]. The island is situated south of the equator (5\({}^{\circ}\)70\({}^{\prime}\) to 6\({}^{\circ}\)50\({}^{\prime}\) S and 39\({}^{\circ}\)18\({}^{\prime}\) to 60\({}^{\prime}\) E). The climate is subject to the effects of the northeast and southeast monsoon winds [42]. In the months of December to March, when northeast monsoon winds are blowing, the climate is relatively hot and dry [42]. The southeast monsoon winds, which blow between April and November, bring heavy rains, which range between 1000 and 2500 mm. The monsoon winds also bring a cool and dry season between June and October. The average rainfall per annum is 1628 mm. Zanzibar's relative humidity ranges from 55% to 99% with an average of 78% throughout the year [42; 43]. The mean monthly temperature is, c., 26 \({}^{\circ}\)C. The majority of Zanzibar's soils are sandy and coralline, with low moisture retention, severe alkalinity, and a hard subsoil, resulting in poor drainage [7; 32]. Fish, salt, limestone, and natural gas are in abundance in the coastal regions. Chwaka Bay is on the east coast of Unguja, c., 34 km east of Zanzibar town. Chwaka Bay has the largest area covered by mangrowes in Zanzibar, i.e., c., 2294 ha [42]. Ten species of mangrowes are found in Chwaka Bay, i.e., _Rhizophora racemose_, _Bruguera gymnorrhiza_, _Ceriops tagal_, _Sonneratia alba_, _Avicennia marina_, _Xylocarpus granatum_, _Xylocarpus molucensis_, _Heritiera littoralis_, _Lummitera racemosa_, and _Pemphis acidula_, [43]. The Menai Bay Conservation Area (MBCA), which is situated in the southwest of Unguja, has 6 islets [43]. Menai's mangrowes occupy, c., 988 ha, but their ecological composition is varied due to intense deforestation. There are numerous stumps and no fully grown trees [42; 43]. The majority of the trees are young _Rhizophora macronata_ trees that are, c., 4-5 m tall. There are a few young _Bruguera gymnorrhiza_ and _Ceriops tagal_ trees at the edge of the forest.
## 4 Implementation of DFDLOF in Personalizing Educational Pathways
### Adaptive Learning Pathway Design
#### 4.3.2 Dynamic Adjustment of Learning Paths and Difficulty Levels  
The essence of DFDLOF lies in its dynamic adjustment of learning paths and difficulty levels, ensuring that each learner's journey is optimally challenging and conducive to learning. This adjustment is a continuous process guided by real-time data analysis and feedback mechanisms. As learners interact with the educational content, the framework monitors their performance, engagement levels, and learning pace, using this information to modify the learning path.  
For instance, if a learner demonstrates mastery of a concept quicker than anticipated, the system can expedite the introduction of more advanced topics, maintaining an appropriate level of challenge. Conversely, if a learner struggles with certain content, the system can slow the pace, provide additional resources, or revisit foundational concepts to reinforce understanding.  
This dynamic adjustment also involves varying activities and assessments presented to the learner, ensuring they cater to different learning styles and preferences. For example, a learner who thrives in problem solving might be presented with more real-world scenariosand case studies. At the same time, another who excels in theoretical understanding might receive more in-depth readings and conceptual analyses.  
The adaptive nature of DFDLOF's learning paths is instrumental in maintaining learner motivation and interest. The framework ensures a more personalized, effective, and engaging educational journey by providing a learning experience that is continually aligned with individual abilities and learning progression. This dynamic and responsive approach to learning paths and difficulty level adjustment is a hallmark of DFDLOF, setting it apart as a sophisticated tool in personalized education.
# Potential Applications of Yeast Biomass Derived from Small-Scale Breweries
## 3. Fermative Yeast Growth  
Yeasts represent a versatile group of fungi broadly used in the beverage industry, especially in beer production. The strains that are the most important to breweries belong to the species _Saccharomyces cerevisiae_ and _Saccharomyces pastorisineus_ (a hybrid of _S. cerevisiae_ and _S. bayanus_). Under the anaerobic conditions used in beverage production, _S. cerevisiae_ ferments sugars to obtain energy for growth. In this metabolic pathway, a hexose molecule (preferentially glucose) is converted into two ethanol molecules and two molecules of carbon dioxide, and two ATP molecules are generated (Figure 2). Since only a small portion of energy is captured by the cells, the majority of sugar available in the growth medium is converted into ethanol, and the biomass gain is limited. In anaerobic conditions, the sugar-to-biomass conversion ratio is below 0.1, while during glucose-limited aerobic growth, this ratio is close to 0.5 [20]. Taking into account the initial sugar concentration in worr ranging between 10 and 15% (_w/v_), in a properly executed process utilizing _Saccharomyces cerevisiae_, approximately 1.5 to 3 kg of yeast slurry that comprises 85 to 90% water can be produced for every 100 L of beer [9]. In this scenario, biomass production in beer fermentation is around 0.15 to 0.75 kg of dry weight per 1 hL of beer produced.  
Yeast cells also absorb nutrients such as free amino acids, short peptides, and vitamins from the word. Amino acids may be divided into three groups according to the order and intensity of assimilation: rapidly assimilated (glutamic and aspartic acids, aspartate, glutamate, serine, threonine, lysine, and arginine), gradually absorbed (valine, methio  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Generated in** & **Accepted by** & **EUNorm for Purified** \\  & **Brewery** & **Treatment Plant** & **Wastewater** \\ \hline BOD 5 [mg/L O\({}_{2}\)] & 1000–50,000 & 100–940 & 25 \\ \hline COD [mg/L O\({}_{2}\)] & 800–38,000 & 109–1668 & 125 \\ \hline TSS [mg/L] & 200–3000 & 78–1260 & 35 \\ \hline Total P [mg/L] & 4–100 & 2–40 & 1–2 \\ \hline Total N [mg/L] & 20–100 & 20–210 & 10–15 \\ \hline Wastewater-to-beer ratio [hL/hL] & 2.2–8.7 & - & - \\ \hline References & [13; 14; 15] & [16; 17; 18] & [19] \\ \hline \hline \end{tabular}  
Ranges based on data presented in Tables S1 and S2 in Supplementary Materials.  
\end{table}
Table 1. Parameters for wastewater streams.  
Figure 2. Main metabolic reactions leading to ethanol formation in brewery yeast.  
nine, leucine, isoleucine, and histidine), and late assimilation (glycine, phenylalanine, tyrosine, tryptophan, alanine) [21]. Proline is incorporated into the yeast biomass to a very limited extent. For the majority of brewery yeast strains, biotin and pantothenate are required for proper growth, although strains requiring no biotin supplementation have been identified [22; 23].