esa-sceva/satcom-chunk-collection huggingface dataset:
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 6 Evaluation
### The Selection of Observation Time  
Our strategy requires setting two parameters, one is the observation time \(T_{observed}\), and the other is the CPU utilization threshold \(H\). The observation time \(T_{observed}\) determines how often the data plane calculates CPU utilization and counts the rate at which the Tx queue receives packets. The concern is whether too high a computation frequency will affect the performance of the data plane. We mentioned in the previous section that the most critical performance metric for the data plane is the maximum throughput rate, followed by the latency. Our latency optimization strategy should not adversely affect the maximum throughput rate of the data plane. Therefore, we evaluate the impact of different observation times \(T_{observed}\) on the maximum throughput rate of the data plane. To make the results more intuitive, we still use the topology as shown in Figure 3b. We measure the maximum throughput rate of the data plane with our design for observation times \(T_{observed}\) equal to 10 s, 1 s, 0.1 s, 0.01 s and 0.001 s, respectively. The experimental results are shown in Figure 11. The standard reference line in Figure 11 is the maximum throughput rate when the data plane is not using our scheme. The experimental results show that the additional overhead incurred by our design using the five observation time parameters described above is insufficient to impact the data plane's maximum throughput rate significantly. The CPU utilization of the data plane also does not fluctuate drastically in a very short time so that the \(T_{observed}\) can be chosen between 0.1 s and 1 s.
## 4 Security Analysis and Discussion
### Participant-Based Security Threats
#### 4.2.2 The Statistical Security Threshold  
Design our protocol employs parameter \(\theta\) as the acceptable threshold for decoy photon measurement error rates. The threshold determination process considers the quantum channel's natural error rate \(\varepsilon\), selecting \(\theta\) slightly above \(\varepsilon\). Current quantum systems exhibit error rates of 0.1-1% [23], providing baseline parameters subject to technological advancement. The relationship between security parameters demonstrates that as the number of decoy photons n increases, statistical significance improves, complicating measurement result falsification for attackers. While the error threshold \(\theta\) is inversely proportional to security, excessively low \(\theta\) values may cause unnecessary protocol termination under normal fluctuations.
## 6 Insights from the Survey and Future Prospects  
This review paper presented, in a rigorous way, image fusion and evaluation strategies for NDT, taking into account various structures (albeit with a particular focus towards composites). It is clear that the studies indicate that using image fusion is promising for improving the reliability of inspections. An important highlight from this study is that various image fusion methods which are well-studied in other domains, such as remote sensing and image recognition, have not been extensively applied to NDT yet.  
Individual fusion techniques' performances with regard to a number of evaluation metrics were presented, including insights on how they have been perceived in studies in the literature. These metrics provide a quantitative means of identifying the best fusion approach for the specific or given inspection case. Each method is capable of revealing different aspects. For example, adding small noise to simulate a more realistic environment can significantly impact each technique with defect variability. Current studies have shown that the results depend on the acquisition system (i.e., being multi-sensor or multi-excitation). Different configurations impact the results. Another critical point is the applicability of the fusion method. This can differ depending on the level of fusion or whether it is pixel-wise or region-based. As one moves towards the use of machine learning tools, the importance of having sufficient datasets (of realistic defects) for training and validation is paramount.  
The widespread use of high-performance AI (ML)-based fusion approaches in aviation has the potential to improve the reliability and profitability of aviation operations. This review paper sheds further light on utilising the fusion of NDT techniques appropriately in this field. This was shown by the highlights of applying fusion in other domains, which can inform the fusion-based inspection of aeronautical structures (particularly composites). The following points are noted from this rigorous survey study:  
* There is no comprehensive review of NDT image fusion (this review paper fills this research gap).
* Fusion applications for composite structures are very limited.
* Low-level (signal or pixel) fusion applications require a precise registration that might be possible to reduce its effect on the higher-level fusion structures.
* General algebraic fusion rules have often been utilised as a main rule in low-level fusion or auxiliary for higher levels.
* PCA and wavelets are the most preferred methods among the current applications, but few relate to AI applications.
* Preprocessing operations before applying fusion are a vital part of the process.
* There is a plethora of evaluation metrics, and a series of these tends to be used in validation studies. This seems to be application-dependent.
* Even though ultrasonics and acoustic emission seem to outshine the other methods in terms of capability for various defect types, each technique has individual characteristics for their sensing ranges.
## 4 Discussion
However, in this study, in the frequency range from 1000 to 10,000 Hz, the most accurate results were obtained using only the explanatory variables obtained from the entire frequency range described in Section 3.2, instead of using explanatory variables obtained by dividing the peaks at each peak of the sweep pulse excitation. This suggests that it is critical to evaluate changes in the noise intensity in thehigh-frequency band due to differences in the [PERSON]'s modulus over a wide range of low to high frequencies, instead of differences in individual peak intensities. In summary, the estimation of the [PERSON]'s modulus of soft materials using machine learning requires a high-quality dataset that is sufficiently averaged for both the teacher and analysis. In future research, a detailed analysis of the vibration frequency spectra of soft materials obtained by L-RFA and the variance of the extracted explanatory variables will be conducted to provide quantitative data acquisition guidelines. The high potential of stiffness measurement by L-RFA, when compared with other methods, is the evaluation of single cells in organ geometry. Therefore, the spatial resolution is the most critical parameter, and a few micrometers of a single cell should be achieved. In this study, the laser-induced thermoelastic excitation of soft materials by L-RFA was visualized using a scanning Doppler vibrometer, as described in Section 3.3. As a result, by analyzing the vibration of higher-order modes, the observation of vibrations of less than one-tenth of the laser irradiation spot size was demonstrated. This result demonstrates that a high spatial resolution can be achieved using higher-order modes, which are characteristics, in that only the central part of the laser beam with high light intensity oscillates. As shown in Figure 8d, even for the same higher-order mode, a high resolution was not achieved when the [PERSON]'s modulus was low. This can be attributed to the propagation of vibrations without attenuation in the soft sample. Although this limits the realization of high spatial resolutions, it suggests that stiffness diagnoses can be conducted using information on vibration propagation. In particular, this method estimates stiffness from the attenuation of vibration intensity by varying the relative excitation and the laser irradiation position for vibration measurements and is expected to be used as CADe for abnormality detection to discriminate between hard tumors and normal soft tissues.
## 1 Introduction
However, these works are mostly conceptual and lack detailed mathematical models and algorithmic descriptions, and limited validation results are presented only in [15]. Authors in [16] or [17] used similar approaches. Other works highlighted the relevance of DT in enhancing microgrid operations [18]. Authors in [19; 20] developed a DT for a grid-connected microgrid powered by RES and BESS and proposed an optimization framework based on reinforcement learning. However, BESS degradation was not considered in either the DT model or the optimization proposed. [PERSON] et al. [21] investigated how data-driven models (DDMs), implemented in digital twins, can improve the operation of RES. Various DDMs were employed to forecast system performance and predict component failure, enabling more effective maintenance strategies. There are multiple examples of DT use in energy systems, as shown above. However, most existing investigations are focused either on individual technologies [4] or overlook the potential synergies between DT and operations optimization for more sustainable industries. Furthermore, few works that explored the use of DT for energy systems optimization often neglect the impact of battery degradation on the optimization decisions. This leaves a research gap for a comprehensive methodology to integrate DT in enhanced decision-support systems based on deterministic optimization approaches. This study, conducted within the framework of a Spanish R&D project called GENIUS, addresses this research gap by proposing a methodology to build an EMS that leverages DT and deterministic optimization to minimize the carbon footprint and operational costs of energy-intensive industries using RES, BESS, and EV fleets. The article overviews the formulations behind four DTs and introduces a novel two-layer optimization strategy. A real case study from a waste management and collection company is used to validate the proposed solution. The article is structured as follows: Section 2 provides a detailed overview of the methodology followed to develop the DT-based EMS. The mathematical foundations of the DT and optimization algorithms are presented in Section 3. Section 4 details the case study specifications. Section 5 presents the results obtained from applying the EMS to optimize system operation, compared with the actual plant operation. Finally, Section 6 summarizes the key findings of the study.
# Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
## 3 Experimental Results
### Surface Inspection for Gold Finger Connectors
In addition to detection, we also provide a simple visualization scheme, as shown in Figure 11, where the original test image is superimposed by the defective blocks. The defective blocks are marked as red, orange, or yellow blocks, depending on their corresponding L2 distance measurements. In this way, the quality of the surface inspection can be directly observed. To show the effectiveness of the proposed algorithm based on the visualization scheme shown in Figure 11, a number of examples for the defective samples and their detection results are revealed in Figure 12. To facilitate the observation, the ground truth of the samples is also included. It can be observed from Figure 12 that, although the diversities of defects are high, they are effectively identified. This is because the AE is able to reproduce defect-free templates from the defective samples. Furthermore, the proposed FCN can accurately identify the gold finger areas from the templates. Figure 10: Examples of segmentation results produced by FCN for the images of gold finger connectors. (**a**) The input images to the FCN; (**b**) the segmentation results, where the white areas are the gold finger area, and the black areas are the area outside gold finger connector. Figure 11: The proposed visualization scheme for the inspection results: (**a**) visualization results for a normal sample; (**b**) visualization for a defective sample; (**c**) colors and their corresponding L2 distances. Figure 12: Examples of the defective samples, their ground truth, and inspection results for gold finger connectors: (**a**) defective samples; (**b**) ground truth; (**c**) inspection results.
# Biosensors-Based In Vivo Quantification of 2-Oxoglutate in Cyanobacteria and Proteobacteria
###### Abstract  
2-oxoglutarate (\(\alpha\)-ketoglutarate; 2-OG) is an intermediate of the Krebs cycle, and constitutes the carbon skeleton for nitrogen assimilation and the synthesis of a variety of compounds. In addition to being an important metabolite, 2-OG is a signaling molecule with a broad regulatory repertoire in a variety of organisms, including plants, animals, and bacteria. Although challenging, measuring the levels and variations of metabolic signals in vivo is critical to better understand how cells control specific processes. To measure cellular 2-OG concentrations and dynamics, we designed a set of biosensors based on the fluorescence resonance energy transfer (FRET) technology that can be used in vivo in different organisms. For this purpose, we took advantage of the conformational changes of two cyanobacterial proteins induced by 2-OG binding. We show that these biosensors responded immediately and specifically to different 2-OG levels, and hence allowed to measure 2-OG variations in function of environmental modifications in the proteobacterium _Escherichia coli_ and in the cyanobacterium _Anabaena_ sp. PCC 7120. Our results pave the way to study 2-OG dynamics at the cellular level in uni- and multi-cellular organisms.  
biosensor; cyanobacteria; _Escherichia coli_; FRET; 2-oxoglutate 2018, 8, 51; doi:10.3390/life8040051
# Fine Dust Creation during Hardwood Machine Sanding
## 2 Materials and Methods
### Particle Size Determination  
Particle size determination and calculation of the content of fine dust particles were carried out according to two complementary methods used in previous studies to determine dust particle size: sieving and laser light scattering described by [38; 40; 64]. Sieve analysis was carried out using an AS 200 (Retsch, Germany) sieving machine. A set of sieves with small apertures of 250 um, 125 um, and 63 um was used due to the fineness of the wood dust. The dust fraction isolated during the sieve analysis in the bottom collector was taken for laser analysis. The results of the sieve analysis were used to calculate the mean arithmetic particle diameter as follows:  
\[\overline{x}=\sum_{i=1}^{n}x_{i}\cdot q_{3i} \tag{1}\]  
where: \(q_{3}\)--particle distribution by mass, \(x\)--mean value of particle size class, and \(n\)--number of particle size classes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Hardwood Species** & **Density [kg.m\({}^{-3}\)]** & **Percentage of the Forest Area in Poland [\%]** \\ \hline
1. & Black alder (_Alrus glutinosa Gaertn._) & 446.3 & 5.6 \\
2. & European ash (_Frazimus excelsior L._) & 621.3 & No data \\
3. & Common walnut (_Iqplas regia L_) & 641.0 & No data \\
4. & Pedunculate oak (_Quercus robur L._) & 686.4 & 7.9 \\
5. & European beech (_Fagus sylvatica L._) & 686.6 & 6.1 \\
6. & Hornbeam (_Carpinus betulus L._) & 753.6 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density of the hardwood species used in this research work.  
Particle-size distribution of the dust fraction with the sieve diameter smaller than 63 \(\upmu\)m was determined by particle size analysis using an Analysette 22 Microtec Plus (Fritsch, Idar-Oberstein, Germany) laser particle size. The laser diffraction method generates a volume distribution of particle size. Assuming a constant density of wood matter, this is identical to the mass distribution [65]. Two particle size ranges were assumed: <2.5 \(\upmu\)m and <10 \(\upmu\)m. The mass content of particles <2.5 \(\upmu\)m and <10 \(\upmu\)m in this sieve dust fraction was calculated based on the results of laser particle size analysis using MaScontrol software ([PERSON], Idar-Oberstein, Germany). Particles in these size ranges were taken into consideration because they cause airborne dust pollution. The calculation of the content of particles <2.5 \(\upmu\)m and <10 \(\upmu\)m in the whole mass of dust created was performed as follows:  
\[c_{i}=c_{563}\cdot c_{Li} \tag{2}\]  
where \(c_{i}\)--dust content in the assumed size range of the whole mass of dust created, \(c_{563}\)--dust fraction isolated during the sieve analysis in the bottom collector, and \(c_{Li}\)--mass fractions of the dust in the assumed ranges determined using laser diffraction analysis in the \(c_{563}\) fraction.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Static Sink-Based Frameworks
#### 2.2.4 Collection Tree Protocol (CTP)  
[PERSON] et al., proposed a tree-based routing protocol called collection tree protocol (CTP) [60]. In CTP, the tree roots are determined after the advertisements of some nodes, and a set of routing trees are created using these roots. A parameter named expected transmission (ETX) value based on the distance from the root is used to calculate the cost of routing. A routing loop problem occurs when a node receives the data packet from another node that has an equal or lower ETX value. Two mechanisms are used to solve this problem. First, upon detecting the inconsistency in the ETX value, CTP broadcasts a beacon frame so that the sender node can receive it and adjust its routes. Second, CTP does not select a route that has an ETX value higher than a predetermined constant value. Another problem of CTP is packet duplication which occurs due to the retransmission of the same packet. To resolve this problem, CTP monitors a packet for duplication before forwarding, upon receiving the packet. A cache of packets is utilized to detect the duplication and the packet is discarded if it is a duplicate. However, due to the routing loop, a node may receive a packet more than once, which creates complications for packet dropping because of duplication. Therefore, a parameter called 'time has lived' (THL) is contained in data frames, which is incremented in each routing loop. The CTP data frame contains the ETX, THL, origin id of the packet, origin sequence number, data payload, higher-level protocol identifier, and two control bits named pull bit and congestion bit. In the CTP routing frame along with the ETX, pull bit, and congestion bit, the node's current parent is provided. For implementing CTP, a link estimator, a routing engine, and a forwarding engine are required. The routing engine calculates the ETX for communicating with single-hop neighbors. The routing engine selects the next-hop neighbor with the help of link estimation and network-level information. The forwarding engine takes the decision of packet sending from the queue of packets.  
Advantages: The main advantage of CTP is that it can send data to the sink nodes using the route with the minimum cost, in which the cost depends on the number of hops. Therefore, the total of hops can be reduced in CTP, which makes a routing protocol efficient. Moreover, it can adapt to dynamic topology changes by adjusting the routes.  
Limitations: CTP is suitable for comparatively low traffic rates and, thus, it may not achieve good performance in a network of high traffic rates. Hence, it is suitable for a WSN environment with a large number of nodes sending packets simultaneously.
## 3 Data Collection  
The dataset was collected in an actual classroom environment [29] using a purposive sampling technique focusing on a first-grade primary school at Sisaket Rajabhat University Demonstration School, Thailand. Specifically, data was obtained for students studying mathematics in the second semester of 2020. The number of students was 24 per classroom, and a PLC teaching approach was used. For the data collection, the educational researcher assigns the teams, designs the layout for the student seating, and sets up a video camera to record all students. The video cameras were placed in front of the whole class on the left and right sides. The top view of the seating layout is depicted in Figure 1a. When a class starts, the photographic specialist records the video until the course ends. Figure 1b shows an actual classroom scene. A SONY Handy Cam 60X (Tokyo, Japan) was used to record the images with a frame rate of 30 frames per second.  
The expert analyzed and collected behavioral clips by cropping the original video file and uploading it to the cloud server storage. This research focused on the student's behavior during lesson activities, and we labeled each clip with one of five categories: hand-raising, interacting, sitting, turning around, and writing. The dataset collection sample for each behavior is shown in Figure 2.  
The PLC teaching approach aims to encourage collaboration among educators to enhance teaching, improve student outcomes, and foster continuous improvement within the school. PLCs strive to create an environment that supports educators in optimizing teaching for diverse student needs, promoting academic success through data-driven decisions, ongoing development, and a student-centered focus [2]. By observing how a particular behavior influences and positively impacts students' learning [1; 2; 3], teachers gain firsthand insights into the effectiveness of student learning. This observation-based approach encourages teachers to analyze the correlation between student behaviors and  
Figure 1: (**a**) Top view of the classroom seating layout. (**b**) A sample image of real-class activities.  
Figure 2: Sample images from the student behavior dataset; (**a**) Hand raising; (**b**) Interacting; (**c**) Sitting; (**d**) Turning around; (**e**) Writing.  
the resulting outcomes in students' understanding. Our collected dataset encompasses five distinct classes of student behaviors observed within a classroom environment employing the PLC teaching method. The classification of student behaviors in our dataset aligns with the core objectives of the PLC [2].
## 2 Methodology
### Model Overview
#### 2.2.1 Design Variables  
Design variables are analogous to knobs used for adjusting design specifications. The following five design variables have been chosen to maintain adequate degrees of freedom:  
* Altitude;
* Altitude difference;
* Number of orbit planes;
* Number of satellites per plane;
* Field of regard.  
The altitude variable is the height of a satellite in ROM represented by a repeat ground track (RGT) ratio. For example, a ratio of 15/1 means that satellite revolves around Earth 15 times in 1 day (Earth rotates once). As seen in Table 2, ratios between 31/2 and 14/1 are sampled such that the resulting ROM has an altitude between 300 km and 1200 km. The procedures for calculating RGT altitudes are provided in Appendix A[39; 40]. The altitude difference is the difference between the GOM altitude the ROM altitude, i.e., (ROM altitude) + (altitude difference) = (GOM altitude). While these first two variables govern the vertical distribution of satellites, the next two variables dictate the horizontal distribution of satellites. Lastly, the field of regard relates to the viewing range, achieved by either maneuvering the whole satellite or the optics subsystem only.
## 4 Algorithm Design
### Car Posture Fusion Solution
#### 4.1.3 Posture Fusion  
Based on the above analysis, it can be concluded that the heading angle yaw cannot be calculated using three-axis acceleration data alone, and using the three-axis gyro data alone will result in a drift after a long period of measurement, so it is necessary to perform posture fusion of the two sources of data.  
\[\left\{\begin{aligned} roll&=roll+(roll_{acc}-roll_{gyro}) \cdot K\\ pitch&=pitch+(pitch_{acc}-pitch_{gyro})\cdot K\\ yaw&=yau_{gyro}\end{aligned}\right. \tag{6}\]  
where \(K\) is the scale factor, which needs to be adjusted according to the actual situation, in this system, where \(K=0.4\).