esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computed Tomography and Aortic Valve
### CT Assessment of the Aortic Valve for Transcatheter Procedures
#### 2.3.4 Coronary Ostia  
MDCT is crucial to identify the distance from the annulus/leaflet hinge point to the left main and right coronary ostia and the length of the corresponding coronary cusp (Figure 6a,b). Multiplanar, three-dimensional techniques allow better visualization and assessment of these complex structures and their relationships. For this reason, CT represents the preprocedural imaging gold standard for the evaluation of the risk of coronary occlusion. Even if there is no threshold value contraindicating the procedure, an increased risk of coronary occlusion was reported in patients with a coronary ostial height from the annulus < 12 mm and a sinus of Valsalva mean diameter < 30 mm. To ensure reproducibility, the height of the coronary ostia perpendicular to the plane of the annulus should be measured with an electronic caliper from the plane of the ring to the bottom of the ostium. No recommendations exist as to whether these measurements should be performed in systole or diastole [24].  
[PERSON] et al. found that coronary occlusion most commonly affects the left coronary (88.6 per cent) and occurs more commonly in women and following balloon expandable TAVI. In a systematic review, several parameters were found to be possible predictors of procedural complications: a low-lying coronary ostium < 10 to 12 mm from the basal leaflet insertion to the coronary ostium as measured by MDCT, mean sinus of Valsalva diameter of <30 mm, and sinus of Valsalva diameter/annular diameter ratio of <1.25. Furthermore, according to this analysis, women appear to be more subjected to coronary occlusion because of smaller aortic root dimensions and lower coronary ostial height [52]. Probably, significantly oversized THV or aortic root dissection, the displacemen  
Figure 6: (a) Right and (b) left coronary ostia height assessment by CT images.  
leaflets, impingement of the coronary ostia by the THV support structure, the embolization of calcium, thrombus, etc., are the mechanisms underlying coronary obstruction.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology
### Neural Network Training Algorithm  
The neural network training process in this study is based on a supervised learning approach using backpropagation and stochastic gradient descent (SGD) optimization. Below is the detailed mathematical formulation of the training algorithm.  
Forward Propagation: Each input image **X** is passed through a sequence of layers, including convolutional, activation, pooling, and fully connected layers. The transformation at each layer is **l** defined as follows:  
\[Z^{(L)}=W^{(L)}P^{(L-1)}+b^{(L)} \tag{13}\]  
where \(W^{(L)}\) is the weight matrix of the layer, \(P^{(L-1)}\) is the activation from the previous layer, and \(b^{(L)}\) is the bias vector of the layer.  
The activation function is applied to introduce nonlinearity:  
\[A^{(l)}=f\Big{(}Z^{(l)}\Big{)} \tag{14}\]  
where \(f(.)\) represents the activation function.  
Loss Function Calculation: The network is trained using categorical cross-entropy loss:  
\[L=\sum_{i=1}^{N}\sum_{K=1}^{K}y_{i,k}y_{i,k}\;log\;\hat{y}_{i,k} \tag{15}\]  
where \(y_{i,k}\) is the ground truth label for class k of sample \(i\), \(N\) is the number of training samples, and \(K\) is the number of classes.  
Backpropagation: To update the network parameters, we compute the gradient of the loss function concerning the weights using the chain rule:  
\[\frac{\partial L}{\partial W^{(l)}}=\frac{\partial L}{\partial A^{(l)}}\times \frac{\partial A^{(l)}}{\partial Z^{(l)}}\times\frac{\partial Z^{(l)}}{ \partial W^{(l)}} \tag{16}\]  
Weight Update Using Gradient Descent: The network parameters are updated using the stochastic gradient descent (SGD) optimization method:  
\[W^{(l)} \gets W^{(l)}-\eta\frac{\partial L}{\partial W^{(l)}}\] \[b^{(l)} \gets b^{(l)}-\eta\frac{\partial L}{\partial b^{(l)}}\]  
where \(\eta\) is the learning rate.  
Iteration until Convergence: The training process iterates over multiple epochs until the loss function converges to a minimal value, ensuring the optimal performance of the model.
## 9 WLAN Integration with 6G: Challenges and Opportunities  
In the previous sections, we have presented a comprehensive analysis of the state-of-the-art indoor WLANs. In this section, we investigate the feasibility and challenges of the integration of these technologies in the 6G ecosystem.  
Integration of different networks to support diverse use cases, applications, and services have been studied extensively. These network technologies include WiFi, 4G, 5G, and optical [113, 114, 115, 116]. The integration of WLANs with cellular networks has also been considered since 4G systems to a great extent [117, 118, 119]. These efforts were mainly focused on integrating WiFi with mobile networks due to the wide availability of WiFi. However, with the development of VLC networks, which coincided with the 5G era, a significant amount of work was carried out on the integration of VLC and WiFi with 5G networks [120, 121]. All of these efforts involved integrating WLANs with the mobile network. Due to the challenging nature of this approach, practical deployments need further investigations. In particular, factors such as security, deployment cost, protocol mismatch, guaranteed QoS, energy-efficiency, and equipment limitations need to be addressed in achieving such fully converged networks when one or more networks are integrated with each other [122, 123, 124].  
Further, compared to previous wireless technologies, 6G is defined by the applications and services and not by the underlying technology [125]. Hence, the 6G ecosystem is expected to be built of heterogeneous networks of different natures [126]. Therefore, in this paper, we propose to use the WLANs in the 6G ecosystem to satisfy the requirements of 6G indoor applications. This kind of convergence in networks is called'superconvergence' [6]. We identified the following research opportunities to overcome the challenges to realize superconvergence between WLAN and 6G to support emerging mobile and IoT applications.
## 2 Materials and Methods
### Description of the LUE-EF Model  
This model was developed mainly based on the principles of the EC-LUE model [9]. Specifically, the regulation of water on GPP is represented by the evaporative fraction (EF), taking advantage of the newly available EF products [31]. In addition, two new modifiers of GPP were added to the original EC-LUE model. The first modifier considers the impact of cloudiness on GPP. The other modifier addresses the fertilization effect of increased CO\({}_{2}\) concentration in the atmosphere.  
The LUE-EF model can be expressed as follows:  
\[GPP = PAR\times FPAR\times F_{CI}\times F_{CO2}\times LUE_{(MAX)}\times \min(T_{S},W_{S}) \tag{1}\]  
where \(PAR\) is incident photosynthetic active radiation (MJ/m\({}^{2}\)) over a period of time; \(FPAR\) is the fraction of \(PAR\) absorbed by the vegetation; \(F_{CI}\) is the regulation of cloudiness on \(GPP\); \(F_{CO2}\) is the regulation scalar of atmospheric CO\({}_{2}\) concentration; \(LUE_{(MAX)}\) is the maximum light use efficiency; and \(T_{S}\) and \(W_{S}\) are regulation scalars respectively for temperature and water stress on GPP, from which the minimum value is taken, following the Leibig law [9], and the expression of \(T_{S}\) is the same as that of EC-LUE model. The determination of model parameters was done as:  
1. \(FPAR\) is in practice approximated by EVI [11], since photosynthetically active vegetation is estimated as a ratio \(\alpha\) of EVI, set to be \(\alpha=1\): \[FPAR_{EVI}=\alpha\ \times EVI;\] (2)
2. Most previous models underestimates of GPP on cloudy days mainly because photosynthesis can be increased by diffuse radiation under cloudy conditions [6; 32]. The regulating effect of cloud cover on GPP was expressed by a cloudiness index (CI) as follow: \[F_{CI}=a\times CI+b\] (3)where \(CI\) is the ratio of PAR to potential PAR (PPAR) [6], and PPAR is PAR that reaches the upper atmosphere. Using the FLUXNET2015 dataset, the coefficients were determined to be a (= 2.9) and b (= 1.2);
3. For calculating the influence of atmospheric CO\({}_{2}\) on GPP, we employed the algorithm in the Frankfurt Biosphere Model (FBM): \[F_{CO2}=f(CO2,T)=\frac{CCL-\Delta(T)}{CCL+2\Delta(T)}\] (4) where \(CCL\) is the internal CO\({}_{2}\) concentration of leaves, and it assumed to be 70% of atmospheric CO\({}_{2}\) concentration [33]. \(\Delta(T)\) is the CO\({}_{2}\) compensation point for gross photosynthesis and photorespiration at temperature \(T\) (\"C) [34]: \[\Delta(T)~{}=~{}40.6e^{\frac{(9.46\times(T-25))}{(T+23\Delta)}};\] (5)
4. The regulation scalar of water on GPP, \(W_{S}\), was expressed as the evaporative fraction (\(EF\)) of the total sensible and latent heat [9]: \[W_{S}=EF=\frac{LE}{LE+H}\] (6) where \(LE\) is latent heat flux (W m\({}^{-2}\)), and \(H\) is sensible heat flux (W m\({}^{-2}\)).
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 6 Emerging Trends and Future Directions
### Potential Future Directions  
1. Improved Event Recognition and Localization Algorithms To address the limitations of current event recognition and localization algorithms, researchers are exploring novel approaches such as contrastive learning and self-supervised learning. These methods can leverage unlabeled video data to learn more discriminative features, improving the performance of event detection and localization. Additionally, integrating temporal context and semantic information can further enhance the accuracy of event recognition.
2. Enhanced Multimodal Integration and Cross-Media Understanding Multimodal integration is key to improving the descriptive capabilities of DVC systems. Future research should focus on developing more sophisticated multimodal fusion techniques that can effectively combine visual, audio, and language features. Moreover, cross-media understanding, which involves linking video content to related text, audio, and other media, holds promise for advancing DVC applications in various domains.
3. More Efficient and Interpretable Models As DVC systems become more complex, the need for efficient and interpretable models becomes increasingly important. Researchers are exploring various techniques to reduce the computational complexity of DVC models, such as model pruning, quantization, and knowledge distillation. Additionally, developing interpretable models that can provide insights into the decision-making process of DVC systems will enhance their trustworthiness and usability.
4. Applications in Video Retrieval, Accessibility, and Content Analysis DVC has a wide range of applications, including video retrieval, accessibility, and content analysis. In video retrieval, DVC can improve the accuracy and efficiency of search results by generating descriptive text for each video segment. For accessibility, DVC can help visually impaired individuals better understand video content by providing detailed descriptions of events and actions. In content analysis, DVCcan provide valuable insights into video data, facilitating tasks such as automatic annotation, classification, and summarization.
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
###### Abstract
By comparing it with several commonly used prediction models, it is shown that the new model has good prediction performance and high prediction accuracy. The fifth part is the summary and prospect, which summarizes the contents of this paper and points out the areas that can be further improved and optimized in the current research results.
# Biosynthesis of Nature-Inspired Unnatural Cannabinoidss
## 1 _Cannabis sativa_ and Cannabinoidss--An Introduction
### Cannabinoid Biosynthetic Pathway  
In order to engineer the biosynthesis of novel cannabinoids, it is vital to first understand the cannabinoid biosynthetic pathway (Figure 1). In _C. sativa_, hexanoyl-CoA is produced by an acyl-activating enzyme, CsAAE1, from hexanoic acid derived through the fatty acid biosynthesis pathway [21]. Thereafter, a type III polyktetide synthase named olive-tol synthase (OLS) elongates one unit of hexanoyl-CoA using three units of malonyl-CoA in a manner characteristic of other PKSs [22]. However, unlike other polyktetide synthases of its kind, OLS does not have a cyclization pocket and hence lacks the ability to cyclize the intermediate, a tetraketide-CoA thioester [23]. Instead, a cyclase enzyme, Olivetolic acid cyclase (OAC), catalyzes the necessary C2 to C7 aldol condensation reaction to produce olivetolic acid (OLA) [24].  
Next, a putative prenyltransferase enzyme transfers the C10 chain from geranyl pyrophosphate (GPP) to C3 of OLA to produce the first cannabinoid, cannabigerolic acid (CBGA). The native prenyltransferase from _C. sativa_ that completes the step was identified as CsPT1 or geranyl pyrophosphate: olivetolate geranyltransferase (GOT) in 1998 [25]. However, this protein is predicted to be a transmembrane protein with a plastid targeting signal due to its plantal origin. Several groups have attempted heterologous expression of a functional CsPT1 without much success. [PERSON] et al. found a functionally active prenyltransferase, named CsPT4, from _C. sativa_ that produces the CBGA from GPP and OLA [20]. GPP is presumably supplied through the 2-C-methyl-D-erythnitol 4-phosphate (MEP) pathway in _C. sativa_ due to the plastid localization tag found on the sequences of subsequent prenyltransferase and cannabinoid synthases.  
Finally, CBGA serves as a branch-point in the pathway where several cannabinoids such as THCA, CBDA or cannabichromeic acid (CBCA) can be produced. This is achieved through differential cyclization of the C10 moiety from GPP via the actions of independent synthases. Categorically, the biosynthetic pathway can be dissected into three distinct functional parts: the polyktetide pathway producing OLA, the isoprenoid pathway together with the prenyltransferase step producing CBGA as the first cannabinoid, and the last cyclization step producing various cannabinoids from CBGA. Discussions on synthetic biology approaches will be organized according to these individual enzymatic steps in the pathway, along with structural and mechanistic insights, to facilitate the subsequent framing of strategies for producing novel cannabinoids.  
Figure 1: Cannabinoid biosynthetic pathway. Polyketide pathway is highlighted in red; isoprenoid pathway is highlighted in blue.
## 3 Method and Model
### Dataset
#### 3.1.3 Student-Recorded Spoken Audio and Teacher Emotional Evaluation Scores  
To implement the proposed oral speech emotion evaluation method in actual teaching scenarios for this research, a total of 45 students recorded 810 oral speech audio samples based on 18 representative dialogues from the IEMOCAP dataset. These audio recordings cover the six emotions contained in the 810 dialogues from the IEMOCAP dataset. During the recording process, students referred to the emotional annotations in the IEMOCAP dataset to express the corresponding emotions as accurately as they could. Additionally, we formed a team consisting of English teachers who were responsible for assessing the emotional richness of the recordings. These teacher ratings served as crucial reference standards for the emotional evaluation model used in model training, evaluation, and optimization. Figure 2 displays the distribution of scores, including the average spoken scores for each emotion category for each student. The scores on the heat map indicate the degree of high or low scores on oral emotional expression, with the more reddish colors corresponding to higher scores, and the more purplish colors corresponding to lower scores. The different colors and shades of colors can better show the students' oral emotional expression scores and their distribution.  
From Figure 2, it can be observed that for the majority of students, their ability to express various emotions is consistent. For the same students, their evaluation scores for six types of emotions are distributed around the same value. Only a few students show excellent performance in expressing a specific emotion, as evidenced by significantly higher evaluation scores for one particular type of emotion compared to others.
# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
###### Abstract  
Novel view synthesis and 3D reconstruction have been extensively studied. Three-dimensional Gaussian Splatting (3 DGS) has gained popularity due to its rapid training and real-time rendering capabilities. However, RGB imaging is highly dependent on ideal illumination conditions. In low-light situations such as at night or in the presence of occlusions, RGB images often suffer from blurred contours or even complete failure in imaging, which severely restricts the application of 3 DGS in such scenarios. Thermal imaging technology, on the other hand, serves as an effective complement. Thermal images are solely influenced by heat sources and are immune to illumination conditions. This unique property enables them to clearly identify the contour information of objects in low-light environments. Nevertheless, thermal images exhibit significant limitations in presenting texture details due to their sensitivity to temperature variations rather than surface texture features. To capitalize on the strengths of both, we propose thermal geometrically accurate Gaussian Splatting (TGA-GS), a novel Gaussian Splatting model. TGA-GS is designed to leverage RGB and thermal information to generate high-quality meshes in low-light conditions. Meanwhile, given low-resolution thermal images and low-light RGB images as inputs, our method can generate high-resolution thermal and RGB images from novel viewpoints. Moreover, we also provide a real thermal imaging dataset captured with a handheld thermal infrared camera. This not only enriches the information content of the images but also provides a more reliable data basis for subsequent computer vision tasks in low-light scenarios.  
Gaussian splatting; thermal image; 3D reconstruction; novel view synthesis 2020 IEEEexample:BSTcontrol
# Visible Light Communication: A System Perspective--Overview and Challenges
## 3 Applications  
The concept of the IOE expands the network connectivity to the intelligent connection of people, data, processes, things, machine and everything. IoE would require internet connectivity for billions and trillions of sensors to provide ubiquitous, seamless services to people, machines, process, and things. All these applications would have a different set of requirements such as high data rate in Giga b/s, reliability, availability and security. The ease of availability, low cost and high data rates of VLC could make it a relevant wireless communication technology that would cater to all kind of future applications. Some of the potential applications of VLC are discussed as follow.
## 8 Simulation Assessment
### Simulation Environment
Figure 12: Configuration of the _unconditioned Scenario_: (**a**) Randomly distributed data rates; (**b**) Unconditioned cluster-tree network. Figure 13: Configuration of the _conditioned Scenario_: (**a**) Data rates distributed in two load zones; (**b**) Conditioned cluster-tree network. For this simulation assessment, we used the ZigBee-based _hierarchical addressing_ scheme, in which each CH has its own sequential address block. Regarding the active period scheduling, we used a typical time division scheme. For the sake of simplification, we have used a bottom-up scheduling scheme, which prioritises the monitoring traffic (from leaf cluster-heads toward the PAN coordinator). Basically, the main difference between bottom-up and top-down scheduling schemes is related to the the protocol constraint, as the top-down scheme imposes more demanding beacon interval restrictions. Regarding the node's features, we have adopted the CC2420 (Texas Instruments/Chipcon CC2420 Datasheet: [[http://www.ti.com/product/CC2420/technicaldocuments](http://www.ti.com/product/CC2420/technicaldocuments)]([http://www.ti.com/product/CC2420/technicaldocuments](http://www.ti.com/product/CC2420/technicaldocuments))). radio model, which is compliant with the IEEE 802.15.4 standard. Furthermore, we adopted a linear energy model provided by Castalia and the initial energy for all nodes was set to 18.720 Joules (typical energy for two AA batteries). We also adopted the _unit disc_ model as the radio propagation model, where the range of the disk was defined to be 55 m. For the interference model, we use a simple interference model provided by Castalia, where concurrent transmissions generate collisions at the receiver. Table 4 summarises the most important configuration parameters used in the simulations.
## 1 Introduction
### Contribution  
As indicated in the previous paragraph, a number of previous studies have focused on using traditional and smart measurements to improve DSSE results. However, they generally have switched between two distinct SE methods considering the types of available measurements. Moreover, the most recent studies in the hybridization of traditional and smart measurements assume that both traditional and smart measurements are available from the initial distribution network operation. As a result, the system cannot be effectively modeled the moment smart measurements are available. In addition, any model training must be redone every time a new sensor is added to the network. This makes their methods non-applicable for grid modernization development; in the real world, advanced measurements are not available from the initial operation of a DN; rather, they are added gradually while considering the requirements and needs of DNs.  
In contrast, this paper assumes that only SCADA measurements are available from the beginning operation of a DN and that PMU devices are added later as part of a grid modernization procedure.  
The main contributions of this paper are summarized as follows:  
1. SCADA measurements and pseudo-measurements were used to perform DSSE calculations based on the WLS method before the installation of PMU devices and additional DGs in ADNs.
2. A multi-output regression model was trained using DSSE results from SCADA and pseudo-measurements.
3. This model predicted SCADA measurements after smart measurement devices were added to the network, when these measurements were not available, to enable synchronization between the traditional and smart sensors despite their different refresh rates.
4. As more data was collected from the measurement devices, the regression model performance improved under the condition that the model continued to be updatedregularly. Thus, the training model became robust and improved DSSE performance even in the presence of more Distributed Generations (DGs) or other dynamic changes.
5. The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. This analysis was applied to two case studies: (1) an IEEE 33-bus case study using loads and generation from a Monte Carlo simulation and (2) an IEEE 69-bus case study using loads and generation from actual data.  
It is worthwhile to mention that, in this work, we aimed to address the absence of SCADA measurements between PMU measurements using a regression model instead of switching between two different DSSE methods. The regression model was updated as more DSSE results were obtained to improve its accuracy. Note that we did not consider execution time in this study.