esa-sceva/satcom-chunk-collection huggingface dataset:
# Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions
## 3 Results
### Performance Analysis of the Phenological Model
#### 3.2.2 Flowering  
For flowering, the SM also reveals higher performance metrics for the Lisboa region when compared to the other wine regions, with average RMSE of four days and EF of 0.75. As for budburst, the Vinhos Verdes wine region reveals less favorable results, with an RMSE of seven days and EF of 0.46. In general, the varieties have an RMSE \(<\) 7 days, except for cv. _Aragonez_ (Douro) and cv. _Azal Branco_ (Vinhos Verdes). The efficiency of the model is equal to or greater than 0.5 for 43 varieties and equal to or greater than 0.75 for 26 varieties.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 5 Discussion
### Overall Performance Analysis of RS-YOLOX-Nano
#### 5.1.4 Actual Detection Results  
It is evident from the detection result images that the improved algorithm demonstrates an enhanced performance compared to its prior version. The original algorithm previously exhibited issues with missing and falsely detecting debris flow targets, which have been substantially ameliorated following the revisions. As illustrated in Figure 12, the enhanced algorithm now detects more accurate targets with greater confidence than its predecessor, yielding superior detection performance.  
Figure 11: Heatmap of feature extraction for debris flows before and after the addition of attention mechanisms.
# Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks
## 4 Results and Discussion
### Tuning of the Network Parameters  
When tuning the DNN architecture described in Section 3.2.2, the tuned parameters are:  
* the dimension of LSTM cells;
* the number of training epochs;
* the look-back factor.  
Despite advancement in the use and understanding of LSTM networks, no analytical procedure is yet available to tune the dimension and training parameters. They are tuned in trial-and-error steps, fixing two parameters and modifying one at a time. The goal is to minimise the selected loss function, the Mean Squared Error (MSE). The MSE, computed at each epoch, is defined as:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(Y_{i}-\hat{Y}_{i})^{2} \tag{10}\]  
where \(\boldsymbol{n}\) is the total number of points provided at each epoch and \((Y_{i}-\hat{Y}_{i})\) represents the difference between the output of the Simulink model and the one of the DNN.  
The dimension of each LSTM layer was tested starting from a dimension of two and increasing the size following an exponential of base two distribution. The optimal network size that minimised the MSE was found to be 128 cells.  
The number of training epochs that produced the best fit to the reference signal without overfitting was 100. This was tuned starting from 10 epochs and doubling the number of epochs while evaluating the MSE and the computational time, which was chosen not to exceed 5 h of training.  
Finally, the look-back factor was tuned. The best performance was obtained when it was 100, meaning that generating the dataset at 10 Hz, the DNN predicted the next output based on the last 10 s of the input signal.  
To carry out the training and validation of the network, eighty percent of the dataset was used for training and the remaining 20% for validation. In order to avoid the saturation of the input gate, the input time series was standardised based on the mean value and the standard deviation of the training set. This avoids any leakage of information coming from the validation set. An example of the model fitting is reported in Figure 5.  
It should be noted that the output time series predicted (corresponding to the training and the validation sets, reported in orange and green, respectively) almost entirely superimpose the Simulink generated set (drawn in blue).  
Figure 5: Simulink output dataset (blue) vs. DNN training prediction set (orange) and DNN validation prediction set (green).  
A zoomed plot, corresponding to the red box highlighted in Figure 5, is reported in Figure 6. This illustrates the section where the two datasets switch: the last input corresponding to the training set is a sinusoidal input, and the first one to constitute the validation set is a ramp with a negative slope. The effect of the look-back factor can also be observed: the first 100 points of the validation set are not predicted since they are needed to estimate the first output value.  
In Figure 7, the loss function is reported as an example for the training of the nonlinear model described by Equation (5), with the optimal network parameters mentioned above.
## Performance Evaluation
### Evaluation Results
As can be observed, the packet delivery ratio of the CRD protocol is showed better than that of the CR-AODV protocol at all speeds. The reason is that CRD protocol provides a stable routing space for routing process and the established route from source to destination is preferred to avoid the  
Figure 11: Control overheads versus node mobility. Figure 12: Delay (in second) versus node mobility. affected region of primary user. In CR-AODV, because the arbitrary node can participate in the route from source to destination, the established route cannot avoid the affected region of the primary user. According to these reasons, the PDR of CRD protocol is higher than that of CR-AODV protocol. Thus, the PDR of the CRD protocol is significantly improved. Figure 14 shows the energy consumption as a function of node mobility. As can be observed, the energy consumption of the CRD protocol is smaller than that of the CR-AODV protocol. In particular, the energy consumption of the CRD protocol is increased from 70 to 120 Joules and the energy consumption of the CR-AODV protocol is increased from 135 to 145 Joules when the speed is increased from 20 km/h to 80 km/h. The reason is that the control overheads and delay of the CRD protocol are smaller than that of CR-AODV protocol resulting in the reduction of energy consumption in CRD protocol.
## 2 3D Neuron Datasets and Morphological Feature Extraction  
### 3D Neuron Datasets  
The 3D morphological structure of neurons can be described by the standard SWC file format, which has been widely used for analyzing neuronal morphologies or sharing neuron classifications [17; 18]. The digitally reconstructed neurons are publicly available at the online NeuroMorpho.Org ([[http://www.neuromorpho.org](http://www.neuromorpho.org)]([http://www.neuromorpho.org](http://www.neuromorpho.org))) [19], which is a neuron database containing the publicly accessible 3D neuronal reconstructions and associated metadata. It collects neuron data from more than 200 laboratories around the world and continuously collects and publishes new data. So far, NeuroMorpho.org is the world's largest publicly accessible neural source database. SWC morphology files can be read by the publicly available tools CVAPP [20] and Neuromantic [21]. In order to verify the effectiveness of the proposed approach, two neuron classification datasets were constructed based on different types of neurons in different animals.  
C. elegans dataset  
This dataset mainly performs different neuron classification within the somatic nervous system region of C. elegans. There are three types of selected neuron, namely interneuron, motor neuron (motorneuron), and somatic neuron. In the constructed neuron classification dataset, the number of samples for each subtype was 64, and the total number of samples was 192. The 2D projections of the morphological structures of interneurons, motoneurons, and somatic neurons are shown in Figure 1.  
2. Zebrafish dataset  
This dataset focuses on different neuronal classifications within the main olfactory bulb region of zebrafish. We selected four types of neuron data for classification including 53 samples of each subtype, a total number of 212 neurons in the dataset, specific data sample names, and the thumbnails provided in NeuroMorpho.org. As shown in Figure 2, we provide the 2D projections of some of the geometric morphologies of neurons for the four types such as large, mitral, output, and small neurons. It can be seen that the morphological structures of different types of neurons are quite similar, and it is very challenging to effectively classify them.  
Figure 1: The 2D projections of geometric morphologies of the C. elegans neurons. (**a**) Interneuron, (**b**) Motoneuron, (**c**) Somatic neuron.  
Figure 2: The 2D projections of geometric morphologies of the zebrafish neurons. (**a**) Large, (**b**) Mitral, (**c**) Output, (**d**) Small.
## 4 Recall Test Results  
Prior to conducting the ANOVA, assumptions of normality and homogeneity of variance were tested. Normality was assessed using the Shapiro-Wilk test, which showed no significant deviation from normality across groups (\(p\) > 0.05). Homogeneity of the variances was verified using [PERSON]'s test, which confirmed equal variances across groups (\(p\) > 0.05), validating the use of one-way ANOVA.  
The recall count and rate for different educational item types are summarized in Table 6. ANOVA was used to examine differences; the Scheffe test was applied for post-verification. The recall count was 3.02 for A-type, 4.46 for B-type, and 4.14 for C-type, with a statistically significant difference (F = 10.441, \(p\) < 0.001, \(\eta^{2}\) = 0.057), indicating a medium effect size. For
## 4 Results
### Evaluation Metrics  
The road networks are commonly evaluated by complexness and correctness [19]. Because the metrics mentioned above can be approximated as precision and recall, the extracted road results are evaluated by the overall accuracy (\(OA\)), precision (\(P\)), recall (\(R\)), \(F_{1}\), and intersection-over-union (\(IoU\)). The F1 score and IoU serve as two comprehensive evaluation metrics. These metrics [34] can be computed as follows:  
\[OA=\left(TP+FN\right)/\left(TP+TN+FP+TN\right) \tag{9}\]  
\[P=\
icefrac{{TP}}{{\left(TP+FP\right)}} \tag{10}\]  
\[R=\
icefrac{{TP}}{{\left(TP+FN\right)}} \tag{11}\]  
\[F_{1}=2\times\frac{P\times R}{P+R} \tag{12}\]  
\[IoU=\frac{TP}{FN+TP+FP} \tag{13}\]  
where TPs (true positives) and TNs (false positives) are the number of road pixels and non-road pixels, respectively, which are correctly classified. FPs (false positives) represent the number of background pixels that are identified as road. FNs (false negatives) are the number of road pixels that are identified as background. Meanwhile, those above evaluated metrics are powerful for the harmonic means.  
Figure 4 exhibits the accuracy and IoU curves of the DPBFN from three validation sets. From the leftmost graph, it can be observed that the accuracy of all three datasets start stabilizing after 20 epochs. The Massachusetts dataset achieves the highest accuracy. The accuracy of the Massachusetts and Deep Globe datasets remains relatively stable, while that  
Figure 3: The loss curves of DPBFN training process from three datasets.  
the GF-2 dataset exhibits more significant fluctuations. However, as shown in Figure 3(b), the Deep Globe dataset achieves the best IoU. The IoU curves of the Massachusetts and GF-2 datasets exhibit a phenomenon of crossing during the convergence process, especially in the beginning of 10 epochs. Moreover, even after reaching a stable state, the IoU value of GF-2 is higher than that of the Massachusetts dataset.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 4 Discussion
### Fault Detection and Diagnosis (FDD) Approaches  
In general terms, algorithms used in FDD are divided into physical model-based and data-driven methods. Both can be used to compare the real and the predicted value of one or several variables and deploy an action when this difference reaches a threshold that has to be calibrated for each application. The action to perform can vary from a simple alarm to a complex control action to minimize safety and performance problems, and it can be applied at the component level but also at the facility level. To summarize the main findings from the works reviewed, in the following, the main pros (+) and cons (\(-\)) of the two approaches are presented.  
As far as physical model-based approaches are conceived:  
(+) They are made up of simple and usually well-known algorithms based on physical laws and equations.  
(+) Once the models are calibrated, online data can be used directly to predict some indicators and compare them with the real situation without the need for training the model periodically.  
(\(-\)) The integration of physical models from different components to create a complete facility model may suppose an important effort to be assumed by developers and testers.  
On the subject of the use of data-driven methods, the approach is slightly different:  
(+) Can be useful to represent complex real phenomena that are not easy to explain with equations based on physics.  
(+) Can be very accurate in the prediction of the behavior of a system.  
(\(-\)) The models have to be trained using usually different datasets for the different states of the system, one for a regular state and one for each possible failure state or even combination of several failures at the same time.  
(\(-\)) It is necessary to have a huge amount of experimental data that in most of the cases are not available, with the consequence to resort to using small not representative experimental datasets or data coming from simulations or physical models.  
By way of conclusion, even though it seems that every time more algorithms used for fault detection are based on machine learning (ML) techniques such as artificial neural networks (ANNs), a recent study [95] concludes that some of these approaches are not useful, since models trained with laboratory data or data coming from simulations do not achieve a good enough performance when working with online data. On the other hand, these kind of FDD applications show a very promising growth and may be a good option to solve complex FDD problems in the near future.
# Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations
## 4 Discussion
### Challenges of IoV-Assisted Smart Grid
At the communication level, there are serious issues regarding the creation of safe and fast payments and interactions throughout the IoV ecosystem [64]. Furthermore, due to the mobility and topological unpredictability of the IoV, maintained privacy is not ensured during the resource-sharing process [65]. Lastly, at the application level, the IoV is subject to cloud service issues, which may result in data loss or inadequate storage capacity [66].
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 2 Two-Electron Repulsion Integrals
The orbitals are often named based on the sum of the azimuthal quantum numbers \(L=\mu_{x}+\mu_{y}+\mu_{z}\). For example, an orbital with \(L=0\) is classified as an s-orbital, which has a spherical shape. Likewise, \(L=1,2,3\) render the p-, d-, and f- orbitals, respectively [39]. Furthermore, within the context of contracted Gaussian basis functions (CGFs), \(K_{\mu}\) is the degree of contraction, and \(\mu_{x}+\mu_{y}+\mu_{z}\) denotes the total angular momentum. The above-described parameters are uniquely determined by both the types of basis functions and the number of atoms. Selecting basis functions is crucial as it significantly influences both the accuracy and computational cost of quantum chemical calculations [40]. For instance, the Slater-type orbital with three Gaussian functions (often termed STO-3G) is a well-known minimal basis set representing molecular orbitals as the linear combination of three Gaussian-type orbitals [41]. For the purpose of research and through evaluations, the community has developed the Basis Set Exchange (BSE) [42], a comprehensive and convenient database of basis sets used in quantum chemistry calculations. As the Hartree-Fock method involves the evaluation of quartet electron repulsion integrals (ERIs), we define the ERIs of the four Gaussian basis functions as follows:  
\[(\mu\
u|\lambda\sigma)\]4where \(G_{\mu_{e}}\), \(G_{\
u_{f}}\), \(G_{\lambda_{g}}\), and \(G_{\sigma_{h}}\) denote Gaussian-type orbitals (GTO-ERIs). Using the above-mentioned formulation, Equation (6) can be rewritten as follows:  
\[(\mu\
u|\lambda\sigma)\]5  
For clarity of exposition, in order to relate notions of basis sets and Gaussian-type orbitals while computing the electronic configuration of molecules, Figure 2 shows the relation between the Basis-ERIs and the GTO-ERIs. In particular, Figure 2 shows the combinations of four Basis-ERIs under symmetry considerations, in which the terms over the row (column) directions represent the _bra_ (_ket_) Basis-ERIs. Each cell of the upper triangular matrix of Figure 2 corresponds to a single Basis-ERI and is the result of the sum of Gaussian orbitals (GTO-ERIs), as expressed in Equation (9).
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 6 Experimental Results and Analysis  
### Performance of Fall Detection Algorithm Based on Improved YOLOv5s  
#### 6.1.1 Dataset  
This paper collected a large number of images of different scenes and different people in a fall posture. The dataset contains more than 6000 images, covering a wide range of people in different occupations, people in different countries, and people of different races. The age span is also relatively large, and the overall data distribution is uniform. A part of the dataset is shown in Figure 13.
## 3 Results and Discussion
### Melting Profiles of Sea Buckthorn and Rosehip Oils  
The fatty acid composition may also influence the melting profiles of the fats. Melting profiles of sea buckthorn and rosehip oils are shown in Figures 2 and 3, respectively. Two endothermic peaks were recorded on the melting curves of sea buckthorn oils at temperatures \(-37.05\)\({}^{\circ}\)C and \(-26.76\)\({}^{\circ}\)C for B2 oil and \(-36.55\)\({}^{\circ}\)C and \(-24.71\)\({}^{\circ}\)C for B1 oil. For sea buckthorn oil B2, an additional distinct peak was recorded at a temperature of \(-8.57\)\({}^{\circ}\)C because this oil contained over 8 percentage points more saturated fatty acids and 17 percentage points less polyunsaturated fatty acids than sea buckthorn oil B1. On the melting curve of rosehip oil R1, one peak was observed with a maximum at a temperature of \(-37.75\)\({}^{\circ}\)C, while in rosehip oil R2, two peaks were recorded with a maximum at a temperature of \(-38.05\)\({}^{\circ}\)C and \(-29.5\)\({}^{\circ}\)C. Rosehip oil R2 was characterized by a higher content of saturated acids and a lower content of polyunsaturated \(\alpha\)-linolenic acid, which may affect the occurrence of the second peak. Generally, the analyzed oils were characterized by the presence of endothermic peaks at low temperatures [24,37]. This corresponded to the triacylglycerglycerol fraction with a high share of mono- and polyunsaturated fatty acids, which were confirmed by the chromatographic analysis of the oils. [PERSON] et al. [32], in the studies on the properties of the lipid fraction obtained from strawberry, cranberry, and blackcurrant seeds, also observed the occurrence of endothermic peaks at low temperatures related to the melting of triacylglycerols containing mainly polyunsaturated fatty acids.  
Figure 1: The distribution of selected fatty acids (palmitic, stearic, oleic, linoleic) in the sn-2 position of triacylglycerglycerols molecules of sea buckthorn and rosehip oils.
## 4 Results
### Development Package  
The purpose of this test suite was two-fold: firstly, to verify that all tasks are carried out as per the user scenarios and, secondly, to assess whether the minimum hardware requirements established in prior tests remain applicable in this instance. Tests included 12 different end-users requesting slightly different tasks. A concise summary of the test results for each scenario based on these hardware specifications can be found in Table 4.  
The service effectively responds to the requested services and establishes seamless communication between various entities. Moreover, the auto-connect provision functions flawlessly, behaving as anticipated in case of connection loss, malfunctioning errors, timeserver faults, and unavailability of the Gateway API server.
# Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications
## 2 Human-Voice Interface System
### Automatic Speech Recognition (ASR)
#### 2.3.2. Convolution Module Design  
The convolution module starts from the rating mechanism [27], which consists of a pointwise convolution layer and a gated linear unit (GLU), followed by a 1D depth-wise convolution layer. The batch-norm is arranged after the convolution layer to facilitate model training, and the pre-norm residual unit is used as in the multi-head self-attention module. The convolutional module architecture is shown in Figure 11.  
As mentioned, the used depth-wise separable convolution [28] is a new computational structure for CNN developed by Google. It aims to reduce the computational effort of CNN by splitting the original convolutional computation into two parts: pointwise convolutions and depth-wise convolutions, each of which is performed without affecting the output structure to reduce the computation.
## 6 Discussion on the Results
Under full-scale seismic excitation (1 g), Specim 2 exhibits a moderate increase in hysteretic response and displacement, resulting in a damage index of approximately 0.34, a value that remains substantially lower than that of the bare frame under similar conditions. These findings not only underscore the superior performance of the retrofitting strategy but also highlight the robustness of the Park-Ang damage index as a composite metric that integrates both deformation and energy-based degradation mechanisms. The ability of this index to reconcile the visual evidence from hysteretic curves with quantitative damage assessments provides a powerful tool for benchmarking and comparing different structural configurations and retrofitting interventions. In essence, by incorporating both indices--displacement and energy--the Park-Ang model offers a comprehensive and nuanced framework for evaluating seismic damage, thus reinforcing our overall conclusions drawn from both experimental observations and numerical simulations. Figure 13 presents photographs of the shake table tests conducted on the selected RC specimens. An analysis of the figure indicates that the initial specimen has experienced considerable damage, in contrast to the second specimen, which appears to be unscathed. The prominent cracking in the upper corners of the first specimen, along with the observed concrete crushing, indicates a significant joint shear failure. Further, significant areas of the concrete overlay have separated, especially in the regions near the base and the junctions between beams and columns. The damage at the joints may have compromised the bond between the reinforcement and the surrounding concrete, leading to further weakening. In contrast, the second specimen exhibited no signs of damage, such as cracks or spalling, suggesting that the TnT BRB performed its function of dissipating seismic energy effectively. The inclusion of the diagonal brace enhanced resistance to lateral forces, which in turn minimized deformation and safeguarded against early failure of the beam-column joints. Thus, it can be deduced that the TnT BRB system played a crucial role in absorbing and redistributing seismic forces, thereby averting significant stress accumulation in the joints. Figure 13: Shake table test results.