esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
According to the summary listed in Table 2, some experimentation needs to be carried out to investigate various setups of the ANN model that will give the optimum prediction results. The first result is a related feature selection, as this is the first stage of data preparation before ANN training. The features were selected based on the correlation score between the independent variable (input features) and the dependent variable (target). Table 3 shows the correlation score for each feature and the target/output variable. The result indicates a strong correlation (R = 0.739) between weighted rainfall (\(x_{1}\)) and the river flow (\(y\)). It can be concluded that the correlation between a min of temperature (\(x_{3}\)) and the target variable (\(y\)) is very low and therefore \(x_{3}\) was removed from the input feature. The lowly correlated feature would degrade the prediction accuracy if it was not removed from the feature. With these four selected features (after removing \(x_{3}\)), the ANN training experimentation proceeds and the evaluation is performed. For the model parsimony reason, further removal of either feature \(x_{4}\) or \(x_{5}\) is also investigated to ascertain whether it affects the model accuracy. This is because these two features are of the same type, i.e., temperatures. The first experimentation is mainly to investigate the number of hidden layer neurons and comparisons between FFNN and CFNN trained with the LM algorithm. Table 4 shows the results of this experimentation. The two numbers in the hidden layer neurons indicated that two hidden layers were used with the corresponding number of neurons in each layer. In the first column of Table 4, the notation in the square bracket indicates the number of hidden layer neurons, for example, [5] meaning there are 5 neurons in 1 layer, \(\{10+10\}\) meaning that there are 10 neurons in two hidden layers, etc. The main finding in this experimentation is that the ANN trained with LM algorithm have a high tendency of overfitting, i.e., good prediction (even perfect, \(R^{2}~{}=~{}1\)) for training data but poor prediction of testing data. This occurs in both models using FFNN and CNNN structure. Some worse cases of this situation are highlighted in gray where the obtained RMSE is very high such that the ANN failed to make predictions, i.e., resulting negative values of \(R^{2}\), marked with '-' in the Table. In addition, increasing the number of neurons (and layers) tends to increase the chance of overfitting. The second experimentation is the same as the first, but the BR training algorithm was used. Table 5 shows the results of this experimentation. In Table 5, the lower RMSEs obtained during model testing are marked by '' and the overfitting situations are highlighted in gray. It can be seen from Table 5 that, generally, CFNN with one hidden layer (5 to 20 number of hidden neurons) sufficiently produced lower RMSEs when it is trained with BR algorithms. Moreover, the increasing number of neurons (and layers) did not give a satisfactory performance as can be seen from both Tables 4 and 5.
# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
## 2 Materials and Methods
### Pointclouds Acquisition with RGBD Camera
#### 2.2.1 Registration Algorithm  
Since the acquisition of a single pointcloud did not allow for the optimal reconstruction of the phantom's surface, especially for areas such as the ears and nose, it was necessary to capture a series of pointclouds from different angles and subsequently register them one respect to the other. The estimation of the rotation and translation of the camera with respect to a reference system is known as camera pose estimation. In this work, RGBD pointclouds were registered using a \(7\times 7\) ChArUco board as a fiducial marker (Figure 5). A ChArUco board is the combination of two types of markers: a chessboard and ArUcos [43; 44] and, in particular, it is composed of a chessboard with white and black cells, where, within the white ones, ArUco markers are present.  
Thanks to its known geometry i.e., number and dimensions in mm of the cells forming the chessboard and number and dimensions in mm of the ArUco markers, it was possible  
Figure 4: Pointcloud obtained from the DICOM images processing.  
Figure 5: ChArUco board used as a fiducial marker for RGBD camera pose estimation. The side length of each square on the board is 15 mm, while the side length of each ArUco marker is 10 mm.  
to estimate the camera pose using this information, the camera intrinsic parameter matrix \(\mathbf{A}\) and the camera distortion coefficient vector \(\mathbf{K}\), defined as:  
\[\mathbf{A}=\begin{bmatrix}f_{x}&0&pp_{x}\\ 0&f_{y}&pp_{y}\\ 0&0&1\end{bmatrix}\qquad\mathbf{K}=[k_{1},k_{2},k_{3},k_{4},k_{5}]^{t} \tag{2}\]  
where: \(f_{x}\) and \(f_{y}\) are the focal length, \(\mathbf{p}_{0}=\begin{bmatrix}pp_{x},pp_{y}\end{bmatrix}^{t}\) is the principal point and \(\mathbf{K}\) values are lens radial and tangential distortion coefficients.  
The camera's pose estimation was evaluated with respect to an axis system defined on the ChArUco as shown in Figure 6. The OpenCV library was used for each acquired RGB frame to identify the ChArUco and evaluate the camera pose. Once the poses for each frame were evaluated, it was possible to register each RGBD pointcloud to each other. Specifically, the pointclouds were registered with respect to the position that the RGBD camera had once it was positioned in front of the phantom, thus defining the reference system \(O_{xyz}^{RGBD}\).
## Appendix A
### Scenario 2
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* _Pozar and Miorouave Engineering_ (2012) [PERSON] _Microwave Engineering_, 4 th ed. ; Wiley: Hoboken, NJ, USA, 2012. * _[PERSON] and [PERSON] (2009)_ [PERSON]; [PERSON] On the Dispersive Nature of the Power Dissipated Into a Lossy Half-Space Close to a Radiating Source. _IEEE Trans. Antennas Propag._**2009**, _57_, 2572-2582. [CrossRef]
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# Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
## 7 Performance Metrics
### Edge-Tolerant IoU  
One very specific characteristic of pavement distress detection is that the defects (the cracks in particular) can be highly irregular and are made up of lines of small diameter. Annotating every little detail of such defect with a sharp pen is too time-consuming for practical purposes. An annotation is therefore often a rough sketch of the annotated defect and contains a substantial amount of perfectly fine road surface. This also means that minor detection mismatches at the edges of a detected defect are not necessarily mistakes at all, neither are they critical to the success of the application. Moreover, they can bias the evaluation measure in the undesired direction.  
Figure 9: An illustration of various metrics. (**a**) The original image: (**b**) TP, FN and FP pixels are depicted by colors of green, blue and red, respectively: (**c**) Three objects obtained by combining the predictions and ground truth instances: (**d**) TP, FN and FP pixels with edge-tolerant intersection over union (IoU) computation.  
To account for that we propose an edge-tolerant object-level IoU--a more complicated approach than the one taken in References [10; 31; 32] where a true positive is simply defined as any labelled pixel in the output segmentation mask that is within 5 pixels of a true label in the ground truth--in which object-level FN and FP pixels are calculated as follows.  
\[\text{eFP}_{j}=\left|Y_{j}\right|-\left|X_{j}^{\prime}\cap Y_{j}\right|, \tag{10}\]  
\[\text{eFN}_{j}=\left|X_{j}\right|-\left|X_{j}\cap Y_{j}^{\prime}\right|, \tag{11}\]  
where \(X_{j}^{\prime}\) is a dilated ground truth instance belonging to the j-th object (using a fixed-size kernel) and \(Y_{j}^{\prime}\) is a similarly dilated set of prediction pixels belonging to the same object.  
An edge-tolerant object-level IoU would appear as  
\[\text{eloU}_{j}=\frac{\text{oTP}_{j}}{\text{oTP}_{j}+\text{eFP}_{j}+\text{e FN}_{j}} \tag{12}\]  
and global edge-tolerant intersection over union (eloU)as  
\[\text{eloU}=\frac{\sum_{j}\text{oTP}_{j}}{\sum_{j}\text{oTP}_{j}+\text{eFP}_ {j}+\text{eFN}_{j}}. \tag{13}\]  
In the example, having the kernel size of 7 pixels, it would mean that \(\text{eloU}_{1}=100\%\), \(\text{eloU}_{2}=6\%\), \(\text{eloU}_{3}=0\%\) and the overall IoU, \(\text{eloU}=66\%\). We can see (Figure 9d) that the only truly affected object by this is object no. 1, which was the original purpose.
# Performance of Molasses Waste as a Cement Replacement to Study Concrete Compressive Strength
###### Abstract  
To reduce the negative environmental impact of cement production and preserve natural resources, an experimental investigation was conducted to study the performance of concrete specimens at different curing ages and to determine the compressive strength of these specimens by replacing cement with molasses. Experimentation was carried out on the concrete specimens at a temperature range of 25 \({}^{\circ}\)C to 30 \({}^{\circ}\)C; six specimens were cast for each replacement ratio, except for 0.75% wt. of cement (86.55 g) and 1% wt. of cement (113.6 g), where five samples were considered for each ratio. The average 28-day compressive strength of the conventional concrete specimens came out to be 29 MPa, but increased to 40 MPa with the addition of 0.25% wt. of cement molasses (28.85 g). It was observed that as the percentage of molasses waste in the concrete mix was further increased by replacing the cement, the compressive strength of the concrete specimens increased gradually and then significantly decreased. The findings shed light on the prospect of using molasses waste instead of cement in the concrete mix. Also, it is worth mentioning that about 30% of the cost-benefit was obtained with reference to that of conventional admixtures available in the market for the production of concrete. However, it is notable that a long-term durability study needs to be conducted before making it viable. This work not only addresses a sustainable and innovative method of waste management (SDG12), but also contributes to low carbon emissions (SDG13). The novelty of this work lies in the fact that no such kind of study has been conducted in Pakistan so far, in addition to the very limited international literature available, and, in particular, no evidence on the compressive strength results at higher molasses dosages, i.e., 1% wt. of cement (113.6 g) and 2% wt. of cement (230.8 g).  
replacement ratio; molasses; sustainable development; compressive strength; cost-effective; low carbon emission; construction material
# The Concept regarding Vehicular Communications Based on Visible Light Communication and the IoT
## 2 Methodology and Design Parameters
### Li-Fi Communication System Proposal
The generation of the light beam that contains that data is related to the dynamics of the environment and the data quantity; the light contains the data that must be provided to the receiver, and the data take a path through the optical channel in free space (see Figure 4). To obtain a more robust structure, this research focused on the development of a system capable of providing information and connectivity with any other device. For the reception side, as can be seen, the collector optical system had a processing component and a processing unit. These components integrate optical filters that adjust the signal to noise ratio (SNR) and remove unwanted components from the optical spectrum. For the optical detection part, a PDA component and PIN photodiodes connected to direct transimpedance circuits were used. For the processing blocks, bandpass filters were introduced with certain cutoff frequencies determined by the spectral densities. The data encoding and decoding process are carried out using quadratic triggers and other types of triggers, and the final signal is ultimately analyzed and received by the ARM Cortex M4-type controller. The final data arrive at the last unit and can be accessed by the end user [52; 53].
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Evaluations  
The field layout in Figure 1 is used in experiments. Among the parameters in _FILS15.4_, the detection interval and the fingerprint values can most influence the detection accuracy. Thus, their optimizations are discussed.
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 4 Simulation Analysis
### Performance Analysis of Pest and Disease Spread Evolution Process Based on the GSPN Model
#### 4.1.3 Transition Utilization Rate  
The transition utilization rate reflects the activity level and resource usage efficiency of each transition node within a specific time frame. In accordance with Equation (19), the utilization rates for each system transition are calculated as shown in Table 10.  
Compared to other transitions, the utilization rates of t\({}_{19}\), t\({}_{20}\), and t\({}_{21}\) are relatively high. The frequent triggering of recovery of health (t\({}_{19}\)) and plant death (t\({}_{20}\)) transitions indicates a strong system demand for evaluating treatment outcomes. This reflects the necessity of continuously monitoring and assessing the health status of plants to ensure the effectiveness of control measures and to promptly adjust strategies to prevent further spread of pests and diseases. Among all, the treatment effectiveness assessment (t\({}_{21}\)) transition has the highest utilization rate. This is because, following the implementation of control measures,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transition** & **Utilization Rate** & **Transition** & **Utilization Rate** \\ \hline t1 & 0.013443 & t12 & 0.289034 \\ t2 & 0.008066 & t13 & 0.011523 \\ t3 & 0.008066 & t14 & 0.011523 \\ t4 & 0.008066 & t15 & 0.011523 \\ t5 & 0.008066 & t16 & 0.011523 \\ t6 & 0.008066 & t17 & 0.011523 \\ t7 & 0.008066 & t18 & 0.011523 \\ t8 & 0.008066 & t19 & 0.278278 \\ t9 & 0.092184 & t20 & 0.278278 \\ t10 & 0.092184 & t21 & 0.188208 \\ t11 & 0.092184 & t22 & 0.084694 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Transition utilization rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Place** & **Idle Probability** & **Place** & **Idle Probability** \\ \hline M1 & 0.986557 & M7 & 0.907816 \\ M2 & 0.986557 & M8 & 0.710966 \\ M3 & 0.986557 & M9 & 0.800269 \\ M4 & 0.986557 & M10 & 0.533514 \\ M5 & 0.986557 & M11 & 0.777223 \\ M6 & 0.991934 & M12 & 0.915306 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Idle probabilities for each place.  
the system must assess their effectiveness to verify that the pest or disease is effectively managed. The high utilization rate of this transition node is critical for ensuring that pest and disease control measures are appropriate, strategies are adjusted in a timely manner, and overall system performance is optimized.
Sensors_**2012**, _12_, 7917-7937; doi:10.3390/s120607917  
_SEZISOS_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Ultra Low Power Signal Oriented Approach for Wireless Health Monitoring**  
**[PERSON] * and [PERSON] *  
Department of Electrical and Electronic Engineering, University College Cork, Cork 11111, Ireland  
* Authors to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] (S.M.);  
[EMAIL_ADDRESS] (E.P.); Tel.: +353-21-490-2027 (S.M.).  
_Received: 27 March 2012; in revised form: 29 May 2012 / Accepted: 29 May 2012 / Published: 8 June 2012_  
**Abstract:** In recent years there is growing pressure on the medical sector to reduce costs while maintaining or even improving the quality of care. A potential solution to this problem is real time and/or remote patient monitoring by using mobile devices. To achieve this, medical sensors with wireless communication, computational and energy harvesting capabilities are networked on, or in, the human body forming what is commonly called a Wireless Body Area Network (WBAN). We present the implementation of a novel Wake Up Receiver (WUR) in the context of standardised wireless protocols, in a signal-oriented WBAN environment and present a novel protocol intended for wireless health monitoring (WhMAC). WhMAC is a TDMA-based protocol with very low power consumption. It utilises WBAN-specific features and a novel ultra low power wake up receiver technology, to achieve flexible and at the same time very low power wireless data transfer of physiological signals. As the main application is in the medical domain, or personal health monitoring, the protocol caters for different types of medical sensors. We define four sensor modes, in which the sensors can transmit data, depending on the sensor type and emergency level. A full power dissipation model is provided for the protocol, with individual hardware and application parameters. Finally, an example application shows the reduction in the power consumption for different data monitoring scenarios.  
**Keywords:** wireless body area network; wake up receiver; mHealth
# Reconstructing the 26 June 1917 Samoa Tsunami Disaster
## 3 Methods and Data
### Tsunami Exposure and Damage Analysis
#### 3.2.2 Building Fragility Model  
Fragility functions relate tsunami hazard intensity (e.g., flow depth) to the conditional probability of a building reaching or exceeding a given damage state [35]. Here, physical building damage is measured from empirical fragility curves representing Samoan buildings damaged in the 2009 SPT [9]. The fragility curves apply a cumulative lognormal function for 'timber','masonry', and'reinforced concrete' construction frame buildings to determine the conditional probability (0-1) of \"light\", \"minor\", \"moderate\", \"severe\", and \"collapse\" damage states (DS) being reached or exceeded for a maximum tsunami inund  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Primary Attribute** & **Secondary Attribute** & **Metric or Value** \\ \hline Construction Frame & Masonry, Steel, Reinforced Concrete, Timber & Text \\ Usually Resident Population & - & Floating \\ Outline Area & - & m\({}^{2}\) \\  & Commercial; Community; Education; Fale; & \\ Use Category & Hotel, Resort; Industrial; Outbuilding; & Text \\  & Religious; Residential Dwelling; Tourist Fale & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of attributes represented in the building exposure data.  
dation depth (Table 2). In the absence of representative fragility curves for some building typologies,'masonry' curves are applied for'steel' construction frame buildings, and DS1 and DS2 fragility curves are applied for timber and reinforced concrete building typologies.
# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
## 3 Methodology  
In this section, a novel framework is presented for distantly supervised relation extraction, which uses the prior knowledge from FrameNet and knowledge graph to provide hierarchical knowledge attention. We denote a KG as \(G=\{(e_{1},r,e_{2})\}\), which consists of many triples \((e_{1},r,e_{2})\). Each triple indicates a relation \(r\) between entity pair \(e_{1}\) and \(e_{2}\).  
Given an entity pair \((e_{1},e_{2})\) in a KG and a training set of sentence bags \(D=\{B_{1},B_{2},...,B_{N}\}\), a relation \(r\) is defined as a semantic property between the entity pair \((e_{1},e_{2})\). For distantly supervised relation extraction, all sentences \(S_{i}\) refer to the entity pair \((e_{1},e_{2})\) are regarded as instances of the relation \(r\). They constitute a instance bag for this entity pair and relation type, denoted as \(B_{i}=\{S_{1},S_{2},...,S_{n}\}\). The target of the distantly supervised relation extraction is to predict the labels for unseen bags. To this end, we need to learn a relation extractor to capture features of the valid sentences in the bag and aggregate them to form the bag-level features. Then we use the bag-level features to train a classifier to predict the relations for the given entity pairs.  
Our framework consists of two modules: Sentence embedding module and multi-instance selection module. The sentence embedding module utilizes the relation indicators from lexical resources as prior knowledge to guide the embedding of sentences, which consists of word-level knowledge attention layer and graph convolutional layer. The multi-instance selection module explores the structural and semantic information from KGs as prior knowledge to guide the selection of multiple valid sentences, which consists of knowledge graph embedding and sentence-level knowledge attention layer. The model leverages hierarchical knowledge attention to attend over instances to alleviate different levels of noise, which can generate more expressive relation representations to enhance the relation extraction.