esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, the mechanical properties and pore structure of BPFRC are evaluated. The following conclusions can be drawn from the study.  
1. Incorporation of a hybrid mixture of BF and PF into concrete has both positive and adverse effects on the mechanical properties of concrete. The synergistic effect of the hybrid fibers is greatest when the BF and PF contents are 0.05% each; the corresponding increments in the compressive strength and splitting tensile strength 5.06% and 9.56%, respectively. The effect of the hybrid fibers on the splitting tensile strength is greater than that on compressive strength. However, when the fiber content is too high, the hybrid fibers have adverse effects on the mechanical properties. Therefore, accurate control of the fiber content of concrete is necessary.  
Figure 8: Fitting of air content and fractal dimension.  
Figure 9: Fitting of spacing factor and fractal dimension.  
2. The pore structure of BPFRC exhibits fractal characteristics. The fractal dimension of the pore structure calculated using a fractal model based on an optical method is in the range of 2.297-2.482, with a high correlation coefficient (R\({}^{2}\) > 0.977); this indicates that the fractal dimension calculated using this model can well-characterize the pore size distribution characteristics of concrete.
3. The fractal dimension of BPFRC is closely related to the air content and spacing factor. As the fractal dimension increases, the air content decreases and the spacing factor increases. Therefore, the pore structure characteristics of BPFRC can be evaluated comprehensively using the fractal dimension. In addition, the fractal dimension has a strong positive correlation with the compressive strength and splitting tensile strength of concrete. That is, the larger the fractal dimension, the higher the compressive strength and splitting tensile strength. This indicates that the complexity of the pore structure is an important factor affecting the macroscopic mechanical properties of concrete.  
[PERSON] and [PERSON] conceived the main concept and contributed the analysis; [PERSON], [PERSON], and [PERSON] conducted experiments; [PERSON] and [PERSON] helped analyzing experimental data; [PERSON] wrote the paper.  
This research was funded by National Natural Science Foundation of China (Grant Nos. 51590914, 51608432, 51808438), and the APC was funded by National Natural Science Foundation of China (Grant No. 51590914).  
This study is financially supported by National Natural Science Foundation of China (No. 51590914, No. 51608432, and No. 51808438).  
The authors declare no conflict of interest.
## 6 Experimental Simulation and Analysis of the Results
### Number of CHs  
Specific to the formation of the CHs, 100 nodes are selected in the simulation experiment, and 100 rounds are extracted for statistics before the death of the first node. Figure 9 shows the comparison of the number of CHs formed under the four protocols, and it is discovered in the traditional LEACH algorithm that the number of CHs is rather dispersed with a large fluctuation. The CNF-Degree algorithm introduces the distance of nodes as the reference, which is superior to the LEACH algorithm; however, PEAFC-CL is rather dispersed by comparison. However, the probability of the nodes near the BS becoming the CH and relay node is large in the EBACR protocol, which makes the number of CHs formed unstable. However, in the PEAFC-CL algorithm, the number of CHs formed is controlled by the sliding window and the number of active nodes in each round; therefore, the number of CHs generated by PEAFC-CL is relatively concentrated, with more than 50% above the expected number of five.
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 3 Our Approach
### Classifier
#### 3.1.5 Rich Disease Representation with Prior Knowledge  
To further improve the accuracy of generated reports, we introduce rich disease representations with prior knowledge. The main idea behind rich disease representations with prior knowledge is to further encode informative attributes about the disease state, such as positive, negative, uncertain, or unmentioned. Formally, let \(k\) be the number of states and the state embedding be \(\text{S}\in\mathbb{R}^{k\times e}\), then the confidence of each disease classification as one of \(k\) disease states is:  
\[p=\text{Softmax}\Big{(}\text{D}_{pri\text{-}fused}\text{S}^{T}\Big{)} \tag{8}\]  
\(S\) is a trainable parameter initialized randomly. \(\text{D}_{fused}\) is used as the feature of multi-label classification, and the classification loss function is as follows:  
\[\mathcal{L}_{p-c}=-\frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{k}y_{ij}\log(p_{ij}) \tag{9}\]where \(y_{ij}\in\{0,1\}\) and \(p_{ij}\in[0,1]\) represent the true and predicted values of the \(i\)-th disease label, respectively. The state embedding \(D_{states}\in\mathbb{R}^{n\times e}\) can be calculated as follows:  
\[D_{states}\bigg{\{}\begin{matrix}yS\,,\,\,\text{Training stage}\\ pS\,,\,\,\text{Other stages}\end{matrix} \tag{10}\]  
Finally, \(D_{enriched}\in\mathbb{R}^{n\times e}\) is the disease representation enriched with prior knowledge, and is composed of disease embedding states, disease name topics, and episodic disease representations with prior knowledge.  
\[D_{pri\text{-}enriched}=D_{states}+D_{topics}+\text{D}_{pri\text{-}fused} \tag{11}\]  
where \(D_{topics}\in\mathbb{R}^{n\times e}\) is randomly initialized to represent the disease or topic to be generated and is a trainable parameter. The rich disease representation with prior knowledge provides a clear and accurate disease description, which provides strong data support for the subsequent generation module.
## 3 Materials and Methods
### Validation Metrics  
Several metrics have been used to validate and compare the models, based on literature recommendations: the coefficient of determination \(R^{2}\) (considered among the most reliable metrics for regression), the \(MAE\), and the \(MAE\%\) defined below [36].  
The coefficient of determination \(R^{2}\) measures the variation in the \(y\) observed values, where \(\hat{y}\) is the mean, and is calculated using the equation below [37,38]:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y})^{2}} \tag{8}\]  
The \(MAE\) represents an average of the absolute prediction error with \(y_{i}\) as the observed value and \(\hat{y}_{i}\) as the predicted value, as computed in Equation [38]:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}abs(y_{i}-\hat{y}_{i}) \tag{9}\]  
Another metric used is the \(MAE\%\) or median absolute percentage error (\(M\)\(A\)\(A\)\(A\)) [36], which measures \(MAE\) as a percentile from the mean of the \(y\) values, as in Equation:  
\[MAE\%=\frac{MAE}{\frac{1}{n}\sum_{i=1}^{n}y_{i}}\times 100 \tag{10}\]  
We used repeated k-fold cross validation with \(k=10\), computing the mean and standard deviation for five repeats, for each of the metrics \(R^{2}\) and \(MAE\).
## 7 Results and Discussion
### Evaluation Metrics
#### 7.1.1 Throughput  
The throughput is the sum of the \(\mathit{Pkt}_{(received)}\) by the base station in the \(T_{(period)}\), where the packet sizes \(\mathit{Pkt}_{(size)}\) are in bits. Equation (26) shows the numerical form of the throughput.  
\[T_{(r)}=\frac{\mathit{Pkt}_{(received)}\times\mathit{Pkt}_{(size)}}{T_{( period)}}. \tag{26}\]
## 3 Fall Detection Methods and Technologies
### Fall Detection Sensor Types  
[PERSON] et al. [40] enumerate 10 various technologies, including inertial sensors and locating systems, vision sensors, sound and infrared sensors, pressure sensors, etc. These technologies are divided into three main classes based on the hardware resources: wearable technologies, ambient technologies, and a mix between wearable and ambient technologies together. Inertial sensors (e.g., accelerometers) and locating systems (e.g., Global Positioning Systems) are two forms of hardware that are used in wearable technology. Vision sensors and sound sensors like cameras and microphones, respectively, are examples of ambient technology. Sensors that assist fall prediction and detection are divided into three categories by [PERSON] et al. [41]: motion sensors, physiological sensors, and environmental sensors. The accelerometer is the key sensor in the motion sensors group for detecting falls by detecting the variation in body acceleration [42]. The accelerometer measures the rate at which an object's velocity changes with respect to time in m/s\({}^{2}\) or G unit in three dimensions: x, y and z, i.e., acceleration. In case of the body acceleration exceeding the predefined threshold value for the fall, there is a possibility of a fall. It is a straightforward approach to detecting a fall using threshold values of the measured acceleration. The gyroscope, on the other hand, is a mechanical device, which monitors angular motion around x, y, and z-axes. The gyroscope detects falls forward/backward or left/right by measuring orientation in pitch, roll, and yaw. Magnetometers detect the direction of a fall event by measuring the geomagnetic field. Physiological sensors monitor the body's vital indicators [43]. In the aftermath of a fall, vital signs are usually altered quickly due to shock. The physiological changes can be used to establish the likelihood of a fall. Electrocardiography is a technique for recording electrical signals generated by the heart muscle and displaying the state of the heart in various scenarios [44]. The procedure of measuring blood volume
# Urban Free-Space Optical Network Optimization
## 2 Problems Statement
### General Properties of the Visibility Graph  
We present a lemma showing the visibility graph model to be as general as a regular undirected graph. As mentioned, the optimization problems of max-leaf and Steiner tree on graphs are NP-hard. In this subsection, we show that every graph can be represented as a visibility graph on some terrain. Moreover, such terrain and its corresponding visibility graphs can be computed efficiently.  
**Lemma 1**: _For any graph \(G<V,E>\), there exists a terrain \(\tau\) (of size \(O(|E|+|V|)\)) and a geometric embedding \(V_{\tau}^{\prime}\) of \(V\) on \(\tau\). The visibility graph of \(V_{\tau}^{\prime}\) over \(\tau\) is the same graph as \(G\)._  
By construction: The visibility graph can be constructed by starting from a null graph, where all the vertices are located on the outer boundary of a thin \(n\)-edges polygon. For each two vertices \(\left(u,v\right)\) that are connected in the graph, a narrow \"window\" (white \"segment\") is created between \(u,v\) to allow visibility between them without changing the other visibility relations. Such construction has an \(O(E)\) size, as each additional edge adds two segments to the polygon. The construction is demonstrated in Figure 6.  
**Corollary 1**: _The visibility graph model is equivalent to the general undirected graph model._  
Figure 6: Visibility graph representation on a simplified terrain. Left: a graph. Mid: the vertices are located on a polygon of \(n\) edges; the black polygon represents a “wall”. Each edge \(e(u,v)\) in the graph is marked as a (blue) segment in the polygon. Right: each visibility segment is marked in white to allow a “visibility window” related to each edge in the graph.
# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods
## 2 Materials and Methods
### Research Methodology
#### 2.2.3 Relative Difference Coefficient  
Due to the differing mechanisms of the algorithms, the interpolation results from the four methods vary across the same grid cells. When comparing two interpolation methods, one is designated as the baseline method, with the other serving as the comparison algorithm [45]. The relative deviation coefficient (RDC) is calculated using Formula (1) as follows:  
\[\alpha=1-\frac{\sum_{i=1}^{n}\Bigl{(}B_{comp}^{i}-A_{base}^{i}\Bigr{)}}{\sum_{i= 1}^{n}\Bigl{(}B_{comp}^{i}-\overline{A_{base}^{i}}\Bigr{)}} \tag{2}\]  
In the formula, \(A_{base}^{i}\) represents the interpolation error value of the benchmark algorithm at the \(i\)th grid cell, \(\overline{A_{base}^{i}}\) denotes the mean of the entire interpolation error results, and \(B_{comp}^{i}\) represents the interpolation error value of the comparison algorithm at the corresponding location.  
Both the benchmark and comparison algorithms are fitted to a straight line with a slope of 1. The RDC quantifies the overall deviation between the comparison and benchmark algorithms. If \(\alpha=1\), the results of both algorithms align, indicating no discrepancy between the methods. If \(0<\alpha<1\), a smaller \(\alpha\backslash\)alpha value indicates a greater disparity between the two methods. If \(\alpha<0\), the methods are not comparable [45].
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Differential Attack  
Differential attack means that the attacker encrypts two plain images with only slight differences, and cracks them by the difference between the ciphertext images [18]. In order to resist differential attacks, it is required that even if the plain image changes slightly, the cipher image needs to be greatly different. NPCR (number of pixels change rate) and UACI (unified average changing intensity) are usually used to measure the degree of difference, where NPCR represents the proportion of different pixel values at the same location in the two images, and UACI represents the difference in pixel values at the same location. The ideal values of the two are 99.6094% and 33.4635% respectively, and the calculation formulas are defined as [19,20]:  
\[\text{NPCR}=\frac{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}D(i,j)}{M\times N} \times 100\% \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{3}{c}{**Plain Image**} & \multicolumn{3}{c}{**Cipher Image**} \\ \cline{2-7}  & **Horizontal** & **Vertical** & **Diagonal** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline Lena & 0.9391 & 0.9682 & 0.9232 & 0.0035 & 0.0028 & \(-\)0.0045 \\ Text & 0.2385 & 0.3664 & 0.0500 & \(-\)0.0094 & 0.0053 & 0.0082 \\ Fruits & 0.9523 & 0.9578 & 0.9288 & \(-\)0.0022 & \(-\)0.0095 & \(-\)0.0170 \\ Peppers & 0.9675 & 0.9723 & 0.9451 & 0.0064 & \(-\)0.0110 & \(-\)0.0088 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation coefficient test results.  
\[\text{UACI}=\frac{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}\lvert C_{1}(i,j)-C_{2} (i,j)\rvert}{255\times M\times N}\times 100\% \tag{23}\]  
\[D(i,j)=\left\{\begin{array}{c}0,C_{1}(i,j)=C_{2}(i,j)\\ 1,C_{1}(i,j)\
eq C_{2}(i,j)\end{array}\right. \tag{24}\]  
where \(C_{1}\) and \(C_{2}\) represents the two images used to test the difference.  
Randomly select a pixel value of the plain image and add 1 to encrypt it. If the selected pixel value is 255, decrease it by 1. Calculate the NPCR and UACI between the new and old cipher images, and repeat 500 times to calculate the average value. The test results are shown in Table 6.  
The NPCR and UACI of this algorithm are very close to the ideal value, which can effectively resist differential attack. The NPCR and UACI values of the algorithm described in [4] are quite different from the ideal values, because the initial chaotic value of the algorithm is not associated with the plain image. The generation of the chaotic initial value  
Figure 10: The correlation of Lena in different directions: (**a**) horizontal direction of plain image and cipher image; (**b**) vertical direction of plain image and cipher image; (**c**) diagonal direction of plain image and cipher image.  
of the algorithm in this paper and those described in [7; 8] is linked to the hash digest of the plain image, so it is extremely sensitive to the plain image.
## 3 Methods
### Definition of Extreme Weather Events
For the criteria of heatwave condition from the viewpoint of agriculture, we referred to the two mainly cited studies by [PERSON] et al. [35] and by [PERSON] et al. [5]. [PERSON] et al. [35] suggested the Heat Stress Intensity (HSI) index that can express the heat stress during the reproductive phase and the yield damage. The HSI index has four categories: low (\(<\)0.05), medium (\(<\)0.15), high (\(<\)0.30), and very high (\(>\)0.30). From our match-up database, only four out of the 3585 records (approximately 0.1%) corresponded to the high or very high category. Alternatively, we decided to adopt the critical temperature suggested by [PERSON] et al. [5]. Different crops have different thermal stress tolerance  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Corn Yield** & **PPT** & **TMIN** & **TMAX** & **TMEAN** & **VHI** & **PDSI** \\  & **(ton/ha)** & **(mm/Month)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(0 to 100)** & **(Untilless)** \\ \hline
2006 & 8.519 & 85.6 & 17.0 & 29.8 & 23.4 & 45.2 & \(-\)0.886 \\
2007 & 8.871 & 103.5 & 16.6 & 29.1 & 22.8 & 49.0 & 1.083 \\
2008 & 9.420 & 83.8 & 15.6 & 28.3 & 21.9 & 58.0 & 2.855 \\
20 levels, and a heatwave limit temperature of 35 \({}^{\circ}\)C has been reported for corn and soybeans [5]. In this study, we defined a heatwave as a period when temperatures exceed 35 \({}^{\circ}\)C for three consecutive days during July and August. We conducted sensitivity analyses for the corn yield predictions according to the count of successive heatwave days.
## 3 Data and Method
### Experimental Settings
#### 3.3.4 Evaluation Metric  
In quantifying and evaluating the prediction performance of the model, the following four common metrics are employed: Precision, Recall, Accuracy, and \(R^{2}\).  
1. Precision quantifies the proportion of correctly predicted positive instances out of all instances predicted as positive. In the context of stock price prediction, a correct prediction implies that an upward price movement was anticipated and the price did indeed rise. The formula for Precision is: \[\text{Precision}=\frac{\text{True Positives (TP)}}{\text{True Positives (TP)}+\text{False Positives (FP)}}\] (25) Here, True Positives (TP) represents the number of correctly predicted positive instances, while False Positives (FP) denotes the number of instances incorrectly predicted as positive.
2. Recall measures the proportion of actual positive instances that were correctly predicted as positive. Its formula is: \[\text{Recall}=\frac{\text{True Positives (TP)}}{\text{True Positives (TP)}+\text{False Negatives (FN)}}\] (26) In this case, False Negatives (FN) are the instances that were incorrectly predicted as negative.  
Accuracy offers insight into the overall proportion of instances that were correctly predicted, regardless of being positive or negative. Its formula is expressed as: \[\text{Accuracy}=\frac{\text{True Positives (TP)}+\text{True Negatives (TN)}}{\text{Total Number of Samples}}\] (27) Here, True Negatives (TN) are the instances correctly predicted as negative. 4. \(R^{2}\), also known as the coefficient of determination, is a statistical measure reflecting the correlation between the actual and predicted values. Its formula is: \[R^{2}=1-\frac{\sum(y_{i}-\hat{y}_{i})^{2}}{\sum(y_{i}-\hat{y})^{2}}\] (28) where \(y_{i}\) is the actual value, \(\hat{y}_{i}\) is the predicted value, and \(\hat{y}\) is the mean of the actual values. 5. In order to rectify for degrees of freedom, the adjusted \(R^{2}\) was utilized, taking into consideration the number of predictive variables within the model: \[\text{Adjusted }R^{2}=1-(1-R^{2})\frac{n-1}{n-k-1}\] (29) Herein, \(n\) denotes the quantity of observations while \(k\) represents the number of predictive variables. By employing the adjusted \(R^{2}\), a more equitable comparison of models incorporating varying quantities of predictive variables is facilitated. The adjusted \(R^{2}\) undertakes a correction for degrees of freedom, ensuring an increase in the \(R^{2}\) value only when the addition of new variables genuinely enhances the predictive capability of the model.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 4 Results and Discussion  
### Site Selection  
The network described in this paper was installed on 20 November 2018 in Las Canadas del Teide in Tenerife, Canary Islands. The gateway was deployed in the Paradox, whilst the rest of the elements (repeater and eight thermometers) were placed on the summit part of Teide volcano, areas A and B (Figure 11), where the main thermal anomalies are located.  
In the Paradox, the gateway was installed taking advantage of the location of a video camera that is integrated into the Canary Islands volcano monitoring system belonging to the Spanish Instituto Geografico Nacional (Figure 3a).  
The repeater was placed ensuring a proper line of sight to the Paradox in order to improve the signal strength of the gateway-repeater link (Figure 11 and link 1 in Figure 1).  
The locations of the thermometers were carefully selected based on data collected by the MultiTeide project [91] ([PERSON] personal communication). Since October 2016, every three months, temperature measurements at 40-cm deep were carried out in the main thermal anomaly areas on the summit part of Teide volcano. Two of them are A and B areas (Figure 11). Area A is located at the southeast zone in the upper part of the Teide edifice, and area B is located inside the Teide crater itself.  
Three main criteria were taken into account to select the final configuration of the eight thermometers, equally distributed between the two areas: (i) points that show higher temperature variations over time, (ii) points with the highest and lowest temperature values, and (iii) monitoring zones that show less stability throughout the year. Considering this, the final thermometer distribution selected is displayed in Figure 12. Four thermometers were place in area A (T1-T4) forming a linear profile, and the other four (T5-T8) were deployed in area B, forming a square.  
Figure 11: Wireless network configuration installed in Las Canadas del Teide, Tenerife. Devices were deployed as follows: the gateway in the Paradox, the repeater at the southern rim of Teide crater, and the eight thermometers on the summit part of Teide volcano (areas A and B).  
It is necessary to point out that the network is scalable, being possible to expand or reduce the number of end devices even after deploying it only by modifying a single parameter in the gateway configuration.