esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
The increasing possibility of LoRa signal interference at GCU can be attributed to nearly all the measurement samples being above the threshold, \(-97\) dBm. During the measurement time, there was a 4.03 min periodic signal with intense power levels. Moreover, transmissions possessed nearly equal power levels, which indicates an origin from the same source. These transmissions violate the 1% DC, requiring a device to transmit only 36 s and wait for 3564 s. In the measurement locations, there were instances of DC violation. The measurement results have shown that signals with intense power levels are more likely to overshadow weak signals. This case was observed at GCU and Rottenrow Garden. As both locations are surrounded by education institutions (GCU, Glasgow College, and the University of Strathclyde), it is more likely that experimental platforms are left to operate out of the transmit regulations. The core downlink channels for LoRa were less occupied. The probability of signal interference in the downlink was insignificant except for Kelvingrove Park, 49.7% and Rottenrow Garden, 51%. Multiple transmissions occured in the same channel in these locations, largely at 969.5 MHz. and irregular and weak signals were observed in the downlink channels. It is essential to underscore that LoRa shares the core channels with other technologies. Transmissions occupying the 868.0-868.6 MHz core LoRa channels for downlink and 869.4-869.65 MHz for the uplink may be LoRAWAN, Sigfox, Wireless-M, and other IEEE 802.15.4 technologies. The end devices must comply with the DC and implement the transmission power control mechanisms to reduce signal interference in the channels. These practices will reduce interference and improve the reliability of LoRa channels as the deployment of IoT networks continue to grow.
## 2 Literature Review  
### Classic Location-Allocation Models  
Serving as a tool for people to analyze and optimize locations of facilities, location-allocation models have been present for more than five decades [22]. There are a set of such models that are termed as the classic location-allocation models [24; 26]. In addition to the p-median model, the classic models also include the maximal covering location model, the location set covering model, and the p-center model. The covering models deal with the coverage of demanders (usually represented by discrete and aggregate locations) within a certain radius of each facility [38]. The maximal covering location model aims to maximize the coverage on the basis of a certain number of facilities [27], whereas the location set covering model is designated to achieve full coverage using the least number of facilities. The p-center model is different from the above models, aiming to minimize the maximal distance from each demand nodes to its nearest facility. It is also known as the minimax problem [24]. To some extent, the p-center model considers equity issues by improving the situation of the remotest demanders.  
The classic models are tailored to approach various policy objectives and have engendered numerous applications [22; 24]. The classic models have also been extended and improved in other instances, e.g., the gravity p-median model that incorporates a gravity rule into the p-median model [39], making them applicable in more complicated real-world contexts. The classic models, however, are faced with several drawbacks [4]. First, they fail to explicitly address the equity issue. Most of the existing location-allocation models only address efficiency-oriented objectives. Second, the assumptions of the spatial interaction between demanders and facilities in these models are relatively simple. Most notably, few have employed realistic accessibility measurement when considering how demanders reach facilities.
# Numerical Simulation of Rock Mass Structure Effect on Tunnel Smooth Blasting Quality: A Case Study
## 3 Results and Discussions
### Layered Structure Rock Mass  
Figure 13 shows the influence curve of various factors of the layered rock mass. Among the six numerical test factors, the average spacing of structural planes had the most significant influence on the overbreak rate of smooth blasting, and the overbreak percentage decreased rapidly with the increase in the average spacing of structural planes. When the average structural planes spacing increased from 1.0 m to 2.0 m, the overbreak rate decreased from 8.11% to 3.65%. The influence of peripheral hole spacing was second only to the average spacing of structural planes. When the peripheral hole spacing was 40 cm, the overbreak rate was 6.89%, and when the peripheral hole spacing increased to 80 cm, the overbreak rate decreased to 4.90%. When the peripheral hole spacing increased by 10 cm, the overbreak rate decreased by 0.71%. The influence of the angle between structural plane trend and working face normal ranked third. With the increase in \(\gamma\), the overbreak rate also increased. When the angle was 60 deg, the overbreak rate was the largest.  
Figure 12: Overbreak or underbreak rate of smooth blasting under different levels of each factor in the massive structure rock mass.  
Through the main effect analysis, the maximum membership degree of the angle between structural plane trend and working face normal, the average dip angle of the structural plane, the average spacing of structural planes, the peripheral hole spacing, the heading hole spacing, and the vertical spacing between the peripheral hole and the heading hole were obtained as follows: \(\hat{F}_{\gamma}=0.209\),\(\hat{F}_{a}=0.213\), \(\hat{F}_{D}=0.284\), \(\hat{F}_{P}=0.244\), \(\hat{F}_{H}=0.205\), and \(\hat{F}_{S}=0.205\), respectively. Considering that \(\hat{F}_{D}>\hat{F}_{P}>\hat{F}_{a}>\hat{F}_{\gamma}>\hat{F}_{H}=\hat{F}_{S}\), the influence weight of these six factors was ordered as the average spacing of structural planes, peripheral hole spacing, average dip angle of the structural plane, the angle between structural plane trend and working face normal, heading hole spacing, and vertical spacing between the peripheral hole and the heading hole. The maximum membership degree of the average spacing of structural planes reached 0.284, which was much greater than that of other factors, indicating that the average spacing of structural planes was the most influential factor. The results showed that the smaller the structural plane spacing of the layered structure was, the greater the degree of rock mass fragmentation, and the more serious the overbreak of smooth blasting was.
## 3 Experimental Results  
Experiments were conducted using the ADOCR database [9], a public and freely available dataset, which contains both printed and synthetically generated Amharic text-line images. Following the network architecture and experimental setups described in Section 2, we implemented our model with the Keras Application Program Interface (API) with a TensorFlow backend, and the model was trained on a GeForce GTX 1070 GPU.  
To select suitable network parameters, different values of these parameters were considered and tuned during experimentation, and the results reported in this paper were obtained using an Adam optimizer employing a convolutional neural network with a feature map that started from 64 and increased to 512, the BLSTM network with two network hidden layers with sizes of 128 each, and a learning rate of 0.001.  
Based on the nature of the dataset, we conducted three experiments. Once we trained our network with the synthetic and some of the printed text-line images, the performance of the model was tested with three different test datasets. In the first and the second experiments, the model was evaluated with synthetic Amharic text-line images generated with the Power Geez and Visual Geez fonts, respectively. The third experiment was conducted using a printed test dataset written with the Power Geez font type.  
In the original dataset, the sizes of the images were 48 by 128 pixels. Considering similar works done in the area, to reduce computational costs during training, we resized the images into sizes of 32 by 128 pixels. For validation, we used 7% of the training dataset, randomly selected, as proposed in the original paper [9]. The network was trained for 10 epochs with a batch size of 200.  
During the testing of the proposed model, character error rates of 1.05% and 3.73% were recorded on the two test datasets which were generated synthetically using the Visual Geez and Power Geez fonts, respectively. The model was also tested with the third test dataset, which had printed text-line images that were written with the Power Geez fonts, and a character error rate of 1.59% was obtained. The results recorded during experimentation are summarized in Table 4.
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
## 3 Modeling of the PV Generation Scheduling Problem
### Formulation of PV Generation Scheduling Problem
The optimization problem to maximize the expected daily revenue of the PV site can be formulated as follows:  
\[\underset{a_{t}}{\text{maximize}}\sum_{s=1}^{S}p^{s}\sum_{t=1}^{T}output_{t }^{s}\times\left(MP_{t}+\alpha_{pv}\times REC+i^{s}_{t}\right) \tag{16}\]  
where \(p^{s}\) is the probability of occurrence for the PV scenario \(s\), \(MP_{t}\) is the market price at time \(t\), which remains constant regardless of the scenarios, \(\alpha_{pv}\) is the REC weight of the PV generator, \(REC\) is the unit price of REC, \(i^{s}_{t}\) is the incentive unit price at time \(t\) for the PV scenario \(s\), \(S\) is the number of PV scenarios that can be considered, and \(T\) represents 24 h.  
MP revenue can be calculated by multiplying the net PV generation, considering ESS operation and PV curtailment by the hourly MP. Moreover, expected MP revenue can be calculated by considering the probability of occurrence. The REC weight can be changed according to the type and regulation of PV generator installation for each energy source. The REC weight and REC unit price have the same constant value for all scenarios, which remains constant at all times. Therefore, these constants do not affect the determinants in the optimization problem-solving process.
## 3 Results and Discussion
### Stability Test Results  
Figure 9 shows the absolute differences in the prediction error resulting from the test to determine the stability of the proposed algorithm with respect to small changes in the selected peak wavelengths. The \(k>3\) experiments for the ideal case are shown to be very stable to fluctuations in peak wavelengths, with most of the error differences much less than 1%. Similar results can be seen for the convolved case, although there is noticeably more variation for the \(k=7\) and \(k=11\) cases, with differences in error reaching \(\sim\)3%. Furthermore, the mean error differences shift away from zero for the \(k=40\) and \(k=60\) cases, reflecting the increases in the prediction error from the Monte Carlo validation study results shown in Figure 6. Given the reduction in complexity and expense offered by the hypothetical device enabled by the proposed algorithm, this potential for modest additional error may be an attractive trade off.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ideal**} & \multicolumn{2}{c}{**Convolved**} \\ \hline
**k** & **At Rest** & **Occlusion** & **At Rest** & **Occlusion** \\ \hline
20 & 83.41 \(\pm\) 3.69\% & 20.90 \(\pm\) 4.20\% & 74.99 \(\pm\) 2.16\% & 26.69 \(\pm\) 3.01\% \\
40 & 88.59 \(\pm\) 3.03\% & 16.73 \(\pm\) 4.57\% & 74.80 \(\pm\) 2.08\% & 27.59 \(\pm\) 2.99\% \\
60 & 87.98 \(\pm\) 2.99\% & 16.17 \(\pm\) 4.40\% & 77.37 \(\pm\) 2.22\% & 26.36 \(\pm\) 3.17\% \\
81 & 90.45 \(\pm\) 2.83\% & 15.60 \(\pm\) 4.44\% & 79.35 \(\pm\) 2.30\% & 26.09 \(\pm\) 3.29\% \\ \hline \hline  & \multicolumn{2}{c}{**Investigator**} & \multicolumn{2}{c}{**Estimated SO\({}_{2}\)**} \\ \hline  & & [PERSON] et al. [45] & 92 \(\pm\) 2.6\% \\  & & [PERSON] et al. [46] & 93 \(\pm\) 1\% \\  & & [PERSON] et al. [47] & 68 \(\pm\) 6\% \\ At Rest & & [PERSON] et al. [48] & 63 \(\pm\) 11\% \\  & & [PERSON] et al. [25] & 91.2 \(\pm\) 5.4\% \\  & & [PERSON] et al. [49] & 73.9 \(\pm\) 5.8\% \\  & & [PERSON] et al. [50] & 87 \(\pm\) 3\% \\ \hline  & & [PERSON] et al. [47] & 48\% \\  & & [PERSON] et al. [51] & 35\% \\ Arterial Occlusion & [PERSON] et al. [52] & 20\% \\  & [PERSON] et al. [25] & 12.3 \(\pm\) 8.9\% \\  & & [PERSON] et al. [49] & 47.3 \(\pm\) 7.6\% \\  & & [PERSON] et al. [50] & 3 \(\pm\) 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 9: Results of the stability testing for (**a**) the ideal case and (**b**) the convolved case. Boxplots are based on the differences in prediction error between the values for the adjusted wavelength sets and those for the exact wavelengths selected by the simulated annealing algorithm.
## 1 Introduction
### Scope and Method of Research  
Road facilities represent essential infrastructure assets, and road construction projects constitute long-term public undertakings involving multiple entities. Throughout their lifecycle, these projects require the continuous acquisition of digitally transformed construction information. While road construction typically follows the sequence of facility planning, design, construction, and operation, the most significant amounts of digital data are generated and processed during the construction phase. Therefore, focusing on the construction phase and developing a construction information classification system would enable the effective collection, classification, and analysis of construction information. This facilitates its utilization throughout the entire construction project lifecycle. Consequently, our study aimed to develop an information classification system that targets key tasks in the construction phase of road construction, applying it to processing, safety, quality, and progressive billing management in highway construction projects.  
Figure 1 illustrates the research flow. To achieve the research objective, we conducted a literature review of construction information classification systems. Currently, the existing systems are being utilized to investigate ways to enhance the usability of road construction information. Based on the literature review, this study established a novel approach that differs from those employed in previous research.  
First, while most existing construction information classification systems are based on construction objects, classifying information based on activities is a more effective means of handling the information generated during the construction process. Second, construction information is organized by dividing it into the construction object information of the target facility and the activity information of the construction process. Third, a combination of hierarchical and facet classification [6] is applied during the information classification process to enhance intuitive understanding and ease of use, depending on the specific needs. Finally, rather than uniformly applying a fixed number of levels in the hierarchical classification, the number of levels is flexibly determined based on the requirements. Consequently, the construction information classification system ensures the user's flexibility to respond to changes in construction information owing to technological development or changes in conditions. According to the direction of development, definitions were established for objects, activities, and JOBs, representing unit work, through connections  
Figure 1: Research flow diagram.  
with objects and activities. Roles, interrelationships, and construction details were digitized by creating corresponding specifications. Using these three concepts, a framework for the construction information classification system was constructed, and a pilot application was implemented in highway construction, performing actual classification.
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved NLM Denoising Algorithm
#### 2.3.1 The Improved Euclidean Distance Function  
In the traditional NLM algorithm, the similarity of two pixels is calculated by using the Gaussian kernel function to calculate the Euclidean distance of the neighborhood matrix centered on the two pixels. The formula of the Gaussian kernel function is shown below:  
\[G(i,j)=\frac{1}{2\pi\sigma^{2}}exp^{-\frac{\sigma^{2}+j^{2}}{2\sigma^{2}}},(- r\leq i,j\leq r) \tag{19}\]where \(\sigma\) is the variance of the Gaussian kernel function. The Gaussian kernel function is used mainly because the distance between each pixel in the neighborhood and the center pixel point is different, and the impact on the center point is different. The closer the distance, the greater the impact, and the farther the distance, the smaller the impact [45]. However, there is a problem that the role of the central pixel does not pay enough attention to, so that the weight of the central point is too low when using Gaussian kernels to calculate. The ideal distance function should highlight the weight of the central pixel, and the closer to the central pixel, the greater the weight distribution, and the farther from the central pixel, the more obvious the weight drops. In that way, the similarity of the two pixels calculated will be more accurate.  
The Euclidean distance function designed by the algorithm is based on the Gaussian kernel function and accumulates Gaussian kernels with different radii. The radius ranges from 1 to \(r\); the time of accumulation is also \(r\), and the size of the Gaussian kernel matrix obtained is \((2r+1)\times(2r+1)\). The calculation formula is as follows:  
\[G(i,j)=\sum_{i=1}^{r}\frac{1}{2\pi\sigma^{2}}exp\left(-\frac{i^{2}+j^{2}}{2 \sigma^{2}}\right),(-t\leq i,j\leq t) \tag{20}\]  
In order to unify the size of each Gaussian kernel matrix, the elements around the matrix with a radius less than \(r\) are filled with 0. Each Gaussian kernel calculated needs to be normalized, and the final normalization is performed after accumulation.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 3 Preliminary
### Certificateless Signature Scheme  
Certificateless signature scheme (CertS) [47] consists of the following procedures.  
* CertS.Setup() computes a master key along with a public system parameters as follows:
* Let \(G\) be an additive group with a prime order \(q\) and \(P\in G\) be a generator based on an elliptic curve.
Choose \(s\in Z_{q}^{*}\) as master secret key and generates the master public key \(P_{pub}=s\cdot P\). * Let \(H_{1}:\{0,1\}^{*}\times G\to Z_{q}^{*}\) and \(H_{2}:\{0,1\}^{*}\times G\to Z_{q}^{*}\) be two cryptographic hash functions. * Output the public parameters \(\mathit{CertS.params}=\{G,q,P,P_{pub},H_{1},H_{2}\}\). * Output \(\langle s,\mathit{CertS.params}\rangle\)
* CertS.Secret(\(id\)) returns a secret value for every identity \(id\) as follows:
* Choose \(x_{id}\in Z_{q}^{*}\) as a secret value, then compute \(P_{id}=x_{id}\cdot P\).
* Return \(\mathit{Sec}_{1}=\langle x_{id},P_{id}\rangle\)
* CertS.PartialK(\(s\), \(id\), \(P_{id}\)) computes a partial private/public key for the given \(id\) as follows:
* Select a random \(r_{id}\in Z_{q}^{*}\) and generate \(R_{id}=r_{id}\cdot P\).
* Compute \(s_{id}=r_{id}+s\cdot H_{1}(id,R_{id},P_{id})\) (mod \(q\)).
* Output \(\mathit{Sec}_{2}=\langle s_{id},R_{id}\rangle\) as the corresponding partial private key.
* CertS.SKey(\(\mathit{Sec}_{1}\), \(\mathit{Sec}_{2}\)) sets \(sk_{id}=\langle x_{id},s_{id}\rangle\) and \(pk_{id}=\langle P_{id},R_{id}\rangle\) representing the private key and public key for \(id\), respectively.
* CertS.Sign(\(m\), \(sk_{id}\)) computes the signature for a given message \(m\) as follows:
* Select a random \(l\in Z_{q}^{*}\) such that gcd(\(l+h\), \(q\)) = 1, where \(h=H_{2}(m,R,P_{id},R_{id})\) and \(R=l\cdot P\).
* Generates \(r=(l+h)^{-1}(x_{id}+s_{id})\) (mod \(q\)).
* Output the signature \(\sigma=\langle r,R\rangle\).
* CertS.Verify(\(m\), \(id\), \(pk_{id}\), \(\sigma\)) provides the signature verification \(\sigma\) for the message \(m\) for the identity \(id\) as follows:
* Generate \(h_{1}=H_{1}(id,R_{id},P_{id})\) and \(h_{2}=H_{2}(m,R,id,P_{id},R_{id})\).
* Check whether \(r\cdot(R+h_{2}\cdot P)\stackrel{{?}}{{=}}P_{id}+R_{id}+(h_{1} \cdot P_{pub})\).
## 2 Theoretical Background
### Constitutive Equations  
The FSDT [29] is considered to establish the constitutive equations of the system. The constitutive equations for the plane strain case can be expressed in terms of the extensional \(a\), bending-extension coupling \(b\), shear \(l\), and bending \(d\) compliance and the axial strain \(\mathrm{e}_{1}\) and curvature \(\mathrm{e}_{2}\) induced by the residual hygthermal effects as:  
\[\begin{bmatrix}\varepsilon\\ \kappa\end{bmatrix}=\begin{bmatrix}a&b\\ b&d\end{bmatrix}\begin{bmatrix}\mathcal{N}\\ \mathcal{M}\end{bmatrix}+\begin{bmatrix}\mathrm{e}_{1}\\ \mathrm{e}_{2}\end{bmatrix},\ \gamma=l\mathcal{Q} \tag{4}\]  
The superscript of \(\mathcal{N}\), \(\mathcal{Q}\), and \(\mathcal{M}\) is here omitted as they can refer to the cracked and the uncracked parts. The above formulas have been derived considering the kinematic assumptions of the FSDT. These kinematic assumptions allow one to express the axial strain (\(\varepsilon\)), the shear strain (\(\gamma\)), and the curvature (\(\kappa\)) as:  
\[\varepsilon=\frac{du}{dx},\ \ \gamma=\frac{dw}{dx}+\phi,\ \ \kappa=\frac{d\phi}{dx} \tag{5}\]  
where \(u\), \(w\), and \(\phi\) represent the axial displacement, the transverse displacement, and the cross-sectional rotation of the middle plane of both sub-beams. Note that only thermal effects are taken into account in this study and the effects of moisture conditions on the beams are omitted. However, one can simply superpose their effects in a similar manner.
## 4 Coordinated Control Example and Simulation
### Coordinated Control Example
#### 4.1.2 Data Distribution of the Examples  
The number of vehicles in the downstream intersection arterial direction was calculated based on the number of vehicles and the flow direction ratio generated by each entrance of the upstream intersection, with a certain degree of randomness. Therefore, it was necessary to further analyze the distribution of the number of car vehicles, the number of bus vehicles, and the ratio of the two types of vehicles in the 178 sets of examples.  
(1) Number of cars  
For the distribution of the number of cars arriving at the downstream intersection arterial direction in the two travel directions, the number of vehicles was between 550 and 950, and the analysis was carried out at an interval of 50 vehicles. The overall distribution of the number of cars was relatively uniform. Among them, the number of vehicles was 550-600, and 900-950 were relatively low, accounting for 6.75% and 8.58%, respectively; the proportions of other quantity ranges were between 13% and 15%.  
(2) Number of buses  
For the distribution of the number of buses arriving at the downstream intersection arterial direction in both directions of travel, the number of vehicles was between 90 and 120, and the analysis was carried out at an interval of 5 vehicles. The overall distribution of the number of buses was relatively uniform, with the proportion of each number interval ranging from 15% to 19%.  
(3) The ratio of the number of buses to the number of cars  
The proportional distribution of the number of buses and cars in the downstream intersection arterial direction. The proportion was between 10% and 20%, which was analyzed at an interval of 2%. The overall distribution was uneven, with more cases of 12-14%.