esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Strength of the Proposed Method  
The test results show that the proposed method has excellent filtering accuracy for forest land, relatively uniform types I and II errors, and a small total filtering error. The  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{5}{c}{**Type I Error**} & \multicolumn{5}{c}{**Type II Error**} & \multicolumn{5}{c}{**Total Error**} \\ \cline{2-13}  & **CSF** & **MF** & **SF** & **Ours** & **CSF** & **MF** & **SF** & **Ours** & **CSF** & **MF** & **SF** & **Ours** \\ \hline Test 1 & 4.15 & 4.30 & 4.36 & 3.61 & 0.93 & 4.57 & 0.58 & 3.27 & 2.80 & 4.45 & 2.79 & 3.46 \\ Test 2 & 23.24 & 3.4 & 8.36 & 18.04 & 5.56 & 56.53 & 33.95 & 7.42 & 13.13 & 22.54 & 33.02 & 11.50 \\ Test 3 & 25.60 & 0.32 & 3.92 & 12.87 & 5.92 & 84.95 & 15.10 & 9.65 & 8.76 & 13.67 & 15.01 & 9.91 \\ Total & & & & & & & & & 7.74 & 12.70 & 15.53 & 7.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error comparison of the filtering method results (unit. %).  
results of experiments on forest point cloud data with different terrain features show that the proposed filtering method performs well in any terrain, especially in steep terrain, multiple ravines, and complex scenes. The filter also shows strong terrain applicability in forest areas. The proposed filtering method was also applied to urban areas for testing, but experiment results showed the method is not effective for urban areas, and will misjudge the roof as a ground point, indicating that the proposed method is more suitable for scenes with an uneven terrain surface.  
Results of the filtering comparison experiment show that the morphological filtering method must set several parameters, such as window size, altitude threshold, and elevation scale coefficient. The slope-based filtering method must set grid size and slope threshold, and the CSF must set terrain mode, distance threshold between the point cloud and cloth simulation point, and cloth rigidity. Meanwhile, the proposed method requires less manual participation and only needs to set a universal height threshold when calculating the height difference at the early stage. Moreover, the subsequent experimental steps do not require any parameter setting, thereby greatly improving the convenience of the method.
## 2 Calculation Model and Solution Method
### Establishment and Solution of the Difference Equation
#### 2.2.2 Boundary Conditions  
As shown in Figure 2, in order to facilitate programming and avoid zeros and negative signs, 1, 2, \(n+4\), and \(n+5\) virtual units are set at both ends of the pile, so that the pile top number is 3 and the pile bottom number is \(n+3\).  
(1) Pile top boundary conditions  
At the pile top, there is a horizontal force \(H_{0}\), bending moment \(M_{0}\), and axial force \(N_{0}=(N)_{3}\).  
From \(\left(M\right)_{3}=EI\left(\frac{d^{2}y}{dz^{2}}\right)_{3}=M_{0}\), there is  
\[y_{4}-2y_{3}+y_{2}=\frac{\lambda^{2}}{EI}M_{0} \tag{5}\]  
and from \(\left(H\right)_{3}=EI\left(\frac{d^{2}y}{dz^{3}}+N\frac{dy}{dz}\right)_{3}=H_{0}\), there is  
\[EIy_{5}+\left(-2 EI+N_{3}\lambda^{2}\right)y_{4}+\left(-2 EI+N_{3}\lambda^{2} \right)y_{2}-EIy_{1}=2\lambda^{3}H_{0} \tag{6}\]  
(2) Pile bottom boundary conditions  
Free bearing at the pile bottom:  
When the lower end of the pile is supported in the general soil layer and the conversion depth is \(\alpha l\geq 2.5\), or when the pile end is supported on the rock surface and \(\alpha l\geq 3.5\), the bending moment and shear force are both zero.  
Figure 2: Schematic diagram of single pile stress calculation in layered soil.  
\[\text{From }(M)_{n+3}=EI\Big{(}\frac{d^{2}y}{dz^{2}}\Big{)}_{n+3}=0,\] \[y_{n+4}-2y_{n+3}+y_{n+2}=0 \tag{7}\]  
is obtained, and from \(\left(H\right)_{n+3}=EI\bigg{[}\Big{(}\frac{dM}{dz}\Big{)}_{n+3}+\Big{(}(N+ \xi z)\frac{dy}{dz}\Big{)}_{n+3}\bigg{]}=0\),  
\[Ely_{n+5}+\Big{(}-2 EI+N_{3}\lambda^{2}+\xi z\lambda^{2}\Big{)}y_{n+4}+\Big{(}- 2 EI+N_{3}\lambda^{2}+\xi z\lambda^{2}\Big{)}y_{n+2}-Ely_{n+1}=0 \tag{8}\]  
is likewise obtained.  
Fixed support at the pile bottom:  
For this case the pile bottom displacement and rotation angle are both zero.  
\[(\varphi)_{n+3}=\bigg{(}\frac{dy}{dz}\bigg{)}_{n+3}=0\]  
so that  
\[y_{n+4}-y_{n+2}=0 \tag{9}\]  
\[y_{n+3}=0 \tag{10}\]
## 5 Implementation and Results
### Implementation Parameters  
LightGBM can perform classification, regression, and ranking by invoking the necessary learning-to-rank (LTR) functions. It is a Microsoft-powered training speed algorithm.  
Low computing consumption, higher reliability, and data scaling are other benefits. We used LightGBM Ranker for our paper's questions. We ranked using Lambda Rank, which is useful for optimizing ranking functions such as nDCG, which is included in our script. The Normalized Discounted Cumulative Gain ranks samples based on their gain [126]. A relevant sample placed at the top of the predicted data has a higher gain than one placed at the bottom of the predicted data, which has a lower gain. LightGBM Ranker can rank pointwise, pairwise, or listwise [123]. The pointwise method examines one item at a time and trains a classifier or regressor to predict relevance. Item scores determine ultimate ranking [117]. Pairwise ranking classifies items into two categories (correctly ranked and incorrectly ranked) based on their optimal ordering [119]. The listwise technique evaluates the whole list of items to find their optimal order using nDCG or by minimizing a loss function [119]. In our case study, we used listwise ranking to find the most popular artworks based on two scenarios. We observed how LightGBM Ranker performed with our data in later experiments.
## 3 Simulated Results and Analysis
### Effects of the Lengths and Widths of the Rectangular Slot  
Typically, the effects of the rectangular slot lengths '\(l_{s}\)' varied whereas other parameter values were kept constant. The following parameter values were constrained throughout the experiment: the positions of the rectangular cavity '\(d_{y}\)', the square patch length '\(l_{p}\)', the rectangular slot width '\(w_{s}\)', the cavity length '\(c_{x}\)', the cavity width '\(c_{y}\)', the cavity height '\(c_{z}\)', the exciting probe length '\(l\)', and the gap between parasitic patches '\(a\)' and '\(b\)'. Figure 5 showed six cases of the measured variation of the rectangular slot lengths \(l_{s}=8.0\) to 9.0 mm versus the frequency (GHz). It could be seen from Figure 5 that the minimal return loss appeared at \(l_{s}=8.8\) mm as expected in Table 1. However, when the rectangular slot lengths '\(l_{s}\)' deviated from 8.8 mm, the return loss value was likely to increase.  
Similarly, the rectangular slot length and the effects of the rectangular slot width varied according to the rectangular slot width '\(w_{s}\)' whereas other parameter values were kept constant. The following parameter values were constrained throughout the experiment: the position of the rectangular cavity '\(d_{y}\)', the square patch length '\(l_{p}\)', the rectangular slot length '\(l_{s}\)', the cavity length '\(c_{x}\)', the cavity width '\(c_{y}\)', the cavity height '\(c_{z}\)', the exciting probe length '\(l\)', and the gap between parasitic patches '\(a\)' and '\(b\)'. Figure 6 showed six cases of the measured variation of the rectangular slot of widths '\(w_{s}\)', ranging from 0.3 to 1.3 mm versus the frequency (GHz). It could be seen from Figure 6 that the minimal return loss appeared at \(w_{s}=0.5\) mm, as expected in Table 1. However, when the rectangular slot of width '\(w_{s}\)' deviated from 0.5 mm, the return loss value was likely to increase.  
Figure 4: Simulated return loss for different values of ‘\(l_{p}\)’.  
Figure 5: Simulated return loss for different values of ‘\(l_{s}\)’.
## 5 Experiments
### Data Distribution Analysis  
To analyze user mobility patterns and the spatial density of location predictions, we visualize the visit frequency and location distribution across two real-world datasets: NYC and TKY. Figure 4 presents the heatmap of visit frequency, where each pixel corresponds to a geographical location \((x,y)\in\mathbb{R}^{2}\), and the color intensity reflects the total number of user visits to the corresponding location prediction.  
The NYC dataset contains \(|L|=3835\) locations and the TKY dataset contains \(|L|=7038\) locations. The redder the region on the heatmap, the higher the accumulated visit frequency. From Figure 4a, it is evident that user activities in NYC are highly concentrated in the Manhattan area, particularly around midtown and the Upper West Side. In contrast, Figure 4b shows that visit frequencies in Tokyo are densely distributed in the _Tokyo Metropolis_, which serves as the central area of user interaction.  
To further examine the spatial density of location predictions, we show the number distribution of locations, where each circular marker represents a spatial cluster of locations, annotated with the count of locations within that region. In particular, Figure 5a shows that the _Upper West Side_ of Manhattan (highlighted with a blue bounding box) contains approximately 286 locations, representing a high local density. Similarly, Figure 5b highlights an area in Tokyo Metropolis that includes 655 locations. These distributions reflect significant spatial clustering and long-tail distributions, where a small number of urban areas contain the majority of location predictions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**City** & **\#Users** & **\#Locations** & **\#Check-Ins** & **\#Sessions** & **Sparsity** \\ \hline NYC & 834 & 3835 & 44,686 & 8841 & 98.61\% \\ TKY & 2173 & 7038 & 308,566 & 41,307 & 97.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic statistic of two heterogeneous datasets (# denotes the number of).  
Figure 4: Heatmap of visit frequency. (**a**) NYC dataset. (**b**) TKY dataset.
## 3 Results and Discussion
### Capillary Porosity  
Figure 7 shows the capillary porosity gotten from the micro-CT and HYMOSTRUC3D models, in which the error bar represents standard deviation. As Figure 7 indicates, the capillary porosity of the cement stone increased with the increase in the _w/c_ ratio and decreased with the increase in hydration time. With the increase of the _w/c_ ratio from 0.40 to 0.44, the capillary porosity of cement stone at 1, 3, 7, and 28 days increased by 6.85%, 11.39%, 6.64%, and 23.07%, respectively. With the increase of the _w/c_ ratio from 0.44 to 0.50, the capillary porosity of the cement stone at 1, 3, 7, and 28 days increased by 3.08%, 6.94%, 10.59%, and 12.81%, respectively.  
When the _w/c_ ratio was 0.40, the daily reduction rates of capillary porosity of the cement slurry from 1 to 3 days, 1 to 7 days, and 1 to 28 days were 13.62%, 6.02%, and 1.91%, respectively. The daily reduction rate of capillary porosity markedly decreased with the hydration time, while the decreasing trend reduced dramatically. In addition, the porosity of cement stone increased with the increase in the _w/c_ ratio, which is contrary to the changing trend of compressive strength.  
Figure 6: Measured and simulated compressive strength of cement stone.  
The relative deviations between the simulated results and measured results were 13.95-20.43%, 13.04-21.73%, and 17.56-25.31%, while the water-cement ratios were 0.40, 0.44, and 0.50, respectively.  
It can be seen from Figures 6 and 7 that the capillary porosity increased with the increase in the _w/c_ ratio at the same hydration time, while the mechanical strength decreased as the _w/c_ ratio increased. In the well cementing industry, a lower _w/c_ ratio should be selected as far as possible to prepare cement slurry and thus improve the early mechanical properties of the cement sheath.  
Comparing the porosity results gotten from the micro-CT and HYMOSTRUC3D models, the micro-CT-measured results were smaller than those of the HYMOSTRUC3D model, while the overall trend of the porosity measured by the micro-CT model is consistent with the porosity simulated by the HYMOSTRUC3D model. The analysis shows that the resolution of the HYMOSTRUC3D model is 1 \(\upmu\)m, while the resolution of the micro-CT model is 3.38 \(\upmu\)m. Therefore, the measured porosity is lower than the simulated porosity, which can be attributed to CT not recognizing the pores between 1 and 3.38 \(\upmu\)m. The limited resolution of the micro-CT model is the main reason for the smaller porosity values compared with the HYMOSTRUC3D simulated results, micro-CT is inadequate for testing nanoscale and sub-micron pores.  
However, as [PERSON] et al. [49] concluded, it is very important to obtain pore structures with large volumes in the cement stone. [PERSON] et al. [59] also demonstrated that the big capillary pores can be correlated to the strength properties of the cement paste. Hence, the following discussion in Section 3.4 is based on the measured results, in which the influence of capillary porosity (pores larger than 3.38 \(\upmu\)m in diameter) on the compressive strength of cement stone is discussed.
## 2 Related Work  
The classification of network traffic is a never-ending process as technology keeps evolving, and as such, the classification approaches and techniques need to be changed [7]. Additionally, routing protocols can overcome classical challenges such as intermittent connectivity or broadcasting storms if traffic conditions can be known in advance, which assists in the development of adaptive protocols [8]. Primarily, in order to classify traffic, a deep understanding of traffic characteristics is necessary. In this context, several fundamental classification methods, categorized as parametric and non-parametric, were used. Currently, researchers have started exploring machine learning algorithms, which reveal more promising outcomes when compared to other parametric classifiers, such as C4.5 decision trees, Bayesian networks, or neural networks [9]. Other methods, such as port-based, IP-based, payload-based, behavior-based, and flow-based algorithms, have produced promising results in various traffic situations [9]. Other studies deployed k-means algorithms from machine learning, a semi-supervised learning approach to clustering [10]. The attempt of a semi-supervised approach to clustering demonstrated better accuracy, significantly improving the categorization of real-time traffic.  
Non-parametric approaches were proposed to compute correlation in the traffic dataset and predict the corresponding classes. These approaches were quoted to improve the classification performance, as they incorporated the correlation information. Research concluded that a similar approach to incorporating the correlation information can be deployed to mine and predict traffic in HETVNETs [11]. This approach to correlation information outperformed the neural network method of traffic classification [5]. Researchers have claimed that most parametric linear machine learning models are less accurate than non-parametric models [5; 12; 13].  
The time complexity concept quantifies the computation time required to run a specific algorithm. Determining the time complexity of a classification or prediction model assists in judging whether this model can be applied online to vehicular ad hoc networks (VANETs) or not. Several studies have referred to the time complexity concept as computational demand, efficiency, or time demand, and have specified that traffic systems require fast forecasting algorithms that process data quickly enough to enable real-time decisions [5; 14]. To reduce computational cost, studies have used data from limited correlated roads, instead of examining all roads. Furthermore, studies have tried to adopt efficient forecasting methods. However, most widely-used models, such as autoregression and neural network-based models, require substantial computation time [15; 16]. In summary, our proposed model is a non-parametric model that has the ability to capture traffic nonlinearity and to address different traffic conditions. We should also focus on models that have a short computation time to allow real-time applications of our proposed model.
## 3 Experimental Results
### Optimal Push-Protocol Selection  
In this study, the hybrid protocol includes one of the selective distribution content protocols that delivers video content to end-users from the original server to the edge server. In a selective hybrid protocol, the sender first establishes a connection with the edge server using a connection-oriented protocol, such as the File Catalyst, Signiant, and FASP. Once the video content arrives to the edge server, the receiver can establish connection with one of the closest edge servers through HTTP. This allows an efficient transfer of data while maintaining the reliability of a connection-oriented protocol.  
An experiment was conducted for one minute of video [34; 35]. This video is segmented according to MP4 Box [36] for a segment length equal to 2 s; the details of the parameters of the experimental process are explained in Table 2. The arrival time is calculated based on a timer from sending the video segment data until received by the edge server. In Figure 5, the arrival time versus packet loss is depicted for the File Catalyst, Signiant, and FASP protocols. The impact of packet loss is considerably observed in the File Catalyst and Signiant protocols due to many retransmissions of the video packets.  
The same test has been conducted for different segment sizes; in this experiment, each segment size has chunked into 10 MB. Figure 6 shows that the FASP recorded better results  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Video size & 270 MB \\ Streaming time & 60 s \\ Total number of clips & 2 \\ Channel used & Wi-Fi and Wire Connection \\ Channel bandwidth & Shaped to varied Mbps \\ Packet size & 1500 bytes (MTU) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental process parameters.  
Figure 5: Arrival time vs. packet loss rate (segment size = 5 MB).  
than the File Catalyst and Signiant. Moreover, the File Catalyst resulted in less delay in transmitting large segments than the Signiant.  
Another test has experimented with 30 video segments with a duration of 2 s per segment that have been sent to monitor the maximum throughput usage of the used protocols (File Catalyst, Signiant, and FASP). A significant variation can be observed in the throughput of the protocols when transmission time is affected by packet loss of 5%. For the File Catalyst protocol, a remarkable delay of the arrived segments has been observed as there were many packets lost. This leads to longer transmission duration as shown in Figure 7.  
Figure 6: Arrival time vs. packet loss rate (segment size = 10 MB).  
Figure 7: Throughput vs. video segments of 2 s.  
While retransmission can help mitigate the effects of packet loss, it can also introduce additional delays and network traffic, as packets are re-sent multiple times. Consequently, it is important to make balance between reliability and performance when implementing retransmission in a network. Figure 8 demonstrates the retransmission number of packets when segments are sent over the network to edge server.
# Influence of the Composition of the Activator on Mechanical Characteristics of a Geopolymer
## 3 Test Results
### Compressive Strength  
Figure 6 shows the statistical compressive strength, \(\sigma\)c, for the 9 different activator compositions. As can be seen, for Type A mixes the highest compressive strength is that of the Base Mix (1:2). As for the other mechanical characteristics, the excess of liquid might be the reason for the reduction of the compressive strength for 1:1.5 and 1:2.5. This was an important reason that leads to the change of composition of the following mixes, Type B mixes, which were obtained by maintaining constant the activator weight.  
For Type B mixes, the highest strength corresponds to 1:1.75 (42.8 MPa) and the lowest to 1:4 (20.5 MPa). Because the composition had small amounts of sodium hydroxide, and therefore low ability to dissolve the constituent materials, the compressive strength would be low. The standard deviation for Type B mixes was always less than 3.1 MPa, which can be considered a good value.  
For OPC mortars the compressive strength measured for 28 days of curing was 21.7 MPa and for 60 days was 23.5 MPa, which corresponds to an 8% increase in strength, as would be expected. From the results, OPC mortar presents lower compression strength when compared to the geopolymers. For the OPC mortars, the maximum standard deviation was 2.6 MPa.  
Figure 5: Stress–strain diagrams for tensile bending tests.  
In order to validate the methodology described in EN 196-1 [33] for the calculation of the tensile bending strength and of the compression strength through \(4\times 4\times 16\) cm OPC mortar specimens, some compressive tests were also performed on 15 cm-wide cube specimens. Such cubes were loaded up to failure, following EN 12390-3 [35]. An average value of 24.1 MPa was obtained for the strength of 28 d old cubes (this value was 11.1% higher than that of small specimens), and therefore was not very different.
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 2 Motivation and Problem Statement
### Energy Waste  
Energy is a rare resource; therefore, it requires an efficient way to avoid wasting energy in low power biosensors. It includes two kinds of energy wastes. The first is that it does not receive the wireless energy efficiently, and the second is that it does not fully utilize the received energy. In each charging period, sensors need to receive as much energy as possible. If the battery is almost full, then the energy may overflow in the next period of wireless charging. The energy overflowing is illustrated in Figure 2. Let the energy that can be received in each period be \(C\), and the battery can store at most \(C\) units of energy. Let the energy left in the battery be \(e\); then, \(e\) units of energy will overflow in  
Figure 1: Periodical wireless charging process.  
the next period as shown in the right part in Figure 2. Energy overflowing is one type of energy waste in this paper.  
Besides the energy overflowing, unused energy is another type of energy waste. Figure 3 demonstrates an example of the energy waste. Given \(n\) packets to be transmitted, packet \(i\) is of size \(s_{i}\), which denotes that it will cost \(s_{i}\) units of energy to transmit this packet. Different transmission orders of the \(n\) packets may result in different energy waste. Let the sizes of the packets to be transmitted be \(\{5,4,4,3,2,2\}\), the battery capacity be 10, and the energy to be received in each period be 10. Assume that the sensor can only receive two periods of wireless energy. If the packets are transmitted in the order of 5, 3, 2, 4, 4, 2, then no energy overflows in the first two periods as shown in Figure 2(a). If the packets are transmitted in the order of 5, 4, 4, 3, 2, 2, then 1 unit of energy overflows and one unit of energy is unused, as shown in Figure 2(b). Except energy overflowing, unused energy should also be avoided. In Figure 2(c), if the packets are transmitted in the order of 2, 2, 3, 4, 4, 5, then three units of energy overflowing and two units of unused energy occurs if the sensor will not receive wireless energy anymore after two charging periods. Different orders of transmission may result in different energy waste.