esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
We proposed a DT framework and built a system to predict cancer progression in patients with PCa. The prediction was constructed using eight predictive models with data from 404 patients. The DT-based prediction model can predict the pathological status as well as BCR. Previous predictive studies using ML related to PCa have limitations. This is because their models were designed only for a specific point in time. However, this study is superior to previous studies because it provides predictions linked to pathology and BCR. The DT-based model achieved good performance (pathology: 98.50%; BCR: 96.25%). In [PERSON] et al. (2016), the prediction of the pathology stage using a neuro-fuzzy-based model reported 81.2% as the area under the curve ([PERSON] et al., 2019). [PERSON] et al. (2019) provided predictions of BCR after a robot-assisted prostatectomy and the accuracy of K-NN, RF, and  
Figure 8: Comparison of the proposed and traditional methods (**left**: learning algorithm; **right**: learning model).  
Figure 9: Results of the predictions of pathological T stage and BCR (**left**: pathological T stage; **right**: biochemical recurrence).  
LR was reported as 0.976, 0.953, and 0.976, respectively [37]. [PERSON] et al. (2016) provided an imaging-based approach using SVM that was superior at predicting BCR (sensitivity: 93.3%; specificity: 91.7%; accuracy: 92.2%) [38]. The performance of our DT-based prediction model was significantly better than the performances reported in previous studies.  
Our study suggested a method that can generate two predictions for pathology T and BCR sequentially using the patient's biopsy information, and the results are excellent. Thus, physicians can prepare the treatment in advance according to the prognosis. In addition, it has the advantage of providing a treatment prognostic result to the patient. Existing studies predict pathology or BCR alone, thus they have limitations as they can only be applied to a specific moment. Each machine learning model configured in the DT-based prediction model provides a future prediction using previous data, and because it operates in a forward chain, clinical decisions can be made regarding the treatment process of PCa.  
Therefore, we conclude that the proposed method will help provide a CDSS related to PCa. Further, the proposed DT framework can be expected to integrate DT with the medical field to improve medical processes, promote health, and reduce medical costs and problems.
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 5 Simulation Analyses
### Comparisons with the Benchmark Methods  
In this section, the performance of the proposed method was compared with three benchmark methods proposed by the authors in [4; 28; 29]. In order to assign the ISLs in GNSS, the authors in [28] used the discrete differential evolution (DDE); the authors in [29] used the genetic algorithm (GA); and the authors in [4] used RWM and RWMWE, as mentioned in Section 1. As these benchmark methods concentrate on GNSS, not all of the indicators used were fit for our study. There were two available indicators. One was the average number of ISLs for all satellites among all slots in the state period; the other was the average path temporal length (APTL). The satellites that are invisible to the ground station were defined as overseas satellites, and the others were called domestic satellites. APTL is the average value for all overseas satellites in all state periods, which can be obtained by:  
\[APTL=\sum_{i=1}^{S_{o}}\sum_{k=1}^{K}PTL_{ik} \tag{18}\]  
where \(PTL_{ik}\) is equivalent to the length temporal of the shortest dynamic path from an overseas satellite \(i\) at slot \(k\) to any domestic satellite at subsequent slots [4]; \(S_{o}\) is the number of overseas satellites; and \(k\) is the number of slots.  
As the satellite constellation introduced in Section 5.1 was identical to the simulated satellite constellations in [4; 28; 29], we just needed to execute a simulation with the same parameters. The main parameters of the simulation setup are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(k\)-Value} & **Number** & **Average Spatial Length** & **Average Temporal Length** & **Average** \\  & **of ISLs** & **of Dynamic Paths** & **of Dynamic Paths** & **DCD** \\ \hline
1 & 409 & 2.758 & 4.556 & _7.7_ \\
2 & 411 & 2.731 & 4.494 & 7.475 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dynamic connectivity properties under different values of \(\kappa\).  
Figure 10: General degree of all satellites under different \(\kappa\)-values.  
Table 4 illustrates the comparison results of the proposed method and three benchmark methods for 10,080 state periods. The average number of ISLs were calculated for all satellites and 10,080 state periods. The proposed method obtained the minimum average number of ISLs, which was 19.91. The APTL of the proposed method was 1.33, which was the second smallest APTL.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 4 Experiments
### Data Preprocessing  
Firstly, we use trilinear interpolation to normalize the CT scans, which can ensure that isotropic resolution in all \(x\), \(y\), \(z\) dimensions. Next, we resize the resolution of images to \(32\times 32\) due to the largest diameter of nodules in the LIDC-IDRI dataset is 30 mm. Then, we normalized the nodules as ProCAN [15]. Finally, we augmented the nodules by rotatingeach nodule around the three (\(x\), \(y\), and \(z\)) axes. Specifically, we rotated each nodule in seven directions (\(0^{\circ},45^{\circ},90^{\circ},135^{\circ},180^{\circ},225^{\circ},270^{\circ}\)) along the each axis.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
###### Abstract  
Accurate forecasting of power plant loads is critical for maintaining a stable power supply, minimizing grid fluctuations, and enhancing power market trading mechanisms. However, the data on power plant generation load (hereinafter abbreviated as load) are non-stationary. The focus of existing load forecasting methods has been on continuously improving the ability to capture the dependent coupling between outputs and inputs, while research on external factors, which are the causes of non-stationary load data, has been neglected. The identification of the causal relationship between external variables and load is a significant factor in accurately predicting load. In the present study, the causal effects of various external variables on load were identified and then quantitatively calculated using various methods. Based on the improved Informer model, a long-time series forecasting model, a hybrid forecasting method was proposed called causal inference-improved Informer (hereinafter abbreviated as Causal-Informer). In the present study, the mutual information method was used to remove insignificant external variables. Subsequently, external factors such as CDP, holidays, ambient temperature, wind speed, power plant maintenance status, and rainfall were selected as input features of the proposed forecasting model. Finally, the proposed Causal-Informer method was evaluated using the historical load of a power plant in East China. Compared with four popular forecasting models, measurements on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for the proposed method were reduced by 89.8 million kwh-672.3 million kwh, 56.8 million kwh-637.9 million kwh, and 5.1-25.4%. The proposed method achieved the most accurate and stable results. The MAPE reached 10.4% and 24.8% in 30 time steps ahead and 90 time steps ahead of forecasts, respectively.  
multi-step load forecasting; causal inference; Informer  
2023  
1
## 2 Bayesian Functional Mapping
### Bayes Factor  
The estimate of the number of QTLs is a very important but difficult issue for QTL mapping. In ML, the QTL number can be estimated on the basis of an assumed number by changing the dimensionality of the mode. The best QTL number is chosen using some model-selection criterion such as [PERSON]'s AIC or by hypothesis testing. However, the theory underlying AIC could not apply in the mixture mode context because of the absence of a single dominating local maximum in the likelihood. The unsuitability of the theory used to construct the null distribution of the likelihood ratio statistic for mixture model means that hypothesis testing is not straightforward in this context. Moreover, because the number of QTL (\(\kappa\)) cannot be estimated as a parameter, it is not possible to estimate the sampling error and confidence interval of the estimate for QTL number. But in the Bayesian paradigm, inference is based on the posterior distribution of the parameters. Inference for \(\kappa\) is then based on the marginal posterior distribution, \(Pr(\kappa=l|\mathbf{y}),l=1,2,...,.\)  
Models \(\kappa=l_{1}\) and \(\kappa=l_{2}\) may be compared via the ratio of their posterior probabilities:  
\[\frac{P(\kappa=l_{1}|\mathbf{y})}{P(\kappa=l_{2}|\mathbf{y})}=\frac{P(\mathbf{ y}|\kappa=l_{1})Pr(\kappa=l_{1})}{P(\mathbf{y}|\kappa=l_{2})Pr(\kappa=l_{2})}=B_{ l_{1}l_{2}}\frac{Pr(\kappa=l_{1})}{P(\kappa=l_{2})}, \tag{28}\]  
where the ratio of marginal probabilities of \(\mathbf{y}\), \(B_{l_{1}l_{2}}=\frac{P(\mathbf{y}|\kappa=l_{1})}{P(\mathbf{y}|\kappa=l_{2})}\), is known as the Bayes factor for comparing \(\kappa=l_{1}\) with \(\kappa=l_{2}\). The Bayes factor does not depend on the prior distribution of \(\kappa\). Recently, the Bayesian analysis of mixtures with an unknown number of components has received great attention [13; 35; 46]. In practice, a Bayes factor larger than 100 can often be regarded as an evidence for the preference of \(l_{1}\) QTLs over \(l_{2}\) QTLs.
## 2 Materials and Methods
### Pollen Sampling Campaigns and Air Exposure Tests  
Pollen was collected in 2019 and 2021 from selected olive trees (_Olea europaea_ L., Cultivar San Felice and Leccino) located in the suburbs of Giano dell'Umbria, a rural area close to Monte Martano, and in the town of Perugia. During the full pollination period, the infloresences were enveloped by pollination bags constituted of lightweight paper, which provided protection from wind dispersion, foreign pollen, and air pollutants. Homogeneous layers of pollen (200 mg) were deposited in a set of Petri dishes (8 cm diameter) fitted inside six passive deposition chambers. Each chamber, constituted by four gridded side walls, contained three Petri dishes with pollen (POL in the following) and one clean Petri dish as a control of the net deposition fluxes (CNT in the following). In this configuration, each Petri dish acts as a collector plate where atmospheric pollutants are accumulated with a specific flux. We placed two deposition chambers at each monitoring site. The chambers were installed on the roof of air-quality monitoring cabins (ARPA-Umbria Regional Environmental Protection Agency) at about 3 m above the surrounding ground (Figure 1). In Perugia (PG), we selected a monitoring station installed in a residential and commercial district (Fontivegge Station headquarter) characterized mainly by traffic pollution [1], while in Terni (TR), we chose an industrial monitoring station located near the AST (Acciai Special Terni) factory, one of the oldest steel plants in Europe and one of the largest in Italy [38]. The background Monte Martano (1094 m a.s.l.) station (MM) is located about 50 km south of Perugia and 45 km north of Terni. All the selected air-quality monitoring stations are equipped with instruments to measure meteorological parameters and air pollutant concentrations (gases and aerosol), including sampling systems for total (wet and dry) depositions. The starting day of exposure was the same for all the chambers at the three sites. The first set of chambers was removed after 7 days, and the others after 15 days. Two exposure experiments were performed in the periods 3-17 July 2019 and 21 July-4 August 2021, respectively. During the 2019 experiment, pollen grains were exposed to air for 15 days only. After sampling, the POL and CNT collector plates were refrigerated at 5\({}^{\circ}\), transported to the laboratory, and stored at \(-\)18 \({}^{\circ}\)C until chemical analyses.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
## 3 Finite Element Model
### Numerical Modelling Procedures  
The gradual unloading method was adopted to simulate the excavation. Figure 3 shows the flow chart of specific simulation process and as follows.  
1. Establish the initial stress conditions using \(K_{0}\) = 0.5. Apply the same amounts of vertical and horizontal pressure as that in the centrifuge test to the formation level and diaphragm wall, respectively.
2. Incrementally increase the gravitational acceleration of the whole model from 1 g to 60 g in four steps, i.e., from 1 g, to 15 g, to 30 g, to 45 g, and finally to 60 g. Simultaneously, apply pressure to the formation level and the wall.
3. Decrease the amounts of vertical and horizontal pressure gradually in each excavation stage to simulate excavation until a depth of 9 m is reached.  
\begin{table}
\begin{tabular}{c c} \hline
**Soil Parameters** & **Values** \\ \hline Critical state friction angle \({}^{(1)}\), \(\varphi_{\rm c}\) & 30\({}^{\circ}\) \\ Granulates hardness \({}^{(1)}\), \(h_{\rm s}\) & 2.6 GPa \\ Exponent \(n\)\({}^{(1)}\), \(n\) & 0.27 \\ Minimum void ratio at zero pressure \({}^{(1)}\), \(e_{\rm d0}\) & 0.61 \\ Critical void ratio at zero pressure \({}^{(1)}\), \(e_{\rm c0}\) & 0.98 \\ Maximum void ratio at zero pressure \({}^{(1)}\), \(e_{\rm i0}\) & 1.10 \\ Exponent \(\alpha\)\({}^{(2)}\), \(\alpha\) & 0.14 \\ Exponent \(\beta\)\({}^{(2)}\), \(\beta\) & 3.0 \\ Parameter controlling the initial shear modulus upon a 180\({}^{\circ}\) strain path reversal and & 5.5 \\ in the initial loading \({}^{(2)}\), \(m_{\rm R}\) & 2.75 \\ Parameter controlling the initial shear modulus upon a 90\({}^{\circ}\) strain path reversal \({}^{(2)}\), \(m_{\rm T}\) & 2.75 \\ Size of elastic range \({}^{(2)}\), \(R\) & \(3\times 10^{-5}\) \\ Parameter controlling rate of degradation of stiffness with strain \({}^{(2)}\), \(\beta_{\rm r}\) & 0.08 \\ Parameter controlling rate of degradation of stiffness with strain \({}^{(2)}\), \(\chi\) & 1.0 \\ Coefficient of at-rest earth pressure, \(K_{0}\) & 0.5 \\ Dry density, \(\rho_{\rm d}\) & 1542 kg/m\({}^{3}\) \\ Void ratio, \(e\) & 0.72 \\ \hline \end{tabular} Note: (1) [PERSON], 1999 [41]. (2) Obtained by fitting test results from [PERSON] (1999) [42] and [PERSON] et al. (1978) [43].  
\end{table}
Table 1: Soil parameters adopted in the hypoplastic model.
## 1 Introduction
This involved analyzing the approaches used in various studies and their effectiveness in addressing the challenges associated with microgrid planning. (iv) Comparison of sequential and simultaneous optimization. The review also contrasted methodologies that employ sequential optimization of sizing and energy management with those that utilize simultaneous optimization. This comparison aimed to highlight the advantages and disadvantages of each approach. This review comprehensively analyzes sizing and energy management strategies tailored to microgrids integrating renewable energy sources. It addresses the gap in the literature by thoroughly comparing sequential and simultaneous optimization techniques, highlighting their effectiveness in balancing energy production, cost minimization, and system sustainability. Additionally, it discusses integrating emerging technologies, such as electric vehicles and smart grids, which present significant potential for enhancing microgrid efficiency. This study emphasizes methodologies and approaches that support decision-making in energy planning and management under varying conditions, including the intermittency of renewable sources and the complexities of non-interconnected regions. Furthermore, it is intended to serve as a pedagogical resource for researchers and practitioners new to microgrid planning, energy management, and sizing. This paper contributes, in a pedagogical manner, to the existing knowledge of microgrid energy planning; its key contributions are as follows:  
* Comprehensive review: The paper thoroughly reviews the current methodologies for microgrid energy planning, highlighting the main strategies for both sizing and energy management. * Methodological comparison: This paper compares sequential and simultaneous optimization approaches, providing insights into their benefits and challenges. * Integration strategies: This study discusses various integration strategies for renewable energy sources within microgrids, emphasizing the importance of efficient energy management to address the intermittency of renewables. * Practical guidance: By synthesizing the findings from numerous studies, this paper provides practical advice for researchers and practitioners on selecting the most appropriate energy planning strategy based on specific needs and objectives. The remainder of this review paper is structured in the following sections: Section 2 remarks on the topology of microgrids, including isolated and interconnected systems, and their operational modes in AC and DC configurations. Section 3 reviews different methodologies for microgrid sizing, including heuristic, mathematical, and hybrid approaches. Section 4 explores various energy management approaches within microgrids, highlighting optimization-based methods and commercial software tools. Section 5 discusses optimization approaches in microgrids' sizing and energy management, and analyses their advantages and challenges. Lastly, Section 6 provides concluding remarks and insights on future work relating to this study.
## 2 Materials and Methods
### Research Methodology  
UAV image data were obtained using nap-of-the-object photogrammetry technology. The aerial survey data were aerial triangulation encryption with DJI Terra 3.4.4 software to generate fine 3D models and point cloud data. The coordinate data collected from orthophoto image Digital Orthophoto Map (DOM), point cloud data, and the digital surface model (DSM) were utilized as reference values. ArcMAP 10.2 and DJI Terra software were utilized to evaluate the accuracy of the measurement results and determine the optimal flight proximity in the Benggang study area [23; 24]. A comparison was made with data obtained using image control nap-of-the-object photogrammetry, with measured RTK coordinate data as a reference. The error source of the 3D model was analyzed; then, the feasibility of control-free-image-based nap-of-the-object photogrammetry in analyzing erosion and collapse walls was investigated, as depicted in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**DJI Phantom 4 RTK UAV**} & \multicolumn{3}{c}{**Basic Parameters of Camera**} \\
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Weight (g) & 1391 & Sensors (in) & 1 \\ Wheelbase (mm) & 350 & Effective pixels & 20 million \\ Maximum flight speed (km/h) & 50 & Resolution (mm) & 5472 \(\times\) 3648 \\ Maximum tilt angle (\({}^{\circ}\)) & 25 & Aperture & f 2.8–f 11 \\ Maximum wind speed (m/s) & 10 & Focal length (mm) & 8.8 \\ Maximum flight time (min) & 30 & Equivalent focal length (mm) & 24 \\ Vision system & Five-way obstacle avoidance & Focusing distance (m) & 1 m\(\rightarrow\)\(\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of UAV and camera.  
Figure 7: Workflow of research.
## 4 Simulation Results and Discussion
### Effects of the OIS's Speed on Various Aspects of Performance  
#### 4.2.1 Connection Hold Rate  
Figure 18 shows the performance of the OIS's connection hold rate in relation to its speed in various channel environments. Figure 18a shows that when there is no fading in the channel (only the path loss is considered), the toss-and-catch algorithm performed better than Control Groups 1 and 2 at all OIS velocities. In the toss-and-catch algorithm, the SBSs are categorized into MSBSs, ASBSs, and OSBSs, and the super mode was applied to the MSBSs. Therefore, after the score-based selection mechanism has made a selection, the connection score of the MSBSs is increased. In addition, if the OIS fails to identify a suitable MSBS for a handover, the overload support mechanism assists to increase the overall connection hold rate of a moving train.  
We further explored the performance of these three methods in various fading conditions. According to the results presented in Figure 18b-d, the toss-and-catch algorithm performed better than Control Groups 1 and 2, regardless of the fading condition employed. Even though the channel was affected by Nakagami-3 fading such that the track coverage of the SBSs was slightly smaller than that in a nonfading environment, most of the track paths can still be covered. In other words, even though the performance of all three methods had decreased slightly, the degree of decline was not substantial. When Nakagami-1 fading is employed, the degree of fading is greater fading is employed and the effective signal coverage of the SBSs is only able to cover fewer track paths. Therefore, when comparing Figure 18b,c, it can be seen that the performance of the two control groups had decreased by around 30%, while the performance of the toss-and-catch algorithm had decreased by around 20%. When Nakagami-1/2 fading is employed, the fading of the channel environment was so severe that the effective signal coverage area of the SBSs was practically unable to cover the track paths. Therefore, under this condition, the connection hold rate is below 50% for all three methods, which signifies that the OIS is disconnected half the time when the train is moving. However, compared to Control Groups 1 and 2, the toss-and-catch algorithm had a better ability to reduce the impacts brought by the harsh environment, and its connection hold rate was even two times higher those of the two control groups. This superiority could be due to the fact that the MSBSs' mode configuration was set to super mode and the ASBSs were used to assist the overload support mechanism to increase the connection performance of the OIS. Hence, the toss-and-catch algorithm had a better performance than the two control groups.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### End-to-End Assessment  
End-to-end (E2E) is an important indicator when developing a system, as it is crucial to know how long the system takes to perform a job, from the start of a workflow to the end. Therefore, the E2E time will be calculated for each communication flow of the developed prototype. All the measurements collected in the computation time and latency sections will be used to obtain approximations of the E2E, i.e., it will be calculated according to the existing processing time and latency in each communication flow. It uses the median values obtained in the computing and networking latency experiments. In total, approximately 4000 measurements were collected across all the conducted assessments. The calculated E2E times for each combination between nodes are reported in Table 11.  
\begin{table}
\begin{tabular}{c c c} \hline
**No Security [ms]** & **DLAPP [ms]** & **MFSPV [ms]** \\ \hline
10.196 & 10.792 & 11.251 \\ \hline \end{tabular}
\end{table}
Table 10: ITS-G5 communications latency measurements between RSU and OBU using different security approaches.  
Figure 20: Cellular and G5 latency measurements comparison for each security approach. The cellular latencies are the average of the ones reported in Table 9.  
The results are analysed from two perspectives: the network segment and security approach. Following this analysis, the applicability of the developed proof-of-concept is briefly discussed based on the results obtained.