esa-sceva/satcom-chunk-collection huggingface dataset:
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 4 Results  
Monitored displacement data are affected by the presence of existing buildings through the soil-structure interaction. For this reason, this paper focuses on the northern area of the Cemetery of Poggioreale (Figure 2b), where only small burial chapels and niches are present. The small dimension of those chapels allows to neglect the soil-structure interaction and continue to use the free-field approximation inherent in Equations (4), (5), and (15). This section is structured into four parts:  
* The stability of the AoI is assessed;
* The displacement maps right after the excavation of the first and second excavation are presented;
* Predicted settlement profiles obtained using Equation (15) are compared to those retrieved from MT-InSAR to assess the reliability of the measurements;
* Monitored data from MT-InSAR, which lie on cross sections orthogonal to the first excavated tunnel axis (the one further east in Figure 2b), are interpolated to gain information on \(\mathrm{V_{L}}\) and \(\mathrm{i_{x}}\). The latter are then used to predict the settlement caused by the second excavation, taking into account the influence of the second excavation on the first one using the superimposition of the effects.
## 1 Introduction
Based on the above mentioned third approach, an alternative method is to rely on existing global long-term CDRs of harmonized narrowband reflectance (FDRs), for example, from the Advanced Very High Resolution Radiometer (AVHRR) instrument [14; 15], and to transform these measurements into broadband quantities [16]. A preceding paper documents the retrieval of instantaneous broadband reflectance from the narrowband AVHRR observations [17] by establishing robust relations between the narrowband AVHRR measurements and the broadband measurements from CERES. Using this AVHRR-derived broadband reflectance, the current paper describes the retrieval of the daily mean RSF (Wm\({}^{-2}\)), which could then be aggregated to monthly means to constitute a long-term, broadband energy balance dataset that would fit the needs of the climate modeling and monitoring communities. This is done on a global grid with a spatial resolution of 0.25\({}^{\circ}\) (Figure 1b). The methods and algorithms described in this article are used for the upcoming third edition of the CM SAF Cloud, Albedo And Surface Radiation dataset from 42 years of AVHRR data, 3 rd edition (CLARA-A3). The first and second editions are described by [18; 19], respectively, and do not yet include TOA radiative fluxes. In addition to the Reflected Solar Flux, the algorithm also estimates the TOA incoming solar radiation (based on daily TSI and Earth orbit parameters). The third component of the Earth radiation budget, the Outgoing Longwave Radiation, is described in [PERSON] et al. [20]. The AVHRR observation density on a given day is not constant. It varies over time, depending on the number of AVHRR-carrying satellites at any particular moment (Figure 2a). It also varies in space because the polar orbit causes the consecutive swaths to overlap towards the poles, thereby increasing the number of observations on a given location (Figure 2b). This requires a flexible algorithm capable of ingesting any number of observations. The daily mean RSF is derived by integrating daytime, twilight, and nighttime fluxes. Twilight RSF is estimated by applying an empirical model. Daytime RSF is estimated by applying an interpolation method that seamlessly fuses observations with an empirical diurnal cycle model, which requires at least one (daytime) observation per 24 h; higher accuracy, however, is obtained with higher observation density (Figure 2). Section 2 provides an overview of the input data necessary for the TOA flux retrieval. Subsequently, Section 3 describes the methodological framework of the TOA flux retrieval. Finally, Section 4 validates the method by comparing the obtained RSF with the state-of-the-art products, CERES SYN1 deg-Day and SYN1 deg-Hour, Ed4.1. Figure 1: Daily mean RSF for 15 June 2008, zoomed in on the Northern Atlantic and Europe, from (**a**) CERES-SYN1 deg, and (**b**) the TOA flux retrieval discussed in this article (and part of CM SAF CLARA-A3).
## 5 Simulation and Numerical Results
The load curve before and after DRP implementation is shown in Figure 8. Herein, the DRP does not change the total value of the load but smooths the load curve. In other words, DRP shifts the load demand from peak to off-peak hours. As can be seen, employing DRP results in alleviating the demands at hours 11-16 and 20-22 and consequently economic savings. As shown in Figure 9, purchasing power during peak  
Figure 6: Optimal operation of CAES in Case 1. Figure 7: Optimal purchased power and gas in Case 1.  
hours 11-15 and 20-22 is reduced after DRP implementation, whereas the purchasing power at valleys is increased. In case 1, the value of the objective function (3) is listed in Table 3 to scrutinize the effects of CAES, P2G, and DRP on the system's operation cost. The results advocate the importance of P2G technology in reducing the cost by 3.4%. More importantly, the operation cost is reduced by 5.25% from Figure 4:06912.73 in the presence of P2G, CAES, and DRP.
## 2 Materials and Methods  
### Study Area  
The study area is situated in Zhuzhou City, Hunan Province (Figure 1). This area is a region of a subtropical monsoon climate characterized by hot and wet summers with sufficient sunlight for rice growth. The mean annual temperature ranges from 16 to 18 \({}^{\circ}\)C. The mean annual precipitation is over 1400 mm. The predominant soil type is orthic acrisol according to the world reference base (WRB) for soil resources [37]. Zhuzhou is a commodity grain production base of China. The main crop type in Zhuzhou is paddy rice, and the predominant variety is Boyou9083, usually transplanted in early June and harvested in late September. Meanwhile, Zhuzhou is also an important industrial city with significant heavy metal pollution. Industrial waste is discharged into the Xiangjiang River, the most polluted river in China, and has large contaminated areas of land along the river. The rice in this region is cultivated in an intensive pattern as follows: The planted rice in the experimental sites is supplied with abundant irrigation water and the right amount of fertilizers and farm chemicals to prevent unwanted stress, including diseases, pests, and water and nutrition deficiency. Thus, heavy metals become the dominant stressors interfering with the growth of rice.  
Four experimental sites, labeled A, B, C and D, were selected to account for differences in the severity of heavy metal stress (Figure 1b). Each of them had a size of 1.5 km \(\times\) 1.5 km. The concentrations of cadmium (Cd), mercury (Hg), lead (Pb), and arsenic (As), which are the main pollutants in Hunan Province, were measured in the soil of all experimental sites (Table 1). In Sites B, C, and D, the concentrations of Cd, Hg, and Pb were all higher than background values and the concentration of Cd was significantly greater than the upper limit value of the National Secondary Standard in the Environment Quality Standard for Soils (GB15618-1995). According to the concentration of Cd, which was considered the main pollutant in the study area, Site A is categorized as \"nonstress\", Site B is categorized as \"moderate stress\", and Sites C and D are categorized as \"severe stress\".  
Figure 1: Location of the experimental sites in Zhuzhou, Hunan Province, China, and the spatial distribution of sample plots.
# Low Carbon Optimization of Deep Foundation Pit Support in Undulating Strata
## 3 Foundation Pit Support Scheme Design
### Numerical Simulation Optimization  
#### 3.2.1 Numerical Simulation Optimization Scheme  
The theoretical calculation and design of the foundation pit revolve around the anchor pile and internal support structure. Due to the length of the pile (i.e., 35 m), the construction is inconvenient and costly. Therefore, it is recommended to use the numerical simulation method to optimize the structure based on local engineering experience. The southeast side of the foundation pit is high, and the corresponding load is large, so the steel pipe piles and anchor support are adopted. The anchor is pulled at the top of the steel pipe pile and 1.5 m underground. The rest of the upper part of the support is supported by reinforced concrete bored piles with a diameter of 1000 mm and a spacing of 1500 mm and jet grouting piles with a diameter of 900 mm and a spacing of 600 mm. The length of the pile is 13 m, and the grade of the concrete is C45.  
The lower part of the foundation pit is supported by a \(\varphi\)168 @ 1500 steel pipe pile with a minimum insertion depth of 1.5 m and a pile length of 16.5 m. Additionally, prestressed anchors using HRB400 steel bars are installed to anchor 14 m below the top of the steel pipe pile.  
The foundation pit is a regularly shaped, elongated pit with a concrete support as the initial support, measuring 1200 \(\times\) 900 in size and made of C30 reinforced concrete. The second and third supports are constructed with \(\varphi\)800 mm and t = 20 mm steel tubes, with a vertical spacing of 7 m and 4 m, respectively, and a horizontal spacing consistent with the theoretical design. The bolts have a horizontal spacing of 1.5 m and an inclination angle of 15\({}^{\circ}\). The specific parameters are presented in Table 5.
## 4 Numerical Examples
### Analysis and Discussion of Model Performance Comparison Using LSE Estimation Parameter Values
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Parameter Estimation Values** & \(MSE\) & \(R^{2}\) & \(RMSE\) & \(KD\) & \(TS\) & \(Bias\) \\ \hline G-O model & \(a=8563.5\); \(b=0.00007\) & 77.12 & 0.7854 & 8.78 & 0.4003 & 29.08 & 6.42 \\ \hline Delayed S-shaped model & \(a=24846\); \(b=0.000893\) & 26.32 & 0.9268 & 5.13 & 0.2116 & 16.99 & 4.56 \\ \hline Infection S-shaped model & \(a=93.807\); \(b=0.013585\); \(\beta=1.0814\) & 87.57 & 0.7563 & 9.36 & 0.4316 & 30.99 & 6.78 \\ \hline Yamada Imperfect-2 model & \(a=227.72\); \(b=0.001534\); \(c=0.025675\) & 42.34 & 0.8822 & 6.51 & 0.2952 & 21.55 & 4.56 \\ \hline P-N-Z model & \(a=165.16\); \(b=0.014861\); \(\beta=6.2516\); \(a=0.008429\) & 35.01 & 0.9026 & 5.92 & 0.2649 & 19.59 & 4.18 \\ \hline Weibull distribution model & \(a=617.45\); \(b=0.000389\); \(c=1.2317\) & 58.21 & 0.838 & 7.63 & 0.3515 & 25.26 & 5.35 \\ \hline Wang model & \(a=220\); \(b=0.00029\); \(\beta=13.574\); \(d=1.9148\) & 79.7 & 0.7782 & 8.93 & 0.4072 & 29.56 & 6.49 \\ \hline Li model & \(a=219.57\); \(b=0.008231\); \(\phi=0.042635\); \(N=10.761\) & 113.92 & 0.683 & 10.67 & 0.4872 & 35.34 & 7.89 \\ \hline PM & \(a=150.02\); \(b=0.004319\); \(\theta=0.000007\); \(d=2.4545\); \(\sigma=0.098155\); \(\beta=0.12636\) & **16.02** & **0.9554** & **4.0** & **0.1732** & **13.25** & **3.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model performance comparison using 100% of the data (DS1-3). Figure 1: _Cont._  
Figure 1: The cumulative number of detected faults over time. (**a**,\(c\),**e**) represent the cumulative number of detected faults using 100% of the data for DS1-1, DS1-2, and DS1-3, respectively. (**b**,\(d\),**f**) represent the cumulative number of detected faults using 85% of the data for DS1-1, DS1-2, and DS1-3, respectively. Table 9-11 show that using 100% of the data (DS3-1, DS3-2, and DS3-3), the _MSE_, \(R^{2}\), _RMSE_, _KD_, _TS_, and _Bias_ values of the PM are 122.31, 0.9907, 11.06, 0.0741, 4.54, and 8.47; 113.84, 0.9053, 0.67, 0.2245, 25.73, and 8.02; and 57.79, 0.804, 7.6, 0.3472, 38.08, and 5.42, respectively. From Table 9, we can see that the _MSE_, _RMSE_, _TS_, and _Bias_ values of the second-placed G-O model are almost 1.23, 0.5, 0.5, and 0.62 times more than those of the PM, respectively. Table 10 shows that the _MSE_, _RMSE_, _KD_, _TS_, and _Bias_ values of the second-placed P-N-Z model are around 1.3, 0.52, 0.57, 0.52, and 0.6 times larger than those of the PM, respectively. The PM has the best fitting performance of all the models. Using 100% of the data (DS3-1, DS3-2, and DS3-3), the next-best performing models are the G-O model, the P-N-Z model, and the ISS model, respectively. The worst is the Li model when run once and the Wang model when run twice. From Figure 3a,c,e, we can see the fitting comparison of models.
## 2 Theoretical Basis
### Calculation of Selected Functionals from the GGI Model
#### 2.2.2 The Gravity Values  
If we neglect the slight differences between the real and normal plumb lines, then by differentiating the disturbing potential (1) along the normal to the equipotential surface at the observation point (hence differentiating with respect to height \(H\)) we get the expression defining the gravity disturbance:  
\[\delta g_{P}=-\frac{\partial T}{\partial H}=-\left(\frac{\partial T_{\Omega}}{ \partial H}+\frac{\partial T_{x}}{\partial H}+\frac{\partial T_{E}}{\partial H}\right) \tag{8}\]  
Now, if we have the position of the point \((\varphi,\lambda,h)\), e.g., from the GNSS measurement, we can determine the value of the normal gravity \((\gamma_{P})\) at the point, and then the gravity value \((gp)\):  
\[gp=\delta g_{P}+\gamma_{P} \tag{9}\]  
These values can be used, for example, to determine system corrections for precise spirit levelling instead of performing gravity measurements.
## 2 Literature Review  
### Brief Review of SCADA System  
SCADA systems connect remote field sites and production equipment to central command centers [10]. They acquire data from sensors and instruments, communicate it to servers, and display information through monitors or human-machine interfaces (HMIs) [11]. Operators or automated controls then respond by sending commands to equipment [12]. Key SCADA capabilities include data acquisition, network communications, analysis, historical trending, and user interfaces [13].
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
## 1 Introduction
An optimization model is constructed to maximize the throughput of all D2D users who communicate through NOMA-enhanced D2D while ensuring all the users' QoS on each channel. * D2D users communicate through cellular users' uplink channels, and the many-to-one matching game is used to allocate channels for D2D clusters. We prove the convergence of the matching game and optimize the channel allocation problem for D2D users. * The successive convex approximation (SCA) method is used to convert the nonconvex optimization problem into a convex problem, and the convexity of the optimization problem is proved based on the negative semidefiniteness of the Hessian matrix. The genetic algorithm (GA) is used to optimize the transmit power of D2D content providers and the convergence of the algorithm is proved. The remainder of this paper is organized as follows. Section 2 shows the system model. Section 3 defines the utility function of D2D content requesters to form D2D clusters, allocates cellular channels for D2D clusters, and optimizes the transmit power for D2D content providers in the cluster. We also prove that the algorithm can converge to a stable solution. Section 4 plots and analyzes the simulation results. Section 5 summarizes the whole paper.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 1 Introduction
Therefore, bundle service times can be easily miscalculated in the current practice, which can increase losses or delays as bundles may be scheduled for contacts that lack a real sufficient capacity. This study examines the problem and proposes a possible solution. The main contributions of this work are as follows:  
1. An analysis of the average performance drop from the optimal response time for a set of bundle flows transmitted over a DTN of known topology and statistical properties for the node contact patterns when inaccurate bundle transmission estimations are used to decide the bundle paths is performed. Rather than scrutinizing the behavior of a specific routing protocol, the methodology abstracts away the protocol details by the use of a stochastic model of the system, which is then applied to solve the stated routing optimization problem using three variations for estimating the service time. 2. A link service time formulation that gives a more accurate estimation of the bundle service time over lossy channels when using LTP as the convergence adapter to implement reliable single-hop transmissions is carried out. This formulation was introduced in a prior work [5] but was not applied to routing optimization. 3. Finally, it is demonstrated the application of the proposed service time expression with the standard contact graph routing. The results show that it is possible to achieve significant performance improvements compared with the baseline response. The rest of this paper is organized as follows. Section 2 presents the work related to this study. Section 3 discusses the system model and analysis considering optimal routing, whose results are numerically evaluated and presented in Section 4. In Section 5, the evaluation with space DTN protocols is discussed, and the results are presented in Section 6. Finally, Section 7 offers our final remarks.
## 4 Performance Evaluation
### Discussion  
The FNR (false negative rate) and FPR of the experimental results for all scenarios are averaged and shown in Table 2. Note that the magnitude of injected faults is the smallest in constant faults and the largest in noise faults. Random faults are distributed in all ranges of the magnitude according to the intensity. For all types of faults regarding all of the intensity and duration AT does not generate any FP for all velocities, but KT does show an average 27% of FPR only at 1.3 m/s. RT shows about 64% FPR for all faults regardless of velocity.  
AT shows 0% FNR for all velocities in noise faults but it has mean 7% FNR for random faults, and mean 12% FNR for constant faults, respectively, except for 0.7 m/s. KT show about 10%, 29.5%, and 38% FNR for noise, random and constant faults, respectively, except 0.7 m/s. RT not only has about 52.3% and 78.5% FNR, respectively, except 0.7 m/s for random and constant faults, but also, about 27% FNR is shown for noise faults. The reason for the poor FNR performance for constant fault injection for all thresholds seems to be that the designed intensity is too small to be distinguished from normal data and transient faults.  
From the above results, we demonstrate that the AT has enough sensitivity to small changes and adaptable to various changes and has the dependability enough to support the safety for CPS. However, the performance of the AT is degraded only at both the lowest velocity and the shortest duration, probably because the variation and the values of the original training data are too small to identify the differences in the large time-window size at the lowest velocity. There is a need for further investigation related to the size of time window as the velocity slows down, which needs to be made smaller than \(w=100\).
## 4 Comprehensive Link Budget Analysis of the BDS-3 ISL  
Various link losses should be calculated to quantitatively assess the signal transmission quality based on the analysis of inter-satellite link topology characteristics. The current link performance is compared with system requirements such as data transmission rate, bit error rate, and ranging accuracy. Through iterative adjustments of signal transmission power and antenna gain, the ISL design is refined to meet the specified performance metrics required by the satellite navigation system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline \(L_{\text{max}(MEO-MEO)}\) & Maximum distance for MEO-MEO links & 55,897.12 & km \\ \(L_{\text{max}(MEO-GEO/IGSO)}\) & Maximum distance for MEO-GEO/IGSO links & 70,477.33 & km \\ \(\alpha_{MEO}\) & Earth occlusion angle for MEO satellites & 14.79 & \(\circ\) \\ \(\alpha_{GEO/IGSO}\) & Earth occlusion angle for GEO/IGSO satellites & 9.84 & \(\circ\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation results of maximum communication distance and Earth obstruction angle.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### ML and IoT Applications in Smart Transportation
#### 2.2.1 Route Optimization
The authors in [60] experiment in finding a possible correlation among traffic congestion, fuel emissions, and the crowdsourced information of Google 'popular times' feature. In the research, traffic data are collected by cameras in specific roads, and a vehicle equipped with a GNSS data logger. Emissions are estimated based on the Vehicle Specific Power (VSP) model, and popular time data are obtained from Google Maps. The results showed that there is a correlation between the crowdsourced data of 'popular times' and emissions, although calibration among the data needs to done, and some adaptive learning algorithm to analyze similar cases would be needed to make accurate correlations. Google was one of the first to use the power of crowdsourcing, in order to create new services. Google Maps application is available for free and is compatible with all modern mobile devices. Mobile devices have integrated GPS, accelerometer, and gyroscope sensors. Into 2009, Google announced a new service that would provide traffic information integrated in Google Maps [61]. The traffic information was not collected by fixed location sensors or other monitoring systems. The end users mobile devices can send anonymous information about their speed and location just by using the maps application. Google Maps can now suggest new routes based on traffic information, in order to avoid congestion. Lastly, in [62], crowdsourcing route planning in performed for the last mile towards a destination. The authors state that the last mile is usually not accurately planned by simple approaches, such as shortest path or shortest time. Locals on the other hand, could share their driving patterns around various destinations, and that would provide more accurate directions at the last segment of the navigation. A mobile application (CrowdNavi) is implemented in the research that uses crowdsourcing to obtain information and make suggestions about the last mile of a trip. Part of the application is to identify the last segment. The first part is suggested using information from services like Google Maps, and the last segment is calculated by crowd data and a method called landmark scoring.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 2 Related Work
### Visual Lips Movements  
It refers the visual lips movements of a speaker must match the sound of the spoken words. If the video and audio displayed at the receiving endpoint are not at the same time then this misalignment between audio and video is referred to as skew. Without ensuring lip sync, audio often plays ahead of video, because the latencies involved in processing and sending video frames are greater than the latencies for audio [16].  
The realignment of two streams is not possible with the information contained in RTP data packets. There should be a mechanism that indicates which of the two streams are to be synchronized. Secondly, time stamping is also required to correlate the two streams. Both of these pieces of information are transported in RTCP packets. RTCP-SR (RTCP-Sender Report) packets carry the NTP timestamp. RTCP-SDES (RTCP-Source Description) packets contain CNAME (Canonical Name) value that uniquely identifies the source and can be used to correlate the streams. In [16] intra-stream synchronization was achieved by considering the audio stream as a master stream and to delay the video stream as necessary to achieve LIP sync. The work described in [17] shown a client-driven approach for synchronizing multimedia data for packet-based video streaming.