esa-sceva/satcom-chunk-collection huggingface dataset:
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 4 A Credit-Based Congestion-Aware Incentive Scheme
### CBCAIS Incentive Scheme Overview  
The CBCAIS scheme is mainly composed of two functional components: a check and punishment mechanism and a message acceptance selection mechanism. The check and punishment mechanism is used to check whether some congested nodes have discarded messages deliberately, and punish them severely if they have. A node will be punished if it has been discarding messages deliberately, but it can decide whether to accept other nodes' messages autonomously before the buffer is full. To obtain more credits, a message acceptance selection mechanism is used to decide whether to accept other nodes' messages according to the congestion degree. If a node is congested, it can increase the acceptance threshold to accept messages that bring more credits. Otherwise it will decrease the threshold to accept more messages.  
Assuming nodes \(N_{i}\) and \(N_{j}\) meet, the overview of credit-based congestion-aware incentive scheme CBCAIS is shown in Figure 3; it includes the following steps:  
1. Both nodes exchange message lists \(L_{i}\), \(L_{j}\).
2. For a message \(m_{i}\) in the list \(L_{i}\) of node \(N_{i}\), nodes \(N_{i}\) and \(N_{j}\) calculate the message delivery delay.
3. If the message delivery delay of \(N_{j}\) is smaller than \(N_{i}\), then node \(N_{j}\) will decide whether to accept message \(m_{i}\) by the message acceptance selection mechanism.
4. If node \(N_{j}\) decides to accept, then message \(m_{i}\) is added to the sending set \(SendS_{i}\), otherwise node \(N_{i}\) will go to Step 2 and select another message to send.
5. If node \(N_{i}\) does not receive the corresponding credits for forwarding message \(m_{i}\), it will require TTP to check if a forwarding node has discarded message \(m_{i}\) deliberately by the check and punishment mechanism. If a misbehavior node is checked, it will be punished for the discarding.
6. The process of sending message \(m_{j}\) of node \(N_{j}\) is the same as message \(m_{i}\).
# Estimating Thermal Material Properties Using Solar Loading Lock-in Thermography
## 2 Materials and Methods  
This work uses two reference plates made out of EN AW 5083 aluminum sized 300 \(\times\) 300 \(\times\) 10 mm and 300 \(\times\) 300 \(\times\) 20 mm respectively. Using literature values for the thermal material properties described by [5]\(\varrho=2660\) kg/m\({}^{3}\), \(\lambda=125\) W/(m\(\cdot\) K), \(c_{p}=900\) J/(kg\(\cdot\) K) results in thermal masses of 2106 J/K and 4212 J/K respectively.  
A concrete block was fabricated out of premixed dry mortar with a size of 300 \(\times\) 300 \(\times\) 300 mm to be used as a thermally thick reference. Material properties were assumed to be [6]\(\varrho=2240\) kg/m\({}^{3}\), \(\lambda=2\) W/(m\(\cdot\) K), \(c_{p}=900\) J/(kg\(\cdot\) K), resulting in a thermal effusivity of \(b_{conc}=2008\) J/(K\(\cdot\) m\({}^{2}\cdot\sqrt{s}\)).  
The brickwort measured in the field experiment is assumed to have the following thermal properties [6]: \(\varrho=1920\) kg/m\({}^{3}\), \(\lambda=0.9\) W/(m\(\cdot\) K), \(c_{p}=800\) J/(kg\(\cdot\) K), resulting in a thermal effusivity of \(b_{brick}=1176\) J/(K\(\cdot\) m\({}^{2}\cdot\sqrt{s}\)). The retaining wall itself is approx. 8 m tall and extends laterally over 100 m, of which a single measurement site of approx. 4 m by 4 m was chosen.  
A custom measurement platform was developed, powered by a rechargeable battery and consisting of an embedded Linux single board computer (RaspberryPi 3B) and the FLIR Boson 640 in USB Video (UVC) mode. The FLIR Boson LWIR camera can directly send raw image data over USB, which was used in all further processing. Thermal simulations were carried out using GNU Octave and MATLAB.
## 4 Discussion
### FDR Response to Soil Properties
We found that temperature correction applied in standard functions may significantly reduce bias, but had a limited effect on precision error. So, a possible bias may be introduced due to temperature effects and it is worthwhile to quantify its magnitude, preferably in the field. The manufacturer mentions possible changes in water content due to temperature effects of up to 0.005 m\({}^{3}\) m\({}^{-3}\)\({}^{\circ}\)C\({}^{-1}\) in clayey soils. The magnitude of the temperature sensitivity changes with water content, so the temperature correction assumes that both the water content and temperature do not vary over the length of the probe's rods [20]. With recalibration we found also that models including temperature correction were rarely supported (using AICs) as best models in the topsoil, but rather in (some) subsoil layers. This may suggest that there is a risk of overfitting the calibration function by including temperature correction given the low number of replicates. A similar conclusion was drawn by [13]. They found that temperature compensation was not satisfactory for most of the layers they calibrated. It may be necessary for soils with high EC (>500 uS/cm) or electrically lossy clays (large surface area and CEC).
# Urban Free-Space Optical Network Optimization
## 2 Problems Statement
### Network with Minimum Edge Cost That Connects All Terminal Points  
We now consider a similar problem to the previous, with the difference that costs are applied on edges rather than on vertices. Applying costs on the edges is motivated by the need to place different FSO hardware for different scenarios (e.g., distance or density).  
**Definition 4**.: _Given a red-blue visibility graph \(G_{rb}=(R,B,E)\) and an edge cost function \(w\left(e\right):E\rightarrow\mathbb{R}\), find a set of edges \(E^{\prime}\in E\) of minimal overall cost that connects all the vertices in \(B\), possibly using some red vertices \(R^{\prime}\in R\). The solution for this problem is a \(\hat{G}_{rb}\) with \(\arg\min_{v\in E^{\prime}}\sum w\left(e\right)\). This problem is also known as the Steiner tree problem for graphs. Note that the problem's optimal solution is not necessarily a graph that connects all terminal points with the minimum possible number of edges. This is illustrated by the example in Figure 5._  
The Steiner tree problem for graphs is NP-hard (on general graphs), and its decision variant is known as one of [PERSON]'s 21 NP-complete problems [33]. The problem has a polynomial-time  
Figure 4: The visibility and optimal graphs of the example in Figure 3: (**a**) the visibility graph, (**b**) an optimal solution, and (**c**) another optimal solution.  
Figure 5: An example of a red-blue edge-weighted graph. The optimal solution (top blue) uses four edges, which leads to an overall edge cost of \(4\); terminal connectivity is achievable with just three edges (bottom green), but with a higher edge cost of \(5\).  
approximation of factor \(\approx\)1.39 [34]. Several heuristic algorithms were proposed for this problem throughout the years [26].
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 6 Conclusions  
In this paper, we present a location-based routing algorithm named RTEIT that provides a new method of selecting the next hop taking advantage of link stability, distance and effective information traffic for choosing the most appropriate relay node. The link utility is used to assess the link status which is based on the distance and stability of the wireless transmission. The effective information traffic mechanism is used to lead a correct path. The experiment of the proposed algorithm is successfully established in a spare scenario in NS-3. Simulation results indicate that RTEIT outperforms both GPSR, MOPR, and MM-GPSR in term of packet loss rate, end-to-end delay and network yield. Our work assumes that global positioning system can provide accurate location information. However, due to the multipath effect and other factors, geographic location information is generally inaccurate, which will make degradation of protocol performance in VANETs [33]. Therefore, in future work, we will consider this situation and make corresponding improvements to adapt to the actual scene.  
Conceptualization, F.B. and J.L.; methodology, F.B.; validation, J.L.; formal analysis, H.W. and S.L.; investigation, X.C. and Y.Z.; writing--original draft preparation, F.B.; writing--review and editing, J.L. and Z.Z.; supervision, X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China 2017 YFC1405203; the National Natural Science Foundation of China under Grant 61972417, 61872385, 61902431, 91938204; the Fundamental Research Funds for the Central University under Grant 18 CX02134A, 19 CX05003A-4, 18 CX02137A, 18 CX02133A and Science and Technology Service Network Plan (STS plan) of Chinese Academy of Sciences: Technological Breakthrough and System Demonstration of Intelligent Facility Agricultural Equipment (KF)-STS-ZDTP-074). The National Defense Basic Scientific Research under Grant JCKY2020604 SSJ5014.  
The authors declare no conflict of interest.
## 3 Results
### Public Stigma  
Twenty-three studies investigated public stigma in IPV survivors (see Table 1). Most of these studies have shown the presence of stigmatizing attitudes toward IPV survivors, which contributed to the development of mental health problems, such as depression and PTSD symptoms [14; 39; 48; 64]. In this sense, examples of negative public reactions are victim blame, shame, and discrediting attitudes [14].  
[PERSON] and colleagues [65] showed the presence of victim-blaming attitudes in undergraduate students, and these results have been confirmed in other studies [33; 42]. In contrast, [PERSON] and [PERSON] [27] showed that most of their participants were unwilling to blame survivors of abuse.  
Stigmatizing attitudes appeared in formal and informal supporters as well: [PERSON] and [PERSON] [49] showed that most of the women in their study reported experiencing harsh behaviors and judgments from court professionals. In this regard, [PERSON] and colleagues [56] confirmed these results with respect to advocates. Despite this, hand therapists have shown low levels of victim-blaming attitudes [60]. This result is encouraging,considering the high possibility that survivors of physical or sexual abuse attend hand therapy clinics [60].  
A factor that appears to play a key role in public stigma is gender: Men endorsed victim-blaming attitudes more often, compared to women [26; 27; 33; 38; 42; 45; 58; 65]. Furthermore, [PERSON] and colleagues [65] have shown that male undergraduate students tended to trivialize IPV episodes more often than females.  
Another factor that appears to affect public stigma is the type of relationship the woman has with the offender. [PERSON] and colleagues [65] showed that participants tended to blame the survivor more when she was dating the abuser compared to when she was married to him. Furthermore, the decision to stay or return to the abusive partner led to increased levels of victim blame [50; 58; 65].  
Another factor that seems to play a key role in public stigma is the adhesion to sexist, Patriarchal, or conservative values [26; 51; 58; 65]. For example, [PERSON] and [PERSON] [58] showed that people with higher values of benevolent sexism (BS) reported that they would not have provided any support to women who do not leave the abuser.  
In conclusion, few studies showed no public stigma toward female IPV survivors [39; 48; 60]. For example, [PERSON] and colleagues [39] reported that women who revealed their IPV experience received more positive than negative social reactions.
## 2 Materials and Methods
### Complex Network Methodology  
#### 2.4.1 Network Construction  
In order to examine the correlation-based topological structure of SSTA variability in the EJS, we apply the complex network methodology to ensemble sets of the same mode IMFs of SSTA. For network construction, we use the Pearson correlation coefficient (PCC) as a criterion for determining the connectivity of a pair of same-mode IMFs. In this case, determining the threshold value of PCC is a very important step. Generally, the PCC of a pair of oscillatory signals shows a sinusoidal pattern with respect to lag time. Thus, if two spatially remote SSTA variabilities show a similar oscillation pattern due to their nearly the same timescale, there can be a lag time due to the physical distance between both locations. By considering this characteristic appearing in cross-correlation behavior between two oscillating time series, we determine the threshold value of PCC in order to ensure that it contains lag time information. In this study, for IMF-1 through IMF-8 with smaller timescales than an interannual scale (~1000 days), the critical value is set to be 0.5 (approximately 1/6 of the corresponding timescale), while for IMF-9 and IMF-10 with longer timescales than 2500 days, the critical value is 0.8 (nearly 1/10 of the corresponding timescale).  
For an ensemble of mode-\(k\) IMFs, \(\{c_{i}^{k}\}\), the PCC matrix \(\{C_{ij}^{k}\}\) is computed as follows:  
\[C_{ij}^{k}=\frac{\sum_{t=1}^{T}\left(c_{i}^{k}(t)-\overline{c_{i}^{k}}\right) \left(c_{i}^{k}(t)-\overline{c_{j}^{k}}\right)}{\sqrt{\sum_{t=1}^{T}\left(c_{i }^{k}(t)-\overline{c_{i}^{k}}\right)^{2}}\sqrt{\sum_{t=1}^{T}\left(c_{j}^{k}(t )-\overline{c_{j}^{k}}\right)^{2}}} \tag{6}\]  
where \(\overline{c_{i}^{k}}\) and \(\overline{c_{j}^{k}}\) denote the time average PCC values of \(i\) and \(j\) nodes, respectively. Next, the diagonal components of \(\{C_{ij}^{k}\}\) are set to zero in order to remove self-loops in a network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**IMF Mode No.** & **Peaked Timescale (Day)** & **Empirical Timescale** \\ \hline
1 & 6\(\sim\)7 & Atmospheric synoptic scale (5\(\sim\)18 days) \\
2 & \(\sim\)13 & Atmospheric synoptic scale \\
3 & \(\sim\)30 & Monthly scale \\
4 & \(\sim\)60 & Inter-monthly and seasonal scale \\
5 & 100\(\sim\)200 & Seasonal and semi-annual scale \\
6 & 300\(\sim\)400 & Semi-annual and intra-annual scale \\
7 & 300\(\sim\)400 & Annual scale \\
8 & \(\sim\)1000 & Biennial scale (2\(\sim\)3 years) \\
9 & 2500–3500 & Inter-annual scale (ENSO’s 3\(\sim\)7 years) \\
10 & 4000–6000 & Decadal scale (8\(\sim\)15 years) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Timescales of IMFs.  
And, by applying the threshold value to all triangular components of \(\{C_{ij}^{k}\}\), we finally obtain the adjacency matrix, \(\{A_{ij}\}\), representing a scale-dependent network.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 3 Security and Trust of Situational Awareness
### SA Components Susceptible to Security Attacks  
Figure 2 shows potential security attacks on different SA components that can negatively affect the integrity and trust of COP. Mainly, the security attacks can be characterized by four main aspects of SA:  
* Attacks on communication links;
* Attacks on sensors;
* Attacks on the computing hardware of SA infrastructure; and
* Attacks on the equipment of dismounted operators and pilots.  
Both the wired and wireless communication links are vulnerable to security attacks. As depicted in Figure 2, it is possible for an attacker to compromise the communication links between: (i) radars and the radar data fusion center, (ii) satellite and UAVs, (iii) satellites and fighter aircraft, (iv) aircraft and the aircraft data fusion center, (v) the ground station and the UAVs, (vi) sensors and the sensor fusion center, (vii) the sensor fusion center and the COP builder, (vii) the radar data fusion center and the COP builder, (ix) the UAV data fusion center and the COP builder, (x) the aircraft data fusion center and the COP builder, (xi) the COP builder and the commander's COP display center, and (xii) the COP builder station and other recipients of COP. Sensors can be attacked to manipulate the physical properties of sensors to provide malicious readings [15]. An attacker can also conduct security attacks on the computing hardware of SA infrastructure including the radar data fusion center, aircraft data fusion center, sensor fusion center, UAV command and control station, and the COP builder. Finally, an attacker can compromise sensors and equipment, such as HUDs and HMDs, that are carried by dismounted operators and pilots.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 2 Related Research
### Earthwork Operational Simulation and Monitoring
For example, the designs, material properties, and other characteristics of earthwork projects may vary [42]. Even for a single construction site, the material properties exhibit dynamics in three dimensions [43]. This highlights the necessity of using accurate design and construction data with advanced data collection methods such as 3D geotechnical investigation, unmanned aerial vehicle (UAV)-based mapping, and laser scanning. On the monitoring side, tracking construction progress involves the acquisition of operational as-built data, processing it into the required format for visualization and analysis, and comparing it with the as-planned data. Conventionally, these tasks were performed manually by surveyors, which is a time-consuming process. In recent years, advanced data-capturing tools and techniques such as laser scanning and photogrammetry have been proposed for construction monitoring [6]. For the comparison of as-planned and as-built models to identify the progress, multiple methods have been proposed, including image vs. model, point cloud vs. model, and model vs. model [44]. The as-built time-lapsed photograph has been used and compared with the BIM-based as-planned model to monitor the construction of a building project [45]. While effective for progress visualization, such methods struggle with quantitative analysis, such as volumetric computation. To address this, the construction images were processed into a point cloud and compared with the as-planned BIM model in an augmented reality (AR) environment for progress quantification [46]. Moreover, the point cloud was converted into an as-built BIM model that enhances model visualization and provides progress quantification [47]. However, such methods still rely on homogeneous surfaces or solid BIM models, which often fail to reflect the actual material condition, particularly for the earthwork construction site. The lack of geotechnical variation and real-time volumetric updates limits the accuracy and applicability of these methods in earthwork construction.
# Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material
## 3 Results
### NMR Analysis of Pore Properties  
NMR analyses were done using an MiniMR-60 magnetic resonance imaging (MRI) analysis system manufactured by Shanghai Newmai Co. Ltd., China. As the samples were very small, three samples were tested for each group, and their mean values were used for analysis to minimize the error. NMR experiments were carried out for 28 d of curing samples of group A and B, and the pore structure parameters of SWCPB were obtained.  
NMR total relaxation (T2) time is related to surface relaxation, loose relaxation of fluid procession, and diffusion relaxation caused by gradient fields [22]. For the water-saturated samples, T2 relaxation time was directly proportional to the pore size and the magnitude of the T2 curve, which directly reflected the porosity of SWCPB samples. Pore size distribution maps were plotted by considering the average amplitude of NMR, as shown in Figures 4 and 5, for groups A and B, respectively.  
Three peaks of T2 relaxation time for group A after 28 d of curing can be observed in Figure 4, in which the first peak emerged at about 0.28 ms, the second peak observed at about 11.50 ms, and the third peak appeared at 204.91 ms.  
The pore ratio of A1 was shown in Figure 4a, in which the total porosity was about 1.99%. The main pores in A1 were micropores (1 st peak accounted for 93.3%), the mesopores accounted for a relatively small proportion (2 nd peak accounted for only 1.91%), and there were also many macropores (3 rd peak accounted for 4.79%). The pore ratio of A2, as depicted in Figure 4b, showed an increase in total porosity as the limestone powder consisted of fewer active ingredients than the cement. The addition of limestone powder increased the micropores but decreased the macropores, and the change of mesopores was not significant. By further increase of limestone powder to about 15%, as shown in Figure 4c, the total porosity, proportion of micropores, and mesopores were increased, while the amount of macropores was found to be reduced. When the addition of stone powder increased and reached about 20% (Figure 4d), the mesopores remained the same while the micropores increased and the macropores decreased, but the total porosity decreased overall.  
The NMR distribution of T2 relaxation time for 28 d of the group B sample showed two peaks in Figure 5, the first peak appeared in the vicinity of 0.30 ms, while the second peak emerged at around 204.90 ms.  
Figure 4: Pore size distribution of group A.  
Due to poor grading of group B (\(Cu>40\)), there was no obvious difference between the micropores and mesopores. Therefore, the curve had two peaks, and the peak areas of the first peak and the second peak in each group were assumed for 95% or higher of the total area. By increasing the limestone powder, the distribution of micropores decreased, but the macropores increased slightly while the total porosity remained constant. However, when the addition of limestone powder reached 20%, the proportion of micropores increased significantly and the macropores decreased.
## 8 Conclusions  
In order to enhance the LVRT performance of the SCIG-based WF, partial installation of the DFIG with the new rotor-side controller based on the FLC is proposed in the present study. Moreover, a comparative study of the proposed and conventional rotor-side controllers is carried out. Based on the simulation results and performance analyses, the following points are of notable significance regarding the proposed method:  
1. The proposed FLC-controlled DFIG of a lower power rating can stabilize the larger power rating of SCIG as well as conventional SGs during fault conditions.
2. The installation cost can be decreased by incorporating a small number of VSWT-DFIGs with the proposed controller and a large number of FSWT-SCIGs into a WF.
3. The proposed FLC controlled DFIG system can maintain its terminal voltage at constant under normal operating conditions by effectively injecting reactive power into the grid.  
Therefore, if the proposed DFIG with a relatively small power rating is installed at a WF composed mainly of SCIGs, its LVRT capability, as well as the stability of a connected power system, can be enhanced.  
**Acknowledgments:** The present study was supported by a Grant-in-Aid for Scientific Research (B) from the Ministry of Education, Science, Sports, and Culture of Japan.  
**Author Contributions:** [PERSON] and [PERSON] developed the theoretical concepts, designed the power system model and proposed RSC controller, and performed the simulation analysis. [PERSON] and [PERSON] developed the fuzzy logic controller. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] revised the manuscript. [PERSON] wrote the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
FSWT  
fixed-speed wind turbine  
SCIG  
squirrel cage induction generators  
WF
wind farm  
LVRT
low-voltage ride through  
VSWT  
variable-speed wind turbine  
DFIG  
doubly fed induction generators  
RSC  
rotor-side converter  
GSC  
grid-side converter  
FLC  
fuzzy logic controller  
SG  
synchronous generator  
STATCOM  
static synchronous compensator  
SMES  
superconducting magnetic energy storage  
ECS  
energy capacitor system  
PMSG  
permanent magnet synchronous generator  
MPPT  
maximum power point tracking  
WRIG  
wound rotor induction generator  
IGBT  
insulated gate bipolar transistor  
PWM
pulse width modulation  
NB  
negative big  
NM  
negative medium  
NS  
negative small  
ZO  
zero  
PS
positive small  
PM  
positive medium