esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The use of ICTs has several advantages, which was also the statement of several respondents. However, as several also stated, the expectations and benefits of implementingICTs should be greater than the factors (in many cases, the concerns) that hinder the implementation of the decision to implement ICT. This research sought to quantify and analyze these implementation factors focusing on cost optimization throughout the life cycle of the construction project and a positive impact on cost management during the life cycle of buildings. Experts in the field answered questions about these implementation factors. They tried to quantify the impact level of implementation factors on the Likert scale based on managing construction projects. Based on the answers, the RII index was used to obtain the ranking of implementation factors resulting from thinking about ICT implementation in construction projects' cost management. The final ranking, which considers the entire life cycle of the construction project and buildings (i.e., pre-design and design stage, project stage, construction phase, and maintenance and use stage of the building, up to its liquidation) is given in detail in Table 5.  
Based on the ranking results, it can be stated that the most extensive relative importance index was achieved for the implementation factor \"Investment Costs for ICTs\", with a value of 0.879, which falls in the interval (H) (\(0.8\leq\text{RII}\leq 1\)), i.e., a high value. The \"legislative framework\" achieved a similar value and the same order in the ranking. These two implementation factors achieved a relative importance index higher than 0.8 and are therefore considered significant. These results suggest a research statement and consideration of the most critical implementation factor. However, for a correct evaluation and conclusion, this result must be evaluated by statistical tests that confirm its statistical significance. In addition, however, the research also yielded other quantitative and qualitative results that need to be discussed within the topic. Significant results, according to the ranking, also included the following implementation factors (factors that reached 0.6 and higher): level of use of ICT by other participants in the construction project; management support; system maintenance costs during its lifetime; system maintenance and service and the need to upgrade the system; the need to recruit IT staff to manage ICTs; ability to embrace innovation and change; compatibility of software solutions; user qualification (training and certificates); and readiness and disinterest of users. Of a total of 16 examined implementation factors, up to 12 (75%) can be considered significant based on ranking. All examined economic factors were identified as significant implementation factors. These fall in the interval of (\(0.6\leq\text{RII}\leq 0.8\)), which can
# \(HM^{3}a1D\): Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model
## 4 Proposed Method: \(HM^{3}aID\)
### Detection Phase  
In the detection phase, as shown in the bottom part of Figure 1, for each program \(P_{l}\) at runtime, first its corresponding system call sequence is collected (by _detection profiler_ module), and then its corresponding action sequence \(O^{l}\) is generated (by _preprocessing_ module). The induced action sequence \(O^{l}\) is given to all learned malware HMMs, \(\{(\lambda_{c_{1}},\mathcal{T}_{c_{1}}),(\lambda_{c_{2}},\mathcal{T}_{c_{2}}),\ldots,(\lambda_{c_{k}},\mathcal{T}_{c_{k}})\}\) and then \(HM^{3}alD\) aggregates their returned results by Algorithm 4. Note that, in Algorithm 4, the forward values are calculated by multiplying small probabilities, and with long action sequences we risk getting underflow. To avoid this, at each time step in the forward algorithm, we normalize the forward values. This technique is presented as Algorithm 5 in Appendix B.  
Algorithm 4 applies HMM forward algorithm, \(\textbf{forward}(O^{l},\lambda_{c_{i}})\) on the induced action sequence \(O^{l}\) from the running of program \(P_{l}\) and it calculates \(LP(O^{l}|\lambda_{c_{i}})\) iteratively. The result will be _\"Malware\"_ when at least one of the malware HMMs return the probability value \(\textit{forward\_value}\) greater than or equal to the corresponding threshold value \(\mathcal{T}_{c_{i}}\), otherwise it will be _\"Benign\"_.
## 4 Field Test Results and Discussion
### CO\({}_{2}\) Concentration Regulation Results  
In practical application, the operation frequency of the control facility is limited by the inherent 0.5 second delay of the solenoid valve. Therefore, the control parameters calculated by the FLC for adjusting the duty cycle and pulse width modulation of the electromagnetic valve was normalized to 1-10 from the original 1-100. In order to achieve the purpose of spreading evenly the compensated pure CO\({}_{2}\) gas, the catheter through the whole greenhouse was opened, with a gas vent spacing at the same fixed distance. Even though, there was still an obvious diffusion delay corresponding to the  
Figure 12: (**a**) The photo of a sensor node in a solar greenhouse; (**b**) The measured CO\({}_{2}\) concentration and luminance in a greenhouse; (**c**) The measured temperature and humidity in a greenhouse.  
overshoot and oscillation, shown in Figure 13. In the field test, the aimed CO\({}_{2}\) concentration is 800 ppm and 1200 ppm, as examples responding to the different requirements of different plants. According to the test results, at least 40 min was required for the oscillation stage. In the 800 ppm control test, when the CO\({}_{2}\) concentration under control was in the stable stage, the fluctuation range was from 762 ppm to 849 ppm, which was acceptable in the actual agricultural application. There was a similar result in the 1200 ppm control test. The CO\({}_{2}\) concentration data in Figure 13 are the average results of the measurement result of each sensor node. In addition, some human-induced mutation was inevitable, because the actual field test environment was time-varying. However, a more reasonable membership and fuzzy rules are probably able to optimize the regulation performance.
[18], a specific approach with the transmitters oriented towards the center of the receiving plane (i.e., the pointing center F) is proposed, when the received power level is maximized due to the line-of-sight components on the focal point. The proposed scheme offers a significant accuracy improvement of up to \(\sim\)66%, as compared to a typical non-tilted transmitter VLP, at a dedicated location within a room, using a low complex linear least square algorithm with polynomial regression. In the last paper on \"_Impact of Transmitter Positioning and Orientation Uncertainty on RSS-Based Visible Light Positioning Accuracy_\", [PERSON] et al. [19] present simulation-based results on the impact of the transmitter's position and orientation uncertainty, on the accuracy of the VLP system based on RSS. Authors focus on several constraining factors for RSS-based algorithms, particularly multipath channel characteristics and set-up uncertainties. The last three papers are devoted to applications of VLC within underwater optical communications (UWOC) and vehicular communications. [PERSON] et al. [20], in the paper on \"_LDPC-Coded CAP with Spatial Diversity for underwater visible-light communication (UVLC) Systems over Generalized-Gamma Fading Channel_\", propose a low-density parity-check (LDPC)-coded CAP modulation with spatial diversity, to mitigate turbulence induced fading in a UVLC channel. Authors derive an approximated bit-error rate (BER) for the CAP modulation scheme, with a spatial diversity and equal-gain combination. They also simulate the performance of the CAP system with the diversity and LDPC for various turbulence conditions. [PERSON] et al. (2011), in their paper on _\"Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links\"_, investigate the use of high spectral efficiency modulation schemes, based on orthogonal-OFDM, namely DC-biased, asymmetrically-clipped, and layered asymmetrically clipped O-OFDM (DCO-, ACO-, and LACO-OFDM), and discuss the limitations in terms of the silicon photo-multiplier-based Rx saturation and the limited dynamic range of the transmitter, which determine the range of operation of the UWOC link, for a given target BER. In the last paper, _\"Performance of Vehicular Visible Light Communications under the Effects of Atmospheric Turbulence with Aperture Averaging\"_, [PERSON] et al. (2020) investigate the performance of a vehicular VLC link with a non-collimated and incoherent light source (a light-emitting diode) as the transmitter, and two different optical receiver types--a camera and photodiode--under the atmospheric turbulence conditions, and by considering the aperture averaging feature. The authors declare no conflict of interest.
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
## 4 Multi-Scenario Physics-Based Tsunami Hazard Analysis: Data and Methods
### Tsunami Hazard Maps for Alexandria
The use of GIS tools allows for the combination of topographic models of coastal areas with the empirical relationship of the attenuation of wave ingression, delimiting the inundated areas by classes of maximum potential scenario run-up values at the coast (Figure 3). \[\text{D}=\text{R}/\text{tg}\alpha \tag{1}\]  
where D is the inundation distance, R is the run-up, and tg\(\alpha\) is the roughness; in this work, it varies between 0.015 and 0.08. High inundation depth and zone are shown on the eastern side of the study area, as indicated in Figure 14. This highly inundated area occurs in the Al Montazah district, one of the busiest tourist districts during the summer. Also, the area around Alexandria Port, the middle of the study area, shows a large extent inundated area. This part of the city is highly commercial, social, and strategic, of great importance since this port is considered the largest in Egypt. However, the area located to the east of Alexandria, which imposes the highest tsunami hazard (Figure 14), has a high topography relative to the east side. Still, if no mitigation measures are applied, the existing water canals may augment or exaggerate the inundated area for the lowlands behind. The west of Alexandria, called Al Sahel Al Shamally, shows a less inundated area than the eastern side because it is naturally protected by a ridge of carbonate material that forms the shoreline. The west side of Alexandria's coast is less hazardous due to the presence of the ridge. The area between the shoreline and the road running along Alexandria's coast can be considered a highly hazardous zone for the people resting, swimming, and vacation along the coast. Figure 14: Joint peak ground acceleration (g) and inundation depth (m) (95 th and 50 th percentile) for the studied area.
## 5 Analyses and Results
### Results of Measurement Model Assessment
#### 5.1.4 Discriminant Validity  
Discriminant validity of the overall measurement model was established by examining the square root of AVE for each individual construct verse with its corresponding correlations, and checking correlation coefficient between any two constructs. Based on Table 4, the correlations between constructs ranged from 0.000 to 0.57 and the correlations among the constructs were less than the the square root of AVE, indicating each construct to be truly distinct from the others.
## 3 Design Formula of Rigid-Flexible Composite Lining Structure in ASTM Standard  
The design of trenchless rehabilitation lining pipes for drainage pipelines is divided into semi-structural repair and structural repair. The design of rigid-flexible composite lining structures in the ASTM standard [26] is based on Formula (13). The long-term creep effect of the pipeline is considered by changing the elastic modulus into the long-term elastic modulus \(E_{L}\), and the influence of geometric defects in the lining pipe is considered by introducing the ellipticity reduction factor \(C\)[28]. Finally, Formula (14) is obtained.  
\[E_{L}I=\frac{P_{cr}ND^{3}}{8\big{(}K_{1}^{2}-1\big{)}C} \tag{14}\]  
\[D=D_{0}-2(h-\overline{y}) \tag{15}\]  
where \(E_{L}\) is the long-term elastic modulus of the liner (MPa); \(I\) is the rotational inertia of the liner (mm\({}^{4}\)/mm); \(E_{L}I\) is the stiffness coefficient of the liner (MPa\(\cdot\)mm\({}^{3}\)); \(D\) is the average diameter of the liner (mm)); \(D_{0}\) is the outer diameter of the lining pipe (mm); \(h\) is the height of the strip profile (mm); \(\overline{y}\) is the distance from the inner surface of the strip to the neutral axis of the strip (mm); \(P_{cr}\) is the water pressure at the pipe bottom (MPa); \(C\) is the ellipticity reduction factor; \(q\) is the ellipticity of the original pipeline (%), but if the ellipticity cannot be measured, take 2%; \(K_{1}\) is the factor determined from the filling angle without grouting.  
The value of \(K_{1}\) in Formula (14) needs to be calculated by Formula (12). However, the relation between \(K_{1}\) and \(\alpha\) in Formula (12) is not clear, which is not conducive to the use of designers.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Surface Model Generation
#### 4.2.5 DSM Fusion  
In the proposed processing chain, the DSM fusion is applied for two main reasons: (1) to combine the information of forward and backward matching in cases when only a single stereo input set exists and (2) to combine multiple forward and backward matchings in cases when multiple input (stereo) sets exist. Thus, the fusion process gets multiple DSMs as input and calculates one final DSM. For simplicity, we stick to a local method [85] which takes all height measurements within a \(3\times 3\) pixel neighborhood and extracts the probability mode of this probabilistic height distribution. The only required parameter is a threshold value of how many input height values have to exist such that an output is generated. To get high coverage this value is set to 1 by default. We extend this method for arbitrary neighborhoods and also incorporate a step size. This step size can be used to generate a lower resolution DSM by, for instance, only extracting the probability mode at every second pixel in column and line direction. In contrast to a straightforward median-based approach, local height errors can better be eliminated using the probability mode [85].
## 2 Study Areas and Data  
The HY-2B satellite is equipped with a traditional dual-frequency (Ku-band and C-band) altimeter. The repetition cycle of the HY-2B altimeter is 14 days, and 386 passes cover the global ocean. The 20 Hz Ku-band echo waveform data included in the standard Sensor Geophysical Data Records (SGDR) provided by the National Satellite Ocean Application Service (NSOAS) are used in this study for waveform retracking. In addition, the SGDR data also contain the original result of the waveform retracking by the MLE4 retracker and the range corrections from different sources [27]. The tide gauge data to validate the accuracy of the retracking results of the HY-2B altimetry waveform are the Permanent Service for Mean Seal Level (PSMSL) data from the University of Hawaii Sea Level Center and include the raw tide information.  
According to the available of tide gauge measurement data and the HY-2B altimeter orbit data, two areas are selected as study areas: the 0-40 km offshore area in the southwest of the Indian Peninsula (area A) and the 0-40 km offshore area in the southwest of the Philippine Islands (area B), as shown in Figure 2.  
Area A is shown in the upper left corner of Figure 2. The pass 323 data of the HY-2B altimeter are selected in this study. Pass 323 is an ascending orbit, and its ground trajectory is parallel to the shoreline, so it has the largest proportion of data affected by coastal land in theory. The corresponding tide gauge station at Cochin (PSMSL Station ID 174, 9.967\({}^{\circ}\)N, 76.267\({}^{\circ}\)E) is located in this area. According to the time match of the tide gauge data and the HY-2B altimetry data, 15 cycles of altimetry data from 16 February 2019, to 9 November 2019, are used in this study (partial cycles are excluded due to the absence of satellite altimetry data or the lack of tide observations).  
Area B is shown in the upper right corner of Figure 2. To verify the influence when the observation mode of HY-2B altimeter transforms from ocean to land and from land to ocean, the pass 196 data of the HY-2B altimeter are selected in this study. Pass 196 is a descending orbit, and the geographical conditions on both sides of the orbit are more complicated. In the range of 13.75\({}^{\circ}\)N to 14\({}^{\circ}\)N, the satellite flies over coastal islands, completing observation mode transition. The corresponding tide gauge station at Subic Bay (PSMSL Station ID 382, 14.765\({}^{\circ}\)N, 120.252\({}^{\circ}\)E) is located in this area. According to the time match of the tide gauge data and the satellite altimetry data, 38 cycles of altimeter data from 28 January 2019, to 10 August 2020, are used in this study.
## 4 Mathematical Formalism of Consensus Through Path-Laplacian Matrices
### Consensus Dynamics on Directed Networks  
Suppose that \(G=(V,E)\) is now a directed network with \(A\) as an adjacency matrix. For each node \(v_{i}\in V\), the _out-degree_ of \(v_{i}\) is defined as \(d_{i}^{\text{out}}=\sum_{j=1}^{n}A_{ij}\) and the _in-degree_ as \(d_{i}^{\text{in}}=\sum_{j}A_{ji}\). Then, the _directed Laplacian_ is given by  
\[L^{\text{out}}=D^{\text{out}}-A, \tag{14}\]  
where \(D^{\text{out}}=\text{diag}(d_{1}^{\text{out}},\ldots,d_{n}^{\text{out}})\). Note that \(L^{\text{out}}\) satisfies \(L^{\text{out}}\mathbf{1}=0\), but, in general, it is not symmetric, so its spectral properties differ from those of the undirected case [45]. We recall that a directed network is _weight-balanced_ if \(d_{i}^{\text{in}}=d_{i}^{\text{out}}\) for all \(1\leq i\leq n\). In this case, we can show that \(W^{-1}L^{\text{out}}W\) is symmetric. Consequently, \(L^{\text{out}}\) has real eigenvalues and a simple zero eigenvalue with the eigenvector \(\mathbf{1}\) (on both left and right) [46]. In the continuous-time case, this guarantees that the total sum of states remains invariant, so the final consensus state is exactly the arithmetic mean of the initial conditions. In contrast, if the network is not balanced, the consensus state will be a weighted average determined by the stationary distribution of the row-stochastic matrix \(W=I-\varepsilon L^{\text{out}}\)[43]. The discrete-time case is similar to the continuous one. The case of multi-hop interactions is expressed through the \(k\)-hop Laplacian defined as  
\[L_{k}^{\text{out}}=D_{k}^{\text{out}}-A^{(k)}, \tag{15}\]  
with \(D_{k}^{\text{out}}\) as the diagonal matrix of \(k\)-hop out-degrees and \(A^{(k)}\).
## 4 Conclusions  
A novel TFC PAN-NF HF membrane was developed to remove natural organic compounds during drinking water treatment. The membrane was modified, based on its physicochemical properties, to obtain a semiaromatic structured membrane and showed superior retention of the target NOM due to its small pore size, large negative charge, and hydrophilicity. Thus, it exhibited good fouling resistance. We further applied CEB to improve the organic-fouling resistance.  
NaClO-assisted CEB can remove most particulates and colloids and overcome the problems involved in removing NOM deposited on the membrane. Compared to the initial water flux of the PAN-NF membrane, the permeability recoveries after NaClO backwashing were 93.3% for HA and 93.7% for SA. Furthermore, the removal efficiencies after NaClO backwashing were 92.7% and 93.7% for HA and SA, respectively. We concluded that the NaClO concentration of 1 mg/L with a backwashing time of 30 s can be selected as the optimal chemical backwashing agent in actual HF NF water treatment applications. We expect that the accumulation of field data on NF using different real surface waters will contribute greatly toward the development of operational guidelines on NF plants and their operation.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11156764/s1](https://www.mdpi.com/article/10.3390/app11156764/s1)]([https://www.mdpi.com/article/10.3390/app11156764/s1](https://www.mdpi.com/article/10.3390/app11156764/s1)), Figure S1: Scanning electron microscopy (SEM) morphologies, Figure S2: Zeta potential of PAN-NF HF membrane, Figure S3: Contact angle measurements, Figure S4: Application of CEB in the PAN-NF HF membrane; Table S1: Membrane module characteristics and operating conditions, Table S2: Parameters of raw water samples from a water treatment plant at Jakarta, Indonesia.  
Conceptualization, H.K.; Methodology, H.K.; Software, H.K.; Validation, M.Z.; Investigation, H.K. and I.S.; Resources, H.K.; Data curation, H.K. and I.S.; Writing-original draft preparation, H.K. and M.Z.; Writing-review and editing, M.Z.; Visualization, H.K. and M.Z.; Supervision, M.Z.; Project administration, H.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the KOREA ENVIRONMENT INDUSTRY & TECHNOLOGY INSTITUTE (KEITI) through the Public Technology Program based on Environmental Policy, funded by the Korean Ministry of Environment (grant number 2018000700001).  
Figure 7: (**a**) Normalized permeate flux and flux recovery of the PAN-NF HF membrane during backwashing using different concentrations of NaClO as a function of time. (**b**) TOC removal efficiency of the PAN-NF HF membrane after backwashing with different concentrations of NaClO. Operating conditions: pressure = 5 bar; stirring speed = 300 rpm; cross-flow velocity = 1 L/min, conductivity = 300 μ/cm; backwashing pressure: 1 bar; backwashing period: 30 s; and temperature = 25 \({}^{\circ}\)C.  
Data Availability Statement: The data presented in this study are available in the Supplementary Materials.