esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Method
### Meshing for Computational Domain  
The meshing configuration with tetrahedron, pyramid, and prism mesh elements is adopted for computational domain. A finer mesh is used at corners and edges to capture the heat transfer effect. The sizing function with proximity and curvature is enabled to produce fine mesh. To trade-off between the accuracy and computational time, the meshing is formulated with five different mesh element numbers ranging from 236,834 to 8,200,752. The mesh independent study is carried out using five different mesh element numbers to establish that the simulated results are independent of mesh sizes with acceptable accuracy. The mesh independence test is carried out for ferrofluid coolant at 25 \({}^{\circ}\)C ambient and coolant temperatures, 0.2 T magnetic intensity considering both magnets, and 0.1 V voltage between electrodes. For various mesh element numbers, the flow characteristics of the Lorentz force and average velocity are simulated. Figure 3 depicts the simulated results for various mesh element numbers. The computed Lorentz force and average velocity converge beyond the mesh-element number of 2,207,945. Therefore, the mesh element number of 2,207,945 is adopted as the final mesh configuration for computational geometry in numerical analysis. Figure 4 depicts the adopted meshing configuration for the computational domain with cooling channel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Fe3O4** & **Water** & **Copper** & **Aluminum** \\ \hline Density, kg/m\({}^{3}\) & 5180 & 998.2 & 8933 & 2700 \\ Specific heat, J/ kg\({}^{\circ}\)C & 670 & 4182 & 385 & 900 \\ Thermal conductivity, W/m\(\cdot\)s & 80 & 0.6 & 400 & 238 \\ Dynamic viscosity, Pa\(\cdot\)s & - & 0.001 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermophysical properties of ferrofluid nanoparticles and water.  
Figure 2: Detailed view of cooling channel with internal fins.
## 3 Results and Discussion  
### Reaction Systems  
The radiolytic degradation of water supplies hydroxyl radical (\({}^{\bullet}\)OH, yield 0.28 umol J\({}^{-1}\)), hydrated electron (e\({}_{\text{aq}}^{-}\), 0.28 umol J\({}^{-1}\)), and hydrogen atom (H\({}^{\bullet}\), 0.06 umol J\({}^{-1}\)) reactive intermediates [28]. Under practical conditions, dissolved air is also present in water treatment, assuring an O\({}_{2}\) concentration of 0.25 mmol dm\({}^{-3}\). e\({}_{\text{aq}}^{-}\) and H\({}^{\bullet}\) in aerated solutions mainly disappear in reaction with dissolved O\({}_{2}\) (Reactions (1) and (2)). In the reactions, superoxide radical anion/perhydroxyl radical pair forms (O\({}_{2}\)\({}^{\bullet-}\)/HO\({}_{2}\), pK\({}_{\text{a}}\) = 4.8, [29]). These low-reactivity radicals mostly terminate in H\({}_{2}\)O\({}_{2}\)-forming reactions (Reaction (3)):  
\[\text{e}_{\text{aq}}^{-}+\text{O}_{2}\rightarrow\text{O}_{2}\text{{}^{\bullet -}} k=1.9\times 10^{10}\text{ mol}^{-1}\text{ dm}^{3}\text{ s}^{-1} \tag{1}\]  
\[\text{H}^{\bullet}+\text{O}_{2}\rightarrow\text{HO}_{2}\text{{}^{\bullet}} k=2.1\times 10^{10}\text{ mol}^{-1}\text{ dm}^{3}\text{ s}^{-1} \tag{2}\]  
\[\text{O}_{2}\text{{}^{\bullet-}}+\text{HO}_{2}\text{{}^{\bullet}}+\text{H}_{ 2}\text{O}\rightarrow\text{H}_{2}\text{O}_{2}+\text{O}_{2}+\text{OH}^{-}\text{ }k=9.7\times 10^{7}\text{ mol}^{-1}\text{ dm}^{3}\text{ s}^{-1} \tag{3}\]  
Therefore, during radiolysis of aerated systems, H\({}_{2}\)O\({}_{2}\) forms with a high initial yield of \(\sim\)0.3 umol J\({}^{-1}\)[22]. The initially linear H\({}_{2}\)O\({}_{2}\) concentration-dose dependence curve at higher absorbed doses (2-3 kGy, 1 Gy = 1 Jy kg\({}^{-1}\)) levels off at a constant value. This leveling off is due to the competition of H\({}_{2}\)O\({}_{2}\) forming and consuming reactions, e.g., Reaction (4) converts e\({}_{\text{aq}}\)\({}^{-}\) to \({}^{\bullet}\)OH [30].  
\[\text{e}_{\text{aq}}\text{${}^{-}$}+\text{H}_{2}\text{O}_{2}\rightarrow\text{{}^{ \bullet}}\text{OH}+\text{OH}^{-}\text{ }k=1.2\times 10^{10}\text{ mol}^{-1}\text{ dm}^{3}\text{ s}^{-1} \tag{4}\]  
Therefore, during wastewater treatment by ionizing radiation in the presence of dissolved air, the chemical transformations are mostly initiated by \({}^{\bullet}\)OH. Water radiolysis may provide the cleanest source of hydroxyl radicals, providing excellent potential for studying their reactions [28].
# Impact and Control of Reflected Noise from an Overpass Bottom
## 2 Materials and Methods  
### Measurement Methods and Investigation Sites  
A Class 1 sound meter (NL-32; Rion Co., Ltd., Tokyo, Japan), which complies with International Electrotechnical Commission (IEC) 61672-1, was used. The measurements were acquired using the reflected noise measurement plan recommended by the WSDOT [34] and the Ohio Department of Transportation [23]. Measurements were conducted in accordance with the method approved by the FHWA [39] and American National Standards Institute (ANSI) S12.8 Standard [40]. Each measurement at each measurement point was taken over a period of 1 hour to obtain a representative traffic noise equivalent sound level (L\({}_{\text{Aeq}}\)). When conducting measurements, the sound meter was in the A frequency-weighting mode and fast time-weighting was adopted.  
Three field investigation sites with reflected noise were selected. The road sections chosen at each site were roughly 200 m long. Suitable measurement locations were established to identify the noise profiles in the area. To determine the noise profiles at different heights, measurement points were at heights of 1.5 and 4.5 m. Two locations were selected at each site. Noise levels were measured at heights of 1.5 m and 4.5 m at each location. Monitoring at the 4 measuring points was conducted simultaneously. To characterize decreases in reflected noise as distance from the noise source of reflected noise increases, cases from Ohio [26] and ANSI [40] methods were used to establish measurement locations at different distances from the source of reflected noise. Since American design regulations are mostly adopted for road design in Taiwan, the deployment planning of this study was conducted making reference to the I-5 Ship Canal Bridge Study of Washington State Department of Transportation [34] and the Measurement of Highway-related Noise of the Federal Highway Administration (FHWA) [39]. The I-5 Ship Canal Bridge Study set up 11 measuring points on both sides of the road within a distance of 1 km to measure the noise level during peak traffic hours. The distance between measuring points was mostly around 200 to 250 m. The area was divided into 4 divisions (NE, SE, NW, and SW) in order to facilitate simulation with the model [34]. If sensitive points at different heights need to be considered, apart from the basic height (1.5 m), measuring points may also be set at the height of 4.5 m and 7.5 m. If noise attenuation needs to be considered, measuring points may be deployed at the distance of 7.5, 15, and 30 m from the centerline of the nearest lane [39].
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 4 Results and Discussion
### Influence of Wellbore Section Length
#### 4.3.2 Influence of Vertical Section Length  
In order to investigate the influence of vertical section length on pressure fluctuations at the downhole, a constant liquid injection scheme and horizontal section length of the well tubing are maintained, while the vertical section lengths are varied to 1000 m, 1500 m, and 2000 m.  
As depicted from the simulation results in Figure 11, with the increase in the vertical section length of the well tubing, the initial value of downhole pressure gradually increases, the amplitude of pressure fluctuations at the downhole gradually decreases, and the rate of increase in downhole pressure slows down. With the passage of time, shorter vertical sections are more susceptible to generating higher downhole pressures. For every additional 500 m in the vertical segment length, the initial value of the downhole pressure increases by approximately 5.145 MPa, and the amplitude of pressure fluctuations decreases by 10-20%.
## 2 Materials and Methods  
As shown in Figure 1, we consider a UAV air-to-air (A2A) model for independent edge communication via 6G, and the UAV swarm is ordered; that is, each UAV has a specified relative position (SRP) which is known to UAVs. Among them, everyone has simple half-duplex communication capability and limited computing power. However, during the flight, due to various reasons, there are always some small deviations from the SRP. Therefore, the UAV swarm maintains an overall relatively orderly state with local fluctuations, which means although SRP is known, the actual location needs to be determined through communication. In addition, this article assumes that the model is in an area where GPS is not available, and there is no external signal to assist communication during the flight, so the UAV swarm performs positioning, formation adjustment, and communication through the A2A internal link. On the other hand, there are only a limited number of UAVs transmitting signals which do not contain valid information, such as the source UAV number, distance, etc., acting as aerial base stations (ABSs) according to certain rules at the same time, and other UAVs act as APs to passively receive the approach angles of signals.
# Depth Estimation of a Deformable Object via a Monocular Camera
## 2 3D Labeled Reconstruction
### SIFT Features to Label the 3D Reconstruction  
Given a two-dimensional image \(I\left(x,y\right)\), the SIFT algorithm [19] is able to extract effective key points through the LoG operator. By computing the gradients in the neighborhood of each key point, a corresponded descriptor vector can be obtained to distinguish the key point. Then, we can use the SIFT algorithm to find a set of feature points (denoted as \(\left\{m_{s}^{i}\right\}_{s=1...S}\)) and their corresponding descriptor vectors (denoted as \(\left\{l_{s}^{i}\right\}_{s=1...S}\)) for the image captured at the \(i^{th}\) instant, which jointly yield a two-tuple set denoted as \(\left\{\left(m_{s},l_{s}\right)\right\}_{s=1...S}\). Executing the same operation to all the images, we can finally get \(\left\{\left(m_{s}^{i},l_{s}^{i}\right)\right\}_{\begin{subarray}{c}i=1...N \\ s=1...S^{i}\end{subarray}}\) (where \(S^{i}\) indicates the total number of feature points derived from the SIFT algorithm for the image captured at the \(i\)th instant).  
Subsequently, we need to attach the descriptor vectors to the corresponding 3D points on the surface of the reconstructed point cloud. It can be deduced from Equations (1) and (2) that  
\[P_{j}=R_{i}^{T}\left[\begin{array}{cc}\frac{d_{j}^{i}}{\tilde{I}}I_{2\times 2 }&O_{2\times 1}\\ O_{1\times 2}&d_{j}^{i}\end{array}\right]\left[\begin{array}{c}m_{j}^{i}\\ 1\end{array}\right]-R_{i}^{T}t_{i}. \tag{5}\]  
By Equation (5), we can determine the 3D point \(P_{s}^{i}\) on the reconstructed point cloud corresponding to the feature point \(m_{s}^{i}\). Thus, we can acquire a two-tuple set \(\left\{\left(P_{s}^{i},l_{s}^{i}\right)\right\}_{\begin{subarray}{c}i=1...N \\ s=1...S^{i}\end{subarray}}.\) For denotation simplification, we define that any \(P_{j}\) that is occluded from the camera view at the \(i\)th instant, or whose corresponding \(m_{j}^{i}\) is not a key point, still has a descriptor vector \(l_{j}^{i}=0\). Therefore, we can get \(\left\{\left(P_{j}^{i},l_{j}^{i}\right)\right\}_{\begin{subarray}{c}i=1...N \\ j=1...M\end{subarray}}\) after the \(N\)th instant. The required 3D labeled reconstruction is the following:  
\[\left\{\left(P_{j}^{*},l_{j}^{i}\right)\right\}_{j=1...M}=\left\{\left(P_{j}^ {*},\bar{I}_{j}\right)\right\}_{j=1...M} \tag{6}\]  
where \(\bar{I}_{j}\) represents the average vector over all the non-zero descriptor vectors (i.e., \(\left\{l_{j}^{i}\left|i\leq N,l_{j}^{i}\
eq 0\right.\right\}\)) related to the 3D point \(P_{j}\) within the \(N\)th sampling instant.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 4 Performance Evaluation
### Experimental Results
#### 4.4.1 The Impact of Probe Selections  
In this simulation, without losing generality, two additional _probe_ selections are used. They are _sequential probe_ that selects an entry from FIB sequentially as a _probe_ and _random probe_ that selects a content name from either PIT or FIB randomly as a _probe_.  
Table 2 demonstrates the accuracy of the content provider and the length of the routing path, respectively.  
Our _probe_-based approaches rely on FIB and SPT to find the best content provider with the shortest path to reach it. However, the result reveals that the expected content provider is less than 20%. Because router routing tables are not centrally computed or maintained in CCN, routing trajectories with high probability are different from what _I router_ expects.  
The better the probe selection for our _probe_-based approach, the higher the expected content provider rate and the shorter the path length. Among the _probe_ selections, _FIB probe_ performs the best, because _FIB probe_ is devoted to improving the highest-cost content name in FIB. However, since _PIT probe_ can reduce the waiting time of interest, _FIB probe_ and _PIT probe_ are used in the following simulations.
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## Abbreviations  
\begin{tabular}{l l} GNSS & Global Navigation Satellite System \\ IMU & Inertial Measurement Unit \\ INS & Inertial Navigation System \\ RTS & Rauch-Tung-Striebel \\ EKF & Extended Kalman filter \\ CKF & Cubature Kalman Filter \\ ESKF & Error state Kalman filter \\ RMS & Root mean square \\
**Symbols** \\ \(X\) & System state \\ \(\widetilde{X}\) & Prediction of the system state \\ \(\widetilde{\mathbf{X}}\) & Update of the system state \\ \(\delta\widehat{x}_{k,N}\) & The smoothed estimate of the state vector \\ \(U_{k}\) & The smoothing gain matrix \\ \(\widehat{P}_{k,N}\) & The covariance of smoothing estimates \\ \(W\) & Gaussian white noise \\ \(Z\) & System measurement \\ \(V_{k}\) & Measurement of Gaussian white noise \\ \(f(\cdot)\) & State functions of nonlinear systems \\ \(h(\cdot)\) & Measurement functions for nonlinear systems \\ \(F_{k}\) & The Jacobi matrix of \(f(\cdot)\) at \(x_{k}\) \\ \(H_{k}\) & The Jacobi matrix of \(h(\cdot)\) at \(x_{k}\) \\ \(p_{k}\) & Covariance matrix of states \\ \(q_{k}\) & Covariance matrix of noise \\ \(\delta\) & Error character \\ \(x_{t}\) & True state \\ \(x\) & Nominal state \\ \(p_{t}\) & Position at time t \\ \(v_{t}\) & Velocity at time t \\ \(q_{t}\) & Quaternion at time t \\ \(R_{t}\) & Rotation matrix at time t \\ \(\theta_{t}\) & The angular vector at time t \\ \(a_{bt}\) & Acceleration bias at time t \\ \(\omega_{bt}\) & Angular velocity bias at time t \\ \(a_{m}\) & Acceleration measurement \\ \(\omega_{m}\) & Angular velocity measurement \\ \(a_{n}\) & Acceleration noise vector \\ \(\omega_{n}\) & Angular velocity noise vector \\ \(a_{\omega}\) & Acceleration bias vector \\ \(\omega_{\omega}\) & Angular velocity bias vector \\ \(\Delta t\) & Time interval from k to k+1 \\ \(w_{\eta k}\) & Velocity Gaussian random noise \\ \(w_{\theta k}\) & Angular Gaussian random noise \\ \(w_{ak}\) & Acceleration bias Gaussian random noise \\ \(w_{\omega k}\) & Velocity biased Gaussian random noise \\ \(W_{\upsilon}\) & Corresponding covariance matrix of \(\mathbf{w}_{\upsilon k}\) \\ \(\upsilon\) & Corresponding covariance matrix of \(\mathbf{w}_{mk}\) \\ \(\sigma_{a_{n}}\) & Acceleration Gaussian white noise \\ \(F_{ck}\) & Error state Jacobi matrix \\ \(F_{wk}\) & Noise state Jacobi matrix \\ \(q_{w}\) & Noise covariance matrix \\ \(U_{k}\) & Smoothing gain matrix \\ \(count\) & Smooth counter \\ \end{tabular}
## 4 Taxonomy of Cooperation in UASNs
### Reputation-Based Cooperation
#### 4.1.2 Indirect Reputation (Second-Hand)  
An indirect reputation is introduced by [56,57] that allows the periodic exchange of the observed behavior of a node with other nodes. In CONFIDENT [58,59] and CORE [56] there is a monitor module that continuously observes the behavior of the neighboring nodes in the network. This module detects  
Figure 5: Taxonomy of cooperation in UASNs.  
the misbehaving nodes and broadcasts an alert about them to other nodes. When other nodes receive such alert, they delete them from their list; so that, they will be gradually isolated from the network. Although, most of the common methods of adjusting the reputation can be either by linear method [55] or by nonlinear method [57], the selfish clever node may maintain a high reputation threshold by forwarding other node's packet which makes it undetected. In [57], a reputation-based method that uses direct reputation information so that each node can maintain the rating and information about the reputation of other nodes is introduced. Furthermore, they designed a reputation-based approach which takes into account the indirect reputation and accepts the modified reputation rating information. A reputation information-based rating with indirect reputation compatibility for trust updating and rating is proposed in [60], where the historical behavior of the opponent node is captured and the expected future behavior for such opponent node is predicted by the use of trust. In reputation based approaches, usually two forms of information are obtained, one is obtained by interacting directly to the neighbor (intermediate) and the other is obtained by the direct neighbors' aggregation.
## 3 Results
### Spatiotemporal Variation in VPD
#### 3.1.2 Characterization of Spatial Variation in VPD  
Figure 3 shows the spatial variations in VPD at every 10 years divided into 0.2 intervals. Overall, the VPD in the Yellow River Basin s gradually increased from west to east and radiated outward from its source (the arid region), where the VPD was highest. In the ecologically fragile areas upstream, the VPD has remained relatively stable (below 0.8) during the past nearly 40 years (below 0.4 in the source area of the Yellow River). In the Loess Plateau and downstream areas, the VPD generally increased, showing a local breakthrough of 1.2.  
Figure 3: Spatial distribution changes in VPD during the growing season in the Yellow River Basin ((**a**) 1982; (**b**) 1990; (**c**) 2000; (**d**) 2010; (**e**) 2019).  
Figure 2: The interannual dynamic of annual mean VPD during the growing season in the Yellow River Basin.  
Figure 4 shows the changing proportions of different VPD intervals over the past nearly 40 years. Within the absolute main body, the VPD spatially ranged between 0.2 and 1.2. During the study period, the 0.2-0.4 spatial range remained relatively stable (~9%), the 0.4-0.6 spatial range slightly decreased, the 0.6-0.8 spatial range significantly decreased (by 38% between 1982 and 2019), the 0.8-1.0 and 1.0-1.2 spatial ranges rose significantly (by 21% and 25%, respectively), and the 1.2-1.4 spatial range, although a relatively small percentage, broke through the 0 value and shows an overall upward trend.
# Development of an On-Line Defect Detection System for EDM Process
## 2 Proposed Methodology
### Anomaly Detection Method
A model built from training data is inputted into the model. The model will calculate each datum, and the model results and actual conditions will generate a result analysis table. There are four conditions:  
TP (true positive): the actual state is normal, and the model judges it to be normal. FP (false positive): the actual state is abnormal, and the model judges it to be normal. FN (false negative): the actual state is normal, and the model judges it to be abnormal. TN (true negative): the actual state is abnormal, and the model judges it to be abnormal. The sensitivity, specificity, false positive rate (Fpr), and false negative rate (Fnr) of the model based on the four conditions are calculated, as shown in Equations (14)-(17). \[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{14}\]  
\[\text{Specificity}=\frac{\text{FN}}{\text{FP}+\text{TN}} \tag{15}\]  
\[\text{Fpr}=1-\text{Sensitivity} \tag{16}\]  
\[\text{Fnr}=1-\text{Specificity} \tag{17}\]  
The higher the sensitivity result, the higher the accuracy rate of the normal processing state model judgement. Otherwise, the higher the result, the lower the missed detection rate (which is ideally zero). The higher the specific result, the higher the abnormal processing state model judgement accuracy rate. On the contrary, the false positive rate will be lower (the ideal is zero).
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## Appendix C Experimental Results for Peak and Off-Peak Hours
### Long-Term Prediction  
For long-term predictions, the results are detailed in Table 15, representing the average performance over a 7-day period. SLIT does not perform as effectively in RMSE, particularly during peak hours. However, SLIT excels in MAE and SMAPE, outperforming all comparative methods during off-peak hours, and shows a performance that is comparable with that of PASS2S during peak hours.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Peak**} & \multicolumn{3}{c}{**Off-Peak**} \\ \cline{2-7}  & **MAE** & **RMSE** & **SMAPE (\%)** & **MAE** & **RMSE** & **SMAPE (\%)** \\ \hline HA [28] & 43.335 & 87.007 & 7.081 & 32.659 & 70.134 & 6.141 \\ LSTM [29] & 43.440 & 89.895 & 6.958 & 29.011 & 69.080 & 5.488 \\ DNN [30] & 41.065 & 84 & 6.861 & 29.219 & 66.087 & 5.681 \\ DE-SLSTM [22] & 41.68 & 84.723 & 6.841 & 29.899 & 68.765 & 5.632 \\ MTSMF [23] & 41.23 & 83.999 & 7.026 & 28.193 & 63.141 & 5.509 \\ DHM [8] & 43.506 & 86.560 & 7.459 & 29.609 & 64.803 & 5.808 \\ TFT [24] & 46.439 & 91.406 & 7.965 & 29.023 & **62.462** & 5.705 \\ PASS2S [5] & **37.596** & **78.879** & 6.211 & 27.105 & 62.947 & 5.197 \\ \hline SLIT & \(37.879\) & \(81.329\) & **6.152** & **26.248** & 63.620 & **4.992** \\ Improvement ratio (\%) & \(-0.753\) & \(-3.106\) & \(0.948\) & \(3.1627\) & \(-1.855\) & 3.935 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison with competitive methods in short-term prediction across peak and off-peak hours.
## 1 Introduction
### FTIR Spectra Vibrations for Ca(OH)\({}_{2}\), -OH, H-OH, and Si-OH  
[PERSON] [54] posits that the H-O-H and Si-OH/H-OH ratios influence the equilibrium constant between Si-OH bonds and hydrogen bonds. How hydrogen bonds influence the absorption of IR light depends on the amount of each reactant used in the synthesis. This suggests that an increase in -OH- production from the alkaline activator (Na/KOH\({}_{\text{aq}}\)) or precipitated Portlandite (Ca(OH)\({}_{2}\)) could lead to an increase in the intensity of -OH and Si-OH groups vibrating at 3500-3600 cm\({}^{-1}\). Bands of vibration between 2750 and 2800 cm\({}^{-1}\) are associated with the asymmetric stretching of H-O-H formed by hydrogen bonding, whereas the bending vibration is associated with 1623-1650 cm\({}^{-1}\), according to other researchers [63, 71].  
Due to the formation of hydrogen bonding from thaumasite interaction (CaSi-OH), this bond is also observed in the IR spectra of the sulfate attack of Na\({}_{2}\)SO\({}_{4}\)[65], but not on MgSO\({}_{4}\) salt. The formation of hydrogen bonds could reduce the frequency of Si-OH bonds. Thus, there was structural retention of hydroxyl bonds in the MgSO\({}_{4}\)-exposed sample [66]. Due to Si-O-H stretching or OH bending, the spectra of low and high NaOH concentrations are distinguished by a shoulder peak at 875 cm\({}^{-1}\)[95]. As noted by [PERSON] et al. [32], the disappearance of H-O-H bending (1789 cm\({}^{-1}\)) was due to the reactivity of acidic silanol (Si-OH) with alkali. Hydrogen-bound hydroxyl (H-OH) was assigned the frequency of 3424 cm\({}^{-1}\), while intermolecular water was assigned the frequency of 3124 cm\({}^{-1}\). Consequently, FTIR could be used to qualitatively distinguish between the concentration of hydroxyl ions and the pH of cementitious binder products.