esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Multi-Attack Reference Hashing
\begin{cases}1&(\mathbf{x}_{i},\mathbf{x}_{j})\in\mathcal{S} \\ -1&(\mathbf{x}_{i},\mathbf{x}_{j})\in\mathcal{D}\\ 0&\text{otherwise}\end{cases} , \tag{20}\]  
Specifically, a pair \((\mathbf{x}_{i},\mathbf{x}_{j})\in\mathcal{S}\) is denoted as a perceptually similar pair when the two images are the same images or the attacked images of a same image, and a pair \((\mathbf{x}_{i},\mathbf{x}_{j})\in\mathcal{D}\) is denoted as a perceptually different pair when the two images are different images or when one suffered from malicious manipulations or perceptually significant attacks. Figure 3: An example of preprocessing. Equation (19) can also be represented as:  
\[P_{1}(\mathbf{W})=\frac{1}{2}\mathbf{tr}\{(\mathbf{W}^{\mathrm{T}}\mathbf{x_{n}}) \:\mathbf{E}\:(\mathbf{W}^{\mathrm{T}}\mathbf{x_{n}})^{\mathrm{T}}\}. \tag{21}\]  
This relaxation is quite intuitive. That is, the similar images are desired to not only have the same sign but also large projection magnitudes, while the projections for dissimilar images not only have different signs but also are as different as possible. Moreover, to maximize the amount of information per hash bit, we want to calculate the maximum variance of all hash bits of all images and use it as a regularization term of the hash function. \[V(\mathbf{W})=\sum_{i=1}^{N}var(\mathbf{h_{i}})=\sum_{N}var(\mathbf{W}^{ \mathrm{T}}\mathbf{x_{i}}). \tag{22}\]  
Due to the indifferentiability of the above function, it is difficult to calculate its extreme value. However, the maximum variance of the hash function is the lower bound of the scale variance of the projected data, so the information theoretic regularization is represented as:  
\[P_{2}(\mathbf{W})=\frac{1}{2}\mathbf{tr}\{(\mathbf{W}^{\mathrm{T}}\mathbf{X}) (\mathbf{W}^{\mathrm{T}}\mathbf{X})^{\mathrm{T}}\}. \tag{23}\]  
Finally, the overall semi-supervised objective function combines the relaxed empirical fitness term from Equation (21) and the regularization term from Equation (23). \[P(\mathbf{W})=P_{1}(\mathbf{W})+\eta P_{2}(\mathbf{W})=\frac{1}{2}\mathbf{tr} \{\mathbf{W}^{\mathrm{T}}(\mathbf{X_{n}}\mathbf{EX_{n}}^{\mathrm{T}}+\eta \mathbf{X}\mathbf{X}^{\mathrm{T}})\mathbf{W}\}, \tag{24}\]  
where \(\eta=0.25\) is a tradeoff parameter. The optimization problem is as follows:  
\[\underset{\mathbf{W}}{max}P(\mathbf{W})\quad s.t.\:\mathbf{W}\mathbf{W}^{ \mathrm{T}}=\mathbf{I}, \tag{25}\]  
where the constraint \(\mathbf{W}\mathbf{W}^{\mathrm{T}}=\mathbf{I}\) makes the projection directions orthogonal. We learn the optimal projection \(\mathbf{W}\) that is obtained by means of eigenvalue decomposition of matrix \(\mathbf{M}\).
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 3 System Model
### Simulation Setup  
Extensive simulations were executed to evaluate and assess the performance of the routing schemes under various device speed scenarios. The network deploys 49 number of devices randomly distributed in a network area of 500 m \(\times\) 500 m. The device movement in the network based on the RWP mobility model and device speed varies between 10m/s and 60 m/s. The constant bit rate (CBR) universal datagram protocol (UDP) traffic was set to generate 20 packets per second with a packet size of 512 bps, and the wireless channel capacity was set to be 11 Mbps. The wireless channel frequency was set to be 2.4 GHz, and the radio transmission distance was set to be about 270 m based on the Wi-Fi parameter setting. A two-ray-ground path loss model with the shadowing means of 4.0 dB was selected and the transmission power was set to be 31.623 mW. The simulations were run for 200 s, with every simulation being run for an iteration of 200 times to calculate the average value of the results. The simulation parameters and values are described in Table 1.
## 2 Laser Phase Noise and Equalization Enhanced Phase Noise
Corresponding to the definition of the laser phase noise from the transmitter and the LO lasers, an effective linewidth \(\Delta f_{Eff}\) can be employed to describe the total phase noise variance in the EDC-based \(n\)-PSK coherent optical communication systems and it can be expressed as follows:  
\[\Delta f_{Eff}\approx\left(\sigma_{Laser}^{2}+\sigma_{EEPN}^{2}\right)/2\pi T_ {S}. \tag{4}\]  
When the impact from laser phase noise and the influence from EEPN give an equal contribution in the \(n\)-PSK optical fiber communication systems, namely \(\sigma_{Laser}^{2}=\sigma_{EEPN}^{2}\), we will have the transmission distance of \(L_{0}=8 cT_{S}^{2}/\lambda^{2}D\). Take the 32-Gbaud \(n\)-PSK coherent optical fiber transmission system as an example, and we assume that the fiber CD coefficient is 17 ps/nm/km and the central wavelengths of the transmitter and the LO lasers are both 1550 nm. In this case, we have \(L_{0}=60.69\) km. It means that at this transmission distance, the laser phase noise and the EEPN will have the same impact on the degradation of the performance of the 32-Gbaud \(n\)-PSK optical transmission systems.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
###### Abstract  
Cache timing attacks, i.e., a class of remote side-channel attack, have become very popular in recent years. Eviction set construction is a common step for many such attacks, and algorithms for building them are evolving rapidly. On the other hand, countermeasures are also being actively researched and developed. However, most countermeasures have been designed to secure last-level caches and few of them actually protect the entire memory hierarchy. Cache randomization is a well-known mitigation technique against cache attacks that has a low-performance overhead. In this study, we attempted to determine whether address randomization on first-level caches is worth considering from a security perspective. In this paper, we present the implementation of a noise-free cache simulation framework that enables the analysis of the behavior of eviction set construction algorithms. We show that randomization at the first level of caches (L1) brings about improvements in security but is not sufficient to mitigate all known algorithms, such as the recently developed Prime-Prune-Probe technique. Nevertheless, we show that L1 randomization can be combined with a lightweight random eviction technique in higher-level caches to mitigate known conflict-based cache attacks.
###### Abstract  
Deep learning-based object detection technology can efficiently infer results by utilizing graphics processing units (GPU). However, when using general deep learning frameworks in embedded systems and mobile devices, processing functionality is limited. This allows deep learning frameworks such as TensorFlow-Lite (TF-Lite) and TensorFlowRT (TRT) to be optimized for different hardware. Therefore, this paper introduces a performance inference method that fuses the Jetson monitoring tool with TensorFlow and TRT source code on the Nvidia Jetson AGX Xavier platform. In addition, central processing unit (CPU) utilization, GPU utilization, object accuracy, latency, and power consumption of the deep learning framework were compared and analyzed. The model is You Look Only Once Version4 (YOLOv4), and the dataset uses Common Objects in Context (COCO) and PASCAL Visual Object Classes (VOC). We confirmed that using TensorFlow results in high latency. We also confirmed that TensorFlow-TensorRT (TF-TRT) and TRT using Tensor Cores provide the most efficiency. However, it was confirmed that TF-Lite showed the lowest performance because it utilizes a GPU limited to mobile devices. Through this paper, we think that when developing deep learning-related object detection technology on the Nvidia Jetson platform or desktop environment, services and research can be efficiently conducted through measurement results.  
deep learning; embedded system; Nvidia Jetson platform; TensorFlow; TensorRT; YOLO
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 2 Bone-Conducted Speech Blind Enhancement Technology
### Bone-Conducted Speech Spectrum Expansion  
The spectrum spreading method assumes that the BC and AC speech have the same harmonic structures or formant structures. Through these similar waveform characteristics, the high-frequency signal loss of BC speech can be reduced without the assistance of AC speech, and the high-frequency signal components of BC speech can be derived and expanded directly from the low-frequency components of BC speech, which can achieve a certain enhancement effect and improve the hearing effect of the enhanced BC speech. According to the research, the low-frequency and high-frequency harmonic structures of BC and AC speech signals are similar [22]. In the experiment, based on this assumption, BC speech signals are recovered at high frequency, and the high-frequency harmonic structures are obtained according to the low-frequency information. According to the theory of the spectrum spreading method, some researchers soon strengthened the high-frequency signal components of the BC speech attenuation by adjusting the pole of the LP coefficient under the premise of the theory [23].  
The advantage of the spectrum spreading method is that it is simple, convenient, and does not require other prior knowledge when enhancing BC speech. The idea of this algorithm has been applied to some BC electronic products. However, this algorithm, in fact, has certain theoretical limitations. Because the bone structure of the human body that generates vibration is different, BC recorded in different parts has the same formant structure as AC voice, which does not fully conform to the actual voice characteristics. For instance, the method proposed by [PERSON] et al. [22] has little enhancement effect on consonants without harmonic structure. [PERSON] et al. conducted relevant research, and the enhancement theory proposed is only for BC speech with a wider enhancement frequency band [23]. For example, the formant structure of the high-frequency segment of BC speech recorded by a laryngeal microphone is seriously missing. When the obtained speech frequency band is narrow, the formant expansion method cannot be used to recover high-frequency information.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 5 Experimental Results
### Evaluation of Traffic Classification
However, federated learning guarantees an improved detection rate and accuracy even when there is an increased number of clients in a large-scale network. The objective of the FedSec leverages the IDS towards the accomplishment of security in the MC-SDN with the help of the federated model along with the GCNN-GRU. Accordingly, the effectiveness of attack detection heavily relies on the enrichment of the traditional deep learning model and the learning of diversified attack patterns. The FedSec accurately recognizes the attacks launched in the SDN, such as the time jack and peer flooding attacks in the enriched dataset. Even though the DoS and probe attacks only have comparatively high attack patterns, the FedSec distinguishes the R2L and U2R attack types from the normal, DoS, probe, and other traffic samples.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 5 Experiments  
The experiments were conducted on four datasets of solar plants from four cities in Brazil. All data correspond to hourly measures of solar irradiance, considering sunlight times, which is generally between 6:00 and 18:00 in Brazil. The data was split into three disjoint sets to perform training and model selection, and the last set was used for testing. To select the model parameters, a grid search procedure was conducted in the parameter space defined in Table 1. Regarding the ARIMA, \(p\) and \(q\) are the AR order and the MA order, respectively. Concerning the SVR, \(\gamma\) is the kernel parameter, \(C\) is the regularization factor, and \(\epsilon\) is the maximum deviation of the observed value.  
The ARIMA model employed a stepwise methodology to select an appropriate model [65]. To perform the predictions, we used 4758 hourly samples of the solar irradiation series so that 60% were used for training (the first 2856 samples), 20% for validation (951 samples), and 20% for testing (the last 951 samples). Moreover, the data sets used in the experiments were scaled to the range [0.1, 0.9] [66].  
Several configurations of the proposed model were tested, using the \(m\) best models varying in the set 1, 3, 5 and the size of the region of competence varying in the set 5, 10, 20. Moreover, static ensemble strategies were also tested using mean and median combination operators using the same models from the pool.
## 5 EKF-Based Approach for AE Source Localization during Quasi-Static Tensile Test
### Mechanical Test and AE Monitoring
During this stage, the AE activity follows the evolution of the microstructural events until the complete fracture of the plate. In general, ductile fracture in metals is initiated by the nucleation of the existing micro-scale voids [59]. As the applied load increases, nucleated and pre-existing voids coalesce to form the crack by growing, changing shape, and getting closer to each other [59; 60; 61; 62]. Figure 9 shows the evolution of the created crack at the time when the AE increases suddenly, namely at \(t\approx 160\) s, \(t\approx 215\) s and \(t\approx 270\) s, respectively. However, despite the sensitivity of AE to the microstructural evolution of the material, the link between the sudden increase of the AE activity and the and the crack propagation velocity is not straightforward. Indeed, Figure 10 shows that the crack growth velocity remains constant (\(\sim\)0.3 mm/s) even after the sudden jump in the cumulated AE energy, which evolves by more than two orders of magnitude at \(t\approx 160\) s. At the same time, the crack growth velocity becomes twice higher (0.68 mm/s) at the time when the cumulated AE energy evolves approximately one order of magnitude at \(t\approx 215\) s. According to literature, the propagation of a ductile tearing crack can be influenced by the interplay between nucleation, growth, and coalescence of voids [59; 63]. At this stage, our tests show that AE signals are sensitive to the creation and change of orientation of the created slanted crack. At the time when the underlying reasons for the initiation and propagation mechanisms of the crack are not yet fully understood [59; 64], we will only focus in the present paper on the location of the different AE sources in order to locate the created cracks and monitor their propagation with the help of the continuous wavelet transform and the EKF method previously presented. Figure 8: (**a**) Notched aluminum plate mounted on the testing machine; (**b**) evolution of the applied load as well the AE activity during the quasi-static tensile test, where AE is almost absent in (1), then a sudden and low increase in the AE activity is recorded in (2), and finally an important increase in the AE activity is recorded in (3). Figure 9: Evolution of the crack at the first (**a**) second (**b**) third (**c**) sudden increase of the AE signals which happen at \(t\approx 160\) s, \(t\approx 215\) s and \(t\approx 270\) s, respectively.
## 3 The Proposed Algorithm
### NC-Euler-ESPRIT-DPD
\right]\in\mathbb{R} ^{2(M-1)\times Q}, \tag{45}\]where the \(q\)th column element of \(\mathbf{T}_{3}\mathbf{H}_{g}\) is  
\[\mathbf{b}_{k3g,q}=\left[ \begin{array}{c}\sin\bigl{(}\varphi_{q}\bigr{)}+\sin \bigl{(}\gamma_{g,q}+\varphi_{q}\bigr{)}\\ \vdots\\ \sin\bigl{(}(M-2)\gamma_{g,q}+\varphi_{q}\bigr{)}+\sin\bigl{(}(M-1)\gamma_{g,q }+\varphi_{q}\bigr{)}\\ \cos\bigl{(}\varphi_{q}\bigr{)}+\cos\bigl{(}\gamma_{g,q}+\varphi_{q}\bigr{)} \end{array} \right]\in\mathbb{R}^{2(M-1)\times 1} \tag{46}\]  
Similarly,  
\[\mathbf{T}_{4}H_{g}=\left[ \begin{array}{c}T_{b4}\text{Re}\bigl{(}\mathbf{A}_{g} \varPhi\bigr{)}\\ T_{b4}\text{Im}\bigl{(}\mathbf{A}_{g}\varPhi\bigr{)}\end{array} \right]\in\mathbb{R }^{2(M-1)\times Q}, \tag{47}\]  
where the \(q\)th column element of \(\mathbf{T}_{4}\mathbf{H}_{g}\) is  
\[\mathbf{b}_{k4g,q}=\left[ \begin{array}{c}\cos\bigl{(}\varphi_{q}\bigr{)}-\cos \bigl{(}\gamma_{g,q}+\varphi_{q}\bigr{)}\\ \vdots\\ \cos\bigl{(}(M-2)\gamma_{g,q}+\varphi_{q}\bigr{)}-\cos\bigl{(}(M-1)\gamma_{g,q }+\varphi_{q}\bigr{)}\\ \sin\bigl{(}\gamma_{g,q}+\varphi_{q}\bigr{)}-\sin\bigl{(}(M-2)\gamma_{g,q}+ \varphi_{q}\bigr{)}\end{array} \right]\in\mathbb{R}^{2(M-1)\times 1} \tag{48}\]  
According to Equations (45) and (47), and the sum difference product formula, we can obtain  
\[\mathbf{T}_{3}\mathbf{H}_{g}=\mathbf{T}_{4}\mathbf{H}_{g}\mathbf{\Xi}_{g}, \tag{49}\]  
where  
\[\mathbf{\Xi}_{g}= \begin{bmatrix}-\cot\Bigl{(}\frac{\gamma_{g,1}}{2}\Bigr{)}\\ -\cot\Bigl{(}\frac{\gamma_{g,2}}{2}\Bigr{)}\\ \ddots\\ -\cot\Bigl{(}\frac{\gamma_{g,Q}}{2}\Bigr{)}\end{bmatrix}  \tag{50}\]  
The following relationship can be obtained from Equations (40) and (49):  
\[\Bigl{(}\mathbf{T}_{4}\mathbf{U}_{g}^{(s)}\Bigr{)}^{+}\mathbf{T}_{3}\mathbf{U}_{g}^{(s)}=\mathbf{ T}^{-1}\mathbf{\Xi}_{g}\mathbf{T}, \tag{51}\]  
where \(\left(\cdot\right)^{+}\) denotes general Moore-Penrose inverse. Then, \(\gamma_{g,q}(q=1,2,\cdots Q)\) can be acquired by performing EVD on Equation (51). According to the above definition, the phase of \(\gamma_{g,q}\) contains the azimuth information of targets. Then, by combining the azimuth information of \(G\) observation stations, we can obtain the position information of targets. The joint spectral function of NC-Euler-ESPRIT-DPD algorithm can be constructed as  
\[f_{NC-Euler-ESPRIT}(\mathbf{p})=1/\sum_{g=1}^{G}\min_{q}\ |-2\text{arccot} \bigl{(}v_{g,q}\bigr{)}-\frac{2\pi d}{\lambda}\frac{\mathbf{u}_{g}(1)-\mathbf{p}(1)}{ ||\mathbf{u}_{g}-\mathbf{p}||}|,\ q\in[1,Q] \tag{52}\]  
where \(v_{g,q}\) represents the \(q\)th diagonal element of diagonal matrix \(\mathbf{\Xi}_{g}\).
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
## 2 Materials and Methods
### Desalination Evaluation Parameters  
In any desalination technique, it is necessary to evaluate certain key parameters that determine the efficiency and feasibility of the process. One of the most important reference parameters is the specific energy consumption (\(SEC\)), which relates to the amount of energy required to produce a specific volume of desalinated water. This parameter is crucial for comparing the energy efficiency across different desalination technologies. It was evaluated following the methodology proposed by [PERSON] et al [32] using Equation (10):  
\[SEC=\frac{(N\ V_{c}+V_{d})\tilde{I}A_{mem}}{N\ Q_{d}}\ \left(\frac{\mathrm{kWh}}{ \mathrm{m}^{3}}\right), \tag{10}\]  
where \(V_{c}\) represents the voltage per unit cell, \(V_{el}\) is the redox reaction potential at the electrodes, \(\tilde{I}\) denotes the average current density within a unit cell, and \(Q_{d}\) refers to the flow rate of desalinated water per unit cell.  
Water recovery (\(WR\)) is used to quantify the ratio between the volume of produced water and the volume of feed water supplied [32]. It is evaluated using Equation (11), where \(Q_{f}\) represents the volumetric flow rate of the feed solution [33].  
\[WR=\frac{Q_{d}}{Q_{d}+Q_{f}}\times 100\ (\%). \tag{11}\]  
The salt removal percentage (\(SR\)) is calculated as shown in Equation (12), where \(\sigma_{d}\) corresponds to the conductivity of the desalinated water, and \(\sigma_{f}\) refers to the conductivity of the feed water [31; 32]. This percentage is determined based on conductivity values measured in mS/cm.  
\[SR=\left(1-\frac{\sigma_{d}}{\sigma_{f}}\right)\times 100\ (\%). \tag{12}\]
## 4 A Combined Algorithm for Listing of Triangles
### Time Complexity  
**Lemma 6**.: _The algorithm runs in time \(\widetilde{\text{O}}(n^{2.3727})\), if \(l<n^{1.3727}\)\(\widetilde{\text{O}}(n^{1.9368}l^{0.31754})\), if \(n^{1.3727}\leqslant l<n^{2.3940}\)\(\widetilde{\text{O}}(n^{1.5}l^{0.5})\), if \(n^{2.3940}\leqslant l<n^{3}\) where \(n\) is the number of nodes, and \(l\) is the number of triangles._  
**Proof.** The calculation on Line 3 can be implemented by using the fast matrix multiplication algorithm in O\((n^{\omega})\) time (Fact 1).  
The loop on Line 4 runs in O\((n^{2})\) time.  
The sorting on Line 11 can be done in O\((n\log n)\) time. We might for example use radix sort [10].  
The sum on Line 12 takes O(n) time.  
Recall that \(l\) is the number of triangles in the graph. Let \(p\) be the number of elements that the loop on Line 20 goes through. For each of them, there are more than \(k\) triangles. Hence, we obtain \(pk\leqslant l\) and consequently \(p\leqslant l/k\).  
The loop on Line 21 has \(n\) iterations, and each iteration takes O\((1)\) time, so the loop takes O\((n)\) time. The loop on Line 20 therefore takes O\((np)\) time, which is O\((nl/k)\), for \(k>0\). In the special case where \(k=0\), we know that \(p\in\) O\((l)\) so the loop on Line 20 takes O\((nl)\) time. The \(k\)-witness algorithm runs in \(\widetilde{\mbox{O}}(n^{\omega}k^{\mu}+n^{2}k)\) time (for \(k>0\)), where \(\mu\approx 0.46530\) (Fact 2). The total time is the sum of these contributions, \(\widetilde{\mbox{O}}(nl/k+n^{\omega}k^{\mu}+n^{2}k+n^{\omega})\).  
In the case \(l<n^{1.3727}\), \(k=0\):  
The first term of the sum can be replaced by \(nl\), so we have  
\[\widetilde{\mbox{O}}(nl+n^{\omega}*k^{\mu}+n^{2}k+n^{\omega})=\]  
\[=\widetilde{\mbox{O}}(n^{2.3727}+n^{2.3727})=\]  
\[=\widetilde{\mbox{O}}(n^{2.3727})\]  
In the case \(n^{1.3727}\leqslant l<n^{2.3940}\), \(k=l^{0.68245}/n^{0.93681}\):  
\[\widetilde{\mbox{O}}(\frac{nl}{k}+n^{\omega}k^{\mu}+n^{2}k+n^{\omega})=\]  
\[=\widetilde{\mbox{O}}(n^{1.9368}l^{0.31754}+n^{1.9368}l^{0.31754}+n^{1.0632}l ^{0.68245}+n^{2.3727})\]  
We have \(n^{1.3727}\leqslant l\); therefore \(n^{2.3727}\leqslant n^{1.9368}l^{0.31754}\).  
We have \(l<n^{2.3940}\); therefore \(n^{1.0632}l^{0.68245}<n^{1.9368}l^{0.31754}\).  
This means that the first two terms dominate, so the time is \(\widetilde{\mbox{O}}(n^{1.9368}l^{0.31754})\).  
In the case \(l>n^{2.3940}\), \(k=\sqrt{l/n}\):  
\[\widetilde{\mbox{O}}(\frac{nl}{k}+n^{\omega}k^{\mu}+n^{2}k+n^{\omega})=\]  
\[=\widetilde{\mbox{O}}(n^{1.5}l^{0.5}+n^{2.1401}l^{0.23265}+n^{1.5}l^{0.5}+n^{2.3727})\]  
We have \(l>n^{2.3940}\); therefore \(n^{1.5}l^{0.5}>n^{2.1401}l^{0.23265}>n^{2.3727}\). This means that the first and third terms dominate, so the time is \(\widetilde{\mbox{O}}(n^{1.5}l^{0.5})\). \(\square\)  
**Proof of Theorem 4.** As follows from Lemma 5 and Lemma 6, the algorithm satisfies the statements of the theorem.
# Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres
## 4 Case Application of a Machining Centre  
The fault propagation behaviour analysis method proposed in this paper has a certain general applicability and can provide guidance for the analysis of fault propagation behaviour of other complex systems. This paper selects the machining centre commonly used by various manufacturing companies as an example to illustrate the specific application.
## 3 Simulation Results and Discussion
### The Sensitivity Investigation of Angle Measurement Based on Direct THG
The weighted wavelengths \(\lambda_{\mathrm{w}}(\theta_{\mathrm{i}})\) are shown in Figure 8(a,b,c,c) by the white dotted lines to quantitatively evaluate peak shifts. The sensitivity is calculated by a linear fitting function to get the slope of \(\lambda_{\mathrm{w}}(\theta_{\mathrm{i}})\). The calculated sensitivities are \(-0.0054\) nm/arcsec, \(-0.0063\) nm/arcsec, and \(-0.0002\) a.u./arcsec for \(\alpha\)-BBO, calcite, and rutile, respectively. The results suggest that \(\alpha\)-BBO and calcite have better measurement performance in the wavelength around 1560 nm. Figure 7: The calculated angle dependence of \(I_{3}(\theta_{\mathrm{i}})\) and \(I_{\mathrm{THW}}\) (\(\theta_{\mathrm{i}}\)) of (**a**) \(\alpha\)-BBO, (**b**) Calcite, and (**c**) Rutile.