esa-sceva/satcom-chunk-collection huggingface dataset:
# TAWC: Text Augmentation with Word Contributions for Imbalance Aspect-Based Sentiment Classification
## 3 Word Selection for Augmentation
### Selective Augmentation Operations  
According to the identified word contributions, we proposed a text augmentation method for ABSA that selects words for each sentence consisting of three operations. In our previous work [12], we applied unimportant words to synonym replacements and important words to synonym insertions, which outperformed the baselines. Motivated by this result, we applied the text editing operation based on the following considerations:  
* Selective Replacement (SR): Select \(n\) words, except _Meaningful_ words from the given sentence and replace them with synonyms. The only thing to keep in mind is that the replacement should not fall within the aspect term. This is ensured by replacing the aspect term with a fixed expression ($t$) before the replacement.
* Selective Insertion (SI): Select \(n\) words, except _Necessity_, and _Irrelevant_ words from the sentence and then insert their synonyms. We must ensure that the aspect words remain unchanged. Replacing the aspect with a single expression ($t$) resolves this problem.
* Selective Deletion (SD): Choose \(n\) words, except aspect term and _Meaningful_ words from the sentences, and then delete them. We must apply a procedure similar to that described above and replace the aspect word with a fixed expression ($t$) to ensure that this expression cannot be selected for deletion.  
Each augmentation operation was performed to generate different types of augmented text to balance the training sets. Regarding synonym replacement and insertion operations, we implemented an alternative resource using a word embedding model to provide high-quality synonyms. The \(n\) was determined by hyperparameter \(\alpha\), representing the augmentation strength. The pseudocode for selective replacement is presented in Algorithm 2.  
```
0: training dataset \(D_{train}\), aspect term, \(\alpha\), fixed expression $$$, Necessity words \(W_{N}\); reward words \(W_{R}\); irrelevant words \(W_{I}\); and Word2 Vec
0: augmented dataset \(D_{aug}\)
1:\(D_{aug}\leftarrow\emptyset\)
2:\(synonym\leftarrow\emptyset\)
3:for sentence \(\in\)\(D_{train}\)do
4: sentence \(\leftarrow\) replace(aspect term, $$, sentence)
5:\(n\leftarrow\alpha\)*len(sentence)
6:\(candidates\leftarrow\)\(candidates\cup W_{N}\cup W_{R}\cup W_{I}\)
7:while\(count(candidates)<n\)do
8:\(keyword\leftarrow\) select random entries in candidates
9: model \(\leftarrow\) vector corpus(Word2 Vec)
10: vocabulary \(\leftarrow\) vocab(model)
11:for each word in vocabulary do
12:\(sim\_score(word)=cosine\_similarity(keyword,word)\)
13:if\(sim\_score(word)>0\)then
14:\(synonym\gets synonym\cup word\)
15:endif
16:\(synonym\leftarrow\) the highest \(sim\_score(word)\)
17:endfor
18: sentence \(\leftarrow\) replace(\(keyword,synonym\), sentence)
19:\(D_{aug}\gets D_{aug}\cup\) sentence
20:endwhile
21:endfor
22:return\(D_{aug}\)
```  
**Algorithm 2** Selective replacement
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 5 Experimental Results and Analysis  
### Experimental Environment  
To demonstrate the superiority of the proposed HTV-Lp model, Figure 2 shows eight different test images downloaded from the publicly available datasets found in [[http://adas.cvc.uab.es/elektra/datasets/far-infra-red/](http://adas.cvc.uab.es/elektra/datasets/far-infra-red/)]([http://adas.cvc.uab.es/elektra/datasets/far-infra-red/](http://adas.cvc.uab.es/elektra/datasets/far-infra-red/)), and [[http://www.dgp.toronto.edu/](http://www.dgp.toronto.edu/)]([http://www.dgp.toronto.edu/](http://www.dgp.toronto.edu/))\(\sim\)\(|\)nmorris/data/IRData/. Infrared images of [[http://adas.cvc.uab.es/elektra/datasets/far-infra-red/were](http://adas.cvc.uab.es/elektra/datasets/far-infra-red/were)]([http://adas.cvc.uab.es/elektra/datasets/far-infra-red/were](http://adas.cvc.uab.es/elektra/datasets/far-infra-red/were)) obtained using an infrared camera (PathFindIR Flir camera) with 19-mm focal length lens. The size of the \"Store.BMP\" is \(506\times 408\) and that of the rest images are \(384\times 288\). The Gaussian blur and motion blur kernels are generated by MATLAB functions. For example, MATLAB function \"fspecial\" (\"gaussian\", \(B\times B\times o\)) generates a \(B\times B\) Gaussian blur kernel with a standard deviation of \(\sigma\). For convenience, the kernel is referred to as (\(G\), \(B\), \(\rho\)).  
\begin{table}
\begin{tabular}{l} \hline \hline
**HTV-Lp Pseudo-Code** \\ \hline \hline
**Input**: observed image \(G\) and fuzzy kernel \(\mathbf{H}\); \\
**Output**: original image \(\mathbf{F}\); \\
**Initialize**: \\ \(\overline{\mathbf{Z}}_{i}^{(0)}\), \(\mathbf{Z}_{i}^{(0)}\), \(\mathbf{F}^{(0)}\), \(\mu_{i}\), \(\beta_{i}\), \(tol\), \(E,k(i=1,2,\cdots,5)\). \\
1: **While \(E>tol\) do;** \\
2: Use (13) to update \(\mathbf{F}^{(k+1)}\); \\
3: Use (15)-(16) to update \(\mathbf{Z}_{i}^{(k+1)}(i=1,2,\cdots,5)\); \\
4: Use (18) to update \(\overline{\mathbf{Z}}_{i}(i=1,2,\cdots,5)\); \\
5: \(E=\|\mathbf{F}^{(k+1)}-\mathbf{F}^{(k)}\|_{2}/\|\mathbf{F}^{(k)}\|_{2}\); \\
6: \(k=k+1\); \\
7: **End While** \\
8: **Return \(\mathbf{F}^{(k)}\) as \(\mathbf{F}\).** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The pseudo-code of the proposed method.  
\"fspecial\" ('motion', \(L\), \(\theta\)) generates a motion blur kernel with a motion displacement of \(L\) and motion angle of \(\theta\), which is referred to as (\(M\), \(L\), \(\theta\)) for convenience. Furthermore, Gaussian noise standard deviations of 1, 3, and 5 are used.
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 4 Optimization Problem Formulation  
In this section, the optimization variables, relay selection matrix and lifetime function are introduced first. Then, an optimization problem considering both energy consumption rate and residual energy of each sensor node, aiming to maximize the network lifetime of WBANs, is described in detail.
# Efficient Enhancement of Second Harmonic Generation via Noninvasive Modulation
## 2 Methods and Experimental Setup
### Experimental Setup  
The second harmonic generation (SHG) was enhanced experimentally by the combination of scattering medium and nonlinear crystal. The experiment setup is depicted in Figure 1. The femtosecond light with wavelength of 820 nm (Coherent, Santa Clara, VA, USA, 35 fs, 80 MHz) was expanded by a telescope system consisting of lenses \(L1\) and \(L2\). The expanded beam was reflected by a spatial light modulator (HAMAMATSU, Hamamatsu City, Japan, X13138-02) loaded with the optimal phase into the scattering medium. In addition, a half wave plate was used to control the polarization direction of the incident light of BBO crystal to maximize the efficiency of the crystal. The scattering light was incident on the barium borate crystal (BBO) to generate a second harmonic signal. After filtering by a set of filters, the second harmonic light was detected by CCD (AVT, Munchen, Germany, PIKE F421B,). In order to better demonstrate the effect of enhancement of SHG during modulation, the spectrometer (Ocean Optics, Orlando, FL, USA, HR 2000) is used to detect the intensity of the second harmonic spectrum before and after optimization. In our experiment, instead of increasing the intensity on a small area, we improved the intensity of the whole second harmonic light field. The total intensity of the second harmonic signal is regarded as a feedback signal in the GA for achieving a desired phase mask on the SLM, which can change phase and phase gradient of speckle field. The objective function (the second harmonic intensity enhancement factor) of GA is defined as [28]:  
\[\eta=\eta_{o}/\eta_{I}=I_{OS}/I_{IS}, \tag{4}\]  
where \(\eta_{o}\) and \(\eta_{I}\) are optimized and initial conversion efficiency of SHG, respectively. \(I_{OS}\) is optimized second harmonic light intensity, and \(I_{IS}\) is initial intensity of second harmonic light. After iteration, the optimal phase was applied on SLM and the value of the feedback factor reached more than unity, indicating that the enhancement of the second harmonic conversion efficiency was realized.
## 2 Materials and Methods
### Image Pre-Processing
#### 2.4.2 Conversion of DN to Radiance  
Because the MicaSense RedEdge imaging sensor records images in 16-bit format, radiometric correction models are required to convert raw pixel values into spectral reflectance values. This is especially true when the desired results are reflectance maps [39]. For this study, the unitless digital number (DN) values of the image were converted into at-sensor radiance, using Equation (1) adopted from [40]:  
\[L(\lambda)=V(x,\ y)\times\left(\frac{a_{1}}{g}\right)\times\frac{DN(\lambda)- DN_{BL}}{t_{e}+a_{2}y-a_{3}t_{e}y} \tag{1}\]  
where \(L_{(\lambda)}\) denotes the spectral radiance (measured in W/m\({}^{2}\)/sr/nm); DN is the raw digital number of a given pixel and \(DN_{BL}\) is the black level value (usually obtained from metadata file; \(a_{1}\), \(a_{2}\) and \(a_{3}\) are radiometric calibration coefficients; \(t_{e}\) is the image exposure time; and \(g\) denotes the sensor gain setting [39].
## 4 Experiments
### Ablation Study
#### 4.4.4 Cost Volume Aggregation  
In this experiment, we compare the cost volume construction method proposed in this paper with two types of aggregation in learning-based multi-view stereo (MVS): 1. variance fusing [6; 7; 28], 2. CNN-based fusing [8; 29].  
Our method primarily establishes semantic correlations in 3D space through cross-attention, enhancing the aggregation of image features from a greater number of input views during the cost volume construction. Additionally, the cross-scale cost volume communication module boosts information utilization, strengthening correlations among cost volumes at different scales. As demonstrated in Table 6, our approach achieves a relative improvement of 12.35%, 16.01%, and 16.19% in accuracy error, completeness error, and overall error, respectively, compared to CNN aggregation. The reconstruction performance of our method significantly surpasses the other two approaches.  
As depicted in Figure 8, compared to the original CasMVSNet [6] (the number of depth samples is 48, 32, 8), the introduced cost volume aggregation module in our approach helps mitigate the impact of errors, resulting in sharper and smoother edges in the depth map. In Figure 8e, it is evident that our model's predicted depth map is more complete, demonstrating superior performance in handling low-texture regions and physical edges.  
In Table 7, we conduct ablation study on two components employed in the cost volume aggregation process. Both components show optimization effects on the Overall metric, as evident from the results. It'snoteworthy that the introduction of the CVIE module leads to higher memory usage, primarily due to the additional space required to store the cross-scale cost volume. This addition also increases computation time. However, given the overall performance improvement, the extra storage space is deemed worthwhile.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Acc. (mm)** & **Comp. (mm)** & **Overall (mm)** \\ \hline CNN Fusion & 0.376 & 0.326 & 0.351 \\ Variance Fusion & 0.353 & 0.317 & 0.335 \\ Ours & 0.351 & 0.279 & 0.315 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative results of different aggregation methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Hypo. Num** & **Sampling Range (mm)** & **Coverage Ratio (\%)** & **Overall (mm)** \\ \hline CasMVSNet & \(48,32,8\) & 21.09 & 0.8441 & 0.355 \\ DDRNet & \(48,32,8\) & 19.24 & 0.8435 & 0.329 \\ Ours & \(16,8,4\) & 13.12 & 0.8468 & 0.320 \\ Ours and DDRS & \(16,8,4\) & 28.43 & 0.8934 & 0.315 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative comparison of ablation experiments on dynamic sampling module in DTU’s test set (This experiment mainly analyzes stage 0).
# A Time-Series Model for Varying Worker Ability in Heterogeneous Distributed Computing Systems
## 3 Estimation of Latent Variable and Parameters
### EM Algorithm  
We define the complete-data likelihood functions as follows:  
\[\begin{split} L(\theta|&\eta_{1:G,0:T},x_{1:G,0:T}, l_{1:G,0:T})=p(\eta_{1:G,0:T},x_{1:G,0:T}|\theta,l_{1:G,0:T})\\ &=p(x_{1:G,0:T}|\theta)p(\eta_{1:G,0:T}|x_{1:G,0:T},l_{1:G,0:T}) \\ &=\prod_{i\in[G]}p(x_{i,0:T}|\theta)p(\eta_{i,0:T}|x_{i,0:T},l_{ i,0:T})\\ &=\prod_{i\in[G]}f_{0}(x_{i,0}|\theta)\prod_{t\in[T]}f(x_{i,t}|x _{i,t-1})\prod_{t\in[T]_{0}}g(\eta_{i,t}|x_{i,t},l_{i,t})\end{split} \tag{9}\]  
Let  
\[l(\theta|\eta_{1:G,0:T},x_{1:G,0:T},l_{1:G,0:T})=\log L(\theta|\eta_{1:G,0:T}, x_{1:G,0:T},l_{1:G,0:T})\]  
be the complete-data log-likelihood function where log represents the natural logarithm.  
The EM algorithm is used to estimate the latent variable \(X_{i,t}\) and parameter \(\theta\) simultaneously, which consists of the following two steps: The first step is called the E-step, which starts by calculating the expected value of the complete data log-likelihood function \(Q(\theta|\theta^{(v)})\), defined as  
\[Q(\theta|\theta^{(v)})=\mathrm{E}\Big{[}l(\theta|\eta_{1:G,0:T},X_{1:G,0:T}, l_{1:G,0:T})|\eta_{1:G,0:T},l_{1:G,0:T},\theta^{(v)}\Big{]}.\]  
where the expectation is over \(X_{1:G,0:T}\), more precisely, \(p(x_{1:G,0:T}|\eta_{1:G,0:T},l_{1:G,0:T},\theta^{(v)})\). Here, \(\theta^{(v)}\) is the \(v\)-th parameter estimate in the procedure of the EM algorithm. Then we have  
\[Q(\theta|\theta^{(v)})=\int l(\theta|\eta_{1:G,0:T},x_{1:G,0:T},l_{1:G,0:T})p (x_{1:G,0:T}|\eta_{1:G,0:T},l_{1:G,0:T},\theta^{(v)})dx_{1:G,0:T}. \tag{10}\]  
Hence, (10) implies that the distribution function for \(x_{1:G,0:T}\) given \(\eta_{i,0:T}\). \(l_{1:G,0:T}\), and \(\theta^{(v)}\) are required to evaluate the \(Q\) function value at each iteration. The methodology for obtaining this distribution will be given in the next subsection. M-step finds the parameter of the next iteration using  
\[\theta^{(v+1)}=\arg\max_{\theta}Q(\theta|\theta^{(v)}).\]  
The aforementioned EM algorithm is summarized in Algorithm 1.  
```
(1) Initialize \(\mathbf{\theta}^{(0)}=(\alpha^{(0)},\beta^{(0)})\). (2) [E-Step] For the \((v+1)\)-th iteration, compute \(Q(\mathbf{\theta}|\mathbf{\theta}^{(v)})\). (3) [M-Step] Find \[\mathbf{\theta}^{(v+1)}=\arg\max_{\mathbf{\theta}}Q(\mathbf{\theta}|\mathbf{\theta}^{(v)}).\] (4) Repeat the E-Step and M-Step until \(\mathbf{\theta}^{(v)}\) converges.
```  
**Algorithm 1** EM Algorithm.
## 4 Support Vector Machine Models
### Kernel Function Provided for SVM  
SVM maps the data in a non-linear map to describe the linear process in the space of the predicted data according to the availability of data. This expression results in a simple linear combination problem attributed to the mapped space [58]. The kernel function allows a classification to be performed to form nonlinear boundaries to model complicated separating hyperplanes [59]. Figure 4 describes a projection from low to high dimension in space data.  
The kernel parameters must be ideal to solve the problem classification according to the data to become separable in the next space. The four principal basic kernel functions are linear, polynomial, radial basis function (RBF) and sigmoid [57; 60].  
* Radial basis function (RBF): this function could perform nonlinear mapping of the samples into a higher dimensional feature space expressed by [61]: \[K(x_{i},x_{j})=exp\Bigg{(}-\frac{\|x_{i}-x_{j}\|^{2}\sigma}{2}\Bigg{)}\] (8) where \(\sigma\) is the kernel weight and \(x_{i}\) and \(x_{j}\) are the inputs to the \(i\)-th and \(j\)-th dimensions, respectively.
* Linear kernel function: According to [57], the linear function to obtain the SVM parameters is described by the following: \[K(x_{i},x_{j})=x_{i}\cdot x_{j}\] (9)  
Figure 3: General architecture of a support vector maching (SVM) model according to [55].  
* Polynomial kernel function: this is a typical example of a global kernel, defined as follows: \[K(x_{i},x_{j})=\left(x_{i}\cdot x_{j}\right)^{q}\] (10) where \(q\) is the degree of the polynom that will be used [62].
* Sigmoid kernel function: this function gives an explanation to practical viability [63]. This function is expressed as follows: \[K(x_{i},x_{j})=tanh(v(x\cdot x_{i})+c)\] (11) where \(v\) and \(c\) are adjustable kernel functions based on the data.
## 2 Methodology
### Influence of Damage on Physical Field Parameters  
According to elastic damage theory, the elastic modulus of the unit is given by:  
\[E=(1-D)E_{0} \tag{5}\]  
where \(E_{0}\) and \(E\) are the elastic moduli before and after damage, respectively. It is assumed that damage and its evolution are isotropic, so \(E_{0}\), \(E\), and \(D\) are scalars. The porosity of the rock layer is related to its stress state and can be expressed as:  
\[\phi=(\phi_{0}-\phi_{r})\exp(-\alpha_{\Phi}\overline{\varphi_{V}})+\phi_{r} \tag{6}\]  
where \(\Phi_{0}\) is the initial porosity; \(\alpha_{\Phi}\) is the stress sensitivity coefficient of porosity; \(\Phi_{r}\) is the limit value of porosity under high compressive stress; \(\overline{\varphi_{V}}\) is the average effective stress, which can be calculated as:  
\[\overline{\sigma_{V}}=(\sigma_{1}+\sigma_{2}+\sigma_{3})/3-\alpha p \tag{7}\]  
where \(\alpha\) is [PERSON]'s coefficient, and \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) are the three principal stresses. Additionally, the permeability after damage can be considered to satisfy the following power function relationship with porosity:  
\[k=k_{0}(f/f_{0})^{3}exp(\alpha_{k}D) \tag{8}\]  
where \(k_{0}\) is the initial permeability (m\({}^{2}\)); \(k\) is the permeability after damage (m\({}^{2}\)); \(\alpha_{k}\) is the influence coefficient of damage on permeability, \(\alpha_{k}=5\).
# A PEKS-Based NDN Strategy for Name Privacy
## 2 Background and Related Work  
### NDN Architecture  
#### 2.1.1 Name  
NDN uses hierarchically structured names, for example, a document produced by Kanazawa University may have the name \"/japan/kanazawa/ac/gnst/info.pdf\". A content consumer uses this name to retrieve data content \"info.pdf\", whose location is Kanazawa University. This states that there is a significant relationship between names and data content. Names must be globally unique to retrieve global data. They are application-specific and not transparent to the network, which means naming schemes do not depend on the network [1; 2; 11]. Therefore, one prefix has many aggregated names. This fact provides not only a faster lookup mechanism but also better routing scalability. The users can easily memorize content names because of readability [12; 13].
## 4 Model Establishment  
### Parameters Set  
In the process of Inception-v3 retraining, iteration was set as 20,000; learning rate was set 0.01. In each iteration, 100 images were selected randomly to train the model, namely the training batch size equals 100. Batch size is limited by computer performance; 10% of the data was set as the test dataset. After 10 iterations, the model was evaluated. All the images were going to be used in the training process. Training accuracy, validation accuracy, and cross-entropy were used to evaluate the model in the training process. Training accuracy is gained by testing the trained dataset with the model; validation accuracy is gained by testing the validation dataset with the model; cross-entropy shows the model performance in identification. The smaller cross-entropy indicates a better performing model. In each training step, the prediction value and target value were measured to update the weight matrix. The geological structures images were cut as the same size before training. As a consequence, there were no strict limitations on the size and resolution of the images. The color images and grayscale images were both used in training, which was able to show the influence of color on the model.  
In the training of CNN, we set two, three, and four convolutional layers and two fully connected layers to establish the model. The convolutional layers were set as 5 \(\times\) 5 and 3 \(\times\) 3, respectively. There were 64 neurons in each convolutional layer. While there were 128 neurons in fully connected layers. The learning rate was set as 10\({}^{-4}\). Batch size was set as 32. The data was split into training data and validation data; 80% of the data was set as training data; 10% data was set as validation data and test data.  
In the model establishment of KNN, ANN, and XGBoost, we used OpenCV [36] to process the raw data into two datasets, namely color images and grayscale images, with the size of 128 \(\times\) 128. The two datasets were used to build images features with origin pixels and pixels histogram. Then we took the pixel vectors and histogram features as the input of KNN, ANN, and XGBoost. The python package Scikit-learn [37] was used in the research to build the three models, and all the parameters of the models were set as in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameters** & **Value** \\ \hline KNN & n\_neighbors & 1 \\  & P & 2 \\ \hline XGBoost & colsample\_bytree & 0.8 \\  & learning\_rate & 0.1 \\  & eval\_metric & mlogloss \\  & max\_depth & 5 \\  & min\_child\_weight & 1 \\  & nthread & 4 \\  & seed & 407 \\  & subsample & 0.6 \\  & objective & multi:softprob \\ \hline ANN & hidden\_layer\_sizes & 50 \\  & max\_iter & 1000 \\  & alpha & 10\({}^{-4}\) \\  & solver & sgd \\  & tol & 10\({}^{-4}\) \\  & random\_state & 1 \\  & learning\_rate\_init & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters in K nearest neighbors (KNN), artificial neural network (ANN), and extreme gradient boosting (XGBoost).
## 3 Comparative Analyses Results
### Rain-Gauge vs. Satellite Precipitation Products  
In this section, we use the same methodology to compare the properties of rain amounts given by the satellite products and RADOME rain-gauge observations. The satellite precipitation products were divided into three categories: IR sensor-based products, MW sensor-based products, and C/R-based products.
## 4 Experiments
### Datasets and Evaluation Metrics  
We evaluate our model on three standard datasets commonly used for monocular depth estimation: KITTI, SUN RGB-D, and NYU Depth v2.  
We use standard metrics for evaluating the performance of our depth estimation framework, which are widely used in previous works to provide a comprehensive assessment of both accuracy and robustness. Abs Rel measures the relative error between the predicted depth \(D_{pred}\) and the ground truth depth \(D_{gt}\).  
\[\text{Abs Rel}=\frac{1}{N}{\sum_{p}\frac{\left|D_{pred}(p)-D_{gt}(p)\right|}{ D_{gt}(p)}} \tag{19}\]  
RMSE computes the square root of the mean squared error between the predicted and ground truth depth.  
\[\text{RMSE}=\sqrt{\frac{1}{N}{\sum_{p}\left(D_{pred}(p)-D_{gt}(p)\right)^{2}}} \tag{20}\]  
Threshold accuracy measures the percentage of predicted depth values where the ratio between the prediction and ground truth is within a threshold \(\delta\). Three thresholds are commonly used: \(\delta_{1}=1.25,\ \delta_{2}=1.25^{2}\), and \(\delta_{3}=1.25^{3}\).  
\[Accuracy(\delta)=\frac{1}{N}{\sum_{p}\parallel\left(max\left(\frac{D_{pred}(p )}{D_{gt}(p)},\ \frac{D_{gt}(p)}{D_{pred}(p)}\right)<\delta\right)} \tag{21}\]  
These metrics provide a well-rounded assessment of both the overall error in depth predictions and the accuracy within specific thresholds.