esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model
### Potential Flow Theory  
#### 2.3.1 Linear Potential Flow Theory  
Linear potential flow theory is used to solve the radiation and diffraction problems. It is assumed that the water around the floating structure is a Newtonian fluid, which is irrotational, incompressible, and inviscid. Since the potential velocity \(\phi\) satisfies the boundary conditions and Laplace equation, the total velocity potential can be written as:  
\[\phi(x,\ y,\ z,\ t)=\phi_{I}(x,\ y,\ z,\ t)+\phi_{D}(x,\ y,\ z,\ t)+\sum_{j=1}^{ 6}\zeta_{j}\phi_{R_{j}}(x,\ y,\ z,\ t) \tag{1}\]  
where \(\ \phi_{I}(x,\ y,\ z,\ t)\) is the incident wave potential; \(\phi_{D}(x,\ y,\ z,\ t)\) is the diffraction wave potential; \(\phi_{R_{j}}(x,\ y,\ z,\ t)\) is the radiation wave potential; \(\zeta_{j}\) represents motion displacement; and \(j=1,2,\,\ldots,6\) represents surge, sway, heave, roll, pitch and yaw, respectively.  
Figure 1: The schematic diagram: (**a**) the design, and (**b**) three-view drawing of the semi-submersible.  
Figure 2: The diagrams of earth-fixed (O\({}_{0}\)-X\({}_{0}\)Y\({}_{0}\)Z\({}_{0}\)) and body-fixed (O-XYZ) coordinate systems.  
The incident wave potential \(\phi_{I}(x,\ y,\ z,\ t)\) can be expressed as:  
\[\phi_{I}(x,\ y,\ z,\ t)=\frac{-iga}{\omega_{o}}e^{i\omega_{o}z}e^{i(k_{0}xcos \theta+k_{0}ysin\theta-\omega_{0}t)} \tag{2}\]  
where \(i\) is the imaginary unit; \(a\) is the incident wave amplitude; \(g\) is gravity; \(k_{0}\) is the wave number; and \(\theta\) is the angle of wave incidence.  
The diffraction wave potential \(\phi_{D}(x,\ y,\ z,\ t)\) can be expressed as:  
\[\phi_{D}(x,\ y,\ z,\ t)=e^{i\omega_{0}x\cos\theta-\omega_{0}t}\int_{C}Q(\eta, \ \zeta)G(y,\ z,\ \eta,\ \zeta;\ k_{0})dC \tag{3}\]  
where the Green function \(G\) is the two-dimensional pulsating source potential of intensity at the point \((\eta,\ \zeta)\) in the lower half of the y-z plane; and \(C\) indicates that the integral is to be taken along the hull-section contour below the calm water surface.  
The first order exciting force \(F^{(1)}\) in \(i\)-th mode can be derived from the following equation by adopting the incident and diffraction potentials:  
\[F^{(1)}=-ip_{w}\omega\iint_{s}[\phi_{I}(x,\ y,\ z,\ t)+\phi_{D}(x,\ y,\ z,\ t)] \cdot n_{i}(x,\ y,\ z,\ t)dS \tag{4}\]  
where \(\rho_{w}\) is seawater density; \(S\) is the wetted body surface of the floating body; and \(n_{i}\) is the \(i\)-th mode normal vector on the wetted body surface.  
The \(i\)-th mode radiation force \(\zeta_{j}\phi_{R}\) can be derived from the following equation by adopting the radiation potentials:  
\[F^{R}_{i}\zeta_{j}=i\rho_{w}\omega\iint_{s}\sum_{j=1}^{6}\zeta_{j}\phi_{Rj} \cdot n_{i}dS=\sum_{j=1}^{6}\Bigl{(}\omega^{2}\mu_{ij}+i\omega\
u_{ij}\Bigr{)} \zeta_{j},\ i=1,\ 2,\ \ldots,\ 6 \tag{5}\]  
\[\mu_{ij}=-\rho_{w}\omega\iint_{s}\phi_{Rj}\cdot n_{i}dS \tag{6}\]  
\[\
u_{ij}\ =-\rho_{w}\omega\iint_{s}\phi_{Rj}\cdot n_{i}dS \tag{7}\]  
where \(\mu_{ij}\) is the added mass coefficient; and \(\
u_{ij}\) is the damping coefficient.
## 4 The RZE Method for Rainfall Detection and Rainfall Intensity Level Retrieval  
The RZE method for rainfall detection is mainly divided into threshold acquisition and rain-contaminated radar image determination. Firstly, the offline observation experiment is carried out. A large number of radar images are used to calculate ZPP and \(\mu\) in the occlusion area, and rainfall information of each radar image is recorded synchronously. The RZE parameters of each image are calculated by the Equation (5), and the threshold T is determined by statistical analysis of the RZE parameters. Then, the ZPP and \(\mu\) are calculated in the occlusion area of a selected radar image, and the RZE parameter is calculated by the Equation (5). Finally, the RZE parameter is compared with the threshold T to determine whether the radar image is contaminated by rainfall.  
For the radar images detected as rain-contaminated, the rainfall intensity level of these radar images is determined. Firstly, the RZE parameters of a large number of radar images are calculated by the same method mentioned above, and the distribution relationship between RZE parameters and rainfall intensity recorded synchronously by each radar image are analyzed statistically. The distribution relationship is screened and fitted to determine the function of rainfall intensity level. Then, RZE parameters of the rain-contaminated radar images are calculated by the same method mentioned above. Finally, the RZE parameters are substituted into the determined fitting function of rainfall intensity level to obtain the rainfall intensity level. The flow chart of the whole method is shown in Figure 6.
# Clinical Applications of Nano-Hydroxapatite in Dentistry
## 3 Results
### Study Characteristics
#### 3.2.2 Dentin Hypersensitivity
No statistically significant differences were noted between the two study products in the reduction of sensitivity from baseline to 4 weeks, for any of the sensitivity parameters. [PERSON] et al. [22] compared the effects of fluoride gel with or without a nanostructured desensitizing agent. The authors did not find significant differences between treatments, which both provided a decrease in mean pain intensity over the 3 months period of observation. [PERSON] et al. [23] evaluated the effectiveness of three desensitizing toothpaste, containing 5% calcium sodium phosphosilicate (NovaMin), 8% arginine, and 15% hydroxyapatite nanoparticles, respectively. A reduction of dentin hypersensitivity in terms of VAS score and Schiff Cold Air Sensitivity Scale was observed in all groups at all timepoints. The most effective desensitizing agent was 15% nano-HAp containing toothpaste, followed by 8% arginine and 5% NovaMin group. [PERSON] et al. [24] compared nano-HAp versus 8% arginine toothpaste in reducing dentin hypersensitivity. Both toothpaste were equally effective in reducing dentin hypersensitivity in the absence of statistically significant differences. [PERSON] et al. [25] compared a nano-HAp 2% gel toothpaste, a fluoride gel toothpaste, and a placebo in reducing dentin hypersensitivity. Nano-HAp treatment resulted in lower sensitivity values at two and four weeks follow-up. Results are summarized in Table 2.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Renewable Energy Integration in Operation
#### 3.3.2 Management of Grid Stability and Power Quality with Intermittent Renewable Sources  
Addressing grid stability and power quality amidst fluctuating renewable energy sources is critical. Study [184] illustrates the utility of ISS-theory-based digital simulation tools for evaluating grid stability across diverse renewable penetration rates. The case study in [28] demonstrates how digital control systems in energy storage that offer synthetic inertia and fault current support can fortify grid stability in renewable-dense networks. Additionally, Ref. [123] showcases the integration of advanced computational methods with digital simulation tools and adaptive rat-swarm optimization (ARSO) for optimizing static VAR compensators (SVC) and power system stabilizers (PSSs). This synergy between traditional digital tools and innovative optimization strategies is vital for ensuring a stable, high-quality power supply in renewable-rich grids, paving the way for a resilient energy future.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 3 Proposed Method
### Proposed Model
#### 3.2.1 Residual Network Block  
In order to reduce the impact of network deterioration on model learning, [PERSON] et al. [28] introduced Res-Net. In Res-Net, the accuracy deterioration, vanishing gradient, and large parameters problems were addressed. Such issues were resolved by the introduction of a skip connection with residual learning. Res-Net uses a shortcut to add the identity to each block's output. It is different from traditional convolutional operations because it concatenates the features map directly into the decoding process. The input image latent representation is learned more effectively using the stacked residual block. The traditional 2D convolution block and residual block are shown in Figure 5.  
In a deep residual learning framework, the residual convolution block aims to learn the output \(F(x)\), and identity \((x)\), which results in \(F(x)+(x)\). Recasting the original mapping into \(F(x)+x\), the network not only learns the features but also the residuals from the previous layers. The addition of identity assists the model to retain the features from the original input which helps in better feature learning. Additionally, the original map features \((x)\) guide the model in backpropagation, which solves the problem of the vanishing gradient.  
Figure 4: Our proposed attention Res-UNet.  
Figure 5: Difference types of convolution blocks. (**a**) Normal convolution block, (**b**) residual convolution block.  
The shortcut connection inside the residual block performs the identity mapping, and their resultant features are added to the outputs of stacked layers. This shortcut or skip connection never adds any extra parameters nor computational complexity. The entire network is fully connected and can be trained in an end-to-end manner with backpropagation. At output, the features are passed through a rectified linear unit (ReLU) activation function, and then to the proceeding layers. Mathematically, identity mapping and shortcuts can be represented as follows.  
\[y=F(x,\ \{Wi\})+x \tag{1}\]  
Here, Equation (1) shows the building block of the residual layer. The \(x\) and \(y\) represent the input and the output vectors of the considered layer. The function \(F(x,\ \{Wi\})\) is the residual mapping, while \(x\) is an identity which is the same as input \(x\). The operation on \(F(x)+x\) is element-wise addition. The \(x\) and \(F(x)\) must be of the same dimensions. If this is not the case, then linear projection can be performed by the shortcut connection to match the dimensions. Hence, the output will be as follows.  
\[y=F(x,\{W_{i}\})+W_{s}x \tag{2}\]  
In Equation (2), the function \(F(x,\{W_{i}\})\) can represent multiple convolutional layers, whereas \(W_{s}\) represents the weights from the previous layer. The form of the residual function is very flexible, as more than two layers are possible within it [28]. The residual layer becomes a simple plain layer when only one layer exists inside the block, but this shows no advantage in feature extraction.
# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 4 Discussion
### Evaluation Metrics and Experimental Details  
To test the performance of the model, we evaluated the model by introducing average precision (AP) and mean average precision (mAP) as in Equations (10) and (11). The AP is calculated using the difference-average accuracy measure, the area under the precision-recall curve. The equation of the precision and recall are shown in Equations (8) and (9):  
\[Precision=\frac{TP}{TP+FP}, \tag{8}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{9}\]  
where \(T/F\) denotes true/false, which indicates whether the prediction is correct, and \(P/N\) denotes positive/negative, which indicates a positive or negative prediction result.  
\[\text{AP}=\big{(}\frac{1}{n}\sum_{(r\in\frac{1}{R},\frac{2}{R},-\frac{n-1}{R},\frac{1}{1})}P_{\text{interop}}(r)\big{)}^{2}, \tag{10}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Items** & **Description** \\ \hline \multirow{4}{*}{H/W} & CPU & Intel(R) Core (TM) i5-11400F \\ \cline{2-3}  & RAM & 16 GB \\ \cline{2-3}  & SSD & Samsung SSD 500 GB \\ \cline{2-3}  & Graphics Card & NVIDIA GeForce RTX 3050 \\ \hline \multirow{4}{*}{S/W} & Operating System & Windows 11 Pro, 64 bit \\ \cline{2-3}  & Programming Language & Python 3.7 \\ \cline{1-1} \cline{2-3}  & Learning Framework & TensorFlow 2.2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Workstation mainframe hardware and software.  
Figure 9: Percentage of ground truth for each object.  
\[\text{mAP}=\frac{1}{n}\sum\text{AP}, \tag{11}\]  
where \(n\) denotes the number of detection points and \(P_{\text{interp}}(r)\) represents the value of the accuracy at a recall of \(r\).  
Figure 10 shows a plot of the loss values during the training of the model and a plot of the validation loss function obtained by validating the model using the validation dataset during the training process. The training was divided into two phases: the freeze phase and the unfreezing phase. For initializing the weight parameterization of the shared convolutional layer, a pre-trained GhostNet model was used. The loss function was CIoU loss [31], the batch size was set at eight and four, and the learning rates were 0.01 and 0.0001, respectively. The total number of training iterations was 100 epochs, which were divided into two phases: frozen training and unfrozen training, where the first 50 epochs froze the backbone network weights and prioritized the training of network weights other than the backbone network, and the second 50 epochs unfroze the backbone network for full network training. This has the advantage of fine-tuning the original weights of the backbone network, thus accelerating the convergence of the network and saving training time.
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 4 Fusion of Telco and IT Services
### Evolution Path of API Services in Telecommunications
API security considerations are also crucial in mobile banking applications, whose services are more and more widespread worldwide [31]. In 2022, [PERSON] and [PERSON] [6] noted that every generation of wireless systems has its own representative industries and companies. Network equipment vendors and operators were at center stage in the 1G era. Feature phones and the corresponding supply chains like Qualcomm and Nokia became dominant in the 2G era. Smartphones and their suppliers like Apple started to rise in the 3G era. And the OTT applications riding the mobile network reaped most benefit in the 4G era. Currently in the 5G era, there are also numerous opportunities open for new players. In general, 5G introduces many IT technologies, and that helps openness and network disaggregation for preventing vendor lock-in (i.e., locking in one vendor's proprietary solutions). In this way, more players can join the supply chain to increase flexibility and diversification. More specifically, 5G involves the combination of Communication Technology, IT (Information Technology) and OT (Operation Technology). Therefore, in addition to the communication network itself, plus all possible new applications, 5G brings many new industry opportunities [2; 3; 4; 5; 6]. The industrial cooperation aspects of Open APIs have been discussed for years [8; 48]. Simultaneously, there has been a significant focus on standardization issues too [1; 62; 63; 64]. The reviewed papers are summarized in Table 1. As shown, the selected API related papers cover various domains of IT and telecommunications. The most intensively discussed topics within the list of the reviewed papers are 4G, 5G, and cloud core. IT and Telco convergence is clearly observed. Notably, in the majority of the papers, various new business models, resource management issues, and security and standardization aspects received significant focus too.
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
###### Abstract  
Most existing studies on pet breeds classification focus on images with simple backgrounds, leading to the unsatisfactory performance of models in practical applications. This paper investigates training pet breeds classification models using complex images and constructs a dataset for identifying breeds of pet cats and dogs. We use this dataset to fine-tune three SOTA models: ResNet34, DenseNet121, and Swin Transformer. Specifically, in terms of top-1 accuracy, the performance of DenseNet is improved from 89.10% to 89.19%, while that of the Swin Transformer is increased by 1.26%, marking the most significant enhancement. The results show that training with our dataset significantly enhances the models' classification capabilities in complex environments. Additionally, we offer a lightweight pet breeds identification model based on PBI-EdgeNeXt (Pet Breeds Identification EdgeNeXt). We utilizes the PolyLoss function and Sophia optimizer for model training. Furthermore, we compare our model with five commonly used lightweight models and find that the proposed model achieves the highest top-1 accuracy of 87.12%. These results demonstrate that the model achieves high accuracy, reaching the SOTA level.  
pet breeds identification; complex background; transfer learning; image classification +
Footnote †: journal: Applied sciences
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 3 Dynamap Approach  
The ultimate goal for deploying a network of noise sensors inside an urban zone is the prediction of traffic noise at time \(t\) at any road stretch \(s\) within the zone. As an attempt to fulfill this goal, we discuss the Dynamap approach, which is illustrated for the pilot zone of the city of Milan, Z9. Therefore, we will assume to have sorted the whole road stretches within Z9 into six groups, as discussed in Section 2.3.3. Each group has associated a dynamical map which can be updated in real time, as illustrated in what follows.  
The absolute level \(L_{\text{eq}}^{(\tau)}(t,s)\) at time \(t=n\tau\), for the time interval \(\tau\), at an arbitrary location \(s\) within Z9, can formally be written using the relation [86]  
\[L_{\text{eq}}^{(\tau)}(t,s)=10\cdot\log_{10}\sum_{i=1}^{6}10^{t_{\text{eq}}^{( \tau)}(t,s(g_{i}))/10}, \tag{5}\]  
where \(L_{\text{eq}}^{(\tau)}(t,s(g_{i}))\) represents the contribution of the traffic noise sources on site \(s\) from all the road arches within group \(g_{i}\) (not to be confused with the BTNM shown in Figure 5, although we may also refer to these contributions as BTNMs). For our purposes, it is convenient to specify the above relation in the particular case \(t=t_{\text{ref}}\) and \(\tau=1\) h, where \(t_{\text{ref}}\in\) (8:00-9:00) a.m., yielding the reference acoustic equivalent levels,  
\[L_{\text{eq}}^{(1h)}(t_{\text{ref}},s)=10\cdot\log_{10}\sum_{i=1}^{6}10^{t_{ \text{eq}}^{(1h)}(t_{\text{ref}}s(g_{i}))/10}, \tag{6}\]  
for each site \(s\) in Z9, in terms of the reference equivalent noise levels contributions from the groups \(g_{i}\) at position \(s\), \(L_{\text{eq}}^{(1h)}(t_{\text{ref}},s(g_{i}))\). These quantities can be used back in Equation (5), which now takes the form,  
\[L_{\text{eq}}^{(\tau)}(t,s)=10\cdot\log_{10}\sum_{i=1}^{6}10^{[\delta^{(\tau) }(t,s(g_{i}))+L_{\text{eq}}^{(1h)}(t_{\text{ref}}s(g_{i}))]/10}, \tag{7}\]  
where  
\[\delta^{(\tau)}(t,s(g_{i}))=L_{\text{eq}}^{(\tau)}(t,s(g_{i}))-L_{\text{eq}}^ {(1h)}(t_{\text{ref}},s(g_{i})). \tag{8}\]  
Thus, Equation (8) provides the dynamic counterpart to the stationary map represented by Equation (6).
## 3 Formulation for Joint Design Problem
### Comprehensive Utility Function
#### 3.1.1 Transmission Delay
The data transmission time of SU \(n_{i}\) is defined as:  
\[\tau_{i}=\ R_{packet}\Bigg{/}\Bigg{[}B\cdot\log_{2}\Bigg{(}1+\frac{h_{ijc}p_{ i}}{\vartheta+\phi_{ijc}^{PU}}\Bigg{)}\Bigg{]}\,, \tag{8}\]  
where \(R_{packet}\) is the packet size, \(B\) is the bandwidth of DTC, \(h_{ijc}\) represents the channel gain between the node \(n_{i}\) and \(n_{j}\), \(\phi_{ijc}^{PU}\) denotes the PU-to-SU interference at the receiver node \(n_{i}\), and \(\vartheta\) is the additive white Gaussian noise (AWGN) power. Consequently, the transmission delay is calculated as:  
\[u_{TD,i}=\delta_{i}+\tau_{i}=\frac{\lambda_{i}^{t}}{\mu_{i}^{t}\big{(}\mu_{i}^ {t}-\lambda_{i}^{t}\big{)}}+\ R_{packet}\Bigg{/}\Bigg{[}B\cdot\log_{2}\Bigg{(} 1+\frac{h_{ijc}p_{i}}{\vartheta+\phi_{ijc}^{PU}}\Bigg{)}\Bigg{]}\,. \tag{9}\]
# Antibacterial and Antiproliferative Potential of Green, Roasted, and Spent Coffee Extracts
## 2 Results and Discussion
### Antibacterial Activity
[51] reported a remarkable effect of reducing cell viability in P388 cancer cells treated with SCG extracts, compared with other coffee samples. Furthermore, studies have demonstrated the antitumor activity of SGC extracts in HT-29 colon cancer cells [11], as well as an induction of the expression of antioxidant proteins in Raw 264.7 cells [52]. Additionally, the induction of apoptosis has been described through DNA fragmentation, the increased expression of proapoptotic proteins, and activation of the caspase pathway [53], as well as through the reduction of reactive oxygen species (ROS) [54]. Some action mechanisms have been described, in terms of the anticancer activities of the bioactive metabolites contained in SCG extracts, such as melanoidins and phenolic compounds: they have antiproliferative and proapoptotic effects on cancer cells. Specifically, the chlorogenic acids and caffeic acid inhibited the phosphorylation of ERK1/2 in hepatic or colon cancer cell lines, this pathway's signaling being related to cell proliferation and apoptosis [55; 56]. Furthermore, it has been described that melanoidins reduce cell proliferation and induce cell cycle arrest in G0 or G1/S in cancer cell lines, where one of the mechanisms described for cell cycle arrest is alteration of the microtubule skeleton [57]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & **Concentration** & **Apoptosis (\%)** & \multicolumn{3}{c}{**Cell Cycle Phases (\%)**} \\ \hline
**Cell** & **Extract** & **Sample** & **(\(\mathrm{\SIUnitSymbolMicro mL^{-1}}\))** & **LC** & **Ap** & **Sub G0/G1** & **G1-S** & **G2-M** \\ \hline \multirow{4}{*}{C33A} & Control & – & – & 93.1 & 4.9 & 42.3 & 33.4 & 16.7 \\  & & GB & 25 & 90.5 & 9 & 32.9 & 35.6 & 23.1 \\  & & GB & 125 & 85.8 & 13.5 & 47.5 & 21.2 & 26.6 \\  & & RC & 25 & 92.8 & 6.8 & 37.6 & 24.4 & 32 \\  & & RC & 125 & 85.5 & 13.9 & 42.7 & 20.7 & 32.2 \\  & & SCG & 25 & 92.4 & 7.4 & 36.6 & 22.8 & 35.1 \\  & & SCG & 125 & 82.0 & 12.6 & 38.3 & 27 & 27.6 \\ \hline \multirow{4}{*}{A549} & Control & – & – & 92.6 & 4.8 & 0.1 & 65.2 & 11.6 \\  & & GB & 12.5 & 84.2 & 12.7 & 0.5 & 49.6 & 17.2 \\ \cline{1-1}  & & RC & 12.5 & 91.3 & 8.7 & 0.2 & 53.8 & 16 \\ \cline{1-1}  & & SCG & 12.5 & 95.1 & 4.7 & 0.9 & 50.1 & 15.5 \\ \cline{1-1}  & & GB & 200 & 94.4 & 4.5 & 0.3 & 64.2 & 12 \\ \cline{1-1}  & & RC & 200 & 91.2 & 7.4 & 0.9 & 62.6 & 17.5 \\ \cline{1-1}  & & SCG & 200 & 87.1 & 10.3 & 0.5 & 58.7 & 14 \\ \hline \end{tabular}  
* LC: Live cells, Ap: apoptosis, GB: green coffee, RC: roasted coffee, SCG: spent coffee grounds, –control DMSO vehicle.  
\end{table} 
Table 3: Determination of apoptosis and cell-cycle phases in C33A and A549 cells treated with the different coffee extracts.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
### Related Works
A multiple sources localization problem is formulated in [28] as a unimodality constrained matrix factorization (UMF) and two rotation techniques have been developed to solve the problem. In [29], an eigendecomposition is first applied to find the local locations of sensor nodes, then a rotation matrix and a translation vector are used to transform them to true locations. [PERSON] et al. in [30] proposed to estimate the positions of nodes by the classic multidimensional scaling algorithm (MDS) [31] using a truncated eigendecomposition. However, the MDS method requires high recovery rate of the EDM which is not guaranteed when working in noisy environments. The localization can be also ensured by the fingerprinting technique. The principal drawback is that an offline radio map construction is needed. Furthermore, due to environment changes this radio map has to be frequently updated. In this paper, we propose to use the trilateration method. It can be applied if we have at least three pairwise known distances between the sensors if the localization is performed in 2D space. At each sensor node to be localized, data fusion is conducted by combining measurements from different nodes to estimate its location.