esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Analysis  
### Dataset and Platform  
The dataset used for the experiments included real sonar images, collected by lake and sea trials, and simulated images, obtained by three-dimensional modeling software, including a sphere, cylinder, truncated cone, and line. The number of each class of objects  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Layer** & **Output Size** & **c** & **_n_** & **s** \\ \hline \multirow{4}{*}{Stage1} & Input & (128, 128, 3) & 64 & 1 & 2 \\ \cline{2-6}  & Conv + BN + ReLU & (64, 64, 64) & 64 & 1 & 2 \\  & Conv + BN + ReLU & (32, 32, 64) & 64 & 1 & 2 \\  & Conv + BN + ReLU & (16, 16, 64) & 64 & 1 & 2 \\ \hline \multirow{4}{*}{Stage2} & DSDCN + Maxpool & (16, 16, 64) & 64 & 1 & 2 \\ \cline{2-6}  & Short Block & (16, 16, 256) & [64,64,256] & 1 & 1 \\  & Conv Block & (8, 8, 512) & [128,128,512] & 1 & 2 \\ \cline{2-6}  & Short Block & (4, 4, 1024) & [256,256,1024] & 1 & 2 \\ \cline{2-6}  & Conv Block & (2, 2, 2048) & [512,512,2048] & 1 & 2 \\ \cline{2-6}  & DSDCN + Avgpool & (1, 1, 2048) & 2048 & 1 & 2 \\ \hline \multirow{2}{*}{Stage3} & Flatten & 2048 & - & 1 & - \\  & Dense + Softmax & k & - & 1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure and parameters of DSDCN-ResNet.  
Figure 8: Framework of the network training and object classification algorithm.  
is shown in Table 2. The simulated images were used for auxiliary training of the model. During the simulation, the grazing angle between the sonar and the object was 30-45\({}^{\circ}\), and the angle between the object axis and the incident acoustic wave was 0-180\({}^{\circ}\). Some experimental images can be seen in Figure 9. During the experiments, 30% of the data were randomly selected to train the model, and the rest of the data were used to test the performance of the network. The GPU of the experiment computer was RTX2070 and the CPU was a 6-core i7-10750H.
## 3 Experiments and Analysis
### Qualitative Comparison
#### 3.4.6 Proposed STFD Method  
The proposed STFD detection method performs excellently across all six datasets. In complex backgrounds such as Seq.1, Seq.2, Seq.3, and Seq.6, it effectively filters out clutter caused by pixel displacement. For weak targets in Seq.4 and Seq.5, it effectively accumulates their energy across multiple frames. Overall, our method delivers the best visual results.
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 3 Partition Method Based on Spatio-Temporal Correlation
### Division of Survival Target Measurement Set  
For an extended target that survives at time \(k\), according to its motion model, the target position at the previous time is correlated with the target position at the current time, and the measurements generated by the extended target at these two times are also correlated. The measurement set at time \(k\) is set to \(Z_{k}=\left\{z_{k}^{(i)}\right\}_{i=1}^{n_{k}}\), \(n_{k}\) denotes the number of measurements at time \(k\), and the prediction of the Gaussian term of the ET-GM-PHD filter is \(\left\{w_{k|k-1}^{i},m_{k|k-1}^{i},P_{k|k-1}^{i}\right\}_{i=1}^{l_{k|k-1}}\), where \(l_{k|k-1}\) is the number of Gaussian components. The process of dividing the living target measurement set is described as follows:  
1. Select the measurement set of the surviving targets. The components with weights larger than 0.5 in the Gaussian term of time prediction are selected, which is denoted by \(G_{\mathrm{s}}=\left\{\hat{w}_{k}^{i},\hat{m}_{k}^{i}\hat{p}_{k}^{i}\right\}_{ i=1}^{l_{k}}\), and their position components are extracted as the centroid estimation of the surviving target, which is denoted by \(x_{k}^{i},i=1,\cdots,l_{k,s}\). Then the candidate measurement set of the \(i\)th extended target at time \(k\) is given by the following: \[\hat{Z}_{k}^{i}=\left\{z_{k}^{i}|z_{k}^{j}\in Z_{k},d\left(z_{k}^{i},x_{k}^{i} \right)<\varepsilon\right\}\] (3)
2. Determine the class of the cluster. If \(\hat{Z}_{k}^{i}=\varnothing\), it means that the \(i\)th extended target at time \(k\) has died, the number of surviving extended targets is decreased by one, and the number of surviving targets at time \(k\) is finally determined as \(c_{k}\).  
3. Cluster the living target measurements. For the set of measurements obtained in step 1, its union is \(\tilde{Z}_{k}\). This dataset is put into the K-means++ clustering algorithm, and the \(i\)th cluster center is obtained as \(\hat{x}_{k,i}^{i}=1,\cdots,c_{k}\), which can be regarded as the centroid estimation of the \(i\)th extended target. At this point, the tracking of the extended target is converted to the tracking of the point targets, and the estimated value of the centroid is brought into the update formula of GM-PHD, and the updated Gaussian term is \(\left\{w_{k}^{i},m_{k}^{i},P_{k}^{i}\right\}_{i=1}^{l_{c}}\).
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 4 Lunar Orbital Filter  
This section describes the implementation of lunar orbit propagation filter sensor fusion techniques. Performances are then presented, alongside trade-offs for their implementation. The desire is to deliver a precision of 30 m, as has been previously discussed.
## 1 Introduction
Given that structural deformations in the inner ear, such as those of the oval window, round window, or basilar membrane, are negligible when compared to the organ's overall size (the oval window is approximately 2.2 by 1.4 mm with a displacement of around \(10^{-8}\) m [7]), a simplified model has been proposed to reduce overall computational time. This paper presents both experimental and numerical research on the dynamic properties of the human RWM and sound transmission within the cochlea. Typically, models of the cochlea are simulated using two mutually coupled solvers--a mechanical FEM and CFD--that exchange information dynamically during the simulation. However, in the specific scenario of incompressible fluid flow in the ear, the difference in density between the fluid and the solid (basilar membrane) is minimal, and the basilar membrane itself is a thin, flexible structure; thus, the solvers must rely on strong coupling methods. This approach requires exchanging data not only between successive time steps, but also during individual iterations within each time step, as mentioned above. This constraint significantly increases computation time and may lead to some challenges in achieving solution convergence. In this research, the authors propose a novel, simplified but reliable approach to address this problem. In the adopted solution method, just one solver on the fluid side (CFD) is used, which helps avoid potential instabilities arising from fluid-solid interactions. In the laboratory tests, the middle ear's response to sound stimulation at the tympanic membrane was recorded using an LDV. Based on these measurements, FRFs of the middle ear were determined. According to [13], in cases of middle-ear measurements, the FRF is defined as the ratio of the vibration velocity of the tested temporal bone (a middle-ear component) to the acoustic pressure applied by sound waves to the ear tympan membrane. Additionally, the authors developed a numerical model of the cochlea to simulate the vibrations of the RWM during oval window stimulation, which represents the motion of the stapes footplate. The structure of the paper is organized as follows. Section 2 outlines the experimental procedure, setup, and the results obtained from the sound stimulation of the system. Section 3 provides a brief description of the numerical model of the cochlea and the oval window. This is followed by simulations of traveling pressure waves through the middle ear and the response of the RWM to the induced pressure changes. Finally, Section 4 presents a discussion of the results and final conclusions.
## 4 Conclusions  
This study investigates moisture infiltration induced by atmospheric precipitation in high-fill loess subgrades during cold periods in Northwest China. Through scaled model testing, we analyze the coupled evolution of the subgrade's humidity field and settlement deformation. The main conclusions are as follows:  
1. The greatest changes in moisture content and settlement occur at a depth of 20 cm. Within the same layer, the settlement and moisture content increase as the distance to the slope foot decreases. Due to the combined effects of water seepage and cold air, the settlement exhibits a clear lag. A settlement greater than 0.1 cm is considered effective. The settlement lag time near the slope foot is approximately 4 days (with a permeability of 8 L).
2. The settlement of the subgrade is primarily concentrated in the slope area due to the increased wetness at the foot of the slope, resulting in a \"bending tipping failure\". Finite element simulations using Plaxis show that the settlement profile follows an inverted \"S\" shape.
3. A quadratic relationship exists between cumulative settlement deformation and the cumulative moisture content change at each monitoring point. The maximum settlement at each layer is approximately 0.8 cm, occurring near the slope foot.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Fitted Equation** &
\begin{tabular}{l} **Coefficient of** \\ **Determination** \\ \end{tabular} \\ \hline At a depth of 40 cm in the foundation & \(S=-0.29w^{2}+1.03w-0.1\) & R\({}^{2}=0.92\) \\ At a depth of 20 cm in the foundation & \(S=-0.01w^{2}+0.17w+0.31\) & R\({}^{2}=0.987\) \\ Interface between the subgrade and foundation & \(S=-0.55w^{2}+1.65w-0.48\) & R\({}^{2}=0.98\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Settlement fitting equations.  
* The addition of a temperature sensor can be used to study the freezing depth of soil under cold conditions, as well as the relationship between temperature, moisture, and settlement and deformation.
* The incorporation of a slope spraying device can be used to simulate the effects of atmospheric precipitation-induced slope infiltration.  
Conceptualization, W.W. and W.L.; methodology, W.L. and P.Z.; software, W.W. and P.Z.; investigation, P.Z.; resources, P.Z. and L.L.; data curation, W.L.; writing--W.W. and L.L.; original draft preparation, L.L.; writing--review and editing, W.W. and W.L. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (42302320). Recipient: L.L.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
All data, models, and codes generated or used during the study appear in the submitted article.  
None.  
Author [PERSON], [PERSON] and [PERSON] were employed by the company Jinan Huanghe Road and Bridge Construction Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 3 Materials and Methods
### Evaluation Criterion  
In the field of medical image segmentation, the Dice coefficient [25] is widely used to evaluate segmentation models and is defined as follows:  
\[\textit{dice}=\frac{2|P\cap G|}{|P|+|G|}\]  
in which \(P\) is the predicted area and \(G\) is the real area (ground truth). The value of the Dice coefficient varies between 0 and 1, in which 0 is the lowest possible accuracy and 1 the highest possible accuracy.  
Segmentation models can also be evaluated pixel by pixel, and for this type of evaluation, the following metrics can be used:  
\[\text{accuracy}=\frac{TP+TN}{TP+TN+FP+FN}\]  
\[\text{precision}=\frac{TP}{TP+FP}\]  
\[\text{recall}=\frac{TP}{TP+FN}\]  
in which:  
* _TP_: are the amount of pixels correctly predicted from the tooth area;
* _TN_: are the amount of pixels correctly predicted from the background area;
* _FP_: are the amount of incorrectly predicted pixels of the tooth area;
* _FN_: are the amount of pixels incorrectly predicted from the background area.  
Finally, we add the amount of trainable parameters (param) in the segmentation model for comparison between the models.
## 4 Experimental Evaluation
### Graph Embedding Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **ATP** & **BoostNE** & **DeepWalk** & **DWNS** & **graphGAN** & **Line** & **NERD** & **NetMF** & **node2 vec** \\ \hline \multirow{4}{*}{BlogCatalog} & \(d=64\) & 3.69 & 3.44 & 41.25 & 30.38 & 151.37 & 6.53 & 9.10 & **0.80** & 19.62 \\  & \(d=128\) & 4.21 & 3.73 & 53.16 & 52.86 & 308.36 & 10.49 & 11.12 & **1.12** & 24.16 \\  & \(d=256\) & 4.88 & 4.22 & 72.91 & 71.50 & 397.20 & 23.03 & 14.02 & **1.85** & 25.15 \\  & \(d=512\) & 6.21 & 4.63 & 106.40 & 90.26 & 579.83 & 36.19 & 30.30 & **3.31** & 26.29 \\ \hline \multirow{4}{*}{Cora} & \(d=64\) & 5.05 & 2.12 & 10.21 & 85.67 & - & 7.65 & 4.73 & 5.64 & **0.26** \\  & \(d=128\) & 5.24 & 2.54 & 12.22 & 116.03 & - & 10.01 & 6.09 & 7.76 & **0.31** \\  & \(d=256\) & 7.44 & 3.98 & 16.82 & 175.93 & - & 18.94 & 13.70 & 9.15 & **0.35** \\  & \(d=512\) & 11.30 & 9.44 & 24.22 & 216.82 & - & 34.62 & 25.94 & 12.38 & **0.61** \\ \hline \multirow{4}{*}{DBLP} & \(d=64\) & 5.85 & 2.42 & 4.88 & 89.76 & 891.96 & 6.55 & 6.01 & 5.63 & **0.17** \\  & \(d=128\) & 6.53 & 2.86 & 6.46 & 101.19 & 1009.23 & 10.24 & 8.01 & 6.56 & **0.22** \\  & \(d=256\) & 7.67 & 3.32 & 8.64 & 150.27 & 1479.23 & 19.37 & 13.61 & 7.33 & **0.26** \\  & \(d=512\) & 12.18 & 5.97 & 12.43 & 175.10 & 2292.45 & 35.67 & 26.87 & 9.17 & **0.38** \\ \hline \multirow{4}{*}{TREC} & \(d=64\) & 52.23 & 5.02 & 61.65 & 369.48 & - & 7.06 & 5.45 & 10.88 & **1.36** \\  & \(d=128\) & 57.86 & 5.84 & 79.98 & 408.59 & - & 10.10 & 7.92 & 11.55 & **1.65** \\  & \(d=256\) & 63.69 & 8.19 & 105.36 & 479.09 & - & 20.77 & 16.45 & 17.75 & **1.96** \\  & \(d=512\) & 76.22 & 9.90 & 144.88 & 639.21 & - & 33.98 & 27.36 & 26.46 & **2.41** \\ \hline \multirow{4}{*}{Wikipedia} & \(d=64\) & 1.03 & 1.00 & 16.20 & 9.73 & 29.55 & 6.65 & 8.88 & **0.26** & 4.92 \\  & \(d=128\) & 1.05 & 1.05 & 18.39 & 11.92 & 32.81 & 11.28 & 10.68 & **0.30** & 5.32 \\  & \(d=256\) & 1.33 & 1.15 & 25.77 & 18.32 & 49.52 & 18.61 & 17.04 & **0.40** & 5.55 \\  & \(d=512\) & 1.85 & 1.78 & 38.35 & 27.6 & 92.38 & 36.66 & 28.35 & **0.80** & 5.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Similarity computation time (minutes) with different values of \(d\) for all datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **BlogCatalog** & **Cora** & **DBLP** & **TREC** & **Wikipedia** \\ \hline \(d=64\) & 4.07 & 21.05 & 17.52 & 72.89 & 0.90 \\ \(d=128\) & 4.51 & 22.45 & 19.87 & 74.07 & 0.92 \\ \(d=256\) & 4.91 & 23.28 & 20.44 & 76.42 & 1.10 \\ \(d=512\) & 6.13 & 25.69 & 23.23 & 83.06 & 1.62 \\ \hline \hline \end
## 2 Results and Discussion
### Effect on Selectivity of E-nose at Maximum Response from Fresh and Dried Herbs  
Figures 7(A and B) show the maximum sensor responses observed from fresh and dried herbs. Higher sensor signals were recorded from fresh herbs as compared to dried. In the case of fresh herbs, rosmary and sage showed the maximum responses _i.e._, 1,183 Hz and 1,092 Hz for \(\alpha\)-pinene, 533 Hz and 292 Hz for limonene, 445 Hz and 338 Hz for eucalypt, 74 Hz and 51 Hz for \(\beta\)-pinene, 170 Hz and 132 Hz for terpinene, 149 Hz and 117 Hz for estragole respectively. The maximum signals recorded from fresh basil were, 281 Hz for \(\alpha\)-pinene, 68 Hz for limonene, 98 Hz for eucalypt, 38 Hz for\(\beta\)-pinene, 77 Hz for terpinene and 55 Hz for estragole, which are lower than for rosmary and sage. This distribution of maximum sensor signals for terpen represented selective attitude of the MIP-QCM based e-nose. Interestingly dried basil showed higher sensor signals for \(\alpha\)-pinene (183 Hz), limonene (130 Hz) and eucalypt (74 Hz), whereas dried rosmary showed maximum amounts of terpinene (36 Hz) and estragole (24 Hz) in terms of sensor signals. Comparable amounts of \(\beta\)-pinene (16-24 Hz) were observed from all dried herbs. Dried sage on the other hand gave higher sensor signals _i.e.,_ 170 Hz for \(\alpha\)-pinene, 108 Hz for limonene and 54 Hz for eucalypt as compared to dried rosmary _i.e.,_ 111 Hz for \(\alpha\)-pinene, 81 Hz for limonene and 44 Hz for eucalypt. These patterns reveal again the selectivity behavior of the QCM based e-nose.  
Figure 7: Selectivity patterns of e-nose, (A) Emanated terpen from fresh herbs, (B) Emanated terpen from dried herbs. Normalized at layer height = 40 nm, actual layer height = 180–240 nm.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Directive CSMA/CA  
Scheduling is a computation intensive approach in mmWave frequencies; consequently, implementing PNCs efficiently on a mobile device is arduous. IEEE 802.15.3 states that a PNC is required to arrange the bandwidth requests from linked devices. Legacy CSMA/CA protocols do not work well with the directional antennas because of the deafness problem in the wireless network nodes [37]. Here, when a transmission uplink and downlink is established between nodes A and C, node B is unable to sense the transmission because node A points towards node C. Thus, a deafness problem arises for directional beamforming at node B as shown in Figure 4.  
To address this issue, a directional CSMA/CA-based MAC protocol was proposed in [37], in which the DEVs always maintain their positions by focusing their beams toward a PNC. This scenario is ensured before any communication link is established. The DEVs in the nodes dispatch a target request to be sent (TTRS) to the centralized PNC. Subsequently, upon finding a DEV that is ready to transmit a target clear to sent (TCTS), the PNC regulates the request accordingly and establishes a connection by overcoming the deafness problem.  
Figure 3: PNC and connecting devices for WPAN.  
A modified version of the above protocol [37] was presented in [38], utilizing spatial reuse by the same authors. In the revised scheme, the DEVs are called stations (STAs), and they form a peer-to-peer link. A few of the STAs are grouped together with an AP, which is basically the same as a PNC. An example is illustrated in Figure 5.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Ablation Experiment  
An ablation experiment is used to explore the impact of various components of the LTS-SEI framework on the recognition performance of long time span-specific emitter signals. Although some experimental data are duplicated from Section 5.5, we must present them again to evaluate the contribution of every component of the LTS-SEI framework to the recognition performance. Tables 7-10 present six ablation studies: (1) Original I/Q, (2) Shallow confrontation, (3) Deep alignment, (4) LTS-SEI (no Center Loss), (5) LTS-SEI (no HOMO3 Loss), and (6) LTS-SEI. Evidently, the proposed LTS-SEI method achieves the optimum results in terms of accuracy, precision, recall, and F1-score on multiple datasets. Shallow confrontation can enhance the recognition effect of specific emitters to a certain extent. On A1-\(\times\)A2 and A1-\(\times\)A3, deep alignment seems to have overfitting after incorporating Center Loss, which results in its recognition accuracy being lower than the previously mentioned deep layer HOMOM domain adaptive method. When the Center Loss or HOMOM3 Loss of LTS-SEI does not participate in model training, the accuracy, precision, recall, and F1-score on most datasets will decrease. For example, incorporating Center Loss can increase the accuracy of the model by up to 2.09%, and incorporating HOMOM3 Loss by 41.41%. Additionally, by comparing shallow confrontation and deep alignment, we can  
Figure 10: Training accuracy curves of LTS-SEI on (**a**) A1-\(\times\)A2, (**b**) A1-\(\times\)A3, (**c**) B1-\(\times\)B2, (**d**) B1-\(\times\)B6, (**e**) B1-\(\times\)B9, and (**f**) B1-\(\times\)B14.  
observe that with adversarial learning, it is easier to obtain domain-invariant RFFs for real data with dynamic changes. However, better results cannot be achieved solely through shallow confrontation and deep alignment. Only when different components of LTS-SEI work together to learn domain-invariant shallow and deep fingerprints can the accuracy of the long time span SEI be considerably increased.
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 4 Hybrid Positioning Approach
### Smart Space Integration  
From smart deployments including ID-card readers for door opening systems, a momentary but very accurate position can be obtained by knowing the installation point. In our case, the accuracy estimation is reduced proportionally to the average walking speed. Since people walk calmly between 1 and 2 m per second, we consider that the accuracy estimation is reduced by 1 m per second in the next 30 s after opening a door. As the access control and positioning systems are independent, the interaction between them is not straightforward. In order to let the smartphone know about a door opening event, a position monitoring online service has been deployed to receive the current position of different users with a certain periodicity. This could be useful for centralized control purposes. As depicted in Figure 1, when a door opening event occurs, the access control system notifies the position monitoring system with the user ID, the card reader ID and the timestamp of the event. The associated location is retrieved from the registered card readers database. Then, the ID-card driven location source returns a new position and its associated accuracy in order to be taken into account in the hybridization module to be combined with the rest of positioning data coming from the other technologies and algorithms.