esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Multidisciplinary Collaboration  
* The workflow requires a continuous exchange of feedback between the satellite imagery analyst and archaeologist, which is proper of the co-working approach. Therefore, to properly perform this type of activity, it is always advisable to organize small teams with at least one expert for each required skill (e.g., image processing, site detection, landscape feature interpretation). However, the size of the teams may vary, depending on variables such as the expected reservoir dam dimension or the variability of the terrain. For small dams like Halabiyeh, a satellite imagery analyst and an archaeologist may be sufficient, while in the case of GERD, two or more figures covering each skill may be necessary. Alternatively, a longer period for data processing and analysis should be scheduled (see below).
* Considering the substantially different expertise of a satellite imagery analyst and an archaeologist, it is advisable that both the professional figures would already have a basic knowledge of the other's subject or that they would receive basic training or introduction.
## 3 Spatial and Temporal Correlation Characteristics  
For a wideband channel, the channel transfer function is derived by performing Fourier transformation to \(h_{qp}(t,\tau)\), i.e.,  
\[H_{qp}(t,f)=\int_{-\infty}^{\infty}h_{qp}(t,\tau)e^{-j2\pi f\tau}\mathrm{d}\tau. \tag{59}\]  
The spatial-temporal CCF between \(H_{qp}(t,f)\) and \(H_{\tilde{q}\tilde{p}}(t-\Delta t,f)\) is defined as [35]  
\[R_{qp,\tilde{q}\tilde{p}}(t,\Delta t)=\mathbb{E}\{H_{qp}(t,f)H_{\tilde{q} \tilde{p}}^{*}(t-\Delta t,f)\} \tag{60}\]  
where \((\cdot)^{*}\) is the conjugate operation and \(\mathbb{E}(\cdot)\) stands for the expectation operation. Based on (5), the spatial-temporal CCF can be expressed as the summation of the LoS and NLoS components, i.e.,  
\[R_{qp,\tilde{q}\tilde{p}}(t,\Delta t)=R_{qp,\tilde{q}\tilde{p}}^{\mathrm{LoS} }(t,\Delta t)+R_{qp,\tilde{q}\tilde{p}}^{\mathrm{NLoS}}(t,\Delta t). \tag{61}\]  
The channel transfer function of the LoS component can be obtained by substituting (19) into (59), i.e.,  
\[H_{qp}^{\mathrm{LoS}}(t,f) =\sqrt{P_{L}}\cdot e^{-j\frac{2\pi}{\lambda}d_{L}}\cdot e^{j\frac {2\pi}{\lambda}(p-1)\delta_{k}\sin(\phi_{L}-\xi_{l})}\] \[\times e^{-j\frac{2\pi}{\lambda}(q-1)\delta_{r}\sin\phi_{L}} \cos(\varphi_{L}-\xi_{r})\cdot e^{j\frac{2\pi}{\lambda}a_{t}\sin(2\pi f_{r}t) \cos(\gamma_{t}^{L})}\] \[\times e^{-j\frac{2\pi}{\lambda}a_{t}\sin(2\pi f_{r}t)\cos( \gamma_{t}^{L})}\cdot e^{j2\pi(\gamma_{t}^{L}+\gamma_{r}^{L})t}\cdot e^{-j2 \pi\tau_{L}(t)f}. \tag{62}\]  
By substituting \(H_{qp}^{\mathrm{LoS}}(t,f)\) and \(H_{\tilde{q}\tilde{p}}^{\mathrm{LoS}}(t-\Delta t,f)\) into (60), the spatial-temporal CCF of the LoS component is given as  
\[R_{qp,\tilde{q}\tilde{p}}^{\mathrm{LoS}}(t,\Delta t)= \mathbb{E}\big{\{}P_{L}\cdot e^{j2\pi(\gamma_{t}+\gamma_{r}) \Delta t}\cdot e^{j2\pi f\left[\tau_{n}(t-\Delta t)-\tau_{n}(t)\right]}\] \[\times e^{j\frac{2\pi}{\lambda}(\Delta t_{t}\sin(\phi_{L})\cos( \xi_{t})+\Delta t_{r}\sin(\phi_{L})\cos(\xi_{r})\right]}\] \[\times e^{j\frac{2\pi}{\lambda}a_{r}\left[\sin(2\pi f_{r}t)-\sin(2 \pi f_{r}(t-\Delta t))\right]\cos\gamma_{r}^{L}}\] \[\times e^{-j\frac{2\pi}{\lambda}a_{t}\left[\sin(2\pi f_{r}t)-\sin( 2\pi f_{r}(t-\Delta t))\right]\cos\gamma_{t}^{L}}\big{\}} \tag{63}\]where \(\Delta d_{t}=(p-\bar{p})\delta_{t}\) is the spacing between \(A_{p}^{T}\) and \(A_{p}^{T}\), \(\Delta d_{r}=(q-\bar{q})\delta_{r}\) accounts for the spacing between \(A_{q}^{R}\) and \(A_{\bar{q}}^{R}\), and \(a_{t}\sim\mathrm{U}(-a_{tm},a_{tm})\), \(a_{r}\sim\mathrm{U}(-a_{rm},a_{rm})\).
# Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments
## 3 Integrating NDNs in Smart Cities
### NDN in the ATCLL  
Our NDN solution was specially designed for IoT infrastructures taking advantage of mobility prediction information. In our project, we developed improvements regarding the Forwarding [3], Caching and Energy-efficiency [23], as well as exploring mobility-aware prediction capabilities [4]. For all these developments, we have used the NDN implementation available in the ndnSIM network simulator, an open source NS-3-based NDN simulator [25]. The ndnSIM offers efficient features for simulating the device faces of the NDN nodes. However, these mechanisms do not work in real communication devices interfaces. In order to appraise our solution in a mobile-aware environment as is the case of the ATCLL infrastructure, first we had to port our adjustments to an adequate format to be installed in constrained mobile devices. In this way, we performed this port in a modular approach, with the aforementioned changes to Forwarding and Caching processes being implemented in a Single-Data request format at the current time, with mobility-awareness capabilities, while the publish-subscribe capabilities should be included in following versions.
# Efficient IoT-Based Formal Model for Vehicle-Life Interaction in VANETs Using VDM-SL
###### Abstract  
VANETs have gained much attention from both industry and academia because of their characteristics, such as dynamic topology. There are various applications of VANETs that are classified on the basis of safety, efficiency, commercial usage, and productive areas. This paper presents an IoT-based formal model for vehicle-life integration enabling RSUs with the help of different approaches. We have developed a model that uses vehicle scenarios in smart transportation systems so that quick data transmission is provided between the source and destination vehicles. Further, fog-based RSUs provide a wide range to communicate with hospitals and emergency vehicles to deal with emergency situations. All the appropriate entities are connected to ensure a consistent traffic flow for the arrival of an emergency vehicle in emergency places. The UML, graph theory, and VDM-SL formal technique are used to represent this system. To model the network and discover appropriate paths for V2V communication, graph theory is applied. The system requirements are designed using a UML diagram. The VDM-SL, an object-oriented model-based formal technique, was utilized for this modeling procedure. This approach assures the safety and accuracy of systems by detecting flaws early in the design process. It also gives an exceptionally important answer to an issue and increases trust in the software's quality.  
VANETs; IoT; fog-based RSUs; formal methods; verification and validation 2022121 January
# Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
## 3 Key Technologies
### Visualization Engine Design  
The use of abstract data makes it difficult to meet the final needs of the users. However, the visualization technology can describe the data graphically to provide users with a visual outlook of these data, which can help them understand and recognize the current space activities. Faced with complex, massive spatial data, efficient data scheduling and flexible operational control are key problems in the visualization process. Therefore, Sino-InSpace divides the visualization engine into two parts: the data engine and rendering engine, as shown in Figure 8.  
The data engine is responsible for the rapid indexing and scheduling of data. Depending on the visual cone parameters delivered by the rendering engine, the visible area is first calculated and then, the tiles, environment data, or graphic names that need to be updated are determined. To improve the rendering of visualization, the data are progressively scheduled from near to far based on the distance to the viewpoints; in addition, a certain part of the data buffer memory is reserved for visualization. For the visualization process, first, we determined whether the data buffer contained the required data; if the data were not available in the buffer, they were imported by multithreading, and the data buffer was then updated. After receiving the data transmitted by the data engine, the rendering engine graphically renders the environment and entity elements in the visual scene.  
The rendering engine also supports user interactions such as mouse-, keyboard-, and gesture-based interactions as well as real-time or non-real-time script drivers to flexibly control the viewpoint, time, and entity target behavior. A script file can be opened and edited directly in text, and each line represents a command and has a fixed format as follows:  
**Command identification** [[_Parameter identification_ Parameter]]  
Table 2 shows some of the commands and parameters. These commands support the switch of viewpoints, control of simulation speed, text display, etc. A simple example is given in Figure 9.  
Figure 8: Visualization engine design.
## 1 Introduction
However, very few attempts to use such an arrangement for an ASP system in a car have been made so far. Therefore, this study focuses on utilizing the ESG-cowl pair as a secondary actuator to implement an ASP control system based on the command-FxLMS in a car. As mentioned at the beginning of this section, however, a secondary path of an ASP system using such an ESG-cowl pair has uncertainty increased with frequency, and this reduces the frequency range of the pure ASP control to enhance sound for maintaining stability. To overcome this limitation, an additive ASP algorithm with a purely feedforward enhancement is proposed, which can generate an additional enriching engine sound with robust stability even at the higher frequencies. The theoretical considerations of the additive ASP algorithm and the control filter design process of the feedforward enhancement are described in detail in the main body of this paper. The ASP system with the proposed algorithm was implemented in an actual car, and the real-time control experiment was carried out for six engine orders when the engine of the test car was accelerated at the neutral mode. Then, the experiment result was analyzed and discussed in detail in terms of tracking performance by comparing the SPLs of each order before and after controls for the target SPL. In this paper, theoretical considerations of the proposed ASP algorithm with the feedforward enhancement are investigated in Section 2. The experimental set-up for real-time ASP control is explained in Section 3. The analysis and discussions about the control experiment results are written in Section 4. The conclusions of this paper are summarized in Section 5.
# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 3 Definitions and Improved Network Framework  
### Related Definitions  
Based on previous work [5, 6, 7, 15], we divide the category set \(C\) into two parts: the base class \(C_{base}\) with a large amount of labeled data and the new class \(C_{novel}\) with only \(K\)-labeled data per category, i.e., \(C=C_{base}\cup C_{novel}\). Among them, \(C_{base}\) comes from the base class dataset \(D_{base}\), \(C_{novel}\) comes from the new class dataset \(D_{novel}\), and the two category sets have no intersection. That is, \(C_{base}\cap C_{novel}=\emptyset\). The learning process of FSOD is usually divided into a base class training stage and a new class fine-tuning stage [3]. For base classes, we have a large number of annotated target images \(D=(i,y),i\in I,y\in Y\), where \(i\) is the training image and \(y\) is the real label corresponding to image \(i\). Specifically, \(y=\{(c_{i},box_{i}),c_{i}\in C_{base},box_{i}\in\{x_{i}y_{i}w_{i}h_{i}\}\}\), \(i\in\{1,N\}\). Image \(I\) contains \(N\) bounding boxes, each consisting of a class label \(c_{i}\) and a bounding box position \(box_{i}\). For the new class, we use the \(K\)-shot method to label the samples (e.g., \(K=1,3,5,10\)). The support image for the new class \(c\in C_{novel}\) is defined as \(F_{c}^{c}=\{(\tilde{i_{i}},box_{i}),\tilde{i_{i}^{c}}\in I,box_{i}\in\{x_{i}y _{i}w_{i}h_{i}\}\}\), \(i\in\{1,K\}\). \(i_{i}^{c}\) represents the training image, \(box_{i}\) is the boundary position of the object with class label \(c\). The ultimate goal of the algorithm is to optimize the detector at different stages, using the base class dataset and the new class dataset to detect the query set and samples, where the query set belongs to a subset of the base class and the new class.
# Inertial Parameter Identification in Robotics: A Survey
## 5 The Output Error (OE) and Input Error (IE) Identification Approaches
### Direct Dynamics Identification Model (DDIM) and Nonlinear Kalman Filtering (NKF)
We here briefly present some of the most popular approaches, namely the Extended Kalman Filter (EKF), the Sigma-Point Kalman Filter (SPKF) and the Particle Filter (PF). In the EKF, the prior estimate is propagated through a first-order linearized version of the robot dynamics. Although the resulting computational cost is low, it should be noted that the first-order linearization induces a bias in the posterior mean and covariance of the estimate. This bias can be non-negligible when the considered system is highly nonlinear. In SPKF implementations such as the Unscented Kalman Filter (UKF) or the Central Difference Kalman Filter (CDKF), a set of deterministic samples of the prior estimate (assumed to be normally distributed) are propagated through the true nonlinear dynamics of the system. In this manner, the nonlinearities can be taken into account up to the second order. Although this method significantly reduces the bias on the estimates of the posterior mean and covariance, it must be noted that it has a higher computational cost than the EKF, although it remains on the same order of magnitude. This aspect is discussed in greater details in [82]. Finally, in a Particle Filter (PF) no prior assumption is made on the nature of the estimate distribution. The latter is in fact sampled using a Monte Carlo method, hence resulting in enhanced robustness, even to severe non-linearities and non-Gaussian noises, but at the expense of computational cost, as the number of particles has a direct influence on the precision of the estimate (c.f. [84; 85]). The full derivation of the EKF, SPKF and PF algorithms being out of the scope of this paper, the reader is referred to [82] for a more in-depth discussion and comparison between the different filters. In the context of parameter identification, it is worth noting that \(\mathbf{\tau}(t_{k})\) in Equation (48a) may either denote the torque measured during the experiments--as is for example the case in [7; 80]--or the control torque applied to the simulated closed-loop system during the time-update step of the Kalman filter. In this manner, parameter identification can still be achieved when torque measurements are not available or available at a low sampling rate, but that the robot control structure and control parameters are known. Note however that in the latter case the control loop is updated at the robot control frequency \(f_{c}\). As a result, several control iterations can potentially be executed between two updates of the Kalman filter. To the best of our knowledge, this was so far never applied to the field of parameter identification.
## 2 Theoretical Background
### The Forward Model
#### 2.2.3 Objective Function  
Within the Bayesian inversion, the objective function calculates the divergence between the values of the previous model and the observed data (\(\mathbf{m}\)). The objective function allows us to adjust the previous model to the observed data \(\mathbf{m}\) to determine the subsequent model, with which we describe the physical structure of the model while providing information about the subsoil properties with greater precision. The maximum likelihood of the likelihood is related to the parameters of the convolution model \(\mathbf{d}\) with the smallest difference between observed data \(\mathbf{m}\) and the acquired data, while the maximum a priori probability is related to the slightest difference from the low-frequency model. Therefore,the maximum a posteriori (MAP) for the inverse problem is the optimal configuration of the model that manages to minimize the following function:  
\[J(\mathbf{m})=\Big{(}(\mathbf{d}-g(\mathbf{m})^{T}\mathbf{\Sigma_{d}}^{-1}( \mathbf{d}-g(\mathbf{m})))\Big{)}+\Big{(}(\mathbf{m}-\mu_{m})^{T}\mathbf{\Sigma_ {m}}^{-1}(\mathbf{m}-\mu_{m})\Big{)}, \tag{7}\]  
where the covariances \(\mathbf{\Sigma_{d}}\) and \(\mathbf{\Sigma_{m}}\) act as the weights in the optimization process. The function \(g(\mathbf{m})\) is a weighted sum of mixture components, where \(g(\mathbf{m})=\sum_{i=1}^{k}w_{i}g_{i}(\mathbf{m})\). Here, each \(g_{i}(\mathbf{m})\) represents a specific mixture component, and the coefficient \(w_{i}\) indicates the weight associated with the \(i\)th component. These weights \(w_{i}\) control the relative contribution of each component to the overall mixture, adhering to the conditions \(0\leq w_{i}\leq 1\) for all \(i\), and the sum of all weights equals one, \(\sum_{i=1}^{k}w_{i}=1\)[31].
# Recent Progress on Aberration Compensation and Coherent Noise Suppression in Digital Holography
## 6 Conclusions  
Aimed at improving the imaging quality in DHM, research was carried out on the tilt phase error compensation, phase aberration compensation, and coherent noise reduction, respectively. Recent research progress was reviewed from different aspects, such as principle, category, advantage, and applicability. In the tilt phase error compensation, the hologram spectrum's centering judgment of side band, as a precondition for the digital reference wave method and spectrum translation method, was discussed. It can be demonstrated that correctly judging the hologram spectrum's centering is of importance for tilt phase error elimination. In phase aberration compensation, the physical and numerical compensation methods were summarized, and their advantages and limitations analyzed, respectively. In coherent noise reduction, the comparisons among coherent noise reduction methods  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantage** & **Limitation** \\ \hline Low spatial or temporal coherent & & Increasing the adjustment \\ source method [77; 78; 79; 80] & Recording one hologram & difficulty of light configuration \\ \hline OSH method [91; 92; 93; 94] & Higher signal-to-noise ratio & Complicated setup and slower \\  & & recording speed \\ \hline FINCH method [95; 96; 97; 98] & Higher signal-to-noise ratio and dynamic measurement & Larger bias buildup for \\ \hline Multiplex holograms & Uncorrelated holograms obtained & \\ method [33; 35; 100; 101; 102; 103; 104; 105; 106; 107] & by different ways & Recording multiple holograms \\ \hline Digital processing & Recording one hologram & Loss of spatial resolution \\ method [108; 109; 110; 111; 111; 112; 113; 114; 115; 116] & Recording one hologram & Loss of spatial resolution \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of coherent noise suppression methods.  
only by one hologram were conducted. At the same time, the influence of coherent noise suppression on the spatial resolution loss of a reconstructed image was also analyzed. In conclusion, the phase aberration compensation and coherent noise suppression are two critical research fields in improving DHM's imaging quality, in which a number of research approaches and achievements have also emerged. The research on the automatic imaging quality improvement method with real-time and non-intervention characteristics, applied in a dynamic measurement such as cell migration, should be a critical development trend in the future.  
This work was supported by the National Natural Science Foundation of China under (Grant No. 61605156), the Scientific Instrument Research Funds of State Key Laboratory for Manufacturing Systems Engineering, and a Key Laboratory Project of the Shaanxi Provincial Department of Education (entitled \"Research on imaging quality improvement method of 3D morphology measurement in DHM\").  
All authors contributed to the research work. [PERSON] wrote the paper, [PERSON] revised the paper  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Validation for the Sensorial System
#### 2.2.2 Repeatability Test Methodology  
The repeatability test was designed to check the repeatability of the sensorial element, for three different calibrated test weights were used: 1 kg, 5 kg and 10 kg--thus also observing the (non)linearity. The measurement protocol for repeatability using a specific test weight is:  
* The steps from the General methodology are conducted up until the conduction of the measurement protocol.
* On the measurement file, one research team member notes the evaluated sensorial element's unique identifier, the environmental conditions (temperature and humidity), and the value of the calibrated test weight used during the test.
* Another research team member places the calibrated test weight on the ground contact element of sensorial element (placed upwards, in this stage), assuring that the whole weight is supported only by the sensorial element.
* The weight is maintained on the sensorial element for 5 s.
* The research team member removes the weight from the sensorial element.
* The previous three steps are repeated ten times, respecting the repeatability conditions--within a short time interval and by the same research team member, without any recalibration or reinitialization of the data acquisition system. Each repetition is carried out as a standalone measurement--another research team member monitors the behaviour of the operator team member to be consistent and constant. The measurement is discarded if the monitoring team member observes deviations from those principles.  
Following the data integrity check, the resulted dataset is subjected to validation and verification to find and eliminate possible random errors.
# Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges
## 7 Conclusions  
This paper presents a semantic segmentation network model for fatigue crack detection based on DeepLabv3+, ResNet50, and active learning. The classification network ResNet50 is re-tuned using the crack image dataset, leading to ResNet50_crack, which is adopted as the backbone network for DeepLabv3+ to construct a crack semantic segmentation network. This network was trained with the assistance of active learning, followed by the optimization of the probability threshold of the pixel category. Compared with the crack detection network based on conventional ResNet50, this model can improve MIoU from 0.6181 to 0.7241. In future research, we are to further improve the detection accuracy of the proposed model.  
Figure 8: Selected fatigue crack detection results using the proposed model.  
model for small cracks and to achieve the automatic and fast calculation of crack width, length, and other information based on the Transformer network.  
Conceptualization, X.J.; Writing--original draft, Y.W.; Writing--review and editing, X.J. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study.  
Author [PERSON] was employed by the company China Construction Second Engineering Bureau 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.