esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of Fresnel-Kirchhoff Integral Method for the Study of Volume Dielectric Bodies
## 1 Introduction  
There is no doubt that optical methods have been applied with success in different fields of engineering such as experimental solid mechanics, fracture mechanics, civil engineering, etc. Fiber-optic sensor technology, for instance, has been widely used by civil engineers for performance monitoring of civil infrastructures. This is basically due to the fact that fiber-optic sensors have the advantages of small dimensions and good resolution and accuracy [1]. Digital image correlation (DIC) has also been employed for the measurement of small deformations, such as those occurring during fluid-structure interaction [2]. Moire interferometry has also been a valuable experimental technique for the understanding of the mechanical behavior of materials and structures [3; 4; 5]. Photoelasticity [6] has also been applied to evaluate the stress and strain field around cracks [7], etc.  
In this work, we introduce another technique to evaluate properties of defects, deformations, cracks, and in general any small-volume structure that can be modeled as a volume dielectric body. The method is based on the Fresnel-Kirchhoff integral [8], which in principle was intended to simulate planar structures. Nonetheless, it also has been applied to simulate volumetric dielectric structures as well [9; 10; 11]. To do this, the structure must be treated as a two-dimensional object. This is achieved by taking into account the phase accumulated by an incident plane wave after passing through the object [12]. The amplitude of the input field in the Fresnel-Kirchhoff integral is assumed to have this phase, and the calculation of the integral provides the field in the output plane. In this work, the accuracy of this method will be analyzed by comparing the results provided by the Fresnel-Kirchhoff approximation for a dielectric cylinder of circular cross-section to the results obtained by rigorously solving the Helmholtz equation.  
Next, the method will be applied to obtain the refractive index of a small-volume body such as a human hair. To do this, the normalized intensity distribution of the diffraction pattern of the hair will be measured by using a CCD camera. Then, the theoretical curve will be fitted to the experimental results by the Fresnel-Kirchhoff method to show the validity of the method. Finally, to demonstrate the potential of the method, this will be applied to obtain the dimensions of a crack artificially created in a piece of plastic.
## 8 Example  
To highlight the effectiveness of the proposed approach, a numerical example is now developed. Consider the 2D singular continuous system in (1) with the following system matrices:  
\[A = \left[\begin{array}{ccccc}-0.7500&0.4250&-0.1750&0.4050&0.3950 \\ 0.0750&-0.2250&0.4275&-0.3750&0.4250\\ -0.2950&1.2150&0.3450&-0.1750&0.4000\\ -0.0250&0.5750&0.4250&0.0750&0.2500\\ 0.2500&0.8750&0.3450&0.0750&0.4000\end{array}\right]\] \[E = \left[\begin{array}{ccccc}1&0&0;&0\\ 0&1&0;&0\\ 0&0&0;&0\\ 0&0&0;&0\end{array}\right],U=\left[\begin{array}{ccccc}0&1&0;&0\\ 1&0&0;&0\\ 0&0&1;&0\\ 0&0&0;&0\end{array}\right]\] \[V = \left[\begin{array}{ccccc}0&1&0;&0\\ 1&0&0;&0\\ 0.&0.&1;&0\\ 0&0&0;&1\end{array}\right],U=\left[\begin{array}{ccccc}0&1&0;&0\\ 1&0&0;&0\\ 0&0&1;&0\\ 0&0&0;&0\end{array}\right]^{\top}\] \[B = \left[\begin{array}{ccccc}1.00&-0.10&2.20&0.50&0.70\end{array} \right]^{\top}.\]  
The open-loop system is not regular, with the corresponding matrix \(\tilde{A}_{22}\) being not invertible.  
By solving the LMI (27), the following feasible solution can be found:  
\[P = \left[\begin{array}{ccccc}1.0018&-0.3591&-0.1420\vdots&0&0\\ -0.3591&1.0482&0.0508\vdots&0&0\\ -0.1420&0.0508&0&0&0\\ \hline 0&0&0&\vdots 0.2649&0.1804\\ 0&0&0&\vdots 0.1804&0\end{array}\right]\] \[G = \left[\begin{array}{ccccc}-0.1079&0.5117&-0.1550&0.1530&-0.0364 \\ 0.5311&-0.0514&0.4227&-0.1214&-0.0478\\ -0.4449&0.0317&-0.5058&-0.3204&-0.1699\\ -0.0463&0.0046&0.1783&0.2721&0.0015\\ -0.2428&-0.0595&-0.0878&-0.0251&-0.3444\end{array}\right]\] \[R = \left[\begin{array}{ccccc}-0.0444&0.0215&-0.3406&0.1722&0.0741 \end{array}\right]\] \[K = \left[\begin{array}{ccccc}0.2712&-2.6693&-3.6971&-4.8858&1.929 0\end{array}\right]\]  
Some simulated results are now presented, with null boundary values for \(T_{1}>50\) or \(T_{2}>50\). To facilitate the visualization, we plot in Figure 1 the evolution of the two following quantities, which correspond to the norm of the generalized vector along the diagonal \(t_{1}+t_{2}=t\), that is,  
\[X^{h}(t)=\int_{0}^{t}\left\|x^{h}(\theta,t-\theta)\right\|^{2}d\theta\quad \text{and}\quad X^{v}(t)=\int_{0}^{t}\left\|x^{v}(\theta,t-\theta)\right\|^{2 }d\theta.\]  
This makes it possible to compress the evolution of the 2D system using 1D plots. It can be seen in Figure 1 that \(X^{h}(t)\) and \(X^{v}(t)\) go to zero as \(\theta\) goes to infinity, indicating that the system is stable.
## 5 Experiments
### Preliminary Exploration and Training Details
#### 5.2.2 Models' Generalization  
With respect to the training of the audio autoencoder, we successfully softened the latent space through dropout regularization applied to the audio decoder, which improved generalization significantly. For the generator, as mentioned in Section 4.2, we included test time dropout regularization between each dense block. This strategy, in addition to helping to generalize the model, induces stochasticity within the generator, enabling greater visual variety in translations. In practice, this technique noticeably enhanced the diversity of the generated images. However, it appears that test time dropout only contributed to improving model generalization up to a certain extent, as translated images were often non-informative. Actually, evaluating the generalizability of the generator is not an easy task, since the synthesized images most likely do not share the visual structure of the images corresponding to the input sound. This visual decoupling implies that even if generated images are semantically coherent with the original sound, visual elements will not necessarily appear on the same position as in the target images. Consequently, pixel loss obtained from the test set becomes useless. In fact, visual matching is rare, and even when it occurs, it is an approximate match. Aware of those limitations, we decided to focus our analysis on two key aspects that we consider suitable for assessing the quality of the generated images, namely, interpretability and semantic coherence, which can both be summarized in the term 'informativity.'  
Figure 6: Illustrative comparison between ground truth (1 st row on top), and images generated by the sequential 13-layer (2 nd row), sequential 25-layer (3 rd row), and the dense 25-layer generator (4 th row) using known sound–image pairs.
## 6 Numerical Experiments
### Illustrative Example of the Result of the Proposed Optimization Framework  
We begin by showcasing the results of the proposed optimization framework using \(N=100\) and \(P=50\) as examples. The random selection of ride requests was performed \(S=2000\) times, resulting in 200,000 trips in total, which means that each trip in the dataset can appear in around 7.5 of the selections on average. After 2000 selections were completed, the corresponding matrices of \(d_{ij}\) were then computed, and the resulting PM models were solved.  
Figure 8 shows the distribution of the minimized total passenger detour distance (denoted as \(Z_{\min}\) herein) for the 2000 selections. By conducting both the Kolmogorov-Smirnov test and the Shapiro-Wilk test, we verify that \(Z_{\min}\) follows a normal distribution. The detailed test statistics can be found in Appendix A, in which we also verify the normality of the density distribution of \(Z_{\min}\) when \(N=50\), 60, 70, 80, and 90, given \(P=50\).  
Figure 8: Distribution of minimized total passenger detour distance among 2000 selections.  
Figure 7: The road network of Shenzhen.  
Figure 9 shows the total frequency of being matched with passengers for all the candidate parcel locker locations among 2000 selections in descending order. The x-axis tells the rankings of all the candidates (from 1 to 421), revealing that the most frequently selected candidate was paired with over 7000 passengers. Figure 9 also indicates that 113 locations have been matched with fewer than ten passengers, with 76 of these locations having no matches at all. We then plot \(P=50\) selected locations for the parcel lockers in Figure 10 (represented by the red triangles), which mainly locate in the downtown areas of the city.
## 4 Case Study
### Obtaining Data from the Networks  
Three datasets containing data for sub route groups, networks, and terminals were used in the experiments. The data set was divided into 20 file batches, each containing over 100K rows of data timestamped events. At this stage of the research, training was performed offline and attempts were made to find anomalies in the dataset (in future work, real-time deployment in a production environment will be implemented). Due to  
Figure 4: The system implementation under the lambda architecture.  
confidentiality, the variables used in this work were anonymised. The sub route groups are called SUB_GRP_X and network NET_X (Table 1).
## 2 Test Materials and Test Methods
### Test Methods  
#### 2.2.1 Triaxial Test  
The Nanjing Soil Instrument Factory TSZ-2 strain control instrument, which is a fully automatic triaxial instrument, was used for the tests, and the test apparatus is shown in Figure 4. This instrument is from Jiangsu Province, China. The granite residual soil with different contents of fly ash was mixed according to the geotechnical test procedure, cylindrical specimens of \(\Phi\)39.1 mm \(\times\) 80 mm were obtained by layered compaction, with fifteen specimens obtained from five groups of three specimens each, with fly ash contents of 0%, 5%, 10%, 15% and 20%. To achieve sufficient strength, cylindrical specimens were fabricated and then cured for 14 d at 25 \({}^{\circ}\)C and 90 \(\pm\) 2% humidity in a curing box. This ensured that the fly ash was thoroughly mixed with the granite residual soil. Then, the specimen was soaked using vacuum pumping in preparation for the triaxial test. A prepared specimen is displayed in Figure 5. The accuracy of the test was ensured by averaging two parallel tests.  
Using a triaxial instrument, a solidification undrained shear test was performed. The peripheral pressure was 100, 200 and 300 kPa, and the shear rate was fixed at 0.01 mm/min.  
Figure 4: TSZ-2 fully automatic triaxial test system.  
Figure 5: Specim of granite residual soil amended with fly ash.  
Because the test was strain-controlled, the specimen was deemed to have been sheared when its axial strain reached 20%.
## 3 Control Design for Transition Phase
### Guidance Law Design
#### 3.1.2 Altitude--\(\Omega_{heave}\) Channel  
This channel is used for the rotary-wing mode, which controls the altitude by varying the lift force by changing the speed of the four rotors to achieve the altitude hold function. The guidance law design is explained as follows.  
The difference between the desired altitude and the current absolute altitude is obtained using Equation (7) to get the altitude error; the altitude error is then used in Equation (8) to generate the climb rate command, and then the climb rate command is converted into the rotary-wing mode hover command \(\Omega_{hrace}\); the hover command is distributed to each rotor to realize the altitude tracking.  
Figure 5: Diagram of altitude—\(\theta\) control.
###### Abstract  
In this study, an activity-based travel demand model of the Usti nad Labem district (Czech Republic) is created. To do this, an advanced travel demand synthesis process is presented by utilizing the Eassim framework, which is a pipeline-processing, initial raw data to simulation step. The framework is extensively modified and extended with several algorithms in order to utilize multiple data points for increasing realism in mobility for travel demand models. Two major extensions are provided. First, the pipeline framework is improved to estimate inbound and outbound trips of the study area, comprising a main city and 23 surrounding municipalities. The extended framework assigns synthetic gates for the study area as hubs for the inclusion of inbound and outbound trips. Second, the pipeline framework is advanced to provide a more compatible match of travel destination and activity location state. To do this, the extended framework assigns a capacity for each facility identified for the study area, the expected number of visitors to each facility, and the number of residents in each building. The resulting demand model is presented and the generated trips are evaluated based on locational, transport mode, and sociodemographic characteristics with origin-destination (OD) bundling. Additionally, distribution analyses of the present model are conducted to understand the matching results on a detailed level. The results demonstrate that the present model provides a reasonable output for transport researchers when testing different mobility scenarios and the provided extensions helps them to reduce implausible reflections of the distribution of travel and activity characteristics in household travel surveys while creating demand models, thus increasing realism. Lastly, open-source playground and code repository for further future improvement of synthetic travel synthesis methods are created, which enhances a deep understanding of the preparatory and methodological backgrounds required for complex activity-based simulations in order to inspire transport planners.  
activity-based approach; synthetic population; travel demand; MATSim model; statistical matching +
Footnote †: journal: Applied  
1 Faculty of Transportation Sciences, Czech Technical University, 110 00 Prague, Czech Republic 1
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 2 Problem Description
Evidently, using the 3D loading method for express cargo can improve the operational efficiency of the whole logistics system. To sum up, this study explores the vehicle path optimization problem with the time window under the pallet 3D loading constraint, considering the constraint of cargo for pallet 3D loading and the time window and cargo demand at the customer point. Influenced by the time window of customer points and various cargo demands, the traditional cargo loading and distribution method have the problems of low vehicle loading rate, violation of distribution time window, and high total cost of logistics operation. The 3D space of distribution vehicle loading is optimized and designed to adjust to maximize the utilization efficiency of logistics resources and improve the vehicle loading rate. In addition, constraints, such as 3D pallet loading and customer service time window, are introduced to build a dual-objective vehicle path optimization model with the maximum loading rate of distribution vehicles and the minimum total cost of logistics operation. The distribution path optimization scheme using traditional 3D loading distribution and optimization based on the 3D loading of pallets and combined with the field research data is compared and analyzed. Figure 2 shows the results, and the color of goods in the figure is only used for distinction. Suppose the speed of the distribution vehicle is 25 km/h, the cost of distribution per kilometer is CNY 4, the cost of violating the time window is 35 CNY/h, and the time of unloading each pallet is 0.083 h. Figure 2a shows the traditional 3D loading distribution, which has the highest loading rate as pallets are not used as loading and unloading carriers. Suppose the volume of goods (16.8 m\({}^{2}\)) is loaded into the vehicle, and the loading rate is 87.5%. In this case, it takes approximately 0.5 h for cargo differentiation and unloading work to reach the customer point. Figure 2b shows the 3D loading scheme for pallets, which is to load the pallets first by site number and then into the compartment, maximizing the space inside the vehicle. Table 1 shows that based on the above conditions, the use of the pallet 3D loading scheme can save more time and cost than the traditional 3D loading scheme in terms of loading and unloading speed and can maintain a high loading rate. Figure 2: Schematic diagram of scheme comparison.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
###### Abstract  
This paper proposes a dynamic phase comparison algorithm for planar direction finding on a high-speed moving satellite radio receiver, treating the moving antenna as equivalent to single-baseline array antennas. Based on a phase interferometer algorithm, this algorithm adjusts the baseline length according to the frequency measurement module and the satellite's high-speed motion to avoid phase ambiguity indirectly. By integrating the traditional amplitude comparison algorithm based on orthogonal dipole antennas, a dynamic fusion direction-finding method is proposed. Simulations demonstrate that this approach method not only covers a broader range of direction finding but also achieves higher accuracy, providing valuable insights for acquiring three-dimensional plasmagrams with space-borne plasma imagers.  
direction finding; interferometer; DOA; amplitude comparison; RPI; dipole antenna +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: _Applied_  
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: _Applied_  
astrometry and geodesy [12], which is also used for observing stations to explore the distribution of radio sources on the solar disk [13].  
Early in the study of the ionosphere, researchers began using interferometers to estimate the DOA (direction of arrival) of electromagnetic echoes [14; 15; 16]. EM waves in the far field can be considered parallel waves. When EM waves from different directions arrive at antennas located at different positions, there is a phase difference in the signals received at the same moment due to their different locations. By analyzing the phase difference between any two received signals, the azimuth angle of the EM waves can be determined, thereby obtaining the direction of the radiation source. Due to the phase ambiguity in interferometers [17], researchers have conducted in-depth research over the years to resolve this ambiguity [18; 19; 20; 21; 22; 23]. In the context of space-borne receivers, several researchers have proposed rotational baseline disambiguation methods based on the spinning motion of satellites [24; 25].  
Hence, to improve the DF system performance of the satellite radio receiver, this paper proposes a dynamic phase comparison algorithm against the backdrop of a high-speed moving satellite based on the phase interferometer algorithm. This algorithm, while offering a broader DF range than the amplitude comparison algorithm, dynamically calculates and adjusts the baseline length to avoid phase ambiguity. By integrating the amplitude comparison algorithm, it further achieves the purpose of improving DF accuracy.
## 3 Computational Approach
### Resource Preference Generation  
To generate a top 'N' resource list based on similarity, a prediction of a user's preference for a resource should be made. For this purpose, this paper employs the \"All But 1\" protocol, as suggested by [PERSON] et al. [38]. In the methodology adopted by this research, one resource related to the target user is randomly selected and excluded from the comprehensive collection of resources. The formula for preference value of a user u for resource r is as follows [27].  
\[Score(u,r)=\frac{\sum_{v\in KNN(u)}(W_{i}(v,r))\cdot sim_{i}(u,v)}{\left|\sum_ {v\in KNN(u)}simK\right|} \tag{5}\]  
In this context, _KNN_(_u_) stands for the collection of '_K_' closest neighbors to the user '_u_'. Meanwhile, _W_i_(_v_,_r_) illustrates each weight vector of the neighbor user '_v_' related to the resource '_r_'. The term _sim_i_(_u_,_v_) designates the measure of similarity between users '_u_' and '_v_', which is computed as described in Equation (4). The preference matrix for resources by users can be visualized, where each row represents an individual user and the columns are indicative of various resources. For clarity and easy reference, Table 5 elucidates every symbol and offers a corresponding interpretation.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 5 Discussion of Experimental Results
### Distributed Mobility Management Experiments  
A mobility management solution requires two identifiers: one that doesn't change as the mobile host moves, so that communication endpoints keep stable identities, and on that does change, reflecting the position of the device in the network. Currently, the only identifier assigned to an entity in a Mobile Host is the IP address, but this single identifier cannot satisfy both requirements simultaneously.  
RINA assigns location-independent application names and location-dependent addresses to IPCPs, resulting in mobility being supported without the need for home routers, foreign routers, tunnels, anchors, or specialized protocols. Managing mobility requires a combination of routing updates, changing addresses of IPCPs and designing the number and size of layers in different parts of the network to accommodate the load, scale, and rate of change of the devices to be supported. Mobile Hosts can roam through multiple provider networks and multiple access media without causing service disruption at the application level: service continuity is preserved as long as the destination applications are reachable [7]. Moreover, different applications may access different services from multiple providers, utilizing multiple underlying physical media simultaneously or as a fail-over.
# Design and Analysis of Independently Adjustable Large In-Pipe Robot for Long-Distance Pipeline
## 2 Overview of Robot
### Design of Crawlers and Pantograph Bracket  
As shown in Figure 2a, instead of mounting the motor on the side of wheels, the gear motor was mounted on the upper of the tracked bracket to minimize crawler's width. Each 90-w DC motor was equipped with a speed reducer and powered with a voltage of 12 V.  
Figure 1: The large in-pipe robot. (**a**) Prototype. (**b**) Solid model.  
When it comes to wall-press in-pipe robots, a flexible linkage mechanism allows the robot to adapt tp various pipeline diameters. Most of them use four suspension-links with one rod connect to an axial sliding bush. For some in-pipe robots, this saves space between the main body and the crawler section, but it brings many new problems. The thrust provided by the screw -ole or spring may not meet the expansion needs of large in-pipe robots. When locking phenomena happen, these parts are easy to damage. In the meantime, the robots may not easily maintain the state where the center of the main body coincides with the center of the pipe, because the actual diameter of the in-pipe robots will be slightly smaller than the pipe diameter. In this way to avoid possible self-locking phenomenon. However, the rugged inner surface of the tube will increase the vibration of the robot, and frequent vibration will cause hurting data performance. The robots and the pipe wall must maintain the proper pressure to avoid these two situations.  
Therefore, the ideal support method can independently adjust the length of each \"legs\" and control the pressure between crawler and the pipe wall actively, which not only ensures the robot always in the center of the pipeline, but also reduces the harm caused by vibration and self-locking to the robot. As shown in Figure 2b, the pantograph consists of one electric putter and two parallel brackets. When the electric putter moves along the orange arrow, the crawler will rise or fall.  
In addition, compared to the traditional screw structure, this design is easy to be replaced, robot can also quickly adjust its diameter according to the pipe diameter in a wide range (by adjusting the position of the connection point between the aluminum frame and the electric putter).