esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Frequency Sound Absorption in Air Pipelines and Water-Filled Pipelines Based on Metamaterials
###### Abstract  
Due to the significant impedance mismatch between water and air, two types of pipeline silencers designed for the same target frequency with different filling materials often have significantly different thickness. This increases the difficulty in designing silencers for multiple pipelines with different filling materials in narrow spaces. The paper reports a metamaterial design paradigm based on a Helmholtz cavity for low-frequency sound absorption for both air pipelines and water-filled pipelines. An asymmetric absorption metamaterial with coupled Helmholtz resonators is proposed to reduce the impact of low-frequency noise in air pipelines. By coupling the absorption mode and reflection mode, the asymmetric absorption metamaterial with a thickness of 71 mm achieves 95.6% absorption at 403 Hz. The tunable absorbing performance in broadband is confirmed by a finite element simulation. Additionally, a composite metamaterial constructed with HRs associated with a rubber layer is proposed for low-frequency broadband sound absorption in water-filled pipelines. An average absorptance of above 0.8 is achieved over the range of 380-508 Hz by coupling four basic composite metamaterial units with a thickness of 31.5 mm. The proposed design paradigm can reduce the complexity of designing multiple pipelines silencers with different filling materials because the muffler should have similar thickness in the same paradigm.  
metamaterials; pipeline noise elimination; low-frequency sound absorption; asymmetric sound absorption Article
## 3 Main Simplifying Hypotheses Assumed in the Optimization Tool
### Electrical Model  
The geometry of the coaxial cable plays a critical role in determining the distribution of current across its layers and phases. An erroneous selection of geometric parameters may result in significant current imbalances or force the superconductor to operate under undesirable conditions. This study considers some simplifying assumptions to reliably estimate the distribution with relatively straightforward analytical formulae.  
First, the HTS tapes wound on each layer are assumed to be connected in parallel and share their corresponding layer current of the module \(\left|\underline{I_{i,j}}\right|\) evenly. Then, it is assumed that the cable connects a direct symmetrical three-phase generator system to a three-phase wye load, having a load factor \(\cos\varphi\). In computing the current distribution, the electrical resistance of each conductive layer is neglected, under the assumption that the  
Having assumed radial symmetry for the cable structure, only the radial (\(B_{r}\)) and tangential components (\(B_{t}\)) of the magnetic field with respect to the tapes' main face have to be computed. These expressions, reported in Equation (9), refer to the field applied at the average radius of the \(k\)th layer of the \(z\)th phase (the midpoint between them being its inner and outer radii, called \(R_{mean,z}\)). Thus, this accounts for the current flowing across all layers. The analytic equations are derived while assuming each HTS layer is a cylinder with an inner radius \(R_{i,j}\) and outer radius \(R_{i,j}+t_{HTS}\), where a current \(I_{i,j}\) which is uniformly distributed across the surface flows [47]:  
\[\left\{\begin{aligned} & B_{r_{k,z}}=\sum\limits_{i=1}^{3}\sum \limits_{j=1}^{nl_{i}}\mu_{0}\big{(}\big{(}R_{i,j}+t_{HTS}\big{)}-R_{mean,z} \big{)}\frac{\left|I_{i,j}\right|\,sin\big{(}\alpha_{i,j}\big{)}}{\pi\big{[} \big{(}R_{i,j}+t_{HTS}\big{)}^{2}-R_{i,j}\big{]}}\\ & B_{t_{k,z}}=\sum\limits_{i=1}^{3}\sum\limits_{j=1}^{nl_{i}} \mu_{0}\frac{\left[R_{mean,z}{}^{2}-R_{i,j}\right]^{2}}{2\ R_{k,z}}\frac{ \left|I_{i,j}\right|\,cos\big{(}\alpha_{i,j}\big{)}}{\pi\big{[}\big{(}R_{i,j}+ t_{HTS}\big{)}^{2}-R_{i,j}\big{]}}\end{aligned}\right. \tag{9}\]  
In Equation (9), \(\mu_{0}\) is the vacuum permeability, while the magnitude and the orientation of the field (\(\theta\) angle), required in the \(I_{c}(T_{HTS},\ |B|,\ \theta)\) function, are calculated as follows:  
\[\left|B_{k,z}\right|=\sqrt{{B_{r_{k,z}}}^{2}+{B_{t_{k,z}}}^{2}};\qquad\theta_{ k,z}=\text{atan}\left(\frac{B_{r_{k,z}}}{B_{t_{k,z}}}\right) \tag{10}\]  
It is worth noting that the radial component of the magnetic field is minimal everywhere, given the arrangement of the HTS tapes on the concentric layers. However, given the significant dependence of the tape's critical current on the radial component, the authors opted not to neglect it.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
###### Abstract
The idea behind the proposition of this intelligent control algorithm with a model-free approach is to achieve a minimum chattering effect, reduce the delay in all angular and translational accelerations, and minimize transient and steady-state issues, i.e., steady-state error, overshoots and settling time, even in the presence of unmodelled dynamic factors and external disturbances [3]. The subject is introduced in Section 1. Section 2 briefs the reader about the previous research contributions. Section 3 presents the study of an underactuated quadrotor dynamic model with unmodelled dynamic factors and unknown external disturbances. Furthermore, the Section 4 demonstrates the proposed model-free single-dimension fuzzy-based sliding mode control (MFSDF-SMC) along with its stability proof using Lyapunov stability theorem. Section 5 shares the software simulations and analysis among the SMC, conventional fuzzy-based SMC, and single-dimension fuzzy-based sliding mode control design with and without model-free approach. The hardware configuration and experimental results can be seen in Section 6, and last but not the least the entire research finding is concluded in Section 7.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 2 User Requirements
### The Requirement Wishes of the Air Traffic Controllers  
Although the integration of adverse weather is intended as a support for advanced scheduling and sequencing, it was usually perceived by controllers as a taking over of additional responsibility. Some of the respondents emphasized that it is the responsibility of the pilot to ensure a safe flight, including evasive maneuvers due to adverse weather [26]. One of the most common comments regarded a possible overload of the controller's display, either with additional information or with its colored presentations. The next point was that the controller's display may only represent the actual state at any time and not a forecasted one. On the other hand, the respondents stressed that displaying severe weather would be beneficial for better planning and less interference with traffic flows. Due to the large differences in the reactions of the pilots, respondents assumed that meaningful and realistic categorization with regard to the dangerousness of a weather situation was impossible. Experience shows that one aircraft can fly on the left side of a convective cell, the next one on the right side, and the last one through some severe weather area depending on the experience of the pilot and the interpretation of available onboard weather radar. Generally, this supporting tool is conceivable for a planning controller. For executive controllers, there might be a risk of visual overload on the traffic display. In principle, it was noted that it is a very good approach to show current weather data in the radar traffic image. However, there should be the possibility of manually switching this information on or off with a button. This should be used by ATCOs if necessary for better planning of traffic in relation to sequence creation. The represented information should always match the actions of the controller. For instance, airspace being \"usable\" or \"not usable\" means that a display  
Figure 1: Age distribution and professional experience of the participating controllers.  
with two possible states should be used without detailed information about meteorological airspace conditions.  
The essence of the results from the requirements analysis for the development of the controller support system in the project can be summarized very well with the phrase _\"less is more\"_. Overall, eight out of the nine controllers that participated in the survey welcomed a way of quickly and directly accessing weather information that is relevant to them. One of their basic requirements is that any type of information, whether it is provided graphically or numerically, must be able to be activated and deactivated quickly and easily on the display. These requirements build the basis for further developments in the SINOPTICA project that can visualize adverse weather on a controller's traffic situation display.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 2 Methodology
### Contour Mission Flight Planning
#### 2.2.7 S7: Boundary Check  
Determine if the entire route is located outside the boundary. If not, proceed to the next route. If yes, end the whole step.  
Through the contour route planning process S1-S7 proposed by the flowchart, the route paths of the whole region can be successfully obtained. This process uses the user-supplied parameters and DEM information to plan a suitable aerial route considering the topographic relief. Our method allows each route to maintain the same flight altitude, resulting in a more continuous GSD and avoiding the UAV's constant vertical displacement. This method is more effective in keeping the smoothness of the route than allowing each aerial point to build its altitude. Through such a process, we can effectively consider the terrain up and down and generate a suitable aerial path, providing a better solution for UAV aerial photography missions.  
This study utilized the ASTER DEM as the terrain reference data, with the coordinate system set to WGS-84. The DEM was extracted based on the user-defined flight area. The flight route was then generated using the defined camera parameters, ground sample distance (GSD), and overlap rate, following the workflow outlined in Figure 3, steps S1-S7. The calculations were performed using Python code, resulting in a KML file format. The IPad's DJI Ground Station Pro app was used to import the KML file and create the flight mission, allowing flight speed selection. Subsequently, the UAV executed the flight mission to capture aerial images.
## 2 Newly Proposed Evaluation Method and Analysis Procedure
### Typical Procedure of Newly Proposed CRITIC-GRA Evaluation Model  
Figure 2 shows a typical procedure of the proposed CRITIC-GRA evaluation model for quantitatively selecting the optimal scheme with effects of multi-indicators. The procedure is divided into four parts: the identification of all possible influential indicators to optimize the desired scheme, the weight analysis of all selected indicators using the CRITIC method (criteria importance through inter-criteria correlation), the statistical analysis of the ranging conditions of each indicator using the GRA method (grey relation analysis) and the correlation analysis of all preset schemes with all the considered indicators and their corresponding value ranges to obtain the optimal one among the preset schemes.
# Traffic Signal Time Optimization Based on Deep Q-Network
## 4 Experimental Studies  
### Experimental Settings  
The proposed algorithm was tested at nine successively interconnected four-way intersections, as shown in Figure 8. The average distance between intersections is approximately 200 m. The average speed of the vehicle is approximately 15 km/h and the maximum speed of the vehicle is about 50 km/h. The average speed of the vehicle was measured low because it included the speed of the vehicles waiting in the intersections. We conducted experiments to analyze the change in performance with varying the distance between the intersections and the speed of the vehicles. We extend the length of the road to from 200 m to 400 m to measure the sensitivity related to the distance and speed of vehicles. When we extend the length of the road, the average speed of vehicles was 30 km/h and the maximum speed was 70 km/h. Through experiments, we confirmed that even if the number of waiting vehicles increases due to the increase in the length of the intersection, the capacity of the intersection is similar.  
The experiment was conducted using the Python API provided by the Simulation of Urban MObility (SUMO) package. SUMO is an open-source city environment and traffic flow simulator designed to handle large networks [20]. In addition, various options such as pedestrian and intersection signals can be provided to simulate the interactions with the environment, vehicles, and people. The pedestrians were not considered in this experiment. The vehicle data used in the experiment were produced in the form of a Poisson distribution using SUMO. The vehicle route data had the following settings: repetition rate of 0.5, period of 0.1, and arrival rate of 10.  
Figure 7: Data flow chart of the proposed model.  
In the experiments, the proposed algorithm was used with mini-batches of size 64 and a replay memory of size 3200. The learning rate of the DQN model was set to 0.001, and the discount factor was set to 0.99. The exploration rate decreased from 1 to 0.001 as the training episode continued. The model calculates the loss function using a stochastic gradient descent algorithm. The agent used k-fold cross-validation to increase the reliability of the data by using all the data for training and validation to reduce the bias of the data of the model and eliminate overfitting.
## 3 Spatial-Spectral-Emissivity Land-Cover Classification Based on Conditional Random Fields (SSECRF)
### Methodology of SSECRF
#### 3.2.3 Construction of the Spatial-Spectral-Emissivity (SSE) Feature Set  
For visible imagery, the digital number (DN) value ranges from 0 to 255, but the emissivity value varies from 0 to 1, and the other feature information changes from 0 to 0.0001, etc. If all the original feature images are integrated directly, some of the characteristics will have an exaggerated influence, while some will be completely ignored. Normalization preprocessing of the feature images can sum up the statistical distribution to uniform samples and does not change the nature of the image. In view of this situation, it is necessary to balance the contribution degree of each feature map to the final fused image with the following formula:  
\[DN_{normalized}=\frac{DN_{i}-DN_{\text{min}}}{DN_{\text{max}}-DN_{\text{min}}} \tag{9}\]  
where \(DN_{i}\) records the feature value at site \(i\), and \(DN_{\text{min}}\) and \(DN_{\text{max}}\) represent the maximum and minimum feature values of the whole feature image. The SSE feature set can be written as a vector:  
\[F_{SSE}=\left(F_{thermal\_ emissivity},F_{spectral},F_{VDVI},F_{texture},F_{ spatial}\right) \tag{10}\]
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Communication Analysis  
To get knowledge about contact times between the ground station and the satellite, three main orbits that fit in previous considerations are being analysed.  
* An orbit with an altitude of 550 km and an inclination of 53 degrees (SpaceX Rideshare /Starlink);
* a sun synchronous orbit with an altitude of 550 km (various vendors, e.g., Vega Small Spacecraft Mission Service (SSMS), SpaceFlight Industries);
* the orbit of the International Space Station (ISS) with an altitude of around 420 km and an inclination of 51.6 degrees (ISS NanoRacks CubeSat deployer).  
The nature of these low Earth orbits (LEO) indicates common similarities. All orbits feature five to six contact windows per day, spanning between seven and nine minutes. Sun synchronous orbits feature two contact windows per day, consisting of two to three orbits. Considering a maximization of communication time, the favoured orbit is the Starlink orbit, averaging 9.1 min per pass with a contact time per day of 53 min. Secondly, the sun synchronous orbit reaches an average 8 min per pass and 45 min per day, respectively. The lower ISS orbit reaches 7.3 min per pass and 38 min per day. With a proposed data rate of 9600 bps, data amounts of 2.5 to 3.4 megabytes per day are expected to be transferred.
# Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
## 5 Conclusions  
The integration between the UAVs and the AI gives rise to more powerful and dynamic network topologies thanks to their advantages and capabilities. This paper presented an adaptive and intelligent cellular structure design for a UAV wireless communication topology using an AI framework, which is a noticeable shift from existing work in the literature. Three main objectives have been pursued to achieve our aim. The obtained results show that the optimized cluster has been well-recognized, which achieves the seven parameters that have been set to optimize the proposed model using the SOM and MSE. The optimized cellular structure design shows better connections as well as less power consumption in comparison to non-optimized predictions. The proposed framework is validated in a proof-of-concept scenario and the results validate that. This emphasizes a practical application for adaptable wireless communication without human intervention that can achieve an efficient and optimized automatic design. As a future work, this work can be extended after securing funds to include practical experiments, where different forms of UAVs can be built and evaluated for further analysis, including connectivity and QoS.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding for practical work.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 3 Separation Calculation Model Based on Track Prediction
### Track Prediction Model
#### 3.1.2 Adam-LSTM Prediction Model  
LSTM is a special type of recurrent neural network structure used for processing sequential data. The overall structure diagram is shown in Figure 2. An individual LSTM internal unit is depicted in Figure 3.  
Figure 1: Flowchart for track prediction.  
By incorporating the Adam optimizer, the learning rate of the LSTM model can be automatically adjusted to achieve rapid convergence.
# Improving Sentence Retrieval Using Sequence Similarity
## 4 Partial Match of Terms Using Sequence Similarity
### Sentence Retrieval with Language Model (LM)  
The language modeling-based method (language model) for document retrieval can be applied analogously to sentence retrieval. The probability of a query \(q\) given the sentence \(s\) can then be estimated using the standard LM approach [6]:  
\[\text{LM}(q,\,s)=\prod_{t\in q}P(t|s)^{c(t,q)} \tag{3}\]  
\[P(t|s)=\frac{c(t,s)}{|s|} \tag{4}\]  
where  
* \(c(t,s)\) is number of appearances of the term \(t\) in a sentence \(s\), and
* \(|s|\) is the sentence length.  
One of the most commonly used methods when it comes to sentence retrieval is Dirichlet smoothing, which, when applied to Method (3), gives:  
\[\text{LM}(q,\,s)=\prod_{t\in q}\frac{c(t,s)+\mu P(t)}{|s|+\mu} \tag{5}\]  
where  
* \(c(t,s)\) is the number of appearances of the term \(t\) in a sentence \(s\),
* \(|s|\) is the sentence length,
* \(\mu\) is the parameter that control the amount of smoothing, and
* \(P(t)\) can be calculated using the maximum likelihood estimator of the term in a large collection: \(p(t,\,C)\) (where \(C\) is the collection) [6].  
In the previous literature, previously presented ranking functions were combined with data pre-processing and stop word removal. Removing stop words is generally considered to be useful, since stop words do not contain any information [24].  
There are several methods for removing stop words, which have been presented by the authors of [25]. Some papers [26] have also proposed time efficient methods. The method we used in this paper is based on a previously compiled list of words. The performance of functions was tested in its basic form with stop word removal.
###### Abstract
They are important processes of river ecosystem metabolism (REM) and regulate nutrient and energy flow [53; 54]. The difference between GPP and ER indicates the overall carbon budget in river ecosystems, it is therefore referred to as net ecosystem production (NEP) [55]. Use of NEP can be used to determine autotrophic or heterotrophic conditions (e.g., [56; 57; 58]). A wide range of environmental factors affects both GPP and ER, primarily including light availability [59; 60; 61; 62], water temperature [63; 64; 62], aquatic species composition [65; 25; 66] and nutrient supply, notably stressors associated with the USS [35; 67]. Overall, considering complex interactions among hydrology, climate, chemistry, and biology, REM has direct implications for water quality and is an excellent functional indicator of ecological response to environmental change [66; 68; 69; 70]. Measurement of REM, therefore, helps to understand the responses of urban river ecosystems to a range of disturbances [53; 54; 57]. Urban rivers are unfortunately characterized by nutrient enrichment, and there are many studies on elevated concentrations in urban rivers (e.g., [71; 72; 73]). There are, however, relatively few studies that have integrated ecological processes that incorporate nutrient fluxes and their involvement in river metabolism [66; 74]. Further, as a megacity, Beijing's waterways have experienced multiple nutrient sources and increased nutrient concentrations, resulting in significant changes in river ecological processes [75; 76]. In our study, three urban tributaries of the River Beiyun, Beijing, China, provide us with natural conditions with elevated nutrient concentrations to examine river ecological processes. We aim to answer the following questions: (1) How do nutrient dynamics at the sediment-water interface (e.g., concentrations and fluxes of NO\({}_{3}\)\({}^{-}\), NH\({}_{4}\)\({}^{+}\), PO\({}_{4}\)\({}^{3-}\) and DOC) and metabolic processes vary between rivers and between sites on the same river? (2) What are the seasonal characteristics of nutrient dynamics and metabolic processes? (3) Are there relationships among nutrient dynamics and river metabolism? (4) What are the potential drivers of these ecological processes?