esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
###### Abstract  
Wireless Multimedia Sensor Networks (WMSNs) are a special type of Wireless Sensor Network (WSN) where large amounts of multimedia data are transmitted over networks composed of low power devices. Hierarchical routing protocols typically used in WSNs for multi-path communication tend to overload nodes located within radio communication range of the data collection unit or data sink. The battery life of these nodes is therefore reduced considerably, requiring frequent battery replacement work to extend the operational life of the WSN system. In a wireless sensor network with mesh topology, any node may act as a forwarder node, thereby enabling multiple routing paths toward any other node or collection unit. In addition, mesh topologies have proven advantages, such as data transmission reliability, network robustness against node failures, and potential reduction in energy consumption. This work studies the feasibility of implementing WMSNs in mesh topologies and their limitations by means of exhaustive computer simulation experiments. To this end, a module developed for the Synchronous Energy Saving (SES) mode of the IEEE 802.15.5 mesh standard has been integrated with multimedia tools to thoroughly test video sequences encoded using H.264 in mesh networks.  
WMSN; IEEE 802.15.5; mesh topology; multimedia; WSN +
Footnote †: journal: Sensors
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 2 Materials and Methods
### Mathematical Model Building
#### 2.3.3 Sediment Model  
The sediment model adopts the MORSELFE model [17], which is based on a triangular unstructured mesh and can be applied to water simulation with complex geometric boundaries and complex topography. The MORSELFE model considers the effects of waves on sediment transport and riverbed evolution. In the suspended sediment module, the transport equation was solved based on SCHISM, the Soulsby formula was used to obtain the settling rate of suspended sediment, and the bed sediment exchange model used the active bed sediment adjustment model. The bedload sediment is calculated using the Meyer-Peter-Muller formula and the Van Rijn formula.
# Generating Fingerings for Piano Music with Model-Based Reinforcement Learning
## 3 Environmental Model
### Quantification of Fingering Transition Difficulty
#### 3.4.1 Stretching Rate  
During single-note playing, finger stretching and contracting frequently occur, and the stretching abilities of different fingers are not equal. This study proposes a quantification method called a \"finger stretching rate\" to measure finger extension, as shown in Figure 4a.  
We first define the finger extension between two fingers: when two fingers are placed on the keyboard, the white key distance between their finger labels \(f_{a}\) and \(f_{b}\) is defined as the natural distance \(d_{nature}=|f_{a}-f_{b}|\). The maximum extension distance can be obtained from the matrix \(\mathbf{M}_{f}\) mentioned earlier, denoted by \(d_{max}\), which represents the maximum extension ability between the two fingers. The actual distance between the two fingers on the keyboard is the white key distance \(d_{k}\) between two pressed notes.  
The stretching rate can be defined as \(r_{s}=(d_{k}-d_{nature})/(d_{max}-d_{nature})\). A value close to 1 indicates that the fingers are approaching their maximum stretching ability, resulting in greater discomfort and a lower reward. Similarly, for finger contraction, we can define the contraction rate \(r_{s}=(d_{nature}-d_{k})/d_{nature}\). A value close to 1 indicates that the fingers are approaching their maximum contraction ability, resulting in stronger discomfort and a lower reward.  
During chord playing, we allocate \(n\) fingers on the keyboard. We can calculate the combinations of fingers used, with the number of combinations being \(C_{n}^{2}\). The overall stretching rate of the chord fingering can be obtained by averaging the stretching rates \(r_{s}\) of each finger combination. Specifically, the formula is \(r_{all}=(\sum\limits_{k=1}^{c}(r_{sk})^{a})/c\). Since lower stretching rates result in less discomfort, the discomfort is usually more noticeable when approaching maximum stretching. Therefore, a parameter \(a>1\) is introduced to slow down the growth of the stretching rate at lower values of stretching.
# Durability of Superamphiphobic Polyester Fabrics in Simulated Aerodynamic Icing Conditions
## Appendix B Aerodynamic Definitions, and Calculations  
### Terminology Related to Aerodynamics  
**Leading Edge:** The first point of contact of a fluid in motion with a solid body.  
**Trailing Edge:** The furthest point on a body in a moving fluid from the leading edge.  
**Chord:** The distance between the leading edge and the trailing edge.  
**Span:** The out-of-plane size of an object in a 2-dimensional airflow.  
**Camber:** Asymmetry between the top and bottom surfaces of an airfoil.  
**NACA 0012:** An airfoil shape described by the National Advisory Committee for Aeronautics (NACA, now NASA) with a 4-digit system. The digits, \"00\" signify a symmetric airfoil, without camber. The digits, \"12\" signify that the maximum airfoil thickness is 12% of the chord length.  
**Stagnation Line/Point:** In a 2-dimensional airflow, the stagnation point is the location where a streamline theoretically stops. The stagnation line in the out-of-plane extension of the stagnation point, and runs along the span of an object.  
**Suction/Pressure Side:** Air along a symmetric airfoil (without camber), travels the same distance over the top side and the bottom side of the airfoil, and therefore with the same static and dynamic pressure. If the symmetric airfoil is tilted, the position of the stagnation point is no longer exactly at the leading edge of the airfoil. In this orientation, air must travel a longer distance over one side than the other side. To maintain continuity, air on the longer side travels faster than on the shorter side. The result is higher dynamic pressure on the longer side and lower static pressure; hence the term, suction side. The pressure side intuitively is so-called due to the higher static pressure. Note that the pressure difference between two sides of an airfoil generates a force called lift, and it is that which makes flight possible.  
**Reynolds' Number:** A dimensionless number that represents the ratio of inertial to viscous forces in the motion of a fluid or gas. It is the most common ratio used to describe flow regimes.  
**Boundary Layer:** A common assumption in fluid dynamics is the \"no-slip condition\" where the velocity of a fluid in motion is null at the interface with a solid body. The flow approaches its freestream velocity with increasing distance from the interface. The distance between the interface and the freestream velocity is the thickness of the boundary layer.  
**Flow Separation:** When a boundary layer flow becomes reversed with respect to the freestream flow direction or when it can no longer remain attached to a body. In aerodynamics, flow separation causes a loss of lift.
## 4 Performance Evaluation
The query processing time was increased in the existing scheme as a query became more complex since the communication and join costs were taken into consideration during query processing. The proposed scheme removed this problem thus that it achieved performance improvements of around 15% on average through the query processing scheme considering the communication and join costs. Table 2 presents the comparison results of query processing time incurred per node between the existing and proposed schemes through the LUBM dataset. Data parsing time was measured in a node that executed the query the first time according to the query processing path while processing time of data received from other nodes and parsed data from the current node was measured in other nodes and compared. The performance evaluation was conducted based on four different queries, the same as in the first performance evaluation. For Q3, query processing was conducted based on two nodes, in which query processing time was compared between N1 and N2. The performance evaluation results showed that processing time was calculated differently by the node according to the query execution path between the existing and proposed schemes, and query processing time was increased according to the data size used in join operation in all nodes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**N1**} & \multicolumn{2}{c}{**N2**} & \multicolumn{2}{c}{**N3**} \\ \cline{2-7}
**(s)** & **Existing** & **Proposed** & **Existing** & **Proposed** & **Existing** & **Proposed** \\  & **Scheme** & **Scheme** & **Scheme** & **Scheme** & **Scheme** & **Scheme** \\ \hline Q1 & 2.578 & 2.557 & 2.532 & 2.542 & 3.421 & 3.411 \\ Q2 & 2.571 & 2.671 & 2.515 & 2.424 & 2.997 & 2.987 \\ Q3 & 3.784 & 3.532 & 3.564 & 3.487 & X & X \\ Q4 & 7.601 & 7.710 & 7.331 & 6.109 & 1.927 & 1.887 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Query processing time per node. Figure 8: _Cont._  
Figure 8: Query processing time: (**a**) LUBM 230,000 dataset; (**b**) LUBM 430,000 dataset; (**c**) LUBM 910,000 dataset; (**d**) DBpedia 300,000 dataset. Figure 9: Data transfer time: (**a**) LUBM 230,000 dataset; (**b**) LUBM 430,000 dataset; (**c**) LUBM 910,000 dataset; (**d**) DBpedia 300,000 dataset.
## 2 Materials and Methods
### Behavior  
Pigs were tested on the novel object recognition (NOR) task using two different delays to assess intermediate and long-term recognition memory. This task has previously been validated for use in pigs by independent labs [34; 35; 36; 37] and the methods used for execution and analysis of this test by our lab have been previously described [21; 38; 39]. Testing consisted of a habituation phase, a sample phase, and a test phase. During the habituation phase, each pig was placed in an empty testing arena for 10 min each day for two days leading up to the sample phase. In the sample phase, the pig was placed in the arena containing two identical objects and given 5 min for exploration. After a delay of 1 or 48 h the pig was returned to the arena for the test phase of the NOR task. During the test phase, the pig was placed in the arena containing one object from the sample phase and a novel object and allowed to explore for 5 min. Between trials, objects were removed, immersed in hot water with detergent, and rubbed with a towel to mitigate odor and the arena was sprayed with water to remove urine and feces. Objects chosen had a range of characteristics (i.e., color, texture, shape, and size). However, the novel and sample objects only differed in shape and size. Only objects previously shown to elicit a null preference were used for testing [35]. Task order was counterbalanced between replicates. Habituation trials began at PND 22 and testing on the sample phase began on PND 24. The recognition index, or the proportion of time spent with the novel object compared to total exploration of both objects, was used to measure recognition memory. A recognition index significantly above 0.50 demonstrates a novelty preference and thus recognition memory. Videos from all experiments were analyzed using a commercially available software package (Ethovision XT 11(r), Noldus Information Technology, Wageningen, The Netherlands). Time spent investigating objects was recorded manually by mapping start and stop conditions to specific keys on a computer keyboard. Experimenters were blind to all treatment conditions during analysis. Investigations were classified as nose-directed behavior such as rooting, mouthing, or sniffing of the objects. Rubbing up against, standing over, standing near, looking at, or sniffing the floor/air near the objects were not counted as investigations.
## 4 Signal Acquisition
### Reciprocating Compressor Signal Acquisition  
Several sensors are coupled to the compressor to measure the acoustic emission, the sound, the pressure, and the vibration signals. Three accelerometers PCB 603C01 oriented vertically are located in the compressor case. A1 is close to the discharge valve. A2  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Description** & **Discharge Pressure** \\ \hline C1 & Reg. discharge valve up to 5.5 bar & \(5.5\pm 0.2\) bar \\ C2 & Reg. discharge valve up to 6.5 bar & \(6.5\pm 0.2\) bar \\ C3 & Reg. discharge valve up to 7.5 bar & \(7.5\pm 0.2\) bar \\ C4 & Reg. discharge valve up to 8.5 bar & \(8.5\pm 0.2\) bar \\ C5 & Reg. discharge valve up to 9.5 bar & \(9.5\pm 0.2\) bar \\ C6 & Reg. discharge valve up to 10.4 bar & \(10.4\pm 0.2\) bar \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental conditions concerning the pressure in the discharge valve.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage and Valve Type** & **Fault Type** & **Fault Label** & **Stage and Valve Type** & **Fault Type** & **Fault Label** \\ \hline All stages & Healthy & P1 & First stage, discharge & Valve seat wear & P10 \\ Second stage, discharge & Valve seat wear & P2 & First stage, discharge & Corrosion of valve plate & P11 \\ Second stage, discharge & Corrosion of valve plate & P3 & First stage, discharge & Fracture of valve plate & P12 \\ Second stage, discharge & Fracture of valve plate & P4 & First stage, discharge & Broken Spring & P13 \\ Second stage, discharge & Broken Spring & P5 & First stage, inlet valve be & Valve seat wear & P14 \\ Second stage, inlet valve & Valve seat wear & P6 & First stage, inlet valve b & Corrosion of valve plate & P15 \\ Second stage, inlet valve & Corrosion of valve plate & P7 & First stage, inlet valve b & Fracture of valve plate & P16 \\ Second stage, inlet valve & Fracture of valve plate & P8 & First stage, inlet valve b & Broken Spring & P17 \\ Second stage, inlet valve & Broken Spring & P9 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Valve faults in the reciprocating compressor.  
is close to the intake valve, and A3 and A4 are close to the B1 roller bearing. The difference is that A3 records the vertical vibration, and A4 records the horizontal vibration.  
As in the centrifugal pump, the analog signals are acquired using hardware and software from National Instruments. In particular, the model NI-9234 compact data-acquisition card (cDAQ), the NI-9188 chassis stream digital signals to a laptop computer, and the LabView NXG 3.1 stores the data in a hard disk for offline processing.  
The vibration signals were acquired under a constant motor rotation frequency of 57.7 Hz, resulting in a crankshaft rotation frequency of 12.8 Hz. There are two crankshaft cycles per compression cycle, which takes approximately 0.156 s. The tank pressure was kept constant at 3 bar. A set of 15 vibration signals for each sensor and each fault condition was collected. A total of 255 signals for the multi-valve dataset were acquired, with a duration of 10 s each, and digitized at a sampling frequency of 50 kHz.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
###### Abstract  
Landmark building models typically exhibit intricate structures, posing challenges for retrieval using generic descriptors. To address this challenge, this paper abstracts and simplifies the model into simple topological structures grounded in topological perceptual theory and introduces a novel categorization of landmark building models. Furthermore, based on view, the quotient distance-angle histogram algorithm is proposed for extracting model topological features. This approach facilitates the identification of intricate landmark building models and enhances retrieval performance. In addition, a new projection method is also proposed. Based on the fact that most landmark building models have correct upright direction and the significant topological features predominantly emerge in the top or side views of these models, this method can accelerate the subsequent extraction and matching of shape descriptors by reducing the extraction of model views. Compared with the existing methods, the proposed projection method is more consistent with human visual perception, and the experimental results verify that the proposed method can improve the retrieval performance when classifying the topological structure of 3D landmark building models.  
topological perception; 3D landmark buildings; categorization; model retrieval; projection; quotient distance-angle histogram +
Footnote †: journal: Applied Sciences  
0000-0002-4611-  
and experiences for city residents and visitors, thereby amplifying the attractiveness and competitiveness of cities. In this context, the retrieval of 3D landmark building models has become an important research direction.  
Shape-based matching methods are preferred over text-based methods because visual cues of 3D landmark models available on the web are usually not sufficiently annotated, and moreover, some misannotations occur [14]. Textual annotations often suffer from ambiguity given their susceptibility to influence from variables such as language, culture, age, gender, and other human-associated factors. Conversely, measurable visual properties like shape, color, and texture can typically be directly extracted from the objects. Hence, this paper concentrates on facile extraction of shape features directly from landmark models to subsequently utilize those features for shape-based retrieval of landmark mode. In the realm of information retrieval, the importance of shape retrieval cannot be overstated. Shape retrieval is increasingly being used as a primary cue for indexing in numerous applications [15]. This holds true for geobrowsers as well: Shape-based model matching proves notably valuable for swiftly identifying landmark buildings using user-provided geometric cues. This is particularly beneficial for individuals who are concerned with and interested in information pertaining to landmark building shapes, including sketchers and architects.
# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
###### Abstract  
Channel modeling of unmanned aerial vehicles (UAVs) from wireless communications has gained great interest for rapid deployment in wireless communication. The UAV channel has its own distinctive characteristics compared to satellite and cellular networks. Many proposed techniques consider and formulate the channel modeling of UAVs as a classification problem, where the key is to extract the discriminative features of the UAV wireless signal. For this issue, we propose a framework of multiple Gaussian-Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural network. The developed system used UAV measurements of a town's already existing commercial cellular network for training and validation. To evaluate the proposed approach, we run ray-tracing simulations in the program Remcom Wireless InSite at a distinct frequency of 28 GHz and used them for training and validation. The results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs.  
channel modeling; deep learning; optimization algorithms; unmanned aerial vehicles (UAVs) IEEEexample:BSTcontrol  
1  
[PERSON], [PERSON], [PERSON], [PERSON], H. Channel Estimation for UAV Communication Systems Using Deep Neural Networks (Drones2022), 6, 236, [[https://doi.org/10.3390/drones6110326](https://doi.org/10.3390/drones6110326)]([https://doi.org/10.3390/drones6110326](https://doi.org/10.3390/drones6110326))
## 2 Related Work
The simulation provides a benchmark to understand whether the system behaves as predicted. The level of MBSE maturity provided by frameworks similar to ESEM blurs the line between design and development. However, the ESEM methodology has been applied to automated systems, not autonomous ones. The work presented in this paper extends the efforts of executable models, providing a simulation framework targeting operational scenarios. The defined simulation framework tests the operations of an autonomous system from its point of view, employing techniques of AI planning. The paper also proposes a new case study, autonomous systems, for applying executable MBSE models. AI planning is the branch of artificial intelligence that focuses on defining an action sequence for an agent to reach a specified goal. Space operations' software uses planning and scheduling algorithms to achieve a high level of autonomy for both goal-oriented autonomy onboard a spacecraft and planning activities in the ground segment [28]. Most of the work employing AI planning focused on enabling long-term autonomous operations, such as in [7; 29; 30; 31]. Some of those algorithms and frameworks were concretized in the Deep Space 1 mission [10; 32] and in the Earth Observing 1 mission [33]. In these studies, automated planning techniques are used during the implementation and operation phases of the life cycle. The planners are developed following a series of requirements defined during the formulation phases. Hence, it can be helpful to leverage the ability of those algorithms to output a series of feasible plans (given a set of desired goals, knowledge of the environment, and the possible actions and resources available) to simulate an operational scenario defined in a ConOps at a conceptual design level. No prior studies have examined possible links or synergies between automated planning techniques and preliminary design for space systems, nor have they analysed the reuse of systems engineering models to define planning domains, as illustrated in Table 1.
## 2 Materials and Methods
### Performance Indicators  
In the present work, the drying rate (\(DR\)) in any period (\(\Delta t\)), which reflects the strength of drying process, is calculated using Equation (29) [41]:  
\[DR=\frac{M_{t+\Delta t}-MC_{t}}{\Delta t}\times 100\% \tag{29}\]  
The energy efficiency (\(\eta_{\mathit{en}}\)) and the specific energy consumption (\(\mathit{SEC}\)) are utilized to assess the energetic performance of the components and the whole system. The \(\mathit{SEC}\) is calculated according to Equation (30) [42]:  
\[\mathit{SEC}=\frac{\dot{Q}+\dot{P}_{\mathit{if}}+\dot{P}_{h}+\dot{P}_{\mathit{ dm}}+\dot{P}_{\mathit{ap}}}{\dot{m}_{\mathit{exp}}} \tag{30}\]  
The exergy efficiency (\(\eta_{\mathit{ex}}\)), exergy destruction ratio (\(r_{d}\)) [43] and stainability index (\(\mathit{SI}\)) [4] are utilized to assess the exergetic performance of the components and the whole system. Generally, the \(\eta_{\mathit{ex}}\) is increased with the reducing exergy destruction. \(\mathit{SI}\) can reflect the impact of exergy efficiency changes on sustainability while \(r_{d}\) can reflect the contribution level of the exergy destruction related to the component to the overall exergy destruction. All performance indicators are defined as follows:  
\[r_{d}=\frac{\dot{E}_{\mathit{xdes},k}}{\dot{E}_{\mathit{xdes},tot}} \tag{31}\]  
\[\mathit{SI}=\frac{1}{1-\eta_{\mathit{exdes}}} \tag{32}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value/Equation** & **Unit** & **Reference** \\ \hline \(A_{\mathit{fir}}\) & 16.6 & m\({}^{2}\) & – \\ \(\sigma\) & \(5.67\times 10^{-8}\) & W\(\cdot\)m\({}^{-2}\).K\({}^{-4}\) & [27] \\ \(\varepsilon_{r}\) & 0.9 & – & [39] \\ \(S\) & 0.126 & m\({}^{2}\) & – \\ \(A_{s}\) & 8.3 & m\({}^{2}\) & – \\ \(\delta\) & 0.05 & m & – \\ \(\gamma