esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of the Methodology
### The Code Phase  
As already stated, in our approach the user is considered an expert programmer, who is aware of the specific parts of her code that can, or cannot, be actually parallelized. However, once she has annotated the sequential code with parallel primitives, these will allow her to distribute the sequential computation among processing nodes residing on remote platforms and even in multi-platform environments.  
Figure 1: The Code-Based Approach workflow.  
The parallel directives used to guide the next transformation phase have well known meaning within the approach, and have been studied to adapt to most of the situations. Also, directives can be nested to achieve several levels of parallelization.  
Primitives can be roughly divided into two main categories:  
* **Data Parallel** primitives, which organize the distribution of data to be consumed among processing nodes. General primitives exist, which do not refer to a specific Parallel Pattern, but most of the primitives are modelled against one.
* **Task Parallel** primitives, which instead focus on the process, and distribute computing loads according to a Pattern based schema. General primitives also exist, but they will need to be bound to a Specific Pattern before the transformations phase. Examples of used primitives are:
* The _data_parallel_region(data, func, *params)_ represents the most general data parallelization directive, which applies a generic set of functions to input data and optional parameters.
* The _producer_consumer(data, prod_func, cons_func, *params)_ directive implies a Producer Consumer parallelization approach. The _data_ input is split into independent chunks which represent the input of the _prod_func_ function. A set of computing node elaborates the received chunk and puts the result in a shared support data structure (Queue, List, Stack or similar), as also shown in Figure 2. Another set of nodes polls the shared data structure and executes _cons_func_ on the contained data, until exhaustion.
* The _pipeline(data, [order_func_list] *params)_ directive represents a well known task parallel paradigm, in which the processing functions to be executed need to be run in the precise order they appear in the _order_func_list_ input list.  
The input _data_ is generally a list of elements, which are fed one by one to the first function, whose output is passed to the second one and so on, until the last result is obtained. While the i th function is in execution, the i - 1 th can elaborate another chunk of data (if any), while the i + 1 th needs to wait for the output from the i th in order to go on. In this way, at regimen, no computational nodes are idle and resources are fully exploited. Figure 3 reports an example of execution of such a Primitive, showing how functions are sequentially executed by Computing nodes and fill the pipeline.  
Figure 2: Producer Consumer.
## 5 Statistical Characterization of Seismic Activity
### Temporal Patterns  
To gain insight into the development of seismic activity in the Red Sea region, the temporal distribution of earthquakes was analyzed across multiple scales, as depicted in Figure 4. Each plot in this figure is tailored to assess earthquake frequency at specific time resolutions, including daily, weekly, monthly, and yearly intervals. Figure 4a sheds light on daily changes, revealing a non-uniform distribution of seismic activity. The daily peak frequency reaches 142 seismic events, while the mean frequency is about 4 events per day. This variability in daily event counts may be indicative of episodic clusters of seismic activity, possibly influenced by underlying tectonic processes. The data reveal that it is somewhat quieter at night in most places. Turning to a weekly seismic activity, Figure 4b illustrates the weekly irregularity of the reported earthquakes. The plot shows a weekly peak of 445 earthquakes, compared with an average frequency of 30 events per week. The range of weekly event counts is quite broad. This significant variability indicates that the region undergoes periodic episodes of increased seismic activity, likely influenced by underlying tectonic interaction processes. Figure 4c presents the observed monthly distributions, highlighting any potential seasonal cycle patterns. These could possibly be linked with geological processes like rifting affecting the area. The reported activity records a high monthly frequency of 849 earthquake events, with an average rate of 134 events per month. This variability in monthly seismic activity suggests the presence of complex temporal dynamics, possibly punctuated by bursts of heightened seismic events. The substantial spread in the number of events, as captured by the standard deviation, indicates that the seismic behavior in the Red Sea region is not uniformly distributed over time, perhaps influenced by episodic tectonic activities or other underlying geophysical processes. Lastly, on an annual scale, Figure 4d includes the yearly distribution, exhibiting multi-year patterns. The annual data indicate a peak of 4982 earthquakes, with a mean incidence of 1572 occurrences each year. The data suggest that seismic activity in the area is not constant over various years, underlining the necessity for long-term monitoring and study to understand the underlying dynamics.  
The analysis of temporal patterns suggests that the Red Sea region displays a consistent level of seismic activity. The distribution over time does not reveal a clear trend of increasing or decreasing seismic events across the observed years; instead, the activity fluctuates. There are noticeable periods of more frequent seismic events, as indicated by clusters of taller bars in the data. Pronounced spikes representing days with exceptionally high activity could correspond to significant seismic occurrences or sequences, such as swarms of earthquakes or series of aftershocks. These findings corroborate the results of several previous studies [5; 8; 56; 57].
## 3 Denoising Transformer-Based Audio Music Genre Classification
### Overview  
A method utilizing pre-training techniques based on Deformer was proposed for audio music genre classification. The proposed method consists of pre-training and fine-tuning stages, as shown in Figure 1. First, unlabeled or labeled audio data are preprocessed into a normalized Mel spectrogram, and a noise-injection operation is applied in the pre-training stage, during which Deformer learns deep representations of audio music from unlabeled audio data. For this, a prior decoder is utilized to restore the denoised Mel spectrogram from the low-dimensional hidden states, which is obtained from Deformer. In the fine-tuning stage, the pre-trained Deformer and classifier are further trained using labeled audio data to perform music genre classification. The flowchart for the proposed method is shown in Figure 2.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 6 Discussion
### The Applicability of the BOMSDM  
The BOMSDM proposes a balanced and fair distribution strategy to address a competition issue related to facility-community-allocation, which may arise in FLPs across different levels of administrative divisions. For instance, public welfare facilities such as high-quality schools, comprehensive hospitals, and large-scale parks may become competitive targets at the street or district level. Even at the city level, competition can arise for scarce public facilities such as commercial centers, theme parks, and transportation hubs. In this context, the model proposed in this study will provide an adjustable tool for FLPs where facility competition exists between administrative units.  
Although our study focuses on an exploratory test of a single case, there is reason to believe that the BOMSDM exhibits broader applicability. First, the efficiency and equity objectives proposed by the model are fundamental optimization goals for FLPs [78; 79], using conventional methods such as coverage and the Gini coefficient. Additionally, the model imposes no restrictions on key factors like community size, housing volume, or the number and increments of facilities, which suggests that the model is likely applicable to facilities at other levels, such as neighborhood-level or district-level facilities. Finally, the data used in this study are easily accessible through public sources, such as maps and real estate websites for road and housing data and government planning documents for administrative boundaries. The number of new facilities is based on government budgets. A key consideration is that facility candidate points are chosen subjectively. In this study, we used the main entrances of communities as candidates, though in practice, factors like population distribution, facility type, and site suitability could lead to multiple candidate points within a community.
## 4 Performance Evaluation
### Clique Topology
Whenever they are finally backlogged, it is very likely that no or only a few other devices are backlogged concurrently; hence, the contention resolution mechanism (for both protocols) is expected to terminate quite fast. In order to confirm our assertion that both the sojourn time and the duty cycle of RI-MAC benefit from the sink's long awake period incurred by the lengthy contention and collision resolution mechanisms, we compare the sink's duty cycle. Figure 9 confirms that the sink's duty cycle for RI-MAC is indeed much higher than that for MAR-RiMAC, even when the number of contenders is very low (only two devices), and the contending devices can sense one another (no hidden terminals), which implies low contention. Next, we examine the performance in the presence of hidden terminals. Figure 8: Clique topology: Mean duty cycle for different numbers of devices and \(K=4\). (**a**) Two devices. (**b**) Five devices. (**c**) Twelve devices. (**d**) Twenty devices.
## 2 Methodology
### Flight
#### 2.2.3 Flight Model  
The values calculated in Sections 2.2.1 and 2.2.2 are used to determine the Thrust, \(F\), and Drag, \(D\), during the LPRE's flight from takeoff to the required altitude, \(H\). For this framework, a time interval, \(\delta t\), of 0.1 s is used when calculating the velocity and height during the flight. The Drag Equation (assuming a fixed drag coefficient (\(C_{D0}\)) of 0.2) and General Thrust Equation are, respectively, used to calculate the Drag and Thrust, as given in the following equations:  
\[D=\frac{\rho_{a}\times v^{2}\times A_{s}\times C_{D0}}{2} \tag{38}\]  
\[F=\dot{m}_{p}\times v_{e}+a_{e}\times(p_{e}-p_{a}) \tag{39}\]  
where \(v_{e}\) and \(R_{g}\) are defined as per [30] using the universal gas constant R assumed to be 8.314 J/Kmol. \(T_{c}\), \(M\) and \(\gamma\) are calculated via Section 2.1 and used as follows.  
\[R_{g}=\frac{R}{M} \tag{40}\]  
\[v_{e}=\sqrt{\left(\frac{2\times\gamma\times R_{g}\times T_{c}}{\gamma-1} \right)\times\left(1-\left(\frac{p_{e}}{p_{c}}\right)^{\left(\frac{\gamma-1}{ \gamma}\right)}\right)} \tag{41}\]  
The exit pressure (\(p_{c}\)) is fixed and assumed at 0.7 bar for this research study. Therefore, for each interval of time, \(p_{a}\) is recalculated as per the equations given in Section 2.2.1 and then used in the Thrust equation. The second term in the Thrust Equation is the pressure component, which varies as \(p_{a}\) changes as the LPRE gains altitude. \(F\), therefore, increases when the nozzle exit velocity, \(v_{e}\), is at a maximum point (as propellant mass flow rate, \(\dot{m}_{p}\) is fixed).  
In the Drag Equation, \(A_{s}\) is assumed as the frontal surface area. This is a fixed value, and as \(C_{D0}\) is kept at a fixed value, changes in \(D\) are only due to changes in \(\rho_{a}\) and velocity, \(v\).  
The system mass at each interval of time and \(D\), \(F\), \(T_{a}\), \(p_{a}\), \(\rho_{a}\) and gravity, \(g_{0}\), calculated earlier are used to determine the system mass, \(M_{sys}\), velocity, \(v\), and subsequently the altitude, \(H\), at each time interval as per the following equations.  
\[M_{sys}=M_{sys}-\dot{m}_{p}\times\Delta t \tag{42}\]  
\[v=v+\frac{F}{M_{sys}}\times\Delta t-g_{0}\times\Delta t-\frac{D}{M_{sys}} \times\Delta t \tag{43}\]  
\[h=h+v\times\Delta t \tag{44}\]
## 1 Introduction: Crack Localization Based on Pressure Wave Propagation
### Effective Structural Health Monitoring  
Considering the advantages and drawbacks of structural health monitoring systems, a new effective Structural Health Monitoring (eSHM) system has been developed [9; 10]. With the proposed eSHM methodology, the flexibility offered by the 3D printing process can be fully exploited to integrate the smart monitoring technology inside the 3D-printed components. The eSHM system is based on the detection of pressure changes in 3D-curved capillaries (see the red channels in Figure 1) that are integrated in the component thanks to additive manufacturing techniques. The capillary channels can be compared with the human nervous system, in which the pain receptors of the body inform the brain by sending signals to it. It is crucial to embed the capillaries in the fatigue critical regions of the component. By using a vacuum pump, the capillaries are set initially at a very low pressure (quasi-vacuum). After the structure has been fatigue loaded, cracks can be initiated and will then propagate through the structure. If the crack encounters a capillary on its way, contact occurs between the atmosphere and the low-pressure volume inside the capillary. Due to the pressure difference between both volumes, the sudden crack-capillary contact induces the propagation of pressure waves through the capillary channel towards both ends. These waves are monitored by two pressure probes (S1,S2) located at both ends of the capillary. As shown in Equation 1, by exploiting the difference in the time of arrival \(t_{2}-t_{1}\) of the front wave at both ends of the duct, the location of the crack \(x_{1}\) w.r.t S1 can be retrieved. This principle currently relies on the constant propagation speed characteristic of acoustic pressure waves (= speed of sound), for which a value of \(c=340\) m/s is considered.  
\[x_{1}=\frac{L-c(t_{2}-t_{1})}{2} \tag{1}\]
## 4 Results and Discussion
### Multi-Objective Optimization
The values of the optimization objectives extend between 25 and 50 kW/kg for specific work and from $270,000 to $370,000 USD for the cost of core equipment. Compared with the simple recuperated cycle, the economic cost and the specific work of the recompression cycle and intercooling cycle are lack of competitiveness. Figure 10: Optimization result of the simple recuperated cycle. To keep things straight, the optimized Pareto set of three cycles are compared and shown in Figure 13. These cycle layouts are compared in the coordinate system of \"cost of core equipment and specific work\". In the \"cost of core equipment and specific work\" coordinate system, the location closer to the lower right corner means more excellent performance. Although the device cost of this study is not fully calculated and the actual system payback period cannot be calculated correctly. However, from Figure 13, we can still see that the simple recuperated cycle has obvious advantages over the recompression cycle and intercooling cycle after optimization. In detail, under the same level of specific work, the cost of the core equipment of the simple recuperated cycle is lower than that of the recompression cycle and intercooling cycle, which can be clearly seen in the figure. However, a previous study has shown that when the central receiver tower with heliostat field is used, the intercooling cycle and recompression cycle will achieve greater efficiency [23]. The reason for this phenomenon may be that the temperature of the S-CO\({}_{2}\) entering turbine is too low when using the parabolic trough solar collector. Because of this bottleneck, the complex system structure cannot produce an obvious advantage of specific work while the increase of its cost is significant. Moreover, complex cycle structures are often used in superposition, such as recompression, intercooling, and reheating. Thus, they are more suitable for higher temperature scenarios. Figure 12: Optimization result of recompression cycle. Figure 13: Comparison of Pareto sets for three cycles.
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 2 Related Work
### Object Detection Methods
#### 2.2.2 Two-Stage Object Detection Method  
Compared to single-stage detectors, two-stage object detection algorithms divide the detection process into two main steps: region proposal generation and object classification. The advantage of this approach lies in its ability to select multiple candidate boxes, effectively extracting feature information from targets, resulting in high-precision detection and accurate localization. However, due to the involvement of two independent steps, this can lead to slower detection speeds and more complex models. [PERSON] et al. [55] introduced the classic R-CNN algorithm, which employed a \"Region Proposal + CNN\" approach to feature extraction, opening the door for applying deep learning to object detection and laying the foundation for subsequent algorithms. Subsequently, [PERSON] et al. [56] introduced the SPP-Net algorithm to address the time-consuming feature extraction process in R-CNN. To further improve upon the drawbacks of R-CNN and SPP-Net, [PERSON] [57] proposed the Fast R-CNN algorithm. Fast R-CNN combined the advantages of R-CNN and SPP-Net, but still did not achieve end-to-end object detection. In 2015, [PERSON] et al. [58] introduced the Faster R-CNN algorithm, which was the first deep learning-based detection algorithm that approached real-time performance. Faster R-CNN introduced Region Proposal Networks (RPNs) to replace traditional Selective Search algorithms for generating proposals. Although Faster R-CNN surpassed the speed limitations of Fast R-CNN, there still remained issues of computational redundancy in the subsequent detection stage. The algorithm continued to use ROI Pooling layers, which could lead to reduced localization accuracy in object detection. Furthermore, Faster R-CNN's performance in detecting small objects was subpar. Various improvements were subsequently proposed, such as R-FCN [59] and Light-Head R-CNN [60], which further enhanced the performance of Faster R-CNN.
## A UGV Platform
### Robot Localization System  
During the inspection it is important to precisely localize the place where a fault occurred. One of the systems that are precise enough for event localization is GPS. It can be successfully used for positioning in an open space with high reliability and easy to use interface. However, in the case of underground mines there is no possibility for GPS communication in the underground conditions, so for such a use case the Ultra-Wideband (UWB) transceivers were proposed for the localization system (see Figure 11).  
The localization system is based on DWM1001 modules equipped with firmware, which ensures bi-directional distance estimation (TWR) and real-time localization system (RTLS). Inside the module, besides the system directly responsible for distance estimation (UWB Transceiver) there is also a Bluetooth(r) Microprocessor Nordic nRF52832 and high-performance three-axis linear accelerometer with digital I2C/SPI serial interface standard output LIS2 DH12 [23]. Configuration of the module is realized via API interface which can be accessed using various interfaces such as UART, SPI, i2C, and BT. Specific modules configured as Anchor (A) play a role of fixed beacons, and a module installed on the robot operates as a tag (T).  
Figure 11: Sensory and location module.  
Figure 10: Mobile platform remote control panel.  
The bi-directional communication and data exchange between the modules as well as the known position in space of each anchor allows us to precisely calculate distances between them and using trilateration algorithms to estimate the position of a tag in space (see Figure 12).  
The inspection itself is carried out by driving the robot along the conveyor keeping close distance to it. Deviation of the measurement system in a vertical direction with respect to the conveyor is expected to be minimal, probably negligible. Hence, the number of localization modules can be very limited.  
Modules configured as anchors have been installed along the conveyor in such spacing that a tag is always in the range of at least one of them, see Figure 13. For the purpose of the experiment the beacon has been placed on a stand (Figure 14). During the inspection the position relative to the unique antenna in parallel to the sensor data. Hence, it is possible to localize the place of any event. Using the proposed system it is possible to achieve an accuracy of 10 cm.
## 3 Results and Discussion
### Effect of Catalytic Conditions
#### 3.3.4 Effect of Pore Size  
Pore size was also an important parameter for the powder catalyst. The PEK-C-ILs powder catalysts with different pore size were prepared by adjusting the polymer concentration in 4 wt %, 6 wt % and 8 wt %, respectively. Using ImageJ software [29], the pore size of the PEK-C-ILs powders was analyzed base on the SEM images of Figure 7. Because the surface flateness of powder is low, it is difficult for the image analysis software to capture the macropore information. We prepared plate PEK-C-ILs films in the same preparation conditions as powders so as to test the pore size. The analysis results were listed in Table 2. It is illustrated that the decrease of surface pore size of PEK-C-ILs powder followed by the increase of polymer concentration.  
Figure 6: Effect of reaction temperature on the TRS of inulin. Hydrolysis conditions: inulin concentration 20%, DCM 1.67. PEK-C-ILs concentration (wt %, the weight ratio of the PEK-C-ILs to water and PEK-C-ILs).  
Figure 7: SEM images of the PEK-C-ILs powder surface. PEK-C-ILs concentration (**a**) 4 wt %, (**b**) 6 wt %, (**c**) 8 wt %.  
Figure 8 depicted the variation of the TRS with an increase in pore size of PEK-C-ILs catalyst. These three kinds of powder catalyst with different pore size exhibited remarkable hydrolysis performance with yields up to 99%. But they showed obvious different in both hydrolysis rate in first 2/3 process and complete hydrolysis time. When reaction time was 25 min, with the increase of mean pore size from 71 nm to 185 nm, the corresponding TRS yields of inulin catalyzed by PEK-C-ILs powders increased from 90.1% to 96.3%. When the pore size of powder catalyst was too small, the molecule transfer resistance in the internal pores increased. Then, the interaction possibility between the active sites of ILs immobilized on the pore wall and the glycosidic bonds decreased, resulting in lower hydrolysis efficiency.  
It is worth mentioning that the PEK-C-ILs powder catalyst with average pore size of 112 nm was used in the hydrolysis experiments of this work, which is obviously larger than the pore size of typical microporous silica materials with 2-5 nm [30]. Therefore, this macroporous powder catalyst perhaps exhibit more efficient catalytic performance in the field of high polymerization degree materials, such as cellulose, due to taking full advantage of the active sites of ILs immobilized on the pore wall.