esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation of Oil Spill  
### Simulation of Oil-Spill Drift  
The wind and current were assumed to affect the centroid of the oil film. The Eulerian-Lagrangian tracking method was adopted to simulate the drift trajectory of the oil film centroid on the water surface as Equation (12) which explained the initial position \(S_{0}\) of the oil film centroid drifting to the new position \(S\) after a time \(\Delta t\). \(U_{L}\) was calculated by Equation (13) and \(a_{w}\) was commonly ranging from 0.03 to 0.04. \(S\) was expressed as Equations (14) and (15). The velocity vector of oil-spill drift is shown in Figure 1.  
\[S\ =\ S_{0}+\int_{0}^{0+\Delta t}U_{L}dt \tag{12}\]  
\[U_{L}\ =\ a_{f}U_{f}+a_{w}U_{w} \tag{13}\]  
\[x\ =\ x_{0}+u\Delta t+i_{w}\cdot U_{w}\cdot sin\theta \tag{14}\]  
\[y^{\prime}\ =\ y_{0}+v\Delta t+i_{w}\cdot U_{w}\cdot cos\theta \tag{15}\]
# YOLOv5 DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs
## 3 Results
### Posture Distribution  
The postures of pigs in a pen (7 to 14 September 2021) were detected using YOLOv5 DA, with three periods monitored each day: approximately 7:00-8:30 in the morning, 12:00-13:30 at noon, and 16:00-17:30 in the afternoon. Figure 9(a) shows the proportion of postures over three periods of eight days, and the results indicated that the standing posture was mainly concentrated in the morning and afternoon and was the least common at noon. Figure 9(b) shows the average proportion of postures over three periods of eight days, and the proportion of standing postures was 47.38% in the morning, 37.18% in the afternoon, and 16.84% at noon. In the morning and afternoon, the proportion of side lying posture was 27.48% and 24.57% lower than those of the prone-lying posture, respectively. However, the proportion of side-lying posture was predominant at noon, surpassing the proportion of prone-lying posture by 29.92% and reaching 59.54%.  
This study detected the posture of pigs in a pen over 8 days, focusing on three specific periods. The results proved the standing posture was predominantly focused in the morning and afternoon, which showed that the pigs are typically more active in the morning and afternoon. The reason may be that the posture of the pigs is influenced by temperature: as the temperature rises, pigs spend less time standing to facilitate faster heat dissipation [49]. When the temperature is highest at noon, pigs choose to lie down instead of standing. The proportion of side-lying postures exceeded the proportion of prone-lying postures by 29.92%, reaching a maximum of 59.54% at noon. The temperature is likely highest at noon, so the pigs adopt the side-lying posture to maximize their body surface contacting the cooler floor, facilitating the dispersion of excess body heat resulting from the elevated ambient temperature [50].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**AP**} & \\ \cline{2-7}
**Models** & **Standing** & **Prone Lying** & **Side Lying** & **mAP** & **GFLOPs** & **Params** \\ \hline Faster-RCNN & 98.9 & 98.6 & 96.7 & 80.7 & 91.01 & 41.13 M \\ YOLOv4 & 99.0 & 98.3 & 96.5 & 82.0 & 70.72 & 63.9 M \\ FCOS & 98.8 & 96.7 & 97.0 & 79.0 & 78.67 & 31.84 M \\ CenterNet & 98.7 & 99.3 & 99.0 & 84.2 & 56.64 & 34.4 M \\ YOLOv5s & 99.4 & 98.7 & 98.0 & 85.1 & 15.8 & 7.02 M \\ YOLOv5 DA & 99.4 & 99.1 & 99.1 & 86.8 & 25.2 & 13.3 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different object detection models on pig posture.  
Figure 10: **(a)** The proportions of pig postures for three periods from September 7 to 14. **(b)** The average proportions of pig postures over three periods of eight days. The blue bar depicts the proportion of standing postures, the green bar depicts the proportion of side-lying postures, and the orange bar depicts the proportion of prone-lying postures.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Metrics Used to Quantify Flexibility Potential  
A common source of diversity among the studies reviewed was how authors quantified flexibility potential estimations. In this context, flexibility potential refers to the estimated demand reduction or temporal shift achievable by a single or group of customers over a defined time period. More than 20 metrics were used to quantify flexibility potential. There  
Figure 1: Schematic of DSM with DR and energy efficiency as two sub-categories. While energy efficiency is out of scope in this work, DR includes two sub-categories of incentive-based and price-based programs. DLC and interruptible programs are both types of incentive-based DR. TOU and CPP are both types of price-based DR.  
is no clear standardization of metrics authors used as most studies defined their own terms with little overlap among studies.  
Although most authors used units of power [3; 7; 10; 11; 16; 18; 21; 23; 35; 36; 37; 38; 39; 40; 43; 46; 50; 51; 52; 53; 43; 47; 54; 55; 58], other authors used units of energy [9; 11; 12; 15; 19; 28; 29; 31; 33; 35; 40; 45; 53; 59; 60; 61], emissions [8; 9; 99], economic measurements [8; 9; 11; 26; 53; 62], or ramping capacity [31; 33; 40; 56]. Examples of other less commonly used units are temperature [30] and area [24] with [22; 27] developing specialized indices. For example, [27] quantified DR potential as a measure between 0 and 1 with 1 referring to high potential.  
With such a diverse set of terms and units used to measure flexibility potential, it is difficult to objectively compare studies and understand how flexibility potential varies across sectors and end-uses. It is also difficult to quantify the total availability of this resource over a region when authors employ different quantification metrics that cannot be easily converted into a common quantity.
# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 2 Methodology
### Date Recognizer  
For the purpose of date recognition, an adapted convolutional recurrent neural network (CRNN) [13] is employed, known for its effective application in recognizing handwritten text. This is because it captures spatial features of characters and the sequential flow of text efficiently. Initially, the pretrained model undergoes fine-tuning with a dataset that comprises a variety of characters, including uppercase, lowercase, and diverse symbols commonly found in dates.  
Figure 3 depicts the structure of the CRNN model.  
1. Convolution layer extracts spatial features from the input using convolution operations;
2. MaxPooling layer reduces the spatial dimensions of the feature maps generated by the convolutional layer, which makes the model computationally efficient and reduces the risk of overfitting;
3. Reshape layer flattens the 2D spatial data into a 1D vector to prepare for the RNN layer;
4. Bidirectional LSTM captures temporal or sequential patterns in the data.
## 1 Introduction
SCW is not used as commonly as SCCO\({}_{2}\) because of its high critical constants, P\({}_{\rm c}\) = 221 bar and T\({}_{\rm c}\) = 374 \({}^{\circ}\)C. These constants make SCW regeneration be an energetically more demanding method than SCCO\({}_{2}\) regeneration; despite this drawback, it has been probed that SCW and subcritical water (150 bar and 350 \({}^{\circ}\)C) are able to efficiently remove phenol [23] and some of its derivates [24] from traditional ACs. Under these conditions, SCW is not only able to remove the retained pollutants but also clean and open the microporosity of the solids, hence increasing the available surface area for subsequent adsorption stages. This work reports the regeneration of an activated carbon fiber (ACF) exhausted with phenol using SCCO\({}_{2}\) and SCW as regenerating fluids. The porous structure of ACFs is similar to that of ACs but their physical morphology provides them with better adsorbent properties than those of ACs. They have larger surface areas because their pores are more uniform and less branched than the pores of ACs, hence their adsorption capacity is greater [25; 26]. However, it is an expensive material so an effective regeneration method is essential to guarantee the economical viability of the purification method based on the adsorption of pollutants on ACFs. In order to meet this requirement, the regeneration conditions were hardened in this investigation, thus reaching pressure and temperature up to 225 bar and 400 \({}^{\circ}\)C in SCCO\({}_{2}\) regeneration and 300 bar and 425 \({}^{\circ}\)C in SCW regeneration. The influence of other parameters (regeneration time, flow of regenerating fluid and number of subsequent adsorption-desorption cycles) on the regeneration efficiency (RE) was also analyzed as well as the ability of the method to regenerate the material after several subsequent adsorption-regeneration cycles. Taking into account the scarce information regarding SCW regeneration, this work also reports the comparison between SCCO\({}_{2}\) regeneration and SCW regeneration of an ACF exhausted with phenol for the first time.
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 2 Materials and Methods
### Poincare Plot Explanation  
Poincare plot is a geometrical representation of a time series in a Cartesian plane. Heart rate dynamic is a nonlinear process, and its pattern can be explained by Poincare plot [36]. By plotting consecutive points of R-peak (QRS) to R-peak time interval into series on a Cartesian plane, a 2D plot was constructed [37]. It is extensively used for qualitative visualization of the physiological signal and commonly applied to assess the dynamics of heart rate variability (HRV) [36; 37; 38].  
Figure 1: Step by step activity classification process.  
Figure 2: The orientation of the laying data (dotted line) after the multiplication with rotation matrix from unoriented position (solid line).  
An ellipse can be fitted to the shape of the Poincare plot. SD1 and SD2, along with semi-minor and semi-major axis, respectively, are used for quantification of the Poincare plot geometry.  
The description of SD1 and SD2 in terms of linear statistics shows that the standard descriptors guide the visual inspection of the distribution. In terms of HRV, it reveals a useful visual pattern of the RR interval data by representing both short- and long-term variations of the signal [39; 40].  
\[\text{SD1}=\sqrt{\frac{\left(\frac{1}{L}\right)\sum((x_{t}-x_{t-1})-(\overline{ x_{t}-x_{t-1}})\ )\ ^{2}}{2}} \tag{1}\]  
\[\text{SD2}=\sqrt{\frac{\left(\frac{1}{L}\right)\sum((x_{t}+x_{t-1})-(\overline{ x_{t}+x_{t-1}}))\ ^{2}}{2}} \tag{2}\]  
\[\text{SDRR}=\sqrt{\frac{\left(\sqrt{\frac{\left(\frac{1}{L}\right)\sum((x_{t} -x_{t-1})-(\overline{x_{t}}-x_{t-1})\ )\ ^{2}}{2}}\right)^{2}+\left(\sqrt{\frac{\left(\frac{1}{L}\right)\sum((x_{t}+x_{t -1})-(\overline{x_{t}}+x_{t-1})\ )\ ^{2}}{2}}\right)^{2}}{2}} \tag{3}\]  
We used this concept to determine the non-linearity of angle series for human activity classification purposes with an accelerometer sensor. Poincare plot derived from an angle can also be effective for the classification of variations of dynamic activities like walking and running.
# Selection of Grid Road Networks Based on Raster Data
## 4 Experiments and Evaluations
### Limitations of the Proposed ARWA Method  
The proposed ARWA method has some limitations, as follows:  
1. In the real world, roads have different distribution patterns, such as radial and irregular patterns, and the proposed ARWA method can only be effectively applied to roads with grid patterns. Methods suitable for different road patterns need to be further studied;
2. The selection of the parameter values is based on the accumulation of experimental experience, which needs multiple manual adjustments. For example, the setting of parameters AT and RT requires multiple fine-tunings;
3. In our experiment, the original road data were vector data, which may have led to a loss of information when using the proposed ARWS method because of the format conversion. However, if the original roads are raster data, such as remote sensing images, this phenomenon can be avoided.
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 4 Assessment of the Adaptive Fault Tolerance Mechanism  
To assess the performance of the AFT mechanism proposed, we have scheduled a number of injection campaigns that we describe in the following. It is interesting to mention that, for simulation purposes, we have set the threshold to detect intermittent errors (i.e., \(\alpha_{\text{TH}}\)) to 5. This is a tradeoff value, big enough to prevent from detecting false positives and small enough to permit detecting faults in the short duration of the workload (2686 clock cycles). In addition, we have set the clock period to 100 ns (nanoseconds), the default value in the original Plasma model.  
Prior to validating the operation on the proposed model, we have analyzed the simulation of the Plasma model, annotating every memory access (including the address and the access time). From this analysis, we have extracted some important features of the workload, such as the number of times that each memory address is accessed, the average access time (i.e., the average separation between accesses), and the time of the last access.
## 4 Experiments and Results
### Discussions
[30] simplifies the computational complexity by converting images to grayscale during processing. However, in our DFU-Helper, the SNN analyzes images while retaining the three RGB channels. Moreover, we used meticulously labeled data to ensure accurate training and validation for similarity learning in our model. The mentioned work does not specify the number of experts whose input was considered to calculate similarity, which contrasts with our algorithm that outputs similarity results. Additionally, in comparison to the approach proposed by [PERSON] et al. [31], which solely focuses on image equivalence without considering image content, our SNN, particularly the utilization of the ViT, places significant emphasis on the semantic content of the image. The DFU-Helper framework can further be enhanced by incorporating additional deep learning architectures. To ensure its suitability for application in the public health domain, it is essential to establish a rigorous validation and testing protocol, thereby making the proposed framework more robust.
## 4 Results and Discussion
### YOLOv11n-seg Model Selection and Comparison with Other Models
#### 4.4.3 Industrial Constraints and Model Suitability  
The conveyor belt in this project operated at high speed (over 1 m per second), thus demanding immediate and precise inference. Additionally, the materials to be identified were very small (ranging from 1 to 4 mm). Therefore, high detection accuracy and fast processing capability were essential in the model selection.  
Considering its processing speed of under 10 milliseconds and high segmentation accuracy, the YOLOv11n-seg model was highly suitable for this industrial application.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 2 Current Status of Lunar Relay Navigation Satellite Networks
Using lunar constellations for exploration can effectively solve these problems and achieve continuous coverage of specific areas of the lunar surface or even the entire lunar surface, which can provide communication, navigation, positioning and other functional support for human beings to comprehensively carry out lunar exploration in the future. Therefore, the construction of lunar communication and navigation satellite systems has gradually become one of the most important planning contents of future lunar exploration by major space agencies. Foreign countries have proposed the LunaNet, Moonlight, Lunar Pathfinder and other special communication and navigation systems for lunar exploration. In 2022, China also launched the demonstration of a lunar communication and navigation system (constellation).
## 2 Basic Measurement Techniques for Localization in WSNs
### Distance Related Measurement
#### 2.2.1 Propagation Time Measurement  
In **one way propagation time measurement**, the principle approach is to measure the difference between the sending time of the transmitting signal and the receiving time of the signal at the receiver. The distance between the transmitter and the receiver is then computed using this time difference and the propagation speed of the signal in the media. Time delay measurement is a relatively mature field. However, a major limitation in implementing the one way propagation time measurement is that, it requires the synchronization between the local time at the transmitter and the local time at the receiver. Any difference between the local times at the transmitter and the receiver will cause large error in estimating distance and consequently the position estimation error will be large. At the speed of light, a very small synchronization error of 1 ns will translate into a distance measurement error of 0.3 m [50]. The accurate synchronization requirement may add extra cost to the sensor nodes, by demanding a highly accurate clock or may add complexity to the sensor network by demanding a sophisticated synchronization algorithm. This disadvantage makes this option less attractive for WSNs localization.  
**Round trip propagation time measurement** measures the difference between the times when a signal sent by a sensor node is returned from the second sensor node to the first sensor node. In this technique, there is no need for time synchronization, since the time difference is measured at the transmitting sensor node using the same local clock. The major source of error in this technique is the delay required in the second sensor node to handle the signal, process it and send back again. This internal delay is either known via a priori calibration or measured at the second sensor node and send back to the first sensor node where it is subtracted. In addition to the synchronization problem, both one way and round trip propagation time measurements are affected by noise, signal bandwidth, non line-of-sight and multipath environment. To overcome some of the limitations, Ultra Wide Band (UWB) signals have been used for accurate propagation time measurements [51]. UWB can achieve very high accuracy because its bandwidth is very large and therefore its pulse has a very short duration. This feature makes fine time resolution of UWB signals and therefore the separation of multipath signals possible.  
**Time difference of arrival measurement** measures the difference between the arrival times of a transmitting signal at two separate receivers respectively, assuming the locations of the two receivers are known and they are perfectly synchronized. This technique requires three receivers to uniquely locate the transmitter location. The accuracy is affected by synchronization error and multipath. The accuracy improves when the distance between receivers are increased because this increases the difference between the times of arrival.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 2 Problem Formulation and Cramer-Rao Lower Bound
### Cramer-Rao Lower Bound
The number of time steps is set as \(T=5\). Figure 3 presents the CRLBs for only RSS, only AOA, and RSS-AOA time series models in the simple localization scenario. It is evident that the CRLB of RSS-AOA is the lowest. Moreover, at four specific target node locations, the FIM in (11) becomes a singular matrix without a matrix inverse, which explains the white spots in Figure 3a,c. It is worth noting that both CRLBs with AOA only and RSS only decrease as the target node approaches the center of the square region, while the CRLB of RSS-AOA reaches its minimum at the projection of the first anchor node in the square region. This is because RSS-AOA measurement only requires one node to achieve 3D localization and the first anchor node is highly accurate, resulting in lower CRLB for RSS-AOA when approaching the node, which means a lower theoretical minimum localization error. Figure 2: Node placement of a simple localization scenario.
###### Abstract  
This study proposes a multi-span greenhouse Building Energy Simulation (BES) model using a Transient System Simulation (TRNSYS)-18 program. A detailed BES model was developed and validated to simulate the thermal environment in the greenhouse under different design parameters for the multi-span greenhouse. Validation of the model was carried out by comparing the results from computed and experimental greenhouse internal temperatures. The statistical analyses produced an R\({}^{2}\) value of 0.84, a root mean square error (RMSE) value of 1.8 \({}^{\circ}\)C, and a relative (r)RMSE value of 6.7%, showing good agreement between computed and experimental results. The validated proposed BES model was used to evaluate the effect of multi-span greenhouse design parameters including thermal screens, number of screens, orientation, covering materials, double glazing, north-wall insulation, roof geometry, and natural ventilation, on the annual energy demand of the greenhouse, subjected to Taean Gun (latitude 36.88\({}^{\circ}\) N, longitude 126.24\({}^{\circ}\) E), Chungcheongnam-do, South Korea winter and summer season weather conditions. Additionally, the proposed BES model is capable of evaluating multi-span greenhouse design parameters with daily and seasonal dynamic control of thermal and shading screens, natural ventilation, as well as heating and cooling set-points. The TRNSYS 18 program proved to be highly flexible for carrying out simulations under local weather conditions and user-defined design and control of the greenhouse. The statistical analysis of validated results should encourage the adoption of the proposed model when the underlying aim is to evaluate the design parameters of multi-span greenhouses considering local weather conditions and specific needs.  
greenhouse modeling; energy demand; thermal screen; shading screen 1
## 3 Adaptive Time-Frequency Analysis
### Empirical Wavelet Transform and Optimization Models  
empirical wavelet transform (EWT) combines the concepts of wavelet analysis with data-driven approaches. It provides an effective way to analyze signals with complex time-frequency structures. This section focuses on EWT and its ensemble-based counterparts like ensemble empirical wavelet transform and empirical Fourier decomposition. We will introduce the basic principles of these methods, their implementation details, and how they can be utilized to extract meaningful information from non-stationary signals.