esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Monte Carlo Simulation
### MCS Architecture Viewpoints  
In the previous section, we derived the MSC milestone Equations (41)-(44). We will explain how to implement these equations in the MSC programme's code. To render this task possible, we need to build a programming construct that can easily handle the computational challenges and be robust and resilient to change when required to handle changes in the mathematical expression of the equations mentioned earlier. To carry out that, we need to author three essential viewpoint diagrams: (1) the context viewpoint diagram (Figure 14); (2) the implementation viewpoint, which requires two diagrams: class and object diagrams (Figure 15)--on many occasions, the coding team might require just the class diagram; (3) the functional viewpoint, which can be expressed by one or more sequence diagrams (Figure 16); and (4) the data/information viewpoint, which will be discussed in Section 4.5. First, we need to describe the classes that will help us explain the inner dynamics of the MCS programme.  
Figure 14: MCS PROJECT—context viewpoint—programme topology layer diagram.  
Figure 15: MCS PROJECT—implementation viewpoint—class diagram.  
To start building reliable software, we must understand the extent of the requirement specifications for the target MCS programme. The first thing to carry out is to identify the MCS programme's topology layers, as shown in the context viewpoint illustrated in Figure 14. Such topology ensures the separation of the programme's main functional concerns, particularly the data visualisation, from the main programme subject layer. Since there are millions of data arrays, we should avoid directly overloading the machine's physical memory to prevent slowing the execution time. Hence, a dedicated data layer must be established to manage the run-time data and save the necessary data chunks into the disc when possible or use object serialisation to make it available during run time. Also, a support utility layer provides general functionality to all other layers. Each layer has dedicated Python libraries and supporting classes, as shown in Figures 14 and 15.  
To implement the topology architecture in Figure 14 and make the MSC programme an efficient running entity, we must implement the classes depicted in Figure 15. The channel class is the most crucial, as it manages the events' random selection and propagation steps of the photon object. In essence, the channel class object (we will call it channel object for short) implements Equations (3) and (4) according to the mode of operations. Hence, it manages the event set and stream of photons travelling through the water channel. We have not fully deployed the optical Tx and Rx objects because the current version of the code is to emulate photon propagation through the water channel only. As shown in Figure 3A, the Tx object provides the optical pulse shape and \(h_{Tx}(t)\) duration. Also, it controls the transmitted optical power (total number of photons per bit time duration, \(\tau\) sec) for a set of modulation rates.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
###### Abstract  
This paper presents a unified speech enhancement system to remove both background noise and interfering speech in serious noise environments by jointly utilizing the parabolic reflector model and neural beamformer. First, the amplification property of paraboloid is discussed, which significantly improves the Signal-to-Noise Ratio (SNR) of a desired signal. Therefore, an appropriate paraboloid channel is analyzed and designed through the boundary element method. On the other hand, a time-frequency masking approach and a mask-based beamforming approach are discussed and incorporated in an enhancement system. It is worth noticing that signals provided by the paraboloid and the beamformer are exactly complementary. Finally, these signals are employed in a learning-based fusion framework to further improve the system performance in low SNR environments. Experiments demonstrate that our system is effective and robust in five different noisy conditions (speech interfered with factory, pink, destroyer engine, volvo, and babble noise), as well as in different noise levels. Compared with the original noisy speech, significant average objective metrics improvements are about \(\Delta\text{STOI}=0.28\), \(\Delta\text{PESQ}=1.31\), \(\Delta\text{twSegSNR}=11.9\).  
Keywords:speech enhancement; parabolic reflector; microphone array; deep neural network; beamformer +
Footnote †: journal: Applied sciences
###### Abstract  
A honeycomb structure is a sandwich structure widely used in fuselage, among which the hexagonal honeycomb core is the most widely used. The energy absorption characteristics and impedance ability of the structure are the main reasons that directly affect the energy absorption characteristics of the honeycomb sandwich structure. Therefore, it is necessary to study the out-of-plane mechanical properties of the composite honeycomb sandwich structure. Based on the numerical simulation results, the energy absorption characteristics of several composite honeycomb sandwich structures are verified by drop hammer impact experiments. The research shows that the transient energy absorption characteristics of the composite honeycomb sandwich structure are mainly related to the cell size of the honeycomb structure. The smaller the size of the front cell, the stronger the overall impact resistance; the strength of the composite honeycomb sandwich structure exceeds that of 7075 aluminum alloy-NOMEX and carbon fiber-NOMEX honeycomb sandwich structures. In this paper, the energy absorption characteristics of composite honeycomb sandwich structures under different impact energy are compared and studied. The displacement, force and energy curves of energy absorption characteristics related to time variables are analyzed. The difference in protective performance between the composite honeycomb sandwich structure and existing airframe structure is compared and studied. The optimal structural design parameters of composite honeycomb sandwich under low-speed impact of drop hammer are obtained. The maximum energy absorption per unit volume of the designed honeycomb sandwich structure is 171.7% and 229.8% higher than that of the NOMEX-AL and NOMEX-C structures. The 6.4 mm and 3 mm cell sizes show good characteristics in high-speed buffering and crashworthiness. The composite honeycomb sandwich airframe structure can improve the anti-damage performance of the UAV airframe structure, ensure the same thickness and lightweight conditions as the existing honeycomb sandwich airframe structure, and improve the single-core bearing mode of the existing airframe structure.  
honeycomb structure; energy absorption; drop hammer impact; sandwich structure +
Footnote †: journal: Applied Sciences
# Fully Open-Source Meeting Minutes Generation Tool
## 3 Proposal of Meeting Minutes Generation System
### Information Extraction Function
#### 3.5.1 Abstractive Summarization  
The _abstractive summarization_ generates summaries while retaining the original semantic contents of the source text [35]. In this study, the _bidirectional and auto-regressive transformers language model (BART LM)_ is employed to achieve accurate results in generating coherent summaries [36]. This selection comes from the results of our previous works to measure the performance of abstractive summarization models [19].  
The _BART LM_ model is downloaded through the _HuggingFace_ implementation [37]. The _BART LM_ model adopts the _bidirectional_ encoder and the _auto-regressive_ decoder. The _bidirectional_ encoder allows the model to understand the contextual representations between words from before and after the current words in the transcribed text. It encodes these data into numerical vectors and heightened significant and flattened uninfluential data using an attention mechanism. The _auto-regressive_ decoder enables the model to produce words by predicting the most potential words after the current words. This iterative process is performed continuously following the depth of the architecture. Finally, the model transforms these numerical vectors back into the text.
# Erythermal Solar Irradiance, UVER, and UV Index from Ground-Based Data in Central Spain
###### Abstract  
The study shows an analysis of a 7-year data set measuring Ultraviolet-B (UVB) irradiance values and ultraviolet index TABLEUVI) values derived from ground-based broadband irradiance measurements, satellite-derived total ozone, and UVB solar irradiance recorded in Valladolid (Central Spain). Ultraviolet-B (UVB) solar irradiance measurements in the range (280-315 nm) carried out during the period 2013-2019 at a continental Mediterranean solar station, located in Valladolid (Spain), were analyzed. UVB data recorded using a YES UVB-1 pyranometer were used to estimate erytimal irradiance, ultraviolet erytimal irradiance (UVER), UVI, cumulative dose, and sun protection. Hourly UVER data in January (minimum values) and June (maximum values) were analyzed as an average year for the measurement station. Differences between UVI values at solar noon and the maximum daily value were minimal. It was found that on certain summer days, maximum daily UVI and SED (cumulative daily dose) could be over 12 and 60, respectively. The cumulative dose on the horizontal surface was calculated at the station for different skin types. It was observed that over 45% of the annual dose is received in summer, about 30% in spring, over 15% in autumn, and less than 10% in winter. In addition, the relationship between the maximum daily UVI and the annual accumulated dose in SEDs was studied to provide information on sun protection under low UVI conditions.  
ultraviolet erytimal irradiance (UVER); ultraviolet index (UVI); cumulative doses; maximum daily UVI; sun protection; Northern Hemisphere +
Footnote †: journal: Applied Sciences
## 3 Data and Methods
### Vegetation Indices
#### 3.2.1 NDVI  
NDVI is the most widely used vegetation index [(37)]. It is based on the solar reflectance in the red (Red, 0.6 \(\upmu\)m) and near infrared (NIR, 0.8 \(\upmu\)m) wavelength bands (Table 2). As its name indicates, the NDVI is a normalized index with values in the range of \(-1\) and \(1\), with full vegetation cover approaching the value of \(1\) and bare soil with values close to \(0\). Negative values usually indicate presence of water. The NDVI has some saturation problems over dense vegetation cover [(38)]. However,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Planet Scope 3-m (nm)** & **Sentinel-2A 10-m (nm)** \\ \hline Blue & 455–515 & 448–546 \\ Green & 500–590 & 537–583 \\ Red & 590–670 & 545–583 \\ NIR & 780–860 & 763–909 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wavelength range of Planet and Sentinel-2A satellites sensors for blue, green, red, and NIR spectral bands.  
it was shown to be very reliable in monitoring vegetation dynamics in Mediterranean forests and woodlands, particularly for Mt Carmel, Israel [23]. It was also successfully used to assess burn severity in Mt Carmel forests and woodlands following the great fire of 2010 [23].
# Generalized Sketches for Streaming Sets
## 3 Problem Formulation  
Let \(\Gamma=e^{(1)},\ldots,e^{(t)},\ldots\) denote the stream of interest, where \(e^{(t)}=(u^{(t)},w^{(t)})\) is the \(t^{\text{th}}\) element of stream \(\Gamma\), which represents a link from a user \(u^{(t)}\) to an interest \(w^{(t)}\). Note that a pair \((u,w)\) may appear more than once in stream \(\Gamma\). Denote by \(U\) the set of occurring users, and \(W\) the set of occurring interests. Let \(N_{u}\) be the interest set of user \(u\), i.e., the set of interests that user \(u\) links to. User \(u\)'s cardinality is defined as the number of its distinct interests, i.e., \(d_{u}=|N_{u}|\). For two users \(u\) and \(v\), we define their common-interest count as \(d_{u\cap v}=|N_{u}\cap N_{v}|\), and their Jaccard similarity as \(J_{u,v}=\frac{|N_{u}\cap N_{v}|}{|N_{u}\cup N_{v}|}\). In this paper, we aim to design a fast method to build a compact data summary (or, sketch) for each user in a single-pass stream \(\Gamma\). For any specific query user \(u\), generated sketches are effective for rapidly searching for nearest neighbors of user \(u\) (i.e., users \(v\in U\) with large Jaccard similarity \(J_{u,v}\)) without enumerating each user in \(U\). More importantly, they are also accurate for estimating the cardinality \(d_{v}\) of any user \(v\), Jaccard similarity \(J_{u,v}\), as well as the common-interest count \(d_{u\cap v}\) of any two users \(u\) and \(v\). For ease of reference, we list the notation used throughout the paper in Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \(\Gamma\) & The stream of interest \\ \hline \(U,W\) & The sets of users and interests, \\ \hline \(r_{w}\) & The random rank value of interest \(w\in W\) \\ \hline \(i_{w}\) & \(i_{w}=|r_{w}k|+1\) \\ \hline \(k\) & The number of registers used for a MinHash, \\  & OPH, or OH sketch \\ \hline \(k_{1}\) & The number of registers used for an HLL sketch \\ \hline \(N_{u}\) & The set of interests of user \(u\in U\) \\ \hline \(N_{u\cap v}\) & The set of common interests of users \\  & \(u\) and \(v\) in \(U\), i.e., \(N_{u\cap v}=N_{u}\cap N_{v}\) \\ \hline \(N_{u\setminus v}\) & The set of interests affiliated with user \(u\) \\  & but not user \(v\), i.e., \(N_{u\setminus v}=N_{u}\setminus N_{v}\) \\ \hline \(N_{u,i}\) & The set of interests \(w\) in \(N_{u}\) with \(i_{w}=i\) \\ \hline \(N_{u\cap v,i}\) & The set of interests \(w\) in \(N_{u\setminus v}\) with \(i_{w}=i\) \\ \hline \(d_{u}\) & The cardinality of user \(u\in U\), i.e., \(d_{u}=|N_{u}|\) \\ \hline \(d_{u\cap v}\) & The common-interest count of users \(u\) and \(v\), \\  & i.e., \(d_{u\cap v}=|N_{u}\cap N_{v}|\) \\ \hline \(d_{u\setminus v}\) & The number of elements in set \(N_{u\setminus v}\) \\  & i.e., \(d_{u\setminus v}=|N_{u}\setminus N_{v}|\) \\ \hline \(d_{u,i}\) & The number of interests \(w\) in \(N_{u}\) with \(i_{w}=i\) \\ \hline \(
## 3 The Principle of CEEMDAN-WP and RDT
### EEMD, CEEMD, and CEEMDAN Algorithms  
The core of the EMD algorithm is decomposition, through which a number of IMFs can be obtained [13]. The IMF components satisfy two conditions: the number of extreme points and zero-crossing points are equal or at most have a difference of one, and the average of the upper and lower envelopes is zero. Following EMD, EEMD mainly focuses on ameliorating the problem of mode mixing [14]. The EEMD algorithm is presented as follows.  
1. Add Gauss white noise \(\omega(t)\) into the original signal \(x(t)\) and thus produce a new signal \(X(t)\). \[X(t)=x(t)+\omega(t)\] (1)
2. Using the EMD algorithm, \(X(t)\) is decomposed into a number of IMF components and a residual component \(r(t)\). \[X(t)=\sum_{i=1}^{N}imf_{i}(t)+r(t)\] (2)
3. By repeating the above two steps, the IMFs \(imf_{ij}(t)\) are obtained, where \(i\) is the iteration number and \(j\) is the mode.  
Figure 3: Flow chart of the combined signal denoising and identifying process.  
Figure 2: The vertical displacement and its corresponding power spectral density (PSD) value.  
4. Compute the average of the IMF components to eliminate the effects of additional white noise. \[imf_{j}(t)=\frac{1}{N}{\sum\limits_{i=1}^{N}imf_{ij}(t)}\] (3)  
Although EEMD can reduce the influence of mode mixing, the noise remains. To solve this problem, a CEEMD algorithm [15] is introduced, which can be expressed as  
1. Add positive and negative white noise \(\omega^{\pm}(t)\) into the original signal \(x(t)\), and generate two new signals \(X^{\pm}(t)\). \[\left\{\begin{array}{l}X^{+}(t)=x(t)+\omega^{+}(t)\\ X^{-}(t)=x(t)+\omega^{-}(t)\end{array}\right.\] (4)
2. Repeat the above step, and decompose the new signals using EMD.
3. Derive two sets of IMF components for the new signals.
4. Calculate decomposition results by averaging multiple components. \[imf_{j}(t)=\frac{1}{2N}{\sum\limits_{i=1}^{2N}imf_{ij}(t)}\] (5)  
To alleviate the computational load and maintain the ability to eliminate mode mixing, [PERSON] et al. [16] proposed a method (i.e., the CEEMDAN method). This method can be divided into the following steps.  
1. Define \(E_{j}(\cdot)\) as the operator which produces the \(j\)-th IMF which has been decomposed based on the EMD algorithm. The first mode \(imf^{\prime}_{1}\) is derived by EMD from the signal \(x(t)+\varepsilon_{0}\omega_{i}(t)\). \[imf_{1}(t)=\frac{1}{N}{\sum\limits_{i=1}^{N}imf_{i1}(t)}\] (6) where \(\varepsilon_{0}\) is the amplitude of the added white noise and \(\omega_{i}(t)\) is the  
Repeat Steps 4-6 until the residue has no more than two extrema, and satisfy the relationship  
\[R(t)=x(t)-\sum_{j=1}^{l}imf^{\prime}_{\ j}(t) \tag{11}\]  
where \(J\) is the total number of modes.  
Then, the original signal \(x(t)\) is defined as  
\[x(t)=\sum_{j=1}^{J}imf^{\prime}_{\ j}(t)+R(t) \tag{12}\]  
From Equation (12), the CEEMDAN method can provide a complete and accurate reconstruction signal. Meanwhile, at each stage, noise of different signal noise ratio (SNR) values can be obtained by adjusting the coefficient \(\varepsilon_{j}\).
# Influence of Drilling Along Pilot Holes on Drilling Performance and Effective Rock Strength +
## 3 Results and Discussion
### Validation of Drilling Performance Through Increased Rotational Speed  
During this research stage, we aim to add the rotating speed (RPM) as a new parameter to validate and further confirm the drilling performance observed in Stage 1. This step was performed to verify the consistency of the data and examine any discrepancies in drilling performance under different drilling conditions. The same technique was used, except raising the RPM to 300 while drilling hole number two (h2). The same equipment configuration and characteristics, such as a constant applied weight on bit (WOB) of 6 KN, were used for the drilling experiments.  
The ROP and torque results from drilling gabbro formation at 300 RPM are shown in Table 5. The data analysis indicated a clear trend: when drilling the top section parallel to the pre-cored formation, the torque was always higher than when drilling the bottom section parallel to the un-pre-cored formation. The observed torque variation resulted from the top section's higher Rate of Penetration (ROP) than the bottom section.  
The steady torque is consistent, which validates earlier conclusions and provides further information on how formation properties impact drilling results. Greater drilling resistance is indicated by the increasing torque values at the top portion, which results from variations in the formation's properties. These results highlight the consistency of the observed drilling performance by confirming the patterns seen in the photographs.  
The comparison shows that raising the rotary speed from 60 RPM to 300 RPM increases the Mechanical Specific Energy (MSE) values in both pre-cored and un-pre-cored forms, as seen in Table 5. This indicates that drilling at a faster rotational speed takes more energy, whether the formation is pre-cored or not. Furthermore, the MSE values are consistently greater in the pre-cored formation than in the un-pre-cored formation, implying that drilling through pre-cored intervals requires more energy.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion
### Experiments on the SIMD Dataset
#### 4.4.2 Comparison Experiments with Other Models  
To verify the superiority of the RS-DETR algorithm in remote sensing image object detection, we compared the algorithm with transformer-based [43; 44] object detection algorithms and the classic two-stage Faster R-CNN algorithm. All experiments were conducted on the SIMD remote sensing dataset, with results shown in Table 2. As detailed in Table 2, our proposed method significantly outperforms the DETRs and Faster R-CNN algorithms in terms of Params, FLOPs, and the mAP50 and mAP metrics. Additionally, compared to the baseline model, our proposed RS-DETR achieves improvements of 2.0% in mAP50 and 1.1% in mAP. Moreover, RS-DETR requires fewer parameters and FLOPs than other models, indicating that it achieves lower model complexity and parameter count while maintaining superior performance.  
Figure 9 shows the detection results of the RT-DETR and RS-DETR algorithms on the SIMD dataset. In the top comparison, our proposed method detected a pushback truck instance that the baseline model missed, reducing missed detections. In the middle comparison, our model performs better in densely distributed scenes, successfully detecting all overlapping targets. In the bottom comparison, our model correctly classified a car that the baseline model misclassified as a van, reducing missed detections. Overall, the improved algorithm not only enhances detection accuracy for dense targets but also addresses missed detections and false detections in remote sensing scenes.
## 3 Results and Discussion
### Effect of pH  
The influence of pH on the removal of Pb(II) was investigated at pH range 1.5-5.5, while other conditions were the same as in the adsorbent dosage tests. As seen from Figure 5, pH does not have a significant effect on the adsorption of Pb(II) onto G-26 resin in the entire explored pH range (1.5-5.5). The removal rate of Pb(II) was slightly low at pH 1.5 for MTS9570. Precipitation of lead was observed at pH > 5.5. Therefore, in the subsequent experiments, the solution pH was kept at 3.5.  
Figure 4: Effect of resin dosage on adsorption of Pb(II) using G-26 and MTS9570 exchange resins at 30 min contact time, 150 rpm agitation speed, pH 3.5, concentration 1000 mg/L, and temperature 20 \({}^{\circ}\)C.  
Figure 3: Effect of agitation time on adsorption of Pb(II) using G-26 and MTS9570 exchange resins at 0.5 g of resin dosage, 30 min contact time, 150 rpm agitation speed, pH 3.5, concentration 1000 mg/L, and temperature 20 \({}^{\circ}\)C.  
resins are both close to 100% in the Pb concentration range of 100-1000 mg/L. At the high metal concentration of 1200 mg/L, the removal percentage of Pb decreased, which is due to less availability of favorable sites for adsorption on the resin beads. The result obtained in this work is consistent to the results reported by [PERSON] et al. (2018) and [PERSON] et al. (2018).