esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Processing of Reflections for Passive-Source Seismology Based on Geometric Design
## 3 Direct Migration of Reflections of 3D Waves from Passive Source Based on Geometry  
### Principle of Calculation of Direct Migration  
The principle of calculating random noise by direct migration can be expressed as follows:  
\[I(x)=\sum_{k=1}^{N_{k}}\int_{0}^{T}R_{K}^{A}(x,t)R_{K}^{B}(x,t)dt \tag{2}\]  
\(R_{K}^{A}(x,t)\) and \(R_{K}^{B}(x,t)\) are the wave fields from the \(k\)th (\(k\) = 1, 2,..., \(N_{s}\)) source at a certain time, and are received by the receivers and on the surface. The two are extended downward, and the underground image of the reflection \(I(x)\) can be obtained through correlation (Figure 16). The final result of imaging is the sum of the results of correlation of a series of sources and receivers in a series of time windows. [PERSON] (2017) summarized the process of calculation of direct migration of data from a passive source as the extrapolation, correlation, and summation of the wave field. All algorithms for migration imaging that involve the extrapolation of the wave field and are conducive for use in the relevant imaging conditions can apply the direct migration method  
We used the one-way wave pre-stack depth migration method to process the original random noise records. It consisted of two steps: recursion of the source and receiver points along the direction of the depth, and imaging (Stein
# Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
## 4 Potential Transfer Trajectories and Case Study

Table 1: Classical orbital elements, which give the characteristics of the Keplerian orbits of Earth, Mars, and Venus, used in the numerical simulations. Data obtained by the JPL Horizon system on 1 July 2024. Figure 8: Results of the optimization process in a 3D Earth–Venus mission scenario when \(a_{c}=0.07\,\mathrm{mm}/\mathrm{s}^{2}\) and \(K=1\). (**a**) Time variation of the components of \(\mathbf{\hat{t}}\) in the RTN reference frame. (**b**) Optimal transfer trajectory (black line). Blue line \(\rightarrow\) is Earth’s orbit; red line \(\rightarrow\) is Venus’ orbit; blue circle \(\rightarrow\) is start point; red circle \(\rightarrow\) is arrival point; blue star \(\rightarrow\) is Earth’s perihelion; red star \(\rightarrow\) is Venus’ perihelion; orange circle \(\rightarrow\) is the Sun.
# Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model
## 2 System Architecture  
### Design  
In order to evaluate the quality of CCs, this paper estimates the human pose in a two-dimensional (2D) image by applying OpenPose. By analyzing the motion features of crucial parts of the human body, this paper assesses the depth, frequency, position, chest rebound and elbow straightness of compression postures. As shown in Figure 2, the system architecture consists of four stages: image acquisition, pose estimation, essential motion feature assessment and quality evaluation. The details of each step are introduced below.  
A camera is installed to record the front view of the CC motions of trainees. The camera's field of view should include the operator's whole shoulders and elbows as well as the upper body of the lying CPR manikin. Sample CCs video is provided in Supplementary Material (Video S1). FFmpeg [14] is a computer program that can record and convert video and audio. We used the FFmpeg package in python to vary video format, resolution and frame rate [15].  
After the format conversion is completed, the pose estimation can be performed. The OpenPose we selected is a 2D human estimation algorithm that detects multiple people's poses in real time and provides valuable information using multi-GPU-based deep learning methods. OpenPose uses the bottom-up method, first returning all the joint points in a picture and returning the part affinity field (PAF) simultaneously. According to the PAF, the joint points are assigned to the corresponding people. Example processed frame is provided in Supplementary Material (Figure S1). Therefore, its computational complexity is not related with the number of people in the image, and its computational efficiency is  
Figure 1: Traditional training mode with a CPR manikin.  
higher than the top-down method. Based on the VGG-19 network [16], OpenPose extracts a feature map. It generates a part confidence map to detect each joint of the human body and to determine the relationship between the extracted joints [17].  
We used OpenPose to recognize the coordinate information of 18 key points and the confidence of the key points of the trainee from the processed video. However, during the experiment, the key points of the CPR manikin are also extracted together. To judge the standardization of the trainee's motions, we must first distinguish the key points of the trainee from them. The value of confidence provided by OpenPose can differentiate the key-point information of the trainee in the video from the mixed coordinate information and can generate the key-point coordinate file simultaneously. As shown in Figure 3b, the multi joints and links of the trainee's body are recognized and generated.
# Adipose Stem Cell Response to Borophosphate Bioactive Glass
## 3 Results
### pH-Neutral BPBGs Attract ASCs, While ASCs Treated with Alkaline BPBGs Attract ECs  
Adipose stem cells possess an inherent ability to migrate to sites of injuries and secrete various chemokines, cytokines, and growth factors that enable them to mediate the regeneration process [41; 42; 43; 44]. To test whether different compositions of BPBGs affected the migration of ASCs, a 5 h transwell migration assay was used. Glasses X0 and X40 increased ASC migration, whereas X60 had no statistical effect (Figure 3A).  
Figure 2: BPBGs influence ASC differentiation. ASCs were induced to differentiate into bone or fat in the absence or presence of BPBG. **(A)** Representative micrographs of 3 ASC donors are shown. Scale bar = 250 μm. Osteogenic (**B,C**) and adipogenic (**D,E**) differentiation were quantified using Fiji software. Mean \(\pm\) SD;^p \(<\) 0.05 compared to no glass control; ^p \(<\) 0.05 compared to X0; ^p \(<\) 0.05 compared to X0; ^p \(<\) 0.05 compared to X40. When stimulated with glass, ASC donors showed a difference in osteogenesis (**C**) and adipogenesis (**E**). Mean \(\pm\) SD;^p \(<\) 0.05 compared to D1; ^p \(<\) 0.05 compared to D2; ^p \(<\) 0.05 compared to D3.  
ASCs are also angiogenic, partially due to their ability to attract endothelial cells (ECs) [36]. To test if BPBG could affect the ability of ASCs to attract ECs, subconfluent ASCs were treated with BPBG for 24 h. The resulting conditioned media was used as an attractant for EC migration. Interestingly, only X60 increased the ASCs' ability to attract ECs (Figure 3B).
# Metabolic Periparturient Diseases in Small Ruminants: An Update
## 1 Introduction  
Small ruminants are typically fed _ad libitum_, whatever the production system. Food intake, as in other species, must cover the energy and nutrient requirements for their maintenance and physiological condition, i.e., lactation, pregnancy, and dry period [1]. When reared in extensive, semi-intensive, or intensive production systems, an economic outcome is sought in accordance with productive purpose. This economic revenue is dependent on their function and productive capacity in terms of gestation, lactation, and growth.  
The physiological transition between pregnancy and lactation is challenging. The emergence of metabolic and nutritional diseases depends on their genetic potential and on the nutritional management or diet availability balanced in water, energy, protein, minerals, and vitamins [2, 3]. Essential nutrients include (a) carbohydrates (sugars, starch and fiber), proteins (amino acids); (b) lipids, macroelements (sodium, calcium, phosphorus, zinc, magnesium and potassium); (c) microelements (iron, iodine, copper, cobalt, selenium, among others), and; (d) fat-soluble vitamins (A, D, E and K) and water-soluble vitamins (B complex and vitamin C) [4], which mainly serve as metabolic co-factor pathways.  
Animals with high productive capacity are subject to a high food demand (input) and metabolism intensification. Potential homeostatic imbalances can occur to achieve the amounts of protein and energy necessary to produce a protein (creatopoietic activity) and milk (lactogenesis), among others (output). This metabolism intensification, which, byinducing functional and lesional changes in body tissues and organs, causes the so-called \"production diseases\" [2; 3; 4]. Nutrient requirements during late pregnancy (around the last six weeks) and lactation (around the first eight weeks) are distinct [5; 6]. Dams' gut and physiological (oxidative status) adaptation also occurs during the transient period [7; 8], exercising additional pressure on metabolic disturbance [9; 10].  
This critical review mainly aimed to address an update on the main interrelated diseases of energy (ketosis) and mineral (hypocalcemia, hypomagnesemia) occurring during the periparturient period of sheep and goats. Major issues related to the veterinary and engineering improvement of efficient production in farms were highlighted. Diagnosis, treatment, and prevention at individual and herd levels are discussed with emphasis on diagnostic and prognostic biomarkers.
# Automatic Evaluation of Piano Performances for STEAM Education
## 6 Conclusions  
One of the reasons that piano learners play the piano is for technical skill practice, for instance, Legato, Staccato, and other articulations. However, learners do not know which techniques and skills are the best for their piano performance improvement. Hence this research aims to explain and compare results for the accurate identification of piano sounds. The performers are four piano teachers and nine students, a total of thirteen people, for training and assessing processes with four different methods: Support Vector Machine (SVM), Naive Bayes (NB), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to evaluate the sounds. Datasets were recorded for all performers.  
In this study, model technique analysis was used to explain the model prediction to create an automatic assessment process for piano performance. The results show that the CNN approach outperformed other approaches in all assessment criteria. Furthermore, this study obtained identification accuracy results for CNN performance in two ways: piano sounds in Legato using the sustain pedal at 93.65% accuracy, and piano sounds in Staccato with detached notes and no use of the sustain pedal at 89.36%. In addition, to evaluate the accuracy, kids' songs were performed to collect data. The results reveal that the accuracy rates were higher than eighty percent.  
However, there are some limitations. The data were collected using scales and songs in the evaluation process. The experimental dataset could be expanded by including performances with other musical instruments and different types of music to include, for instance, Classical, Jazz, Pop, Blues, Punk, Disco, Rock, or Heavy Metal. In addition, our current experiment tool is the piano; future studies could include other instruments such as strings, woodwind, brass, or percussion to test accuracy for different tones, rhythms, and musical styles. Furthermore, we achieved robust correctness accuracy for piano sound data with each method which used six scales, major and minor keys, and various key signatures together with two major types of musical articulation (Staccato and Legato), which helps to validate this approach. Moreover, this research can be of benefit to the music industry and could be used to enhance music teaching, study, and testing in the future.  
Conceptualization, V.P. and W.-H.T.; methodology, V.P. and W.-H.T.; software, V.P.; formal analysis, V.P.; investigation, V.P.; resources, V.P.; data curation, V.P.; writing--original draft preparation, V.P.; writing--review and editing, V.P. and W.-H.T.; visualization, V.P.; supervision, W.-H.T.; project administration, V.P. and W.-H.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, under grant MOST-106-2221-E-027-125-MY2.  
Figure 25: Confusion Matrix of classifying piano performance of Twinkle Twinkle with the overall played.
## 3 Results
As can be seen in Figure 6, the number of nodes was increased for the continuation of the experiments. P-WOA outperformed competing approaches when compared in aggregate. In some phases, p-WOA overlapped with those of other approaches due to the randomness of the algorithms; however, this may be readily fixed by adopting a probabilistic approach and applying intelligent self-adaptation weights during the succeeding iteration. C. Packet Delivery Ratio  
The average packet delivery ratio shows how many packets were sent from the source and how many were received at the destination. Because it allows us to evaluate the efficacy of any given network, it is an essential metric [38]. Using Equation (11), we can determine  
Figure 5: Comparison of 30 Nodes on a Grid with Different Numbers of Clusters. Figure 6: Comparison of 40 Nodes on a Grid with Different Numbers of Clusters. that a network with a higher average packet delivery ratio is more reliable than one with a lower ratio:  
\[\textit{Average PDR}=\frac{\mathbf{\Sigma}\ \textit{Number of packets received}}{\mathbf{\Sigma}\ \textit{Number of packets sent}} \tag{11}\]  
Comparing p-WOA's PDR to that of other conventional approaches, as shown in Figure 7, it can be seen that p-WOA performed better than ALO and GWO. D. Latency  
Transferring a data packet requires some processing time, which is referred to as \"latency\" (packet). The term \"latency\" is used to describe the delay experienced by data as they travel over a network. The time it takes for a datum to travel from its origin to its destination and back again is often referred to as the \"round trip delay.\" When 30-100 cars are taken into account, as shown in Figure 8, the average delay was about one minute. In comparison to other approaches, the figure demonstrates that p-WOA had the lowest latency [39]. Figure 8: Latency for 30–100 Nodes. Figure 7: Packet Delivery Ratio for 30–100 Nodes. E. Average Throughput  
The average throughput is the rate at which data are transported between the source and the destination. We know that a higher throughput number [40] will boost our network's performance. Equation (12) allows for its determination:  
\[Throughput=\ \frac{\mathbf{\Sigma}(no.\,of\ packets)*(packet\ size)}{(transmission\ time)} \tag{12}\]  
For 100 nodes and a grid size of 1 km \(\times\) 1 km, Figure 9 displays that the maximum throughput attained with p-WOA was 7 Mpbs, whereas the average throughputs of GWO and ALO were 6 Mpbs and 4 Mpbs, respectively.
## 5 Conclusions  
This paper proposed an enhancement to the DV-Hop localization algorithm by the introduction of the IPSA-DV-Hop algorithm, which incorporates weighted hyperbolic refinement. In the first step, the hop distance was refined by correcting the RSSI. During the hop distance calculation phase, a weighted hyperbolic algorithm based on DCL replaced the original least squares method. The optimized PSA further reduced the localization error. When extended to 3D scenarios, the optimal node subset strategy, based on the PROSAC algorithm, effectively reduced beacon node hop distance errors. Position calculation was performed using the 3D hyperbolic algorithm, which was augmented with DCP for improved accuracy. Simulation experiments were conducted in 2D using random, C-shaped, H-shaped, S-shaped, and X-shaped anisotropic network distributions. In 3D, simulations were performed with random, hilly, valley, and rugged terrains. By comparing localization accuracy across various complex network environments, the proposed algorithm demonstrated superior accuracy in both isotropic and anisotropic networks. Despite a slight increase in computational complexity, it was demonstrated to be robust, exhibiting  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithms** & **Cubic Terrain** & **Rugged Terrain** & **Hilly Terrain** & **Valley Terrain** \\ \hline
3D DV-Hop & 3.6 & 5.2 & 6.3 & 6.9 \\
3D PSO-DV & 18.5 & 19.9 & 21.2 & 21.4 \\
3D SSA-DV & 28.9 & 28.5 & 29.6 & 30.2 \\
3D PSA-DV & 32.8 & 31.5 & 33.2 & 38.3 \\ \hline \end{tabular}
\end{table}
Table 4: The computational complexity comparison in 3D scenarios.  
Figure 13: Comparison of localization errors under different communication radii in 3D scenarios. (**a**) Three-dimensional variable beacon ratio. (**b**) Three-dimensional variable communication radius.  
low sensitivity to network topology and maintaining strong stability under irregular radio communication conditions.  
D.Z.: Supervision, project administration, resources, writing--review and editing, data validation; P.L.: Conceptualization, investigation, formal analysis, data curation, and writing--original draft; B.H.: Validation and software. All authors have read and agreed to the published version of the manuscript.  
Project ZR2023 MF058 was supported by the Shandong Provincial Natural Science Foundation.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methodology Overview
### Theoretical Background  
The most relevant concepts in this problem are the AMS and RMS, which are both defined in the moment space. Additionally, the AMS is derived from the admissible control set (ACS) and the effectiveness matrix. Recalling all relevant definitions from [8; 9]:  
* Control Space: A Cartesian coordinate system in the \(\mathbb{R}^{m}\) space, with one control effector as a variable on each axis. \(m\) is the number of effectors. Each point in this space represents a combination of effector positions.
* Admissible Control Set: (\(\mathbf{\mathcal{A}}_{\mathbf{c}}\)): A set in the control space of all possible combinations of effector positions given their respective limits. Since the effectors are actuated independently, it figures a cuboid or hyper-cuboid in the \(\mathbb{R}^{m}\) control space.
* Moment Space: A Cartesian coordinate system in the \(\mathbb{R}^{k}\) space, with generalized forces on each axis as variables. The generalized forces do not necessarily have to be moments but also, e.g., angular accelerations, load factors or direct forces. The name \"Moment Space\" is more of a legacy concept at its initial definition. \(k\) is the number of generalized forces.
* Attainable Moment Set: (\(\mathbf{\mathcal{A}}_{\mathbf{M}}\)): A set in the moment space of all possible combinations of generalized forces that can be produced from the effector combinations in the ACS. In this paper, we consider a linear mapping from ACS to AMS, with the so-called effectiveness matrix \(\mathbf{B}\in\mathbb{R}^{k\times m}:\mathbf{\mathcal{A}}_{\mathbf{c}}\to\mathbf{\mathcal{A}}_ {\mathbf{M}}\). Since \(\mathbf{\mathcal{A}}_{\mathbf{c}}\) is convex, this convexity is preserved by this linear transformation [10], making the AMS geometrically a convex polytope in the \(\mathbb{R}^{k}\) moment space. The \(\mathbf{B}\) matrix is inherently defined by the configuration especially the effectors, e.g., the moments produced by a propeller is dependent on its lever arm with respect to center of gravity and the angle of installation.
* Required Moment Set: A set in the moment space, incorporating all the combination of generalized forces desired to fulfill the prescribed system requirements and missions. These requirements and missions could include fulfillment of certain trajectory profiles, bandwidths of control reaction or disturbance rejections. The RMS is inherent from major system parameters such as mass and moment of inertia, as well as the prescribed requirements, and will remain unchanged once the concept of operations and preliminary design are fixed.
## 2 Problem Formulation
### Optimal Capacity and Power Factor  
The optimal capacity of each DG and CB is calculated using the minimum and maximum limits in Equations (5) and (6). In addition, the total active power of all DGs (\(P_{Gi}^{total}\)) should not exceed the total active load (\(P_{L}^{total}\)), and the total reactive power of all CBs (\(Q_{Gi}^{total}\)) should not exceed the total reactive load (\(Q_{L}^{total}\)). This is determined by considering the constraints in Equations (9) and (10).  
Normally, the power factor of DGs is assumed to be unity. However, the power factors of inverter-based DGs can be adjusted with converters. Thus, both active and reactive power can be provided to the power system with only DGs, without CBs. The optimal power factor of each DG is determined by using all the constraints in Equations (5)-(10). Both optimal capacity and optimal power factor are calculated within the constraints iteratively by the power flow analysis and proposed optimization algorithm. The flowchart of the optimal installation of DGs and/or CBs with AOA is in Figure 3, and it is summarized in the following steps.  
Figure 1: The flowchart for determining the optimal location of DGs and/or CBs.  
Figure 2: Two-bus distribution power system.  
Figure 3: The flowchart of AOA.  
**Step 1:** Read and define the bus and line data of radial distribution systems.  
**Step 2:** Run the power flow and get the initial active and reactive power losses, bus voltages and phase angles, and line flows.  
**Step 3:** Choose the allocation problem (only DGs, only CBs, DGs, and CBs simultaneously, and DGs at optimal p.f) and specify the AOA parameters (population size, maximum iteration, upper or lower generation limits of DGs and CBs, etc.).  
**Step 4:** Randomly generate a candidate solution (location, size, and/or p.f).  
**Step 5:** Calculate the objective function (total power loss and voltage deviation) and determine the best solution.  
**Step 6:** Update MOA and MOP, determine the search phase, use the necessary arithmetic operators, and increase \(C_{iter}\).  
**Step 7:** Apply the power flow and get the new active and reactive power losses, bus voltages and phase angles, and line flows.  
**Step 8:** Obtain the best solution (location, size, and/or p.f).  
**Step 9:** Check the current iteration (\(C_{iter}\)) if (\(C_{iter}<M_{iter}\)) returns **Step 5**.  
**Step 10:** Display the best solution (location, size, and/or p.f).