esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Data Augmentation  
[PERSON] et al. [4] divided the approaches to small sample learning into three categories: model fine-tuning, data augmentation, and transfer learning. Data augmentation methods involve enhancing the features and expanding the target data sets with auxiliary data or information. Based on the learning method, they further categorized learning methods into three kinds: based on unlabeled data, data synthesis, and feature enhancement by data augmentation. [PERSON] et al. [5] have proposed a data augmentation method based on knowledge to monitor oil conditions using sparse and small samples. Furthermore, data augmentation was carried out using particle filtering algorithms after data decomposition.  
However, this method has a high computational complexity, and it may be hard to efficiently compute the solution. [PERSON]. [PERSON] et al. [6] have put forward a noise-based spatial clustering method based on DBSCAN and AICc density for small sample clustering. It utilizes a Maximum P-Value method to estimate whether each sample distribution is unimodal or multimodal and creates virtual samples based on the sample distributions. [PERSON] et al. [7] proposed generating virtual data to improve sample diversity. The method jointly trains the generation model and classification algorithms by means of an end-to-end strategy combined with meta-learning methods. Timestamps are used as an input in the T-CGAN method in order to handle irregular time intervals [8]. [PERSON] et al. have combined an RNN with GAN in RGAN and RCGAN to generate multi-dimensional time-series data; however, this method suffers from insufficient parallelization, and further validation of its generalization ability is required.
## 2 Status of Smart Grids
### Popularization of the Smart Grid
#### 2.1.3 Oceania and Asia  
Since its approval in December 2013, a plan to implement a localized green smart grid electricity network in the Tonsley Park redevelopment in Australia has been realized [19]. Sydney, in partnership with the Australian government, has implemented a smart grid and smart city program.  
In Korea, the smart grid market got off to a slower start than in developed countries but is being promoted rapidly under government initiatives. Taking advantage of the country's small land mass, the government's objective is to build the world's first nationwide intelligent power network by 2030, spending about $6 billion on technical development and $18 billion on infrastructure. In the first phase, the government carried out pilot projects to establish base cities for each of the seven major metropolitan areas ending in 2016, plans for the second and final stages are to build a smart grid covering all of the metropolitan areas by 2021 and extend it nationwide by 2030. Specifically, the plan is to complete consumer intelligence by 2020 and a nationwide intelligent power grid by 2030.
## 2 Pull-Out Test
### Fatigue Loading  
The material and component dynamic fatigue testing machine (landmark370.25) of American MTS Company was used to fatigue load the test piece. The upper clip of the fatigue testing machine clamped the top of the free end of the planting bar, and the lower clip clamped a T-shaped steel plate which served as a test piece placement platform upon which to affix the test piece to the T-shaped steel plate.  
At present, there is no unified standard for the fatigue loading system of single rebar planting. As shown in Figure 4, the fatigue loading of the rebar planting specimen was achieved by affecting the free end of the rebar. The reciprocating load of axial pull-out was transmitted from the free end of the rebar to the embedded hole, which affected the anchorage performance of the rebar planting system. However, as a kind of connection and anchorage method, both ends of planted rebar should be in a constrained state in practical application, which is different from the boundary conditions of the single rebar planting specimen. Therefore, the influence of the free-end rebar on the performance needed to be reduced as much as possible during the fatigue loading of the single rebar planting specimens, so as to achieve test results in line with actual working conditions. The research shows that the fatigue loading frequency has no obvious effect on the performance of steel bars at 3\(\sim\)10 Hz [35]. [PERSON] et al. adopted a fatigue loading system of 0.2\(\sim\)0.45 \(P_{u}\) (\(P_{u}\) is the ultimate drawing load) for 20 single rebar planting specimens with a diameter of 16\(\sim\)25 mm [28]. Referring to the fatigue loading system of the above similar tests and the actual situation of this test, the 6 mm and 8 mm single rebar planting specimens were fatigue loaded with a sine wave load with an average load of 0.25 \(P_{u}\), a loading amplitude of 1 kN and a frequency of 2 Hz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Diameter (mm)**} & **Average Yield** & **Average Yield** & **Average Ultimate** & **Mean Ultimate** \\  & **Tension (kN)** & **Strength (MPa)** & **Tension (kN)** & **Strength (MPa)** \\ \hline
6 & 12.08 & 427 & 17.35 & 614 \\
8 & 22.22 & 455 & 30.33 & 603 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured values of rebar strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test Items**} & **Standard** & **Performance** & **Standards** \\  & **Requirements** & **Data** & **Standards** \\ \hline Splitting Tensile Strength & \(\geq\)8.5 MPa & 12.5 MPa & GB 50728-2011 \\ \hline Bending Strength & \(\geq\)50 MPa & 67.2 MPa & GB/T 2567-2008 \\ \hline Compressive Strength & \(\geq\)60 MPa & 87.3 MPa & GB/T 2567-2008 \\ \hline Bond Strength Between & & & \\ Rebar and Concrete under & C60, C25, L = 125 mm & \(\geq\)17 MPa & 26.4 MPa & GB 50728-2011 \\ Constraint Conditions & & & \\ \hline Heat Distortion Temperature (HDT) & \(\geq\)60 \({}^{\circ}\)C & 66.1 \({}^{\circ}\)C & ISO 75-2: 2003 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured values of planting adhesive.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 5 Performance Analysis  
Several key factors, including optimum system architecture, economic issues, wireless issues, and environmental issues, are highlighted in this section. The HOMER is chosen to analyze the proposed hybrid supply system's optimal design and technical requirements for on-grid situations to minimize the cost of electricity and greenhouse gas emissions over 20 years. Using MATLAB-based Monte Carlo simulations, the wireless network was fully assessed. Throughput and energy efficiency measures were considered as performance metrics.
# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 3 Methodology
### Data Collection and Data Processing
#### 3.1.3. Geo-Coding  
For spatial data analysis, geocoding plays a vital role. It assigns the respective latitude and longitude to the location for better visualization and better prediction of crime patterns. There exist many techniques of geo-coding in Python such as geocoder, geo-py, etc. We used the geo-py 2.0.0 module along with the Pandas module of Python for geo-coding because it is the best tool to deal with columns and rows while geo-coding [29]. The flowchart given below shows the whole information extraction process of news in Figure 1.  
Figure 1: Flowchart of information extraction process with example.
## 4 Discussion
### Practical Applications  
The present findings provide quantitative insights into the biomechanical loading experienced during Latin dance, which could guide programing to harness its potential osteogenic and neuromuscular benefits for mitigating age-related decline, like sarcopenia and fall risk, in older adults. The observed distal-to-proximal gradient of impact magnitudes, with the highest loads on the knees and ankles, but remaining predominantly low to moderate (\(\leq\)4 g), suggests that Latin dance could strengthen lower extremity bones and muscles without excessively overloading the joints or soft tissues.  
For practical implementation, dance instructors can strategically manipulate music characteristics, like tempo and genre, to modulate the session intensity according to the older participants' fitness levels and desired training stimuli. Programing faster, rhythmic styles, like salsa and cumba, interspersed with occasional slower genres, like rumba and bolero, can offer a high exertion variability within a single session. The use of wearable inertial sensor technology enables the practitioners to objectively monitor external workloads, which could inform personalized programing and recovery prescription. For instance, a higher sustained ankle load could signify an appropriate osteogenic stimulus for some, but potentially excessive strain for others struggling with choreography or balance. An understanding of normative multi-joint impact profiles could facilitate exercise modification for safe yet effective person-centered training.  
Alongside Latin dance's documented cognitive and social enrichment benefits, its dynamic, multi-planar choreography purposefully challenges older adults' musculoskeletal and neuromuscular systems in ways known to enhance strength, balance, and physical function. Continued research efforts to establish dose-response relationships and delineate acute versus chronic workload effects in larger older cohorts will enable the development of definitive guidelines. Such evidence could position Latin dance as a sustainable, lifelong training modality to empower healthy, active aging through integrating the physical and psychosocial exercise domains.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 3 Methodology
### RL Controller Architecture  
The neural RL controller is an actor-critic style architecture, as shown in Figure 3. Given the sequential images \([\mathbf{O}_{t-2},\mathbf{O}_{t-1},\mathbf{O}_{t}]\) as observations, the actor network and the critic network share the perception layer, which consists of the spatial and temporal feature encoders. This shared perception layer is designed to extract the spatial and temporal features of the dynamic target. The rest of the actor network and the critic network calculate the continuous normalized desired speed commands \([\bar{V}_{fb},\bar{V}_{gaw}]\) and the estimated value of the observation, respectively.  
This end-to-end perception and control method maps the high-dimensional raw images to the high-level actions directly. The mapping is learned via deep RL in the virtual environment, which saves the effort typically required for ground-truth labeling for perception learning and parameter tuning for simultaneous perception and control. The layers with a specific function improve the interpretability of the RL controller.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 4 Discussion
### Future Works  
For improvement of the energy yield prediction of VIPV, the following works are to be done.  
1. Improvement of the shading model. The current model is useful but too simplified, especially in the urban area. Note that more than 45\({}^{\circ}\) of the average shading height is not allowed, because the maximum height becomes more than 90\({}^{\circ}\). Even in the area of Miyazaki, the average height of 15.5\({}^{\circ}\) means that the maximum height should be 31\({}^{\circ}\). However, there are many buildings of more than 31\({}^{\circ}\). Possibly, we need to develop a curved trend, namely the two parameters model.
2. Modeling of partial shading validated to the measured data. To do this, we need to start monitoring the partial shading on the car roof and car body.
3. Validation of energy yield using the real curved PV module.
4. Validation of the shading model by several areas with different shading height and shading density.
5. Development of the spectrum model for predicting energy yield by multi-junction solar cells on the car roof and car body.
## 2 State of the Art  
A large number of energy-efficient MAC protocols have been proposed for wireless sensor networks to reduce energy consumption by avoiding the essential sources of energy consumption at the MAC level, which are: _overhearing_ that is the reception of unwanted traffic, _collisions_ that is the simultaneous reception of more than one frame, _idle listening_ that is being active without neither receiving nor transmitting, and _overhead_ that is the exchange of non application related data. These MAC protocols can be categorized into three broad families: contention-based protocols using a CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) algorithm that is based on the IEEE 802.11 CSMA/CA [1], TDMA-based (Time Division Medium Access-based) protocols where time slots are assigned for collision free data communication, and hybrid protocols that try to benefit from the advantages of both. In what follows we present a quick overview of some existing protocols and we focus on the different slot assignment algorithms.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Model Development
#### 3.7.4 Scope of Predictions  
It is important to clarify that all model development and validation in this study was conducted on historical data up to 2023. The predictions generated by these models are confined to the data within this period, meaning that the models are not yet used to project crime rates beyond the timeframe of the dataset. This design decision ensures that we are evaluating model accuracy against known outcomes (historical crime data), rather than attempting speculative future predictions. While the modeling framework is forward-compatible and could be adapted for true time-series forecasting (using past data to predict future crime rates), such an application lies outside the scope of the current phase. Future work will address this lack by extending the model to forecast crime trends in upcoming years.
###### Abstract  
The chemical content of essential oils (EO) obtained from the leaves of four Pinaceae (_Abies alba_, _Pica abies_, _Pinus cembra_ and _Pinus mugo_) was investigated by SPME-GC-MS technique. The vapor phase was characterized by the monoterpenes with values higher than 95.0%. Among them, \(\alpha\)-pinene (24.7-48.5%), limonene (17.2-33.1%) and \(\beta\)-mycrene (9.2-27.8%) were the most abundant. The monoterpenic fraction prevailed over the sesquiterpene one (\(\geq\)74.7%) in the EO liquid phase. Limonene was the major compound in _A. alba_ (30.4%), _P. abies_ (20.3%) and _P. mugo_ (78.5%), while \(\alpha\)-pinene in _P. cembra_ (36.2%). Regarding the phytotoxic properties, EOs were studied at different doses (2-100 \(\mu\)L) and concentrations (2-20/100 \(\mu\)L/mL). All EOs were found to be significantly active (\(p\)-value < 0.05) against the two recipient species in a dose-dependent way. In pre-emergence tests, germination of _Lolium multiform_ and _Sinupis alba_ was reduced by up to 62-66% and 65-82%, respectively, as well as their growth by up to 60-74% and 65-67%, due to the effects of compounds in both the vapor and liquid phases. In post-emergence conditions, at the highest concentration, the phytotoxicity of EOs caused heavy symptoms and, in the case of _S. alba_, _A. alba_ EO completely destroyed (100%) the treated seedlings.  
Abies alba; Pica abies; Pinus cembra; Pinus mugo; chemical volatile composition; SPME-GC-MS; allelopathy; weed management Article
## 2 Experimental Design and Setup
### Instrumentation and Data Sampling  
A three-component force sensor (FC3D80, produced by Forcechina Measurement Technology Co., Ltd., Shanghai, China) installed as shown in Figure 4b was used to measure the wave/wave-current forces on the pile model. The calibrated sensor had the following performance-related characteristics: \(F_{x}\) and \(F_{y}\) were in the range of \(\pm\)150 N at a resolution of 0.015 N, and \(F_{z}\) was in the range of \(\pm\)300 N at a resolution of 0.03 N, where \(F_{x}\) is the inline force in the direction of wave propagation, \(F_{y}\) is the transverse force perpendicular to the direction of wave propagation, and \(F_{z}\) is the vertical force.  
To investigate the wave propagation, five capacitance-type wave gauges (WG1-WG5) with a precision of 0.1 mm and a sampling rate of 200 Hz were arranged at different positions in the water flume, as shown in Figure 2a: WG1 was 4.3 m upstream of the pile model to measure the incident waves; WG2, WG3, and WG5 measured wave fluctuations around the model; WG4 measured waves 0.15 m from the side wall of the flume and at the horizontal position of the center of the model.  
As shown in Figure 2a, an acoustic Doppler velocimeter (ADV) [labeled CM1 in Figure 2a] with an accuracy of 0.5% and a sampling frequency of 100 Hz was used to measure the velocity of the current. The ADV, the three-component force sensor, and all the wave gauges were calibrated prior to the experiments.
## 5 Numerical Examples
### Buried Thrust Fault Model
Finally, a precise pre-processing integration method was applied to extract the virtual wavefield from the diffusion field, as shown in Figure 11. It is observed that, with the increase in square wave  
Figure 10: The \(\mathbf{b}_{2}\) with low-resistivity anomalies, with low-resistivity anomalies and relative anomaly for differential pulses: (**a-c**) differential pulses with pulse widths 0.4 ms; (**d–f**) differential pulses with pulse widths 2 ms; (**g–i**) differential pulses with pulse widths 5 ms.  
Figure 9: The \(\mathbf{b}_{2}\) with low-resistivity anomalies, with low-resistivity anomalies and relative anomaly for square waves: (**a-c**) square wave with pulse widths 0.4 ms; (**d–f**) square wave with pulse widths 2 ms; (**g–i**) square wave with pulse widths 5 ms.  
pulse width, there are no significant changes in the shallow subsurface features, while the deep subsurface features gradually intensify, though they remain insufficiently pronounced. Figure 12 shows the virtual wavefield corresponding to differential pulses with different pulse widths. Figure 12a,b clearly depict the interface features of the shallow ductile rock layer fold, but it is difficult to capture the interface information of the deep buried thrust fault. As the pulse width increases to 5 ms, Figure 12c reveals the interface information of the deeper strata. As previously mentioned, the virtual wavefield's phase axes can only intuitively reflect the morphology of the shallow strata and cannot accurately depict the characteristics of deeper formations. Therefore, migration imaging is required to correctly position these features spatially. Figure 13 presents the migration imaging results for square waves with different pulse widths. It can be observed that the geometric characteristics of the shallow subsurface  
Figure 11: Virtual wavefields for square waves with different pulse widths: (**a**) pulse width of 0.4 ms; (**b**) pulse width of 2 ms; (**c**) pulse width of 5 ms.  
Figure 12: Virtual wavefields for differential pulses with different pulse widths: (**a**) pulse width of 0.4 ms; (**b**) pulse width of 2 ms; (**c**) pulse width of 5 ms.  
layers show little change. As the pulse width of the square wave increases, the deep subsurface features are somewhat enhanced, but the stratigraphic interfaces remain difficult to clearly delineate. Figure 14 shows the migration imaging for differential pulses with different pulse widths. It can be observed that the migration imaging results for differential pulses with varying pulse widths effectively characterize the interface morphology of strata at different depths (Figure 14a-c). By stacking the imaging results from different differential pulses, a high-resolution imaging profile can be obtained (Figure 14d), where the red dashed line represents the theoretical model's electrical interface. However, due to effects such as volume effects, there are discrepancies between the migration imaging of the shallow fold and the theoretical model.
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 6 (\(t_{5}<t<t_{6}\), Figure 6f)  
At the time \(t=t_{5}\), the auxiliary switch \(S_{a1}\) is turned off. The primary voltage \(v_{p1}\) is \(-d_{m1}V_{in}/(1-d_{m1})\), so that the secondary voltage \(v_{s1}\) is also negative. At this time, the resonant current flows through the capacitor \(C_{ds1}\) to force it discharging, and the voltage \(v_{ds1}\) decreases. The equivalent circuit is as shown in Figure 6f. In this state, the voltage \(v_{ds1}\) and the leakage inductor current \(i_{Lr1}\) can be expressed as:  
\[v_{ds1}(t)\ =\ V_{in}-[V_{in}-v_{cc1}(t_{5})]\cos\omega_{1}(t-t_{5})+i_{Lr1}(t_{5} )Z_{1}\sin\ \omega_{1}(t-t_{5}), \tag{20}\]\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{5})\cos\omega_{1}(t-t_{5})+\frac{V_{in}-v_{cc1}(t_{5})}{Z_{1 }}\sin\ \omega_{1}(t-t_{5}), \tag{21}\]  
where \(\omega_{1}\ =\ \frac{1}{\sqrt{\zeta_{cd1}(L_{r1}+L_{ml})}}\), and \(Z_{1}\ =\ \sqrt{\frac{(L_{r1}+L_{ml})}{\zeta_{cd1}}}\). This state ends when the voltage \(v_{ds1}\) drops to \(V_{in}\), and the time it takes is as follows:  
\[t_{6}-t_{5}\ =\ \frac{1}{\omega_{1}}\tan^{-1}\left[\frac{V_{in}-v_{cc1}(t_{5})} {Z_{1}i_{Lr1}(t_{5})}\right]. \tag{22}\]
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## 4 RMS Delay Spread  
A wideband multipath channel can be described by its dispersion in the delay domain, which is usually expressed in terms of the RMS DS, \(\sigma_{\tau}\). The RMS DS is defined as:  
\[\sigma_{\tau}=\sqrt{\frac{\sum\limits_{k}P(\tau_{k})\tau_{k}^{2}}{\sum\limits_ {k}P(\tau_{k})}-\left(\frac{\sum\limits_{k}P(\tau_{k})\tau_{k}}{\sum\limits_{ k}P(\tau_{k})}\right)^{2}}. \tag{4}\]  
In Equation (4), \(\tau_{k}\) are the delay values and \(P_{k}\) the PDP values in linear units. Figure 9 shows the RMS DS for a threshold of 30 dB, using the full bandwidth.  
The RMS is observed to be increasing with distance, and as before, a simple 1-slope model can be derived:  
\[DS=DS_{0}+10n_{DS}\times\log_{10}(d)+X\sigma(\mathrm{ns}), \tag{5}\]  
where:  
\(DS_{0}\) (in ns) and \(n_{DS}\) are adjustment parameters;  
Figure 8: Measured K-factor.  
Figure 9: Full bandwidth root mean square of the delay spread (RMS DS) versus the Tx-Rx distance in logarithm units.  
Usually, when the RMS DS is measured in several positions, its mean and standard deviation are obtained because they are two of the main parameters of the RMS DS' statistical characterization. In Table 8, the mentioned parameters are shown for the WRC bands and they are compared with values found in the literature. To fairly compare the values of Table 8 the type of environment is also shown. As seen in Table 8, the mean and standard deviation of the RMS DS corresponding to large indoor environments, as large offices, industrial scenarios and auditoriums, are larger than the values found in a small open indoor environment, as the one measured in this work. This fact is due to the high number of large delay multipath components found in large rooms, whereas a fewer number of large delay components are present in smaller scenarios.  
The multipath effect produces significant values of RMS DS which is one of the main factors that limit the system bit rate. Traditionally, the performance of the wireless system can be improved thanks to equalization algorithms; furthermore, the use of directional antennas along with circular polarizations can reduce the delay spread as indicated in [34].