esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Signal Model  
Let us consider a uniform linear array with \(M\) elements that receives a total of \(V\) source signals from different directions as shown in Figure 2. The sources are assumed to be static points from which the signal energy is originated. For example, the received signals may be emission of electromagnetic energy from transmitters at different locations as in the case of passive RADAR systems (bistatic RADAR system). It is also assumed that the sources are in the far-field. For simplicity, the sources and antenna array are considered as co-planar. The received signals are modeled as:  
\[\mathbf{x}(t)=\mathbf{A}\mathbf{s}(t)+\mathbf{w}(t)=\sum_{v=1}^{V}\mathbf{a}( \theta_{v})s_{v}(t)+\mathbf{n}(t) \tag{1}\]  
In Equation (1), \(M\times 1\) vector \(\mathbf{x}(t)=[x_{1}(t),x_{2}(t),\dots,x_{M}(t)]^{T}\) contains the received signals, \(V\times 1\) sized vector \(\mathbf{s}(t)=[s_{1}(t),s_{2}(t),\dots,s_{V}(t)]^{T}\) contains \(V\) narrow band source signals, \(M\) elements of vector \(\mathbf{n}(t)\) are additive white Gaussian noise (AWGN). Similarly, \(M\times V\) matrix \(\mathbf{A}\) denotes a steering matrix which is modeled as:  
\[\mathbf{A}=[\mathbf{a}(\theta_{1}),\mathbf{a}(\theta_{2}),\dots,\mathbf{a}( \theta_{V})] \tag{2}\]  
The \(v\)-th column \(\mathbf{a}(\theta_{v})\) in the steering matrix \(\mathbf{A}\) is called a steering vector that corresponds to the signal \(s_{v}(t)\) arrived from \(\theta_{v}\) direction:  
\[\begin{split}\mathbf{a}(\theta_{v})=\left[1,e^{-2\pi\left(\frac{ d}{\lambda}\right)\cos(\theta_{v})},e^{-2\pi\left(\frac{M}{\lambda}\right)\cos( \theta_{v})},\dots\right.\\,e^{-2\pi\left(\frac{(M-1)d}{\lambda}\right)\cos(\theta_{v})} \right]\end{split} \tag{3}\]  
In Equation (3), the parameter \(\lambda\) represents the wavelength and parameter \(d\) denotes the distance between sensors. Similarly, the \(v\)-th source signal, i.e., \(s_{v}(t)\), is a frequency modulated (FM) signal, given as:  
\[s_{v}(t)=\alpha_{v}(t)e^{i\phi_{v}(t)}, \tag{4}\]  
where \(\alpha_{v}(t)\) and \(\phi_{v}(t)\) are the instantaneous amplitude and the instantaneous phase of \(s_{v}(t)\). The IF of \(s_{v}(t)\) is given as:  
\[f_{v}(t)=\frac{1}{2\pi}\frac{d\phi_{v}(t)}{dt} \tag{5}\]  
Figure 2: Uniform linear array receiving signal from multiple sources.
## 4 Discussion
### Educational Framework Evaluation  
To evaluate and improve the educational framework surrounding the haptic effects design tool, it is essential to consider its alignment with established learning theories.  
The Revised Bloom's Taxonomy categorizes educational goals and objectives from lower-order thinking skills, such as remembering, understanding. and applying, to higher-order skills, such as analyzing, evaluating, and creating [55]. The tool supports this taxonomy by enabling students to remember basic haptic concepts, understand their impact on perceived sensation and their relationship with user experience, apply their knowledge in the design process and practical development scenarios, analyze peer feedback during collaborative discussions, evaluate the effectiveness of different haptic designs and their use in various application scenarios, and create innovative haptic patterns that enhance immersion and user experience in interactive applications.  
[PERSON]'s Taxonomy of Significant Learning [56] complements this perspective by also emphasizing deep learning and higher-order thinking, while highlighting integration, the human dimension, and caring, elements that foster deeper personal and social transformation and enhance students' understanding and application of haptic design in real-world scenarios. In the context of integration, the proposed haptic feedback design tool enables designers to interlink haptic feedback design principles with other aspects of the interactive design, such as visual and audio feedback, interaction mechanics, and user experience. Caring is promoted by empowering designers to rapidly create more immersive and interactive applications and focus on the impact of haptic feedback on user engagement and overall experience. Through the human dimension, designers can reflect on how their haptic feedback designs influence user's behavior, emotions, thoughts, and interactions, as well as focus on understanding their needs. These components can guide students towards building responsibility and empathy, motivating them to create designs that are not only functional but also meaningful and inclusive for diverse populations, including individuals with impairments.  
The tool's practical and reflective nature aligns well with both Constructivist Learning Theory [57] and [PERSON]'s Experiential Learning Theory [58], as it emphasizes active experimentation and learning through hands-on experience. Users of this tool iteratively create, preview, and modify haptic effects, which enables them to engage in reflective observation and abstract conceptualization. This process allows them to construct an understanding of haptic design principles by directly interacting with sensory feedback.
## 1 Introduction
To solve this problem, this paper proposes a computationally effective SFO estimation scheme for the OFDM-based HomePlug GP system, which does not require a priori knowledge on pilot symbols nor channel side information (CSI). The proposed estimation scheme exploits a repetition structure inherent in the HomePlug GP signal using repeat coding, thus removing the use for decided data or dedicated pilots. To accommodate to the tone mask requirement, the proposed frequency-offset estimation scheme considers the concept of carrier grouping together. The mean square error (MSE) is theoretically derived to verify the feasibility of the proposed estimation scheme. We demonstrate from simulation results that the proposed estimation scheme with repeat coding and subcarrier grouping achieves accurate synchronization and relieves the computational burden. The outline of the paper is as follows. Section 2 presents the signal model in the baseband PLC system using ROBO scheme. In Section 3, we briefly introduce the existing SFO estimation algorithms, having some limitations when applied to the HomePlug system. In Section 4, a complexity effective SFO estimation scheme is proposed using inherent redundant information in the HomePlug GP signal. Section 5 presents the simulation results proving the effectiveness of the proposed SFO estimation scheme. Section 6 concludes this paper.
## 3 Mobile Communications Background
### Evolution of Mobile Technology
#### 3.1.3 Third Generation (3G)  
The 3G was introduced in 2001 to standardize the network protocol that manufacturers use. The standardization of the \"data packets\" that drive web connectivity allows users of 3G technology to access data from anywhere in the globe. The popularity of new services such as Voice over Internet Protocol (VoIP) and video streaming has increased as a result of 3G's better data transmission capabilities. A vast coverage area of 384 kbps and a narrow coverage area of 2 Mbps were the data speeds that 3G systems were intended to provide [41; 43].
# Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership
## 2 Literature Review  
### Understanding Ride-Hailing Ridership with Different Data Sources  
The literature has attempted to understand ride-hailing ridership patterns and their influencing factors based on various kinds of data [5; 13; 14], among which questionnaire survey is one of the most common data sources. For instance, [PERSON] and [PERSON] [14] explored the factors of ride-hailing trip frequency based on the National Household Travel Survey. The findings show that younger and well-educated people tend to use ride-hailing services more frequently. [PERSON] et al. [13] found that temperature and the built environment are also determinants of ride-hailing ridership, except for socio-economics, based on survey data from Chicago. Apart from the above analyses based on large-scale survey data, some researchers have explored ride-hailing ridership patterns based on surveys of ride-hailing users [13; 15]. Using two separate survey datasets for ride-hailing and taxi users, [PERSON] et al. [5] demonstrated significant differences in user characteristics and wait time between the two categories of users. [PERSON] et al. [15] surveyed ride-hailing users through the ride-hailing platform and demonstrated the impact of ride-hailing usage on travel behavior. In another example, an online questionnaire survey was conducted for Uber users, and the impact of ride-hailing usage on vehicle kilometers traveled (VKT) was investigated [16]. The results showed that ride-hailing usage caused increases in VKT.  
Apart from survey data, emerging big data (e.g., order data) provide a different perspective for understanding ride-hailing ridership patterns. Although it is widely believed that emerging big data contain more activity information, TNCs are not willing to share their data [17]. Aiming to better understand ride-hailing ridership patterns, some companies have released a part of their data in recent years. Based on operation data from Didi, [PERSON] [17] used multi-day ride-hailing order data to understand spatiotemporal mobility patterns from both the regional and driver's perspectives. [PERSON] et al. [18] analyzed the spatiotemporal variations in ride-hailing demand in Haikou. Using the ride-hailing order data collected in Chengdu, [PERSON] et al. [19] identified areas with high travel intensity and further explored the relationship between points of interest (POIs) and the number of ride-hailing trip orders. However, most prior studies analyze ride-hailing demand based on order data from one specific TNC, which cannot reflect the real demand for ride-hailing due to the state of multi-company competition. Moreover, the literature usually neglects the role of taxi ridership in affecting ride-hailing ridership.
## 5 Solution and Analysis of Examples
### The Performance of Travel Distance Fair Strategy  
The K-means clustering result was redistributed by travel distance fair strategy based on the improved ant colony algorithm to balance the travel distance covered by each rider. The clustering result by constraining the travel distance difference within 0.02 was presented in Table 5 and Figure 10, where 0.02 represents the relative threshold for differences in path lengths between riders. The 0.02 tolerance (2% route difference) aligns with real-world delivery standards. As can be observed from Figure 11, compared with the clustering result of the K-means algorithm in Figure 6, four nodes in cluster 1 and one node in cluster 4, which are close to center 3, have been redistributed to cluster 3.  
To balance the travel distance of different riders, researchers optimized the route to traverse all nodes in the cluster using the improved ant colony algorithm after executing the travel distance fairness strategy. The travel distance fairness strategy would be executed several times until the optimal route travel distance difference satisfies the constraint requirement. The final optimal route is illustrated in Figure 9. As can be noticed from Figure 9b. the K-means algorithm clustering result was redistributed several times to adapt the travel distance difference.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & **Number of Nodes** & **Center Location** & **Optimal Route Length (km)** &
\begin{tabular}{c} **Standard Deviation of the Route Lengths (km)** \\ \end{tabular} \\ \hline
1 & 20 & (126.651025, 45.732542) & 11.23 & 0.0052 \\
2 & 20 & (126.622107, 45.725252) & 13.47 & 0.0035 \\
3 & 20 & (126.649186, 45.713749) & 11.71 & 0.0047 \\
4 & 20 & (126.672893, 45.716256) & 14.80 & 0.0044 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The clustering result redistributed by order quantity fair strategy.  
Figure 10: The clusters redistributed by order quantity fair strategy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & **Number of Nodes** & **Center Location** & **Optimal Route Length (km)** &
\begin{tabular}{c} **Standard Deviation of the Route Lengths (km)** \\ \end{tabular} \\ \hline
1 & 16 & (126.652989, 45.733311) & 11.23 & 0.0042 \\
2 & 23 & (126.623411, 45.725812) & 13.48 & 0.0038 \\
3 & 21 & (126.650481, 45.714486) & 11.24 & 0.0037 \\
4 & 20 & (126.672893, 45.716256) & 14.57 & 0.0044 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The clustering result redistributed by travel distance fair strategy.
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 3 Foundation Model-Enhanced Coastal Land Cover Recognition
### Data Augmentation  
Data augmentation is an important strategy for enhancing the robustness and transferability of a foundation model regarding feature extraction and recognition [26; 27]. However, some previously reported data augmentation methods might not be helpful for coastal land cover recognition. Considering the characteristics of coastal land covers in the optical satellite image, this paper applies six data augmentation methods: rotation, flip, scale, contrast, brightness, and cloud and fog noises, the details of which are listed in Table 1.  
References [27; 28] reported the impacts of rotation and scale variation on visual recognition. Moreover, scale is the fundamental attribute in a remote-sensing data analysis. Thus, this paper generated new training samples through rotating, flipping, and scaling the original image. Atmospheric conditions and the long distance between ground surfaces and satellite sensors always lead to some losses in the electromagnetic energy obtained by sensors. The loss of electromagnetic energy corresponds to several abnormal states in the contrast, bright degree, and intensity distribution. Thus, this paper generated new images by modifying the brightness and changing the contrast of each original image. Finally, to raise the robustness of a deep learning model to deal with noises, pepper noises, Gaussian  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Augmentation** & **Methods** & **Demo** \\ \hline Rotation & Generating 36 new images through rotating the original image every 10 degrees. & \\ \hline Flip & Generating 2 new images through flipping the original image over the horizontal dimension and vertical dimension. & \\ \hline Scale & Generating 4 new images through scaling the original image by 1:4, 1:2, 2:1, and 4:1. & \\ \hline Contrast & Generating 4 new images through modifying the original image with separate weighting parameters of 0.2, 0.4, 0.6, and 0.8. & \\ \hline Brightness & Generating 4 new images through randomly modifying the brightness of the original image. & \\ \hline Cloud and fog noises & Generating 4 new images through randomly adding different cloud noises to the original image. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lists of data augmentation techniques used in this manuscript.  
noises, and mosaics are commonly used for data augmentation. However, these noises are rarely observed in the rectified optical satellite images sent to end-users; rather, cloud and fog are two major types of noises possibly shown in the optical satellite image. Thus, this paper randomly added clouds and fog into the original optical satellite image rather than signal noises and mosaics.
## 1 Introduction
### Thesis Statement and Research Questions  
The following hypothesis and research questions were derived from the problem described in the problem statement.  
Hypothesis: Web crawling and natural language processing technologies can make the search for and basic understanding of domain specific technology more efficient and effective than the use of popular online search engines.  
Independently of the results regarding our hypothesis, the following research questions will be answered to support a potential falsification or acceptance of the hypothesis.  
RQ1--Feasibility: Is it possible to build a prototype of a technology recommender system based on natural language processing and web crawling technology using programs in Python?  
RQ2--Effectivity: Can such a technology recommender system yield satisfactory results in terms of the quality of recommendations? Quality means that the recommendations are easy to understand and suited to the selected application field.  
RQ3--Efficiency: Is the quantity of results satisfactory? By \"satisfactory\" we mean that the recommender system delivers at least the same number of results as a manual web search including a suitable summarization but in a more time-efficient manner?
# The Impact of Automated Vehicles on Road and Intersection Capacity
## 2 Methods
### Analysis of Intersection Capacity  
In order to calculate the traffic capacity of intersections in mixed environments and analyze the impacts made by autonomous vehicles in different proportions on signal-controlled intersections, it is necessary to obtain the basic saturation flow rate of the road, and then obtain the revised saturation flow rate, by converting the basic saturation flow rate, and finally calculate the traffic capacity.  
In order to calculate the basic saturated flow rate of a road in a mixed environment, that is, the maximum traffic volume passing through a certain section of the road, it is necessary to rely on the flow-density relationships in the basic traffic flow diagram model, the following model of artificial and autonomous vehicles, and the equilibrium analysis of traffic flow.
# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 4 Experimental Verification and Analysis
### Optimization Path Planning for Multi-View Recognition  
In order to test the performance of the proposed multi-view target optimal view path planning method in barrier free environment, this section takes the medicine box as the recognition object and generates the training set and verification set. Different starting points and different numbers of view angles are set to verify.
## 4 Materials and Methods
### Berry Titrable Acidity, Malic Acid, Anthocyanins, and Volatile Measurements
Frozen whole berries (25-50 g) were destemmed and maceratedfor one min using a chilled 250 mL stainless steel Waring blender. Berry maccates (5 g per vial, performed in duplicate) were transferred to 20 mL amber SPME vials (Sigma-Aldrich, St Louis, MO, USA) along with 3 g of NaCl, 5 mL 0.1 M pH 7.0 phosphate buffer, and 50 \(\upmu\)L internal standard cocktail (initial concentrations prior to dilution = 118 mg/L d12-hexanal (CDN Isotopes, >98% purity; 99.1% isotopic purity), 8 mg/L d2-(E)-2-hexanal (Sigma-Aldrich, St. Louis, MO, USA), >90% purity; >99% isotopic purity), 9 ug/L d3-IPMP (aromaLAB), >98% purity; >99% isotopic purity). Identification of native compounds was performed with commercially purchased hexanal (Sigma-Aldrich, \(\geq\)97% purity), (E)-2-hexanal (Sigma-Aldrich, \(\geq\)95% purity), and 2-isopropyl-3-methoxypyrazine (IPMP) (Sigma-Aldrich, 97% purity). HS SPME analyses were performed using a 1 cm, 50/30 \(\upmu\)m divinylbenzene-carboxoxen-polydimethylsiloxane (DVB/CARB/PDMS; Supleco, Bellafonte, PA, USA) with a pre-extraction incubation temperature of 60 \({}^{\circ}\)C for 15 min followed by a 15 min HS-SPME extraction. The SPME fiber was desorbed for 3 min in a split/splitless injector in splitless mode at a constant temperature of 230 \({}^{\circ}\)C, a purge time of 3 min and a purge flow of 50 mL/min. The GC column was a Varian Factor Four VF-WAXms (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) (Varian, Palo Alto, CA, USA) with helium as a carrier gas at a flow rate of 0.76 mL/min. The GC oven was held for 5 min at 40 \({}^{\circ}\)C, then ramped to 195 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, then ramped to 240 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min and held for 5 min. The MS was operated in EI mode with an ionization energy of 70 eV. MS data were collected from _m_/_z_ 25-250. Data processing was performed using Shimadzu GCMS Solutions Post-Run Analysis software. Concentrations of each volatile were calculated by determining the peak area ratio of the native analyte to its respective deuterated standard, and assuming that the response factor for the native compound was identical to its deuterated analogue. The quantifier ions as follows: for (E)-2-hexanal, _m_/_z_ 83; for d2-(E)-2-hexanal, _m_/_z_ 85; hexanal, _m_/_z_ 72; for d12-Hexanal, _m_/_z_ 79; for IPMP, _m_/_z_ 152; and for d3-IPMP, _m_/_z_ 155. To confirm selectivity, the ratios of the quantifier ion to the next two major ions (qualifier ions) were compared against the ratios for the authentic standards. To evaluate repeatability, selected samples (_n_ = 5) were run in duplicate, and %RSD values < 30% were observed.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
###### Abstract  
Nano-modified concrete has attracted wide attention due to its improved mechanical properties. Among them, compressive strength is the most critical indicator. However, testing nano-concrete is costly and complex because it requires control over many factors, such as nanoparticle content and dispersion. Machine learning offers a data-driven way to predict compressive strength more efficiently. It reduces trial-and-error efforts and supports mix design optimization. Currently, machine learning is more adept at handling complicated datasets than experimental and traditional statistical models. In this article, the development of machine learning research in predicting the strength of concrete enhanced by nanoparticles is reviewed. First, we systematically outline a three-phase ML framework encompassing data curation, model development, and validation protocols; next, popular algorithms and their uses in predicting the strength of nano-modified concrete are evaluated, such as Artificial Neural Networks, K-Nearest Neighbor, Random Forest, etc. Ultimately, the article offers a forward-looking perspective on how future machine learning advancements can foster and accelerate the development of nano-modified concrete.  
Keywords:concrete; machine learning; algorithm; nanoparticles; prediction