esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 4 Datasets
### RadioML2016.10a  
The signal data in the dataset include 11 modulation modes, namely, BPSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK and WBFM, in which 8 of them are digital modulation modes and 3 analog modulation modes. It contains 220,000 communication signal data after sampling. Each modulation mode has 2000 signal data. Each signal is sampled as vector data of \(2\times 128\), where 2 represents I and Q signal data and 128 represents 128 time nodes. All SNR ranges from \(-20\) to 18 dB and is included in the serial number and test tag. The number of samples per symbol parameter is a modulation characteristic that specifies the number of samples that represent each modulation symbol. The display parameters of the dataset are described in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **RadioML2016.04c** \\ \hline Number of modulation mode & 11 \\ \hline Number of digital modulation mode & 8 \\ \hline Number of analog modulation mode & 3 \\ \hline Modulation mode & 8 PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, \\  & PAM4, QAM16, QAM64, QPSK, WBFM \\ \hline Format of each sample & \(2\times 128\) \\ \hline Number of samples & 220,000 \\ \hline Samples per symbol & 8 \\ \hline SNR (dB) & \(-20\):2:18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Description of display parameters for Radio2016.04c dataset.  
This dataset is generated using GNU radio. The specific data generation method is divided into the following four steps. The first step is to select the source alphabet. The analog modulation uses a publicly available copy of serial episode 1. Digital modulation uses the ASCII code of [PERSON]'s entire Gutenberg work. In order to equalize the symbol and bit of the data, a whitening randomizer is used in digital modulation. The second step is modulation. In order to form a normalized symbol rate in all digital modulation, each symbol value has a normalized sample. Different modulation modes have different usage and transmission modes. The third step is channel simulation. The production of this set of radio signals mainly uses the block layer of GNU's wireless dynamic channel model. The fourth step is data storage. The data are standardized first. Based on the unit energy in each 128 sample data vector, each stored signal sample is scaled. Then, using numpy and cpickle, the data are stored as an n-dimensional vector. Next, the time period sampled from the analog output stream is stored in the output vector. The last step is to classify the signals by machine learning.
## 5 Interference Analysis
### NLOS-LAPs Interference  
The NLOS-LAPs interference at the recipient V-N is computed as \(L_{I_{X}}(s)\), wherein \(X\) is \(N\) and is derived by considering a similar procedure of (38) and is given as  
\[L_{I_{N}}(s)=\exp\left(-2\pi\lambda_{N}\int_{h_{U}}^{\infty} \mathfrak{f}\mathfrak{p}_{N}\bigg{(}\sqrt{t^{2}-h_{U}^{2}}\bigg{)}\Bigg{(}1- \Bigg{(}1+\frac{sP_{N}\eta_{N}^{-1}\mathcal{G}_{N,t}\mathcal{G}_{N,r}\|z_{ \mathbf{d}}-z_{N,t}\|^{-\alpha_{\mathbf{d}}}}{m_{N}}\Bigg{)}^{m_{N}}\Bigg{)} dt\right). \tag{39}\]
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 2 Methods
### Conducting the Review  
We chose to use Google Scholar rather than subscription platforms such as Web of Science for our systematic search. We did this to ensure our approach would be broadly reproducible and avoid access limitations. However, to further develop our database, we supplemented our systematic search within Google Scholar with a purposive search conducted with coffee mapping experts. We also expanded our search terms as we proceeded, reflecting initial limitations in gathering sufficient relevant literature.  
We began by searching Google Scholar for all articles since 2010 using the search terms in Table 2. We divided these terms into three categories and combined terms from each category into the search phrases entered in Google Scholar. The results of the searches were then reviewed manually by title to identify their relevance to our research aim. If the title proved relevant, we then reviewed the abstract to screen for additional articles that fulfilled the criteria listed above. Given the scarcity of resulting articles, we also pursued three additional approaches to supplement our article database. First, we systematically reviewed all resulting articles for additional references that matched our search criteria. Second, we expanded our search to included peer-reviewed publications, grey literature, and reports from the mid-2000s onward, as well as approaches for mapping cacao. We selected cacao based on its similarities to coffee vis-a-vis mapping challenges, the similarity of geographies and countries in which cacao and coffee are grown [54], and the diversity of smallholder production modalities [13]. Third, we presented our initial results at an expert workshop (described in Section 2.3) to gather feedback and additional references.  
Our Google Scholar search yielded 23 articles, while our purposive sampling approach produced 19 articles, 11 of which came from experts from the workshop. We also received one additional paper during the peer review process. Most articles were in English, though we did receive three studies in Portuguese and two studies in Spanish. We carefully and systematically reviewed each article to understand the mapping approach and implemented methods. As we reviewed the articles,we categorized the relevant information into three matrices (Tables S1-S3 in the Supplementary Materials) to capture the classification method, the validation method, and the information related to choosing the best method in future research. We then aggregated the information in the matrices to identify common themes, including the prevalence of various classification approaches, the number of studies that used certain sensors, or gaps in the literature.
## 2 Materials and Methods
### Dynamic Environment
#### 2.6.3 Relative Sunlight Angle  
The relative sunlight angle is crucial for accurately calculating the solar power output of the UAV. It determines the efficiency of solar energy capture by the UAV's panels, as the power output is directly influenced by how directly sunlight strikes the panels. A more perpendicular angle (closer to 0 degrees) between sunlight and the UAV surface maximizes energy absorption, while an acute or obtuse angle reduces it [51]. This calculation, incorporating the angle of incidence and later potential cloud cover reduction, allows for precise estimation of available power, critical for energy management and flight path optimization in solar-powered UAVs [13]. The relative angle between the sunlight direction and a UAV normal vector is calculated using the dot product:  
\[angle\ =\ \arccos\left(\frac{\sum\_direction\cdot\ uav\_normal}{\parallel\ \sum\_ direction\parallel\ \cdot\ \parallel\ uav\_normal\parallel}\right) \tag{26}\]
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
###### Abstract  
Researchers have explored multiple Wi-Fi features to estimate user locations in indoor environments in the past decade, such as Received Signal Strength Indication (RSSI), Channel State Information (CSI), Time of Arrival (TOA), and Angle of Arrive (AoA). Fine Time Measurement (FTM) is a protocol standardized by IEEE 802.11-2016, which can estimate the distance between the initiator and the station using Wi-Fi Round-Trip Time (RTT). Promoted by Google, such a protocol has been explored in many mobile localization algorithms, which can provide meter-level positioning accuracy between Wi-Fi RTT-enabled smartphones and access points (APs). However, previous studies have shown that the Wi-Fi RTT measurements are sensitive to environmental changes, which leads to significant errors in the localization algorithms. Such an error usually varies according to different environments and settings. Therefore, this paper investigates the error in Wi-Fi RTT distance measurements by setting multiple experiments with different hardware, motion status, and signal path loss conditions. The experiment results show that four categories of errors are found in RTT distance measurements, including hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. Comparison and analysis are carried out to illustrate the impact of the different errors on Wi-Fi RTT distance.  
indoor positioning; fine time measurement; round-trip time; Android; Google; smartphone; distance measurement +
Footnote †: journal: 2022, 15, 464.
## 3 Numerical Experiments  
### Numerical Experiments  
Six synthetic models were used to validate the proposed imaging method. For this purpose, we constructed an area with a height of 20 m and a width of 30 m (Figure 7) to simulate near-surface seismic exploration. The seismic data came from numerical simulations based on staggered-grid finite differences in the time domain [29,31]. A perfectly matched layer (PML) was used to absorb reflections on the left, right, and bottom boundaries of the model. The upper boundary of the model was a free boundary.  
According to the near-surface geological conditions, the background P-wave velocity was set as 1500 m/s, the S-wave velocity was set as 700 m/s, and the density was set as 1.8 g/cm\({}^{3}\). A 600 Hz Ricker wavelet was used as the source at the ground surface. Twenty shots were deployed with an interval of 1 m, and 151 receivers were evenly arranged at a 0.2 m intervals across the top of the model.  
The signal time length was 0.05 s. According to the size of the area and background velocity, the estimated P-wave travel time of a vertically down and up path was 0.026 s and the travel time associated with the far-right source, reflection at bottom left, and far-right receiver pair was 0.045 s (Figure 8). Therefore, the target P-wave signal integrity was guaranteed, which meant that a 0.05 s signal would be long enough to cover all the reflected objectives and the reflection of most part of the area could be received by multiple receivers.  
Figure 8: Illustration of travel time.  
Figure 7: Sketch map of the geological model.  
All parameters in the numerical experiments are shown in Table 1. Note that the sampling frequency was \(2\times 10^{5}\) which is consistent with the time step of the numerical simulation. In the numerical experiments, adapting a shorter recorded signal can help save computational resources. However, in real cases, the recorded signal can be longer in order to obtain more information.  
The receivers are velocity sensors that collect vibrations perpendicular to the ground surface. The geological models are produced by a random near-surface model generation algorithm (Figure 9). For example, horizontal parallel layers (1), folds (2), random fluctuations (3), faults (4) and caves (5) were incorporated into the models. More details on the model generation methods can be found in Appendix B.  
The structural details of each model are shown in Table 2. The models in group A contained cave-type geological anomalies. The models in group B were composed of multiple strata. The models in group C were combinations of cavities and multiple strata. The medium parameters are shown in Table 3.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.1.2 MODIS Remote Sensing Data  
In this study, six vegetation indices were employed, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), global vegetation moisture index (GVMI), normalized difference drought index (NDDI), and near-infrared reflectance for vegetation (NIR\({}_{\text{V}}\)). These vegetation indices were calculated using the reflectance bands from the moderate resolution imagingspectroradiometer (MODIS). The MODIS data used in this study correspond to the same years as the flux site data.  
These reflectance bands were retrieved from the MODIS MCD43A4 Version 6 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset ([[https://lpdaac.usgs.gov/products/mcd43a4v006/](https://lpdaac.usgs.gov/products/mcd43a4v006/)]([https://lpdaac.usgs.gov/products/mcd43a4v006/](https://lpdaac.usgs.gov/products/mcd43a4v006/)), accessed on 21 May 2021), which is generated daily using 16 days of Terra and Aqua MODIS data at a 500-m resolution. The retrieved data were processed using the following two steps to obtain daily records:  
1. Pixel values with snow or cloud cover were removed, i.e., only pixel values with a 'Reliability' band value of 0 or 1 were retained;
2. For unavailable daily data, gaps were filled by linearly interpolating the closed available data over time.
## 2 Background and Related Research
Thus, the insight they provide is rich for each individual research topic and at the same time limited to a specific AI application segment, which does not allow for the exploration of business opportunities arising from data exchange in overlapping segments, e.g., the use of forecasting models for predictive financial reporting, the effects of the deployment of digital assistants on decision-making in the organizational setting or the outcomes of the application of conversational agents in the early stages of employee onboarding. In addition, due to the fast pace of AI development and the surge of investments in AI research in recent years, some of the literature reviews conducted on sources up to the year 2020 no longer provide a complete view of the available technology in a specified segment. Three systematic literature reviews [3; 4; 52] will also be considered since they approach the literature investigation by incorporating a wide range of AI applications and thus mitigate the segmentation of the resulting insights. A comparison of [3; 4; 52] with the current systematic literature review is presented in Table 2. It is pertinent to discuss the limitations of previous systematic literature reviews. Highlighting these limitations is helpful to the research community, as well as to the authors of this review in presenting a more thorough view on the key factors affecting the adoption of AI in MIS. The literature review conducted by [PERSON] et al. [3] cited a total of 41 primary studies from a variety of journals and conferences; however, its focus was on specific AI interactions with organizational strategy, unlike that of the current literature study, which investigates the challenges and opportunities for AI adoption, platforms used, and contributions made by researchers when implementing AI in MIS. [PERSON] et al. [4] focused their review on the subject definition and business value identification of AI in information systems. Their study provides valuable insight into the concept and classification of AI tools in information systems; however, their journal selection process excluded some valuable literature sources, e.g., the QI journal _Knowledge-Based Systems_ focused on AI applications in MIS and the QI _Journal of Supply Chain Management_ focused on information systems research. The review conducted by [PERSON] and [PERSON] [38] is narrowly focused on the context of user interaction with AI tools and provides insights into the effects of such interactions; however, it is primarily focused on the outcomes in terms of user behavior rather than business value. The outlined limitations of previous systematic literature reviews allow for a knowledge gap to be recognized with respect to the identification of key factors affecting the process of AI adoption in MIS. The current literature review aims to minimize this knowledge gap by identifying some of these factors: challenges of AI adoption at the organizational level of automation, existing AI capabilities and platforms, business value, and contributions made by researchers in the field of AI adoption in MIS.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 3 The Crypto-Engine
### Security Analysis  
The security analysis of ECTAKS has been formally carried out in [47] with respect to an attacker that can compromise one of the nodes of the network and obtain full access to the node's secret information. According to typical approaches available in the literature, the security proof of the scheme is obtained _by reduction_, i.e., showing that an attacker to the scheme can be efficiently turned into a solver for a believed-intractable mathematical problem. Two attack scenarios have been considered: in the first one the attacker's goal is to determine the secret shared by other nodes in the network; in the second scenario the goal is to distinguish the shared secret from a random element. As an example, consider the minimal setting of Figure 4 and assume that the attacker gaining access to node 3 wants to attack the key exchange from node 1 to node 2.  
Figure 4: An ANT composed of 3 nodes (1, 2, 3). Node 3 is controlled by an attacker.  
It has been proven that there exists a polynomial-time reduction which turns an attacker able to recover the ECTAK into an attacker which solves the _Computational Diffie-Hellman Problem_ (_CDHP_) in the corresponding elliptic curve group. Similarly, it has been shown that there exists a polynomial-time reduction which turns an attacker able to distinguish a correctly generated ECTAK from a random element into an attacker which solves the _Decisional Diffie-Hellman Problem_ (_DDHP_). Provided that the scheme parameters are set in such a way that CDHP and DDHP are hard in the corresponding elliptic curve group, the previously mentioned results show that there exists no efficient attacker against the secret recovery and indistinguishability of ECTAKS. Therefore, in the considered attack scenario, this makes ECTAKS suitable for providing military-grade security when used along with a safe elliptic curve; see [64] and a secure symmetric-key block cipher (e.g., AES).
## 3 Model Verification
The maximum torque error obtained for the case of the clutch with 5.5 mm height magnets is 1.28%, and the mean absolute error is 0.99%. The maximum axial force error, obtained also for the case of the clutch with 5.5 mm height magnets, is 4.28%, and the mean absolute error is 3.1%. Structure parameters of the investigated coupling are listed in Table 3, modeling environment and the model details are specified in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Height of Motor Side Half-Clutch**} & \multicolumn{2}{c}{**Maximum Axial Force, N**} & \multirow{2}{*}{**Error,**} \\ \cline{2-3} \cline{5-4}
**Magnets, mm** & **Experiment** & **Simulation** & **\%** \\ \hline
5.0 & 51.12 & 52.97 & 3.62 \\
5.5 & 51.78 & 54.00 & 4.28 \\
6.0 & 53.81 & 54.50 & 1.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the maximum axial force values obtained experimentally and by the numerical simulation. Figure 5: Measurement results presenting the maximum axial force vs. height of motor side half-clutch magnets. \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Symbol**} & \multicolumn{1}{c}{**Quantity**} & \multicolumn{1}{c}{**Value**} \\ \hline \(R_{i}\) & Inner radius of the magnets & 10 mm \\ \(R_{o}\) & Outer radius of the magnets & 30 mm \\ \(R_{s}\) & Radius of the supports & 32 mm \\ \(R^{\prime}_{s}\) & Radius of the shrouding supports & 33.5 mm \\ \(h_{m}\) & Magnets thickness (symmetrical case) & 5 mm \\ \(h_{m1}\) & Magnets thickness of the first half-clutch & variable \\ \(h_{m2}\) & Magnets thickness of the second half-clutch & variable \\ \(\Delta h_{m}\) & Magnets thickness deviation & variable \\ \(h_{ag}\) & Air-gap length & variable \\ \(p\) & Number of pole-pairs & 6 \\ \(l_{adj}\) & Distance between adjacent magnets & 1 mm \\ \(B_{r}\) & Remanence of magnets & 1.3 T \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Structure parameters of the magnetic coupling. The same parameters of the clutch as listed in Table 3 and model settings shown in Table 4 were used in the further presented simulations.
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
###### Abstract  
Dedicated multi-project wafer (MPW) runs for photonic integrated circuits (PICs) from Si foundries mean that researchers and small-to-medium enterprises (SMEs) can now afford to design and fabricate Si photonic chips. While these bare Si-PICs are adequate for testing new device and circuit designs on a probe-station, they cannot be developed into prototype devices, or tested outside of the laboratory, without first packaging them into a durable module. Photonic packaging of PICs is significantly more challenging, and currently orders of magnitude more expensive, than electronic packaging, because it calls for robust micron-level alignment of optical components, precise real-time temperature control, and often a high degree of vertical and horizontal electrical integration. Photonic packaging is perhaps the most significant bottleneck in the development of commercially relevant integrated photonic devices. This article describes how the key optical, electrical, and thermal requirements of Si-PIC packaging can be met, and what further progress is needed before industrial scale-up can be achieved.  
Keywords:photonics packaging; silicon photonics; integrated optics; optoelectronics; photonic integrated circuits (PICs) +
Footnote †: journal: Applied Sciences
# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 3 Results
### Payback Period Analysis by Building Cluster  
Figure 5 presents the payback period for each retrofit intervention across the three building clusters. This visualization reveals substantial variations in the time required to recover initial investments, ranging from approximately 5.5 years (Smart Home Systems in Moderate-High-consumption buildings) to over 15 years (Window Replacement across all clusters). HVAC System Upgrades consistently demonstrate favorable payback periods  
Figure 5: Payback period for each retrofit intervention across the three building clusters.  
(8.0-10.6 years) across all clusters with High-consumption buildings showing the shortest recovery time (8.0 years).  
This can be attributed to their combination of relatively high energy use and moderately aged HVAC systems, creating an optimal intervention point. Smart Home Systems also show attractive payback periods, particularly for Moderate-High-consumption buildings (5.5 years), suggesting that digital control systems offer a cost-effective pathway for buildings with better baseline efficiency. Window Replacement interventions exhibit the longest payback periods (>15 years) across all clusters, reflecting their high initial cost relative to energy savings potential.