esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 2 Proposed Method
### Compatibility Analysis of 5G and BeiDou B1 Signal
#### 2.1.1 BeiDou B1 Signal System
BOC modulates the data component, and the quadrature multiplexed binary offset carrier (QMBOC) modulates the pilot frequency component. The B1C signal structure is shown in Table 3.  
The complex envelope of the BeiDou B1C signal is generated by combining the data component \(S_{BIC-data}\) and the pilot frequency component \(S_{BIC-pilot}\) according to the power ratio of 1:3. In terms of IQ modulation in communication, the complex envelope expression of the B1C signal is:  
\[S_{BIC}(t)=S_{BIC-data}(t)+jS_{BIC-pilot}(t) \tag{2}\]  
\[S_{BIC-data}(t)=\frac{1}{2}D_{BIC-data}(t)\cdot C_{BIC-data}(t)\cdot SC_{BIC -data}(t) \tag{3}\]  
\[S_{BIC-pilot}(t)=\frac{\sqrt{3}}{2}C_{BIC-pilot}(t)\cdot SC_{BIC-pilot}(t) \tag{4}\]  
where \(D_{BIC-data}\) is the data component message data, \(C_{BIC-data}\) is the PRN code of the data component, and \(C_{BIC-pilot}\) is the PRN code of the pilot frequency component. The B1C data component is modulated by the binary offset carrier (BOC), a modulation method based on the original BPSK-R with a square wave as a subcarrier. \(SC_{BIC-data}\) is the subcarrier of the data component, and its expression is:  
\[SC_{BIC-data}(t)=sign(\sin(2\pi f_{sc-BIC-a}t)) \tag{5}\]  
where \(f_{sc-BIC-a}\) is 1.023 MHz. The B1C pilot frequency component is \(QMBOC(6,1,4/33)\) modulated by BOC(1,1) and BOC(6,1) distributed in a power ratio of 29:4 and modulated in two mutually orthogonal phases. Thus, the subcarrier expression of the pilot component \(SC_{B1C-pilot}\) is: \begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Modulation** & **Phase Relationship** & **Power Ratio** \\ \hline \(S_{B1C-data}(t)\) & SineBOC(1,1) & 0 & 1/4 \\ \(S_{B1C-pilot-a}(t)\) & QMBOC(6,1,4/33) & SineBOC(1,1) & 90 & 29/44 \\ \(S_{B1C-pilot-b}(t)\) & SineBOC(6,1) & 0 & 1/11 \\ \hline \end{tabular}
\end{table} 
Table 3: The B1C signal structure. Figure 2: Diagram of BeiDou signal distribution. \[SC_{B1C\_pilot}(t)=\sqrt{\frac{29}{33}}sign(\sin(2\pi f_{sc\_B1C\_a}t))-j\sqrt{ \frac{4}{33}}sign(\sin(2\pi f_{sc\_B1C\_b}t)) \tag{6}\]  
where \(f_{sc\_B1C\_b}\) is 6.138 MHz. Quadrature multiplexed binary offset carrier (QMBOC), which adopts the method of modulating the BOC(n,n) component and BOC(m,n) component in two orthogonal phases, is a relatively novel modulation method for navigation signals, which is different from both CBOC and TMBOC. The use of this modulation method in BeiDou signal design can ensure good compatibility and coexistence with GPS L1 and Galileo E1 signals at the same frequency point while avoiding patent disputes.
## 4 Conclusions  
The study of the results has led to the following conclusions:  
1. Moderate CBA content enhances durability, as a 30% CBA replacement reduces the Cantabro mass loss. However, a higher CBA content weakens the structure and increases the mass loss;
2. The resilient modulus of the CBA mixtures decreased as the content of CBA increased. The reduction was minimal, less than 10%, with up to 20% CBA replacement. An increased CBA content resulted in a reduced modulus, which decreased the elastic stiffness of the mixture;
3. The permanent deformation increased up to 50% CBA replacement. This characteristic of CBA enhances interparticle friction, stabilizing the mixture and diminishing the strain under sustained loading, resulting in improved permanent deformation resistance;
4. CBA improved the tensile strength of asphalt mixtures in both wet and dry conditions. Up to 50% CBA satisfied the minimum TSR requirement of 80%, but higher CBA contents reduced the moisture resistance.  
Conceptualization, S.A.M.; methodology, S.A.M.; formal analysis, S.A.M. and S.K.; investigation, S.A.M., A.M.B. and F.H.K.; resources, S.K.; writing--original draft preparation, S.A.M.; writing--review and editing, S.A.M., S.K., M.H.M.I.; supervision, S.K.; funding acquisition, S.K., S.A.M., A.M.B., M.H.M.I. and F.H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data used in this research can be provided upon request.  
The authors would like to acknowledge the Universiti Malaysia Perlis and Universiti of Malaya.  
The authors declare no conflicts of interest.  
Figure 15: Tensile strength ratio (TSR) of CBA mixtures.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Hardware and Software Configuration  
The entire model training process was implemented by renting a \"Tianchi Cloud\" shared GPU server. The server's CPU is an Intel(R) Xeon(R) E5-2686 v4 @ 2.30 GHz with 60 GB of memory (Intel, Luoyang, China). The operating system used was Ubuntu 20.04, 64 bit, with Python 3.10, PyTorch 2.0.1, CUDA 11.8, and cuDNN 8 used for model training. A GPU (NVIDIA RTX A4000, NVIDIA, Santa Clara, CA, USA, 16 GB VRAM) was utilized to optimize the training speed. For experiments establishing the lawn working environment, a handheld depth camera and a laptop were used. The test model's frames per second (FPS) value was evaluated using an NVIDIA GeForce RTX 3060 Laptop GPU (6 GB) (NVIDIA, Santa Clara, CA, USA) and an Intel Core 12 th i7-12700H CPU (Intel, Luoyang, China).  
Figure 11: Annotations for different types of images in the lawn dataset.  
Table 2 provides detailed parameters for model training, such as the maximum iterations, learning rate, and the number of classes.
# Future of IoT Networks: A Survey
## Future IoT Network
### IoT Gateway
#### 5.6.3 Mobility Control  
Mobility is already an important issue in legacy networking and communications. Various devices including cell phones have attributes of mobility, and many studies have proposed mobility control in both the cellular network and IP network. The cellular network, which is proposed to provide plenary communication anywhere, solves some critical problems by reporting its location to the mobility management server. Nonetheless, a cellular network is based on mobility control, which is possible only with an enormous number of servers and extra-large infrastructure. In addition, there is a problem that cellular-based devices consume significant battery resources to maintain connectivity.  
Figure 6: The concept of ID-based routing.  
In terms of IP networking, some solutions [107; 108] such as mobile IP, have been proposed to support mobility. These solutions are difficult to adopt because of message authentication, connection delay, and the complicated management of IP. Thus, mobility control in an IP network is performed only when the client attempts to reconnect when the connection is broken. Although it is a very simple solution, seamless handover and connectivity are not guaranteed. Further, this may lead to degradation of network performance and Quality Of Service (QoS) in the end.  
Therefore, a novel concept of mobility support is required. Moreover, it is advantageous to support mobility control by the network management object in terms of reliability instead of constructing a mass server or new infrastructure such as a cellular network. Because the device normally maintains continuity when it moves except during power-off, the nearby network management object can control the mobile devices with ease. For example, when a mobile device identifies that it is moved, it sends the mobility message to its network management object. The network management object then forwards the server's data to nearby objects, and the mobile device can receive the data seamlessly.
# Pathway to Carbon Neutrality in the Cement Industry: CO2
## 4 Co\({}_{2}\) Capture of RAs
### Recycled Concrete Aggregates from Recycling Plants  
The XRD diffractograms of RCAs collected from recycling plants are presented in Figure 3. Due to the higher amount of cement fraction, the presence of portlandite is evident in these RCAs. The other main mineralogical compounds identified are calcite, quartz, microcline, ettrigite, gypsum, muscovite, hydrocalumite, kaolinite and albite. In general, the RCA is mostly composed of calcite and quartz, as well as gypsum, sanidine and albite [69], which are not likely to carbonate. However, [PERSON] et al. [70] found unhydrated cement remaining in RCAs and also MRAs, which are prone to carbonate. Another research [71] also characterized RCAs through XRD and verified that, besides the presence of carbonation products, such as calcite, aragonite and vaterite, unhydrated cement compounds, such as ettringite and calcium hydroxide, were not yet carbonated. Therefore, it shows that recycled aggregates contain compounds that can carbonate.  
The evolution of the CO\({}_{2}\) content of the RCAs at different carbonation periods (3 h, 5 h, 12 h, 24 h) is presented in Figure 4. It was observed that the maximum CO\({}_{2}\) capture by RCAs occurred at 12 h of carbonation.  
The maximum CO\({}_{2}\) captured was 1.0%, 0.6%, 0.9% and 4.1%, for RCA-RP1, RCA-RP2, RCA-IS1 and RCA-IW1, respectively. The uptake of RCA-IW1 stands out from the other RCAs, indicating a higher carbonation potential, due to its higher cement content and not having been subject to previous natural carbonation.  
As in MRAs, in RCAs, only part of the Rc fraction contributes to carbonation. Considering that Rc is 57.8%, 89.0%, 62.1% and 90.4% of RCA-RP1, RCA-RP2, RCA-IS1 and RCA-IW1, respectively, and taking into account that only 11%-16% of Rc contributes to carbonation, these aggregates can capture between 5.2% and 34.8% of CO\({}_{2}\), per fraction of cement paste. This corresponds to an absorption between 52 kg and 348 kg of CO\({}_{2}\) per tonne of cement paste.  
Figure 3: XRD pattern of uncarbonated RCAs: RCA-RP1, RCA-RP2, RCA-IS1 and RCA-IW1. Notation: M—Muscovite; E—Ettring; Hy—Hydrocalumite; G—Gypsum; K—Kaolinite; Fk—Microcline; P—Portlandite; Q—Quartz; Fa—Albite; C—Calcite.  
Other studies also quantified the CO\({}_{2}\) capture of RCAs. [PERSON] and [PERSON] (2019), reported that RCAs from recycling plants could capture approximately 11 kg of CO\({}_{2}\) by 1 tonne of aggregate. In another study, RCAs from recycling plants showed a CO\({}_{2}\) uptake capacity of 50 kg per tonne of aggregate ([PERSON] et al., 2019). [PERSON] et al. (2019) state values of CO\({}_{2}\) uptake up to 56% for smaller particles with larger specific surfaces. Such high values of CO\({}_{2}\) uptake may be due to carrying out forced carbonation of RCAs under 100% of CO\({}_{2}\). [PERSON] et al. (2019) also quantified the CO\({}_{2}\) uptake of RCAs from recycling plants. On average, the RCAs captured 5.2 kg of CO\({}_{2}\) per tonne of aggregate.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Disruption Identification  
A disruption of the system resilience, also known as a potential threat, can be beneficial for the improvement of the resilience level of the system. Only if a disruption is identified effectively can the resilience be quantitatively measured; however, the disruptions are different from risks and causal factors contributing to accidents. Actually, only part of a disruption is able to ultimately cause accidents, with evolution to risk and causal factors. Therefore, it is difficult to identify all disruptions practically. In the present study, the disruptions identified from safety meetings, accident reports, interviews, and literature reviews are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sea** & **March (Million km2)** & **September (Million km2)** & **Seasonal Changes** \\ \hline Barents Sea & 0.855 & 0.128 & 85\% \\ Kara Sea & 0.830 & 0.266 & 68\% \\ Laptev Sea & 0.536 & 0.196 & 63\% \\ East Silerian Sea & 0.770 & 0.516 & 33\% \\ Chukchi Sea & 0.595 & 0.196 & 67\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average ice-covered waters in the marginal seas of the NSR region [57].  
Figure 5: The Arctic Northeast Route [11].  
As shown in Table 3, 15 kinds of disruptions were identified in the present study. Subsequently, a safety meeting was conducted. After consulting experts, such as experienced mariners, senior scholars, and safety managers associated with the Arctic NSR, all disruptions were finally divided into four groups: navigation services out of order (\(N=3\)), malfunction/failure of machinery (\(N=4\)), harsh nautical environment (\(N=5\)), and other disruptions (\(N=3\)). The classification results are presented in Figure 6. Furthermore, classifying all the disruption factors into different groups can facilitate the calculations for the CPT of the Bayesian network in Section 3.4.
# Pixel Interaction Model for Contrast Enhancement: Bridging Social Science and Image Processing
## 3 Proposed Method
### The Threshold Parameter \(u\)  
In this approach, the threshold parameter \(\mathrm{u}\) plays a pivotal role in defining the sensitivity to pixel intensity changes. It establishes the cutoff point for categorizing pixel intensities as similar or distinct. As a result, \(\mathrm{u}\) fine-tunes the algorithm's ability to respond to varying levels of image complexity, thereby shaping the level of detail and granularity in the final output. A higher or lower \(\mathrm{u}\) value directly affects how the algorithm distinguishes between subtle intensity differences.  
In this study, the value of \(\mathrm{u}\) used for the influence model depends on the distance \(\mathrm{d}\) between the pixels being compared. The value of \(\mathrm{u}\) is defined by the following function represented in Equation (5):  
\[\mathrm{u}=\left\{\left(\begin{bmatrix}2\cdot\left(\frac{\mathrm{d}}{255} \right)\end{bmatrix}^{2}\right)\cdot 70\quad\text{if $\mathrm{d}\leq 128$}\right. \tag{5}\]  
The behavior of the function presented in Figure 5 demonstrates how the value of \(\mathrm{u}\) changes in relation to the difference \(\mathrm{d}\) between pixel intensity values. Initially, for small values of \(\mathrm{d}\), the value of \(\mathrm{u}\) remains low, indicating that contrast adjustments are applied aggressively to pixels with similar intensities. This increases the perceived contrast in these areas. As the distance approaches the midpoint of the pixel intensity range, \(\mathrm{d}=128\), the function applies a threshold of 70. Under this behavior, as the intensity difference increases, \(\mathrm{u}\) also increases, reducing the impact of contrast correction. This is because large intensity differences already create sufficient contrast, and further enhancement isunnecessary. Consequently, the function primarily focuses on amplifying the contrast for pixels with similar intensities while reducing the corrections for already distinct intensities.
###### Abstract  
Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks.  
footprints; light use efficiency; gross primary production; parameter optimization Article How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production [PERSON], X., [PERSON], [PERSON], [PERSON], X., [PERSON], M., [PERSON], [PERSON], [PERSON], [PERSON], How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production X.
# Wideband Versatile Receiver for CubeSat Microwave Front-Ends
## 2 Related Works  
The recent research activities concerning CubeSats are focused on a wide set of topics, e.g., the development of compact and high gain antennas, either stationary or composed of deployable structures, and able to increase the radiation area after reaching the correct CubeSat position and attitude with respect to the Earth [9; 10; 11; 12; 13]. Many valuable contributions are instead focused on technological aspects related to the CubeSat position and attitude. In [14], the NASA/NORAD two-line element (TLE) position and velocity calculation were validated by means of an on-board Global Positioning System (GPS) receiver, whereas in [15], the attitude of the UltraViolet & infrared Sensors at high Quantum efficiency was determined by means of a tri-axial attitude determination (TRIAD) method and then improved by means of an extended Kalman filter (MEKF). Recent works have also focused on the proper determination of the power link budget, on the improvement of the efficiency of solar panels and on the enhancement of the energy accumulation systems performance [16; 17].  
In the scientific literature, the topic of designing premium performance versatile receivers is of great interest, although a very wide frequency microwave downconverter has still not been addressed. The availability of wide-bandwidth downconverters would have great potential in a huge number of applications, e.g., satellite communication, radars, etc., where they might be used in the receiving stages [18; 19; 20; 21; 22; 23].  
As an example, in [24], both the down-converting and up-converting sections were designed for GPS applications, with optimum performance in terms of output power and low-level path losses. However, the downconverter was specifically designed for GPS systems, thus with a very narrow operating bandwidth centered at the frequencies of 433 MHz and 1575.42 MHz (L1 band). In [25], a CubeSat transceiver designed, realized and tested to work in the S-band (in detail from 2.025 GHz to 2.29 GHz) with the purpose of transmitting images and videos between a satellite and a ground station was studied. Particular attention was devoted to antennas and filters.  
In [26], a prototype radio frequency (RF) receiver for Cubesats was designed and fabricated. Even though it was realized by exploiting COTS components, it was designed to operate within a very narrow bandwidth, i.e., 2025-2110 MHz. However, the characterization was particularly deserving since it was performed both with a board-level approach concerning the verification of the functional requirements and with a component-level approach to accurately characterize each specific component.  
This literature review highlights the lack of wideband receivers in the scientific literature, justifying the efforts of the authors in the present study.
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 3 Experimental Setup
### Feature Extraction  
As a first preprocessing step, all audio considered for this work was downsampled to 8 kHz and converted to a single-channel input. As input features for the proposed neural network-based SAD system, we consider log Mel-filter bank energies. We use 64 log Mel-coefficients concatenated with the log energy of the frame. Considering an audio input sampled at 8 kHz, Mel filters span across the frequency range between 64 Hz and 4 kHz. Features are computed every 10 ms using a 25 ms Hamming window. As a final step, the mean and variance at feature level are used to normalise the corresponding file. The set of features described in this section is shared among all experiments described in this paper.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method
### Feature Fusion  
In the feature fusion sub-network, we adjust the utilization of position encoding by separating the traditional position encoding from the binding relationship with the feature sequence. This separation allows for a more comprehensive interaction of concatenated features during the feature fusion stage [34]. The exemplar attention module implements visual motion representation learning in the encoder-decoder structure. The encoder is responsible for the feature fusion of the concatenated features. At the same time, the decoder is utilized for visual motion representation learning, enhancing the understanding of the tracked target images. The overall structure is illustrated in Figure 4.  
Figure 3: Architecture of the spatial scale attention module.
## 3 Results and Discussion
### Other Uses for Smartphone in Fire Emergency  
In Question 9 it was important for the researcher to understand whether occupants/ interviewees will consider using their smartphone for any purposes during the fire emergency other than contacting family and friends. A large proportion of interviewees indicated that they would not have time to use their smartphone to contact anyone else during the evacuation other than friends and family. Firstly, because they believe they do not have time and, secondly, checking on family members was more important to them than any other matter during the emergency. However, it is worth noting that two interviewees stated that occupants in general could use their smartphone to record what is happening or take photos for future reference. It was clear from the responses that interviewees' priorities will be to use their smartphone to gather information and to receive or make calls. One interviewee stated that they will use their smartphone to locate other family members within the building.  
Three other interviewees stated that they would call emergency services if they had not been called and to make sure they are on their way to evacuate the building. Moreover, one interviewee explained that they would use their smartphone to check social media quickly to see if any useful information regarding the situation has been posted. However, the interviewees also expressed concerns about time. It was clear that the smartphone will be used to receive or make calls, in addition to gathering information and possibly locating other family members. The word \"time\" occurred in all interviewee responses, which clearly indicates that time was the most important factor that affected the usage of the smartphone. Interviewees explained that making sure family and neighbours evacuated the building is very important, as well as calling fire emergency services.