esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approach
### Demodulation  
The demodulation process of this system is based on the visible light vehicle-to-vehicle communication system using modulated taillights by [PERSON] et al. [1]. The proposed system is intended to be used for communication between two vehicles driving in succession on the highway. A public key fingerprint is transmitted from the first car by modulating its taillights to the second car, which is receiving the signal using a camera. As the system should be used to establish a secure connection between two vehicles, this communication needs to happen before those two cars decrease the distance between them to build a  
Figure 10: Blooming affecting width of resulting stripes.  
platoon. Thus, the communication distance is the usual safety distance between two cars on the highway, i.e., between 30 m and 80 m, depending on the current speed.  
For receiving the signal the proposed system uses a camera with a common CMOS image sensor recording 30 frames per second (FPS). The shutter speed, or exposure time, respectively, is set manually to a very low level between 1/1000 of a second and 1/8000 of a second, to enable the use of the rolling-shutter effect. Every single frame captured by the camera is needed to detect the taillights of the car in front and to recognize their current states to demodulate the signal correctly.
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 4 Implementation Criteria
### Two Implemented Versions
#### 4.2.2 Version 2, Minimal Area  
This version has been designed with minimal diode \(W\times L\). Its calculated optimal \(R_{in}\) at room temperature is roughly 10 k\(\Omega\) again with \(N\) = 60 diode stages, resulting in a theoretical \(r_{DS}~{}=~{}1.2\) M\(\Omega\) and an actual \(r_{DS}~{}=~{}1.1\) M\(\Omega\) for each diode. The dimensions of the diodes are \(W\) = 120 nm and \(L\) = 100 nm. The values for the filtering and coupling capacitors are \(C_{gate}=C_{drain}=C_{gnd}\) = 13 fF implemented as 1.2-V poly-on-pplus capacitors. The simulated \(C^{\prime}_{in}\) is 0.62 pF. \(R_{bias}\) has been implemented as a diode-connected MOSFET with a null \(V_{GS}\). According to simulations, factor \(k\) is roughly 0.75, around the same as for version 1 since junction losses are lower due to a lower diode area, but non-perfect coupling of the input signal \(V_{RF}\) to the internal gate and drain nodes is more significant due to smaller coupling capacitors. The dimensions of \(M4\) are \(W\) = 450 nm and \(L\) = 100 nm, whereas those of \(M3\) are \(W\) = 450 nm \(\times\) 2, \(L\) = 100 nm and \(R_{PTAT}~{}=~{}1.4\) M\(\Omega\). Again, a 100 nA biasing current in the baseband amplifier following the ED is supposed in order to assume \(NF\) as roughly equal to unity in all cases. The PTAT block, start-up included, consumes 14 nW at 20 \({}^{\circ}\)C, 3.9 nW at \(-\)40 \({}^{\circ}\)C and 33.8 nW at 120 \({}^{\circ}\)C.
## 4 Experimental Results and Analysis
### Experimental Details and Guided Wave Dataset
Moreover, to further visually exhibit the differences between the baseline signal and damage signals, the scattering signals corresponding to the original signals are given in Figure 8b, which are the differences between the baseline signal and the damage signals from D1 and D4, respectively. Similarly, as shown in Figure 8c, they are baseline signal and damage signals which are transmitted by PZT-4 and received by PZT-9 passing by D2 and D3 region before and after damage, respectively. Moreover, their corresponding scattering signals are given in Figure 8d. It is apparent from Figure 8b,d that the scattered waves of the damage vary significantly from different damage areas, especially in the first few reflected/scattered waves, and there are significant differences in these different scattering signals corresponding to different damage regions. Therefore, the proposed LCANet can intelligently and efficiently extract these discriminative differences to form features for damage identification. The experimental environment of the network model: Python3.7, PyTorch.10, Torchvision 0.3.0, Intel(R) Core(TM) i7-6500U CPU, NVIDIA GeForce 940M GPU, 12 GB memory and 932 GB storage. In the training, we use the Adam optimizer with 100 epochs and the initial learning rate is set as 0.0005. The proposed LCANet is trained offline by the histori  
Figure 8: (a) Baseline wave signal and damage signals transmitted by PZT1 and received by PZT6; (b) Scattering signals corresponding to damage 1 and damage 4; (c) Baseline wave signal and damage signals transmitted by PZT4 and received by PZT9; (d) Scattering signals corresponding to damage 2 and damage 3.  
cally collected guided Lamb wave signals. After LCANet is well trained, it can be invoked to detect the damage from the real-time collected data. Moreover, in the experiment, we selected two damage-detection methods and a popular network as contrastive methods:  
* 1D-CNN [21]: The model is built using conventional one-dimensional convolutional layers with pooling and fully connected layers, and directly using the collected 1D guided wave data as input. * 2D-CNN [32]: The model consists of four conventional two-dimensional convolutional layers paired with pooling layers and four fully connected layers. In the experiments, the input to this model is the same as our proposed method, which is processed 2D data. * ResNet [34]: ResNet introduces the residual structure and batch normalization between convolutional layers. This improvement solves the problem of gradient anomalies and degradation that occurs as the network deepens. In the experiments, the inputs to this model are the same as in our proposed method. In our experiments, we use accuracy, parameters, and inference speed to evaluate the performance of the proposed model. Accuracy is the value of the number of samples judged correctly by the model as a percentage of the number of all samples. The parameters represent the total parameters of the model. The inference speed represents the time spent per sample at the time of testing.
## 5 Linear Methods
### Supply Chain Optimization  
The food industry's supply chain is characterized by its complexity and sensitivity to disruptions, such as those caused by climate change or geopolitical instability. Linear methods provide robust solutions for optimizing transportation, inventory management, and distribution networks, ensuring cost-effectiveness and reliability.  
For example, LP models have been successfully applied to optimize transportation routes for food products, reducing fuel consumption and delivery times. A case study in Europe revealed that optimizing delivery routes for perishable goods using linear methods reduced transportation costs by 12% and carbon emissions by 8% [46]. Such improvements not only enhance economic performance but also contribute to the environmental goals outlined in SDG 13 (Climate Action).  
In inventory management, linear methods play a crucial role in minimizing holding costs and preventing overstocking or stockouts. For instance, a linear optimization model applied to a frozen food manufacturer helped balance inventory levels, reducing storage costs by 18% while ensuring consistent product availability. This efficiency directly supports the goals of SDG 12 (Responsible Consumption and Production) by minimizing waste and optimizing resource use [47].
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
###### Abstract  
Existing space application networks in space stations are mainly fiber-optic cable-based networks due to their low size, weight, and power (SWaP) values. While fiber networks in space stations offer data transmission at high speeds with minimal signal loss, their major disadvantage is the lack of flexibility and mobility when new and unplanned space scientific equipment is added to the network. To enhance the flexibility of space networks while increasing their throughput, this paper introduces the hybrid space network (HSN), a new space network architecture that incorporates an optical wireless link, to meet the ever-increasing demands for larger bandwidth and higher mobile access capabilities in space scientific experiments. To best utilize the HSN's system performance, we propose a multi-priority-based network scheduling scheme, which can dynamically adapt to the requirements of mass tasks and select the best transmission procedure. Through simulations, we find that by adding optical wireless communication (OWC) links to the state-of-the-art deterministic FC-AE-1553 space network, the HSN's bandwidth can be increased by 20 times with an average latency reduction of 87.3%. We believe that the proposed HSN's architecture may ultimately shape the future of space stations' wireless connectivity, and in the meantime, innovate many advanced space applications with larger data rates and mobility requirements.  
optical wireless communication; fiber channel; FC-AE-1553; hybrid; space network +
Footnote †: journal: _photonics_
## 5 Conclusions  
This study used OCO-2 SIF data, MODIS products, and meteorological data to explore trends in SIF and the relationship between SIF (and MODIS products) and meteorological variables in three ecosystems (i.e., forest, cropland, and grassland). Our results led us to draw several innovative conclusions:  
1. In different ecosystems, the relationships between the SIF and MODIS products show different correlations. At both a regional and footprint level, EVI demonstrates a close relationship with SIF, indicating that EVI has potential for the simulation of SIF.
2. With regard to the sensitivity of ecosystems to temperature, forests appear to be more sensitive to daily minimum temperatures, whereas cropland and grassland areas are more sensitive to the former 16 d minimum temperature. In grassland areas, SIF showed a significant correlation with precipitation, although a closer correlation was identified with the former 16-day precipitation. It was concluded that, because of regional differences in meteorological factors, relationships between SIF and meteorological factors are regionally specific.
3. We took OCO-2 SIF as the observed value to check the accuracy of the simulated SIF products and found different simulation biases for different ecosystems. At the regional scale, R\({}^{2}\) values were higher than 0.9 (for GOSIF) and higher than 0.8 (for HRGCSIF). At the footprint scale, the R\({}^{2}\) values were different for different ecosystems. We can conclude that SIF must be modeled differently for different ecosystems.  
Methodology, M.G. and J.L. ([PERSON]); software, J.L. ([PERSON]); validation, J.L. ([PERSON]), C.Z. and F.Z.; formal analysis, M.G. and J.L. ([PERSON]); data curation, C.Z. and F.Z.; writing--original draft preparation, M.G.; writing--review and editing, J.L. ([PERSON]); visualization, J.L. ([PERSON]); supervision, J.L. ([PERSON]); funding acquisition, M.G. and [PERSON]. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (41871103 and 41771179).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank the NASA Goddard Earth Science Data and Information Services Center for the use of the OCO-2 data for this study. We would also like to thank the National Meteorological Information Center of China for access to the meteorological data used herein.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 2 Theory Introduction
### DS Evidence Fusion Theory  
The DS evidence theory is a mathematical theory that was established by [PERSON] and his student [PERSON] in the late 1960s and early 1970s [41; 42]. This theory enables inter-evidence fusion without a priori information to improve the diagnostic accuracy. The DS evidence theory mainly consists of a discriminative framework, a basic probability distribution function, and a confidence function.  
The discriminative framework is denoted by \(\Theta\), which is the set of the class, and the set is mutually exclusive between \"2\". The structure of the discriminative framework is shown below [43].  
\[2^{\Theta}=\{\varnothing,\theta_{1},\theta_{2},\ldots,\theta_{N},\theta_{1} \cup\theta_{2},\ldots\theta_{1}\cup\theta_{2}\cup\theta_{3},\ldots,\Theta\} \tag{4}\]  
The basic probability distribution function is denoted by \(m\), which is the probability that each state in a category has a corresponding probability. It has a mapping relationship with the discriminative framework, as shown in Equation (5) [44] as follows:  
\[\left\{\begin{array}{l}0\leq m(A)\leq 1\\ M(\varnothing)=0\\ \sum\limits_{A\subseteq\Theta}m(A)=1\end{array}\right. \tag{5}\]  
wherein, \(A\) denotes any subset of the discriminative framework and \(m(A)\) is the underlying probability of \(A\).  
In order to fuse the multiple independent information sources, it is necessary to fuse the underlying probabilities from multiple sensors in order to obtain the final comprehensive and accurate results using the DS fusion rules. The fusion rule is shown in Equation (6) as follows:  
\[\left\{\begin{array}{l}m(\varnothing)=0\\ m(A)=\frac{1}{1-k}\sum\limits_{\cap A_{i}=A}\prod\limits_{1\leq i\leq N}m_{i}( A_{i}),A\
eq\varnothing\\ k=\sum\limits_{\cap A_{i}=\varnothing}\prod\limits_{1\leq i\leq N}m_{i}(A_{i}),k \
eq 1\end{array}\right. \tag{6}\]  
wherein, \(k\) is the conflict factor.
## 6 Limitations and Future Work  
The findings of this study have to be seen in light of some limitations. In this paper, in order to simply study the propagation effect of fractures in hard and soft layered rock samples, artificial rock samples were used for the experiment. However, there are structural planes such as joints and bedding in the actual rock samples. Therefore, in future research work, real rock samples should be used to conduct experiments, so as to study the influence of different structural planes on fracture propagation, which is closer to the actual situation. In addition, there is still a lack of further theoretical research and quantitative analysis on the mechanism of fracture propagation induced by SC-CO\({}_{2}\) jet fracturing. In the next step, we need to carry out theoretical modeling in this aspect and quantitatively analyze the fracture initiation and propagation through numerical solution.  
Conceptualization, F.L.; methodology, Y.H.; validation, F.L.; writing-original draft preparation, F.L.; writing-review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number 52174004 and 51804318, and the Fundamental Research Funds for the Central Universities grant number 2042022 kf1025.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Case Study
### Prefabricated Component Model Library and Model Data Processing
#### 4.1.2 Processing and Conversion of Model Data  
A vital issue in BIM and VR-integrated technology is resolving the compatibility of different data formats, since the model data processing and conversion are crucial. The VR software Fuzor 2020 chosen for this case supports multiple formats such as.fbx,.che,.exe, and.skp. As shown in Figure 3, different professional data models were constructed by Revit software at first, and then the model data were directly imported into the VR software via an add-on module. The viewpoints of the BIM model and VR scene model were synchronized, and the model data could be modified and updated in real time from both directions.
## 4 Conclusions
Through the use of these technologies, IoT systems in smart cities can work efficiently to provide high-quality services of traffic management, security, surveillance, etc. In addition to this, this study also examined the different applications of IoT systems based on 6G technology in smart cities. These applications include the 15 Minute City, Industrial Town, and ITS. Furthermore, more applications of IoT and 6G are likely to be researched and developed in the future, considering the high pace at which they have been adopted by researchers and experts in this field. Besides this, the trends in research and development in IoT and 6G technology show that future smart cities will be more efficient and effective, as they will be able to handle data at great speeds. These systems are likely to solve sustainability problems and make smart cities more environmentally friendly. In addition, the recent trends show the researchers' significant focus on security and data transmission speed and the performance of the systems, which is likely to make IoT and energy-efficient 6G-based smart cities highly effective. The application of IoT devices to 6G in smart cities provides a 100 Gbps data rate, <0.1 ms latency rate, up to 1000 km/h mobility rate, 100 bps/Hz spectral efficiency, and 1000 GHz frequency. This is most promising in resolving the issues of energy inefficiency and other concerns in classical communication networks. Conclusively, the use of energy-efficient 6G in smart cities via IoT devices is promising and is likely to solve various problems that are encountered by existing smart city systems.
## 4 Metabolic Pathway and Host Engineering for Advanced Biofuels Production
### Isoprenoid-Based Biofuels
For example, production of limonene, a promising diesel fuel precursor [76], has been significantly improved by introducing a heterologous MVA  
Figure 6: Isoprenoid pathways ((A) MEP and (B) MVA pathways) and biofuel compounds derived from isoprenoid pathways. DXS: deoxyxylulose-5-phosphate (DXP) synthase, MEP: methylerythnitol phosphate, DXR: DXP reductase, HMGS: 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) synthase, HMGR: HMG-CoA reductase, MK: Mevalonate kinase, PMK: Phosphomevalonate kinase, PMD: Phosphomevalonate decarboxylase. pathway and optimizing GPP synthase from _Mentha spicata_ and limonene synthase from _Abies grandis_[69]. A step-by-step optimization led to over 100-fold titer increase from the initially reported titer using the MEP pathway [77] up to 450 mg/L and further engineering using proteomics analysis of the pathway enzymes improved the titer about 40% [26]. For a jet-fuel precursor pinene production, initial titer in _E. coli_ (5.44 mg/L in flasks [78]) has been significantly improved by combinatorial fusion of GPP synthase and pinene synthase, reporting the highest titer of 32 mg/L when GPP synthase and pinene synthase from _A. grandis_ were co-expressed as a fused protein [27]. In addition to limonene and pinene, microbial production of sabinene was also reported with the titer of 82.18 mg/L [79]. Production of isoprenoid-derived alcohols were achieved by co-expression of phosphatases: isopentenol [80; 81; 23; 82], geraniol [83] and farnesol [73]. Particularly, isopentenol, a promising biofuel and a precursor for commodity chemicals such as isoprene, was produced at a titer of 2.2 g/L from 10 g/L glucose (70% of apparent theoretical yield) [23]. This significant improvement in isopentenol production has been achieved by the \"fine-tuning\" of the upstream MVA pathway [81] and the increased availability of NudB, which is required for hydrolysis of IPP into isopentenol [23].
## 2 Materials and Methods
### Cell Culture of Human Dental Pulp Stem Cells (hDPSCs) and Osteogenesis Induction  
Human dental pulp stem cells (hDPSCs) were obtained from Lonza (Basel, Switzerland), which were isolated from adult third molars collected during the extraction of a donor's \"wisdom\" teeth. These cells express CD105, CD166, CD29, CD90, and CD73, and they do not express CD34, CD45, and CD133 markers. After being thawed from cryopreserved tubes, the cells were maintained in DPSC BulletKit medium with 10% FBS. The osteogenesis of hDPSCs was induced by adding osteogenic supplements (100 \(\upmu\)m ascorbic-2-phosphate, 10 mM \(\beta\)-glycerophosphate, and 100 nM dexamethasone) to the DPSC BulletKit medium.
## 2 Materials and Methods  
### Reagents  
Primary antibodies (BDNF, p-APP, \(\beta\)-actin) for performing Western blot analysis were acquired from Cell Signaling Technology (Danvers, MA, USA). Primary BACE-1 antibody for Western blotting was acquired from Abcam (Cambridge, MA, USA). Secondary antibodies (anti-rabbit or anti-mouse monoclonal antibodies) were obtained from Jackson ImmunoResearch (West Grove, PA, USA). Additionally, 10\(\times\) phosphate buffer saline (PBS) was obtained from ELPIIS BIOTECH (Daejeon, Republic of Korea) and bovine serum albumin (BSA) was obtained from MPbio Chemicals (Irvine, CA, USA). The Total BDNF ELISA kit was obtained from R&D Systems (Minneapolis, MN, USA). AChE and A\(\beta\) ELISA kits were purchased from NOVUS Biologicals (Centennial, CO, USA). Donepezil (purity 98%) and scopolamine (purity 95%) were acquired from Sigma Aldrich (St. Louis, MO, USA).
## 3 Ultra-Wideband Channels Distorted by Rain  
In this section, we recall the theoretical flowchart of the digital receivers. Figures 6 and 7 show the baseband receiver in ideal conditions and in rain conditions. \(S(f)\) is the two-sided spectrum of the Nyquist pulse, \(\sqrt{S(f)}\) is the matched filter, \(n(t)\) is the receiver total additive Gaussian white noise.  
In the ideal case (Figure 6), the amplitude at the sampling time is normalized to unity. In other words, a hard decision gives \(\mp 1\) by sampling the pulse peak at the sampling time \(t=0\).  
In the rain fade case (Figure 7), we suppose that, at any time \(t\), the received signal is amplified by multiplying it by \(10^{\frac{A(f)}{20}}\) to remove the variable rain attenuation at the carrier frequency \(f_{c}=80\) GHz (this is not related to the outage condition discussed above, which must consider the rain attenuation at 85 GHz). After this multiplication, only the in-band signal variations of the carrier frequency are left, which is the effect we want to study. At the output of the sampler, before the hard decision, we find the algebraic sum \(s_{sd}+s_{sq}\) of direct and orthogonal channel samples at the reference time \(t=0\), corresponding to the peak value of the direct (cosine) and orthogonal channel (sine) pulses present simultaneously in the two orthogonal channels. In general, however, both values are affected by self-channel (ISI) interference due to the tails of the previous and following pulses and to the interference coming from the orthogonal channel (QI).  
Figure 5: SST simulated event at Tampa, 27 September 1997, started at 7:34:27 AM, local time. (**Upper panel**): phase delay \(T(t)\) (picoseconds) at 80 GHz (circular polarization). (**Lower panel**): relative phase delay at the extremes of a 10 GHz bandwidth. Sampling time is 1 min.  
Figure 6: Flowchart of the baseband receiver in ideal conditions. \(S(f)\) is the two-sided spectrum of the Nyquist pulse, \(\sqrt{S(f)}\) is its matched filter, \(n(t)\) is the receiver total additive Gaussian white noise.  
From these equations, as shown in [4], we can calculate the baseband equivalent transfer functions at frequency \(f\), with \(H_{ed}(f)\) for the direct channel and \(H_{eq}(f)\) for the orthogonal channel (Figure 7), whose averages for Spino d'Adda, Madrid and Tampa are reported in [4].  
In the next section, we report the statistics of interference due to the direct channel (ISI) and to the quadrature channel (QI). These results are fundamental for assessing the feasibility of the ultra-wideband radio link.