esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Source Codes
### Achievable System Rate Analysis
Finally, candidates satisfying (8) over the whole considered range of \(p\) for a given \(R_{s}\) are chosen, which can maintain a satisfactory PAS performance for varying \(p\). \[I_{s}^{\text{ev}}(i,j) = \Phi\Big{(}x_{i,j}^{s}\Big{)}\,J_{\text{BSC}}\bigg{(}\frac{1}{2} \sum_{k\
eq j}\left(x_{k,j}^{s}\Big{[}J^{-1}\big{(}I_{s}^{\text{av}}(k,j)\big{)} \Big{]}^{2}\right)+\frac{1}{2}\Big{(}x_{i,j}^{s}-1\Big{)}\left[J^{-1}\big{(}I _{s}^{\text{av}}(i,j)\big{)}\right]^{2},p\bigg{)} \tag{9}\] \[I_{s}^{\text{ec}}(i,j) = \Phi\Big{(}x_{i,j}^{s}\Big{)}\Bigg{[}1-J\Bigg{(}\sqrt{\sum_{k\
eq j }\left(x_{i,k}^{s}\big{[}J^{-1}\big{(}1-I_{s}^{\text{ac}}(i,k)\big{)}\big{]}^ {2}\right)+\left(x_{i,j}^{s}-1\right)\left[J^{-1}\big{(}1-I_{s}^{\text{ac}}( i,j)\big{)}\right]^{2}}\,\Bigg{)}\Bigg{]}\] (10) \[I_{s,\text{APM}}(j) = J_{\text{BSC}}\bigg{(}\frac{1}{2}\sum_{k}x_{k,j}^{s}\Big{[}J^{-1} \big{(}I_{s}^{\text{av}}(k,j)\big{)}\Big{]}^{2},p\bigg{)} \tag{11}\]  
Note:  
* The choice of \(\Delta\) influences the system design. On the one hand, a small value of \(\Delta\), which means a small gap to the Shannon limit, leads to a small number of candidates and hence limits the search for a source code with good source decoding thresholds. On the other hand, increasing \(\Delta\) can enlarge the search space for source codes with a good error-floor performance at the expense of the PAS performance. As a consequence, the proposed Algorithm 1 can strike a balance between the PAS performance and the error-floor performance by adjusting the value of \(\Delta\). * The value of \(\delta\) is related to the step size of \(p\) in Algorithm 1. We use \(\delta=0.02\) in our search process based on trial and error. ```
0:\(p_{ini}=0.5,\delta,R,m_{s},n_{s},e_{s},\mathcal{A},\Delta\)
1:while\(H(p_{ini})\geq R_{s}\)do
2:\(p_{ini}\gets p_{ini}+\delta\); \(\backslash*\) find the lowest \(p\) for \(R_{s}*\backslash\)
3:endwhile
4:compute \(R\) using (2); \(\backslash*\) target system rate \(*\backslash\)
5:for\(p=p_{ini};p<1\)do
6:\(\frac{E_{s}}{N_{0}}\Big{|}_{\max}\leftarrow\Big{(}2^{2H(p)R}-1\Big{)}\Big{/}(2 R)\cdot 10^{\Delta/10}\);
7:\(\mathcal{W}_{\Delta}(p)\leftarrow\emptyset\);
8:for every \(W\in[1,n_{s}e_{s}]^{m_{s}-1}\)do
9: compute \(P_{W}(X)\) using (5) for all \(X\in\mathcal{A}\);
10:\(\alpha\leftarrow\left(\frac{RN_{0}\cdot\frac{E_{s}}{N_{0}}\big{|}_{\max}}{ \sum_{X\in\mathcal{A}}P_{W}(X)|X|^{2}}\right)^{1/2}\); \(\backslash*\) scaling amplitudes \(*\backslash\)
11: compute \(C_{W}\) using (6) with \(P_{W}(X)\) and \(P_{a}(Y|X)\);
12:if\(C_{W}\geq C\)then
13:\(\mathcal{W}_{\Delta}(p)\leftarrow\mathcal{W}_{\Delta}(p)\cup\{W\}\). 14:endif
15:endfor
16:\(p\gets p+\delta\);
17:endfor
18:Selecting the common elements \(\mathcal{W}_{\Delta}\) in all \(\mathcal{W}_{\Delta}(p)\)
19:return\(\mathcal{W}_{\Delta}\). ```  
**Algorithm 1** Search for the row weight distribution of the source code
# Joint Transmit Waveform and Receive Mismatched Filter Design to Suppress Range Sidelobe
###### Abstract  
Pulse compression technology can augment the likelihood of target discernment without degradation and without amplifying system hardware requisites. However, radar-communication integrated waveforms may cause mismatches in reception due to communication modulation, leading to increased loss in processing gain (LPG). This method aims to achieve communication transmission while suppressing near-range sidelobe interference (NRSI) with a minor sacrifice in LPG. An environment-based weighted mismatched filter (EWMF) design methodology is proposed to attenuate NRSI to the requisite level, with further control of LPG possible by adjusting communication modulation parameters. Moreover, the alternating direction method of multipliers is employed to jointly optimize the integrated waveform and filter design. The effectiveness of this method is demonstrated using the average sidelobe level over a specified region as the performance metric. Theoretical evaluation and experimental results confirm the applicability of waveforms using EWMF, effectively suppressing NRSI, and this method is suitable for all waveforms based on pulse compression processing. Notably, it offers cost-reduction advantages without requiring modifications to the radar transmitter or receiver.  
r 11 November 202420 December 20243 January 2025 Published: 7 January 2025  
Acedemic Editor: [PERSON]  
11 November 202420 December 20243 January 2025  
Submitted to: 17 January 2025
# Intelligent Total Transportation Management System for Future Smart Cities
## 2 Materials and Methods
### Sensing and Technologies in Total Transportation Management
* passive monitoring of the operational conditions--built in the environment from a distance to the vehicles or the operators, monitored objects, using passive and active sensors like video cameras, signal transmitters, or eye tracking, infrared cameras, microwave radars
* passive monitoring by sensors integrated into the working environment for direct sensing of the operators' behaviors, such as heart rate; skin resistance; built in the operators' control elements or their clothes; integrated into the infrastructure as road construction, rail tracks, bridges, or tunnels to measure the size of the vehicles, their weight, or to detect the deformation in the structural elements
* semi-active sensing and monitoring--measuring some characteristics as the reaction of operators on some signals/information, the action of vehicles while passing small obstacles, load, stress and/or deformation of bridges under heavy vehicles
* active monitoring and detection--passive or active sensors that measure the reactions of the vehicles/operators on the specially generated signals (including signals initiated to test the vehicle systems, transportation infrastructure, or operators). The challenging scenario has made IoT concepts obligatory to be implemented in smart cities since they create a connection between the objects and intelligently communicate to humans [5; 54; 55]. The classified monitoring and detection methods must be harmonized with the introduced novel concept of total transportation management (Figure 4). Having some ideas on the size of the system is recommended to study the EU's research and results supported by the Smart Santander project [23]. This project established a unique world city-scale experimental research facility to investigate and demonstrate the possible Internet-of-Things-driven service for future smart cities [24; 25]. A large flexible scale experimental area was set in a city, Santander, in a real environment. More than 12,000 IoT devices were applied and integrated into the experimental area. A special application was developed for users that provides an augmented reality service with information about more than 2700 places in the city of Santander including tourist information offices, shops, monuments, buildings, beaches, parking lots, and bus stops and real-time availability of traffic, weather, short-term demands or even availability of parking places. Moreover, the users' mobile phones were included in the information system as other sensing sources. The users' mobiles transmitted information on, e.g., GPS position, noise, and temperature. By this, the SmartSantander project showed the possible realization of the extensive integrated transportation system management based on the available IoT solutions and real-time information sensed by a large number of distributed sensors providing information on, e.g., the vehicles, the infrastructure, or the operator.
# Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
## 1 Introduction
In terms of research content, existing achievements mostly remain at the level of single-parameter description, with insufficient exploration of the coupling relationships between parameters such as wavelength, amplitude, channel bifurcation angle, and bifurcation spacing, as well as their sedimentary dynamic significance. Addressing these research gaps, this study utilizes the global coverage advantage of the Google Earth platform to systematically collect sedimentary parameters (wavelength, amplitude, channel bifurcation angle, bifurcation spacing) from eight typical braided river deltas, including the Saloum River, Ruffji River, Lena River, and Poyang Lake. To address the above issues, this study constructs a dataset of eight end-member braided river deltas using tectonic activity (high/low), climatic humidity (high/moderate/low), and provenance type (terrigenous/mixed-source) as screening dimensions. The cases include the Rufiji delta in the East African Rift Zone (high tectonic activity, tropical climate), the Po delta in the Apennine foreland basin (low tectonic activity, humid climate), and the Lena delta in the polar tundra region (low tectonic activity, cold climate), among others.
## 5 Field Tests
### Results and Discussion  
The steer-axle weights are inferred from the derived acceleration responses to 5-axle trucks crossing the Sentvid bridge. Figure 17a shows the histogram of actual steer-axle weights of trucks that crossed the bridge within the interval mean of 20 \({}^{\circ}\)C and the corresponding inferred weights using the acceleration-based BWIM system.  
Figure 16: Measured internal temperature of the Sentvid bridge in 2014.  
Figure 17: **(a)** Histograms of actual weights and the inferred steer-axle weights of trucks at 20 \({}^{\circ}\)C internal bridge temperature; **(b)** Normal distribution fits of inferred steer-axle weights with different mean internal temperatures.  
The same analysis is performed for different intervals of bridge temperature to assess the sensitivity of the statistical properties of the BWIM system to temperature. Figure 17b shows the normal distribution curve fits to the histograms of inferred steer-axle weights for different temperature intervals. It can be seen in Figure 17b that there are reasonable changes in both the mean and the standard deviation with temperature.  
The change in the standard deviation with temperature is illustrated in Figure 18. It can be seen that the standard deviation of the inferred weights increases almost linearly with the increase in the temperature, with an R-squared value of 89.7%. The standard deviation increases by about 10% due to a 10 \({}^{\circ}\)C increase in the bridge internal temperature which means that statistically, the acceleration-based BWIM data change with the change in bridge stiffness. Previously, the percentage change of standard deviation obtained in the numerical analysis of the one-dimension bridge beam due to global loss of stiffness is seen to be more significant (Figure 10) as compared to the percentage change in the field analysis (Figure 18). Despite the reduced performance, it is encouraging to see field evidence that this parameter, through most of the temperature range, provides a reasonable indication of a change in the bridge stiffness.
## 2 Materials and Methods  
The experiment was implemented at the Agricultural Experimental Station of the University of Life Sciences, where about 350 dairy cows with an average yield of more than 10,000 kg of milk per 305-day lactation are kept in a free-stall barn. The animals are fed TMR twice daily ad libitum. The ingredient composition of the TMR (kg/day DM) was as follows: maize silage, 12.00; alfalfa silage, 4.00; corn silage, 2.00; soybean meal, 2.20; pasture ground chalk, 0.20; salt, 0.05; rapeseed meal, 1.80; and magnesium oxide, 0.06. Cows were fed twice a day. For the experiment, 105 cows (55 multiparous cows that were in their second laction, as well as 50 primiparous cows) were selected from the entire herd and assigned to one of four groups: healthy primiparous (PH; 30 cows), healthy multiparous (MH 32 cows) or ketotic primiparous (PK; 20 cows), ketotic multiparous (MK; 23 cows) based on their symptoms, and blood serum BHBA concentrations at 5 \(\pm\) 2 days post-partum; each group was then kept separately. Dry matter intake (DMI) was determined by weighing remaining orts. Body condition score (BCS) was assessed via the BCS-5 method described by [PERSON] et al. [25]. Symptoms of ketosis were as follows: reduced feed intake, poor body condition score, and BHBA > 1.2 mmol/L.  
Milk and blood samples were taken from the animals for a period of three weeks at weekly intervals on the same day. All samples were taken at the following time intervals: between the fifth and seventh days of lactation for the first collection, the eighth and fourteenth days of lactation for the second collection, and the fifteenth and twenty-first days of lactation for the third collection. The basic composition of the milk and the fatty acids content were determined for the collected milk samples, while BHBA levels were determined via blood samples.  
The milk samples (250 mL) were obtained from each cow using milk samplers (from morning and evening milking) and mixed. Blood samples (10 mL) were taken from each cow via jugular vein puncture using tubes (Vacuette, Kremsmunster, Austria) containing potassium-EDTA (K3 EDTA, 1.8 g/L of blood) as an anticoagulant. Blood samples were centrifuged at 1800\(\times\)_g_ at 4 \({}^{\circ}\)C for 15 min, and the supernatant was immediately transported to the Veterinary Centre of WULS for the analysis of blood plasma metabolites (BHBA).
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 4 Proposed Trust Model
### Indirect Reputation
#### 4.2.1 Evidence Consistency  
Distance suggests how far two objects are away from each other in practical life. The distance of BPAs shows the conflicts or differences between two BPAs. The shorter distance means that the two BPAs are more likely to support the same target (service provider). For trust estimation models in MASs, we adopt average distance to capture the consistency of some evidence. Assume that the demander received BPAs from \(L\) different witnesses \(w_{k}\), each of which is represented by \(m_{kj}^{d}\). As a result, the consistency (the difference) between these pieces of evidence is explored below. First, the average distance of \(m_{kj}^{d}\) is represented by \(Dis(m_{kj}^{d})\), which is defined as  
\[Dis(m_{kj}^{d})=\frac{\sum_{p=1,p\
eq k}^{L}d(m_{kj}^{d},m_{pj}^{d})}{L-1} \tag{14}\]  
where \(d(m_{kj}^{d},m_{pj}^{d})\) indicates the distance of \(m_{kj}^{d}\) and \(m_{pj}^{d}\), and \(Dis(m_{kj}^{d})\) represents the average distance to the evidence provided by the witness \(w_{k}\), which also expresses its conflict or difference in the group of \(L\) evidence. Obviously, the distance between two pieces of evidence falls in the range \([0,1]\) in this paper. At the same time, we claim that the opposite of difference is similarity. Consequently, we can express the similarity by the following operation:  
\[Sim(m_{kj}^{d})=1-Dis(m_{kj}^{d}) \tag{15}\]  
where \(Sim(m_{kj}^{d})\) represents the consistency towards the service provider \(s_{j}\) from the perspective of the witness \(w_{k}\) in the group of \(L\) witnesses.
## Materials and Methods
### Methodology  
The temporal dynamics in vegetation growth can be reflected by the changes in the physiological activity, density, or greenness of vegetation cover. The flux-based GPP data reflect the physiological activity of the underneath vegetation and measure the carbon flux, which can be applied to model the amount of carbon dioxide absorbed through photosynthesis in a unit time. GPP begins to increase significantly with the onset of green-up and decrease rapidly with the onset of dormancy. Thus, the physiological activity reflected by GPP can provide a valuable indicator to estimate the vegetation growth cycle, which can be further used to derive the dates of vegetation phenological events.  
Similarly, spatio-temporal GPP dynamics derived from remote sensing technology can be applied to map vegetation phenology over a large area. For any given location, the best curve fit for time-series GPP (usually within a year) contains critical information for one growing cycle of vegetation from which vegetation phenology can be extracted. The inflection point detection method, which locates the dates corresponding to the inflection point in the vegetation GPP curve, was applied to identify the phenological dates for SOS and EOS (Figure 2). Two steps, time-series data preparation and location of phenological dates, were involved. The objective of data preparation was to prepare high-quality data that captured the temporal changes for GPP using smoothing functions (i.e., Savitzky-Golay or SG smoothing), because the original data may inevitably contain noise, which causes abnormal values. The second stage was to locate the phenological dates. The DOY corresponding to the characteristic point in the smoothed curve (e.g., the point of maximum curvature, the point of maximum first-order derivative, or the point of maximum relative change, and so on) is the phenological metrics [36]. Detail processes are listed as below.  
Figure 1: Global distribution of vegetation types and locations of FLUXNET (Flux) sites, where land cover type products from MCD12Q1 are reclassified into five groups, including forest(s) (evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, and mixed forests), shrub (open shrublands and closed shrublands), savannas (woody savannas and savannas), grass (grasslands), and crop (croplands and cropland/natural vegetation mosaics).
###### Abstract  
Simultaneous monitoring of temperature and vibration of electromechanical systems, public buildings, and volcanic terrain is an important indicator of preventing accidents. This work showcases the simultaneous temperature and vibration measurement using a single-fiber Bragg grating (FBG) sensor. The concurrent interrogation of vibration and temperature by the FBG sensing system can be integrated with free space optics (FSO), which saves on the costs associated with fiber optic cables and overcomes terrain barriers. Furthermore, the real-time measured vibration frequencies and different temperature levels can be recognized by a deep neural network (DNN) sequential model. In addition to the PSO support, the sensing system can be arranged in multiple sensing locations to form a wide range of measurements, offering a highly cost-effective solution.  
fiber Bragg grating vibration measurement; temperature measurement; free space optics; deep neural network +
Footnote †: journal: Applied sciences
## 3 Calculation Results and Analysis
### Analysis of the Optical-Electrical-Thermal Coupling Effect of Output Characteristics
#### 3.3.3 Output Power  
Besides the short-circuit current and open-circuit voltage, the filling factor and maximum power also change with the installation position of the cell (Figure 7c,d). When Z = 112 mm, the illumination non-uniformity is the strongest and the filling factor is the lowest, which is about 50%. At the position away from the focal plane, the filling factor is close to 90%. The filling factor is mainly closely related to the non-uniformity of concentration. In the case of non-uniform illumination, the current generated in the highly illuminated central area of the cell flows to the darker edge area through the grid line where the transverse current is generated and the effective series resistance is increased [23,44]. The conversion efficiency is lower than that in the case of current matching and the filling factor also decreases. The filling factor has an obvious negative correlation with PAR. Opti  
Figure 10: (**a**) Open-circuit voltage of three sub cells; (**b**) Open-circuit voltage of sub cells and open-circuit voltage of triple-junction solar cell.  
mizing the distance can improve the non-uniformity of the illumination so as to improve the filling factor. Figure 7d shows the variation in the maximum output power of the triple-junction cell along the optical axis. The minimum output power at the focal plane is 1.21 w, while the maximum output power at both sides of the focal plane (Z = 108 mm, 114.5 mm) is 2.06 wand 1.85 w. When the cell is placed at the focal plane, the maximum output power decreases by 35%. The main reason is the decrease in the short-circuit current, open-circuit voltage and filling factor caused by the non-uniform illumination as well as the negative impact of comprehensive optical-electrical-thermal coupling. Compared with the short-circuit current and open-circuit voltage, the filling factor fluctuates more significantly. The filling factors at 108 mm, 110 mm and 114.5 mm in the optical axis direction are 76%, 62.6% and 68.8%, respectively. The optical-electrical-thermal coupling intensifies the negative effect of the non-uniformity near the focal plane, causing the cell temperature to increase by 15%, the open-circuit voltage to decrease from 2.7 v to 2.63 v and the maximum output power to decrease further by 1.2%. It can be seen that the coupling effects of the non-uniformity of the focusing, the spectral mismatch between the focusing spectrum and the multijunction cell design and the increase in cell temperature are the key reasons for the decline in the output power of the multijunction cell near the focal plane.
###### Abstract  
To simultaneously address dual demands on the spectral efficiency and the data transmission rate of maritime communications, in this paper, a novel maritime communication technology based on time-delay overlapping modulation (TDOM) is proposed. In TDOM, multiple carriers are delayed in turn and directly overlapped in the time domain to form the communication symbol. In this way, the strict orthogonality between carriers can be broken through, indicating that the frequency interval between carriers can be much narrower than ever. In other words, TDOM can achieve non-orthogonal communication and realize both higher spectral efficiency and a higher maritime communication rate. The system architecture is designed, and its performance is analyzed and compared with that of other typical maritime communication technologies such as the very-high-frequency data exchange system (VDES). It is shown that TDOM can reach up to 20 times the peak communication rate and the spectral efficiency of VDES. As a digital modulation technology implemented in the physical layer, the proposed TDOM can be extended to other communication systems such as satellite communications or 4G/5G based off-shore communications.  
Article  
Academic Editor: [PERSON]  
14 January 2025  
Revised: 6 February 2025  
Accepted: 11 February 2025  
Published: 13 February 2025  
**Citation:** [PERSON]; [PERSON] A Time-Delay Overlapping Modulation-Based Maritime High-Speed and High-Spectral-Efficiency Communication Technology. _J. Mar. Sci. Eng._**2025**, _13_, 345. [[https://doi.org/10.3390/jmse13020345](https://doi.org/10.3390/jmse13020345)]([https://doi.org/10.3390/jmse13020345](https://doi.org/10.3390/jmse13020345))  
**Copyright:** 62025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).