esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we propose new complex and real pair-wise detection for conventional differential space-time modulations based on quasi-orthogonal design with four transmit antennas for general QAM. Since the new complex and real pair-wise detections allow the independent joint ML detection of two complex and real symbol pairs, respectively, the decoding complexity is the same as or lower than conventional differential detections. Simulation results show that the proposed detections exhibit almost identical performance with an optimum maximum-likelihood receiver, as well as improved performance compared with conventional pair-wise detections, especially for higher modulation order.  
space time codes; differential space time modulation; differential detection; pair-wise detection; maximum likelihood detection +
Footnote †: journal: _Electronics_  
_Acticle_  
Improved Pair-Wise Detections of Differential Quasi-Orthogonal Space-Time Modulation with Four Transmit Antennas
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Building Trust Frameworks
Best results:  
Combining blockchain and IoT presents a promising solution for creating secure and transparent trust frameworks, particularly in supply chains, by ensuring reliable data, automated risk management, and end-to-end visibility. This integration not only restores consumer confidence but also enhances the efficiency of risk mitigation, as studies demonstrate significant improvements in monitoring devices and systems, enabling the swift detection and resolution of potential threats or malfunctions. While these frameworks are currently applied in agriculture and livestock, their broader applicability extends to other sectors where data integrity and consumer trust are critical, showcasing their versatility and potential for wider adoption.
# DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index
## 1 Introduction  
The Baltic Dry Index (BDI) is a freight index created by the London-based Baltic Exchange. This index indicates shipment costs for dry bulk cargoes consisting of commodities such as grain, coal, iron, ore, and copper. The BDI is a composite of three sub-indices, namely Capesize, Panamax, and Supramax. Those indices have different bulk-carrier capacities, 180,000, 74,000, and 58,000 dwt, respectively. The BDI has been widely used as a world-trade economic indicator [1]. Many stakeholders make serious efforts to forecast it, precisely, so as to be able to make smart investment and trading decisions. However, the volatility, non-stationarity, and complexity of the BDI is known to be more intractable than stock prices. Therefore, it is a challenging task to perform predictions against BDI values.  
The BDI is regarded as a barometer not only of the shipping industry and international trade, but also of the global economy [2]. Investors, speculators, and researchers have long found it to be useful, theoretically challenging, and relevant when projecting future profits. However, because many managerial decisions are based on future prospects, forecasting accuracy is essential for organizations and companies in order to avoid market risk. Recent advances in both analytical and computational methods have resulted in a number of new ways of mining freight-index time-series data.  
Ship owners, stakeholders and investors need to be concerned about not only short-term prediction of time-series data but also long-term prediction. Predicting a long-term sequence of time-series data is more difficult than short-term prediction [3]. For example, in making a decision on a vessel, there are multiple options available to the vessel's owners. If the BDI trend is increasing,the vessel's owners wait for the right time to sell the vessel to get the maximum profit. Instead, they will immediately sell it if the BDI index tends to decline. In other cases, if the BDI index has an upward trend, the vessel's owner will not rent the vessel to the market. Instead, they will operate the ship by themselves. Conversely, if the BDI trend decreases, they will rent the vessel. Therefore, in this study, we developed an analytic method for accurate prediction of short- and long-term values of the BDI for helping vessel's owners.  
The rest of this paper is organized as follows. Section 2 provides the background of our research and discusses the relevant BDI-related work. Section 3 introduces our proposed method. Section 4 analyzes the experimental results and compares them against econometrics and machine learning methods. Finally, Section 5 draws conclusions and anticipates future work.
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 3 Results
### Testing the Okada-GA Inversion Model Using the Observation Data
#### 3.2.1 The 2011 Tohoku Oki Earthquake Event
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline \(t_{GA}\) (s) & 49.13 & 26.485 & 48.99 & 19.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The GA computational time required (\(t_{GA}\)) in the case of the 2011 Tohoku Oki earthquake, for each fault model tested. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline length (km) & 308 & 425 & 668 & 202 \\ width (km) & 153 & 171 & 234 & 202 \\ rake (\({}^{\circ}\)) & 116 & 99 & 94 & 75 \\ slip (m) & 24.777 & 12.999 & 10 & 24.598 \\ _Mw_ & 8.97 & 8.908 & 9.054 & 8.926 \\ _error\_disp_ (m) & 0.087 & 0.108 & 0.141 & 0.085 \\ _rmse\_disp\({}_{N}\)_ (m) & 0.076 & 0.075 & 0.096 & 0.07 \\ _rmse\_disp\({}_{E}\)_ (m) & 0.102 & 0.151 & 0.195 & 0.1 \\ _rmse\_disp\({}_{Z}\)_ (m) & 0.081 & 0.08 & 0.112 & 0.083 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The estimation results of the 2011 Tohoku Oki earthquake’s source parameters using real observation data.
###### Abstract  
This study assesses the efficacy of the Heliosat-2 algorithm for estimating solar radiation, comparing its outputs against ground measurements across seven distinct countries: the Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, the study utilizes two distinct satellite data sources--Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries--and spanning the time between January 2022 and April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all of the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median values of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from \(-27.5\) to \(+10.2\) W\(\cdot\)m\({}^{-2}\). Notably, the median root mean square error (RMSE) on clear days is significantly lower, with values ranging from \(24.8\) to \(108.7\) W\(\cdot\)m\({}^{-2}\), compared to cloudy days, for which RMSE values lie between \(75.3\) and \(180.2\) W\(\cdot\)m\({}^{-2}\). In terms of R\({}^{2}\) values, both satellites show strong correlations between the estimated and actual values, with a median value consistently above \(0.86\) on a monthly scale and over \(92\%\) of daily data points falling within \(\pm 2\) standard deviations.  
BSRN, Heliosat-2; Himawari-8; Meteosat-MSG; renewable energy; solar radiation; validation study 2024
## 4 Mechanical Calculation and Analysis of Key Strata-Inclined-Bearing Structure
### Mechanical Analysis of Full-Trapezoidal Hinged Arch Load-Bearing Structure
\begin{array}{l}M_{\rm Z2\text{-}1}=T_{\rm A2}\big{(}h_{4}-L_{\rm Z2 }\sin\theta_{\rm Z2}-\frac{\theta}{2}\big{)}-T_{\rm A1}\big{(}h_{4}-L_{\rm Z1 }\sin\theta_{\rm Z1}-\frac{\theta}{2}\big{)}\\ M_{\rm Z2\text{-}2}=-Q_{\rm Z1}(L_{\rm Z1}\cos\theta_{\rm Z1}+h_{4}\cot\beta_{ \rm Z1})\\ M_{\rm Z2\text{-}3}=\frac{(P_{\rm Z1}+P_{\rm Z1})(L_{\rm Z1}\cos\theta_{\rm Z1 }+h_{4}\cot\beta_{\rm Z1})+(P_{\rm Z4}-P_{\rm Z3}-P_{\rm Z4})L_{\rm Z3}}{2}\\ M_{\rm Z2\text{-}4}=-(P_{\rm Z2}+P_{\rm Z2})\Big{(}L_{\rm Z3}+\frac{L_{\rm Z2 }\cos\theta_{\rm Z2}+h_{4}\cot\beta_{\rm Z2}}{2}\Big{)}\\ M_{\rm Z2\text{-}5}=Q_{\rm Z2}(L_{\rm Z3}+L_{\rm Z2}\cos\theta_{\rm Z2}+h_{4} \cot\beta_{\rm Z2})\end{array} \right. \tag{9}\]  
According to Formula (1), \(\sum F_{\rm Zx}=0\) and \(\sum F_{\rm Zy}=0\), can be derived as follows:  
\[T_{\rm A1}+T_{\rm A2}=0 \tag{10}\]  
\[Q_{\rm Z1}+Q_{\rm Z2}+P_{\rm Z4}=P_{\rm Z1}+P_{\rm Z2}+P_{\rm Z3}+P_{\rm Z1}+P _{\rm Z12}+P_{\rm Z3} \tag{11}\]  
According to the breaking characteristics of rock strata, key block C has been fully broken and collapsed, and then:  
\[P_{\rm Z4}=P_{\rm Z3}+P_{\rm Z3} \tag{12}\]  
Incorporating it into Formula (7), which is the second formula, and Formula (9), which is the third formula, yields:  
\[M_{\rm Z1\text{-}2}=(P_{\rm Z2}+P_{\rm Z2})\bigg{(}L_{\rm Z1}\cos\theta_{\rm Z 1}+h_{4}\cot\beta_{\rm Z1}+\frac{L_{\rm Z3}}{2}\bigg{)} \tag{13}\]  
\[M_{\rm Z2\text{-}3}=\frac{(P_{\rm Z1}+P_{\rm Z1})(L_{\rm Z1}\cos\theta_{\rm Z 1}+h_{4}\cot\beta_{\rm Z1})}{2} \tag{14}\]  
Substituting Formulas (6) and (12) into Formula (11) yields \(Q_{\rm Z1}\) as follows. \[Q_{\rm Z1}=P_{\rm Z1}+P_{\rm Z2}+P_{\rm Z1}+P_{\rm Z2}-\frac{M_{\rm Z1\text{-} 1}+M_{\rm Z1\text{-}2}+M_{\rm Z1\text{-}3}}{L_{\rm Z1}\cos\theta_{\rm Z1}+h_{ 4}\cot\beta_{\rm Z1}+L_{\rm Z3}+L_{\rm Z2}\cos\theta_{\rm Z2}+h_{4}\cot\beta _{\rm Z2}} \tag{15}\]  
Substituting Formulas (9) and (10) into Formula (8) yields \(T_{\rm A1}\) and \(T_{\rm A2}\), where the positive and negative signs in the equation represent the direction of the force. \[\left\{ \begin{array}{l}T_{\rm A1}=\frac{M_{\rm Z2\text{-}3}+M_{\rm Z2\text{-} 3}+M_{\rm Z2\text{-}4}+M_{\rm Z2\text{-}5}}{L_{\rm Z1}\sin\theta_{\rm Z1}-L_{ \rm Z1}\sin\theta_{\rm Z1}}\\ T_{\rm A2}=-\frac{M_{\rm Z2\text{-}3}+M_{\rm Z2\text{-}3}+M_{\rm Z2\text{-}4}+M_{ \rm Z2\text{-}5}}{L_{\rm Z1}\sin\theta_{\rm Z1}-L_{\rm Z2}\sin\theta_{\rm Z2}} \end{array} \right. \tag{16}\]According to the masonry arch theory and the S-R criterion, it is necessary to ensure the stability of the full-trapezoidal hinged arch structure. The arch foot must satisfy the following:  
\[T_{\text{A1}}\tan\varphi\geq Q_{\text{Z1}} \tag{17}\]  
\[T_{\text{A2}}\tan\varphi\geq Q_{\text{Z2}} \tag{18}\]  
where \(\tan\varphi\) is the friction coefficient, take 0.5. At this time, the arch height \(h_{\text{q}}\) of the fully trapezoidal hinged arch structure is:  
\[h_{\text{q}}=\max\{L_{\text{Z1}}\sin\theta_{\text{Z1}},L_{\text{Z2}}\sin\theta_ {\text{Z2}}\} \tag{19}\]
## 2 Methods
### Search Strategy and Selection  
The search was carried out on PubMed, Web of Science, SCOPUS, and Cochrane databases. The search strategy was formulated based on combinations of three concepts: embodiment AND brain-machine interface AND sensorial feedback. Due to the diversity of nomenclature, the embodiment concept was also searched in the form of volition, ownership, agency, body experience, and presence. The brain-machine interface concept was also searched as brain-computer interface. And finally, the sensorial feedback concept was also searched in the form of sensory stimulation, multisensory, visual, tactile, haptic, vibrotactile, auditory, sound, temperature, virtual reality, and virtual immersion. Search results from each database were merged and sorted for the removal of duplicates. Afterwards, titles and abstracts were screened according to the inclusion/exclusion criteria. The screening process was performed by the authors. The full text of the selected papers was obtained for closer inspection. Any disagreement concerning whether to include a specific study was discussed among all the authors.  
The studies selected in the review were based on the following inclusion criteria: (i) studies with the full text published in English; (ii) studies were original research; (iii) studies were only carried out with experiments related to humans; (iv) studies integrated SoE variables; (v) studies integrated BMIs (note that studies not using an actual BMI but only giving participants the impression of using it were also excluded from the search); and (vi) studies integrated at least one sensory feedback modality. Additionally, studies were excluded if they (i) were reviews, (ii) were conference papers, (iii) were not peer-reviewed material, or (iv) did not have accessible full text (also, see Figure 1).  
The searching strategy returned 493 articles, 223 of which were duplicates. A total of 2 were in foreign languages, 86 were conference papers, and 1 was not full-text available. From the remaining 181 articles searched, 119 did not have any type of sense of embodiment measures, 33 were reviews or conference papers, and 7 did not use brain-machine interface devices. After full-text reading, 2 papers were excluded for using just a simulation of a BMI. This resulted in a final list of 20 papers in our review. Details of the process are described in the flowchart (Figure 1).  
Figure 1: Flowchart of the selection process.
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 3 Proposed Solution
### Blockchain-Based Lightweight Trust Management in MANETs  
With the above design goals in mind, we adopted a blockchain architecture to manage trust evaluation and maintenance in a MANET environment. Specifically, we implemented a public blockchain architecture to eliminate the intensive resource consumption and long validation times of current blockchain technology in a dynamic and latency-sensitive environment. Based on the requirements of MANETs, the proposed blockchain-based distributed trust establishing system can be divided into the components presented in Figure 5.
## 3 Lenslet Array Homogenizer
### The Use of the Second LA  
A parallel incident beam is required before the LA to guarantee the desired overlay range, as can be seen from the LA homogenizer's introduced working principle. However, in actual applications, most light sources cannot be well collimated due to their Etendues [23]. As a result, not all beamlets divided by the LA are superposed within the designed range, but overfill it. This leads to a waste of optical energy in the system. To solve this, a second LA can be applied.  
Figure 3: Working principle illustration of a lenslet array (LA) homogenizer, the LA is composed of three lenslets.  
The second array is completely the same as the first, but set oppositely to it at the focal plane of the first LA. Thus, the deviation of the beamlets caused by the initial divergent angle can be revised, so that all beams can be superposed to the same area by the relay lens. Figure 4 shows an illustration of this revision while using a single lenslet from each LA.  
Figure 4 illustrates a limitation of applying the second LA. When the incident beam divergence is larger than \(\sigma\), this beamlet is no longer focused on the relevant lenslet on the second LA. This situation is detrimental to the beam homogenization. Since the optical system in Figure 4 is approximately a paraxial system, ABCD matrix can be applied to figure out this limitation. The ABCD matrix for the \(\sigma\) calculation is shown below:  
\[\left[\begin{array}{c}\frac{d}{2}\\ \theta\end{array}\right]=\left[\begin{array}{cc}1&f_{1}\\ 0&1\end{array}\right]\times\left[\begin{array}{cc}1&0\\ \frac{1}{-f_{1}}&1\end{array}\right]\times\left[\begin{array}{c}0\\ \sigma\end{array}\right] \tag{4}\]  
Therefore, when the light emitted from the light source cannot be ideally collimated, an additional LA can complement the homogenization performace. Moreover, in some cases where \(f_{1}\) is not too long, merging the two LAs into one optical element with the thickness of \(f_{1}\) and curvatures on both sides is practical.
# Approaching Breakthrough: Resource-Efficient Micropolitan Removal with MBR-GAC Configuration
## Appendix A  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Substance** & \begin{tabular}{c} **Average** \\ **Recovery** \\ \end{tabular} & \
\begin{tabular}{c} **Average LOD** \\ **(ng/mL)** \\ \end{tabular} &
\begin{tabular}{c} **Average LOQ** \\ **(ng/mL)** \\ \end{tabular} \\ \hline
**Pharmaceuticals** & & & \\ Atenold 1 & 109\% & 1 & 4 \\ Carbamazepine 1 & 106\% & 1 & 3 \\ Ciprofloxacinloxacin 1,2 & 104\% & 6 & 20 \\ Citalopram 1 & 79\% & 1 & 4 \\ Clarithromycin 1,2 & 106\% & 1 & 4 \\ Diclofenac 1 & 107\% & 3 & 10 \\ Erythromycin 2 & 93\% & 1 & 4 \\ Fluconazoleide 1 & 110\% & 1 & 3 \\ Furosemide 1 & 67\% & 5 & 17 \\ Ibuprofen & 87\% & 10 & 34 \\ Ketoconazole & 43\% & 6 & 19 \\ Losartan 1 & 104\% & 3 & 8 \\ Methotrexate & 106\% & 6 & 21 \\ Metoprolol 1 & 107\% & 1 & 3 \\ Naproxen & 127\% & 5 & 14 \\ Oxazepm 1 & 131\% & 2 & 6 \\ Paracetamol & 144\% & 6 & 20 \\ Proportional 1 & 91\% & 1 & 3 \\ Sertraline & 51\% & 2 & 5 \\ Sulfamethoxazole 1,2 & 108\% & 2 & 5 \\ Tramadol 1 & 50\% & 3 & 9 \\ Trimethoprim 1,2 & 108\% & 1 & 3 \\ Verlafacing 1 & 61\% & 1 & 4 \\ Zolpidem & 104\% & 1 & 3 \\ \hline
**Per- and polyfluoroalkyl substances (PFAS)** & & & \\ Perfluorobutane sulfonic acid (PFBS) & N/A & 0.05 & 0.15 \\ Perfluoropentanoic acid & N/A & 0.06 & 0.17 \\ Perfluorohexane & N/A & 0.06 & 0.17 \\ Perfluorohexanoic acid & N/A & 0.08 & 0.24 \\ Perfluoroheptanoic acid & N/A & 0.08 & 0.26 \\ Perfluorooctane sulfonic acid (PFOS) & N/A & 0.06 & 0.17 \\ Perfluorocotanoic acid & N/A & 0.07 & 0.22 \\ Perfluororonanoic acid & N/A & 0.14 & 0.46 \\ \hline
**YEFA1** & & & \\ \hline \hline \end{tabular}  
* 1—persistent pharmaceutical. 2—antibiotic.  
\end{table}
Table 1: Analyzed organic micropollutants.  
**Figure A1. Concentration of analyzed pharmaceuticals and sum of the sixteen most persistent pharmaceuticals in the four sampling locations over the pilot (MBR IN, MBR permeate, and the effluent of GAC L1 and GAC L2) for sample occasions (calendar week). Only quantifiable concentrations are presented.**  
**Figure A2. Concentration of PFAS11 in the four sampling locations over the pilot (MBR IN, MBR permeate, and the effluent of GAC L1 and GAC L2) for sample occasions (calendar week). The total column height corresponds to \(\Sigma\)PFAS11. Only quantifiable concentrations are presented.**
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 4 Recent Developments of Shearographic NDT
### Simultaneous Measurement of Deformation and the First Derivative  
As mentioned above, the data obtained by the shearography technique is the strain information, that is, the first derivative of the deformation. Sometimes it is necessary to quickly calculate the deformed data to achieve accurate positioning and quantification of the flaws, but the inverse calculation is very complicated. In 2013, [PERSON] et al. developed an SPS-DS setup for simultaneous measurement of deformation and first derivative, which obtains phase information recorded by one camera through the introduction of two carrier frequencies [45; 46]. Thus, the phase information obtained by the holography and shearography optical paths can be separated from a single intensity map. Figure 10 shows the SPS-DS setup and the results of simultaneous measurements with the device.
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 4 Study on Vibration Isolation Trench Measures for Depot  
Based on the vibration assessment of the building floor slabs, it is found that the vibration levels of some slabs in the over-track building exceed the standard limits. Therefore, appropriate vibration isolation measures should be implemented to reduce the vibration levels of the slabs. In this study, the method of vibration isolation through propagation paths is adopted to mitigate the floor vibrations of the over-track building. The effects of open trenches and infilled trenches on the vibration isolation performance of the over-track building are investigated in detail.  
To compare the vibration isolation effects before and after the excavation of vibration isolation trenches, the acceleration attenuation coefficients are used for presentation. The specific calculation formula for the acceleration attenuation coefficients is shown as follows.  
\[A_{r}=\frac{A_{0}}{A_{1}} \tag{4}\]  
where \(A_{r}\) represents the attenuation coefficient of the acceleration root mean square (RMS) in each frequency band. \(A_{0}\) is the 1/3 octave band acceleration RMS value of the floor slab after vibration isolation, and \(A_{1}\) is the 1/3 octave band acceleration RMS value of the floor slab before vibration isolation. When \(A_{r}=1\), it indicates no vibration isolation effect. When \(A_{r}>1\), it indicates that the vibration in this frequency band is amplified under the influence of the isolation trench. When \(A_{r}<1\), it indicates that the vibration in this frequency band is attenuated.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 2 Geolocating GNSS Reflections from Land
### Land Geolocation Example
These local surface maps are calculated for each estimated land reflection point using a global (1 km resolution) SRTM surface height map, cropped around the initial geolocation estimate. This adds considerably to the processing overhead for geolocating each measurement, but is necessary to provide confidence in the predicted surface location of the received signal power, especially as the terrain becomes more varied. In some extreme cases (i.e., mountain regions), the local area maps often reveal highly irregular and discontinuous surface regions which are not always in agreement between each of the three confidence checks. In these cases the geolocation is classified as low confidence and a detailed individual DDM analysis would be required to recover the observations at these locations. Figure 2: **(top)** Example Cyclone Global Navigation Satellite System (CYGNSS) reflection track over changing terrain. Image background is Shuttle Radar Topography Mission (SRTM) height map in meters. CYGNSS track shown as white arch traveling from west to east, green stars along track indicate Delay Doppler Map (DDM) locations. Second 11 is the western most green star, Second 120 is the eastern most. Water regions shown in black. **(bottom)** Example DDMs at points along the track from various terrain conditions. For the DDMs showing coherent reflection (such as seconds 60 and 120), the received power is received from a small area (approx. 1 km) near the predicted geolocation center pixel. The actual surface resolution in the case of coherent reflections is investigated further in Section 4.