esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Blockchain and Consignment Contracts to Optimize Food Supply Chains Under Uncertainty
## 1 Introduction
### Research Questions  
1. Is adoption of blockchain technology important for the food industry? Answer: This research investigates the answer in several ways. Blockchain can help the food supply chain in many aspects, such as transparency, food safety, quality control, certification, fraud reduction, smart contracts, and consumer trust. Blockchain can provide a smooth and transparent record of each stage of the food supply chain. Customers can track a transparent report of a food contamination outbreak, which ultimately increases customer safety. Blockchain can verify several certifications, such as fair-trade or organic labels, which can reduce the cases of fraud. Moreover, due to the original information about the origin of food and company practices, the customer trust level automatically becomes higher.
2. How is a consignment contract helpful for the food industry supply chain? Answer: The food industry has to deal with many issues, such as food safety, contamination, and deterioration. Perishable food items are one of the greatest concerns of a food production, packaging, and supply chain. In the food industry, the consignment contract can play a crucial role. Under this contract, the manufacturer does not use its warehouse or storage facility to stock the food product. On the other hand, retailers have the opportunity to store and sell the products without purchasing the entire lots. Transactions are only conducted for sold units. This reduces the financial risk of stocking perishable items that might not sell before their expiration dates.
* How can a company deal with uncertain demand for perishable food items? Is adopting blockchain really helpful in addressing extreme demand fluctuation? Answer: In the food industry, companies have to pay extra attention to perishable food items. The decay of food items is one of the biggest problems for the industry as the lifetime of these products is limited. Thus, with the production of extra quantities or a fewer number of items, in either case, a company shall face huge losses. This research identifies the strategies to deal with demand uncertainty and provides recommendations to companies on how much to produce. Adopting blockchain technology can potentially offer several benefits to the perishable food industry supply chain, especially in managing extreme demand fluctuations. The ways in which this technology can help deal with demand fluctuation are transparency, traceability, and real-time data sharing. This research investigates how food industry and associated retail channels can improve profitability in various demand patterns.
# The Quanta Image Sensor: Every Photon Counts
## 3 Imaging Characteristics  
### Hurter-Driffield Characteristic Response (D-LogH)  
In the QIS, the statistical nature of the arrival of photons and the photoelectrons they produce are well described by Poisson arrival statistics. For convenience we define the quanta exposure \(H\) as the average number of photoelectrons collected by a jet over an integration period, which depends on factors such as the incident photon flux, effective jet area, quantum efficiency, carrier collection efficiency and integration time. The probability of there being \(k\) photoelectrons is given by the Poisson mass function:  
\[\mathbb{P}[k]=\frac{e^{-H}Ht^{k}}{k!} \tag{1}\]  
The probability that there are no photoelectrons is \(\mathbb{P}[0]=e^{-H}\), and the probability of at least one photoelectron is \(\mathbb{P}\left[k>0\right]=1-\mathbb{P}\left[0\right]=1-e^{-H}\). In the single-bit QIS, reading out no photoelectrons is a logic \"0\" and reading out one or more photoelectrons is set as a logic \"1\". Essentially, the full-well capacity (FWC) of the single-bit QIS is 1 e\(-\). The bit density \(D\) of jets (fraction per bit ensemble that have logic value \"1\") gives rise to an S-shaped curve if \(D\) is plotted as a function of \(\log(H)\), reflecting the relationship [3; 9]:  
\[D=1-e^{-H}\] (2a) This characteristic QIS-response curve is shown in Figure 3a. This behavior is similar to the behavior of an ensemble of avalanche detectors in a Si photomultiplier and also observed in SPADs and in subsequent analysis of binary sensors [33; 34; 41]. The standard deviation of \[D\] (or noise, \[\sigma\]) is given by [9]: \[\sigma=\sqrt{e^{-H}\ \left[1-e^{-H}\right]}\] (2b) as shown in Figure 4.  
The statistical nature of photoelectron counting (or essentially photon counting if the efficiency factors above are close to unity) is the same as that which gives rise to the \(D\)-\(\log(H)\) nature of film exposure reported by [PERSON] in 1890 [37] due to the statistical exposure of film grains, as shown in Figure 3b. _Henceforth, the asymptotic response for Figure 3a,b is referred to as the Hurter-Driffield response curve in their honor, whether referring to film, SPADs or QIS jets_. In the case of Ag-I film grains, about 3 photoelectrons are required to result in exposure leading to slightly different slopes [9; 33; 42]. In fact, many photographers and cinematographers desire this non-linear behavior that results in a response curve with good overexposure latitude [54]. The curve shape is determined by the underlying statistical physics of exposure and threshold for creating jets with value \"1\", and is not influenced strongly by circuit or device performance. Further, the exposure-referred signal-to-noise ratio (\(SNR_{H}\)) as a function of exposure is well-behaved [9], unlike that which is found in conventional image sensors operating in high dynamic range mode, and which suffer from one or more large dips in \(SNR_{H}\) as the exposure is increased (e.g., [7]).
# Ear Centering for Accurate Synthesis of Near-Field Head-Related Transfer Functions +
## 3 Evaluation with Calculated HRTF Datasets
### Results  
All panels in Figure 4 show synthesis errors calculated with (20) and displayed in a logarithmic scale, from \(-30\) dB to \(0\) dB, to contrast with the perceivable HRTF dynamic range of around \(30\) dB, as reported in [32]. The black-dashed curves highlight the \(-3\) dB values and are used as an indicator to compare among panels. We use the \(-3\) dB indicator because this value is commonly considered as a perceivable difference. The black-dashed lines indicate \(f_{\text{max}}\) as formulated in (16).  
In Figure 4, panels (a), (d), and (g), correspond to synthesis with NEC; panels (b), (e), and (h), to synthesis with PEC; and panels (c), (f), and (i), to synthesis with SEC. Panels (a), (b), and (c) correspond to synthesis from \(P=252\) points (\(q=5\), \(N_{\text{g}}=14\)); panels (d), (e), and (f), to synthesis from \(P=42\) points (\(q=2\), \(N_{\text{g}}=5\)); and panels (g), (h), and (i), to synthesis from \(P=12\) points (\(q=1\), \(N_{\text{g}}=2\)).  
When comparing panels along rows in Figure 4, it is observed that applying SEC outperforms the accuracy across all spherical resolutions when contrasted with PEC and NEC. The enhancements of SEC are more noticeable at near distances and their benefits extend even beyond the corresponding \(f_{\text{max}}\). A closer inspection of the black-dash curves (\(-3\) dB) at distances close to the head (around \(20\) cm) shows that, for the same error level, SEC yields a bandwidth improvement of around \(2\) kHz when compared to PEC. At the same  
Figure 4: Synthesis error calculated with (20). Black-dashed curves indicate \(-3\) dB values. Black-dashed lines indicate \(f_{\text{max}}\) in (16). Left column: NEC. Center column: PEC. Right column: SEC.  
distance, we can obtain an accurate synthesis up to a higher frequency; in other words, at the same accuracy, we can obtain a synthesis up to a distance closer to the head.  
Panels in Figure 5 show the overall synthesis errors across distances calculated with (21) and displayed in a logarithmic scale, from \(-15\) dB to \(0\) dB. The black-dashed lines indicate \(f_{\text{max}}\) in (16) once again. Panel (a) corresponds to synthesis from \(P=12\) points (\(q=1\), \(N_{\text{g}}=2\)), panel (b) to synthesis from \(P=42\) points (\(q=2\), \(N_{\text{g}}=5\)), and panel (c) to synthesis from \(P=252\) points (\(q=5\), \(N_{\text{g}}=14\)). Yellow curves correspond to synthesis with NEC, red curves to synthesis with PEC, and blue curves to synthesis with SEC.  
When comparing the curves in each panel in Figure 5 at \(f_{\text{max}}\), we observe the following approximate improvements. In panel (a), PEC outperforms NEC by 3 dB, and SEC outperforms PEC by 2 dB. In panel (b), PEC outperforms NEC by 3 dB and SEC outperforms PEC by 3 dB. In panel (c), PEC outperforms NEC by 2 dB, and SEC outperforms PEC by 2 dB.  
In summary, in the whole distance range from 20 to 100 cm and at frequencies below \(f_{\text{max}}\), applying SEC yields an approximate improvement of 2 dB when compared to PEC.
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 3 Experimental Platform for Underwater Polarization Imaging
### Experimental Platform and Method  
The underwater polarization imaging experimental platform consists of five parts: polarization light source, polarization camera, target, experimental water tank and computer. Figure 6 is a schematic diagram of underwater polarization imaging composed of five parts.  
In Figure 6, a polarized light source is generated by rotating the polarizer in front of the LED light source. Reflected light is generated when polarized light shines on the surface of underwater target. The reflected light is received by the polarization camera to complete the polarization detection. Then, the polarization image of underwater target can be obtained. The corresponding degree of the polarization image can be calculated using Formulas (1) and (2).  
In this paper, three objective image quality evaluation indexes, namely image detail enhancement measure (EME), information entropy H and average gradient \(\overline{G}\), are introduced  
Figure 5: Software of polarization imaging system based on split focus plane.  
Figure 6: Experimental platform for underwater polarization imaging.  
to quantitatively analyze the experimental results of underwater polarization imaging. In general, when the image EME value is larger, the image shows more details; when the information entropy H value is larger, the image contains more information; when the average gradient \(\overline{G}\) is larger, the image contains richer texture details and higher definition.
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 2 Adaptive Noise Cancellation Based on Time Delay Estimation  
In most applications, low frequency magnetic signals are used as the communication signals, received by a primary loop antenna. Then the reference noise can be electric field component of noise received by electric antennas at the same position or magnetic field component of noise received by loop antennas at different locations. As shown in Figure 1, suppose that **d** is the desired signal at primary input and **x** is the noise signal at the reference input. So we get:  
\[\textbf{d}=\textbf{s}+\textbf{n}_{0} \tag{1}\]  
\[\textbf{x}=\textbf{n}_{1} \tag{2}\]  
where, **s** is the interesting signal, \(\textbf{n}_{0}\) and \(\textbf{n}_{1}\) are the low frequency correlated noises. The adaptive filter output is obtained by:  
\[\textbf{y}=\textbf{W}^{T}\textbf{x} \tag{3}\]  
where, **W** is the adaptive filter coefficient vector. With appropriate cost function, the additive noise can be subtracted from the primary signal by adaptive noise canceller.  
Widely used to model the non-Gaussian signals in brain images [27], power-line communication [28], pattern recognition [29], and so on, the \(\alpha\)-stable distributions can well represent the impulsive noise in low frequency communication. Lack of a closed-form expression for the probability density function (PDF), the \(\alpha\)-stable distributions are usually described by the characteristic function [30], as:  
\[\Phi\left(t\right)=\exp\left\{j\delta t-\gamma|t|^{\alpha}\left[1+j\beta sign \left(t\right)\omega\left(t,\alpha\right)\right]\right\} \tag{4}\]  
where,  
\[\omega\left(t,\alpha\right)=\left\{\begin{array}{ll}\tan\frac{\alpha\pi}{2} &\quad\alpha\
eq 1\\ \frac{2}{\pi}\log|t|&\quad\alpha=1\end{array}\right. \tag{5}\]  
\[sign\left(t\right)=\left\{\begin{array}{ll}1&\quad t>0\\ 0&\quad t=0\\ -1&\quad t<0\end{array}\right. \tag{6}\]  
Thus, the \(\alpha\)-stable distributions are completely characterized by four parameters and can be denoted by \(S_{\alpha}\left(\beta,\gamma,\delta\right)\). \(\alpha\in\left(0,2\right]\) is the characteristic exponent and determines the shape of the distribution. As \(\alpha\) is closer to 0, the variables have higher levels of impulsiveness and the PDF has heavier tail. \(\beta\in\left[-1,+1\right]\) is the skewness parameter. When \(\beta<0\), the distribution is left-skewed, while \(\beta>0\) means the distribution is right-skewed. When \(\beta=0\), the distribution is symmetric. \(\gamma>0\) is the scale parameter and \(-\infty<\delta<+\infty\) is the location parameter, analogous to the scale and location in Gaussian distribution respectively. Note that when \(\alpha=2\), the value of \(\beta\) is irrelevant and the distribution is Gaussian distribution. When \(\alpha=1\) and \(\beta=0\), the distribution reduces to a Cauchy distribution.
## 1 Introduction
This can be used for UAV route planning, as this has the potential to return good-fitting routes (maximized reward) according to the actions (i.e., route plan). This research proposes a location-aware RL-enabled UAV path planning for remote sensing in smart farms. The study's novelty is based on its contribution to optimize the use of UAVs for farming practices. This is explored specifically from a combination of power-aware UAV path planning and location-based reinforcement learning, which also responds to key factors in cost-effective UAV flights and dynamic field labeling and partitioning processes. Such an approach enables us to explore optimized practical methods for remote sensing in the farming applications. This research utilizes the Q-learning technique for reactive path planning on-the-fly over a labeled region-based farm. For this, the algorithm dynamically labels a grid-partitioning farm according to the static or mobile sensory targets such as plant, crop and/or farm animals. In turn, it utilizes an energy-aware exploration-exploitation paradigm to interact with the farm and learn the remote sensing target regions. This returns a table of routes/directions according to a weighting function of path length (euclidean distance) and consuming energy. Finally, it forwards the UAV through the optimal routes (shortest with minimized consumed energy) to capture the sensory recordings. The proposed approach offers the following contributions:  
* To take the benefits of RL to interact with the environment and dynamically route the UAV. * To propose an energy-aware routing by labeling the farm zones as no-fly, safe-fly and target to avoid blind walk and/or loop. * To use a weighting function of path length and consumed energy to minimize the remote sensing cost. * To utilize a grid-based field partitioning/labeling to avoid overlapped data collection. * To compare and contrast the performance of the proposed approach with infrastructure-based and to heuristic UAV path planning to highlight their superiorities and/or inferiorities. This paper is organized into six sections, as shown here. Section 2 outlines a review of UAV route-planning algorithms and applications in smart farming. This addresses the existing drawbacks and advances in this field of research. Section 3 introduces the RL enabled UAV path planning approach. This highlights the key features and techniques of the proposed approach. Section 4 explains the research simulation plan and shows how the performance of the proposed approach is tested and evaluated. Section 5 discusses the evaluation of the proposed approach according to four key metrics: (1) average End-to-End delay (ETE), (2) average number of captured data, (3) average battery consumption and (4) average traversed distance. The performance of the proposed approach is compared with two well-known conventional protocols IEMF [13] and TBID [14]. Section 6 summarizes the benefits of the proposed approach and highlights the key points of this research that have the potential to be addressed as further work.
# Shear Behavior of Superposed Perfobond Connectors Considering Lateral Constraints
## 3 Experimental Results
### Test Parameter Analysis
#### 3.5.2 Load-Holding Capacity  
According to the test results and load-slip curve analyses of the perfobond connectors, the crucial factors affecting the connectors' ability to maintain loads after the peak load are the lateral constraints and areas of the concrete dowel and those lateral constraints primarily provided by the perforating rebar, transverse reinforcement, and concrete wrapping.  
Figure 14 shows that the increase in the perforating rebar's diameter not only enhances the shear capacity but also significantly improves the load-holding capacities of the connectors. Specimens P-1 and CP-2 feature connectors with 16 mm-diameter perforating rebars, and Specimens P-2 and CP-1 feature connectors with 20 mm diameter perforating rebars; their capacity decreases slowly after reaching the peak load, and the load-slip curves all remain fairly stable until the end of loading; thus, the load-holding capacity of the connectors is reasonably good. On the other hand, when the bearing capacities of the P-3 and CP-3 connectors (without perforating rebar) reach their peak value, the bearing capacity declines prominently as loading progresses, soon falling to 80% of the peak value. Hence, the load-holding capacity of these connectors is poor.  
By comparing Figure 14a,b, the concrete strength can be seen to have little influence on the load-holding capacity of the perfobond connectors. This illustrates that the load-holding capacity of connectors with different concrete strengths is approximately uniform.  
As can be seen from Figure 15, when other parameters remain unchanged, the reinforcement of the concrete slab has a distinct effect on the load-holding capacities of perfobond connectors. Beyond the ultimate load, the bearing capacity of the NP-1 specimen (without reinforcement in the concrete slab) decreases rapidly, soon falling to 80% of the peak load. Thus, it has a poor load-holding capacity. Meanwhile, Specimens P-1 and SP-1 (with ordinary and segmental reinforcement, respectively) achieve better load-holding capacities beyond the peak value, and they do not exhibit the fall to 80% of the peak load thereafter. It can be concluded that the lateral constraints provided by the transverse reinforcement markedly enhance the load-holding capacities of the perfobond connectors. On the one hand, the shear contribution of the lateral constraints increases; on the other hand, the concrete dowel and perforating rebar give full play to the vertical shear function.  
As shown in Figure 16, the hole shapes of Specimens P-5, LP-1 and HP-1 are circular, long, and high hole, respectively. Owing to the approximate areas and identical lateral constraints, the load-holding capacities of these three types of connectors are similar and promising. However, they all measure lower than that of Specim P-1, which has a smaller hole area. Therefore, under identical lateral constraints, the load-holding capacity of the perfobond connectors decreases slightly under an increase in opening area.
## 3 Results and Discussion  
### Textural and Chemical Characterisation of Activated Carbons  
The four original ACs, after being submitted to acid and basic treatments, were texturally characterised by N\({}_{2}\) adsorption, at 77 K. Representative isotherms are shown in Figure 1. The isotherms obtained on the modified ACs are presented in Figures S1 and S2 (Supplementary Information). The textural characteristics, obtained by the application of the \(\alpha_{\mathrm{s}}\), DR and BET methods, of the N\({}_{2}\) adsorption isotherms are presented in Table 1.  
The four ACs presented close textural properties, with apparent superficial areas varying between 927 and 975 m\({}^{2}\) g\({}^{-1}\), micropores volumes varying from 0. 22 and 0.23 cm\({}^{3}\) g\({}^{-1}\) and mean pore size varying from 0.97 and 1.11 nm. Textural properties do not seem to be very determining in the performance of ACs in the removal of phenolic compounds. However, the physical shape of the ACs influences their performance in future applications. It should be noted that the Norit ACs were in granular form and the Merck and Panreac ACs were powdered. The four ACs originally presented a basic character, with a pHpzc varying between 7.27 and 10.49. Merck's AC presented a pHpzc closest to seven, and for this reason, it was chosen to undergo acid and basic treatments. The Merck AC treated with NaOH presented a high basic character (pHpzc from 7.76 to 8.67), which is helpful for the removal of phenolic compounds from the liquid phase [32; 33]. The elemental analysis did not reveal any significant change in the ACs modified with NaOH. However, the ACs treated with HNO3 presented a noteworthy reduction in the C content, varying from 85.2%, in the original AC, to 70.5% and 71.8%, in the modified ACs. An increase in the oxygen percentage, which was reflected in an increase in the acid functional groups, on the ACs surface, offset this reduction. FTIR analysis also confirmed the presence of acidic groups, on the surface of the ACs modified with nitric acid (Figures S3 and S4, in Supplementary Information). All of the spectra are almost similar in the absorption region, between 3200-3600 cm\({}^{-1}\). The principal bands and peaks are related to alkyl groups, namely CH3, CH2 and CH, more specifically with anti-symmetric and symmetric stretching, in the range 2800-3000 cm\({}^{-1}\). The band found in the original ACs in the region of 3400 cm\({}^{-1}\) was attributed to an O-H stretching mode of the hydroxyl group in alcohols, phenols or absorbed water. This peak was slightly broader in the modified ACs, in which the band shifted slightly, to 3450-3500 cm\({}^{-1}\). The peak near 1700 cm\({}^{-1}\), present on the ACs modified with HNO3, indicated C=O groups, in mode stretch, or the carboxyl group, responsible for their pronounced acid character. Simultaneously, the intensity of the band at 1410 cm\({}^{-1}\) increased due to the presence of a lactone structure. To summarise, as a result of the nitric acid treatment, the quinone functional groups were reduced and carboxylic acid groups were introduced.
## 6 Results and Discussion
### Bit Error Rate for Different Pulses' Width  
In Table 2, we present the bit error rate results considering various values of the duty cycle (\(D\)), defined in Section 4.1. The amplitude, frequency, and bias sensitivity remained constant across all scenarios. Considering the duty cycle, values close to 0.5 yielded better results than those near the extremes. Given that the demodulator counted transitions of events to decode data, as explained in Section 4.2, having duty cycle values close to the extremes made the duration of pulses (\(\tau_{\text{on}}\)) or the duration of the absence of pulses (\(\tau_{\text{off}}\)) very short, respectively. This short duration made it less likely for the receiver to capture the fast amplitude changes, thus failing to detect the start of a frame or the transmitted symbol itself, ultimately degrading the overall system performance.