esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## 5 Conclusions  
A semi-empirical QKD model for satellite-based systems that simplifies the practical simulation of communication links has been proposed. In contrast to other pure theoretical models, our semi-empirical approach was based on atmospheric extinction coefficients common for ground stations at mid-latitude. These coefficients were determined experimentally by means of real ground-based receivers developed by the authors.  
To verify our method, the Xinglong ground station's peak sifted key rate was simulated and compared to the published experimental data from the Micius satellite for specific satellite passages. The lower and upper boundaries of the peak sifted key rate were appropriately estimated by our model, employing best-choice phenomenological parameters. Then, using this model for ground stations with 300 and 600 mm aperture telescopes, we simulated the secure key generation from the Micius satellite.  
To conclude, we proposed and verified a simple practical method for estimating the performance of ground-based stations, which are one of the key elements of future QKD networks. We believe that the developed model will be useful for quick, right-on-the-spot estimation of satellite QKD links and the analysis of future satellite-to-ground QKD systems.  
Conceptualization, V.L.K. and A.V.K.; methodology, A.V.K., E.LI and A.V.M.; investigation, A.V.K. and A.V.D.; writing--original draft preparation and editing, A.V.K. and A.V.M.; supervision, V.L.K. and Y.V.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Education and Science of the Russian Federation in the framework of the Program of Strategic Academic Leadership \"Priority 2030\" (Strategic Project \"Quantum Internet\").  
\begin{table}
\begin{tabular}{c c c} \hline
**Passage** & **Sifted Key Length, \(l_{sift}\) (kbit)** & **Secret Key Length, \(l_{sec}\) (kbit)** \\ \hline
2021-10-31 & 547/1207 & 40/182 \\ (\(\theta_{El}^{max}=32.5^{\circ}\), \(\Delta t=221\) s) & & \\
2022-03-23 & 859/1897 & 101/357 \\ (\(\theta_{El}^{max}=42^{\circ}\), \(\Delta t=260\) s) & & \\
2022-03-09 & 1263/2790 & 187/600 \\ (\(\theta_{El}^{max}=57.5^{\circ}\), \(\Delta t=283\) s) & & \\ Zenith passage & 1586/3505 & 260/803 \\ (\(\theta_{El}^{max}=90^{\circ}\), \(\Delta t=285\) s) & & \\ \hline \end{tabular}
\end{table}
Table 4: Sifted key length and secret key length were simulated for the satellite-to-ground QKD with 300 mm/600 mm ground stations for four typical satellite passages. The communication time of \(\Delta t\) corresponds to elevation angles greater than 20\({}^{\circ}\).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank [PERSON] and [PERSON] for helpful discussion.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
3-Hydroxypropionic acid (3-HP) is an important bulk chemical with a wide range of applications in chemical synthesis. Since it contains two reactive groups--hydroxy and carboxyl--one of the important applications of 3-HP is its polymerization to yield poly(3-hydroxypropionate) (P-3 HP). Due to its biodegradability and biocompatibility, P-3 HP is one of the widely studied substitutes for petrochemical plastics, with great application value and development prospects [1]. In addition, 3-HP can undergo redox reactions to produce 1,3-propanediol (1,3-PDO), acrylic acid, malonic acid, acrylamide, acrylonitrile, etc. [2], which are widely used in the production of adhesives, plastic packaging, fibers, and cleaning agents. In particular, acrylic acid occupies the highest market, with a value of USD 12 billion in 2020, which is projected to be around USD 19.2 billion by 2030 [3, 4]. Due to the significant commercial application potential of 3-HP, the market value and size are estimated to be more than USD 10 billion/year and 3.6 million tons/year, respectively [3].Currently, 3-HP production is mainly based on chemical synthesis from fossil feedstocks, but chemical production is costly and environmentally unsafe. 3-HP was listed as one of the most promising bulk chemicals that can be obtained from renewable materials by the U.S. Department of Energy [5, 6], which can be achieved using engineered microorganisms. In the past few years, several industrial parties, such as OPX Biotechnologies, Cargill, Novozymes, and BASF, have developed pilot plants for the bio-based production of 3-HP and its chemical conversion to AA [4, 7, 8, 9]. A series of genetically engineered microbial cell factories to convert renewable substances into 3-HP have been constructed via different biosynthetic pathways [3, 10, 11]. However, microbial 3-HP synthesis processes still has a certain distance to travel meet the market demand.  
The most important strategy for developing microbial 3-HP synthesis processes is the selection of suitable microbial chassis strains, effective metabolic pathways and economical substrate, but the effects of fermentation processes on overall process economics should also not be ignored. In this paper, we give a brief overview of the main 3-HP biosynthesis pathways that have been used for the development of microbial cell factories. Notably, we summarize the excellent cases of microbial 3-HP production from both chassis and substrate aspects. In this way, readers will have a clearer understanding of the current status of 3-HP production, i.e., what are the 3-HP production pathways, chassis and substrates, and researchers who want to establish 3-HP microbial cell factories will be able to better select and combine them. In terms of chassis, we summarize the excellent chassis used for 3-HP production and introduce their characteristics in the production process. In terms of substrates, we not only summarize the excellent cases of common carbon sources (glycerol and glucose) in 3-HP production, but also outline the cases of other emerging carbon sources, such as xylose, acetate, fatty acids, ethanol, and CO\({}_{2}\).
## 2 Literature Review
### Data Gathering by AUV Path Planning  
As mentioned above, the UWSN environment is involved in a variety of applications. Pipeline monitoring was addressed with a UWSN using an AUV [37]. The sensed data were collected by AUV and then forwarded to the surface sink. The AUV followed a linear sensor network segment model to gather the data sequentially. If the sensed data were emergency data, then the AUV skipped the entire intermediate sensor and reached the event-sensed sensor to gather its data. Therefore, the emergency data were provided with higher priority; however, the use of a single AUV was not sufficient to support a large-scale network, and also it increased waiting time for delivery of data. Data gathering was presented with an evolutionary algorithm for the path planning of an AUV [38]. The PSO was used in estimating the optimal waypoint at which the data have to be collected. This PSO algorithm was slow in convergence, which took more time in optimal selection.  
A hybrid optimization algorithm was designed for predicting an optimal solution for AUV tours [39]. The work proposed an integrated, quantum-behaved PSO with improved ant colony optimization (ACO) to predict a shorter tour path. Initially, the lowest number of waypoints was generated, and the points to be visited were selected using the algorithm. The shortest path was chosen using ACO algorithm, which extended the network lifetime by reducing the trajectory length. The conventional problem in this ACO is that it is slower in convergence. Heuristic, adaptive sink sensor set selection (HAS\({}^{4}\)) was designed to enrich network lifetime [40]. This HAS\({}^{4}\) was developed for centralized and distributed environments in which the sensor lifetime plays a key role. Sensor lifetime was the only metric computed and sorted among all the sensor nodes and then a set of sensors was selected, which the AUV visited to collect data. The AUV tour selected sensor nodes by minimum lifetime, which tended to increase the tour length.  
A swarm hyper-heuristic algorithm (SHH) was associated with local path planning and global path planning [41]. The global path was identified using an A* algorithm, and the local path prediction was subsequently performed using time, obstacles and AUV motion. In general, this A* algorithm involves multiple computations that increase the complexity of planning the path of AUVs. A prediction-based delay optimization data collection algorithm (PDO-DC) was presented to mitigate the collection delay [42]. The clusters were created and then they were updated; further, an AUV path was predicted. According to this work, the data collected at CH had to request that the AUV collect the gathered data. Data collection performed in this way certainly increased the traveling length, and also more than one CH could request at a time. By analyzing the existing works, the major demerits in UWSNs used for routing and data gathering were identified and resolved with particular solutions.
# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 4 Proposed Hybrid DL Model
### Dataset Construction  
To train and assess the model, olive leaf images were gathered from an olive grove. The area of the study lies in the Al Jouf region in the Kingdom of Saudi Arabia at an altitude of 37\({}^{\circ}\)23\({}^{\prime}\)57\({}^{\prime\prime}\) N and a longitude of 3\({}^{\circ}\)24\({}^{\prime}\)47\({}^{\prime\prime}\) W. The location enjoys a Mediterranean climate with mild-wet winters and serious summer droughts. The average annual rainfall is 100 mm and the annual temperature is of 25 \({}^{\circ}\)C. The area in which the test is conducted lies in an olive grove having a surface of 30 hectares. It comprises one thousand olive trees which were planted in 2006. A flat rectangle of 560 m \(\times\) 280 m that contained around 100 olive trees was used as the object of our study. An aerial view or the proposed concept is shown in Figure 4.  
For the aerial inspection mission, a drone, on which all hardware modules for the sender were mounted, was used. The drone is a fixed-wing drone which can cover large geographic surfaces with a low consumption of energy. A strip's approach was used to plan the flight path of the considered drone in order to improve the efficiency of energy use. Figure 5 shows the use of Mission software calibrated by remote control. Clearly, the figure  
Figure 2: The architecture of the MobileNet model.  
shows a take-off point of the drone to start its aerial inspection journey, as well as an end point where the drone finishes the task of dedicated test area. A rechargeable battery that enabled flying for about half an hour was installed on the considered drone that could fly at an altitude ranging from 25 to 30 m. Thus, the camera that was fixed onboard the drone was used to capture images and to send them to the cloud for storage and analysis. Then, administrators could access the stored images by using the proposed model to evaluate the status of the olive tree leaves and identify any diseases and insects.  
Figure 3: Schematic view of the proposed olive diseases detection method.  
To build the dataset and to use it later in this study, 5400 varied images of olive leaves were gathered from the above-mentioned geographical area. The varied images contained a variety of olive diseases. Further, with the help and support of an agricultural engineer, the gathered data were classified into four different classes (Aculus Olearius, olive scab, peacock spot, and healthy), as shown in Figure 6. Then, the dataset was divided into two major groups; 80% of the dataset was used for training and the remaining 20% of the dataset was used for testing. Table 2 presents the image classes and the number of objects per class.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 2 Background and Methodology  
### Study Area Background  
The Yangtze River, with a length of 6397 km, is the largest river in Asia. It flows from west to east, ultimately reaching the East China Sea at Shanghai. The river's geographic coordinates range from E90\({}^{\circ}\) to E122\({}^{\circ}\) longitude and from N25\({}^{\circ}\) to N35\({}^{\circ}\) latitude. The upper reach of the river is divided at Yichang, and it encompasses several major tributaries, including the Yalong River, Min River, Tuo River, and Jialing River. The segment of the mainstream that is upstream of YiBin is also known as the Jinsha River.  
This paper proposes a river runoff estimation method primarily based on historical hydrological data from the Jinsha River and Min River basins, which converge at YinBin to form the Yangtze River. The Jinsha River spans a length of 2136 km, with a watershed area of 473,200 km\({}^{2}\). Its largest tributary, the Yalong River, is located approximately 550 km upstream of YinBin. Xiangjiaba has served as the control hydrological station for the Jinsha River since the construction of the Xiangjiaba dam in 2012. Prior to that, the control station was Pingshan, situated around 30 km upstream of Xiangjiaba. The Min River, on the other hand, stretches for 735 km, with a watershed area of 135,800 km\({}^{2}\). Its largest tributary is the Dadu River, located 135 km upstream of YinBin. Gaochang serves as the control hydrological station for the Min River, situated 25 km upstream of YinBin.  
Due to the objectives of water supply and electricity generation, numerous dams have been constructed since 2000. As of 2020, there were four major dams in the Jinsha River watershed: Xiajiaba, Xiloudu, Ertan, and Jinping 1 st, with the former two located along the mainstream. According to the latest cascaded reservoir plan, two additional dams, Baihetan and Wudongde, will be added along the mainstream in the next two years, while Lianghekou will be constructed on the Yalong River, a tributary of the Jinsha River. Similarly, as of 2020, four major dams have been built in the Min River watershed: Zipingpu, located on the mainstream, and Pubugou, Houziyan, and Changheba, situated on its largest tributary, the Daduhe. In the next five years, an additional major dam, Shuangjiangkou, is planned for the Daduhe. Detailed information and the distribution of the aforementioned dams are shown in Table 1 and Figure 1.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Interpolation-Based SR Method  
Interpolation-based techniques of SR include nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, and Lanczos interpolation [44]. These methods generate the initial HR estimate of the output image by utilizing interpolation methods. In this step, the interpolation stage entails employing upsampling techniques and generating the HR output.  
In the nearest neighbor interpolation method, the concept underlying such algorithms is that comparable image are near in terms of distance measurement [45]. The nearest neighbor interpolation approach can be represented mathematically as follows in Equation (2):  
Given a collection of datasets \((L_{1},M_{1}),(L_{2},M_{2}),...,(L_{n},M_{n})\) and a position \(L\), the estimated value \(M\) at \(L\) is  
\[M=M_{i}\text{ if }L_{i}\leq L<L_{i+1},\text{ \ for }i=1,2,\ldots,n-1,M=M_{n}; \text{ if }L\geq L_{n} \tag{2}\]  
In this case, \(M_{i}\) indicates the \(M\) value of the data point closest to \(L\), while \(L_{n}\) represents the data set's greatest \(L\) value [46].  
Apart from the nearest neighbor interpolation method, bilinear interpolation is one of the well-known techniques for producing HR output [47]. The bilinear interpolation approach is accustomed to generating an HR output straightforwardly and efficiently. A bilinear interpolation is one of the comprehensive techniques for two-dimensional linear interpolation. The value at one place inside the rectangular grid is calculated using a weighted average of the values at the four closest grid points using bilinear interpolation. The distance between the interpolated location and the surrounding grid point positions determines the weights applied to each grid point.  
On the other hand, bicubic interpolation is a method of interpolating data points on a two-dimensional regular grid that is an extension of cubic spline interpolation. This approach is often used in SR tasks due to computational efficiency and comparably better performance than other interpolation methods. Apart from these methods, Lanczos interpolation is a well-known method for upscaling the LR images [48]. The intuition behind the method is the Lanczos Kernel, which is a windowed sinC function with superior frequency domain features compared to conventional interpolation kernels. In particular, the Lanczos method was utilized for sharpness preservation and is customizable.
## 4 Experimental Results and Analyses
### Comparison with Other Methods
#### 4.2.3 Experiments on the URCAL Dataset
FIDTM replaces the density map with a focal inverse distance transform map for point regression, producing high-quality outputs. Nonetheless, its feature extraction limitations result in missed objects, reducing overall accuracy. In contrast, MS-WFFNet leverages the weighted mechanisms in the EEMA and MGFA modules to enhance its robustness in complex backgrounds. This design effectively improves both the model's counting accuracy and the quality of its generated density maps, making it better suited for challenging scenes.
## 4 Discussion
### Influence of the Proposed Model's Parameters on Balancing Performance and Clustering Efficiency
#### 4.3.3 Merging Distance Threshold  
The merging distance threshold \(\lambda\) governs the model's ability to address defect merging (Category 2). Smaller \(\lambda\) values effectively minimize false positives by restricting matches to closely spaced features. However, this strict matching criterion may fail to capture actual matches in the case of significant interactions between adjacent defects, leading to reduced recall. Conversely, larger \(\lambda\) values enhance recall for these scenarios, but they may introduce false matches in segments with fewer interactions. These false positives become particularly problematic when the clustering setup is unsuitable, causing inappropriately formed clusters to be classified as defect merging problems (Category 2). In such cases, the merging distance threshold may inadvertently associate unrelated features, undermining the model's precision.
## 2 Materials and Methods
### Kernel Density Estimation  
Kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights [14; 15]. KDE answers a fundamental data-smoothing problem where distributions about the population are carried out [16]. For the basic definition of KDE, let (\(x_{1}\), \(x_{2}\), \(\ldots\), \(x_{n}\)) be independent and identically distributed samples drawn from a specific distribution with an unknown density \(f\) at any given point \(x\). Its kernel density estimator can be defined using Formula (1).  
\[\hat{f}_{h}(x)=\frac{1}{nh}\cdot\sum_{i=1}^{n}K(x-x_{i};\ h) \tag{1}\]  
In Formula (1), \(K(x-x_{i};h)\) is the kernel function, whose outcomes are non-negative values. There exists a range of kernel functions being used, such as cosine, linear, normal, etc. [14; 15]. The positive variable \(h\) is called the bandwidth, which is a smoothing parameter and exhibits a strong influence on the resulting estimation.
## 3 Numerical Simulations
### Influence of Excitation Frequency on Vibration Characteristics
In order to further explore the influence of the excitation frequency on the output of the piezoelectric energy harvester, keep \(A=14\) m/s\({}^{2}\) unchanged, in the excitation frequency range studied, and take \(f=6\) Hz, \(f=9\) Hz, and \(f=12\) Hz for comparative analysis. Figure 8a,b shows the simulation results of time-domain velocity versus the displacement and voltage-time diagram with the conditions of \(A=14\) m/s\({}^{2}\) and \(f=6\) Hz, respectively. It can be seen from Figure 8 that the BPEH system performs small-amplitude intrawell periodic oscillation, and the IBPEH system performs chaotic motion. In some periods, the response speed and vibration displacement amplitude of IBPEH are larger. Under this  
Figure 7: RMS voltage diagram with excitation frequency as parameter: (a) BPEH; (b) IBPEH. excitation condition, the RMS voltage produced by IBPEH is 16.96 V, while that BPEH is only 2.65 V.  
Increase the excitation frequency to \(f=9\) Hz, and keep \(A=14\) m/s\({}^{2}\) unchanged. Figure 9a,b shows the simulation results of time-domain velocity versus the displacement and voltage-time diagram under this condition. It can be seen from Figure 9 that compared with the small-amplitude intrawell motion state of BPEH, IBPEH can realize a large-interwell periodic motion state, and the response displacement and speed have been greatly improved. In this case, IBPEH can yield a higher voltage, and the RMS voltage is 6.21 times that of BPEH. Continue to increase the excitation frequency to \(f=12\) Hz, and keep \(A=14\) m/s\({}^{2}\). Figure 10a,b shows the simulation results of time-domain velocity versus the displacement and voltage-time diagram, respectively. From this figure, it can be seen that both BPEH and IBPEH systems can achieve large-amplitude interwell period motion, and the response displacement and speed of the system have been greatly improved. Compared with BPEH, the RMS voltage of the IBPEH is increased by 23.51%. Figure 8: Simulation results of time-domain velocity versus the displacement and voltage-time diagram when \(A=14\) m/s\({}^{2}\) and \(f=6\) Hz. (**a**) Simulation results of time-domain velocity versus the displacement; (**b**) voltage–time diagram. Figure 9: Simulation results of time-domain velocity versus the displacement and voltage–time diagram when \(A=14\) m/s\({}^{2}\) and \(f=9\) Hz. (**a**) Simulation results of time-domain velocity versus the displacement; (**b**) voltage–time diagram. It can be seen from the above analysis that different excitation frequencies have a significant influence on the output efficiency of the piezoelectric energy harvester. Compared with BPEH, IBPEH can adapt to lower excitation frequency and has wider operating frequency bandwidth. Piezoelectric energy harvesters are capable of generating higher and more intensive electrical energy in large-amplitude interwell period motion than in chaotic motion.
## 3 Materials and Methods
### Method Validation Results: Precision and Accuracy  
For aroma compounds, results on quantified analytes referred to a fairly good precision with inter-week CV% values never exceeding 25% (results reported in Supplementary Table S1 (ST1)); this outcome was consistent with validation parameters included in the Commission Implementing Regulation (EU) 2021/808 in the case of analytes present at ppb levels (\(\mu\)g/L). Within aroma compounds, those with the best performances over the six-week validation period were _hexanal_ (10.09%), (_E,Z_)-_2,6-nonadienal_ (12.21%), _phenylacetaldehyde_ (13.54%), and _geraniol_ (14.25%). The most volatile odorants, i.e., Strecker aldehydes 3-_methyl butanal_ and _2-methyl butanal_, reported CV% values close to the acceptability threshold with 24.56 and 22.13%, respectively.  
In the case of taste-active compounds, results concerning precision were all within the acceptable range established by the Commission Implementing Regulation (EU) 2021/808; CV% values were always below 18%. The higher value was obtained for flavan-3-ols with an inter-week CV% of 14.55% while a very good intermediate precision was obtained for theaflavin (5.16%), _caffeine_ (6.0%), and flavonol derivates (10.60%).  
Accuracy was determined by spiking suitable amounts of analytes within the working range. Percent recovery (% Rec) never exceeded the range \(\pm\)20%, with better performances, as expected, for non-volatile compounds (average Rec % 103).