esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed System
### Setup  
TA first picks a random \(s\in Z_{q}^{*}\) as its master secret and runs _Setup()_ algorithm to generate public system parameters \(params:=\langle\mathbb{G}_{1},\mathbb{G}_{2},q,\hat{e},g,h,g_{1},g_{2},g_{3}, H_{1},H_{2},H_{3}\rangle\). TA also issues id-based  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(\mathbb{G}_{1},\mathbb{G}_{2}\) & bilinear map groups of a prime order \(q\) \\ \(e:\mathbb{G}_{1}\times\mathbb{G}_{1}\to\mathbb{G}_{2}\) & bilinear map \\ \(g,h\in\mathbb{G}_{1}\) & generators of \(\mathbb{G}_{1}\) \\ \(s\in Z_{q}^{*}\) & master secret key of TA \\ \(g^{s}\) & public key of TA corresponding to \(s\) \\ \(RSU_{i}\) & identity of a roadside unit \\ \(sRSU_{j}\) & identity of a socialspot RSU \\ \(rsk_{j}\) & id-based secret key for an \(RSU_{j}\) \\ \(pid_{di}\) & \(i\)-th pseudonym of a vehicle \(V_{d}\) \\ \(vsk_{di}\) & id-based private key of a vehicle \(V_{d}\) for \(pid_{di}\) \\ \(sk_{X}\), \(pk_{X}\) & private and public key of \(X\) for CL-PRE \\ \(rk_{V_{d}\to sRSU_{j}}\) & re-encryption key of \(V_{d}\) to \(sRSU_{j}\) \\ \(ts\) & current timestamp \\ \(Enc_{\{i\}}()\) & symmetric encryption under the key \(k\) \\ \(Dec_{\{i\}}()\) & symmetric decryption under the key \(k\) \\ \(MAC_{\{i\}}()\) & message authentication code under the key \(k\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and descriptions.  
secret keys to RSUs and vehicles registered to the system. Note, at this phase, we assume that id-based secret keys can be issued to RSUs and vehicles through an out-of-band secure channel before they participate in VANETs. Let \(\mathcal{R}=\{RSUI_{1},...,RSU_{m}\}\) be the RSUs subordinated by the TA and \(\mathcal{V}=\{V_{1},...,V_{n}\}\) be the registering vehicles. For each \(RSUj\in\mathcal{R}\), TA generates \(RSUj\)'s id-based secret key \(rsk_{j}\gets idKeyGen(s,RSUj)\) and preloads \(rsk_{j}\) to each \(RSUj\) securely. Then, \(RSUj\) sets its private key as \(sk_{RSUj}=(rsk_{j},a_{j},b_{j})\leftarrow\)_SetPrKey(rsk\())_ and public key \(pk_{RSUj}=(g_{3}^{a_{j}},g^{b_{j}})\leftarrow\)_SetPubKey(sk\()_{RSUj})_ for CL-PRE.  
On the other hand, for a vehicle \(V_{d}\in\mathcal{V}\), TA chooses \(w+1\) pseudonymous identities \(PID_{d}=\{pid_{d1}|0\leq i\leq w\}\) for \(V_{d}\) after checking the eligibility of \(V_{d}\). TA issues \(V_{d}\)'s id-based secret keys \(vsk_{d}\gets idKeyGen(s,pid_{d})\) for each \(pid_{d1}\in PID_{d}\). In our system, \(pid_{d0}\) is used for re-encryption procedure while \(\{pid_{d1},...,pid_{dw}\}\) are used for VANET communications. When obtaining id-based keys for the pseudonyms, \(V_{d}\) sets its private key and public key for proxy re-encryption procedure as \(sk_{V_{d}}=(vsk_{d0},a_{d},b_{d})\leftarrow\)_SetPrKey(rsk\()_{d0})_ and public key \(pk_{V_{d}}=(g_{3}^{a_{d}},g^{b_{d}})\leftarrow\)_SetPubKey(sk\()_{V_{d}})_ for CL-PRE. Then, each \(V_{d}\) and each \(RSUj\), respectively, store \((pid_{d0},pk_{V_{d}})\) and \((RSUj,pk_{RSUj})\) to the cloud storage so as for them to retrieve the public key of each others.
## 3 Results and Discussion
### Verification Test under Optimum Conditions  
Based on Equations (2)-(5), the appropriate extraction condition (independent variables) proposed by the Design Expert software was as follows: extraction time 20.4 min, ultrasonic power 202.5 W and liquid-solid ratio 26.2 mL/g. From the consideration on the yield of flavonoids and operation, extraction time, ultrasonic power and liquid-solid ratio were respectively modified as 20 min, 200 W and 26 mL/g. Based on these conditions, the yields of QU, KA, GI and SC were respectively 0.109, 0.406, 0.031 and 0.355 mg/g obtained by UHRE. The above experimental values showed agreement with fitting values (RSD < 1.72%) according to the predictive RSM models.
## 3 Results
### Results of Segmentation, Element Geometric Transformation and Texture Synthesis  
Four examples are shown in Figure 4 of the results of segmentation on a lake, river, and island. In each example, the original image is shown on the left, and in the middle, the k-means segmentation results are shown, and the final processed segmented result is on the right.  
After the geometric boundary of the elements was obtained, we carried out a geometric transformation on the geometric boundary of the elements. In Figure 5, the leftmost side of each line is the original scene image, and then the geometric transformation of zooming in, shrinking, translation, and rotation is shown successively from left to right.  
Figure 4: Results of segmentation after processing on lake, river and island.  
Figure 3: Examples from the NWPU-RESISC45 data set. (Each class shows four images).  
The results of texture synthesis are as follows. Four examples are shown in Figure 6.  
Final Results of Filling the Element Polygon with the Textures and Comparison of the Results of GANs  
In this Section, we provide the results of tests conducted to examine the effectiveness of conventional GAN network models in RSSI generation and compared them with our proposed approach.  
Figure 7 shows the comparison of the effects of RSSI samples generated by mainstream GAN networks and by our proposed method.  
We compared images generated by our method with the image generated by the conventional GAN network. It may be observed that for most images generated by the GAN network, there are problems with blur and distortion. In the ACGAN network generation experiment, there was a significant gradient-vanishing issue, and the model did not achieve a degree of convergence that fulfilled the requirements. The image generated by WGAN did not have enough detailed information. The effect of the image generated by other GAN networks was better than the former two, but the size of the generated image was strictly limited. It required more time to train an image of the same size as the original data set, and these times were often excessive. The RSSIs generated by our proposed method were not only higher in resolution than the images generated by the GAN network, but also our method was able to quickly generate an image of any size.  
Figure 5: Results of geometric transformation on elements of lake, river, and island.  
Figure 6: Results of texture synthesis; the smaller ones are samples selected from the data set and the bigger ones are the synthesized texture images.  
After extracting and transforming the geometric boundaries of the elements in the scene image, we used the synthetic texture to fill in the geometric contours of the elements and obtained the following results.
# Personalized-Template-Guided Intelligent Evolutionary Algorithm
## 3 Results
### Comparison of PTG with Other Algorithms
#### 3.3.2 Analysis of Experimental Results of CEC2022 Test Function  
The comparison results of PTG and other algorithms on CEC2022 are shown in Tables 5 and 6. In the table, we take the average of the optimal solutions obtained by the algorithm in 30 independent runs as the mean of the algorithm. Calculate the standard deviation of these 30 solutions to measure the average deviation degree of data points from the mean. The larger the value, the more dispersed the data distribution. The 10 algorithms are ranked based on their standard deviations and means. The higher the ranking, the better the performance of the algorithm. It can be seen from the table that PTG ranks first among all 12 algorithms and has the highest solution accuracy. Compared with other algorithms, PTG has the smallest standard deviation and the best stability. In the F1 unimodal function, this algorithm is significantly superior to the comparison algorithm, proving that this algorithm has a strong global search ability. In the F5 multimodal function, the algorithm is one order of magnitude better than other algorithms, which means that the algorithm has the ability to traverse the multi-peak terrain so that the population can continuously find better areas. In the mixed function F6, the solution result of the algorithm is significantly better than that of other algorithms, indicating that the algorithm has the ability to solve complex problems. Since CEC2022 contains a variety of test functions, we record the time required for the algorithm to run once on each function in Tables 5 and 6. As can be seen from the  
Figure 4: Box diagram of PTG and 9 optimization algorithms in CEC2005(F1–F23). table, the running time of PTG is longer, only better than that of HO, and it runs more than 2 s longer than other algorithms. After analysis, the algorithm's main time is spent in the template generation stage. At this stage, after the template determines the base point and the extended interval, the dimension information of the particles is integrated to generate the template. Through the ablation experiment, it can be concluded that this design is reasonable and effective. The generated template integrates historical information to guide particles to optimize efficiently during the template guidance stage, while eliminating the need for custom parameters in the algorithm and solving the difficulty of setting algorithm parameters. The experiments in the next section also prove that the algorithm has high accuracy and high stability.
# An OCARI-Based Wireless Sensor Network for Heat Measurements during Outdoor Fire Experiments
## 3 Experimental Protocol
### Experimental Plot
Hence, we did not investigate the network failures as a communication channel occupation and possible congestion due to several nodes [26]. We focused on the ability of the WSN to conveniently record heat data and the relevant timestamped data when compared to a standard wired system. The fire is ignited from a fire line on the downwind edge of the plot using a constant quantity of alcohol and a fire torch. It is important to consider that the experimental configuration leads to variations in the distance in terms of the height from fire to sensors, load, and wind conditions. The aim involves promoting a large set of analogue signals from the WSN and the wired datalogger to compare the same and does not involve investigating different incident radiations from fire to the sensing target. The results are presented in the next section and focus on the influence of measurement conditions and sensor sensitivity on signals gained by WSN when compared with the ones gained on the wired datalogger. The physics of the heat radiation in outdoor fires is not discussed in the present study. Readers can refer to publications focusing on this subject [2; 3; 23] for details. Another important point concerns the thermal protection of each node and their analogue sensor: each node is inserted into a shield pocket made with ceramic wool. Into their thermal shield of ceramic wool, the sensor nodes--i.e., the transducer and the radio-communication node--can show resistance to a distant heat irradiance as to a prolonged contact with the fire. However, if the shape and size of the shield of the sensor nodes can be set up according to an incident heat radiation, this is not the case when these sensor nodes enter into contact with the fire. The direct contact between fire and target promotes a mixed heat environment including radiative and convective contributions of heat transport from flames to the node. If the irradiation is well known (the heat flux sensors were calibrated against a black body source in the same conditions), this is not the case for the convective heat transfer. Typically, if a fire causes irreversible damages to the shielded nodes embedded, we would not be able to quantify the level of heat flux responsible of these damages because the convective heat flux cannot be measured (since heat flux sensors are calibrated against a radiant source). For instance, in Reference [6], we observed that a fire across a 10 kg/m\({}^{2}\) load of vegetal fuel causes the destruction of nodes. However, we could not quantify the amount of heat causing this destruction because of the uncertain contribution to the heat convection. This is why we do not embed sensor nodes into the fire in the present study as we did in Reference [6], to remain under the influence of a sole heat radiation for which shields are dimensioned and avoiding the sensor destruction.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Data Processing, Analysis and Validation
#### 3.3.1 Manual Mapping  
The manual mapping of flooded areas is made by drawing on QGIS software the polygon of the flooded area directly on the georeferenced satellite image. The image is obtained with the composition of multispectral bands in true or false color. In the case of co-flood imagery, the identification is simple; however, for post-flood data, the creation of an enhanced color image is necessary to identify the traces of flooding. For the manual mapping of the flooded area, we also try to use the SAR amplitude images of the Sentinel-1, geocoded with ESA SNAP software.
## 5 Experimental Results
### Software Used
#### 5.3.3 Application  
The application was realized in TinyOS 2.1 with Crossbow TelosB development platforms. An application was created for the Coordinator and another one for the Network Device; therefore each platform could only perform one of the two behaviors. This alternative does not allow the established network to be an ad-hoc network, but it is a solution that notoriously reduces memory consumption in platforms.  
There were three Network Devices, two of them transmitting the environment temperature information, by means of the SHT11x sensor integrated in the TelosB. The remaining Network Device made the implementation of the Pan Tompkins algorithm, described on Section 3.1. When the node completes its association process, a periodical sampling of the ECG signal is configured. When this signal is sampled, the Pan Tompkins algorithm initiates. Each algorithm block was developed in a different task and every corresponding task was posted at the end of the signal sampling.  
This alternative was selected to allow synchronous events, such as the arriving of a beacon or data, could be attended without major delays. When concluding the filtering process, the Network Device transmitted the according information of each phase.  
The network Coordinator received this information and transmitted it through the serial interface to the Computer.
## 4 Findings Results  
The data used in this study were up to date as of October 2023. The first phase of the subsequent steps involved an initial descriptive analysis to gain a better understanding of the reviewed articles. This analysis examined the papers across several dimensions to uncover various aspects of the literature. Key components of this examination included the words used in titles, key terms in abstracts, years of publication, citation counts, bursts of interest for specific topics, centrality measures indicating the importance of papers within a citation network, and sigma values representing the overall impact of the publications. Additionally, specific terminology and thematic clusters were identified through this analysis. Each paper's disciplinary field was assessed, and distinctions were made between article types (e.g., research articles versus reviews), as well as tracking their distribution across different academic journals. This approach provided the researchers with a multifaceted understanding of the literature, revealing important patterns, themes, and focal points within the discipline.
# Stabilization of Transverse Modes for a High Finesse Near-Unstable Cavity
###### Abstract  
We develop a method to lock a high-finesse near-unstable Fabry-Perot (FP) cavity (F = 7330) to a frequency stable dye laser operating at 605.78 nm using the Pound-Drever-Hall technique. The experimental results show the feasibility of locking this cavity to different transverse modes. This method links the external FP cavity to the dye laser cavity, and a 379 kHz final linewidth of the FP cavity is achieved. Such a near-unstable cavity is potentially useful for cavity-enhanced spontaneous parametric down-conversion to generate narrow-band single photon or photon pairs in different transverse modes.  
Keywords:PDH technique; transverse modes; cavity +
Footnote †: journal: Applied Physics Letters
## 4 Evaluation of Model Developments
### Observational Data to Evaluate Model Developments
#### 4.2.5 Lsa SAF LST  
Land Surface Temperature (LST) is taken from the Satellite Application Facility on Land Surface Analysis (LSA-SAF). LST is derived from measurements by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation (MSG). The LST retrievals are based on the generalized split-window technique [81] and are available every 15 min for all clear-sky pixels within the MSG disk with a resolution of 3 km at the nadir. Following previous work on model evaluation with satellite retrievals [37; 82], the data is aggregated to a coarser grid (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) only keeping the valid pixels (fraction of clear sky). For the comparison with model simulations, clear sky conditions, synchronous in time are considered: (i) at least 70% of the fraction of valid pixels and (ii) total cloud cover lower than 30% [82]. For each day and pixel, when at least 75% of the 3-hourly data was available (6 out of 8), the daily maximum and minimum LST are extracted to compute the diurnal range.
## 5 Decision Support Workflow
### Building and Validating a Simulation Model  
After identifying the above factors, we analyzed their impact on delivery precision by discrete event simulation (DES). Note that DES was already routinely applied in our plant for validating and evaluating layouts. Here, a DES model of the mini-factory was validated and run in a number of scenarios characteristic to the variations of the above factors. The model of the existing mini-factory was built in the professional Plant Simulation software, with product-dependent stochastic operation and cycle times and sequence-dependent change-over times (characterized by product family, diameter, and stroke length). The layout and the corresponding operations of the mini-factory are shown in Figure 4 above.  
Next, different simulation scenarios were generated by tweaking both external and internal factors. To keep experimentation manageable, from the factors discussed above, only the most characteristic ones were selected. Hence, typical external factors were the product code and the daily ordered volume, while internal factors were both structural (buffer sizes, operator, and machines' availability) or behavioral, such as lot-sizing and sequencing policies. To execute the sensitivity analysis, the ultimate outputs of the simulation model were the completion dates of the orders and the total makespan of the daily production executed in three shifts.  
In the next step, validation of the model was performed to demonstrate that this DES model can truly reproduce the operation of the original system. During the validation, the DES model was connected and fed with the real-life systems' inputs, processing times, and buffer levels, thus forming a digital twin [22] of the mini-factory. Specifically, for a one-week  
Figure 7: Factors influencing CDD performance.  
period, we compared five points of the production flow's real volume results with the simulated volume figures, and the duration of real operation times with the simulated duration times of operations (after cutting, cylinder and rod manufacturing, washing, and assembly). Whenever significant deviation was detected, we checked the root causes, and adjusted and fine-tuned the parameters of the simulation model. As an example, Figure 8 shows the distribution of the order lead times for different randomized simulation runs for the same scenario, compared to the measured data (black line). Note that at the time of the measurements, the mini-factory worked daily in two shifts only.
## 2 Machine Configuration and Operating Principle  
### Machine Configuration  
The configuration of the proposed generating system is shown in Figure 1. The designed model consists of a stator, an adjustable modulator ring, and a rotor. A three-phase seven-pole-pair winding is placed inside twelve stator slots. A set of seven-pole-pair PMs is placed on the rotor. The PMs on the rotor are tangentially magnetized for flux concentration. The rotor is connected to the wind turbine through a gearbox. A twelve-pole-pair adjustable modulator ring is placed between the stator and rotor. The modulator is connected to the servo motor, which can be adjusted within a limited angle for flux weakening.
# Flavour Volatiles of Fermented Vegetable and Fruit Substrates: A Review
## 3 Results
### Alcohols  
Alcohols, with their characteristic aromas, comprised the largest volatile group detected in 33 of the 35 studies reviewed. Alcohols are produced from carbohydrate degradation or amino acid catabolism [52]. Across the 33 studies, the alcohols most commonly detected after fermentation were ethanol, 3-methyl-1-butanol (isoamyl alcohol/isopentyl alcohol), 2-methyl-1-butanol (amyl alcohol), 3-methyl-3-buten-1-ol (isopentyl), 2, 3-butanediol, 2-ethylhexanol, 1-hexanol, 2-hexanol, 2-hexen-1-ol, 3 hexen-1-ol, 2,6-dimethyl-1-4-heptanol, benzyl alcohol (phenyl methanol/benzene methanol), 2-phenylethyl alcohol (2-phenyl ethanol/benzene ethanol), 4-ethylphenol, 2-(4-methylphenyl)-2-propanol, 1-octanol, 1-octan-3-ol, (Z)-1,5-octalen-3-ol, octanol, 2-octenol, 1-nonanol, (Z)-3-nonen-1-ol, 2-undecanol, 3,7,11-trimethyl-1-dodecanol, and 2-tridecanol. Ethanol is synthesised from sugars naturally present in plants; LAB utilise sugars via the phosphoketolease (PK) pathway, and yeast utilise sugars through the Embden-Meyerhof-Parnas (EMP) pathway [13]. In mango slurry, fermentation involving the yeast _Saccharomyces cerevisiae_ (S. cerevisiae) generated 30-100 times more ethanol than LAB fermentation [41]. When _Williopsis sativurus_ var. _saturus_ (_W. saturus_) yeast was combined with LAB for fermentation, there was also a marked increase in the ethanol concentration (six-fold increase) compared to LAB alone in durian pulp [30]. However, ethanol can be generated by heterofermatative LAB, which possess the alcohol dehydrogenase enzyme that converts acetaldehyde into ethanol [26]. In kiwifruit juice (cultivars of _Actinitia delicosa_ cv. Xuxiang and _Actinitia chinensis_ cv. Hongyang), the ethanol concentration was 10,316.5, 17,249.2, and 8652.7 ng/mL in Xuxiang cultivar juice fermented by either _L. acidophilus_, _L. helicticus_, or _L. plantarum_, respectively, compared to 6242.9 ng/mL in the unfermetted juice. However, the ethanol concentration was 13,042.5, 7004.2, and 9551.9 ng/mL in Hongyang cultivar juice fermented by either _L. acidophilus_, _L. helicticus_, or _L. plantarum_, respectively, compared to 19,642.6 ng/mL in the unfermetted juice [49]. The different ethanol concentrations produced from the two kiwifruit cultivars after LAB fermentation could be a result of the different substrate compositions of the cultivars, which are subjected to various metabolic pathways by LAB. After fermentation of orange pomace by _L. rhamnas_, 0.3 ug/mL of ethanol was detected in a distillate prepared using vacuum distillation to extract VOCs. However, ethanol was not detected in a fermented orange pomace distillate prepared using a simple distillation method. In the same study, the ethanol concentration detected in distillates prepared from _L.