esa-sceva/satcom-chunk-collection huggingface dataset:
## Results
### Construction of Aroma Fingerprinting of Flat Peaches from Xinjiang
#### 3.3.4. Identification of Characteristic Aroma Fingerprint of Flat Peaches from Xinjiang  
To verify the aroma fingerprinting of flat peaches from Xinjiang, hexanal, 2-hexenal, nonanal, decanal, benzaldehyde, 2,4-decadienal, dihydro-\(\beta\)-ionone, 6-pentylpyran-2-one, 2-hexenyl acetate, ethyl caprylate, \(\gamma\)-decalactone, and theaspirane, which had OAVs greater than 1, constituted the aroma fingerprinting of flat peaches from Xinjiang (Figure 9). Twelve volatile key compounds from nectarines (RN), apricots (DB), and grapes (CS) were introduced and their concentrations were compared (Supplementary Table S4). The shaded area shows the confidence interval for the major aromas in the flat peaches from Xinjiang, and the solid black line shows the average value (Figure 9). The aroma fingerprinting of RN, mainly including benzaldehyde, 6-pentylpyran-2-one, and ethyl caprylate, was similar to the aroma fingerprinting of the flat peaches, to some extent. What is interesting about the data in this figure is that 2,4-decadienal, 2-hexenyl acetate, and theaspirane were only found in flat peaches, and these concentrations were consistent with the confidence intervals of the four flat peaches, so three compounds could be aroma characteristic substances identifying flat peaches from Xinjiang. Taken together, these results suggest that flat peaches have a unique aroma and provide a theoretical basis for the rapid identification of flat peaches with other peach varieties and quality control of the flavor of flat peach products.  
The odor characteristics of flat peaches are summarized in Supplementary Figure S4. The results showed that the aroma characteristics of flat peaches were basically the same; the odor characteristics mainly showed \"fruit\", \"Ioral\", \"peach-like\", and \" coconut-like\" aromas, with the \"fruit\" aroma being more prominent. It should be noted that QP had significantly higher \"tea\" and \"fatty\" aromas in the model. Among the flat peaches, QP had the highest OAV, followed by WP and R4, and the lowest was R2.  
Figure 9. The aroma fingerprint of flat peaches from Xinjiang. The confidence interval and average values of 12 volatile compound concentrations in flat peaches, including Qingpan (QP), Wanpan (WP), Ruipan 2 (R2), and Ruipan 4 (R4). Yellow line is nectarine (_P. nicepiscia_ L.), yellow nectarine (YN); red line is apricot (_P. armeniaca_ L.), Dabiayou (DBY); blue line is grape (_V. vinifera_ L.), Cabernet Sauvignon (CS); green line is R4 (flat peach).
## 2 _Introduction and State of Research_
With increasing shielding gas flow, coating irregularities increase. On the other hand, the depth of the heat-affected zone decreases with increasing shielding gas flow due to the higher cooling rate caused by the higher shielding gas flow. Due to the higher cooling rate, the microhardness increases. The study clearly illustrates the influence of the process gases on the coating properties. However, only geometrical and mechanical properties are investigated based on the permutation of two gas settings (carrier gas: 4 L/min and 10 L/min and shield gas: 10 L/min and 15 L/min). A reliable statement cannot be made due to the low number of samples investigated. Furthermore, the influence of the gas settings on the gas velocity was investigated by means of a CFD simulation. This does not allow final conclusions to be drawn about the spatial oxygen concentration. An increase in microhardness could also be due to an increase in oxidation. Since no element analysis was carried out, the statement of this paper is not unambiguous. Finally, the influence of the nozzle geometry on the oxygen concentration was not investigated; this will be investigated in the following study in more detail. [PERSON] and [PERSON] [8] performed a simulation and parameter study looking at different gas types (He and Ar) with different three-jet nozzles while characterizing their performance regarding powder particle speed, oxygen concentration and shielding capabilities of both process gases. It was shown that changing the carrier gas species has an effect on the oxygen concentration. Due to its lower molar mass compared to Ar, He has a smaller influence on the protective gas flow at the same carrier gas flow and thus leads to a lower oxygen concentration. Furthermore, it can be seen that the shielding gas bell is increasingly disturbed with an increase in the carrier gas flow. However, the oxygen concentration was only measured exactly below the nozzle, so no spatial oxygen concentration can be drawn experimentally. Furthermore, only two different three-jet nozzles were investigated and no comparison with other nozzle geometries (e.g., a coaxial nozzle) was provided. The aims and objectives of this study differ from the existing studies in the following points:  
1. Specification of the gas dynamics and the resulting present oxygen concentration over a larger parameter range;2 Description of the influence of different nozzle geometries (in particular a coaxial nozzle) must be investigated in more detail;
3 Simulation of the coaxial nozzle in terms of resulting oxygen concentration depending on different gas flow settings.
# Reconfiguration for the Maximum Dynamic Wrench Capability of a Parallel Robot
## 4 Examples
### Kinematic Parameters and Differential Evolution Variables
The rotation matrix \(\overset{N}{L}\)**R** with subscripts \(L\in\{B,i(i=1,...,6)\}\) is defined as  
\[\overset{N}{L}\textbf{R}=\left[ \begin{array}{ccc}c\psi c\phi&-s\psi c\phi+c \psi s\theta s\phi&s\psi s\phi+c\psi s\theta c\phi\\ s\psi c\phi&c\psi c\phi+s\psi s\theta s\phi&-c\psi s\phi+s\psi s\theta c\phi\\ -s\phi&c\theta s\phi&c\theta c\phi\end{array} \right]_{L} \tag{33}\]  
where \(c\left(\cdot\right)\) is a shorthand notation for \(\cos\left(\cdot\right)\) and \(s\left(\cdot\right)\) for \(\sin\left(\cdot\right)\). The Euler angles vector \(\gamma=\left[ \begin{array}{ccc}\psi&\theta&\varphi\end{array} \right]^{T}\) is used to specify the orientation, which means that the inertial frame is rotated about the \(Z\) axis with \(\psi\) first, resulting in the primed frame; then about the \(y^{\prime}\) axis with \(\theta\), resulting in the double primed frame; and finally about the \(x^{\prime\prime}\) axis with \(\phi\), resulting in the local frame. Moreover, the corresponding velocity transformation matrix  
\[\textbf{C}_{\textbf{L}}=\left[ \begin{array}{ccc}-s\phi&0&1\\ c\phi s\phi&c\phi&0\\ c\phi c\phi&-s\phi&0\end{array} \right]_{L} \tag{34}\]
## 3 Two-Echelon Two-Indenture System Modelling
### Mathematical Model Solution  
In reality, the joint solution of LORA and spare parts stocking is a sequential optimization. First, the costs with failure number and repaired costs can be optimized by LORA. Then, the spare parts stocking can be dealt with VARI-METRIC, based on the LORA's results. Finally, the location of spare parts in the repair network can be found to achieve a target availability of capital goods with the lowest possible spare parts holding costs [30].  
The proposed iterative algorithm is shown in Figure 4. It is obvious to find that the sequential approach is two of the same building blocks.  
By solving the LORA model, the cost (maintenance resources cost \(\sum\)\(rc_{r,j}\cdot Y_{r,j}\) and maintenance cost \(\sum\)\(\sum\)\(vc_{i,j,d}\cdot X_{i,j,d}\)) is minimized when the solution \(\left(X_{i,j,d},Y_{r,j}\right)\) satisfies the constraint conditions. According to the results, the maintenance ability \(r\) of the component at the Base location is calculated. \(r\) is taken as the input parameter of the spare parts inventory METRIC model, and the inventory solution \(S_{i,j}\) is obtained.  
The \"intermediate\" decision contains move decision, while the \"final\" decision includes repair and discard decision. Thus, only the costs of final decision should be focused on. We define \(h^{k}_{i,j,d}\) as the decision cost of component \(i\) added to LORA model in the \(k\)-th iteration:  
\[h^{k}_{i,j,d}=\frac{\sum\)\(fc_{i,j}\cdot s_{i,j}}{\lambda_{i,j}} \tag{28}\]  
where \(s_{i,j}\) is the result from iteration \(i-1\).  
The cost coefficient of maintenance decision is:  
\[vc_{i,j,d}=\left\{\begin{array}{c}vc_{i,j,d}+h^{k}_{i,j,d},d\in\{repair\}\\ vc_{i,j,d},d\in\{move\}\end{array}\right. \tag{29}\]  
As mentioned above, the holding costs as the inputs of LORA should be changed in \(i\)-th iteration only if the corresponding repair/discard decision was selected in the former iteration. This is a way to gradually estimate the resultant holding costs for all  
Figure 4: Joint iterative optimization method.  
corresponding repair/discard decision. Then, the proposed algorithm will find optimal total costs: LORA costs and holding costs resulting from the spare parts stocking analysis.  
The stop condition of the proposed algorithm is the solution of LORA and is identical to the previous iteration. If there are two different LORA solutions with the same costs, the solution shown in both iterations is selected, and the algorithm terminates.
# Temperature Stable, High-Quality Factor Li2 TiO3-Li4 NbO4F Microwave Dielectric Ceramics
## 1 Introduction  
With the rapid development of wireless and mobile communication, new microwave dielectric ceramics with a suitable dielectric constant (\(\epsilon_{\mathrm{r}}\)), high microwave quality factor values (Q/f, low dielectric loss), and near-zero temperature coefficient of resonant frequency (\(\tau_{\mathrm{f}}\approx\) 0 ppm/\({}^{\circ}\)C) are desired for microwave device applications [1; 2; 3; 4; 5; 6]. Recently, lithium-based microwave dielectric ceramics with rock salt, such as Li2 TiO3, Li3 NbO4, Li2 WO4, and Li2 CoO3, have gained plenty of attention because of their relatively low sintering temperature and excellent dielectric properties [7; 8; 9]. Among these ceramics, Li2 TiO3 ceramics sintered at 1300 \({}^{\circ}\)C for 2 h showed superior microwave dielectric properties with a \(\epsilon_{\mathrm{r}}\) of 22, Q/f value of 63,500 GHz (8.6 GHz), and \(\tau_{\mathrm{f}}\) value of +20.3 ppm/\({}^{\circ}\)C [10]. However, its practical applications were hindered because of the high sintering temperature as well as the positive \(\tau_{\mathrm{f}}\) value. In a previous study, B2O3 was added to Li2 TiO3-Li3 NbO4 ceramics to decrease the sintering temperature, and the results showed that the sintering temperature was decreased to 900 \({}^{\circ}\)C with the deterioration of the Q/f value to 44,000 GHz [11]. On the other hand, LiF, as a kind of sintering aid, was reported to successfully decrease the sintering temperature in several microwave dielectric ceramic systems [12; 13; 14]. As reported, Li4 NbO4F with a high Q/f, low sintering temperature, and negative \(\tau_{\mathrm{f}}\) was studied extensively [15]. Therefore, in this work, Li4 NbO4F ceramics were implemented as a sintering aid to adjust the \(\tau_{\mathrm{f}}\) value and decrease the sintering temperature for Li2 TiO3 ceramics. (1-x)Li2 TiO3-xLi4 NbO4F (x = 0.05, 0.10, 0.15, 0.20) ceramics, compared with non-lithium based ceramics [16; 17; 18], were investigated in order to reduce the sintering temperature and achieve a near-zero \(\tau_{\mathrm{f}}\) value as well as a high Q/f value. Their outstanding properties made the widespread application in a satellite communication and global positioning system antenna possible to achieve [19]. The phase structure, microstructure,and microwave dielectric properties of (1-x)Li\({}_{2}\)TiO\({}_{3}\)-xLi\({}_{4}\)NbO\({}_{4}\)F (x = 0.05, 0.10, 0.15, 0.20) were studied in detail.
# Quaternary Segmentation Characteristics of the Hunhe Fault, Northeast China
## 3 Methods
### Field Investigation  
The geological and geomorphological survey focuses on areas where the fault intersects with bedrock outcrops and areas with noticeable fault-related geomorphological changes within Quaternary coverage. Attention is given to critical fault locations such as structural terraces, intersection points, bifurcation points, and turning points. Detailed tracing investigations, observations, and studies are further conducted in Quaternary geological bodies, geomorphological surfaces, and other geological and geomorphological featuresthat show structural influences, aiming to assess their changing characteristics, formation mechanisms, distribution patterns, and the geological, geomorphological, and Quaternary trends. Field measurements and delineations of potential displacement indicators influenced by fault activity are carried out for geological bodies, geomorphological surfaces, and the Hunhe River system where the fault intersects. These measurements determine horizontal or vertical displacements resulting from fault activity over different periods.  
The study analyzes the control exerted by the fault during the development of the Quaternary structural basin in the Hunhe Valley, examining the relationships and magnitudes of misalignment between fault activity and various types, grades, and formation eras of geomorphological units. It categorizes structural geomorphological phenomena such as fault valleys, fault scarps, displaced terraces, and strip-shaped alluvial fans, assessing fault activity traces, geometric deformations, and discontinuities in structural zones. This work seeks to determine the spatial locations, scales, and geometric and kinematic characteristics of faults, creating conditions for necessary trench excavations and seismic geological surveys.  
Field geological and geomorphological investigations of active faults are conducted using a combination of tracing and traversing methods, achieving a positioning accuracy of \(\leq\)3-5 m. For late Pleistocene to Holocene active faults and observed surface rupture phenomena, the tracing method is primarily utilized, while stratigraphic and rock boundaries are generally controlled using a traversing method. The density of observation points is determined based on the significance of the observation object and its spatial distribution, typically set at around 50-200 m, to meet the required accuracy for the results.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 5 Security Analysis  
In this section, we analyze the security, the computation and communication overhead of the proposed scheme and compare it with some of the recent existing authentication protocols [19; 20; 21; 22].  
**Authentication and Integrity:** Assuming an adversary intercepts or modifies the message \(M_{1}:\{\phi_{1}=H(Auth_{v}||\overrightarrow{R}^{\prime}_{v}||AID_{v}||t_{1}), \overrightarrow{R}^{\prime}_{v},AID_{v},t_{1}\}\), then sends the modified message to CA. CA can detect the attack by the equation \(\phi_{1}=H(H(R_{Fv})\|\overrightarrow{R}^{\prime}_{v}||AID_{v}||t_{1})\), where \(Auth_{v}=H(R_{Fv})\). Therefore, message integrity is guaranteed.  
**Physical Protection:** In the proposed protocol, instead of storing a secret key in TPD's permanent storage, PUF generates a secret response \(R\) for generating a secret key. Furthermore, when a challenge \(C\) is inputted, the PUF provides a response \(R\). As the secret response \(R\) is exclusively generated by the PUF upon request, attackers are unable to extract any responses from the memory of a vehicle or RSU. Even if an attacker manages to acquire a challenge \(C\), the unclonable nature of the PUF prevents them from deducing the response \(R\) from challenge \(C\). Consequently, any endeavor by an adversary to obtain the physical secret key will not be successful.  
**Anonymity:** In the registration phase of the vehicle, the CA generates the pseudonym of a vehicle using the formula \(AID_{v}=H(sk_{c}||r_{v})\oplus ID_{v}\). Subsequently, the true identity of the vehicle is concealed within this pseudonym. To deduce the real identity from \(AID_{v}\), RSUs and other vehicles would need access to both \(sk_{c}\) and \(r_{v}\). However, this crucial information is exclusively stored within the CA's database and is accessible solely by the CA. Consequently, neither RSUs nor other vehicles can obtain this information, rendering them incapable of deducing the real identity from \(AID_{v}\). Thus, anonymity is effectively ensured.  
**Traceability:** Upon the dispute of a message, the CA possesses the capability to extract the true identity of the vehicle. Given that the secret key \(sk_{c}\) and the random value \(r_{v}\) are stored within the database of CA, the CA is able to ascertain the vehicle's real identity through the computation of \(ID^{f}_{v}=H(sk_{c}||r^{f}_{v})\oplus AID^{f}_{v}\). After obtaining the real identity \(ID^{f}_{v}\) of the malicious vehicle, you can revoke the vehicle from the CA; for further details, please refer to Section 4.8. Other entities do not have the ability to revoke and track the malicious vehicle, because they do not have the main private key \(sk_{c}\) of the system and random number \(r^{f}_{v}\)  
Finally, the security comparison results presented in Table 1 demonstrate that our protocol offers superior advantages.
## 2 Problem Statement and Preliminaries  
In this work, we propose a rebalancing methodology that reduces the situations in which bike sharing stations are unusable because of critical status. Specifically, we aim to plan online rebalancing operations subject to the constraint that such actions are performed in a short amount of time to limit any new critical situation or station unavailability. The rebalancing operations are planned by analysing historical data about the occupancy of the stations under analysis. In the following paragraphs, we formalize the addressed problem.  
Let \(\mathcal{S}\) be the set of stations of the bike sharing system under analysis. Each station \(s\in\mathcal{S}\) is characterized by its geographical position.  
Let \(\mathcal{D}\) be a structured dataset containing the historical information about the number of bicycles and free slots available for each station \(s\in\mathcal{S}\). The schema of \(\mathcal{D}\) is \(\{t,s,used\_slots,free\_slots\}\), where \(t\) is a timestamp, \(s\in\mathcal{S}\) is a station, \(used\_slots\in\mathbb{N}\) is a natural number that represents the number of bicycles available in \(s\) at time \(t\), and \(free\_slots\in\mathbb{N}\) is the number of free slots available in \(s\) at time \(t\).  
Let \(\mathcal{B}\) be a structured batch of records, with the same schema of \(\mathcal{D}\), containing the information about the number of bicycles and free slots available for each station \(s\in\mathcal{S}\) at the new timestamp \(t_{new}\).  
Let \(t_{reb}\) be the time needed for a truck to reach the set of stations we need to rebalance and move bicycles from them to apply the planned rebalance action.  
Problem statementGiven the input data \(\mathcal{S}\), \(\mathcal{D}\), and \(\mathcal{B}\), the problem addressed in this work consists of planning at time \(t_{new}\) which rebalancing actions should be made within at most \(t_{reb}\) minutes from time \(t_{new}\) to minimize the number of stations in a critical situation at time \(t_{new}+t_{reb}\).  
To limit the amount of time during which some bicycles are unavailable because they are on a truck that is moving them from one station to another, the rebalancing problem is subject to the constraint that only local rebalancing operations can be planned, where each local rebalancing operation moves bicycles only among neighbouring stations, i.e., among stations at a maximum distance of \(d\) meters from each other.  
Before describing the proposed methodology, we provide preliminary definitions.
## 2 Materials and Methods
### Research Area and Data Source
#### 2.1.3 Basis of Sample Data and Authentication Data  
Classification accuracy is affected by the sample number and the accuracy; thus, the number of training samples should be sufficiently large and representative. The ground objects in the research area are classified into five categories: impervious surface, water body, walnut, other orchards, and other crops. This research selects 225 sample points via a visual interpretation of high-resolution images in Google Earth to obtain sample data. In addition, sample data are evenly distributed, from which 110 walnut sample points are selected. The other sample points are water bodies, impervious surfaces, and other vegetation. The stratified random sampling method is selected by comprehensive consideration: 70% of samples are set as the training set of the classification model, and 30% of samples are set as the test set to verify the classification accuracy. The basis for sample selection is shown in Table 3, and authentication data were obtained from the walnut planting area data released in the Statistical Yearbook of Alar City.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 4 Case Analysis
### Error Analysis  
The relative error between the predicted value and the actual value obtained by the MWP-DLP model is shown in Figure 9, and the comparison between some predicted values and the actual value is shown in Table 3. The analysis shows that, as can be seen from Figure 9 and Table 3, the absolute value of the maximum error between the predicted value and the actual value was 12 \(\times\) 10\({}^{4}\) m\({}^{3}\), and the corresponding serial number was 7 (the first red dot in the figure), which appeared on 23 November of the first year. The reason may be that the temperature decreased on 23 November, the solar radiation value decreased, and the user felt cold and began to use gas heating, resulting in a sudden increase in the daily gas load. However, in the algorithm, the response to the sudden load change was slow, which resulted in a large difference between the model prediction and the actual value. The maximum relative error of the predicted result was \(-9\%\), corresponding to the  
Figure 8: \(D_{i}\)–daily gas load linear relationship diagram.  
Figure 7: Predicted value and actual value change curve with \(D_{i}\).  
Figure 9: STD–daily gas load linear relationship diagram.  
serial number 59 (the second red dot in the figure), which appeared on 27 February of the following year. The reason may be that the temperature suddenly increased on 27 February, the solar radiation increased, the residents felt that the temperature was suitable, and the gas consumption was reduced, resulting in a rapid decline in the daily gas load. On 24 th, the daily gas load decreased by 147 \(\times\) 104 m\({}^{3}\) compared with 27 th, and the genetic algorithm could not quickly find the optimal solution when solving the model, resulting in increased error.