esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Virtual Water Trade in Gansu Province from 2007 to 2012
#### 3.2.3 Variations and Rationality Analysis of Inter-Provincial Virtual Water Trade in Gansu Province  
A net import trend was observed for all virtual water trade in Gansu province in 2007 and 2012; however, in 2010 virtual water displayed a net export trend (Table 4). Gansu province received 104 million m\({}^{3}\) of virtual water in 2007 because the import volume in the secondary and tertiary industry was higher than the export volume of the primary industry. The 'develop-the-west' strategy required the mid-eastern regions to provide facilities, resources, and techniques for the west during that period [55]. The volume of virtual water imported from the primary industry in 2010 was similar  
Figure 4: Spatial pattern of interprovincial flow of virtual water from Gansu province from 2007 to 2012.  
to that of 2007, yet the import volume from the secondary and the tertiary industries increased by 52.09% and 78.47%, respectively.  
The export volume of virtual water for the three industry categories combined was two-times greater in 2010 than in 2007; net output of virtual water in 2010 was 506 million m\({}^{3}\). There are two primary reasons for this; first, although the number of export destinations were reduced, the export volume is extremely large, and second, Gansu increased intermediate inputs and final product exports to other provinces.  
Net imports of virtual water were observed in 2012, with a virtual water import volume of 1.235 billion m\({}^{3}\). The output of the three industry classes combined was twice that of 2010, with the import of the second and tertiary industries, respectively, reaching 2.92 and 7.41 times that of their 2010 counterparts. This is likely related to the policy orientation, industry distribution and state of development of Gansu province, along with the important position of Gansu province in the construction of the \"One Belt and One Road\" [55].  
The distribution of water resources in China is extremely unbalanced. Water resources are more plentiful, and the average annual precipitation is much higher, in coastal areas than in inland areas [79]. The main receivers of Gansu's virtual water exports include Zhejiang, Guangdong and other water-abundant areas, as well as Inner Mongolia, Chongqing, Shaanxi; all regions with more water storage than Gansu [72; 80]. Conversely, Ningxia, Beijing, Shanghai and other regions with low water reserve capacity have been exporting water resources to Gansu province. These patterns reflect the regional failure to match the inter-provincial flow of virtual water trade with water source storage.
## 3 Results
### Assembly & Mass customisation
#### The Implications of Complexity  
Mass customization brings a great deal of complexity to assembly operations, which affect key elements of the system as well as other nearby areas, such as quality, supply chain or maintenance.Assembly complexity has can been evaluated from different perspectives: the number of product variants [32], induced task differences [33] or product configuration [34]. Complexity has a negative effect on quality, which could be minimized by using cognitive automation [35]. The increasing number of product features to be controlled makes the necessary new advanced quality management systems [36]. Supply chain implications of mass customization assembly range from assembly line feeding problems [37] and modularity-specific issues [38] to assembly supply chain configuration [39] and whole manufacturing networks [40]. Using Automated Guided Vehicles (AGVs) can be used efficiently to feed mixed-model assembly lines [41; 42]. Maintenance resource allocation also needs to be prioritized to minimize the negative effects of increased complexity [43].  
**In conclusion**, assembly complexity reaches outside the boundaries of assembly operations and needs to be considered jointly with supply chain, quality, maintenance and IT/IS.
###### Abstract  
In 5 th generation mobile communication networks, the frequency band of the millimeter band of 30-100 GHz is supported to provide a transmission speed of 20 Gbps or higher. Furthermore, a huge transmission capacity, i.e., up to 1 Tbps, is one of the main requirements for the 6 th generation mobile communication networks. In order to meet this requirement, the terahertz band is considered a new service band. Hence, we consider multi-band network environments, serving the sub-6 Hz band, mmWave band, and additionally sub-terahertz band. Furthermore, we introduce the transmission point selection criteria with multiple connections for efficient multi-connectivity operation in a multi-band network environment, serving and receiving multiple connections from those bands at the same time. We also propose a point selection algorithm based on the selection criteria, e.g., achievable data rate. The proposed point selection algorithm attains lower computational complexity with a 2-approximation of the optimal solution. Our simulation results, applying the channel environment and beamforming in practical environments defined by 3 GPP, show that selecting and serving multiple transmission points regardless of frequency band performs better than services through single-connectivity operation.  
mobile communication networks; multi-band; multi-connectivity; transmission point selection; user association
## 4 The Proposed AAC-IoT System Model
### Registration and Authentication
#### 4.2.1 DO Registration  
Step 1: Let \(\mathcal{D}_{1}\) be a DO. It initially sends a request to the CA with the unique identity and timestamp. The registration of the DO is \(R_{1}\)\(\rightarrow\)\(\{Id_{1},T_{0}\}\), where \(Id_{1}\) is the owner's identity, \(R_{1}\) denotes the first request from the DO, and \(T_{0}\) is the initial timestamp.  
Step 2: The CA receives \(R_{1}\), and if \(T_{0}\)\(<\)\(\Delta T\), where \(\Delta T\) is the timestamp for receiving the request message, the identity is considered for registration. If the timestamp is not validated, then the expired communication link of the owner and the CA will also be taken in account for registration.  
Step 2.1: Then the certificate is generated for each DO and transferred securely in the form of encryption. For encryption, a lightweight PRESENT algorithm is used which consumes limited computations [48].  
Step 2.2: Using addRoundkey generation, the certificate is encrypted into cipher text. The key lengths are 80-bit and 128-bit, and the block length of 64-bits is used. This  
Figure 2: The Proposed AAC-IoT system model.  
block cipher algorithm is handled in 31 rounds. The key for each round is \(K_{i}\), where \(1\leq i\leq 32\) and it uses an XOR operation. The sBoxLayer and pLayer state; hence it uses an S/P network.  
Step 2.3: Then, the CA sends \(R_{2}\)\(\rightarrow\)\(E\{Id_{1},E(Ct_{1})\),\(T_{1}\}\) to \(\mathcal{D}_{1}\) with the encrypted certificate \(E(Ct_{1})\) and timestamp \(T_{1}\).  
Step 3: The \(\mathcal{D}_{1}\) receives \(R_{2}\) and checks timestamp \((T_{1}-T_{0})<\Delta T\). If valid, the certificate is decrypted and then sends signature \(SO_{xx}\) generation requests to CA. The \(R_{3}\)\(\rightarrow\)\(\{Id_{1},SO_{xx},T_{2}\}\) where \(R_{3}\) is the request from owner, \(SO_{xx}\) denotes the unknown signature, and \(T_{2}\) is the timestamp.  
Step 4: CA validates \((T_{2}-T_{1})<\Delta T\), then generates a signature for the \(\mathcal{D}_{1}\) and sends it to the owner. The security credentials generated for all \(\mathcal{D}_{N}\) are updated in the HLF blockchain of the system.  
The designed DO registration workflow is depicted in Figure 3 which demonstrates all the three entities involved during the registration using multiple security credentials.
## 3 Results
### Kinematics
#### 3.2.2 Joint Angles during Landing Phase  
Peak joint angles and ROM are presented in Table 3. The majority of the parameters which significantly changed were larger under fatigue conditions, such as the peak inversion angle and ROM of the ankle joint in the frontal plane (\(p\) = 0.003, ES = 0.65; \(p\) = 0.001, ES = 0.87), the peak external rotation angle and ROM of the angle joint in the horizontal plane (\(p\) = 0.004, ES = 0.52; \(p\) = 0.005, ES = 0.62), the peak abduction angle and ROM of the knee joint in the frontal plane (\(p\) = 0.032, ES = 0.64; \(p\) = 0.000, ES = 1.60), but the peak abduction angle of the hip joint was smaller when fatigue occurred (\(p\) = 0.025, ES = 0.39). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Joint**} & \multirow{2}{*}{**Plane**} & \multicolumn{2}{c}{**Pre**} & \multicolumn{2}{c}{**Post**} & \multicolumn{2}{c}{**Post**} \\  & & **Max** & **Min** & **ROM** & **Max** & **Min** & **ROM** \\ \hline Ankle (\({}^{\circ}\)) & Sagittal & 29.31 (5.17) & \(-\)22.82 (5.81) & **52.13 (3.32) \({}^{\star}\)** & 30.02 (6.14) & \(-\)19.98 (3.30) & **50.01 (3.79) \({}^{\star}\)** \\  & Frontal & **7.62 (2.58) \({}^{\star}\)** & \(-\)9.02 (5.97) & **16.64 (6.36) \({}^{\star}\)** & **9.41 (3.03) \({}^{\star}\)** & \(-\)9.63 (5.73) & **19.04 (5.79) \({}^{\star}\)** \\  & Horizontal & **9.43 (6.69) \({}^{\star}\)** & \(-\)5.57 (9.95) & 15.00 (7.95) & **6.07 (8.33) \({}^{\star}\)** & \(-\)6.19 (6.83) & 12.26 (2.31) \\ Knee (\({}^{\circ}\)) & Sagittal & **-1.69 (2.19) \({}^{\star}\)** & \(-\)9.47 (0.18) (6.36) & 95.70 (10.87) & **-4.44 (4.83) \({}^{\star}\)** & \(-\)6.45 (26.96) & 92.01 (2.453) \\  & Frontal & 8.12 (4.15) & **-3.08 (4.66) \({}^{\star}\)** & **11.20 (2.07) \({}^{\star}\)** & 11.12 (9.34) & **-8.30 (10.60) \({}^{\star}\)** & **19.42 (8.20) \({}^{\star}\)** \\  & Horizontal & 9.48 (7.37) & \(-\)9.28 (5.11) & 18.76 (5.12) & 10.89 (19.15) & \(-\)8.78 (16.03) & 19.65 (3.26) \\ Hip (\({}^{\star}\)) & Sagittal & 60.70 (19.42) & \(-\)1.07 (3.82) & 61.77 (2.30) & 60.09 (23.88) & 1.08 (4.45) & 59.91 (21.10) \\  & Frontal & 3.71 (4.28) & **-9.38 (3.63) \({}^{\star}\)** & **13.09 (3.13) \({}^{\star}\)** & 3.79 (6.10) & **-4.67 (4.18) \({}^{\star}\)** & **10.26 (2.29) \({}^{\star}\)** \\  & Horizontal & 6.45 (6.07) & \(-\)7.36 (3.06) & 13.81 (4.78) & 8.34 (15.20) & \(-\)5.36 (16.80) & 13.70 (3.89) \\ \hline \end{tabular}
\end{table} 
Table 2: Peak joint angles and range of motion (ROM) of pre-fatigue and post-fatigue during the push-off phase.
## 3 Simulation Results
After the higher-energy electrons at the tail overtook those in the front (\(\Delta\varepsilon_{x}>0\)), the \(e\)-beam energy chirp reversed to positive, and finally, the \(e\)-beam longitudinal energy spread began to increase. Figure 4a-c shows this process in detail, as well as the evolution of the energy spread of both \(e\)-beams (Figure 3c), indicated in the LWFA stage. The minimum energy spread of the first \(e\)-beam was approximately 2.4% (Figure 4b), and that of the second \(e\)-beam was approximately 1.8% at \(x=4.96\) mm (Figure 4c). At \(x=5.44\) mm (Figure 4d), the energy spread of the second \(e\)-beam increased and its energy chirp was reversed to positive. When the acceleration mode changed to PWFA, the first \(e\)-beam became the main driver of the wakefield. As shown in Figure 4e, the second \(e\)-beam was located at the tail of the wakefield and it experienced a negative-dispersion wakefield (\(E^{\prime}<0\)), which again compensated for its positive energy chirp. Since the phase velocities of the two-color \(e\)-beams were almost the same, the acceleration phase of the second \(e\)-beam could be maintained in the negative-dispersion wakefield for a long time. Finally, after undergoing the hybrid acceleration process over a distance of 7.04 mm, the second \(e\)-beam obtained a peak energy of 649.1 MeV with an energy spread of 1.2%, while the driving \(e\)-beam lost energy up to 244.3 MeV with 59.6% energy spread, as shown in Figure 4f. These results were in reasonable agreement with the experimental results. To realize the transition from the LWFA to PWFA in our scheme, two-color \(e\)-beams should first be generated and the charge of the first \(e\)-beam must be larger than the second one. In our experiments, by controlling the gas pressure of the two gas nozzles and the horizontal span between them accurately, the position, the width and the maximum density of the density bump could be adjusted, which made it possible to control the injection position and the charge of the \(e\)-beams with a relatively good stability. Figure 4: On-axis acceleration field \(\text{E}_{x}/\text{E}_{0}\) and the corresponding electron space-phase distribution at (**a**) \(x=2.4\) mm (**b**) \(x=4.72\) mm (**c**) \(x=4.96\) mm (**d**) \(x=5.44\) mm (**e**) \(x=5.84\) mm (**f**) \(x=7.04\) mm. Besides, in order to further achieve the energy chirp compensation in the PWFA process, the second \(e\)-beam should be located at the negative-dispersion wakefield driven by the first \(e\)-beam, which means that the distance between the two-color \(e\)-beams \(\Delta x\) should be approximately equal to the driven bubble size \(r_{pm}\) of the first \(e\)-beam. The maximum radius of wakefield structure for a particle beam driver is \(r_{pm}\approx 2\sqrt{\Delta c}/\omega_{p2}\), where \(\Lambda\) is the peak normalized charge per unit length and depends on the density and spot size of the \(e\)-beam [24; 29; 30].
# The Effect of Apple Juice Concentration on Cider Fermentation and Properties of the Final Product
## Materials and Methods
### Cider Fermentation  
Apple juice and reconstituted apple juice concentrate (400 mL) were distributed into sterile 500 mL Duran bottles (DWK Life Sciences; Mainz, Germany). Each bottle was inoculated (\(5\times 10^{6}\) CFU mL\({}^{-1}\)) with a chosen yeast starter culture (Lallemand, Inc.; Quebec, QC, Canada). The yeast starter cultures used in the study were as follows: Y1 (_S. buyanus_; red and white wine yeast for demanding conditions), Y2 (_S. cerevisiae_; white wine yeast), Y3 (_S. bayanus_; sparkling wine yeast), Y4 (_S. cerevisiae_; red, rose, and white wine yeast, selected through evolutionary adaptation), Y5 (_S. cerevisiae_ with killer factor), Y6 (_S. cerevisiae_; white wine yeast selected through directed breeding). The fermentation vessels were sealed using screw caps with septums pierced with a syringe needle (20 G x 1'', 0.9 x 25 mm; TerumoMedical Corporation, Somerset, NJ, USA) coupled with a microfilter (Milker PTFE Vent filters; Merck, Darmstadt, Germany) to vent carbon dioxide. In the headspace of each vessel, a piece of lead acetate paper strip (Whatman; GE Healthcare, Chicago, IL, USA) was placed to monitor hydrogen sulfide formation. The changes in color were assessed visually and grouped in comparison to blank paper strip and relatively to each other on a scale from \"no color change\" to \"high-intensity color formation\". It needs to be emphasized that the use of lead acetate paper strips has certain limitations. The change in color intensity of the strip as a function of hydrogen sulfide content in the headspace is not linear which makes even relative quantification impossible--the strips become saturated after which no further change in color can be observed despite hydrogen sulfide still potentially being produced by the yeast. Three nutritional strategies were applied: no additional nutritional supplementation (control); 9 g hL\({}^{-1}\) of diammonium phosphate (DAP; Sigma Aldrich, St. Louis, MO, USA) at the start of fermentation as inorganic nitrogen supplement; 40 g hL\({}^{-1}\) of organic supplement Fermaid O (Lallemand Inc, Montreal, QC, Canada). Fermentations were carried out at 18 \(\pm\) 1 \({}^{\circ}\)C and 30 \(\pm\) 1 \({}^{\circ}\)C by following carbon dioxide dissipation (mass loss) every 24 h. Fermentations were completed when the weight of fermentation vessels remained constant for three consecutive days. Experiments were conducted in duplicates. The total number of fermentations was thus 144 (2 fermentation matrices \(\times\) 6 yeasts \(\times\) 2 temperatures \(\times\) 3 nutritional strategies \(\times\) 2 biological replicates).
## Appendix B Building Correct BBCPs  
It is crucial to ensure that the schema provided with the BBCP [33] is correctly utilized. This means that, when creating objects within a category, they should be placed within the appropriate array. For instance, if the user were to declare two operations in the \"Operations\" category, they would need to create two new JSON object literals, each containinga copy of their respective properties and sub-categories, as demonstrated in Algorithm 1. In addition to the correct creation of objects, it is important to be wary of the constraints involved in the logical nesting of certain categories, such as expectations. Expectations contain cycles and events, which, at the same time, contain timed expectations. It is crucial for timed expectations to always be within a cycle or an event. Furthermore, it is mandatory to provide each object literal with a copy of all the properties that correspond to it, even if none of them will be used. An example of timed expectations can be found in Algorithm 2. Finally, we have prepared Table A2 to guarantee that the values per property available in the BBCP are correct by providing a possible value, type, and format for each one. ```
1:Operations:
2:{
3:Operation_ID: \"1\",
4:Resource_Usage:
5:{
6:CPU_Usage: \"0.3\",
7:RAM_Usage: \"100\",
8:Storage_Usage: \"200\",
9:Network_Usage: \"0.5\",
10:Run_Constraints:
11:
12:Minimum_Run_Time: \"30\",
13:Maximum_Run_Time: \"300\",
14:Quota: \"3600\",
15:Cooldown: \"1200\",
16:Countdown: \"0\"
17: }
18: }
19: }
20: {
21:Operation_ID: \"2\",
22:Resource_Usage:
23:CPU_Usage:
24:CPU_Usage: \"0.3\",
25:RAM_Usage: \"100\",
26:Storage_Usage: \"200\",
27:Network_Usage: \"0.5\",
28:Run_Constraints:
29: {
30:Minimum_Run_Time: \"30\",
31:Maximum_Run_Time: \"300\",
32:Quota: \"3600\",
33:Cooldown: \"1200\",
34:Countdown: \"0\"
35: }
36: }
37: }
38: }
39: }
```  
**Algorithm 1**Creating multiple operations in the corresponding category ```
1:Expectations:
2:{
3:Cycles:[
4:{
5:Days:[5-7],
6:Scale:7\",
7:Timed_Expectations:[
8:{
9:Time_Range:[\"9-12,20,23]\",
10:Run_Probability_Override:\"0.3\",
11:Stop_Probability_Override:\"0.7\",
12:Elasticity:
13:Elasticity:
14:Elasticity:
15:Minimum_Run_Probability:\"0.1\",
16:Maximum_Run_Probability:\"0.7\",
17:Minimum_Stop_Probability:\"0.0\",
18:Maximum_Stop_Probability:\"0.3\",
19:Positive_Run_Probability_Modifier:\"0.001\",
20:Negative_Run_Probability_Modifier:\"0.001\",
21:Positive_Stop_Probability_Modifier:\"0.001\",
22:Negative_Stop_Probability_Modifier:\"0.001\"
23:]
24:Run_Constraints:
25:{
26:Minimum_Run_Time:\"30\",
27:Maximum_Run_Time:\"60\",
28:Quota:\"0\",
29:Cooldown:\"0\",
30:Countdown:\"0\"
31:]
32:]
33:]
34:]
35:]
```

**Algorithm 2**Creating timed expectations within its parent object
# Galerkin's Spectral Method in the Analysis of Antenna Wall Operation
## 1 Introduction
When the condition of periodicity of such an antenna and its excitation with linear, uniform phase modulation is met, the problem of scattering on such a structure is reduced to the analysis of one basic antenna cell, usually in the spectral space spanned by a complete system of corresponding Floquet harmonics [13; 14; 15]. Nevertheless, for antenna arrays composed of a dozen or so basic cells of regular shapes, this method gives good results. Other known methods of solving the scattering problem on larger periodic structures, such as modifications of the above-mentioned direct method by limiting the number of basic functions and appropriately selecting their courses [16; 17; 18] or the iterative method imposed on the solution by successive application of the fast Fourier transform, are approximate, slowly convergent and can be used only in certain simple cases convenient for analysis. The analysis presented in this paper is based on the concept of the window function [6; 10; 19; 20; 21; 22; 23; 24] imposed on the sought current distribution and incorporated into the structure of the radiation problem of a periodic unconstrained structure. The generalized spectral analysis, for an unconstrained one, is, after some adaptations, particularly convenient for carrying out the concept of adaptation of the window function due to the full system of Floquet harmonics used in solving the problem. The sequence of successive solution steps presented in the paper should lead to an exact solution, considering the anticipated difficulties with convergence of the applied procedure. In Section 2, the factorization of the scattered field in the spectral space is derived in an exact manner, specifying the grid coefficient function and indicating the possibility of its use in the approximate analysis of the scattering problem on a limited antenna structure. Section 3[25; 26] contains careful specification of the solution of the scattering problem on an unconstrained structure in terms of direct application of this formalism to the case of an antenna of limited dimensions. The procedure for adapting this formalism is presented in Section 4[27], with the necessary modifications introduced by an appropriately defined window function. The accuracy of the solution method is verified by the proposed iterative procedure using a system of algebraic equations for the spectral coefficients of the distribution of field quantities and currents. In this paper, the use of window functions is proposed, and the full Floquet harmonic system is also used to solve the problem. A sequence of subsequent solution steps is also proposed to lead to a strictly numerically convergent solution.
## 1 Introduction
In the past several years, the apparent warming climate caused early flowing and maturity and consequently shortened the reproductive growth stage of maize [29]. The mean temperature as one of the thermal conditions is closely related to the sowing and seedling emergence time [30; 31; 32]. The accumulated temperature is routinely used to determine crop planting schedules, so the accumulated temperature shift will lead to the change in maize growth stages [33]. Moisture is an essential requirement for maize growth, so precipitation has effects on the growth of maize. It was reported that the correlations between precipitation and the length of various maize growing phases were positive [26; 34]. Furthermore, geographical position is a vital factor for maize growth [35; 36]. [PERSON] et al. found that for every 1\({}^{\circ}\) increase in latitude, northward, the growth durations of sowing to emergence and emergence to silking were significantly increased by 0.7 and 1.25 d, respectively [37]. This means the maize phenology has been changed in different regions. It can be clearly seen that the phenology of maize varied with the changes in annual average temperature, accumulated temperature, precipitation, and geographical location. On a large scale, with a great difference in the environment of different regions, the phenologies of maize are different, which lead to the difference in the spectral characteristics of summer maize in the remote sensing image. There is, therefore, the phenological differences of maize caused by various environmental factors being combined with the multi-temporal MODIS EVI images to map the cultivated area of summer maize for better extraction accuracy on a large scale. In the present study, multiple linear regression equations were constructed to simulate the summer maize phenology through analyzing the relationship between environmental factors and summer maize phenology over the Huanghuaihai Plain. The simulated phenology of summer maize was obtained through environmental factors data (longitude, latitude, annual average temperature, annual precipitation, and \(\geq\)10 \({}^{\circ}\)C accumulated temperature), which acted as an independent variable to modify the summer maize MODIS EVI time series curve and generate the standard maize EVI time series curve over Huanghuaihai Plain. Combined with the mean absolute distance (MAD) and the P-tile algorithm, the summer maize planting area was discriminated successfully in the study area. It provides a novel idea for improving the accuracy of maize area extraction using multi-temporal remote sensed data on a large scale.