esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Fermentation Monitoring  
Cider makers monitor the progression of fermentation to inform their decisions regarding the timing of racking events. For the three ciders monitored in this study, specific gravity (SG) was measured with an Anton Parr EasyDens digital gravity and alcohol tester. SG and pH were measured every 48 h throughout fermentation and again at the end of maturation. Between the end of alcoholic fermentation and maturation, the three ciders were not sampled to reduce the potential for contamination. As Figure 3 depicts, an overall increase in pH (3.28-3.72) and a decrease in SG from 1.049 (juice) to 0.996 (cider) was observed, as expected. After the cider was racked (day 25), specific gravity continued to drop, and the pH of the ciders rose slightly to 3.51. The end of primary fermentation was on day 47 when the averaged specific gravity was \(\leq\)1.003 and the average pH was 3.56. The three ciders were not racked through maturation and as a result rested on the remaining lees for 88 days to increase the concentration of fruity, volatile esters. During maturation, the specific gravity decreased from 1.003 to 0.996 on day 135. The pH increased from 3.51 to 3.70 during this same time frame. This increase in pH is due to the production of ethanol throughout fermentation, which has been shown to increase the dissociation of weak acids present in the cider [22]. Similarly, the SG results were consistent with a successful fermentation. As the yeast consumes the sugars present, the density of the solution decreases, and SG values below 1.000 indicate that the fermentable sugars have been converted to ethanol and other fermentation products.  
Figure 2: The electron ionization fragmentation observed for the molecular ions of \(\beta\)-myrcene (**A**) and \(\beta\)-myrcene-d6 (**B**).
###### Abstract  
In downhole drilling systems, self-excited torsional vibrations caused by the bit-rock interactions can affect the drilling process and lead to the premature failure of components. Especially self-excited oscillations of higher-order modes lead to critical dynamic loads. The slim drill string design and the naturally limited drilled borehole diameter limit the installation space, power supply and lead to numerous potentially critical self-excited torsional modes. Consequently, small and robust passive damping concepts are required. The variety of possible downhole boundary conditions and potential damper designs necessitates analytical solutions for effective damper design and optimization. In this paper, two nonlinear passive damper concepts are investigated regarding design and effectiveness to reduce self-excited high-frequency torsional oscillations in drill string dynamics. Based on a finite element model of a drill string, a suitable minimal model based on the identified critical mode is generated and solved analytically using the Multiple Scales Lindstedt-Poincare (MSLP) method. The advantages of MSLP compared to conventional MS methods are shown for this example. On the basis of the analytical solution, parameter influences are determined, and design equations are derived. The analytical results are transferred to self-excited drill string vibrations and discussed using time domain simulations of the drill string model.  
self-excitation; damping; multiple scales Lindstedt-Poincare; drill string; torsional vibration +
Footnote †: journal: Applied Sciences
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 1 Introduction
Moreover, utilizing feature variables across the entire growth period of winter wheat for yield prediction obscures the potential variations in predictive capabilities across different growth stages, thereby limiting the timeliness of governmental decision-making. [PERSON] et al., considering both spectral features and agronomic trait parameters, assessed the impact of different growth stages on yield prediction outcomes [17]. [PERSON] et al., employing inputs such as cumulative biomass, climate adaptability indices, and extreme climate indices in a statistical regression model, predicted wheat yields in the North China Plain and evaluated the performance of yield prediction models concerning different growth stages [34]. While the previous studies explored the predictive performance across different growth stages, they all employed a single machine learning model for yield prediction, overlooking the impact of the model itself on the predictive potential across various growth stages. Additionally, the yield prediction results in the past were mostly presented at the county level and did not downscale county-level yield data to pixel-level resolution. Pixel-level yield information is crucial in helping the government take necessary measures in the agricultural production process to achieve yield maximization. To address the aforementioned issues, this study utilized eight parameters, namely normalized difference vegetation index (NDVI), land surface temperature (LST), gross primary productivity (GPP), enhanced vegetation index (EVI), fraction of photosynthetically active radiation (Fpar), potential evapotranspiration (PET), actual evapotranspiration (ET), and leaf area index (LAI). Combining these parameters with winter wheat yield statistics, an ensemble voting model based on gradient boosting, random forest, and ridge algorithms was constructed. This study analyzed the yield prediction potential across different growth stages of winter wheat and established a spatialization model for winter wheat yield at both county and pixel levels (Figure 1).
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation  
This section provides an evaluation of the proposed task-scheduling framework, the CMS, and its supporting architectures. The evaluation is organized around identifying key features of the framework and examining how each feature addresses the main challenges identified in Section 2. Each subsection focuses on one specific feature, detailing how it is enabled in the framework and architectures, and mapping it to the challenges it aims to resolve. This evaluation process is summarized in two tables: Table 7, which identifies the key features of the framework and its associated architectures, and Table 8, which shows how each feature addresses the corresponding challenges. By establishing these connections, the evaluation demonstrates how the proposed framework effectively tackles existing limitations and advances the field of integrated network operations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features** & \multicolumn{2}{c}{**Integrated OPS in**} & \multicolumn{2}{c}{**Integrated OPS in**} & \multicolumn{2}{c}{**Coordinated Ops**} & \multicolumn{2}{c}{**Adaptive Ops**} & \multicolumn{2}{c}{**CMS Framework**} \\ \cline{2-6}  & **SCC 1** & **PCC 2** & **Coordinated Ops** & **Adaptive Ops** & **CMS Framework** \\ \hline MANO integration & & & X & X & & \\ \hline Aggregating/Dispatching modules & X & X & X & X & X & X \\ \hline
3 GPP constraints & & & & & & X \\ \hline Open-source solutions & X & X & X & X & X & X \\ \hline Unified control & X & X & X & & & \\ \hline Generalized framework & & & & & X \\ \hline Autonomous operation & X & X & X & & X \\ \hline AI / ML algorithms & & & & & X \\ \hline Closed-loop optimization & X & X & X & X & X \\ \hline Constraint satisfaction programming & & & & & X \\ \hline Multi-objective optimization & & & & & X \\ \hline Network topology prediction & & & & X \\ \hline Prototype ready & X & & & X \\ \hline SNO integration & X & & & X \\ \hline \hline \end{tabular} 1  
\end{table}
Table 7: Identified features of the CMS framework and its architectures.
# Toward the Utilization of 3D Blading in the Cantilevered Stator from Highly Loaded Compressors
## 3 Effects of 3D Blading on the Cantilevered Stator
### Effects of the Blade Dihedral
#### 3.2.1 Effects of the Dihedral Height
The increase of incidence angle at the hub region indicates that the positive dihedral will restrict the flow capacity at the blade inlet, while increasing the dihedral height will amplify the effect. On the other hand, the mass flow coefficient at the outlet of the cantilevered stator distributes differently with the variation of the dihedral height. For scheme C1D3, the flow capacity below 20% blade height is increased significantly due to the weakening of the corner separation flow, Meanwhile, the mass flow coefficient in the areas above 20% span witnesses a slight reduction owing to the enhancement of the trailing edge separation, the deviation angle also rises correspondingly. With the increase of the dihedral height (C2D3 and C3D3), the flow capacity above 20% blade height suffers from a significant reduction because of the strengthening of the radial flow migration in the blade channel, thus bringing adverse effects to the aerodynamic performance of the cantilevered stator. Figure 18 presents the variation of the leakage mass flow rate. An interesting phenomenon is that increasing the dihedral height will first increase and then decrease the leakage mass flow rate at the blade leading edge. Consequently, the uniformity of the leakage flow characteristic along the blade chord is first decreased and then increased. Moreover, although the slight positive dihedral (C1D3) could increase the mass flow rate of the leakage flow along the axial direction, increasing the dihedral height will incur a significant reduction of leakage flow at the blade leading edge, thus weakening the effect of the 3D blading technique. Figure 17: Radial distribution of aerodynamic performances for the cases with different dihedral heights. (**a**) \(\varphi\) = 0.52; (**b**) \(\varphi\) = 0.46. Figure 18: Comparison of the streamwise leakage mass flow rate under different dihedral heights (\(\varphi\) = 0.46).
## 3 Results and Discussion
### Thermal Analysis  
Figure 8 shows the results of the thermal analysis. In the temperature range of 30-180 \({}^{\circ}\)C, free water (interlayer water in the C-S-H gel), decomposition of ettrinite/C-S-H [6; 31; 34], and a large amount of bound water were lost, resulting in weight loss. The weight loss around 500-750 \({}^{\circ}\)C was due to the decomposition of calcite [16; 25]. As the amount of CSAe increased, the weight loss in the 30-180 \({}^{\circ}\)C range also increased. This may have been due to the increased formation of ettrinite. In the XRD analysis results in Figure 3, the increase in the ettrinite peak due to the increase in the substitution rate of CSAe is explained, and the thermal analysis supported this trend.  
Regardless of the substitution rate of CSAe, the weight loss in the 30-180 \({}^{\circ}\)C temperature range increased as the substitution rate of SF increased to 0%, 5%, and 10%. The reason for the increased weight loss was that SF promoted the formation of ettrinite or C-S-H gel. The weight loss in the 30-180 \({}^{\circ}\)C temperature range was the largest in the 30% CSAe samples (Figure 8e,f), followed by those in the 20% CSAe (Figure 8c,d) and 10% CSAe (Figure 8a,b) samples (in order of decreasing weight loss). The enhanced production of ettrinite by the addition of SF improved the expansion force and reduced the drying shrinkage [54]. In addition, due to the filler effect of SF and the pore filling effect by ettrinite, a dense matrix was formed, which partially contributed to the strength improvement.  
Figure 8: _Cont._  
28d. In Figure 8f, the weight loss tends to change in the order of G70F0C30 > G65F5C30 > G60F10C30 when 1d and G70F0C30 > G65F5C30 > G60F10C30 when 28d. The weight loss observed in the region below 200 degrees Celsius is complex and unclear. This makes it difficult to distinguish clearly because hydration products are generated by SF and simple evaporation of moisture occurs at the same time [55, 56]. Particularly, very fine particles of SF require a large amount of bound water [55, 56, 57, 58]. Therefore, some of the water required for the hydration reaction of slag or CSAe particles is bound to the surface of the SF particles, which can be considered as an unclear cause of weight loss that hinders the hydration action.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 2 Related Work
In the EP, the coordinator sends energy packets using radio frequency transfer technology to recharge the device batteries, which can harvest this energy while remaining in a sleep state. Finally, during the vUP, the devices transmit their remaining packets using the assigned slots. [PERSON] et al. reported that their proposal, called PAH-MAC, shows significant advantages compared to protocols such as CSMA/CA and AEE-MAC [PERSON] et al. (2018). When the packet size is small, it behaves like a contention-based protocol, whereas with larger packets, it resembles a slot-allocation-based scheme, achieving higher data rates, lower collision rates, and lower energy consumption per transmitted byte. Table 1 summarizes the most important characteristics of the works described above. From the works described in this section, we can observe that except for [PERSON] et al. [PERSON] et al. (2018), the rest of the proposals use, in their respective contention stages, variants of CSMA/CA and ALOHA protocols, which exhibit adequate performance when the number of simultaneous contending users and the overall traffic load are both low. However, they are expected to suffer from congestion as the traffic load and the number of devices increases [PERSON] et al. (2018). Even in the work by [PERSON] et al., where the length of contention period is optimal for each cycle, the random selection of slots by the contending devices could lead to a high number of collisions in a multiple access scenario. In contrast to the previously described works, in this paper we present a hybrid MAC protocol that employs a different approach during the contention stage. This strategy is described in the following section. \begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Year** & **Authors** & **Reservation Mechanism** & **Channel Access Method** & **No. of Devices** & **Data Rate** &
\begin{tabular}{l} **Payload** \\ **Size [bytes]** \\ \end{tabular} \\ \hline
2013 & Y. [PERSON] et al. [PERSON] et al. (2018) & \(p\)-persistent CSMA & TDMA & 1–500 & 1.7 Gbps & 1728 \\
2014 & [PERSON] et al. [PERSON] et al. (2018) & CSMA & DCF within TDMA slots & 1–500 & 1.5 Gbps & 1500 \\
2017 & [PERSON] et al. [PERSON] et al. (2018) & NP-CSMA & TDMA & 100, 200, 500 & NA & NA \\
2018 & [PERSON] et al. [PERSON] et al. (2018) & Random slot reservation & TDMA & 1–300 & 11 Mbps & 1000 \\
2018 & [PERSON]. [PERSON] et al. [PERSON] et al. (2018) & s-ALOHA & TDMA & 1–1200 & 256 kbps & 16 \\
2020 & [PERSON]. [PERSON] et al. [PERSON] et al. (2018) & \(p\)-persistent CSMA & TDMA & 500, 800, 1200 & 1.7 Gbps & 1728 \\
2020 & [PERSON]. [PERSON] et al. [PERSON] et al. (2020) & s-ALOHA & TDMA & 1000 & 256 kbps & 16 \\
2022 & D. [PERSON] et al. [PERSON] et al. (2020) & CSMA & TDMA & 3–15 & 5 Mbps & NA \\
2024 & X. [PERSON] et al. [PERSON] et al. (2018) & CSMA/CA & TDMA (if necessary) & 1–100 & 256 kbps & 50–500 \\ \hline \hline \end{tabular}  
NA: not available.  
\end{table} 
Table 1: Summary of hybrid MAC mechanisms and protocols for machine-to-machine (M2M) communications.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 4 Software Implementation
### Network Monitoring Mechanism  
The beacon interval of the protocol is controlled by each device independently based on its local perception on the consistency of the network (i.e., the changes to the local neighborhood detected by the device). At a random time during each interval (to ensure low congestion at the moment of transmission), devices check the number of consistent beacons received so far and decide whether to send their own beacon or not. Every device additionally piggybacks on the data from different applications requested according to the concept of ULA [40]. On the other end, upon receiving the beacon, devices unpack the data and deliver them to the registered receivers.  
An additional beaconing mechanism is implemented so that the network monitoring mechanism adjusts its operation to the duty cycling rates by querying the duty cycling services. Duty rate and duty period are used across the whole network to identify possible missing beacons. The extremely lower number of messages used to detect neighbors in the initial implementation allowed us to relax this requirement and benefit from using the beacon repetition mechanism presented above. The \(k\)-parameter is used to control the transmission and adaptation rate is auto-adjustable so the operation of our protocol is not affected by environments with variable device densities. As a result, the time difference between the two consecutive transmissions is either the sleep period or the period set by the neighboring discovery protocol's interval. To implement this property, we added a timer based event to be scheduled after each beacon broadcast, called duplicate_transmission, that given a payload rebroadcasts it after the predefined time. The performance evaluation indicates that the resulting component is not affected by the existence of duty cycling, as sleeping devices will get a chance to receive the missing beacons for every transmission, while message loss is also limited due to the increased number of transmissions.
## 2 Fault Feature Extraction Based on Improved Fast Spectral Correlation
### Establishment of Fault Feature Vector  
When different types of faults occur on the bearings, the corresponding enhanced envelope spectrums obtained using the improved fast spectral correlation will also show differences. The characteristic cyclic frequencies will be among the rotating frequency \(f_{r}\), the characteristic frequency of outer-race fault \(f_{o}\), the characteristic frequency of inner-race fault \(f_{i}\) and the characteristic frequency of rolling element fault \(f_{b}\). So, the amplitudes of the four mentioned cyclic frequencies of the enhanced envelope spectrum were selected to form the fault feature vector. The establishment of the fault feature vector using the improved fast spectral correlation approach mainly includes two steps:  
Figure 1: The simulated signal: (**a**) time waveform of \(x_{1}(t)\); (**b**) time waveform of \(x_{2}(t)\); (**c**) time waveform of \(x_{3}\) (\(t\)); (**d**) time waveform of \(x(t)\); (**e**) envelope spectrum of \(x(t)\).  
Figure 3: The processing results of \(x(t)\) via improved fast spectral correlation: (**a**) the improved fast spectral correlation spectrum; (**b**) the corresponding enhanced envelope spectrum.  
1. The improved fast spectral correlation was utilized for dealing with the fault signal of rolling bearing to obtain the corresponding fast spectral correlation spectrum and enhance envelope spectrum.
2. Select the amplitudes (i.e., \(a_{r}\), \(a_{o}\), \(a_{i}\) and \(a_{b}\)) of the four cyclic frequencies (i.e., \(f_{r}\),\(f_{o}\),\(f_{i}\) and \(f_{b}\)) in the enhanced envelope spectrum to constitute the fault feature vector \(\mathbf{A}=[a_{r}\,a_{o}\,a_{i}\,a_{b}]\).
# RF Energy Harvesting Wireless Communications:
## 2 Urban and Semi-Urban RF Environment
### RF Distribution Features
#### 2.2.3 Temporal Features  
Although the power density of ambient RF signal varies with locations and frequencies, it is relatively stable in the time-domain; especially in an outdoor environment, there are only small fluctuations in the RF power density over time. This can be observed in Figure 6, where the power density measured in a certain frequency forms a monochromatic line over time. According to the data published in [42], in an urban area, the total power density in the 680 MHz-3.5 GHz frequency band just has around 3 dBm/m\({}^{2}\) fluctuation in the daytime. (In an indoor environment, the RF signal concentrates in the frequency band of a wireless local area network (WLAN). According to the data collected by [43; 44], the strength of RF signals is quite stable during the late night and early morning, but varies a lot in the daytime due to human activities. In addition, as reported in [43], the average intensity of receiving signals measured in the daytime is about 8 dBm higher than that in the night and morning.)
# Failure in the Tension Zone around a Circular Tunnel Excavated in Saturated Porous Rock
###### Abstract  
Rock failure during tunnel excavation is still a matter of concern. The influence of groundwater is generally taken into account along discontinuities or in \"soil-like\" formations. However, brittle saturated porous rocks can be subject to undrained conditions during tunnel excavation. Negative effective stresses develop close to the tunnel boundary. This study aims at identifying a limit pore pressure in the rock around the tunnel, which induces failure in the tension zone. A discussion related to the strength parameters in the tension zone, with the Hoek and Brown criterion, is presented. A comparative analysis with different far-field stresses and rock properties indicates that the limit pore pressure decreases with the depth of the tunnel. The limit pore pressure is directly proportional to the uniaxial compressive strength and inversely proportional to the constant \(m\). When the uniaxial compressive strength is close to the state of stress around the tunnel, the role of \(m\) reduces. Numerical models set up with FLAC indicate that the tension zone around the tunnel has a thickness of about 1 m. Due to uncertainties in the far-field stresses, hydro-mechanical behavior, and properties of the rock, the tension zone requires a careful investigation, in order to avoid stability problems.  
porous rock; tunnel; pore pressure; tension zone; Hoek & Brown criterion; FLAC
## 1 Introduction
The main limitations of current systems, which hinder such processes, stem from the lack of visibility on flight progress beyond the ATFCM region [30]. Moreover, existing in-block time prediction models predominantly rely on local airport meteorological data [32; 33; 34], neglecting full-scale en-route conditions along the computed FTFM profiles. Current research also focuses primarily on turnaround and off-block time predictability, with limited emphasis on en-route forecasts [34], including velocity data, being incorporated into the Airport Operations Database (AODB) [35], which could otherwise help anticipate potential changes in flight time. Airport operators (AOs) urgently need their own tools for flight predictability, particularly for long-haul flights. This is partially due to airlines' flight planning behavior, in which schedule buffering plays a significant role. Although meteorological conditions along the projected route are considered when submitting the route to NM, economic and business pressures incentivize airlines to perform on time. Based on the authors' own experience, it is clear that the initially estimated time over the destination aerodrome may not always be realistic. To address this, airports could develop their own prediction engines that compute estimates based on flight, trajectory, and meteorological data. Integrating such engines with the AODB, which contains all pertinent information, could significantly improve resource allocation planning. The data used in this study consist of information about arriving flights at Vaclav Havel Airport Prague, Czech Republic. It is important to note that the data are from 2019, the busiest year on record for Prague Airport. During this year, the airport served 17.8 million passengers, marking a 6% increase compared to 2018, and offered direct connections to 165 destinations through 71 airlines [36]. To provide the most current context, in 2023, Prague Airport handled 13.8 million passengers, connecting to 167 destinations via 69 airlines, with 118,046 aircraft movements, which represents a 17% year-on-year increase [37].