esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Quality of the Rebalancing Operations
#### 5.5.3 Effect of Minimum Support  
As mentioned in Section 5.4, a small increase of _minSupport_ is likely to cause a significant decrease in the number of rules mined and, consequently, decrease the performance and quality of the rebalancing methodology we proposed because fewer rebalancing operations can be planned. This trend is shown in Figure 9. As the minimum support threshold increases, the total number of fixed stations drops in all the considered data partitioning  
Figure 8: Reference configuration performances in terms of fixed stations per movement on different data partitioning approaches.  
Figure 7: Reference configuration performances in terms of absolute number of station fixed on different data partitioning approaches.  
approaches. It was observed that a small drop of fixed stations, for example in the per day of the week and timeslot strategy, was due to the fact that many of the first rules in the ranking applied by the proposed methodology are also the most frequent ones, which are therefore not affected by the increasing value of _minSupport_. This consequence is confirmed by Figure 10, in which the fixed stations per movement is plotted. A reduction in the number of rules enforces the framework to apply rules with higher support, which can be associated with stronger patterns. We underline also the fact that the per day of the week and per day of the week and timeslot approaches are the most effective ones. Even though increasing the minimum support threshold increases the quality in terms of fixed stations per rebalancing operation, we opted to prioritize the absolute total number of fixed stations to maintain overall a better quality of service.
# Performance Analysis of Tunnel Boring Machines for
## 4 Case Study: The Moncenisio Base Tunnel
### Application of Models Taking into Account the Geological Aspect  
The geological context along the longitudinal profile of the Moncensiio base tunnel is characterized, as already observed, mainly by schists and gneiss, especially along the third and fourth stretches. The correlation models previously obtained between the mechanical parameters of TBMs are applied below, which take into account the geological aspect. The aim is always to evaluate the power, the torque and the thrust, according to the available equations, for the different types of machines operating in schists and gneiss. For calculations, a diameter of 11 m is considered.  
The models that correlate the diameter of the TBM (D) to the power supplied to the excavation head (P) and to the torque (C) in case of single and double shield TBMs, in shale formations, are listed below:  
\[\text{Single shield TBM}:\ C\ =\ 26.161D^{2.8067} \tag{1}\]  
\[\text{Double shield TBM}:\ P\ =\ 620.07D-1720.5 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Estimated Values** & **Experimental Results** & [42] & **Real Value** \\ \hline Cutterhead Power P (kW) & 3387 & - & 4900 \\ \hline Torque C (kNm) & 14,047 & 11,336 & 8888 \\ \hline Thrust S (kN) & 70,858 & 49,270 & 123,800 \\ \hline \hline \end{tabular}
\end{table}
Table 6: P, C, S values estimated with the experimental models and real values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Developed in the Present Study** & [42] \\ \hline P = 94,279D\({}^{1.4792}\) & - \\ \hline C = 110.42D\({}^{2.0014}\) & C = 90,478D\({}^{1.9951}\) \\ \hline S = 1364.3D\({}^{1.6314}\) & S = 2261.5D\({}^{1.2726}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation equations, obtained for single shield TBMs, between the power supplied to the excavation head (P), the torque (C) and the thrust (S) as a function of the diameter.  
\[C\ =\ 273.24e^{0.4356D} \tag{3}\]  
Through Equations (1)-(3), considering a diameter D of 11 m, the minimum head power and torque values necessary for the two types of TBM are calculated (Table 7).  
The models that link the diameter D of the TBM to the power of the excavation head (P), the torque (C) and the thrust (S) in the case of open TBM operating in rock formations with a prevalence of gneiss are shown below:  
\[\text{Open TBM}:\ P\ =\ 483.01D-1173.6 \tag{4}\]  
\[C\ =\ 1427.3D-5223.9 \tag{5}\]  
\[S\ =\ 3124.6e^{0.1979D} \tag{6}\]  
By inserting in the Equations (4)-(6) a diameter of 11 m, the minimum values of P, C, and S with which the open TBMs must operate (Table 8) are obtained.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 2 The Architecture of a Smart Farming Monitoring System
In this layer the most efficient data management and data mining techniques are adopted to obtain accurate predictions and support regarding field operations such as optimized pesticide application, disease detection, efficient irrigation management. Data processing is supported by Decision Support Systems (DSS) that take care of the overall management of available collected information from the fields towards increasing productivity, optimizing crop yield, maintaining quality and saving resources. It is well known that farmers suffer great economic losses due to incorrect weather forecasting or incorrect irrigation methods. Data analysis is the most important component of IoT agricultural systems resulting in efficient pesticide use and protection against diseases. This layer can be considered to be an additional layer regarding the OSI Model enabling artificial intelligence advancements to the overall system. * The Application Layer, referring to the Application Layer of the OSI Model, includes all suitable application module interfaces for implementing fertilizer and irrigation control, disease and animal detection, alerts regarding the cultivation process and visualization of statistical data. This layer enables the farmer to monitor and manage his fields in a user-friendly way. Data visualization techniques such as graphs, heatmaps, orthomosaics, and three-dimensional models are employed, among others, to allow easy and intuitive representation of the knowledge acquired from the field monitoring. The farmer can inspect the results produced by the services of the system and take action accordingly. Energy-saving mechanisms and data privacy and security techniques are also considered to be very important in the context of Smart farming and should be applied vertically in all the layers of the architecture. To support the efficiency and effectiveness of a smart farming monitoring system energy consumption should be kept under control. Due to the limited battery life and constrained resources of sensor nodes, energy-saving techniques must be applied across the sensor and network layer accordingly. Energy-saving techniques deal with the active and inactive operational time in each sensor node, the scheduling of information transmission and the routing process of data packets. Moreover, security mechanisms are vital for maintaining the privacy of the collected data and safeguarding farmers' personal information exchange.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 5 Justification of EOSSA  
### Algorithm EOSSA  
Consider time series with signals in the form \(s_{n}=\sum_{j=1}^{r}A_{j}\mu_{j}^{n}\). This means that we are considering the case where all signal roots of the characteristic polynomial of the LRR controlling the time series are different.  
Here and below we will use the following notation: for a matrix \(\mathbf{A}\) and a set of column indices \(G\), we denote by \(\mathbf{A}[G]\) the matrix composed of the columns with numbers from \(G\).  
Note that Algorithm 2 of EOSSA contains the step of clustering the signal roots, which will be discussed later in Section 5.2.  
Input: matrix \(\mathbf{Y}\) and its SVD \(\mathbf{Y}=\sum_{l=1}^{t}\sqrt{\lambda_{i}}U_{l}V_{l}^{\mathrm{T}}\) (\(\mathsf{Y}=\mathcal{T}^{-1}\circ\Pi_{\mathcal{H}}(\mathbf{Y})\) is a signal estimate), window length \(L\), way of clustering the signal roots. Result: time series decomposition \(\mathsf{Y}=\mathsf{Y}^{(1)}+\cdots+\mathsf{Y}^{(k)}\).  
1. Construct the matrices \(\mathbf{P}=[U_{1}:\cdots:U_{t}]\) and \(\mathbf{Q}=[\sqrt{\lambda_{1}}V_{1}:\cdots:\sqrt{\lambda_{r}}V_{t}]\).
2. Compute the shift matrix \(\mathbf{M}=\mathbf{P}^{\mathrm{t}}\overline{\mathbf{P}}\) (then \(\mathbf{M}\): \(\overline{\mathbf{P}}\approx\mathbf{P}\mathbf{M}\));
3. Find the matrix \(\mathbf{T}\) from the eigendecomposition \(\mathbf{M}=\mathbf{T}\operatorname{diag}(\mu_{1},\ldots,\mu_{t})\mathbf{T}^ {-1}\).
4. Apply the method of clustering to the set of pairs \((\operatorname{Re}(\mu_{i}),|\operatorname{Im}(\mu_{i})|)\); the result is \(k\) clusters of signal roots. Hereby, we get a grouping \(\{1\ldots t\}=\bigsqcup_{j=1}^{k}G_{j}\). Note that the conjugate eigenvalues belong to one cluster together.
5. For \(j=1,\ldots,k\), compute a basis of \(\operatorname{\mathrm{colspace}space}(\mathbf{T}[G_{j}])\) and constitute the matrix \(\widetilde{\mathbf{T}}_{j}\) of the basis vectors as columns. Denote by \(\widetilde{\mathbf{T}}\) the matrix with \(\widetilde{\mathbf{T}}[G_{j}]=\widetilde{\mathbf{T}}_{j}\) and put \(\mathbf{\Phi}=\mathbf{P}\widetilde{\mathbf{T}}\) and \(\mathbf{\Psi}=\mathbf{Q}(\widetilde{\mathbf{T}}^{-1})^{\mathrm{T}}\). Then \(\mathbf{Y}=\mathbf{\Phi}\mathbf{\Psi}^{\mathrm{T}}\).
6. Set \(\mathbf{\Phi}_{j}=\mathbf{P}\widetilde{\mathbf{T}}_{j}\), \(\mathbf{\Psi}_{j}=(\mathbf{Q}(\widetilde{\mathbf{T}}^{-1})^{\mathrm{T}})[G_{ j}]\).
7. \(\mathsf{Y}^{(j)}=\mathcal{T}^{-1}\circ\Pi_{\mathcal{H}}(\mathbf{\Phi}_{j} \overline{\mathbf{T}}_{j}^{\mathrm{T}})\), \(j=1,\ldots,k\).  
**Algorithm 2** EOSSA.
# Experimental and Numerical Investigations on the Slate Shearing Mechanical Behavior
## 3 Test Results
### Failure Pattern of Slate  
The failure patterns of slate specimens under varying \(\sigma_{\rm n}\) during shear failure are shown in Figure 8. For slate with \(\beta\) of 0\({}^{\circ}\), integral shear failure along the bedding plane is dominant. At \(\sigma_{\rm n}\) = 5 MPa, local protrusions emerge on the shear section. The cracks on the section due to friction become more pronounced with increasing \(\sigma_{\rm n}\). For slate with \(\beta\) ranging from 30 to 60\({}^{\circ}\), varying degrees of bedding plane cracking and matrix shear composite failure are observed. Notably, at \(\beta\) = 30\({}^{\circ}\) and \(\sigma_{\rm n}\) = 15 MPa, stepped tensile cracks are evident, and a concave crack forms along the bedding plane at \(\beta\) = 45\({}^{\circ}\) and \(\sigma_{\rm n}\) = 10 MPa. Cracks along the bedding plane become particularly prominent when \(\beta\) = 60\({}^{\circ}\) and \(\sigma_{\rm n}\) = 15 MPa, displaying distinct scratches and fragmented characteristics. At \(\beta\) = 90\({}^{\circ}\), a crack forms due to the shear failure of the matrix and bedding plane. Also, local cracking of the vertical bedding plane is observed; with the increase in \(\sigma_{\rm n}\), the scratches on the crack surface become more prominent.  
In both intact and cracked rocks, the shear strength in a specific area near the crack tip is weakened by sliding as the shear crack extends. Different anisotropic rock masses exhibit distinct signs of slip weakening (SW) [10]. At \(\beta\) = 0\({}^{\circ}\), shear weakening along the bedding plane is more pronounced when \(\sigma_{\rm n}\) > 5 MPa. When \(\beta\) = 90\({}^{\circ}\), the overall SW phenomenon is weak. The shear SW is also associated with the spacing of bedding planes. At 45\({}^{\circ}\), the bedding plane width (the gray band shown in Figure 2a) in the shear core area is larger,  
Figure 8: Fractured slate specimens after direct shear tests.  
Figure 7: Correlation between shear strength parameters (\(c^{*}\) and \(\varphi^{*}\)) and \(\beta\).  
and the brittleness is more evident at failure. This results in a less evident slip-weakening phenomenon, leading to higher shear strength. For slate with \(\beta=30^{\circ}\) and \(60^{\circ}\), shear action induces tensile cracks developing along the bedding plane, leading to local failure. Subsequently, these tensile cracks propagate along the bedding plane during shearing and sliding, creating an overall rupture zone where shear and tensile cracks interconnect. Cracks along the bedding plane contribute to increased roughness on the shear rupture plane, which explains why the shear strengths of slate with \(\beta\) values of \(30^{\circ}\) and \(60^{\circ}\) closely resemble those of vertically bedded slate.
## 3 The Use of the TIBuck DC/DC Converter as a VLC Driver
### Effect of HB-LED Temperature Dependency  
Figure 7 and Equations (9) and (10) show the values of \(\rm V_{1}\) and \(\rm V_{2}\) that maximize resolution for tracking the output voltage of the TIBuck DC/DC converter acting as a VLC driver, which uses the maximum amplitude of the communication signal. These values depend on the threshold voltage \(\rm nV_{th}(T)\) and the maximum allowed voltage \(\rm nV_{max}(T)\) of the HB-LED load. But, as mentioned and shown in Figure 1, these values depend on the temperature. Taking this into consideration, Figure 9 shows the effect of temperature changes on the resolution for tracking the output voltage (for sake of simplification, sinusoidal waveforms are used as communication signals). This figure represents the I-V characteristic curve of the HB-LED load for two different temperatures \(\rm T_{1}\) (in orange) and \(\rm T_{2}\) (in green), where \(\rm T_{1}>T_{2}\). The design is optimized for the lower temperature \(\rm T2\), meaning that \(\rm V_{1}(T_{2})\) and \(\rm V_{2}(T_{2})\) are obtained using Equations (9) and (10) as follows  
\[V_{1}(T_{2})=n\cdot V_{max}(T_{2}), \tag{15}\]  
\[V_{2}(T_{2})=n\cdot V_{th}(T_{2}). \tag{16}\]  
If the temperature of the HB-LED load rises to \(\rm T_{1}\), the voltages \(\rm V_{max}(T)\) and \(\rm V_{th}(T)\), which maximize the resolution of the duty cycle, also change. Even though \(\rm V_{2}\) can be controlled though \(\rm d_{bu}\), according to Equation (14), and is able to match the tracking of the communication signal allowing its maximum amplitude, there is no control over \(\rm V_{1}\) (\(\rm V_{1}\) temperature changes could only come from control changes of previous stages of the power supply chain). This means a loss of the useful duty-cycle range of the TIBuck DC/DC converter (in blue in Figure 9), and, therefore, a reduction in the resolution for tracking the output voltage. Moreover, the higher the temperature rise of the HB-LEDs, the lower the resolution. This is the price to pay for the simplification compared to other solutions that split power with two DC/DC converters (Figure 2).  
The temperature also has a negative effect on switching losses. As previously mentioned, the most significant power losses of the TIBuck DC/DC converter, acting as a VLC driver, are the switching losses due to its high switching frequency. The switching losses depend on the voltage stress of switches (i.e., V1-V2). While V1 is kept constant and V2 decreases with the rise in the temperature, the switch voltage stress increases when the temperature of the HB-LEDs increases. Therefore, the switching losses increase when the temperature rises. This drawback is common in all solutions that split power based on two converters for designing VLC drivers (Figure 2).
# Potentials for Pressure Gain Combustion in Advanced Gas Turbine Cycles
## 3 Results and Discussion
### Thermal Efficiency and Specific Work
Recuperation and combined intercooling and recuperation reduced the efficiency gains for PGC compared to conventional isobaric combustion. Still, the advanced cycles achieved higher absolute efficiencies for all types of combustion compared to the simple gas turbine cycle. For a temperature constraint of 1200 K, the discussed effects were even stronger (see the same figure). All realistic PGC models increased the efficiency gain for an intercooled GT (+4 PP compared to only +2.7 PP for isobaric combustion) and lowered the gain for recuperated and intercooled-recuperated engines. Especially, the latter was negatively affected. At 1200 K, the efficiency gain was 1 PP less for PGC in an intercooled-recuperated engine than for 1100 K. It can be concluded that higher acceptable blade temperatures improved the potentials for PGC in an intercooled engine,  
Figure 5: Thermal efficiency for various models and cycles at \(T_{mat}\) of 1000 K, 1100 K, and 1200 K. PP, percentage point. but reduced them in an intercooled-recuperated engine. Efficiency gains of the recuperated cycle did not seem sensitive to different blade temperature constraints. Results of the optimizations at a blade temperature constraint of 1000 K in Figure 5 show that PGC reduced the efficiency gains in the combined intercooled-recuperated cycle. On average, the reduction was similar to 1200 K. For the intercooled cycle, efficiency gains were still present for [PERSON]'s model and the Mix ref model, but smaller. Figure 6 depicts the specific work of the gas turbine for the different cycles and combustion types. PGC could increase specific work by almost 50% in advanced cycles. Particularly, intercooling and combined intercooling and recuperation achieved high improvements, as already announced in the Introduction. Large specific work gains were found compared to the simple and recuperated cycle. However, comparing the specific work for different combustion models was difficult, as no trend was visible. Apparently, the behavior of the different combustor models led to completely different operating points in terms of the combustor temperature ratio and pressure ratio for optimum cycle efficiency.
###### Abstract
[18] conducted the droplet size test of the GP-81A (Civil Aviation Xuzhou Equipment Plants, Xuzhou, JS, China) series aviation nozzles for fixed-wing aircraft in the high-speed wind tunnel, then carried out the field spraying test. The results showed that under the similar spray pressure conditions, the droplet size of the same nozzle has some differences in the two environments, but the distribution trend is consistent. [PERSON] et al. [19] conducted a wind tunnel test and field test with CP air nozzle (CP Products, Inc., Tempe, AZ, USA), and studied the effects of four different additives on the atomization characteristics and drift on fixed-wing aircraft aerospace spray droplets. Wind tunnel and field tests determined the difference in particle size distribution and drift reduction of different additives, but affected by meteorological conditions and field sampling methods, the field test data was more variable, and it also exposed the problem of poor repeatability in the field test. [PERSON] et al. [20] tested the droplet size of XR-Teejet 110015 flat-fan nozzle (Spraying System Co., Chicago, IL, USA) used in quadrotor drones in wind tunnels and field. The results showed that at the wind velocity of 2.5 m/s, the ratio of droplet size in field test was consistent with that of wind tunnel test, but the value of droplet size was significantly high compared with that in the wind tunnel test. At present, most studies on the droplets size distribution characteristics are focused on the high-velocity range applicable to manned agricultural fixed-wing aircraft and the low-velocity range applicable to agricultural drones [21; 22], few scholars have researched the middle and low-velocity range of manned agricultural helicopters [23; 24]. In China, most of the helicopters and ancillary equipment for aviation spraying come from abroad, there is a lack of actual application performance testing for various types of machines and equipment. Due to the lack of study on the applicability of these devices in China [25], it is necessary to find out the spraying regular of manned agricultural helicopters under medium-low velocity category. The objective of this study was to draw on the respective advantages of wind tunnel test and field test, understand the droplet size distribution characteristics of the manned agricultural helicopters and their associated flat-fan nozzles commonly used in China, and try to find the relationship between droplet size spectra and stains/impacts dimension. Finally, it is expected to optimize the aviation application mode, provide data support for the nozzles selection of helicopter under medium-low velocity, and provide a reference for precise spray application.
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 3 Establishment of the FAST MST Model
### Types and Parameters of FAST Cables and Connecting Nodes  
FAST uses the OVM.ST high-stress amplitude cable (see Figure 4), the main technical performance parameters of which are as follows:  
1. The pull-down cable uses specification No.1 in Table 1 (OVM.ST15-1), while the main cable uses specification Nos. 2-21 in Table 1.
2. The elastic modulus of the cable: E = (190 \(\pm\) 10) GPa;
3. The linear temperature expansion coefficient: \(\alpha\) = 1.2 \(\times\) 10\({}^{-5}\);
4. Other parameters of the cables are shown in Table 1.  
Figure 4: Sectional view of finished FAST cables. 1: Outer HDPE; 2: galvanized steel wire; 3: epoxy-coated steel strand; 4: inner HDPE; 5: stuffing rope; 6: high-strength polyester tape.
## 4 Discussion
### Perturbation of AaRS Fidelity Leads to Rapid Acquisition of the AlaRS C666A Variant  
One striking observation from this work was the accumulation of AlaRS C666A suppressors without the necessity for mutagenic manipulation. It has previously been reported that increased levels of mistranslation, either facilitated by an error-prone aaRS [35] or imbalances in initiator tRNA levels [36] lead to increased SOS-dependent mutation sampling. This model proposes that increased levels of mistranslation cause elevated Lon protein levels, ultimately leading to the RecA-dependent degradation of LexA causing SOS induction. The authors of the aforementioned work first noted decreased antibiotic sensitivity to the DNA gyrase inhibitor ciprofloxacin during increased protein mistranslation.  
Previous work from our group has shown that defects in AlaRS proofreading lead to increased sensitivity to ciprofloxacin [13]. Similar analysis comparing a proofreading-deficient ThrRS _E. coli_ strain suggested a slight trend towards improved sensitivity to ciprofloxacin compared to wild type, although the observed trend was not statistically significant [13]. Together these observations may suggest that while SOS induction may be occurring in response to increased mistranslation, the cost of a specific error may affect the potential benefit of this early stress response induction.  
Total proteomic analysis of the _E. coli_ AlaRS C666A strain reported a \(\sim\)1.7\(\times\) increase in RecA levels compared to wild type [13]. While predicted correlating trends in both Lon and LexA were not observed, it is possible that the observed changes in RecA correspond to other observations of mistranslation causing SOS-dependent mutant sampling. This mechanism may be responsible for the propensity for the AlaRS C666A suppressor to arise that were identified in this report.