esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Influence of Soil Physicochemical Properties on MOC  
The pH in different grassland types increased with the decrease in the MAP except the MP transect MS. The pH increased with increasing temperature in all three grassland transects. The MOC content was negatively correlated with the pH (r = \(-\)0.69). To some extent, it explained the pattern of less MOC content in MP-MS. In terms of soil texture, we found significant differences in the clay content in the three transects. The MOC and SOC were positively correlated with the proportion of clay in the soil [56; 57]. The MOC content can be enhanced by increasing carbon input. The moist soil with a higher fine silt content (soil, sand, and clay content) was more favorable for the accumulation of organic carbon. LP soils with high Sa and Saf values had the lowest weathering intensity. The content of clay grains and Fe/Al oxides gradually increased with increasing weathering intensity, which is in concordance with the findings in [58]. Also, metal oxides have been shown to be particularly effective in the sorption and stabilization of SOC [59; 60]. The CaO content in LP soil was higher than the other two transects, which may be the reason for the highest MOC/SOC value in the LP transect. When CaO dissolves, calcium ions are released, which attach to soil particles and improve flocculation. Microaggregates may be formed in soils with a high clay content and cation exchange capacity [61], thus indirectly promoting organic carbon binding to minerals and improving SOC stability [62]. Also, the interaction between the pH and CaO had a positive effect on the stabilization of SOC [63]. It has been shown that the increase in calcium concentration in soils with a higher pH could effectively increase the attraction between clay and organic matter. Colloids could flocculate to a large extent due to electrostatic forces, which were the basis for the formation of agglomerates [64]. In agriculture, lime had been used to improve slabby soils [65; 66], and CaO had also been added as an exogenous substance to soil amendments for application, thereby increasing the carbon sequestration capacity of soils [67].
# Double Unloading Gas Control Technology for Fracturing Soft Coal Seams in Overlying Key Strata
## 3 Analysis of Simulation Results
### Drilling and Extraction Rules before and after Stress Relief of Key Strata
#### 3.3.3 Optimization of Hole Drilling Arrangement Parameters  
Through a numerical simulation study of single-hole extraction, it has been concluded that the effective extraction radius and gas pressure drop rate of coal seam hole drilling are significantly larger compared to ordinary drilling. Furthermore, after fracturing the key strata of the overlying rock layer, the effective extraction radius of hole drilling becomes even larger. Based on this, it is determined that hole drilling should be adopted and the process of drilling holes should be optimally arranged. The simulation in Figure 14 shows the use of single-row and double-row drill holes, with varying spacing of 4 m, 5 m, 6 m, and 7 m. The double-row drill holes are arranged in a 'triple-eyelet' configuration, with a spacing of 2.5 m between the upper and lower rows. According to Figure 14a,b, it can be seen that the effective range enhancement of borehole extraction is greater than 2 times  
Figure 12: Variation curve of extraction time and gas pressure. (**a**) Hole drilling. (**b**) Ordinary drilling.  
Figure 13: Fitting curve between effective gas extraction radius and overburden stress.  
the effective extraction radius, due to the superposition effect of the effective extraction radius of the borehole. Additionally, the use of the triangular double-row layout expands the extraction range of the holes, effectively addressing the gas control blank area at the top and bottom plates of the coal seam, which is not adequately addressed by a single row of holes.  
The results shown in Figure 14c indicate that the gas pressure at the center of the adjacent drill holes is 0.32 MPa, 0.34 MPa, 0.38 MPa, and 0.41 MPa for drill hole spacings of 4 m, 5 m, 6 m, and 7 m, respectively. The maximum pressure relief range is found to be 21 m. Similarly, in the case of Figure 14d with double rows of holes, the gas pressures at the center of the adjacent holes are 0.17 MPa, 0.19 MPa, 0.21 MPa, and 0.23 MPa for drill hole spacings of 4 m, 5 m, 6 m, and 7 m and the maximum pressure relief range is also 21 m. It can be concluded that the pressure relief range of double-row holes is greater in the longitudinal direction, while remaining consistent with that of the single-row holes in the transverse direction. Additionally, the gas pressure decreases faster in double-row drill holes within the same extraction time, with the lowest gas pressure reaching 0.17 MPa. This suggests that double-row drill holes can more effectively reduce the gas level in the coal seam below the safety threshold.
# Network Modeling of Murine Lymphatic System
## 4 Quantitative Characterization of Lymph Flows through the LS  
The estimated values of the lymph flow through various vessels of the murine LS are specified in Figure 3. The upper panel shows the flow intensity in the major section of the LS, draining the left and lower parts of the body. The baseline values vary from about 20 to 420 \(\mu\)L/h. The lower panel characterizes the flow intensity in the minor section of the LS, draining the upper right part of the body. The baseline values vary from about 20 to only 100 \(\mu\)L/h.  
The radius of the thoracic duct is known to be around 300 \(\upmu\)m, while the radius of the lymphatic vessels afferent to popletal nodes is around 20-40 \(\upmu\)m (Table 1). For the human LS it is known that the largest lymphatic vessels have a diameter of about 2 mm and the diameter reduces to approximately 10-60 \(\upmu\)m for initial lymphatic capillaries [10]. Due to the lack of detailed anatomical and physiological data in mice on the diameters of all lymphatic vessels, we explored three complementary assumptions on the distribution of the radii of the edges of the lymphatic graph, as specified in the following three scenarios:  
* Scenario 1. All radii in the graph are assumed to be the same, equal to 150 \(\upmu\)m (half of the radius on the thoracic duct).
* Scenario 2. Edge radii decrease linearly with distance from the outlet vertices (jugular veins) to the inlet vertices. On the thoracic duct, the radius is assumed to be 300 \(\upmu\)m, on the most distant edges (from the hindlimbs) it is assumed to be 41 \(\upmu\)m. Therefore, on other edges from the inlet vertices, the value of the radius is equal to 41 \(\upmu\)m and increases linearly when approaching the vein. On the subgraph collecting lymph into the right jugular vein, the radii are set symmetrically, equal to the radii in the left subgraph.
* Scenario 3. Edge radii are distributed so that the cross-sectional area of incoming and outgoing vessels for each vertex of the graph is preserved. On all inlet edges, the radii are assumed to be the same and are estimated so that the radius on the thoracic duct would be equal to 300 \(\upmu\)m.  
The histograms of the vessel radii distribution for the above scenarios are presented in the left column of Figure 4. They clearly indicate that the median value of the vessel lumen decreases as we move from the first to the third scenario (from 150 \(\upmu\)m (Scenario 1), to 115 \(\upmu\)m (Scenario 2), to 60 \(\upmu\)m (Scenario 3)). Note, that the estimated lymph flows shown in Figure 3 refer to Scenario 2.
## 4 Actual Project Study
### Comparative Analysis of Simulation Results with Different Parameters  
#### 4.6.1 Influence Analysis of Excavation Area of New Single Tunnel  
The excavation area A of the new tunnel is calculated by 0.75S, 1.0S, 1.25S, 1.5S, 1.75S, and 2.0S (S is the excavation area of the tunnel in this project, m\({}^{2}\)). Figure 13 shows the mechanical response of the existing structure. From this figure, it can be observed that the maximum settlement (\(S_{\text{max}}\)) and the maximum additional bending moment (\(M_{\text{max}}\)) of the existing structure vary linearly with the excavation area (\(A\)), indicating an increase in both as the excavation area increases. Based on [PERSON]'s [48] proposed relationship between \(S_{\text{max}}\), \(A\), and the stratum loss rate, reducing the excavation area can effectively control the deformation of the existing structure.  
Figure 11: Comparison curve of floor settlement.  
Figure 12: Additional bending moment of the bottom plate.  
4.6.2. Analysis of the Impact of the Clear Distance Between the New Single-Hole Tunnel and the Bottom Plate  
The net distance \(S_{\mathrm{v0}}\) between the tunnel crown and the bottom plate is taken as 1.5 m, 2.25 m, 3.0 m, 4.5 m, 6.67 m, and 8.74 m, respectively, for calculation.  
Figure 14 shows the relationship of clear distance between the newly built tunnel and the floor and the forces and deformations. From this figure, it can be seen that as the clear distance between the new tunnel and the existing structure floor increases, the forces and deformations on the existing structure decrease. When the net distance \(S_{\mathrm{v0}}\) of the bottom plate rises from 1.5 m to 6.67 m, the decay of deformation and force in the existing structure is relatively rapid; from 6.67 m to 8.74 m, this decay slows down. This suggests that the impact of the new tunnel on the existing structure can be effectively mitigated when \(S_{\mathrm{v0}}\) exceeds twice the tunnel span. As illustrated in Figure 13c, the maximum bending moment \(M_{\mathrm{max}}\) reaches 214.9 kN\(\cdot\)m at \(S_{\mathrm{v0}}\) = 6.67 m and jumps to 480.96 kN\(\cdot\)m at \(S_{\mathrm{v0}}\) = 3.0 m, marking a 2.24 times increase and a rapid growth. Hence, in this project, it is advisable to maintain a spacing between the tunnel and the existing structure of at least 3.0 m; otherwise, reinforcement of the bottom slab is necessary to guarantee structural safety.  
Figure 13: The impact of the new tunnel excavation area on the existing structure: (**a**) floor settlement curve, (**b**) variation curve of additional bending moment of floor, and (**c**) influence of tunnel spacing \(L\) on \(S_{\mathrm{max}}\) and \(M_{\mathrm{max}}\).
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 4 Case Analysis
In order to ensure that the controller has a large gain near the fundamental frequency \(f_{0}\) (49.8-50.2 Hz), it is necessary to satisfy \(\omega_{\rm i4}<\omega_{\rm 12}\), so that its characteristic curve is similar to that of the PR controller, and \(\omega_{\rm i4}=0.1\) can be obtained by the estimation method. At the same time, based on the idea that the fundamental frequency is dominant and the frequency fluctuation obeys the normal distribution, the quasi-resonance coefficient of the QPIR controller is mainly allocated reasonably to make full use of its respective advantages. Take \(\alpha=1/2001\), \(\beta=300/2001\), \(\gamma=1400/2001\), \(\eta=300/2001\). Obviously, there is no coupling between the \(\alpha\) axis and \(\beta\) axis in Figure 3, and there is duality. The following is an analysis of the system taking only the \(\alpha\) axis as an example. According to the control block diagram of Figure 3, the open-loop transfer function of the system can be obtained as follows:  
\[T(s)=\frac{H_{\rm 2}K_{PWM}G_{\rm i}(s)}{s^{3}L_{1}L_{2}C+s^{2}L_{2}CH_{\rm i 1}K_{PWM}+s(L_{1}+L_{2})} \tag{17}\]  
Figure 8 is a Bode diagram of the loop gain \(T(s)\) of different controller systems plotted according to the parameters in Table 1. In the figure, \(T_{\rm PR}(s)\), \(T_{\rm QPR}(s)\), and \(T_{\rm QPR}(s)\) represent the loop gain using the PR, QPR, and QPIR controllers, respectively. With the same parameters, the phase margin of the PR controllers is \(67.5^{\circ}\), and that of the QPR and QPIR controllers is \(56.1^{\circ}>45^{\circ}\); their amplitude margin is \(4.2\) dB \(>3\) dB, which meets the design requirements. The cutoff frequency\(f_{\rm c}\) is \(1756\) Hz, and the resonance frequency\(f_{\rm r}\) is \(4125\) Hz. The gain (\(104\) dB) at the fundamental frequency \(f_{0}\) with the QPIR controller is much higher than that with the QPR (\(76.2\) dB) controller. At the same time, the QPIR controller can also meet the requirements of adapting to the power grid frequency fluctuation in the range of \(\Delta f=0.5\) Hz, similar to the QPR controller.
## 4 Grid Voltage Feedforward Control
### Feedforward Design  
When traditional single-L or LC grid-connected inverters experience unbalanced grid voltage sag, they can worsen the imbalance in the VSG's grid-connected current, causing the VSG's LVRT to fail. To solve this problem, this paper proposes a grid voltage feedforward approach that adds an extra control path between the grid voltage and the grid-side inductor, reducing the effect of unbalanced grid voltage sag on the grid-connected current during the VSG's LVRT. This is depicted in Figure 9,The red-lined part represents the feedforward design of the grid voltage.  
Figure 8: VSG Current Loop Control Block Diagram.  
Figure 9: Improved VSG Inner Current Loop Control Block Diagram.  
Based on Figure 9, the expression for the grid-connected current can be derived as follows [25; 26]:  
\[i_{g}=\frac{Q_{PR}(S)K_{pum}}{sL+R+Q_{PR}(S)K_{pum}}i_{Lref}+H(s)v_{g} \tag{19}\]  
where, \(K_{pum}=1\).  
\[H(S)=\frac{(N-sC)Q_{PR}(S)+(M-s^{2}CL-sCR-1)}{sL+R+Q_{PR}(S)} \tag{20}\]  
To eliminate the influence of the grid voltage, we can set \(H(s)=0\).  
\[\left\{\begin{array}{l}M=s^{2}CL+sCR+1\\ N=sC\end{array}\right. \tag{21}\]  
After incorporating the feedforward functions \(M\) and \(N\), the grid-connected current of the VSG can be expressed as follows:  
\[i_{g}=\frac{Q_{PR}(S)}{sL+R+Q_{PR}(S)}i_{Lref} \tag{22}\]  
Equation (22) illustrates that the enhanced grid voltage feedforward control, introduced in this paper, enables the VSG's grid-connected current to precisely match the inverter's output current reference. This eliminates the disruption caused by an unbalanced grid voltage, allowing the VSG to maintain current balance and offer grid support during voltage says, thus ensuring effective LVRT performance.  
We obtain the overall block diagram for balanced current control during the VSG's LVRT, as shown in Figure 10 below [27; 28].
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 4 Specifying Model Formally Using VDM-SL
### Static Model
The _invariant_ is described as: the power of gateway is high, and the mode is idle. The fire info will be received if and only if sensors detect the fire, then the gateway becomes activated and transmits a signal to the control room for immediate action. The gateway is connected to the control room if and only if the neighbor set is not empty. Order : monitoring_ordr:bool

send_adrone:bool order_received:bool;

ControlRoom:remote:NODE

order:Order

con rchrs:set of NODE

inv mk_ContRoom(crmode, aorder, con_crmbrs) == crnode.Power = <High>

and if crnode.signal = <SIGNAL RECIEVED> then

order.monitoring_ordr = true and aorder.send_adrone = true

else aorder.monitoring_ordr = false and aorder.send_adrone = false and

crnode.connect = true <=> con_crmbrs <> {} or

crnode.connect = false <=> con_crmbrs = {};

The gateway then communicates with the control room, and the composite entity is named ControlRoom and has five variables. The crnode characteristics of the object node are being accessed. The order shows that the control room can send instructions to drones. The con_crmbrs is used to demonstrate the control room's relation to other nodes. The _invariant_ is described as: the power of the crnode is high, and if a signal from the gateway node is received, control will give instructions to the monitoring and actor drone; otherwise, no order will be issued. **Composite objects of drones**

WdroneMode = <IDLE>|<WORDING>|

MDroneAction = <REALTIME_VISUALIZATION>|<FIRE_CONFIRMD>|<NOFIRE_FOUND>;

MODE:indnode: NODE

con_mdnds: set of NODE

order:ControlRoom

rdronemode: MdroneMode

mdroneaction: MdroneAction

inv mk_Mdrone(mndode, con_mdnds, order, mdronemode, mdroneaction) ==

mmode.Power = <High> and if order.aorder.ordered_received = true

then mdronemode = <WORKING> and and mdroneaction = <REALTIME_VISUALIZATION>|

and mdroneaction = <FIRE_CONFIRMD> or mdroneaction = <NOFIRE_FOUND> else

mdronemode = <IDLE> and mdnode.connect = true <=> con_mdnds <> {} or

mdrone.connect = false <=> con_mdnds = {};

The monitoring drone is defined as the composite object consisting of six fields. Mdnode is used for accessing the characteristics of the object Node. The con_mdnds is used to show that the link of a drone is enabled or disabled. Order is accessing the ControlRoom to check whether the order is received or not. The mdrone mode shows that the drone is either idle or working. The mdroneaction field describes which action a monitoring drone will perform. The _invariant_ is described as: the power of mdnode is high, and if an order is received from control room, then the mode of the drone becomes working. A monitoring drone's actions include real-time visualization and confirmation that it is their fire, or no fire found; otherwise, the mode would stay idle.
## 1 Introduction
Security protocols typically allow the negotiation of some parameters to be used during operations. Such negotiations might be related to cryptographic and digital signature algorithms. In order to guarantee full interoperability among smart objects, it is necessary to define a set of mandatory options that all objects must implement for minimal support. The algorithms supported by an object are declared in a negotiation phase and a proper choice is then agreed upon by the two communicating parties. It is not necessary that the mandatory algorithms are standard algorithms used in the Internet, but can be targeted for constrained environments. A final remark should be made about the heterogeneous nature of smart objects, whose characteristics can vary significantly with relevant differences with respect to those of conventional hosts. This means that the adoption of a suite of security protocols and cryptographic algorithms is a need and a challenge  
Figure 1: Comparison between the IoT and the Internet protocol stack for OSI layers. at the same time. Standardization can lead to full interoperability, yet it is extremely difficult to agree on a set of common protocols and algorithms supported by all devices. This paper aims at providing an overview of the cryptographic algorithms and security protocols suitable to deployment of smart objects. The rest of this paper is organized as follows. In Section 2, an overview of security protocols is presented. Section 3 is dedicated to the description of several lightweight cryptographic algorithms relevant for IoT scenarios. In Section 4, the focus is on key distribution and management. In Section 5, secure data aggregation is discussed. Section 6 describes the problem of authorization and protection of RESTful services in constrained environments. Finally, in Section 7 some conclusions are drawn.
###### Abstract  
Meat products are highly susceptible to contamination with _Listeria monocytogenes_, a foodborne pathogen associated with high mortality. To mitigate this risk, this study explored the use of _Poliomintha longiflora_ oregano essential oil, both in its pure (PEO) and fractionated (FIV, fraction IV obtained at 140 \({}^{\circ}\)C) forms, as part of a hurdle technology combining natural antimicrobials with mild thermal treatments. In vitro thermal inactivation experiments were conducted at 52, 54, 57.5, and 63 \({}^{\circ}\)C using a simulated meat medium. The FIV group, characterized by 60.23% carvacrol and 21.17% thymol, exhibited significantly enhanced bactericidal activity, achieving up to 5.5 log-reductions in _L. monocytogenes_ at 57.5 \({}^{\circ}\)C within 3 min, compared to <2 log-reductions for the control group. Inactivation kinetics were well described by the Weibull-Mafart model. The \(\delta\)-values, defined as the time required to achieve a 1-log reduction in bacterial population, were consistently lower for FIV compared to the control across all tested temperatures (e.g., \(\delta_{\rm 52}{}^{\circ}\)C = 0.64 min vs. 8.47 min for control). The estimated z-values, which represent the temperature increase required to achieve a tenfold change in \(\delta\)-value, were 5.75 \({}^{\circ}\)C (control), 5.20 \({}^{\circ}\)C (PEO), and 5.00 \({}^{\circ}\)C (FIV), suggesting a consistent thermal sensitivity but enhanced inactivation efficacy with the essential oils. These findings suggest that fractionated oregano essential oil is a promising hurdle to shorten thermal treatments in meat products, thereby lowering _L. monocytogenes_ contamination risk while preserving product quality.  
Keywords:thermal inactivation; predictive microbiology; foodborne pathogens; food safety; hurdle technology; natural antimicrobials +
Footnote †: journal: Applied sciences
## 2 Remote Sensing Applications in Archaeology, Geography, and the Earth Sciences
They indicated the prevailing challenges associated with the utilization of high-spatial-resolution remote sensing images in understanding geographic scenes. The main challenge stems from the use of high-spatial-resolution remote sensing data, which provides finer landscape details but simultaneously exacerbates the complexity of data processing for intelligent image interpretation.