esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Setup Phase (SP)
#### 3.2.1 Routing Construction Period (RCP)  
The CH broadcasts CH_DEC messages to guide surrounding nodes to build cluster units. Its frame consists of the following fields: frame control (2 bytes), extended address (8 bytes), broadcast address (1 byte), and frame check sequence (FCS) (2 bytes).  
The non-cluster head nodes that received CH_DECs select the best CH according to the Received Signal Strength Indicator (RSSI), and reply to the JC_REQ message to join the cluster through CSMA/CA. The JC_REQ message consists of frame control (2 bytes), the node's extended address (8 bytes), CH's extended address (8 bytes) and FCS (2 bytes).  
Each CH waits for a sufficiently long time to ensure that all JC_REQ requests are received, then the number of member nodes is determined based on the number of JC_REQs received.  
Each CH allocates a short address (1 byte) for itself and each member node, which is used to reduce control overhead in future communications. Each CH can be associated with up to 255 member nodes, which is sufficient for most applications. In WSNs with a cluster tree topology (hierarchical cluster topology), if there are too many member nodes in a cluster, traffic conflicts among them are likely to occur. If the network is to scale up, increasing the number of CHs (i.e., the number of clusters) is often more advantageous and effective than increasing the number of member nodes per cluster.  
Figure 3 shows the structure of a CS_AOC message. It is used to broadcast short addresses and control slots of member nodes. The \(S_{i}\) field indicates the control slot number allocated by CH to node \(i\). The Control Slot Length (\(L\_CS\)) field indicates the duration of a single control slot. The \(TOT\_CS\) field indicates the total number of control slots allocated,  
Figure 2: Network phase design.  
which is also the total number of member nodes. The length of CS_AOC is 10\(N\) + 7 bytes, where \(N\) is the number of member nodes.  
After receiving CS_AOC, the non-cluster head node checks the extended address field. If the field has its extended address, it indicates that the node has successfully joined the CH. The start time of the control slot of node \(i\), \(ST\_CS(i)\), can be calculated as \(ST\_CS(i)=(S_{i}-1)\times L\_CS\).
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## 3 Theory and Calculations
### Progressive Flow Method Theory
While the CM involves modulating the position of the partner valve to keep unchanged the flow rate measured at the CBV installed on the index (reference) path, the PFM allows the invariance of the downstream flow rates to be granted by means of simply keeping constant the pressure across the terminal itself, without introducing additional CBV pressure drops. In addition, control of the reference flushing condition can be obtained by alternative ways other than adjusting the branch (partner) valve only, e.g., by varying the pump speed, by opening or closing other upstream branches, or by a combination of all these actions. If the procedure starts with all the terminals belonging to a specific branch in a closed position, the branch flow rate grows step by step, where each step counts the nominal flow rate of the newly activated terminal, according to the denomination of \"progressive flow method\". As already pointed out in Section 2.2.1, the procedure described in this way allows any branch to be balanced, provided that the farthest terminal is also the disadvantaged one. If this is not the case, the compensated method proposes a subroutine involving measurements and the re-balancing of all terminals. Considering the most common hydronic system architectures, there are situations in which the position of the disadvantaged terminal is not decisive and an adapted version of the procedure can still be applied without the need to retroactive sub-routines; manifold-based systems and reverse return systems belong to this category. Appendix B examines these systems and shows how the procedure can be adapted and applied. All other cases (i.e., direct return water systems) point to a situation that is rather common in air systems, which the PFM has had to deal with since its very first formulation. This critical issue has been addressed from a conceptual perspective and the conclusion is that, given the presence of valves in the proper positions, the general balancing theory can be applied in a more extensive manner and an extended procedure can be applied without the enforcement of any retroactive subroutines.
## 4 Development and Implementation
### Schedule with Consideration of the Model  
A mathematical model was developed to determine the actual number of man-hours for the analysed project. The number of man-hours verified by the model, according to the assumption made in the study, is the basis for determining the real number of workers who, on the basis of the adopted standards of material expenditures, will implement the project within the contractual period for the completion of the construction works. The data for the model for this case and the results obtained are presented in Table 6.  
The final output of the model for the project in example one was \(Z=115.15\). The specific coefficients for each group were \(Z_{1}=110\), \(Z_{2}=119\), \(36\), \(Z_{3}=100\) and \(Z_{4}=128\). Based on the results, it is apparent that design and technological factors were the most important for this project. The completion date was set just before the Christmas period,  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Factors** & **Factor Weight** & **Means of Factor Impact** & **Specific Results** & **Final Result** \\ \hline Type of works & Low (31.25) & Neutral (0) & \\ \hline Employee qualifications & Average (60.23) & Positive (1) & Increase a little (\(Z_{1}=110\)) \\ \hline Physical conditions at the site & Low (12.50) & Neutral (0) & \\ \hline Construction works technology & Average (73.86) & Positive (1) & \\ \hline Degree of the prefabrication of elements & Low (43.75) & Neutral (0) & Increase (\(Z_{2}=119.36\)) \\ \hline Degree of the mechanisation of the works & Low (37.50) & Positive (1) & \\ \hline Project management & Low (43.75) & Neutral (0) & \\ \hline Cooperation between the contractor and the designer & Low (6.25) & Neutral (0) & Do not change (\(Z_{3}=100\)) \\ \hline Availability of employees & Low (28.13) & Positive (1) & \\ \hline Implementation deadline & Low (68.75) & Positive (1) & \\ \hline Amount of works & Low (59.38) & Neutral (0) & Increase (\(Z_{4}=128\)) \\ \hline Contract value & Low (3.13) & Positive (1) & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model results.  
Figure 5: General work progress schedule (preliminary).  
20 December. The contract was signed in November, hence the time to prepare and plan the works was short. Some of the works were implemented in the contractor's workshops, so the influence of technological factors is also justified in this case. The factors in group \(Z_{2}\) relate to technology, prefabrication and mechanisation of construction works.  
Figure 6 shows variant I of the schedule, based on the results of the model, which shows the change in man-hours introduced for all works.  
Due to the changes made to the duration of the works, without interfering with the other assumptions on which the initial schedule was based, the project deadline was exceeded. The critical path has also changed, which in this variant includes works 5-4-2. The critical time is 20 working days, and the maximum daily employment is 6 people.
## 3 Results and Discussion
### The Influence of the Boundary Conditions  
There were three different cases for the support conditions of the walls. In the first case, the base of the wall was assumed to be restrained. In the second case, the whole perimeter of the wall was restrained, and in the third case, the three sides of the wall (bottom and sides) were restrained. Figure 11 shows the maximum stress distribution for different support conditions in the wall models. Clearly, the greater the restraints of the sides of the wall (case b), i.e., the better the restraining of the wall to the structural components of the building, the lower the stress in the wall. As such, in case \"a\" with the base of the wall constrained, a large surface of the wall had stress ranging from \(2.3\times 10^{4}\) to \(5.2\times 10^{4}\) kgf/m\({}^{2}\). In case \"c\" with the base of the wall and the sides restrained, a large surface of the wall had the stress of \(1.2\times 10^{4}\) to \(4.8\times 10^{4}\) kgf/m\({}^{2}\). Finally, in case \"b\" with the whole sides of the wall restrained, a large surface of the wall had the stress of \(3.2\times 10^{3}\) to \(3.3\times 10^{4}\) kgf/m\({}^{2}\).  
According to Figure 11a, shear failure was obvious in the wall with one-way behavior. Two-way performance of the wall, as in Figure 11b, led to a decrease in the deformations compared to the other BCs. However, it increased the induced stress to the elements. As was anticipated, the boundary conditions had significant effects on the response of the walls made with AAC, similar to other masonry walls [22; 23; 24].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Thickness (cm)** & **Stand-Off Distance (m)** & **Charge Weight (kg)** & **Maximum Stress (kgf/m\({}^{2}\))** \\ \hline Model-1 & 15 & 2 & 5 & \(7.470\times 10^{4}\) \\ Model-2 & 15 & 5 & 5 & \(6.261\times 10^{4}\) \\ Model-3 & 15 & 10 & 5 & \(5.494\times 10^{4}\) \\ Model-4 & 15 & 2 & 7 & \(1.071\times 10^{5}\) \\ Model-5 & 15 & 5 & 7 & \(9.522\times 10^{4}\) \\ Model-6 & 15 & 10 & 7 & \(5.793\times 10^{4}\) \\ Model-7 & 20 & 2 & 5 & \(1.122\times 10^{5}\) \\ Model-8 & 20 & 5 & 5 & \(6.234\times 10^{4}\) \\ Model-9 & 20 & 10 & 5 & \(9.829\times 10^{4}\) \\ Model-10 & 20 & 2 & 7 & \(4.617\times 10^{5}\) \\ Model-11 & 20 & 5 & 7 & \(6.715\times 10^{4}\) \\ Model-12 & 20 & 10 & 7 & \(1.367\times 10^{4}\) \\ Model-13 & 25 & 2 & 5 & \(1.474\times 10^{5}\) \\ Model-14 & 25 & 5 & 5 & \(3.933\times 10^{4}\) \\ Model-15 & 25 & 10 & 5 & \(6.075\times 10^{4}\) \\ Model-16 & 25 & 2 & 7 & \(1.783\times 10^{5}\) \\ Model-17 & 25 & 5 & 7 & \(5.638\times 10^{4}\) \\ Model-18 & 25 & 10 & 7 & \(6.591\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum stress values in the models.
## 3 Results and Discussion
### The Chemical Structure of the Prepared Capsules  
Figure 3 shows the FTIR spectra of the neat shell material, the neat CAR, and crushed nanocapsules. The FTIR spectrum of neat SAN shows the characteristic peaks at 2237 and 1600 cm\({}^{-1}\) corresponding to C\(\equiv\)N and C\(=\)C in styrene ring bending, respectively [24; 36]. All the absorption of SAN can be observed in spectrum, such as 700, 2900, 3025 cm\({}^{-1}\) for CH, 2852 cm\({}^{-1}\) for CH\({}_{2}\) and 540, 760, 1490 cm\({}^{-1}\) for CH aromatic ring [37].  
Characteristic bands of CAR are observed at 1740 cm\({}^{-1}\) for ester groups and small twin peaks at 1604, and 1584 cm\({}^{-1}\) corresponding to C\(=\)C stretching vibration of the aromatic ring originated from phthalate groups that formed the alkyl resin (as shown in the plausible structure of the alkyl resin in Figure 2). In addition, the aromatic C-H bending arising from this aromatic functional group appears at 720 cm\({}^{-1}\) as a sharp peak. The broad stretching bands at 3455 cm\({}^{-1}\) confirm the presence of free hydroxyl and carboxyl groups, while the peaks at 2850 and 2923 cm\({}^{-1}\) are attributed to C-H aliphatic stretching. Peaks are also observed at 1008-1240 cm\({}^{-1}\) for the C-O-C stretching of ester that supports the structure of CAR [15].  
The FTIR spectrum of crushed nanocapsules shows all the characteristic peaks of neat SAN and CAR, while no more peak can be discerned. It can be concluded that both SAN and CAR are available in the prepared nanocapsules, considering no reaction occurred between them during the encapsulation process.  
The practical core content of the prepared nanocapsules was measured to be 32 wt.% using the extraction method, while the theoretical core content was calculated to be 46 wt.% according to the feed rate of the shell and core solutions. Therefore, the encapsulation yield can be obtained from Equation 2) at 69%, confirming the high efficiency of the electrospray method in comparison to other researches with an encapsulation yield of 10-20% [38].  
Figure 3: FTIR spectra of (**a**) neat SAN nanocapsules; (**b**) neat CAR; (**c**) and crushed nanocapsules obtained from coaxial electrospraying.
## 2 Materials and Methods
### Demonstration of the Model's Potential for Application as a Decision Support Tool  
#### 2.2.1 General Approach  
To demonstrate the performance of the model, a series of scenarios was simulated of the application of the SIT method for local control of the medfly at three hypothetical sites located in a fruit-growing region in Naoussa Valley, Central Macedonia, northern Greece, with a resident low-density medfly population. The simulated scenarios were chosen solely for the purpose of illustrating the model's features and its ability to reveal and quantify performance differences between simulated sites and SIT implementation options.
## 5 Conclusions  
This paper focused on the moisture safety related to energy efficient wood frame wall structures. The objective of this study was to evaluate whether the R-value of the wall structure influences the drying-out ability of the structure. The need for the research was based on the controversial conclusions in the earlier studies, concerning the possible moisture risks with highly insulated structures. The research evaluated the drying-out ability and the hygothermal behavior of the wall structure based on two significant moisture sources, built-in and leakage moisture.  
The results suggest that highly insulated wall structures promote healthy buildings, as their drying ability under severe moisture loads is at a high level. High mould indexes during the drying phase in highly insulated walls are unlikely, even with a considerable amount of built-in moisture or rain leakages. The moisture problems reported earlier concerning the hygothermal behavior of highly insulated walls under water leakages are probably connected to the use of vapor tight materials, such as medium density fiberboard (MDF), outside the wood frame. A more vapor open wind barrier mineral wool outside the wood frame was used in this paper. The use of vapor tight rigid boards outside the wood frame must be avoided. The use of plywood as a weather barrier in structureswith build-in moisture might result in slight mould growth. With rigid boards, the use of a thermal insulation board as the outmost layer will reduce the moisture risks. A vapor open thermal insulation board with a thickness of 50-100 mm, installed on the cold side of the wood frame, contributes to an increased drying ability of the wall and decreased probability for microbial growth in the structure, which is consistent with earlier studies.  
The study results support the following topics for future research:  
* Study the hygothermal behavior of highly insulated wood frame structures with significant moisture loads, further considering moisture convection and water vapor diffusion from elevated indoor humidity levels;
* Detailed analysis on the moisture transfer in highly insulated structures after water leakages;
* Study the influence of more hygroscopic thermal insulation, for example wood fiber insulation, to the drying ability of highly insulated structures under high moisture loads.  
Conceptualization K.V. and X.L.; methodology, validation, formal analysis, visualization, and writing--original draft preparation K.V.; project administration, writing review, and editing X.L.  
This research was funded by the Foundation for Aalto University Science and Technology. In addition, the research was funded with the PhD-scholarship from SNIL-scholarship Fund.  
The authors would like to thank the Foundation for Aalto University Science and Technology and SNIL ry for the funding of this study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 7 Production Results  
The basic units of the hardware implementation are: scheduling diagrams of the basic variables, the overall architecture, and the bit-width definition. Indeed, after bit-width definition, the scheduling diagrams of the basic variables (\(V\) and \(n\)) are evaluated. In this state, all aspects have been considered to achieve an HDL digital code. On the other hand, the overall architecture is presented to show the controlling sequence of the digital implementation.  
To validate the proposed DSSN model and compare this model with other implementations, it is essential that a digital hardware is selected for realizing these models on digital platforms. In this paper, the original and proposed neuron models are implemented on Xilinx Spartan-3 FPGA Board (Model: XC3S50-TQ144 Package) for validating the proposed method. On the other hand, the proposed neuron model is compared by the DSSN model that is implemented in other similar papers [96]. Using the pipelining method, the number of 250 connected neurons can be implemented on this FPGA platform by resource utilization that is presented in Table 6. Two basic factors must be emphasized in this issue: first is the maximum frequency of the digital design and second is the overall saving in FPGA resources (in case of maximum number of implemented neurons on a uniqe FPGA core). As can be seen from Table 6, these two parameters are in the better form in comparison to the main DSSN model and also in comparison to the model presented in [96]. As previously mentioned, since in the proposed DSSN model, all of nonlinear parts (such as multipliers and quadratic functions) are replaced by digital SHIFTs, ADDs, and SUBs, the final frequency of the digital system will be increased, significantly. Moreover, by removing the multiplier operations in the proposed model, the overhead costs will be reduced compared to other similar models. As a result, one of the basic parameters in the realization of neural networks is the large-scale implementation. In this approach, the overall saving in the FPGA resources is an essential issue. In this paper, the overall saving in FPGA resources is higher than the original and other paper model [96]. On the other hand, using an FPGA board, the maximum number of implemented proposed neurons is higher than other models because in the Spartan-3 board that we used, the number of resources are less than the Spartan-6 FPGA board that is used in [96]. Consequently, the proposed model is in the better state in the case of large numbers of implemented neurons compared to other methods. As a result, the final FPGA-based output signals can be achieved as depicted in Figure 11. As it is illustrated in this figure, the proposed signals (implemented on FPGA board) are in the high similarity state in comparison with the original output voltage signals.
## 3 Results and Discussion
### Isothermal Crystallization and Kinetic Analysis
#### 3.3.3 Avrami Modeling of Crystallization Kinetics  
The Avrami equation was applied to model the isothermal crystallization kinetics of neat P3 HBHHx and selected nucleated formulations. This approach provides quantitative insights into the nucleation mechanism and crystal growth dimensionality by evaluating the evolution of relative crystallinity over time. The relative crystallinity, \(\chi(t-t_{0})\), was calculated from the isothermal DSC exotherms using Equation (2), as follows:  
\[\chi(t-t_{0})=\int_{t_{0}}^{t}{(dH/dt)dt}\ /\int_{t_{0}}^{\infty}{(dH/dt)dt} \tag{2}\]  
where \(dH/dt\) is the heat flow rate, and \(t_{0}\) is the induction time, defined as the time at which crystallization begins. This approach normalizes the area under the exothermic peak to the total crystallization enthalpy, thereby enabling the determination of the crystallization degree as a function of time.  
When plotted, the resulting curves exhibited a characteristic sigmoidal shape, as shown in Figure 12, which is typical of semi-crystalline polymers and reflects the distinct stages of nucleation and crystal growth [41; 42]. These curves were fitted to the Avrami equation as follows:  
\[1-\chi(t-t_{0})=\exp\bigl{[}-Z(t-t_{0})^{n}\bigr{]} \tag{3}\]  
where \(Z\) is the temperature-dependent crystallization rate constant, and \(n\) is the Avrami exponent, which provides insight into the nucleation mechanism and dimensionality of crystal growth.  
To facilitate comparisons across systems, the normalized crystallization rate constant \(k\) was calculated as  
\[k=Z^{\frac{1}{k}} \tag{4}\]  
This normalization ensures that \(k\) has consistent units of s\({}^{-1}\), allowing for direct comparisons across different nucleating agents and temperatures.  
The crystallization half-time (\(\tau_{1/2}\)) was calculated as follows:  
\[\tau_{(1/2)}=\left(\frac{\ln{2}}{Z}\right)^{1/n} \tag{5}\]  
Both Avrami exponent \(n\) and the rate constant \(Z\) were determined by plotting \(log[-ln(1-\chi(t-t_{0}))]\) against \(log(t-t_{0})\), which yielded linear fits, as illustrated in Figure 13. In this representation, the \(x\)-axis corresponds to the logarithm of the time elapsed since the onset of crystallization, while the \(y\)-axis represents a transformed expression of relative crystallinity. The kinetic parameters deduced from the Avrami analysis, including \(\tau_{1/2}\) and \(k\), are listed in Table 7.