esa-sceva/satcom-chunk-collection huggingface dataset:
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 3 Degree-Constrained Spanning Problems
### Minimum Spanning Trees Under Budget-Like Degree Constraints  
Any minimum cost solution of spanning problems under budget-like degree constraints, if it exists, is a tree. Trivially, cycles are useless in any optimum. Deleting an edge from a cycle, the node set remains covered. The basic problem was formulated as follows.  
**Definition 3** (Degree-constrained minimum spanning tree (DCMST) problem).: _In an undirected graph \(G\) with non-negative costs \(c(e)\) on the edges and with positive integer degree bounds \(D(v)\) the problem consists in finding a spanning tree in which the degree of any node \(v\) is at most \(D(v)\) and the total cost is minimized._  
The degree bounds can be uniform or not in the node set.  
The DCMST problem is NP-hard [7], and there is not always a solution. As observed in [8], a spanning tree can exist if and only if \(\sum_{v\in V}D(v)\geq 2\cdot|V|-2\). The hardness of the problem has also been demonstrated using a reduction from the traveling salesman problem. In [3], the authors present the problem as a generic bi-criteria optimization where the first objective corresponds to the respect of the budget (degree) constraints and the second to the cost minimization. The paper also specifies the sub-graphs to solve the problem. The investigated classes are spanning trees, Steiner trees, and generalized Steiner trees. The authors give some negative results on the approximations. There is no polynomial-time \(\rho\)-approximation algorithm for the DCMST problem minimizing the cost when respecting the degree constraint, even when the same upper bound is supposed for all nodes. Heuristic solutions were proposed in several works (cf. [2; 3; 9; 10; 11; 12; 13]). In unweighted graphs constrained by a uniform degree bound \(MB\), an \((1,MB+1)\)-approximation algorithm has been proposed by [PERSON] and [PERSON] [14]. The results are generalized to weighted graphs in [13].  
Figure 1: Illustration of a hierarchy with discontinuous lines in an undirected graph \(G\) and the corresponding labeled tree \(T\).  
An interesting idea to compute the solution can be based on uniformly generated spanning trees (cf. [15]) followed by the selection of the tree satisfying the constraints. In [16] the DCMST problem in uncertain networks is discussed. The nondeterministic model mainly concerns the edge costs. Note that uncertainties can characterize the degree bounds.  
In the mentioned papers, the constraints are _budget_ constraints, and spanning trees are the solutions. In the case of degree bounds corresponding to _capacities_ of the nodes, we suppose that the solution can contain several times any node, and the capacity of each occurrence of a node is the same. The model has been presented in [6] for uniform degree bounds.
## 4 Mapping Results
Moreover, we observed a trend towards the covering of non-functional requirements. As shown in Figure 1, of the primary studies, 32% of them were focused on the requirements elicitation phase, 22% on analysis, 20% on specification, and 19% on research on non-functional requirements. On the other hand, requirements validation (5%) and management (2%) were the least investigated phases in the scientific literature. Table 4 presents the primary studies, classified according to the phases introduced in Section 2. The RE phase covered by each primary studied is highlighted with the symbol \(\checkmark\). The last column of Table 4 shows the total score obtained by each primary study. To calculate this score, we assigned one point for each of the RE phases implemented in each primary study. The article with the highest score is highlighted in bold. Only one proposal covered the five phases for RE and supported NFRs; this was the work presented in [30], named _A Technology to Support the Building of Requirements Documents for IoT Software Systems_. The closest one to this was _An Improved RE Framework for IoT-Oriented Smart Applications using Integrated Approach_[31], which covered four phases. RQ-3. What RE techniques have been implemented/developed within the phases of RE in the context of IoT systems development? Considering the classification presented in Section 2, from the 24 primary studies obtained, the most applied techniques for RE were extracted. These were metamodels, UML use cases, misuse cases, interviews, surveys, workshop sessions, questionnaires, goal-oriented language, UML profiles, prototypes, UML activity diagrams, test cases, requirements specification documents, and interaction matrices. Figure 1: Classification scheme of primary studies according to the RE activities introduced in Section 2. Figure 2 shows the techniques found for the RE phases in each primary study. First, there seemed to be a trend towards applying UML use cases, as 8 of 24 primary studies (33.33%) implemented this technique for some phase in RE in relation to IoT systems
## 3 Results and Discussion
### The Bed Average Pressure, Temperature and Uptake
Consequentially, in this case, the rate of increase of the COP was reduced as shown in Figure 9b. In the case of the composite thickness of 5 mm, the COP was enhanced at all cycle times compared to the case of 2 mm composite thickness. This was in response to the decrease in the number of tubes from 309 at 2 mm to 95 at 5 mm composite thickness. In the case of the composite thickness of 10 mm, a higher cycle time was needed to overcome the increase in both interparticle and intraparticle mass transfer resistances associated with higher composite thickness and improve the system performance. The best SDIP of 11 kg\({}_{\text{ice}}\cdot\)kg\({}_{\text{ads}}^{-1}\cdot\)day\({}^{-1}\) and the best COP of 0.263, in this case, were attained at a cycle time of 1230 s. Although the performance was the worst in this case, the lower number of tubes used can considerably reduce the flow rates of the heating and cooling water, which may be needed with an adsorption system of large capacity to reduce the pumping power. Generally, the results showed a discrepancy between the effect of varying the composite thickness and cycle time on both the system SDIP and COP. This highlights the importance  
Figure 9: The specific daily ice production and coefficient of performance of the system under different cycle times and composite thicknesses of (**a**) 2 mm, (**b**) 5 mm, and (**c**) 10 mm. of concurrent solution of mass and heat transfer resistances in evaluating the overall performance of the adsorption ice maker that uses the activated carbon composite with additives in a consolidated form. One can choose the best combination of the composite thickness with the corresponding cycle time to achieve either the best COP or the best SDIP depending on the primary concern and application.
## 4 Evaluation Method
### Weight Calculation Method
#### 4.1.5 Entropy Weight Method (EWM)  
The EWM serves as a multi-criteria decision-making technique that is mainly utilized to tackle the challenges associated with multi-index evaluations. This method harnesses information from data entropy, which encompasses the quantity of available information, to facilitate weight computations. It proves to be particularly useful in contexts marked by fluctuations in data, viewing these changes as a type of information. By establishing the entropy value for each metric, the EWM effectively quantifies and integrates the information related to each unit under assessment, thereby providing a relatively impartial weight for each metric. The EWM evaluates the importance of a collection of information through the lens of entropy, reflecting the extent of variability present within information collection [118]. A lower value of information entropy is associated with a greater level of dispersion, leading to an elevated weight for the index during the overall evaluation [119]. The process of the entropy weight method starts with the standardization of the initial data to remove the influence of varying dimensions across different indicators. Following this, the method computes the relative contribution of each indicator in relation to all evaluation subjects. The entropy value for each indicator is obtained from these relative contributions; indicators with lower entropy values signify a higher degree of uncertainty, thereby enhancing their influence (or weight) on the overall evaluation. Next, the variation index for each indicator is calculated based on the entropy value, which is essential for determining each indicator's weight. In the final step, weighted calculations are executed to derive the overall evaluation indicator [120,121].  
The EWM serves as an unbiased technique that assigns weights according to the extent of data variability [122], thereby effectively removing subjective influence. Nonetheless, this method might neglect the importance of specific indicators, which could lead to a higher weight being assigned to less critical ones. Furthermore, the EWM mainly represents weights based on the information from evaluation factors, failing to consider the relationships among these evaluation factors [4] (Figure 5).  
In practical applications, the EWM is a very commonly used objective weight evaluation method. [PERSON] et al. [11] utilized the EWM to determine the weight coefficients of different factors in particular soil conditions, and then applied the variable fuzzy set method to GSEUUS. Similarly, [PERSON] et al. [75] used the EWM to determine the weights of indicators, which helps reduce the bias commonly linked with weighting processes in Nanjing. The combination of the EWM with other methodologies, such as the AHP and the G\({}_{1}\) method, provides a holistic consideration of both subjective and objective elements, thereby improving the precision and scientific robustness of the weight evaluations [36,123] (Figure 6a,c,d,f and Table 1).
# Thermal Evaluation of a Hybrid Dryer with Solar and Geothermal Energy for Agroindustry Application
## 3 Results
### Experimental Testing of the Hybrid Dryer under Environmental Conditions  
Experimental tests of the operation of the dryer were carried out in a 12 h operation period, from 6:30 a.m. to 6:30 p.m., and we recorded the air velocity at the 1.7 m/s exchanger outlet and the air velocity in the chamber was 1.3 m/s. Figure 7 shows the temperature of the drying chamber and the thermal gain of the collector depending on the irradiation. Moreover, it can be observed that the output temperature of the aerothermal was kept stable and does not depend on irradiation. It should be mentioned that it defines thermal gain as the difference between the output temperature of the collector and the temperature of the collector (output temperature of the aerothermal system). The main result noted was that in the hours of low irradiation, the energy input will depend mainly on the aerothermal system.  
However, in the hours of increased irradiation, the dominant factor will be the solar collector. The system stabilization time at the operating temperature (50 \({}^{\circ}\)C) was 50 min from 10 a.m.  
The energy balance of the system determined that the experimental thermal energy of the hybrid solar dryer varied between 1300 W and 2000 W when working in a solar radiation range of 400 W/m\({}^{2}\) to 1000 W/m\({}^{2}\), respectively. Figure 8 presents the results of the useful heat gained in the sustainable dryer.  
Figure 8: Heat gained in hybrid drying.  
Figure 7: Operation of the solar dryer and the aerothermal system.  
The efficiency of the hybrid dryer is determined by the useful heat ratio of the drying chamber divided by the sum of energy resulting from the incident radiation in the chamber, solar collector, and exchanger. The influence of solar radiation related to the efficiency of the dryer is presented, and as can be seen in the results, the prototype showed an efficiency of around 60% (Figure 9).
## 2 System Model
### Information Transmission Phase  
In the first information transmission phase, by applying the NOMA protocol, S transmits the superimposed signal \(\sqrt{\alpha_{N}}x_{N}+\sqrt{\alpha_{F}}x_{F}\) to \(R\), where \(x_{N}\) and \(x_{F}\) denotes the signal of NU and FU, respectively, with \(\mathbb{E}[|x_{N}|^{2}]=\mathbb{E}[|x_{F}|^{2}]=1\); \(\alpha_{N}\) and \(\alpha_{F}\) are the power allocation with \(\alpha_{N}<\alpha_{F}\) and \(\alpha_{N}+\alpha_{F}=1\). Thus, the received signal at \(R\) is given by  
\[y_{R}=g_{SR}(\sqrt{\alpha_{N}}x_{N}+\sqrt{\alpha_{F}}x_{F})\sqrt{P_{S}}+n_{R} \tag{8}\]  
where \(n_{R}\) denotes the additive white Gaussian noise with \(\mathcal{CN}(0,N_{0})\). The signal-to-interference-plus-noise ratios (SINRs) at R when detecting \(x_{F}\) and \(x_{N}\) are given, respectively, as  
\[\gamma_{R\to x_{F}} =\frac{P_{S}e^{2}\alpha_{F}|\hat{g}_{SR}|^{2}}{P_{S}\alpha_{N}e^{2} |\hat{g}_{SR}|^{2}+P_{S}(1-\epsilon^{2})\Omega_{SR}+N_{0}}\] \[=\frac{\vartheta e^{2}\alpha_{F}|\hat{g}_{SR}|^{2}|g_{PS}|^{2} \rho_{PB}}{\alpha_{N}e^{2}|\hat{g}_{SR}|^{2}\rho_{PB}+\vartheta(1-\epsilon^{2 })\Omega_{SR}|g_{PS}|^{2}\rho_{PB}+N_{0}} \tag{9}\] \[\gamma_{R\to x_{N}} =\frac{\vartheta\alpha_{N}e^{2}|\hat{h}_{SR}|^{2}|h_{PS}|^{2} \rho_{PB}}{\vartheta(1-\epsilon^{2})\Omega_{SR}|h_{PS}|^{2}\rho_{PB}+1} \tag{10}\]  
where \(\rho_{PB}=\frac{P_{PB}}{N_{0}}\) denotes the average signal-to-noise ratio (SNR).  
Figure 2: The operational design of the time of the system.  
In the second information transmission phase, R decodes the signal from S and transmits a superimposed signal to two users NU and FU. The received signal at NU and FU are given, respectively, by  
\[y_{NU} =g_{RN}(\sqrt{\alpha_{N}}x_{N}+\sqrt{\alpha_{F}}x_{F})\sqrt{P_{S}}+n _{NU} \tag{11}\] \[y_{NF} =g_{RF}(\sqrt{\alpha_{N}}x_{N}+\sqrt{\alpha_{F}}x_{F})\sqrt{P_{S}}+ n_{FU} \tag{12}\]  
where \(n_{NU}\) and \(n_{FU}\) are the AWGN with \(\mathcal{CN}(0,N_{0})\). The SINR at FU when detecting its own signal \(x_{F}\) is given by  
\[\gamma_{FU\to x_{F}} =\frac{P_{R}e^{2}\alpha_{F}|\hat{g}_{FU}|^{2}}{P_{R}\alpha_{N}e^{2}| \hat{g}_{FU}|^{2}+P_{R}(1-\varepsilon^{2})\Omega_{FU}+1}\] \[=\frac{\theta\varepsilon^{2}\alpha_{F}|\hat{g}_{PR}|^{2}|\hat{g}_ {FU}|^{2}\rho_{PB}}{\theta\alpha_{N}\varepsilon^{2}|\hat{g}_{PR}|^{2}|\hat{g}_ {FU}|^{2}\rho_{PB}+\theta(1-\varepsilon^{2})\Omega_{FU}|\hat{g}_{PR}|^{2}\rho _{PB}+1} \tag{13}\]  
By adopting the SIC process, NU first detects the signal of FU, i.e., \(x_{F}\), then detects its own signal, i.e., \(x_{n}\). Thus, the SINRs at NU when detecting signals \(x_{F}\) and \(x_{N}\) are given, respectively, by  
\[\gamma_{NU\to x_{F}} =\frac{\theta\varepsilon^{2}\alpha_{F}|\hat{g}_{RU}|^{2}|\hat{g}_ {NU}|^{2}\rho_{PB}}{\theta\alpha_{N}\varepsilon^{2}|\hat{g}_{PR}|^{2}|\hat{g}_ {NU}|^{2}\rho_{PB}+\theta(1-\varepsilon^{2})\Omega_{NU}|\hat{g}_{PR}|^{2}\rho _{PB}+1} \tag{14}\] \[\gamma_{NU\to x_{U}} =\frac{\theta\alpha_{N}\varepsilon^{2}|\hat{g}_{PR}|^{2}|\hat{g}_ {NU}|^{2}\rho_{PB}}{\theta(1-\varepsilon^{2})\Omega_{NU}|\hat{g}_{PR}|^{2} \rho_{PB}+1} \tag{15}\]
# Experimental Study of 3D Concrete Printing Configurations Based on the Buildability Evaluation
## 3 Experimental Program
### Basic Testing Methods  
The initial setting times of the prepared pastes were determined by the Vicat needle apparatus according to ASTM C191. The fluidity of the fresh mortar was evaluated using the flow table test according to ASTM C230. ImageJ, a Java-based image processing program commonly used in the biomedicine domain for microscopic measurements, was used to measure layer height and leaning angle.  
The printing components were of different geometries; then, the generated toolpath might include various motion speeds, non-extruding motions, or rapid changes in direction. In addition, neighbored filaments had an inter-supporting effect of increasing the layer stacking ability, as shown in Figure 4a. Thus, this study was conducted on the basis of single-walled hollow cylinders, as in Figure 4b, with which the 3D printing would have a stable motion speed, a constant extruding speed, and a non-stop printing toolpath with a spiral-up printing method [26]. Furthermore, the toolpath did not have sudden corners to avoid collapse due to the material being pulled by the nozzle motion. Therefore, data accuracy of the _BI_, _HRR_, and LA would be guaranteed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **W/C** & **OPC/g** & **SAC/g** & **Fine Sand/g** & **HRWR/g** & **HPMC/g** & **DFA/g** & **PFB/g** \\ \hline OSS1 & 0.37 & 1440 & 960 & 1600 & 19.2 & 8 & 2 & 40 \\ OSS2 & 0.37 & 1680 & 720 & 1600 & 19.2 & 8 & 2 & 40 \\ OSS3 & 0.37 & 1800 & 600 & 1600 & 19.2 & 8 & 2 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportions of the printing mortar.  
Figure 3: The 3D Printing program.  
A printing height ratio (\(PHR\)) was employed to evaluate the height quality of printed products, as shown in Equation (1) and Figure 4b, referring to the actual average height of the printed product divided by its designed height \(H_{designed}\). The printed product was considered acceptable (Pass) if the \(PHR\geq 90\%\), whereas it was unacceptable (Fail) if the \(PHR<90\%\).  
\[PHR=\frac{h_{1}+h_{2}+h_{3}+h_{4}}{H_{designed}\times 4}\times 100\% \tag{1}\]
# Joint Transmit Waveform and Receive Mismatched Filter Design to Suppress Range Sidelobe
## 2 Design of NLFM-CPM Integrated Waveform and Signal Processing Methods
The target range cell and its neighboring cells are highlighted in green, showing the distribution of their received sample sequence from \(S_{0}\) to \(S_{N-1}\). These samples are utilized in the processing stage for accurate target detection. The orange area on the left represents other cells near the target range cell, while the blue area on the right represents range cells further from the target. The data from these side areas have similarities with the target cell but are temporally shifted, potentially causing sidelobe interference. The output of the convolution of \(\mathbf{x}_{i}\) with \(\mathbf{h}_{\text{MF}}=\mathbf{x}_{i}^{s}\) can be expressed as  
\[\mathbf{psf}_{\text{MF}}=\mathbf{\Lambda}(s)\cdot\mathbf{h}_{\text{MF}}. \tag{15}\]  
Figure 1: A schematic diagram of received data from different range cells. The expression of the EWMF output is given by  
\[\mathbf{psf}_{\text{EWMF}}=\mathbf{\Lambda}(s)\cdot\mathbf{h}. \tag{16}\]  
where \(\mathbf{h}\) is the EWMF coefficient. (15) and (16) can also be represented in terms of range cells as  
\[\mathbf{psf}=[y_{-N+1},\cdots,y_{-1},y_{0},y_{1},\cdots,y_{N-1}]^{\text{T}} \tag{17}\]  
where \(y_{0}\) represents the main peak value of the mainlobe, and the values in the sidelobe region corresponds to \(y_{i}(i=-N+1,\ldots,-1,1,\ldots,N-1)\). Joint Design Algorithm of NLFM-CPM Waveform and Environment-Based Weighted Mismatched Filter (EWMF)
## 3 Experimental Section
### Quantitative Real-Time RT-PCR (qRT-PCR)  
qRT-PCR assays were performed according to the protocol described previously [60]. All primer sequences used in this study are listed in Table 4. The BmEF-2F1 and BmEF-2R1 primers were used to amplify elongation factor-2 (_EF-2_) as an internal standard for quantification [61].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Target gene** & **SiRNA** & **Sense** & **Antisense** \\ \hline _SETD2_ & 393 & UGCACGUCUGAGUCUGAUUCAU & AUUGAUCAGACUCAGGCUGGCA \\  & 490 & CAGUGUGAGCUCAAAGAAUATT & UAUCUCUCUGAGUCACACUGTT \\ _DOT1L_ & 243 & UUCCAAAGCAUCACUACGAAUCGAAU & CAUCGAUUCUGUGUGUGUGUGUGUGUGA \\  & 353 & AUUUACUGCGCUUUACUUUGTT & CAAAAGUGAAGGGAUATT \\ _EZH2_ & 38 & GACAACCCAACAGGUACACGAUAGAGAUGAGAGA & UCUUAUUGGUACCGUGUGUUGUGUGUG \\  & 224 & CGACGGAAAGUGCAUGUGGAUAAA & UUUAUACACACUGACACUUCCCG \\  & 157 & GACCGGCCUCUAUGUCAAAUUCCAAGA & UUUGAUUUGAUGACGGCGCUC \\ _ASH2_ & 167 & UAGUCAAAAUCAAGCCACUGUA & UACAGUGGCUCUUGAAUUUGACUA \\ \hline \end{tabular}
\end{table}
Table 2: siRNA sequences used in RNAi experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Target gene** & **Primers** & **Sequence (5’ to 3’)** & **Denature** & **Annealing** & **Elongation** & **N° cycles** \\ \hline _Bmdsk_ & FDSX-F2 & GGCCTTACCGGACAGGCAG & 98 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & \\  & FDSX-R4 & GCGCAGGTGTCGCTACAG & 10 s & 30 s & 60 s & \\ _Imp\({}^{M}\)_ & BmIMPF1 & ATGGACGGTGCATGTCTCAAG & 98 \({}^{\circ}\)C & 55 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & \\  & BmIMPR1 & TCATCCGCCTCAGCAGGTTG & 10 s & 30 s & 90 s & \\ _Imp\({}^{C}\)_ & IMPE4F1 & TCCCATAATAATCTCATTGGAC & 98 \({}^{\circ}\)C & 55 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & \\  & IMPE7R1 & AATGTGACGGTGGTCCG & 10 s & 30 s & 90 s & \\ _Actin3_ & BA3F1 & AGATGACCAGATCATGTTG & 98 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & \\  & BASR1 & GAGATCCCATCTGTTGGAA & 10 s & 30 s & 30 s & \\ _Bm-Sxl_ & BmSxIF1 & ATTAATCATAATAAGCTACG & 98 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & 57 \({}^{\circ}\)C & \\  & BmSxIR1 & AATCCGTAACTGTAGCCAGTC & 10 s & 30 s & 30 s & \\ \hline \end{tabular}
\end{table}
Table 3: Primer sequences and PCR conditions utilized in this study.  
\begin{table}
\begin{tabular}{c c c} \hline
**Target gene** & **Primers** & **Sequence (5’ to 3’)** \\ \hline _Bmdsx_ & Bmdsxprof1 & TGCATGTTTTTTATAAATCAGCTAG \\ promoter & Bmdsxprof1 & GTAAAATTTCGTAAAAAGCTGACCAG \\ _Bmdsx_ E1 & ChIPEx1F & CCTGTACCACCAGGTGGAAG \\  & ChIPEx1R & CTGACGGGGGTGGAGCGTTAG \\ _Bmdsx_ I2 & ChIP2F & CTACATGAACAGTACCAG \\  & ChIP2R & GTAAGTACAACTAAAATAGCTTTC \\ _Bmdsx_ E3 & ChIP2R3 & GTCGAAGGTACGGAGGAAG \\  & ChIPEx3R & TGTGATGCATGTACTGTCC \\ _Bmdsx_ I3 & ChIP3F & GTAACTGACCTTCTTGTCTAATC \\  & ChIP3R & CTGTGCCAT