esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 4 Computational Complexity
The pseudo-code for this mapping is given in Algorithm 2. ```
Input: Solution of \(\mathbf{K}^{\star}\)\(\mathcal{U}^{\prime}\subseteq\mathcal{U}\) such that \(|\mathcal{U}^{\prime}|=C\) and \(|\mathcal{U}^{\prime}\cap\mathcal{U}_{c}|=1,\ \forall\ c\) Output: Solution of MCP \(\mathcal{T}^{\prime}\)
1 for\(j\gets 1\)to\(m\)do
2 if\(U_{jc}\in\mathcal{U}^{\prime}\) for some \(c\)then
3\(T_{j}\in\mathcal{T}^{\prime}\)
4 end if
5
6 end for
```  
**Algorithm 2**Pseudo-code for mapping a solution of \(\mathbf{K}^{\star}\) to a solution of the MCP  
To complete the proof, what is left to prove is that the constructed solution \(\mathcal{T}^{\prime}\) is indeed the optimal solution of the MCP. We prove this by contradiction as follows. Let us assume that \(\mathcal{T}^{\prime}\) is not the optimal solution of the MCP. This implies that there exists a set \(\mathcal{T}^{\prime\prime}\subseteq\mathcal{T}\) such that \(|\mathcal{T}^{\prime\prime}|\leq k\) and  
\[\left|\bigcup_{T_{j}\in\mathcal{T}^{\prime\prime}}T_{j}\right|>\left|\bigcup_{ T_{j}\in\mathcal{T}^{\prime\prime}}T_{j}\right|. \tag{5}\]  
If (5) is true, then we can construct another solution to \(\mathbf{K}^{\star}\), \(\mathcal{U}^{\prime\prime}\) using \(\mathcal{T}^{\prime\prime}\) as follows. Let \(\mathcal{T}^{\prime\prime}=\{T_{j_{i}},\ldots,T_{j_{i}}\}\) with \(\ell\leq k\) and let \(j_{1}<j_{2}<\ldots<j_{\ell}\). We construct the set \(\mathcal{U}^{\prime\prime}\) as follows  
\[\mathcal{U}^{\prime\prime}=\{U_{j_{1}1},U_{j_{2}2},\ldots,U_{j_{\ell}\ell},U_{ 1(\ell+1)},\ldots,U_{1C}\}. \tag{6}\]  
Then, by Definition 1, the following hold true:  
* \(|\mathcal{U}^{\prime\prime}|=C\);
* For all \(c\in[C]\), \(|\mathcal{U}^{\prime\prime}\cap\mathcal{U}_{c}|=1\);
* \(|\bigcup_{U_{jc}\in\mathcal{U}^{\prime\prime}}U_{jc}|>|\bigcup_{U_{jc}\in \mathcal{U}^{\prime}}U_{jc}|\),  
This contradicts our assumption that \(\mathcal{U}^{\prime}\) is the optimal solution of \(\mathbf{K}^{\star}\). This implies that there does not exist any set \(\mathcal{T}^{\prime\prime}\) such that \(|\mathcal{T}^{\prime\prime}|\leq k\) and \(|\bigcup_{T_{j}\in\mathcal{T}^{\prime\prime}}T_{j}|>|\bigcup_{T_{j}\in \mathcal{T}^{\prime}}T_{j}|\). Therefore, \(\mathcal{T}^{\prime}\) is indeed the optimal solution of the MCP. Algorithm 2 maps a solution of \(\mathbf{K}^{\star}\) to a solution of the MCP in constant time (\(\mathcal{O}(C)\) assignments). Thus, a polynomial-time solution for \(\mathbf{K}^{\star}\) also provides a polynomial-time solution for the MCP. This is not possible unless P = NP. This implies that no polynomial-time algorithm exists for solving \(\mathbf{K}^{\star}\), and therefore, \(\mathbf{K}^{\star}\) is an NP-hard problem. Since the MC multicast resource allocation problem is NP-hard, we cannot construct a polynomial-time algorithm to determine its optimal solution. Therefore, in the following section, we construct approximation algorithms that provide some performance guarantees.
## 6 Conclusions  
This paper presents modular energy-efficient paradigms that comprise two archetypes: the operational medium access control protocol and pheromone termite model. The O-MAC reduced the overhearing and congestion and improved the energy efficiency by deploying semi-synchronization, the anycast methodology, randomization, dynamic node selection and determination of the node's information prior to leaving the communication. O-MAC also involves an ODFF model for the carrier sense multiple access portion. This model reduces the channel access time, data forwarding time, acknowledgement time, and retry transmission time. As a result, there is a substantial reduction in the energy consumption and improvement of the QoS parameters. The PT paradigm provided robust and reliable routing for single and multiple paths in disaster recovery scenarios. Two important features of the PT model are introduced: the packet generation rate and pheromone sensitivity. These features helped avoid congestion and improve the QoS. The performance of O-MAC with the PT model is a tradeoff between energy efficiency and QoS provisioning. Thus, several metrics were investigated and demonstrated the improvement achieved by our proposed paradigms.  
To investigate the effectiveness of paradigms, the ns3.10 network simulator with Ubuntu 14.04 was used to demonstrate the strengths of O-MAC with the PT model in a realistic disaster recovery scenario. O-MAC with PT improved the throughput and reduced the latency and energy consumption in the mobile and static scenarios. Furthermore, we compared O-MAC with known low-duty-cycle protocols (X-MAC, LPL and IEEE 802.15.4) and found improvements in terms of time savings and energy efficiency. These results demonstrate that O-MAC with PT could be used for several application areas over large-scale WSNs. These paradigms have marginal limitations at the transport level. Thus, in the future, we will lessen these limitations by introducing transport-level synchronization to re-order the packets, guaranteeing consistent pre-packet delivery for different packet types and faster recovery in the case of packet loss.
## 5 Electrical Characterization
### Temperature Testing
#### 5.4.3 Uniqueness  
To test the uniqueness of the MRAM PUF at different temperatures, we conducted the same tests as those performed at room temperature. Since the resistive state of the MRAM responses did not correlate to the \(HD_{inter}\), only high-resistance states were tested. As mentioned earlier, at 23 \({}^{\circ}\)C, the average \(HD_{inter}\) was **0.4849** for unfiltered responses and **0.4999** for filtered responses in a high-resistance state.  
When the MRAM PUF was exposed to temperatures of 0 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, no significant change was observed in the uniqueness of its responses. At 0 \({}^{\circ}\)C, the average \(HD_{inter}\) was **0.4820** for unfiltered responses and **0.4999** for filtered responses. Similarly, at 80 \({}^{\circ}\)C, the average \(HD_{inter}\) was **0.4753** for unfiltered responses and **0.4997** for filtered responses.  
The results of these tests can be seen in Table 5. From these results, we can conclude that temperature had no significant impact on the \(HD_{inter}\) for this PUF configuration.
## 2 Fundamentals of Pricing Schemes: Uniform, Zonal, and Nodal Pricing
### Pricing Mechanisms
#### 2.2.1. Locational Marginal Price (LMP) and Zonal Marginal Price (ZMP)  
The LMP is a widely adopted pricing mechanism in most existing electricity markets. This mechanism reflects the cost of satisfying an incremental unit of demand at each node by incorporating the cost of energy and its delivery (including losses and congestion).  
The LMP provides the highest granularity and calculates the price for each node in the system [33]. This is calculated using Equation (7):  
\[LMP_{i}=\lambda(1-\frac{\partial P_{Loss}}{\partial P_{i}})+\sum_{k\in K}\mu_{k} \times PTDF_{k,i},\ \ \ \ \ \forall i\in I \tag{7}\]  
where \(\lambda\) is the marginal cost of the reference node, \(\frac{\partial P_{Loss}}{\partial P_{i}}\) is the marginal loss factor at node \(i\), and \(\mu_{k}\) is the Lagrangian multiplier related to the \(k\)th transmission constraint.  
Theoretically, ZMP is similar to LMP; however, it simplifies the approach by aggregating nodes into zones [3]. Whereas the LMP calculates the prices for each node, the ZMP determines a single price for each zone.  
Notably, although these market prices are determined by marginal costs, generators may incur financial losses owing to quasi-fixed costs, such as no-load and start-up costs. As a result, make-whole payments (MWPs) are necessary to compensate generators for these losses.
## 2 Materials and Methods
### Climatic Conditions in the Study Area  
Historical climatic data were collected for the study area over the past 20 years (2002-2022), as sourced from the National Center for Meteorology (NCM) in Saudi Arabia ([[https://ncm.gov.sa/ar/Pages/default.aspx](https://ncm.gov.sa/ar/Pages/default.aspx)]([https://ncm.gov.sa/ar/Pages/default.aspx](https://ncm.gov.sa/ar/Pages/default.aspx)), accessed on 10 November 2024). The collected data included air temperature, humidity, wind speed, precipitation, and solar radiation. The data were analyzed to obtain averages of the entire period, and the averages were compared to a comprehensive overview of climatic parameters across the different locations (S1 to S20) in the study area that were recorded during the months of June, July, and August 2022. The climatic data collected during the summer of 2022 were consistent with the average climatic parameters recorded over the past 20 years in the Al-Baha region.  
Figure 4: Dust-collection instruments and their deposition mechanisms: (**A**) Big Spring Number Eight (BSNE) Dust Collector, capturing airborne dust at various heights; (**B**) Surface Dust Collector (SDC), collecting surface dust directly affected by wind; and (**C**) Marble Dust Collector (MDCO), utilizing marble trays to capture deposited dust.  
However, it is important to note that there may have been variations in these meteorological parameters over time that could influence the current conditions.  
A detailed analysis of meteorological parameters revealed important variations that influence soil erosion and deposition. Most locations exhibited a noticeable increase in humidity from June to August. Wind speeds remained relatively consistent across most sites, averaging between 2.8 and 3.4 m s\({}^{-1}\), with a slight decline from June to August. This consistency in wind speed is critical for understanding the dynamics of soil erosion, as stable wind conditions can result in predictable patterns of dust deposition and soil loss. Precipitation varied significantly across the study sites. While some locations, such as S1, recorded low precipitation in June (0.7 mm) and July (0.1 mm), August experienced a marked increase (5.2 mm). This rise in precipitation during August plays a crucial role in soil moisture accumulation, potentially mitigating some of the effects of wind erosion during this period.
## 3 Results
### Analysis of N, P, K Fertilizer Contribution to Broccoli Yield
#### 3.2.2 Analysis of P Fertilizer Contribution to Broccoli Yield  
The sub-models of yield related to both ZSHL and HL-5 were obtained by dimensionality reduction, in which N and K factors were set to 0 in the regression equation.  
Figure 1: Nitrogen fertilizer contribution to broccoli yield.  
Y-P(ZSHL) = 3480.1106 + 23.2894X2 - 0.1236X2^2 \[\text{Y-P(HL-5)} = 4027.7660+85.6018X2 - 0.4592X2^2\] (6)  
The linear graph of these two regression equations (Figure 2) can be used to analyze the changes in the yield contribution of the P fertilizer amount on two different maturity broccoli varieties. Let \(\text{dy}_{1}/\text{dx}_{1}=0\) (i = 2), then the X\({}_{2}\) values corresponding to the extreme points of the two regression equations can be obtained, respectively. For early-maturing broccoli, ZSHL, when X\({}_{2}\) = 150.0 (equivalent to 150.0 kg/ha of P application), the yield contribution reached the maximum value; for late-maturing broccoli, HL-5, when X\({}_{2}\) = 96.0 (equivalent to 96.0 kg/ha P fertilizer), the yield contribution reached the maximum value.  
For the early-maturing variety, ZSHL, its contribution to the yield slowly increased with the elevation of the P fertilizer application level, and the curve that reached the maximum value was gentle. For the late-maturing variety, HL-5, its contribution curve to the yield also had a parabolic shape with the increase in the P fertilizer application level. Its contribution to the yield initially increased and then decreased with the increase in the P fertilizer application level.
# The Clique-Width of Minimal Series-Parallel Digraphs
## 3 Directed Clique-Width of Minimal Series-Parallel Digraphs (MSP DAGs)
: _Figure 5 illustrates the application of Algorithm 1 for the digraph \(G\) and its binary decomposition tree \(T(G)\) shown in Figure 3:4 The inner nodes are visited according to a post-order traversal of \(T(G)\) which is defined by the symbols \(\alpha_{i}\), \(1\leq i\leq 9\), written near every internal node of \(T(G)\). Every subfigure in Figure 5 shows a digraph \(G[\alpha_{i}]\), \(1\leq i\leq 9\), defined by expression \(t_{1}(\alpha_{i})\oplus t_{2}(\alpha_{i})\) of Algorithm 1. Digraph \(G[\alpha_{9}]\) obviously corresponds to digraph \(G\)._  
**Remark 2**. : _A useful property of Algorithm 1 is that the number of relabelings of the vertices is bounded. A relabeling of a vertex is done if and only if the state (non-isolated source, non-isolated sink, neither source nor sink, isolated) of the vertex is changed. Since the state of a vertex can change at most twice and an auxiliary label 5 or 6 is given at most once to every vertex, every vertex changes its label at most three times._  
The problem of finding a decomposition tree \(T(G)\) for some special graph \(G\) in polynomial time is a well studied problem. For every MSP DAG and also for every directed co-graph, a decomposition tree can be found in linear time [14; 20]. The reverse direction, i.e. constructing \(G\) in linear time from \(T(G)\) seems to be unsolved for MSP DAGs. Our shown 6-expression allows to solve this problem. **Corollary 2**. : _Every MSP DAG \(G\) can be constructed from its binary decomposition tree \(T(G)\) in time \(\mathcal{O}(|V(G)|+|E(G)|)\)._  
Proof. : Let \(T(G)\) a binary decomposition tree for some MSP DAG \(G\). By Corollary 1 from \(T(G)\) we can define a directed clique-width 6-expression \(X\) for \(G\) in time \(\mathcal{O}(|V(G)|)\). In order to access vertices of a given label \(i\) we maintain in each node \(u\) of the 6-expression-tree (see Section 4) for \(X\) an array containing at \(i\) a pointer to a list of the vertices  
Figure 3:5 Application of Algorithm 1 on the binary decomposition tree \(T(G)\) shown in Figure 2b for the MSP DAG \(G\) shown in Figure 2a. For every inner node \(\alpha_{i}\) of binary decomposition tree \(T(G)\) we give the digraph \(G[\alpha_{i}]\) defined by expression \(t_{1}(\alpha_{i})\oplus t_{2}(\alpha_{i})\) of Algorithm 1. The indices of the vertices represent their clique-width label. (**a**) Digraph \(G[\alpha_{1}]\), (**b**) digraph \(G[\alpha_{2}]\), (**c**) digraph \(G[\alpha_{3}]\), (**d**) digraph \(G[\alpha_{4}]\), (**e**) digraph \(G[\alpha_{5}]\), (**f**) digraph \(G[\alpha_{6}]\), (**g**) digraph \(G[\alpha_{7}]\), (**h**) digraph \(G[\alpha_{8}]\), and (**i**) digraph \(G[\alpha_{9}]\). of label \(i\) in the digraph defined by subtree with root \(u\) for \(1\leq i\leq 6\). Our expression \(X\) only inserts new arcs, such that these operations use time \(\mathcal{O}(|E(G)|)\) and by Remark 2, the number of relabelings is at most three for every vertex. Thus, \(G\) can be constructed from \(T(G)\) in time \(\mathcal{O}(|V(G)|+|E(G)|)\).
# The Use of Volcanic Powder as a Cement Replacement for the Development of Sustainable Mortars
## 2 Materials and Methods
### Impedance Spectroscopy  
The pore structure evolution of the studied specimens was also analyzed using the non-destructive impedance spectroscopy. This technique shows advantages as compared with others, since it is possible to analyze global information of the pore structure in the samples [35; 36], and it is also possible to study the microstructural variations of the same sample throughout the period of time studied [37].  
An Agilent 4294A analyzer (Agilent Technologies, Kobe, Japan) was used for the impedance spectroscopy. This device takes capacitance measurements ranging between 10\({}^{-14}\) and 0.1 F, with a maximum resolution of 10\({}^{-15}\) F. In this research, circular electrodes (\(\O=8\) cm) were used, which consisted of flexible graphite attached to a piece of copper with the same diameter to obtain the impedance spectra. Through these electrodes, frequencies oscillated between 100 Hz and 100 MHz. Regarding the measurement methods, contacting and non-contacting ones were applied [35].  
An example of an impedance spectrum that resulted from the measurement of one of the samples studied is depicted in Figure 4a. To validate the measurements, the Kramers-Kronig (K-K) relations were used [38] and a differential impedance analysis was applied to them [39]. According to the plot shown in Figure 4b, it was noted that the measured spectra contained two time constants, revealed by the two maxima in the plot. From this finding, the experimental data can be fitted to the equivalent circuits proposed by [PERSON] et al. [36] (see Figure 5a,b), which includes two time constants. These circuits, comprised of various resistances and capacitances, make it possible to relate different characteristics of the pore network of cementitious materials [36]. The R\({}_{1}\) resistance gives information about the percolating pores in the sample, whereas the R\({}_{2}\) resistance provides data related to the pores in general. The C\({}_{1}\) capacitance provides information about the solid fraction of the specimen, and the C\({}_{2}\) capacitance is related to the surface of the pores in contact with the electrolyte that fills the microstructure of the material [36]. Figure 5 shows that the impedance parameters R\({}_{2}\), C\({}_{1}\), and C\({}_{2}\) can be obtained using both contacting and non-contacting methods. In this study, due to their greater accuracy, only the values of the parameters R\({}_{2}\), C\({}_{1}\), and C\({}_{2}\) with the non-contacting method were analyzed. The R\({}_{1}\) resistance can be only determined with the contacting method.  
Figure 4: (a) Nyquist plot noted for a VP10 specimen at 200 days using the contacting method; (b) differential impedance analysis of the impedance spectrum represented in 0a.  
In this work eight different discs approximately 1 cm thick were analyzed for each mortar type. The changes with time of the impedance parameters over time were recorded up to 400 hardening days.
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 5 Conclusions  
In this study, an arithmetic optimization was used to optimize the route discovery process of the AOMDV protocol. A fitness function was constructed by integrating four metrics: the link holding time, residual energy, congestion degree, and hop count. The optimal fitness function was employed for path selection. The simulation results demonstrate that the AO-AOMDV protocol not only adapts well to the high mobility of the UAV nodes and drastic changes in topology but also provides better service quality and significantly prolongs the lifetime of UAV networks. Although the algorithms involved in this research introduce certain parameters as routing selection criteria, the covered metrics are not comprehensive. As part of our future work, we will consider incorporating indicators such as available bandwidth and jitter to design routing protocols.  
Conceptualization, H.W. and Y.J.; methodology, Y.L.; software, Y.Z.; validation, H.W., Y.J. and T.H.; formal analysis, Y.L.; investigation, H.W.; resources, Y.J. and T.H.; datacuration, H.W. and Y.Z.; writing--original draft preparation, H.W.; writing--review and editing, Y.Z. and Y.L.; project administration, Y.J. and T.H.; funding acquisition, Y.J. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2021 State Grid Corporation of the China Science and Technology Program, grant number 5700-202141454A-0-0-00.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### ATP-Related Compound Analysis  
ATP-related compounds are widely used to evaluate the freshness of fish [8]. From Figure 2a-f, the concentrations of ATP (1.2 umol/g) and ADP (0.46 umol/g) of tuna decreased obviously with increased storage time. The concentrations of ATP and ADP degraded remarkably within 2 days. The concentrations of ATP in tuna at 0 \({}^{\circ}\)C and 4 \({}^{\circ}\)C decreased to 0.2 and 0.18 umol/g, respectively. Moreover, the level of ATP and IMP in tuna at 4 \({}^{\circ}\)C decreased more than at 0 \({}^{\circ}\)C. The initial concentrations of IMP (5.07 umol/g) decreased to 2.53 umol/g on the 6 th day of storage at 0 \({}^{\circ}\)C, and to 2.35 umol/g at 4 \({}^{\circ}\)C. However, the concentrations of HxR and Hx appeared to increase. The concentrations of Hx increased slowly with increasing storage time and were still measured at a low concentration at day 6. With the degradation of nucleotides, HxR and Hx were important off-flavour contributors in fish muscle produced during cold storage [10]. The result of tuna obtained in the present study is  
Figure 1: Changes in drip loss (**a**), \(L^{*}\) (**b**), \(a^{*}\) (**c**) and histamine concentrations (**d**) at 0 \({}^{\circ}\)C and 4 \({}^{\circ}\)C for 6 day (each point is the mean value of three determinations). \({}^{a,b,c,d,e,f,g}\) Means in the same column with different superscripts are significantly different (\(p<0.05\)).  
similar to that reported by [PERSON] et al. (2017) [PERSON] et al. (2017), who reported that the concentrations of ATP and IMP of white muscle and dark muscle from the common carp (_Cyprinus carpio_) significantly decreased, and the concentrations of HxR and Hx accumulated with storage time increasing as good indicators to evaluate the freshness of the fish. ATP-related compounds were detected to be varied and contributed to the flavour during cold storage. Based on previous study [PERSON] et al. nucleotides play the role in contributing to the flavour of aquatic products.
## 3 Results and Discussion
### Stage 1--LF-DSS Treatment and M-BGS Production
#### 3.1.4. Pollutant Removal Rate
In turn, the M-BGS system analyzed by [PERSON] et al. (2021) [32] ensured 98% efficiency of COD removal from synthetic wastewater and removal efficiencies of biogenes at 78% for nitrogen compounds and 71% for phosphorus compounds [32]. It has also been proved that increased concentrations of biodegradable organic compounds in the environment modify the microalgae metabolism pattern into mixotrophic or even heterotrophic, which enhances the removal of carbon substances from wastewater [48; 49].
## 5 Results
### Comparison of Rural-Macro (RMa), Urban-Macro (UMa), and UMi Scenarios  
In Figure 8, the performance of FDD cell-free massive MIMO is compared in different scenarios. Under the condition of high-precision quantization, the RMa scenario has the highest spectral efficiency due to its smaller path loss and better propagation conditions. This means that, when deployed in practice, UMi and UMa scenarios require more dense AP deployment or design of AP placement to achieve similar performance to RMa scenarios. Compared with UMa and UMi, the 95%-likely spectral efficiency in the RMa scenario is approximately 6 bit/s/Hz higher, while the spectral efficiencies of RMa and UMi are similar. With five quantization bits, the above conclusion still holds, but the performance gap is narrowed. This is because the quantization error becomes larger and therefore results in a loss of performance. With one quantization bit, the spectral efficiency is almost the same in all three scenarios.