esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Particle-Swarm-Optimization Algorithm for a Prediction Model of Steel Slab Temperature
## 2 The Prediction Model of the Slab Temperature  
### The Structure of a Reheating Furnace  
The heat transfer processes in the furnace mainly consist of radiation heat transfer and convection heat exchange. The main function of reheating furnaces is to help the slabs reach the desired discharging temperatures for the next rolling process. The slab passesthrough the preheating zone, multistage heating zone and soaking zone from the furnace's inlet to the outlet, as shown in Figure 1. The multi-stage heating zone can be divided into four subzones (upper zones 1 and 2; bottom zones 4 and 5), and the soaking zone is divided into two subzones (upper zone 3, bottom zone 6). The key components of a subzone are a series of regenerative burners, the corresponding furnace nozzle temperatures \(TN_{j}\) and the furnace zone temperature \(TP_{k}\). Here, \(TN_{j}\) is easily obtained by the thermocouples nearby the location of burner's nozzle \(j\) and \(TP_{k}=\frac{1}{n}\sum_{j=1}^{n}TN_{j}\), which stands for the average value of the nozzle temperatures \(TN_{j}\) in zone \(k\).
## 3 Results and Discussion
### Cell Cycle Progression Changes in LoVoC Cells Treated with Ru(III) Complexes
In conclusion, increased concentrations of Ru(III) complexes seem to induce a dose-dependent decrease in cell distribution found in the S cell cycle phase. The results suggested that cell cycle arrest mostly took place in the G0/G1 phase and in a lower proportion also in the G2M phase, which might represent an important cause for the anti-proliferative effect of Ru(III) complexes, similar to CisPt, but hopefully less toxic. The arrest in G0/G1 and G2-M phases are some cell cycle-regulating mechanisms related to cellular damage and, in particular, to DNA damage. In that case, DNA repair mechanisms are activated to ensure the replication of an intact DNA. When the reparation is successful, cells start again cycling; otherwise, apoptosis mechanisms are activated [16]. The results obtained during the study showed increased levels of cell lysis and apoptotic events, which are both dose-dependent in LoVo cancer cells treated with the Ru(III) complexes with quinolone antibiotics. In addition, data showed a major decrease in cell proliferation, since percentages of cells distributed in the S cell cycle phase diminished, and a G0/G1 cell arrest was observed. Therefore, our results strongly suggest that the newly synthesized Ru(III) complexes might play an important role in future chemotherapeutic approaches, since their activity is based on diminishing cell proliferation, induction of apoptosis, and modulation of cell cycle phases.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 6 Energy Efficiency and Resource Allocation Algorithms
### Selected Papers  
Resource allocation algorithms with EE optimization consider the trade-off between EE and bandwidth utilization. Thus, joint optical and radio resource and transmission power allocation algorithms are used to minimize the total consumed power in the entire network. For instance, in [45], the NP-hard problem of the joint uplink resource allocation of small cells, spectrum resources, and transmission power is decomposed into a potential game for small cell selection and a non-cooperative game for power allocation. The EE maximization problem is formulated in [77] in terms of number of bits delivered per unit of Joule subject to the QoS rate threshold for each user, and an alternating descent algorithm is applied to separate the energy efficiency optimization problem into two sub-problems of EE maximization problem and user throughput fairness. In [78], the EE maximization problem is modeled as a class of optimization problems called fractional programming to minimize the total power consumption of the entire system. With the increased use of dense small cell deployments due to mmWave characteristics, activating and deactivating these small cells with sleep modes based on different parameters, such as the number of APs and network loads [79], flow arrival rates [81], under the presence of a macro cell [82] has become a frequently used energy saving method. In [79], the system to optimize transmission and sleep periods is modeled with a network formation game, in which every AP is a player, establishing connections with its neighbors to create energy efficient routes. The minimization of the total power consumption in user association is modeled as a capacitated facility location problem and solved with the selection and repetition based heuristic algorithms for sleep mode decisions in [82]. Dual-connectivity network architectures with both macro cell and mmWave small cell options are also considered for the trade-off analysis of joint energy efficiency and
## 2 Materials and Methods
All measurements were performed under standard conditions in a thermostated box (T = 298 \(\pm\) 2K and \(p\) = 750 Torr). The needle diameter was 0.51 mm. Distilled water and diethylene glycol (DEG) were used as test liquids with different polarities. The droplet volume was constant and equal to \(\sim\)1.5 \(\upmu\)L. The contact angles (CA) were measured in the sessile drop mode. The drop profile shape of the samples was determined in two ways: the Young-Laplace method and the ellipse approximation method. The final values of the contact angles were calculated as the average of three measurements. The calculation of the surface free energy (SFE) was carried out using two methods: the Newman (equations of state) [50] and Owens-Wendt [51]. Additional detailing of the SFE characteristics of the samples with the calculation of the polar and dispersive components was carried out using the Owens-Wendt method. The microhardness (H), Young's modulus (E), coefficient of elastic recovery (R) of the BC\({}_{\text{x}}\)N\({}_{\text{y}}\) films, as well as the adhesion of the films to the Si(100) substrate were determined using nanoindentation measurements and sclerometry test according to ISO 14577 with scanning nano-hardness tester NanoScan-3D (TISNCM, Troitsk, Moscow, Russia). The procedure for measuring and calculating the values of the mechanical properties of films is given in [52]. The applied load was gradually increased from 1 to 70 mN. Each sample was probed at 10 different locations. Average values of both hardness and elastic modulus were calculated from the curves of the dependence of load from penetration depth with the method proposed by [PERSON] & Pharr. The dependence of the effective hardness of the \"film-substrate\" system on the depth of the indenter penetration was analyzed. The obtained dependence was approximated using [PERSON]'s model [53] for averaging the experimental data and determining the film's hardness without the substrate influence. The typical dependences of hardness and Young's modulus on the indentation depth for the BC\({}_{\rm x}\)N\({}_{\rm y}\) sample are shown in Figure S1. The adhesive strength of the film was evaluated by scratching the sample surface with a pyramidal indenter of the Berkovich type forward with the edge and the face of the indenter at a continuously increasing load from 0.1 to 100 mN. The AFM method was used to control the residual trace topology after the scratching test.
# Low-Cost Active Anomaly Detection with Switching Latency
## 3 The DMSC Policy
### Performance Analysis  
We now analyze the asymptotic performance of the DMSC policy in terms of the expected cumulative cost as the error constraint \(\alpha\to 0\). Note that according to (13), that the error constraint approaches zero implies that the threshold \(B\rightarrow\infty\) and the sample size required to declare the state of each process approaches infinity. In another word, the detection procedure will not be terminated.  
According to (4), the expected cumulative cost \(E(C|\Gamma)\) can be denoted by the summation of the cost caused by observations, which can be denoted as  
\[E(C_{d})=E\left(\sum_{m\in\mathcal{H}_{1}}cN_{\tau_{m}}^{d}\right) \tag{22}\]  
and the cost caused by the switchings  
\[E(C_{s})=E\left(\sum_{m\in\mathcal{H}_{1}}csN_{\tau_{m}}^{s}\right). \tag{23}\]  
Thus, it is necessary to analyze the total number of observations and switchings. In our proposed policy, the states of the \(L\) abnormal processes or the \(M-L\) normal processes are declared one by one, which splits the entire detection procedure into \(L\) or \(M-L\) stages. Thus, one important part of the analysis of the DMSC policy is analyzing both the observation times and the switching times of each stage for two cases (detailed analysis can be found in Appendix A.2).  
Besides the number of observations and the number of switchings, the ratio \(s\) is also an important parameter for the objective function of the expected cumulative cost. It should be noted that \(s\) determines the relative contribution of a single switching compared to a single observation to the cumulative cost. According to the order of the ratio of \(s\) to \(B\), we analyze the optimality of the proposed policy in the following two scenarios:  
**Scenario 1:** The ratio of the single-switching delay to the single-observation delay \(s\) is negligible to the threshold \(B\) in the asymptotic regime of \(\alpha\to 0\), in another words, \(\lim_{\alpha\to 0}\frac{s}{B}=0\), i.e., \(s=o(B)\).  
**Scenario 2:** The ratio of the single-switching delay to the single-observation delay \(s\) is comparable to the threshold \(B\) in the asymptotic regime of \(\alpha\to 0\), in another words, \(\lim_{\alpha\to 0}\frac{s}{B}=O(1)\), i.e., \(s=\Omega(B)\).  
Note that \(B\) is the threshold relative to the error probability \(\alpha\) and determines the expected sample size required to declared the state of processes, where the expected sample size is \(B/D(g||f)\) for the abnormal processes and is \(B/D(f||g)\) for the normal processes. Based on (22) and (23), the ratio of \(s\) to \(B\) can find a connection with the ratio of the cumulative switching cost to the cumulative observation cost.  
Next, we consider the above two scenarios and provide the performance analyses of the DMSC policy on the expected cumulative cost. Firstly, we focus on Scenario 1 and prove that the DMSC is asymptotically optimal in this scenario as \(\alpha\to 0\). Then, similarly, we prove that the DMSC policy is order-optimal in Scenario 2 in the asymptotic regime.
## 3 Results
### Comparison of Kinetic Analysis  
As we previously observed [19], AtSgpp shows affinity over a broad-range of sugar phosphate substrates being D-ribose-5-phosphate, 2-deoxy-D-glucose-6-phosphate, and D-mannose-6-phosphate the preferred substrates amongst the phosphosugar species tested in our experimental set up [19]. The substrate specificity of MBP-AtSgpp3\(\Delta\) was further investigated for phosphatase activity and Pi release, from hydrolyzed substrates under the same conditions, and compared with the normal protein MBP-AtSgpp. The apparent \(K_{\text{m}}\) and \(V_{\text{max}}\) values for phosphoesters/Mg\({}^{2+}\) were determined by linear regression from spectrophotometric data (Figure S3). In the case of MBP-AtSgpp, the corresponding slopes of the Lineweaver-Burke plot indicated lower \(K_{\text{m}}/V_{\text{max}}\) values for D-ribose-5-phosphate, 2-deoxy-D-glucose-6-phosphate, and D-mannose-6-phosphate, than for D-glucose-6-phosphate, DL-glycerol-3-phosphate, and D-fructose-6-phosphate (Figure S3A). The values of the substrate specificity constant \(k_{\text{cat}}/K_{\text{m}}\) for D-ribose-5-phosphate, 2-deoxy-D-glucose-6-phosphate, D-mannose-6-phosphate, D-glucose-6-phosphate, DL-glycerol-3-phosphate, and D-fructose-6-phosphate are in the range of 2.5-10.7 \(\times\) 10\({}^{3}\) M\({}^{-1}\)s\({}^{-1}\), being the highest \(k_{\text{cat}}/K_{\text{m}}\) value for D-ribose-5-phosphate (10.7 \(\times\) 10\({}^{3}\) M\({}^{-1}\)s\({}^{-1}\)) and the lowest for D-fructose-6-phosphate (2.5 \(\times\) 10\({}^{3}\) M\({}^{-1}\)s\({}^{-1}\)) (Table 1). Similar to MBP-AtSgpp, the \(K_{\text{m}}/V_{\text{max}}\) value for D-ribose-5-phosphate of MBP-AtSgpp3\(\Delta\) remained low (Figure S3B). Conversely, MBP-AtSgpp3\(\Delta\) enzymatic kinetic was progressively affected, particularly for DL-glycerol-3-phosphate (Figure S3B). The \(k_{\text{cat}}/K_{\text{m}}\) for D-ribose-5-phosphate, 2-deoxy-D-glucose-6-phosphate, D-mannose-6-phosphate, D-glucose-6-phosphate, DL-glycerol-3-phosphate, and D-fructose-6-phosphate are in the range of 0.5-8.3 \(\times\) 10\({}^{3}\) M\({}^{-1}\)s\({}^{-1}\), being the highest \(k_{\text{cat}}/K_{\text{m}}\) value for D-ribose-5-phosphate (8.3 \(\times\) 10\({}^{3}\) M\({}^{-1}\)s\({}^{-1}\)) and the lowest for DL-glycerol-3-phosphate (0.5 \(\times\) 10\({}^{3}\) M\({}^{-1}\)s\({}^{-1}\)) (Table 1).
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 4 Energy Efficiency Optimization
### Optimal Power Allocation for Given \(P\) and \(N\)  
To solve the optimization problem, we first calculated the optimal \(\theta\) for given \(P\) and \(N\). Given \(P\) and \(N\), \(\mathcal{P}_{1}\) can be converted to  
\[\begin{split}\mathcal{P}_{2}:&\max_{\theta}& \tilde{\eta}_{\text{EE}}\\ & s.t.& 0\preceq\theta\preceq 1\\ &\theta_{1}+\cdots+\theta_{k}+\cdots+\theta_{K}\leq 1.\end{split} \tag{17}\]It is straightforward to prove that \(\tilde{\eta}_{\rm EE}\) achieves the maximal only when \(\theta_{1}+\cdots+\theta_{k}+\cdots+\theta_{K}=1\). Thus, we can rewrite \(\mathcal{P}_{2}\) as  
\[\begin{split}\mathcal{P}_{3}:&\max_{\theta}\quad \quad\quad\tilde{\eta}_{\rm EE}\\ & s.t.\quad\quad 0\preceq\theta\preceq 1\\ &\quad\quad\quad\theta_{1}+\cdots+\theta_{k}+\cdots+\theta_{K}=1. \end{split} \tag{18}\]  
**Theorem 2**.: _Given \(P\) and \(N\), the equal power allocation is optimal which can maximize the EE \(\tilde{\eta}_{\rm EE}\); i.e.,_  
\[\theta_{k}=\frac{1}{K},k=1,\cdots,K. \tag{19}\]  
Proof.: When \(N\) is large enough, the EE \(\tilde{\eta}_{\rm EE}\) can be approximated by  
\[\tilde{\eta}_{\rm EE}=\frac{\sum_{k=1}^{K}\log_{2}\left(\frac{p\theta_{k}N}{ \sigma^{2}}\left(\frac{r_{k}^{2}+r^{2}}{\sigma^{2}}\right)^{\frac{p}{2}-1} \right)}{P_{0}+NP_{c}+KP_{c}+\frac{p}{\xi}}. \tag{20}\]  
Letting \(\mathcal{I}_{k}=\frac{\left(\frac{r_{k}^{2}+r^{2}}{\sigma^{2}}\right)^{\frac{ p}{2}-1}}{\left|r_{k}^{2}-r^{2}\right|^{p-1}}\), \(\mathcal{P}_{3}\) can be reformulated as  
\[\begin{split}\mathcal{P}_{4}:&\max_{\theta}\quad \quad\quad\frac{K\log_{2}\left[\frac{pN}{\sigma^{2}}\left(\prod_{k=1}^{K} \theta_{k}\right)^{\frac{1}{K}}\left(\prod_{k=1}^{K}\mathcal{I}_{k}\right)^{ \frac{1}{K}}\right]}{P_{0}+NP_{c}+KP_{c}+\frac{p}{\xi}}\\ & s.t.\quad\quad 0\preceq\theta\preceq 1\\ &\quad\quad\quad\quad\theta_{1}+\cdots+\theta_{k}+\cdots+\theta_{ K}=1.\end{split} \tag{21}\]  
Then, \(\mathcal{P}_{3}\) is equivalent to maximizing \(\prod_{k=1}^{K}\theta_{k}\) with the constraint \(\theta_{1}+\cdots+\theta_{k}+\cdots+\theta_{K}=\frac{1}{K}\). It is straightforward that \(\theta=1\). This completes the proof.  
**Remark 1**.: _It can be observed from Theorem 2 that the optimal \(\theta^{*}\) that maximizes the EE for different \(P\) and \(N\) is the same, so \(\theta^{*}\) is also the optimal solution of \(\mathcal{P}_{1}\)._
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 3 Temporal Logic Planning  
### Product Automaton  
The search region is divided into \(n\times n\) square cells of the same size. Let \(\Gamma\) be the index set of all cells. Each cell is denoted by \(C_{q}\), where \(q\in\Gamma\). Define a map \(o\) such that, for any position \(p_{k}\in C_{q}\), we have \(o(p_{k})=q\). \(C_{q_{i}}\cap C_{q_{j}}=\varnothing\) for all \(q_{i},q_{j}\in\Gamma\) and \(q_{i}\
eq q_{j}\)[12]. Then, we give the finite-state transition system for the robot as  
A finite-state transition system is a tuple \(T=(Q_{T},Q_{0},\delta_{T},\Pi_{T},\zeta_{T},\omega_{T})\), where \(Q_{T}\) is a finite set of states representing the set of nodes in the transition system, \(Q_{0}\) is the initial state set, \(\delta_{T}\subseteq Q_{T}\times Q_{T}\) is the set of transition representing the transition relationships between states, \(\Pi_{T}\) is an observation set, \(\zeta_{T}:Q_{T}\to 2^{\Pi_{T}}\) is the observation map, and \(\omega_{T}:\delta_{T}\to\mathbb{R}^{+}\) is a positive weight function whose value represents the cost of transition between states in \(Q_{T}\).  
It should be pointed out that if \(q_{i}\to_{T}q_{j}\), \(\omega_{T}(q_{i},q_{j})=1\); otherwise, \(\omega_{T}(q_{i},q_{j})=\infty\). An infinite sequence \(\mathbf{q}=q_{0}q_{1}q_{2}q_{3}...\) generated by the transition system \(T\) is accepted if and only if a given formula is satisfied. Moreover, one can use ltl2 ba toolkit to convert the LTL formula to a Buchi automaton. A Buchi automaton is a tuple \(B=(Q_{B},Q_{B}^{0},\Pi_{B},\delta_{B},F_{B})\), where \(Q_{B}\) is a finite set of states, \(Q_{B}^{0}\subseteq Q_{B}\) is the set of initial states, \(\Pi_{B}\) is the input alphabet, \(F_{B}\) is a set with accepted states, and \(\delta_{B}:Q_{B}\times\Pi_{B}\to 2^{Q_{B}}\) is the observation map.  
Further, one can construct the product automaton according to the finite-state transition system and the Buchi automaton. According to the system \(T=(Q_{T},Q_{0},\delta_{T},\Pi_{T},\zeta_{T},\omega_{T})\) and a Buchi automaton \(B=(Q_{B},Q_{B}^{0},\Pi_{B},\delta_{B},F_{B})\), a product automaton \(P=(Q_{P},Q_{P}^{0},\delta_{P},\omega_{P},F_{P})\) can be constructed by \(T\times B\), where \(Q_{P}=Q_{T}\times Q_{B}\), \(Q_{P}^{0}=Q_{0}\times Q_{B}^{0}\), \(\delta_{P}\subseteq Q_{P}\times Q_{P}\) is the set of transitions defined by \([(q_{i},s_  
In order to effectively search for signal sources, any cell in the search area can be regarded as the initial state in the finite-state transition system \(T\). Based the acceptable node set \(\Delta\), the corresponding product automaton can be further simplified. It should be pointed out that the above work can be performed offline, which means that the process of generating a product automaton cannot influence the calculation performance of robots.
# A Method to Reduce Eddy Current Loss of Underwater Wireless Power Transmission by Current Control
###### Abstract  
In recent years, wireless power transmission (WPT) technology based on magnetic resonance has been extensively studied. However, in contrast to that in the air, wireless power transmission in seawater medium will be accompanied by inevitable energy loss, that is, eddy current loss (ECL), which will increase with the frequency and coil current. In this article, an equivalent circuit model of the eddy current loss of underwater wireless power transmission is established, two methods to reduce the eddy current loss are proposed, and the optimal modulus ratio for the coil current of the dual-coil wireless power transmission system to reduce eddy current loss is calculated. Electromagnetic field (EMF) simulation software verifies the correctness of the two methods, and it is concluded that increasing the phase difference of the coil current or controlling the coil current ratio to ensure that the optimal modulus ratio is in a certain range can reduce the eddy current loss effectively and improve the energy transmission efficiency of the system by about 4-5%.  
wireless power transmission (WPT); current control; eddy current loss (ECL); electromagnetic field (EMF) +
Footnote †: journal: Applied  
Article
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 6 Conclusions  
In this paper, we present HCEL, a novel, energy-efficient, and reliable routing protocol specifically designed to enhance the overall performance of WBANs. By integrating static and dynamic clustering routing techniques in the parent node selection process, HCEL significantly improves the stability period and extends network lifetimes, thereby maximizing the reliability of WBANs. By conducting thorough simulations using Networkx, we provide empirical evidence showcasing the superiority of HCEL over both the SIMPLE and ERRS protocols across various performance metrics. HCEL substantially enhances network stability periods, network lifetimes, throughput, residual energy, path loss, and expected transmission counts. Particularly noteworthy is the remarkable 56% and 40% reduction in end-to-end delay achieved by HCEL compared to the SIMPLE and ERRS protocols, respectively, highlighting its efficiency and reliability as a routing solution for WBANs. To further advance the capabilities and applications of WBANs in healthcare and beyond, our future work will focus on incorporating mobility and security considerations. By addressing these key issues, we can further enhance the versatility and robustness of WBANs, enabling their effective deployment in a wide range of real-world scenarios.  
Conceptualization, H.H. and E.S.; Methodology, H.H. and S.H.; Software, H.H.; Validation, F.S., M.A.S., S.H., M.H. and H.K.; Formal analysis, H.H. and S.H.; Data curation, F.S.; Writing--original draft, H.H.; Writing--review & editing, F.S., M.A.S. and M.H.; Funding acquisition, M.A.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data sharing is not applicable to this article. The authors declare no conflicts of interest.
## 3 Results and Discussion
### Microstructure Characterization
The typical width of the intermixing zone varied from 10 to 40 \(\upmu\)m. Significant grain refinement was observed in the intermixing zone. Such zones have been reported in dissimilar joints, as a result of the diffusion of elements such as Ni, Fe, and Ti across the weld interface. [PERSON] et al. reported similar results for the friction welding of IN718 and SM45C steel [10]. Figure 12b depicts the EDS line scan data analysis of the SS304L/IN718 ST conditions. In the friction stage, where higher temperatures (1080-1100 \({}^{\circ}\)C) are available, and the materials are plasticized [30], diffusion of the elements across the interface, as well as mechanical intermixing of these components, is very likely. Figure 12c depicts the rapid compositional shift in Nickel and Iron at the interface. The Ni and Fe composition variations observed at the weld interface suggested the formation of a mechanical intermixing zone at that location. The dark elongated vertical lines seen in Figures 10 and 12 at the weld interface resulted from the etching. The weld interface consists of the intermixing zone, in which parallel banded alternative layers of SS304L and IN718 formed as a result of the softening and squeezing of the material at higher rotational speed during the friction welding. During the etching process, which was performed to reveal the microstructure, the IN718 in the intermixing zone reacted aggressively with the etchant relative to the SS304L. Hence, the regions of IN718 in the intermixing zone are observed as dark lines parallel to the weld interface.