esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Interference and Network Optimization Difficulties
#### 3.9.1 Interference
In trade-offs for networks such as 5G, where the quality and capacity must be high, the solution of incorporating a multi-beam antenna system such as mMIMO was sought, as explored in detail in [88]. This technique identifies the best route to providing optimal performance to a user. This approach helps compensate for transmission attenuation losses, especially in millimeter-wave communication. In this case, the base station generates multiple narrow beams of mainly RF energy in all directions of the coverage area. This causes a spatial division of multiple beams, introducing interference. Adjacent beams cause this interference from the same cell or a neighboring cell. * Multi-user Interference: Multi-user interference is due to the industry's quest for higher data rates in applications and the dramatic increase in subscribers to wireless communications [89]. The techniques used for this type of technology are given by MUMIMO, a 5G generation technique that helps increase the capacity and performance of wireless broadcasting systems. Multi-user interference occurs when several users try to transmit their information requests at the same time.
# Exploring Metalloprotease from _Dunaliella_ sp.: Production, Regulation, and Structural Insight
## 3 Results and Discussion
### Molecular Investigation
#### 3.5.1 Identification of _FtsH2_ Genes in _Dunaliella salina_  
To identify the genes encoding FtsH metalloprotease proteins in _Dunaliella salina_, a BLASTp (e-value \(\leq\) 1 \(\times\) 10\({}^{-10}\)) search was carried out using 12 At FtsH amino acid sequences as queries. After removing redundant sequences, we used CD-search and Pfam to identify genes containing the structural domains AAA, AAA+ lid domain and peptidase M41, which are exclusive to the _FtsH_ gene family. In total, we identified seven _FtsH_ gene family members in _Dunaliella salina_. As shown in Figure 4a, only two genes (Dusal.1123s00001.1 and Dusal.0121s00013.1) contain the characteristic domains FtsH_ext (PF06480), AAA (PF00004), AAA+ lid domain (PF17862) and the active peptidase_M41 (PF01434). However, the other genes, Dusal.0894s00002.1, Dusal.0167s00018.1, Dusal.047s00008.1, Dusal.0502s00002.1, and Dusal.0274s00008, lack the active zinc-binding motif (HEXH) in their peptidase_M41 domain and the FtsH_ext domain. Similarly to the _FtsH_s of _Glycine max_[115] and _Medicago truncatula_[116], the _DSFtsH_ genes structure analysis showed multiple exons and introns whose numbers and lengths were similar in genes clustered in the same evolutionary tree branch (Figure 4a). The Multiple Em for the Motif Elicitation (MEME) database was used for putative motif identification in the DsFtsH protein sequences. Interestingly, ten motifs were identified, all of which are presented in the Dusal.1123s00001.1 and Dusal.0121s00013.1 proteins. A phylogenetic analysis using MEGA-11 software was used to elucidate the relationship between FtsH proteins from different species and to identify the _DSFtsH2_ gene (Figure 4b). The species included _Arabidopsis thaliana_ (12 proteins), _Glycine max_ (9 proteins), _Oryza sativa_ (9 proteins), and _Chlamydomonas reinhardtii_ (4 proteins). Based on the results, 43 _FtsH_ genes were clustered into four groups: Groups A, B, C, and D. The largest group (A), with 15 genes, contains the two characteristic _DSFtsHs_ (Dusal.1123s00001.1 and Dusal.0121s00013.1), which exhibit a close phylogenetic relationship with the _FtsH2_ and _FtsH1_ genes, respectively, found in _Chlamydomonas reinhardtii_, _Arabidopsis thaliana_, and _Oryza sativa_. According to this phylogenetic analysis, Dusal.1123s00001.1 was renamed hereafter _DSFtsH2_. Although the _FtsH2_ gene is the most highly conserved among different species [117], there is a lack of literature regarding its structural and functional characteristics in photosynthetic organisms, particularly in microalgae.
## 7 Experimental  
A simple experimental setup was considered in [49; 61] to validate results of the Risley prisms simulations (Figure 12a).  
All components are commercially available [63], with two Risley prisms that can be rotated separately, with a minimum step of \(2^{\prime}\). The collimated laser beam passes through them and is projected on a screen (not shown in the figure). A certain ratio \(M\), Equation (1), is simulated by producing incremental angular movements \(\Delta\varphi_{1}\) and \(\Delta\varphi_{2}\) of the two prisms (where Prism 2 is, as in the simulations, the one close to the screen); therefore, \(M=\Delta\varphi_{2}/\Delta\varphi_{1}\).  
In Figure 12 four examples were considered, two of them for \(\mid\!M\!\mid\!=4\) (b) and the other two for \(\mid\!M\!\mid\!=8\) (c). Satisfactory agreement between simulations and experiments could be observed, as we also concluded in [49; 61]. A comparison between simulated (blue for \(M\!>\!0\) and red for \(M\!<\!0\)) and experimental (black) scan patterns can be made in Figure 12, as well as the difference between the \(M\!>\!0\) case (1) and the \(M\!<\!0\) case (2), for the same \(\mid\!M\!\mid\). To better distinguish between simulations and experiments, the corresponding scan patterns were presented slightly rotated with regard to one another.  
The observed differences in size between experimental and simulated patterns are caused by difficulties in aligning the components of the setup, as well as in precisely adjusting the \(e\) and \(L\) distances (between the two prisms and from the second prism to the screen--Figure 2), as well as in the difference from the refractive index of the prism considered in simulations and the actual one, for the utilized laser wavelength. Current work in our group includes the development of mechanized and compact setups for more accurate experimental studies. Even within the limitations above, the relative errors related to the experimental radius \(y^{e}_{max}\) of the FOV/scan pattern (Figure 2) is 2 to 4% (Table 3), with regard to the simulated radius, of \(y_{max}\)= 71.265 mm (for \(e\) = 25 mm, \(L\) = 1 m, and \(n\) = 1.517). To complete this analysis, the experimental radiuses \(y^{e}_{max}\) were measured for each of the \(\mu\) loops of the patterns in Figure 12.  
In addition to this (approximate) match between simulations and experiments, a perfect match can be obtained between simulations and theory (i.e., for the maximum and minimum linear deviations deduced in Section 3) if the effective width of the prisms is considered (by adjusting the radius \(b\) of each prism by considering the prism angle \(\theta\)).  
Figure 12: Experimental study of the scan patterns of two rotational Risley prisms: (**a**) setup built with commercially-available Thorlabs components. Superposed, but slightly rotated with regard to each other, simulated (blue and red) and experimental (black) scan patterns, determined for (**b**) \(\mid\!M\!\mid\!=4\) and (**c**) \(\mid\!M\!\mid\!=8\), with (**1**) \(M\!>\!0\) and (**2**) \(M\!<\!0\).
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 5 Experiments  
In this section, we experimentally compare the presented algorithms among themselves. The reason for this is that, to the best of our knowledge, there is no other published clustering algorithm in the literature for data streams implemented on top of the MapReduce model (which provides scalability, reliability, resilience, and flexibility) that can estimate the natural number of clusters from a data stream automatically. We analyzed the experimental results qualitatively, through the Adjusted Rand Index (ARI) [28], and in total processing time. The ARI returns the value of 1 when the compared partitions are identical and is adjusted to 0 when comparing random partitions.
# Non-Destructive pXRF on Prehistoric Obsidian Artifacts from the Central Mediterranean
## 4 Obsidian Sourcing in Europe and the Mediterranean  
There are four obsidian sources in the Central Mediterranean, from volcanic formations on the Italian islands of Sardinia [11; 13; 32; 33; 34; 35], Palmarola [36], Lipari [37; 38], and Pantelleria [3; 39; 40; 41; 42] (Figure 5). People with agriculture lived on both Sardinia and Lipari from the start of the Early Neolithic (ca. 6000 BCE), with the use of obsidian for stone tools starting at the same time. No one settled on the tiny island of Palmarola, while it appears that Pantelleria was not occupied until about 3000 BCE [43]. There is also Melos in the Aegean with two subsources [44], and one Carpathian source that was used, in southeastern Slovakia [45; 46]. Geological surveys and analytical research were conducted on each, assessing the quantity and quality of obsidian from multiple outcrops and the ability to distinguish between these sources. For the four Italian islands, analyses were conducted by the author, using INAA, LA-ICP-MS, and EDS-XRF, defining multiple subsources for each island, which is important for the study of archaeological artifacts and our interpretation of prehistoric access and collection of obsidian [2; 35; 47].  
Non-destructive analyses by pXRF of obsidian artifacts are also able to distinguish Monte Arci (Sardinia) subsources (Sardinia A, Sardinia B1, Sardinia B2, Sardinia C), as well as for Lipari (Gabellotto, Canneto Dentro, Monte Guardia) and Pantelleria (Lago di Venere 1 and 2, Balata dei Turchi) (Figures 6-9). At least with the elements currently measured and calibrated, we cannot distinguish the three Balata dei Turchi subgroups, nor the three subsources on Palmarola. Given the circumstances in which obsidian would have been obtained on these two islands during the Neolithic period, in particular by visitors rather than residents, these distinctions are not considered important.  
Figure 5: Map showing obsidian sources in Europe and the Mediterranean.  
Figure 6: Distinguishing European obsidian sources with selected trace elements. Not all geological samples tested are shown.  
Figure 8: Two prehistoric Lipari obsidian groups used for tools, using geological samples collected by the author.  
Figure 7: Separation of multiple subsources for Monte Arci (Sardinia) using a selection of geological samples collected by the author.
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 3 Implementation
### Caching Hit Method  
Based on the above settings, we propose a caching strategy based on Double DQN. A flowchart of the method is shown in Figure 4, where a detailed introduction to the algorithm is provided as follows:  
1. Initialization: The local fog satellite selects \(k\) contents with a high request frequency as the cache content, while an adjacent satellite caches other \(k\) contents outside of the local fog satellite's cached content.
2. In each time slot, observe the current state, \(s(t)\), calculate the Q value, and obtain the action, \(a(t)\), taken in that state. After executing the action, observe the new state, \(s(t+1)\), and obtain the reward, \(r(t)\).
3. Store the tuple \((s(t),a(t),r(t),s(t+1))\) as a sample in the replay buffer.
4. After samples of the buffer have reached a certain number, a random batch of them is selected for training. The TD target of all contents is calculated, and the weight parameters of the evaluation network will be updated with the gradient descent method.
5. After every certain number of steps, copy the weight parameters of the evaluation network to the target network.
## 2 Materials and Methods
### Atomic Force Microscopy  
In order to obtain quantitative information regarding the surface profile (i.e., surface topography), we employed atomic force microscopy (AFM) to image with high resolution the surface, before and after laser treatments. Every sample was carefully ultrasonicated and cleaned with distilled water and 70% ethanol. Contact mode AFM images were obtained using a Nanowizard JPK Bioafm (Bruker) and a Zeiss optical microscope (Axio Zoom V16) to find and position the AFM probe onto the area of interest. CSG11-A cantilevers (Mikro-Masch), with a nominal spring constant K = 0.1 N/m, were used for acquisitions. Images were collected in pure water over a 70 \(\times\) 70 \(\upmu\)m\({}^{2}\) area at a resolution of 256 \(\times\) 256 pixels, a constant deflection of 150 nm, and a scan rate of 0.4 Hz. We selected at least 3 images for each sample for roughness analysis. Data were processed using the Mountains(r) software Premium 9.2.9994 courtesy of Digital Surf, France, and a plane correction process was performed on all the AFM topographical images [26]. The changes in roughness parameters and the surface topography of each sample were investigated before and after the titanium surface interaction with the three lasers used. The selected roughness parameters are the most widespread among relevant amplitude parameters and were calculated following the ISO 25178 standard as reported below [27; 28].  
Roughness average:  
\[Sa=\dots\frac{1}{MN}\sum_{k=0}^{M-1}\sum_{i=0}^{N-1}\lvert z(x_{k},y_{i})\rvert\]  
where \(M\times N\) = total number of scan points.  
Root mean square:  
\[Sq=\sqrt{\frac{1}{MN}}\sum_{k=0}^{M-1}\sum_{k=0}^{N-1}\left[z(x_{k},y_{k}) \right]^{2} \tag{1}\]  
Peak-to-peak height: \(Sz=z_{max}-z_{min}\), defined as the height difference between the highest and lowest pixel in the image.  
The results of the quantitative analysis were expressed as mean value \(\pm\) standard error. Statistical analyses were performed using one-way ANOVA plus [PERSON]'s post-test and \(p\)-values \(\leq\) 0.01 were regarded as statistically significant.
## 3 The Proposed Architecture
### Data Center
#### 3.1.2 Node and Request Classification  
The entry values in the performance table are collected with different methods, e.g., the available memory size, CLWQ and CPU utilization rate, are gathered by Ganglia by actively polling resource nodes. The bandwidth is gathered by the TCP/IP network sensor in the NWS. The Information Manager periodically retrieves the performance table to calculate the performance scores, _i.e._, the fourth from last column of Table 1, for all resource nodes in the system. The score of a node \(x\), denoted by Score(_x_), is defined as:  
\[Score(x)=\sum_{k=1}^{p}SC_{\textit{x}} \tag{1}\]  
which was proposed by [PERSON] _et al_. in [21] where \(p\) (= 5) is the number of resource features collected in the performance table, _i.e._, columns 2 to 6, and SC_x_ is the score of the _k_th resource feature (\(1\leq k\leq p\)). With _Score(_s_), the nodes in the underlying grid system are classified into three performance levels (_i.e._, the third from last column of Table 1). Those with _Score()_\(\geq\) belong to level 1, those with \(16\geq Score()\geq 15\) (_Score()_\(\leq 14\)) are level 2 (level 3). Each level has its corresponding serving user requests, e.g., IPTV due to its tight time constraint belongs to level 1.  
Furthermore, at any moment each resource node can be in one of the three states: idle, busy and isolated. A node N in idle state means that it is currently executing no jobs. As stated above, when Resource Broker/Scheduler allocates a resource node to a request, the request becomes a job to be served by the node. N in its busy state means that N is executing a job. The isolated state indicates that N does not share its resources with others, and a node in isolated state cannot send requests to acquire resources. N in its isolated state may occur in two cases: when N is executing some particular jobs, e.g., processing private information, and when it is temporarily inconvenient for the user of N to share his/her own resources with others. To isolate N from the system, we can terminate N's Gmond tool [8]. Then Resource Broker will not allocate N to serve user requests.  
User requests are also classified into three types.  
(1) Type 1: Real-time requests which will become time-constrained jobs, e.g., requesting a video TV program through IPTV.  
(2) Type 2: Normal processes, e.g., ordinary applications.  
(3) Type 3: Non-real-time data transmission and data storage, e.g., FTP and email delivery.  
A type-_i_ request when sent to Resource Broker will be enqueued in a type-i global queue, \(i\) = 1, 2, 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Bandwidth** & **Score** \\ \hline
11 Mbps \(\leq\) Bandwidth & 3 \\
5 Mbps \(\leq\) Bandwidth \(<\) 11 Mbps & 2 \\ Bandwidth \(<\) 5 Mbps & 1 \\ \hline \end{tabular}
\end{table}
Table 6: The score table of bandwidths.
## 2 Innovative System Configuration, Dynamics Equations, and the Simulator
### System Dynamics Equations
#### 2.2.2 Derivation of System Dynamics Equations
The dynamics equation was formulated as: \[\dot{T}_{p5}=\frac{\dot{m}_{p3}C_{v,w}\big{(}T_{p3}-T_{p5}\big{)}-\dot{m}_{p5}C _{v,w}\big{(}T_{p5}-T_{pd}\big{)}-h_{p5}A_{p5}\big{(}T_{p5}-T_{amb}\big{)}}{ \rho_{w}V_{w,p5}C_{v,w}}\] (7). 6. Fuel cell coolant channel and body: for the fuel cell coolant channel, the coolant temperature was influenced by the input energy from pipe 4, the output energy to pipe 1 and the heat transferred from the system body to the coolant, as shown in Equation (8). For the transient fuel cell body temperature, it was effected by the generated waste heat, the heat convection to the atmosphere, and the heat transferred to the coolant channel, as formulated in Equation (9). \[\dot{T}_{w,fc}=\frac{\dot{m}_{fc}C_{v,w}\big{(}T_{p4}-T_{w,fc}\big{)}-\dot{m}_{ ffc}C_{v,w}\big{(}T_{w,fc}-T_{p1}\big{)}+h_{w,fc}A_{w,fc}\big{(}T_{b,fc}-T_{w,fc} \big{)}}{\rho_{w}V_{w}C_{v,w}}\] (8) \[\dot{T}_{b,fc}=\frac{\dot{Q}_{fc}-h_{b,fc}A_{b,fc}\big{(}T_{b,fc}-T_{w,fc} \big{)}-h_{fc}A_{b,fc}\big{(}T_{b,fc}-T_{amb}\big{)}}{\rho_{fc}V_{fc}C_{v,fc}}\] (9) where \(\dot{Q}\) was the heating power, subscript fc means fuel cell. 7. Battery coolant channel and body: for the battery coolant channel, its temperature was decided by the input energy from pipe 4 as well, the output energy to pipe 2 and the heat sent from the system body to the coolant channel, as shown in Equation (10). For the battery body temperature, it was influenced by the generated waste heat, the heat convection to the atmosphere, and the heat transferred to the coolant channel expressed in Equation (11): \[\dot{T}_{w,bat}=\frac{\dot{m}_{bat}C_{v,w}\big{(}T_{pq}-T_{w,bat}\big{)}-\dot{m }_{bat}C_{v,w}\big{(}T_{w,bat}-T_{p2}\big{)}+h_{w,bat}A_{w,bat}(T_{b,bat}-T_{w,bat })}{\rho_{w}V_{w}C_{v,w}}\] (10) \[\dot{T}_{p1}=\frac{\dot{m}_{fc}C_{v,w}\big{(}T_{w,fc}-T_{p1}\big{)}-\dot{m}_{fc }C_{v,w}\big{(}T_{p1}-T_{p3}\big{)}-h_{p1}A_{p1}\big{(}T_{p1}-T_{amb}\big{)}}{ \rho_{w}V_{w,p1}C_{v,w}}\]0 (11) where subscript bat means battery. 8. Coolant pump and the heat exchanger: for the outer loop, as valve 2 was switched on, the input energy (coolant mass flow) from pipe 3 flew to the coolant pump and the heat exchanger, where the output energy brought by the coolant was delivered to pipe 4. Note that most of the waste heat was removed by the radiator on the heat exchanger. The temperature dynamics was formulated as: \[\dot{T}_{p1}=\frac{\dot{m}_{fc}C_{v,w}\big{(}T_{w,fc}-T_{p1}\big{)}-\dot{m}_{fc }C_{v,w}\big{(}T_{p1}-T_{p3}\big{)}-h_{p1}A_{p1}\big{(}T_{p1}-T_{amb}\big{)}}{ \rho_{w}V_{w,p1}C_{v,w}}\]1 (12) where subscript \(he\) represents heat exchanger.
## 2 Materials and Methods
### Chemosensitivity Tests  
In the first stage of the study, we assessed the toxicity of doxorubicin (DOX) towards two cell lines: sensitive (MCF-7/WT) and resistant to doxorubicin (MCF-7/DOX). The cells were seeded in 96-well plates, allowed to attach for 24 h and subsequently exposed to drugs for 10 min, then cells were incubated with DMEM to assess the long-term influence of different concentrations on cell viability. The 3(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT assay) test was used for the assessment of cell viability. MTT assay was performed at 24, 72 and 120 h. Then the medium of each well was replaced with 10 \(\upmu\)L of 0.5 mg/mL MTT stock solution diluted in 90 \(\upmu\)L phosphate-buffered saline (PBS). MTT assay is a colorimetric method that estimates the rate of metabolism in viable cells. After 2 h of incubation, isopropanol with 0.04 M HCl was added (100 \(\upmu\)L/well). The absorbance was determined using a multiwell scanning spectrophotometer at 570 nm (EnSpire(r) Multimode Plate Reader, Perkin Elmer, Waltham, MA, USA). Mitochondrial function was expressed as a percentage of viable cells under treatment relative to vehicle control.  
Cells were treated with aqueous DOX solution in a concentration range of 0.017 \(\upmu\)M-170 \(\upmu\)M. For further study with EP 1.7 \(\upmu\)M, DOX concentration was selected. The cells were seeded into 96-well microculture plates (Nunc, Biokom, Warsaw) at a concentration of \(150\times 10^{3}\) cells/well incubated for 24 h, \(100\times 10^{3}\) cells/well incubated for 72 h and \(50\times 10^{3}\) cells/well incubated for 120 h at 37 \({}^{\circ}\)C 5% CO\({}_{2}\).
## 4 Application of HSOS-LO to CSLP problem
### The HSOS-LO Procedure for CSLP Problem  
The HSOS-LO procedure for CSLP problem is described as follows. First, the algorithm parameters are determined. Then, the algorithm starts by generating a random population or ecosystem _Pop_ based on the population size and the number of design variables. One solution vector represents a solution, _Pop_\(i\), with a corresponding fitness value, _f(Pop_\(i\))_. The evaluation process is then conducted by calculating the objective function for the corresponding solution vector of each solution. The decoding method for calculating the fitness value is explained in Section 4.1. Next, the population of the solution is iteratively updated through the mutualism phase, commensal phase, and parasitism phase.  
This study used the sort function to transform each of the three basic SOS update phases into discrete variables. The sort function returns the sorted element of the real-valued variable \(X\) in ascending order. The mode of conversion for the SOS mutualism, commensalism, and parasitism phases adopted in this study are as follows:  
\[(1)\ \text{Mutualism phase}:\ Pop_{i}=sort\{X_{i}+r_{1}\times(Pop_{ best}-MV\times BF_{1})\} \tag{5}\]  
\[Pop_{j}=sort\{X_{j}+r_{2}\times(Pop_{best}-MV\times BF_{2})\} \tag{6}\]  
\[(2)\ \text{Commensalism phase}:\ Pop_{i}=sort\{X_{i}+(2r_{1}-1)\times\left(Pop_{ best}-X_{j}\right)\} \tag{7}\]  
\[(3)\ \text{Parasitism phase}:\ Pop_{pv}=Fix\{x_{jmin}+r_{1}\times\left(x_{ jmax}-x_{jmin}\right)\} \tag{8}\]  
where \(r_{1},r_{2}\in[0,1]\), \(MV\) is mutual vector and \(BF_{1}\) and \(BF_{2}\) denote the benefit factors.  
The local search improvement is employed after finishing the movement in each phase to improve the performance of SOS. As explained in Section 4.2, the local search procedure involves three neighbourhood operators, namely swap, insertion, and reverse. Neighborhood local search operators play an important role in exploiting the search space around the current solution. The \(Pop_{best}\) is updated if a new best solution has been found during SOS phases or local search improvement. Finally,the procedure is repeated until it meets the maximum number of function evaluations. Then the best result in that iteration, \(Pop_{best}\), is appointed as the final result of the optimization process.