esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Generation of Axenic Flies  
_Drosophila_ was depleted of its microbial interaction partners by dechorionation in bleach as described previously [91; 92; 93]. Briefly, 300-500 flies were mated 1-3 h before the start of a dark cycle, in a mating chamber containing an oviposition plate topped with a thin lawn of freshly made, inactivated brewer's yeast paste. The morning after, flies were disposed of and eggs were harvested in a sieve. After this point, all steps were conducted in laminar flow cabinets using aseptic techniques to avoid any contamination while working with AX or gnotobiotic material. Eggs were sterilized 3 times in 0.6% bleach for a total of 5 min and washed 3 times in sterile, deionized water to remove residual bleach. Dechorionated, sterilized eggs were deposited on sterile fly food (\(\pm\)60-100 per Falcon) and allowed to develop to the adult stage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Remarks** & **Reference** \\ \hline _Bacillus subtilis_ & & \\ indicator strain & ATCC 6051 & [87] \\ _hipA7_ & MG1655 zde-264::Tn10 _hipA7_, mutant of MG21 with increased persistence & [88] \\ MG21 & MG1655 zde-264::Tn10, parental strain of _hipA7_; also called wild type in figures/text & [88] \\ _hipA7_ pTimer & _hipA7_ chemically transformed with pTimer & This study \\ MG21 pTimer & MG21 chemically transformed with pTimer & This study \\ \multirow{2}{*}{X4} & \multirow{2}{*}{A BW25513 related strain that contains a _tsr-venues_ tag in the _lacZ_ gene along with a} & [89] \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\ \end{tabular}
\end{table}
Table 1: Bacterial strains used in this study.  
During development, the AX state was confirmed in various ways. First, fly food was inspected visually. In case growth of yeast or mold was detected, the vial was disposed of. AX larvae also churn up the 10/10 food differently in comparison with conventional (CONV) larvae. They do not penetrate as deep into the food and preferentially churn at the side of vials. Additionally, the consumed food is darker and more humid than is the case for CONV larvae. Together, this creates a distinct, black layer of consumed food in vials with AX _D. melanogaster._ Vials without such layering were closely monitored. In the pupal stage, vials were checked for their germ-free state by sampling the 10/10 food with a sterile probe (e.g., toothpick, micropipette tip, pestles). Similarly, homogenates of freshly enclosed, female flies were sampled. Both samples were streaked on LB and mMRS agar and incubated at 25 \({}^{\circ}\)C (up to 1 week). Only when all parameters were positively evaluated, batches of flies were used and considered AX.
## 2 System Architecture Documentation  
Compared with PAM-4, DB PAM-4 changes from 4 level to 7 level. When the output swing is the same, the eye height becomes half, the eye width is reduced, the eye diagram is compressed, and the CDR is increased judgment difficulty. The recovered quarter-speed clock and 3-bit data of the CDR under DB PAM-4 modulation are shown in the Figure 1. The recovered data is equivalent to the rightmost waveform.  
The ADC + DSP architecture used by CDR as shown in Figure 2, and the working principle is as follows. The input data is an equalized 112 Gbps DB PAM-4 and sampled by a 56 GS/s 8-bit ADC, driven by the PI. The 64 \(\times\) 8 bit data obtained by sampling and the previous group of data is stored in 2 \(\times\) 8 bit data, and are converted into 66 \(\times\) 8 bit data with a single line rate of 875 Mbps through the combination of the first in first out (FIFO) module. MM PD takes 3 UI data as the minimum phase-detection unit, completes parallel detection with 66 \(\times\) 8 bit data, and obtains 64 sets of phase-detection information. The 16 bit temperature code is obtained through DSP processing, and the PI is controlled to perform a clock phase shift so that the sampling clock falls near the optimal sampling point. The best DB PAM-4 signal is obtained, and the phase-locking process of the CDR loop is completed.  
The ADC is an 8-bit precision digital-to-analog converter driven by a 4-phase clock for sampling to obtain 64 channels of parallel data. The FIFO combines data information. The MM PD consists of the same 64 sets of MM phase-detection modu  
Figure 1: Schematic diagram of DB PAM-4 CDR principle.  
Figure 2: Architecture of DB PAM-4 CDR based on MM PD.  
the voter, digital filter, phase integrator, and bandwidth controller. The PI is controlled by a 16-bit temperature code, works on a four-phase 14 GHz clock, equivalent to a 56 GHz clock, and generates a 4-phase sampling clock through vector synthesis.  
The BBPD has a unique locking point, while the locking point of MMPD is a phase region, and the unique locking point has little phase redundancy. The MMPD avoids the inherent phase jitter problem of BBPD due to small phase error. In order to improve the insufficient CDR phase-detection density at high loop speed, the MMPD is redesigned for DB PAM-4. With reference to the CDR loop of DB PAM-4 based on BBPD [16], this MMPD is introduced later.
## 1 Introduction
Furthermore, using EHR records, ML methods have been applied to augment risk prediction for glaucoma risk factors, cataract surgery complications, and diabetic retinopathy [23; 24; 25; 26]. Studies have examined the relationship between risk factors and postoperative visual acuity [27; 28]. [PERSON] et al. have reported automated glaucoma detection using fundus images [29]. Additionally, a systematic review article has reported various ML methods used for risk assessment, progression prediction, and the diagnosis of glaucoma using image techniques [26]. Image processing methods can report lesions that have already occurred at the molecular level. In a recent study, [PERSON] et al. applied deep learning methods and demonstrated about 90% accuracy in detecting glaucoma from fundus images captured using a smartphone and handheld ophthalmoscope [30]. In contrast, the use of EHR records predicts the onset of glaucoma based on the history of comorbidities. More recently, [PERSON] et al. reported machine learning-based predictive modeling of glaucoma surgical intervention using EHR records and demonstrated that the prediction of the need for surgical intervention for glaucoma management six months in advance has a certain degree of success [31]. Very few studies have been conducted to investigate the early predictive model for glaucoma onset using EHR data obtained from multiple clinical centers. In the future, a predictive model must focus on a large cohort of a study population representing heterogeneity and diversity, thus bringing clinical value to ophthalmologists.
# Optimization of a Coherent Dual-Beam Array Feed Network for Aperiodic Concentric Ring Antennas
Recently, early work on non-uniform apertures working together with coherent feeding systems in circular antenna arrays was addressed in [20] and a mono-beam investigation in concentric rings was analyzed in [21]. This paper presents a dual-beam analysis of an antenna system's performance that exploits a non-uniform aperture with concentric ring antennas, employing a minimalistic architecture based on coherently radiating periodic structures with multi-beam steering capabilities. In this approach, the antenna system is optimized in such a way that the radii on the antenna array are improved by a well-known evolutionary optimizer. Furthermore, a benchmarking study with a periodic aperture and the same feeding system with contrasting directivity (\(D\)) and sidelobe level (SLL) is implemented and validated by the antenna synthesis' numerical experiments. The remainder of this paper is structured as follows: the mathematical expressions of a non-uniform layout on concentric ring antennas, the general theory behind coherent periodic structures acting as a beamforming network, and the methodology to optimize the antenna system are presented in Section 2. Section 3 addresses the numerical examples set out for the two-beam feeding system configuration and the parameters used in the metaheuristic optimizer, and at the end, the numerical examples for the dual-beam array feed are assessed and reviewed. Some concluding remarks are presented in Section 4.
## 5 Conclusions  
With the present study, we definitely proved that the enrichment of a defect site with CM, EVs and PEI-EVs substantially modifies, often accelerating, bone remodeling kinetics and the related mineralization process during defect healing. Moreover, different biomaterials (COL or PLA) in combination with stem cells of different origin (hPDLSCs or hGMSCs) and their own CM, EVs and PEI-EVs products were shown to exhibit different mineralization kinetics. Indeed, after six weeks from grafting in a calvarial defect, while increased mineralization was found in COL/hPDLSCs/PEI-EVs compared to the other COL-based groups, PLA/hGMSCs/PEI-EVs showed the presence of lower mineralization levels than some of the other PLA-based groups. However, PLA/hGMSCs/PEI-EVs had also a wider density distribution variance than in the control sites, suggesting the need of longer times for completing the mineralization process. Conversely, PLA/hGMSCs/CM samples were shown to reach high levels of mineralization, confirming better osteogenic capacity than constructs without the conditioned medium, as shown in previous studies.  
conceptualization, A.G. and O.T.; data curation, E.M., A.F. and O.T.; formal analysis, A.G.; funding acquisition, A.P. and O.T.; investigation, A.G., G.S., G.T. and F.D.; methodology, A.G., G.S. and G.T.; project administration, A.P.; resources, E.M., A.F. and F.D.; software, A.G. and G.S.; supervision, E.M., A.P. and O.T.; validation, A.G., A.F., F.D. and O.T.; writing--original draft, A.G. and O.T.; writing--review and editing, ALL. All authors have read and agreed to the published version of the manuscript.  
The ELETTRA synchrotron experiments were funded by the Program \"Support to the Italian Users of ELETTRA\". This research received no external funding.  
Authors thank the ELETTRA Synchrotron Facility for the allocated beamtime at the SYRMEP beamline. Authors also thank [PERSON] for technical support during imaging data elaboration.  
The authors declare no conflict of interest.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 2 System Model
### Transmitted Signal  
As shown in Figure 2, the transmitted data require a special frame structure for channel equalization. The data bit sequence \(\mathbf{a}=\{a_{-N_{t}},\ldots,a_{0},\ldots,a_{N_{m}-1},\ldots,a_{N_{m}-N_{t}-1}, \ldots,a_{N_{c}-1}\}\) is \(N=N_{m}+N_{t}+2\times N_{c}\) long, where \(N_{m}\) is the transmitted information length, \(N_{t}\) is the tail symbol length, and \(N_{c}\) is the CP length. CP is used to construct the cyclic matrix for FDE channel estimation. The tail symbol ensures that the phase continuity of the CPM signal is not destroyed after adding CP. The channel estimation algorithm used in this paper is the orthogonal matching pursuit (OMP) algorithm, which is adapted for CPM signals.  
Figure 1: Block diagram of CPM UWA communication system.  
According to Laurent decomposition [13], the amplitude-normalized CPM signal is expressed as follows:  
\[u(t)=\exp(j\phi(t;\mathbf{a})), \tag{1}\]  
where the symbols belong to the \(M\)-ary alphabet \(a_{n}\in\{\pm 1,\pm 3,\ldots,\pm(M-1)\}\). The excess phase is represented as [13]  
\[\phi(t;\mathbf{a})=2\pi h\sum_{n=-N_{c}}^{N-N_{c}-1}a_{n}q(t-nT), \tag{2}\]  
where \(h\) is the modulation index, and \(T\) is the symbol period. The phase shaping function \(q(t)\) is formulated as follows:  
\[q(t)=\int_{0}^{t}g(\tau)d\tau, \tag{3}\]  
where \(g(\tau)\) is the frequency-shaping pulse of length \(LT\), and \(L\) is defined as the memory length of the CPM signal after Laurent decomposition.  
The CPM signal with Laurent decomposition is expressed as the superposition of \(2^{L-1}\) pulse amplitude modulation signals in the following equation [13]:  
\[u(t)=\sum_{n=-N_{c}}^{N-N_{c}-1}\sum_{p=0}^{2^{L}-1}\exp(j\pi h\alpha_{n,p})c_ {p}(t-nT), \tag{4}\]  
here  
\[c_{p}(t)=c(t)\prod_{n=1}^{L-1}c(t+(n+L\beta_{n,p})T) \tag{5}\]  
and  
\[\alpha_{n,p}=\sum_{m=0}^{n}a_{m}-\sum_{m=1}^{L-1}a_{n-m}\beta_{n,p}. \tag{6}\]  
\(\beta\) is the binary representation of \(p\). Then,  
\[c(t)=\begin{cases}\frac{\sin(2\pi h\alpha(t))}{\sin\pi h},&0\leq t\leq LT\\ \frac{\sin(\pi h-2\pi h\alpha(t-LT))}{\sin\pi h},&LT\leq t\leq 2 LT\\ 0,&\text{else}\end{cases}. \tag{7}\]
## 3 Test Results and Analysis
### Feature Analysis  
As shown in Figure 5, there is a quadratic function relationship between the strength of the mixture and the dosage of AOD slag. The correlation coefficient (r\({}^{2}\)) reaches 0.90; however, the strength of the mixture with AOD slag is increased by 94.9%. The mechanical properties of the mixture with AOD slag and fly ash are better than those of the single-doped mixture.  
The functional relationship between the elastic modulus (\(E\)) calculated using JTG D50-2017 [40] and R was established, and the results are shown in Figure 6. There is a quadratic function relationship between the elastic modulus and the dosage, and the correlation coefficient r\({}^{2}\) = 0.93. The variation in elastic modulus is the same as that of peak stress, which increases first, and then decreases. The maximum increase in the elastic modulus of AOD slag is 43.1%.
## 6 Effectiveness of FttC-Backhaul  
In this section we now briefly discuss on the transport capacity provided by present and the evolved FttC network for future 5G services. We do not focus on specific 5G services but our considerations are confined to the available link capacity for fronthaul as well as on the possibility of using the CPRI or the FAPI interfaces.  
From coverage results provided in the previous Section, we can observe that due to the reduced available bandwidth for the case of US direction, in VDSL2 transmissions, it is not possible to use CPRI interface to transport signal samples in the US direction when vectoring is not implemented. Even in the vectoring case, if the number of available pairs (bonded or using phantom) per node is below 8, CPRI cannot be supported. Thus, for transmissions in the US direction on FttC-fronthaul, FAPI and then MAC/PHY splitting seems to be the only viable choice. However, for the DS direction we have evidenced the possibility to support CPRI rates even for relevant percentage of wireless 5G nodes having bit rates about or above 1 Gbit/s in the DS direction. For these nodes the mixed CPRI/FAPI mode discussed in the previous Section could be applied. When considering only the MAC/PHY protocol split option, the achievable fronthaul bit rates for the DS case and the US case give an indication of the gross maximum radio link capacity that can be supported by the single radio node as well as by the radio interface(s) implemented/programmed in the node. To evaluate the net traffic data rate that can be supported on the fronthaul we need to remove the signaling information of the FAPI interface. In general, we observe that vectoring is mandatory for supporting 5G envisaged bit rates. Considering for example results in Figure 5 corresponding to group 1, the target bit rate of 1 Gbit/s in the DS case for each node can be achieved in the 80% of the area. Assuming a uniform distribution of nodes in 1 km\({}^{2}\) area and considering the density of the nodes for group 1 in Table 1, the 5G node can provide about 1 Gbit/s on a circular area with radius of about 60 m and this is true for the 80% of radio nodes. In the non-vectoring case this percentage falls down to 3% when 6 copper pairs are assigned to the node. In the non-vectoring case the only way to improve capacity is to increase the number of copper pairs assigned to the radio node. Similar considerations apply to the US case. In this case the sum bit rate corresponding to a coverage of 80% is about 400 Mbit/s. The two considered evolution strategies for the FttC network are helpful to improve the coverage in the (residual) service area still served by FttC. When adopting the second strategy, coverage at 1 Gbit/s can be incremented between 90% and 95% (see Figure 5) depending on the selected \(d_{OE}\) which in turns determines the cost for the evolution of the FttC network. In this case, \(d_{OE}=200\) m provides the best performance for 1 Gbit/s at reduced costs with respect to the cases of \(d_{OE}=400\) m and \(d_{OE}=600\) m. Similar considerations apply for cities in group 2 and group 3. For brevity, they are omitted.
# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 3 Current Status of Supply Chain Network Modeling Research  
The supply chain network is a critical component of modern economic systems, encompassing multiple nodes, such as suppliers, manufacturers, warehouses, distribution centers, and retailers. Given the escalating complexity and globalization of supply chain management, modeling techniques for supply chain networks are becoming increasingly important for optimizing resource allocation, reducing operational costs, and enhancing supply chain efficiency. Current modeling approaches for supply chain networks can be broadly categorized into two main types:  
* Complex Network-Based Modeling: This approach adopts a top-down perspective, focusing on the static structure and relationships within the supply chain network;
* Agent-Based Modeling: This method takes a bottom-up approach, emphasizing the dynamic behaviors and decision-making processes within the supply chain [46; 47; 48; 49].  
In the study of supply chain resilience, network modeling methods offer numerous advantages over empirical studies. Network modeling provides a macro perspective, allowing analysis of the supply chain network's topological structure and its impact on system performance. This approach helps identify critical nodes and connection patterns, enabling the anticipation of potential vulnerabilities within the supply chain [50]. Unlike static empirical studies, network modeling dynamically simulates the evolution of networks under varying conditions, accounting for the temporal evolution of node connections and the integration of new nodes. By applying concepts from graph theory and network science, this approach facilitates a higher level of abstraction in understanding the internal relationships and performance dynamics of supply chains.  
Moreover, network modeling supports system optimization and decision-making by identifying which node failures might lead to the collapse of the entire supply chain, providing scientific guidance for designing resilience strategies [51]. With its ability to offer a macro-level perspective, dynamic simulation, complexity analysis, and system optimization support, network modeling comprehensively and profoundly reveals the resilience of supply chain systems from multiple angles.
## 3 Antibiotics Having Direct Interactions with tRNAs  
### Alteration of tRNA Conformation  
tRNA structures can be stabilized by metal ions, such as Mg\({}^{2+}\), and positively charged polyamines, such as spermidine, which bind non-specifically to nucleic acids. Aminoglycos antibiotics mimic polyamines due to the high number of positive charges and this allows them to bind to the negatively charged backbone of RNA [45, 46]. In addition, the flexible structure of these molecules allows them to penetrate the binding pockets and make specific contacts with RNA. One such example is that of tobramycin (Figure 2), which binds to the A-site on the 16S rRNA and thus interferes with the conformational change that occurs at the A-site upon the correct tRNA-mRNA codon interaction [47]. Importantly, tobramycin has also been shown to interact with yeast tRNA Asp and fluorescence anisotropy experiments demonstrated this binding destabilizes the native conformation of the tRNA. Tobramycin binding also leads to a substantial 30-fold decrease in the catalytic affinity (or \(K_{\rm m}\)) of aspartyl-tRNA synthetase (AspRS) for its tRNA Asp substrate resulting in inhibition of the aminoacylation reaction [48]. Detailed studies using single nucleotide resolution SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) chemistry have further elucidated how specifically tobramycin binding results in the loss of interactions between the T and D loops and eventually the structure of the D-stem [47, 49]. Another aminoglycososide Neomycin B (Figure 2) binds to the upper part of the anticodon stem in _E. coli_ tRNA Phe at positions G20, A44 and G45 [50]. It obstructs the region of the tRNA involved in the aminoacylation reaction thus inhibiting the phenyl-tRNA synthetase activity.  
Another compound, pentamidine (Figure 2) (an aromatic diamine) has been used in the treatment of protozoan infections and binds to tRNAs non-specifically at higher concentrations than those of other antibiotics [51]. Rather than employing electrostatic interactions to bind to tRNA, pentamidine forms hydrophobic interactions and inserts itself into the helical regions of tRNA. This can disrupt the tRNA secondary structure, mask the anticodon loop, and has been shown to lead to the inhibition of aminoacylation in the case of tRNA Leu [52].
## 2 Method
### Optimization Framework
#### 2.3.2 Initialization  
The setting of initial value \(z_{0}\) is also critical to our reconstruction. An initial guess close enough to the optimal solution can avoid image artifacts and speed up the phase retrieval process. Previous research [35; 38] demonstrated that various spectral initializers were ineffective for Fourier measurements, although were successful for Gaussian measurements and coded diffraction measurements (CDP) [39]. [PERSON] et al. used the captured data under normal incident light (center image) as the initial value for their Fourier ptychography setup [40; 41]. A random matrix has also been employed previously as the coarse initialization [23]. In this work, the setting of initial value was similar to the research [35; 42], which is more reliable for Fourier measurements. The HIO is first run 50 times using random initialization to obtain 50 estimates of the original data. We then choose the HIO estimate value with the lowest residual as an initialization for the HIO algorithm. The HIO is run a further 50 times, and the result is used to initialize the proposed reconstruction algorithm. The whole algorithm for ptychographic imaging correlography is described in Algorithm 1.  
The convergence of the proposed algorithm with different initializations is validated on a series of images. The comparison methods include random initialization, spectral methods, and our initialization (HIO results). Because of the limited space, here we only show the results of four test images, including letters, numbers, and simple geometric figures. Figure 6 shows the original test images. The structural similarity index (SSIM) is used to quantitatively evaluate the reconstruction quality. Table 1 is the SSIM and iterations of the proposed algorithm with different initialization methods. The best reconstruction result for each test image is bolded in the Table 1. It is clear that the initialization method has a great impact on the reconstruction results. The spectral method mostly fails for the proposed imaging model, although occasionally a good result is obtained. The random matrix and the proposed initialization produce similar reconstructions, but the proposed initial method requires fewer iterations. Since the random initial value is far away from the truth data, it is difficult for the random method to find the steepest descent direction. The proposed initial method converges much faster than other methods. Actually, it can achieve relatively satisfactory results in about 20 iterations.