esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Global Genome Repair (GGR)
### UvrA Mediates a Match between Damaged DNA and UvrB  
UvrA is not only the first responder to the presence of damaged DNA but also the molecular matchmaker that enables delivery of UvrB to the precise site of the lesion. Molecular matchmakers are proposed to be special catalysts that apply ATP binding and hydrolysis on a pair of molecules to enable conformations competent to bind each other; absent the matchmaker, interactions between this pair of molecules is not favorable [112]. In this scheme, UvrA make a match between UvrB and damaged DNA. The process of discrimination and subsequent tight binding of damaged DNA represents one half of the match, while binding of UvrB represents the second half. The order of binding to the matchmaker is not constrained; either damaged DNA or UvrB may be bound by UvrA first. Atomic structures of both halves of the match: UvrA bound to damaged DNA [19] and UvrA bound to UvrB [21] have been described. The configuration of the UvrA\({}_{2}\)B\({}_{2}\)-complex on DNA, however, remains to be visualized.  
The structure of the UvrA\({}_{2}\)B\({}_{2}\) complex [21] unexpectedly revealed a dimeric arrangement of UvrA that is distinct from that observed in every structure of isolated UvrA [19; 37; 44; 45; 46]. An important consequence of this distinct configuration is the alteration of the UvrA surface that binds DNA from a relatively open shape (termed the 'open tray' dimer) compatible with the widely varying conformations of damaged DNA to a 'closed groove' shape that appears solely compatible with native DNA (Figure 3, left).  
Damage discrimination was suggested to feature detection of shape differences between native and lesion-containing DNA. UvrA was also found to bind to two UvrB molecules at the periphery of the ensemble, far from the expected position of the lesion. Recent analysis of the UvrA\({}_{2}\)B\({}_{2}\) complex using negative stain electron microscopy at 25 A resolution confirmed the UvrB-UvrA-UvrA-UvrB disposition of subunits. Computational fitting studies with existing UvrA and UvrB models led the authors to conclude that UvrA, within the complex, adopted the 'open tray' dimeric form [20] (Figure 3, right). This is an intriguing finding that lends support to the idea that UvrA may interconvert between two dimeric states during NER but also one that will require higher resolution studies to more firmly establish. Additional structural studies will be required to further reveal conformation switching by UvrA within its complex with UvrB and whether the DNA is wrapped around UvrB [113; 114] within the complex.
###### Abstract  
The strategic planning of open-pit mining projects is highly influenced by geological variability, economic fluctuations, and operational uncertainties. Traditional deterministic optimization models often fail to account for these uncertainties, leading to potentially misleading economic evaluations. This paper proposes a hybrid model that integrates deterministic and stochastic optimization methods, following the principles of the ISO 31000 risk management framework, to comprehensively quantify uncertainty through key parameters affecting strategic mine planning. Monte Carlo simulations were applied to generate probability distributions of input parameters, including metal prices, mining and processing costs, and ore grade variability, allowing for a more robust financial assessment. The results demonstrate that while the deterministic approach estimates an NPV of USD 130.8 million, the stochastic model yields an average NPV of USD 155.5 million with a standard deviation of USD 76.5 million, highlighting the significant variability in financial outcomes. Risk assessment using Value at Risk (VaR) and Conditional Value at Risk (CVaR) further quantifies potential financial losses, revealing a 3% probability of project unprofitability. The developed methodology provides a structured approach to integrating uncertainty into mine planning, enabling more reliable economic evaluations and improving decision-making in strategic mining operations.  
hybrid risk management; pit limit optimization; Monte Carlo simulations; decision support in mining; uncertainty modeling; economic risk analysis; economic value of the block
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 4 Result  
### Overall DEA  
For calculating the PSEI, it is assumed that the objective of Productivity-Safety Effectiveness is more focused on effectively decreasing accident rates. Therefore, we adopt an input-oriented model, applying the DEA. It is noted that by using the DEA, the integrated value is presented regarding six indexes, divided into three inputs and three outputs. The DEA is conducted with average values of input and output variables from 2015 to 2018, as shown in Table 2. We can prioritize the industries based on PSEI.  
As summarized in Table 5, the average score of PSEI in manufacturing industries is identified and the rank of PSEI is ordered in terms of CCR and BCC. Except for the non-metallic mineral industry, most industries have a consistent pattern in both CCR and BCC. The result shows that the industry that has the highest level of PSEI is _precision instruments_, followed by _basic metal_ and _electrical equipment_ for both CCR and BCC models. In contrast, the _wood_ industry has the lowest level of PSEI for CCR, followed by _non-metallic mineral_ and _metal fabrication_, but for BCC, it is followed by _metal fabrication_ and _rubber and plastic products_. The non-metallic mineral industry has a big difference between CCR (14 th) and BCC (6 th) models, showing a rapid increase in output compared to the other industries.  
The weights of each input and output, which present the information on the optimal state, are summarized in Table 6. The weight value of the variable close to 100 indicates that this variable is more critical to increasing the PSEI. For instance, for the _food and beverage_ industry, SR and PC have the highest value among safety performance inputs and productivity outputs, respectively. It means that this industry has better SR compared to other input variables and better PC compared to other output variables. In overall industries, the SR and the OR are outperformed among other inputs and outputs in general,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measures** & **Explanation** \\ \hline Efficiency & When the efficiency of DMU is higher, the lower the safety performance values, the higher the productivity values. \\  & When the weight of one variable of inputs (outputs) is higher than that of other variables of inputs (outputs) in the industry \(i\), it means that this variable has relatively more influence on increasing PSEI \\  & of the industry \(i\) compared to other industries. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Meaning of DEA measures for PSEI.  
but some industries have more effective variables for increasing PSEI such as IR (_coal and petroleum products, basic metal_), FR (_metal fabrication, transportation equipment_), LP (_general machinery and equipment, precision instrument_, _transportation equipment_), and PC (_food and beverage, automobile_). These results imply that the industry should focus on improving inputs and outputs that have lower weight values for PSEI. The benchmarking will be illustrated in the Section 5.1.2.
## 3 Preliminaries
### Energy Model  
The standard first-order radio model, as described in [30; 31], is the foundation for this article's estimation of the nodes' energy consumption. Equation (1) gives the transmitting node's energy usage, which is referred to as \(E_{TX}(l,d)\). This computation is useful when a node sends \(l\) bits of data to a receiving node that is located \(d\) away. Furthermore, Equation (2) establishes the receiving node's energy consumption, which is represented by the symbol \(E_{RX}(l)\).  
\[E_{TX}(l,d)=\begin{cases}IE_{ellec}+le_{fs}d^{2},d\leq d_{0}\\ lE_{ellec}+le_{mp}d^{4},d>d_{0}\end{cases} \tag{1}\]  
\[E_{RX}(l)=lE_{ellec} \tag{2}\]  
Here, \(\epsilon_{fs}\) and \(\epsilon_{mp}\), respectively, represent the energy consumption coefficients of the amplifier circuit for the free-space fading model and the multipath fading model. Furthermore, the transmission and receiving circuits' energy consumption coefficient is denoted by \(E_{elec}\). Equation (3) establishes the threshold, which is represented as \(d_{0}\) and acts as the border between the two attenuation models. The transmission uses the free-space fading model if the distance d is less than \(d_{0}\), which results in an energy consumption of  
Figure 1: Cluster-based WSN with CHs.  
\(\epsilon_{fg}d^{2}\) for the amplifier circuit. On the other hand, the multipath fading model is applied where \(d>d_{0}\), resulting in an energy consumption of \(\epsilon_{mp}d^{4}\) for the amplifier circuit.  
\[d_{0}=\sqrt{\frac{\epsilon_{fs}}{\epsilon_{mp}}} \tag{3}\]  
Data are received by the CH from the cluster, and it uses some energy to transfer the aggregated data to the BS. Consequently, it becomes imperative for the CH to possess a sufficient reservoir of energy to effectively relay data to the BS. The energy level of the CH is represented by the letter \(E_{CH}\),  
\[E_{CH}=l\cdot\frac{n}{k}\cdot(E_{elec}+E_{DA})+l\cdot\epsilon_{mp}\cdot d_{toBS}^ {4} \tag{4}\]  
where \(E_{elec}\) is the energy used by the transmitter. \(E_{DA}\) is the energy necessary for data aggregating, and a data packet is represented by the symbol \(l\). When transmitting over long distances to a base station, as shown by \(d_{toBS}^{4}\), the transmitter amplifier's energy usage is represented by \(\epsilon_{mp}\). The energy consumption of non-cluster heads is shown by \(E_{NCH}\).  
\[E_{NCH}=l\cdot E_{elec}+l\cdot\epsilon_{fs}\cdot d_{toCH}^{4} \tag{5}\]  
Within a specific context, the symbol \(l\) denotes a data packet, \(E_{elec}\) denotes the transmitter's energy consumption, \(\epsilon_{fs}\) denotes a transmitter amplifier in a free condition, \(d_{toBS}^{2}\) denotes the CH's short distance, and \(d_{toBS}^{4}\) denotes the BS's long distance.
## 4 Results and Discussion  
In the numerical calculations the following parameters were used: permittivity of substrate \(\varepsilon_{1}=3.5\), PyC film conductivity \(\sigma=4.5\times 10^{14}\) s\({}^{-1}\) (\(5.0\times 10^{4}\) S/m), lateral waveguide dimensions are \(a=7.2\) mm, \(b=3.4\) mm. The measured and calculated reflectance, transmittance and absorbance are presented in Figure 3 for films being 75 nm and 110 nm thick and in Table 2 for thinner films (9 nm, 20 nm and 30 nm). Comparison of experimental data with results of calculations in the frequency range 26-37 GHz shows the metal-like behavior of PyC. One can notice that in the \"PyC + quartz\" geometry, 110 nm thick PyC film is capable to absorb not more than 40% of incident power, whereas in the \"quartz + PyC\" geometry, the absorption losses are as high as 50%-55%. Thus the latter geometry can be employed to improve sensitivity of the detectors of MW radiation. Figure 3: Calculated (solid lines) and measured (circles, squares and triangles) reflectance, transmittance and absorbance spectra of 110 nm (**a1**, **a2**) and 75 nm (**b1**, **b2**) thick films in the \"PyC + quartz\" (**a1**, **b1**) and “quartz + PyC” (**a2**, **b2**) geometries. The performed waveguide measurements with 110 nm thick film revealed (see Figure 3) that increase of frequency from 26 GHz to 37 GHz results in increasing of transmittance from 15% to 25% which is accompanied by decreasing of reflectivity. Similarly in \"quartz + PyC\" geometry, 30 nm thick PyC film (see Table 2) reflects 20% of EM energy at 26 GHz and only 13% at 36 GHz. The observed frequency dispersion of transmittance and reflectance is due to the dependence of the effective incidence angle on frequency, \(\cos\theta_{off}=cK_{0}/\omega=\sqrt{1-\left(c\pi/\omega a\right)^{2}}\). This is confirmed by numerical simulation of EM wave interaction with a free standing PyC film in waveguide (see Figure 4a). It is worth noting, however, that in free space, numerical simulation of reflectance and transmittance at normal incidence for PyC film deposited on quartz substrate revealed almost no frequency dependence (see Figure 4b). This is because in contrast to the waveguide propagation, the characteristic interaction length in free space \(l_{\sigma}=c\left(1+\sqrt{\varepsilon_{quartz}}\right)/4\pi\sigma\) is frequency independent. Therefore in free space, the frequency dispersion can only originate from the interference caused by multiple reflections on the substrate boundaries. Since in our experiment, the substrate thickness (0.5 mm) is much smaller than the wavelength (about 1 cm), this interference can hardly influence reflectance, absorbance and transmittance that show a weak frequency dependence (Figure 4b). Along with conductivity and thickness, roughness can also influence the EM response of the thin film. It could be the case when both thickness and roughness are compatible with the skin depth. However one can see from Table 1 that in our experiment, both film thickness and moreover average roughness (1.25 nm for the thickest film) were much smaller than the skin depth of PyC in the K\({}_{a}\)-band.
## 4 Beam Transport and Delivery Considerations
Other work by [PERSON] _et al._[76] compares three novel approaches (aimed at cost effectiveness) for particle radiotherapy: dielectric wall accelerator based therapy systems, superconducting accelerator therapy systems and laser proton accelerator therapy systems. Other new developments in the field of laser driven proton therapy have been discussed recently by [PERSON] _et al._[77]. The work concentrates on collection and focusing of laser produced particles using simulated results in the high intensity Radiation Pressure Acceleration (RPA) regime. They present a scaling law for the \"chromatic emittance\" of the collector and apply it to the particle energy and angular spectra of the simulation output. The collector in this case is a solenoid magnetic lens. For a 10 Hz laser system, the authors find that particle collection by a solenoid magnet satisfactorily provides the requirements of intensity and emittance as needed for depth scanning irradiation in a clinical situation. The authors conclude that without ion focusing in the laser case such clinical beam intensities cannot be provided. The importance of this paper is that it addresses the RPA mechanism which is not yet commonly studied but does provide insight for the near future. A more detailed subsequent paper of RPA simulations was presented by [PERSON] _et al._[78]. This paper concentrates on how a beam of about 10\({}^{10}\) protons per pulse can be used to irradiate tumors. A controlled \(>\)2 Gy dose on a small tumor can be achieved by a small number of laser pulses in seconds. Spot scanning for conformal tumor irradiation requires laser pulse repetition rates exceeding 10 Hz. The conclusions of this simulation show that a combination of an RPA model, solenoid, energy selection aperture and scatterer can result in a well characterized beam suitable for therapy applications. Finally a number of authors (e.g., [PERSON] and [PERSON][79]) have cautioned that much research still needs to be done in various areas in order to implement laser driven therapy in a clinical environment and that this can take many years. They base this on the need for increasing proton energy, reducing the broad energy spread (spectral width) of protons, possible limits in the pulse repetition rate and the number of accelerated particles per pulse. [PERSON], [PERSON] and [PERSON][80] do however point out that the short pulse nature of the laser processes may be ideal tools for motion adaption during radiotherapy.
## 3 Methodology
### Model Training and Optimization  
Training involves minimizing the autoencoder to minimize the reconstruction error and learn effective representations of normal drilling conditions. Hyperparameters like the learning rate, batch size, and layer architecture were optimized for improved performance. The Adam optimizer improved the convergence speed and avoided overfitting. Figure 3 illustrates the data flow through the LSTM autoencoder architecture, emphasizing the encoding, compression, and reconstruction process necessary for anomaly detection.  
To distinguish between normal and anomalous drilling patterns, a fixed threshold was applied to the reconstruction error. This threshold, determined empirically from the validation set distribution, was set to \(T\) = 0.3. It effectively separates normal data from stuck pipe anomalies, as will be confirmed in Section 4. Although some marginal overlap remains, the threshold strikes a practical balance between precision and recall to ensure reliable detection while minimizing false alarms.
## Materials and Methods
### Gravity Driven Membrane Filtration System  
Figure 1 shows the schematic of the submerged GDM filtration system which was operated in a dead-end filtration in the intermittent operation mode. The GDM reactor was equipped with vertically installed membrane modules. The feed tank was initially filled with feed water at the height of 97.5 cm and the filtration carried out until the water level decreased to 32.5 cm. The remaining water was discarded and the tank was filled again to start the next cycle. This intermittent mode was used to simulate a practical GDM filtration operation [24; 25; 26]. The hydraulic pressure varied from 0.075 bar to 0.01 bar during the filtration (i.e., one cycle), calculated from the height of feed in the tank at 97.5 cm and 32.5 cm, respectively. The permeate line was located at a height of 22.5 cm. The GDM reactor (97.5 cm \(\times\) 31 cm \(\times\) 31 cm, 93 L of feed water volume) was operated without any back flushing and membrane cleaning for 99 cycles for PES-UF (or 354 days) and 116 cycles for PVDF-UF and PTFE-MF (or 416 days). Each cycle usually took 3-4 days. Note that this long-term experiment was interrupted a few times due to the lack of feed water supplement. The feed water tank was cleaned twice a month to minimize sedimentation and accumulation of particles on the tank bottom.  
Lake water, which was taken from the influent to the drinking water treatment plant located in Gwangju (Korea), was used as the feed water. The feed water characteristics during the GDM operation were; dissolved organic carbon (DOC) of 2-4 mgC/L, dissolved oxygen (DO) of 6-12 mg/L, turbidity of 1.1-4.4 NTU, pH of 6.5-8.8, and temperature of 20 \(\pm\) 2 \({}^{\circ}\)C. The permeate flux (L/m\({}^{2}\)/h; LMH) was calculated by measuring the volume of permeate water collected in volumetric flasks within a certain filtration time (5-7800 s) and dividing by the effective membrane area (520 cm\({}^{2}\)). The permeate flux was measured every operation day according to the change in the water level (from 0.075 bar to 0.01 bar). The mean permeability (LMH/bar) was used as an average value of permeate flux divided by hydraulic pressure during one cycle.
# Extended Field Interactions in [PERSON]'s Equation Revision
## 3 Potential Applications
### Generation of Electron Beams Carrying Orbital Angular Momentum
However, it is important to note that these equations are not necessarily an improvement over existing methods for describing OAM in electromagnetic waves. Instead, they offer an alternative framework for understanding and analyzing electron beams carrying OAM, especially in the context of electron holography and other experimental techniques, but they can be useful for certain applications and may help researchers gain new insights into the behavior of electron beams with OAM. For instance, in electron microscopy [69], these equations can potentially lead to an improvement in the analysis of the electron beam's interaction with samples: when an electron beam with OAM interacts with a sample, the helical phase front can be influenced by the sample's structure and composition, leading to a change in the OAM distribution [74]. Utilizing the derived equations to describe the helical phase front and the divergence of the axial vector enriches the understanding of how electron beams interact with samples. This insight aids in unraveling the impact of the sample's structure on the distribution of the electron beam's orbital angular momentum (OAM), facilitating more precise interpretations of electron microscopy images. Furthermore, these equations could inform the design of innovative electron holography experiments or techniques that leverage electron beams' OAM properties. Manipulating an electron beam's OAM could pioneer new approaches to examine specific structural characteristics of a sample or enhance the resolution of electron microscopy [75; 76; 77]. For instance, consider an application in which researchers are working with twisted light beams (optical vortices) carrying OAM in optical communication systems [75; 76; 77; 78; 79; 80; 81; 82; 83]. They could use these equations to analyze the effects of different media on the propagation of the twisted light beams and their OAM properties. By understanding the changes in the axial vector and its divergence, researchers could optimize the design of optical communication systems to minimize the loss of OAM information during transmission, thus improving the efficiency and reliability of these systems. Similarly, these equations can be applied in the field of plasmonics, where the interaction of light with metallic nanostructures to confine and manipulate EM waves on the nanoscale is of importance [84; 85; 86]. By using these equations, we can analyze the impact of different nanostructures on the OAM properties of the incident EM waves and design nanostructures that can efficiently manipulate the OAM properties for various applications, such as optical tweezers, super-resolution imaging, or quantum information processing [87; 88; 89; 90; 91].
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 4 Suitability of Legacy Techniques and Standards for 5G and Upcoming Networks
### IEEE 802.21  
This standard provides a conjoint policy where the upper layer can interact with the lower layers [28; 45]. Next, this standard offers media-independent handover (MIH) services, including events, commands, and information services [46]. By using the stated benefits, this protocol facilitates the upper layers in the protocol stack to acquire information that is obtainable on MAC and link layers for guaranteeing continuous connectivity among domains and, therefore, improves the quality of experience and quality of service [27; 28; 29]. We identify the following advantages and drawbacks in the light of defined parameters in Table 2.  
**Advantages**  
- There is an option for user equipment (UE) to attach to more than one access point [28; 47].  
- The UE can also switch between various RATs [47; 48; 49] with seamless handoff capability.  
**Drawbacks**  
- We need to update the existing protocol stack so that communicating devices may be able to use the features of the IEEE 802.21 standard [26; 49].  
Hence, in light of the stated discussion regarding IEEE802.21, it can be inferred that the IEEE802.21 standard fulfills AL4 (Table 2) requirements for the adaptability (privilegeof connection with more than one access point) point of view. Similarly, the DL4 (Table 2) parameter of dependability (privilege of seamless handover among various access points) is also fulfilled by this standard.