esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The efficient separation of chalcopyrite from molybden is crucial for the utilization of copper-molybdenum ones and is inevitably influenced by the presence of metal ions in the pulp. However, the underlying impact mechanisms remain unclear. This study systematically investigated the influence of Cu\({}^{2+}\) on the flotation separation of chalcopyrite and molybden, with sodium thioglycolate (STG) as a depressant. Flotation experiments revealed that Cu\({}^{2+}\) in the STG system significantly reduced molybden recovery and hindered selective separation. Characterization using contact angle, zeta potential, and UV-Vis analysis demonstrated that the presence of Cu\({}^{2+}\) triggered STG adsorption on the molybden surface, thereby increasing its hydrophilicity. XPS analysis showed that Cu\({}^{2+}\) adsorbed onto the molybden surface as Cu(I), creating reactive sites for the -SH or -COO\({}^{-}\) groups in STG. This interaction forms a stable molybden-Cu(I)-STG complex through chemisorption, significantly suppressing molybden flotation. DFT calculations further demonstrated that Cu\({}^{2+}\) enhanced the reactivity between STG and molybden. These findings provide a comprehensive understanding of the influences of Cu\({}^{2+}\) on the flotation separation between chalcopyrite from molybden, providing valuable insights into further optimizing copper-molybdenum flotation separation processes.  
molybdenum; chalcopyrite; copper ions; flotation; sodium thioglycolate; impact mechanism Article
## 3 Ground Loss Effect Factors
### Quantitative Analysis  
Ground loss \(g\) and several parameters of Table 1 can be brought into Equation (8) to calculate ground loss displacement, and the counter map of ground settlement are shown in Figure 2. As we can see it, ground settlement at 4.5 D (D for diameter of tunnel) behind shield tail is stable to maximum 10.51 mm, and its value at 4.5 D in front of shield tail is only 10% of maximum. Therefore, it can be considered that the influence range of ground loss is within 4.5 D behind and in front of shield tail. As for deep soil layer, when the vertical distance from tunnel vault is greater than 0.5 D, the settlement has little changes. However, while it is less than 0.5 D, the settlement increases rapidly, as shown in Figure 3a. This is because that with the redistribution of stress, a pressure arch will be formed around the tunnel. The soil in the arch is a loosening zone, which has a greater displacement, and the soil out of the arch tends to be stable. As we can see from Figure 3b, horizontal displacement of deep soil is mainly distributed in 1 D from the bottom of the tunnel.  
Figure 3: Displacement of stratum: (**a**) Variation curve of settlement maximum; (**b**) Counter map of horizontal displacement.  
Figure 2: Counter map of ground settlement.
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### Data Structures  
The data structures of this protocol can be classified into those handling the auto-configuration mechanism and those belonging to the OLSR routing protocol. OLSR stores internally a routing table which is updated periodically. This table contains information about the route to each node, stored in the following fields:  
* _R_dest_addr_: IP address of the destination node.
* _R_next_addr_: IP address of next hop in the route.
* _R_dist_: Distance to the destination node.
* _R_iface_addr_: IP address of the outgoing interface to the destination node.  
The structures necessary for auto-configuration mechanism are:  
* IP addresses of the node interfaces.
* Netmask.
* Free_IP_Blocks: A table of free block from each node in the network. It will have the following form:
## 3 Proposed Distributed Secondary Control Technique
### Case Four: Proposed Control DER Plug-and-Play Capability
In the third stage, it is observed from the results that the reconnection of DER 4 results in precise current sharing with the four DERs in operation, attaining a consensus and steady-state current of 9.7 A within 1 s without any current overshoot or undershoot. The bus voltage is promptly restored to its steady-state value within 0.6 s following a sudden voltage overshoot of 4.8% caused by the connection. Furthermore, the convergence of the correction signal of DER 4 to a consensus value is evident, aligning with the correction signals of the other three active DERs. Irrespective of the disconnection and connection events of the DERs, the proposed FDSC approach effectively guarantees the control objectives, and this serves as a demonstration of its reliable and robust plug-and-play capability. In addition, the communication Topology \(X\) utilized is a balanced communication graph. Choosing a balanced graph as the communication topology is essential because the proposed algorithm can provide accurate estimates of currents as the true average of steady-state currents from all DERs only under this condition. It is important to note that the disconnection of DER 4, which can occur due to the failure of its converter in a real-world scenario, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **At Stage 2** & **At Stage 3** \\  & (\(t=3\) s) & (\(t=6\) s) \\ \hline Settling Time & \(\leq\)0.9 s & \(\leq\)0.6 s \\ \hline Overshoot & none & 4.8\% \\ \hline Undershoot & 5.2\% & none \\ \hline \hline \end{tabular}
\end{table} 
Table 10: FDSC transient response at the DC bus voltage under case four. Figure 21: Signal output of proposed FDSC under plug-and-play. can also imply the loss of all communication links associated with DER 4 [38]. Therefore, in the event of a communication link failure, as observed in this case, the disconnection of DER 4 at \(t=3\) automatically makes the communication links \(k_{24}\) and \(k_{34}\) inoperable. Nonetheless, the remaining links continue to constitute the connected graph to ensure that the graph still maintains its balance. As seen in Figure 20, when a communication link within the microgrid fails, although the communication topology remains intact, the system experiences unbalanced current generation leading to a slight fluctuation in the bus voltage before returning to a stable state (its nominal value). Figure 19 also illustrates that following the communication link failure, the output current of the remaining DERs demonstrates an increase while DER 4 experiences a complete loss of output current. The distribution of current subsequently aligns with the remaining capacity of each DER. Similar results were obtained in [7; 36; 38]. By employing Topology \(X\), communication redundancy can be minimized, and uninterrupted system connectivity is guaranteed even in the event of a communication link failure. Hence, the proposed FDSC is reliable and robust by ensuring that the microgrid is resilient to communication link failure.
## 4 Discussion
These results are also confirmed by research conducted in Rzeszow [43,49], in which authors concluded that rainwater is unsuitable for applications requiring drinking water quality due to a large number of psychrophilic, mesophilic and faecal bacteria. The best microbiological quality was obtained for rainwater collected from roofs in autumn and spring [43]. Worldwide research most often focuses on the collection and use of rainwater for housing purposes due to water shortages. These studies covered both urban and rural areas. In this publication, we present the issue of protecting water resources in environmentally important areas, which include national parks. Climatic changes were also observed in the RNP, such as the drying of Ponds Echo [31]. Fortunately, the favorable water balance in 2020-2022 largely offset the effects of drought in previous years. These effects are still visible in natural phenomena, but almost imperceptible in hydrological phenomena. Taking into account the forecasts of changes determined by regional climate models [50], the temperature increase will continue, especially in winter to 3 \({}^{\circ}\)C. Forest stand dynamics forecast models assume a warm dry climate scenario. This results in maintaining the status quo for pine stands. On the other hand, changes in the species structure of these stands are forecasted via an increase in the number of beech, fir and hornbeam trees. A decrease in the importance of species sensitive to water shortage (alder and spruce) is expected. The species to gain importance will be hornbeam and linden [51]. The year 2021 was the second consecutive year when the ecosystems of the Swierszcz catchment did not suffer from a lack of water during the growing season. Two consecutive wet years caused natural effects. Bark beetle gradations in pine and spruce stands ended and the process of fir dieback slowed down. A clear improvement was also noted in the degree of hydration in peatland ecosystems. The condition of peat moss improved, and undesirable expansive species began to withdraw from water-dependent natural habitats. The water flow in the Swierszcz River, which was much lower than the long-term average, resulted from the extremely low supply associated with the lowering of the groundwater level in the main Cretaceous aquifer. Significant reconstruction of the power supply took place only in the second half of the hydrological year and was associated with increased precipitation. Regarding this aspect, the protection of groundwater abundance, and to a greater extent, the acquisition and use of rainwater also in nature-protected areas, is gaining in importance. Moreover, attention should be paid to the quality of water in the Swierszcz River, which, for some parameters, was worse than rainwater. This indicates that steps can be taken to improve its quality here as well. Examples of improvement methods can be found, e.g., in [52].
# A Unified Database Solution to Process BIM and GIS Data
## 3 Results
### Evaluation of the Adopted Procedure  
The results confirmed the validity of the initial assumption: that it was possible to implement the IFC model in a relational database in a way that allows the use of the IFC data saved in the database by BIM applications, without needing to significantly change the data operation methods that these applications currently perform. At the same time, such an approach allows GIS applications to operate on the same data. The experiment proved that the IFC model can be implemented in a relational database in a way that makes it possible to perform data writing and reading, which is currently done by other methods using BIM applications.  
During the research, the advantages and disadvantages of the two methods of converting data within BIGI-D were analyzed: copying data with schema changes and dynamic data conversion to another schema \"on the fly\". The advantages of the solution based on data copying include the ease of access to the spatial data saved in the IFC schema by BIM applications (direct reading of IFC data, only saved in a different format). The main disadvantages of this approach are the data redundancy, the time needed for copying, and the required additional computer memory resources. The solution using dynamic data conversion ensures that the data are stored only once in the BIGI-S. CityGML and IFC schemas are created only through perspectives and stored procedures (and, thus, virtually). The main advantage of this solution is, therefore, the elimination of data redundancy. The disadvantage, however, is the necessity to create rather complicated perspectives that translate selected data between schemas in the case of spatial data conversion into the IFC, while requiring very fast access to these data. It was decided that the advantages of the second approach were greater; therefore, this method was used to build the prototype.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 3 Background
### 1-Out-Of-2 Oblivious Transfer  
In the 1-out-of-2 oblivious transfer (OT) protocol, there are two parties: a sender and a chooser. There are two private messages at the sender: \(M_{0},M_{1}\), and the chooser holds a private choice bit, \(\sigma\in\{0,1\}\). At the end of the 1-out-of-2 OT protocol, the sender learns nothing, and the chooser learns \(M_{\sigma}\) only.  
Let \(p=2q+1\) denote a safe prime number; _i.e.,_\(q\) is also a prime number. Let \(Z_{p}^{*}=\{1,2,3,4,\ldots,(p-1)\}\), which denotes the set of integers that are relatively prime to \(p\). Let \(G\) denote a subgroup of \(Z_{p}^{*}\), where \(|G|=q\). Let \(g\) denote the generator for \(G\).  
The sender randomly chooses an element, \(C\in G\), and sends it to the chooser. Note that the discrete logarithm of \(C\) is unknown to the chooser. The chooser randomly selects an integer, \(k\) (\(1\leq k\leq q\)), and sets \(PK_{\sigma}=g^{k}\ mod\ p\), and \(PK_{1-\sigma}=C\times(PK_{\sigma})^{-1}\ mod\ p\). The chooser sends \(PK_{0}\) to the sender. Note that \(PK_{0}\) does not reveal the choice bit \(\sigma\) to the sender.  
The sender calculates \(PK_{1}=C\times(PK_{0})^{-1}\ mod\ p\) on its own, and randomly chooses two elements, \(r_{0},r_{1}\in G\). Let \(h(x)\) denote the output of the hash function (e.g., SHA, which stands for secure hash algorithm) on input \(x\). Let \(E_{i}\) denote the encryption of \(M_{i}\), \(\forall i\in\{0,1\}\). Then, the sender calculates \(E_{i}=\{(g^{r_{i}}\ mod\ p),(h(PK_{i}^{r_{i}}\ mod\ p)\oplus M_{i})\}\), and sends both \(E_{0}\), \(E_{1}\) to the chooser.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{P}\) & \(\mathbf{Q}\) & \(\mathbf{R}\) \\ \hline \(E_{\omega_{0}^{0}}[E_{\omega_{1}^{1}}[\omega_{4}^{1}]]\) & \(E_{\omega_{0}^{0}}[E_{\omega_{3}^{1}}[\omega_{5}^{0}]]\) & \(E_{\omega_{4}^{1}}[E_{\omega_{5}^{1}}[\omega_{6}^{0}]]\) \\ \(E_{\omega_{0}^{0}}[E_{\omega_{1}^{0}}[\omega_{4}^{0}]]\) & \(E_{\omega_{1}^{1}}[E_{\omega_{3}^{0}}[\omega_{5}^{0}]]\) & \(E_{\omega_{4}^{0}}[E_{\omega_{5}^{0}}[\omega_{6}^{0}]]\) \\ \(E_{\omega_{0}^{1}}[E_{\omega_{1}^{1}}[\omega_{4}^{1}]]\) & \(E_{\omega_{2}^{0}}[E_{\omega_{3}^{0}}[\omega_{5}^{0}]]\) & \(E_{\omega_{4}^{1}}[E_{\omega_{5}^{0}}[\omega_{6}^{1}]]\) \\ \(E_{\omega_{0}^{1}}[E_{\omega_{1}^{0}}[\omega_{4}^{1}]]\) & \(E_{\omega_{2}^{1}}[E_{\omega_{3}^{1}}[\omega_{5}^{1}]]\) & \(E_{\omega_{4}^{0}}[E_{\omega_{5}^{1}}[\omega_{6}^{1}]]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Garbled Tables for Gates \(P,Q,R\).  
The chooser decrypts \(E_{\sigma}\) to obtain \(M_{\sigma}\) as follows. Let \(l_{1}=g^{\tau_{\sigma}}\ mod\ p\) and \(l_{2}=h(PK_{\sigma}^{\tau_{\sigma}}\ mod\ p)\oplus M_{\sigma}\) denote the first and second numbers respectively in \(E_{\sigma}\). The chooser calculates \(M_{\sigma}\) using the relation, \(M_{\sigma}=h(l_{1}^{k}\ mod\ p)\oplus l_{2}\). Note that since the discrete logarithm of \(C\), and hence \(PK_{1-\sigma}\), is unknown to the chooser, it cannot retrieve \(M_{1-\sigma}\) from \(E_{1-\sigma}\).
## 5 Simulation and Analysis  
### Simulation Environment Settings  
(1) Construction of real trajectory of HTV-2 trajectory  
In this paper, the trajectory model of hypersonic target is simulated, and the real water drift trajectory model is constructed. Using the characteristic data of HTV-2 given by the United States, the mass of the aircraft was 907 kg, the aerodynamic reference area \(S=0.4837\) m\({}^{2}\), and the gravity acceleration \(g\) was taken as 9.8 m/s\({}^{2}\). In order to obtain the motion state of the hypersonic target, the initial test state of the target was set as follows: the geocentric distance was 6426 km, the longitude and latitude were 0 degrees, the initial velocity of the target was 17 Mach, the target trajectory inclination angle was 0.25 rad, and the trajectory deflection angle was \(-0.42\) rad. The angle of attack setting followed that presented in [28]:  
\[\alpha=\begin{cases}a_{\text{max}},&V_{1}<V\leq V_{e}\\ a_{\text{mid}}+a_{\text{bal}}\sin\Big{[}\frac{(V-V_{\text{mid}})\pi}{V_{1}-V_{2 }}\Big{]},&V_{2}\leq V\leq V_{1}\\ \alpha_{\text{max}(K)}&V_{f}\leq V<V_{2}\end{cases} \tag{47}\]  
\[\begin{cases}\alpha_{\text{mid}}=\Big{(}\alpha_{\text{max}}+\alpha_{\text{ max}(K)}\Big{)}/2\\ a_{\text{bal}}=\Big{(}\alpha_{\text{max}}-\alpha_{\text{max}(K)}\Big{)}/2\\ V_{\text{mid}}=(V_{1}+V_{2})/2\end{cases} \tag{48}\]  
where the maximum angle of attack \(\alpha_{\text{max}}\) was set to 20 degrees, and the angle of attack \(\alpha_{\text{max}(K)}\) was set to 11 degrees at the maximum lift-drag ratio. The target motion trajectory can be obtained through the fourth-order Runge-Kutta method simulation, as shown in the Figure 5.  
Radar simulation condition setting  
The coordinates in the radar longitude, latitude, altitude coordinate system were \((37.45^{\circ},-15^{\circ},0\) m), the detection range of the single-station radar was 400 km, the standard deviations of the angle measurement and range were 500 m and 0.5 degrees, respectively, the radar sampling period was 1 s, and the tracking period was 710 s-1060 s. The tracking track is shown in the red track part in Figure 5.
## 2 Overview of Positioning Principles Using UWB and Wi-Fi RTT Radio Signals
### TOA/TDOA-Based Positioning Principle  
TOA and TDOA are alternative positioning techniques applicable to UWB and Wi-Fi RTT ranging measurements, both requiring precise time synchronization. As illustrated in Figure 5a,b, both methods involve recording the response time of each base station upon receiving a signal from the mobile device to determine the distance. Successful positioning necessitates at least three base stations. The key difference between TOA and TDOA is that TOA also requires knowledge of the signal's transmission time to directly calculate the distance between the mobile device and each base station, necessitating precise time synchronization between the transmitter and all base stations. Since both methods heavily rely on accurate time acquisition between the base station and the mobile device, any clock instability can significantly affect the precision of time measurements, thereby impacting overall positioning accuracy.  
Figure 4: LOS, NLOS, and multipath components in an indoor positioning context.  
Utilizing multiple ranging measurements, several observation equations are formulated to resolve the position of a mobile carrier (e.g., a tag or smartphone). In a simplified two-dimensional scenario, assuming the carrier transmits the signal at time \(t_{0}\), and the coordinates of three base stations are represented as \((x_{i},y_{i})\) with corresponding response times \(t_{i}(i=1,2,3)\) and considering the speed of light as \(c\), the TOA observation equation is as follows:  
\[\sqrt{\left(x-x_{i}\right)^{2}+\left(y-y_{i}\right)^{2}}=c(t_{i}-t_{0}),i=1,2,3 \tag{2}\]  
where \((x,y)\) is the coordinate the mobile carrier to be resolved.  
The method described above is known as the trilateration algorithm, which essentially involves finding the intersection point of three circles, as shown in Figure 5a. However, due to the inherent errors in measurements, the least squares solution of the equation is commonly employed for more accurate results.  
As shown in Figure 5b, TDOA does not require the synchronization of the clocks between the mobile carrier and base stations, and it requires only the synchronization between several fixed base stations, which significantly reduces the technical complexities compared to the TOA approach. In a simplified two-dimensional scenario, the observation equation for the TDOA algorithm is as follows:  
\[\begin{cases}\sqrt{\left(x-x_{1}\right)^{2}+\left(y-y_{1}\right)^{2}}-\sqrt{ \left(x-x_{2}\right)^{2}+\left(y-y_{2}\right)^{2}}=c(t_{1}-t_{2})\\ \sqrt{\left(x-x_{1}\right)^{2}+\left(y-y_{1}\right)^{2}}-\sqrt{\left(x-x_{3} \right)^{2}+\left(y-y_{3}\right)^{2}}=c(t_{1}-t_{3})\end{cases} \tag{3}\]  
where all symbols are same as those in Equation (2).
# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 2 Materials and Methods  
### Study Sites  
Three shallow coastal study sites were selected within a recently declared 8000 km\({}^{2}\) marine sanctuary \"Arrecifes del Sureste\" (Latitude: \(\sim\)17.5\({}^{\circ}\)-18.8\({}^{\circ}\) N, Longitude: \(\sim\)67.8\({}^{\circ}\)-69.35\({}^{\circ}\) W). This sanctuary covers 120 km of coast and is a primary tourism hub, receiving over 4 million visitors annually (Figure 1). With coral reef-based tourism being a major part of the local Dominican Republic economy, a park management plan is currently being developed to monitor and protect these coastal ecosystems. We chose benthic classes to conform to those used by conservation practitioners who manage these habitats within the Dominican Republic. Our research provides an opportunity to test object-based shallow benthic composition mapping across a wide range of benthic types, geomorphic zones, and bathymetric ranges. Such products can provide baseline data, monitor changes, and inform adaptive management actions within the park.
# Monte Carlo Study of Imaging Plate Response to Laser-Driven Aluminum Ion Beams
## 4 Fading Effect  
As time elapses after activation, some of the electrons in the metastable states spontaneously decay to the Eu\({}^{3+}\) state and emit PSL. This phenomenon is called the fading effect, and the resulting PSL loss when the IP is scanned subsequently should be taken into account in the analysis. Several studies have measured fading curves for electrons [13; 17], protons [14], X-rays [5; 7; 15; 26], and \(\gamma\)-rays [14]. Although the radiation sources used in fading measurements are different in each experiment, fading curves are not very sensitive to the type and energy of radiation. [PERSON] et al. find that fading signals are nearly independent of the radiation type with less than 10% differences between photons and protons [14]. [PERSON] et al. also report that the fading effect is similar for electrons and for protons regardless of their kinetic energies [40]. The known parameters contributing to fading effect are ambient temperature and scanner type [7]. Although the fading effect becomes bigger as the ambient temperature increases, its change can be considered negligible for a small temperature fluctuation [7].  
[PERSON] et al. [13] report different fading behaviors between their data measured by BAS-1800 II and the data of [PERSON] et al. [17] measured by BAS-5000. Their findings suggest that PSL signals measured using different scanners can be quite different [13]. [PERSON] et al. also compared BAS-1000 and BAS-5000 and observed that fading of BAS-5000 is larger than that of BAS-1000 [40]. Therefore, we need to apply a fading model benchmarked using the same IP with the same scanner. [PERSON] et al. [14] and [PERSON] et al. [7] have used the same FLA-7000 scanner as in our measurements, and their fading functions are quite comparable each other. They have both used two exponential functions to fit their data, and we have adopted the fading function of [PERSON] et al. as shown below [7].  
\[f(t)=0.535\;e^{\frac{-t}{\tau_{1}}}+0.465\;e^{\frac{-t}{\tau_{2}}}, \tag{2}\]  
where \(\tau_{1}=23.812\) (min), \(\tau_{2}=3837.2\) (min). In our experiment, fading loss is expected to be about 10% at \(t=5\) (min), and thus the experimentally measured PSL values have been scaled upwards to the values that would have been measured at time zero.