esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mineralogical Analysis  
XRD analysis is a well-defined technique, and used in most studies (e.g., [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [25], [PERSON] and [PERSON] [18], [PERSON], [PERSON], [PERSON] and [PERSON] [32] and [PERSON], [PERSON] and [PERSON] [14]) to assess the fly ash's mineralogical composition. In this study, we also conducted an XRD analysis of samples. The XRD analysis was provided to acquire details on the fly ash's mineralogical composition (Figure 3). According to this analysis, the UB sample is really rich in quartz although other mineral phases like magnetite as well as mullite exist. In contrast, the PL sample is quite rich in Magnetite. Often, the non-magnetic components contain quartz and mullite as the substantial mineralogical phases. The peaks related to quartz are very intense in whole non-magnetic fractions. There is no likelihood for quantitative correlation between the particle size and the quartz content as XRD analysis is only related to qualitative approximation. However, it generally appears that the more quartz content, the lower the particle size. it is owing to refractory mullite (\(\theta=\)16.4). As the magnetic components indicate in this figure, magnetite and hematite produce high-intensity peaks. The point that each component contains magnetite is confirmed by good results obtained from the X-ray diffractogram tests showing an authentic sample of magnetite. These observations can be used to initiate a promising technique for the assessment of the finer magnetic fractions, that is normally essential in the preparation of coal.  
Trace element analysis using ICP-MS analysis is given in Table 3. The results reveal the presence of Cr, Mn, Mg, Co, Zn, Ni, Cu, Cd, Mo, and Pb in the fly ash samples even having different concentrations (e.g., elements mentioned) in samples. Considering all the present trace elements, it is demonstrated that Pb, Mn, Cr, Cu, Zn, and Co are present with considerably higher proportions. As particle size decreases, the concentration reduction in case of Co, Mn, and Mg all of which was displaying limited to moderate concentration impact with a decrease in particle size. Cu, Cr, and Pb are adversely proportional to the size of the particle. Other elements show no significant variation in concentration with a particle size decreases.  
Figure 2: Magnetization vs. applied magnetic field for samples particles at 300 K.
# Quantum Private Comparison Based on Four-Particle Cluster State
## 4 Security Analysis
### Participant Attacks
#### 4.2.3 Attacks from Conspiring Participants  
Conspiring attacks can be categorized into three types: three-user collusion, two-user collusion, and cross-group conspiracy. In the three-user collusion scenario, we assume that [PERSON] collaborates with [PERSON] and [PERSON] to uncover [PERSON]'s secrets. Even if these three users are privy to the quantum sequence sent from the STP to [PERSON], as well as the secret key \(X_{i}\), they will not succeed. This is because the quantum sequence \(SE_{B}^{\prime}\) sent to the STP is encoded with both \(X_{i}\) and \(K_{B}\), with \(K_{B}\) remaining unknown to them. In the two-user collusion scenario, we consider [PERSON] and [PERSON] conspiring to ascertain the secrets of [PERSON] and [PERSON]. However, this attack is impractical due to their lack of knowledge regarding the quantum sequences that [PERSON] and [PERSON] receive and transmit. In the cross-group conspiracy scenario, we posit that [PERSON] and [PERSON] conspire to uncover the secrets of [PERSON] and [PERSON]. Although [PERSON] and [PERSON] have access to the quantum sequences \(S_{1}\) and \(S_{3}\), as well as the secret keys \(X_{i}\) and \(Y_{i}\), they cannot succeed, as the secret keys \(K_{A}\) and \(K_{D}\) remain unknown to them.  
Therefore, the proposed protocol can resist attacks from colluding participants.
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
## 2 Materials and Methods
### Planning and Printing of the Transfer Trays  
The transfer trays were designed using the Bonding Tray 3D module (OnyxCeph3 TM Lab software). A base covering the teeth and molds for the brackets and tubes was designed. In detail, the base had a thickness of 1 mm and covered all the occlusal/incisal surfaces as well as half of the lingual and about 1/3 of the labial surfaces of every tooth. The molds had a 0.6 mm thickness and covered the occlusal part of the brackets/tubes and overlapped the slot by 0.04 mm. Undercutting parts (up to 0.5 mm) were filled by the software.  
Each tray was printed twice, one time with a hard and another with a soft printing material (Figure 2C,D): the hard transfer trays were printed horizontally in a polyjet printing process (Object30 Dental Prime, Stratasys, Eden Prairie, MN, USA) using the high-speed mode (28 micron resolution) and biocompatible MED610 (shore D hardness 83-86; Stratasys, Eden Prairie, MN, USA) with support material SUP705 (Stratasys, Eden Prairie, MN, USA). After printing, the support material was removed by water jet cleaning and a hard toothbrush. The soft transfer trays were printed horizontally by means of a digital light processing (DLP) using the MoonRay Printer (SprintRay, LA, USA) with a 100 micron resolution and the biocompatible NextDent Ortho IBT (shore A 85 hardness; NextDent B.V., Soesterberg, The Netherlands).  
After printing, the trays were washed for 20 min with Isopropanol (FormWash, FormWash, Berlin, Germany) and light cured for 60 min at 45 \({}^{\circ}\)C (Curebox, Wicked Engineering, East Windsor, NJ, USA). For those trays that were printed with the DLP technology, an occlusal overlay plane was designed to avoid the need of adding support structures and increase the adhesion to the platform as it became possible to locate them horizontally.
## 4 Discussion
### Limitations  
For a reforestation program, the ecosystem service assessment will illustrate their multiple effects, as well as inform management decisions. While we quantified the change in ecosystem services during the first round of the GPP, and analyzed the effects of afforestation choices on ecosystem services and their trade-offs using the popular InVEST model and scenario simulation, our study has limitations primarily due to the availability of data. One shortcoming was the evaluation accuracy of the four regulating services. The InVEST model used for the qualification of ecosystem services and simplified in terms of mechanisms, with results closely depending on LULC types. The input parameters of the GGP-related land covers for InVEST model are very relevant for the evaluation accuracy. Several parameters were localized by field-sampled data and management practices (e.g., aboveground biomass carbon storage of economic forest was estimated by tree density and diameter a breast height) (Supplementary Table S1). For flood mitigation, as it is difficult to validate against observed data [30], we used observed yearly water yield from local hydrological stations to indirectly test the accuracies (relative root mean squared error is 26.59%; Supplementary Table S2). In addition, the simulated values of water purification and soil retention were compared to the previous research results [55; 56], showing generally consistent ranges of the estimated values. Since the relevant literatures concerning economic forests is very limited, their parameters for ecosystem service evaluation need to be modified based on field-based studies and observations in specific regions [57].  
Another limitation involves the provisioning service (seed yield). The seed yield of Korean pine shows much annual and spatial variations [42], and is influenced by forest management practices. Due to the complex controls of seed yield of Korean pine, its accurate evaluation is a difficult task. Here, we simplify that (1) the seed yield of multi-year average was employed, (2) the management practices are on the same level, and (3) the seed yield is primarily determined by the site condition. Because the seed yield was estimated from a household survey, subjective records would be introduced, leading to uncertainty in the evaluation of the seed yield. In addition, the equation of Mongolian pine was employed to map site conditions for Korean pine, which can also introduce uncertainty into seed yield evaluation. Although some simplifications were introduced, comparable magnitudes of the seed yield across the landscape could be obtained. Overall, available field-based data and specific yield models are required for accurately evaluating the provisioning service of economic forests.
## 4 Equivalent Consumption Minimization Strategy (ECMS)
### Method 1: Hamilton-Jacobi-Bellman Equation  
The Lagrange multiplier \(\lambda_{k}\) required for evaluating the ECMS algorithm can be obtained by means of DP in Equations (21)-(24). Due to the high computational effort of DP, it must be precomputed based on a driving cycle. This driving cycle can only be predicted and needs to approximate the future driving behavior as well as possible. In order to determine \(\lambda_{k}\), the Hamilton-Jacobi-Bellman equation is used [43]:  
\[\frac{\partial J^{*}(q,t)}{\partial t}=\max_{\mathbf{u}}\{-\dot{m}_{\mathrm{f} }(\mathbf{u})\underbrace{-\frac{\partial J^{*}(q,t)}{\partial q}}_{\lambda}. \dot{q}(\mathbf{u},q)\}. \tag{25}\]  
This equation describes the connectedness between the DP and PMP, where the right side represents the Hamilton function (Equation (19)). Accordingly, the partial derivative of the optimal accumulated costs \(J^{*}\) from the battery charge \(q\) corresponds to \(\lambda\). Due to the discrete functional principle of DP, the partial derivative must be approximated by the following means for \(k=0\ldots N-1\) and \(i=1\ldots n\):  
\[\lambda_{i,k}=-\frac{J_{i+1,k}-J_{i,k}}{q_{i+1}^{\mathrm{g}}-q_{i}^{\mathrm{g }}}. \tag{26}\]  
The states \(q_{i}^{\mathrm{g}}\) correspond to the elements within the state variable grid \(\mathbf{q}^{\mathrm{g}}=[q_{1}^{\mathrm{g}}\ldots q_{\mathrm{n}}^{\mathrm{g}}]^ {T}\) used for carrying out DP and the costs \(J_{i,k}\) for the elements within the cost matrix \(\mathbf{J}\), describing the result of the backward computation (Equation (21)). Since each index \(k\) is assigned to an instant of time \(t_{k}\), it can be also assigned to a vehicle position \(z_{\mathrm{veh},k}\) by integrating the vehicle speeds \(v_{\mathrm{veh},k}\) given by the considered driving cycle. Moreover, the elements in the state variable grid \(\mathbf{q}^{\mathrm{g}}\) can be converted into a state of charge according to Equation (10). Consequently, from Equation (26), \(\lambda\) is the result of a function of \(z_{\mathrm{veh},k}\) and \(SoC_{k}\), as shown in Figure 5.  
Figure 6 shows an example for the Lagrange multipliers \(\lambda\) determined according to Equation (26). The upper figure shows \(\lambda\) as a function of \(z_{\mathrm{veh}}\) and \(SoC\). It also contains the optimal characteristics of the state of charge \(SoC^{*}\) for the initial values \(SoC_{0}=80\%\) (red line) and \(SoC_{0}=30\%\) (green line). Both characteristics were the result of DP, which was applied on the same driving cycle used to determine \(\lambda\). The lower figure shows the Lagrange multipliers \(\lambda\) belonging to \(SoC^{*}\). Both characteristics were obtained from the upper diagram. If the battery is constantly discharged during vehicle operation, the values for \(\lambda\) will be lower than if the state of charge is kept constant or the battery is charged. In the algorithm of the ECMS shown in Figure 5, the function for \(\lambda\) is implemented as a map, which corresponds to the upper part of Figure 6.
## 5 Numerical Results and Discussion
### Performance Analysis
#### 5.1.3 Performance Analysis of SU Packets  
Figure 3 depicts the variation trends of SU packets' blocking rate \(B_{S}\), loss rate \(L_{S}\), and throughput \(T_{S}\) under the proposed communication failure and repair mechanism.  
As shown in Figure 3, SU packets' blocking rate \(B_{S}\) and loss rate \(L_{S}\) increase while throughput \(T_{S}\) decreases if the communication failure rate \(f\) is enhanced. When the repair rate \(r\) increases, \(B_{S}\) and \(L_{S}\) decrease while \(T_{S}\) increases. As \(f\) increases, more SU packets enter the cache after their transmission is suspended by the communication failure. As a result, the cache space decreases. In that case, the newly arrived SU packets have less chance to enter the cache and the blocking rate increases. When there is no free space in the cache, SU packets can only leave the system if they are interrupted. Therefore, the loss rate also rises, and the amount of SU packets with completed transmission per unit time decreases accordingly. When \(r\) increases, the probability of the system state being normal increases, the probability of SU packets being transmitted grows, and the probability of SU packets being blocked and leaving decreases. The probability of SU packets being interrupted due to communication failure decreases, and the probability of being forced to leave the system because of interruption also decreases correspondingly; thus, the amount of SU packets with finished transmission increases.  
From Figure 3, we can observe that as PU1 packets' arrival rate \(\alpha_{11}\) grows or PU2 packets' arrival rate \(\alpha_{12}\) grows, SU packets' blocking rate \(B_{S}\) and loss rate \(L_{S}\) increase while throughput \(T_{S}\) decreases. With the increase in \(\alpha_{11}\) or \(\alpha_{12}\), the probability that PU packets are present in the channel rises, and so does the probability of SU packets' transmission being interrupted. Therefore, the probability of SU packets being blocked and lost rises, so the number of SU packets transmitted completely reduces.
## 1 Introduction
The technology requires extremely precise beam directing and is compatible with low-complexity, easy-to-manage satellite multi-wavelength optical switching designs. As shown in Figure 1, we propose a novel optical controlled phased array for non-regenerative LEO satellite networks. To our knowledge, it is the first architecture proposed to support multi-hop bent-pipe satellite networks using the DVB-S2X beam-hopping protocol [14]. Thanks to AWGR-assisted millimeter wave-phased array, we make it possible for transparent satellites to achieve beam hopping. We investigate in detail an AWGR-controlled phased array construction that satisfies the requirements of the constellation. The AWGR-based optically assisted phased array may provide a number of cutting-edge features for broadband millimeter-wave communications: in order to extend interplanetary routing over the air to ground coverage, satellite network designs can easily incorporate wavelength division multiplexing, which allows for a skew-free beam scan at large angular beam pointing, ultra-high precision beam scan intervals, adjustable beam pointing, and reduced satellite processing complexity redundancy. We provide insight into LEO satellite network requirements in Section 2. In Section 3, the idea of using an optically controlled phased array-based subarray was proposed and we theoretically demonstrate its enhancement of the bandwidth performance of broadband phased arrays. The feasibility of the optically controlled millimeter wave phased array structure based on the arrayed waveguide grating routers (AWGR) structure is experimentally demonstrated in Section 4. Section 5 provides a summary and outlook.
# Elasto-Inertial Particle Focusing in Microchannel with T-Shaped Cross-Section
## 2 Materials and Methods
### Sample Preparation and Experimental Setup  
In this research, PEO (polyethylene oxide) solution was used as the viscoelastic solution. To make 500 ppm PEO (PEO500) solution, PEO (Mw = \(\sim\)2 MDa, Sigma-Aldrich, Burlington, MA, USA) was dissolved in DI water. Fluorescent polystyrene (PS) particles (Thermo Scientific Inc., Waltham, MA, USA) with 5 \(\upmu\)m and 13 \(\upmu\)m diameters were placed in the experimental solution (0.05-0.1 wt% concentration). To prevent particle aggregation during the particle focusing and separation experiments, Tween 20 (Sigma-Aldrich, Burlington, MA, USA) was added to the solution at 0.1 wt% as a surfactant. The estimated mean viscosity of the PEO500 solution was about 1.85 mPa-s (DI water: 0.89 mPa-s), and its relaxation time was estimated from the previous empirical relaxation time (\(\lambda_{\text{e}}\)), measured with a capillary breakup extensional rheometer (CaBER) [9; 16]. The relaxation time for the PEO500 solution was estimated to be 3.78 ms on the basis of the empirical formula [9].  
The particles in the microchannel with a T-shaped cross-section were observed using optical microscopes (BX-60, Olympus, Tokyo, Japan). The flow rates were controlled from 1 to 150 \(\upmu\)L/min with a syringe pump (LEGATO 111, KD Scientific Inc., Holliston, MA, USA), and the focusing positions of the particles were recorded and captured at 1 cm, 4 cm and 7 cm downstream from the inlet using a CMOS camera (ZWO Co., Ltd., Suzhou, China). For the precise analysis of the particle focusing behavior, bright-field and fluorescent images were captured from the top and side of the microchannel with exposure times of 20 ms and 500 ms. All the analyses and postprocessing of the images were achieved using the open-source ImageJ software. In this research, the fluorescent intensity profiles across the microchannel were obtained and fitted with a Gaussian distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Top Layer (\(W\), \(H\))**} & **Bottom Layer** \\ \cline{2-5}  & **Type 1** & **Type 2** & **Type 3** & **(\(W_{t}\), \(H_{t}\))** \\ \hline Photoresist & SU-8 2050 & SU-8 2075 & SU-8 2075 & SU-8 2075 \\ \hline Coating speed (rpm) & 3000 & 3000 & 2100 & 1300 \\ \hline Width (\(\upmu\)m) & 50 & 50 & 50 & 150 \\ \hline Height (\(\upmu\)m) & 35 & 52.5 & 70 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions for SU-8 patterning and microchannel dimensions.  
Figure 1: **(a)** Schematic 3D view of fabrication process; **(b)** design parameters between two PDMS molds (top and bottom) in microchannels with T-shaped cross-sections.
## 5 Performance Evaluation
### Experiment Setup
This means that our approach efficiently distributes the inference workload, resulting in more instances of faster inference times. We believe that these results can be attributed to our algorithm for two main reasons. Firstly, our algorithm performs reasonable allocation by deploying heavier tasks on satellites with more available resources, thus reducing the frequency of CPU thread switches and ultimately improving inference speed. Secondly, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenarios** & **Satellite** & **Strategy** & **Mean Value** & **Median Value** & **Percentage** \\ \hline \multirow{6}{*}{CPU-memory occupation: 12–38, 50–45, 2–32, 50–43} & COIN-LEO & 63.524 & 59.0 & 49.58\% \\ \cline{2-6}  & \multirow{2}{*}{1} & Equally splitting & 252.695 & 251.0 & 210.92\% \\ \cline{3-6}  & & Randomly splitting & 123.876 & 119.0 & 100\% \\ \cline{3-6}  & & COIN-LEO & 41.512 & 33.0 & **9.40\%** \\ \cline{3-6}  & \multirow{2}{*}{2} & Equally splitting & 1585.0 & 1600.0 & 455.84\% \\ \cline{3-6}  & & Randomly splitting & 417.2 & 351.0 & 100\% \\ \cline{3-6}  & & COIN-LEO & 1939.063 & 1940.0 & **715.86\%** \\ \cline{3-6}  & \multirow{2}{*}{3} & Equally splitting & 1408.962 & 1557.0 & 574.53\% \\ \cline{3-6}  & & Randomly splitting & 279.388 & 271.0 & 100\% \\ \hline \multirow{6}{*}{4} & COIN-LEO & 49.706 & 49.0 & **2.97\%** \\ \cline{3-6}  & & Equally splitting & 366.084 & 363.0 & 22.04\% \\ \cline{1-1} \cline{2-6}  & & Randomly splitting & 1644.699 & 1647.0 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Time statistics under different strategies on different satellites. our algorithm generates relatively smaller traffic sizes. This also reduces data-transmission time requirements, enhancing system throughput. As a result, the CDF plot confirms that our strategy is more effective in minimizing the overall inference time and improving the performance of the distributed deep-learning inference system. As shown in Figure 11, we also conduct a comprehensive comparison of traffic sizes under different strategies. The flow of data in our experimental scenarios is derived from the intermediate results of inference generated by each satellite. We carefully analyzed and recorded the traffic sizes for each strategy in all four scenarios. Our COIN-LEO strategy demonstrated its advantage in optimizing traffic size by efficiently distributing  
Figure 10: CDF diagrams for different scenarios. (**a**) CPU memory occupation: 36–46, 26–45, 25–46, 26–46; (**b**) CPU memory occupation: 12–38, 50–45, 2–32, 50–43. computational tasks across the satellite network. This is because our designed reward function tries to reduce the time for complete collaborative inference, which leads to reduced transmission delay and traffic size. These results underscore the effectiveness of our proposed algorithm in minimizing the overall traffic burden and enhancing the overall performance of the distributed deep-learning system.
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 2 Literature Review
### Map-Based Region Partitioning for Content Dissemination
More specifically, predictive routing based on analysis of historical trajectories, or geocasting [88, 89] which aims to route specific messages (e.g., promotional campaigns or emergency notifications) to target locations. Map segmentation according to the road network of large cities also appeared in [90, 91] as the basis of their works. Ref. [91] divides the map recursively into multiple levels by road hierarchy, but this is not enough to reflect administrative features. In our method, we will make an effort to align the segmentation result with administrative subdivisions. Ref. [90] extracts the road skeleton by image processing techniques, with the sacrifice of metadata describing properties of vector objects. By contrast, segmentation of our method is purely through processing on vector data, and metadata can be preserved for future use.