esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance Evaluation of Dark Fiber Topology  
Figure 6 illustrates the evaluation of SKR for the P2P link, as a function of fiber distance between the REC and RE nodes, for the case where dark fiber is used for the quantum link.  
It is evident that in this topology the dedicated link offers a feasible transmission with sufficiently long-distance range. Both photon counters QKD setups show an identical behavior for fiber segments up to approximately 70 km. Even for the case of fast key refresh times of 1.4 s, acceptable SKR values can be achieved for both types of photon counter at ~50 km, a longer distance than 17 km, which corresponds to round-trip latency barrier as was calculated in Appendix A. Therefore, for relatively short distances, which correspond to fronthaul segments where the low-latency requirement is crucial, the InGaAs setup can offer the same performance as the upconverted silicon SPADs. The advantage of using CMOS SPADs is revealed only at distances longer than 70 km, where the difference between the DCR that the two types of photon counters exhibit, as stated in Section 2.1, causes its impact. Obviously, these long fiber lengths exceed the maximum fronthaul distances considered for 5G-oriented environments. Nevertheless, as we shall see below, the use of CMOS SPADs could benefit the QKD performance even at very short fiber lengths, in shared fiber topologies where the presence of intense classical signals can severely contaminate the quantum passband.  
The SKR calculation for the P2 MP scenario is depicted in Figure 7 by considering \(N=4\), \(N=16\) and \(N=64\) users, respectively.  
In this P2 MP scenario, as the number N of terminal users increases, the photon loss stemming from the cascaded splitter stages increases too, leading thereby to a lower SKR. This performance is resembled at the maximum key rates that can be distilled at sub-km fiber segments: the ~10 kbps/user for the case of \(N=4\) users is reduced down to ~800 bps for the case of \(N=64\) users. The SKR benefits raised using silicon SPADs are evident only at a distance longer than approximately 27 km for the case of \(N=64\) users (i.e., the maximum length at which the InGaAs QKD setup can distill secret keys) and at even longer fiber segments for the case of \(N=16\) and \(N=4\) users, respectively. At these long fiber distances of P2 MP links, which could be applied to 5G-oriented services where the low-latency requirement is not critical, the silicon SPADs operating at lower DCR allow for secret key  
Figure 6: Secure Key Rate (SKR) as a function of fiber length, for the P2P dark fiber link, for both InGaAs and upconverted CMOS-based Quantum Key Distribution (QKD) setups. The horizontal lines with different styles correspond to the Advanced Encryption Standard (AES) limits for key refresh times equal to 5.14 min (0.83 bps), 1 min (4.3 bps) and 1.4 s (183 bps), respectively.  
distillation with higher rate (i.e., lower key rotation times) than the InGaAs do. For shorter distances, where the low-latency requirement is met (\(<\)\(\sim\)17 km), the SKR behavior is almost identical for both types of detector.
## 2 Signal and Channel Model  
### Signal Model  
Consider an access point that serves a point-to-point (P2P) vehicular MIMO network using mmWave technology, as shown in Figure 1, where a transmit vehicle is operating at mmWave frequency bands with \(N_{t}\) transmit antennas. The user vehicle is equipped with \(N_{u}\) receive antennas. Let the transmit and user vehicles be autonomous, which are assumed to be fully connected with different modules and sensors as the power module is used as a battery to supply power to the other module. The perception module is recognized for driving environments and detected objects using sensors such as light detection and ranging (LiDAR), radars, cameras, a global positioning system (GPS), and inertial measurement unit (IMU), and other sensors as shown in Figure 2. A WiFi-based mmWave transceiver is also intelligently connected to the transmit and receive vehicles in Figure 2. We consider an IEEE 802.11 ad Wi-Fi chips with the number of 8 antennas to communicate at the 60 GHz frequency band.  
A blockage scenario is considered in the system model where the direct link between the transmit and user vehicles is blocked owing to the critical propagation conditions. Hence, the RIS is deployed in the transmission model to assist and expand the service coverage of the P2P vehicular MIMO communication. The received signal vector \(\mathbf{r}\in\mathbb{C}^{N_{u}\times 1}\) can be modeled as  
\[\mathbf{r}=\mathbf{H}_{e}\mathbf{x}+\mathbf{z}, \tag{1}\]  
where \(\mathbf{x}\in\mathbb{C}^{N_{t}\times 1}\) is the transmitted signal vector, which satisfies \(\mathbb{E}[\mathbf{x}\mathbf{x}^{H}]=\mathbf{I}\), \(\mathbf{H}_{e}\) is the \(N_{u}\times N_{t}\) effective channel from the transmitting vehicle to the user vehicle, \(\mathbf{z}\sim\mathcal{CN}(0,\sigma_{z}^{2}\mathbf{I})\) is the \(N_{u}\times 1\) additive white Gaussian noise vector and \(\sigma_{z}^{2}\) denotes the noise variance.  
After applying a hybrid precoding, \(\mathbf{x}\) is given by  
\[\mathbf{x}=\mathbf{F}_{A}\mathbf{F}_{D}\mathbf{s}, \tag{2}\]  
where \(\mathbf{F}_{A}\in\mathbb{C}^{N_{u}\times N_{RF}}\) is the analog radio frequency (RF) beamforming matrix, \(\mathbf{F}_{D}\in\mathbb{C}^{N_{RF}\times N_{s}}\) is the digital precoding matrix, \(||\mathbf{F}_{A}\mathbf{F}_{D}||_{F}^{2}\leq P\), \(P\) is the transmit power, \(\mathbf{s}\in\mathbb{C}^{N_{s}\times 1}\) is the transmitted symbol vector that satisfies \(\mathbb{E}[\mathbf{s}\mathbf{s}^{H}]=\mathbf{I}\), \(N_{s}\leq\min(N_{RF}^{t},N_{RF}^{n})\) when \(N_{t}>>N_{RF}^{t}\), \(N_{u}>>N_{RF}^{n}\), \(N_{RF}^{t}\) denotes the number of RF chains for the transmit vehicle side, and \(N_{RF}^{t}\) denotes the number of RF chains for the user intelligent vehicle side.  
Setting (2) in (1) and the received signal vector \(\mathbf{\hat{s}}\) is given after combining  
\[\mathbf{\hat{s}}=\mathbf{\tilde{H}}_{e}\mathbf{s}+\mathbf{\tilde{z}}, \tag{3}\]  
where \(\mathbf{\tilde{H}}_{e}=\mathbf{W}_{D}^{H}\mathbf{W}_{A}^{H}\mathbf{H}_{e} \mathbf{F}_{A}\mathbf{F}_{D}\) is the \(N_{s}\times N_{s}\) effective equivalent channel, \(\mathbf{\tilde{z}}=\mathbf{W}_{D}^{H}\mathbf{W}_{A}^{H}\mathbf
## 4 Discussion  
There is significant improvement in object detection accuracy from Algorithm 1 to Algorithms 2 and 3, though the improvement is not consistent over all the samples (Figure 8). Macro-averaged F1 score, micro-averaged F1 score and the voting model are in consensus regarding the order of algorithms.  
Looking at precision, Algorithm 1 performs better than Algorithms 2 and 3. In sensitivity, Algorithms 2 and 3 perform considerably better than Algorithm 1, comprising the differences visible in the F1 scores.  
Visual inspection confirmed that Algorithms 2 and 3 perform better in two regards: clump separation and low-intensity object segmentation. Algorithm 2 is both more sensitive to darker objects and is a bit stronger in clump-handling than Algorithm 3; this results in the best F1 score of the three. During the creation of Algorithm 1, the clump separation module was a key step in creating an algorithm that would perform consistently on samples of different object densities. Algorithms 2 and 3 were intrinsically better in this regard: no performance decrease of notable measure was detected in relation to the density of objects on the samples, though Algorithm 3 could still be improved in this regard (there is possibility to fine-tune this behavior). Comparing the algorithms in this dimension might be part of a future investigation.  
Interestingly, the complex but still classical method of Algorithm 2 performs best of the three on this sample set, though only very slightly--keeping in mind that Algorithm 3 can still be fine-tuned to this dataset (annotation with more information is needed).  
It is important to note that clumps of objects are often comprised of objects of inhomogeneous intensities. It is also important to mention that low-intensity objects (on which Algorithms 2 and 3 perform significantly better) are usually apoptotic/necrotic cell nuclei that do not take part in the ploidy analysis result itself, and are filtered from the population during the later stages of the data analysis process.  
Considering all the above: replacing Algorithm 1 with any of the other two is an improvement.  
Based on the close results, the option for more fine-tuning and its extendibility, we chose Algorithm 3 to proceed to the population-level evaluation.
# Determination of Corrosion Rate in GAlvanized Pipes in Centralized Hot Water Supply Systems
## 3 Results and Discussion
### Assessment of the Impact of Iron-Containing Sediment on Zinc Corrosion
Interacting with Fe\({}^{3+}\) iron ions in equilibrium with the precipitate, the zinc coating is destroyed at the anode and passes into water in the form of zinc ions by reaction. \[\text{Zn-}-\text{Zn}^{2+}+2\text{e}.\]  
Iron (III) ions act as a depolarizer [9], take an electron, and are reduced to Fe (II). \[\text{Fe}^{3+}+\text{e}=\text{Fe}^{2+}\]  
And then iron hydroxide in the form of a precipitate is released at the cathode. \[\text{Fe}^{2+}+\text{O}_{2}+\text{H}_{2}\text{O}=\text{Fe(OH)}_{3}.\]  
To determine the thickness of the zinc coating, the longitudinal section of the pipe wall of one part of the cut pipe was subjected to grinding. The measurement was performed using a scanning electron microscope. The smallest thickness of the zinc layer, equal to 17-19 microns (Figure 12), was found at the boundary where the suspension was divided into water and sediment. This boundary is clearly observed in Figure 11. If the average thickness of the coating is 30 microns, the corrosion rate of the zinc coating can be calculated. Calculations show that the corrosion rate was 0.031 mm/year (or 31 microns/year), which is equal to the coating thickness. Figure 11: Sample surface after exposing to the sediment of Fe(OH)\({}_{3}\). Figure 12: Thickness of zinc coating after 28 days of contact. Anions, especially chlorides, play an important role in corrosion reactions. To confirm the mechanism of the effect of chlorides on corrosion, we present the results of a sediment study after three days of contact (Figure 13). Figure 13 shows a micrograph of the sediment with the results of the determination of the chemical composition. The table in the figure shows that in the sediment (the area highlighted in red), there was a locally increased chloride content of up to 9.72 wt% and zinc of up to 20.11 wt%, while in other randomly selected areas of the same sample, the chloride content did not exceed 1 wt%, and zinc was 2-3 wt%.
## 4 Description of the Application Problem and Employed Multi-Fidelity Models
### Conceptual Modeling  
#### 5.1.1 Operating Conditions: Definition of Model Parameters and Their Range  
The developed method starts the conceptual modeling stage by identifying the model parameters, as stated in Table 1. In this case, one only needs to identify the boundary conditions employed in the 3D CFD model (\(x_{HF}\)), as described in Section 4.3, and the inputs of the 0D model (\(x_{LF}\)), as depicted by Figure 3. By doing so, Table 3 is obtained, providing \(N=9\) parameters. Notice that \(N_{LF}=7\), as the parameters for the LF model are the same as the HF one save for the turbulent intensities at both inlet and EGR ducts. Furthermore, three levels are selected for each parameter, with values covering a typical range of working points in a piston engine featuring LP-EGR that may produce condensation. The rightmost column of Table 3 includes the matrix of eligible parameter values \(x_{i}^{\text{f}}\) that will be employed for conducting the different designs of experiments. Notice that, in this case, the geometry of the junction has been kept constant so that the exploration is performed in terms of operating conditions.
# Generalized Sketches for Streaming Sets
## 5 Our Framework SimCar
### Jaccard Similarity Estimation and Nearest Neighbor Search  
**Jaccard Similarity Estimation.** Let \(\vec{x}_{u}^{(*)}\) be the DOH sketch of OH sketch \(\vec{x}_{u}\), \(u\in U\), which is computed using the optimal densification technique introduced in Section 4. Similar to optimal densification [22], we find that \(P(x_{u,i}^{*}=x_{v,i}^{*})=J_{u,v}\) for any \(1\leq i\leq k\). Then, we estimate the Jaccard similarity of \(J_{u,v}\) as  
\[\hat{J}_{u,v}^{*}=\frac{\sum_{i=1}^{k}\mathds{1}(x_{u,i}^{*}=x_{v,j}^{*})}{k}.\]Following the error analysis in [22], we find that \(\hat{J}_{u,v}^{*}\) is an unbiased estimate of \(J_{u,v}\) and has the same accurate performance as the optimal densification of OPH.  
**Nearest Neighbor Search.** A variety of LSH methods [16; 23; 24; 25; 26] have been developed to solve the task of nearest neighbor search with sub-linear time. The basic idea behind these LSH methods is to place similar users into the same bucket of a hash table with high probability. Following the principle of LSH, our nearest neighbor search method consists of two basic phases:  
* **Pre-processing phase.** We first construct \(b\) hash tables \(\mathbb{H}_{1},\ldots,\mathbb{H}_{b}\) for all users in \(U\). Let \(k=bc\), where \(c\) is an integer parameter used to determine the performance of LSH. Let \(\vec{x}_{u}^{(*,i,c)}\), \(1\leq i\leq b\), denote the vector consisting of the \(((i-1)c+1)^{\text{th}}\) to \((ic)^{\text{th}}\) elements of DOH sketch \(\vec{x}_{u}^{*}\), i.e., \(\vec{x}_{u}^{(*,i,c)}=(x_{u,(i-1)c+1}^{*},\ldots,x_{u,ic}^{*})^{\top}\). Then, we insert each user \(u\) and its densification sketch \(\vec{x}_{u}^{*}\) into hash tables \(\mathbb{H}_{1},\ldots,\mathbb{H}_{b}\) as \[\mathbb{H}_{i}[\vec{x}_{u}^{(*,i,c)}]\leftarrow\mathbb{H}_{i}[\vec{x}_{u}^{( *,i,c)}]\cup\{u\},\quad 1\leq i\leq b.\]
* **Retrieving phase.** Given a specific user \(u\), we search for its similar users. Instead of scanning all users in set \(U\), we only probe \(b\) different buckets \(\mathbb{H}_{1}[\vec{x}_{u}^{(*,i,c)}],\ldots,\mathbb{H}_{b}[\vec{x}_{u}^{(*,i, c)}]\) from \(b\) hash tables, respectively, and report the users in any of these buckets as the potential candidates. Last, nearest neighbors of user \(u\) are detected by enumerating all users in set \(\mathbb{H}_{1}[\vec{x}_{u}^{(*,i,c)}]\cup\ldots\cup\mathbb{H}_{b}[\vec{x}_{u} ^{(*,i,c)}]\), and computing and sorting their Jaccard similarity estimations.
## 2 Materials and Methods
### Prediction of Rockfall Trajectories Induced by Rock Mass Instability
#### 2.4.1 Theoretical Framework of the Rockfall Simulation Model
In modeling energy dissipation, RAMMS (Rapid Mass Movements Simulation) incorporates two resistance mechanisms: viscoplastic drag and velocity-dependent sliding resistance. These mechanisms respectively simulate viscoplastic deformation upon impact with soft surfaces and kinetic energy loss during sliding. The viscoplastic drag is integrated into the dynamic equations as a term proportional to the square of the sliding velocity, effectively capturing the absorption and dissipation of kinetic energy by ground materials. In the RAMMS module, this resistance is defined as a function of the squared sliding velocity and is expressed as follows:  
\[F_{v}=-\frac{1}{2}mC_{v}v_{s}^{2} \tag{9}\]  
In the equation, \(F_{v}\) denotes the viscoplastic resistance force. \(m\) denotes the mass of the rock block. \(v_{s}\) denotes the sliding velocity of the rock block along the terrain surface. \(C_{v}\) denotes the terrain resistance coefficient, which reflects the relative stiffness or softness of the surface material. This model indicates that heavier and faster-moving rock blocks undergo more intense deformation in weaker soil substrates, leading to greater energy dissipation. RAMMS::ROCKFALL is based on rigid-body dynamics and hard-contact theory, enabling precise simulation of the complete rockfall process--including sliding, rolling, and bouncing--on three-dimensional terrain. It overcomes the limitations of traditional two-dimensional models in capturing block geometry, rotational motion, and complex terrain features. It supports both single-trajectory dynamic analysis and multi-trajectory statistical simulation and provides output parameters including bounce height, velocity, rotational energy, and total energy variation. This provides a physics-based, high-precision dynamic foundation for rockfall hazard assessment and the optimization of protective engineering measures.
# Air-Polishing Powders' Effect on the Color of CAD/CAM Restorative Materials
###### Abstract  
Air-polishing powders are used to remove stains from the enamel and various restorative materials, but their effect on the discoloration of CAD/CAM blocks remains scarce. Therefore, this study investigated the effect of various air-polishing powders on the color changes in different CAD/CAM blocks to predict the esthetic outcomes. Specimens were prepared from CAD/CAM blocks (Vita Mark II, Paradigm MZ100, Lava Ultimate, Cerasmart, Vita Enamic) and divided into five groups (n = 10) according to the air-polishing powder: sodium bicarbonate; aluminum trihydroxide; calcium carbonate; glycine; and erythroid. Color parameters were measured with a spectrophotometer before and after air-polishing. The color difference was calculated with the \(\Delta\)E\({}_{00}\) formula. Data were statistically evaluated with one-way ANOVA, Tukey, and two-way ANOVA tests (\(\alpha\) = 0.05). The CAD/CAM block type and the air-polishing powder type significantly influenced the \(\Delta\)E\({}_{00}\) value, whereas their interactions did not affect it significantly. Calcium carbonate and aluminum trihydroxide significantly increased the \(\Delta\)E\({}_{00}\) values of Lava Ultimate and Cerasmart. Although none of the groups exceeded the acceptability threshold (\(\Delta\)E\({}_{00}\) = 1.8), most exceeded the perceptibility threshold (\(\Delta\)E\({}_{00}\) = 0.8). Consequently, dentists should avoid air-polishing or should repulsion with care, depending on restorative material knowledge, to maintain color stability when uncertain about the material encountered clinically.  
CAD/CAM blocks; air-polishing: color stability; resin-matrix ceramics; air-polishing powders Article
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
###### Abstract  
This paper investigates secure transmission in unmanned aerial vehicle (UAV) relay-assisted millimeter wave (mmWave) networks, where the selected UAV relay performs secure transmission in both the on-off and non-on-off schemes. Meanwhile, there are multiple eavesdroppers randomly distributed on the ground and attempting to wiretap the transmission. Leveraging the air-to-ground channel model and the tools of stochastic geometry, the novel expressions of transmit probability (TP) and secrecy outage probability (SOP) are derived in both the on-off and non-on-off transmission schemes with perfect beam alignment. The secrecy performance improvement is demonstrated in the on-off transmission scheme, and we find that there exists an optimal altitude of UAV relays to achieve the best TP. In addition, due to the limitations of UAV carriers, such as its low computational capacity and high mobility, the perfect beam alignment is difficult to achieve in the mmWave networks aided by UAV relays, and the effect of beam alignment error on the secrecy performance is investigated in the considered networks. Analyzing the numerical and simulation results, we find that the SOP will not have obvious deterioration when the beam alignment error is relatively small, and the SOP can be improved by using the antennas with a large number of elements. However, in high beam alignment error regime, the antenna arrays with a smaller number of elements will provide the better SOP.  
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Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 3 Results and Discussion
### Identification and Determination of Volatile Compounds in Forty Apple Cultivars
#### 3.2.2 Aldehydes  
Aldehydes were the second most abundant volatiles in this study, accounting for between 8.25% (Jonagold, JNG) and 69.23% (COP) of the total volatile content in the apple cultivars (Figure 2; Table S4). More than 25 aldehydes have been identified in apples [8; 36]. In this study, 12 types of aldehyde compound were identified (Table 2). Aldehyde content varied greatly among the apple cultivars and ranged from 662.21 \(\pm\) 80.12 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW (18.59% of total volatiles) in F) to 4905.73 \(\pm\) 520.41 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW (22.60% of total volatiles) in Jiguan (JG) (Table 4; Table S4). Hexanal (A1) and (E)-2-hexenal (A4) were the most predominant constituent aldehydes (average content \(>\) 200 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW) in all apple cultivars in this study, which was in agreement with a previous report [28].  
Hexanal is an important contributor to the characteristic fish-like sweet odours and confers a green aroma to apples [22]. HY had the highest content of hexanal (A1), at 1050.38 \(\pm\) 85.45 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW, followed by JNG (756.56 \(\pm\) 82.36 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW) and QG (517.39 \(\pm\) 45.28 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW). In contrast, the hexanal content in Ruiyang (RX, 29.29 \(\pm\) 1.86 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW) and FJ (42.77 \(\pm\) 8.65 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW) was much lower than in other cultivars (Table S3). (E)-2-Hexenal confers a green leafy sensorial attribute to apple flavour [37]. The highest content of (E)-2-hexenal (A4) was 4435.22 \(\pm\) 500.52 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW in JG, while the lowest was 569.95 \(\pm\) 26.95 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW in FJ. In addition, nonanal was detected in 24 apple cultivars, providing a strong smell of grease and a sweet orange flavour [38]. On the other hand, (E,E)-2,4-Heptadienal (A9) was found only in cultivar Indo (ID) and GS (Table S3).
## 3 Materials and Methods
### Li\({}_{2}\)S\({}_{6}\) Symmetric Cell Assembly and Testing  
A Li\({}_{2}\)S\({}_{6}\) electrolyte solution (0.1 M) was prepared by dissolving S and Li\({}_{2}\)S in a molar ratio of 5:1 in lithium-sulfur electrolyte (1 M LiTFSI dissolved in a 1:1 volume ratio of DOL/DME, with 1 wt% LiNO\({}_{3}\)) and stirring at room temperature for 24 h in a glovebox. Carbon paper with a diameter of 12 mm was used as both the working electrode and the counter electrode. Two MoS\({}_{2}\)-NG/PP, MoS\({}_{2}\)-G/PP, and MoS\({}_{2}\)/PP separators were placed with the modified layers facing the working and counter electrodes, and 25 \(\upmu\)L of Li\({}_{2}\)S\({}_{6}\) electrolyte was added to each side to assemble symmetric cells. Cyclic voltammetry (CV) tests were conducted with a voltage range of \(-\)1 to 1 V at a scan rate of 10 mV s\({}^{-1}\) to record the current-voltage curves.