esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The flexural strength values (Mean \(\pm\) SD) of the ceramic-manufacturers' liquid mixtures ranged between 67.2 \(\pm\) 10.2 and 85.8 \(\pm\) 12.8 MPA (NR \(<\) VT \(<\) IV \(<\) CR), while the flexural strength values (mean \(\pm\) SD) of the ceramic-water mixtures were between 72.2 \(\pm\) 6.9 and 95.2 \(\pm\) 12.7 MPA (CR \(<\) NR \(<\) VT \(<\) IV) (Table 3).  
When the H\({}_{2}\)O mixture of each ceramic system was compared to its corresponding manufacturer's liquid mixture, there were no significant differences for all four ceramic types.  
Within the H\({}_{2}\)O mixtures, IV showed significantly higher flexural strength values compared to the other ceramics; namely, Creation (\(p=0.00007\)), Noritake (\(p=0.006\)), and Vita (\(p=0.018\)). Within the liquid mixtures, NR had the significantly lowest flexural strength values compared to the other ceramics, namely Creation (\(p=0.003\)) and InLine (\(p=0.0431\)), and no difference with Vita (\(p=0.122\)) (Figures 1-3).  
When flexural strength was evaluated, Weibull distribution presented lower shape values for the groups IV (8.3 vs. 4.3), VT (9.0 vs. 6.0), and CR (11.0 vs. 7.8) when mixed with H\({}_{2}\)O compared to their corresponding liquids, and higher shape values for the group NR (10.0 vs.10.6). Weibull analysis showed more reliable flexural strength for the groups IV, VT, and CR when mixed with H\({}_{2}\)O compared to their corresponding liquids (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ceramic Type** & **Flexural Strength (Mean \(\pm\) SD)** & **Flexural Strength (Mean \(\pm\) SD)** & **Min–Max FS (95\% CI)** & **Min–Max FS (95\% CI)** \\  & **Water (W)** & **Liquid (L)** & **Water (W)** & **Liquid (L)** \\ \hline CR & 72.2 \(\pm\) 6.9 \({}^{\mathrm{a,A}}\) & 85.8 \(\pm\) 12.8 \({}^{\mathrm{a,A}}\) & 67.3–107.9 & 63.1–86.7 (79.0–92.6) \\ \hline IV & 95.2 \(\pm\) 12.7 \({}^{\mathrm{b,c,B}}\) & 83.1 \(\pm\) 16.9 \({}^{\mathrm{a,c,B}}\) & 68.5–121.1 & 45.6–132.8 (88.4–102.1) & (74.1–92.1) \\ \hline NR & 76.3 \(\pm\) 6.3 \({}^{\mathrm{a,d,C}}\) & 67.2 \(\pm\) 10.2 \({}^{\mathrm{b,d,e,C}}\) & 45.9–89.0 (72.9–79.7) & 38.5–76.7 (61.8–72.6) \\ \hline VT & 81.7 \(\pm\) 12.1 \({}^{\mathrm{a,c,D}}\) & 79.7 \(\pm\) 14.6 \({}^{\mathrm{a,c,e,D}}\) & 56.7–108.7 (75.3–88.1) & 60.2–98.9 (71.9–87.5) \\ \hline \end{tabular}
\end{table}
Table 3: The mean flexural values (MPa \(\pm\) standard deviations) of IV, NR, VT, and CR, and their related modeling liquids. 95% confidence interval of mean values of IV, NR, VT, and CR, and their related modeling liquids. The same superscript lowercase letters in the same column indicate no significant differences and uppercase letters based on the experimental group (\(p<0.05\)).  
Figure 3: Mean and standard deviation for the various tested ceramics groups CR, IV, NR, and VT, mixed with H\({}_{2}\)O or their corresponding modeling liquids.  
Figure 2: Mean of MPA interaction of the various tested ceramic groups CR, IV, NR, and VT, mixed with H\({}_{2}\)O or their corresponding modeling liquids.  
Figure 1: Boxplot-best of the groups CR, IV, NR, and VT, mixed with H\({}_{2}\)O or their corresponding modeling liquids.
## 4 Mitigating Quantization Quality Degradation Based on the Observation
### Quantization-Free Prefix (QFeP)  
Orthogonal to the QFeM, we propose the Quantization-free Prefix (QFeP), which mitigates the quantization errors by precomputing the prefix (or short prompt) corresponding to activation spikes. This method is based on the observations presented in Section 3.2, which indicate that significant quantization errors result from the overestimated scale factor of the first instance within the restricted token set. Inspired by this occurrence pattern of activation spikes, we aim to construct a prefix that stabilizes the quantization scale factor of the tokens that come after the prefix. In other words, once the prefix is fixed at the beginning, the activation spikes consistently occur within the prefix. Afterward, we employ the key-value (KV) caching mechanism to process the activation spikes in advance. In practice, KV cache is utilized to optimize the decoding speed of causal language models by storing precomputed key and value states of the previous tokens [45; 46]. This approach provides a bypass of the quantization including activation spikes, while preserving the context of the prefix through the KV cache. The KV cache for the prefix is precomputed once through the offline inference of the LLM without quantization. Then, this KV cache is exploited in the quantization phases, such as calibration or dynamic quantization, even for quantized inference. The process of QFeP is illustrated in Figure 3. We verified that our prefix design is effective in mitigating the quantization errors through a prefix ablation study (Section 5.4).
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 4 Experiments and Performance Analysis
### Performance on Different Applications  
In this section, we compare the performance of the proposed strategy on different applications, wherein the basic setting is shown in Section 4.1. It is noted that, for an arbitrary node, it will randomly select another node to perform one of the designed applications from the table. Therefore, to reduce the bias caused by such randomness and  
Figure 4: The topology of the SDN network (with 10 nodes) generated by mininet during simulation. For an arbitrary node, it will select another node to perform one of the designed applications in Table 3 randomly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applications** & **Interval** & **Size** & **Priority** & **Latency** \\ \hline Periodic AMI & 15 s & 123 & 1 & \(\leq\)15 s \\ \hline AMI management & 300 s & 4000 & 2 & \(\leq\)1 s \\ \hline Periodic power quality & 3 s & 3000 & 1 & \(\leq\)3 s \\ \hline Power management & 300 s & 4000 & 2 & \(\leq\)1 s \\ \hline Requested AMI & On-demand & 123 & 3 & \(\leq\)5 s \\ \hline Requested power quality & On-demand & 2000 & 3 & \(\leq\)5 s \\ \hline Video surveillance & Constant & 250 KB/s & 0 & \(\leq\)100 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Smart grid applications setting.  
make the experimental results more accurate and robust, we have conducted experiments on each kind of topology ten times and adopted the average value as the final result.  
Figure 5a shows the end-to-end delay comparison of data packets for different service flows (AMI/power management, requested AMI/power quality, periodic AMI/power quality, video surveillance). From Figure 5a, as the network node number increases, the network load also increases. At this point, the time of packet staying in the queue also increases. The QCORS considers the extent of the congestion in the middle forwarding link, and leads the packet to the lighter path according to the link load. Thus, the proposed algorithm reduces the queuing time of the packet, and leads to different end-to-end delays of different priorities. It can also guarantee the delay and reliability of the higher-priority data, and reduce the congestion degree of the network.  
Figure 5b shows the variation of the average packet loss rate of four different priorities in the neighborhood network. It can be seen from the figure that in the case of fewer network nodes, the mechanism can guarantee the QoS requirement of different service flows. When the network congestion is aggravated, the QCORS mechanism guarantees QoS of packets by prioritizing the higher-priority packets. Therefore, the higher priority, the smaller packet loss rate.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 7 Conclusions  
The BB84 protocol is the most representative protocol in quantum cryptography. The protocol uses a single quantum channel to transfer quantum encoding states. Despite an outstanding security performance, it still allows the possibility to steal the key by interfering with the mentioned quantum channel under individual attacks by an eavesdropper. Since its development, many other BB84-like protocols have been developed, many of them called by specific names despite their clear similitude to the BB84 one. There is not a unique line of development, instead, they commonly attend to some improvements in the protocol such as economy, security, and so forth.  
In the present work, a protocol for the settlement of QKD using double teleportation and quantum processing together has been proposed. The procedure generates an entangled multipartite system among three parties plus a control system. The involved entanglement, together with local control, still allows us to manipulate the global quantum state on different parts to those exerting it. The process involved in the double teleportation plus post-processing has been shown to have non-locality activation, thus stating quantum correlations. Thus, an asymmetric post-processing scheme is proposed to generate and assemble a quantum state on a selected basis (defined by the state to teleport) on one of those parts. Then, it is shaped under the proper control, finally setting the QKD protocol.
# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 5 GridKG--Graph-Based Representation of Distribution Grid
### System Events
#### 5.3.1 Example C  
This example shows how **GridKG** can be used to derive a list of outage events that occurred downstream from a specific _Element-Asset_. This provides details of the outages, as well as a graph representing a fragment of the system with information about the components. For illustrative purposes, the response is limited to 10 outages. Query produces a list of outages, including details related to their causes and times of incidence, as shown in Table 1. Additional information on the types of involved _Element-Assets_ and their electrical specifications is included in a graphical view of the obtained data in Figure 5. It also provides details related to the customers affected by the outages. The figure shows that one of the elements involved was _Fuse_, connected to phase \(A\) of a 14 _kV_ line that was involved in seven outages. Three outages were caused by wildlife and four were of unknown causes. Other outages were associated with a transformer, a switch, and another fuse. More information about specific outages, involved _Elements-Assets_, as well as affected customers can be easily obtained and examined through interaction with GridKG.  
Figure 4: Primary Switch/protective devices located on the upstream path from the top left element to the primary breaker (bottom right corner).
# Calculation of Blasting Damage Zone Radius of Different Charge Structures in Burnt Rock
## 5 Radius Calculation of Burnt-Rock-Blasting Damage Area
### Calculation of Fracture Zone Radius of Burnt Rock
#### 5.2.2 Non-Coupling Explosive Fill  
(1) The range of the fracture zone under the action of a stress wave  
The range of the crack zone under the action of a stress wave after an explosive explosion with the uncoupled charge structure is similar to that under the action of a stress wave with the coupled charge structure described above. However, due to the different impact pressure of the two charge structures on the blasthole wall, the range of the crushing zone under the action of a shock wave is different. Therefore, according to the [PERSON] criterion, the following equation can be established with the uncoupled charge structure:  
\[(1-D)\sigma_{td}=\frac{1}{\sqrt{2}}\cdot\sigma_{R}\cdot B\cdot\left(\frac{R_{2 (1)}^{2}}{R_{1}^{2}}\right)^{-(\frac{\sigma_{2}}{1-D})} \tag{44}\]  
The initial radius \(R_{2(1)}^{2}\) of the crack zone caused by the stress wave with the uncoupled charge structure is  
\[R_{2(1)}^{2}=R_{1}^{2}\cdot\left[\frac{B\sigma_{R}}{\sqrt{2}\sigma_{td}}\right] ^{\frac{1-D}{\sigma_{2}}}=R_{1}^{2}\cdot\left(\frac{\sigma_{cd}}{\sigma_{td}} \right)^{\frac{1-D}{\sigma_{2}}} \tag{45}\]  
(2) The range of the fracture zone under the action of an explosive gas  
With the uncoupled charge structure, the detonation gas penetrates into the cracks generated by the stress wave to form a quasi-static stress field, and the pressure \(P_{d}^{2}\) on the borehole wall is given as  
\[P_{d}^{2}=\left(\frac{P_{0}}{P_{k}}\right)^{\frac{\gamma}{k}}\cdot\left(\frac {V_{c}}{V_{b}}\right)\cdot P_{k} \tag{46}\]  
In this formula, \(P_{0}\)--detonation pressure of the explosive, MPa; \(P_{k}\)--the critical pressure used in the static pressure calculation, \(P_{k}\) = 200 MPa; \(\gamma\)--expansion adiabatic index, the value is usually 3; \(k\)--explosive gas isentropic index, k = 3; \(V_{c}\)--explosive volume, m\({}^{3}\); and \(V_{b}\)--hole volume (excluding mud volume), m\({}^{3}\).  
Combining Equations (39), (40) and (46) gives  
\[S_{te}(1-D^{\prime})=\frac{P_{d}^{2}}{1-D^{\prime}}\cdot\left(\frac{R_{2(1)}^ {2}}{R_{2(2)}^{2}}\right)^{2} \tag{47}\]  
Therefore, the radius \(R_{2(2)}^{2}\) of the fracture zone of the burnt rock with the uncoupled charge structure is  
\[R_{2(2)}^{2}=\frac{1}{(1-D^{\prime})}\cdot\sqrt{\frac{P_{d}^{2}}{S_{te}}}\cdot R _{2(1)}^{2} \tag{48}\]  
The above derivation shows that the blasting failure zone of the burnt rock mass has a strong relationship with the initial damage \(D_{0}\) of the rock mass.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 4 Object Model Analysis
### 3D Surface Model Processing for Grasp Planning  
By calculating the convex hull of the model, its vertices, which are the potential rotational vertices used for pivoting, can be obtained (see Figure 4a). The rotational vertices are the supporting feet of objects during pivoting gait. In addition, the edges, which are the potential rotational edges used for tumbling, can be obtained from the convex hull.  
Superimposed segments are implemented to process a mesh model for contact and grasp planning. It analyzes the object model by peeling it into facets where the facets are allowed to be overlapped by each other. In addition to superimposed segments, the ray-shooting method [52] and simple segments [42] can also be used for analyzing mesh models. The ray-shooting method samples a contact point on the mesh surface and finds the candidate counter contact point by shooting a ray from the sampled point along the reversed normal direction. The simple segments method is similar to the superimposed segments while the difference lies in if the overlapping of facets is considered. The comparison among the three methods has been researched in [4] which showed that the superimposed segments can find more grasps while the time cost of it is faster than that of the ray-shooting method and is not significantly worse than that of the simple segments.  
In superimposed segments, given a triangulated CAD model [53], a seed triangle is initialized and is compared with the surrounding triangles by evaluating the differences between surface normals of triangles. If the difference is smaller than a threshold, the neighboring triangle is clustered into the same facet. Otherwise, a new facet based on the neighboring triangle is built. After dealing with the initial facet, the algorithm selects a new seed and repeats the clustering until all the triangles are scanned. As a result, a set of facets are generated by the superimposed segments, where each face can be a candidate contact surface with the table in SP. When placing an object on the table, we define SP if the vertical projection of the object's CoM on the table is included in the support area. During SP, the robot is allowed to regrasp the object without influencing the stability of the object.  
In addition, a set of contact points with the EEFs are computed by sampling the surface of the object model. To evenly distribute the contact points on the surface, sampling is initially performed over the entire surface. The sampled points are then repeatedly distributed as contact points on the superimposed facets. Further, some of the undesired sampling points are removed based on the following: (1) they are too close to the boundary of the facet, (2) they are close to each other, and (3) there is undesired collision between the object and the EEF. Examples of the sampled contact points are shown in Figure 4b.
# Theoretical Investigation for Angle Measurement Based on Femtosecond Maker Fringe
## 3 Simulation Results and Discussion
### The Simulation Setup
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Nonlinear Crystal** & \(A\) & \(B\) & \(C\) & \(D\) & \(E\) \\ \hline BBO & & & & & & \\ \(N_{\text{o}}\) & 2.7539 & 0.01878 \(\upmu\)m\({}^{2}\) & 0.01822 \(\upmu\)m\({}^{2}\) & 0.01354 \(\upmu\)m\({}^{-2}\) & * \\ \(N_{\text{e}}\) & 2.3753 & 0.01224 \(\upmu\)m\({}^{2}\) & 0.01667 \(\upmu\)m\({}^{2}\) & 0.01516 \(\upmu\)m\({}^{-2}\) & * \\ LiNbO\({}_{3}\) & & & & & \\ \(N_{\text{o}}\) & 4.91296 & 0.116275 \(\upmu\)m\({}^{2}\) & 0.048398 \(\upmu\)m\({}^{2}\) & 0.0273 \(\upmu\)m\({}^{-2}\) & * \\ \(N_{\text{e}}\) & 4.54528 & 0.091649 \(\upmu\)m\({}^{2}\) & 0.046079 \(\upmu\)m\({}^{2}\) & 0.0303 \(\upmu\)m\({}^{-2}\) & * \\ KDP & & & & & \\ \(N_{\text{o}}\) & 2.259276 & 13.005221 & 400 \(\upmu\)m\({}^{2}\) & 0.0108956 \(\upmu\)m\({}^{2}\) & (77.26408)\({}^{-1}\)\(\upmu\)m\({}^{2}\) \\ \(N_{\text{e}}\) & 2.132668 & 3.22799241 & 400 \(\upmu\)m\({}^{2}\) & 0.008637494 \(\upmu\)m\({}^{2}\) & (81.42631)\({}^{-1}\)\(\upmu\)m\({}^{2}\) \\ Ag\({}_{3}\)AsS\({}_{3}\) & & & & & \\ \(N_{\text{o}}\) & 9.220 & 0.4454 \(\upmu\)m\({}^{2}\) & 0.1264 \(\upmu\)m\({}^{2}\) & 1733 \(\upmu\)m\({}^{2}\) & 1000 \(\upmu\)m\({}^{2}\) \\ \(N_{\text{e}}\) & 7.007 & 0.3230 \(\upmu\)m\({}^{2}\) & 0.1192 \(\upmu\)m\({}^{2}\) & 660 \(\upmu\)m\({}^{2}\) & 1000 \(\upmu\)m\({}^{2}\) \\ \hline \end{tabular}  
* The \(\backslash\)’ means that the Sellmeier equations of BBO and LiNbO\({}_{3}\) aren’t related to parameter \(E\).  
\end{table} 
Table 1: The parameters of the Sellmeier equation for the calculation of refractive indices. \begin{table}
\begin{tabular}{c c c c c} \hline
**Point Group** & **Nonlinear Crystal** & \multicolumn{3}{c}{**Effective Nonlinear Coefficient**} & **Nonlinear** \\ \hline  & & **p-s Case** & **p-p Case** & **s-s Case** & **Coefficient** \\ \hline  & BBO & & & & \(d_{22}\) = 2.3 pm/v \\  & LiNbO\({}_{3}\) & \(d_{22}\)cos\(\theta\)cos\(3\varphi\) & \(d_{31}\)(cos\(\theta\)sin\(2\theta\) + cos\({}^{2}\theta\)sin\(\theta\)) + & \(d_{22}\)cos\(3\varphi\) & \(d_{31}\) = \(d_{22}\) = 2.1 pm/v \\  & Ag\({}_{3}\)AsS\({}_{3}\) & & & \(d_{22}\) = 10.4 pm/v \\  & & & & \(d_{31}\) = \(-\)16.6 pm/v \\ \hline \(\overline{4}\)2 m & KDP & \(d_{36}\)sin\(2\theta\)cos\(2\varphi\) & \(d_{36}\)(sin\(2\theta\)cos\(\theta\)sin\(2\varphi\) + cos\({}^{2}\theta\)sin\(2\varphi\)) & 0 & \(d_{36}\) = 0.39 pm/v \\  & & & & & \\ \end{tabular}
\end{table} 
Table 2: The effective nonlinear coefficients \(d_{\text{eff}}\) of the investigated nonlinear crystals.
## 1 Introduction
The GFAS is currently using the MODIS instrument on the Terra and Aqua satellites for fire observations [25]. Although the method of estimating pollutant emissions based on FRP has achieved relatively good results in many studies [27; 28; 29; 30], the emission inventory based on MODIS is suspected to underestimate pollutant emissions [33; 34]. MODIS only provides four observations per day under ideal conditions and has a \"bow tie\" effect as well as not being sensitive to small fire (low FRP) detection. Although high-spatial-resolution polar-orbit sensors such as VIIRS can provide the ability to identify AFs (active fires) at an increased number and with lower FRP values (, they can still only capture the flames burning in the clear sky at the satellite overpass [34]. Previous studies have found that fire activity exhibits strong Gaussian diurnal cycle characteristics and spatio-temporal variation [35; 36; 37]. Obviously, due to the highly dynamic nature of fire, the low time resolution of polar-orbiting satellites cannot accurately describe the fire diurnal cycle, which leads to large errors in the process of integrating FRE. Geostationary satellites with high time resolution can make up for the above shortcomings and more accurately capture the changes in the process of fire burning, so as to improve the accuracy of estimating combustion emissions. This study focuses on a better understanding of the diurnal variation and spatio-temporal dynamic characteristics of fire, so as to improve the estimation results of fire combustion emissions. We use the new fire diurnal cycle information obtained from the geostationary satellite Himawari-8, to reduce the error caused by the low-time-resolution monitoring results of the medium resolution imaging spectrometer. The spatial variation of this fire diurnal cycle and its drivers is discussed in detail and is combined with the twice-daily FRP information provided by the VIIRS product. We aimed to reduce the deviation and uncertainty caused by using only one FRP data type, to take into account the burning even in the short term and to reduce the omission of active fire detection. We used land use data to mask the study area and chose the most suitable smoke emission factor in order to calculate the final total fire emissions based on the estimated value of DMB obtained by FRE. The paper is organized as follows: Section 2 introduces the study area and data characteristics used in this study. Section 3 introduces the methods of estimating biomass combustion emissions. Section 4 shows the relevant experimental results and comparative analysis. Section 5 concludes with the major findings and problems of this study.
## Experimental Results
### Feasibility of Subset Assumption  
In order to answer **RQ1**, we first evaluate the subset learning and rating aggregation in SaSLM to show how well the subset assumption models the set-level interaction. Specifically, we generate the set ratings by using subsets learned from the true item ratings. We used the same data split as described in Section 5.3. Rather than predicting item ratings, we used the true ratings of items by users to predict set ratings. This was for verifying the intuition of subset assumption. Note that using item ratings here is not contradictory to SPL settings, where item ratings are predicted by set ratings. This experiment aimed to verify the fact that when users are mainly affected by some salient items when they rate a set, no matter whether they like these items or not. These items, which are more influential in users' set-rating process, consist of what we call \"subsets\". By using the item ratings to simulate the set-rating process, we prove that it is necessary to learn personalized subset selection and aggregation for rebuilding users' set-rating process. For a comparison, this experiment was also performed on ARM, VOARM, and ESARM, with different rating aggregations. Results are reported in Table 3. As shown in the table, SaSLM performed best on both explicit and implicit RealSet. In comparison with ARM and VOARM, which do not learn subsets, the subset learning in SaSLM can well reflect users' underlining patterns of set-rating behaviors. Compared with ESARM, which learns extremal subsets, the subset learning in SaSLM is more effective, since it learns personalized subsets for users. The results reported in Table 3 also show the performance ceilings of the methods for set-rating prediction.