esa-sceva/satcom-chunk-collection huggingface dataset:
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 3 Policy-Dividing and Combination Mechanism in SUDC
### SRv6 Policy Pushing
#### 3.2.2 BitString Mapping  
BitString mapping involves determining the BitString encoding for each policy distribution SID block in ascending order of sequence number. First, identify the SRv6 policies to which the policy distribution SID block belongs and determine all target nodes to which this policy distribution SID block needs to be multicast. Then, the BitString encoding of the policy distribution SID block is set in such a way that the corresponding bits of all target nodes are set to 1, and the remaining bits are set to 0. After BitString mapping is completed, each policy distribution SID block has its own BitString encoding that indicates the target node set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distribution** & **Sequence** & **SID** & **Belonging** \\
**Serial Number** & **Number** & **List** & **SRv6 Policy** \\ \hline
101 & 1 & 1->2->3->6->7 & A, B \\ \hline
101 & 2 & 9->5->4->8 & A, C \\ \hline
101 & 3 & 3->2 & C \\ \hline \hline \end{tabular}
\end{table}
Table 1: The policy distribution SID blocks of the example.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 2 Laser Crosslink Mission
### Orbit Scenarios  
The mission lifetime is composed of three phases: the launch and early orbit phase (LEOP); the drift recovery and station-keeping phase (DRSKP); and the normal operation phase (NOP). These phases comprise several modes of system check-out, telecommunication, hardware commissioning, and maneuvers. The concept of operations (ConOps) of the nanosatellites is presented in Figure 2. After being ejected in orbit, they drift several thousand kilometers away and operate independently. Through orbit maneuvers in the DRSKP, the inter-satellite distance is reduced. During the NOP, they sequentially adjusted the inter-satellite distance from 50 km to 1000 km and conducted laser crosslink tests. Figure 3 shows the various inter-satellite distances over the mission lifetime. The details of ConOps are as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Identification** & **Description** \\ \hline TMR.001 & Mission lifetime shall be longer than 12 months. \\ TMR.002 & The laser crosslink shall be established for the inter-satellite distance up to 1000 km. \\ TMR.003 & The data capacity shall be faster than 1 Gbps at \(1\times 10^{-3}\) of \\  & uncoded BER and \(1\times 10^{-9}\) of coded BER. \\ TMR.004 & A precision of residual line-of-sight jitters shall be smaller than 1 \(\upmu\)rad while the laser crosslink is established. \\ TMR.005 & Entire laser crosslink systems shall be contained in the 6U \\  & nanosatellite platforms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top-level project requirements and constraints.  
Figure 2: Concept of operations for the VISION mission. After the early orbit operations, the two CubeSats adjust their distances. For each specific distance, they maintain over 10 days to conduct the laser crosslink tests.
## 5 Results and Discussion
### Case Study Configuration  
To compare the two thermal designs proposed in Figure 8c,d, a case study is configured to investigate their thermal performances at the system, component, and cell levels. The cell (INR 21700) and motorcycle parameters are presented in Tables 1 and 2, respectively. As shown in Figure 9, the drive cycle used was obtained through on-site measurements at a test track, and it is very drastic, with maximum velocity being 221.9 km/h and maximum acceleration being 13.30 m/s\({}^{2}\). The instantaneous power demand is calculated from the data in Figure 9 and Table 2 using standard resistance to motion equations. In the case study, the motorcycle is required to repeat the drive cycle until the battery SOC hits 10%. To further challenge the TMS, the base case ambient temperature is chosen as 35 \({}^{\circ}\)C, and the base case coolant flow rate is set at 10 L/min, while these parameters are varied in the sensitivity analysis presented in Section 5.4. The time step used in the model is 10 ms.
## 4 Performance Evaluation of Parameter Estimation  
In the previous section, we explained the principle of the proposed method for multipath parameter estimation using processes of segmentation and averaging. This section gives the simulation results in comparison with the direct-FFT method.
## 6 Evaluation Metrics of the Network Performance
### Data Traffic  
The data traffic quantity carried by the network is referred to as the load [51]. It employs the most efficient network to alleviate congestion. However, because all available resources are overutilized and overworked, the network may experience severe congestion. The network load was computed by the developers by determining the ratio of data received to the maximum data fluctuating over the simulation period [51].The average time for packets to transit across the collaborative network from sender to receiver is the E2E latency.
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 5 Discussion  
To evaluate the performance and efficiency of an expressed mechanism, a confusion matrix has been utilized that has expressed the four feasible results; in which, the genuine datum point tags rendered via a proficient have been compared to the relating datum point outcomes obtained via an exhibited classification mechanism. In this regard, the four feasible results consist of: false alarm rate (AR), correct reject (CR), hit rate (HR), and miss rate (MR). The confusion matrix has been expressed in Table 3 to make a better perception of four mentioned criteria. The declared anomaly detection mechanism is able to engender each of the four mentioned determinations in Table 3 as false negative (FN), false positive (FP), true negative (TN), and true positive (TP). These determinations have been made according to the expressed anomaly detection design repercussion and the genuine system datum.  
Multiple instance tests are utilized to consider the validity of expressed FSVs method in FDI attack detection. The FDIA pattern has been applied to consider the proficiency of the declared detection model and the assessment results are illustrated in Tables 4 and 5. Table 4 gives the expressed detection pattern with the number of testing data for compromised and common data as 1573 and 1394, respectively. As shown in Table 4, 56 tests of 1573 of compromised tests, and 29 tests of 1394 of common tests have not been detected correctly; 1517 tests of 1573 of compromised tests and 1365 tests of 1394 of common tests have been identified correctly. As a result, the detection precision of compromised and common tests are 96.44% and 97.93%, respectively. Table 5 shows the confusion matrix of the expressed detection pattern and comparison with other methods. Furthermore, the Hit Rate of True Positive and Correct Rejection Rate of True Negative of the suggested detection method are 96.44% and 97.93%, respectively. Additionally, False Alarm Rate of False Positive and Miss Rate of False Negative of the suggested detection method are 2.07% and 3.56%. Overall, it could be noticed from Tables 4 and 5 that the expressed method can identify the FDIAs with detection exactitude above 96% which illustrated the proficiency and performance of the expressed detection model to identify the FDIAs.  
The average detection time of suggested methods (FSVs), DNN with WT and HHT are 10 ms, 3.5 ms and 50 ms, respectively. The DNN training time of DNN with WT method [32] is 2713.2 s. As shown in Table 5, DNN with WT has a less average detection time in comparison of FSVs and HHT but FSVs and HHT methods do not have the training time and, therefore, make them faster than DNN with WT method; average detection time of FSVs (10 ms) is less than HHT (50 ms). Hence, the suggested method-based FSVs has a better performance in comparison of DNN with WT, HHT and Shallow model methods.
## 3 Results and Discussion
### Chemical and Phase Compositions  
The mapping made using SEM-EDS exhibited local segregation of yttrium (Y) in the ceramics under study. Thus, the larger grains were enriched in Y in both materials and no significant variation with sintering temperature was observed (Figure 5). Previous research confirmed the existence of the monoclinic (m), tetragonal (t), and cubic (c) ZrO\({}_{2}\) phases in these ceramics, enriched with different amounts of Y [39; 40; 42]. It was found in [39; 42] that the larger grains are m-ZrO\({}_{2}\), but a remainder of the c-ZrO\({}_{2}\) phase (yttrium-enriched c-ZrO\({}_{2}\) particles) is situated on their boundaries. This is consistent with our results (Figure 5a,b). In contrast, according to [42], tiny yttrium-depleted t-ZrO\({}_{2}\) particles united into agglomerates are situated separately, similarly to the results given in Figure 5a,c.  
Figure 5: Chemical analysis of the 6 YSZ ceramic sintered at 1500 \({}^{\circ}\)C: (**a**) SEM-BSE image and EDS maps of O, Y, and Zr with chemical compositions measured for (**b**) coarse and (**c**) fine grains.  
Using XRD patterns for the studied ceramics presented in our previous research [39,40], we constructed the dependences of phase fractions on the sintering mode (Figure 6). As mentioned above, three phases (m, t, and c-ZrO\({}_{2}\)) are present in these materials in various ratios.  
In summary, with the sintering temperature decreasing:  
* For the 3 YSZ ceramic, a slow decrease in the tetragonal ZrO\({}_{2}\) phase percentage and a steeper increase in the c-ZrO\({}_{2}\) phase fraction occurred; a comparatively high fraction of the t-ZrO\({}_{2}\) phase (above 59%) is characteristic for this material;
* For the 6 YSZ ceramic, a steeper decrease in the tetragonal ZrO\({}_{2}\) phase percentage and a slow increase in the c-ZrO\({}_{2}\) phase fraction occurred; a comparatively high yttria percentage provides better stabilization of the c-ZrO\({}_{2}\) phase in the studied temperature range; a higher sintering temperature facilitates cubic to tetragonal and monoclinic zirconia phase transitions.
# The Local Integrity Approach for Urban Contexts:
## 3 Experimental Validation and Data Analysis
### Post-Processing Analyses for Effective UER Estimation  
The feasibility of the local integrity approach in the selected urban scenario was validated by assessing multiple experimental measurements. Ad hoc routines were developed in a MATLAB(r) environment in order to parse and post-process the NMEA data logs obtained from the receivers under test. The local integrity algorithm was implemented according to Equations (2) to (5), thus including both the ensemble estimate of effective UER and the computation of instantaneous Local Protection Level values (ATPL, CTPL, VPL).  
Focusing on the effective UER estimation, a data base was built from the available observations of pseudorange residuals. An ACU reference frame was adopted, taking into account the local orientation of the road segments with respect to the North direction. A suitable spatial grid was then defined with a resolution of 15 m, as shown in Figure 4a (blue dotted lines). This figure also reports the position information logged during multiple consecutive loops by one receiver under test (red line, obtained from the uBlox EVK-6T [16]) and by a reference receiver (green line, from the Novatel receiver in RTK + IMU mode).  
Figure 4: Results of an experimental GNSS data collection: (**a**) Positions logged and spatial grid for combining the measurements, shown in Google Maps(r); (**b**) obtained values of effective UERER, in meters.  
The pseudorange residual measurements collected in a time window of 5 min were separately combined for each cell of the spatial grid, aiming at properly estimating the effective UERE in each location of the selected path. As an example, \(\hat{\sigma}_{\text{UERE},eff}\) values obtained processing the measurements of the uBlox EVK-6T receiver are reported in Figure 4b. These values were computed for each cell of the path, combining all the observations of residual vectors \(\mathbf{w}_{n}\) available during the selected 5 min time window from all the satellites used in PVT; the obtained \(\hat{\sigma}_{\text{UERE},eff}\) values range from 6.5 to 208.6 m, clearly denoting different qualities of the pseudoranges in different locations along this path.  
It can happen that different receivers located in a specific cell of the urban grid see dissimilar subsets of the expected satellites in view, due to their slightly different locations and signal reception conditions. For this reason and for the sake of flexibility, instead of directly storing aggregated \(\hat{\sigma}_{\text{UERE},eff}\) values in the data base, the information about the pseudorange quality has been kept separate for each available satellite. Thus, for each cell of the grid, the averaged covariance matrix of the residuals \(\overline{\Sigma}_{w}\) was stored as computed from Equation (2), having as diagonal elements the estimated residuals covariance \(\hat{\sigma}_{\mathbf{w}_{j}}^{2}\) for each \(j\)-th satellite.
# A Review of Validation Methods for the Intracranial Response of FEHM to Blunt Impacts
## 4 Further Research into Validation
### Investigation into Validation Protocol
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{4}{c}{**Peak Accelerations**} \\ \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Reference**} & **Impact** & **Linear** & **Rotational** & \multirow{2}{*}{**Weight**} \\  & & **Location** & **(g)** & **(rad/s\({}^{2}\))** & & \\ \hline \multicolumn{5}{c}{**Intracranial Pressure**} \\ \hline C288-T3 & [PERSON] et al. [36] & Occipital & 236 & 24,206 & 0.8 \\ C241-T6 & [PERSON] et al. [36] & Occipital & 127 & 5792 & 1.0 \\ C064-T4 & [PERSON] et al. [36] & Occipital & 122 & 4456 & 1.0 \\ C380-T4 & [PERSON] et al. [36] & Temporal & 196 & 14,962 & 0.8 \\ C380-T5 & [PERSON] et al. [36] & Parietal & 77 & 6358 & 0.8 \\ C380-T6 & [PERSON] et al. [36] & Parietal & 147 & 9797 & 0.8 \\ C393-T3 & [PERSON] et al. [36] & Temporal & 159 & 11,584 & 0.8 \\ C393-T4 & [PERSON] et al. [36] & Temporal & 180 & 9671 & 0.8 \\ \hline \multicolumn{5}{c}{**Relative Brain Displacement**} \\ \hline C755-T2 & [PERSON] et al. [35] & Occipital & 22 & 1882 & 0.7 \\ C755-T5 & [PERSON] et al. [35] & Frontal & 12 & 843 & 0.7 \\ C383-T1 & [PERSON] et al. [35] & Frontal & 62 & 2592 & 0.9 \\ C383-T4 & [PERSON] et al. [35] & Occipital & 108 & 10,364 & 0.9 \\ C288-T3 & [PERSON] et al. [36] & Occipital & 236 & 24,206 & 1.0 \\ C241-T5 & [PERSON] et al. [36] & Occipital & 194 & 8688 & 1.0 \\ C241-T6 & [PERSON] et al. [36] & Occipital & 127 & 5792 & 1.0 \\ C064-T4 & [PERSON] et al. [36] & Occipital & 122 & 4456 & 1.0 \\ C380-T4 & [PERSON] et al. [36] & Temporal & 196 & 14,962 & 1.0 \\ C380-T5 & [PERSON] et al. [36] & Parietal & 77 & 6358 & 1.0 \\ C380-T6 & [PERSON] et al. [36] & Parietal & 147 & 9797 & 1.0 \\ C393-T3 & [PERSON] et al. [36] & Temporal & 159 & 11,584 & 1.0 \\ C393-T4 & [PERSON] et al. [36] & Temporal & 180 & 9671 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental cases passing the selection criteria proposed by [PERSON] and [PERSON] [105] during critical evaluation of the empirical data from [PERSON] et al. [34], [PERSON] et al. [35], [PERSON] et al. [38] and [PERSON] et al. [36] for validation againstICP, relative brain displacement and brain deformation. Each impact is summarised by its location, the peak resultant linear acceleration and the peak rotational acceleration about a single axis. Where impact cases have been selected for multiple validation criteria, repeats have been indicated through italics. parameters proposed by [PERSON] and [PERSON] [105] have however been adopted during FEHM validation and investigation analyses [20; 90; 112].
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
## 2 Numerical Calculation Method
### Validation of the Simulation Method  
Comparing the numerical model with experimental results of a similar nature, we have verified the correctness and feasibility of the model. [PERSON] et al. [31] use high-speed photography experiments to capture the unsteady flow characteristics of a cavitation jet under three different expansion angles. Based on the structure parameter of the experiment, the model and method introduced above were used for simulation. The map of the jet axial velocity distribution and the axial vapor volume fraction distribution at \(d/2\) with three expansion angles of the nozzle are shown in Figure 8. The results show that the nozzle with an expansion angle of 60\({}^{\circ}\) has the best cavitation effect, which is consistent with the experimental results.  
As shown in Figure 9, the development process corresponds to the first 350 \(\upmu\)s period of cavitation. The blue area indicated by the dotted line in the figure indicates the main area of the bubble collapse [31]. Through the transient solution, the generation and shedding of the cavitation cloud are observed, as illustrated in Figure 10. Its development process is consistent with the experimental phenomenon. It has been established that cavitation vortex shedding was taking place in a periodical manner [13].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Parameters** & **Value** & **Unit** \\ \hline Temperature & 298.15 & K \\ Water density & 997.0 & kg/m\({}^{3}\) \\ Water kinematic viscosity & 8.93 \(\times\) 10\({}^{-7}\) & m\({}^{2}\)/s \\ Water dynamic viscosity & 8.9 \(\times\) 10\({}^{-4}\) & Pa-s \\ Saturated vapor pressure & 3169 & Pa \\ Vapor density & UDF & kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of the fluid.  
Figure 8: Effect of expansion angle on cavitation. (**a**) Axial velocity; (**b**) vapor volume fraction.
## 3 Results
### RQ3--How Have These Proposals Contributed to Addressing Different Levels of Violence?
#### 3.4.2 Benefits and Technical Limitations  
Among the key benefits, real-time monitoring (e.g., S1) enables the continuous surveillance of social media environments, facilitating the rapid identification of suspicious activities such as terrorism. Early attack detection (e.g., S4, S8, S10, S19, S21, S22, S27,S35, S44) has strengthened intrusion prevention and improved the resilience of critical infrastructures in smart cities. Additionally, enhanced cybersecurity in IoT networks (e.g., S3, S11, S14, S16, S17, S22, S23, S31, S36, S37, S39) has mitigated vulnerabilities in connected devices, reducing the risk of cyberattacks and unauthorized access.  
Another advantage of these technologies is their ability to automate the classification of critical events, streamlining incident prioritization and improving response efficiency for security agencies. Advanced fraud and theft detection (e.g., S5, S33) has been achieved through the integration of artificial intelligence models capable of analyzing behavioral patterns and predicting crimes in high-risk areas. Furthermore, the combination of IoT and deep learning has optimized security resource management, minimizing operational costs and expanding coverage in densely populated urban areas.  
Despite these advantages, significant challenges remain. One major limitation is the dependency on the data used to train models, which can impact their generalization and effectiveness in real-world scenarios (e.g., S6, S13, S15, S16, S18, S24, S26, S30, S31, S32, S43, S45). Another limitation relates to the high computational requirements of these systems (e.g., S27, S38, S39), which hinder their adoption in resource-constrained environments. Many solutions require specialized hardware, such as high-performance GPUs, increasing implementation costs and restricting their use in budget-limited security infrastructures. Additionally, optimizing large-scale data processing (e.g., S9) remains a challenge, as urban surveillance systems generate massive data streams that must be processed in real time without compromising latency or operational efficiency.  
The configuration of intrusion detection systems (e.g., S11, S20) is another critical factor, requiring continuous adjustments to minimize false alarms and ensure accurate threat detection in IoT networks. Moreover, bias in artificial intelligence models (e.g., S44) can lead to discriminatory behavior in suspect identification, raising ethical and legal concerns regarding the application of these technologies
## 4 Results
### Spatiotemporal Characteristics of the Land Use Conflict
#### 4.2.2 Spatial Patterns of the Land Use Conflict  
During the study period, the conflicts in the Su-Xi-Chang region have intensified (Figure 7). The spatial distribution characteristics of the conflict types are obviously different. From 1990 to 2018, the spatial conflict index of the fringe zone was high and its level from intense gradually turned to severe, while the conflict level in the core zone was moderate. With the expansion of the core into the fringe zone, its land use conflict type began to change from a moderate to an intense one. Mild conflicts were clustered in two types of areas: water (Tai Lake, Yangcheng Lake, etc.) and large tracts of forest or grass areas. Severe conflicts were scattered in ecotones of arable land, urban land, and water, which were located in the suburban zone around Suzhou-Kunshan and Changzhou-Lianyungang. Due to the rapid expansion of urban areas and the influence of Shanghai, moderate conflict units in Kunshan, Taicang, and Changshu spread rapidly and exhibited the tendency to converge in urban centers of Suzhou, Wuxi, and Changzhou. Overall, land use conflict in the Su-Xi-Chang region generally showed a significant increasing trend, especially in areas closest to urban ones. The higher increase rate of conflict levels in the fringe zone can be explained by the rise in complexity and fragmentation caused by abrupt changes in land use.  
In our research, spatial distribution characteristics of LUCI from 1990 to 2018 were obtained by employing the method of Global Moran' I and hot spot analysis (Figure 8) [51; 52]. During this period, the Global Moran' I of LUCI in the study area was 0.188, 0.181, 0.262, and 0.284 (\(p<0.001\)), indicating that spatial units show a certain tendency to cluster. Hot spot analysis demonstrates that both hot spots and cold spots of the LUCI show the spatial pattern of a cluster and belt agglomeration. On one hand, hot spots present clusters of intense and severe conflict located in two typical conflict zones, i.e., the core zone of built-up areas, including urban ones in Suzhou, Wuxi, Changzhou, and other major cities and the Su-Xi-Chang urban-rural interface, which includes metropolitan suburbs of Suzhou and the districts and counties of Kunshan and Taicang closer to Shanghai. On the other, cold spots represent clusters of mild and moderate conflict. They are concentrated in two types  
Figure 7: Spatial patterns of LUCI in (**a**) 1990, (**b**) 2000, (**c**) 2010, and (**d**) 2018.  
of areas, i.e., the contiguous water and lake areas, including Tai, Yangcheng, and Jingji Lake and forests and grassland where development and their utilization is prohibited, mainly in the southwest of the Su-Xi-Chang region. Overall, land use conflict in the Su-Xi-Chang region shows intensified trends and the intensification or alleviation of conflicts is relatively evenly distributed. However, it is worth mentioning that a ring-shaped mitigating area located between the urban-rural interface of major cities was present over the study period.