esa-sceva/satcom-chunk-collection huggingface dataset:
# S2p: Provenance Research for Stream Processing System
## 9 Related Work  
There exist some provenance solutions such as PASS [48] and SPADE [49] to track the provenance at the OS level. These provenance-tracking systems work at a low system level, so that it will cause a great burden if they are applied to capture and manage data relations at a high level for big data systems. X-Trace [50] provides a holistic view of the data transformation on the network among applications. Nevertheless, it lacks the ability to track the data when there is no network transfer involved. Dapper [51] goes further than X-Trace with additional monitoring and sampling strategies. However, it brings large latency when analyzing monitoring data, which is not suitable for DSP systems. In the following part, we will focus on the work exclusively about provenance for general-purpose big data systems.  
For state-of-the-art solutions, data provenance for big data applications is achieved by operator instrumentation, which enriches the source data and intermediate data with additional provenance-related annotations [23]. We can briefly divide these works into two categories, i.e., batch-processing-oriented provenance vs. stream-processing-oriented provenance, according to the types of systems that they target on.
# Hydraulic Vibration and Possible Exciting Sources Analysis in a Hydropower System
## 2 Research Object Description  
The research project in this paper involved two turbines with a capacity of 1015 MW per unit. This set up consists of two parallel water diversion penstocks and tail branches, a downstream surge tank, and a D-shaped tail tunnel with a maintenance tail gate shaft located next to the tail water. Figure 1 is a sketch of the plan layout and detailed division information of the hydropower system.  
Table 1 lists the details of each pipe segment, including length and equivalent diameter, in the hydropower system.  
The specific parameters of the prototype turbine are as follows: the rated output \(P_{\rm r}=1015\) MW, the rated head \(H_{\rm r}=202\) m, the rated discharge \(Q_{\rm r}=545.49\) m\({}^{3}\)/s, and the rated rotational speed \(n_{\rm r}=111.1\) r/min. The maximum and minimum head of the prototype turbine are \(H_{\rm max}=243.1\) m and \(H_{\rm min}=163.9\) m, respectively. The test rig consists of a spiral casing, a scaled model turbine runner with 15 blades, and a draft tube.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & **L(m)** & **D(m)** & **No.** & **L(m)** & **D(m)** & **No.** & **L(m)** & **D(m)** \\ \hline
1 & 41.0 & 15.925 & 11 & 24.55 & 17.145 & 21 & 61.1 & 11.460 \\
2 & 166.446 & 10.999 & 12 & 93.25 & 16.456 & 22 & 60.85 & 17.145 \\
3 & 47.124 & 10.203 & 13 & 41.0 & 15.925 & 23 & 24.55 & 17.145 \\
4 & 104.50 & 10.203 & 14 & 167.938 & 10.999 & 24 & 74.0 & 16.456 \\
5 & 47.124 & 10.203 & 15 & 47.124 & 10.203 & 25 & 103.745 & 17.628 \\
6 & 31.88 & 9.053 & 16 & 104.50 & 10.203 & 26 & 972.805 & 17.628 \\
7 & 42.4 & 8.600 & 17 & 47.124 & 10.203 & 27 & 100.0 & 17.605 \\
8 & 20.0 & 11.460 & 18 & 31.88 & 9.053 & 28 & 122.166 & 17.605 \\
9 & 61.1 & 11.460 & 19 & 42.4 & 8.600 & 29 & 397.048 & 17.605 \\
10 & 60.85 & 17.145 & 20 & 20.0 & 11.460 & & & \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the pipe in the hydropower system.  
Figure 1: Sketch of the plan layout of the hydropower system.  
Figure 2 shows the comprehensive characteristic curves of the turbine. Here, \(Q_{11}\) and \(n_{11}\) stand for the unit discharge and unit speed, respectively, which are defined as \(Q_{11}=Q/(D_{1}^{2}\sqrt{H})\) and \(n_{11}=nD_{1}/\sqrt{H}\). From Figure 2, the optimal operating conditions for the turbine are guide vane opening \(a=18.8^{\circ}\), unit speed \(n_{11}=53.07\) r/min, unit flow rate \(Q_{11}=0.579\) m\({}^{3}\)/s, and peak efficiency \(=95.07\%\).
# Practical Grammar Compression Based on Maximal Repeats +
## 3 Analysis of RePair
### RePair and Maximal Repeats
We denote \(r[1]\) and \(r[|r|]\) as \(\dot{x}\) and \(\dot{y}\), respectively. There are the following two cases to consider:  
**(i)**\(\#\text{occ}(z\dot{x})<f\)**and**\(\#\text{occ}(\dot{y}w)<f\). If \(|r|=2\), the replacement of \(p\) directly corresponds to the replacement of the most frequent maximal repeat, since \(p=r\). If \(|r|>2\), after \(p\) is replaced with a variable \(v\), \(r\) is changed to \(xvy\). This occurs \(f\) times in the updated text, and according to Lemma 2, the frequency of every pair occurring in \(xvy\) is still \(f\). Because the maximum frequency of pairs does not increase, \(f\) is still the maximum frequency. Therefore, we replace all pairs contained in \(xvy\) in the following steps, whereas \(z\dot{x}\) and \(\dot{y}w\) are not replaced. This holds for every occurrence of \(p\), implying that replacing the most frequent pairs while the maximum frequency does not change, corresponds to replacing all pairs (old and new) contained in the most frequent maximal repeats of the same frequency until they are replaced by a single variable. Then, \(s\) can be generated by replacing \(r\). **(ii)**\(\#\text{occ}(z\dot{x})=f\)**or**\(\#\text{occ}(\dot{y}w)=f\). We consider the case where \(\#\text{occ}(z\dot{x})=f\). Note that \(\#\text{occ}(zxpy)<f\) according to the assumption that \(xpy\) is a maximal repeat. Suppose RePair replaces \(z\dot{x}\) by a variable \(v\) before \(p\) is replaced. Note that according to Lemma 2, there is a maximal repeat occurring \(f\) times and including \(z\dot{x}\) once (we denote the maximal repeat as \(r^{\prime}\)), and \(r^{\prime}\
eq r\) by assumption. According to Lemma 3, the length of the overlap of \(r\) and \(r^{\prime}\) is at most \(1\), and then, only \(\dot{x}\) is a symbol present in both \(r\) and \(r^{\prime}\). After that, \(xpy=r\) is no longer the most frequent maximal repeat because some of its occurrences are changed to \(vr[2..|r|]\). However, \(r[2..|r|]\) still occurs \(f\) times in the updated text. Since \(\#\text{occ}(zxpy)<f\) and \(\#\text{occ}(xpy)=f\), \(\#\text{occ}(vr[2])<f\) and \(r[2..|r|]\) is a maximal repeat. Then, \(r[2..|r|]\) will become a variable in subsequent steps, similarly to **(i)**. Here, \(r^{\prime}\) would also become a variable. Thus, we can generate \(s\) by first replacing \(r^{\prime}\) and then replacing \(r[2..|r|]\). Similarly, this holds for \(\dot{y}w\) when \(\#\text{occ}(\dot{y}w)=f\) and \(\#\text{occ}(z\dot{x})=\#\text{occ}(\dot{y}w)=f\). \(\qed\)
## 2 Synthesis of 1,3,4-Oxadiazole Precursors
### Reactions with Aryl Bromides  
Other popular methods of arylation of the 1,3,4-oxadiazole ring include reactions using aromatic bromides (Scheme 5). However, in contrast to the previously described aryl iodide transformations, they require a palladium catalyst and a bifunctional ligand [50; 51; 52]. Among them is the method described in 2015 by [PERSON] and others, who prepared 2,5-diphenyl-1,3,4-oxadiazole from 2-phenyl-1,3,4-oxadiazole and bromobenzene using palladium(II) acetate and 4,5-bis(diphenylphosphino)-9,9-dimethylxanthene in excellent yield (Scheme 5; **11**; 98%) [50]. Another example is the work of [PERSON] et al. describing an effective catalytic method for the direct arylation of 2-substituted 1,3,4-oxadiazoles with bromo(hetero)arenes using low loading of the Pd/PTABS catalyst system (Scheme 5; **14a-p**; 57-92%) [51]. More recently, a range of N-heterocyclic carbene palladium(II) allyl complex precatalysts was prepared and applied in the direct C-H bond arylation of 2-aryl-1,3,4-oxadiazol with 4-bromotoluene (Scheme 5; **15a,b**; 94%) [52]. An interesting approach for the synthesis of conjugated 1,3,4-oxadiazole arrangements is the possibility to conduct arylation reactions under microwave irradiation. In 2016, [PERSON] et al. developed a domino carbo-palladation/C-H activation approach for the synthesis of the 2,5-diphenyl-1,3,4-oxadiazol framework containing an amido functionality for subsequent transformations by trapping the alkyl/vinyl-palladium intermediate with 2-phenyl-1,3,4-oxadiazole (Scheme 5; **12a,b**; 68-79%) [53]. Two years later, [PERSON] and co-workers published the efficient and time-saving methodology for the preparation of 2-phenyl-1,3,4-oxadiazoles conjugated to porphyrins (Scheme 5; **13**; 75%) [54]. The C-C coupling proceeded in the presence of a Pd/Cu catalytical system under the action of microwave irradiation.
# Influence of Recycled Precast Concrete Aggregate on Durability of Concrete's Physical Processes
## 3 Results and Analysis
### Freeze-Thaw Cycle  
Table 5 shows the weight loss results in the corresponding freeze-thaw cycles applied for the different SCCs. Weight loss at 7, 14, 28, 42, and 56 cycles was determined. The results show two different behaviors depending on the strength of the SCC, HR-30 and HR-45. For concretes with a higher _w/c_ ratio (0.45), corresponding to HR-30, the weight loss as the cycles progress is significantly greater. For the dosage of lower ratio _w/c_ (0.35), corresponding to the strength of HR-45, although weight loss is also seen, it is not greater than 10% of the initial weight. This greatest weight loss for the dosages with a higher _w/c_ ratio of the concrete with RA is in line with the results obtained in other studies [46].  
Figure 10 specifies the compressive strength results before and after freeze-thaw cycles as a function of the effective _w/c_ ratio.  
Decreases in strength for concretes with higher _w/c_ ratios are observed, while for lower water/cement ratios, the tendency is toward increasing strength. For the higher _w/c_ ratio (HR-30), strength losses were observed between 8% for the ratio of 20%, and 30% for the substitution of 100% RA with respect to the specimens taken as reference. However, for the dosages of HR-45, there is a slight increase in strength, between 5% and 7%, compared to the reference SCCs. In this regard, there are studies that reflect this better behavior [47, 48]. [PERSON] and [PERSON] [48] observed that the durability of concrete made with crushed concrete and clean recycled aggregate is better or similar to the durability of concrete with natural aggregates, when subjected to freeze-thaw cycles.  
\begin{table}
\begin{tabular}{l c c c c c} \hline MP-35-56 & \(\gamma\) 1.2 days & 18 days & 23 days & 30 days & 30 days \\ \hline HR-30-0\% & 1.49 & 2.91 & 4.52 & 8.37 & 11.42 \\ HR-30-20\% & 0.41 & 0.83 & 1.55 & 2.60 & 4.74 \\ HR-30-50\% & 0.51 & 0.89 & 1.22 & 8.95 & 12.57 \\ HR-30-100\% & 2.1 & 4.63 & 9.56 & 12.30 & 15.87 \\ HR-45-0\% & 0.61 & 0.74 & 1.54 & 3.08 & 4.68 \\ HR-45-20\% & 0.28 & 0.67 & 0.86 & 1.86 & 3.16 \\ HR-45-50\% & 0.71 & 1.99 & 2.58 & 4.51 & 8.14 \\ HR-45-100\% & 0.79 & 2.18 & 3.47 & 5.23 & 9.50 \\ \hline \end{tabular}
\end{table}
Table 5: Weight loss results in freeze-thaw tests.  
Figure 9: Correlation between shrinkage and compressive strength at 28 days’ ageing.  
The behavior under the freeze-thaw cycles was more favorable for the concrete with a lower _w/c_ ratio, even achieving resistance values significantly higher than those of the control concrete and reference specimens. For the higher _w/c_ ratio, in general, the results reflect a trend of poorer behavior as we increase the degree of substitution.
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 3 Description of Data
### Outage Data  
The electrical power company in South Korea records the data regarding power outage events in its branch offices. The outage information includes the details of the head/branch office, time of failure, failure component, cause of failure, number of affected customers, the time required for restoration, and type of failure. The outage is related to the failure components; the location of the failures is not exactly specified in the system topology. Despite this limitation, a wider geographical area managed by each branch office can be evaluated from the data.
## 4 Experiments
### Experimental Environment
The architecture of the experimental environment shows the configuration of the CCTV cameras, the edge devices based on Jetson NX Xavier and Jetson AGX Orin, the integration control server (ICS), and the GPU servers. The green callouts represent the modules dedicated to analyzing pedestrian abnormal behavior within each unit. For the fall detection model, we stack \(T\) vectors containing the coordinates of the pose estimation results for each frame into a matrix and analyze the presence of falls through the network shown in Figure 6.  
The common preprocessing model for both models, object detection, uses the YOLOv7x model, a more accurate member of the YOLOv7 model family than the model used in the edge. As in the edge, YOLOv7x is trained based on the learning method of [18,19]. Since the assault and fall analysis models require preprocessing with a pose estimation model, we add the AlphaPose [25] model for real-time analysis. Since each GPU server is equipped with two GPUs, two analysis modules can be uploaded per GPU server for six analysis modules. The scenarios in this experiment are designed to evaluate the ability of the proposed system to accurately detect pedestrian misbehavior and provide appropriate notifications under various environmental conditions and real-world situations. For example, we examine how the system can effectively respond to various situations, including an assault scenario in a park at night and an older adult falling. In these situations, the system detects the events in real time and immediately generates notifications so that users or administrators can quickly recognize them. One of the primary metrics measured in the experiments is the latency from event detection to notification, which is an essential criterion in evaluating the responsiveness and efficiency of the system. This is discussed in detail in Section 4.2. With this approach, the proposed system is expected to exhibit practicality and effectiveness by demonstrating how quickly and accurately it can respond to various safety threats that may occur in real-world environments. Figure 5: Network structure of the assault event detection model.
## 3 Methodology
### Feature Selection Based on Reliability Analysis  
Feature selection techniques were employed to reduce the dimensionality of the feature space by eliminating unnecessary features. In this study, we used two distinct feature selection methods: MC and reliability analysis, based on two different datasets. Both techniques were performed using the SPSS software package v.23.  
A reliability analysis was conducted using Cronbach's alpha to assess the reliability of all features in the two datasets. Furthermore, it is crucial to determine whether the combined features contribute to the assessment of the same construct. While there is no specified lower bound, a higher Cronbach's alpha coefficient (approaching 1) indicates greater internal consistency of the factors [34]. An \(\alpha\) value of 0.7 or higher is indicative of strong internal consistency in assessing the construct's reliability.
## 6 Analysis of the CRB(\(\boldsymbol{\
abla}_{long}\))  
In this section, we compute the CRB, using Equation (50), for different simulation cases. Our goal is to study the effect of the received signal's satellite elevation span covered during the observation time (\(\Delta\theta_{el}\)), as well as the impact of the GNSS signal modulation, over the theoretical achievable precision in the estimation of the parameters in \(\boldsymbol{\
abla}_{long}\).  
The variation of \(\theta_{el}\) during the observation time has the following implications:  
1. The propagation path difference \(\Delta\rho\) will change due to the displacement of the specular reflection point. In addition, the angle of arrival of both the LOS and the reflected component to the antenna will also vary.
2. If the reflection coefficient \(\Gamma\) is assumed to depend on \(\theta_{el}\), this will also change during the observation time.  
Up to this point, we have not selected any specific model for the reflection coefficient \(\Gamma\). From now on, we will use the model for a single-layer bare soil introduced in [7], which we will call the Zavorotny-Larson model (Z-L). This model is described in more detail in Appendix B, and it allows us to specify \(\Gamma\) as a function of \(\theta_{el}\) and the following parameters:  
\[\boldsymbol{s}_{\Gamma}=\left[\varepsilon_{r},\sigma\right]^{T}, \tag{51}\]  
where \(\varepsilon_{r}\) and \(\sigma\) are the real part of the surface's relative permittivity and the surface conductivity, respectively, which are assumed constant over the entire observation interval. Notice that this model for \(\Gamma\) is used as an example in our analysis and that the approach proposed to obtain \(\mathbf{J}(\boldsymbol{\
abla}_{long})\) can accommodate other models for \(\Gamma(\theta_{el};\boldsymbol{s}_{\Gamma})\).  
In addition, the gain of the receiving antenna will have an important impact over the CRB. To assess this impact, we consider two possible antenna cases in our analysis:  
1. An ideal case with an isotropic antenna model.
2. A Leica GNSS AR25 antenna [74] model. Only the antenna gain's amplitude has been modeled. The antenna phase center is assumed to be located at the distance \(h\) perpendicular to the reflection surface. The antenna boresight is pointing upwards, parallel to the surface normal vector (i.e., an elevation of \(90^{\circ}\)). We have used the pattern model provided as part of the open source GPS multipath simulator [75].  
In both cases, we make the assumption that the antenna complex gain is accurately known and that all of the available signal's energy is considered by defining the receiver's front-end bandwidth as \(B_{fe}=B_{Tx}/2\).
# Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control
## 6 Discussion
### Computational Efficiency  
The proposed optimisation framework was developed using MATLAB. All optimisation problems were executed on a laptop PC equipped with an Intel i7-8750H CPU, utilising parallel processing across four cores. The number of elements in these examples ranged from 5000 to 8000. On average, each optimisation iteration required approximately 10 s of CPU time, allowing the problems to be solved within one or two hours. However, as the number of nodes and elements increases, the computational demand grows exponentially. For instance, numerical experiments show that in optimising a compression column with three-level connectivity, the number of nodes and elements rises to 24,673 and 29,024, respectively, resulting in an average CPU time per iteration exceeding 5 min. While both linear static analysis and eigenvalue buckling analysis are completed within seconds, sensitivity analysis is the most time-consuming step, followed by the updates of design variables. This is due to the need to loop over all constrained nodes for displacement constraints, all elements for stress constraints, and all buckling modes for stability constraints as described by Equations (11), (19) and (30). This presents significant challenges for complex, large-scale structures, such as spatial frames, where the number of candidate elements increases dramatically with finer domain discretisations and higher levels of member connectivity.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 5 Discussion  
For systems that monitor the whole WSNs with a single movable platform, their technical development is hindered in the several aspects: Firstly, low efficiency. Due to hardware and energy consumption, sensor nodes can only transmit signals within tens of meters to hundreds of meters at the same frequency and usually use CSMA/CA (carrier sense multiple access with collision avoidance) protocol. So when one node couple communicates, other nodes within its communication range will listen or fall asleep. Even if broadcast information is sent and communicates with surrounding nodes, only one node can be detected. But the sensing would fail if there are sleeping nodes. In theory, as long as time T that is large enough is given, the platform can go through all nodes within the network, and we can always detect all path information for the network in fixed routing path. However, as network keeps expanding, especially for network models with over 1000 orders of magnitude, it takes a long time for this method to work, and network routing path would change, resulting in invalid detection results. Secondly, incomplete detection information. Compared with traditional spot monitoring system, single platform is not able to obtain data of the same moment within the whole network or the whole-process path information of data packets but is only able to infer according to the models built. Thirdly, poor flexibility. The platform uses the method of reproducing the whole path of network using the known path information but such method is only applicable to target networks using location-based routing protocols at present. Lastly, this method cannot achieve good results  
Figure 18: Node Couples the Platform Detects after Finishing Sampling Completely.  
without constant iteration, and too many iterations will cause overload. Next, we will study how to prevent overload, and bring up effective methods to detect the topology of WSNs in clustering topology structure.
# Investigation of the Behaviors of Methanol Spray Impingement and Wall Wetting
## 3 Results and Discussion
### Fuel Adhesion Formation
When \(P_{inj}\) was further increased to 500 kPa, \(T_{f}\) and \(M_{f}\) were lower than those at 400 kPa, as a result of which the droplet velocity increased and the kinetic energy when hitting the wall was greater; more droplets tended to splash rather than adhere. At 100 ms ASOI, under 300 kPa, 400 kPa and 500 kPa \(P_{inj}\), the percentage of adhesive fuel mass in the total injection volume was 1.5%, 2.2% and 1.9%, respectively. Figure 19 illustrated the contribution indices of different boundary conditions in \(A_{f}\) (Figure 19a), \(T_{f}\) (Figure 19b) and \(M_{f}\) (Figure 19c), respectively, where \(A_{f}\), \(T_{f}\) and \(M_{f}\) under 62 mm \(D_{imp}\), 293 K \(T_{wall}\), 500 kPa \(P_{inj}\) were set as the base values, that is, \(K_{A}\), \(K_{T}\), and  
Figure 17: Pseudo color images of fuel film on the wall with temperature 293 K under different \(P_{inj}\) (300 kPa to 500 kPa). Figure 18: (a) Fuel film area \(A_{f}\), (b) Fuel film thickness \(T_{f}\) and (c) Fuel film mass \(M_{f}\) on the wall under different \(P_{inj}\) (300 kPa to 500 kPa). Figure 19a showed that among the three boundary conditions, only \(T_{null}\) had a negative correlation with \(A_{f}\). Other boundary conditions were positively correlated with \(A_{f}\). The \(T_{f}\) was affected by complex factors, so it did not show a strict positive or negative correlation for any boundary condition, but was largely affected by the relative size of \(A_{f}\) and \(M_{f}\), as shown in Figure 19b. As shown in Figure 19c, \(D_{imp}\) was positively correlated with \(M_{f}\). When \(D_{imp}\) was less than 373 K, \(M_{f}\) decreased with the increase of \(D_{imp}\), but when \(D_{imp}\) reached 423 K, \(M_{f}\) rebounded. In fact, the temperature difference between the wall and the droplet mainly affects the evaporation rate of the adhesive fuel film. However, whether the impinging spray is splash or adhesion depends on the Weber number of the droplet [32]. When the Weber number is higher, the spray tends to splash rather than adhere. Here, the decrease of the impingement distance and increase of the injection pressure led to higher We, thereby the spray splash part increased, and the adhesion part decreased, which can be evidenced by the observation that with the reduction of the impingement distance (Figures 7 and 14) and the increase of the injection pressure (Figures 11 and 18), the impinging spray height \(H_{1}\) increased and adhesive fuel reduced.
## 3 High-Capacity DWDM PDM-16 QAM Optical Switch  
This section describes the first experimental demonstration of a high-capacity dense wavelength division multiplexing (DWDM) optical packet switching node using PDM-16 QAM modulated optical packets and electro-optical PLZT switches.