esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Data Reconciliation with Gross Errors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Working** \\ **Condition** \\ \end{tabular} & **Gross Error Detection** & **Criterion Value of _r_** & **Design Value** & \begin{tabular}{c} **Measured Value** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Reconciliated** \\ **Value** \\ \end{tabular} \\ \hline Condition 3 & TS17 (Temperature, \({}^{\circ}\)C) & 2.559 & 380,330 & 383.021 & 380.658 \\ Condition 4 & PS05 (Pressure, MPa) & 2.198 & 24.000 & 23.282 & 23.653 \\ Condition 5 & MF805 (Mass flow, kg/s) & 2.713 & 62.640 & 73.651 & 63.759 \\ Condition 6 & MF804 (Mass flow, kg/s) & 2.345 & 132.530 & 149.173 & 131.808 \\ \hline Condition 7 & TS17 (Temperature, \({}^{\circ}\)C) & 2.376 & 380,330 & 382.612 & 380.418 \\ \hline Condition 8 & \begin{tabular}{c} MPS05 (Mass flow, kg/s) \\ \end{tabular} & 2.688 & 62.640 & 73.067 & 63.347 \\ \begin{tabular}{c} MPS04 (Mass flow, kg/s) \\ \end{tabular} & 2.147 & 132.530 & 149.819 & 133.867 \\ \hline Condition 9 & \begin{tabular}{c} MPS05 (Mass flow, kg/s) \\ \end{tabular} & 2.599 & 62.640 & 72.613 & 63.273 \\ \begin{tabular}{c} TS17 (Temperature, \({}^{\circ}\)C) \\ \end{tabular} & 2.586 & 380.330 & 383.332 & 380.946 \\ \begin{tabular}{c} PS05 (Pressure, MPa) \\ \end{tabular} & 2.042 & 24.000 & 23.208 & 23.653 \\ \hline Condition 10 & \begin{tabular}{c} MPS05 (Mass flow, kg/s) \\ \end{tabular} & 2.512 & 62.640 & 72.865 & 63.803 \\ \begin{tabular}{c} TS17 (Temperature, \({}^{\circ}\)C) \\ \end{tabular} & 2.206 & 380.330 & 382.842 & 380.806 \\ \
\begin{tabular}{c} MPS04 (Mass flow, kg/s) \\ \end{tabular} & 2.094 & 132.530 & 150.383 & 134.755 \\
\begin{tabular}{c} PS05 (Pressure, MPa) \\ \end{tabular} & 2.004 & 24.000 & 23.211 & 23.651 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Identification with gross errors of the working conditions. Figure 8: Distribution of standard deviation in Condition 10. Figure 9: Distribution of uncertainty in Condition 10.
## 3 Results
### Void Area and Equivalent Diameter  
Table 3 presents the quantitative analysis of void characteristics, including void areas and equivalent diameters, for the five CPAM formulations.  
The data indicate a direct relationship between void count, void area, and overall void ratio. Among the tested formulations, CPAM1 shows the most favorable void characteristics, with the lowest void ratio and smallest average equivalent diameter. CPAM2 and CPAM3 exhibit intermediate values, while CPAM4 and CPAM5 display the highest void ratios, however, with distinct differences in average equivalent diameters.  
This variation in void characteristics stems from differences in aggregate gradation and binder content, as confirmed by previous performance tests. Excessive coarse aggregates tend to increase the void diameter, while properly sized fine aggregates (particularly 2.36 mm gradation) effectively fill voids, reducing both the void diameter and count. Figure 9 shows the relationship between the equivalent void diameter and the cumulative void ratio distribution. The \(x\)-axis represents the equivalent diameter of individual voids (ranging from 0 to 5 mm), and the \(y\)-axis represents the cumulative distribution probability--i.e., the proportion of total voids with diameters less than or equal to a given  
Figure 8: Relationship between void ratio and residual rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Digital** & **Void** & **Total Void** & **Average** & **Equivalent** & **Maximum** \\
**Number** & **Void Ratio** & **Count** & **Area** & **Void Area** & **Diameter** & **Void** \\
**CPAM1** & 7.15\% & 3928 & 6088.4 & 1.55 & 1.41 & 3.79 \\
**CPAM2** & 9.53\% & 5778 & 9707.04 & 1.68 & 1.66 & 5.97 \\
**CPAM3** & 9.98\% & 6059 & 100,57.94 & 1.66 & 1.59 & 6.28 \\
**CPAM4** & 13.88\% & 105,56 & 185,78.56 & 1.76 & 1.83 & 7.60 \\
**CPAM5** & 15.37\% & 134,38 & 217,69.56 & 1.62 & 1.45 & 4.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation results of voids of CPAM specimens.  
value. Most void diameters fall within the 0-3 mm range. The superior void characteristics of CPAM2 and CPAM3 result from their higher 2.36 mm aggregate content, which efficiently fills internal voids. In contrast, CPAM5's more uniform gradation distribution and lack of >9.5 mm aggregates lead to fewer large voids and consequently a lower average equivalent diameter than CPAM4.  
These results emphasize the essential role of optimized gradation in regulating void characteristics, demonstrating the significance of balanced aggregate distribution for achieving optimal microstructural properties.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 4 Practical Engineering Application
### Comparative Analysis of Monitoring Data
#### 4.3.1 CSP Strain  
The YK47 + 355 section of the tunnel face was excavated on 29 May 2018. Installation of strain gauges was completed by 5 June 2018, followed by the pouring of the secondary lining on 5 July 2018. Figure 14 illustrates the arrangement of surface strain gauge measuring points on the CSP monitoring section. In this study, we focus on two specific measuring points, namely 2-3 and 5-2, to track changes in surface strain over time.  
From Figure 14, the following observations can be made: (1) Following the completion of the corrugated steel initial support structure, the CSP strain undergoes rapid changes and gradually stabilizes after 2 weeks. Specifically, the strain at 2-3 stabilizes at \(-\)280 \(\mu\epsilon\), while at 5-2, it stabilizes at \(-\)300 \(\mu\epsilon\) (negative values indicate compressive strain); (2) upon completion and stabilization of the CSP initial support construction, the strains at each measuring point are relatively consistent, indicating balanced stress distribution in the CSP initial support; (3) during the 1 week of secondary lining pouring, CSP strain fluctuates significantly: the strain at 2-3 increases and gradually stabilizes, whereas at 5-2, it initially decreases to \(-\)250 \(\mu\epsilon\) before fluctuating again; and (4) three weeks after completion of the secondary lining pouring, CSP strain values tend to stabilize further, with final strains  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\theta\)/rad & \(-\)3 & \(-\)**0.55** & \(-\)**0.04** & **0** & **0.042** & **0.45** & **3** \\ \hline \(M\)/kN m & \(-\)96.1 & \(-\)96.1 & \(-\)59.2 & 0 & 42.1 & 53.9 & 53.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rotation connector parameters in Abaqus.  
at 2-3 and 5-2 reaching \(-351\)\(\mu\epsilon\) and \(-303\)\(\mu\epsilon\), respectively (negative values indicating compressive strain).  
Based on the numerical simulation results, the strain of the CSP at specific positions extracted from the odb file of Abaqus FE software (ABAQUS 2020) is compared with the field monitoring results. The comparative findings are presented in Table 5.  
From Table 5, it is evident that the relative error between the numerical simulation results and the monitored values of CSP strain at measuring points 2-3 and 5-2 for the initial support of CSP is 2% and 14%, respectively. Moreover, the relative errors at other measuring points are also below 15%, falling within the acceptable range.
# Real-Time Kinematic Positioning Using Multi-Frequency Smartphone Measurements +
## 3 Static Tests
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Base**} & \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**TTFF** [s]**} & \multirow{2}{*}{**Fix \%**} & \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**Vertical**} & \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**Vertical**} \\  & & & **Accuracy [m]** & **Accuracy [m]** & **Precision [m]** & **Precision [m]** \\ \hline CN09 & 27,741 & 28 & 99.5 & 0.011 & 0.016 & 0.008 & 0.015 \\ \hline CGU2 & 27,741 & 29 & 95.9 & 0.013 & 0.020 & 0.012 & 0.020 \\ \hline GUAS & 27,741 & 205 & 91.1 & 0.051 & 0.043 & 0.026 & 0.052 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Patch antenna (CN09: 1 km, CGU2: 5 km, GUAS:25 km). Figure 6: Mean C/N\({}_{0}\) versus elevation for the test with the smartphone’s antenna.
## 1 Introduction
The aim of this study is to assess how the performances of classical MFCC-based speaker recognition models vary in the presence of noise and reverberation, and if the addition of RASTA-style features to a hybrid set can help to build a more generalized model. The experimental setup will involve one-vs.-all speaker verification models based on different acoustic feature sets (MFCCs, RASTA sets, or both). These are implemented with various classifiers and finally tested on raw and augmented (noisy/reverberated) audio, with a focus on the advantages and disadvantages of each feature family. The paper is organized as follows: after a review of related works, the Section 3 presents the dataset, the data processing pipeline, and the augmentation procedures applied to the validation data. Then, we present MFCC and RASTA features and briefly introduce AI selection and classification.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 2 Related Work
Additionally, they developed a flexible Link Quality Estimation technique to dynamically switch between PHYs depending on the current propagation characteristics of the link. Another interesting work is the approach of [PERSON] et al. They proposed two multi-PHY designs: (i) a _multi-template_ design where slower PHYs are scheduled to have logical slots spanning multiple real slots, and (ii) a _single-template_ design where the slot size is based on the slowest PHY [3]. Their multi-template design is similar to the slot bonding approach presented in our previous work. The real hardware evaluation of [PERSON] et al. applies their multi-template design by assigning management traffic to the slower, more reliable PHY, while data are transmitted over the faster, less reliable PHY. As such, they do not not discuss a PHY selection mechanism and their results primarily focus on the advantages of using a multi-PHY in terms of range, channel utilization and synchronization accuracy. The TSCH slot bonding firmware implementation presented in this work is a combination of a new easy-to-configure implementation, developed in the latest version of the Contiki-NG repository, merged with insights from the open-source multi-PHY work from both [PERSON] et al. and [PERSON] et al. To the best of our knowledge, this work is the first to evaluate a parent and PHY selecting mechanism in a TSCH slot bonding, multi-hop network deployed on a real hardware testbed.
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 2 Shortest Path Problems and Algorithms for Solving Them
### All Pairs  
Solving the all pairs shortest path problem in a graph involves populating a matrix \(\mathbf{D}_{n\times n}\), where each element \(D_{ij}\) holds the length of the shortest \(v_{i}\)-\(v_{j}\)-path. A very elegant approach for this problem is the  
Variants of the single-source single-target shortest path problem have also been studied by [PERSON] [16] and [PERSON] [17]. [PERSON] has proposed an algorithm for finding the \(K\) shortest \(s\)-\(t\)-paths, where \(K\) is a parameter supplied by the user. The method operates on graphs with nonnegative weights and starts by first identifying the shortest \(s\)-\(t\)-path. For \(i=2,\ldots,K\), the \(i\)th shortest \(s\)-\(t\)-path is then determined by modifying the graph according to the first \(i-1\) shortest \(s\)-\(t\)-paths and calculating the shortest \(s\)-\(t\)-path of this new graph. Using [PERSON]'s algorithm with a binary heap, this algorithm has a complexity of \(\mathcal{O}(Kn(m\lg n))\). The methods of [PERSON], meanwhile, are used to produce pairs of edge-disjoint \(s\)-\(t\)-paths in a graph. After determining the shortest \(s\)-\(t\)-path, modifications are made to the graph by deleting some edges of the path and negating the weight of others. A subsequent run of an appropriate shortest path algorithm then produces the desired second path. Similar techniques can also be used to produce vertex-disjoint paths [17].
# 47.5 GHz Membrane-III-V-on-Si Directly Modulated Laser for Sub-pJ/bit 100-Gbps Transmission
## 3 Results
### Laser Characterization
As it can be seen from these results, a 3-dB bandwidth of more than 45 GHz is obtained at RT, with a continuous response between \(f_{R}\) and \(f_{PPR}\) being above the 3-dB threshold. Moreover, by comparing these results to previous membrane DML-on-Si with a similar structure and without the DBR-2 section (dashed line in Figure 4a) [6], an enhancement of the \(f_{R}\) is confirmed owing to the combination of DL and PPR effects. Based on this comparison, the estimated enhancement factor was \(\chi_{DL}\approx\) 1.4. As far as the experimentally-obtained data of Figure 4b are concerned, we could achieve flat EO responses of around 47.5 GHz and 42.5 GHz at 25 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, respectively. At these conditions, the applied bias currents and DC voltages were 9.1 mA and 2.15 V, respectively, at 25 \({}^{\circ}\)C, and 15.1 mA and 2.56 V, respectively, at 50 \({}^{\circ}\)C. These  
Figure 3: Spectral characteristics: (**a**) Contour plot of the experimentally-obtained optical spectra Figure 25 \({}^{\circ}\)C; (**b**) Longitudinal mode analysis at a bias current of 9.1 mA (25 \({}^{\circ}\)C operation is assumed). The reflectance of DBR-2 is also shown as a reference. (**c**) Experimental and simulated spectra at a bias current of 9.1 mA and 25 \({}^{\circ}\)C; (**d**) Experimental spectra as in (**c**), but including also a 60 GBaud PAM-4 RF modulation. (**e**) Experimental spectra at a bias current of 15.1 mA and 50 \({}^{\circ}\)C; (**f**) Same as (**e**), but including also a 60 GBaud PAM-4 RF modulation. results translate to DC power consumptions of 19.565 mW at 25 \({}^{\circ}\)C and 38.656 mW at 50 \({}^{\circ}\)C, and fiber-coupled wall-plug efficiencies of 2.73% and 1.78%, respectively. The wall-plug efficiency using wide-apparatus photodetection without fiber was 6.44% at 25 \({}^{\circ}\)C. Moreover, based on these bias currents and Figure 3a-b, at the 9.1 mA/25 \({}^{\circ}\)C case, the second long-wavelength side-lobe of the DBR was used (i.e., mode #3 in Figure 3b) for the PPR generation, while for the 15.1 mA/50 \({}^{\circ}\)C the third long-wavelength side-lobe of the DBR was used (i.e., mode #4 in Figure 3b) instead.
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 2 Modular Bus Scheduling Model
### Mathematical Formulation  
We now formulate an arc-based model for the MB unit scheduling problem, which will be exploited in the Dantzig-Wolfe decomposition posited in the next section.  
\[min\sum_{h\in H(i,j)\in A}c_{ij}\cdot x_{ijh}\cdot\delta_{h} \tag{1}\]  
\[\sum_{j\in N^{-}(i)}x_{jih}=\sum_{j\in N^{+}(i)}x_{ijh}\leq 1\qquad\forall i\in K \cup D\cup F,\forall h\in H \tag{2}\]  
\[\sum_{j\in N^{+}(o)}x_{ojh}=\sum_{j\in N^{-}(s)}x_{jhh}\leq 1\qquad\forall h\in H \tag{3}\]  
\[q_{ih}=0\qquad\forall i\in\{o\}\cup F,\forall h\in H \tag{4}\]  
\[q_{ih}=\sum_{j\in N^{-}(i)}\Bigl{(}q_{jh}+p_{ji}\cdot\delta_{h}+p_{i}\cdot \delta_{h}\Bigr{)}\cdot x_{jih}\qquad\forall i\in K\cup D,\forall h\in H \tag{5}\]  
Figure 2: An example of the scheduling pictorial graph.  
\[q_{ih}+\sum_{j\in N^{+}(i)}p_{ij}\cdot\delta_{h}\cdot x_{ijh}\leq Q \cdot\delta_{h}\cdot\mu\ \ \ \ \ \forall i\in N,\forall h\in H \tag{6}\]  
\[\sum_{h\in Hj\in N^{-}(i)}x_{jih}\cdot\delta_{h}\geq\lceil d_{i}/M\rceil\ \ \ \ \ \forall i\in K \tag{7}\]  
\[\sum_{h\in Hj\in N^{-}(i)}x_{jih}\cdot\delta_{h}+\sum_{a=1}^{a_{max}}\sum_{h\in H \rangle\in N^{-}(\gamma_{i}^{a})}x_{j(\gamma_{i}^{a})h}\cdot\delta_{h}\leq U\ \ \ \ \ \forall i\in F \tag{8}\]  
\[x_{jih}\in\{0,1\}\ \forall(i,j)\in A,\forall h\in H \tag{9}\]  
The objective function (1) aims at minimizing the total system cost inclusive of various components such as dispatching costs, operation costs, and charging costs. Constraint (2) ensures that each MB platoon \(h\) remains balanced with respect to incoming and outgoing arcs at any given node \(i\in K\cup D\cup F\). Constraint (3) specifies that once dispatched, an MB platoon \(h\) must depart from source \(o\) and return to node \(s\). Constraints (4)-(6) define the energy limitations and charging plans for platoon \(h\). Specifically, Constraint (4) states that the cumulative energy consumption at depot \(o\) and charging nodes should be zero, indicating that module platoon \(h\) can reset its energy through strategic charging decisions. Constraints (5) formulate \(q_{ih}\) at the nodes \(i\in K\cup D\), determined jointly by the preorder node \(j\) and energy consumption on arc \((j,i)\) and node \(i\). The energy limitation of each module platoon should be met, and thus, Constraint (6) applies. A minimum number of MB units that serve trip \(i\) is imposed by constraints (7), where for coupling/decoupling action, the same location required is fulfilled by the node itself, and the time restrictions have been handled in the arc feasibility rules. Constraint (8) considers the charging station capacity, determined by the total number of module vehicles accessing node \(i\in F\) and in the previous \(a_{max}\) charging nodes.
# Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids
## 1 Introduction
[PERSON] et al. [15] proposed the HEED (Hybrid Energy-Efficient Distributed Clustering) protocol where primary and secondary parameters were designed. The primary parameter represents the probability that the node is selected as the cluster head in the real situation, and the secondary parameter is used as the measurement of the communication overhead of the nodes in the cluster, which introduces the average lowest power in the cluster as the evaluation value. The operation process of heed protocol is as follows. In the first step, each node determines the value of its own primary parameter; In the second step, first check whether the temporary cluster head is included in the adjacent nodes around each node. If so, this node will compare the value of the secondary parameter with the alternative adjacent node, and the node with the lowest value of the secondary parameter is determined as the cluster head. If not, the probability that this node is selected as the cluster head is calculated with reference to the main parameter value; If the above conditions are not consistent, repeat the above operations and start the next round of algorithm iterations at the same time. Although the HEED protocol also pays attention to  
Figure 1: Schematic diagram of IoT-based microgrids. the energy of nodes, the key operation part is the algorithm iteration, which will increase the overall complexity of the algorithm and increase the amount of algorithm calculation. In the process of research on traditional wireless sensor networks, the LEACH protocol stands out because of its energy efficiency and load balance. It was first proposed in the wireless sensor network and is also the one of the most classic clustering algorithms [16; 17; 18]. Many subsequent sensor network routing protocols are established on the basis of LEACH. Therefore, studying the LEACH protocol helps better understand the routing algorithm protocol in sensor networks, and lays the foundation for proposing better protocols in the future. The LEACH protocol has many disadvantages. Due to the static cluster head ratio and cluster radius setting and the uncertainty mechanism of randomly selecting cluster heads, the LEACH protocol lacks consideration of node location and remaining energy, resulting in poor network stability and life cycle. To address the problems of the LEACH protocol, the paper improves the LEACH protocol under the system model of static nodes, and proposes a Stationary-node Energy-based Routing Protocol (SERP). This paper mainly discusses the Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids. This paper has four main sections: (1) In Section 1, we introduce the IoT-based microgrids, LEACH protocol and HEED protocol; (2) In Section 2, we analyze the main problems in LEACH protocol and propose solutions; (3) In Section 3, we introduce the system model of this paper and the improvement of LEACH protocol; (4) In Section 4, we summarize the conclusions of this paper.
## 3 Materials and Methods
### Sentinel-2 Image Gathering  
To estimate and correlate the field data with the remote sensing data, we used Copernicus Sentinel-2 mission images provided by the European Space Agency. Sentinel-2 comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180\({}^{\circ}\) to each other. As they provide a ten-day revisit time with one satellite and five days with both satellites under cloud-free conditions, we gathered both Sentinel-2 A and Sentinel-2 B images. Therefore, the images available for the summer of 2021 include the following dates: 12 th and 27 th of June; 2 nd, 12 th, 17 th, 22 nd and 27 th of July; 1 st, 6 th, 11 th, 16 th, 21 st, 26 th, and 31 st of August; 5 th and 29 th of September. The initial and final dates were used for the verification of the regression models. The rest of the data were used for the generation of models.  
We used both 10 m and 20 m resolution images. Sentinel-2 offers information at three scales, 10 m, 20 m, and 60 m. At each scale, information from different bands is accessible. We consider that the images with resolution at 60 m have a too low spatial resolution to be used in this study due to the heterogeneity of the land covers in the golf course. The 10 m resolution images were available for band 2 (490 nm), band 3 (560 nm), band 4 (665 nm), and band 8 (842 nm) spectral bands. Meanwhile, the 20m resolution images were available for the previous bands and band 5 (705 nm), band 6 (740 nm), band 7 (783 nm), band 8a (865 nm), band 11 (1.610 nm), and band 12 (2.190 nm). Although we consider that more accurate information will be obtained with information with a spatial resolution of 10 m, the facto of having more available information (more bands) at 20 m justifies its inclusion. The statistical analyses do not join bands with different spatial resolutions to avoid the problems linked to uncertainties regarding differing pixel sizes.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 3 Simulation Results
### Link Utilisation Analysis  
OLBT (Overall Link Busy-Time) and OLWT (Overall Link Wait-Time) are two performance measures used in this paper. A link's busy time is estimated by the total simulation iterations in which a node was using that link. This estimated link busy time is calculated for each node and their summation estimates the overall link busy-time (OLBT). Overall link wait-time (OLWT) is calculated in the same way by summation of the iterations in which a node was waiting to access a link. Both measures are in milliseconds (a fixed time unit) instead of simulation iterations, which is a varying time unit. Moreover, OLBT is compared to a hypothetical non-overhead data transfer mechanism. This is an indicator of the efficiency of the communication protocol, as it shows how much communication was dedicated to real data instead of overhead, re-transmissions, ACK packets, etc. To make it possible to compare the results, the experiments are grouped in terms of data size and comm/comp ratio. It should be noted that in all experiments FFTTM is used; therefore, the results reflect the combination of task model-specific behaviours, communication protocol and network topology. Other task models are likely to produce a completely different set of results.