esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
Also, additional versions of FROG, including those that use the nonlinear processes, self-diffraction (which is mathematically equivalent to the alternative arrangement of TG FROG, not considered herein), and third-harmonic generation, would also benefit greatly from the RANA approach, and their ability to reliably discern instability should be determined. Also, the RANA approach can utilize any FROG algorithm (not just GP) as its kernel, and we believe that algorithm performance for both stable and unstable trains would be vastly improved using it in conjunction with those algorithms. Lastly, no technique, including that described herein, can determine the precise _type_ of instability present in the pulse train. Controlled experiments could, in principle, be done using ultrafast electro-optic phase modulators, but they would be difficult. Alternatively, lasers perturbed in known manners could be used. As the parameter space involved in such a study is vast, this is a challenge that is unlikely to be achieved any time soon, but we mention it here to inspire future generations. **Author Contributions:** Conceptualization, R.T.; Software, R.J.; Formal analysis, R.J.; Writing--original draft, R.J. and E.G. ; Writing--review & editing, R.J., E.G. and R.T.; Supervision, R.T. All authors have read and agreed to the published version of the manuscript. **Funding:** National Science Foundation #ECCS-1609808; Georgia Research Alliance. This work was completed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07 NA27344. **Institutional Review Board Statement:** Not applicable. **Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. **Conflicts of Interest:** [PERSON] owns a company that sells pulse measurement devices, although not the algorithm described herein. [PERSON] consults for this company. [PERSON] does not have any conflicts of interest.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 3 The TYCOS Dataset  
### The Image Acquisition System  
Based on the shortcomings observed in the existing datasets, this work established a semi-physical acquisition system, depicted in Figure 1. The system comprised three main components: the observation system, the sun simulation system, and the target system. The observation system involved mounting the camera on the front end of a moving vehicle, utilizing the vehicle's sliding track to achieve the continuous translation and rotation of the camera. The sun simulation system employed lighting devices fixed on tripods to illuminate the satellite model from various angles. The target system secured the satellite on a test platform, utilizing the rotation axis of the satellite model for movement. Details of  
Renowned for its robust performance, this camera excels in harsh environments, including extreme temperature variations and low-illumination conditions. The main parameters of the camera are detailed in Table 3.  
Sun simulation system: The sun simulation system primarily comprised lighting devices and a fixed bracket. The lighting device utilized LED light sources with three adjustable brightness levels, high, medium, and low, thus effectively emulating normal illumination, low illumination, and high saturation in space. The main parameters of the lighting device are detailed in Table 4. The angle between the light rays and the target satellite was adjusted through moving the fixed bracket.
## 4 Experiments  
### Dataset and Parameters  
We validate the proposed scheme by performing extensive experiments using LITIS Rouen Dataset [14], which includes 19 classes of real acoustic scenes categories with 3026 clips of 30 s length. It is noteworthy that the LITIS Rouen dataset is open dataset, which provide a standardised way to present and compare results. Figure 7 shows the distribution of audio samples among classes. Sounds are recorded at 22.05 kHz sampling rate and 16-bit depth. 20-fold splits are provided to partition data into 80%-training/20%-test sets. In our experiments, we set Fourier analysis window length to 30 ms with half overlapping. 60 Mel-filters were applied to extract Mel-spectrogram. To obtain CQT spectrogram, the number of bins per octave was set to 48 and local region size, number of orientations were set to 8 \(\times\) 8 and 8, respectively. 2D smoothing was performed to TFRs through convolving with Gaussian kernel and kernel parameter was set to 3. At acoustic subspace extraction, we set contribution rate \(\eta_{K}^{\prime}\) to 0.99. Thresholds for events detection of \(\varepsilon_{kurt}\) and \(\varepsilon_{skew}\) were set to 7 and 1, respectively. At classification stage, Gaussian kernel parameter was set to 0.1.
# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 4 Methodology
### Construction of the Model for Coal Identification  
After eliminating redundant features, the feature extraction capability of CNN is utilized to achieve the coal identification task of coal type data from coal-fired boilers in TPPs. The redundant features of CNN learning, which may affect model accuracy, are effectively addressed. The model workflow is illustrated in Figure 3. The pseudo code of the algorithm is shown in Algorithm 2. The details are as follows:  
1. Data acquisition: The experimental data used in this study are related to the actual application of coal in a coal-fired boiler in a TPP, consisting of a total of 288,000 data.
2. Data pre-processing: Directly collected data from TPPs often contain missing and duplicated data caused by the industrial environment and process flow, making them unsuitable for direct use. Therefore, data cleaning is performed to remove or correct missing data, normalize data to reduce the computational load, and improve data labeling to meet the supervised learning conditions of 1D-CNN.
3. Data set partitioning: The processed data are divided into training and test sets.
4. MIC-CNN model construction: The input data are fed into the model for training and testing, and model accuracy is validated using the validation set. In addition, the model is compared with back propagation (BP) neural network and 1D-CNN methods.  
```
Input: A data matrix \(\mathbf{D}=(D_{1},D_{2}...D_{n})\) Output: An array \(R\); // Classification Result // Complete data preprocessing Step 1:Def function (data preprocessing): Return Data cleaning, Normalization, Match Label; // Complete data partition Step 2:Def function (data partition): Training Set: 70%, Testing Set: 30% Return Training Set, Testing Set; // Complete modeling Step 3:Def function (MIC): // feature selection Select best \(k\) features \(\tilde{\mathbf{D}}_{\mathbf{i}}=(\hat{d}_{l1},\hat{d}_{2}...\hat{d}_{in})\), \(i=1,2,\ldots,k\); Return \(\mathbf{D_{new}}=(D_{1}^{\prime},D_{2}^{\prime}...D_{n}^{\prime})\); // New data Step 4:Def function (CNN): Pooling Layer, Dropout Layer, Full Connected Layer, Output Layer Initialization, Set Epochs, Set BatchSize Calculation Error \(E\)
```  
**Algorithm 2** Coal identification algorithm based on MIC-CNN.  
Figure 3: Flowchart of the proposed method.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 6 Simulation Results
### Network Performance at Nonuniform Traffic Load
For \(M=1\), the LETPO algorithm only balances the residual energy of the sender and a single cooperative node. Therefore, from the global viewpoint, the effect of network energy balance cannot compensate the residual energy differences caused by non-uniform traffic transmissions. For \(M=2\), the LETPO algorithm can balance the residual energy of the sender and two cooperative nodes during each data packet transmission, thereby accelerating the effect of balancing the whole network energy consumption. More specifically, it is more likely to select the cooperative nodes with both good channel quality and sufficient residual energy for cooperation transmissions, which takes full advantage of both diversity gain and the cooperative nodes with more residual energy. Therefore, it resolves the unbalance problem of energy consumption due to non-uniform traffic transmissions and extends the network lifetime efficiently. As shown in Figure 14, when \(M=2\), the energy utilization of the PO-CMAC protocol reaches 0.95. If \(M\) is further increased, there is no room for improving energy utilization, and instead, there will be more energy overhead for each data packet transmission. Therefore, when \(M=3\), although the energy utilization of the PO-CMAC protocol nearly reaches 1, its network lifetime and average number of data packets transmitted by each node become lower than those when \(M=2\). From the above results, it can be seen that the PO-CMAC protocol outperforms the DT and EE-CR protocols in terms of the network lifetime, the average number of data packets transmitted by each node and energy utilization at both uniform and non-uniform traffic load. In the PO-CMAC protocol, the parameter \(M\) can be changed according to the traffic load to achieve more satisfactory performance. Generally, the PO-CMAC protocol with \(M=1\) is the best in the presence of uniform traffic load, and the PO-CMAC protocol with \(M=2\) is suitable for non-uniform traffic load.
# Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs
## 5 Performance Simulation and Analysis
### Simulation Setting  
We use Aqua-sim [41], the built-in underwater acoustic module in NS-2 [42], for simulation to evaluate the performance of OCMR. In the simulation, the sensor nodes are deployed in the region of 800 m \(\times\) 800 m \(\times\) 800 m randomly. There is one source node and one destination node in the network scenario, the position of the source node is set to (100, 300, 0), and the position of destination node is set to (400, 400, 800). The node transmission power is 2 W, receiving power is 0.75 W, and idle power is 8 mW. The simulation time is 500 s, the node minimum velocity is 0.2 m/s, the \(V_{Acoustic}\) is 1500 m/s underwater, and the transmission range is 100 m. The radius of the routing pipe of the VBF and the HH-VBF is 70 m. The global simulation parameters are shown in Table 3.  
Figure 7: (**a**) Routing strategy diagram (single-hop process). (**b**) Routing strategy diagram (multi-hop process).
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 7 AVISPA Simulation
### Result Analysis of AVISPA Simulation  
We show the results of the AVISPA simulation using Constraint-Logic-based ATtack SEarcher (CL-AtSe) and On-the-Fly Model Checker (OFMC) to verify the security of our protocol. The CL-AtSe assessed the security of the protocol to replay attacks. The CL-AtSe verifies whether a legitimate user could perform the scheme by executing a search for a malicious adversary. Furthermore, the OFMC verifies the security of the proposed protocol to MITM attacks. The results, shown in Figure 9, demonstrate that the proposed protocol is secure against both MITM and replay attacks. The OFMC verification shows that the search time was 1.12 s for visiting 130 nodes, and the CL-AtSe verification analyzed three states with 0.08 s to translate.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 6 Performance Evaluation
### Scenarios Description  
To compare the performance of the link quality based forwarding strategies, a set of scenarios are proposed. Two evaluation scenarios (A and B) are conducted in both environments, real and simulated one, while Scenario C, a mobile scenario to prove the adaptability of the routing strategy in dynamic scenarios, is only evaluated in the simulation environment.  
Scenario A is illustrated in Figure 12. This scenario is composed of 3 USVs, where one acts as a Gateway and the other two act as sending nodes. All USVs are fixed and USVs 1 and 2 send packets to USV 3 (the Gateway node). USVs 1 and 3 are not directly connected, which means that USV 2 acts as a connection point between USV 1 and 3. USV 2 is positioned 20 meters away from USVs 1 and 3.  
The Scenario B topology is illustrated in Figure 13. In this scenario all USVs are static, similarly to the previous scenario, and USVs 1 and 2 send packets to USV 3 that is acting as Gateway. USVs 3 and 4 do not send packets, which means that USV 4 acts only as a relay node. USVs 3 and 1, and USVs 4 and 2 are not directly connected. In this scenario, the nodes are positioned on the vertices of a 30 m square. The packets transmitted from USV 1 have two possible paths to reach the gateway: Path P1, \(1\to 2\to 3\), and Path P2, \(1\to 4\to 3\).  
The Scenario C topology is illustrated in Figure 14. In this scenario, all USVs are mobile. USV 3 acts as a Gateway while USVs 1, 2 and 4 generate packets to be transmitted to USV 3. Figure 14a represents the connectivity graph on a set of timestamps observed during the simulation where we can see the available paths for the packets to be transmitted. Figure 14b shows the mobility progress of each USV for the same timestamps whose connectivity map is represented in Figure 14a.  
Figure 12: Aquatic Scenario A.  
Figure 13: Aquatic Scenario B.  
The Scenario D topology is illustrated in Figure 15. In this scenario, all USVs are mobile. USV 3 acts as a Gateway while USVs 1, 2, 4, 5, 6, 7 and 8 generate packets to be transmitted to USV 3.  
Finally, the Scenario E topology is illustrated in Figure 16. In this scenario, all USVs are mobile. USV 3 acts as a Gateway while USVs 1, 2, 4, 5, 6, 7, 8, 9, 10, 11 and 12 generate packets to be transmitted to USV 3.  
Each topology scenario is used to assess the performance of the proposed forwarding strategies. Table 5 summarizes the characteristics and objectives of each test when in realistic or simulated environment.  
Figure 14: Aquatic Scenario C.  
Figure 16: Position progress of each USV in Scenario E.  
Figure 15: Position progress of each USV in Scenario D.
# BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization
## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **\# Parameters** & **R1** & **R2** & **RL** & **BERTScore** \\ \hline LEAD-2 & \(\times\) & 27.72 & 11.66 & 19.62 & 70.25 \\ TextRank & \(\times\) & 22.63 & 8.0 & 15.48 & 68.4 \\ LexRank & \(\times\) & 26.96 & 10.94 & 18.94 & 69.94 \\ \hline IT5-base & 220 M & 32.88 & 15.53 & 26.7 & 71.06 \\ BART-IT & 140 M & 37.31 & 19.44 & 30.41 & 75.36 \\ \hline mT5 & 390 M & 35.04 & 17.41 & 28.68 & 74.69 \\ mBART & 610 M & 38.91 & 21.41 & 32.08 & 75.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summarization evaluation results on the IIPost dataset. The # parameters column is empty for unsupervised baseline models since they do not include any trainable parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **\# Parameters** & **R1** & **R2** & **RL** & **BERTScore** \\ \hline LEAD-2 & \(\times\) & 15.63 & 3.32 & 10.51 & 63.01 \\ TextRank & \(\times\) & 15.35 & 3.04 & 9.84 & 62.12 \\ LexRank & \(\times\) & 15.96 & 3.3 & 10.48 & 62.81 \\ \hline IT5-base & 220 M & 37.98 & 24.32 & 34.94 & 77.14 \\ BART-IT & 140 M & 42.32 & 28.83 & 38.84 & 79.28 \\ \hline mT5 & 390 M & 40.6 & 26.9 & 37.43 & 80.73 \\ mBART & 610 M & 39.32 & 26.18 & 35.9 & 78.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summarization evaluation results on the WITS dataset. The # parameters column is empty for unsupervised baseline models since they do not include any trainable parameters. between source and target languages, but they might be less effective in learning to produce a summary of long and diverse documents. The empirical analyses reported in this section show that BART-IT is competitive yet efficient in tackling the Italian document summarization task, especially when compared with language-specific abstractive models. The model also outperforms the IT5 sequence-to-sequence model with a similar number of parameters and is competitive against multilingual models having significantly larger model sizes. Figure 1 compares the performance of the models in terms of a combined evaluation score mixing effectiveness (evaluated using the syntactic ROUGE-2 score) and efficiency (defined by the number of summaries generated per second on a single NVIDIA A6000 GPU). BART-IT achieves the best performance mix, addressing summary generation more efficiently than all the other tested models, outperforming IT5 and mT5 in terms of ROUGE-2 scores, and being competitive against mBART. In view of the achieved results, BART-IT can be deemed as an efficient yet effective solution for tackling the abstractive summarization task for the Italian language.
## 1 Introduction  
The materials and methods used to repair aged pavement are evolving [1, 2]. Because pavement repair requires a short-term interruption of traffic flow, there is considerable interest in the use of rapid-set concrete-based repair materials [3, 4]. However, the high heat of hydration during the development of early strength in rapid-set concrete can result in microcracks that compromise the durability of the entire repaired layer [5, 6]. Specifically, microcracks increase the permeability of the repaired pavement structure, resulting in rapid degradation [7, 8]. To mitigate this issue, a latex-modified rapid-set cement concrete was developed as a pavement material for emergency repair [9, 10]. The addition of latex reduces the risk of cracking due to the heat of hydration [11, 12]. During the hydration process, latex bonds to hydrated cement particles to form an interconnected network and continuous material as latex solids fill micropores within the concrete structure. This significantly reduces the occurrence of cracks and material deterioration [13, 14]. However, the on-site preparation of such a mixture requires a mobile mixer truck. Self-leveling latex-modified prepacked concretes that do not require a mobile mixer truck have been studied as alternative materials [2, 3]. In such a system, an emergency pavement repair is initiated with the removal of the old pavement. A coarse aggregate is then placed into the excavated hole.  
The latex and rapid-set binder are mixed using a standard mixer and then poured into the voids within the coarse aggregate. A vibratory press compactor is used instead of a roller compactor. Once the pour and compaction are complete, the pavement repair is finished [2]. A large mobile mixer is not required provided that the coarse aggregate is deposited into the repair area before adding the self-leveling latex-modified rapid-set cement concrete [2]. Furthermore, the use of a vibratory press compactor significantly enhances the durability of the repaired pavement layer [2]. However, self-leveling latex-modified preplaced concrete does not completely solve the problem of microcrack formation and subsequent concrete degradation. This study addresses this problem with the addition of reinforcing fibers to effectively reduce crack formation, thereby increasing the strength and durability of the concrete mixture. Crack formation in materials containing reinforcing fibers occurs by separation of the fibers from the cement matrix, fiber pull-out, or fracture of the fiber itself [15; 16; 17; 18]. This study investigated natural jute, poly (vinyl alcohol) (PVA), and nylon fibers, as well as hybrid fiber mixtures consisting of two types of reinforcing fiber at a 1:1 weight ratio. The effects of fiber type on the properties of latex-modified rapid-set cement preplaced concrete were evaluated toward the improvement of current materials used for emergency pavement repair.
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 4 Performance Analysis of SSPSWs
### Energy Dissipation Performance  
The energy dissipation analysis revealed a direct correlation between the structural stiffness and energy absorption capacity across all the SSPSW models. The bilateral-joined configurations demonstrated superior ultimate displacement performance, maintaining elastic behavior up to a 5% inter-story drift (3% below the collapse thresholds), whereas the quadrilateral-joined systems exhibited a reduced energy dissipation capacity (15-18% lower than their bilateral counterparts). Notably, the non-slotted shear walls (SPSWs) reached their collapse limits at a 2% drift--60% earlier than the optimized SSPSWs. The quantitative comparisons of the equivalent viscous damping ratios and cumulative energy dissipation metrics are detailed in Table 5, conclusively validating the seismic performance advantages of slit-configuration designs.
###### Abstract  
Expansive soils exhibit pronounced behaviors of swelling, shrinkage, and overconsolidation, leading to significant engineering challenges. To address these issues, this study investigated the influence of different cementation solution concentrations on the swelling and mechanical properties of microbially induced calcium carbonate precipitation (MICP)-treated expansive soils. Additionally, a series of wet-dry cycling experiments were conducted to analyze the performance degradation of the MICP-treated soils under cyclic environmental conditions. The results showed that as the cementation solution concentration increased, the free swell ratio and unconfined swell ratio of the soil samples decreased by 39.29% and 71%, respectively. The unconfined compressive strength, cohesion, and internal friction angle of the MICP-treated soils also exhibited significant improvements, with increases of 391.67%, 33.76%, and 91.67%, respectively. A cementation solution concentration of 1 M was found to be the optimal for achieving substantial enhancements in the soil properties. However, under continuous wet-dry cycling, the mechanical properties of the MICP-treated soils experienced degradation, with the unconfined compressive strength decreasing by up to 62.24%. These findings highlight the potential of MICP as an effective and eco-friendly technique for stabilizing expansive soils.  
expansive soil; microbially induced calcium carbonate precipitation (MICP); swelling characteristics; mechanical properties; wet-dry cycling +
Footnote †: journal: Applied Sciences  
Article