esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction of Geosynthetic-Reinforced Pavements and Evaluation of Their Impacts
## 1 Introduction
Geogrids have also been tested in the base layer and base subgrade interfaces, demonstrating a notable reduction in rutting depth and permanent deformation [18]. In a subsequent study on the same trial section, it was demonstrated that at least a 20% reduction in rutting depth could be provided by installing geogrid at the interface of the base and subgrade [19]. A further review of full-scale studies using geosynthetic-reinforced flexible pavements found that they could reduce vertical deformation and delay rutting [20]. When used in pavement rehabilitation, geogrids placed underneath asphalt overlays were shown to reduce rutting in the surface course [21]. Trials on the Qinghai-Tibet highway demonstrated that the drainage effect of geotextiles also reduced the frost penetration and thawing depth, thus preserving the underlying permafrost and minimizing further thaw [22]. This structure was better suited to prevent moisture flow from soil to the gravel layer and provide better drainage effects. [PERSON] and [PERSON] (2003) conducted a full-scale test on an unpaved road with geogrids laid over non-woven geotextiles embedded at the interface of granular base and subgrade layers; the outcomes of this study were used to supplement the design of unpaved roads for the United States Army Corps of Engineers [23]. A field trial with geocomposite serving as an interlayer treatment was evaluated by testing cored samples in the laboratory using an interlayer shear test [24]. In general, it can be deduced that the proper installation of geosynthetics is crucial, and improper placement could affect the post-construction evaluation of the resilience of the materials [25; 26]. Nevertheless, there are limited studies examining the performance of geosynthetic-reinforced pavements in the real world and with full-scale studies. Asphallayers with geogrid reinforcement are rarely discussed in the context of full-scale field studies, and are even less commonly studied with sophisticated instrumentation and sensors to monitor the actual pavement response during geogrid installation. Limited research has been conducted on geogrid composites during installation thus the impacts of these interlayer systems under construction are still unexplored. In this paper, a practical field study regarding the instrumentation of geosynthetic-reinforced pavements was presented; the installation of both geosynthetics and sensors is introduced, and the effect of these interlayer systems is investigated during construction. In this large-scale study, geogrid reinforced asphalt and geogrid composite on the subgrade were investigated in real-time using pressure, strain temperature, and moisture instrumentation. Post-construction deflectometer measurements were conducted to verify instrument readings and demonstrate the resultant benefits of these interlayer systems.
## 3 Analysis of the Transition between Shock Reflections
### Position (a)
However, due to the generation of strong blast waves, the duration of the disturbance affecting the flow is increased and the transient Mach reflection remains longer during the regeneration process. Because of this effect, the time required for the complete transition is 1.5 times longer than in the previous conditions. Figure 11 shows the results for when the deposited energy is increased to 420 mJ. Different shock regeneration characteristics from the previous conditions are observed. In these conditions, the disturbance influences the oblique shock wave that is generated in the lower wedge as the intensity of the blast wave increases (T = 30 us). As the blast waves reaching the lower wedge are reflected, superimposition and reflection of the complex shock waves develop near the wedge surface, which does not occur in the previous cases. The change of the reflection characteristics of the shock wave in this region is considered to play a major role in forming multiple normal shock waves downstream. Compared to the two preceding cases with lower laser energy, the main difference is in the location where the final interaction occurs. In previous cases, multiple shock interaction between the blast wave and reflected shock occurs from T = 40 us to T = 120 us, but it lasts to 230 us for this case. The interaction is almost finished and the shock structure becomes stable before it contacts the last blast wave, which propagated upstream. Thus, the only effect of the intersect wave is changing the impinging shock angle. However, in the case of the highest energy, there is still much interaction left when the last blast wave passes the interaction region, and a strong Mach reflection develops in the contraction zone. Because there is no reason for the transition, the Mach reflection remains in the domain. In addition, the interaction between the blast wave and shock wave does not continue in the expansion zone and the interference with the rarefaction wave generated at the tip of the wedge is not observed. This was assumed to contribute to the transition by changing the flow speed. It is concluded that even if the energy is deposited at the same position, the characteristics of shock wave interactions can be different for varying energy levels. In particular, the transition does not occur when too much energy is deposited to avoid Mach reflections near the von Neumann criteria of the dual solution domain and it could also be speculated that the transition could be induced efficiently by decreasing the energy.
## 4 Evaluation
### Failure Recovery Evaluation--Results and Analysis  
We conducted experiments with the datasets outputted by FDM for evaluating, regarding RMSE, several interpolation techniques that allow replacing the outliers with 'accurate' data. Figure 4 shows that the Nearest Neighbor technique obtained the worst RMSE; Nearest Neighbor selects the nearest datum's value without considering neighboring data values, which tends to increase noise, especially in the Per_Month dataset. From a conceptual perspective, the Cubic Spline should offer better results than the Linear [61]. Still, our experimental results indicate a lower RMSE with the Linear, which is due to the data points in our datasets are closely near (our data samples are measurements of temperature). Thus, the connection between each data point by a straight line allows for achieving high-accuracy interpolation.
# CATSWoTS: Context Aware Trustworthy Social Web of Things System
## 4 Experimental Validation and Results Discussion
### Effect of QoS Constraints  
The results presented in this section are based on the experiments conducted in Section 4.1. The lists generated based on various values of context parameters were then used to evaluate the shortlisted service providers based on QoS attributes.  
The \(x\)-axis shows the number of requests generated. The \(y\)-axis shows the QoS level of a service where \"required\" level manifests the level desired by a service seeker and maximum \"received\" level, a service provided from among the identified services in each request. The real dataset used in the evaluation acts as a ground truth. For testing purposes, we have considered three attributes (reliability, availability, and latency). A service seeking node entered the weight of each criterion based on the requirements. For example, the SS wanted the services of a commerce company always available for them. Therefore, the SS gave more weight to availability than reliability. The shortlisted services are then evaluated based on Equation (5) and trust level is calculated for each service based on Equation (6). The CATSWoTS gave the sorted list of service providers that were close to the proximity of the SS. The same process of evaluating all the shortlisted service providers for different values of the QoS attributes was completed in this phase. It was observed that not all the shortlisted services fulfilled the criteria required by the SS. Figure 6(a) shows the final results for the commerce category. It is significant to note that level 1 shows that the identified service fulfills only one QoS criterion required by the SS, level 2 manifests that the identified service fulfills two QoS criteria, whereas level 3 symbolizes that the identified service fulfills all three criteria of QoS. It is evident that seven queries recommended one service at least, which was above the criteria. Four queries presented at least one service that fulfilled the exact criteria, whereas four queries were not able to identify any required service. Similarly, Figure 5(b),c show the queries' results for the business and management categories, respectively.  
Though the experiments are carried out on three attributes of QoS, more attributes like throughput and response time can be added in CATSWoTS. In short, adding more attributes in CATSWoTS does not require any additional effort, but service providers can be evaluated on more criteria. The evaluation of service providers in the specific context of trust along with the criteria of QoS is a contribution of the CATSWoTS in finding a trustworthy Web of Things.  
Figure 6: Context aware evaluation.
# Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
## 4 Experimental Results
### Evaluation of Key Components  
#### 4.3.1 Evaluation of the Proposed RDCL Module and ADCL Module  
In this paper, a representation-level dual contrastive learning module and an assignment-level dual contrastive learning module were developed to discover the clustering partitions of unlabeled 3D shapes by jointly learning consistent 3D shape representations and clustering assignments. To validate the effectiveness of the two modules, evaluations were conducted on the ModelNet10 and ModelNet40 datasets. The results are shown in Table 2, in which \"_w/o_ ADCL\" indicates the proposed method without the ADCL module and \"_w/o_  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**ModelNet10**} & \multicolumn{6}{c}{**ModelNet40**} \\ \cline{2-9}  & **ACC** & **NMI** & **ARI** & **F-score** & **ACC** & **NMI** & **ARI** & **F-score** \\ \hline CoMVC* & 0.6703 & 0.6520 & 0.5920 & 0.6408 & 0.4191 & 0.5769 & 0.3608 & 0.3841 \\ \hline EAMC* & 0.7040 & 0.6583 & 0.5890 & 0.6455 & 0.4563 & 0.5403 & 0.3259 & 0.3685 \\ \hline DMSC* & 0.7638 & 0.7291 & 0.7426 & 0.7766 & 0.5516 & 0.6847 & 0.5236 & 0.5556 \\ \hline DCL-Net & **0.7932** & **0.8218** & **0.7646** & **0.7934** & **0.6120** & **0.7246** & **0.5761** & **0.6022** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of results from different clustering methods on the ModelNet10 and ModelNet40 datasets.  
Figure 2: The t-SNE visualization results of the latent 3D shape features on the ModelNet10 dataset.  
RDCL\" indicates the proposed method without the designed RDCL module. When the ADCL module was removed, the clustering results could not be directly predicted by the model, hence the k-means algorithm [57] was introduced to perform clustering on the consistent 3D shape representations obtained by the RDCL. As shown in the table, the performance of the _\"w/o_ ADCL\" method dropped significantly on both two datasets compared to the DCL-Net and the ACC value dropped sharply by more than 10% on ModelNet10. This was mainly because the removal of the ADCL module disconnected the procedure of the representation learning and clustering, thus the obtained 3D shape features were irrelevant to the subsequent clustering. Similarly, the _\"w/o_ RDCL\" method also obtained unsatisfactory clustering performances on both two datasets. The main reason was that removing the RDCL module made it difficult to ensure the feature consistency between different views and the corresponding point cloud, thus damaging the intra-cluster compactness and inter-cluster separation of the latent 3D shape features for clustering. Comparatively, the clustering results of the DCL-Net were consistently improved on both two datasets when using the RDCL and ADCL modules simultaneously. This sufficiently reflected the significance of both the representation-level dual contrastive learning and the assignment-level dual contrastive learning for multi-modal 3D shape clustering.
## 3 Results and Discussion
### GFET Based Flexible Receiver
#### 3.5.2 Frequency Translation Block
Low pass filtering is necessary at output to filter out higher order harmonics that are also produced. The gain of the demodulator is \(\sim\)-34 dB (Figure 10b,c). The gain performance is on the low end for the current devices due to parasitics from the source and drain contacts and interconnects. The GFET demodulator, resistors and capacitors (C\({}_{\mathrm{c1}}\) and C\({}_{\mathrm{c2}}\) surface mount device components) are mounted on the flexible polyimide substrate. Figure 10: _Cont._  
Figure 9: **(a)** GFET Doubler; and **(b)** Power Spectrum (adapted from [16], Copyright 2013, permission granted by IEEE).
## 1 Introduction
In a past paper [18], the intermittent and combined operation of the CCHP system was considered, a robust optimal scheduling method was proposed to reduce the disturbance of uncertainty, however, only the description of the uncertainty in the scheduling cycle was considered, and the coupling problem of the uncertainty between the time period and the emergency was ignored. In different past papers [19; 20], the optimal power flow problem of an energy system was studied, which had multiple EHs that integrate electricity and natural gas systems. In another past paper [21], the cold, heat, and electricity multi-energy flow optimization of microgrids involving renewable energy was studied. In a past paper [22], coordinated optimization was discussed for a combined cold, heat, and power supply system considering electricity, gas, cold, and thermal energy flows. In another previous paper [23], a mixed integer linear programming method was proposed to calculate the optimal power flow of multicarrier energy systems. A linear EH model based on state variables was presented to avoid introducing scheduling factor variables which were traditionally applied to optimal power flow problems. Furthermore, it is worth pointing out that the hierarchical optimal operation of IES is very important if energy production and consumption are to be coupled. However, to the best of the authors' knowledge, most of the existing results pay more attention to _CHP_/CCHP, there are few results considering the day-ahead hierarchical optimal operation of IES based on EH, not to mention considering the scenario of the joint steady state optimal operation and unify management and scheduling of different systems at the same time, which are the main motivations of our paper. Based on the above discussions, in this paper, for the aim of optimizing the operation of coupling natural gas systems, electricity transmission systems, and district heating systems, the hierarchical optimal operation mode considering one objective function of the day-ahead operating cost for EHbased on peak-valley time-of-use power/heat prices and the other objective function of day-ahead fuel cost for IES based on EH, is proposed and solved effectively.
# Panoptic Segmentation-Based Attention for Image Captioning
## 6 Experimental Results
### Evaluation
#### 6.3.2 With Stuff versus without Stuff  
Stuff regions play an important role in image captioning, as they provide the context information (scene, location, etc.) to the model. As shown in Table 1, when comparing PanopticSegAtt with InstanceSegAtt, PanopticSegAtt further improves the CIDEr score by 2.1, which shows that using features of stuff regions also enhances performance. To qualitatively demonstrate the superiority of using features of stuff regions, we show the example captions generated by InstanceSegAtt and PanopticSegAtt in Figure 8. Compared with the InstanceSegAtt model that does not have the features from stuff regions, PanopticSegAtt can generate captions with richer scene information. For example, in the first column of Figure 8, the segmentation regions generated by panoptic segmentation contain the brick wall region in the image and provide the feature of brick wall to the captioning model. Thus, the caption generated by PanopticSegAtt contains the background information \"brick wall\". Similarly, in the second column of Figure 8, the purple area provides the context information of the image, so the PanopticSegAtt can generate the phrase \"with plants\". In the third column of Figure 8, while InstanceSegAtt does not generate the scene of the photo, PanopticSegAtt correctly generates the scene phrase \"in a field\". In the fourth column of Figure 8, the purple area provides the location of the train to the PanopticSegAtt model which generates a more accurate scene word \"mountain\" while InstanceSegAtt generates the scene word \"field\".  
The above results demonstrate that, with the aid of stuff regions, our panoptic segmentation-based attention method can generate captions with richer context information.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **ROUGE-L** & **METER** & **CIDEr** \\ \hline DetectionAtt & 76.9 & 61.0 & 45.5 & 35.7 & 57.0 & 26.6 & 112.2 \\ InstanceSegAtt & 78.4 & 62.6 & 48.3 & 37.0 & 57.5 & 27.2 & 117.8 \\ PanopticSegAtt & **79.2** & **63.8** & **49.5** & **38.1** & **58.2** & **27.6** & **120.9** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results obtained on the MSCOCO dense annotation test split. Higher is better in all columns. Scores are multiplied by a factor of 100.
## 4 A Look Toward Privacy in 6G Core Networks
Such considerations are seemingly left up to system designers, which is a cause for concern. This mirrors our concerns about the assumptive trustworthiness of activities within the 5G core environment with regard to user privacy protections and data handling. Figure 4: Proposed application sectors for 6G technology extrapolated from the findings in [51]. The authors in [51] bring to light three new sectors that could be introduced in 6G technology that are the intersections of the current 5G applications: mULC (Massive Ultra-Reliable Low-Latency Communication), ULBC (Ultra-Reliable Low-Latency Broadband Communication), and uMBB (Ubiquitous Mobile Broadband). One of the driving factors in the innovations sought by 6G technology is the amalgamation of intelligent systems in cellular networks.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 4 Experimental Validation
### Analysis of the Prediction Results of Abnormal Gearbox Operation Data  
The fault sample test set data are the SCADA data from 0:00 to 8 a.m. on 19 September 2022. As shown in the above experiments, the IALO-GRU model training based on normal running data showed better fitting ability and a smaller threshold range. The theoretical performance should have a better fault identification ability. To confirm this point of view, the abnormal sample test experiment's results are shown in Figure 12, as well as the  
Figure 10: Range of the different model thresholds.  
Figure 9: Residual results of the different models.  
IALO-GRU model on the 17 th. And the alarm's frequent residual beyond the threshold range in the following time confirmed that the model can effectively predict a fault and alarm in time. The frequent alarms in the other models were relatively late. The PSO-GRU model detected the residual oil temperature beyond the threshold at 1 a.m. on the 18 th. The GRU model alerted at 6 a.m. on the 18 th. The IALO-LSTM alerted at 8 am on the 18 th, and ALO-GRU triggered the alarm at 10 am on the 18 th. However, the IALO-LSTM and PSO-GRU had failed alarms during normal operation, probably because of improper threshold range setting in these models, resulting in poor fitting with abnormal samples and triggering false positives.  
Based on the results of the five models with normal and abnormal gearbox samples, the IALO algorithm has a higher fitting degree and a smaller threshold range, giving it a better sensitivity and alarm effect. Compared with the LSTM network model, the GRU model has a better fit effect for processing the high-dimensional temporal data.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 4 Optimization Problem of the Proposed System
### Mathematical Model of the Constraints
#### 4.2.3 Operation Constraints of the Storage Battery  
The constraints that govern the operation of the SB are as follows:  
* State of Charge Constraint \[W_{SB-min}^{t}\leq W_{SB}^{t}\leq W_{SB-max}^{t}\] (39)
* Charging and Discharging Power \[U_{SB-ch}^{t}\cdot P_{SB-ch,min}^{t}\leq P_{SB-ch}^{t}\leq U_{SB-ch}^{t}\cdot P _{SB-ch,max}^{t}\] (40) \[U_{SB-dis}^{t}\cdot P_{SB-dis,min}^{t}\leq P_{SB-dis}^{t}\leq U_{SB-dis}^{t} \cdot P_{SB-dis,max}^{t}\] (41) \[U_{SB-ch}^{t}+U_{SB-dis}^{t}\leq 1\] (42) where \(U_{SB-ch}^{t}\) and \(U_{SB-dis}^{t}\) are zero/one variables that are utilized to prevent the SB from charging and discharging at the same time.
## 2 Tunnel Description and Geological Conditions
### Effectiveness Analysis of Grouting Treatment
The water discharge situation of inspection hole and coring result are shown in the Figure 13. The slurry was well filled, with good densification, no obvious fractures were found, and the slurry had a high coagulation strength with the rubble mass. After grouting, the construction unit conducted a water pressure test on the inspection hole, and the depth of the inspection hole is generally slightly less than 10m, located within the range of the grouting reinforcement circle. The decreasing permeability of inspection holes is shown in Table 3. The average permeability of the inspection holes after grouting was 3.32 Lu, compared with the average permeability of 4.39 Lu before grouting, the permeability was reduced by 24%, which indicated that the rock mass was reinforced to some extent after grouting. The inspection hole on the right side of the tunnel face changed from flowing water to dripping water after grouting, and there was no water gushing in the later stage. Multiple boreholes are drilled in the lower left corner of the tunnel face, and the water gushing significantly decreases or stops after the diffusion of slurry. Figure 12: Comparison of mechanical parameters of surrounding rock in the grouting section before and after grouting. Figure 13: Water gushing situation of inspection hole: (**a**) Inspection hole of CGJ10-32; (**b**) Inspection hole of CGJ11-32. The grouting technique achieved the dual effects of reinforcement and water plugging, and it effectively prevented and controlled the water inrush disasters in the tunnel adjacent to the fault.