esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mycoremidation of TCE in Soil  
_P. ostreatus_ and _P. erygii_ were able to grow in the soil spiked with TCE at a concentration of 140 mg kg\({}^{-1}\), which is twice the threshold concentration for industrial soil and 20 times the threshold concentration for urban soil according to the Spanish legislation [32]. The percentage of TCE removal in the control soil and the two mycoremediation treatments are shown in Figure 4. The removal of TCE followed the same pattern in all the three treatments at the beginning of the assay. However, the percentage of removal was significantly higher (\(p<0.05\)) for the two mycoremediation treatments by comparison with  
Figure 3: Laccase (Lac) and MnP activity of _P. ostreatus_ (**A**) and _P. erygii_ (**B**) in the absence and presence (14 mg kg\({}^{-1}\) and 140 mg kg\({}^{-1}\)) of TCE for 14 days in malt extract liquid media. Error bars represent the standard deviation (n = 3).  
the control after the second week. During the second week of treatment, _P. erygii_ achieved an increment in the removal of TCE of 8% and 32%, respectively, when compared with _P. ostreatus_ and the control. Demonstrating that _P. erygii_ was faster at removing TCE than _P. ostreatus_. However, after 2 weeks incubation both species of fungi achieved a percentage removal greater than 70%. The results obtained at the third week of the assay showed the highest efficiency of _P. erygii_ for removing TCE with respect to _P. ostreatus_. At the final sampling time, both species of fungi had achieved 100% TCE removal, demonstrating the high effectivity of the mycoremediation procedure for this contaminant. At this point, the two mycoremediation treatments had increased the removal of TCE by 44% when compared to the control and were statistically significant (_p_ < 0.05).  
Table 2 shows the ligninolytic activity of the two mycoremediation treatments. Ligninolytic activity was not detected in the control treatment during the assay. By contrast with the liquid media assay, lacase was not detected in either mycoremediation treatments. The unique ligninolytic enzyme detected was MnP. No significant differences (_p_ > 0.05) were found between _P. ostreatus_ and _P. erygii_ during the four weeks of incubation.
## 4 Result and Discussions
Meanwhile, in the case of deterministic models, each environment has to undergo the process of computation. Figure 3 shows the percentage improvement calculated from RMSE values when either CNN, CNN + LBP, or CNN + GLCM features were used in combination with numeric features over when numeric features were used alone. The addition of CNN + GLCM features to the numeric features had the highest improvement of 9.4272%, followed by the addition of CNN + LBP, which resulted in 8.8667%, and lastly, the addition of CNN features improved it by 8.3802%, as shown in line Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & & & & \\
**Extraction** & **MAE (dB)** & **RMSE (dB)** & **MAPE (\%)** & **R\({}^{2}\)** & **\% Improvement** \\
**Method** & & & & \\ \hline No Image & 2.9862 & 4.0500 & 2.2985 & 0.9227 & NA \\ LBP & 3.1914 & 4.2985 & 2.4589 & 0.9137 & \(-\)6.135
## 2 Materials and Methods  
### Experimental Setup  
On April 2019, clonal plants of the same age of costmary (_Tanucetum balsamita_), with average fresh weight of 10.5 g, were transplanted, one in each pot (\(\mathcal{O}\) = 20 cm; h = 20 cm) filled with a commercial substrate (Vigorplant Lodi, Italia) generally used in organic farming (pH = 7; electric conductivity = 0.4 dS m\({}^{-1}\); total porosity = 87%) and placed under a black polyethylene net (net hole 1.4 \(\times\) 1.4 mm) with 40% shading intensity in the nursery area of the Italian National Research Council, in Central Italy (Sesto Fiorentino; lat. 43 deg49 deg4.2'' N; lon. 11 deg12'06.6'' E; 55 m a.s.l.). No chemical fertilizers were added to the plants. The plants were treated with EM, activated in dilution 1:100 [21] provided by CREA-OF (Pescia, Italy) or with OMW (Table A1), provided by a Tuscan oil mill, working with a three-phase process (Pieralisi, Firenze, Italy). OMW was left to settle for a week, at 4 \({}^{\circ}\)C, in the dark, to let the solid fraction to deposit in the bottom and the supernatant was used for the treatment.  
The treatments were performed by diluting stock solutions with tap water as follows: O1 (OMW 2.5% \(\
icefrac{{\
u}}{{\
u}}\)); O2 (OMW 5% \(\
icefrac{{\
u}}{{\
u}}\)); E1 (EM 5% \(\
icefrac{{\
u}}{{\
u}}\)); E2 (EM 10% \(\
icefrac{{\
u}}{{\
u}}\)); only water (Control). The EM and OMW concentrations applied are in agree with the literature [21; 22; 23]. Five pots per replicate were arranged in a randomized complete block design with three replications per treatment for a total of fifteen plants per treatment. The pot plants were manually irrigated every 15 days with 1 L of water (Control) or treatment solution to maintain the substrate water content near the field capacity. The experiment ended after 75 days after transplant (DAT), at the balsamic time (at the end of springtime, in June, at this latitude) [24]. Climate conditions were continuously monitored by a weather station (Decagon Em50; Decagon Devices Inc., Pullman, WA 99163, USA). The average minimum and maximum temperatures recorded in the period were 12.95 \({}^{\circ}\)C and 25.06 \({}^{\circ}\)C, respectively, and the mean relative humidity was 65%. The leaves were collected at intervals: date of data collection reported 15 th April (T0), 15 th May (T1), 15 th June (T2) and 30 th June (T3).
# Second-Harmonic Generation in Mie-Resonant GaAs Nanowires
## 5 Conclusions  
To summarize, we have investigated SH generation from Mie-resonant GaAs nanowires. When judiciously designed, nanowires are able to boost the conversion efficiency of SH generation up to \(10^{-5}\) using a \(\sim\) 1 GW/cm\({}^{2}\) pump intensity, similar to the efficiency previously reported in 3D confined dielectric antennas made of GaAs and AlGaAs. We have found a doubly-resonant condition in which both the pump and the SH fields excite normal modes of the nanowire, a scenario that can be achieved in a large range of pump wavelengths by changing the nanowire radius. When the nanowire is under doubly-resonant conditions, the SH light is coupled to a multipolar normal mode with large quality factor (the bandwidth of the SH scattering peak is as narrow as \(\sim\)15 nm). Such narrow spectral features may be exploited for sensing devices, tunable filters, and modulators.  
Our results suggest that, due to the resonant nature of SH generation in GaAs nanowires, on one hand, SH scattering in the opaque region of GaAs may be as efficient as in the transparency region, and, on the other hand, SH absorption in the transparency region may be as strong as in the opaque region of GaAs.  
Conceptualization, D.d.C., L.C., M.A.V., C.D.A., M.S.; methodology, D.d.C., L.C., M.A.V., C.D.A., M.S.; software, D.d.C., L.C.; validation, M.A.V., C.D.A., M.S.; formal analysis, D.d.C., L.C., M.A.V., C.D.A., M.S.; investigation, D.d.C., L.C., M.A.V., C.D.A., M.S.; resources, D.d.C., L.C., M.A.V., C.D.A., M.S.; data curation, D.d.C., L.C., M.A.V., C.D.A., M.S.; writing--original draft preparation, D.d.C.; writing--review and editing, L.C., M.A.V., C.D.A., M.S.; visualization, D.d.C., L.C., M.A.V., C.D.A., M.S.; supervision, D.d.C., C.D.A., M.S.; project administration, M.A.V., D.d.C., L.C.; funding acquisition, D.d.C., L.C., M.A.V., C.D.A., M.S.  
This research was funded by the US Army Research Office, grant number W911 NF-18-1-0424.  
Research of [PERSON] was partially funded by the Rita Levi-Montalcini Program. L.C. acknowledges STARSG project PULSAR. Research of [PERSON] was sponsored by the RDECOM-Atlantic, US Army Research Office, and Office of Naval Research Global, and partly performed within Project \"Internet of Things: Sviluppi Metodologici, Tecnologici E Applicativi\", cofunded (2018-2022) by the Italian Ministry of Education, Universities and Research (MIUR) under the aegis of the \"Fondo per il finanziamento dei dipartimenti universitari di eecellenza\" initiative (Law 232/2016). D.d.C. thanks [PERSON] for fruitful discussions.  
The authors declare no conflict of interest.
## 3 Novel Design of a Cross Axis Wind Turbine (CAWT)
### General Arrangement of the CAWT  
Figure 3 shows the general arrangement of the CAWT. The CAWT consists of three main vertical blades and six horizontal blades. The vertical blades are connected to the horizontal blades via connectors and the horizontal blades are mechanically joined to the upper and lower hubs through the axle holes. Both hubs are attached to a center shaft with a relative distance of 100 mm. The horizontal blades are connected to the vertical blades via the top and bottom connectors. A total of six connectors were used in the design, three top connectors were used to connect the top horizontal blades to the upper part of the vertical blades through the axle hole of the connectors and the three bottom connectors were used to connect the lower horizontal blades to the lower parts of the vertical blade by screwing through the axle hole of the connectors.  
To maximize the interaction of the vertical flow, the lower horizontal blades are offset by 60\({}^{\circ}\) from the upper horizontal blades. Offsetting the upper horizontal blades from the lower horizontal blades will enable the free flow of the vertical wind stream from the bottom of the turbine to the upper horizontal blades. The drive shaft of the CAWT can be fixed directly to the generator which converts the kinetic energy in the wind to mechanical energy at the shaft and then to electrical energy. The upper and lower horizontal blades can be pitched at different pitch angles (\(\beta\) = 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\), and 15\({}^{\circ}\)) to allow for better performance of the CAWT. The purpose of the pitch angle is to maintain a near uniform rotor speed under different wind conditions in order to maximize the power output from the turbine. For this study, the pitch angle of the horizontal blades is set at 10\({}^{\circ}\). The choice of  
Figure 3: General arrangement of the CAWT.  
the pitch angle was arrived at after conducting an experimental study on four different pitch angles (\(\beta=0^{\circ}\), \(5^{\circ}\), \(10^{\circ}\), and \(15^{\circ}\)) to ascertain the optimum pitch angle for the horizontal blade of the CAWT. The result showed that the \(10^{\circ}\) pitch angle is the optimum pitch angle for the horizontal blades. The whole system is attached to a small pole and can be mounted on a building of any roof shape.  
For benchmarking, the performance of a conventional straight-bladed vertical axis wind turbine (VAWT) is tested under the same experimental conditions. The CAWT and VAWT used for this study are demonstrated in Figure 4. The lab scale VAWT model used for the comparison consists of a straight-bladed H-rotor, and it has a 50 mm chord length and 300 mm height. A flat plate metal was used as the connecting strut for the VAWT. The lab scale CAWT and VAWT model which are used in the experiment are depicted in Figure 4a,b. The material selection for the CAWT and VAWT is as shown in Table 1, while the parameter comparison between the two lab scale wind turbine models is presented Table 2.
## 7 Conclusions  
A network resource allocation method based on awareness-prediction joint compensation was proposed to address the problem of the low reliability of network resource allocation caused by the poor timeliness of network resource awareness and accuracy of resource status prediction in LEO satellite networks. This method utilizes INT to perceive the status of satellite network resources, taking the awareness latency as the prediction step. The node network resource status prediction model is based on an LSTM. A dynamic compensation model was proposed, which dynamically compensates for prediction results using awareness data based on awareness latency and resource prediction accuracy. Based on compensation data, an accelerated ADMM resource allocation method was proposed to improve the convergence speed of the algorithm and achieve flexible resource allocation. The simulation results demonstrate that the method can minimize the discrepancy between compensation data and the actual onboard resource status, enhance the timeliness and accuracy of the data, and thereby facilitate efficient and reliable network resource allocation.  
Further, the method presented in this paper emphasizes the awareness and prediction of resource status on satellites, as well as resource allocation for differentiated services across the entire network. However, considering the dynamics and complexity of constellation topology, we will continue to explore flexible resource allocation method for local areas based on the method in this paper in subsequent research, further improving the practicability and scalability of the algorithm.  
Conceptualization, H.D. and T.D.; methodology, H.D.; software, H.D. and S.W.; validation, H.D. and Z.L.; formal analysis, T.D.; investigation, T.D.; resources, S.W.; data curation, D.Z.; writing--original draft preparation, H.D.; writing--review and editing, Q.Z. and D.Z.; visualization, H.D.; supervision, T.D.; project administration, T.D.; funding acquisition, T.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the National Natural Science Foundation of China (62331027), and in part by the Young Elite Scientists Sponsorship Program by CAST (2022 QNRC001).  
Figure 8: Comparison of convergence speed of proposed ADMM algorithm.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** All authors were employed by the company Space Star Technology Co., Ltd. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 2 Related Work
### Clustering  
The clustering of mobility flows is a research field that creates algorithms to identify trip patterns. There are two main types of clustering in urban scenarios: (i) trajectory-based clustering: the whole trajectory of the moving entity is known, and used [9; 10]; and, (ii) Points-of-Presence based clustering: only a few points that are related to the moving entity are known [11; 12; 13]. Their main difference is the sampling rate of observed points. In this work, we focus on exploring datasets with data regarding pick-up and drop-off locations only; thus, our proposed algorithm fits into the second type of clustering.  
[PERSON] et al. [11] proposed a method to cluster the pick-ups and drop-offs of taxi trips. They spread the points in a fine-grained matrix and write each cell's frequency in it, which is decomposed and factorized. The resulting values are plotted as a heatmap over the city's map. Their method identifies hotspots of trips in the city, which allows them to evaluate their correlation with public transportation stations, business areas, and airports. The structure of the outcomes is the main difference to our solution. Their method focuses only on identifying regions, and ours in identifying the regions and the flow between them, which is an important aspect in the route selection.  
Stepwise Spatio-Temporal Flow Clustering (SSTFC) [14] is a spatio-temporal flow clustering technique that identifies mobility trends. SSTFC has spatial and temporal steps and it works likewise other proposed mechanisms in tge literature [15]. In the spatial step, a minimum neighborhood and a size coefficient are used with a custom distance metric to decide whether to merge or not neighboring clusters. Similar parameters exist in DBSCAN [16], namely _min_samples_ and _eps_, which are also used to evaluate the density of a region and create clusters. After the spatial step, SSTFC uses the same method to create temporal clusters inside the spatial ones, but using a third parameter, a temporal threshold, to merge neighboring clusters. In [14], it is assumed that SSTFC is robust, but there is no quantified assessment. As clusters get merged several times, flows with varied directions are mixed in the same group, which is sub-optimal when trying to identify mobility trends. Our algorithm first locates the functional departure and arrival zones, which limits the flow directions. These flows can have different angles, but they always flow from one region to another, while, in their technique, flows going forward/backward might end at the same cluster.
## 3 Results and Discussion
### Anti-Digestibility of GE  
Prebiotics intake is inevitable of going through human mouth and stomach. Therefore, it needs to resist the hydrolysis of salivary amylase and gastric acid, and then arrive at intestinal tract to participate in metabolism of probiotics groups.  
The normal pH of human mouth is 6-7, but it changes temporarily when intaking different kinds of food [19]. Therefore, the anti-digestibility assay of GE to \(\alpha\)-amylase was implemented at pH 4-8 (37 \({}^{\circ}\)C), and the result was shown in Table 2. The DHs of GE were all low during these pHs. When the pH was 4 and 5, almost no hydrolysis of GE was observed. When the pH was 6, 7, and 8, there was only a slight increase of DH within initial 2 h (\(p<0.05\)), and then a balance occurred. The DH was the highest at pH 7, but it was only 2.72%, indicating an excellent anti-digestibility of GE to \(\alpha\)-amylase.  
The normal pH of human gastric acid is about 2, but it rises to 3-5 in 2 h after meal for the dilution of gastric acid. The food remains in stomach for 4-6 h [20]. Thus, the anti-digestibility of GE to simulated gastric acid at pH 1-5 (37 \({}^{\circ}\)C) was investigated, and the result was shown in Table 2. The DHs of GE at different pHs were diverse. It was the highest at pH 1, but only 4.95%, showing a strong resistance of GE to acid. As a result, it was indicated that GE has a good anti-digestibility to gastric acid.  
Based on the results above, GE can resist the hydrolysis of salivary amylase and gastric acid, and go through mouth, stomach, and then enter successfully into intestinal tract, meeting various probiotics in these digestive organs.
## Materials and Methods  
### Materials and Description of Projects  
In this section, a brief overview of two projects, the Lawari Tunnel (Project 1) and Neelum Jhelum Hydropower project (Project 2), are given. The 8.5-km long Lawari Rail Tunnel (LRT) project, now termed the Modified Road Tunnel (MRT), is the only ultra-long road tunnel in Pakistan. The original plan involved a rail tunnel that was proposed with a small cross-sectional area termed as LRT. After the completion of the excavation work in 2009, the same tunnel was chosen for trade with Central Asia, and the horseshoe tunnel was enlarged for two-way road traffic. This was termed as MRT (11.17 m in span) and was completed at the start of 2016.  
The Neelum Jhelum Hydropower project (Project 2) comprises a headrace tunnel with a total length of 28.5 km, which is a combination of single and twin tunnels. These tunnels pass through the Murre formation, which includes alternating beds of sandstone, siltstone, mudstone, and shale. The initial sections of the twin tunnels are excavated in sandstone (9.68 m in span) by the conventional method (drill and blast method).
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 4 Experiment and Results
### Effectiveness of Artifact Noise Reduction and Vizualiation  
The effectiveness of artifact noise reduction is highlighted in Table 3, which focuses exclusively on models utilizing the fNIRS modality. In the table, the column labeled \"Val Acc w/o AF\" presents the validation accuracy achieved by our model when trained without any artifact noise filtering. This baseline result allows for a clear comparison of how much the filtering process impacts overall model performance. Notably, artifact noise filtering significantly improves accuracy. This improvement is particularly pronounced in the \"(Video + fNIRS) B-1\" model, where the application of noise filtering increased the overall accuracy by 2.67%. This demonstrates the critical role of effective noise reduction in enhancing model performance, especially in multimodal systems where signal quality can vary significantly between sources.  
In addition to our noise filtering approach, we also compared MMAPA with traditional noise reduction techniques such as ICA, as summarized in Table 5. While ICA-based preprocessing improved the fNIRS model accuracy to 58.00%, our proposed MMAPA method achieved a slightly higher validation accuracy of 58.67% and a marginally better F1 score. This comparison highlights that while classical techniques like ICA can mitigate some artifacts, integrating task-specific multimodal processing and attention mechanisms, as seen in MMAPA, further enhances robustness and classification performance.  
To identify and mitigate noisy channels, we employed the K-means clustering algorithm, which enabled us to differentiate between noisy and non-noisy signals. As shown in Figure 7, the centroids for the noisy and non-noisy channels were calculated as 29.77 and 1.24, respectively. The threshold between the two clusters was determined to be 15.5. Any channel with a standard deviation of gradient below 15.5 was classified as non-noisy, while channels with a standard deviation exceeding this value were deemed noisy. This systematic classification enabled us to process and filter the noise from a significant portion of the channels, totaling 393 channels. The K-means-based approach for noise detection proved to be an effective method for isolating noisy channels, allowing for more precise artifact noise filtering and contributing to the overall enhancement of model accuracy. The use of this noise reduction technique is essential for improving the reliability of models trained with fNIRS data, as it reduces the impact of sensor and environmental interference.
# Uncovering Hidden Community Structure in Multi-Layer Networks
## 4 Hidden Community Detection Method
### Baseline Community Detection Algorithm  
The ease of the proposed method is that it can work well with any disjoint com- munity detection algorithm as the base to detect a weaker overlapping community structure in the layers of a network. In this work, for the baseline algorithm, we chose the Louvain method, a heuristic algorithm; because of its robustness and scalability to fit well in large networks [3], we find it more suitable. The Louvain method is an efficient, simple and easy-to-implement method for identifying communities in large and different kinds of networks; also, it has been observed that it unveils hierarchies of communities and allows us to go deeper within communities to discover sub-groups, sub-sub-groups, etc., which is the basic aim of our work, i.e., to discover underlying weaker or hidden structure of communities. The inspiration for this method of community detection is that it is a fast greedy method which has the ability to optimize modularity by iteratively changing the allotment of the node community and collapse the community into meta-nodes. In the Louvain Method of community detection, first, small communities are found by optimizing modularity locally on all nodes, then each small community is grouped into one node and the first step is repeated. There are various overlapping community detection methods that can be used as the baseline algorithm for further evaluation of this work in future.