esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
As seen in Table 3, almost all tested metal ions in the concentrations 1 mM and 2 mM were enzyme activators. On the other hand, the highest increase in enzyme activity (over a three-fold increase in activity) was observed with CuCl\({}_{2}\) at the highest studied concentration (2 mM). [PERSON] et al. [35] and [PERSON], [PERSON], and [PERSON] [11] have reported that the activating effect of metal ions (Mg\({}^{2+}\) and Cu\({}^{2+}\)) can be explained by the stabilization of the enzyme structure by them causing an increase in affinity to the substrate. In contrast, some metal ions (Ca\({}^{2+}\), Na\({}^{+}\), and K\({}^{+}\)) inhibit the enzyme activity by changing the conformation of the enzyme through the changing chemical bonds in the active site. Moreover, [PERSON] et al. have observed that adding Fe\({}^{2+}\) and Na\({}^{+}\) into the substrate-enzyme system increased the PPO activity. The plant PPO is suggested to have one binuclear Cu site per protein molecule for the reaction centre and various metal ions may affect the substrate by combining this reaction centre [36]. Similar dependencies were observed in our results (1 mM and 2 mM solutions of FeCl\({}_{2}\) and NaCl). [PERSON] et al. [11] reported that CuSO\({}_{4}\) (in the same concentration) also activates the PPO activity isolated from fennel (by 1.5-fold). In general, the way metal ions interact with the enzyme activity is strictly dependent on the concentration used. The addition of metal ions while extending the shelf life of low-processed foods may contribute to the prevention of nutritional deficiencies. PPO is an enzyme containing copper atoms in the active site that are essential for its catalytic activity. This could explain why the copper ions activate the enzyme. Cu\({}^{2+}\) could cause an increase in enzyme stability and activity by supporting more enzyme-substrate contact [37].
# How to Isolate Non-Public Networks in B5G: A Review
## 6 Open Research Challenges
### Data Isolation of Software-Defined CNs  
The CN of an NPN is software-defined and runs, in general, on a hardware platform, which is the trend in the B5G era. As a result, the physical boundaries among NPNs' CNs are broken, which introduces a great risk for data security [37]. This means that data theft and tampering among the CNs will be possible as there are no physical boundaries. Therefore, data isolation is needed to prevent the data of each CN from being accessed across the CNs. To achieve data isolation, hardware and software security can be improved through research on cloud platforms. Furthermore, protocol security still needs to be further studied [38; 39]. Protocols with endogenous security capabilities are a hot topic in the context of sixth-generation mobile communication networks, for which zero trust architecture [40; 41], heterogeneous redundancy construction [42] and blockchain are potential technologies [43].
# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 4 System Architecture
### Modular Approach and Re-Usability  
As observed by [PERSON] et al. [PERSON] et al. (2018), the orthogonality of each of the tasks involved in the system inspires a modular architecture that allows a fast reconfiguration of software for different applications and robotic set-ups.  
Figure 5: The activity diagram of the Frontier Manager node to find the next goal.  
Although this architecture caters to a rotary-wing holonomic UAV, non-holonomic UAVs, and autonomous underwater vehicles (AUVs) can run the system on-board just as readily. The most significant change lies within the hardware, which can be adapted in a plug and play fashion. To exemplify, Figure 6 shows how the components would change for an AUV adopting the LSTS (Laboratorio de Sistemas e Tecnologia Subaquatica) toolchain [50]. The vehicle becomes a Light AUV (LAUV) equipped with a Multibeam and running on-board DUNE (Uniform Navigational Environment). Additionally, the same Upboard processor used in the UAV field experiments described in [44] is supported by OceanScan's LAUV to run the exploration system on-board. As DUNE already provides path following for the LAUV, the component to command the vehicle would only need to interface the standard commands from ROS.  
The considerations for a non-holonomic vehicle are confined to two components: The waypoint selection within the path planner and the operational requirements embedded into the frontier management.
## 5 Aerial Imaging  
To access the lateral extent of the distinct lithoststratigraphic units and to reveal the internal unconformities that developed during salt rise, a photogrammetric survey was performed using a DJI Phantom 4 + drone. The flight plans were designed and controlled using the Drone Deploy application. The survey resulted in the capturing of more than 3500 georeferenced images covering an extended area of 1.06 square kilometers (Figure 10). The resolution of the acquired images ranged from 1.8 to 2.7 cm/pixel.  
For the processing of the data, Pix4D software 4.8 was used. The processing stage contained three subsections: (a) Initial Processing, which automatically extracts key points from the imported aerial images to compute the internal and external camera parameters using the software's advanced Automatic Aerial Triangulation (AAT) and Bundle Block Adjustment (BBA). A sparse 3D point cloud was computed and a low-resolution DSM and orthomosaic were generated and displayed in an initial quality control process. (b) The second stage of the analysis was to produce a dense 3D point cloud and a 3D textured mesh. (c) Finally, a Digital Surface Model (DSM) was produced accompanied by an orthomosaic image of the covered area. The results of the analysis and the interpretation from a geological point of view are presented in Figure 11.  
According to the photogrammetric analysis, the Eocene and Senonian thick- to massively-bedded horizons can be traced at a distance of more than 1 km. Concerning the lithost stratigraphic correlations of these horizons, the analysis indicated a normal type of contact with the underlying and overlying lithost stratigraphic units. On the contrary, the Early Cretaceous Vigla limestones are characterized by internal angular unconformities of the thick- to massively-bedded horizons with the surrounding calcareous mudstones. Unconformity angles range from 15 to 25 degrees (Figure 4b). The lateral extent of the calcidebritcalciturbiditic beds is similar to that of the Senonian and Eocene formations. The presence of internal unconformities in Vigla limestones, together with the identification for the first time of calcidebrites/calciturbidites horizons in this formation, shows indications of platform steepening due to salt activity and possible combination with tectonic activity.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 4 Consecutive Error Prevention Performance: Evaluation
### Number of Packet Collisions
These desirable properties are maintained over a wide range of traffic densities.
## 4 Experiments
### Experimental Settings  
#### 4.1.1 Datasets  
In this paper, we report experiments conducted on several datasets widely used in RSSC. The details of these datasets can be found in Table 1.  
The NWPU-RESISC45 dataset [22] is a publicly available benchmark dataset for RSSC task. It contains 45 types of remote sensing scene images with an overall size of 31,500. Each scene class of this dataset contains 700 images with \(256\times 256\) pixels. The common training ratio of this dataset is 10% and 20%. The Aerial Image Dataset (AID) dataset [23] contains 10,000 scene images divided into 30 remote sensing scene classes. Each scene class of the AID dataset contains 200 to 400 images with \(600\times 600\) pixels. The common training ratio of this dataset is 20% and 50%.  
The UC-Merced dataset [25] contains 2100 scene images divided into 21 land-use classes. Each scene class of this dataset contains 100 images with \(256\times 256\) pixels. The common training ratio of this dataset is 50% and 80%.  
PatternNet [24] is a large-scale benchmark dataset for remote sensing image retrieval. Different from above datasets, PatternNet is designed specially for the task of remote sensing image retrieval. The images of this dataset were collected from Google Earth imagery or via the Google Maps API for US cities. It contains 38 remote sensing scenes. Each scene class of this dataset contains 800 images with \(256\times 256\) pixels.  
The NAP (NWPU-RESISC45+AID+PatternNet) dataset is an unlabeled dataset for self-supervised pretraining. It is a combination of the AID, NWPU, and PatternNet datasets. The total number of images in this dataset is 71,900.  
The NAP+ dataset is a large-scale unlabeled remote sensing image dataset built on top of the NAP dataset. Since unlabeled data are easier to collect than labeled data, we collected numerous unlabeled remote sensing images from the Google Maps API with the aim of exploring the boundaries of SSL. The total number of images is 200,000 in this dataset.
# Numerical Fit Modeling for Temperature Mitigation in
## 1 Introduction
### Modeling Options  
Urban climate modeling can have many goals, such as finding the effects on local air temperatures due to urban geography or individual buildings, the effects on air pollution, and determining the effect of the urban area on the atmosphere. For example, air is more turbulent in urban areas due to ground roughness and thermal differences. This causes the urban boundary layer to be higher [13]. These methods focus on the microclimate, meaning on the scale of a neighborhood. The purpose of the current research is not to analyze the weather at a fine micro-scale or a macro- or synoptic scale but on a meso- or regional scale.  
The goal of the modeling is to find surface temperatures due to the mitigation. Three main types of models exist for weather prediction: (1) three-dimensional models, (2) single-column models that are one-dimensional, and (3) one-dimensional energy budget equations. All methods have difficulties in matching actual weather observations. All models are simplifications of the processes that occur in the atmosphere. A problem with all modeling methods is acquiring and validating the necessary data.
# Validation of Seismic Performance of Stainless Press-to-Connect Piping System under Cyclic Loadings
## 2 Experimental Test of Component Level  
Threaded and grooved systems as non-welding joints in pipelines are commonly used in the transportation of energy resources; however, these joint systems are prone to leaks that can cause a pressure drop and inconsistent pipeline behavior. These issues in the joint system can cause significant damage to critical structures during and after strong earthquakes. To overcome the drawbacks of threaded and grooved joints, press-to-connect systems with O-ring seals at the end of pipes, fitted using a hand-held press, have been developed in recent years. Typically, the failure mode is observed at the elbow of the piping system owing to seismic ground motion. Therefore, the seismic performance of an application of the press-to-connect elbow system must be evaluated. A press-to-connectsystem that represents an elbow of a piping system was set up for the experiment, as shown in Figure 1.  
In this study, the joint system was applied using a double-ring method that uses O-ring seals with a stainless-steel O-ring for precise compression and a rubber O-ring to ensure a permanent leak-free condition. As shown in Figure 1, the elbow is a piping system component made from stainless steel (STS 304) and composed of a 100A (114.3 mm) diameter and SCH 10S (thickness 3.0 mm). To allow for rotation of the elbow, a pin joint connected to both jigs at the ends was applied and then a 90\({}^{\circ}\) press-to-connect elbow system was set up. In addition, a sinusoidal loading protocol (60 mm/min) by a universal testing machine (UTM) was applied using the displacement control method. An internal pressure of 2 MPa was applied by filling the elbow joint with water during the experiment to detect the leakage limit state of the piping system.
# Bonding Behaviors of GFRP/Steel Bonded Joints after Wet-Dry Cyclic and Hydrothermal Curing
## 5 Conclusions  
This paper examined the bond behaviors of GFRP/steel double-strap joints after harsh environmental curing (wet-dry cyclic curing and hygrothermal pretreatment). Experimental studies were carried out by quasi-static loading, and failure mode, ultimate load, stiffness, strain development, and interfacial fracture energy were discussed. Based on the experimental results, the following conclusions were made:  
1. In addition to the cohesive failure mode of the reference specimen, mixed modes of cohesive failure and debonding of the adhesive-steel interface were found in the WD series specimens. Additionally, mixed failure modes of cohesive failure and the delamination of GFRP composites were found in HY series specimens. The changes of failure mode indicate that the bonding performance of the GFRP/steel bonded specimens are affected by curing conditions under moisture diffusion and thermal effects.  
Figure 14: Normalized interfacial fracture energies of the cured specimens.  
Figure 13: Interfacial fracture energy of the test specimens.  
2. Linear brittle behavior was observed for all of the bonded specimens. Based on the investigation on the ultimate load of the specimens, a significant reduction of tensile capacities was observed for specimens cured in harsh environments, especially for longer exposure duration and higher curing temperature. Further investigation on stiffness of the bonded joints shows that the stiffness was reduced by curing in the harsh environments.
3. A two-stage strain development was found for all bonded specimens, and the threshold stain values are the key parameter to qualify the failure process. As compared with the reference specimen, the harsh cured specimens had smaller threshold stain values, indicating the negative effect of moisture diffusion and heat transfer.
4. A method to calculate the interfacial fracture energy of the bonded specimens is proposed. According to the calculation and comparison, a more severe reduction in interfacial fracture energy could be caused by longer curing and higher curing temperatures.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Conceptualization, J.L.; Data curation, J.L.; Funding acquisition, T.G.; Investigation, J.L.; Methodology, J.L., T.G. and M.H.H.; Project administration, M.H.H.; Resources, T.G.; Supervision, T.G.; Validation, L.W.; Visualization, Z.L.; Writing--original draft, J.L.; Writing--review and editing, T.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Foundation of China [Grant Number: 51978156] and the Scientific Research Foundation of the Graduate School of Southeast University [Grant Number: YBJJ1551].  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 4 Results and Discussion
### Unsupervised Learning Algorithms  
As shown in all the codification outcomes, clustering algorithms that do not require a predefined number of clusters, such as DBSCAN, SOM, and Mean Shift, encountered significant challenges in this study. Unlike K-Means and Agglomerative Clustering, which explicitly define the number of clusters, these methods dynamically determine cluster boundaries based on the data distribution. This approach often resulted in an inaccurate number of clusters, created by grouping either too many or too few samples.  
In the case of DBSCAN, the algorithm struggled to identify the four distinct musical modes due to its sensitivity to the eps parameter, which defines the maximum distance between points in a cluster. Depending on the density of the input data, DBSCAN sometimes produced fewer clusters, merging modes into a single group, or fragmented clusters, leading to over-segmentation. Similarly, Mean Shift faced difficulties in automatically selecting an optimal bandwidth, resulting in inconsistent cluster formation between different feature sets. SOM also failed to cluster effectively in most cases, classifying all data into a single cluster regardless of the predefined centroids. Notably, SOM only achieved some separation when the UMAP reduction was applied to the dataset.  
These limitations highlight the importance of algorithms such as K-Means and Agglomerative Clustering when the number of clusters is known a priori. These algorithms consistently aligned with the dataset's structure by specifying the number of clusters (in this case, four), accurately representing the distinct modes. Consequently, for tasks requiring the classification of predefined categories, algorithms with configurable cluster counts are more reliable and effective.
# Supporting Immersive Video Streaming via V2X Communication
###### Abstract  
With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6 DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method's effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types.  
V2X; CF XL-MIMO; bandwidth allocation; immersive video; 6G +
Footnote †: journal: Electronics Electronics
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
###### Abstract  
With the growing interest in the Internet of Things (IoT), research on massive machine-type communication (mMTC) services is being actively promoted. Because mMTC services are required to serve a large number of devices simultaneously, a lack of resources during initial access can be a significant problem when providing mMTC services in cellular networks. Various studies on efficient preamble transmission have been conducted to solve the random access problem of mMTC services. However, supporting a large number of devices simultaneously with limited resources is a challenging problem. In this study, we investigate code-expanded random access (CeRA), which extends the limited preamble resources to the code domain to decrease the high collision rate. To solve the existing CeRA phantom codeword and physical uplink shared channel (PUSCH) resource shortage problems, we propose an optimal preamble codeword set selection algorithm based on mathematical analysis. The simulation results indicate that the proposed code-expanded random access scheme to enhance success probability (CeRA-eSP) achieves a higher random access success rate with a lower access delay compared to the existing random access schemes.  
random access; code-expanded random access; massive machine-type communications; Internet of Things 20222227959. [[https://doi.org/10.3390/s22207959](https://doi.org/10.3390/s22207959)]([https://doi.org/10.3390/s22207959](https://doi.org/10.3390/s22207959))
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 2 Problem Description and Proposed Solution
### Physical Constraints
#### 2.3.1 Angular Velocity Constraint  
High values of the angular velocity would cause unexpected system behaviors, such as large deformations of flexible appendages (so that the satellite could no longer be treated as a rigid body). Furthermore, star trackers could not be able to work if the angular velocity exceeds their operative threshold. Thus, a restriction on angular velocity is imposed and it is expressed as an inequality constraint given by  
\[|\omega_{i}(t)|\leq\omega_{max},\quad i=x,y,z, \tag{4}\]  
where \(\omega_{max}\) is the maximum allowed angular velocity.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 3 Exhaust Manifold Analysis
### Results and Discussion
#### 3.6.5 Discussion  
The following table summarizes the results of a comparative analysis of the various turbulence models employed in simulating fluid flow within an exhaust manifold. The parameters examined included velocity (ms\({}^{-2}\)), temperature (K), pressure (Pa), and TKE (m\({}^{2}\)s\({}^{-2}\)).  
In Table 4, we have presented a consistent synthesis of our simulation results for the five turbulence models. Each turbulence model depicts variations in these parameters across the manifold (\(y\)-axis position). The k-kl-\(\omega\) model predicts the highest maximum velocity at 64.599 m/s, suggesting that using the length scale variable of this turbulence model helps to handle transitional flows more efficiently, reflected in the wider velocity range. Conversely, we found that the Reynolds stress and k-\(\omega\) turbulence models give the lowest maximum velocity at 62 m/s, reflecting their detailed and conservative approaches to turbulence modeling. Temperature projections are consistent among the exhaust manifold, with all models showing maximum temperatures of around 810 K and slight variations in minimum temperatures, indicating accurate thermal behavior capture. Pressure predictions show minor differences, with the k-kl-\(\omega\) model predicting a slightly lower minimum pressure and the k-omega STD model predicting the highest maximum pressure. The most significant differences are observed in the TKE values, with the k-omega STD and SST transition models predicting significantly higher TKE, indicating heightened turbulence levels, particularly in the latter part of the manifold. These insights are crucial for optimizing exhaust manifold design to ensure efficient exhaust flow, minimize pressure losses, and accurately predict the thermal and flow dynamics within the system.  
Figure 11: Turbulent kinetic energy comparison.