esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Conclusions
### Future Research Directions
#### 6.2.5 Interoperability and Integration with Existing and Emerging Networks  
The integration of ICN's new components with existing TCP/IP-based network architectures and protocols while maintaining backward compatibility is crucial for a smooth transition. Issues related to protocol translation, inter-domain routing, and unified management frameworks must be addressed to ensure consistent QoS and the overall user experience during the integration process. ICN implementation solutions often rely on clean slate deployment [131; 132] due to its straightforward design, but practical implementation is challenging due to ICN being newer than TCP/IP. Therefore, integrating ICN with existing IP infrastructure through overlay/IP [133], underlay/IP [134], and coexistence/IP [135] modes should be explored despite potential drawbacks, such as IP-related issues and the additional overhead from ICN/IP duality [136]. Additionally, while the pull communication model is commonly used, Ref. [136] suggests further investigation into push and publish-subscribe models [22; 137] for their valuable applications in IoT.  
Several studies have investigated the interaction between ICN and emerging networking paradigms, highlighting its natural alignment with the shift toward non-IP-based networking, super convergence, and data-centric communication in 6G. ICN's fundamental focus on named data, along with its in-network caching, improves 6G performance by accelerating content delivery, particularly when combined with Multi-access Edge Computing (MEC), which brings computation and storage closer to users [138]. Additionally, ICN's inherent support for mobility complements the dynamic nature of future networks, while semantic routing can enable more intelligent and efficient content delivery. The convergence of ICN with in-network computing further optimizes resource utilization and reduces latency for demanding applications like extended reality (XR) and holographic communication [139]. Furthermore, dynamically created cloud instances and vehicular cloud architectures can leverage ICN to enable efficient multimedia streaming and data retrieval [140]. However, several research challenges remain, including enabling ICN to provide contextual information about users, applications, and services to MEC for improved QoS in 6G use cases [138]. Additionally, understanding how ICN-MEC integration can effectively interact with key 6G technologies, such as terahertz (THz) waves, optical wireless technology, dynamic spectrum management, and intelligent reflecting surfaces, presents open challenges for future research.
# Algorithm of Smart Building Supervision for Detecting and Counting Column Ties Using Deep Learning
## 3 Literature Review  
### BIM Technology  
NBS [4] defines BIM as _'a process for creating and managing information on a construction project across the project lifecycle'_. In short, BIM can be described as the process of managing, exchanging, and sharing construction project information among participants by creating a digital model for the entire life cycle of a building. The process starts with architectural design and includes demolition, the final stage of the building life cycle.  
By utilizing BIM in the design phase, masses are designed to fit the concept and surrounding environment, design is performed with 3D modeling, and optimized designs can be derived based on an analysis of energy, structure, etc., through various simulations. This enables real-time stakeholder collaboration. A 3D drawing made in this way not only facilitates the output of planes, elevations, and sections to 2D drawings, but also directly transmits information to a factory for the production of building members, improving the efficiency in manpower and time, while reducing human error.  
Before construction, it is possible to shorten the construction time through automation and construction simulation, and this can facilitate the linkage of facilities, structures, and civil engineering, as well as reduce construction damage by reviewing interference in advance, and be used for construction safety. In the maintenance of buildings with construction, BIM can be used to utilize materials and members' information for building utilization, remodeling, and reconstruction.  
It is the trend of the building and construction industry that the use of BIM alone has resulted in the saving of manpower, money, and time in design and construction as well as the saving of risk, but its combination with other technologies is creating greater synergy. [PERSON] et al. [5] tried to reduce the risk of prefabricated house construction (PHC) and improve schedule performance by utilizing the integrating technology of RFID and BIM.  
In the integration between BIM and GIS, [PERSON] et al. [6] tried to minimize the constraints of the infrastructure and environment by designing the transport infrastructure through BIM and GIS data integration. Moreover, [PERSON] and [PERSON] [7] proposed an integrative way between BIM and GIS to plan and predict the infrastructure demand that is created in line with the creation and expansion of cities in the planning phase of smart cities. Furthermore, [PERSON] et al. [8] showed its effectiveness by suggesting the use of the integrated technology of MR, utilizing the advantage of VR, AR, and BIM. As such, the combination of advanced technology and BIM expands more possibilities and leads to efficiency in each field. The combination of Deep-learning technology and BIM is also being tried, but it has been difficult to find any research in the supervision field.
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 7 Result  
In this work, we have presented a new pipeline based on an underwater acousto-optical fusion imaging system, specifically designed based on an underwater optical system. Firstly, a hierarchical clustering algorithm with adaptive threshold was proposed, which automatically determines the threshold of clustering by using the maximum inter-class variance method, which is more convenient for automatically extracting key frames. Secondly, in order to enhance the quality of the image underwater, we proposed an image enhancement algorithm based on the red channel prior and histogram equilibrium methods, which was verified as being effective. In addition, a more efficient rotation averaging algorithm was devised in our paper, and the efficiency of our method was verified by comparing it with the L1 RA-IRLS method on the same dataset. At last, we performed an experiment in a swimming pool using our method, and the dense point cloud of the model was given in the article.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 1 Introduction
We present a workflow designed to process a large amount of data from multiple sensors in order to produce high quality maps of glacier surface velocity. Using this new workflow, we first explore Sentinel-2's ability to map the surface speed of glaciers on a regional and multi-temporal scale for 3 mountain ranges containing small glaciers: the European Alps, the Cordillera Blanca and the Southern Alps of New Zealand. The quality of the maps produced is then evaluated using statistical analysis, as well as by comparing the results obtained with in-situ measurements and maps made with other sensors having either a lower resolution (Landsat 8) or a higher resolution (Ven\(\upmu\)s, Pleiades, PlanetScope).
## 5 Implementation and Deployment
### Database  
Databases enable the effective organization, storage, and management of data. Compared to relational databases, non-relational databases handle unstructured data, support high concurrency, and accommodate the storage of massive amounts of data [39]. We selected MongoDB to support the database function. MongoDB, a document-oriented Non-relational SQL (NoSQL) database, is an ideal choice for storing object-type data and supports intricate query operations.
## 1 Introduction
They demonstrated that the network can maintain its remarkable object localization capability until the final layer, thus allowing it to easily identify discriminating image regions in a single forward pass for a wide variety of activities, even those for which the network was not originally trained. Inspired by the Class Activation Maps (CAM) proposed by [PERSON] et al. [14], we propose to expand the proposed (3+2)D FPN CNN with a mechanism that allows visualizing the time interval of the time series that contribute to the determination of the class for each pixel. With the (3+2)D FPN we want to be more precise in answering the question _\"where a specific crop is located\"_ inside an image, but we do not know how to answer the question _\"when that crop was present\"_ inside a time series. For this reason we have added to the network a mechanism to predict when the class is present in the input time series without needing to supply more ground-truth to the network. In a similar way to what happens in [14], where the network is adapted to understand _\"which area of the input image\"_ contributes most to the determination of the output class, we have proposed a solution to predict for each pixel _\"in which time interval\"_ a specific class is present in the input time series dataset. Exactly as it happens for the CAM, where we can say which area of the image contributed to determining the output class and say nothing about the other classes, we use the CAI to tell when the class was active in the input time series without saying anything about the other classes on that pixel. This is to say that our approach is multi-class on every pixel that the model sees as input, but it is not multi-label. The novelties we propose in this paper are listed below:  
* We propose a new semantic segmentation model suitable for remote sensing time series. * We add to the proposed CNN a mechanism that allows visualizing the time interval of the time series that contribute to the determination of the class for each pixel. * We exceed by about 4% the state of the art on a public dataset with satellite time series.
# PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations
## 5 SHM System and Benchmark Model of the Canton Tower  
In order to study the feasibility of the PTMD on vibration control of civil structures under wind load and earthquake excitations, a benchmark model of the Canton Tower is selected as the host structure. The Canton Tower is located in Guangzhou, China. It has a total height of 600 m, includes a 454 m high main tower and a 146 m high antenna mast. The composite main tower has a reinforced concrete tube covered by a steel lattice. A sophisticated SHM system consisting of 16 types of more than 700 sensors has been installed in the tower. The SHM system has continuously acquired response data and loading data, such as temperature and wind speed, since 2010. As of today, the SHM system has successfully recorded the dynamic responses of the structure under several severe events, such as earthquakes and typhoons. Sets of ambient data have been adopted to verify the correctness of the full-order and reduced-order models in the previous study [43]. Particular wind speed data and wind rose diagrams drawn based on the monitor on top of the main tower during typhoons are shown in Figure 5. In the following simulation of this paper, two sets of the wind load data will be extracted from selected typhoons to verify the control effectiveness of the proposed PTMD device.  
Figure 4: Comparison of the improved and traditional bounding force models.  
Figure 5: Wind speed and wind rose diagrams obtained during typhoons.  
The benchmark problem for this tower originates from the structural health monitoring and damage detection problem of super-high-rise structures. Both a full-order FE model and a corresponding reduced-order model are established by the first author of this paper. Since the purpose of this paper will focus mainly on the global dynamic responses of the structure, the reduced order FE model is adopted in this study. The reduced-order model has been validated in the previous study [43] by field test data to have great precision and can describe the dynamic characteristic of the structure well. In the reduced-order model, the whole structure is modeled as 37 beam elements (27 for the main tower and 10 for the mast) and 38 nodes. Considering five DOFs for each node, which are the two horizontal translational and three rotational DOFs, a total number of 185 DOFs are included. The damping ratio of the structure was assumed to be 0.004 [44; 45]. The layout of the model [46] is shown in Figure 6. Directions \(x\) and \(y\) represent the direction along the long axis and the short axis, respectively.
## 2 Operational Strategy of BESS and Prediction System  
The microgrid can be operated off-grid and grid-connected [23; 24]. As shown in Figure 1, the main components of the microgrid are distributed generations (DG), including photovoltaic (PV), wind turbines (WT), fuel cells, (FC), diesel generators (DG), and BESSs [7].
## 6 Application of Plasma Treatment in Advanced and Emerging Packages  
The relentless drive for miniaturization, increased functionality, and improved performance in electronic devices has led to the development of advanced and emerging semiconductor packaging technologies. While these new packaging paradigms present unique challenges in terms of materials, interconnect density, and manufacturing processes, their processing is still highly dependent on the principles of plasma processing outlined in this paper.  
A group of advanced packaging relies on stacking and interconnecting multiple dies, often with very fine features and complex architectures. This necessitates extremely clean surfaces and precise surface modification to achieve reliable electrical connections and mechanical integrity. Similarly to the situation with lead-frame-based semiconductor system, cleaning methods, such as wet chemical cleaning, fall short of the requirements needed for such packaging manufacturing; therefore, plasma cleaning needs to be used.
## 2 Methods
### InSAR-DIB Dynamic Prediction Model and Parameter Solution
Assume that we have obtained the surface deformation information from time \(t_{\text{j}}\) to \(t_{\text{j+1}}\), and assuming that the characteristic parameters of the goat at time \(t_{\text{j}}\) are \(B_{\text{ggg}}\) = [\(m\), \(H_{0}\), \(\alpha\), \(D_{\text{3 gj}}\), \(D_{1}\)], the characteristic parameters of the goat at time \(t_{\text{j+1}}\) are \(B_{\text{ggg}+1}\) = [\(m\), \(H_{0}\), \(\alpha\), \(D_{\text{3 gj}+1}\), \(D_{1}\)], and the LOS between the two periods is \(\Delta d_{\text{LOS}}\). From the previous section, we can see that during the mining process, the DIB model can be used for dynamic prediction at any time, that is, the parameters of the DIB model at times \(t_{\text{j}}\) and \(t_{\text{j+1}}\) are unchanged. The cumulative deformation value expected from the LOS direction of pixel \(i\), representing the surface point \(t_{\text{j}}\) at any time after mining, can be expressed as follows:  
\[r^{\prime}_{iLOSt_{j}}=-U_{iSNt_{j}}\sin\theta_{i}\cos(\alpha_{i}-\frac{3\pi} {2})-U_{iEWt_{j}}\sin\theta_{i}\sin(\alpha_{i}-\frac{3\pi}{2})+W_{it_{j}}\cos \theta_{i} \tag{20}\]  
Then, the cumulative deformation value expected from the LOS direction of pixel \(i\), representing the surface point \(t_{\text{j+1}}\) at any time after mining, can be expressed as follows:  
\[r^{\prime}_{iLOSt_{j+1}}=-U_{iSNt_{j+1}}\sin\theta_{i}\cos(\alpha_{i}-\frac{3 \pi}{2})-U_{iEWt_{j+1}}\sin\theta_{i}\sin(\alpha_{i}-\frac{3\pi}{2})+W_{it_{j+ 1}}\cos\theta_{i} \tag{21}\]  
\(\Delta d^{\prime}_{\text{LOS}}\) predicted between the two periods can be expressed as follows:  
\[\Delta d^{\prime}_{\text{LOS}}=r^{\prime}_{iLOSt_{j+1}}-r^{\prime}_{iLOSt_{j}} \tag{22}\]  
Therefore, the parameter cost function of the DIB prediction model can be constructed as follows:  
\[F(X)=\sum(\Delta d^{\prime}_{\text{LOS}}-\Delta d_{\text{LOS}}) \tag{23}\]  
By using a multi-population genetic algorithm, a set of \(X\) can be obtained by minimizing the above formula, and the obtained \(X\) value can be used for the next step of the dynamic prediction. Based on the above ideas, a technical roadmap for InSAR-DIB parameter inversion is proposed, as shown in Figure 2. A parameter-finding program was compiled based on a multi-population genetic algorithm.
# Fractional Order Viscoelastic Model for Stress Relaxation of Polyvinyl Chloride Geomembranes
## 5 Discussion
### Residual Stress  
The residual stress, \(\sigma_{\infty}\), refers to the constant relaxation stress after sufficient relaxation time. As a consequence of the internal stress of polymers, it is composed of two parts: the residual stress \(\sigma_{ir}\) induced by the thermal history during the forming process, and the internal stress \(\sigma_{id}\) caused by the deformation during the stress relaxation process.  
\[\sigma_{\infty}=\sigma_{ir}+\sigma_{id} \tag{16}\]  
The residual stress can be derived from the stress relaxation curves at different tensile rates and initial strains. The residual stress \(\sigma_{ir}\) can be determined as follows: Firstly, plot the \(-\mathrm{d}\sigma/\mathrm{d}lnt\) versus \(\sigma\) curves in Figure 10 and establish the maximum of \(-\mathrm{d}\sigma/\mathrm{d}lnt\). Secondly, plot \((-\mathrm{d}\sigma/\mathrm{d}lnt)_{\mathrm{max}}\) against \(\sigma_{0}\) of the longitudinal and transverse specimens in Figure 11a,b. Finally, connect the points at different initial strains but the same tensile rate and fit them with a straight line, and its intercept is regarded as \(\sigma_{ir}\). This is because the polymers should first overcome \(\sigma_{ir}\) before the relaxation activation energy barrier under constant strains. In other words, as the initial stress less than \(\sigma_{ir}\) is applied, the polymers fail to deform. Therefore, the residual stress \(\sigma_{ir}\) corresponds to the initial stress \(\sigma_{0}\) when \((-\mathrm{d}\sigma/\mathrm{d}lnt)_{\mathrm{max}}\) extrapolated to zero. Figure 11a,b display the relationship between \((-\mathrm{d}\sigma/\mathrm{d}lnt)_{\mathrm{max}}\) and \(\sigma_{0}\) of longitudinal and transverse specimens, respectively. The initial stresses of transverse specimens at different tensile rates were close to zero, indicating that no residual stress exists or has been relaxed in the transverse direction. However, those of longitudinal specimens were approximately \(-0.98\) Mpa, indicating that \(\sigma_{ir}\) in the longitudinal direction is \(0.98\) Mpa. This may be explained by the fact that as the PVC GMBs in the longitudinal direction are subjected to the traction by roller crimping, molecular orientation appears at a high temperature. Nevertheless, it does not return in time as the temperature quickly reduces at the extrusion exit, resulting in the stored stress in the material. Due to no crimping traction in the transverse direction, the PVC GMBs are not constrained, and hence no molecular orientation exists as the temperature reduces.  
Due to the polymer cross-linked amorphous structure of PVC GMBs, no crystallization occurs, or \(\sigma_{id}=0\). Therefore, the total residual stress is primarily the residual stress induced by the thermal history in the forming process. The discrepancy in residual stress between the longitudinal and the transverse specimens essentially accounts for the difference in relaxation stress during the stress relaxation process.
# Preventing Black Hole Attacks in AODV Using RREQ Packets
## 8 Conclusions  
Among the various routing protocols proposed for MANET architectures, AODV is a very powerful option; however, the characteristics of MANETs allow the participation of unauthorized terminals in the network. Thus, a malicious attacker can use AODV to launch a black hole attack, which can significantly degrade network performance. Therefore, in this paper we have proposed a method to defend MANETs using the AODV routing protocol against black hole attacks. The proposed method focuses on the fact that black hole nodes behave differently from normal terminals in order to implement an extended AODV protocol that actively detects black hole nodes using dummy RREQ packets. This enables our approach to prevent black hole attacks in a simple manner and send data to the destination via an accurate route. The proposed method is applicable to small environments with few nodes, as verified by a preliminary experiment, as well as to large  
Figure 7: False recognition occurs when an unsupported node exists.  
environments with many nodes, as verified by further experimentation. In addition, as expressed in the discussion section, the algorithm has high practical potential owing to its simplicity. As we have demonstrated, the performance of the standard AODV protocol can be significantly improved, and the proposed method can be incorporated into many previously proposed AODV-derived routing protocols to increase their security. In the future, we plan to consider methods to enable more efficient routing in networks with frequent link breakage and networks containing nodes that do not implement the AODV protocol on which the proposed method is based.  
Conceptualization, Y.N. and T.M.; investigation, Y.N.; writing--original draft preparation, Y.N.; writing--review and editing, Y.N. and T.M.; visualization, Y.N.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Test Results and Discussion
### Behavioral Analysis
#### 3.3.4 Particle Shape Analysis  
It is well-known that the engineering properties (shear strength, compressibility, and permeability) of soil mixtures are affected by the shape of soil particles [35]. The particle size and shape of the mixture can be estimated by crystallographic structure, surface area, and particle volume [36]. In this study, the particles of CEN sand are generally isometric and have angular shape, and the clay particles (kaolinite) have a hexagonal plate shape (Figure 10).  
In the mixture of 10% KC in Figure 11, we can see the smooth surface of a sand particle under kaolinite particles. However, with the mixture which has the amount of kaolinite up to 25%, the kaolinite particles were found to take over the sand grains--findings consistent with previous research [22]. In Figure 11, the particle shape in all mixtures was with 10,000\(\times\) (i.e., 10,000 times magnification) and 100,000\(\times\) magnification by SEM.  
Figure 10: Particle shape: (**a**) CEN standard sand, (**b**) Kaolinite Clay.  
Figure 9: Relationship between the granular compression index and maximum dry unit weight.  
Figure 11: _Cont._  
Additionally, also observed in SEM photos (100,000\(\times\)), the present kaolinite particles were found to be large as kaolinite content increased. Moreover, with the content of kaolinite up to 30%, the kaolinite particles were contacted one to another creating flat surfaces (blue solid line) from small to large according to the percentage of kaolinite as can be seen in photos with 10,000\(\times\) magnification. For this reason, the fines particles could not fully fill in the voids between sand particles which also leaves high local porosity between the kaolinite particles themselves resulting in an effect on the packing density of CEN sand. Therefore, with the high percentage of kaolinite (60% and 70% KC), the sand grains were assumed to float in the fines grain network and were regarded as void in the mixture.