esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 3 Advances in Physical Modeling Method
### 1-g Modeling
#### 3.2.2 Soil Deformation During Caisson Construction in Dry Sand  
By using a small-scale laboratory study, [PERSON] and [PERSON] (2023) [64] investigated the processes of soil deformation during caisson construction in dry sand; the setup for the experiment is depicted in Figure 5. To assess the influence of critical caisson geometric parameters and the efficiency of external cofferdams in reducing soil displacement, the results of seven tests were analyzed. The findings revealed that a compressive \"bearing\" front below the cutting face and a \"frictional\" contribution above the cutting face are the main mechanisms causing ground movements.  
Figure 4: Detail of shaft test setup [63].  
Furthermore, it was demonstrated that the normalized radial settlement profile was insensitive to the normalized caisson embedment depth, and that the settlement zone of effect reached a maximum of 0.25 diameters below the caisson cutting edge. Moreover, studies have demonstrated the significant efficacy of an exterior cofferdam in mitigating soil settlements. With an increase in cofferdam depth from 0.25 to 0.5, quantitative analysis shows a considerable decrease in soil settling; the results were consistent across soil elevations.  
The laboratory findings identified two principal mechanisms causing construction-induced ground motions outside the caisson: (1) a compressive bearing front largely beneath the cutting face, and (2) a frictional contribution above the cutting face. The current free-field test revealed that the bearing mechanism causes the majority of soil settlement, with a secondary, but consistent, influence of friction as the caisson passes the elevation of interest. Ref. [26] provided useful insights into open caisson-induced ground movements, but further work was required, particularly for understanding the magnitude and distribution of induced ground movement during the installation of caissons in heterogeneous soils.
# Quantum Game Application to Recovery Problem in Mobile Database
## 2 State-of-the-Art
Additionally, their approach enables recovery from a base station different than the one where it failed, lowering handoff costs, log replication costs, and the time required to recover from failure. The main benefit of their log management method is its ease of implementation, whereas the primary drawback is likely the recovery time if the home agent is situated a great distance from the mobile unit [28; 29]. The authors of [30] described a recovery technique that is database and mobile device synchronization-dependent. As a consequence, the replication process guarantees that all organizations have consistent data. One drawback of this method is that, although it utilizes hash functions, it does not guarantee data integrity during transmission to the server, since both ends store the hash values in a database table.
## 3 Results
### Descriptive Statistics  
In Figure 4 and Table 5, the descriptive statistics of the performance of all examined models (Random Forest, ANN, Logistic Regression, SVM, LightGBM, CatBoost, and XGB Boost) are presented for all considered metrics (Accuracy, Precision, Recall, F1-Score, and ROC-AUC). These statistics were computed under two cross-validation protocols: the conventional 10-fold cross-validation, providing 10 estimates (represented by red bars), and a 10-times repeated 10-fold cross-validation, yielding 100 estimates (illustrated by blue bars). (To maintain clarity, results for the 5 \(\times\) 2 cross-validation procedure are not displayed in Figure 4 but can be found in Figure 3).  
When the two approaches are compared, it is apparent that their outcomes are relatively consistent, although the repeated cross-validation procedure produces slightly lower results on average. Moreover, for all models except SVM, the ROC-AUC metric values deviate substantially from the mean trends observed in the other metrics. It should also be noted that the Logistic regression model performs worse on all metrics and that its results show the highest variability within the metric as well as a higher standard deviation compared to the other models. This higher variance is likewise evident in Table 5, which provides confidence intervals among other statistical details.  
Additionally, the LightGBM model yields the most stable results for the majority of metrics--aside from ROC-AUC--reporting values around 0.80, thereby indicating a relatively robust and consistent performance profile.
## 3 Results and Discussion
### Sorption Study
#### 3.1.4 Effect of Initial Concentration  
The impact of initial concentration is a substantial parameter on the adsorption process. Some batch experiments were conducted to examine the influence of the initial concentration of yttrium ions on the adsorption using 50 mg of AHIBC. These experiments were performed by using 50 mL of different concentrations of Y(III) solutions ranging from 25 to 600 mg\(\cdot\)L\({}^{-1}\) with pH 6.0. The adsorption process continued for 60 min agitation time at room temperature.  
The result presented in Figure 1d reveals that the initial ions concentration was increased; the amount of Y(III) adsorbed from aqueous solution (q\({}_{\text{e}}\) in mg\(\cdot\)g\({}^{-1}\)) increased gradually with raising the initial ions concentration in the solution until it achieved a maximum adsorption efficiency at initial Y(III) concentration of 200 mg\(\cdot\)L\({}^{-1}\). The loaded yttrium ions amounts (q\({}_{\text{e}}\)) remained constant thereafter, 200 mg\(\cdot\)L\({}^{-1}\). It was expressed that the working adsorbent reached to its saturation capacity. That is because all the active sites of the studied AHIBC were occupied and blocked with the metal ions from the aqueous solution. The result affirms that the adsorption capacity of the metal ions loaded on the AHIBC adsorbent was 171.32 mg\(\cdot\)g\({}^{-1}\).
## 2 Related Works
### Moving Pattern of Mobile Sink
#### 2.4.1 Mobility without Purpose Pattern  
We talk about two conditions in this case. The first is that sink moves randomly in network. In [25], proposed by [PERSON] et al., sink moves randomly. To make source data follow sink's steps, milestone node is introduced. No matter sink moves straight or changes its direction, milestone nodes can help sensory data to be routed to the sink. In algorithm of [26], source nodes establish virtual grid structure, and sink moves randomly to collect data. In Ring routing [27], proposed by [PERSON] et al., the sink just needs to tell its current location to the ring structure. Then data always can be routed to the sink which moves randomly. In this condition, there is no process of planning moving trajectory, hence the system maintenance is very simple. However, the sink may move to areas where much energy has been consumed or areas where source nodes are rare. In the second condition, the sink moves along a predetermined trajectory. In some algorithms, the sink moves around a boundary of network area. In BTDGS, the sink moves along the boundary of the circular network area. The sink may park in each sector for a period of time. When the sink moves in to a sector, it will park in the sector until the count number reaches the threshold. The time of parking in each sector is always different. In [29], proposed by [PERSON] et al., the network area is the same as BTDGS. The sink also moves around the boundary area, and the moving path is also a circle. The sink moves along the circle path to collect data. In VGDRA, the sink also moves around the boundary of the network area. In some other algorithms, sink traverses in network. In [41], proposed by [PERSON] et al., sink travels through the middle of network, and the trajectory is fixed. Sensory data will be routed toward this trajectory. In [42], proposed by [PERSON] et al., the algorithm proposed is used in the application which involves a set of isolated urban areas (e.g., urban parks or building blocks) covered by sensor nodes. Mobile sinks are placed on public transport with fixed trajectories to collect data. So the trajectory sink moves are fixed. Similar to [41; 42], algorithm in [40] also adopts a fixed trajectory. A tree structure is created among nodes. Nodes will upload data toward the trajectory by the tree structure. The sink will park in each grid for some time. These algorithms all adopt fixed trajectory to collect data. This way can make trajectory planning easy. However, the trajectory is so fixed, that sink's movement cannot adapt to the change of network.
## 4 Results and Analysis
### Fixation
#### 4.2.2 Fixation Maps Analysis  
Drivers' AOI reflects the degree of the drivers' attention while driving. Drivers' visual field in the tunnel's environment consists of two sections: central view section and peripheral view section [57], see Figure 9. To describe the tunnel's inside visual environment accurately, the peripheral view areas were divided into five spatial regions.  
* Region A (upper region): it represents the top wall;
* Region B (right region): it represents the right interior wall;
* Region C (lower region): it represents the road surface;
* Region D (left region): it mainly includes the left interior wall;
* Region E (dashboard): it mainly includes Vehicle's Instrument Panel/Car Dashboard.  
Figure 8: Original and calibrated images.  
Figure 9: Drivers’ visual space and visual region division.  
In this study, a MATLAB surface-mapping program was developed to create color fixation maps. The program calculating the fixation points' centroid and spread distance of fixation points aims to compare subjects' visual behavior in three luminance level scenarios. Fixation maps were generated based on the fixation points extracted from the calibrated scene images. On the fixation map, warmer colors denote a higher concentration of fixation points, while colder colors show a lower concentration.  
Figure 10 presents fixation maps extracted from the eye-tracking frames of six participants for the three luminance levels: (a) 2 cd/m\({}^{2}\), (b) 2.5 cd/m\({}^{2}\), (c) 3 cd/m\({}^{2}\). The results show that the portion with high gaze frequency is concentrated on the road surface (region C), the top wall surface (region A), and with somewhat less frequency on the steering wheel (region E). Figure 10d illustrates the percentage of fixation points in each area for the three luminance levels. The results in Figure 10d show that region C has the largest fixation points for the three luminance levels, followed by region A, then region E.  
Figure 11 gives the percentage of fixation point numbers in each region under three luminance levels. It can be concluded that the gaze frequency in region C significantly decreases as the luminance level increases. Likewise, the gaze frequency in regions A and O decreases as the luminance level increases. However, the gaze frequency in regions B and D increases as the luminance level increases.  
Figure 10: Fixation map of the six participants for the three-luminance levels: (**a**) 2 cd/m\({}^{2}\); (**b**) 2.5 cd/m\({}^{2}\); (**c**) 3 cd/m\({}^{2}\); (**d**) illustrates the percentage of fixation points in each region for the three luminance levels.
# An Admission Control Mechanism for 5G LWA
## 3 System Model
### Challenges on an Unlicensed Band  
The WLAN is the most popular and successful technique for providing wireless services on unlicensed bands. With the low costs and high date rates, WLAN systems have already been a dominant player in all unlicensed bands at 2.4 GHz and 5 GHz. However, due to the limited spectrum, WLAN network operators are feeling the crunch in providing service in crowded areas such as public buildings, universities, hotels, hospital, restaurants, stadiums, etc. A lot of research has been done to improve WLAN capacity [26; 27]. In this situation, if LWA directs LTE traffic to WLAN networks, the unlicensed bands will be more congested. This will result in degradation of network performance in both networks. Hence, proper measurement to handle congestion and interference across different networks should be introduced to optimize system performance. In order to facilitate this, two important challenges need to be addressed; they are as follows.  
_Challenge 1: How many LTE UEs need to be transferred to LWA?_  
It seems that an LTE network will obtain the maximum benefit when it transfers the maximum number of its UEs for LWA activation. However, this is not always true. A WLAN AP works on an unlicensed band that is highly unreliable. As LWA technology uses unlicensed bands for downlink, the delay led by out-of-order packets and multiple retransmissions may be intolerable, compared to an LTE system. Additionally, the active WLAN-only UEs in the AP can add additional delays in accessing the channel. If the difference between the delays in packet delivery in LWA and LTE is too large, the throughput will be worse when using both interfaces than if using a single interface. Therefore, eNB hardware needs to be very careful in transferring LTE UEs for LWA activation and should guarantee a minimum rate for LWA UEs. Moreover, the operation of LWA should not affect ongoing transmissions of WLAN-only UEs.  
_Challenge 2: Which LTE UEs should be transferred?_  
In order to maximize total system throughput, the opportunity for LWA activation must be given to the UEs that make the best use of it. Intuitively, the assignment of LWA service is very beneficial for the system if an LWA opportunity is given to the UEs getting lower throughput from LTE networks. However, that is not always the best case. Since, we are offloading LTE traffic to a WLAN, the traffic conditions and signal quality of the WLAN network play an important role. Let us consider a situation where the LTE users are getting low throughput from LTE. Those UEs might be connected with either a heavily loaded WLAN AP or a distant WLAN AP with very low signal strength. In this situation, it is a bad idea to offload the LTE UEs to a WLAN AP where throughput is lower than the LTE network. Hence, consideration of both network conditions is imperative for performance improvement. The UEs with the lowest LTE instantaneous throughput and highest throughput from a WLAN work best for LWA activation.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 1 Introduction  
The IMT-2030 (6G) Promotion Group released the White Paper on 6G Network Architecture Vision and Key Technology Outlook in September 2021. It outlines the development of future 6G network architecture, proposing that the 6G communication system should offer all-weather, three-dimensional network access services available worldwide [1; 2] in the future. SAGINs is a key network architecture to realize the vision of ubiquitous 6G coverage, which comprises the terrestrial network and the NTN, consisting of space-based platforms and satellites. Of these, NTN achieves ubiquitous coverage in remote areas, and terrestrial networks realize regular coverage in non-remote areas [3]. As a key component of the NTN, LEO satellites are once again attracting the interest of re-searchers and industry. This paper will focus on radio resource management for LEO satellites.  
Due to their limited payload capacity, current low Earth orbit (LEO) satellites are un-able to simultaneously serve all users within their field of view. The current approach to addressing this limitation involves providing services to as many users as possible through methods such as frequency division multiplexing, space division multiplexing, and time division multiplexing. The most promising method is beam-hopping technology originating from the resource management of the GEO satellites which has emerged as a hot topic in current research on LEO satellite communication.  
Beam-hopping technology is a resource allocation technique, that is based on flexible devices for the dynamic allocation of communication resources between beams. Thus, this technique can better overcome the phenomenon of scarcity or excess of offered communication resources caused by uneven ground traffic distribution. Moreover, the efficient utilization of on-broad payloads can reduce the mass of satellites and the cost of launch.
## 1 Introduction
### Objectives  
The objective of 5G SENSOR@SEA is to design, deploy and evaluate the \"5 GT System\" solution (5G Global Tracking System), constituted by an NB-IoT framework on top of a hybrid cellular-satellite network, providing real-time information coming from cargo-containers to a OneM2M IoT platform [3]. This continuous monitoring of cargo containers is performed across seas in a port-to-port service scenario, even in deep-sea travel.  
The 5 GT System also includes part of the network functions (especially related to satellite networks) deployed in the cloud as virtualized functionalities, thereby fostering higher cost efficiency for network deployments. Cloud virtualization technology and Software-Defined Networking (SDN) capabilities ensure the seamless integration of the satellite components in the upcoming 5G network systems.  
Moreover, the satellite component of the 5 GT System includes an onboard satellite transmitter using a protocol which includes a packet optimization module.  
The objectives of this experimental research are to compare the communication performance of the system in terms of latency and message loss in the different operational contexts, i.e., when the containers are ashore in the yard, on the ship near the coast, and on the ship in the open sea. Furthermore, another goal is to validate the reliability of the operation of the whole system through a field trial involving an intercontinental liner cargo ship.
## 3 Results and Discussion
### Level Analysis of Influencing Factors of SSCFC's Fluidity and Compressive Strength
#### 3.4.2 Effect of SS Substitution Rate on Fluidity and Compressive Strength of SSCFC
Above a 15% SS substitution, hard metal oxide phases (FeO, MnO) act as microaggregates. SS's angular morphology and multi-graded particle size distribution [67; 68] optimize cement particle packing via the Andreasen model, enhancing the matrix density. Unreacted SS particles enhance interfacial transition zone (ITZ) mechanics through mechanical interlocking, allowing physical reinforcement to outweigh cement dilution effects.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 1 Introduction
Aiming at the problems of existing methods, this paper proposes a spatial small target detection method based on a multi-scale feature fusion pyramid to improve the detection performance of spatial small targets. First, we propose the CST (a CNN fused with a Swin Transformer) module of a CNN (Convolutional Neural Network) fused with a Swin Transformer as the feature extraction module of the feature pyramid network to enhance the extraction capability of target features. Then, we improve the SE (Squeeze-and-Excitation) attention mechanism and construct the CSE (Convolution-based Squeeze-and-Excitation Attention) module to find the attention region in the dense star map background. Finally, we introduce the ISPP (improved spatial pyramid pooling) module to fuse more features to increase the sensory field to obtain multi-scale object information and improve detection performance for small targets. The rest of the paper is organized as follows. Section 2 presents related work. Section 3 describes the construction of the dataset and the improvement of the algorithm. Section 4 provides the details of the experiments and test results, which are analyzed and discussed. Finally, Section 5 draws conclusions.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 8 Oss Domain  
Finally, for the OSS domain, the technology radar addresses two different dimensions: OAM and capability exposure. On the one hand, the _OAM_ dimension refers to the set of activities related to network slice life cycle management. On the other hand, the _capability exposure_ dimension touches on the need for providers to make network slice capabilities available for consumption to B2B customers, through easy-to-use service APIs.
### Conceptual Description of Transport Processes by Hopping
Transport of sediment particles by saltation has received extensive attention [28; 29; 30; 31], being a significant subject in the field of the hydraulic and river engineering, as it relates to many hydraulic engineering challenges such as channel bed and bank destabilization [32], as well as central for the stability of hydraulic infrastructure against scour (such as bridge piers and abutments [33; 34; 35; 36]). Understanding sediment hop dynamics can lead to insights into the formation of more coherent morphological structures on the bed surface, such as pebble clusters [37], ribs, and riffle pools [38]. Particle hop metrics are essential in describing bedload transport in saltation mode and can be used to understand the complexity of sediment dynamics, by using a range of derived variables such as hop length (\(\Delta\)x), hop travel time (\(\Delta\)t), and mean instantaneous particle velocities (\(V_{mean}\)). Hop length (\(\Delta\)x) is defined as the stream-wise distance travelled by one saltating particle, from the start of its motion to the end of its rebound. Hop travel time (\(\Delta\)t) is the time of particle transport elapsed between the start of its motion and the end of its rebound.
## 2 Materials and Methods
### Measurement of Quantum Yields  
Quantum yield (\(\Phi Y\)) of the synthesized CDs was estimated using a general refractive index of 1.384 from literature (1) by comparing the CDs with quinine sulfate as a reference compound (fluorophores). Quinine quantum yield reported as 0.53 when dissolved in 0.1 M H\({}_{2}\)SO\({}_{4}\) has a refractive index \(\eta\) = 1.346. The quinine sulfate solution was prepared and diluted to have an optical density (OD) = 0.00, 0.02, 0.04, 0.06, 0.08, 0.1, and the synthesized CDs treated in the same manner. The \(\Phi Y\) of CDs in water having refractive index \(\eta\) = 1.384 was estimated using Equation (3) obtained from Equation (2) because the absorbance values were components of the gradients:  
\[\Phi Ys=\frac{I.F.I_{s}}{I.F.I_{ST}}\times\frac{A_{s}}{A_{ST}}\times\left( \frac{\eta_{s}}{\eta_{ST}}\right)^{2}\times\,\Phi Y_{ST} \tag{2}\]  
\[\Phi Ys=\frac{G_{rad_{s}}}{Grad_{ST}}\left(\frac{\eta_{s}}{\eta_{ST}}\right)^{ 2}\times\,\Phi Y_{ST} \tag{3}\]  
where \(\Phi Y\) = the quantum yield, IFI is the integrated fluorescence intensity, \(Grad\) is the gradient from the plot of integrated fluorescence intensity versus absorbance, \(A\) = absorbance, \(\eta\) the refractive index, and the subscripts \(ST\) and \(S\) denote standard and test sample, respectively [1, 26, 46, 72].  
Figure 2: Furnace for the process of carbonization of PKS to CPKS [57].