esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ultra-high-performance concrete (UHPC) has the advantages of high strength, excellent durability performance, etc., and its compressive strength is several times that of normal concrete (NC). Due to the role of materials such as steel fibers, the tensile strength of UHPC is higher than that of NC. For steel-tube concrete columns in corrosive seawater environments, UHPC-NC columns with welded ring reinforcement inside the steel tube are proposed to strengthen the interfacial bonding performance, and the effects of seawater corrosion of steel-tube concrete are studied. Eight steel-tube UHPC-NC specimens were designed for push-out tests. The steel tubes were internally constructed with glossy unconstructed and reinforcing rings, with the core concrete with UHPC used below and the C40 plain concrete used above. By examining push-out load, slip displacements, and steel-tube wall strains, this study analyzed the influence of different factors on the bond behavior and failure mechanism of bond-slip in shear-resistant reinforcing ring connectors. The push-out simulation of the steel-tube concrete was carried out using ABAOUS 2021 software, and the simulation results were compared with the experimental results, which showed good agreement. The results show that the bond strength of the steel tube-concrete column interface can be significantly improved by using the construction measures of internally welded reinforcement rings; for specimens with the same percentage of core concrete UHPC and C40 thickness, the bond strength of the two rings was significantly improved by approximately 33% over that of the one-ring reinforcement ring; corrosive environments will degrade the bond strength of the steel tube-concrete column interface.  
steel-tube concrete; internally welded reinforcement rings; UHPC; bond strength; push-out tests +
Footnote †: journal: Applied Sciences
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 2 Underground MI Communication System  
In this section, we develop a point-to-point MI communication model and evaluate the performance of an MI communication system for single-hop communication. Further, we extend the model to multihop communication and discuss system parameters.
# Volumetric Modulated Arc Therapy for Radiosurgery of Brain Metastases: A Single-Center Study
###### Abstract  
Whole-brain radiation therapy and stereotactic radiosurgery are two treatment modalities commonly utilized to treat brain metastases (BMs). The aim of this study is to retrospectively analyze the main radio-oncologic and clinical-demographic aspects of a cohort of BM patients treated with Volumetric Modulated Arc Therapy for radiosurgery (VMAT-RS). This is a cross-sectional observational design study with a retrospective review of the medical records of patients with brain metastases treated with VMAT-RS between 2012 and 2018. Clinical and demographic data, with special attention to sex, age, primary tumor, brain tumor-related epilepsy (BTRE), number and brain location of BMs, Karnofsky Performance Status (KPS), the updated DS-GPA prognostic index, and the survival estimated according to the Kaplan-Meier model from the date of radiosurgery, were analyzed. One hundred and twenty-one patients with 229 BMs were treated with VMAT-RS. Patients presented 1-4 BMs, which were treated with five non-coplanar VMAT arcs. Sixty-eight percent of the patients had lung cancer, and 35% of the BMs were in the frontal lobe. The proportion of local control was 88.5%. BTE prevalence was 30.6%. The median survival time (MST) was 7.7 months. In the multivariate analysis of the Cox regression model, KPS \(\geq\) 70 (HR\({}_{\text{KPS}}<\tau_{0}=2.59;\,p=0.001\)) and higher DS-GPA (HR\({}_{\text{DS-GPAII}}=0.55,\,p=0.022;\,\text{HR}_{\text{DS-GPAII-IV}}=0.38,\,p=0.006\)) were associated with improved survival. In the univariate analysis, primary tumor, age, and the presence of metastases in the posterior fossa (PFBMs) were also significant. In conclusion, the VMAT-RS is a technique with an overall survival rate comparable to other radiosurgery techniques. The median survival is significantly longer for those with higher KPS and DS-GPA. Other variables, such as the type of primary tumor, age, and PFBMs, could also influence survival, although further studies are needed.  
VMAT; stereotactic radiosurgery; brain tumor-related epilepsy; overall survival; brain metastasis; brain location +
Footnote †: journal: Applied Sciences
## 3 Dual-Stack Single-Radio Communication Architecture (DSSRCA)
### Experimental Evaluation
#### 3.4.1 Best-Effort Protocol Experimentation
The measurement consists in the time difference between the moment the packet is sent to the transceiver upon receiving the ACK at MAC level. The dark line in the graph denotes the polynomial trend. It is possible to observe that the average message latency is about 15 ms from 5 to 80 m. Moreover, the latency increases at distances greater than 80 m since the link quality decreases and more re-transmissions are necessary. This experiment is important in order to highlight the boundaries of a good data collection mechanism. With this information, one can tune the protocol to optimize data gathering performance. Figure 5: Messages received by the UAV along the experiment. Figure 6: Histogram of messages collected by the bicycle.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Iterative Decoding Feedback-Based MP Detector
#### 4.4.1 From the MP Detector to the LDPC Decoder  
Within the \(n_{out}\) outer loop, when the inner-loop iterations of the MP detector reach the maximum iterations \(N_{in,MP}\), the symbol-level outer information output by the detector is as follows:  
\[M_{e,MP}^{n_{out}}(x_{i}=a_{j})=p_{i}^{N_{in,MP}}(x_{i}=a_{j}), \tag{26}\]  
where \(x_{i}\) represents the \(i\)th symbol after OTFS modulation (\(i=1,2,\ldots,NM\)), A represents the set of modulation symbols, \(a_{j}\in\mathrm{A},j=1,2,\ldots,Q\), and \(Q\) is the size of the modulation symbol set A (i.e., \(Q=16\) when the 16 QAM is used for modulation). Furthermore, \(p_{i}^{N_{in,MP}}(x_{i}=a_{j})\) denotes that when the inner-loop iterations of the MP detector reaches the maximum number of iterations \(N_{in,MP}\), the probability of the symbol \(a_{j}\) in the quadrature amplitude modulation (QAM) alphabet is calculated within the MP detector. This probability is the symbol-level output of the MP detector.  
The symbol-level soft information output by the MP detector \(M_{e,MP}^{n_{out}}(x_{i}=a_{i})\) passes through the LLR converter to obtain the bit-level soft information.  
\[L_{e,MP}^{n_{out}}(c_{i,k}=a_{j,k})=ln\frac{\sum\limits_{x_{i}\in\mathrm{A}_{ k}^{0}}M_{e,MP}^{n_{out}}(x_{i}=a_{j})}{\sum\limits_{x_{i}\in\mathrm{A}_{ k}^{0}}M_{e,MP}^{n_{out}}(x_{i}=a_{j})}, \tag{27}\]  
where \(a_{j,k}\) represents the bit value (0 or 1) of the \(k\)th bit in the \(j\)th element of the modulation symbol set.  
Each modulation symbol \(a_{j}\in\mathrm{A}\) corresponds to a binary sequence with a length of \(log_{2}|Q|\). Therefore, \(c_{i,k}\) represents the bit value (0 or 1) on the \(k\)th subcarrier of the \(i\)th symbol, where \(k=0,1,\ldots,log_{2}|Q|\). \(\mathrm{A}_{k}^{1}\) (\(\mathrm{A}_{k}^{0}\)) represents the modulation symbol set in which the \(k\)th subcarrier in the modulation symbol set A is \(1(0)\).  
Figure 4: The structure of IDF-MP.  
The bit-level soft information \(L_{e,MP}^{n_{out}}(c_{i,k}=a_{j,k})\) output from the LLR in the \(n_{out}\) outer loop is de-interleaved to obtain the input information \(L_{pr,LDPC}^{n_{out}}(c_{i,k}=a_{j,k})\) of the LDPC decoding part. \(L_{pr,LDPC}^{n_{out}}(c_{i,k}=a_{j,k})\) serves as the prior information of the LDPC decoder in the \(n_{out}\) outer loop.
## 4 Experiments
### Effects of Different Settings for BCCCC  
We further investigate the effects of different settings for BCCCC. There is a parameter \(k\) that governs the relative weight of the category-based CC component of BCCCC (see Equation (5)). In the above experiments, \(k\) is set to 0.5. It is interesting to investigate whether this parameter is difficult to set (i.e., whether performance of BCCCC changes dramatically for different settings for \(k\)).  
Figure 7 shows performance of BCCCC with ten different settings for \(k\) in [0.1-1.0]. It is interesting to note that performance in each evaluation criterion (i.e., MAP, and Average P@X) does not change dramatically. BCCCC with \(k=0.5\) does not have significantly different performance than BCCCC with some of the other settings for \(k\), especially, when \(k\) is in [0.3-0.5], all performance differences are not statistically significant. Therefore, it is not a difficult task to set the parameter \(k\) in practice. Setting \(k\) as [0.3-0.5] may be good for BCCCC.  
Figure 6: An example to show how BCCCC successfully reduces the similarity between the target article (ID: 22707478) and a less related article (ID: 17850458).  
Figure 7: Effects of setting different weights for the citation-based similarity component of BCCCC (i.e., \(k\) in Equation (5), and ‘\(\bullet\)’ indicates that performance difference produced by setting \(k\) to the value and 0.5 is statistically significant with \(p<0.01\)).  
Another different setting for BCCCC is the way to compute the citation-based similarity. In the above experiments, BCCCC employs category-based CC (see Equation (4)). Another setting for BCCCC is to replace Equation (4) with _article-based_ cocitation (i.e., article-based CC), which is a traditional cocitation measure defined in Equation (12) [2; 29], where \(I_{a1}\) and \(I_{a2}\) are the sets of articles that cite articles \(a_{1}\) and \(a_{2}\), respectively (i.e., in-link citations of \(a_{1}\) and \(a_{2}\), respectively).  
\[CC_{article}(a1,a2)=\frac{|I_{a1}\cap I_{a2}|}{|I_{a1}\cup I_{a2}|} \tag{12}\]  
Therefore, article-based CC can be seen as a \"constrained\" version of category-based CC, as cocitation is counted only if two references are cited by the same article (rather than articles in the same category). Figure 8 shows the performance of the different settings. BCCCC with category-based CC performs significantly better than BCCCC with article-based CC in all evaluation criteria. The results justify the contribution of category-based CC, which provides additional helpful information even when two references are not cocited by the same article. It is also interesting to note that, as BCCCC with article-based CC still performs better than the baselines, as seen in Figures 2 and 8, it can be a good version as well, especially when no categories of articles are provided in practice.
# Behavior-Based Control Architecture for Legged-and-Climber Robots
## 4 Control Architecture
### Complementary Modules
#### 4.4.3 Kinematics Calculation  
This module obtains the direct and inverse kinematic of a leg, with the reference system in the robot center. It is completely dependent on the robot, so this module must be changed if another robot is used. Using the tests with ROMHEX, we present the forward and inverse kinematics of this robot, obtaining the algebraic solution.  
Following Figure 1, forward kinematics is calculated in (6)-(8), where \(A_{coxx}\) is the angle between the first motor origin and the femur. \(P_{x}\), \(P_{y}\) and \(P_{z}\) denote the position of the end-effector with respect to the leg coordinate system, \(L_{coxx}\), \(L_{femur}\), and \(L_{tibia}\) denote the link lengths, while \(q_{1}\), \(q_{2}\) and \(q_{3}\) denote the joint angles. Furthermore, thanks to Figure 1a itis possible to obtain the transformation matrix between the body center and each leg origin. These transformation matrices must be applied over the body and legs reference systems.  
\[P_{x}=L_{coxa}\cdot cos(A_{coxa}+q_{1})+L_{femur}\cdot cos(q_{2}) \cdot sin(q_{1})+\] \[L_{tibia}\cdot cos(q_{2}+q_{3})\cdot sin(q_{1}) \tag{6}\] \[P_{y}=L_{coxa}\cdot sin(A_{coxa}+q_{1})+L_{femur}\cdot cos(q_{2} )\cdot cos(q_{1})+\] \[L_{tibia}\cdot cos(q_{2}+q_{3})\cdot cos(q_{1})\] (7) \[P_{z}=L_{coxa,z}+L_{femur}\cdot sin(q_{2})+L_{tibia}\cdot sin(q_{ 2}+q_{3}) \tag{8}\]  
With respect the inverse kinematic, the solution is found in (9)-(11), where variables \(B,A_{1}\), etc. are calculated in (12)-(16).  
\[q_{3}=\left\{\begin{matrix}B-\pi&if\ third\ link\ up\\ \pi-B&if\ third\ link\ down\end{matrix}\right. \tag{9}\]  
\[q_{2}=\left\{\begin{matrix}(A_{1}+A_{2})-\frac{\pi}{2}&if\ third\ link\ up\\ \frac{\pi}{2}-(A_{1}+A_{2})&if\ third\ link\ down\end{matrix}\right. \tag{10}\]  
\[q_{1}=\frac{P_{x}}{P_{y}}-\frac{L_{coxa,x}}{LP+L_{coxa,y}} \tag{11}\]  
\[B=arccos(\frac{HF^{2}-L_{femur}^{2}-L_{tibia}^{2}}{-2\cdot L_{femur}\cdot L_{ tlibia}}) \tag{12}\]  
\[HF=\sqrt{LP^{2}+(P_{z}-L_{coxa,z})^{2}} \tag{13}\]  
\[LP=\sqrt{P_{x}^{2}+P_{y}^{2}-L_{coxa,x}^{2}}-L_{coxa,y} \tag{14}\]  
\[A_{1}=arctan(\frac{LP}{|P_{z}-P_{coxa,z}|}) \tag{15}\]  
\[A_{2}=arccos(\frac{L_{tibia}^{2}-L_{femur}^{2}-HF^{2}}{-2\cdot L_{femur}\cdot HF }) \tag{16}\]
###### Abstract  
This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid metaheuristics optimisation techniques to predict the axial load bearing capacity of fully grouted rock bolting systems. For this purpose, a comprehensive dataset of 72 pull-out tests was built, considering various influential parameters such as three water-to-grout (W/G) ratios, five ranges of curing time (CT), three different grout admixtures with two different fly ash (FA) contents, and two different diameter confinements (DCs). Additionally, to find out the effect of the mechanical behaviour of grouts on the performance of fully grouted rock bolting systems, seventy-two uniaxial compression strength (UCS) samples were cast and tested simultaneously with pull-out samples. The UCS samples were prepared with the same details as the pull-out samples to avoid any inconsistency. The results highlight that peak load values generally increase with longer curing times, lower W/G, and higher UCS and DC values. The main novelty of this paper lies in its data-driven approach, using various XGBoost models. This method offers a time-, cost-, and labour-efficient alternative to traditional experimental methods for predicting rock bolt performance. For this purpose, after building the dataset and dividing it randomly into two training and testing datasets, five different XGBoost models were developed: a standalone XGBoost model and four hybrid models incorporating Harris hawk optimisation (HHO), the jellyfish search optimiser (JSO), the dragonfly algorithm (DA), and the firefly algorithm (FA). These models were subsequently evaluated for their ability to predict peak load values. The results demonstrate that all models effectively predicted peak load values, but the XGBoost-JSO hybrid model demonstrated superior performance, achieving the highest R-squared coefficients of 0.987 and 0.988 for the training and testing datasets, respectively. Sensitivity analysis revealed that UCS values were the most influential parameter, while FA content had the least impact on the maximum peak load values of fully cementitious grouted rock bolts.  
fully grouted rock bolt; XGBoost; axial load bearing capacity; machine learning; prediction +
Footnote †: journal: Applied Sciences
## 2 Related Work  
### Extended Reality (XR) and Mixed Reality (MR) Interaction Systems  
Extended Reality (XR) is defined as an umbrella concept encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), providing new forms of user experiences by blurring the boundaries between reality and virtuality [8,9]. Among these, MR has gained attention for its potential applications in various fields including medicine, education, manufacturing, and cultural content by supporting real-time interactions between real-world structures and virtual objects. The core of XR interaction systems lies in precisely recognizing the user's position, gaze, and hand gestures in physical space and mapping them to virtual objects to implement physically consistent real-time interactions. To achieve this, technologies such as SLAM (Simultaneous Localization and Mapping) [10], depth sensing, spatial mapping, and hand and gaze tracking are actively utilized, with 3D engines like Unity and Unreal Engine supporting the development of these interactions [11,12]. With the emergence of high-performance XR devices such as Meta Quest 3, Apple Vision Pro, and Microsoft HoloLens 2, XR interaction systems have expanded their research domains beyond simple visual overlays to precise interactions with physical structures and real-time physics simulations [13,14]. XR-based surgical simulations, virtual fitting systems, and smart manufacturing systems are representative application cases reflecting these technological advancements. However, most existing XR systems focus on functional interactions such as object placement, UI manipulation, and gesture-based triggers, while showing relatively limited approaches to real-time simulation and collision processing of deformable soft body objects (e.g., cloth, skin, soft materials) [15]. In particular, deformable objects like cloth require high-performance parallel computing and optimization techniques due to their high computational complexity arising from non-linear deformations and collision processing [16]. This research proposes a system that overcomes these limitations by implementing real-time precise physical interactions between deformable objects such as cloth and scanned real-space meshes in XR environments.
## 3 Materials and Methods
### SPE, DLLME, and SPME Conditions
#### 3.5.3 SPME Settings  
The 96-well SPME procedure with a polystyrene-divinylbenzene (PS-DVB) resin and a 2.2 mL polypropylene deepwell storage plate was employed. The activation of SPME brushes was carried out with 1 mL of MeOH/H\({}_{2}\)O (1:1; _v/v_) for 30 min. Then, the brushes were washed with 1 mL of deionized water (10 s). The homogenates (200 \(\mu\)L) were diluted with water or a 0.1% FA water solution to 1 mL, loaded onto the SPME plate, and left for adsorption for 90 min at room temperature, while the tray was shaking (speed 850 rpm). Subsequently, the SPME brushes were rinsed with deionized water (10 s) to remove impurities. The desorption of the NTs was carried out with 1 mL of MeOH or a 1 mL solution of MeOH enriched with 20 ng/mL of IL (90 min, speed: 850 rpm, at room temperature). The desorption solution was further evaporated to dryness (centrivap, 45 \({}^{\circ}\)C, 1 h). Finally, the residue was dissolved in 50 \(\mu\)L of 2 mM sodium tetraborate, centrifuged at 10,000 rpm for 5 min, and analyzed by MEKC.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method
### Baseline Localization Using a Particle Filter  
We were inspired to adopt a particle filter by existing methods [24; 25; 26] with a particle filter for estimating the user's position in a map image. Additionally, particle filters are used in a wide range of tasks, such as multiple target tracking [27], magnetic particle tracking [28], and wireless tracking of magnet position [29]. The Kalman filter is also a promising algorithm for the localization task [30; 31].  
We manually create the passage region, which consists of 0 or 1 values, from the image of the information board. Given a stride length \(l\) and a heading direction \(\theta_{h}\in[0,2\pi]\), one particle follows Equation (1) to update three parameters: the two-dimensional position \((x,y)\) in the map image, the scale of the map \(mpp\) (meter-per-pixel), and the offset of the heading direction \(\theta_{o}\). This converts the coordinates of the smartphone to the map image. \(\epsilon\) denotes noise from a normal distribution. Likewise, the sine function is used for updating \(y\). Furthermore, \(mpp\) and \(\theta_{o}\) are updated by adding noise obtained from normal distributions. Finally, the position is defined as the average of the positions of all the particles. The number of particles is 2000. Particles will disappear if they are not in the passage region. Resampling is performed to keep the total number of particles constant. We followed the work in [7] to determine the number of particles. The proposed method removes particles if they are not in the passage regions. Thus, localization will fail if all the particles are removed at once. The experimental results verified that localization was performed successfully. All particles did not disappear at once. Therefore, the number of particles was sufficient.  
\[x=x+\frac{l}{mpp+\epsilon_{mpp}}\cos\left(\theta_{h}-\theta_{o}+\epsilon_{o}\right) \tag{1}\]
# Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment--Using MEC/SDN
###### 1 Introduction
This work considers developing a framework for an IoT/5G system, in which heterogeneous IoT networks can be integrated with the 5G cellular system. The system mainly considers the dense deployment of IoT devices and even cellular supported devices in 5G networks. The proposed system considers MEC, D2D and SDN technologies for such integration. Energy and the latency-efficient offloading scheme are developed for the proposed structure to manage data offloading among network parts in a way that achieves the previously mentioned requirements. Our main contributions can be summarized in the following points:  
1. Integrating heterogeneous IoT networks with the 5G networks;
2. Enabling dense deployment of IoT networks;
3. Enabling dense deployment of 5G cellular systems;
4. Developing a framework for IoT networks able to support ultra-low latency applications;Developing an energy-efficient offloading scheme that offloads computing tasks in a way that preserve devices' energy without affecting the quality of service (QoS);
* Achieving high system scalability of IoT and 5G networks. Paper organization: The article is structured as follows: Section 2 introduces the current literature and compares the related works with the proposed IoT/5G system. The novelty of the proposed work is well defined in this section. Section 3 presents the proposed framework for the IoT/5G system. The system structure is introduced, and the main parts are clearly discussed. Section 4 introduces the developed energy-aware, and latency-aware offloading scheme for the proposed IoT/5G system Section 5 is introduced for performance evaluation and result in a discussion of the proposed system. Section 6 is introduced for conclusions and future directions.