esa-sceva/satcom-chunk-collection huggingface dataset:
# The Problem of Stability in Mechanical Systems Using the Example of Mine Hoist Installations
## 2 Dynamic Analysis of the Hoist Installation
Citing the solution of [PERSON]'s equation of the second type, the following was obtained for this system [24; 25; 26]:  
\[\frac{me_{2}^{2}+I}{l^{2}}\ddot{x}_{\rm A}+\frac{me_{1}e_{2}-l}{l^{2}}\ddot{x}+ k_{\rm g}\big{(}x_{\rm A}-x_{\rm g}\big{)}+2h_{\rm g}\big{(}\dot{x}_{\rm A}-\dot{x}_{ \rm g}\big{)}=0, \tag{1}\]  
\[\frac{me_{1}^{2}+I}{l^{2}}\ddot{x}_{\rm B}+2k\big{(}x_{\rm g}-x_{1}(t)\big{)}- k_{\rm g}\big{(}x_{\rm A}-x_{\rm g}\big{)}-2h_{\rm g}\big{(}\dot{x}_{\rm A}-\dot{x}_{ \rm g}\big{)}+2h\big{(}\dot{x}_{\rm g}-\dot{x}_{1}(t)\big{)}=0, \tag{2}\]  
\[m_{\rm g}\ddot{x}_{\rm g}+2k\big{(}x_{\rm g}-x_{1}(t)\big{)}-k_{\rm g}\big{(}x _{\rm A}-x_{\rm g}\big{)}-2h_{\rm g}\big{(}\dot{x}_{\rm A}-\dot{x}_{\rm g} \big{)}+2h\big{(}\dot{x}_{\rm g}-\dot{x}_{1}(t)\big{)}=0, \tag{3}\]  
\[m_{\rm d}\ddot{x}_{\rm d}-k_{\rm d}(x_{\rm B}-x_{\rm d})+2k(x_{\rm d}-x_{1}(t+ \tau))-2h_{\rm d}\big{(}\dot{x}_{\rm B}-\dot{x}_{\rm d}\big{)}+2h\big{(}\dot{ x}_{\rm d}-\dot{x}_{1}(t+\tau)\big{)}=0, \tag{4}\]  
where:  
\(x_{\rm A}=x+e_{1}\), \(x_{\rm B}=x+e_{2}\). \(\varphi\)--the angle of the skip hopper rotation around its center of gravity (c. o. g.)  
\(x\)--horizontal displacement of the skip hopper mass  
\(m\)--the skip hopper mass with the payload  
\(I\)--inertia moment of a loaded skip hopper  
\(m_{\rm g}\)--the skip head mass  
\(m_{\rm d}\)--the bottom frame mass  
\(2h\), \(k\)--(linear) damping and elasticity factors of sliding and rolling shoes  
\(2h_{\rm g}\), \(k_{\rm g}\)--damping and shear elasticity factors of a flexible connector between the head and hopper  
\(2h_{\rm d}\), \(k_{\rm d}\)--damping and shear elasticity factors of flexible connectors between the bottom frame and skip hopper  
\(x_{\rm g}\), \(x_{\rm d}\)--horizontal displacements of the face of the skip head and bottom frame, respectively  
\(x_{1}(t)\), \(x_{1}(t+\tau)\)--function governing the irregularities of cage guides' front surfaces  
\(\tau\)--time to ride along the path equal to the distance between the front guide shoes (on the head and the bottom frame)  
\(e_{1}\), \(e_{2}\)--the distance between the hopper c. o. g. and front shoes on the top and at the bottom, respectively  
\(l\)--distance between the top and bottom guide shoes  
The previous work on this topic [26] have shown that irregularities and misalignments on the guide strings are random values. Citing [PERSON]'s work [27], it was assumed that irregularities and misalignments on the guide strings \(x_{1}\) and \(x_{2}\) could be taken into consideration as a random stochastic process, for which the power spectral density is expressed as:  
\[S_{\rm x}=\frac{2D_{\rm x1}\alpha}{\alpha^{2}+\omega^{2}}\left[\rm m^{2}s \right], \tag{5}\]  
\(D_{\rm x1}\), \(\alpha\)--parameters.
# DSCope: Development of Automatic Program for Detecting Fractures and Measuring Dip Angles
## 1 Introduction  
Groundwater containing oxygen flowing underground can lead to changes in the underground environment. Drilling investigations are generally conducted for geological surveys of the deep underground, which is analyzed based on the drilling cores identified during drilling investigations. The types of rocks underground, the tectonic features and the attitude of discontinuities can be verified through drilling. Various characteristics can be analyzed through borehole logging and by capturing photographs during the drilling operation. In particular, photographs of boreholes are generally used to analyze the distribution or attitude of discontinuities. The distribution characteristics of discontinuities are examined to predict the movement of surface water or groundwater as well as the distribution of the results of tectonic movements such as faults or folds.  
In most cases, excluding the one in which the borehole is captured in a photograph during drilling, the recovered drilling core is analyzed and organized with the naked eye to identify the distribution and attitude of any discontinuities. The dip angle among the elements indicating the attitude of the discontinuities can play a key role in predicting the flow of surface water or groundwater [1, 2, 3]. The most commonly used method is to drill a borehole, insert a camera inside and capture the wall of the borehole in a photograph to investigate the discontinuities. However, this method requires specialists and is expensive. It is therefore inconvenient for the investigator to directly measure the discontinuities ofthe lifted drilling cores with the naked eye. It is easy to visually analyze a shallow drilling core, but that is not the case with a deep drilling core.  
Some researchers published papers analyzing the value of rock quality designation (RQD) or the distribution of fractures using photos of core samples [4; 5]. Some of these methods can be analyzed only when expensive software is used [6]. However, the method in this study used simple tools that investigators could carry around to take pictures and used them to analyze the slope angles of drilling cores in the field easily and quickly.  
This study develops an automated program written in Python for accurate and rapid analysis of drilling cores, thereby addressing the inconvenience of visual inspection in determining the dip angle of discontinuities for drilling cores. This automated program can quickly and accurately measure the dip angle of discontinuities in an indoor space by using photographs of the drilling cores. The reliability of the program is verified by applying it to the representative granite of South Korea. The results show that the developed program is satisfactorily reliable and can measure the dip angle of discontinuities quickly and accurately.
## 3 Network Model  
We model the xHaul access network by means of graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), where \(\mathcal{V}\) represents the set of remote and hub nodes, and \(\mathcal{E}\) is the set of links between the nodes. We assume that each link consists of a number of optical fibers, and the fiber resources are sufficient to establish the connections between the remote sites and the hub. Let \(\mathcal{W}\) be the set of WDM channels (wavelengths) that can be carried in an optical fiber. \(W=|\mathcal{W}|\) denotes the capacity of the WDM transmission system.  
The set of traffic demands is denoted by \(\mathcal{D}\), where demand \(d\in\mathcal{D}\) represents a remote node requesting a certain number of wavelength connections, denoted by \(W(d)\), to be established between the remote node and the hub. The connections are realized by means of optical TPs, where each connection has assigned a wavelength on its TP. We distinguish two types of TPs, namely the following:  
* A direct TP--where the remote node is connected with the hub by means of a direct (dedicated) fiber connection (i.e., without any OADM introduced on the TP) that carries only the wavelengths originated at the node and aggregated by means of a MUX.
* A traversing TP--where the optical fiber connection between the end remote site and the hub goes through some intermediate remote nodes at which the aggregation of wavelengths originated at these nodes is realized by means of OADMs (e.g., as illustrated in Figure 1); in this case, the TP serves the wavelength requests both of the end node, for which the TP is established, and of the intermediate nodes.  
Let set \(\mathcal{P}(d)\) comprise candidate TPs of demand \(d\in\mathcal{D}\). Path \(p\in\mathcal{P}(d)\) is a fiber route going through a subset of network links (\(p\subseteq\mathcal{E}\)), which connects the remote node of demand \(d\) with the hub. Let \(D(p)\) denote the length of TP \(p\). Let \(Q(d,\bar{d})\) be the set of candidate TPs of demand \(\bar{d}\) that go through the remote node associated with demand \(d\) and which may serve (aggregate) the wavelength connections of \(d\) by means of an OADM.  
Let \(\mathcal{M}\) and \(\mathcal{O}\) be the sets of available types of MUXs and OADMs, respectively. Set \(\mathcal{M}\) includes also a fictitious type of the MUX supporting one channel, which facilitates the modeling of direct fiber connections in the MILP formulation. This fictitious MUX does not induce any cost and has no attenuation. Let \(W(m)\) denote the number of wavelength channels multiplexed/demultiplexed by MUX \(m\in\mathcal{M}\) and let \(W(o)\) be the number of wavelengths added/dropped by OADM \(o\in\mathcal{O}\). Eventually, let \(o(d)\) denote the type of OADM selected for demand \(d\).
## 2 Related Work
In real-world applications, it is often unknown whether a signal contains hidden information, let alone the extent of embedding. Therefore, future research should aim to develop detection methods that do not depend on predefined embedding rates. To tackle this problem, [PERSON] et al. [24] proposed a detection method based on Dempster-Shafer theory (DST), which fuses results from multiple classifiers trained on different embedding rates. These classifiers use the R-SCPP feature set to support detection under varying conditions. Similarly, [PERSON] et al. [25] introduced a method that combines K-means clustering and ensemble learning. During training, samples are grouped by embedding intensity, and each cluster is analyzed with an XGBoost classifier, improving adaptability to unknown embedding rates. Despite these advancements, existing steganalysis techniques [24; 25] still present notable challenges. Most approaches rely on manually designed features, which may not effectively capture intricate steganographic patterns. Additionally, many methods handle different embedding rates as independent cases, disregarding their underlying correlations and missing critical contextual relationships. This limitation reduces their ability to adapt to real-world scenarios where embedding characteristics can vary dynamically. Consequently, there remains a need for more robust detection frameworks capable of recognizing covert modifications without prior assumptions about embedding configurations.
# Zero-Shot Image Classification with Rectified Embedding Vectors Using a Caption Generator
## 4 Experimental Results
### Qualitative Study  
Figure 4 presents examples of the zero-shot image classification on the CUB dataset. In the table in Figure 4, the upper part shows the prediction result obtained by using only the image-feature vector, and the lower part shows the case of using our proposed rectified vector. In the example of the bird, a Laysan Albatross is characterized by a brown part of the body. A brown creeper, which the upper model predicted as the rank-1 answer, also has a brown part of the body. The upper model here overly relied on the brown body feature of the image, causing incorrect prediction. Our proposed model (lower part) modifies the prediction of the original model by providing additional information from generated sentences so that it does not depend only on large image features. As a result, the provision of additional textual information through the generated description in our proposed model improves classification accuracy. Although the generated sentences are sometimes not grammatically correct (the right in Figure 4), they still can provide meaningful information correcting the wrong prediction.  
One limitation of the proposed method is that the caption generator does not always generate good semantic textual information. Figure 5 illustrates some examples of generated sentences. While the sentences of the left examples are semantically matched with the images, the right examples reveal that some words in the generated sentences are inaccurate. This kind of generated text that semantically mismatched with the image may lead to poor performance. The use of a better generation model may reduce this problem and avoid performance degradation due to the misunderstanding of images.  
Figure 4: Zero-shot image classification results on the CUB dataset. Given the single test image, we compare classification results between using only the image-feature vector (upper) and using the proposed rectified vector (lower).
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 5 Experimental Results
(**b**) Impact of the target area radius on the deployment solution (local). overlapping in certain areas within the target area, increasing power consumption and also intensifying signal interference. The CCPO strategy is also a feasible method, characterized by guaranteed full coverage and minimal overlapping area. Compared to the previous two methods, the system transmission power of the UAV-BS is reduced by an average of 14.75% and 6.52%, respectively. The results indicate that the CCPO strategy proposed in this paper can effectively solve the site planning and deployment problems of UAV-BSs, and also provide support for the development of UAVCN technology.
# A Brief Review on Micro-Implants and Their Use in Orthodontics and Dentofacial Orthopaedics
## 3 Results  
### The Success Rate of Micro-Implants  
The overall success rate of skeletal anchorage using micro-implants was ranged from 79% to 98.2% [4], described by other studies as being 85.0% [6]. A systematic review published in 2010 [7] included fourteen clinical trials and described the mean overall success rate of 83.8 \(\pm\) 7.4%, with no significant differences regarding the patient's sex. Little diameters of the mini-screws from 1 to 1.1 mm had lower success rates than the greater ones from 1.5 to 2.3 mm; screws less than 8 mm in length and 1.2 mm in diameter should be avoided [7]. One study reported significantly lower success rates for 6-mm vs. 8-mm long mini-screws (72% vs. 90%) [7]. The recommended diameter and length for a micro-implant placed in the alveolar bone were 1.2 to 1.6 mm respectively 6-7 mm in another study [8]. Other authors [9] concluded that the success rate did not depend on sex, age, and side of placement but significantly increased as total bone density and cancellous bone density increased. The OMIs' success rate was not significantly correlated with the cortical bone density.
###### Abstract  
To maximize the limited spectrum among primary users and cognitive Internet of Things (IoT) users as we save the limited power and energy resources available, there is a need to optimize network resources. Whereas it is quite complex to study the impact of transmission rate, transmission power or transmission delay alone, the complexity is aggravated by the simultaneous consideration of all these three variables jointly in addition to a channel selection variable, since it creates a non-convex problem. Our objective is to jointly optimize the three major variables; transmission power, rate and delay under constraints of Bit Error Rate (BER), interference and other channel limitations. We analyze how total power, rate and delay vary with packet size, network size, BER and interference. The resulting problem is solved using a branch-and-cut polyhedral approach. For simulation of results, we use MATLAB together with the state-of-the-art BARON software. It is observed that an increase in packet size generally leads to an increase in total rate, total power and total transmission delay. It is also observed that increasing the number of secondary users on the channel generally leads to an increased power, delay and rate.  
network resource optimization; Wireless Sensor Networks (WSNs); Cognitive Radio Wireless Sensor Networks (CR-WSNs); primary user (PU); secondary user (SU); transmission power; transmission rate; Transmission delay; Bit Error Rate; Signal-to-Interference Noise Ratio +
Footnote †: journal:
## 3 Method Used
### Modeling Methodology  
The modeling methodology of this study was carried out through several main steps described as follows (Figure 5):  
1. Loading the as-obtained dataset and dividing it into two parts. The first part, including 70% of the data, is used to train and construct the AI \"black-boxes\", whereas the remaining 30% of data was used for validation of the models. The input parameters were coarse and fine aggregates (wt.%), AC-60/70 (wt.%) or PMB I (wt.%) binders, and cellulose fibers (wt.%). The output of the AI numerical tools was MS (kN), MF (mm) and MQ (kN/mm).
2. Construction of the models using the training dataset. In the PSOANFIS, the PSO was first used to optimize the consequent and antecedent parameters of the ANFIS with the best number of particles and the inertia weight were set as 25 and 0.01, respectively. The optimal parameters optimized by the PSO were then used to train the ANFIS model for generating the PSOANFIS. For the GAANFIS, the GA was first used to optimize the consequent and antecedent parameters of the ANFIS with the crossover rate, the best number of individuals and mutation rate were set as 0.4, 25, and 0.7, respectively. The optimal parameters optimized by the GA were then used to train the ANFIS model for generating the GAANFIS. With respect to the SVM, the cubic algorithm was used to train and construct the model. A k-fold cross-validation was applied to assess the performance of SVM with the number of 10 folds.
* Validation of the models using testing data set was performed in this step. Various criteria namely R, RMSE, MAE were used to validate the three developed models in both the training and testing datasets.
* Monte Carlo analysis and asymmetric distribution were finally used to validate the robustness of the developed models. In this step, the uniform distribution was used to generate random sampling of the training dataset for Monte Carlo simulation.
* Predicting the MF and MQ of the SMA materials: Using the results of Monte Carlo analysis, asymmetric distribution and other validation criteria, the best model will be determined, this model is then used to predict other important parameters of the SMA materials namely MF and MQ.  
Figure 5: Methodological chart of the present study.
###### Abstract  
The increasing demand for enhanced communication systems, driven by applications such as real-time video streaming, online gaming, critical operations, and Internet-of-Things (IoT) services, has necessitated the optimization of cellular networks to meet evolving requirements while addressing power consumption challenges. In this context, various initiatives undertaken by industry, academia, and researchers to reduce the power consumption of cellular network systems are comprehensively reviewed. Particular attention is given to emerging technologies, including Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Cloud-Radio Access Network (C-RAN), which are identified as key enablers for reshaping cellular infrastructure. Their collective potential to enhance energy efficiency while addressing convergence challenges is analyzed, and solutions for sustainable network evolution are proposed. A conceptual architecture based on SDN, NFV, and C-RAN is presented as an illustrative example of integrating these technologies to achieve significant power savings. The proposed framework outlines an approach to developing energy-efficient cellular networks, capable of reducing power consumption by approximately 40 to 50% through the optimal placement of virtual network functions.  
cellular network; C-RAN; green; NFV; power efficiency; SDN +
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0000-0001-6507-2278][PERSON]  
1 Department of Electrical and Photonics Engineering, Technical University of Denmark, 2800 Kgs Lyngby, Denmark 1
## 5 Summary and Future Work
### Limitations
#### 5.2.2 Transferability  
The experiments investigates the behavior of a single joint type with a circular cross-section, limiting the exploration of other cross-sectional shapes. Additionally, specific lengths of mechanisms were not investigated, affecting stiffness and eigenfrequency. Future research will address these limitations by analyzing the transferability of findings to other  
Figure 11: Total mobility of the human thoracic and lumbar spine. The underlying images were taken from the Human Anatomy Atlas [31] and superimposed in Gimp to form a composite illustration.  
joint types and exploring the effects of different cross-sections and lengths on mechanical properties. This discussion highlights avenues for further investigation in subsequent experiments, as outlined in the outlook section.  
Furthermore, the experimental investigation of the torsional oscillations and their resonance behavior presented in this work is limited to a straight continuum joint configuration. Torsional oscillations with a bent continuum are not investigated. Clearly, the deformation state of the continuum will be asymmetric for large bending angles, yielding potentially coupled oscillation with the same excitation, which potentially influences the found resonance behavior. Furthermore, the resonance behavior with respect to a bending excitation is not investigated, as was performed in, e.g., [19] for stiffness identification or [20] for open loop control.
# Impact of LGE-MRI in Arrhythmia Ablation
## 6 Limitations  
Some limitations need to be addressed. First, consistent methodological and analytical standards defining fibrotic tissue characterization are needed to achieve reproducibility of results between centres. The use of the same values and methods to define fibrotic tissue, will promote the implementation in clinical practice. Second, there is also no homogenous method for integrating MRI-3D into the navigation system. Each group used different structures for merging (pulmonary veins, right ventricle, aortic roof, pulmonary artery, etc.). A more standardized method for using MRI to facilitate ablation would be beneficial for increasing its practical use. In the area of VT ablation, the assessment of ablation lesions has been investigated in small series [28,84,85]. More research is required to confirm and evaluate the usefulness of LGE-MRI in the evaluation of ventricular ablation lesions. Finally, despite new MRI scans, spatial resolution could be insufficient to detect small areas of fibrosis in the atrium wall due to its thickness. In the same line, some types of interstitial fibrosis could be underdetected with conventional LGE, and T1 mapping needs to be improved for clinical practice.  
Conceptualization: [PERSON], [PERSON] and [PERSON], writing: [PERSON]; review: [PERSON] and [PERSON]; and supervision: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Hospital Clinic de Barcelona/Instituto de Salud Carlos III (ISCIII) PI20/00693/CB16/11/00354.  
Not applicable.  
Conflicts of Interest: [PERSON]-[PERSON] have served as consultants for Boston Scientific and Abbott Medical. T.A. has received research grants for investigator-initiated trials from Biosense Webster.