esa-sceva/satcom-chunk-collection huggingface dataset:
# Geomechanical Basis for Assessing Open-Pit Slope Stability in High-Altitude Gold Mining
## 2 Materials and Methods
### In Situ Testing of Rock Mass Conditions  
During the development of the deposit, detailed investigations of the strength properties of rocks and rock contacts are carried out [16]. For this purpose, rock prisms are prepared at the quarry, with the cutting method depending on the type of rock being tested. The shear testing of the prepared rock prisms is performed using a hydraulic jack.  
For in situ shear testing of the rock prisms, a special test rig is assembled at the quarry site. This setup includes a 40-ton hydraulic jack installed inside a steel casing with fastening bolts and shear plates (Figure 2). To generate shear force in the system, an electric hydraulic power unit is used. The cutting of rock prisms in granodiorites and marbleized limestones is carried out using a drilling rig equipped with a small-diameter bit.  
Figure 1: Condition of the northern (left) and southern (right) pit walls of the high-altitude open-pit mine.  
During the shear testing process, the applied force is recorded using a pressure gauge mounted on the hydraulic jack. First, the maximum shear force, Qmax, corresponding to the point of failure, is recorded, followed by the minimum shear force, Qmin, at which sliding occurs along the failure plane. The linear and angular dimensions of the rock prisms are measured using a measuring tape and a geological compass.  
Based on the test results, the shear surface area (S), the actual forces, Qmax and Qmin (adjusted with the hydraulic system's calibration coefficient), and the volume of the sheared prism (P) are calculated. Using these data, the shear stress (\(\tau\)) and normal stress \(\sigma_{\text{N}}\) are determined. These parameters are used to construct the rock strength envelope (Mohr-Coulomb diagram), from which the cohesion (c) and internal friction angle (\(\varphi\)) are graphically derived. In some cases, the values of c and \(\varphi\) are also calculated analytically directly from the experimental data [17].  
The obtained physico-mechanical properties of the rocks are used in the assessment of bench slope stability.  
During the in situ shear testing of prisms cut from strong rocks (granodiorites and marbleized limestones), difficulties arise in the preparation of specimens. Initially, the prism contours are formed using a small-diameter borehole perforator or a diamond saw, followed by final trimming using a drilling rig. The obtained cohesion values for these rock types reflect the composite mechanical behavior of the prisms, accounting for the presence of internal fractures within the samples.  
Figure 2: Design of the shear testing setup and shear scheme for a rock prism: (**A**)—schematic diagram of rock prism shearing; (**B**)—electric hydraulic power unit; (**C**)—photograph of the prism shearing process; and (**D**)—shear schemes of the rock prism in plan and elevation views.
## 2 Materials and Methods  
### Sampling  
Serial images of 32 patients who underwent CBCT three times for mandibular bone block grafts between 2017 and 2019 were retrospectively collected, which were available from our dental hospital. All surgeries were performed by two clinicians following the same surgical protocol. Of the 60 cases, 32 had clear CBCT images from all the three scans and were, thus, selected for further analysis. The CBCT images were obtained before surgery (T0), 1 day after surgery (T1), and 4 months after surgery (T2). The mean time between T1 and T2 was 4.6 \(\pm\) 1.65 months. The CBCT images obtained 1 day after the surgery (T1) were used to evaluate the amount of harvested bone and determine whether the intraoperative fixation was adequate. All the patients were treated with intraoral mandibular body bone grafts because of severe alveolar bone loss in the expected implant placement site. Patients with serious health problems, such as uncontrolled diabetes mellitus or hypertension, and heavy smokers were excluded from the study. After performing a crestal incision and elevation of the mucoperiosteal flap, the mandibular body was exposed. Osteotomies were performed from the buccal aspect of the first and second mandibular molars, and blocks of the bone were harvested (Figure 1A-C). After placement of the mandibular bone blocks onto the defects, they were fixed using mini-screws. Hemostasis was performed for the donor sites without grafting, followed by primary wound closure (Figure 1D). CBCT images were obtained using PaX-i3D (Vatech Co., Hwaseong-si, South Korea) at 105-114 kVp and 5.6-6.5 mA with a 160 \(\times\) 160 mm field of view and a 0.2 mm voxel size. The CBCT images were saved in DICOM file format using DICOM 3.0 software on a computer with Windows 10 operating system (Intel Core i7-4770, 32 Gigabyte). Figure 2 shows the flowchart of the subsequent image processing and analysis procedure.
# An Architecture for the Performance Management of Smart Healthcare Applications
###### Abstract  
The sixth-generation (6G) network intends to revolutionize the healthcare sector. It will offer smart healthcare (s-health) treatments and allow efficient patient remote monitoring, exposing the high potential of 6G communication technology in telesurgery, epidemic, and pandemic. Healthcare relies on 6G communication technology, diminishing barriers as time and space. S-health applications require strict network requirements, for instance, 99.999% of service reliability and 1 ms of end-to-end latency. However, it is a challenging task to manage network resources and applications towards such performance requirements. Hence, significant attention focuses on performance management as a way of searching for efficient approaches to adjust and tune network resources to application needs, assisting in achieving the required performance levels. In the literature, performance management employs techniques such as resource allocation, resource reservation, traffic shaping, and traffic scheduling. However, they are dedicated to specific problems such as resource allocation for a particular device, ignoring the heterogeneity of network devices, and communication technology. Thus, this article presents PRIMUS, a performance management architecture that aims to meet the requirements of low-latency and high-reliability in an adaptive way for s-health applications. As network slicing is central to realizing the potential of 5G-6G networks, PRIMUS manages traffic through network slicing technologies. Unlike existing proposals, it supports device and service heterogeneity based on the autonomous knowledge of s-health applications. Emulation results in Mininet-WiFi show the feasibility of meeting the s-health application requirements in virtualized environments.  
s 2020, 20, 5566 ; doi:10.3390/s20195566
###### Abstract
Certainly, the use of CI for an autonomous system does not simulate an individual's expertise in decision-making; however, it generally enters a new dimension of intelligence by means of the cooperative understanding of sensory inputs and language patterns driven by the autonomous application through video, audio, text, and so on. In the late 2010s, deep learning (DL) models were used for feature extraction [8]. A hand-crafted feature is optimum since it expresses and extracts feature values through the intended model-based knowledge of the researcher. The DL method could automate feature extraction and is efficient for image detection [9]. DL has attained remarkable outcomes in overall object-detection competitions and the use of image detection needed for autonomous driving (namely semantic segmentation and object recognition) [10]. Numerous object-detection models are available in the literature, which are limited in several aspects [9; 10]. Different DL-based models have been developed that could consider different objects, such as dynamic road environments, background lighting conditions, position, dimension, colour, shape, size, etc. Autonomous vehicles require a high capacity for the real-time object detection of road targets. At the same time, the DL models involve several hyperparameters (learning rate, batch size, momentum, and weight decay), and determining the optimal parameters in such a high dimensional space is not a trivial challenge. To resolve this issue, metaheuristic optimization-based hyperparameter tuning techniques are needed. Therefore, this study focuses on the object detection and classification model by the use of hyperparameter tuned DL models. This study introduces an efficient computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique primarily derives a new object detection model using a krill herd (KH) algorithm with a multi-scale Faster RCNN model. Additionally, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was utilized to categorize the recognized objects effectively. The simulation analysis of the CIWHO-ORC technique was examined against benchmark datasets, and the results were inspected under several evaluation measures.
## 2 Experimental Setup  
Image data acquisition and processing are carried out on the experimental setup as shown in Figure 1a. The main elements of the setup are the computer, belt conveyor, cameras, lighting, Arduino microcontroller, laser generator, proximity sensor sensor, and motor controller. The laser generator is installed vertically above the conveyor and the conveyor belt, located in the center of the whole frame width direction, which is used to produce two laser lines perpendicular to each other, as shown in Figure 1a (Laser 1 and Laser 2). The Arduino microcontroller and a proximity sensor are used to detect the presence of material on the conveyor belt and signaled a camera to acquire the images. The acquired images are processed both offline and in real-time using Matlab and Labview. Figure 1b shows the structure model of the conveyor tail drum. Here are four main parts: roller shaft, bolt, bearing chock, and adjusting bracket. The adjusting bracket is fixed on the conveyor frame, the roller shaft is mounted in the bearing chock, and the bolt is connected to the bearing chock by a nut fixed to the bearing chock. Then the movement of the bearing chock in the adjusting bracket can be realized by rotating the bolt, so as to realize the movement of roller shaft.  
In this paper, the feasibility of the device and algorithm is verified by the above experimental setup. Each experiment was divided into four stages: running normally, manufactured running deviation artificially, corrected running deviation artificially, and running normally. In the running normally stage, the conveyor was correctly identified, with no deviation phenomenon. During the test, the roller shaft of the tail drum of the conveyor was adjusted manually by turning the bolt clockwise, so that its axis was not perpendicular to the centerline of the conveyor frame, to cause the deviation. After running for a period of time, we turn the same bolt counterclockwise to correct the conveyor belt back to the normal position. We only adjusted the bolt on one side of the conveyor instead of the two sides during the experiment. The reliability of the algorithm is verified by the above experimental device.
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 2 System Model and Basic Assumptions
### A&F Signal Model
At the destination node, the received signal (4) is processed by the RF matrix \(\mathbf{M}_{\mathrm{DR}}\in\mathbb{C}^{\mathrm{N}_{\mathrm{R}}^{\mathrm{D}} \times\mathrm{N}_{\mathrm{R}}^{\mathrm{D}}}\) and baseband matrix \(\mathbf{T}_{\mathrm{BB}}\in\mathbb{C}^{\mathrm{N}_{\mathrm{A}}\times \mathrm{N}_{\mathrm{R}}^{\mathrm{D}}}\), which are equivalent to the inverse processing performed by the NR-UE. The received signal is converted to baseband frequency and the baseband signal and physical channels are demodulated and decoded, respectively. Finally, the estimated signal of the destination can be expressed as  
\[\mathbf{\hat{S}} =\mathbf{T}_{\mathrm{BB}}^{\mathrm{H}}\mathbf{M}_{\mathrm{DR}}^{ \mathrm{H}}(\mathbf{H}^{2}\mathbf{X}_{\mathrm{AF}}+\mathbf{N}_{\mathrm{AF}}^ {2})\] \[=\mathbf{T}^{\mathrm{H}}\mathbf{H}^{2}\mathbf{M}_{\mathrm{RT}} \mathbf{G}_{\mathrm{AF}}\mathbf{M}_{\mathrm{RR}}^{\mathrm{H}}\mathbf{H}^{1} \mathbf{G}\mathbf{S}+\mathbf{N}_{\mathrm{D}}, \tag{5}\] \[=\mathbf{S}+\mathbf{N}_{\mathrm{D}}\]  
where \(\mathbf{T}=\mathbf{T}_{\mathrm{BB}}\mathbf{M}_{\mathrm{DR}}\), \(\mathbf{G}=\mathbf{M}_{\mathrm{ST}}\mathbf{G}_{\mathrm{BB}}\), and \(\mathbf{N}_{\mathrm{D}}=\mathbf{T}^{\mathrm{H}}(\mathbf{H}^{2}\mathbf{M}_{ \mathrm{RT}}\mathbf{G}_{\mathrm{AF}}\mathbf{M}_{\mathrm{RR}}^{\mathrm{H}} \mathbf{N}_{\mathrm{AF}}^{\mathrm{I}}+\mathbf{N}_{\mathrm{AF}}^{2})\). In (5), the first term is the desired signal and the second term is the noise, which denotes the noise in the backhaul and access links at the co-operative system, respectively.
# Multi-Objective Order Scheduling via Reinforcement Learning
## 1 Introduction
To address the challenges of multi-objective order scheduling, we need to answer the following three questions: (1) how to formulate the multi-objective order scheduling problems? (2) how to construct policy for perform order scheduling? and (3) how to obtain a Pareto-optimal policy of order scheduling to meet the multiple requirements of order scheduling? For the first question, we formulate the problem as a Multiple-Objectives Markov Decision Process (MOMDP), in which we represent the states using a disjunctive graph representation. This discrete graph structure effectively captures the characteristics  
Figure 1: The (**left**) picture presents traditional reinforcement learning with a single objective, while the (**right**) picture presents multiple-objective reinforcement learning. of the order scheduling problem. For the second question, we introduce a Graph Neural Network (GNN) to construct a robust policy network. The GNN utilizes fixed-dimensional embeddings to represent nodes in the disjunctive graph, enabling effective decision-making for order scheduling. By learning the graph structure data, extracting relevant features, and exploring patterns within the graph, the GNN provides valuable insights. For the last question, we employ policy gradient methods to train the network and overcome the limitations associated with traditional approaches i order to obtain high quality Priority Dispatching Rules (PDRs). However, in multi-objective reinforcement learning, designing the reward function with subjective weight settings for each index often leads to imbalanced objective optimization. To address this issue, our algorithm employs a linear embedding technique that achieves a more balanced solution. Furthermore, to ensure the best optimization of all objectives, we introduce the convex hull algorithm to guarantee that the weight vector yields a maximized linearly scalarized value function. To the best of our knowledge, no previous work has specifically addressed the same problem as we do here. In this paper, we focus on the unique requirements of communication work order dispatching systems while considering the needs of both operators and customers. By developing a multi-objective deep reinforcement learning approach, it is possible to objectively optimize each crucial objective based on the available data.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 4 System Verification
### Parallelism Measurement  
The verification of the proposed parallelism system was also conducted by using one square block, one dial indicator, and two linear guideways (both 400 mm) in a well air-conditioned laboratory. Figures 18 and 19 show the verification setup of the square block and the proposed system, respectively.  
Figure 17: Verification setup of perpendicularity measurement using the proposed system.  
Figure 18: Verification setup of parallelism measurement using a square block.  
The linear guideway was first aligned parallel to each other using a square block and then measured by the proposed system, as shown in Figures 17 and 18. The average parallelism error measured by the square block and the proposed system were 11.3 \(\pm\) 0.5 \(\upmu\)m and 11.2 \(\pm\) 0.5 \(\upmu\)m, respectively.  
Table 4 shows the verification results of the long-travelling geometric error measurement system; obviously, the measurement difference \(\varepsilon_{Dif}\) between the commercial instrument/tool and the proposed system was very small, except for the vertical straightness error (>5 \(\upmu\)m). This phenomenon may originate from the vertical ground vibration of the laser source, which mainly depends on the measurement environment, as discussed in the above uncertainty analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Measurement** \\ **Distance** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Commercial Instrument/Tool** \\ **Eroro (\(\varepsilon_{L}\), \(\upmu\)m)** \\ \end{tabular} & \
\begin{tabular}{c} **Proposed System** \\ **Repequability (\(\varepsilon_{L}\), \(\upmu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Droped System** \\ **Ero (\(\varepsilon_{L}\), \(\upmu\)m)** \\ \end{tabular} \\ \hline Straightness (Horizontal) & 1000 & 7.2 & \(\pm\)0.2 & 8.2 & \(\pm\)0.5 & 1.3 \\ Straightness (Vertical) & 1000 & 5.3 & \(\pm\)0.3 & 9.2 & \(\pm\)1.7 & 5.3 \\ Perpendicularity & 400 & 4.0 & \(\pm\)0.8 & 2.7 & \(\pm\)0.9 & 1.2 \\ Parallelism & 400 & 11.3 & \(\pm\)0.5 & 11.2 & \(\pm\)0.5 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Verification results of the proposed system.  
Figure 19: Verification setup of parallelism measurement using the proposed system: (**a**) Reference axis and (**b**) follower axis.
# An AC/DC LED Driver with Unity Power Factor and Soft Switching
## 2 Proposed Circuit Topology and Operation Analysis
### Operation Analysis
#### 2.2.1 Mode I (\(t_{0}<t<t_{1}\); in Figure 3a)  
Prior to this mode, \(S_{1}\) is on and the DC link voltage \(V_{dc}\) supplies the buck-inductor current \(i_{b}\). Mode I begins at the instant of turning off \(S_{1}\). Thereby, \(i_{b}\) would discharge the parasitic capacitance (\(C_{DS2}\)) between the drain and source of \(S_{2}\). As soon as the voltage across \(C_{DS2}\) decreases to \(-0.7\) V, \(D_{S2}\) is forward-biased to conduct \(i_{b}\). The gated voltage \(v_{GS2}\) becomes high level after the short deadtime and the current flowing through \(D_{S2}\) diverts to flow from the source to drain of the MOSFET \(S_{2}\). By this  
Figure 2: Proposed AC/DC LED driver.  
way, the voltage across \(S_{2}\) is clamped at near zero voltage. The voltage across the buck inductor \(L_{b}\) is minus of the output voltage \(V_{o}\) and hence, \(i_{b}\) decreases linearly.  
\[v_{b}(t) = -V_{o} \tag{1}\] \[i_{b}(t) =i_{b}(t_{0})-\frac{V_{o}}{L_{b}}(t-t_{0}) \tag{2}\]  
Since \(S_{2}\) is on, the voltage across the buck-boost inductor \(L_{p}\) is equal to \(v_{rec}\).  
\[v_{p}(t) = v_{rec}(t)=V_{m}|sin(2\pi f_{L}t)|, \tag{3}\]  
where \(f_{L}\) and \(V_{m}\) respectively represent the frequency and the amplitude of the input-line voltage.  
Since the buck-boost converter is operated at DCM, the buck-boost current \(i_{p}\) linearly increases from zero.  
\[i_{p}(t) = \frac{v_{rec}(t)}{L_{p}}(t-t_{0})=\frac{V_{m}|sin(2\pi f_{L}t)|}{ L_{p}}(t-t_{0}) \tag{4}\]  
In this mode, \(i_{b}\) is higher than \(i_{p}\). There are two current loops for \(i_{b}\). Parts of \(i_{b}\) flow through \(S_{2}\), while the rest of \(i_{b}\) is equal to \(i_{p}\), which flows through \(L_{p}\) and the line-voltage source. This mode ends when \(i_{p}\) becomes higher than \(i_{b}\).
## 4 Conclusions  
The gait classification model developed in this work demonstrates a diagnosis accuracy of 95.40% in diagnosing osteoporosis using force profiles derived from a standard smart walker. The Random Forest classifier was used to obtain this performance, and it was further improved by adjusting the hyperparameters for the best results.  
The experimental results confirm the possibility of utilizing machine learning to distinguish between normal and osteoporotic walking patterns with the help of assistive devices. Additionally, these findings highlight the potential for improving diagnostic methods. The utilization of machine learning in gait analysis shows potential for the advancement of more accurate and effective diagnostic instruments, not only for osteoporosis but also for a wider range of musculoskeletal illnesses.  
As we explore the cutting-edge field of machine learning-based gait analysis, it is crucial to highlight the importance of further study. Thorough validation of these findings and an investigation into the feasibility of deploying machine learning-based gait analysis in clinical settings are essential next stages. These efforts will not only strengthen the dependability of our model but also facilitate groundbreaking progress in the early detection and treatment of osteoporosis and other musculoskeletal conditions.  
Conceptualization, N.E., S.J.K., S.-D.S. and F.A.; methodology, N.E., S.J.K., M.A. and E.T.; software, N.E., R.P., A.P., F.A. and S.J.K.; validation, E.T., R.P., A.P. and F.A., formal analysis, N.E., S.J.K., F.A. and M.A.; investigation, N.E., S.J.K. and F.A.; resources, S.J.K., F.A., M.A. and N.E.; data curation, N.E., S.J.K., A.P. and R.P.; writing--original draft preparation, N.E.; writing--review and editing, S.-D.S., R.P. and F.A.; visualization, S.J.K., M.A. and N.E.; supervision, S.J.K. and F.A.; project administration, N.E., S.J.K. and F.A.; funding acquisition, S.-D.S., E.T., A.P. and R.P. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Erasmus+, with activity ERASMUS-EDU-2023-CBHE-STRAND-2, under the proposed title of 'Capacity building in biomedical engineering education for digital transformation and industry 4.0/5.0 technologies (also known as BIOMED5.0)'.  
The study was conducted in accordance with the Declaration of Ziauddin University and approved by the Institutional Review Board (or Ethics Committee) of Ziauddin University (the protocol code is ERC/ZUFESTM/002 and the date of approval is June 2023) for studies involving humans.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors. The dataset is not publicly available due to the ethical privacy of participants involved in this study.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest to report regarding the present study.