esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Influence of the Sensor Position on the Balance Predictors for Different Activity Types  
The logistic regression models conducted separately for two activity types (static and dynamic) and different sensor placements revealed varying relationships between movement characteristics and balance. Table 4 summarizes predictors for each sensor location and task type combination. Our observations were as follows:  
* For IMU attached to the head, vertical acceleration (Z-axis) was associated with better balance during static tasks but poorer balance during dynamic tasks, suggesting different control strategies for different task contexts. The overall results were different from when using the IMU at the lower back.
* For IMU attached at the torso, lateral acceleration (Y-axis) was negatively correlated with fall risk in dynamic tasks, while the movement volume and anterior-posterior jerk (X-axis) were positively correlated with fall risk.
* Larger movement volume from the IMU attached at the torso was associated with better balance in only dynamic tasks but not static ones. This was different from the results for the IMU at the lower back when pooling all tasks together.
* Total path length was an important predictive parameter when attaching the IMU to the torso, similarly to the lower back. However, the total path length could not help predict the risk of falls if the IMU was attached to the head.  
These findings emphasize the importance of considering both sensor placement and task context when interpreting IMU data for balance assessment. These varying relationships between movement characteristics and balance across different body locations and task types suggest that a comprehensive balance assessment may incorporate multiple sensor locations, and that static and dynamic tasks should be analyzed separately.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Motion Law Analysis of the PV Panel Cleaning Robot  
The motion of the PV panel cleaning robot follows a certain regularity. It always moves from the left side (right side) of one PV panel to the right side (left side) with a particular attitude. It then spans the distance between PV panels to reach the left side (right side) of another PV panel before finally moving to the right side (left side) with a different attitude. The PV panel cleaning robot operates in a cyclical manner, moving back and forth to complete its cleaning tasks. This process occurs sequentially through three distinct states: translation, spanning, and translation again. It may be beneficial to define the law of motion of the cleaning robot, which sequentially executes three phases, translation, spanning, and spanning, as a work cycle. The start (end) of a work cycle corresponds to the end (start) of  
Figure 1: The physical model: (**a**) the three-dimensional model of the PV panel cleaning robot; (**b**) the physical model of the PV panels.  
the previous (next) work cycle, while the end of a work cycle marks the commencement of the next one.  
In this paper, the relative positions between PV panels are found to be of different sizes based on the actual inspection of desert PV power plants, as illustrated in Figure 2. One of the most complex situations can arise between two neighboring photovoltaic (PV) panels. This scenario involves angular projections on three coordinate planes and translations along three coordinate axes. Specifically, the cleaning robot may rotate by angles \(\psi\), \(\theta\), and \(\phi\) around the X, Y, and Z axes of a fixed coordinate system, as well as translate along the X, Y, and Z axes. It is during this relatively complex phase of spanning motion that the non-coplanar trajectory must be analyzed and resolved. A diagram illustrating the pose transformation is presented in Figure 3.
## 3 Results and Discussion
### SVR Model Results  
Figure 22 offers an analysis of the SVR model's predictive performance for \(\sigma_{min}\) and \(\gamma_{max}\) across both training and test datasets. The training set exhibited a coefficient of determination (R-squared) of 0.9554 for \(\sigma_{min}\) and 0.9967 for \(\gamma_{max}\), along with an RMSE of 17.26 for \(\sigma_{min}\) and 0.069 for \(\gamma_{max}\), and an MAE of 10.79 for \(\sigma_{min}\) and 0.0237 for \(\gamma_{max}\). Despite being less accurate than previous models in the training set, SVR demonstrated acceptable predictive capabilities. In the test set, SVR performed slightly better than DTR and nearly matched RFR's performance, evident in R-squared values of 0.972 for \(\sigma_{min}\) and 0.9936 for \(\gamma_{max}\). The RMSE and MAE values (14.6 and 11.37 for \(\sigma_{min}\), 0.102 and 0.0488 for \(\gamma_{max}\)) confirmed SVR's effectiveness in providing reasonably accurate predictions on independent data. However, SVR exhibited some discrepancies from the ideal line, highlighting its limitations compared to RFR, GBR, and XGBo models. While SVR can deliver accurate predictions, there were instances where data points deviated significantly from the ideal line, akin to the limitations observed in the DTR model.  
Figure 21: Comparing the actual and predicted values for the training and test datasets in the XGBoost model for (**A**) \(\sigma_{min}\) and (**B**) \(\gamma_{max}\).  
In direct comparison to RFR, GBR, and XGB models, SVR fell short in its predictive ability. Despite providing reasonably accurate predictions, the noticeable deviations from the ideal line suggest caution in using SVR for stress and dry unit weight predictions. Given its relative inferiority, it is advisable to prioritize RFR, GBR, and XGB models for more accurate and reliable predictions in applied stress and dry unit weight scenarios.
# A Spatial 4-DOF Laser Collimation Measurement System
## 4 Experiment and Results  
To verify the reliability and validity of the method, an experimental setup was established according to the schematic diagram shown in Figure 1, as shown in Figure 14. CMOS uses Hikvision's MV-CS050-10 GM with a resolution of 2048 \(\times\) 2448, an imaging area of 8.8 mm \(\times\) 6.6 mm (H \(\times\) V), and a pixel size of 3.45 \(\upmu\)m \(\times\) 3.45 \(\upmu\)m. The laser uses Sorebo's LP633-SF50 with a laser center wavelength of 633 nm, the collimator uses Lubang Optoelectronics' RFCAG-3.9-PC, and the theoretical spot diameter is 3.9 mm. The distance between the light source and the moving unit is 500 mm. Distance and focal length do not affect the measurement accuracy, only the measurement resolution. A series of experiments were carried out under laboratory conditions.
## 2 Background and Related Work
### Risk Management Frameworks  
This section presents two risk management frameworks the IEC 80001-1:2010 and the AAMI TIR57 which are widely used for developing healthcare applications. This section also outlines why they are not directly applicable to WBAN applications, even though they are specific to healthcare applications.  
* IEC 80001-1:2010: IEC 80001-1--Application of risk management for IT-networks incorporating medical devices was introduced in 2010 to address risks associated with medical devices when connecting to IT-networks [19]. The framework aims to help organizations define the risk management roles, responsibilities, and activities to achieve medical device safety and security. IEC/TR 80001-2-2 [20] is a technical report that provides background processes to address security risk related capabilities for connecting medical devices to IT-networks.
* AAMI TIR57: AAMI TIR57 provides guidance for manufacturers to perform information security risk management to address security risks within medical devices [21]. AAMI TIR57 was developed with guidelines provided by ISO 14971 [22] and NIST SP 800-30 Revision 1--security risk management process developed for traditional IT systems [23]. The goal of AAMI TIR57 is to assist manufacturers with the following key outcomes: (1) identification of assets, threats and vulnerabilities, (2) estimation and evaluation of associated security risk, (3) selection of security risk controls and (4) monitoring the effectiveness of the security risk controls.  
The risk management frameworks mentioned above are not directly applicable to WBAN applications for the following reasons:  
* IEC 80001-1:2010 was primarily developed for applications which operate within a healthcare delivery organization's IT-network, whereas WBAN applications may operate in a public, open network using short-range communication media.
* A WBAN application consists of resource constrained sensor devices which have limited memory and computational power and cannot accommodate complex security solutions like traditional healthcare applications. Neither framework provides any guidance for managing security and privacy risks for resource constrained sensor devices.
###### Abstract
The problem of evaluating interference in radio links with beamforming for vehicles is complicated by following factors: significant (tens of dB) dependence of the instantaneous signal-to-noise-plus interference ratio (SINR) on the current spatial [1] and angular [16] separation of the VUE; interdependence of channel coherence time and beamwidth in vehicular channels [39; 40; 41]; dependence of beamformed radio link capacity on UE positioning uncertainty [42]; employed beam tracking approaches [43; 44]; beam alignment techniques [45; 46; 47; 48] and location-aided channel estimation [49; 50; 51; 52; 53]. Recent investigations [16; 17; 18; 19; 20; 21; 22], concerning LAB in 5G UDN for mmWave links, treated only stationary gNB and UE cases. The aim of current research is to evaluate interference level in 5G mmWave UDN scenarios with Location-Aware Beamforming, when gNB perfectly knows VUE coordinates and points its pencil beam directly to VUE during its movement in various scenarios, which are characterized by terrestrial and angular separation. One of the first works, concerning LAB for fixed UE [54], revealed, that using position enables in creating transmit precoding BF vector for line-of-sight (LOS) scenarios without signaling overhead. In particular, proposed approach was to limit beamforming to spatial area, bounded by angular sector with inaccuracy of the GPS positioning up to 20 m.  
Fundamental purpose of the project \"Location aware beamforming in mm-wave band ultra-dense radio access networks\" [55] is to develop scientifically grounded methodology for ARP control, based, on the positioning of UE for scenarios of a separate link, two links, a set of links of one cell and a set of UDN cells. To numerically substantiate the terrestrial and angular separation of neighboring transceivers in a given set of conditions for networks development and scenarios for the device operation, signal-to-interference ratio (SIR) evaluation should be taken into account, which will make it possible to scientifically substantiate the theoretical and practically realizable limits of separation for simultaneously operating in the spatial multiplexing mode transceiver devices with LAB in 5G and B5G UDN. Motivated by the described state-of-art, this work aims to evaluate interference in 5G mmWave UDN with LAB for scenarios of two links. The paper is organized in the following order. Section 2 describes background for location-aware beamforming in 5G ultra-dense networks. Section 3 presents publicly released open-source simulator for LAB in 5G UDN and performs interference evaluation through link level simulation. Finally, paper is concluded in Section 4.
# K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Name** & & **K-RBBSO** & **RBBSO** & **SA** & **ABC** \\ \hline \multirow{4}{*}{Iris} & Average & 0.553 & 0.553 & 0.627 & 0.178 \\  & S.D. & 0.000 & 0.000 & 0.000 & 0.260 \\  & Min & 0.553 & 0.553 & 0.627 & \(-\)0.020 \\  & Max & 0.553 & 0.553 & 0.627 & 0.586 \\ \hline \multirow{4}{*}{Wine} & Average & 0.564 & 0.564 & 0.582 & \(-\)0.009 \\  & S.D. & 0.002 & 0.002 & 0.000 & 0.028 \\  & Min & 0.562 & 0.562 & 0.582 & \(-\)0.031 \\  & Max & 0.567 & 0.567 & 0.582 & 0.059 \\ \hline \multirow{4}{*}{Glass} & Average & 0.556 & 0.553 & 0.027 & \(-\)0.036 \\  & S.D. & 0.051 & 0.059 & 0.000 & 0.002 \\  & Min & 0.479 & 0.450 & 0.027 & \(-\)0.039 \\  & Max & 0.595 & 0.595 & 0.027 & \(-\)0.033 \\ \hline \multirow{4}{*}{Vowel} & Average & 0.381 & 0.363 & \(-\)0.009 & N/A \\  & S.D. & 0.010 & 0.021 & 0.000 & N/A \\  & Min & 0.363 & 0.331 & \(-\)0.009 & N/A \\  & Max & 0.392 & 0.388 & \(-\)0.009 & N/A \\ \hline \multirow{4}{*}{Cloud} & Average & 0.461 & 0.458 & \(-\)0.006 & N/A \\  & S.D. & 0.014 & 0.012 & 0.000 & N/A \\  & Min & 0.449 & 0.450 & \(-\)0.006 & N/A \\  & Max & 0.478 & 0.478 & \(-\)0.006 & N/A \\ \hline \multirow{4}{*}{CMC} & Average & 0.441 & 0.442 & \(-\)0.002 & N/A \\  & S.D. & 0.002 & 0.002 & 0.000 & N/A \\ \cline{1-1}  & Min & 0.439 & 0.439 & \(-\)0.002 & N/A \\ \cline{1-1}  & Max & 0.444 & 0.444 & \(-\)0.002 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimated values of the silhouette coefficient. higher silhouette coefficient corresponding to more data types. In contrast, the SA algorithm and ABC algorithm obtained relatively low evaluation values corresponding to a large proportion of the data. Moreover, the ABC algorithm was unable to derive any results corresponding to Vowel, Cloud, and CMC data. The SA algorithm also generated negative evaluation values corresponding to Vowel, Cloud, and CMC data. The termination condition was removed for the SA and ABC algorithms corresponding to Glass, Vowel, Cloud, and CMC data, and the algorithms were repeated until results comparable to those of RBBSO and K-RBBSO were achieved. However, even after 12 h, the results derived were not appreciably better than those listed in the table. The execution times of the K-RBBSO, RBBS, S.A, and ABC algorithms are presented in Table 3. K-RBBSO was fast, corresponding to all data. Because of its specialized termination condition based on its algorithm's characteristics, the SA algorithm was also fast. However, as evidenced in Table 2, it was unable to derive a suitable evaluation value. The ABC algorithm was unable to obtain results corresponding to Vowel, Cloud, and CMC data. However, the quantity of data was proportional to the difference between the execution times of K-RBBSO and RBBSO. Corresponding to CMC data, K-RBBSO exhibited an execution time of 2096.927 s, and RBBSO recorded an execution time of 2932.954 s.
# An Interoperable UMLS Terminology Service Using FHIR
## 3 Background
### FHIR Standard
#### 3.2.2 FHIR ConceptMap  
The FHIR ConceptMap captures the mapping between a set of codes in a code system (e.g., SNOMED) to one or more codes in another coding system (e.g., MeSH, SNOMED, etc.). Figure 4 shows the ConceptMap as a UML class diagram from FHIR documentation [34]. The ConceptMap has a variety of attributes to describe the purpose of the mappings. A group (Group class) captures the mapping between the source code (SourceElement class) and the target code(s) (TargetElement class), and ConceptMap can have multiple groups. The equivalence attribute of the TargetElement class captures the exact nature of the mapping between the codes. Experts can specify more information about the mapping using the OtherElement class. For example, the SourceElement can be Atrial Fibrillation (code-C0004238, display--Atrial Fibrillation) with UMLS as its source, TargetElement is AFib (code 427.31, display--Atrial Fibrillation) from target ICD9 CM and these codes are equal (equivalence in TargetElement).
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Packet Size
#### 4.1.6 Simulation Results of Range Propagation Model regarding Packet Size  
The mRSU shows better performance in terms of the packet delivery ratio in the range propagation model since the range propagation model is based on the principle of delivering specific power in a specific range, so the probability of packet reception is higher in the range propagation model, especially in the mRSU, where a certain RSU moves together with the nodes.
## 4 Model Verification  
The model is verified for different soil conditions, including uniform sand, loose sand overlying clay, and dense sand overlying clay. All the centrifuge tests used for verification were conducted at the University of Western Australia (UWA), which normally uses super fine sand and kaolin clay. As the sand used in UWA centrifuge tests has a friction angle (\(\phi_{c}=32\sim 34\)) very close to that of [PERSON] et al. (2013) (\(\phi_{c}=33\)), \(k_{shear}^{p}=11\%\) and \(k_{shear}^{c}=14\%\) are selected for the simulation similarly. Moreover, the kaolin clay used in UWA centrifuge tests has a sensitivity of 3 ([PERSON] et al., 2014).
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 1 Introduction  
In recent years, traffic congestion and accidents have become important global issues caused by road traffic [1]. To deal with these problems, the Cooperative Intelligent Transport System (C-ITS) is attracting a lot of attention, and the Wireless Access in Vehicular Environments (WAVE) has been developed and researched to support C-ITS [2; 3; 4; 5]. The WAVE system is a radio communication system that provides standardized services and interfaces to enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications [7; 8]. In WAVE, the physical (PHY) and medium-access control (MAC) layers are defined using the IEEE 802.11p and IEEE 802.11p/IEEE 1609.4 standards, respectively [9; 10]. This system has the potential to significantly increase road safety, and it is also expected to solve many traffic issues. In practical C-ITS applications, the fairness andreliability of the communications between vehicles using the WAVE system is very important. Fairness problems arising from IEEE 802.11p MAC affects the throughput and packet drop rate; thus, various MAC schemes have been proposed to solve these problems [11]. In terms of reliability, the accuracy of channel estimation plays a critical role in improving the reliability of communications in vehicular environments because it generally affects the system performance, including equalization, demodulation, and decoding performance.  
In vehicular environments, wireless channels have a high Doppler shift and large delay spread [12; 13; 14]. As a result, the channel shows time and frequency selective fading characteristics due to motion and multipath components. The channel variations that occur in vehicular environments affect the transmitted packet; thus, the channel coefficient can significantly change between the start and end of the packet. To adapt to these rapid channel variations, IEEE 802.11p PHY was designed based on IEEE 802.11a PHY [15]. The 802.11p has a bandwidth of 10 MHz which is halved bandwidth of 802.11a and the carrier frequency is 5.9 GHz, higher than 802.11a. Except for the bandwidth and carrier frequency, the 802.11p PHY has the same structure as 802.11a; thus, 802.11p uses four pilot subcarriers in each symbol portion of the packet. However, these pilot subcarriers are insufficient for tracking the channel variations in vehicular environments [12; 13]. This leads to poor system performance in realistic vehicular environments. Thus, robust and accurate channel estimation is necessary to obtain excellent communication performance.
# Predicting Hard Disk Failure by Means of Automatized Labeling and Machine Learning Approach
## 4 Proposed Approach
### Automated Labeling Stage
#### 4.1.3 Pre-Processing  
To suitably train the proposed method, all incorrect measures associated with errors, missing values, exceeding values, etc., must be preliminarily removed. The collected measurements may be corrupted as errors caused by the probe, in the internal network, in the file system, etc., may occur. Another typical example of a corrupt measurement can be an out-of-range temperature measurement, which can happen because the temperature sensor of a hard disk broke, but this has not affected the correct functioning of the storage device. Moreover, a disk can be turned off for several days before being switched on again. Disks disappeared from the list of monitored devices and belonging to multiple removals (due  
Figure 3: Hard disks’ lifetimes in a decommissioned host at time \(t_{3}\).  
to maintenance, dismantling of hosts machine or similar) have to be excluded from the set labeled as broken units. In addition, entire measures may be missing in some days because of software errors, busy firmware, database problems, etc.  
An example of the considered conditions are schematically presented in Figure 3; in particular, the hard disk \(d_{2}\) (more specifically, its SMART tuples) are not present during a time interval, a gap.  
Let us define \(S\) as the whole dataset of available hard disks in the storage system, and let \(I_{\mathrm{g}}:=[g_{1};g_{2}]\), with \(g_{2}>g_{1}+1\) be a generic days gap experienced during SMART tuples acquisitions. This way, the subset \(S_{r}\in I_{\mathrm{g}}\) of hard disks replaced during the gap has to be removed from the dataset because no assumptions can be made about their possible failure and the classification rule for broken disks is not applicable; a specific hard disk is dropped from the list of monitored devices since its host machine has been completely removed, and hard disks possibly operating have also been dismissed. On the other hand, disks normally running after the gaps are kept in the dataset. Disks replaced during an interval \(S_{r,k}\in I_{\mathrm{g}+k}:=[g_{2};g_{2}+k]\) need to be excluded as well because a replacement \(\hat{S}_{r,k}\) that happened in the interval \(I_{\mathrm{g}+k}\) does not have the \(k\) days of measures needed for a consistent train dataset. Thus, the dataset becomes \(S_{d}=S-\{S_{r}\in I_{\mathrm{g}},I_{\mathrm{g}+k}\}\).  
Afterward, a classification needs to be performed on the dataset according to Definition 1. The system must be able to recognize and process the typical measures of the last \(K\) useful days before disk replacement. To this aim, a preliminary analysis has to be carried out to understand how many days before the replacement, the SMART measures of a disk, typically, begin to significantly change.
## 5 Influence of the Seismic Intensity and Structural Span Number  
### Span Number of the RC-Frame  
The influence of the span number on the SSI effect has commonly been neglected while investigation of the influence of the span number on the SSI effect is of great significance to accurately evaluating the seismic demands of structures with different spans. To investigate the influence of the span number on the seismic demands of different structures, 3-story, 9-story, and 15-story RC-frames with different spans were designed and are shown in Figure 15, in which the span numbers were, respectively, set as 1, 2, 3, 4, and 5; and the periods of the fixed-base structure with different spans and the same height were the same. The seismic record RSN-587 with a peak acceleration of 100 gal was used as seismic input, and the values of vs. were set as 150 and 250 m/s, respectively.  
It can be seen from Figure 16 that the maximum influences of the span number on the values of \(e_{bs}\) and \(e_{sd}\) are, respectively, up to 25% and 40% when vs. is 150 m/s and reduced to 15% and 30% when vs. is 250 m/s. With the increase in the span number, the values of \(e_{bs}\) and \(e_{sd}\) decrease in most cases and increase in some specific cases. The results point out that the influence of the span number on the SSI effect can be significant under the condition of soft soil, which decreases with a lager soil shear velocity in general. The influence of the span number on the maximum base shear and story drift has no obvious laws, which is closely related to the structure frequency, soil shear wave velocity, and spectral characteristic of the seismic record.  
Figure 14: Comparisons of \(e_{sd}\) and \(e_{bs}\) under different pile diameters.  
Figure 15: Finite element models of RC-frames with different spans.