esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cellular Network Reconstruction and Validation
### Considered Datasets  
_OpenCellID_. It is a collaborative community project that collects measurements and cell towers' data by means of an API and a ready-to-use mobile phone application. In spring 2017, the project was acquired by Unwired Labs, a geolocation service provider enterprise. This step changed privacy policies and also the kind of published data. Regardless, we worked on a dataset downloaded from the site project in April 2017. The data are in _csv_ tabular format, and each measurement is characterized by the following attributes: _mcc_, _net_, _area_, _cell_, _lon_, _lat_, _signal_, _measured_, _created_, _rating_, _speed_, _direction_, _radio_, _ta_, _rnc_, _cid_, _psc_, _tac_, _pci_, _sid_, _nid_, and _bid_. Clearly, depending on the specific technology, some features may not be available, and their meanings may even be different. The problem of missing data is also exacerbated by the fact that the hetereogeneous devices which contributed to the dataset may have provided different subsets of information. The original dataset includes 42,952,377 measurements based on three different cellular technologies: GSM (26,896,809), UMTS (6,195,903), and LTE (9,859,665). The dataset covers the entire world, as can be seen in Figure 9a, where different densities in different areas can also be appreciated. OpenCellID dataset makes no distinction between serving and neighbor cells; thus, all measurements are considered as to be distinct, and all entries are serving cells.  
_u-blox_. For further testing the generality of our proposal, we extended the OpenCellID data with a proprietary dataset gathered by the company u-blox. For privacy reasons, we have obtained only information about the cellular networks, and not any details about devices and users. The dataset was assembled by parsing raw measurements logs. Each measurement contains a set of cells that are explicitly identified as serving and neighbors. Differently from OpenCellID, this dataset includes only GSM and UMTS cell information. GSM serving cells might contain a TA value, whereas UMTS neighbor cells do not usually have the logical parameters but only the physical ones. Measurements include the GNSS position with the time to first fix (TTFF), and the number of detected satellites. Overall, the dataset includes 12,492,545 measurements, partitioned into GSM (11,998,811) and UMTS (493,734). Figure 9b shows that, with respect to the OpenCellID dataset, u-blox data have lower worldwide coverage, although some areas are more densely sampled--for instance, South Africa. A high density of observations is also present in Europe, which, as we shall see, is useful for comparison and integration with data from OpenCellID.
## 5 Simulation Results and Analysis
### The Impact of Maximum Speed  
The purpose of the simulation experiment in this part was to investigate and analyze the impact of maximum speed on the network performance of these protocols. The number of nodes was 100 in the UANET. The minimum speed of the nodes was 3 m/s. The  
Figure 8: The effects of network density on PDR.  
Figure 7: The effects of network density on E2E delay.  
maximum speed change of the nodes ranged from 5 m/s to 25 m/s. The packet-sending rate was 5 Hz.  
Figure 9 presents the relationship between the maximum speed and the average E2E delay. As shown, the average E2E of the DLGR-2 DQ was lower than that of the other protocols. The reason was that the DLGR-2 DQ considered the total waiting time. When the buffer of a neighboring node of the current forwarding node was under a heavy load, or the packet transmission rate was very small due to a poor SNR, this increased transmission time in the neighboring node. Eventually, the E2E delays increased.  
Figure 10 depicts the impacts of maximum speed on the PDR. It was evident that DLGR-2 DQ performed better than the other protocols at different maximum speeds. As the nodes' maximum speed increased, the trends of the PDR for DLGR-2 DQ decreased. This was because the larger the speed difference between adjacent nodes, the greater the Doppler shift, the worse the link quality, and the lower the packet delivery rate. The GPSR, QNGPSR, and TQNGPSR did not consider link quality, so the packet loss increased. TQNGPSR was slightly better than QNGPSR. That was because TQNGPSR considered the number of packets in the buffer of neighboring nodes and added the relevant penalty term to the Q-value.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 5 Implementation and Results
### Implementation
#### 5.1.3 Policy Enforcement Point  
The policy enforcement point GEri is named Wilma ([[https://catalogue.fiware.org/enablers/pep-proxy-wilma](https://catalogue.fiware.org/enablers/pep-proxy-wilma)]([https://catalogue.fiware.org/enablers/pep-proxy-wilma](https://catalogue.fiware.org/enablers/pep-proxy-wilma)) [Last accessed on 3 October 2017]) and has been fully developed by the authors of this manuscript, using Node.js. In the context of FIWARE, it is usually referred to as _PEP Proxy_, to emphasize the fact that it intercepts requests to the service it has been deployed on top of (as explained in Section 4.4). Wilma can be configured to check three levels of security:  
1. Authentication: Using this level of security, the _PEP_ just checks if the client has been correctly authenticated against the _IdP_. Thus, at this level, every user with an active account would be able to access the protected resources. The check is performed by sending a validation request to the _IdP_.
2. Basic authorization: In this case, the _PEP_ also checks if the client has the required roles to perform the corresponding action (defined by an HTTP verb) in the corresponding resource (defined by an HTTP path). The check is performed by sending a policy request to the _PDP_ module.
3. Advanced authorization: This is the most complex, powerful case, because the authorization check is not only based on the HTTP verb and path, but also on other more advanced, customizable parameters, such as the request body or headers. To perform the check, a custom XACML policy request is sent to the _PDP_.  
The main challenge when implementing this component was related to the necessity of having a single PEP component for each registered application in the platform. Thus, the registration and the authentication mechanisms of the component for being able to validate requests was a new feature not present in other existing frameworks.
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 3 Variable Voltage Regulator for LCRs to Balance the String LED Currents
### Master and Slave Control for Current Balance of Multiple string N-LEDs  
In this study, using an array of ICs to acquire identical MOSFETs for serving multiple-string \(N\)-LED control, is vital work. The circuit scheme for the multiple LCRs respectively loaded with one string \(N\)-LED, is outlined in Figure 7, in which the master and slave controls for the current balance are built. Since the devices and components, in practice, can be selected as almost identical to one another, the current balance between the adjacent string LEDs can then be realized as predictions. The control reference is preset at the master LCR and wires together with the other slave LCRs. The \(VV_{dc}\) of VVR is basically synthesized by the loop parameters of the master LCR, as per Figure 5b, including the \(v_{DS}\) of the MOSFET, \(v_{R}\) on source \(R_{S}\), \(Nv_{D}\) on the \(N\)-LED, and the specified low voltage dropout \(V_{Q}\). To implement the VVR, a reference voltage (\(V_{Q}+V_{k}\))\({}_{\text{ref}}\) is initially preset, where \(V_{k}\) depends on the state of the gate drive \(v_{GS}\). After comparing the instantly detected voltage (\(v_{k}+v_{DS}\)) with the (\(V_{Q}+V_{k}\))\({}_{\text{ref}}\) in the master loop, the synthesized \(VV_{dc}\) is then adapted to supply all multiple string LEDs through the LCRs, and results in the same currents for adjacent LEDs, while holding all \(v_{DS}\)'s positions greater than the lowest \(V_{Q}\). Once the control is finished in the master LCR, all slave LCRs with the updated supply \(VV_{dc}\), can mirror the master drain current on their flat \(v_{GS}\)-characteristic curves in the \(i_{DS}\)-\(v_{DS}\) planes, and then all slave string LED currents are almost the same as one another.  
Figure 6: Current balance control for multiple-string \(N\)-LEDs using multiple identical \(i_{DS}\)-\(v_{DS}\) planes, where all of the MOSFETs and LCRs are assumed to be identical with \(VV_{dc}\) supply.  
Figure 7: Configuration of master and slave control for the current balance of multiple string \(N\)-LED.
## 3 Results and Discussion
### Analysis of Synergistic Interactions in Antioxidant Activity  
Based on the experimental results from Section 3.9.3, peptides 3 and 6 were selected for evaluation of their synergistic effects. Considering the antioxidant activities of these two peptides, DPPH radical scavenging ability, hydroxyl radical scavenging ability, and superoxide anion scavenging ability were chosen as the indicators for assessing their synergistic effects.  
Figure 15: Fe\({}^{2+}\)- chelation rates at various concentrations for each synthetic peptide.  
As shown in Figure 16, with increasing concentrations, these two peptides demonstrated a certain degree of synergistic effect overall. In the ABTS radical scavenging experiment and superoxide anion scavenging experiment, the actual values for peptides 3 and 6 at concentration gradients of 0.1 to 1 mg/mL were higher than the theoretical values. Similarly, in the DPPH radical scavenging experiment and hydroxyl radical scavenging experiment, peptides 3 and 6 also exhibited notable synergistic effects, with the most pronounced effect observed at a concentration of 0.1 mg/mL. At this concentration, the DPPH radical scavenging ability of peptides 3 and 6 increased from a theoretical value of 20.65% to an actual value of 21.53%, surpassing theoretical values by 0.88% [54]. Under the same concentration, their hydroxyl radical scavenging ability also increased from 24.96% to 27.12%, surpassing theoretical values by 2.16% [55].  
In a word, it was concluded that peptides 3 and 6 exhibited a certain degree of synergism in antioxidant activity experiments at different concentrations.  
Figure 16: Analysis of synergistic effect of synthetic peptides. ABTS\({}^{+}\) scavenging activity (**a**), DPPH-scavenging activity (**b**), OH\({}^{-}\)-scavenging activity (**c**) of GCPs, O\({}_{2}\)\({}^{-}\)- scavenging activity (**d**) with different concentration of theoretical value and actual value.
## 5 Transient Performance of the System in DC Faults
#### 5.1.3 The Transient Overvoltage Analysis During DC Faults  
This section compares the overvoltage characteristics across the DC line as a result of DC faults in two cases.  
The voltage across the converter in a pole-to-pole fault is presented in Figure 14. In Case-1, the voltage dip is not large due to the presence of voltage across the circuit breaker during the fault. In addition, it was confirmed that the voltage in the normal state recovered to 160 kV within 100 ms after the peak overvoltage of 175 kV occurred during the reclosing operation.  
Figure 14: The waveform of pole-to-pole DC voltage in the case of pole-to-pole fault.  
Figure 13: The waveform of the DC line current and voltage across the CB in the transient state: (**a**) the DC line current; (**b**) the voltage across the CB.  
On the other hand, in Case-2, a reverse voltage was applied across the DC system due to the blocking operation of the FB-MMC to prevent the AC system from feeding in to the DC fault. In the case of reclosing, an overvoltage of 205 kV was obtained. In addition, the recovery time to a DC-rated voltage of 160 kV after reclosing was much longer than that of Case-1, and it was confirmed that the steady-state voltage was reached within 10 s, compared to 100 ms in Case-1.  
Figure 15a shows the overvoltage graph of Case-1, showing the maximum overvoltage of 90 kV during the reclosing process. However, in Case-2, a maximum overvoltage of 150 kV and a longer time to reach the steady state was observed, as shown in Figure 15b.  
System components are expected to carry larger currents as well as bear higher overvoltage stress during fault transience in Case-2.
## 3 Circuit Level Design
### Gate Driver Circuit Design  
The gate driver circuit bridges low-voltage control signals to the high-voltage transistor gate, thus enabling its operation. The utilized gate driver, whose topology can be seen in Figure 3, is designed following the recommendations of [43], initially for Si applications. However, adapting this design to the SiC technology is not straightforward due to the different operating voltages and process parameters.  
The design process of the gate driver follows a structured approach given by [43]. Initially, the application for the driver is defined. This decision sets the stage for one of three design scenarios: (I) \(V_{H}\sim V_{DD}\), (II) \(V_{H}\ll V_{DD}\), or (III) \(V_{H}\gg V_{DD}\), where \(V_{H}\) and \(V_{DD}\) are gate driver power supply connections and \(V_{H}\) determines the high level of the output signal in the gate driver \(V_{G1}\). Given the objective of converting low-voltage signals to higher-voltage signals, the third scenario, \(V_{H}\gg V_{DD}\), is targeted. The primary operational criterion is to ensure the switching of the cross-coupled transistors when \(V_{S1}\) changes from high to low, as this triggers the switching of the subsequent inverter stages. In the context of this topology, a simple criterion is checked, i.e., when \(V_{DS1}=V_{TP}\) (threshold voltage PMOS), it is verified that \(I_{D2}>I_{D1}\), where \(I_{D1,2}\) correspond to the drain currents of transistors \(M_{D1,2}\) in the circuit shown in Figure 3. If this holds true, a state change in the crossed couple is ensured and consequently in the gate driver. Synthesizing the aforementioned points, a relationship between the sizes of \(M_{D2}\) and \(M_{D1}\) can be formulated using the long-channel equations [36] combined with the operational conditions necessary for the proper functioning of the gate driver. This relationship is expressed by (4), where \(W_{1,2}\) and \(L_{1,2}\) are the widths and lengths of \(M_{D1,2}\), and \(\mu_{P,N}\) are the PMOS/NMOS carrier mobilities. This provides a starting point for transistor design. The final sizing is achieved by adjusting the theoretically derived dimensions in the simulation until the desired design characteristics are met. The primary criteria for these adjustments are the gate driver output current, the symmetry of the output signal, and the total area used.  
\[\frac{W_{1}/L_{1}}{W_{2}/L_{2}}=\frac{\mu_{N}}{2\mu_{P}}\cdot\frac{\big{(}(V_{ DD}-V_{TN})^{2}\big{)}}{(V_{H}-|V_{TP}|)\cdot|V_{TP}|} \tag{4}\]
###### Abstract  
Background: The aim of this study was to determine whether the elite handball (HB) athletes with glenohumeral internal rotation deficit (GIRD) present differences in the mechanical properties in the trees major muscle, and strength and extensibility of the posterior shoulder tissues of the throwing shoulder (TS) compared to the non-throwing (non-TS) and non-HB athletes. Methods: A cross-sectional study was carried out, with sixty male participants: 30 HB athletes with GIRD and 30 age-matched healthy non-HB athletes. Mechanical properties of the teres major muscle were measured with MyotonPRO; also, extensibility of the posterior shoulder tissues and maximum isometric internal rotation (IR) and external rotation (ER) strength were recorded. Results: The teres major muscle of the TS in the HB group achieved a higher tone (\(\Delta\) 0.34; 95% CI: 0.15, 0.53) and stiffness (\(\Delta\) 30.86; 95% CI: 23.04,38.68), and a lower relaxation time compared to the non-TS of the same group (\(\Delta-0.69\); 95% CI: \(-1.15\), \(-0.24\)) and to the TS of the control group for the tone (\(\Delta\) 0.36; 95% CI: 0.02, 0.70) and for the stiffness (\(\Delta\) 27.03; 95% CI: 15.24, 38.83). The extensibility of the TS of the HB group presented a statistically significant decrease compared to the control group (\(\Delta-7.83\); 95% CI: \(-12.42\), 3.23). A between-groups ER/IR ratio imbalance was found for the TS (\(\Delta-12.18\); 95% CI: \(-25.59\), \(-1.23\)) and the non-TS (\(\Delta-13.01\); 95% CI: \(-25.79\), \(-0.25\)). Conclusions: HB athletes with GIRD present a higher tone and stiffness of the teres major muscle and lack of extensibility of the tissues of the posterior part of the shoulder compared to the non-TS and to healthy non-HB athletes.  
handball; myotonometry; GIRD; cross-sectional Article
# A Semantic and Knowledge-Based Approach for Handover Management
## 3 SIM-Know  
HM allows an end-user device to keep an active connection when moving from one network's coverage area (BS or AP coverage) to another [9]. HM comprises the initiation, selection and execution phases [17; 26; 27]. Handover Initiation gathers all the information needed to identify and determine the neighboring networks and their current and future statuses (e.g., data about network performance and available services). Network Selection chooses the best available network from a ranking created by taking into account a single criterion or multiple criteria. Handover Execution connects and disconnects end-users to and from a network, involving resource allocation and releasing [28].  
SIM-Know introduces SIM and KBP for improving HM. SIM allows SIM-Know to make context-aware handover decisions. In turn, the distributed KBP provides local and global intelligence to make rule-based cognitive decisions about network connection and disconnection. SIM and KBP envision diminishing the number of handovers and the number of throughput drops and, as a consequence, have a positive impact on several network performance metrics (delay, jitter, packet loss, and throughput).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Description** & \multicolumn{2}{c}{**Making-Decision**} & \multicolumn{1}{c}{**Wireless Technology**} \\ \cline{3-5}  & **Control** & **Method** & \\ \hline
[18] & A framework, based on data analytics, context extraction, user profiling and pre-processing contextual information, is presented to score the available BSs and to improve network access selection & NAHO & Fuzzy Logic & 5G \\ \hline
[19] & The AP or BS selection is improved by using AHP for weighting selection criteria coming from the user and networks’ context and TOPSIS for ranking the available networks & NCHO & AHP-TOPSIS & LTE, WLAN \\ \hline
[20] & A mechanism is proposed for selecting the radio access network that best meets the end-user needs by considering the on/off state and battery level of the mobile device and the available bandwidth in the target and serving network & NCHO & Policy & LTE, WLAN \\ \hline
[21] & A versatile modeling methodology is introduced for evaluating proactive and reactive vertical handover approaches & NCHO & Policy & 5G, LTE, WLAN \\ \hline
[22] & Two co-operating algorithms with adaptive thresholds are introduced for performing network selection while avoiding network congestion and meeting user preferences regarding monetary cost, Quality of Service (QoS), security and energy consumption & NAHO & Policy & 5G \\ \hline
[24] & A multiattribute decision handover making scheme, centered in the triggering phase and based on SDN and Fuzzy Logic, is proposed for increasing the network throughput and reducing unnecessary handovers and total handover delay in femto-access points and device-to-device communications & NAHO & TOPSIS, Fuzzy Logic, AHP \\ \hline \hline \end{tabular} PROMETHEE: Preference ranking organization method for enrichment evaluation—SAW: Simple Additive Weighting Algorithm.  
\end{table}
Table 1: Cont.
## 1 Introduction
Other studies are focused on the analysis of complex hub geometries, for instance, a hub with an exponential variation of the thickness with the radial distance was proposed in [28]. Other design options, such as considering a variable radial interference [27; 29] or using hubs of functionally graded materials [8; 30], have been also studied. The estimation of the contact pressure at the interference is a difficult task since it cannot be measured directly at the hub-shaft interface. Therefore, the contact pressure can be quantitatively estimated by finite element method (FEM) numerical simulations [6; 27; 31; 32; 33; 34]. Within this framework, a key issue in this field is to find the optimal values of modified hub geometry using chamfer hubs that allow reducing the stress concentration at the hub edge such that the stress distribution is uniform and, hence, design equations can be used for estimating the real stress state at the shaft-hub interface. To fill this gap, in this study, an analysis of the influence of the geometry of chamfer hubs on the stress concentrations was carried out in order to determine the optimal geometry to be considered in the design of interference fits. To achieve this goal, two different chamfer hub geometries were analyzed and compared with a conventional hub (Figure 1a): (i) partial-chamfer hubs with chamfer heights lower than the hub thickness, \(t\) (Figure 1b) and (ii) full-chamfer hubs with a chamfer height (\(l_{\rm r}\)) equal to the hub thickness (Figure 1c). Thus, multiple simulations by means of FEM were carried out, varying the parameters that define the geometries (chamfer angle, \(\alpha\), hub thickness, \(t\), and chamfer height, \(l_{\rm r}\)) to reveal the stress concentrations at the shaft-hub interface and quantitatively establish the reduction of stress concentrations for each geometry. From the analysis of the obtained results, a simple design recommendation of the optimal geometry of chamfer hubs can be derived that could be useful in the design of interference fits using chamfer hubs. By using these design recommendations in terms of chamfer hub geometric parameters, the stress concentrations can be reduced and an almost uniform distribution of contact pressure can be obtained, thereby making the values given by the theoretical expressions used in the design of interference fits realistic.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 1 Dynamic Modeling of Integrated Bearing Systems
### Dynamical Differential Equations of Spindle  
During the operation of the integrated bearing, the displacement degree of freedom of the spindle in the (\(x_{i}\)) direction, as well as the rotational degrees of freedom in the (\(y_{i}\)) and (\(z_{i}\)) directions, are constrained by external factors. The spindle is subjected to the combined effects of the steel balls and external loads. The differential equations governing the core shaft are as follows:  
\[\begin{split} F_{r}-\sum\limits_{j=1}^{Z}&\left[ \left(F_{R\eta ij}\sin\alpha_{ij}+Q_{ij}\cos\alpha_{ij}-T_{\eta ij}\sin\alpha _{ij}\right)\cos\varphi_{j}+\right.\\ &\left.\left(F_{R\xi ij}-T_{\xi ij}\right)\sin\varphi_{j}\right]+ m_{i}g=m_{i}\ddot{y}_{i}\end{split} \tag{13}\]  
\[\begin{split}\sum\limits_{j=1}^{Z}&\left[\left(F_{R \eta ij}\sin\alpha_{ij}+Q_{ij}\cos\alpha_{ij}-T_{\eta ij}\sin\alpha_{ij} \right)\sin\varphi_{j}+\right.\\ &\left.\left(F_{R\xi ij}-T_{\xi ij}\right)\cos\varphi_{j}\right] =m_{i}\ddot{z}_{i}\end{split} \tag{14}\]  
In the equations, (\(\ddot{y}_{i}\)) and (\(\ddot{z}_{i}\)) represent the accelerations of the core shaft's center of mass, while (\(F_{a}\)) and (\(F_{r}\)) denote the axial and radial forces exerted on the bearing.