esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The new inventions in health care devices have led to a considerable increase in the human lifespan. Miniaturized bio-sensing elements and dedicated wireless communication bands have led to the development of a new arena called Wireless Body Area Network (WBAN) (IEEE 802.11.6). These Implantable Medical Devices (IMDs) are used for monitoring a chronic patient's medical condition as well as therapeutic and life-saving functions. The aim of this study is to improve the dynamic channel selection algorithm for an increased Out Patient-Body Network Controller (OP-BNC) medical device during visits to the hospital. There is a fixed number of licensed spectra allocated to the In Patient-Body Network Controller (IP-BNC) and Out-Patient Body Network Controller (OP-BNC). When there is an increase in the OP-BNC, there is an availability of idle spectrum in the IP-BNC. An existing rank-based algorithm is used in the allocation of idle spectrum to the increased OP-BNC. This ranking method takes more time for the processing and selection of an idle channel to the registered user. To avoid it, we proposed an EFPOC model to select from the free idle channels of the IP-BNC licensed spectrum. We also discussed the algorithm complexity of the proposed Enhanced Flower Pollination-based Optimized Channel selection (EFPOC) algorithm and obtained a complexity of O(n\({}^{2}\)), which is a significant improvement over the existing algorithm rank-based algorithm complexity. Our experimental result shows that the proposed EFPOC algorithm improves the Tier-2 systems lifetime by 46.47%. Then, to prove that the proposed model is time efficient in channel selection, a simulated experimented is conducted. When selecting a number of channels from a Look-Up Table (LUT), the proposed EFPOC method takes 25% less time than the existing algorithms.  
c 2020 8 2189 10.3390/math8122189
## 3 Results and Discussion
### Biomarker Analysis  
#### 3.2.1 Catalase Activity  
A comparison of CAT activities at the two studied sites shows that this activity is more important at the Ghannouch site (Figure 4). Indeed, the results of the ANOVA indicate that there is a significant difference between the two studied sites for the hepatopancreas (F = 71.25; \(p<0.05\)) and for the gills (F = 17.9; \(p<0.05\)). The data also showed statistically significant differences between the two tissues within the same site (Djerba: F = 114.68; \(p<0.01\) and Ghannouch: F = 24.8; \(p<0.05\)), with higher activities found in the gills. Depending on the type, concentration, and exposure duration of the metal, antioxidant enzymes may be stimulated or inhibited. These results may be in favor of an induction by pollution, caused by industrial discharges in Ghannouch, on the antioxidant activity of CAT. This is in agreement with several previous works that showed an increase in CAT enzymatic activities in _Carcinus menemas_, which were exposed to pollutants in the Monastir region [72] and in the Bizerte region [52], and in crab species exposed to metals [73; 74]. These results can be explained by the fact that exposure to certain pollutants such as TMEs leads to an increase in the production of reactive oxygen species (ROS), such as the superoxide anion (O2* -), hydrogen peroxide (H2O2), and the hydroxyl radical (*OH), which can alter the defense system of aquatic organisms [75]. In such a situation, increases in certain antioxidant enzymes may occur. These enzymes participate in the neutralization of these ROS and thus in the reduction of the damage caused by the contaminants. In this respect, [PERSON] et al. [76] affirmed that the enzymatic activity of CAT increases after exposure to certain pollutants. In addition, our results also show a significant difference in CAT activity between the two studied tissues. Indeed, it is higher in the gills than in the hepatopancreas, with activity levels of about 4.7 umol H2O2/min/mg protein and 3.49 umol H2O2/min/mg protein, respectively, in Ghannouch, and 2.15 umol H2O2/min/mg protein and 1.58 umol H2O2/min/mg protein, respectively, in Djerba. In fact, our results show a profile in the same direction between the enzymatic activity of CAT and the accumulation of heavy metals at the level of the gills. These results are in agreement with several other scientific studies, such as the study by [PERSON] et al. [77], which showed an increase in this activity in the gills of _Callinectes ammicola_ crabs exposed to industrial effluents.
## 2 Field Measurement of Rainfall from OEL
### Experimental Setup  
To assess the performance of rainfall inversion by OEL, a real earth-space link is established in the Jiangning district of Nanjing, China (31\({}^{\circ}\)58\({}^{\prime}\)27\({}^{\prime\prime}\)N, 118\({}^{\circ}\)48\({}^{\prime}\)52\({}^{\prime\prime}\)E). The signal of this link is obtained from AsiaSat 5 that is a GEO satellite located above 100.5\({}^{\circ}\)E and emits C- and Ku band signal towards east Asia. Furthermore, a dish antenna in 1.2 m diameter receives the 12.32 GHz with vertical polarization. Based on the received data, the values of SNR are estimated by the corresponding terminal which outputs the estimations once every 6 s for 24 h a day. Moreover, a modern laser disdrometer, OTT-Parsivel (OTT), is deployed near the antenna as a comparison. The OTT can measure the rain intensity with 1-min resolution. This link has been working since July 2019, and the rainfall measurements will be used to evaluate the feasibility and potential of OEL for rainfall field reconstruction.
## 2 A Literature Review
### Industry 4.0 Technologies
The availability of knowledge [48; 51; 52], potential cost [48; 52], and external pressure [53; 54] can also affect the adoption of Industry 4.0 technologies. The second category is related to the internal factors of the organizations. Perceived benefits were investigated by numerous scholars and were affirmed as a critical indicator [41; 55; 56; 57]. Additionally, the organizations' IT infrastructure conditions and strategies are essential in the acceptance of Industry 4.0 technologies [53; 56; 58; 59; 60], which are indispensable in connecting different devices and determining organizations' future directions. Moreover, the standards of occupational safety and health will be updated, thereby affecting the adoption of Industry 4.0 technologies [56; 59]. Finally, the third category is the characteristics of organizations. The organizational sector and organization size are the most widely explored variables associated with the organizations' characteristics in technology adoption [53; 55; 61]. The varying characteristics could influence the competitive advantages and challenges faced by organizations. The third stream of the literature focused on the applications and roles of Industry 4.0 technologies. From an economic point of view, Industry 4.0 technologies can be utilized to optimize production processes [62], boost productivity and efficiency [63; 64], and customize advanced products [65]. From the dimension of ecology, these technologies play a crucial role in mitigating gas emissions [66], reducing the usage of resources [67], and reducing the amount of waste [68]. In terms of society, employees' satisfaction can be enhanced because their efficiency is improved by intelligent and interactive devices [66]. Nevertheless, the reduced job opportunities call for additional attention from governments and managers to tackle the imminent challenges [55; 69].
## 5 Methodology
### Components  
In the following, we provide more details about the components and configurations involved in the testbed. The components involved in the testbed are shown in Figure 5.  
* **5G NSA Network** We use the Amarisoft Callbox Classic [37] as a 5G NSA network. The Callbox provides a closed-source 3 GPP compliant enodeB, a gnodeB for the RAN, and an EPC and 5 GC for the core network. It can be configured to act as a mobile network for different generations and standards (i.e., 5G SA, 5G NSA, LTE M, LTE NB-IoT).
* **Fake 4G Network** Since 5G NSA is based on LTE control traffic, a fake 4G network can act as a fake BS for a UE connected to the 5G NSA network. We have set up the malicious enodeB by adapting the srsRAN [38] software package. srsRAN is an open-source software radio suite for 4G implemented in C/C++. The software runs on a Linux-based PC (Intel Core i5 7 th Gen laptop). A USRP B210 [39] acts as the radio front end for our fake 4G network. It is connected to the host-based PC running the modified srsRAN software. The fake BS needs to be configured to broadcast Mobile Number Code (MNC) and Mobile Country Code (MCC) numbers identical to the 5G NSA BS. It must also be operated using higher power than nearby enodeBs to lure the victim UE to connect to it. Our laboratory setup consists of only one 5G NSA BS under our control, operating at a particular frequency. Operating the fake 4G BS at this frequency could be considered analogous to a real-world situation, where the attacker usually operates the fake BS at the highest priority frequency.
* **2G Network** We have set up a 2G network using the OpenBTS [40] framework. It runs on a Linux-based PC (Intel Core i5 7 th Gen laptop). OpenBTS is a C++ application that implements the GSM cellular stack. A USRP B210 acts as the radio front end for our 2G network. It is connected to the host-based PC running the OpenBTS software.  
Figure 4: Schematic representation of the implemented experimental testbed, featuring a 5G NSA network, a 5G NSA UE, a fake 4G network, and a 2G network.  
Figure 5: Our experimental testbed consisting of an Amarisoft Call Box Classic (**left**), two SDRs, two personal computers (PCs), test SIM cards, and Commercial Off-the-Shelf (COTS) UE (**right**).  
* **COTS UEs** We used the following eight 5G NSA COTS UEs for the tests: Samsung A40, Huawei P40, Huawei Mate40 Pro, Oppo Find X3 Neo 5G, Google Pixel 7 Pro, Samsung Galaxy S20 FE, Samsung Galaxy S21 FE and iPhone 12. The UEs are connected to the Amarisoft Callbox using test sim cards that were shipped with the system.
# Computationally Efficient PM Power Loss Mapping for
## 5 Modification in the Full Operational Envelope
### Electrical Resistivity Correction Factor  
Although two-dimensional FEA can calculate the PM loss relatively accurately and quickly, it cannot consider the effect of the PM end and the influence of PM segmentation. According to [19], based on the idea of an equivalent electric rent, the PM loss has a certain rule between three-dimensional and two-dimensional FEA calculation results, which can be expressed by the width and length of the PM. Assuming that the resistivity of the PM is isotropic, the electrical resistivity correction factor can be expressed as  
\[\text{F}=\frac{P_{3D}}{P_{2D}}=\frac{3}{4}\frac{L^{2}}{w^{2}+L^{2}} \tag{14}\]  
where \(P_{3D}\) is the PM loss calculated by three-dimensional FEA, \(P_{2D}\) is the PM loss calculated by two-dimensional FEA, \(L\) is the axial length of the PM, and \(w\) is the width of the PM.  
Figure 8: The relationship between operating temperatures and the results calculated by the fitting formula in Equation (12) and the FEA. (**a**) Constant-torque area (n = 1039 rpm). (**b**) Constant-power area (n = 1400 rpm).  
However, the above correction factor is only applicable to the correction of the PM loss caused by the armature reaction. When the PM loss caused by the slotting effect is too large, the errors become larger. According to the method proposed by [14], the ratio of the PM loss of the reference speed open-circuit condition can be calculated by 2-D and 3-D FEAs as the correction factor of the PM loss caused by the slotting effect, which can be expressed as follows:  
\[\eta=\frac{P_{PM-SE-3D}}{P_{PM-SE-2D}} \tag{15}\]  
where \(P_{PM-SE-2D}\) is the PM loss calculated by 2-D FEA at a reference speed under open-circuit conditions and \(P_{PM-SE-3D}\) is the PM loss calculated by 3-D FEA at the reference speed under open-circuit conditions.  
The whole PM eddy current fitting formula is fitted by the PM loss value of the PM at a special operating point calculated by two-dimensional FEA. It can be improved by \(\eta\) and F, and a more accurate fitting formula is obtained as follows:  
\[P_{PM-AL}=\frac{1}{\left(1+\beta_{1}(T-T_{0})+\beta_{2}(T-T_{0})^{2}\right)} \Big{(}P_{PM-SL}\Big{(}n^{2},i_{d},B_{r},\eta,F\Big{)}+P_{PM-EL}(n,N,M)F\Big{)} \tag{16}\]  
where  
\[P_{PM-SL}\Big{(}n^{2},i_{d},B_{r},\eta,F\Big{)}=\left(\Big{(}{a_{i}}_{q}{}^{2 }+{b_{i}}_{d}{}^{2}\Big{)}\eta+\Bigg{(}c\dot{i}_{d}\bigg{(}\frac{B_{r}}{B_{r0 }}\bigg{)}+d\bigg{(}\frac{B_{r}}{B_{r0}}\bigg{)}^{2}\Bigg{)}F\Bigg{)}\left( \frac{n}{n_{w}}\right)^{2} \tag{17}\]
## Appendix B  
### [PERSON] and [PERSON] Window-Based Method  
The procedure introduced by [PERSON] and [PERSON] [10] for the detection of aftershocks is based on specific magnitude dependent space-time windows. It is known as the window-based method, and it is one of the simplest forms of aftershock identification. For each earthquake with magnitude \(M\), the subsequent events are assigned as aftershocks if they occur within a temporal window \(t(M)\) and a spatial interval \(d(M)\), respectively. Foreshocks are treated as aftershocks when a larger earthquake occurs later in the sequence. The event is considered as an aftershock and the algorithm is repeated based on the largest magnitude.  
We give in Equation (10) the functional form of the spatial and temporal windows suggested in [PERSON] and [PERSON] [10], which are denoted as GK1. Additionally, in Equations (11) and (12) we present alternative window parameter settings that can be found in [PERSON] et al. [75]. We denote them as GK2 and GK3, respectively.  
\[d=10^{0.1238*M+0.983}\left(\text{km}\right)\text{and }t=\left\{\begin{array}{ll}10^{0.032*M+2.7389}&M \geq 6.5\\ 10^{0.5409*M-0.547}&M<6.5\end{array}\right\} \tag{11}\]  
\[d=e^{1.77+\sqrt{0.037+1.02*M}}\left(\text{km}\right)\text{and }t=\left\{\begin{array}{ll}10^{2.8+0.024*M}&M\geq 6.5\\ e^{-3.95+\sqrt{0.62+17.32*M}}&M<6.5\end{array}\right\} \tag{12}\]  
\[d=e^{-1.024+0.804*M}\left(\text{km}\right)\text{and }t=e^{-2.87+1.235*M}\text{ days} \tag{13}\]
# A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis
## 6 Discussion
### Isolating Critical Subsystems  
Depending on applications and stakeholders' interest, some components can be considered more critical than others. For example, safety applications emphasise more on driving functionalities, while privacy applications focus more on data-related components. Putting more security resources on these critical components will not be enough to secure them, given the connections between attack surfaces can bring unknown threats from other vulnerable surface as shown in previous sections.  
Therefore, it is also important to isolate these critical parts from other vulnerable surfaces or at least to create a secured shield around them by putting proper controls in their connections.
## 4 Discussion
### The Variable Importance  
The measurements used to build the neural network model were purposely chosen to represent as much cranial variability as possible by keeping only the essential amount of morphological information. Those choices aim to increase the chances of application (e.g., when crania are only fragmentary) but also to reduce the chances of overfitting. In choosing the measurements to include in the dataset, we also made sure to include traits that were previously recognised as sexually dimorphic in modern humans. How the variables are allowed to interact in the model is hard to pinpoint; in fact, neural networks and other algorithms are referred to as \"black boxes\" [60]. This means that the way in which the model is classifying the crania (e.g., the nature and degree of interaction across the metric traits used) is not straightforward (especially because we used a non-linear activation function). Nevertheless, we can make assumptions regarding the model based on the measured variable importance, which represents an approximation of the influence of each trait on the estimation.  
The variable deemed to be the most relevant in the estimation was the geometric mean (Figure 6). This variable was computed from the 10 metric traits to represent the size of the cranium. Size was virtually erased from the other measurements thanks to the Mosimann transformation [48]; thus, we can expect that the sexual differences in overall dimensions were gathered in this single feature. The observed importance of geometric mean suggests that size is an important aspect of human cranial dimorphism, although it must be highlighted that, in the model, size may have been interacting with other features; thus, it could be important only in association with other sources of morphological variability.  
Following size, the second most important feature was the bizygomatic breadth, followed by mastoid height and biauricular height (Figure 6). Bizygomatic breadth was analysed in several studies [17, 22, 61, 62, 63, 64], and in each, bizygomatic breadth measurement positively discriminated sex. Moreover, in other studies using different approaches than the one adopted here, the shape of zygomatic arches resulted in a diagnosis of sex [14, 65].  
lesser known than the bizygomatic breadth for its sex-related differences, but previously reported in the literature [17, 64, 61].  
All measurements selected for our model are noted in the literature [17, 61, 62, 63, 64, 66], except for the orbital height. This measurement is not considered sexually dimorphic by various authors [17, 61, 64]; however, it presented a non-negligible influence on our model. This result is in agreement with the use of orbital shapes for se
# Effectiveness of Electrode Design Methodologies for Fast EDM Slotting of Thick Silicon Wafers
## 3 Results and Discussion
### Effects of Hollow Electrodes on Machining Speed with Varied Wall Thickness
#### 3.2.3 Analysis of the Quality of Machined Features on Silicon  
It can be seen from Figure 21 that silicon workpieces machined using block electrodes have signs of chipping on the machined square holes, especially at the exit side, compared to those machined using hollow electrodes. It is found that hollow electrodes with a 0.04\({}^{\prime\prime}\) wall thickness have the least amount of chipping at the exit side of the silicon workpiece. It has been found that with the decrease in the wall thickness of hollow electrodes, there is an improvement in the quality of the machined parts in silicon workpieces. The irregular or zagged edges and signs of chipping are more obvious in the image of machined features on the exit side, as shown in Figure 21b. The machined silicon workpiece for 0.08\({}^{\prime\prime}\) and 0.12\({}^{\prime\prime}\) can be seen in Appendix B as Figure A3.  
From Table 12 and Figure 22, it can be seen that the overall percentage of deviation is decreased with the reduction in the wall thickness from the block electrode (1\({}^{\prime\prime}\)) to 0.12\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.04\({}^{\prime\prime}\) wall thickness electrodes. The overall percentages of deviation (%) are 3.957, 3.399, 3.255, and 2.394 for the block electrode (1\({}^{\prime\prime}\)), 0.12\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.04\({}^{\prime\prime}\) hollow electrodes, respectively. The average percentages of deviation (%) for the length and width are 3.244, 3.085, 3.355, and 3.095 and 3.371, 3.355, 3.083, and 1.985 for the block electrode (1\({}^{\prime\prime}\)), 0.12\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.04\({}^{\prime\prime}\) hollow electrodes, respectively, at the entry side. The average percentages of deviation (%) for the length and width are 4.544, 3.514, 3.498, and 2.795 and 4.669, 3.514, 3.084, and 1.701 for the block electrode (1\({}^{\prime\prime}\)), 0.12\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.04\({}^{\prime\prime}\) hollow electrodes, respectively, at the exit side. Due to chipping at the exit side of the through hole, the percentage of deviation is higher at the exit side of the silicon workpiece compared to the entry side, especially for the block electrode. The characterization of the block electrode, the hollow electrodes of 0.12\({}^{\prime\prime}\), 0.08\({}^{\prime\prime}\), and 0.04\({}^{\prime\prime}\) wall thicknesses, and the mirror image of these electrodes on the silicon workpiece (square holes) is shown in Appendix A as Table A2.  
Figure 20: Variation in MRR for block and hollow electrodes with different thicknesses.  
Figure 21: (**a**) Entry side and (**b**) exit side of the block and hollow electrode with 0.04\({}^{\prime\prime}\) wall thickness.
## 4 Computation of PDs of Surface-Wave Group Velocities
### Hemi-Analytical Method  
In the fully analytical method, the crucial second-order partial derivative term \(\frac{\partial^{2}c}{\partial\omega\partial p_{t}}\) in (10) is substituted by (11) and then solved via some recursive procedures. In contrast, the hemi-analytical method approximates this term with a central finite-difference scheme [36], a procedure which will be briefly reviewed in the following.  
Suppose that the partial derivative \(\partial U/\partial p_{t}\) is desired at the angular frequency \(\omega_{0}\) and that the phase velocity \(c_{0}\) and group velocity \(U_{0}\) are computed at \(\omega_{0}\). Define two other frequencies around \(\omega_{0}\): \(\omega_{+1}=\omega_{0}e^{+\delta}\) and \(\omega_{-1}=\omega_{0}e^{-\delta}\). \(\delta\) is a small quantity, denoting a perturbation of \(\log\omega\); see [36] for a more detailed explanation. And suppose the quantities \(c_{+1}\), \(U_{+1}\), and \(\partial c_{+1}/\partial p_{t}\) are corresponding to \(\omega_{+1}\) and have been obtained. The same hypothesis is applied to the quantities \(c_{-1}\), \(U_{-1}\), and \(\partial c_{-1}/\partial p_{t}\) at \(\omega_{-1}\). Then the PD of the group velocity at \(\omega_{0}\) can be computed via  
\[\partial U_{0}/\partial p_{t}=\frac{U_{0}}{c_{0}}\bigg{(}2-\frac{U_{0}}{c_{0} }\bigg{)}\frac{\partial c_{0}}{\partial p_{t}}+\frac{U_{0}^{2}}{c_{0}^{2}} \bigg{(}\frac{\partial c_{+1}/\partial p_{t}-\partial c_{-1}/\partial p_{t}}{ 2\delta}\bigg{)} \tag{16}\]  
We call (16) 'hemi-analytical' due to the fact that all the first-order partial derivatives and \(U_{0}\) in (16) are computed by the analytical approach discussed previously, leaving only \(\frac{\partial^{2}c}{\partial\omega\partial p_{t}}\) approximated by the central finite-difference.