esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Nutritional Requirements  
#### 3.3.1 How to Measure Energy Requirements in Adult COPD Patients?  
**Recommendation 12**.: _In well-nourished and stable patients, GARIN advises the use of the following: WHO or Harris-Benedict Equation \(\times\) Activity Factor (AF); Adjusted weight if BMI \(>\) 30. In advanced-stage malnourished patients (GOLD 3 or GOLD 4: E): Harris-Benedict Equation \(\times\) FA \(\times\) Disease Factor (DF) (1.3)._  
Grade of recommendation GPP, (94%) Strong consensus.  
Comment:  
Several studies estimate an increase in basal energy expenditure (BEE) of 15-26% above the requirements of healthy individuals [85; 86; 87; 59; 88]. This increased metabolism has been associated with reduced food intake, weight loss, and muscle wasting and is considered an independent predictor of morbidity and mortality [85].  
Although indirect calorimetry (IC) is the recommended method, when it is not available, predictive equations are a good option.  
Research that has compared different predictive equations with IC or the doubly labeled water (DWL) method has found that they overestimate or underestimate the REE. Thus, most of those studies recommend the Harris-Benedict and WHO equations as the ones that have best matched the values of the reference method used [59; 86]. [PERSON] [86] compared the resting energy expenditure (REE) measured by IC and the Harris-Benedict equation in mechanically ventilated patients. The IC-measured REE was approximately 45.0% higher (49.1% in males; 36.8% in females). In this study, the REE, according to the Harris-Benedict equation multiplied by 1.5 in men and by 1.4 in women, was close to the values obtained by IC.  
GARIN members reaffirm their interest in deepening the morphofunctional assessment of COPD patients and the possibility that, in the future, predictive equations may incorporate body composition data to allow a better adjustment of energy intake in these patients' diets [88; 89; 90].
## 2 Material and Methods
### Preprocessing  
#### 2.3.1 Intensity Normalization  
The MRI scans were linearly normalized between 0 and 1 to approach a coherent intensity range and facilitate deep learning by minimum-maximum normalization as given by:  
\[\alpha_{i}^{new}=\left(\frac{\alpha_{i}^{old}-\alpha_{min}^{old}}{\alpha_{max }^{old}-\alpha_{min}^{old}}\right)\left(\alpha_{max}^{new}-\alpha_{min}^{new} \right)+\alpha_{min}^{new},\]  
where \(\alpha_{i}^{new}\) and \(\alpha_{i}^{old}\) represent normalized and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tumor Class**} & \multirow{2}{*}{**Patients**} & \multirow{2}{*}{**MRI Scans**} & \multicolumn{2}{c}{**Planer Geometry Details**} \\ \cline{5-5}  & & & **Anatomical Plane** & **Scans Division** \\ \hline \multirow{3}{*}{Glioma} & \multirow{3}{*}{89} & \multirow{3}{*}{1426} & Transverse & 494 \\ \cline{3-4} \cline{5-5}  & & & Coronal & 437 \\ \cline{3-4} \cline{5-5}  & & & Sagittal & 495 \\ \hline \multirow{3}{*}{Meningioma} & \multirow{3}{*}{82} & \multirow{3}{*}{708} & Transverse & 209 \\ \cline{3-4} \cline{5-5}  & & & Transverse & 268 \\ \cline{3-4} \cline{5-5}  & & & Sagittal & 231 \\ \hline \multirow{3}{*}{Pituitary} & \multirow{3}{*}{62} & \multirow{3}{*}{930} & Transverse & 291 \\ \cline{3-4} \cline{5-5}  & & & Coronal & 319 \\ \cline{3-4} \cline{5-5}  & & & Sagittal & 320 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CE-MRI dataset with 3064 instances (233 patients), 512 \(\times\) 512 pixels (pixel: 0.49 mm \(\times\) 0.49 mm).  
Figure 2: Intra-class variation existing in three different brain tumors for transverse view: (**a,d**) Glioma, (**b,e**) Meningioma, and (**c,f**) Pituitary tumors’ variation in CE-MRI dataset.  
original intensity values for the \(i\)th pixel, respectively, \(\alpha^{old}_{max}\) and \(\alpha^{old}_{min}\) represent maximum and minimum original intensity values, respectively, \(\alpha^{new}_{max}=1\) and \(\alpha^{new}_{min}=0\) used to define maximum and minimum normalized intensity values. The images, resized to 224 \(\times\) 224, speed up the training process and address the out-of-memory problem especially when running on average-GPU price-based portable systems. Figure 3a,b compares images of the glioma tumor.
## 4 Experimental Methodology and Measurement Results
### Link Performance
#### 4.1.1 Downlink Performance  
Table 2 lists the measured EVM values corresponding to different DL fiber lengths. Due to the high bit-error rate (BER) tolerance of sigma-delta modulated signals [23], there is little performance degradation when the optical transmission distance increases. While CPRI requires a BER less than \(10^{-12}\)[24], a BER higher than \(10^{-4}\) can be tolerated in SDoF applications; the corresponding signal quality is about \(-45\) dB EVM [23]. The constellation diagram of the worst case--with a 100 m MMF added between the break-out fibers--can be found in Figure 7a.  
\begin{table}
\begin{tabular}{l l} \hline Data bandwidth & 40.96 MHz \\ Subcarrier spacing & 320 MHz \\  & 128 subcarriers (index: \(-64\) to \(63\)) \\ Pilot subcarrier indices & \(\pm 11\), \(\pm 25\), \(\pm 33\) \\ DC subcarrier indices & \(\pm 1\), \(0\) \\ Null subcarrier indices & \(-64\) to \(-59\), \(59\) to \(63\) \\ Number of data subcarriers & 108 \\ Cyclic prefix (CP) size & \(1/4\) (0.78 us) \\ Modulation scheme & 256-QAM (quadrature amplitude modulation) \\ \hline \end{tabular}
\end{table}
Table 1: OFDM Signal Parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**DL (DU to RRU) Fiber Length**} \\  & **Back-to-Back** & **30 m MMF** & **100 m MMF** \\ \hline EVM & 2.725\% (\(-31.29\) dB) & 2.752\% (\(-31.21\) dB) & 2.765\% (\(-31.17\) dB) \\  & & & Constellation: Figure 7a. \\ \end{tabular}
\end{table}
Table 2: Downlink performance in EVM.
## 3 Experimental Investigation
### Dynamic Properties  
As a part of OMA, structural dynamic properties were obtained. In the following section, each test is described in detail. Operational modal analysis was performed by using piezoelectric accelerometer (PCB Piezotronics, type 393B31) with the nominal sensitivity of 10 V/g (PCB Piezotronics, Depew, NY, USA) an analyzer (Bruell&Kjaer, type 3560C, Bruell&Kjaer, Naerum, Denmark) and associated software. The measurement was performed in total 64 points, 32 points per floor (1 st and 2 nd) in two perpendicular, x and y, directions (Figure 5) during 128 s in each point. In this way, 128 degrees of freedom were obtained, the sum of which corresponds to two horizontal directions. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{\(f_{\mathbf{n}}^{exp}\)} & \multicolumn{2}{c}{\(E^{exp}\)} & \multicolumn{2}{c}{\(f_{\mathbf{y0}}^{exp}\)} \\  & \multicolumn{2}{c}{[**MPa**]} & \multicolumn{2}{c}{[**MPa**]} & \multicolumn{2}{c}{[**MPa**]} \\ \hline \multirow{2}{*}{**Floor,**} & \multicolumn{2}{c}{**Measurement Point**} & \multicolumn{2}{c}{**Measurement Point**} & \multicolumn{2}{c}{**Measurement Point**} \\ \cline{2-7}  & **FJ-m-1** & **FJ-m-2** & **FJ-m-1** & **FJ-m-2** & **FJ-m-1** & **FJ-m-2** \\ \hline GR & 0.54 & 0.43 & 4020 & 4122 & 0.171 & 0.825 \\
1F & 0.59 & - & 3470 & - & 0.134 & \\ \hline \multicolumn{7}{c}{Mean value} & \multicolumn{2}{c}{3850} & \multicolumn{2}{c}{0.377} \\ \hline \end{tabular} Legend: GR—ground floor; 1E—first floor.  
\end{table} 
Table 2: Experimentally determined existing compressive stress \(\left(f_{0}^{exp}\right)\), modulus of elasticity \(\left(E^{exp}\right)\) and shear strength without compressive stress \(\left(f_{V0}^{exp}\right)\) in megapascals [MPa] for different floors (m). Figure 5: Arrangement of measurement points for determination of the structural dynamic properties at the (**a**) first and (**b**) second floor. and numerically determined dynamic parameters to create and analyze a linear numerical model. Walls and slabs were modelled with shell elements, slabs reinforced with ribs are modelled using coupled shell elements, and a slab reinforced with beams is modelled with a shell element that is connected with a beam using rigid elements. Columns and beams are modelled using beam elements. Boundary conditions under the load bearing walls are assumed to be fixed, while for the columns it is proposed that they are clamped. The dimensions of the elements are taken from the drawings. Material properties assigned to the modelled elements are listed in Table 4.  
To consider the effect of the roof, i.e., its weight, a sub-model was created (Figure 8b). The basic mechanical properties of the material are taken from the experimental investigation, while the dimensions are defined according to the available drawings. The elements of the roof are modelled using the beam elements. By calculating the sub-model of the roof,
## 4 Analyses
### Discussion
#### 4.1.2 Kolmogorov-Smirnov Test
As displayed on the right of Figure 3, regarding the whole wind farm for the following signals the KS test showed \(p\)-values below 0.05 at least for one turbine: the temperature inside nacelle (1\(\times\)), both generator bearing temperatures as well as the generator stator temperature (each 1\(\times\)), the top box temperature (1\(\times\)), voltage phase B (1\(\times\)), wind direction (2\(\times\)), wind speed (2\(\times\)), and yaw angle (1\(\times\)). Thus, for the last mentioned group of signals, resampling to 600 s could alter the data in a way such that the underlying distribution of the data was no longer the same as for the raw data. One additional signal that also came close to our significance level with a \(p\)-value of 0.082 once within the scope of all turbines was the ambient temperature.
# Firewall Best Practices for Securing Smart Healthcare Environment: A Review
## 2 Security Challenges for Smart Healthcare Environment  
In this section, we provide the security challenges for smart healthcare environments. Before describing the security challenges, we would like to provide a typical smart healthcare architecture.  
During the past decade, there have been tremendous advancements and developments in the field of information and communication technologies (ICTs), providing many benefits to society. Moreover, these intelligent and interconnected systems, where information is constantly shared between hospitals, doctors, and patients, require proper monitoring and safeguarding against threats and attacks [8]. Healthcare has always been a very important part of our lives, as the patients' health can be ensured by using the services of medical practitioners and the healthcare industry. Smart health monitoring devices provide facilities for paramectics to monitor and track patients remotely and provide relevant diagnostics [9]. Smart healthcare systems, which consist of various components, such as smart sensors, Radio Frequency Identification (RFID) devices, are constantly sending medical data from patients to local workstations located at the hospital [10]. However, when dealing with patient data, the underlying privacy on the data requires proper safeguarding before transmitting via interconnected network components. A typical smart healthcare architecture is presented in Figure 1. The integration of smart healthcare devices between hospitals, paramedics, and the internet is facing significant cybersecurity challenges. Smart healthcare devices are connected to share data and are linked with other devices using the internet, to upload and download data to and from cloud servers. Therefore, the placement of a specific firewall is the key component that defends against internal and external attacks and threats.  
The demand for healthcare systems is growing because of aging populations and an increase in chronic diseases [11]. Moreover, the adoption of online healthcare advice and the services provided are heavily dependent on connected devices and network services. Healthcare systems become a key target for cybercriminals due to the value of the information they contain; therefore, security is a must for healthcare devices to protect sensitive data and critical infrastructure [12; 13]. Lack and poorly implemented security in healthcare applications have negative impacts and consequences on patient data, privacy, and vulnerability to various attacks. Therefore, failure in maintaining the security for healthcare devices can result in the loss of integrity, availability, and data, which ultimately risks patients' lives [14; 15].
# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
## Methods
### Support Vector Machine  
Another classification method which is being used in the considered classification problem is the support vector machine (SVM) [21]. This method performs well in many linear and non-linear problems; it is also able to process high-dimensional and large data sets.  
The basic idea of SVM is building a hyperplane which splits the training set into the necessary number of classes (in our case, two). When the hyperplane is built, a testing sample is placed on the plane. Depending on its position relative to the hyperplane, we infer the class affiliation of the testing sample.  
Formally, the problem can be formulated as follows: begin with a training set of N points (\(x_{i},y_{i}\)), where \(x_{i}\in\ \mathbb{R}^{n}\) and \(y_{i}\in\ \mathbb{R}\) are the \(i\)-th input and output samples, respectively. SVM constructs a classifier, which can be represented by a function:  
\[f(x)=sign\left[\sum_{i=1}^{N}\alpha_{i}y_{i}K(x,x_{k})+b\right]\]  
where \(x_{k}\) are support vectors, \(\alpha_{i}\) are positive real numbers, while \(b\) (bias) is a real number. The kernel \(K\) can be used in various forms: \(x_{k}^{T}x\) (linear kernel), \(\left(\gamma x_{k}^{T}x+r\right)^{n}\) (polynomial kernel of degree \(n\)), \(e^{-\gamma\left\|x-x^{\prime}\right\|^{2}}\)(radial basis function kernel), or \(\tanh\left[\gamma x_{k}^{T}x+r\right]\) (sigmoid kernel), where kernel parameters \(\gamma=\frac{1}{2\alpha^{2}}\) and \(r\) are real constants.  
The learning of SVM lies in finding \(f(x)\), which minimizes the following objective:  
\[Obj=C\sum_{i}^{N}l(f(x_{i}),\ y_{i})+\Omega(\alpha_{i},b)\]  
where the first term \(l\) is a loss function, \(l(\hat{g},y)=0\) if \(\hat{g}=y\), and the second term \(\Omega\) is a penalty function of our model parameters. The parameter \(C\) specifies the desire of avoiding the misclassification of each training sample. For example, a large value of \(C\) will reduce the width of the hyperplane margin, which will potentially cause fewer errors on the training set. At the same time, a low \(C\) will widen the hyperplane margin, as shown in Figure 6.  
Figure 5: Example of \(k\)-NN when \(k=3\).  
Figure 6: Examples of regularization parameter influence in the case of the two-dimensional classification problem. Red and blue points are training samples. The double arrow is hyperplane margin.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 2 Quantum Key Distribution Systems and Networks
### Quantum Security and Practical Vulnerability Issues
The bits are encoded into the arrival times of consecutive pulses, where an empty state followed by a coherent state represents a \"0\" bit, while the opposite order represents a \"1\" bit. The decoy states used to prevent PNS attacks are instead represented by consecutive coherent states. This protocol was tested over more than 300 kms in [33, 34, 35]. Other examples of attacks exploit the physical working principle of the devices in the system, as shown in [28], where they were able to trick the BB84 protocol by injecting power at the detector. Other approaches are reported in [36, 37, 38]. For this reason, the design trend in new protocols is to achieve device independency (i.e., preventing any security dependency on device implementation and imperfections). The first proposal of this kind of protocols was made in 2012 and is known as measure-device-independent (MDI) protocol [39], where they introduced an intermediate node to which both the partners send their photons. The photons are jointly projected in a base according to the same mechanism as in BB84, but using four maximally entangled Bell states. As in the case of regular QKD, any eavesdropping would lead to a perturbation of the measurement that could be detected by the communicating partners. Since the first proposal of the MDI protocol, many experimental and theoretical works have emerged, improving the distance reach, information rate, robustness, and compatibility with classical optical systems [40, 41, 42, 43, 44].
## Fault Diagnosis of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China; E-Mails: [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (Z.H.)  
2 Technology Center, CNPC Logging CO., LTD, Xi'an 710077, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-187-1071-4266; Fax: +86-29-8266-3689.  
Academic Editor: [PERSON]  
_Received: 11 August 2015 / Accepted: 13 October 2015 / Published: 23 October 2015_  
**Abstract:** The demountable disk-drum aero-engine rotor is an important piece of equipment that greatly impacts the safe operation of aircraft. However, assembly looseness or crack fault has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and fault diagnosis technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured vibration data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then vibration signal is processed by customized ensemble multiwavelet transform. Next, normalized information entropy of multiwavelet decomposition coefficients is computed to directly reflect and evaluate the condition. The proposed approach is first applied to fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack fault of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault detection of aero-engine rotor. Moreover, the robustness of the multiwavelet method against noise is also tested and verified by simulation and field experiments.  
**Keywords:** fault diagnosis; vibration signal analysis; customized ensemble multiwavelet; aero-engine rotor
## 6 Performance Analysis
### Results and Discussion
#### 6.3.1 Experiment I  
Figure 5 shows the average throughput and latency for GetBallot and CastVote functions at varying transaction send rates. The results indicate that as transactions are sent at higher rates, the scheme reaches a peak throughput, at which point latency begins to rise and the throughput is unable to match further increases to the send rate. This maximum throughput also is dependant on the transaction type: around 200 tps for GetBallot, a read function, and around 75 tps for CastVote, a write function. As seen in Figure 5 the average latency does not rise until the send rate eclipses the scheme's peak throughput. These results are generalized as follows:  
* A large number of voters, 200 per second, can concurrently fetch ballots without significant delay.
* Fewer voters, 75 per second, can concurrently cast votes without significant delay.
* The type of transaction will affect scheme performance.
# Stability of Reinforced Retaining Wall under Seismic Loads
## 2 Assumptions and Formulation of the Horizontal Slice Method (HSM)
### Formulation of the HSM  
HSM was proposed by [PERSON] and [PERSON] [22] and was aimed at overcoming the difficulties in analyzing reinforced soil structures by the vertical slice method (Figure 1). In Figure 1, \(H\) is the height of the retaining wall (in m); \(S_{yj}\) denotes the vertical distance between the reinforcement of layer \(j\) and layer \(j\)+1 (in m), and it is usually treated as a certain value in the design; \(S_{t,i}\) and \(N_{N,i}\) are the shear force and normal force acting on the \(i\)th slice, respectively; \(l_{ai}\) is the length of reinforcement in the slip zone behind the wall; and \(l_{ei}\) refers to the length of reinforcement in the anchorage zone. Figure 2 zooms in on the \(i\)th slice taken from Figure 1, which contains one reinforcement. In the figure, \(F_{N,l}\) and \(F_{N,i+1}\) represent the normal force acting on the top and bottom of the \(i\)th slice, respectively; \(T_{i}\) refers to the tensile force of the \(i\)th reinforcement [33]; \(Q_{ii}\) and \(Q_{vi}\) denote horizontal and vertical seismic force, respectively, acting on the top of the slice; \(\alpha_{i}\) is the inclination angle of slip surface of the slice in the horizontal direction; and \(W_{i}\) is the weight of the \(i\)th slice. It should be noted that the slip surface is assumed to be a log spiral shape passing through the toe of the wall [34]. The sliding wedge is assumed to be in a limited equilibrium state, while both horizontal and vertical seismic loads are applied on the center of gravity of the slice. Then the safety for each slice is:  
\[F_{S}=\frac{\tau_{f}}{\tau_{r}} \tag{1}\]  
where \(\tau_{f}\) and \(\tau_{r}\) refer to shear strength and shear stress on the sliding surface, respectively.
## 4 Experiments and Results
### Complexity Comparison  
The complexity of an algorithm plays a crucial role in determining the working time in underwater environments where energy replenishment is challenging. Excessive complexity leads to increased energy consumption and may result in communication failure. Among the algorithms studied in this work, the ZF algorithm has a complexity of \(O(N^{3})\). The MMSE algorithm is formed based on ZF but considers the noise in the system. Although the complexity of MMSE is also at the \(O(N^{3})\) level, the presence of noise makes it slightly more complex than ZF. ML requires a traversal search in all transmitted signals, and its complexity is closely related to the number of transmitting antennas and the modulation order. The complexity of ML can be approximated as \(O(M^{N})\), where \(M\) is the modulation order, and \(N\) is the number of transmission antennas. The modulation order determines the complexity of the AWOA, the number of transmission antennas, the  
Figure 11: BER in 4 \(\times\) 6 MI-MIMO.  
Figure 12: BER in 4 \(\times\) 8 MI-MIMO.  
Figure 10: BER in 2 \(\times\) 4 MI-MIMO.  
volume of transmitted data, and the number of iterations. The complexity of the AWOA is \(O(N*I*U*M*L)\), where \(U\) is the population number, \(I\) is the maximum number of iterations, \(M\) is the modulation order, and \(L\) is the signal transmission length. It is worth noting that the complexity of the AWOA is lower than that of ML, as the number of antennas increases due to the influence of the initial population size and the number of iterations.  
This paper presents the number of significant multiplication calculations for the four algorithms studied in Figure 13. It is evident that, for the same number of antennas, the complexity of the AWOA is lower than that of ML and higher than that of ZF and MMSE. Furthermore, we investigated the computation time of the AOWA for different iterations and population sizes, as shown in Figure 14. It is evident that the number of populations and iterations directly impact the complexity. Regarding complexity, ZF and MMSE algorithms are the most computationally efficient. In contrast, the AWOA detection algorithm and ML algorithms use random selection and traversal concepts, leading to increased complexity.