esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Intelligent Learning Method Based on ResNet  
### Convolutional Layer  
A convolutional neural network (CNN) was originally invented to deal with the variability of 2D shapes. A basic CNN is composed of a convolutional layer and a pooling layer [33]. Each level consists of a certain number of feature maps, meaning that CNN possesses good hierarchical feature representation ability from the lower level to higher level [34]. Through the propagation of CNN, the feature maps' size will decrease layer by layer and the extracted features are more global.  
Let a time-domain signal to be processed be represented as \(x(t)\). After the multi-scale decomposition, the resulted signals can be represented as \(\mathbf{x}_{S}=[\mathbf{x}_{S}^{1},\mathbf{x}_{S}^{2},\dots,\mathbf{x}_{S}^{L}]\), where \(S\) is the number of the training samples and \(L\) is the decomposition level. The corresponding network output can be written as \(y=[y_{1},y_{2},\dots,y_{S}]\). Each \(y_{j}\) means a specific output class from the finite set of classes. Defining \(w_{ji}^{l}\) as the filters with a sliding filter bank and \(b_{j}^{l}\) as the bias, the convolutional layer output feature maps can be expressed as  
\[g_{j}^{l}=relu\left(\sum_{i=1}^{m}x_{i}^{l-1}*w_{ji}^{l}+b_{j}^{l}\right), \tag{18}\]  
where \(i\) means the \(i\)-th input feature map; \(j\) means the \(j\)-th output feature map; \(l\) means the \(l\) layer ; and \(relu(\cdot)\) means the activation function in the network is rectified linear units (ReLU).  
An example of the convolutional layer is shown in Figure 7. The input of the layer is filtered images using 2D-DTCWT. After the sliding filtering, several feature maps are acquired according to the filter setting.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 4 Proposed Method: Adopting a Signal-Processing Technique
### Instantaneous Phase  
Raw signal data are recorded as a real value of time series. However, during analysis, the signal can be converted into complex series by adding the Hilbert transform of the real signal to an original signal series. The mathematical expressions of the complex signals of time series are  
\[c(t)=s(t)+i\ H\{s(t)\} \tag{5}\]  
\[c(t)=s(t)e^{i2(t)} \tag{6}\]  
\[c(t)=s(t)\{\cos(\varnothing(t))+i\sin(\varnothing(t))\} \tag{7}\]  
where _c_(_t_) is a complex signal, _s_(_t_) is a real signal time series, _H_[s(_t_)] is a Hilbert transform of the real part of the time series signal, and \(\varnothing(t)\) is an instantaneous phase of the time series. The instantaneous phase \(\varnothing(t)\) is taken as an arc tangent of the Hilbert transform of _s_(_t_) and _s_(_t_):  
\[\varnothing(t)=arc\ tan\bigg{(}\frac{H\{s(t)\}}{s(t)}\bigg{)} \tag{8}\]  
The instantaneous phase attribute is an amplitude-independent signal attribute; hence, this attribute is commonly used to identify structure rather than properties. The strata of the medium become more detailed by displaying this attribute. As this attribute is not dependent on amplitude, the attribute does not indicate the internal properties of the layers. However, this attribute can highlight a good record of the strata of the medium. It is predicted that if this attribute is adopted for medical imaging analysis, it can produce a different visualization between normal and osteoporosis bone, as the osteoporosis mechanism can change the physical geometry of bone. If osteoporosis can change bone structure, it is expected to be revealed in this attribute.
## 4 Game Clustering Model
### Profit and Loss Analysis
#### 4.2.2 Cost Model  
The loss of selected CH \(C_{CH}(i)\) primarily includes receiving data packets from cluster members, fusing data packets of cluster members, and the energy consumed by transferring data packets to the BS. In this study, the free space channel is assumed to be utilized for intra-cluster communication, and the multipath fading channel is presumed to be used for communication between CHs and the BS. Thus, \(C_{CH}(i)\) is defined as  
\[C_{CH}(i)=C_{Tx(CH,BS)}+C_{Rx(CH,i)}+C_{aggr} \tag{12}\]  
where \(C_{Tx(CH,BS)}\) is the energy required to transmit data packets from CH to the BS, \(C_{Rx(CH,i)}\) is the energy consumed by receiving data of cluster members, \(C_{aggr}\) is the energy consumption of data fusion through CH, and \(C_{Tx(CH,BS)}\) is given by  
\[C_{Tx(CH,BS)}=kd_{(CH,BS)}^{2}\varepsilon_{mp}+kE_{dec} \tag{13}\]  
where \(d_{(CH,BS)}\) represents the distance between CH and the BS, and \(\varepsilon_{mp}\) denotes the power amplification constant under the multipath fading channel model. \(E_{dec}\) is the power consumption of sending and receiving 1 bit data, and \(k\) is the packet size. Moreover, the energy consumption of the packets acquired and fused by CH is proportional to the packet size. Hence, \(C_{Tx(CH,i)}\) and \(C_{aggr}\) are obtained by  
\[C_{Tx(CH,i)}=(N_{m}-1)kE_{dec} \tag{14}\]  
\[C_{aggr}=kE_{aggr} \tag{15}\]  
where \(N_{m}\) is the number of nodes in the cluster, and \(E_{aggr}\) is the energy consumption of merging data of a node.  
Therefore, the cost of the selected CH is described by  
\[C_{CH(i)}=kd_{(CH,BS)}^{2}\varepsilon_{mp}+N_{m}kE_{dec}+kE_{aggr} \tag{16}\]  
In addition, the cost of cluster members is depicted by  
\[C_{CM}(i)=kd_{(CH,i)}^{4}\varepsilon_{fs}+kE_{dec} \tag{17}\]  
where \(d_{(CH,i)}\) is the distance between cluster member \(i\) and CH, and \(\varepsilon_{fs}\) is the power amplification constant of free space channel model.
# Seven-Phase PMSM Drives Operation Post Two Types of Faults
## 2 Operation of the Seven-Phase Drive Post the Speed Sensor Failure
### Algorithms to Extract the Rotor Position in Seven-Phase PMSMs  
The inductances of the stator winding of the seven-phase PMSM are modulated by the saturation saliency of the main flux. This modulation appears in the derivative of the stator currents of the seven-phase PMSM when any of the IGBTs in the inverter are switched on or off. Hence, the speed and position of the rotor can be extracted by measuring the current derivative due to the IGBTs switching action in one PWM waveform. The technique to extract the rotor position and speed of the seven- phase inverter can be found in [38].
# Graphene Oxide Adsorption Enhanced by Attapulgite to Remove Pb (II) from Aqueous Solution
## 3 Results and Discussion
### Thermodynamic Parameters  
The thermodynamic parameters \(\Delta H^{0}\), \(\Delta G^{0}\), and \(\Delta S^{0}\) of Pb\({}^{2+}\) adsorption on the as-prepared GO can be calculated according to the temperature adsorption isotherms. The standard free energy, \(\Delta G^{0}\), can be obtained by the following equation [43]:  
\[\Delta G^{0}=-R\mathrm{Tln}K_{0}=\Delta H^{0}-T\Delta S^{0} \tag{6}\]  
where \(R\) is the universal gas constant, 8.314 J/(mol\(\cdot\)K), and \(T\) is the absolute temperature in K. \(K_{0}\) is the thermodynamic equilibrium constant in mg/L, which can be calculated by \(q_{\rm e}/C_{\rm e}\). Based on Equation (6), the standard enthalpy change, \(\Delta H^{0}\), and standard entropy change, \(\Delta S^{0}\), can be obtained from the slope and intercept of the plot of ln\(K_{0}\) vs. 1/T by the following equation:  
\[\ln K_{0}=\frac{\Delta S^{0}}{R}-\frac{\Delta H^{0}}{RT} \tag{7}\]  
By calculation, the negative values of \(\Delta G^{0}\) are \(-\)4.19 kJ/mol at 298 K, \(-\)4.68 kJ/mol at 308 K, and \(-\)5.78 kJ/mol at 318 K, indicating that adsorption of Pb\({}^{2+}\) by the as-prepared GO is a spontaneous process [44; 45]. \(\Delta H^{0}\) is 19.40 kJ/mol, and \(\Delta S^{0}\) is 78.17 J/(mol\(\cdot\)K), indicating that the adsorption of Pb\({}^{2+}\) by the as-prepared GO is an exothermic process [46].
## 4 ACE and Blockchain-Based IoT Knowledge Sharing Scheme  
### Access Control Model of the IoT Knowledge Sharing  
To address the varied privacy requirements of distinct IoT users, clients within the HBCE-FL system can be categorized into different levels of privacy protection. Clients at low-security levels should be restricted from acquiring knowledge from those at higher security levels, mitigating potential privacy concerns arising from model inversion attacks. In light of potential gradient leakage, the model aggregation task for high-security level clients should be entrusted to a more secure cloud server. Simultaneously, high-security level servers should possess the capability to aggregate a larger number of local models, thereby enhancing services for users at higher security levels. Our approach incorporates mandatory access control to describe the accessibility for IoT knowledge flow.  
Mandatory access control surpasses discretionary and role-based access control in terms of security. The Bell-La Padula (BLP) model, a classic model in mandatory access control, classifies security levels based on information sensitivity. It enforces one-way information flow through communication rules [39] and ensures data confidentiality with a \"no read/no write\" principle [40]. Essentially, the BLP model restricts users with lower security levels from reading highly sensitive information and prevents the writing of such information to low-security areas. This aligns seamlessly with the privacy needs of IoT knowledge flow in our scheme.  
There are \(n\) security levels of senders \(S_{i}\) and receivers \(R_{j}\) in the HBCE-FL system (\(i,j\in[1,\ldots,n]\)). The predicate \(P:[n]\times[n]\rightarrow\{0,1\}\) indicates whether communication is allowed between the sender and the receiver. \(P(i,j)=1\) means that \(S_{i}\) is allowed to send to \(R_{j}\), while \(P(i,j)=0\) means it is not allowed. The access control policy of IoT knowledge flow can be defined as: \(P(i,j)=1\leftrightarrow i\geq j\).  
For example, in an HBCE-FL system with three security levels, the security levels are classified as Level 1: top-secret; Level 2: secret; and Level 3: public. Level 1 is the highest security level, and then successively lowered. The connectivity of the IoT knowledge flow between the different security levels is shown in Table 1.
# 3-D Numerical Study on Progressive Failure Characteristics of Marbles under Unloading Conditions
## 4 Discussions
### Influences of Preexisting Cracks' Distributions  
Based on the previous numerical results and experimental data [32; 65; 66], there were five different cracks, i.e., wing crack, horsetail crack, anti-wing crack, inclined secondary crack, and coplanar secondary crack, observed from the 3-D numerical simulations by discrete element method in this research. The schematic diagram of crack type is shown in Figure 11. Wing crack, horsetail crack, and anti-wing crack belong to the tensile cracks. On the other hand, the inclined secondary crack belongs to the mixed tensile-shear crack and coplanar secondary crack is a type of shear crack, as shown in Figure 11. At the same time, three different modes of crack coalescence, including tensile crack coalescence, mixed tensile-shear crack coalescence, and shear crack coalescence, in the rock bridge areas are also drawn in Figure 12.  
Figure 11: Five different kinds of crack appear in the present numerical simulations: (**a**) Tensile cracks and (**b**) shear cracks.  
Figure 12: Three modes of crack coalescence in the rock bridge region. (**a**) Tensile crack coalescence, (**b**) mixed tensile–shear crack coalescence, and (**c**) shear crack coalescence.  
To better understand the influence of initial fissure length \(a\), initial fissure inclination angle \(\alpha\), ligament length \(b\), and ligament inclination angle \(\beta\) on the mechanical behaviors of pre-fissured marble specimens under unloading conditions, relationships between numerically predicted axial deviatoric stress and other geometric factors are summarized in Figure 13. It can be observed from Figure 13 that the numerically predicted axial deviatoric stress decreased as values of the geometrical parameters, i.e., \(a\), \(b\), \(\alpha\), \(\beta\), increased. As shown in Figure 13, influences of pre-existing fissure length \(a\) and ligament length \(b\) on the peak strength was smaller than that of initial fissure inclination angle \(\alpha\) and ligament inclination angle \(\beta\). Moreover, it can be also found from Figure 13 that the unloading rate \(V_{U}\) had an important influence on the deviatoric strength effect. With the increase of the unloading rate, the peak value of axial deviatoric stress decreased, as shown in Figure 13. This shows the same pattern as the results of previous studies [8; 10; 20]. However, due to the different materials and loading conditions, the geometrical parameters of the sample had different effects on the partial stress strength.
## 6 System Evaluation
### Xbee ZB PRO S2 Radio Module
#### 6.1.3 Transmission and Mobility Tests  
The goal of the mobility tests is to measure the amount of data that two radio modules are capable of exchanging when one of them is moving. This test simulates the scenario where the sensor node deployed in the railway infrastructure (static node) sends information to a radio module located on the train (mobile node). Tests simulate the train with a car driving at a controlled speed. The vehicle drives along a straight road with a length of 2.1 km, as shown in Figure 12. The sensor node located on the floor continuously transmits information to the car. The car starts from a location where the radios are out of range, passes next to the sensor node on the floor and continues until the radios are again out of range. An antenna of 2 dBi has been used for the receiver/transmitter respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Transmitter**} & \multicolumn{2}{c}{**Receiver**} & \multirow{2}{*}{**Distance**} \\ \hline
**Antenna Gain** & **Height** & **Antenna Gain** & **Height** & \\ \hline Small (2 dBi) & Floor & Small (2 dBi) & Floor & 60 m \\ Small (2 dBi) & 2 m & Small (2 dBi) & 2 m & 200 m \\ Small (2 dBi) & 2 m & Small (2 dBi) & 2 m & 450 m \\ Big (5 dBi) & Floor & Big (5 dBi) & Floor & 600 m \\ Big (5 dBi) & 2 m & Big (5 dBi) & 2 m & 4000 m \\ \hline \end{tabular}
\end{table}
Table 3: Maximum communication range of Xbee PRO S2.  
Figure 12: Evaluation scenario for the range tests.  
Figure 13 shows a graph generated from the data obtained, varying the speed of the vehicle. At a speed of 80 km/h, the data received is less than 8 Kb. This amount of data is relatively low and is partly due to the fact that these modules require a long association time of about 5 s. Given that a high-speed train reaches speeds of over 300 km/h, these modules are clearly not suitable for node-to-train communication.  
However, these modules have a good performance under static conditions at a competitive price compared to the Xbee 868 modules. This module is, therefore, a good candidate for the static nodes deployed in the railway infrastructure (as proposed in Section 4.1).
## 3 Results and Discussion
### Polymer Modification  
PLGA-PEG-FA was successfully synthesized in two conjugation steps. FT-IR studies were carried out to confirm the presence of amide linkage in polymer (Figure 2). Characteristic bands for folic acid at 1452 and 1608 cm\({}^{-1}\) are presented in functionalized copolymer and could be due to stretching vibrations of C=C in the backbone of aromatic ring present in folic acid [57; 58]. The most significant FT-IR absorption peak in PLGA-PEG-FA polymer was caused by the presence of -CONH- linkage. The carbonyl (C=O) and amine (N\(-\)H) groups present in amide linkage exhibited bands at 1623 and 1571 cm\({}^{-1}\) respectively [45; 58]. Previous works with similar polymer modifications reported characteristic FT-IR absorption peaks obtained in the present work. For example, [PERSON] et al. [57] hybridized folic acid with layered double hydroxide by ion-exchange reaction. In the same way, [PERSON] et al. [58] reported FA-chitosan conjugates and [PERSON] et al. [45] synthesized PLGA-PEG-FA. [PERSON] et al. work on FT-IR spectrum results matches the spectrum obtained with copolymer prepared in the present work.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 1 Introduction  
In recent years, the telecommunication and wired computer communication communities have rapidly developed several mobile technologies. Consequently, different skills and huge efforts are required to counter attacks malicious users particularly on mobile technologies, such as global system for mobile communication (2G-GSM), code division multiple access (3G-CDMA), and long term evolution (4G-LTE) communication systems. This accordingly made mobile security systems a key field that requires great attention and more study. Meanwhile, there is no unique solution to counter the attacks on mobile technologies despite the increasing cooperation between the telecommunication and the Internet industries. For instance, the first Generation 1G mobile telephone systems providedessentially no security features. The second Generation 2G GSM system was designed such that it provides security similar to that of eavesdropping in fixed phones, and to protect against cloning of mobile identities [1]. Thus, various weaknesses on GSM mobile security systems were revealed by theoretical and practical works (See Section 2.1). The weaknesses found on 2G GSM were taken into account with the emergence of the 3G or CDMA security system. The 3G security system offers new security features and services. In addition, it is important to stress that the 3G security architecture is not to provide a completely secure system, but to build a system that is flexible and adaptable to new challenges [2].  
As far as LTE is concerned, despite developed and standardized by the 3 GPP and considered the most secure cellular system by providing security enhancements in comparison with GSM and CDMA cellular system, the LTE security architecture still has issues, particularly in key management techniques, such as the lack of privacy-preservation and key forward/backward secrecy. Moreover, most of the existing studies of mobile communication protocols have focused on confidentiality and authentication requirements. The public key infrastructure (PKI) meant that communication entities use two different keys, and was supposedly a good solution for the mobile communication security due to its flexibility and security features. It could be tailored with the new technologies such as 4G and 5G despite their high communication characteristics (Band width, Data rate, etc.) in order to open a new direction for PKI-based security [3].
# Compressing BERT for Binary Text Classification via Adaptive Truncation before Fine-Tuning
## 4 Truncating BERT before Fine-Tuning
### The Compression Method  
Now, we describe how to use CSM as a proxy to truncate BERT before fine-tuning, and hence reduce resources to be consumed in both fine-tuning and inference without a significant loss in classification accuracy.  
Let \(\mathbb{S}_{tr}\) be the training set of a BTC task. The maximum and minimum of the CSMs of the embedding sets generated from \(\mathbb{S}_{tr}\) by encoders with different numbers of Transformer layers are denoted by \(CSM^{*}\) and \(CSM_{*}\), respectively. That is,  
\[CSM^{*}=\max_{m}\{CSM(h_{m}(\mathbb{S}_{tr}))\}, \tag{9}\]  
\[CSM_{*}=\min_{m}\{CSM(h_{m}(\mathbb{S}_{tr}))\}, \tag{10}\]  
where \(h_{m}(\mathbb{S}_{tr})\) is the embedding set generated by \(h_{m}\), a pre-trained encoder consisting of \(m\) Transformer layers. The encoder indices/subscripts corresponding to \(CSM^{*}\) and \(CSM_{*}\) are denoted by \(m^{*}\) and \(m_{*}\), respectively. As the goal of compression is to shrink the model under consideration as much as possible without significantly diminished accuracy, we define \(\rho(\in[0,\ 1])\) to be the _maximally tolerable ratio of (relative) accuracy decline_, and determine the number of Transformers to be preserved during truncation, denoted by \(m^{\triangle}\), as follows  
\[\Delta_{CSM}=CSM^{*}-CSM_{*}, \tag{11}\]  
\[m^{\triangle}=\min_{CSM(h_{m}(\mathbb{S}_{tr}))\geq CSM^{*}-\rho\Delta_{CSM}}\{ 1,\dots,m^{*}\}. \tag{12}\]  
By definition, \(\rho\) should be a small constant. Otherwise, significant decreases in accuracy may occur. In our experiments (see Section 5.3.1), we find \(0.20\leq\rho\leq 0.25\) to be a good choice that ensures very small accuracy loss while achieving a considerable compression ratio. As an additional advantage, \(\rho\) makes the balance between accuracy loss and model size reduction somewhat controllable, and hence provides the possibility to implement compression on-demand.  
To summarize, truncating BERT before adaptively fine-tuning for a specified BTC task can be implemented in three steps: (i) Vectorize each example in the training set \(\mathbb{S}_{tr}\) of the target task as \(\mathbf{x}_{n}(n=1,\dots,N)\). (ii) Feed each \(\mathbf{x}_{n}\) into a pre-trained BERT, and collect all the hidden states, each of which is denoted as \(\mathbf{z}_{n}^{(m)}(m=1,\dots,12)\), generated by each Transformer layer into an embedding set \(Z^{(m)}\). Namely, \(Z^{(m)}=h_{m}(\mathbb{S}_{tr})=\left\{\mathbf{z}_{1}^{(m)},\dots,\mathbf{z}_ {N}^{(m)}\right\}\). (iii) Calculate the CSM of each embedding set \(Z^{(m)}\) for \(m=1,\dots,12\), and find the truncation position according to Equation (12), described above.