esa-sceva/satcom-chunk-collection huggingface dataset:
# Multicell Interference Management in Device to Device Underlay Cellular Networks
## 1 Introduction
### Related Work  
RB allocation approaches in the uplink of Long Term Evolution (LTE) networks have gained a lot of interest in the literature [13; 14; 15]. In this setting, the eNB needs to determine in an efficient and timely manner the assignment of RBs to the users of the network. The optimization objective of the RBs assignment process could be the total throughput maximization, the interference mitigation [16; 17], or the spectral efficiency improvement.  
The approaches above concern the traditional cellular networks, where the users communicate via the eNB. Towards 5G networks and enabling the D2D communication capabilities of the users [18; 19; 20], respective RB allocation approaches can be found in [21; 22; 23]. In [21] the importance of RB allocation approaches in various and emerging network architectures is stressed via presenting different approaches in LTE and LTE-Advanced networks. In [22] the authors propose scheduling algorithms for the problem of RB allocation in D2D enabled networks taking into account each user's delay requirement. Here the eNB needs to determine the optimal assignment of resources to the users at time scales of milliseconds according to 3 rd Generation Partnership Project (3 GPP) specifications including devices that may communicate directly. In [23] the authors are proposing an efficient resource allocation scheme in clustered D2D enabled networks.  
Focusing on the uplink RB allocation problem for the D2D enabled networks most of the related work is restricted to single cell environments [7; 8; 9; 10; 11; 12], while very little attention has been paid so far to the respective multi-cell case. Specifically, in [24; 25] the D2D RB selection problem is analysed as a game, in a simple scenario with one cellular user per cell. The players of the game are the BSs and they compete with each other in order to serve the D2D users with less harm. However, the existence of the Nash Equilibrium (NE) point of the game is not analytically proven, and it is only examined via numerical simulations.  
The previous works focus on maximizing the total sum of rates in the network via game theoretic approaches and pricing for the resources. In [26], the authors address only the problem of RB allocation in a D2D underlay multicell network, via formulating a potential game with the objective of optimizingthe total sum of rates in the network. In [27] the problem of total throughput maximization for the D2D users in a multicell D2D underlay cellular network is addressed under a Multiple Input Multiple Output (MIMO) setting. In [28] the problem of both RB and power allocation is addressed. Initially, the authors aim at minimizing the interference caused by the D2D links to the cellular links of the network, and subsequently a power control approach is applied in order to maximize total system throughput.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 3 Frequency Domain Simulation
### Design Rules  
With the increase of the frequency, the bonding parameters have a great influence on the characteristics of microwave transmission. Therefore, it is important to analyze, optimize and design the parasitic characteristics of high-power bonding wires under high frequency. The appropriate design parameters should be selected according to different working frequency bands. When the working frequency is too high, the transmission quality of the signal may be very poor. Combined with the practical project application, in order to reduce the parasitic characteristics while improving the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bonding Wire Loop** & **M** & **Q** & **S** \\ \hline Min S21 (Insertion Loss)/dB & \(-9.59\) & \(-9.29\) & \(-8.71\) \\ Corresponding frequency/GHz & 17.73 & 17.68 & 17.38 \\ Passing signal/incoming signal/\% & 33.15 & 34.32 & 36.69 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of S21 extremum under different loop modes.  
Figure 11: (a) S11 of bonding wire with different loop modes; (b) S21 of bonding wire with loop modes.  
Figure 10: Bonding wire loop modes: (a) Q-loop bonding wire; (b) S-loop bonding wire; (c) M-loop bonding wire.  
frequency characteristics and electromagnetic properties of bonding wires, the optimization measures are obtained from the following five aspects (as shown in Table 7).
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 3 Application  
### Flight Data of UAV  
The flying parameter system of a UAV records the entire process data from the start to the stop of the UAV, including attitude, altitude, power, navigation parameters, and other indicators. With the development of machine learning technology, data-driven diagnosis technology has become an important part of fault diagnosis. For UAVs, flight data are the basis for fault diagnosis and prediction. But in the actual flight data, there are two problems. First, the flying data set is huge. Airborne equipment usually records data in milliseconds, and the number of samples recorded during a flight may reach hundreds of thousands. Therefore, before using the flight data for analysis, the data should be compressed at the variable level. Second, there are a large number of flight status indicators in flight data, ranging from dozens to hundreds. The relationship between indicators is complicated, and the correlation is serious. Therefore, it is necessary to reduce the dimensionality of the data before using the flying parameter data for modeling. Using the idea of interval data and CICA method can solve these two problems well.  
Figure 1: The structure of three-layer neural network.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 4 Evaluation
### Relation of Metric for Energy Efficiency
#### 4.2.1 Importance of Balance between Metrics  
Figure 7 shows the relationship of WT and reliability. For measurement, we set the WT per 100 ms. Then the placement measurement is conducted 200 times.  
As shown in Figure 7, waiting time is increased while the number of failure VMs decreased. The result of the test indicate that we can find that WT is related to performance metrics such as service ET. Thus, we have to consider various metrics, according to objectives.
## 3 Results and Discussion
### Analysis of the Erosion Evolution Using CT and XRM
In addition to the computed tomographic measurements and AI evaluation, surface analysis using the fringe light projection method (3D profilometer) was carried out to generate an overview of the specimen surface and to document surface defects in their three-dimensional shape. The initial test specimen state is shown in Figure 19. Figure 19a shows a smooth surface based on the image recordings, which has a certain inhomogeneity in the form of an undefined waviness. This is also clearly evident from the 3D profilometer height representation in Figure 19b. In addition, individual defects introduced in the coating can be seen in the height image using slightly indicated local elevations on the surface, but not in the visual image. Figure 20 shows the damaged test specimen state after loading with rain. In both the visual image and the height image, the defects on the surface of the coating caused by rain erosion are visible. Small defects appear on the surface along the entire specimen, which is hypothesized to result from initially introduced defects since they weaken the material structure. Significant damage due to rain erosion is visible in the area of the marking hole of the GFRP specimen (about 1 cm to 1.5 cm from the left end of the specimen). In addition, a coating peeling is visible in the front area of the specimen (up to 3 cm from the left edge). These large-area coating peels are due to adhesion restrictions of the individual applied coating layers on this specimen. Figure 19: (**a**) Visual image and (**b**) 3D profilometer height image of a test specimen with voids in the coating before rain loading. Figure 20: (**a**) Visual and (**b**) 3D profilometer height image of a whole test specimen with voids in the coating after rain loading. In conclusion, the evaluated CT and XRM data results show that initial defects not visible from the outside are weak points where the material is destroyed at first, and the defects grow during rain loading. Furthermore, the surface defects appear to be smaller when observed from the outside as they are inside the specimen, so in both cases, a visual inspection of the leading edge of a GFRP rotor blade is not suitable for assessing the entire damage, as the full extent of the degraded area cannot be determined. Therefore, a measurement method for in-situ applications is necessary, which can visualize both the damaged state of the rotor blade leading edge as well as the damaged state beneath the surface.
## 1 Introduction
The combination of cyclostationarity and ANN can be extended to implement an adaptive frequency shift (FRESH) [19; 20; 21; 22] filtering at the receiver to extract the signal of interest (SOI) through successive interference cancellation (SIC). Most multicarrier wireless signals are appended with a cyclic prefix (CP) to combat the inter-symbol interference (ISI) caused by frequency selective fading channel. It is assumed that the CP length used by the PU-OFDM is different from that used by the SU-NOMA FC-FB system. Hence the FRESH filter can take advantage of the cyclostationarity induced by the CP of PU-OFDM to extract the PU-OFDM signal first. The extracted PU-OFDMA signal can then be subtracted from the received signal mixture, yielding an initial estimate of the FC-FB based SU-NOMA. Filter-bank demodulation can be performed on the initial estimate to recover the CP-NOMA blocks corrupted by channel noise. The signal can be subject to a second FRESH filter which can extract the desired SU-NOMA signal. Such receiver side neuromorphic processing can prove beneficial in cognitive NOMA scenarios under very low SNR conditions, where multiple PU and SU units attempt to coexist together. Due to the non-Gaussian nature of the residual signal, the ANN based cyclic FRESH receiver structure can prove beneficial in estimating the NOMCR individual signal components.
# Functional Magnetic Resonance Imaging in Research on Dog Cognition: A Systematic Review
## 3 Results
### Research Articles on Canine fMRI Without Stimuli (Resting State Network)  
Among the five articles on resting state networks, three (60.0%) were observational cross-sectional studies, while one article each (20.0%) represented an observational cohort study and an experimental non-randomized trial, respectively. The size of study groups was large, ranging from 10 dogs [14] to 40 dogs [15]. In total, 143 dogs were examined for fMRI resting state network assessment. Of these, 95 dogs (66.4%) across three studies were assessed without anesthesia [15; 16; 17], while 48 dogs (33.6%) across two studies were examined under general anesthesia [14; 18].  
[PERSON] et al. mapped whole-brain connectivity patterns [17] and functional networks [16] in conscious dogs, identifying primary areas of activation within networks involving the prefrontal cortex, anterior cingulate, posterior cingulate, and hippocampal regions [17]. Additionally, they identified two highly connected, lateralized multi-region networks [16]. Other research groups mapped functional networks [14] and functional connectivity [18] in anxious dogs, finding activation to be related to anxiety within amygdala-hippocampus, amygdala-mesencephalon, amygdala-thalamus, frontal lobe-hippocampus, frontal lobe-thalamus, and hippocampus-thalamus pathways [18]. Recently, [PERSON] et al. investigated resting-state functional connectivity to predict trainability in working dogs, discovering functional connectivity between the brainstem and the frontal cortex [15].  
The cognitive findings from canine fMRI studies performed without stimuli are summarized in Table 1 and are detailed in the referenced articles.
## 5 The Computational Results and Analysis
### Performance Analysis of the QL-Driven ABC Algorithm with Learning and Deteriorating Effects  
In this section, the IQABC-LDE algorithm with a learning and deteriorating effect is evaluated on the designed benchmarks completed with random learning and deteriorating indexes generated from a uniform distribution \(\mathcal{U}[0,1]\). The results of the 1100 executions are summarized in Table 6 and compared to those of the ABC-LDE reported in [54]. The ABC-LDE follows the same steps as the ABC-LE, with the addition of the deteriorating effect. The ARPD values reported in 6 are calculated on the basis of the \(C_{max}\) values given in Formula (5).  
Once again, the IQABC-LDE is superior to the ABC-LDE, proving the effectiveness and robustness of the QL-based neighbor selection mechanism when combined with the ABC metaheuristic. As with the learning effect, we observe a significant difference in the results obtained with the IQABC algorithm if the learning and degradation effects are not taken into account, thus emphasizing the need to integrate these effects into the study. In fact, the deteriorating effect, as opposed to the learning effect, extends processing times. It is therefore important for manufacturers to implement an efficient maintenance strategy to prevent machines from deteriorating and additional delays.  
Figure 8: Box plot of the IQABC_LE algorithm with SF, LF, and FPM for both maintenance modes.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{2}{c}{**M1**} & \multicolumn{2}{c}{**M2**} \\ \cline{2-5}  & **ABC-LE(FPM)** & **IQABC-LE(FPM)** & **ABC-LE(FPM)** & **IQABC-LE(FPM)** \\ \hline
20x5 & 4.4 & 2.95 & 3.38 & 3.44 \\
20x10 & 13.69 & 11.42 & 14.20 & 12.23 \\
20x20 & 25.11 & 23.38 & 27.28 & 24.84 \\
50x5 & 0.76 & \(-\)0.47 & 1.24 & \(-\)1.48 \\
50x10 & 7.63 & 4.51 & 9.86 & 4.39 \\
50x20 & 17.66 & 13.44 & 20.96 & 14.34 \\
100x5 & \(-\)1.47 & \(-\)1.24 & \(-\)1.55 & \(-\)3.02 \\
100x10 & 1.84 & 0.47 & 1.52 & \(-\)0.38 \\
100x20 & 9.9 & 7.70 & 9.30 & 7.05 \\
200x10 & \(-\)1 & \(-\)2.01 & \(-\)2.10 & \(-\)2.74 \\
200x20 & 3.45 & 2.40 & 3.19 & 2.41 \\
500x20 & \(-\)0.52 & \(-\)0.16 & \(-\)2.49 & \(-\)1.61 \\ \hline average & 6.79 & 5.19 & 7.06 & 4.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance offset from [PERSON]’s upper bounds of IQABC-LE and ABC-LE considering random learning indexes per machine.
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 2 Methodology
### Dual-Frequency Processing Methods
\begin{array}{l}s_{c1}(\tau,t_{m})=A_{3}\cdot X(\tau-\Delta t)\cdot dec(\frac {v\cdot(1+\cos\alpha)}{\lambda_{1}})\cdot\\ \exp\left\{-j\cdot 2\pi\cdot\frac{v\cdot(1+\cos\alpha)}{\lambda_{1}}.(t_{m}+\tau- \Delta t)\right\}\\ \\ s_{c2}(\tau,t_{m})=A_{3}^{\prime}\cdot X^{\prime}(\tau-\Delta t)\cdot dec(\frac {v\cdot(1+\cos\alpha)}{\lambda_{2}})\cdot\\ \exp\left\{-j\cdot 2\pi\cdot\frac{v\cdot(1+\cos\alpha)}{\lambda_{2}}.(t_{m}+ \tau-\Delta t)\right\}\\ \end{array}  \tag{10}\]  
Since the signals of the two frequencies \(f_{c1}\) and \(f_{c2}\) are transmitted by the same satellite, the bistatic angle \(\alpha\) is same, and the phase difference in the echoes of the two frequency signals is mainly caused by the difference in wavelength. The phase scaling factor \(k\) is defined as shown in Equation (12). \[k=\frac{\lambda_{2}}{\lambda_{1}}=\frac{f_{c1}}{f_{c2}} \tag{12}\]  
The amplitudes of the two frequency echo signals in Equations (10) and (11) are added to obtain the joint amplitude \(AA\), as shown in Equation (13). The phase of the echo signal at frequency \(f_{c2}\) is multiplied by the scaling factor \(k\) and subtracted from the signal at frequency \(f_{c1}\) to obtain a differential phase \(\Delta\varphi\), as shown in Equation (14). After subtraction, the fluctuation of the single-frequency signal over time \(t\) is canceled out. The differential phase \(\Delta\varphi\) is close to zero, with only small amplitude random noise \(\varphi_{noise}\). \[AA=A_{3}\cdot X(\tau-\Delta t)\cdot dec(\frac{v\cdot(1+\cos\alpha)}{\lambda_{1 }})+A_{3}^{\prime}\cdot X^{\prime}(\tau-\Delta t)\cdot dec(\frac{v\cdot(1+ \cos\alpha)}{\lambda_{2}}) \tag{13}\]  
\[ \begin{array}{l}\Delta\varphi=\left\{2\pi\cdot\frac{v\cdot(1+\cos\alpha)}{ \lambda_{1}}.(t_{m}+\tau-\Delta t)\right\}-k\cdot\left\{2\pi\cdot\frac{v\cdot (1+\cos\alpha)}{\lambda_{2}}.(t_{m}+\tau-\Delta t)\right\}\\ =\varphi_{noise}\\ \end{array}  \tag{14}\]  
Then, a new signal \(\Delta s(\tau,t_{m})\) is composed of joint amplitude \(AA\) and differential phase \(\Delta\varphi\), as shown in Equation (15). It fully utilizes the amplitude of dual-frequency signals and eliminates the fluctuation of the signal phase over time, allowing for long-term coherent accumulation to improve signal SNR. \[\Delta s(\tau,t_{m})=AA\cdot\exp\{-j\cdot\varphi_{noise}\} \tag{15}\]
###### Abstract
[PERSON] carried out triaxial strength tests on saturated silt in the temperature range 0-35 \({}^{\circ}\)C and found that the soil strength decreases with increasing temperature [20]. Moreover, high temperature has been reported to reduce the soil strength and enhance crack formation [21]. [PERSON] found that higher temperature leads to a jump in pore water pressure [22]. We determined that the strength of the red clay and expansive soil decrease with increasing temperature (from \(-4\) to 60 \({}^{\circ}\)C), and red clay, in particular, exhibits higher heat sensitivity [23]. Moreover, [PERSON] described the effect of temperature on the SWCC under isothermal environment [24]. However, the above-mentioned studies focus on the influence of temperature on the strength of clay, silt, red clay, and expansive soil. Studies on the effect of temperature on the C-M-S strength are still scarce. Meanwhile, extensive studies have indicated that changes in soil macroscopic mechanics are generally related to the microstructural features [25; 26]. Furthermore, water is a vital cause of mechanical deterioration in unsaturated soils [27]. In this regard, scanning electron microscopy (SEM) has become the most commonly used geotechnical technology in the study of geotechnical microstructure. The microstructure and morphological characteristics of CMS along the shear plane for different water contents are studied via SEM and direct shear tests [28]. CMS contains a large number of clay minerals, which have expansive characteristics [29]. In addition, surface soil degradation caused by seasonal dry-wet cycles destroys the surface structures used for slope protection. Thus, it is important to understand the soil-failure mechanism via analyzing the C-M-S microstructures and the variation in the soil shear-strength parameters under dry-wet cycling. Moreover, the coupling effects of dry-wet cycling and temperature on the microstructural features of CMS have not, thus far, been investigated in detail. This paper firstly performs surveys on the shear strength and microscopic parameters of coal measure soil under dry-wet condition and seasonal climate in the mountains of Southern China. In order to further explore the effects of dry-wet cycling and temperature on the shear strength and microscopic parameters of CMS, an unconsolidated undrained triaxial test and SEM test were carried out. Firstly, the effects of dry-wet cycling and temperature on the shear strength of C-M-S samples were investigated. Then, the microscopic parameter changes in the CMS after dry-wet cycling was studied. Finally, the crack development relationship with the temperature-induced stress after dry-wet cycling and the failure mechanism of the K209 landslide on the Chang-li highway were discussed.
## 3 Results and Discussion
### Tri-Layer Planar Heterojunction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & **Response Wavelength** & \(R\) **(mA/W)** & **References** \\  & **(nm)** & **(26)** \\ \hline n\({}^{+}\)-Si/SiO\({}_{2}\)/CuPc/PbPc:PTCDA/Au & 808 & 322 mA/W & [26] \\ n\({}^{+}\)-Si/SiO\({}_{2}\)/PbPc/pentacene/Ag & 808 & 123 mA/W & [27] \\ n\({}^{+}\)-Si/SiO\({}_{2}\)/PbPc/pentacene(2 nm)/Au & 808 & 505 mA/W & [31] \\ n\({}^{+}\)-Si/SiO\({}_{2}\)/PbPc/Au & 850 & 12.08 mA/W & [38] \\ n\({}^{+}\)-Si/SiO\({}_{2}\)/C\({}_{60}\)/PbPc:AlClPc:PTCDA/Au & 850 & 580 mA/W & [39] \\ n\({}^{+}\)-Si/SiO\({}_{2}\)/pentacene/PbPc/C\({}_{60}\)/Au & 850 & 1415 mA/W & this work \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of device performance. The air stability of Dev-D was measured and shown in Figure 5. As can be seen, after 120 h, the \(R\), \(P_{\rm max}\), and \(\mu\) of Dev-D increased from 1415 mA/W, 1.2 \(\times\) 10\({}^{4}\), and 2.4 \(\times\) 10\({}^{-2}\) cm\({}^{2}\)/V-s to 1500 mA/W, 1.38 \(\times\) 10\({}^{4}\), and 2.56 \(\times\) 10\({}^{-2}\) cm\({}^{2}\)/V-s by 6%, 15%, and 6.7%, respectively. The results indicated that Dev-D has superior air stability to Dev-B, and air-sensitive \(C_{60}\) has almost no effect on the air stability of Dev-D. This is because the C\({}_{60}\) layer located on the PbPc layer would play a key role in passivation and prevent O\({}_{2}\) and H\({}_{2}\)O molecules from permeating towards PbPc to some extent. It is an interesting phenomenon that the device performance may deteriorate quickly when C\({}_{60}\) acts as the channel layer in an OPT, whereas the device performance can be maintained well when C\({}_{60}\) functions as the top-level passivation layer. This indicates that the air stability of the OPTs containing the C\({}_{60}\) layer is linked to the position of the C\({}_{60}\) layer. To sum up, C\({}_{60}\) acts as a blocking layer, an exciton dissociation layer, and a passivation layer in Dev-D.
# Transforming Group Codes in Mealy Finite State Machines with Composite State Codes
## 3 Peculiarities of FSMs Based on Composite State Assignment
The function assembler generates functions (13) and (14). The FSM outputs (14) enter the other blocks of the digital system. The IMFs (13) enter the flip-flops of the register RG. The outputs of the flip-flops determine both the group and partial state codes. In [11], the results of various experiments are shown. The experiments allowed for a comparison of the characteristics of CSC-, MBC- and OHC-based FSMs. These results show that CSC-based FSMs have better LUT counts than are obtained using either MBCs or OHCs. It is very important that reducing the LUT count does not reduce the values of the maximum operating frequencies. The best case for using CSC-based FSMs was determined in [11]. These conditions are the following:  
\[R_{k}=\lceil log_{2}NS_{k}\rceil. \tag{6}\]0  
\[R_{k}=\lceil log_{2}NS_{k}\rceil. \tag{6}\]1  
If condition (15) is violated, then practically, there is no need to use FD-based approaches. The corresponding FSM circuits are mostly single-level [11]. If condition (16) is true, then there is a single level of LUTs in the function assembler circuit. If both conditions (15) and (16) hold, then there are two levels of LUTs in the circuits of \(U_{2}\)-based FSMs. As follows from the analysis of systems (13) and (14), the violation of condition (16) is connected with the existence of a direct dependence between the functions \(f_{i}\in D\cup Y\) and group variables \(\tau_{r}\in\tau_{G}\). So, to reduce the number of levels in the function assembler circuit, it is necessary to eliminate this direct dependence. This will allow for the deletion of the conjunctions \(G_{1},\dots,G_{KC}\) from functions generated by the function assembler. One of the possible approaches for solving this problem is proposed in our current article.