esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Identification of Modal Frequencies and Damping Ratios  
VMD [42] is adopted as the primary signal decomposition technique owing to its capability to separate mixed signals into mutually independent components (MICs). In contrast to traditional approaches--which are often affected by problems such as mode mixing and end effects--VMD iteratively minimizes the bandwidth of each modal component while simultaneously determining its center frequency. The decomposition process is formulated as a constrained variational optimization problem, expressed as follows [42]:  
\[\begin{split}\min\biggl{\{}\sum\limits_{k=1}^{N}\left\|\partial_{t }[(\delta(t)+\frac{j}{\pi t})\times\mu_{k}(t)]e^{-j\,2\pi\
ot f_{k}t}\right\| _{2}^{2}\biggr{\}},\\ \sum\limits_{k=1}^{N}\mu_{k}=x(t)\end{split} \tag{1}\]  
where \(\mu_{k}\) represents each mode, \(f_{k}\) is the center frequency, \(\delta(t)\) is the Dirac distribution, and \(x(t)\) is the input signal.  
After decomposition, each single-mode component is processed using NExT to obtain the free decay response. Subsequently, the SDESA method is employed to identify the modal frequency as follows [44]:  
\[\begin{cases}\psi_{d}(x(n))=[x(n)]^{2}-x(n-1)x(n+1)\\ y(n)=x(n)-x(n-1)\\ \Omega_{c}(n)=\cos^{-1}\Bigl{[}1-\frac{\psi_{d}(y(n))}{2\psi_{d}(x(n))}\Bigr{]} \\ f(n)=\Omega_{c}(n)f_{s}/2\pi\end{cases}, \tag{2}\]  
where \(x(n)\) is the discrete signal, \(f_{s}\) is the sampling frequency, and \(f(n)\) devotes the actual frequency of \(x(n)\). This approach mitigates end effects, ensuring precise identification of natural frequencies.  
To identify damping ratios, the HCEO [45] is applied to the free decay signals of each mode. This approach calculates the energy dissipated per vibration cycle and derives the damping ratio \(\xi\) as follows [45]:  
\[\begin{cases}\begin{split} E_{i}=\int_{t_{1}}^{t_{i}+T_{d}/2}x^{2 }(t)dt\\ C=\ln\Bigl{[}\frac{E_{1}+E_{3}+\cdots+E_{2N-1}}{E_{2}+E_{4}+\cdots+E_{2N}} \Bigr{]},\\ \xi=\frac{C}{\sqrt{4\pi^{2}+C^{2}}}\end{split}\end{cases} \tag{3}\]  
where \(E_{i}\) is the energy of each half-cycle.  
While the original VMD-SH [41] was designed for frequency and damping ratio identification, this study proposes an extended approach to also identify mode shapes, as detailed in Section 3.2.
## 1 Introduction
In the strict FFR, the entire frequency band can not be fully used in an entire cell. Some portion of resources are used only for CUEs or cell center users and the remaining portion is allocated to DUEs or cell edge users. Thus, the waste of resources and the loss of throughput are inevitable in the strict FFR. On the other hand, the entire frequency can be used for the entire users in the soft FFR, but the interference occurs from CUEs to DUE-Rx. The interference can prevent a DUE-Rx from receiving data from a CUE on transmitting mode (CUE-Tx) in cell edge areas. In this paper, a hybrid mechanism of fractional frequency reuse and almost blank sub-frame (FFR-ABS) schemes are proposed to mitigate inter-cell interference caused by CUEs of neighboring cells to D2D pairs in cell edge areas. The proposed scheme can guarantee the Quality-of-Service (QoS) of both CUE and D2D pairs in terms of throughput and signal to interference and noise ratio (SINR), and increase the throughput of UEs in the cell edge area. The rest of this paper is organized as follows: Section 2 describes proximity services and resource allocation for D2D in LTE-A standards. The system model and proposed scheme are discussed in Section 3. Mathematical modeling, analysis, and discussion of the performance evaluations are provided in Sections 4 and 5, respectively. Finally, concluding remarks are summarized in Section 6.
## 2 Architectural Framework of IBSN and Its Components
### Communication Strategies
#### 2.5.1 Medium of Communication  
Communication between the nodes in IBSN can happen in three different manners, namely, in-body to in-body, in-body to on-body, in-body to off-body (a base station). Unlike the Body Area Network (BAN) and Personal Area Network (PAN), communication in IBSN happens between the implanted nodes to a base station placed outside or on the human body. Moreover, BAN and PAN networks have limited use of the latest implantable medical devices, such as deep brain stimulators. This is partly due to the limited capabilities of BAN and PAN, which cannot support the requirements of implantable medical devices in terms of power-consumption, reliability, and quality-of-service parameters [22].  
International regulation of the frequency band for life-critical medical devices will benefit the user, the wireless medical industry, and regulators. It will also positively impact on cost-saving, quality, reliability and delivery of health-care. Medical Implant Communication Service (MICS) is accepted globally for the use of medical devices and is standardized [23]. To this end, an IEEE 802.15.6 task group was set up to standardize the in-body communication together with the off-body and on-body communication. One of the main aspects of this standard is to standardize the physical layer with three different radio configurations, namely narrow band, ultra-wide band, and human body communications.  
Out of the three radio configurations, MICS band (under the narrow band) was dedicated to the in-body communication, which operates at 402-405 MHz with 10 channels of 300 KHz bandwidth. Body-worn applications are not allowed in the MICS band [22]. The 402-405 MHz frequencies have propagation characteristics conductive to the transmission of radio signals within the human body. Also, tissue absorption is reduced by using a lower transmitting frequency, which has two advantages, one being to improve signal propagation within human body, the other being to reduce the thermal overload caused to the surrounding tissues due to the absorption of radio waves [24].
## 3 Materials and Methods
### Scanning System
#### 3.4.1 Data Post Processing  
Before starting the detector optimization, the training data size is expanded to increase the accuracy as more resources are available for the neural network optimizer in the detector. This is achieved through data augmentation techniques that do not require more samples. Instead, linear and non-linear transformations methods are applied to the images in the dataset to distort some of the visual information of the image while preserving its structure. The data augmentation process not only provides additional samples, but the variance it adds to the dataset helps in decreasing overfitting [20].  
Figure 5: Schematic of the linear actuator circuit.  
Different types of transformation can be applied to augment the data, including image rotation, shearing, affine transformation, and multiple color transformations, such as contrast manipulation, blurring, and histogram equalization [21]. We had some considerations to take into account before selecting proper transformation. First, we recorded the raw data videos for barcodes at different pitch and yaw angles, depth, and positions. Thus, performing image projection or displacement cannot increase the variance of the data. Additionally, we avoided texture transformation since distorting the barcode strips can badly affect the system performance. Instead, we only used reflection and contrast mapping across different color channels. Since bounding boxes are assigned earlier, the bounding boxes are displaced after reflection. Bounding boxes correction is applied as follows:  
* Define a bounding box as \(B=[b_{1},b_{2},b_{3},b_{4}]=[x,y,width,height]\) for an image with \(m\) rows and \(n\) columns.
* For reflection over the \(x\)-axis, the following holds: \[b_{2,new}=b_{2}-2(b_{2}-\frac{m}{2})-b_{4}\] (9)
* For reflection over the \(y\)-axis, the following holds: \[b_{1,new}=b_{1}-2(b_{1}-\frac{n}{2})-b_{3}\] (10)  
Meanwhile, random color transformation is applied for each original image, image reflected on \(y\), and image reflected on \(x\) as follows:  
* An image is defined as \(I=[R,G,B]\) for the RGB color layers.
* A random channel is selected each time. Supposing that \(R\) is selected, then for each pixel \(x_{C}\) in a channel \(C\), the following holds: \[x_{R}=\left\{\begin{aligned} 0.15,&\quad 0\leq x_{R} \leq 0.15\\ x_{R},&\quad 0.15<x_{R}\leq 1.0\end{aligned}x_{G,B}= \left\{\begin{aligned} 0.25,&\quad 0\leq x_{G,B}\leq 0.25\\ x_{G,B},&\quad 0.25<x_{G,B}\leq 1.0\end{aligned}\right.\] (11)  
The augmentation process results in increasing the dataset six times. For each sample, the transformations shown in Figure 6 are applied. The total size of the final dataset is 31,500 samples. No additional transformations are applied to avoid redundancy or excessively increasing the training time. The optimized model required about 40 h to finish 12 epochs over the complete dataset, using a GTX 1060 GPU.  
Figure 6: Reflections and their color transformations for one sample.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 3 Results and Discussion
### Methods to Improve Fiber Weave Effects
#### 3.4.2 NE-Glass Fiber Weaves  
NE-glass (new electronic glass), a product of Nittobo, shows improved electrical and mechanical performance over traditional E-glass and is widely used in some low-loss laminate products (for example, it was used in Nelco's N4000-13 SI and Meterowave 2000, TUC's TU872 SLK SP, and Panasonic's Megtron 6 NE and Megtron 7 NE). NE-glass shows better temperature stability, lower Dk, and lower loss than the equivalent E-glass. The reduced Dk of NE-glass yields important benefits for high-performance signaling applications [10]. Materials that using NE-glass have less variation between the Dk of resin. With a ratio of glass/PPO resin as 46:54, we could calculate the nominal laminate Dk to be 3.6 with E-glass, but with NE-glass would be 3.35.  
Figure 13 shows the impedance fluctuation (Z0) of traces routed on NE-glass. When compared to Figures 7 and 8, it was found that traces routed on NE-glass showed much lower impedance fluctuations (Z0).  
Figure 14 shows phase differentials for differential pairs produced with NE-glass and E-glass. The results indicated that the \(\Delta\)Phase of NE-glass was much lower than that of E-glass. Due to its excellent performance, NE-glass has been widely used in low-loss, very low-loss, and ultralow-loss laminates.
## 2 Summary of Cracking Types and Effect Factors of High Arch Dams
### Cracking Factors  
Many factors lead to the cracking of super-high arch dams, including different concrete materials, concrete temperature control measures, geological condition of dam foundation, seepage, geological exploration, arch dam profile, and so forth. The cracking factors of arch dams are summarized as follows.  
1. Concrete materials. Different concrete materials have different properties such as hydration heat and tensile strength. High-strength concrete generally has a large content of cement, leading to high hydration heat. When the external temperature changes sharply or the temperature control measures are not appropriate, high-strength concrete can easily crack. Concrete materials should be selected according to different dam structures and high-strength concrete should not be used blindly.
2. Site selection of the arch dam. The complex geological conditions of the dam foundation directly affect the stress and deformation distribution of the arch dam. The uneven deformation of both abutments and different stiffness between arch dam and foundation can easily lead to arch dam cracking. Appropriate reinforcement methods are important for reducing the cracking risk of arch dams.
3. Temperature control and maintenance. Concrete temperature control measures are directly related to concrete thermal stress. The sharp increase or decrease of the external temperature has more influence on the dam abutment, heel, and outlets. The temperature control methods should be designed and implemented before the arch dam is built. For the special structures such as outlets, it is necessary to consider the cracking caused by the cavern drafts flowing and the outlets should be closed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
\begin{tabular}{c} Concrete hydration heat \\ and temperature \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Summary of cracking types in high arch dams.  
* Dam profile design. Profile design needs to consider specific geological conditions. Arch dam profile is directly related to the stress distribution of the dam body. Outlets and dam heel should be considered especially. The effects of different profiles on the stress, deformation, overall stability, and cracking of the arch dam can be comprehensively compared by using the method of dividing load of the arch beam, FEM, and geomechanical model.
# Review on the Recent Numerical Studies of Liquid Atomization
## 4 Particle Tracking
### Breakup Models
#### 4.2.4 Kelvin-Helmholtz Model  
The Kelvin-Helmholtz (KH) model is suitable for droplet Weber number greater than 100 [53]. The radius of the newly formed droplets is proportional to the wavelength \(\Lambda\) of the fastest-growing unstable surface wave on the parent droplet, given by:  
\[r=B_{0}\Lambda \tag{25}\]  
where \(B_{0}\) is a constant equal to 0.61. The rate of change of droplet radius \(a\) in the parent parcel is given by:  
\[\frac{da}{dt}=-\frac{(a-r)}{\tau} \tag{26}\]and \(\tau\) is:  
\[\tau=\frac{3.726B_{1}a}{\Lambda\Omega} \tag{27}\]  
where \(B_{1}\) is an empirical breakup time constant in the range between 1 and 60. The maximum growth rate of the surface wave \(\Omega_{\text{KH}}\) and its corresponding wavelength \(\Lambda_{\text{KH}}\) were deducted by [PERSON] [53] by numerically analyzing the KH instability and can be expressed as:  
\[\frac{\Lambda_{KH}}{a}=9.02\frac{\left(1+0.45 Oh^{0.5}\right)\left(1+0.4 Ta^{0.7 }\right)}{\left(1+0.87 We_{2}^{1.67}\right)^{0.6}} \tag{28}\]  
\[\Omega_{KH}\sqrt{\frac{\rho_{1}a^{3}}{\sigma}}=\frac{0.34+0.38 We_{2}^{1.5}}{ \left(1+Oh\right)\left(1+1.4 Ta^{0.6}\right)} \tag{29}\]  
where the dimensionless groups have been defined in Table 1.
## 4 Discussion  
The current study focused on the assessment of facial emotion recognition in 54 stable patients with diagnosed major depression as compared to 54 healthy controls matchedfor age, gender, and educational level. A previously validated tool, using non-immersive virtual reality with DVFs, was administered to conduct the assessment. The following is a discussion of the hypotheses put forward on the basis of the findings obtained.  
Comparison of Recognition Scores and Reaction Times for the Depression and Healthy Groups in Emotion Recognition (H1)  
In accordance with the predictions of previous studies [11; 33], it was observed that individuals diagnosed with MDD exhibited a diminished capacity for recognizing emotions in virtual emotional expressions, as well as prolonged reaction times when compared to healthy controls across all emotions assessed. The disparities between the two groups were particularly pronounced for certain negative emotions, such as fear, sadness, and disgust. Within the MDD group, the emotions that were least recognized were fear, sadness, and disgust (with recognition rates of 48.9, 63.0%, and 66.9%, respectively). On the other hand, the most recognized emotions were neutral (90.3%), surprise (89.6%), and joy (84.5%).  
Despite the growing interest in recent years in facial recognition of emotions with DVFs, a recent review on the recognition of emotions through VR revealed little research on the subject, a lack of validity of the current tools, and the difficulty in finding studies that analyze all basic emotions [34]. The comparison of our results with previous similar studies is, therefore, complicated, which is why we present them against results with static stimuli. In this way, it is known that the deterioration of the recognition of facial emotions in depression is a solid finding, consistent with previous studies [33; 35; 36].  
Although it is difficult to know the causes of these differences, some authors have suggested that this deficit may be supported by the tendency to motor slowness and indecision shown as a symptom of MDD [32] and by a relationship between emotion recognition and alexithymia, defending the idea that the difficulties in recognizing and categorizing emotions may be mediated by another prior deficit in the identification and naming of even one's own emotions [37; 38].
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 5 Comparisons
### Total Computational Load  
This section compares the total FLoat-point OPeration (FLOP) of the MPFA and other frequency domain imaging algorithms, including the RDA, CSA and RMA. The basic operations employed by these algorithms including the FFT/IFFT, complex multiply and interpolation. The computational loads of these basic operations are summarized in Table 1. Table 2 lists the computational load of the MPFA step by step.  
There would be two major differences if the other algorithms such as the RDA, CSA and the RMA, instead of the MPFA, were used for imaging. Firstly, without the 2D dechrip to alter the range and azimuth bandwidths, the sampling rate should be set as \(F_{\mathrm{sT}}\) in Equation (
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 2 EH Circuits
### Proposed H-Bridge Rectifier Circuit for PEH in Human Motion Application  
The H-Bridge circuit proposed in this study for converting DC voltage from PD excited by low frequency human motion is shown in Figure 8. The H-Bridge circuit was designed using MOSFETs in order to reduce the forward voltage drop suffered by diodes used in conventional rectifier circuits. This leads to lower conversion losses and thus higher power conversion. Another significant advantage of the proposed H-Bridge circuit is its self-powering ability. It does not require an external power to turn on the MOSFETs in the circuit [31]. Therefore, no additional component, like polarity detector and logic gates, is required. As a result, it is also cost effective.  
The key difference between the conventional FBR and the H-Bridge circuit is that the FBR circuit comprises of four diodes, while the H-Bridge circuit comprises of four MOSFETs (PMOS: P-Channel MOSFET, NMOS: N-Channel MOSFET). The diodes possess two terminals, namely the anode and the cathode, where the MOSFETs have gate (G), drain (D) and source (S) terminals. The advantage of MOSFET is that the drain to source voltage can be controlled by applying PD voltage on the gate terminal. In addition, the MOSFETs allow flow of current in both directions (D \(\leftrightarrow\) S) while diodes only allow current in one direction. In addition, each transistor consists of an internal diode is shown in Figure 8, which provides protection against reverse leakage current in reverse bias. The four modes of operation of the H-Bridge circuit in both positive and negative cycles of PD is summarised in Table 1. A flow chart summarising its operation is also presented in Figure 8. Further details and explanations are available in [32].  
Notably, the H-Bridge circuit is proven to be more effective than the FBR circuit in AC-DC conversion for PEH at high frequency and low voltage application. In this study, its applicability in human motion induced PEH is investigated. It is anticipated that the use of H-Bridge circuit will improve the output voltage and output power of PD, as well as stabilise the output DC voltage (considering the input voltage AC voltage is not stable), for the sake of charging storage device or powering LED displays.
# Multi-Blockchain Structure for a Crowdsensing-Based Smart Parking System
## 5 Performance Evaluation
### Security Analysis  
The three security advantages of the MCS-based smart parking system constructed on the multi-blockchain structure proposed in this paper are as follows. First, the availability and safety of the smart parking service increases through the use of the public-private blockchains in conjunction. Second, the data contributors enable secure data delivery via transmission of data over a secure session. Third, the reward information for the contributors can also be safely delivered while providing integrity and confidentiality through the blockchains. Each safety feature is described in detail as follows.  
Many systems are integrating blockchain technology to provide transparency and safety to information management. The public blockchain provides openness, in the sense that anyone can participate in the formation of the blockchain, and thus it fits well with the characteristics that many data contributors need to participate in the mobile crowdsensing system. However, controlling data access rights or setting restrictions on participation is difficult in the consensus process of block generation. The private blockchain can limit participants, and thus many unspecified participants may be unable to provide the latest sensing values. Therefore, in the system proposed in this paper, data contributors participate in data collection on a public blockchain, with the openness and transparency of information management on the public blockchain assuring the safe delivery of incentive information for reward claims. In addition, the providers and users of the smart parking service can provide access authority processing and safety by allowing only authorized users to participate in service provision and data use through a private blockchain.  
Second, the contributors must deliver unstructured data, i.e., photos, to provide data for the MCS system. Even though the contributors remove, as much as possible, any personally identifiable information contained in the photos that they will be sending, delivering these unstructured data in the block of the blockchains is still inappropriate, and thus the contributors need a secure session to deliver the data to the service providers. Therefore, in this paper, the request for session information of the contributor is delivered to the service provider though two blockchains, and the session information encrypted with the contributor's public key is safely replied to the contributor.  
Finally, the incentive information of the data contributors calculated according to the data evaluation results of service providers and data users should also be safely transmitted. Accordingly, in the proposed system, the information is encrypted with the public key of the contributor and safely transmitted to the public blockchain. When the contributors claim their rewards, they decrypt the encrypted incentive information with their private keys, and receive the reward in cryptocurrency.