esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed PEADO-OFDM Scheme and Full-Duplex Communication
### Dual-Branch PEADO-OFDM Structure  
In O-OFDM, HS is performed before the IFFT to obtain the real-value signals, where the second half of the subcarrier information is the conjugate symmetry of the first half, which greatly reduces the spectral efficiency. Thus, PEADO-OFDM is proposed without HS, so the time-domain signals after the IFFT are complex-value. Then, the real and imaginary branches of the obtained signals will be divided and transmitted independently. For the ACO-OFDM branch, after the real-imaginary division, the negative signals of both the real branch and the imaginary branch will be clipped. For the DCO-OFDM branch, the clipped real and imaginary branches of the ACO-OFDM signals should be combined to the complex-value signals and converted to the pre-distortion signals through the FFT. Then, the operation of pre-distortion, IFFT, S/P and real-imaginary division are performed sequentially. The DC biases are added to both the real and imaginary branches of the pre-distorted DCO-OFDM signals with all the remaining negative signals clipped to zero, which are:  
\[B_{DC,Re}=\mu_{Re}\sqrt{E\left\{y_{n,DCO,Re}^{2}\right\}}, \tag{12}\]  
\[\text{and}\ B_{DC,Im}=\mu_{Im}\sqrt{E\left\{y_{n,DCO,Im}^{2}\right\}}, \tag{13}\]  
where \(y_{n,DCO,Re}\) and \(y_{n,DCO,Im}\) represent the real and imaginary branches of the pre-distorted DCO-OFDM signals, respectively, and \(\mu_{Re}\) and \(\mu_{Im}\) represent the bias index of the real and imaginary branches, which can be defined as \(\beta_{Re}=10 log_{10}(1+\mu_{Re}^{2})\ [dB]\) and \(\beta_{Im}=10 log_{10}(1+\mu_{Im}^{2})\ [dB]\), respectively. Hence, by superimposing the real branch from the ACO-OFDM and the DCO-OFDM and the same superimposing operation at the imaginary branch, the real and imaginary branches of the transmitted signals are obtained as:  
\[z_{n,ADC,Re}=x_{n,ACO,Re}^{\prime}+y_{n,DCO,Re}^{\prime}, \tag{14}\]  
\[\text{and}\ z_{n,ADO,Im}=x_{n,ACO,Im}^{\prime}+y_{n,DCO,Im}^{\prime}. \tag{15}\]  
Without HS operation, the number of subcarriers conveying information is \(N-1\), while in the HS-based O-OFDM scheme, the number of effective subcarriers is \(N/2-1\). Therefore, the spectral efficiency of the proposed transmission scheme is significantly improved.
# An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing
## 4 Methodology
### GWR Regression Model  
House prices reflect to some extent the distribution of wealth in a city. An analysis of the spatial association between house prices and the accessibility indicator was based on the results of the analysis of the accessibility of various types of food in cities. This analysis can contribute to further strengthening the equity of food accessibility of cities. In addition, an exploration of the relationship between the construction time of communities and accessibility can reveal how the pattern of evolution in urban food accessibility is affected during urban development.  
[PERSON] [51] proposed the geographically weighted regression (GWR) method to study food accessibility. GWR is based on the idea of local smoothing and utilizes samples around the center for auxiliary regression to overcome the problems caused by nonsmooth spatial processes. Under GWR, the regression coefficients are generally considered to be spatially smooth to a certain degree and vary smoothly. The vicinity of the centroid can be regarded as approximately stationary, since the similarity of locations is inversely proportional to the distances between them, consistently with the first law of geography. The geographically weighted regression model is:  
\[y_{i}=\beta_{i0}+\sum_{j=0}^{m}\beta_{j}(u_{i},v_{i})x_{ij}+\varepsilon_{i} \tag{4}\]  
Figure 8: Enhanced two-step algorithm flow.  
In Equation (4), \(\beta_{i0}\) is the intercept coefficient at location \(i\), \(y_{i}\) is the dependent variable; \(x_{ij}\) is the \(j\)th predictor variable; \(\beta_{j}\) (\(u_{i}\), \(v_{i}\)) is the \(j\)th regression coefficient; and \(\varepsilon_{i}\) is the random error term [52].  
\[\hat{\beta}(i)=\big{[}X^{\prime}W(i)X\big{]}^{-1}X^{\prime}W(i)y \tag{5}\]  
In Equation (5), \(X\) is an explanatory variable by \(k\) matrix, \(W(i)=\textit{Diag}[w1(i),\ldots,\textit{wn}(i)]\) is a diagonal weight matrix in which weights are based on the distance between observation and location \(i\); \(\hat{\beta}(i)\) is the coefficients vector with \(k\) by 1, and y is a \(k\) by 1 observations vector of the dependent variable.
## 3 Case Study: Experimental Verification and Comparison Results
### Comparison Studies
#### 3.2.1 Comparison of Model Performance by Signal Processing Methods and the Learning Algorithms  
The accuracy of the proposed method was compared with that of using only a small number of target domain data and using the source and target domain data simply together without transfer learning. In addition, the comparison of accuracy when using different signal processing methods is also presented in each case. We also used a fully-connected neural network with 100-50-25 neurons. A total of 12 handcrafted features extracted from each frequency band through wavelet decomposition were used to train the fully-connected neural network: minimum, maximum, mean, absolute mean, root mean square (RMS), peak-peak, peak to RMS, variance, kurtosis, skewness, entropy, and RMS error of estimation.  
The results are listed in Table 3. In most cases, the diagnosis accuracy was improved by additionally using the source domain data compared to only using the target domain data. By comparing the diagnosis accuracy of the models trained using same ResNet50 structure and different training algorithms, it was verified that the accuracy can be increased through proposed method. On average, when the source and target data were treated as a single training dataset for model training, the average accuracy increases by 1.34% compared to when only using target data. However, when the proposed method is used, the average accuracy increases by 4.33% compared to when only using target data. From this result, it is possible to confirm the excellent effectiveness of the transfer learning and the fine-tuning strategy to use the knowledge of the source domain systematically. In addition, the performance of the deep learning-based method is quite superior to the traditional machine learning method using handcrafted features, and transfer learning can be a good application method to develop deep learning-based methods.  
The highest accuracy of 98.33% was derived using the proposed method and STFT, which is 5.66% higher than 92.67% when only target data are used. The average accuracies of the proposed method using WPD and WPD with spectral subtraction are 97.22% and 96.67% respectively, which decrease 1.11% and 1.67% compared with the STFT. It is inferred that the time-frequency image created using STFT is the most suitable to be used in the proposed method in this case study.
## 4 Discussion
### Economic Efficiency
[PERSON], [PERSON] and [PERSON] [22] simulated a CPH system for residential buildings in Italy and demonstrated that such a system enabled a reduction of operating costs of up to 20.9%. In [PERSON] and [PERSON] [35], the operational cost reduction of 5.7% was achieved for an outpatient building. This study further implied that the operational cost could be saved with a higher ratio of the thermal load from the CHP operation and that cost was an important factor when evaluating the performance of a CHP system.
## 6 Computing in Vehicular Networks
### Cloud  
Due to the limitation of resources for computing and energy, it is common to employ cloud computing to allow vehicles to offload computation tasks consuming a lot of available resources to other nodes for processing [123]. As a consequence, cloud servers are generally located considerably far from vehicles, thereby resulting in significant latency induced by data transfer via core and backbone networks. For this reason, applications that are highly sensitive to latency, as well as reliability, are not appropriate to be offloaded to a cloud [124]. This category of applications includes emergency braking and collision avoidance or obstacle recognition. On the other hand, applications that are not latency-sensitive may be efficiently offloaded, which will result in conserving resources and a reduced computation time for the offloaded task. Preserved resources can then be used to promptly solve tasks that cannot be offloaded [125]. An illustration of a system utilizing cloud computing alongside onboard computing is provided in Figure 7.
# Advancements in Data Analysis for the Work-Sampling Method
## 2 Materials and Methods
### The Recording Process of the Work-Sampling Method  
Before the actual recording, we must study and prepare the necessary steps in detail, which include the following phases [26; 27]:  
1. Pre-recording preparations: Determining the scope and location of the workplaces, preparing the recording team (a single person can collect 400 to 600 observations per day), informing the workers, making a list of activities, drawing up a plan for random visits to the workplaces (the observation route), and defining the forms--usually recording and collecting sheets. Each analyst draws up the observation schedule randomly and according to the outline of the group of workplaces to be observed (the departure time for observation, the workplace where it starts, and the direction of movement) for each recording day.
2. Recording--collecting observations: preliminary (pilot) recording (usually 3 to 5 days; we check the adequacy of the list of activities and the schedule of random observations; we calculate the proportion of time spent on each activity) and the main (full) recording (we collect the number of observations required according to the most typical or important activity).  
With the work-sampling method, a confidence level of 95% and a precision level of 0.05 of the results are completely sufficient, so that the required number of observations is as follows:  
\[N=\frac{1.96^{2}\cdot(1-p_{i})}{0.05^{2}\cdot p_{i}}=1537\cdot\frac{(1-p_{i})}{ p_{i}}, \tag{1}\]  
where  
\(N\)--denotes the number of observations required,  
1.96--number of standard deviations from the mean reflecting the 95% level of confidence,  
\(p_{i}\)--proportion of time spent on the major activity (between 0 and 1).  
The equation for determining the upper and lower control limits (\(M_{i}\)) for a confidence level of 95% is as follows:  
\[M_{i}=p_{i}\pm 1.96\cdot\sqrt{\frac{p_{i}\cdot(1-p_{i})}{N}}, \tag{2}\]
## 1 Introduction
### Hypotheses, Aim and Contribution  
Three-dimensionally printed prosthetics is an emerging field that would benefit from improved socket designs to fit users' anatomies because the current adjustable designs lack features to control tightening [12]. Socket fit has always been a problem in traditionally manufactured sockets, and it is a key factor for the acceptance of prosthetic devices [2; 3; 4]. Advances in 3D printing and 3D scanning allow for new personalized socket designs to be proposed that very accurately fit users' stump morphologies [23]. These personalized sockets present several advantages compared to 3D-printed adjustable sockets, such as more comfort, a lower risk of skin problems, no need for shape modifications, and more stability. Nevertheless, it is key to listen to users' experiences and perceptions to propose new socket designs because personal and contextual factors are critical determinants of prosthesis acceptance [38]. However, there is a gap in the studies that analyze functional [12] and esthetics-related aspects of upper limb sockets. In fact, no agreement between professionals and prosthesis users has been reached [39].  
Therefore, the aim of this work is to define and apply a specific questionnaire to study prosthetic arm users' and potential users' perceptions by focusing on the socket part. To achieve this, this work presents and provides a specific questionnaire (SOCKET-BIOEMO survey) as supplementary data and reports the results obtained from respondents with upper limb amputations or congenital disorders. The questionnaire also comprises an analysis of aspects related to the socket manufacturing process, perceived comfort, functionality, esthetics and durability by integrating questions adapted from standardized questionnaires, such as QUEST 2.0 [33] or OPUS [34]. Finally, the results and their implications are discussed, and the main lines of action using current 3D printing technologies are outlined to meet the reported user preferences.  
The results reported in this work may help to detect the problems faced by current traditional socket users, which would contribute to creating 3D-printed socket designs that meet final users' needs. Furthermore, it may provide the prosthetics field with insightful data about aspects to reconsider or improve in current socket designs that are applicable to both 3D-printed and traditionally manufactured sockets. Apart from this, the results of this study may contribute to understanding the aspects that lie behind prosthetic arm rejection and may help move toward user-centered designs and increase users' quality of life.
# Dual-Band Frequency Selective Surface-Backed Reflectarray for High-Speed Ka-Band Satellites
## 4 Experimental Verification  
### Fabrication of the Dual-Band Reflectarray  
The reflective surfaces with 33-unit cells along their diameter are printed on a 200 mm \(\times\) 200 mm Roger 5870 and Rogers 3003 printed circuit boards by chemical etching technique. Top and bottom views of the boards are given in Figure 11. All conductors are copper with a 35 \(\upmu\)m thickness. Screwing holes with 5 mm diameter have been added to the corners of each reflective surface. A holder is fabricated by additive manufacturing technique using polylactic acid (PLA) material. By using the fabricated holder, the reflectarrays and the feed antenna are fixed to their positions as given in Figure 12. A 1 mm air gap between the reflectarrays is preserved by utilizing foam, which has a relative permittivity very close to air. The 18-40 GHz broadband double-ridged horn feed antenna is attached to the holder at \(\theta=20^{\circ}\) in the \(\phi=0^{\circ}\) plane. Reflectarrays and the foam is attached to the planar surface of the holder by four 5 mm screws.
# Shape Optimization of Discontinuous Armature Arrangement PMLSM for Reduction of Thrust Ripple
## 4 Optimal Design of the End Edge Auxiliary Teeth  
### Setting of Design Parameter  
A significant change in the air gap generated at the end edge induces a greater detent force than when the mover is aligned with the armature. Therefore, auxiliary teeth in a trapezoidal shape, which can induce a gradual change in the air gap at the end edge of the armature, were installed, and each shape parameter was optimized. Figure 15 illustrates the shape of these auxiliary teeth.  
The auxiliary teeth were installed as far apart as the width of the armature and slot. Additionally, the length of the top of the tooth (\(A_{t}\)), the length of the tooth in the moving direction of the mover (\(A_{b}\)), and the height of the bottom of the tooth (\(A_{e}\)) were selected as the main shape parameters. The auxiliary teeth were composed of laminated silicon steel, similar to the armature core. The range of the shape parameters was set as 0-30 mm, considering the tooth height and armature spacing.
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
## 4 The Rectifying and Boost Circuit Characteristics  
A mathematical approach such as Runge-Kutta (RK) has been used to analyse the DC behaviour of the circuit as in Figure 4, and the complete analysis is shown in [2]. A comparative analysis could have also been applied in analysing the diode behaviour [13]. In this work, the calculation of the DC output voltage after the rectifier circuit, \(v_{\eta f}\) is done using Equation (10). The bridgeless rectification has been developed and the output voltage of the rectification circuit with the filter capacitor value of 10 \(\mu\)F is measured and reported in Figure 8.  
A boost converter is accomplished in the proposed power processing circuit by switching a reactive component. Two n-channel MOSFET (NMOS) devices are used as the switches. Triggering the devices ON and OFF is done by a periodic signal generated by the push-pull circuit. When both switches are turned ON a current is flowing through the inductance \(L_{4}\). The output voltage vout is identical to \(v_{\eta f}-v_{Rb}\) where \(v_{Rb}\) is the voltage drop due to the internal push-pull resistance. The diode is unbiased, and the output voltage vout is the voltage over the capacitor \(C_{7}\). When the switch is turned OFF, the voltage, \(v_{L4}\) is developed to keep the current flowing. The diode is now forward biased and the voltage \(v_{out}=v_{\eta f}-v_{Rb}+v_{L4}\) is transferred to the capacitor. As the switch is turned ON again, the diode is unbiased. A resistive load is draining the charge from \(C_{7}\). Upon opening the switch, \(C_{7}\) is recharged again.
## 4 Security Attacks
### Consensus and Incentive Layer
#### 4.3.7 Selfish Mining  
Selfish mining [27; 30; 32; 42] occurs when an adversarial miner withholds mined blocks from the public network. Instead of immediately broadcasting a newly mined block, the selfish miner keeps it private, extending a private chain. The attacker's goal is to strategically release the withheld blocks to force honest miners to work on an outdated chain. This causes regular miners to waste their computational resources, while the selfish miner reaps disproportionate rewards.  
Figure 5 illustrates the workflow of a selfish mining attack, a strategy where a malicious miner manipulates the block publication process to gain more rewards than they would fairly earn. The attack begins with a selfish miner (top box) who chooses to mine blocks privately instead of immediately broadcasting them to the network. These withheld blocks are kept secret, giving the selfish miner a temporary lead over honest miners, who are unaware of the hidden progress. At a carefully chosen moment, the attacker strategically releases the withheld blocks, shown as broadcasted blocks, to the blockchain network.  
At this point, the blockchain network, now receiving conflicting information from both honest and selfish miners, experiences confusion and waste. Honest miners may have unknowingly mined on a shorter chain, causing their work to be discarded when the longer withheld chain is revealed. This results in network disruption (shown with a warning symbol), which ultimately benefits the selfish miner. By manipulating the timing of block releases, the attacker can force honest miners to waste computational resources and thereby secure increased rewards for themselves, gaining an unfair advantage without necessarily controlling the majority of the network's hash power.
## 2 System Architecture and Design
### Mask Layout Integration and Design Methodology  
The design of complex integrated circuits, such as our SiP matrix, requires an efficient design flow and accurate design tooling to facilitate interactions between multiple designers and compatibility between multiple technologies [16]. An open-source photonic IC design framework-Nazca-Design [17] was used as the layout design environment. The tool enabled a hierarchical methodology in which different designers contributed components, sub-circuits and test structures to a single project. Nazca-Design provided a direct interface between the designer and the foundry through process design kits (PDKs), accessed via name-spaces so that a mix of technology and packaging information could be exchanged during the mask layout integration. The tool facilitated the exchange of components in Python-script and in a mask layout (e.g., GDSII) format. The Nazca-Design framework is open source but does protect proprietary information inside the PDKs and designers' IP. A foundry, or others, can securely distribute black box design information (e.g., with modulator or laser designs) to be replaced at the foundry side upon manufacturing. Beyond the state-of-the-art in PIC design, the tool includes a methodology for dealing with PDK versioning and a focus on a robust exact copy philosophy for high-fidelity volume production design work. Figure 4 shows our final reticle design that we manufactured at the AMS foundry. It includes our 64 element SiP matrix circuits and various test structures provided by the designers in this project. Figure 5 depicts our unit cell layout for WDM  
Figure 3: L3 MATRIX integration scheme. L3 MATRIX is a 2D SiP matrix with (4 \(\times\) 16) cells, with each cell being optically and electrically isolated from its neighbors. The unit cell is a full SiP transmitter or receiver with an embedded DFB laser. The lasers in each row have a CWDM wavelength, and the output of each column is multiplexed and fiber coupled. The 2D SiP is back illuminating, with a Silicon microlens assembled on the back surface to assist in fiber coupling. The analog chip drives the Mach–Zehnder Interferometer (MZI) modulator and supplies DC bias to the DFB lasers.  
and CWDM. The IEEE 802.3 LN WDM grid was finally selected, in which the 4 channel wavelengths correspond to 1295.56 nm, 1300.05 nm, 1304.58 nm and 1309.14 nm, because the reduced 20-nm bandwidth imposes a more realistic specification on the gain bandwidth, compared to a 20 nm channel spacing (80-nm bandwidth).
## 4 Results and Analysis
### Control Oil Pressure  
Under the conditions of a rotation speed of 2488 rpm, the maximum control oil pressure is set to 1.3 MPa, 1.5 MPa, and 1.7 MPa, and the corresponding oil film thickness response curves are illustrated in Figure 5a-c, respectively. The gap deviation coefficient can be calculated by Equation (18), and the corresponding result is illustrated in Figure 5d. The results of the engagement time, disengagement time, and gap deviation coefficient after the friction element separation for different oil pressures are demonstrated in Table 3.  
As the control oil pressure increases, the engagement time decreases from 1.459 s to 1.2389 s, a decrease of 0.2201 (15.1%); the disengagement time increases from 2.6864 s to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rotation Speed/rpm** & **Engagement Time/s** & **Disengagement Time/s** & **Gap Deviation** \\  & & & **Coefficient** \\ \hline
888 & 0.8789 & 2.7044 & 0.6267 \\
1688 & 1.0882 & 2.7053 & 0.6238 \\
2488 & 1.3126 & 2.7065 & 0.6198 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the engagement time, disengagement time, and gap deviation coefficient after the friction element separation for different rotation speeds.  
Figure 5: The response curves of the oil film thickness for (**a**) 1.3 MPa, (**b**) 1.5 MPa, (**c**) 1.7 MPa, and (**d**) the gap deviation coefficient.  
2.7109 s, an increase of 0.0245 (0.91%); and the gap deviation coefficient after the friction element separation increases from 0.5774 to 0.627, an increase of 0.0496 (8.6%). Consequently, the control oil pressure has a negligible effect on the disengagement time. As the control oil pressure increases, the contact pressure of the friction pair increases, the asperities contact plastic deformation is more serious, and the disengagement force decreases, which results in deteriorating friction elements separation uniformity.