esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
SVCT reconstruction has become a hot research topic because of its huge promise in terms of radiation dose reduction. The PICCS theory has made a great contribution to the research area. Nonetheless, the traditional PICCS-based CT reconstruction algorithms still use TV sparse framework, and the gradient \(L_{1}\)-norm may cause blurred edges and lose image details. To resolve the limitation of the PICCS-based methods, this paper presents a novel improved PICCS algorithm for SVCT reconstruction. Firstly, the method utilizes the advantages of PICCS by introducing a prior image, which preserves more details and structures. Moreover, the algorithm incorporates the gradient \(L_{0}\)-norm into the framework, which improves the ability of retaining edge and suppressing noise. The reconstruction results show that the proposed algorithm outperforms methods such as FBP, OS-SART, TV, PICCS, and TVPI-G.  
Although the method achieves better performance, there still exist some problems. Firstly, the optimal parameters are selected by an empirical approach in this paper. Thus,  
Figure 16: Parameters selection of the abdomen image: (**a**) parameter \(a\); (**b**) parameter \(\gamma_{1}\); and (**c**) parameter \(\gamma_{2}\).  
Figure 15: Absolute difference image of different methods from 48-views. (**a**) FBP, (**b**) OS-SART, (**c**) TV, (**d**) PICCS, (**e**) TVPI-G, and (**f**) NPICCS.  
how to automatically optimize the parameters needs to be investigated. Moreover, a good-quality prior image is often not available, and we get the prior image from a full-view projection with noise by direct FBP reconstruction, but noise still exists in the prior image that will affect the final image quality. In addition, it is also hard to obtain a full-view projection reconstruction image. Therefore, how to achieve a better prior image remains to be investigated. These problems will be studied in our next work. In the future, it will be fascinating to combine deep learning techniques with mathematical optimization models to improve CT image quality.  
Conceptualization, X.L.; methodology, X.L. and X.S.; software, X.L., F.L. and X.S.; validation, X.L., F.L. and X.S.; resources, X.L. and F.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation for Young Scientists of Shanxi Province (202203021212455), the Shanxi Key Research and Development Program (201703D22-1033-3), and the Shanxi Agricultural University Young Science and Technology Innovation Program (2019021).  
Not applicable.  
Data Availability StatementData in this paper are available here. [[http://wwwaapm.org/GrandChallenge/LowDoseCT/](http://wwwaapm.org/GrandChallenge/LowDoseCT/)]([http://wwwaapm.org/GrandChallenge/LowDoseCT/](http://wwwaapm.org/GrandChallenge/LowDoseCT/)), accessed on 2016.  
The authors declare no conflict of interest.
## 3 Methodology  
In this section, we depict our model, which wraps around the syntactic attention module and the semantic enhancement module. The model first accesses sentences as input from the three datasets and extracts the aspect words using BERT for the sequence labeling task. Then, BERT is utilized to obtain context-rich vector representations from sentences and identified aspect terms, and the obtained vector representations are used as input to continue to the next step. The syntactic attention module (SA) reconstructs the ordinary dependency parse tree to generate a new tree in which aspect terms act as the root, so as to better guide the model in terms that are closely related to aspects, as well as encode the dependencies through GAT, mapping them to vector representations to obtain dependency features. The semantic enhancement module (SE) extracts the semantic information in the sentences in depth by using self-attention mechanisms to generate attention matrices as the neighbor matrix and encoding processing on the GCN. Then, the two parts of the features are fused through a multi-head cross-attention mechanism, simultaneously learning the interaction between the two features and classifying their sentiment. The diagram of our model structure has been depicted in Figure 2. We will discuss each module in further detail. Specifically, let a sentence be \(S=\{w_{1},w_{2},\cdots,w_{n}\}\), denoting a sentence containing \(n\) words, and let aspect \(A=\{w_{a+1},\cdots,w_{a+m}\}\), denoting mentioned aspects of a sentence and made up of \(m\) words.
## 2 Configuration
### Straightness Error Measurement  
A schematic diagram of the straightness error measurement based on laser collimation is shown in Figure 3. The light transmitted by PBS1 passes through a corner cube retroreflector (RR1), and the outgoing beam is parallel to the incident beam. The beam is finally received by the CMOS5 camera after passing through BS2.  
When the moving unit exhibits straightness errors in the x and y directions, respectively, the position of the spot on the CMOS5 camera changes correspondingly. According to the characteristics of the RRs, when there is a straightness error, the displacement of the spot is doubled, which can be expressed as  
\[\Delta x =\frac{\Delta X_{CMOS5}}{2}, \tag{2}\] \[\Delta y =\frac{\Delta Y_{CMOS5}}{2},\]  
where \(\Delta x\) and \(\Delta y\) represent the straightness errors of the moving unit in the x and y directions, respectively; and \(\Delta X_{CMOS5}\) and \(\Delta Y_{CMOS5}\) denote the displacement of the output laser beam spotted on the CMOS5 cameras.  
The horizontal straightness error can also be measured by other beams in Figure 4. The transmitted p-polarized light from PBS1 is reflected by RR1, BS2, M2, and RR2, and received by the CMOS1 camera  
Figure 3: Measurement principle of straightness error.  
Figure 2: Measurement principle of pitch and yaw.  
after passing through PBS2. The reflective surfaces of BS2 and M2 are placed at 45\({}^{\circ}\) with the vertical plane, causing the incident beam to be parallel to the direction of the outgoing beam. In addition, the light reflected by PBS2 travels in the opposite direction, which can also be used for the horizontal straightness error measurement.  
Each beam passes through the RR twice compared to Figure 3. When there is a horizontal straightness error \(\Delta x\), the displacement of the spot is four times, which can be expressed as  
\[\Delta x=\frac{\Delta X_{CMOS1}}{4}=\frac{\Delta X_{CMOS2}}{4}, \tag{3}\]  
where \(\Delta X_{CMOS1}\) and \(\Delta X_{CMOS2}\) denote the displacement of output laser beams in the x direction, which are spotted on the CMOS cameras.  
According to Equations (2) and (3), the horizontal straightness can be expressed as  
\[\Delta x=\frac{3\Delta X_{CMOS1}+3\Delta X_{CMOS2}+4\Delta X_{CMOS5}}{12}. \tag{4}\]
## 3 Implementation  
We constructed the harmonic interference resilient backscatter system prototype using the USRP and customized backscatter tags, as illustrated in Figure 10a. Below, we outline the implementation details of these components.  
**Reader:** Our reader is designed based on the USRP RFID transceiver developed in Ref. [13] and operates on the USRP X310 platform. The reader program operates within an Ubuntu 18.04 virtual environment. The UHD version used is 3.14.1.1, and the GNU Radio version is 3.7.13.4. The USRP X310 is equipped with two vertical omnidirectional antennas, both capable of operating within a frequency range from 824 to 960 MHz, with a gain of 3 dBi. One antenna serves as the transmitting antenna, sending commands and carrier signals to the tags, while the other functions as the receiving antenna, capturing the backscattered signals from the tags at their respective frequency points to evaluate the effectiveness of frequency shifting and harmonic interference suppression. Furthermore, the USRP is connected to a LEGION laptop via a gigabit Ethernet cable, enabling the transfer of waveform data received by the RF front end to MATLAB R2023b on the computer for data analysis.  
**Tags:** We implemented the relevant functionalities on customized tags with a simple hardware architecture, primarily composed of a radio frequency switch, a crystal oscillator, and a microcontroller unit (MCU). The MCU, an AGLN250, is a low-power FPGA. The tags generate square waves with varying duty cycles using a custom square wave generation module. An XOR operation is applied to produce an adaptive pulse-width square wave, which serves as the frequency-shifting component. The EPC-related encoded data is modulated onto this frequency-shifting component, and backscattering is controlled via  
Figure 10: Overview of the harmonic interference resilient backscatter system prototype. (a) Harmonic interference resilient backscatter system prototype. (b) Key components of the tags.  
Figure 9: Illustration of the communication process between the reader and the tag. (a) Communication process between the reader and the tag. (b) RN16 data packet reflected by the tag.  
On-Off Keying (OOK) by toggling the RF switch connected to the tag's antenna. Figure 10b illustrates the key components of our tags.
## 7 Conclusions  
In this work, the incorporation of the DSM strategy for effective optimal configuration is presented as a transition pathway towards accelerating uptake and management of variable renewable energy sources for smart microgrid. The system model considered in this work consists of the PV, WT and BESS without and with FDR scheduling. Due to the variability of the renewable energy sources, the mismatch between the generation and load demand profiles tends to increase. Thus, FDR scheduling technique that attempts to minimize the instantaneous mismatch between the load demand and generation profiles is proposed towards reducing the overall system planning cost. The effectiveness of the proposed methodology was investigated and verified on a real Kenyan microgrid. The Kenyan microgrid was used as case study to demonstrate the proposed methodology and techniques due to the accessibility of the required data. However, the research parameter and simulation are obtained from general specification in existing literature. Hence, the procedure can be replicated anywhere in the world for any given smart energy hub or systems.  
The objective is to determine the optimum size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. The results of short-term power generation forecasts are utilized to improve and effectively schedule the FDRs. Two simulation scenarios, without and with the consideration of DSM, were compared. From the simulation results obtained, the application of FDR scheduling resulted in a significant cost saving of about 12.41% of the investment costs. The FDR shifting programs time-shifted the flexible energy demands from deficit generation periods to surplus generation periods, thus; resulting in credible system component size reduction. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand. Hence, this paper has shown the potential cost-benefit of FDRs scheduling DSM strategy consideration for effective optimal component sizing for current and future VRE-based smart microgrid developments.  
**Author Contributions:** Conceptualization, M.K.K.; methodology, M.K.K., O.B.A. and M.E.L.; validation, T.S., T.A. and K.V.K.; formal analysis and investigation, M.K.K., O.B.A. and M.E.L.; writing-original draft preparation, M.K.K. and O.B.A.; writing-review and editing, M.K.K., O.B.A. and M.E.L.; supervision, project administration and funding acquisition, T.S.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors wish to acknowledge the Japan international cooperation agency (JICA) for the support provided in the form of African business education (ABE) scholarship to the main author towards the success of this research work. The authors also wish to appreciate the effort of [PERSON] of REA, Kenya office for supplying the principal data needed for this project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 3 The Proposed Method
### Fault Diagnosis Process  
The designed model (Figure 2) is applied to a rotating machinery fault diagnosis system; its application procedure is illustrated in Figure 3, and the implementation steps are outlined below:  
_Step 1_: The vibration signal of rotating machinery is acquired by the data collection system.  
_Step 2_: The vibration signal is normalized, and each sample is obtained by the sliding window method within 1024 data points. The samples are separated into three parts for training, validating, and testing.  
_Step 3_: The CNN model is built, the ReLU activation function in the traditional CNN is replaced with Linear and SELU activation functions, and the cross-entropy loss is replaced by the L2-SVM loss. Simultaneously, two new parameters are introduced, namely, the dynamic adjustment parameter and the modulation factors. Then, initialize the parameters in the model.  
_Step 4_: The samples for training and validating samples are imported to the model for iterative training. The output label of each sample is predicted, and its estimation error compared to the real one is calculated using the improved L2-SVM loss.  
_Step 5_: Judge whether the model converges. If it converges, jump to step 7; otherwise, turn to step 6.  
_Step 6_: Back propagate the error value obtained in step 4 to each node layer by layer; then, update the weights and biases and repeat steps 4 to 5 until the network converges.  
_Step 7_: The testing samples are imported to the trained model to verify the effectiveness of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**NO.** & **Layer Type** & \begin{tabular}{c} **Kernel** \\ **Size/Stride** \\ \end{tabular} & \
\begin{tabular}{c} **Kernel** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Output Size** \\ **(Width \(\times\) Depth)** \\ \end{tabular} & **Padding** \\ \hline
1 & Conv(C1) & \(16\times 1/1\times 1\) & 16 & \(1024\times 16\) & \(\surd\) \\
2 & Pooling(P1) & \(2\times 1/2\times 1\) & 16 & \(512\times 16\) & \(\times\) \\
3 & Conv(C2) & \(3\times 1/1\times 1\) & 32 & \(512\times 32\) & \(\surd\) \\
4 & Pooling(P2) & \(2\times 1/2\times 1\) & 32 & \(256\times 32\) & \(\times\) \\
5 & Conv(C3) & \(3\times 1/1\times 1\) & 64 & \(256\times 64\) & \(\surd\) \\
6 & Pooling(P3) & \(2\times 1/2\times 1\) & 64 & \(128\times 64\) & \(\times\) \\
7 & Conv(C4) & \(3\times 1/1\times 1\) & 64 & \(128\times 64\) & \(\surd\) \\
8 & Pooling(P4) & \(2\times 1/2\times 1\) & 64 & \(64\times 64\) & \(\times\) \\
9 & Fully-connected1 & 100 & 1 & \(100\times 1\) & \(\times\) \\
10 & Fully-connected2 & 10 & 1 & 10 & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the proposed 1 DCNN-L2 SVM.
## 3 Results
### The Data Structure and Functionality of AGEM-S  
The AGEM-S model is based on the farm as a logical unit, and its data structure consists of five main modules (Figure 1), including farm-level livestock data, nutrition, housing technology, manure storage, and manure application modules, which are presented to the user via the user interface.  
Each module contains input data, reference data, and calculated data. The tables and the manure stores are the basic units where the N-related processes take place and are identified by their names. The flow of N and TAN between the modules is taken into account continuously, with the output of one module serving as the input of the next one.  
AGEM-S includes both interface and evaluation checks. The interface checks are run in connection with the \"Save\" button, and in the case of missing, incorrectly entered, or inconsistent data, an error message alerts the user to correct the data. The data can only be saved after being corrected/completed. The evaluation check is available at the end of the data entry, under the \"Results\" menu, where the data content of the fields is compared. The \"Evaluation Document\" will only be active if the check has not found any errors. Otherwise, the user will have to follow the instructions in the error list displayed to correct the data entered.  
Figure 1: General structure of AGEM-S.  
The results of the farm-level ammonia emission estimation are displayed in a downloadable pdf file called \"Farm Evaluation Document\". The user can access downloadable information under the documents menu.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### MCS Selection
#### 3.6.1 One TX Case  
The method to adjust the MCS depends on the used feedback scheme. For No feedback, the gNB does not have information about actual BLER at the receivers. Thus, to provide high reliability, the gNB selects a robust MCS to take into account possible channel fluctuations.  
In particular, the authors of [30] propose a simple method (called eOLLA) that subtracts a positive constant \(\Delta(N)\) from \(MCS_{SINR}\) to take into account possible degradation of SINR at UEs: \(MCS_{1}=MCS_{SINR}-\Delta(N)\). The exact value of \(\Delta(N)\) is selected based on long-term experiments as the value providing the required reliability for a given multicast group size \(N\).  
For the schemes with UE feedback, the MCS can be dynamically adjusted using an Outer Loop Link Adaptation (OLLA) algorithm [36]. While OLLA is a widely used algorithm for unicast, below we propose its multicast version.  
With multicast OLLA, the gNB keeps a single subtraction \(\Delta_{olla}\) for a multicast group, and the MCS is selected as \(MCS_{1}=MCS_{SINR}-round(\Delta_{olla})\). The gNB updates \(\Delta_{olla}\) based on the obtained HARQ feedback. Specifically, \(\Delta_{olla}\) is increased by a constant \(\delta_{+}\) if the transmission fails as defined at the beginning of Section 2. Otherwise, it is reduced by a constant \(\delta_{-}\). The average BLER provided by the multicast OLLA algorithm converges to \(p_{olla}=\frac{\delta_{-}}{\delta_{-}+\delta_{+}}\). Therefore, in the One TX case, we can set \(p_{1}=PLR^{QoS}\) and accordingly select OLLA parameters.
# Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO
###### Abstract  
To address the impact of load variations, external environmental changes, and the tuning of the parameters on Permanent Magnet Synchronous Motors (PMSMs) used in ships, this study proposes an Active Disturbance Rejection Control (ADRC) strategy for PMSMs, optimized by the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, based on the PMSM model, the study addresses the limited disturbance rejection capability of the traditional fal function in the Extended State Observer (ESO) of conventional ADRC. To improve the accuracy of the state observer, the faln function is introduced as a replacement for the traditional fal function. Second, due to the numerous parameters in ADRC, which are difficult to tune, the QPSO algorithm--known for its strong global search capabilities and fast convergence speed--is utilized for parameter optimization. Additionally, the position update formula within the optimization algorithm is revised and optimized. Finally, simulation experiments are conducted using the Matlab/Simulink platform, where practical conditions, such as load fluctuations and random noise, are incorporated. The simulation results demonstrate that, compared to PSO-ADRC control, IPSO-ADRC control, and ICFO-ADRC control, the proposed method offers a superior dynamic response. Specifically, the speed control accuracy is improved by 46.7%, torque ripple is reduced by 50.8%, and harmonic distortion decreases by 23.1%. These results highlight the significant advantages of this method in enhancing system robustness, dynamic response speed, and steady-state accuracy, making it particularly suitable for PMSM control systems in complex dynamic environments, such as those encountered on ships.  
permanent magnet synchronous motor for ships; active disturbance rejection control (ADRC); nonlinear state observation; quantum-behaved particle swarm optimization Article
# Genetic Variants in Patients with Premature Ovarian Insufficiency in a Mexican Cohort
## 3 Results
### Gene Variants  
The genetic variants of _WT1_ (NM_024426.6), _NR0B1_ (NM_000475.5), _NR5A1_ (NM_004959.5), _LHX9_ (NM_020204.3), _ZNF275_ (NM_001080485.4), and _NRIP1_ (NM_003489.4) were determined with bidirectional Sanger sequencing. Figures 3-8 illustrate the molecular analysis in patients with POI and healthy subjects. Figure 3 depicts the three genetic variants in exon 1b of the _WT1_ gene: GG, TT, and GT at position 213 in the cDNA, which encodes for a synonymous variant Pro-71 (NM_024426.6c.213G>T; P.P71=); two variants in exon 1e: CC and TC at position 609 in the cDNA, which encodes for a synonymous variant Asn-203 (NM_024426.6c.609T>C; P.N203=); two variants in exon 3: GG and GA at position 873 in the cDNA, which encodes for a synonymous variant Arg-291 (NM_024426.6c.873A>G; P.R291=); three variants in exon 7: GG, AA, and GA at position 1122 in the cDNA, which encodes for a synonymous variant Arg-374 (NM_024426.6c.1122 G>A; P.R374=). In Figure 4, we identified one non-synonymous and one synonymous variant in the _NR0B1_ gene. There were two genotypic variants in exon 1b: CC and CT at position 353 in the cDNA, which encodes a non-synonymous variant Ala118 Val (NM_000475.5: c.353C>T; P.A118V); three variants in exon 1c: AA, GG, and GA at position 498, which encodes for a synonymous variant Arg-166 (NM_000475.5: c.498G>A; P.R166=). The variant sequencing results for the _NR5A1_ gene are shown in Figure 5. In exon 4b, two GG and GC variants were identified at position 437 in the cDNA, which encode for a non-synonymous change Gly146 Ala (NM_004959.5: c.437G>C; p.G146A). A non-coding genotype (CC, TT, and CT) was localized to IVS4\(-\)20 (NM_004959.5: IVS4\(-\)20C>T). The automated sequencing in Figure 6 depicts two different GG and GC non-coding changes in intron 2 (NM_020204.3: IVS2\(-\)12G>C) and other non-coding genotypes IVS3+13 CC, TT, and CT (NM_020204.3:IVS3+13C>T) of the _LHX9_ gene; in addition, in exon 4, there were two different TT and TC genotypes at position 741 in the cDNA, which encode for a synonymous variant Asn-247 (NM_020204.3: c.741T>C; P.N247=). Figure 7 illustrates the two different TT and CT genotypes at position 969 in the cDNA, which encodes for a synonymous variant Cys-323 (NM_001080485.4: c.969C>T; P.C323=) in exon 3 of the _ZNF275_ gene, and Figure 8 describes the two different CC and CT genotypes at position 3403 in the cDNA, which encodes for a non-synonymous variant Arg1135 Cys (NM_003489.4: c.3403C>T; p.R135C) of the _NRIP1_ gene.