esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 5 Results and Discussion
Furthermore, as the minimum required data rate was increased, the total power consumed slightly decreased, as can be seen from the column for the conventional scheme in \[\varphi(h_{\text{d}})=3.2\log_{10}(11.75h_{\text{d}})^{2}-4.97 \tag{15}\]1 The reason for this is that the possibility of the higher data rate requirement being satisfied is relatively low under heavy  
\[\varphi(h_{\text{d}})=3.2\log_{10}(11.75h_{\text{d}})^{2}-4.97 \tag{15}\]2 Fairness indexes over time for the RT user type and NRT user type under the proposed scheme. traffic load conditions, such that given the FPA design, some sessions may be forced to continuously queue in the scheduling queue, in which case they will not consume power at the BS transceivers. Essentially, the advantages of the proposed scheme indicated by the above experimental results spring from the fact that the proposed scheme was based on the radio resource and power allocation approach previously proposed by [PERSON] and [PERSON] [8]. The primary aim of their approach was to ensure efficient control of the fairness of resource allocation, and we have simply modified that approach further to fit the purposes of our proposed model. Simply put, the proposed scheme provides powerful power-saving ability, while also satisfying the respective minimum required data rates of all the user sessions and efficiently controlling the fairness of resource allocations to different types of users. More specifically, by monitoring fluctuations in the traffic load and providing dynamic activation/deactivation of the SCCs of the BS accordingly, the proposed scheme significantly reduces any unnecessary consumption of power. Stated differently, the proposed approach allows for efficient use of system resources with regard to power usage, bandwidth efficiency, user fairness, and computational time. Based on the above observations, it can be firmly concluded that the proposed scheme would constitute an effective means of reducing power consumption on the BS side of cellular systems.
## 3 Control Mechanism for LCC-HVDC with a Synchronous Condenser  
### Control Mechanism for LCC-HVDC  
The control system of the LCC-HVDC is consistent with the CIGRE benchmark model [12]. The system operates in the constant current control mode with a minimum firing angle at the rectifier end, and adopts a constant extinction angle control mode at the inverter end, which is also equipped with a constant current control mode and a current error controller (CEC) mechanism. The constant current control at the inverter side is used as a backup control mechanism during severe AC and DC disturbances. It contains a voltage-dependent current order limiter (VDCL) function, which helps in limiting the DC overcurrent and avoiding the stress on thyristor valves during different fault conditions. Figure 2 presents the overall control mechanism for the LCC-HVDC system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **LCC-HVDC** & **SC** \\ \hline Rated AC voltage/kV & – & 20 \\ Rated AC current/kA & – & 2.88 \\ Rated capacity/MW(Mvar) & 1000 & 100 \\ DC voltage/kV & 500 & – \\ DC current/kA & 2 & – \\ Rated ratio of converter & 345/213.5(Rectifier) & \\ transformer/(kV/kV) & 230/209.2(Inverter) & 20/230 \\ Short circuit impedance of & 18(Rectifier) & \\ coupling transformer/\% & 18(Inverter) & 8 \\ Smoothing reactance/H & 0.15 & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of LCC-HVDC with a SC.  
Figure 1: Schematic diagram of the LCC-HVDC system with a synchronous condenser.
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 4 Energy Efficiency Optimization
### Optimal \(N\)  
After obtaining the optimal power allocation and the total transmit power for the given number of antennas. It can be seen from (31) that the maximized EE \(\hat{\eta}_{\text{EE}}^{*}\) is a function w.r.t. \(N\). Thus, we can obtain the optimal number of antennas by one-dimension exhaustive search. It is noteworthy that the NP-hard problem is solved by simple one-dimension exhaustive search w.r.t. \(N\).
## 3 Performance Evaluation Results and Discussion  
### Simulation Environment  
We carried out our simulation work in the underwater acoustic simulator Aqua-sim-NG, aiming to compare and analyze the FLRAF routing protocol we proposed with existing traditional protocols, including VBF, HH-VBF, ALRP, and GTRP. We conduct a thorough assessment to determine whether the proposed protocol offers advantages by analyzing the effects of two scenario parameters--specifically, the number of nodes and their maximum movement speed--on four common network performance metrics. To ensure a fair comparison and consistency with previous studies, the simulation parameters are set based on [25; 26], where similar settings have been validated. The main network simulation scenario parameters are set as shown in Table 4.  
Additionally, all protocols are tested under identical network configurations and simulation durations to eliminate bias in performance evaluation. The selected performance metrics include average end-to-end delay, packet delivery ratio, average energy consumption, and lifetime, which comprehensively reflect the protocol's adaptability, reliability, and efficiency in dynamic underwater environments. Through this simulation setup, we aim to rigorously verify the practical effectiveness and robustness of the FLRAF protocol under varying network conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Routing protocol & VBF, HH-VBF, ALRP, GTRP, FLRAF \\ MAC layer protocol & Broadcast MAC \\ Propagation model & Underwater Propagation \\ Channel & Underwater Channel \\ Number of nodes & 100–600 \\ Node transmission range & 100 m \\ Simulation time & 500 s \\ Size of simulation scene & 800 m \(\times\) 800 m \(\times\) 800 m \\ Moving model & RandomWalkMobilityModel \\ Maximum moving speed of node & 2–7 m/s \\ Application layer data rate & 3 kbps \\ VBF, HH-VBF radius of pipe & 70 m \\ Node transmit, receive and idle power & 2.0 W, 0.75 W, 0.008 W \\ \(k\), \(T_{delay}\) in ALRP & 7, 1 s \\ \(\beta\) in GTRP & 10\({}^{6}\) \\ \(\alpha\) in FLRAF & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Major parameters used in the simulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Packet Format Serial** & **Task Type/Information Carried by the Packet** \\
**Number** & Initiate LQI acquisition task/Current forwarder’s location \\ Format 1 & Initiate data collection task/Sink’s location, query type \\ Format 2 & Initiate data collection task/Node \(i\)’s forwarding area \\ Format 3 & Initiate fuzzy logic reasoning task/Receiver’s ID \\ Format 4 & Report reasoning result/Receiver’s ID, node priority \\ Format 5 & Report node selection result/Forwarder’s ID, data collected \\ Format 6 & Terminate redundant transmissions and initiate data collection of a new round/node \(i\)’s forwarding area, node \(j\)’s forwarding area \\ \hline \hline \end{tabular}
\end{table}
Table 3: Packet format in the FLRAF routing protocol forwarding process.
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 4 Discussion  
### Main Findings  
In this study, we apply multiple Deep Learning (UNet, Attention UNet, PSPNet, DeepLabv3+, and a dedicated network for MRI images) to model the segmentation of tomographic abdominal organ images. We extend the Chaos dataset by rotation and Cutout and perform ablation experiments using multiple Deep Learning models. The CI-UNet designed in this paper was finally obtained, and we found that the CI-UNet model outperformed other network models in all metrics. Meanwhile, we applied the CI-UNet designed in this paper to spinal segmentation to provide a solution for calculating the Cobb angle for the Guizhou branch of Beijing Jishuitan Hospital.
## 2 Results
### Differential Expression Analysis of Unigenes  
To reveal transcriptional differences between tissues, we performed a differential expression analysis of the 40,850 _A. paniculata_ unigenes. This identified 7962 DEGs among the leaf, stem, and root (Figure 4A). The leaf vs. root comparison had the most DEGs (5309), with 2912 upregulated in the leaf (Figure 4B; Table S6). Leaf vs. stem showed 2166 DEGs, with 960 more highly expressed in the leaf (Figure 4C; Table S7). Stem vs. root had 5424 DEGs, including 3012 upregulated in the stem (Figure 4D; Table S8). There results show little difference between the leaf and stem but more between the leaf and root and between the stem and root. This suggests that there may be a significant difference in the content of secondary metabolites between the leaf and root and between the stem and root.  
Figure 3: Database annotation. (**A**) KEGG classification enrichment analysis. (**B**) GO classification.  
There were 20,359 unigenes coexpressed in the leaf, stem, and root, while 13,173 unigenes were expressed in only one tissue (Figure S2). In the comparison of differences among groups, the leaf vs. stem, leaf vs. root, and stem vs. root groups shared 417 DEGs. (Figure 5A). In the KEGG database, 547 DEGs of the leaf vs. stem were enriched in 103 KEGG pathways (Figure 5B). The main pathways were Starch and sucrose metabolism (29), Plant hormone signal transduction (29), Glyoxylate and dicarboxylate metabolism (18), Phenylpropanol biosynthesis (17), and Galactose metabolism (16). There were 1492 DEGs annotated in the leaf vs. root, enriched in 117 KEGG pathways (Figure 5C). The top five pathways were Plant hormone signal transduction (77), Phenylpropanol biosynthesis (48), Photosynthesis (32), Starch and sucrose metabolism (64), and Photosynthesis-antenna proteins (19). The stem vs. root comparison had 1415 DEGs mapped to 117 KEGG pathways (Figure 5D), including Plant hormone signal transduction (75), Starch and sucrose metabolism (59), Plant-pathogen interaction (40), Phenylpropanol biosynthesis (37), and Pentose and glucuronate interconversions (28).  
Figure 4: Histogram of DEGs in different tissues of _A. paniculata_ and volcanic distribution map of DEGs. (**A**) Histogram of DEGs. (**B**) Volcanic map of DEGs in Leaf vs. Root. (**C**) Volcanic map of DEGs in Leaf vs. Stem. (**D**) Volcanic map of DEGs in Stem vs. Root.
# Optimizing Investment Portfolios with Bacterial Foraging and Robust Risk Management
## 2 Bacterial Foraging Optimization (BFO)
### Mathematical Foundations
#### 2.2.1 Chemotaxis  
Chemotaxis represents the primary movement mechanism guiding bacteria toward nutrient-rich regions in the search space. The position of each bacterium is updated based on a balance of directed movement and random perturbations. The update rule is expressed as follows:  
\[x_{i}(t+1)=x_{i}(t)+C_{i}\cdot\phi_{i} \tag{1}\]  
where:  
* \(x_{i}\left(t\right)\) is the position of bacterium i at iteration t.
* \(x_{i}\left(t+1\right)\) is the updated position of bacterium i at iteration t + 1.
* \(C_{i}\) is the step size, controlling the magnitude of the movement.
* \(\phi_{i}\) is a unit vector representing the random search direction, typically chosen as a normalized random vector: \(\phi i=\frac{\overset{\rightarrow}{r}}{\left\|r\right\|}\), where \(\overset{\rightarrow}{r}\) is a vector of random values and \(\left\|r\right\|\) denotes the Euclidean norm of \(\overset{\rightarrow}{r}\).  
This movement in Equation (1) allows bacteria to balance exploration (randomness) and exploitation (nutrient gradient).
## 3 Background
### Current Challenges with Conventional Authentication Methods
#### 3.5.4 Human Aspect on Security  
The saying \"A chain is only as strong as its weakest link\" is especially true in the context of cybersecurity, which can be seen when looking at social engineering attacks that target the human aspect, which is often the weakest link [49]. When authentication processes become too complex or require too much effort, people often try to cut corners and skip critical practices for maintaining high-security levels [50].  
In a typical single-factor authentication with username-password combinations, users might find using the same username and password as convenient and natural as using other services [51]. This practice of reusing credentials creates a risk for the user's accounts and data on that particular service if another service, where the user has used the same credentials, is compromised or if the credentials are collected in a phishing attack [52]. Despite the risk, recent research suggests that almost every third user reuses the same password across different services and that more than 80% of online users use passwords across very similar services [53]. While the exact numbers are only based on sample checks through academic studies, these numbers still indicate that many users do not follow the best-practice password guidelines.  
Over the years, services have continuously enhanced security levels for single-factor authentication implementations. Strict password requirements have been put in place and vary from service to service, making a valid password in one service invalid in another, and much research has gone into guiding users into improving the strength of passwords [54; 55; 56; 57]. Additionally, some services, especially in corporate environments, have a sunset period for the password's validity, prompting the user to change the password occasionally [58]. Furthermore, as a common practice, databases containing leaked user credentials are being integrated with services to forbid the reuse of already compromised passwords [59]. Nevertheless, research suggests that even despite these improvements, the human is still the weakest link, and as already mentioned, phishing attacks are one of the most common and easiest attacks today [55, 60, 61].  
At the same time, we see a general trend outside single-factor authentication, which is that users, especially non-technical ones, tend to be slow in adopting new security processes and technologies. This is a general problem that all new technology and processes need to consider when designing new solutions [62, 63].
## 2 Structure of TRUM-70H  
Figure 1 shows the structure of the 70H prototype motor via a three-dimensional model and a sectional view. The motor comprises a stator, a rotor, a base, and a preload nut. The stator is fixed to the base with a locknut. The rotor is located in a bearing that is assembled in the base and is in contact with the stator. The preload nut provides a preload between the stator and the rotor through the threads on the rotor. The piezoelectric ceramics (PZT-8, 0.5 mm thick) and the friction materials (PTFE composites, 0.25 mm thick) are glued to the stator and the rotor with epoxy adhesive. Compared with the mainstream solid-shaft traveling wave motor, 70H has a larger inner hole with the ratio of aperture to outer diameter reaching 53%. Therefore, the design is more difficult because the design space of the stator and rotor is relatively smaller, the supporting web of the stator is narrower, and the amplitude of the stator may be smaller. However, 70H has obvious advantages too: the structure is more compact, the volume and mass are smaller, and the torque density is greater. In Figure 1, \(H\) is the stator thickness, \(H_{1}\) is the stator tooth height, \(h=(H+H_{1})/2\) is the distance from the stator tooth surface to the neutral plane, \(H_{2}=H-H_{1}\) is the stator base thickness, \(r_{h}\) is the rotor inner radius, and \(r_{d}\) is the stator inner radius.  
Figure 1: Hollow ultrasonic motor, \(\emptyset\)70 mm: (**a**) 3D structure of 70H; (**b**) some essential size parameters of 70H.
# Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
## 2 Related Works  
### Time Series Forecasting with Traditional Methods  
Time series forecasting has been studied for a long time. Most of the existing work can be divided into statistical methods and machine learning and deep learning methods. Such as Autoregressive Integrated Moving Average Model (ARIMA) [6] and a hybrid ARIMA and multilayer perception model (VARMLP) [13]. ARIMA obtained future forecast values by constructing polynomials of historical information and adding noise. VARMLP modeled linear and nonlinear data combining ARIMA with an artificial neural network model (ANN) [14]. Neural networks are often used to capture nonlinear relationships in time series forecasting [15; 16]. However, the main limitations of ARIMA models are the pre-assumed linear patterns and the requirement for high stationarity of the data. Therefore, ARIMA is not suitable for modeling problems of multivariate time series. Furthermore, Linear Support Vector Regression (SVR) [17] treats the prediction problem as a typical regression problem with parameters changing over time. The vector autoregressive model (VAR) [18] extends the AR model and was a commonly used econometric model. However, these models are difficult to extend to multivariate time series forecasting problems.
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 3 Signal Processing  
In this section, the signal model of PB-GB-SAR is established based on the geometries in Figure 4. Then, BPA- and RMA-based methods are modified for PB-GB-SAR imaging. At last, the displacement estimation formula is derived.
## 4 Performance Evaluation and Analysis
### Comparison of Algorithm Performance Under Fixed Loads
Using 24 h simulation data, we estimated network lifetime, considering the lifetime of the entire constellation as a whole, with a fixed amount of life consumed every 24 h. It is assumed that the service life of the satellite network under SP is five years, and its reliability is calculated based on the simulation results. GreenRP could extend the lifetime to 13.8 years. The enhancement in network lifetime by GreenRP was substantial, showing an increase of 176% over SP, 80% over GreenSR, and 33% over SGA, and the improvement of this indicator is based on the premise that the network quality has not declined significantly. This further proves that when designing and operating satellite networks, the SP strategy consistently shows a higher network lifecycle consumption rate in all periods compared with energy-saving strategies. It further indicates that the temporal variation in traffic distribution is crucial for optimizing network lifespan and efficiency. Otherwise, just like the SP strategy, the consumption rate of network lifespan will remain at a high level regardless of changes in traffic demand, leading to unnecessary loss of network lifespan.
## 2 Data Acquisition and Preprocessing
### Feature Selection Method  
The independent variables in Table 1 play a key role in describing the fluid behavior and thermodynamic process, and the prediction accuracy can be effectively improved through the reasonable selection and optimization of characteristics. We determined the Pearson correlation coefficient method from a large number of experiments, before which we tried RF-RFE, Brouta, and other feature selection methods. Finally, according to the experimental results, Pearson screened features were selected for follow-up study.  
The Pearson correlation coefficient method is widely employed in this field as a feature selection technique. Unlike many other methods, the Pearson correlation does not rely on machine learning algorithms but instead filters features based on statistical relationships or other metrics between the input features and the target variable [18]. Due to its simplicity and computational efficiency, this filtering approach is often utilized in the early stages of feature selection to help eliminate redundant or irrelevant features. The calculation formula is as follows:  
\[r=\frac{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})(y_{i}-\overline{y})}}{ \sqrt{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})^{2}\sum\limits_{i=1}^{n}{(y_ {i}-\overline{y})^{2}}}}} \tag{1}\]  
where \(X_{i}\) and \(y_{i}\) represent the sample data points of the sum of the two variables, respectively, \(\overline{X}\) and \(\overline{y}\) are the mean values of the two variables, respectively, and the value of \(r\) is between \(-1\) and \(1\).
## 3 Results and Discussion  
### Accelerated Oxidation Test with OXITEST and Kinetic Study  
Table 1 report the induction period (IP) of oxidation for each breadstick's formulation. Both the amount of GPP addition and temperatures affected the onset oxidation of breadsticks. B0 had higher IP at all temperatures compared to B5 and B10, and similarly B5 to B10. [PERSON] et al. (2017) reported a IP of 39 h (almost 1.5 times greater than B0) for breadsticks with olive oil [13]. These differences could rely on the different types of olive oil and the manufacturing process of breadsticks used in the current study. Moreover, the increasing temperature in the oxidation chamber led to a significant drop of IP values, indicating an acceleration of the oxidation process as expected and confirmed by the rate of oxidation \"k\" (Table 1). Indeed, B10 showed a faster rate of oxidation for all tested temperatures compared to B5 and B0.  
OXISoftTM calculated the linear regression between the logarithm of the IP and the temperature (\({}^{\circ}\)C) to estimate shelf-life (IP in days) at 25 and 20 \({}^{\circ}\)C (Table 2). All linear regressions had a coefficient of determination (R\({}^{2}\)) greater than 0.98. Results indicated that B0 had the most extended estimated shelf-life while B10 accounted for the lowest. Under this point of view, B0 had almost two-fold shelf-life days compared to B10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**IP**} & \multicolumn{3}{c}{**Rate of Oxidation \(k\)**} \\  & \multicolumn{3}{c}{**(min)**} & \multicolumn{3}{c}{**(I/Day)**} \\ \cline{2-7}  & **90 \({}^{\circ}\)C** & **100 \({}^{\circ}\)C** & **110 \({}^{\circ}\)C** & **90 \({}^{\circ}\)C** & **100 \({}^{\circ}\)C** & **110 \({}^{\circ}\)C** \\ \hline B0 & 1465.5 \(\pm\) 48.8 \({}^{\mathrm{a}}\) & 747.5 \(\pm\) 29.0 \({}^{\mathrm{b}}\) & 419.5 \(\pm\) 7.8 \({}^{\mathrm{f}}\) & 0.2053 \(\pm\) 0.0012 \({}^{\mathrm{a}}\) & 0.4132 \(\pm\) 0.0025 \({}^{\mathrm{d}}\) & 0.7710 \(\pm\) 0.0047 \({}^{\mathrm{g}}\) \\ B5 & 776.5 \(\pm\) 44.5 \({}^{\mathrm{b}}\) & 386.5 \(\pm\) 6.4 \({}^{\mathrm{d}}\) & 218 \(\pm\) 4.2 \({}^{\mathrm{g}}\) & 0.2307 \(\pm\) 0.0015 \({}^{\mathrm{b}}\) & 0.4890 \(\pm\) 0.0027 \({}^{\mathrm{e}}\) & 0.8201 \(\pm\) 0.0052 \({}^{\mathrm{h}}\) \\ B10 & 698.5 \(\pm\) 9.2 \({}^{\mathrm{c}}\) & 331.5 \(\pm\) 7.8 \({}^{\mathrm{e}}\) & 201 \(\pm\) 8.5 \({}^{\mathrm{h}}\) & 0.3010 \(\pm\) 0.0020 \({}^{\mathrm{c}}\) & 0.5185 \(\pm\) 0.0032 \({}^{\mathrm{f}}\) & 0.9295 \(\pm\) 0.0064 \({}^{\mathrm{l}}\) \\ \hline \hline \end{tabular} Values with different superscript letters are statistically different for \(p\) < 0.05. B0: control breadstick; B5: breadsticks fortified with 5% of GP; B10: breadsticks fortified with 10% of GP.  
\end{table}
Table 1: Induction period (IP) and rate of oxidation \(k\) of control and fortified with grape pomace (GP) breadsticks. OXITEST was set at temperatures 90, 100, and 110 \({}^{\circ}\)C.