esa-sceva/satcom-chunk-collection huggingface dataset:
# A Circularly Polarized mmWave Dielectric-Resonator-Antenna Array for Off-Body Communications
## 2 Design Methodology
### A Single DRA Element  
Figure 1 illustrates the antenna's configuration, with dielectric constants and loss tangents of \(\varepsilon_{\mathrm{r}}\) = 9.9, tan\(\delta\) < 0.0001 and \(\varepsilon_{\mathrm{s}}\) = 3.66, tan\(\delta\) < 0.0027 for the DRA and substrate, respectively. The DRA dimensions were determined using the dielectric waveguide model (DWM), as well as a CST eigenmode solver for calculating the resonance frequencies of the transverse electric, TE\({}_{mnp}\), resonance modes [23].  
In order to achieve the CP radiation, the single DRA element was excited by a cross slot while the energy coupling was controlled by a stub-length extension (\(l_{stub}\)) with a recommended initial value of 0.25\(\lambda_{\mathrm{g}}\), where \(\lambda_{\mathrm{g}}\) is the guided wavelength. The cross-slot was fed using a 50-ohm microstrip line. The choice of cross-slot arm lengths and widths is crucial for maximizing the impedance and AR bandwidths [24]. The lengths of the 1 st and 2 nd cross-slot arms were optimized as 1.7 mm and 3.2 mm, respectively, with an identical width of \(w_{\mathrm{s}}\) = 0.35 mm for each arm. The antenna parameters are listed in Table 1. In addition, the higher-order modes of TE\({}_{113}\) and TE\({}_{133}\) were simultaneously excited at 27 GHz and 30 GHz, respectively, together with the fundamental resonance mode of TE\({}_{111}\) at 25 GHz. Table 2 presents the resonance frequencies of the transverse electric modes, which are supported by the designed DRA within the considered frequency range.
## 2 Materials and Methods
where \(k\) = correction factor for rotating masses \(\left(k\approx 1+I/R^{2}m\right)\)  
\(R\) = tire radius, _m(ft)_  
Typical values of k for passenger cars range from 1.05 to 1.15 in high gear to 1.3 to 1.5 in low gear. The corresponding values for trucks are from 1.03 to 1.06 and from 1.25 to 1.6, respectively. Braking power \(P_{b}\) is equal to braking energy divided by the time t during which braking occurs. \[P_{b}=d(E_{b})/dt,\ \ Nm/s(lbft/s) \tag{4}\]  
If the deceleration a is constant, then the velocity \(V(t)\) is given by the following:  
\[V(t)=V_{1}-at,\ \ m/s(ft/s) \tag{5}\]  
where \(a=\) deceleration \(m/s^{2}\left(ft/s^{2}\right)\)  
T = time, s  
Equation (3) through (5) yield the brake power as follows:  
\[P_{b}=kma(V_{1}-at),\ \ Nm/s(lbft/s) \tag{6}\]  
Equation (6) reveals that braking power does not stay constant during the braking process. At the beginning of braking (\(t\) = 0), the brake power is at a maximum, and it decreases to zero when the vehicle stops. The time \(t_{s}\) for the vehicle to come to a stop is calculated as follows:  
\[t_{s}=V_{1}/a,\ \ \ \ \ \text{second} \tag{7}\]  
The average braking power \(P_{b}\) over the braking time \(t_{s}\) for a vehicle coming to a stop is calculated as follows:  
\[P_{b}=\frac{kmaV}{2},\ \ \ Nm/s(lbft/s) \tag{8}\]  
Equation (8) shows the basic general calculations of the energy absorption. The tire slip, heat distribution, brake area, and percentage braking of brake pads should all be considered. Tire slip is defined by the ratio of the difference between vehicle forward speed and circumferential speed to vehicle forward speed. The friction energy varies by deceleration, which can be controlled by the load and rpm of the friction experiment. The conditions of the friction test are listed in Table 3. The test was conducted for a total of 7200 cycles for 60 min based on 120 rpm. This is 40 min based on 180 rpm, 30 min based on 240 rpm, and 24 min based on 300 rpm. For the ELPI+ equipment, airborne particles were captured and separated into stages according to their sizes. The concrete size of each stage is listed in Table 4. The experimental data were set by obtaining the difference between the average of the data values stabilized after the run-in phase and the average of the data values before the initial experiment. An example used to extract data values is shown in Figure 2. \begin{table}
\begin{tabular}{c c} \hline Test pressure & 0.25 MPa, 0.66 MPa \\ Test rpm & 120, 180, 240, 300 \\ Temperature & 292–295 K \\ Cycle & 7200 \\ Humidity & Under 30\% \\ \hline \end{tabular}
\end{table} 
Table 3: Friction test conditions.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 6 Proposed Algorithm  
This section introduces a scheduling algorithm that uses weights to determine to which access point a user is assigned. The probable users are chosen based on the distance and received signal strength between the user and the access point.
## 5 Conclusions  
In the existing ERA parameter identification process, the conjugate constraint relationship of the angular frequencies is not fully considered, which leads to a decrease in the accuracy of the SSO parameter identification results in actual scenes. Using the ERA's advantages, such as easy solutions, low computational complexity, and anti-noise solid capability, a fitting-based ERA parameter identification method for SSO is proposed, enabling dynamic real-time monitoring of SSO in the modern power system. However, the fitting-based ERA successfully addresses this issue by introducing an angular frequency fitting equation to solve the conjugate constraints of these angular frequencies, making the parameter identification results more precise and reliable and thereby realizing the ability to use a 200 ms data window for SSO parameter identification. This paper also validated the fitting-based ERA method using synthetic and actual PMU data. The validation results indicate that compared with the existing ERA, the fitting-based ERA can achieve accurate parameter identification results with a 200 ms data window, enhancing its application value in SSO analysis of power systems.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare that they have no known competing financial interests or personal relationships which could have appeared to influence the work reported in this paper. [PERSON], [PERSON] and [PERSON] are employees of the State Grid Sichuan Electrical Power Research Institute, who provided funding and technical support for this work. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
###### Abstract
Understanding electromagnetic pollution and SAR is essential for conducting thorough risk assessments, establishing safety guidelines, guiding technology advancement, and promoting public awareness regarding the potential health effects of RF-EMF exposure. Continued research in this field helps ensure that individuals can make informed decisions about their RF-EMF exposure and adopt measures to minimize potential risks.
## 4 Proposed System
### Trajectory-based Message Delivery
#### 4.4.2 Immediate Message Forwarding  
If a message is distributed to contact point RSUs, each RSU stores the received messages and forwards them when a target vehicles for the message passes by RSU's coverage before \(ttl\) is expired. Suppose that a vehicle \(V_{d}\) enters \(RSU_{i}\)'s coverage and \(M_{jd}\) sent from \(sRSU_{j}\) for \(V_{d}\) is kept by \(RSU_{i}\). The followings describe message forwarding protocol from \(RSU_{i}\) to \(V_{d}\).  
Figure 3: Message forwarding from an RSU to a destination vehicle.  
1. \(RSU_{i}\) periodically broadcasts beacon message containing \(pid_{di}\) specified as a destination of \(M_{jd}\).
2. If \(pid_{di}\) is found in the beacon message, \(V_{d}\) sends a message request \(\{req,pid_{di},S^{\prime}_{d}\}\) to \(RSU_{i}\), where \(req\) is a metadata for message requesting and \(S^{\prime}_{d}\leftarrow\mathit{idSig(vsk_{di},req|pid_{di})}\).
3. Upon receiving the request message, \(RSU_{i}\) authenticates \(V_{d}\) by verifying \(S^{\prime}_{d}\) as \(idVrf(pid_{di},\)\(req|pid_{di},S^{\prime}_{d})\). If it holds, \(RSU_{i}\) sends \(M_{jd}\) to \(V_{d}\).  
When \(M_{jd}\) is downloaded, \(V_{d}\) checks the authenticity of the received message \(msg\) to be convinced whether the message actually originated from \(V_{d}\)'s desired \(sRSU_{j}\) as follows:  
1. Parse \(M_{jd}=\{pid_{di},sRSU_{j},msg,ts^{\prime},S_{j},ttl\}\).
2. Verify the signature \(S_{j}\) as \(idVrf(sRSU_{j},pid_{di}|sRSU_{j}|msg|ts^{\prime},S_{j})\); and, if it holds,
3. Accept \(msg\) as a valid message from \(sRSU_{j}\).
## 2 Methodology
### Loss Functions, Optimizers, and Schedulers
#### 2.3.3 Schedulers  
Learning rate schedulers were added to fine-tune the optimization process further. The schedulers used in our experiments are given below:  
* StepLR gradually decreased the learning rate at set intervals, nudging the model toward convergence as training progressed.
* ReduceLROnPlateau watched the validation loss closely and reduced the learning rate only when improvements stalled--helpful for escaping local minima.  
* CosineAnnealingLR and CosineAnnealingWarmRestarts [29] follow a cosine-shaped curve to gently reduce the learning rate and occasionally reset it, encouraging the model to explore new possibilities during training.  
All optimizer and scheduler settings, including learning rates, momentum, and weight decay, were carefully tuned through experimentation. This setting ensured the models could not only learn robust features effectively but also stay robust against class imbalance and other training challenges.
## 6 Conclusions  
Accurate prediction of ice crystal trajectory is a precondition for icing numerical simulation. In this paper, a numerical simulation method for ice crystal motion trajectory based on a Lagrangian method was proposed, and the effects of different parameters on the calculation results were discussed. The governing equations of ice crystal motion were established and the solution method of the governing equation was studied. In order to verify the correctness of the numerical simulation method of this paper, the particle trajectories and collection coefficient distributions under various conditions were calculated using this method. The calculation results were compared with reference results and icing wind tunnel experimental results, which proved the validity of the model. Based on the research method in this paper, the impact characteristics of ice crystals under different model parameters and conditions were studied. The main conclusions are as follows:  
1. The numerical simulation method in this paper could calculate the motion trajectories of ice crystal, and the collection coefficient results were consistent with the experimental results, meeting the requirements for engineering calculation;
2. The particle density has a certain effect on the collection coefficient, and the smaller the particle density, the smaller the collection coefficient;
3. Some of the current drag coefficient models have an effect on ice crystal trajectories and collection coefficients, but the effect is small;
4. An increase in particle diameter and inflow velocity leads to an increase of ice crystal collection coefficient and the expansion of impact range. However, when the ice crystal diameter reaches 200 \(\upmu\)m, the collection coefficient is almost unchanged;
5. As the angle of attack changes, the range of the ice crystal collection coefficient also changes, but the values do not change significantly.  
Conceptualization, Z.W. and C.L.; methodology, Z.W.; software, W.Z.; validation, Z.W., C.L. and W.Z.; writing--original draft preparation, Z.W. and C.L.; writing--review and editing, Z.W. and C.L.; supervision, H.Z.; project administration, H.Z.; funding acquisition, Z.W. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No.51806105), the Postgraduate Research and Practice Innovation Program of Jiangsu Province and the Scientific Research Foundation of Nanjing Institute of Technology (Grants No. YKJ202009).  
Not applicable.  
All data have been shown in the paper.  
The authors declare no conflict of interest.  
Figure 14: Comparison of ice crystal collection coefficients for different angles of attack.
# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Performance Analysis
#### 4.3.4 Model Poisoning Scenario and Results (Non-IID)  
The evaluation of defense mechanisms against sign-flipping attacks in non-IID CIFAR settings reveals distinctive performance characteristics. While baseline performance remained comparable across frameworks, varying resilience emerged under attack conditions. The OC-SVM implementation maintained stability, achieving 74.12% accuracy under moderate attack conditions and 73.54% under 20% malicious participant scenarios. This performance trajectory notably surpassed state-of-the-art approaches, particularly Krum, which exhibited significant vulnerability with accuracy declining to 42.36% under maximum attack conditions. The results for this scenario are shown in Table 12.  
We further extended our experiments across dataset architectures to demonstrate the defense capabilities against sign-flipping attacks. In the F-MNIST evaluations, the advanced frameworks maintained robust performance, with OC-SVM achieving 85.75% accuracy under moderate attack conditions. The MNIST implementations revealed enhanced resilience, consistently exceeding 98% accuracy under standard conditions and maintaining above 97% effectiveness even under maximum attack scenarios. These findings demonstrate meaningful advancement in preserving model integrity across varying data distributions and malicious user attendance. The results mentioned are given in Table 13.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**F-MNIST**} & \multicolumn{3}{c}{**MNIST**} \\ \hline  & **\%0** & **\%10** & **\%20** & **\%0** & **\%10** & **\%20** \\ \hline Krum [14] & 64.46 & 60.61 & 63.62 & 85.39 & 82.83 & 81.74 \\ Median [16] & 79.12 & 78.15 & 76.17 & 97.01 & 96.01 & 87.37 \\ TMeans [16] & 86.24 & 82.57 & 78.22 & 97.66 & 97.56 & 97.2 \\ FLDefender [21] & **86.59** & 81.59 & 78.02 & **98.69** & 98.05 & 94.86 \\ Ours OC-SVM & 86.33 & **82.75** & **81.15** & 98.65 & 98.11 & **97.64** \\ Ours Agg-C & 85.88 & 81.33 & 80.27 & 98.48 & **98.17** & 96.44 \\ Ours k-NN & 83.58 & 80.57 & 77.25 & 97.36 & 97.25 & 89.14 \\ \hline \hline \end{tabular}
\end{table}
Table 10: OAcc performance against label-flipping attack for non-IID F-MNIST and MNIST.
###### Abstract  
The ubiquitin-proteasome system (UPS) participates in the degradation of proteins which play an important role in regulating the cell cycle, apoptosis, and angiogenesis, as well as in the immune system. These processes are important in carcinogenesis. Transitional cell carcinoma (TCC) is one of the predominant types of bladder cancer. The relationship between the ubiquitin-proteasome system and cancer progression has become a topic of increasing interest among researchers. In this work, we propose an application of surface plasmon resonance imaging (SPRi)-based biosensors for the detection of 20S proteasome and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) in the blood serum and urine of patients with TCC. The aim of the study was to determine 20S proteasome and UCH-L1 concentrations and to correlate the results with clinicopathological parameters. The group of subjects consisted of 82 patients with confirmed TCC, in addition to a control group of 27 healthy volunteers. It was found that 20S proteasome and UCH-L1 concentrations were significantly elevated in both the serum and urine of TCC patients, compared with the healthy subjects. There was a correlation between 20S proteasome concentrations in serum and urine, as well as between serum proteasome and UCH-L1 concentration. The SPRi biosensor sensitive to 20S proteasome using PSI inhibitor as the receptor, and the SPRi biosensor sensitive to the UCH-L1 protein using the protein-specific antibody as the receptor is suitable for the determination of 20S proteasome and UCH-L1 in body fluids and can serve as useful tools in the investigation of cancer biomarkers.  
proteasome; ubiquitin carboxy-terminal hydrolase L1 (UCH-L1); bladder cancer; surface plasmon resonance imaging biosensor Article
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 3 Proposed Method  
The research method employed in this study comprised four steps. First, historical stock data, such as opening, closing, highest, and lowest prices, as well as trading volumes, were collected from Yahoo Finance, as listed in Table 1. These were used to generate the technical indicators and price limits, and the closing price signals were decomposed into several subsignals to create three distinct datasets. Second, the data were preprocessed through dimensionality adjustments, feature extraction using Pearson's correlation coefficient, and normalization. Third, the data were divided into training and test sets at a ratioof approximately 8:2. Finally, the predictions of all systems were integrated using CSA (crow search algorithm) to obtain the final prediction.
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.12. Telemedicine and Digital Health Development in Locomotor System  
The evolution of telemedicine in Orthopedics and trauma in Poland has driven the evaluation of pandemic-induced teleconsultation adoption and post-surgical care, particularly after total hip arthroplasty (THA) [164]. Research has focused on 3D telediagnostic screening for scoliosis and postural disorders [165]. A web survey examined \"red flags\" for serious low back pain among Internet users, highlighting the need for tele-triage [166]. During COVID-19 (2021-2023), studies have focused on orthopedic teleconsultations, documenting diagnoses, and effectiveness [167,168]. Surveys have explored surgeons' methods for assessing fracture healing [169]. Literature reviews have proposed telemedicine-based postsurgical care models for THA recovery [170]. Systematic reviews have identified technologies for postsagittal THA care [171]. Early telemedicine focused on diagnostic screening and patient self-reporting. The pandemic has accelerated the adoption of telecon-sultation, proving its effectiveness. Research has emphasized post-hospital telemedicine care, including rehabilitation monitoring, for THA patients. Future efforts should enhance organizational frameworks and patient outcomes through telemedicine.
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
###### Abstract  
The ongoing Zika virus (ZIKV) in the Americas has been a serious public health emergency since 2015. Since Zika is a vector-borne disease, the size of the vector population in the affected area plays a key role in controlling the scale of the outbreak. The primary vectors for Zika, the _Aedes Agypti_ and _Aedes Albopictus_ species of mosquitoes, are highly sensitive to climatic conditions for survival and reproduction. Additionally, increased international travel over the years has caused the disease outbreak to turn into a pandemic affecting five continents. The mosquito population and the human travel patterns are the two main driving forces affecting the persistence and resurgence of Zika and other vector-borne diseases. This paper presents an enhanced dynamic model that simulates the 2013-2014 French Polynesia Zika outbreak incorporating the temperature dependent mosquito ecology and the local transit network (flights and ferries). The study highlights the importance of human travel patterns and mosquito population dynamics in a disease outbreak. The results predict that more than 85% of the population was infected by the end of the outbreak and it lasted for more than five months across the islands. The basic reproduction number (\(R_{0}\)) for the outbreak is also calculated using the next-generation-matrix for validation purposes. Additionally, this study is focused on measuring the impact of intervention strategies like reducing the mosquito population, preventing mosquito bites and imposing travel bans. French Polynesia was chosen as the region of interest for the study because of available demographic, climate and transit data. Additionally, results from similar studies for the region are available for validation and comparison. However, the proposed system can be used to study the transmission dynamics of any vector-borne disease in any geographic region by altering the climatic and demographic data, and the transit network.  
a 2018; 1, 31; doi:10.3390/asi1030031
## 2 Materials and Methods  
### Coded Excitation  
Coded excitation describes the variation in the stimulation of an ultrasonic transducer using a code sequence instead of a default sine wave or impulse stimulation. There are many parameters that can be varied, including the signal shape, length, amplitude or the minimal and maximal pulse width. Coded excitation sequences evaluated in this work include pseudo noise sequences (PN) [24], Barker codes [25], variations of chirp signals [15] as well as derived custom signals. The pseudo noise sequences were generated using linear feedback shift registers (e.g., PN 8 and 24) [26]. The Barker codes are defined as digital sequences with ideal autocorrelation properties. Only the three longest codes were tested because it is known that the ambiguity of their CCF decreases with increasing code length (e.g., Barker 7, 11 and 13) [25]. For the chirp signals the factor between the lowest and the highest frequency and the direction of the frequency adaption are used to describe the sequence (e.g., Chirp Up-Down Freq. 1-4). The lookup tables for chirp sequences are generated with a frequency factor of 2 or 4 and linear or quadratic frequency increments according to the formula in the literature converted to create discrete signals [15, 27]. The initial frequency, final frequency and duration of the signal are influenced by the measurement setup, which will be further elaborated in Section 2.2.  
Limits in the variation of the waveforms were chosen taking into account the limitations of the testing system. The output stage of the ultrasonic system can only assume three states: positive output voltage, zero output voltage and negative output voltage. The output stage controls a MOSFET stage through which the burst voltage is switched to the transducer. The system can generate pulses with a width between 25 ns and 1 us.  
The code length must be limited to avoid overlay effects between the incoming and reflected waves in the specimens. To achieve an optimal comparability between the different experiments and simulations, the length of the echoes was limited to the frame size of the CCF (512 samples) while matching the center frequency of 4 MHz and the bandwidth of the transducer.