esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 2 Related Work
### Ship Emissions under the Pandemic  
The global shipping industry has been affected by the COVID pandemic, which has ramifications for ship emissions in port regions. Several analysts have examined the influence of the COVID pandemic on ship emissions. [PERSON] et al. [29] analysed local shipping activities in the Strait of Gibraltar during the early phases of the pandemic. They utilised a brand-new mathematical model that considered weather and sea conditions. Their analysis yielded a 12% reduction in greenhouse gas emissions in the region, because ship operations were mainly Ro-Pax vessels.  
The COVID pandemic has altered the number of ships calling at numerous ports [30; 31]. There is a decline in regional emissions for the port of Shanghai, whose principal activity is container-handling [14]. They determined how the COVID pandemic might affect ship emissions in the region. The number of ships and their berthing periods were evaluated. It was deduced that, due to the severe preventative measures adopted, the number of ships entering the port were reduced, and there was a consequent reduction in emissions.  
The COVID pandemic affected the number of port-wide ships and their operations [32]. The study examined the COVID pandemic's effects on maritime traffic and emissions, using the port of Barcelona and adjacent waterways as an example. Moreover, the COVID pandemic considerably impacted people's travel preferences; for instance, in Danish waters, fewer people chose cruise ships as a form of transportation following the pandemic, resulting in lower emissions [28].  
In recent years, many researchers have also looked for ways to decarbonise ports. Using the Port of Ravenna as an example, [PERSON] et al. [33] propose a framework to develop a multi-phase Smart Port Management Strategy (SPMS) through a Local Integrated Partnership (LIP). This approach pioneers an innovative process for the sustainable and secure management of ports. In addition to this, approaches to implementing sustainable port development include the application of alternative fuels and the electronic transformation of terminals, which have been shown to reduce pollutants by more than 70-80% in a study conducted by [PERSON] et al. [34].  
Although there are several research studies on the COVID pandemic's influence on ship emissions, none discusses the relationship between the number of COVID pandemic infections and changes in ship emissions. Some ports have introduced shore power to limit ship emissions. These studies are mentioned, but need to establish the policy's effectiveness.
# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
## 1 Introduction
Compared to data cleaning models applied in distribution networks and power systems, models for cleaning mine ventilation data are relatively scarce. Some existing models in the field of mine ventilation directly remove anomalous data, thereby compromising data integrity. Although the Stacked Denoising Autoencoder (SDAE) model can ensure data integrity, it typically requires preprocessing of missing values before feature learning, which may introduce additional errors. To address these issues, this paper proposes a mine wind speed sensor anomaly cleaning model based on LOF-GMM and SGAIN. The LOF-GMM method is used to identify anomalous and faulty data, which is then removed. Following this, SGAIN is applied to the cleaned data to generate reasonable imputed values for the missing data via adversarial training between the generator and discriminator. This approach eliminates the need for data preprocessing, ensuring data integrity and offering a novel method for mine ventilation data cleaning.
# Exploring ISAC: Information-Theoretic Insights
## 5 Network ISAC with Sensing Distortion
### Multi-Access ISAC: Collaborative Sensing and Suboptimality of Symbolwise Estimators
#### 5.2.2 Results
: _The capacity-distortion region \(\mathcal{CD}\) of an ISAC MAC system includes any rate-distortion tuple \((R_{1},R_{2},D_{1},D_{2})\) that for some choice of pmfs, \(P_{U_{0}},P_{U_{1}|U_{0}},P_{U_{2}|U_{0}},P_{X_{1}|U_{0}U_{1}},P_{X_{2}|U_{0}U _{2}},\)\(P_{V_{1}|U_{0}U_{2}X_{1},X_{2},\prime},P_{V_{2}|U_{0}U_{1}X_{2},X_{2}}\) satisfies Inequalities (49) below (where \(\underline{U}:=(U_{0},U_{1},U_{2})\))_  
\[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]0 Block Markov strategy of the ISAC multi-access scheme in [68]. \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]1 (49a) \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]2 \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]3 \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]4 \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]5 \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]6 \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]7 \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]8 \[R_{2} \leq I(X_{2};YZ_{1}Z_{2}|X_{1}QQ_{2}),\]9 (49b) \[R_{1}+R_{2} \leq I(X_{1}X_{2};YZ_{1}Z_{2}|QQ_{Z}),\]0 (49c) \[R_{1}+R_{2} \leq I(X_{1}X_{2};YZ_{1}Z_{2}|QQ_{Z}),\]1 \[R_{1}+R_{2} \leq I(X_{1}X_{2};YZ_{1}Z_{2}|QQ_{Z}),\]2 \[\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\It is rather straightforward to identify further ways of obtaining improved multi-access ISAC schemes. For example, one could add additional coding layers as in Marton coding in a way that the Rx is not required to decode all sensing information. In fact, in certain scenarios, sensing information is useless at the Rx, and moreover, the Rx has worse decoding capabilities than the Txs. Moreover, joint source-channel coding methods could be applied for the transmission of sensing information. In fact, the sensing information sent at the two Txs are correlated and it is well known that in such a scenario, a joint source-channel coding approach can achieve improved performances. In an upcoming section, we briefly discuss a joint source-channel coding approach for the two-way channel, i.e., for device-to-device (D2D) communication.
## 3 Results and Analysis
### Simulation Results
For a given structure, rotating the polarization angle changes the alignment between the electric field and the resistor position, thereby altering the strength of the coupling between the incident wave and the resistive load. As a result, small shifts in the absorption frequency are observed as the polarization angle changes. This effect is evident in Models A and B, where variations in \(\varphi\) lead to corresponding shifts in the frequency at which maximum absorption occurs (see Figure 2). The second case simulation which is also carried out in this paper is that the via and resistor positions are varied at angles (\(\varphi_{p}\)) of 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) from the center of the patch, as shown in Figure 6. The simulation results of this case of voltage at the lumped resistor are shown in Figure 7. For each model in the second case, voltage dip appears when the lumped resistor is placed at a 90\({}^{\circ}\) angle to the electric field seen from the center of the unit cell. It can be seen that the voltage dips shift with changing lumped resistor positions. This means that there is also a relationship between the position of the lumped resistor and the central point of the patch in capturing RF wave energy. The relationship between the dip angle and the geometric angle of the structure is shown in \[P_{load}=\frac{V^{2}}{2R} \tag{1}\]0 Additional simulations were also performed for other angles (15\({}^{\circ}\), 45\({}^{\circ}\), 75\({}^{\circ}\)) as well to verify the observed relationship. This condition shows that there will always be a dip wherever the resistor is placed, which means polarization-independence will not be possible with this design. There are two points emphasized by these results: (1) the dip angle does not depend on frequency but is determined by the geometry of the structure itself; (2) there is a clear relationship between the position of the resistor relative to the center of the patch and the resulting dip angle. To further validate this observation we  
\[P_{load}=\frac{V^{2}}{2R} \tag{1}\]1 The proposed second case unit cell of metasurface with variations of \(\varphi_{p}\). (**a**) Front view, (**b**) back view. also simulated a circular patch structure using one via and a resistor, and we confirmed a similar relationship between the resistor position from the patch center and the dip angle.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
###### Abstract  
Developing reliable protection systems is critical for the advancement of medium-voltage direct-current (MVDC) grids. This paper highlights the significance of fault detection in MVDC grids, especially in ensuring the reliability and efficiency of renewable energy systems. This paper provides a comparative analysis of fault detection algorithms, including overcurrent, undervoltage, rate of change of current (ROCOV), rate of change of voltage (ROCOV), differential, and inductor voltage derivative methods. The performance of these algorithms is quantified by metrics such as the detection speed, accuracy, and sensitivity under diverse scenarios. The authors assess these algorithms within a multi-terminal MVDC grid designed for renewable hydrogen production, evaluating the detection speed across various fault types (bus and link faults) and conditions, including the variation in the fault location and resistance. The results reveal that the UV, ROCOC, ROCOV and LIVRD methods achieve detection speeds as high as 0.01 ms, outperforming other techniques under low-resistance fault conditions. By using uniform fault scenarios, we identify the most effective algorithms for rapid fault detection, aiming to enhance the protection strategies for MVDC grids. These findings underline critical performance differences between methods, guiding the design of tailored protection schemes that address specific fault challenges in renewable-powered grids. Additionally, the practical implications of these findings for designing resilient protection schemes in renewable-powered grids are discussed. Simulations are conducted using PSCAD/EMTDC V4.6 software, ensuring the consistency and accuracy of the performance comparison. The insights gained provide a concrete understanding of each algorithm's trade-offs, enabling informed decisions when selecting optimal fault detection methods to ensure MVDC grid reliability.  
fault detection; fault diagnosis; power converters; power electronics +
Footnote †: journal: applied sciences
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 5 Model Fitting Results
### Example 3
Again, let us look at where the fitted models cross the noise threshold level. At both frequencies the MLE fitted model for NLOS matches the true PL distribution at \(\overline{PL}=PL^{*}\) and \(\overline{PL}(d)\pm 1.96\sigma=PL^{*}\). These distances are 140, 390, and 1100 m at the 2 GHz frequency and 110, 290, and 800 m at 28 GHz. Similarly to examples 1 and 2, the OLS fitted model overestimates these as 220, 820, and 3000 m in the 2 GHz case and as 170, 640, and 2400 m at 28 GHz. The accuracy of the model at the threshold level becomes relevant when these models are used in the positioning phase to define likelihood functions for BSs with \(PL>PL^{*}\).
# A Multi-Band LNA Covering 17-38 GHz in 45 nm CMOS SOI
## 4 Conclusions  
In this work, a multi-band LNA is designed and implemented in a 45-nm CMOS SOI process, which is composed of the differential cascode amplifier, RC feedback link, and source degeneration inductor. The LNA consists of three stages in order to achieve high gain, broadband, and low NF, and adopts baluns for the input/output and interstage impedance matching. Meanwhile, by adding switch capacitances, the proposed LNA realizes the switching of three operating bands, 17-21 GHz, 21-28 GHz, and 28-38 GHz. Measurement results show that the maximum gain is 23.0 dB, and the minimum NF is 4.0 dB, while occupying a chip area of 0.94 \(\times\) 0.34 mm\({}^{2}\). This work has a certain reference value for multi-band LNA design.  
Conceptualization, F.H., X.L. ([PERSON]) and Z.L.; data curation, Q.Q. and Z.L.; formal analysis, F.H., X.L. ([PERSON]) and C.W.; funding acquisition, X.L. ([PERSON]) and X.L. ([PERSON]); investigation, F.H., X.L. ([PERSON]), C.W. and X.L. ([PERSON]); methodology, X.L. ([PERSON]) and Q.Q.; project administration, X.L. ([PERSON]), X.L. ([PERSON]) and Z.L.; supervision, Q.Q., X.L. ([PERSON]) and Z.L.; validation, X.L. ([PERSON]) and Z.L.; visualization, F.H., C.W. and X.L. ([PERSON]); writing--original draft, C.W.; writing--review and editing, F.H. and X.L. ([PERSON]). 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 under Grant 62101038 and Grant 61801027, the Beijing Institute of Technology Research Fund Program for Young Scholars under Grant XSQD-202105006, and the Beijing Nova Program of Science and Technology under Grant Z191100001119078.  
The authors declare no conflict of interest.
## 4 Determination of Full Anchor Cable Support Parameters
### Determine the Support Scheme  
In summary, among the analyzed schemes, when implementing support schemes 4, 6, and 8 from Table 2, the roadway exhibits better surrounding rock stress states, smaller surrounding rock displacements, and a smaller range of plastic failure. When considering the economic aspect, scheme 6 in Table 2 requires fewer bolts and anchor cables compared to scheme 4 due to the larger row spacing. Furthermore, when compared to scheme 8 in Table 2, scheme 6 has a larger row spacing and shorter length, resulting in a reduced requirement for bolts and anchor cables. Therefore, scheme 6 in Table 2 can be considered the plan with the most comprehensive support function, the best effect, and the scheme that conforms to the principle of economy and practicability among the analyzed schemes. To ensure the anchoring quality of the anchor cable, the diameter of the anchor hole, anchor cable, and resin cartridge should be properly matched when using the MSK2550 resin cartridge anchoring agent for anchor cable installation. Therefore, the design diameter of the anchor hole was set to 28 mm.
## 5 Test and Verification
### Condition Detection via the Proposed Method  
Figure 9 shows the procedure of the fault diagnosis method proposed in this paper. First, the vibration signal is decomposed via the VMD method introduced in Section 2. Figures 10-13 show the decomposition results of the collected vibration signals of the bearing in each state. Each state has four corresponding decomposed IMF components, and 300 \(ApEn\) values are calculated for each IMF. An eigenvector is created from the \(ApEn\) of each component. To explain the efficiency of \(ApEn\), we compare the sensitivity of \(ApEn\) with RMS, skewness, and kurtosis by the detection index method (DI) [26]. Suppose that x\({}_{1}\) and x\({}_{2}\) are the SP values calculated from the signals measured in state 1 and state 2, respectively, and their average and standard deviation are \(\mu\) and \(\sigma\). The DI is calculated by \begin{table}
\begin{tabular}{c c} \hline \hline
**Contents** & **Parameters** \\ \hline Bearing outer diameter & 52 mm \\ Bearing inner diameter & 25 mm \\ Bearing width & 15 mm \\ Bearing roller diameter & 7 mm \\ The number of the rollers & 11 \\ Contact angle & 0 rad \\ Outer defect & 0.3 \(\times\) 0.05 mm (width \(\times\) depth) \\ Inner defect & 0.3 \(\times\) 0.05 mm (width \(\times\) depth) \\ Roller element defect & 0.3 \(\times\) 0.05 mm (width \(\times\) depth) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bearing parameters for verification. Figure 9: Fault diagnosis process. \(ApEn\): approximate entropy; PSO: particle swarm optimization; CV: cross validation; KELM: kernel extreme learning machine. \[DI=\frac{|\mu_{1}-\mu_{2}|}{\sqrt{\sigma_{1}+\sigma_{2}}} \tag{32}\]  
The Distinction Rate (DR) is defined as  
\[DR=1-\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{-DI}\exp(-\frac{\mu^{2}}{2})d_{\mu} \tag{33}\]  
It is obvious that the larger the value of the DI, the larger the value of the DR will be, and therefore, the better the SP will be. Thus, the DI can be used as the index of quality to evaluate the distinguishing sensitivity of the SP. Table 2 lists the DI values of each SP. The distribution information of the \(ApEn\) is shown in Table 3. From Table 2, the DI values of \(ApEn\) are higher than RMS, skewness, and kurtosis; that is to say, the sensitivity of \(ApEn\) for bearing diagnosis is higher than that of other SPs. Figure 11: Outer ring defect state signal VMD. Figure 10: Normal state signal VMD. The KELM parameters are optimized via the PSO algorithm to obtain the KELM training model with optimal parameters. The process of KELM model training and optimization of the parameters \(C\) and \(\sigma\) is shown in Figure 14. Assume that a particle swarm contains 50 particles, the acceleration factors \(\eta_{1}\) and \(\eta_{2}\) are 2, and the maximum number of iterations is 1000. In order to minimize possible effects of data outliers, a fivefold cross validation method (5-CV) is adopted for parameter optimization
## 4 Experimental Results
### Evaluation of ROI Extraction Module  
As mentioned in Section 3.3.1, a pre-processing step is needed before extracting the bounding boxes. Considering the contact-based HKPU database, the pre-processing step maps gray-scale palmprint images of \(384\times 284\) to suitable ones to be fed into our CNN architecture. To this end, the inputs were rescaled to \(284\times 284\) square images and thereafter were represented in RGB. Finally, the images were resized to \(224\times 224\times 3\) as the input of the network for the ROI extraction task.  
In REM, we set the learning rate in two modes: a constant value of \(L_{r}=10^{-4}\) and an adaptive one in which it starts with a predefined value (\(10^{-3}\)) and decays by a factor of 0.96 during steps. Experimental results showed the same performance for both values, therefore, we report the results of the constant learning rate.
## 3 Proposed Method  
### System Overview  
The proposed method, presented in Figure 1, consists of two main steps: enhanced berth mapping and multimodal localization. The mapping step involves generating a lightweight map using a series of discrete nodes. Each node includes three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated based on the positions of the key berth's corner points. In the multimodal localization step, the corresponding localization method is adapted based on the predicted pose of the vehicle. If the predicted pose falls within the range of the lightweight map, the vehicle pose is determined based on multi-scale feature matching with the map. If not, the vehicle pose is determined using odometry with prior pose.
## 5 Analysis of Numerical Model Results
### Analysis of the Separate Effects of Blast Load and Water Pressure
As can be seen from Figure 8b, the stress at measurement point #1 gradually increases under water pressure; from \(t=5000\) us onwards, the stress value accelerates to \(P_{\text{S}}=0.32\) MPa, and then the stress value starts to decrease sharply to \(P_{\text{S}}=-0.45\) MPa. After that, the peak stress shows an increasing trend again. The stress curve analysis shows that the blast load and water pressure have a sequential effect on the damage to the wall, with the blast load first causing damage fissures in the wall and water pressure acting as the dominant effect on the wall after the blast stress wave has decayed, leading to the expansion of the damage fissures and the formation of a water inrush channel. Figure 7: Rock wall stress under the independent action of blasting load and water pressure (\(d=1.2\) m).
# Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts
## 3 Methodology
### Tradeoff between Platoon Safety and String Stability  
Sections 3.4 and 3.5 investigate the collision risk and string stability of the spacing and velocity difference bidirectional control models. In this section, we synthesize the findings and discuss the tradeoff between platoon safety and stability.  
First, we direct our attention to the spacing bidirectional control model. The analyses conducted in Sections 3.4 and 3.5 indicate that a bidirectional control model exhibiting the in-phase (IP) effect has the potential to reduce the risk of rear-end collisions and enhance string stability. Conversely, a model featuring the opposite-phase (OP) effect can increase the likelihood of rear-end collisions and worsen instability. These observations are summarized in Table 2, highlighting that incorporating spacing information with the IP effect can effectively improve both platoon stability and safety.  
Next, our attention turns to the evaluation of the velocity difference bidirectional control model. The analysis conducted in Sections 3.4 and 3.5 reveals notable observations: the IP effect can decrease the risk of rear-end collisions, albeit at the cost of worsening stability. Conversely, the OP effect can increase the risk of rear-end collisions but has the potential to improve string stability. Table 3 summarizes these findings, emphasizing that the utilization of velocity difference information can only enhance either safety or stability, but not both simultaneously.
## 5 Testing Experiment: Applying the Tuned Recommendation Pipeline to the Sequestered Data
### Some Prediction Examples  
To illustrate a real-world use case, we present the profile of Patient X (a pseudonym used to preserve compliance with MIMIC-III data use agreements), a 74-year-old white male admitted to the ICU under Medicare coverage. By hour 15 of admission, when the system generated predictions, the patient exhibited signs of severe physiological instability. His base excess had dropped to \(-13.0\) and pH to 7.20, indicating significant metabolic acidosis. He was also hypoxemic, with oxygen saturation as low as 74% and elevated lactate levels (6.1 mmol/L). Glucose was critically high (a peak of 395 mg/dL), while renal function was altered, with creatinine ranging from 3.2 to 3.7 mg/dL and BUN exceeding 90. Liver enzymes, including AST, also showed marked elevation.  
Given this clinical presentation, the system predicted a mix of continuations of treatment and new interventions. Notably, it recommended the continuation of regular insulin, norepinephrine, midazolam, vasopressin, and drotrecogin. For medications not yet administered at the time of prediction, such as fentanyl, dopamine, and dextrose, DataToCare suggested their use within a window of 1 to 4 h. Fentanyl, for example, was predicted and administered four hours later, while dopamine and dextrose were initiated within the next hour.  
These predicted treatments spanned a wide range of usage frequencies in non-sequestered patient admissions, from very common to relatively rare. Drotrecogin, used in select cases of severe sepsis, is a rare treatment, appearing in only 81 treatment administrations and falling in the 24.7 th percentile of treatment frequencies. In contrast, vasopressin and dopamine are used moderately (65.5 and 73.2 percentiles, with 1581 and 2442 applications). By contrast, common treatments such as regular insulin and midazolam rank above the 90 th percentile, with 18,271 and 31,739 occurrences, respectively.  
This case demonstrates DataToCare's ability to recommend timely and contextually appropriate treatments ranging from common to rare. By forecasting likely interventions in advance, the system can support more responsive, proactive, and informed clinical care, particularly for complex and critically ill patients like this.