esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of rGO/CeO\({}_{2}\)/Polymer Monoliths
#### 3.2.4 CO\({}_{2}\) Adsorption
Moreover, groups like -COOH will improve the affinity toward CO\({}_{2}\) significantly [50]. The different amines also offer different affinities, depending on their chemistry. Primary, secondary, and then final tertiary amines show a high affinity toward CO\({}_{2}\)[51]. Finally, the capacity is also favored by the ester groups [52]. In PAA, the functional group is -COOH; for the PNVCL, it is R1-N-R2-R3 and the ketone R1-C(O)-R2; and for the PMETAC, it is the ester R1-C(O)-O-R2 and -N\({}^{+}\)R3. By comparing the results for the CO\({}_{2}\) adsorption, functionalities, and porosity values, it can be concluded that this is a complex procedure where many parameters can affect the final adsorption. For the GCePN monolith, it can be said that it had the highest total pore volume below 0.75 nm where CO\({}_{2}\) was adsorbed. In addition, it had the highest percentage of N atoms and a relatively high SSA. The slight basic character of the ketone [53] present in the PNVCL also had a positive aspect for this material. The low oxygen functionality and the almost-zero value for the zeta potential show that the particles were not fully covered with the polymeric chains, which probably helped the particles become exposed and offer active sites for CO\({}_{2}\) adsorption. The GCe monolith did not have relatively high elemental functionalities but did have a high SSA and the highest total pore volume, leading to an extensive porous network that helped CO\({}_{2}\) be adsorbed. The GCePA sample had the highest O functionality and a relatively high total pore volume below 0.75 nm. This monolith probably had the highest coverage of the polymeric chains that would likely be affected more by the acidic nature of the CO2, leading to the possible globular conformation of the polymeric chains. All this combined led to relatively medium-to-high adsorption compared with the poor properties found in the N\({}_{2}\) adsorption-desorption technique, the initial monolith (lowest total pore volume and SSA), and via the H4 hysteresis loop showing narrow, slit-shaped pores. Finally, the GCePM showed the lowest adsorption, which could explain the medium values in almost all the characterization techniques. These included a low total pore volume below 0.75 nm, medium O functionality, and the -N\({}^{+}\)R3 group that did not help the adsorption, since CO\({}_{2}\) is practically neutral.
# Residential Demand Response Scheduling with Consideration of Consumer Preferences
## 5 Conclusions and Discussions  
In this paper, a new model for scheduling demand response has been proposed. The model has taken the preferences of participating households into account and has been defined as a multi-objective optimization problem having objective functions corresponding to energy production costs and average household satisfaction. For the proposed model, a MIP has been developed. The model has been evaluated using real-world data based on the use of appliances in different types of households.  
In the proposed model, customers can set up their preferences for each appliance, e.g., through smart energy management systems, and the scheduling system will continue to operate until the end of the day. If customers make any changes, they will be executed in the next day. Therefore, customers do not need to set the preference matrix each day, but they have to update it if necessary. The computational experiments have shown that coupling the consumers' preference levels with the associated job descriptions can be beneficial, both for the customer and the utility company. To be exact, by giving this additional information, it is possible to achieve a significant level of savings in production costs while maintaining a high degree of satisfaction for the participating households. It has also been shown that by allowing a higher level of maximal dissatisfaction for households, further savings can be achieved. This type of information can be exploited in designing pricing models for consumers. The results also showed that the reduction in utility company operations can also be reflected in customer bills by means of incentives.  
In the future, we are going to explore the potential of incorporating preference levels into an online version of the original problem, which will allow customers to be able to change their preferences during the day. We will also compare the two systems, online _vs._ offline, in terms of cost and customer satisfaction. Another direction of future research will be the extension of the model to a stochastic environment by further involving renewable generators and adapting it to a longer scheduling period.  
We would like to thank the Department of Energy & Climate Change, United Kingdom, and ICF International for providing the survey data used in evaluating the proposed model. The authors worked equally on the development of the ideas presented in this paper. The simulation work has been conducted by [PERSON].
## 3 Results and Discussion
### Manual Design of Model: Effects of Layers and Hyperparameters
#### 3.2.3 Initial Learning Rate  
The initial learning rate is one of the key factors that determines model performance. The learning process speed is high when the model is trained with a higher learning rate, but there will be an increase in the loss function. The loss function can be decreased with a lower learning rate. Hence, it is essential to set the appropriate learning rate when training a model. [PERSON] et al. [35] performed a study on pest classification and varied the initial learning rate between 0.0006 and 0.0014; the best performance was obtained at an initial learning rate of 0.0001. In our experiment, the proposed model was evaluated with various initial learning rate values, such as 0.000001, 0.00001, 0.0001, 0.001, and 0.01. The number of epochs and size of mini-batches were set to 100 and 64, respectively, during the evaluation of the model. The performance evaluation is shown in Table 3. It can be observed from Table 3 that the proposed model performed better when the initial learning rate was set to 0.001.
## 1 Introduction
The modulated symbols of a split sub-band are pre-coded by MB-Hadamard matrix to compress the peak signals of the DMT, which would mitigate the signal distortions of the following clipping operation. The received SNRs and BERs are also calculated for each split bands. The results demonstrated that the proposed scheme reduces the PAPR significantly and improves the BER performance effectively. Moreover, it matches well with the bit-loading technique to enhance the system capacity. In addition, some other famous PAPR reduction approaches are also compared and discussed. The remainder of this paper is organized as follows. In Section 2, we briefly present the PAPR characteristics of the DMT. In Section 3, the effective SNR of the whole IM/DD channel considering both the clipping and the quantization is analyzed. In Section 4, a hybrid PAPR reduction scheme is proposed to compress the peak signals and then limit the PAPR to a given threshold level. Section 5 presents the results and discusses them, and Section 6 concludes the paper. Notations: the time and frequency domain vectors are represented by lower and upper boldface italic letters (e.g., \(x\) and \(\mathbf{X}\)), respectively. The \((n+1)\)th element of the column vector \(\mathbf{x}\) is denoted as \(x(n)\). Upper-case boldface letters (e.g., \(\mathbf{\mathrm{F}}\)) denote matrices, and \(\mathbb{E}\{\cdot\},(\cdot)^{T},(\cdot)^{*},\)\(|\cdot|\), \(\mathbb{R}(\cdot)\) and \(\mathcal{Z}(\cdot)\) are the expectation, the transpose, complex conjugate, absolute value, real component, and symmetric conjugate operator, respectively. Let \(\mathcal{N}\big{(}\mu,\,\sigma^{2}\big{)}\) denotes the Gaussian distribution with a mean of \(\mu\) and a variance of \(\sigma^{2}\).
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 2 Related Work
### Calculation Method of Ship Emissions  
For mitigating emissions from ships, it is vital to calculate them accurately. The calculation method of ship emissions can be divided into top-down and bottom-up approaches [19; 20]. The former calculates shipping emissions on the basis of fuel consumption, whereas the latter employs information on each ship's engine and distance sailed [21].  
About a decade ago, the top-down technique was dominant, since counting data for each ship was impossible. [PERSON] and [PERSON] [22] used the top-down method to quantify emissions from commercial and passenger ships in the Turkish Strait waters. This method was also utilised by [PERSON] et al. [23] to assess and anticipate vessel emissions in the Houston area. In 1991, the EPA (US Environmental Protection Agency) conducted statistics and studies on vessel data, such as vessel engine power and emission parameters, and listed vessel activity data for port and fishing vessels in various sailing modes to refine the predicted outcome. In addition, emission estimates have been derived using ship-specific data, such as vessel type, engine power, and operating status. However, they discovered that collecting such vast data was complex and required substantial workforce investment and time. Consequently, the top-down method was the predominant method of calculating ship emissions at the time, even though it was impossible to identify the distribution of ships with precision and, consequently, had a significant degree of error.  
Nevertheless, with the advent of satellite communications and the acceleration of the digital field, ship AIS equipment has become widespread in the shipping industry [3]. This device can provide vast information about a ship, such as a ship's voyage and kind, among other details. The regular reporting of AIS data has made up for the deficiencies of the top-down method since the debut of AIS technology. [PERSON] and [PERSON] [24] suggested a bottom-up methodology based on vessel activity, increasing the precision of ship emissions calculations. [PERSON] et al. [25] have calculated ship emissions over the Baltic Sea using this method. To make the precise calculation, the operation pattern and dynamic distribution of ships in this study are considered. This method was used by [PERSON] [26] to calculate ship emissions on Australian seas. [PERSON] et al. [27] utilised this method to calculate Ningbo-Zhoushan port-wide ship emissions, and performed a geographical and temporal analysis. [PERSON] et al. [28] computed emissions from cruise ships on Danish seas and conducted a variability analysis using the approach.
# Mechanical Properties and Freeze-thaw Durability of Basalt Fiber Reactive Powder Concrete
## 3 Results and Discussion
### Mechanical Properties
#### 3.1.1 Compressive Strength  
Figure 4 shows that the effects of basalt fiber contents and fiber type on the compressive strength of RPC. It can be known from Figure 4 that the addition of basalt fiber could improve the compressive strength of RPC and the compressive strength of RPC increases with the increase in basalt fiber content, which is consistent with the conclusion of [PERSON] et al. [28]. When the basalt fiber content is 12 kg/m\({}^{3}\), the compressive strength of BFRPC reaches the maximum value of 149.40 MPa, which is 6% higher than that of PRPC. [PERSON] et al. [35] pointed out that evenly distributed basalt fiber inside the RPC could inhibit the initiation of cracks and bear part of the load, thus improving the compressive strength of RPC. Besides, basalt fiber is composed of oxides such as silica, alumina and magnesia, and its chemical composition is similar to cement. The bond strength between basalt fiber and cement paste is great [36], thus improving the compressive strength of RPC. Compared with SFRPC, the compressive strength of BFRPC with a basalt fiber content of 12 kg/m\({}^{3}\) is 4.3% lower, which indicates that basalt fiber is not as effective as steel fiber in improving the compressive strength of RPC.
The fatigue curve of Laves-phase-strengthened steel typically consists of three phases [33]: an initial phase of pronounced strengthening (which makes up for about 1% of the fatigue life), a stable phase (which lasts until approximately half of the fatigue life) and a comparatively protected damage phase (making up for approximately the other half of fatigue life). During the damage phase, a comparatively small decrease in stress range per cycle is observed because cyclic plasticization dislocations are generated, leading to both non-permanent strain hardening and accelerated nucleation of intergranular Laves phase particles. As a result of the effective blocking of the dislocations by the deformation-induced Laves phase particle precipitation (under the experimental conditions lined out in [33], the total number of Laves phase particles in Crofer\({}^{\circledR}\)22 H, for example, increased from 0 to 4576 during testing from 10 to 2000 TMF cycles), part of the dislocation-induced strain hardening remains practically permanent. For a detailed explanation of the mechanisms and the complete evolution of intragranular Laves phase precipitates in Laves-phase-strengthened ferritic steel during TMF loading, [33, 34] may be consulted. Furthermore, high chromium ferritic steels generally possess good resistance to (high-temperature) corrosion due to the formation of a protective duplex Cr\({}_{2}\)O\({}_{3}\)/(Mn, Cr)\({}_{3}\)O\({}_{4}\) scale on the component surface [35]. The chromium content of HiperFer was adjusted to 17 wt.% to exclude the undesired formation of the FeCr \(\sigma\)-phase at temperatures above 600 \({}^{\circ}\)C [25], and thus, the potential danger of embrittlement due to long-term service at elevated temperatures. With this property profile, an alloy that is custom designed for additive manufacturing on the basis of HiperFer is a good candidate to replace typical austenitic grades, such as AISI 316L, at much lower material and processing costs. A suitable way to make the dynamic strengthening properties of HiperFer steel accessible for in-build and in-servicestrengthening of AM-processed components is tuned precipitation kinetics. Based on the outlined mechanisms, a first HiperFer\({}^{\text{AM}}\) (additive manufacturing) alloy was created specifically for additive manufacturing processes, which ideally should utilize both the \"dynamic strengthening\" and the so-called \"temper bead-effect\" [36, 37, 38, 39] for in-build heat treatment (to make downstream heat treatments obsolete). In this instance, a suitable in-build heat treatment was aimed at keeping the residual stresses below the yield strength of the material and simultaneously generating Laves-phase-strengthened precipitates. In addition, the paper outlines the obtained mechanical property data, such as tensile strength, elongation at rupture and minimum creep rates (depending on the build direction). Furthermore, the active deformation mechanisms in additively processed material were compared to conventionally manufactured (casting, rolling, heat treatment) material.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 4 Channel Estimation Method Based on Deep Learning
### Residual Channel Estimation Framework for Super-Resolution Denoising Based on Deep Learning  
In the SDR channel estimation framework, data preparation should be carried out first and the collected data should be preprocessed. Then, the OMP algorithm is used to estimate the signal, followed by model fusion to obtain the score. Finally, the signal is estimated by integrating the above results to accurately obtain the channel information and achieve efficient and accurate channel estimation.
## 3 System Model
### Optimization Model  
The total time consumption for completing \(r_{j}\) includes: (1) the time consumed by executing in the mobile cloud, if \(r_{j}\) is selected to be executed at C; (2) the time consumed by computation offloading, if \(r_{j}\) is selected to be offloaded to MEC-BS; (3) the time consumed by computation offloading, if \(r_{j}\) is selected to be further offloaded to \(n_{k},\forall k\in\mathcal{K}\). In (1), the time consumption is generated by transmitting the offloading request of \(r_{j}\) from \(m_{i}\) to MEC-BS, transmitting the offloading request of \(r_{j}\) from MEC-BS to C, executing \(r_{j}\) at C, transmitting the offloading response of \(r_{j}\) from C to MEC-BS, and transmitting the offloading response of \(r_{j}\) from MEC-BS to \(m_{i}\), that is, \((1-\sum_{k\in\mathcal{K}}\beta_{i,j,k}-\alpha_{i,j})(t_{i,j}^{\text{OMT2 BS}}+t _{i,j}^{\text{OBS2C}}+t_{i,j}^{\text{C}}+t_{i,j}^{\text{O2 BS}}+t_{i,j}^{\text {OBS2 MT}})\). In (2), the time consumption needs to be divided into two situations: (1) MEC-BS has the data caching required by task \(r_{j}\); (2) MEC-BS does not have the data caching required by task \(r_{j}\), and it needs to send the data caching request to C to get the data. The two cases are discussed separately as follows:  
(1) The time consumption is generated by transmitting the offloading request of \(r_{j}\) from \(m_{i}\) to MEC-BS, executing \(r_{j}\) at MEC-BS, and transmitting the offloading response of \(r_{j}\) from MEC-BS to \(m_{i}\), that is, \(\alpha_{i,j}\sigma_{j}(t_{i,j}^{\text{OMT2 BS}}+t_{i,j}^{\text{BS}}+t_{i,j}^{ \text{OBS2 MT}})\). (2) The time consumption is generated by transmitting the offloading request of \(r_{j}\) from \(m_{i}\) to MEC-BS, transmitting the data caching request of \(r_{j}\) from MEC-BS to C, transmitting the data caching response of \(r_{j}\) from C to MEC-BS, executing \(r_{j}\) at MEC-BS, and transmitting the offloading response of \(r_{j}\) from MEC-BS to \(m_{i}\), that is, \(\alpha_{i,j}(1-\sigma_{j})(t_{i,j}^{\text{OMT2 BS}}+t_{i,j}^{\text{DBS2C}}+t_{i, j}^{\text{DCE2 BS}}+t_{i,j}^{\text{DCE2 MT}})\). In (3), the time consumption needs to be divided into two situations: (1) \(n_{k}\) has the data caching required by task \(r_{j}\); (2) \(n_{k}\) does not have the data caching required by task \(r_{j}\), and it needs to send the data caching request to C to get the data. The two cases are discussed separately as follows:  
(1) The time consumption is generated by transmitting the offloading request of \(r_{j}\) from \(m_{i}\) to MEC-BS, transmitting the offloading request of \(r_{j}\) from MEC-BS to \(n_{k}\), executing \(r_{j}\) at \(n_{k}\), transmitting the offloading response of \(r_{j}\) from \(n_{k}\) to MEC-BS, and transmitting the offloading response of \(r_{j}\) from MEC-BS to \(m_{i}\), that is, \(\sum_{k\in\mathcal{K}}\beta_{i,j,k}\pi_{r,k}(t_{i,j}^{\text{OMT2 BS}}+t_{i,j,k}^ {\text{OBS2 NBS}}+t_{i,j,k}^{\text{DCE2 NBS}}+t_{i,j,k}^{\text{DCE2 NBS}}+t_{i,j }^{\text{DOSE2 MT}})\).
## 5 Discussion
### Provision of Quality-of-Service (QoS)
#### 5.4.1 Request to Lower Priority  
The devices of vehicle and VRU, that are on the verge of collision, broadcast a special message requesting other nodes to lower the _priority_ of transmission of safety messages while maintaining their own priority. This leads other nodes to lower the priority of their message temporarily (e.g., for next scheduled transmission). This allows the crash-prone devices a higher chance of wireless channel access ensuring the guarantee of message delivery.
## 2 Materials and Methods
### Clinical Implementation  
The pipeline combines anatomically aware processing with computational efficiency:  
* Data layer: DICOM-to-PNG conversion preserving 16-bit depth and OCT metadata.
* Preprocessing: Retinal layer alignment using SLO-guided registration.
* Hardware: NVIDIA A100 GPUs with FP16 acceleration.
* Reproducibility: Seed locking (PyCaret session_id = 123).
* Deployment: ONNX runtime integration for clinical systems. Learning curves monitored diagnostic stability through generalization gaps: \[\Delta_{gen}^{(t)}=\text{AUC}_{train}^{(t)}-\text{AUC}_{val}^{(t)}<0.1\quad \text{(convergence criterion)}\] (13) The computational workflow was implemented as a sequential pipeline: \[\mathcal{P}:D_{train}\xrightarrow{f_{eval}}(D_{train},D_{val},D_{test}) \xrightarrow{f_{eval}}(X_{train},y_{train},X_{val},y_{val},X_{test},y_{test}) \xrightarrow{f_{eval}}m^{*}\] (14) Image integrity was preserved through calibrated preprocessing transformations: \[I_{processed}=T_{standardize}(T_{resize}(T_{augment}(I_{raw})))\] (15) where \[T_{standardize}\] normalizes pixel intensities, \[T_{resize}\] ensures dimensional compatibility with the feature extractor, and \[T_{augment}\] applies clinically plausible variations to enhance model generalization.  
The training phase incorporated early stopping with patience \(p=10\) epochs to prevent overfitting while maximizing validation AUC:  
\[\text{stop at }t\text{ if }\max_{i\in\{t-p,\ \cdots,\ t-1\}}\text{AUC}_{val}^{(i)}>\text{AUC}_{val}^{(t)} \tag{16}\]  
Performance evaluation extended beyond standard metrics to include clinically relevant indicators:  
\[\text{Clinical Utility} =\omega_{1}\cdot\text{PPV}+\omega_{2}\cdot\text{NPV}+\omega_{3} \cdot\text{F1}, \tag{17}\] \[\text{where PPV} =\frac{\text{TP}}{\text{TP}+\text{FP}},\quad\text{NPV}=\frac{ \text{TN}}{\text{TN}+\text{FN}},\quad\text{F1}=\frac{2\cdot\text{Precision} \cdot\text{Recall}}{\text{Precision}+\text{Recall}} \tag{18}\]The complete pipeline including data preprocessing, feature engineering, and model selection was implemented in Python 3.9 using TensorFlow 2.8 for feature extraction and PyCaret 3.0 for automated ML workflows. Computational reproducibility was ensured through version-controlled dependencies and fixed random seeds across all stochastic processes.