esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrafast Modulation of Magnetization Dynamics in Ferromagnetic (Ga, Mn)As Thin Films
## 4 Discussion  
As shown in Figure 2, with the pump intensity varying from 1.8 \(\upmu\)J/cm\({}^{2}\) to 28 \(\upmu\)J/cm\({}^{2}\), the joint thermal and non-thermal effects associated with the incident pulses cause the precession frequency to decrease by about 17.5 GHz. Although it is difficult to distinguish the accurate proportion of the thermal and nonthermal effects, the 3 GHz slowdown by 4\(\pi\)_Meff_ could be considered as the nonthermal contribution due to photoexcited carriers. We can therefore estimate that at least 17% of the observed effect on the precession frequency can be ascribed to nonthermal causes.  
As already shown in Figure 2, the increase of precession amplitude is accelerated near the same pump intensity as the accelerated reduction of precession frequency. We simulated the change of magnetization precession under different in-plane and out-of-plane magnetic anisotropy fields. The extracted oscillation amplitude presents the change of amplitude with \(H_{z}\) and \(H_{x\text{-}y}\) in opposite directions, as shown in Figure 6. When pump intensity increases from 1.8 \(\upmu\)J/cm\({}^{2}\) to 28 \(\upmu\)J/cm\({}^{2}\), the decrease of in-plane magnetic anisotropy field is accompanied by a linear increase in precession amplitude. Thus, the nonlinear variation of precession amplitude seen in Figure 2 cannot be ascribed only to the change of in-plane magnetic anisotropy field. Recall that the increase of pump intensity decreases the out-of-plane magnetic anisotropy field through laser heating, but increases it through the increase of carrier concentration. In addition to the calculated results in Figure 6, only the increase of out-of-plane magnetic anisotropy field can lead to an increase of the oscillation amplitude. Thus, the accelerated increase of precession amplitude shown in Figure 2 can result from the increase of the out-of-plane magnetic anisotropy field. It is therefore suggested that non-thermal effects have the distinct advantage over thermal effects for manipulating the out-of-plane magnetic anisotropy field through pump intensity variation. This conclusion also supports our analysis above, i.e., that non-thermal effects due to photo-carrier excitation could effectively influence magnetization precession by their effect on the out-of-plane magnetic anisotropy field.  
Figure 5: (Color online) LLG simulation of magnetization precession frequency as a function of the modulated value of out-of-plane magnetic anisotropy field (\(\delta H_{z}\)) for the annealed compressively-strained (Ga,Mn)As film. The inset shows details of precession frequency changes near zero field.
## 4 Continuous Casting-Hot Rolling Scheduling Model Considering Reheating Conversion Mode  
### Problem Assumptions  
Due to the complex production process, multi-variety, and small batch production mode of steel enterprises, it is necessary to consider various factors in the scheduling of continuous casting-hot rolling to promote production coordination, reduce energy consumption, and achieve maximum hot delivery and hot charging.  
Therefore, after considering the product delivery time and maximizing the hot-charging ratio, this section cites the concepts of hot-rolling batch and integrated batch planning and scheduling on paper [30], to group production contracts and generate a hot-rolling batch with batch types (DHCR, HCR, or CCR). It is used in the hot rolling stage as its minimum scheduling unit to participate in the continuous casting-hot rolling scheduling to improve the executability of the contract plan at the operation level. In general, integrating the casting schedule of the continuous casting machine with the batch schedule of the hot-rolling machine, considering the waste of gas during the cold- and hot-charging slabs conversion in the reheating furnace, and ultimately forming the overall production schedule plays an important role in energy savings and improving the production efficiency of the steel plant.  
In the modeling of the continuous casting-hot rolling scheduling problem, the assumptions used are  
1. The hot-rolling batches and the casts contained in each batch are known in advance;
2. In order to facilitate the scheduling of hot-rolling batches, several reheating furnaces are set with the same specifications and consistent steps, which means that the furnaces here are considered as an expanded capacity reheating furnace. The slabs in each batch are evenly distributed to each reheating furnace for heating operations;
3. The transportation time between processes is not separately considered and is evenly distributed throughout the processing time;
4. The number of hot-rolling machines in the steel plant is one, which means that the reheating furnace and hot-rolling machines have a many-to-one relationship.
## 3 Results  
### Determination of Pollution Indicators  
Utilizing pre-human-engineering-activity baseline values from the basin, pollution indicators for karst water quality at 54 water source locations were established based on test results (Table S1). These results were derived from seven water sources in the study area, which included seven population drinking water source areas (Baishuitang, Yanjinggou, Wuzhe Shuiku, Zuyuandi, Aobushan, Wulang Haizi), as well as one karst spring water source (Pijiazhai Daquan). The establishment of these baselines followed the guidelines of the report presented in [43] in conjunction with the Standard Examination Methods [42].  
The Nemero index method was used to calculate the degree of water pollution and the water quality grade in the study area (Figure 4). The results are as follows:  
Figure 4: Research area watershed scope and water quality.  
(i) The value of \(P_{i}\) was calculated, and the contribution degree of the water quality grade was analyzed. The identified pollution indicators include total bacterial count, total coliforms, total hardness (CaCO\({}_{3}\)), HCO\({}^{3-}\), oxygen demand (COD), pH, Ca\({}^{2+}\), Mg\({}^{2+}\), chroma, odor, visible objects, and turbidity, ordered by their individual factor contribution. Collectively, the first eight factors account for 89.82% of the cumulative pollution affecting the water quality in the area.  
Notably, total hardness (CaCO\({}_{3}\)), HCO\({}^{3-}\), oxygen demand (COD), pH, Ca\({}^{2+}\), Mg\({}^{2+}\), chroma, odor, visible objects, and turbidity align with the chemical characteristics of carbonate rocks in rocky desertification karst areas [43]. In addition, ORP as a comprehensive water quality indicator should also be considered. Consequently, the laboratory experiments prioritized pathogen-microbes (total bacterial count, total coliforms), COD, and pH as the target characteristic pollution indicators.  
(ii) Under the influence of long-term agricultural activity, the thin surface soil layer not only fails to purify surface water but also acts as a repository for pollution. Field surveys revealed that the primary sources of karst groundwater pollution in the area are domestic sewage, livestock breeding, and agricultural cultivation, which significantly pollute both water resources and soil layers.
The periodic electrical injection of the surface p-electrode caused the quantum wells in the active region to generate periodic gain differences to realize the gain coupling effect and enable the device to achieve stable single longitudinal mode output [21; 22; 23]. Surface gratings can provide optical feedback as well as modulate the spectrum, enabling wavelength tunability and improving single longitudinal mode performance [24; 25; 26]. This method not only avoids complex epitaxial re-growth, nano-grating, and other time-consuming processes but can be realized by using ordinary i-line lithography technology, which reduces costs and greatly improves the yield. In this paper, we apply periodic p-electrodes and surface gratings to a 1550 nm DFB semiconductor laser. AlGaInAs multi-quantum wells with smaller linewidth enhancement factor and improved temperature characteristics compared to InGaAsP equivalents are selected, making it easier to achieve narrow linewidths [27; 28]. In addition, since the output power of the traditional 1550 nm tunable DFB laser only reaches tens of milliwatts or even a few milliwatts [29; 30], we added a tapered waveguide to increase the output power [31]. In this paper, the design of the device is described in Section 2 and the measurement results and analysis are characterized in Section 3. Finally, the conclusion is presented in Section 4.
# AI-Based Approach to Firewall Rule Refinement on High-Performance Computing Service Network
## 3 Methodology
### Machine-Learning Algorithms
#### 3.2.2 KNN  
For KNN, we used the _KNeighborsClassifier()_ provided by scikit-learn 1.4.0 with _neighbors_ (\(k\)). The main equation of the KNN algorithm [7] allows computation of the distances between the new data point and all of the data points in the training set, the selection of the \(k\) nearest neighbors based on these distances, and performing predictions based on the labels (for classification) or values (for regression) of these neighbors. In this experiment, we used this equation, mathematically expressed below, for classification (in Equation (2)):  
\[\hat{y}_{\text{new}}=\text{argmax}_{y}\sum_{i=1}^{k}I(y_{i}=y) \tag{2}\]  
Here, \(\hat{y}_{\text{new}}\) is the predicted class label for the new data point \(x_{\text{new}}\); \(y_{i}\) are the class labels of the \(k\) nearest neighbors; \(I\) is the indicator function that returns 1 if the condition inside the parentheses is true and 0 otherwise; \(y\) is the class label being considered; and \(\text{argmax}_{y}\) returns the class label with the highest count among the \(k\) nearest neighbors. In this experiment, we tested \(k\) from 1 to 10, and \(k\) = 3 showed the highest score. While KNN is a simple and versatile algorithm without a training phase, and it does not assume the underlying data distribution, this algorithm has limitations related to computational complexity, memory usage, and sensitivity to feature scaling, in addition to exhibiting the curse of dimensionality and requiring an appropriate choice of the number of neighbors \(k\).
## 3 Performance Analysis of the PSI Technique
### Testing PSI Performance with WCDMA, DVB S2, 4G, and 5G Signals  
Wideband code division multiple access (WCDMA) is one of the earliest signals that experienced high PAPR [43]. Here, a \(20 MHZ\) bandwidth signal was generated and tested with PSI technique. This test was also performed with Digital Video Broadcasting Satellite Second Generation (DVB S2) Signal, a typical 4G orthogonal frequency division multiplexing (OFDM) signal, and a 5G signal based on orthogonal frequency multiplexing division access (OFDMA) signals [44].  
The necessity of applying PAPR reduction on DVB S2 signal is due to the need for maximising the power efficiency of amplitude phase shift key (APSK) transmission in satellite channels, as the bandwidth requirements are very limited, and as power efficiency is mostly effected by high power amplifiers (HPAs), and as explained about efficiency section, PAPR determines the efficiency of HPAs in modern signals. Therefore, it is beneficial to show that the PSI technique is usable in the DVB S2  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LTE** & **Number of** & **Signal** & **LTE** & **Spectrum** & **Spectrum** \\
**Windowing** & **Resource Blocks** & **Bandwidth** & **IFFT/FFT** & **Leakage** & **Leakage** \\
**Size** & **(NRB)** & **(MHz)** & **Size** & **(No Re-Sampling)** & **(Re-Sampling Factor = 2)** \\ \hline
4 & 6 & 1.4 & 128 & 1.16 & 0.94 \\
6 & 15 & 3 & 256 & 0.22 & 0.08 \\
4 & 25 & 5 & 512 & 0 & 0 \\
6 & 50 & 10 & 1024 & 4.83 & 4.14 \\
8 & 75 & 15 & 2048 & 3.66 & 9.29 \\
8 & 100 & 20 & 2048 & 6.98 & 7.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: LTE Signal structure [30; 42].  
case and for other types of signals. In order to have comparable test, the signals were simulated with close characteristics; for example, length of a inverse fast Fourier transform (IFFT) in three cases of WCDMA, DVB S2, and 4G signals was kept to 2048 in order to fulfil bandwidth of \(20 MHZ\). It was captured that by testing about 1000 symbols of these signals, the PAPR value fell on about 10.3 dB. As shown in previous test results, this value increases by extending the test to \(10,000\) symbols, as shown in Figures 8b and 10. However, it should be noted that length of IFFT in 5G signals start at 4096, which effects the probability of PAPR value the same way that increasing bandwidth does, and therefore, as seen in Figure 15, the PAPR value of reference 5G signal is about 12 dB at CCDF of \(10^{-3}\). The probability of PAPR after the PSI technique is shown by purple colour plots in Figure 15. It is observed that in cases of WCDMA, DVB S2, and 4G signals, PSI reduces the PAPR by about 2.4 dB at CCDF of \(10^{-3}\); it should be noted that this performance increases by looking at CCDF of \(10^{-4}\). Here, due to time limitation, the simulation was performed only 1000 times.
## 3 Calculation of emissions due to the operation of the electricity distribution network.
Taking into account the studies by [25; 29], the emissions from an ICV are presented in Table 6.  
The calculation of losses in the grid was considered similar for all interconnection modes, although it is expected that the V2G mode will have a greater impact due to the increase in power flow (bidirectional). However, as the data used only considered 10% of EV for this purpose, in this study it was considered that the value did not increase significantly compared to other modes. This will no longer be acceptable if the percentage of EVs operating in V2G mode is high. For NC and WCNC modes, emissions due to manufacturing, charging and losses in the distribution grid are considered, resulting in a total of 119.22 gCO2e/km (best case) to 155.92 gCO2e/km (worst case). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Life Cycle**} & \multicolumn{3}{c}{**Infrastructures**} & \multicolumn{2}{c}{**Total EV**} & \multicolumn{1}{c}{**Total ICV**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Communications**} & \multicolumn{2}{c}{**Grid Losses**} & & \\ \cline{3-10}  & & **Manufacturing** & **Charge** & **G2V** & **V2G** & **-** & **G2V** & **V2G** & \\ \hline \multirow{2}{*}{EV} & Worst case & 66.7 & 76 & 41.35 & 66.69 & 13.22 & 197.27 & 222.61 & \\  & Best case & 46 & 60 & 19.66 & 31.68 & 13.22 & 138.88 & 150.9 & \\ \hline \multirow{2}{*}{ICV} & Worst case & 35 & 143 & - & - & - & - & - & 178.0 \\  & Best case & 33.3 & 143 & - & - & - & - & - & 176.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the calculation of total emissions (in gCO2e/km) for EVs (G2V and V2G mode) and for ICV.
## 3 Data and Methods
### Flash-Flood Potential Assessment
#### 3.3.1 Flash-Flood Inventory  
In order to generate the flash-flood potential index (FFPI) for the two reference years, an essential step was the inventory of the previous flash-flood events that took place in the study area. It should be mentioned that the location of historical flash-flood events was based on data provided by the General Inspectorate for Emergency Situations (GIES) of Romania. Thus, two data sets were established, one for 1989 and the other for 2019. For the year 1989, the phenomena produced between 1965 and 1989 were taken into account, while for the year 2019, the events produced between 1990 and 2019 were considered. It should be mentioned that the year 1965 was chosen because this year was the first in which the flash-flood events were quantified by the national authorities. The year of 1990 was chosen because it is the first year of the period 1990-2019 being also the one immediately after the first reference period 1965-1989, namely the collectivization period. Within the first period (1965-1989) the most destructive flash-flood events occurred in 1970, while within the second period (1990-2019), the most destructive flash floods were recorded in 2005. Due to this fact, the vast majority of flash-flood locations belong to these two years. For a greater objectivity of the present study, from the total number of flash floods collected, an equal number of flash-flood locations for each of the two analyzed periods were selected. Thus, 127 flash-flood locations were quantified (Figure 1), which were subsequently divided into training (70%) and validating (30%) datasets [(89)]. Further, in order to ensure a highaccuracy of the results [90], 127 non-flash-flood locations were generated inside of areas where the flash floods were absent and in which there was a very low probability of flash flood occurrence. These were located in the forested areas with very low slopes in general, and were divided into training (70%) and validating (30%) datasets [39]. It should be mentioned that for a higher objectivity of the results, the same dataset regarding the absence of the phenomena was used for both periods.
###### Abstract  
There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models.  
text classification; CNN; Bi-LSTM; attention mechanism +
Footnote †: journal: Applied Sciences
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 4 Differential Privacy
### Mechanisms of Differential Privacy
#### 4.1.3 Exponential Mechanism  
It is well known that not all query functions are able to return numerical values in their output. A more general approach to handling and responding to qualitative queries was proposed by [PERSON] and [PERSON] [39]. This mechanism deals with non-quantitative queries.  
**Definition 4**.: _Given a set of \(N\) of acceptable outputs and a utility function (\(u:N\times O\rightarrow\mathbb{R}\)) that quantifies the desirability of every outcome (\(n\in N\)), given a dataset (\(O\)), this mechanism [40] probabilistically selects an output (\(y\)) to ensure \(\epsilon\)-differential privacy:_  
\[P(O)=\Pr[n\mid O]\propto\exp\left(\frac{\epsilon u(n,O)}{2\Delta u}\right) \tag{9}\]  
where \(u(n,O)\) is the utility of output \(n\) given dataset \(O\)[35]. \(\Delta u\) is the maximum sensitivity, which measures how much the utility function (\(u(n,O)\)) can change when one element of \(O\) is changed. It determines the scale of possible changes in utility across datasets. Similarly, the amount of noise added depends on the privacy parameter (\(\epsilon\)).
## 3 Results and Discussions
### Damage Classification Using the Deep Learning Approach
#### 3.3.2 Classification of AE Signals Based on the Configuration of the Specimen  
The same observation was made for the AE signals categorized based on their configurations (Txy, Tz, T45). Looking at the results presented in Table 5, a simultaneity is observed with the first damage-based categorization, where the accuracy increases when the sigmoid activation function is replaced with ReLu. Again, let us observe that the accuracy is still 33.3% lower for AE signals from different specimen orientations. By increasing the input data, a particular observation was made on the new model, the accuracy increases by 66.7% compared to alexNet and SqueezeNet. Finally, by changing the sigmoid activation function to ReLu, an accuracy of 100% is achieved.
## 2 System Model
### Communication Model
For UE \(n\) belonging to slice \(m\), its SNR in direction \(d\) can be expressed as  
\[\gamma_{m,n}^{d,x}=\frac{P_{m,n}^{d,x}H_{m,n}^{d,x}}{N_{0}} \tag{2}\]  
Note that \(x\) is always \(nr\) for downlink transmission. \(N_{0}\) is the power of additive white Gaussian noise. Assume that each resource block (RB) has a bandwidth \(b\), then the achievable rate of UE \(n\) in direction \(d\) on one RB can be expressed as  
\[r_{m,n}^{d,x}\leqslant b\times\log_{2}\Bigl{(}1+\gamma_{m,n}^{d,x}\Bigr{)}. \tag{3}\]  
The rate that the UE \(n\) of slice \(m\) in direction \(d\) can achieve in each period of resource-slicing is denoted as \(R_{m,n}^{d}\).
# Optimal Trajectories of Diffractive Sail to Highly Inclined Heliocentric Orbits
## 5 Conclusions  
Reaching orbits with a high inclination relative to the Ecliptic in order to observe the polar regions of the Sun requires the use of unconventional propulsion systems to simplify the interplanetary trajectory design, i.e., to avoid the use of a number of planetary gravity-assist maneuvers. Solar sails seem to offer an excellent option for such challenging missions and, in that context, diffractive sails show several advantages, especially in terms of simplicity of attitude control, over the more common reflective counterparts.  
The numerical simulation of the minimum-time trajectory to transfer a diffractive sail to the final circular orbit around the Sun is rather complex, but this paper has shown that a near-optimal solution can be obtained in a sufficiently simple way by splitting the transfer flight into two phases: (i) a first approach phase to a circular orbit of the same radius as the final orbit but with a smaller inclination; and (ii) a second cranking phase, in which the orbital plane is rotated until the desired inclination is reached. During the first phase, the minimum distance from the Sun does not fall below a minimum value compatible with the maximum thermal load that the diffractive sail can withstand. Simulations have shown that this strategy is indeed near-optimal, in the sense that flight times differ by less than 5% from those achievable with an optimal strategy.  
Conceptualization, G.M.; methodology, A.A.Q.; software, A.A.Q.; writing--original draft preparation, G.M.; writing--review and editing, A.A.Q. and G.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.