esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 5 Result and Discussion
### Video Fairness  
Figure 3 depicts the fairness performance of the proposed technique. It is clear that the PrOEXPRule approach is superior to other techniques in terms of fairness because it provides better fairness and fixed throughput for all users, despite their increasing number. At 30 users, the EXPRule reached 0.31782, while the proposed PrOEXPrule achieved 0.32795 in throughput fairness for video flow; nevertheless, at 5 users all algorithms obtained close results between 0.39991 and 0.39989. The difference in results increase as the number of users increased, ranging from 0.13831 for the PF algorithm to 0.32795 for PrOEXPrule for 30 users. The PF algorithm has a low fairness index because throughput is compromised for fairness and vice versa. MLWDF, EXP/PF, and EXPRule presented a higher fairness index than PF. Hence, these algorithms have a satisfactory fairness index for video services at a specified level.  
Figure 3: Packet fairness of video flow.  
Figure 2: Packet loss ratio for video flow.
## 3 Proposed Protocol
### System Model  
For a better understanding of the routing protocol, we first explain the system model as follows:  
The node-set of \(N\) is constituted by the vehicles-set of \(V=\{v_{1},v_{2},\cdots,v_{n}\}\) and the RSU-set of \(I=\{rsu_{1},rsu_{2},\cdots,rsu_{m}\}\). The node-set \(N\) is defined as \(N=V\cup I\) where \(V\cap I=\emptyset\). Therefore, each node can be placed only in one set. The set of wireless links between RSUs and OBS is denoted by \(E=\{l_{1},l_{2},\cdots,l_{k}\}\). The network graph is represented by \(G(N,E)\) for all nodes \(s\in N\) and for all links \((x,y)\in E\).
## 2 Materials and Methods
### Dataset  
ISC data utilizes the already mature Global Artificial Impervious Area (GAIA) data for impervious surface coverage mapping. Subsequent ecological vulnerability analysis employs Fractional Vegetation Coverage (FVC) as a proxy indicator for vegetation growth and Land Surface Temperature (LST) as an indicator of climate change, both of which are derived from MODIS satellite data. Specifically, LST data were obtained from the MOD11A1 Version 6 product, which provides daily observations at a spatial resolution of 1 km. We utilized the daytime LST (LST_Day_1 km) values in this study. All data processing was conducted on the Google Earth Engine (GEE) platform, which facilitates large-scale time series analysis and automatic cloud masking. By incorporating all available daily observations throughout each year and computing their annual average, we ensured both the temporal representativeness and robustness of the LST values used in our analysis. Using elevation data from the L-band Phased Array Synthetic Aperture Radar (PALSAR) data of the Advanced Land Observing Satellite (ALOS), elevation, topographic relief, and slope information are extracted. Land use data comes from CLCD data products with a spatial resolution of 30 m from 1985 to 2023, derived from over 335,000 Landsat images on the Google Earth Engine platform. A random forest classifier was trained using samples from the Chinese Land Use Dataset and visually interpreted points. A post-processing step ensured spatial-temporal consistency. The dataset achieved an overall accuracy of 79.31%, averaged across all years based on 5463 validation samples, and outperformed other global land cover products such as MCD12Q1, ESA-CCI, and GlobeLand30.  
Population data is derived from gridded population data with a resolution of 1 km. To maintain consistency with all data, we resampled all raster data to 1000 m resolution using the nearest neighbor method. All datasets used are summarized in Table 1.  
Figure 1: Study area location. (**a**) Geographical location of China. (**b**) DEM map of Beijing–Tianjin–Hebei region derived from NASA global 30 m SRTM elevation data.
###### Abstract  
The biosynthetic pathway from linoleic acid to 1-octen-3-ol in _Agaricus bisporus_ has long been established, in which linoleic acid is converted to 10-hydroperoxide (10-HPOD) by deoxygenation, and 10-HPOD is subsequently cleaved to yield 1-octene-3-ol and 10-oxodecanoic acid. However, the corresponding enzymes have not been identified and cloned. In the present study, four putative genes involved in oxylipid biosynthesis, including one lipoxygenase gene named _AbLOX_, two linoleate diol synthase genes named _AbLDS1_ and _AbLDS2_, and one hydroperoxide lyase gene named _AbHPL_ were retrieved from the _A. bisporus_ genome by a homology search and cloned and expressed prokaryoticly. _AbLOX_, _AbLDS1_, and _AbLDS2_ all exhibited fatty acid dioxygenase activity, catalyzing the conversion of linoleic acid to generate hydroperoxide, and _AbHPL_ showed a cleaving hydroperoxide activity, as was determined by the KI-starch method. _AbLOX_ and _AbHPL_ catalyzed linoleic acid to 1-octen-3-ol with an optimum temperature of 35 \({}^{\circ}\)C and an optimum pH of 7.2, whereas _AbLDS1_, _AbLDS2_, and _AbHPL_ catalyzed linoleic acid without 1-octen-3-ol. Reduced _AbLOX_ expression in antisense _AbLOX_ transformantsums was correlated with a decrease in the yield of 1-octen-3-ol. _AbLOX_ and _AbHPL_ were highly homologous to the sesquiterpene synthase Cop4 of _Coprimus cinerea_ and the yeast sterol C-22 desaturase, respectively. These results reveal that the enzymes for the oxidative cleavage of linoleic acid to synthesize 1-octen-3-ol in _A. bisporus_ are the multifunctional fatty acid dioxygenase _AbLOX_ and hydroperoxide lyase _AbHPL_.  
keywords: button mushroom; 1-octen-3-ol; linoleic acid; enzymatic pathway +
Footnote †: journal: Fungal
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 5 Numerical Results and Analysis
The NMSEs of DA ML and M\({}_{2}\)M\({}_{4}\) for \(M\) = 2, 4, 8 are plotted in Figure 7. The estimation performance improves as the CPM modulation order becomes smaller. Figure 5: NMSE comparison of DA ML and M\({}_{2}\)M\({}_{4}\) in different IPOINT. Figure 6: NMSE comparison of DA ML and M\({}_{2}\)M\({}_{4}\) in different lengths of information. The three figures show that the three parameters have no influence on the estimation performance when SNR is greater than 8 dB. The observation points of the receiver can be expressed as  
\[K=\frac{length(bit)*IPOINT}{\log_{2}(M)}. \tag{43}\]  
We can obtain the conclusion by considering the three parameters. The more observation points the receiver has, the better the two estimators performs. The number of observation points has no influence on the estimation performance when the SNR is greater than 8 dB. When the channel environment is poor in practical applications, better estimation performance can be obtained by increasing the number of sampling points per symbol, increasing the length of the transmitted information sequence, and reducing the CPM modulation order. SNR estimators are the main research content of this paper, and we only select the modulation orders as the example to illustrate the principle of ACM control loop. The length of the source information sequence is set to 1000 bit, and the sampling points per symbol are set to \(\text{IPOINT}=10*\log_{2}(M)\). For the CPM modulator, the modulation index is set to h = 1/2, the pulse shaping function is set to RC, L = 2, and the modulation orders are set to \(M\) = 2, 4, 8, respectively. The average value of the SNR estimators is calculated with 1000 frames. Figure 4.0 BER in different \(M\). Figure 4.1 NMSE comparison of DA ML and M\({}_{2}\)M\({}_{4}\) in different \(M\). The BER for different modulation orders are shown in Figure 4.2 For the same SNR, the BER of the system decreases as the modulation order becomes larger. The target BER is assumed to be \(10^{-4}\). When the SNR is less than 10.6 dB, all the three modulation orders can't achieve the target BER, we can choose a low BER method as the modulation order of the system, that is \(M=8\). When the SNR is within the range of 10.6 dB to 11.4 dB, only \(M=8\) can achieve the target BER, so we still choose \(M=8\) as the modulation order of the system. When SNR is in the range of 11.4 dB to 14 dB, both \(M=4\) and \(M=8\) can achieve the target BER. Because of the higher frequency band utilization rate of \(M=4\), we select \(M=4\) as the modulation order of the system. When the SNR is greater than 14 dB, all the three modulation orders can achieve the target BER. Because of the highest frequency band utilization in \(M=2\), we select \(M=2\) as the modulation order of the system. When SNR grows the modulation order is decreased by the ACM control loop, when SNR drops down higher modulations have to be used.
## 3 Materials and Methods
### Proposed Patient Monitoring Scheme
In addition, encryption algorithms using public encryption keys such as Rivest-Shamir-Adleman (RSA) or elliptic-curve cryptography (ECC), or symmetric key algorithms such as Advanced Encryption Standard (AES) or Data Encryption Standard (DES), are crucial to ensuring secure data transmission and communication [22]. Furthermore, key management systems (KMSs) are used for the generation, distribution, and management of secret keys between the different communicating entities [23]. To illustrate a possible secure communication scenario, data collected from healthcare sensors, such as blood pressure or glucose level, are encrypted using an encryption protocol (AES-256, for example). The encrypted readings and the digital signatures of the sensors are securely transmitted over secure communication channels using the Transport Layer Security (TLS) protocol. At the recipient end, the digital signature is verified and the received data are decrypted [21]. With the high volumes of medical data being captured and sent over the IoMT, privacy is of paramount importance due to the presence of personal identifiable information (PII) [20]. Laws for data privacy and protection of medical data are enforced in countries to support fundamental privacy objectives like fairness, purpose fulfillment, proportionality, and accountability. Among these laws and initiatives are the Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology for Economic and Clinical Health Act (HITECH), Personal Information Protection and Electronic Document Act (PIPEDA), and others [24]. Privacy protection mechanisms are applied in different aspects of the IoMT: data collection, data access, and data usage. Protection mechanisms for data collection include consent, opt-in, and fairness. Data access mechanisms dictate the pseudonymity (or anonymity) of the data owner, notification of when data are being collected, auditability (for controlled access to the data), and accountability. Finally, data usage privacy mechanisms should be guaranteed during the re  
Figure 2: Proposed patient monitoring scheme. case of possible incidents or breaches in the handling of PII [20]. In this proposed model, the sensor readings collected from a pregnant woman and the XAI recommendations are encrypted and transmitted over a secure channel to the data centers, hospitals, and/or physicians. The pregnant woman's identity should remain anonymous on all records kept in the data centers and the hospital, as these records could be used later for further analysis to study the health conditions of a region.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### Low-Pass Flat Negative Group Delay Circuit  
The circuit structure is shown in Figure 1. When the values of the components in the circuit are as shown in Table 8, the circuit has a low-pass negative group delay function, as shown by the blue lines in Figures 3 and 4.
## 4 Response Analysis of the Wind Turbine Coupling Structure
### Stress Analysis of Blades  
The internal stress responses at blade roots and downwind displacement distribution along the blade span under normal wind and typhoon loads are shown in Figures 22 and 23. Downwind displacements at the blade tips are shown in Table 7. It was found that: (1) under normal wind and typhoon loads, the internal stresses at the root of Blade A were significantly higher than those of Blade B and Blade C. The numerical values of Blade B and Blade C were similar. Moreover, the internal stresses at the blade roots under typhoon loads were significantly higher than those under normal wind loads. The maximum increase of the shearing force was 41.3%. The maximum increase of the circumferential bending moment was 39.6%. The maximum increase of the meridian bending moment was 39.5%. They were all achieved by Blade C; (2) The downwind displacements of Blade B and Blade C along the span were basically same under normal wind and typhoon loads. However, they were significantly different from those of Blade A. With the extension along the blade span, the downwind displacement of Blade A was higher than those of Blade B and Blade C, and then became smaller. Next, the numerical values that were close to the middle of the blades were the same. However, the numerical values that were close to the middle of Blade A exceeded those of Blade B and Blade C. The numerical values of the three blades were equal at about 35 m of the blade span under normal wind, and at about 30 m of the blade span under typhoon loads; (3) The downwind displacements of the blades were larger under typhoon loads than those under normal wind loads. The maximum increase of downwind displacement was 49.3%, which was achieved at the tip of Blade A.  
Figure 21: Internal stress responses at the tower bottom of the wind turbine system under normal wind and typhoon loads. (**a**) Meridian axial force Ty/(N); (**b**) Shearing force Tyx/(N); (**c**) Circumferential bending moment Mx/(N\(\cdot\)m); (**d**) Meridian bending moment My/(N\(\cdot\)m).
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 3 Proposed Algorithm for Static UE Scenario
### Model Training and Validation  
The network is trained in offline mode using a large dataset collected by a diverse range of channel instances under various requirements for end-users. It is designed to minimize the mean absolute error (MAE) and the mean square error (MSE) between the optimized vectors derived from the alternating approach, \(\mathbf{V}\), and estimated vectors derived from the trained model \(\hat{\mathbf{V}}\). The mathematical definitions of MAE and MSE for the proposed model are, respectively, given by  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert\mathbf{V}_{i}-\hat{\mathbf{V}}_{i}\rvert, \tag{16}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(\mathbf{V}_{i}-\hat{\mathbf{V}}_{i})^{2}. \tag{17}\]  
During training, cross-validation is adopted in the model to fine-tune its network parameters and evaluate its performance. Cross-validation is an important evaluation method for assessing the performance of ML models and avoiding overfitting issues on new data sets [34]. In this work, \(K\)-fold cross-validation is employed, which is the most widely used technique and fits well with the data collected. Under the \(K\)-fold cross-validation, the entire data set is divided into \(K\) randomly sized subsets (folds), one of which is used for validation, and the \(K\)-1 remaining folds are used for training the model. The test MSE or MAE is calculated for the validation subset to be a performance indicator. A \(K\) times repetition of this procedure is performed, validating each fold subset exactly once. Finally, the model with the fewest validation errors is chosen.
## 3 Mathematical Model of This Study
### Symbols  
Table 3 shows the symbol definitions of the developed model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Decision Variable** & **Description** \\ \hline \(x_{ijk}\) & The binary variable of 0 or 1 \\  & 0: other situations, 1: unmanned vehicle \(i\) travels from \(j\) to \(jk\) \\ \hline \multicolumn{3}{c}{**Parameter definition**} \\ \hline \(W_{1}\) & The weight of logistics service provider’s profit \\ \(W_{2}\) & The weight of the total distance \\ \(W_{3}\) & The weight of penalty \\ \(i\) & Unmanned vehicles with different models and capacities, \(i=1\ldots,D\), we assumed that it was only one in this study \\ \(j\) & Customer in match state, \(j=1\ldots,C\) \\ \(jk\) & Customer in the real-time state, \(k=1\ldots,C\) \\ \hline \(M\) & Customer number (a big constant); we assumed that it was 999 in this study \\ \(p\) & Delivery price (Fees charged by logistics service providers to customers) \\ \(Q_{i}\) & The capacity of unmanned vehicles \(i\) \\ \(F_{C}i\) & Fixed travel cost of unmanned vehicles \(i\) \\ \(VC_{i}\) & Variable travel cost of unmanned vehicles \(i\) \\ \(T_{i,0}\) & The initial time of unmanned vehicle \(i\); we assumed that it was 0 in this study \\ \(T_{i,max}\) & Maximum travel time of unmanned vehicle \(i\) \\ \(q_{j}\) & Customer’s requirement \\ \(q_{k}\) & Customer’s rush requirements in real-time \\ \(\tilde{l}_{i}\) & The load of the unmanned vehicle is inversely proportional to the flight time \\ \(d_{k}\) & Unit distance from the customer in real-time state \\ \(v_{i}\) & The unit distance required speed of unmanned vehicle \(i\) \\ \(c_{j,k}\) & The early fees of an unmanned vehicle arriving before the start of customer \(j\)’s time window \\ \(c_{j,x}\) & The delay fees of an unmanned vehicle arriving after the end of customer \(j\)’s time window \\ \(c_{j}\) & The fees of the unmanned vehicle \\ \(T_{(i,d)}\) & Total operating time of unmanned vehicle \(i\) \\ \(T_{(i,d)}\), \(T_{(k,a)}\) & The time arrived the customer in the real-time state of the unmanned vehicle (\(a\)-arrive) \\ \(T_{(k),b^{\prime}}\), \(T_{(j)}\) & Time window lower limit of customer in real-time state \\ \(T_{(k),a^{\prime}}\), \(T_{(j)}\) & Time window upper limit of the customer in the real-time state (\(b\)-begin; \(e\)-end ) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The variables used in the developed model.  
Figure 3: The analysis process of the decision-making platform.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 5 Experiment and Analysis
### Experiment Setting
#### 5.1.2 Implementation Details  
The model was implemented using the open-source deep learning framework PyTorch (v1.9.0, created by the Meta Platforms, Menlo Park, CA, USA), and it was run on a server equipped with an AMD EPYC 7773X 64-core processor (manufactured by AMD, Santa Clara, CA, USA) and an Nvidia GeForce RTX 3090 GPU (NVIDIA, Santa Clara, CA, USA). The network was optimized using the Adam optimizer with a learning rate of \(1\times 10^{-4}\). A validation set was used to select the best model for testing.
## 4 Results
### Performance Evaluation
#### 4.1.1 Comparison to Related Work  
As mentioned earlier, there is limited work on pairwise evaluation based on the small and noisy dataset. In order to compare our results with other methods, additional experiments were reproduced in order to imitate as closely as possible earlier work settings, that were (i) based on different classifiers such as SVM [17] and RF [37] and (ii) based on other evaluation methods, that is, the use of the BLEU score [4,17].  
Figure 3 shows the overall Kendall \(\tau\) for the different approaches. The proposed DL schema has achieved comparable performance to the models proposed in earlier works. The SVM classifier succeeds in a strong positive relationship between the two classes for C1_EN-EL: 0.7, and moderate positive relationship for C2_EN-EL: 0.4, C1_EN-IT: 0.4 and C2_EN-IT: 0.6, while the RF classifier reached a moderate positive relationship for the \(C1\) corpus (0.4 for EN-EL/0.6 for EN-IT) and for the \(C2\) corpus (0.4 for EN-EL/0.6 for EN-IT). When the BLEU score information is used, the model achieved a moderate positive relationship. Kendall \(\tau\) reached its highest value when the proposed schema uses the simple embedding layer, the feature set of 86 dimensions, and the NLP set for both language pairs (EN-EL: 0.7 for \(C1/0.6\) for \(C2\) and EN-IT: 0.6 for \(C1/0.5\) for \(C2\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MT Model** & \multicolumn{2}{c}{**10 Fold CV**} & \multicolumn{2}{c}{**70\% Per. Split**} \\
**MCC/Corpus** & **C1** & **C2** & **C1** & **C2** \\ \hline EN-EL\_simple emb layer & **0.32** & 0.17 & 0.29 & 0.22 \\ EN-IT\_simple emb layer & 0.10 & 0.12 & 0.10 & **0.15** \\ EN-EL\_pre-trained emb & **0.20** & 0.15 & 0.18 & 0.17 \\ EN-IT\_pre-trained emb & 0.11 & 0.13 & 0.10 & **0.14** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy performance (MMC) in different cross validation options.  
Figure 2: Human correlation. Simple embedding layer vs Pre-trained embeddings.
# Optimizing Multidimensional Pooling for Variational Quantum Algorithms
## 4 Materials and Methods  
In this section, we discuss the proposed quantum pooling methods. Pooling, or downsampling, is a critical component of CNNs that consolidates similar features into one [2]. The most commonly used pooling schemes are average and maximum (max) pooling [30], where the two differ in terms of the sharpness of the defined input features. Max pooling typically offers a sharper definition of input features while average pooling offers a smoother definition of input features [30]. Depending on the desired application or dataset, one pooling technique may be preferable over the other [30].  
Average and max pooling can be represented as special cases of calculating the \(p\)-norm or \(\ell^{p}\) norm [31], where the \(p\)-norm of a vector \(\mathbf{x}\in\mathbb{C}^{N}\) of size \(N\) elements is given by (12) for \(p\in\mathbb{Z}\)[31]. More specifically, average and max pooling can be defined as the 1-norm and \(\infty\)-norm, respectively; see (13) and (14). Since max pooling (\(p=\infty\)) is difficult to implement as a quantum (unitary) operation, a pooling scheme defined by the \(p\)-norm where \(1<p<\infty\) could establish a balance between the average and max pooling schemes. Therefore, we introduce an intermediate pooling technique based on the 2-norm/Euclidean norm named as the quantum Euclidean pooling technique; see (15). The proposed Euclidean pooling technique is limited to processing positive real data and is only compatible with the 2-norm, as norms for \(p>2\) suffer from some similar challenges to max pooling in terms of being non-unitary and thus unwieldy to implement in a quantum context.  
\[\left\|\mathbf{x}\right\|_{p}=\left(\sum_{i=0}^{N-1}x_{i}^{p}\right)^{\frac{1} {p}} \tag{12}\]\[\bar{\mathbf{x}}=\frac{1}{N}\|\mathbf{x}\|_{1} \tag{13}\]  
\[\max\left(\mathbf{x}\right)=\|\mathbf{x}\|_{\infty} \tag{14}\]  
\[\epsilon(\mathbf{x})=\frac{1}{\sqrt{N}}\|\mathbf{x}\|_{2} \tag{15}\]  
In this work, we propose quantum pooling techniques for average pooling (\(p=1\)) and Euclidean pooling (\(p=2\)). We implement average pooling using the QHT, a highly parallelizable quantum algorithm for performing multilevel decomposition/reduction in multidimensional data. For the implementation of Euclidean pooling, we employ partial measurement to perform dimension reduction with zero circuit depth. The average and Euclidean pooling techniques are described in greater detail in Sections 4.1 and 4.2, respectively. As detailed further in Section 5, we validated our proposed quantum pooling techniques using 1-D audio data [32], 2-D black-and-white (B/W) images [33], 3-D color (RGB) images [33], and 3-D hyperspectral images [34].