esa-sceva/satcom-chunk-collection huggingface dataset:
## Case Study
### Results and Discussion  
#### 5.2.1 Case 1: Optimization Results without ESS  
Figure 5 shows the optimal power flow of the microgrid without access to ESS in a daily basis.  
It can be observed from the simulation result that the main grid plays an important role in providing power for load during the valley-price periods. While during peak-price periods, most of the load demand is satisfied by DE instead of by purchasing electricity from the main grid. During flat-price  
Figure 4: Time-of-use electricity prices.  
Figure 5: Optimal power flow without ESS.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Treatment Cost (yuan/kg)**} & \multicolumn{3}{c}{**Pollutant Emission Coefficient (g/kW)**} \\ \cline{3-6}  & & **WT** & **PV** & **DE** & **GRID** \\ \hline CO\({}_{2}\) & 0.21 & 0 & 0 & 680 & 889 \\ SO\({}_{2}\) & 6 & 0 & 0 & 0.306 & 1.8 \\ NO\({}_{x}\) & 8 & 0 & 0 & 10.09 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environmental parameters.  
periods, the load demand of microgrid is satisfied by both DE and the distribution network, but DE output accounts for a larger proportion relatively.
## 2 Methods
### Participants and Protocol  
Eleven healthy young males (age: 28.57 \(\pm\) 3.3 years; height: 1.70 \(\pm\) 0.01 m; weight: 70.0 \(\pm\) 0.02 kg) participated in the experiment. None of the participant had any history of injuries in their lower extremities or falls. The subjects signed informed consent forms and the protocols were approved by the institutional review board of Nagoya University. An a priori power assessment of %DET between normal and robotic walker-assisted walking revealed that at least nine individuals were needed to provide a minimum of 80% power to detect differences in the %DET along the forward direction.  
IMUs were attached to the right leg of each subject on the posterior side of the shank and on the spine at the T9 vertebra level. As shown in Figure 1b, each subject walked along an indoor corridor (4 m wide and 80 m long) for 1 min at their preferred walking speed as consistently as possible. Three condition were investigated: Normal walking (NW, condition 1), rollator-assisted walking (RW, condition 2), with \(\alpha=0\), and robotic walker-assisted walking (RRW, condition 3) with \(\alpha=1\). In condition 2, the robotic walker acted like a rollator, while the motors were activated to provide assistance in condition 3. No subject reported feeling uncomfortable during assisted walking  
Figure 1: Photograph of **(a)** the robotic walker provided by Panasonic Co., Japan and **(b)** walking experiment.  
Figure 2: Data measurement process from the robotic walker and accelerometers.  
(conditions 2 and 3). Experiments in each condition were repeated three times, and the experiments were performed in a random order. After each 1 min-walking trial, the subjects rested for 1 min to avoid the effects of fatigue. The acceleration data of the right shank and T9 in the three axes were sampled at a frequency of 100 Hz, and the ranges of the sensors were set to \(\pm\)8 and \(\pm\)2 g. In each trial with the robotic walker, the average walking speed was calculated from the wheel rotational velocities. In each trial of normal walking, the average walking speed was calculated from the total distance and measured with a measuring wheel against the one-minute time frame.
## 3 Results and Discussion
### Volatile Compound Analysis of Black Tea Under Different Shaking Combinations
#### 3.2.3 Key Aroma Compounds of Black Teas  
The relative odor activity values (rOAVs) were calculated to evaluate the contribution of specific volatiles to black tea aroma (Table S2). Eight compounds (rOAV > 1) were identified as the most important contributors to black tea aroma under different shaking combinations (Table 2), including linalool, cedrol, 3-methyl-butanal, trans-\(\beta\)-ionone, dimethyl sulfide, benzeneacetaldehyde, \(\alpha\)-cedrene, and nonanal. Among these, linalool, cedrol, 3-methyl-butanal, and trans-\(\beta\)-ionone had average rOAV values > 10, indicating their substantial contributions to black tea aroma.  
Compared to CK, the rOAV values of key aroma components increased significantly after shaking combinations, particularly in YLS, which had the highest rOAVs for all aromatic compounds, suggesting superior aroma quality, further corroborated the results of sensory evaluation. Trans-\(\beta\)-ionone in HYS (78.50 \(\pm\) 32.76) and dimethyl sulfide in S (12.35 \(\pm\) 4.39) also showed high rOAVs. Referring to the previous literature for the aroma description (Table 2), linalool, trans-\(\beta\)-ionone, \(\alpha\)-cedrene, and nonanal were identified as key floral aroma substances, with their highest rOAV values in YLS, indicating that the increase in floral aroma compounds is crucial for aroma quality improvement under YLS treatment.
## 4 Results
### Boundary Layer Influence  
Simulations were conducted exclusively using the twoPhaseEulerFoam solver to assess the influence of boundary layers. Wall functions were applied at the domain boundaries to model turbulence fields. Three boundary layers were tested, each with a maximum height of 3 mm, corresponding to the minimum cell height in the D24 mesh. An expansion ratio of 1.1 was used to define the height decay of successive cells. These boundary layers consisted of \(4,8\) and \(16\) cells, and all tests were based on the D24 mesh.  
Table 1 summarizes the differences in mean dynamic pressure at five distinct locations for meshes with and without boundary layers. The maximum observed difference was less than 4.2%, and no correlation emerged between the number of boundary layer cells and pressure variations.  
Y+ values were analyzed at the bottom boundary to evaluate boundary layer effects further. These values, which depend on the distance from the jet's centerline, reflect the flow's complex behavior. For the D24 mesh without a boundary layer, y+ values measured ranged from 30 to 200 in the jet centerline to \(r/D_{i}=0.15\), and for \(r/D_{i}>10.0\). Introducing a 16-cell boundary layer reduced the maximum y+ value to around 170 near the jet but maintained values above 30 in regions far  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(r/D_{i}\) (-)} & \multicolumn{3}{c}{**Number of Cells in the Boundary Layer**} \\ \cline{2-4}  & **4** & **8** & **16** \\ \hline
0.00 & \(-1.0\%\) & 4.2\% & \(-1.9\%\) \\
0.25 & \(-2.4\%\) & 2.7\% & \(-1.5\%\) \\
0.35 & \(-3.2\%\) & 1.4\% & \(-1.6\%\) \\
0.69 & \(-0.1\%\) & \(-1.2\%\) & \(-1.3\%\) \\
1.04 & 0.3\% & \from it. Similar y+ trends were observed in all meshes with boundary layers for distances beyond \(r/D_{i}=10.0\).
# Image-Based Radical Identification in Chinese Characters
## 2 Materials and Methods  
### Indexing Radical Identifier  
Hanzi is a composition of a single or several smaller sub-lexical units with fairly regular morphology across different characters. Even though some bushou radicals have different shapes according to their location in the hanzi (as exemplified in Figure 2), the morphological features of the indexing radicals are regular and individual. Thus, if one considers the hanzi simply as images, the characters of the same radical should all have shared morphological features, namely the strokes that compose the bushou block.  
In this project, an indexing radical identifier based on convolutional neural networks (CNN) was implemented with Keras and TensorFlow backend [27] to learn and recognize common features of the radical block for several indexing radicals. As represented in Figure 3, the identifier model has two layers with Leaky Rectifier Linear Unit (ReLU) activation function, and the input data is a 1-D grayscale image of 28 \(\times\) 28 pixels that contains a single hanzi. The training sessions have a dropout of 50%, and 15% of the training samples are separated to validate the model parameters in each epoch. The prediction loss over these validation samples is defined as the early stopping parameter to avoid overfitting. Lastly, the output layer has 15 neurons with Softmax activation; each neuron represents a class (radical).  
Figure 2: Different shapes of the indexing radicals (**a**) fire, (**b**) heart, and (**c**) water are on the left side or at the bottom of the hanzi. The indexing radical block is red.  
Given the low complexity of the input data and the reduced number of output classes (more details in Section 2.2), a simple CNN with two hidden layers was preferred over a model with a lot of hidden units, which shall be required once the input data grows in complexity or new functions are added to the proposed framework, increasing the number and type of output information.
## 3 Adaptive Routing Optimization Algorithm
### Analysis of Node Relations
#### 3.2.3 Proposed New Metric  
Both the social feature and the historical encounter can reflect the relationship between nodes, but they are different from a logical point of view. The former is stable and can be obtained according to the investigation before network deployment, and it is related to the social relationship and physical features of the carrier, while the latter is dynamic and calibrated and maintained at scheduled time intervals, and it depends on the physical position of nodes. Therefore, they are complementary and can be combined to use as a hybrid relationship as follows:  
\[m_{ij}=\alpha\times\frac{w_{ij}-w_{\min}}{w_{\max}-w_{\min}}+(1-\alpha)\times \frac{F_{ij}-F_{\min}}{F_{\max}-F_{\min}} \tag{6}\]  
where, \(w_{ij}\) is the new metric which is combined historical encounters with social features of nodes by normalized processing, \(\alpha\) is an adjustment parameter to control the influence of historical encounters and social features on the relationship between nodes.
## 2 Materials and Methods
### Data Analysis and Quality Control  
All sorption/desorption experiments were performed in duplicate. Confirmation of estrogens in samples was based on the MRM ion transitions and comparing the retention time of each peak to the corresponding standard. The method's performance was evaluated by considering both water matrices' response linearity, method detection and quantification limits, recovery (Table S3) and repeatability (intra- and inter-day variations).
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 3 Results  
### Spatio-Temporal Development of Water Provisioning  
#### 3.1.1 Temporal Analysis Results  
The total water storage showed a fluctuating curve, with the total volume dropped from 26.95 billion cubic meters in 1998 to 20.95 billion cubic meters in 2018. The fluctuations strengthened year by year, indicating the stability of water storage gradually decreased. Figure 9 shows that the total lake surface area and the total lake water storage have similar development curves.  
According to the Mann-Kendall test, \(|Z_{c}|<Z\) (\(1-\alpha/2\)), and \(p>0.05\), which means we cannot reject the null hypothesis. The total lake water storage in Minnesota has no statistically significant monotonic trend from 1998 to 2018 (Table 5), even though the curve shows a slight downward trend visually.
## 3 Numerical Results and Discussion
### Al Doping Dependence of the Band Gap Energy in CFO  
The band gap is a crucial parameter governing the optoelectronic characteristics of materials, making its controllability highly important. The capability to tune the band gap across a broad energy spectrum has established perovskites as promising materials for diverse applications, including photovoltaics, lasers, light-emitting devices, photodetectors, and high-energy particle detectors. In particular, semiconductors with narrower band gaps are essential for improving absorption within the visible solar spectrum. Delafossite CFO is a p-type oxide semiconductor with an approximate band gap of 1.5 eV [17], attracting  
Figure 1: (Color online) Ion doping concentration dependence of the magnetization \(M\) in CFO for \(h=20\) kOe with (1) Al (\(J_{d}=1.2J\)); (2) Ga (\(J_{d}=0.9J\)); (3) In (\(J_{d}=0.8J\)). The \(J_{d}\) values are valid for all figures.  
considerable attention for solar energy conversion and oxide electronic devices. The band gap in CFO mainly arises from charge-transfer transitions between O-2p and Fe-3d electronic states. In this study, we focus on the effects of Al\({}^{3+}\) doping at the Fe sites in CFO. As previously noted, the substitution induces compressive strain, leading to an increase in the exchange interaction constant (\(J_{d}>J\)). Using Equations (11) and (12), we derive the doping dependence of the band gap \(E_{g}\) in Al-doped CFO, observing that the magnetization \(M\) increases with the doping concentration \(x\). The results, depicted in Figure 2, reveal a reduction in the band gap energy \(E_{g}\) with increasing Al content, resulting in enhanced transparency within the visible spectral region. This finding aligns well with experimental observations reported by [PERSON] et al. [18], validating the effectiveness of the proposed theoretical model in capturing the electronic and magnetic behavior of Al-doped CFO.  
Nevertheless, comprehensive experimental investigations and advanced theoretical analyses remain necessary to fully elucidate the underlying mechanisms by which Al doping influences the structural, magnetic, and electronic properties of CFO. Such studies will contribute to optimizing CFO-based materials for future optoelectronic and spintronic applications.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 3 System Model
### Experimental Channels
#### 3.2.2 Watermark NOF1 Channel  
In order to simulate a real experiment, we consider the Watermarkchannel [18] that implements a replayed channel simulator driven by at-sea measurements of the time-varying CIR. The Watermark channel is a realistic simulation tool including a direct-replay channel simulator and test channel document freely available to the UWAC community. The simulation tool includes Single-Input Single-Output (SISO) and Single-Input Multiple-Output (SIMO) modes. The principle of the simulator consists of distorting input waveforms by convolving them with measured channels [19]. Such an operation known as channel replay can be expressed as:  
\[r\left(t\right)=\int_{-\infty}^{\infty}\hat{h}\left(t;\tau\right)s\left(t- \tau\right)d\tau+n\left(t\right) \tag{10}\]  
where \(s\left(t\right)\) is the input signal, \(\hat{h}\left(t;\tau\right)\) the recorded Time-Varying Impulse Response (TVIR) estimates, \(n\left(t\right)\) a noise term and \(r\left(t\right)\) the distorted output signal. On the other hand, a channel file is represented by a MATLAB (.mat) file with a continuous single-hydrophone TVIR estimate. The collection represents consecutive soundings (TVIR measurements). To reproduce at-sea conditions, Watermark also includes Doppler shifts due to both platform motion and Carrier Frequency Offset (CFO).  
The selected Watermark channel for this paper is NOF1 (Norway - OsloFjord) with a signaling range of 750 m and a water depth of 10 m. The channel coherence time for a normalized CIR autocorrelation value of 0.75 is found to be 2.56 s. The NOF1 CIR estimated from the preamble sequence is shown in Figure 4b, and the basic parameters are listed in Table 1.
# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 4 Experimental Design
### Training Data Oversampling  
In this research, for constructing a multiclassification model, the predicted labels are divided into five categories (see Section 4.3). Through our pre-analysis of the dataset in training period 1, we found that about 55% of the labels could be regarded as small fluctuations, according to which the classifier would not provide correct direction prediction and simulation trading for the large magnitude predictions. In addition, there is a minority sample of only 8% of this training dataset in one of the other four classes. It has been evidenced that the presence of the class imbalance in the raw training dataset might result in poor classification performance of minority categories in some binary or multiclassification classification tasks [46; 47]. To solve this problem, a commonly used oversampling technique, named Synthetic Minority Oversampling Technique (SMOTE) [27], is integrated into the proposed approach. SMOTE is a heuristic oversampling technique that aims to balance the class distribution of the dataset to improve the efficiency of the classifier, while it can also alleviate the overfitting problem caused by random oversampling methods [47]. Rather than replicating existing observations, the SMOTE creates and inserts new synthetic samples in the minority class [26]. Specifically, suppose that the sample of the minority class in the training dataset is \(S_{\text{min}}\), the steps of SMOTE is to generate a class-balanced dataset follow:  
Step 1: A random minority sample \(x\) is chosen from \(S_{\text{min}}\). Then, calculate the Euclidean distance of \(x\) from the other samples in \(S_{\text{min}}\), and its \(k\) nearest neighbors are identified.  
Step 2: A random sample \(x_{n}\) is selected from \(k\) nearest neighbors, and a new sample \(x_{new}\) is created by the following rule:  
\[x_{new}=x+\omega\times|x-x_{n}| \tag{13}\]  
where \(\omega\) is a random weight chosen from the range [0, 1].  
Step 3: Repeat steps 1-2 until the number of minority samples is satisfied.
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 2 Related Work
### Anti-UAV Detection  
Numerous methods have been proposed for image-based UAV detection. For example, several state-of-the-art object detection models have been applied to UAV detection datasets [17; 32; 33; 34; 35]. Faster R-CNN [20], combined with feature extraction backbones such as ResNet50, ResNet18, or VGG16, achieves a mean average precision (mAP) greater than 0.6. Its inference speed, measured in frames per second (FPS), can reach up to 19.4 [23]. These results demonstrate that Faster R-CNN can effectively perform UAV detection. However, its FPS is relatively low for real-time applications. To address this, faster models such as YOLOX [36] and SSD [19] have been utilized. Although these models achieve lower mAP scores ranging from approximately 0.4 to 0.63, they offer significantly higher inference speeds, with FPS values reaching up to 53.7 [23].  
These findings indicate that state-of-the-art object detection models can be successfully applied to UAV detection after training on relevant datasets. Nonetheless, these models still struggle with accurately detecting small UAVs. To address this challenge, [PERSON] et al. [37] proposed a transformer-based model called ST-Trans, which improves small object detection performance in UAV detection tasks. Despite its improved accuracy, ST-Trans still faces limitations in achieving real-time inference speeds.  
Currently, there are no publicly available datasets for UAV semantic segmentation, and consequently, no existing models specifically designed for this task. However, state-of-the-art semantic segmentation models from related detection tasks can be adapted for UAV semantic segmentation. In this paper, we propose a novel lightweight model tailored for UAV semantic segmentation, optimized for both higher mean intersection over union (mIoU) and real-time performance. The proposed model is evaluated against existing state-of-the-art segmentation models retrained on our newly proposed dataset.
## 1 Introduction
More recent publications (2024) have also reported ray-tracing simulations aiming to optimize the working parameters in tower-type solar thermal power generation systems: (1) the simulation results allowed the authors to conclude that the average thermal power output per unit mirror area can be increased by reducing shading between the heliostats [16]; (2) the optical efficiency of the mirror field is the primary factor determining the overall system's power generation efficiency [17]. In this manuscript, computer modelling studies of heliostat fields are reported, specifically looking into the optical aspects determining their overall efficiency (in a subsequent publication, equivalent studies covering mechanical aspects will be reported). Models are introduced to simulate the most common heliostat mirror curvatures, to handle shading, blocking, and radiation collection. Several different layouts to represent realistic heliostat fields are then suggested. Ray-tracing computer simulations of these layouts permit detecting and measuring even small optical losses due to shading and blocking effects, which are then reported and discussed. The software used was specifically written for these studies, providing maximum control, flexibility, efficiency, and accuracy, thus increasing our knowledge and our error detection capacity. This software includes the algorithms recently reported [18] to calculate the sun position (as a function of time, date, latitude, and longitude) and to simulate its non-parallel rays. Figures of merit to measure and rank the efficiency of the various heliostat fields studied, based in shading, blocking, and night effects, are then calculated and discussed.
# Longer Ubiquinone Side Chains Contribute to Enhanced Farnesol Resistance in Yeasts
## 1 Introduction
The specificity of this enzyme determines the number of isoprenoid units present (i.e., side chain length) and, therefore, it is believed that the isoform of this enzyme varies among different species [17; 18]. Polyprenyl diphosphate synthase is encoded by a single gene _COQI_ in both _S. cerevisiae_[19] and _C. albicans_[20] while its homolog in _Arabidopsis thaliana_ is identified as _At2g34630_[21]. The variability of ubiquinone side chain length is often taxonomically useful [22] but functional differences due to differing UQ side chain lengths have rarely been reported. Thus, we were intrigued by taxonomic studies which showed that _S. cerevisiae_ produces UQ6 while most _Candidia_ species produce UQ7 except for _C. albicans_ and _C. dubliniensis_ which have UQ9 [23; 24]. Significantly, _C. albicans_ and _C. dubliniensis_ are major human fungal pathogens which cause life-threatening systemic infections in humans and they are the only two species in the _Candidia_ clade known to excrete large quantities of farnesol [8; 9]. As would be expected for organisms using farnesol in this fashion, the growth rates of _C. albicans_ and _C. dubliniensis_ were not affected by 300 uM [2; 6] and 150-200 uM [25; 26] farnesol, respectively. These observations led us to hypothesize that a longer isoprenoid chain length may provide an advantage to farnesol-producing _Candidia_ species, allowing them to cope with the oxidative stress exerted by the ROS generated by farnesol. In this study, we tested this hypothesis by comparing the farnesol sensitivity of yeasts, using a recombinant _S. cerevisiae_ which produces UQ9 instead of UQ6 [21]. Our findings suggest that a longer isoprenoid side chain length protects yeasts from farnesol and excess ROS by a mechanism independent of transcriptional regulation. Thus, synthesizing UQ9 instead of UQ7 could serve as a pathogen-adapted structural trait in _C. albicans_.