esa-sceva/satcom-chunk-collection huggingface dataset:
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 3 Proposed Target Selection Strategies
### Embedding-Based Target Selection  
The embedding-based target selection strategy is summarized in Figure 4. As can now be seen, \(I=X+N+Y\), where \(X\) is still the source of interest and \(N\) is still the noise to be removed, but now another speech source \(Y\) is present.  
Figure 3: A decoding layer in the Demucs-Denoiser model.  
Figure 2: An encoding layer in the Demucs-Denoiser model.  
It is assumed that a previous recording of the target speech is known, so that an embedding of their speech can be extracted a priori. This embedding is then concatenated channel-wise to the input of the sequence modeling stage. However, the dimensions of the output of the sequence modeling stage are kept at \(2^{L-1}H\), so as to not differ from its original operation. The objective of this concatenation is to \"nudge\" the focus of the sequence modeling towards the target speech, instead of the loudest speech source.  
The embedding codifier chosen for this strategy is ECAPA-TDNN [21], which is based on the x-vector architecture [22; 23]. The latter uses a time-delay neural network [24] trained for a speaker classification task, and employs the outputs of the \"bottleneck\" layer (the one preceding the final fully-connected layer) as the codified embedding. The ECAPA-TDNN technique further extends this idea by:  
* Pooling channel- and context-dependent statistics to be used as part of its temporal attention mechanism, to focus on speaker characteristics that occur in different time segments.
* Creating a global context by joining local features with the input's global properties (mean and standard deviation), so that the attention mechanism is more robust against noise.
* Rescaling features from each time segment to benefit from the previously described global context.
* Aggregating all the aforementioned features, as well as the ones from the first layers of the TDNN, and achieving this by way of summation into a static embedding size (in this case, 192 dimensions), regardless of the input size.  
The ECAPA-TDNN technique has been successfully employed, not only for speaker verification tasks [25; 26], but also for speech synthesis [27], for depression detection in speech [28], and speech stuttering detection [29]. This indicates that ECAPA-TDNN is quite versatile in its ability to encode the speaker identity in an embedding.  
It is important to mention that this type of target selection scheme is not new. The works of [30; 31], as well as of [32], carry out a similar strategy. However, their underlying speech enhancement techniques rely on time-frequency masking, while the Demucs-Denoiser model used in this work completely relies on time domain. In [13] it was shown that this provides low response times and better performance against reverberation compared to other time-frequency-masking speech enhancement techniques.  
Figure 4: Embedding-based target selection strategy.
# Ontology-Based Spatial Data Quality Assessment Framework
## 2 Materials and Methods
### Ontology  
Ontology is a widely recognized term that serves as a standard for domain modeling [30; 31]. One of the primary advantages of ontology-based applications is their ability to promote interoperability and reusability of frameworks.  
Quality management frameworks are designed for evaluating and representing quality results based on the rules of the application domain, and can leverage these benefits. In the context of the Semantic Web, for rules, the W3C SWRL [32] and SQWRL submissions are utilized in this study.  
OWL stands for Web Language for Ontology. In decidable ontologies, reasoning ends with certainty. OWL DL is a dialect that guarantees decidability. Datalog is an alternative to Prolog and is more compatible with open-world reasoning and ontologies. SWRL combines OWL DL and a special subset of Datalog [32]. The Open-World Assumption is maintained in the SWRL. Therefore, no new clause will change the previous truth values; unknowns can become known.
## 7 Model-Based Renyi Quadratic Entropy  
Consider a mixture model:  
\[f(x)=\sum_{i=1}^{k}\alpha_{i}f_{i}(x)\text{ with }\sum_{i=1}^{k}\alpha_{i}=1 \tag{12}\]  
Here \(f\) could correspond to one level of a mutiple resolution transformed signal. The number of mixture components is \(k\).  
Now take \(f_{i}\) as Gaussian:  
\[f_{i}(x)=f_{i}(x|\mu,V)=\left((2\pi)^{-\frac{m}{2}}|V_{i}|^{-\frac{1}{2}} \right)\exp\left(-\frac{1}{2}(x-\mu_{i})V_{i}^{-1}(x-\mu_{i})^{t}\right) \tag{13}\]  
where \(x,\mu_{i}\in\mathbb{R}^{m},V_{i}\in\mathbb{R}^{m\times m}\).  
Take  
\[V_{i}=\sigma_{i}^{2}I \tag{14}\]  
(\(I\) = identity) for simplicity of the basic components used in the model.  
A (i) parsimonious (ii) covering of these basic components can use a BIC approximation to the Bayes factor (see section 2) for selection of model, \(k\).  
Each of the functions \(f_{i}\) comprising the new basis for the observed density \(f\) can be termed a _radial basis_[27]. A radial basis network, in this context, is an iterative EM-like fit optimization algorithm. An alternative view of parsimony is the view of a sparse basis, and model fitting is sparsification. This theme of sparse or compressed sampling is pursued in some depth in [17].  
We have:  
\[\int_{-\infty}^{\infty}\alpha_{i}f_{i}(x|\mu_{i},V_{i})\cdot\alpha_{j}f_{j}(x| \mu_{j},V_{j})\;dx \tag{15}\]  
\[=\alpha_{i}\alpha_{j}f_{ij}(\mu_{i}-\mu_{j},V_{i}+V_{j}) \tag{16}\]  
See [13] or [26]. Consider the case - apropriate for us - of only distinct clusters so that summing over \(i,j\) we get:  
\[\sum_{i}\sum_{j}(1-\delta_{ij})\alpha_{i}\alpha_{j}f_{ij}(\mu_{i}-\mu_{j},V_{ i}+V_{j}) \tag{17}\]  
Hence  
\[H_{R2}=-\log\int_{-\infty}^{\infty}f(x)^{2}\;dx \tag{18}\]  
can be written as:  
\[-\log\int_{-\infty}^{\infty}\alpha_{i}f_{i}(x|\mu_{i},V_{i})\cdot\alpha_{j}f_ {j}(x|\mu_{j},V_{j})\;dx \tag{19}\]  
\[=-\log\sum_{i}\sum_{j}(1-\delta_{ij})\alpha_{i}\alpha_{j}f_{ij}(\mu_{i}-\mu_{ j},V_{i}+V_{j}) \tag{20}\]  
\[=-\log\sum_{i}\sum_{j}(1-\delta_{ij})f_{ij}(\mu_{i}-\mu_{j},2\sigma^{2}I) \tag{21}\]  
from restriction (14) and also restricting the weights, \(\alpha_{i},\alpha_{j}=1,\forall i\
eq j\). The term we have obtained expresses interactions beween pairs. Function \(f_{ij}\) is a Gaussian. There are evident links here with Parzen kernels [28; 29] and clustering through mode detection (see e.g., [30; 31] and references therein).  
For segmentation we will simplify further Equation (20) to take into account just the equiweighted segments reduced to their mean (cf. [28]).  
In line with how we defined mutiple resolution entropy in Section 6, we can also define the Renyi quadratic information of wavelet transformed data as follows:  
\[H_{R2}=\sum_{s=1}^{S}H_{R2}^{s} \tag{22}\]
## 4 Hardware Implementation and Tests of Proposed RMEDS-SP Simulation Model
### Test Environment  
We further construct the test environment to test the performance of DPS modeling by RMEDS-SP through the hardware. The test environment is shown in Figure 16. As Figure 16 shows, the test environment consists of the Test Signals Generating Module (TSGM), the Channel Response Module (CRM), and the Performance Analysis Module (PAM).  
* TSGM is used to generate the test signal, which are the signals of LEO-SIN. In our test, the frequency of test signals ranges from Ka-band, with a bandwidth of 100 K to 400 M. In the test, one signal generator is exploited.
* CRM is the above channel response simulation module, which can generate and superpose the response of DPS and other channel responses, such as large-scale fading, shadowing fading, and delay.
* PAM module is used to collect the running data from the test and analyze the performance. In the test, the frequency spectrum analyzer is exploited.  
All the test configurations would be set in the main control computer, which connects to the main control board with the Field-programmable Gate Array (FPGA). The main control board loads all the hardware design of channel response and outputs the real signal.
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Home Range Analyses  
To estimate the home range of the satellite-tracked reef manta rays, we utilized only the GPS position data due to its superior accuracy compared to the Argos data. We applied an optimally weighted Autocorrelated Kernel Density Estimator (AKDE) [42] to the processed and filtered GPS positions using the 'ctmm' R package [43] to estimate the home range (95% utilization distribution--UD). AKDE has been shown to outperform conventional home range estimators by accounting for autocorrelation in movement data [44]. It has also been previously applied to satellite tracking data from reef manta rays [23], where satellite tracking data are often irregular due to the unpredictable surfacing behavior of this species. The 95% UDs for each individual were then calculated in square kilometers (km\({}^{2}\)), excluding any island areas within the UDs. Furthermore, the 95% UDs of all the individuals were combined in each region to assess their overlaps with the existing marine protected areas (MPAs) in Indonesia. These processes were undertaken in QQIS 3.22.
## 2 Materials and Methods
### Osf-Rcnn  
Deep learning-based object detection methods can be divided into two categories: two-stage approaches based on region proposals and one-stage approaches based on regression. The two-stage method first generates candidate bounding boxes in the processed image, and then classifies these proposals and performs precise positional regression to obtain detection results. Representative algorithms of this approach include the Region-Based Convolutional Neural Network (R-CNN) series [31]. In contrast, one-stage methods treat all possible locations in the image as potential candidate bounding boxes, directly performing object classification and bounding box regression to obtain detection results, with the You Only Look Once (YOLO) series being representative examples [32]. YOLO is a single-stage object detection algorithm that formulates detection as a regression problem. It processes the entire image in a single forward pass and directly predicts bounding boxes along with class probabilities, enabling real-time performance. Since the one-stage method exhibits lower localization accuracy and poorer performance in multi-object detection [33], and this study requires the precise localization of multiple GNSS interference sources, we propose improvements to the Faster R-CNN algorithm to adapt it to the application scenario of this research.  
To address the problems outlined in Section 2.3.2, this paper proposes an OSF-RCNN framework based on the Faster R-CNN algorithm for processing C/N\({}_{0}\) distribution maps  
Figure 6: Examples of interference source targets with different antenna types at an equivalent radiated power of \(-15\) dBm.  
to achieve the effective localization of multiple GNSS interference sources. Built upon the Faster R-CNN framework, the proposed OSF-RCNN can effectively learn and recognize the features of different types of interference sources. To account for the rotational characteristics of directional antennas, an Oriented RPN was introduced to regress the orientation angles of directional antennas [34]. Additionally, the SE mechanism [35] was integrated into key positions of the network to enhance attention to weak interference signals. Furthermore, FPN was employed to fuse multi-type and multi-scale feature information, thereby improving the detection accuracy of small targets and making the method more suitable for weak GNSS interference detection and localization. The experimental results demonstrate that the proposed method achieves accurate and reliable localization of multiple GNSS interference sources, effectively addressing challenging scenarios such as the presence of multiple directional interferences, increasing diversity and quantity of GNSS interference, and the detection of multiple weak interference signals.
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## 4 Satellite-to-Ground QKD Analysis
### Satellite-QKD Model and Parameters Verification
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mu\) & \(\mathbf{p_{s}}\) & \(\mathbf{f\,(Hz)}\) & \(\mathbf{\chi_{0}^{300}}\) _(/pulse)_ & \(\mathbf{e_{det}^{300}}\) \\ \hline
0.8 & 0.5 & \(10^{8}\) & \(2.5\times 10^{-6}\) & 0.9 \% \\ \hline \(\
u\) & \(\mathbf{p_{d}}\) & \(\mathbf{p_{v}}\) & \(\mathbf{\chi_{0}^{600}}\) _(/pulse)_ & \(\mathbf{e_{det}^{600}}\) \\ \hline
0.1 & 0.25 & 0.25 & \(5\times 10^{-6}\) & 0.5 \% \\ \hline \end{tabular} _Note : The superscript of \(Y_{0}\) and \(e_{det}\) denotes the aperture of a ground station._  
\end{table} 
Table 3: Key parameters of the QKD experimental setups. To verify our semi-empirical model with a phenomenological parameter \(\varkappa\), we compared the simulated results for the Xinglong ground station with published experimental data collected with the Micius satellite [13] and modelled findings by [PERSON] et al. [22]. Figure 6 illustrates the peak sifted key rate on the shortest distance between the Micius satellite and the Xinglong ground station for specific satellite passages between September 2016 and May 2017. The Xinglong ground station is equipped with a 1000-mm-aperture telescope, where the obstruction of the telescope's secondary mirror is approximated to be 80%. In order to compare the models, we calculated the atmospheric extinction coefficient \(\varkappa_{Vergoossen}=0.80\), which is equivalent to a \(-3.2\) dB Vergoossen atmospheric absorption loss at zenith. The other inputs for the key rate calculations are provided in [PERSON] et al. (Table 3 [22]). Thus, the peak sifted key rate in our model with the atmospheric extinction parameter \(\varkappa_{Vergoossen}\) agrees well with the model prediction provided by [PERSON] et al. [22]. Meanwhile, the experimentally obtained atmospheric extinction coefficient for mid-latitude ground stations under favourable weather conditions provides a better evaluation of the Micius data with the shortest distances of 530.33 km, 591.56 km, 711.94 km, and 929.67 km. Moreover, the atmospheric extinction coefficient for foggy, hazy weather conditions, together with less than 3 dB loss due to pointing error [13], is suitable for the assessment of the lower bound of the peak sifted key rate. Hence, we have verified our model with the real experimental data. As a result, our semi-empirical model with phenomenological parameters is robust and closely matches the satellite-to-ground QKD experiment.
## 3 Double-Beam System Traversed by a Moving Force
### Mathematical Formulation  
The equations of motion of the secondary beam and the primary beam can be expressed as  
\[E_{2}I_{2}w_{2}^{\prime\prime\prime\prime}+\rho_{2}A_{2}\ddot{w}_{2}-k_{\rm e}(w_ {1}-w_{2})=0 \tag{49}\]  
\[s_{\rm k}E_{2}I_{2}w_{1}^{\prime\prime\prime\prime}+s_{\rm m}\rho_{2}A_{2} \ddot{w}_{1}+k_{\rm e}(w_{1}-w_{2})=-f\delta(x-vt) \tag{50}\]  
The sum of Equations (49) and (50) is  
\[E_{2}I_{2}(s_{\rm k}w_{1}^{\prime\prime\prime}+w_{2}^{\prime\prime\prime})+ \rho_{2}A_{2}\big{(}s_{\rm m}\ddot{w}_{1}+\ddot{w}_{2}\big{)}=-f\delta(x-vt) \tag{51}\]  
Applying the MS method to Equation (51), one can obtain the following ordinary differential equation regarding modal coordinates [42; 43]  
\[\ddot{q}_{n}^{(i)}(t)+\Big{(}\omega_{n}^{(i)}\Big{)}^{2}q_{n}^{(i)}(t)=-\frac {f\varphi_{1,n}^{(i)}(vt)}{M_{nn}^{(i)}},\quad n=1,\;2,\;3\ldots,\;i=1,\;2 \tag{52}\]  
where  
\[M_{nn}^{(i)}=\int_{0}^{l}s_{\rm m}\rho_{2}A_{2}\Big{[}\varphi_{1,n}^{(i)}(x) \Big{]}^{2}{\rm d}x+\int_{0}^{l}\rho_{2}A_{2}[\varphi_{2,n}(x)]^{2}{\rm d}x \tag{53}\]
## 4 Numerical Simulations
### Newborn Vaccination  
An initial total population of \(N(0)=4\times 10^{7}\) individuals with the following demographic parameters: \(A=0.005\left(\text{individual}\times\text{day}\right)^{-1}\) and \(\mu=0.005\,\text{day}^{-1}\) was assumed. The model was evaluated under normalization to unity. The initial conditions of the subpopulations were \(S(0)=1-1/N(0)\), \(E(0)=R(0)=0\), \(I(0)=1/N(0)\). The evolution in the absence of vaccination is displayed in Figure 2. The basic reproduction number was \(R_{0}=6.7\), and the solution converges to the endemic equilibrium point which is asymptotically stable with an infectious proportion of \(I^{*}=0.05\). Figure 3 exhibits the evolution of the infectious for different levels of vaccination effort. Note that for \(q>q_{c}=0.85\), the disease-free equilibrium point again becomes a stable attractor.  
It is now evaluated how to update the vaccination effort to the proportion of susceptible individuals through time. The basic idea is to increase the effort as the susceptible individuals increase. For that  
Figure 3: Evolution of the infectious proportion under different levels of vaccination effort.  
Figure 2: Evolution of the SEIR model in the absence of vaccination.  
purpose, the following updating formula is used for the vaccination effort which adapts the vaccination effort to the proportion of susceptible through time:  
\[q(S(t))=(q_{\max}-q_{\min})S(t)+q_{\min} \tag{40}\]  
Figures 4 and 5 show the model evolution with \(q_{\max}=0.90\), and \(q_{\min}=0.55\) and \(q_{\min}=0.775\), respectively, under the newborns vaccination effort designed with the above formula. The endemic proportion of infectious is \(\varGamma^{*}=0.015\) in the first case and \(\varGamma^{*}=0.0036\) in the second one. Note that Figure 5 exhibits an improvement in the maximum infectious level at the expense of larger vaccination efforts according to the above formula.
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 4 Experimental Setup and Results
### Performance Evaluation
This clear distinction between normal and anomalous reconstruction errors demonstrates the model's strong ability to effectively differentiate anomalies from normal behavior. In contrast, the VAE with LSTM layers struggles to achieve the same level of separation. A significant portion of anomalous samples fall below the threshold, resulting in a higher false negative rate. Furthermore, the reconstruction error for normal data is more spread out, leading to a slightly higher false positive rate. These results indicate that while the VAE with LSTM layers can detect some anomalies, it is less effective than our VAE, especially for vehicle-targeted position offset anomalies. Figure 4 illustrates the reconstruction error distributions for vehicle-targeted speed offset anomalies, comparing our VAE model with a traditional autoencoder. The VAE  
Figure 3: Reconstruction error distribution comparison between (**a**) our VAE and (**b**) the VAE with LSTM layers for vehicle targeted position offset. model shows a well-defined separation between normal and anomalous reconstruction errors. The normal data exhibits a narrow distribution concentrated below the threshold, with minimal overlap with the anomalous distribution. The reconstruction errors of the anomalous data are significantly higher than those of the normal data, ensuring a low false negative rate and robust anomaly detection for speed offset anomalies. The traditional autoencoder, on the other hand, exhibits a much less distinct separation. The reconstruction error distribution for anomalous data overlaps with the normal data, resulting in a higher rate of both false positives and false negatives. The threshold fails to effectively separate the two distributions, showing the limitations of the autoencoder in detecting vehicle-targeted speed offset anomalies. These findings further highlight the superior capability of our VAE model in distinguishing anomalies with greater precision and reliability.
###### Abstract  
_Salvia miltiorrhiza_ (_S. miltiorrhiza_) Bunge is one of the most economically important medicinal crops in China. In traditional Chinese medicine, its root is used as an important ingredient in formulas for treatment of atherosclerosis-related disorders. The continuous cropping of _S. miltiorrhiza_ increases the proportion of dried seedlings and decreases the biomass of the shoots and roots and the contents of active components. In this study, three field experiments were conducted to investigate the effects of _Glomus mosseae_ (_G. mosseae_) inoculation on the growth and contents of active ingredients and nutrients in continuously cropped _S. miltiorrhiza_. The results showed that inoculation with _G. mosseae_ increased the shoot biomass of _S. miltiorrhiza_ by 48.1% and the root biomass by 39.2%, and decreased the dried seedling rate by nearly 75%. Inoculation with _G. mosseae_ also increased the salvianolic acid B concentration by 21.9% in the shoots and 9.2% in the roots of _S. miltiorrhiza_, and also significantly increased Mn concentration in the roots and shoots (by 65.1% and 93.4%, respectively) and Fe concentration in the roots (by 75%). The accumulation of salvianolic acid B, Mn, and Fe in _G. mosseae_ inoculated _S. miltiorrhiza_ may be a mechanism that imparts tolerance to continuous cropping. Inoculation of _S. miltiorrhiza_ with _G. mosseae_ can serve as an effective approach of biocontrol to improve the performance of continuously cropped _S. miltiorrhiza_.  
**Keywords:**_Salvia miltiorrhiza_; continuous cropping; arbuscular mycorrhizal fungus; _Glomus mosseae_; salvianolic acid B; growth
## 3 Methodology
### Composite Metric Development
Compute the PCA for Metrics: PCA was applied to the metrics (CUQ, UES-SF, response time, or error scores) to identify components, where each component \(Ci\) explains a variance (Variance\({}_{C_{i}}\)) in the data. PCA outputs the component loadings (weights, \(Wij\)) for each metric \(Mj\) and their explained variance. 2. Calculate the Variance-Weighted Contribution for Each Metric: For each component \((Ci)\), the weight \((Wij)\) of each metric \((Mj)\) was determined using Equation (4): \[\text{Weight}_{\text{Mij}}\ =\ Wij\times\text{Variance}_{C_{i}}\] (4)
3. Normalize Combined Weights: To enable comparisons on a standardized scale, weights were normalized to sum to 1 using Equation (5): \[\text{$ normalized\_$Weight}_{\text{Mj}}\ =\ \frac{\sum_{i=1}^{n}\text{Weight}_{\text{Mij}}}{ \sum_{j=1}^{m}\sum_{i=1}^{n}\text{Weight}_{\text{Mij}}}\] (5) where \(m\) is the number of metrics. 4. Construct the Final Composite UX Metric:  
The overall composite UX metric (S) was derived by aggregating the metrics using the normalized weights obtained in Step 3, as defined in Equation (6):  
\[S=\sum_{j=1}^{m}\text{Mj}*Normalized\_\text{Weight}_{\text{Mj}} \tag{6}\]  
The proposed variance-weighted, multi-dimensional metric is particularly suited for educational chatbots, where usability and task performance are often prioritized. While engagement and efficiency play supporting roles, this framework is adaptable for broader applications in educational contexts. By integrating multiple dimensions into a single composite UX metric, this approach enables nuanced insights into chatbot performance, facilitating targeted improvements and comprehensive evaluations.