esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Multilevel Shared Decoder
#### 3.2.2. Upsampling by UpCSPN-k  
With relatively adequate resources, a complexity-moderate upsampling module called UpCSPN-\(k\) can be selected for high-accuracy applications. UpCSPN-\(k\) utilizes CSPNs over the layers of the decoder to iteratively produce the refined depths. As shown  
Figure 4: The structure of the \(n\)th upsampling block (UP\({}_{n}\)) of SimpleUp, where \(\oplus\) expresses the element-wise addition.  
Figure 3: Architecture of the multilevel shared decoder with multi-scale input features of depth and RGB.  
in Figure 5, the input RGB feature \(r_{n}\) and depth feature \(d_{n}\) with a size of (\(H\), \(W\), \(C\)) in the \(n\)th layer are passed through a 5 \(\times\) 5 convolution then divided into two branches, the _convolution_ branch and the CSPN branch.  
In Figure 5, the convolution branch performs one 3 \(\times\) 3 standard convolution with batch normalization to output features of size (\(H\), \(W\), \(C\)/2-1). Meanwhile, the _CSPN_ branch is composed of two prediction heads, which produce a coarse depth map \(d_{n}^{c}\) with a size of (\(H\), \(W\), \(1\)) and a predicted affinity map \(\hat{\kappa}(x)\) with a size of (\(H\), \(W\), \(k^{2}\)-1), where \(k\) is the kernel size of the CSPN. For each pixel \(x\) in \(\hat{\kappa}(x)\), the convolution weights of the channels can help to convey the information of pixels through the spatial average.  
Thus, the refined depth \(d_{n}^{r}\) through CSPN processing in each decoding layer can be obtained as  
\[d_{x}^{r}=\kappa_{x}(x)\odot d_{x}^{c}+\sum_{x_{n}\in\mathcal{N}_{x}}\kappa_{ x}(x_{n})\odot d_{x_{n}}^{c}, \tag{1}\]  
where \(x\) denotes the pixel indices, \(x_{n}\) denotes the local neighborhood \(N_{x}\) around \(x\) and \(\odot\) is the element-wise production. The refined affinity of \(x\) is given as  
\[\kappa_{x}(x)=1-\sum_{x_{n}\in\mathcal{N}_{x}}\kappa_{x}(x_{n}), \tag{2}\]  
where the normalization of the predicted affinity map \(\hat{\kappa}(x)\) is performed by  
\[\kappa_{x}(x_{n})=\delta_{x}(x_{n})/\sum_{x_{n}\in\mathcal{N}_{x}}|\delta_{x}( x_{n})|. \tag{3}\]  
After CSPN processing, the refined depth is concatenated with the features of the convolution branch, and then the spatial resolution of the concatenated results is doubled by a bilinear interpolator for each decoding layer. Although the upsampling of UpCSPN-k is more complex than that of SimpleUp, it can provide much more precision than SimpleUp.  
Note that, instead of general convolution and concatenation, the element-wise addition and depthwise separable convolution in SimpleUp and UpCSPN-k can efficiently fuse due RGB features and depth features without performance degradation through extensive testing.  
Figure 5: The structure of the \(n\)th upsampling block (UP\({}_{n}\)) in UpCSPN-\(k\), where \(\oplus\) denotes the element-wise addition.
## 2 EDOB and FDOB  
### The EDOB Built in the Acceleration Control Loop  
DOB as a disturbance compensation feed forward method, has commonly been used in industrial control [20; 21]. As it does not need to add an additional sensor and basically does not affect the stability of the system, it is very suitable for the small OCS whose model can be recognized by spectrum fitting and seldom changes in motion. [PERSON] first introduced the DOB disturbance feed forward control into the OCS and later proposed a modified method called EDOB [13; 22]. Figure 1 presents the structure of EDOB. Compared with the DOB, EDOB changed the compensation object \(G_{a}\) to be the open-loop transfer function \(C_{a}G_{a}\) and optimized the controller design.  
The closed-loop acceleration is given as follows.  
\[a=uG_{a}+\theta_{d}S^{2} \tag{1}\]  
\[u=\left[a_{ref}-a-(a-u\widetilde{G}_{a})C_{f}\right]C_{a} \tag{2}\]  
As \(u\) is a substitutable factor, after substitution, we get  
\[a=\frac{C_{a}G_{a}}{1+C_{a}G_{a}+C_{f}C_{a}(G_{a}-\widetilde{G}_{a})}a_{ref}+ \frac{(1-C_{f}C_{a}\widetilde{G}_{a})s^{2}}{1+C_{a}G_{a}+C_{f}C_{a}(G_{a}- \widetilde{G}_{a})}\theta_{d} \tag{3}\]  
Since we only focus on the line-of-sight stabilization, \(a_{ref}\) could be set as 0. The acceleration loop's DS transfer function is as Equation (4).  
\[E_{EDOB}=\frac{a}{\theta_{d}s^{2}}=\frac{1-C_{f}C_{a}\widetilde{G}_{a}}{1+C_{ a}G_{a}+C_{f}C_{a}(G_{a}-\widetilde{G}_{a})} \tag{4}\]  
Theoretically, in Equation (4), if \(C_{f}\) is set to be \(\left(C_{a}\widetilde{G}_{a}\right)^{-1}\), \(E_{EDOB}\) would become 0. However, due to model mismatch and noise effects, the DS improvement is limited, especially in low frequency. The reason is that the observed disturbance in low frequency from the MEMS accelerometer is not  
Figure 1: The EDOB method. \(G_{a}\) is the acceleration open-loop transfer function. \(\widetilde{G}_{a}\) is the approximate model of the platform. \(C_{a}\) and \(C_{f}\) is respectively the acceleration controller and the controller of disturbance compensation. ACC respects the MEMS accelerometer. \(a_{ref}\), \(a\) is the given acceleration and the output acceleration. \(\theta_{d}\) is the external disturbance.  
accurate with a big drift and much noise and would lead to poor effects of compensation. In order to get a satisfying low-frequency DS, we first need to acquire a relatively accurate acceleration signal depending on the existing sensors, decreasing the influence of the drift and noise.
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
## 4 Results and Discussion
### The Permeability Behavior of Coal in the Full Stress-Strain Process
Based on the experimental conclusions, it is essential to continuously monitor the gas concentration at the working face during field construction to determine whether mining disturbances have caused a sudden increase in permeability of the coal seam at the working face, thereby minimizing the risk of disasters such as coal and gas outbursts. Figure 8: Stress–permeability temporal curve of coal under cyclic dynamic load. The increase in permeability of the coal mass during different cyclic dynamic loading processes is illustrated in Figure 9. As the amplitude of the cyclic dynamic load increases, the increment in permeability of the coal mass during the application of the dynamic load also rises. This indicates that, with the increase in the dynamic load amplitude, the damage within the coal mass becomes more pronounced, leading to the more extensive development of fractures. The coupling curve of permeability increases, and the yield strain of coal under different cyclic dynamic loading is illustrated in Figure 10. As the amplitude of dynamic loading increases, the yield strain during the dynamic loading process also grows. This indicates that the coal body is subjected to further compression; however, an increase in permeability is observed. The reason for this is that, during this process, fractures radially expand further, leading to a continuous rise in permeability. Figure 10: Permeability increase and yield strain curve of coal under different cyclic dynamic loads. Figure 9: Increase in coal permeability under different cyclic dynamic loads. Following cyclic loading, the maximum permeability of the coal body during failure also increases, as illustrated in Figure 11. When the cyclic loading amplitudes are 2.5 MPa, 3.75 MPa, 5 MPa, and 6.25 MPa, the maximum permeability values of the coal body are 0.0255 \(\upmu\)m\({}^{2}\), 0.0286 \(\upmu\)m\({}^{2}\), 0.0321 \(\upmu\)m\({}^{2}\), and 0.0386 \(\upmu\)m\({}^{2}\), respectively. Compared to the baseline value of 0.0135 \(\upmu\)m\({}^{2}\), these represent increases of 88.9%, 111.9%, 137.8%, and 185.9%, respectively. It is evident that the application of dynamic loading not only enhances the current permeability of the coal body but also significantly contributes to the increase in its maximum permeability upon failure.
## 4 Numerical Results and Discussions  
In this section, the proposed Doppler estimation algorithm and channel estimation algorithm are validated using the superimposed-LFM-based preamble signal.  
Figure 6 shows the designed superimposed-LFM-based preamble signal, where Figure 6a,b are the waveform and the spectrogram, respectively. In the designed preamble signal, \(f_{\text{L}}\) = 23 kHz, \(f_{\text{H}}\) = 27 kHz, and \(T_{\text{P}}\) = 40 ms. Notably, these parameters are used in the sea trial. The sampling frequency at the receiver, \(f_{\text{s}}\), is set as 128 kHz. From Figure 6, we observe that the up-sweeping and down-sweeping LFM signals can still be identified in the frequency domain, although they are superimposed in the time domain.
###### Abstract  
We present a comparative study of the capacity increase brought by bit- and power-loading discrete multi-tone (DMT) modulation for low-cost colorless transmitters. Three interesting reflective semiconductor optical amplifier (RSOA) based colorless transmitter configurations are compared: First, an amplified spontaneous emission (ASE) spectrum-sliced source; second, a self-seeded RSOA fiber cavity laser (FCL) and third, an externally seeded RSOA. With bit- and power-loaded DMT, we report record high line rates of 6.25, 20.1 and 30.7 Gbit/s and line rates of 4.17, 10.1 and 24.5 Gbit/s in a back-to-back and in a 25 km nonzero dispersion shifted fiber (NZDSF) transmission experiments for the three transmitter configurations, respectively. In all the experiments, BER (bit error ratios) below an FEC (forward error correction) limit of 7.5 \(\times\) 10\({}^{-3}\) were achieved.  
bit- and power-loading; colorless transmitter; discrete multi-tone (DMT); reflective semiconductor optical amplifier (RSOA); WDM-PON +
Footnote †: journal: Applied Sciences
## 6 Problems, Challenges, and the Future of Phage Therapy
### Regulatory and Standardization Barriers  
The clinical translation of mycobacteriophage therapy faces considerable regulatory barriers. A major obstacle is the absence of harmonized national and international guidelines, alongside the lack of publicly accessible global phage banks. These gaps hinder standardized development, approval, and distribution processes. In the European Union, mycobacteriophages are classified as biological medicinal products and are subject to rigorous standards for safety, quality, and efficacy--comparable to those governing vaccines. This classification necessitates lengthy and expensive clinical trials. Regulatory ambiguity further complicates matters, particularly in the case of locally administered preparations such as aerosols. It remains unclear whether such therapies should be classified as innovative medicinal products or as personalized interventions, which affects their regulatory pathways. As a result, phage therapy in many EU countries is restricted to experimental or \"compassionate use\" contexts--permitted only in narrowly defined cases where conventional treatments have failed. Similar regulatory limitations are observed in the United States and parts of Asia, including India and China [11; 152; 153]. Compounding these issues is the absence of standardized procedures for phage isolation, purification, and quality control. Given the high specificity of phages to particular bacterial strains, therapies often require personalized formulations. This level of customization challenges reproducibility and limits integration into current pharmaceutical regulatory models.  
Unlike traditional antibiotics, phages are self-replicating biological entities capable of co-evolving with their bacterial hosts. This unique nature raises additional concerns regarding quality control and regulatory oversight [154]. Although agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have begun to develop specific regulatory pathways, these remain limited and inconsistent across jurisdictions [155]. One of the most pressing regulatory issues is also the personalized character of phage therapy. As phage cocktails often need to be tailored to individual patients' infections, traditional approval models based on fixed compositions are inadequate [156]. Moreover, therapeutic phage formulations may require frequent updates as bacterial resistance emerges, further complicating regulatory compliance.  
Despite these challenges, interest in mycobacteriophage-based treatments is growing rapidly. The rise in drug-resistant mycobacterial infections has fueled a surge in preclinical research, early-phase clinical trials, and patent activity [157; 158]. Establishing harmonized regulatory frameworks and production standards is essential for improving access, ensuring safety and efficacy, and facilitating broader clinical adoption.
## 1 Introduction
### SMART DShot: Our Solution Approach  
This paper introduces SMART DShot, a novel ML-enhanced framework that seamlessly integrates adaptive timing correction and anomaly detection directly within the DShot protocol stack. Figure 1 illustrates the overall system architecture, showing how ML-enhanced signal processing enables real-time attack detection and adaptive timing correction. Our approach synthesizes techniques from mathematical optimization, ML, and embedded systems engineering to enable real-time detection and mitigation of previously undetectable attack vectors.
# GPSMAP 66 sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning
## 3 Assessment of Observation Quality and Antenna Calibration  
In preparation of centimeter-accurate positioning, we analyzed the observation data with respect to the completeness, data quality of dual-frequency code and carrier-phase observations (Section 3.1), and the integer property of the carrier-phase observation in baselines to geodetic-grade equipment (Section 3.2). Since ambiguity fixing could be performed successfully, we were able to calibrate the antennas with respect to their mean phase center and with respect to phase-center variations (Section 3.3).
## 3 Quantification of the Model Uncertainty Associated to the Application of EBM  
As discussed before, an approximate result is obtained using the EBM. The EBM curve completely depends on the static pushdown curve (see Figure 2b). Considering it is an approximate approach, it is therefore important to quantitatively assess the performance of the EBM, i.e., quantifying its model uncertainty through comparison to the more accurate dynamic analysis results. To evaluate this model uncertainty, the following three major steps are applied (see also the flowchart for the model uncertainty quantification of the EBM shown in Figure 3):  
Figure 2: (a) Column–removed building and (b) energy balance in the energy–based method.  
1. Initially, the appropriate random variables and a column removal scenario are selected. 2. Subsequently, both the non-linear static and dynamic analyses are carried out. In case of the former, the pushdown curve is subsequently used to derive the EBM curve and the corresponding dynamic resistance for every realization (the left branch in Figure 3). In case of the latter, incremental dynamic analyses (IDA) are executed to accurately determine the dynamic resistance for every realization (the right branch in Figure 3). 3. Eventually, the model deviation for the EBM comparing to the IDA are calculated for both the resistances and the corresponding displacements: \[K_{EBM}=\frac{R_{EBM}}{R_{IDA}}\text{ or }\frac{D_{EBM}}{D_{IDA}}\] (2) where \(K_{EBM}\) is the ratio of EBM/IDA. \(R_{EBM}\) is the ultimate load-bearing capacity according to the EBM, while \(R_{IDA}\) is the ultimate capacity according to the IDA. \(D_{EBM}\) is the maximum displacement calculated according to the EBM, while \(D_{IDA}\) is the maximum displacement calculated from the IDA.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 2 Related Work
### Multiview-Based Methods  
The Multiview Convolutional Neural Network (MVCNN) [4] projects point clouds or 3D shapes onto 2D images, thereby subsequently employing Convolutional Neural Networks (CNNs) for processing the projected 2D images. This methodology integrates feature information from multiple viewpoints into a compact 3D shape descriptor via convolutional and pooling layers. These aggregated features are then fed into a fully connected layer for classification. [PERSON] [12] proposed the Multiview PointNet (MVPointNet), where the views are acquired through a Transformation Network (T-Net) [7] to generate transformation matrices that determine multiple views captured at identical rotational angles, thereby ensuring the network's robustness against geometric transformations.  
Despite the efficacy of projecting point clouds into multiple views for point cloud segmentation and classification tasks compared to conventional manual feature extraction methods, notable limitations persist. Firstly, predetermined viewpoints are required when projecting 3D point clouds into multiple 2D views. View variations result in differential contributions to the final shape descriptor; similar views yield akin contributions, whereas significantly distinct views offer advantages for shape recognition. Secondly, 2D projections are confined to modeling the surface attributes of objects, which are unable to capture the 3D internal structure adequately. This partial representation disrupts intrinsic geometric relationships within 3D data, thus failing to exploit contextual information comprehensively within 3D space and incurring information loss, which is particularly unsuitable for large-scale scenes. Furthermore, feature extraction via multiview approaches often necessitates pretraining and fine-tuning, thereby consequently escalating workload demands.
## 3 Processing Methods
### Spectrum Reconstruction  
The spectrum reconstruction process of the interferogram data cube consists of two steps: First, the interference information of the target gas in the interferogram data cube is subjected to radiance spectrum reconstruction, converting it into the target radiance spectrum representing radiant flux density. Subsequently, the radiance spectrum is modeled using a radiance transfer model to obtain the absorbance spectrum, which reflects the composition and concentration information of the target gas. The detailed steps are explained below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Spectral region & 1.5–5 μm \\ Spectral resolution & 4 cm\({}^{-1}\) \\ Detector array size & 640 \(\times\) 512 \\
1 st filter transmission range & 2030–2133 cm\({}^{-1}\) \\
2 nd filter transmission range & 2223–2268 cm\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of SMWIR-IFTS.  
Figure 1: (**a**) Internal core structure of SMWIR-IFTS; (**b**) external 3D structure of SMWIR-IFTS.
# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
## 2 Probabilistic Prediction Models
### Multi-Head Self-Attention Network Model  
The output Y of the BiLSTM layer is input into the multi-head self-attention (MHSA) layer, which enhances the performance of the model by computing multiple attention heads in parallel. Each attention head learns different information through different linear transformations, thereby capturing a variety of different contextual relationships and dependencies in the sequence. In this paper, there are h attention heads. First, the sequence Y is linearly transformed using different weight matrices to generate query (\(Q_{i}\)), key (\(K_{i}\)), and value (\(V_{i}\)) vectors. Each attention head has an independent weight matrix. The formulas for calculating the query, key, and value vectors are as follows:  
\[Q_{i} =YW_{Q_{i}} \tag{1}\] \[K_{i} =YW_{K_{i}}\] (2) \[V_{i} =YW_{V_{i}} \tag{3}\]  
The value of \(i\) ranges from 1 to h, and \(W_{Q_{i}}\), \(W_{K_{i}}\), and \(W_{V_{i}}\) are weight matrices.  
The self-attention mechanism is then executed on each attentional head to obtain the output of each \(head_{i}\), calculated as follows:  
\[head_{i}=Attention(Q_{i},K_{i},V_{i})=Soft\text{max}(\frac{Q_{i}K_{i}^{T}}{ \sqrt{d_{k}}})V_{i} \tag{4}\]  
where \(d_{k}\) is the dimension of the key vector used to scale the scores.  
Figure 1: BiLSTM network architecture.  
Finally, the outputs of all the attention heads are concatenated to obtain the output \(M\), as shown in Equation (5).  
\[M=MultiHead(Q_{i},K_{i},V_{i})=Concat(head_{1},\dots,head_{h})W_{O} \tag{5}\]  
where \(W_{O}\) is the output linear transformation matrix.  
Through the above formulas and steps, the MHSA can process the output sequence Y of the BiLSTM layer in parallel. Each attention head of the MHSA layer learns features in different subspaces, which enables the model to understand the relationships in the input sequence from different perspectives. By merging the attention information from different heads, the model can comprehensively consider different types of contextual information and more comprehensively understand the dependencies in the sequence. Compared with single-head attention, the multi-head attention mechanism obtains richer representations by focusing on different parts multiple times, thus reducing information loss and helping capture more details and semantic information. This mechanism plays a vital role in the probability prediction model presented in this paper. The output of the MHSA layer is \(M\), whose dimensions are (batch_size, timesteps, lstm_size \(\times\) 2).
## 3 Results
### Antimicrobial Activity  
The in vitro antimicrobial activity of RGEO against nine bacteria and fungal strains was evaluated qualitatively and quantitatively by the presence or absence of inhibition zones, MIC, MBC, and MFC values. The diameters of the inhibition zone of RGEO, which include the diameter (6 mm) of the paper disk against the microorganisms tested, are shown in Table 6. The diameters of the inhibition zone induced by RGEO against the tested microorganism strains ranged between 15.21 \(\pm\) 0.14 mm and 20.61 \(\pm\) 0.21 mm, suggesting that the oil exerts low to moderate antimicrobial effects. The results revealed that S, pyogenes, S, aureus, and S. mutans were the most susceptible tested strain to the RGEO action, followed by _C. albicans \(>\) C. parasilosis \(>\) E. faecalis \(>\) P. aeruginosa \(>\) E. coli \(>\) K. pneumoniae \(>\) S. enterica \(>\) S. flexneri._ Our results agree with previous studies [6, 45, 49], which reported that RGEO exhibited antimicrobial activity against S. aureus, _E. faecalis, E. coli, K. pneumoniae_, and _C. albicans_. The recorded MICs, MBCs, and MFCs for the tested strains were 1.25, 2.5, and 5 mg/mL, respectively. According to [PERSON] et al. [60], a strong MIC EOs can hold up to 0.5 mg/mL, moderate for MIC 0.6-1.5 mg/mL, and low for MIC above 1.5 mg/mL. The RGEO exhibited a moderate MIC for S. mutans and S. pyogenes and showed low activity against the rest of the analyzed bacteria. Overall, RGEO showed low efficiency in inhibiting the Gram-negative strains compared to Gram-positive strains, following previous studies [6, 27, 49, 61, 62]. These differences in susceptibility could be associated with different rates of penetration of EO constituents into the cell wall and cell membrane structures. Therefore, the ability of EO to disrupt the permeability barrier of cell membrane structures and the accompanying loss of chemiosmotic control are the most likely reasons for its lethal action [63].