esa-sceva/satcom-chunk-collection huggingface dataset:
# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 3 Materials and Methods
### Ribosome Profiling Data Extraction
#### 3.1.4 Identification of Significantly Reproducible Ribo-seq Profiles  
Our strategy consists in inspecting each row of the \(p\)-value matrix. We define reproducible the Ribo-seq profiles referring to those rows featuring all the \(p\)-values below a chosen significance threshold. To cast our strategy into a more rigorous statistical framework, we exploit the False Discovery Rate (FDR) concept and the Benjamini-Hockberg (BH) correction method for multiple tests. In this experiment, for any given row of the \(p\)-value matrix, we set an FDR threshold of 0.01. This means that we accept that 1% of profiles are reproducible by chance. Then, we counted how many \(p\)-values in each row resulted significant according to the BH method, and we defined reproducible those Ribo-seq profiles associated with the rows where 80% of the \(p\)-values are significant. Following this strategy, we found that, out of 3588 genes that are common to the eight datasets, 40 genes, listed in Table 3, have a significantly reproducible Ribo-seq profile. \begin{table}
\begin{tabular}{c c c c} \hline  & **Dataset 1 vs. Dataset 2** & **Dataset 1 vs. Dataset 3** & **Dataset 1 vs. Dataset 4** \\ \hline
**alr** & 0.769298564 & 0.122368427 & 0.632263895 \\ \hline
**modB** & 0.165522551 & 0.056591384 & 0.601754757 \\ \hline
**cysZ** & 0.005770742 & 0.00011569 & 0.2021111 \\ \hline
**dfp** & 0.002343099 & 0.000384015 & 0.093624025 \\ \hline
**fruB** & 0.566785395 & 0.85548442 & 0.381131384 \\ \hline \end{tabular}
\end{table} 
Table 2: Excerpt of the \(p\)-value matrix. Each column corresponds to a pairwise comparison between two datasets while each row contains the gene ID. For the sake of readability, only three columns and five rows are reported here. As an example, Figure 3 shows the profile across all datasets of the gene ompC (EG10670). OmpC, also known as outer membrane (OM) protein C, is a porin of gram-negative bacteria tightly associated with the peptidoglycan layer. It has been recognized to have a crucial role in the non-specific diffusion of small solutes such as sugars, ions and amino acids across the outer membrane of the cell [39]. To highlight which specific regions within the Ribo-seq profiles are similar to each other, we built a _consensus sequence_. The consensus sequence is a character string representing the nucleotides of the reference ORF and, in Figure 4, it is colored red in those positions where a peak is present in at least 80% of profiles (i.e., the digitalized profiles values are \(+1\) and the ribosome proceeds slower), and green where a valley is located. The black color, instead, will be used in all other cases and the label assigned to these regions will be 0.
## 4 Results and Discussions
### Performance of Hybrid Filter under Perturbations in Balanced and Unbalanced Non-Linear Loads  
In the proposed power system model, the performance of the hybrid filter is investigated under balanced and unbalanced non-linear loads. Figures 8 and 9 show the steady-state performance of hybrid filters. In Figure 8, the steady-state performance is analyzed under balanced non-linear loads. The simulation results in Figure 8 shows the waveforms of load voltage, PMSG current, filter current, load current, and DC link voltage, and in Figure 8 shows the waveforms of mechanical torque, wind speed, actual and reference current of PV and SOC percentage. The SOC limits and the charging  
Figure 7: Dynamic performance under a change in wind speed and solar irradiations with the state of battery charge (SOC) percentage less than 100%. Simulation results showing (**a**) load voltage, wind turbine current, load current, filter current, real and reference PV current, colors blue, green, red represents phase a, b, and c for VL, similarly for IWT colors green, red, blue indicates -phase a, b, c; for iL red, blue and green for phase-a, b and c; Brown, red and blue for phase a, b and c of iFtr; blue, red shows IPV, IPVref. (**b**) mechanical torque, wind speed, DC link voltage, and SOC percentage.  
discharging rates should be considered as the energy constraints of the battery, 10%\(\leq\) SOC \(\leq\) 90%. In Figure 8a,b, a balanced non-linear load is connected at the load bus. The voltage and frequency are regulated under such large load perturbations. Simultaneously, the hybrid filter is operated under an unbalanced non-linear load and is tied at a load bus. The voltage and frequency are regulated at their rated values. The simulation results are shown in Figure 9a,b. The percentage of THD analysis is provided in Table 1, and a comparative graphic showing the harmonic distortion analysis of the proposed system with DWPT is shown in Figure 10. From the comparison chart and table, it is observed that the hybrid filter using the proposed technique provides better performance compared to the conventional one for improvement in PQ.  
Figure 8: Steady-state performance under balanced non-linear loads showing (**a**) load voltage, WT current, filter current, load current, DC link voltage, the colors red, blue and green represents phase a, b and c (**b**) mechanical torque, wind speed, real and reference PV current, and SOC percentage colors blue, red represents IPV and IPVref.  
Figure 9: Steady-state performance under un-balanced non-linear loads showing (**a**) load voltage, WT current, filter current, load current, DC link voltage, colors red, blue and green represents phase a, b and c. (**b**) mechanical torque, wind speed, real and reference PV current, and SOC percentage colors blue, red indicates IPV, IPVref.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Scenario** & **DWPT** & **AMDWPT** \\ \hline Balanced load & 2.58 & 2.11 \\ Unbalanced load & 4.87 & 3.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total harmonic distortions (THD) analysis in percentage.
## 2 Materials and Methods
### Orbit Estimation with an Unscented Batch Filtering Method
#### 2.2.1 Sigma Points Sampling Strategy  
Symmetric sampling is a widely used strategy in UT-based algorithms, where \(2n+1\) sigma points are generated to approximate the probability distribution of an \(n\)-dimensional state vector. However, as the state dimension \(n\) increases, the computational cost rises sharply. Therefore, optimizing the sigma-point selection strategy becomes crucial, aiming to reduce the number of sigma points while maintaining the accuracy of the distribution approximation. [PERSON] and [PERSON] [32] showed that representing the posterior distribution of an \(n\)-dimensional state distribution vector requires at least \(n+1\) sigma points. [PERSON] et al. [33] conducted a comparative analysis on the performance of UT and UKF using various sigma point sets, including the symmetric set, the rho-minimum sigma set, and a new minimum sigma set. Considering the trade-offs between computational cost, UT performance, and the complexity of parameter tuning, this study adopts the rho-minimum sigma set introduced by [PERSON] et al. [31] as the preferred sampling strategy. For clarity and reference, the construction of the \(n+1\) sigma points for an \(n\)-dimensional state vector with mean \(\overline{\mathbf{X}}\) and the covariance matrix \(\mathbf{P}_{xx}>0\) is presented in the following:  
1. Choose the tuning parameter \(\omega_{0}\) such that \(0<\omega_{0}<1\);
2. Determine the weight matrix \(\mathbf{W}\) with \(\omega_{1}\); i = 1,..., n \[\rho=\sqrt{\frac{1-\omega_{0}}{n}},\] \[\mathbf{C}=\sqrt{I_{n}-\rho^{2}[1]_{n\times n}},\] \[\omega_{l}=\left(\omega_{0}\rho^{2}\mathbf{C}^{-1}[1]_{n\times n}\Big{(}\mathbf{C}^{T }\Big{)}^{-1}\right)_{ii},\text{for }i=1,\ldots,n\,,\] (5) \[\mathbf{W}=\left[\begin{array}{ccc}\omega_{1}&0&0\\ 0&\ddots&0\\ 0&0&\omega_{n}\end{array}\right].\] (6)
3. Construct the sigma set \[[\mathbf{x}_{0}\ \ \ldots\ \ \mathbf{x}_{n}]=\left[-\sqrt{\mathbf{P}_{xx}} \frac{[\rho]_{n\times 1}}{\sqrt{\omega_{0}}}\ \sqrt{\mathbf{P}_{xx}}\mathbf{C}\Big{(}\sqrt{\mathbf{W}} \Big{)}^{-1}\right]+[\overline{\mathbf{x}}]_{\text{L:}n+1}.\] (7) The square root of a matrix is calculated through eigenvalue decomposition.
# Efficient Fine-Tuning of Large Language Models via a Low-Rank Gradient Estimator
## 4 Method
### Low-Rank Gradient Estimator  
The core idea of LoGE is to replace the full-rank weight in the backward pass with a low-rank representation and update one low-rank matrix. In the backward pass, we substitute the full-rank matrix with its low-rank approximation to compute the gradient of the activation. However, updating both components of the low-rank approximation prevents the weight update from being directly represented by the updates of the components. Given the low-rank approximation \(\mathbf{BA}\), we can formulate the update of weight \(\delta_{\mathbf{W}}\) as follows:  
\[\delta_{\mathbf{W}}=(\mathbf{B}+\delta_{\mathbf{B}})(\mathbf{A}+\delta_{ \mathbf{A}})-\mathbf{BA}=\delta_{\mathbf{B}}\mathbf{A}+\mathbf{B}\delta_{ \mathbf{A}}+\delta_{\mathbf{B}}\delta_{\mathbf{A}} \tag{7}\]  
The \(\delta_{\mathbf{W}}\) requires full-rank storage, making this approximation memory-inefficient. Inspired by [17], we try to fix one low-rank matrix and only update the other. This approach maintains the update in a low-rank format, thereby reducing the memory usage required for optimization states and gradients. For simplicity in computation, we frame thelow-rank update in the adapter format, following the design principles of LoRA [13]. Given a full-rank weight \(\mathbf{W}\in\mathbb{R}^{m\times n}\) and \(n\geq m\), the calculation for LoGE is defined as follows:  
\[\mathbf{O}=(\mathbf{W}_{0}+\mathbf{B}\mathbf{A})\mathbf{x}\quad\partial\mathbf{ O}/\partial\mathbf{x}=\delta_{\mathbf{O}}\mathbf{B}(\mathbf{C}+\mathbf{A}) \tag{8}\]  
Here, \(\mathbf{B}\in\mathbb{R}^{m\times r}\) and \(\mathbf{A}\in\mathbb{R}^{r\times n}\) represent the low-rank approximations of \(\mathbf{W}\) used during the backward pass, with \(\mathbf{A}\) fixed during training. The matrix \(\mathbf{C}\in\mathbb{R}^{r\times n}\) is the trainable parameters and represents the updates to \(\mathbf{A}\). For cases where \(n<m\), we fix the \(\mathbf{A}\) and reformulate the calculations to the following:  
\[\mathbf{O}=(\mathbf{W}+\mathbf{C}\mathbf{A})\mathbf{x}\quad\partial\mathbf{O} /\partial\mathbf{x}=\delta_{\mathbf{O}}(\mathbf{B}+\mathbf{C})\mathbf{A} \tag{9}\]  
The subsequent sections will concentrate on the case where \(n\geq m\) to explore LoGE further. Orthogonal decomposition matrices can simplify the calculation of importance for structured sparsity. Therefore, we utilize Singular Value Decomposition (SVD) to decompose the matrix. Given the pre-trained weight \(\mathbf{W}_{0}\), this process can be formulated as follows:  
\[\mathbf{W}_{0}=\mathbf{U}\Sigma\mathbf{V}^{T}\quad\mathbf{B}=\mathbf{U}_{[idx] }\quad\mathbf{C}=\Sigma_{[idx]}\mathbf{V}_{[idx]}^{T} \tag{10}\]  
Here, \(idx\) refers to the selected index vector. The select strategy is discussed in the next subsection.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 3 Methods
### Loss Function  
The loss function for object detection and recognition comprises three components: regression loss, classification loss, and confidence loss. Regression loss is typically measured using IoU-based metrics (e.g., GIoU loss), while classification and confidence losses often  
Figure 6: Structure of the WTConv module.  
employ Cross-Entropy Loss or Focal Loss. For regression loss, we utilize the Generalized Intersection over Union (GIoU) loss [39], defined as  
\[\text{L}_{\text{Giou}}\ =1-\text{IOU}+\frac{|\text{C}\smallsetminus(\text{A}\cup \text{B})|}{|\text{C}|} \tag{1}\]  
where IOU represents the intersection-over-union ratio between the predicted bounding box A and the ground truth box B; C denotes the smallest enclosing convex shape covering both A and B. Giou extends the standard IOU by introducing a penalty term for non-overlapping boxes, guiding the predicted box to converge toward the ground truth even when no overlap exists initially.  
In ship detection tasks, class imbalance is prevalent, where the number of samples across classes (e.g., background vs. rare ship types) varies significantly. Traditional Cross-Entropy Loss tends to overfit majority classes (e.g., background) while underperforming on minority classes (e.g., specific ship targets). To address this, we introduce Adaptive Threshold Focal Loss (ATFL) [40] into YOLOv11's loss computation. The ATFL formula is defined as  
\[\text{ATFL}=\begin{cases}-(\lambda-\text{p}_{\text{t}})^{-\ln\left(\text{p}_{ \text{t}}\right)}\log(\text{p}_{\text{t}})&\text{p}_{\text{t}}\leq 0.5\\ -(1-\text{p}_{\text{t}})^{-\ln\left(\text{p}_{\text{t}}\right)}\log(\text{p}_{ \text{t}})&\text{p}_{\text{t}}>0.5\end{cases} \tag{2}\]  
where \(\gamma\) is a modulating factor. Derived from Focal Loss, ATFL assigns higher weights to hard-to-classify samples and lower weights to easy samples, mitigating class imbalance. Unlike standard Focal Loss, ATFL incorporates an adaptive threshold mechanism that dynamically adjusts the focal parameters based on class distribution during training. Specifically, ATFL calculates the sample proportion for each class and adaptively scales the modulating factor, ensuring minority classes receive greater emphasis in loss computation. By integrating ATFL into the loss module, the model prioritizes feature learning for minority classes while maintaining stable overall detection performance.
# Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
## 3 Results  
Figure 4 shows radar images after range compression (before azimuth compression) with varying baselines \(B\). Parabolic signals returned from three corner reflectors are clearly seen when \(B\) is small (0.27 m) while reduced at larger \(B\) (0.5 m and 1 m) due to the strong uni-directional backscattering characteristics of the trihedral corner reflectors (CR). Coherent integration length (\(X\)) is also reduced accordingly with increasing \(B\) as described in Equation (6). The power of the returned signal was also reduced due to the non-squint setup of this experiment, i.e., the footprint of Tx and Rx did not match completely with each other. This is partly intended to simulate the satellite bistatic configuration with no designated target area or squint mode.  
Figure 3: Doppler rate as a function of range with different baselines \(B\).  
Figure 5 is the comparison of SAR-focused images generated by (a) RDA and (b) BPA when \(B\) = 0.27 m. Rows of classroom chairs and desks, a teacher's computing desk and a blackboard hung on the wall have been successfully imaged. Three corner reflectors on top of desks were also clearly shown. Image degradation occurs at near range (less than 4 m) of RDA due to the approximation used for this frequency-domain focusing algorithm, as described in the previous section.  
Figures 6 and 7 also show the same images with increasing \(B\). Image degradation at the near range worsened with increasing \(B\), and the power and azimuth resolution were  
Figure 4: Range-compressed images for (**a**) \(B\) = 0.27 m, (**b**) 0.5 m, and (**c**) 1 m. Note: trihedral corner reflector signals (CR) are completely lost when \(B\) = 1 m.  
Figure 5: Bistatic GB-SAR focused image by (**a**) range-Doppler algorithm (RDA) and (**b**) back-projection algorithm (BPA) for \(B\) = 0.27 m. CR represents trihedral corner reflector.  
also reduced due to the reduced coherent integration length. Signals from the three CRs were lost completely when \(B=1\) m (Figure 7) as expected, while those from other objects such as chairs, desks, and blackboard, still remained strong due to their bi-directional scattering property.
## 2 Literature Review  
There are five major categories for the target tracking solutions [1]: tree-based tracking, cluster-based tracking, prediction-based tracking, mobicast message-based tracking, and hybrid tracking. Studies have shown that the cluster-based tracking algorithms have better network scalability and resource utilization compared with those in other categories. Prediction-based tracking rely on tree-based and cluster-based tracking in addition to the prediction method, but the tracking accuracy cannot be guaranteed. Mobicast message-based tracking method depends on prediction, which is a multi-cast method in which message is delivered to a group of nodes that change with time according to estimated velocity of moving entity. Scheduling strategies vary in target tracking protocols and time synchronization may be needed to set the wake up and sleep timings of sensor nodes.  
Since the proposed approaches fall into the category of cluster-based tracking, we focus on the research results of this category. The following subsections briefly describe the current literature of target tracking with respect to the number of tasking sensors.
## 8 Security Considerations
In this case, we propose that the 6 BR, which takes the role of the collaborator on the proposed distribution model, check for every incoming packet from the HIP peers that the sequence number is strictly higher from the last received sequence number. Elsewhere, the 6 BR (collaborator) drops any received packets with a previously used sequence number. _Man in the middle attack (MITM)_: The leakage of the digital signatures in HIP DEX can cause an MITM attack for the R1 packet. This attack is treated on the R2 packet with the state machine. We reinforce our solution by the configuration of the HIT Access Control List (ACL) in the initiator, the responder, the CTTP, and all network appliances (router, firewalls). ACLs define the legitimate list of HITs and HIs. In addition, we propose to add the password used to authenticate the responder and the CTTP (see Section 4.1.2) in the ACL rather than storing it on a database on the base station. Thus, HIP peers discard any packet if the sender's HIT (or HI) is not assigned in the ACL. Lunching a link-layer attack can be lessened by the minimization of the frame length and of the communication overheads we worked on in this paper. This optimization on the communication between the initiator and the responder lets the attacker not having enough time to capture exchanged packets on the communication channel. In addition, the adversary can not hold the HIT's responder, since we proposed to revoke it. Hence, the initiator, when receiving the R1 packet from the adversary, identifies that it is not from the legitimate responder and discards it. For more precision, an indicator must be communicated between the HIP peers to indicate the removal of the HIT's responder so that a malicious packet containing a HIT value will automatically be discarded by the initiator. _Node capture_: The collaborator 6 BR, on the LC-DEX solution, has a crucial role and if an attacker succeeds to attack it by a node capture attack, the hole network is damaged. Hence, we propose to not fix a collaborator for eternity. The collaborator on an IoT HIP domain changes dynamically in a short certain period. So, if an attacker has access to the collaborator, he loses authority on it after a very short period.
## 4 Case Study  
### Case Background  
This study focuses on the design method of the segmented lining structure based on a CAES project in Inner Mongolia. The proposed underground gas storage facility is designed as a tunnel structure with a circular cross-section, a clear internal diameter of 10 m, and a working depth of 146 m. According to the geological survey report, the surrounding rock is classified as a medium-quality rock mass, with a deformation modulus of \(E=6\) GPa, a cohesion value of \(c=1.5\) MPa, and an internal friction angle of \(\varphi=45^{\circ}\). The rock mass has a Poisson's ratio of \(\
u=0.28\) and a density of \(\gamma=26.2\) kN/m\({}^{3}\). Additionally, the hydraulic fracturing stress measurements show the presence of tectonic stress in the area, with a vertical stress of 3.9 MPa, a horizontal principal stress of 3.5 MPa, and a lateral pressure coefficient \(\lambda\) of 0.9.
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 3 Evolution of Substation Architectures and Digital Modernizations for Smart Substations
### Comparasion of a Conventional P&C Systems vs. CPAC Approach
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criteria/Test Feature** & **Conventional P\&C System** & **CPAC Approach** \\ \hline Flexible Configuration & Flexibility is limited by the need to configure each device individually. & High flexibility with rapid, centralized reconfiguration capabilities. \\ \hline Commissioning and Maintenance & Requires time and effort for each individual device. & Centralized configuration and maintenance streamline the process, enhancing efficiency. \\ \hline Startup Costs & Lower initial costs due to reliance on existing devices and mature technology. & Requires an initial investment in new technology and staff training. \\ \hline Life-Cycle Cost & High maintenance and upgrade costs due to the distributed nature of the system. & Centralization leads to cost reduction and life-cycle simplification. \\ \hline Reconfiguration & Time-consuming and costly; each device needs individual reconfiguration. & Quick and easy reconfiguration from a central point. \\ \hline Communication Infrastructure & Leverages existing infrastructures but may face capacity and speed limitations. & May require upgrades to support centralization and increased data traffic. \\ \hline Resistance to Change & Less resistance due to reliance on established technologies. & Potential resistance due to the need for adopting new technologies and modifying established processes. \\ \hline Protection and Control & Relies on discrete, distributed devices, each responsible for specific protection and control tasks. & Centralizes protection and control, consolidating multiple functions into a single management point for enhanced coordination. \\ \hline System Complexity & Simpler initial configuration, but maintenance and upgrades are challenging due to dispersed devices. & More complex initial configuration, but significantly easier maintenance and upgrades due to system centralization. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Conventional P&C system vs. CPAC approach. Figure 10: Substation architecture alternatives. Redundancy protocols like PRP and HSR introduce zero recovery time upon failure for enhancing system reliability. Figure 11 illustrates this. Expanding on the concept of hybrid architectures, Figure 12 shows an example of hybrid architecture with Numerical Protection Relays (NPR). In this configuration, all relays must be capable of handling the Sampled Analogue Values (SAV) communication profile of IEC 61850-9-2. This enables them to function as merging units for an existing feeder. This hybrid architecture combines relays and merging units to provide process-level information to the CPAC unit, allowing for the implementation of separate process units for critical and redundant feeders to channel
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 3 Experimental Data Acquisition  
The experimental data were captured using a smartphone, and a total of 200 data images were collected from blister tablets with defects such as breakage, scratches, and stains. Figure 1 shows samples of each type of defect, and the dataset was expanded to 2000 using added noise, mirroring, and changing contrast. These were manually labelled using Labelme version 5.4.1 software and divided into a training set of 1200 sheets, a validation set of 400 sheets, and a test set of 400 sheets.