esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the performance analysis of the LCAA, LCPAA, LRAA, and LCCAA beamformers is compared with the ODL, FDL, VDL, and NVL techniques. The proposed robust NVL technique showed a better performance. The conventional technique is applied to LCAA, LCPAA, LRAA, and LCCAA, but it cannot detect the interference and an optimal technique has been applied to peak the actual signal and detect the interference. This proposed beamformer has been further analyzed by applying robust techniques, namely ODL, FDL, and VDL, which reduce the beam width. The LCCAA beamformer outperforms others, with a beam width of \(12.55^{\circ}\) and a sidelobe level of \(-15.1\) dB in the conventional techniques. With an output power of \(-85.59\) dB, LCCAA surpasses other beamformers with the optimal method. In the robust FDL approach, the LCCAA beamformer has a beam width of \(10.3^{\circ}\) at an output power \(-20\) dB and a sidelobe level of \(-1.35\) dB, which is better than the other beamformers. In the robust ODL approach, the LCPAA beamformers have a greater beam width performance than the other beamformers. With an output power of \(-20\) dB and a sidelobe level of \(-15.1\) dB, the LCPAA beamformer has a beam width of \(12.26^{\circ}\). The LCPAA beamformer leads other beamformers in terms of beam width in the robust VDL method, with a beam width of \(12.51^{\circ}\) at an output power of \(-20\) dB and sidelobe level of \(-15.1\) dB. In the robust NVL approach, the LCPAA beamformer outperforms the others with a beam width of \(12.53^{\circ}\) at an output power \(-20\) dB and sidelobe level of \(-17.1\) dB. Moreover, the NVL technique provides the least beam width of \(33.11^{\circ}\). The NVL approach SINR outperforms the others, with \(1^{\circ},2^{\circ}\), and \(3^{\circ}\) disparities, the SINR of the LCAA beamformer is \(40.30\) dB, \(40.23\) dB, and \(40.11\) dB; the SINR of the LCPAA beam-former is \(40.29\) dB, \(40.18\) dB, and \(39.98\) dB; the SINR of the LCAA beamformer is \(40.29\) dB, \(40.18\) dB, and \(39.99\) dB; and the SINR of the LCCAA beamformer is \(40.29\) dB, \(40.18\) dB, and \(39.99\) dB, respectively. All of the beamformers with no disparity have an SINR of \(40.45\) dB. The LCPAA beamformer performs better in the NVL robust approach, with output powers of \(-0.01955\) dB, \(-0.078\) dB, and \(-0.178\) dB, respectively. Finally, analyzing the simulation results, we can conclude that the proposed NVL technique is better than any other technique and provides an optimum performance for reducing the beam width, sidelobe level, and interference cancellation.
## 2 Materials and Methods
### Effect of the Generated (Reactive) CAP Species on the Biofilms
#### Depth of Penetration of the Generated CAP Species  
To visualize the biofilms before and after CAP treatment, 3D images (at zoom = 1\(\times\) or 2\(\times\)) were taken using a Nikon A1R confocal laser scanning microscope (CLSM). Fluorescent dyes were used to visualize both the biofilm-associated cells and the matrix. Calcofluor white (Sigma-Aldrich, St. Louis, MO, USA) was used to stain the extracellular (\(\beta\)-)polysaccharides present in the biofilm matrix [33]. For the visualization of the cells, a distinction was made between living and dead/damaged cellsusing SYTO9 (green) and propidium iodide (red) (LIVE/DEAD(tm)BacLight(tm) Bacterial Viability Kit for microscopy, Thermo Fisher Scientific, Waltham, MA, USA). SYTO9 is able to enter both living and dead cells, while propidium iodide is only able to penetrate dead (or injured) cells with a damaged cell membrane [34]. Therefore, 2D images at certain depths of the biofilm can provide information concerning the ability of the specifically generated CAP species to penetrate into the deeper layers of the biofilms. Moreover, the CLSM images can be used to examine the effect of CAP on the structure of the model biofilms.  
For this specific experiment, biofilms were (i) removed from the incubator, (ii) rinsed three times with sterile PBS solution, (iii) allowed to dry, (iv) CAP treated (if applicable), and (v) stained with a mixture of the three different stains. The protocol used for staining was adapted from [PERSON] et al. [35]. The authors refer to the study of [PERSON] et al. [17] for a detailed explanation of the applied chemicals, concentrations, and incubation times for CLSM visualization of the studied biofilms. Afterwards, biofilms were covered with a cover slide (35 mm, No. 1.5 glass thickness, MatTek In Vitro Life Science, Bratislava, Slovakia), which was glued on the Petri dish with nail polish and protected from sunlight. During the visualization of the biofilms, calcofluor white was excited with a 405 nm laser and its emission was measured between 425 and 575 nm. For SYTO9 and propidium iodide, (i) excitation wavelengths of 488 and 560 nm were used and (ii) emission wavelengths were captured at 500-550 and 570-620, respectively. Images were taken using NIS Elements and further analyzed using ImageJ.
# Improved U-Net++ with Patch Split for Micro-Defect Inspection in Silk Screen Printing
## 2 Related Work  
### U-Net and U-Net++  
U-Net is an end-to-end, fully convolution-based model proposed for segmentation. The sliding window method used in existing CNNs has two disadvantages. The first is the unnecessary redundancy of overlapping patches and the second is the tradeoff between localization accuracy and context use. U-Net is divided into a contraction path, which is the left part where the size of the activation map decreases based on the center, and an expansion path, which is the right part where the size of the activation map increases, as shown in Figure 1. There are many similarities to a convolutional autoencoder (CAE) [21; 22]. CAE is used in various fields such as generator models, denoising, super-resolution, and self-supervised learning, by encoding a small feature map through a convolution layer and then returning to the original size using a deconvolution layer [23]. However, the utility of CAE is limited because of the performance limitations of the decoder in upscaling. However, in U-Net, this limitation allows the capturing of context of the CNN image in the contraction path and the expansion path upsamples the reduced feature map to increase it to a size similar to the original image, whereby a segmentation map with accurate location information can be obtained [24]. The symmetric architecture with skip-connection allows precision analysis with increased upscaling ability, as applied in the biomedical field.  
As shown in Figure 2, U-Net++ differs from U-Net in two major ways. The first is the redesigned skip pathways. Although the original U-Net has skip-connection, U-Net++ borrows the idea of DenseNet and connects the semantic gap between the encoder and decoder. The second method is deep supervision. This method averages the output of each branch and uses it as a result. U-Net++ does not use the skip-connection as a simple add operation as in U-Net, but adds a layer to the connection to further improve the upscaling ability, enabling precise analysis. Inference can be implemented at high speed. In particular, the convolution layer, which is the first layer of the ImageNet pretraining model, requires color information for data treated in three channels.
# On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
## 1 Introduction
The investigations focus on the development of an optimal scheduling strategy and the evaluation of the impact of integrating GENESIS into VGOS experiments on the estimation of geodetic parameters. In a previous study, [PERSON] et al. [18] integrated observations of LAGEOS-1/-2 into VLBI experiments with a legacy S/X antenna network. The authors notice no negative impact on the estimation of geodetic parameters. In comparison, this work significantly enhances the investigation of the integration of GENESIS by considering its actual orbit, utilizing a modern VGOS antenna network, and conducting comprehensive scheduling. In contrast to prior studies, this investigation addresses the trade-off between the negative impact of GENESIS on geodetic parameter estimates and the estimated frame tie accuracy between the VLBI and GENESIS frames. The frame tie accuracy is evaluated in the form of station position estimates from observations of GENESIS. It is established from 24 h experiments as well as over an extended period of up to 12 months. Section 2 outlines the concept of this work, while Section 3 presents the methodology. Section 4 shows the results, which are then discussed in Section 5.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 3 Related Work  
In this section, we review the existing receiver-initiated handshaking MAC protocols and survey the related work on the network traffic prediction.
## 3 Results
### Comparison of HO-ADRC and PID Control Performance with Sinusoidal Speed Signal over Zero
As can be seen from Figure 10c, the residual disturbance using HO-ADRC is 0.001045 rad/s; in contrast, the residual disturbance using the PID method is 0.002562 rad/s. The denoising ability of the HO-ADRC is also better than PID in Figure 10d. Therefore, in the case of the same \"maximum ability\", the HO-ADRC is better than PID. The HO-ADRC has a fast response speed and almost no overshoot when tracking square wave signals. When tracking the sinusoidal speed signal, using the HO-ADRC has less tracking bias and a better ability to suppress perturbations. The tracking accuracy of the control system is increased from 0.146 rad/s to 0.077 rad/s, with an improvement of 89.61%. The disturbance inhibition effect is increased from 0.002562 rad/s to 0.001045 rad/s, an improvement of 145.17%. It can be seen from the above simulation verification that the classical PID is inferior to the HO-ADRC in both stability, accuracy, and rapidity.
## 4 Methodology of APSO-LS  
This section presents the proposed methodology for dynamic UAV task allocation and path planning with energy management. The approach integrates a rolling horizon framework with an Adaptive Perturbation Particle Swarm Optimization algorithm with Local Search (APSO-LS). The rolling horizon framework enables the system to adapt todynamically evolving task demands, while the APSO-LS algorithm efficiently optimizes task allocation and path planning at each stage, considering both mission makespan and energy consumption.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.12 Smart Wireless Climate Sensor Node  
The proposed smart wireless climate sensor node [30] was designed for indoor comfort quality-monitoring applications. Pertaining to the processor and memory specifications, the node was developed around the ATMEGA-4808 microcontroller with 48 kB of flash memory and 256 bytes of EEPROM. The Wi-Fi module (WINC15100) was utilized for communication purposes.  
The node supports an SHT sensor module that is capable of measuring temperature and relative humidity levels. It is powered by either a supercapacitor or rechargeable Li-ion battery. A battery charger unit is also incorporated. Concerning the power consumption, the sensor node consumes 755.86 mW of power when transmitting both temperature and humidity, and 94.65 mW in the low-power mode.
In contrast, in [17] we devised a generalized design approach suitable for arbitrary irregular LDPC codes as defined in many standards (e.g., IEEE 802.11 or DVB-S2) without any modification of the LDPC code itself. We proposed an intermediate processing step called _message alignment_ which we first applied in the context of information-bottleneck channel quantizers for higher-order modulation schemes [18] and distributed information-bottleneck sensor design [19]. Caused by the high coding rate required in optical communication systems, respective LDPC codes incorporate very high node degrees. In this paper, we show that existing construction techniques cause an undesirable growth in memory demand when they are applied to high rate codes, due to a large number of look-up tables. In detail, this paper contains the following main contributions:  
* We extend the decoder construction framework from [13; 19] to be applicable to arbitrary irregular LDPC codes also with high code rates. * We introduce a novel tree-like look-up pattern. With this strategy, the relation between the number of look-ups required per iteration and node degree changes from linear to logarithmic. * We derive the underlying information-theoretic problem formulation and explain how the intermediate optimization technique called _message alignment_ can be incorporated. * We construct a 4-bit information bottleneck decoder for irregular LDPC codes with a code rate \(R=0.8\), where all conventional arithmetic in the nodes is replaced by simple look-up tables and only 4-bit integer-valued messages are passed. * Our proposed decoder achieves error-rates superior to min-sum decoding and only 0.15 dB away from double-precision belief propagation decoding. The paper is organized as follows. The information bottleneck method and LDPC codes are briefly reviewed in Section 2. In Section 3, we propose _message alignment_, resulting in a general information bottleneck decoder design approach for arbitrary irregular LDPC codes. In Section 4 we provide detailed insights concerning the internal structure of the look-up tables replacing the arithmetic operations. Based on these considerations, a more efficient design strategy is proposed. In Section 5, numerical simulations comparing the performance of our proposed decoder with several reference systems are provided. Section 6 concludes the paper.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR
### The Probabilistic Routing Table Set-Up Phase
#### 4.1.3 Residual Energy Distribution  
The sender node selects the next-hop with higher remaining residual energy to relay collected data, in order to balance the energy and ensure more lifetime for the network. This distribution has the aim of giving priority to nodes with higher residual energy. However, on the other side, this distribution depends on the location of the nodes, and the nodes with low battery may be used as a relay node. Let \(E_{j}\) be the residual energy of the node \(n_{j}\) such that; \(n_{j}\)_is_ the forwarder of \(n_{i}\); \(E_{i}=\left(E_{i,1},E_{i,2},\ldots,E_{i,m_{i}}\right)\), _and_\(n_{i}\) has \(m_{i}\) neighboring nodes denoted by the set \(\mathbb{N}_{i}\). Let \(\overline{E_{j}}\) be the normalized function of these nodes, given by Equation (9).  
\[\overline{E_{j}}=1+\left(\frac{E_{j}}{E^{*}}\right)\forall n_{j}\in\mathbb{N} _{i} \tag{9}\]  
Let \(\overline{E}_{i,j}\) be the mass probability of the residual energy distribution of forwarding data from the sender node to its neighboring nodes, such that the greater probability is given to the nodes with greater residual energy, as expressed in Equation (10).  
\[\tilde{E}_{i,j}=\overline{E}_{i,j}=\frac{\overline{E}_{i,j}^{\gamma}}{\sum_{ l=1}^{m_{i}}\overline{E}_{i,l}^{\gamma}},\quad\forall n_{j}\in\mathbb{N}_{i} \tag{10}\]  
To determine the impact of the residual energy distribution on the other two distributions defined in the proposed PDTR, we defined in Table 1, a residual energy control parameter denoted by \(\gamma\) such that: \(\gamma\geq 0\), as shown in Equation (10).
## 2 Background and Related Work
### Deep Learning on 3D Point Clouds  
By providing a simpler, denser, and more close-to-reality representation, 3D point clouds are prevalent in representing both static and dynamic 3D objects. By definition, a 3D point cloud is a set of points \(\{p_{i}\}_{i=1}^{n}\) embedded in the 3D space and carrying both geometry and attribute information [2]. Given a Cartesian coordination system, the geometry information refers to the point position, which can be expressed as a coordinate tuple \(c_{i}=(x_{i},y_{i},z_{i})\). The attribute information is used to describe the visual appearance of each point, and it may have different formats according to various user cases, such as color value tuple \((R,G,B)\) and normal vectors \((n_{x},n_{y},n_{z})\).  
As a dominating technology in artificial intelligence (AI), deep learning on point clouds has been thriving with an increasing number of solutions to 3D point cloud applications, and typical examples are 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation [1]. Regarding 3D shape classification, the whole point cloud file is used to extract a global shape embedding, which is then input into several fully connected layers of the neural network to achieve the classification task [8]. In 3D object detection scenarios, a 3D object detector firstly processes the point cloud of a frame, and then, it produces a set of detected objects with 3D bounding boxes [9]. As a 3D object detection algorithm can detect the locations of target objects in the first frame, 3D object tracking methods can use the embedded rich information of point clouds to estimate their state in subsequent frames [10]. Given the understanding of the global geometric structure and fine-grained details of each point, 3D point cloud segmentation methods can be classified into three types: semantic segmentation, instance segmentation, and part segmentation [1].
# Effective RNA Regulation by Combination of Multiple Programmable RNA-Binding Proteins
## 3 Results and Discussion  
### RNA-Binding Properties of Programmable RNA-Binding Proteins  
Four PUF proteins, PUF (A), (B), (C), and (D), targeting different RNA sequences, RNA (a), (b), (c) and (d), respectively, were designed by assembling PUF repeats corresponding to each RNA base in order (Figure 1d). EMSA revealed the high affinity and specificity of PUF proteins to target RNA sequences. In this assay, the binding of PUF proteins reduced the mobility of the target RNAs, allowing the evaluation of RNA-binding abilities of proteins. PUF (A)-(D) proteins were expressed in _E. coli_ cells and purified via affinity chromatography using a Ni-NTA column. PUF (A), (B), (C), and (D) showed band-shifts for RNA (a), (b), (c), and (d), respectively (Figure 2, Figure S2a). The apparent dissociation constants between each PUF protein and corresponding RNA were 10.1 nM (PUF (A)/RNA (a)), 5.3 nM (PUF (B)/RNA (b)), \(<\)1 nM (PUF (C)/RNA (c)), and 9.1 nM (PUF (D)/RNA (d)) (Table 1, Figure S2b). Although the \(K_{\text{d}}\) values were somewhat different between PUFs, they were within the reported range of engineered PUF proteins (10\({}^{-10}\)-10\({}^{-8}\) M) [23; 31]. Importantly, all PUF (A)-(D) proteins showed high specificity to their corresponding RNA sequences (Figure S2a). RNA (a) showed clear shift bands only when incubated with PUF (A) but not with the other three PUFs. Similarly, RNA (b)-(d) showed clear shift bands only when incubated with their corresponding PUFs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\mathbf{K_{D}}\) **(nM) 1**} \\ \hline  & **RNA(a)** & **RNA(b)** & **RNA(c)** & **RNA(d)** \\ \hline PUF (A) & 10.1 \(\pm\) 1.4 & n.d. & n.d. & n.d. \\ PUF (B) & n.d. & 5.3 \(\pm\) 2.7 & n.d. & n.d. \\ PUF (C) & n.d. & n.d. & \(<\)1 & n.d. \\ PUF (D) & n.d. & n.d. & n.d. & \(9.1\pm 1.4\) \\ \hline \hline \end{tabular}  
* mean \(\pm\) SD, n.d.; not detected.  
\end{table}
Table 1: RNA binding of engineered PUF proteins to specific RNA sequences.  
Figure 2: RNA binding of engineered PUF proteins to target RNAs. In the electrophoretic mobility shift assay (EMSA), RNA oligonucleotides, RNA (a)–(d), were incubated with corresponding PUF proteins, PUF (A)–(D), respectively, at the following concentrations (left to right): 0, 3.7, 11, 33, 100, 300 nM, and the mixtures were electrophoresed in native PAGE. “F” and “B” indicate protein-free RNA and protein-bound RNA, respectively. RNA (a) showed two protein-free bands (shown by *).
## 3 Application of Hyperspectral RS into Plant RS and Military Actions
### Research Advances of Plant Hyperspectral RS Technology
obtained the spectral bands and first spectral derivatives of beetle-infected pine trees from hyperspectral images and completed a quantitative discriminant analysis of pine beetle damage [68]. [PERSON] et al. extracted three features (e.g., original spectrum, reflectance derivative, and vegetation index) of insect-infected pine trees in drone hyperspectral images and used a convolutional neural network to construct a recognition system that can distinguish healthy, infected, or dead pine trees [69].
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 4 Results and Discussion
### Energy Consumption  
Based on splits made in the \(EC\) data, prediction results are separately presented for \(EC_{\text{dense}}\) and \(EC_{\text{sparse}}\).
## 3 In Situ Monitoring Program  
### Principle of Monitoring  
A coupling monitoring technology of DOFS and MPBXs was used to realize real-time monitoring of the internal motion deformation parameters of whole columnar overburden in the process of coal mining. On account of its continuity and high sensitivity, DOFS can collect the micro-strain of any rock strata inside the whole borehole. However, since its non-deformability is rather limited, once the overburden deformation of a certain stratum in the borehole exceeds the strain limit of the optical fiber, it will be broken, thus failing to monitor the overburden deformation below the breakpoint. On the other hand, MPBXs cannot monitor the deformation of all strata in the borehole because of its discreteness, but it has the characteristics of a large range and is not easy to break by overburden deformation.  
Figure 2: The key strata distribution of LC1 borehole and No. 6 drainage hole based on logging data: (a) LC1 borehole; (b) No. 6 drainage hole.  
In this sense, MPBXs can continuously collect the overlying rock deformation data at the set stratum. The coupling in situ monitoring technology of DOFS and MPBXs can provide a good research method for mastering the movement law of overlying strata.
## 2 Materials and Methods
### Developed Model
#### 2.6.1 Hyperparameter Fine-Tuning  
Hyperparameter tuning was systematically conducted to optimize the performance of the IndoBERT-based ABSA model. The process involved experimenting with various configurations of batch size (16, 32, and 64), learning rate (2 \(\times\) 10\({}^{-5}\)), number of epochs (5 and 10), and dropout rate (0.1, 0.3, 0.5, and 0.7). The optimal configuration of the batch size 32, a learning rate of 2 \(\times\) 10\({}^{-5}\), 10 epochs, and a dropout rate of 0.4 resulted in the highest aspect extraction accuracy 0.97 and sentiment classification F1-score 0.95. Early stopping and cross-validation were employed to prevent overfitting and enhance model generalization. These hyperparameters were systematically tuned to achieve the best performance. Smaller batch sizes allowed for more granular gradient updates, while larger batch sizes improved computational efficiency during training. The learning rate of 2 \(\times\) 10\({}^{-5}\) was chosen based on preliminary experiments that showed stable convergence without overfitting or underfitting. Dropout rates were adjusted to randomly deactivate units in the network, mitigating overfitting and improving the robustness of new data.  
Experimental Setup and Results  
Experiments were conducted to identify the optimal hyperparameter configuration. Each combination was evaluated using standard metrics, including accuracy and F1-score, to determine its performance in aspect extraction and sentiment classification. The results of hyperparameter tuning are summarized in Table 10 below:  
The experiments achieved the best performance with a batch size of 32, a 2 \(\times\) 10\({}^{-5}\), 5 epochs learning rate, and a dropout rate of 0.4. This configuration improved aspect accuracy to 0.97 and sentiment F1-score to 0.95, showing significant gains compared to the baseline model.