esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Feature Extraction
### GlRLM and Textural Features
#### 2.3.1 GlRLM  
Run-length statistics extract the coarseness of a texture in the different directions. A run is defined as a string of consecutive pixels that have the same gray level intensity along a specific linear orientation. Fine textures contain more short runs with similar gray levels, while coarse textures have more long runs with significantly different gray levels.  
For a given image, the pair \((i,j)\) of a run-length matrix \(Q(i,j)\) is defined as the run-number of gray level \(i\) and run length \(j\), as described in Figure 9. Hence, the RLM measures how many times there are runs of \(j\) consecutive pixels with the same value, with j going from 2 to the length of the longest in a fixed orientation. Even though many GLRLMs can be defined for a given image, normally four matrices are computed, for the horizontal, vertical and diagonal directions. The matrix P has the size \((M\times N)\), where \(M\) is equal to the maximum gray level and \(N\) is the possible maximum run length in the corresponding image. The typical directions are \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\), and calculating the run-length encoding for each orientation will produce a run-length matrix.  
\begin{table}
\begin{tabular}{c l c} \hline \hline  & **Textural Features** & **Value** \\ \hline
1 & Energy & 0.1386 \\
2 & Entropy & 2.0915 \\
3 & Contrast & 0.9960 \\
4 & Correlation & 0.5119 \\
5 & Homogeneity & 0.7213 \\
6 & Sum average & 2.3260 \\
7 & Sum variance & 2.8829 \\
8 & Sum entropy & 1.5155 \\
9 & Difference average & 0.4980 \\
10 & Difference variance & 0.5542 \\
11 & Difference entropy & 1.0107 \\
12 & Information measures of correlation feature 1 & \(-\)0.3713 \\
13 & Information measures of correlation feature 2 & 0.7840 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GLCM features of an image.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 3 Methodology  
Our model, called Adaptive Dual-Modal Learning (ADML), processes EEG data through two complementary pathways. The first pathway analyzes the temporal patterns in raw EEG signals, while the second pathway examines visual patterns created from these signals using the Markov Transition Field technique. This dual-stream approach allows us to capture different aspects of seizure activity that might be missed by traditional single-stream methods. Figure 1 illustrates the architecture of our proposed model.  
Figure 1: Overview of the Adaptive Dual-Modal Learning (ADML) framework. The temporal stream (**top path**) consists of two parallel branches: 1. A Transformer encoder that captures long-range temporal dependencies in the raw EEG signal through self-attention mechanisms. The input EEG signal is first projected to a higher dimensional space (128-d) and combined with positional encodings to preserve temporal order information. 2. A frequency analysis branch is implemented as a Fully  
Convolutional Network (FCNN) that extracts spectral characteristics through hierarchical convolutional operations. This branch applies consecutive 1D convolutions with increasing receptive fields to capture multi-scale frequency patterns. The spatial stream (**bottom path**) processes the MTF images through the following: A ResNet backbone that extracts hierarchical spatial features through residual learning. The network progressively increases the receptive field while maintaining fine-grained information through skip connections. A Vision Transformer (ViT) that captures global dependencies in the image space by treating the image as a sequence of patches. Each patch is linearly projected and processed through multi-head self-attention layers.  
Our model consists of two main streams working in parallel: a temporal stream for processing raw EEG signals and a spatial stream for analyzing MTF images. The temporal stream employs a Transformer encoder with eight attention heads and three Transformer layers, each with a dimension of 128. A positional encoding layer is added to capture sequential information. For frequency characteristics, we use two 1D convolutional layers with kernel sizes of 3 and channel dimensions of 32 and 64, respectively. The spatial stream processes MTF images through a dual-path architecture. The CNN path utilizes a ResNet-18 backbone pretrained on ImageNet, with the final fully connected layer modified to output 128-dimensional features. The ViT path divides input images into 16 x 16 patches and processes them through six Transformer layers with eight attention heads. Both paths maintain the same output dimension of 128 to facilitate feature fusion.
## 2 Underwater Sensor Nodes
The authors are satisfied with the accuracy of the system compared to reference hydrometer readings. It had good response even in the noisy environment caused by sea waves. The Ocean Bottom Seismometer (OBS) presented by [PERSON] _et al._ in [7] has been designed to be used in long duration seismic surveys. It has low power consumption and it is able to store large data with high resolution and Signal-to-Noise Ratio (SNR). One of its key points is the noise level of the acquisition system. Throughout the deployment of the marine seismometer prototype, calibration standards (such as \"IEEE Std-1057 Standard for Digitizing Waveform Recorders\" and \"IEEE Std-1241 Standard for Terminology and Test Methods for Analog-to-Digital Converters\") have been used in order to let them know the uncertainties in the measurements Moreover, the geophone has been designed to obtain maximum sensitivity. In a seismic survey, a series of OBSs are placed on the seabed of the area under study, where they record either natural seismic activity or acoustic signals generated by compressed air-guns on the ocean surface, and the motion of the ocean floor in order to study offshore seismicity and to explore the Earth's crust. The resulting data sets are subsequently used to model both the earthquake locations and the crustal structure. The deployed OBS can be transported and manipulated in a small ship, and an oceanographic vessel is not needed. Moreover, it can be used in civil engineering, submarine surveying, and mine prospecting. The effects on ecological productivity and biogeochemical cycling can be evaluated by determining how land-use changes affect dissolved organic carbon (DOC) concentrations and bioavailability within aquatic ecosystems. In [8], [PERSON] _et al._ present a case study of a submersible UV-Vis absorbance spectrophotometer to determine stream organic carbon dynamics within a headwater catchment. The experiment was placed near Campbell River (on the eastern side of Vancouver Island, British Columbia, Canada). Field measurements were made before and after forest harvest. The system has high temporal resolution compared to traditional grab sampling and laboratory measurements, which allows resolving variations at small time scales and the observation of concentration changes over longer time periods, and thus demonstrating DOC concentration dynamics over the pre and post harvest periods. One of its main advantages is the ability to remotely monitor the system through a wireless connection. The system is able to react when environmental or power disturbances cause system interruption and data loss.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 3 Experimental Validation and Results Analysis
### Robustness Evaluation of the Improved Algorithm
#### 3.6.2 Comparative Analysis before and after Improvement  
To verify the robustness of our algorithm, comparative experiments were conducted between YOLOv5s and CCCS-YOLO on different weed datasets, using the same equipment and environment with identical parameter settings. Comparative results are presented in Table 5, while Figure 10 illustrates the Precision-Recall (PR) curve for the experimental process.  
In Table 5 and Figure 10, it is evident that on the sesame weed dataset, our improved algorithm exhibits significant improvement compared to YOLOv5s. There is a notable increase of 1.5% in [EMAIL_ADDRESS] and a 2.5% enhancement in accuracy. This demonstrates that our algorithm performs well on other weed datasets, confirming the robustness of the improved algorithm.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline YOLOv5s & 87.2\% & 57.3\% & 82.5\% & 81.2\% \\ \hline CCCS-YOLO & 88.7\% & 58.9\% & 83.1\% & 83.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative experiment on sesame weed dataset.  
Figure 9: Comparison of detection results in different environments. (**a**–**e**) represent five different soil conditions, respectively.
## 2 Related Research
### Estimation of the Muscle Contraction Force  
Monitoring the muscle contraction force is possible using the estimation method. Several studies have introduced potential modalities for estimating the muscle contraction force. A study compared the conventional method of Hill's musculoskeletal actuator type with the EMG-based estimation method for reaching and grasping a cylinder object [28]. They confirmed that the EMG-based estimation, a more direct approach, showed promising performance. A study developed an estimation system using joint angles from goniometers and EMG signals [29]. Another study combined EMG and joint angle to predict muscle force [30]. The aforementioned studies have shown that EMG is a viable modality due to its direct relationship with muscle activity. Nevertheless, EMG-based approaches are limited by the precision of the recording, as they have a low signal-to-noise ratio (SNR) and a complicated self-calibration procedure. Furthermore, EMG installation is limited to experts, which makes it less practical for patients to use at home.
## 3 Results and Discussion
### Vertical Density Profile  
As seen in Figure 12, vertical density profiles of the cup-plant PB and the spruce PB were quite different. Single-layer cup-plant PB showed a flat density profile without distinct surface peaks. With spruce particles present in the surface layers, in an otherwise cup-plant PB, the density profile was also altered. It was found that the core layer density of the three-layer cup-plant PB was higher than that in the surface layers. It was evident that the density profile altered the mechanical performance of the cup-plant PBs. In [PERSON] et al. [49] it was found that the density profile was like the one measured for spruce PBs, which is beneficial to MOE and MOR. Likewise, a flat density profile measured for the single-layer cup-plant PB was commonly connected to reduced bending properties, as shown with own data. The three-layer cup-plant PB had a higher density in the core layer, which at first glance should provide better IB for the three-layer cup-plant PB. Interestingly, this is not shown with our data. It must be noted that commonly not only the core layer, but the transition zone (TZ) between the surface and core layer is more prone to fail during an internal bonding test [50] (Figure 6). As seen in Figure 3, the three-layer cup-plant PB's transition zone had a higher proportion of pores and could be seen as a \"weak layer\", with a density similar to the core layer of the single-layer cup-plant PB. Thus, internal bonding of the 3 LCP did not improve over the other panel types. The three-layer spruce particleboard had the highest surface layer density, which explains the high MOR and MOE values of this PB type.  
Figure 11: Thickness swelling (TS) and water absorption (WA) of produced particleboards (PBs). 3 LCP—three-layer cup-plant PB, 3 LSP—three-layer spruce PB, 1 LCP—single-layer cup-plant PB, and 1 LSP—single-layer spruce PB.
## 2 Materials and Methods
### Data Flow and Storage  
The data flow from the sensors to a storage solution is summarized in Figure 3. The sensor emits a payload with stored data to the gateway which is registered to a network server that manages sensors and gateways. The network server is The Things Stack, an open source LoRaWAN network server that can be hosted free of charge for small networks. All payloads are decoded to an ASCII readable format, i.e., JSON. The data are intercepted via a webhook. The webhook is a web-reachable page with a server-side script in PHP that redirects the data to a PostgreSQL database server. The data are stored in a JSON binary format (JSONB class in PostgreSQL) in a table with three columns: a timestamp column, a device id, and the JSON payload. The table does not have any indexing, triggers, or constraints in order to limit any overhead in data insertion. This table with raw data is then used to create derived tables using views and materialized views that provide the right formats and units for using them in further calculations. For example, in some  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Make, Model, (Abbreviation)** & **Sensors** & **N.** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Synetica enLink Air-X \\ (Syn-Air) \\ \end{tabular} } & temperature, relative humidity, barometric & \\  & pressure, ozone, oxygen, carbon dioxide, & \\  & nitrogen dioxide, volatile organic compounds & 3 \\  & (VOC), and particles as PM 1, 2, 5, 4, 10 & \\  & (quality measurement) & \\ \hline \multirow{2}{*}{\begin{tabular}{c} DecentLab DL\_ATM22 \\ (DL-Atm) \\ \end{tabular} } & temperature and wind speed and & \\  & direction vectors & 3 \\ \hline \multirow{2}{*}{\begin{tabular}{c} DecentLab DL\_PYR \\ (DL-Pyr) \\ \end{tabular} } & \multirow{2}{*}{pyranometers for measuring solar radiation} & \multirow{2}{*}{7} \\  & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Iotsens Sound Level \\ (Iot-Snd) \\ \end{tabular} } & noise level & 3 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Davis Pro weather station \\ (DP-Wst) \\ \end{tabular} } & temperature, barometric pressure, humidity, & \\  & rainfall, solar and UV radiation, wind speed, and & 1 \\  & wind direction & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Sensedge Senstick \\ (Sen-Stk) \\ \end{tabular} } & temperature, humidity, and acceleration & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of sensors and models, with the number (N) installed in the garden.  
Figure 2: Three sensors positioned at 1.5 m with reference to Table 1 from left to right: Sen-Stk, DL-Pyr, DL-Atm. Circles define the specific position of each sensor model.  
sensors barometric pressure is provided in the unit of inches of Hg (mercury) and further processing requires millibars.
###### Abstract  
Rock temperature reflects the adjustment in crustal stress, and the fluctuation of ionospheric electron concentration is closely related to short-term disturbances of the stress field. Their coupling may reveal short-term effects before strong earthquakes. This study explores the rock temperature changes and mechanical-electrical coupling in the lithosphere-ionosphere before the Jiashi \(M_{\text{s}}\)6.4 earthquake on 19 January 2020. The observed data were detrended by general polynomial piecewise fitting; three observation points within 150 km of the epicenter were found to show significant temperature fluctuations in the 15 days before the earthquake. The peak occurred synchronously five days before the earthquake, and the variation range was approximately \(10^{-3}\) orders of magnitude. Five days before the earthquake, the electromagnetic satellite Zhangheng-1 synchronously observed an anomalous electron concentration in orbit near the epicenter, with a maximum value of \(2.01\times 10^{10}\) m\({}^{-3}\). The loading/unloading response ratio (LURR) was calculated using small earthquakes within 100 km of the epicenter; it showed that the large changes in rock temperature and the ionosphere occurred at high LubRR, indicating high-stress accumulation in the region. Various anomalies appeared simultaneously and may indicate fault rupture, which may be caused by an acoustic-gravity wave, indicating a synchronous coupling between the lithosphere atmosphere and the ionosphere.  
Jiashi \(M_{\text{s}}\)6.4; rock temperature; ionosphere; earthquake anomaly; cross-layer coupling +
Footnote †: journal: Physics Letters B
## 1 Introduction  
This paper presents a survey of routing protocols, based on ant-like mobile agents, which are intensively used in Mobile _Ad Hoc_ Networks (MANET). MANET is a network that consists of wireless mobile nodes such as PDAs, laptop computers, and smart phones [1]. They have wireless communication capability that dynamically form a temporary network without using any existingnetwork infrastructure such as wireless access points connected to a wired network. The only infrastructure in MANET is comprised of the independent wireless communication interfaces on the devices. In such a circumstance, since the system lacks infrastructure such as routers, if some of the wireless devices are not within wireless range of each other, then multi-hop routing of data communication is necessary for messages to reach their final destination. A node that wants to start communication with other nodes that are not within its one-hop wireless transmission range has to request intermediate nodes to forward their communication packets to the destination. MANET is used in various applications including sensor networks and rescue and guidance systems in disaster situations [2; 3; 4].  
Current routing protocols have certain intrinsic shortcomings. Since proactive routing policies such as Destination Sequenced Distance Vector Routing (DSDV) continuously updates the routing tables, they tend to waste network resources just for exchanging large amounts of routing table data [5]. It can be a serious problem because the network resources in MANET are assumed not to be robust. Too much internal activity may restrict the capability to transmit communication data for the actual users. On the other hand, reactive routing protocols such as Dynamic Source Routing Protocol (DSR) performs the route discovering procedures upon transmitting demands and may produce unacceptable delays in establishing the forwarding route [6]. In order to ameliorate such disadvantage in reactive routing protocol, _Ad Hoc_ On Demand Distance Vector (AODV) was proposed as a result of integrating favorable features in DSDV into DSR [7].  
Ant-like mobile software agents, which are analogous to the ones used in the Ant Colony Optimization (ACO) framework, are employed for discovering network topologies and thus efficient routing in the networks [8; 9; 10; 11; 12]. Ant-like mobile agents are an effective means to discover the network topology in particular in circumstances such as MANET in which the network topology frequently changes [9]. Unlike a proactive routing protocol, routing based on ant-like agents does not require frequent exchanges of update messages for routing tables. As the population of the network becomes dense, an ant-like agent becomes more effective for load balancing in the network. Ant-like agents are a known means to mitigate congestion [13].
## 2 State of the Art  
### Model-Based System Engineering and Safety Assessment  
During system design, engineers create representations that they use for different intents. We study the consistency of system engineering and safety assessment representation in this work, especially model-based ones. System engineering is an interdisciplinary approach for designing and integrating complex systems throughout their life cycle, from their design to their end of life. It is achieved by representing the system's requirements, environment, life-cycle, functions, and architecture. Safety assessment aims at asserting that the system complies with its safety requirements. Whereas traditional approaches have been document-centered, industrial practices shift towards model-based approaches. Model-based approaches provide new tools that better consider complexity in system design. This change in modeling paradigm induces new engineering disciplines.
# Multi-Feature Guided Low-Light Image Enhancement
## 3 Materials and Methods
### Enhancement Network
#### 3.3.1 Low-Level Feature Extraction  
The enhancement network needs to consider the local and global features of the input image at the same time. Extracting local features of an image can better obtain information such as image contrast, lighting, and texture details, and extracting global features can obtain image brightness and scene information. The lighting information of the local features reflects the change of the local light, while the brightness of the global feature represents the overall style of the image, such as dark or normal brightness. Therefore, in order to better enhance the effect, we layer the network to better extract global and local information (see the right part of Figure 2). Before that, we designed a coding network to learn low-lever features.  
The encoding network receives the low-resolution version of the input image and combines the noise feature map obtained by the noise estimation network and the illumination map output by the illumination awareness network for low-level feature extraction. The coding network consists of five fully convolutional layers, followed by the ReLu function for nonlinear activation. It is worth noting that we use a combination of conventional convolution and dilated convolution for feature extraction, where the first to fourth layers use conventional convolution methods, the size of the convolution kernel is \(3\times 3\), and the step size is \(2\). For the last layer, we use dilated convolution with convolution kernel size of \(3\times 3\), interval of 2, and step size of 1. In this way, we can reduce the parameters of image and expand the receptive field of the convolution kernel.
# Multiple Facies Metamorphic Rocks: A Rare Example from the East Qinling Orogen, China
## 4 Results
### Zircon U-Pb Dating  
The zircon LA-ICP-MS U-Pb isotope data are reported in Table 2. Twenty-three spots were analysed on zircons from the gneiss, showing 9 to 693 ppm U, 9 to 987 ppm Th, and Th/U ratios of 0.05 to 1.53 (Table 2). The data yield spot ages between 794 and 376 Ma (Figure 5B). The concordant zircons define major age populations at 400-370 Ma, and 790-720 Ma, and the \({}^{206}\) Pb/\({}^{238}\)U ages are between 750 and 370 Ma (Figure 5A). The CL images show that most of the zircons have core-rim textures, with cores dated at 790-720 Ma that show oscillatory zoning with high Th/U ratios (>0.4). These Neoproterozoic ages for the cores are in agreement with recently published zircon U-Pb dates by [25]. The rims yield Paleozoic ages of 400-370 Ma and low Th/U ratios of 0.04-0.11 interpreted as metamorphic overgrowths. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Spot No.
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
## 3 Anti-Icing Mechanisms of SHSs  
Adopting diverse and tunable micro-nanostructures fabricated by ultrafast lasers, a large number of applications have been explored [48; 107; 108]. Among that, ULSASs have been extensively researched in recent years. Many new phenomena have been discovered to deepen the understanding of superhydrophobic anti-icing properties. In this section, we combine recent reports and our understanding to explain the relationship between superhydrophobicity and icephobicity and give the analysis of common failure factors of superhydrophobic anti-icing surfaces.