esa-sceva/satcom-chunk-collection huggingface dataset:
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 2 Methodology
### Method Overview
#### 2.2.3 Subregion Feature Extraction and Visibility Evaluation  
After obtaining the sub-regions, the features of the subregions are extracted to provide input variables for subsequent visibility estimation. The image of the sub-region contains lots of information and features, including features related to visibility estimation and other redundant information due to interference. If all the information of the sub-region image was directly mapped with the visibility value, the amount of data calculation for training was too large, and the redundant information may cause errors in the final results. Therefore, feature extraction was an indispensable and important step for the image of the subregions.  
Feature extraction was one of the most important operations in image processing. It considered the image as a data set, and pixels' features as elements. The process aims at finding the elements in the data set that could give the best representation of the data characteristics. According to different requirements of image classification and parameter evaluation, the method and dimension for feature extraction could be greatly different. In this paper, four common deep learning feature extraction methods based on the Keras platform were used. These methods were (VGG)-16 network, (VGG)-19 network, DenseNet network and ResNet_50 network. The extracted features were used as the input variables for the visibility evaluation model, and the variables were used as input for the Support Vector Regression model, and then visibility evaluation was performed on the effective area.
## 2 Background and Related Literature
### CNNs for Semantic Segmentation  
Recent developments in the field of image analysis, driven by the utilization of clusters and Graphics Processing Units (GPUs) along with advancements in algorithms, have substantially reduced processing times. Consequently, deep learning methods have garnered significant attention, as noted by [24]. Specifically, the study of semantic segmentation using CNNs can be categorized into two primary sets of techniques: patch-based and pixel-based approaches [25]. In patch-based techniques, patches surrounding each pixel within the input image are extracted, and a single label is predicted for each patch using a CNN, effectively classifying the entire image [26]. While this category of techniques has contributed significantly to image segmentation, it is associated with certain drawbacks, including limited receptive fields, computational inefficiency, and substantial memory requirements.  
In contrast, pixel-based techniques involve predicting a label for each individual pixel within the entire image. A notable advancement in this realm is the Fully Convolutional Network (FCN) [27]. An FCN represents a classical model that deploys convolutional layers instead of fully connected layers, making it adept at upscaling coarse segmentation outputs into more precise results. This is achieved through the utilization of transposed convolutional layers. Various CNN models have adopted the FCN architecture, characterized by an encoder-decoder structure, and have achieved commendable results. However, it is worth noting that one drawback associated with the FCN architecture is the potential for significant factor up-sampling, which can introduce classification ambiguities.  
To address the challenge of classification ambiguities due to large factor up-sampling, various models have been proposed. One such model is the DeepLab-CRF, presented by [28], which incorporates a fully connected CRF. This model introduces \"atrous\" convolutions to mitigate the impact of eliminating pooling layers. To achieve smoother raw segmentation results, the fully connected CRF is integrated with the responses from the final CNN layer. Another approach is the DeconvNet [29], which employs a multi-layer deconvolution network to replace bilinear interpolation in the upscaling stage. This network incorporates un-pooling (the reverse of max-pooling) and deconvolution layers to enhance the upscaling process. Similarly, the SegNet [30], which is conceptually similar to DeconvNet but offers a simplified architecture, has the advantage of being trainable end-to-end and features a significantly smaller parameterization, making it a computationally efficient choice for image segmentation tasks.
## 3 Survey of Techniques for Updating
### Update Fault Detection and Recovery
#### 3.4.1 Software Update Recovery for Wireless Sensor Networks (SUR)  
This work [82] identifies a single, critical, failure symptom--_loss-of-control_--that complements exception-based schemes, and supports failsafe recovery from faults in software updates. It includes a software update recovery mechanism that uses loss-of-control to provide high-reliability, low energy, recovery from software update failures. It has two phases: a _trial_ phase (with lower latency) for use immediately following an update, and an _operational_ phase (with lower energy) for use once an update has been determined to be stable. A _beacon_ injected from a management station and _polycast_ throughout the network, using a dedicated flooding algorithm (Flooding with Duplicate-suppression, Missing packet regeneration, and Timeouts), is used to determine loss-of-control. Nodes run in one of four states: OPERATIONAL, TRIAL, FALLBACK, and MAINTENANCE. In the FALLBACK state, a previous version of the software is run; in the MAINTENANCE state, a high-reliability image is executed which supports the software update process (allowing further updates to be made). Transition between the states is controlled by commands in the _beacon_ and watchdog timeouts. A protocol is defined for controlling and monitoring the software update process, which allows version information to be propagated, and separates code dissemination from activation. Low energy feedback on the software status of nodes is provided through overhearing; aggregated status is piggybacked onto the _beacon_, and used to report via the gateway node back to a management station.  
Most of the components of the UFDR model are covered, but support for ensuring unique access to the Watchdog timer, or persistent data storage for the recovery process, are not included. Also, the entire fault detection software is placed in the O/S image, potentially reducing the reliability of the detection and processes.
# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 4 The Application of CT in Loess Microstructure
### The 3D Characterization of Loess Microstructure
[117] employed continuous sectioning to study the distribution and connectivity of pore sizes in loess from various regions of the Loess Plateau, categorizing the microstructure into three types. [PERSON] et al. [118] reconstructed the 3D microstructure of loess using an enhanced continuous sectioning method and quantitatively analyzed the 3D microstructural characteristics of particles and pores by examining statistical parameters, such as the equivalent diameter, sphericity, morphology, and orientation angle. In recent years, [PERSON] et al. [119] have utilized different amounts of slag micro-powder to amend loess and industrial CT to perform micro-scale experiments on the amended loess, analyzing aspects such as pore distribution, shape, and size to understand the impact of slag micro-powder on the loess's pore structure, offering insights into the mechanical property changes in loess. [PERSON] et al. [120] focused on a typical soil profile from the Loess Plateau, using CT scanning to analyze the 3D structure of the plow layer, plow pan, and heart soil layer, and explored how layered soil pore parameters vary with the soil layer structure and the characteristics of the interface layer's pores. [PERSON] et al. [121] selected the loess in the southern plateau of Jingyang as the research object, combining CT scanning with mercury intrusion porosimetry to analyze the loess sample's structure, qualitatively analyze the variation in fine and micro pore structure characteristics with the buried depth of loess from 2D and 3D levels, and quantitatively analyze the pore structure based on statistical analysis parameters.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 4 Reconfigurable and Smart Antennas  
### Overview  
Fundamental limitations in the characteristics and bandwidth of compact antennas have been extensively investigated. For example, in [210], new limits, added to those found in [211], were presented for the minimum radiation achievable with an antenna whose conductor arrangement corresponds to a spherical surface. It is preferable to use a technique that allows several different radiators to share the same volume, and to enable the change of the antenna's performance characteristics to bypass such limits as operating resonance frequencies, polarizations, impedance bandwidths, and radiation patterns independently. This technique is called reconfigurability in antennas, and it may be implemented electrically using electronic switching components to redirect antenna surface currents, or optically using photoconductive switching elements, or even physically by changing the geometrical structural aspects of the antenna. Additionally, using the smart material technique, reconfigurable antennas can be implemented using such materials as ferrites and liquid crystals.  
Tunable or reconfigurable antennas have received increasing attention, for example, in [212; 213], where electronic switches such as PIN diodes were used, and in [214], where the field effect transistors (FET) were used. The authors of [215] exploited electromechanical switches such as relays and micro-electromechanical systems (MEMS) switches, and later the same in [216]. Electromechanical systems in which electrostatic actuation causes a global deformation of the radiator are also used to design reconfigurable antennas as in [217; 218]. In addition, the design of such antennas can include silicon substrates in which plasma regions with high conductivity are created by the injection of a DC current [219].  
In general, the most important task was to control the antenna's electromagnetic characteristics, but paying attention to the system-level performance is similarly important [220]. The reconfigurability itself provides degrees of freedom readily exploitable during the design process, so iterative EM analysis/geometry correction is possible in order to come up with an optimal geometry. This was one of the goals of the work [221]. The paper presents a novel patch antenna geometry that is well suited to the frequency reconfigurability achieved through the use of switches connecting metal portions to the ground plane. The optimization procedure combined a global optimizer (GA) with a local optimizer (1-bit neighborhood) for the geometry, and an exhaustive search for the switching patterns. During the optimization, the characteristics and antenna performance were evaluated using MoM simulation. The resulting structure had good characteristics, such as small size, space filling, and good return loss.
## 2 Methodology
By using these datasets, we succeeded in working with high spatial resolution images (3 m), took advantage of the near-infrared band in addition to the visual spectrum, and finally chose cloud-free days, due to the high frequency revisiting time. The near-infrared band (845-885 nm) was assigned to the Red color, the red band (650-680 nm) was assigned to the Green color, and the green band (547-583 nm) was assigned to the Blue color, generating pseudocolor image maps. The latter provides a nice contrast between areas that are covered with vegetation due to the high reflectance of chlorophyll and objects on the earth's surface that consist of concrete (buildings damaged or not, disposal sites), asphalt (paved roads, debris) or synthetic material (tents, containers, hovels) [19; 20; 21]. These remote sensing observations included multi-temporal, geo-referenced imagery interpretations of the same areas acquired some days before the February earthquake and almost four months later. The purpose of using these methodologies was to capture the dimensions, the properties, and the actions that were taking place within these sites and the potential presence of human activity and structures (buildings and infrastructure) as well as natural ecosystems that could be adversely affected by the operation of these sites.
## 2 System Modeling and Optimization Problems  
### IRS-Assisted HST Communication Models  
This paper considers an IRS-assisted HST communication system model, as shown in Figure 1, where a mobile relay (MR) located on top of the train is utilized to receive signals to avoid train penetration losses [27]. The system consists of an M-antenna base station (BS), an IRS with N reflecting elements, where the distance between the reflecting elements \(d_{z}=d_{y}=\lambda/2\lambda\) is the wavelength [22], and a single-antenna MR. The IRS is deployed along the railway on the side of the BS. It is assumed that the line-of-sight (LOS) component between the BS and MR is obstructed by obstacles, and the IRS is used to establish an auxiliary link to enhance the received signal strength. Furthermore, it is assumed that both the BS and IRS have perfect channel state information (CSI) [28].  
Considering multiple equally sized time slots denoted as t within a time period T, each slot maintains constant signal characteristics. This assumption holds when the train's velocity remains constant [29]. Let \(x(t)\in\mathbb{C}\) represent the information symbols transmitted by the base station (BS) in time slot \(t\), subject to constraints: \(\mathbb{E}\{x(t)\}=0\) and \(\mathbb{E}\Big{\{}x(t)x(t)^{H}\Big{\}}=1\). Moreover, \(\mathbf{f}(t)\in\mathbb{C}^{M\times 1}\) denotes the BS's transmit beamforming vector in time slot \(t\). Consequently, the received signal at the mobile relay (MR) in time slot \(t\) is given by:  
\[y(t)=\Big{(}\mathbf{h}_{r}^{H}(t)\mathbf{\Theta}(t)\mathbf{G}(t)+\mathbf{h}_{d}^{H}(t)\Big{)} \mathbf{f}(t)x(t)+\mathbf{n}(t), \tag{1}\]  
where \(\mathbf{h}_{d}(t)\in\mathbb{C}^{M\times 1}\) represents the channel gain of the BS-MR link in time slot \(t\), and \(\mathbf{G}(t)\in\mathbb{C}^{N\times M}\) and \(\mathbf{h}_{r}(t)\in\mathbb{C}^{N\times 1}\) represent the channel gains of the BS-IRS and IRS-MR reflection links in time slot \(t\), respectively. \(\mathbf{n}(t)\sim\mathcal{CN}(0,\sigma^{2})\) is the additive white Gaussian noise with power \(\sigma^{2}\). The reflection coefficient matrix of the IRS is denoted as \(\mathbf{\Theta}=\text{diag}\big{\{}\beta_{1}e^{\beta_{1}},\cdots,\beta_{n}e^{ \beta_{n}},\cdots,\beta_{N}e^{\beta_{N}}\}\). Let \(\mathbf{V}=[v_{1},\cdots,v_{n},\cdots,v_{N}]^{H}\), where \(v_{n}=\beta_{n}e^{\beta_{n}}\), \(j\) is imaginary units. \(\beta_{n}\in[0,1]\) and \(\theta_{n}\in[0,2\pi)\) represent the amplitude and  
Figure 1: IRS-assisted HST communication system model.  
phase shift of the nth reflecting element of the IRS, typically set as \(\beta_{1}=\beta_{2}=\cdots=\beta_{N}=1\) and \(|\tau_{n}|=1\)[22], respectively. Due to significant path loss, multiple reflections from the IRS are neglected [28].  
Therefore, the signal-to-noise ratio (SNR) of the MR's received signal in time slot \(t\) is:  
\[\gamma(t)=\frac{\left|\left(\mathbf{h}_{r}^{H}(t)\mathbf{\Theta}(t)\mathbf{G}(t)+\mathbf{h}_{d} ^{H}(t)\right)\mathbf{f}(t)\right|^{2}}{\sigma^{2}}, \tag{2}\]  
where \(|\cdot|\) indicates the absolute value of a complex number.
## 2 Theoretical Aspects
### Improved Variational Mode Decomposition (IVMD)
The specific algorithm for adaptive determination of K using MI method is as follows:Step 1: Initialize \(n=n+1\), assign \(K=1\);  
Step 2: \(K=K+1\), perform outer circulation;  
Step 3: Initialize \(m_{k}^{1},w_{k}^{1},\lambda^{1}\) and \(n\), assign \(n=0\);  
Step 4: Order \(n=n+1\) to execute the inner loop;  
Step 5: For all \(w\geq 0\), according to Equations (9) and (10), \(m_{k}\) and \(w_{k}\) are updated, respectively;  
Step 6: Update \(\lambda\) according to Equation (11);  
Step 7: For a given discriminate accuracy \(e>0\), if the iteration condition \(\sum\limits_{k}\frac{\|m_{k}^{n+1}-m_{k}^{n}\|_{2}^{2}}{\|m_{k}^{n}\|_{2}^{2}}<\) e is satisfied, the process is terminated, otherwise loop step 2 to step 6;  
Step 8: Circulate step 2 to 7 until the set threshold \(\sigma\) is greater than the normalized mutual information \(\sigma_{1}\), that is, if \(l(f-\sum m_{k},f)<\sigma\), end the cycle. The flow chart of the above calculation steps is shown in Figure 1.  
Figure 1: Flow chart for adaptive determination of K.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 3 Design of _uw_-Cellular Network  
### Application Scenarios  
The growing interest in a large number of applications, such as offshore oil and natural gas pipelines security, coastal surveillance and port security, has created the urgent need for underwater wireless monitoring network. As shown in Figure 1, the underwater wireless monitoring network is kind of UWSN which focused on the underwater target monitoring. Usually, the monitoring network need cover a wide underwater area from hundreds to thousands square kilometers. In order to cover such wide area and obtain various types of monitoring data, a large number of heterogeneous network nodes with different functional need to be deployed in the target area. Fixed nodes, such as surface and subsurface buoys, are deployed at different depths in underwater to acquire data from different locations. Mobile nodes are required to navigate over a specified or the entire area to enable flexible monitoring and data acquisition tasks independently or collaboratively as a cluster. This kind of network needs to provide the adaptive interaction links for different kinds of data transmission. For example, early warning data and command data need to be provided with a stable and reliable link and have a high demand for real-time performance. Instead, we can tolerate long latency and even packet loss for general monitoring data.  
In response to the above issues, we design and implement a hierarchy based UWSN called _uw_-Cellular Network. It can not only cover a larger area but also accommodate more different types of nodes. In this application scenario, network nodes, whether they are fixed or mobile, can join the network either as clusters or as independent individuals for different tasks. It is worth pointing out that as mobile nodes join the network, frequent changes in the network topology and low degradation in communication success rate have put the information interaction within the network to a greater test. The _uw_-Cellular network provides reliable data transfer and conflict avoidance strategies based on the characteristics of mobile in nodes in the network. Simultaneously, in this network, users are able to interact with the network system via controlling the behavior, checking the status and setting the parameters of the designated network nodes.  
Figure 1: Underwater wireless monitoring network is used in port security scenarios.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 4 The Proposed Method
### Vector Representation of Alarms Using DK-Word2 vec
#### 4.2.2 DK-Word2 vec
Since we propose to simultaneously learn the category information provided by domain knowledge during the training of the skip-gram model, we now define a category loss function as  
\[\text{Category\_loss}=\sum_{i=1}^{N}\left(\delta_{i}+\gamma_{i}\right), \tag{7}\]  
where \(\delta_{i}\) and \(\gamma_{i}\) are evaluation values under the \(i\)-th classification criterion, and \(N\) is the total number of classification criteria. We combine the category loss function and the skip-gram loss function. The resulting new loss function is referred to as Optimized_Loss, and it is formulated by the following equation:  
\[\text{Optimized\_loss}=\left(1-\lambda\right)\times\text{Raw\_loss}+\lambda \times\text{Category\_loss}, \tag{8}\]  
where \(\lambda\) is the weight of category loss. The method of obtaining vector representations through the proposed method is referred to as DK-Word2 vec in this paper. The weighted average of two loss functions provides a balance between learning the relationships among neighboring alarms and the category information. A higher value of \(\lambda\) implies a higher proportion of category information in the vector representation of the alarm.
## 2 Challenges, Trends and Constraints in WSAN Hardware Design
### Open-Source-Hardware Architectures versus Open-Architecture Systems  
Undoubtedly, OSH architectures have been seen as a significant way to avoid having to design hardware systems from scratch. For WSANs systems, the adoption of the OSH expandable multi-board architectures is very convenient, due to the fact that the developers (engineers and researchers) can test, evaluate and integrate several wireless connectivity solutions coming in the form of plug-in modules together with several MCUs' main-boards alternatives. This approach increases the degree of freedom and decreases the development cycle time in sake of the applications deployment.  
In the following subsections we identify and present several constraints associated with the existing OSH expandable architectures in order to emphasize the need for new solutions that could help the open-source approach to make the next step, namely the step from the prototyping to the optimization and reliability.