esa-sceva/satcom-chunk-collection huggingface dataset:
# Space-Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin
## 5 Conclusions and Prospects  
With the globally increasing demand for the utilization of water resources and the mitigation of flooding hazards, advanced systems for monitoring water and sediments in efficient and environmentally friendly ways are essential. A space-ground remote sensing network, combining satellites, drones, and ground-based hyperspectral stations, is proposed for monitoring the Yellow River watershed. As the recent demonstrations show in this work, this system would be expected to provide fine sustainable water and sediment monitoring and management applications for the Yellow River basin from the upper-reach small watersheds, main river channel, and reservoirs to its estuary in a continuous spatiotemporal dynamic manner.  
Through the integrated remote sensing monitoring network, not only can water and sediment be monitored and warned, but the water quality monitoring of the Yellow River basin and estuarine waters can also be conducted in the future. Parameters such as chlorophyll concentration, algae blooms, transparency, chemical oxygen demand (COD), and other parameters can be retrieved, and the assessment of water body functions can be performed to enable more rational water resources development, utilization, and management. And it could be expected that more sensor instruments and algorithms with more reliable performance will be developed with more applications of the space-ground remote sensing network in future.  
Conceptualization, Q.X.; methodology, Q.X., Y.H., M.A. and X.L.; software, Y.H., M.A. and Q.X.; formal analysis, Y.H. and Q.X.; resources, Q.X. and Z.H.; writing--original draft preparation, Q.X. and Y.H.; writing--review and editing, all authors; project administration, Q.X. and Z.H.; funding acquisition, Q.X. and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research received financial support from the Seed Project of Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences (YICE351030601); the Key R&D Program of Shandong Province, China (2022 CXPT019); the Instrument Developing Project of the Chinese Academy of Sciences (YJKYYQ20170048); and the National Natural Science Foundation of China (42076188, 42476175).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors acknowledge the support of the 2024 Shandong Province Central Guidance Fund for Local Science and Technology Development project \"Research and Development of the Integrated Intelligent Monitoring and Early Warning System for Water and Sediment in Qingshui River(Ningxia)\". The authors are thankful to the anonymous reviewers for their useful suggestions. The satellite data were kindly provided by GSCloud, USGS and CRESDA.  
The authors declare no conflicts of interest.
# Watermarking Protocols: A Short Guide for Beginners
## 3 Security Primitives
### Commutative Encryption  
An encryption scheme \(E\) is _commutative_ if it satisfies the following properties [41; 42; 43; 44]:  
\[E_{k_{1}}(E_{k_{2}}(m))=E_{k_{2}}(E_{k_{1}}(m))\quad\text{and}\quad D_{k_{1}}( D_{k_{2}}(E_{k_{1}}(E_{k_{2}}(m))))=m\]  
with \(D\) representing the decryption function corresponding to \(E\) for any two keys \(k_{1}\) and \(k_{2}\) and any message \(m\).  
The above properties mean that a message \(m\) can be encrypted more than once using different public keys; that is, there is no need to decrypt \(m\) before re-encrypting it. Moreover, \(m\) can be recovered from the corresponding ciphertext without having to take into account the order of the public keys used in encrypting it. More precisely, \(m\) can be decrypted by applying the private keys in any order. In practice, the results achieved by applying a commutative encryption scheme are not affected by the order of keys used in encryption and in decryption.  
The properties of commutative encryption schemes can be very useful to implement a wide variety of applications [45; 46; 47; 48; 49]. However, they are particularly suitable for watermarking protocols that enable content providers to exploit \"security delegates\", such as cloud computing platforms, to apply protections to digital contents distributed on the Internet [10; 11]. In such cases, the protection schemes are based on double encryption. More in detail, content providers apply a first encryption to their digital contents so as to release them in a protected form to cloud platforms. Then, cloud platforms can re-encrypt the received contents without having to first decrypt them. At this point, it is possible to embed watermarks directly into the encrypted domain even in the presence of a double encryption, thus supporting the protection schemes based on security intermediaries [50; 51; 52].
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 2 Improved FraSegNet Algorithm
### Model Optimization
#### 2.2.2 Introduction of the SE Attention Mechanism  
In the nodule recognition task, rock body images often have rich background information (e.g., rock texture and noise) that can interfere with the model's ability to accurately identify nodule regions. The original FraSegNet uses a uniform weighting approach for feature maps, which does not capture the importance of different channels dynamically, leading to poor differentiation between nodule and background features. The SE module improves the expression of key features and reduces redundant information by adjusting the weights of the feature channels.  
The SE module has three main steps: (1) Squeeze: This operation pools the global average of each feature map, turning 2D feature maps into 1D channel descriptors to capture global spatial information. (2) Excitation: A fully connected layer learns the relationships between channels, followed by a sigmoid activation to create a channel-level attention map. (3) Recalibration: The generated channel weights are multiplied with the original feature map, adjusting the importance of each channel.  
Incomplete connections in the fully connected layer during the Excitation of the SE module can lead to the following: 1. Failure of the attentional mechanism: The fully connected layer is used to learn the dependencies between channels, and if the connections are incomplete, the information of some channels may not be modeled correctly, leading to an imbalance in the allocation of attention. This may result in abnormal attention weights (too high or too low) for some channels, affecting the performance of the model. 2. Impaired gradient propagation: The fully connected layer of the SE module relies on parameter learning to adjust the channel weights, and if the connection is incomplete, the gradient may not be propagated correctly. This will affect the training of the whole model, making the SE module unable to effectively improve the feature expression ability. 3. Abnormal numerical computation: If the parameters of the fully connected layer are not initialized correctly or the connection is lost, this may lead to abnormal values such as NaN (Not a Number) or Inf (Infinity), which will affect the training and inference of the whole network. 4. Inability to correctly generate the channel attention maps: The output of the Excitation process is a channel-level attention vector that is used to weigh the input feature maps. If an error is made in the FC layer computation, it may result in the following: (i) The attention vector becomes all-zero (all channels are suppressed). (ii) The attention vector tends to be all-ones (no real adjustment effect). (iii) The attention values are not regularly distributed, resulting in the model not being able to focus on important features correctly. The structure of Squeeze-and-Excitation is shown in Figure 3.  
Figure 3: Squeeze-and-Excitation.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Opportunities of Data Fusion for SAR-Based Snow Cover Detection
#### 5.6.3 Combination of SAR with Optical Imagery  
Although many studies referred to the benefit of synergizing cloud-penetrating SAR with the high spatial/temporal resolution of optical imagery, so far there are only very few studies available that actually followed this approach. In all reviewed studies, only 12 performed a fusion of optical- and SAR-based snow cover classifications. This implies that there is still some potential to develop a dedicated algorithm combining optical and SAR data for snow cover detection.  
So far, most studies relied on optical-based NDSI calculation to map total SCE and then apply [PERSON]'s method to map wet snow. Subsequently, dry, wet and total SCE can be derived [220; 221; 222]. This simple areal calculation, however, is not a genuine fusion of the same SCE type sensed by different sensors. A possible integration is to assign weights to both SAR-based and optical-based snow cover classifications as pursued by [PERSON] et al. [151], [PERSON] et al. [33] and [PERSON] et al. [68]. In these studies, time-series of SAR-based total SCE's were merged with total snow cover derived from MODIS by applying (1) confidence values assigned to each sensor's snow cover result, and (2) a time-dependent function defining how quickly the confidence value would decay over time. Ultimately, the algorithm would select the snow cover result with the highest confidence value for each pixel. Nevertheless, as those key parameters were selected by finding the optimal solution through trial and error, the case-dependency hinders transferability to other locations and periods. A more universally applicable combination procedure, however, is still lacking.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 2 Materials and Methods
### Fault-Tolerant Image Processing Algorithm  
The realization of visual perception with flexible devices is promising and essential, which is also critical for greatly broadening applications of the flexible devices. Generally, image classification tasks are composed of pre-processing and feature processing phases, which are used to enhance the features extraction and classification, respectively. As for the pre-processing phase, the widely used Difference-of-Gaussian (DoG) algorithm [11] is adopted to extract and enhance edges of pictures. Additionally, as for the classification phase, multi-layer perception (MLP) [20] is selected because it contains the common multiply-accumulate units and non-linear activation units of neural network, without loss of generality.  
Besides, the computation burden of the Gaussian convolution is higher and the mask size is larger with the increasing resolution of image, and it becomes a tough mission for digital processors, because the same multiplication and addition operations are performed on every single pixel and with neighbor pixels. Moreover, neural network is also a computation-intensive task, for its large number of computations between every two layers, which could not be performed on the general-purpose processors with real-time processing. Therefore, the DoG and MLP algorithm are sufficient to verify  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **CMOS** & **A-Si:H TFTs** & **A-Oxide TFTs** \\ \hline
**Carrier Mobility (cm2/Vs)** & 480–1350 & \(<\)1 & 10–50 \\
**Transparent** & \(\times\) & \(\surd\) & \(\surd\) \\
**Soft** & \(\times\) & \(\surd\) & \(\surd\) \\
**Biocompatible** & \(\times\) & \(\surd\) & \(\surd\) \\
**Process Variation** & Small & Medium & Large \\
**Cost** & High & Medium & Low \\
**Manufacture Temperature** & High & \(\cong\)110 \({}^{\circ}\)C & Room temperature \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons among different devices [17; 18; 19].  
our architectures, which solves the contradiction between low mobility, low stability device versus complex computation task.  
Moreover, the fault-tolerant capability of the selected algorithms could tolerate the error that is introduced by unstable devices and analog computations. The feature descriptors of DoG are generated from local statistical information, in which the exact original data does not make much sense; and, the training process of neural network can gradually eliminate the error that is caused by imperfect computation. Details of the DoG and MLP are introduced as follows.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Signal Multi-Feature Extraction and Combination
#### 2.5.6 SVD  
SVD provides an effective way to extract the main features from data. By retaining the most significant singular values, the data's most important structures and information can be preserved, thus achieving the dimensionality reduction of the data [43]. The decomposition formula for SVD is as follows:  
\[\mathrm{A}=\mathrm{U}\Sigma V^{H} \tag{6}\]  
Let \(\mathrm{A}\in C^{m\times n}\). There exist unitary matrices \(\mathrm{U}\in C^{m\times m}\) and \(\mathrm{V}\in C^{n\times n}\) such that \(\mathrm{A}=\mathrm{U}\Sigma V^{H}\), where \(H\) denotes the conjugate transpose. It follows that \(U^{H}U=UU^{H}=I\), illustrating the preservation of orthonormality within these transformations.
## 5 Thermal Damage
### Definition of TD and Its Influencing Parameters  
The TD is often used to characterize the degree of damage to rock after heating, based on variations in the P-wave velocity with temperature, defined in Equation (1) as [42; 43]  
\[\mathrm{TD_{in}=1-(Vp_{T}/Vp_{0})^{2}} \tag{1}\]where \(\rm V_{\rm PT}\) stands for the P-wave velocity (\(\rm Vp\)) of samples after heating treatment, and \(\rm Vp0\) stands for the \(\rm Vp\) of the same specimens at room temperature. There are other definitions of thermal damage, but Equation (1) is chosen since P-wave velocity correlates quite well with the crack evolution, which reflects the amount of damage experienced by the sample.  
Among the six parameters studied in Section 4, P-wave velocity and tensile strength show a good correlation to the \(\rm TD_{\rm in}\). Figure 3 shows the negative correlation between the predicted \(\rm TD_{\rm in}\) and P-wave velocity (Figure 3a) and tensile strength (Figure 3b). Generally, the intact rock sample has a higher P-wave velocity than the fractured one because cracks, air, or fluid gaps slow down the waves. Hence, the trend is that intuitive thermal damage often degrades material properties, reducing the ability to transmit seismic waves. The coefficient of determination \(\rm R^{2}\) is 0.85 and 0.90 for P-wave velocity and the tensile strength, respectively, which shows a strong inverse relationship between these two parameters. Moreover, tensile strength has a higher negative correlation to the \(\rm TD_{\rm in}\). Hence, tensile strength is a good parameter to define \(\rm TD_{\rm in}\), indicating that strong tensile strength prevents TD in rocks. High-temperature treatment can transform brittle material into a more ductile material. Therefore, it is important to employ all key influencing parameters when evaluating the thermal damage.
# Evaluation of Metal-Organic Frameworks as Potential Adsorbents for Solar Cooling Applications
## 1 Introduction
Furthermore, comparative studies related to MOFs and other adsorbent materials commonly used for cooling applications are reviewed. The comprehensive plan to achieve the objectives of this research can be summarized as:  
* Description of alternative cooling technologies that are both energy efficient and environmentally friendly. * Research on thermal cooling systems for their development and promotion. * A detailed review of available advanced adsorbent materials that can be employed for adsorption based solar cooling. * Introduction to MOFs as an alternative to other adsorbents. * The recent development of MOFs and their adsorption characteristics. * Future needs for development and advancements of MOF-based cooling technology. It is predicted that the use of MOF materials for both adsorption-based cooling technology will help for their development and promotion on a commercial scale. A suitable MOF can be selected based on the desired outcome and further tuned if needed. The use of these novel structures as an adsorbent for adsorbent-based cooling may perform better as compared to other materials that are widely used for these applications. In short, to find out this potential through a comprehensive review of adsorbent-based cooling systems, desired adsorbent behavior for these technologies and comparative adsorption properties of MOFs is the aim of this paper.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
###### Abstract  
Vehicle Ad-hoc Networks (VANETs) have experienced significant development in recent years, playing a crucial role in enhancing the driving experience by enabling safer and more efficient inter-vehicle interactions through information exchange. Vehicle-to-Vehicle (V2V) communication is particularly vital as it not only helps to prevent collisions and improve traffic efficiency but also provides essential situational awareness to drivers or autonomous driving systems. Communication is typically supported by roadside units (RSUs); however, in practical applications, vehicles may exceed the communication range of RSUs, thus exposing them to various malicious attacks. Additionally, considering the limited computational resources of onboard units (OBUs) in vehicles, there is a high demand for designing lightweight security protocols that support V2V communication. To address this issue, this paper proposes an efficient anonymous V2V identity-authentication protocol tailored for scenarios that lack RSU support. The proposed protocol was formally assessed using the Scyther tool, demonstrating its capability to withstand major typical malicious attacks. Performance evaluations indicate that the proposed protocol is efficient in terms of communication and computational overhead, making it a viable solution for V2V communication.  
internet of vehicles; mutual authentication; vehicle-to-vehicle communications; security 2024245376. [[https://doi.org/10.3390/s24165376](https://doi.org/10.3390/s24165376)]([https://doi.org/10.3390/s24165376](https://doi.org/10.3390/s24165376))
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 5 State-of-the-Art Works Comparison  
For the modelling of the proposed antenna, CST MW Studio is used for designing and simulating the proposed antenna. Figure 11 illustrates the two-dimensional radiation pattern produced by the antenna integrated within the smart watch. Antenna stability can be shown in Figure 5, Figure 6, and Figure 10 when it is simulated in free space and on the watch strap. A total radiation efficiency of over 82% was found for the antenna and may be tested both in free space and on the rubber strap. Table 2 compares the proposed antenna to literary studies that were recently published. This piece has an antenna that is smaller than most of the others, making it stand out from the others. As a result, the antennas described in [13; 14; 15] have a limited bandwidth and poor strength, even if they are tri-band compact. Researchers [16; 17; 18] reported huge antennas with dual-band operation and a limited bandwidth. Thus, the triband antenna's superior radiation properties, such as a broad bandwidth, large antenna gain, greater radiation efficiency, as well as a simple and a compact size, make it suitable for 5G mm-wave devices. Figure 14 describes the comparison of the \(S_{11}\) between the free space and on the phantom.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. No.** & **Antenna Size (mm\({}^{\$}\))** & **Bandwidth (GHz)** & **No. of Bands** &
\begin{tabular}{c} **Operating** \\ **Frequency (GHz)** \\ \end{tabular} & **Peak Gain (dB)** \\ \hline
[13] & \(10\times 5\times 0.51\) & 3, 5, 3 & Tri-band & 45, 57, 66 & 5.6 \\ \hline
[14] & \(4\times 5\times 0.2\) & 0.5, 0.9, 0.4 & Tri-band & 24.4, 28, 38 & 6.5, 7, 5 \\ \hline
[15] & \(8\times 8\times 1.6\) & 2.5, 1.6, 7 & Tri-band & 26, 35, 54 & 5.8, 4.5, 6 \\ \hline
[16] & \(14\times 12\times 0.38\) & 2.6, 2.1 & Dual band & 28, 38 & 1.27, 1.83 \\ \hline
[17] & \(55\times 110\times 0.05\) & 0.8, 1.5 & Dual band & 28, 38 & 7.9, 8.2 \\ \hline
[18] & \(20\times 20\times 1.95\) & 4.8, 3.6 & Dual band & 28, 38 & 5.7, 7.2 \\ \hline
[19] & \(8\times 8\times 0.79\) & 2.3, 5.1, 15 & Tri-band & 28, 38, 55 & 6.6, 7, 7.35 \\ \hline
**[This work]** & \(4\times 3\times 0.25\) & \(10,1.5,6\) & **Tri-band** & **28, 38, 60** & **5.29, 7.49, 9** \\ \hline \end{tabular}
\end{table}
Table 2: Analysis of the planned study in comparison to relevant work.  
Figure 13: SAR values vs. frequency graph over phantom using mathematical equations.
## 1 Introduction
This extra phase \(\delta_{ex}\) can be controlled by the pulse width, which manifests itself in a pulse width dependence of the PAD. This will eventually open a new avenue to the coherent control of the continuum wave packets. (In this connection, see [45] for the control of the resonant two-color two-photon excitation yield and [46] for the control of the photoelectron angular distributions of the nonperturbative resonant multi-photon ionization with ultrashort polarization-shaped pulses. See also a very recent review article for photoelectron angular distributions [47].) In this paper, we extend the study and investigate the dependence of the PAD and \(\delta_{ex}\) on pulse parameters in more detail. In our previous study [44], we have chosen He as a target atom for the following reasons: first, its single-electron excitation energies, e.g., 21.218 eV for \(1s2p\,^{1}P\) and 23.087 eV for \(1s3p\,^{1}P\)[48], coincide with the 13 th and 15 th harmonic photon energies of a Ti:Sapphire laser, respectively, and also with the typical wavelength range (50-61 nm) of EUV FELs such as the Spring-8 Compact SASE Source (SCSS) [18], the Free-electron LASer at Hamburg (FLASH) [17], and FERMI [49]. Second, its simple electronic structure allows for exact time-dependent numerical analysis [50; 51; 52; 53; 54], in great contrast to alkali atoms. Basically the same effect, however, is also expected for other atomic and molecular species. In the present study, in addition to He, we examine the case of a hydrogen atom, for which simulations are much less demanding and the scattering phase shift is precisely known as the Coulomb phase shift. We also investigate the case where the photon energy is close to the ionization threshold and show that the transition from 2 PI involving Rydberg-manifold excitation to above-threshold 2 PI is smooth. The Rydberg manifold behaves in a similar manner to the continuum, even when only a modest numbers of levels are excited by the pulse. Intense high-order harmonic and self-amplified spontaneous emission (SASE) FEL pulses are rarely a single coherent pulse; the former is often a train of a few pulses temporally separated by a half cycle of the driving laser, and the latter fluctuate from shot to shot [55; 56; 57; 58; 59]. Hence, we examine how such a multi-component nature, either coherent or chaotic, affects the PAD. This paper is organized as follows. In Section 2, we discuss the PAD by a Gaussian pulse, based on the second-order perturbation theory and TDSE simulations. Then, in Section 3, we examine the PAD by high harmonic pulses containing multiple harmonic orders and the effects of the chaotic nature of SASE FEL pulses. Conclusions are given in Section 4.