esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
## 2 State-of-the-Art
### Authentication in 5G Networks
In addition, as the number of connected devices increases exponentially in 5G networks, scalability and distribution are also essential concerns to be considered. Finally, as 5G networks integrate with various non-3 GPP networks, secure and efficient cross-network authentication mechanisms (high interoperability levels) are also required [32]. In this context, Self-Sovereign Identity (SSI) [33] appears as a promising approach for authentication in 5G networks, as it offers enhanced privacy, security, and user control, while guaranteeing the required scalability in massive IoT environments [74; 75]. Regarding the use of SSI in 5G, on the one hand, SANS (Self-Sovereign Authentication for Network Slices) has been proposed as an innovative authentication protocol for 5G network slicing [76]. This approach grants users full control over their data by allowing them to prove their right to access specific services without leaking personal information. It provides non-linkable protection for issued information and prevents Slice Operators (SOs) or eavesdroppers from tracking users' activities. SANS uses Zero-Knowledge Proofs (ZKPs) [77] to achieve these privacy-preserving features while maintaining robust authentication. On the other hand, other authors [78] have explored the application of SSI in privacy-preserving contact tracing apps for 5G networks. This approach offers user control over shared information and privacy-preserving authentication with verifiable credentials and a decentralized storage of sensitive data (e.g., COVID-19 test results). In addition, other authors have highlighted the potential of SSI to enhance security and privacy in specialized 5G applications [79]. They investigated the use of SSI for identity management in ship-based 5G devices, where they evaluated the control overhead, verification delay, and identity reconciliation in SSI-based 5G identity management systems, and analyze use cases, technologies, and challenges for SSI in Industrial Internet of Things (IIoT) contexts. Finally, SSI has been proposed as a solution for secure cloud-to-cloud data migration in 5G networks [80]. This approach aims to enhance data security during migration processes by providing user-controlled authentication and authorization and leveraging Blockchain technology for secure, decentralized identity management. All these works demonstrate the potential of SSI technology for different aspects of 5G. However, its use for authenticating users among different operators in offloading processes has not yet been considered. As 5G evolves towards 6G, research is ongoing to further integrate SSI into next-generation networks [81] to address current limitations and expand the capabilities of privacy-preserving authentication in mobile networks.
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 2 Methodological Models
### Pruning Process  
Figure 6 shows the specific pruning process. As illustrated below, channel pruning of a single convolutional layer is implemented to reduce the width of feature map B whilst maintaining the output quality of feature map C. Specifically, once channels in feature map B have been pruned, the corresponding filter channels--denoted by the dashed lines in W--can be eliminated. Similarly, the filters that generate these channels, indicated by the dashed lines from feature maps A to B, are also removable. The channel pruning process comprises two main steps: The first is channel selection, where the Maximal Channel Pruning (MCP) regression method is employed to discard redundant channels, retaining only those that are representative. The second step is the reconstruction of the output, achieved through linear regression to restore or reconstruct the output of the remaining channels, thereby ensuring that the network performance is unaffected by pruning. Through this process, although the width of feature map B is diminished, the output of feature map C is consistently preserved. The use of identical colors for blocks A and C represents the corresponding layers before and after compression, consistently maintaining the final output dimensions.
## 2 Train SINS/GNSS Integrated Navigation System Model
### SINS State Equation
\begin{array}{l}\delta\dot{\mathbf{V}}^{n}=\begin{bmatrix}\delta\dot{V}_{E }\\ \delta\dot{V}_{N}\end{bmatrix}=f^{n}\times\phi+\mathbf{C}_{b}^{n}\
abla= \begin{bmatrix}0&-f_{z}^{n}&f_{y}^{n}\\ f_{z}^{n}&0&-f_{x}^{n}\end{bmatrix}\begin{bmatrix}\phi_{E}\\ \phi_{N}\\ \phi_{U}\end{bmatrix}+[\mathbf{C}_{b}^{n}]_{2\times 2}\cdot\begin{bmatrix} \
abla_{x}\\ \
abla_{y}\end{bmatrix}\\ \end{array}  \tag{13}\]  
where \(c\) represents \(\cos\), and \(s\) represents sin. Because \(f_{z}=g\), the pitch and roll misalignment angles have strong observability from above observability analysis, while the heading misalignment angle is unobservable when the train is in a state of constant linear motion due to the zero acceleration output. During constant linear motion, the coefficients in front of \(\
abla_{x}\) and \(\
abla_{y}\) will never be zero, so the bias is always observable. To avoid being influenced by irrelevant coefficients in the inertial navigation error equation and to better focus on the problem, a simplified model is chosen for problem analysis. The main estimation parameters are the train's position, velocity, and heading angle. Specifically, both the velocity error caused by heading error \(\phi\) and the position error caused by velocity integration do not contain heading error components. They cannot be estimated or corrected for by velocity or position aiding measurements. Therefore, in the navigation state where the train, especially high-speed trains, mostly operates in a state of constant linear motion, the heading is unobservable in the GNSS/SINS integration framework, and it will continuously diverge. Additionally, since the integration of the vertical gyroscope drift \(\omega_{z}\) results in heading error \(\phi\), the vertical gyroscope drift will also diverge. In the case of GNSS signal availability, heading error \(\phi\) does not affect the train's requirements for speed and position estimation. However, once GNSS fails and the system switches to an inertial-based navigation mode, heading error directly leads to inaccurate velocity and position estimates. When the train is in a state of acceleration or deceleration \(f_{x}^{b}\), \(f_{y}^{b}\) and are not zero. When the train passes through curved sections, there are tangential acceleration and centripetal acceleration, and the projection in the b-frame also makes \(f_{y}^{b}\), \(f_{y}^{b}\) not zero. Therefore, the heading error is observable when the train passes through curved sections. The summary of heading observability analysis for different motion modes and track curvatures is shown in Table 1.  
Gyroscopes and accelerometers are modeled as first-order Markov processes [35], and the models are listed in Appendix A. The F and G matrices of system equations are listed in Appendix B.
# Progress in Improving Photovoltaics Longevity
## 4 Results  
### PV Degradation Mechanisms  
After their installation and commencement, the interaction between PV modules and their ambient is inevitable. The effect of prolonged exposure to environmental and weather conditions and the limited viability of construction materials lead to the gradual degradation of the module. This decline can severely impact the efficiency, performance parameters, operating characteristics, and estimated lifespan over time. Gaining a clear insight into these degradation processes and developing effective mitigation strategies is crucial for enhancing the sustainability and cost-effectiveness of solar energy technologies.  
Recent surveys and research have made remarkable progress in identifying the key degradation mechanisms impacting PV modules and implementing measures to enhance their longevity. According to Table 1, these mechanisms can be broadly categorized into three main types: intrinsic degradation in the dark, extrinsic degradation caused mainly by environmental stress factors, such as photo-induced degradation and chemical reactions with water and oxygen, and other factors, and specific degradation from mechanical load and other modes. In the present subsection, an analysis of the internal, external, and specific degradation mechanisms that affect a PV module is examined.
## 3 ABER Analysis of the SISO TOW Link
### SISO TOW Link with IM/DD OOK  
The OOK scheme is the most widely used modulation format for FSO communication systems, mainly due to its simplicity, ease in installation, simple receiver design, bandwidth efficiency, low cost, and immunity to the innate nonlinear ties of the laser and the external modulator [57; 61]. However, when OOK is employed through a transmission medium of varying characteristics, in order to optimally operate, the knowledge of an adaptive threshold for each specific channel state is necessarily required [2].  
Assuming an IM/DD TOW SISO link with OOK, errors may be occurred at the receiver in determining the actual symbols transmitted, because of the presence of AWGN [2]. Thus, the instantaneous BER is estimated through the following expression [47; 50; 52]  
\[P_{e}=p(0)P(e|0)+p(1)P(e|1), \tag{13}\]  
where \(p(0)\) and \(p(1)\) are the probabilities of transmitting the bits \"0\" and \"1\" respectively, while \(P(e|0)\) and \(P(e|1)\) are their corresponding conditional bit-error probabilities [47; 62]. Without loss of generality, assuming that \(p(0)=p(1)=1/2\) and \(P(e|0)=P(e|1)\), and assuming \(m=1\), because SISO link is considered, and \(\overline{P}_{S}=1\) mw/MHz, the BER is given as [47; 50; 52]  
\[P_{e}(h)=P(e|0,h)=P(e|1,h)=P\bigg{(}n>\frac{\eta h}{2}\bigg{)}=P\bigg{(}n<- \frac{\eta h}{2}\bigg{)}=\frac{1}{2}\text{erfc}\bigg{(}\frac{\eta h}{2\sqrt{N_ {0}}}\bigg{)}, \tag{14}\]  
where _erfc_(.) stands for the complementary error function [59], (8.250.4). By averaging (14) over \(h\), the ABER, \(P_{e,av}\), is obtained as [47; 52]  
\[P_{e,av}=\int_{0}^{\infty}P_{e}(h)f_{h}(h)dh. \tag{15}\]  
Thus, by substituting (10) into (15) we get  
\[P_{e,av}^{\text{OOK}}=\frac{1}{2}\frac{\psi^{2}h_{l}^{\psi^{2}-1}}{\big{(}A_{ \theta}\big{)}\psi^{2}}\int_{0}^{\infty}erfc\bigg{(}\frac{\eta h}{2\sqrt{N_{0 }}}\bigg{)}h^{-\psi^{2}}dh, \tag{16}\]  
and by using then \(\int_{0}^{\infty}t^{z-1}erfc(t)dt=\frac{1}{\sqrt{\pi z}}\Gamma\Big{(}\frac{z+1 }{2}\Big{)}\),Re\((z)>0\)[63], (06.2721.0132.01), we get after some calculations the following ABER expression for the IM/DD OOK TOW link, by taking into account the skin-induced attenuation and NZB pointing errors  
\[P_{e,av}^{\text{OOK}}=\frac{\psi^{2}\exp\Bigl{(}-\frac{1}{2}\alpha(\lambda) \delta\Big{)}^{\psi^{2}-1}}{2\sqrt{\pi}(1-\psi^{2})\big{(}A_{\theta}g\big{)} ^{\psi^{2}-1}}\bigg{(}\frac{\eta}{2\sqrt{N_{0}}}\bigg{)}^{\psi^{2}-1}\Gamma \bigg{(}\frac{2-\psi^{2}}{2}\bigg{)}. \tag{17}\]  
Furthermore, by substituting (12) into (17) we can alternatively express the ABER as a function of the average electrical SNR, \(\mu\)  
\[P_{e,av}^{\text{OOK}}=\frac{\psi^{2}\Bigl{[}\mu\big{(}\psi^{2}+2\big{)}\Bigr{]} ^{\frac{\psi^{2}-1}{2}}\Gamma\Big{(}\frac{2-\psi^{2}}{2}\Big{)}}{2\psi^{2} \sqrt{\pi}\psi^{\psi^{2}-1}\big{(}1-\psi^{2}\big{)}\big{(}A_{\theta}g\big{)} ^{2\psi^{2}-1}\Big{(}\overline{P}_{s}\Big{)}^{\frac{\psi^{2}-1}{2}}}. \tag{18}\]
# Effective Assessment of Inelastic Torsional Deformation of Plan-Asymmetric Shear Wall Systems
## 1 Introduction
They showed that the torsional behavior of an asymmetric structure can be minimized if its strength center is located between the stiffness and mass centers. [PERSON] and [PERSON] [15] investigated the inelastic seismic behavior of asymmetric multistory buildings using the concept of story shear and torque histories. [PERSON] and [PERSON] [16] proposed a two-degrees of freedom analysis model consisting of a single lateral force resisting column and a corresponding rotational spring. [PERSON] et al. suggested an accurate two-dimensional cyclic beam-truss model for reinforced concrete shear walls [17], and it was later extended to a three-dimensional model [18]. [PERSON] et al. [19] proposed a modified three-dimensional capacity spectrum method to efficiently evaluate the seismic behavior of a building structure with asymmetric walls. The application of plan-asymmetric analysis techniques to multi-story building structures was discussed by several researchers [20; 21; 22] by explicitly considering the nonlinear torsional and higher-order effects. [PERSON] et al. [23] published an excellent review paper on the earthquake induced torsion in buildings by grouping various publications into a number of subtopics. Considering all these issues, this paper proposes a simple method that can accurately assess the inelastic lateral displacement and rotation of plan-asymmetric wall structures. The relationship between the lateral displacement and rotation is obtained by considering the inelastic behavior of asymmetric structures and is presented and interpreted in the displacement-rotation (D-R) coordinate. This coordinate system is very convenient to understand the inelastic torsional behavior of the asymmetric structure. The validity of the proposed method is investigated by comparing its predictions and inelastic time-history analysis results for a model problem. The outline of this paper is as follows. Following the introduction, Section 2 introduces and summarizes several current seismic code provisions on the torsional design of plan-asymmetric structures. In Section 3, the generalized form of the inelastic lateral displacement-rotation relationship of the structure is derived by following a step-by-step procedure. In Section 4, the effectiveness of the inelastic torsional response assessment procedure proposed in Section 3 is verified by comparing its results with those of time history analyses. Finally, the summary and concluding remarks are provided in Section 5.
## 2 Methodology
### Target Decomposition for DCP Data  
Recently, several target decomposition theories for compact polarimetric SAR data have also been proposed. There are three main groups, which differ in the type of the symmetry assumptions that they made about the observed media and/or the matrix used in decomposition: decomposition deduced by the Stokes vector [20]; 2 \(\times\) 2 coherency matrix [10]; and 3 \(\times\) 3 pseudo-coherency matrix [21]. Here, we note that the decomposition deduced from the 3 \(\times\) 3 pseudo matrix was not adopted in this study since the assumed relationship between the correlation coefficient and cross-polarization ratio would not always be valid [21]. Thus, we employed two decomposition approaches, one based on the Stokes vector and the other on the 2 \(\times\) 2 coherency matrix, which carry identical information [20]. The \(H/\tilde{\alpha}\) decomposition and the three-component decomposition are explained below.
## 2 Structural Characteristics, Classification, and Nomenclature of GSLs
This database features entries for each GSL species, presenting both the systematic nomenclature and the commonly used name. It also incorporates information about the identifiable sphingoid base and N-linked fatty acids for each GSL species. A shorthand notation based on Liebisch et al. [109] is used for annotating MS-data-derived lipid structures at three levels: species level, molecular species level, and full structure level. When considering the ganglioside GM3 (d18:1/24:0), it is named according to this shorthand notation:  
(1) NeuAcHex2 Cer, 42:1:02, at the species level (when the number of hydroxyl groups within the sphingoid base is uncertain, the total count of N-linked fatty acyl and sphingoid bases should be identified as the total carbon number; double bonds total; ceramide moiety oxygen atoms number; unidentified monosaccharide); (2) NeuAcHex2 Cer, 18:1:02/24:0, at the molecular species level (if the fatty acid and long-chain base structure are both known, with the exception of the double bond or stereochemistry and position); (3) NeuAc-Gal-Glc-Cer, 18:1(4E):3 OH/24:0 (GM3), at the full structure level (if both stereochemistry or double bond and position and are identified). This widely acknowledged representation applies exclusively to the oligosaccharide headgroups comprising a maximum of two monosaccharide units; it does not encompass the more intricate glycosphingolipids (GSLs). Taking into account all these observations, the advantages and limitations of the presented GSL nomenclatures, their combination would be appropriate to annotate structures of complex GSL structures resulting from MS data.
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
## 1 Introduction
To address the second challenge, we propose an acoustic hand-tracking-smoothing algorithm with an Interaction Multiple Model (IMM) Kalman Filter. The smoothing algorithm utilizes nonlinear tracking state equations to perform the optimal estimation on the hand trajectory. Therefore, we can obtain low-error hand trajectories to improve the accuracy of acoustic-based hand gesture recognition with a commercial microphone array. To address the third challenge, we propose a novel classification algorithm without training. We test five types of hand gestures for TaGesture, including swipe left, swipe right, push forward, pull back, and draw a circle. The classification algorithm relies on the unique characteristics of each gesture to realize acoustic-based hand gesture recognition without training. The main contributions of this paper are listed as follows. * TaGesture employs an inaudible acoustic signal to realize device-free and training-free hand gesture recognition, with a commercial speaker and microphone array. We believe TaGesture can be widely deployed on smart devices in the real world. * We propose a novel acoustic hand-tracking-smoothing algorithm with IMM Kalman Filter, which can eliminate localization angle ambiguity of hand tracking. Furthermore, we propose a classification algorithm to realize acoustic-based hand gesture recognition without training. * We conduct comprehensive experiments to evaluate the performance of TaGesture. Results show that the total accuracy of acoustic-based hand gesture recognition is 97.5%, and the furthest sensing distance is 3 m.
# Emerging Perspectives of Blockchains in Food Supply Chain Traceability Based on Patent Analysis
## 4 Discussion
### Further Prospects
A most recent study providing content-based analysis of the academic literature [77] revealed that BC application in the agrifood sector is a relatively recent topic that started to gain traction in 2018, when the research community started intensively dealing with this issue, with more than 75% of identified related records in the academic literature being published in 2020 and 2021. The identified sharp increase in the number of patents and patent applications related to BC application in FSCs is a reflection of vigorous research activities, resulting in ready-to-implement solutions in this field. The obtained results will support research community members in making decisions related to the development of high-readiness level products in the field of BC application in FSCs. The main limitation related to present research is the lag period of patents, which has certainly contributed to incomplete results within the past year. The second limitation is related to the fact that patent application does not necessarily result in an approved patent, which was taken into consideration, but, due to the low number of approved patents, they had to be taken into account for this analysis. However, the clearly identified increasing trend in the number of patents suggests that these limitations most probably do not affect the obtained results to a great extent. However, the development of BC applications in FSCs is closely related to integration with other information and communication technologies, in particular IoT. Finding that combination of BC technology with other information and communication technology, and IoT in particular, has been adopted for the improvement of FSCs was already emphasized in [59].
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 5 Challenges in Designing a Flexible Antenna
### Deterioration in the Gain  
Some flexible antennas contribute to a low gain. The gain of such an antenna should be improved using various techniques including artificial magnetic conductors, metamaterial integrated surfaces, etc. [84]. For applications involving long-distance communication, the flexible antenna should show a comparatively high gain. So, the high gain antenna is a design consideration in the implementation of a flexible antenna. Other gain enhancement techniques are EBG, FSS, DGS, etc. The use of the defected ground plane makes variations in the antenna parameters since the antenna's ground plane comes in close contact with the body. However, the DGS integrated with a reflector can be used to increase the gain of the wearable antenna.
# Air Conditioning Load Forecasting and Optimal Operation of Water Systems
## 3 Results
### Analysis of Water System Operation Optimization Results  
(1) Optimal operation control strategy of a groundwater system with different load rates  
The actual operating parameters of the system under different load rates are obtained from the data derived from the energy management system. Based on the load-forecasting results, the operating parameters of water system equipment are optimized. The results are shown in Table 3.  
It can be seen in Table 3 that within the range of a 25-90% load rate, the relative error of load-forecasting results is within \(\pm\)10%. The actual total load of 14 working conditions in the table is 22,761.8 kW, the predicted total load is 22,913.2 kW, and the relative error is 0.64%. The load-forecasting effect is good. In terms of equipment operation parameters, with the increase in load rate, the outlet temperature of chilled water decreases from 12.00 \({}^{\circ}\)C to 9.30 \({}^{\circ}\)C. When the load rate is below 80%, one cooling water pump and cooling tower can meet the load demand, and when the load rate is above 80%, two can meet the load demand. The start and stop of the cooling tower fan are interlocked with the cooling water pump; one set of chilled water pumps can meet the load demand when it is operated at a 25% load rate, and two sets can meet the load demand under other working conditions.  
(2) Energy consumption analysis of the water system  
It can be seen in Table 3 that the optimization strategy has a certain energy-saving effect in the range of a 25%-90% load rate, and the energy-saving effects of the optimization strategy are different under different load rates. When the load rate is 25.4%, the system energy-saving rate is the highest, which is 29.45%. When the load rate is 80.37%, the system energy-saving rate is the lowest, which is 0.43%. The average energy-saving rate of the optimization strategy under different load rates is 12.40%.