esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Security Architecture for WSN-Based Applications in Smart Grid
## 1 Introduction
### Problem Statement  
The result of different communication technologies in the SG is a potential increase in the security risk. Apart from that, the security of WSNs-based applications in SG needs attention for many reasons. The open nature of WSNs makes them vulnerable to different attacks, such as the Denial of Service (DoS), selective forwarding, sinkhole, and Sybil [5]. Furthermore, WSNs are deployed in large solar and wind farms. Additionally, many research efforts interested in the security of WSNs-based applications in SG focus more on the consumer side. The power generation side is broadly neglected. However, the existence of internal or external attacks on WSNs should not affect the availability of the SG services and the energy generation. Hence, security is a critical problem in WSNs applications and many security requirements should be considered. Non-repudiation is one of the important requirements, it prevents sensor nodes from denying their actions and messages. Integrity is another requirement; it ensures that the transmitted data are not altered by an illegitimate entity [5]. The authentication validates that the identity of the sensor node that sent a message is what it claimed. Additionally, confidentiality protects sensitive data from being disclosed [6]. Distinct efforts have been made to design security schemes for WSNs in SG. The existing schemes are categorized as encryption-based and non-encryption-based solutions, and this depends on the nature of the threats. Non-encryption-based solutions target only some types of attacks and provide only some security requirements. However, the encryption-based solutions mitigate a larger set of attacks and ensure many security services, such as confidentiality and integrity. PKI is a potential mechanism that provides the most security requirements for WSNs in SG [5]. The authentication and the non-repudiation can be satisfied by a digital signature, the integrity is ensured using a keyed hash function and confidentiality is guaranteed using encryption [6]. However, PKI has some limitations, particularly the availability and the scalability of the CA due to its centralized architecture. The CA represents a single point of failure, once the CA is compromised, the WSNs will be compromised as well. The compromise of the CA will affect the availability of the certification services in the network. Furthermore, the CA should be able to serve a high number of sensors at the same time and keep the same robustness, independently of the size of the network.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### Secrecy of the Network  
NOMA technology is receiving an increasing amount of attention as a result of its various benefits, including high data rates, improved spectrum and energy efficiency, broad connectivity, and reduced latency. However, because wireless channels are typically open, secure data transfer in wireless communication systems continues to be a major difficulty. Physical layer security (PLS) techniques have recently been introduced to improve the stability of NOMA systems and address wireless transmission problems. Users who are closer to the channel are typically given lower power levels than users who are farther away and have worse channel conditions. In order to obtain their own signals, near users will therefore need to block the high-power transmissions (signals of far users). To facilitate the effective deployment of NOMA, these major security issues must be resolved [192]. The problem of protecting NOMA utilising a physical layer has recently been the focus of various studies in the literature. Because it depends on the randomness and dynamics of the physical layer and uses straightforward operations to guarantee the security of communicated data, PLS is a novel security approach that has proven to be more effective and robust than existing upper-layer security schemes [193].  
Secrecy of the network is an essential problem that must be addressed in each wireless communication generation [194; 195; 196; 197; 198]. Because it is subject to eavesdropping, a signal sent using a wireless channel requires special attention. Security remains an unresolved question for the NOMA method, particularly in the cases of MIMO and mMIMO. In wireless networks, providing secure communication has always been a priority. Traditionally, security has been handled at the protocol stack's top levels, using encryption methods that are unaffected by the physical features of wireless channels [198] However, with the rapid expansion in the number of low-complexity, low-power, and computationally challenged devices in 5G and IoT networks, the notion of PLS is gaining traction. PLS uses the unpredictability of wireless channels to ensure that a hostile eavesdropper cannot decipher the sent data [199].
## 3 Results
### EACEE Inhibited Excess ROS Production in LPS-Stimulated RAW 264.7 macrophages  
Previous studies have demonstrated a strong relationship between oxidative stress and inflammation [15]. To investigate the inhibitory effects of EACEE on LPS-induced redundant ROS production in macrophages, we conducted an oxidative stress assay using a cell analyzer. First, for the establishment of proper conditions, the cells were incubated with LPS for various times; then, the proportion of ROS-positive cells was measured using the Muse(r) oxidative stress kit. LPS-stimulated cells showed upregulated intracellular ROS levels compared with vehicle-treated normal cells after 3 h (\(p<0.001\)). However, pretreatment with EACEE significantly inhibited this excess ROS production in LPS-stimulated RAW 264.7 macrophages (Figure 3).  
Figure 2: Effect of EACEE on NF-xB activation in LPS-stimulated RAW 264.7 macrophages. Cells were pretreated with EACEE for 1 h and stimulated with LPS for 30 min. Total protein was isolated and the expression of (**A**) p1 xB-\(\alpha\) and lxB-\(\alpha\) was detected by Western blotting. (**B**) Nuclear and cytosolic proteins were isolated and the levels of p65 in each fraction were determined. Densitometric analysis was performed using ImageJ software. Data are shown as mean \(\pm\) S.D. of three independent experiments. ***\(p<0.001\) vs. LPS-unstimulated macrophages; ***\(p<0.01\) and ***\(p<0.001\) vs. LPS-stimulated macrophages.  
EACEE Enhanced Nrf2 and HO-1 Protein Expression and Inhibited Overexpression of Nox4 in LPS-Stimulated RAW 264.7 Macrophages  
The Nrf2/HO-1 signaling pathway has anti-inflammatory therapeutic potential [16]. To observe the effects of EACEE on oxidative stress-mediated Nrf2 signaling in LPS-stimulated macrophages, we confirmed the protein expression of Nrf2, HO-1, and Nox4. The expression of Nrf2 protein was lower in LPS-stimulated cells than in vehicle-treated normal cells in the nucleus fraction, but this expression was significantly enhanced by EACEE (25 and 50 \(\mathrm{\SIUnitSymbolMicro m}\)/mL) treatment (Figure 4A). Additionally, EACEE (50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) treatment significantly upregulated HO-1 protein expression compared with that in LPS-stimulated cells (Figure 4B). Moreover, the protein expression of Nox4 was significantly higher after LPS stimulation than after vehicle treatment (1.00 \(\pm\) 0.21 vs. 0.48 \(\pm\) 0.06). In contrast, treatment with EACEE significantly inhibited the overexpression of Nox4 protein in a dose-dependent manner in LPS-stimulated cells (Figure 4C).  
Figure 3: Effects of EACEE on ROS generation in LPS-stimulated RAW 264.7 macrophages. (**A**) Cells were incubated with LPS for various times and the levels of ROS were measured using the Muse® oxidative stress kit. (**B**) Cells were pretreated with or without indicated concentrations of EACEE for 1 h and incubated with LPS for 180 min. Data show relative percentages of ROS-negative and ROS-positive cells. Data are shown as mean \(\pm\) S.D. of three independent experiments.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Tracking Techniques
#### 2.5.3 Simultaneous Localization and Mapping (SLAM) Algorithm  
The SLAM algorithm is the process by which the robot (mobile node) can build a map of the environment and simultaneously determine its position on the map. The trajectory of the platform and the location of all landmarks are estimated in real time, without any a priori knowledge. The mobile nodes are sensors with time-varying states, such as position and pose, and the landmarks have fixed or slowly changing states. Specifically, the probabilistic SLAM involves the estimation of an appropriate representation for the observation and motion model, which allows for efficient and consistent computation of the prior and posterior distribution. The observation model is given by:  
\[P(x_{k},m|Z_{0:k},U_{0:k},x_{0}), \tag{25}\]  
where \(x_{k}\) is the state vector, \(m\) is the landmark vector, \(Z_{0:k}\) is the set of all landmark observations, \(U_{0:k}\) is the history of control inputs, and \(x_{0}\) is the robot's initial state. The motion model is given by  
\[P(z_{k},m|x_{k},m), \tag{26}\]  
where \(z_{k}\) is the landmark observation at time instant \(k\), and \(m\) is the landmark vector. In SLAM, the correlations between landmark estimates improve and never diverge, so the joint probability density on all landmarks is proportional to the number of observations. For instance, the robot vacuum cleaners, which are used to randomly clean floors, utilize the SLAM tracking, while the newer versions operate in a much more intelligent approach using the vision simultaneous localization and mapping (VSLAM). In VSLAM, the infrared cameras are also used for taking snapshots of the area for building the whole picture of the environment. By \"remembering\" the layout of the different rooms, future cleaning becomes more effective [67]. Nevertheless, the tracking camera is sensitive to light strength changes [59]. In addition, SLAM can improve the accuracy of the position estimations by using the inertial measurement unit (IMU). The IMU reduces the cost of the system at the expense of increasing the computational complexity and limiting applicability to the particular area/environment used [8]. The IMU-SLAM can be used for pedestrian tracking in indoor navigation, which is useful in emergency scenarios, such as terrorist attacks or buildings on fire. For instance, in [68], the IMU-SLAM was effectively used by the \"FeetSLAM\" algorithm, which is a collaborative SLAM technique operating in GNSS-denied and infrastructure-less indoor areas. In particular, the inertial data were derived from the 3D accelerometer and gyroscope and combined with UKF for estimating the pedestrian orientation.
## 2 Status of RIS and LC-RIS  
### Methods to Achieve RIS Inspired by LCRA and LCOS  
Current approaches to realize RIS are categorized in Figure 2. Interestingly, the technological candidates identified here are mostly identical with those used to achieve microwave and millimeter-wave phase shifters [18], except for digital/programmable metamaterials [19] and software-controlled metasurfaces [9; 12]. Extensive research and excellent reviews on these candidates have been presented in previous publications on LC-based phase shifters [20] and phased array beam steering [21], which agree with our argument that the \"new\" concept of RIS could arguably be seen as being driven by LC, more specifically by LC phase shifters based on phased array [22] or LC reflectarray [23; 24].  
Figure 1: Technological progression of LCOS, LCRA, and LC-RIS.  
iquid crystal on silicon (LCOS) technology [25; 26; 27; 28] has been in existence for several decades, and it is primarily used in display technology. LCOS works by reflecting light off a mirror coated with a layer of liquid crystals that can be electrically controlled to change the angle of the reflected light to generate high-resolution images. LCOS displays are known for their high brightness, contrast, and color accuracy, making them suitable for a wide range of applications, including projectors, head-mounted displays, and augmented reality devices. While LCOS can modulate light waves, its performance in modulating microwaves or radio waves is limited due to the size of the liquid crystal pixels and the operating frequency range. However, LCOS technology has recently been leveraged to drive the emergence of LC-RIS.  
LC-RIS, on the other hand, uses a surface coated with an array of small liquid crystal pixels that can be individually controlled electronically to manipulate the phase, amplitude, and polarization of incident electromagnetic waves (including radio waves, microwaves, millimeter waves, THz, and light waves), producing customized wavefronts that can be used to redirect, reflect, or scatter incoming signals in real time and, thus, exhibiting potential applications in wireless communication (by enabling more efficient and secure transmission of data), as well as radar and imaging systems. Numerous examples of RIC-aided networks are illustrated in [14]. Arguably, the emergence of RIS has provided a new way to think about displays and communication systems. RIS technology has the potential to revolutionize the way we interact with our environment and improve many aspects of our lives. As the research in this area continues, we can expect to see even more exciting applications of RIS technology in the future.
###### Abstract  
The adaptive beamforming algorithm can realize interference suppression and navigation signal enhancement, and has been widely used. However, achieving high-precision real-time estimation of the direction of arrival (DOA) parameters of navigation signals in strong-interference scenarios with low complexity is still a challenge. In this paper, a blind adaptive beamforming algorithm for a Global Navigation Satellite System (GNSS) array receiver based on direction lock loop is proposed without using the prior information of the DOA parameter. The direction lock loop is used for DOA tracking and estimation after interference suppression, which uses the spatial correlation of the array beam pattern to construct a closed direction-tracking loop. The DOA estimation value is adjusted in real time based on the loop errors. A blind beamformer is constructed using the DOA estimation results to provide gain by forming a beam in the satellite direction. This method improves the accuracy and dynamic adaptability of DOA estimation while significantly reducing the computational complexity. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.  
GNSS; blind adaptive beamforming; direction lock loop; array antenna; DOA estimation
# ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
## 2 Background
### KREONET: Korea Research Environment Open Network  
KREONET is the representative research and education network in Korea. The goal of KREONET is to provide a collaborative research environment for a variety of research areas such as high-energy physics, astronomy and space observation, genomics and biology, weather/climate, education, future networking, and supercomputing. Since 1988, KREONET has been providing networking services, and it is currently focusing on realizing a science big data super highway through ScienceDMZ, ScienceLoRa, and SDN (software-defined network). More than 200 organizations in Korea, including universities, research centers, and government institutes, are connected through KREONET. KREONET currently operates and manages 17 regional centers in Korea and 4 international Points of Presence (PoPs) (i.e., Hong Kong, Seattle, Chicago, and Amsterdam). Particularly for international networking services, KREONET has been participating in the GLObal RIng network for Advanced Application Development (GLORIAD) project since 2005. Figures 3 and 4 show the network topologies of KREONET and GLORIAD, respectively.
## 1 Introduction
### Addressing RRI and RTI Collisions  
DTS-AC employs a new TDMA notification system to mitigate RTI by allocating specific time slots to each reader, ensuring that they can operate independently without interfering with one another.  
Additionally, by incorporating FDMA-based frequency resource management, DTS-AC effectively resolves RRI collisions. This approach assigns different frequency channels to individual readers, minimizing collisions during tag interrogation.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 4 Estimation of Forwarder Search Delays
### Discussion on Waiting for the \(k\)-First Replying Forwarders  
As discussed in [48] for instance, the times, at which the \(k\)-first forwarders will reply, corresponds to the order statistics [49] of the random wake-up time of nodes. If a node has \(M\) possible forwarders,the time at which they will wake-up is given by the \(M\) random variables \(Y_{1},\ldots,Y_{M}\). The _sorted_ sequence of these wake-up times is the _order statistics_ and is denoted \(Y_{(1)}\leq Y_{(2)}\leq...\leq Y_{(M)}\). Now, we compute the average value of \(Y_{(k)}\) in two specific, but largely applicable, cases:  
* If each of the \(Y_{i}\) is exponentially distributed, THEN: \[E(Y_{(k)})=\sum_{i=M-k+1}^{i=M}\frac{1}{i}=\frac{1}{M}+\frac{1}{M-1}+\ldots+ \frac{1}{M-k+1}\] Furthermore, because even for dependent variables \(U\) and \(V\), we have \(E(U-V)=E(U)-E(V)\), therefore the mean delay between the \(k\)-th and \((k+1)\)-th reply is: \[E(Y_{(k+1)}-Y_{(k)})=E(Y_{(k+1)})-E(Y_{(k)})=\frac{1}{M-k}\] It results that for instance, \(E(Y_{(2)})-E(Y_{(1)})=\frac{1}{M-1}\) and we have \(E(Y_{(1)})=\frac{1}{M}\). One fundamental result is that \(E(Y_{(2)}-Y_{(1)})>E(Y_{(1)})\), hence a node will wait on average (slightly) longer for the 2-nd reply than from the first. Intuitively, the difference comes from the fact that, after the first reply, there are only \(M-1\) nodes which could reply. Naturally, if \(M\) is large, the mean delay between the first and the second reply is small. **Remark 1**.: _The distribution of \(Y_{(k+1)}-Y_{(k)}\) is even given in [50], it is exponentially distributed as: \(\mathrm{Exp}(\frac{1}{M-k})\)._
* If each of the \(Y_{i}\) is uniformly distributed in [0,1], THEN the order statistic \(Y_{(k)}\) has a beta distribution (using here the notation \(n=M\)): \[Y_{(k)}\sim\mathrm{Beta}(k,n+1-k)\] and then: \(E(Y_{(k)})=\frac{k}{n+1}\). Thus: \(E(Y_{(1)})=\frac{1}{n+1}\) and \(E(Y_{(k+1)}-Y_{(k)})=\frac{1}{n+1}\). This means that a node will wait on average as long for the second reply after having received the first, as it will wait for the first reply.
# An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
## 3 Results
### Experimental Data
Figure 16b shows an example of interference fringes with position errors, in which the azimuth error is \(-\)500 m and the distance error is \(-\)500 m. It can be seen that the interference fringes with position errors are shifted in both azimuth and distance directions compared with the interference fringes without errors. The second dataset is generated by artificially adding yaw angle error in the process of simulating interference fringes to simulate the real attitude error in flight, where the yaw angle error is between plus and minus 30\({}^{\circ}\), the error interval is 1\({}^{\circ}\), and there is a total of 61 pieces. Figure 16c shows an example of interference fringes with attitude error, where the yaw angle error is 30\({}^{\circ}\). It can be seen that the interference fringes with attitude errors have certain rotation and translation compared with the interference fringes without errors. Figure 15: Experimental data. (**a**) The corresponding SAR image of the experimental area. (**b**) Real interference fringe with noise. (**c**) Real interference fringe after noise removal. (**d**) Real interference fringe after removing the leveling effect.
# New Space Engineering Design: Characterization of Key Drivers
## 3 Results and Discussion
### General Discussion of New Space Design Drivers and Enablers
#### 3.1.3 Category C: Sustainability and Technological Independence
This trend is facilitated in the New Space era by the emergence of specialized space suppliers around the world. For example, while the United States has historically dominated the production of fluidic components such as space-qualified valves and regulators, new suppliers have emerged across Europe, Japan, and India, broadening global access to these critical components [84]. Although not always explicitly considered, this approach is particularly effective during periods of global supply chain disruptions, such as in times of conflict or geopolitical instability. Anticipating these risks at the pre-design stage can help save both time and costs during later phases of development. A more conscious approach to life cycle management, built on sustainability, safety, and autonomy, can be key to ensuring a company's success by accelerating time-to-market. This is achieved, for example, by reducing operational delays typically associated with the handling of hazardous materials during pre-launch phases and by mitigating export-related bureaucratic constraints during commercialization.
## 5 Result and Discussion
### Evaluation Metrics  
The performance of the BSL recognition using BenSignNet was measured by accuracy, precision, F1-score and recall, based on the true positive (TP), false positive (FP), true negative (TN) and false negative(FN) defined as below:  
The ratio between the total count of correctly predicted classes and total count of sample is called accuracy was computed using Equation (8).  
\[Accuracy=\frac{TP+TN}{P+FN+FP+TN}\times 100 \tag{8}\]  
The ratio between the total count of correctly predicted positive sign classes and total count of classes is called precision or sensitivity was computed using Equation (9).  
\[Precision=\frac{TP}{TP+FP}\times 100 \tag{9}\]  
The ratio between the total count of correctly predicted positive classes and total count of positive classes is called precision or sensitivity was computed using Equation (10).  
\[Recall=\frac{TP}{TP+FN}\times 100 \tag{10}\]  
The harmonic mean between the precision and recall is called the F1 score was calculated by the Equation (11).  
\[F1 score=\frac{2\times Precision\times Recall}{Precision+Recall}\times 100 \tag{11}\]