esa-sceva/satcom-chunk-collection huggingface dataset:
# Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil
## 1 Introduction
The Caatinga region located in northeast Brazil (Figure 1) is a suitable biogeographical area to test the usability of SIF as a proxy for photosynthesis seasonality and abiotic stress due to its heterogeneous vegetation, its pronounced water-dependent seasonality, and a recent extreme drought reported there in the year 2012 [34]. Accordingly, our specific objectives were to:  
* Describe seasonal and spatial patterns of chlorophyll fluorescence dynamics as estimated by the GOME-2 orbital instrument in a eleven-year period from February 2007 until December 2017;
* Model Sun-induced fluorescence (SIF) and spectrally adjusted SIF, as functions of environmental parameters, testing their responses to climate in the period;
* Compare the responses of vegetation from the different ecoregions of the Caatinga, as defined by [PERSON] et al. [35], to the observed environmental variation in the period. Our main hypothesis was that SIF is a good proxy to photosynthesis seasonality and drought-responses, even when measured from heterogeneous vegetation and, especially when combined with different spectral data like vegetation indices.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 2 Related Work
### Security Vulnerabilities in 5G-AKA  
5G-AKA is the standardized Authentication and Key Agreement protocol in the latest 3 GPP 5G security architecture TS 33.501 [2]. Evolved from the architecture of EPS-AKA in the LTE security architecture, 5G-AKA aims to ensure the authenticity between UE, the serving network, and the home network. However, some security vulnerabilities in the 5G-AKA have recently been disclosed, making it less secure than has been claimed. For example, Ref. [3; 5] found that 5G-AKA suffers from linkability attacks, by which adversaries can use synchronization error messages (MAC_FAIL and SYNC_FAIL) to detect if the UE is currently located in a certain area. Additionally, 3 GPP TR 33.846 [7] found that 5G-AKA fails to prevent denial-of-service (DoS) attacks because the 5 GC has no way to justify if the SUCI is a replayed message. The 5G-AKA is a centralized protocol that heavily relies on the authentication functional entities of AUSF/UDM, so it could be vulnerable to Distributed DoS (DDoS) attacks and single-point-of-failure issues in the AUSF/UDM. Moreover, [4] found that 5G-AKA fails to provide perfect forward secrecy and post-compromise secrecy due to the use of the long-term symmetric keys and sequence numbers. In fact, according to the 3 GPP TS 33.501 [2], the device anonymity protection of UE in 5G-AKA is also vulnerable. For example, network operators can opt out of the encryption in the Subscription Concealed Identifier (SUCI) that encrypts the Subscription Permanent Identifier (SUPI) of UE. It is also known as a \"null-scheme\". Thus, the UE will send the cleartext of its SUPI through wireless channels, which could be dangerous for IMSI-catching attacks. In some emergent situations, UE also sends its SUPI directly to initiate authentication procedures. To conclude, 5G-AKA is vulnerable to many network attacks, including but not limited to linkability attacks, DoS attacks, and IMSI-catching attacks. The lack of perfect forward secrecy also makes 5G-AKA vulnerable to session data recovery if the long-term key (LTK) is compromised at any time.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 4 Traffic-Aware Clustering-Based Routing Protocol (TACRP)
### Vehicle Laving the Cluster  
VANET is a network with a dynamically moving topology. It means that vehicles in clusters get periodical replacement according to vehicle mobility. If the vehicle CM wants to leave the present cluster it has to send a request message to the neighbor CH. After getting the approval the leaving CM gets disconnected and then joins the new cluster [20]. The CH needs to find a replacement as soon as possible. Then the routing table gets updated according to the degree of connectivity. Consequently, the CM with maximum connectivity is chosen as the next CH member. Then the CH rebuilds the cluster to achieve the maximum number of CMs in a decentralized manner. So, the CM which leaves a cluster transmits the leaving message to the corresponding CH and the other CM vehicles in the cluster and then the routing table gets updated. The leaving CM resets its CH's ID to null to communicate with the new CH.
## 3 Results and Discussion
The RABE prototype transmitting with 4-tone QPSK modulation, with 1 MHz frequency separations. On the other hand, it is worth mentioning that the development of the PCB was taking into account the CubeSat standard, with the idea of possible inclusion as a payload in a satellite platform and carrying out transmission tests from space. To ensure that the designed device has a correct performance in space, it is necessary to carry out different types of tests so that the prototype can withstand the working conditions (electromagnetic radiation, thermal gradient) in space and being put into orbit (vibration). Among the tests that the prototype will undergo, we can mention those of electromagnetic compatibility and susceptibility, Total Ionizing Dose (TID), vibration and thermo-vacuum. Some simulations were carried out, and they were obtained according to the PCB of the manufactured prototype, with the aim of comparing and knowing the radiation generated by inducing an electric current in the transmission lines. These simulations were carried out using the Keysigth (\"EMPro\" software based on the Finite Difference Time Domain Method (FDTD) because it is one of the numerical analysis techniques best used to simulate electrodynamics, as manifested in [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. The simulation was carried out taking into account the Transmission Lines (TL) designed on the RABE prototype PCB towards the RF output. The near field for the present prototype is defined as maximum \(1\lambda\) (wavelength). To perform the simulations, the frequency was defined at 2.3 GHz; therefore, the radiation levels emitted by the TL for the \(\vec{E}\) field can be seen in Figure 13a and for the \(\vec{H}\) field in Figure 13b. It can be seen graphically that the radiation levels emitted by the TL of the RABE transmitter do not act as an antenna towards the outside of the PCB with the \(\vec{E}\) and \(\vec{H}\) fields. However, when obtaining the Poynting vector with (3), the case is different, because here it can be seen that the radiation pattern exceeds the limits of the PCB. \[\vec{S}=\vec{E}\times\vec{H} \tag{3}\]  
With the simulations of the \(\vec{E}\) and \(\vec{H}\) fields, the Poynting vector can be obtained, as can be seen in Figure 14, where \(\vec{S}\) gives the measure of rate of energy flow per unit surface area at a point in a uniform plane wave. Figure 12: Spectrum radiated by RABE at 2.300 GHz. Image obtained from the signal analyzer with the prototype transmitting a single tone with a frequency of 2.300 GHz.
###### Abstract  
The theoretical paradigm of geographic automata systems (GAS) underpins a wide range of studies to represent dynamic complex geospatial phenomena. Specifically, cellular automata (CA) were used extensively over the past 40 years for geospatial applications, though primarily for modeling urban growth. Currently, the hyper-specialized and fragmented geospatial technology ecosystem supporting CA model implementation often necessitates programmed solutions or use of disconnected programs with graphical user interfaces (GUIs) separate from common geographic information systems (GIS) software. Therefore, the main goal of this study is to present a general-purpose, GIS-based CA modeling framework and extension for Esri's ArcGIS Pro software environment. The methodological approach centered around (1) developing generic functions for building binary or multi-class CA models to capture various spatiotemporal systems and (2) enabling end-to-end CA modeling projects that can incorporate built-in functionality available in ArcGIS Pro versions 3.1 and newer. Two case studies demonstrate the add-in capabilities to support geosimulation model-building activities and exploration of new hybrid models. This research contributes to advancing flexible, transparent spatiotemporal modeling tools within existing GIS software. The proposed approach addresses the lack of streamlined geospatial technologies capable of simulating numerous dynamic geospatial phenomena, exploring human and environmental processes, and examining possible futures with CA in research, decision making, or educational settings.  
geographic automata; geographic cellular automata; geospatial spatiotemporal modeling tools; geographic information systems software extensions; ArcGIS Pro add-in; geospatial technologies for geosimulation
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 2 Background  
In previous works that aim at realizing power savings, the main target scenario was the distributed sensor network, with low traffic arrival rates in each STA. In distributed sensor network, since the sensors are static after they have been implemented, the tree construction mechanism as proposed in [18] is one of the main systems of these networks. In order to reduce power consumption in transmission procedure, previously proposed transmission schemes are based on the duty-cycled power saving operation to reduce the idle listening, which refers to the consumption of power while in the idle state without actually transmitting or receiving data. However, in the duty-cycled operation, which involves periodically turning on and off the transceiver, there are tradeoffs between the consumed power and the average data transmission delay. To resolve the tradeoff problem and meet the various requirements in different kinds of sensor traffic, wake-up receivers and related data transmission schemes for sensors and IoT devices have been developed.
###### Abstract  
The long-term three-phase imbalance in distribution systems can lead to increased energy losses during transmission, reduced efficiency in energy utilization, and have serious implications for power supply security, power quality, and economic operations of the power system. The automatic load commutation device is an automatic device designed to address the issue of three-phase load imbalances in low-voltage distribution networks. By adjusting the phase of users without interrupting the power supply, this automatic device evenly distributes the load across all phases and effectively resolves the three-phase imbalance problem. Therefore, aiming at the above issue, a robust optimization method is adopted to address the control problem of automatic load commutation devices for a low-voltage distribution network. First, using historical data from photovoltaic and wind power generation systems as well as user load, a forecast analysis is conducted and uncertainty models for renewable energy and load demand are established. Then, a robust control strategy for the automatic load commutation device is proposed, which considers uncertainties of both source and load using the robust optimization method. The proposed model is then linearized using second-order cone technology and strong dual theory, and the column-and-constrained generation (C&CG) algorithm is employed to solve the problem iteratively. Finally, a modified IEEE 33-bus system is taken to verify the effectiveness of the proposed strategy. The simulation results show that the proposed load commutation device robust control model can enhance the ability of the distribution network to respond to load demand and renewable energy fluctuations while ensuring the economic operation of the distribution network. In addition, by adjusting the deviation amount and uncertainty parameters of load demand and renewable energy, a good balance between the robustness and economy of the proposed model can be achieved.  
low-voltage distribution network; uncertainty; load commutation device; robust optimization
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 7 Antimicrobials
### Animalarial and Antivials  
Most commonly used antiviral and antimalarial agents possess a stereogenic carbon atom. The chiral antimalarial drugs chloroquine (**37**) and its hydroxyl analog hydroxychloroquine (**38**) are also known for their antiviral activities against human immunodeficiency virus type \(1\) (HIV-1) and acquired immunodeficiency syndrome (AIDS) virus [106]. These drugs have shown preliminary inhibitory effects against the new pandemic Coronavirus 19 (COVID-19) [107] and apparent efficacy in clinical studies [108]. Cabotegravir (**39**) is an HIV integrase strand transfer inhibitor with potent antiviral activity and very useful in HIV prevention in uninfected women [109]. The antiviral remdesivir was the first FDA-approved treatment for COVID-19 and is a chiral aryloxy-phosphoramidate prodrug, as it is metabolized into nucleoside triphosphate (active drug form) inside the cell through sequential reactions by ester-mediated-hydrolysis [110]. In a recent study on _Opuntia focus-indica_, a plant with interesting biological activities widespread worldwide in tropical and subtropical regions, including the Mediterranean Sea [111], chiral phytochemicals extracted from this plant were suggested as interesting protease inhibitor candidates for anti-COVID-19 disease acting as inhibitors of the main protease (M\({}^{\text{pro}}\)), which is essential for viral replication [112].
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 4 Experiment
### Model Evaluation Indicators  
In the analysis of the comparison of experimental results of signal detection, the following three performance indicators [2] are mainly used in this paper.  
1. Detection probability \(P_{d}\): \(P_{d}\) is the ratio of the number of detected real signals to the total number of actual signals. Let the total number of actual signals to be detected by \(N\) and the number of detected target signals be detected by \(M\), which can be expressed as \(M\)=\(M_{T}\)+\(M_{F}\), where the number of real signals is \(M_{T}\), and the number of false results with no signal is \(M\), then \(P_{d}=M_{F}\) / \(N\).  
2. False alarm probability \(P_{f}\): \(P_{f}\) is the ratio of the number of detected spurious signals to the total number of detected signals, that is, \(P_{f}=M_{F}\) / \(M\).  
3. The average error of signal parameters \(E_{avg}\): \(E_{avg}\) is to count the frequency, bandwidth, and burst start and end time parameters for all detected real signal targets: \(f_{c0}^{i}\), \(B_{w0}^{i}\), \(t_{s0}^{i}\), and \(t_{c0}^{i}\) with the corresponding real parameters: \(f_{c}^{i}\), \(B_{w}^{i}\), \(t_{s}^{i}\), and \(t_{c}^{i}\), then the average relative error between them is calculated by the formula (11).  
\[E_{avg}=\frac{1}{4M_{T}}\sum_{i=1}^{M_{T}}\big{(}\frac{\big{|}f_{c}^{i}-f_{c0}^ {i}\big{|}}{B_{w}^{i}}-\frac{\big{|}B_{w}^{i}-B_{w0}^{i}\big{|}}{B_{w}^{i}}- \frac{\big{|}t_{c}^{i}-t_{s0}^{i}\big{|}}{T^{i}}+\frac{\big{|}t_{c}^{i}-t_{c0 }^{i}\big{|}}{T^{i}}\big{)} \tag{11}\]  
where \(T^{i}\) is the true signal duration, and \(B_{w}^{i}\) is the true signal bandwidth.  
In experiments where the performance gap is obvious, we will directly use mean Average Precision (mAP) [33] as the evaluation index, as shown in the following formula:  
\[P(k)=\frac{TP}{TP+FP}\times 100\% \tag{12}\]  
\[R(k)=\frac{TP}{TP+FN}\times 100\% \tag{13}\]  
\[\text{m}AP=\frac{1}{C_{s}}\sum_{M=i}^{N}P(k)\Delta R(k) \tag{14}\]  
where \(TP\) is the number of correctly identified signal samples, \(FP\) is the number of incorrectly or unidentified signal samples, and \(FN\) is the number of incorrectly identified signal sample targets. \(C_{s}\) is the number of categories of signal samples; \(M\) and \(N\) represent the number of IOU thresholds and IOU thresholds; \(P(k)\) and \(R(k)\) is the precision and recall rates.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 4 Routing Algorithm
### Complexity Analysis  
In this section, we will investigate the complexity of Algorithm 1. We assume that the number of neighbors of the sender is n. In Algorithm 1 the sender needs to calculate the distance of each node to the destination firstly for forwarding mode selection. The time complexity of this process is O(\(n\)). In the greedy forwarding mode, according to Formula (16), additional calculation of link utility \(E(l)\) is required. As shown in Formula (15), \(E(l)\) is defined as an integral Formula, where \(f(\Delta v_{j}-\Delta v_{i})\) follows Gaussian distribution. Therefore, the complexity of (15) is O(1) using Newton-Leibniz formula to the computation, and for all neighbors the complexity is O(\(n\)). Due to the effective information flow is maintained by each node and sent to the neighbors with hello packet, the complexity of \(I(t)\) is O(1). As shown in Formula (16), in order to obtain the value of vehicle utility, it is necessary to calculate the variance of \(E(l)\) and \(I(t)\), so the computation complexity is O(1). In general, the computing complexity of each node in greedy forwarding mode is O (\(n\)), and the complexity of the whole routing process is O (\(n^{2}\)). In the recovery mode, the right-hand rule is the core of the algorithm. A no-crossing algorithm based on RNG or GG network structure is used in this mode, which requires O(\(n^{2}\)) complexity for each node. For the whole routing path the complexity is O(\(n^{3}\)). As shown in the Table 2, compared with the classical routing protocol, our protocol does not increase the complexity.
## 4 Results
### Operation Ticket Generation  
In the process of obtaining temperature data, sometimes the sensor is encountered by the wind and snow. We found that there were a lot of incomplete, inconsistent, and abnormal values in the original temperature profile data from the ice-tethered, seriously affecting the efficiency of the algorithm. During data mining, the original data preprocessing process was also viewed as data cleaning [21]. Figure 13 shows the process of obtaining a heating operation ticket in the cabin. It is roughly divided into three parts:  
1. Input sample data: data acquisition, then data preprocessing, unusable data culling, and filtering after thresholding.
2. Determination of the prediction method: Separate the amount of data by 80% and 20%. After normalization and regularization, it is input as a training set and test set. During the training and testing of five neural networks (BPNN, ELM, Bi LSTM, SAELSTM, and LSTM), no overfitting occurred in each system. Loop three times and select the algorithm corresponding to the smallest error.  
Figure 12: Bi LSTM model predicts the results of the next 24 h of data.  
According to the corresponding temperature prediction, based on the current temperature and the temperature in the next two hours, a heating operation ticket is generated, and the lower temperature is used as the predicted temperature to represent the average temperature in the next hour. The prediction result is compared with the prediction result of the Bi LSTM model, and then the result with a lower temperature in the next hour is selected as the parameter generated by the temperature difference. Then check the table and heat accordingly.
# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 3 Application of the Proposed Approach to Insulator Detection
### Description of the Proposed Approach  
The block diagram in Figure 1 shows the individual stages of the conducted research. The first stage involved an inspection flight along an electric power line with the use of an unmanned aerial vehicle and a camera recording a digital image at a resolution of \(3840\times 2160\). Then, the recorded video material was divided into single digital images with a specific time interval. The selected images containing the examined object or objects were pre-processed and then tagged.  
Figure 1: Subsequent stages of the research experiment.  
In the next stage, the selected images were divided into training datasets consisting of 15, 30, and 60 frames, and a test dataset consisting of 283 frames containing 2560 regions of interest (ROIs) representing a power insulator. In the in-depth study for testing of selected CNNs (the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN)), training datasets were also prepared, comprising five, 10, 20, 25, 35, 40, 45, 50, and 55 frames. A detailed description of the dataset preparation process with its sample elements is described in more detail in the next section. For each prepared dataset (15, 30, 60 frames), the process of training for the selected convolutional neural network was conducted. The inference process for an identical test dataset was performed, and, in the last stage, the obtained results were compared.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 2 Materials and Methods  
### Dataset  
A publicly available database, AMIGOS, was used in this work [7]. This database was preferred over other available databases due to its relatively high number of participants, recent publication, large number of signals and experiments, standardized data collection methods, and detailed and high-resolution annotation. This database includes EEG, ECG, galvanic skin response (GSR), and face video data from 40 participants recorded during two experiments. The first experiment (short videos) consisted of individually watching 16 short videos of less than 250 seconds each. The second experiment (long videos) consisted of watching 4 long videos of around 14 min each in groups (see Figure 2).  
Experiments were done in a lab-controlled environment to record changes in physiological signals when exposed to the video stimuli. The face video recording was subsequently externally score-annotated by experts in arousal and valence dimensions with a time resolution of 20 s (length of each annotated time window) for both short and long experiments. In addition, in the case of short experiments, a self-annotation was carried out by means of a questionnaire after each short-video presentation.