esa-sceva/satcom-chunk-collection huggingface dataset:
# Attack Classification Schema for Smart City WSNs
## 2 Background and Related Work
### Wireless Sensor Networks
#### 2.2.1 General Overview  
Wireless sensor networks are networks that communicate using wireless technology, where nodes, also known as motes, are equipped with one or several sensors to capture information about their environment.  
In smart cities, it is common for motes to have autonomous cooperative communication to send values read by their sensors to a device at the edge of the WSN known as the gateway or base station. Gateways are equipped with several communication interfaces with the aim of transmitting WSN data to the smart city data centers through a conventional and reliable network (e.g., the Internet).  
Wireless personal area networks (WPAN), which are the type of wireless sensor networks that are normally used to transmit sensor data, are considered especially vulnerable. Relevant technologies included in this category are ZigBee [13] and Low power Wireless Personal Area Networks (6 LoWPAN) [14]. These are made of low-power devices, low-throughput and short-range communication modules, which rely in many cases on multi-hop capabilities to build an extensive network and deliver packets from the most remote nodes to the base station. Besides, motes are frequently battery powered and, therefore, are designed also with restrained processing capacity to save power. Therefore, in this paper, we focus on this type of WSN to perform attacks and intrusion analysis. However, results are generalizable to the other types of WSN.  
In WSNs, multi-hop capabilities allow three basic topologies shown in Figure 2: star, tree and mesh. In these topologies, we can see three types of nodes: gateways, motes with routing capabilities and leaf motes. Gateways and routing nodes consume large amounts of energy forwarding packets, and thereby, they are generally plugged into the electrical grid. However, leaf nodes can be battery powered, because their sole responsibility is sensing the environment and sending their own packets towards the gateway.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion
### Xrd  
Figure 2 shows the XRD spectrum of the prepared catalyst and SAPO-34. In the figure, it can be seen that 9.47\({}^{\circ}\), 12.84\({}^{\circ}\), 16.04\({}^{\circ}\), 17.72\({}^{\circ}\), 20.52\({}^{\circ}\), 25.03\({}^{\circ}\), 25.88\({}^{\circ}\), and 30.58\({}^{\circ}\) were the characteristic peaks of the SAPO-34 molecular sieve support [13], which was a typical CHA structure with a high crystallinity. The corresponding characteristic peaks of the metal oxides were also detected in the experiment. The characteristic peaks detected at 35.3\({}^{\circ}\) and 38.5\({}^{\circ}\) were associated with CuO [14], 28.2\({}^{\circ}\) and 33.1\({}^{\circ}\) were associated with CeO\({}_{2}\)[15], and 33.8\({}^{\circ}\), 50.7\({}^{\circ}\), and 60.1\({}^{\circ}\) were associated with ZrO\({}_{2}\)[16]. Five catalysts showed the same characteristic peaks of SAPO-34, while their peaks were slightly different. Comparing the characteristic peaks of 4 Cu-4 Ce/SAPO-34, SAPO-34, and 4 Cu-4 Ce-4 Zr/SAPO-34, it was found that the loading of Cu and Ce reduced the characteristic peaks of SAPO-34. After the Zr modification, the characteristic peaks of SAPO-34 were enhanced, which proved that a small amount of Zr could promote the crystallization growth of the SAPO-34 zeolite molecular sieve to a certain extent. It also enhanced the CHA skeleton structure of SAPO-34 [14]. However, the characteristic peaks of SAPO-34 of 4 Cu-4 Ce-6 Zr/SAPO-34 and 4 Cu-4 Ce-8 Zr/SAPO-34 almost disappeared. It was speculated that a large number of Zr oxides appeared in the channel and outer surface of the crystal, resulting in a decrease in its crystallinity, the disappearance of the characteristic peaks, and the destruction of the crystal structure [17; 18]. The characteristic peaks of Cu, Ce, and Zr were not detected in the five catalysts, indicating that the above metal species were dispersed on the surface of the catalyst in an amorphous form, which was conducive to the NH\({}_{3}\)-SCR reaction [19].
# Testbed for Experimental Characterization of Indoor Visible Light Communication Channels
## 4 Simulation Results
Figure 9: Received power and illuminance at receiver plane for mockup illustrated in Figure 2: (**a**) Power balance. (**b**) Illuminance. (**c**) Contour map of LOS component percentage in the total received power. (**d**) Contour map of illumination level. Once we know the power balance in the mockup, we can estimate the approximate data rate, which could be achieved throughout the receiver plane. Figure 11 shows the estimated throughput in bits per symbol (BPS) when adaptive OFDM is used as communication scheme (see Section 3.2). We have considered a fourth-order low-pass channel response with cut-off frequency at 65% of the communication bandwidth (which roughly fits typical VLC channel responses of experimental studies [24; 25; 32]) and a maximum SNR of 25 dB (at low frequencies) for the location at the center of the mockup receiver plane. Maximum SNR at other locations in the mockup are calculated based on the simulated received power at that location with respect to this reference level at the receiver plane center. For a target BER of \(3.8\times 10^{-3}\), two situations are evaluated: receiver pointing upward and receiver oriented as defined by Equation (14) to maximize received power. We can see how, in the second case, a more uniform throughput with higher values at edge areas is observed but still vertical orientation constitutes a simple and effective solution as previously mentioned. Figure 10: Simulated impulse responses at two locations of the mockup: (**a**) Impulse response under lamp \(1\) (close to a window). (**b**) Zoom of impulse response under lamp \(1\). (**c**) Impulse response at the center of the mockup. (**d**) Zoom of impulse response at the center of the mockup.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 1 Introduction
However, due to the technical limitations of the time, it was significantly more challenging to achieve two-point contacts with similar parameters compared to a RF SQUID with only one point contact. Consequently, RF SQUIDs became the standard device in the ensuing years. As thin-film fabrication processes advanced, DC SQUIDs began to supplant RF SQUIDs from the 1970s and 1980s onward. DC SQUIDs typically offer higher sensitivity, lower noise, greater versatility, and improved stability  
Figure 1: In a DC SQUID, the variation in terminal voltage is directly correlated with changes in the magnetic flux, where an increase across one voltage period corresponds to the addition of a single flux quantum. compared to RF SQUIDs [9]. These characteristics render the DC SQUID ideal for high-resolution measurements across a wide array of fields. A typical DC SQUID device comprises four main components: two Josephson junctions, consisting of two thin potential barriers connected to a superconducting loop; a superconducting loop, often made of the underlying superconducting material Nb in niobium-based SQUIDs, commonly referred to as the \"washer\"; a superconducting ring, serving as the primary component of the SQUID; an input coil, utilized to convert the measured magnetic field into a flux detectable by the SQUID; and a feedback coil, employed to provide feedback magnetic fields during operation, thereby linearizing the SQUID's response. Washer coils in SQUIDs play a crucial role in their functionality and performance [29]. Their shape and size significantly impact the overall performance and sensitivity of the device. Primarily, washer coils amplify the magnetic field within the SQUID loop. By increasing the area that captures magnetic field lines, these coils markedly enhance the device sensitivity. Additionally, they serve to shield against external electromagnetic interference, with well-designed washer coils effectively reducing noise [30]. Washer coils also influence various SQUID parameters, including inductance and critical current. Through optimization of the shape and size, washer coils can amplify the magnetic field inside the ring, enhance the magnetic flux collection, and effectively suppress the external magnetic flux and potential resonance to minimize external noise [31]. Therefore, optimizing washer design is pivotal for enhancing the performance of SQUID devices. This study aims to investigate methods for enhancing SQUID performance by optimizing the shape and size of the washer coil through simulations. By leveraging advanced computational tools and optimization algorithms, we seek design solutions to improve the sensitivity and efficiency of the device. Our simulation findings will contribute to extending the application of SQUIDs across various environments.
## 3 Low-Power Electronic Converters
### Voltage Multipliers (Doublers and Quadruplers)
#### 3.2.3 Maximum Point Power Tracking  
As shown in Formula (34), to drain the maximum possible power from the piezoelectric generator, it is necessary to match the impedances of the generator and the load. Normally this is achieved by implementing the MPPT (Maximum Point Power Tracking) control technique, often in the DC-DC stage. When the impedance of the piezoelectric generator is considered real (active resistance), this can be easily implemented [13]. In some cases, the impedance of the piezoelectric generator could be a complex number [62]. In order to compensate the generator, an inductance connected in parallel could be used, but the value of this inductance could reach some tenth of Henrys. Such large inductance is highly recommended to be avoided in wearable electronic devices due to the practical realization considerations. There are some works discussed in detail in [62] that show possible techniques to solve this problem. The common solution is to use a small inductance with a switch and a suitable control algorithm. The common solution is to use a small inductance with a switch and suitable control algorithm. In Figure 17, one of the possible solutions is shown. It is the so-called synchronized switch harvesting on inductor (SSHI). In the presented case, the inductor is connected in parallel with the piezoelectric element. A series connection is also possible.  
The SSHI is used to optimize the energy extraction from the piezoelectric generator. The theory of operation in this circuit is complex and well described [63]. In general, a switching synchronized with the mechanical vibrations of the generator is used in order to extract the maximum power from the source.
## 4 Parametric Studies
### Influence of the Technique Parameter on the Applicability of the Proposed Method  
In this paragraph, we analyze the effect of the parameter related to the techniques proposed--that is, \(k_{\!c}\) and \(\mathrm{COV}_{\mathrm{R}\mathrm{P}}\)--on its applicability. Figure 11 shows heatmaps that present the optimized initial SF and \(EC_{\!tot}\) of the proposed method. The heatmaps of the conventional method are drawn in white, which we hereafter call the boundary line, as they are independent of the parameters, \(k_{\!c}\) and \(\mathrm{COV}_{\mathrm{R}\mathrm{P}}\), and become constant. Note that \"optimized\" is defined as the point of the minimal cost in each method, as shown in Figure 9.  
A smaller \(\mathrm{COV}_{\mathrm{R}\mathrm{P}}\) and \(k_{\!c}\) obviously result in both lower optimal SF and \(EC_{\!tot}\). The boundary line of the \(EC_{\!tot}\) deviates more to the left than that of the SF because the same initial SF results in the additional countermeasure (CM) cost. Even if \(k_{\!c}\) is greater than 1, meaning that the cost of the CM is greater than the target building cost, the proposed method can still be adopted when \(\mathrm{COV}_{\mathrm{R}\mathrm{P}}\) is lower than 0.2. This may seem unnatural at first, but it is because the implementation of CMs is judged based on observations, and as a result, the total cost can be small.  
Figure 12 shows the probability of CMs when \(EC_{\!tot}\) is optimized. A higher countermeasure cost (\(k_{\!c}\)) and lower variation in estimation (\(\mathrm{COV}_{\mathrm{R}\mathrm{P}}\)) reduce the countermeasures (lower \(P_{\!c}\)) and raise the optimized initial SF to avoid additional costs.  
The gray area on the lower right of Figures 11 and 12 was excluded because it did not have a minimal value of \(EC_{\!tot}\) (see Figure 9). This is because the expected total cost in the gray area is not a downward convex function and the expected total cost decreases when the SF is too low. To prevent this, the cost of the countermeasure could be a function of the observed value itself, rather than a constant. However, it is difficult to calculate a realistic cost ratio for the preparation and planning of countermeasures, and more specific works need to be considered. We believe that \(\mathrm{k_{\!c}}=0.7\) is not too large and is realistic enough to consider. Therefore, the gray area is left for future research.  
Figure 11: Relationship between the parameters of the proposed method (\(\mathrm{COV}_{\mathrm{R}\mathrm{P}}\) and \(k_{\!c}\)) and SF and \(EC_{\!tot}\) when \(k_{\!b}=1.0\) and \(k_{\!f}=80\). The median color of the heatmaps represents the optimum value for the conventional method.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 7 Current Situation and Development Trend
### Development Trend
#### 7.2.3 QoS Assuring on WSN  
The QoS refers to the capability of a network that can provide promising services (including ensuring the transmission bandwidth, reducing the transmission delay, reducing the packet loss rate) for specified network communications using various underlying technologies. Since the traditional OSI model does not apply to wireless networks. Moreover, WSN combines sensor technology and distributed information processing technologies. The traditional QoS system is not applicable to WSNs. More improved QoS system frameworks are needed for WSN. For example, energy conservation and scalability should be emphasized for the physical layer, and the dormancy mechanism should be considered for the sensor to save energy. The network layer should consider the whole network energy consumption balance. In our references, the proportion of literature considering QoS quality is relatively small; thus, how to solve the QoS problem in the WSN is a direction that can be studied.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 5 Research Challenges of IRS-Assisted UAV Communications
### Security Vulnerabilities  
UAVs can easily be detected by visual or radar scanning, which makes them susceptible to jamming attacks. Therefore, research on safe transmission technologies is required to enhance the security of the IRS-assisted UAV communications system [116]. As anti-jamming techniques, cooperative jamming, as well as conventional beamforming will be effective to degrade the quality of eavesdropping channels [117; 118]. In addition, it is required to make the CSI estimation protected from and robust to the jamming attacks and reflect incomplete CSI on the eavesdropping channel in the design of the PLS system [119].
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 2 PRAN-Net Fabric Defect Detection Method Based on Faster R-CNN
### Feature Extraction Based on Multi-Scales Feature Maps  
As shown in Figure 2, we use the typical deep residual network ResNet-101-FPN [25] as the backbone to extract features of fabric defects. The whole network is divided into bottom-upline and top-down line, which consists of several convolutional layers and upsampling layers. These two lines are laterally connected with \(1\times 1\) convolutional layers, which are used to reduce the channel dimensions. For the bottom-upline, the classical FPN obtain five scales feature maps. To reserve enough information of tiny defects while getting rid of redundant information and increasing efficiency, we only retain three feature maps with strides of [4; 8; 32] defined as [C1, C2, C3], respectively. For the top-down line, the high-level feature map is fused with the low-level feature map. M3 is the third scale feature map in the merged maps obtained by the \(1\times 1\) convolution of C3. The upsampled M3 and the  
Figure 1: The fabric defect detection process of the PRAN-Net.  
\(1\times 1\) convolution result of C3 are summed to get M2. The same operation is done to M2 and C2 to get M1. To reduce the aliasing effect of upsampling, {M1, M2, M3} are convoluted by a \(3\times 3\) convolution kernel to get three new feature maps {P1, P2, P3}. The top-down line operation enables to reserve more detailed information of defects by interlayer fusion, which is especially beneficial to detect tiny defects.
## 3 Materials and Methods  
This research was carried out in three stages: (1) carbon pools initialization, (2) model simulation and (3) validation stage (Figure 2). In the carbon pools initialization stage, the statistical models were built for different forest types to produce the Landsat-based forest AGB map of 2008 based on the field-based forest LAI and AGB data in 2012 and 2013. Due to the lack of field data of 2008 in the study area, we assumed the empirical relationships between the vegetation index and field-based AGB were universal for the same study area with same forest types, and produced the baseline forest AGB map of 2008 by applying these statistical models. The baseline forest AGB were allocated into different carbon pools of individual trees, including leaf, wood (i.e., stem and branch), and coarse and fine root to parameterize the BEPS model [40]. In the model simulation stage, Landsat-based LAI time series maps with eight-day interval from 2009 to 2012 were generated by combining MODIS-based LAI seasonal variation curves and one available Landsat TM/ETM+ image of each year from 2009 to 2012. The LAI time series images, combined with the forest types, meteorological, soil type, CO\({}_{2}\) data were all used to drive the ecological process-based BEPS model to conduct the numerical simulation of daily and annual NPP and annual AGB variations. In the validation stage, the forest AGB estimates obtained from ALS and field measurements were used to validate estimates from the BEPS-based method independently.  
Figure 2: The flowchart of mapping and updating forest AGB by combining multi-source remotely sensed data and ecological process-based model.
# Impact of Audio Delay and Quality in Network Music Performance +
## 7 Conclusions and Future Work
Furthermore, in future studies we plan to introduce compressed audio, using the Opus codec [3], especially after finding that musicians are quite tolerant of reduced audio quality. We will use the Perceptual Evaluation of Audio Quality (PEAQ) tool [35] and possibly the more advanced Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) [36] to objectively measure how compression and encoding impact perceived QoE in a performance context, while also analyzing the additional delays from low-latency encoders. This approach should help us better determine which types of audio compression are most likely to affect human musicians in an NMP setting. Conceptualization, K.T. and G.X. ; methodology, K.T. and I.D. ; software, K.T. ; validation, K.T., G.X. and I.D. ; writing--original draft preparation, K.T. ; writing--review and editing, K.T. and G.X. ; visualization, K.T. ; supervision, G.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original data presented in the study are openly available in GitHub, at [[https://github.com/mmlab-aueb/nmp/tree/master/Datasets/audio](https://github.com/mmlab-aueb/nmp/tree/master/Datasets/audio)]([https://github.com/mmlab-aueb/nmp/tree/master/Datasets/audio](https://github.com/mmlab-aueb/nmp/tree/master/Datasets/audio)), accessed on 18 July 2025. The authors declare no conflict of interest.
# A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge
## 2 Methodology
### Gated Recurrent Unit Neural Network  
GRU neural network is an advanced version of recurrent neural network (RNN) which can mitigate the problem of gradient vanishing and has a better capacity to model relationships among long sequences [33; 34]. Compared to analogous neural networks like LSTM (long short-term memory) neural network and attention-based neural network, it can achieve comparable performance with fewer parameters [33]. Considering the computational burden caused by the introduction of the Newmark-beta method inside the model, GRU neural network is chosen as the basic module of the proposed model. The diagram of the GRU cell is shown in Figure 1, where \(x_{i}\) denotes the input at ith time interval; \(\mathbf{r}_{i}\) and \(\mathbf{z}_{i}\) represent the reset gate and update at ith time interval gate, respectively; \(\mathbf{h}_{i}\) and \(\mathbf{\widetilde{h}}_{i}\) represent the hidden-state vector and the candidate hidden-state vector at \(i\)th time interval, respectively; and tanh, \(\sigma\) and \(\otimes\) denote the hyperbolic tangent function, sigmoid function and element-wise multiplication, respectively. The calculation inside the GRU cell can be presented as follows:  
\[\mathbf{r}_{i}=\sigma(\mathbf{W}_{rih}\mathbf{h}_{i-1}+\mathbf{W}_{rx}x_{i}+ \mathbf{b}_{r}) \tag{11}\]  
\[\mathbf{z}_{i}=\sigma(\mathbf{W}_{zih}\mathbf{h}_{i-1}+\mathbf{W}_{zx}x_{i}+ \mathbf{b}_{z}) \tag{12}\]  
\[\mathbf{\widetilde{h}}_{i}=\text{tanh}(\mathbf{W}_{hh}(\mathbf{r}_{i}\otimes \mathbf{h}_{i-1})+\mathbf{W}_{hx}x_{i}+\mathbf{b}_{h}) \tag{13}\]  
\[\mathbf{h}_{i}=\mathbf{z}_{i}\otimes\mathbf{\widetilde{h}}_{i}+(1-\mathbf{z}_ {i})\otimes\mathbf{h}_{i-1} \tag{14}\]  
where \(\mathbf{W}\) denotes the learnable weight of the GRU cell and \(\mathbf{b}\) denotes the learnable bias of the GRU cell. As shown in the aforementioned formulas, the reset gate controls the importance of the previous hidden-state vector in the calculation of the present candidate hidden-state vector, and the update gate controls the importance of the previous hidden-state vector in the calculation of the present candidate hidden-state vector. With the introduction of the reset gate and the update gate, the information flow along the sequence can be managed more adaptively.
# Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
## 5 Conclusions and Limitations  
### Conclusions  
In this paper, we presented the design of the beamforming of an IRS- and UAV-aided DM network in order to fully exploit the SR performance benefit from an IRS and a UAV. Two beamforming methods, called Max-SR-SLNR and MRT-NSP-PA, were proposed. The simulations in Section 4 indicated that the Max-SR-SLNR and MRT-NSP-PA achieved an obvious SR performance gain over the no IRS and random phase, especially in the large-scale IRS. Moreover, the SR gains harvested by the two methods increased gradually as \(N_{s}\) increased. In the small-scale and medium-scale IRSs, the proposed Max-SR-SLNR method was better than the MRT-NSP-PA method in terms of the SR, and the latter approached the former as the IRS moved to a larger scale. However, the latter was at least one to two orders of magnitude lower than the former when the IRS size ranged from small to large. In addition, the proposed Max-SR-SLNR and MRT-NSP-PA struck a good balance between the rate performance and the complexity.