esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 3 Greedy with Migration Pool
### Migration  
The Greedy-MP policy triggers migration when a specific block's erase count exceeds a threshold calculated using (1), which mirrors the formula employed in the PWL static wear leveling policy [20]. Equation (1) dictates that during the initial phase, when the average erase count of blocks is relatively low compared to the maximum erase count, migration occurs infrequently due to the threshold being set relatively high in relation to the average block erase count. However, as the average erase count of all blocks increases over time, the gap between the threshold and the average block erase count diminishes, leading to more frequent migration. Essentially, as the average erase count of all blocks rises, so does the likelihood of bad block occurrences, necessitating more frequent migration for wear leveling.  
\[\text{threshold}=\frac{\text{avg\_erase}+\text{max\_erase}}{2}. \tag{1}\]  
During migration, it is necessary to identify cold data from other blocks and copy it to the worn-out block. However, since individual pages do not store the timestamps of their last modification, accurately determining cold data is challenging. Storing the timestamp for each page would require significant memory space. Therefore, we assume that the valid data in the block with the lowest erase count are cold data. A low erase count indicates that the invalidation ratio of the block has been consistently low for a time, implying that the valid pages in that block have remained unchanged for a long time. If multiple blocks have the same lowest erase count, the block with the lowest invalidation ratio is selected for migration. Once the target block for migration is determined, its valid pages are copied to the worn-out block. This migration process continues until the clean pages of the worn-out block are exhausted. In other words, the process of finding the target block for migration is repeated until the worn-out block has no clean pages. Figure 2 illustrates the pseudocode for the migration process.  
Figure 1: The normal pool and the migration pool, each comprising \(N+1\) linked lists.  
Figure 2: The pseudocode for the migration process.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 6 Simulation Results and Discussion  
### Simulation Environment  
In the simulation, we assumed that there were 100 STAs associated with the AP. Each STA produced a data packet for single-user uplink transmission, with the inter-arrival time following an exponential distribution mean value \(T_{D}\). When the AP needed to collect the data from N users, the transmission procedure to request and receive the sensor data began. The transmission procedure was performed until the data from all N users were received successfully.  
The power consumption in each STA was measured only when the wake-up and transmission procedure for that STA was processed. For the MU transmission case, the transmission power was assumed to be similar to that of the single-user case. Other parameters are described in Table 2.
# Quantum Key Distribution in Kubernetes Clusters
## 3 Quantum Software Stack 2.0
### Asynchronous Approach  
The first version of the QKS used a multithreaded synchronous approach This choice was poor considering that the standard _CPython_ interpreter limits the execution of multiple threads at a time by exploiting the _Global Interpreter Lock (GIL)_[18]. Besides, the QKD Manager, the core component within the QKS, is an I/O bound application, meaning that it spends a consistent amount of time waiting for results from other applications. Thus, the synchronous choice consistently slowed the whole system down. Because of this, we introduced a new version of the QKS entirely based on an asynchronous pattern and the asyncio Python library. In more detail, the QKD Manager exposes REST APIs leveraging the Quart [19] framework, which supports asyncio, and the Hypercorn [20]; asynchronous web server to serve incoming requests. Even the Routing module (Section 3.3), the available QKDMs, and the clients for MongoDB, Vault, and Redis now adopt an asynchronous approach and specific libraries. This new strategy and the adoption of Hypercorn allow scaling of the application both vertically and horizontally. Indeed, when the number of requests to the QKS grows, we could decide either to statically allocate additional workers on the Hypercorn side or dynamically allocate more replicas of the QKD Manager. Since we designed all the components according to the principles of the cloud-native applications, we could also deploy our QSS on Kubernetes, thus making the horizontal scaling process automated.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 4 Performance Analysis  
### Secrecy Outage Analysis  
In this subsection, we investigate the secrecy outage performance of the considered system. According to [13], considering all of the possible states, the secrecy outage probability of the system is given by  
\[P_{out}^{\star}\left(\gamma_{th}\right)=\sum_{n=1}^{N}\mathbf{\pi}_{n}^{\star}P_{ out,n}^{\star}\left(\gamma_{th}\right), \tag{20}\]  
where \(N=\left(L+1\right)^{M}\) denotes the total number of states, \(\mathbf{\pi}_{n}^{\star}\) and \(P_{out,n}^{\star}\left(\gamma_{th}\right)\) denote that when the state is \(s_{n}\), the corresponding stationary probability and the secrecy outage probability. \(\gamma_{th}\overset{\Delta}{=}2^{2R_{s}}\) represents the secrecy outage threshold. Besides, to make the following analysis traceable, we define \(\gamma_{E1}^{\star}=\left(1+\gamma_{SR_{M_{1,n}}^{\star}}^{\star}\right)/ \left(1+\gamma_{1,E}^{\star}\right)\), \(\gamma_{E2}^{\star}=\left(1+\gamma_{SR_{M_{2,n}}^{\star}}^{\star}\right)/ \left(1+\gamma_{2,E}^{\star}\right)\) and the noise variance is \(\sigma^{2}=1\).
## 4 Proposed Network Model
### Nodes Placement  
Nodes deployment pattern makes a significant positive impact on the performance of a wireless sensor network. A sensor node is deployed either by placing it in a precise location depending on the application scenario, or by deploying it randomly. A sensor node is also positioned based on the monitored data. For example, in a remote agriculture application, a sensor node is placed manually in the field. If node placement changes continuously, a routing protocol will suffer from the frequent creation and broken of many links. Moreover, if nodes are deployed densely, a single node has many neighbors that can be directly connected, which will consume more power energy and add more burden to the Media Access Control (MAC) protocol due to the repetitive sending to a large-range.  
To overcome the above issues, topology control is applied. The idea behind using topology control is to control a set of neighbor nodes. Sometimes, a topology is controlled by placing a set of neighbor nodes manually. Topology can also be controlled by creating hierarchies in a network or clustering, by controlling transmission power. A topology is considered based on the used communication technology and the system network requirements. In some scenarios, a hybrid topology is the best option.  
The proposed protocol is a hybrid topology, and a combination of centralized and distributed approaches is adopted. Moreover, the topology is controlled by placing a set of neighbor nodes manually. The proposed network model is discussed in more detail in Section 4.2, Section 4.3, and Section 6.2.
## 4 Discussion
### Limitations  
First, the WUE data used were derived from moderate-resolution MODIS imagery, which may have limited the capture of finer details because of its relatively low resolution. Second, the study area was limited to Anhui Province; although this region has diverse terrain and soil characteristics, extending the scope to a larger area could enhance the generalizability of the findings. Third, we understand the possible effects of different refined soil types (e.g., sandy loam and black soil) on the WUE. However, because of the relatively low resolution of the dataset in this study, if a detailed soil classification was adopted, the sample data of many soil types would be too small, which would significantly affect the accuracy of the fractal analysis and reliability of the results. Finally, although multifractal analysis revealed the complexity of WUE in different topographic regions, it lacked a detailed comparison of the fractal characteristics of diverse terrains (mountainous and plain areas), particularly the analysis of multifractal characteristics and their driving factors across different topographic or climatic regions. In follow-up research, these areas will be important research directions for coping with complex environmental changes and providing a regional reference for agricultural water use.
## 3 Analytical Expressions
### Energy Consumption  
Both architectures are based on always-on technique. Figure 4 displays a simplified timing diagram of channel listening and WuPt. The transmission node (TX-node) is sending a packet and the WuRx (RX-node) is continuously listening to the channel for the WuPt. After data receiving, the LF wake-up receiver chip is matching the address before sending the interrupt to the MCU.  
During a wake-up interval \(\alpha\), the time of channel listing \(t_{\text{listen}}\) the energy consumption of listening mode \(E_{\text{listen}}\) can be calculated as shown in Equation (1).  
\[E_{\text{listen}}=(P_{\text{AS3933\_listen}}+P_{\text{LF\_Amp}}+P_{\text{MCU\_ LLPM}}+P_{\text{RF\_LPM}})\cdot t_{\text{listen}} \tag{1}\]  
where \(P_{\text{AS3933\_listen}}\), \(P_{\text{MCU\_LPM}}\), and \(P_{\text{RF\_LPM}}\) are the power consumption of low-power mode during listening mode of the LF wake-up receiver chip, microcontroller, and RF transceiver, respectively. \(P_{\text{LF\_Amp}}\) is the active current of the LF amplifier.  
\[E_{\text{RX\_WuPt}}=(P_{\text{AS3933}}+P_{\text{LF\_Amp}}+P_{\text{MCU\_ LPM}}+P_{\text{RF\_LPM}})\cdot t_{\text{WuPt}} \tag{2}\]  
\(E_{\text{RX\_WuPt}}\) represents the energy required for WuPt reception, as expressed in Equation (2). The supply power of BJT or transimpedance amplifier (TIA) is written as \(P_{\text{LF\_Amp}}\) and \(P_{\text{AS3933}}\) is the power of AS3933 in detecting mode. The energy of receiving the WuPt is determined by \(t_{\text{WuPt}}\). The energy requirement during sending the WuPt is displayed in Equation (3) with a transmission power of the transceiver \(P_{\text{RF}}\).  
\[E_{\text{TX\_WuPt}}=(P_{\text{MCU}}+P_{\text{RF}})\cdot t_{\text{WuPt}} \tag{3}\]
# Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution
###### Abstract  
The classical least-squares migration (LSM) translates seismic imaging into a data-fitting optimization problem to obtain high-resolution images. However, the classical LSM is highly dependent on the precision of seismic wavelet and velocity models, and thus it suffers from an unstable convergence and excessive computational costs. In this paper, we propose a new LSM method in the imaging domain. It selects a spatial-varying point spread function to approximate the accurate Hessian operator and uses a high-dimensional spatial deconvolution algorithm to replace the common-used iterative inversion. To keep a balance between the inversion precision and the computational efficiency, this method is implemented based on the strategy of regional division, and the point spread function is computed using only one-time demigration/migration and inverted individually in each region. Numerical experiments reveal the differences in the spatial variation of point spread functions and highlight the importance to use a space-varying deconvolution algorithm. A 3D field case in Northwest China can demonstrate the effectiveness of this method on improving spatial resolution and providing better characterizations for small-scale fracture and cave units of carbonate reservoirs.  
Keywords:least-squares migration; imaging domain; point spread function; global space-varying deconvolution +
Footnote †: journal: Applied
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 4 V2X Communication Architecture
### V2X Communications Protocols
#### 4.1.4 Hybrid V2X DSRC and C-V2X  
The majority of research that has been published is concentrated on a single V2X technology and discrete use cases.Studies show that the two main V2X technologies, DSRC and C-V2X, tend to perform worse when more devices are added to the system. Therefore, enabling hybrid network architecture solutions that integrate DSRC and C-V2X is of significant importance. Advocates of multiple radio access technology (multi-RAT) heterogeneous vehicles argue that no single technology can meet all requirements. As a result, the higher bandwidth needs of new cooperative sensing and perception requirements can only be satisfied by various solutions that include a broad spectrum of communication technologies such as DSRC, existing LTE-4G, 5G, enabled C-V2X [97]. Hybrid ITS networks consist of base stations, road infrastructure and moving devices. It enables V2V, V2I, and V2X communication to provide services such as road safety, transport productivity, travel reliability, informed travel choices, environment protection, and traffic resilience. Nevertheless, it is essential to solve security issues, such as preventing data fraud and privacy protection, while sharing vehicle data. Here, V2X technology is to be developed based on the network load, quality, application, or other critical parameters and is not limited by the hardware. In addition, hybrid vehicular network design presumes vehicles with dual interfaces to switch between network architecture using RAT allocation algorithms and vertical handover [87]. Hybrid architecture can be classified as either hierarchical or flat. Hierarchical architecture is founded on the idea of classifying network devices into different levels. DSRC-based networks are used for V2V and V2I communication. C-V2X is used for communication of V2N, V2P and V2X, and can also be used for access to the Internet. Devices assigned to levels that do not change are called static. In a dynamic hierarchical architecture, vehicles update levels in response to network conditions [98]. The C-V2X network is used for level-to-level, level-to-infrastructure, and infrastructure-to-infrastructure, whereas the DSRC network is used for V2V devices in the same group [81]. A hierarchical system helps reduce network load, whereas the grouping process is lengthy and hard to implement in populated areas. On the other hand, the type of connection used to link devices is not limited by a flat architecture. Instead, the network load, quality, and distance are considered when deciding the device's connection type. A high-level comparison of attributes for V2X protocols has been illustrated by [PERSON] in [88].
# Mixed-Carrier Communication for Technology Division Multiplexing
###### Abstract  
Recently, research on sixth-generation (6G) networks has gained significant interest. 6G is expected to enable a wide-range of applications that fifth-generation (5G) networks will not be able to serve reliably, such as tactile Internet. Additionally, 6G is expected to offer Terabits per second (Tbps) data rates, 10 times lower latency, and near 100% coverage, compared to 5G. Thus, 6G is expected to expand across all available spectrums including terahertz (THz) and optical frequency bands. In this manuscript, mixed-carrier communication (MCC) is investigated as a novel physical layer (PHY) design for 6G networks. The proposed MCC version in this study is based on visible light communication (VLC). MCC enables a unified transmission PHY design to connect devices with different complexities, simultaneously. The design trade-offs and the required signal-to-noise ratio (SNR) per individual modulation schemes embedded within MCC are investigated. The complexity analysis shows that a conventional optical OFDM receiver can capture the high-speed bit-stream embedded within MCC. For a forward error correction (FEC) bit-error-rate (BER) threshold of \(3.8\times 10^{-3}\), MCC is optimized to maximize the spectral efficiency by embedding 2-beacon phase-shift keying (2-BnPSK) within an MCC envelope on top of 12 bits per beacon position modulation (BPM) symbol.  
6G; complexity; VLC; mixed-carrier; multiplexing; OFDM; PPM; PSK; spectral efficiency Article
## 4 EHA Power Matching
### Characteristics Analysis of Motor Output Power  
Another advantage of using the ALS-EHA is that the motor power can be better matched to the load, thereby shrinking the motor size [25; 26; 27].  
For a given heat consumption, the motor has a power curve. the power curve is limited by the current at low speed when the copper loss of the motor constitutes most of the thermal load. As the speed augments, both viscous and non-viscous damping will generate some heat. Therefore, the total current must be cut down and the total output torque is lessened, because the damping will also consume some torque. As the speed continue to increase, this trend reaches a point. After this point, the output power of the motor becomes a voltage limit, which is sufficient high for the motor speed, so the power supply voltage is not high enough to overcome the back electromotive force, nor can a large current be formed to make motor overheat. the power curve of the motor is shown as the blue curve in Figure 12, which can be approximated to estimate the motor power by two straight lines as shown in Figure 12. the intersection of the two straight lines is the peak power point of the motor. the motor speed and torque corresponding to the maximum power point are \(\omega_{p},T_{p}\) respectively. Therefore, the approximate power curve equation of the motor is indicated in the following Equation (30).  
\[T_{l}=\left\{\begin{array}{ll}T_{\max}-\left[\left(T_{\max}-T_{p}\right)/ \omega_{p}\right]\omega_{m}&0\leq\omega_{m}\leq\omega_{p}\\ \left[T_{p}+T_{p}\omega_{p}/\left(\omega_{\max}-\omega_{p}\right)\right]- \left[T_{p}/\left(\omega_{\max}-\omega_{p}\right)\right]\omega_{m}&\omega_{p} \leq\omega_{m}\leq\omega_{\max}\end{array}\right. \tag{30}\]  
where \(T_{\max}\) and \(\omega_{\max}\) are the maximum output torque and speed of the motor, respectively, generally called stall torque and no-load speed,\(P_{M\max}\) is the maximum power  
Since the load of EHA applied to aviation is aerodynamic load, it can be simplified to elastic load. the load characteristic curve (a plot of force versus velocity) is an ellipse as shown by the blue dotted line in Figure 13. Essentially, the load characteristic curve is the power curve required by the actuator, and can also be approximate representation of two straight lines. Therefore, the required power equation of the actuator can be expressed as follows  
\[F_{l}=\left[F_{\max}^{2}-\left[\left(F_{\max}/v_{\max}\right)v_{a}\right]^{2} \right]^{\frac{1}{2}} \tag{31}\]  
Figure 12: Motor power curve.  
where \(F_{\rm{Imax}}\) is the maximum output force (load force). According to the extremum condition of the ellipse areas, the peak power output by the actuator is at this point, (\(F_{lp}=\sqrt{2}/2F_{\rm{Imax}},v_{p}=\sqrt{2}/2v_{\rm{max}}\)), resulting in \(P_{\rm{max}}=F_{lp}v_{p}=1/2F_{\rm{Imax}}v_{\rm{max}}\).  
**Remark 4**.: _According to different application fields, the load characteristic curve is also different. Here, EHA is used in aeronautics, so it is an elastic load characteristic._
## 3 Proposed Handover Scheme  
The proposed handover scheme has the following modules: (1) position tracking; (2) position prediction; and (3) handover decision. In this section, we first give an overview of the proposed scheme and then elaborate the three component modules.
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
###### Abstract  
A bioflocculant produced by a _Bacillus_ species was assessed with regards to its physiochemical properties and flocculating efficiency. Identification of the bacteria through 16S rDNA sequencing revealed it to have 99% similarity to _Bacillus pumilus_ strain ZAP 028. The optimum culture conditions for bioflocculant production by the bacterial strain were inoculum size of 4% (\(\upsilon/\upsilon\)), maltose as a sole carbon source, multiple nitrogen source (yeast extract, urea and ammonium sulfate) and medium initial pH 7. The bioflocculant was thermostable with high flocculating rate for kaolin suspension at low dosage 0.1 mg/mL over a wide pH range (3-11). Fourier-transform infrared (FTIR) spectroscopy analysis result of the purified bioflocculant showed that hydroxyl, amino and carboxyl groups were the main functional moieties in its molecular structure. The bioflocculant was composed of sugar (75.4%), protein (5.3%) and uronic acid (15.4%). Scanning electron microscopy (SEM) showed a dendritic bioflocculant structure and the energy dispersive X-ray spectroscopy (EDX) analysis revealed that the purified bioflocculant had weight fractions of elements as follows: 22.71% of C, 11.56% of N, 41.60% of O, 0.51% of S and 7.98% of P. The bioflocculant produced had strong flocculating activity and high thermal stability, which affords its utilization in industrial processes.  
Bacillus pumilus; bioflocculant; flocculating activity; thermostable +
Footnote †: journal: Applied Sci. 2016
# Multi-Term Attention Networks for Skeleton-Based Action Recognition
## 5 Experiments and Analysis
### Ablation Experiment
#### 5.3.3 Feature Concatenation and Weighted Feature Fusion  
In MTANs, we select the appropriate weights to fuse temporal features with different time scales, which is extracted from multiple LSTMs with different terms, respectively. We designed an experiment to compare feature concatenation method and the weighted feature fusion method.  
Table 4 shows the performance of the model with feature concatenation and the model with weighted feature fusion on the NTU RGB+D dataset when processing the temporal features with different time scales. It indicates that when concatenating multiple temporal features with different time scales in our model, the accuracy is lower than the model based on weighted feature fusion. This proves the superiority of the weighted feature fusion method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **CS Acc. (\%)** & **CV Acc. (\%)** \\ \hline Model with feature concatenation & 80.86 & 89.36 \\ Model with weighted feature fusion (MTANs) & 84.74 & 93.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison between a model with feature concatenation and a model with weighted feature fusion on the NTU RGB+D dataset.  
Figure 4: Distribution of attention weights and visualization of keyframes. The picture shows the frames with top 3 weights in the “waving” action.