esa-sceva/satcom-chunk-collection huggingface dataset:
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 3 Related Works  
EKFs have commonly been used as the standard technique for state estimation in CL. There are EKF-based methods that achieve localization utilizing stereo-vision and sensor fusion [28] and can be further extended to target tracking [29]. However, these methods require maximizing the overlap of each agent's field of view to enhance localization. Many localization techniques rely on a central system or single/multiple \"leader\" agents responsible for sensor fusion [30; 31; 32]. Distributed or decentralized methods have been investigated [33; 34; 35] to ameliorate the computational bottlenecks associated with centralized systems. While these methods can be efficient and effective, they come at the cost of accuracy. A combination of centralized-decentralized CL in ad hoc networks shows great promise in improving localization without prior knowledge of an environment [14; 36]. A distributed CL utilizing an EKF with low communication path requirements can also provide centralized-equivalent performance [37]. It is also possible to model the CL problem as a graph model to further utilize additional algorithms [38].  
EKF-based CL algorithms are popular due to their ease of execution and computational efficiency. However, linearizing an extremely nonlinear system can lead to inaccuracies and numerical instabilities. Unscented Kalman Filter (UKF)-based methods were introduced in [39] with the advantage of not requiring any linearization. The UKF is deemed to be more robust [40] since it does not rely on the computation of the Jacobian matrix and is an excellent option for self-localization and tracking [41; 42; 43; 44]. On the other hand, the UKF has a much higher computation cost compared to the EKF, especially considering the Cholesky factorizations at every step. As an alternative, the Squared Root UKF [45] does help mitigate the computation cost. However, implementation of UKF-based CL algorithms and simultaneous measurements is very complex and has not been fully explored. Table 1 lists some of these related works along with their advantages and limitations.
## 2 Related Work
### Distributed On-Board Processing  
In the last few years, on-board processing has become a remarkable research trend, mainly motivated by the limited bandwidth of the communication links between satellites and Earth facilities. In parallel, the search for highly efficient and fast solutions for edge processing has also been a hot spot in the field. Activities combining both research lines can be found in the literature. For instance, a high-efficiency system based on Zynq SoPC devices for hyperspectral image classification was presented in Reference [32]. Other on-board application scenarios have covered Synthetic Aperture RADAR (SAR) image processing [33; 34] or Support Vector Machines (SVM) for cloud detection [35].  
The appearance of CubeSats can be also considered a major breakthrough in the field. The fact that these devices are usually low-cost, and therefore limited in performance, has encouraged the research around Space Information Networking (SIN), where on-board processing in satellite applications is enhanced by the clustering of satellites [12]. With this approach, satellites operate as nodes of the same network, sharing resources in a distributed-computing approach.  
One of the main challenges in this type of solutions is to achieve scalable behavior when adding more processing nodes to the system. In this regard, communications play an important role, since their overheads may not be negligible. Different satellite network architectures have been proposed taking into account efficiency and flexibility regarding new technologies, rendering overheads in the order of nanoseconds [36; 37]. Moreover, satellite communications have been also envisioned for FPGA usage [38], being flexibility, reconfigurability and reliability the main parameters assessed.  
Going back to the on-board processing capabilities of CubeSats, the use of hybrid and reconfigurable computing has been found the most suitable, especially when taking into account factors such as power consumption, price, reliability, and flexibility. In the literature, it is possible to find many research works related to on-board processing in SmallSats, with applications ranging in a wide spectrum that covers, for instance, signal processing for interferometric satellites using FPGAs [39], or machine learning for image processing on a Xilinx FPGA [40]. Nevertheless, there is still a need for scalable execution in satellite clusters.  
With regard to alternative state-of-the-art solutions, the proposed implementation provides not only low cost, reduced energy consumption, and flexibility, but also scalability at both the device and node level. These features are required for the distributed on-board processing scenario, and have been enhanced by using the ARTICo [3] architecture, which provides a run-time adaptable working point trading off between energy consumption, performance, and fault tolerance.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 3 Pretreatment of Feedback to Enhance Biogas Production
### Biological Pretreatment  
Biological pretreatments mainly depend on using microorganisms, such as bacteria and fungi, to degrade the lignin fraction [3; 62; 71]. Such pretreatment is gaining popularity because of its cost-effectiveness as a natural process that does not require chemicals or energy, in addition to being an environmentally friendly method. However, careful selection of the suitable microbial consortium for efficient pretreatment of biomass is a critical step [72]. Additionally, it is important to highlight that the process is slow and requires longer residence times, which is often not practical for large-scale applications [3; 62; 71; 73].  
The effect of bacterial-based biological pretreatment on liquefaction of microalgae _Chlorella vulgaris_ with cellulose-secreting bacteria prior to anaerobic digestion was studied [74]. The results showed that bacterial pretreatment enhanced the bioavailability of biomass, and hence methane generation, with methane yield being almost double that of control. Furthermore, the BMP test to evaluate the potential effect of enzymatic pretreatment on pulp mill biosludge with protease from _B. licheniformis_ found that an increase of 26% in biogas yield could be achieved under the studied conditions [75].
## 4 Fault Simulation and Analysis
### Evaluation Indexes of Shifting Quality  
During the shift process, the separation of the clutch C\({}_{1}\) would lead to the decrease of the tractor speed. The engagement of the clutch C\({}_{2}\) would lead to the increase of the tractor speed. Therefore, the speed of the tractor would fluctuate during the shift process, and the speed drop could be used as the first evaluation index of the shifting quality:  
\[v_{drop}=v_{o}-v_{min} \tag{30}\]  
where \(v_{drop}\) is the speed drop of the tractor during the shift, r/min; \(v_{o}\) and \(v_{min}\) are the theoretical driving speed and the minimum driving speed of the tractor during the shift, respectively, r/min.  
The research of [PERSON] [30] showed that the shifting quality that the human body can feel is closely related to the peak acceleration of the tractor. Therefore, this study took the maximum value of the acceleration during the shift as the second evaluation index of the shifting quality:  
\[a_{peak}=\text{Max}\bigg{(}\frac{dv_{t}}{dt}\bigg{)} \tag{31}\]  
where \(a_{peak}\) is the peak acceleration, m/s\({}^{2}\); \(t\) is the time, s.  
In addition, the clutch would produce energy loss in the process of sliding; if the energy loss were too large, the service life of the clutch would be shortened. Therefore, the power and energy loss of the clutch during the shift were taken as the third and fourth evaluation indexes of the shifting quality, respectively:  
\[P_{loss}=\text{Max}\bigg{(}\frac{T_{c}\Delta\omega}{9550}\bigg{)} \tag{32}\]  
\[W_{loss}=\int\limits_{t_{1}}^{t_{2}}\frac{T_{c}\Delta\omega}{9550}dt \tag{33}\]  
where \(P_{loss}\) is the maximum power loss of the clutch during the shift, kW; \(W_{loss}\) is the accumulated energy loss of the clutch during the shift, kJ; \(t_{1}\) and \(t_{2}\) are the starting time and ending time of the shift, respectively, s.
## 4 Results and Analysis
### Consistency Assessment with MODIS Products
#### 4.3.2 Comparison with 16-Day MODIS NDVI Product  
Figure 11 shows a scatter plot of all the comparable samples selected from the HJ 16-day composited NDVI and the 16-day MODIS NDVI products. Overall, the samples were generally distributed around the 1:1 line, and good agreement was found between the HJ-1A/B NDVI and  
Figure 10: Comparison between the enlarged details of 8-day composited HJ-1A/B TOA reflectance (upper) and the 8-day MODIS MOD09A1 surface reflectance product (lower). The HJ-1A/B composites are shown with bands 3, 2, 1 and the MODIS MOD09A1 are shown with bands 1, 4, 3 in red, green and blue.  
the MODIS NDVI, with the R\({}^{2}\) reaching 0.9312. In addition, the correlation was strongly linear, and the slope of the regression line was close to 1. The good agreement between the HJ-1A/B NDVI and the well-verified MODIS NDVI product suggested that the overall quality of HJ-1A/B NDVI was high. However, there is a systematic bias that the MODIS NDVI is on average 0.067 greater than HJ TOA NDVI. Such systematic bias can be partly explained by the fact that the MODIS NDVI product is derived from atmospherically corrected data while the HJ TOA NDVI is calculated from TOA reflectance. Similar results had been found in the comparison between surface and TOA NDVI of WELD done by [PERSON] _et al._[49], which demonstrated that surface NDVI was on average 0.1 greater than the TOA NDVI for \"vegetated\" surface. In addition, the systematic bias may also include an effect from the different sensor's Spectral Response Function (SRF) between HJ and MODIS (Figure 2). Previous studies pointed out that NDVI is sensitive to the sensor's SRF, and the difference of the SRF should be considered for the comparison between different sensors [50; 51].
## 4 The IDRR Traffic Control Algorithm
### General Description
This is controlled by not permitting more than two AGvs at the same TX-node, which allows maneuvering therein, and more than three AGvs in adjacent TX-nodes, which allows to conduct resolutions in at least one of them; the latter is verified through a path saturation condition [16]. In order to better illustrate the working principle of IDRR, a simplified flowchart of it for a specific case is presented in Figure 6: namely, \(N_{x}=T_{ij}\), \(N_{y}=T_{ij+1}\) are two adjacent TX nodes to be visited by AGV \(R_{i}\), which is assumed to be at the exit CP of node \(N_{x}\), i.e., the CP of \(N_{x}\) that it is closest to \(N_{y}\), waiting to be allocated at \(N_{y}\). The flowchart describes the procedure followed by IDRR to this end. The reader is referred to the original work [16] for a detailed description of the general case. Notice that allowing multiple AGVs to access shared routes results in lower waiting times when compared to DRR. Another major difference is that IDRR does not need AGVs to head to a parking station after a drop-off and before undertaking a new task. In fact,  
Figure 6: IDRR flowchart for \(N_{x}=T_{ij}\), \(N_{y}=T_{ij+1}\) adjacent TX nodes to be visited by AGV \(R_{i}\), which is at the exit CP of \(N_{x}\), i.e., the CP of \(N_{x}\) that is closest to \(N_{y}\), waiting to be allocated at \(N_{y}\). IDRR moves idle vehicles from drop-off points to parking stations just in case the list of pending tasks is empty. Overall, the combined effect of both features results in major improvements in task completion times [16]. However, IDRR's traffic management policy outperforms DRR's regardless of the parking effect; this was shown in [16] using the so-called IDRR with parking (IDRRP) algorithm, which works exactly as IDRR but always heads AGVs to park between task assignments. Hence, IDRRP will also be used in this paper to further enlighten the comparison between DRR and IDRR. As for the role of discretization and the control period in task completion times, the discussion is the same as for DRR, namely, higher node densities should have a positive effect on reducing waiting times, but also entail a higher number of permission requests and acceleration/deceleration instances, while shorter control periods should result in faster ending times.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 1 Introduction
While the memory and byte overhead increases from pre-quantum to post-quantum, our results show that the post-quantum Kyber is more efficient in terms of time and energy than pre-quantum PKE/KEM. The rest of the paper is organized as follows: Section 2 describes the motivation and background, which highlight the rationale and context of the research. We discuss the related work in Section 3. While Section 2 describes the background that is more foundational and established, Section 3 describes the state-of-the-art research. Section 5 describes our implementation and experimentation procedure. Section 6 focuses on the experimentation analysis, presenting the results and observations. Section 7 concludes the paper, summarizing the key findings, contributions, implications of the study, and future directions.
# Ras Isoforms from Lab Benches to Lives--What Are We Missing and How Far Are We?
## 5 Missing Pieces in the Ras Puzzle
### Differential Access and Preference to Activators and Effectors  
The functional specificity of Ras isoforms can also be attributed to the differential access to or affinity of the isoforms for activators and effectors. This specificity is cell-type and activating receptor-dependent in most cases. [PERSON] et al. elucidated the comparative activation of effectors PI3K and Raf1 by Ras isoforms: PI3K was activated to a higher extent by H-Ras when compared to K-Ras, which activated Raf1 [154]. The affinity of GAP NF1 is four-fold higher for H-Ras when compared to N-Ras [155]. Different groups have published similar cell types and context-dependent isoform-specific preferential activation of an effector [21]. Functional specificity of Ras isoforms may arise from:  
* Different activation kinetics of Ras isoforms under the same receptor;
* Spatiotemporal segregation of signaling complex, with activators and effectors having different space, time, and thresholds of activation;
* Specific enrichment of substrates for activators or effectors in the microdomains containing specific Ras isoform.  
For example, the enrichment of domain-containing any of the Ras isoforms with phosphatidylinositol 4,5-bisphosphate (PIP2) may increase its affinity for PI3K [156], whereas a phosphatidic acid-rich microdomain resident Ras isoform would preferentially activate more of Raf 1 [157].  
Figure 6: Ras isoforms are distributed and signals from not only the plasma membrane but also from other signaling platforms like Golgi, endoplasmic membrane, mitochondria, endosome, etc. Once produced in the cytosol, Ras undergoes a series of post-translational modifications in the endoplasmic reticulum and Golgi. Ras is farnesylated by protein farnesyl transferase (PFT), further proteolytically cleaved by Ras converting enzyme (RCE), and carboxymethylated by isoprenylcysteine carboxyl methyltransferase (ICMT). H/N-Ras enters the Golgi to be palmitoylated and subsequently targeted to the plasma membrane. while K-Ras goes addition of lysine residues and takes a Golgi-independent route to the plasma membrane. The presence of different Ras isoforms has been detected from the mitochondria, E.R, Golgi, endosome, and the nucleus.
# Combined Optical Fiber Transmission System Based on QNSC and BER-LM
## 3 Integrated System of QNSC and BER-LM  
Figure 4 shows the system schematic diagram of QNSC and BER-LM. The raw data, negotiation data, and operation key are generated by [PERSON]. After bit mapping of TNIT, encrypted data can be obtained. The encrypted data are sent to [PERSON] through the optical transmitter, and [PERSON] receives the signal through the optical receiver. [PERSON] uses the running key to obtain the received data and negotiation data through bit demapping. [PERSON] and [PERSON] use the loopback negotiation data to calculate the BER. The BER samples are quantized and  
Figure 3: (**a**) Encryption transmission principle of optical fiber communication. (**b**) Principle of transmission and negotiation integrated transformation.  
Figure 2: BER of loopback fiber channel measured by [PERSON] and [PERSON].  
encoded to obtain the seed key. Then, the seed key generates the running key using the random number generator.  
The specific operation steps of QNSC and BER-LM are shown in Figure 5:  
* Step 1: the locally randomly generated negotiation data and the original data are mapped with the key and sent out.
* Step 2: the key is used to demap the received data.
* Step 3: the data obtained from the demapping are mapped again and then returned to the opposite end.
* Step 4: demap the returned data and compare the obtained negotiation data with the locally randomly generated negotiation data to calculate the BER.
* Step 5: obtain a consistent running key based on the calculated BER.  
This integrated system of QNSC and BER-LM can realize data encryption and key distribution simultaneously in the same fiber channel. It not only saves equipment costs but also makes full use of spectrum resources, which is suitable for large-scale popularization.  
The system device diagram of a loopback optical fiber channel is shown in Figure 6. A 1550 nm laser is used, and the data are generated by the digital signal processing (DSP) module at the sending end. DSP includes a series of processes and algorithms such as analog-to-digital and digital-to-analog conversion, dispersion compensation, frequency offset compensation, clock synchronization, channel equalization, carrier phase recovery,  
Figure 4: System schematic diagram of QNSC and BER-LM.  
Figure 5: The specific operation steps of QNSC and BER-LM.  
QNSC, etc. [20]. After the IQ modulator, the signal arrives at the variable optical attenuator (VOA) and is amplified by an erbium-doped fiber amplifier (EDFA) before entering the fiber channel. At the Bob side, the signal is amplified by EDFA and enters the coherent optical receiver. The DSP module at the Bob side restores the encrypted signal to the original data and negotiation data. The negotiation data are further processed to obtain the key. Figure 6 shows the received PSK/QNSC signal and the decrypted QPSK signal.
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 2 The Design of the Metamaterial-Integrated Dipole Antenna
### The Dipole Array Antenna with the Proposed Metamaterial Structure  
After achieving a good response from the metamaterial unit cell for the targeted frequency band, the proposed metamaterial structure is employed at the top side of the dipole array, at both the front and back side, as presented in Figure 7, to enhance the performance, particularly in terms of high gain. The dipole array antenna is evaluated with a different number of unit cells for the best gain performance. Figure 8 contains the gain response of the dipole antenna with a different number of metamaterials. While Figure 8a shows the gain response of the variation in the cells in the horizontal position, Figure 8b presents the gain response of the varying cell numbers in the vertical arrangement. It can be observed from the gain graphs that the proposed \(5\times 7\) metamaterial structure offers the best gain performance, with a maximum gain of 11.21 dBi. The minimum and maximum gain responses of different metamaterial cell arrangements along with their corresponding frequency responses are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metamaterial**} & \multicolumn{2}{c}{**Gain (dBi)**} & \multirow{2}{*}{**Frequency Response (GHz)**} & \multirow{2}{*}{**Metamaterial**} & \multicolumn{2}{c}{**Gain (dBi)**} & \multirow{2}{*}{**Frequency Response (GHz)**} \\ \cline{2-2} \cline{5-7}  & **Min.** & & & & & **Min.** & & **Max.** \\ \hline \(3\times 7\) & 5.12 & 10.3 & 23.5–40.1 & \(5\times 5\) & 6.5 & 10.39 & 23.3–40.9 \\ \(5\times 7\) & 7.28 & 11.21 & 23.1–44.8 & \(5\times 7\) & 7.28 & 11.21 & 23.1–44.8 \\ \(7\times 7\) & 6.4 & 10.63 & 23.5–40.6 & \(5\times 9\) & 7.72 & 11.43 & 23.2–36.4 \\  & & & & & & & 40.7–44.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gain and frequency responses for various configurations of metamaterials.  
Figure 7: The geometry and design parameters of the proposed metamaterial-integrated dipole array antenna.
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
## 5 Prototyping and Performances Exploration
### Validation of the Proposed Prototyping  
We will take into account a network of six Arduino-based SNs (one CH and five nodes) to ensure the suggested protocol is robust and reliable. According to Section 4.3, we divide the process into three \"3\" stages:  
Figure 6: Sensor node built with the Arduino MEGA board.  
Figure 7: Real testbed demonstrator.  
**Network topology stage**: At this point in the network's development process, each proposed sensor node must initialize its network device, IPv6 stack, UDP, and data-transfer timer before it can begin sending data to the rest of the network. The CH can detect the sensor node immediately after startup. This work will focus on a cluster with six nodes, one of which will serve as the CH while the other five will be responsible for monitoring leak detection. Each node is based on the Arduino platform as described in Figure 6. The network topology is shown in Figure 8.
2. **Routing construction stage**: The CH is responsible for calculating the PRSA and determining the next hop for all nodes within the cluster. Thus, in the event of a leak, each sensor node is fully aware of the next-hop (preferred relay) IP address to connect with. When five nodes are added to the network, the routing structure shown in Figure 9 functions as intended. This second graphic shows us that there are three distinct ways for nodes to reach the CH: direct transmission (from nodes 1 and 2), two-hop transmission (from nodes 3 and 5), and three-hop transmission (from node 4).
3. **Packet delivery stage**: in this phase, if a sensor node detects a leak, it must immediately notify the CH. A sensor node's preferred relay will forward the alert message to its preferred relay, and so on until the alert message reaches the CH. Future works will explain a practical implementation of the intended use case.  
Figure 8: Network topology for N = 6.  
Figure 9: Routing construction for N = 6.