esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Inception Model
#### 3.1.4 Model Construction and Training Parameter Settings  
This study used the Tensorflow platform for model training and prediction, with the evaluation units being grid units. Tensorflow has a rich neural network code library provides a complete technical solution for model training, prediction, error analysis, and other tasks. Grid units are like raster data structures in GIS, making it more intuitive and straightforward to import factor data into the model and facilitate fast reading and computation by computers.  
Model training requires a dataset of positive and negative samples, where positive samples are historical landslide points and negative samples are points where landslides have not occurred. The positive samples in this study were directly obtained from the \"Global Landslide Points and Landslide Areas Dataset (1915-2021)\", totaling 221 landslide points in the study area. The sampling method for negative samples mainly followed the technique for negative samples [36] in regional landslide risk assessments, randomly sampling 221 negative sample points outside a specific range buffer of the positive samples.  
Before training the model, the landslide factor dataset was preprocessed to generate data that met the model's input requirements. According to the First Law of Geography, objects closer together have a stronger correlation. In this study, we selected a region measuring 47 by 47 units centered around each sample point, both positive and negative, for cropping purposes. The landslide factor dataset was divided into multiple datasets of shape (47,47,15), labeled with corresponding \"landslide\" and \"non-landslide\" labels, shuffled, and divided into training and testing sets at a ratio of 8:2. These datasets were then imported into the model for multiple rounds of training and fitting validation. By setting different training parameters, the optimal parameter combination was eventually obtained. The data processing process is shown in Figure 8 below:  
Through repeated experiments to improve model accuracy [37], the parameters for all four models were ultimately set as follows: learning rate of 0.001, number of training epochs of 25, and batch size of 16. The specific parameters are shown in Table 3.  
Figure 8: Data processing process.  
Based on the parameter configuration mentioned above, 150 independently trained models were batch-generated for each model type (Inception, BPNN, ResNet, CNN, VGG-16) to explore the adaptability and generalization performance of the different architectures for the \"landslide susceptibility\" task under the same training conditions.
# Biosensors-Based In Vivo Quantification of 2-Oxoglutate in Cyanobacteria and Proteobacteria
## 2 Materials and Methods
### Protein Production, Purification and Spectroscopy  
_E. coli_ BL21(DE3) cells carrying pET derivative plasmids were grown at 37 \({}^{\circ}\)C in Lysogeny Broth to an OD\({}_{600}\) of 0.5, and gene expression was induced by the addition of 100 uM IPTG for 12 h at 17 \({}^{\circ}\)C. Proteins were purified by immobilized metal ion chromatography as previously described [35]. Spectroscopic analyses were monitored in 96-well plates using a TECAN Infinite(r) 200 series microplate reader (Hannedorf, Switzerland) equipped with a dual injector head for kinetic measurements. The excitation and emission bandwidths of the instrument are 9 nm and 20 nm, respectively. For in vitro and in vivo experiments, fluorescence emissions were recorded using an excitation at 425 nm, and emissions at 530 nm and 480 nm for YFP and CFP, respectively. The FRET ratio was calculated as the 530/480 intensity ratio (YFPm/CFPem) and reflects the level of excitation of YFP by the CFP, which is directly linked to the distance between the two fluorophores. The relative FRET ratio was calculated as the FRET ratio for a specific 2-OG concentration relative to the same ratio without 2-OG. The data were analyzed by the PRISM 5 program (GraphPad Software, San Diego, CA, USA). For in vitro experiments, fluorescence emissions were recorded 5 min after ligand addition.
## 4 Conclusions  
The preliminary laboratory results achieved in this study are very encouraging as they showed that the use of packaging glass cullet, cattle bone flour ash, vegetable biomass ash, spent coffee ground, degreased from biomass of prepupae of Black Soldier Flies, and volcanic scraps (pumice) in the production of fertilizer glasses and lightweight aggregates allows for obtaining sustainable ceramic materials without causing further impacts on humans and the environment. The leaching tests showed that Al is the element most solubilized, but it is in line with the release of a conventional clay in the ground while only traces of heavy metals are found. In agreement with Italian legislation, the materials obtained were not hazardous. Besides, it was demonstrated that the release of the main nutrients (P, K, N) confirmed the feasibility to orient their use in outdoor or indoor cultivation purposes. Data acquired through cytotoxic assessment are in line with the synthesis of materials that are not harmful to human health through dermal contact, whereas antibacterial results highlighted the ability of the sintering and vitrification technologies to produce inert materials. Further analyses are needed to deeply unravel the ability of the synthesized materials to be resistant to bacteria attachment, as well as an in-depth study on the durability of materials over time.  
Conceptualization, L.B., I.L. and F.A.; formal analysis, G.D.P., S.P. and F.A.; data curation, M.C., G.D.P., S.P., F.A. and L.B.; writing--original draft preparation, L.B. and M.C.; writing--review and editing, L.B., S.P., I.L. and M.C.; supervision, L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: Representative images of untreated NIH-3T3 cells and investigated materials-treated NIH-3T3 cells, acquired by Inverted Phase Contrast Brightfield Zeiss Primo Vert Microscope at 1, 3, and 6 h of exposure.
## 4 Monitoring System Testing and Analysis
### Measurement Performance Test and Analysis of State Parameters
#### 4.2.3 Temperature Test  
The experiment was conducted in a temperature-controlled chamber under existing experimental conditions to test the temperature sensing status monitoring. The temperature of the temperature-controlled chamber was controlled at 21.6 \({}^{\circ}\)C by setting this as the average temperature, and temperature sensors measured the temperature for 118 moments. The measurement results of the temperature sensor are shown in Figure 7. The error range of temperature sensing monitoring is within \(\pm\)1 \({}^{\circ}\)C, which meets the temperature sensing monitoring design requirements.  
\begin{table}
\begin{tabular}{c c} \hline
**Output Voltage \(U_{2}\) (V)** & **Input Voltage \(U_{1}\) (V)** \\ \hline
0.30 & 3 \\
0.62 & 6 \\
0.93 & 9 \\
1.26 & 12 \\
1.54 & 15 \\ \hline \end{tabular}
\end{table}
Table 6: Data read from voltage test.  
Figure 7: Measurement results of the temperature sensor.
## 1 Introduction
However, the joining of static sensor subnetworks with mobile ones shifts the load of correcting the heterogeneity of the coverage that static subnetworks inherently exhibit, to the nodes of the mobile subnetwork. This compensation increases the energy need, thus decreasing the efficiency. A recently emerged novel concept of Sycopphant Wireless Sensor Networks (SWS) was developed as static sensor networks that are equipped with limited mobility ability and these are overviewed in Section 4. A SWS network is a clandestine traveler that passively uses the mobility of carrying agents to collect, process, and communicate data harvested during the agent's navigation. However, it cannot in any circumstance guide or influence the motion of the carrying agents. Nowadays, mobile sensor networks are increasingly gaining new abilities with emerging new hardware, software and biologically inspired approaches. In turn, many future scientific challenges exist in the networked mobile sensor research area such as biologically inspired self-organizing robot networks, completely decentralized controller networks, scalability, heterogeneous robot networks which can cooperate with humans and intelligent environments. In this paper, we provide a comparative survey of existing approaches based on the span of their focus and their advantages or disadvantages brought to this field of interest and we dwell on potential future challenges of networked mobile sensors in Section 5.
## 2 Problem Analysis and Modeling
### Constraints  
The observation time of each task should be within VTW of the task:  
\[t_{i}^{s}-tw_{i}^{s}\geq 0 \tag{3}\]  
\[tw_{i}^{e}-t_{i}^{e}\geq 0 \tag{4}\]  
\(tw_{i}\): the VTW of \(task_{i}\).  
\(tw_{i}^{s}\): the start time of \(tw_{i}\).  
\(tw_{i}^{e}\): the end time of \(tw_{i}\).  
\(t_{i}^{s}\): the actual start time of \(task_{i}\).  
\(t_{i}^{e}\): the actual end time of \(task_{i}\).  
The actual observation of each task should meet the required duration of the observation:  
\[t_{i}^{e}-(t_{i}^{s}+l_{i})\geq 0 \tag{5}\]  
\(l_{i}\): the duration time required for \(task_{i}\) to be observed.  
The time interval between the two adjacent observation tasks \(task_{i}\) and \(task_{j}\) in the priority must be longer than the transition time \(trans_{ij}\):  
\[t_{j}^{s}-(t_{i}^{e}+trans_{ij})\geq 0 \tag{6}\]  
\(trans_{ij}\): the transition time from \(task_{i}\) to \(task_{j}\) where \(task_{j}\) is the next task of \(task_{i}\).  
The total storage occupied by the observation tasks should be within \(M\).  
\[M-\sum_{i}^{N}m_{i}\geq 0 \tag{7}\]\(M\): the total storage of the satellite.  
\(m_{i}\): the storage required for a successful observation \(task_{i}\).  
The total power consumed by the observation task should be within \(E\).  
\[E-\sum_{i}^{N}e_{i}\geq 0 \tag{8}\]  
\(E\): the power of the satellite.  
\(e_{i}\): the power required for a successful observation \(task_{i}\).
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 5 Results and Discussion
### Performance Evaluation
Based on the results of the NF-based features and speech mask, \(ResNet_{F+NF+M}\) could be a better solution, which is expected to weight different kinds of features adaptively in different frames. Specifically, for background-dominant frames, NF-MFBE features can be adopted and MFBE can be adopted in speech-dominant frames. Surprisingly, however, the results indicate that we do not obtain better performance by incorporating more features as network input. Especially in low SBRs, the accuracy is even worse than both \(ResNet_{F+NF}\) and \(ResNet_{F+M}\). A possible explanation is that both NF-MFBE features and speech mask lose/distort information in low SBR conditions and then add up to further degradation. To take full advantage of the trained networks, we propose two alternatives to tackle this problem: \(M\_{NF\_}{\textit{vote}}\) and \(Meta\_learner\). The first alternative \(M\_{NF\_}{\textit{vote}}\) is the average voting by \(ResNet_{F}\), \(ResNet_{F+M}\) and \(ResNet_{F+NF}\). It shows competitive improvement in all SBRs. The second alternative, the meta-learner, uses a fully connected layer for the combination of three models shows the largest improvement. The comparison of the \(ResNet_{F}\) baseline and \(Meta\_learner\) is presented in Figure 3. At 20 dB SBR, an absolute improvement of 9.11% is observed. For the small-footprint model, the following key points can be concluded:  
* NF-based features bring benefits to certain acoustic scenes, thus can be utilized as an extra input feature for ASC;
## 3 Dynamic Routing Algorithm for Inter-Satellite Network Based on Q-Learning  
The multi-path routing technique suggested in the second part is no longer appropriate since many satellites are constantly moving at a rapid speed and occasionally it is impossible to obtain the global topology of the inter-satellite network. There are many studies on service networks using machine learning and reinforcement learning, including service category, routing decision, service function chain scheduling and resource optimization, etc. [11; 12; 13; 14; 15; 16; 17]. This section suggests a Q-learning-based dynamic routing technique for inter-satellite networks as a result.
# Design of Time-Synchronized Switch Based on Zynq
## 4 Clock Synchronization Module Design
### NIC Module  
The NIC module is responsible for transmitting Ethernet frames between the Linux system running on the PS side and the SFP interface on the PL side. It includes a frame buffer and two RAM blocks (TX descriptor memory and RX descriptor memory), which are used to store the descriptors of received frames and frames to be transmitted. The frame buffer stores all frames received from the physical port of the switch, as well as frames that the CPU intends to send to the switch port.  
When the software running on the CPU wants to send an Ethernet frame, it must first write the frame into the frame buffer and then store the TX descriptor describing this frame in the TX descriptor memory. Conversely, the software must first write an empty RX descriptor into the RX descriptor memory. It must describe the offset and length of the area in the frame buffer where the NIC module can store the received frames. When a new frame is received, the NIC module will fill the RX descriptor and set the empty space to 0.
# Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11 ax Networks +
The clusters are scheduled based on the proportional fair-based approach to enhance network throughput performance without severe degradation in fairness. In this paper, we focus on IEEE 802.11 ax networks characterized by simultaneous uplink transmission; however, the proposed scheduling scheme can be applied to any Wi-Fi network, including a Wi-Fi-based sensor/IoT network where the simultaneous uplink transmission is potentially adoptable to improve spectral efficiency. The contributions of this study are summarized as follows:  
* In dense IEEE 802.11 ax networks, the uplink multi-user transmission scenario specified in the standard is an efficient solution for improving network throughput performance because of reduced competitions among a large number of stations. However, because block ACK is supposed to be used for uplink multi-user transmissions, the channel would be underutilized when differences in transmission delays of scheduled stations are significant. The proposed method creates clusters of stations so that stations with similar transmission delays are grouped together for uplink multi-user transmissions and enhances the uplink channel use. The proposed method also considers both network throughput and fairness performance by following the proportional fair approach. The trade-off between network throughput and fairness can be easily adjusted by fair proportional parameters. * The proposed proportional fair-based uplink multi-user scheduling method requires no standard modifications, only simple calculations for scheduling at the AP. High compatibility with the IEEE 802.11 ax standard increases the feasibility of using the proposed method in real-world scenarios. Through the software-defined radio (SDR)-based testbed, we verify the performance of the proposed method. The remainder of this paper is organized as follows: In Section 2, an overview of related work on the uplink multi-user transmissions is provided. In Section 3, the uplink transmission scenario specified in IEEE 802.11 ax and the system model are presented. In Section 4, we explain the proposed station clustering method, and then in Section 4, we propose a proportional fair scheduler based on station clustering. In Section 6, the performance evaluations of the proposed method are presented, and the concluding remarks follow in Section 7.
## 2 CRKG in Three Typical Duplex Modes
### In Band Full Duplex (IBFD) Mode  
IBFD communications enable users to transmit and receive simultaneously over the same frequency band. Therefore, it is not constricted by aforementioned problems in TDD and FDD modes. Even without considering these non-reciprocity issues, it also has attracted significant interest to study CRKG in IBFD mode, because it offers the potential to double the key rate, as measured by the number of secret key bits generated per second per Hz. In some scenarios, the growth may be slightly lower than double, due to the time correlation of adjacent channel measurements. Furthermore, IBFD provides additional protection against eavesdroppers because they observe the superposition of simultaneous transmissions over the two-way channel.  
Due to the close proximity of the transmitter and the receiver antennas, simultaneous transmission and reception of signals emanates a key issue of self-interference (SI) [36]. For a full-duplex base-station to achieve the link SNR equal to that of a half-duplex counterpart, it needs to suppress SI by about 106 dB. Therefore, some residuals will remain in practice. The inevitable residual self-interference (RSI) is asymmetric due to different self-interference channels and the hardware deviation. Therefore, it is the most challenging issue that hinders the practical implementation of CRKG in IBFD mode. One solution comes from the fact that the SI channel changes slower than the channel between users.  
At this point, theoretical CRKG approaches in IBFD mode are proposed in [37; 38], while practical CRKG testbed with IBFD capability universal software radio peripheral (USRP) devices and near field communication (NFC) devices are demonstrated in [38; 39], respectively. IBFD communications can noticeably boost the secret key rate in an operationally interesting regime of parameters.  
Figure 4 shows the secret key rate for systems operating at TDD, FDD and IBFD modes. It is observed in the figure that, with lower mobility, smaller frequency separation and smaller RSI, TDD, FDD and IBFD achieve higher secret key rate, respectively. It is also seen that with appropriate techniques for SI cancellation, that IBFD outperforms its half duplex counterparts. The secret key rate is calculated as bits per channel use since the channel impulse response within the coherence time is considered to be not varying. Mobility poses a negative effect on channel reciprocity for TDD mode, and thus the secret key rate for \(v\) = 50 m/s is lower than that of \(v\) = 10 m/s. However, the channel sampling rate can be improved for high mobility scenarios, since the coherence time is reduced. The secret key rate per unit time might increase, which will be studied in the future.
## 3 Results and Discussion
### Structure Analysis
#### 3.1.2 Micro Structure of PVDF Membrane  
Figure 3a shows an SEM image of PVDF, where it can be seen that the PVDF has a smooth surface and compact texture. From the SEM image of GO/PVDF (0.75 wt%) in Figure 3b, it can be seen that GO of the slice layer is dispersed in the PVDF, but the dispersion is not uniform and GO, with a two-dimensional structure, will precipitate on the surface of the PVDF. As can be seen from the SEM image of CGO/PVDF (0.75 wt%) in Figure 3c, the CGO is coated by PVDF and there is no precipitated two-dimensional structural material of CGO on the surface of the sample.  
In addition, a cross-sectional SEM image of CGO/PVDF is shown in Figure 3d, which demonstrates that the membrane is dense and compact and the CGO is uniformly embedded in the PVDF matrix. For further verification of the dispersion of CGO in PVDF, TEM images of CGO/PVDF are shown in Figure 3e. It can be seen from the images that the two-dimensional black lamellar material is CGO, the shadowy gray part is PVDF matrix and the CGO is relatively evenly dispersed in the PVDF, which is consistent with the SEM test results. Such a uniform and compact structure lays a foundation for the separation performance of water.  
Figure 2: (**a**) FTIR of GO and CGO, (**b**) XRD patterns of GO and CGO, (**c**)XPS spectra of GO, (**d**) XPS spectra of CGO, (**e**) zeta potential and (**f**) water contact angle of GO and CGO.
## 3 Driving Force Distribution and Control Strategy
### Upper Layer Controller for Vehicle Motion Control
#### 3.2.2 Yaw Rate Control  
The yaw rate controller was designed to calculate the desired resultant yaw moment to force the vehicle to follow the desired yaw rate using PID control as follows.  
\[M_{desire}=I_{z}\dot{\omega}_{d}, \tag{17}\]  
where  
\[\dot{\omega}_{d}=\ [K_{p}(\omega_{d}-\omega)+K_{i}\int(\omega_{d}-\omega)dt+K_{d }\frac{d}{dt}(\omega_{d}-\omega)].\]
## 5 Sea Experiment  
### Experiment Setup  
To further demonstrate the effectiveness of the proposed BS-CS algorithm, the experimental data was collected from a shallow water field at Wuyuan bay, Xiamen, China, to perform the channel estimation and equalization. Figure 5 shows the deployment of four receivers and one transmitter at a communication distance 1000 m in the experiment. The water depth of the transmitter is 2 m, as the water depth of experiment area is 10 m. Moreover, a four-element vertical receiver array was deployed at a water depth from 2 m to 8 m with an element spacing of 2 m.  
In our experiment, a QPSK modulation signal is transmitted at a bit rate of 1 kbps and a carrier frequency of 16 kHz. Figure 6 shows the response of four channels obtained in our experiment. We observe that these four channels exhibit block sparse structure and different degrees of time-varying characteristics at a time delay of 16 ms. Moreover, to perform the communication performance evaluation of different algorithms, the channel estimation-based decision feedback equalizer (CE-DFE) [31] is adopted in this study and a periodic training scheme is taken to avoid error propagation. In particular, we divide the received data into several data blocks, while the received data include 6000 information bits and 1500 training bits. The parameters of sea experiment are given in Table 1. In addition, our experiment's communication performance metrics are the symbol-to-reconstruction error ratio (SRER), the bit error rate (BER), and the constellation. The SRER is defined by:  
\[\text{SRER}=10\text{log}_{10}\frac{||\mathbf{s}||_{2}^{2}}{||\mathbf{s}- \mathbf{\tilde{s}}||_{2}^{2}}, \tag{21}\]  
where \(\mathbf{s}\) is the transmitted symbol and \(\mathbf{\tilde{s}}\) is the soft output from the CE-DFE receiver.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Description** & **Value** \\ \hline Carrier frequency & 16,000 Hz \\ Bandwidth & 5000 Hz \\ Channel impulse response duration & 60 ms \\ Length of discrete observation window & 30 \\ Length of feedforward filter & 120 \\ Length of feedback filter & 59 \\ Error tolerance of LS & \(10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the sea experiment.  
Figure 5: The deployment of receivers and transmitter.
## 2 System Model
and the received data symbols \(\chi_{dd}^{data}(l_{d},k_{d})\) are given by:  
\[\chi_{dd}^{data}(l_{d},k_{d})=\sum_{i=1}^{p}\sum_{j=1}^{N-1}[h_{i}\alpha_{j}e^{ j2\pi\frac{(k_{i}+k_{j})(l_{d}-l_{i})}{MN}}X_{dd}^{data}((l_{d}-l_{i})_{M},(k_{d}-k_ {i}+j)_{N})] \tag{10}\]  
where, \(\alpha_{j}\) represents the IDI caused by the fractional Doppler, which can be specifically expressed as follows:  
\[\alpha_{j}=\frac{e^{-j2\pi(-j-\kappa_{i})}-1}{N(e^{-j2\pi(-j-\kappa_{j})}-1)} \tag{11}\]