esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Antibiotics by Biochars: A Critical Review
## 4 A Critical Review of Biochar-Based Adsorption Processes  
Currently, four different processes are applied to remove antibiotics from water sources, including the conventional process (i.e., filtration and sedimentation, followed by biological processing), oxidation (advanced oxidation), disinfection (chlorination), and adsorption, as well as combined processes [12]. The conventional wastewater treatment process consists of primary mechanical (filtration and sedimentation) and secondary biological (activated sludge) processes. Antibiotics are partially removed through these processes. The activated sludge process is the main process for the removal of organics in WWTPs. Alternative biological processes, such as fixed-bed bioreactors, moving-bed biofilm reactors, and membrane bioreactors, are less common compared to the activated sludge process [60]. Some wastewater treatment plants also apply a final disinfection with UV irradiation, or by using chlorine in the final step of the water treatment. These latter processes have been shown to be effective in the removal of antibiotics. Advanced oxidation processes based on the generation of reactive radicals such as hydroxyl or sulfate radicals [61] are highly effective against organic pollutants such as antibiotics. Biochars can be integrated into the AOPs as activators for the generation of radicals [61; 62]. This latter approach seems to be particularly promising for the simultaneous removal of heavy metals and antibiotics [63; 64].  
Adsorption has certain advantages over conventional and oxidation processes, such as high efficiency at low concentrations, being easy to scale up, low cost, and the possibility of utilizing a wide range of waste materials, including plastics [12; 65; 66].  
Adsorption also has disadvantages, such as the production of concentrated waste and the generation of secondary pollution when the adsorbent is modified to enhance the adsorption, as in the case of doping the biochars with metals [42]. Yet another disadvantage of the adsorption is the time-consuming separation of the adsorbent from water [67].  
It is possible to apply a wide range of adsorbents for the removal of antibiotics from water resources, including clay and minerals, metal oxides, polymeric resins, polymers, chitosan, gels, carbon-based materials, and metal-organic framework (MOF) materials [65].  
Carbon-based materials (mainly in the form of activated carbons, carbon nanotubes, graphene, and biochars) are commonly used for the adsorption of antibiotics [12; 53] because of their four characteristics that contribute to adsorption:  
1. Specific surface area;
2. Micro- and mesopore structures;
3. Surface functional groups;
4. Mineral content and composition.  
These characteristics are determining factors in the adsorption of antibiotics, along with the antibiotic properties and the adsorption conditions [12]. Therefore, they are evaluated below for biochar adsorbents.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 3 Systematical Design of the Receiver
### Linearity  
Input 1 dB compression point (\(IP_{\rm 1 dB}\)) and input third-order intercept point (IIP3) are both indicators that represent linearity performance. \(IP_{\rm 1 dB}\) and IIP3 should be greater than \(-75\) dBm and \(-65\) dBm at the minimum cascaded gain according to the general specification [10]. Usually, IIP3 is about 10 dB higher than \(IP_{\rm 1 dB}\)[17]. Therefore, \(IP_{\rm 1 dB}\) is allotted first in this section. It is supposed that when \(IP_{\rm 1 dB}\) is \(-75\) dBm, the in-band signal can be linearly amplified by each sub-circuit. The in-band power (IBP) at the output of each sub-circuit is obtained in Table 1. Next, interference is considered. For the BDS-3 and GPS L1 bands, the nearest interference is at 1710 MHz from the LTE channel. According to the technical specification [18] issued by 3 GPP, the maximum transmission power is 23 dBm. After a communication distance of 3.5 m with an ideal attenuation of 48 dB, the interference power is \(-25\) dBm, which is so large that it is needed to add an external SAW filter to suppress the out-of-band interference. In order to prevent the SAW filter from worsening the NF, an external monolithic LNA will be added in front of the SAW. So, coupled with a SAW filter (SAFFB1G57 KE0 FoA of Murata) and an external LNA (SW7121D of Silicon Wave), the interference power arriving at the RF input is less than \(-56\) dBm. When only the RF gain of 51 dB is considered, the out-of-band interference power (OBIP) at GMT's output is \(-5\) dBm. But after GMT, the interference frequency is 134.58 MHz from the center frequency. So, taking into account the out-of-band attenuation provided by GMT, the OBIP at GMT's output is less than \(-25\) dBm. An out-of-band rejection of 75 dB at 1710 MHz is easily achieved by CBPF. So the OBIP at the programmable gain amplifier's (PGA's) output is less than \(-100\) dBm, which does not affect the linear capability at all. To summarize, when considering in-band signals, \(IP_{\rm 1 dB}\) should be greater than \(-75\) dBm; when considering out-of-band interference, \(IP_{\rm 1 dB}\) should be greater than \(-56\) dBm. So, all in all, \(IP_{\rm 1 dB}\) should be greater than \(-56\) dBm. In other words, as long as the interference power at the RF input is less than \(-56\) dBm, there is no need for the off-chip SAW and LNA.  
Combined with previous product experience, the \(IP_{\rm 1 dB}\) breakdown of each sub-circuit is shown in Table 1, and the cascade \(IP_{\rm 1 dB}\) is about \(-51\) dBm, considering 5 dB margins, which caters to the requirement in [10]. The IIP3 values for each sub-circuit are also summarized in Table 1. Using the cascade IIP3 calculation equation from [19], the cascade IIP3 is \(-43.39\) dBm.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 7 LoRaWAN Improvements
### Network Scalability Assessment and Improvement Methods
Interestingly, the highly channel variations will decrease ADR convergence time for large networks (4000 nodes). This is explained with the fact that high channel variations introduce randomness in RSSI values, increasing the impact of capture effect. By optimizing different parameters of the ADR mechanism (ADR_ACK_DELAY and ADR_ACK_LIMIT), authors of [103] showed that only the ADR_ACK_DELAY decrease will show an improvement in ADR convergence time. By reducing the ADR_ACK_DELAY the duration of each individual step to increase transmit power and SF is shortened, speeding up the convergence time. In general, main factors that cause a packet to be lost are interference, gateway receiver saturation and network outage. It was exhibited that for highly congested LoRaWANs the main factor is the receiver congestion rather than network outage [98]. Thus, in such cases, ADR algorithms or retransmissions mechanisms will not improve the performance, in opposite they will worsen it further. Authors of [46] proposed an improvement in the current ADR mechanism implemented at the network server side in order to increase network performance. Instead of considering the maximum SNR value from a number of uplink packets in the past, authors proposed to take the average SNR of that window. This improvement helps to alleviate the impact of variable channel conditions. The proposed ADR mechanism achieves at least 30% better packet deliver ratio compared to basic one in cases or moderate variable channels. Another ADR algorithm that maximizes the data extraction rate (DER) of end nodes was presented in [104]. Authors proved that the fairness index between end nodes using Aloha will decrease by increasing the number of end nodes in the network. This will favor transmissions from end nodes that are closer to gateways compared to far away end nodes. Proposed ADR algorithm in [104] distributes the SFs and transmit powers to end nodes by maximizing the DER fairness index among the end nodes. The implementation is tested in LoRaSim simulator [41]. The impact of downlink traffic in the network performance was studied in [40; 50; 97; 98] and was proved to be deteriorating for the uplink traffic performance in case of large number of end nodes. In [97], it is shown that, by increasing the ratio of high priority end nodes (asking for confirmed uplink traffic), the total network throughput is decreased by worsening the throughput for low priority end devices. Same relation is found even with the number of retransmissions per packet. In [40], it is demonstrated that the number of missed receive windows will increase with the increase of uplink traffic that requires downlink confirmation due to gateway duty cycle.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
###### Abstract  
Artificial neural network (ANN) is powerful in the artificial intelligence field and has been successfully applied to interpret complex image data in the real world. Since the majority of images are commonly known as private with the information intended to be used by the owner, such as handwritten characters and face, the private constraints form a major obstacle in developing high-precision image classifiers which require access to a large amount of image data belonging to multiple users. State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a substantial overhead of computation and data traffic for the data owners, and are restricted to approximation models by low-degree polynomials which lead to a large accuracy loss of the trained model compared to the original ANN model in the plain domain. Consequently, it is still a huge challenge to train an ANN model in the encrypted-domain. To mitigate this problem, we propose a privacy-preserving ANN system for secure constructing image classifiers, named _IPPNN_, where the server is able to train an ANN-based classifier on the combined image data of all data owners without being able to observe any images using primitives, such as randomization and functional encryption. Our system achieves faster training time and supports lossless training. Moreover, _IPPNN_ removes the need for multiple communications among data owners and servers. We analyze the security of the protocol and perform experiments on a large scale image recognition task. The results show that the _IPPNN_ is feasible to use in practice while achieving high accuracy.  
image classifier; neural network; privacy-preserving; non-interactive; function encryption; mask matrix +
Footnote †: journal: Applied  
Article  
Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification  
doi:10.3390/app122412873
## Nomenclature  
\begin{tabular}{l l} \(a\) & Cracks size \\ \(a_{0}\) & Equivalent initial micro-defect size \\ \(a_{1}\) & Parameter of P-S-N curve \\ \(a_{2}\) & Parameter of P-S-N curve \\ \(a_{f}\) & Crack size of final failure \\ \(a_{sc}\) & Critical crack size for identifying long and short cracks \\ \(b_{1}\) & Parameter of surface condition modification factor \\ \(b_{2}\) & Parameter of surface condition modification factor \\ \(C\) & Material constant in Paris law \\ \(c\) & Material constant in Basquin's relation (\(NS^{n}=c\)) \\ \(E\) & Elastic modulus \\ \(G\) & Shear modulus \\ \(h\) & One-sided tolerance factor \\ \(k_{a}\) & Surface condition modification factor \\ \(K_{f}\) & Fatigue stress concentration factor \\ \(K_{t}\) & Stress concentration factor \\ \(N\) & Number of cycle \\ \(N_{p}\) & Crack propagation life \\ \(N_{i}\) & Crack initiation life \\ \(N_{f}\) & Fatigue life (number of cycles to failure) \\ \(n\) & Material constant in Basquin's relation(\(NS^{\text{st}}=c\)) \\ \(n_{s}\) & Sample size \\ P-S-N & Probabilistic stress-life \\ \(R_{a}\) & Average surface roughness \\ \(R\) & Survival probability \\ \(\varsigma\) & Stress amplitude \\ S-N & Stress-life \\ \(S_{v}\) & Endurance limit of a machine part \\ \(\hat{S}_{e}\) & Endurance limit of the rotary-beam test specimen \\ \(S_{at}\) & Minimum tensile strength \\ \(x_{RL}\) & Confidence lower limit \\ \(\overline{x}\) & Mean of the experimental fatigue life \\ \(a\) & Parameter in Equation (4) \(N_{i}=\beta(K_{t}S)^{a}\) \\ \(\beta\) & Parameter in Equation (4) \(N_{i}=\beta(K_{t}S)^{a}\) \\ \(\gamma\) & Confidence level \\ \end{tabular}
## 2 Integrated Navigation System Model
### Coordinate System Definition  
This paper employs a geodetic coordinate system based on the east-north-up (ENU) convention, with the vehicle's centroid serving as the origin, designated as the n-frame. The b-frame is centered on the IMU mounted on the vehicle and is rigidly affixed to the vehicle, moving in synchrony with it. The absolute positioning of satellites is represented in the Earth-Centered, Earth-Fixed (ECEF) coordinate system, known as the e-frame. The interrelations between the coordinate systems utilized in this study are depicted in Figure 1.
## 2 Experimental Setup and Methods  
### Experimental Setup  
The experiments were conducted at the Daxing experiment base of the China Institute of Water Resources and Hydropower Research. The experiment flume, as depicted in Figure 1, measures 50 m in length, 1 m in width, and 1.2 m in height, with an adjustable slope range of 0-0.01 and a 200 m\({}^{3}\) underground reservoir. The flume is equipped with a sophisticated measurement and control system that adjusts the flow rate through a computer-controlled frequency converter with feedback closed-loop control and measures the flow rate using an electromagnetic flow meter before flow enters the flume. The flow level and depth along the flume are monitored by eight ultrasonic-level meters mounted above the flume and twelve steel straightedges affixed to the flume sides. The transparent glass sides of the flume enable clear observation of sediment particle movement on the bed surface and the dynamics of dune movement. The slope gradient refers to the glass flume inclination angle. The slope gradient was adjusted using lifts, which automatically set the flume to the desired inclination before the experiment.  
Approximately 25 m upstream of the flume, a transition section consisting of a 10 cm thick layer of 20 mm pebbles was installed, and the experiment section was situated about 20 m downstream of the flume. A funnel (length: 1 m; width: 1 m; depth: 0.8 m) was positioned between the end of the experiment section and the tailgate to collect sediment. The sediment captured in the funnel during the experiment was classified as bedload particles. The experiment sediment comprised uniformly sized crushed quartz sand particles with a median grain size of 0.83 mm, as depicted in the grading curve displayed in Figure 2.  
Figure 1: Schematic of experiment flume.  
Figure 2: Cumulative sediment curve of experiment sand.
# A Real-Time Sound Field Rendering Processor
## 4 Performance Estimation  
To verify and estimate system performance, the register transfer level (RTL) model and cycle-accurate simulator of the processor were developed using VHDL and C programming language. Furthermore, the prototype machine was investigated and implemented using a processor-based FPGA machine TD-SPP3000 from Tokyo Electron Device Ltd., and sound propagation in a small shoe-box type room was examined. In the defined room, both length and width were 1.28 m, and height was 0.64 m; the cube grid size \(\Delta l\) was 4 \(\times\) 10\({}^{-2}\) m; the reflection factor of the boundaries was 0.95; and the incident and observation points were at the middle of the room. Therefore, the small room was discretized into a mesh with 32 \(\times\) 32 \(\times\) 16 grids. The hardware development environment was a 64-bit Windows 7 platform with FPGA tools Xilinx ISE 14.3 and ModelSim SE 10.1d. For comparison, the counterpart system was also developed by C++ programming language, and executed on a personal computer (PC) with 32 GB RAM and an Intel i7-6800K six-core processor running at 3.4 GHz. The software environment of the PC was 64-bit CentOS 7.0 with gcc 4.9.4. The reference C++ codes were compiled and optimized by using the command g++ with the option -O3, mcmodel = large, and -fopenmp to use all six cores in the PC. Data were 32-bit fixed-point in the prototype machine while they were integer in the software simulations.  
The prototype machine consisted of two FPGA boards, and each board contained two XC5 VLX330T-FFI738 FPGA chips from Xilinx. A high-speed A/D board (ADS5474) was attached to the FPGA1 on the board 1 to sample the incident signals. Then, the sampled data were processed by the rendering processor implemented by the FPGA1 on the board 1. The sound pressures at the observation point were transferred to the D/A board (DAC568 ZZ) on the board 2 through the advanced telecommunication computing architecture (ATCA) bus and output to drive the speakers directly. The rendering processor and the A/D and D/A boards all ran at 200 MHz. In the rendering processor, each PE processed the sound rendering in a sound space with 4 \(\times\) 4 \(\times\) 4 grids, and all PEs worked in parallel to carry out rendering at the whole sound space. Thus, computation at each time step took 64 cycles, and each cycle was 5 (1/(200 \(\times\) 10\({}^{6}\))) ns.  
Figure 6: Data transmission.
## 3 Simulation Results and Analysis
### The Influence of Inter-Stage Distance on the Selection of Current Direction
Figure 14 displays the optimal exit velocity curves for the two configurations with different adjusted inter-stage distances. It can be observed from Figure 14 that as the inter-stage distance of the drive coils in the SICL increases, the optimal exit velocity of the PP configuration device gradually decreases and tends to stabilize, while the optimal exit velocity of the PN configuration device gradually increases and tends to stabilize. The intersection point of the two curves represents the threshold value of the inter-stage distance, which is 7.5 mm in this case, accounting for 37.5% of the length of the drive coils. When the inter-stage distance is smaller than this threshold value, the coil launching device using the PP configuration will achieve a better armature exit velocity. Conversely, when the inter-stage distance is larger than this threshold value, the coil launching device using the PN configuration will achieve a better armature exit velocity. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Inter-Stage** & **2.5 mm** & **5 mm** & **7.5 mm** & **10 mm** & **12.5 mm** & **15 mm** & **17.5 mm** & **20 mm** & **22.5 mm** \\
**Distance** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** \\ \hline PP: N = 2 & 41.76 & 40.66 & 39.78 & 39.57 & 39.05 & 39.20 & 39.22 & 39.27 & 39.48 \\ PN: N = 2 & 38.53 & 40.53 & 42.23 & 43.71 & 44.61 & 45.52 & 46.13 & 46.57 & 46.99 \\ PP: N = 3 & 57.68 & 54.90 & 53.66 & 52.95 & 52.06 & 52.02 & 51.68 & 51.98 & 52.08 \\ PN: N = 3 & 47.48 & 50.86 & 53.34 & 55.84 & 57.94 & 59.66 & 60.82 & 61.64 & 63.05 \\ PP: N = 4 & 70.78 &
## 3 Early Warning Model
### Support Vector Machine  
A support vector machine (SVM) is a supervised machine learning technique for constructing the optimal hyperplane based on the principle of structural risk minimization. It maps the input vectors into high dimensional feature space by non-linear transformation and finds the optimal classification hyperplane in high dimensional feature space, which separates the data into two groups with a maximized classification interval. Suppose a training sequence \(\{x_{i},y_{i}\}\), \(i=1,2,\)..., \(l\); \(x_{i}\in R^{n}\), \(y_{i}\in\{-1,+1\}\); where 1 is the number of samples, and n is the dimension of \(x_{i}\). In the case of linearly separable values, a classification hyperplane \(\mathbf{w}+b=0\) can be found to separate \(\{x_{i},y_{i}\}\) into two groups. For the nonlinearity situation, \(x_{i}\) is mapped from a low dimension feature space to a high dimension feature space by a nonlinear mapping function \(\mathbf{\Phi}(x)\). Then, the classification hyperplane can be expressed as \(\mathbf{w\Phi}(x)+b=0\), where \(\mathbf{w}\), \(b\) are constants to be determined. Finding the classification hyperplane equals maximizing \(2/\|\mathbf{w}\|\). This problem can be solved by the Lagrange multiplier method:  
\[\begin{cases}\min\frac{\|\mathbf{w}\|^{2}}{2}+C\sum\limits_{i=1}^{l}\xi_{i},\\ s.t.\begin{array}{c}y_{i}(\mathbf{w};x_{i}+b)\\ \xi_{i}^{\prime}\geq 0,i=1,2\ldots,l\end{array}\end{cases}\begin{cases}1 \\ 2\end{cases}\begin{array}{c}\min\frac{  
\[f(x)=\text{sign}\Bigg{\{}\sum_{i=1}^{l}\alpha_{i}y_{i}[\mathbf{\Phi}(x_{i})\cdot\mathbf{ \Phi}(x_{i})]+b\Bigg{\}} \tag{20}\]  
If the kernel function \(K(x_{i},x_{i})=\mathbf{\Phi}(x_{i})\cdot\mathbf{\Phi}(x_{j})\) is found here, the function operation in Equation (20) can be simplified to the inner product of the vectors. The commonly used kernels are the linear kernel function, polynomial kernel function, radial basis (Gauss) function, and sigmoid kernel function.
###### Abstract  
Wind power is currently the most mature representative of sustainable energy generation technology, which has been developed and utilized on a large scale worldwide. The random and fluctuating nature of wind power output poses a threat to the secure and stable operation of the system. Consequently, the transmission of wind power has garnered considerable attention as a crucial factor in mitigating the challenges associated with wind power integration. In this paper, an artificial-intelligence-aided frequency coordination control strategy applicable to wind power transmission systems based on hybrid DC transmission technology is proposed. The line commutated converter (LCC) station at the sending end implements the strategy of auxiliary frequency control (AFC) and automatic generation control (AGC) to cooperate with each other in order to assist the system frequency regulation. The AFC controller is designed based on the variable forgetting factor recursive least squares (VFF-RLS) algorithm for system identification. First, the VFF-RLS algorithm is used to identify the open-loop transfer function of the system. Then, the AFC controller is designed based on the root locus method to achieve precise control of the system frequency. The DC line power modulation quantity is introduced in the AGC to automatically track the active power fluctuation and frequency deviation of the system. The AGC utilizes the classical proportional-integral (PI) control. By selecting the integrated time absolute error (ITAE) performance index to construct the reward function, and using a deep Q-network (DQN) for controller parameter optimization, it achieves improved regulation performance for the AGC. The voltage source converter (VSC) station at the receiving end implements an adaptive DC voltage droop control (ADC)strategy. Finally, the effectiveness and robustness of the proposed frequency control strategy are verified through simulation experiments.  
high-voltage direct current (HVDC) transmission; automatic generation control; auxiliary frequency control; coordinated control; adaptive DC voltage droop control +
Footnote †: journal: Applied Sciences