esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Client Selection in Satellite Federated Learning
## 4 Algorithm
### Federated Learning  
As Figure 6 shows, we consider a PS with \(N\) satellite clients. A typical implementation of federate learning is FedAvg [13]; the PS will randomly choose \(K\) clients from \(\mathcal{I}\) to participate in training, which is denoted as set \(\mathcal{K}\). The parameters of the global model are aggregated by these clients. Every client has a local dataset \((x_{k},y_{k})\in\mathcal{A}\), where \(x_{k}\) is the sample data and \(y_{k}\) is the label data, and the size of \(\mathcal{A}\) is \(D_{k}\). For a client that can participate in the learning, the loss function can be denoted as \(f_{k}(\omega)\), where \(\omega\) is the model parameter.  
The goal of FL is finding the \(\omega\) that can minimize the global average loss, and the optimization function is given by  
\[\min_{\omega\in R}F(\omega)=\frac{D_{k}}{D}\sum_{k=1}^{|K|}F_{k}(\omega) \tag{9}\]  
\[F_{k}(\omega)=\frac{1}{D_{k}}\sum_{i\in D_{k}}f_{i}(\omega) \tag{10}\]  
where the \(D\) is the data size of the total dataset. For client \(k\), it updates the model parameters by stochastic gradient descent:  
\[\omega_{t+1}^{k}=\omega_{t}^{k}-\eta\
abla f_{k}(\omega^{k}) \tag{11}\]  
where \(\eta\) is the learning rate.  
The PS aggregates the parameters and updates the global model from clients \(\mathcal{K}\) by  
\[\omega_{t+1}=\frac{D_{k}}{D}\sum_{k=1}^{|K|}\omega_{t+1}^{k} \tag{12}\]  
Figure 6: Architecture of SFL.  
```
1:Initialize model parameter \(\omega_{0}\) randomly from a uniform or normal distribution, and send to clients.
2:for each round \(t=1,2,\times s\)do
3: select \(K\) clients as set \(\mathcal{K}\)
4:for each client \(k\in\mathcal{K}\) in parallel do
5:\(\omega_{t+1}^{k}\leftarrow\) Client Update\((k,w_{t})\)
6:endfor
7:\(\omega_{t+1}\leftarrow\sum\limits_{k=1}^{K}\frac{D_{k}}{D}\omega_{t+1}^{k}\)
8:endfor Client Process For client \(k\), update \(\omega^{k}\)
9:for\(i\in E\)do
10:for\(b\in B\)do
11:\(\omega_{b+1,i}^{k}\leftarrow\omega_{b,i}^{k}-\eta\
abla I\Big{(}\omega_{b,i}^ {k};b\Big{)}\)
12:endfor
13:endfor
14:return\(\omega^{k}\)
```  
**Algorithm 1** FedAvg [13]. The chosen \(K\) clients are indexed by \(k\); \(B\) is the local minibatch size and \(E\) is the number of local epochs
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 2 TiCl\({}_{4}\) Hydrolysis Process and Mathematical Model
### Mathematical Model
#### 2.2.4 PSD Calculation  
According to [27] and the associated reference materials utilizing Aspen Plus, the particle size distribution formula adheres to the normal distribution model.  
\[f(D_{t})=\frac{1}{\sqrt{2\pi\sigma}}\text{exp}(-\frac{\left(D_{t}-D_{50}\right) ^{2}}{2\sigma^{2}}) \tag{20}\]  
In Equation (20), \(D_{50}\) symbolizes the median or average particle size (\(\mu\)m), \(f\) stands for the particle size distribution function (mass fraction), and \(D_{t}\) is a random variable that holds when the particle size is greater than zero (\(0<t<t_{\infty}\)). The standard deviation of particle size (\(\mu\)m) is represented by \(\sigma\). This formula can directly depict the nucleation and growth behavior of TiO\({}_{2}\) particles. Moreover, based on the normal distribution model, it can accurately compute the particle size distribution of TiO\({}_{2}\) particles.
# Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing
## 2 System Model  
### System Overview  
In this paper, a multi-user uplink system is considered as depicted in Figure 1a. Here, \(N_{\text{u}}\) single-antenna users simultaneously transmit to a central BS, where a uniform linear array is employed with a very large number of receive antennas \(N_{\text{rx}}\!\gg\!N_{\text{u}}\) and an antenna distance of \(d_{\text{a}}\). The users are assumed to be located in the far-field, i.e., the (radial) user distance \(d_{u}\) meets the far-field condition with respect to the physical antenna array size \(D\!=\!N_{\text{rx}}d_{\text{a}}\) at the BS and the wavelength \(\lambda\) according to \(d_{u}\!>\!2D^{2}/\lambda\)[32]. Consequently, plane waves impinge at the receiver from various angular directions \(\phi\) (measured with respect to the normal of the array axis) depending on the user positioning and characteristics of the propagation channel. In order to recover the individual data streams transmitted by the users, noncoherent detection schemes are applied at the BS. This requires the per-user received power induced at the antenna array of the BS to be spatially separated, which is not the case due to the far-field assumption. To this end, beamspace preprocessing is utilized in the digital domain after coherent downconversion and analog-to-digital conversion. Digitally and simultaneously generated beams pointing into different angular directions within a predefined field of view (FoV) focus the energy of the incoming waves to a smaller subset of the overall receive paths (see Figure 1a).  
In the following, a joint digital beamspace preprocessing of all \(N_{\text{rx}}\) receive branches is denoted as the full-array architecture. This system can exploit its full potential in terms of angular resolution and power-focusing capability. However, to form beams into a desired direction, a fully coherent demodulation of the received branches is required. The complexity of such a high-quality LO distribution network scales with the number of receive branches. In order to relax the design requirements, the full-array architecture can be divided into \(N_{\text{sub}}\) sub-arrays of equal size (see Figure 1b), where coherency of the receive channels can be provided more easily and where one LO with proper distribution network can be employed instead. Consisting of \(N_{\text{rx,sub}}\!=\!N_{\text{rx}}/N_{\text{sub}}\) antennas or rather receive branches, each sub-array can perform digital beamspace preprocessing individually, i.e., forming beams in the digital domain within a predefined FoV. Noteworthy, due to the reduced aperture size \(D^{\prime}\!=\!D/N_{\text{sub}}\), the (radial) user distance could be less to meet the far-field condition in the sub-array case.  
Further details regarding digital beamspace preprocessing can be found in Section 2.4. First, the underlying channel model and the noncoherent detection scheme are briefly reviewed.
## 3 Biochemical Profiling of _W. somifera_
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bioactive** & **Part of Plant** & **Derivatives** & **Yield** & **Uses** & **References** \\ \hline \hline \multirow{4}{*}{Saponins} & Roots & & & Anti-inflammatory, & \\  & Berries & Sitoindoside VII And VIII & 3.02–5.26 \(\mu\)g/gm & anti-metrobial and & \\  & & & & antitrival, used in & \\  & & & & detergents and & \\  & & & & molluscicides & \\ \hline \multirow{4}{*}{Flavonoids} & Roots & Quercetin, 7-hydroxyflavone & & Anticancer, & \\  & Stems & Kaempfer & antioxidant & & \\  & & & & antimicrobial & \\  & & & & strengthen capillary & \\  & Roots & Quercetin, 7-hydroxyflavone & & walls, improves & \\  & Stems & Kaempfer & blood cholesterol & & \\  & & & & levels as well as & \\  & & & & reduces the risk of & \\  & & & & cancer, osteoporosis, & \\  & & & & and coronary heart & \\ \hline \multirow{4}{*}{Steroids} & \multirow{4}{*}{Roots} & \(\beta\)-sitosterol, Cholesterol, & & Aphrodisiac, reduces & \\  & & Disogenin, Ergostane, & cholesterol levels, & & affects the immune & \\  & & Sitomadsideş, X, & & system, and tumor & \\  & & Stigmastadien, Stigmasterol & & & cells & \\ \hline \multirow{4}{*}{Steroidal lactones} & \multirow{4}{*}{Roots} & Withaferin–A, Withanone & & Anti-inflammatory, & \\  & & & & A-Y, Withasomniferine, & & anti-anxiety, and \\  & & & Withasomniferools A-C & & improves fertility & \\ \hline \multirow{4}{*}{Phenolic} & Roots & \
\begin{tabular}{c} Coumaric acid, caffe acid, \\ chlorogenic acid, gallic acid, \\ fertile acid, and catechin. \\ \end{tabular} & 90.325 mg/gm &
\begin{tabular}{c} Anti-inflammatory, \\ anti-oxidants, \\ anti-cancer, and \\ anti-septic \\ \end{tabular} & [43; 44] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._as withhanolides, 14\(\alpha\), 15\(\alpha\), 179, 22R withanolide F, and withhanolide J have been evaluated, but other plant parts have not yet been studied.
## 4 Experimental Verification
### Validation of Predictive Models  
In order to verify the effectiveness of the ANN prediction model used in this paper, the ANN model was compared with three classic regression prediction models: logistic regression (LR) [48], SVM [16], and AdaBoost [19]. The models are trained with the augmented data of GRW-DBA and tested with the source dataset.  
Figure 9 shows that the R\({}^{2}\) of the ANN model combined with the GRW-DBA method reaches 0.986, which is higher than other models. Figure 10 shows the comparison of the  
Figure 8: Comparison of the prediction accuracy of models trained on augmented data obtained using the GRW-DBA method proposed in this paper and other classical methods.  
predicted value of the four comparison models based on 500 times augmented data. From Figure 10, it can be found that compared with the classical prediction model, the ANN model combined with the GRW-DBA data augmentation method can better predict the fatigue life of concrete.
## 3 DID-Based Authentication
### DID Network Configuration  
The structure of the system proposed in this study is presented as shown in Figure 6. The DID distributed network requests DID registration from the DID mobile card, and the DID mobile card authenticates the DID documents after authentication. If authentication for the DID mobile card proceeds in the mobile card distributed network, the DID mobile card sends a notification that authentication has been made. After each DID distributed network request and mobile card distribution network authentication request to the DID mobile card, the DID mobile card system proceeds with the authentication process through Face ID, and the VP (verifiable presentation) system requests and receives a response.  
Figure 5: UML-based flowchart with entities.  
Figure 6: The architecture of the proposed technique.  
The VP consists of presentation metadata, verifiable credentials, and proofs, and the VP does not require users to directly submit VC (verifiable credentials) to prove their identity but includes their VC in the VP and then processes and submits it again. VC is used as a means for including identity attributes such as identification, career certificates, driver's licenses, and other certificates, and a single VP may include multiple VCs. If a VC is continuously loaded and stored, it can become vulnerable to leakage when a hacking attempt is made on it later.  
After that, the DID mobile card system responds to the DID distributed networks and the mobile card distribution network with authentication.  
In the system proposed in this study, the authentication information is transmitted to the network and then the validation is performed. Blocks in the current blockchain can be saved after checking block validity, chain validity, and genesis block validity as shown in the source code below (Algorithm 1).  
```
const isValidNewBlock = (newBlock: Block, previousBlock: Block): boolean => { if (isValidBlockStructure(newBlock)) { console.log('invalid structure'); return false;} if (previousBlock.index + 1!== newBlock.index) { console.log('invalid index'); return false;} else if (previousBlock.hash!== newBlock.previousHash) { console.log('invalid previoushash'); return false;} else if (calculateHashForBlock(newBlock)!== newBlock.hash) { console.log(typeof (newBlock.hash) +'+ typeof calculateHashForBlock(newBlock)); console.log('invalid hash: '+ calculateHashForBlock(newBlock) +'+ newBlock.hash); return false;} return true;} const isValidChain = (blockchainToValidate: Block[]): boolean => { const isValidGenesis = (block: Block): boolean => { return JSON.stringify(block) == = JSON.stringify(genesisBlock); }; if (isValidGenesis(blockchainToValidate[0])) { return false; } for (let i = 1; i < blockchainToValidate.length; i++) { if (isValidNewBlock(blockchainToValidate[i], blockchainToValidate[i - 1])) { return false; }} return true;}
```  
**Algorithm 1** Blockchain Based Authentication Code
## 2 Materials and Methods
### Microbial Quality Evaluation  
The microbial quality of the halophytes, _Disphyma crassifolium_, _Inula crithmoides_, _Mesembranthemum nodiforum_, and _Sueda maritima_, cultivated using a soilless technique in three different concentrations (35, 200, and 465 mM of NaCl) was evaluated through the enumeration of aerobic microorganisms at 30 \({}^{\circ}\)C and 6.5 \({}^{\circ}\)C, filamentous fungi and yeasts, _Escherichia coli_, and coagulase-positive staphylococci, according to the International Organization for Standardization (ISO) standards ISO 4833-1:2013 [24], ISO 17410:2019 [25], ISO 21527-1:2008 [26], ISO 16649-2:2001 [27], and ISO 6888-1:2003 [28], respectively. For each analysis, 10 g of each plant was aseptically collected, mixed with 90 mL of sterile Ringer solution (Oxoid, Basingstoke, UK), and homogenised for 2 min (Model 400 Circulator, Seward, Norfolk, UK). Serial dilutions were performed using a sterile Ringer solution. The aerobic microorganisms at 30 \({}^{\circ}\)C were determined after the incorporation of aliquots of 1 mL in Plate Count Agar (PCA) (Scharlau, Barcelona, Spain) and incubated at the mentioned temperature for 3 days. The psychrotrophic aerobic microorganisms (6.5 \({}^{\circ}\)C) were determined by spread-plating aliquots of 0.1 mL onto the surface of the PCA, following incubation for 10 days.  
Filamentous fungi and yeasts were counted on Dichloran Rose Bengal Chloramphenicol Agar Petri dishes (Scharlau, Barcelona, Spain) incubated at 25 \(\pm\) 1 \({}^{\circ}\)C for 2 to 5 days. To enumerate _E. coli_, aliquots of 1 mL were inoculated in Chromocult Triptone Bile X-glucuronide Agar (TBX) (Merck, Darmstadt, Germany), following incubation at 44 \({}^{\circ}\)C for 24 h. The enumeration of the coagulase-positive staphylococci was performed by inoculating Baird-Parker medium (Oxoid) with 0.1 mL of the dilutions, followed by incubation at 37 \({}^{\circ}\)C during 24-48 h, according to the ISO 6888-1 ([PERSON] et al., 2017). The Staphylase Test (Oxoid) was used to test for the coagulase reaction of typical and atypical colonies. Aliquots of the dilutions were inoculated in duplicate, for all determinations.  
The results were expressed as Log CFU/g of plant fresh weight. Experimental values are expressed as means \(\pm\) standard deviation.
## 2 Materials and Methods  
### Electrical and Optical Investigations  
For the investigations, two different experimental setups were designed. The experimental setup used for the assessment of the discharge parameters consisted of two electrodes made of stainless steel (diameter 4 mm, length 32 mm). These electrodes were fixed to an electrode holder and positioned about 3 mm above the surface of the liquid in a beaker with a volume of 250 mL (Figure 1). The electrode holder was a round shape disk made of polycarbonate with a diameter of 120 mm and a thickness of 80 mm. The electrodes were connected to the output transformer of a commercial high-voltage source (Neon-Pro NP 10000-30) operating the discharge at a frequency of approximately 25 kHz and ignition voltages of about 2-3 kV (amplitude). The high voltage source was driven by a variac (RFT LTS 002) connected directly to the net. Details of the electrical parameters are given in the electrical characterization section.  
The beaker with the electrode holder was placed on a magnetic stirrer (Thermo Scientific Cimarec i Mono) and the water was stirred permanently. The electrical data were monitored with high voltage probes (Tektronix P6015A) and current probes (Pearson 2877) and recorded with a digital oscilloscope (Tektronix DPO 4104, Tektronix, Beaverton, OR, US). The power dissipated into the high voltage source was measured by a voltage meter (PCE-PA 6000). The discharges were optically investigated with a fiber spectrometer (Ocean Optics HR4000, Ocean Optics, Largo, FL, US) via an orifice in the beaker.  
For the plasma-treatment of water, the electrode configuration described above was doubled, as described in [28]. Thus, four electrodes were positioned with a holder on top of a beaker with a volume of 0.5 L or 1 L. Pictures of the discharge configurations (Figure 2) were taken with a digital camera (Canon EOS 70D with a macro lens Tamron XR Di II). Tap water, physiological saline solution (distilled water with 0.85% NaCl), and distilled water were treated for up to 30 min.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 4 Performance Optimization of the OpenCV Object Tracking Algorithm
### Improving the Occupancy
#### 4.1.2 Securing Sufficient Wavefronts  
As described in Section 2.2, an SIMD unit can handle 64 work-items, which is referred to as a wavefront. Since a CU has four SIMD units and each SIMD unit can concurrently execute ten wavefronts, a CU can theoretically activate up to 40 wavefronts simultaneously. The APU device, one of the environments used in this paper, has eight CUs. Therefore, the APU device can activate 320 wavefronts at the same time and process 20,480 work-items (320 \(\times\) 64 work-items) simultaneously. This characteristic is important in achieving  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variables Moved to LDS Memory** & **VGPR** & **Occupancy** \\ \hline _Baseline_ & 76 & 30 \\ ngroups & 59 & 40 \\ sumstep & 57 & 40 \\ nofs0 & 51 & 40 \\ normarea & 58 & 40 \\ invarea & 58 & 40 \\ nofs & 55 & 40 \\ ngroups, sumstep & 57 & 40 \\ ngroups, nofs0 & 51 & 40 \\ ngroups, invarea & 50 & 40 \\
**nofs0, nofs** & 47 & 50 \\ ngroups, sumstep, nofs0, normarea, invarea, nofs & 44 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Occupancy change of the runHaarClassifier kernel.  
high occupancy because if the global work size is lower than 20,480 work-items, the GPU hardware has no more wavefronts to fill up all the wavefront buffers.  
Most OpenCV computer vision algorithms perform parallel data operations to the individual pixel datum of the input images. Therefore, the input image size determines the number of kernel's work-items. For example, 307,200 work-items can be created when a 640 \(\times\) 480 VGA image is the input. Most OpenCV algorithms do not need to consider the number of work-items because many wavefronts are already active.  
However, some kernels failed to activate all wavefronts provided by the GPU despite increasing the occupancy in Section 4.1.1; the runHaarClassifier kernel is an example of this case. As depicted in Section 3.1, the runHaarClassifier kernel generates only 96 active wavefronts. Although the input data size is large to process at once, the kernel configures a global work size (i.e., 6144 work-items) too small to utilize the total wavefronts supported by the GPU. Therefore, due to the necessity to generate more wavefronts in the kernels, we expand the global work size to 20,480 so that the runHaarClassifier kernel generates a total of 320 wavefronts. For this, we modified the fixed value assigned to the variable deciding the global work size. Note that when the global work size is set to generate more wavefronts, the algorithm's performance will be improved more because the SIMD units are more utilized. However, when the global work size is set to generate more than 320 wavefronts, the performance will not be significantly improved. This is because instruction buffers in SIMD units are fully occupied, so increasing the global work size does not improve the algorithm's parallelism.
## 4 Model Formulation
### Lower-Layer Model
#### 4.3.2 First Vehicle Trajectory Planning in the CAV Queue
Equations (29) and (30) define the moment \(t_{0}^{\omega}\) at which CAV \(\omega\) enters the passing zone and the moment \(t_{pass}^{\omega}\) at which it leaves the stop line of the variable virtual waiting zone. Equation (31) sets the upper and lower speed limits of CAV \(\omega\) during its operation. Equation (32) constrains the values of acceleration, \(a^{U},0,a^{L}\), that is, the maximum deceleration process, the acceleration of the uniform process, and the maximum acceleration of the acceleration process. Equations (33)-(35) define the deceleration, uniformity, and acceleration of the three processes in the velocity-acceleration formula. Equations (36)-(38) define the deceleration,  
\[\mathit{mint}_{pass}^{\omega} \tag{27}\]7 Acceleration profile. uniformity, and acceleration of the three processes of the velocity-displacement formula. Equation (39) specifies the division of time. Equation (40) is used to calculate the holding time of the variable virtual waiting zone. Equation (41) ensures that CAV \(\omega\) can cross the junction within the period of time when there is a green light for that phase. It should be noted that the velocity \(v_{0}^{\omega}\) of CAV \(\omega\) at the time of entering the passing zone is not known, and the magnitude of its value is between the minimum \(v_{0}^{L}\) and the maximum \(v_{0}^{L}\). In actual operation, since vehicles prefer to operate at a faster speed, \(v_{0}^{\omega}\) is probably equal to \(v_{0}^{U}\) in the trajectory planning. The trajectory optimization model \(T_{3}\) can be solved by the method in the Appendix A of this paper, as shown in Figure 12, when the CAV enters the passing zone at time \(t_{0}^{\omega}\). The trajectory planning of the fleet will be adjusted according to the actual entry speed.
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 4 Detectability of Planets in Alpha Centauri
### Astrometry  
Given the proximity of the Alpha Centauri system, we assessed the potential influence of hypothetical planets on the apparent motion of the stars in the celestial plane. This assessment supplements the radial velocity (RV) measurements discussed earlier. Building on the stability regions identified, we considered a low-mass, rocky planetary system akin to the inner Solar System to determine if its astrometric signals could be detected with current technology. We replicated the methodology from the previous section, thus simulating the stellar system displacement from the initial position \(\delta r=r(t)-r_{0}\) with and without planets over time. In Figure 12, we show the astrometric deviations caused by the mutual gravitational perturbations between stars and planets around Alpha Cen (upper row)--as opposed to a configuration without any planets (middle row). We found that the deviations (lower row) due to the presence of the other stars were around 17 arcseconds (as) over a binary period, whereas the astrometric deviations due to the presence of planets were much more subtle, thereby ranging between milliarcseconds (mas) and tens of mas.  
It is worth noting that Gaia's astrometric measurement precision enables the detection of these subtle deviations. As a matter of fact, Gaia boasts a sensitivity of up to 7 microarcseconds (\(\mu\)as) for bright stars, thus implying that the milliarcsecond-scale\({}^{5}\) deviations induced by rocky planets in Alpha Centauri are within its detection range. Gaia's sensitivity not only corroborates our estimates but also paves the way for future observations that could directly confirm the existence of such planetary systems.
## 1 Introduction
The rest of this paper is organized as follows. Section 2 presents the non-linear satellite channel model. Section 3 gives the design and analysis of 7-QAM constellation. In Section 4, we combine 7-QAM with NB-LDPC codes over GF(7). The simulation results and analyses are reported in Section 5. Section 6 finally concludes the paper.