esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Strategic Development of Ultrafiltration Membrane
### Protein Concentration  
High concentration of feeds (peptides) allows the development of severe concentration polarization and fouling. Subsequently, this can lead to either increase or decrease of retention factor (RF). First, RF increase will be due to the compact and less permeable layer is formed which in turn increasing filtering capacity and decreasing permeate flux Figure 7. This can be also attributed to the lower MWCO of the membrane. On the contrary, the decrease of RF can be as consequence of viscous polarization layer that prevents counteraction of solute accumulation by retro-diffusion [194; 195]. It is interesting to note that in binary model solution, both mass flow and RF of a compound will follow the trends of other peptides which are highly concentrated. For example, myoglobin of tuna concentrate was 10-fold more than its hemoglobin content; thus the mass flow of hemoglobin was higher at pH 8.6 (isoelectric point (IEP) of myoglobin), not at pH 7.3 (IEP of hemoglobin) [196].  
The pattern of RF reduction was sometimes not linear along with the increase of protein concentration. As being shown here, whey milk concentrate (WPC) 80% processed by using 30-kDa PS membrane suggested a unique manner of protein concentration toward flux. Permeate flux increased as following 2%-wt \(<\) 1%-wt \(<\) 3%-wt protein solution since at some point, the deposition rate may lower than the removal rate like in 3%-wt protein, and thus increasing concentration does not merely trigger up the fouling formation [198]  
Figure 7: The effects of various parameters on UF flux (**a**) protein concentration and ionic strength; (**b**) pH; (**c**) TMP. Membranes in a1, a3: RC 5 kDa [197], a2: PS 30 kDa [198], b1: RC 5 kDa [197], b2: ceramic membrane ZrO\({}_{2}\)-TiO\({}_{2}\) (0.045 m\({}^{2}\)) [177], b3: PS hollow fiber 100 kDa (this data) after EDBM with Electrocell AB (eff electrode area 100 cm\({}^{2}\)) [199], c1: tubular PES 4 kDa [194], c2: tubular ceramic 4 kDa [200], c3: hollow fiber PS 100 kDa [201].  
(Figure 7a). In some researches, denatured proteins could also affect the performance of UF. One research analyzed UHT milk which contains denatured whey protein more than the pasteurized milk, but with the same native whey protein concentration. There, UF of pasteurized milk using 50 kDa ceramic membrane in a rotating disk module led to the higher permeate flux. This trend was supported by another work that used M5 Carbosep membrane 10 kDa to fractionate UHT skim milk and low-heat skim milk powder [202].  
Feed concentration also affects the migration rate of protein. For instance, in EDUF system, linear increase of peptide concentration in feed leads to higher migration rate as performed in peptide solution containing Asp, Glu, His, and Arg that reached maximum of 16.2 and 7.8 g m\({}^{-2}\) h\({}^{-1}\) at 4% feed concentration for cationic and anionic compartments, respectively. In this work, energy consumption also declined from 17.4 to 3.53 W h g\({}^{-1}\) while peptide concentration climbed up from 0.5% to 4% [203].
## 5 Discussion
### Discussion  
This section discusses the insights into why certain models perform better than others and also presents the practical implications of our findings. Specifically, we address the trade-off between accuracy, latency, and computational complexity in the context of IoT environments. In terms of accuracy vs latency, while the CNN-BiLSTM model showed superior accuracy and other performance metrics, its increased computational complexity and latency might pose issues for real-time intrusion detection in IoT environments. This observation aligns with findings from [48], who also noted that deep learning models, although accurate, often require significant computational resources that may not be feasible for time-sensitive applications. The model's extensive architecture, combining convolutional layers with bidirectional LSTM layers, demands significant computational resources and time, which may not be feasible for systems requiring immediate threat response. Lightweight models such as logistic regression and k-nearest neighbors demonstrated competitive accuracy with much lower computational requirements. These models are suitable for real-time applications where latency is critical, as they can process data and generate alerts much faster than deep learning models [3]. For accuracy vs computational complexity, the CNN-BiLSTM model's complex structure leads to a higher accuracy but also demands more computational power, memory, and processing time. This complexity can be a limiting factor in resource-constrained environments like IoT. A similar conclusion was drawn by [49], who discussed the trade-offs between model complexity and feasibility in IoT contexts. A practical implication involves deploying lightweight models for preliminary detection to ensure timely responses, while more complex models like the CNN-BiLSTM models can be used for detailed analysis and configuration of threats. Models, such as decision trees and random forests, although not as accurate as CNN-BiLSTM, offer a good balance between performance and computational complexity. They are easier to implement and require fewer resources, making them suitable for environments with limited computational capacity.
## 2 Materials and Methods
### CASA Model  
The CASA model is a remote sensing model that operates based on the intricate relationship between ecosystem productivity and the dynamics of carbon and nitrogen within the soil [32]. This model comprehensively considers environmental conditions and plant characteristics and integrates remotely sensed data with temperature, precipitation, solar radiation, vegetation type, and soil type information to efficiently harness light energy. In this model, corn NPP is contingent on variables such as absorbed photosynthetically active radiation (APAR) and vegetation light energy utilization (\(\epsilon\)) [20]. The formula used to calculate the model is as follows:  
\[NPP(x,t)=APAR(x,t)\times\varepsilon(x,t) \tag{1}\]  
where \(NPP(x,t)\) denotes the net primary production of image \(x\) in month \(t\)[33], the variable \(APAR(x,t)\) denotes the amount of photosynthetically active radiation absorbed by image \(x\) during month \(t\), and the variable \(\varepsilon(x,t)\) represents the actual use of light energy by image \(x\) during month \(t\)[34; 35; 36; 37]. The determination of APAR relies on two variables: fractional photosynthetically active radiation by vegetation (FPAR) and total solar radiation (SOL). The calculation formula is as follows:  
\[APAR(x,t)=FPAR(x,t)\times SOL(x,t)\times 0.5 \tag{2}\]  
where \(FPAR(x,t)\) denotes the effective photosynthetic radiation ratio of image \(x\) in month \(t\) and \(SOL(x,t)\) denotes the total solar radiation of image \(x\) in month \(t\) (MJ/m\({}^{2}\)).  
The light energy conversion rate was calculated as follows:  
\[\varepsilon(x,t)=\varepsilon_{\text{max}}\times T_{\varepsilon}(x,t)\times W_{ \varepsilon}(x,t) \tag{3}\]  
where \(\varepsilon_{max}\) denotes the maximum light energy conversion rate, \(T_{\varepsilon}(x,t)\) denotes the temperature factor of image element \(x\) in month \(t\), and \(W_{\varepsilon}(x,t)\) denotes the precipitation factor of image element \(x\) in month \(t\)[38].  
Within a specific range, FPAR exhibits a robust linear correlation with both the normalized vegetation index (NDVI) and the ratio vegetation index. To enhance data precision, this study employed static parameters tailored to the characteristics of corn in the Fen River Basin [31].
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 4 Pending Issues
### Bad Weather Conditions  
Autonomous driving tests are, up to now, handled mostly in sunny environments, such as California or Texas. However, the quality of the detection under fog, rain and snow, especially if they are extreme, becomes severely degraded, especially regarding range [142] due to the absorption and scattering events induced by water droplets. This introduces a large number of false detection alarms from the backscattered intensity, reducing the reliability of the sensor [85; 143]. Further, snowflakes, fog and rain droplets have different shapes, distributions and sizes and affect different aspects of the detection, complicating a precise modeling [144]. Managing lidar imaging in extreme weather conditions is a pending subject of the technology which needs to be tackled in the near future for commercial deployment of automated vehicles.
# Intuitionistic and Interval-Valued Fuzzy Set Representations for Data Mining
## 2 Background
### Uncertainty Representations
#### 2.1.5 Rough Set Theory  
The core concept of rough sets is an indiscernibility relation IR on the domain D [24]. A rough set N is specified by using the upper, \(R^{u}N\), and lower approximations, \(R_{i}N\), of N.  
* The lower approximation of N is the set \(R_{i}X=\{a_{i}\in D/[a_{i}]_{R}\subseteq N\}\).
* The upper approximation of N is the set \(R^{u}X=\{a_{i}\in D/[a_{i}]_{R}\cap N\
eq\emptyset\}\).  
where \([a_{i}]_{R}\) denotes the equivalence class of the indiscernibility relation R containing \(a_{i}\).  
In summary, the lower approximation of a set is a conservative approximation comprising only elements which can definitely be determined to be members of the set. The upper approximation is a _liberal_ approximation, including all elements that may be members of the set.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 10 Open Challenges with Wi-Fi 6
### Challenges
#### 10.1.1 Control of Complex Wi-Fi 6 Deployments  
Wi-Fi 6 has many parameters that impact performance. As highlighted in the previous sections, many algorithms have been proposed to derive the optimal values according to the environment (traffic, signal strength, density, etc.). A centralized controller-based approach may help to collect statistics on the network and adapt accordingly the values of the parameters [198].  
A WLAN controller [199] monitors and manages a set of wireless access points. The controller typically collects the channel quality from the access points to determine the reservation parameters and the required channel time for transmissions to configure each access point accordingly. In particular, it assigns channels to each access point to minimize interference. It possibly assigns multiple channels to heavily-loaded access points (channel bonding) to reduce the contentions in their BSSs. It may balance the load among the different BSSs by assigning stations to the different access points. For instance, reducing the transmission power of a heavily loaded access point may force some stations to hand off. However, the controller has also to maintain a global coverture. Moreover, setting specific \(OBSS\_PD\_level\) values can give higher priority to the most loaded access point, which increases its capacity compared to the neighboring BSSs. Additionally, a controller can help to optimize energy efficiency [200] and fairness [201]. As an option, such a controller can be executed in the cloud [202].  
The authors of [203] extended the work in [204] and proposed an optimized centralized control architecture to steer dense Wi-Fi networks. It focused on three objectives: flexibility (SDN), scalability (optimization framework), and extensibility (unified control interface).  
Each access point also has to select the most accurate MCS for each of its active stations. Rate adaptation algorithms have been proposed in [205] for legacy Wi-Fi. The rate increases or decreases if the packet loss rate exceeds or falls behind a threshold value. However, the number of MCS is larger in Wi-Fi 6 and many parameters also impact transmission reliability (MU MIMO, transmission power, etc.). Therefore, it is essential for real deployments to investigate strategies for optimal MCS adaptation. This task can also be supported by a WLAN controller.  
As a result, the control of a Wi-Fi 6 network may yield a multi-criteria optimization problem. Unfortunately, many parameters depend on the environment. For instance, the efficiency of spectrum reuse depends on the location of interfering stations, making optimization even more complex. Environmental models are needed for better Wi-Fi control and must be validated by large-scale experimental studies. They can result in novel controller algorithms for continuous optimization of large Wi-Fi deployments.
## 5 Outlook  
Landsat change detection is applied in a range of scientific and nonscientific fields, not limited to the remote sensing community. Notable increases in the application of Landsat data in change detection research came as a result of the free data policy in 2008 and developments in cloud computing platforms, such as GEE and AWS. Recent  
Figure 21: The trend in reference data collection techniques (office reference data sampled from Google Earth, high-resolution images, or existing land cover maps).  
improvements in data standards, computation power, and storage abilities have allowed for the development of algorithms capable of processing large amounts of data. This means Landsat-based change detection research can now be implemented at large geographical and timescales, which were infeasible a decade ago. Future trends will likely rely on the use of multiple sources of data and the virtual constellation comprising of Landsat-8, -9, and the Sentinel satellites.
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 3 The Proposed GGS-MAF Algorithms  
The devised algorithms, which are named as generalized group-sparse mixture adaptive filtering (GGS-MAF) algorithms, propose a novel gain allocation scheme for the MEC algorithm, which will be derived in detail. We first proposed a mixed error criterion (MEC) algorithm to give a resistant to the impulsive noise in the context of the SI. Then, a family of constructed GGS-MAF algorithms is presented in detail.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Discussion and Analysis Based on Performance Metrics
#### 5.2.4 Analysis Based on MSE  
In this part, the values of the MSE are discussed. Table 18 shows the details of the papers' MSE. In this table, papers [22, 24] used a CNN-based model; papers [32, 33, 35, 38, 39, 43, 59] used an LSTM-based model; and papers [70, 72] used a DNN-based model. Paper [30] used an RNN-based model, and paper [81] used the reinforcement learning based model.  
It could be found that paper [59] performed the best, which used the LSTM model; its MSE was smaller than 0.01. Papers [24, 33, 40, 42, 81] also had good performance; however, papers [35, 38] did not have low MSEs.  
Among the papers that used MSE as a performance measurement, the only CNN paper was in the range of 0.1-1; 40% of the LSTM papers were in the range of 0-0.01; 20% of the LSTM papers were in the range of 0.01-0.1; 10% of the LSTM papers were in the ranges of 0.1-1, 1-10, 10-100, and above 100 each; and 50% of the DNN papers were in the ranges of 0.01-0.1 and 0.1-1 each. The only RNN-based model and reinforcement learning-based model were in the ranges 0-0.01 and 0.01-0.1 respectively.
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 4 Results and Discussion
### Experiment Analysis and Discussion  
#### 4.2.1 Effect of Temporal Features on the Predictive Models  
A total of 24 consecutive hours of the working day were divided into 8 time periods according to Section 3.1, and 24 consecutive hours during holidays that were divided into 5 time periods. The system predicts the waiting time for taxi passengers on weekdays and holidays. The MAE values are shown in Table 7 below, while the main patterns are illustrated in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline
**Time Lice** & **MAE (s)** \\ \hline work day: 4:00–6:59 & 71.32 \\ work day: 7:00–8:59 & 56.78 \\ work day: 9:00–11:59 & 24.12 \\ work day: 12:00–14:29 & 21.5 \\ work day: 14:30–17:29 & 30.96 \\ work day: 17:30–20:59 & 33.85 \\ work day: 21:00–22:59 & 19.67 \\ work day: 23:00–3:59 & 34.63 \\ holiday: 00:00–4:59 & 55.5 \\ holiday: 5:00–8:59 & 37.49 \\ holiday: 9:00–13:59 & 32.46 \\ holiday: 14:00–17:59 & 34.33 \\ holiday: 18:00–23:59 & 62.24 \\ \hline \end{tabular}
\end{table}
Table 7: MAE value of each time segment.  
It can be seen in Figure 8 that among the prediction effects of different time segments, the worst MAE value is 71.32 s, that is, passengers' waiting time can be satisfactory predicted at any time segment. The average error of time prediction is always less than 1.5 min, and it can be considered that passengers can obtain reasonable predicted waiting time in different time periods.  
Through the trend chart, one can find that whether it is a weekday or a holiday, the forecast error of the passenger waiting time during the day is relatively small. This may be caused by the relatively fixed behavior pattern of urban taxis during the day. Furthermore, the waiting time for taxis in the daytime itself may be less, so there will be no excessively large forecast deviations. In addition, in most time periods, the waiting time prediction error during holidays is higher than that of working days. The reason may be that different holidays have inconsistent influences on the behavior of taxis. For example, on National Day, a large number of foreigners will flood into the city, leading to a shortage of urban taxi supply. On the tomb-sweeping day, although residents travel more, there are fewer taxis as a way of travel, so the supply of taxis is excessive. Therefore, the further optimized model needs to analyze different holidays.
###### Abstract  
The photo-response non-uniformity (PRNU) noise of imaging sensors significantly aids digital forensics and judicial identification, as it can be used as the fingerprint for uniquely identifying individual imaging devices. During the PRNU fingerprint extraction, it is very important for source camera identification to estimate the natural noise from real-world images. Most existing deep learning-based neural networks have a large number of model parameters, and they may not be practical in realistic scenarios such as deploying the model on small devices like smartphones and remote forensics equipment. In this paper, we present a lightweight PRNU fingerprint extraction algorithm based on an invertible denoising network (InvDN) for source camera identification. Specifically, to reduce the number of parameters, the deep network uses a constant amount of memory to compute the gradient and employs the same parameters for both forward and backward propagation. In addition, by introducing an information-loss-less reversible network, more complete PRNU fingerprint information can be extracted. Experimental results show that this algorithm exhibits superior PRNU fingerprint extraction performance and generalization ability compared to the state-of-the-art PRNU fingerprint extraction algorithms.  
photo-response non-uniformity; source camera identification; invertible denoising network; image forensics +
Footnote †: journal: Applied sciences  
1
Footnote 1: [[https://doi.org/10.3390/app15010319](https://doi.org/10.3390/app15010319)]([https://doi.org/10.3390/app15010319](https://doi.org/10.3390/app15010319))
## 5 HPSO: Best-Fit Solution for Adaptive Filtering  
It can be seen from the above experiments that the performance of HPSO was superlative as compared to other optimization techniques. HPSO is an effective approach for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SNR** & **LMS** & **PSO-CCF** & **PSO-VCF** & **HPSO** \\ \hline
0 & \(-\)0.06568 & 4.9835 & 5.02463 & 12.2088 \\ \hline
50 & \(-\)2.159 & \(-\)4.41707 & \(-\)5.6075 & \(-\)8.31691 \\ \hline
100 & \(-\)3.2676 & \(-\)4.499 & \(-\)5.8928 & \(-\)8.6452 \\ \hline
150 & \(-\)3.6319 & \(-\)4.622 & \(-\)5.894 & \(-\)8.6042 \\ \hline
200 & \(-\)3.9244 & \(-\)4.4589 & \(-\)5.8535 & \(-\)8.6863 \\ \hline
250 & \(-\)4.1297 & \(-\)4.4170 & \(-\)5.93592 & \(-\)8.6065 \\ \hline
300 & \(-\)4.70443 & \(-\)4.41785 & \(-\)5.85384 & \(-\)8.6042 \\ \hline
350 & \(-\)4.8275 & \(-\)4.2939 & \(-\)5.89628 & \(-\)8.76025 \\ \hline
400 & \(-\)4.78296 & \(-\)4.2527 & \(-\)5.77185 & \(-\)8.604 \\ \hline
450 & \(-\)4.786296 & \(-\)4.37602 & \(-\)5.689 & \(-\)8.8095 \\ \hline
500 & \(-\)4.745 & \(-\)4.25287 & \(-\)5.6077 & \(-\)8.6863 \\ \hline \hline \end{tabular}
\end{table}
Table 10: MSE performance for the nonlinear channel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SNR** & **LMS** & **PSO-CCF** & **PSO-VCF** & **HPSO** \\ \hline
0 & \(-\)0.0515 & 1.4433 & 5.051546 & 5.15464 \\ \hline
50 & \(-\)5.2062 & \(-\)11.289 & \(-\)13.9691 & \(-\)18.0928 \\ \hline
100 & \(-\)8.6082 & \(-\)11.753 & \(-\)14.3299 & \(-\)18.0928 \\ \hline
1  
channel adaptive equalization, leveraging its global search capability to optimize equalizer coefficients and enhance the performance of communication systems by mitigating the effects of channel distortion and inter-symbol interference. This section will elaborate on the advantages, issues, and challenges faced by the HPSO in the optimization of adaptive filters.
## 7 Conclusions
Future works will consider the implementation of a physical-based dynamic stall model such as the one of Beddoes-Leishman, which is able to capture unsteady aerodynamics and separation physics even at small angles of attack. Eventually, the implementation of the streamtube expansion model will be also considered, in view of improving the accuracy of performance predictions for large-scale VAWT applications. Conceptualization and methodology, A.G.S., V.D. and G.P. ; software, A.G.S. ; formal analysis A.G.S. and G.P. ; writing-review and editing, A.G.S., V.D. and G.P. ; supervision, V.D. and G.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request. The authors declare no conflict of interest. **Nomenclature** \begin{tabular}{l l} \multicolumn{2}{l}{**Nomenclature**} \\ \(a\) & induction factor \\ \(C_{D}\) & drag coefficient \\ \(C_{L}\) & lift coefficient \\ \(C_{M}\) & torque coefficient \\ \(C_{N}\) & normal force coefficient \\ \(C_{P}\) & power coefficient \\ \(C_{T}\) & tangential force coefficient \\ \(C_{X}\) & thrust coefficient \\ \(c\) & airfoil chord \\ \(dh\) & infinitesimal blade height \\ \(F\) & Prandtl tip loss factor \\ \(F_{X}\) & thrust force \\ \(H\) & blade semi-span \\ \(i,j,k\) & vertical, azimuthal and radial indices \\ \(L_{p}\) & pole length \\ \(N\) & number of blades \\ \(P\) & turbine power \\ \(R\) & turbine radius \\ \(Re\) & Reynolds number \\ \(R_{sl}\) & strut maximum radius \\ \(r\) & local rotor radius \\ \(r_{p},D_{p}\) & pole radius, diameter \\ \(s\) & helical wake pitch \\ \(T_{pole}\) & pole passive torque \\ \(T_{st}\) & strut passive torque \\ \(U\) & tangential blade speed \\ \(V\) & upstream disc wind velocity \\ \(V^{\prime}\) & downstream disc wind velocity \\ \(V_{D}\) & wind velocity at disc \\ \(V_{e}\) & equilibrium wind velocity \\ \(V_{\infty_{i}}\) & free stream wind speed \\ \(V_{\infty},V_{0}\) & free stream wind speed at equator \\ \(W_{rel}\) & relative wind speed \\ \(x_{0r}\) & non dimensional hook point distance \\ \(x,y,z\) & Cartesian coordinates \\ \(z_{EQ}\) & equatorial height \\ \(\alpha\) & angle of attack \\ \(\alpha_{0}\) & zero lift angle \\ \(\alpha_{fc}\) & flow curvature angle of attack \\ \(\dot{\alpha}\) & angle of attack change rate \\ \(\alpha_{w}\) & wind shear coefficient \\ \(\beta\) & rotor axis tilt \\ \(\beta_{c}\) & stagger angle \\ \(\delta\) & sweep angle of Troposkein rotor \\ \(\Delta\alpha\) & dynamic stall angle of attack delay \\ \(\lambda\) & tip speed ratio \\ \(\rho\) & air density \\ \(\theta\) & azimuthal blade angle \\ \(\Omega\) & rotational speed \\ \end{tabular}