esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock
## 3 Mf Signal Impairments
### Sky-Wave Self-Interference
In contrast, when the signals are out of the phase (\(-\pi\) or \(\pi\)), the interference is destructive and the signal strength decreases. Nevertheless, for positioning purposes, the estimated phase or phase error respective to the true value is more important than the amplitude itself. By looking at the phase error represented in Figure 3b, we can observe that with the decrease in GSAR, the phase error increases, in the absolute value sense. If GSAR \(\leq\) 1, the error always increases monotonically with the increase in the sky-wave phase. The maximum absolute value error is \(\pi\) or half the wavelength. In such a case, the sky-wave is stronger than the ground-wave, which means that the receiver will finally track the sky-wave, severely corrupting the range estimation. This is clearly the worst possible situation. For GSAR \(>\) 1, we can observe that the phase error has a maximum, in the absolute value sense, between 0 and \(\pi\). This maximum depends on the value of the GSAR and is given by the following formula  
\[\theta_{\text{err}}=\tan^{-1}\left(\frac{1}{\sqrt{\text{GSAR}^{2}-1}}\right). \tag{9}\]  
The reader can find the derivation of Equation (9) in the Appendix A. The mitigation of the sky-wave interference poses a real challenge to the usage of the MF R-Mode system, and research in this field is ongoing. One of the possible solutions would be to redesign the signal waveform modulation in order to have a broader signal bandwidth, which would allow the receiver to distinguish between the ground-wave and the reflected wave at the receiver side, as suggested in [26]. An alternative approach is to design specific antenna patterns in order to attenuate signals arriving with high elevation  
Figure 3: Amplitude (**a**) and phase error (**b**) for different GSAR and \(\theta_{\text{SW}}\). angles, which most likely are sky-waves [18]. The transmitter antenna pattern is generally optimized to transmit the signals horizontally. Therefore, the sky-wave is negligible in the vicinity of the transmitter, due to the transmitter antenna pattern, even if the receiver antenna is assumed to be isotropic. The sky-wave becomes stronger at larger distances where the reflected wave, generated by the transmitter antenna, presents a dependency on the elevation angle. Thus, having a non-isotropic receiving antenna with a specific pattern could help in mitigating the influence of the sky-wave.
## 2 Materials and Methods
### Definition and Design of the System's Components
#### 2.2.2 PD Driver
The amplitude of the received signals was 500 mV\({}_{\mathrm{pp}}\) at a distance of 50 cm between the receiver and transmitter, as evident from the 11 MHz frequency sinusoidal signal received. Given the BP 104 S OSRAM PD has a response range from 400 nm to 1100 nm, and the IR HSDL-4250 LED emits at 870 nm, it was decided to use the BP 104 S OSRAM PD and the PD driver based on three amplification stages for the IR uplink. As shown in Figure 14, the bandwidth of the BP 104 S OSRAM PD and the PD driver based on three amplification stages using the IR HSDL-4250 LED reached 12.27 MHz.
# Fast and Effective Tag Searching for Multi-Group RFID Systems
## 7 Performance Evaluation
### Simulation Setting  
In our simulations, the communication parameters settings follow the specification of the EPCglobal C1G2 standard [23]. The transmission rate between the reader and the tag is asymmetric because of the physical implementation. Generally speaking, the transmission rate of tag-to-reader is 40\(\sim\)460 kb/s for FM0 and the transmission rate of reader-to-tag is 5\(\sim\)320 kb/s for a Miller-modulated subcarrier. In our paper, we adopt 62.5 kb/s as the tag-to-reader and reader-to-tag date transmission rate. Thus, the time taken by the reader and the tags to send 1-bit data are 0.016 ms, denoted by \(T_{c}\). It is well known that any two consecutive transmissions are separated by a 0.184 ms time interval, denoted by \(T_{i}\), whether reader-to-tag or vice versa. To sum up, we can set \(T_{s}=0.2\) ms and \(T_{id}=2.42\) ms.
## 3 Results  
The four models were trained separately using the preprocessed dataset from the Huangyang Reservoir. Two models with 96 (sixteen days; four hours in a time step) and 336 (fifty-six days; four hours in a time step) output time steps, respectively, were trained for each model. The results are shown in Figures 5-7 and the detailed results are shown in Table 2. The purpose of training the models with different output time steps is to verify the effectiveness of the models for long time-series prediction and to analyze the effect of different output time steps on the models. After the model training was completed, the divided test set of water quality data of the Huangyang Reservoir was used as an input for the trained model for prediction and compared with real data values obtained from the National Automatic Integrated Water Quality Supervision Platform.  
From the table above, it can be seen that the Linear model has the smallest values of MSE and MAE, and the Linear model has reduced the MSE by 16.8% and the MAE by 17.3% for the short-term prediction of DO concentration (time step 96; 16 days) compared to the best results of the other models. The Linear model has reduced the MSE by 9.7% and the MAE by 3.7% for the long-term prediction of DO concentration (time step 336; 56 days). The MSE for the short-term prediction of pH concentration by the Linear model decreased by 2.9%, and the MAE decreased by 23.9%. The MSE for the long-term prediction of pH concentration decreased by 1.6%, and the MAE decreased by 4.9%. The MSE for the short-term prediction of Turbidity concentration by the Linear model decreased by  
\begin{table}
\begin{tabular}{c c c} \hline
**Methodologies** & **Advantages** & **Drawbacks** \\ \hline \multirow{4}{*}{LSTM} & The utilization of the gate & \\  & mechanism in its long-term & The obtained results are \\  & memory function partially & unsatisfactory, lacking \\  & addresses the issues of & parallelism and exhibiting \\  & gradient explosion and & poor landing execution. \\ \hline \multirow{4}{*}{Informer} & This study demonstrated a & \\  & significant potential for & \\  & long-distance-dependent & \\  & expression of the high & \\  & memory usage. & \\ \hline \multirow{4}{*}{Linear} & The modeling process is & It should be noted that the \\  & efficient, as it does not require & model does not fit well with \\  & complex computations, and & nonlinear data. In order to \\  & remains fast even when & ascertain the linear \\  & dealing with large datasets. & relationship between the \\  & & variables, it is necessary to \\  & conduct an initial analysis. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: Comparison of DO for each model at different time steps.  
15.4%, and the MAE decreased by 10.2%. The MSE for long-term prediction of Turbidity concentration was reduced by 6.5%, and the MAE was reduced by 3.1%. This leads to the conclusion that the Linear model is the most effective and that it has good applicability.
# Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
###### Abstract  
The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.  
Keywords:blockchain; federated deep learning; anomaly detection; audit; performance +
Footnote †: journal: Accepted: 14 December 2018; Published: 18 December 2018
# From Feed to Fish--Nutrients' Fate in Aquaculture Systems
## 3 Results
### Nutrient Output in Fishponds
#### 3.4.5 Sedimentation  
Sedimentation was evaluated in 15 of the 23 budget studies (Table 10). Only three studies evaluated the relative importance of sedimentation for C, N, and P [13; 17; 31]. Based on the studies in our dataset, the percentages of C, N, and P varied widely among and within cultivation systems. Carbon sedimentation contributed between 9 and 81% of the total carbon output (Table 10). The lowest contribution was reported in a study conducted in an in-pond raceway polyculture pond, where carbon sedimentation represented only 9% of the total output, representing the third major carbon loss in the studied system [16]. In the intensive monoculture of _Microperus salmonides_, sediment accumulation was the second main carbon output, contributing an average of 16% to the total output. Here, the relatively low sedimentation rate was related to the presence of eco-substrates placed in different depths below the water surface, trapping suspended organic matter, hence decreasing the settlement [31]. On the other end of the spectrum are integrated cultivation systems where carbon sedimentation contributes more than 60% to the total output. In integrated fishponds (IAA), 81% of the C output accumulated in the sediment due to high organic matter from the human excreta and the crop residues that were added to the ponds as fertilizers [13].  
Sedimentation was also an important sink for N, representing 19 to 60% of the total output. The lowest contribution was found in _Colsosoma macroopum_ ponds cultivated together with prawns [24]. As for P sedimentation, it represented 24 to 82% of the total output. However, percentages above 60% were observed in ponds that had substrates for periphyton growth placed in the water column--e.g., bamboo, geotextile, and Aquanat(r)[22; 31]. Similarly, in fish cultivated with prawns (IMTA) and in monoculture ponds, it accounted for a high share of total P output [17; 18; 27].
## 4 Discussion
### Mechanism of Crystallinity Enhancement in Cellulose Fibers by Plasma Treatment
Gas flow rates were optimized for the plasma gas compositions with Ar (8 L min\({}^{-1}\)) and O\({}_{2}\) (10 sccm). Computational fluid dynamics (CFD) simulations help us to better understand the gas flow patterns that can be achieved, which provide support in optimizing the design of reactors for efficient and homogeneous treatment [54]. In industrial applications of DBD plasma treatment for natural fibers, scalability is a major concern. The reactor design must be modified to accept higher fiber loads and a continuous feed. Utilize a moving tray system to investigate the potential for treating higher volumes of material per pass. Roll-to-roll systems show promise for the large-area treatment, where fibers are fed continuously between rotating electrodes through a plasma zone, which have also shown promise for large-scale treatment [55]. Several of the process improvements can be applied to increase the activity and controllability, as well as ensure reliability in DBD plasma treatment for hemp fiber surface modification. One large player in ascension is real-time monitoring and control systems of the plasma treatment process, which can greatly enhance the efficiency and consistency. The continuous monitoring of critical parameters such as the voltage, current, and gas flow rates help in making instantaneous corrections to achieve the ideal treatment conditions. The results and discussion show the characterization of the plasma-treated fibers. Our study used the response surface methodology (RSM) to find the best combination for producing composite materials with the maximum tensile strength using hemp-fiber-reinforced epoxy composites, via an approach that consists of the treatment time followed by discharge power for maximizing the tensile strength of hemp-fiber-reinforced epoxy composites (Figure 9 and Table 4). By going a step further and incorporating these conditions into a closed-loop control system, a consistent surface modification across various fiber batches can be achieved with a reduced process variability. Secondly, standardized protocols and quality control measures need to be developed for the successful translation of surface modification at an industrial scale. Therefore, detailed working procedures, especially regarding the fiber preparation, parameters of plasma, and post-treatment, can help decrease the fluctuations in processing. Thus, with the combining of these improvements in a process by taking advantage of optimal conditions explored in this work for designing DBD plasma treatment processes, significant enhancement can be achieved on both efficiency and reproducibility throughout bettering the hemp fiber surface modification. This will enable the industrial use of plasma-treated hemp fiber in high-performance composite applications.
## 7 Conclusions  
We have proposed a novel CAD system for the early diagnosis of lung cancer. We have used a database of 100 sputum color images for different patients collected by the Tokyo center of lung cancer. The new CAD system can process sputum images and classify them into benign or malignant cells. For the color quantization, it was found that, the higher the color space resolution, the more accurate the  
\begin{table}
\begin{tabular}{c c c} \hline
**Performance** & **Previous CAD System** & **Proposed CAD System** \\ \hline Sensitivity & 93\% & 97\% \\ Specificity & 70\% & 96\% \\ Accuracy & 85\% & 97\% \\ \hline \end{tabular}
\end{table}
Table 7: Performance criteria of the computer aided diagnosis (CAD) systems.  
Figure 16: ROC curve obtained using ANN and SVM classifiers.  
detection and extraction of the sputum cell. It was found that the Bayesian classification is better than the heuristic rule-based classification as the former achieved an accuracy of 98%. The comparability of the performance with regard to the color format reveals close scores for histogram resolution above 64. In the segmentation process, it was demonstrated that the mean shift approach significantly outperforms the HNN technique, especially after acquiring additional information such as pixel space coordinates. The mean shift has a reasonable accuracy of 87%.  
In the classification process: it was found that the performance of the SVM is superior compared to the ANN classifier. The SVM classifier allows a clear separation and classification of the cells into benign and malignant classes. Therefore, it is more stable and reliable for the proposed CAD system. The SVM achieved an accuracy of 97%. The experimental result shows that the proposed CAD system is able to detect the false positives and false negatives correctly. The new system has achieved a good performance in terms of sensitivity, specificity and accuracy equal to: 97%, 96% and 97% respectively. In addition, the use of extreme SVM as a learning model increased the accuracy of detecting the malignant cells. The new CAD system will be useful in screening a large number of people for lung cancer while helping pathologists to focus on candidate samples, and reducing the pathologists fatigue to a great extent.  
With respect to the experimental work described in this paper, there is also considerable further work which could be undertaken. To solve the problem of inhomogeneity in the cytoplasmic region, we plan to use active contour snake segmentation which has the ability to work with images that have an overlapped nucleus and cytoplasm. This will be done by using Otsu's automated thresholding selection method to segment the dysplastic sputum cells due to high inhomogeneities between the cells. We plan to extend the research to address these limitations as well as use a more extended data set of available sputum images.
## 4 SDP Solution Method for the FFP-IM
### The SDP Solution Method
#### 4.2.3 SDP Method for Estimating HDMR First-Order Terms  
In Equation (15), each term is contingent upon the input variable \(Z_{i}\) and may be characterized as a state-dependent parameter.  
Comparing the first-order HDMR in Equation (15) with the definition of the SDP model in Equation (16), and acknowledging that the inputs and outputs are usually deterministic concerning each other, there is no recursive term of the output variable in the signal processing (i.e., \(m_{1}=0\)) as well as the lagging and delaying of the input signal (i.e., \(m_{2}=0,\omega=0\)), there are \(\mathbf{H}_{t}^{\rm T}=\mathbf{Z}_{t}^{\rm T},\mathbf{p}_{t}=b_{0}(\mathbf{Z}_{t})\). In addition, each term in Equation (15) functions as a dependent on a single input variable, implying that each state-dependent parameter \(b_{0,i}\) is contingent solely upon its corresponding input variable \(Z_{i}\), which is designated as \(p_{i,t}=b_{0,i}(\mathbf{Z}_{t})=b_{0,i}(Z_{i,t})\) Therefore, to maintain the broad applicability of the model, each term in Equation (15) may be redefined as \(\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;HDMR. To estimate \(p_{i,t}(i=1,2,\cdots,n)\) in Equation (18), it is typically necessary to describe its variation through a stochastic approach. Within the framework of the SDP method, the generalized random walk (GRW) class of non-stationary processes is commonly employed to model the variation in each state-dependent parameter \(p_{i,t}\). Notably, within the GRW processes, the integrated random walk process is chosen for its ability to ensure that the estimated SDP relationship maintains the smoothness characteristic of cubic spline interpolation, thereby providing enhanced outcomes for estimating state-dependent parameters. In-depth discussions regarding the SDP model and its criteria for parameter estimation are available in Refs. [32; 33].  
After obtaining each state-dependent parameter \(p_{i,t}(i=1,2,\cdots,n)\) in Equation (18), due to \(g_{i}(Z_{i,t})=p_{i,t}Z_{i,t}\), the first-order HDMR model of the response function, i.e., the conditional expectation \(E(Y|Z_{i,t})(i=1,2,\cdots,n)\) of the response model \(Y=g(\mathbf{Z})\), can be obtained.
## 5 Empirical Analysis  
We evaluate how well the proposed algorithms deal with invalid actions using the Gym-\(\mu\)RTS environment [24]. It is a perfect testbed for our experiments because the space of invalid actions increases exponentially with the map size. We take four maps with different sizes for experiments (see Figure 3), and the environment setup is the same as [PERSON] et al. [23].  
Figure 3: Four screenshots of Gym-\(\mu\)RTS with different map sizes. The grey circular units are workers. Green square grids are resource mines from where workers can extract resources to produce more units. White square grids are bases that can produce workers. We train agents to control units at the top left to harvest resources, and the units in the bottom right will remain stationary.  
In the Gym-\(\mu\)RTS environments, an agent attempts to control the unit at the top left of the map to harvest resources as fast as possible. The agent receives a reward \(+1\) when the unit harvests a resource or returns a resource to the base. The action space is designed as the MultiDiscrete action space [19]. A composite action comprises eight discrete actions, Source Unit, Action Type, Move Parameter, Harvest Parameter, Return Parameter, Produce Direction Parameter, Produce Type Parameter, and Attack Target Unit. The first component is the location of the selected unit to perform an action. Since only the worker and base can be selected as Source Unit, selecting other grids on the map are usually invalid. This action space enables us to verify the scalability of invalid action masking.  
We will compare On-PIAM, Off-PIAM, and CO-IAM algorithms on different maps. We further evaluate the performance of original policies with Masking Removed (i.e., On-PIAM-MR, Off-PIAM-MR, and CO-IAM-MR). To illustrate the superiority of invalid action masking, we also compare it with the invalid action penalty, which assigns a negative reward \(r_{\text{inv}}=\{-0.01,-0.1\}\) for choosing an invalid action (called Penalty-0.01 and Penalty-0.1). All the agents are trained for 1,000,000 time steps, and the maximal length of each episode is 200 iteration steps. All experiments were run with four random seeds.
## 2 Materials and Methods
### Mathematical Model
The calibration parameter was the hydraulic conductivity, the initial value of which was derived from a few hydraulic tests that were performed in some of the wells that were located in the model domain during the drilling phase. The available data have been interpolated in order to generate the initial hydraulic conductivity distribution for the four layers. The spatially distributed parameter was then the object of the automatic calibration process. Once the model was calibrated, the attention was focused on the area where flooding occurred in 2014 for the evaluation of the feasibility of groundwater level control through pumping wells. Some wells were then implemented in the model and were optimized, first using PEST to minimize their number and pumping rate, which was then done manually in order to set their position. The goal of the optimization was to maintain the groundwater depth around 9 m below the ground (level constraint), in steady state condition. This level was defined considering a safety distance from the underground structures, which in this zone generally reaches a depth of 4.5-5 m from the ground. A global objective function was constructed in order to take into account the level constraint and the minimization of the pumping rate. This second objective was obtained allowing PEST to set zero as the minimum possible flow rate per well and thereby, at the same time, to optimize the number of wells (which was constrained to a maximum of 10). The position of the wells was then manually arranged according to the area that was effectively available for their installation. The mathematical model was then calibrated in transient mode as well, using the hydraulic heads that were registered in the monitoring points, and was finally used to forecast the system behaviour in three different scenarios, namely Scenario1 (2014 rainfall and no interventions), Scenario2 (2014 rainfall and continuous extraction), and Scenario3 (2014 rainfall and three months extraction). The unsteady calibration was performed taking into account 1 year piezometric data that was collected during the project development (November 2015-November 2016) and has been carried out to define the effective porosity value and the recharge term for the first layer. During this second calibration phase, the still calibrated hydraulic conductivity distribution was not modified.