esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Impact of Plasma Treatment on the Hardness and Brittleness  
Hardness and brittleness are critical factors in assessing the texture of potatoes [24]. The softening of potatoes during storage directly affects their quality, subsequently influencing attributes such as taste and appearance. The impact of plasma treatment on potato hardness is depicted in Figure 5a. Potato hardness exhibits a general decreasing trend throughout the storage period, with significant differences observed during the later stages. There was no significant difference in hardness between the control and treated groups from the very beginning to the fourth day. From the fourth to the sixteenth day, the trend of decreasing hardness became slower in the treated group compared to the control group. On the sixteenth day, the hardness of the treated group increased by 6.06% compared with that of the control group, likely due to water consumption during potato sprouting, which causes a breakdown of cell structure and a subsequent decrease in hardness [25].  
Brittleness changes are shown in Figure 5b. Both the control and treatment groups showed a decreasing trend, but throughout the period, the control group was much lower than the treatment group. The brittleness of treated potato slices decreased by only 24% over 16 days, compared to a 37.19% reduction in the control. This indicates that the optimal plasma treatment better preserves the brittleness of the potatoes.
## 4 Experimental Results
The more accurate the prediction is, the closer the D1 value is to zero. Therefore, the more accurate the pixel value prediction, the greater the frequencies at the highest and second-highest peak points in the histogram of the difference sequence. Thus, the greater the number of confidential data bits that can be embedded. When the ARDHA technique was applied, D1 was very small because the mean of prediction errors is very small as shown in Table 1. Thus, the ARDHA technique greatly increases the frequency at the highest and second-highest peak points in the histogram of a difference sequence. Therefore, the number of confidential data bits that can be embedded in the cover image is greatly increased. The prediction hit ratio in the fifth column of Table 1 was the same as the value calculated using P.H.R and N.P in the last seven columns. The number of predictions in the seventh column of Table 1 was equal to the sum of the NPs in the last seven columns. In addition, when the threshold (\(\beta\)) was 5 or more, the total number of prediction times for V1 and V2 in the APDLS technique was the same as the total number of prediction times for V1, V2, V3V, and V3C in the ARDHA technique. These points could be easily seen in the flowchart of pixel value prediction in Figure 6. That is, if \(\beta\) is greater than 0 and dev1 is less than \(\beta\), the predicted pixel value is determined between V1, V2 in the APDLS technique, and the predicted pixel value is determined among V1, V2, V3V, and V3C in the ARDHA technique. Therefore, the number of predictions for V1 and V2 in the APDLS technique was the same as the number of predictions for V1, V2, V3V, and V3C in the ARDHA technique. As shown in the fourth column of Table 1, in the ARDHA technique, increasing \(\beta\) from 0 to 20 decreased the PSNR value of the stego-image. However, we could observe in Figure 13 that the visual quality of the stego-image remained very good. Thus, it was not possible to visually discern whether confidential data are embedded in the stego-image. As shown in Figure 13, the cover image and the stego-image were visually identical. As shown in the eighth column of Table 1, when confidential data was embedded in the cover image using the ARDHA technique, the number of hidden bits increased by 29.3% and 24.5% at the maximum compared to the APD and APDLS techniques, respectively. By applying the proposed technique, it is possible to hide confidential data in the cover image at various levels while maintaining excellent visual quality of the stego-image. The confidential data can be extracted without data loss from the stego-image, and the original cover image can be recovered without distortion from the stego-image. Therefore, the proposed technique can be very useful for digital image watermarking, military, medical applications.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology  
The reimplemented architecture has several processes for detecting anomalies. The first component of the defensive system is an intrusion detection system with databases hidden behind the firewall (network data is preprocessed). Following preprocessing, it is necessary to identify any replacement of the null values with alternate values after the system checks for any missing values. Default consideration is given to average values, after which duplicate values are purged from the dataset. Dimensional reduction is applied to the encoded data to facilitate data management. Anomaly detection is aided by feature optimization, which is performed to extract the best characteristics from the data.  
The filtered data is then moved on to the next stage, where the method known as CFS-RF is used only to choose the affected features for the outcomes. In order to distinguish between legitimate activity and potential attacks, the system employs the reimplemented hybrid adaboosting bagging algorithms (HABBAs) as classifiers. The system's intricate construction is shown in Figure 2.  
It comprises several phases, each of which completes a particular task in a series of sequential stages. The previous step serves as input for the following stage. These phases and steps are thoroughly discussed. After reading the collected NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019 datasets, the three primary processes of the preprocessing stage (filtration, transformation, and normalization) are carried out.
###### Abstract  
This paper is devoted to evaluating the combined coherent and incoherent sources (CCIS) technique for different applications in the optical domain and future optical code division multiple access (OCDMA) networks. Spectral amplitude coding (SAC) has gained significant attention in optical processing systems due to its increased capabilities in dealing with multiple-access interference (MAI) efficiently. Fixed right shift (FRS) is adopted as a signature code in this study. Furthermore, performance analysis is studied in terms of bit error rate (BER) for the system using CCIS in both the free space optics (FSO) and sky mesh network using an aerial altitude platform system (AAPS). Simulation results confirmed that a CCIS design significantly improves system performance with moderate cost. An acceptable BER value of \(10^{-9}\) at 1.25 Gbps data rate and 60 km, 38 km, and 6 km distances for the laser, CCIS, and LED sources, respectively, can be supported. In particular, at Q-factor \(\sim\)4.5, the FSO ranges under low haze, moderate haze, and heavy haze are, respectively, 3.7 km, 2.5 km, and 1.5 km. The reason is that a CCIS design causes an increase in the effective transmitted power. It can be summarized that a CCIS design can provide reliable solutions and an affordable cost for future optical fiber and wireless network applications.  
CCIS; FSO; AAPS; SAC-OCDMA; bit error rate
# High-Resolution, Broad-Range Detection Setup for Polarimetric Optical Fiber Sensors
## 3 Results
### Influence of the Quantization Errors  
A simple computer model was created in MathCAD (MathCAD v. 15, PTC, Boston, MA, USA), following the description in the previous Section. The model describes an ideal area image sensor with \(2^{K}\) pixels in a row and an ideal \(N\)-bit A/D converter. Parameters \(K\) and \(N\) were assumed to be positive integers.  
Calculations were performed for 256, 512, and 1024 pixels in a row, corresponding to \(K\) = 8, 9, and 10. For each value of \(K\), the number of bits \(N\) in the A/D converter was varied from 8 to 10 to 12. The phase difference \(\varphi\) was changed in the range corresponding to the shift of intensity pattern (9) by one pixel, i.e., \(2\pi/2^{K}\) rad, with an increment equal to 0.001 of the range.  
First, the phase error of an ideal area image sensor with 256 pixels in a row and an ideal \(N\)-bit A/D converter was calculated using the model. The range of the phase difference \(\varphi\) in that case was 24.5 mrad. Calculation results are presented in Figure 5.  
From the data presented in Figure 5, it follows that the magnitude of the error does not exceed 880 \(\upmu\)rad for an 8-bit A/D converter, which is \(36\cdot 10^{-3}\) of the range corresponding to one pixel. For a 12-bit A/D converter, the magnitude of error is below 72 \(\upmu\)rad, i.e., \(2.9\cdot 10^{-3}\) of the range.  
The phase error of an ideal area image sensor with 512 pixels in a row and an ideal \(N\)-bit A/D converter was then calculated. The range of phase difference \(\varphi\) in that case was 12.3 mrad. The phase error is shown in Figure 6.  
Based on the data presented in Figure 6, it can be concluded that the magnitude of the error does not exceed 474 \(\upmu\)rad for an 8-bit A/D converter, which is \(39\cdot 10^{-3}\) of the range corresponding to one pixel. For a 12-bit A/D converter, the magnitude of error is below 55 \(\upmu\)rad, i.e., \(4.4\cdot 10^{-3}\) of the range.  
Finally, the calculations were repeated for an image sensor with 1024 pixels in a row and an ideal \(N\)-bit A/D converter. The range of phase difference \(\varphi\) in that case was 6.14 mrad. The phase error is shown in Figure 7.  
The magnitude of the error did not exceed 350 \(\upmu\)rad for an 8-bit A/D converter, which is \(57\cdot 10^{-3}\) of the range corresponding to one pixel. For a 12-bit A/D converter, the magnitude of error was below 41 \(\upmu\)rad, i.e., \(6.7\cdot 10^{-3}\) of the range.  
Figure 5: Phase error of image sensor with 256 pixels in a row.  
While this indicates that the presented detection setup can perform measurements with a sub-pixel resolution, the influence of non-idealities and noise in the image sensor and in the A/D converters may degrade this resolution substantially.
# The Potential of Visible and Far-Red to Near-Infrared Light in Glaucoma Neuroprotection
###### Abstract
Scopus and PubMed research databases were used to perform an electronic search from January 2011 to May 2021. The following filters were utilized in Scopus: human, classical article and review, engineering, medicine, biochemistry, genetics, and molecular biology. In PubMed, we used the following filters: humans, classical article, and review. Abstracts, case reports, and conference presentations were not included. The following medical subject headings (MeSH), terms, or keywords were combined to identify the most relevant research studies: \"glaucoma\" AND \"photobiomodulation\" OR \"visible light\" OR \"far-red light\" OR \"near-infrared light\" OR \"low energy photon irradiation\" AND \"mitochondria\" OR \"mitochondrial respiratory chain activity\" OR \"redox signaling\" OR \"oxidative stress\" AND/OR \"retinal ganglia cells\" OR \"nerve lamina region neural progenitor cell niche\". Only English publications regarding human studies were selected, except for eight papers regarding in vitro and in vivo animal model studies that were recovered from the reference lists of some selected articles. An additional nine papers were considered, even though they were published before 2011. A total of 549 publications were selected through the research databases and carefully taken into consideration. Publications and duplicated articles that did not meet inclusion criteria were excluded; the remaining 66 articles were reviewed together with two patents retrieved using the following keywords: red light ocular treatments or visual system function (Figure 1). At present, the published studies have demonstrated the beneficial effects of photobiomodulation in in vitro human models, or in in vitro and in vivo animal models. This review aims to illustrate the most recent evidence supporting and encouraging the design of clinical trials that exploit photobiomodulation in the treatment of glaucoma.
# The Analysis and Verification of Unbiased Estimator for Multilateral Positioning
## 5 Experiments and Results  
Both simulation and SX1280 based experiments are carried out to verify the effectiveness of our unbiased estimation. As for positioning error, the commonly used positioning accuracy evaluation indexes are the root mean square error and the error cumulative distribution function. Root mean square error (RMSE) refers to the sum of the square error between the measured value and the corresponding true value [26]:  
\[\text{RMSE}=\sqrt{E\left[\left(x-x^{\prime}\right)^{2}+\left(y-y^{\prime} \right)^{2}\right]} \tag{12}\]Cumulative Distribution Function (CDF) can accurately represent the probability distribution of different random variables, and the cumulative distribution function curve can more clearly represent the positioning error of the positioning system. The cumulative distribution function is defined as follows:  
\[F(x)=P\;(X\leq x) \tag{13}\]
# Human Skeleton Data Augmentation for Person Identification over Deep Neural Network
## 6 Experimental Evaluation and Results
### Four Benchmark Methods  
For performance comparison, we implemented four person identification methods: (1) C-SVM, (2) nu-SVM, (3) \(k\)-NN, and (4) MLP. Figure 11 shows the block diagram for the training and testing phases of the four methods. Each classifier was trained to learn the person identification task. The anthropometric features, as described in Section 4, were extracted from the input skeleton sequence. They were inputted to each classifier in a form of vector. The four methods are detailed as follows:  
* C-SVM and nu-SVM: To implement C-SVM and nu-SVM that support multi-class classification, we used LIBSVM--an open-source library for SVMs [47]. According to the recommendation in [48], we used the radial basis function (RBF) kernel for C-SVM and nu-SVM. C-SVM has a cost parameter, denoted \(c\), whose value ranges from 0 to \(\infty\). The nu-SVM method has a regularization parameter, denoted \(g\), with a value range \([0,1]\). The RBF kernel has a gamma parameter, denoted \(\gamma\). The grid search method was used to find the best parameter combination for both C-SVM and nu-SVM.
* \(k\)-NN: According to the results of the previous studies [22; 23; 24; 25], \(k\)-NN achieved the best performance in the task of person identification. To implement \(k\)-NN, we used the MATLAB function fitcknn. To determine the best hyperparameter configuration, we performed hyperparameter optimization supported in the function fitcknn.
* MLP: According to the results of the previous studies [22; 23; 24; 25], MLP showed the worst performance in the task of person identification. In [22], MLP had 10 hidden units. In [23], the number of hidden units was 20. Conversely, in [24; 25], MLP, which had 40 hidden units, was used. For performance comparison, we implemented the three MLPs using TensorFlow [49]. However, as mentioned in Section 3, in the experiments, we observed that the training set accuracy of each MLP was lower than 10% when each MLP comprised only a single hidden layer. Therefore, to overcome this issue and to improve the training set accuracy, the batch normalization layer, activation layer, and dropout layer were successively added after the hidden layer of each MLP. For simplicity, we called the three MLPs: MLP-10, MLP-20, and MLP-40, respectively. Here, the suffix number denotes the number of hidden units in the hidden layer.
## 3 Results and Discussion
### FTIR Analysis  
FTIR spectra of (A) freeze-dried _G. biloba_ leave extract, (B) CMC and (C) film based on CMC and freeze-dried _G. biloba_ leave extract are shown in Figure 4. FTIR spectra shows the existence of specific functional groups in the samples. Figure 4A depicts the characteristic bands of _G. biloba_ leaf extract, which appear at 1694, 1646 and 1068 cm\({}^{-1}\). The absorption at 1068 cm\({}^{-1}\) was attributed to C-OH stretching vibration in alcohols and carboxylic acids [42]. The absorption at 1647 cm\({}^{-1}\) was attributed due to the C=C and C=N stretching vibration and the absorption at 1694 cm\({}^{-1}\) was assigned to C=O [43]. In Figure 4C, the broad absorption peak at 3364 cm\({}^{-1}\) was due to the stretching vibrations of the hydroxyl group (OH) for FTIR spectra of CMC. The peak located at 2931 cm\({}^{-1}\) was attributed to the stretching vibration of C-H [44; 45]. A comparison Figure 4A-C for fast dissolving films based on CMC and _G. biloba_ ethanol extract signified that the hydroxyl group (OH) and C-H group appeared in both figures at 3364 cm\({}^{-1}\) and 2931 cm\({}^{-1}\), respectively. According to reported data, the peaks at wavelength of 1068, 1647 and 1694 cm\({}^{-1}\) represented in freeze-dried _G. biloba_ leaf extract were also determined in sample of fast dissolving film based on CMC and freeze-dried _G. biloba_ leaf extract.  
Figure 3: HPLC—PDA chromatogram of identified flavonoids in freeze-dried _Ginkgo biloba_ extracts (1-quercetin, 2-kaempferol, and 3-isorharnetin).  
Figure 4: FTIR spectra of a GFD (**A**), CMC powders (**B**) and sublingual film containing FRG (FRG —15 formulation) (**C**).
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 4 System Control
### Segmented Hybrid Motion/Force Control
In this direction, the manipulator tracks the desired magnitude of the constrained force. Figure 5 illustrates the schematic diagram of the hybrid impedance controller for the manipulator, where the controller structure is divided into the inner loop of hybrid motion-force control and the outer loop of impedance control. The inner loop is a force/position hybrid control method, with the control signals from both controllers being allocated to different degrees of freedom directions by the mutually orthogonal subspaces \(\mathrm{P}\) and \((\mathrm{I}-\mathrm{P})\). In the outer loop of impedance control, the measured contact force \(\mathrm{F}_{\mathrm{ext}}\) and the desired contact force \(\mathrm{F}_{\mathrm{d}}\) are introduced into the impedance model. Subsequently, the concentrated contact force in the workspace is transformed into the generalized active force in the joint space. From the equation, it is evident that when the force tracking error \(\mathrm{F}_{\mathrm{e}}=(\mathrm{I}-\mathrm{P})\mathrm{F}_{\mathrm{d}}-\mathrm{ F}_{\mathrm{ext}}\) is zero, the trajectory correction generated by the manipulator arm impedance model is zero.
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 2 Design of Cascaded Adaptive Vector Tracking Loop
### Signal Tracking Loop Based on KF
The main calculation steps are:  
\[S_{k}=I_{k}^{2}+Q_{k}^{2} \tag{7}\]  
The mean and variance of \(S_{k}\) are:  
\[\overline{S}=mean(S_{k}) \tag{8}\]  
\[\sigma_{S}^{2}=\text{var}(S_{k}) \tag{9}\]  
Then the average carrier power can be defined as:  
\[\left(\frac{NA}{2}\right)^{2}=\sqrt{\overline{S}^{2}-\sigma_{Z}^{2}} \tag{10}\]  
where \(N\) is the number of samples of the time series \(S\), and \(A\) is the signal amplitude output by the front end. For the noise part of the I and Q branches, the covariance of the noise integral term \(\sigma_{IQ}^{2}\) is:  
\[\sigma_{IQ}^{2}=\frac{1}{2}\left(\overline{S}-\left(\frac{NA}{2}\right)^{2}\right) \tag{11}\]  
The estimated value of the C/N\({}_{0}\) is:  
\[C/N_{0}=10\log_{10}\left(\frac{(NA/2)^{2}}{2T\sigma_{IQ}^{2}}\right) \tag{12}\]
## 2 Methods  
The operation process of the Argo buoy is shown in Figure 1. After the buoy runs a profile and floats up to the sea surface, it uploads the collected deep-sea data, positional information, buoy status information, and other information to the satellite through the BTTT. The shore-based terminal receives the information through the satellite and then sends it to the upper computer for handling and display. The upper computer can send remote commands to the shore-based terminal for uploading to the satellite. The terminal receives the information via satellite and transmits it to the buoy to change the parameter settings for the next profile. Accordingly, remote communication occupies a significant role in buoy-based marine detection. Throughout the operational cycle of the buoy, multiple communications are required to be conducted by the BTTT. The direction for the design of low-power terminals is oriented towards shortening the duration of communication and reducing the power consumption of the terminal.  
The BTTT is optimized to improve the speed and accuracy of the transmitted data. The original BDM protocol is hidden, requiring users to parse it separately. To facilitate user operation and ensure data transmission speed and accuracy, an extended design is made for the BDM. Each function of the terminal corresponds to a specific command. Users only need to send short commands to achieve complex functions. The system analyzes Beidou communication information, extracts message content, parses the content again, and accurately transmits the information to the user.  
The remote communication process of the Argo buoy [17] is shown in Figure 2. The BTTT depends on the Beidou transparent transmission control board, BDM, and antenna to achieve buoy remote communication functions. The design of the terminal is comprised of hardware design, control program design, and data compression algorithm design. Because the buoy belongs to limited energy equipment, the requirements for power consumption are very high. Through many revisions and optimizations, the following measures are taken to realize the ultra-low power consumption design of the terminal: First, selecting ultra-low power microcontrollers and using stop mode to reduce static power consumption; second, designing a reasonable power management process; and finally, compressing data to shorten communication time, improve communication speed, and reduce power consumption.  
Figure 1: Operation flowchart of Argo buoy.
## 5 Materials and Methods
### Experimental RSSI Analysis Protocol  
To determine the range capabilities of the on-board BLE radio, we used the received signal strength indicator (RSSI). From the collected data, we determined how the range affected the strength of the signal and at which point the signal was too weak to trigger a wake up. RSSI was measured on a scale from 0 dBm (closest) to \(-127\) dBm (farthest). The signal power can be theoretically determined by considering the signal waves as electromagnetic waves obeying the mechanisms of reflection, diffraction, and scattering [71].  
A simplified equation for the RSSI (Pr) on the receiver side is as follows:  
\[Pr=-10\eta\cdot log_{10}(d)+A\]  
where \(Pr\) is the strength of the received signal; \(\eta\) is the signal propagation exponent; \(d\) is the relative distance between devices; and \(A\) is the nominal transmission power at 1 m.  
The experiment was conducted indoors in an empty room, limiting external disturbances and the causes of wave reflections (objects, furniture, etc.). We measured the RSSI levels between two identical boards that were laid flat on the ground, separated by different experimental distances. One board periodically sent data (1 Hz), while the other one was in reception mode, recording RSSI levels for later analysis. We measured the RSSI levels with this configuration over 40 s for each fixed distance from 10 to 500 cm, with a variable incremental step. Until 100 cm, the incremental step was 10 cm. Above 100 cm, the incremental step was 50 cm. The variation chosen was linked to the high variability in short-distance RSSIs (usually below 100 cm). For each fixed distance, we calculated the RSSI by averaging 40 data points.