esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In previous research, most remote sensing SIF retrieval methods could only be used for the quantification of the SIF at individual bands. However, the shape of the SIF spectrum is also very important in many applications. In this paper, we proposed a new PCA-based full-spectrum spectral fitting method (F-SFM) for the retrieval of SIF spectra. We used PCA to extract the features of both the reflectance and the SIF spectra, and used an iterative process to improve the accuracy of the reflectance estimations.  
The F-SFM algorithm was tested using both simulated and field-measured datasets that had different SRs and SNRs. The results obtained using the simulated test dataset with an SR of 0.3 nm and without noise showed that the RRMSE was less than 14% within the full spectral region that was studied; they also showed that the peak-value ratios (SIF\({}_{735}\)/SIF\({}_{685}\)) could be accurately estimated. Compared with the 3 FLD and iFLD methods, F-SFM was less sensitive to the SR or SNR and performed well under all the SR and SNR conditions that were tested. The testing of the field data showed that the F-SFM can provide credible full-spectrum results for data obtained by the QE Pro spectrometer, which has an SR of 0.3 nm and SNR of more than 1000. The results for the data obtained by the ASD FieldSpec4 spectrometer (which has an SR of 3 nm and SNR greater than 4000) showed the same trend as the results for the data obtained by the QE Pro, which indicates that the F-SFM algorithm is also applicable to data obtained by spectrometers with relatively low SRs. Therefore, the F-SFM algorithm is suitable for accurate retrieval of full-spectrum SIF information, even at relatively low SRs and SNRs, and shows promise for use in applications involving SIF information relating to both the intensity and the spectral shape.
## 5 Experiment, Results and Discussion
### Experimental Results and Discussion
#### 5.2.1 Network Model Training Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Using the Data Generated** & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**f1-Score**} & \multirow{2}{*}{**Support**} \\ \cline{3-3} \cline{5-6} \multicolumn{1}{c}{**by the Generator**} & & & & & \\ \hline \multirow{3}{*}{NO} & LOS & 0.89 & 0.96 & 0.93 & 2092 \\  & NLOS & 0.80 & 0.56 & 0.66 & 542 \\  & accuracy & & & 0.88 & 2634 \\ \hline \multirow{3}{*}{YES} & LOS & 0.91 & 0.95 & 0.93 & 2092 \\  & NLOS & 0.78 & 0.65 & 0.71 & 542 \\ \cline{1-1}  & accuracy & & & 0.89 & 2634 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of our trained MLP models. Figure 15: Variation of the discriminator loss function during the training. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Using the Data Generated** & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**f1-Score**} & \multirow{2}{*}{**Support**} \\ \cline{3-3} \cline{5-6} \multicolumn{1}{c}{**by the Generator**} & & & & & \\ \hline \multirow{3}{*}{NO} & LOS & 0.93 & 0.96 & 0.94 & 2092 \\  & NLOS & 0.82 & 0.72 & 0.76 & 542 \\  & accuracy & & & 0.91 & 2634 \\ \hline \multirow{3}{*}{YES} & LOS & 0.94 & 0.95 & 0.94 & 2092 \\  & NLOS & 0.79 & 0.77 & 0.78 & 542 \\ \cline{1-1}  & accuracy & & & 0.91 & 2634 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of our trained CNN models.
## 5 Coating Materials with Embedded Antibiotics and Other Antibofilm Agents
### Biofilm Control by Inhibitors or Modulators of Bacterial Signaling Processes
#### 5.5.3 Biofilm Disruption by Targeting the Stringent Response Alarmone (p)ppGpp  
(p)ppGpp is a second messenger essential for virulence and long-term persistence of pathogenic bacterial biofilms. Three AMPs, namely 1018, DJK-5, and DJK-6 (see Figure 7 for the amino acids sequences) were shown to inhibit the synthesis and trigger degradation of (p)ppGpp in both Gram-positive and Gram-negative bacteria resulting in a reduction of biofilm development and increased susceptibility towards AMPs [74,75]. [PERSON] et al. [76] demonstrated that treatment with peptides DJK-5 and 1018 suppressed the activity of the spoT promoter in a _P. aeruginosa_ murine cutaneous infection model causing reduction of the size of the skin lesions and decreasing the bacterial burden inside the abscess. [PERSON], [PERSON] et al. [77] proved that the application of a combined treatment using peptide 1018 and the classical oral disinfectant chlorhexidine on hydroxyapatite greatly reduces the volume of the dental plaque consisting in highly antibiotic-resistant multispecies biofilms. Similarly, the peptide DJK-5 alone or mixed with EDTA increases the antibiofilm activity of root canal irrigants such as sodium hypochlorite in endodontic therapy [78].  
[PERSON] et al. reported significant inhibition of the synthetase activity of the bifunctional Rel enzyme by the acetylated (AC) and, respectively, acetylated benzoylated (AB) guanosine derivatives shown in Figure 7. Rel protein is responsible for the synthesis and degradation of the (p)ppGpp alarmone in mycobacteria. The authors used a _Mycobacterium smegmatis_ strain cultured in the presence of AC and AB compounds for in vivo assessment of the effect of these compounds on (p)ppGpp synthesis. They found a sharp drop in the (p)ppGpp levels corroborated with significant disruption of biofilm formation [79]. The two guanosine synthetic analogs also inhibited biofilm formation by the pathogen _Mycobacterium tuberculosis_. Toxicity tests, namely MTT assay with H460 cells and hemolysis assay on healthy red blood cells (RBCs), showed compounds AC and AB as being nontoxic. In comparison to relacin, a previously developed (p)ppGpp production inhibitor [80,81], AC and AB showed improved biofilm disruption performances. To the best of our knowledge, no coating formulations have been developed yet for any of the substances in the present paragraph.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 5 CoSiWiNeT Evaluation
### Clock Offset Error  
The clock offset error was calculated as a difference between filtered or corrected clock offset and ideal clock offset values for each measurement sample. As the raw and uncorrected input offset values are increasing or decreasing over time, the ideal clock offset profile was considered to follow a straight line passing through raw offset values. Therefore, the clock offset error should be zero for the ideal clock synchronization algorithm. In practice, the synchronization algorithm with minimum offset error is considered to be the best algorithm.  
Figure 14: Trace 2—Statistical distribution of predicted frequency offset.  
Figure 15: Trace 1—Comparison of clock offset error.  
The clock offset error profiles of KF, LS, and CoSiWiNeT for network traces 1 and 2 are shown in Figures 15 and 16, respectively. Both error profiles show that KF and CoSiWiNeT produce lower offset errors compared to LS. The magnified views on both figures reveal this fact more clearly.  
To quantify the error performance of synchronization algorithms, a statistical parameter called root mean square error (RMSE) was used. RMSE is the standard deviation of the residuals (prediction errors), and it informs how concentrated the filtered offset data is around the line of the best fit. RMSE for raw input as well as output offset values of various synchronization algorithms is shown in Table 3. In network traces 1 and 2, the RMSE of CoSiWiNeT is lower than RMSE of raw and synchronization algorithms KF and LS. In the case of traces 3 and 4, the RMSE of CoSiWiNeT is lower than RMSE of raw and LS, but it is comparable with KF.
## 4 Radiometric Processing Workflow
### Radiometric Block Adjustment  
Radiometric block adjustment can be used in cases when the area of interest is covered by multiple overlapping images, such as image blocks captured with 2D or pushbroom imaging sensors. In the photogrammetric (geometric) processing of image blocks, the block adjustment is used to determine the best geometric fit over the entire image block. Radiometric block adjustment is based on a similar idea. The approach is to model the radiometric imaging process, i.e., the model between the object reflectance and the image DN, and then solve the parameters of this model using optimization techniques utilizing redundant information from the multiple overlapping images [44,163]. The outputs of the process are the parameters of the radiometric model, which can be used in the following processes to produce radiometrically corrected image products, such as reflectance mosaics, reflectance point clouds, or reflectance observations of objects of interest [44,163]. Similar approaches have previously been used with aircraft images [177, 178, 179, 180].  
The model between a DN and reflectance by [44] accounts for the variability of the radiance measurement and the BRDF effects, and determines the absolute transformation from DN to reflectance using the ELM. In the adjustment process, a set of radiometric tie points are determined, observation equations are formed utilizing the DN observations of each radiometric tie point in multiple images. In addition to the radiometric tie points, other observations can also be included. In the current implementation, radiometric control points (e.g., reflectance panels, c.f. Section 4.4.3) and the a priori values of relative differences in the irradiance of different images can be included as observations [163]. Relevant model parameters are selected for each adjustment task. For example, during overcast conditions, it is not necessary to use the BRDF parameters, whereas under stable conditions, the relative correction parameters are not usually necessary. Furthermore, a comprehensive weighting strategy is used to reach the optimal results in the combined adjustment mode [163]. The definition for the reflectance outputs calculated as a result of this procedure is the hemispherical directional reflectance factor (HDRF). Many of the steps in Figure 5 are thus integrated into the radiometric block adjustment process.
# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
## 3 Problem Formulation
### Enhancement for Time Complexity  
The BBU pool in the C-RAN architecture carries out all computations, including that of the optimization problem. Due to the high computing power of C-RAN as well as the use of sophisticated methods for handing binary/integer optimization variables, such as branch and bound with sequential linearization, solving the optimization problem does not take a significant amount of time; the results of the time complexity evaluation are discussed in Section 4. However, to achieve the scalability of the proposed methods, we propose a simple addition to the ALGO-2 to reduce the processing time required for the optimal association at the expense of memory space.  
As mentioned above, the proximity matrix \(\mathbf{N}\) is computed to identify which IoT devices are within the transmission range of which RRHs. If we have the matrices for the two consecutive time slots, i.e., \(\mathbf{N_{t-1}}\) and \(\mathbf{N_{t}}\), we can identify the changes in the proximity information. The easiest way is to compute the following: \(\|\mathbf{N_{t}}-\mathbf{N_{t-1}}\|_{1}=\sum_{\forall r\in\mathcal{R}}\sum_{ \forall i\in\mathcal{I}}|n^{t}_{r,j}-n^{t-1}_{r,j}|\) where \(\mathbf{N_{t}}=[n^{t}_{r,j}]_{r\in\mathcal{R},j\in\mathcal{I}}\) and \(\mathbf{N_{t-1}}=[n^{t-1}_{r,j}]_{r\in\mathcal{R},i\in\mathcal{I}}\). At time \(t\), if there is no change in \(\mathbf{N_{t}}\) compared to \(\mathbf{N_{t-1}}\), the network can reuse the solution \(\mathbf{X_{t-1}}\) found in the previous time slot. When the IoT devices are stationary or when they are moving at a slow speed, this approach can significantly reduce the computational time. Even in the case when the IoT devices are fast-moving objects, some optimal solutions can still be valid for the next time slot if the length of a time slot is short.  
The third proposed algorithm ALGO-3 runs as follows. It stores both the previous optimal association, \(\mathbf{X_{t-1}}\), and the previous proximity matrix, \(\mathbf{N_{t-1}}\). For each time \(t\), it checks if there are any changes in the proximity information. If \(\|\mathbf{N_{t}}-\mathbf{N_{t-1}}\|_{1}=0\), ALGO-3 terminates and reuses \(\mathbf{X_{t-1}}\), which is the previous optimal association decision. If not, ALGO-3 computes P. (3) to find the optimal association rule with the fewest handovers. ALGO-3 uses a matrix of all 0 values for \(\mathbf{N_{0}}\).
## 2 Materials and Methods
### Schemes of Experiments, Laser Sources, and Spectrometers  
To determine fluorescence spectra at different wavelengths, we used generation modes of tubers with and without mechanical damage, laser sources, and spectrometers presented in Table 1.  
The lidar is based on a pulsed Nd: YLiF4 diode-pumped solid-state laser (Laser Compact, DTL-319 QT: 527 nm, 5 ns, 1 kHz, 200 uJ/imp).  
For recording the fluorescence spectra, use was made of a compact fluorimetric spectroscopy lidar system. Its schematic diagram is shown in Figure 4.  
The principle of the experimental installation for recording fluorescence spectra based on the fluorimetric lidar is as follows:  
A laser beam is directed to the studied object through two rotating prisms. The scattered radiation from the object is collected by a quartz lens and focused on the entrance slit of the spectrograph. The system of signal registration consists of a compact spectrograph (Spectra Physics, MS127i) equipped with a CCD camera with a strobable brightness amplifier (Andor iStar).  
A low dispersion (400 lines/mm) diffraction grating and a 250 \(\upmu\)m wide entrance slit were used to improve the signal-to-noise ratio. To prevent damaging the CCD matrix by scattered laser radiation, an OS-13 band-pass filter was used when recording signals.  
As for the RAMAN lidar location and integration in the mechanism, it was based on the previously built-in fluorimetric lidar (Figure 5). RAMAN lidar was constructed using 785 nm RAMAN laser (Bruker, USA) and signal reception system including a dark box with sensors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **A-Series** & **B-Series** \\ \hline Source & Nitrogen Pulsed AIL-3 Laser & Pulsed Diode-Pumped Nd: YLiF\({}_{4}\) \\  & Nitrogen Pulsed AIL-3 Laser & Solid-State Laser (Laser Compact, \\  & DTL-319 QT) \\ \hline Generation mode & Pulsed & Pulsed \\ \hline \(\lambda\), nm & 307 & 527 \\ \hline Average power, mW & 3 & 3 \\ \hline Pulse Energy, \(\upmu\)J & 30 & 200 \\ \hline Frequency, Hz & 100 & 1000 \\ \hline Pulse, ns & 5 & 5 \\ \hline Spectrometer & AvaSpec-2048 & Spectra Physics, MS127i \\ \hline \hline \end{tabular}
\end{table}
Table 1: Schemes of experiments, laser sources, and spectrometers.  
Figure 4: Schematic diagram of experimental installation for recording fluorescence spectra based on the Raman lidar.
## 4 Simulation Results
### Performance Analysis of Signal Detection
We can see from the solid lines in Figure 1a-c that the false detection probability of the proposed method is lower than those of the methods published in [11; 13; 14]. Under the situation with relatively high \(E_{b}/N_{0}\), the false detection probability of the proposed method is higher than that of the method in [12]. However, it can also be seen from the corresponding missed detection probability that the low false detection probability of the method in [12] is achieved at the cost of a very high missed detection probability. Thus, even though the false detection probability of the method in [12] is lower than that of the proposed method under higher \(E_{b}/N_{0}\), its corresponding missed detection probability is much higher than that of the proposed method, which indicates that the signal detection performance of the method in [12] is not better than the proposed method under low SIR and \(E_{b}/N_{0}\). In addition, we can also see from the dashed lines in the three figures of Figure 1 that the missed detection probability of the proposed method is the lowest among these methods, except the one published in [11] under different SIRs and \(E_{b}/N_{0}\)s. For the method  
Figure 1: False detection probability and missed detection probability of the proposed method and published methods under different SIRs and \(E_{b}/N_{0}\)s. (**a**) SIR = \(-10\) dB, (**b**) SIR = \(-6\) dB, (**c**) SIR = \(-2\) dB. The false detection probabilities are plotted with solid lines, whereas the missed detection probabilities are plotted with dashed lines. published in [11], we can see from Figure 1a,b that its missed detection probability is the lowest under the situations of SIR = \(-10\) dB and \(-6\) dB. However, we can also see that the corresponding false detection probabilities of this method are almost 1, which indicates that its low missed detection probability is achieved at the cost of a very high false detection probability. Thus, the signal detection performance of the method in [11] is also not better than the proposed method under the low SIR and \(E_{b}/N_{0}\). To sum up, we can conclude that the signal detection performance of the proposed method is the best compared to the published ones, which demonstrates the good application prospects of the proposed method in interference environments.
## 2 The Materials and Methods
### Machine Learning Model Setup  
To predict the tribological properties of lithium disilicate, an ANN model was employed. The structure of the ANN was optimized using a GA, which was used to determine the optimal architecture for the network, including the number of layers, number of neurons per layer, and activation functions. The GA ensures that the network structure is highly adapted to the specific problem, allowing for the accurate prediction of wear and friction properties based on various input variables, such as material composition, filler content, and operational conditions. The ANN was trained on a dataset containing both experimental data and data sourced from the relevant literature. Input features for the model included material composition, processing conditions, normal load, and linear velocity, while the output properties were the wear rate and friction coefficient. The model was implemented using MatLab2017, and the GA was used to fine-tune the model parameters. The following pseudo code provides a clear overview of the entire process of training and optimizing an artificial ANN using a GA (Table 2). In this study, an ANN was used to model the relationship between tribological parameters (normal force and sliding velocity) and the resulting wear rate and coefficient of friction. ANNs were chosen due to their ability to approximate complex functions and generalize relationships between input and output data. The structure of the used ANN model is three-layered: an input layer, one hidden layer, and an output layer. The input layer consists of two neurons corresponding to the tribological parameters (normal force, F\({}_{n}\), and velocity, v) while the output layer has one neuron for predicting the wear rate (W) or coefficient of friction (COF). To optimize the parameters of the ANN model, such as the number of neurons in the hidden layer, type of transfer function for the hidden and output layer, number of epochs, etc., a GA was applied. The GA is a method based on natural selection and evolution, which is used for searching large and complex parameter spaces to find the optimal set of parameters for the ANN. The GA was applied to optimize the ANN structure with the objective function to minimize the error function (MSE), thereby improving the model's performance. This approach allows for better generalization of the ANN model, reducing issues like overtraining and overfitting. The parameters being optimized include the number of hidden neurons, transfer function for the hidden layer, transfer function for the output layer, training validation test data split ratio, training coefficient, number of epochs, and training function. For the optimization of these parameters, the lower and upper bounds were applied as defined in Table 3.  
The performance of the ANN models was evaluated using the MSE for each subset (training, validation, and testing). The models that best predict the wear rate and coefficient of friction under different surface conditions were selected based on these criteria.
## 3 IoT Standard Architectures
### ESTI M2M Reference Architecture  
The European Telecommunications Standards Institute (ETSI) IoT Standard, also known as the ESTI M2M Reference Architecture (as shown in Figure 5), is the high-level functional architecture that consists of Device and Gateway Domain and Network Domain [23]. The lower domain includes the M2M device that runs applications, the M2M gateway, and the M2M Area Network. The upper domain has an Access network, a Core network, M2M Service Capabilities, M2M Applications, a Network Management Functions, and M2M Management Functions. These Two domains can communicate in three ways: direct connectivity, gateway as a network proxy, and multiple gateways as a network proxy. ESTI is the basic standard on which many M2M standards have been built. It gives the requirements, architecture, application programming interface (API), security solutions, and interoperability for M2M and IoT technologies.