esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 3 Experiments and Analysis of Results
### Multiple Machine Learning Classifiers for Multi-Scale Deep Features  
As shown in Figure 5, multi-scale deep features were extracted and classified using different machine learning algorithms. Table 3 summarizes the classification results of various algorithms on Dataset A, and detailed classification results for each category can be found in the confusion matrix shown in Figure A2 of the Appendix A. Table 4 presents the classification results on Dataset B, with the category-specific results also shown in the confusion matrix in Figure A3 of the Appendix A. The results indicate that the KNN classifier achieved the highest classification accuracy for all spectral features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Feature** & **Accuracy (\%)** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{Dataset A} & log-power spectrogram & 92.35\% & 0.9162 & 0.8944 & 0.9013 \\  & log-Mel spectrogram & 95.61\% & 0.9508 & 0.9503 & 0.9503 \\  & log-wavelet spectrogram & 90.05\% & 0.9020 & 0.8857 & 0.8921 \\ \hline \multirow{4}{*}{Dataset B} & log-power spectrogram & 93.06\% & 0.9304 & 0.9255 & 0.9271 \\  & log-Mel spectrogram & 94.31\% & 0.9415 & 0.9366 & 0.9377 \\ \cline{1-1}  & log-wavelet spectrogram & 90.69\% & 0.9028 & 0.8990 & 0.8993 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline Classification with Single-Scale Feature with Single Classifier on Different Spectrograms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Method** & **Accuracy (\%)** & **Precision** & **Recall** & **\(F1\) Score** \\ \hline \multirow{6}{*}{Muti-scale log-power spectrogram spectrogram deep features} & LR & 93.44\% & 0.9330 & 0.9217 & 0.9266 \\  & KNN & 95.89\% & 0.9605 & 0.9515 & 0.9556 \\  & NB & 92.24\% & 0.9168 & 0.9062 & 0.9107 \\ \cline{1-1}  & DT & 83.82\% & 0.8223 & 0.8143 & 0.8179 \\ \cline{1-1}  & RF & 92.11\% & 0.9241 & 0.8996 & 0.9096 \\ \cline{1-1}  & XGBoost & 91.93\% & 0.9176 & 0.9011 & 0.9081 \\ \cline{1-1}  & CatBoost & 92.99\% & 0.9282 & 0.9098 & 0.9172 \\ \cline{1-1}  & MLP & 93.42\% & 0.9323 & 0.9243 & 0.9280 \\ \cline{1-1}  & SVM & 93.93\% & 0.9378 & 0.9216 & 0.9285 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of our methods (multi-scale feature extraction with multiple classifiers) used on Database A.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 3 Materials and Methods
### Classification
#### 3.5.4 Sequential Minimal Optimization (SMO)  
SMO is an algorithm used to train the support vector machine (SVM). In this classifier, feature space is classified according to the hyperplane that splits the Stress and relaxed/non-stress state by using the class labels. It uses the iterative algorithm that iterates the optimization problem into a series of mini subproblems until all its conditions are satisfied.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 4 Proposed Scheme
### Preamble Codeword Set Selection Algorithm  
In order to select an optimal preamble codeword set to improve the RA success rate, it is necessary to consider all the RA factors. As mentioned in Section 1, there is a trade-off relationship between the collision rate, which is also called the preamble success rate, and the codeword ambiguity. In addition, the device needs to be allocated a dedicated PUSCH resource in order to succeed in RA; it is also necessary to consider the degradation of RA performance due to PUSCH resource limitations. To consider all of the RA factors, we first design the mathematical analysis model of the preamble transmission success rate, preamble codeword utilization rate, and PUSCH timeout rate analysis. The first two metrics are related to the trade-off between the collision rate and codeword ambiguity, and the last metric is used to calculate the PUSCH non-allocable preamble codeword rate. After the analysis model description, we describe the proposed algorithm to select the optimal preamble codeword set considering the three metrics mentioned above.
## 3 Results  
### Comparison of Semi-Rigid Node Indicators Under Completely Similar Conditions  
This section looks for the distribution of similar errors by analyzing the errors between geometrically incompletely similar models and completely similar models. To study the error, the data of the completely similar model must be obtained first. To examine the completely similar results, a model that strictly adheres to the 1:2 geometric scale of the prototype node is established, and the parameters of the scaled model are determined based on the fully similar conditions derived. Similar conditions can be judged from the stress state of the key parts of the node.  
As can be seen from Figures 4-8, the stress distributions of the nodes are almost exactly the same under the completely similar situation, including the stress distribution of beam-column members, end-plates, and the core area of the nodes. The stress data of the six typical points, such as the upper flange of the beam, the outer flange of the column, the partition on the column, the upper part of the column web, the core area of the node, and the upper and middle part of the end-plate, are listed in Figures 6-8. It can be seen that with the progress of the loading process, the stress changes of the upper flange of the beam, the core area of the joint, and the upper and middle parts of the end-plate increase monotonically, while the change trend of the stress at the outer flange of the column first decreases and then increases, and the trend of the stress change at the upper part of the column web is to maintain flatness and then increase monotonically. After superimposing the above-mentioned point stress trends of the prototype and the completely similar model (Figure 8), it can be seen that the curves of the two are almost identical, which proves the correctness of the complete similarity of the two and the previously derived completely similar conditions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Position** & **Prototype (mm)** & **Model (mm)** \\ \hline Beam & 30 & 15 \\ Column & 30 & 15 \\ End-plate & 8 & 4 \\ Bolt & 2.8 & 1.4 \\ \hline \end{tabular} The dimensions in the table are the mesh sizes in the finite element models.  
\end{table}
Table 6: Model and prototype unit dimensions.  
Figure 3: Boundary conditions of the model.  
\begin{table}
\begin{tabular}{c c c} \hline
**Index** & **Bolt** & **Beam and Column** \\ \hline Yield strength & 940 & 373.2 \\ Ultimate strength & 1130 & 540.7 \\ Yield strain & 0.05 & 0.08375 \\ Ultimate strain & 0.1 & 0.3 \\ \hline \end{tabular}
\end{table}
Table 5: Material properties of two steels.  
Figure 4: Prototype stress distribution.  
Figure 5: Model stress distribution.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Heavy Metals
#### 5.1.4 Chromium  
Chromium is a common heavy metal used in many industrial applications, such as leather tanning, electroplating, metal finishing, chromate preparation, dye manufacturing, paint, paper, and aluminum manufacturing. Chromium is present in the environment  
Figure 4: Schematic illustration of the preparation process for gelatin-hydroxyquinoline-Chitosan/ion-imprinted polymers for copper ions [52]. (Reproduced under permission from Elsevier, doi:10.1016/j.jcis.2019.01.081).  
in two oxidation states, hexavalent Cr(VI) and trivalent Cr(III). Cr(VI) is more toxic than Cr(III) and can lead to digestive and lung cancer.  
A photoelectrochemical (PEC) sensor based on nano-structured hybrid formate anion incorporated in graphitic carbon nitride (F-g-C\({}_{3}\)N\({}_{4}\)) is smartly integrated with a Cr(VI) ion-imprinted polymer (IIP@F-g-C\({}_{3}\)N\({}_{4}\)) was applied for the determination of Cr(VI) in water samples, as shown in Figure 5. The linear response to Cr(VI) concentrations in the range of 0.01 to 100 \(\mu\)g L\({}^{-1}\) was observed with a correlation coefficient of 0.9998, and a detection limit of 0.006 \(\mu\)g L\({}^{-1}\) (6 ppt). It is lower than those of Flame Atomic Absorption Spectrometry (FAAS) (0.8 ppb) and electrocatalytic detection (5.2 ppb). The method was used to detect Cr(VI), Cr(III), and the total chromium level in aqueous solution through the oxidation of Cr(III) to Cr(VI) and the determination of the total chromium as Cr(VI) [53].  
In another study, a fluorescence sensor was developed incorporating ion-imprinted polymer (IIP) as recognition element and Mn-doped ZnS quantum dots (QDs) as fluorophores to determine Cr(VI) within a linear range of 20 \(\mu\)g L\({}^{-1}\) to 1 mg L\({}^{-1}\); the detection limit was 5.48 \(\mu\)g L\({}^{-1}\). The prepared QDs-IIP sensor was highly specific for Cr(VI) with respect to Cr(III), Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), PO\({}_{4}\)\({}^{3-}\), and MoO\({}_{4}\)\({}^{2-}\)[54].
## 2 Materials and Methods
### Isolation of Fungal and Bacterial Communities of Apricot Fruits  
Epiphytic yeasts and bacteria were isolated from 15 fruits randomly chosen from each of three batches (5 fruit per batch). From each fruit, 25 g of the skin were cut with a sterile knife, put in a stomacher bag containing 225 mL of peptone water, and homogenized for 3 min in a stomacher blender (Stomacher Seward 400-Laboratory Blender, Seward, Worthing, West Sussex, UK). To isolate the endophytic microbiota, 15 fruits chosen, as previously described, were washed in sterile distilled water, soaked for three minutes in a 3% sodium hypochlorite solution, rinsed with sterile water, immersed for 1 min in a 70% ethyl alcohol solution, and washed 3 times with sterile distilled water. Twenty-five grams from each sterilized fruit were aseptically mashed and homogenized with a stomacher blender in 225 mL peptone water.  
Homogenized samples were serially diluted in peptone water and plated onto Plate Count Agar (PCA, VWR International, BDH Chemicals, Leuven, Belgium) to determine the total mesophilic count, Rose Bengal Agar (RBA, VWR International, BDH chemicals, Leuven, Belgium) to determine the fungal count, Man Rogosa Agar (MRS, VWR International, BDH chemicals, Leuven, Belgium) to determine the lactic acid bacteria (LAB) count, FLAB medium to determine the fructophilic LAB count [20]. PCA and FLAB plates were incubated in aerobiosis for 7 days at 30 \({}^{\circ}\)C, MRS plates were incubated in anaerobic conditions (Anaerocult\({}^{\text{\textregistered}}\) A, Mercks, Darmstadt, Germany) for 7 days at 30 \({}^{\circ}\)C, and RBA plates were incubated for 7 days at 25 \({}^{\circ}\)C.  
To specifically stimulate the growth of fermentative yeast species in _Bisuciu_ fruit, two enrichment culture protocols were carried out by using fresh apricot puree (FP). In the \"aerobic growth enrichment\" (AGE) protocol, 300 mL flasks filled with 50 mL of FP, to secure avoid headspace with a high level of air/oxygen, were incubated in shaking conditions (150 rpm) at 30 \({}^{\circ}\)C for 3 days. In the \"fermentation growth enrichment\" (FGE) protocol, 300 mL flasks were filled with 300 mL of FP, to avoid any residual headspace, and incubated in static conditions at 25 \({}^{\circ}\)C for 3 days. Subsequently, 10 g of puree resulting from AGE and FGE protocols were put in sterile stomacher bags containing 90 mL of buffered peptone water (VWR International, BDH chemicals, Leuven, Belgium) and homogenized for 3 min in a stomacher blender. Then, decimal serial dilutions were set up and 100 \(\upmu\)L of each solution were spread in YEPD (yeast extract 1%, peptone 2%, D-glucose 2%) plates and incubated at 30 \({}^{\circ}\)C for 7 days.
# Equivalence between Fuzzy PID Controllers and Conventional PID Controllers
###### Abstract  
This paper proposes the equivalence between fuzzy Proportional-Integral-Derivative (PID) controllers and conventional PID controllers. A well-designed conventional PID controller, with the help of the proposed method, can be rapidly transformed to an equivalent fuzzy logic controller (FLC) by observing and defining the operating ranges of the input/output of the controller. Furthermore, the knowledge base of the proposed equivalent fuzzy PID controller is represented as a cube fuzzy associative memory (FAM), instead of a combination of PD-type and PI-type FLCs in most research. Simulation results show the feasibility of the proposed technique, both in continuous and discrete time. Since the design techniques of conventional linear PID controllers have matured, they can act as preliminary expert knowledge for nonlinear FLCs designs. Based on the proposed equivalence relationship, the designer can further tune the membership functions of fuzzy variables in the control rules to exhibit the nonlinearity of a FLC and yield more satisfactory system responses in an efficient way.  
equivalence; conventional PID controller; fuzzy logic PID controller +
Footnote †: journal: Applied Sciences
## 3 MMA Design Analysis
The value of bottom-layer thickness \"tn\" that was investigated from 5 nm to 25 nm is shown in Figure 4d, where it did not affect the absorption curve because the lowest values of nickel thickness are above the skin depth. Figure 5 shows that the proposed metamaterial absorber performance has been investigated with different materials. The absorption spectra are analyzed at the different dielectric, as illustrated in Figure 5. Alumina, gallium arsenide, polycarbonate and aluminium nitride were chosen to examine the absorption rate of the proposed design. It is seen that the absorption spectrum for aluminium nitride (AIN) shows better absorption at the desired wavelength range from the visible to infrared region. Alumina has similar absorption to AlN but decreases in the visible optical region. However, polycarbonate shows less absorption in the visible optical region, which is not computable with the proposed work. However, AlN shows better absorption for both visible and near-infrared spectrum for this design due to its material characteristics such as dielectric constant, electrical tangent, thermal conductivity and unit cell geometrical pattern. Figure 4: The absorption performance of different design parameters. (**a**) unit cell period (p); (**b**) height of the top of the hexagonal ring resonator (tw); (**c**) height of the substrate (ts); (**d**) the height of the bottom layer (tn).
## 2 Related Work on Bridging Between Sensors and Applications  
Goal of the Sensor Web research field is to bring sensor resources on the Web and make them available to applications. To achieve this middleware technologies, which help to manage the heterogeneity of sensor resources and make them usable on the application level, have been developed. This section gives an overview of the broader research area, comes up with a categorization of different middleware classes, lists selected approaches, and points out their characteristics. Some of the listed approaches use the Sensor Web Enablement standards, other solutions incorporate non-standardized interfaces. The categorization below provides an overview and helps readers to find solutions that fit the needs of their use cases.  
The Sensor Web can be considered as a middleware between sensors and applications. This implies three main architectural layers. First, there is the _sensor layer_, where the actual hardware devices reside and various kinds of proprietary or standardized communication protocols are used by different sensor types (e.g., WPAN protocols, IEEE 1451). Second, there is an intermediary _Sensor Web layer_ providing functionality to bridge between sensor resources and applications. On top, there is the _application layer_ where direct interaction with clients (human end users or computers) takes place. Applications could run on various client devices ranging from cell phones to server machines. The three main layers are further divided into sub-layers depending on the architectural design of middleware systems.  
Figure 1 shows the described layer stack and places four identified middleware classes on their positions within the layer stack. Note that the borders of those middleware classes are drawn fuzzy since their functionalities might overlap and some middleware approaches offer functionalities belonging to multiple classes. Also, middleware solutions can be built upon each other to realize the entire Sensor Web layer stack. The four identified middleware classes are described in the following.
## 2 System Model
\begin{split} x(t)=&\sum_{j=1}^{H}\omega_{j}s\big{(} t-\tau_{\eta j}(t)\big{)}+n(t)+C^{k}(t-\tau_{d}(t))D^{k}(t-\tau_{t}(t))\\ &\times\Big{(}\sqrt{P_{d}}e^{2\pi f_{0}(t-\tau_{d}(t))}+\sqrt{P_ {t}}e^{2\pi f_{0}(t-\tau_{t}(t))}e^{i\varphi_{c}}\Big{)}.\end{split}  \tag{4}\]  
Since there is no reference channel for the FSR system, it is necessary to perform preprocessing on the received signal before radar processing. The preprocessing flow chart is shown in Figure 3[23]. In the tracking loop, the code phase and the carrier spectrum are accurately estimated by the code tracking loop and the carrier tracking loop, respectively. I/Q demodulation is often used to realize the tracking loop. According to [24], code correlation and symbol elimination are performed on the received signal after the down conversion, and the correlation result after signal processing can be obtained as  
\[x_{t1}(t)=G_{d}+G_{T}A_{T}e^{2\pi f_{0}(\tau_{d}(t)-\tau_{t}(t)-\varphi_{c})}+ \sum_{j=1}^{H}\omega_{\eta j}e^{2\pi f_{0}\big{(}\tau_{\eta j}(t)-\tau_{d}(t) \big{)}}+n^{\prime}(t), \tag{5}\]  
where \(G_{d}\) and \(G_{T}\) are the code correlation gain of the direct wave and target echo, respectively. \(\omega_{\eta j}\) stands for the amplitude of the \(j\)th multipath interference, and \(n^{\prime}(t)\) is the Gaussian white noise with mean zero. The output results after processing are shown in Figures 4 and 5. Figure 2: Geometric model of forward scatter system. Notch filter refers to a filter that can rapidly attenuate the signal at a certain frequency to remove the signal at this frequency. It belongs to a bandstop filter, and its stopband is very narrow. Due to the target motion, the target echo signal presents Doppler change. In the forward scattering scenario, it can be assumed that the arrival time of direct wave and target echo to the receiver is basically the same, and that there is no distance deviation between the two signals. Then the output of the code correlation processing of the two signals can be considered to be coherent, and the amplitude of the code correlation results can be obtained. According to [24], since each processing cycle is within a few milliseconds of single-chir integration time, we can regard the frequency of direct signal and echo signal as constants. For direct signals, this assumption is widely used in traditional navigation satellite signal processing; therefore, the suppression of direct wave signal can be completed by a notch filter. In this case, the received signal can be given by  
\[x_{r}(t)=G_{T}A_{T}e^{2\pi f_{0}(\tau_{d}-\tau_{l}-\varphi_{c})}+\sum_{j=1}^{H} \omega_{\eta j}e^{2\pi f_{0}(\tau_{\eta j}-\tau_{d})}+n^{\prime}(t). \tag{6}\]  
Figure 4: Output signal after code correlation. Figure 3: Flow chart of forward scatter signal preprocessing.
## 4 Proposed Monitoring Approach
### Localization of the Subject  
People detection has been successfully addressed in the past, with state-of-the-art models reaching excellent performance in terms of Average Precision (AP) [55]. Faster R-CNN (Regions with CNN features) is considered a state-of-the-art object detector, successfully applied to the detection of human subjects [56]. It falls in the category of two-stage neural detection models. During the first stage, a list of object proposals is generated in the form of bounding boxes coordinates. In the second stage, the neural features corresponding to each proposed region are brought to common size and classified into a defined set of classes. For our solution, we trained the detection model focusing on only the \"person\" class, which makes the classification stage reject false positive detections that were generated at the proposal stage. At inference time, we resort to selecting only the largest detected subject, which is supposed to be the closest to the camera, for the subsequent step of action recognition. In future developments, we will consider the integration of person re-identification techniques [57] to ensure the execution of a proper analysis.  
As we will show with proper experiments (Section 5.2), however, the same models quickly degrade in performance when tasked with detecting subjects in horizontal position or lying down. We consider this a critical aspect for our application, where correctly detecting the presence of a lying subject is a potential trigger for an alerting situation. Based on the hypothesis that this behavior is not an inherent limit of people detection models, but lies in the training data used for learning, we used digitally rotated images for training and testing the people detector. We chose this strategy to exploit the inherent richness and high cardinality of existing datasets for people detection, which however contain little to no samples of people lying down.
## 3 Industrial Case Study
### Pilot System Configuration and Deployment  
To support the industrial scenario, within the pilot environment described above, the system presented in Figure 4 has been deployed in the pilot area of the spoolers' workstation. For the implementation of the computer vision system, one area scan, GigE interface, and camera were used (Table 1). A consistent setup was utilized both during the training and testing phase of the images. The pilot case involved the placement of a camera approximately 2 m above the waste bins, in close proximity to a wall, on a stable iron structure, utilizing a camera mount bracket with a 360/90 degree rotational capability. The camera was connected to a computer machine via an ethernet cable for the purpose of triggering the software and storing the captured images. The location of the waste bin remained constant in accordance with factory regulations, and the bins were always situated within a specific frame formed on the floor, with an acceptable deviation of approximately 10 cm.  
The following software modules have been developed and deployed (Figure 4):  
* _Camera Control App_: A python application that is used to control, configure, manage, and trigger the camera for capturing images in a pre-set periodic manner (one image per five minutes). The Camera Control App receives the captured images and then stores them into the IIoT platform through REST API calls and the publish-subscribe mechanism.
* _IIoT platform_: An extension of the IIoT platform developed in Java and initially presented in [2] has been used for the needs of this pilot. The platform has been extended to implement the Asset Administration Shell (AAS) model [20] for storing and managing information collected from sensors in the shop-floor or legacy systems.
* _DL Classification App_: This application subscribes to image publishing events and when a new image is published by the Camera Control App it applies the classification model and returns the result, in JSON format, to the IIoT platform. The application is developed in Python and uses Tensorflow ML library. The development of the DL model is described in detail in chapter 3.3.  
Figure 3: Waste bins (**a**) Spoolers’ scrap 20% fill-in level for the bottom bin and (**b**) Spoolers’ scrap 100% fill-in level for bottom bin.  
* _Legacy Systems Connector:_ In order to provide a holistic insight into the status of the shop floor, the information provided by the vision system is combined with additional information regarding the status of the activities on the shop floor and is loaded to the IIoT system.
* _Waste Management Web Application:_ The waste management web application presents the bin fill level and different types of Key Performance Indicators in a dashboard (Figure 5) for monitoring purposes by the engineers.