esa-sceva/satcom-chunk-collection huggingface dataset:
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 5 Ultrasound Treatment
### Ultrasound-H\({}_{2}\)O\({}_{2}\) Combined Treatment  
Several studies have shown that the oxidizing effects of H\({}_{2}\)O\({}_{2}\) can be further increased when combined with physical treatments, such as microwave or ultrasonic irradiation. [PERSON] et al. have shown that the combination of US and hydrogen peroxide can be applied for effective aniline degradation, and the effects were stronger than those obtained for the standalone US or H\({}_{2}\)O\({}_{2}\) treatments [76]. It was also reported that with this combined treatment method, pesticides in an aqueous solution can be reduced [77], as well as bisphenol A [78]. In a recent 2021 study, [PERSON] et al. investigated the effects of US-H\({}_{2}\)O\({}_{2}\) treatment on excess sludge destruction. They found that with an initial pH level of 11.0, 0.5 mmol/L H\({}_{2}\)O\({}_{2}\) concentration, 17 g/L sludge concentration and 15 min US treatment (40 kHz), \(\Delta\)SCOD reached 3662.78 mg/L, with a DD of 28.61% and a sludge reduction rate of 19.47%. [79].  
Table 2 summarizes the results of these studies in regards to different efficiency indicators while applying ultrasonication alone or combined with chemical treatments.
# Quasi-Static Load Sharing Characteristics of a Planetary Fear Set with Planet Journal Bearings
## 3 Load Sharing Analysis Method
### Journal Bearing Stiffness  
Equivalent meshing integrated stiffness of the planetary gear set is composed of three parts: the sun-planet meshing pair, the planet-ring meshing pair, and the planet gear supported by a journal bearing. Therefore, it is necessary to transform these three elements to the meshing line of a sun-planet gear pair. Thus, the effective planet support stiffness \(k_{\Sigma}\) can be estimated as [28]  
\[\frac{1}{k_{\Sigma}}=\frac{1}{k_{p}}+\frac{1}{k_{sp}+k_{pr}}. \tag{28}\]  
Furthermore, the journal bearing stiffness can be significantly different under various operating conditions.  
Under different loads, the center of the planet gear changes, as shown in Figure 5. As the load increases, the eccentric angle \(\theta\) decreases, the planet center moves from \(O^{\prime}\) to \(O^{\prime\prime}\). The projection of the planet center on the load action line moves from \(p\) to \(p^{\prime}\). Then, the bearing support stiffness is given by [29]  
\[k_{p}=\frac{\partial W}{\partial L_{Op}}, \tag{29}\]  
where, \(W\) is the load and \(L_{Op}\) is the distance between \(O\) and \(p\). \(L_{Op}\) can be calculated by  
\[L_{Op}=e\cos\theta=c\varepsilon\cos\theta. \tag{30}\]  
Substituting Equation (30) into Equation (29), the numerator and the denominator in Equation (29) are simultaneously divided by partly derivative of the eccentricity ratio. Then the bearing stiffness can be described as  
\[k_{p}=\frac{\frac{\partial W}{\partial c}}{c\big{(}\cos\theta-\varepsilon \frac{\partial b}{d\varepsilon}\sin\theta\big{)}}. \tag{31}\]  
Figure 5: The variation of the planet gear center.  
Since the dynamic pressure distribution in the journal bearing is obtained from the numerical iterative computation, the dimensional loads in both horizontal and vertical directions can be calculated by  
\[\left\{\begin{array}{l}W_{h}=\int_{0}^{L}\int_{0}^{2\pi r}p\cos\varphi dqqd \lambda=\frac{p_{0}Lr}{2}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}P_{i,j}\cos \varphi_{i}\Delta\varphi\Delta\lambda\\ W_{v}=\int_{0}^{L}\int_{0}^{2\pi r}p\sin\varphi dqd\lambda=\frac{p_{0}Lr}{2} \sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}P_{i,j}\sin\varphi_{i}\Delta\varphi \Delta\lambda\end{array}\right.. \tag{32}\]  
Thus, the load is given by  
\[W=\sqrt{{W_{h}}^{2}+{W_{v}}^{2}}. \tag{33}\]  
The eccentric angle is expressed by  
\[\theta=\arctan\frac{W_{v}}{W_{h}}. \tag{34}\]
## 4 Signal Processing Algorithm for Classification of 17 Motions  
In this section, the proposed signal processing algorithm to classify 17 motions is described. In this method, the pEMG signals are transformed from time domain to frequency domain by performing fast Fourier transform (FFT) that uses a hamming window function. Next, power spectrum densities (PSDs) are calculated from the FFT results. Feature points that best characterize each motion are selected from the power spectrum densities for each channel to form feature vectors. Finally, the 17 motions are classified by feeding the feature vectors into the artificial neural network (ANN) classifier. The details of the proposed method are described in the following sections.
# Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression
## 6 Conclusions  
In this study, we examined the impact of weight pruning and channel pruning methods after KD. To evaluate the efficiency of these methods, we introduced a formula called \"efficiency\" that takes into account the number of parameters and the performance of the pruned models. Through our experiments, we observed that weight pruning consistently outperformed channel pruning when combined with knowledge distillation. This finding was consistent across various model architectures, including ResNet, DenseNet, and EfficientNet, over a total of 10 cases. Weight pruning demonstrated superior efficiency by significantly reducing the number of parameters while maintaining high model performance. Channel pruning, on the other hand, showed comparatively lower efficiency in terms of achieving both compression and performance enhancement. These results highlight the effectiveness of weight pruning as a pruning method when incorporating knowledge distillation. The combination of knowledge distillation and weight pruning offers a powerful approach for achieving efficient model compression without sacrificing performance.  
Conceptualization, L.M.; methodology, L.M.; software, L.M.; validation, L.M.; formal analysis, L.M.; investigation, L.M.; resources, L.M. and G.H.; data curation, L.M.; writing--original draft preparation, L.M.; writing--review and editing, L.M. and G.H.; visualization, L.M.; supervision, G.H.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This paper was funded by the Osnabruck University \"Open Access Publizieren\" of the \"Deutsche Forschungsgemeinschaft\" (DFG) with fund number DFG-4321.  
Figure 6: Efficiency comparison between channel and weight pruning using the \(t\)-test.  
Data Availability Statement:For researchers seeking access to the data utilized in this study, collaboration opportunities, and data access approval, we kindly request direct contact with the corresponding author. Additionally, any potential collaboration involving data access is subject to the approval of the institutional review board. This collaborative approach ensures transparency and adherence to ethical considerations. It is important to note that the authors declare the absence of conflicts of interest associated with the study. The funders did not contribute to the design, data collection, analysis, interpretation, manuscript composition, or publication decision of this research. The study's support was solely provided by the respective institutions of the authors. This declaration underscores the commitment to maintaining research integrity, independence, and impartiality throughout the entirety of the research process.  
The authors state that there are no conflict of interest related to the study. The funders played no role in the study's design, data collection, analysis, interpretation, manuscript writing, or decision to publish. The study was solely supported by the authors' institutions. This declaration ensures transparency and independence in the research process.
## 4 Numerical Application
### Initial Model Dependency  
We next evaluated the effectiveness of our proposed Physics-Informed Recurrent Neural Network with First-Arrival Constraints for Full Waveform Inversion (PIRNN-FAC-FWI, abbreviated as FAFWI) method in mitigating the initial model dependency through a comparative analysis using the Marmousi model. As a nonlinear optimization problem, FWI results are highly sensitive to the quality of the initial velocity model. When there are significant differences between the initial and true models, FWI tends to fall into local minima, leading to inaccurate inversion results. To verify that our proposed FAFWI method can effectively reduce the dependency on the initial models, we used a constant-velocity model as the initial model for full waveform inversion. This initial model differs significantly from the true velocity model, which lacks both low-frequency and well-log information and thus presents a challenging initial model for FWI. Furthermore, to ensure experimental consistency, the Adam optimization algorithm was used for the various FWI methods, with the time threshold \(\Delta\)tthreshold set to 2.5 ms, and the learning rate was uniformly set to 40 and kept constant. Numerical experiments were conducted using a 2D acoustic Marmousi model.
## 3 Results
### Supervised Learning Classification Results
According to Table 11, our results show that the performance (91.83% accuracy, 89.71% macro-P, 88.86% macro-R and 89.16% macro-F1) of KNN classification algorithm is the best among the three classification algorithms. However, the performance of SVM is not high in any kind of feature set. In fact, this is perfectly understandable that a larger sliding window contains richer information about fatigue and that fatigue itself affects human physiology, psychology and behavior. Smaller time windows lead to a lack of deeper understanding of sample features around time. In terms of overall performance, the KNN algorithm shows very good performance in all three feature sets. After exploring different feature sets, we also found that fatigue detection is very much influenced by feature variation. From the results, it can be concluded that the best results are obtained with multiple feature sets. Compared to feature set 1, feature set 2 will work better. This will be a major help for future in-depth exploration to extract features about the mouse. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Classification Performance (\%)**} \\ \cline{2-6}  & **10 s** & **20 s** & **30 s** & **40 s** & **50 s** \\ \hline SVM & _87.42_ & 79.68 & 69.05 & 68.72 & 66.45 \\ KNN & _91.83_ & 82.49 & 72.69 & 69.65 & 66.92 \\ RF & _89.25_ & 79.87 & 73.26 & 72.47 & 67.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Feature set 3—different steps. From the confusion matrix in Figure 5, it can be seen that the KNN algorithm has good performance in detecting all three fatigue classes. Although all three algorithms are very accurate at fatigue level 2. The accuracy of identifying fatigue level 1 and fatigue level 3 is low. The possible reason is that there is a difference in performance between the awake and tired states.
# Field-Applicable Pig Anomaly Detection System Using Vocalization for Embedded Board Implementations
## 3 Results
### End Point Detection  
To detect sound-based pig anomalies, the first process involves localizing the sound generated from the sound signal acquired through the sound sensor installed in the pig house. In this study, the VAD algorithm proposed by [PERSON] and [PERSON] [18] was used for that purpose, allowing the detection of all sounds generated in pig houses. The values of the settings used to detect sound were as follows: the length of the fast Fourier transform (FFT) window was 512, window size was 0.025 s, hop size was 0.01 s, and threshold was set to 0.75. Figure 6 depicts the 12.669 s long signal that represents five pig coughs and the result of the coughing sound detection in the signal. The results indicated that continuous coughing sounds such as 1, 2, 3, and 4, and coughing sounds with a small signal sound size such as 5 were effectively detected. In addition, the time taken to detect a sound region in a sound signal of 12.669 s in length in the TX-2 embedded board (CPU: ARM Cortex-A57, GPU: Pascal with 256 CUDA cores, and RAM: 8 GB) was 4.391 s. The sound region detected through this process was converted into a spectrogram, and then, input to the MnasNet-based abnormality detector. Librosa Python package 0.7.2 [43] with its default setting values was used to convert the sound signals to spectrograms. At this stage, the time required to convert a 2.005 s sound signal into a spectrogram in the TX-2 board was 1.095 s.  
Figure 5: Examples of pig sound signals: (**a**) Normal (healthy cough); (**b**) Normal (grunt); (**c**) mycoplasma hypopreunoniae (MH); (**d**) postweaning multisystemic wasting syndrome (PMWS); (**e**) porcine reproductive and respiratory syndrome (PRRS); and (**f**) Scream. The horizontal axis denotes the time in seconds and the vertical axis denotes the sound signal in dB.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 2 Zone-Based Registration  
Let us consider zone-based registration with multiple zones. First, let us take a brief look at zone-based registration using two zones (2Z).  
If UE uses two zones, as shown in Figure 1, location registration will not occur if the UE leaves and returns to zone X where it was initially registered. In this case, however, the network knows that the UE is in zone Y, where it was registered most recently. Therefore, the network starts paging zone Y first. If there is no response in this zone, it then pages zone X to receive an acknowledgement. As a result, location registration decreases, but paging load increases compared to zone-based registration with one zone (1Z). In order to obtain an accurate paging load, the probability of being in zone X, not the most recently registered zone, must be determined [1].  
Let us now look at zone-based registration with three zones (3Z), which is the main subject of this study. If the UE uses three zones, as shown in Figure 2, location registration will not occur when the UE leaves and returns to zone Y where it had already been registered. On the other hand, the probability of returning to stored zones (X or Y) against staying in the most recently registered zone (Z) increases compared to a 2Z system, which increases paging load. As a result, location registration is reduced, but the paging load is increased compared to 1Z or 2Z systems.  
In this study, a semi-Markov process model was developed to present an accurate mathematical model for a 3Z system.  
Figure 1: Zone-based registration with two zones.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 3 UWB Localization System Design  
### High Level Design  
Our solution aims to minimize two shortcoming in currently available UWB solutions, namely, (i) the need for a wired backbone and (ii) the large energy consumption of the anchor nodes.  
* To remedy the need for wired access points, some UWB solutions [12] have proposed the use of WiFi based multi-hop mesh networks. However, a multi-hop wireless networking approach has several disadvantages. (i) Multi-hop networking protocols are scalable towards only a limited number of hops and are thus not suitable for very large warehouse environments, (ii) data transmissions required multiple hops thereby reducing the available throughput and impacting reliability and (iii) WiFi cards typically have high energy consumption, thus limiting their suitability for battery powered anchor nodes. Instead, our solution connects UWB anchor nodes using a backbone of low-power sub-GHz communications which have a much larger range, of up to several kms [21]. In addition, these long ranges can even be extended using RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) [22], which has been shown to be more scalable than WiFi ad-hoc routing protocols, with network scaling up to hundred of devices [23; 24].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Infrastructure** & **Anchor Powering** & **Release** \\ \hline
[17] Sewio & Wi-Fi/Ethernet & PoE & commercial \\
[18] eliko & Wi-Fi/Ethernet & PoE & commercial \\
[19] Tracktio & Ethernet & DC Power & commercial \\
[20] openRTLS & Zigbee/Ethernet & PoE/DC Power & commercial \\
[12] & Wi-Fi & DC Power & academic \\
[13] & Ethernet & DC Power & academic \\
**Our work** & None & Battery / energy harvester & academic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of infrastructure requirements of existing UWB indoor Positioning solutions.  
* To reduce the anchor node energy consumption, we observe that most UWB positioning systems use always-on UWB radios, which are highly energy consuming. Since drones will not be present continuously at each location, we propose to turn off the UWB radio when no drones are nearby. Instead, UWB anchor nodes continuously listen using their low power sub-GHz radio. The drone will activate anchor nodes using an low-energy sub-GHz activation beacon when he is nearby, but only (i) when he actually wants to range with the anchor node and (ii) during a small TDMA based time slot. As such, anchor nodes remain in a low-power mode most of the time, and only activate the UWB radio during short timeslots when drones are nearby.  
In Figure 1, our proposed solution is summarized using a high-level system diagram.
## 4 Overview of the Type of Classifiers Used for MR
### Critical Analysis on Different Classifiers for MR  
Table 3 presents the summary of the review presented in Sections 4.1-4.5.  
As shown in Table 3, machine learning performs efficiently on small data sets and requires low implementation costs. However, the complexity of ML increases and leads to performance degradation when large data sets and complex features are used for recognition. ML cannot be applied in real-time environments. SVM and KNN classifiers can produce higher classification rates than ANNs. KNN classifiers are preferred because they are less complex than SVMs. The performance of DT is lower than that of ANN, SVM, and KNN. However, DT is simple to design and implement and can classify a wide range of modulation schemes by adding many decision points without retraining the classifier. Clustering classifiers are sensitive to noise but have high computational complexity.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference(s)** & **Classifier(s)** & **Merits** & **Demerits** \\ \hline
[35; 52; 53; 58; 59; 63; 71; 74; 88; 89] & DT & Simple implementation. \\  & & & This method can accommodate many modulations by adding additional decision branches \\  & & & High classification rate. \\
[43] & ANN & Reduced computational complexity & Ineffective when a large number of input features are used. \\
[76] & ANN & Improved accuracy & Computational. \\  & & & Small-order modulation types \\
[72; 94] & ANN & Reduced computational complexity through PCA. & Small-order modulation types \\  & & Improved accuracy & \\
[99] & GP with SVM & Improved classification performance & Two modulation types, namely, 16 QAM and 64 QAM, are used \\
[29] & PSO with SVM & Robust to noise. \\  & & Good classification & Computationally complex \\
[34] & Rough set theory with SVM & Reduced computational complexity & Poor discrimination of M-ASK at lower SNR \\
[54] & GP with KNN & Reduced complexity & Small set of modulation schemes \\
[84; 85] & Clustering & A priori information about signal is not required. & Requires high SNR \\ \hline \end{tabular}
\end{table}
Table 3: Summary of the merits and demerits of classifiers.
## 2 Materials and Methods
### Enzymatic Hydrolysis Procedure  
The sample powder was suspended in deionized distilled water (DDW) at 5% concentration (\(w/v\)), and the pH was adjusted to 8.0 using 0.1 N sodium hydroxide, considering the pH ranges for optimal activation (pH 8.0 for trypsin and pH 7.0-8.5 for Alcalase) [8]. For each porcine organ, the sample mixture was divided into three 500 mL volumes and assigned to non-hydrolyzed control, Alcalase-hydrolyzed treatment, and trypsin-hydrolyzed treatment groups. Two types of proteases, Alcalase 2.4 L (with a declared activity of 2.4 AU/kg, density of 1.18 g/mL, Novyzymes, Bagsvaerd, Denmark) and trypsin from porcine pancreas (EC 3.4.21.4, 1500 U/mg, Sigma-Aldrich, Saint Louis, MO, USA) were added in a 1:100 enzyme-substrate (protein concentration) ratio (E:S) [8]. For Alcalase-hydrolyzed treatments, the sample mixture was enzymatically hydrolyzed in a 50 \({}^{\circ}\)C incubator for 2 h, whereas the trypsin-mediated hydrolysis was performed at 37 \({}^{\circ}\)C for 2 h. Next, the samples were heated in an 85 \({}^{\circ}\)C water bath for 20 min to deactivate the proteases. The hydrolyzates were cooled at room temperature for 30 min and filtered through a mesh sieve (size 500 \(\upmu\)m/35, line thickness 315 \(\upmu\)m, 885705, Chung-Gye Sanggong Sa, Seoul, Republic of Korea) to remove the insoluble residues. The filtrates were freeze-dried and the sample powder was vacuum-packaged and stored at \(-\)20 \({}^{\circ}\)C to await further experiments. Three batches underwent this process on different days.
# TCNformer Model for Photovoltaic Power Prediction
## 5 Conclusions  
In this paper, a TCNformer model was proposed for photovoltaic power prediction, and we can draw the following three conclusions based on the experiment results:  
1. The TCNformer model adopts the Transformer structure and introduces the sparse attention mechanism into the Informer model. The experimental results show that the photovoltaic output prediction accuracy is improved effectively.
2. The VS module, LSTFE module, and one-step TCN decoding extract more efficiently the impact of multiple time series features and other weather factors on photovoltaic power by classifying the data based on the time series, periodicity, and correlation.
3. Compared with the LSTM model and the Transformer series model, the TCNformer model has a higher level of accuracy in multistep prediction, but there is still room for optimization when the prediction range is further enlarged. In the follow-up study, we will focus on ways to solve the multistep prediction problem with a further increase in the time dimension.  
Methodology, S.L.; Software, S.L.; Validation, S.L.; Investigation, J.M.; Writing--original draft, S.L.; Writing--review & editing, D.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research and development of large dispatching level data acquisition and monitoring control system grant number E212641B01 and AI assisted optimization of hybrid energy system and techno-enviro-economic analysis of green hydrogen supply chain grant number PT19797.  
Not applicable.  
The authors declare no conflict of interest.