esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Material Monitoring in Harsh Steelplant Environments
## 3 Data Acquisition and Preprocessing Methods
### Audio Frames with Noise
#### 3.3.1 RMSe Outlier Detector  
The signal is high pass filtered with a cutoff frequency of 1250 Hz and the short-term RMS is computed with a sliding Blackman window on a frame width of 25 ms length. In the first step, all samples with a RMS value greater than the threshold \(T\) as defined in Equation (2) are marked as outliers.  
\[T=\alpha\cdot\sigma_{y} \tag{2}\]  
\(\alpha>1\) is defined as the fixed threshold value and \(\sigma_{y}\) the standard deviation of the high-pass-filtered 10-s impulse denoised signal. In the second step, the outlier detection signal is extended on the right side of the last detected sample until the RMS value reaches the given level \(T_{1}=\sigma_{y}\). If the corresponding distance between two detected impulses is smaller than 400 samples, all in-between samples are marked as outlier samples. Then the detected disturbances are removed. In Figure 8, an example of a sequence of impulses and the detection with RMSe is shown. The peaks at \(n=2900\) and \(n=3400\) are indicative to the material sound and should therefore remain. The green target line represents the required detection samples.  
Figure 7: An example of an audio recording corrupted with impulse noise (**red**).  
After detection, the gaps caused by the removal of outlier samples are filled with a frame obtained from uncorrupted sequences of signal \(y(n)\). The uncorrupted sequences from \(y(n)\) are truncated into frames of gap length \(M\). Then, the corresponding RMS value for each frame is computed. The frame with maximum \(RMS\) chosen from the uncorrupted \(N\) frames of \(y(n)\) is used to fill the gap. Finally, the transitions are crossfaded with 50 samples to prevent abrupt changes between signal subsequences.
## 1 Introduction
In Section 2, we describe the uplink of a two-cluster C-RAN system. In Section 3, we review baseline uplink reception strategies with no or conventional inter-BBU cooperation strategies. We propose an improved cooperation scheme based on layered compression in Section 4, where we also discuss the signal processing optimization of the proposed scheme. In Section 5, we provide numerical results that check the convergence property of the proposed algorithm and the performance gains of the proposed scheme compared to the baseline schemes discussed in Section 3. We close the paper in Section 6. Throughout the paper, we use the following notations. The circularly symmetric complex Gaussian distribution with zero mean and variance \(\sigma^{2}\) is denoted by \(\mathcal{CN}(0,\sigma^{2})\). \(I(X;Y)\) denotes the mutual information between two random variables \(X\) and \(Y\). The transpose and Hermitian transpose of a vector or matrix are denoted by \((\cdot)^{T}\) and \((\cdot)^{H}\), respectively, and \(\mathbb{C}^{M\times N}\) denotes the set of all \(M\)-by\(N\) complex matrices. We denote the Euclidean 2-norm of a vector by \(||\cdot||^{2}\).
## 6 Discussion
### System-Level Performance Evaluation  
#### 6.3.1 Phase-Synchronized P-V Behavior  
The diagnostic framework confirms stable cyclic operation across configurations (Figure 8), with pressure transitions at critical stroke points (Point 1: 39.298 MPa; Point 2: \(-\)0.348 MPa) demonstrating minimized closure hysteresis. The maintained circulation area validates operational integrity, while phase-correlated expansion/compression profiles reflect liquid compressibility and cylinder wall elasticity interactions.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 1 Introduction
### Related Works  
The concept of NOMA is well-known from the information theoretic literature [24], and its application to 5G dates back to [25], where authors demonstrated for a single-cell scenario that superimposing multiple users in the same resources achieves superior performance with respect to conventional LTE networks, provided that the resulting interference is properly taken care of. The extension of NOMA to multi-cell networks is presented in [26], which addresses several multi-cell NOMA challenges, including coordinated scheduling, beamforming, and practical implementation issues related to successive interference cancellation.  
In contrast with most of the works on NOMA, which deal with homogeneous traffic conditions (see [27] for a recent review), here the focus is on NOMA techniques in the context of heterogeneous networks, such as the forthcoming 5G wireless systems, as discussed in [28; 29; 30]. In fact, NOMA represents an attractive solution to meet the distinct requirements of 5G services, as it improves spectral efficiency (eMBB), enables massive device connectivity (mMTC), and allows for low-transmission latency (URLLC) [31].  
In [29] a communication-theoretic model was introduced to investigate the performance trade-offs for eMBB, mMTC and URLLC services in a single-cell scenario under both OMA and NOMA. This single-cell model has been later extended in [32] to the uplink of a multi-cell C-RAN architecture, in which the BBU communicates with multiple URLLC and eMBB users belonging to different cells through geographically distributed ENs. In the C-RAN system studied in [32], while the URLLC signals are locally decoded at the ENs due to latency constraints, the eMBB signals are quantized and forwarded over limited-capacity digital fronthaul links to the BBU, where centralized joint decoding is performed.  
None of the aforementioned works considers the coexistence of different 5G services in a C-RAN architecture based on analog fronthaul links which is the focus of this paper.
# 3 MSF: A Multi-Modal Adaptation of the 6 TiSCH Minimal Scheduling Function for the Industrial IoT
###### Abstract  
Although wireless devices continuously gain communication capabilities, even state-of-the-art Industrial Internet of Things (IIoT) architectures, such as Internet Protocol version 6 over the Time-Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4 (6 TSCH), continue to use network-wide, fixed link configurations. This presents a missed opportunity to (1) forego the need for rigorous manual setup of new deployments; and (2) provide full coverage of particularly heterogeneous and/or dynamic industrial sites. As such, we devised the Multi-Modal Minimal Scheduling Function (3 MSF) for the TSCH link layer, which, combined with previous work on the routing layer, results in a 6 TSCH architecture able to dynamically exploit modern multi-modal hardware on a per-link basis through variable-duration timeslots, simultaneous transmission, and routing metric normalization. This paper describes, in great detail, its design and discusses the rationale behind every choice made. Finally, we evaluate three basic scenarios through simulations, showcasing both the functionality and flexibility of our 6 TSCH implementation.  
6 TSCH; 6 top; IEEE 802.15.4; IIoT; IPv6; multi-hop; multi-modal; RPL +
Footnote †: journal: _Acticle_  
_Article_  
3 MSF: A Multi-Modal Adaptation of the 6 TSCH Minimal Scheduling Function for the Industrial IoT.
# Farm Monitoring System with Drones and Optical Camera Communication
###### Abstract  
Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30% compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On-Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under \(10^{-3}\) in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture.  
sensor network; optical camera communication; drone; LED; point-to-multipoint communication 2024246146[[https://doi.org/10.3390/s24186146](https://doi.org/10.3390/s24186146)]([https://doi.org/10.3390/s24186146](https://doi.org/10.3390/s24186146))
## 2 Study Area and Data Sources
### Data  
#### 2.2.1 SIF Data  
Several satellites are currently capable of directly observing SIF, but the SIF products they generate often lack spatial continuity (e.g., GOSAT, OCO-2, TanSat) and typically have coarse spatial resolution (e.g., SCIAMACHY, GOSAT, GOME-2). For this study, which focuses on estimating carbon sinks from 2011 to 2018, we selected the Global SIF dataset (GOSIF) constructed by [PERSON] and [PERSON] [31]. This dataset, based on discrete OCO-2 data, MODIS data, and meteorological data, offers an 8-day time resolution and a spatial resolution of 0.05\({}^{\circ}\). Compared to other SIF data with coarser resolution, GOSIF provides finer spatial resolution, global continuous coverage, and a longer time series. The stability and applicability of GOSIF data have been validated in previous studies [24; 32].
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 2 Related Work  
Some traditional blind source separation methods provide solutions for single-channel blind source separation, but there are some disadvantages. Independent component analysis [9] performs well in over-determined and positive-definite blind source separation, but it performs poorly in under-determined blind source separation, especially in single-channel scenarios. Sparse Component analysis (SCA) [18] based on clustering method requires the number of mixed signals to be known. The time-frequency mask method [15] improves the separation accuracy, but there is amplitude and phase decoupling, and the short-time Fourier transform requires a higher resolution frequency decomposition window, which limits its applicability in low-delay systems. Non-negative matrix factorization (NMF) [10] can perform signal decomposition in the time domain, but it is weak in generalization ability. Single channel separation based on Kalman filter [19], LCL-FRESH filter [20] and cyclic Wiener filter [21] needs high computational complexity, and the practical effect needs to be improved.  
With the development of big data and the improvement of computing power, deep learning achieves great success in time series signal processing such as speech recognition, speech separation [12; 15; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38], and communication signal modulation recognition [39]. These tasks demonstrate the powerful feature extraction and timing signal processing capabilities of deep learning. However, the application of deep learning in communication signal processing is mostly seen in conventional modulation recognition and classification tasks, and is involved in complex tasks such as single-channel communication signal separation. Most of the research focuses on speech signals and EGG signals. In the TasNet [11], the traditional recurrent neural network is used. Due to the difficulty of optimization, it is impossible to effectively model such a long sequence, so the separation effect is not good. ConvTasNet [12] can complete the modeling of the long sequence on the basis of the large receiving field to meet the long-term dependence of blind source separation. However, due to the limitation of the receiving field, the sequence length cannot be increased indefinitely. When the reception field of the one-dimensional convolutional neural network (1-D CNN) is small, the speech-level sequence modeling cannot be performed. Wave-U-Net [13] processes the time series context by repeatedly performing downsampling of the feature map and convolution, combining high-level and low-level features on different time scales. In each feature map generated by convolution, the sampling rate of the original signal is used as the resolution, so the memory consumption is high.
## 2 Methodology  
### Modelling Process of LRA  
The main objective of this study was to analyze the impact of landslide spatial aggregation on landslide hazard evaluation and to conduct an LRA study for the KKH. The process of an LRA study consists of six steps, as shown in Figure 1.  
1. Establishment of landslide database. We identified the predisposing factors for the formation of landslides in the study area by field investigations and literature research. Based on remote sensing interpretation and field investigation data, a database containing information on the spatial distribution of landslides and the characteristics of related predisposing factors was established. In this study, the hydrological slope unit was adopted as the basic evaluation unit.
2. Analysis of correlations between landslide occurrence and predisposing factors. We established the connection between landslide occurrence and the predisposing factors through the DFR method. Based on this, an index system for landslide risk assessment was established.
3. Landslide hazard map. We conducted the LHA of the study area using the AHP and RF methods. In the process of calculating the landslide hazard, the frequency ratio and the dual-frequency ratio were the input variables.  
Evaluation of LHA models. Based on the ROC curve and distribution characteristics of various levels of landslide hazard, we analyzed the LHA outputs obtained by different methods for the study area.
5 Vulnerability assessment. We used the AHP method to obtain the vulnerability zoning map of the study area for the calculation of the landslide risk.
6 Landslide risk map. Based on the above assessment of the landslide hazard and vulnerability, we calculated the landslide risk of the study area. During this procedure, the landslide hazard data with the highest accuracy were selected as the input variables for landslide risk calculation.
# MOCA: A Network Intrusion Monitoring and Classification System
## 5 Measurement Results
### Comparing MOCA with Previous Works  
Table 5 compares the results of MOCA with previous works. Because previous works used recall or decision rate, it was necessary to only compare their recall values with ours. From the table, the values for MOCA are higher than previous approaches. For example, SGM only achieved a slightly high recall value in Web Attack XSS. This observation implies that MOCA can efficiently address the effects of imbalanced malicious attacks on classification accuracy.
# Preliminary Study on Greywater Treatment Using Nonwoven Textile Filters
## 1 Introduction
Non-woven or woven filters are used in various technological systems: as part of the first stage of treatment, e.g., in the form of composting sieves, mainly used for separation of solids from raw wastewater [23], as a second stage of treatment [13; 24], and simultaneously as the first and second stage--mechanical and biological wastewater treatment [15; 25]. Most of the research was carried out using geotextiles as a biomass carrier immersed in aerated [26] or oxygenated reactors [27]. Often, nonwoven fabrics were wrapped on membranes or aerated media. As a result, better contact with oxygen was achieved than through deep aeration of the liquid, which is the reactor content. In this respect, the principle of operation is similar to that of membrane bioreactors (MBR) [28]. Advantages of nonwoven fabrics noted by many authors include: laying the supporting media for the development of the biofilm, creating conditions for the growth of long age biomass and differentiation of oxygen conditions inside the nonwoven fabric (especially of a greater thickness), enabling the growth of various groups of organisms (heterotrophic and autotrophic) and the occurrence of many unit processes (appropriate for aerobic, anoxic, and anaerobic conditions) [18; 29]. The characteristic properties of biomass covering non-woven filters were identified during the studies carried out on septic tank effluent bio-filtration (heterogeneous, granular structure inside the nonwoven fabric, more homogeneous and more strongly compacted, yet the structure of the filter cake on the filter surface being easy to detach). Relatively high removal efficiency of total suspended solids (TSS): 67.3-79.3%, chemical oxygen demand (COD\({}_{\text{Cr}}\)): 69.5-76.0%, and biochemical oxygen demand (BOD\({}_{5}\)): 51.3-73.1%was observed during the previous research carried out on septic tank effluent (STE) treatment by filtration. The results showed that filters for STE treatment can be a very useful technology as the second stage of an on-site wastewater treatment plant, especially in the conditions of treated wastewater discharge to surface waters [30]. Filters used for STE treatment meet the requirements of the regulation of the Ordinance of the Minister of Environment of 18 November 2014 on conditions to be met when introducing wastewater into waters or into the ground and on substances particularly harmful to the aquatic environment [31] in terms of not exceeding the maximum permissible values of pollution indicators in terms of total suspended solids, COD\({}_{\text{Cr}}\), and BOD\({}_{5}\) (eight-layer filters). However, several studies carried out on the domestic wastewater treatment using textiles as biomass supporting media or activated sludge separation barrier exist in the literature, there are no reports related to the greywater treatment by textile filters [18]. The objective of the study was to evaluate the usefulness of novel nonwoven textile filter technology and construction for greywater treatment.
# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 4 Results and Discussions
### Results Evaluation Using BraTS 2018 Dataset  
The model is trained in the experiments using an SGD optimizer over the course of 50 epochs. The training and validation accuracy curves are steadily improving after each epoch, as shown in Figure 10. Our model reaches 96.75% and 97.50% for training and validation accuracy, respectively, on the 15 epochs.  
The classification report of our model using testing set is shown in Table 1. The proposed model effectively classifies BT such as G (glioma) with a ratio of 96%, HG (high-grade glioma) with 98%, LG (Low-grade glioma) with 96%, and M (meningioma) with 97% as shown in Table 1, which demonstrates the effectiveness of our model using the BraTS 2018 dataset. The proposed model confusion matrix using the BraTS 2018 dataset is provided in Figure 11, where the ratio of positive predicted samples is high for each category.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline G & 0.96 & 0.98 & 0.969 & 96.0 \\ HG & 0.98 & 0.97 & 0.974 & 98.0 \\ LG & 0.96 & 0.96 & 0.96 & 96.0 \\ M & 0.97 & 0.97 & 0.97 & 97.0 \\ Average & 0.967 & 0.970 & 0.968 & 96.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification report of our model using BRATS’2018 dataset.  
Figure 11: Confusion matrix of the proposed model using BRATS’2018 dataset.  
Figure 10: Training-validation accuracy and loss curves of our model using BRATS 2018 dataset.  
The proposed model is compared with four different state-of-the-art models DR LBP features + k-NN [65], Inception V3 + SVM [65], PSO features + Softmax [65], and Two-Channel DNN [66], as given in Table 2. In the comparison, the DR LBP features + k-NN obtain 85.10% accuracy, which is the lowest in this experiment. The proposed model surpasses the DR LBP features + k-NN by achieving 11.65% higher accuracy. The Inception V3 + SVM, PSO features + Softmax, and Two-Channel DNN achieve accuracy of 87.40, 92.50, and 93.69, respectively. The proposed model surpasses Inception V3 + SVM, PSO features + Softmax, and Two-Channel DNN by obtaining 9.35%, 4.25%, and 3.36% higher accuracy, respectively. The accuracy of our models is 96.75%, which is higher than other models. Our model is able to perform at a level that is superior to that of the other models.
# Possibilities of Evaluating the Effectiveness of Noise Barriers in Slovakia
## 2 Materials and Methods
### Measurement by the Direct Method According to Standard ISO 10847  
With the direct method [28], measurement is performed before and after the construction of the noise barrier. This method can only be used if the barrier has not yet been installed, or it can be removed so that a barrier-free measurement can be performed. Sound pressure levels are measured at the reference point and at the point of reception without a barrier and subsequently after its installation. In both cases, the same reference point and point of reception must be used. The equivalence of resources, terrain profiles, obstacles, if any, and terrain surfaces must be met.  
For a valid determination of the insertion loss, the noise source must be equivalent to the measurement without a barrier and the measurement after its installation. An unregulated natural resource for which a noise barrier is constructed is usually the best source. However, it is usually very difficult to ensure its stability. Therefore, a regulated natural source can be used, in which one or more test vehicles are selected as typical sources and used as a regulated natural source for without barrier and with barrier measurements. It is also possible to use a speaker with typical traffic noise.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & \(DL_{\mathbf{n}}\) **(dB)** \\ \hline D0 & Not specified \\ D1 & \textless{}14 \\ D2 & 16–27 \\ D3 & 28–36 \\ D4 & \textgreater{}36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Airborne sound insulation categories [26].  
A regulated artificial noise source is used when the equivalence of a natural source cannot be ensured. An artificial source must be used for both measurements with and without a barrier. The operating parameters of the artificial source must be as close as possible the characteristics of the natural source.  
The microphone reference point is the point where the sound of the source is minimally affected by the noise barrier installed or planned. The reference microphone is placed at a point in the vertical plane containing the barrier so that the equivalence of the source can be checked. The height of the reference microphone must be at least 1.5 m above the upper edge of the barrier.  
The insertion loss of the noise barrier is given by Equation (4):  
\[D_{IL}\ =\ \left(L_{ref,A}-L_{ref,B}\right)-\left(L_{r,A}-L_{r,B}\right) \tag{4}\]  
where:  
\(D_{IL}\)--insertion loss of the noise barrier;  
\(L_{ref,\ B}\)--sound pressure level at the reference point without a barrier;  
\(L_{r,B}\)--sound pressure level at the reception point without a barrier;  
\(L_{ref,A}\)--sound pressure level at the reference point with a barrier; and  
\(L_{r,A}\)--sound pressure level at the point of reception with a barrier.  
An alternative method is the indirect measurement method using levels measured at an equivalent location without a barrier.