esa-sceva/satcom-chunk-collection huggingface dataset:
# Municipal Sewage Sludge Disposal in the Republic of Poland
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Parameter** & **Total Solids TS** & **Volatile Solids VS** & **Fat** & **Phosphorus** & **Protein** & **Cellulose** & **Nitrogen** & **Potassium** & **pH** & **Reference** \\ \hline Unit & \% & \% TS & \% TS & \% TS & \% TS & \% TS & \% TS & - & \\ \hline Primary sludge & 2-8 & 60–80 & 5-8 & 0.8–2.8 & 20-30 & 8–15 & 1.5–4.0 & nd & 5.0–8.0 & [1] \\ \hline Excess activated sludge & 0.4–1.2 & 60–85 &
\begin{tabular}{l} 5–12 \\ \end{tabular} & 1.5–3.0 & 32–41 & nd & 2.4–7.0 & nd & 6.5–8.0 & [1] \\ \hline Primary sludge & 4-9 & 65–80 & nd & 0.6–2.9 & 18-30 & 8–16 & 1.4–4.2 & nd & 5.5–8.0 & [8] \\ \hline Excess activated sludge & 0.6–1.2 & 60–85 & nd & 3–10 & 30–40 & nd & 2.5–5.0 & nd & 6.6–8.0 & [8] \\ \hline Mixed sludge & 14.6–67.7 & nd & nd & 2.1–2.6 & nd & nd & 1.1–1.66 & 0.29–0.58 & 6.7–7.6 & [9] \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristics of municipal sewage sludge. In addition, sewage sludge may contain numerous pollutants such as pathogens [10; 13], microplastics [14; 15], pharmaceuticals [16], hormones [17], PFAS [18] and PAHs [19; 20]. The main objectives of sludge processing are as follows [2; 3; 4; 21]:  
* To reduce the volume of sludge (removing water from it)--thickening, dewatering and drying;
* To stabilize sludge (limiting its putfaction, eliminating unpleasant odors, and reducing the content of organic substances);
* Hygienization of sludge (covering processes that remove pathogenic organisms from the sludge and obtain a sanitary-safe product);
* Preparation of sludge for its final form of management or disposal. The processes of treatment of municipal sewage sludge are presented in diagram Figure 1.  
According to the Polish Central Statistical Office data [22], the amount of sewage sludge produced in municipal sewage treatment plants in 2022 was 580.7 thousand Mg of total solids. Sewage sludge produced in municipal sewage treatment plants constitutes waste with code 19 08 05--stabilized municipal sewage sludge. It is subject to the rules of procedures specified in the Act on Waste of 14 December 2012 [23] and requires disposal processes. In 2022, the most popular method of sewage sludge management was its use in agriculture (27.1%). On the other hand, approximately 18% of the sludge from municipal sewage treatment plants (105.2 thousand Mg of total solids) was thermally transformed. A significant part of sewage sludge is also used for crops, compost production, or land reclamation [22]. The choice of the method of recovery or disposal of municipal sewage sludge is difficult. It should be based on a detailed technical and economic analysis taking into account the following factors [2]:  
* The amount of waste and the size of the municipal sewage treatment plant;
* Sewage treatment technology;
* Type of catchment area (city, commune, village);
* The size of the catchment area;
* The characteristics of the catchment area--presence of industrial plants and type of sewage system in the catchment area;
# A Deep Neural Network Model for Speaker Identification
## 3 Results
### Evaluation Metrics  
The performance evaluation of speaker verification system mainly depends on two parameters, namely false acceptance rate (FAR) and false rejection rate (FRR) [4]. FAR refers to an error in identifying a non-target speaker as a target speaker. FRR is an error caused by misrecognizing the target speaker for a non-target speaker. However, in a closed set speaker identification system, the correct recognition rate (accuracy rate) and top-N correctness [57] are commonly used to evaluate the performance of the system. Recognition rate refers to the probability of that the speech to be recognized can be correctly found the corresponding speaker from the target set. The speech to be recognized is usually identified as the speaker with the greatest similarity to the target speaker set, and its recognition accuracy ratio can also be called the top-1 recognition accuracy rate. There is another evaluation method called top-N recognition accuracy rate: If the N recognition speakers with the largest similarity include the correct speaker. Then the recognition result is considered to be correct.  
Since in this speaker identification task, the accuracy rate refers to the probability of correctly identifying the speaker. The total number of successfully recognized voices (TNSV) is divided by the total number of tested voices (TNTV), and it can be formulated as below:  
\[Accuracy=\frac{TNSV}{TNTV}. \tag{19}\]
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 2 Traffic Adaptive Dynamic Meshed Backhaul Construction For Outdoor Dynamic Crowd
### Network Architecture and System Models  
For such environment like ODC, it is essential to deploy at hotspot areas a number of mmWave SC-BSs (or APs) overlaid on the current macro cells to effectively offload traffic, especially in peak hours as stated in [5]. However, introducing more SC-BSs will incur higher CAPEX (capital expenditures) and OPEX (operating expenses). Self-backhauling i.e., employing the same mmWave bands of access for backhaul can circumvent this issue since it radically avoids deployment cost of wired backhaul such as optical fiber or Ethernet cable. In addition, another benefit of mmWave meshed backhaul in reducing OPEX is its flexibility to reallocate backhaul resources via beam switching, multi-route multiplexing and deactivation of unused wireless interfaces [28]. Such functionalities are especially suitable for the time-variant and spatially non-uniform traffic occured in ODC. Fortunately, such flexible control of the mmWave meshed network can be done easily over out-band control plane (C-plane) e.g., LTE [29] owing to the maturity of nowadays SDN technologies.
## 4 Conclusions  
In our study, we showed that tetraethoxysilane/polyvidone nanofibers (TEOS/PVP NFs) and their modified version with carbon nanotubes (CNT-TEOS/PVP NFs) have an impact on the mechanical and microstructural characteristics of cementitious materials. We make the following conclusions considering our findings:  
* According to results from mechanical strength testing, adding TEOS/PVP NFs and CNT-TEOS/PVP NFs to cementitious materials results in an increase in compressive strength of 28% and 38% and in toughness characteristics of 54% and 66%, respectively;
* We observed our proposed nanofibers' morphology using SEM and TEM studies. We discovered that TEOS/PVP NFs had a hollow structure by examining TEM images. Additionally, the existence of CNTs was clear;
* We discovered the bridging effect of fibers inside the matrix through SEM images. Additionally, through our EDS and TGA observations, we assessed the change in cement hydrates' proportions and the more compacted structures of the modified cement pastes. Based on these microstructure observations, we showed that a higher percent  
Figure 17: TGA/DTG observations of TEOS/PVP NFs-blended cement paste.  
Figure 18: TGA/DTG observations of CNT-TEOS/PVP NFs-blended cement paste.  
age of cement hydrates combined with the more compacted structure is commensurate with, the higher compressive strength of our survey's samples.  
Above all, we regarded the process of adding electrospun nanofibers to the pastes as a useful strategy for strengthening cementitious materials. Our findings can be considered a foundation for increasing cement quality while lowering overall usage, thereby minimizing its environmental impacts. However, more studies are required to optimize the performance of the composite cement manufacturing process and determine the impact of these nanofibers on the other qualities of cement paste and concrete to put this product to use in practical applications.  
Conceptualization, J.J.K.; methodology, T.H.H. and J.J.K.; software, T.N.M.N. and J.K.P.; validation, T.H.H. and J.K.P.; formal analysis, T.N.M.N. and J.K.P.; investigation, T.H.H. and J.J.K.; data curation, J.J.K. and T.N.M.N.; writing--original draft preparation, T.N.M.N. and J.K.P.; writing--review and editing, T.N.M.N., J.K.P., T.H.H. and J.J.K.; visualization, T.H.H.; supervision, J.J.K.; project administration, T.H.H.; funding acquisition, T.H.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Marine Science & Technology Promotion (KIMST), funded by the Ministry of Oceans and Fisheries (20220364).  
Not applicable.  
The authors declare no conflict of interest.
# Microplastics' Detection in Honey: Development of Protocols in a Simulation
## 2 Materials and Methods
### Material Characterization  
#### 2.6.1 Raman Spectroscopy and Mapping  
Raman measurements were conducted at room temperature using a LabRAM HR Evolution Confocal Raman Microscope (LabRAM HR; HORIBA FRANCE SAS, Lille, France). Raman excitation was achieved with a selection between two laser lines: 532 nm and 785 nm. The lasers had maximum powers of 100 mW and 400 mW, respectively. A 600 groves/mm grating resulted in a Raman spectral resolution of around 2 cm\({}^{-1}\).  
Each sample filter and sediment were observed under the Raman microscope for about 5 h, switching between two objective lenses, 10\(\times\) (MPlan 10\(\times\)/0.25, OLYMPUS Corporation, Tokyo, Japan) and 50\(\times\) (LMPlanFLN 50\(\times\)/0.5, OLYMPUS Corporation, Tokyo, Japan) to get a broad picture of the dimensions of MPs. The Raman mapping sample area was randomly chosen near the center of the filter. For Protocol 1, Raman mapping refers to the scanning of the selected area, in 3600 points, that lasted for 15 measurement h. In each point, the Raman spectral signal was acquired, with an acquisition time of 5 s, 3 accumulations per point, 3 mW laser intensity (10% from a maximum power of 30 mW on the samples), and a spectral range of 200-1850 cm\({}^{-1}\) with \(\lambda\) = 532 nm [38] using the 50\(\times\) lens. For Protocol 2, Raman mapping was carried out for each sample for 18 measurement h, step 10 \(\upmu\)m, acquisition time 5 s, 3 accumulations per point, 10 mW laser intensity (25% from a maximum power of 40 mW on the sample), and a spectral range of 2000-1850 cm\({}^{-1}\) with \(\lambda\) = 532 nm using the 10\(\times\) lens. For quantification of each Raman mapping area, ImageJ-win64.exe software [39] was used, as shown in Figure S9 of the Supplementary Section.  
For Protocol 3, Raman measurements were carried out using both laser sources. Firstly, Raman excitation was achieved with a \(\lambda\) = 532 nm laser source, within a power range of 0.3-3 mW, on the sample. Due to fluorescence, a 785 nm laser source was preferred with 100 mW laser intensity on the sample. The acquisition settings were set to 10 s acquisition time, 3 accumulations per point, spectral range between 200-1850 cm\({}^{-1}\), and 600 grooves/mm grating. Here, three objective lenses were used a custom-made 4\(\times\) (NA 0.25), and the two aforementioned objective lenses: 10\(\times\) (NA 0.25) and 50\(\times\) (NA 0.5) for MP observation.  
Raman maps were generated through the LabSpec software (LabSpec LS6, Horiba France SAS, Longjumeau, France). Particularly for LS6 following a complete area scan, it provides the capability to enhance the visualization of a scanned area by depicting specific and selected Raman peaks. This enables an intensity-dependent visualization of up to three Raman peaks (chemical bonds) in different colors based on the Raman peak intensity or chemical bond concentration.
## 4 Discussion
### Discussion of Findings
#### 4.1.2 Discussion of Real-Vehicle Result  
Real-vehicle testing on a Honda CRV with a 0.5 engine-to-alternator transmission ratio and 5 kHz sampling frequency demonstrated the system's robustness. The measurement error remained below 0.4% under typical operating speeds, outperforming the vibration analysis methods (\(\pm\)1.5% [33]) and OBD-II tools v2.4.7 (SAE J1979-compliant, ELM327 chipset, Texas Instruments, USA) (\(\pm\)0.8% [32]). Dual-channel acquisition via the STM32F407 microcontroller (STMicroelectronics; sourced from Shenzhen JLCPCB Technology Development Co., Ltd., China) enabled signal amplitude optimization, while the MSF algorithm enhanced the signal-to-noise ratio (SNR) from 28 dB (FFT) to 42 dB by smoothing amplitude and phase discontinuities (Figure 9). Phase unwrapping and continuous phase calculations (Equations (6) and (7)) further ensured the precise determination of frequency (Equation (9)), which is critical for real-time applications requiring \(<\)10 ms latency. In contrast, vibration-based systems, though non-invasive, suffer from sensitivity to environmental noise and \(>\)200 ms latency due to their high computational demands [33], while OBD-II tools v2.4.7 (SAE J1979-compliant, ELM327 chipset, Texas Instruments, USA) are constrained by proprietary protocols and lack raw signal access [32].
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
###### Abstract  
In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of \(-15\) dB and above ([EMAIL_ADDRESS] \(>\) 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods.  
satellite TT&C signal detection; complex electromagnetic environment; attention mechanism; Transformer; Hungarian algorithm Article
## 3 Verifications  
### Feasibility of the Proposed Method  
To verify the proposed method, we derived the equivalent fatigue damage stress-strain curves at specified cycle ratios for fatigue specimens tested by [PERSON] et al. [31].  
The two specimens tested at a maximum stress level of 0.75 for concrete compressive strength grades of 26 Mpa and 84 Mpa were selected for comparison. The monotonic loading constitution (abbreviated as the Kim model) and maximum fatigue strain data were reported in the literature [31]. The theoretical behavior of these specimens was predicted by the equivalent fatigue damage constitutive model (with appropriate fatigue damage evolution equation parameters from Table 2) and is shown in Figure 14 as various dashed lines at given cycle ratios.  
The monotonic stress-strain relationships of the concrete are shown as solid lines in Figure 14. It is observed that the strains corresponding to the maximum fatigue stress in the ascending and descending branches of the Kim model are approximate to the measured maximum fatigue strains during the first and last loading cycles. Since the Kim model can describe the mechanical behavior of these two specimens reasonably well, the equivalent fatigue damage model was derived using the descending branch of the Kim model as the fatigue residual strength envelope curve.  
Figure 14 shows the specimens' predicted fatigue damage constitutive curves after specific loading cycles. These equivalent stress-strain curves indicate that the fatigue damage evolution equation constructed based on maximum fatigue strains could effectively describe the degradation behavior of stiffness, strength, and residual strain accumulation. When the ascending branch of the equivalent fatigue stress-strain curve reaches the maximum fatigue stress, a theoretical strain is obtained at a given cycle ratio. These theoretical strains are then compared to the measured maximum fatigue strains, shown as red dots in Figure 14. The comparison is depicted in Figure 15 for more details.  
As demonstrated in Figure 15, the maximum relative error between the theoretical and experimental values is 12%, and more than 95% of the relative errors do not exceed 5%. Regression analysis of the results shows that the root mean square error (RMSE) between experimental and theoretical values in Figure 15a is 0.00011, and the RMSE in Figure 15b is 0.00003. This comparison verifies that the proposed equivalent fatigue damage constitutive model can effectively explain the stress-strain relationship of concrete after considering fatigue damage.  
Figure 14: Equivalent fatigue damage constitution for specimens from the literature [31]: (**a**) S-75-C-26; (**b**) S-75-C-84.
## 3 Proposed Fault Diagnosis Method
So, if there is a symmetry in the current patterns, then the four diagnostic variables will be, approximately, equal to one. If there is an asymmetry due to a short or open-circuit failure, then the diagnostic variables will be no longer near one. So, for an open-circuit failure, the diagnostic variable associated with the faulty power device will change from one to zero. The other diagnostic variables will also change, but for a positive value higher than one. In case of a short-circuit failure, the diagnostic variable associated with the faulty power device will change from one to a positive value higher than one. The other three diagnostic variables will change from one to a positive value lower than one. Taking into account the defined diagnostic variables, it is possible to establish a decision table where the different failure conditions are characterized. In Table 1 those characteristics are presented for the several conditions associated with the switches of phase \(A\). Since the quantities provided by the diagnostic variables are very well defined and independent of the load and speed, it is quite easy to define threshold values associated with each failure mode. It should be noted that the proposed fault diagnosis method was only developed for the power semiconductors of the machine drive. However, asymmetries in the winding resistances could affect the choice of the threshold values. So, if the machine has a current control, then the current waveforms of the windings will be the same even when there are windings with different resistances. However, if there is not current control of the windings, then asymmetries in the current waveforms of the windings will appear. In this case, the current waveform associated with the winding with higher resistance will have lower amplitude. Since the method is based on the definition of indexes related to the pattern of the winding currents, then a difference between the diagnostic variables (_SI_ index) will appear. However, since in the case of a fault in the power semiconductors will severely affect the current waveform of the winding associated with the leg under fault, then changes in the resistors by for example 20 % will not require to set up thresholds. In fact, those thresholds should be chosen considering winding resistances superior to 50%. One of the limitations of the method is when their faults associated with all the phases. In this case, since the proposed method is based on the definition of indexes related to the pattern of the winding currents, and taking into consideration that in this situation all the current waveforms will be the same, all the diagnostic variables (_SI_ index) will present the same value indicating that there is not a fault.
## 4 Visual-Inertial Odometry
### Optimization of Visual-Inertial Systems
#### 4.3.2 Marginalization  
As time goes on, there will be more and more landmark points and camera poses, leading to an increasing amount of computation. To achieve large-scale Gauss-Newton non-linear optimization, the number of optimization variables needs to be limited. The Schur-Complement method is used to marginalize out older keyframes and all variables related to that keyframe continuously. When marginalizing the visual factors between two keyframes, the method proposed by [PERSON] et al. [37] is used. Firstly, all points in the keyframe are marginalized, and then remove the residual terms that affect the sparsity of the system. To maintain the consistency of the system, it is necessary to evaluate the Jacobian matrix for the marginal factors related variables and ensure that their values are the same. For visual factors, the Jacobian matrix can be evaluated at the linearized points. When computing the inertial factors, it is necessary to determine the evaluation points for all variables related to the marginal factors and evaluate their Jacobian matrices. As shown in Figure 3b, it can be intuitively understood how marginalization affects the connection between the factors.
# Identification of Topographic Seismic Site Periods in Sloping Terrains
## 3 Results and Discussion
### Identification of Limits \(X_{a}\) and \(X_{b}\) beyond Which \(T_{t}\) Is No Longer Present  
Considering the presented results, it is clear that the central block is influenced by the vibration of the adjacent blocks; in turn, they will be influenced by the self-vibration of the central block. The limits of this influence towards each lateral block would be confined within distances \(X_{a}\) and \(X_{b}\) (Figure 2), beyond which the amplification peak associated with the \(T_{t}\) period no longer exists.  
Given that, towards the base of the slope, the amplitude of the amplification ratio linked to \(T_{t}\) is reduced compared to that observed at the crest (Figure 3), it was noted that, even for distances \(X_{a}\) less than \(H\), the \(T_{t}\) amplification peak was not perceptible.  
On the other hand, towards the crest, this information can be of most interest for analyzing the response of structures located at the crest of a sloping area, since the identification of the distance \(X_{b}\) can be a relevant parameter for evaluating potential topographic effects. For this case, and as observed in a representative analysis case shown in Figure 7, it is found that, from an equivalent distance of approximately \(3H\), measured from the crest of the sloping area (\(X_{b}\)), the amplification peak associated with \(T_{t}\) is significantly reduced. It should be noted that, for the cases corresponding to \(H\) values of 90 m and 120 m, the presence of the amplification peak associated with \(T_{t}\) decreases for distances shorter than \(3H\), which is expected considering the significant magnitude of the distance \(X_{b}\) in these cases.  
Figure 7: Horizontal distances from the crown of the sloping terrain, \(X_{b}\), beyond which \(T_{t}\) is no longer present (\(H_{a}=50\) m; \(H_{b}=110\) m; \(\beta=20^{\circ}\); \(V_{S}=350\) m/s).