esa-sceva/satcom-chunk-collection huggingface dataset:
# Influences of Temperature and Substrate Particle Content on Granular Sludge Bed Anaerobic Digestion
## 2 Materials and Methods
### Sample Analysis  
Influent and effluent samples were regularly analyzed during the course of the experiment. Total solids (TS), total suspended solids (TSS), volatile solids (VS), and volatile suspended solids (VSS) were determined in accordance with the American Public Health Association standard method APHA 2540 [25]. Total and soluble COD (chemical oxygen demand) of influent and effluent samples was analyzed using test kits and a spectrophotometric method in accordance with APHA standard method 5220 D. Feed and effluent pH was measured using a Beckman 300 pH meter equipped with a Sentix-82 pH electrode. Ammonium-nitrogen content (NH4\({}^{+}\)-N) was measured according to APHA 4500-NH3. Both COD and NH4\({}^{+}\)-N concentrations were measured using commercially available Merck test kits and a Spectroquant Pharo 300 spectrophotometer (Darmstadt, Germany). Volatile fatty acids (VFA) were analyzed using gas chromatography (Hewlett Packard 6890) with a flame ionization detector and a capillary column (free fatty acid phase (FFAP) 30 m, inner diameter 0.250 mm, film 0.25 \(\upmu\)m). The oven was programmed to go from 100 \({}^{\circ}\)C, held for one minute, to 180 \({}^{\circ}\)C at a rate of 30 \({}^{\circ}\)C/min, and then to 230 \({}^{\circ}\)C at a rate of 100 \({}^{\circ}\)C/min. The carrier gas used was helium at 245 mL/min. The injector and detector temperatures were set to 200 and 250 \({}^{\circ}\)C, respectively.
## 2 Background
### Case Study: Intelligent Transportation Systems
Therefore, the self-protection of the whole system is required. However, very hard security policies have a penalty in efficiency, especially in energy and performance, two typical requirements of these applications. Therefore, the security policy to be applied in each situation must be negotiated to make a trade-off between the necessary security and the efficiency. Additionally, as these situations can change quickly, these policies have to be (re)negotiated. For instance, in the case where malicious software is suspected in a given WSN (detected by intruder detection algorithms [17]), the security needs to be increased, and the suspect nodes must be isolated. To the contrary, on roads where the main interest is to preserve the lifetime of the system and there is no risk of malicious attacks, the security can be a little bit weaker. Similarly, in situations where communication performance is required (e.g., high traffic density), the security could also be relaxed. On the other hand, in exceptional circumstances, the interception of data sent by certain vehicles (e.g., a presidential motorcade) has to be totally avoided. In this case, the security policy applied to these specific nodes must be modified. Furthermore, other special situations, such as emergencies or attempted terrorist attacks, require another kind of global security and/or intrusion detection.
## 5 Conclusions  
This paper has illustrated the potential of microwave sensing as a monitoring platform for a broad spectrum of commercial applications, with a focus on the system developed by the authors, namely, the real-time monitoring of bacterial biofilm growth in milk processing plants. While this paper has discussed the simulated performance of microwave sensors in milk processing plants, it is also important to remember the wide spread of application domains that may be covered. Furthermore, the microwave sensors demonstrated by the authors can be operated without the need for chemical reagents. Our simulations demonstrated that even a 1 \(\upmu\)m thin biofilm shifted the frequency response of the milk transportation pipe, holding tubes, and pasteurizer. It is suggested that a novel approach to biofilm monitoring, namely using specially designed microwave cavity sensors, could lead to success in the development of an advanced platform capable of the real-time detection of a biofilm presence with superior sensitivity. The multipurpose nature of microwave sensors means that they lend themselves to many other future applications, not least those within emerging areas of interest.  
Methodology, R.B.; Validation, K.C. and J.B.; Formal analysis, R.B.; Investigation, R.B.; Writing--original draft, R.B.; Writing--review & editing, K.C. and J.B.; Supervision, K.C.; Funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by IGEM HERC, \"Food and Dairy Innovation Center at Boise State University\" (2021-2024). This work was also supported by Centers of Biomedical Research Excellence (COBRE)--P20 GM148321.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
Figure 9: Variation in resonance frequency of the pasteurizer model with biofilm thicknesses of 1 to 10 \(\upmu\)m. Only milk and biofilm thickness variation of 1 \(\upmu\)m to 10 \(\upmu\)m with a step value of 1 \(\upmu\)m was simulated. A clearance difference in resonance frequencies was observed.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 3 Methods
### Improved Informer Model for Non-Stationary Data
The original Informer model chooses the Mean Squared Error (MSE) loss function, which is not suitable for load forecasting of power plants that have large deviations in data distribution under extreme operating conditions, such as shutdown conditions. The MSE loss function is typically tailored to fit the distribution pattern of data and tends to penalize outliers, resulting in a biased prediction towards them. To address such issue, the SmoothL1 Loss function [28] was employed in the present study instead of the original MSE loss function. The SmoothL1 Loss function is less sensitive to outliers, leading to a more balanced predictive outcome. When the predicted value is close to the actual value, the SmoothL1 Loss is similar to the MSE loss function to speed up the model convergence. The formula of SmoothL1 Loss is as follows:  
\[\text{loss}(x_{i},y_{i})=\left\{ \begin{array}{l}\frac{1}{2}(x_{i}-y_{i})^{2 },if|x_{i}-y_{i}|<\delta\\ |x_{i}-y_{i}|-\frac{1}{2},otherwise\end{array} \right. \tag{11}\]  
where \(\delta\) is an artificially set hyperparameter. In addition, given the unsteady nature of load, better results may be obtained by regarding data fluctuation as a feature in model construction. In the present study, the residuals of load were constructed as a feature with the following formula:  
\[Y_{\text{{residuals}}}=Y_{t}-Y_{t-1} \tag{12}\]  
The effects of Informer with improvements on non-stationary data (Informer_nos) and original Informer were tested and analyzed as described in Section 4.
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 3 Transmission Schemes
### Outage Probability and Transmitted Power
#### 3.1.2 As  
In this case only one antenna is active at both transmitter and receiver. The pair of antennas can be chosen during the transmission of pilot symbols prior to each frame, in which the best antenna at the receiver is selected based on the highest received SNR, whereas the best antenna at the transmitter is selected via a feedback channel. The outage probability of AS is given by [23; 25]  
\[\mathcal{O}_{\text{AS}}=\left[\frac{\gamma\left(\frac{\xi_{0}}{\zeta},m \right)}{\Gamma\left(m\right)}\right]^{N_{\text{t}}N_{\text{r}}}, \tag{11}\]  
yielding the following required transmitted power  
\[P_{\text{out}}^{\text{(AS)}}=\frac{\xi_{0}\,N_{0}\,B\,F_{\text{rx}}}{\kappa\, \gamma^{-1}\big{(}\Gamma\left(m\right)\left(\mathcal{O}^{\star}\right)^{\frac{ 1}{N_{\text{t}}N_{\text{r}}}},m\big{)}}. \tag{12}\]
# An Improved Skewness Balancing Filtering Algorithm Based on Thin Plate Spline Interpolation
## 2 Materials and Methods
### The Classical Skewness Balancing  
The classical skewness balancing filtering algorithm was proposed by [PERSON] et al. [36]. According to the central limit theorem, naturally measured samples will lead to a normal distribution [38]. [PERSON] et al. [36] made the initial assumption that ground point elevation is normally distributed. A further assumption is that nonground point elevations disturb the abovementioned normal distribution. Thus, by removing object points from raw points until the remaining points are normally distributed, the ground points are obtained. To describe the asymmetry of a distribution, the skewness is adopted and is defined as Equation (3):  
\[sk=\frac{1}{N\sigma^{3}}\sum_{i=1}^{N}(Z_{i}-\mu)^{3}, \tag{3}\]  
where \(sk\) refers to the skewness, \(N\) is the number of points, \(Z_{i}\) is the \(i\)-th point's elevation, and \(\mu\) and \(\sigma\) are the arithmetic mean and standard deviation, respectively. They are defined as Equations (4) and (5):  
\[\mu=\frac{1}{N}\sum_{i=1}^{N}Z_{i}, \tag{4}\]  
\[\sigma=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(Z_{i}-\mu)^{2}}, \tag{5}\]  
Figure 3: Sketch map of the structure element.  
Although skewness balancing has no requirements of point cloud maximum size and data format, this method requires an implicit assumption that the number of ground points must dominate the point clouds to ensure a normal distribution [39]. Thus, the skewness balancing needs to meet a criterion of a minimum number of ground points, which is defined as Equation (6):  
\[n_{\text{min}}=\left(\frac{Z_{\alpha/2}\sigma_{0}}{E}\right), \tag{6}\]  
where \(Z_{\alpha/2}\) is the value for the alpha level, \(\sigma_{0}\) is the standard deviation of the population and \(E\) is the acceptable margin of error. It can be estimated that the minimum number of ground points is 841 [38]. It is obvious that most test areas meet this requirement.
# Real-Time Visual Feedback in Singing Pedagogy: Current Trends and Future Directions
## 3 The Oscillatory Subsystem
### Flow Glottograms  
Glottal adduction can be observed by means of a real-time inverse filter unit connected to a pressure transducer in a flow mask. When appropriately tuned, the inverse filter displays the oscillation of glottal airflow, henceforth the flow glottogram (FLOGG), which can be visualized by an oscilloscope, for example [50; 51]. Tuning the inverse filter is done by frequency nobs that introduce antiresonances and, thus, cancel the effects of the vocal tract resonances on the signal [50; 52; 53].  
An example of a typical FLOGG for a male singer is displayed in Figure 13. The right panel shows the FLOGG [18; 54]. Its ascending part corresponds to the airflow increase during the glottal opening and the descending parts correspond to the airflow decrease during glottal closing. The peak amplitude is strongly related to the amplitude of the voice source fundamental, a parameter relevant to voice quality and phonation type [55].  
The real-time visual feedback provided by a FLOGG has assisted both trained and untrained singers to achieve specific phonation types [33]. Figure 14 shows examples of audio, FLOGG and EGG signals typical of three phonation types--breathy, flow and pressed. FLOGG and EGG metrics reflect different, but related, aspects of phonation, as shown in this figure [56]. Breathy phonation is produced with a substantial airflow as the vocal folds fail to close completely. Thus, the quasi-closed phase is short. The EGG signal, therefore, has a long non-contact time. Pressed phonation, by contrast, is produced with firm glottal adduction and with a complete glottal closure, resulting in minimal airflow and pulse amplitude and a long, closed phase. The EGG reveals a knee-like shape in its descending part. For flow phonation, the vocal folds allow generous airflow, as shown  
Figure 12: Voice maps of a female amateur singer displayed by FonaDyn software. (**Left panel**): normalized peak derivative (\(Q_{\Delta}\)), with red and green indicating high and low values, respectively. (**Right panel**): index of contact (\(I_{\rm c}\)), with red and blue indicating high and low values, respectively.  
by the large pulse amplitude, combined with complete glottal closure. The EGG lacks the sharp knee of pressed phonation.  
As mentioned above, changes in lung volume are associated with changes in tracheal pull, which, in turn, tends to affect glottal adduction, a crucial parameter for phonation type [18]. From a pedagogical point of view, it should be mentioned that a student singer may be advised to avoid a pressed voice (hyperfunctional) at high pitches by producing them at high lung volumes, i.e., after a deep inhalation, or, by practicing exercises with a descending melodic pitch direction. The opposite will apply for students tending to produce a breathy phonation at higher pitches [35].
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 2 Background
### Scalable Video Coding: H.264 SVC and H.265 Scalable HEVC (SHVC)  
The H.264 SVC is an H.264 advanced video coding (AVC) scalable extension that combines spatial, temporal, and quality scalabilities simultaneously [4]. Because of the scalable feature, SVC supports multiple screen resolutions, frame rates, and video qualities within a single bitstream file that consists of multiple layers, as shown in Figure 2.  
In the layered structure of the SVC encoder, the original high quality video input is spatially down-sampled for lower layers, and each layer encodes the input video using an inter-layer prediction. Due to the layering architecture, SVC has the following advantages: (i) it can reduce server-side disk storage and overall network bandwidth without the use of the kind of transcoding that has high computational complexity; (ii) because the layers of SVC have priorities (the lower layer is more important than the higher layer), the UEP method can be applied to each layer packet; (iii) SVC can support the diverse screen resolutions of user devices as well as network bandwidth (BWs.)  
The scalable high efficiency video coding (SHVC) standard of joint collaborative team on video coding (JCT-VC) is as shown in Figure 3. It has a low computational complexity for enhancement layers (ELs) by adding the reconstructed and up-sampled base layer (BL) picture to the reference picture lists (RPLs) in EL. Additionally, the SHVC uses multiple-loop decoding to make a decoder chipset simple while the SVC uses single-loop decoding. SHVC also has a standard scalability with supporting AVC standard with BL and high efficiency video coding (HEVC) standard with EL.  
Figure 3: Conceptual diagram of H.265 scalable high efficiency video coding (SHVC). EL, enhancement layer; BL, base layer; MUX, multiplexer.  
Figure 2: The layered feature of H.264 SVC.
## 2 Experimental Pullout Test  
The pullout interaction mechanisms between particles and geogrid reinforcements are more complex than those between particles and sheet reinforcements. This is because the pullout resistance of geogrids includes two main components: the passive resistance that develops against the transverse ribs and the interface shear resistance that takes place along the longitudinal ribs and also, but to a lesser extent, along transverse ribs. Although the interface shear component can be quantified using parameters obtained from direct shear tests, the passive resistance can only be evaluated using a pullout test.  
The factors affecting pullout behavior, such as the stiffness of the loading plate, the distance between the first transverse ribs and the front wall, the shape of the geogrid specimen (the number of ribs and the transverse direction), geogrid length, external clamping, wall friction, surcharge load and pullout velocity, were described by [PERSON] et al. [22], [PERSON] [23] and [PERSON] [24]. [PERSON] and [PERSON] [25] found that the internal friction angle between the soil and the reinforcement may be seriously overestimated because there is friction on the internal front wall of the box in small-scale tests. Therefore, these authors suggested lubricating the front face of the test box and increasing the scale of the test. Therefore, a large box measuring 400 mm width \(\times\) 600 mm length \(\times\) 400 mm height was used in our pullout tests, which is four times larger than the typical small box, as shown in Figure 1. The test box was filled with 140 kg graded ballast with a mean size (\(D_{50}\)) of 40 mm and a coefficient of uniformity (\(C_{u}\)) of 1.4, whereas the initial density was approximately 1458 kg/m\({}^{3}\). The geogrid layer was placed at the middle depth of the box and extended out of the box through the slot on the right side of the box. A thin plastic membrane was placed on both sides of the geogrid near the slot to prevent the geogrid from generating additional resistance between the geogrid junction and the slot. The pullout force was measured and analysed to evaluate the interlocking of the geogrid-ballast system. A higher pullout force represents more effective interlocking between the geogrid and the ballast, from which one can expect the effective prevention of the lateral spread of the ballast on railway tracks. More details on the experimental tests can be found in previous studies [17].
# Reducing the Cost of Implementing Filters in LoRa Devices
## 3 Simulation Results
### Bit-Error Rate  
The BER tests were performed for filtered LoRa signals with and without quantization to see how they would perform under the effects of noise in an AWGN channel. The bandwidth was set to a fixed value of 125 kHz, while the spreading factor was varied from 6 to 12 for all tests. The BER results for each case are shown in Figure 5. The BER was tested at each desired signal-to-noise ratio (SNR) level by transmitting 175,000 data bursts containing 10 LoRa symbols each. The transmit signal is normalized to have unit power and hence the noise power is calculated based on the desired SNR level as \(P_{\text{noise}}=10^{\frac{-\text{SNR}}{10}}\).  
The results for the non-quantized case can be corroborated by the simulated BER measurements of typical LoRa systems given in both [6; 14]. It is evident that the results for most of the quantized cases match the non-quantized results, with the exception of the case with two-bit fractional precision. As such, it can be concluded that quantizing LoRa chirp signals to a certain degree does not affect the symbol decoding capabilities of LoRa devices in the presence of noise from an AWGN channel.
## 3 Results and Discussion
### Optimization of Single HAP Conditions
#### 3.1.6 Concentration of Spiramycin  
The result presented in Figure 3f indicated that an initial slight decrease of the MPS of spiramycin was followed by a sharp enhancement of MPS when the spiramycin concentration increased from 5 mg/mL to 30 mg/mL, and 30 mg/mL to 60 mg/mL. For interpreting this phenomenon, three points were listed as follows: Firstly, higher supersaturation can be formed with a higher spiramycin concentration, which is conducive to promoting the nucleation rate and leads to small particle size [39; 40]; secondly, the viscosity of the spiramycin solution also increased with the increase of concentration to 30 mg/mL, which retards the diffusion between solution and antisolvent, thereby facilitating the particles being aggregated [41; 42]; finally, a large number of nuclei can be formed at the interface of the antisolvent and solvent at high concentration, 30 mg/mL, thus decreasing the diffusion from solvent to antisolvent and accelerating agglomeration [40; 41]. In view of this, a 10-30 mg/mL spiramycin concentration was selected for further optimization experiments.
## 2 Results
### Saponin Profile  
Following our previous UHPLC-MS/MS analyses, the saponin profiles of _I. aquifolium_ and _I. paraguariensis_ were compared under the conditions described above [8]. By comparing the saponin profiles of holly and commercial mate, we found that the saponin pattern in _I. aquifolium_ is less complicated than in _I. paraguariensis_, as shown in Table S3 (available in Supplementary Materials). Therefore, we have tentatively designated 29 and 53 compound respectively, as triterpene glycosides or glycoside esters.  
Qualitatively, the profiles were quite different: 16 compounds were found in both species, while 50 were unique to one or the second. In _I. paraguariensis_, one compound (911, 12.54 min; only in mate), tentatively assigned as matesaponin 1, was the dominant one, while another compound (911, 13.98 min; in both species) appeared to be the main saponin in _I. aquifolium_. However, numerous already-recognized saponins in the _Ilex_ genus match the MS fragmentation of the last saponin, for example, itlexaponin B2, latifoloside A, latifoloside B, or latifoloside D; thus, we cannot speculate on dereplication.  
Considering the compounds classified as the same in both holly and mate, only six of sixteen had quite small differences below 10 RA% between species (911, 6.81 min; 825, 7.74 min; 927, 7.97 min; 911, 9.25 min; 1073, 9.53 min; 895, 14.68 min); these compounds, moreover, were also of generally small intensity. Four compounds differed between species in the range of 10-25 RA% (927, 8.30 min; 1219.61, 9.15 min probably matesaponin 4; 1057, 10.85 min; 1057, 12.15 min), while six others had a difference greater than 25 RA%.  
Taking into account the MS fragmentation of saponins, the following three types of triterpene glycones were found based on MS/MS-resulting ions with \(m/z\) 455 for [C\({}_{30}\)H\({}_{48}\)O\({}_{3}-\)H]\({}^{-}\) (an equivalent of monohydroylated triterpene acid, such as oleanolic or unsolic acid), 469 for [C\({}_{30}\)H\({}_{46}\)O\({}_{4}-\)H]\({}^{-}\) (an equivalent of didehydrogenated dihydroxylated triterpene acid) and 471 for [C\({}_{30}\)H\({}_{48}\)O\({}_{4}-\)H]\({}^{-}\) (an equivalent of dihydroxylated triterpene acid) in both species.