esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### PAPR Performance and Bellhop Channel Impulse Response  
The PAPR performance is evaluated with the help of a complementary cumulative distribution function (CCDF) for the proposed model in this section. The PAPR is compared between the addition of clipping and without clipping. It is observed that the proposed method can reduce the PAPR, although this proposed FFB method is a receiver-based PAPR distortion mitigation technique. The reason for adding a clipping block is to verify the performance of the proposed scheme with clipping. In Figure 7, the blue curve is the original OFDM signal. The red line shows the PAPR reduction with the TFNN method; similarly, the green and yellow lines represent the PAPR reductions with and without clipping, respectively. The PAPR is reduced from 11.2 (original) to 7.6 dB for the proposed scheme 64-QAM constellation used at \(10^{-3}\) CCDF, as shown in the brown curve. It can be observed from Figure 7 that the FFB model performs significantly better by adding clipping and in comparison to the TFNN method, i.e., 8.1 dB. However, the computational complexity is lower in the proposed neural network deep learning model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Surface Height** & **Transmitting Side** & **Receiving Side** & **Distance between** & **Frequency** \\  & **(m)** & **Depth, TX (m)** & **Depth, RX (m)** & **TX and RX (km)** & **Range (kHz)** \\ \hline
**Data** & 100 & 30 & 50 & 2 & 10–12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bellhop channel configuration.  
Figure 6: Power amplifier (PA) solid-state model used at the transmitter.  
Figure 8 illustrates the impulse response of the channel at 2 km, which is based on the sound speed profile used in the simulation. The sound speed was saved in the range of 1480 to 1500 m/s. The time delay can be observed in the figure with the normalized amplitude.
# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
## 3 Results and Discussion
### LoRaWAN Performance
This finding prompted the replacement of the included Sense Cap antenna (Antenna A) with a 4.5 dBi LoRa antenna, 868 MHz, from Paradar, UK (Antenna B). An initial investigation revealed promising results for Antenna B with a range of approximately 1.5 km and with no obvious data loss. As a cross-comparison for data drop rate, the tests carried out with Antenna A were reproduced over a period of 12 days with Antenna B, maintaining the  
Figure 6: Boxplots of data transmission delay (**a**) 5 min sampling rate, N = 485; (**b**) 10 min sampling rate, N = 825; (**c**) 30 min sampling rate N = 86 and (**d**) 60 min sample rate N = 49; whiskers are calculated as 1.5 times the IQR (interquartile range) and the circles represent the outliers. same distances from the gateway. Given fixed data sampling frequencies, the total number of packages can be estimated and the drop rate can be calculated as:  
\[drop\ rate=\frac{\text{total number of packages $-$ number of received packages}}{\text{total number of packages}} \tag{2}\]  
It was found there is no significant difference between the two antennas for the range tested and that the drop rate increases with range (Table 3). While Antenna A had a smaller drop rate at shorter ranges it shows a higher drop rate at higher ones. The % drop rate for the two antennae is consistent with previous reports when LoRaWAN is used for environmental applications [37]. Data loss through operational issued or sensor drift can have a deleterious effect on the overall sensor network performance and is not desirable. Two options are available to mitigate data loss: increase the overall data transmission performance or use imputation algorithms to introduce missing values and maintain the sample size [37].
# Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements
## 2 Measurement Setup  
A self-developed high-precision measuring system as shown in Figure 1 is used. All the measurement equipment is placed inside a thermally insulating box which decouples the devices from the environment outside. Nevertheless, the entire measurement setup is located in an air-conditioned room in order to keep unnecessary interferences as low as possible. This allows for cycling four cells in parallel in a temperature-stabilized environment. The air is blown by a fan through the cooling fins of the cycler, past the heating elements attached to the cycler, and past the cell. To keep the temperature constant at 40 \({}^{\circ}\)C,the waste heat from the cycler is used and, in addition, electrical heating elements provide the necessary residual heat. The electrical heating elements compensate for the known temperature fluctuations of the cycler with the aid of a feedforward control. Any other disturbances are compensated by using a PI controller.  
The block diagram used for cell measurements is shown in Figure 2. The cell voltage is measured using four-wire sensing technique. All four cyclers are controlled by a PC and therefore both measurement programs and measurement parameters can be individually adapted.  
Each measurement system can cycle single cells within a voltage range between 0 to 5 and a current between 0 to \(\pm\)1 A. By adapting the current measurement shunt, the current measuring range could even be increased up to \(\pm\)6 A. The current can be output and measured with a resolution of 24 bits at 48 kHz sampling rate. Due to the fast current output frequency, the hardware can also be used for electrochemical impedance spectroscopy in a range from a few \(\mu\)Hz up to several kHz.  
Figure 1: The four cyclers and cells under test are exposed to a circulating air stream, which is temperature controlled. A temperature sensor is attached to each cell.  
Figure 2: Schematic measurement setup: Cycler and cell are located in a temperature-stabilized box. The cell is measured using four-wire sensing to ensure good measurement precision. The cycler is controlled by a PC, so that measurement programs can be adapted and performed as desired.  
To suppress the 50 Hz mains hum, these measured values are averaged in \(\frac{1}{50\,\mathrm{Hz}}\)-steps, i.e., 20 ms blocks with 960 individual measured values. To ensure the drift stability of the measurement system, the current measurement shunt and the voltage reference of each cycle are separately temperature controlled. In addition, offset- and gain-calibration for the voltage and current measurement is performed several times per second. Because the offset and gain calibration is carried out alternately with the measurements, the total measurement rate is 20 Hz.
# Numerical Integration-Based Performance Analysis of Cross-Eye Jamming Algorithm
## Appendix A Taylor Approximation of Cross-Eye Gain  
Cross-eye gain is a nonlinear function shown in a fractional form, it is difficult to develop the mean operation analytically. Therefore, by applying the Taylor approximation, which approximates this function as a polynomial function for \(a\) and \(\phi\), analytical expansion of the mean operation is performed.  
\[f(x,y)^{(\text{approx}1)}\approx f(a,b)+\left.(x-a)\frac{\partial f(x,y)}{ \partial x}\right|_{\begin{subarray}{c}x=a\\ y=b\end{subarray}}+(y-b)\left.\frac{\partial f(x,y)}{\partial y}\right|_{ \begin{subarray}{c}x=a\\ y=b\end{subarray}} \tag{1}\]  
\[\begin{split} f(x,y)^{(\text{approx}2)}\approx f(a,b)+\left.(x- a)\frac{\partial f(x,y)}{\partial x}\right|_{\begin{subarray}{c}x=a\\ y=b\end{subarray}}+(y-b)\left.\frac{\partial f(x,y)}{\partial y}\right|_{ \begin{subarray}{c}x=a\\ y=b\end{subarray}}\\ +\frac{1}{2}\Bigg{(}\left.(x-a)^{2}\frac{\partial^{2}f(x,y)}{\partial x^{2}} \right|_{\begin{subarray}{c}x=a\\ y=b\end{subarray}}+2(x-a)(y-b)\frac{\partial f(x,y)}{\partial x\partial y} \right|_{\begin{subarray}{c}x=a\\ y=b\end{subarray}}+(y-b)^{2}\frac{\partial^{2}f(x,y)}{\partial y^{2}}\Big{|}_{ \begin{subarray}{c}x=a\\ y=b\end{subarray}}\Bigg{)}\end{split} \tag{2}\]  
Equations (1) and (2) are the first-order [PERSON] approximation and the second-order [PERSON] approximation, respectively. When Equations (1) and (2) are applied to cross-eye gain, \(x\), \(y\) become \(a_{\text{real}}\), \(\phi_{\text{real}}\) and \(a\), \(b\) becomes \(a_{\text{real}}\), \(\phi_{\text{nominal}}\). So \(f(a,b)\) changes to \(\text{GC}_{\text{nominal}}\). Similarly, \((x-a)\) and \((y-b)\) change to \((a_{\text{real}}-a_{\text{nominal}})\) and \((\phi_{\text{real}}-\phi_{\text{nominal}})\), which are \(\delta_{a}\) and \(\delta_{\phi}\). According to this, the first-order [PERSON] approximation and the second-order [PERSON] approximation of cross-eye gain can be expressed  
\[\text{GC}_{\text{real}}^{\text{approxi}=1}=GC_{\text{nominal}}+\frac{-2\big{(} \cos\phi_{0}a_{0}^{2}+2a_{0}+\cos\phi_{0}\big{)}}{\big{(}1+2\cos\phi_{0}a_{0}+a _{0}^{2}\big{)}^{2}}\delta_{a}+\frac{-\big{(}2a_{0}\sin\phi_{0}(a_{0}^{2}-1) \big{)}}{\big{(}1+2\cos\phi_{0}a_{0}+a_{0}^{2}\big{)}^{2}}\delta_{\phi} \tag{11}\]  
\[\text{GC}_{\text{real}}^{\text{(approxi=2)}}=GC_{\text{nominal}}+\bigg{(}\frac {-2\big{(}\cos\phi_{0}a_{0}^{2}+2a_{0}+\cos\phi_{0}\big{)}}{\big{(}1+2\cos\phi_ {0}a_{0}+a_{0}^{2}\big{)}^{2}}\delta_{a}+\frac{-\big{(}2a_{0}\sin\phi_{0}(a_{0} ^{2}-1)\big{)}}{\big{(}1+2\cos\phi_{0}a_{0}+a_{0}^{2}\big{)}^{2}}\delta_{\phi} \bigg{)}\]  
In Section 2, each coefficient of \(\delta_{a}\) and \(\delta_{\phi}\) are simply expressed as \(\alpha\) and \(\beta\).
## 3 Discussion of the Results
By contrast, the higher size of the DOC allowed it to deal better with HC mass transfer limitations and took advantage of the temperature increase throughout the cycle to increase its HC conversion efficiency from medium- to extra-high-speed phases. Nevertheless, its HC conversion efficiency was damaged with respect to the baseline ATS configuration, as for CO abatement, and the benefits of the pre-DOC were partially degraded when evaluating the entire exhaust line layout. Despite the increase in overall CO and HC conversion efficiency with pre-turbine pre-DOC configuration, the parallel increase of the engine-out CO and HC emissions canceled these improvements out. Figure 13 shows the variation of the cumulative engine-out and tailpipe CO and HC emissions brought by the pre-turbine pre-DOC configuration in each WLTC. Even though the increase of the engine-out emissions were completely countered in low-speed phases of cold WLTC, the total cumulative tailpipe emissions increased for CO and HC. Nevertheless, there are two relevant remarks. First, the percentage penalty was always lower in tailpipe than engine-out emissions. In addition, the penalties were lower in cold conditions (8.1% for CO and 24.4% for HC) than in warm WLTC (20.8% for CO and 37.6% for HC) despite the similar percentage increase of engine-out emissions. These two trends confirm the potential for cold-start conversion efficiency improvement of pre-turbine ATS provided that the effects on the engine operation were minimized. Figure 12: HC conversion efficiency in each WLTC phase in (**a**) cold and (**b**) warm tests: baseline (DOC) and pre-turbine pre-DOC (overall and contributions from pre-DOC and DOC) configurations.
# A Survey of Vehicular VLC Methodologies
## 2 Visible Light Communication
### Energy-efficient and inexpensive  
VLC systems remain substantially less expensive compared with conventional radio communication. According to research [51], a Bluetooth piece with a maximum data transfer rate of one-megabyte costs at least USD 5.00, while a VLC with a transfer rate of roughly four megabytes costs only about USD 1.00 per piece. Furthermore, due to the excellent energy consumption of LEDs, the VLC-based LED is emerging as the best friendly connection method solution. LEDs utilize less energy [14] and produce less heat than traditional lighting systems. LED lighting consumes \(1/4\) and \(1/10\) the power of conventional and fluorescent illumination.
## 3 Positioning Based on DVB-T
### Range Measurement
#### 3.2.5 Final Considerations  
It is well known that the minimum number of reference points for performing the trilateration process in a 3-D space is four. In practice, in most applications, three reference points could be enough, as the intersection of three spheres gives two solutions, one of which is not realistic and can be discarded. Moreover, some aiding data can be provided during the initialization phase, such as the synchronism, and the user altitude, further reducing the minimum number of DVB-T emitters.  
In a more realistic situation, three or two DVB-T signals should be sufficient, considering that in most applications, some data can be considered quite constant (_i.e._, altitude) during short/medium GNSS outages, especially in a vehicular scenario. Moreover, if the hybrid GNSS/DVB receiver can track its position in an area where GNSS satellites randomly appear and disappear, the position provided by DVB-T can be used to allow a very hot start of the GNSS-based positioning, even if the GNSS satellites are visible in a very short time interval. In general, we can say that a hybrid GNSS/DVB receiver could be used for both a hybrid trilateration and for mutual assistance of the two separate positioning systems. This paper is devoted to this second aspect, and the simulation experiments with four emitters have been  
Figure 6: The flow diagram of the re-acquisition stage.  
done to prove the feasibility of a positioning method based on synchronous OFDM DVB-T signals, while the role of GNSS is only the initialization of the DVB-T-based position computation.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 2 Sensor Technologies
### Measurement of Mechanical Parameters
#### 2.3.2 Vibrating Wire Sensors  
Vibrating wire sensors can measure the stresses occurring in a structure and its surrounding environment. It is a frequency sensor with an internal metal string as the sensing component. The sensor's output frequency signal changes according to the the stress on the metal string. The device is durable and simple in design and has a strong anti-interference ability [50]. The metal string vibrates as a result of excitation when an external force is applied, changing the tension in the wire, which changes the frequency of vibrations. The dependence between the vibration conductor and the vibration frequency is calculated from the equation:  
\[f=\frac{1}{2L}\sqrt{\frac{F}{m}}, \tag{1}\]  
where \(m\) is the mass of the vibrating string, \(L\) is the length of the vibrating string, \(F\) indicates the pressure on the sensor, and \(f\) is the resonant frequency of the string [54]. These sensors are suitable for applications in geothermal or oil drilling wells [52].
###### Abstract  
Asphalt migration is one of the significant detrimental effects on asphalt pavement performance. In order to simulate the state after the occurrence of asphalt migration amid asphalt pavement layers and further investigate the effects of asphalt migration on the dynamic modulus of asphalt mixture, samples with different asphalt contents layers were firstly separated into the upper and lower half portions and then compacted together. By conducting the dynamic modulus test with the Superpave Simple Performance Tester (SPT), the variation laws of the dynamic modulus (\(|\)E\(\"\)) and the phase angle (\(\delta\)) at different testing temperatures and loading frequencies were analyzed in this paper. Further, the dynamic modulus and the stiffness parameter (\(|\)E\(\"\)/\(\sin\)\(\delta\)) at the loading frequency of 10 Hz and testing temperature of 50 \({}^{\circ}\)C were illustrated. Simultaneously, the master curves of the dynamic modulus and phase angle of asphalt mixtures under different testing conditions were constructed to better investigate the effects of asphalt migration on the dynamic modulus by means of Williams-Landel-Ferry (WLF) equation and Sigmoidal function. Results show that, after the asphalt migration, the dynamic modulus of asphalt mixtures increase with the increasing loading frequency while they decrease with the increasing testing temperature; the dynamic modulus and the stiffness parameter are the highest when asphalt mixtures have the optimum asphalt content layers, and then decrease with the incremental difference of asphalt content in the upper and lower half portions. Besides this, different from the master curves of dynamic modulus, the master curves of phase angle firstly increase with the increase of loading frequency to the highest point and then decrease with the further increase of loading frequency and are not as smooth as that of dynamic modulus. It can be concluded that the asphalt migration has compromised the mixture's mechanical structure, and the more asphalt migrates, the weaker the mechanical properties of asphalt mixture will be. Additionally, based on the shift factors and master curves in the time-temperature superposition principle (TTSP), the effects of asphalt migration on the dynamic modulus and the variation laws of the dynamic modulus of asphalt mixture after the occurrence of asphalt migration can be better construed at the quantitative level.  
**Keywords:** asphalt migration; dynamic modulus; master curve; simple performance test; stiffness parameter; phase angle
# Can Biochar Improve the Sustainability of Animal Production?
## 3 Animal Feed  
Animal nutrition has significant impacts on feed and growth efficiency as well as the properties of their excreted waste. Growers seek to maximize weight gain with the least amount of inputs and additives. The undigested macro and micronutrients pass into the manure and contribute to gaseous emission production. Approximately 60-85% of phosphorus, 70-80% of nitrogen, and 80-90% of potassium in animal feed are excreted as manure [70]. Biochar as a feed additive is not a new concept, and its chemical and physical properties have been hypothesized to reduce nitrogen content in exceta, decrease risk of pathogens, and strengthen the immune system of livestock and poultry among other benefits as shown in Figure 3[71].  
Figure 3: Summary of benefits of biochar as an animal feed additive.  
Recent reviews, e.g., by [PERSON] et al. [72] and [PERSON] et al. [73], focus on diet impacts of biochar on animal performance, such as weight gain, and less on the mechanisms of nutrient uptake or efficiency. A smaller subset of literature has focused on biochar as an animal feed additive for the purpose of reducing emissions in excreted manure.  
While there are numerous publications on the use of biochar as a feed additive, few of them provide a useful characterization of the biochar, including the source and the manufacturing process [73]. This gap limits our ability to understand the mechanisms involved in biochar impacts on feed digestion and nutrient absorption.
# The Potential of Indole Alkaloids in Bone Health and Osteoporosis Management
## 5 Indole Alkaloids
### Mechanisms of Action of Indole Alkaloids in Osteoporosis  
While research is still ongoing, several potential mechanisms have been proposed for the anti-osteoporotic effects of indole alkaloids:  
1. Modulation of bone turnover [57]  
* Influence on osteoblast activity:  
\(\bigcirc\)  Indole alkaloids can stimulate osteoblast proliferation and differentiation, leading to increased bone formation.  
\(\bigcirc\)  They might regulate the expression of bone-specific genes involved in matrix synthesis and mineralization.
* Inhibition of osteoclast activity: \(\bigcirc\)  Some indole alkaloids can suppress osteoclast differentiation and function, reducing bone resorption. \(\bigcirc\)  They may interfere with osteoclast-mediated bone resorption by inhibiting key enzymes or signaling pathways. 2. Anti-inflammatory effects [58]
* Reduction in inflammatory cytokines: \(\bigcirc\)  Chronic inflammation is linked to bone loss. Indole alkaloids can downregulate the production of pro-inflammatory cytokines, such as tumor necrosis factor alpha (TNF-\(\alpha\)) and interleukin-1 (IL-1), which contribute to osteoclastogenesis.
* Inhibition of NF-\(\kappa\)B Pathway: \(\bigcirc\)  Indole alkaloids may inhibit the NF-\(\kappa\)B signaling pathway, a key regulator of inflammation and bone resorption. 3. Antioxidant properties [42]
* Protection against oxidative stress: \(\bigcirc\)  Oxidative stress contributes to bone loss by damaging bone cells. Indole alkaloids with antioxidant properties can neutralize reactive oxygen species, protecting bone tissue. 4. Hormonal modulation [41]
* Estrogen-like effects: \(\bigcirc\)  Some indole alkaloids exhibit estrogenic activity, which can be beneficial in postmenopausal osteoporosis, as estrogen deficiency is a major risk factor.
* Vitamin D-like effects: \(\bigcirc\)  Certain indole alkaloids may influence vitamin D metabolism or receptor signaling, contributing to bone health. 5. Other potential mechanisms [41]
* Regulation of bone-related signaling pathways: \(\bigcirc\)  Indole alkaloids might interact with various signaling pathways involved in bone metabolism, such as Wnt, BMP, and Notch pathways.
* Influence on bone microenvironment: \(\bigcirc\)  These compounds could affect the composition and function of bone cells and the extracellular matrix.
## 3 Design of an Adaptive Compound Controller for Reference Model
### Adaptive-Layer Controller
The evaluation index \(\rho\) is closely correlated with the adaptive coefficient \(z\). By properly adjusting the adaptive coefficient \(z\), the external yaw moment in the upper-layer controller can be regulated. The parameter tuning method is summarized as follows. The phase plane diagram is divided into three regions based on the value of the evaluation index \(\rho\): Type I (\(0\leq\rho<0.25\)), representing the energy-saving region; Type II (\(0.25\leq\rho<0.75\)), representing the transition region; and Type III (\(0.75\leq\rho<1\)), representing the critical stability region. These three regions correspond to three distinct control methods, designated as Type = 0, 1, and 2, respectively. In the energy-saving region, the vehicle reaches a highly stable state and does not require an external yaw moment. In the critical stability region, the vehicle's steering maneuverability is insufficient, so the actual external yaw moment \(M_{z}^{\prime}\) is exactly equal to the external yaw moment \(M_{z}\) obtained from the upper-layer controller. In the transition region, the evaluation index \(\rho\) is scaled between 0.25 and 0.75, with less external yaw moment being added, i.e., \(M_{z}^{\prime}=2M_{z}(\rho-0.25)\). When performing phase plane analysis, it is also necessary to determine the adaptive coefficient \(z\). The adaptive coefficient \(z\) influences both energy consumption and vehicle stability. For a fixed SOC, with an increase in \(z\), the energy-saving region expands, and the difference between \(M_{z}^{\prime}\) and \(M_{z}\) increases, leading to a reduction in the external yaw moment. Conversely, as \(z\) decreases, the energy-saving region narrows, and the difference between \(M_{z}^{\prime}\) and \(M_{z}\) decreases, resulting in an increase in the external yaw moment. In this section, a fuzzy controller is used with \(I_{\text{actual}}^{*}\) and SOC as inputs and \(z\) as the output. The value of \(I_{\text{actual}}^{*}\) is derived by substituting \(\beta_{\text{ss}}\) and \(\gamma_{\text{ss}}\) into Equations (41) and (42). The values of \(I_{\text{actual}}^{*}\) and SOC are classified into three levels: L (Low), M (Medium), and H (High), while the value of \(z\) is classified into five levels: VL (Very Low), L (Low), M (Medium), H (High), and VH (Very High). The fuzzy rules are presented in Table 3 and Figure 7, where the adaptive coefficient \(z\) is determined as a function of the stability evaluation index \(I_{\text{actual}}^{*}\) and the SOC. The surface reflects the controller's ability to dynamically balance lateral stability and energy savings.