esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### In Situ Biological Field Test Results
#### 3.2.2 In Situ Water Quality Parameter Variation  
Because OW1 was closer to the IW, OW2 and CW were selected for comparing underground environments and water quality parameters before and after injection. The results are presented in Table 1. OW2 had family vegetable gardens in its peripheral areas; thus, the initial nitrate concentration in the groundwater was higher than that at CW. Because the groundwater at the in situ field testing site was flowing, the pH value of OW2 after injection did not decrease at a level comparable to the microcosm test results. The TOC results, as stated previously, indicated that chemicals were transmitted to OW2 only after day 90. Thus, only an ORP decrease was observed before day 90. Prior to day 90, the nitrate and nitrite concentrations did not decrease, and the iron (II) concentration did not increase. On day 140, the nitrate concentration of OW2 decreased substantially, the iron (II) concentration increased to 3.6 mg/L, and sulfide was observed at a concentration of 0.22 mg/L. The results from the CW indicated that the groundwater was originally in an anaerobic state; however, little difference in water quality was observed between day 0 and day 140 because of the lack of electron donors.  
Figure 6: TOC concentration variations before and after injection for each monitoring well in the in situ field testing.
# Differential Signalling in Free-Space Optical Communication Systems
## 6 Combined Atmospheric Turbulence and Pointing Errors  
### Weak Turbulence and PE  
The PDF of \(h_{i}=h_{a}h_{p}\), where \(h_{a}\) is the fading coefficient due to turbulence and \(h_{p}\) represents the effect of PE under log-normal (weak turbulence) channel is given by  
\[f_{h_{i}}(h_{i})=\frac{\gamma^{2}e^{u_{a}}}{2{(A_{o})}^{\gamma^{2}}}h_{i}^{ \gamma^{2}-1}\;erfc\left(\frac{ln\frac{h_{i}}{A_{o}}+u_{b}}{u_{c}}\right), \tag{30}\]  
where \(u_{a}=\frac{r_{b}^{2}}{\sigma_{j}^{2}}+2\sigma_{h}^{2}\gamma^{2}+2\sigma_{h}^ {2}\gamma^{4}\), \(u_{b}=\frac{6r_{b}^{2}}{w_{z_{ab}}^{2}}+2\;\sigma_{h}^{2}+4\;\sigma_{h}^{2}\; \gamma^{2}\), and \(u_{c}=\sqrt{8\left(\frac{4\;r_{b}^{2}\sigma_{j}^{2}}{w_{z_{ab}}^{4}}+\sigma_ {h}^{2}\right)}\). The \(n\)th order moment of the above PDF is  
\[E[h^{n}]=\frac{{(A_{o})}^{n}\gamma^{2}}{n+\gamma^{2}}\text{exp}\left(-2\sigma _{h}^{2}n+2\sigma_{h}^{2}n^{2}-\frac{nr_{b}^{2}}{(n+\gamma^{2})2\sigma_{j}^{2 }}\right), \tag{31}\]  
and therefore  
\[Mean(h_{i})=\frac{A_{0}\gamma^{2}}{1+\gamma^{2}}exp\left(-\frac{r_{b}^{2}}{2 (1+\gamma^{2})\sigma_{j}^{2}}\right), \tag{32a}\] \[Var(h_{i})=\frac{A_{0}^{2}\gamma^{2}}{2+\gamma^{2}}\text{exp}\left(4\;\sigma _{h}^{2}-\frac{2r_{b}^{2}}{2(2+\gamma^{2})\sigma_{j}^{2}}\right)-\frac{A_{0}^ {2}\;\gamma^{4}}{\left(1+\gamma^{2}\right)^{2}}\text{exp}\left(-\frac{r_{b}^{ 2}}{(1+\gamma^{2})\sigma_{j}^{2}}\right). \tag{32b}\]  
Therefore, in terms of both \(h_{a}\) and \(h_{i}\), Label (32a) is re-written as  
\[Mean(h_{i})=E\big{(}h_{a}h_{p}\big{)}=E(h_{a})E\big{(}h_{p}\big{)}=\frac{A_{ 0}\gamma^{2}}{1+\gamma^{2}}exp\left(-\frac{r_{b}^{2}}{2(1+\gamma^{2})\sigma_{ j}^{2}}\right). \tag{33}\]  
Considering the assumption that \(h_{1}\approx h_{2}\), similar to the other channel conditions, the average of DTL will be zero. Then, knowing that \(\text{Var}(h_{i})=\text{Var}\big{(}h_{a}h_{p}\big{)}=E\big{[}h_{a}^{2}\big{]} E\big{[}h_{p}^{2}\big{]}-\big{(}E[h_{a}]E\big{[}h_{p}\big{]}\big{)}^{2}\), \(\text{Var}(h_{i})\) is determined using Label (23) and Label (27), which is already given in Label (32b). Finally, using Label (6) and Label (32b), \(\text{Var}(V_{thresh})\) is obtained as given in Table 1. Deriving the expression for the BER in weak turbulence with the presence of PE provides a very complex closed-form expression, which is not convenient to provide here; however, the instruction for a numerical approach to calculate BER is given in Appendix A.
## 4 Discussion
### Implications for Practical Deployment  
The findings of this study carry several implications for the practical deployment of TPE systems using low-cost PIR sensors. CCFHT demonstrates superior robustness to both sensor mismatch and environmental noise, making it a more viable option for real-world implementations where hardware constraints and variability are common. The mathematical framework developed in this study enables the quantitative prediction of estimation performance under different conditions. This allows system designers to assess expected error margins and choose parameters accordingly.  
Despite the simplicity and low cost of the PIR-based system, the analytical and experimental results indicate that accurate speed estimation is achievable within typical traffic speed ranges (20-100 km/h), with mean estimation errors below 3.5% when using CCFHT.  
While this paper focuses on two correlation-based techniques, it is important to note that other TPE methods have been proposed in the literature. For instance, generalized cross-correlation (GCC) [13] incorporates frequency domain weighting to improve delay detection in multipath or noisy environments, while wavelet-based approaches [22; 36] leverage time-frequency analysis to handle non-stationary signals. Though potentially more accurate, these methods often demand greater computational resources, making them less suitable for embedded or low-power platforms such as those built around PIR sensors.  
Future work may consider integrating lightweight machine learning models [37] to further improve performance, or multi-sensor fusion with complementary sensors (e.g., accelerometers, ultrasonic, or light sensors) to increase robustness under complex traffic and environmental conditions.
# Plume Divergence and Discharge Oscillations of an Accessible Low-Power Hall Effect Thruster
## 1 Introduction  
Hall effect thrusters (HETs) are a subcategory of in-space electric propulsion devices that harness electric energy to accelerate a gas to provide thrust [1]. For in-space applications, the electrical energy is typically acquired by solar panels and stored in batteries. That energy is then converted into charged particle kinetic energy through the use of carefully designed magnetic and electric fields. The primary advantages to utilizing HETs are high propellant exhaust velocities and high thrust-to-power ratios [2]. Exhaust velocities for HETs utilizing xenon propellant are typically between 10 and 20 km/s, resulting in high specific impulses (\(l_{sp}\)) and thus, increasing the change in velocity (\(\Delta V\)) a spacecraft can achieve for a given propellant mass [1]. This enables a spacecraft propulsion system to produce a greater net impulse for a set propellant mass, or, conversely, reduces the necessary propellant for a given mission allowing increased payload mass. Albeit relatively low compared to conventional chemical propulsion, the thrust produced by an HET is sufficient to perform satellite station-keeping, slow orbit transfers, and attitude control maneuvers. Modern mission concepts are even utilizing HETs for deep space exploration missions [3].  
The Western Hall Thruster, with a 44-mm outer diameter channel wall (WHT44), was designed as a 200 W nominal testing platform by an undergraduate capstone group in 2015/16. Details regarding the design and initial characterization experiment can be found in references [4; 5], and a brief overview of the design process will be described here. The WHT44 has a channel width of 5 mm and a channel anode-to-exhaust plane length of14 mm. The goal of the original project was to demonstrate that an HET could be designed, simulated, and manufactured using resources available to a wider number of university students outside of the very high research activity (R1 Carnegie classification), and well funded institutions. The SPT-70 was used as the baseline, and dimensions were initially scaled down before parameterization began in COMSOL Multiphysics software to achieve the desired magnetic lens shape [6]. This desired magnetic lens was achieved by iterating the parametric model in order to emulate the field line shape and centerline strength profile given in reference [1]. The predominate magnetic circuit material and geometry were select to prevent saturation and to minimize cost. The WHT44 discharge anode doubles as both an electrode and as a propellant gas injection manifold. Propellant injected into the discharge channel is done so that the propellant is azimuthally uniform prior to reaching the ionization region of the discharge channel. The magnetic circuit, anode, and discharge channel were machined and assembled in-house at no cost to the project. The WHT44 was completed with a budget of under $2100, including an allotment for xenon gas to perform initial characterization testing.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 4 Network Traffic Forecasting Based on ARIMA-BPNN  
### ARIMA-Based IMF Forecasting  
#### 4.1.1 ARIMA  
To address the problem of the LRD laws of satellite network traffic, EMD is utilized to decompose traffic into multiple IMFs and residuals. The autoregressive integrated moving average model (ARIMA) is a forecasting method proposed by [PERSON] and [PERSON] in the 1970s [9] that has advantages in short-term forecasting [8]. Thus, the EMD-ARIMA combined model is constructed, where ARIMA is utilized to forecast the IMFs obtained via EMD.  
Take IMFs as input, ARIMA(\(p\), \(d\), \(q\)) is utilized to forecast the IMFs in turn. The ARIMA(\(p\), \(d\), \(q\)) model is given by  
\[\
abla^{d}X(t)=\sum_{i=1}^{p}\phi_{i}\
abla^{d}X(t-i)-\sum_{j=1}^{q}\theta_{j} \varepsilon(t-j)+\varepsilon(t) \tag{9}\]  
\[\text{s.t.}\left\{\begin{array}{l}\phi_{p}\
eq 0,\theta_{q}\
eq 0\\ E(\varepsilon(t))=0,Var(\varepsilon(t))=\sigma_{\varepsilon}^{2}\\ E(\varepsilon(t)\varepsilon(s))=0,s\
eq t\\ E(\
abla^{d}X(s)\varepsilon(s))=0,\forall s<t\end{array}\right. \tag{10}\]  
where \(\
abla^{d}X(t)\) denotes the value of the original traffic at time slot \(t\) after the differencing of order \(d\), \(p\) denotes the highest autoregressive order of the differenced series data, \(q\) denotes the highest moving-average order of the differenced series data, \(\phi_{i}\) denotes the \(i\)-th order autoregressive term coefficient, \(\theta_{j}\) denotes the \(j\)-th order moving-average term coefficient, \(E(\varepsilon(t))\) denotes the mean of a random series \(\varepsilon(t)\) and \(Var(\varepsilon(t))\) denotes the variance of \(\varepsilon(t)\), i.e., \(\sigma_{\varepsilon}^{2}\).  
The IMFs of satellite network traffic are converted to a smooth series by the means of difference operation. According to the smooth data decomposition theorem, the network traffic data can be decomposed into deterministic smooth data and a stochastic white noise, which can be indicated equivalently by ARIMA(\(p\), \(d\), \(q\)).
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 3 Simulation Tool and Settings
### Main Settings
#### 3.2.2. IEEE 802.11p with LTE Physical Layer  
In order to focus on the performance of the MAC layer, we also simulate a version of IEEE 802.11p where the PHY layer is assumed with the same data rate and reliability as LTE-V2X. Please remark that this assumption, corresponding to a redefinition of the PHY of the Wi-Fi technology, also reflects what is targeted by IEEE 802.11 bd (see Section 2.1.1).  
More specifically, the MAC procedure remains the same as in standard IEEE 802.11p, including the sensing and backoff procedure, the duration of the inter-frame spaces and the MAC overhead. Unchanged is also the first part of the message, where the training sequence and the control part are transmitted (which is also compliant with the retro-compatibility addressed by IEEE 802.11 bd). Like in standard IEEE 802.11p, all transmissions always occupy the full bandwidth.  
Differently from the standard version, the following two metrics are taken from Table 3, i.e., from the numbers of LTE-V2X: (1) the data rate adopted to calculate the duration of the payload transmission; and (2) the SINR threshold. In general, these assumptions mean shorter transmissions and/or higher reliability than standard IEEE 802.11p. Clearly, the number of possible MCSs is the same as with LTE-V2X.
## 2 Theory of UWA Communication Using CCVTRM  
### Principles of Signal Coding  
Pattern time delay shift coding scheme [24; 25] is a kind of pulse position coding scheme. It modulates the digital information in the time delay shift value of the pattern, rather than in the code waveform. And different time delay shift values represent different information. Figure 1 shows the scheme of pattern time delay shift coding.  
As shown in Figure 1, \(\tau_{di}(i=1,2,3,\ldots)\) is the time delay shift value, means the position of pattern in the encoding time window. \(T_{p}\) is the pattern pulse width and \(T_{0}\) denotes the length of one symbol. The encoding time window for information coding is \(T_{c}=T_{0}-T_{p}\). The quantization unit of time delay shift is \(\Delta\tau=T_{c}/(2^{n}-1)\) if one symbol carries \(n\) bits digital information, thereby the time delay shift value is \(\tau_{d}=k\cdot\Delta\tau\), where \(k=1,2,\ldots,2^{n}-1\). The duty ratio is \(\eta=T_{p}/T_{0}\), whose value is smaller than 1. It means this scheme can save the power during the communication, which is very valuable for UWA communication system.
## 3 Results and Discussion
### Reaction Kinetics
Figure 11: Removal of **(a)** ammonium, **(b)** MEA, and **(c)** TMAH by the MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) process, respectively (C\({}_{\text{o}}\) = 100 mg/L, V = 0.5 L, solid 1 g 0.25 mmol O\({}_{3}\)/min, 0.125 mmol H\({}_{2}\)O\({}_{2}\)/min). In general, a model with a smaller RMSE value is a better fit model. According to RMSE results (Table 2), both pseudo-first-order equation and liquid film diffusion models seem to fit the removal process of ammonium and MEA well with smaller RMSE values (1.31 and 0.43 for ammonium and MEA, respectively). This result is similar to the findings reported by Wang et al. [38], who reported that liquid film diffusion was probably the rate limiting step in an MnCe-AC/O\({}_{3}\) process for the removal of humic acid in solution. On the other hand, the intra-particle diffusion model can better describe the removal process of TMAH (with lower RMSE = 0.38). The pseudo-second order model was unable to fit these systems as indicated by a larger RMSE value. Based on the above simulations, liquid film diffusion is probably the rate limiting step for the removal of ammonium and MEA. Both ammonium and MEA probably transported from the bulk of the solution into the solid phase through the liquid film. For the removal of TMAH, it seemed that intra-particle diffusion played an important role in the process. TMAH was first adsorbed onto the MnCe-GAC surface, and then oxidized via catalytic reactions. This result is similar to the results presented by Fan et al. (2006), who reported that the intra-particle diffusion model was the best fitted model for GAC/H\({}_{2}\)O\({}_{2}\) and FeGAC/H\({}_{2}\)O\({}_{2}\) processes for the removal of acid black 24.
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 3 Framework for Continuous PoI Detection in Smart Cities
### Outlier Detection Aspects  
An outlier is, as explained in Section 1, a representation of a point that is beyond the limits of a well-defined population. The detection of outliers is a first step to be worked upon in our proposed framework, as outliers can lead, in data aggregation, to inaccurate values.  
Outliers may occur due to pervasive sensing errors, e.g., some malfunctioning of the equipment or software, or even due to migration of data from user devices to the edge or cloud. However, they may also occur due to some individual change in the citizens' mobility behaviour.  
Outliers require detection and correction through ML algorithms. Outlier detection is the focus of this work and is, therefore, addressed in detail in Section 4.
###### Abstract  
This paper presents a novel polarization-insensitive dual-band frequency-selective surface (FSS)-based electromagnetic shield. The miniaturized FSS unit cell consists of a modified Jerusalem crossed loop and a corner-modified square loop. These FSS elements are arranged in a co-planner configuration over a single-layer Rogers 5880 substrate and simultaneously offer effective shielding in the X- and Ku-bands. Moreover, the FSS manifests polarization-independent and angularly stable band-reject filter characteristics over various oblique angles of incidence for both the TE and TM polarizations with virtuous attenuation at both resonances. In addition, the FSS structure is modelled into an equivalent lumped circuit to better analyze the phenomenon of EM wave suppression. A finite prototype of FSS is fabricated and tested. The simulated and measured results are in good agreement, thus making it a potential candidate for RF shielding/isolation applications.  
Keywords:angularly stability; EM interference; frequency-selective surface; polarization insensitive; shielding effectiveness; spatial filter +
Footnote †: journal:
## Appendix B Arrhenius Equation
### Application to Global Kinetic Theory  
In the case of a particle \(A\) transformed to \(B\). This transformation can be described through transition state theory. This description uses an intermediate species called the activated complex noted \(A^{\ddagger}\). This theory indicates that particle \(A\) needs to be activated before the transformation can occur. Furthermore, the transition between \(A^{\ddagger}\) and B is considered instantaneous in front of the equilibrium formed by the transition \(A\) to \(A^{\ddagger}\). Then, the speed of the reaction can be written as:  
\[v=kN_{A}=k^{\ddagger}N_{A^{\ddagger}} \tag{100}\]  
Here, \(v\) represents the reaction speed, \(k\) is the reaction rate of the reaction \(A\) to \(B,k^{\ddagger}\) is the reaction rate for the transformation \(A\) to \(A^{\ddagger}\). The Equation (100) gives:  
\[k=k^{\ddagger}K^{\ddagger}_{c} \tag{101}\]  
With the equilibrium constant \(K^{\ddagger}_{c}=\frac{N_{A^{\ddagger}}}{N_{A}}\).  
To describe \(N\) in a specific state (here \(A\) or \(A^{\ddagger}\)), we can use the macroscopic scale, but we need the means to transition from the microscopic scale to the macroscopic scale. In the case of molecules, this transition is made with the Boltzmann statistical distribution (see the previous section). By using Equation (100) to describe the equilibrium constant, we obtain the following equation:  
\[k=k^{\ddagger}\frac{Z_{A\ddagger}}{Z_{A}}e^{-\beta(\Delta E_{0})} \tag{102}\]  
The activated energy noted \(E_{act}\) is \(E_{act}=\Delta E_{0}\). Furthermore, the pre-exponential part can be represented by \(A^{pf}\) and then, Equation (102) gives:  
\[k=A^{pf}e^{-\frac{E_{act}}{kT}} \tag{103}\]  
The Equation (103) is the same form as the Arrhenius equation experimentally determined in 1884 [20].
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 2 Materials and Methods
### HS-SPME GC-MS Analysis  
Headspace solid-phase microextraction (HS-SPME) was employed to extract volatile compounds from fish sauce samples. Briefly, 5 g of each fish sauce sample, 2 g of sodium chloride, and 5 \(\upmu\)L of cyclohexanone were transferred to a 20 mL headspace vial. The vial was sealed with a PTFE silicone cap and shaken to ensure thorough mixing. A dynamic equilibrium between absorption and release of volatile compounds was established by heating the samples in a magnetic stirrer (DF101S, Yuhua Instrument Co., Ltd., Shanghai, China) at 60 \({}^{\circ}\)C for 20 min. The DVB/CAR/PDMS (Supelco Analytical, Bellefonte, PA, USA) extraction head was inserted for 40 min, followed by desorption at 250 \({}^{\circ}\)C in the GC/MS (7890B-5977A, Agilent Science and Technology (China) Co., Ltd., Beijing, China) injector port for 10 min.  
Volatile compounds from the fish sauce samples were analyzed using GC-MS. The GC oven was initially set to 50 \({}^{\circ}\)C for 3 min, then increased to 170 \({}^{\circ}\)C (hold for 3 min) at a rate of 3 \({}^{\circ}\)C/min, and finally to 230 \({}^{\circ}\)C (hold for 5 min) at 4 \({}^{\circ}\)C/min. The temperatures of the MS transfer line and ion source were set to 280 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. The ion source was FID, and MS detection was performed over the range of 35-500 \(m/z\). Volatile compounds were identified based on RI and the mass spectrometry databases NIST 14. Semi-quantification of fish sauce was performed by dividing the specific GC peak area of each volatile by the cyclohexanone peak area, then multiplying the result by the cyclohexanone quantity [16].
# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 2 Related Work
### Research on Target Detection for Transmission Lines
[39] introduced SPD convolution into the YOLOv7 model, optimizing hyperparameters to achieve a detection accuracy of 92.2% for cranes, excavators, bulldozers, tower cranes, trucks and nests. [PERSON] et al. [40] made improvements based on the YOLOv4 model, generating anchor boxes suitable for this dataset using k-means clustering, replacing the network's SPP module with an SPPF module, and substituting the SiLU activation function for the Leaky ReLU activation function, achieving an average detection accuracy of 97.57% for balloons, nests, kites, and trash. [PERSON] et al. [41] combined Transformer V2 with YOLOX, utilizing the STCSP feature extraction layer in the backbone network and employing the Hybrid Spatial Pyramid Pooling (HSPP) module and the RepVGGBlock. This approach achieved a detection accuracy of 96.7% for nests, kites, and balloons. In research on transmission line components and environment, [PERSON] et al. [42] improved a YOLOv5s model for key-component detection of power transmission lines. [PERSON] et al. [43] proposed a detection algorithm called P2E-YOLOv5 to detect a small target. [PERSON] et al. [44] proposed an optimized YOLOv7-tiny model for smoke detection in power transmission lines. The existing foreign-object detection mainly focuses on four types of foreign objects, namely balloons, bird nests, kites, and plastic trash. However, in the plateau mountainous environment of Yunnan Province, China, slender twigs and large birds also seriously interfere with the regular operation of high-voltage transmission lines. Therefore, slender twigs and birds are added to our detection database. The existing methods cannot effectively solve the problems faced by the Yunnan Branch of China Southern Power Grid Co., Ltd.