esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data  
### Study Area  
The study was conducted in areas of two counties in the southern North China Plain (NCP) (Figure 1). One is situated in Yucheng County, Shandong Province (Site 1, centered at 116\({}^{\circ}\)39\({}^{\prime}\)E, 36\({}^{\circ}\)26\({}^{\prime}\)N). Yucheng is an alluvial plain in the lower reaches of the Yellow River; it has an average altitude of 28 m above sea level and enjoys irrigated agriculture. It is characterized by a mean annual temperature of 13.2 \({}^{\circ}\)C and precipitation of 528 mm, approximately 60% of which occurs in July and August. The other study area is located in Guantao County, Hebei Province (Site 2, centered at 115\({}^{\circ}\)23\({}^{\prime}\)E, 36\({}^{\circ}\)37\({}^{\prime}\)N). Here, arable land accounts for approximately 70% of the county's total area. Guantao is dominated by a typical warm, temperate, semi-humid continental monsoon climate, and the yearly frost-free period and sunshine duration are approximately 200 days and 2557 h, respectively. The Zhang and Yunwei Rivers, which lie across the county, provide irrigation water for agriculture. The study areas were chosen because they are typical planting regions of winter wheat, which ranks third among the most produced food crops in China.  
The traditional tillage practice in the study areas is dominated by the two-season cropping of winter wheat and summer maize. In general, wheat is sown in mid or late October and harvested in late May or early June of the following year, and maize is planted in mid or late June and harvested in late September or early October.
## 4 Results
### Sensitivity Analysis and Environmental Factors  
#### 4.3.1 Primary Parameter Sensitivity Analysis  
A sensitivity analysis was carried out to pinpoint which variables exert the most influence on the model's predictions. Each variable was altered individually while holding the others stable, and the resulting sensitivity coefficients appear in Table 7:  
The above chart (Figure 16) indicates that **total building occupancy** (0.842) is the most impactful variable, followed by **time of day** (0.731) and **gender ratio** (0.695).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Sensitivity Coefficient** \\ \hline Total building occupancy & 0.842 \\ Time of day (hour) & 0.731 \\ Gender ratio & 0.695 \\ Day of the week & 0.583 \\ Weekend & 0.527 \\ Rolling average usage (past 1 h) & 0.489 \\ Total floor area & 0.412 \\ Temperature & 0.376 \\ Holiday & 0.354 \\ Type of business & 0.328 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sensitivity coefficients of key parameters.  
Figure 15: Spider chart—ML model improvement over queueing theory.  
Key Findings  
* Occupancy (0.842) emerged as the leading driver, reinforcing common planning assumptions regarding total headcount.
* Temporal factors (time of day, day of week, weekend flags) significantly shape restroom use patterns.
* Gender ratio (0.695) highlights the importance of tailored fixture allocation for different genders.
* Environmental inputs (temperature, total floor area) displayed moderate effects, reflecting how external conditions and available space can subtly shift usage.
* Type of business (0.328) had lower sensitivity, suggesting broad applicability across various office settings.  
In Figure 17, spline fits and 95% confidence intervals demonstrate how each top parameter alters fixture estimates:  
* Occupancy: Demand rises linearly with occupant count, with narrow confidence intervals suggesting consistent performance.
* Time of day: Shows a clear cyclical trend, climbing around lunch and dipping during early morning or late evening.
* Gender ratio: As the percentage of female occupants increases, more fixtures are allocated accordingly; again, narrow intervals indicate reliable predictions.  
Figure 18 provides a heatmap of building occupancy versus time of day, highlighting peak usage periods in darker shades of red. This visual helps facility managers plan targeted interventions when demand is highest.  
Figure 16: Sensitivity coefficients of key parameters.
## 3 Results and Discussion
### Adsorption Isotherms  
The adsorption process was characterized on the basis of Freundlich adsorption isotherms determined at 288.15, 313.15 and 338.15 [K], at pH 2, for both adsorbents.  
Freundlich isotherms were presented in a linear form and on their basis constants \(K\) and \(n\) were determined.  
The Freundlich equation in the linear form has the following form:  
\[log\frac{X}{m}=logK+\frac{1}{n}logC \tag{2}\]  
where:  
* \(X\)--amount of adsorbed substance [mg]
* \(M\)--adsorbent mass [g]
* \(C\)--equilibrium concentration [mg/dm\({}^{3}\)]
* \(K\), \(n\)--isotherm constants  
The chromium(VI) adsorption isotherms for tested carbons are shown in Figures 4 and 5.  
Tables 2 and 3 present isotherms constants \(K\), \(n\) and the degree of matching (coefficient of determination R\({}^{2}\)) to experimental conditions.  
Figure 3: Chromium (VI) final concentration in solution at pH 2.  
The analysis of the isotherms allows to conclude that matching the Freundlich mathematical model to experimental values was very good for both carbons, regardless of the solution temperature, as evidenced by high determination coefficients R\({}^{2}\) (above 0.9).  
Comparing the values of coefficients \(K\), it can be concluded that they were much higher for WD (HCl) modified carbon, which indicates the possibility of obtaining larger adsorption capacities for the same equilibrium concentrations. According to literature data, these values are comparable with other activated carbons adsorbing chromium Cr (VI) compounds [11].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature** & \(N\) & \(K\) & **Coefficients R\({}^{2}\)** \\ \hline
288.15 K & 0.972 & 1.035 & 0.97 \\ \hline
313.15 K & 0.973 & 1.238 & 0.93 \\ \hline
338.15 K & 1.028 & 2.032 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of constants \(n\) and \(K\) of Freudlich isotherms for WDA.  
Figure 4: Freudlich adsorption isotherms for WDA, at temperatures: 288.15; 313.15; 338.15 [K].  
Figure 5: Freudlich adsorption isotherms for WD(HCl) at temperatures: 288.15; 313.15; 338.15 [K].  
For WDA and WD (HCl) a slight temperature effect on the removal of chromium was noticeable, however, along with its growth, the adsorbents showed better adsorption properties. For both tested carbons, the largest adsorption capacities were obtained for the highest temperature of 338.15 K. In turn [PERSON] et al. [29] found that for activated mango kernel the sorption capacity slightly decreased with the increasing temperature. Analyzing the obtained isotherms, it can be concluded that the chromium (VI) adsorption on WD (HCl) was much better. This is clearly confirmed by constant values \(n\) and \(K\). The maximum capacity was \(P_{\text{max WD (HCl)}}=114\) mg Cr (VI)/g.  
Comparing WD (HCl) (under static conditions) with other carbons, it showed a good Cr(VI) adsorbent, sometimes achieving adsorption capacities several times higher [22; 23; 24; 27; 29].
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
###### Abstract  
Although quantifying the massive exchange of carbon that takes place over the Amazon Basin remains a challenge, progress is being made as the remote sensing community moves from using traditional, reflectance-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), to the more functional Photochemical Reflectance Index (PRI). This new index, together with satellite-derived estimates of canopy light interception and Sun-Induced Fluorescence (SIF), provide improved estimates of Gross Primary Production (GPP). This paper traces the development of these new approaches, compares the results of their analyses from multiple years of data acquired across the Amazon Basin and suggests further improvements in instrument design, data acquisition and processing. We demonstrated that our estimates of PRI are in generally good agreement with eddy-flux tower measurements of photosynthetic light use efficiency (\(\epsilon\)) at four sites in the Amazon Basin: r\({}^{2}\) values ranged from 0.37 to 0.51 for northern flux sites and to 0.78 for southern flux sites. This is a significant advance over previous approaches seeking to establish a link between global-scale photosynthetic activity and remotely-sensed data. When combined with measurements of Sun-Induced Fluorescence (SIF), PRI provides realistic estimates of seasonal variation in photosynthesis over the Amazon that relate well to the wet and dry seasons. We anticipate that our findings will steer the development of improved approaches to estimate photosynthetic activity over the tropics.  
MAIAC; MODIS; Amazon; tropical forest; drought; photosynthesis; GPP; light use efficiency; Sun-induced fluorescence; eddy-flux 2017
## 2 Wireless Sensor Network Technology  
WSN technology enables distributed sensing through efficient data communication between a multitude of environmental sensors [14; 37; 38]. WSN is still a relatively new area of research, but the communication technology used for low-cost, low-power wireless networks has advanced greatly in recent decades [39]. A well-designed WSN is in principle infinitely expandable and data acquisition can be dynamically adapted to external influences. For example, a soil moisture WSN can be connected to a rain gauge, and the frequency of sampling can be automatically increased during rain events to allow the study of highly dynamic processes with efficient energy use. An important advantage of long-term sensor network measurements compared with conventional measurement campaigns with mobile sensors, such as, e.g., [31], is that the soil moisture probes can remain continuously in the soil. In this way, comparisons among different points in time are not affected by different measurement locations or changes in probe properties (e.g., due to different calibration quality).  
Low-power wide-area technology (LPWA) is an umbrella term for various technologies that enable WSN communication at a relatively low cost and with reduced power consumption. Many LPWA technologies have emerged in both licensed and unlicensed markets, e.g., ZigBee, SigFox, Long Range (LoRa), and Narrow Band (NB)-IoT. In the following section, the most recent and widely used LPWA technologies, i.e., ZigBee, LoRa and NB-IoT, are discussed in more detail.
## 4 Payload Development
### Long-Form Communication Testing  
To test the payload's downlink capability over the duration of the mission and ensure robustness to connectivity issues, a 12-h long-form functional test was conducted. Simultaneously, the payload acquired images. Subsequently, image acquisition delays were analyzed to determine the optimal manual delay values that could be implemented between images to mitigate any unanticipated system delays.
## 4 Conclusions  
Highly dense microstructures of barium cerates and barium zirconates were successfully formed by e-beam vapor deposition at low deposition rates and 500 \({}^{\circ}\)C. The formed thin films consisted of oriented columnar grains. It was determined that the yttrium dopant reduces the grain size of the formed thin films. XRD measurements revealed that the  
Figure 4: XRD patterns of (**a**) BZO thin films and initial powder; (**b**) BZY20 thin films and initial powders; and (**c**) BZCY thin films and initial powder.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Formed Thin Film**} & \multicolumn{4}{c}{**Powder**} \\ \hline  & **Crystallite** & \multicolumn{4}{c}{**Lattice Parameter, Å**} & **Crystallite** & \multicolumn{4}{c}{**Lattice Parameter, Å**} \\  & **Size, nm** & \(a\) & \(b\) & \(c\) & **Size, nm** & \(a\) & \(b\) & \(c\) \\ \hline BCO & 106.2 & 8.8116 & 6.2793 & 6.2580 & 122.0 & 8.7742 & 6.2339 & 6.2127 \\ BCY10 & 24.2 & 8.6764 & 6.2473 & 6.3033 & 29.5 & 8.7031 & 6.1464 & 6.2138 \\ BCY20 & 10.3 & 4.4393 & & & 16.8 & 4.4027 & & \\ BZO & 12.0 & 4.2436 & & & 125.0 & 4.1915 & & \\ BZY20 & 8.6 & 4.2446 & & & 23.9 & 4.2169 & & \\ BZCY & 2.4 & 4.3022 & & & 24.1 & 4.2437 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lattice parameters and crystallite sizes of the formed thin films in comparison to the initial powder material’s lattice parameters and crystallite sizes.  
formed barium cerate films had a predominant orthorhombic phase. In addition, yttrium dopant had an influence on the lattice parameters, the crystallite sizes, and the preferential crystallographic orientation. The crystallite size values for barium cerate decreased with the content of yttrium from 106.2 nm for undoped BCO to 10.3 nm for BCY20. Contrary, the crystallite size values for barium zirconate increased with the yttrium content from 2.4 nm for BZCY to 8.6 nm for BZY20. Barium zirconates formed at 500 \({}^{\circ}\)C were composed of a mixture of phases, consisting of the oxides and carbonates. A higher power, a higher temperature during the deposition, or a smaller thickness are necessary to enhance the diffusion and crystal growth for these materials.  
Conceptualization, M.G. and G.L.; formal analysis, M.S.C.C., M.S., K.B., A.P. and R.V.; investigation, M.S.C.C. and M.S.; writing--original draft preparation, M.S.C.C., A.P. and R.V.; writing--review and editing, K.B. and G.L.; supervision, M.G. and G.L.; funding acquisition, M.G. and G.L. All authors have read and agreed to the published version of the manuscript.  
The research was financially supported by Research Council of Lithuania (LMTLT), agreement No. S-LL-18-3 and the National Science Centre, Poland project No. 2017/27/L/STS/03185.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Laboratory Experiment
#### 3.2.2 Number of Errors  
The results shown in Table 2 represent the number of errors made by workers during the assembly of products P1 and P2 at traditional and smart manual assembly workstations. It has been shown  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Product Name** & **Traditional Manual Assembly Workstation** & **Smart Manual Assembly Workstation** \\ \hline \hline  & Final time (SD) (s) & Final time (SD) (s) \\ P1 & 216.8 (59.5) & 175.3 (53.5) \\ P2 & 187.4 (39.2) & 159.7 (51.6) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured times of experiments at traditional and smart manual assembly workstation.  
that with the implementation of a smart algorithm that controls smart technologies and tools at the workstation and guides the worker through the assembly process, the number of errors is reduced for more than 72%. Errors at the traditional manual assembly workstation are usually caused by workers who have assembled the wrong parts or the right parts in the wrong place. All workers have discovered and successfully corrected the errors themselves. However, errors on the smart manual assembly workstation occurred when the right component was assembled in the wrong product location. All workers discovered the errors themselves and corrected them successfully. At the smart manual assembly workstation, the errors where a worker reaches into the grab containers for parts were eliminated. On the contrary, the errors that occur during the assembly process itself are still present. In further work, the segment of error elimination will be addressed by upgrading with the AR technology.
## 3 Results' Comparisons
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Size (\(\lambda_{0}\))** & **Substrate** & **IBW (\%)** & **ARBW (\%)** & **Peak Gain (dBi)** \\ \hline
[9] & 1.02 \(\times\) 1.02 \(\times\) 0.028 at 5.5 GHz & Rogers 5880 & 25.8 & 20.6 & 8 \\
[10] & 1.45 \(\times\) 1.45 \(\times\) 0.028 at 5.4 GHz & Rogers 5880 & 15.9 & 11.8 & 12.5 \\
[11] & 1.43 \(\times\) 1.43 \(\times\) 0.032 at 6.15 GHz & FR4 & 37.4 & 27.6 & 10 \\
[12] & 1.67 \(\times\) 1.67 at 5.4 GHz & FR4 & 52 & 31 & 7.5 \\
[14] & 2.07 \(\times\) 1.79 \(\times\) 0.01 at 5.6 GHz & FR4 & 78.5 & 35.7 & 14.2 \\
[15] & 0.4 \(\times\) 0.4 at 1.63 GHz & Rogers 4003 & 31.9 & 21.56 & 5 \\
[18] & 2.02 \(\times\) 2.33 \(\times\) 0.18 at 6.16 GHz & FR4 & 64.28 & 54.28 & 10.84 \\
[19] & 1.72 \(\times\) 1.72 \(\times\) 0.22 at 7.55 GHz & Rogers 5870 & 55 & 54.9 & 12.1 \\
[21] & 0.86 \(\times\) 0.86 \(\times\) 0.03 at 5.75 GHz & FR408 & 29 & 15.5 & 8.25 \\
[24] & 1.26 \(\times\) 1.26 \(\times\) 0.046 at 5.9 GHz & Rogers 4003 & 55.6 & 41.67 & 12.08 \\
[25] & 1.64 \(\times\) 1.64 \(\times\) 0.072 at 6.15 GHz & F4B, Duriod & 62.7 & 56.7 & 14.1 \\
[27] & 1.6 \(\times\) 1.6 \(\times\) 0.065 at 9.6 GHz & Rogers 4003 & 41.45 & 33.16 & 13.5 \\
[28] & 1.85 \(\times\) 1 at 5.15 GHz & F4B & 74 & 49.5 & 10.2 \\
[31] & 0.12 at 1.78 GHz & FR4 & 66.7 & 44.7 & 6 \\
[33] & 1.63 \(\times\) 1.63 \(\times\) 0.13 at 2.5 GHz & FR4 & 28.4 & 23.6 & 13 \\
[34] & 0.4 \(\times\) 0.4 \(\times\) 0.17 at 1.5 GHz & FR4 & 66.2 & 47.8 & 7.1 \\
[36] & 1.21 \(\times\) 1.75 at 6.8 GHz & Rogers 4003 & 34.38 & 15.17 & 12.15 \\
[37] & 1\(\times\)1\(\times\)0.07 at 5.5 GHz & F4B & 28.2 & 20.9 & 9.7 \\ This Work & 1.31 \(\times\) 1.31 \(\times\) 0.21 at 4.5 GHz & FR4 & 77.67 & 69 & 11.1 \\ \hline \hline \end{tabular} \(\lambda_{0}\) refers to the wavelength of the corresponding center frequency in free space.  
\end{table} 
Table 1: Comparison of the proposed antenna to previous antennas. Figure 9: Simulated and measured results of (**a**) S\({}_{11}\). (**b**) AR curves. (**c**) Gain curves.
## 1 Introduction
This initiative is designed to leverage technology as a bridge between citizens and their local government, fostering an environment of mutual trust and engagement. By integrating citizens directly into the data collection and policy-shaping process, we ensure that local governance is responsive and attuned to the community's needs and perspectives. This endeavor is not only about enhancing the quality of life through smart city innovations, but also about reinforcing the social contract by demonstrating a genuine commitment to considering citizen input. Accordingly, this aligns with and actively contributes to the Sustainable Development Goals, promoting well-being and ensuring inclusive participationin all facets of urban life. Gathering information from citizens in a smart city is essential for institutions to understand what measures to take to enhance the quality of life of its residents. Various methods can obtain this information, including observation, interviews, bibliographic study, IoT, public cameras, websites, etc. Lately, totems have been frequently used, to offer online bureaucratic services to the public to simplify and speed up procedures [12]. However, concerns about protecting citizens' data are significant, focusing on potential exploitation within the framework of neoliberal ideologies [13]. Recently, the term \"smart street furniture\" has been used to describe interactive equipment [14]. Interactive kiosks find practical applications in various sectors such as transportation systems [15], tourism [16], healthcare [17], and enhancing societal value [18]. In the case study of Balaguer, citizen data was collected through questionnaires displayed on a smart street furniture device known as a totem, a device installed at the town hall entrance in the city's most central square. The totem allowed a two-way communication channel between citizens and the city governmental institutions. This information is crucial for developing effective social policies, fostering trust in institutions [19; 20; 21; 22], and meeting the needs of citizens. Our study leverages a multifaceted approach that integrates quantitative data analyses--derived from on-site sensors and simulations--with qualitative insights from citizen science activities. The quantitative analyses provide the empirical evidence needed to understand energy demand patterns, while the citizen science component enriches this data with human insights, making the research outcomes more holistic and actionable. This interconnected approach ensures that policy interventions are not only data-driven but also aligned with citizen perceptions and behaviors, enhancing the likelihood of successful implementation and positive outcomes regarding sustainability and quality of life improvements.
## 2 Materials and Methods
### Signal Preprocessing  
Under ideal conditions, the sEMG signal is superimposed by the signals of the muscle units involved in the body movement, and they would not crosstalk each other [26]. Signal analysis-based force estimation methods depend on extracting the biological signal components. However, most sEMG signals are composed of many different components, including background noise, resistive noise, and scatter-shot noise. Therefore, the sEMG signal is a time series signal coupled with the nonlinear and non-stationary feature, which has the characteristics of weak strength, a low-frequency range, and large randomness. This dynamically stochastic feature of the sEMG signal affects the reliability and accuracy of the muscle force estimation. It is often difficult for the traditional signal analysis-based force estimation methods to identify the different signal components and extract effective features.  
To decompose the nonlinear and non-stationary part, we introduce the moving average filter to separate the raw sEMG sequence for more convenience and effectiveness. After that, the nonlinearity data are normalized by using the sliding window technique.  
Suppose the sEMG acquisition system collected the raw sEMG time series which is noted by: \(S_{t}(l)=\left\{S_{1}^{l},S_{t}^{2},\ldots,S_{t}^{l}\right\}\).  
The nonlinearity series part of the raw sEMG series can be obtained by the sliding average filter, noted by: \(MS_{t}(l)=\left\{MS_{1}^{l},MS_{1}^{2},\ldots,MS_{t}^{l}\right\}\),  
\[MS_{t}^{k}=\left\{\begin{array}{l}\frac{S_{t}^{l}+S_{t}^{l}+\ldots+S_{t}^{l} }{k},\quad k=1,2,\ldots,w_{1}-1\\ \frac{S_{t}^{k-w_{1}+S_{t}^{k-w_{1}+S_{t}^{l}+S_{t}^{l}}}}{w_{1}},\,k=w_{1}, w_{1}+1,\ldots,L\end{array}\right. \tag{1}\]  
where \(w_{1}(w_{1}<L)\) is the sliding window size; \(S_{t}^{l}\) denotes the raw sEMG data at time step \(l\); \(MS_{t}^{k}\) denotes the sEMG nonlinearity data at time step \(k\).  
Muscle contraction is a continuous process, so we adopted a sliding window with an overlapping window to divide the entire sEMG series into multiple discrete time series by sliding window. The process of this algorithm is shown in Figure 3. Considering that the nonlinear sequence has strong characteristics of time series, and historical data hide rich operating experience, we presumed that \(W_{x}\) is window size, and sEMG time-series signals are divided into several segments by the window. Let, \(x_{i}=S_{t}(l),\overline{\chi_{t}}=MS_{t}^{l}\),  
\[x_{i} =\left(\overline{\chi}_{t-T_{x}+1},\overline{\chi}_{t-T_{x}+2}, \ldots,\overline{\chi_{t}}\right) \tag{2}\] \[t =T_{x},T_{x}+\Delta t,T_{x}+2\Delta t,\ldots\]  
where \(\Delta t\) is the slide step, \(t\) is the window size.  
Figure 3: Fix–length slide window algorithm.