esa-sceva/satcom-chunk-collection huggingface dataset:
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 3 Methodology
### Measurements  
Simulation results are verified with an antenna prototype produced based on the simulation model. Antenna reflection coefficient measurements were conducted using an Agilent Vector Network Analyzer 8720 ES, with the SOLT-calibration for the frequency range of 2-11 GHz with 50-ohm coaxial cables, and with an input reference power of 5 dBm. The antenna's efficiency was measured with Satimo Starlab antenna measurement system and with SH2000 dual ridge antenna.  
A hand phantom of IXB-090 provided by IndexSAR company was used in the measurements. The phantom hand was vertically oriented along the X-axis in the Satimo coordination system, and the antenna was located on top of the hand on XY-plane and approximately on 1 mm distance to the surface of the hand. The feed cable was installed in the Z-axis direction connected to the 90-degree turn connector, and finally the antenna, where the antenna connector was pointing to +Y-axis direction and the antenna open end to the Y-axis direction, as presented in Figure 4, where the 90 degree turn connector was mechanically supported by the paper tape. The total antenna efficiency was measured both in the free space and on the hand. Moreover, the S11 parameter of the antenna was measured with a female volunteer on the hand area as well as on the abdomen area. The Ethical  
Figure 3: (**a**) Vertical cross-cut illustrating the air gaps between the skin and the antenna due to pixels of the voxel model, (**b**) Smoothening layer replacing air gaps.  
Committee of the Human Sciences of University of Oulu has granted approval for the antenna measurements on the human abdomen area with the permission number 14/2018.
## 3 Results
### The Effect of Cutting Was Studied by Photographic Color Extraction
#### 3.3.2 Scintillation  
Gem scintillation is a kind of visual effect caused by the light changes of the different small facets. The greater the brightness, the greater the difference between light and dark, the stronger the scintillation degree of color. The scintillation is directly related to the L* value of the small facet, so when we study the scintillation of colored gemstones, the saturation is excluded and we only study the effect of the L* value on the scintillation.  
First, we study the influence factors of the scintillation degree in the standard round drill cutting (Figure 12). Scintillation is shown as the strength of contrast in static photographs, so we use Photoshop to increase the contrast of the picture from the left to right by 50. As seen transversely in Figure 12a-c, the more to the right, the stronger the scintillation, the histogram shows the differences in L. Therefore, the larger the difference between the brightest value and the darkest value, the stronger the scintillation. We keep the number of pixels of different gray levels close to each other, if there are only a small number of bloom points, the overall scintillation is not strong. In the vertical contrasts of Figure 12a-c, we find that the higher the L\({}_{\text{average}}\), the stronger the flicker perceived by the human eye. In conclusion, the higher the L\({}_{\text{average}}\) value and the \(\Delta\)L value, the stronger the scintillation. In the brightness histogram, which counts the number of pixels under different L values, the image is divided into about 2 million pixels. To ensure that it is a suitable visual area, the value of \(\Delta\)L begins with the value of L above 100 pixels.  
Figure 12: Cont.  
Then, we apply to tsavorite samples (Figure 13), Tsa-5, Tsa-29, and Tsa-31, that have \(\Delta\)L values of 212, 162 and 125 in the histogram, respectively, and their scintillation decreases gradually.  
L**+** in colored gems can affect the color saturation, although when L**+** becomes higher, the flicker degree is stronger, but the color is lighter. The color saturation in colored gems is more important than the flicker degree, so for colored gems' cuttings, we only judge \(\Delta\)L, the L value is evaluated in the color rating section. We divide the scintillation into three grades, when \(\Delta\)L \(<\) 130, the scintillation is weak, \(\Delta\)L \(\in\) 130-190, the scintillation is excellent, and \(\Delta\)L \(>\) 190, the scintillation is strong.
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 3 Security Analysis
### Attacks by the Dishonest Controller
#### 3.4.1 Entanglement Substitution  
A dishonest controller Charlie\({}_{1}\) does not prepare the agreed-upon hyperentangled Bell state \(\ket{\Phi}_{AB}^{PS}=\ket{\varphi^{+}}^{P}\otimes\ket{\varphi^{+}}^{S}\) but instead uses a different hyperentangled state \(\ket{\Phi}_{ABC}^{PS}\):  
\[\ket{\Phi}_{ABC}^{PS}=\frac{1}{\sqrt{2}}(\ket{\mathrm{HHH}}+\ket{\mathrm{VVV}} )_{ABC}^{P}\otimes\frac{1}{\sqrt{2}}(\ket{a_{1}b_{1}c_{1}}+\ket{a_{2}b_{2}c_{2 }})_{ABC}^{S} \tag{20}\]  
Subsequently, [PERSON]\({}_{n}\) sends the sequence \(S_{A}\) to [PERSON] and \(S_{B}\) to [PERSON]. [PERSON]\({}_{n}\) then attempts to steal [PERSON] and [PERSON]'s secret messages by measuring the sequence \(S_{C}\) using the measurement basis. However, in Step 3, [PERSON] prepares new qubits based on the string to be transmitted and uses a permutation operator to shuffle the order of the particles. This shuffling prevents [PERSON]\({}_{n}\) from knowing the quantum states of the encoded Code particles. Consequently, [PERSON]\({}_{n}\) cannot deduce [PERSON]'s secret information \(M_{B}\).
# Compliant Detachment of Wall-Climbing Robot Unaffected by Adhesion State
## 3 Robot System
### Control System Setup of Wall-Climbing Robot  
An online impedance adaptation controller is proposed to remove the trade-off between PVS attachment strength and ease of detachment. To realize the motion controller, a reproducible control system is adopted in IBSS-8, as shown in Figure 4. The robot's main controller, a Raspberry Pi 4 Model B, is responsible for motion control, including movement gait planning, joint movement parameter generation, and sensor data acquisition. The main controller communicates with the robot joint motors (Dynamixel XM430-W350), which support position control mode and torque control mode, via RS-485 interface. Depending on the control mode, the target torque or position is sent to the motors, which provide the current position, angular velocity, and torque as feedback. To ensure flexible motion in torque control, the communication frequency is set to 1 kHz (USB transmission limits). This motion control system is stable and efficient in practical operation and meets the requirements of most current control algorithms.  
The main controller can receive input information from control peripherals (e.g., handles) via Bluetooth. The communication frequency of this link is 10 Hz to save computing power. Inertial measurement unit data are read through a serial port to obtain robot attitude information. The communication frequency of this link is set to 200 Hz to obtain the robot pose accurately and in real time.  
As for the parameter in control method, based on the geometric parameters of the robot's single leg and adhesion foot, we test the foot trajectories with different lengths and heights and obtain the trajectories of the maximum length of the legs under the condition of stable detachments. Table 2 shows the trajectory parameters selected in this paper.  
Figure 4: Hardware framework of wall-climbing robot control.  
Equation (9) shows the parameters \(a,b\) (see Equation (8)) for joints 1-3 (see Figure 2b). The parameter \(a\) of joint 1 is bigger than that of joint 2 and 3, which can make the feet more compliant in the normal direction.  
\[a=\left[\begin{array}{ccc}1.6&0&0\\ 0&1.0&0\\ 0&0&1.0\end{array}\right],b=\left[\begin{array}{ccc}0.5&0&0\\ 0&0.5&0\\ 0&0&0.5\end{array}\right]. \tag{9}\]
## 5 Discussion
It should be noted that the satellite remote sensing-based FAPAR was used in our parameter optimization procedure, and uncertainty in the remote sensing FAPAR may be attributed to the uncertainty of the optimized parameters in some sites, which in turn generates uncertainty in the estimated GPP. Improvement on remote sensing FAPAR is necessary for increasing the accuracy in global cropland GPP estimation using LUE type models.
# Transitioning Broadcast to Cloud
## 6 Conclusions  
In this paper, we have studied the differences between on-premise broadcast and cloud-based online video delivery workflows and identified means needed for bridging the gaps between them. Such means include: improvements in cloud ingest, signal processing stacks, transcoder capabilities, and most importantly, a broadcast-grade cloud playout system. To implement a cloud playout system, we have suggested an architecture employing intra-only mezzanine format and associated processing blocks that can be easily replicated and operated in fault-tolerant fashion. We finally considered possible evolutions of broadcast and cloud-based video systems and suggested several possible hybrid architectures, with different degrees of offloading of processing in cloud, that are likely to emerge in the future. Examples of operational statistics observed in today's mass-deployed cloud-based media delivery systems were also shown. These statistics confirm that such systems can indeed handle the load required for transitioning of additional elements of broadcast systems to cloud.  
Conceptualization, Y.R. and J.C.; methodology, Y.R., J.C. and B.Z.; software, J.C. and B.Z.; validation, Y.R., J.C. and B.Z.; formal analysis, Y.R.; investigation, Y.R. and J.C.; resources, Y.R., J.C. and B.Z.; data curation, B.Z. and Y.R.; writing--original draft preparation, Y.R.; writing--review and editing, J.C. and B.Z.; visualization, Y.R. and B.Z.; supervision, Y.R.; project administration, Y.R.; funding acquisition, Y.R.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.  
Figure 15: The numbers of concurrent streaming players pulling the content.
## 1 Introduction
[19] added hemp biochar to a wetland, resulting in reduced PO4\({}^{3-}\) concentration, from 15.5 mg/L to less than 2 mg/L (PO4\({}^{3-}\)). Another key approach has been to reduce the amount of nonpoint source phosphorus runoff from agricultural lands [20]. [PERSON] et al. [21] used 4.5 \(\times\) 10\({}^{7}\) g of aluminum treated slag steel in a 72 m by 1.5 m trench to sorb 27% of dissolved PO4\({}^{3-}\) from farm areas. [PERSON] et al. [22] used a 1.5 m deep, 1.5 m side, and 10 m long limestone barrier to obtain a 13% phosphorus removal efficiency in a riparian buffer. Although effective, both the aforementioned trench and buffer systems required a large footprint, and the media was not refreshable. Other research has used WTR as an amendment in buffer strips. [PERSON] et al. [23] found that if the rain event created a water velocity of with a travel time of approximately 3 s over the buffered area, there was insufficient time for the WTR amendment to bind PO4\({}^{3-}\). [PERSON] et al. [24] also found that effectiveness of amended buffer strips was dependent on flowrate, size of the buffer strip and time of service. While not all of the above mentioned experiments used WTR and none used small-scale, rechargeable instream gate structures such as those evaluated in this research, they do corroborate the hypothesis that this approach can provide long-term effectiveness as well as contribute to the actual removal of sequestered PO4\({}^{3-}\) from the system completely. The objectives of the current research were to: 1. design a full-scale passive treatment system utilizing WTR to adsorb PO4\({}^{3-}\) from surface water and 2. determine the in-field effectiveness of the treatment system. Design of the treatment system involved the selection of appropriate material to 'bag' the WTR as well as a series of cartridges to house the mesh bags filled with WTR material. This research builds upon laboratory testing using batch sorption-desorption isotherms and a continuous flow column of Al-WTR for PO4\({}^{3-}\) removal. Batch experiments determined a cumulative adsorption of 33.93 mg PO4\({}^{3-}\)/Kg-WTR. The continuous column experiments which utilized more water had a higher PO4\({}^{3-}\) uptake of 123 mg PO4\({}^{3-}\)/Kg-WTR. Full details of the lab scale assessment of the WTR can be found in [PERSON] and Cutright [25].
## 5 Multi-Parameter Analysis of Angular and Linear Deviations
### Multi-Parameter Analysis of Linear Deviations
As a remark, the (minimum) value \(e\) equal to 25 mm was chosen to avoid the situation when the  
Figure 11: **(a)** Minimum linear deviations/radiuses \(\Delta_{j}(\theta)\), \(j=a,\ b,\ c,\ d\) of the blind spot, i.e., of the central disk of the scan patterns (Figure 2(a,b,c,d,d)): study carried out for identical prisms (with \(n_{1}=n_{2}=n\) and \(\theta_{1}=\theta_{2}=\theta\), therefore for \(k=1\)) for each of the four configurations, (**a**) Equation (15), (**b**) Equation (21), (**c**) Equation (27), (**d**) Equation (35). Characteristics of the study on columns: (**1**) Exact radiuses \(\Delta_{j}(\theta)\), \(j=a,\ b,\ c,\ d\) compared with the approximate/linearized radius \(\Delta_{j}^{lin}(\theta)\), \(j=a,\ b,\ c,\ d\) given by (**a**) Equation (16), (**b**) Equation (22), (**c**) Equation (28), and (**d**) Equation (36) for \(n=1.517\), \(e=25\) mm, and \(b=12.7\) mm; (**2**) Exact radiuses for three values of the distance between the prisms \(e_{1}=25\), \(e_{2}=50\), and \(e_{3}=75\) mm, for \(n=1.517\) and \(b=12.7\) mm; (**3**) exact radius for three values of the refractive index of the prisms \(n=1.517\), \(n^{\prime}=1.7\), and \(n^{\prime\prime}=1.9\) mm, for \(e=25\) mm and \(b=12.7\) mm; (**4**) exact radius for two values of the radius of the prisms, \(b_{1}=12.7\) and \(b_{2}=25.4\) mm, for \(n=1.517\) and \(e=25\) mm. prisms were getting in contact even for commercially-available prisms with the maximum common deviation \(D=10^{\circ}\), for a \(1^{\prime\prime}\) diameter of the prisms. To increase \(\Delta\), increasing \(e\) is the best solution for all configurations. (3) Exact radiuses were obtained for three values of the refractive index of the prisms, \(n=1.517\), \(n^{\prime}=1.7\), and \(n^{\prime\prime}=1.9\) mm (for \(e=25\) mm and \(b=12.7\) mm). For all configurations, \(\Delta\) increases with \(n\), but this is a less effective solution. A useful remark is that a minimum \(\Delta\) is obtained for the first, most common \(n\) (corresponding to the BK7 optical glass). (4) Exact radiuses were studied for two values of the radius of the circular prisms, \(b_{1}=12.7\) and \(b_{2}=25.4\) mm (with \(n=1.517\) and \(e=25\) mm). For the smaller \(b\), \(\Delta\) is slightly higher for configurations (a) and (b), higher for configuration (c), and lower for configuration (d). However, to be cost-effective, one must consider that prisms with \(b=12.7\) mm (i.e., with \(1^{\prime\prime}\) in diameter) are commercially-available and therefore less costly.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 2 Materials and Methods
### GPP and NEE  
We obtained GPP derived from MODIS (MOD17A2H 006) for the years 2018 to 2022 from Google Earth Engine. This product utilizes MODIS land remote sensing data such as reflectance and chlorophyll indices, along with meteorological data like temperature and radiation, to estimate vegetation photosynthetic activity and productivity. It has temporal and spatial resolutions of 8 days and 500 m, respectively, and we also aggregated it to a monthly scale [34]. GPP measures the rate at which plants convert solar energy into organic substances through photosynthesis, thereby indicating the overall efficiency of photosynthesis in plants. It can also be roughly understood as the total energy fixed by plants per unit area or unit time, serving as an important indicator of plant productivity.  
We use the net ecosystem exchange (NEE) indicator to describe the carbon exchange between ecosystems and the atmosphere. This data originates from the SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange, Version 7 ([[https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)]([https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)), accessed on 25 March 2024), a dataset provided by NASA's Soil Moisture Active Passive (SMAP) satellite. It employs the baseline algorithm, combining ground observations with satellite remote sensing data, such as SMAP's microwave observations and meteorological data, to estimate carbon fluxes in terrestrial ecosystems. This product provides daily NEE data globally, utilizing a 9 km resolution EASE-Grid. Like other datasets, we compute averages to obtain monthly scale data.
## 3 Semi-Automated Image Analysis on ADC Maps
### Tumor segmentation on T1 wCE MR images  
All tumors were segmented on T1 wCE images via a semi-automated method using the [PERSON]'s thresholding algorithm [36] and seeded region growing [37]. First, radiologists drew a line from inside of the tumor to the outside of the tumor on the approximate center slice of the tumor. Then intensity values along the line were collected to form a bimodal histogram, and the [PERSON]'s thresholding method was used to find the optimal thresholding value. Afterwards, a 3D seeded region growing was applied to obtain the segmentation results in the whole volume. Threshold-based segmentation methods are a standard approach to calculation of tumor volume.  
The concept behind the [PERSON]'s thresholding method [36] is to find the threshold that minimizes the weighted within-class variation \(\sigma_{w}^{2}\) as in equation (2), considering the two-class segmentation into object and background:  
\[\begin{array}{c}\sigma_{w}^{2}(t)=q_{1}(t)\sigma_{1}^{2}(t)+q_{2}(t)\sigma_{ 2}^{2}(t)\\ q_{1}(t)=\sum_{i=1}^{t}P(i)\hskip 20.0 pt\sigma_{1}^{2}(t)=\sum_{i=1}^{t}[i- \mu_{1}(t)]^{2}\frac{P(i)}{q_{1}(t)}\hskip 20.0 pt\mu_{1}(t)=\sum_{i=1}^{t} \frac{iP(i)}{q_{1}(t)}\end{array} \tag{2}\]  
with \(q(t)\) as the class probability, \(\mu_{c}(t)\) as class mean, \(\sigma_{c}(t)\) as class variance, c = 1,2 as two different classes, t is variable for the intensity value, and P(.) is the probability density function. Given an initial class mean and variance, the algorithm will do an exhaustive search by altering the thresholding value to find the optimal thresholding value.  
Afterwards, seeded region growing [37] using the optimal thresholding value was applied to get the tumor contours in the 3D volume. Radiologists reviewed the results and made manual corrections when necessary. Figure 1(A) shows an example of a segmented tumor on a T1 wCE image.  
Figure 1: **(a)** An example of the tumor segmented on a T1 wCE image; **(b)** An example of the tumor ROI mapped from T1 wCE to ADC map; **(c)** An example of the tumor ADC histogram fitted by two-component Gaussian mixtures.
## 4 Computer Simulations
In the next series of simulations, we compare the performance of the proposed two schemes with [PERSON]'s approach [19]. Figure 4 plots the MSE curves _versus_ the input SNR when \(\epsilon_{d}=0.3\) for all of the considered schemes. As expected, the MSE is shown to decrease as we increase the input SNR. The proposed iterative estimator requires five iterations to reach a performance comparable to our proposed search-based estimator while achieving the substantial reduction of computational complexity shown in Section 3. It can be found that [PERSON]'s method fails to offer a reliable estimate when the estimate of the channel order \(L\) is not correctly provided. The MSE characteristics in response to the normalized CFO are presented in Figure 5 given SNR = 30 dB. It can be observed that the MSE performance is almost invariant to the actual CFO values regardless of the employed methods. However, in case an erroneous estimate on the channel order is made, there is significant performance degradation in the MSE of [PERSON]'s estimator. Figure 6 presents the MSE curves _versus_ the RMS of the channel delay spread for all of the considered schemes. It shows that the MSE performances for both of the iterative and search-based estimators are nearly the same and gradually degrade while \(T_{RMS}\) is getting larger. This is because the interpolated and actual channel responses do not coincide when the linear interpolation prevents the proposed schemes from acquiring a perfect time-domain channel response. The other way round is that [PERSON]'s method is strongly dependent on \(T_{RMS}\). Especially, for the case with a large \(T_{RMS}\), say \(T_{RMS}>3\), the proposed schemes have much better performances than that using [PERSON]'s scheme. Note that there is a distinguished degradation in the MSE performance once again for using [PERSON]'s method when the channel response order \(L\) is not well estimated in Figure 6.  
Figure 3: Convergence behavior of the proposed iterative CFO estimation for different RMSs of channel delay spread, \(\tau\). \(N\) = 256, \(K\) = 64, SNR = 30 dB and \(\epsilon_{d}\) = 0.3. Figure 4: MSE performance of the proposed and [PERSON]’s CFO estimators for different SNRs. \(N\) = 256, \(K\) = 64, \(\epsilon_{d}\) = 0.3, \(\tau\) = 4 and \(J\) = 5. Figure 5: MSE performance of the proposed and [PERSON]’s CFO estimators for different CFOs. \(N\) = 256, \(K\) = 64, SNR = 30 dB, \(\tau\) = 4 and \(J\) = 5.
## 3 Results and Discussion
### Adsorption Performance
#### 3.2.2 Effects of Time and Temperature  
The influence of contact time and temperature on the adsorption of high-concentration m-cresol (20 g L\({}^{-1}\)) using 0.15 g of resin was investigated to simulate conditions typical of coal-based industrial effluents. As shown in Figure 8b-e, the adsorption process exhibited a biphasic pattern: rapid initial uptake followed by a slower approach to equilibrium. This behavior is attributed to the abundant availability of active sites during the early stages and subsequent site saturation over time. Notably, the adsorption process reached near-equilibrium within approximately 240-480 min, as shown in Figure S1, depending on temperature, with higher temperatures accelerating the approach to saturation. Temperature significantly impacted both the adsorption rate and equilibrium capacity. At 30 \({}^{\circ}\)C, the adsorption capacity was 493.3 mg g\({}^{-1}\), which progressively increased to 556.3 mg g\({}^{-1}\) at 60 \({}^{\circ}\)C (Table S1). Correspondingly, removal efficiency improved from 65% to 71%. These results clearly indicate that the adsorption process is endothermic in nature. Elevated temperatures enhanced molecular diffusion, reduced boundary layer resistance, and increased the interaction energy between m-cresol and functional groups on the XDA-1G resin surface.  
Figure 8: Effect of temperature and contact time on m-cresol adsorption. **(a)** Equilibrium adsorption capacity as a function of temperature; **(b–e)** adsorption kinetics at 30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C using 0.15 g of XDA-1G resin.  
These observations are well supported by material characterization. The XDA-1G resin's high BET surface area (1439 m\({}^{2}\) g\({}^{-1}\)) and hierarchical porosity (as confirmed by N\({}_{2}\) adsorption-desorption and DFT analysis) offer numerous pathways for intraparticle diffusion. In addition, FTIR and XPS analyses confirmed the presence of surface hydroxyl and carbonyl groups, which facilitate \(\pi\)-\(\pi\) interactions and hydrogen bonding with m-cresol. These interactions are thermally activated, explaining the improved adsorption at higher temperatures.  
In summary, the results affirm that increasing temperature enhances both the kinetics and capacity of m-cresol adsorption. Combined with the XDA-1G resin's structural attributes, these findings demonstrate its suitability for thermally optimized treatment of concentrated phenolic wastewater streams.