esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
UVSQ-SAT was launched on 24, January 2021 by the Falcon 9 rocket in the frame of the \"Transporter 1\" mission (Figure 1 right). Since then, the satellite is on a sun-synchronous orbit around the Earth at an altitude of about 533 km (515 km at perigee), an inclination of 97.5010\({}^{\circ}\), an eccentricity of 0.0014455, a right ascension at the ascending node of 128.424\({}^{\circ}\), and an argument at perigee of 94.1573\({}^{\circ}\). The duration of an orbit is about 95.18 min. The selected orbit presents eclipses, which allow to realize calibrations with observations towards the deep space or only towards the Earth. This manuscript presents first UVSQ-SAT in-orbit measurements of the Earth's outgoing radiation. Section 2 presents the objectives of the UVSQ-SAT mission and the importance of measuring the Earth's radiation budget. The expected scientific requirements on the measurement of the Earth's energy imbalance will be presented. They are given for the UVSQ-SAT demonstrator and the future constellation (Terra-F) which must be disruptive. Section 3 presents the methods used to reconstruct the maps of the albedo and the OLR at the TOA. They are applicable to the observations by the UVSQ-SAT's instruments and the model used. Section 4 shows the first observations made by the UVSQ-SAT mission and the preliminary results. Section 5 highlights the perspectives related to this study and finally in Section 6, the conclusions are presented. Figure 2: UVSQ-SAT environmental tests and calibrations carried out during the year 2020. (**a**) Vibration. (**b**) EMC tests. (**c**) Calibration with a Xenon lamp and a black body. (**d**) Thermal vacuum and thermal balance tests. (**e**) Magnetic cleanliness verification of the satellite. (**f**) End to end tests with the UHF/VHF LATMOS ground-based station (telemetry at 437.020 MHz, telecommand at 145.905 MHz).
# The Removal of Crystal Violet from Textile Wastewater Using Palm Kernel Shell-Derived Biochar
## 4 Conclusions  
The feasibility of the agricultural by-product PKS for removing CV from textile wastewater was investigated in this study. Kinetic studies revealed that chemisorption is the key limiting factor for the rate of CV adsorption. The equilibrium data were better fitted to the Langmuir model, indicating that CV adsorbed on BC-PKS as a monolayer with uniform CV molecules adsorption. Thermodynamic studies revealed that CV adsorption to BC-PKS was a spontaneous, endothermic process with increasing CV adsorption to BC-PKS at higher temperatures. The increase of solution pH changed the surface charge of BC-PKS, which served favorable adsorption sites for cationic CV dyes via electrostatic attraction. When the BC-PKS loading rate was increased, the percentage of CV removal was found to increase due to the increase of available adsorption sites, but the adsorption capacity began to decrease because of the decline of the flux gradient. The presence of cations in the wastewater decreased the CV adsorption to BC-PKS because of increased competition for adsorption sites. It can be concluded that biochar prepared from the agricultural byproduct has adequate adsorption capacity and can be used as an efficient and cost-effective adsorbent for the removal of CV from textile wastewater.  
P.P.K.: Experiment, Data curation; J.O.Q.: Formal analysis, Visualization, Writing-original draft; C.-G.L.: Investigation, Validation; J.-K.M.: Conceptualization, Writing-review & editing; S.-J.P.: Conceptualization, Writing-review & editing, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1D1A1B03030649).  
The authors declare no conflict of interest.
## 3 Results
### Evaluation of Classification Accuracy Based on XGBoost for Different Altitude Tree Species
#### 3.4.3 Altitude 2000 m  
As shown in Table 12, unlike the previous two study areas, only two dominant conifer species, _Pinus armandii_ and _Larix gmelini_, were screened at an altitude of 2000 m, with user accuracy (UA) values of 100% and 81.82%, respectively. It was also found that there was serious confusion among the broad-leaved species, for example, the UA of _Cyclobalanopsis glauca_ was only 33.33%, which was easily confused with _Fargesia spathacea_ and _Quercus aliena var. acutiserrata_, etc. In addition, _Larix gmelini_ was also easily confused with several other broad-leaved species. Figure 7 shows the tree species classification results.
## 6 Conclusions and Outlook on Future Work  
In this paper, a thick ellipsoid state estimation procedure was presented which allows for a computation of outer and (if the uncertainties are not too large) inner ellipsoidal enclosures. The advantage of the presented technique over other ellipsoidal bounding approaches such as those in [19; 56] is the applicability to general (differentiable) nonlinear state equations (in this paper, in quasi-linear form) as well as its reduced complexity by using a solution technique in which complex minmax optimization procedures are replaced by simple one-parameter optimizations which only require the online computation of eigenvalues.  
In future work, a combination of stochastic and set-valued uncertainty models can be investigated using the novel thick ellipsoid definitions. Other existing techniques such as those presented in [61] also make use of a Kalman filter-like structure. However, they are mainly focused on linear measurement equations. This can be circumvented by the proposed technique if the system's output equations are reformulated in a quasi-linear form and the influence of nonlinearities is mapped onto the uncertainty that represents the measurement noise.  
In addition, the simulation results in Section 5 have shown that the observer approach is capable of reconstructing bounded disturbances. This property can be exploited in future work also for an observer-based parameter identification. Finally, the thick ellipsoid-based state prediction can be employed directly for a numerical evaluation of classical Luenberger-like observers and Extended Kalman Filters. With these results, it will become possible to perform a stability analysis of these estimation schemes when applied to nonlinear dynamic systems. The procedure will be similar to the one proposed in [33], where a centered-form representation of discrete-time dynamic systems was employed for the derivation of stability contractors.  
The algorithm was designed jointly by [PERSON], [PERSON], and [PERSON] and implemented as well as validated numerically by [PERSON] The illustrations were made by [PERSON] and [PERSON]. The paper was jointly written by [PERSON], [PERSON], and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 13: Comparison of the estimation results with noisy measurements and true system states for the case of small uncertainties (92). (**a**) Estimated bounds \(\left[x_{1,k}\right]\) vs. measured and true velocities \(y_{\text{m}1,k}\) and \(x_{1,k}\). (**b**) Estimated bounds \(\left[x_{2,k}\right]\) vs. measured and true velocities \(y_{\text{m}2,k}\) and \(x_{2,k}\). (**c**) Estimated bounds \(\left[x_{3,k}\right]\) vs. measured and true velocities \(y_{\text{m}3,k}\) and \(x_{3,k}\). (**d**) Estimated bounds \(\left[d_{3,k}\right]\) vs. true disturbance \(d_{3,k}=0.05\).
# Methods for Determining Polycyclic Aromatic Hydrocarbons (PAHs) in Milk: A Review
## 3 PAH Detection Methods in Milk and Dairy Products
### Instrumentals Detection Methods
#### 3.2.1 HPLC
They obtained satisfactory detection limits for all hydrocarbons except acenapthlylene. Recovery they obtained ranged from 90.12-93.78%. They estimated that chrysene and benzo(k)fluorene were found only in pasteurized and UHT milk samples, suggesting that the heath treatment used certainly influenced the PAH formation. [PERSON] and co-workers [94] used HPLC combined with UV-VIS and FLD for the analysis. They obtained a recovery rate for all PAHs of more than 79%. Evaluating commercially available milk powders, they concluded that the products in Brazil and Argentina had alarmingly high levels of hydrocarbon contamination.
# An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems
## 2 Design of the System Architecture  
The AHS, which was designed to adapt inventoried sensor devices, mainly included an ultralow-power MCU system, a communication module, and a power module. The overall architecture is shown in Figure 1. The AHS took the ultralow-power MCU system as its core, and enabled and controlled the boost chip to turn the working power supply of the sensor device on or off. It obtained the data acquired by the sensor device or configured its relevant parameters by adapting the 485 interface communication protocol; connected and controlled its communication module through UART; and exchanged data with the server through ultralow power wireless transmission, which included uploading the data acquired by the sensor device and receiving the control command parameters sent by the server to control the workflow of the system.
## Materials and Methods
### Catalyst Characterisation  
X-ray photoelectron spectroscopy (XPS) was performed on a Thermo Scientific K-alpha\({}^{+}\) spectrometer. Samples were analysed using a monochromatic Al X-ray source operating at 72 W (6 mA \(\times\) 12 kV), with the signal averaged over an oval-shaped area of approximately 600 microns \(\times\) 400 microns. Data was recorded at pass energies of 150 eV for survey scans and 40 eV for high resolution scan with a 1 eV and 0.1 eV step size respectively. Charge neutralisation of the sample was achieved using a combination of both low energy electrons and argon ions (less than 1 eV), which gave a C(1s) binding energy of 284.8 eV. All data were analysed using CasaXPS (v2.3.17 PR1.1) using Scofield sensitivity factors and an energy exponent of \(-\)0.6. Particle size distributions and mean particle size were obtained by means of transmission electron microscopy (TEM) using a JEOL JEM 2100 TEM operating at 200 kV. Samples for examination were prepared by dispersing the catalyst in high purity ethanol. A drop of the suspension was allowed to evaporate on a holey carbon film supported by a 300-mesh copper TEM grid. Samples were subjected to bright field diffraction contrast imaging experiments. Mean particle sizes and particle size distributions were determined by measuring the size of over 200 particles from different selected areas. Scanning electron microscope (SEM) images were taken on Hitachi TM3030 PLUS equipped with a Quantax70 energy-dispersive X-ray spectroscope (EDX) in order to study the morphology and determine the palladium content of the samples (fresh and used catalysts). BET surface area was determined from the N\({}_{2}\) adsorption-desorption at liquid nitrogen at 77 K using a Quantachrome NOVA 2200e instrument. Samples were outgassed for 3 h under vacuum at 227 \({}^{\circ}\)C. Total surface area was determined using the BET (Brunauer-Emmett-Teller) equation and the multi-point method.
## 5 Discussion and Practical Tests
### Portfolio Risk Forecasting  
This section conducts our first practical test: portfolio risk forecasting. The risk of a portfolio involves two key elements: volatilities and correlations. This study employs various models for dynamic carbon-energy volatilities and correlations, including the GARCH, DCC, and SWARCH models. The question arises: would state-dependent volatilities and correlations involved in our bivariate SWARCH model help with portfolio risk forecasting, compared with the simple time-dependent volatilities and correlations in the conventional bivariate GARCH and DCC models?  
To answer this question, we construct the equal-weighted energy-carbon volatilities and correlations for each of the three models (bivariate GARCH-CCC, bivariate GARCH-DCC, and bivariate SWARCH); we then calculate the model-implied variance of portfolio return residuals at each time \(t\) over the sample period. It should be noted that, since there are four different volatility state combinations at time \(t\) in our bivariate SWARCH model, we first compute the model-implied standard deviation for each volatility state (i.e., LV-LV, HV-LV, LV-HV, and HV-HV) and then determine the weighted average standard deviation based on the probabilities of realizing the four state combinations.  
We calculate MAE (Mean Absolute Error) and MSE (Mean Square Error), the two most prominent forecasting performance measures, for each model and present the results in Table 7. The table shows that the bivariate SWARCH model with state-varying correlations is associated with the smallest MSE and MAE. Moreover, we use the GARCH-CCC model as a benchmark to calculate the t-statistic for the difference in MSE and MAE. Notably, the statistic is significant at 1% for the SWARCH model. Our conclusion is clear. Our proposed regime-switching volatilities and correlations may help with EUA-WTI portfolio risk forecasting.  
The MSE and MAE are defined as follows:  
\[MSE=T^{-1}\sum_{d=1}^{T}\left(r_{p,t}^{2}-\sigma_{p,t}^{2}\right)^{2},\ MAE=T^{-1}\sum_{d=1}^{T}|r_{p,t}^{2}- \sigma_{p,t}^{2}|\]  
where \(r_{p,t}\) is the return of the equal-weight portfolio of energy and carbon at time \(t\), and \(\sigma_{p,t}\) is the estimated standard deviation of the respective carbon-energy portfolio return residuals according to each of the models.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 4 Analysis
### Quality of FCB Products  
We used observation data from 152 globally distributed MGEX stations to estimate GCE FCBs, in addition to evaluating the daily stability of NL FCBs, as shown in Figure 3. NL FCBs in Galileo were the most stable, with all NL FCBs remaining within 0.1 cycles in a day. Among GPS satellites, the G23 satellite showed a range of 0.3 cycles, while other satellites remained stable within 0.1 cycles. For BDS-3, the stability was relatively poor. Satellites C34 and C46 had the largest changes, up to 0.6 cycles, while other satellites had changes within 0.4 cycles. Overall, BDS-3 exhibited the worst NL FCB stability among the three systems. The average STD of the NL FCBs for G, C, and E were 0.016, 0.054, and 0.014 cycles, respectively.
## 4 Bringing CRKG into the Field: Securing Practical IoT Communications
### Open Research Challenges  
* **Narrow-band IoT (NB-IoT).** NB-IoT is a new technology standard, designed to broaden the future of IoT connectivity. However, no CRKG prototype has been developed for NB-IoT yet. One major issue is that Release 13 NB-IoT only supports FDD mode.
* **Protocol limitation.** Some IoT protocols are not flexible to provide ideal condition for channel sounding. For example, LoRaWAN is the most popular MAC layer protocol for LoRa and the class-A is compulsory for all the implementation. There will be a one-second interval between the uplink and downlink transmissions, which will impact the correlation of the channel measurements.
* **Static Devices.** Many IoT devices, e.g., unattended devices, can be fixed and the channel randomness is limited in this case. The channel variations induced by the environment changes might not be significant and it is difficult for the low cost IoT devices to be sensed. This challenge can be tackled by leveraging beamforming, helper devices or artificial randomness [59].  
Figure 8: Received power of [PERSON] and [PERSON] operating with WiFi and LoRa.  
* **Frequency Hopping.** Frequency hopping spread spectrum (FHSS) technology is widely used in military and civilian IoT communications, e.g., Bluetooth. However, it is very challenge to apply CRKG in a FHSS system because the devices change the carrier frequency and the channel reciprocity is impacted.
* **Massive Connectivity.** The performance of CRKG is impaired for massive connectivity scenarios. The overhead of CSI acquisition scales with the number of IoT devices and becomes extremely large with massive IoT devices. Meanwhile, some IoT applications only transmit short packages. The huge overhead makes CRKG impractical. Besides, when non-orthogonal pilot signals are employed among different devices, it may bring interference to degrade the correlation of channel observations.
###### Abstract  
The preservation of the alveolar ridge has gained increasing importance for various types of rehabilitation, including dental implant placement. Consequently, researchers have explored different bone grafts, such as mineralized dentin matrix grafts. However, a comprehensive review of the efficacy of autogenous mineralized dentin (AMD) for alveolar ridge preservation remains lacking. In this review, we evaluated the efficacy of AMD as a method for alveolar ridge preservation in cases of delayed implant placement. A comprehensive search through PubMed, Google Scholar, Cochrane Library, and B-on repositories was conducted without time constraints up to July 2024 to identify peer-reviewed human studies. These studies assessed the percentage of newly formed bone and residual graft following bone regeneration with AMD grafts after tooth extraction, specifically in the context of delayed implant placement. Our analysis included four selected studies involving 55 patients and 67 sockets. The findings suggest that AMD grafts resulted in an average (and 95% confidence interval) of 43.8% [36.6%, 50.8%] newly formed bone, and delayed implant placement was a feasible surgical option for all patients. Although the available literature is scarce, AMD grafting has yielded promising outcomes as a method for bone reconstruction. Nevertheless, additional randomized controlled trials with larger sample sizes and longer follow-ups are required to substantiate these findings.  
marginalized dentin matrix; dentin graft; dental implants; alveolar ridge preservation Article  
and osteogenic fibers form, contributing to the formation of osteoid tissue at the base of the socket [5; 6]. Non-calcified bone spicules become identifiable by day seven, and immature bone forms within two weeks post-extraction. This immature bone will later be replaced by mature bone [3; 6]. Osteoid tissue and non-calcified bone spicules play a crucial role in bone mineralization during the following two to three weeks, a process that begins at the base of the alveolus and continues toward the coronal portion, lasting up to six months post-extraction [4; 7]. The final phase, modeling and remodeling, involves significant bone shape and architecture changes. During this phase, bone resorption occurs (modeling), along with the replacement of cancellous bone by lamellar bone (remodeling), without altering the overall shape [3]. Bone modeling continues for several months, with the most pronounced changes occurring within the first three months [8]. These phases are critical for understanding the tissue dynamics involved in post-extraction socket healing and provide a foundation for optimizing clinical outcomes in dental extractions.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 4 Secure WiFi Fingerprint Localization
### Offline Update Phase  
Due to the online phase and the offline phase having a certain time difference, during which the AP is likely to be impaired, resulting in the RSSI being biased against the existing RSSI in the database, if we do not detect the trusted value of APs, the impaired AP will have a greater impact on the localization results. Therefore, in order to determine the credibility of APs, the algorithm proposed in this paper votes for APs, then updates the fingerprint information in the database and the weight information of the APs.
## 2 MFC and Power Management System Specification
### MFC Construction and Characterization  
Four MFCs were fabricated from an acrylic anode and cathode chambers. One 1000 mL MFC and three 240 mL MFCs were constructed, the two different sizes were used to simulate MFCs having different output power levels. A Proton Exchange Membrane (PEM) (Nafion 117\({}^{\mathrm{TM}}\), Ion Power Inc., New Castle, DE, USA) was used to separate the anode and cathode chambers from each other, while selectively allowing proton generated by bacteria to cross over to the cathode chamber to complete the electrochemical reaction to produce power. The anode was a carbon felt (Morgan, Durham, CT, USA) and the cathode was a carbon cloth containing 0.5 mg/cm\({}^{2}\) of Pt catalyst on one side (ElectroChem, Inc., Woburn, MA, USA) for all MFCs. Both anode and cathode were connected through a titanium wire to a 1 k\(\Omega\) load resistor that was placed between them to allow the electrons produced by bacteria to flow. The schematic diagram of the MFC is shown in Figure 1. The electrical model in Figure 1 is further discussed in Section 2.2.  
All MFCs were inoculated with anaerobic activated sludge collected from the Austin Wastewater Treatment Plant for the initial growth of the bacteria on anodes. Then growth medium containing acetate (1.0 g/L) in Nutrient/Mineral/Buffer (NMB) solution was used (10 mL/L mineral base 1, 10 mL/L mineral base 2, and 1 mL/L nutrient base). The solution was replaced with a fresh one when the voltage across the load resistor dropped below 50 mV [28; 37; 38; 39]. The constructed two-chamber MFCs are shown in Figure 2.  
The MFC voltages were monitored in real time in volts (V) against time in seconds (s) [40] by using a digital multimeter (Fluke 79 series II, Everett, WA, USA) through a multiplexer (Agilent 34970A, Santa Clara, CA, USA). Once the voltage production level was stable, power and voltage at maximum power point were obtained by varying a testing load resistor value between the electrodes in the range of 5-20 \(\Omega\)[41]. Table 1 summarizes the specifications and typical power production of all MFCs.  
Figure 1: Microbial Fuel Cell (MFC) device description (**a**) schematic and (**b**) electrical model.