esa-sceva/satcom-chunk-collection huggingface dataset:
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models  
PPP exhibits a range of implementation modes contingent upon various determinants, including accessible constellations, management of the ionospheric delays, the decision to fix phase ambiguities, and the number of operational GNSS frequencies.  
While ionospheric delays are frequently mitigated using IF measurement combinations, leveraging ionospheric corrections sourced from either local or global networks can expedite the convergence of the solution. Concerning ambiguity resolution (AR), the ambiguities might be estimated as float values or as fixed to their integer counterparts. Float PPP remains a prevalent choice, especially for real-time applications, due to the complexity of fixing integer ambiguities within the PPP context. Nevertheless, ambiguity-fixed PPP, or PPP-AR, has the advantages of shorter initial convergence time and improved accuracy, especially in the east direction [7,68]. PPP-RTK is a state-of-the-art technique that combines the complementary characteristics of PPP and RTK to achieve centimeter-level positioning in a short time [69].  
Classified by the number of employed GNSS frequencies, implementations encompass single-frequency (SF), dual-frequency, and triple-frequency (TF) PPP. SF-PPP utilizes observations from a single GNSS frequency, which is useful when using low-cost SF GNSS receivers. SF-PPP has the advantages of low-cost hardware and shorter convergence time but to a limited accuracy range of the decimeter to submeter level, especially in the kinematic mode [70,71]. Recently, the availability of DF observations in low-cost GNSS receivers, even smartphones, has grown, reducing the interest in SF-PPP. In TF-PPP, observations from three GNSS frequencies are employed, e.g., GPS L1, L2, and L5. The third frequency's main contribution is to shorten the convergence time and allow faster ambiguity resolution. Because of this, positioning accuracy can be improved in the short term due to faster convergence, but the contribution to positioning accuracy after convergence will be limited [72].  
One limitation of the TF-PPP is that it is still not supported by all GNSS receivers in the market, especially low-cost ones [10].  
The focus of this review is the implementation of DF-PPP. This section starts with a description of the GNSS code and carrier-phase observations and some of their valuable linear combinations. Then, these observations, in addition to the error sources correction and modeling discussed in Section 2, are used to build the standard DF-PPP and the ambiguity-fixed DF-PPP models. Finally, the PPP-RTK model is introduced.
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 3 Problem Description and Formulation of the VRP-CS
### Customer Satisfaction Function  
In the VRP-CS, the optional delivery locations and variable delivery time are effective means to improve customer satisfaction. Customer satisfaction is influenced by the psychological factors of customers, who are more sensitive to poor delivery services. This trend can be well assessed using the loss aversion concept of prospect theory.  
Therefore, a nonlinear satisfaction change curve based on prospect theory is provided. For deliveries that are later than the psychological reference point, a penalty is applied (i.e., the satisfaction value is negative). Conversely, when the vehicle arrives early, a reward is given (i.e., the satisfaction value is positive). Combining the above research, the customer satisfaction function is defined as follows:  
\[f(i,a)=\left\{\begin{array}{cc}\frac{1}{\lambda}&r_{ia}\leq e_{ia}\\ \rho_{1}\bullet(\frac{w_{ia}-r_{ia}}{A\tau_{ia}})^{\alpha}&e_{ia}<r_{ia}\leq w_ {ia}\\ -\lambda\bullet\rho_{2}\bullet(\frac{r_{ia}-w_{ia}}{T_{ia}-\Delta t_{ia}})^{ \beta}&w_{ia}<r_{ia}\leq l_{ia}\\ -1&l_{ia}<r_{ia}\end{array}\right.\quad\forall i\in N,a\in L,k\in K \tag{14}\]  
The \(\varepsilon_{ia}\) represents the satisfaction benefit obtained by customer \(i\) when receiving delivery at location \(a\) calculated using formula (14), with a value range of (\(-1\),\(1\)), and the value of \(\varepsilon_{ia}\) for unused location is \(0\). The moment when the customer receives the service is represented by \(r_{ia}\), the customer's psychological reference point for receiving service at location \(a\) is \(w_{ia}\), we use \(T_{ia}=l_{ia}-e_{ia}\) to represent the time window width of this location, \(\Delta T_{ia}=w_{ia}-e_{ia}\) represents the time span from a to b, \(\alpha\) and \(\beta\) are the risk attitude coefficients, which usually take on values between \(0\) and \(1\). The larger the value, the more likely the individual is to take risks. \(\lambda\) represents the loss of aversion coefficient, which is greater than \(1\), indicating that the individual is more sensitive to losses. It should be noted that \(\rho_{1}\) and \(\rho_{2}\) are used to ensure that \(f(i,a)\in[-1,1]\). The satisfaction distribution is shown in Figure 2.
# Organic Crystals for THz Photonics
## 5 Applications of Organic THz Sources
### Food and Agriculture  
THz waves offer a non-ionizing non-destructive testing method to detect foreign bodies within food products with low water content such as chocolate or powdered eatables, e.g., sugar or flour. In addition, the chemical analysis of food samples such as edible oils is also possible with THz spectroscopy. THz interaction with food samples provides intramolecular information linked with the existence of certain characteristic bonds indicating the presence of certain molecules or groups of molecules as well as intermolecular information such as hydrogen bonding, alignment of molecules, amorphous or crystalline structure [216]. Because THz waves are very sensitive to water molecules, THz-TDS is an excellent tool to determine the water content within the food samples such as butter, vegetable oils or flour. A DSTMS-based THz spectroscopic system was used to obtain the absorption coefficient of five different edible oils from different plants in the frequency range from 1.5 THz to 3.5 THz [179]. By using additional chemometrics, also distinguishing between different edible oils was possible. The use of chemicals such as additives and hormones in food production is becoming a critical food safety issue not only for regulatory agencies but also for consumers. Some studies on hormones have already been performed using THz spectroscopy. One such example performed by a DSTMS-based THz system investigated the melatonin [178], which can be used as a dietary supplement. The experiment confirmed that the melatonin has a unique spectral feature at around 3.2 THz, which can be used to detect and quantify it within the dietary samples in order to control its authentication. Similarly, other chemicals such as pesticides and antibiotics can also be analyzed. Since pesticides have characteristic spectral response in the THz frequency range, the THz-TDS method can be used to detect pesticides residue in crop samples [216]. Furthermore, qualitative and quantitative analysis of food additives or antibiotics due to veterinary drugs can be performed by THz-TDS.
## 2 Materials and Methods
### Data Source
#### 2.2.5 MODIS Data  
The MODIS products, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-infrared Reflectance (NIR), Land Surface Temperature (LST \({}^{\circ}\)C), Land Cover type (LC), and Gross Primary Productivity (GPP, gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\)) were adopted in this research ([https://e4 ftl01.cr.usgs.gov/](https://e4 ftl01.cr.usgs.gov/), accessed on 10 June 2021). NDVI, EVI, and NIR were monthly data with 0.05\({}^{\circ}\) of spatial resolution from the MOD13C2 product. LST was monthly data with 0.05\({}^{\circ}\) of spatial resolution from the MOD11C3 product. LC based on the International Geosphere-Biosphere Programme (IGBP) classification scheme is an annual dataset with 500 m resolution from the MCD12Q1. GPP was an 8-day data with 500 m of spatial resolution from the MOD17A2H product. The monthly GPP data with 0.05\({}^{\circ}\) spatial resolution used in this study were synthesized, converted from 8-day data, and weighted based on the number of days in each month [33]. Moreover, the Near-infrared Reflectance of Vegetation (NIRv) adopted in this study is obtained by multiplying NDVI and NIR [34].
## 5 Discussion  
### Interpretation and Comparison of Experimental Results  
The interpretation and comparison of the experimental results shed light on the effectiveness of the proposed plastic waste classification system. The achieved classification accuracy of 91.7 mAP for PET and PET-G samples moving at a speed of 2 m per s demonstrates the system's high precision in distinguishing between different types of plastics. This accuracy is crucial in ensuring proper sorting and recycling of plastic waste materials.  
Comparing the achieved accuracy with existing manual sorting processes reveals the superiority of the developed system. Manual sorting processes often suffer from human error and subjective judgment, leading to lower accuracy rates. The automated nature of the proposed system eliminates these drawbacks, providing consistent and reliable classification results.  
Moreover, the real-time performance of the learning algorithm enables prompt decision making in waste-sorting operations. The system keeps pace with the conveyor belt's speed, ensuring that samples are classified on time. This efficiency is essential for maintaining the productivity of waste management facilities and optimizing the recycling process.
## 3 Results and Discussion  
In our previous study [29], a polymerizable ionic liquid (IL) was utilized as a functional monomer to synthesize molecularly imprinted anion exchange hydrogels with excellent affinity and a fast binding rate toward the BSA template. In addition, [PERSON] et al. [30; 31] reported imprinted anion exchange polymers synthesized using other charged monomers. However, applying randomly oriented charged molecules as functional monomers could lead to the formation of charged moieties of non-imprinting active sites outside the imprinted materials, resulting in significant non-specific binding. In this study, anchored charged functional groups were used as active sites to immobilize the template through the anion exchange mechanism to ensure that binding sites were only formed inside the imprinted materials. In this approach, the residual charged moieties were completely embedded after polymerization. Consequently, non-specific binding from the residual charged moieties could be avoided, leading to an enhanced imprinting efficiency for the imprinted materials after the template was removed.  
Herein, we report a novel and facile approach, anion exchange affinity-based controllable surface imprinting, for the efficient imprinting of BSA. This strategy is schematically presented in Figure 1. Poly(DVB-VBDC) nanoparticles with surface-bound dithiocarbamte groups were first synthesized by dispersion polymerization using microwave irradiation. Then, linear poly(4-CMS) was grafted onto the poly(DVB-VBDC) surface using photoinifer-ter polymerization. Subsequently, the obtained PVC nanoparticles were modified using TMA via a benzyl group reaction to introduce positively charged TAA at the substrate surface. Subsequently, the ionic state of the BSA molecules (pH 7.0, PI = 4.7) could be immobilized on the PVC@TAA surface through an anion exchange affinity-based interaction [32; 33]. After adding the functional monomer (Aam) and cross-linker (MBAA) to generate a size and shape that is a complementary match to binding cavities with template proteins, the polymerization reaction was carried out using ultraviolet radiation under low-temperature conditions. During polymerization, the attached dithiocarbamte groups on the surface of the PVC nanoparticles acted as initiators for the surface-initiated activator generated by SI-PIMP. This approach allowed the precise control of the imprinted film thickness, where the cross-linked polymer matrix could be propagated from the multireactive nanoparticle substrate in proportion to the polymerization time [34]. Finally, imprinted nanoparticles were obtained by removing the template protein from the imprinted films using solvent extraction.
# Neurorotrophic Factors in Glaucoma and Innovative Delivery Systems
## 3 Advances in Drug and Gene Delivery Systems for Neuroprotection in Glaucoma
### Gene Delivery for Neurotrophic Factors in Glaucoma
However, from a multicenter, sham placebo-controlled phase II clinical trial for AD, AAV2-NGF was not able to improve cognition [155]. A recent post-mortem analysis conducted on three patients from the phase I dose-escalation trial revealed that NGF gene expression had persisted for at least seven years at sites of AAV2-NGF injection; however, the mean distance of AAV2-NGF spread had been only 0.96 \(\pm\) 0.34 mm. Particularly, NGF had not directly reached cholinergic neurons at any of the 15 injection sites [154]. Thus, gene transfer could overcome the obstacles associated with delivering NTFs to the brain, and even possibly to the posterior segment of the eye, but further insight is recommended as available evidence is limited and inconclusive. The eventuality of insertional mutagenesis and the immunogenicity concerns associated with the use of viral vectors led to the development of non-viral gene delivery techniques for NTFs in the eye. Interestingly, eye drops of poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO) polymeric micelles demonstrated effective potential for noninvasive, topical ocular gene delivery in in vivo studies [156]. In the same direction, polyamidoamine (PAMAM) dendrimer-driven expression of BDNF and NT-3 was achieved in vitro in rodent and human stem cells [157]. In addition, a cationic cell-penetrating peptide was studied for ocular delivery (POD) conjugated with polyethylene glycol (PEG-POD). PEG-POD nanoparticles, containing glial cell line-derived neurotrophic factor transgene (PEG-POD-GDNF), were injected into the subretinal space of adult murine retina, showing a significant reduction in light-induced photoreceptor apoptosis at 14 days post exposure compared to control-injected animals [86]. Direct intravitreal injection of BDNF cDNA was studied in mice models of optic nerve transection, followed by in vivo electroporation, and it resulted in increased survival of axotomized RGCs [158]. Another interesting application of NTs delivery involves the use of artificial substrates for promoting axonal repair. A graft made from a silicone tube enriched with cultured Schwann cell, extracellular matrix, NGF, BDNF, and NT-4 was tested in murine models subjected to axotomy, resulting in regeneration of blood vessels, RGC, and related axons [159] (Table 1).
# A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz-2.4 GHz Range
## 2 Materials and Methods
### RF Technologies
#### 2.2.2 450 MHz Body-Coupled Communication and On-Body Monopoles
In the end, the measured received power is 3 to 6 dB lower that the TX settings (the 0 dBm output power). Those losses are mainly due to the on-board matching network centered around 433 MHz. All tests are performed three times under the same conditions for repeatability purposes and then average for plotting. We explored multiple distances between electrodes on a human arm for 10 different frequencies. In order to compare the impact of the human body on the communication channel, free-space measurements (with the same BCC electrodes) were also made. The same subject was standing with both arms along the body (vertically). No other pre-gelled electrode but the two required ones were on the body for each measurement (see Figure 4d,e). The tests with the antennas provided by the manufacturer were performed under the same conditions.
# Compliance of Distribution System Reactive Flows with Transmission System Requirements
## 1 Introduction  
### Background and Motivation  
Distributed Energy Resources (DERs) in Active Distribution Networks (ADNs) create challenges for the Transmission System Operators (TSOs) and the Distribution System Operators (DSOs) but also provide opportunities to improve network operation and services [1]. In the future, active and reactive power flows between Distribution Networks (DNs) and Transmission Networks (TNs) can change more rapidly due to the intermittent nature of renewable generation and higher load uncertainty. Therefore, more active and shorter-term control of reactive power flow between these networks is needed to minimise voltage fluctuations and losses at the TN level. DER units with inverter-based interfaces are potential flexibility resources capable of providing services without additional investments in new network assets/components next to traditional voltage control and congestion management related services.  
In the context of TSO/DSO collaboration, it can be stated that ADNs are desired to provide local (up to the DSO level) and system-wide (up to the TSO level) ancillary services (AS) through DERs. One local AS is reactive power control through DER units for satisfying the TSO's requirements for reactive power flow exchange. In some practicalcases, excessive reactive power flows are penalised by TSOs, thus it is beneficial to find an optimal way to control DER reactive power support.
## 5 Discussion  
For O\({}_{3}\) estimation, a variety of methods have been proposed and implemented, but certain limitations persist, primarily concerning spatial resolution, the comprehensiveness of factors, and the precision of estimations. The majority of studies have reported results with a resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) or coarser [67,68], while only a few have produced surface ozone products with a finer resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)[69,70]. Concurrently, the high spatial resolution observations of NO\({}_{2}\) and HCHO from S5P-TROPOMI are seldom utilized in current surface ozone estimation research [69,70]. Therefore, it is essential to utilize the high spatial resolution precursor data (S5P-TROPOMI NO\({}_{2}\) and HCHO) to estimate the surface ozone over large regions, thereby achieving higher accuracy, certainty, and improved spatial resolution in the estimation results. Furthermore, studies employing deterministic models to estimate surface ozone at regional and national scales often produce results for a relatively short period [71], and there is a need to improve the estimation accuracy [72,73]. There is also significant variability in the accuracy of estimations across different regions [74]. This highlights the necessity for more sophisticated models and methods that can address these challenges and enhance the reliability and precision of ozone estimations. In this study, we have addressed these limitations by using high spatial resolution S5P-TROPOMI NO\({}_{2}\) and HCHO data, combined with the XGBoost model, to estimate surface ozone at a finer resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). This approach not only improves the spatial resolution, but also enhances the accuracy (\(R^{2}\): 0.856) and comprehensiveness of the estimations, distinguishing our work from previous studies. By comparing our results with those obtained from other methods, we demonstrate the advantages of our approach in terms of precision and spatial detail, thereby contributing new insights and advancements to the field of surface ozone estimation.
## 2 Determining \(G\) for I-II Mixed Mode Crack
### Numerical Analysis of 2D Crack
#### 2.1.2 The Theoretical Basis of Energy Release Rate Definition  
A cracked plate in plane stress has an infinitesimal kink at an angle \(\theta\) from the plane of the crack, as illustrated in Figure 3a-c. The energy release rate \(G\), as a crack extension force, is a measure of the energy available for an increment of crack extension, which can be calculated approximately as:  
\[G=-\frac{dU}{da}=-\underset{a_{2}\to a_{1}}{lim}\frac{(-U_{2})-(-U_{1})}{a_{2} -a_{1}}\approx\frac{U_{2}-U_{1}}{\Delta a} \tag{2}\]  
Irrespective of whether the crack extension is in its initial plane or kinked, the angle is \(\theta\), where \(U_{1}\) and \(U_{2}\) are the strain energy before and after kink extension, respectively, and \(a\) is the length of the kink, which is measured from the original crack tip.  
For an edge crack in the linear elastic body, the J-integral computed along a contour surrounding the crack tip is equal to the energy release rate (Figure 3d), which is revalidated by simulation work. The strain energy release rate and \(J\)-integral for a kink can be evaluated based on the output data of ABAQUS, as shown in Figure 4. It shows that energy release rate is in good agreement with the \(J\)-integral evaluated along the contour starting at one kink's crack surface and ending at the other kink's crack surface under arbitrary loading and kink angle conditions.  
1. Obtain the values of \(J\) and strain energy at the points \(a_{0}=0\) mm, \(a_{1}=0.1\) mm, \(a_{2}=0.3\) mm, \(a_{3}=0.5\) mm, \(a_{4}=1\) mm.
2. The energy differences between \(a_{0}\) and \(a_{1}\), \(a_{0}\) and \(a_{2}\), \(a_{0}\) and \(a_{3}\), \(a_{0}\) and \(a_{4}\) are calculated and divided by \(a_{1}-a_{0}=0.1\) mm, \(a_{2}-a_{0}=0.3\) mm, \(a_{3}-a_{0}=0.5\) mm, \(a_{4}-a_{0}=1\) mm. It represents the energy release rate for the crack at 0.05 mm, 0.15 mm, 0.25 mm, 0.5 mm.
3. Determine that the minimum value of kink length for calculation is 0.1 mm; since the kink crack becomes infinitesimally small, the values from the energy difference deviate from the \(J\) integral.  
This validates the assumption made in the connection with strain energy release rate. \(J\)-integral will be used in the following research as it is reliable and convenient.  
Figure 2: Verification of simulation accuracy. (**a**) Verification of simulation value output from ABAQUS; (**b**) Errors between the two values of each loading.  
Figure 4: Determination of energy release rates as the length of kink crack approaches zero.  
Figure 3: Cracked plane with kink extension. (**a**) Initial crack; (**b**) Kink crack \(a_{1}\); (**c**) Kink crack \(a_{2}\); (**d**) J integral.