esa-sceva/satcom-chunk-collection huggingface dataset:
# Neurorotrophic Factors in Glaucoma and Innovative Delivery Systems
###### Abstract  
Glaucoma is a neurodegenerative disease and a worldwide leading cause of irreversible vision loss. In the last decades, high efforts have been made to develop novel treatments effective in inducing protection and/or recovery of neural function in glaucoma, including neurotrophic factors (NTFs). These approaches have shown encouraging data in preclinical setting; however, the challenge of sustained, targeted delivery to the retina and optic nerve still prevents the clinical translation. In this paper, the authors review and discuss the most recent advances for the use of NTFs treatment in glaucoma, including intraocular delivery. Novel strategies in drug and gene delivery technology for NTFs are proving effective in promoting long-term retinal ganglion cells (RGCs) survival and related functional improvements. Results of experimental and clinical studies evaluating the efficacy and safety of biodegradable slow-release NTF-loaded microparticle devices, encapsulated NTF-secreting cells implants, mimetic ligands for NTF receptors, and viral and non-viral NTF gene vehicles are discussed. NTFs are able to prevent and even reverse apoptotic ganglion cell death. Nevertheless, neuroprotection in glaucoma remains an open issue due to the unmet need of sustained delivery to the posterior segment of the eye. The recent advances in intraocular delivery systems pave the way for possible future use of NTFs in clinical practice for the treatment of glaucoma.  
glaucoma; neurotrophic factors (NTFs); neurorotrophins (NTs); neuroprotection; drug delivery systems; microspheres; gene therapy; polymers; nanoparticles; implants +
Footnote †: journal:  
has the potential to establish survival permissive conditions [10; 11]. Neurotrophins (NTs) are key modulators of multiple signaling pathways essential for neuronal survival and differentiation of central and peripheral nervous systems, as well as for synaptic plasticity and axonal regeneration [12; 13; 14]. The family of NTs consists of nerve growth factor (NGF), brain-derived neurotrophic factor (BNDF), neurotrophin 3 (NT3), and neurotrophin 4/5 (NT4/5). The glial cell-derived neurotrophic factor (GDNF) family and the neuroopoietic cytokines have also been shown to regulate development and maintenance of neuronal cells [15].  
Currently, clinical use of NTFs in glaucoma has been hindered by the difficulty to provide enough evidence of a safe, steady, controlled in vivo delivery to RGCs. Therefore, the potential of neuronal survivals might be further enhanced from advancement in intraocular delivery devices, enabling sustained drug release to the retina and optic nerve and improved safety profile.  
In this paper, we review available knowledge on the use of NTFs for the treatment of glaucoma and related innovative delivery methods, as promising research strategy for preventing RGCs degeneration.
## 2 Materials and Methods
### Spectroscopic Investigations
#### 2.3.2 Front-Face Fluorescence Spectroscopy  
Front-face fluorescence (FFF) spectra were recorded using an ISA Fluoromax II photon counting spectrofluorometer with xenon arc lamp, equipped with a device for reflectance measurements with a cell holder designed to set the incident angle of the excitation beam at 31\({}^{\circ}\)[49]. The angle of reflectance was regulated and optimized [47; 48] in order to eliminate or reduce self-absorption effects, light-reflected, and scattering phenomena [30; 50]. All spectra of both honey samples and standard solutions were acquired by inserting the sample in a quartz cell (1 cm \(\times\) 1 cm) with 1 cm of optical path. In the case of measurements on honey samples in the bulk, honey was previously heated in a water bath at a temperature less than 30 \({}^{\circ}\)C in order to homogenize the sample. For each honey sample in the bulk, emission spectra (em), excitation spectra (ex), and synchronous (syn) spectra were recorded. The excitation and emission slits were fixed to 2 nm and 5 nm, respectively. After optimization, the integration constant time was chosen at 0.5 s; the wavelength increment was 1 nm. The intensity of the emission spectra was determined as the ratio between the emission signal (counts per second, cps) and the intensity of light from the excitation monochromator (mA). In most of the cases, as reported in the text, spectra were normalized and arbitrary units (a.u.) used. For each honey sample, emission spectra with \(\lambda_{\text{ex}}\) ranging between 250 nm to 550 nm, excitation spectra with \(\lambda_{\text{em}}\) fixed in correspondence with the maximum position of fluorescence emission spectra, and synchronous spectra with fixed wavelength interval, \(\Delta\lambda\), ranging from 20 nm to 120 nm, were recorded. Before the spectral analysis and spectral deconvolution, emission spectra were corrected taking into account the light scattering contribution, estimated by using a light diffuser (dried Na\({}_{2}\)SO\({}_{4}\)) as previously reported [49].
# A Review of Degradation and Life Prediction of Polyethylene
## 5 Prediction Techniques for Polyethylene Materials
### Equal Conversion Rate Method
#### 5.3.4 Kissinger Method and Kissinger-Akahira-Sunose (KAS) Method  
The original Kissinger method was proposed in 1957, and [PERSON] made the premise that the experimental conditions barely affect the reaction rate and that it reaches a maximum at a temperature \(T_{p}\) that corresponds to a specific conversion rate \(\alpha\). In this instance, only non-isothermal conditions are suitable for determining the manifest activation energy \(E_{a}\) of the crystallization process, which is proportional to the slope of the maximum value corresponding to the crystal temperature. The heating rate \(\beta\) often affects the conversion rate \(\alpha\). The Kissinger equation is shown in Equation (23) [135]:  
\[\ln(\frac{\beta}{T_{p}^{2}})=\ln(\frac{AR}{E_{a}})-\frac{E_{a}}{RT_{p}} \tag{23}\]where \(T_{p}\) is the peak temperature, and the activation energy \(E_{a}\) can be obtained from the slope of the line \(\ln(\beta/T_{p}^{2})\) to \(1/T_{p}\).  
According to the Kissinger-Akahira-Sunose (KAS) technique, the activation energy is assumed to be constant for a specific conversion rate [136, 137]. The method is based on Equation (24) [138, 139]:  
\[\ln(\frac{\beta}{T_{p}^{2}})=\ln(\frac{AR}{E_{a}f(\alpha)})-\frac{E_{a}}{RT_{p}} \tag{24}\]  
where the \(\ln(\beta/T_{p}^{2})\) versus \(1/T_{p}\) curve is a straight line, and the slope and intercept can be used to calculate the activation energy \(E_{a}\) and the value of pre-exponential factor \(A\) for a given type.  
The Kissinger-Akahira-Sunose (KAS) method corrects some biases in the Ozawa-Hynn-Wall (OFW) method by using the Coats-Redfer approximation, and the Kissinger-Akahira-Sunose (KAS) method provides a more accurate estimate of the activation energy [133, 134].
## 5 Simulation Results and Analysis
### Initial Deployment of UAV  
Initially, UAVs take flight from the base station, as shown in Figure 8, where UAVs start scanning the area. UEs were randomly distributed in the AoI. UAVs scatter in the area immediately and connect the undiscovered UE. Once a UAV discovers the UE, they start sending a CH request packet to their neighbor UAV and start creating a cluster network. When the network is established, the redundancy of data is checked and the redundant drones put on sleep to keep their battery life for later use, as shown in Figure 9. 200 nodes are distributed for simulation purposes. 30 green nodes were declared as UAV; these nodes were mobile nodes that move with 10 m/s, and among them '0' node was declared as a BS. In a scenario, 200 nodes are distributed randomly. UAV nodes were scattered in the AoI; green UAV nodes are active nodes; the nodes in yellow color present similar data put tosleep as shown in the Figure 8, whereas the red nodes area's CH controls the client UAV and information that reaches the BS through the CH.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & NS2(v-2.34) \\ Time of simulation & 100 s \\ Range of UAV & 500 m \\ Deployment Area & 1000 \(\times\) 1000 \\ Number of Nodes & 80–100 \\ Distribution of Nodes & Random \\ Trace of Movement & Off \\ No of UAV & 42 \\ CHs & 10–12 \\ Followers & 7 nodes/CH \\ Channel & PL: path loss, \\ Distance between Nodes (Range) & 8–10 \\ Distance between CH and Nodes & 2–12 \\ Between CHs & 10–12 \\ CH and BS & 10–16 \\ UAV & Quadcopter \\ Altitude & 10–40 \\ Frequency Range & 0.97 \\ Sound Sig. & OFDM \\  & 1 monopole antenna on GS \\ Antenna and Mounting & for TX, 1 monopole on \\  & aircraft for RX \\ Maximum Speed of UAV & 10 m/s \\ Packet Size & 512 bytes \\ Channel & Wifi \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 8: Final result without obstacles.
## 2 Literature Review
### Some Considerations about Manual Assembly
Historically, workers have performed these inspections visually. However, the limitations of humans as visual inspectors have long been recognized [43; 44]. For this reason, automated visual inspection (AVI) has long been used in the manufacturing industry for quality control and monitoring [45; 46]. As gains in computational power yield enhanced image acquisition, processing and analysis capabilities, automation replaces human visual inspection more and more often [42; 47]. For example, a robot can take pictures of the final product to detect deviations and nonconformities. Such a system can validate the quality of final assembly based on its ability to perform optical characteristics recognition, such as detecting whether a specific component is absent. However, even though machine vision has been used in quality inspection for several years, it seems that technological challenges remain. According to [PERSON] [48] \"Many of the advances we take for granted in modern computing--ubiquitous connectivity, unlimited data storage in the cloud, insights drawn from massive unstructured data sets--have yet to be applied systematically to the factory floor in general and to computer vision specifically\". One drawback of centralized automated inspection at the end of the assembly line is that the identification of nonconformities is made too late and the cost of reworking may be considerable. Lean approaches seek to ensure the quality of the assembled object before it leaves the workstation to minimize costs and delays by eliminating waste [49]. As machine vision continues to evolve, it would be reasonable to expect that automated visual inspection might become sufficiently flexible to come to the assembly workstation. Thus, a centralized automated inspection would shift to an in-process, ubiquitous automated inspection carried out with a smart assistance system. These systems could combine, for example, wearable augmented reality with automated visual inspection [50; 51] or use collaborative robots for automated visual inspection [52; 53].
## 3 Results
### Relative Variable Importance
#### 3.3.2 Grouping by Cropland and Crop Rotation  
Figure 7 displays the relative importance of the variables selected by MGFS from the sub-models built on different subsamples. There were significant differences in the selected variables and their rankings between the two grouping strategies and also between the  
Figure 6: The relative variable importance for scenarios 1 (a), 2 (b), 3 (c), 4 and 5 (d), 6 (e), and 7 (f). The prefixes of the satellite composites represent the order in the compositing sequence, ranging from 0 to 39 for radar and 0 to 13 for optical imagery. The suffixes of crop rotation maps indicate the production year and the systems, and for the main crop rotation, 1, 2, and 3 indicate the paddy, vegetable, and orchard systems, respectively. The dots of green, blue, yellow, brown, and dark brown represent radar, optical, crop rotation system, cropland type, and soil variables, respectively.  
models built on different subsamples. For grouping by crop rotation, only Sentinel composites participated in predicting the soil pH without the crop rotation systems themselves, and the optical composites contributed more. All the important variables for the paddy and vegetable subsample were Sentinel-2 composites, including many historical images, while both sensors were used for the orchard subsample. As for grouping by cropland type, the crop rotation systems were selected by MGFS alongside the Sentinel composites. The VHVV and VH backscatters were listed as the most important variables. The rainfed subsample used more variables, which were also more diverse than those in the irrigated subsample.
## 4 Computational Experiments  
Our computational experiments have been aimed to evaluate whether the use of predictions made by ML techniques can be beneficial for a routing heuristic in a time-dependent context. To this purpose we have implemented both the baseline heuristic (NNP) and the ML-enhanced heuristic (referred to as ML-NNP in the following). Python (version 3.6) was used for both. The Multilayer Perceptron Regressor implementation was taken from the _sklearn_ neural network library (method MLPRegressor) while the k-means implementation came from the _sklearn_ cluster library (k-means method). The training instances were solved to optimality (or near-optimality) using a Java implementation of the branch-and-bound scheme proposed in [19]. A time limit of an hour was imposed. All the codes have been tested on a Linux machine clocked at 2.67 GHz and equipped with 8 GB of RAM. We first describe the instances we have used in our experimentations.
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 3 Introduction to Signal Quality Index for Photoplethysmographic Signals
### Applications  
We define as a quality index an algorithm taking a PPG signal in entry and assigning it a score or label indicating its quality. A signal quality index can be used in several applications:  
* Reducing false alarms during patient monitoring;
* Presenting clean signals for experts' interpretation;
* Cleaning datasets for machine learning applications;
* Suppressing irrelevant signals to maintain performance of physiological variable predictions;
* Integrating the SQI into the signal processing algorithm to evaluate or improve its performance.  
The general purpose of an SQI consists in increasing the reliability of the application in which it is implemented. We can distinguish two ways to use an SQI: as an indicator for keeping or removing a signal and as an indicator to improve the performance of the signal processing chain.
# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 1 Introduction
The accuracy of segmentation results directly affects the follow-up treatment effect. Medical images mainly come from different imaging technologies, including ultrasound, X-ray, magnetic resonance imaging (MRI), computer tomography (CT), and positron-emission tomography (PET). On the one hand, different segmentation methods should be selected according to different imaging technologies and imaging parts. On the other hand, medical images are different from natural images and have high complexity. Due to the single pixel of the image, the boundary, shape and other information of the lesion are also fuzzy. In addition, automatic preprocessing without human involvement also reduces human errors and overall time and cost [8]. Considering the slow process and the complexity of manual segmentation methods, there is a great demand for a fast and accurate computer method for fully automatic segmentation [9]. To meet the need for more accurate medical image segmentation, inspired by the attention mechanism and residual unit, we propose Residual-Attention UNet++, an extension of the UNet++ architecture using a residual unit and attention mechanism. The contributions of this work can be summarized as follows:  
* The residual unit and attention mechanism were introduced to UNet++ to increase the weight of target areas and to solve the degradation problem. * The proposed model Residual-Attention UNet++ was introduced for medical image segmentation. * The experiments conducted on three medical imaging datasets demonstrated better performance in segmentation tasks compared with existing methods. * Residual-Attention UNet++ could increase the weight of the target area and suppress the background area irrelevant to the segmentation task. * Comparison against some UNet-based methods showed superior performance. * The pruned Residual-Attention UNet++ enabled faster inference at the cost of minimal performance degradation. The remaining content is organized as follows. Section 2 discusses some related work, Section 3 presents the introduction of the proposed architectures, Section 4 describes the datasets, experimental details, and results, and finally, Section 5 concludes this paper.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 3 Hard SyDR
### Current Architecture  
The architecture currently implemented, shown in Figure 3(b), significantly differs from the envisioned architecture. These differences, however, are primarily introduced deliberately to arrive at a minimally viable, intermediate design that nevertheless proves the practicality of our platform concept faster. First, the resulting design integrates only a single channel. This prevents parallel processing of multiple channels and positioning results, but it is sufficient for benchmarking intra-channel algorithms, such as the DSP-heavy acquisition and tracking stages. Second, the design does not implement a decoding block as we consider this less important in our current development process.  
Third, and most importantly, the design does not integrate the acquisition module originally envisioned to be present in all channels, as shown in Figure 3(a). This is mainly due to (1) difficulties related to hardware implementation of the FFT, namely its memory resource utilization, which would prevent simultaneous implementation of multiple channels, and (2) high noise levels resulting from zero-padding, which affect the acquisition performance, as will be detailed in Section 5. We consider two strategies to resolve this issue: (1) performing benchmarking of the acquisition and tracking operations separately by loading/unloading hardware layouts through the PYNQ flow during processing and (2) having a limited number of acquisition blocks that multiple channels share. Of these strategies, the second appears most attractive as the first option prevents easy re-acquisition if a channel's tracking fails.  
Figure 4: Overview of the hardware architecture for Hard SyDR accelerated on the Zynq platform.
# Lichens as a Natural Source of Compounds Active on Microorganisms of Human Health Interest
## 2 Materials and Methods
### Screening of the Antimicrobial Activity of Lichen Extracts  
The sensitivity of microorganisms to lichen extracts was analyzed by the agar well diffusion assay [41]. In detail, the dried extracts obtained from cyclohexane and acetone solvents were named LC1 and LC2, respectively, dissolved in 5 mL of dimethyl sulfoxide (DMSO, Sigma Aldrich St. Louis, MO, USA). Then, 100 \(\upmu\)L of each solution in the concentration of 1000 \(\upmu\)g/mL was tested on wells of about 6 mm in diameter hollowed out in TSA plates. All plates were placed under a biosafety cabinet to allow a correct diffusion of the solution. On each plate, 6 mL of soft agar containing 10\({}^{6}\) CFU/mL of each indicator strain was inoculated, and plates were incubated for 24 h at 37 \({}^{\circ}\)C or 30 \({}^{\circ}\)C. A clear zone of inhibition around wells quantified the antimicrobial activity. The assay was repeated at exact time intervals (7, 15, and 30 days) to determine the stability of the antibacterial activity over time of lichen extracts stored at room temperature. All experiments were performed in triplicate, and a DMSO solution (diluted 1:10 in sterile distilled water) was used as a negative control.
## 1 Introduction
These findings demonstrate the effectiveness of reinforcement learning utilizing semantic information like key-value and triple relationship-based rewards in improving the robustness and generalization of baseline SLMs as well as LLMs for Cypher query generation in practical graph database applications. The contributions of this paper are summarized as follows:  
1. We propose a novel refining training method for Text2 Cypher, which works well even on baseline SLMs (pretrained and instruction-tuned generative small language models). The proposed refining training method shows that it can effectively deal with the semantic gap between input texts and output Cypher queries with reinforcement learning, leveraging semantic information rendered during training. To the best of our knowledge, this paper is the first to apply reinforcement learning to Text2 Cypher;
2. In order to reduce the semantic gap more efficiently, we propose a GRPO-based reinforcement learning optimization strategy, which can effectively utilize the semantic information of key-value and triple relationships extracted from output responses for Text2 Cypher, which takes the best reward based on averaging several output responses;3. We propose a simple prompting approach applied to a baseline LM to extract semantic information alongside Cypher query generation. The guidance embedded in the input prompts directs the model to extract supplementary information, which enhances the Text2 Cypher task. The structure of this paper is as follows: Section 2 explains background technologies and reviews related work on Text2 Cypher. Section 3 describes our proposed method, including the training strategy. Section 4 outlines the experimental setup, evaluation results, and discussion. Finally, Section 5 concludes this paper and outlines directions for future work.
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
###### Abstract  
We present an analysis of clock recovery algorithms in both polarization division multiplexing systems and mode division multiplexing systems. The impact of inter-polarization time skew and polarization mode dispersion in single-mode fibers, as well as the combined impact of mode mixing and mode group delay spread in multi-mode fibers under different coupling regimes are investigated. Results show that although the clock tone vanishing has a known solution for single-mode systems, in multi-mode systems even for low group delay spread, strong coupling will cause clock tone extinction, making it harder to implement an effective clock recovery scheme.  
optical communications; clock recovery; space-division multiplexing; coherent communications +
Footnote †: journal: Future Internet