esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This could be related to the stimulation of the production of reactive oxygen species (ROS) and the stimulation of molecules and antioxidant mechanisms. UV-C can also trigger and regulate signalling pathways due to its effect on ROS production [18]. Microwave radiation is a component of the electromagnetic spectrum that ranges from 300 MHz to 300 GHz. Microwave radiation can cause different biological effects depending on the field strength, wave form, modulation and duration of the exposure. The positive effects of microwave radiation on the germination of seeds and tubers have been demonstrated. Microwave radiation modifies the germination process and reduces crop infections. The exposure of seeds to microwave radiation appears to be beneficial for increasing the germination, growth and vigour of emerging seedlings and the accumulation of biomass in various plant species. The susceptibility of plants to microwave radiation depends on environmental conditions. MW radiation in living tissues causes ion movement, dipole rotation and the distortion of the electronic orbit, which results in rapid and selective heating. Non-destructive MW treatments are widely used to disinfect seeds before sowing or storage [18]. IR radiation is a part of the electromagnetic spectrum within the wavelength range of 0.5 to 1000 mm and is mainly used in food processing. Infrared (IR) radiation is used as an effective method for the disinfection and drying of seeds and the postharvest disinfection of fresh fruit and vegetables. Gamma (\(\gamma\)) radiation is a type of infrared radiation that can penetrate and interact with living tissues. Gamma radiation is used to remove microbial contamination or control insect pests and pathogens, thereby preventing plant diseases. \(\Gamma\) radiation is also used to delay fruit ripening and vegetable germination by inhibiting the activation of key enzyme activities, which results in the extended shelf life of crops. \(\Gamma\) rays are used by plant breeders to obtain the desired characteristics of plants or develop new varieties. The biological effects of \(\gamma\) rays are strongly dependent on the intensity, dose and duration of the exposure. Irradiation with lower doses of \(\gamma\) rays has a positive effect on the morphological features of plants and improves seed germination efficiency. Low-dose irradiation also has a beneficial effect on the production characteristics of crops. Low doses of \(\gamma\) radiation have a stimulating effect on enzymatic activity and the synthesis of nucleic acids and proteins in treated seeds [18]. The use of the above-mentioned physical methods can contribute to satisfying consumer demand for healthy fruit and vegetables with extended storage and storage times. The objective of this research was to study the effects of treatment with a 100 uT MF on the quality and shelf life of apples after storage that were kept at room temperature.
## 3 Results and Discussions  
### Single-Spot Investigations on Silicon and Biln-Si  
Generally, a single-spot investigation is required to obtain a detailed analysis of the effect of laser fluence on the formation process before the line-like or area-like generation of LIPSSs. Consequently, bare silicon and silicon coated with a Bi-In film (Biln-Si) were irradiated with different laser fluences. Figure 2 shows the SEM images of the LIPSSs on bare silicon and Biln-Si under laser fluences varying from 1.02 to 2.55 J/cm\({}^{2}\) with a fixed laser pulse number \(N=200\). At the laser fluence of 1.02 J/cm\({}^{2}\) (Figure 2a), surface modification occurred and the obtained structures were not well-defined grating structures. However, when irradiated with higher laser fluences (Figure 2b-d), the bare silicon surfaces were damaged due to the excessive energy. For silicon with a pre-deposited Bi-In film, as shown in Figure 2e, the alloy film on the silicon was removed with only a laser fluence of 1.02 J/cm\({}^{2}\). When the laser fluence \(F=1.53\) J/cm\({}^{2}\), ablation appeared on the silicon surface (Figure 2f). Grating structures started to be generated on the silicon surface at the laser fluence of 2.04 J/cm\({}^{2}\) (Figure 2g). Better-defined ripple structures with an orientation perpendicular to the laser beam polarization were generated as the laser fluence was further increased to 2.55 J/cm\({}^{2}\) (Figure 2h). As shown in Figure 2, high-quality periodic ripple structures were hard to generate on bare silicon when the laser energy varied from 20 to 50 nJ with a small interval of 10 nJ. However, distinct ripple structures were formed on Biln-Si at the laser fluences of 2.04 and 2.55 J/cm\({}^{2}\) due to the excellent thermal conductivity contributed by free electrons in the Bi-In film that allowed for avoiding energy localization. Moreover, the Bi-In film enabled a wider range of laser fluences to generate periodic structures, providing higher stability for fabrication.
## 3 Methodology  
### Satellite Image Processing  
#### 3.1.1 Radiometric and Geometric Corrections  
Satellite imagery such as Landsat or RapidEye are generally stored in a database and classified by the degree of processing. This ranges from Level 0, a raw, uncorrected image up to Level 4, where all corrections have been applied and a series of analyses (such as vegetation indices) have been applied to the imagery. The RapidEye imagery used in this study was provided at Level 3a, meaning that all radiometric and sensor corrections had been performed. Level 3a imagery has also been aligned to a cartographic map projection and geometric corrections applied using fine DEMs with a post spacing of between 30 and 90 m and Ground Control Points (GCPs). RapidEye satellite images are collected with a bit depth of up to 12 bits and stored on-board the satellites. They are then scaled to a 16 bit dynamic range. Scaling converts relative pixel DNs into values directly related to absolute at-sensor radiances. A scaling factor is applied so that these post-processed single DN values correspond to 1/100 th of a W/m\({}^{2}\) sr \(\upmu\)m. The digital numbers of the RapidEye image pixels represent the absolute calibrated radiance values for the image.
## 4 Results and Discussion
### Sensitivity Analysis  
Whereas the results presented in this paper are based on certain assumptions, a sensitivity analysis is recommendable to highlight possible weaknesses and/or potentials for the trailer combinations, combined to an EV. An overview is given of examples which are thought to influence the results of this exercise.
## 5 Discussion
### Radiometric Results  
A key advantage of using NeRF-series methods for data processing lies in their ability to fully utilize color information for the 3D reconstruction of UAV remote sensing images, thus enabling the comprehensive detection of radiation information (also known as \"radiation measurement\"). Figure 5 shows the measurement results of the reflectance, shadows, and transient objects for the same scene under different viewing angles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Config.** & & & \\ \hline
**cos\(\lambda\)** & **PE** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **Alt MAE[m]**\(\downarrow\) \\ \hline  & & 24.936 & 0.887 & 0.86 \\ ✓ & & 25.168 & 0.892 & 0.80 \\  & ✓ & 25.321 & 0.895 & 0.79 \\ ✓ & ✓ & 25.684 & 0.903 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study on DengFeng Area#1.  
Figure 5: The UAV image, albedo, shadow scalar \(s\), and transient scalar \(\beta\).  
Firstly, the method's albedo attempts to restore the original feature attributes and color information that are not disturbed by atmospheric imaging conditions or shadows, which is highly important for feature recognition and image denoising. In previous remote sensing methods, it has often been necessary to combine specific spectral information to restore the reflectivity of ground objects. However, carrying a spectral detector is relatively difficult and expensive for UAVs. Therefore, the UAV-NeRF method for ground object radiation measurement via visible image reconstruction has more application potential.  
Secondly, our method can extract shadows efficiently. In the \(S\) diagram in Figure 5, darker areas indicate a greater impact from shadow interference, while lighter areas represent a higher exposure to sunlight. Shadows are extracted from various surfaces, including ground areas directly shaded by buildings, the facades of the buildings, and vegetation shading from the sun. Shadow extraction is important for recovering the properties of ground objects, which increases the accuracy of albedo measurements.  
Thirdly, our method can effectively extract transient objects that are unaffected by shadows and are mainly dynamic objects in different phases. Therefore, change detection based on UAV-NeRF is also a potential research direction. In addition, we observed several interesting phenomena: (1) vegetation edges were often detected as dynamic objects due to varying wind conditions at different phases and angles; (2) certain areas of buildings were identified as transient objects because color changes occur at different angles; and (3) these features were easily detected as transient objects at a navigation height of 300-500 m (when the reconstruction accuracy was at the submeter level) because of the small size of the pixel road sign line, the limited pixels in corresponding images, and insufficient light-focusing accuracy.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.5 Nines  
Energy and grid management are issues also addressed by the \"NINES\" project, carried out in the UK. The project aims at improving and stabilizing the electricity grid in the island of Shetland. Through the so-called \"Active Network Management\" system, the HV network can be monitored. Storage devices, like storage heaters and water tanks are introduced, while the usage of renewable energy sources is also addressed [102].
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 2 Materials and Methods
### Methods
#### 2.2.1 Encoder and Decoder  
As shown in Figure 1, the noisy STDCT spectrum \(X\) is simultaneously fed into the speech and noise encoders, aiming to extract speech-related and noise-related feature representations. The encoders in both branches include multiple two-dimensional (2D) convolutional (Conv2d) blocks. The detail of the Conv2d block is illustrated in Figure 2a, comprising a 2D convolutional layer, a 2D batch normalization layer, and a PReLU layer.  
Correspondingly, a decoder structurally symmetrical to the encoder is employed in both the speech and noise branches. The decoder processes the output of the recurrent module and reconstructs a spectrum mask of the same size as the input spectrum \(X\). The decoders in both branches consist of multiple 2D deconvolutional (DeConv2d) blocks. As shown in Figure 2b, each DeConv2d block includes a 2D deconvolutional layer, a 2D batch normalization layer, and a PReLU layer. Note that the non-linear activation function of the last DeConv2d block is a modified Tanh function like [37].  
Figure 1: Overall architecture of unidirectional information interaction-based dual-branch network (UnilInterNet).  
In the speech branch, the encoder and decoder, respectively, adopt causal convolutional and deconvolutional layers, which are achieved through asymmetric zero-padding and frame-discarding operations in the time dimension. The kernel size and stride in the frequency and time dimensions are (5, 2) and (2, 1). In the noise branch, the kernel size and stride of all non-causal convolutional and deconvolutional layers are (5, 3) and (2, 1). This improves the noise modeling capability. In addition, the output channel of each convolutional layer in the encoders is {16, 32, 64, 128, 160}, and the output channel of each deconvolutional layer in the decoders is {128, 64, 32, 16, 1}.
# _electronics_
## 3 Coded Packages Naming Lookup and Cache Placement
### The Location of the Coded Packages  
The placement of the number of satellite-coded packages is expressed as \(O_{w_{st,i}}=\left\{O_{w_{st,1}},O_{w_{st,2}},\ldots,O_{w_{st,N}}\right\}\), where \(O_{w_{st,i}}\) represents the number of coded packages of the \(i\)th file stored on the \(t\)th satellite of the \(s\)th cluster.  
\[\min U=\sum_{i=1}^{N}p_{i}\frac{m_{i}}{k}(k-\min\left\{\sum_{s=1}^{u}\sum_{l=1 }^{p}O_{w_{st,i}},k\right\}) \tag{6}\]  
\[\min T=\sum_{i}p_{i}Rt\left(1-\text{sgn}(\sum_{i=1}^{v}O_{w_{st,i}}-k)\right) +p_{i}Ht\,\text{sgn}(\sum_{i=1}^{v}O_{w_{st,i}}-k) \tag{7}\]  
\[f_{\min}=\min[U,T] \tag{8}\]\[s.t.\left\{\begin{array}{l}\sum_{i=1}^{N}O_{w_{s,i,j}}\dfrac{m_{i} }{k}\leq C\\ O_{w_{s,i,j}}=\{0,1,\ldots,k\}\\ \sum_{s=1}^{u}\sum_{t=1}^{v}O_{w_{s,i,j}}\leq O_{i}\end{array}\right. \tag{9}\]  
where \(sgn\) is the step function, when \(\sum_{t=1}^{v}O_{w_{s,i,j}}-k\geq 0\), the function value is 1, which means that the required coded block can be obtained in a satellite cluster. Assuming that the number of hops in a cluster is \(H\), when \(\sum_{t=1}^{v}O_{w_{s,i,j}}-k<0\), the function value is 0, which means that it is necessary to obtain code blocks from other clusters or content servers. Assuming that the number of hops passed at this time is \(R\), \(H<R\), \(t\) represents the transmission delay of one hop of the inter-satellite link.  
The contents are arranged in descending order according to popularity, starting from the most popular content and placing it in sequence. Based on satisfying the cache capacity constraints, we solve the location of each node's coded package to minimize the backhaul link load and content acquisition delay.
## 3 Data  
### GRACE/GRACE-FO Data  
The GRACE/GRACE-FO RL06 spherical harmonic (SH) solutions (truncated to a degree and order 60) were derived from the Center for Space Research at the University of Texas at Austin (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL) and Institute of Geodesy at Graz University of Technology (ITSG), respectively. Among them, GRACE SH solutions are from January 2003 to June 2017, and GRACE-FO SH solutions are from June 2018 to December 2020. Before calculating the monthly TWSC gridded data, the SH solutions must be preprocessed. Firstly, we used C\({}_{20}\) of satellite laser ranging and the results from [PERSON] et al. to replace C\({}_{20}\) and degree-1 coefficients of SH solutions [27; 28]. Then, considering the correlated and high frequency errors, we used 300 km Gaussian filter and P3M6 polynomial filter to process SH coefficients [29]. Finally, the multiple scale factor method (MSF) was used to recover the signal attenuation due to degree truncation and filter process [30], and the further details of scale factors calculation are available in Section 4.2. Except for SH solutions, we can obtain the monthly TWSC gridded data from Mason solutions. The advantage of Mason solution is that the monthly TWSC gridded data can be obtained directly without additional data processing. In our study, we used the Mason solutions derived from CSR and JPL.  
Since GRACE and GRACE-FO data are two identical data, GRACE and GRACE-FO were collectively called GRACE in our study. For convenience, the four GRACE SH solutions and two Mason solutions were termed as CSR-SH, GFZ-SH, JPL-SH, ITSG-SH, CSR-M and JPL-M.  
Figure 1: The digital elevation models of the ARB.
## 3 Results and Discussion
### Nanoparticle Characterization
#### 3.2.1 UseNPs' FTIR Analysis  
The use of FTIR for UseNPs can help identify potential active components which are responsible for the reduction of selenium ions and NP formation or those involved in stabilizing the NPs produced. The FTIR spectrum was recorded in the wavelength region from 613.5960 to 3435.0658 cm\({}^{-1}\) (Figure 2), showing an absorption band at approximately 3435.0658 cm\({}^{-1}\), which represented the hydroxyl group stretching vibration and indicated the presence of a hydroxyl functional group [50]. The highest absorption rate observed was at the 3346.5464 band. The shift in the spectral profile of _U. lactuca_ extract to 3286.8142 cm\({}^{-1}\) could be attributed to interactions between functional groups responsible for this band and USeNPs [51]. Similarly, the shift in the broad hydroxyl band could be explained by interactions between hydroxyl groups of polyphenols or sugars in the _U. lactuca_ extract and the resulting USeNPs [50]. The occurrence of a unique peak at 2933.1506 cm\({}^{-1}\), which represented the C-H alkenes stretching vibration, could indicate the involvement of hydrocarbon chains in the stabilization of USeNPs. These peaks also demonstrated the presence of a biopolymer, which was likely derived from algal cell walls and was linked to the SeNPs [52]. The bands at 1659.3297 cm\({}^{-1}\) and 1536.7124 cm\({}^{-1}\) represented the stretching vibration of the (NH) C=O group (Amide band II) for the N-H bending of the amines 1 ry and 2 ry, respectively, may suggest the involvement of amide groups (which could be from amino acids, peptides, or proteins). The band at 1444.9020 cm\({}^{-1}\) was marked by N-H stretching vibrations found in protein amide linkages. Previous studies have indicated that these functional groups play a role in the stability/capping of SeNPs [53; 54]. According to the literature, another absorption band at 1118.1212 cm\({}^{-1}\) shows the superposition of in-plane C-H bending and C-N stretching vibrations of aliphatic amines, as well as the distinctive Se-O stretching vibration [55]. Some bending vibrations of the Se-O bond are highlighted from 852.7263 to 613.5960 cm\({}^{-1}\).
## 3 Methods
### Validation and Accuracy Assessment  
The change points detected by the proposed method were verified by the NFSC data and the manually collected samples from TimeSync-plus tool. Spatially, accuracies of both seasonal and trend change points were evaluated in terms of Kappa coefficients, user's and producer's accuracy (UA and PA) and overall accuracy (OA). The Kappa coefficient evaluates the consistency between the detected change points and validation data. UA and PA determine the commission and omission errors for seasonal and trend change points. OA reflects the ratio of correctly detected change points to the total validation samples. Temporally, we used interval intersection to measure the proximity between the timing of changes and the validation data to manifest the BEAST-derived dates as being close to the actual events.
###### Abstract  
Chlorophyll fluorescence is a useful indicator of a plant's physiological status, particularly under stress conditions. Remote sensing is an increasingly adopted technology in modern agriculture, allowing the acquisition of crop information (e.g., chlorophyll fluorescence) without direct contact, reducing fieldwork. The objective of this study is to improve the monitoring of olive tree fluorescence (F\(\
u^{\prime}\)/Fm\({}^{\prime}\)) via remote sensing in a Mediterranean environment, where the frequency of stress factors, such as drought, is increasing. An advanced approach combining explainable artificial intelligence and multispectral Sentinel-2 satellite data was developed to predict olive tree fluorescence. Field measurements were conducted in southeastern Italy on two olive groves: one irrigated and the other one under rainfed conditions. Sentinel-2 reflectance bands and vegetation indices were used as predictors and different machine learning algorithms were tested and compared. Random Forest showed the highest predictive accuracy, particularly when Sentinel-2 reflectance bands were used as predictors. Using spectral bands preserves more information per observation, enabling models to detect variations that VIs might miss. Additionally, raw reflectance data minimizes potential bias that could arise from selecting specific indices. SHapley Additive exPlanations (SHAP) analysis was performed to explain the model. Random Forest showed the highest predictive accuracy, particularly when using Sentinel-2 reflectance bands as predictors. Key spectral regions associated with F\(\
u^{\prime}\)/Fm\({}^{\prime}\), such as red-edge and NIR, were identified. The results highlight the potential of integrating remote sensing and machine learning to improve olive grove management, providing a useful tool for early stress detection and targeted interventions.  
Keywords:random forest; drought; satellite; SHAP; remote sensing; olive; machine learning
## 3 Therapeutic Potential of Exosomes in Different Diseases
### Therapeutic Potential of Exosomes in Neural Diseases
Therefore, the integrity of the vascular network plays a key role in peripheral nerve regeneration and a therapeutic option in PNI management [94]. Insults to the neural tissue caused by spinal cord injury increase the number of complement factors. Bone Mesenchymal stem cells (BMSCs) inhibit the levels of C4-C6 as well as C4 binding proteins and complement factor H in rats [21]. Furthermore, the application and use of neural stem cell-derived extracellular vesicles significantly reduce the severity and extent of spinal cord injury by reducing neuronal apoptosis, activation of microglia, and neuroinflammation in rats [23]. Interestingly, MSC exosomes promote angiogenesis through paracrine activation leading to vascular remodeling. Through the P13K/AKT signaling pathway, exosomes are induced by the pluripotent Mesenchymal stem cells which activate local angiogenesis. [95]. Furthermore, vascular plasticity is through MSC exosomes and endothelial cell-transferred proangiogenic miRNAs. Additionally, other studies observed activation of angiogenesis by MSC-derived exosomes decreasing neurological deficits [96]. Intravenous injection of MSC-derived exosomes activates neurite remodeling, neurogenesis, and angiogenesis leading to functional recovery [52]. The same effects were explained in another study in which MSC exosomes decreased inflammation in rat models by activating the endogenous angiogenesis including neurogenesis [95]. In summary, reports from all studies revealed that MSC-derived exosomes are likely to be regulators by promoting communication with vascular endothelial cells thereby enhancing vascular plasticity after nerve injury [96]. Nanoparticles provide beneficial therapeutic effects such as those enhanced by MSC and may be a promising adjuvant for treatment due to the inherent advantages they possessed over the MSCs [97]. MSC-derived exosomes play an important role in the intercellular communication [80] by utilizing a number of neurotrophic factors, protein, and genetic materials to axons. The introduction of MSC-derived exosomes can inhibit the problems arising from stem cell transplantation [86; 98].