esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications
The actual deviation differs for the two years for both QuikSCAT products. The difference in days (between -15 and +15) was in nine of ten cases smaller for the diurnal cycling method [12] than the threshold based method [16]. The number of samples were however too low to draw general conclusions. Snowmelt timing statistics for the ten years of data availability based on [12] are shown in Figure 4.  
[PERSON] _et al._[36] assessed different spring timing products from the Siberia II project. The Siberia II project dealt with multi-sensor concepts for greenhouse gas accounting in central Siberia [46]. The different approaches within the project identified three different phases of the spring thaw, specially identifying the end of snow melt from active microwave data (SeaWinds, [12]), free snow condition from passive microwave data (SSM/I, [47]) and the onset of green up/budburst based on optical multispectral data (SPOT-VGT, [48]). Whereas the SeaWinds product provides daily information, SSM/I was averaged over 5 days and the SPOT-VGT was available in 10 day intervals. These differences in temporal resolution in addition to issues of spatial resolution allow only limited comparability. Considering a \(\pm\)10 day variation, 80% of the central Siberian study region (3 Mio km\({}^{2}\), stretching from Taimyr to Lake Baikal) showed agreement between SeaWinds and SSM/I end of snowmelt [36]. SSM/I snowmelt timing tends to be earlier than the end of daily freeze/thaw cycling. The average offset between the SPOT-VGT greening up dates was 1-2 weeks, indicating that the end of diurnal thaw and refreeze from SeaWinds is also representative for budburst within Taiga forest with moderate terrain. [PERSON] _et al._[23] compared QSCAT derived snowmelt with the daily 24 km resolution Interactive Multisensor Snow and Ice Mapping System (IMS) product and a snow water equivalent (SWE) product (SSM/I, [49]) based on passive microwave. Best performance was found for IMS and QSCAT, although the duration of the melt period has been assumed to be static.
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
## 2 Characterization of Multipath Interference in G.654E SMF
A more rigorous MPI characterization could also bear in mind the coupling between \(\text{LP}_{11a}\) and \(\text{LP}_{11b}\) modes and the fact that they propagate at slightly different speeds, causing temporal dispersion in high-speed systems, which can be relevant in mode-division multiplexing systems.
## 4 The Proposed Mechanisms
### The PIF Mechanism  
The first proposed mechanism is called Probabilistic Interest Forwarding (PIF). With PIF (Algorithm 1), a node forwards interest packets with a probability of \(p\) if the node has a centrality degree (\(g\geq G\)), where \(G\) is the centrality threshold. Otherwise, nodes that have a centrality degree (\(g<G\)) forward interest packets according to the default NDN forwarding mechanism. The interest packet forwarding probability (\(p\)) is the same for all nodes. Each node has a centrality degree (\(g\)) that refers to the number of neighbors that a node has at the time of the interest packet forwarding decision. The centrality threshold (\(G\)) is defined based on the network density. It stands for the number of neighbors considered to decide whether the node has high centrality or not, and it is the same for all nodes.  
With PIF, we aim to reduce the number of interest packets sent by nodes with a high degree of centrality, which means nodes with a high number of neighbors. Preliminary experiments suggest the definition of a forwarding criterion based on the centrality degree. We observed more nodes contending for the medium just after an interest packet forwarding by a node with more neighbors.  
These nodes are trying to send copies of the same interest packet. Besides, a neighbor of a node with a high degree of centrality has a high probability of having already received that interest from another node, because interest packets are sent in a broadcast.  
```
Data: (\(p\), \(g\),\(G\), \(pc\),\(\mathit{k\_interest}\).) begin if\(g>=G\)then \(\lambda\leftarrow\mathit{Random}()\); if\(\lambda=<p\)then \(\mathit{Forward}(\mathit{pc\_interest})\) else \(\mathit{Discard}(\mathit{pc\_interest})\);  end if else \(\mathit{Forward}(\mathit{pc\_interest})\)  end if  end for
```  
**Algorithm 1**Probabilistic Interest Forwarding (PIF) Mechanism.  
Figure 2 presents a scenario where a consumer node requests content from a given producer. At time \(t=1\), node \(X\) has already received the interest packet sent by the consumer and then broadcasts it. After this, at time \(t=2\), nodes \(C\) and \(F\) receive the interest and then broadcast it to their neighbors. Let us suppose that \(A\) is a central node and decides to not forward the interest packet based on the probability (\(p\)) defined by PIF. In this case, we have less contention for the medium. Otherwise, if \(A\) forwards this interest, \(C\),\(F\),\(E\),\(D\) and \(B\) can be impacted. In this example, we observe that suppressing some interest packets in central nodes reduces the medium contention. Therefore, we increase the forwarding probability of other interest packets or contents in the transmission channel and thus, increase the network throughput.
## 1 Introduction
In contrast, a direct immobilisation of Con A will reduce the amount of (expensive) protein used in the preparation of the biosensor as well as preventing any loss of this potential toxic molecule should the sensor membrane be compromised [30]. It will also permit the detection of any target molecules that are freely suspended in the surrounding environment should any related applications be explored. Although protocols for a direct immobilisation of Con A on the sensor surface exist [31; 32] the immobilisation process may change the binding affinity of the protein [33]. It is therefore important to map the binding properties of any immobilized Con A as an integral part of the biosensor calibration. The main variables which have to be taken into account considering immobilisation on silicon oxide based ceramics, is the surface roughness (effective surface area) and the number of oxygen molecules associated with Si (and the corresponding number of active bond sites available). The immobilisation starts with a silanisation process that forms an initial anchor point to the surface active OH groups. The choice of silane depends on the selection of the coupling group (e.g., amine, thiol, carboxylic acid) of the biomolecule to be immobilised and the type of crosslinker (homobifunctional or heterobifunctional) that is used [34]. The active group of Con A is an amine where both homobifunctional and heterobifunctional crosslinkers can be employed. Successful protocols exist for both silicon oxide based ceramics [35; 36] and metal surfaces [31], although thiols are used at the initial attachment molecule for gold [37]. However, most immobilisation protocols involve elaborate steps and require the use of harmful chemicals [36]. In this paper, we present a simple and safe protocol for rapid Con A immobilisation on quartz surfaces based on the formation of a covalent linkage between the carboxylic acid on the immobilisedsilane (N-[(3-Trimethoxysilyl)propyl] ethylenediamine triacetic acid trisodium salt) and an amine group of Con A. The binding characteristics of carbohydrates to the immobilised lectin as well as its distribution per unit area was assessed by titration studies based on the competitive binding between glucose and fluorescent labelled dextran. The binding capacity of the immobilised Con A was quantitated based on the observed fluorescent intensity, whereas structural assessment was performed by atomic force microscopy (AFM). The latter approach was employed for monitoring changes in surface topography after each immobilisation step. This affinity assay will be suitable for implementation in a gravimetric glucose sensor based on acoustic wave resonators.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 1 Introduction
### Scope of the Study and Research Objectives  
In this research, we aim to contribute to a change in traditional construction methods through the use of tools that allow automating and connecting design and manufacture of personalized structures through 3D concrete printing. To do it, the following software were used: Autodesk RevitTM, DynamoTM for Revit, and a KUKATM robotic arm, along with a robust computer.  
In addition, the equipment used in this research corresponds to a robotic arm, specifically the KUKA | Quantec KR120 R2500, which has six axes of movement, which gives it the possibility of printing detailed and complex construction elements. The robotic arm is connected to a concrete pump by means of a nozzle through which the concrete used for the printed element circulates.  
The use of these tools allows not only automating a process that for years has been carried out in a traditional and little mechanized way but also optimizing the use of resources by reducing construction time, maximizing the use of the material, and avoiding the use of formworks. Most of the unused material--95% to 98%--can be recycled [10].  
Thus, the main research objective consists of designing communication protocols between BIM-designed elements and their 3D concrete printing. The specific objectives are: (a) to develop a code that allows constructive elements to be linked to a KUKATM robot; (b) to design printing paths allowing the correct 3D printing process of BIM-designed elements; (c) to check that a KUKATM robot correctly executes the parameters entered in the code; (d) to run the code into the KUKATM robotic arm to test different printing paths.
# Forecasting the Impacts of Prescribed Fires
## 3 Results
### Evaluation of Burn Day Forecasts  
The statewide model was used for burn impact forecasting in 2015, while county-specific models were used in 2016. The 2015 burn day forecasts were repeated with the county-specific models, and the \(F1\) scores calculated for each county were compared to those obtained with the statewide model. Using county-specific decision trees, burn day forecasting performance improved significantly for the 2015 burning season, compared to the use of a single, statewide model (Figure 2).  
Figure 2: January–April 2015 burn day forecast \(F1\) scores by county for the (**a**) statewide and (**b**) county-specific decision tree models.  
We used the Hazard Mapping System (HMS) Fire and Smoke Analyses by NOAA [26] for qualitative evaluation of our forecasts. Every day, we compared our burn forecasts to the daily analyses by NOAA and gave them a rating based on the agreement in regard to the location and density of the fires. Figure 3 shows an example where the rating was \"excellent\". This subjective rating was given based on the agreement both in location and density of the fires between the forecast and the analysis, everywhere except in Northeastern Georgia where the cloud cover may have obstructed the satellite's view. It should also be kept in mind that other factors, such as the forest canopy, can also interfere with the satellite retrieval. We used the highest impacts of the burns on PM\({}_{2.5}\) concentrations (shown in purple in Figure 3b) as a proxy for the locations of the burns. Also, since the burn forecast is for Georgia only, the fires observed in other states were ignored for the purposes of this evaluation.  
This qualitative evaluation is conducted as a first indication of the suitability of the burn forecast. Clearly, the ability to correctly predict the locations of the burns influences the accuracy of the fire impact forecasts at specific air quality receptors. A \"good\" or better rating warrants more detailed analysis, including qualitative evaluation. The number of good or better agreement days increased in 2016 with the county-specific models compared to 2015 when the statewide model was used for PB impact forecasting, especially in March when the burn activity peaked.
## 3 Results and Discussion
### Soil Enzyme Profile  
Microorganisms play a crucial role in the nutrient cycling of the soil, which in turn is related to the nutrition of the crops growing in it. Enzyme activities provide an overview of the conditions in which the different microorganisms present in the soil are immersed. The enzyme profile analysed through the API ZYM(r) system of the sampled soils is shown in Figure 2.  
With regard to the activity of the various enzyme families analysed here, the following order could be observed: phosphatases > aminopeptides > esterases-lipases > glycosyl hydrolases. The enzymes trypsin and \(\alpha\)-chymotrypsin from the peptidase family were not detected, nor was the enzyme lipase (Figure 2).  
A higher activity of the enzyme's acid phosphomonoesterase and phosphohydrolase reflects the acidic soil conditions. However, it is important to clarify that the enzyme alkaline phosphomonoesterase was present according to the detection. The concentrations detected by the API ZYM(r) system in relation to the family of phosphatase enzymes  
Figure 2: Enzyme profile of the sampled soils from the Bajio region of Guanajuato. Acámbaro (Ac1, Ac2, Ac3, Ac4); Salamanca (Sa1, Sa2, Sa6); Irapuato (Ir1, Ir2, Ir3).  
are mainly due to the low solubility of P in the soil, as well as the biological need of microorganisms to acquire this element to incorporate it into their membranes and use it as an energy compound for their metabolism. Consequently, they release extracellular enzymes [55,56].  
The activity of aminopeptides is related to the breakdown of simple N compounds, which is linked to the chemical fertilisation processes used. Fertilisation compounds are easily broken down so that they can be taken up by plants. Increased availability of these compounds triggers the response of microorganisms to the release of extracellular enzymes [55]. This could explain why enzymes related to the peptidase family were not detected, since, with a high availability of nitrogenous compounds, the microorganisms would not need to metabolize complex proteins.  
The esterase-lipase enzyme family is linked to the breakdown of water-soluble C compounds (ester bonds and organic acids). However, it is important to note that a higher concentration was observed in Ac2 and Sa6 soils, which showed higher values for the indicators qCO2 and MBC, suggesting the existence of better nutritional conditions for the microorganisms, compared to the other soils.  
In the same context, the glycosyl hydrolase enzyme family is related to the decomposition of C compounds, and high activity of these enzymes is associated with high OM concentrations [53]. The lack of detection of these enzymes in most of the sampled soils could be attributed to the presence of long-chain C compounds or difficult access to the OM present in the soil, possibly influenced by high Na concentrations and conventional agricultural practices [41]. The enzyme profiles of all soils were observed to focus on the mineralization of N and P in the soil, which are essential elements for the development and growth of microorganisms and plants.
## 3 Results  
### E-Field Estimation  
The computational models were employed to estimate the E-field intensity on the cortex for TMS stimulation equal to 120% of the LT (Figure 1). The stimulus intensity for each subject S1-S5 was respectively 38%, 36%, 36%, 34% and 32% of the MSO, the maximum E-field value was 127, 115, 121, 105, and 90 V/m\({}^{2}\) and the average E-field was 33, 27, 29, 29, and 27 V/m\({}^{2}\). The similarity between the computed E-fields was assessed by averaging the field values per Brodmann area and then estimating the Pearsons' correlation. The average correlation was 0.94, while the minimum and maximum correlation values were 0.86 (S4-S5) and 0.97 (S1-S3), indicating a high similarity of E-fields on average, but with substantial case-to-case variability.
## 4 Experimental Section
### Simulation Experiments
#### 4.2.4 Simulation Validation  
Validating the performance of a Deep Q-Network (DQN) model in pushing and grasping tasks using CoppeliaSim becomes more effective with the use of image masks. Image masks function to separate objects from the background in images captured by visual sensors, providing cleaner and more focused input to the DQN model. The first step is to ensure that the simulation environment in CoppeliaSim is correctly configured, including the installation of necessary plugins and APIs to connect the DQN model with the system. This environment should include elements such as the robotic arm, objects to be manipulated, and visual sensors capable of generating real-time image masks. Variations in object shape, material, and initial position should be simulated to test the model's adaptive capabilities.  
With image masks, the DQN model can more accurately identify and manipulate objects as visual distractions from the background are minimized. A series of experiments were conducted to evaluate the model's performance across various pre-set scenarios. Performance metrics such as the grasping success rate, accuracy of the final object position after pushing, and task completion time should be recorded. The use of image masks can enhance the model's precision in detecting objects and determining the appropriate actions, especially in complex or cluttered environments. Error analysis from the experimental results helps identify model failures and their causes, enabling further refinement and fine-tuning. Through iterative testing and optimization based on experimental data, the use of image masks in validating the DQN model's performance can ensure the model operates optimally in real-world applications, enhancing the efficiency and effectiveness of pushing and grasping tasks. Machine learning simulation and optimization can improve effectiveness and application in the field of manufacturing and industrial automation [38; 39].  
Figure 10: Pushing and grasping with a mask in the CoppeliaSim application.
# Characterization of the Aroma of an Instant White Tea Dried by Freeze Drying
## 2 Results and Discussion
### GC-MS Analysis of Volatile Compounds in SDE Extract of IWT  
It was reported that the main volatile compounds in the white tea were alcohols and aldehydes, including (\(Z\))-3-hexen-1-ol (30.2 \(\mu\)g/g), linalool (49.4 \(\mu\)g/g), (\(E\))-linaloxide (furanoid) (58.6 \(\mu\)g/g), (\(Z\))-linalool oxide (furanoid) (29.2 \(\mu\)g/g), benzyl alcohol (18.4 \(\mu\)g/g), 2-phenylethanol (31.8 \(\mu\)g/g), (\(E\))-2-hexenal (24.6 \(\mu\)g/g), hexanal (34.7 \(\mu\)g/g) and benzeneacetaldehyde (19.8 \(\mu\)g/g) [20; 26]. Among them, hexanal, (\(E\))-2-hexenal and (\(Z\))-3-hexen-1-ol were the characteristic compounds of a white tea [26]. Twenty-five volatile compounds were identified in the SDE according to the retention indices (RI) and mass spectrum by using Rtx-5 ms and Rtx-wax columns. These volatiles included 10 alcohols, 2 aldehydes, 4 ketones, 5 oxides and 4 others. Among the 25 volatiles, 20 compounds were quantitatively analyzed by using their respective standard curves, and the relative content of other 5 compounds were estimated by the internal standard method due to lacking of standards (Table 1). The main volatile compounds of the SDE extract were benzyl alcohol (179.7 mg/L), linalool (128.7 mg/L), hotrienol (70.0 mg/L), geraniol (64.7 mg/L), \(\alpha\)-terpineol (63.0 mg/L), coumarin (45.3 mg/L), camphene (42.1 mg/L), benzeneacetaldehyde (38.0 mg/L), 2-hexanone (31.4 mg/L), _cis_-jasmin lactone (24.6 mg/L), phenylethyl alcohol (19.4 mg/L), \(\beta\)-ionone epoxide (16.2 mg/L) and _cis_-linalool oxide (9.0 mg/L) (Table 1). By comparison, some characteristic aldehydes and alcohols in white teas, including hexanal, (\(E\))-2-hexenal and (\(Z\))-3-hexen-1-ol had not been detected in the SDE extract. This phenomenon could be attributed to aldehydes and alcohols being apt to evaporation and oxidation during preparation of instant tea as reported in a previous study [19]. In the future, more in-depth studies are needed to elucidate the transformation pathway. Figure 1: Sensory evaluation of simultaneous distillation–extraction (SDE) extracts and instant white tea (IWT) samples. SDE extracts and IWT samples showed significant difference (\(p<0.05\)) in the sweet note.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 2 Fixed Wireless Access
### Planning Tools for FWA Designs
#### 2.2.2 Research Focused Tools
The OpenAirInterface is a network simulator that integrates the core network with the RAN to perform complex simulations that can support 3 GPP Rel 8 and other improvements such as 5G NR and OpenRAN [44]. Finally, the Vienna 5G simulator performs system-level simulations abstracting the physical and MAC layer to model the complete protocol stack in a simplistic object-oriented implementation. By doing this, the Vienna 5G simulator can simulate thousands of 5G nodes in complex scenarios, including 3D channel modelling, MaMIMO and mmWaves [46; 47]. Unfortunately, none of the network planning tools described in this subsection are capable of analysing human exposure, much less planning based on electromagnetic exposure restrictions.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 4 Discussion
### Segmentation Performance: NTUH vs. BraTS Dataset
For example, a patient with brain metastases may also have a small meningioma, which may be stable and will not be labeled and treated by radiosurgery. If an algorithm detects that meningioma is this rare, decreased precision and dice score can be expected. However, from the clinical experience of our expert neurosurgeons and radiation oncologists, the rate of intentionally ignored meningiomas and pituitary adenomas was estimated around 1%. This estimation was in parallel with the reported prevalence of intracranial incidentaloma. On the other hand, the estimated rate for ignored brain metastases was much higher (5%), because our clinical experts might decide not to treat small lesions (less than 5-10 mm or visible only on one axial slice) in patients with multiple brain metastases [41; 42]. As a result, this should not impede the training due to its rarity, and most meningiomas were labeled.