esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Integrity
#### 3.2.1 Safe Channel  
The Safe Channel pattern [28] ensures integrity on the transport layer, making use of certificates. Communicating through public (i.e., unsafe) networks allows for interceptions, making the transmitted messages open to tampering.  
The file channel integrity tool in Apache Flume ([[https://flume.apache.org/](https://flume.apache.org/)]([https://flume.apache.org/](https://flume.apache.org/)) (accessed on 2 December 2020)), a distributed system for efficiently collecting, aggregating, and moving large amounts of log data from different sources to a centralized data store, is an example of implementation of the Safe Channel pattern. The Transmission Control Protocol (TCP) protocol also provides a (non-cryptographic) checksum to aid in detecting data corruption, while not protecting against reordering of data nor recalculation of the checksum. Finally, the Minefield is a relevant lower-level pattern that will be detailed below.
# BODIPY-Based Nanomaterials--Sensing and Biomedical Applications
## 1 Introduction  
### Nanotechnology and Nanomaterials in Medicine  
Miniaturization processes of bulk materials reduced to the nanoscale have led to the development of structures with interesting physicochemical properties up to 100 nm in size, called nanoparticles. Nanotechnology brings many opportunities for the application of nanoparticles in numerous fields, including medicine and pharmacy, biotechnology, electronics, optics, and even automotive and construction industries [1, 2, 3, 4, 5, 6].  
Nanotechnology conducted in life sciences is defined as nanobiotechnology and its benefits are further displayed in the field of medicine and pharmacy, especially in the early diagnosis of diseases and the improvement of treatment methods. Nanotechnology has impacted the development of nanomedicine, especially in terms of drug delivery approaches for potential use in therapy and diagnostics [7]. Nanomedicine is now consideredin many medical applications: (i) diagnostics, (ii) imaging techniques, (iii) biomaterials for internal and external use, (iv) new pharmaceuticals and drug delivery systems for targeted therapies, and (v) clinical and toxicological aspects of nanodrugs utilization (Figure 1) [8].  
Progress in nanotechnology is strictly related to new discoveries in the nanomaterials field. According to European Commission guidelines, the term \"nanomaterial\" means both natural and artificial material where, for at least 50% or more of all particles, one or more external dimensions is in the size range 1-100 nm [9]. Nanoscale dimensions result in special physicochemical as well as optical features of nanomaterials. Nanomaterials are known for their high surface-area-to-volume ratio, which allows a vast number of molecules to be attached to their surface [10]. Another significant property is the quantum size effect, which allows nanomaterials to display individual electronic band structures as molecules on the macroscale [10]. In addition to this, nanomaterials also reveal some other interesting features: (i) increased molecular adsorption and surface tension force, (ii) enhanced both biological and chemical (catalytic) activities, (iii) fast clustering tendency, (iv) lower melting points, and (v) significant mechanical strengths (Figure 2) [10; 11]. However, for most applications, raw nanoparticles have to be physically or chemically modified before being used [12].  
Figure 1: The nanomedicine scope of interest [8].  
Figure 2: Unique features of nanomaterials [10; 11].
## 4 Results and Analysis  
In this section, we analyze the channel characteristics, present the channel characteristic models, and compare the channel characteristic model parameters between the sub-6 GHz, mmWave, THz, and visible light bands. Firstly, the path loss is analyzed and the distance dependency of the path loss is characterized by the FI model. Secondly, the RMS delay spread is analyzed with regard to the distance dependency, and the CDF of the RMS delay spread is modeled. Finally, the azimuth AS is analyzed, and the CDF model of the AS is presented.
## 5 Simulation
### One LOS and One Reflected Path Environment  
In the simulations below, we compare the performance of the DP-SIMLE algorithm with the RHCP-R1-MLE and the RHCP-MEDLL algorithms, which are designed for the RHCP antenna. In the first simulation, we construct the most basic multipath environment with one reflected signal. Multipath 1 (MP1) shifts 1 m further away from the LOS signal smoothly every 10 s such that we could evaluate the multipath effect within a particular range. We also fix the relative carrier phase of the MP to the LOS signal as 0 or \(\pi\) to obtain the maximum code multipath error to assess the worst case. Table 3 shows the parameters of this two-path scenario. The duration of the simulation is 400 s when MP1 shifts 40 meters away from the LOS signal.  
Figure 9: The diagram of the connections between the IF generator, the software receiver and the post-processing unit of simulation platform. The top left part is the IF data generator, the bottom left part is the post-processing unit with multipath mitigation algorithms, and the software receiver is on the right.  
Figure 10 shows the residual multipath errors of the three algorithms as with the shifting of MP1 when constructive multipath presents (above zero) or destructive multipath presents (below zero). The data of the first 5 s are discarded for the pull-in procedure of the tracking loop. The DP-SIMLE (in red) outperforms the RHCP-MEDLL (in blue) after the MP signal is 10 m (constructive) or 5 m (destructive) further away from the LOS signal. Figure 11 illustrates the RMSE of the proposed DP-SIMLE algorithm: in the entire simulation, the DP-SIMLE algorithm reduces 26% more of the multipath bias than the RHCP-MEDLL algorithm ((2.85 m-1.68 m)/4.42 m); when it comes to the short delay range, the DP-SIMLE algorithm reduces 38% more of the multipath bias than the RHCP-MEDLL algorithm ((3.61 m-2.1 m)/4.02 m).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **LOS** & **MP1 1** \\ \hline RHCP power & 0 dB & \(-6\) dB \\ LHCP power & \(-\)Inf 2 & \(-6\) dB \\ path delay & 0 m & 0 to 40 m 3 \\ carrier phase & 0 & 0 or \(\pi\) \\ \hline \end{tabular}
\end{table}
Table 3: The parameters of the one LOS and one reflected path signals.  
Figure 11: The RMSE of the three multipath mitigation algorithms. (**a**) RMSE in the entire simulation; and (**b**) RMSE in the short delay range.  
Figure 10: The residual multipath error of the three multipath mitigation algorithms. MP1 shifts 1 m every 10 s, so we can divide the scale of \(x\) axis by 10 to obtain the true path delay of MP1 relative to the LOS signal conveniently.
## 5 Case Study
### Experimental Results
#### 5.3.2 Estimation of the Computational Complexity  
The second experiment set aims to estimate the computational complexity of CPU and memory usage as well as the number of _Rules_ created considering large time window sizes, from 10 min to 190 min. A total of 950 runs were made, and the results shown correspond to the average of 10 runs per query and time window size. We also used four curve fits to estimate the computational cost: linear, polynomial, logarithmic, and exponential. As  
Figure 8: Processing time results considering query construction and search time using short time window sizes.  
mentioned earlier, the _left_ operator was also used to estimate the trend lines. In this version of the experiments, the operators involving more than one object are configured on all the objects that match the query, even partially. Therefore, the tests were extensive and explored all the combinations to find out their processing capacity at high workloads.  
Figure 9 shows the results of the CPU processing time considering the construction of the NOP query and the search time. Please observe that the _left_, _conj_, and _seq_ operators are expensive because they evaluate pairs of objects within the time window size. In particular, the _left_ operator is more costly because it evaluates the spatial positions of the objects and their _Attribute_ values. In this application, positioning changes are constant because the objects are moving. In turn, the _objects_ and _count_ operators are the least expensive because the _objects_ operator only evaluates one object at a time, and the _count_ operator only adds the counter when an object appears in the time window. These results suggest that the computational complexity grows polynomially. Therefore, this approach performs better than others in the literature.  
Figure 10 presents the results regarding memory usage, keeping in mind that the current version of the experiment does not have any garbage collector when the objects and the respective NOP elements are not used. This strategy was defined to test the application in a CPU-intensive and memory-consuming setting. It was observed that the _left_, _conj_, and _seq_ operators require more memory because they evaluate pairs of objects. Once again, it can be observed that the _objects_ and _count_ operators perform with constant computational cost because the dataset contains a similar distribution of objects across time window sizes.  
Figure 11 presents the amount of _Rules_ produced. The _Rules_ are created when a new object matches, even partially, with the query expression. It means that the number of _Rules_ created was high due to the lack of a garbage collector. If some strategy for clearing unused or \"old\" NOP elements from memory were used, the final number of _Rules_ would be smaller, but the real potential would not be known. In this way, it is possible to see how the system behaves with a large mass of data processed.  
Figure 9: CPU processing time results considering the NOP Query construction and search time using large time window sizes.
## 3 Results
### Solvent-Solvent Extraction  
In the solvent extraction of MEL, the assessment of liquid-liquid partition coefficients is extremely important to understand the distribution of MEL in an organic-solvent-water system. Various organic solvents were screened at different pH levels for the extraction of MEL from broth. The higher partition coefficient was used as the selection index. According to the results, the partition coefficient was high for mid-polar solvents like ethyl acetate and methyl tert-butyl ether and for a binary mixture of ethyl acetate and acetonitrile (9:1). The nonpolar solvents like hexane showed a significantly low partition coefficient of 0.33. An increase in the partition coefficient was proportional to the increase in the concentration of MEL extracted. MEL extraction using binary solvents like ethyl acetate and acetonitrile (9:1) allowed an excellent recovery due to their having the highest partition coefficient in a ratio of 3.1. Hexane gave a low level of MEL because of its lower efficiency of solvation. The  
Figure 4: Time course of MEL production from acid oil by _Pseudozyma antarctica_. Data for periodic addition of substrate are represented in the following formats: MEL titer g L\({}^{-1}\) (\(\blacklozenge\)), cell mass g L\({}^{-1}\) (\(\blacksquare\)), and, for one-time addition of substrate, MEL titer g L\({}^{-1}\) (\(\blacklozenge\)) and cell mass g L\({}^{-1}\) (\(\Leftarrow\)). Data points are the averages of data from three independent cultivations.  
extraction process was found to be strongly affected by variations in the pH. Graphically, it was observed that the partition coefficient of MEL declined with an increase in the pH (Figure 5).  
A progressive increase in the partition coefficient due to a decreased pH resulted from the improved solubility of MEL in the solvent. When pH value was increased from 2 to 6, the extraction titer of MEL decreased due to weak covalent bonding between MEL and the solvent at a higher pH. Solvent extraction in an acidic medium was also successfully reported by [PERSON] et al. [28]. This study reported having high surfactin recovery in the acidic medium and a low distribution ratio at a neutral pH. Therefore, the type of extraction solvent, its polarity, and its pH may have significant impacts on the quantity of MEL extracted. The highest extraction of MEL was typically achieved using a solvent with intermediate polarity, which exhibited better solvation efficiency due to interactions, such as hydrogen bonds, between the polar sites of the compounds and the solvent when compared to nonpolar solvents [29]. Here, the recovery rates of MEL from broth using ethyl acetate, MTBE, and Ethylacetate: acetonitrile (9:1) were 69%, 73.6% and 75.6%, respectively. Therefore, binary solvents are also useful for recovering MEL.
# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 3 Experimental Results and Performance Analysis
### Ablation Study  
This subsection incrementally introduces the RLNet based on RepELAN, the CMI module, and the TFusion module to explore the contribution of each component to the performance of abandoned object detection. The experimental results, as shown in Table 6, clearly demonstrate the performance enhancements contributed by each module.  
Initially, the model experienced a 47.7% and 41.9% reduction in the number of parameters and computational load, respectively, a 16% increase in FPS, and a 2% increase in both AP50:95 and AP50 metrics by replacing the backbone network of RT-DETR-R18 with the RepELAN-based RLNet. This significant performance enhancement is primarily attributed to the simple and efficient structure of RepELAN, which effectively extracts crucial information during training and employs fewer residuals and a faster cascade architecture during inference, thereby optimizing overall model performance. Further, introducing the TFusion module in the encoder improves the model's detailed information, resulting in a 3.5% and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kernel Number** & **Params (M)** & **FLOPs (G)** & **AP50:95** \\ \hline
2 & 14.4 & 44.8 & 40.9 \\
3 & 14.4 & 44.9 & 41.2 \\
4 & 14.5 & 45.0 & 42.6 \\
5 & 14.5 & 45.2 & 41.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different numbers of kernels.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Params (M)** & **FLOPs (G)** & **AP50:95** & **AP50** & **AP75** & **AP\_s** & **AP\_m** & **AP\_1** \\ \hline
1.RT-DETR-R18 & 19.9 & 57.0 & 40.0 & 82.5 & 31.7 & 20.1 & 43.3 & 34.9 \\
2.RT-DETR-R18 + TFusion & 19.9 & 58.2 & 40.7 & 84.1 & 33.4 & 21.2 & 43.6 & 36.8 \\
3.RT-DETR-R18 + CMI & 23.9 & 66.8 & 41.2 & 83.8 & 35.3 & 19.6 & 44.2 & 37.4 \\
4.RT-DETR-R18 + TFusion + CMI & 24.0 & 68.1 & 41.6 & 84.3 & 35.6 & 18.9 & 44.8 & 37.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of the efficient EHMencoder.  
8.2% increase in AP50:95 and AP75, respectively. Subsequently, integrating the CMI module at the beginning of the encoder resulted in a 2.9% increase in AP50:95, demonstrating the necessity of multi-scale feature interactions in complex highway scenarios. This module employs a variety of sizes of DW convolution to expand multi-scale feature representations under different receptive fields. Finally, the collaborative effects of these modules enable the LE-DETR model to achieve real-time capabilities while reducing the number of parameters and computational load by 27.1% and 21.1%, respectively, compared to the baseline model. The model improves detection accuracy across various scales, with increases of 6.5% in AP50:95, 2.2% in AP50, and 18% in AP75.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 5 Results and Discussion
### Discussion  
In the following part, the discussion of our semantic fusion method will be arranged in three aspects: the down-sample rate, the parameter chosen for the k-nearest neighbor algorithm, and the decision strategies between soft and hard.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Pixel Accuracy(\%)**} \\ \hline
**Method** & **Building** & **Vegetation** & **Vehicle** & **Road** & **All** \\ \hline
2D prediction & 95.60 & 89.85 & 97.95 & 87.91 & 85.66 \\
3D baseline & 97.51 & 90.06 & 99.76 & 75.59 & 87.76 \\ Local & 96.20 & 91.38 & 99.74 & 68.61 & 88.24 \\ Global & 96.16 & 91.40 & 99.45 & 71.44 & 88.21 \\ \(Global+Local\) & 96.19 & 91.40 & 99.76 & 68.16 & 88.40 \\ \hline \hline \multicolumn{5}{c}{**F1-Score(\%)**} \\ \hline
**Method** & **Building** & **Vegetation** & **Vehicle** & **Road** \\ \hline
2D prediction & 96.53 & 73.99 & 33.15 & 61.42 \\
3D baseline & 97.00 & 74.69 & 63.66 & 75.79 \\ Local & 97.13 & 74.87 & 62.72 & 75.63 \\ Global & 97.15 & 74.69 & 73.17 & 75.03 \\ \(Global+Local\) & 97.85 & 76.07 & 81.40 & 76.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of different methods for semantic categories.  
Figure 6: (**a**) is the Confusion Matrix for 2D segmentation, (**b**) is the Confusion Matrix for re-projection images. Four categories are evaluated, which are _Building_, _Vegetation_, _Road_ and _Vehicle_. It shows that the re-projection map from 3D semantic points behaves higher accuracy compared with 2D segmentation, due to considering multi-view information.
# PTG-HEFA Hybrid Refinery as Example of a SynBioPTx Concept--Results of a Feasibility Analysis
## 2 Materials and Methods
### Economic Assessment  
As part of economic feasibility, specific fuel production costs have been calculated referring to the guideline 6025 of the Association of German Engineers' (VDI) [33] and by using the dynamic annuity model that takes all relevant cost items into account:  
1. Capital-linked costs: single and total investments for the different plant designs for the PTG-HEFA hybrid refinery;
2. Consumption-linked costs: feedstocks, electricity, and auxiliaries (e.g., water, catalysts, natural gas and biomethane for steam reforming);
3. Operation-linked costs: (plant staff, maintenance);
4. Other costs: administration, insurance.  
Revenues from by-products (here naphtha, fuel gas and diesel) are subtracted from the total costs. These total net-costs divided by the total amount of HEFA-SPK produced (or for scenario 10 for diesel) yield the specific fuel production costs.  
A detailed method description for the economic assessment is presented in [12]. The main assumptions can be obtained from Table 3.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Methodology Step** & **Assumptions for PTG–HEFA Hybrid Refinery** \\ \hline _Goal and scope definition_ & \\ \hline Considered impact categories & Global warming potential (GWP) \\ Functional unit & 1 M] fuel ex hybrid refinery \\ System boundary for LCA & Well-to-tank-chain including feedstock production (w/o direct or indirect land use changes), biomass collection of UCO and fuel production. \\  & No consideration of infrastructure (i.e., built up of plants, components, and vehicles) \\ Consideration of by-products & According to European Renewable Energy Directive (2008/29/EC) allocation of by-products (here, the subdivision of emissions and demands along the production chain between HEFA-SPK and naphtha, fuel gas, and diesel) and according to their energy content (lower heating value) \\ \hline _Inventory calculation_ & \\ \hline Input/output analysis & Consideration of all relevant input and output streams (i.e., energy and feedstock inputs, auxiliaries and utilities, products and by-products, and waste) within the system boundary \\  & Concepts based on process simulation, own data, and EcoInvent Version 3.3 [30] \\  & External electrical power based on country specific mixes for 2015, emissions according to Gemis [31] \\ \hline _Impact assessment_ & \\ \hline Approach & Evaluation of data resulting from input/output analysis regarding potential environmental impacts by means of so called characterising factor aggregation with regard to one reference substance \\ GHG emissions & According Forth IPCC Assessment Report (AR4) \\  & CH\({}_{4}\) with 25 CO\({}_{2}\)-eq, N\({}_{2}\)O with 298 CO\({}_{2}\)-eq \\  & (w/o consideration of process-related biogenesis CO\({}_{2}\) emissions) [32] \\ \hline _Result interpretation_ & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Methodology and assumptions for life cycle assessment (LCA).
## 7 Results and Discussion
### Evaluation Metrics  
We evaluated the proposed algorithm in terms of throughput, residual energy, packet delivery ratio, end-to-end delay and network lifetime, concerning different mobility speeds of the network and compare it with the existing protocol. We presented two schemes: CRP-GR (i.e., based on game theory and reinforcement learning) and CRP-G (i.e., based on game theory). The results were generated at a learning rate \(\alpha=0.9\) and regulatory factor \(\beta=0.9\). Table 3 shows the simulation parameters.
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
## 2 Procedure Framework  
In the research carried out so far by the authors, some existing buildings were chosen and analysed as case studies of vertical extension with IIS [32; 33]. The case studies include low-rise masonry buildings in Pozzuoli, an Italian town close to Naples, where a recent plan has been issued for reconstructing the upper floors (demolished nearly forty years ago) of several buildings of the historic centre. These buildings, therefore, appear as optimal candidates for investigating vertical extension with IIS.  
The procedure for the design and assessment of vertical extension with IIS consists of four main steps, fully described in [31] and briefly recalled here. The steps concern:  
1. Examination of the existing building;
2. Parametric response spectrum analysis on simplified lumped mass model;
3. Selection and design of one or more solutions;
4. Advanced analyses on refined models of the design solution(s).  
The proposed procedure is described in the following sections by selecting a masonry existing building as a case study [31]. For the sake of brevity, the results of the dynamic and static analyses, performed on the simplified and refined models of both the reference existing building and the design solutions, are only discussed for the weakest direction of the case study.