esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Time-Kill Kinetics  
Another way to assess the activity of the peptides is to measure the killing activity of peptides over time rather than over a range of peptide concentrations, as in MIC. The more rapidly a peptide kills bacteria, the more active it is likely to be. Since HRZN-15 showed strong antimicrobial activity, we measured its time-killing kinetics. HRZN-15 peptide was tested at 1\(\times\) MIC, 5\(\times\) MIC, and 10\(\times\) MIC, considering the MIC to be 8 \(\mathrm{\SIUnitSymbolMicro\text{$\mu$g}}\)/mL for AB5075 and 4 \(\mathrm{\SIUnitSymbolMicro\text{$\mu$g}}\)/mL for BAA-1710. Bacterial killing was assessed for AB5075 and BAA-1710 over three hours. Polymyxin B was used as a control.  
The killing kinetics of HRZN-15 against _A. baumanniii_ (Figure 1A,B) revealed the peptide's ability to completely kill at a 1\(\times\) MIC concentration after 3 h, irrespective of the strain tested, which is very fast. At 5\(\times\) MIC, HRZN-15 eliminated AB5075 after 1 h  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Peptide/Organism**} & _A. baumanniii_ & _A. baumanniii_ & _A. baumanniii_ & _A. baumanniii_ \\  & **AB5075** & **BAA-1710** & **BAA-1794** & **BAA-1800** \\ \hline HRZN-13 & 32 & 32 & 64 & 64 \\ \hline HRZN-14 & 16 & 32 & 64 & 32 \\ \hline HRZN-15 & 4–8 & 4 & 4 & 4 \\ \hline HRZN-16 & 16 & 16 & 16 & 16 \\ \hline HRZN-17 & 32 & 32 & 32 & 32 \\ \hline LL-37 & 8 & 32 & 16–32 & 8 \\ \hline Polymyxin B & 0.25–0.5 & 0.5 & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The MIC of HRZN peptides against three multidrug-resistant strains of _A. baumannii_: AB5075, BAA-1710, BAA-1794, and BAA-1800.  
and BAA-1710 after 30 min. The peptide also showed rapid killing activity within 10 min at 10\(\times\) MIC against AB5075. HRZN-15 was equally effective in time-dependent killing activity when 10\(\times\) MIC was tested against BAA-1710 compared to 5\(\times\) MIC, both at 30 min. The \"drug of last resort\", polymysxin B, only killed AB5075 (Figure 1C) within 3 h at the highest concentration (5 \(\mathrm{\SIUnitSymbolMicro g/mL}\)), but it failed to kill the BAA-1710 strain within 3 h at all tested concentrations (Figure 1D). These results demonstrate that HRZN-15 acts very quickly compared to polymysxin B. Recent reports have demonstrated similarly rapid killing kinetics of other peptides [64; 65].
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 4 Simulation
### Analysis of Experimental Results
#### 4.2.2 Performance Comparison between the Five Algorithms
The distribution of standard deviation from the other three algorithms is sparser, which means the accuracy of the experiments on DTCM and Spray and wait is higher than those on the other three algorithms. Figure 10 illustrates performance comparison in term of network overhead. The network overheads from FCNS and P&F are higher than those from the other three methods, because they consume more processing and storage resources with a higher complexity. The network overhead from DTCM always keeps medium horizon among those from the five routing methods, with an average value of 400. As the cache space increases from 10 to 35 Mb, each node under DTCM can store, process, and forward more information, and network resources are constantly consuming. To this end, the network overhead from DTCM rises from 325 to 450, and it reaches the maximum as cache space is 35 Mb. As we can see in the Figure 10, the network overhead from FCNS rises slowly. When the cache space is 20 Mb, the node can obtain more information about network state, and network overhead is at the lowest value of 320, because DTCM uses an adaptive method to reduce energy consumption and avoid data congestion. With a simple structure and the flooding model, Spray and Wait and Epidemic need less network resources to transmit messages, so they demonstrate a relatively low horizon of network overhead. Figure 11 shows the distributions of mean and error of network overhead from DTCM. As we can see, the error from the experiments as the parameters of the cache are 10, 15, and 20 Mb higher than those when the parameters of the cache are 25, 30, and 35. In other words, with the situation of a larger setting for cache, the experimental results have a greater reference value. Figure 9: Distribution of standard deviation from the experiments on delay comparison. To sum up, through several experiments and different parameter settings, we comprehensively compare the performance of the five routing algorithms from three indicators. Moreover, we analyze the error, mean, and standard deviation of these experiments. Our experimental results demonstrate that within the error interval of 0.05-0.1, DTCM improves delivery ratio by nearly 20% and decreases end-to-end delay by approximate 25% as compared with the Epidemic algorithm, and the network overhead from DTCM is in the middle horizon.
# Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption
## 2 Materials and Methods
### Implementation
#### 2.2.2 Length of Arrays  
* Layer: The size of this array is given by the value of num_layer, that is, the number of layers.
* Index and position: The size of this array is given by the value of num_neurons, that is, the number of neurons in the network.
* Stride: The size of this array is given by the number of neurons that have outgoing connections, i.e., all the neurons except the output layer. \[size(stride)=num\_neurons-layer[num\_layer-1]\] (1)
* Weight: The size of this array is determined by the number of outgoing connections that can exist, which is determined by the num_weight variable: \[num\_weights=\sum_{i=0}^{num\_layers-1}layer[i]*layer[i+1]\] (2)  
Once we have gathered all the necessary information, we still need to know how to access a weight by referencing it with the input and output neurons. We can perform this using the formula below:  
\[w_{ij}=weight[stride[i]+index[j]] \tag{3}\]  
Figure 3 shows the values of the different arrays used in the proposed method for a neural network.  
Figure 3: Array values using the proposed method for the example in Figure 1. The index of the arrays “index” and “position” and “stride” refer to the number of neurons to which it refers. The valuesof the \"index\" array indicate the position that a neuron occupies within a layer. The array \"position\" values indicate the layer number to which a neuron belongs. The array \"weights\" values are the values of the network connections. The \"stride\" values indicate at which position in the array weight the outgoing weights of a neuron start.  
Finally, it remains to be checked if all this auxiliary information that we added improves the memory consumption used in the code. To know the amount that is consumed, they are used in the equations shown below (the _sizeof(int)_ and _sizeof(float)_ simply imply the value of the size of an integer and a float, respectively):  
\[\begin{split} Traditional&=(2+num\_neurons)*sizeof( int)\\ &\qquad+num\_neurons^{2}*sizeof(float)\end{split} \tag{4}\]  
\[\begin{split} Proposed&=(3+3*num\_neurons+num\_layers+\\ &\qquad layer[num\_layers-1])*sizeof(int)\\ &\qquad+num\_weights*sizeof(float)\end{split} \tag{5}\]  
In light of these equations, the comparative calculation to estimate the difference in consumption between the traditional and the proposed method is not a trivial issue. While in the former it is only necessary to know the number of neurons (\(num\_neurons\)), our method incorporates another series of variables that depend directly on the architecture of the MLP network under analysis.
## 3 Methodology
### Design of the Data Service Container
#### 3.1.1 UDX Model  
The UDX model was proposed for the structured description of multi-source heterogeneous geographic data [29]. UDX represents the content information of geographic data through the combination of data nodes, as shown in Figure 2. Each UDX node represents a specific data type, such as \"list\" type, \"float/real\" type, \"int\" type, and so on. By organizing these nodes according to certain logic, arbitrary data content can be expressed. In Figure 2, the pollutant flux data stored in Excel are represented by two nested \"DTKT_LIST\" nodes.  
As can be seen from Figure 2, unlike a specific data format, the UDX model is a descriptive model, which is only responsible for describing the content of the data, not how the data are organized and stored. In this way, users can obtain the data they want based on UDX, such as obtaining the pollutant data of \"Yuxi River\" in January, which can be obtained directly by accessing the UDX data node, without reading the Excel file and extracting the required data from it.  
In addition, in the traditional data-sharing process, after users receive the raw data, they often need to configure the corresponding development environment to process and operate the data to meet their specific data needs, such as reading and writing Excel files requires downloading the corresponding development libraries and configuring the development environment. This process is often cumbersome and time consuming, and the data-processing methods developed are difficult to share with other users. However, if the user obtains UDX data, they can use the data read and write interface provided by UDX to process the data. This is obviously much easier than using the underlying data read and write interface, and the UDX-based data-processing methods are also easier to share with other users.  
Therefore, for data sharing based on UDX, data providers, data users, and data processors will be separated. The different roles only need to focus on their responsibilities: the data provider only needs to be responsible for describing what data they share, the data user only needs to focus on how the data are processed to meet their data needs, and the data processor only needs to focus on how to implement UDX-based data processing and then share the processing method with the other two roles.  
The construction of data services in this paper is also based on a UDX model, aiming to provide users with a general view of data operation, without paying attention to the details of the organization and storage of the underlying data, thus helping to improve the efficiency of data sharing.
# A Framework for Security Transparency in Cloud Computing
## 1 Introduction  
Cloud computing (CC) provides numerous benefits such as cost reduction and a scalable environment that stimulate organisations for wider cloud adoption. Nevertheless, as more and more mission critical data and applications are migrated to the cloud, it is critical to protect them from potential risks [1]. Concerns are constantly raised by Cloud Service Customers (CSCs) about Cloud Service Providers' (CSPs) capability to protect their critical assets. Security transparency is an essential means that provides sufficient visibility of security operations and practices implemented by a CSP to protect users' data and applications [2; 3]. Recently, it has received a lot of attention by both industry and research communities. For instance, virtual machine monitoring and Service Level Agreement (SLA) management are considered transparency related activities as indicated in the work of [4; 5]. However, efforts related to virtual machines monitoring mainly focus on security management of CSP services rather than serving as a means of providing transparency to the CSCs. Service Level Agreements (SLA) monitoring is mainly CSP-centric as it mainly emphasizes areas where CSPs feel more confident to render such as service performance rather than overall view of the user migrated assets. Leveraging the benefits of CC requires a detailed understanding of CSP controls foradequate protection of CSC critical data and applications. It is therefore crucial to develop a systematic framework for analysing cloud transparency needs based on CSCs' context as well as devising means for meeting such needs.  
Within the above context, the novel contribution of this paper is a framework that supports the analysis of security transparency in CC from three different levels of abstraction; namely, conceptual level, organisational level and technical level. The approach of using different levels of abstraction is aimed at supporting users in the understanding and alignment of security transparency needs in accordance with CSP offerings for supporting such needs. The presented framework is based on basic transparency principles. It uses concepts from the existing requirements engineering domain and combines them with concepts relating to transparency principles so that the framework can support extraction of user needs, identification of requirements to support these needs and checking of CSP offerings to address these requirements. To demonstrate the applicability of our work, we consider a real migration use case to analyse security transparency using our framework.  
The paper is structured as follows: Section 2 covers a description of related works. Section 3 outlines transparency basics, while Section 4 covers security transparency. Section 5 describes the proposed security transparency framework, while Section 6 demonstrates the approach through a real work migration use case. Finally, Section 7 concludes the paper and provides a direction for future research.
# Recycled Eco-Concretes Containing Fine and/or Coarse Concrete Aggregates. Mechanical Performance
## 3 Results
### Hardened-State Properties
#### 3.2.3 Flexural Strength  
Figure 11 shows the flexural strength at 28 days of the formulated concrete, as well as the estimated strength according to the expression included (\(\mathrm{f_{ct,m,fi}=1.6-h/1000}\mathrm{f_{ct,m}}\)) in point 3.1.8 of the CodE. Additionally, the flexural strength has been estimated from the compressive strength, using the expression included in article A19.3.1.8, combined with the expression for average tensile strength and taking 90% of the compressive strength obtained in the cubic samples as \(\mathrm{f_{ck}}\). The resulting expression is shown in the Equation (3)  
\[\mathrm{f_{ct,m,fi}=0.405\cdot fcm^{2/3}} \tag{3}\]  
In the mixes that only substitute the coarse fraction of the aggregate, lower strengths were observed, with losses of between 7.41% and 10.73%, with very similar values regardless of the substitution percentage. In the case of fine aggregate, the decrease in strength is greater as the amount of recycled aggregate increases, with losses of between 4.37% and  
Figure 11: Flexural strength.  
Figure 10: Failure mode in HR-100+50 mix.  
9.01%. In the mixes that incorporate both fractions, the values are similar except for mixes with 50% fine aggregate, which generally present a higher strength loss, although all values remain below 20%. These values are similar to those recorded by other authors. [PERSON] [73] and [PERSON] [74] found losses of 4.1% and 10% for 50% and 100% substitution of the coarse fraction, respectively. Regarding the fine fraction, [PERSON] [70] observed a loss of 14% by substituting 50% of the fine aggregate with recycled aggregate.  
[PERSON] [70] and [PERSON] [75] state that the reduction in strength occurs due to the presence of micro-cracks in the adhered mortar in the recycled fractions, as well as the intrinsic properties (thickness and micro elastic) of the ITZs old mortar/new mortar and old aggregate/old mortar, whose values are worse than those shown by new aggregate/new mortar.  
It should also be noted that in all mixes, regardless of their granular skeleton, the failure mechanism consisted of a single large crack that was initiated in the flexural span and with a normal orientation to the tensile stresses generated due to flexure [76].  
From a statistical point of view (Table 7), the effect of adding the coarse aggregate is negative, with a non-significant value for the HR-25 mix. Regarding the mixes that incorporate only the fine fraction, there is only one significant factor for the HR-0+50 mix, so there is no clear trend. Likewise, there is no interaction, so the combination of the coarse and fine fractions has no significant effect on the model studied.
## 2 Materials and Methods
### Signal-Processing Framework for Ultrasound Compressed Sensing Data  
Although CS signals are advantageous for acquisition and storage processes in terms of the time and the space they save, most signal-processing frameworks used for temporal and spectral analysis require an uncompressed time-domain signal, and cannot be directly applied to CS data. The signal-processing framework for ultrasound CS data we propose in this paper involves the direct computation of the analytic representation of a measured signal, and estimation of its spectral properties without full reconstruction in the time domain. This is possible due to CS theory, which dictates that a basis matrix can be used to convert original signals into sparse signals. If an appropriate basis matrix is selected, the sparse signal's coefficients will contain the spectral information necessary for analysis, avoiding the need for reconstruction and transformation [18].  
In Figure 2, two signal-processing procedures for CS data are summarized, with the blue dotted line representing the traditional framework, and the red solid line representing the proposed framework. For simplicity, a signal is recovered from sparse data using the \(l_{1}\)-minimization  
In our CS framework, the Gaussian random matrix was utilized as a measurement matrix to randomly sample the signal. The Basis Pursuit was a principle for decomposing a signal into a sub-optimal superposition of dictionary elements which have the smallest \(l_{1}\)-norm of coefficients among all decompositions, and used as the reconstruction algorithm in the proposed framework [37]. For correct operation of the devised framework, the coefficients of the basis matrix should contain physically relevant spectral information, a fact that restricts the possible candidates for selection. To verify the effect of the basis matrix on the framework's operation, we compare the performances of the DFT and DCT matrices when applied for measurement and reconstruction.
## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.1 Top Three Most Cited Papers--Overview  
The top three most cited documents worldwide are presented in detail in Table 6, having information about the article, number of authors, region, total citations (TCs), total citations per year (TCY), and normalized TC (NTC), which explains the number of citations for each author who cites the document.  
The paper authored by [PERSON] et al. [61] stands out as the most frequently cited, amassing a substantial citation count of 3785, in contrast to the second most cited paper, authored by [PERSON] and [PERSON] [85], which has garnered 683 citations, as indicated in Table 6. Moreover, the TCY indicator associated with the paper authored by [PERSON] et al. [61] exhibits a notably higher value when compared to the other top three selected papers, registering at 235.56.  
The remaining papers featured in the list of the top three most cited papers each exhibit a substantial citation count, surpassing 340 citations. Furthermore, the top three selected papers achieve a TCY exceeding 30 and an NTC exceeding 9.  
Notably, all the papers listed in the top three most cited papers have arisen from collaborative efforts among multiple authors.  
The predominant geographical affiliation of the authors of the top three most cited papers is the United States (USA), followed by Spain (Table 6). This result was expected given the position of the USA in the top 20 countries with the most citations list provided in Figure 13.
# Delay Bounded Multi-Source Multicast in Software-Defined Networking
## 5 DBMS Routing Algorithm
### Complexity Analysis for Algorithm 3  
We analyse the time complexity of Algorithm 3 in the following steps. Firstly, we have to calculate the least delay cost paths from all destination nodes to all potential source nodes and among the \(\left\lvert D\right\rvert\). The number of the least delay cost paths will be \((|S||D|+\frac{||D||D-1|}{2}\). The time complexity of calculating one delay cost path is \(O(|V|^{2})\)[32]; therefore, the time complexity for a complete graph would be \(O((|S||D|+\frac{||D||D-1|}{2})\times|V|^{2})\). The time complexity of forming the minimum spanning tree \(T\) is \(O((|D|+1)^{2})\). For creating the steiner tree, the time complexity is \(O(|V|)+O(|V|)+O(|V|^{2})+O(|E|)+O(|V|^{3})+O(|V|^{4})=O(|V|^{4})\). The overall time complexity of our Algorithm 3 therefor is \(O(|V|^{4})+O((|D|+1)^{2})=O((|V|+1)^{4})\).  
```
Data:\(G\) = (\(V,E\)), \(\left\lvert S\right\rvert\), \(\left\lvert D\right\rvert\) and the \(\delta\). Result: A DBMS-Forest, formed by the minimum delay cost trees rooting at each potential \(\left\lvert S\right\rvert\), the \(\left\lvert D\right\rvert\) that do not satisfy the \(\delta\).
1 begin
2 Create Graph \(G\) with a virtual source node
3 Create set \(Q\) which include the virtual source node
4 Connect all nodes in set \(\left\lvert S\right\rvert\) to the virtual source node, with \(\delta(u,v)=\infty\)
5 Call _DBMS-DelayPath_ to get the minimum delay cost path between the virtual source node and \(\left\lvert D\right\rvert\)
6 Compare the total delay cost of the path \(P\), from the virtual source node to \(\left\lvert D\right\rvert\) with the delay bound \(\delta\), according to equations (2) and (3)
7 if\(\delta(P)>\delta\), then
8 The path to the destination node does not satisfy the delay bound.
9 end if
10 else
11 Add nodes to set \(Q\).
12
13 end if
14 Call _DBMS-MST_ on set \(Q\) to get the minimum spanning trees
15 Return the DBMS-Forest
16
17 end for
```  
**Algorithm 4**_Pseudocode for DBMS algorithm_
## 2 Materials and Methods
### Parameter Specification  
Users begin by specifying the full set of variables to be simulated. For each variable, the user defines a descriptive name, a code name (for internal reference), and a variable type (continuous, binary or categorical). All parameters are entered through a standardized input sheet within the Excel interface.  
For continuous variables, users must specify the mean (\(\mu\)), standard deviation (\(\sigma\)), and minimum and maximum values. These inputs define the expected statistical properties and plausible clinical bounds of the variable. A probability distribution function--such as normal, log-normal, or beta--is selected to determine the underlying distribution used by the patient cohort generator algorithm.  
For binary variables, only the mean (interpreted as the probability of positive outcome) is required. Minimum and maximum values default to 0 and 1, respectively. The standard deviation is calculated from the Bernoulli distribution using the following expression:  
\[SD=\sqrt{\mu(1-\mu)}\]  
For categorical variables, list each category in a separate row of the input sheet and assign proportional weights to each category such that the sum equals 1 for every variable group. As with binary variables, the minimum and maximum values default to 0 and 1, and the standard deviation for each category is computed using the same Bernoulli-based formula.  
The patient-cohort-generator algorithm also supports the definition of correlations between continuous variables through a correlation matrix. These correlations can be entered manually or imported from a built-in empirical database [16]. The built-in empirical database derives from [PERSON] et al.'s (2022) study of 803,614 individuals, containing 221 physical examination indicators with 7662 documented correlations across healthy and disease states (hypertension, diabetes, etc.) [17].