esa-sceva/satcom-chunk-collection huggingface dataset:
# SMARTEN--A Sample-Based Approach towards Privacy-Friendly Data Refinement
## 1 Introduction
This is therefore not an option either, especially for sensitive data [22]. To this end, we introduce **SMARTEN**, a **s**ample-based **a**pproach towards **p**ivacy-friendly **d**ata **refinement**. With SMARTEN, users are enabled to share sensitive data with smart services without the risk of unwanted insights being gained during data processing. In other words, we _smarten up_ smart services in a privacy-friendly manner. To this end, we make the following three contributions:  
Figure 1: Data pyramid illustrating the relationships between data, information, and knowledge. We adapt the **data preparation phase** of the data refinement process. Data processors are not granted access to the full data stock, but rather to **representative samples** that comply with the privacy requirements. They then define data wrangling activities on these samples. These definitions are generalized as data pre-processing rules and applied to the entire data stock. 2. We adapt the **data processing phase** of the data refinement process. By means of **privacy filters**, different versions of the pre-processed data are generated. According to the privacy requirements, a specific version is used for the analysis, and the resulting insights are provided to the data consumers (e.g., smart services). As a result, each use case can be provided with the required knowledge without exposing any sensitive information. 3. We enable data owners to annotate their data with **privacy requirements** by means of **digital signatures**. These privacy requirements define how profoundly a data processor and a data consumer can inspect the data. Analogous with the data refinement process, the privacy requirements are also structured in two parts to determine in which manner access to the data are granted during pre-processing and what insights can be gained during the analysis. The remainder of this paper is structured as follows: We initially discuss the data refinement process, describe its two phases as well as the tasks to be carried out, and identify key players in data processing in Section 2. Furthermore, we also address legal aspects that have to be considered in data processing and must be ensured by technical and organizational measures. In Section 3, we discuss such measures and how they resolve the privacy paradox. Based on these findings, we present our proposed solution SMARTEN in Section 4, which addresses shortcomings in related work. Our main focus is to ensure that all privacy requirements of a data owner are respected by the data processor for both the data preparation phase and the data processing phase of the data refinement process. In Section 5, we evaluate SMARTEN by means of a feature assessment to document its capabilities, by means of a user study to prove the applicability of our approach, and by means of a performance measurement to determine its overhead in terms of runtime and memory usage. Finally, we wrap up our work in Section 6.
# On the Statistical Characterization of Lightning-Induced Voltages
## 2 Lightning-Induced Voltage: AESs
### Step-Function Current  
In the case of the step-function current (Figure 2a), the exact analytical solution was derived in [13; 15]. With reference to the geometry of Figure 1, for \(x=0\), namely the point closest to the lightning channel, the solution reads as follows:  
\[v(0,t)=\tfrac{\zeta_{0}}{4\pi}I[v_{1}(h)-v_{1}(-h)], \tag{1}\]  
with  
\[v_{1}(h)=-ln\biggl{[}-\beta(\beta ct-h)+\sqrt{\left(\beta ct-h\right)^{2}+d^{ 2}/\gamma^{2}}\biggr{]}-\beta ln\biggl{[}\left(\beta ct-h\right)+\sqrt{\left( \beta ct-h\right)^{2}+d^{2}/\gamma^{2}}\biggr{]}.\]  
where \(\zeta_{0}\) is the free space characteristic impedance, \(I\) is the peak value of the return stroke current (Figure 2a), \(\beta\) is the ratio between the return stroke speed and light speed (c), \(t\) is the time, \(h\) is the line height, \(d\) is the line to stroke distance, and \(\gamma=1/\sqrt{1-\beta^{2}}\). Equation (1) is valid for \(t>t_{0}\), where \(t_{0}=\sqrt{d^{2}+h^{2}}/c\), with the symbols' meaning inferable by Figure 1. In Figure 3a, an example  
Figure 1: Lightning-induced voltage model.  
of induced voltage due to the step-function current at \(x=0\) (\(h=10\) m, \(d=50\) m, \(I=10\) kA, \(\beta=0.4\)) is shown.
# Numeric Evaluation of Game-Theoretic Collaboration Modes in Supplier Development
## 4 Model Description
### Settings  
In the proposed multi-manufacturer and a single-supplier structure, we include the relationship of the manufacturers. Manufacturers have different options for communication to contract their supplier development investment. Regarding the game theory settings mentioned in Section 2, manufacturers are able either to invest in a supplier together (cooperative game) or to invest in a supplier individually (non-cooperative). In the latter case, they can choose between deciding on investing at the same time or sequentially. Note that our strategy in this setting is a non zero-sum game since utilizing manufacturers' investment; both will gain profit regardless of the imposed strategy. Previous studies categorized and defined the relationships among partners in the supply chain in different ways [37; 38; 39; 40]. In this study, for the first time, we adopt these four types of relationship based on game theory in the background of supplier development from a dynamic investment perspective. These different communication methods focus on the procedure of information sharing for supplier investment decision making. As an instance, in the full-cooperation setting, manufacturers share all information about their investment strategies and their supplier development programs with each other and pursue the shared goal to maximize their overall profit, cf. Figure 2(a). The non-cooperative setting describes partners who tend to work separately without any communication, as outlined in Figure 2(b). Collaboration means that the manufacturers contribute to develop the supplier while they are seeking to increase their profit out of the investment.  
Nevertheless, the partners still try to maximize their key performance indicators. In these settings, partners can share information during a decision making step (simultaneous setting), shown in Figure 3(a), or they can share their final decisions (sequential setting), shown in Figure 3(b). Apart from using different system models, the scenarios differ in which parts of the vector \(u\) are optimized by the different manufacturers.  
Figure 3: Cooperative (**a**) and Non-cooperative (**b**) supplier development.
# The Status of Space Weather Infrastructure and Research in Africa
## 4 Gaps in Infrastructure and Human Resources  
### Gaps  
There are many countries in Africa where space weather research is non-existent or infrastructure for space weather monitoring is lacking. In some cases, institutions in some African countries may be hosting facilities whose data they do not use at all, and hence they take little interest in ensuring the functionality of the facilities. This situation is equivalent to cases where there are no facilities installed at all because the result is the same, namely, there is no data to facilitate research. Gaps in infrastructure are evident in regions like Chad, Central African Republic, Somalia, Eritrea, and South Sudan. Incidentally, these countries lie on or very close to the geomagnetic equator, where ionospheric conditions are very dynamic and have potentially adverse consequences for the technological systems in use. This lack of ground-based observing facilities makes the understanding of space weather dynamics over Africa difficult. Non-deployment of space weather monitors may have been occasioned by political instability in those countries. In some other countries, the infrastructure exists but is not operating optimally due to erratic electric power supply, low internet bandwidths, poor maintenance, or lack of funds to replace ageing or obsolete monitors. Another aspect of the gaps observed is the non-uniform spread of different types of monitors across the continent. The reason for this is partly because collaborators would normally deploy their facilities where they already have points of contact or in a region of interest depending on the relevant space weather campaign at that time. This aspect informs the observed trends in the distribution of equipment across Africa. The gaps in infrastructure also go hand in hand with the lack of or inadequate human resources in many countries. Many countries do not have well-established departments of physics where space weather research could thrive. This badly limits postgraduate research in space weather. However, despite the gaps in the deployment of monitors, some current and upcoming projects will help improve the situation (see Table 11). This table may not be exhaustive, but it indicates that more facilities are coming to Africa.
## 3 Results and Discussion
### Real Water Samples Analysis  
The proposed method was finally used to determine CBs from real water samples (1#, 2#, 3#), which were collected from the river located in Puding County, Guizhou Province.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analytes**} & \multirow{2}{*}{\begin{tabular}{c} **Linear Ranges** \\ **(ng L\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **LODs** \\ **(ng L\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **LOQs** \\ **(ng L\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**RSDs (\%)**} \\  & & & **(ng L\({}^{-1}\))** & **(ng L\({}^{-1}\))** & **(ng L\({}^{-1}\))** & **(n = 7)** & **(\# Fiber-to-Fiber)** \\ \hline
135-TCB & 0.25–2500 & 0.9989 & 0.003 & 0.010 & 8.0 & 9.1 \\
124-TCB & 0.25–2500 & 0.9974 & 0.086 & 0.288 & 9.7 & 12.6 \\
123-TCB & 0.25–2500 & 0.9948 & 0.033 & 0.108 & 9.4 & 10.3 \\
1245-TCB & 0.25–2500 & 0.9865 & 0.007 & 0.024 & 5.3 & 7.5 \\
1234-TCCB & 0.25–2500 & 0.9822 & 0.016 & 0.053 & 7.7 & 9.5 \\ PeCB & 0.25–2500 & 0.9933 & 0.005 & 0.016 & 8.5 & 8.9 \\ HeCB & 0.25–2500 & 0.9996 & 0.002 & 0.007 & 6.1 & 8.2 \\ PeCNB & 0.25–2500 & 0.9945 & 0.024 & 0.083 & 7.2 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analytical performance of the CDs/g-C\({}_{3}\)N\({}_{4}\) SPME coating towards CBs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Sorbents**} & \multirow{2}{*}{
\begin{tabular}{c} **Linear Range** \\ **(ng L\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{**LODs**} & \multirow{2}{*}{**LOQs**} & \multirow{2}{*}{**LODs**} \\  & & & **(ng L\({}^{-1}\))** & **(ng L\({}^{-1}\))** & **(ng L\({}^{-1}\))** \\ \hline \multirow{4}{*}{SDME/GC-FID} & 1-Octyl-3- & & & \\  & Methylimidazolium & 1000–1 \(\times\) 10\({}^{7}\) & 100–500 & – & [45] \\  & hexafluorophosphate & & & & \\  & Toluene & 500–50,000 & 10–50 & 40–440 & [46] \\  & HSSE/GC-MS & PDMS coated stir bar & 10–200 & 0.4–1.4 & 1.4–4.7 & [47] \\  & & 10–500 & 0.10–1.03 & – & [48] \\  & Nanoporous carbon & 2.5–100 & 0.01–0.1 & – & [49] \\ HS-SPME/GC-MS & Nano-structured ZnO & 0.05–1000 & 0.01–0.1 & – & [49] \\  & Nitrogen/Porous & & & & \\  & biochar & 1–1000 & 0.007–0.079 & 0.023–0.261 & [50] \\  & SDME/GC-MS & CDs/g-C\({}_{3}\)N\({}_{4}\) & 0.25–2500 & 0.002–0.086 & 0.007–0.288 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons with other analytical methods for the determination of CBs in water.  
The analysis results for the three collected water samples are listed in Table 3. It was found that no CBs were detected in the sample 1#; however, 135-TCB and 123-TCB were detected in samples 2# and 3#, which were collected from a location that is near to a port. Recoveries were obtained, ranging from 73.4 to 109.1%, for eight CBs from water samples with spiked concentrations of 10 ng L\({}^{-1}\). Satisfactory recoveries illustrate the applicability of the proposed method for the analysis of trace CBs in real water samples.
## 4 Conclusions  
A low-cost, high-gain low-SLL, differential-fed, SIW-based SAA with zero beam squint is presented, fabricated, and tested. The proposed antenna is realized on a single-layered Taconic TLY-5 substrate. By employing the differential feeding structure, the proposed antenna achieved a stable broadside main beam with a low SLL across the entire operating band. The experimental results demonstrate that the proposed antenna has a \(-10\) dB reflection bandwidth of 7.15% (9.57-10.28 GHz) and a gain ranging from 20.30 to 21.92 dB. In addition, a peak SLL \(\leq\)\(-\)29.1 dB and a cross-polarization level of less than \(-\)30 dB are obtained at 10 GHz. With these advantages, the proposed low-SLL, differential-fed, SIW-based SAA is an excellent candidate for several applications, such as satellite communication and radar systems.  
Conceptualization, T.V.T., S.T.-V., K.-Y.L., Y.Y., and K.C.H.; methodology, T.V.T., S.T.-V., and K.C.H.; simulation, T.V.T. and S.T.-V.; validation and formal analysis, T.V.T., S.T.-V., K.-Y.L., Y.Y., and K.C.H.; investigation, T.V.T. and S.T.-V.; resources and data curation, T.V.T. and S.T.-V.; writing--original draft preparation, T.V.T., S.T.-V., and K.C.H.; writing--review and editing, T.V.T., S.T.-V., and K.C.H.; supervision, K.-Y.L., Y.Y., and K.C.H.; and project administration, K.C.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government Ministry of Science and Information and Communication Technologies (MSIT) (no. 2020-0-00261, the development of a low-power/low-delay/self-power sulphide RF simultaneous information and power transfer system and stretchable electronic epineurium for wireless nerve bypass implementation).  
Not applicable.  
All data have been included in study.  
The authors declare no conflict of interest.
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method
### Dataset Types
#### 3.1.2 Synthetic Dataset  
Synthetic data are generated algorithmically from actual data by statistical properties. However, the synthetic dataset for DDoS attacks in SDN, which injects attack activity into the dataset, depends on tunable attributes such as the number of nodes attacked [26]. Moreover, the synthetic dataset should include accurate values to guarantee privacy and anonymity to avoid the fact that synthetic traffic causes a normality model, which may lead to incorrect results [27]. In this section also, we mention some articles that used more than one dataset source as the following:  
* The study [28] worked with two datasets, CICDoS2017 and CICDDoS2019.
* The work of [29] proposed a multilayer perceptron (MLP) neural network to detect HTTP-based slow-rate attacks. For their proposed method, they used three different datasets: CTU dataset, WIDE dataset, and generated MANET dataset.  
Several approaches utilize public tools, such as Scapy and Hping3, to generate synthetic datasets for testing DDoS attack detection and prevention mechanisms in software-defined networking (SDN). These approaches are often fast in their calculation and are simple to analyze. However, there are some limitations to consider. For example, the datasets generated by these approaches may be small in size, potentially leading to insufficient or inaccurate results. Additionally, the number of features extracted may not be sufficient to cover all attack behavior, which could impact the overall effectiveness of the approach.
## 4 Experimental Evaluation of Performances  
To extensively research the performance of CAnom (Algorithm 1), we compared it with two state-of-the-art anomaly detection algorithms. Before giving the results of this comparison, we briefly introduce the competing methods.  
* CODA [10] is one of the most famous models used to detect anomalies in social network communities. The model detects communities and identifies anomalous nodes in one step. It uses the full attributes set of the nodes.
* ConSub [22] is based on a statistical selection of a subset of nodes' attributes that show dependencies with the graph structure. The selection of a subset of attributes combines with a distance-based outlier model called DistOut to detect anomalous nodes in the communities.
## 2 Materials and Methods
### Electrical Conductivity Measurements  
Electrical conductivity tests were performed on both irradiated CF yarns and CFRP, utilizing a two probe Keithley 2611B (Keithley Instruments, Cleveland, Ohio, United States) source meter. For the CF yarns, conductive silver paste was laid at the CF yarn's edges, and conductive copper tape was positioned over the silver paste, thus creating a uniform connection between the probes and the CF yarn. For the CFRP specimen, two different out of plane measurements were performed in order to examine the electrical conductivity of the fabricated laminates: (i) out of plane through volume conductivity and (ii) out of plane through thickness conductivity. The different probe contacts for each case are depicted in Figure 4. For the calculation of the electrical conductivity, the following equation was used:  
\[\sigma=\text{G}\frac{\text{L}}{\text{A}} \tag{6}\]  
where \(\sigma\) is the electrical conductivity (S/cm), L is the length between the two contacts, G is the conductance (S), and A is the cross-sectional area (cm\({}^{2}\)).
## 1 Introduction  
Vehicular communication is a vital realization technology of automatic driving and intelligent transportation systems [1]. Various candidate technical solutions have been proposed, such as cellular vehicular communication and dedicated short-range communication (DSRC) based on IEEE 802.11p [2, 3]. DSRC has the disadvantages of short coverage distance and high infrastructure cost [4, 5]. Despite the deployment of DSRC-based vehicular communication prototypes in the United States, inherent issues with DSRC and recent developments in cellular technology have encouraged research and industry research on cellular-based vehicular communications [6]. 5G Automotive Association (5 GAA), as a global and cross-industry organization, was created in September 2016 and has presented the clear position of supporting cellular vehicular communication as the feasible solution for future mobility and transportation services [7]. Different regions such as the United States, Europe, and Japan have carried out pilot construction of cellular vehicular communication in actual operation [8]. Through cellular vehicular communication, road safety and traffic efficiency can be improved [9]. Therefore, cellular vehicular communication has attracted more attention from industry and academia. 3 GPP Release 14 introduced modes 3 and 4 for vehicular communication [10]. These modes enable vehicles to directly communicate via licensed cellular frequency bands, bypassing the BS. Modes 3 and 4 both support direct vehicular communications, but their radio resource allocation methods are different. In mode 3, the BS allocates resources to vehicle users, while in mode 4, vehicles autonomously select radio resources [10, 11]. Mode 3 can be divided into two types: overlay method and underlay method. In the overlay approach, V2I users and V2V users transmit in different frequency bands, while in the underlay method, V2V users reuse spectrum resources allocated to V2I users, which improves the spectrum utilization but interferes with V2I users. Therefore, in the underlay approach of mode 3 how to flexibly allocate resources to meet the low latency requirements of V2V users while increasing the throughput of V2I users is challenging.
## 4 Proposed Method of Sensitivity Analysis
The gradient of the limit-state function \(\
abla_{\mathbf{x}^{*}}g(\mathbf{d},\mathbf{x}^{*})\) in Equation (17) is then computed as  
\[\
abla_{\mathbf{x}^{*}}g(\mathbf{d},\mathbf{x}^{*})=\left[\frac{\partial g( \mathbf{d},\mathbf{x}^{*})}{\partial x_{1}^{*}},\ldots,\frac{\partial g( \mathbf{d},\mathbf{x}^{*})}{\partial x_{k}^{*}},\ldots,\frac{\partial g( \mathbf{d},\mathbf{x}^{*})}{\partial x_{n}^{*}}\right]^{T} \tag{21}\]  
Application of the CS approximation to Equation (21) yields  
\[\
abla_{\mathbf{x}^{*}}g(\mathbf{d},\mathbf{x}^{*})=\tfrac{1}{h}\cdot \Im[g(\mathbf{d},\mathbf{x}^{*}+\mathrm{i}h\mathbf{e}_{1}),\ldots,g(\mathbf{ d},\mathbf{x}^{*}+\mathrm{i}h\mathbf{e}_{k}),\ldots,g(\mathbf{d},\mathbf{x}^{*}+ \mathrm{i}h\mathbf{e}_{n})]^{\mathrm{T}} \tag{22}\]  
Note that taking a real part of \(g(\mathbf{d},\mathbf{x}^{*}+\mathrm{i}h\mathbf{e}_{k})\), \(k=1,\ldots,n\) in Equation (22) results in a value of \(g(\mathbf{d},\mathbf{x}^{*})\). This indicates that both the value and sensitivity of the limit-state function can be obtained from Equation (22). Using Equation (8), the sensitivity of the first-order probability approximation with respect to a vector of design variables is obtained by computing  
\[\
abla_{\mathbf{d}}P_{f}=-\varphi(\beta).\frac{\Im[g(\mathbf{d}+\mathrm{i}h \mathbf{e}_{1},\mathbf{x}^{*}),\ldots,g(\mathbf{d}+\mathrm{i}h\mathbf{e}_{1}, \mathbf{x}^{*}),\ldots,g(\mathbf{d}+\mathrm{i}h\mathbf{e}_{2},\mathbf{x}^{*}) ]^{\mathrm{T}}}{h\left|\
abla_{\mathbf{x}^{*}}g(\mathbf{d},\mathbf{x}^{*}) \right|\mathbf{t}^{-1}_{\mathbf{x}^{*}}(\mathbf{x}^{*})\right|} \tag{23}\]  
Note that the gradients in Equation (21) are also utilized in the FORM to compute a failure probability of a limit-state function. When the improved Hasofer and Lind algorithm [50] is utilized to solve Equation (3), a sequence of points \(\mathbf{u}\) by using a recursive formular is identified as  
\[\mathbf{u}^{i+1}=\mathbf{u}^{i}+\lambda^{i}\mathbf{s}^{i},\;i=1,2,3,\ldots. \tag{24}\]  
where \(\lambda^{i}\) is a search scale factor and \(\mathbf{s}_{i}\) is the search direction vector obtained by  
\[\mathbf{s}^{i}=\left(\frac{G(\mathbf{d},\mathbf{u}^{i})}{\
abla G\left( \mathbf{d},\mathbf{u}^{i}\right)}+\alpha^{i}\mathbf{u}^{i}\right