esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward an SDN-Based Web Application Firewall: Defending against SQL Injection Attacks
## 5 Conceptual Design  
In this section, we present the conceptual design of our WAF and explain the stages that the traffic passes through in the SDN. The implementation process of these stages is illustrated in Figure 3. Our WAF goes through the following stages:  
1. _Reception stage:_ The controller receives all traffic within the network from the data plane using the OpenFlow protocol, and filters the traffic of interest. This stage analyzes the sender/receiver address in layer 3 to identify the address of the web server. After that, it verifies if the port is for the HTTP protocol for either the sender or the receiver. If these conditions are met, the packet is moved to the next stage.
2. _Processing stage:_ At this point, the packet is processed to convert it into a textual format. This stage standardizes the traffic that will be taken to the next stage for inspection and for searching for suspicious patterns.
3. _Inspection stage:_ This stage is the primary function of the WAF, as it focuses on inspecting traffic for matching suspicious patterns. In order to achieve this, all traffic is transferred to the controller for inspection.
4. _Containment stage:_ When a match occurs in the inspection stage, the process moves to the containment stage, where the suspicious packet is first dropped and then the IP address of the machine responsible for the packet is blocked. After that, the suspicious packet is recorded on the controller for future inspection and investigation by information security personnel.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 5 Verification of the NOMA Simulator  
This section deals with the abilities of the NOMA simulator by illustrating some of the simulated results produced as part of our recent research. The performance metrics presented in this section are the BER, throughput, and spectral efficiency. Simulations were carried out according to the parameters listed in Table 1. All simulated results are produced considering 16 quadrature amplitude modulation (QAM) and quadrature phase-shift keying (QPSK) for NEU and FEU, respectively, in the presence of AWGN with a Rayleigh fading channel. The given simulated results are mostly related to link-level analysis; however, the system-level simulator results will be discussed in the upgraded version of this simulator.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP & 3 rd Generation Partnership Project 6G & Sixth-generation (communication networks)  A3C & Asynchronous advantage actor-critic  AoI & Age of information  BCD & Block coordinate descent  BPP & Bin-packing problem  BS & Base station  CSI & Channel state information  DDPG & Deep deterministic policy gradient  DRL & Deep reinforcement learning  FedAvg & Federated averaging  FedSGD & Federated stochastic gradient descent  FGPR & Federated Gaussian process regression  FL & Federated learning  FRL & Federated reinforcement learning  G-FML & Group-based federated meta-learning framework  GMM & Gaussian mixture model  HFL & Hierarchical federated learning  HMM & Hidden Markov model  i.i.d. & Independent and identically distributed  ITS & Intelligent transportation system  LoS & Line of sight  MBS & Macro base station  MCC & Mobile cloud computing  MDP & Markov decision process  MEC & Mobile edge computing  Non-i.i.d. & Non-independent and identically distributed  NLoS & Non-line-of-sight  OFDM & Orthogonal frequency division multiplexing  OTFS & Orthogonal time frequency space  PFL & Personalized federated learning  QoS & Quality of service  RL & Reinforcement learning  SINR & Signal-to-interference noise ratio  TTI & Transmission time interval  UAV & Unmanned aerial vehicle
## 2 System Design
### Software Design
#### 2.3.2 Development of Object Anomaly Analysis Software  
The anomaly analysis software was primarily developed using MATLAB. After the aforementioned echo signal was acquired and processed, the impact-echo signals stored in an audio file were translated to time domain and frequency domain programs to execute analyses using MATLAB software. The goal of the time domain analysis was to obtain the consecutive reflected waveforms from the impact device after the first reflected wave to evaluate the velocity of the soundwave. As for the frequency domain, substantial peaks were observed in the frequency graph. The established time frequency curve in the frequency graph was used to evaluate the structural anomaly in the object, which further confirms the accuracy of the analysis.  
1. Time domain interface program design: Two string signals were tested. Their frequencies were 25 Hz and 200 Hz, and their amplitudes were 1.5 and 2.0, respectively. The discrete signal was sampled at 1 KHz, and 1000 samples were obtained. A portion of the codes for writing this signal and the acquired signals are presented in Figure 7.
2. Frequency domain interface program design: After conducting a Fast Fourier transform (FFT) of the time domain signals, we obtained frequency signals as shown in Figure 8. The frequency graphs revealed that at 25 Hz and 200 Hz, the amplitudes were 1.5 and 2.0, respectively.  
When using FFT to obtain the spectrum of discrete signals, the maximum frequency that could be analyzed was half of the sampling frequency; when this frequency was exceeded, spectrum aliasing occurs, resulting in reading difficulties.  
Figure 6: GoldWave acquired soundwave signals through the sound card.
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
###### Abstract  
The aim of this paper is two-fold. First, the trade-off between directivity, beam-width, side-lobe-level, number of radiating elements, and scanning range of planar antenna arrays is reviewed, and some simple ready-to-use formulas for the preliminary dimensioning of equispaced planar arrays are provided. Furthermore, the synthesis of sparse planar arrays, and the issue of their reduction in directivity, is analyzed. Second, a simple, yet effective, novel approach to overcome the directivity issue is proposed. The presented method is validated by several synthesized layouts; the examples show that it is possible to synthesize sparse arrays, able to challenge with equispaced lattices in terms of directivity, with a significant reduction of the number of radiators.  
antenna arrays; sparse arrays; radar; pattern synthesis; signal processing +
Footnote †: journal: Accepted: 28 April 2020; Published: 30 April 2020
# Precise Calculation of Inverse Kinematics of the Center of Gravity for Bipedal Walking Robots
## 2 Materials and Methods
### Converging Center of Gravity Algorithm  
For the precise calculation of the CoG, joint angles are first calculated using the current positions. Using these angles, the CoG of the robot is calculated using Equation (19). The vector from the current to the reference CoG is found using Equation (20).  
\[{{(\mathbf{{G}}_{\text{error}})}_{n}}=\epsilon_{\mathbf{{{}_{{\mathbf{B}}}}} \mathbf{{{}_{{\mathbf{F}}}}}}-{{(\epsilon_{\mathbf{{{}_{{\mathbf{B}}}}}} \mathbf{{{}_{{\mathbf{F}}}}})}_{n-1}} \tag{20}\]  
Equation (21) is utilized to calculate the unit vector of this vector.  
\[{{(\mathbf{{{}_{{\mathbf{G}}}}}\mathbf{{{}_{{\mathbf{error}}}}})}_{n}}=\frac{{{ (\mathbf{{{}_{{\mathbf{G}}}}\mathbf{{{}_{{\mathbf{error}}}}})}_{n}}}}{{{|{( \mathbf{{{}_{{\mathbf{F}}}}\mathbf{{{}_{{\mathbf{error}}}}})}_{n}}|}}} \tag{21}\]  
Using Equation (21), the hip connected to the foot that is in contact with the floor is moved to a new position by multiplying the unit vector by a certain coefficient \(\beta\) (0.001 in this study).  
\[{{{\left({{}^{0}}\mathbf{{{}_{{\mathbf{O}}}}}\right)}_{n}}}={{{\left({{}^{0}} \mathbf{{{}_{{\mathbf{O}}}}}\right)}_{n-1}}}+\beta{{.(\mathbf{{{}_{{\mathbf{G}} }}}\mathbf{{{}_{{\mathbf{error}}}}})}_{n}} \tag{22}\]  
The hip connected to the foot in motion is also moved accordingly. The joint angles and the CoG of the robot are calculated again based on these new hip positions. This process continues until the difference between the reference and current CoGs becomes smaller than the desired value. In our study, the error margin is taken as 0.001 m. The calculation is first made for the fixed foot. Afterward, the indices of the fixed and moving feet are changed, the foot in motion is assumed to be fixed, and the hips are moved. In this way, the masses and positions of all robot components are utilized.  
The pseudo-code of the process is given in Algorithm 1.  
In this method, the error between the reference and current CoGs is first calculated.  
Figure 5 displays the graphical representation of the legs of the robot. The right leg is shown in blue, and the left leg is shown in red. The CoG at the beginning of the trajectory calculations is called the current CoG and is shown as a red circle. The reference CoG is called the target CoG and is shown as a green circle.  
The result of running the algorithm given in Algorithm 1 is shown as the blue track in Figure 5. The walking trajectory is obtained by running the algorithm for all values on the trajectory.
## Abstract
[14; 21; 22], for simplicity, in this study, only the uncertainty associated with debris flow event location is investigated. The focus herein is the impact of uncertainty on the determination of principal environmental predisposing factors, factor classes, and resulting susceptibility analyses. The debris flow predisposing environmental factors initially employed are those that are commonly associated with debris flows [23; 24; 25; 26; 27; 28; 29; 30]; they are acidity, climate, depth to bedrock, elevation, fault density, landcover, landform, lithology, topsoil percent clay, precipitation, slope, soil drainage, soil type, and soil thickness. The study herein is focused on the European continent, as a case study. While all fourteen environmental factors were inputs to the Maximum Entropy (MaxEnt) models, only a subset of these debris flow predisposing factors are used to demonstrate the uncertainty issues. The factors further investigated are those with the highest gain (contribution) to debris flow susceptibility as defined by MaxEnt modelling. They are precipitation, fault density, and soil type.
# Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area
###### Abstract  
The growth in the utilization and development of rail transport within urban networks is crucial for transitioning towards a more sustainable form of mobility. However, challenges related to discomfort and noise pollution arising from rail traffic must be addressed and mitigated to foster a harmonious coexistence between residents and trains. This study focuses on analyzing an experimental campaign conducted on the surface metropolitan network of Porto to study and identify the frequency content and pressure levels associated with light rail traffic. The presented experimental campaign holds significant relevance as it comprises various and distinct circulation conditions within the railway network, enabling a comprehensive characterization of railway noise. The collected data indicates a noticeable increase in sound pressure levels as the speed of circulation rises, particularly emphasizing the 1/3 octave band centered around 1000 Hz. The choice of tracks with components having a limited capacity for absorbing acoustic energy leads to a significant rise in noise levels compared to track solutions with elements exhibiting excellent acoustic energy absorption. Furthermore, the study highlights a substantial increase in noise levels (10 dBA) associated with small radius curves, even at low speeds. These findings underscore the importance of considering the track characteristics and geometric features in noise assessment within rail networks. Therefore, the insights gained from this experimental campaign contribute significantly to the understanding and comprehensive characterization of railway noise under diverse circulation conditions within the railway network.  
experimental campaign; railway noise; railway noise characterization Article
## 2 Materials and Methods
### OCT Images and Biospeckle Contrast  
The speckles arise from the random interference of the scattered light when an optically rough surface or a scattering medium object is subject to the illumination of a coherent light source such as a laser source. If the object is stationary with no motion of the surface structure of the object, the intensity of the speckle patterns remains temporally stable. However, when the object is dynamic, i.e., there is continuous movement of the scattering centers, as in the case of biological samples such as skin, the speckle pattern varies, and there is a fluctuation in the intensity. This dynamic speckle pattern is a specific characteristic of biological tissues and, thus, has been named \"biospeckle\" and can be used to characterize the activity of the biological sample. In x-z OCT image scans, speckles appear in the structural images and have a granular appearance degrading the image; thus, they are considered to be noise and are usually averaged out.  
The temporal variation of the collected OCT scan images was evaluated using a method called biospeckle contrast (\(\gamma\)). \(\gamma\) is defined as the ratio of the standard deviation of the intensity of a pixel across the temporal axis, all scans to the mean value of the images along the temporal axis, or over all the scans. The definition is given as in Equation (2).  
\[\begin{array}{c}\gamma(x,y)=\frac{1}{<_{\mathrm{Iot}}(x,y)>}\left[\frac{1}{N} \sum_{j=1}^{N}\left\{\mathit{l}_{\mathrm{oct}}(x,y;t_{j})-<\mathit{l}_{ \mathrm{oct}}(x,y)>\right\}^{2}\right]^{\frac{1}{2}}\\ <\mathit{l}_{\mathrm{oct}}(x,y)>=\frac{1}{N}\sum_{j=1}^{N}\left\mathit{l}_{ \mathrm{oct}}\left(x,y;t_{j}\right),\end{array} \tag{2}\]  
Figure 3: The protocol describing the application of heat stress with a USB heat pad.  
where \(x\) and \(y\) stand for the coordinates of a single pixel, j for the scan number, and N for the total number of scans. In our experiments, N was set to 100. A higher biospeckle contrast corresponds to higher temporal fluctuations, indicating higher internal activity within the measured region, and low biospeckle contrast corresponds to lower temporal fluctuations, indicating less internal activity. Therefore, biospeckle contrast can be used as a parameter to evaluate the response within the skin to external environmental heat stress.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 3 Trust Establishment and Trust-Based Monitor Selection
### Trust Establishment and Link Duration Estimation  
A UAV \(A\) is considered friend of another UAVs \(B\) and \(C\) if the UAV \(A\) has a high trust value from the perspective of \(B\) and \(C\), and the communication medium is mostly stable between them, as illustrated in Figure 3. Therefore, node \(A\) is included in the friends table only if its Friend Score (FS) is higher than a predefined threshold; this node will also be removed from the table once its \(FS(i,j)\) value goes below the same predefined threshold. To compute FS, we use the following equation:  
\[FS(i,j)=Trust(i,j)\times LD(i,j) \tag{1}\]  
A node is considered as a friend when its FS is greater than a certain threshold \(FS_{th}\). In addition, \(Trust(i,j)\) and \(LD(i,j)\) represent the trust evaluation of a UAV \(i\) about another UAV \(j\), and the link duration between these two nodes, respectively. Details about the computation of both metrics are provided in the following sections.  
Figure 2: Vanilla NDN hierarchical data authentication process [27].
# Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater--Making the Right Choice
###### Abstract
However, they conclude that the increased reliability of centralised water supplies and the economics of scale mean that decentralised systems should only be used as a stop-gap solution to allow low-income communities to better transition to larger scale centralised systems. [PERSON] et al. also discuss the benefits and disadvantages of decentralisation [15]. They acknowledge that the inherent greater flexibility of decentralisation, with large numbers of small-scale investments, allow decentralisation strategies to react faster to the water supply challenges brought about by climate change. However, despite offering several examples of the successful deployment of decentralised systems, they caution against wide spread decentralisation, noting that the disadvantages in safe supply reliability are largely borne by the most disadvantaged sections of society, potentially placing decentralisation at odds with the UN Sustainable Development Goals. In contrast, [PERSON] et al. argue in favour of decentralisation, noting that existing centralised systems are facing significant challenges relating to ageing infrastructure and are inherently more vulnerable to natural and man-made threats [16]. Their arguments are supported by [PERSON] et al., who also favour decentralisation, especially in rural areas, where the initial capital investments required make centralised systems prohibitively expensive [17]. An interesting pilot study was conducted by [PERSON] et al. [18] who demonstrated the use of solar powered membrane technologies in a decentralised water supply system in Namibia. However, the authors themselves note that such high technology systems are especially difficult to maintain in developing countries. In order to form a truly sustainable water supply strategy for a region, the treatment technologies applied need to be well understood and readily available locally. This paper presents an example of a water supply analysis for two neighbouring municipalities in Vojvodina, where the currently exploited water resources have elevated arsenic concentrations. All available options are evaluated, taking into account the number of inhabitants covered, socioeconomic aspects, quality of the treated water and efficiency of the treatment required, techno-economical aspects of the possible technological solutions, potential availability of arsenic-free water sources in the vicinity, and possibility of agglomerating separate settlements into connected microregional water supply systems.
# The Effect of Curing Conditions on Selected Properties of Recycled Aggregate Concrete
## 3 Results and Discussion
### Compressive Strength  
The compressive strength results of concretes made of CEM I and CEM III after both 28 and 90 days in relation to RCA content are presented in Figure 4. Compressive strength differences calculated on the base of 28- and 90-day results are shown in Figure 5. The results of [PERSON]'s post-hoc test for 90-day strength are presented in Table 6. Wet conditions of hardening were better for concretes made of both CEM I and CEM III, but after 90 days they were more effective in the case of CEM I used as a binder. PEV can be observed to be a more effective way of curing for both cements in the case of 0% and 100% RCA, while less effective is the influence on the compressive strength of concrete with 50% RCA. Probably, the amount of water delivered during pre-soaking for samples with 50% RCA is not sufficient to prevent water consumption during hydration processes. After some time, the water no longer reaches the paste from the outside or from the recycled aggregate. In such a situation, the process of hydration for samples treated with PEV seems to be similar to the process of hydration for samples treated under wet conditions--with a water deficit, which contributes to lower strength. On the other hand, increasing the share of water from pre-soaking in the series with 100% RCA (from 11 to 22 kg/m\({}^{3}\)) eliminates this unfavorable effect.  
Figure 4: Compressive strength of concrete after 28 and 90 days.  
Figure 3: Average density of 90-day concrete.  
Dry conditions of hardening turn out to be more hazardous for the compressive strength of concretes made of CEM III than for those made of CEM I. Statistically, wet conditions of hardening made compressive strength higher (the strength of samples of all six series treated under wet conditions exceeded the value of 50 MPa), while dry conditions made the compressive strength lower. In samples with a 100% level of recycled concrete aggregate, there was a similar 90-day compressive strength for concrete cured with the use of PEV and for concrete cured in water with both cements applied as binders (both series of concretes treated with the PEV method and with 100% RCA constituted one homogeneous group with the corresponding series of concretes of the wet group with 50% and 100% RCA). This effect is connected with the internal curing (self-curing) process, because of the bigger (than for 50% RCA) amount of water taken up by RCA during the pre-soaking procedure.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 4 An Algorithmic Metatheorem for Log-Depth Circuits and Its Applications
### Applications: Visibly Pushdown Languages
This means that the overall \"shape\" or \"outline\" of the stack during the computation can be computed easily (in a class called \"\(\mathrm{TC}^{0}\)\", to be precise) _without actually running the automaton._  
Next, this \"shape\" of the stack gives rise to a graph in a natural way as shown on the right: each position where a stack symbol could go is a vertex and edges \"connect stack symbols at the same height in adjacent steps\", \"fork during a push\", and \"join during a pop\". The example graph in Figure 5 is the one resulting for the automaton for deciding the language of balanced parentheses for the input string ([()][]). We make three observations concerning this graph:  
1. It is \"easy to compute\" since, as explained earlier, the height of the stack at each horizontal position can be computed just by doing a simple subtraction. Clearly, adding the edges is also very easy to achieve. 2. The graph has tree width 2, that is, three detectives suffice to corner [PERSON] wherever he might start: two detectives position themselves at the \"forking vertex\" and at the \"joining vertex\" of an area containing [PERSON] and then the third detective moves to an intermediate forking or joining vertex to cut of more of the graph. Note that not only does the graph have tree width 2, we can actually _compute the tree decomposition just as easily as the graph itself_ since this tree decomposition just reflects the nesting structure of the stack. 3. In order to decide whether the automaton accepts the input word, we use a formula that _existentially guesses the contents of the stack at each moment_, using unary predicates, and then verifies that the changes of the stack contents along edges are correct (mostly, this means that there may not be any change, except for the top of the stack, where the change must match the automaton's behavior). With these three observations, applying this section's algorithmic metatheorem, Theorem 9, gives the claim. Figure 5: **(Left)** once more, the stack contents from Figure 4a for the automaton that checks whether parentheses are balanced in an example input word; (Right) a graph of tree width 2 that represents the stack “outline” for the stacks shown left. While this outline is easy to compute (by \(\mathrm{TC}^{0}\)-circuits, to be precise) for visibly pushdown automata, the _contents_ of the stack is harder to compute and we use a second-order quantifier to “guess”. The containment \(\mathrm{VPL}\subseteq\mathrm{NC}^{1}\) can also be proved directly [22], but the proof using the algorithmic metatheorem is not only simpler, but generalizes more easily. For instance, just as for logarithmic space, there is also a \"counting version\" of the theorem and plugging in the same construction as in the above proof yields that counting the number of accepting computations of a visibly pushdown automaton can be done in #\(\mathrm{NC}^{1}\)--a result that is hard to prove directly [23].