esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Treatment of Low-a\({}_{w}\) S. Typhimurium with Light Pulses Emitted from the LED  
The LED heads of JL3 series (111 \(\times\) 70 \(\times\) 128 mm\({}^{3}\); six high-intensity LEDs) emitting light of wavelengths 365 nm (ultraviolet-A), 395 nm (near-visible ultraviolet) and 455 nm (blue) compatible with a controller unit (CF3000, Clearstone Technologies Inc., Hopkins, MN, USA) were used in this study. The irradiance of the LEDs was measured using a laser energy meter (7Z01580, Starbright, Ophir Photonics, [PERSON], JRS, Israel) connected to an irradiance and dosage sensor (PD300 RM-8 W, Ophir Photonics, A Newport Corporation Brand, [PERSON], JRS, Israel) at a distance of 4 cm from the LED head and at 60 or 80% power level as mentioned in our previous studies [29; 30]. The 365, 395 and 455 nm LEDs emitted light pulses with irradiance values of 0.06, 0.30 and 0.35 W/cm\({}^{2}\), respectively, at 80% power level, and the irradiance values of the 365 and 395 nm LEDs at 60% power level were 0.05 and 0.23 W/cm\({}^{2}\), respectively.  
For the determination of cell membrane damage in low-a\({}_{w}\)_S_. Typhimurium, the bacteria prepared with longer drying and equilibration periods were used. Ten milligrams of low-a\({}_{w}\)_S_. Typhimurium was weighed in a small disc (7.07 cm\({}^{2}\)) composed of polylactic acid filament and treated with the LED emitting light pulses of wavelengths 365 and 395 nm at 60% power level and at 4 cm from the LED head. The treatment times chosen were 10 and 60 min corresponding to treatment doses of 28.9 and 188.1 J/cm\({}^{2}\) for 365 nm and 138.8 and 834.4 J/cm\({}^{2}\) for 395 nm LED treatments, respectively. To determine the oxidative stress induced by the treatment with light pulses emitted from the LEDs, low-a\({}_{w}\)_S_. Typhimurium prepared with shorter drying and equilibration period was used. Ten milligrams of low-a\({}_{w}\)_S_. Typhimurium was treated with the LEDs emitting light pulses at room conditions (\(\sim\)23 \({}^{\circ}\)C, 48% relative humidity). The low-a\({}_{w}\) bacteria were treated with treatment doses of \(\sim\)217, \(\sim\)217 and \(\sim\)250 J/cm\({}^{2}\) corresponding to 60, 12 and 12 min treatment times with 365, 395 and 455 nm LEDs, respectively, at 80% power level and at 4 cm distance from the LED heads. Ten milligrams of low-a\({}_{w}\)_S_. Typhimurium with no LED treatment was considered as the control. Enumeration of the viable cell counts was performed by 10-fold serial dilution in 0.1% peptone water and spread plating in TSAYE plates, followed by incubation at 37 \({}^{\circ}\)C for 24 h.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 5 Performance Evaluation
### Static Weak Signal Environment Simulation  
In this section, the tracking sensitivity and convergence speed of the proposed method are evaluated. Under the static weak signal conditions, nine channels are simulated in this software receiver. The signal simulator emulates the controlled attenuated GPS signal as weak signals.
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 4 Discussion
### Novel Techniques and Modifications in Scaffold Fabrication  
The selection of the scaffold design, stem cells, growth factors and environmental conditions suitable for vascularization are key aspects for successful tissue regeneration, which [PERSON] et al. termed the Diamond concept of healing [139]. Several modifications have shown great potential to enhance the scaffold properties, as well as the growth, of stem cells. Modifications are conducted in different ways.  
Nanotopography, decellularization, melt-electrowriting technique, or microencapsulation of stem cells are all processes which impact surface structure of scaffolds, their mechanical strength, cell proliferation, osteogenic properties, degradation, and vascularization of scaffolds. So far, however, nanotopography in the dental pulp complex regeneration is not yet fully investigated [39,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,221,222,232,241,242,253,264,275,286,297,298,209,211,223,243,265,287,299,222,254,293,294,295,296,297,298,200,209,211,224,215,216,217,218,223,219,225,226,227,228,233,244,The foam replication method is one of the methods to create novel scaffolds for bone augmentation. [PERSON] et al. (2021) compared the traditionally used Cerabone and novel scaffolds, produced by the aforementioned method, to outline future research directions [146].  
The osteogenic potential is a very important factor in achieving complex and functional construction of cell-seeded scaffolds. The technology that uses 3D-printed molds to create teeth-shaped fibrin gel implants may be very helpful for further exploring and testing [147].  
Scaffold-based techniques have shown very positive results in bone and other tissue regeneration processes. On the other hand, studies on scaffold-free techniques are scarce. [PERSON] et al. evaluated scaffold-free techniques (an approach using exosomes, hypoxia-based MSCs, strategic use of heat-shock proteins) in single repair processes, and investigated their potential in regenerating smaller lesions. [PERSON] used scaffold-free sheets and spheroids for the complex regeneration of dental pulp [148]. On the other hand, [PERSON] et al. also highlighted and confirmed higher potential and better functionality of scaffold-based techniques in the repair of large, damaged lesions when compared to scaffold-free techniques [149].  
Several novel techniques have recently been enabled by the current significant advances in technologies, such as optical scanning [150], AI segmentation [131; 151], and micro-CT methods [152], which are potentiated by advances in material analytical methods, such as attenuated total internal reflection coupled with Fourier transform infrared spectroscopy, X-ray fluorescence, and many others.
## 3 SDMob Architecture
### Filter Design
In this step, the particle filter defines obstacles or feasible areas for the MN by simply removing the samples that come to be outside the legit area. Consequently, an estimate of the mean of the posterior is calculated using simple weighted averaging. Finally, the filter selects one of the anchors to become the new preferred parent and initiates a route-projection packet, containing either a SET or UNSET rule. The SET rule is sent to the new parent to suggest accepting the MN's data packets and the UNSET rule signals the anchor to stop relaying. If an UNSET packet gets lost, it may cause the network to carry multiple instances of the same data packet and congest the network. But one can arrange such a multi-parenting scenario deliberately to have redundant anchors for a mobile node. Studying multi-parent routing is out of the context of this paper. SDMob sends the UNSET packet only if the preferred parent has changed, and the SET packet is transmitted every filtering interval. With the probabilistic approach that particle filter takes, some of the samples may lose their importance and cause the filter to diverge. The filter should eliminate those irrelevant particles, and increase the number of credible particles. This can be performed in the re-sampling step, and to avoid the overheads it is carried out only when the number of effective samples drops below a certain threshold. There are a few alternative algorithms that the filter can select as re-sampling strategies such as systematic, stratified, and residual re-sampling. In SDMob we use systematic re-sampling in which \(N\) points are selected (with even distances) in the whole area and then randomly moved. It is believed that systematic re-sampling reduces the computational complexity while giving identical or improved estimates[45]. The number of effective samples can be defined using Equation (9). Figure 3:1  
All the mentioned steps are performed for each MN, and each MN has its own set of particles. Each RSSI measurement contains the IP address of the MN, so the filter can easily associate RSSI values to the particle sets. The overall procedure is presented in Algorithm 1. ```
1:Initialize a number of particles with random values for positions and velocities
2:
3:\(\chi_{0}^{i}\gets p(x_{0})\)
4:while Termination condition not reached do
5: Wait for the filter timer
6:for each Mobile Node do
7:if\(N_{eff}\leq N/2\)then
8: Re-sample
9:endif
10: Update weights:
11:for Each particle \(i\)do
12:\(\mathcal{L}(y_{i}|x_{i-1}^{i})\gets N(H(x_{i}),\theta)\)
13:\(\tilde{w_{i}}\gets w_{i-1}^{i}\mathcal{L}(y_{i}|x_{i-1}^{i})\)
14:endfor
15: Predict samples:
16:for Each particle \(i\)do
17:\(\chi_{i}^{i}\gets F(x_{i-1}^{i})\)
18:endfor
19: Estimate by weighted averaging
20: Send SET/UNSET packets to the Mobile Node
21:endfor
22:endwhile
```  
**Algorithm 1** SDMob's algorithm with Particle Filter
# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 1 Introduction
### TCP/IP Layer Based DDoS Attack  
TCP/IP layer based DDoS attack comprise GET/POST floods, low and slow-speed attacks, potential Windows or Open BSD attacks, Apache-driven attacks, and more. Such  
Figure 2: Basic types of DDoS attacks.  
attacks seem to be legitimate and innocent applications, and they target the web server. The extent is measured by requests per second.  
The number of attacks and the associated traffic volume continue to increase dramatically. With such traffic intensity, the network infrastructure upstream of the intended victim is also severely impacted, so attack traffic must be filtered as close as possible to the sources of attack. However, it is difficult to predict and identify such nodes, as attacks originate from widely distributed nodes and spread across multiple locations. To successfully respond by disrupting traffic, the mitigation approach must detect malicious traffic and respond with minimal impact on legitimate traffic. The attacker launches a new attack, known as increasing DDoS attack and proxy DDoS attack. We develop a detection algorithm to solve this problem. The detection algorithm uses deep-learning techniques to detect malicious traffic and separate legitimate traffic from malicious traffic. The algorithm classifies traffic into three categories: (1) normal traffic (2) suspicious traffic (3) malicious traffic. The main contributions of this study are summarized below.  
1. We addressed novel application layer DDoS attack by analyzing the characteristics of incoming data packets including size of HTTP frame packets, number of IP addresses sent, constant mappings of ports, and number of IP addresses using proxy IP.
2. We analyzed the client's behavior in public attacks using standard datasets, CTU-13 dataset, real web logs (dataset) from our organization, and experimentally created datasets from DDoS attack tools such as Slow Lairs, Hulk, Golden Eyes, and Xerex.
3. A deep learning classification algorithm, multilayer perceptron (MLP), is proposed to evaluate the effectiveness of attack detection based on metrics.
4. Our proposed MLP classification model provided the lowest value of false positives as compared with conventional classifiers such as Naive Bayes, Decision Stump, Logistic Model Tree, Naive Bayes Updateable, Naive Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.  
The rest of the article is organized as follows. Section 2 briefly describes the literature review; the problem motivation is discussed in Section 3. Chart flow and research methodology are presented in Section 4. The proposed attack classification model is briefly described in Section 5 and simulation results are elaborated upon in Section 6. Finally, Section 7 concludes this study along with future work.
## 3 Optimization System
### Optimization Constraints  
The optimization constraints were based on a set of intuitive geometric dependencies that can be easily determined from the solution parameters:  
* Distances of electrodes from the bottom wall \(D_{e1,bru}\), \(D_{e2,bru}\), \(D_{e3,bru}\) greater than the assumed margin,
* Distances of electrodes from the left wall \(D_{e1,bru}\), \(D_{e2,bru}\), \(D_{e3,lu}\) greater than the margin,
* Distances of electrodes from the right wall \(D_{e1,ru}\), \(D_{e2,ru}\), \(D_{e3,ru}\) greater than the margin,
* Distances of electrodes from the front wall \(D_{e1,fur}\), \(D_{e2,fur}\), \(D_{e3,fw}\) greater than the margin,
* Distances of electrodes from the back wall \(D_{e1,bw}\), \(D_{e2,bw}\), \(D_{e3,bw}\) greater than the margin,
* Immersion depths \(D_{e1}\), \(D_{e2}\), \(D_{e2}\) greater than the margin.  
Some optimization algorithms have constraints built into their structure. However, the vast majority of algorithms, including those used in the presented research and a large group of popular population algorithms, do not have this mechanism directly implemented. The solution to this constraint is to include the constraint in the form of a penalty function in the optimization criterion. In the discussed solution, the penalty function for each constraint described by the geometric dependence \(D_{\gamma}\) is described by the following equation:  
\[P_{\gamma}(D_{\gamma})=\begin{cases}(M-D_{\gamma})^{b}&\text{if }D_{\gamma}<M\\ 0&\text{if }D_{\gamma}\geq M\end{cases} \tag{15}\]  
where M is the margin and \(b\) the exponent of the strength of the penalty, which is equal to 2 in this study.  
If a violation of geometric dependencies has occurred, then the total criterion \(C_{T,j}\) returns the sum penalty of functions and the maximum theoretically possible value of the criterion. Electromagnetic calculations are not performed for this defective geometry.  
\[C_{T,i}=\begin{cases}C_{max,i}+\sum\limits_{\gamma}P_{\gamma}&\text{if }\sum\limits_{\gamma}P_{\gamma}>0\\ C_{i}&\text{if }\sum\limits_{\gamma}P_{\gamma}=0\end{cases} \tag{16}\]  
where \(C_{max,i}\) is the highest possible value of the criterion in the allowable parameter space. For criterion \(C_{1}\), it is a value 0 for the location of the heat generation center at the upper surface of the bath. For criterion \(C_{2}\), this is the value of the criterion for the location of the center in one of the upper corners of the bath (for a positive coefficient \(a\)).  
\begin{table}
\begin{tabular}{c} \hline \hline
**Parameter** \\ \hline Immersion of electrode 1 \\ \hline Immersion of electrode 2 \\ \hline Deflection X of electrode 1 \\ \hline Deflection X of electrode 2 \\ \hline Deflection Y of electrode 1 \\ \hline Deflection Y of electrode 2 \\ \hline Position X of electrode 1 \\ \hline Position Y of electrode 1 \\ \hline Position Y of electrode 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization parameters for a 2-electrode furnace.
# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 1 Introduction
For example, medical applications related to cardiac [8], dermatology [9], chest X-ray [10], mammography [11], cervix [12], pancreas [13], liver CT [14], ultrasound nerve [15], lung diseases [16], or brain MR [17] providing open-source codes. The most commonly architecture was inspired by convolutional networks for biomedical image segmentation [18]. As mentioned, the segmentation techniques available are multiple and diverse, and it is often difficult to know a priori which will be the best for the medical study of a particular type of ROI. That is why researchers are often forced to try several of these techniques until they find the one that provides best performance in a specific problem. In addition, some techniques have parameters that need to be adjusted in order to obtain the best result in a given image. All this often means that this pre-processing of the images ends up being laborious and time consuming, when in reality the important part for the study is the post-processing where a perfect segmentation of the region of interest is important for their research purposes. The objective is twofold, firstly to create a test platform for comparing segmentation tools, and secondly to generate segmented images but create ground truths that can then be used for future purposes as artificial intelligence tools. This novel tool has been developed with the main purpose of providing the community with a graphical user interface (GUI) interface that can be used to manage a big amount of images using a GUI and allows to generate a ground truth data for AI purposes. For this reason, a new application (MedicalSeg) based on different segmentation approaches is presented in this work using directly Digital Imaging and Communications in Medicine (DICOM) images [19], and also standard image formats (jpeg, png, etc.). Moreover, MedicalSeg includes a set of pre-processing tools and segmentation algorithms that can be easily and quickly applied to different types of medical images. Furthermore, an automatic system saving all the results in a defined structure is also implemented to facilitate the users and generate a dataset for post-processing purposes. Finally, facilities to execute this application using different modalities are available. In this paper, a related works, methodology, a case of use of this application and a discussion section with conclusions are presented.
# A Heuristic Approach to Solving the Train Traffic Re-Scheduling Problem in Real Time
## 5 Experimental Application and Performance Assessment  
### Experimental Set-Up  
For the experimental evaluation, the approach was applied to a number of disturbance scenarios occurring within a selected sub-network in the south of Sweden, namely the railway stretch between Karlskrona city to Malmo, via Kristianstad and Hassleholm (see Figure 4, below). From Karlskrona to Hassleholm, the railway line is single-track, and from Hassleholm to Malmo, the line is double-track with a small portion having four tracks between Arlov and Malmo. This stretch consists of approximately 90 segments, with a total of 290 block sections. For the regional trains that operate between Karlskrona and Malmo (and even further, into Denmark via the Oresund Bridge), there is a travel time of 1 h and 31 min between Karlskrona and Kristianstad, and a travel time between Kristianstad and Malmo of 1 h and 5 min. In line with the categorization suggested in [29], three types of disturbance scenarios are used:  
* Category 1 refers to a train suffering from a temporary delay at one particular section, which could occur due to, e.g., delayed train staff, or crowding at platforms resulting in increasing dwell times at stations. * Category 2 refers to a train having a permanent malfunction, resulting in increased running times on all line sections it is planned to occupy. * Category 3 refers to an infrastructure failure causing, e.g., a speed reduction on a particular section, which results in increased running times for all trains running through that section. All disturbance scenarios occur between 16:00 and 18:00, which is during peak hours. The re-scheduling time horizons are 1 and 1.5 h time windows, respectively, counting from when the disturbance occurs. The scenarios are described in Table 3 below. The experiments were tested on a laptop with 64-bit Windows 10, equipped with an Intel i7-CPU, 2.60 GHz, with 8 Gigabytes of RAM.
## 2 Materials and Methods
### Kinetic Model Development and Determination of Kinetic Parameters
#### 2.5.1 Cell Growth Model  
In general, microorganisms gave a sigmoidal growth curve including lag phase, log phase, and stationary phase. The logistic function (Equation (6)), which is a sigmoidal equation includes biologically relevant parameters instead of mathematical parameters. When the inhibitory impacts of ethanol and substrate are absent, the cell growth rate follows the well-known exponential relationship as defined by following differential equation (Equation (1)).  
\[\frac{dX}{dt}\ =\ \mu X \tag{1}\]  
where \(\mu\) is the specific cell growth rate (h\({}^{-1}\)), which is given by the following equation (Equation (2)).  
\[\mu\ =\ \frac{\mu_{max}S}{K_{S}+S} \tag{2}\]  
where \(\mu_{max}\) is the maximum specific cell growth rate (h\({}^{-1}\)), \(K_{S}\) is the saturation constant (g/L), and \(S\) is the residual sugar concentration (g/L). Equation (2) can be employed to define the cell growth rate. When Equation (2) is written in Equation (1), Equation (3) is obtained as follows.  
\[\frac{dX}{dt}\ =\ \frac{\mu_{max}S}{K_{S}+S}X. \tag{3}\]  
When Equation (3) is integrated with the limits \(X=X_{0}\) when \(t=0\) and \(X=X_{t}\) when \(t=t\) as follows, Equation (5) is yielded.  
\[\int_{X_{0}}^{X_{t}}\frac{dX}{X}\ =\ \int_{0}^{t}\frac{\mu_{max}S}{K_{S}+S}dt \tag{4}\]  
\[X_{t}\ =\ X_{0}e^{\frac{\mu_{max}S}{K_{S}+S}t}. \tag{5}\]  
The cell growth model is expressed by a logistic equation in many fermentation systems, which represents the cell growth from the beginning of the log phase to the stationary phase (including the deceleration phase) [34]. The logistic equation (Equation (6)) is written as follows.  
\[\frac{dX}{dt}\ =\ \mu_{max}\bigg{(}1-\frac{X}{X_{max}}\bigg{)}X. \tag{6}\]  
where \(X_{max}\) is the maximum biomass concentration (g/L). The following equation (Equation (7)) is obtained by integrating Equation (6) with appropriate initial conditions (\(X=X_{0}\) at \(t=0\)).  
\[X_{t}\ =\ \frac{X_{0}e^{\mu_{max}t}}{1-\frac{X_{0}}{X_{max}}(1-e^{\mu_{max}t})}. \tag{7}\]  
Or, with the rearrangement of Equation (7), Equation (8) is obtained as follows.  
\[ln\bigg{(}\frac{X}{X_{max}-X}\bigg{)}\ =\ \mu_{max}t-ln\bigg{(}\frac{X_{max}-X_{0}}{X _{0}}\bigg{)}. \tag{8}\]  
\(X_{max}\) can directly be used from the experimental data. When \(ln\Big{(}\frac{X}{X_{max}-X}\Big{)}\) is plotted versus \(t\), \(\mu_{max}\) and \(X_{0}\) are calculated from the slope and the \(y\)-shift of the plot, respectively (Figure 1A,D,G,J,M).
# Assessment of the RACPC Performance under Diffuse Radiation for Use in BIPV System
## 3 Simulation Performance Analysis  
Prior to carrying out the performance evaluation through an outdoor experiment, the electrical output generated from the RACPC prototype under diffuse conditions was predicted through an optical concentration analysis. According to the optical concentration gain definition, the optical gain can be expressed in the same way as the opto-electronic gain using the total power at the detector with and without an optical concentrator.  
\[C_{op-elct}=\frac{P\text{ }_{with\text{ }OC}}{P\text{ }_{without\text{ }OC}} \tag{1}\]  
However, the optical losses are included due to the definition of the optical concentration gain. The analysis evaluates the gain performance of the RACPC when it is exposed to rays coming from all directions by a light source, in this case, a dome with a thickness of 1 mm and a 380 mm radius. The dome was created by [PERSON] [34] using AutoCAD and then it was imported into an optical analysis software called ZEMAX(r). Figure 3 shows the created ray source.  
The dome shape was implemented with the aim of simulating diffuse light conditions. For this purpose, 1 million rays were emitted by the source at a power of 1000 W. The detector size was 100 mm\({}^{2}\). A layer of index matching gel and the concentrator are positioned inside the dome with a distance  
Figure 3: Light source for diffuse light analysis.  
between the dome and the concentrator of 350 mm as illustrated in Figure 4 where the concentrator is displayed in black and orange respectively.  
Diffuse light is characterised by rays coming also from the side profile of the concentrator, however, even with the additional rays, the optical concentration gain is lower than that of the direct light simulation performed by [PERSON] et al. [32] who achieved an optical concentration gain of 3.34 while the optical gain for diffuse light is 2.12.  
The optical efficiency for diffuse light of 58% was calculated using the optical concentration gain definition (Equation (1)). However, for an optical efficiency value, the rays must come only through the entrance aperture not from the sides as it was calculated.