esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principle and Theory of Coding
In one hand, if such frequency resolution is hardly reached with classical TDS systems, it can be easily obtained using continuous wave (CW) systems whose frequency resolution goes typically down to 100 MHz. In that case, \(\delta f\) is no more limited by the reader but only by the FWHM of the transmitted peaks (for a given number of layers), i.e., by the layers' absorptions. On other hand, considering realistic dielectric materials with absorption of about 1 cm\({}^{-1}\) around 0.3 THz, we can estimate, by using the transfer matrix method, that the peaks width in such a structure would present a FWHM of 2.2 GHz (see insert of Figure 4). According to Figure 4, an eligible coding capacity of 60 bits can be then obtained, leading to theoretical spatial coding density of about 300 bits/cm\({}^{2}\). Such a performance is several orders of magnitude better than coding density obtained with a chipless RFID system [4; 5; 6; 7; 8], mainly since THz wavelengths are a hundred times smaller than in the \(RF\). In order to analyse the effect, and consequently the performance limitations by the material used to fabricate the tag, we plot in Figure 5 the variation of the minimum channel bandwidth \(\delta f\) (blue curve) and then the corresponding coding capacity \(\rho\) (red curve) versus the absorption coefficient of the materials. We can easily see that \(\rho\) rapidly drops for absorption coefficient from 0.1 to 10 cm\({}^{-1}\). However, as many dielectric materials exhibit low to moderate absorption in the frequency range typically below 1 THz, a coding capacity bigger than 30 bits can easily be considered with common plastic materials. Let's noticed that these theoretical results have been obtained by taking into account the 19 layers' structures described previously. Obviously, for a structure made of fewer layers, as long as this number remains sufficient to maintain an exploitable PBG, these results can be considered as underestimated. Even though THID tags represent a potentially high coding capacity solution for identification applications, they will be of interest only if they are able to be reliably and rapidly read. Practically, THz signals recorded from the THz detector remain small; it follows that the corresponding signal to noise ratio drastically depends on the averaging rate, i.e., to the global acquisition time. Thereby, it exists as a speed-reliability compromise, which is discussed in the following section, where we focus on the evolution of reading reliability via the bit error rate (BER), when measurement duration is decreased down to allow video-rate reading. Figure 4: Prediction of the coding capacity of THID tags versus the channel width \(\delta f\) and considering a PBG bandwidth of \(\Delta f=130\) GHz centered at 320 GHz. Inset: focus of the second peak in the 1 st PBG, simulated by taking into account material absorption of 1 cm\({}^{-1}\).
# Kilohertz Pixel-Rate Multilayer Terahertz Imaging of Subwavelength Coatings
## 3 Results  
### Terahertz Performance  
Two representative waveforms--a reference waveform and a normalized sample waveform of the three-layer-system--are shown in Figure 2a. The sample waveform already indicates the sub-wavelength nature of the applied layers, as the echoes of the individual layers overlap and are not readily separated by the eye. The acquired waveforms comprise a time interval of about 160 ps (not fully shown in Figure 2a), which would be sufficient for measuring samples even of millimeter thicknesses. Admittedly, the paint  
Figure 1: (**a**) Sketch and (**b**) photograph of the experimental setup. Coated disks with diameters of 300 mm are mounted on an encoder-equipped stepper motor. A linear stage radially translates the terahertz measurement head, giving rise to a spiral-shaped imaging pattern of the disk. On the photograph, metallic resolution targets are placed on the coated disk. PC: polycarbonate; rpm: revolutions per minute; PPR: Pulses per revolution (encoder resolution).  
layers investigated in this study have a thickness of the order of a few 10 \(\upmu\)m. Nevertheless, the 160 ps range allows for measuring samples even in applications where the distance from the measurement head to the sample surface varies by as much as \(\sim\)20 mm (as long as the Rayleigh length of the utilized optics is sufficient as well). This can be particularly useful when measuring the thicknesses of multilayer foils or webs in production.  
The single-shot dynamic range of the ECOPS system is shown in Figure 2b. The peak dynamic range of 40 dB is measured at a frequency of about 1 THz. The useable bandwidth exceeds 3 THz. These values, obtained at the full measurement rate of 1.6 kHz, are sufficient for evaluating multilayer structures [22]. The symmetry of the water vapor absorption lines points towards the good linearity of the time axis that the ECOPS system provides. TDS systems with nonlinear time axis errors typically exhibit an amplitude overshoot at the edges of absorption lines [23], which is not the case with the system presented.
# Effects of Organophosphorus Flame Retardants on the Dissipation Factor of Flame-Retardant Polymers
###### Abstract  
To understand the effect of the hydroxyl group and processing temperatures on dielectric losses of flame retardants and flame-retardant polymers, the performance difference between 6-methyldibenzo[c,e][1,2]oxaphosphinine 6-oxide (DOPO-Me) and 6-(hydroxymethyl)dibenzo[c,e][1,2]oxaphosphinine 6-oxide (DOPO-HM) has been investigated, respectively, in non-polar and polar polymers at 7-20 GHz. DOPO-HM and DOPO-Me differ by only one OH group. The former demonstrates a lower dissipation factor (Df) than the latter, owing to hydrogen bonds. In polystyrene and crosslinked polyphenylene oxide, both flame retardants increase a dielectric loss of flame-retardant polymers, with DOPO-HM being less detrimental because of its higher crystallizability and lower plasticization. In polar poly(methyl methacrylate) (PMMA), conformational changes in PMMA main chains caused by flame retardants and high processing temperatures lead to an early Df drop of PMMA at low loadings of the flame retardants. At high loadings, a change in the physical form of flame retardants from a primitive crystalline state to an amorphous state increases a dielectric loss of flame retardant PMMA, with DOPO-HM resulting in a slightly higher dielectric loss than DOPO-Me. These results prove that the effect of a hydroxyl group in organophosphorus structures on the dielectric loss of flame-retardant polymers is crucially dependent on its interaction with the polymer matrix.  
dissipation factor; conformation; crystallizability; PMMA; PS; PPO; flame retardant +
Footnote †: journal:  
0000-0002-0002-3199][PERSON]  
1 School of Chemistry and Chemical Engineering, Jiangxi University of Science and Technology,  
Ganzhou 341000, China; [EMAIL_ADDRESS]
## 3 Wireless Sensor Networks  
As mentioned before, the climate variables, movement signal, walls temperature, and additionally the state (open/closed) of windows and doors is measured by a Wireless Sensor Network. In previous work this has been accomplished by the Tmote Sky WSN platform [23; 25], connected to the required sensors. This platform, as well as most commercial off-the-shelf WSN (wireless sensor network) solutions, is not appropriate for this specific application due to several reasons:  
* economic: they are not cheap, in particular for large deployments;
* redundancy: their general-purpose nature drove the design to include many unnecessary components for specific applications;
* energy consumption: energy consumption would need too frequent battery replacement or hardwiring to the electric network;
* maintenance: changing batteries in large installations is unfeasible;
* engineering: a significant amount of work is required to integrate specific sensors; wiring for power restricts available placement locations;
* ergonomic: the nodes are too large for integration with the required components.  
In order to solve the above issues, the objective here is to design, implement and test WSN nodes in which:  
* to avoid wiring for power or having to periodically change batteries, energy should be harvested from the environment and stored in a battery;  
Figure 1: Schematic diagram of the IMBPC HVAC approach. IMBPC: Intelligent Model-Based Predictive Control; HVAC: Heating, Ventilating and Air Conditioning; SR: solar radiation; TA: atmospheric air temperature; RH: relative humidity.  
* to enable easy and aesthetically pleasant installations in larger types of spaces typically found in buildings, their size should be small;
* to reduce the size and decrease the energy consumption, only the necessary components should be used, employing Ultra Low Power (ULP) components whenever possible;
* to allow a viable and marketable product that promotes the energy efficiency of buildings, they should be cheap to produce.  
Besides the advantage of being autonomous, these nodes permit installation in existing buildings and homes without requiring infrastructure changes and unpleasant, undesirable wiring. Three different types of devices were created (Receiver, Repeater and Transmitter).
# Distributed-Satellite-Clusters-Based Spectrum Sensing with Two-Stage Phase Alignment
## 3 The Proposed Two-Stage Phase Alignment Frame  
### Phase Compensation Analysis  
In order to achieve carrier phase synchronization at the master satellite, each concomitant satellite in DSC adjusts its beamforming weighted phase \(\phi_{i}(t)\) independently and iteratively. However, different methods can be used for different phase deviations. Herein, we conceive a two-stage phase alignment method for SS_DSC system so as to improve the strength of the sensing signal. To be specific, in the first stage, the phase compensation is to eliminate the phase dynamics caused by satellite motion, such as the aforementioned \(a_{i}(t)\) and \(b_{i}(t)\), while the second stage is to eliminate the random phase deviations caused by satellite disturbance and non-ideal synchronization, e.g., \(c_{i}(t)\), \(\varphi_{i}(t)\) and \(\xi_{i}(t)\). Therefore, for \(a_{i}(t)\), the phase compensation can be realized by broadcasting reference signal, i.e., the received sensing signal \(s_{m}(t)\) from the master satellite to the concomitants. The phase compensation \(\overline{a_{i}(t)}\) between the two signals is estimated in real time, which can be expressed as  
\[\overline{a_{i}(t)}=\arccos\left(R_{s_{m}s_{i}}(t)\bigg{/}\sqrt{R_{s_{m}s_{m} }(0)*R_{s_{i}s_{i}}(0)}\right) \tag{9}\]  
where \(R_{xy}(\cdot)\) denotes the correlation operation between \(x\) and \(y\). Further, for \(b_{i}(t)\), the phase compensation \(\overline{b_{i}(t)}\) can be obtained by calculating the distance change \(\Delta d_{im}(t)\) between the master satellite and its concomitants through the ephemeris, which can be expressed as  
\[\overline{b_{i}(t)}=\frac{2\pi}{\lambda}\Delta d_{im}(t) \tag{10}\]However, in the second stage, it is difficult to calculate the random phase deviations directly through prior information. Thus, we need to design the adaptive weight component \(\overline{u_{i}(t)}\) to eliminate randomness so as to achieve carrier phase synchronization at the master satellite. In this paper, we propose a random phase compensation algorithm based on two-bit feedback (RPC_TF) to obtain the value of \(\overline{u_{i}(t)}\) without the knowledge of \(c_{i}(t)\), \(\varphi_{i}(t)\) and \(\tilde{\xi}_{i}(t)\). In addition, it should be emphasized that there will still be some slight errors between satellite phases after \(\overline{a_{i}(t)}\) and \(\overline{b_{i}(t)}\) compensation, so \(\overline{u_{i}(t)}\) is also their further compensation.
## 3 Experimental Results
### Data Acquisition
It reveals three main features: a peaked contribution shaded in blue, a broader feature shaded in green and another broad contribution almost spreading out to the edge of the detector shaded in orange. We attribute the strongest feature (shaded blue) to low kinetic energy N\({}_{2}^{+}\) ions and predissociation of the N\({}_{2}^{+}\) C\({}^{2}\Sigma_{u}^{+}\) state [51]. The contribution shaded in green has previously been assigned to dissociation from the F\({}^{2}\Sigma_{S}^{+}\) state, and the features inthe area shaded in orange are associated with dissociation from the highly-excited Rydberg-like states of N\({}_{2}^{+}\) and the H band [51; 52; 53]. Figure 6c,f shows the photoelectron momentum distribution and the photoelectron kinetic energy spectrum, respectively. From the photoelectron spectrum, it is apparent that the resolution of the spectrometer is limited by the bandwidth of the harmonics (\(\sim\)0.5 eV) rather than by the electron imaging. Figure 6f contains the assigned contributions originating from the X\({}^{2}\Sigma_{g}^{+}\), A\({}^{2}\Pi_{u}\) and B\({}^{2}\Sigma_{u}^{+}\) states, after taking their respective branching ratios, cross-sections [54; 55] and the relative XUV intensity at the photon energies contained in the XUV spectrum into account. The ratio of the contributions is displayed by a stacked bar plot, where each state has a specific color assigned. The much weaker contributions of the other cationic and diatomic states are not shown for clarity [53; 54; 56; 57]. The signal at kinetic energies smaller than 1 eV can be assigned to highly excited inner valence states of N\({}_{2}^{+}\) and possibly the first electronic states of N\({}_{2}^{2+}\)[52; 53; 57]. As mentioned earlier, in order to extract information about the correlation between the different single-shot datasets, it is important that the sets are acquired simultaneously and can be analyzed in a synchronized fashion. That this is indeed the case for the acquired dataset readily verified by studying the color-coded scatter plots in Figure 6g-i, indicating a high degree of correlation between the XUV intensity measured by the photodiode and the other three detector signals.
## 2 Related Works
### Advanced Message Queuing Protocol (AMQP)  
Advanced Message Queuing Protocol (AMQP) is an open wire Internet protocol for business messaging comprised of several layers [24]. The one located at the lowest level is used to interchange binary information on a peer-to-peer basis, whereas the immediately higher one is used to send and receive messages by using an abstract message format with specific standardized encoding. A higher level layer is used to contain a peer-to-peer transport-like protocol and the highest one makes use of either Transport Layer Security (TLS) or Simple Authentication and Security Layer (SASL) for security purposes such as encryption and authentication. The messaging level makes use of a messaging format, and another one is defined for transactions at a higher level. AMQP works in a way that itmakes use of a communications broker to process all the requests that are performed. The broker provides communications under a Publish/Subscribe paradigm: publishers will send topics to the broker and they get organized in queues that are accessed by subscribers. There are several broker implementations that have been developed, such as Apache Qpid [25] or RabbitMQ [26].  
AMQP offers a very detailed set of PDUs for information transmission at the data level (rather than being bits or packages). The main disadvantage of AMQP is that challenges like low bandwidth or the unreliability of the transmission medium have not been specifically taken into account, so it is not an advisable option to use in the context of this manuscript. Adding security at this level of data transmission can also be a major challenge, since encrypting data tends to result in rather verbose communications. Lastly, AMQP relies on some communication and transport protocols (such as TCP) that cannot be guaranteed to be available in an underwater level.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 6 Conclusions  
The framework's ability to identify banking malware and its variants were demonstrated by the empirical analysis conducted during this research. The research showed that the methodology and framework used for this study can identify both older and newer versions of the Zeus banking malware. It is possible that this approach can be used to detect a large number of banking malware variants without having to examine each one in order to understand its characteristics. This is because the framework and technique developed during this research identified key features that could be used and may also predict other banking malware variants. Also, this research showed that a reduced set of features can be used for detecting banking malware, and this should help to increase the performance and time required for training and testing machine learning or deep learning algorithms, especially for large datasets. This research will also benefit other researchers, as they should be able to adopt this approach in their own research and will have a good base to begin conducting experiments of a similar nature.  
It may be possible to advance this research in the future by improving the methodology to include more banking malware variants, especially variants belonging to a different banking malware family. Additionally, more research may be conducted to identify other malware types and increase the prediction accuracy of these predictions. The results of this study may also be utilized by researchers to develop an intrusion detection system (IDS) that can identify a variety of malware, and by anti-virus manufacturers to support their development of malware detection tools. Once an infection has been identified, action can also be taken against malicious communications. Researchers can improve their work by using the results of this study to create their own malware prediction systems.  
This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The author declares no conflicts of interest.  
Figure 19: Results of [45] compared to the results of this research.
## 3 A Fractiona-Order Sliding Mode Controller (FOSMC)
### Application to a Photobioreactor  
The simplified model of the photobioreactor in Equation (3) represents its dynamics well if disturbances such as \(I(t)\), \(pH(t)\), etc. are kept regulated. However, these and other disturbances vary unexpectedly in experimental photobioreactors, making its control very difficult. Even if the expanded model in Equation (7) can better represent the complex dynamic behavior of the experimental system, the disturbance function is a priori unknown. Consequently, this model cannot be used to design a robust controller. Moreover, developing a control law using this expanded model is difficult. Instead, in this paper, we use a simplified model that only considers the dynamics of the measured variable (biomass concentration) to design a FOSMC to achieve robust control of the photobioreactor. This approach has been successfully used before for robust bioreactor control [9; 40], assuring asymptotic stability of the main state, while the rest of the system is kept bounded. In this paper, we design a FOSMC to achieve robust tracking of \(x_{1}(t)\) to a reference signal while keeping the substrate \(x_{2}(t)\) and the volume \(x_{3}(t)\) bounded.  
The reduced model used to design the control is given by \[\dot{x}_{1} = \mu_{m}x_{1}-\frac{x_{1}}{x_{3}}u+\Sigma x_{1}, \tag{24}\] \[\mu_{m} = \frac{a_{1}x_{2}}{a_{2}+x_{2}},\]  
where \(x_{1}(t)=x_{1}\geq 0\), \(x_{2}(t)=x_{2}\geq 0\), \(x_{3}(t)=x_{3}\geq 0\), \(u(t)=u\geq 0\), and \(\Sigma(t)=\Sigma\). Thus, when considering an optimal reference biomass dynamics \(x_{1,r}\), the following robust FOSMC is proposed:  
\[u = -(\mu_{m}x_{1}+\eta(x_{1},x_{1,r}))\frac{x_{3}}{x_{1}},\] \[\eta(x_{1},x_{1,r}) = -k_{2}I^{\beta}sgn(s)-k_{3}s-\dot{x}_{1,r}-k_{1}e,\] \[e = x_{1,r}-x,\] \[s = e+k_{1}.\int_{0}^{t}\!e(\tau)\,d\tau. \tag{25}\]  
The structure of the designed controller is shown in Figure 2. The numerical simulations carried out when using this fractional-order controller are given in Section 5.
## 2 Project Overview and In-Situ Monitoring
### Monitoring Arrangement
As shown in Figure 9, monitoring of the internal forces of the four bar lattice girders were obtained using steel strain gauges; the monitoring elements at each monitoring point were installed in pairs. The side facing away from the surrounding rock is defined as the inner side, and the side nearest to the surrounding rock is defined as the outer side, the definition holds true here in after. The stresses in the concrete of the secondary supports were monitored by the concrete strain gauges (see Figure 10). Based on the monitored values, the internal forces in the secondary supports were calculated from Equation (1):  
Figure 6: Layout of the multi-point extensometers. Figure 7: Layout of the earth pressure cells. \[ \begin{array}{l}N=\frac{1}{2}(\sigma_{inner}+\sigma_{outer})bh;\\ M=\frac{1}{2}(\sigma_{inner}-\sigma_{outer})bh^{2}\end{array}  \tag{1}\]  
where, \(N\) and \(M\) are the axial force and bending moment to be calculated, respectively; \(\sigma_{inner}\) and \(\sigma_{outer}\) denote the stress of the inner side and outer side of the secondary supports, respectively; \(b\) represents the distance between the two adjacent main bars in the longitudinal direction; \(h\) is the thickness of the secondary support. After comprehensive consideration of the design of the support and excavation stages, the layout of the monitoring points was carried out, as shown in Figure 11. According to the location of the monitoring points, Points A, B and C, D and E, F and G, H to L were defined as the crown, the spandrel, the haunch, the arch springing and the inverted arch, respectively. Figure 8: Layout of the ring dynamometers. Figure 10: Layout of the concrete strain gauges. Figure 9: Layout of the steel strain gauges.
## 5 Discussion
### Role of SHAP Method in Model Training  
In this study, the introduction of the SHAP method not only enhances the interpretability of the model, but also provides strong support for feature selection and optimization. The SHAP method makes the decision-making process of the model more transparent by quantifying the marginal contribution of each feature to the model prediction and revealing the importance of the key variables such as the dam length, the storage volume, and the area of the upstream catchment area in the prediction of weir life. The results of this analysis provide a clear, data-driven direction for optimizing the input feature set by adjusting the weights of features based on their importance as determined by SHAP values. Features with higher SHAP values receive more weight in the model, enhancing their influence on the predictions. This optimization process improves model training efficiency by focusing on the most relevant features while reducing the impact of less important features. The clarity of the optimization direction was measured by evaluating the improvements in model performance after adjusting feature weights. In addition, the SHAP approach further advances the model's ability to understand complex data patterns by revealing the interactions between features, thus theoretically enhancing the model's prediction performance. More importantly, this method provides a quantitative basis forpractical engineering applications; e.g., by identifying the significant influence of dam size, it can provide scientific support for resource allocation and management decisions in weir design and maintenance. Therefore, the SHAP method is not only a powerful tool for model interpretation, but also an important means to guide data processing and to optimize the modeling process, which can provide valuable experience for the construction and optimization of complex prediction models.