esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Water Treatment Methods Modifications
#### 4.2.2 Switching to Chloramines for Secondary Disinfection  
Transitioning to chloramines for secondary disinfection presents an effective strategy for mitigating DBP formation, such as of THMs and HAAs. Unlike free chlorine, chloramines do not generate THMs and HAAs during chlorination. Moreover, maintaining residual disinfect levels in the distribution system is more manageable with chloramines due to their enhanced stability compared to chlorine. However, it is vital to meticulously regulate the dosages of chlorine and ammonia to prevent issues such as di- and trichloramine formation, as well as THMs and HAAs. Inadequate control may result in the absence of residual disinfectants, fostering nitrification and microbial regrowth. One notable byproduct of chloramination is N-nitrosodimethylamine (NDMA), which is subject to specific drinking water quality standards such as 9 ng L\({}^{-1}\) in Ontario and a notification level of 10 ng L\({}^{-1}\) in California [6].  
The effectiveness of precursor removal is site-specific and can vary depending on various factors, including the characteristics of the source water and the treatment methods employed. Aluminum sulfate (alum) and iron chloride (ferric) salts exhibit variable capabilities in removing NOM. In the case of alum, the pH range of 5.5-6.0 is considered optimal for NOM removal. Adding alum tends to lower pH levels, potentially facilitating in achieving the optimal pH range without the need for additional acid. However, in cases where water has extremely low or high alkalinity, adding either a base or an acid may be necessary to attain the ideal NOM coagulation pH [11].
## 3 Validation of CCPD Model
### Validation of the CCPD Model
Figure 13 shows the relationship between \(S_{br}\) obtained by the three models and \(S_{pu}\) measured by the experiment. For a certain laser fluence, models A and B respectively show the largest and smallest rupture areas, whereas model C has moderate-sized rupture area. Figure 11: Pressure distribution on the wall of the median curvature (stdGC = 41.3 mm\({}^{-2}\)) vessels after laser irradiation (\(E\) = 12 J/cm\({}^{2}\), \(t_{p}\) = 6 ms). Figure 12: Pressure distribution on the wall of the median curvature (stdGC = 64.5 mm\({}^{-2}\)) vessels after laser irradiation (\(E\) = 13 J/cm\({}^{2}\), \(t_{p}\) = 6 ms). Figure 10: Pressure distribution on the wall of small curvature (stdGC = 20.5 mm\({}^{-2}\)) vessels after laser irradiation (\(E\) = 11 J/cm\({}^{2}\), \(t_{p}\) = 6 ms). As shown in Figure 13, the CCPD model (model C) predicts a more significant linear relationship between \(S_{br}\) and \(S_{pu}\), which is in accordance with the histopathological analysis of patients with PWS [36; 37]. After comprehensive consideration with our analysis in Figures 11-13, the CCPD model is the best predictive model. The linear relationship between \(S_{br}\) and \(S_{pu}\) by model C fitted in Figure 13 is expressed as:  
\[S_{pu}=1.01S_{br}+0.0279. \tag{14}\]
###### Abstract  
High-resolution seismic processing involves the recovery of high-frequency components from seismic data with lower resolution. Traditional methods typically impose prior knowledge or predefined subsurface structures when modeling seismic high-resolution processes, and they are usually model-driven. Nowadays, there has been a growing utilization of deep learning techniques to enhance seismic resolution. These approaches involve feature learning from extensive training datasets through multi-layered neural networks and are fundamentally data-driven. However, the reliance on labeled data has consistently posed a primary challenge for deploying these methods in practical applications. To address this issue, a novel approach for seismic high-resolution reconstruction is introduced, employing a Cycle Generative Adversarial Neural Network (CycleGAN) trained on authentic pseudo-well data. The application of the CycleGAN involves creating dual mappings connecting low-resolution and high-resolution data. This enables the model to comprehend both the forward and inverse processes, ensuring the stability of the inverse process, particularly in the context of high-resolution reconstruction. More importantly, statistical distributions are extracted from well logs and used to randomly generate extensive sets of low-resolution and high-resolution training pairs. This training set captures the structural characteristics of the actual subsurface and leads to significant improvement of the proposed method. The results from experiments conducted on both synthetic and field examples validate the effectiveness of the proposed approach in significantly enhancing seismic resolution and achieving superior recovery of thin layers when compared with the conventional method and the deep-learning-based method.  
neural network; CycleGAN; data processing; high-resolution reconstruction
## 2 Models and Methodology
### Specifications of HCE and PTC System  
In this paper, the popular commercial Schott PTR70 solar receiver and EuroTrough collector are selected as the studied HCE and PTC system. By referring to the real outdoor testing conducted by [PERSON] et al. [24], the total length of small-scale PTC system is determined as 72 m, the detail specifications of the HCE and PTC system used in simulations are presented in Table 2. In addition, the other parameters used in simulations are same with the experimental parameters in literature [24], including optical efficiency (\(\eta_{\rm o}\)) in the PTC system of 0.768, and ambient temperature (\(T_{\rm a}\)) of 20 \({}^{\circ}\)C, with solar molten salt used as the heat transfer fluid in this paper.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 4 Solar Irradiance Forecasting Based on Deep Learning Techniques
### Solar Irradiance Forecasting Model Based on GRU  
Ref. [141] employed LSTM and GRU up to an hour ahead of solar irradiance forecasting. Both univariate and multivariate forecasting models were developed to utilize exogenous meteorological factors and historical solar irradiance data. These solar data were based on an international airport in Phoenix, Arizona, USA (1 January 2004-31 December 2014). The findings showed that multivariate LSTM and GRU are more accurate than their univariate equivalents. Further, ref. [142] suggested a short-term forecasting horizons solar irradiance forecasting model integrated with GRU and Attention Mechanism, such as 5/10/20/30 min, for use in four climates in the Nevada desert, USA. The dataset used was obtained by the University of Nevada, Las Vegas, USA, from the year 2014 onwards, and it consists of peak wind speed and averaged historical solar irradiation data. A hybrid deep learning model presented by [143] was based on encoder-decoder networks of LSTM, Bidirectional LSTM, RNN, and GRU models for short-term solar irradiance forecasting. The model training was performed on NREL's fifteen-year dataset for Kharagpur, India, which spanned the years 2000 to 2014. A 10-kilo meter grid of meteorological and solar irradiance data were gathered. In addition, the suggested model was evaluated against other previously used machine learning models, such as Feed-Forward Neural Networks (FFNN) and Gradient-Boosted Regression Trees (GBRT), and showed superior performance.
# A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor
## 2 Physical Model
### Drill String Dynamics Model for Toolface Adjustment
The lateral force can be expressed as  
\[F=\frac{\sqrt{\big{[}(2T_{2}+W_{t})\sin\frac{\gamma}{2}+W_{n}\big{]}^{2}+W_{b} ^{2}}}{\sqrt{1+\mu_{a}^{2}}} \tag{9}\]  
In Equations (8) and (9), \(T_{1}\) and \(T_{2}\) are the axial tension at the upper and lower ends of the unit, respectively. \(W_{t}\), \(W_{b}\), and \(W_{n}\) are the components of gravity in a tangential direction, main normal direction, and sub-normal direction, respectively. \(\gamma\) is the dogleg degree of the drill string element. \(\mu_{a}\) is the component of the friction coefficient in the axial direction. The components of gravity in each direction can be expressed as  
\[\left\{ \begin{array}{l}W_{t}=W\frac{\cos\alpha_{2}+\cos\alpha_{1}}{2\cos( \gamma/2)}\\ W_{n}=W\frac{\cos\alpha_{2}-\cos\alpha_{1}}{2\sin(\gamma/2)}\\ W_{b}=W\frac{\sin\alpha_{1}\sin\alpha_{2}\sin\alpha\varphi}{\sin\gamma}\end{array} \right. \tag{10}\]  
where \(W\) is the floating weight of the element. \(\alpha_{1}\) and \(\alpha_{2}\) are the well deviation angles at the upper and lower ends of the element, respectively. \(\Delta\varphi\) represents the azimuth angle difference between the upper and lower ends of the element. The relationship between the axial friction coefficient, torsional friction coefficient, and overall friction coefficient can be expressed as  
\[\left\{ \begin{array}{l}\mu_{a}=\frac{\mu_{a}}{\sqrt{\
u_{a}^{2}+\
u_{t}^{2}} }\\ \mu_{t}=\frac{\mu_{pv}}{\sqrt{\
u_{a}^{2}+\
u_{t}^{2}}}\end{array} \right. \tag{11}\]The overall friction coefficient between the wellbore and the drill string is related to the movement speed, and this phenomenon can be described by the Stribeck friction curve. \[\mu=\mu_{d}+(\mu_{s}-\mu_{d})e^{-(|v_{r}|/v_{s})^{2}} \tag{12}\]  
where \(\mu_{s}\) represents the static friction coefficient; \(\mu_{d}\) represents the dynamic friction coefficient; \(v_{r}\) represents the resultant velocity; \(v_{s}\) is a characteristic velocity, which determines the speed at which \(\mu\) approaches \(\mu_{d}\) as \(v_{r}\) increases.
# Detection and Prevention of DDoS Attacks on the IoT
## 2 Scenarios of DDoS Attack
### System Resource Consumption DDoS Attacks  
System resource consumption is caused by system transmission vulnerabilities or fake IPs, which exhaust system memory or CPU resources, and eventually lead to service suspension or interruption.
# Ride Comfort Prediction on Urban Road Using Discrete
## 2 Roughness Indexes and Ride Comfort Standard
### Whole-Body Vibration  
ISO 2631-1 [5] recommends the weighted root-mean-square acceleration (WRMSA) for evaluating human comfort and predicting health risk. The WRMSA is a comprehensive indicator taking into account the effects of frequency bands, positions, and acceleration directions. Since the uneven road affects the fluctuation of vertical vibration instead of longitudinal and transverse vibrations, we only consider the vertical vibration in this work.  
Figure 2: Discretized road profile.  
The definition of WRMSA is expressed in Equation (9):  
\[a_{w}=\left[\frac{1}{T}\int_{0}^{T}a_{wz}^{2}(t)dt\right]^{\frac{1}{2}} \tag{9}\]  
where T is the duration of the measurement and \(a_{wz}\) is the vertical frequency-weighted acceleration, computed by Equation (10):  
\[a_{wz}=\left[\sum_{i}(W_{i}a_{i})^{2}\right]^{\frac{1}{2}} \tag{10}\]  
where \(W_{i}\) denotes the weighting factor for the \(i\)-th one-third octave band given in ISO 2631, and \(a_{i}\) denotes the vertical acceleration for the \(i\)-th one-third octave band. Moreover, since we mainly consider the pavement roughness of short road sections, the vibration in the vehicle appears to be more like a transient vibration. The ISO standard provides an index to describe transient vibration: the maximum transient vibration value (MTVV). The MTVV is defined based on the WRSMA and expressed in Equations (11) and (12).  
\[a_{w}(t_{0})=\left\{\frac{1}{\tau}\int\limits_{t_{0}-\tau}^{t_{0}}\left[a_{wz} (t)\right]^{2}dt\right\}^{\frac{1}{2}} \tag{11}\]  
\[\text{MTVV}=\text{max}[a_{wz}(t_{0})] \tag{12}\]  
where \(a_{wz}(t)\) is the instantaneous frequency-weighted acceleration in the time domain, \(t_{0}\) is the time of observation, and \(\tau\) is the integration time for the moving average, and it is recommended to use \(\tau=1\).  
In Table 1, the ISO standard also provides the approximate indications of likely comfort reactions to vibration in public transport, which can be utilized to estimate the pavement roughness thresholds in the following sections.
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 6 Results
### Results from Dual-Frequency Modulation  
In this section, using dual-frequency modulation based on parallel phase demodulation, the bias error and the standard deviation are investigated. The conditions of the experiment were determined by the combination of the two frequencies. In this case, combinations of 31.25 and 34.375 MHz, 21.875 and 34.375 MHz, and 3.125 and 34.375 MHz were used. The unambiguous ranges of each frequency combination are shown in Table 2 which can be calculated using Equations (8) and (11). The distance was measured from 1 to 10 m at intervals of 0.5 m. For each distance, 10,000 measurements were conducted. The remaining factors were fixed; i.e., the sampling frequency at 625 MS/s, the modulation voltage amplitude at 12 V, eight samples for one-point distance measurement, and an integration time of 9.6 \(\upmu\)s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Comparison Item**} & \multicolumn{2}{c}{**1 m-Distance Object**} & \multicolumn{2}{c}{**1.3 m-Distance Object**} & \multicolumn{2}{c}{**1.6 m-Distance Object**} \\ \cline{2-7}  & **Integration** & **Standard** & **Integration** & **Standard** & **Integration** & **Standard** \\  & **Time** & **Deviation** & **Time** & **Deviation** & **Time** & **Deviation** \\ \hline SR-4000 [30] & 3.5 ms & 0.016 m & 8.5 ms & 0.008 m & 16.75 ms & 0.008 m \\ CamCube3.0 [30] & 0.05 ms & 0.008 m & 0.1 ms & 0.010 m & 0.25 ms & 0.009 m \\ This proposed method & 9.6 \(\upmu\)s & 0.0011 m & 9.6 \(\upmu\)s & 0.0015 m & 9.6 \(\upmu\)s & 0.0018 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard deviations of conventional cameras and the proposed method.  
Figure 8: Standard deviation for each modulation voltage amplitude.  
Figure 9a shows that the bias error when using only single low frequency is much larger than that when using dual frequencies. Specifically, the maximum value of bias error when using single low frequency is higher than 0.4 m. On the other hand, the maximum value of bias error for dual-frequency cases is about 0.1 m. Figure 9b shows that the standard deviation when using only single low frequency is much larger than that when using dual frequencies in general. It should be noted that as higher frequencies are combined, the standard deviation becomes smaller. This trend is also shown in earlier work [25]. However, in case of combination of 31.25 and 34.375 MHz, there is an abrupt increase in the standard deviation at around 9.0 m, which was predicted in Equations (27) and (28) [25]. Consequently, as shown in Figure 9b, the best combination of dual frequencies is the intermediate frequency and high frequency; i.e., 21.875 and 34.375 MHz.
# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 3 Results
### Study Characteristics
#### 3.2.1 Data Acquisition and Datasets  
From the selected studies, a total of 23 datasets adhered to the eligibility criteria of 2D images and were considered for further evaluation. It is important to note that due to the challenges posed by speckle decorrelation theory in real tissue, some studies included phantom datasets [14; 17; 23], while the remainder of the studies employed clinical datasets. As presented in Figure 2, the clinical datasets show a variety of different anatomies, paving the way for diverse clinical applications. Across the datasets included, there is a large range in the number of sequences utilized (from 9 to 619, \(\mu=194.08\) and \(\sigma=213.29\)), which is also applicable to the number of frames and subjects. This directly influences the data volume available for model training and testing and the anatomical variety between sequences. Seventeen datasets reported on the trajectory motions in the sweep, with seven describing basic linear motions. Four datasets described more complex sweeps encompassing multiple motion types (like wave, tilt, or shift) [19; 21; 22]. Additionally, variations in speed and sudden movements were occasionally noted. Another important dataset characteristic concerns the length of the sweeps, which was reported for 13 out of the 23 datasets. These datasets provided either a range or an average of sweep lengths, spanning from 20 mm to 323.96 mm (\(\mu=154.60\) and \(\sigma=77.06\)).
# Sub-Band Backdoor Attack in Remote Sensing Imagery
###### Abstract  
Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in the data. Backdoor attacks have been extensively studied in machine learning-based computer vision applications with natural images. However, much less research has been conducted on remote sensing imagery, which typically consists of many more bands in addition to the red, green, and blue bands found in natural images. In this paper, we first extensively studied a popular backdoor attack, BadNets, applied to a remote sensing dataset, where the trigger was planted in all of the bands in the data. Our results showed that SOTA defense mechanisms, including Neural Cleanse, TABOR, Activation Clustering, Fine-Pruning, GangSweep, Strip, DeepInspect, and Pixel Backdoor, had difficulties detecting and mitigating the backdoor attack. We then proposed an explainable AI-guided backdoor attack specifically for remote sensing imagery by placing triggers in the image sub-bands. Our proposed attack model even poses stronger challenges to these SOTA defense mechanisms, and no method was able to defend it. These results send an alarming message about the catastrophic effects the backdoor attacks may have on satellite imagery.  
deep learning; backdoor attack; remote sensing monitoring systems; backdoor defense +
Footnote †: journal: _Algorithms_
## 4 Results and Discussion
### TBM Muck as a Partial or Full Replacement of Concrete Coarse Aggregates  
Three random muck samples were subjected to the tests mentioned in Section 3.3.1. Table 3 presents the average results obtained from each test.  
Table 3 indicates that the raw TBM muck extracted from the Gold Line meets the requirements the coarse aggregates, except for the water absorption, loss by Magnesium sulphate soundness, loss by Los Angeles abrasion and the acid-soluble sulphate. However, the effect of these properties on the overall behavior of concrete mixtures should be investigated by replacing different percentages of concrete aggregates with TBM muck and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Test Method**} & \multicolumn{3}{c}{**Average Test Result**} & \multirow{2}{*}{**QCS 2014 Limits**} \\ \cline{3-4}  & & **4-10 mm** & & **10-20 mm** \\ \hline Particle density & BS/EN 1097-6 & 2.80 & 2.75 & Min. 2.0 \\ \hline Water absorption (\%) & BS/EN 1097-6 & 7.73 & 7.07 & Max. 2.0 \\ \hline Clay lumps and friable particle (\%) & ASTM C 142 & 2.80 & 2.05 & Max. 2.0 \\ \hline Shell content (\%) & BS/EN 933-7 & Nil & Nil & Max. 3.0 \\ \hline Flakiness index (\%) & BS/EN 933-3 & 4.33 & 8.33 & Max. 35 \\ \hline Aggregate drying shrinkage (\%) & BS/EN 1367-4 & 0.03 & 0.03 & Max. 0.075 \\ \hline Loss by magnesium sulphate soundness (\%) & BS/EN 1367-2 & 53.67 & 50.67 & Max. 15 \\ \hline Loss by Los Angeles abrasion (\%) & BS/EN 1097-2 & 40.00 & 41.00 & Max. 30 \\ \hline Acid-soluble chloride (\%) & BS/EN 1744-5 & 0.020 & 0.020 & Max. 0.03 \\ \hline Acid-soluble sulphate (\%) & BS/EN 1744-1 & 1.30 & 1.20 & Max. 0.3 \\ \hline Lightweight particles (\%) & ASTM C123 & \textless{}0.1 & \textless{}0.1 & Max. 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the properties of TBM muck and QCS 2014 (specifications of concrete coarse aggregates).  
studying the effect of such replacement on the fresh and hardened properties of concrete. This will be part of the future work that will commence as part of this research project.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 2 Cross-Media Communication Model
### RF Model  
Cross water-air interface acoustic and electromagnetic integrated communication (AEIC) technology estimates the WSAW parameters by detecting the delay change in the target echo. The initial distance from the calm water surface to the radar is \(d_{0}\). In order to conveniently conduct research, the sea wave interference, \(d_{sea}(t)\), is formed by the superposition of a series of sinusoidal interference. The millimeter-wave radar illuminates the center of the vibration of the water's surface; then, the instantaneous distance, \(d(t)\), from the radar to the water's surface can be expressed as:  
\[d(t)=d_{0}+d_{sea}(t)+\delta(t) \tag{4}\]  
where \(t\) is the slow time.  
In order to achieve high phase resolution while eliminating the interference from other reflectors in the environment, AEIC uses an FMCW-based wideband radar. The transmitted signal can be described as:  
\[S_{t}(t)=A_{0}e^{i2\pi(f_{0}t+\frac{\mu^{2}}{2})+\Phi_{0}},t\in\left[-\frac{T }{2},\frac{T}{2}\right] \tag{5}\]  
where \(A_{0}\) is the amplitude of the transmitted signal; \(f_{0}\) is the center frequency of the transmitted signal; \(\mu\) is the chirp rate; \(\phi_{0}\) is the initial phase; and \(T\) is the time width of the sweep frequency.  
Considering the influence of the WSAW and sea wave interference, the time-delay function is \(\tau(t)=2d(t)/c\). The received signal can be expressed as:  
\[S_{r}(t)=K_{r}A_{0}e^{i2\pi[f_{0}(t-\tau(t))+\frac{\mu}{2}(t-\tau(t))^{2}]+ \Phi_{0}},t\in\left[-\frac{T}{2},\frac{T}{2}\right] \tag{6}\]  
where \(K_{r}\) is a constant related to the water surface reflection coefficient and spatial propagation attenuation.  
The received signal and the transmitted signal are mixed and filtered to obtain the beat signal. Considering the actual situation, the vibration speed of the WSAW is much slower than the speed of light. Then:  
\[S_{b}(t)=\frac{1}{2}K_{r}A_{0}{}^{2}\mathrm{e}^{2\pi(\frac{2\pi d(t)}{\varepsilon} )t+\frac{4\pi q_{0}}{\lambda}+\frac{4\pi(\delta(t)+d_{\mathrm{sat}}(t))}{ \lambda}-\frac{4\pi q_{0}}{\varepsilon^{2}}} \tag{7}\]  
The time evolution of the phase change of the beat signal, \(\phi(t)\), can be described as:  
\[\phi(t)=4\pi\bigg{(}\frac{d_{0}+\delta(t)+d_{\mathrm{sat}}(t)}{\lambda}\bigg{)} \tag{8}\]  
where \(\lambda\) is the wavelength of the radar's transmitted signal. The above phase change includes the WSAW and the sea wave interference. Based on the above, a novel sea wave interference suppression method is proposed, which eliminates the sea wave component \(d_{sea}(t)\) and retains the WSAW component \(\delta(t)\).