esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fundamentals of the AES-128 Encryption/Decryption Algorithm
Thus, each round of the decryption process consists of the following operations:  
* Inverse Sub Bytes
* Inverse Shift Rows
* Inverse Mix Columns
* Add Round Key  
A further difference between the encryption and decryption processes is the order of functions performed within a single round; in the decryption process, the first step is the Inverse Shift Rows, followed by Inverse Sub Bytes, Add Round Key and finally Inverse Mix Columns. In particular, the Inverse Shift Rows cyclically shift to the right by the same offset of the Shift Rows step but in the opposite direction. To invert the Mix Column operation, the Inverse Mix Columns step employs the corresponding inverse matrix. The 4-byte columns of the state array are multiplied for the inverse \(4\times 4\) matrix featured by constant entries for producing the output bytes; all operations involved in the matrix multiplication are performed in GF\(\left(2^{8}\right)\) or equivalently by multiplying each column, modulo \(z^{4}+1\), with a fixed polynomial \(b(z)=0 Bz^{3}+0 Dz^{2}+09z+0E\), where \(b(z)=c(z)^{-1}mod\left(z^{4}+1\right)and\)\(c(z)\) is the polynomial used in the _Mix Columns_ step of the encryption. The Inverse Substitute Bytes function is carried out similarly to the Substitute Bytes but using a different Sbox (Figure 3), obtained applying the inverse affine transformation to the Sbox followed by the multiplicative inverse in GF\(\left(2^{8}\right)\). Since the encryption and decryption operations are not the same, a significant disadvantage from the implementation point of view is obtained; however, there is an equivalent version of the decryption algorithm, which involves the inverse functions in the same order as the encryption algorithm. In particular, since the Inverse Shift Rows step changes the sequence of the bytes of the state array, leaving the content unchanged, whereas the Inverse SubBytes step changes the content but not the sequence of the bytes, their order has not relevance anymore, thus it can be exchanged. Moreover, the Add Round Key and Inverse Mix Columns transformations, considering the key as a sequence of words, both operate on the state array, column by column; therefore, the Inverse Mix Columns operation can be applied to the phase key \(W[i]\), before adding it to the current state array, so obtaining the data packet decryption with the same sequence of operations of the encryption algorithm. Figure 3: Sbox involved in Inverse substitute bytes transformation.
## 5 Experiments and Discussions
### Experiments on Data Preprocessing Capability  
During the data preprocessing, the number of worker nodes in the Spark cluster is altered to measure the changes in the pre-processing time (excluding the time required to import into the HBase database). This experiment uses a total of 54 GB NYC Taxi data span about 30 months (from January 2014 to June 2016). All the observed preprocessing times are shown in Figure 12. From the figure it can be seen that the increase in the number of Spark worker nodes leads to obvious reduction in pre-processing time, from 360 min to 160 min, while the efficiency is improved by about 1.3 times. Therefore, by using more computing nodes, the Spark cluster has more worker nodes available to share the pre-processing tasks, thereby significantly improving the pre-processing efficiency. However, as the Spark node number is over eight, the speedup efficiency increases very slowly. This phenomenon coincides with the well-known Amdahl's law and shows there exist some pre-processing operations which cannot be parallelized between nodes, i.e., I/O communication.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 5 Results and Discussion
### Rock-Fall Risk Assessment Result  
The method outlined in Section 4.4 (Equation (12)) of this study was applied to calculate the rock-fall risk probability. The Python environment was used as a simulation tool. The settings and configurations used for the simulation are listed in Table 5.  
In this study, we used the normal distribution of vehicle traffic for twenty-four hours a day and assumed the value of the vulnerability of the vehicle to be equal to one, considering that any rock-fall event will cause damage to cars. The result in Figure 8 shows the rock-fall risk probability per hour during the day. The findings indicate that the values for the highest and lowest rock-fall risk probability were, respectively, \(1.51\times 10^{-3}\) and \(7.98\times 10^{-6}\). Moreover, the relationship between the local time and the rock-fall risk probability (Figure 8) shows that the high-risk probabilities are concentrated in the local time period between 12 p.m. and 6 p.m.  
In order to classify the risk values into three levels, the outcomes were compared to the triangle of safety-critical regulation and management thresholds, which is shown in  
Figure 8: The rock-fall risk probability.  
Figure 7: The ROC curve for the validation dataset).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Average daily number of vehicles on the road (\(N_{V}\)) & 8325 vehicles \\ Average vehicle lengths & 5.4 m \\ Brake engagement time & 2 s \\ Driver reaction time & 0.4 to 2 s \\ Average acceleration & 10 m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation setups.  
Figure 9 (ALARP) [39]. The result shows that the risk probability values were spread among all ALARP levels. Overall, 29.1% of values are unacceptable, and the remaining risk values are divided between acceptable and tolerable levels by 16.6% and 54.2%, respectively.
## 4 Results and Discussion
### Radiation Patterns Obtained with Rings  
The simulated steering pattern as resulted with the defined seven circular rings for 10 GHz, 11 GHz, 12 GHz, 13 GHz, 14 GHz and 15 GHz are shown in Figure 5a-f for various angles of rotation of the WR-75 waveguide. This figure shows the drop in value of the peak main beam from 11.9 dB, 11.2 dB, 10.8 dB, 10.6 dB and 10.1 dB for 0\({}^{\circ}\), 30\({
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 2 Medical Imaging Methods
### Ultrasonography and Contrast-Enhanced Ultrasonography  
Ultrasonography is an important tool widely used in the study of the anatomy and morphology of the liver. Ultrasonography can be used to obtain sonograms of every section of the organ, which allows the detection of morphological and histological changes of organs or lesions to facilitate diagnosis. Furthermore, by injecting contrast agent (e.g., microbubbles) into patients, the contrast-enhanced ultrasonography (CEUS), in which the contrast of structures or fluid within the patient can be further enhanced, is often applied for the diagnosis of liver lesions. But, due to the operator dependence and the difficulty in detecting the lesion at the top of the liver, and small liver cancer is sometimes missed, so it needs to be integrated with other imaging methods.  
All these imaging technologies provide quantitative information from different aspects of tumor lesions. The geometrical parameters of tumors, such as lesion size, capsule integrity, lesion blood flow and other morphological features, are dependent on Ultrasonography, CT or MRI imaging. On the other hand, \({}^{18}\)F-FDG PET is the main medical imaging technology used to study the characteristics of tumor metabolism. However, limited information is available from a single method. For example, ultrasound and contrast-enhanced ultrasound have high diagnostic efficacy in detecting intrahepatic lesions and they allow dynamic visualization of lesions from multiple sections, with particular utility in the detection of small lesions less than 1 cm, but they could not generate data on the spatial structure of lesions and their spatial relationship with the surrounding hepatic artery and portal vein system. Whereas, CT or MRI provide information on the relationship between the spatial location of lesions and the surrounding structure, but do not allow dynamic observation of liver lesions from multiple angles. Therefore, the application of integrated information from a fusion of different medical imaging technologies in clinical diagnosis not only achieves the cross-validation of data from different imaging methods, but also improves the detection of tumor lesions, facilitating early diagnosis and the development of effective treatment plans.
## 7 Discussion and Results of FE Simulation
### Comparing the Types of Systems  
Comparing the hysteresis curve of the FE models, Figure 14 confirms that connecting the cross_flexural plated to the main plate led to the issues described in Section 5.2. As shown in the figure, upon adding the cross plate to the damper, the ratio of normalized shear strength to plastic shear strength of the main plate exceeded 1. This implies that the cross plate not only changes the main plate's behavior from flexural to shear but also contributes resisting the applied loading. Therefore, the assumption in Section 2.2 is confirmed. The results are listed in Table 2.  
Adding the cross plate to M_P models increased the strength and stiffness for all types of main plate buckling. Referring to the Table 4, for a plate with plastic buckling (\(\lambda\leq 1.12\sqrt{K}\)), the cross_flexural plate increased the strength and stiffness by 2 and 1.06, respectively. This reveals a negligible effect on the stiffness for \(\lambda\leq 1.12\sqrt{K}\). For a plate with elastic buckling (\(\lambda>1.4\sqrt{K}\)), the strength and stiffness due to adding the cross plate increased by 1.2 and 2.64, respectively. Hence, by increasing \(\lambda\), the effect of the cross plate on strength was reduced in comparison to stiffness. Connecting the plate to the main plate led to considerable increases in the strength and stiffness.  
Figure 11: The yielding status in the plates of the dampers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{\(\mathbf{V_{u}}\) **(kN)**} & \multirow{2}{*}{\(\mathbf{K_{d}}\) **(kN/mm)**} & \multicolumn{2}{c}{_Results of damper i_} \\ \cline{3-5}  & & & _Danger with flexural plates_ \\ \cline{3-5}  & & & \(V_{u}\) & \(K_{d}\) \\ \hline M\_P & 251.603 & 441.33 & & \\ M\_I & 268.273 & 583.47 & & \\ M\_E & 223 & 528.57 & & \\ \hline M\_P\_St & 503.558 & 469.56 & 2.00 & 1.06 \\ M\_I\_St & 512.196 & 1081.748 & 1.91 & 1.85 \\ M\_E\_St & 267.6 & 1394.80 & 1.20 & 2.64 \\ \hline S\_P\_St & 604.681 & 1985.16 & 2.40 & 4.50 \\ S\_I\_St & 660.454 & 918.60 & 2.46 & 1.57 \\ S\_E\_St & 630.256 & 938.71 & 2.83 & 1.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ultimate strength and elastic stiffness of the damper.  
Figure 12: The yielding of all dampers.  
Figure 14: Hysteresis curves of FE models.  
Figure 13: Hysteresis curves of FE models.
## 4 Calculation Example of Slope Stability under Freeze-Thaw Conditions
### Analysis of Calculation Results
#### 4.4.4 Effect of Grading Series on Slope Stability under Freeze-Thaw Conditions  
In cold regions, when the spring temperature rises, the freezing and thawing active layer on the slope melts, the slope is loose, then the cohesion, bearing capacity, and shear resistance are reduced. During this time, shallow freezing and thawing disasters are more likely to occur. In order to improve the stability of the slope, the slope platform is usually set up at a certain height of the slope to form a secondary or multi-grade slope.  
It can be seen in Figure 6 that when the slope grade increases, the slope safety factor \(Fr\) also increases. The safety factor \(Fr\) of the first-grade slope is 1.26, the safety factor \(Fr\) of the second-grade slope is 1.63, and the safety factor \(Fr\) of the third-grade slope is 1.71. The reason for the change in the slope safety factor is that when the slope grade increases, the number of platforms also increases. The platforms between slopes can not only share the load, but also prevent the slope from being washed and damaged by rain. When a shallow landslide occurs in the slope after freeze-thaw cycles, the resistance provided by the platform between the slopes slows down the sliding of the slope. In other words, the more the slope grades, the greater the resistance to shallow sliding, the better the stability of the slope and the greater the safety factor \(Fr\). Therefore, designing the slope grades reasonably can effectively improve the safety factor \(Fr\) of the slope. The setting of graded slopes and platforms is an effective engineering measure to prevent freezing and thawing disasters in cold regions.
## 2 Materials and Methods
### Wing Model with Multiple Flaps and Optical Fiber Sensors
The lead wires of the servomotors, the pressure sensor tubes, and the optical fibers were routed to the basement of the wind tunnel and connected to the controller and the interrogators. Figure 1: Wing model. **Left**: Photograph in wind tunnel taken from upstream and lower side of wing. **Right**: Schematic of flaps and sensors viewed from upper side of wing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Segment No.**} & **Position Z of Flap** & **Span-Wise Flap** & **Chord Length** & **Flap Width** \\  & **Center (m)** & **Length (m)** & **(mm)** & **(mm)** \\ \hline
1 & 0.277 & 0.472 & 297 & 54 \\
2 & 0.749 & 0.472 & 283 & 51 \\
3 & 1.221 & 0.472 & 271 & 48 \\
4 & 1.686 & 0.458 & 258 & 45 \\
5 & 2.144 & 0.458 & 243 & 43 \\
6 & 2.602 & 0.458 & 226 & 40 \\
7 & 3.025 & 0.388 & 208 & 39 \\
8 & 3.413 & 0.388 & 186 & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Wing geometry. Figure 2: Cross-section of wing model with locations of pressure ports and spars. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Port** & **Position** & **Position** & **Normal Direction** \\  & \(X/C\) & \(Y/C\) & **(deg)** \\ \hline (leading edge) & 0 & 0 & 180 \\ u1 & 0.02 & 0.024 & 125 \\ u2 & 0.03 & 0.037 & 117 \\ u3 & 0.06 & 0.047 & 110 \\ u4 & 0.08 & 0.057 & 107 \\ u5 & 0.13 & 0.067 & 102 \\ u6 & 0.19 & 0.077 & 98 \\ u7 & 0.24 & 0.084 & 96 \\ u8 & 0.29 & 0.088 & 94 \\ u9 & 0.42 & 0.088 & 90 \\ u10 & 0.52 & 0.084 & 87 \\ u11 & 0.61 & 0.077 & 84 \\ u12 & 0.70 & 0.064 & 82 \\ u13 & 0.79 & 0.047 & 80 \\ u14 & 0.87 & 0.034 & 79 \\ u15 & 0.93 & 0.020 & 77 \\ b1 & 0.07 & \(-\)0.030 & 260 \\ b2 & 0.17 & \(-\)0.040 & 267 \\ b3 & 0.29 & \(-\)0.044 & 270 \\ b4 & 0.42 & \(-\)0.040 & 273 \\ b5 & 0.56 & \(-\)0.034 & 275 \\ b6 & 0.70 & \(-\)0.020 & 276 \\ b7 & 0.79 & \(-\)0.013 & 276 \\ b8 & 0.87 & \(-\)0.0067 & 274 \\ (trailing edge) & 1 & 0 & 0 \\ (main spar) & 0.30–0.40 & \(-\)0.043–0.091 & - \\ (aft spar) & 0.74–0.76 & \(-\)0.017–0.057 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pressure port geometry. \(C\) represents chord length. Figure 3: Pressure coefficient distribution.
# Digestate Management and Processing Practices: A Review
## 5 Digestate Processing
### Solid-Liquid Separation  
Separation of the DG into a solid and a liquid fraction is a simple and low cost-effective technology [93], which is usually carried out before any further post-treatment of the DG [94]. Solid-liquid separation reduces the volume of the DG and is usually performed by screw press [90; 93; 95] or by thickening [91; 95]. Thickening can be a static (gravity settling) or a dynamic process (filtration, air flotation, or centrifugation) that concentrates DG to as low as up to 5 to 10% suspended solids [91]. Due to the amounts of  
Figure 3: Most commonly applied DG valorization processes.  
undigested OM and extracellular polymeric substance (EPS) from microbial metabolites, DG is hardly dewatered effectively. Therefore, polyelectrolytes can be added to increase the concentration of suspended solids [95]. The dense slurry can be further dewatered to obtain a cake with a solids content of up to 35%. Dewatering can be performed by mechanical methods such as filtration and centrifugation, or by electrochemical and physicochemical methods such as electrocoagulation and chemical coagulation [91].  
Solid fraction, often referred to as pressed cake, which concentrates dry matter (between 20 and 30%) and nutrients, mainly organic N and P [96], is generally used as an organic amendment [93]. Liquid fraction, with a dry matter content of around 2 to 6% [96,97] concentrates mineral N above all in the form of ammonia and therefore can be used as a fertilizer in substitution for mineral fertilizers [93]. Due to its high fertilizer and humus value, the solid fraction is regarded as cost-effective to transport, while the liquid fraction has limited market potential due to its high water content and low organic content [96].
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 5 3 GPP V2X-NDN Forwarding Strategy
### Interest Processing at the Receiving UE  
When the UE receives an Interest on a 3 GPP network interface, the following processing is performed.  
Figure 3: Proposed signaling workflow for name-aware resource allocation.  
The UE first looks in the CS. If a matching is found, it will answer with a Data packet over the same interface it has received the request. Specifically:  
* If the outgoing interface is PC5 Mode 4, the UE directly broadcasts the Data packet. To avoid collisions with other provider UEs, smart deferral schemes can be implemented similarly to those deployed for NDN-IEEE 802.11 networks, as described in Section 3.2.
* If the outgoing interface is PC5 Mode 3 or LTE-Uu, the UE sends a grant request for Data transmission to the eNodeB. In both cases, the request piggybacks the name of the Data and, therefore, the eNodeB decides if granting resources according to the name-aware resource allocation scheme previously described.
* If the CS matching fails, then the UE looks in the PIT. If a matching is found, it updates the entry by recording the incoming interface and discards the Interest.
* If the PIT matching also fails, the UE has to decide whether to further relay the Interest or not and, therefore, it looks in the FIB. If a name matching is found, the Interest is forwarded accordingly, otherwise it is discarded. Depending on the outgoing interface, the UE may broadcast the Interest over the PC5 while implementing smart deferral schemes to avoid collisions in Mode 4, or it may send a named grant request to the eNodeB for the Interest transmission over PC5 Mode 3 or LTE-Uu. Again, the eNodeB may grant or refuse the transmission according to the name-aware resource allocation scheme.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 4 Proposed Hybrid Deep Learning-Based IDS (DL-IDS)
### Hybridization of DAE and DANN Models (DAE-DANN)
The average out-of-boundaries \(z_{i}loss_{diff}\) of given samples within a time window, \(w\), is calculated, the resulting value is stored as the new attack threshold (\(attack_{2}\)_threshold_), and \(y\) is updated by adding a new label for the recently detected attack. The DANN will be retrained again based on the updated dataset. DANN was used to classify known behaviors, based on which the model was trained much faster and produced a higher classification accuracy. ```
1 Input: (\(\mathbf{X}\), \(\mathbf{y}\)): Training set; \(\mathbf{Z}\): test set Output: \(l\): Test sample labels
2 Function DAE_Train (\(\mathbf{X}\), \(\mathbf{y}\)): Train DAE with on (\(\mathbf{X}\), \(\mathbf{y}\)), where \(y=0,\ 0:\) normal traffic label
4 Compute the average \(normalLoss_{threshold}\)
5 Return \(normalLoss_{threshold}\)
6 Function DANN_Train (\(\mathbf{X}\), \(\mathbf{y}\)):
7 Train DANN with on (\(\mathbf{X}\), \(\mathbf{y}\)) to obtain optimal values of weight and bias and reduce loss
8 Return optimal weights & biases
9
10 Function DAE_Classifier (Z):
11\(z_{i_{predict}}=DAE.predict(z_{i})//\)Feed the DAE with new traffic instances
12\(loss_{z_{i}}=Loss_{Diff}(z_{i_{predict}},z_{i})//\)Compute \(loss_{diff}\) by Equation (4). 13 If \(loss_{z_{i}}\in\{Normalloss_{Threshold}\), \(attack_{l}threshold,...,attack_{l}threshold\}\) boundaris
14 Use Supervised DANN classifier
15 Else
16 Add \(loss_{z_{i}}\) to a list V
17 Within time window w, compute the \(Average\_loss_{z}\) in V
18 Update the number of classes (labels): \(y=y+1\)
19\(attack_{l}threshold=Average\_loss_{z}//\)new class threshold
20 Update (\(\mathbf{X}\), \(\mathbf{y}\)) with new samples and label
21 Retrain DANN with updated (\(\mathbf{X}\), \(\mathbf{y}\))
22 Return \(attack_{l}\)_threshold_
23
24 Function DANN_Classifier (Z):
25 Predict the labels \(l\) using the DANN classifier
26 Return \(l\)
```  
**Algorithm 4** Training and Classification phases of DAE-DANNThe following is a description of the architecture and configuration of the DAE and DANN used in the IoT-IDS system.
## 3 LRM/LRMM/LRRM Deviation
### LRRM Derivation
From the first two equations of (51), \(\Gamma_{\rm R1}\) and \(x\) are solved as function of \(\Gamma_{\rm R2}\), and the results are as follows:  
\[x=\frac{w_{12}^{\prime\rm L}}{t_{21}^{\rm L}+t_{22}^{\rm L}\Gamma_{\rm R2}-w_{ 22}^{\prime\rm L}} \tag{52}\]  
\[\Gamma_{\rm R1}=\frac{(w_{11}^{\prime\rm L}-t_{21}^{\rm L})(t_{21}^{\rm L}+t_{ 22}^{\rm L}\Gamma_{\rm R2}-w_{22}^{\prime\rm L})+w_{21}^{\prime\rm L}w_{12}^{ \prime\rm L}}{t_{22}^{\rm L}+t_{21}^{\rm L}+t_{22}^{\rm L}\Gamma_{\rm R2}-w_{ 22}^{\prime\rm L})}. \tag{53}\]  
Then, by substituting (52) and (53) into (47), the following first-order rational expression about \(\Gamma_{\rm R2}\) is obtained:  
\[\Gamma_{\rm M}=\frac{f_{1}\Gamma_{\rm R2}+f_{0}}{e_{1}\Gamma_{\rm R2}+e_{0}}. \tag{54}\]The coefficients in (54) are all known by calibration standard definitions and the measurement quantities as shown below. \[\left\{ \begin{array}{l}e_{1}=(t_{22}^{\text{L}})^{2}w_{1}^{\text{M}}\\ e_{0}=t_{22}^{\text{L}}(t_{21}^{\text{L}}-w_{22}^{\text{L}})w_{1}^{\text{M}}+t_ {22}^{\text{L}}w_{2}^{\text{M}}w_{12}^{\text{{}^{\prime}}\text{L}}\\ f_{1}=t_{22}^{\text{L}}(w_{11}^{\text{L}}-t_{21}^{\text{L}})w_{1}^{\text{M}}+t_ {22}^{\text{L}}w_{2}^{\text{M}}w_{12}^{\text{{}^{\prime}}\text{L}}\\ f_{0}=(w_{11}^{\text{L}}-t_{21}^{\text{L}})(t_{21}^{\text{L}}-w_{22}^{\text{{}^ {\prime}}\text{L}})w_{1}^{\text{M}}+w_{1}^{\text{M}}w_{21}^{\text{{}^{\prime} }\text{L}}w_{12}^{\text{{}^{\prime}}\text{L}}\end{array} \right. \tag{55}\]  
The broadband reflection coefficient of the match standard, \(\Gamma_{\text{M}}\) is determined from the relationship between \(\Gamma_{\text{R2}}\) and \(\Gamma_{\text{M}}\) by (54). Similar to the previous work [26], the impedance of the match standard at low frequencies is approximated by its DC resistance, which then allows the open capacitance to be determined at low frequencies. Once the open capacitance is known, the broadband reflection coefficient of the match standard \(\Gamma_{\text{M}}\) can be calculated. A nonlinear fitting is performed for fitting \(\Gamma_{\text{M}}\) with a physical load model [26]. The fitting returns the parasitic load inductance and capacitance, which then allows the fitted \(\Gamma_{\text{M.fit}}\) to be calculated. \(\Gamma_{\text{M.fit}}\) is used as the load definition. The rest of the unknown variables \(\Gamma_{\text{R2}}\), \(\Gamma_{\text{R1}}\), \(x\), \(y\), and \(z\) can be subsequently calculated. At this point, the LRRM calibration is done.