esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Prediction of Girder Design Strength for a Regression Algorithm
### Introduction of Regression Algorithms  
Various regression algorithms can be used to establish a relationship between the design strength of a structure and a certain geometric parameter. The present study compared the performances of models based on five popular algorithms: multiple linear regression (MLR), decision tree (DT), boosting tree (BT), support vector machine (SVM), and Gaussian process regression (GPR). A description of each algorithm is as follows.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Input Data**} & \(\mathbf{L_{\textit{girder}}}\) & \(\mathbf{W_{\textit{bridge}}}\) & \(\mathbf{t_{\textit{slab}}}\) & \(\mathbf{N_{\textit{girder}}}\) & \(\mathbf{S_{\textit{girder}}}\) & \(\mathbf{H_{\textit{girder}}}\) & \(\mathbf{\phi M_{n}}\) \\ \cline{2-7}  & **(mm)** & **(mm)** & **(mm)** & **-** & **(mm)** & **(mm)** & **(kN m)** \\ \hline Max. & 50 & 37,000 & 270 & 16 & 3090 & 2600 & 41,261 \\ Ave. & 40 & 16,199 & 241 & 4 & 2690 & 2013 & 26,380 \\ Min. & 27 & 10,000 & 240 & 4 & 2000 & 1100 & 11,880 \\ St. Dev. & 6 & 6111 & 3 & 2 & 200 & 450 & 7800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statical properties of bridge design document data.  
Figure 3: Distribution of data information. (**a**) Length of girder. (**b**) Width of bridge. (**c**) Thickness of slab. (**d**) Number of girders. (**e**) Space of girder. (**f**) Right of girder. (**g**) Design strength.  
process used to create new classification rules by focusing on misclassified data. Compared to DT, it has the advantage of low error but has the disadvantages of a slow speed and overfitting.
# In-Band Full-Duplex Relaying for SWIPT-Enabled Cognitive Radio Networks
## 5 Conclusions  
In this paper, we considered a sum rate maximization problem for the primary and secondary systems assisted by an FDR with energy harvesting. A design problem based on the TSR protocol for joint optimization of energy harvesting and information transfer was established as a non-convex problem. By using the ICA framework, we derived the optimal solution for the problem. Remarkably, the proposed iterative algorithm was shown to provide a low computational complexity per iteration. Numerical results indicated that the proposed scheme and algorithm provided a spectral efficiency 0.5 to 2 bps/Hz higher than other schemes in the medium to high transmit power at PT by exploiting the benefits of multiple antennas and time fraction optimization. Moreover, we demonstrated a trade-off between the SR rate and the PR rate, as well as the effect of the number transmit and receive antennas at FDR on the sum rate.  
**Author Contributions:** Conceptualization, H.V.N. and V.-D.N.; methodology, H.V.N., V.-D.N., and O.-S.S.; simulation, H.V.N.; validation, H.V.N., V.-D.N., and O.-S.S.; writing, original draft preparation, H.V.N.; writing, review and editing, H.V.N., V.-D.N., and O.-S.S.; supervision, O.-S.S.; project administration, O.-S.S.; funding acquisition, O.-S.S. All authors read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MOE and MSIT) (No. 2017R1D1A1B03030436, No. 2017R1A5A1015596 and No. 2019R1A2C1084834).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 2 Impedance Characteristics Analysis of D-PMSG
### Simplified Model of D-PMSG  
The structure diagram of D-PMSGs integrated in a weak AC grid is shown in Figure 1. The wind farm in Xinjiang consists of hundreds of 1.5 MW D-PMSGs. Every unit is radially connected to 35 kV bus through a 0.69/35 kV transformer. Then, the electric power is collected at the 35-kV substation and next transmitted along higher voltage level transmission lines, to the 750-kV power grid 200 km away. The thermal synchronous power units are also connected to the common bus. Extra power is transmitted along HVDC to Eastern China. Since the transmission line has a long-distance and multi-stage transformers boost the voltage, the grid side equivalent impedance is as large as 0.4 pu [21]. The SCR is 2.5 (\(<\)3) and the grid wind farm integrate into belongs to weak AC grid [27].  
Figure 1: Structure diagram of D-PMSGs integrated in a weak AC grid.  
As shown in Figure 2, grid-connected D-PMSG unit consists of wind turbine, permanent magnet synchronous generator, machine side converter (MSC), and grid side converter (GSC). The MSC controls the rotor speed to track the maximum wind power. The GSC is controlled to maintain the DC-link voltage and the output power factor.  
It is well understood that the small signal disturbance from the GSC cannot transmit to the MSC since the DC capacitor decouples the interactions between two converters. Therefore, the generator and the MSC is usually simplified as controlled current source when applying the small signal stability analysis. On the other hand, the outer loops of VSC control, DC voltage control, and reactive power control are proved not to take part in the dynamics under frequency band of SSO, 10-100 Hz approximately [26]. Consequently, the impedance model constructed in this paper mainly concerns the influence of GSC control parameters, operating condition. The structure diagram of simplified grid-connected D-PMSG system is shown in Figure 3. For the reasons above, \(V_{dc}\) is assumed constant in this study and the active and reactive current references (\(I_{drf}\) and \(I_{qref}\)) are assumed constant.  
Phase voltages are denoted as \(v_{a}\), \(v_{b}\), and \(v_{c}\), while phase currents as \(i_{a}\), \(i_{b}\), and \(i_{c}\). In \(d\)-\(q\) axis, recall the current as \(i_{d}\), \(i_{q}\). The \(ABC/dq\) transformation reference angle \(\theta_{pll}\) is the output of PLL, whose function is obtaining the phase angle of AC voltage. The block diagram of PLL is depicted in Figure 4, where \(H_{PLL}\)(s) is the PLL regulator which uses a PI controller and an integrator. Current errors are adjusted by the current controller and added with feedforward compensation. The results \(v_{drf}\), \(v_{qref}\) are the dq-domain voltage references, which are transformed to \(v_{drf}\), \(v_{brf}\), and \(v_{crf}\) for PWM.  
Figure 3: Circuit and control of grid-connected D-PMSG.  
Figure 2: Schematic diagram of D-PMSG.
## 3 Three-State Hidden-Markov Model
### BER Thresholds  
In order to analyze the link quality, we need to define when a link is assumed to be in \"good\", \"medium\", and \"bad\" state. For this reason, we set the following thresholds to the observed BER:  
* Good state: \(BER<0.012\);
* Medium state: \(0.012<BER<0.025\);
* Bad state: \(BER>0.025\).  
With these thresholds, considering a Hamming (7,4) Forward Error Correction (FEC) and a packet size of 16 bytes without FEC (i.e., 28 bytes with FEC), the resulting Packet Error Rate (PER) can be computed analytically as follows. If we define the probability of having no more than one error in 7 bits as  
\[\mathbb{P}_{succ}=(1-BER)^{7}+7\cdot BER(1-BER)^{6}, \tag{1}\]  
we can obtain  
\[PER=1-(\mathbb{P}_{succ})^{224/7}. \tag{2}\]  
To check that these results are correct we verified them via simulation. Given a topology, a link, and its empirical BER observed at a fixed time \(t_{x}\), a sequence of 224 uniform random values in \([0,1]\) are extracted. Each of the values has been compared with the respective BER empirical value to generate a logical array with \"0\" in the cells where the number generated by the RNG was greater than the BER value, and \"1\" in the other positions.  
This array can be interpreted as our 224 bit packet, where the bits set to \"1\" are wrong and the bits set to \"0\" are correct. Therefore, since we have adopted Hamming (7,4), the packet is scanned with a 7-bits step: since Hamming (7,4) cannot correct more than one error every 7 bits, whenever the sum of the bits in a block is greater than 1 we mark the block as compromised and the whole packet is considered corrupted. The process is iterated for \(N=1000\) times and the PER value is given by the number of corrupted packets divided by \(N\).  
We can observe in Figure 3 the PER-BER relationship obtained analytically (red line) and via simulation (blue crosses) for the three links presented in Figure 2.  
With the BER thresholds presented above, the corresponding PER thresholds follow:  
* Good state: \(PER\leq 0.09\);
* Medium state: \(0.09<PER\leq 0.32\);
* Bad state: \(PER>0.32\).  
We can finally observe that the stable link from node 4 to node 2 observed in topology 2 is 95% of the time in Good or Medium states, the average-performance link from node 3 to node 2 observed in topology 2 is only 80% of the time in Good or Medium states, and the challenging link from node 1 to node 3 observed in topology 1 is in Bad state 45% of the time.  
With these fits, we can compute the generic probability that a link is in one of the three-states. Nevertheless, this is not enough to model the variability of the channels.
# The Vicious Cycle of Obesity and Low Back Pain: A Comprehensive Review
## 4 Spinal Changes Related to Obesity  
Epidemiological studies have consistently shown a strong correlation between elevated BMI and the prevalence of LBP (Figure 2). [PERSON] et al. found higher LBP and IDD risk in overweight and obese individuals, but no significant link with spondylosis or cervical disorders [63]. Additional factors, such as waist circumference, body fat percentage, and fat-free mass (i.e., all non-fat components of the body), were causally linked to increased risks of IDD and sciatica. Adjusting for BMI, whole-body fat-free mass maintained a significant association with the risk of IDD [64].  
A systematic review of twin studies by [PERSON] et al. [65] examined the relationship between obesity, LBP, and lumbar IDD, highlighting that while obesity correlates with these conditions, genetic and early environmental factors also play a significant role. Similarly, a prospective study by [PERSON] et al. [66] assessed the relationship between BMI and spinal pathologies, including IDD, disc herniation, and spinal stenosis. Their findings showed BMI modestly predicted IDD but was more strongly linked to disc herniation and spinal stenosis, especially in the upper lumbar spine. Obesity-related LBP involves mechanical stress, systemic inflammation, and metabolic disturbances, contributing to degeneration of the IVD, facet joints, paraspinal muscles (PSMs), and epidural fat, which are further discussed below (Table 1).  
Figure 2: Detrimental effects of obesity on spine structures. Abbreviations: AF (annulus fibrous); CEP (cartilaginous endplate); CNS (central nervous system); ECM (extracellular matrix). NP (nucleus pulposus). PSM (parasipinal muscle).
# A Multi-Granularity Word Fusion Method for Chinese NER
## 4 Experiments
### Ablation Study  
We devise ablation experiments to evaluate the effectiveness of the MW-NER model's two modules: N-grams aggregator and word encoder. The following are the settings for ablation experiments:  
* MW-NER. MW-NER is the proposed method, as depicted in Figure 1.
* MW-NER without N-grams aggregator (w/o NA). The N-grams aggregator of MW-NER is removed. There is only character and word information in the model, but no multi-granularity N-grams information.
* MW-NER without Word encoder (w/o WE). The word encoder is further removed. In this case, the model has degenerated into a character model.  
The N-grams aggregator module requires some hyper-parameters to be adjusted, i.e., utilizing different combinations of N-grams, among the experiments. The Resume dataset is still chosen as the experimental dataset. The other settings remain the same as in the previous experiments.  
In Figure 5, the numbers in parentheses indicate different combinations of N-gram in the N-grams aggregator module. For example, \"MW-NER(2,3)\" indicates that MW-NER uses the 2-gram and 3-gram in the N-grams aggregator module. The experimental results could be divided into three groups, and the first group has multiple bars (filled with green) in the figure due to different hyper-parameters. By removing the N-grams aggregator, the model's best result falls from 95.87% to 95.41%, which demonstrates that the N-grams aggregator could improve the model's comprehension of phrases and sentencesby enriching semantics. When the word encoder is removed from the model, performance suffers even more, illustrating the importance of incorporating word embeddings into the model.  
We further analyze the parameters used by the N-grams aggregator. When comparing the models using different N-grams information, as shown in the first four bars in Figure 5, we discover that as the number of N-grams increases, the model's performance does not improve. On the contrary, the model based on 2,3,4-grams outperforms the model based on 2,3,4-grams. This could be explained by the fact that the positive gain introduced by 5-grams is less than the negative gain introduced by it. Some official data supports this explanation. According to statistics, there are around 120,000 words in Chinese dictionaries, with over 80,000 two-character words, 20,000 three-character words, 20,000 four-character words, and only about 2500 five-character words. When we employ 5-grams, we run the risk of introducing a large number of errors, lowering the model's performance.
# Wireless Remote Control for Underwater Vehicles
## 2 Underwater Communication Technologies  
Nowadays, the technologies used for underwater wireless communications are acoustics, RF, Magneto-Inductive (MI), and optical. In this section, we analyze these technologies by presentingthe pro and cons of each system, some details on their expected performance, and the lessons learnt from the actual performance we experienced during sea trials.
## 2 Design and Analysis of the Proposed Optical System
### Design of the Proposed Optical System
To satisfy a concentration ratio of 20, where solar flux entering an area of 100 mm \(\times\) 100 mm is concentrated onto a Si PV cell measuring 5 mm \(\times\) 100 mm, the light flux guided to the edge of the lightguide is directed to the cylindrical compound parabolic concentrator (CCPC) end face by a CCPC structure. Figure 3 illustrates the cross-sectional shape of the CCPC and the parameters defining its shape. The relationship between the maximum acceptance angle and the parameters of the CPC shape is defined by Equation (1), and in this study, considering a maximum acceptance angle, \(\theta_{i}\) = 27\({}^{\circ}\), and both \(b_{1}\) = 10 mm and \(b_{2}\) = 5 mm, the x-axis length of the CCPC, \(L\), was set to 14.7 mm [24]. \[\tan\theta_{i}=\frac{b_{1}+b_{2}}{2L} \tag{1}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Target** & **Remark** \\ \hline Pitch & mm & 10 & CFL array plate with the dimension of \\ \hline Width & mm & 100 & 100 \(\times\) 100 mm\({}^{2}\). The plate is composed of \\ \hline Length & mm & 100 & 10 CFL structures \\ \hline Designed wavelength & nm & 550 & Considering the peak of the solar spectrum \\ \hline Refractive index & – & 1.4936 & Refractive index at 550 nm of PMMA \\ \hline Effective focal length & mm & 11.5 & Considering a compact optical module \\ \hline Optical power efficiency & \% & \textgreater{}80 & Considering the Fresnel loss of CFL \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Design specification of the cylindrical Fresnel lens (CFL) array plate. Figure 2: Non-sequential ray tracing for the designed CFL array plate with the solar direct normal incidence (DNI) condition.
## 3 Results
### Remaining Useful Life (RUL) Prediction
In the initial phase (initial wear), which spans from 0 to 0.1 mm, the wear exhibits a nonlinear function, indicating rapid degradation based on the input parameters and the insert material. This phase is characterized by the tool's settling process, during which the cutting insert adjusts to the material being machined, effectively eliminating surface irregularities and micro-defects. Physically, this phase may reveal visible wear at the tool's cutting edges, a slight increase in surface roughness, and noticeable changes in cutting sounds and forces. The second phase (middle wear), occurring within the wear range of 0.2 to 0.3 mm, presents a more stable and predictable wear rate. By this point, the insert has transitioned beyond the settling stage, resulting in more uniform wear. The relationship between time and wear becomes increasingly linear, enabling interpolation to predict wear at intermediate points, such as 0.25 mm. This threshold is critical, as it is the point at which tool replacement should be considered to prevent issues like diminished cut quality, increased cutting forces, and vibrations that could adversely impact the final product. During this phase, the tool's edge begins to round off more significantly, and while the cut quality starts to decline, it remains within acceptable limits. As wear approaches 0.3 mm, operators enter a critical zone where wear accelerates again, marking the beginning of the third phase where severe wear occurs. At this stage, it is imperative to replace the tool, as prolonging its use could result in severe failures, such as tool breakage or damage to the workpiece. Operators may experience a marked increase in cutting forces, elevated temperatures, and alterations in sound, all indicating that the tool is under considerable stress. The quality of the machined surface may deteriorate rapidly, displaying visible signs of wear such as rough edges or even deformation of the workpiece. This phase is characterized by flank wear and potentially abrasive wear, making it essential not to exceed the recommended wear limits for inserts (between 0.25 and 0.3 mm).
## 6 Results  
For all the performed tests, the UEs disconnected from the 5G network and connected to the fake BS, which was verified by the procedure described in Section 5.2. Additionally, the respective reject messages were also visible in the pcap file related to srsEPC. The behavior of the tested UEs upon receiving the reject message is described below.  
The IMSI Leak attack was verified on four COTS UEs. All of them leaked their IMSIs. The Numb attack was tested on eight COTS UEs. Only four of these examined UEs had implemented a mitigation strategy to immediately reconnect to the 5G network once the fake base station was shut down. This result is particularly interesting since most of the 4G UEs tested before [22, 43] did not quickly recover from such attacks.  
The Downgrade attack was tested on eight COTS UEs. Among these, four of the tested UEs did not recover from the attack and remained linked to the 2G base station. Conversely, the remaining four UEs promptly re-established their connection with the 5G network once the fake base station was deactivated. The results of all three attacks are summarized in Table 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**UE**} & \multirow{2}{*}{**IMSI Leaked**} & \
\begin{tabular}{c} **Numb Attack** \\ **(Quick Recovery)** \\ \end{tabular} &
\begin{tabular}{c} **Downgrade Attack** \\ **(Quick Recovery)** \\ \end{tabular} \\ \hline Samsung Galaxy A40 & Yes & No & No \\ Huawei P40 & Yes & Yes & Yes \\ Huawei Mate40 Pro & Yes & Yes & Yes \\ Oppo Find X3 Neo 5G & Yes & No & No \\ Google Pixel 7 Pro & Not tested & Yes & Yes \\ Samsung Galaxy S20 FE & Not tested & No & No \\ Samsung Galaxy S21 FE 5G & Not tested & Yes & Yes \\ iPhone 12 & Not tested & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for the IMSI Leak, Numb, and Downgrade attacks.
## 3 Mathematical Modeling  
The first cycle of DLSRG operation is divided into two stages, excitation stage and generation stage. There is essential difference between DLSRG and traditional generator. DLSRG operates independently in three-phase operation process. Its electromagnetic force equation is as follows:  
\[f(i_{k},x)=\frac{1}{2}i_{k}^{2}\frac{dL_{k}}{dx},\quad k=1,2,3 \tag{1}\]  
where \(i_{k}\) is the phase current of phase \(k\); \(L_{k}\) is the self inductance coefficient of the \(k\) phase of the generator. This equation shows that the electromagnetic force is related to the square of the current and the rate of change of the self-inductance. When \(dL_{k}/dx>0\), the transverse magnetic pull force is in the same direction as the external mechanical force, and the generator is in the excitation stage, when \(dL_{k}/dx<0\), the transverse magnetic pull force is opposite to the external mechanical force, and the generator is in the generation stage.  
The \(k\) phase total flux equation of generator is:  
\[\psi_{k}=L_{k}i_{k},\quad k=1,2,3 \tag{2}\]  
where \(L_{k}\) is the \(k\) phase inductor; \(i_{k}\) is the phase current of phase \(k\). This equation shows that the total flux of phase \(k\) is proportional to the inductance of phase \(k\) and the current of phase \(k\).  
The \(k\) phase terminal voltage equation of generator is as follows:  
\[u_{k}=R_{k}i_{k}+\frac{dy_{k}}{dt},\quad k=1,2,3 \tag{3}\]  
Among them, \(R_{k}\) and \(i_{k}\) are generator phase \(k\) resistance and current; \(\Psi_{k}\) is the total flux of \(k\) phase of generator. This equation states that the \(k\) phase terminal voltage consists of two parts, one part consists of the voltage on the resistance of phase \(k\), the other is the voltage generated by the rate of change of the total flux of phase \(k\).  
According to the structure and operation characteristics of DLSRG, when the DLSRG is placed vertically, the normal force on the DLSRG actuator is basically zero, and the effect on the generator operation can be neglected in case of neglecting magnetic flux leakage. Therefore, the friction force can be neglected when considering the motion equation of the rotor. The motion equation of the generator rotor is as follows:  
\[F=m\frac{d^{2}x}{dt^{2}}\pm f\pm mg \tag{4}\]  
where \(F\) is the input of mechanical force, \(m\) is the mass of the rotor, \(x\) is the displacement of the rotor, \(t\) is the moving time of the rotor, and \(f\) is the electromagnetic force of the generator. When the generator is in the excitation stage, \(f\) is in the same direction as the external mechanical force and when the generator enters the generation stage, \(f\) is opposite to the external mechanical force.  
To sum up, the mathematical model of DLSRG is represented by the following equations:\[u_{k}=R_{k}i_{k}+\frac{dt_{k}}{dt},k=1,2,3\] \[f(i_{k},\mathbf{x})=\frac{1}{2}i_{k}^{2}\frac{dL_{k}}{dx},k=1,2,3\] \[F=m\frac{dx}{dt^{2}}\pm f\pm mg\]  
The three formulas in Equation (5) have been explained in the above article.
###### Abstract  
A secure data stream is important for the real time communications of Internet of Things (IoT). A stream cipher with the characteristics of simple and high speed is suitable in the secure communications of IoT for its security. Some stream ciphers based on coupled map lattices (CML) were proposed. However, the original coupled map lattice shows evidence of correlation between the contiguous points. In this paper, we present an improved CML with a counter to overcome the weakness. The proposed scheme has the advantage of simplicity and suits the resource constrained IoT environment. We implement the proposed improved CML and analyze the proposed ciphers against some attacks. For the balance performance analysis, the numbers of 0 and 1 in the keystream are almost balanced, and the difference rates show that the proposed schemes have high key sensitivity. Finally, we present some experimental results of statistical random number tests for the output keystreams. Under the pass rates of the statistical test of NIST SP800-22, the proposed ciphers with improved CML, and compound CML are at least 95% and 97%, respectively. All the pass rates of the proposed stream ciphers are 100% for the statistical test of FIPS PUB 140-1.  
secure IoT; stream cipher; compound coupled map lattices; chaotic map +
Footnote †: journal: Applied Sciences