esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 2 Related Work
### Bio-Inspired Methods  
Most bio-inspired approaches for synthetic gait generation are based on central pattern generators (CPGs). CPGs are oscillators that can generate rhythmic patterns from non-rhythmic signals or no inputs at all. When applied to legged locomotion [30; 31; 32; 33], the rhythmic output of CPGs corresponds to the gait pattern, in response to inputs as a gait velocity command and the proprioceptive sensor information from the limbs of the robot; this means that sensory information plays an important role in CPG-based gait generation [33]. The biggest difficulty with regard to CPGs is determining the correct range of values for the input as well as tuning the internal oscillator parameters, so that the output corresponds to the desired movement of the limbs and the transition between different gaits is smoothly performed [34]. Tuning such parameters is usually performed by trial and error, and some authors have even turned to GAs to tune CPGs [35]. Furthermore, in a decentralized scenario, where there are as much CPGs as limbs, an additional higher-level control is required [36]. In recent works, CPGs can generate a dynamic walking pattern, based on the turning radius of the desired trajectory and switch from tripod, ripple, and tetrapod gaits [37].
## 5 Energy Efficiency and Sustainability
### Energy Efficiency in the Context of RIS and HAP Integration
#### 5.1.4 Reducing Interference and Enhancing Signal Quality  
By controlling the propagation of signals, RISs can also minimize interference, a common problem that requires additional energy expenditure in traditional systems to overcome [103; 105]. RISs can improve the SNR by directing signals away from unintended receivers and toward the intended targets. This means that signals require less power to achieve the same level of quality and reliability, further enhancing energy efficiency.
## 5 Analysis of the Protocol
### Performance Analysis  
Based on Section 4.1, it is easy to verify that our collaborative computation of the optimal parameter for linear regression is correct. For the computational complexity of the scheme, each party needs to compute \(X_{i}^{T}X_{i}\) and \(X_{i}^{T}\vec{y}_{i}\), \(i=1,2\), respectively. This is necessary even if computations are performed by only one party using all the data in one set. The only possible extra work is that every party needs to invert the matrix \(X_{1}^{T}X_{1}+X_{2}^{T}X_{2}\) to obtain the solution of \(\vec{\beta}\). The dimension of the matrix to be inverted is \((m+1)\times(m+1)\), where \(m\) is the number of features used in the model, which is usually much smaller than the number of data \(n\).  
This step can also be done by one party (i.e., the server) who then sends the solution \(\vec{\beta}\) to the other parties (i.e., local users). In this way, the total computational work is the same as the computational work required to be done when all the data are at one site.  
For communication complexity, our proposed scheme sends only aggregated data to the other party. The size of the aggregated data is only \((m+1)^{2}+(m+1)=(m+1)(m+2)\) numbers for each party having \(n\) records of data. The communication complexity is proportional to the number of features \(m\), which is independent of \(n\). When \(n>(m+1)\), this is less than the size of the dataset, which is \((n(m+1)+n)\).
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 1 Introduction
### LTE-U and LTE-LAA  
The LTE-unlicensed (LTE-U) initiative by Qualcomm has led to a standard for operating LTE-A over the unlicensed spectrum. The LTE-U Forum focuses on supplemental downlink (SDL) mode of LTE in the unlicensed spectrum paired with a licensed LTE spectrum by using carrier aggregation (CA) [12]. Since stand-alone access to unlicensed spectrum may be unstable due to the unknown interference, 3 GPP has worked on a single global solution framework for LTE licensed-assisted access (LTE-LAA) to unlicensed spectrum, targeting fair coexistence with existing WiFi networks in the 5-GHz spectrum [13]. To determine whether a channel of the unlicensed spectrum is occupied or not, it is necessary for LTE operators to use listen-before-talk (LBT), which is one of the clear channel assessment (CCA) mechanisms for carrier sensing before transmission [13]. Although European and Japanese regulations mandate LBT features in the unlicensed bands, other countries such as the United States, China, and South Korea are still considering it [14].
## 8 Conclusions  
In comparison to measurement methods based on casing current excitation and single-pole excitation methods for drilling wells, the dual-electrode current excitation method offers several advantages:  
1. The dual-electrode current excitation MWD method eliminates the need for a ground-based current excitation system and reflux electrode, thereby reducing operating costs and time. This makes it particularly well-suited for target well location operations with safety risks, especially in the context of relief well operations at space-constrained drilling platforms.
2. The dual-electrode current excitation mode through the upper/lower electrodes to form a current loop in the formation can adjust the electrode distance and the electrode length combination according to the actual ranging environment to meet the requirements of the MWD power supply.
3. Since the central control unit of the MWD system can detect the amplitude and phase of the excitation current and the ranging system can realize the synchronous measurement of the phase between the excitation current and the induced magnetic field, there is no uncertainty problem of 80\({}^{\circ}\) in the calculation results of the relative azimuth of the two wells, and the orientation of the target well can be determined by a single point measurement.
4. The central control unit of the MWD system is capable of detecting the amplitude and phase of the excitation current. Additionally, the ranging system can achieve the synchronous measurement of the phase between the excitation current and the induced magnetic field. As a result, there is no uncertainty problem with a 80\({}^{\circ}\) calculation error in determining the relative azimuth of two wells. Furthermore, the orientation of the target well can be accurately determined through a single point measurement.
5. During drilling operations for relief well connections or cluster well obstacle avoidance, particularly in special scenarios near the bottom position of the drilling tools or the casing in the target well, the dual-electrode current excitation ranging method proves to be effective. In contrast, other active electromagnetic ranging methods may encounter challenges in attaining comparable results, primarily due to the necessity for drilling equipment to make frequent trips and their inability to conduct real-time ranging operations.  
X.D.: formal analysis (equal); software (equal); and writing--original draft (equal); X.Y.: conceptualization (equal); L.H.: methodology (equal); and H.L.: visualization (equal) and review and editing (equal). All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Tangshan University PhD Innovation Fund] grant number [1402003].  
Data Availability Statement: Data available on request due to restrictions e.g., privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to [The follow-up study is still classified and can't be made public].  
The authors declare no conflict of interest.
# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
## 2 System Model
### FSS Satellite Antenna Model  
According to IMT-2020, for circular or elliptical beam spacecraft antennas with a single feed source in satellite fixed services, the following radiation patterns should be used as design indicators outside the coverage area:  
\[G(\psi)=\left\{\begin{array}{ccc}G_{m}-3(\psi/\psi_{0})^{\alpha},&\psi\leq\psi \leq a\psi_{0}\\ G_{m}+L_{S}+20\log_{10}z,&a\psi_{0}\leq\psi\leq 0.5b\psi_{0}\\ G_{m}+L_{S},&0.5b\psi_{0}\leq\psi\leq b\psi_{0},\\ X-2.5\log_{10}\psi,&b\psi_{0}\leq\psi\leq Y\\ 0,&Y\leq\psi\end{array}\right. \tag{6}\]  
In the above equation, \(Y=b\psi_{0}10^{0.04(G_{m}+L_{S})}\), \(G(\psi)\) represents the gain (dBi) at an angle deviating from the main beam direction, \(G_{m}\) represents the peak gain of the main lobe (dBi), \(\psi_{0}\) represents half of the 3 dB beam width, \(L_{S}\) represents the paraxial sidelobe level (dB) relative to the peak gain required by the system design, and z represents the (principal/secondary axis) of the radiation beam. The radiation pattern of a single feed circular beam antenna can be represented as follows:  
\[G(\psi)=\left\{\begin{array}{ccc}G_{m}-3(\psi/\psi_{0})^{2},&\psi_{0}\leq \psi\leq a\psi_{0}\\ G_{m}+L_{s},&a\psi_{0}\leq\psi\leq b\psi_{0}\\ G_{m}+L_{s}+20-25\log_{2}(\psi/\psi_{0}),&b\psi_{0}\leq\psi\leq\psi_{1}\\ 0,&\psi_{1}<\psi\end{array}\right., \tag{7}\]  
In the formula, \(\psi_{1}\) represents the value \(\psi\) of time \(G(\psi)\) = 0 dBi. Table 2 shows the values of the sidelobe levels \(L_{S}\) for \(-20\) dB, \(-25\) dB, and \(-30\) dB.
## 4 Proposed Adaptive Linear Quadratic Regulator with Fractional Integral (LQR-FI) Control Structure
### Augmented State-Space Model  
According to the proposed control loop structure, as shown in Figure 2, the LQR controller optimizes the states of the augmented system, which contains the states of the original system and the states of the fractional-order pre-compensator. The proposed augmented system can be expressed in state-space form as,  
\[\begin{array}{l}\dot{\mathbf{z}}(t)=A_{new}\mathbf{z}(t)+B_{new}u(t)\\ y(t)=C_{new}z(t)+Du(t)\end{array} \tag{6}\]  
The \(A_{new},B_{new}\) and \(C_{new}\) are the augmented matrices and obtained as  
\[\dot{\mathbf{z}}(t)=\left[\begin{array}{c}\dot{\mathbf{x}}(t)\\ \dot{\mathbf{e}}(t)\end{array}\right],\;A_{new}=\left[\begin{array}{cc}A&A^{ \prime}\\ 0&A^{\prime\prime}\end{array}\right],\;B_{new}=\left[\begin{array}{c}B\\ B^{\prime}\end{array}\right],\;C_{new}=\left[\begin{array}{c}C\;C^{\prime} \end{array}\right] \tag{7}\]\[\left[\begin{array}{c}\dot{x}(t)\\ \dot{\mathbf{e}}(t)\end{array}\right]=\left[\begin{array}{cc}A&A^{\prime}\\ 0&A^{\prime\prime}\end{array}\right]\left[\begin{array}{c}x(t)\\ e(t)\end{array}\right]+\left[\begin{array}{c}B\\ B^{\prime}\end{array}\right] \tag{8}\] \[u(t)y(t)=[C\ C^{\prime}]\left[\begin{array}{c}x\\ \mathbf{e}\end{array}\right]+Du(t)\]  
Here, \(A^{\prime}\), \(A^{\prime\prime}\), \(B^{\prime}\), and \(C^{\prime}\) are non-zero matrices, and \(\mathbf{e}(t)\) are the states that are added by the pre-compensator. We can see that the obtained new matrices, \(A_{new}\),\(B_{new}\),\(C_{new}\) contain a non-zero effect on the states and output of the pre-compensator. The objective of the LQR is to find a state feedback control law for the augmented system as in the equation as follows:  
\[u(t)=-\mathrm{K}_{lqr}\mathbf{z}(t) \tag{9}\]  
which minimizes the quadratic performance cost function  
\[\mathrm{J}=\int_{0}^{\infty}\left(\mathrm{z}^{\prime}\mathrm{Q}_{new}\mathrm{ z}+\ u^{\prime}\mathrm{Ru}\right)dt \tag{10}\]  
Here, \(\mathrm{K}_{lqr}=\mathrm{R}^{-1}B_{new}\mathrm{T}\mathrm{P}\) can be obtained by solving the algebraic Riccati equation (ARE) for the system.  
\[\mathrm{PA}_{new}+A_{new}\mathrm{T}\mathrm{P}+\mathrm{Q}-\mathrm{PB}_{new} \mathrm{R}^{-1}B_{new}\mathrm{T}\mathrm{P}=0 \tag{11}\]  
Here, \(\mathrm{Q}_{new}\) is the augmented system state weighted matrix, which is calculated simultaneously with \(\mathrm{R}\), \(\lambda\), and \(\mathrm{K}_{I}\) by the PSO algorithm. PSO algorithm adaptively changes the gains of the proposed controller according to the designed constrain parameter vectors. Then, the cost function maintains the stability of the controlled system and obtains zero tracking error.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 7 Conclusions  
In this study, a modified non-isolated high gain quadratic boost topology is proposed and studied, which can achieve high voltage gain with a lower duty ratio that enables its potential application in the renewable energy sector as well as other different applications. The obtainable voltage gain for this topology is verified by both the theoretical and experimental results (i.e., in this study Vin 48 V is stepped up to 400 V output voltage). A base 94.5% efficiency is achieved for 150-W output power from the prototype circuit. The performance of the proposed high gain quadratic boost converter is validated by the experimental prototype, which is also verified by the SIMPLIS software simulation results. The gain and phase plot of the system obtained from PSIM justify that the control can be easily implemented. Further, the efficiency can be improved by reducing the switching loss (i.e., using more efficient power MOSFET) and the loss due to the ESR of the passive component.  
Conceptualization, A.S.J.; Methodology, A.S.J.; software, A.S.J. and T.-H.K.; Validation, A.S.J.; Formal analysis, A.S.J.; PCB design, A.S.J. and T.-H.K.; Investigation, C.-H.L.; Resources, C.-H.L.; Data curation, A.S.J.; Writing--original draft preparation, A.S.J.; writing--review and editing, C.-H.L.; project administration, C.-H.L. and C.-H.C.; funding acquisition, C.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, R.O.C., grant number MOST 110-2221-E-011-081 and MOST 110-3116-F-011-002.  
Not applicable.  
The authors sincerely appreciate the support from the Taiwan Building Technology Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.  
The authors declare no conflict of interest.  
Figure 27: Open-loop Gain plot (in dB) and Phase plot (in Hz) vs. frequency.
## 2 Materials and Methods  
### Plant Material, Treatments, and Sampling  
The susceptible to powdery mildew _C. pepo_ L. \"Kompokololokitho\" zucchini cultivar (Doulgerakis, Rethimno, Greece) was used. Zucchini seeds were sown in trays with individual 5 cm pots containing sterile peat substrate for germination and were transplanted to 2 L pots containing sterile Klasmann-TS2 soil (Klasmann-Deilmann, Geeste, Germany) one week later. Plants were grown under controlled greenhouse conditions (20-25 \({}^{\circ}\)C; Relative Humidity (RH) 70-80%; 16 h light) and fertilized twice (every 10 days) with 0.3 g/L of Nutri-Leaf 20:20:20 fertilizer (Miller Chemical & Fertilizer, Hanover, PA, USA).  
The Regalia 200 SC 5% \(v/v\) formulation of _R. sachalinensis_ plant extract (provided by Syngenta, AG, Basel, Switzerland) was applied at 0.5% \(v/v\) (5 mL of formulation per L water) twice. The first application was performed on 21-day-old _C. pepo_ plants and the second, 5 days later. Mock applications to control plants were made with water.  
_C. pepo_ plants were artificially inoculated with an aqueous conidial suspension of _P. xanthii_ following a previously standardized protocol for infection [30; 57]. Briefly, freshly collected conidia were suspended in de-ionized water 0.01% Tween-20, to a concentration of 0.5 \(\times\) 10\({}^{6}\) per ml of water. Plants were sprayed with the conidial suspension or with water 0.01% Tween-20 for mock controls, 2 days after the second foliar spray treatment application (developmental stage of 6 true leaves).  
Four sample groups (cases) were formed: (a) control plants (C) sprayed only with water, (b) plants inoculated with _P. xanthii_ conidia (PX), (c) RS sprayed plants (RS), and (d) RS sprayed plants inoculated with _P. xanthii_ conidia (RSPX). Each individual plant constituted a single biological replicate. Leaf material (all 6 fully developed leaves) was collected from 4 individual plants for each of the control and treated groups, 36 h post inoculation. The leaves from each plant were piled, cut into smaller pieces (approximately 2 \(\times\) 2 cm squares), mixed and wrapped in aluminum foil, subsequently frozen in liquid nitrogen, and stored at \(-80\)\({}^{\circ}\)C before use.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 3 Proposed Method
### KFCM Clustering  
In the training stage, a better classification can achieve the appropriate divisions of the training dataset. In each division the features will be close each other and fit for illuminant modeling. We use some above-mentioned features to classify the training dataset by the KFCM method.  
KFCM is derived from fuzzy c-means (FCM). FCM is a classification algorithm based on fuzzy sets, which results in clustering partitions with the maximum similarity among samples in the same cluster, while the minimum similarity among different clusters [50]. As an improved version of the common c-means algorithm, FCM provides flexible fuzzy partitions since the training samples are clustered in terms of the membership functions.  
For the standard FCM, a cost function based on the Euclidean distance is minimized to solve the cluster centroids and the membership degrees of all samples:  
\[J_{FCM}(\mathbf{U},\mathbf{V})=\sum_{k=1}^{n}\sum_{i=1}^{c}\mu_{ki}^{m}|x_{k}-v_{ i}|^{2}, \tag{13}\]  
where \(|.|\) is the Euclidean norm to measure the similarity between sample data and cluster centroid; \(\mu_{ki}\) is the degree of membership for the sample \(x_{k}\) in the cluster \(i\), \(\mathbf{U}=[\mu_{ki}]\); \(v_{i}\) is the centroid of the cluster \(i\), \(\mathbf{V}=[v_{i}]\); \(n\) is the total number of samples; \(m\) is the exponent of the membership; and \(c\) is the cluster number. Furthermore, a kernel function can be introduced into FCM by a nonlinear mapping \(\Phi:x\rightarrow\Phi(x)\), which maps the low dimension vector \(x\) into a high dimension space. Then the kernel-based FCM, i.e., KFCM, is to minimize the following cost function:  
\[J_{KFCM}(\mathbf{U},\mathbf{V})=\sum_{k=1}^{n}\sum_{i=1}^{c}\mu_{ki}^{m}|\Phi (x_{k})-\Phi(v_{i})|^{2}, \tag{14}\]  
where  
\[|\Phi(x_{k})-\Phi(v_{i})|^{2}=K(x_{k},x_{k})+K(v_{i},v_{i})-2K(x_{k},v_{i}). \tag{15}\]  
Here \(K(x,\
u)\) is an inner product kernel function, and \(K(x,\
u)=\Phi(x)^{T}\Phi(\
u)\). In this study, we adopt the Gaussian function as a kernel function, i.e., \(K(x,\
u)=\exp\left(\frac{-|x-\
u|^{2}}{\sigma^{2}}\right)\), where \(\sigma\) is the kernel width; then we can get \(K(x,x)=1\) and \(K(\
u,\
u)=1\). According to Equation (15), Equation (16) can be rewritten as:  
\[J_{KFCM}(\mathbf{U},\mathbf{V})=2\sum_{k=1}^{n}\sum_{i=1}^{c}\mu_{ki}^{m}(1-K (x_{k},v_{i})). \tag{16}\]  
From Equation (16), we can see that the cost function is minimized when the first-order partial derivatives with respect to \(\mu_{ki}\) and \(v_{i}\) both are zeros. By these two necessary conditions, the final clusters and their centroids are iteratively obtained as follows:  
\[\mu_{ki}=\
# Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model
## 5 Conclusions  
This study is a proof of concept for the detection of DDoS attacks with Deep Learning (DL) and a Gaussian Mixture Model (GMM). As a solution to the Open Set Recognition (OSR) problem in DDoS detection, the proposed framework consists of Bi-Directional Long Short-Term Memory (BI-LSTM), GMM, and incremental learning. The Bi-LSTM has demonstrated itself as a practical approach for the discrimination of malicious and legitimate traffic sampled from the distribution of the training data set. The GMM has shown to be an effective measure for the differentiation of novel instances and trained samples. Unknown traffic can be captured by the GMM and labeled by data engineers, and then fed back to the BI-LSTM and the GMM for incremental learning. Both the new traffic and the old traffic can be handled by the updated model correctly and gracefully. The feasibility and effectiveness of the proposed framework has been validated by a series of experiments on data sets CIC-IDS2017 and CIC-DDoS2019.  
BI-LSTM is fully capable of performing what it has been trained to do, such as detecting known DDoS attacks. However, when confronted with novel attacks, the system performance degrades severely. The recall drops from 99.8% to 41.2% for Dataset CIC-IDS2017/Wednesday and Friday. Unknown traffic can be captured by GMM and then labeled by traffic engineers. After that, incremental learning regains detection rates up to 95.3% and 99.8% for Dataset CIC-IDS2017/Wednesday and Friday.  
To explore the full potential of the proposed BI-LSTM-GMM framework there are some research directions that deserve further investigation: validation on more datasets, auto-configuration of BI-LSTM and GMM, and the elimination of intervention traffic engineers.  
Conceptualization, C.-S.S.; methodology, C.-H.C.; software, C.-H.C.; validation, W.-W.L. and T.-T.N.; writing--original draft preparation, C.-S.S.; writing--review and editing, W.-W.L. and T.-T.N.; supervision, M.-F.H.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Minister of Science and Technology, Taiwan, under the grant number 109-2637-E-992-006, MOST 109-2622-E-992-033, and 109-2221-E-992-073-MY3, and the Ministry of Education, Taiwan, under the grant code of University-Industry Innovation R&D (RSC).  
The authors would also like to thank the Editor and Reviewers for their constructive comments which led to improvements to the presentation of the manuscript.  
The authors declare no conflict of interest.
## 2 Beam Bending Vibration and FE Models
### 3D FE Model of Healthy and Damaged Beam  
In this section, a 3D FE model of healthy and damaged beam with clamped-free boundary condition is proposed. The models are implemented in COMSOL Multiphysics. In this context, we emphasize that a damage has a physical geometry.  
The beams are meshed with tetrahedral elements (6 DOFs per node). The mesh density is _in fine_ controlled by COMSOL. Typically, converged models with damage have 22,000 DOFs. A high mesh density is applied close to the damage area. Figure 2a,b represent 3D healthy and damaged FE models, respectively. A zoomed view of the rectangular geometry damage is shown on Figure 2b.  
Natural frequencies of 3D healthy and damaged models are obtained using COMSOL Multiphysics' own solver.  
Figure 2: 3D FE mesh of the cantilever beam (**a**) healthy state (**b**) and example of damaged state with zoom in the damage area.
## 1 Introduction
This study similarly utilizes an amplifier board as a sensitivity augmentation method, because both photodiodes and LEDs mainly consist of PN junctions; the board mainly comprises operational amplifiers and transistors. This methodology concurs with that of [PERSON]'s group [11] that amplified a PEDD's photovoltaic signal while using an operational amplifier. In the other sensitivity enhancement approach, the LED sensitive/active area was increased by increasing the number of LED chips. In both sensitivity augmentation methods, our LEDs were in the photovoltaic configuration--similar to elsewhere [12; 13]. Stated otherwise, we did not apply any biasing voltage to the LEDs. Applying a negative voltage to the LEDs (photoconductive configuration) would leadto the detection of dark currents, which would make it difficult to distinguish the low diagnostic radiation-induced signal from the dark current. Therefore, this study aims at enhancing the sensitivity of LEDs to diagnostic X-rays by amplifying its radiation-induced current.