esa-sceva/satcom-chunk-collection huggingface dataset:
# Manipulator Control of the Robotized TMS System with Incurved TMS Coil Case
## 8 Discussion  
The adhesion between the subject's head and TMS coil was improved by using the characteristics of the coil's floor, which can help reduce the effect of small movements of the subject's head on the robotized TMS task. Calibration and trajectory planning were performed to achieve this goal. The transformation matrix from the camera coordinate system to the robot manipulator's base coordinate system was calculated from the calibration work. Using this matrix, the robot manipulator's base coordinate system can express the coordinate data from the navigation system. While generating the trajectory, a sphere, including the subject's head, is generated to prevent collision between the head and coil by preventing the coil from invading the sphere. After following the planned trajectory, several force/torque control strategies are applied for better adherence: compliance control for the interaction between the coil and head and proportional torque control to make the center of the contact area between the head and coil near the center of the coil's floor. When using these strategies, the magnitude of the force used is scheduled by \(e\) to reduce \(e\) faster. Through this scheduling, a low \(e\) can be maintained with a lower force after applying a large force for a short time.  
In the system used in our experiment, a force/torque sensor embedded inside the UR5e manipulator was used without additional external sensors. Therefore, this study can be utilized without external force/torque sensors. However, more precise sensors would be helpful for a low-desired force control of their robot manipulator because of their better precision.  
The interaction force between the coil and head during the force/torque control in this study was larger than that in existing studies [21, 22, 26, 27]. However, because the floor of the coil case used in previous studies was flat, the contact surface area during the interaction between the coil and the head was larger in this study than in previous studies. Therefore, the pressure transferred to the head during the force control was not significantly higher than in previous studies.  
In future work, real-time tracking will be applied to the present system using the control strategy developed in this study. The real-time system will be realized by sending the head and target point of the coil periodically from neuronavigation system to the robot controller. By embodying this function, subjects of our system may feel less pressuret to  
Figure 19: Results of the tracking experiment: (**a**) Measured reaction force between the TMS coil and the dummy head while the head is moving; (**b**) The error between the current coil position and new target position after moving the dummy.  
fix their head. Additionally, to mimic the moving of the human head, the moving dummy stage will be developed using servomotors and the additional structures. The clinical experiments with real patients will also be conducted.
## 1 Introduction  
Colon cancer is a major health problem globally and one of the three most common cancers diagnosed in both men and women worldwide. Its incidence rapidly rises in cities with economic development. Colon cancer can be caused by accumulating mutations and epigenetic alterations [1]. Presently, various therapies have been used clinically for colon cancer, including chemotherapy. However, chemotherapy resistance remains one of the serious problems and can also cause adverse side effects. Thus, the development of anticancer natural extracts could possibly be an alternative treatment for future colon cancer patients to overcome those problems.  
Rice (_Oryza sativa_ L.) is the most important crop and staple food source consumed by over half of the world population. For Thailand, rice is the most important crop that makes the country a major reporter in the world rice market. Recently, riceberry rice has become popular among health-concerned people due to its health-promoting and potential anticancer activity. Moreover, riceberry rice has been reported to have hypoglycemic, hypolipidemic, antioxidant and antiinflammation properties. The active ingredients of riceberry aretocopherol, locotrienol, \(\beta\)-Sitosterol, \(\gamma\)-oryzanol, \(\beta\)-Carotene and some proteins. These substances were found in most of the pericarp, also known as rice bran [2]. From previous studies, riceberry rice bran extract was shown to be a significant inhibitor of colonic carcinoma (Caco-2), breast adenocarcinoma (MCF-7) and acute myeloid leukemia (HL-60) by suppressing cell growth and proliferation, inducing cell cycle arrest and promoting cellular apoptosis [3]. However, the use of protein hydrolysate from riceberry rice bran on colon cancer inhibition remains limited. This is despite the rice bran being rich in proteins such as albumin, globulin, glutelin and prolamin with high nutritional value in terms of minerals, unsaturated fat and vitamins. The highest phytate content in rice bran was found in albumin and the lowest in prolamin [4; 5; 6], which can be a high potential source for protein hydrolyses using the Alcalase enzyme. According to a previous study, Alcalase is an alkaline endo-peptidase that can give higher protein yields, and it demonstrates its activity in hydrolyzing gluten (MW 57 kDa), which is the main protein accounting for 60-80% of the total protein in rice [7; 8].  
Thus, the purpose of this study is to investigate the anticancer activity of hydrolyzed riceberry bran extract on non-metastatic and metastatic colon cancer cell lines. This investigation may give useful information that leads to future application in medical research and treatment for colon cancer patients.
## 2 Materials and Methods
### Inverse Dynamics  
For the hand and forearm, an anatomical local coordinate system was made according to the ISB recommendations [16]. The following bony landmarks on the throwing arm were used: third proximal interphalangeal, ulna processes styloid, radius processes styloid, lateral humeral epicondyle, and medial humeral epicondyle. Positions of the centers of mass and the moments of inertia were estimated according to [PERSON] (2002) [17] and [PERSON] et al. (1996) [18]. The baseball was modeled with a mass of 0.145 kg attached to the hand, where the ball mass linearly reduced by 10% over the last ten samples (0.025 s) before ball release. The elbow joint torque was expressed in the anatomical local coordinate system of the elbow, located in the middle of the lateral and medial humeral epicondyle. Using this anatomical coordinate system with the segment data and with the scaling factors of [PERSON] et al. (1996) and [PERSON] et al. (1990), the kinetics of the segments were calculated [17; 18; 19]. Elbow joint torques were calculated using the top-down inverse dynamic analysis based on the Newton-Euler equation of motions, starting in the hand of the throwing arm. The external elbow valgus torque was calculated as a time series for each single pitch between foot contact and ball release. Subsequently, the peak external valgus torque was obtained from this time series.
# Experimental Study of Complex Coupled Deep Tillage Mechanism and Drag Reduction
## 3 Results
### Analysis of Field Trial Results
#### 3.2.2 Experimental Validation and Comparative Testing  
Based on the range and variance analysis, the optimal scheme A\({}_{3}\)B\({}_{1}\)C\({}_{1}\) was not included in the nine experiments presented in the orthogonal table, necessitating further experimental validation. Field tests were conducted in the corn fields at the Corn Research Center of Inner Mongolia Agricultural University, following the A\({}_{3}\)B\({}_{1}\)C\({}_{1}\) scheme: an air pressure of 2.2 MPa, a working speed of 3.31 km/h, and a subsoiler width of 150 mm. The experiment was carried out three times, and the results were averaged, yielding a drag ratio (SDF) of 3.12, as shown in Table 6.  
After a comparison test with the original air-blown deep pine subsoiler of the group in [31], using the test method in the text, under the same test factors, and recording the traction resistance and the disturbance of the soil after using the deep pine subsoiler, it can be seen that the traction resistance of the complex coupling deep pine subsoiler compared to the original deep pine subsoiler is reduced by 10.18%. After calculation and analysis, it can be seen that the specific resistance of deep pine SDF is reduced by 5.4%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment Number** & **A: Air Pressure (MPa)** & **B: Working** & **C: Subsoiler** & **Y: SDF** \\ \hline
1 & 2.2 & 3.31 & 150 & 3.13 \\
2 & 2.2 & 3.31 & 150 & 3.07 \\
3 & 2.2 & 3.31 & 150 & 3.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental validation of optimal scheme.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment Number** & **A: Air Pressure (MPa)** & **B: Working** & **C: Subsoiler** & **D: Blank** & **Y: SDF** \\ \hline
1 & 1.4 & 3.31 & 150 & 1 & 4.33 \\
2 & 1.4 & 3.83 & 175 & 2 & 7.43 \\
3 & 1.4 & 7.32 & 200 & 3 & 11.53 \\
4 & 1.8 & 3.31 & 175 & 3 & 4.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
###### Abstract  
The emergence of low-cost connected devices is enabling a new wave of sensorization services. These services can be highly leveraged in industrial applications. However, the technologies employed so far for managing this kind of system do not fully cover the strict requirements of industrial networks, especially those regarding energy efficiency. In this article a novel paradigm, called Low-Power Wide Area Networking (LP-WAN), is explored. By means of a cellular-type architecture, LP-WAN-based solutions aim at fulfilling the reliability and efficiency challenges posed by long-term industrial networks. Thus, the most prominent LP-WAN solutions are reviewed, identifying and discussing the pros and cons of each of them. The focus is also on examining the current deployment state of these platforms in Spain. Although LP-WAN systems are at early stages of development, they represent a promising alternative for boosting future industrial IIoT (Industrial Internet of Things) networks and services.  
Low-Power Wide Area Networks (LP-WAN); Machine-to-Machine (M2M) communications; Industrial Internet of Things (IIoT); Internet of Things (IoT); wireless sensor networks +
Footnote †: journal: Sensors
## 5 UbiPriSEQ: System Evaluation
Figure 4 depicts the performance of UbiPriSEQ and CMDP for the same four metrics as in Figure 3 against the task and data sizes (SpMV computations for matrices of sizes ranging between 10 KB and 40 MB). UbiPriSEQ outperforms CMDP, on average, for PrivacyMetric by \(1.12\times\), SINR by \(1.25\times\), latency by \(1.20\times\), and utility by \(1.18\times\). Figure 3: UbiPriSEQ: Performance vs. Time (**a**) PrivacyMetric, (**b**) SINR, (**c**) Latency, (**d**) Utility. These results illustrate that UbiPriSEQ was able to defend and recover from security issues in the network. UbiPriSEQ is successful in selecting the best offload and migration policies that ensure improved privacy, security, and QoS including latency, energy-efficiency, and reliability. The dynamic learning process in both the modules enables both the fog devices as well as the edge devices to optimize the offloading strategy while preserving user privacy and security. This multi-agent learning enables the devices to learn and decide the offloading decisions based on the strategies taken by the network module to provide security. Similarly, at the same time, the network module learns from the effects of the strategies taken by the device module to provide a better security (jamming, noise, etc.) strategy while maintaining the required privacy. This process of learning by modules from each other provides theUbiPriSEQ framework a better performance compared to the CMDP-based approach [44]. Moreover, as we have mentioned earlier, the CMDP-based approach [44] has only focussed on privacy, while we propose a holistic framework to optimise multiple design goals, and this differentiates our work from all the state-of-the-art relevant approaches.
## 2 Background
### Linear Detectors Based on Iterative Methods  
In this subsection, the signal \(\mathbf{\hat{x}}\) is equalized by avoiding inverse of the matrix using several iterative methods such as the SOR, the GS, the JA, and the RI. The concept of an iterative method depends on two major procedures: initial solution and iterative updates. The concept of iterative methods is that the initial solution is refined iteratively for few iterations to obtain a better and modified estimation. The iterations are repeated until the best performance is achieved.  
The SOR iterative method depends on the relaxation parameter (\(\omega\)). The estimated signal is expressed as  
\[\hat{\mathbf{x}}_{(n+1)}=\left(\mathbf{D}-\frac{1}{\omega}\mathbf{L}\right)^{-1} \left(\mathbf{y}_{MF}+\left(\left(1-\frac{1}{\omega}\right)\mathbf{D}+\frac{1} {\omega}\mathbf{U}\right)\hat{\mathbf{x}}_{(n)}\right), \tag{9}\]  
where \(\mathbf{U}\) and \(\mathbf{L}\) are the upper and the lower triangular matrices, respectively. Appropriate selection of \(\omega\) impacts positively the performance and the computational complexity. If \(\omega=1\), the SOR method is called the GS iterative method where the estimated signal is given as  
\[\hat{\mathbf{x}}_{(n+1)}=\left(\mathbf{D}-\mathbf{L}\right)^{-1}\left(\mathbf{ y}_{MF}+\mathbf{U}\hat{\mathbf{x}}_{(n)}\right). \tag{10}\]  
In general, the SOR and GS methods have a good convergence rate.  
In the RI iterative method, \(\mathbf{H}\) is utilized to find the estimated signal as  
\[\hat{\mathbf{x}}_{(n+1)}=\hat{\mathbf{x}}_{(n)}+\omega\left(\mathbf{y}_{MF}- \mathbf{H}\hat{\mathbf{x}}_{(n)}\right). \tag{11}\]  
It requires a large number of iterations to converge. Thus, high computational complexity is required.  
In the JA iterative method, the received signal is given as:  
\[\hat{\mathbf{x}}_{(n+1)}=\mathbf{D}^{-1}\left(\hat{\mathbf{x}}_{MF}+\left( \mathbf{D}-\mathbf{A}\right)\hat{\mathbf{x}}_{(n)}\right). \tag{12}\]  
In general, the JA method has a low convergence rate in comparison with the SOR and GS methods.  
In this paper, the initial solution depends on \(\mathbf{D}\) and \(\mathbf{S}\) and is expressed as one of the following solutions:  
* Using a stair matrix: \(\hat{\mathbf{x}}_{(0)}=\mathbf{S}^{-1}\mathbf{y}_{MF}\),
* Using a diagonal matrix: \(\hat{\mathbf{x}}_{(0)}=\mathbf{D}^{-1}\mathbf{y}_{MF}\).  
\(\hat{\mathbf{x}}_{(0)}\) is refined iteratively and the new solution is updated until the best performance is obtained.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### Low-Pass Flat Negative Group Delay Circuit
#### 2.2.1 Low-Pass Negative Group Delay Circuits with Different \(C_{1}\) Values  
When the values of \(L_{1}\), \(R_{1}\), \(L_{2}\), \(R_{2}\), and \(L_{3}\) in the circuit remain unchanged, as shown in Table 8, and the values of \(C_{1}\) are sequentially changed to 5 pF, 10 pF, and 15 pF, we observe the changes in the circuit transfer function and group delay. The circuit results in these three cases are shown in Table 9 and Figures 17 and 18. Through observation, it is found that changing the value of capacitor \(C_{1}\) does not change the group delay of the low-pass circuit at 0 MHz. However, as the frequency increases, the larger the value of capacitor \(C_{1}\), the smaller the negative group delay value. The change in capacitor \
# Quantum Neural Networks Approach for Water Discharge Forecast
## 2 Methodology and Data Series
### Quantum Neural Network
#### 2.2.3 Computational Process of QNN  
The computational process of a QNN consists of the following steps [55; 56]:  
1. Mapping Classical Data to Quantum States. The input data \(x\) are transformed into a quantum state using a Quantum Feature Mapping Circuit: \[\left|\,\psi(x)\right\rangle=U_{encode}(x)\left|\,0\right\rangle\] (5)
2. Quantum Circuit Processing. The Variational Quantum Circuit (VQC) applies quantum gate operations to the quantum state: \[\left|\,\psi(x,0)\right\rangle=U(\theta)U_{encode}(x)\left|\,0\right\rangle\] (6) where \(U(\theta)\) is composed of multiple trainable parameterized quantum gates.
3. The expectation value of quantum states is measured: \[f(x,\theta)=\langle\psi(x,\theta)|Z|\psi(x,\theta)\rangle\] (7) Measurement results are used for loss function calculation.
4. Parameter Optimization. Classical optimization algorithms update the quantum circuit parameters, \(\theta_{t}\), using Equation (8), \[\theta_{t+1}=\theta_{t}-\eta\
abla_{\theta}L(f(x,\theta),y)\] (8) where \(L\) is the loss function, \(\eta\) is the learning rate.
5. Training and Convergence. The above process is iterated until the loss function (mean standard error--MSE, or mean absolute error--MAE) converges, leading to optimized parameters \(\theta\).  
Quantum algorithms are a core component of quantum computing, designed to leverage the unique advantages of quantum computers to solve complex problems that are challenging for classical computers. These algorithms exploit quantum parallelism and coherence, achieving exponential acceleration over classical algorithms, thereby providing novel solutions in cryptography and number theory [32]. Quantum search algorithms, such as [PERSON]'s, demonstrate unique advantages in searching unstructured databases by achieving quadratic speedup. By utilizing quantum superposition and interference, [PERSON]'s algorithm enhances search efficiency and holds potential applications in information retrieval and optimization problems [32].  
The three main challenges of QNN are quantum decoherence, error correction, and scalability. Decoherence leads to computation errors. Therefore, the computation should be completely executed in the qubit coherence time, which is relatively short. The errors correction is based on the topological approach. Quantum computing may scale better than the classical one, but achieving this is challenging due to limited qubit connections. Moreover, most scaling techniques for classical computers do not apply to current quantum computers. In summary, quantum computing presents significant challenges for scalability [57].
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 2 Related Work  
### The Authentication of the Industrial Internet of Things  
The SCADA system is usually realized by integrating the Modbus industrial communication protocol. However, Modbus is designed to be fast and convenient, and only considers its use in the local network and the use of plaintext to transmit packets. With the increasing demand for automated industrial control systems, SCADA has moved from a strictly isolated network to a highly interconnected Internet. Therefore, the advantages of Modbus protocol in the past will cause serious security breach in modern application scenarios. Furthermore, the infrastructure is exposed to information security risks, which may be attacked by hackers at any time, causing significant economic losses. The authors of [17] proposed a mechanism for the authentication of industrial control systems. During the transmission of Modbus TCP/IP packets, the hash code of authentication is generated by intercepting the header fields of the TCP packet to confirm the identity of the device. Obviously, in the SCADA system, it is not sufficient to use only Modbus TCP/IP packet content for authentication. The authors of [18] proposed an automated validation of Internet security protocols and applications (AVISPA) tool that is used in the authentication and key agreement protocol of the Industrial Internet of Things (Industry 4.0). AVISPA is based on a layered approach protocol design, and uses ECC (Elliptic Curve Cryptography), PUF, hash function, concatenation, and XOR operations to provide mutual authentication between IoT nodes and centralized nodes. AVISPA can effectively resist DoS, replay and spoofing attacks. In the resource-constrained smart meters application scenario, the authors of [19] use fully hashed menezes-qu-vanstone key exchange mechanism along with Elliptic curve cryptography and one-way hash functions to provide trust, anonymity and mutual authentication, and reduce energy, communication and computing costs. In [20], this research work further proposed a lightweight remote device mutual authentication and key exchange model, which is applied in IIoT, named MAKE-IT. MAKE-IT uses symmetric and asymmetric key encryption, hash and timestamp technologies to perform mutual authentication and effectively defend against replay attacks, modification attacks and man in the middle attacks. It shows that in the security issues of the Industrial Internet of Things, how to use a lightweight authentication mechanism to effectively resist attacks is an important key factor.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 3 Results and Interpretation
### GNSS-Derived Tidal Heights
Therefore, to further explore the cause of these lower Q1 percentages, the track (East-West versus North-South), individual positional components (North-South, East-West and Up-Down) and vertical accelerations for tidal cycle A and tidal cycle B are plotted in Figures 3-8 respectively (Q1 solutions are shown in green, and Q2 solutions shown in orange).
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 1 Introduction
In Section 2, we describe the proposed SWIPT-aided OFDM-IDMA model. The analysis on the iterative MUD, as well as the problem formulation are also included in this section. We then propose the sub-optimal algorithm in Section 3, along with the discussion related to the impacts of the three readily optimized parameters on the system performance. Simulations are provided in Section 4 to evaluate the performance of the proposed algorithm. And the concluding remarks and future works are drawn in Section 5.