esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Testing of Passive Linear TMD for Postural Tremor Attenuation
## 1 Introduction
The tremor assistance device was able to mitigate the fluctuation in the water level. In this paper, an experimental arm is fabricated in the form of a rectangular beam to reflect the flexion-extension motion of the hand in the horizontal direction. The system is excited by a mechanical shaker, using Electromyography (EMG) signals, measured for the extensor carpi radialis (ECR) muscle involved in the generation of tremors [12; 13]. TMDs designed in the form of thin cantilever beams with concentrated masses are used to test the ability of passive absorbers to reduce the amplitude of the system due to the excitation signal measured for a real patient. The mass of the TMDs respects the efficient mass range of 5-25% of the total mass of the system of interest [14]. Similar to most research work related to orthoses, the efficiency of the tremor controller is analyzed depending on the reduction in the power spectral density (PSD) of the measured signals [4]. The TMD is designed to reduce the amplitude of the system at 6.64 Hz, which represents the critical peak frequency of the measured tremor signal of the patient. The operating frequency of a TMD, having a 5.7% mass ratio, changes within a bandwidth of 5.52-7.57 Hz as the position of its proof mass changes. The corresponding mean value of modal damping ratio, for the used stainless steel beam material of the TMD, is estimated to be 0.33% using the half-power bandwidth method and 0.21% using the continuous wavelets method. The optimal modal damping ratio calculated numerically for a similar TMD is 0.99%. The best positions for the proof mass of the one TMD system, determined through experimental measurements, were those corresponding to an operational frequency between 6.44-6.66 Hz representing a 2 mm wide displacement range. Three similar TMDs, with a mass ratio of 15.7%, are attached to the experimental arm to provide a 69% reduction in the amplitude of the velocity signal measured using a vibrometer. Measurements usingan IMU show that the reduction in the acceleration, displacement, angular velocity, and angular displacement signals, is 29%, 79%, 67%, and 82%, respectively. The rest of the paper is organized as follows. Section 2 describes the experimental setup used to simulate the measured tremor of the patient and the fabricated TMD used as a passive controller and gives information about the sensors used to provide the processed signals. Section 3 provides the results of the measurements for the behavior of the TMD and its effect in reducing the amplitude of the system. Section 4 includes a discussion of the results in addition to a comparison with the literature. Section 5 provides the conclusions and perspectives.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 4 The Taxonomy of Malware Analysis and Detection Approaches
### Malware Detection Approaches
#### 4.2.3 Behavioral-Based  
After monitoring the executable files in an isolated environment and collecting the exhibited behaviors, features extraction techniques have been developed to extract the sensitive features by which the developed model can classify the known malicious behaviors as well as any behavior that seems to be similar to them with respect to false positive behaviors. The ability to identify novel malware behaviors in addition to the known ones based on collecting behaviors during run-time has made this approach more valuable than the signature-based approach. As a result, the majority of the studies in the literature review focused on using behavioral-based approaches in the form of continuous, sequential, and common behaviors to increase malware detection ratios.  
Some studies have been conducted based on the extracted continuous behaviors which, are represented by machine activities. The authors of [41] was interested in the Windows platform and used the Cuckoo sandbox to extract machine activity data (CPU, memory, received, and sent packets). After that, the observations were transformed into vectors, which were used to train and assess classification algorithms. Most of the previous studies were concerned with extracting API calls, system calls, opcodes, and others to form them sequentially (sequential behaviors) or into ordered patterns to understand the malicious functionalities. The sequential or ordered patterns can be API calls, registry data, and network data [13; 40; 45] or opcode sequences [76]. Moreover, the common behaviors that are performed by malware and benign samples can be used as an indicator to classify the investigated file between malware or benign classes in the binary classification models. In addition, those common behaviors can be observed in each malware family in the case of multi-classification models. The time of the matching process has been reduced from where the developed models classify the test files based on only the common behaviors. Common behaviors graph-based malware detection and classification models have been proposed by [10; 77] in their work through observing the most frequent behavior graphs in each malware family. Additionally, ref. [78] presented binary and multi-classification models using (LSTM) long-short term models based on the common API call sequences offered by each malware family.
# Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights
## 2 Smart Streetlights and Experimental Datasets  
### Smart Streetlights  
As shown in Figure 1, the camera and computing unit are integrated into the ready-made streetlight to form an intelligent streetlight. After the camera in the intelligent streetlight identifies the type and number of vehicles, the traffic data collected by the computing unit is transmitted to the cloud strategy platform through the wireless communication, power line carrier communication or visible light communication system. According to the information from smart streetlights, the cloud strategy platform could provide a variety of application services, such as traffic guidance and emergency rescue. Because the streetlight system is a complete infrastructure, cable layout and routing have cost advantages. In addition, streetlights can provide sufficient brightness for video and image detection to ensure the accuracy of detection.  
Furthermore, the number of nine kinds of vehicles in the field of vision of each streetlight can be summed up, and the ratio of the road area occupied can be accurately calculated according to the area of the road projected by each type of vehicle. This index is very important for illegal parking assessment, traffic diversion and traffic light strategy formulation, which is also difficult to achieve by other technologies (satellite remote sensing, microwave radar).
## 2 Modelling Methods
### VOF Method  
The sea surface with wave was presumed as an air-water interface, and the oil spill from underwater to water surface was a three-phase oil-water-air flow. Therefore, the VOF was adopted to track the free surface of the water [19]. The VOF method tracked the fluid flow in each control cell by constructing a fluid volume fraction function to inform the free surface shape with its function and derivative values [20]. When the specified phase fluid was located at the lower left of the grid element, the lower left vertex was the datum, while the fluid was in another orientation, and the datum was obtained by coordinate flipping. The outward normal in a free surface grid was taken as the unit of the outward normal vector. The horizontal direction was the X axis and the vertical direction was the Y axis. The fluid volume fraction function \(F_{q}\) satisfied Equation (1) [21]. If \(F_{q}~{}=~{}1\), the entire cell consisted of the \(q\)-th phase fluid; if \(F_{q}\ =\ 0\), there was no \(q\)-th phase fluid in the cell; and if \(0<F_{q}<1\), the cell was considered as an interface cell. \(F_{q}\) was a continuous function of time and space, and the sum of the volume ratios \(\sum F_{q}\) of each phase \(q\) was \(1\). In the VOF method, the physical parameter \(\varphi\) was calculated by Equation (2).  
\[\frac{\partial F_{\mathrm{q}}}{\partial t}+\frac{\partial\Big{(}uF_{q}\Big{)} }{\partial x}+\frac{\partial\Big{(}vF_{q}\Big{)}}{\partial y}\ =\ 0\,\sum F_{q}\ =\ 1 \tag{1}\]  
\[\varphi\ =\ \sum\varphi_{q}F_{q} \tag{2}\]
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 3 GNSS Performance Characterization
### Error Modelling
#### 3.1.3 Receiver Thermal Noise  
The signal tracking module in any receiver comprises a Delay-Lock -Loop (DLL) which dynamically tracks the code delay of each incoming signal. Thermal noise at the receiver front-end perturbs this process and induces errors on the code pseudo-range measurement estimates, the magnitude of which is dependent on the employed DLL discriminator. The variance of the thermal noise ranging error is given by [24]:  
\[\sigma_{noise}^{2}=c^{2}\frac{B_{n}(1-0.5B_{n}T_{I})\int_{-\frac{B_{f_{c}}}{ 2}}^{\frac{B_{f_{c}}}{2}}G_{s}(f)\sin^{2}(\pi f\Delta)df}{N_{0}\left(\int_{- \frac{B_{f_{c}}}{2}}^{\frac{B_{f_{c}}}{2}}G_{s}(f)\sin(\pi f\Delta)df\right)^ {2}}\cdot\left[1+\frac{\int_{-\frac{B_{f_{c}}}{2}}^{\frac{B_{f_{c}}}{2}}G_{s} (f)cos(\pi f\Delta)df}{T_{I}C/N_{0}\left(\int_{-\frac{B_{f_{c}}}{2}}^{\frac{B_ {f_{c}}}{2}}G_{s}(f)cos(\pi f\Delta)df\right)^{2}}\right] \tag{15}\]  
under the following assumptions:  
* The Early Minus Late Power (EMLP) DLL discriminator is used.
* The receiver's front-end filter is approximated by a rectangular band-pass filter centred at zero frequency and having two-sided bandwidth.
* The thermal noise is white with power spectral density \(B_{n}\) is the DLL bandwidth (Hz); \(G_{\rm s}(f)\) is the normalized signal power spectral density (/Hz); \(C/N_{0}\) is the carrier to noise ratio; \(T_{I}\) is the integration time; \(\Delta\) is the early-late chip spacing; \(B_{fe}\) is the bandwidth of the front-end.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 3 Methods
### Object Motion Prediction Module  
Mainstream methods to address distractor objects mainly consider pixel-level motion. This often leads to errors, especially when dealing with object occlusion or fast motion. Inspired by [20], we introduce a novel object motion prediciton module (OMPM) beyond  
Figure 4: Implementation of shape spatio–temporal constraints module.  
the pixel level. Part (b) in Figure 2 displays the structure of this module. Figure 5 shows the difference between our method and the pixel-level method. In video frames (\(I_{t}\)), where \(t\) denotes the \(t\)-th frame, we use binary masks to represent the positional information of objects. The binary mask is denoted as \(\mathbf{m}_{t}^{k}\), where \(k\) represents the \(k\)-th object. This module learns the motion from \(\mathbf{m}_{t-n}^{k}\) to \(\mathbf{m}_{t-1}^{k}\) to predict the positional information of the object \(\mathbf{m}_{t}^{k}\). In our scenario, given any number of frames, the module predicts one frame forward (\(\mathbf{m}_{t}^{k}=OMPM(\mathbf{m}_{t-n:t-1}^{k})\)), where \(OMPM\) represents the object motion prediction module.  
Inspired by conditional variational autoencoder (CVAE) dynamics modeling [29], in the training process, an encoder is utilized to understand the motion signatures extracted from the Gaussian distribution of latent variables \(\mathbf{z}_{t}\) used for prediction. Meanwhile, we store key motion signatures in the memory bank \(\mathbf{M}\). In the inference process, these stored patterns refine incomplete information from (\(\mathbf{m}_{t-n:t-1}^{k}\)), incorporating object-level motion details to predict the next frame \(\mathbf{m}_{t}^{k}\).
###### Abstract  
Modern systems of intelligent sensors commonly use radio data transmission. Hand movement acquisition with the use of inertial sensors requires the processing and transmission of a relatively large amount of data, which may be associated with a significant load on the network structure. Network traffic limitation, without losing the quality of monitoring parameters from the sensor system, is therefore important for the functioning of the radio network which integrates both the telerransmission sensor system and the data acquisition server. The paper presents a wearable solution for hand movement acquisition, which uses data transmission in the Wi-Fi standard and contains 16 MEMS (Micro Electro Mechanical System) sensors. An adaptive algorithm to control radio data transmission for the sensor system has been proposed. The algorithm implemented in the embedded system controls the change of the frame length, the length of the transmission frame and the frequency of its sending, which reduces the load on the network router. The use of the algorithm makes it possible to reduce the power consumption by the sensor system by up to 19.9% and to limit the number of data transferred by up to about 91.6%, without losing the quality of the monitored signal. The data analysis showed no statistically significant differences (\(p>0.05\)) between the signal reconstructed from the complete data and processed by the algorithm.  
transmission control algorithm; wireless sensor; wearable system; hand MoCap; MEMS; IoT 22, 9866. [[https://doi.org/10.3390/s22249866](https://doi.org/10.3390/s22249866)]([https://doi.org/10.3390/s22249866](https://doi.org/10.3390/s22249866))
## 4 Discussion
### Ex Vivo Measurement  
The endosteal surface of the human tibia sample was tracked with and without PAC methods; however, more endosteal surface locations were retrieved when PAC was used. The cortical thickness measured by ultrasound was consistent with the reference value measured by HR-pQCT. The cortical sound velocity of (2857 \(\pm\) 53) m/s was in the range of the cortical speed of sound values typically found in human cortical bone [17]. Our previous study showed a dependency of cortical speed of sound on cortical porosity (_Po_) \(Ct.v_{1}\)\({}^{\text{fit}}\) = 0.39\(\cdot\)Po\({}^{2}\) - 51.4\(\cdot\)_Po_ + 3485 [m/s], Figure 6a in [19]). By inserting the reference cortical porosity value of 15.1% obtained from HRpQCT into this equation a speed of sound value of \(Ct.v_{1}\)\({}^{\text{fit}}\) = 2804 m/s was determined for the human tibia sample. The MF-based estimation was in the range of the expected speed of sound value; however, this observation needs to be confirmed in a larger sample size. In conclusion, the ex vivo measurement on a human tibia sample suggests the ability to measure cortical thickness and speed of sound using the MF approach by incorporating PAC methods.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### MDP Model for Expected Total Rewards
#### 3.2.3 Discretization of System States and Actions  
The optional values of the system states and actions should be finite in MDP model. However, the system states include links and the battery states, and the relay power actions are continuous valuesin the wireless relay network. Therefore, it is necessary to discretize the system states and relay power actions. The relay system states consist of channel fading values and battery levels, which can be given as \(s_{i}\triangleq\left\langle{{{h_{{\rm{S}R}^{i}}},{{h_{{\rm{S}D}^{i}}},{{h_{{\rm{R }D}^{i}}}},{{\epsilon}(i)}}}\right\rangle}\). We discretize the channel states by reference to the method in literature [28]. Denoting \(H=\{H_{1},H_{2},\ldots H_{3}\}\) as the set of channel fading values, which is an equal-difference sequence. The probability when the channel fading is \(H_{k}\) with \(k\in\{1,2\cdots K\}\) can be calculated as:  
\[p\big{[}{{h_{j}^{i}}={H_{k}}}\big{]}=\frac{1}{K}\forall i\in \mathbb{N},j\in\{SR,SD,SRD\},k\in\{1,2\cdots K\}. \tag{29}\]  
We divide the battery into M + 1 energy level. And the battery states set is taken as \(\varepsilon(i)\in\varepsilon=[0,1,\cdots,m_{t},\cdots M]\). The real-time energy level of the battery can be calculated by  
\[\varepsilon(i)=m=\bigg{\lfloor}\frac{{{E_{i}}M}}{{{E_{\max}}}} \bigg{\rfloor}. \tag{30}\]  
Denote \(A_{i}^{\prime}=[0,\frac{{{p_{{\rm{R}}}^{\max}}}}{L},\ldots,{{p_{{\rm{R}}}^{ \max}}}]\) as the action set, which is also an equal-difference sequence. Actually, the value of the relay transmit power is constrained by the battery level. Thus, the action set is given as \(A_{i}=[0,\frac{{{p_{{\rm{R}}}^{\max}}}}{L},\ldots,\min({{p_{{\rm{R}}}^{\max}} },{{p_{{\rm{x}}}^{i}}})]\), where \({{p_{{\rm{x}}}^{i}}}\) is calculated by:  
\[{{p_{{\rm{x}}}^{i}}}=\bigg{\lfloor}\frac{{2\varepsilon(i){{E_{\max}}}{L}}}{M \overline{{{\rm{T}}}{{p_{{\rm{R}}}^{i}}}}}\bigg{\rceil}*\frac{{{p_{{\rm{R}}}^ {\max}}}}{L}. \tag{31}\]  
\(\lfloor x\rfloor\) is the function that rounds the variable x down.
# Solving Multidimensional Partial Differential Equations Using Efficient Quantum Circuits
## 4 Materials and Methods  
In this section, we present the materials and methods associated with our proposed algorithm to solve Partial Differential Equations (PDEs). The proposed work leverages the Finite Discrete Method (FDM) for the discretization of PDE. After discretization, Variant 1 of the proposed algorithm uses Classical-to-Quantum (C2Q) encoding [21] and numerical instantiation [22], while Variant 2 employs C2Q encoding with Column-by-Column Decomposition (CCD) [23, 24]. Here, we have used the multidimensional Poisson equation as a case study. For preprocessing steps, we have employed polar decomposition and Singular Value Decomposition (SVD). For detailed information regarding these preprocessing steps, refer to the Appendix B. Some of the major contributions of our work are as follows:  
* Proposed an accurate, scalable, and time-efficient algorithm for solving multidimensional Partial Differential Equations (PDEs).
* Evaluated the proposed algorithm on noise-free and noisy simulators, as well as on hardware emulators and real quantum hardware from IBM-Qiskit.  
Figure 3: T2 time of individual qubits in FakeSherbrooke and ibm_shebrooke. (a) T2 time of hardware emulator (FakeSherbrooke). (b) T2 time of real quantum hardware (ibm_shebrooke).  
* Compared the proposed work with a variational algorithms-based PDE solver in terms of accuracy, scalability, and total execution time, using the Poisson equation as a case study.
* Presented the theoretical execution time comparison of the circuits generated by our algorithm for hardware emulator and real quantum hardware with the decoherence times of these systems.
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
## 2 System Model
### Path loss Model
The mean path-loss exponent, denoted as \(n\), is determined by estimating the average slope of the samples in relation to the logarithmic distances to the GW. This estimation is achieved through Least Squares Linear Regression. The plot of these measured RSSI samples against the log-distance to the GW is depicted in Figure 6. Organizing the samples based on log-distance enables the estimation of the slope of the linear log-distance line, expressed as: \(10\times n\times log_{10}\left(\frac{d}{d_{0}}\right)\). Figure 6 illustrates the estimated mean-path-loss exponent, featured in the legends of each measurement dataset, along with their respective 95% confidence intervals for each channel. The 95% confidence intervals show that the mean path-loss exponent can vary from a value of \(n=-2\) all the way up to \(n=-5.5\), with an overall mean value of \(n=-4\) averaged across channels. This variation is attributed to differences in terrain, surrounding buildings, and interference levels. In numerical results, we assume a mean path-loss exponent value of \(n=-4\) and log-normal shadow fading with a standard deviation \(\sigma=1.4\) dB (ref. Figure 7). This simplifies the model. In Figure 6, the term \(\Delta\) represents the additional path-loss coming from the hardware losses such as RF insertion losses, as the signal travels from the antenna port to the hardware's chipset port. The field measurements taken in an urban area such as Westchester, LA, show that the shadow fading standard deviation is estimated to be \(\sigma=1.4\) dB averaged across locations (ref. Figure 7). This estimate is based on measurements within 400 m to the GW, where the RSSI is represented in full range to cover the PSR of 100% down to 0%. In Figure 7, on each box, the central red line indicates the median, and the bottom and top edges of the box indicate the 25 th and 75 th percentiles of the measurements, respectively. The whiskers extend to the most extreme data points not considered as outliers, and the outliers are plotted individually using the '+' symbol. Figure 8 illustrates the probability mass function of the RSSI samples collected at two distinct positions (depicted in Figure 8a,b). In the plot, the terms \(\mu\) and \(\sigma\) represent the mean and standard deviation of the RSSI samples, respectively. The results suggest that both the average RSSI value and its standard deviation are impacted by factors such as location, environment, and proximity to the GW. Figure 6: Path loss measurements plotted against the logarithm of the distance from the GW in an urban neighborhood. Figure 7: RSSI sample standard deviation across [902, 902.2, 915, 927] MHz channels, analyzed with respect to distance to the GW.
# A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array
## 3 Calibration Method
### Influence of Array Placement Error on Calibration  
During the calibration process, the composite module array needs to be placed along the X direction of the machine tool. If the array cannot be placed to the ideal position due to the array placement error, as shown in Figure 5, the calibration data at this time will produce errors. Therefore, it is necessary to analyze the influence of array placement errors on the calibration.