esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Signal Evaluation Approach under the Local Despreading Waveform Mismatch
### Formulation and Statistical Properties of Correlator Output
It can also be derived that the modulus of correlator output \(|\delta_{i}|\) has Rician distribution. According to Equations (11) and (12), the signal to noise and interference ratio (SNIR) can be obtained as:  
\[\rho(\tau_{i})=\frac{|c_{ss}(\tau_{i})|^{2}}{\text{Var}(c_{ws})}=\frac{TC_{s} \left|\beta_{-\beta_{r}/2}^{\beta_{r}/2}e^{-i2\pi f(\epsilon-\tau_{i})} \mathcal{X}_{s,\hat{s}}(f)df\right|^{2}}{\int_{-\beta_{r}/2}^{\beta_{r}/2}G_{ w}(f)G_{\delta}(f)df} \tag{15}\]  
Consider the prompt branch \(\delta_{\text{P}}\), where \(\tau_{\text{P}}=0\), then Equation (15) can be used to evaluate the performance of acquisition and data demodulation, where unmatched receiving is supported. In stable tracking where the random error of TOA estimation caused by noise and interference can be treated as zero, there is always \(e>>0\). Then moving the \(TC_{s}\) to the denominator from Equation (15), at \(\tau_{\text{P}}=0\), the first decoupled evaluation criterion is provided as:  
\[\eta_{\text{mis}}=\frac{\rho(\tau_{\text{P}})}{TC_{s}} \tag{16}\]  
where \(\eta_{\text{mis}}\) is defined as the correlation loss under unmatched receiving, used to characterize the performance of acquisition and demodulation. Different from the traditional evaluation criterion for acquisition in [10; 11] and the new unmatched criterion Equation (15), Equation (16) decoupled from the integration duration \(T\) and average signal power \(C_{s}\). In addition, when only WGN exists in \(w(t)\), \(\eta_{\text{mis}}\) is further decoupled from the WGN strength \(N_{0}\) or \(C_{s}/N_{0}\). Therefore, Equation (16) can better describe the performance of tracking without the effect of the receiver and environment. As a special case, when the local signal is exactly conjugated with the received signal, Equations (15) and (16) degenerates to the existing evaluation criterion for acquisition provided in [10; 11]. In this case, it explains the power loss caused by band limiting, while result Equation (16) considers the factors of band limiting and local code mismatch.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 6 Nonlinear Characterizations of Rigid Multilink Mechanism with Clearances
### Driving Speed Effect on Mixed Clearance Rigid Multilink Mechanism Nonlinear Characteristics
Figure 25: Phase diagram at the joint clearance of revolute pair: (**a**) phase diagram of clearance A\({}_{1}\) in the \(X\)-direction; (**b**) phase diagram of clearance A\({}_{1}\) in the \(Y\)-direction; (**c**) phase diagram of clearance A\({}_{2}\) in the \(X\)-direction; (**d**) phase diagram of clearance A\({}_{2}\) in the \(Y\)-direction. As can be seen from Figure 25, the phase diagram region with mixed clearances at the revolute joint clearances exceeds that with compound revolute joint clearances, indicating a higher degree of chaos and a more pronounced tendency of expansion. The corresponding Poincare maps in Figure 26 show that the Poincare mapping points at the revolute joint clearances under mixed clearances are more divergent and disordered. In contrast, the Poincare mapping points at the revolute joint clearances under compound revolute joint clearances are more concentrated, have a higher degree of repetition, and are relatively more concentrated and ordered. From this, it can be inferred that the introduction of the prismatic joint clearance F makes the nonlinear characteristics at the revolute joint clearances more pronounced, with greater chaotic properties, and the motion accuracy of the mechanism is affected.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Number of Chunks  
Minimal data chunks with an optimal size are advantageous as they reduce the computation overhead. Furthermore, a balanced number of chunks leads to better storage consumption at the fog server and cloud. Balanced data chunks improve bandwidth consumption and throughput, which is essential, especially in the context of the healthcare sector. In the case of the proposed technique, it dynamically adjusts the size of the chunks on the basis of the healthcare data. Furthermore, the usage of appropriate delimiters to segment the data and the identification of appropriate breakpoints ensure that the chunks are stable in number and optimal in size. The RAM has a slightly larger number of data chunks because it depends on extreme data values existing in the data to find a breakpoint. The RAM is sensitive to data variations, analyzing fixed and variable windows to determine valid breakpoints. In cases where the data readings exhibit frequent variations, the scheme generates smaller chunks than required. The CWCA and WCA-BF + ASE schemes maintain a better number of chunks by dynamically setting the chunk size and focusing on the minimum and maximum threshold values for the window size. Figure 2 illustrates the generated number of chunks throughout the deduplication mechanism. The input data string consists of different sizes, ranging from 10,000 to 50,000 bytes. DSW utilizes two sliding windows with variable intervals to segment the data. This segmentation generates many smaller-sized chunks, making the overall number of chunks slightly higher. By ensuring that the chunks are neither too small nor too large, the DICA enhances the efficiency of data storage and transmission, minimizing the number of chunks and the associated processing costs. To check the efficiency of the proposed scheme, the average number of chunks was considered. The simulation results demonstrate that for a string of  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Duration of Simulation & 400 s \\ Size of Data Packet & 300 B \\ Transmission Power at Node & 0.928 microjoules \\ Receiving Power & 0.052 microjoules \\ Nature of Channel & Radio \\ Type of Channel & Wireless Physical \\ Category of Mac & 802.11n \\ Nature of Queue & Priority Queue \\ Link Layer Type & Link Layer \\ Type of used Antenna & Omnidirectional \\ Maximum Data Packets in Line & 55 \\ Agent Trace & ON \\ Router Trace & ON \\ Trace of Mac & OFF \\ Number of Multipliers (\(\Downarrow\)) & 1–5 \\ Time Span & 0.1–1.0 s \\ Input String (Bytes) & 10,000–50,000 Bytes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters with corresponding values.  
20,000 bytes, the RAM generated 33.9, CWCA produced 31.3, WCA-BF + ASE produced 32.8, DSW attained 32.1, and the proposed DICA generated 30.5 chunks on average.
# An Automatic Embedded Device Registration Procedure Based on the OGC SensorThings API
## 4 Results
### A Real-world Experiment--Underground Metro Mall Environment Monitoring  
Besides the aforementioned applications demonstrating the capabilities of the proposed solution, we have also conducted a real-world experiment to evaluate the performance of the proposed solution. This experiment is an indoor monitoring application, where the testing site is at the East Metro Mall in Taipei. The East Metro Mall is a 725 m long underground public space with many restaurants and stores, which is usually crowded during rush hours, meal times, and weekends.  
We applied the Arduino Uno, DHT22 temperature and relative humidity sensor, and XBee communication module to construct 34 SW-IoT devices. With the message-forwarding capability of ZigBee, we only deployed one gateway (i.e., a laptop) to receive sensor observations. Figure 20 shows the floor plan and the locations of sensors (i.e., black dots) and the gateway (i.e., red star). The number besides each black dot represents the ID of each device. Figure 21 shows some photos of the deployed devices, where the white boxes contain the devices.  
The time period of the testing is from 27 September 2018 to 2 November 2018, where the sampling frequency for each device was set as 15 min. According to the sampling rate, we should receive 117,504 observations in the 37 days of the experiment. However, the gateway was accidently turned off on 11 October 2018. By excluding that day, we should receive 117,540 observations for both temperature and relative humidity. In reality, we have received 108,328 temperature observations and 108,328 relative humidity observations. Therefore, the overall successful return rate is 92.19%.  
All the devices can successfully upload sensor observations periodically. Figures 22 and 23 show the time-series observations of device 24 between 12 October 2018 to 2 November 2018 retrieved from the SensorThings API service. As the temperature is ranging between 22 to 26 degree Celsius, it shows a periodical trend that fits with the schedule of air conditioning. On the other hand, while the relative humidity shows a similar behavior with the temperature, it also captured the dry days from 10 October 2018 to 30 October 2018.  
Figure 20: The distribution of the sensors and the gateway of the real-world experiment.
## 3 Results and Discussion
### Optical Properties
This trend in optical bandgap variation concurs with the previously observed changes in the fractions of sp\({}^{2}\) and sp\({}^{3}\), and it aligns with the findings of a study conducted by [PERSON] [37], which reported an increase in the photon bandgap of amorphous carbon films with an increasing sp\({}^{3}\) fraction. These results underscore the significance of nitrogen doping in regulating the optical properties and microstructural characteristics of N-DLC films, offering valuable insights for potential optical applications in the field of diamond-like carbon thin films. The absorption coefficient and extinction coefficient images of the thin films under different gas flow rates were plotted, as shown in Figure 10. The extinction coefficient (K) is determined by K = \(\frac{\alpha\lambda}{4\pi r}\), where \(\alpha\) is the extinction coefficient and \(\lambda\) is the wavelength of light. The absorption coefficient decreases as the wavelength value increases. Compared with DLC without nitrogen doping, DLC has a larger range of numerical changes. The variation trends of the extinction coefficient and absorption coefficient are similar. We can also calculate the refractive index of N-DLC thin films using Equation (3), where R is the optical reflectance, and K represents the extinction coefficient [22]. The variations in the refractive index with wavelength for different samples are depicted in Figure 11. We can see that in the UV range, the values of the maximum refractive index do not differ much, only the corresponding wavelengths are shifted. In the visible range of 600-700 nm, several refractive index curves have more intersections and the refractive index of the film changes significantly as the gas flow rate is changed. These variations are attributed to changes in the polarization of light induced by alterations in the content and clusters of sp\({}^{2}\) within the films. \[\text{n}=\left[\left(\frac{4\text{R}}{\left(1-\text{R}\right)^{2}}\right)- \text{K}^{2}\right]^{1/2}-\frac{\text{R}+1}{\text{R}-1} \tag{3}\]  
Figure 10: The absorption coefficient and extinction coefficient of the sample. (**a**) Absorption coefficient plot of the sample; (**b**) Partial enlarged image of the Absorption coefficient of the sample; (**c**) Extraction coefficients diagram of the sample.
# InP-Based Foundry PICs for Optical Interconnects
## 2 InP-Based Generic-Foundry Photonic Integrated Circuits  
The purpose of generic foundries is to offer external designers access to the fabrication process of PICs in the form of regular MPW runs. In a generic-foundry concept, external PIC designers create the layout of their PIC by selecting the optical components from a list of standardized- and well-proven devices (also called building blocks or BBs) provided by the foundry in the form of a photonic design kit (PDK). The PDK contains all the information for PIC designers to design their PIC, including the design rules, and the simulation- and layout software. The PIC designer routes the BBs together using the available passive waveguides to finalize the PIC layout. Similar to electronic foundries, generic foundries for photonic ICs provide the following advantages to PIC designers: low cost PICs due to sharing of wafer space amongst several designers, reduced design time with the help of calibrated simulation software and extensive BB libraries, and fast turn-around time without the need for additional process development. In order to attract as many applications and PIC designers as possible, foundry-service providers aim to develop a fabrication process that can realize as many BBs and functionalities as possible.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 4 Evaluation
### Rating Prediction  
First, we uniformly sample the same number of negative implicit feedback as the positive implicit feedback. Then we set \(\lambda=0.02\), \(W_{ij}=0.5\)\(K=3\), \(d=3\) and validate the convergence of our model on both datasets. As the number of iterations increases, the RMSE decreases for models using both datasets, and they both achieve convergence after around 100 rounds of iterations, as shown in Figure 4.  
Furthermore, there are two important parameters in the loss function. One is \(\lambda\), which adjusts the proportion of each part in the loss function. The other is \(W\), which represents the weight of the negative implicit data. By varying the values of these two parameters and placing the RMSE results of the experiment on the contour plot as Figure 5, we can observe that when the model is training on _FilmTrust_, the RMSE reaches its minimum when \(W=0.5\) and \(\lambda=0.02\), and for _Epinions_ the minimum RMSE is obtained when \(W=1\) and \(\lambda=0.06\).  
Figure 4: The RMSE of _FilmTrust_ and _Epinions_ decreases as the number of model iterations increases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **SVD++** & **DMF** & **WRMF** & **PMF** & **DPMF** \\ \hline Computation & \(|R|\cdot K\) & \(|R|\cdot K\cdot d\) & \(m\cdot n\cdot K\) & \(|M|\cdot K\) & \((|R|+|M|)\cdot K\cdot d\) \\ \hline Communication & - & \(|R|\cdot K\cdot d\) & - & - & \((|R|+|M|)\cdot K\cdot d\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Complexity Comparison.  
Based on this result, we further explore how our method compares with other state-of-the-art methods. Specifically, we set \(\lambda=0.02\), \(W_{ij}=0.5\), \(d=3\) for conducting experiment on _FilmTrust_ and \(\lambda=0.06\), \(W_{ij}=1\), \(d=3\) for conducting experiment on _Epinions_. As shown in Figure 6, for the experiments on both datasets, the RMSE for all three models decreases as the size of the latent factor vector \(K\) increases and our model consistently outperforms the DMF and the SVD++ model. This is mainly because our model fully uses the implicit data which helps to train a matrix co-factorization model.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 4 System Design
### Feature Extraction  
A proper feature is critical in classification tasks. Generally, the moving speed of a person is constant in a short period, and some periodicity exists when the person is moving. For instance, when the person walks, two steps construct a period. However, it is a challenging task to analyze the periodicity directly from the waveform of the wireless signals. During our early exploration, we find that besides time-domain features, frequency-domain features can better characterize the waveforms in intrusion detection. As a result, in order to explore a scenario independent feature, we utilize time\(-\)frequency analysis on the waveform. Continuous Wavelet Transform (CWT) combined with wavelet variance is a proper tool to analyze the periodicity of the waveform. First, the wavelet coefficient of the first principle component of the CSIs after low-pass filter (_cpl_) is calculated utilizing CWT in Equation (5):  
\[W_{t}(a,b)=\int\limits_{-\infty}^{\infty}x(t)\frac{1}{\sqrt{a}}\psi(\frac{t-b }{a})dt, \tag{5}\]  
where \(x(t)\) is the first principle component of the CSIs after low-pass filter (_cpl_), \(a\) and \(b\) are scale and time, respectively. \(\psi()\) is the wavelet function, and db6 (Daubechies) wavelet [42] is selected as it provides the best performance after we have tried different wavelet functions.  
As shown in Figure 3, it can be clearly seen that some periodicity exists in the waveform after we conduct continuous wavelet transform. However, it is necessary to quantitatively calculate the significance of the periodicity to confirm that the periodicity is caused by human behaviors.  
Wavelet variance is widely used in meteorology to calculate the periodicity of precipitation. It reflects the distribution of the power of the wavelet coefficients of various scales. As a result, it can also describe the significance of the periodicity of human motion. The wavelet variance is calculated as Equation (6):  
\[\mathrm{var}(a)=\int_{-\infty}^{+\infty}\left|W_{f}(a,b)\right|^{2}db, \tag{6}\]  
where \(\left|W_{f}(a,b)\right|^{2}\) is the power of the wavelet coefficient of scale \(a\) at time \(b\).  
During our experiment, we find that the distribution of the wavelet variance is different among whether there is human motion as shown in Figure 4. In consequence, the wavelet variance is a proper feature for intrusion detection.
# Performance Evaluation of Bluetooth Low Energy: A Systematic Review
## 3 BLE Performance
### Range  
The maximum range a BLE RF module may reach is strictly linked to type of antenna used, e.g., PCB, chip or whip [93; 94] and to the _path loss_, i.e., a measure of how much the power of the radio signal is reduced along the path starting from the transmitter and ending in the receiver. The _path loss_ is defined as the difference between the radio power of the transmitter, described in Section 2.1.1, and the receiver sensitivity, both measured in dBm [31; 55].  
The correlation between the _path loss_ and the distance (\(d\)) is described in [55] with Equation (6) and shown in Figure 26. This equation is valid only for an isotropic antenna and ignores any losses, reflection, noise or obstacles existing in the environment [55].  
\[pathloss=40+25\times log(d) \tag{6}\]  
Figure 25: Average latency for _one-way_ and _round-trip_ message exchanges, for various _connInterval_ and BER values. Adapted from [50].  
According to the BLE specification [53], a BLE receiver must have a sensitivity lower than or equal to \(-\)70 dBm, while a transmitter could have its radio power included in a range from \(-\)20 dBm (0.01 mW) to \(+\)20 dBm (100 mW).  
Concretely, an STMicroelectronics BLE IC could set its radio power between \(-\)18 dBm and \(+\)8 dBm [69]. For example, with a transmitter using a power of \(+\)8 dBm and a receiver having a sensitivity of \(-\)70 dBm, the _path loss_ is \(+\)78 dB. With this value of _path loss_, this BLE communication can theoretically reach a distance of \(\sim\)33 m.  
Relative to the estimation of the maximum distance reachable by a BLE link, there is not a practical evaluation in the literature that analyzes the effective range of a BLE RF module.  
BLE is also used to detect the distance between different nodes of the network. In fact, RF modules in general provide a feature called Received Signal Strength Indicator (RSSI) that indicates the power strength of the Tx signal received by the Rx node. Several studies indicate empirically the relationship between RSSI and distance ([23; 29; 30; 33; 34]), but [22] finds a model that can be used to calculate the distance directly from the RSSI, as described in the following equation:  
\[RSSI=-10\times N\times log(d)+a \tag{7}\]  
where \(N\) is a constant assumed as one, \(d\) is the distance in meters between the two devices and \(a\) is the power of the Tx at a one-meter distance.  
In [31; 32; 91], a more precise method is described to compute the distance using the RSSI value. In particular, [91] also analyses and evaluates the operation and performance of BLE, changing the main parameters of _advertising_ and _scanning_. They also show how the cut-off frequency of the signal filtering influences the uncertainty in the position estimation.
## 6 Conclusions, Limitations, and Future Work  
This study investigates the potential of deep learning models for recognizing human activities, regardless of the user's position. We propose a novel deep residual network, Att-ResBiGRU, to achieve high accuracy in activity identification. This network incorporates three key components:  
* Convolutional blocks: These blocks extract spatial features from the sensor data. Each block uses a 1D convolutional layer to capture patterns, followed by batch normalization for stability and pooling to reduce data size while preserving key information.
* ResBiGRU block: This block captures temporal features by considering both past and future data points in the sequence. This bidirectional approach effectively models the dependencies and dynamics within the data.
* Attention mechanism: This mechanism assigns greater importance to informative features, enhancing the model's ability to distinguish between similar activities and improving recognition accuracy.  
We evaluated Att-ResBiGRU on three public datasets, achieving overall F1-scores of 86.69%, 96.44%, and 96.23% for position-independent HAR on Opportunity, PAMAP2, and REALWORLD16, respectively. Ablation experiments confirmed the effectiveness of each component, demonstrating the model's superior ability to differentiate between similar actions.  
Furthermore, our proposed model outperformed existing state-of-the-art techniques, showcasing its exceptional performance in position-independent HAR. Notably, Att-ResBiGRU surpassed the current leading method, establishing itself as a robust single classifier for this task. We also investigated the model's practical utility in situations without regard to position or subject, an area with considerable promise that has yet to see much research.  
Figure 13: Visualization of the Grad-CAM method.