esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of LEO Satellite Downlink
To quantify the difference between the two cases, we defined \(\beta\), which is the average difference between the two models expressed as (5):  
\[\beta=\frac{1}{N}\sum_{k=1}^{N}\left|P_{s}(k)-P_{f}(k)\right| \tag{5}\]  
where \(N\) is the number of elevation angle points, \(P_{s}\) is interference power of each elevation angle with the space environment, and \(P_{f}\) is interference power without the space environment. When the J/S = 0 dB, \(\beta\) is 3.53, which is due to the high atmospheric loss and bore-sight error at the low elevation angle of \(\theta_{gs}\) = 10\({}^{\circ}\). Figure 8b presents the regression model when J/S is changed to 5 dB, and it was observed that \(\beta\) is 2.56 dB in this case. Figure 8c,d illustrate the regression models with J/S = 10 dB and J/S = 15 dB, where \(\beta\) are 2.16 dB and 1.63 dB, respectively. Figure 9 illustrates J/S with \(\theta_{gs}\) = 60\({}^{\circ}\), which is the result of seeing the effect of the space environment when the elevation angle \(\theta_{gs}\) is increased. In Figure 9a, the solid and  
dashed lines indicate the regression models with and without the space environment when J/S is 0 dB. A reduced \(\beta\) of 0.02 was observed in this case because the atmospheric loss and bore-sight error decrease as the elevation angle \(\theta_{gs}\) approaches 90\({}^{\circ}\). Figures 9b, 9c and 9d present the regression models when J/S is changed to 5 dB, 10 dB, and 15 dB, respectively. For each case, \(\beta\) were 0.37 dB, 0.62 dB, and 0.14 dB, respectively. The detailed values of J/S results are listed in Table 2. As can be seen from the table, the smaller the elevation angle, the larger the overall \(\beta\). The results demonstrate that it is necessary to consider the space environment to accurately analyze the LEO satellite downlink, especially at the low elevation angle of the satellite.
## 3 Target State Prediction with Uncertainty Estimation
### Intention Inference and State Prediction
\right]^{T }\\ \text{where,}&=p_{k-1|k-1}+v_{k-1|k-1}\cdot dt\\ v_{k|k-1}&=v_{k-1|k-1}+q_{k-1|k-1}\cdot dt\\ a_{k|k-1}&=a_{k-1|k-1}\Big{(}1-\frac{dt}{v_{x}}\Big{)}+a_{max}\left[1-\left( \frac{v_{k-1|k-1}}{v_{des}(p_{k-1|k-1})}\right)^{\delta}\right]\cdot\frac{dt}{ v_{x}}\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\the probability update process of the IMM filter. First, the mixing probability of the \(i\)-th model is calculated as follows:  
\[\mu_{i|j}(k-1|k-1)=\frac{1}{c_{j}}p_{ij}\mu_{i}(k-1) \tag{7}\]  
where \(c_{j}\) is the normalizing factor and \(\mu_{i}\) is the \(i\)-th model probability. \(p_{ij}\) is the transition probability from the \(i\)-th model to the \(j\)-th model under the assumption of the Markov chain. The mixed state is calculated as follows:  
\[\hat{x}_{oj}(k-1|k-1)=\sum_{i=1}^{m}\hat{x}_{i}(k-1|k-1)\times\mu_{i|j}(k-1|k- 1)) \tag{8}\]  
Based on the mixed state, filtering using the IDM-based EKF is performed. The model probability is updated with the likelihood ratio \(\Lambda_{j,k}\) as follows:  
\[ \begin{split}&\mu_{j,k}=\frac{1}{\epsilon}\Lambda_{j,k}\sum_{i}p_{ i,j}\mu_{i,k-1}\\ &\text{where, }\Lambda_{j,k}=N(r_{j,k};\,0,\,S_{j,k})\end{split}  \tag{9}\]  
In Equation (9), \(r_{j,k}\) is the residual of the \(j\)-th model, and \(S_{j,k}\) is the covariance matrix of the residual. The remaining parts and details of the IMM filter can be found in [27]. The process update of each IDM-based EKF has been conducted within the prediction horizon to predict the future states of the target vehicle. In this study, the fused behavior model has been derived by using the individual behavior model based on the estimated probability of each model. Therefore, the desired velocity profile of the fused behavior model has been defined as follows:  
\[v_{des,fused}(s)=\sum_{j}\mu_{j}\cdot v_{des,j}(s) \tag{10}\]  
The fused behavior model, \(v_{des,fused}\)(s), has been used as a desired velocity profile of the IDM to predict the future state of the target vehicle based on the sensor measurements. The example of state prediction with an estimated probability is summarized in Figure 7. As shown in Figure 7, the proposed predictor provides more accurate prediction results than the single model prediction case. Figure 7: Target state prediction results.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 3 Design and Implementation of Access Control Method
### Access Control Method Implementation  
#### 3.2.1 System Initialization  
The consumer needs to obtain the access permission of the content to obtain the content when the user first visits, and the consumer user issues the signed interest package. The format of the interest package name can be \"ndn/user/number/i/registration,\" and the registration is completed and the permission is obtained. In the process, the content publisher obtains the consumer user attribute and also authenticates the user's identity through the attribute. The definition and retrieval of attributes is transparent to NDN. After receiving the interest package requesting the content, the content publisher verifies the integrity and authenticity of the registered interest package. If the interest package is authenticated, the content publisher will send a registration confirmation packet to the consumer, and the publisher will verify the user and complete the user registration process (see Figure 2). The initialization process needs to execute the following functions:  
1. Global Setup \((\lambda,U)\rightarrow\{GP,uid,aid\}\)  
Global initialization function, CA first selects a system security parameter \(\lambda\) and an attribute domain \(U\), and finally generates a global public parameter \(GP\), \(AA_{i}\) identity _aid_ and user identity _uid_.  
2. Authority Setup \((aid)\)\(\rightarrow\)\(\{PK_{aid},SK_{aid},\ \{PK_{sk}\}_{aid\in IA}\}\)  
Figure 2: User registration process.  
\(AA_{i}\) initialization function that is executed by each \(AA_{i}\). Enter aid, output a pair of \(AA_{i}\) public and private keys \(PK_{aid}\) and \(SK_{aid}\), and generate an attribute public key \(\{PK_{sk}\}_{aid\in LA}\) for each attribute managed by the \(AA_{i}\).
## 2 Materials and Methods
### Improvement of Cross-Polarization
The connecting metallic vias are located on the two sides of the z-axis along the x-axis; the horizontal distance between the magnetic dipole and the connecting metallic vias is represented as \(L_{\text{st}}\). Thus, the electric field generated by the shorted metal via at an arbitrary observation point P in the far field can be found to be  
\[\overset{\rightarrow}{E}_{hole}=\frac{1}{j\omega\varepsilon\mu}\
abla\times \
abla\times\overset{\rightarrow}{A}_{x}=-j\frac{k^{2}K_{2}}{\omega\mu \varepsilon}(\hat{\theta}\cos\theta\cos\varphi-\hat{\phi}\sin\varphi)\frac{e^{ -jkr}}{r} \tag{2}\]  
where \(k=2\pi/\lambda\) is the wave number, and \(K_{2}=\mu_{0}I_{0}L_{2}/4\pi\) is the total amplitude of the vector potential function. Then, the total electric field in far field produced by the magnetic dipole and the two shorted metal holes can be derived as  
\[\overset{\rightarrow}{E}_{total}=\overset{\rightarrow}{E}_{aperture}+e^{- j(k\frac{L_{\text{st}}}{\cos\theta_{1}}\cos\left(\theta-\theta_{1}\right)+\delta_{0})} \overset{\rightarrow}{E}_{hole1}+e^{-j(k\frac{L_{\text{st}}}{\cos\theta_{1}} \cos\left(\theta+\theta_{1}\right)+\delta_{0})}\overset{\rightarrow}{E}_{hole2} \tag{3}\]  
when \(\theta=0^{\circ}\), \(\varphi=0^{\circ}\), the E-field along the +z axis is  
\[\overset{\rightarrow}{E}_{total}=-\hat{\theta}(jkK_{1}+j\frac{k^{2}K_{2}}{ \omega\mu\varepsilon}e^{-j(kL_{\text{st}}+\delta_{0})})\frac{e^{-jkr}}{r} \tag{4}\]  
It is seen that the vertical electric field is mainly controlled by the length \(L_{\text{st}}\) of the DSPSL. When \(kL_{\text{st}}+\delta_{0}=\pi\), the electric fields generated by the magnetic dipole and the connecting metallic holes are out-of-phase in the vertical direction, the amplitude of the vertical component electric field has a minimum value of \(|E_{1}|-|E_{2}|\). Thus, the cross polarization of our proposed antenna has the optimal value. The orthogonal gain components \(\text{Gain}(\theta)\) and \(\text{Gain}(\varphi)\) of Antenna A and the proposed Antenna B for various \(L_{\text{st}}\) are shown in Figure 8a,b, respectively. For Antenna A, as the length \(L_{\text{st}}\) of the DSPSL increases, the amplitude of \(\text{Gain}(\varphi)\) will be reduced, while the amplitude of \(\text{Gain}(\theta)\) only has a small change. A similar change can be observed for the \(\text{Gain}(\varphi)\) of Antenna B with that of Antenna A. However, the amplitude of the \(\text{Gain}(\theta)\) can be effectively reduced by adjusting the length of DSPSL; the proposed antenna can achieve a lower cross-polarization than the conventional SIW-fed dipole antenna. Figure 7: The equivalent source’s model for the SIW-fed folded dipole antenna.
## 2 Materials and Methods
### Procedure  
The ADL-TTD measurement procedure includes several steps to ensure accurate calibration and setup. Each participant stands or sits in front of the device, depending on their ability to stand. A box, attached to a cable, is placed on a horizontal tabletop at the participant's pelvic level (as shown in Figure 3). The device is then recalibrated in this position. A screen in front of the ADL-TTD displays the measurement sequence.  
The test begins with a five-second preparation phase, during which the participant lifts the box to a height of 15 cm above the tabletop, with the rope limiting any further lift. In the following five seconds, the participant performs a maximum voluntary contraction (MVC), lifting the box as forcefully as possible while keeping it upright. This is followed by a 10-second recovery period, allowing the box to be returned to the tabletop. The process is repeated two more times.  
Figure 3: Measurement position when using ADL-TTD.  
Figure 2: Six directions of tilt across the x-, y-, and z axes for the box. (In this figure, the right hand is the AH.)  
Participants perform a bimanual lifting task using a box attached to the ADL-TTD. The task is divided into two performance zones:  
* Max Zone: Lifting tasks are performed at an intensity level above 80% of the MVC. This zone evaluates upper limb strength and positional performance during levels of force control tasks.
* SubMax Zone: Lifting tasks are performed at an intensity level of force control below 80% of MVC. This zone assesses positional performance during moderate levels of force control tasks, which are closer to real-world daily living activities.  
During these tasks, IMU sensors attached to the box measure six position parameters: anterior and posterior tilt, affected hand (AH)/non-affected hand (NAH) forward rotation, and AH/NAH downward tilt. In this study, we assess positional performance using IMUs, detected along x-, y-, and z-axes in degrees\({}^{\circ}\). Positional performance is evaluate at various levels of force control (very low to very high MVC) to understand how children with neurodevelopmental disabilities manage object positioning in a bimanual box-lifting task.
## 3 Results and Discussion
### Validation of the GC-MS Method  
The selected validation parameters (such as linearity, correlation coefficient, LODs, and ILOQs) for the identified analytes were studied in spiked water samples and are collected in Table 1. The spiked water samples were analyzed by HS-GC-MS. A seven-point calibration curve was constructed by extracting seven spiked water samples containing all the analytes at different concentration levels in the range from 0.01 to 10 \(\upmu\)g/mL. For each calibration level, three replicate headspace samplings were performed. Acceptable linearity was obtained for all the studied VOCs over the whole studied concentration range with correlation coefficients (r2) higher than 0.990. The high correlation coefficient demonstrates a directly proportional relationship between the concentration of the analytes in the HS phase and the initial concentration of the sample. The LODs based on a signal-to-noise ratio (S/N) of 3 ranged from 0.19 to 4.02 \(\upmu\)g/L, which is below regulated limits for these compounds in drinking water. The validation parameters are shown in Table 1. The obtained LODs are lower than the established maximum permissible levels of these contaminants in water destined for public supply of 1.0 mg/L (toluene), 0.7 mg/L (ethylbenzene), 10 mg/L (total xylenes), and 0.1 mg/L for chlorobenzene.  
Figure 2: The effect of sampling time on the efficiency of toluene, ethylbenzene, chlorobenzene, and xylene transfer to the gas phase. The dependence of the peak areas on the sampling time.
## 4 Experimental Setup
### Algorithm Parameter Settings  
To enhance the performance of the IGWOHHO algorithm and ensure the robustness of its parameters, a sensitivity analysis was conducted on eight key parameters. Figure 7 illustrates the results using a heat map, where the color gradient ranges from black to white, with darker colors representing lower values and lighter colors corresponding to higher values. The analysis shows that the initial step size \(S_{0}\) has minimal impact on the objective function, indicating strong robustness, while the perturbation factor, switching thresholds, and elite retention ratio \(P\) exhibit greater sensitivity and require further tuning.  
Figure 6: Flowchart of the IGWOHHO algorithm.  
Since the algorithm's performance tends to decline as the problem size increases, this study employed the Taguchi orthogonal design method to identify suitable parameter combinations through preliminary experiments. A signal-to-noise ratio (S/N ratio) analysis was performed using Minitab 19 software, and the main effect plot of the S/N ratio is presented in Figure 8. A higher S/N ratio indicates greater stability and improved solution quality.  
Based on the analysis, the population size is set to 150, the maximum number of iterations to 500, the perturbation factor to 0.4, the minimum switching threshold to 0.025, the maximum switching threshold to 0.09, and the elite retention ratio to 0.08.
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 3 Methodology
### Thresholding for Anomaly Detection  
#### 3.4.1 Static Thresholding for Anomaly Detection  
Static thresholding is a widely used approach for anomaly detection, particularly in scenarios where the anomaly ratio is known or can be estimated. This method involves computing a fixed threshold based on the distribution of reconstruction errors (e.g., the Mean Squared Error or MSE) derived from the training data. The threshold is typically set as the \((100-\alpha)\)-th percentile of the error distribution, where \(\alpha\) represents the expected proportion of anomalies in the data. Formally, the threshold \(\tau\) is defined as follows:  
\[\tau=Q_{100-\alpha}(\mathcal{E}_{\text{train}}), \tag{16}\]  
where \(Q_{100-\alpha}(\mathcal{E}_{\text{train}})\) denotes the \((100-\alpha)\)-th percentile of the reconstruction errors \(\mathcal{E}_{\text{train}}\) computed on the training set. Anomalies in the test set are then identified by comparing the reconstruction errors \(\mathcal{E}_{\text{test}}\) to the threshold \(\tau\):  
\[\text{Anomaly}_{\text{static}}(x)=\begin{cases}1&\text{if }\mathcal{E}_{\text{test}}(x)> \tau,\\ 0&\text{otherwise.}\end{cases} \tag{17}\]  
This approach is computationally efficient and straightforward to implement. However, it assumes that the anomaly ratio \(\alpha\) is constant and that the distribution of reconstruction errors remains stable over time. These assumptions may not hold in dynamic environments, leading to suboptimal performance.
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
###### Abstract  
5G technology is spreading extremely quickly. Many services, including Voice Over Internet Protocol (VoIP), have utilized the features of 5G technology to improve their performance. VoIP service is gradually ruling the telecommunication sector due to its various advantages (e.g., free calls). However, VoIP service wastes a substantial share of the VoIP 5G network's bandwidth due to its lengthy packet header. For instance, the share of the packet header from bandwidth and channel time reaches 85.7% of VoIP 5G networks when using the IPv6 protocol. VoIP designers are exerting considerable efforts to solve this issue. This paper contributes to these efforts by designing a new technique named Zeroize (zero sizes). The core of the Zeroize technique is based on utilizing the unnecessary fields of the IPv6 protocol header to keep the packet payload (voice data), thereby reducing or \"zeroizing\" the payload of the VoIP packet. The Zeroize technique substantially reduces the expanded bandwidth of VoIP 5G networks, which is reflected in the wasted channel time. The results show that the Zeroize technique reduces the wasted bandwidth by 20% with the G.723.1 codec. Therefore, this technique successfully reduces the bandwidth and channel time of VoIP 5G networks when using the IPv6 protocol.  
5G network; IPv6; VoIP; bandwidth utilization; header overhead +
Footnote †: journal: Applied System
# Deterministic Coresets for \(k\)-Means of Big Sparse Data +
## 7 Comparison to Existing Approaches
### Results  
The results of experiment for \(k=15,20,25\) on small datasets for offline computation are depicted on Figure 2, where it's evident that error of kmeans computation fed by our coreset algorithm results outperforms error of uniform and non-uniform sampling.  
For streaming computation model our algorithm is able to provide results which are better than other two as could be explored in Figure 3. In addition, existing algorithms suffer from \"cold start\" as common in random sampling techniques: there is a slow convergence to the small error, compared to our deterministic algorithm that introduces small error already after a small sample size.  
Figure 2: Offline coresets computation for small datasets (uniform sampling, non uniform sampling and our algorithm).  
At Figure 4 presented results of the experiment on Wikipedia dataset for different values of \(k=32,64,128\), as it could be easily observed proposed coreset algorithm provides good results of big sparse dataset and provides lower energy cost compared to uniform and non-uniform approaches.  
Figures 5-7 show the box-plot of error distribution for all the three coresets in the offline and streaming settings. Our algorithm shows a little variance across all experiments, its mean error is very close to its median error, indicating that it produces stable results.  
Figure 4: Comparison of uniform sampling, non uniform sampling and our algorithm based on Wikipedia in distributed setting with 16 servers.  
Figure 5: Error (y-axis) box-plots for real-data sets, offline computation model.  
Figure 3: Streaming coresets computation for small datasets (uniform sampling, non uniform sampling and our algorithm).  
Figure 8 shows the memory (RAM) footprint during the coreset construction based on synthetically generated random data. The oscillations corresponds to the number of coresets in the tree that each new subset needs to update. For example, the first point in a streaming tree is updated in \(O(1)\), however the \(2^{i}\)th point for some \(i\geq 1\) climbs up through \(O(\log i)\) levels in the tree, so \(O(\log i)\) coresets are merged.
## 4 Modeling of Multi-Parameter Degradation
### Types of Copula Function and Determination  
There are many kinds of binary Copula functions, such as Frank, Gumbel, and Clayton. The three different widely used binary Copula functions are shown in Table 1, where \(\mu\) and \(\
u\) are the independent variables which are the marginal distribution functions of a product, such as \(\mu=F_{1}(t)\) and \(\
u=F_{2}(t)\), and \(\theta\) values are the parameters describing the correlation between binary degradation performances.  
For Frank and Clayton functions, the closer the \(\theta\) value is to 0, the weaker the correlation between variables. On the other hand, for the Gumbel function, the closer the \(\theta\) value is to 1, the stronger the correlation.  
Different Copula functions will lead to different results, and for making the prediction results more reasonable and accurate, it is necessary to determine the appropriate Copula function. As the Akaike Information Criterion (AIC) value is a quantitative method widely used to evaluate the goodness-of-fit, it can be applied to the determination of the Copula function. The AIC criterion is defined as:  
\[AIC=-2 lg(A)+2m \tag{18}\]  
where \(lg(A)\) is the log-likelihood function, and \(m\) is the number of estimated parameters of the model. If the AIC value is smaller, the selected model is more appropriate.
## 4 Experiment Evaluation
### Results and Discussion
Thus, for any match ratio, the input stream generated using the ClamAV type 1 pattern set contained fewer patterns than that using the Snort pattern set, resulting in greater access to states at lower depths for the ClamAV type 1 pattern set and higher throughput values. As shown in Figure 6, the HBM and FHBM algorithms for the ClamAV type 3 pattern set exhibited a different throughput-head size relationship compared to the other two pattern sets. Maximum throughputs for both algorithms were initially obtained at a head size of 10,000 states--much smaller than that shown in Figures 4 and 5. This is explained by the approximately 10,000 states found at the lowest eight depths for the ClamAV type 3 pattern set. As discussed earlier, most state access occurred at lower depths, therefore head size increase exerted little impact on throughput when head sizes exceeded 5000 states. Second, when the match ratios were 8% or 32%, a large head size resulted in decreased throughput--a decrease that was more obvious at 32%. As shown in Table 4, 94.8% of the ClamAV type 3 patterns were long. For high match ratios, states at higher depths were accessed more frequently, resulting in main memory access when the head size exceeded the cache size. Last, for the ClamAV type 3 pattern set, both algorithms achieved higher throughput values than the other two at the same head size, since the head part was capable of storing states at higher depths. Accordingly, most state access occurred in the head part rather than the body part, resulting in shorter access time.
# When Social Networks Meet D2D Communications: A Survey
###### Abstract  
In the last few years, one of the main characteristics of the current technological development is the constantly increasing need for data exchange among various types of devices, both mobile and fixed. Within this context, the direct communications between devices has the potential to create new, location-based peer-to-peer applications and services, as well as to help offload traffic from the congested traditional cellular networks. The main hurdles for this kind of Device to Device (D2D) communications are throughput, spectral efficiency, latency and fairness. Most of these hurdles can be overcome by the use of the new Social IoT (SIoT) paradigm, of things and people involved together in the network, guided autonomously by social relationships following the rules set by their owners. This paper aims to investigate the state of the art of socially-driven D2D communications. Upon an initial analysis, we perform an in-deep literature investigation of the main directions in which social ties can improve D2D communication, draw conclusions and identify the research topics left open.  
D2D communication; social; IoT; 5G