esa-sceva/satcom-chunk-collection huggingface dataset:
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 4 Covariance Analysis
### Measurement Covariance Modelling
#### 4.5.1 LCNS Covariance Model  
The uncertainty of the LCNS measurements due to thermal noise can be modelled using the same methodology as for Earth GNSS assuming the pseudorange noise is determined by the DLL, while the pseudorange-rate measurements are determined by the FLL. The uncertainty can be modelled using Equations (13) and (14) for, respectively, the pseudorange and pseudorange-rate [46].  
\[\sigma_{DLL}=\lambda_{\mathrm{C}}\sqrt{\frac{B_{DLL}d}{2C/N_{0}}\bigg{(}1+ \frac{2}{T_{i}C/N_{0}(2-d)}\bigg{)}} \tag{13}\]  
\[\sigma_{FLL}=\frac{\lambda_{S}}{2\pi T_{i}}\sqrt{\frac{4B_{FLL}}{C/N_{0}}\bigg{(} 1+\frac{1}{T_{i}C/N_{0}}\bigg{)}} \tag{14}\]  
The parameters used in those equations (i.e., settings of the DLL and FLL) are provided in Table 7, while the signal characteristics are given in Table 2.  
The \(C/N_{0}\) (carrier-to-noise density ratio in dB-Hz) was evaluated considering a complete link budget from satellite to receiver (refer to Equation (1)), including the transmission and reception antenna pattern using the ESA Lunar Navigation simulator also used in previous publications (e.g., [28]).  
The definition of \(\Sigma_{pp}\) and \(\Sigma_{pp}\) is given in Equations (15) and (16), respectively, considering the uncertainty at the tracking level as well as the accuracy of the LCNS ODTS (note that \(\sigma_{\mathrm{x}p}\) and \(\sigma_{\mathrm{x}p}\), respectively, represent the satellite position and velocity uncertainty, while \(\sigma_{\mathrm{x}\beta t}\) and \(\sigma_{\mathrm{x}\beta t}\), respectively, represent the satellite clock bias and drift).  
\[\Sigma_{pp}=\begin{bmatrix}\bigg{(}\sigma_{DLL,1}^{2}+\sigma_{ \mathrm{x}\mathrm{x},p}+\sigma_{\mathrm{x}\mathrm{x},\beta t}\bigg{)}&&\\ &\ddots&\\ &&\Big{(}\sigma_{DLL,m}^{2}+\sigma_{\mathrm{x}\mathrm{x},p}+\sigma_{\mathrm{x }\mathrm{x},\beta t}\Big{)}\end{bmatrix} \tag{15}\]  
\[\Sigma_{pp}=\begin{bmatrix}\bigg{(}\sigma_{PLL,1}^{2}+\sigma_{ \mathrm{x}\mathrm{x},p}+\sigma_{\mathrm{x}\mathrm{x},\beta t}\bigg{)}&&\\ &\ddots&\\ &&\Big{(}\sigma_{PLL,m}^{2}+\sigma_{\mathrm{x}\mathrm{x},p}+\sigma_{\mathrm{x }\mathrm{x},\beta t}\Big{)}\end{bmatrix} \tag{16}\]
###### Abstract  
A robot manipulator is a multi-degree-of-freedom and nonlinear system that is used in various applications, including the medical area and automotive industries. Uncertain conditions in which a robot manipulator operates, as well as its nonlinearities, represent challenges for fault diagnosis and fault-tolerant control (FDC) that are addressed through the proposed FDC technique. A machine-learning-based neural adaptive, high-order, variable structure observer for fault diagnosis (FD) and adaptive, modern, fuzzy, backstepping, variable structure control for use in a fault-tolerant control (FC) algorithm, are proposed in this paper. In the first stage, a variable structure observer is proposed as an FD technique for the robot manipulator. The chattering phenomenon associated with the variable structure observer(VSO) is solved using a high-order variable structure observer. Then, the dynamic behavior estimation performance in the high-order variable structure observer is improved by incorporating a neural network algorithm in the FD pipeline. This adaptive technique is also effective in improving the robustness of the fault signal estimation. Moreover, support vector machines (SVMs) that can derive adaptive threshold values are used to categorize faults. To design an effective fault-tolerant controller (FC), an adaptive modern fuzzy backstepping variable structure controller is used in this study. First, a new variable structure controller is designed. Next, to increase robustness and reduce high-frequency oscillations in uncertain conditions, a backstepping algorithm is used in parallel with the variable structure controller to design the backstepping variable structure controller. To design an effective hybrid controller, a fuzzy algorithm is integrated into the backstepping variable structure controller to create a fuzzy backstepping variable structure controller. Then, to improve the robustness and reliability of the FC, a neural adaptive. high-order. variable structure observer is applied to the fuzzy backstepping variable structure controller to design a modern fuzzy backstepping variable structure controller. An adaptive algorithm is used to fine-tune the variable structure coefficients and reduce the effect of faults on the robot manipulator. The effectiveness of the selected algorithm is validated using a PUMA robot manipulator. The neural adaptive. high-order variable structure observer improves the average performance for the identification of various faults by about 27% and 29.2%, compared with the neural high-order variable structure observer and variable structure observer, respectively.  
**Keywords:** fault diagnosis; variable structure observer; backstepping algorithm; neural network; machine learning technique; support vector machine; adaptive method; fuzzy logic approach
## 4 Research Questions  
The following research questions were identified, focusing on improvement using passive strategies and positions of its implementation in the sports centers [49];  
RQ1: Is it feasible to implement energy-retrofitting strategies specifically for the sports center administration, excluding the sports hall, to achieve a significant reduction in overall energy consumption to upgrade the energy class of the building by one level?  
RQ2: Can energy-retrofitting strategies be effectively implemented in a sports hall within a sports center without extending these measures to the entire building, thereby achieving a significant reduction in energy consumption while maintaining or enhancing occupant comfort?  
The first question explores the potential for significant energy savings by applying retrofitting strategies to the sports center as a whole, but specifically excluding the sports hall, having in mind two separate thermal zones. This inquiry seeks to determine whether comprehensive retrofitting of other areas within the sports center while leaving the sports hall, as different thermal zones, unchanged, can result in meaningful reductions in energy consumption to improve the energy class of a building in accordance with legislation and the law on energy efficiency by one level [39].  
These research questions aim to investigate the effectiveness and implications of targeted energy-retrofitting strategies within a sports center.  
The second question examines whether it is possible to achieve substantial energy savings and enhanced comfort by applying retrofitting measures solely to the sports hall without necessitating the implementation of similar measures throughout the entire facility. The focus here is on understanding whether localized improvements can yield significant energy benefits while ensuring that the sports hall's operational performance and occupant comfort are preserved or enhanced. It has to be mentioned that the same improvement measures were tested on a facility belonging to the same category but of a different form, an object of dispersed form, in the broader context of the research [15].  
Both questions are designed to provide a nuanced understanding of how targeted energy-retrofitting measures, thermal insulation in this case, can balance energy efficiency with occupant comfort and operational efficacy, contributing to more informed decision making in the context of sports facility management and renovation.
## Description
### Environmental Impact
#### 2.1.2 Noise  
Aviation noise has the characteristics of spatiotemporal discontinuity and accumulation, so it is necessary to construct a coordinate system when evaluating the noise level. In this paper, the Cartesian coordinate system is constructed: the origin is the midpoint of the airport runway; the X-axis is through the origin and perpendicular to the runway centerline, and the right side of the approach route is positive; the Y-axis is consistent with the runway centerline, and it is positive after the runway threshold; the z-axis is the vertical axis through the origin, the runway entry elevation is zero and the value higher than the entry plane is positive, as shown in Figure 1.  
In China, we use weighted equivalent continuous sensible noise level (WECPNL) [27] to measure the noise level. The calculation formula is as follows:  
\[L_{WECPNL}=\overline{L_{EPN}}+10\cdot\lg(N)-39.4, \tag{2}\]  
\[\overline{L_{EPN}}=10\cdot\lg\left[\left(\sum_{i}\sum_{j}\sum_{k}10^{0.1\cdot L _{EPNLijk}(x,y)}\right)/N\cdot T_{0}\right], \tag{3}\]  
\[N=N_{1}+3\cdot N_{2}+10\cdot N_{3}. \tag{4}\]  
where, N\({}_{1}\) is the number of aircraft from 07:00 to 19:00 during the day, N\({}_{2}\) is the number of aircraft from 19:00 to 23:00 in the evening, N\({}_{3}\) is the number of aircraft from 23:00 to 07:00 at night, T\({}_{0}\) = 10 s is the reference time, \(\overline{L_{EPN}}\) is the average effective perceived noise level, \(L_{EPNLijk}(x,y)\) is the single event noise level generated by aircraft \(i\) on the measuring point (\(x\),\(y\)) when flying in the k segment of the route \(j\), which value can be calculated by regression analysis of the Noise-Power-Distance (NPD) data from Aircraft Noise and Performance Database (ANP) [28].  
Currently, most researches take the coverage of noise level as the evaluation indicator, but it cannot directly and accurately reflect the impact of aviation noise on the surrounding residents. This paper refers to the noise exposure population evaluation method proposed by [PERSON] [13; 14], divides the covered area of the terminal area into several grids with an area of S, and takes the center of the grid as the noise measuring point. According to the density distribution of residents, the total population of different noise level was calculated. The population which are exposure to greater than noise level \(L\) dB can be expressed as follows:  
\[Q(L)=\sum_{G(x,y)\in AS}[q(x,y)\big{|}L_{WECPNL}(x,y)\geq L], \tag{5}\]  
\[q(x,y)=\rho(x,y)\cdot S. \tag{6}\]  
where, \(G(x\),\(y)\) represents the grid with the center coordinate of (\(x\),\(y\)), \(\rho(x\),\(y)\) represents the population density within \(G(x\),\(y)\), person/m\({}^{2}\), \(q(x\),\(y)\) represents the population quantity within \(G(x\),\(y)\), \(L_{WECPNL}(x,y)\) represents the noise value of \(G(x\),\(y)\), and \(AS\) represents the evaluation range of the terminal area.
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 5 Experimental Setup
### Datasets  
**Data for DL training:** We used 16 kHz sampled data from Timit ([[https://catalog.ldc.upenn.edu/LDC93S1](https://catalog.ldc.upenn.edu/LDC93S1)]([https://catalog.ldc.upenn.edu/LDC93S1](https://catalog.ldc.upenn.edu/LDC93S1)) (accessed on 1 September 2022)) and Librspeech ([[http://www.wopenslr.org/12/](http://www.wopenslr.org/12/)]([http://www.wopenslr.org/12/](http://www.wopenslr.org/12/)) (accessed on 1 September 2022)) datasets in English, as well as some data in Spanish from Albayzin, Speechdatcar, Domolab, Tostar, Mavir, and some hours of Spanish TV emissions for a total of approximately 120 h of clean speech. These data were augmented by adding randomly stationary and non-stationary noises from the Musan dataset [33], SNR = 0-30 dB, including music and speech, and scaling the time axis at the feature level.  
**Data for speech enhancement:** We created a simulated noisy dataset with 11,976 speech utterances using clean read phrases in Spanish, phonetically balanced, from the laboratory sessions of the AV@CAR dataset [34]. The dataset is sampled at 16 kHz and includes 20 male and female speakers. The clean data were corrupted with different types of stationary and non-stationary additive noise:  
* Babble: Noisy pattern from the talking of many people. It is a special case of non-stationary noise, very difficult to handle because it is highly correlated with the target voice since it is also voice.
* Traffic: Noise from the traffic at a random street, including cars, klaxon, street noise, etc.
* Cafe: Mixture of environmental noises in a cafe, including people talking, noise from cutlery, etc.
* Tram: Environmental noise in a tram station, including some stationary segments when the tram arrives.  
In order to have a representation of several noise levels according to different scenarios of application, each noisy subset was evaluated at \(SNR=0,5,10,15,20\) dB.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 4 Sybil Attack Detection System
### Motion Detection  
Since the stationary node amplitude characteristics are the same, we only need to detect the time interval of the node movement. It is a challenge to determine the beginning and end of the movement. Figure 6a shows the 4000 packet amplitude data at the transmitter from standstill to walking straight with a speed of 1 m/s. The distance between transmitter and AP with Intel 5300 NIC is 4 m. Because the distance between the peaks is usually higher than 200 when there are movements, we set window size \(ws=200\), in order to reduce the computation time and to get accurate resolution of movement. The input data can be expressed as V. When the data are less than \(ws\), they are unavailable data.  
\[V=\left\{V_{1},V_{2},\cdots,V_{i}\right\},V_{i}=\left[a_{i},a_{i+1}\cdots,a_{ i+ws-1}\right]\,,i\geq ws,a_{i}\in R \tag{5}\]  
\(a_{i}\) is the amplitude of CSI. \(R\) is the sequence of the entire amplitude. We calculate the variance \(x\) for each \(V_{i}\) of the collected data. As shown in Figure 6b, it can be seen that the variance increases obviously with the fluctuation in the window. Small differences in the amplitude of the device at rest can still cause the variance to fluctuate. Therefore, we use the variance rate of variability to identify motions.  
\[k(i)=\frac{\Delta_{\sigma_{i}}}{\Delta_{i}}=\frac{\sigma_{i}-\sigma_{i-1}}{i- (i-1)}=\sigma_{i}-\sigma_{i-1} \tag{6}\]  
As shown in Figure 6c, when the environment is stationary, the variance increment is almost zero, and when the node moves, the variance changes drastically. Therefore, set the threshold for \(K\); when  
Figure 6: Motion detection of different packets. (**a**) Client from static to moving; (**b**) variance; (**c**) variance rate of changes.  
greater than \(K\), then it is the beginning of the movement. The start and end points can be determined as follows:  
\[Startpoint:\ [|k(i)>K|+ws/2] \tag{7}\] \[Endpoint:\ [|k(i)\leq K|+ws(-1)]\]  
According to the attack detector, we can accurately detect whether the node is moving, so we can use the static detection algorithm to calculate the AoA by the phase feature or detect the attacks with the special amplitude feature by the dynamic detection algorithm.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion
### Noise Cyclostationary Behavior
#### 3.3.2 Summary of the Results for the Rural Site  
Figure 13 gives the pseudo Wigner-Ville transform analysis for the rural site in the time-frequency domain. In that case, the channel exhibits a periodic time-varying response. Moreover, it is composed of a cyclo-stationary noise component. This periodic impulsive noise appears in the form of bursts every 10 ms. It represents the periodic impulsive noise which is synchronized with the mains' frequency.  
The spectrograms of the measured signals show that periodic noises are typically generated at frequencies lower than 30 kHz. Periodic impulsive noise can also be observed at frequencies higher than 300 kHz. However, the PSD of these disturbances remains at a low level in comparison with the low-frequency disturbances.  
Figure 12: Pseudo Wigner-Ville transform analysis for the sub rural site in the time-frequency domain.
# Environmental-Based Speed Recommendation for Future Smart Cars +
## 4 Experimental Results
### Evaluation Scenarios
#### 4.3.2 Scenario 2  
The second experiment was performed in order to explore the dynamic changes of the surrounding environment of a vehicle and validate the proposed speed from weather- and route-based information. To do so, we intended to push the car to its limits, so that we can achieve braking signals activation (such as ABS or ESC). However, driving in an urban area, the intended activation of ABS or ESC can be dangerous and unwanted. For that reason, Scenario 2 was performed in the test track field in the Ford Development Center in Dearborn, MI, USA, where an experienced and professional driver was responsible for all the actions. During the experiment, we replicated wet road conditions and the driver was performing maneuvers to avoid obstacles. That way, we verify how effective is the proposed speed recommendation in real conditions. It is worth mentioning that the driver intentionally drove aggressively in order to force the braking systems to intervene and the experiment was conducted intentionally on a wet road surface.  
Figure 7(a) presents the estimated engine speed mismatch, compared to the theoretical nominal value. The calculation is based on an approximation formula that was specifically tailored for the conditions of the experiment (specific vehicle characteristics, no inclination of the ground). The red line in Figure 7(a) indicates Electronic Stability Control (ESC) activation, which is depicted as a binary (0-1) value (inactive-active). In Figure 7(a), it is observed that, before the activation of ESC, engine mismatch had a significant sudden increase. However, there are cases where the value of mismatch was high, but ESC was not activated. This phenomenon occurred because of the approximation procedure that was adopted in order to estimate the engine speed mismatch. Figure 7(b) depicts the speed that the test vehicle had during the experiment (blue line). The red line reflects the activation of Electronic Stability Control (ESC) as it was described above. The green line in Figure 7(b) illustrates the proposed speed that the presented framework was recommending, based on weather and on-route information. The proposed framework estimates the current road class as \"Wet\" and evaluates the extracted PoIs in order to provide the recommended speed (green line). The presented methodology, however, is able to identify dangerous driving conditions and recommends a suitable speed range in a pro-active manner. As a result, a smart vehicle that is equipped with the presented framework could have possibly avoided the activation of the braking signals, adjusting its speed accordingly. It is worth mentioning that the proposed methodology alerts the driver with a message that indicates the recommended speed. However, it is the driver's final decision to either follow the recommendation or act independently. The proposed framework will be used as a basis for speed recommendation systems in autonomous vehicles.
# Cooling Effectiveness of the Sustainable Cooling Solution for Cattle: Case Study in Poland
## 2 Materials and Methods
### Environmental Indicator of Heat Stress  
Environmental conditions inside and outside the barn, including the temperature and relative humidity of the air, were continuously measured with a 2 min sampling time using the meteorological station, equipped with an NTC AKO015561 (AKO, Barcelona, Spain) ambient temperature sensor (accuracy rating \(\pm\) 1 \({}^{\circ}\)C) and a SONEE16F6A21 (Pego, Rovigo, Italy) relative humidity sensor (accuracy rating \(\pm\) 3%). The sensors were installed at about 1.5 m from floor level. Collected data were used to designate the Temperature Humidity Index (Equation (1)) as the environmental heat stress indicator during the experiment.  
The THI was calculated according to the National Research Council formula [26]:  
\[\text{THI}=(1.8\times\text{T}_{\text{db}}+32)-(0.55-0.0055\times\text{RH}) \times(1.8\times\text{T}_{\text{db}}-26) \tag{1}\]  
where  
Tdb--dry bulb temperature (\({}^{\circ}\)C);  
RH--relative humidity (%).  
Figure 4: Three types of beddings that were used during the experiment: the innovative cooling water mattress (CM), the commercial water mattress (M), and the conventional straw bedding (S). The last two are the referenced ones.
## 4 Experiments and Results
### Results of Autoencoder-Based Denoising  
The dataset for each channel scenario was examined independently to evaluate our Convolutional Denoising Autoencoder (CDAE) performance in reconstructing the clean spectrograms. Overall, 10,000 transmitted spectrograms make up the clean dataset, while 10,000 received spectrograms make the noisy dataset. Each spectrogram image is resized to \(128\times 128\).The dataset was split, 80% for training and the remainder for testing the model.
# A Bounded Scheduling Method for Adaptive Gradient Methods
## 3 Basdam: Bounded Scheduling Method for Adam
### Algorithm Overview  
Based on the above analysis, in this subsection, we propose a new variant of the optimization methods, named Basdam, which can maintain the fast convergence of the algorithm in the early stage and obtain a good finally generalization capacity.  
Empirically, the number of epoch in the first phase is the same as that in the second phase and the number of epoch in the third phase should be less than that in the former two phases. Specifically, if the total number of training epochs is \(T\), the allocation of the number of epochs for three phases are \(\frac{2T}{5}\), \(\frac{2T}{5}\) and \(\frac{T}{5}\), respectively. The details of Basdam are illustrated in Algorithm 1, where \(max\_lr\) and \(min\_lr\) can be found by the method mentioned in Section 3.2, \(\beta_{1}\), \(\beta_{2}\) and \(\eta\) is the hyper-parameters of Adam itself, \(data\_loader()\) is a function that combines a data set and a sampler and provides an iterable process over the given data set.  
```
1:total epochs \(T\), \(max\_lr\), \(min\_lr\)
2:\(w_{0}\), \(\beta_{1}\), \(\beta_{2}\), \(\eta\)
3:set \(v_{0}\) = 0, \(m_{0}\) = 0, \(k\) = 0
4:for\(t\) = \(\{1,2,...,\frac{2T}{5}\}\)do
5:\(ascending\_lr\) = \(ascending(t)\)
6:for\(data\) in \(data\_loader()\)do
7:\(k\) = \(k+1\)
8: compute gradient \(g_{k}\)=\(\
abla f(w_{k-1})\) on \(data\)
9:\(m_{k}\) = \(\beta_{1}m_{k-1}+(1-\beta_{1})g_{k}\)
10:\(v_{k}\) = \(\beta_{2}v_{k-1}+(1-\beta_{2})g_{k}^{2}\)
11:\(lr\) = \(Clip(\frac{\sqrt{1-\beta_{2}^{k}}}{1-\beta_{1}^{k}}\cdot\frac{\eta}{\sqrt{v_{k }+e}},ascending\_lr,max\_lr)\)
12:\(w_{k}\) = \(w_{k-1}-lr\cdot m_{k}\)
13:endfor
14:endfor
15:for\(t\) = \(\{1,2,...,\frac{2T}{5}\}\)do
16:\(ascending\_lr\) = \(descending(t)\)
17:for\(data\) in \(data\_loader()\)do
18:\(k\) = \(k+1\)
19: compute gradient \(g_{k}\) = \(\
abla f(w_{k-1})\) on \(data\)
20:\(m_{k}\) = \(\beta_{1}m_{k-1}+(1-\beta_{1})g_{k}\)
21:\(v_{k}\) = \(\beta_{2}v_{k-1}+(1-\beta_{2})g_{k}^{2}\)
22:\(lr\) = \(Clip(\frac{\sqrt{1-\beta_{2}^{k}}}{1-\beta_{1}^{k}}\cdot\frac{\eta}{\sqrt{v_{k }+e}},min\_lr,descending\_lr)\)
23:\(w_{k}\) = \(w_{k-1}-lr\cdot m_{k}\)
24:endfor
25:endfor
26:for\(t\) = \(\{1,2,...,\frac{T}{5}\}\)do
27:\(lr\) = \(min\_lr\)
28:for\(data\) in \(data\_loader()\)do
29:\(k\) = \(k+1\)
30: compute gradient \(g_{k}\)=\(\
abla f(w_{k-1})\) on \(data\)
31:\(w_{k}\) = \(w_{k-1}-lr\cdot g_{k}\)
32:endfor
33:endfor
```  
**Algorithm 1** Basdam.