esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 3 PSR Protocol
### PS-DF Scheme
\begin{array}{ll}\hat{x}_{r}&=\frac{y_{i}}{M}\\ &=\sqrt{P_{\rm{e}}x_{\rm{r}}}+\frac{n_{i}^{[a]}+n_{i}^{[c]}}{\frac{M}{M}}\\ \frac{\Sigma}{m-1}\,h_{i,m}\end{array}  \tag{12}\]  
(For realizing this assumption, interested readers may consult [24; 25]). Denoting by \(\gamma_{\rm{ri}}\) the instantaneous SNR of \(\hat{x}_{r}\) at T\({}_{i}\), it can be shown that:  
\[ \begin{array}{ll}\gamma_{\rm{ri}}&=\frac{P_{\rm{e}}\left|\frac{M}{m-1}\,h_ {i,m}\right|^{2}}{\sigma_{a}^{2}+\sigma_{c}^{2}}\\ &=\frac{\eta\rho M\left(P\sigma^{2}+\sigma_{\rm{a}}^{2}\right)\left|\frac{M}{ m-1}\,h_{i,m}\right|^{2}}{\sigma_{a}^{2}+\sigma_{c}^{2}}.\end{array}  \tag{13}\]  
Defining \(\beta:=\left|\sum\limits_{m=1}^{M}h_{i,m}\right|^{2}\) and using (p. 48, [27]) and (Equations (5)-(8), [28]), the PDF of \(\beta\) can be expressed as:  
\[f_{\beta}(x)=\frac{1}{M\sigma^{2}}\exp\left(-\frac{x}{M\sigma^{2}}\right);\ x \geq 0. \tag{14}\]  
From Equations (13) and (14), we have:  
\[f_{\gamma_{\rm{ri}}}(x)=\frac{\sigma_{\rm{a}}^{2}+\sigma_{\rm{c}}^{2}}{\eta \rho M^{2}\sigma^{2}(P\sigma^{2}+\sigma_{\rm{a}}^{2})}\exp\left(-\frac{\left( \sigma_{\rm{a}}^{2}+\sigma_{\rm{c}}^{2}\right)}{\eta\rho M^{2}\sigma^{2}(P \sigma^{2}+\sigma_{\rm{a}}^{2})}x\right);\ x\geq 0. \tag{15}\]  
Following the same procedure as in (9), the average BER associated with \(\gamma_{\rm{ri}}\) is obtained as:  
\[ \begin{array}{ll}\overline{P}_{\rm{e,ri}}&=\frac{M\sigma\left(\sigma_{\rm{ a}}^{2}+\sigma_{\rm{c}}^{2}\right)\left|\eta\left(P\sigma^{2}+\sigma_{\rm{a}}^{2 }\right)\right|^{1/2}}{4\left[\eta\rho M\sigma^{2}(P\sigma^{2}+\sigma_{\rm{a}} ^{2})+\sigma_{\rm{a}}^{2}+\sigma_{\rm{c}}^{2}\right]^{3/2}}\\ &\quad\times_{2}\mbox{F}_{1}\Big{(}1,\frac{3}{2};2;\frac{\sigma_{\rm{a}}^{2}+ \sigma_{\rm{c}}^{2}}{\eta\rho M\sigma^{2}(P\sigma^{2}+\sigma_{\rm{a}}^{2})+ \sigma_{\rm{a}}^{2}+\sigma_{\rm{c}}^{2}}\Big{)}.\end{array}  \tag{16}\]  
At the end node T\({}_{i}\), the intended signal \(x_{j}\) (\(j=1,2;j\
eq i\)) can be finally recovered by performing bit-level network decoding of \(\hat{x}_{r}\) with its own signal \(x_{i}\), and the corresponding end-to-end BER can be obtained as:  
\[ \begin{array}{ll}\overline{P}_{\rm{e}}&=\overline{P}_{\rm{e, XOR}}\big{(}1-\overline{P}_{\rm{e,ri}}\big{)}+\overline{P}_{\rm{e,ri}}\big{(}1- \overline{P}_{\rm{e,XOR}}\big{)}\\ &=\overline{P}_{\rm{e,XOR}}+\overline{P}_{\rm{e,ri}}-2\overline{P}_{\rm{e, XOR}}\overline{P}_{\rm{e,ri}}\end{array}  \tag{17}\]  
where:  
\[ \begin{array}{ll}\overline{P}_{\rm{e,XOR}}&=\overline{P}_{\rm{e,Ir}}\big{(} 1-\overline{P}_{\rm{e,2r}}\big{)}+\overline{P}_{\rm{e,2r}}\big{(}1-\overline{P} _{\rm{e,Ir}}\big{)}\\ &=\overline{P}_{\rm{e,Ir}}+\overline{P}_{\rm{e,2r}}-2\overline{P}_{\rm{e,Ir}} \overline{P}_{\rm{e,2r}}\end{array}  \tag{18}\]  
is the average BER of \(\hat{b}_{1}\oplus\hat{b}_{2}\).
# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 4 Method Overview
### Image Acquisition  
Images are captured from a Ricoh Theta S and are scaled to 1280 \(\times\) 640. Before tracking, we run SPHORB, a new fast and robust binary feature detector and descriptor for spherical panoramic images [27]. In contrast to the state-of-the-art spherical features, this approach stems from the geodesic grid, a nearly equal-area hexagonal grid parametrization of the sphere used in climate modeling. It enables us to directly build fine-grained pyramids and construct robust features on the hexagonal spherical grid, avoiding the costly computation of spherical harmonics and their associated bandwidth limitation. The SPHORB feature also achieves scale and rotation invariance. Therefore, each feature point has a specific spherical descriptor.
# Stationary 3D Scanning System for IoT Applications
## 4 Mechanical Construction of the Test Stand, e.g., Scanning System  
The design of the stationary 3D scanning system is based on two movements: rotary and linear along the \(z\)-axis. Linear displacement is accomplished by guides moving on  
Figure 2: The determination of a single point on the surface of the object, taking into account the rotation of the table.  
linear rollers supported on both sides. Both movements are driven by two stepper motors, which, using an appropriate configuration of microsteps, ensure the accurate positioning of the head on which the infrared sensor is mounted through a screw mechanism. All the necessary elements of the structure are marked with the help of the 3D model in Figure 3. It ensures stability, ease of installation, and compactness.  
The material of all parts that are not standardized or covered by any standard is polylactide (PLA). This material is used in a 3D printing method called FDM (\"Fused Deposition Modeling\") due to its low density while maintaining high strength. Non-standard assembly parts and components used to make the device from a particular material include the control system housing, the stepper motor housing, the connecting part of the linear guides, and the base itself. The aforementioned component parts of the design are not subject to any significant loads beyond their own weight and the influence of temperatures above ambient temperature, which allowed the prototype to be made of PLA material, which clearly reduced the cost of manufacturing while maintaining correct operation [17]. When considering the location of the measuring system in an environment with constant temperature conditions and the accuracy of the device, the material used should not have a large impact on the measurement results. The appearance of the measurement system is shown in Figure 4.  
Figure 4: Main PCB layout.  
Figure 3: Design of a 3D scanning system.  
The stationary embedding of the platform with the motor limits the dimensions of the scanned object. The object should have a maximum height of 250 mm and a width of 220 mm. To obtain the highest measurable value of the width of the object, it should be placed in such a way that the center of the longest horizontal dimension coincides with the center of rotation of the platform while maintaining adequate stability.
## 5 Classification  
For the classification task, we use a Bayesian maximum a posteriori classifier (MAP). For comparison, the classification results are also calculated using support vector machines (SVM) and decision trees (DT). Nevertheless, the probabilistic Bayesian MAP method has the following advantages:  
1. If the features follow Gaussian distribution the classifier is straightforward to implement. Furthermore, the output is optimal in a probabilistic sense, and it is possible to derive a proper confidence measure for the classification result.
2. Adaptation can be performed by changing the parameters of the class distribution.  
3. The Bayesian approach allows a straightforward implementation of recursive filters. For example, data from multiple users can be combined recursively from the same geolocation.  
With the underlying Gaussian assumption, the pattern recognition problem simplifies to a discrimination between \(p\) multivariate normal populations. First, a training data set with known states is collected to obtain  
\[\mathbf{z}_{j}\sim N(\boldsymbol{\mu}_{j},\Sigma_{j}) \tag{5}\]  
the distribution of the observed \(q-by-1\)-vector \(\mathbf{z}\) given that the observation comes from the class \(j\). At this stage, the mean vector \(\boldsymbol{\mu}_{j}(q\times 1)\) and the covariance matrix \(\Sigma_{j}(q\times q)\) are here assumed to be perfectly known (very representative training data set). We also assume that for all classes \(\Sigma_{j}>0\). If Equation (5) holds, the density function of \(\mathbf{z}_{j}\) is  
\[f_{\mathbf{z}_{j}}(\mathbf{z};\boldsymbol{\mu}_{j},\Sigma_{j})=\frac{1}{(2\pi )^{q/2}\sqrt{|\Sigma_{j}|}}\text{exp}[-\frac{(\mathbf{z}-\boldsymbol{\mu}_{j}) ^{T}\Sigma_{j}^{-1}(\mathbf{z}-\boldsymbol{\mu}_{j})}{2}] \tag{6}\]  
A new observed \(\mathbf{z}=\mathbf{z}_{x}\) can then be classified by maximizing Equation (6) over all the classes \(j=1...p\). To assign a probability to the classification result, we need to use unconditional prior probabilities \(P(C=j)\), and assume that all the possible classes are included. Then the probabilities for all the classes are obtained from the [PERSON] rule:  
\[P(C=j|\mathbf{z}_{x})=\frac{f_{\mathbf{z}_{j}}(\mathbf{z}_{x})P(C=j)}{f_{ \mathbf{z}}(\mathbf{z}_{x})} \tag{7}\]  
where \(f_{\mathbf{z}}\) is the unconditional density function for the observation \(\mathbf{z}\). However, Equation (7) may not work well in practical classification problems as the Gaussian assumption does not necessarily hold. In addition, the class parameters \(\boldsymbol{\mu}_{j},\Sigma_{j}\) are only estimates obtained based on incomplete training data and the list of classes is not complete - we do not have data from all the possible activities or environments. For these reasons it is necessary to modify this basic model.
## 1 Introduction
Replacement of corn silage with triplicate and wheat at 10% of the diet dry matter (DM) did not affect DM intake, but decreased milk yield and its components compared to corn silage. At the same time, milk fat from alternative forage diet had a higher level of 4:0, 6:0, and 18:0 and the lower concentration of total trans-fatty acids. Digestibility of neutral and acid detergent fiber was increased in triplicate silage diet. Emission of methane was higher for triticale and CO\({}_{2}\) was reduced by both triticale and wheat silages, concluded that triticale or wheat double cropped with corn many be providing significant cropping strategy [25]. Triticale is one of the available options for swath grazing to extend the fall grazing and produce as silage or hay for beef cattle producers in Canada [26]. Ensiling of pre-mature cereal of barley, wheat, triticale, and rye with LAB could serve as alternative forages [27]. Triticale has an excellent impact on higher meat production as well as gives good responses for dairy and sheep milk production in Algeria [28]. Few reports have been available on the improvement of nutritional values of triticale silages using LAB; recently many researchers have been focused on silage production using triticale plant and LAB due to its crude proteins and positive fermentative capabilities, respectively. Thus, the production of high-quality silage from early heading and heading stages of triticale is a good source due to its rich crude proteins. The use of LAB as an inoculum has been proposed as effective strategies to improve feed palatability and be stored for prolonged periods. In the current study, we isolated effective _Lactobacillus_ strains (_Lactobacillus__rhamnosus_ and _Lactobacillus paracasei_) and demonstrated its potential effects on fermentation of triticale at different moistures. Furthermore, probiotic potential was also studied.
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 1 Introduction  
Data collected by the US National Highway Traffic Safety Administration have shown that rear-end collisions constitute the highest percentage of collisions of motor vehicles in transport, and rear-end collisions often lead to multiple injuries and property damage [1]. To address the rear-end collision problem, Collision Warning Systems (CWS), such as radar systems that detect and warn drivers about potential rear-end collisions, are often used [2]. Radar systems are highly accurate in determining the inter-vehicle distances via detecting the rear-end of vehicles; however, under poor weather conditions such as fog, rain, or snow, its accuracy is significantly affected. [PERSON] et al. showed that the transmit power density of laser radar is large, and \"the high reflectivity of snowflakes generates a false alarm rate of up to 2.5/s\(\cdot\)m\({}^{2}\) in a range of 5 m in front of the sensor\" [3].  
In the early 2000s, the study of wireless network for data exchange in vehicular environments became popular in the field of Intelligent Transportation Systems (ITS). A commonly used technology in ITS is the dedicated short-range communication (DSRC), which is a wireless communication technology for Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication, compliant with the IEEE 802.11p (physical layer protocol) standard and the IEEE 1609 (network layer protocol) standard [4]. DSRC-based collision warning systems have been proposed to reduce the number of accidents under poor weather conditions [5].  
Although DSRC-based systems, similar to radar-based systems, allow communication among vehicles and warn drivers in real-time, low visibility conditions are still a major concern. Weather conditions have an impact on human-related factors such as response time. Further, though human factors significantly impact collision estimations; however, most collision warning methods do not consider human factors adequately [6], thus resulting in high rates of false alarms or missing alarms.  
In this work, our goal is to design a system that can not only predict vehicle collisions, but also dynamically lengthen the prediction horizon (number of future time slots for prediction) such that the system can enhance safety through providing early warnings and giving drivers enough time to react to future possible collisions.  
This article is organized as follows: Section 2 presents related work on collision warning models, human factors, and vehicle speed prediction. Section 3 presents the proposed Visibility-based Collision Warning System design. Section 4 gives the experiment results. Section 5 concludes the article with some future work.
## 3 Modeling of Data Fusion Algorithm  
In this work, the data fusion modeling is built using the Kalman algorithm. The Kalman filter is suitable for data fusing Kalman (1964). The Kalman filter is a kind of linear filtering recursive algorithm for a discrete signal. For a discrete system:  
\[x(k)=A(k)x(k-1)+B(k)u(k-1)+w(k) \tag{1}\]  
\[y(k)=H(k)x(k)+v(k) \tag{2}\]  
\(x(k)\) is the system state vector. \(y(k)\) is the system observation vector. \(u(k)\) is the system input vector. \(A(k)\) is the \({}^{n\times n}\) system state matrix. \(H(k)\) is the \({}^{m\times n}\) system observation matrix. \(B(k)\) is the \(1\times n\) system input matrix. \(w(k)\) is the process noise vector. \(v(k)\) is the observation noise vector.  
Assumptions \(w(k)\) and \(v(k)\) were independent, and the noise is normal distribution white noise, so \(w(k)\) and \(v(k)\) is expressed as the following:  
\[E\big{[}w(n)w^{T}(k)\big{]}=\begin{cases}Q(k)&n=k\\ 0&n\
eq k\end{cases} \tag{3}\]  
\[E\big{[}v(n)v^{T}(k)\big{]}=\begin{cases}R(k)&n=k\\ 0&n\
eq k\end{cases} \tag{4}\]  
\[E\big{[}w(n)v^{T}(k)\big{]}=0 \tag{5}\]  
where \(Q(k)\) is the noise covariance matrix and \(R(k)\) is the observation noise covariance matrix.  
The first step of the Kalman filter is predicting the next state of the system. If the system state is \(x(k)\), then the next time system state is:  
\[x(k+1\big{|}k)=Ax(k\big{|}k)+B(k)u(k) \tag{6}\]  
The update status covariance matrix is:  
\[P(k+1\big{|}k)=A(k)P(k\big{|}k)A^{T}(k)+Q(k) \tag{7}\]The state at \(k+1\) time is:  
\[x(k+1\big{\|}k+1)=x(k+1\big{\|}k)+K_{g}(k)[y(k+1)-H(k)x(k+1\big{\|}k)] \tag{8}\]  
where \(K_{g}(k)\) is the Kalman gain:  
\[K_{g}(k)=\frac{P(k+1\big{\|}k)H^{T}(k)}{H(k)P(k+1\big{\|}k)H^{T}(k)+R(k)} \tag{9}\]  
\[P(k+1\big{\|}k+1)=[I-K_{g}(k)H(k)]P(k+1\big{\|}k) \tag{10}\]  
The Kalman filter estimation algorithm has five formulas. If giving the initial value of state \(x(0)\) and state covariance matrix \(P(0)\), the system state can gradually be estimated by the recursive method.  
The vehicle side slip angle fusion algorithm based on GPS/INS is shown in Figure 6. The main parameters in GPS measurement are heading angle \(\psi_{GPS}\), azimuth angle \(\theta_{GPS}\) and speed \(\upsilon_{GPS}\), and the main parameters of INS measurement are yaw rate \(\gamma_{ gyro}\), longitudinal acceleration \(a_{x,acc}\) and lateral acceleration \(a_{y,acc}\).  
GPS and INS combination methods can be divided into the kinematics method and the dynamics method. The kinematics method is based on the motion relations of a car, and it does not rely on the estimation vehicle dynamics model. Because there is no model error, measurement accuracy depends on the accuracy of the testing device and the installation position, so this method is very robust [24].
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 5 Results and Discussion
### Performance Evaluation
#### 5.3.2 Performance of Large-Footprint Model  
Table 4 presents performances of the extensions based on the large-footprint model. Comparing the \(ResNet_{F}\) baseline with the small- and large-footprint, we found that the large model extracts more ASC information and improves the accuracy of all tested conditions. This conclusion also holds for other extensions. In other words, increasing model size is a consistent way of improving classification accuracy. When extending the \(ResNet_{F}\) baseline with NF-MFBE features, it is surprising that the small- and large-footprint model behave differently. In the large model, though \(ResNet_{NF+F}\) still outperforms the corresponding system in the small model, it shows poorer performance than the large \(ResNet_{F}\) baseline. We do not find evidence indicating that NF-based features can be beneficial to ASC in the large-footprint model. The reason might be that sufficient ASC information can already be extracted from MFBE features with a larger model, and no extra benefits could be brought by noise floor estimate in this condition. Model voting with NF-based features could still make slight improvements in \(NF\_vote\_1\) and \(NF\_vote\_2\), but it could also be attributed to the increase of system complexity.  
By incorporating speech presence information from cruse-net in the large-footprint model, a significant improvement is obtained by \(ResNet_{F+M}\) in almost all conditions, except a very slight 'decrease' in the clean condition. \(M\_vote\) further improves the system performance. For the combination of three features in the large model, \(ResNet_{F+NF+M}\) tends to bring larger distortion than \(ResNet_{F+M}\) in all SBRs. \(M\_NF\_vote\) could still improve the system performance compared with the \(ResNet_{F}\) baseline. It is interesting to note that \(M\_vote\) and \(M\_NF\_vote\) show almost identical performance, indicating again that no additional information may be attributed to the NF-based features. To conclude, both \(ResNet_{F+NF+M}\) and \(M\_NF\_vote\) show that noise floor estimate does not bring further improvement to the large-footprint model. The best performance is achieved by the \(Meta\_learner\) for the large model. The absolute improvement is 8.8% in 20 dB conditions. For the large-footprint model, the following key points can be concluded:  
* Statistical noise floor estimate does not convincingly help ASC, presumably because the model is big enough that relevant information can be faithfully extracted from the feature of mixed signal;
* For the large model, incorporating DL-based speech mask estimates consistently improves the classification accuracy;  
Figure 3: The performance of both small- and large-footprint ResNet baseline and the proposed best systems.  
* Ensemble methods could further bring benefits to the large-footprint model.
## 3 RAR: Reliability-Aware Routing
### Data Routing  
When data is ready for transmission, a sensor node sends it directly to the sink node if the latter locates within its communication range. Otherwise, the node chooses multi-hopping. For multi-hopping, the sensor nodes select relay nodes in their neighborhood. Relay nodes posses the  
Figure 2: Strategy for sharing ID, depth and neighbors information among one-and-multi-hop neighbor sensor nodes.  
minimum depth. Selecting a relay node is accomplished by every node that has to advance data to another node. In multi-hopping, every senor node, that has to transmit data, computes all the paths from itself to the water surface through relay nodes. A path in which a relay node is absent from the sensor node to the water surface is discarded. It is because such a path does not provide connectivity to the water surface. This lack of connectivity results in loss of data packets along such a path, if selected. The information about the connectivity of a path and the availability of the relay nodes is already obtained during the exchange of hello packets.  
After a sensor node selects all the connected paths from itself towards the water surface, the probability of error is estimated for every link that involves calculation of the average signal-to-noise ratio (SNR). Two nodes that are separated by a distance \(d\) and communicate using an acoustic signal of frequency \(f\) have an average SNR \(\gamma(d,f)\) as [19]  
\[\gamma(d,f)=\frac{E_{b}/A(d,f)}{N(f)\times B} \tag{1}\]  
where \(E_{b}\), \(A(d,f)\), \(N\) and \(B\) represent the average energy required in transmitting a single bit, attenuation, noise power density and the acoustic bandwidth, respectively. Fading in underwater environment can be modeled by the small scale Rayleigh fading [19]. Since, the channel fading inherently exhibits the exponential probability density function, the corresponding average SNR also exhibits the exponential probability density function, which for a specific value \(\Gamma\) of the SNR, is defined by [19]  
\[f_{\gamma}(\Gamma)=\frac{1}{\gamma}e^{\frac{-\Gamma}{\gamma}} \tag{2}\]  
for which the error probability, \(p_{e}\), is the integral of the product of the error probability at a particular value of the SNR, \(p_{e}(\Gamma)\), times the corresponding density function at that SNR [19], i.e.,  
\[p_{e}=\int_{0}^{\infty}p_{e}(\Gamma)f_{\gamma}\left(\Gamma\right)d\,\Gamma. \tag{3}\]  
The proposed schemes use BPSK modulation as in [19] for which the error probability is  
\[p_{e}=\frac{1}{2}\bigg{(}1-\sqrt{(\frac{\bar{\gamma}}{1+\bar{\gamma}})}\bigg{)} \tag{4}\]  
and the corresponding probability of successfully delivering a single packet between nodes separated by a distance \(d\) is [19]  
\[p_{s}=(1-p_{e})^{k} \tag{5}\]  
where \(k\) is the size of a single packet. For data routing, a sensor node selects the link that is connected from itself towards the water surface and has the highest probability of successfully delivering data packets.