esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 3 Noise Model
### External Noise
#### 3.1.4 Weather Noise  
The signal attenuation caused by weather conditions is incorporated into the path loss log-normal shadowing channel model. Let \(\gamma_{\rm{weather}|\rm{weather}-\{\rm{rainfall,\,\,show,\,\,fog}\}}\) represent the level of attenuation caused by severe weather conditions, including heavy rainfall, wet snow, and fog. The total signal attenuation induced by inclement weather, denoted as \(L_{\rm{weather}}\), may be determined using the following formula:  
\[L_{\rm{weather}}=d\times\gamma_{\rm{weather}}, \tag{18}\]where \(d\) (km) represents the distance between the receiver and emitter of the two sensors.  
The received signal strength indicator (RSSI) (dBm) refers to the measurement of signal intensity at the receiver sensor, which is located at a distance \(d\) from the transmitter sensor. The expression for the value of RSSI at a given distance \(d\) is as follows:  
\[\text{RSSI}(d)=P_{\text{tx}}-P_{\text{loss}}(d_{0})-10\eta_{\text{pl}}\log_{10} \frac{d}{d_{0}}-L_{\text{weather}}-X_{\sigma}, \tag{19}\]  
where \(P_{\text{tx}}\) and \(X_{\sigma}\) represent the transmitted power and a zero-mean Gaussian distribution with variance \(\sigma\) due to multipath and shadow fading. Moreover, \(P_{\text{loss}}=20\log_{10}(4\pi d_{0}/\lambda)\) represents the path loss over a reference distance of \(d_{0}=1\)(m) [27], while \(10\eta_{\text{pl}}\log_{10}\frac{d}{d_{0}}\) denotes the additional path loss due to the distance between the transmitter and receiver, where \(\eta_{\text{pl}}\) is the path loss coefficient.  
The threshold value of the RSSI is denoted as \(\text{RSSI}_{\text{threshold}}\). In this research, we established a value of \(-80\) dBm as the threshold for the RSSI [28]. In the event that the RSSI of the receiver sensor falls below the predetermined threshold value, the receiver sensor is unable to successfully retrieve the signal that was originally provided by the sender. Consequently, the packets that are received are dropped. In summary, we may model this information as follows:  
\[S_{\text{receive}}=\begin{cases}0,&\text{RSSI}<\text{RSSI}_{\text{threshold}}, \\ S_{\text{receive}},&\text{RSSI}>\text{RSSI}_{\text{threshold}}.\end{cases} \tag{20}\]
## 2 Experimental Approach
### Machine Learning Methods
It has several hyperparameters that can be adjusted to enhance performance. Due to its adaptability and simplicity of usage, it has been widely used in many applications [45]. Another boosting approach for regression tasks is called AdaBoost Regressor (ABR), which uses multiple weak learners (simple models) combined to produce a powerful predictor of the target variable. It is especially skilled at handling noisy data [46] and can adapt to nonlinear interactions between the input and output variables. It can process both categorical and numerical data and is computationally efficient. It can, however, be sensitive to outliers and requires enough data to prevent overfitting [47]. Table 4 presents a general summary of each method's advantages and disadvantages. Which approach is best relies on some variables, including the type of data, available computing power, interpretability standards, and intended prediction performance. \begin{table}
\begin{tabular}{l l l} \hline \hline
**ML** & **Advantages** & **Disadvantages** \\ \hline DNN & High capacity for complicated pattern recognition & Extensive datasets are necessary for training \\  & Able to pick up hierarchical & Training and inference at a computational cost \\  & characteristics automatically & \\  & robust to erratic data & If not sufficiently regularized, it is prone to \\  & overfitting & \\ \hline ANN & Adaptable, appropriate for a range of jobs and & Sensitive to hyperparameter tuning and \\  & data types & architecture design \\  & Able to simulate nonlinear connections & Requires meticulous data preparation \\  & Excellent results with text, image, and & In particular for deep structures, training times \\  & sequence data & might be lengthy \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of ML algorithms by advantages and disadvantages.
## 3 Results and Discussion
### Minerals
Mn values varied from 0.29 \(\pm\) 0.03 to 0.45 \(\pm\) 0.01 for untreated palm dates and from 0.26 \(\pm\) 0.03 to 0.45 \(\pm\) 0.01 for treated ones. According to Figure 5 iii, after SDBD treatment, Zn values increased for _Maghol_ from 93.50 \(\pm\) 6.10 to 105.30 \(\pm\) 5.30, _Swkari_ from 93.47 \(\pm\) 5.06 to 96.88 \(\pm\) 6.04, and _Khadari_ from 79.33 \(\pm\) 6.09 to 80.33 \(\pm\) 5.60, but decreased for the remaining palm date varieties. Moreover, in the case of Zn, untreated samples ranged from 79.33 \(\pm\) 6.09 to 101.80 \(\pm\) 3.01; on the other hand, they ranged from 80.33 \(\pm\) 5.60 to 105.30 \(\pm\) 5.30 for treated ones. Cu values of control palm dates ranged from 1.64 \(\pm\) 0.13 up to7.17 \(\pm\) 0.29. Figure 5 iv shows that the Cu values of treated palm dates using SDBD plasma for 3 min ranged from 1.71 \(\pm\) 0.12 to 7.11 \(\pm\) 0.46. Cu values of palm dates has decreased after SDBD treatment for Ajwa, and Swkari varieties from 2.15 \(\pm\) 0.01 to 2.14 \(\pm\) 0.16 and from 7.17 \(\pm\) 0.29 to 7.11 \(\pm\) 0.46, respectively. Moreover, Cu values of the _Maghole_ date cultivar did not change after SDBD treatment. However, Cu values were increased for the rest of the palm date treated varieties. Statistical analysis showed that there are no significant differences (\(p\)? 0.05) in macro and microelement content between palm date varieties treated using SDBD plasma and untreated ones. These findings support the conclusion of [PERSON] et al. (2013) that non-thermal plasma has no effect on the trace elements of brown rice snack bars [75].
## 2 Spectral Analysis
### Preconditioning
Here, we extend the spectral distribution results in the case where \(\mu(x,y)\) is not bounded and even not integrable. More precisely, as in Corollary 1, we consider the case of a power singularity. Assume that there exist positive constants \(c\leq C\) for which  
\[c/|x+\mathrm{i}y|^{\gamma}\leq|\mu(x,y)|\leq C/|x+\mathrm{i}y|^{\gamma}.\]  
for every \(x,y\in[0,1]\). Consider the preconditioner \(P_{\mathbf{n}}=\tau(T_{\mathbf{n}}(t_{a}))\). Then, for every \(\gamma\in[0,1)\) and for every \(\alpha\in(1,2))\), we have  
\[ \begin{array}{ll}c1&\{P_{\mathbf{n}}^{-1}h^{\alpha}D_{\mathbf{n}}(\mu)\}_{n }\sim_{\lambda}0;\\ c2&\{P_{\mathbf{n}}^{-1}\big{[}\hat{B}_{\mathbf{n}}+h^{\alpha}D_{\mathbf{n}}( \mu)\big{]}\}_{n}\sim_{\lambda}1.\end{array} \]  
Proof. : We rely on Theorem 2, on Corollary 1, and on a standard symmetrization trick. First of all, we observe that the eigenvalues of \(P_{\mathbf{n}}^{-1}h^{\alpha}D_{\mathbf{n}}(\mu)\) and \(Y_{n}=P_{\mathbf{n}}^{-1/2}h^{\alpha}D_{\mathbf{n}}(\mu)P_{\mathbf{n}}^{-1/2}\) are the same because the two matrices are similar. The same holds for  
\[P_{\mathbf{n}}^{-1}\big{[}\hat{B}_{\mathbf{n}}+h^{\alpha}D_{\mathbf{n}}(\mu) \big{]}\]  
and \(X_{n}+Y_{n}\) with  
\[X_{n}=P_{\mathbf{n}}^{-1/2}\hat{B}_{\mathbf{n}}P_{\mathbf{n}}^{-1/2}.\]  
Now \(X_{n}\) is real symmetric, and in fact positive definite and so is \(\hat{B}_{\mathbf{n}}\). As proven in [1], the spectral distribution function of \(\{X_{n}\}_{n}\) is \(1\) thanks to a basic use of the GLT theory. Furthermore, the minimal eigenvalue of \(P_{n}=\tau(T_{\mathbf{n}}(t_{a}))\) is positive and tends to zero as \(h^{\alpha}\), since \(t_{a}\) has a unique zero of order \(\alpha\) at zero (see, e.g., [17]). Therefore,  
\[\|h^{\alpha}P_{\mathbf{n}}^{-1/2}\| = \frac{h^{\alpha}}{\lambda_{1}(\tau(T_{\mathbf{n}}(t_{a})))}\leq Dh ^{1/2},\] \[\|P_{\mathbf{n}}^{-1/2}\| = \frac{1}{\lambda_{1}(\tau(T_{\mathbf{n}}(t_{a})))}\leq Dh^{-1/2}.\]  
As a consequence of Corollary 1 we deduce  
\[\|Y_{n}\|_{2}\leq D^{2}\|D_{\mathbf{n}}(\mu)\|_{2}=\sigma(n) \tag{11}\]  
if and only if \(\gamma<1\). In conclusion, the desired result follows from Theorem 2 with \(d_{n}=n^{2}\) and \(f=1\). Theorem 4 cannot be sharp since the estimate in (11) would hold also if the preconditioner is chosen as \(P_{\mathbf{n}}=h^{\alpha}I_{n^{2}}\). A more careful estimate would require considering that the eigenvalues of \(\tau(T_{\mathbf{n}}(t_{a}))\) are explicitly known, and in fact, we will see in the numerical experiments that the spectral clustering at 1 of the preconditioned matrix-sequence is observed also for \(\gamma\) to be much larger than 1.
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
## 2 System Model
### Channel Access
Each beacon is sent in one channel. Hence, the beacon channel shuffler is only drawn once every beacon interval. The data transmissions in UL can be performed in one out of 156 slots. Hence, the data channel shuffler is drawn 156 times within each beacon. This way, the GW can also determine which channels to listen to during UL. The previous paragraphs described how a synchronized connection is established between an EN and a GW based on the beacons. As part of the mechanism to minimize interference, non-beacon packets (i.e., the data packets) use the LBT mechanism. When an EN has a packet that needs to be transmitted in the queue, it first randomly selects one of the next four transmission slots relative to the beacon start time. It then selects one out of the six LBT sub-slots by drawing from a geometric random distribution. It will then schedule the LBT operation to occur at the correct time based off of the time slot and LBT sub-slot. When the scheduled LBT operation occurs, the EN first listens to determine if anyone else is actively transmitting a packet by entering in preamble detection mode. If the medium is available, i.e., when there is no signal detected from other devices, then the EN starts its UL data transmission. If the medium is detected to be busy, then the EN backs off for a random duration by selecting one of the next sets of four transmission slots. From there, it will select an LBT sub-slot where it will repeat the LBT process. This is illustrated in Figure 3. The connection to a GW is established via the successful discovery of the beacon in the DL and when the GW registers the EN by receiving the UL packets from it. This channel access scheme reduces the chances of packet collisions within the WAN as ENs join the network, but it does not guarantee a collision-free medium. An analysis of such capacity will be presented in Section 6. The analysis shows that the system capacity depends on the number of channels available for FHSS, the number of ENs in the desired WAN, the number of ENs in the neighboring WAN, the number of GWs in the area, and the airtimes of the packets (beacon interval, beacon duration, UL packet durations, and UL packet rates).
## 1 Introduction
On the other hand, while the space-time-lagged correlation analysis yields only the time-mean propagation velocities, the OF computes \"instantaneous\" velocities between the pairs of consecutive satellite images, so given the large number of images, it is well suited for estimating the temporal evolution of motion. In oceanographic applications, this might be particularly useful in regions where the propagation velocities of mesoscale eddies are strongly affected by the variable background ocean circulation. In this work, we focus on the South Atlantic Ocean and the adjacent regions of the Southern Ocean (Figure 1). This region is characterized by complex dynamics and, therefore, it is particularly representative for testing the OF method. The same region was used by [22] to estimate eddy propagation velocities using a space-time-lagged correlation method, which we also use for validation. The mean dynamic topography contours displayed in Figure 1 indicate the streamlines of the surface flow (with the high SSH to the left of the flow direction in the Southern Hemisphere). The ocean circulation here is dominated by a system of western boundary currents (the Brazil and Malvinas Currents), the eddy-rich eastward-flowing ACC, and the Agulhas Retroflection, which produces prominent eddies that propagate into the South Atlantic. The ACC transports 130-140 Sv (1 Sv = 10\({}^{6}\) m\({}^{3}\) s\({}^{-1}\)) of water through the Drake Passage [32] and supplies the Malvinas Current that carries 30-40 Sv northward along the Argentine coast [33; 34; 35]. The southward branch of the South Atlantic subtropical gyre along the Brazil coast is comprised of the Brazil Current with a mean strength of 14 Sv and a standard deviation of about 9 Sv at 34.5\({}^{\circ}\)S [36]. The Brazil and the Malvinas currents collide at approximately 39\({}^{\circ}\)S forming one of the most energetic regions of the World Ocean--the Brazil-Malvinas Confluence (Figure 1; ref. [37]). A very strong anticyclonic (anticlockwise) barotropic flow transporting approximately 100 Sv exists around a topographic anomaly known as the Zapiola Rise at about 45\({}^{\circ}\)S and 45\({}^{\circ}\)W [38; 39]. This flow is also associated with the anticyclonic propagation of mesoscale eddies [22]. By applying the OF technique to SSH data in the South Atlantic Ocean, we provide a realistic description of eddy propagation velocity validated by several other methods and previous studies.
###### Abstract  
Waste biomass generated during agricultural production is a popular source of energy used in many developed and developing countries, due to economic factors and easy availability. Pellets produced from waste biomass generated during the cultivation of plants for industrial purposes are a good substitute for fossil fuels, the consumption of which should decrease for environmental reasons. This article presents the results of research on the use of waste biomass generated during the cultivation of plants for industrial purposes, such as sunflower, tobacco, and Jerusalem artichoke for the production of pellets. In addition,iferous sawdust was used for the production of pellets. Mechanical, calorimetric, and thermogravimetric properties were tested. It was noted that pellets made of Jerusalem artichoke biomass (1591.45 N) were the most resistant to mechanical damage. The calorific value of the tested fuels ranged from 16.35 to 17.70 MJ\(\cdot\)kg\({}^{-1}\), and the ash content was below 5%. In addition, during the combustion of pellets, the lowest emissions of nitrogen oxides were recorded for pellets made of tobacco stalks--45.56 mg\(\cdot\)m\({}^{-3}\) and sulfur dioxide for pellets consisting of a mixture of coniferous sawdust and tobacco stalks--1.88 mg\(\cdot\)m\({}^{-3}\). The addition of coniferous sawdust to each type of biomass tested resulted in a reduction in the emission of sulfur dioxide, carbon monoxide, and carbon dioxide, and an increase in the emission of nitrogen oxides. Based on the research, we found that the waste biomass generated during the cultivation of the tested plants for industrial purposes is a suitable raw material for the production of pellets used for industrial and non-industrial purposes.  
industrial crops; alternative fuel; waste; pellets +
Footnote †: journal: Applied Sciences
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 3 Experiment
### Quantitative Comparison
#### 3.5.1 Asd  
We quantitatively compared the performances of our method with 20 state-of-the-art methods: SR [13], AC [14], RC [7], FT [15], GS [10], RBD [11], DSR [12], SF [8], HS [41], GBMR [23], BSCA [30], MC [24], IDCL [9], TLSG [16], GC [43], GR [44], SWD [39], GU [43], MSS [45], and FES [46]. The performances of MC [24], GBMR [23], and RBD [11] methods are at top in a recent saliency benchmark study [47].  
The average precision-recall curves in Figure 13a show the proposed method performs better than other approaches for the ASD dataset. As shown in Figure 13b, compared to the diffusion-based methods GBMR [23], BSCA [30], MC [24], IDCL [9], TLSG [16], our method can obtain a higher precision and F-measure but a slightly lower recall. In addition, the MAE of our method is lowest in these methods, which means the tiny difference between the saliency map and the ground truth.  
Figure 13: The comparison results on the ASD dataset. (**a**) Precision-recall curves. (**b**) Precision, recall, and F-measure at adaptive threshold. (**c**) Mean absolute error (MAE).
## 2 Materials and Methods
### Methods
#### 2.2.6 Standards and Limitations  
While there are no standards specifying mechanical performance requirements for non-ferrous metallurgical slags as SCMs, EN 15167 [39] requires a minimum 28-day strength activity index (SAI) of 70% when 50% of the cement is replaced by ground granulated blast furnace slag. Despite the substantial differences in the source and composition of the slags studied in this research with ground granulated blast furnace slag (GGBFS) generated in pig iron production, such a criterion appears a reasonable minimum requirement for SCMs used in concrete and is thus adopted here as the minimum SAI for assessment of the slags. In addition, there are no requirements yet set forth for the R3 test results of SCMs (i.e., 7-day heat release or bound water). The R3 results of the slags were compared with those of few conventional SCMs (GGBFS, coal fly ash and calcined clay) and a typical quartz powder to put the values in prospective.  
For the evaluation of environmental performance, the limitations set forth by the Flemish legislation VLAREMA (17 February 2012--Order of the Government of Flanders adopting the Flemish regulation on the sustainable management of material cycles and waste) for the use of waste as resources in construction applications were used. The heavy metal leaching limitations are provided along with the results for each slag later in the Section 3.
## 5 Discussion
Therefore, we introduced a dynamic linear regression into our algorithm, in order to supplement the function of time prediction. At the same time, due to the limitations of dynamic linear models, our multi-step prediction may be more suitable for spatio-temporal data with periodic and trend changes. Comparing the simulation and actual data, good prediction results were observed. Some limitations of the FUKSS and its modified, as presented, is the assumption regarding the initial parameters. First, we assume that \(c_{0}(x)\) is a zero-mean process with a covariance function \(E[c_{0}(x_{a})c_{0}(x_{b})]=\alpha k(x_{a},x_{b})\). Of course, the known non-zero mean can be obtained from all the data vectors, but this requires a lot of extra work for the general covariance function. We chose [26], as it proposes two most likely cases, which do not add significant complexity to the description of the algorithm. Those two cases are as follows: (1) \(c_{0}(x)\) is known, such that \(\alpha=0\); and (2) \(c_{0}(x)\) is determined by the definition, in which case it can be simplified to the length of the running time. In practical application, we prefer the second case, which is simulated as shown in Figure 4. On the other hand, we prefer to take a smaller value than zero if enough data points are available. Second, our algorithm takes advantage of the characteristics of Kriging space statistics and does not require fixed observation points. For the additional sample data, the computation requirement of our algorithm will be greatly increased. Finally, as for selecting parameters for dynamic linear regression, we need prior knowledge to obtain better results, which can lead to extra workload. Due to the characteristics of the cubic dynamic linear regression model and the certain smoothing of the historical data by our FUKSS model, rough parameter selection can be used to obtain relatively good results.
# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
## 4 UAVS Path Planning Through IPKO
### Objective Function  
The objective function of drone path planning is to assess the quality of various paths with the goal of selecting the optimal flight path. Typically, this function considers factors like total flight distance, obstacle avoidance cost, and adherence to the designatedboundaries. These combined elements influence the direction of optimization and the ultimate choice of the drone's path. The objective function is as follows:  
\[\min(W)=\min(V_{c}+T_{c}+E_{c}) \tag{26}\]  
where \(V_{c}\) represents the total cost of flight distance, \(T_{c}\) signifies the expense associated with obstacle avoidance, and \(E_{c}\) represents the cost incurred by adhering to designated boundaries during flight.  
The cost of flight distance \(V_{c}\) primarily considers the total flight distance, which is the sum of the individual arc segments \(L_{i}\). If the total flight path consists of \(n\) segments, the expression for calculating the cost of total flight distance is as follows:  
\[\left\{\begin{array}{l}V_{c}=\sum\limits_{i=1}^{n-1}L_{i}\\ L_{i}=\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i}\right)^{2}+ \left(z_{i+1}-z_{i}\right)^{2}}\end{array}\right. \tag{27}\]  
The terrain cost Tc is primarily intended to ensure the avoidance of hazardous areas along the drone's flight path, achieved by adjusting the value of \(T_{c}\). Specifically, when the altitude \(Z_{i}\) exceeds the obstacle height \(Z(x_{i},y_{i})\) in the terrain, \(T_{c_{0}}=0\); When \(Z_{i}\) is below \(Z(x_{i},y_{i})\), \(T_{c_{i}}=\infty\). This summation effectively enables obstacle avoidance for the drone's flight trajectory, including potential obstacles such as the sea surface. The expression is formulated as follows:  
\[\left\{\begin{array}{l}T_{c}=\sum\limits_{i=1}^{n}T_{c_{i}},\ \ T_{c_{0}}=0\\ T_{c_{i}}=\left\{\begin{array}{ll}0,\ Z_{i}>Z(x_{i},y_{i})\\ \infty,\ \ otherwise\end{array}\right.\end{array}\right. \tag{28}\]  
where the \(E_{c}\) is meticulously crafted to guarantee that the drone remains within the designated airspace, primarily by controlling the value of \(E_{c}\). When within the airspace, \(E_{c}=0\); when outside the airspace, \(E_{c}=\infty\). This summation confines the particles within the specified boundaries. The expression is as follows:  
\[\left\{\begin{array}{l}E_{c}=\sum\limits_{i=1}^{n}E_{c_{i}},\ \ E_{c_{0}}=0\\ E_{c_{i}}=\left\{\begin{array}{ll}\infty,\ \ otherwise\\ 0,\ x_{i}\in[0.x_{\max}]\cap y_{i}\in[0,y_{\max}]\cap z_{i}\in[0,z_{\max}] \end{array}\right.\end{array}\right. \tag{29}\]