esa-sceva/satcom-chunk-collection huggingface dataset:
# A High Throughput Hardware Architecture for Parallel Recursive Systematic Convolutional Encoders
## 3 Results
### Impact of the Parallelism Degree on the Data Rate: Case Study
The same simulation was exploited to estimate the relation \(\Omega_{D^{\prime}}[k]\) for the matrix \(D^{\prime}\). Such a trend is difficult to derive by simply exploiting Equations (11) and (16). Nevertheless, it possible to realize an estimator \(\overline{\Omega_{D^{\prime}}[k]}\) through a machine learning approach. First of all, the model described in Equation (27) was used:  
\[\overline{\Omega_{D^{\prime}}[k]}=\|\theta_{0}+\theta_{1}\cdot k\|, \tag{27}\]  
where \(\|\cdot\|\) indicates the rounding operation; \(\theta_{0}\) and \(\theta_{1}\) are the learning parameters. The relation, previously calculated, reporting the maximum number of unitary elements respectively in the rows of and \(D^{\prime}\) matrix for each value of \(k\) in the range \((1,...,100)\) was used to realize a dataset. The latter was randomly partitioned into a train and a test datasets, whose dimensions are respectively \(\frac{2}{3}\) and \(\frac{1}{3}\) of the original one. The values of the learning parameters \(\theta_{0}\), \(\theta_{1}\) were estimated through a mean square error approach on the training set, without considering the rounding operation. A total of 20 iterations were performed; during each iteration the random partition of the total dataset was changed and the accuracy of the estimator is calculated on the test dataset. In particular, accuracy was calculated as the percentage of right predictions on the test dataset. At the end of the procedure, the learning parameters relative to the partition with maximum accuracy on the test dataset were considered. The best accuracy on the test dataset was of 87.87% and the obtained learning parameters are shown in Table 3.  
\(\overline{\Omega_{D^{\prime}}[k]}\) was used for the estimation of the parameters \(T_{A}\) and \(T_{B}\). To increase the number of data to use for the interpolation process, we also exploited the values of the maximum clock frequency for the networks relative to the matrix \(D^{\prime}\) for such values of \(k\) for which the system critical path was \begin{table}
\begin{tabular}{c c} \hline \(\theta_{0}\) & 0.988904449419594 \\ \hline \(\theta_{1}\) & 0.571261448964218 \\ \hline \end{tabular}
\end{table} 
Table 3: Learning parameters for the \(\overline{\Omega_{D^{\prime}}[k]}\) estimator. included in the network describing the rows of another matrix. Table 4 reports the \(T_{A}\) and \(T_{B}\) values derived through the described methodology. Figure 7 shows the estimated \(f_{clk_{MAX}}[n]\) trend and data used for the interpolation. Equation (28) sums up the expression for the \(R_{IN}[k]\) trend. \[R_{IN}[k]=k\cdot f_{clk_{MAX}}[k]=\frac{k}{T_{A}+T_{B}\cdot\lceil log2(\|\theta _{0}+\theta_{1}\cdot k\|)\rceil}. \tag{28}\]
## 1 Introduction
To address these problems, this article focuses on designing conditions that enable the closed-loop system to perform output regulation and tolerate actuator faults. The proposed approach is presented through UI observers, whose gain is computed using Linear Matrix Inequalities (LMIs). The approach is based on the linear approximation of nonlinear systems; the stabilizer gain and the fault estimator are computed through LMIs, and a pair of matrix equations calculates the rest of the controller scheme. The observation algorithm is tested through simulations and real-time experiments on the Furuta pendulum system to verify the viability of the proposed approach. It is worth noting that the developments in this article are based on the theory presented in [24], where an HGO provides an estimation of the reference signal, as well as a mathematical model for the exosystem derived from its structure. Additionally, the reason behind using LMIs is that they allow the designer to naturally include performance specifications such as speed convergence (the faster the observer converges, the faster its estimations can be fed to the control signal). Of course, this includes taking care of the transient behavior (similar for the controller, as it stabilizes the system so it can track signals) [18]. Other performance specifications are related to bounds on the input/output. In our case, bounding the input signals prevents damage to the actuators. The article is organized as follows: Section 2 presents the general problem to be solved, while Section 3 outlines the methodology employed throughout the work. Then, Section 4 presents the main results, which include the LMI conditions. Section 5 presents the performance of the proposal using the Furuta pendulum system in both simulation and real-time settings. Finally, Section 6 presents the paper's conclusions and outlines some potential future work.
## 4 Results and Discussion  
In order to validate the hybrid numerical model and also assess its advantages, the predicted results are compared with available measure values [38], together with the data from the lumped model, as shown n the comparison of in-cylinder pressure variation in Figure 3. The experimental measurements performed by reference [38] were typical and valuable for testing the thermodynamic cycles of reciprocating compressors, and the experimental results have been widely cited by many researchers for comparing their numerical results. Generally, both models can give similar pressure variation trends that follow well with the measuring history. Especially during the expansion and compression stages, numerical model results are consistent with the measured values. However, the merits of the hybrid model predicting the thermodynamic cycle can be clearly explained by examining in detail the pressure changes in the suction and discharge phases. As can be seen in Figure 3, here the hybrid model predicts the in-cylinder pressure oscillations much better than the lumped model by comparing with the experimental ones, which directly corresponds to the pressure pulsation waves in the suction and discharge chambers. The lumped model is mainly limited in not evaluating the pressure pulsations, while the above detailed analysis confirmed effectively the feasibility of the developed computational model allowing the compressor-pipeline interaction. It is also worthwhile to notice that the developed model is able to simulate accurately the pipeline pressure pulsations with large amplitude, which is much more advantageous than the current acoustic wave models.  
The following results presented in this work are for a reciprocating natural gas compressor with the specifications mentioned in Table 1. The effects of pressure pulsations are investigated in separate sections.  
Figure 3: In-cylinder pressure comparison between hybrid model and experimental results [38], together with lumped model data.
# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
## 3 Results and Discussion
### Breast Cancer Classification Comparative Analysis  
In this experiment, the INbreast benchmark was utilized to evaluate the classification performance of the proposed CNN. The ability of the CNN architecture to classify breast cancer cases is evident from the confusion matrix presented in Figure 4. The proposed CNN model using the FTS activation function accurately identified 105 malignant, 116 benign, and 342 normal cases, demonstrating high precision across all categories. Despite its exceptional accuracy, the model had a few misclassifications: only three cases were incorrectly labeled as 'Malignant' and two were incorrectly labeled as 'Benign'.  
Based on the values from the confusion matrix, the precision, recall, F1-score, and accuracy of each model were calculated and are presented in Table 2. This comparative analysis of the breast cancer diagnosis results highlights the effectiveness of various frameworks. The Faster RCNN approach by [PERSON] et al. (2018) achieved a precision of 84.36%, a recall of 85.23%, an F1-Score of 84.70%, and an overall accuracy of 87.80%. Meanwhile, the YOLO V3-based model proposed by [PERSON] et al. (2018) marked an improvement, yielding a precision of 90.94%, a recall of 89.83%, an F1-Score of 90.36%, and an accuracy of 92.16%. Further advancements were observed in the YOLO V5-based framework by [PERSON] et al. (2018), which accomplished a precision of 92.08%, a recall of 91.31%, an F1-Score of 91.67%, and an accuracy of 93.72%. The YOLO-LOGO model by [PERSON] et al. (2018) achieved 95.53% precision, 95.47% recall, 95.50% F1-Score, and 96.51% accuracy. We evaluated the impact of various activation functions on the classification accuracy of our proposed CNN model. By replacing traditional activation functions--specifically, Leaky ReLU--with a state-of-the-art activation function, namely, FTS, we observed a notable improvement. The implementation of the FTS activation function resulted in an increase in the classification accuracy by 0.52% compared to the baseline model utilizing Leaky ReLU. The proposed YOLO-BCD CNN architecture using the FTS activation function outperformed all, exhibiting a remarkable  
Figure 4: Confusion matrix of proposed CNN architecture.  
precision of 97.13%, a recall of 97.93%, an F1-Score of 97.51%, and the highest accuracy of 98.08%. These results illustrate the enhancement in breast cancer classification accuracy through advanced deep learning models, concluding in the superior performance of the proposed architecture.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 3 Deep-Reinforcement-Learning-Based Cooperative Downloading Scheme
### MDP Formulation  
To obtain the optimal cooperative downloading policy maximizing the amount of downloaded data from satellites to GS, we formulate an MDP consisting of state, action, and reward.
## 2 Applications
### Underwater Exploration  
The concept of IoUT can be applied to the underwater lost-treasure discovery. For instance, the discovery of the Titanic in 1985 (by the Woods Hole Oceanographic Institution) benefited from the use of autonomous underwater vehicles (AUVs) [13]. By using UWSN technologies, IoUT can also be applied to tracking of fishes [13]. Moreover, underwater natural-resource discovery, such as minerals [17], metals, corals, and coral reefs [18], can all benefit from the infrastructure of UWSNs as well.  
* [PERSON] and [PERSON] present an overview of UWSNs in [13]. They provide a valuable survey of various UWSN architectures, and indicate how the architectures facilitate unmanned underwater exploration. As mentioned in this paper, a number of successful lost-treasure discoveries were made with the help of UWSNs.
* The objective of [17] is to develop advanced communication techniques for efficient real-time investigation of large uninhibited oceans. To explore the oceanic environment, the authors propose a large-scale system that uses UWSNs. In particular, to investigate the underwater resources, the proposed sensor modules are installed with video capturing devices. Accordingly,the proposed system can be used to discover and excavate the mineral resources underwater. The simulation results are encouraging as the proposed system is helpful in large-scale surveillance and exploration.
* [PERSON] et al. carried out a practical research [18] in Australia. The research reports on the lessons learned from two years of operation of UWSNs deployed at seven coral reefs along the Great Barrier Reef in north-eastern Australia. The authors emphasize that \"cool technologies\" should be used in an extremely careful way, and should never overshadow the basic process of collecting data; the key is to strike a balance between what actually works in an operational sense versus what is the latest and greatest.
## 4 CTP and Probabilistic Routing  
As stated previously, CTP suffers from the hot spot problem. Best routes are rapidly overloaded leading to fast energy depletion and network partitioning. This problem can be diminished by several load balancing schemes to route information by other well connected routes. One way to address load balancing is to introduce new parameters to be measured by the control plane (state information transmitted to keep the topology of the routing protocol). These techniques introduce new traffic to the network and usually require blacklisting techniques to avoid bottleneck nodes. Blacklisting usually require application specific configuration in order to avoid network partitioning [18].  
In order to reduce the hot spot problem, we propose to use probabilistic techniques to determine routes. The idea behind our work is to select a route based on probabilities. Eligibly routes will be selected taking into account CTP routing metric and the neighbour table information of a node. Probabilities will be assigned to possible paths considering local information of each node. In order to avoid pure randomized choice (as Random Walks [19]), a biased selection pattern is introduced. In our work two metrics have been studied, the first one calculates distances between possible paths and assigns a probability proportional to the quality of that path with respect to others. The second technique uses a probability distribution function that assigns a probability to each possible path without requiring in-node calculations. Both techniques give more probability to the best route (_i.e._, represented by less ETX) in order to do not heavily compromise the reliability of the network.
# A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications
## 3 Algorithm Description  
### System Configuration  
In this section, we are going to describe the architecture of the whole system, the parts of the system and how these parts are connected. Figure 1 illustrates the network architecture adopted in this work. In detail, the network encompasses three different kinds of devices: the sink node (SN), the cluster head, (CH) and the end device (ED), modeling a cluster with a variable number of EDs.  
Pertaining to the position-placing of the employed nodes, the SN is always located in any place on the outer cycle, the CH is placed on the center of the inner cycle, while the moving EDs can be positioned anywhere inside the outer cycle. The circular sectors depicted with dashed lines at the top of Figure 1 constitute the boundaries between the different SF zones (7-12) defined by LoRaWAN. Therefore, each ED depending on the distance it has from the SN sends its LoRa message using the appropriate SF as part of the inherent ADR mechanism. The CH, which is responsible for orchestrating communication between the EDs and the SN, and the EDs are both equipped with a LoRa transceiver as well as a WuR transceiver, allowing pure asynchronous operation. More precisely, the system is able to support bidirectional communication between the CH and the EDs through the WuR equipment, allowing both types of devices to trigger each other via wake up beacon messages (WuB), while simultaneously assuring energy efficiency as the EDs enable their main LoRa radio on demand only, avoiding continuous transmissions.In addition, the EDs can realize single-hop unidirectional transmissions with the SN using their LoRa transceivers. The SN, on the other hand, is equipped only with a LoRa transceiver permitting long-range bidirectional transmissions with the CH in order to directly gather data from the latter as well as the EDs and to update the network's parameters.  
Figure 1: Network architecture.  
The system architecture is primarily based on the maximum transmission distance of the latest WuR technology as we consider that the maximum diameter of the external cycle is 9 km [21] and it can be utilized with ease in outdoor environment as opposed from existing works that have an additional circuitry like Bluetooth [22]. Keeping this in mind, it can be easily perceived that the moving EDs (either terrestrial or aerial and no matter where they are located) and the CH are always reachable with each other for WuB message exchange.
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 4 Algorithm Design and Description
### Basic Steps of FQGA-SR
Taking \(H_{1}\) and \(H_{2}\) quantified as \(c_{1}\) and \(c_{2}\), where the range is \([0,1]\). By inputting \(H_{1}\) and \(H_{2}\) into the quantum rotation the gate phase is obtained \(\Delta\theta\) along with mutation probability \(P_{m}\). Adaptive adjustment strategy of quantum rotation gate and mutation:  
The chromosome update is realized by quantum rotation gate phase transformation and mutation operation, which makes the algorithm possess the ability of expansion and exploration, so as to ensure the rapid convergence of the algorithm. By adjusting the phase of the quantum rotation gate \(\Delta\theta\) and mutation probability \(P_{m}\), it can adjust the value of \(H_{1}\) and \(H_{2}\). Assuming \(\Re\) is a positive real number, if \(H_{1}<\Re\) and \(Fit(X_{c})>Fit^{*}(X_{b})\), it represents that the fitness of the current individual \(Fit(X_{c})\) is better than the highest fitness value \(Fit^{*}(X_{b})\). At this time, FL is used to increase \(\Delta\theta\) and \(P_{m}\), in order to increase the value of \(H_{1}\), while if \(H_{1}>\Re\) and \(Fit(X_{c})<Fit^{*}(X_{b})\), it represents that the fitness of the current individual, \(Fit(X_{c})\), is lower than the optimal fitness value \(Fit^{*}(X_{b})\). Then, the FL algorithm is used to adjust the \(\Delta\theta\) and \(P_{m}\) respectively, which means decreasing the \(\Delta\theta\) while increasing \(P_{m}\), so as to reduce the value of \(H_{1}\). Quantum mutation operation:  
Applying quantum rotation gate to quantum mutation operation can effectively avoid converging into local optimum. Then, according to the mutation probability \(P_{m}\), the chromosome coding is selected, and some qubits are randomly selected. If \(Fit(X_{c})<Fit^{*}(X_{b})\), then the qubits of an individual are selected to transform the quantum gate and exchange the two quantum probability amplitudes of the qubits. Otherwise, \(N_{m-i}^{(t)}\) is selected, and its quantum probability amplitude is exchanged.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 3 Preliminary Studies
### Different Types of UAVs in Space
#### 3.2.4 Flapping Wing Drone
This UAV uses a CO\({}_{2}\) nuclear motor, while carbon dioxide is the main ingredient of Mars' atmosphere. In 2017, the University of Alabama in Huntsville conducted a study to examine the potential of a flapping wing aerobot to achieve flight on Mars. The concept of the aerobot was influenced biologically by the bumblebee [88]. Humanity can now develop technologies that will facilitate human habitation on Mars thanks to the robotic expedition. Artificial intelligence and Machine Learning for supporting connectivity of robots, including UAVs, can achieve common tasks effectively and efficiently [89; 90; 91; 92]. In February 2017, the NASA Johns Hopkins University Applied Physics Laboratory suggested a mission concept for UAV exploration of Titan. Since the surface of Titan would be investigated by a pathfinder, the balloon would act as a flagship. Upon alteration to the initial concept, using a UAV was suggested. The UAV, called the Dragonfly, used an eight-bladed rotor configuration with vertical take-off and landing capabilities. Both Titan's crust and atmosphere will be able to be explored by the Dragonfly [93]. Table 4 shows the comparison between different types of UAVs used till now for planetary exploration.
## 5 Experiments and Results
We obtained the data from driving on a road in Seoul, South Korea, for about 1 h, including during the day and night. Since the image processing algorithms have difficulties with diverse light sources and different intensity problems at night, the data sets were divided to compare the performances between day and night. Consequently, we divided the collected data into 10 sets, of which five were collected during the day and five were collected during the night. Each data set had 100 images, and the 1000 images were tested with the proposed method. The feature points were classified manually to be used as the ground truth. In addition, we tested the previous method [27] that uses only the image to compare its results with the proposed method. The classification errors of the two methods are shown in Figure 13.  
The results show that the proposed method is improved in classification accuracy over the previous work. The proposed method shows 98.19% accuracy in the day data set, better than the previous work's 95.85%. Moreover, the accuracy of the proposed method for the night data set is 90.33%, compared to the previous work's 85.93%. Table 1 shows the average error rates of the tested algorithms for each data set.
# Spatial Connections between Microplastics and Heavy Metal Pollution within Floodplain Soils
## 2 Materials and Methods  
### Study Area and Geospatial Sampling  
Field work and sampling was conducted within the floodplains of the Lahn River located in central Germany. Profound details about sampling sites and geospatial sampling approach were made available in [PERSON] and [PERSON] (2020) and [PERSON] et al. (2021). The Lahndrains an area of approx. 5924 km\({}^{2}\) with a river length of 245.6 km and an approximate floodplain area of 88.9 km\({}^{2}\) in the Federal State of Hesse, Germany [47]. The floodplain soils are mostly formed by organic-rich silt and loam, which was deposited by frequent historical and recent floods in the floodplain area. As with other floodplain landscapes, especially in central Europe, the Lahn catchment is affected by historical mining, subsequent industry, and general human use (smaller urban spaces and infrastructure: 7.0% of total land use) [47; 48]. Previous studies within the Lahn catchment area show that heavy metals (Cu, Pb, and Zn) are frequently deposited in floodplain soils as consequence of historical mining and related industry [49; 50]. The current land use of the catchment area (housing, infrastructure, and agriculture) favors the release of plastics and microplastics into the Lahn River, resulting in historical and recent depositions of (micro-)plastics within the Lahn floodplain. Based on these circumstances, the Lahn floodplain offers the opportunity to investigate the interrelationships between both the deposition of (micro-) plastics and the enrichment of metal(oid)s in floodplain soils.  
The geospatial sampling approach was applied at four floodplain cross-section transects, representative of different floodplain soilscapes (floodplain soils and floodplain landscapes) of the Lahn River [45]. Each transect site is located within regularly flooded areas (yearly flooding to 5.5 years) and shows no anthropogenic interruptions ([PERSON] et al., 2021).  
At each transect, two (upper catchment) or four (lower catchment) sampling plots containing two soil profiles (distance: 5 m) were drilled via pile core driving (100 mm and 80 mm core diameter; maximum depth: 2 m) and sampled according the following depth sections: 10 cm sections (for upper 0.5 m), 25 cm sections (for 0.5-1.5 m), and 50 cm sections (for 1.5-2.0 m) [45]. Sampling plots represent the floodplain cross-section with its diverse morphological forms, showing surface altitude changes of \(\pm\)0.5 m with interruptions from flood channels and a general slope from higher natural levees to the lower floodplain edge with the partwise occurrence of back swamps (further information on floodplain morphology can be found in [PERSON] et al., 2021).  
Root density was estimated in the field according to the German guidelines of soil mapping [51]. Stratigraphy and pedogenesis of each soil profile were documented according to the German soil classification [51] and the FAO Guidelines for soil description [52], as well as the WRB 2015 [53].