esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improved MPC-Based Trajectory Tracking Controller Design
### Solution to Proposed Algorithm  
The optimization problem that is expressed in Equation (23) can be converted into the following quadratic programming (QP) problem with the following constraints:  
\[\begin{split}\min&\mathbf{J}=[\Delta\mathbf{U}(k)^{T},\varepsilon] ^{T}\mathbf{H}[\Delta\mathbf{U}(k)^{T},\varepsilon]+\mathbf{G}\big{[}\Delta \mathbf{U}(k)^{T},\varepsilon]\\ & s.t.\quad\Delta\mathbf{U}_{min}\leq\Delta\mathbf{U}(k)\leq\Delta \mathbf{U}_{max}\\ &\mathbf{U}_{min}\leq\mathbf{S}\Delta\mathbf{U}_{k}+\mathbf{1}_{N_{c}} \mathbf{\tau}(k-1)\leq\mathbf{U}_{max}\end{split} \tag{28}\]  
where, \(\mathbf{H}=\begin{bmatrix}\Theta^{T}Q\Theta&0\\ \mathbf{0}&\rho\end{bmatrix}\), \(\mathbf{G}=\begin{bmatrix}\mathbf{2}\mathbf{e_{t}}^{T}Q\Theta&\mathbf{0}\end{bmatrix}\), the definition of matrix \(\Theta\) is shown in the Appendix A. \(\mathbf{e_{t}}=\mathbf{\Psi}\xi(k)\) is the tracking error in the predictive horizon, and the definition of matrix \(\mathbf{\Psi}\) is shown in Appendix A.  
During each control period, the solution to Equation (28) can be obtained and expressed, as follows:  
\[\Delta\mathbf{U}_{k}=[\Delta\mathbf{\tau}_{k},\Delta\mathbf{\tau}_{k+1},\cdots,\Delta \mathbf{\tau}_{k+N_{c}-1}]^{T} \tag{29}\]  
In this control series, the first element, as the practical controlling input increment, is applied to the robot system. The real control input is \(\mathbf{\tau}(k)=\mathbf{\tau}(k-1)+\Delta\mathbf{\tau}(k)\).  
The MPC-based trajectory tracking control will be implemented in a horizontal fashion. The control algorithm is summarized in Algorithm 1.  
```
1:Set the reference trajectory \(p(t)\);
2:Discretize the reference trajectory \(p(t)\) by t = K \(\times\) T, obtain a series of reference point and form a matrix \(P\);
3:Initialize the state of the robot and weighting matrices \(R\) and \(Q\);
4:Compute the state error \(\widetilde{\mathbf{x}}(k)\);
5:Solve the MPC problem (28), obtain the first element of the optimal solution, \(\Delta\tau\);
6:\(\tau(k)=\Delta\tau+\tau(k-1)\)
7:Input \(\tau(k)\) into the robot;
8:Measure the current state of the robot;
9:At next sampling time instant, then repeat from step 4;
```  
**Algorithm 1** MPC-based trajectory tracking control algorithm
# Conserved Plastid Genomes of _Pourthiaea_ Trees: Comparative Analyses and Phylogenetic Relationship
## 4 Discussion
### Phylogenetic Relationship of Pourthiaea  
To explore the phylogenetic relationships of _Pourthiaea_, we used 78 shared plastid protein-coding sequences and 29 nrDNA sequences, and two methods to conduct phylogenomics analyses. As expected, all phylogenetic analysis results support the monophyly of the 13 _Pourthiaea_ species. Previous studies divided _Pourthiaea_ into three sections: sect. _Amphidoxae_, sect. _Impressiveuae_, and sect. _Pourthiaea_[6]. However, the monophyly of the three sections was supported in nrDNA phylogenies, but not in plastome phylogenies. For the discordances, the nrDNA phylogeny was usually better supported by morphological evidence than the plastome phylogeny [6]. Morphological characteristics distinct to each section include shrubs, occasionally small trees, shorter peptides, and umbellate infloresences or cormypose in sect. _Pourthiaea_; tometose branchlets, adaxial leaf blade, petioles, pedicles, peduncles, and sepals in sect. _Amphidoxae_; and ferruginous villous pulsoesc on the leaf blade, petiole, branchlet, hypanthium, and inflorescences in sect. _Impressiveuae_. The cytometera discordance, common in angiosperms, is mostly explained as caused by incomplete lineage sorting (ILS) and/or hybridization [26; 90; 91]. In the case of _Pourthiaea_, our study implies that the conflicting nuclear and plastome phylogenies are likely the result of hybridization [6]. _Pourthiaea_ belongs to Maleae, and the hybridization event within this tribe is widespread [7]. The origin or expansion of some genera is related to hybridization such as _Micromeles, Pseudocydonia, Phippsioneles_, and _Rhaphiolepis_[12; 92; 93]. Therefore, the observed cytometera discordance likely stems from hybridization events within _Pourthiaea_, reflecting complex evolutionary dynamics within the genus. This finding highlights the necessity of using nuclear and plastid markers for phylogenetic studies to fully unravel the evolutionary history of these species. Future research could focus on resolving the cytometera discordance of _Pourthiaea_ through whole-genome approaches.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 4 Statistical Characterization of the Aggregated Interference Power
In the case of Figure 10, the calculated \(\bar{\omega}\) for the AP heights of \(h=\{2,5,10,100\}\) m are \(\bar{\omega}=\{32,355,13070,858160\}\) respectively, and in Figure 11, the \(\bar{\omega}\) for AP height \(h=5\) m for two cases of \(N_{c}=\{1,16\}\) are \(\bar{\omega}=\{355,5688\}\), respectively. Based on the figures, it can be noted that these approximations are close to the real breaking points of the curves. Figures 12 and Figure 13 (corresponding to AP height \(h=2\) m and \(h=30\) m, respectively) validate the proposed approximate model, with respect to true CF of a UCA described in (19) and the \(\alpha\)-stable distribution model [11] that coincides with (19) for \(h\to 0\). It can be seen that the stable distribution might be appropriate only if the AP height and UE density are not very large. By increasing the UE density \(\lambda\), for large AP height \(h\), the approximate mixture model (40) outperforms the stable model. The difference in the median between the approximate model (dashed curves) and the true model (solid curves) in the dB scale, for \(h=2\) m, is in the order of 0.1 to 0.3 dB, and for \(h=30\) m, it is in the order of 1.1 to 1.3 dB, while the curves corresponding to stable distribution are dashed lines with a marker. From the computation complexity point of view, depending on the parameters, the approximate model (40) is computed very fast compared to (33). The computational complexity of Monte-Carlo simulations, on the other hand, depend on the UE density \(\lambda\), while the true and approximate analytical models do not depend on the density \(\lambda\). In practical systems, the height is known, but other parameters, such as the density of active users \(\lambda\), are not known, and some inaccuracies with respect to the ideal model might occur. We believe that the knowledge of a reasonable approximation of the distribution of the aggregated interference enables the measurement of the approximating alpha-stable distribution during multiple idle times of the communication intervals using any unsupervised learning method [53], which is a practical on-the-fly method. \[\Psi_{I}(\omega)=\exp(\pi\alpha\lambda\Xi(\alpha,\omega)), \tag{33}\]2 Comparison of the CDF of the aggregated interference power, within \(R_{max}=400\) m using a UCA of size \(N_{c}=16\) positioned at AP height \(h=2\) m, for different interferer density \(\lambda=\{10^{-3},10^{-2},10^{-1}\}\) m\({}^{-2}\), path-loss exponent \(2b=2.6\), transmit power \([P_{tx}]_{dBm}=20\) dBm, central frequency \(F_{c}=28\) GHz and \(|\beta_{o}|=\sqrt{P_{tx}}\,4\pi F_{c}/c\), for three cases: (a) true CF model described in (19); (b) approximate mixture model described in (40); and (c) \(\alpha\)-stable distribution.
## 4 Experiments  
### Experimental Settings  
#### 4.1.1 Baselines and Evaluation Metrics  
In this experiment, we treat the odor descriptors prediction of odor molecules in the dataset (introduced in Section 3) as a multi-label classification problem. We compare our proposed model HGAFMN with the DREAM Winner model [4], GNN-QSOR model [5], and DNN-CNN model [6].  
DREAM Winner model [4]: It is a random forest (RF)-based model that predicts odor perception from chemical feature descriptors of odor molecules.  
GNN-QSOR model [5]: It treats atoms as nodes, chemical bonds as edges, and constructs an odor molecule as a graph structure. It predicts odor perception from the molecular structure graph of odor molecules.  
DNN-CNN model [6]: It uses a simple DNN component to learn descriptive features from the chemical feature descriptors of odor molecules uses the Xception-based CNN component to learn structural features from the molecular structure graph of odor molecules, and fuses these two components to predict odor perception.  
We use precision, recall, F1-score, and AUROC commonly used in classification problems to evaluate the performance of the above models. The higher the value of precision, recall, F1-score, and AUROC, the better the model performance. We randomly divided the entire dataset into three parts: 80% for training, 10% for validation, and 10% for testing.
## 3 Results and Discussions
### Future Vulnerability Assessment
#### 3.6.2 Second Scenario  
The results in Figure 11b indicate the future distribution of the vulnerability in relationship to the second scenario (increase the operating rate) for the year 2030 according to the DRASTIC method. A very important and remarkable displacement of the low vulnerability class over a period of 30 years (2030) compared to the reference groundwater vulnerability map (2010) due to overexploitation was observed. The map shows the presence of two important zones for this class located in the east (north of Hammamet city) and in the southern part (west of Bekkaria city). The increase in the area has a rate of 5.3% and 3.1% compared to the reference and the first scenario maps, respectively. In this study and based on the modeling conditions applied to the MODFLOW model, the variations in climate conditions (very low recharge rate) do not significantly influence the groundwater vulnerability using the DRASTIC method in the Tebessa-Morsott alluvial aquifer; conversely, overexploitation in this area can decrease the risk of vulnerability. Moreover, the piezometric level changes have a strong relationship and directly influence groundwater vulnerability.  
The new proposed approach considering the hybridization between a numerical model (MODFLOW model) and a groundwater vulnerability method (DRASTC) to predict actual and future groundwater vulnerability represents a step forward from the canonical concept of static representation of vulnerability. The possibility to forecast short- and long-term situations according to the most suitable climatic and socioeconomic scenarios is a valuable tool for groundwater management. At the regional scale, this proposed concept can be easily adapted for different aquifer types (kar  
Figure 11: Future DRASTIC classification according to the two simulated scenarios.  
climatic regions. On the other hand, the concept's suitability for regional-scale assessment is currently the main conceptual drawback, since the numerical simulation requires detailed aquifer information that is not available everywhere, especially on the national scale. Moreover, it is of paramount importance to include in the assessment the pollutants' transport dynamic, nitrogen formation and removal processes in both vadose and saturated zones [53], along with the role of fracture and sinkholes in nitrogen leaching [54], to ensure proper management of land and water resources.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 3 Results and Discussion
### Microbial Community Composition
#### 3.1.1 Bacteria  
The inocula I-LRS, I-BGP, and I-LRD showed a high abundance of _Frimicutes_ within the bacterial population. I-LRS had the highest ratio with 49%, followed by I-LRD with 40% and I-BGP with 39%. [PERSON] et al. [35] described the capability of _Frimicutes_ to degrade complex materials like cellulose. It was also shown, that _Frimicutes_ grow when fed with maize silage [36, 37]. Additionally, all inocula contained a high number of _Clostridia_. As much as 40.5% of all bacteria in I-LRS were classified as _Clostridia_, as well as 33% in I-BGP and I-LRD. The classes _Clostridia_ and _Bacillus_ (phylum _Frimicutes_) are able to decompose protein, cellulose, fat, and carbohydrates to acetic, propionic, and butyric acids [38, 39].  
However, members of the class _Clostridiales_ are also reported to replace bacteria of the phylum _Bacteroidetes_. Most _Bacteroidetes_ are acid producers and are capable of performing acidogenic-, acetogenic-, and syntrophic acetate oxidation breakdown [40]. A reduction of activity caused by self-inhibition of produced acid can occur. The high ratio of _Bacteroidetes_ for I-LRS, I-BGP, and I-LRD (22.5%-29.2%) could reveal a condition without high volatile fatty acid (VFA) as reported from [PERSON] et al. [40] for increasing the amount of poultry manure on feeding.  
Inocula WWP showed a different bacteria composition and a higher diversity. Members of the phylum _Frimicutes_ (15%-19%) and _Bacteroidetes_ (13%) were less abundant and additionally the phyla _Clacimonetes_ (11%-12%), _Patescobacteria_ (5%-6%), _Spirochaetes_ (7%-9%), _Proteobacteria_ (10%-11%), _Actinobacteria_ (5%), _Chloroflexi_ (6%-7%) and _Thermotogae_ (3%-4%) were identified. Members of the phylum _Cloacimonetes_ are responsible for acetogenesis, and the degradation of amino acids, sugars, and alcohol. A lower abundance of this phylum could result in fewer educts of acetogenesis. The abilities of the phyla _Atescibacteriam_ and _Spirochaetes_ have not yet been described in the literature [41, 42].  
Figure 2: Microbial community of inoculum from laboratory reactor (I-LRS), laboratory reactor, diluted (I-LRD), biogas plant (I-BGP), and wastewater treatment plant 7 d and 14 d degassed (I- WWP7, I-WWP14).  
[PERSON] et al. [43] showed that some members of the phylum _Patescibacteria_ are more abundant in wastewater sludge compared to the digestate of agricultural feedstock. _Proteobacteria_ are described as secondary degraders of polysaccharides [44]. The availability of more complex substrates should increase their population, which are common in wastewater sludge. The phyla _Actinobacteria_ and _Chloroflexi_ were previously described as highly abundant in wastewater sludge [41]. _Thermotogae_ are found in several habitats. They can resist high temperatures and degrade a large diversity of organic sources [45].
## 2 Materials and Methods
### Hardware Architecture  
The hardware/software system uses a microprocessor Broadcom based on low-energy ARM quad-core architecture energy consumption, which is then used by the Raspberry Pi(r) board. The serial communication and GPIO signals (general purpose input/output) were used for interacting with on-board sensors and for sending data. In the first phase of development, experimentation was performed through the use of a Breadboard to test the functionality of the microprocessor and check for any critical issues. The assembly allowed the starting of software development for specific board components, before getting to the final hardware prototype. The board was directly assembled using optimized instrumentation for surface mounting technology (SMT). The hardware board of the Smart Capsule included a 16 \(\times\) 2-character LCD to display information from on-board sensors; a multicolored LED (red, green, blue) to inform the user about the system status; three wired sensors for measurement of the temperature in the different compartments of the Smart Capsule, including external and internal temperatures and humidity.  
The hardware has the possibility of connecting additional sensors via Bluetooth Low Energy (BLE) to wirelessly connect temperature sensors; LTE-TDD/LTE-FDD/HSPA and GSM/GPRS/EDGE multi-band data connectivity for connection to the data network via different protocols was provided through a SIM7600 module by SIMCom. GNSS geolocation satellite receiver is also possible, with reception of GPS, BeiDou and GLONASS signals and guaranteeing correct positioning of the Smart Capsule. Management of an electric lock with an opening command and closing status was implemented. The electronics were equipped with power modules that were already compatible with the CEI EN 55022 standard, dedicated to information technology equipment, which specifies the limits in the frequency range of 9 kHz-400 GHz. Furthermore, the radio sets are also certified according to the Radio Equipment Directives (2014/53/EU). The electronic components used complied with the RoHS (Restriction of Hazardous Substances Directive) which imposes restrictions on the use of certain dangerous substances in the construction of various types of electrical and electronic equipment.  
The electronics were designed in a compact board embedding all the needed electronic hardware, allowing the easy integration inside the Smart Capsule.
# Binaural Rendering with Measured Room Responses: First-Order Ambisonic Microphone vs. Dummy Head
## 3 Experiment II: Dummy-Head MOBRIR vs. ARIR
### Results and Discussion  
The results of the listening experiments with nine participants (all male experienced listeners with normal hearing, and between an age of 27 and 57) are depicted in Figure 6 and are discussed in detail below. _Timbre:_ While most of the conditions are rated significantly poorer than the reference, the following are not: LISHPh with \(30^{\circ}/2\)k and ARIR rendering with the ASDM-upscaled orders 3 and 5, cf. upper triangle in Table 3. ARIR rendering with 5 th order is generally rated best; however, it is not significantly different to the \(30^{\circ}/2\)k and 3 rd-order conditions. The timbral quality decreases with both orientation resolution and lower order, and thus the \(60^{\circ}/1\)k and 1 st-order conditions yield significantly lower quality. The broadband linear interpolation conditions received the poorest ratings and significantly differ from all other conditions, with the exception of \(60^{\circ}/1\)k and 1 st order. Figure 6: Median (markers) and 95% confidence interval (solid lines) of ratings from all nine subjects for testing the perceived difference to the reference (linearly interpolated BRIRs on a \(1^{\circ}\) resolution). Settings of the algorithm are indicated by \(\Delta\varphi/f_{c}\), where _lin_ denotes a broadband linear interpolation. _Spatial Mapping:_ While the general trend is similar to the _timbre_ results, for _spatial mapping_ only the 1 st order and all linear interpolations are significantly poorer than the reference, cf. lower triangle in Table 3. Again, the 5 th-order ARIR rendering is rated highest, albeit not significantly better than the 3 rd-order ARIR and all LISHPh conditions (\(p>0.68\)). The 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\) linear interpolations are significantly outperformed by all other conditions except the 1 st-order ARIR rendering. Please note that we tested static directions different from _Experiment I_ and even though the trend is similar to Figure 3a, the 30\({}^{\circ}\)/2k is not significantly different from the reference condition here. This can be addressed to participants not always rating the reference highest in _Experiment II_. _Continuity:_ The ratings of the _continuity_, i.e., robustness of source position and timbre to head rotations, are depicted in Figure 6b and Table 4, respectively. Tendentially, quality ratings are higher for music compared to pink noise as source signal. Independent of the source signal, the 5 th-order and 3 rd-order ARIR conditions as well as all LISHPh conditions do not significantly differ from the reference condition (\(p>0.15\)). Again, all linearly interpolated conditions and the 1 st-order condition perform poorly, are significantly different to all other conditions, and are similar to each other.
## 7 Practical Application of the CNM Precision Validity Score
At the departmental level, the calculation is only valid for large cities like Paris or Lyon if you want to maintain the precision of \(\delta=100\) kbit/s. In Figure 3, the scores are calculated once for the larger regions (left) and once for individual departments (right) in France. Everything is shown on two maps next to each other to highlight where enough measurements were made for which case. The figure depicts the average downlink throughput with different colors. All areas with sufficient measurements are colored in blue, as the precision here corresponds to the target value of at least \(\delta^{*}=100\) kbit/s, i.e., the actual average throughput lies within an interval of 100 kbit/s. In this case, the _Val_. _Score\({}_{\text{prec. }}\)_ is calculated with Bootstrapping and Percentile Confidence Intervals to take into account the real distribution of all downlink measurements in our data set for a region or department. The annotations include the number of measurements and the associated validity score in percent, which shows whether enough CNM samples were taken or whether more measurements need to be made to avoid throughput calculations with large ranges. Especially for the investigation of the average downlink throughput according to departments, the number of measurements is not sufficient to guarantee the precision due to the smaller division compared to regions. As a result, there are enough measurements available for the analysis by region, but not for the analysis by department. The Auvergne-Rhone-Alpes region is the third largest region, and it contains, for example, 13 individual departments. Our data set contains 21 million throughput measurements for the entire region. The validity score is '\(\geq\)100%' with a mean throughput value according to the measurement samples of 25.49 Mbit/s and a calculated mean value between 25.46 Mbit/s and 25.51 Mbit/s. For department Ain, 111k measurements are available, with a sample average throughput of 22.37 Mbit/s. If you calculate \(n_{\text{abs}}^{\text{min}}(\delta^{*})\) here, about 509k measurements are required to maintain the precision. The validity score  
Figure 2: Amount of measurements, precision, and validity scores for \(\delta^{*}=100\) kbit/s for selected regions and islands of France. In the top row, regions with sufficient measurements are shown. All regions have a validity score of \(\geq\)100% and precision according to an interval with at most \(\overline{y}_{S}\pm 50\) kbits/s for average throughput in CNM data \(S\). In the bottom row, islands and regions are listed for which too few measurements are available to determine average downlink value with an precision of at least 100 kbit/s. is 21.80%, which indicates that too few measurements were made for the consideration at the department level. The right subfigure shows three more departments in the southwest of France that have too few measurements; see yellow labels. Only the departments around Paris, Lille, Lyon, and Marseilles contain enough data to maintain the target precision at the departmental level.