esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Algorithms and Parameter Settings  
In this subsection, HE-DEPSO is compared with five algorithms, including the PSO and DE algorithms, as well as three representative and advanced variants of the DE algorithm, named CoDE [30], SHADE [32], and DEPSO [39]. Among these algorithms, CoDE and DEPSO incorporate modifications mainly within the mutation operator of the DE  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Type** & **Index** & **Function Name** & **Optimum (\(F_{min}\))** \\ \hline Unimodal & 1 & Shifted and Rotated Bent Cigar & 100 \\ Functions & 3 & Shifted and Rotated Zakharov & 300 \\ \hline  & 4 & Shifted and Rotated Rosenbrock & 400 \\  & 5 & Shifted and Rotated Rastrigin & 500 \\ Simple & 6 & Shifted and Rotated Expanded Schaffer F6 & 600 \\ Multimodal & 7 & Shifted and Rotated Lunacek Bi-Rastrigin & 700 \\ Functions & 8 & Shifted and Rotated Non-Continuous Rastrigin & 800 \\  & 9 & Shifted and Rotated Levy & 900 \\  & 10 & Shifted and Rotated Schwefel & 1000 \\ \hline  & 11 & Hybrid Problem 1 (N = 3) & 1100 \\  & 12 & Hybrid Problem 2 (N = 3) & 1200 \\  & 13 & Hybrid Problem 3 (N = 3) & 1300 \\ Hybrid & 14 & Hybrid Problem 4 (N = 4) & 1400 \\ Functions & 15 & Hybrid Problem 5 (N = 4) & 1500 \\  & 16 & Hybrid Problem 6 (N = 4) & 1600 \\  & 17 & Hybrid Problem 7 (N = 5) & 1700 \\  & 18 & Hybrid Problem 8 (N = 5) & 1800 \\  & 19 & Hybrid Problem 9 (N = 5) & 1900 \\  & 20 & Hybrid Problem 10 (N = 6) & 2000 \\ \hline  & 21 & Composition Problem 1 (N = 3) & 2100 \\  & 22 & Composition Problem 2 (N = 3) & 2200 \\  & 23 & Composition Problem 3 (N = 4) & 2300 \\ Composition & 24 & Composition Problem 4 (N = 4) & 2400 \\ Functions & 25 & Composition Problem 5 (N = 5) & 2500 \\  & 26 & Composition Problem 6 (N = 5) & 2600 \\  & 27 & Composition Problem 7 (N = 6) & 2700 \\  & 28 & Composition Problem 8 (N = 6) & 2800 \\  & 29 & Composition Problem 9 (N = 9) & 2900 \\  & 30 & Composition Problem 10 (N = 3) & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of benchmark functions used in the experiments.  
algorithm, while SHADE modifies the control of the \(F\) and \(Cr\) parameters. The performance comparison of the HE-DEPSO algorithm with the previously mentioned algorithms was performed in two dimensions, \(D=10\) and \(D=30\), on the CEC 2017 test function set.  
In order to ensure a fair comparison, the parameter settings in common to all algorithms were assigned identically: the maximum number of iterations, \(t_{max}\), was set to 1000, the population size, \(NP\), to 100, and 31 independent runs were performed. The configuration of each algorithm's parameters followed the values suggested by their respective authors, as presented in Table 3. The proposed HE-DEPSO algorithm was implemented in Python version 3.11, and all algorithms were executed on the same computer with 8 GB of RAM and a six-core 3.6 GHz processor.
## 4 Conclusions  
A heliostat control system based on a SCADA system was designed and implemented in order to monitor and control the orientation of an Azimuth-Elevation heliostat. The proposed system allows a single registered operator to visualize and control the operation status of the heliostat in order to detect any failure in its operation. The system consists of a supervisory unit with a graphical user interface, a remote terminal unit implemented in a low-cost microcontroller, a wireless communication network, and a mobile app. Besides, a fuzzy logic controller embedded in the remote terminal unit orients the heliostat to the desired position.  
The stand-alone RTU performs all the calculations and tasks of the orientation control by only a control command sent by the supervisory unit. Therefore, this reduces the network traffic between the SU and the RTU. The experimental results show a similar behavior between the orientation control of the DC motors with no load and the heliostat, with an MSE less than 0.1\({}^{\circ}\) for the orientation control of the heliostat, despite the backlash in the axis mechanisms and the load of the wind over the mechanical structure. Therefore, the control algorithm with fuzzy logic can guide the heliostat to the desired angles using the same parameters of the position control of the DC motors with no load, and it is not necessary to tune the controller parameters if the dynamics of the system changes, as in the traditional PID controllers.  
As future work, the system can be applied to control an entire heliostat field just by modifying the GUI in order to send the corresponding control command to each heliostat by using the broadcast model of the RF modules, and the same program code for each RTU, only by updating the data of the geographical position and the distance to the target of each heliostat. Besides, the FLC can orient all the heliostats by using the same control parameters, because it is robust in front of changes in the dynamics of the process. However, higher resolution encoders would be necessary for farthest heliostats from the central tower. The system can also be adapted in order to control other two-axis solar-tracking systems that only use the solar-tracking system, such as photovoltaic, parabolic trough, or solar dish systems.  
Conceptualization, E.S.-P. and M.T.-A.; methodology, E.S.-P., E.A.R.-A., and R.V.C.-S; software and hardware, E.S.-P.; validation, E.S.-P. and R.V.C.-S; project administration, M.T.-A. This research received no external funding. The authors would like to thank Consejo Nacional de Ciencia y Tecnologia (CONACYT-Mexico) for supporting this research. The authors declare no conflict of interest.
# A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
## 3 Experiments, Results and Discussion
### Test Results  
After optimizing the data imbalance in the previous section, the XGBoost algorithm could be used for fault diagnosis. The data set was divided into a training set and a test set and divided according to the ratio of 3:7. The results of the confusion matrix are shown in Table 6.  
To better calculate the performance of the model, precision, recall rate and F1-score were calculated, and the calculation results are shown in Table 7.  
Tables 6 and 7 show the classification results of the model for five faults of the steam turbine. As is well-known, for a classification model, if precision, recall and F1-score have higher values at the same time without considering other factors, the model is considered to have better performance. The model based on the XGBoost classifier had high accuracy in fault diagnosis of steam turbines and could identify the different types of faults.
# Resource Indexing and Querying in Large Connected Environments
## 2 Motivating Scenario
### Connected Environment Setup
#### 2.1.3 Data Retrieval  
Members of the environmental team need to retrieve data from any sensing device or zone. They can retrieve data directly from the device (when they know its identifier). However, sometimes this is not possible, and queries need to be sent to a zone (without knowing who is in charge of managing it). Devices must be able to forward a given query in case the current zone is not the corresponding requested zone. When the latter is uncovered, the device will be able to return an explicit response (noting that it is an uncovered zone, i.e., a zone without a device). Moreover, to comply with different querying needs, several query types exist. For instance, a user needs to quickly retrieve the temperature from any device covering a targeted zone, while another user wants to be sure that no fire is detected within another specific zone. If a quick response is needed, she can execute a low priority query call. In another case, the user wants to detect that there is a probability that a fire could happen. Then, a high-priority query must be sent. Finally, the user may want to send a query with a moderate priority to test the temperature for experiments. During the querying process, a query may take some significant time to respond (such as high priority queries), while others can return a response quickly (such as queries with a low-level priority). Queries having a moderate urgency will return a response with a time between low-level and high-level queries. We note that we will define the different levels of query urgency in the upcoming sections.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for MAC Layer
#### 3.1.2 [PERSON] et al. in [11] proposed the QoS-Aware Energy Efficient MAC (QAEE-MAC) protocol for energy harvesting WSNs. This protocol is intended to provide a mechanism so that the data transmission for data packets that are urgent can be delivered faster than the ordinary data packets. The mechanism is based on Carrier Sense Multiple Access (CSMA). At the time of wake up, each node that wants to send data will send Tx-beacon that contains the priority information of the data to be sent. The receiving node must wake up early to collect all Tx-beacon packets and determine the sender with the highest priority, then sends Rx-beacon to all senders containing the ID of the selected node. The selected sender can then send data packets, while other senders can sleep until the next Rx-beacon period.  
The QAEE-MAC protocol has been validated through simulation. As the result, it successfully prioritizes the data delivery for packets that are urgent and each node in the network can set its wake up period according to its energy level. The QAEE-MAC protocol weakness is its limited implementation to a small number of the receiver. Moreover, the sender only implements a single hop communication. The priority setting mechanism also causes a high idle time in the receiver to collect the beacon of all senders.
## 3 Results
### RCS, Extinction, Backscatter Coefficients, and Angstrom Exponent Profiles
For the upper layer, identified as a cloud, the LIDAR ratio is considerably smaller and, moreover, different for the two wavelengths, comparable with line-mode particulate scatterers. Note that a transition is visible at about 3 km within the  
Figure 25: Atmospheric properties on 25 August 2021 (D-III), during a moderate intrusion of calima. **Left**: Logarithm of RCS in the two AN channels: 355 nm (pink), 532 nm (green), intentionally operated at a very low gain. The measurement was performed at a zenith angle \(\theta=37^{\circ}\) with \(N_{sh}=900\). **Center**: The extinction coefficient profile of aerosols retrieved from elastic channels using Klett inversion with a supposed LIDAR ratio of 50 sr. A Savitzky–Golay filter with a moderate filter width of 150 m has been applied. **Right**: The Angström exponent profile retrieved from the extinction coefficients. The points show the profile obtained with a minimum height resolution of 40 m, whereas the cross shows the average from the upper layer only, with a resolution of 200 m.  
upper layer from larger scatterers (higher LIDAR ratio at 532 nm) to smaller ones (LIDAR ratio approaching the one of 355 nm). From the combined results of both ratios and knowledge of the local environment, we can conclude that the lower aerosol loading is composed of volcanic ash that was dispersed from the Cumbre Vieja volcano plume, while the upper part must be due to a cloud with typical characteristics for the La Palma atmosphere at those altitudes. Even though, during the LIDAR measurement, the plume is found below the thick cloud layer, completely covering it from the satellite view, the clockwise motion of the plume, visible in multiple consecutive satellite pictures, suggests that it was located above the ORM during the time of measurement. Figure 26: Atmospheric properties on 22 September 2021 (D-IV), during a cloudy night with the Tajogaire volcano dust plume traveling over the observatory. **Upper left**: Logarithm of RCS in the two AN channels: 355 nm (pink), 532 nm (green), intentionally operated at a very low gain, and the Raman channel at 387 nm (blue). The measurement was performed at a zenith angle of 37\({}^{\circ}\) with \(N_{sh}=900\). **Upper right**: The extinction coefficient profile of aerosols retrieved from elastic channels using iterative Klett inversion with LIDAR ratio retrieval, and the Raman channel retrieval (Equation (50)). A Savitzky-Golay filter with a moderate filter width of 50 m has been applied. **Lower left**: The backscatter coefficient profile retrieved from the combination of elastic and Raman channels using Equation (51). **Lower right**: The LIDAR ratio profile retrieved from the extinction and backscatter coefficients. The points show the profile obtained with a minimum height resolution of 100 m, whereas the crosses show the average from the full layer. The measurement was performed at a zenith angle of 20\({}^{\circ}\) with 2001 laser shots at 10 Hz rate.
## 4 Experimental Results and Analysis
### Category Accuracy  
To verify the accuracy of this method on object detection, we compare 20 types of VOC 2007 with some typical object detection algorithms. As shown in Table 2, we compare the category scores of YOLO, YOLOv2-v5, SSD, DSSD, and our algorithm.  
It can be seen from Table 2 that our algorithm not only has an excellent detection effect on normal objects but also has a greater improvement on the detection of small objects, such as, the detection accuracy of aero, boat, bird, and bottle are 89.4%, 72.9%, 81.7%, 65.3%, which are improved by 9.9%, 3.3%, 5.7%, and 14.8% respectively, and it has been comprehensively improved which compare with the traditional SSD algorithm. However, due to the small number of images in some categories of the dataset, the general effect of the detection of categories such as a boat, bottle, chair, and potted plant was not ideal, but the accuracy in these categories of these algorithms is still better than other algorithms. Although there is a certain gap between us and the recent series of YOLOv5 detectors, we will research small object detectors based on the YOLOv5 structure in the future.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **Train** & **Input Size** & _mAP (\%)_ & **FPS** \\ \hline Faster RCNN [13] & ResNet-101 & VOC 2007 + 2012 & 600 \(\times\) 1000 & 76.4 & 5 \\ R-FCN [35] & ResNet-101 & VOC 2007 + 2012 & 600 \(\times\) 1000 & 80.5 & 9 \\ YOLO [14] & GoogleNet-9 & VOC 2007 + 2012 & 448 \(\times\) 448 & 63.4 & 45 \\ YOLOv2 [19] & Darknet-19 & VOC 2007 + 2012 & 544 \(\times\) 544 & 78.6 & 40 \\ YOLOv3 [20] & Darknet-53 & VOC 2007 + 2012 & 640 \(\times\) 640 & 78.3 & - \\ YOLOv4 [36] & Darknet-53 & VOC 2007 + 2012 & 640 \(\times\) 640 & 84.3 & - \\ YOLOv5, [37] & Darknet-53 & VOC 2007 + 2012 & 640 \(\times\) 640 & 81.9 & - \\ SSD300 [15] & VGGNet-16 & VOC 2007 + 2012 & 300 \(\times\) 300 & 77.2 & 46 \\ DSSD321 [16] & ResNet-101 & VOC 2007 + 2012 & 321 \(\times\) 321 & 78.6 & 9.5 \\ FSSD300 [18] & VGGNet-16 & VOC 2007 + 2012 & 300 \(\times\) 300 & 78.8 & - \\ ScratchDet300 [38] & Root-ResNet-34 & VOC 2007 + 2012 & 300 \(\times\) 300 & 80.4 & 17.8 \\
**OURS** & VGGNet-16 & VOC 2007 + 2012 & 300 \(\times\) 300 & **80.7** & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection results on PASCAL VOC dataset.  
Figure 7: Training loss and test accuracy of the model. (**a**) Relationship between loss and iteration. (**b**) The _mAP_ calculated on VOC2007 test set.
## 2 Experimental
### Multiband Imaging  
Multiband imaging was carried out using a modified Canon D60 camera (IR/UV filters removed) (Canon USA Inc., Melville, NY, USA), with a Coastal Opt UV-Vis-IR 60 mm macro lens (The Jenoptik AG, Jena, Germany). The following lens filters were used:IDAS-UIBAR (ICAS Enterprises, Tokyo, Japan) for visible (VIS); IDAS-UIBAR + a Kodak 2E Wratten (Eastman Kodak Company, Rochester, NY, USA) or IDAS-UIBAR + Schott 418 filter (Schott Company, Mainz, Germany) for ultraviolet-induced visible luminescence (UVL); X-Nite BP1 + X-Nite 330 filter for ultraviolet-reflectance (UVR); X-Nite 830 filter for infrared-reflectance (IRR) and visible-induced infrared luminescence (VIL) (All X-Nite filters: Llewellyn Data Processing (LDP LLC), Carlstadt, NJ, USA). UVR, IRR, and VIL monochrome images were created in Adobe Camera RAW with the black and white function. False color (FC) images were generated in Adobe Photoshop (PS) (Adobe Inc., San Jose, CA, USA). A pair of three-channel (UV-VIS-IR) LED fixtures were used for illumination. VIS source: 5000k with 99 CRI/RA, ISO 3664:2009 (viewing booth quality), approx. 1500 Lumens (per fixture) and approx. 17W. IR source: peak output at 940 nm and approx. 19 W (per fixture). UV source: peak output at 365 nm and 18W (per fixture). A pair of red LED strip lights (peak output of 630 nm; 640 lumens per 4' fixture, 18 WL) were used for VIL.  
Since the sphinx could not be moved from the gallery, a mobile dark room was built around the sculpture to block out ambient light using sheets of black foam core board, taped together to create four sides and a ceiling (Supplementary Materials: Figure S1). The interior was large enough to accommodate lights and a camera on a tripod, yet the structure was easily dismantled for reuse.
## 3 Results and Discussion
### Case-Study: Simulation of the Test of a Farm Tractor
#### 3.7.3 Quasi-Static Test (Considering Also the Tyres Deflection)
From the same figures, it is also possible to read the maximum loads reached by every single wheel (reported also in Table 7), recorded when that wheel is in the lowest position on the sloping  
Figure 21: (**a**) forces normal to the four quadrants that support the vehicle during a complete rotation of the turntable for \(\alpha=20.00^{\circ}\); (**b**) percentage decomposition of the total weight on the four quadrants/wheels for \(\alpha=20.00^{\circ}\). Figure 22: (**a**) forces normal to the four quadrants that support the vehicle during a complete rotation of the turntable for \(\alpha=33.95^{\circ}\); (**b**) percentage decomposition of the total weight on the four quadrants/wheels for \(\alpha=33.95^{\circ}\). In the lower part of the picture there are two enlargements of the Cartesian planes close to the abscissae axis. support plane, or the minimum load, reached when that wheel is in the highest position on the sloping support plane. Observing the data in Table 7 and the homologous data in Table 5, it can be observed that:  
* for a 20\({}^{\circ}\) inclination of the support, with the assumption of tyre deformability, the maximum loads are higher than 5% whilst the minimum loads are lower than 10%, a clear sign of the worsening of the situation due to the deformation of the tyres downstream, which has affected the position of the \(CoG\) and caused a load transfer;
* for an inclination of the support at the limit of incipient rollover for the tractor (respectively of 37.73\({}^{\circ}\) and 33.95\({}^{\circ}\) in the two examined cases), the maximum loads recorded under the assumption of tyre deformability are greater by 2-3%, due to the lower inclination of the support platform, which results in a greater normal component on each wheel.
## 3 Methodology
### Exergy Analysis  
The exergy analysis predicts the irreversibilities of a component quantifying its exergy destruction. After evaluating the system from the energy point of view, the exergy rate of each current is calculated. The exergy destruction for each component is calculated as the difference between the exergy rate of the fuel and the product. The exergy rate of product and fuel for each component are defined by applying the methodology explained in reference [25]. The equations for each component are summarized in the Table 5. The ambient temperature and pressure were established at 25 \({}^{\circ}\)C and 1 bar, respectively.  
The solar block's energy source is solar radiation. The exergy provided by this radiation can be expressed using Equation (10), which is known as the Petela's formula [26].  
\[\dot{E}x_{s}=\dot{Q}_{s}\left(1-\frac{4}{3}\frac{T_{0}}{T_{s}}+\frac{1}{3} \left(\frac{T_{0}}{T_{s}}\right)^{4}\right) \tag{10}\]  
where \(\dot{Q}_{s}\) is the total power incident in the heliostat field, \(T_{s}\) is the sun surface temperature (5700 K) and \(T_{0}\) is the ambient temperature.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Direct Normal Irradiance \(DNI\) & 950 W/m\({}^{2}\) \\ Concentration ratio C & 1000 \\ Receiver absorptivity \(\alpha\) & 095 \\ Receiver emissivity \(\epsilon_{rec}\) & 0.85 \\ View factor \(F\) & 1 \\ Heat transfer coefficient \(h_{conv}\) & 5 W/m\({}^{2}\cdot\) K \\ Heat transfer coefficient heat transfer coefficient from & 10 kW/m\({}^{2}\cdot\) K \\ receiver surface to fluid \(U\) & 0.6 \\ Heliostat field efficiency \(\eta_{hot}\) & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Input parameters assumed for the calculation of the solar to thermal efficiency of the solar block [19].  
The overall exergy efficiency of the system is expressed by Equation (11)  
\[\psi=\frac{\dot{W}_{N}}{Ex_{s}} \tag{11}\]
## 3 Discussion
### Clade-Specific Volatile Patterns  
We detected a clear differentiation of floral scent among clades and species within _Jabrosa_, associated with particular floral syndromes previously reported in the literature [61]: S-volatiles (carrion mimicry; [75; 76; 77; 78]), aliphatic alcohols/esters (fermentation; [79]), aromatic alcohols/N-volatiles (fecal mimicry; [62]), aromatic alcohols/esters (reward-based pollination, including sphingophily; [71; 80]), isoprenoid hydrocarbons (generalized pollination; [1]), and isoprenoid alcohols (reward-based pollination, especially sphingophily; [15; 70]).  
The floral scents of hawkmoth pollinated _Jabrosa_ species (_J. integrifolia_, _J. odonelliana_ and _J. runcinata_) were chemically simple, featuring large amounts of relatively few compounds (i.e., oxygenated sesquiterpenes or aromatic volatiles) shown to be nearly ubiquitous in sphingophilous flowers in temperate South America [13; 49; 67; 71; 81] and elsewhere [15; 65]. Interestingly, scent composition among sphingophilous _Jabrosa_ species from the Subtropical lowland clade was divergent rather than conservative, as volatile emissions were dominated by methyl salicylate in _J. integrifolia_, benzaldehyde in _J. runcinata_ and (E)-nerolidol in _J. odonelliana_ (Tables S1 and S2). This trend reflects larger patterns among congeneric hawkmoth-pollinated species in the Nyctaginaceae [82], Cactaceae [49] and elsewhere in the Solanaceae (genus _Nicotiana_ L.; [13]): loosely convergent evolution of certain volatile classes nested within species-specific blend composition. Behavioral assays performed with _Manduca sexta_ ([PERSON], 1763), a widespread species thought to be representative of the hawkmoth guild present in lowland warm-temperate Argentina [83], confirm that a variety of volatile terpen, aromatics and N-compounds attract these moths to flowers [49; 72; 84; 85; 86]. These findings indicate that convergent evolution is not constrained to a narrow sensory bias for _M. sexta_ and ecologically similar hawkmoths, and suggest that species-specific blends may be learned by moths to promote floral constancy [87; 88].  
In parallel, the four saprophilous fly-pollinated species (_J. leucotricha_, _J. laciniata_, _J. rotacea_ and _J. sativa_) generally were characterized by the presence of universal volatile components (e.g., oligosaccharides, cresol, indole) indicative of carrion- or dung-mimicking flowers, fungi and mosses [62; 63; 64; 89]. Specifically, most of the Andean clade species emitted DMDS and DMTS, behaviorally active compounds that are common to rotting meat and carrion-mimicking flowers worldwide [60; 59; 26]. However, beyond these core volatiles, composition of the full scent bouquets among these taxa was divergent, dominated by oligosaccharides in _J. rotacea_, thioesters and short, branched chain aliphatic esters in _J. sativa_, monoterpenes and ringed N-compounds in _J. laciniata_, and cresols and saturated monoterpenes in _J. leucotricha_.