esa-sceva/satcom-chunk-collection huggingface dataset:
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 1 Introduction
After an algorithm is applied to the synthetic data, one can gauge the performance of the algorithm by comparing the algorithm results to the initial earth system simulation from which the synthetic data was created (i.e., the OSSE \"truth\"). In order to generate synthetic observations globally over a realistic dynamic range, a 2-year high-resolution mesoscale free-running atmospheric simulation, the Goddard Earth Observing System version 5 (GEOS-5) Nature Run (G5 NR) [12], was completed. The GSNR has been validated against nature [13], and provides a realistic representation of global aerosol concentrations and climate. As the GEOS-5 Nature Run includes aerosols, we are able to extend OSSEs beyond traditional weather forecasting applications. Several studies have explored the ability of geostationary observations to better constrain spatial and temporal variations of CO and O\({}_{3}\)[14; 15; 16; 17], and have shown that geostationary satellites have the potential to address the limitations of the current observing system. In comparison, only [PERSON] et al. [18] has presented an OSSE for aerosols. They found that hourly geostationary AOD observations over Europe improved the ground level particulate matter analysis and forecast. The goal of this paper is to describe the aerosol nature run, GSNR, and its' application to forward model calculations for generating synthetic observations for aerosol OSSEs. These synthetic data will be used to test AOD retrieval algorithms over realistic conditions, and to understand the impact of the geostationary constellation on global aerosol data assimilation and forecast products. Aerosol OSSEs require detailed radiative transfer modeling of top of the atmosphere (TOA) reflectance to generate synthetic observations rather than averaging kernel regression methods, such as those used for CO and O\({}_{3}\) OSSEs, because aerosol distributions have higher spatial variability, and operational AOD retrievals rely on multi-step retrieval approaches (that can vary depending on the type of aerosol detected) rather than formal optimal estimation to retrieve AOD. In Section 2, we describe the TEMPO, GEMS, and SENTINEL-4 instruments, as well as the G5 NR aerosol simulation. This is followed by a description of the methodology used to generate synthetic TOA reflectance with VLIDORT (Vector Linearized Discrete Ordinate Radiative Transfer), the forward radiative transfer model. In Section 3, we present examples of simulated observations for TEMPO, GEMS, and SENTINEL-4 and describe an estimate of their uncertainty in Appendix A. Finally, Section 4 contains a summary of the work and future perspectives.
## 3 Results and Discussion
### Microstructural Characterisation  
Figure 21 shows microstructural figures of samples with a W/C ratio of 0.6 and different UFS replacement ratios for 25\(\times\) magnifications.  
Figure 21: Microstructural analysis under 25\(\times\) magnification: pores, cracks and cavities of UFS mortars with 0% (**a**), 25% (**b**), 50% (**c**), 75% (**d**) and 100% (**e**) substitution.  
Figure 21 shows that mortars with 0% and 25% UFS have a homogeneous paste without any visible pores. This could be because the low UFS replacement allows adequate workability despite the high absorption of the UFS. On the other hand, for 50% and 75% UFS, pores can be observed in the mortar paste. Finally, for 100% UFS, it can be observed that the paste has large cavities, probably because the UFS absorbed most of the water.  
Figure 22 shows microstructural figures of samples with a W/C ratio of 0.6 and different FSR ratios for 500\(\times\) magnifications.  
Figure 22 shows how the paste and aggregate interact in the mixture. For UFS of 0 and 25%, good bonding between the aggregate and the paste can be observed. It can also be seen that as the UFS replacement increases, the bonding between the paste and the aggregate becomes poorer as the space between the aggregate and the paste increases.  
Figure 22: Microstructural analysis under 500\(\times\) magnification: bond between paste and aggregates of UFS mortars with 0% (**a**), 25% (**b**), 50% (**c**), 75% (**d**) and 100% (**e**) substitution.
## 3 Experiments
### Evaluation Metrics  
Our experiment used the Clear Matrix [54] and IDF1 [55] for the evaluation of multi-object tracking performance. The overall performance assessment was based on the multiple-object tracking accuracy (MOTA) and IDF1 score (IDF1), as depicted in the following equations:  
\[\mathrm{MOTA}=1-\frac{\sum_{\mathrm{t}}(\mathrm{FN_{t}}+\mathrm{FP_{t}}+ \mathrm{IDSW_{t}})}{\sum_{\mathrm{t}}\mathrm{GT_{t}}}, \tag{9}\]  
\[\mathrm{IDF1}=\frac{2\times\mathrm{IDTP}}{2\times\mathrm{IDTP}+\mathrm{IDFP}+ \mathrm{IDFN}} \tag{10}\]  
MOTA is a comprehensive metric that evaluates the overall tracking performance by considering multiple aspects, such as false positives, false negatives, identity switches, and fragmentation. It provides a unified measure of tracking accuracy, where a higher value signifies better tracking performance.  
IDF1 is a single-value metric that combines the precision and recall of the tracking system. Precision measures the ratio of correctly identified objects to the total number of identified objects, while recall measures the ratio of correctly identified objects to the total number of ground truth objects. IDF1 combines these two metrics to provide a balanced evaluation of tracking accuracy, where a higher IDF1 score indicates better precision and recall performance.  
Additionally, the following quantitative metrics were employed as supplementary performance measures: false negatives (FNs), which refer to the number of missed detections, false positives (FPs), which represent the number of incorrect detections, identity  
Figure 7: Our tested dataset included three categories: vehicles, vessels, and aircraft. The red boxes represent the ground truth bounding boxes of objects. Subset of videos was utilized for the model comparison experiments.  
switches (IDSWs), which quantify the number of times the tracker incorrectly switches the identities of objects, mostly tracked trajectories (MTs), and mostly lost trajectories (MLs). Specifically, MTs and MLs are utilized to assess the tracking performance of the tracking model in capturing the complete target trajectories. The MT is defined as the percentage of trajectories for which the tracking model's output covers more than 80% of its full trajectory, while the ML is defined as the percentage of trajectories for which the output only covers less than 20% of its full trajectory. The statistical results of the MTs and MLs reflect the consistency and robustness of the tracking model in tracking full-target trajectories. These performance metrics provide valuable insights into the accuracy, robustness, and consistency of multi-object tracking methods, enabling the effective evaluation and comparison of different tracking approaches.
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
## 5 Experimental Results  
In this section, we present some experimental results based on real Iridium data collected in the weak signal environment. The IRIDIUM NEXT data are collected by a static receiver under a dense forest of BUAA university for 30 min, as shown in Figure 4. The Iridium signals are sent into the software in PC for analyzing and positioning after down-conversion and conversion from analog to digital signals by a RF front end and an Iridium antenna. Compared with the open sky environment, the average power loss of the Iridium signal in this case reaches 8 dB. The IF data collector receives the raw data with a sampling rate of 112 MHz at a RF center frequency of 1626.25 MHz, and the center frequency of the recorded data is 28.25 MHz. The received signals are from two downlink-only channels which are ring alert signal (7 channel) and primary message signal (11 channel). Two channel signals are used for positioning, respectively. The results of Iridium signal Doppler-shift estimation and Iridium SOPs positioning are presented, as described below.
## 3 Results and Discussion
### Fungal Community Analysis in CTS and Fermented Dough  
High-throughput sequencing of 18S rRNA gene was performed to analyze the fungal communities in CTSs and fermented doughs. Sequencing quality, community diversity, and community richness were evaluated by the rarefaction curve, the Shannon index, and the Chao1 index, respectively. The sequencing and diversity results of fungi are shown in Table S2 and Figure 2A,B. The number of sequences for each sample was in the range of 35,555-64,086, and the rarefaction curves tended toward horizontal lines, indicating that the sequencing depth was sufficient. There were no significant differences among the fungal Shannon indexes of the CTS, AFD, and FD groups. The fungal Chao1 index of the FD group was significantly higher than that of the CTS group. The results indicated that dough fermentation did not alter the diversity but promoted the richness of the fungal community.  
Figure 2: Microbial diversity indexes, including the Shannon and Chao1 indexes of CTS, FD, and AFD. (**A**) Chao1 index of fungi. (**B**) Shannon index of fungi. (**C**) Chao1 index of bacteria. (**D**) Shannon index of bacteria. * \(p\) \(<\) 0.05; ** \(p\) \(<\) 0.01.  
As shown in Figure 3A, five genera belonging to the phylum Ascomycota (Figure S1A) were identified in CTSs and fermented doughs, including _Saccharomyces_, _Aspergillus_, _Hyphopichia_, _Fusarium_, and _Pichia_. Consistent with previous research, Ascomycota was the predominant phylum in CTSs, _Saccharomyces_ was the dominant genus in the CTS group (98.1%), and their relative abundance increased further in the AFD group [3]. The fungal composition of the FD group was similar to that of the AFD group. Five species were identified in CTSs: _S. cerevisiae_, _Aspergillus_ penicilloides_, _Hyphopichia burtonii_, _Fusarium graminetarum_, and _P. kudriwavezii_ (Figure 3B). _S. cerevisiae_ is the most important fungus that influences the dough fermentation properties, forming large populations in fermented dough and acting as the major CO\({}_{2}\) contributor to expand the dough in fermented wheat food preparation [18]. _P. kudriwavezii_ is a frequently encountered yeast in sourdough ecosystems [19]. _A. penicilloides_ was the subdominant species in terms of relative abundance in the LK (7.8%) and YC (6.7%) CTSs, and it was reported to be the predominant fungus in _sufu_, a traditional Chinese appetizer of fermented soybean curd [20]. _H. burtonii_ causes \"chalk mold\" on bread [21], and _F. graminetarum_ is a pathogenic fungus that causes wheat scabs [22]; even though they may lead to the deterioration of steamed bread quality or the accumulation of a variety of mycotoxins in steamed bread, they almost disappeared in the mature doughs fermented by CTSs.
## 5 Fabrication and Testing
Accounting for about 0.6 dB loss, which is expected from imperfect connections between the antenna and the waveguide feed and also from the connected cables, the remaining 0.4 dB can be attributed to the additive manufacturing process. The simulated and measured worst case cross polarization ratio for the flared SBF antenna with the foam lid, which were 20.54 dB, and 19.95 dB, respectively, are very close in agreement. To demonstrate the superiority of 3D printing over the conventional milling fabrication process, we measured the weight of a PLA (thermoplastic) antenna and an aluminum prototype. The weight of the PLA antenna was 252 g, while that of the aluminum prototype was 576.5 g. This implies a 56.3% reduction in mass for the PLA antenna, which is crucial in applications like satellite communications and remote sensing where increased mass can lead to a significant increase in cost. Figure 10:1 The flared SBF antenna with the foam lid.
## 4 Results and Discussion
### Ablation Studies for Assessing the Effect of the Image Quality Aware Data Augmentation Strategy  
We conducted an ablation study using the different datasets in Table 3. For this study, we trained only the RetinaNet object detector following the procedure described in Section 4.3, monitoring both learning curves and, specifically, the losses or the training and validation sets.  
The results of this study are shown in Figure 10, which depicts the training loss curves for the models trained with the augmented and quality aware datasets (pink and green lines) and the validation losses for the augmented (blue line) and quality aware (red line) datasets.  
As can be observed, both strategies significantly reduce the validation loss, but a clear improvement can be seen with the latter, which is also reflected in the performance of the trained detectors, as summarized in Table 6. The table also shows the results for the model using the original dataset proposed in [25], for which very similar (but slightly superior) results were obtained (in the first row of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric/Class** & **D01 (Long Crack)** & **D11 (Lat. Crack)** & **D20 (Alligator)** & **D40 (Pothole)** \\ \hline UT Dataset & 0.8030 & 0.8225 & 0.8351 & 0.9233 \\ Augmented & 0.8420 & 0.8550 & 0.8759 & 0.9534 \\ Quality Aware & **0.9148** & **0.9511** & **0.9522** & **0.9823** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean average precisions obtained through the ablation study; note that the results for both the UT and augmented dataset are better than those in [25], and the quality aware model outperforms them by about 10%.  
Figure 9: Some examples of detection results for the D01, D20, and D43 road damage classes.  
the table). These results are consistent with other works that make use of RetinaNet [23], as this object detector was designed to focus on \"hard examples\", making it more robust, particularly in terms of recall, as we will see in Section 4.3 (see Table 7).
## 3 Energy Management Strategy
### Adaptive Equivalent Consumption Minimization Strategy with Target SOC
#### 3.2.2 Adaptive ECMS  
In this paper, we use an A-ECMS, which is one of the various optimal power distribution control methods. The target SOC derived from the road segment information is reflected in the equivalence factor (EF). This allows for the charge-discharge tendency to  
Figure 12: Main ideas of target SOC decisions: (**a**) inside of the boundary; (**b**) outside of the lower limit; (**c**) outside of the upper limit.  
Figure 13: Flowchart of the target SOC decision algorithm.  
be adjusted according to the information of the road segment. The equivalent factor \(s\) of a conventional ECMS and an A-ECMS is as follows.  
\[s=\begin{cases}s_{0}\\ s_{0}+K_{p}\big{(}SOC_{target}-SOC_{0}\big{)}+K_{i}\big{(}SOC_{target}-SOC_{0} \big{)}\end{cases},\begin{array}{c}ECMS\\ Adaptive\ ECMS\end{cases} \tag{14}\]  
where \(s_{0}\) is the initial EF and \(K_{p}\) and \(K_{i}\) are the coefficients of the PI controller.  
Next, a quasistationary model of the vehicle was used to calculate the energy consumption required from fuel and the battery in relation to the driver's required torque [23]. In this paper, the control input candidates (\(u_{grid}\)) used 61 grids. As a result, the power candidates \(P_{f,grid}\) for fuel and \(P_{b,grid}\) for the battery are calculated, and the cost function J is defined as follows.  
\[J=P_{f,grid}+sP_{b,grid} \tag{15}\]  
The control input for which the cost function J has a minimum value is  
\[u^{*}=\underset{u_{grid}}{\text{arg min}}J \tag{16}\]  
where \(u^{*}\) is the optimal power distribution ratio. Therefore, the engine torque command \(T_{eng,cmd}\) and motor torque command \(T_{MG,cmd}\) are calculated from the required torque \(T_{req}\) as follows.  
\[T_{eng,cmd}=u^{*}T_{req} \tag{17}\]  
\[T_{MG,cmd}=\left(1-u^{*}\right)T_{req} \tag{18}\]  
\(T_{eng,cmd}\) and \(T_{MG,cmd}\) are sent to the controllers of the engine and motor, respectively. The workflow of the A-ECMS with target SOC is shown in Figure 14.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 2 State of the Art
### Management and Identification of Devices  
Identifying things presents a particular challenge. Today devices can be identified through different maps (e.g., Unique Identifier, UID, or level of communication identifiers such as, Medium Access Control, MAC, and IP addresses). In order to find information and services related to things,it is necessary to have a resolution infrastructure which links identifiers of things to other devices on the network. Probably the clearest example of such infrastructure is the so called Object Name Service (ONS) in the Electronic Product Code (EPC) global network [22].
## 3 Results of Simulation Experiments and Discussion
### Performance Analysis with Small-Scale Problem
#### 3.2.3 Runtime Analysis  
Figure 8 shows the relationship between the runtime of the NBS-ISPS method (when time limit = 100 s) and our proposed methods with different numbers of tasks and robots. In Figure 8a, we can find that the runtime of the task assignment (NBS solver) always exceeds the time limit, which means that the task assignment can hardly find optimal solutions within 100 s. In addition, the runtime of the NBS-ISPS method generally grows as the number of tasks increases. When the number of robots increases from 5 to 10, the runtime of these methods does not change too much.  
By comparing Figure 8a vs. Figure 8c or Figure 8b vs. Figure 8d, we find that the NBS-ISPS method consumes a significant amount of time, while our proposed methods only require a very small runtime. For example, when \(r=5,n=60\), the runtime of NBS-ISPS is 147.623 s and the runtime of RSS is only 0.254 s. In real manufacturing conditions, the runtime for the NBS-ISPS method is almost unbearable. This result also reveals the practical value of our methods.
# An Active Irradiation System with Automatic Beam Positioning and Focusing for a Medical Cyclotron
###### Abstract  
A novel active focusing system was developed for enhancing the irradiation performance of the 18 MeV medical cyclotron in operation at the Bern University Hospital in view of the production of non-conventional medical radioisotopes using solid targets. In several cases, such as the production of \({}^{43}\)Sc and \({}^{44}\)Sc, the beam has to be kept stable within a very small target of about 5 mm diameter. For this purpose, we conceived and realized an apparatus based on a compact focusing and steering magnet system followed by a two-dimensional beam monitoring detector and a specific feedback software that drives the magnet to optimize the beam for a given irradiation set-up. We report on the design, realization and validation beam tests performed using the research beam transfer line of the Bern cyclotron. We demonstrated that the beam spot can be kept on target thanks to the fact that the system automatically reacts to perturbations. Compactness is one of the key features of this system, allowing its use in accelerator facilities with limited space, such as medical cyclotrons for radioisotope production.  
particle accelerator; medical cyclotron; automatic irradiation system; beam diagnostics; solid targets; medical radioisotope production +
Footnote †: journal: Physics Letters B  
Article An Active Irradiation System with Automatic Beam Positioning and Focusing for a Medical Cyclotron  
[PERSON]  
1 Albert Einstein Center for Fundamental Physics (AEC), Laboratory for High Energy Physics (LHEP), University of Bern, Sallesterstrasse 5, 3012 Bern, Switzerland; [EMAIL_ADDRESS] (P.H.); [EMAIL_ADDRESS] (C.B.-A.); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (P.S.)
# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 4 Clustering Applications of the HSA
### Fuzzy Clustering Applications  
[PERSON], in [71], proposed a new fuzzy method using the HSA to find the optimal parameter values for the zone structure problem. The problem was solved based on three guidelines, namely residual failure, parameter change, and structural distinction. A mathematical version supplied in the literature was determined to improve the overall performance of the proposed HSA. Moreover, a sensibility evaluation was performed to study the performance of the HSA for numerous businesses of solution parameters. The obtained results demonstrated that the proposed HSA is a powerful method for performing simultaneous clustering.  
In [73], [PERSON] et al. introduced a new integrated method, called Fuzzy Harmony Search Clustering (FHSClust), by employing a fuzzy approach to enhance the HSA. The results of FHSClust were fed into a Fuzzy C-Means (FCM) algorithm, which then practiced its convergence speed, to improve the solution. This method merged the advantages of fuzzy HSA with the convergence acceleration of the fuzzy C-means technique. The obtained results showed that FHSClust obtained a nearly optimal solution. Furthermore, it converged quickly to the optimal-recognized optimum. [PERSON], in [101], proposed a hybrid design method based on hybridizing K-means and HSA to overwhelm the K-means problems. The proposed hybrid method obtained better results and a suitable interval length.  
[PERSON] and [PERSON], in [93], introduced a hybrid optimization approach based on the use of a fuzzy suitable judgment method and HSA for solving the clustering protocols in WSNs. The proposed method, which used a fuzzy strategy and improved HSA, is called FLIHSBC. The FLIHSBC algorithm exhibited reduced power consumption and assisted in increasing the lifetime of the network. The obtained results confirmed that FLIHSBC performs well with regard to increasing the lifespan of WSNs.  
[PERSON] et al., in [94], introduced a fuzzy kernel clustering technique with a new differential HSA to manage the classification process. First, a self-adaptive solution approach and differential evolution update procedures were proposed to improve the basic HSA. Second, differential HSA was employed to the kernel fuzzy clustering technique to help the clustering technique in reaching more leading results. Later, the fuzzy kernel, combined with differential HSA, was used for water entertainment arrangements. The proposed approach was then compared with other similar methods. The acquired results revealed that the proposed fuzzy kernel clustering combined with the differential HSA demonstrated a superior overall performance with regard to water diversion arrangements. Regarding the fuzzy clustering, several HSAs have been used for solving the different problems using fuzzy techniques, which means that fuzzy clustering applications still have space for further investigations and analysis.