esa-sceva/satcom-chunk-collection huggingface dataset:
# Localizing the First Interstellar Meteor with Seismometer Data
## 1 Introduction  
The first interstellar meteor (IM1), CNEOS ([[https://cneos.jpl.nasa.gov/](https://cneos.jpl.nasa.gov/)]([https://cneos.jpl.nasa.gov/](https://cneos.jpl.nasa.gov/)) 8 January 2014), was detected by U.S. Department of Defense (DoD) sensors through the light that it emitted as it burned up in the Earth's atmosphere off of the coast of Papua New Guinea in 2014 [1]. The material strength of IM1 appears to be higher than all other 272 meteors in the CNEOS catalog [2]. A forthcoming expedition aims to recover fragments from the ocean floor with an expedition to Papua New Guinea [3, 4]. Micrometeorites have previously been recovered from deep-sea sediment samples [5, 6, 7, 8]. The area associated with the DoD-reported localization box for the fireball is \(\sim\)120 km\({}^{2}\) but, given practical constraints, the expedition cannot search an area that large. Hence, it is crucial to improve the precision of the fireball localization. Seismic signals have been detected from previous fireballs [9, 10, 5, 11]. Here, we use data from a nearby seismometer, AU MANU, to set new constraints on the fireball location.
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 4 Case Analysis
### Step Response Analysis Based on the Improved QPIR Controller  
In order to verify the coupling relationship between active power and reactive power in the two-phase stationary coordinate system, the active power and reactive power jumped at 0.2 s and 0.3 s, and the power waveform is shown in Figure 15.  
In Figure 15, \(P\) represents active power and \(Q\) represents reactive power. At 0.2 s, the active power suddenly jumps from 0 to full load (40 kW). It can be seen that when the active power jumps, the reactive power is hardly affected. At 0.3 s, the reactive power suddenly jumps from 0 to 40 kvar, and when the reactive power suddenly changes, the active power is hardly affected.  
Figure 16 is a waveform of active power and reactive power following the reference power. At 0.2 s, when the active power reference quantity \(P_{\text{ref}}\) jumps, the active power output by the inverter can quickly follow the target value without overshoot. At 0.3 s, when the reactive power reference value \(Q_{\text{ref}}\) jumps, the reactive power output by the inverter can quickly follow the target value without overshoot.  
Figure 14: Reactive power waveforms controlled by PR, QPR, and QPIR controllers (at 50.5 Hz).  
Through the comparison between Figures 15 and 16, it can be seen that there is no coupling relationship between active power and reactive power in the two-phase static coordinate system, and the improved QPIR controller can quickly follow the target value, which verifies the correctness of the previous analysis.
## 4 Results
### Evaluation with the Altimeter Products  
As mentioned in the Introduction, WW3 has good efficiency for wave simulations over global seas, and the influence of wave hindcasting by the WW3 model on sea surface temperatures (SSTs) was assessed in our recent study [30]. Although there were some gaps in the fusion wind field data, the simulations by WW3 for the gaps were filled using linear interpolation. Figure 12 shows the monthly average SWH maps for July 2020 that were derived from two sources over global seas, i.e., the WW3 simulations and the average values from the HY-2B and Jason-3 products. It can be observed that the wave patterns from the WW3 simulation are consistent with those from the altimeters. Again, this indicates that the fusion wind derived from the remote-sensed products was suitable for simulating waves using the WW3 model.  
Furthermore, an error assessment was systematically conducted by comparing the WW3-modeled waves with monthly average products from the altimeters. A seasonal analysis of the monthly average error between the WW3 model and the altimeters is shown in Figure 13 for March 2020, June 2020, September 2020, and December 2019. We note that the gaps for the Southern Ocean were related to a lack of data. It was found that the differences in the SWHs (the altimeter results minus the WW3 results) were within \(\pm\) 1.5 m for March and June. The differences for September became large for the  
Figure 11: The comparisons of the SWAN-simulated SWHs for June\(-\)September in 2020 with (**a**) the observations of the NDBC buoys and (**b**) the ERA-5 waves.  
Figure 12: The monthly average SWH maps for July 2020: (**a**) the WW3 simulations and (**b**) the average values from the HY-2B and Jason-3 altimeters.  
coastal waters. We determined that this was likely caused by the extreme waves induced by the strong winds of the cyclonic season. The differences for December were significant--up to \(\pm\) 3 m--especially for latitude \(>\) 40\({}^{\circ}\)N and latitude \(<\) 40\({}^{\circ}\)S. In the winter, the coverage of the sea ice gradually grew in the Arctic Ocean, whereas it was reduced in the Antarctic Ocean. Under this circumstance, the reliability of remote-sensed products deviated in the winter, resulting in a large error in the simulation. This is because the measurements from the Jason-3 altimeter are only used here, leading to apparent gaps in Figure 13d, which can be improved by abundant data in the future.
## 1 Introduction
The suite is composed by two applications: _acquisition_, which collects signals from the available sensors while a human subject is performing activities, and _labeling_, which allows the user to specify which type of activity he/she is carrying out, such as walking, running, or sitting. The two applications are designed to run on two different smartphones; thus the user can specify each time an activity performed and its beginning and end timings using a smartphone without having to handle the other smartphone that is acquiring the data. This solution allows signals and assigned labels as synchronous as possible with the exact activity carried out by the subject to be obtained. Indeed, the smartphone that records signals is not moved from its position when the subject specifies the beginning and the end of each activity with the labeling smartphone. The suite discovers, independently of the hosting device, the available sensors and all their settings. The definitions of the types of activities that the user is requested to perform are easily configurable, as their definitions are stored in external files. Moreover, UniMiB AAL also collects information related to the sensor types and sampling rates that can be used to perform any kind of normalization that may be required to standardize the data with other data acquired with different devices. Finally, in addition to the use for which the suite was designed, it can also be used as a tool to label data from wearable devices that are not directly connected with smartphones and that provide data from different kinds of sensors, including those related to health. Because these devices are widely used, this could allow a potential amount of labeled data to be obtained. This can be achieved by placing the device (smartphone or smartwatch) on which the acquisition application is running next to the wearable device. The data acquired by the two devices is then analyzed in order to identify time series that refer to the same activity. The labels captured by the labeling application are then assigned to the wearable device data on the basis of these analyses. If the wearable device can be synchronized with a reference time (e.g., through the Network Time Protocol (NTP) (2019)), only the labeling application is required, because the labels can be assigned directly to the data acquired by analyzing the timestamps only. The article is organized as follows. Section 2 discusses differences between some existing applications and the UniMiB AAL suite; Section 3 describes the method that guided the design and implementation of the suite; Section 4 describes the applications that are included in the UniMiBAAL suite; Section 5 describes how signals and labels are stored and managed; Section 6 provides information about the usability tests we performed; finally, Section 7 sketches the conclusions.
## 4 Machine Learning Methodology
### Algorithm Training and Validation  
In order to verify the accuracy of the algorithm [38], the dataset was divided into two groups, training data and test data, using a percentage ratio of 90:10 or 50:50 to observe how the amount of training data influenced the accuracy of the algorithm. Because the observations in the training data differed in each execution of the algorithm, even with the same ratio, the algorithm was executed five times with the same ratio.  
Accuracy (\(A\)) was obtained in each execution of the algorithm to validate the trained model. \(A\) is calculated as:  
\[A(\%)=\left(\frac{TP+TN}{TP+TN+FP+FN}\right)\cdot 100 \tag{5}\]  
where \(TP\) is the number of predicted true positives, \(TN\) is the number of predicted true negatives, \(FP\) is the number of predicted false positives, and \(FN\) is the number of predicted false negatives.  
The accuracy results based on the percentages of training and test data are listed in Table 7. In addition, Table 7 lists the confusion matrices obtained in each case. It is important to keep in mind that a transformer misclassified as contaminated (false positive) would be evaluated with high C\({}_{2}\)H\({}_{2}\) limits (Table 2). Thus, its status assessment would go unnoticed in terms of C\({}_{2}\)H\({}_{2}\).  
From Table 7, it is observed that a larger training group resulted in fewer false positives and negatives.  
Since the accuracy results shown in Table 7 were correct, the trained algorithm was validated; otherwise, it would be necessary to modify the parameters defined in the algorithm development to improve the algorithm accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Run No.**} & \multirow{2}{*}{**Class**} & **90\% Training** & **80\% Training** & **70\% Training** & **60\% Training** & **50\% Training** & **50\% Training** & **and 10\% Test** & **and 20\% Test** & **and 30\% Test** & **and 40\% Test** & **and 50\% Test** \\ \hline \multirow{4}{*}{1} & 0 & 35 & 0 & 0 & 70 & 1 & 0 & 104 & 1 & 0 & 139 & 2 & 0 & 174 & 1 & 2 \\  & 1 & 0 & 4 & 0 & 0 & 8 & 0 & 0 & 10 & 2 & 0 & 14 & 3 & 0 & 18 & 2 \\  & 2 & 0 & 0 & 2 & 0 & 0 & 4 & 0 & 3 & 4 & 0 & 2 & 6 & 0 & 4 & 7 \\ \cline{2-16}  & \multicolumn{1}{c}{} & A (\%): 100 & A (\%): 98.80 & A (\%): 95.16 & A (\%): 95.78 & A (\%): 95.67 & \multicolumn{1}{c}{} & A (\%): 95.67 \\ \hline \multirow{4}{*}{2} & 0 & 35 & 0 & 0 & 70 & 0 & 0 & 104 & 1 & 0 & 140 & 1 & 0 & 174 & 2 & 0 \\  & 1 & 0 & 4 & 0 & 0 & 8 & 0 & 0 & 13 & 0 & 1 & 16 & 0 & 0 & 18 & 3 \\  & 2 & 0 & 0 & 2 & 0 & 0 & 5 & 0 & 3 & 3 & 0 & 2 & 6 & 0 & 2 & 9
## 5 Discussion  
Various attacks can be brought against steganographic encoding methods. Each data hiding technique that is published gives rise to a series of papers describing attacks that are successful against it, and each of those attacks, in turn, stimulates the development of appropriate countermeasures [11, 12].  
The adversary is assumed to know about the existence of the basic protocol for information embedding, but to have no access to the configuration of the SRR system, _i.e.,_ all the parameters that influence the encoding/decoding, including the choice of a catalog of optimization with their associated thresholds, pre-processing and post-processing operations, and dictionaries. All this information is assumed to have already been shared (by means of some unspecified protocol) between the parties wishing to communicate covertly. As with most steganographic systems, the hidden message itself can be encrypted with a secret key, making it harder for the adversary to perform guessing attacks on the secret message (as this message will result in a pseudorandom bitstream).  
In order to show that a message is hidden by SRR in a HTML document, the adversary would have to prove that the partially optimized page could not be plausibly generated by ordinary optimization, by an HTML generation program, or even by hand. Even if the adversary had reasonable grounds to suspect that selective optimizations had been done, the adversary would still have to prove that this behavior is not simply the result of a legitimate choice of user-definable options in the HTML generation program, in some optimization program, or both.  
The adversary might look for internal inconsistencies in the document. Simultaneous use of long and short tags, for example, can be interpreted as a signal that selective optimization was applied. However, the base of HTML documents shows that such internal inconsistencies are frequent and depend on the configuration of the generating program, on user preferences, or on programming style. Thus, it would not be enough for the adversary to mark as suspicious all the pages that do not correspond exactly to a few patterns of tag use. The rate of false positives, thus, can be quite high. Analogously, the adversary cannot flag as suspicious all HTML documents that do not exactly match the redundancy pattern of known HTML editors. There are three reasons for this. The first is that there are many tools that generate HTML code, and each of them must be taken into account. The second is that each of these systems may offer its users many options that affect the redundancy patterns in the output. The third is that some of these tools are open-source and are thus amenable to minor or substantial modifications in their behavior, as the result of amendments to the source code done by someone wishing to adapt the program to their particular requirements. Needless to say, there is no reason why such customized systems must be made public.
# Rotating Lorentz Force Magnetic Bearings' Dynamics Modeling and Adaptive Controller Design
## 5 Simulation Results and Analysis
### Tracking Error for Diagonal Velocity  
The adaptive controller also safeguards the rotor from collisions by tracking diagonal velocity to ensure that the RLFMB has an endpoint velocity of zero.  
As can be seen in Figure 12, the adaptive controller can nearly instantly follow the angular velocity of the RLFMB rotor. The adaptive controller has a smaller overrun when a specific signal has just been applied, guaranteeing that the RLFMB drives the load compartment more smoothly. After stabilization, the adaptive controller's diagonal velocity tracking may achieve zero error, guarantee that the angular velocity of the RLFMB driving the load chamber is always under control, and prevent load chamber instability brought on by the rotor of the RLFMB striking the stator.  
Figure 11: Comparison of anti-interference performance under pulse signals.
## 5 Experiments and Results  
To evaluate the effectiveness of the proposed MO-MPO algorithm for multi-objective process parameter optimization, experiments were conducted on two datasets:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Actor network architecture & (256, 256, 256) \\ Critic network architecture & (256, 256, 256) \\ Batch size & 128 \\ \(\epsilon_{\mu}\) (mean KL bound) & 0.0001 \\ \(\epsilon_{\Sigma}\) (covariance KL bound) & 0.0001 \\ Discount factor \(\gamma\) & 0.99 \\ Number of episodes & 1500 \\ Episode length & 500 \\ Optimizer & Adam \\ Activation function & ELU \\ Target update period & 100 \\ Prediction step & 200 \\ Objective priorities & 0.1 for all objectives \\ Stability constant & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters used for the actor and critic networks of the RL agent.  
* A pinion manufacturing dataset generated using Simufact simulations (cf. Section 3.1).
* A continuous-flow manufacturing dataset available at [[https://github.com/nicolasj9](https://github.com/nicolasj9)]([https://github.com/nicolasj9](https://github.com/nicolasj9)) 2/industrial-ml-datasets (accessed on 30 March 2025).  
These experiments were designed to assess the algorithm's capability to handle conflicting objectives inherent in real-world industrial processes. For instance, in the pinion manufacturing task, increasing the flange radius often leads to a reduction in part thickness, which highlights a typical trade-off scenario in multi-objective optimization. The optimized process parameters obtained through the MO-MPO algorithm were evaluated using predictions from the trained surrogate models on both the test set and an independent hold-out split. As explicit Pareto fronts were not provided for these use cases, Pareto optimality was assessed using a half-space validation approach to verify the quality and trade-off behavior of the obtained solutions.
## 7 EGTL Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Task Difficulty Level** & **Total Experiments** & **Succeeded Experiments (\%)** & **Avg. Model Work Time (s)** & **Avg. Output Correctness (\%)** & **Avg. Prompt Size (Symbols)** & **Avg. Model Output Size (Symbols)** \\ \hline gemini-1.5-flash & 1 & 10 & 100.0 & 1.03 & 94.0 & 798.2 & 45.9 \\ gemini-1.5-flash & 2 & 10 & 100.0 & 0.93 & 100.0 & 852.8 & 95.1 \\ gemini-1.5-flash & 3 & 10 & 70.0 & 1.78 & 98.0 & 982.7 & 212.6 \\ gpt-3.5-turbo & 1 & 10 & 100.0 & 0.96 & 100.0 & 436.4 & 110.0 \\ gpt-3.5-turbo & 2 & 10 & 100.0 & 3.68 & 100.0 & 490.9 & 160.3 \\ gpt-3.5-turbo & 3 & 10 & 100.0 & 2.63 & 100.0 & 618.1 & 322.0 \\ gpt-4 & 1 & 10 & 100.0 & 3.14 & 100.0 & 436.0 & 133.5 \\ gpt-4 & 2 & 10 & 60.0 & 6.35 & 100.0 & 491.6 & 177.8 \\ gpt-4 & 3 & 10 & 100.0 & 9.16 & 100.0 & 619.5 & 291.7 \\ mistral-small & 1 & 10 & 90.0 & 1.38 & 98.0 & 798.1 & 67.4 \\ mistral-small & 2 & 10 & 72.7 & 2.29 & 94.0 & 854.3 & 139.7 \\ mistral-small & 3 & 10 & 70.0 & 4.57 & 100.0 & 1009.5 & 384.4 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{Summary for all models} & Task Difficulty experiments & Total experiments (\%) & Succeeded experiments & Avg. model work time (s) & Avg. output correctness (\%) & Avg. prompt size (symbols) & Avg. model output size (symbols) \\ \hline Difficulty level 1 & 1 & 40 & 97.5 & 1.63 & 98.0 & 618.0 & 89.2 \\ Difficulty level 2 & 2 & 40 & 83.2 & 3.31 & 98.5 & 673.0 & 143.2 \\ Difficulty level 3 & 3 & 40 & 85.0 & 4.54 & 99.5 & 807.5 & 302.7 \\ \hline All levels & & 120 & 88.6 & 3.16 & 98.7 & 700.0 & 178.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance metrics of EGTL staging store data transformation experiment. To conclude, the EGTL experiment was successfully executed on the Hyperloop Decision-Making Ecosystem (HDME), a web-based prototype designed to streamline decision making in Hyperloop technology projects. The EGTL (extract, generate, transform, load) model was integrated with Snowflake data warehouses, utilizing three specialized stores--Fusion, Staging, and Alliance. Through automated Python scripts, the experiment simulated near-real-time data processing, evaluating several GenAI models, including gemini-1.5-flash, GPT-3.5-turbo, GPT-4, and mistral-small, across five difficulty levels. The results demonstrated a robust performance at lower complexity levels, though success rates decreased as task difficulty intensified. Overall, the experiment highlighted GenAI's potential to improve data workflows, especially in extraction, transformation, code generation, and business analysis tasks. The HDME prototype, EGTL experiment code, and detailed results are included in the Supplementary Materials Section.
# A Methodological Approach for the Smartification of a University Campus: The Smart ESI Use Case
## 4 Smart ESI: A New Model of School
### System Architecture
#### 4.3.1 Physical Layer: Sensors and Actuators
For energy consumption monitoring purposes, we acquired professional devices, versatile and robust, which are intended for tertiary and industrial buildings and designed to operate in multiple failure situations, e.g., data transmission failures. These devices are the Sennet IoT DL271 Datalogger Long Net and 2 Sennet IoT Xtend M6, designed and manufactured by the Spanish company Satel Iberia, and shown in Figure 5. The IoT DL271 Datalogger is a measurement concentrator that allows us to configure and deploy a complete monitoring system for a building. In our use case, the purpose of this device will be the energy monitoring of the Smart ESI campus, including the three buildings and the data center, which is one of the most energy consumers. This datalogger includes a display and keypad, RFNet radio frequency, 3G/GPRS router, Ethernet, RS232 and RS485 serial interfaces, digital inputs and outputs and it integrates 3 three-phase electrical analyzers (or 9 single-phase analyzers) in the same equipment, and it has the capacity to manage up to 100 m by radio frequency. It has an SD card for data storage in the event that the network is not available, which automatically will resend data when enabled. The second one is the Sennet IoT Xtend M6, which consists of 6 expansion modules to extend with one only click the range of the datalogger to a total of 15 three-phase analyzers. At the moment of writing these lines, these devices were turned off for maintenance purposes. Figure 5: Sennet IoT DL271 Datalogger Long Net (the first device on the left) and 2 Sennet IoT Xtend M6.
## 4 Reduce Redundancy in Trail-Based Search
### Redundancy Reduction Scheme  
The analysis given in Section 4.1 shows that there is nontrivial transmission redundancy in the chase phase of TS. This motivates us to propose a new scheme named _TS-R_ to reduce the transmission redundancy in TS.  
The solution is based on a greedy strategy: selecting the node closest to the recorded location of actor as the next-hop relay at each hop in the chase phase. To realize this idea, more information is required to be saved in the trail update packet and the search packet. In the trail maintenance, the actor is required to write the coordinate of its current location into the trail update packet. Moreover, in the transmission of search packets, the host should record its location into the search packet and then broadcast it. It is important to note that, only the location of the previous hop node is recorded in the search packet, thus the overhead of TS-R is still low.  
When a host finds it obtains an effective footprint, it first obtains its distance to the recorded location of the actor, denoted as \(d_{h,A}\). Then it checks its neighbor table \(\mathbf{N}(h)\). Consider each sensor node \(i\in\mathbf{N}(h)\), the host derives node \(i\)'s distance to the previous hop node \(d_{i,p}\) and that to the actor \(d_{i,A}\)  
Figure 3: Transmission redundancy in trail-based search (TS).  
If there is no node in the neighbor table \(\mathbf{N}(h)\) that satisfies both \(d_{i,p}\leq R\) and \(d_{i,A}<d_{h,A}\), the host will continue to broadcast the search packet. Otherwise, it will discard the search packet.  
Figure 4 shows an example that demonstrates how TS-R reduces the transmission redundancy in the chase phase. When node \(p\) broadcasts the search packet, node \(i\), \(j\), and \(k\) can receive this packet. For node \(i\), since \(d_{i,A}>R\), it does not satisfy the condition \(\alpha_{i}<\alpha_{p}\) and thus discards the packet. For node \(k\), it finds that there is a node \(j\) in \(\mathbf{N}(k)\) that satisfies the condition \(d_{j,A}<d_{k,A}\). Thus node \(k\) does not forward the packet either. Only the node \(j\) continues to forward the search packet, because it satisfies \(d_{j,p}\leq R\) and it is closer to the actor than any other nodes in both \(\mathbf{N}(p)\) and \(\mathbf{N}(j)\), i.e., the node \(i\) and \(k\). Therefore, TS-R scheme can reduce the transmission redundancy in the chase phase, and improve the energy efficiency of the network. Simulation results will be provided in Section 6.2.3 to prove the efficiency of TS-R.