esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A Primer on Multi-Link Aggregation in IEEE 802.11 be
### Multi-Link Architecture: STR and Non-STR Cases
Due to an in-device interference under the non-STR condition, a station affiliated to an MLD cannot perform medium detection while the other station within the same MLD is transmitting, even the station can transmit along with the other station, and thus the transmission opportunity can be constrained. The second problem is the reception hindrance problem, shown in Figure 2b. In the conventional 802.11, a station just needs to defer its transmission (that is, freeze its backoff counter) until the end of the other station's transmission. On the other hand, if a station on the same MLD under non-STR condition is receiving, other stations within the same MLD have to defer transmission even if the medium is sensed as idle. This is because attempting transmission occurs in-device interference, and thus the ongoing receiving operation may be corrupted. To sum up, there can be severe cases that worsen the chances of transmission in the case of non-STR. So the multi-link DCF should take this non-STR case into account. A typical solution to avoid the above-mentioned hindrance problems is to align end times of data transmissions among the stations within the same MLD. Through transmission alignment, transmission opportunities may not be hindered from the ongoing transmission. Furthermore, since the transmissions from stations within the same MLD will end simultaneously and there will only be simultaneous transmissions or simultaneous receptions within the same MLD, the reception hindrance problem will also not occur.
## 4 Discussion
### Analysis of the Evaluated Metrics for Each Method
#### 4.1.2 Crest Factor  
The crest factor, which measures the ratio between peak power and its RMS value, indicates that V2 GGlide demonstrates a more stable behavior with consistently lower values compared to V2 GGSUN and V2 GSmooth, especially at high SoC levels (>80%). This result suggests that V2 GGlide is better suited for applications where minimizing power peaks is a priority, as it ensures smoother transitions and reduced peak stress on the grid.
# Preparations for Galileo PRS in Poland
## 1 Introduction  
Many sectors of the European economy (transport, logistics, telecommunications, and energy) depend on precise location and timing. The European satellite navigation market is growing steadily, and consequently, the satellite navigation aspects are becoming more important, also due to the fact that the Galileo system is to become fully operational in 2023. The GNSS systems are not 100% reliable. There may be periods of outage or a significant decrease in their quality (e.g., as a result of unintentional or deliberate interference, such as jamming and spoofing), which may potentially result in life-threatening situations and, in some aspects, even threats to public safety. In the context of that, the following facts and incidents can be mentioned. First of all, the GPS is the US military system, and its availability for civilian users may be temporarily limited or even disabled in certain situations and areas. Secondly, during the well-known Galileo outage incident in July 2019, following a combination of several circumstances, the system was completely unavailable for seven consecutive days. Finally, the multiple reports of intentional and long-term interference with the GPS signals, which initially occurred in northern Europe territory, and since February 2022, has also been observed in eastern parts of Europe.  
In the context of increasing threats to the integrity and availability of GNSS systems, related to unintentional or deliberate activities, there is a need for a service more resilient and robust than the initial GNSS open services. The Galileo Public Regulated Service (PRS), dedicated for authorized European governmental users, is one of the most promising solutions. The PRS service will be encrypted and more resistant to typical spoofing and jamming attacks, and it will be crucial for the safety and security of the European Union as an alternative to open GNSS systems vulnerable to deliberate interferences.
# A Study on Improving the Machining Performance of Scrolls
## 5 Experimental Results and Discussion
### Experiments on Chatter Suppression  
After the rough machining processes, the semi-fine and fine machining processes were implemented to improve the surface roughness and precision of the scrolls. In this part of the study, experiments on chatter suppression were conducted. We used a microphone to detect the audio signals and the developed program to illustrate real-time spectrum diagrams and monitor the machining conditions. When chatter occurred, the command to change speed was given to the spindle through the connection between the Ethernet and the controller of the milling machine to suppress the chatter. The cutter used in these experiments was a Nachi List 6212, and the microphone was an Audio-Technica AT9942. The specifications of the cutter and the machining parameters are listed in Table 4. The experimental results are shown in Table 5 and Figure 14.  
The matter frequency (2293 Hz) shown in Table 4 was obtained by observing the cutting edge; the regenerative waviness was detected from the surface of the workpiece, and the vibration type can be thought of as a self-excited one. The results in Table 5 also verified that, when the multiple factor \(K\), which is expressed in Equation (5), was an integer, there was no chatter. As shown by the experimental results, the proposed methods could reduce and disperse the energy of the chatter frequency, and the machining conditions of the system could be stabilized by changing the real-time spindle speed. As signal interference from noise and the variation in the workpiece stiffness in the machining processes might shift the stability lobes, it is suggested that an increase in the spindle speed could be first adopted to avoid unstable machining conditions and to achieve chatter suppression. To conveniently demonstrate the effects of chatter suppression, Figure 15 shows the surface of the block of aluminum alloy with and without the occurrence of chatter, respectively. In the figure, the left side of the block without chatter suppression reveals a significant chatter effect, and the right side with a smooth surface shows the result of chatter suppression. In this example, the transition time for the variations in spindle speeds was about 80 milliseconds (ms), and the transition length on the workpiece  
Figure 14: Experimental results displayed in the operation interface of the developed program: (**a**) \(S=5000\) rpm, (**b**) \(S=6879\) rpm.  
surface was about 1.05 mm. The obtained results also verified the feasibility of the proposed methods for chatter suppression.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## 3 Cramer-Rao Lower Bounds for Exponentially Decaying Phasors  
Due to the noise, only an estimate for \(\chi\) can be determined. For any given estimate of the state vector \(\chi\), we can calculate the resulting estimates \(\mu_{n}\) for the sampled signals \(z_{n}\) at times \(n\cdot T\):  
\[\mu_{n}=\tilde{b}e^{n\cdot(-\alpha+\hat{\omega})T+\hat{\varphi}\hat{\varphi}}. \tag{27}\]
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 2 Theoretical Analysis of Displacement-Dependent Damping Inerter System (DDIS)
### Mechanical Model
#### 2.1.1 Model of Displacement-Dependent Damping Element (DDE)  
The damping force of the DDE is dependent on the displacement rather than on the velocity response, and it is characterized by a stable and non-degrading mechanical behavior. The DDE is of significant engineering interest; several control devices have been proposed, which exploit the DDE characteristics (such as shape-memory alloys, wire-cable isolators, metallic dampers, and friction-based dampers) [45; 46]. The bilinear model in Figure 2 is widely used to describe the DDE and give it a clear and simple physical meaning. When this model is used to represent the force-deformation relationship of the DDE, the restoring force \(F_{d}\) is given by:  
\[F_{d}(u_{d},z_{d})=\alpha k_{d}u_{d}+(1-\alpha)k_{d}z_{h} \tag{2}\]  
Figure 1: Schematic of the inerter.  
where \(k_{d}\) is the initial stiffness of the DDE, \(\alpha\) is the ratio of the post-yielding stiffness to pre-yielding stiffness, \(u_{dy}\) is the elastic limit displacement or yielding displacement of the corresponding bilinear model, and \(u_{d}\) and \(z_{h}\) are the deformation of the DDE and the hysteretic variable, respectively, which are related as follows:  
\[\dot{z}_{h}=\dot{u}_{d}\Big{[}\big{(}1-\text{sgn}\big{(}\dot{u}_{d}\big{)}\text {sgn}\big{(}z_{h}-u_{dy}\big{)}-\text{sgn}\big{(}-\dot{u}_{d}\big{)}\text{sgn} \big{(}-z_{h}-u_{dy}\big{)}\big{)}\Big{]} \tag{3}\]  
where \(\text{sgn}(\cdot)\) is the signum function.
# General Moving Object Localization from a Single Flying Camera
## System Design
### Calibration  
#### 3.2.1 Camera Calibration  
In order to obtain an accurate estimation of the depth of the object, the camera needs to be calibrated. We want to be able to recover the angle subtended by an object from the camera axis based on the pixel offset of the object in the image. Figure 3 illustrates the scenario. The drone is at the bottom of the figure with its camera pointing up the page. An object is at the top-right hand corner of the figure, at a depth \(Z\) from the camera and a horizontal displacement of \(P\) from the camera axis. The object subtends an angle \(\theta\) from the optical center axis and causes an image offset of \(p\) from the image center. We want to obtain \(\theta\) from \(p\).  
The computation \(\theta\) from \(p\) is dependent entirely on the focal length of the camera and the image resolution (dots per inch or dpi). To make the computations simpler, we introduce a new parameter \(R\) where:  
\[\frac{p}{Z}=\frac{p}{R}=\tan\theta. \tag{1}\]  
The value of \(R\) is a constant for a particular camera. We can obtain this value empirically by placing an object at a depth \(Z\) from the camera with a horizontal displacement \(P\) from the camera axis in the drone's Field Of View (FOV) and then measuring the image offset \(p\). Table 1 shows the data measured from our experiment.  
With this ratio \(R\), when the pixel offset \(p\) of an object from the image center is provided, we can immediately calculate the angle \(\theta\) subtended:  
\[\theta=\arctan\left(\frac{p}{R}\right). \tag{2}\]
## 6 Conclusions  
The structure of a MIMO-OFDM system for V2V channels was proposed as a result of the generalization of the IM developed in this study. Its performance in terms of BER is superior to that of more-advanced approaches requiring complex products \(O(N^{2})\) thanks to the incorporation of IM. The integration achieved in the modulation, channel estimation, and data detection stages of the proposed system has not been reported elsewhere in the literature, establishing this approach as a suitable solution that achieves the mitigation of ICI and the exploitation of spatial diversity for MIMO-OFDM in V2V channels with reduced computational complexity and improved BER performance compared to a conventional MIMO-OFDM system. Possible areas for future work include studying these techniques in massive and non-orthogonal MIMO systems.  
Figure 7: BER-vs-\(E_{b}/N_{0}\) comparison of different detection algorithms for MIMO-OFDM-IM and MIMO-OFDM with \(N_{t}=4\), \(N_{r}=4\) under conditions of high transmission rates.  
**Author Contributions:** Conceptualization, J.A.D.P.-F., F.R.C.-S., C.A.G., and F.P.-C.; methodology, J.A.D.P.-F., F.R.C.-S., C.A.G. and F.P.-C.; software, J.A.D.P.-F.; validation, F.R.C.-S., C.A.G., and F.P.-C.; formal analysis, J.A.D.P.-F. and F.R.C.-S.; investigation, J.A.D.P.-F., F.R.C.-S., C.A.G., and F.P.-C.; resources, J.A.D.P.-F., F.R.C.-S., C.A.G., and F.P.-C.; writing--original draft preparation, J.A.D.P.-F., F.R.C.-S., C.A.G., and F.P.-C.; writing--review and editing, J.A.D.P.-F., F.R.C.-S., C.A.G., and F.P.-C.; supervision, J.A.D.P.-F., F.R.C.-S., C.A.G., and F.P.-C.; project administration, J.A.D.P.-F., F.R.C.-S., C.A.G., and F.P.-C.; funding acquisition, J.A.D.P.-F. All authors read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to thank Universidad Panamericana, for their support through the program \"Fomento a la Investigacion UP 2022\", and project \"Diseno de sistema de comunicacion vehichuculo a vehiculio MIMO-OFDM basado en modulacion por indicre\" UP-CI-2022-GDL-ING. And to the Universidad Panamericana Direccion Institucional de Investigacion.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Growth Trend Analysis of Wheat by 1 km SIF  
Previous applications of satellite SIF products in crop growth monitoring and yield estimation have been somewhat more limited. To further assess the applicability of downscaled SIF products in crop growth analysis, spatial SIF values were extracted from wheat-growing areas in Shandong Province. Using the GLASS LAI remote sensing products and long-term observation of climatic records from agro-meteorological stations, we defined the inflection points and peaks of the key climatic periods of each crop. These key nodes of the LAI characteristic curves were identified by the inflection point and threshold methods, thus accurately capturing the growth dynamics of wheat in a year. In addition, the SIF, LAI, NDVI, and EVI data at the same spatial and temporal scales were selected to analyze the response of their changes to the growth status of wheat over eight days. The results showed that the trends of the SIF values closely followed those of the LAI, NDVI, and EVI, with SIF changes exhibiting greater stability than the other vegetation indices (Figure 15). Specifically, October marked the sowing period of wheat. From November to January, during winter, wheat's vegetation index fluctuated significantly due to temperature effects, and the SIF values were also influenced by temperature and snow cover, which reduced sunlight absorption, reaching their lowest point in January. As temperatures increased from February to April, the vegetation index gradually rose, and in April, wheat growth reached its peak, coinciding with the highest SIF values. By May, as the wheat was harvested, both the SIF and vegetation index values began to decrease gradually. The wheat growth cycle showed similar trends in both years.  
Furthermore, we conducted a correlation analysis between downscaled 1 km SIF and three vegetation indices. Figure 16 shows that the correlations in both years were high (R\({}^{2}\) > 0.8), with the strongest correlation observed with the LAI (R\({}^{2}\) > 0.95). This indicates that downscaled SIF not only possesses similar structural characteristics to traditional vegetation indices but also can outperform them to a certain extent due to its mechanism of spontaneous luminescence based on sunlight stimulation. Therefore, the accuracy of downscaled SIF and its potential and effectiveness in this application area was verified through crop growth.  
Figure 14: Correlation analysis of SIF with a GPP density plot. (**a**) 2018–2019. (**b**) 2019–2020.
## 3 Related Works  
[PERSON] et al. [13] utilized ANN, which was trained with genetic algorithm to predict dengue fever outbreak in Puerto Rico and some areas in the coast of Mexico. They concluded that the model they developed using ANN had a good predictive ability.  
[PERSON] et al. [14] compared two machine learning methods, which were Artificial Neural Networks multilayer perceptron (ANN-MLP) and Support Vector Machine (SVM) as the tools to assist medical diagnosis. ANN-MLP produced a better result with an average of 96% accuracy, 96% sensitivity, and 97% specificity. In conclusion, ANN-MLP could be used as a classifier to diagnose dengue infection with high accuracy, sensitivity, and specificity.  
[PERSON] et al. [15] compared Seasonal Autoregressive Integrated Moving Average (SARIMA), Generalized Additive Models (GAM), Artificial Neural Networks (ANN), naive model, and ensemble model to predict dengue cases in Sao Paulo for one month ahead.  
[PERSON] et al. [16] developed a novel hybrid model, which was a combination of Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregressive (NNAR). The model was used to analyze time series dengue data of three dengue endemic regions, which were San Juan, Iquitos, and Filipina. They concluded that the proposed hybrid model was easy to interpret, had an excellent performance in forecasting dengue epidemic for three dengue time series data from different regions, and had better forecasting accuracy compared to the other methods used in previous researches, such as traditional methods or other hybrid methods.  
[PERSON] et al. [17] developed models that enable forecasting of outbreaks of dengue, giving medical professionals the opportunity to develop plans for handling the outbreak, well in advance. They utilized several methods, one of them is ANN. Based on their results, the ANN model had two advantages: easy to be used in incremental learning and can learn to ignore irrelevant attributes.  
[PERSON] et al. [18] applied Quadratic Discriminant Analysis (QDA) in mapping the incidence of dengue into five areas in Palembang based on significant factors, such as age, gender, blood group, etc. The overall correct percentage of the mapping results was 66.7%.  
[PERSON] et al. [19] utilized Principal Component Analysis (PCA) and DA to predict the patients' clinical dengue positivity. The DA method yielded accuracy between 93-98%, sensitivity between 75-89%, and specificity between 94-100%.  
ANN and DA have been widely used to predict dengue incidences. That is why we decided to use both methods separately in our research to develop models that can predict the severity level of dengue based on the laboratory test results.
## 5 Conclusions  
Rock slopes pose costly and life threatening hazards in many parts of the world. TLS volumetric change analyses enables the development of relationships between rockfall volume (magnitude) and frequency of release (e.g., [57, 58]) for risk assessment of these slopes. However, existing surface modeling methods can be difficult to employ, particularly on surfaces exhibiting significant morphologic complexity. In this paper, we present a new, fully automated approach to perform change detection and identify individual rockfalls on complex rock slopes. This new approach provides a streamlined method to analyze time series point clouds to isolate and quantify individual failure clusters (rockfall events) on the rock slope to generate magnitude-frequency relationships.  
The approach was applied to several highly active, rock slope study sites in Alaska, U.S.A., resulting in magnitude-frequency relationships consistent with established theory (R\({}^{2}>\) 0.7) at each site. The effects of hole filling and model resolution on the analysis were also evaluated. The TPS hole filling technique successfully created complete surface models that are fully consistent with the topography. The hole filling technique was also effective in capturing larger rockfall events that were erroneously separated into several smaller events using the traditional approach of not filling holes. The combined hole-filling and change-detection compared well with field observations and high-resolution photographic records from the study sites.  
In general, model resolution plays a pivotal role in the ability to detect smaller rockfall events when developing magnitude-frequency relationships. The minimum detectable rockfall volume was found to be a function of the modeling resolution, assuming that sufficient resolution has been obtained during acquisition. The total volume estimates are also influenced by model resolution, but were comparatively less sensitive. In contrast, hole filling had a noticeable effect on magnitude-frequency relationships but to a lesser extent than modeling resolution. However, hole filling yielded a modest increase in overall volumetric quantity estimates.  
Hence, it can be concluded that optimal results in estimating both magnitude-frequency relationships and volumetric quantities occur when appropriately balancing high modeling resolution with an appropriate level of hole filling.
## 2 Related Work and State-Of-The-Art Technologies
### Integrating a Communication and Coordination Infrastructure Using a Message Oriented Middleware  
For larger transdisciplinary, multi-domain data processing and co-simulation workflows, data exchange between executables and some coordination logic is essential. As depicted in Figure 3, a distributed messaging infrastructure is integrated into the framework for allowing communication between the framework processes and providing means for coordination. Redis and Apache Kafka, both of them having efficient and very scalable messaging functionality, can be utilized as a communication interface for the framework and were tested for providing the message functionality.  
As Table 1 shows, Redis is a bit different from Apache Kafka in terms of its storage and various functionalities. As mentioned above, the framework requires fast real-time data processing with low communication or I/O overhead. At its core, Redis is an in-memory data store that uses its primary memory for storage and processing which makes it much faster than the disk-based Apache Kafka. Beside this, Redis can be used as a high-performance database, and a message broker, which better matches the framework's requirements, since most of the data in the framework needs to be transferred quickly, and it is not necessary to store the data persistently on a hard disk. Redis features publish/subscribe messaging which is push-based while the publish/subscribe messaging of Apache Kafka is pull-based. That means that messages published to Redis will be automatically delivered to subscribers instantly, while in Apache Kafka data/messages are never pushed out to consumers; the consumer will ask for messages when the consumer is ready to handle the message. In the case that consumers do not process data fast enough, Apache Kafka will have to read from a disk and not from memory which will slow down its performance. Moreover, In most cases, the framework deals with small short lived messages with a minimal latency. Redis, using an in-memory storage can store the small messages within the limit of memory allocated to Redis. However, since the memory storage used by Redis is typically smaller than a disk, it is necessary to clear it regularly and making room for new data.  
Therefore, in summary, compared to Apache Kafka, as a generic message-oriented in-memory-based communication interface, Redis is a better solution for the framework to establish communication between the executables and the _I/O_ adapters (see Figure 3).  
\begin{table}
\begin{tabular}{l l l} \hline
**Framework Requirements** & **Apache Kafka** & **Redis** \\ \hline fast storage & in-memory and disk-based & in-memory \\ \hline a database and & & can be used as a database, \\ a message broker & a message service & cache, and message broker \\ \hline short lived messages & can keep flushing data for & retain data for short period \\  & longer period of time & of time \\ \hline small amount of data & large data size & small data size \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of framework requirements to the functionalities of Apache Kafka and Redis.