esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage
## 3 STMaps Visualization Tool
### SILKNOW's Data and Ontology  
SILKNOW's ontology is a DO based on the CIDOC CRM. The selected implementation is the Erlangen CRM/OWL [71], a RDFS/OWL ontology. SILKNOW's knowledge graph stores records of silk heritage mainly from the 15 th to the 19 th century. The records are mostly limited to a European geographical scope. However, there are some non-European records in the database and also data providers from the Americas.  
SILKNOW's knowledge graph currently stores more than 38,000 instances of E22 Man-Made Object class [72] (CIDOC model) and more than 67,000 images, although it is still growing. The main properties stored for each item are:  
* Production place;
* Production time;
* Type of object;
* Material (silk, wool, cotton, gold, chenille etc.);
* Weaving technique (brocatelle, damask, brocade, lampas, espolin etc.);
* Depiction;
* Museum that provided the record.  
All but the last two properties ('depiction' and'museum', which are used for filtering purposes only) are the ones that we want to show in a spatio-temporal representation. The knowledge graph also stores images of the items (when available) and some other textual information, such as a description of the object. The overview of SILKNOW's ontology is shown in Figure 1. As can be seen, there are many textile technique classes, which extend the E22 Man-Made Object class from the CIDOC model, and the Weaving class, which extends the E12 Production class [73] from the CIDOC model.  
The granularity of the spatial data provided by museums and organizations is diverse. Some records have a very specific location, whereas others indicate the first-order administrative division (country) or the second-order division (region/province/state). For this reason, we use the GeoNames database [74]. This free API and database contains more than 25 million geographic names, organized in 9 categories and 645 subcategories. GeoNames provides integration for its use in ontologies and semantic webs. Each place in GeoNames is associated to a unique Uniform Resource Identifier (URI). This URI provides access to all the information of the geographic feature. This allows identifying places that have more than one name and deal with different spatial granularities.  
A similar problem occurs with the granularity of time data. Some items in SILKNOW are labeled as belonging to a particular century. However, some records provide more specific periods than others do. For this reason, a similar approach as the one used for spatial data is used.
# Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
## 6 Simulation Research
### Simulation Results
#### 6.2.3 Performance of the Freeway  
In this paper, the performance of the freeway is evaluated from the aspect of TTS, TTD, TE and TF. Table 3 lists these indicators with three control methods, namely fixed speed limit, MPC_SOO and MPC_CPDMO-NSGA-II. For reducing the effects of random seeds, this paper repeats five experiments for each method, and calculates the mean value of the performance indicators and traffic variables.  
Figure 11: The control solutions of the freeway with MPC_SOO and MPC_CPDMO-NSGA-II.  
From Table 3, it is known that the efficiency of the freeway is the worst with fixed speed limit. It also aggravates the burdens of environment and economics. By contrast, MPC_SOO and MPC_CPDMO-NSGA-II greatly improve the performance of the freeway, especially in the aspect of TE and TF, reduced more than 60%. In MPC_SOO, TTS is 15.9% shorter than fixed speed limit, while TTD rises by 4.6%. It implies that vehicles passed the freeway increase, or their speed increases significantly. Compared to MPC_SOO, the presented method reduces more TTS, TE and TF, and greater improves TTD, reaching to 4.9%.
## 3 Results and Discussion  
### Comparison of Different k-mer Features  
In this study, we used a 5-fold cross-validation method to reliably evaluate the performance of our CSpredR predictor [33]. Specifically, the benchmark dataset was divided into five equally sized subsets. Four of these subsets were used for model training, while the remaining subset was used as test data for model performance evaluation. This process was repeated five times, with each subset being selected as the test data in turn. In our experiments, we found that the value of k for k-mers had a significant impact on the results. To determine the most suitable k-mer value, we conducted a series of experiments using different k-mer lengths: k = 1, 2, 3, 4, 5, and 6.  
The experimental results, as shown in Figure 3, indicate that the model's prediction performance is relatively poor with smaller k values, such as k = 1, k = 2, and k = 3. This may be because these shorter k-mers fail to fully capture the critical information in the RNA sequences, leading to lower model accuracy. However, the best prediction performance was observed with k = 4, suggesting that a k-mer length of 4 can better capture the contextual information in RNA sequences, thereby improving model accuracy. Consequently, we chose k = 4 as the optimal k-mer value for further analysis.  
It is noteworthy that using longer k-mer values, such as k = 5 and k = 6, did not further improve the model's prediction performance. This might be because longer k-mers could introduce noise or excessive features, reducing the model's generalization ability. Choosing k = 4 is a trade-off that allows for effective capture of contextual information in RNA sequences while controlling the complexity of the de Bruijn graph to ensure computational efficiency. It also reduces the information insufficiency caused by smaller k values and the noise and cost introduced by larger k values. Therefore, k = 4 is a reasonable and efficient choice. In summary, in this study, we ultimately determined that a k-mer length of k = 4 is the optimal choice for mRNA subcellular localization prediction based on word2 vec and fasttext encoding. The research results also indicate that when k = 4, the k-mer length performs best in balancing information capture and feature dimension control [34].  
Figure 3: Experimental results with different k values. (**A**) Performance evaluation of different k-values in terms of ACC and Average precision. (**B**) Performance evaluation of different k-values in terms of one-error, Ranking loss and Hamming loss.
# Advancing Digital Twin-Based Collision Avoidance:
## 4 Functional Safety for Wireless Systems
### Background
#### 4.1.2 Reliability of Wireless Networks
The technology's notable features, including high scalability, extensive coverage, and low power consumption, make it particularly promising for diverse use cases in vast remote areas. Defining an outage probability for a communication network proves challenging. On the application level, a system may not necessarily experience an outage. However, it is possible to design a system in such a way that brief interruptions in the communication channel can be bridged with heuristics and specific safety mechanisms.
# An Introduction of NoSQL Databases Based on Their Categories and Application Industries +
## 3 The Categories of NoSQL Databases
### Wide Column Store  
This category of NoSQL databases has a complex table schema described as follows [5; 12; 13; 14].  
1. A row key is an identification that has a unique value used to identify a specific record, similar to the primary key of a relation in RDB.
2. A timestamp (abbreviated as ts) is an integer used to identify a specific version of a data value.
3. At least one column family that has the format of \"Family: Qualifier = Value,\" where \"Family\" is the name of a column family, \"Qualifier\" is the name of a column qualifier, and \"Value\" is a real value of a column qualifier stored in text.
4. The name of a column family need to be defined when the table is created, but the name of a column qualifier does not.
5. Users can find the actual data value through the value of a specific row key, the name of a specific column family, the name of a specific column qualifier, and the value of a specific timestamp.  
An example is illustrated as follows. An inventory table of 3C products in a wide column store database is shown in Table 3, where:  
1. Products_Inventory is the name of the inventory table, which contains two column families, products, and inventory, and has three records with the product codes P001, P002, and P003 as the values of three row keys, respectively;
2. An increasing integer \(t_{i}\) (\(i\) = 1, 2,..., 18) is the value of timestamp for each column qualifier when a data value of a column qualifier is inserted into the table;
3. Column family products includes four column qualifiers: Classes, title, descriptions, price, and their data values, for example, are \"TV\", \"SONY 55 inch 4K OLED Smart Networked TV\", \"TBD\", and \"24999\", respectively;
4. Column family inventory includes two column qualifiers: Quantity, place, and their data values, for example, are \"10\" and \"1A\", respectively.  
According to the statistics of the DB-Engines Ranking website [15], Apache Cassandra and Apache HBase are the more widely discussed ones of the wide column store databases.
## 1 Introduction
However, this is far lower than the data rate offered by WLAN (up to 11 Mbps for IEEE 802.11b and up to 54 Mbps for 802.11g), and even Bluetooth (up to 3 Mbps for Bluetooth 2.0). While actuator nodes typically have stronger computation and communication capabilities and more energy budget relative to sensors, resource constraints apply to both sensors and actuators. 2. WSANs are highly dynamic in nature. The network topology may possibly change over time due to node mobility, node failure, node addition, and exhausted battery energy. The channel capacity may also change because of the dynamic adjustment of transmission powers of the sensor/actuator nodes. 3. WSANs feature inherent node heterogeneity. Having different functionality, sensors and actuators do not share the same level of resource constraints. The coexistence of sensors and actuators makes WSANs and WSNs fundamentally distinct. 4. WSANs typically operate in unpredictable environments. With wireless radio as the medium for data transmission, most WSANs suffer from diverse radio interferences. This problem will become increasingly severer as wireless technologies are incorporated in more and more (consumer) products that are expected to become pervasive. Furthermore, query-driven and event-driven applications can also cause the traffic load on the network to vary unpredictably. This paper deals with QoS management in WSANs. A fuzzy logic control based QoS management (FLC-QM) paradigm will be developed to facilitate QoS support in resource-constrained WSANs operating in dynamic and unpredictable environments. This approach is by no means an almighty solution to all of the above challenges; it is, however, the first attempt to explicitly address the impact of unpredictable variations in traffic load on the QoS of WSANs. The variability of traffic loads over wireless connections may be a natural result of network topology changes, ambient interferences, and/or system reconfiguration, just to mention a few. The deadline miss ratio for data transmission is used as a metric to measure the QoS of WSAN. A fuzzy logic controller is designed to dynamically adjust the sampling period of relevant sensor in a way that the deadline miss ratio is kept at a desired level. Taking advantage of the feedback control technology, the FLC-QM can provide QoS guarantees while achieving predictable application performance. This solution is generic, scalable, and easy to implement. It can simultaneously address multiple QoS problems such as delay, packet loss, and network utilization. Simulation results will be given to demonstrate the effectiveness of the proposed FLC-QM scheme. The rest of this paper is organized as follows. Section 2 reviews some related work. The architecture of the FLC-QM scheme is described in Section 3. In Section 4, the fuzzy logic controller is designed. Comparative simulations are conducted in Section 5. Section 6 concludes the paper.
## 2 Material and Methods
### Data Processing and Analysis  
The GC-MS signals were collected by the MassHunter Acquisition software (Agilent Technologies, Santa Clara, CA, USA). The Automatic Mass Spectral Deconvolution and Identification System (AMDIS-32) software and the National Institute of Standards and Technology (NIST) mass spectra library (version 2.2) were used to identify chemical compounds. The Kovat's retention index was used to assist identification. Data sorting and linear regression were processed by Microsoft Excel 2016. The averages of the compound areas were statistically analyzed by Metaboanalyst 4.0 ([[http://www.metaboanalyst.ca/faces/upload/StatUploadView.xhtml](http://www.metaboanalyst.ca/faces/upload/StatUploadView.xhtml)]([http://www.metaboanalyst.ca/faces/upload/StatUploadView.xhtml](http://www.metaboanalyst.ca/faces/upload/StatUploadView.xhtml))), using hierarchical cluster analyses (heatmap) [20]. Samples were uploaded to Metaboanalyst 4.0 as columns (unpaired); data filtering was characterized using the mean intensity value. Sample normalization, data transformation and data scaling were specified as a \"NONE\" mode. Heatmap parameters were as follows: distance measure = Euclidean; clustering algorithm = ward; and standardization = auto scale feature. The heatmap statistical model was the t-test/ANOVA. The LOD was calculated by the linear regression method [21] using Equation (1):where S is the standard deviation of the linear response of the GC-MS and b is the slope of the calibration curve.
## 5 Conclusions  
In this work, a prototype of a novel digital chaotic communication system utilizing FM-ACSK modulation was successfully developed on an FPGA platform. By integrating frequency modulation with ACSK, the system improved resilience against channel nonlinearities and carrier frequency offsets, while retaining the benefits of a coherent-detection chaotic communication scheme.  
This study demonstrates the feasibility of and serves as a proof-of-concept for the proposed FM-ACSK chaotic communication system, which holds potential for applications in IoT and wireless networks. Although the current prototype is not fully optimized, it provides a flexible framework for future advancements. The findings lay a foundation for further research aimed at enhancing system performance, increasing noise immunity, and expanding functionality in subsequent iterations.  
Conceptualization, F.C. and A.L.; funding acquisition, A.L.; methodology, D.K.; project administration, A.L.; resources, R.B.; software, F.C. and R.B.; supervision, D.K.; validation, D.C.; writing--original draft, F.C.; writing--review and editing, D.C. and D.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Riga Technical University's Doctoral Grant program.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
This research was performed at Riga Technical University, Space Electronics and Signal Processing Laboratory--SpacESPro Lab.  
The authors declare no conflicts of interest.
## 2 KUBeetle
### Dynamic Model
Either an open-loop or a closed-loop extended dynamic model could be used as the starting reference in system identification with the gray box model approach. The open-loop extended dynamic model could be retrieved simply by setting controller gain parameters on the last row of the system matrix to zero and changing the last parameter of the input matrix to 30.91, according to Equations (4) and (5). Moreover, it should be noted that velocities along the x- and z-axes were not measured. They could not be used for system identification, which makes the system identification more challenging. Parameters of the filters, servo motor, and PD controller were fixed, because their values were already known and verified [34]. The gravity, integrator, and zero constants were also fixed. Thus, only nine stability derivatives and three control derivates were refinable in system identification. Each parameter had different physical characteristics and an effect on the dynamic of the KUBeetle [87; 88; 89]. Based on CFD analysis [89], \(X_{u}\), \(Z_{w}\), and \(M_{u}\) should be negative and have relatively large magnitudes; \(X_{w}\), \(Z_{u}\), \(Z_{q}\), and \(M_{w}\) should be close to zero; and \(X_{q}\) and \(M_{q}\) should have relatively small magnitudes. Overall, \(X_{u}\), \(X_{q}\), \(Z_{u}\), \(M_{u}\), and \(M_{q}\), the five parameters of stability derivatives, and \(X_{\gamma p}\), \(Z_{\gamma p}\), and \(M_{\gamma p}\), the three parameters of control derivatives, were mainly focused on for system identification. During system identification, these eight parameters were refined with a relatively wide range of restriction. Conversely, non-focused parameters of stability derivatives were kept close to zero with a narrow range of restriction. Restrictions were adjusted considering computational fluid dynamics (CFD) results [87; 88; 89] and current KUBeetle design specifications to maintain the integrity of the identified dynamic model in relation to the physical system. While relaxing parameter restrictions might improve the result of system identification, some refined parameters might not match the real physical system.
# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 5 Applications of DMC  
In this section, we present successful applications of Direct Mobile Coaching in different scenarios. While two applications were aimed at investigating the effects of feedback on the ability of athletes to adhere to performance tasks, two further systems were developed to help athletes with their pacing. Furthermore, we present a system for increasing the motivation of students to exercise more.  
In two recent studies [6; 25], DMC/PEGASOS has been used as part of the investigations into the effects of different types of feedback on the ability of athletes to adhere to heart-rate, power or speed targets during cycling or running activities. Due to the fact that both studies investigated the effect of visual, tactile or auditory concurrent feedback, they could not have been modelled with MobileCoach or RADAR. A further factor for the selection of DMC as the underlying model was that both MobileCoach and RADAR do not have a concept of a session or trial that was necessary for the studies and the data analysis. Parts of the studies could have been implemented with GCIS. However, modelling the data analysis would not have been possible.  
Furthermore, a feedback system providing athletes with a pacing strategy during uphill cycling time trials has been developed using DMC/PEGASOS, and its usefulness has been investigated as part of several studies [60; 68]. As shown above, for [60] and also for [68], the feedback system used in the evaluation study cannot be fully modelled directly using the frameworks from related work. Another feedback system with the aim of helping runners to improve their running performance by providing them with a pacing strategy based on their current level of fitness was also developed using DMC/PEGASOS [69]. For these studies, DMC proved useful as the complexity of the model was reduced. Furthermore, the usage of PEGASOS allowed for rapid prototyping of the system and reduced overall lines of code.  
As part of the \"Te(a)chIn Sport\" project, a system allowing young people and students to compete against each other in running games was created [70]. In order to make these games more appealing, points were awarded based on relative performances, i.e., performances were scaled based on estimated fitness levels. These estimations were based on a graded exercise test performed by the athletes. Tests and games were modelled using DMC and implemented using PEGASOS. An implementation with GCIS, MobileCoach or RADAR would not have been possible due to--among other reasons--the fact that modelling multi agent systems with interactions is not feasible with these systems.
## 3 A Taxonomy for Planar FSS
### Combined Designs  
All the designs discussed in the previous sections, including cross, patch, loop, ring, and meander-based FSSs, can be combined in more complex structures to enhance performance further. By integrating different design strategies, such as multilayer configurations, active elements, or intricate geometries, various improvements can be realized, including:  
* Multiple Resonant Frequencies: Combining different basic unit cells or adding resonant elements can enable FSS designs that operate at multiple frequencies. This is particularly useful in multiband applications where signals at various frequency bands need to be filtered or passed selectively.
* Size Reduction: By incorporating compact design strategies such as meandering lines, fractal geometries, or integrating active elements (e.g., PIN diodes or varactors), the size of the FSS unit cells can be significantly reduced, achieving the same or even improved performance while minimizing the spatial footprint.
* Stability Across Wide Range of Incident Angles: Combining self-complementary structures or adding symmetrical elements can enhance the angular stability of FSS designs, ensuring consistent performance across a broad range of incident angles, which is critical in practical applications like radomes, where signal reflection or transmission must remain stable even when the angle of incidence changes.
* Broader Bandwidth: By stacking layers or using more complex arrangements such as nested loops, meanders, or convoluted geometries, FSS designs can achieve larger bandwidths. This feature is essential in applications that require attenuation or transmission over wide frequency ranges, such as in shielding or broadband communication systems.  
For example, single-layer structures with nested loops, fractal designs, or meander lines could provide multiple resonances, high angular stability, and reduced size without the complexity of multilayer systems. Alternatively, multilayer configurations, where each layer provides a complementary function (such as polarization conversion, impedance matching, or angular stability), could be designed for broader bandwidths and enhanced performance.  
Thus, the combination of the various design approaches from previous sections offers a powerful toolkit for creating highly optimized FSS structures that meet specific requirements in wireless communication, radar, and electromagnetic shielding applications. References [77; 78; 79; 80; 81; 82] provide several examples of such advanced designs, where these techniques are used in various combinations to achieve enhanced FSS performance.