esa-sceva/satcom-chunk-collection huggingface dataset:
# NewSQL Databases Assessment: CockroachDB, MariaDB Xpand, and VoltDB
## 2 Related Work  
There are not many works related to the evaluation of NewSQL databases and their performance. This section presents some of the related works that evaluate NewSQL database performance.  
In Reference [21], the authors compared three NewSQL databases, VoltDB, MemSQL, and NuoDB, using the YCSB benchmark. Comparing the results, MemSQL had better results in most workloads, followed by VoltDB. NuoDB had some problems with data ingestion, having worse results.  
Another group of authors in Reference [22] performed experiments with four NewSQL databases, VoltDB, NuoDB, CockroachDB, and MemSQL. They applied a set of operationsto evaluate read, write, update latency, and query execution time among the databases. NuoDB was the database with the best performance, followed by MemSQL, VoltDB, and CockroachDB being the database with the highest latencies among the four databases.  
In Reference [23], the authors performed a benchmarking test against MySQL and Spanner, a NewSQL database. The authors used datasets provided by the Autonomous City of Buenos Aires. The results proved that when measuring its behavior by increasing the number of records and query complexity, Spanner performed better than MySQL.  
In Reference [24], the authors performed an evaluation of two NewSQL databases, MemSQL and VoltDB, using TPC-H benchmark queries with 1 GB of data. During the evaluation, MemSQL performed better than VoltDB.  
In Reference [25], the authors compared CockroachDB, MemSQL, NuoDB, and VoltDB with two benchmarks, YCSB and Voter. Comparing the results with both benchmarks, MemSQL performed better, having a higher throughput and lower latency. VoltDB and NuoDB performed similarly, despite NuoDB restrictions with the open source version. CockroachDB presented the worst results, with a lower number of transactions per second.  
Another group of authors in Reference [26] performed a benchmarking test with weather data collected from Romanian cities between 2008 and 2020 using several NewSQL databases. The assessment was set with minimum resources, focusing on the read, write, and update latencies, as well as query execution. CockroachDB and FaircomCO presented higher values with write latency. Regarding query execution time, Citus, TIBCO, and VoltDB presented good results, with HarperDB being the database with the best results. Moreover, with update latency, CockroachDB presented inferior outcomes, while other databases had some difficulties with the number of records. Lastly, the read latency test showed that, once more, CockroachDB had the worst outcomes, while Citus and VoltDB presented lower read latencies.  
The work conducted in this paper differs from that of the other authors presented in this section because we use a reliable benchmark (SSB) and a methodology (OSSpal) to choose the best NewSQL database. Furthermore, we use the latest available versions of the NewSQL databases.
## 3 Materials and Methods
### Model Design  
#### 3.6.1 Preprocessing  
Figure 6 depicts the dataset preprocessing process performed in this study. Dataset preprocessing comprises a series of steps on raw data to ensure their quality and suitability before they can be used for analysis or modeling. In this research, the dataset includes temperature, humidity, and electric current data, with attributes as shown in Table 4. Subsequently, this dataset is processed by calculating the average values of temperature and humidity attributes over time (T). Meanwhile, the electric current attribute (I) is calculated to obtain its energy value (W) by multiplying voltage (V) with electric current  
and time, as indicated in Equation (2) [38]. As a result, a new attribute called energy is added to the dataset, rendering the electric current attribute obsolete. Unlike temperature and humidity attributes with averaged values, the energy attribute is summed according to periods (T).  
\[W=V\times I\times T \tag{2}\]  
After obtaining the new dataset, the next step is to scale the training and testing data. The fitting process is carried out in the training data by applying the MinMaxScaler method [39]. This method helps to transform the data range to distribute it on a scale between 0 and 1 [40]. The use of MinMaxScaler on the training data is intended to balance the influence of each variable on the model. Additionally, the testing data are scaled to adapt the testing data to the same transformation as the training data, and this is performed to maintain a balance between the training and testing data.
## 3 Results and Discussion
### Comparison between the Numerical and the Experimental Results  
A comparative analysis of the numerical and experimental results of temperature difference was performedin order to validate and optimize the numerical model. The experimental results reproduced here as well as the details of the experimental test set-up can be found in [26]. As shown in Figure 9, the temperature differences deviate from the experimental results for the same geometry, and the temperature was measured in similar positions.  
Figure 8: Trajectory of the fluid in the collector and respective distribution to the channels at a fluid flow of 100 mL/min and at a scale of 5.  
Figure 7: Temperature evolution throughout each channel, from the entrance to the exit, with a fluid flow of 1 mL/min.  
The discrepancies observed (for instance, the fact that the temperature difference is about 5 \({}^{\circ}\)C higher for the numerical simulation under low flow rates) could be explained by the simplifications in the numerical model, such as in the boundary conditions. For instance, in the numerical simulation, considering the side walls as adiabatic could have contributed to an enhanced heat flux fraction. The flow is adiabatic, which is a reasonably good approximation given the range of temperatures and the small variation in the enthalpy.  
In addition, factorssuch as not considering the contact resistances in the numerical model could have promoted the increase in the discrepancies. Similarly, the experimental assays also have associated errors that contribute to the increase in the uncertainty interval.
# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 3 Methodology  
### Overview  
This section provides a comprehensive overview of the algorithmic workflow, focusing on key components following dataset acquisition. The operational principles of the radar section for positioning FOD are well-documented in prior studies [43; 44; 45]. Figure 2 presents the proposed architectural framework schematic. The backbone network extracts hierarchical features, including 1/8 scale feature maps to preserve shallow spatial information, alongside 1/16 and 1/32 scale maps capturing transitional representations. These correspond to the layer 3, 4, and 5 feature maps of the backbone output, respectively, with a supplementary 1/64 scale semantic feature map generated through progressive downsampling to enhance semantic richness. The neck network employs a multi-scale integration algorithm, computationally mapping deep feature semantics onto shallow high-resolution maps, effectively infusing high-level semantics into spatially detailed shallow features. To further enhance feature discriminability, the MDPCA module simultaneously models channel-wise dependencies and spatial correlations within input features. For the Transformer network, a reconfigured encoder optimizes feature propagation and long-range dependency modeling using a lightweight feedback-enhanced feedforward network, with the Threshold Selection (TS) algorithm applied to filter non-essential elements from the encoder-generated attention sequences, reducing attention dispersion. Finally, the CEL function is proposed to improve regression accuracy, particularly for small targets. This integrated approach ensures a robust and efficient framework for FOD detection, with each component contributing to a cohesive workflow from feature extraction to refined output prediction.  
Figure 2: Overview of the proposed architecture: The overall implementation framework of improved Dab-Deformable. The backbone initiates multi-layer feature map extraction, followed by the Hotspot Sense module, which identifies salient regions. Subsequently, the Space Mapping module, jointly with MDPCA, extracts multi-directional attention to enhance spatial and channel-wise dependencies. These enriched features are integrated into multi-scale representations and fed into the encoder, where the FBFFN optimizes feature propagation. The encoder output undergoes TS Module filtering to refine attention focus. Finally, the processed features are directed to the decoder; this component leverages attention mechanisms and learnable anchor boxes to generate detection outcomes.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 6 Obstacle Avoidance
### Obstacle Detection from Depth Information  
[PERSON] et al. have introduced an innovative approach to obstacle detection, leveraging dense disparity images accumulated along image columns [30]. This approach entails the transformation of the disparity image into a U-disparity map (U-map) through the accumulation of disparity values along columns. The U-map serves as a fundamental tool for identifying objects and grouping them into distinct obstacles. The technique for detecting obstacles relies on U-V disparity maps [31], which partition the disparity image into two histograms: a U-map (accumulated along image columns) and a V-map (accumulated along image rows). This partitioning improves robustness against noise and inaccuracies in depth measurements. Obstacles are identified as connected components within the U-map and subsequently projected back into the world frame.  
In dynamic environments, integrating obstacle detection into NavMesh-based navigation requires continuous path replanning to accommodate changing obstacles. Figure 15 illustrates a scenario where a static obstacle--a chair--blocks the robot's original path. Upon detection, the NavMesh structure is updated to define new walkable areas, with the chair visualized as an orange column and the newly computed path represented by a yellow curve.  
Dynamic obstacles also influence navigation performance. In Figure 15(a), the robot successfully avoids a static obstacle; however, the introduction of a dynamic obstacle--a pedestrian--results in multiple obstructions, initially leaving no feasible path. This scenario triggers the clearance of prior obstacle configurations (Figure 15(b)), followed by NavMesh regeneration to accommodate the new environment. Finally, as shown in Figure 15(c), a new path is planned based on the robot's updated pose (green) and the positions of both static and dynamic obstacles.
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Sensing Layer
#### 4.1.4 Sparse Networks  
From another perspective, IoT applications are usually deployed using dense WSNs in which the data collected from the sensors are redundant and highly correlated. Thus, exploring the redundancy in the reading each of sensor can be used to minimize its duty cycle, thus, prolonging its lifespan and subsequently the network lifetime. Furthermore, the high correlation between the measurements can be used to enable a scheme where only a few sensors operate in an active mode for each time slot. By taking advantage of these facts, [PERSON] et al. [86] presented CS based activation scheme for water distribution in IoT networks, the proposed method selects a few numbers of sensors to send their data to the sink at each time slot, which renders a remarkable reduction in the power consumption. Besides, a recent active node selection framework aims to improve signal acquisition performance, network lifetime, and the use of spectrum resources has been proposed in [87]. The proposed approach exploits the temporal correlation to select the active nodes based on the support of the data reconstructed in the previous time slot. In addition, the correlation between the sensor's measurements were exploited in [88], in which a 1-bit DCS-WSN framework was proposed to achieve more data compression while retaining an acceptable performance using the joint sparsity between the sensors reading. [PERSON] et al. [89] presented another idea for saving energy in WSN based on regression and CS. The main idea is to divide the sensors into different clusters, where at each cluster only one sensor (reference) node works in a periodically sampling mode, whereas all other nodes in the cluster exhibit a CS-based acquisition. After receiving data from all nodes, the sink node runs prediction algorithm to roughly estimate the signal series of all nodes based on the signal of the reference node.  
A comparative study has been conducted [86] to compare the performance of different data gathering approaches in term of the achieved network lifetime. The investigated scenario includes a network of 100 sensors deployed randomly over a specific area, in which only 20 sensors are active at each time slot and all the sensors transmit their data to a single node. The lifetime achieved by the approach proposed in [86] is about 5 and 2.5 times the CDG and CSF lifetime, respectively, whereas the lifetime achieved by the CDC algorithm is about 80% of the achieved lifetime using the proposed algorithm in [86].
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 2 Virtual Power Plant (VPP)  
The VPP can participate in the electricity market as an individual energy generation system. It is a combination of energy storage systems, energy generation units and manageable loads. The VPP purchases power from the grid and charges the energy storage system when the electricity price is lower and supplies the loads by discharging the battery when the electricity price is higher in the market.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref. No.** & **Contribution** & **Techniques** & **Remarks** \\ \hline \multirow{2}{*}{[51]} & \multirow{2}{*}{Offering strategy was developed for VPP in unified electricity market.} & Stochastic adaptive robust optimization method & The uncertainty of wind power production and market prices was modeled as decision-making problem. \\ \hline \multirow{2}{*}{[52]} & \multirow{2}{*}{Optimal DA market offering model was developed for VPP.} & Four-level robust optimization method & Scenario-based DA price uncertainty, box-like uncertainty set for wind uncertainty and two types of VPP operators, RA (risk-averse) and PS (profit-seeking), were considered. \\ \hline \multirow{2}{*}{[53]} & \multirow{2}{*}{Optimal bidding strategy was presented for profit maximization of commercial VPP.} & Three-stage stochastic bi-level optimization & The uncertainty was considered for real-time VPP-W production and VPP-HVAC consumption. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._Generators used in the VPP are based on renewable energy sources such as PV generation and wind turbines. Due to the intermittent nature of renewable generation, the output power is unpredictable. Therefore, the bids and the grid requirements cannot be met by renewable generation. The aggregation of the power generation resources in the VPP should maintain the network security. Each element of the VPP is tied up with an energy management system (EMS). The power flow between DERs is also controlled by an EMS.  
The data from controllable loads, all generating units and storage units connected to the VPP are collected by the EMS. Then, the EMS sends the control signals to each element of the VPP according to the available data. With a bidirectional communication system, the EMS can receive the present status and send a control signal to each unit. Three operating modes exist for the EMS, such as the grid export mode, the grid import mode and the no power exchange mode. The excess power generated from the VPP is fed back to the grid in the grid export mode. The power deficiency of the VPP is met by the grid in the grid import mode.  
The controllability and visibility of DERs have been limited to the consumers as they need to be connected through the internet [2]. Now, with the VPP concept, all the DERs are controllable and viable to a wide range of customers, ranging from small to medium renewable sources in the electricity market. The schematic diagram of the VPP is shown in Figure 1.
## 1 Introduction
### Directions of Research Work
Mobility can be further increased by introducing a wireless communication module into the sensor. It has been shown that the obtained electricity allows the sensor to work and suffices to meet the needs of the wireless communication module [36]. Apart from the research directions mentioned above, currently, the most widespread research is in the propagation and damping of vibrations accompanying rail transport, dynamic compaction, explosions, and pile-driving foundations [22]. The vibration monitoring system presented in this publication combines elements from different suppliers to work together smoothly and provide reliable measurement data. A significant difference from commercial systems available on the international market is the flexible structure of the measurement system, adapted to the requirements of the [37], with the possibility of easy adaptation to other standard requirements. International corporations producing vibration-recording systems have no business interest in creating systems that qualify vibration values under locally applicable standards. Introducing an international standard in a small country is easier than getting support from an international hardware and software manufacturer for the local standard. The data presentation has two levels--system administrator- and user-level access to measurement data. The obtained measurements instantly provide the user with information on the vibration classification (SWD range) and its potential impact on the structure (Section 3). Remote access with limited user privileges is provided. This type of data access is rare in commercial solutions. Commercial research systems are typically designed to be operated by qualified employees with extensive permissions. The cost of the presented, dedicated monitoring system (consisting of sensors, a data acquisition card, and software) is much lower than the cost of a closed system provided by recognised suppliers of this type of solution (e.g., Bruel and Kjaer, Svantek).
###### Abstract  
This paper presents an analytical study of the bit error rate (BER) and signal-to-noise ratio (SNR) performance in simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) relaying systems over Rayleigh fading channels. A power-splitting (PS) protocol is employed at the energy-constrained relay to divide the received signal for concurrent energy harvesting and information processing. Closed-form and asymptotic BER expressions are derived based on exact and bounded moment-generating functions (MGFs), offering insights into how the SNR balance between the source-relay (SR) and relay-destination (RD) links influences system performance. An asymptotic BER expression further reveals that a SWIPT AF relay system can be interpreted as a generalized AF relaying model, sharing the same diversity order as conventional AF systems. Based on this interpretation, an optimization method for the PS factor is proposed, effectively reducing the BER by reinforcing the weaker link. Simulation results confirm the tightness of the derived expressions and the effectiveness of the optimization strategy. Moreover, the analytical framework is extended to multiple SWIPT relaying systems, where multiple relays operate with individually optimized PS ratios. For such configurations, approximations for the system BER, outage probability, and channel capacity are derived and validated. Results demonstrate that increasing the number of relays significantly improves system performance, and the proposed analysis accurately captures these performance gains under varying channel conditions.  
BER; SWIPT; AF relay; PS; MGF +
Footnote †: journal:
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 4 Discussion and Directions  
The proposed DDPG-MPCC, designed to boost MPCC performance in dynamic network scenarios, integrates a deterministic policy network and Q-network. This reinforcement learning agent demonstrates superiority over MPCC, excelling in adaptive learning, handling complex state spaces, providing enhanced stability, balancing exploration-exploitation, and optimizing policies. In this section, we will discuss how our experimental results reveal improved utility and reward over MIs, showcasing its potential for dynamic network optimization. However, effective real-world implementation requires careful consideration of reinforcement learning parameters and extensive further research with thorough evaluations.  
Figure 8: Evolution of the average reward (\(R_{t}\)) of the trained DDPG over the number of MIs, while DDPG-MPCC and MPCC for utility maximization as demonstrated in Figure 6.  
Figure 7: Evolution of the average utilities of MPCC [5] and DDPG-MPCC over the number of MI (network scenario c, compared to Figure 6).