esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Replace the Backbone Network Convolution  
The standard convolution operation is a core component in constructing convolutional neural networks (CNNs). It effectively extracts feature information from images through sliding windows and parameter sharing, overcoming the inherent limitations of fully connected layers in terms of parameters and computational efficiency. However, this operation is also accompanied by issues such as large model parameters and high computational costs. The spatial attention mechanism, as an important attention technology, focuses on the spatial dimensions of images, namely the interrelationships between pixels. Through training, the model can learn how to assign varying weights to different regions within the image, effectively prioritizing and focusing on the key information. The combination of this mechanism with standard convolution enables more efficient extraction and processing of image information in CNNs. However, traditional spatial attention mechanisms often fail to fully consider the spatial features of the entire receptive field. Therefore, when dealing with large convolutional kernels (such as 3 \(\times\) 3 convolutions), the advantages of parameter sharing are not fully leveraged, which somewhat limits their effectiveness.  
Receptive-Field Attention convolutional operation (RFAConv) [18] focuses on the spatial features information of the receptive field and addresses the issue of parameter sharing in convolutional kernels by introducing a receptive field attention mechanism.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **Params 10\({}^{6}\)** \\ \hline YOLOv8n & 93.5 & 64.3 & 3.0 \\ Add a Small Target Layer & 94.7 & 65.5 & 2.9 \\ Improve the Small Target Layer & 94.4 & 65.2 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Results of the Improved Small Target Layer.  
RFAConv ensures minimal computational overhead and parameter count, resulting in significant improvements in detection performance. Coordinate Attention (CA) is a mechanism that incorporates spatial location information into channel attention. By introducing CA's spatial attention mechanism into the spatial features [19] of the receptive field, we obtain Receptive-Field Coordinate Attention (RFCA). The framework of the network is attractively portrayed in Figure 3, offering a clear visual representation. By matching the spatial attention of the receptive field's spatial features with convolution, we generate the Receptive-Field Coordinate Attention convolutional operation (RFCACConv) to replace standard convolution, fully resolving the issue of convolutional parameter sharing. At the same time, it considers long-range information to some extent, enhancing convolutional performance. In the present study, RFCACConv is utilized to replace some of the standard convolutions in the backbone network, resulting in increased model performance.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Experimental Results
#### 6.1.1 Test A  
Figure 6 shows the user interface (UI) of this system. The current load is 976 W. In the previous step (\(t\)-1), the predictor forecasted the next load to be 947 W (yellow point), i.e., 479 W. Therefore, according to the results of the proposed pattern matching-based anomaly detector, both the real load of 976 W and the prediction result of 947 W are associated with pattern 1. Therefore, the power usage was normal. Moreover, a moving window was used to select the historical load data over the previous 24 h and update the predictor; the prediction indicated that the load at the next step would be 967 W. The prediction results first be used to check the remaining power of the battery to decide if it is necessary to shut off the power of some households. Then, it can also serve as a reference for the next detection step.  
Figure 5: Wiring diagram of the third village (from Google Maps). The third village is a rural area in Tanzania with more than 50 households and nearly 150 occupants.  
Figure 6: The UI of the proposed method—test A.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
###### Abstract  
Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localization task uses smooth-L1 loss with IoU to estimate the object's location, and the classification task identifies the object/class category inside each 3D bounding box. Localization suffers a performance gap in cases where the predicted and ground truth boxes overlap significantly less or do not overlap, indicating the boxes are far away, and in scenarios where the boxes are inclusive. Existing axis-aligned IoU losses suffer performance drop in cases of rotated 3D bounding boxes. This research addresses the shortcomings in bounding box regression problems of 3D object detection by introducing an Improved Intersection Over Union (IIoU) loss. The proposed loss function's performance is experimented on LiDAR-based and Camera-LiDAR-based fusion methods using the KITTI dataset.  
3D; 2D; IoU; neural network; small objects; KITTI; object detection; point-cloud +
Footnote †: journal: _Future Internet  
0
Footnote 0: Copyright © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# IoT-Enabled Smart Cities: Evolution and Outlook
## 4 Open Challenges for IoT-Enabled Smart Cities
### Evolve towards Flexible IoT Platforms  
Currently, each smart city looks different with respect to infrastructure deployed, applications available, but often also the IoT platforms utilized. This makes it difficult to reuse anything developed for one city in another and constitutes an impediment with respect to creating a smart city market. Furthermore, it is not economically viable to develop custom-made platforms for each city [91].  
As described in Section 3.3, a number of vendors have started to offer IoT platforms for smart cities, typically based on what has been developed for a pilot city. As a result, these platforms are often not a perfect fit for other cities and cities are also afraid of vendor lock-in, i.e., being restricted to this one vendor for any future additions regarding infrastructures and applications as everything has to be customized to the vendor's platform.  
The direction to go is to move from monolithic proprietary platforms to modular platforms based on standards (e.g., NGSI-LD [73], oneM2M [71], OMA Lightweight M2M [72]). It is especially important to agree on the interfaces and the modelling of information which OASC is defining as minimum interoperability mechanisms. Furthermore, costs can be reduced by relying on common open-source components as a basis, e.g., as provided by FIWARE. These can be complemented by proprietary vendor components. Nevertheless, the use of standards at the interface level prevents vendor lock-in.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 1 Introduction
### State of the Art
#### 1.2.6 Comparison and Research Gap
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method/Approach** & **Type** & **Precision** & **Comput. Cost** & **Scalability** & **Input Requirement** \\ \hline Greedy Reduction [8] & Heuristic & Medium & Low & High & Source \\ Delta Debugging [9] & Diff Analysis + Fault localization & High & Medium & Low & Source + Outcome \\ Test Slicing HSFal [10] & Static Slicing + Fault localization & Medium & High & Low & Source + Spectrum \\ Dynamic Slicing [11] & Dynamic Slicing & High & High & Medium & Execution Trace \\ Genetic Algorithms [12] & Search-Based & Medium & Medium-High & Medium & Coverage \\ ILP-Based TSM [13; 14] & Optimization & High & High & Medium & Dependency Graph \\ Doc2 Vec + HDBSCAN [15] & Clustering & Medium & Medium & Medium & Requirements \\ RL Prioritization [16] & RL-Based & Medium & Medium & Medium & User Logs \\ RLFUZZ [18] & Fuzzing + DDPG & Low & High & High & Execution Trace \\ XCSF + Replay Buffer [20] & RL-based Classifier & Medium & Medium-High & Medium & Logs + Coverage \\ RL + DGC [21] & RL for TSP & Medium & Medium & Medium & Coverage + History \\ LSTM [24; 25] & Deep Learning & Medium & High & Low & Historical Logs \\ ANFIS [26] & Neural Network & Medium & Medium & Low & Meta Data \\ SBLA-AdaBoost-CNN [27] & DL + Boosting & High & High & Medium & Execution History \\ LoM-Score [28] & Heuristic + Slicing & Medium & Medium & Medium & Change Impact + Slice \\ Our Method & RL-based + & High & Medium & Medium & Coverage + API Trace \\  & Fine-Grained TSM & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the capabilities of classical, machine learning, and hybrid methods for TSO across dimensions such as precision, computational cost, scalability, and required input artifacts. This analysis highlights that few existing techniques simultaneously achieve high precision, reasonable computational cost, and applicability in low-information environments. This defines the research gap our work addresses: the need for a reinforcement learning-based method capable of synthesizing compact and effective test cases without relying on formal specifications, and capable of scaling to open-source C++ libraries with diverse API structures and incomplete documentation.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
###### Abstract  
Precise Point Positioning (PPP) technique enables stand-alone receivers to obtain cm-level positioning accuracy. Observations from multi-GNSS systems can augment users with improved positioning accuracy, reliability and availability. In this paper, we present and evaluate the GPS/BDS combined PPP models, including the traditional model and a simplified model, where the inter-system bias (ISB) is treated in different way. To evaluate the performance of combined GPS/BDS PPP, kinematic and static PPP positions are compared to the IGS daily estimates, where 1 month GPS/BDS data of 11 IGS Multi-GNSS Experiment (MGEX) stations are used. The results indicate apparent improvement of GPS/BDS combined PPP solutions in both static and kinematic cases, where much smaller standard deviations are presented in the magnitude distribution of coordinates RMS statistics. Comparisons between the traditional and simplified combined PPP models show no difference in coordinate estimations, and the inter system biases between the GPS/BDS system are assimilated into receiver clock, ambiguities and pseudo-range residuals accordingly.  
multi-GNSS; BDS; precise point positioning; MGEX; ISB 1  
[EMAIL_ADDRESS]; Tel.: +86-21-347-5223
# Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
###### Abstract  
Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results.  
c pacs: 1
## 1 Introduction
(2013) stated that cumulative evaporation decreased by 5.4-12.0, and the field capacity (FC) increased by 7.2-15.9% at the surface layer of the column, and the results demonstrated that applying biochar to the soil at rates of 5, 10, 15, and 20 g kg\({}^{-1}\) resulted in an obvious effect on soil water retention capacity, which increased by 8.9%, 17.6%, 28.1%, and 30.9%, respectively. In a recent study on the use of macro-biochar and compost by [13], results showed that the use of biochar, compost, and the biochar-compost combination affected the pattern of soil moisture distribution. Furthermore, applying date palm waste biochar at rates of 1%, 2%, 3%, and 4% to the top 10 cm layer significantly decreased cumulative evaporation by 5.8%, 10.8%, 12.8%, and 16.1%, respectively. [PERSON] et al. [18] reported on the influence of the different dosages of biochar on a sand-clay soil composition and observed that the application of the biochar enhanced the pore spaces, field capacity, the greatest amount of water available, stability ratio, and saturated water content in the soil, whereas [PERSON] et al. [19] could not find any evidence of hardwood biochar having significant effects on saturated hydraulic conductivity. Considered a vital food source and the principal crop in terms of efficiency and area cultivated, the date palm is a tree of enormous importance in the Kingdom of Saudi Arabia. A total of 32 million date palm trees were grown in the Kingdom's agricultural region in 2018, producing an estimated 1,539,775 tons of produce [20]. The effect of employing waste date palm biochar of nanoparticle size on the sandy soil properties was not well understood, and very little research has been conducted on the preparation of nanoparticles from date palm to be used in improving sandy soil physical properties. As a result, the main aim of this study was to determine the influence of date palm waste biochar with different particle sizes (macro and nano) and the position of biochar on selected hydro-physical properties of the sandy soil (_Typic Torripsamments_).
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 2 The Mathematical Model and Characteristics of the B1C/L1C Signal  
China, the United States and the European Union proposed a new implementation of MBOC-modulated signals on civil interoperability signals, realizing that BDS B1C signals are interoperable at the user level with GPS L1C and Galileo E1 OS. These three satellite navigation systems now have broadcast MBOC (6,1,1/11) signals. The MBOC signal modulated on the GNSS system, denoted as MBOC (6,1,1/11), is specified by its power spectral density (PSD), which is a mixture of the PSDs of \(BOC_{\sin}(1,1)\) and \(BOC_{\sin}(6,1)\) with an appropriate weighting factor \(1/11\). \(G_{BOC_{s}(m,n)}(f)\) is the normalized PSD of a sine-phased BOC spreading modulation, which is defined by Equation (1) as follows [35]:  
\[G_{BOC_{\sin}(f_{y},f_{c})}(f)=\frac{\sin^{2}(\pi fT_{C})\sin^{2}(\pi fT_{S})}{T_ {C}{\left[\pi f\cos(\pi fT_{S})\right]}^{2}} \tag{1}\]  
where \(T_{C}\) stands for the code period and \(T_{S}\) stands for the subcarrier period. Considering the definition of the MBOC (6,1,1/11) signal, the normalized Power Spectral Density (PSD) (pilot and data channel) can be derived by Equation (2) as follows:  
\[G_{MBOC}(f)=\frac{10\cdot\sin^{2}(\pi fT_{C})\sin^{2}(\pi f^{T_{C}/2})}{11\cdot T _{C}{\left[\pi f\cos(\pi f^{T_{C}/2})\right]}^{2}}+\frac{\sin^{2}(\pi fT_{C}) \sin^{2}(\pi f^{T_{C}/12})}{11\cdot T_{C}{\left[\pi f\cos(\pi f^{T_{C}/12}) \right]}^{2}} \tag{2}\]  
The power spectrum of the MBOC (6,1,1/11) modulation signal is obtained by mathematical model simulation. Figure 1 shows that the idea behind MBOC (6,1,1/11) modulation is to increase the power of the higher-frequency (6 MHz) on the BOC (1,1).  
The LIC signal is also flying on the QZSS satellite, which has the exact same format as the GPS-L1C signal, except for the QZS1 satellite. Both the data and pilot of Japan's QZS1 satellite are loaded with BOC (1,1) signals. Since the MBOC (6,1,1/11) definition is only in the frequency domain, the BOC (n, n) component and BOC (m, n) component can be combined in any multiplexing way, as long as the final PSD meets the limitation of express Equation (2). GPS/QZSS and BDS have designed three different schemes.  
The implementation schemes of \"BOC (1,1) + QMBOC (6,1,4/33)\" and \"BOC (1,1) + TM-BOC (6,1,4/33)\" were designed for B1C and L1C. According to the power distribution factor and phase relationship in Table 1, the time domain expression can be deduced in Equations (3) and (4) as follows:  
\[S_{B1C}(t)=\frac{1}{2}D(t)\cdot C_{B1 Cd}(t)\cdot sub_{B1 Cd}(t)\cdot\cos(2\pi f _{B1}t)-\frac{\sqrt{3}}{2}C_{\rm sec}(t)\cdot C_{B1 Cp}(t)\cdot sub_{B1 Cp}(t) \cdot\sin(2\pi f_{B1}t) \tag{3}\]  
\[S_{L1C}(t)=\frac{1}{2}D(t)\cdot C_{L1 Cd}(t)\cdot sub_{L1 Cd}(t)\cdot\cos(2\pi f _{L1}t)+\frac{\sqrt{3}}{2}C_{\rm sec}(t)\cdot C_{L1 Cd}(t)\cdot sub_{L1 Cp}(t) \cdot\cos(2\pi f_{L1}t) \tag{4}\]  
Figure 1: Normalized power spectral density of the MBOC (6,1,1/11) signal.
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
###### Abstract  
Raman spectral imaging is an effective method to analyze and evaluate the chemical composition and structure of a sample, and has many applications for food safety and quality research. This study developed a 1064 nm dispersive Raman spectral imaging system for surface and subsurface analysis of food samples. A 1064 nm laser module is used for sample excitation. A bifurcated optical fiber coupled with Raman probe is used to focus excitation laser on the sample and carry scattering signal to the spectrograph. A high throughput volume phase grating disperses the incoming Raman signal. A 512 pixels Indium-Gallium-Arsenide (InGaAs) detector receives the dispersed light signal. A motorized positioning table moves the sample in two-axis directions, accumulating hyperspectral image of the sample by the point-scan method. An interface software was developed in-house for parameterization, data acquisition, and data transfer. The system was spectrally calibrated using naphthalene and polystyrene. It has the Raman shift range of 142 to 1820 cm\({}^{-1}\), the spectral resolution of 12 cm\({}^{-1}\) at full width half maximum (FWHM). The spatial resolution of the system was evaluated using a standard resolution glass test chart. It has the spatial resolution of 0.1 mm. The application of the system was demonstrated by surface and subsurface detection of metanil yellow contamination in turmeric powder. Results indicate that the 1064 nm dispersive Raman spectral imaging system is a useful tool for food safety and quality evaluation.  
Raman spectral imaging; 1064 nm; spatially offset Raman spectroscopy; food safety Article  
A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 2 Virtual Power Plant (VPP)
### Aggregator  
The aggregator is responsible for market operations that deal with portfolio aggregations [10]. The VPP aggregator communicates directly in real-time with the CVPP and TVPP to provide a profile and forecast of energy production from diverse and geographically distributed sources between distribution and transmission networks [13]. The process includes many details internally, such as prediction algorithms. Its main feature is error reduction through the knowledge of uncertainties, since for a trader interested in selling energy, this experience allows accurate energy forecasts to be traded within the stipulated trading period. It can group numbers of small or large generators to generate economies of scale in market access. It is also responsible for balancing a VPP's entire system as a power supplier and buyer of locally generated electricity [20].
## 4 MAC Protocol for Scheduling Concurrent Transmissions
### Validating a Scheduled Transmission  
Suppose that a node (called \"free transmitter\") wins the channel and is transmitting to a one-hop neighbor (called \"free receiver\"). When a node near the free transmitter identifies itself as an exposed terminal, it can validate the feasibility of its concurrent transmission with a probabilistic approach based on location information and the shadowing channel model. We call such a node a \"scheduled transmitter\" and its target receiver \"scheduled receiver\" if the concurrent transmission is successfully scheduled.  
When testing the feasibility of concurrent transmission, we focus on the case of two (free and scheduled) transmitters and two (free and scheduled) receivers. This simplification can be justified by the fact that the chance of having multiple interfering transmitters in the neighborhood of a tagged receiver is usually small in many wireless networks. When the path loss exponent is large, usually the interference power from the nearest one dominates the overall interference power. If an identified exposed node has a frame to transmit, it will evaluate the following four probabilities:  
* the success probability of the free DATA frame;
* the success probability of the scheduled DATA frame;
* the success probability of the free ACK frame;  
Figure 3: The IEEE 802.11 PHY frame structure when DSSS is used in PHY.  
- the success probability of the scheduled ACK frame.  
Under log-normal shadowing channels and with location information available, each of the probabilities can be calculated using (11). Table 1 shows the validation procedure validate_schdTx(), where \(P_{th}\) is a prescribed threshold. In order to schedule the concurrent transmission, each of the above probabilities should be greater than \(P_{th}\). As \(P_{th}\) is increased, there will be fewer scheduled transmissions allowed.
## 5 Performance Evaluation  
Network Simulator 3.26 was used to evaluate the proposed routing protocol. The UAVs started from the southwest corner of the reconnaissance area. All experiments were repeated 30 times to obtain reasonable statistical confidence. The main simulation parameters are presented in Table 2. Moreover, we compared the performance of LECAR with some existing routing protocols: Spray and Wait [12] as a DTN-based routing protocol and LAROD-LoDIS [29] and GPSR [30] as hybrid routing protocols. We also implemented a modified version of GPSR so that the UAVs can store the packets in the buffer until they locate a suitable custodian. We call this protocol GPSR-Q. In addition, we implemented a modified version of LECAR and called location estimation-based routing (LER). The LER has all the functionality of LECAR, except it does not consider the buffer occupancy for selecting the custodian.  
We compared LECAR with the considered routing protocols in terms of the packet delivery ratio, hop count per packet, number of copies per packet, number of transmissions per packet, delay per packet, total overhead, and total consumed energy. For all cases, we compared two buffer sizes: 25 and 50 MB. Each UAV generated 1 MB of data packets per minute during the experiment.