esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Study of Test Case and Test Framework Presence in Public Projects on GitHub
## 1 Introduction
Mentioned issue of detecting test cases in a code could also be related to the correlation of the number of the word \"test\" and other project attributes, such as activity or popularity. This paper answers the following questions:  
**RQ 1**. : _Is there a typical number of the word \"test\" occurrences across a project that is observed in a large number of projects? If so, what is the reason behind such specific number?_  
**RQ 2**. : _Is occurrence of the word \"test\" related to the creation date, date of last commit, number of commits, or number of watchers of a project?_  
As a part of our previous work, we also developed an automatic identification approach for test cases with 97% accuracy. This allows us to extend the analysis to all GitHub projects from May 2019 with Java as a primary language. To find out how many projects use tests that could be used as possible sources of information about the production code, we will use the mentioned automatic identification approach in this paper. We need to investigate how many tests and testing frameworks are used and where developers place important information useful for program comprehension. Therefore, the aim of this study is not to analyze how tests change during the project life cycle, but whether they are present in the projects, how to find the projects that contain tests, and find how tests describe the production code given the framework used. In this paper, we also answer the following questions:  
**RQ 3**. : _What is the ratio of open-source projects that contain at least one test case?_  
**RQ 4**. : _What is the general correlation between the number of occurrences of the word \"test\" in all files' content of a project and the number of actual test cases in the project?_  
**RQ 5**. : _Which test frameworks and libraries are used and how are they combined in projects?_  
In the following sections, we describe the data gathering process, results, and threats to validity.
## 2 Methods
### Related Work
#### 2.1.3 Attention Mechanism  
[PERSON] et al. [18] introduced the \"Squeeze-and-Excitation\" block, a noteworthy architectural unit, which is extensively used in the classification of hyperspectral images through the channel attention mechanism. The channel attention mechanism is extensively employed in HSIs. This SE block effectively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels. Ref. [18] redistributes feature weights through three operations. Firstly, the Squeeze process compresses features across the spatial dimension, using GAP to transform each two-dimensional feature channel into a single value that has a global receptive field. Further, the Exception operation generates weights for each feature channel through parameters, where parameters are learned to explicitly model the correlation between feature channels. Finally, the Reweight operation uses multiplication to weigh each channel of the previous features and keep the dimension the same. Ref. [15] introduces a new visual attention-driven technique for the HSI classification.  
The transformer for HSI classification is introduced by SpectralFormer [17]; the author first adopts purely transformers, which include two main modules (GSE and CAF) that improve the fine-grained spectral difference capture ability. SpectralFormer is suitable for both pixel-wise hyperspectral image classification and spatial-spectral classification. Ref. [28] proposed HSI-BERT, which consists of a bidirectional encoder representation used to obtain a global receptive field. Ref. [29] proposed the SSFTT method, which combines spectral-spatial feature extraction, Gaussian-weighted feature tokenization, and transformer encoding to efficiently capture spectral-spatial and high-level semantic features. Ref. [30] introduced a Multimodal Fusion Transformer (MFT) network with multihead cross patch attention (mCrossPA) for hyperspectral image classification, leveraging complementary data sources for improved generalization.
# On Transient Stability Problems in DC Microgrids
## 2 Transient Stability Problems in AC and DC Microgrids
### Transient Stability Problems in DC Microgrids  
Figure 2b shows a simplified model of the DC microgrid and grid-tied DC generator, where the two voltage sources model the DC generator and DC grid, respectively. Specifically, \(v_{\rm c}\) and \(v_{\rm g}\) represent the converter and grid voltages, respectively. \(i_{\rm c}\) and \(R_{\rm c}\) denote the converter current and line resistance, respectively. It is straightforward to derive the injected power from Figure 3b as  
\[p_{\rm c}=\frac{v_{\rm c}(v_{\rm c}-v_{\rm g})}{R_{\rm c}}, \tag{2}\]  
where \(p_{\rm c}\) designates the power injected from the converter to the DC grid.  
Figure 3: _Cont._  
Figure 2: Schematics of AC and DC microgrids for transient stability analysis.  
In DC microgrids, we control the converter voltage \(v_{\rm c}\) for power regulation and require a positive relationship between \(v_{\rm c}\) and \(p_{\rm c}\). Referring to Figure 3b and (2), we can observe that a positive relationship always holds valid when the converter voltage is larger than the grid voltage, i.e., \(v_{\rm c}\)\(>v_{\rm g}\). In this case, transient instabilities will not occur. However, when the converter absorbs power beyond a certain extent that causes an excessive voltage drop, a decrease of \(v_{\rm c}\) will no longer increase power absorption, giving rise to transient stability problems in DC microgrids. By taking the derivative of (2) and letting it equal 0, we can derive the critical voltage \(v_{\rm c}\) = 0.5 \(v_{\rm g}\) and minimum power injection \(p_{\rm c,min}=-v_{\rm g}^{2}/(4R_{\rm c})\).  
The above analysis is applicable to both the steady and quasi-steady state analyses of DC microgrids. Notably, control parameters play a decisive role during system dynamics, as will be discussed.
## 5 Future Directions  
Industrial robot control is entering a period of convergence in which heterogeneous enabling technologies--Artificial Intelligence, digital twins, ultra-reliable low-latency 5G/URLLC connectivity, and emerging quantum/quantum-inspired computation--inject with next-wave control paradigms (hybrid AI, edge/distributed learning, bio-inspired/soft control, and cyber-resilience frameworks) to extend scalability, autonomy, and sustainability. This interplay is already evidenced in recent DT reconfiguration studies showing measurable responsiveness gains under disturbance [112], human-centric DT ecosystems piloted in Industry 5.0 contexts [113], maturing 5G non-public (industrial) networks for time-critical control [133], quantum-inspired controllers demonstrating precision/energy advantages in articulated arms [134], and URLLC/edge-AI integration benchmarks targeting <10 ms end-to-end control latency for IIoT workloads [135].  
Figure 6 situates these interactions in a strategic \"technology convergence\" map: top-tier digital enablers (AI, quantum computing, digital twins, 5G connectivity) feed an intermediate layer of emerging control stacks (hybrid AI control, bio-inspired control, edge computing, cybersecurity frameworks), which in turn drive next-wave industrial applications (e.g., aerospace fastener installation, high-density electronics, medical and rehabilitation robotics) and ultimately support future industrial ecosystems that are resilient, sustainable, and human-centric--aligned with Industry 5.0 policy roadmaps [113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Challenge/Technology** & **AI/DRL** & **Digital Twins** & **5G/URLLC** & \
\begin{tabular}{c} **Quantum-** \\ **Inspired** \\ **In**y** \\ \end{tabular} &
\begin{tabular}{c} **Bio-Inspired** \\ **Control** \\ \end{tabular} \\ \hline Scalability & \(\blacktriangle\) (compute load) & & & & \(\blacktriangle\) (prototype HPC) & \(\blacktriangle\) (cost) \\ \hline Energy Efficiency & \(\blacktriangle\) (high demand) & – & & & & \\ \hline Safety & \(\blacktriangledown\) & & & & – & \\ \hline Cybersecurity & \(\blacktriangle\) (needs shielding) & & & \(\blacktriangle\) (protocol gaps) & & – \\ \hline Ethics & – & – & – & – & & \\ \hline AM Integration & \(\blacktriangledown\) & & & \(\blacktriangle\) (limited deployment) & \(\blacktriangle\) (integration issues) \\ \hline \hline \end{tabular}  
* Symbols: \(\blacktriangledown\) = Mature/industry-validated evidence; \(\blacktriangle\) = limited/prototype or constrained by cost, compute, or standardization gaps; \(-\) = unproven or insufficient evidence. Representative references: Scalability [20; 30; 31; 40; 45; 72]; Energy [119; 63; 37; 48; 36]; Safety [46; 47; 48; 50; 120]; Cybersecurity [21; 22; 22; 24; 26; 34; 64; 95]; Ethics [1; 2; 114; 116; 117]; AM Integration [27; 28; 29; 30; 31; 32].  
\end{table}
Table 3: Symbolic mapping of robotics challenges (rows) versus emerging technologies (columns).
## 5 Conclusions  
This study proposed the MIMO C-OOK scheme for the mobility environment using a Deep Learning network. With deep learning algorithms, we applied a scheme not only for LED detection and tracking but also for decoding data.  
Firstly, we employed a deep learning neural network to detect multi-LEDs. With the rolling-shutter effect, the LED displayed images as black and white strips, which complicated the clear detection of LEDs compared with RoI detection. Besides that, the SNR measurement with different distances was measured at different exposure times. Therefore, we were able to analyze the relationship among the following three parameters: communication distance, exposure time, and Signal to Noise values.  
Furthermore, a deep learning neural network for data decoding will be helpful for long communication distances, particularly concerning the mobility effect. The measurement of Bit Error Rate of the MIMO C-OOK scheme using a Deep Learning decoder with different distances was proposed compared with the Matched filter decoder. Based on our proposed approach, we found that the BER can be reduced in the mobility environment.  
The following supporting information can be downloaded at: [[https://youtu.be/pljkRXj2](https://youtu.be/pljkRXj2) Ulk]([https://youtu.be/pljkRXj2](https://youtu.be/pljkRXj2) Ulk). The supplementary material video shows the implementation of our proposal scheme at the distance of 2 m.  
All authors contributed to this paper: [PERSON] proposed the idea and implemented the methodology; [PERSON] reviewed and edited the paper; [PERSON] performed all experiments; [PERSON] supervised the work and provided funding support. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-0-01396) supervised by the IITP (Institute for Information & communications Technology Promotion) and the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No.2022R1A2C1007884).  
The authors declare no conflict of interest.
## 5 Conclusions and Future Works  
IoT is the primary technology that powers the data collection and intelligent control systems in intelligent buildings. IoT devices have been integrated more and more recently in almost every smart environment in general and intelligent building environments in particular. The integration of these devices not only provides ease but also automates the process and reduces human effort. However, it poses some serious issues concerning security that directly affect energy optimization and user comfort. The energy optimization of intelligent buildings is an important part of green and sustainable buildings. Therefore, in this paper, we worked on the energy optimization of intelligent buildings by taking the security of the IoT devices into account. We, first of all, defined various variables concerned with the security of IoT devices and energy optimization of intelligent buildings. With the help of IoT gadgets such as sensors and other IoT control systems, we collected data on these variables. After that, we applied one of the multi-objective evolutionary algorithms called the niche genetic algorithm for security insurance and energy optimization. This paper not only focused on the security of IoT devices and energy optimization but also on the comfort of occupants of intelligent buildings. We proposed a state-of-the-art technique  
Figure 12: Total power utilization diagram.  
to enhance life comfortability inside intelligent buildings by consuming the least amount of energy possible. Our presented method makes a tradeoff between energy-saving optimal control technology and IoT security of intelligent buildings, and this study discovered an optimal solution for the problem of resource waste in intelligent buildings. Compared with traditional energy-saving design methods, our proposed method significantly improves the IoT devices' security and tradeoff between energy consumption and residence comfortability. The proposed security solutions will need to be improved and expanded in the future to keep up with the development of IoT threats and vulnerabilities in intelligent buildings. Additionally, by investigating adaptive control techniques and using cutting-edge technologies such as edge computing and 5G networks, researchers can go further into optimizing energy use. As part of ongoing efforts, substantial real-world case studies and pilots can be carried out to verify the viability of the suggested approaches in various intelligent building situations and to gather useful information for broader application.  
Conceptualization, R.L. and L.H.; Software, L.H.; Formal analysis, Y.X.; Writing-original draft, L.H.; Writing-review & editing, Y.X.; Project administration, Y.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the project FJ2020C051 from Social Science Planning Program of Fujian Province in 2020.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
The authors declare no conflict of interest.
## 2 Peripheral Ultrasonic Neuromodulation--Technical Specifications
### Biological Preparations to Assess Peripheral US Neuromodulation  
Peripheral US neuromodulation has been studied both in humans and in preclinical animal models. Human studies benefit from the direct verbal feedback from the subjects, while preclinical animal studies allow mechanistic investigations in vivo, as well as in vitro or ex vivo with reduced and isolated systems. Studies that implement whole-animal in vivo preparations require to maintain anesthesia, the level of which can directly affect the detection of US neuromodulation [20]. In addition, the interpretation of the results from in vivo studies can be confounded by indirect effects of US on surrounding muscles, blood vessels, and immune cells. In vitro cultures of dissociated sensory neurons from the DRG have also been used as a model to study the effects of US neuromodulation [69]. Although the sensory cell somata in the DRG differ significantly from the nerve axons and endings in fundamental electrophysiological properties [59], neurites grown in cultured DRG have similar dimensions as axons and nerve endings and thus could potentially be an adequate model for studying peripheral neuromodulation. Nonetheless, cultured neurites in the absence of Schwann cells lack clustering of sodium channels [70] and thus differ significantly from the bundled axons in peripheral nerves in the physiological situation. In contrast, ex vivo studies on isolated peripheral nerves offer a physiologically relevant model for the direct assessment of US neuromodulatory effects, while avoiding the potential confounding factors of in vivo or in vitro studies. It is worth mentioning that many studies described isolated peripheral nerves as in vitro preparations, but they are considered as ex vivo preparations in this review to distinguish them from in vitro cultured DRG preparations.
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 3 Factors Affecting Dust Generations
### Effect of Indoor Temperature and Relative Humidity  
Temperature and relative humidity (RH) are inversely proportional to each other. Increased temperature decreases RH and is directly influenced by ventilation rates within poultry houses [70]. Temperature and RH change seasonally and depend on weather conditions and experimental house design. During the winter, ventilation rates are decreased, and heaters are turned on to make a room warm, reducing RH. A decrease in RH increases PM concentration. However, ventilation rates are increased during the summer season to bring cold air or moisture from outside (cooling pad). The moisture from outside makes RH higher inside the house and decreases PM concentration by making heavy PM settle down. According to [PERSON] et al. (2017), PM\({}_{2.5}\) and PM\({}_{10}\) concentrations depend on RH due to ambient air [71]. Similarly, houses or rooms attached more to the outside environment possess higher RH due to individual room effects. [PERSON] et al. (2020) tested the effect of different temperatures (21.1, 23.8, 26.4, and 29.2 \({}^{\circ}\)C) and RH (49.5, 74.7, 78.8, and 80.0%) on PM concentrations and found that PM\({}_{1}\), PM\({}_{2.5}\), PM\({}_{10}\), and TSP were significantly lower in higher RH and lower temperature treatments because RH could affect litter moisture [64]. In addition, several experiments used liquid spray (oil or water) to reduce PM concentration by increasing LMC and RH [72; 73; 74; 24; 75; 76]. In addition, [PERSON] et al. (2022) found that RH directly affects LMC and varies between rooms and within rooms [77]. Higher LMC within rooms results in lower PM levels [78; 24]. That is why temperature and RH have a direct influence on PM levels.
## 4 Cause Estimation Algorithm
### Feature Extraction
#### 4.2.1 Features Significance  
To determine the significance of each feature of a scan response, first, we find the correlation between the scan response RTT and other features of the scan response. The correlation coefficient \(corr\) for all features in the scan response is calculated as in Equation (1), where \(N\) is the number of samples in the scan response, and \(x\) and \(y\) represent the individual sample points from the scan response RTT and other features, respectively. \(\Sigma x\) and \(\Sigma y\) are the sample mean of each feature (i.e., the mean sample of the scan response RTT and the mean sample of other features).  
\[corr=\frac{N\Sigma xy-(\Sigma x)(\Sigma y)}{\sqrt{[N\Sigma x^{2}-(\Sigma x)^{2 }][N\Sigma y^{2}-(\Sigma y)^{2}]}}. \tag{1}\]  
Each feature that is correlated to the scan response RTT is extracted from the scan response. For this, strongly positive or moderately positive correlated features are extracted.  
It is possible that a feature may have a high correlation coefficient but contribute less to the factor estimation; therefore, a process to determine the significance of each extracted feature is necessary. In order to determine the significance of each extracted feature when combined with the scan response RTT, statistical multiple linear regression is utilized. In this case, the coefficient of determination (R-squared (\(R^{2}\))) from the linear regression needs to be close to 1. If the \(R^{2}\) is close to 1, the features are considered significant to the factor cause estimation. Otherwise, they are considered as not significant.  
Furthermore, the mode of our factor cause estimation algorithm is divided into two, as shown in Figure 2: uncorrelated mode and correlated mode. In the uncorrelated mode, it is expected that the selected feature has a high/moderate positive correlation coefficient and \(R^{2}\) close to 0 (e.g., \(R^{2}\) = 0.18). In the correlated mode, however, it is expected that the selected feature has a high positive correlation coefficient and \(R^{2}\) close to 1 (e.g., \(R^{2}\) = 0.8). Based on these characteristics, the mode of our factor cause estimation is decided. If there are no correlated features, then the uncorrelated mode of the algorithm is used for the failure factor estimation. Additionally, in a situation where the algorithm mode is decided to be an uncorrelated mode, the extracted feature is optimized by calculating the K-nearest neighbor (KNN) [20]. The KNN of the selected feature (i.e., the average value of the distances calculated as in [4]) is combined with the scan response RTT before being passed into the estimation algorithm.
###### Abstract  
The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15%, an mAP of 51.46% and an inference time of 16.44 ms with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5 ms.  
vision system; object detection; fruit detection; machine learning; SSD benchmarking; robotics vision +
Footnote †: journal: Sensors  
0021217 May 2021
## 4 Quality-Aware Routing and Channel Assignment in Multi-Channel Multi-Gateway WOBAN
### Generating the Planar Subgraph of the Conflict Graph  
After constructing the conflict graph, we use _vertex deletion_ to get the planar subgraph of the conflict graph, which is described in Algorithm 1. We first check the planarity of the conflict graph \(G\) in linear time by using the Boyer and Myrvold planarity test [44]. If \(G\) is non-planar, we generate its planar subgraph of \(G\) as follows. The vertex with the highest degree is first removed from \(G\) and placed in _genetic-colored-list (GCL)_. Then, the planarity condition is checked again on the remaining graph (lines 3-6 in Algorithm 1). This vertex deletion process is repeated until the remaining graph becomes planar. The vertices from the remaining graph are stored in a separate list called _four-colored-list (FCL)_. On the other hand, GCL consists of the vertices that are removed from \(G\) to make it planar.  
Figure 4: Illustration of (**a**) a five node connectivity graph and (**b**) its corresponding conflict graph.  
```
1:GCL = FCL = NULL;
2:Sort \(v_{i}\in G\) in decreasing order of vertex degree;
3:while\(G\
eq\) PLANAR do
4:\(G=G\setminus v_{i}\), \(v_{i}\) is of maximum degree in \(G\); \(\triangleright\) Vertex with highest degree is removed from \(G\)
5:GCL = GCL \(\cup v_{i}\); \(\triangleright\) And placed in GCL
6:endwhile
7:FCL = \(G\); \(\triangleright\) Vertices of the planar graph \(G\) is stored in FCL
8:return GCL and FCL;
```  
**Algorithm 1** Function Vertex Deletion (Input graph \(G\)).