esa-sceva/satcom-chunk-collection huggingface dataset:
# Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development
## 3 Methods  
### Overview  
Figure 1 provides an overview of the proposed approach. The method takes the CCSD project as an input and predicts whether it will receive a solution. The output is a binary decision predicting the status (_successful_ (1) or _unsuccessful_ (0)) of the project. We collect the history data of CCSD projects from _TopCoder_ using REST API ([[https://tcapi.docs.apiary.io](https://tcapi.docs.apiary.io)]([https://tcapi.docs.apiary.io](https://tcapi.docs.apiary.io)), accessed on 15 March 2023). Each project information includes _Challenge Name_, _challenge type_, _challenge ID_, _detailed requirements_, _technology_, _prize_, _platforms_, and _Current Status_. The proposed approach works as follows:  
* First, it performs input formatting for BERT. Proper input formatting is essential for BERT to generate accurate and meaningful contextualized embeddings for classification. The input formatting process involves tokenizing the text input and adding special tokens to mark the beginning and end of the text. It also involves padding and truncating the input to a fixed length and creating attention masks to differentiate between padding and actual input.
* Second, it fine-tunes the BERT classifier for the project's success prediction task.
* Finally, it predicts the success of an upcoming software project using a fine-tuned BERT classifier.  
The following sections provide further details on the proposed approach.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 3 Materials and Methods  
C-band Sentinel-1 SAR, as all active microwave systems, is sensitive to different physical phenomena that occur in a given landscape. Over bare soils and sparsely vegetated fields, the parameter that governs the C-band backscatter value, in addition to soil roughness, is the soil water content due to its high dielectric constant at microwave frequencies [43; 55]. An increase in the soil's water content will increase the SAR backscatter value [40; 41]. However, when ice begins to form in the soil, the dielectric constants decreases to an extent which causes the C-band backscatter values to drop significantly [20; 23; 24; 32]. The backscatter response can also vary significantly with respect to soil and crop types [44; 45]. SAR backscatter can increase or decrease between two satellite acquisition dates due to the development of the vegetation cover [42; 56]. SAR backscatter is also sensitive to soil roughness, which can increase significantly due to field plowing. It is has been proven that an increase in soil roughness can also increase the backscatter [39; 57]. Based on these sensitivities, we propose a surface state detection algorithm that discriminates between frozen and unfrozen surface states. The algorithm is a variant of the threshold algorithm [16; 20; 32; 33; 34], combined with a variant of the moving average algorithm, which flags a surface as frozen or unfrozen based on the average of the previously acquired backscatter values [35; 36; 37; 38]. The algorithm developed in the present study is based on the difference between the S1 backscattering coefficient at current date '\(d_{i}\)' and a reference S1 backscattering coefficient calculated from S1 backscattering coefficients acquired in the n-days preceding the S1 acquisition at current date '\(d_{i}\)'. If the difference between the two backscatters (\(o^{0}\) at date \(d_{i}\) - the reference \(o^{0}\)) is greater than a threshold that remains to be determined, then it is highly probable that the surface state is frozen at acquisition date '\(d_{i}\)' of the S1 SAR backscatter. Moreover, since in the event of freezing, the magnitude of decrease in S1 SAR backscatter is highly correlated with the severity of the freezing event [16], in this study, we used two thresholds to distinguish between mild-to-moderate and severe freezing events. The threshold values were based on the land cover type, as in the event of freezing the decreases in the S1 SAR backscatter are also dependent on the land cover type [44; 45]. Our approach can be divided into two independent parts. The first part allows the calculation of the threshold values for each land cover type, and needs to be performed only once, as the same thresholds can be used for different study sites with the same land cover types. The second part consists of the algorithm for the near real-time frozen/unfrozen detection at plot scale, the output of which will indicate the plot state as one of three possible outcomes: (1) unfrozen, (2) mild-to-moderately frozen, and (3) severely frozen.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
###### Abstract  
This paper presents a computationally efficient haze removal and image enhancement methods. The major contribution of the proposed research is two-fold: (i) an accurate atmospheric light estimation using principal component analysis, and (ii) learning-based transmission estimation. To reduce the computational cost, we impose a constraint on the candidate pixels to estimate the haze components in the sub-image. In addition, the proposed method extracts modified haze-relevant features to estimate an accurate transmission using random forest. Experimental results show that the proposed method can provide high-quality results with a significantly reduced computational load compared with existing methods. In addition, we demonstrate that the proposed method can significantly enhance the contrast of low-light images according to the assumption on the visual similarity between the inverted low-light and haze images.  
image enhancement; dehazing; random forest; principal component analysis; low-light image enhancement; supervised learning Article
## 3 Results
### Evaluation Metrics  
We evaluated the results of our method using four metrics: BLEU [26], METEOR [27], ROUGE-L [28] and CIDEr [29]. These metrics are used to compute the similarity between two sentences. They are first introduced in the domain of machine translation and brought into image captioning to measure the similarity of the generated captions and the ground truth. Each metric computes the similarity in a different way. BLEU [26] uses the n-gram precision to calculate the similarity score. The higher n is, the more precise the generated captions need to be in order to get high scores. We report the BLEU metric with n from 1 to 4. METEOR [27] calculates the score using matching method. Synonym and stem are also considered in the matching method. ROUGE-L [28] calculates based on the longest common subsequence (LCS) between the generated captions and the ground truth. CIDEr metric uses n-gram combined with TF-IDF to represent a sentence as a vector and then computes the cosine similarity between the vectors.  
We use these metrics to measure the similarity between the generated captions and the ground truth in the dataset. The implementation of these metrics are also provided along with the MSCOCO dataset. For comparison, we use the same implementation of these metrics as in some other methods.
# Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance
###### Abstract  
Due to their excellent performance, the inductor-capacitor-capacitor-series (LCC-S)-compensated topologies are extensively used in inductive power transfer (IPT) applications. However, perfect alignment of the system's contactless couplers is difficult, which leads to serious deterioration of the system output characteristics. In this paper, the influence of the coupler misalignment on the performance of the conventional resonant system is studied. To obtain stable output against varying couplings and loads in a certain range, a novel parameter design method based on the multi-objective particle swarm optimization (MOPSO) algorithm is introduced. The multi-objective optimization framework is developed to analyze the Pareto trade-offs between three conflicting performance metrics, namely output current/voltage ripple, reactive power transmission and component stress. Optimization results depict that misalignment tolerances in both constant current output (CCO)-type and constant voltage output (CVO)-type LCC-S-compensated IPT systems are improved, while a wider load range is suitable for a CCO-type system using the method of compensation parameter optimization. Experimental results are highly consistent with the design, achieving a current fluctuation of no more than 10.5% with a load range from 50 \(\Omega\) to 100 \(\Omega\) and a voltage fluctuation of less than 10.4% with a narrow load from 90 \(\Omega\) to 100 \(\Omega\) over 100% of coupling variations (from 0.25 to 0.5).  
Keywords:inductive power transfer (IPT); misalignment tolerance; particle swarm optimization (PSO); LCC-S compensation topology; zero-voltage switching (ZVS) +
Footnote †: journal: Applied Sciences
# Blockchain-Based Decentralized Architecture for Software Version Control
###### Abstract  
Version control is an important component of configuration management, and most enterprise-level software uses different tools and technologies to manage the software version control such as CVS, Subversion, or Perforce. Following the success of bitcoin, the first practical application of blockchain, it is being implemented in other fields such as healthcare, supply chains, financial management, real estate, electoral systems, and so on. Blockchain's core features include decentralization, immutability, and interminability. Most version control repositories are centralized and can be modified by external sources, implying that they are in danger of being corrupted or controlled. In this study, we present the BDA-SCV architecture for implementing a version control system in blockchain technology. Our proposed approach would replace the necessity for a centralized system, with a decentralized approach implemented in the blockchain using distributed file storage, for which we will use the InterPlanetary File System (IPFS), which is a distributed file system. The proof of authority (PoA) consensus algorithm will be used to approve the developer communicating modifications to the private blockchain network; the authority will only provide permission and will not be able to add, edit, or delete code files. For each change, a ledger block will be created with a reference to the file stored in the distributed repository. A block cannot be manipulated once it has been created. Smart contracts will be used to register developers, create blocks, and manage the repository. The suggested model is implemented using the Hyperledger Fabric network, and the developer and authorizer ends are built into the dontet web application.  
version control; blockchain; change management; IPFS; smart contract +
Footnote †: journal: Applied Sciences
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 4 Experimental Results Analysis
### Comparison of P2 Detection Head Integration  
Initial experiments with the YOLOv8n model on dataset II demonstrated that, while YOLOv8n performs well on medium and large objects, its performance on small objects is relatively limited. Given that small dangerous objects are prevalent in under-vehicle scenarios, there is a need to enhance YOLOv8n's detection capability for these smaller targets. To address this, the PSP-YOLO algorithm introduces a P2 small-object detection head, which is derived from the P2 layer. Experimental results show a significant improvement in performance. As indicated in Table 2, the model's [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 increased to 90.6% and 67.7%, respectively, reflecting an enhancement of 1.9% and 3.0%. The inclusion of the small-object detection head notably improved detection precision for the parcel and bottle categories. Specifically, the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 for these categories increased by 2.0% and 4.4% for parcels and by 2.9% and 6.7% for bottles, respectively.  
To visually demonstrate the performance before and after adding the P2 detection head, this paper uses the Grad-CAM [15] method to generate performance heatmaps, as shown in Figure 7. In these heatmaps, the blue areas indicate low confidence in the detected regions, while the red areas represent high confidence. In the heatmaps shown in Figure 7a, before the P2 detection head was added, the red areas are sparse and even present in some non-target regions. Some target areas appear yellow-green, indicating that the original model struggled to detect small objects effectively, with instances of missed detections. After incorporating the P2 detection head, the heatmaps show an increase in small red areas  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**[EMAIL_ADDRESS]\%**} & \multicolumn{2}{c}{**[EMAIL_ADDRESS]-0.95\%**} \\ \hline  & **YOLOv8n** & **YOLOv8n + P2** & **YOLOv8n** & **YOLOv8n + P2** \\ \hline All & 88.7 & 90.6 & 64.7 & 67.7 \\ Knife & 84.5 & 85.7 & 64.3 & 66.8 \\ Gun & 86.2 & 86.3 & 69.8 & 70.6 \\ Parcel & 94.5 & 96.5 & 64.5 & 67.4 \\ Bottle & 89.3 & 93.7 & 59.4 & 66.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection head comparison experiment.  
corresponding to previously undetected targets, indicating that the model successfully identified more small objects. Figure 7b highlights another scenario where, prior to adding the P2 detection head, the red areas are concentrated in large non-target regions, with only a small amount of red in the target regions. This suggests that the original model had issues with false detections. After adding the P2 detection head, the red areas are more focused on the target regions, showing that the model better avoided both missed and false detections.  
Overall, these results demonstrate that the P2 detection head effectively addresses the issues of missed detections and low detection accuracy in the original model.
## 3 Results
### 3.1.1 Filtration Rate
#### 3.1.2 Effect of Coagulation Time  
The effect of coagulation time on the filtration rate of the lignin-containing solution is shown in Figure 3. After 60 and 120 min of coagulation, the filtration rates were poor, 25.1 and 33.4 dm\({}^{3}\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\), respectively. The filterability was increased to 2608 dm\({}^{3}\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\) when the coagulation time was increased to 180 min. This is logical because giving enough time for the lignin molecules to sufficiently coagulate can be beneficial for the filtration step. As discussed before, when the concentrate is acidified, more molecules of lignin with a low molecular weight are precipitated. These low molecular weight molecules require time to agglomerate and form larger particles. The particles of lignin should have a reasonable size in order to obtain a high filtration rate, and this can be achieved by allowing sufficient time for the coagulation step. As a result, a considerable increase in the flux is achieved. Furthermore, this will reduce the size of the filter and will allow savings in the capital cost of the filtration unit.  
Figure 2: Effect of sulfuric acid concentration and temperature on the filtration flux.
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 5 Simulation Experiment
### Simulations and Analyzation
: _As shown in Figures 6 and 7, they demonstrate the capability of the four algorithms in terms of the system utility and the system fairness under the condition of increasing spectrum number and fixed SU number, respectively, where the available channel number distributes in \(M=[10,30]\) and the number of SUs is fixed at \(N=15\)._  
_Figure 6 shows the trend of the system utility with the increasing of the available channel while the number of SUs is fixed. From the figure, we can see that all algorithm curves augment with the increase of available spectrum. Because more available channels will allow more SU access to the system to create higher utility. In Figure 6, IGSA always performs better than other algorithms; PSO and CSGC are slightly lower._  
_Figure 7 shows the trend of the fairness among SUs with the increasing of the available channel under the condition of fixed SU. As we can see, the fairness curves decline with the increase of available spectrum, because more available channels offer more channel access opportunities for SUs, which reduces collision probability. IGSA always outperforms GSA, PSO or CSGC._
## 2 State-of-the-Art Methods
Their model effectively identifies various plant diseases by analyzing leaf images, demonstrating the capability of transformer architectures in handling complex image classification tasks. The study's findings indicate that transformer-based models can be adapted for OCR applications, particularly in scenarios requiring the recognition of intricate patterns. Finally, a more advanced solution involved a CNN ensemble for handwritten kanji recognition [12], which serves as a baseline for this research. This study not only set a benchmark but also highlighted the Preact ResNet-18 architecture, which was chosen for the present work. Preact ResNet-18 strikes a balance between resource efficiency and high performance, making it ideal for processing small images without compromising accuracy. Although transformer-based models [9; 10; 11] excel at capturing global dependencies, their computational costs can be higher, and convolutional architectures, like ResNet, remain highly effective and efficient for image classification tasks, particularly character recognition, where local features are crucial. Although our focus is on recognizing static handwritten characters derived from hand movements, related research addresses the complexities of interpreting the dynamic hand activity itself, such as real-time pose estimation or gesture recognition, often for human computer interaction or robotic applications. These tasks frequently rely on multisensor fusion techniques to achieve robustness against challenges like sensor noise or occlusion. For instance, [PERSON]. [13] developed an adaptive sensor fusion method using Kalman filters and multiple leap motion controllers for stable hand pose estimation, even during occlusion. Similarly, [PERSON]. [14] tackled hand gesture recognition for robotic teleoperation using multi-sensor fusion guided by an LSTM-based recurrent neural network to handle interference and classify dynamic gestures. These approaches, leveraging temporal modeling (Kalman filters and RNNs) and different sensor modalities for dynamic tracking and gesture classification, represent a distinct but related area compared to our work on classifying static character images using convolutional architectures. Considering the unique challenges of handwritten kanji recognition and the insights gained from these studies, this work aims to develop an artificial intelligence model capable of recognizing handwritten kanji with an accuracy rate exceeding 96.43% and supporting more than 150 distinct kanji characters.
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
###### Abstract  
Display field communication (DFC) is a frequency-domain unobtrusive display-to-camera (D2C) communication, in which an electronic display serves as a transmitter and a camera serves as a receiver. In this paper, we propose a machine learning-based DFC scheme and evaluate its performance in a lab test scenario. First of all, we adopt the Discrete Cosine Transform (DCT) to transform a spatial-domain image into its spectral-domain equivalent. To reduce the computational complexity during the data-embedding process, addition allocation and subtraction data retrieval techniques are used. Moreover, channel coding is applied to overcome the data error caused by the optical wireless channel. In particular, robust turbo coding is used for error detection and correction. Afterward, we perform the experiments to validate the performance of the proposed system. After capturing the displayed image with a camera, data restoration is done using a deep learning technique. Extensive real-world experiments were performed considering various geometric distortions, noise, and different standard input images. As a result, we found that by increasing the transmit display image size (upsampling), the overall error rate can be reduced. In addition, real-world noise analysis is performed and it is notified that the actual noise is dominant in the low-frequency region of an image. The experimental results confirm the robust performance of the proposed DFC scheme and show that an error-free performance can be achieved up to a distance of 1 m in the given lab test environment setting.  
discrete cosine transform; display-camera communication; display field communication; spectral transformation; machine learning; object detection; turbo coding +
Footnote †: journal: Medical Imaging
## 3 A Short Overview of Selected Examples of Measuring and Monitoring Systems on Sea Ships
### Temperature Measurements in the Ship's Hazardous Areas  
Hazardous areas occur on almost all the ships. However, in certain ships, such as tankers (crude oil, chemicals, gas), mobile oil rigs or other offshore ships the hazardous areas are common. Due to technical conditions, the measurement of different parameters has to be made in the hazardous areas.  
Hazardous areas are the places where the risk of appearing mixture of flammable materials with oxygen from air might occur [20]. According to International Electrotechnical Committee (IEC) approach hazardous areas are divided into three zones for gas hazard: zone 0, zone 1, zone 2 and three zones for dust hazard: zone 20, zone 21 and zone 22. Depending on the zone, adequate explosion-proof equipment should be used.  
The IEC approach is commonly accepted by the majority of state maritime administrations and maritime classification societies [21; 22; 23].  
Electrical equipment placed in the hazardous areas can be protected by different methods. For measurement and control, intrinsically safe solution \"Ex i... \" is the most adequate explosion-proof protection [24; 25].  
In case of long distance measurement or control lines, which are typical on ships especially for deck monitoring and control, a special approach has to be used in comparison to the standard solution. The basic assumption of intrinsically safe solution is that inside of the intrinsically safe circuit, the accumulated electrical energy is so low that in the case of its realization in a form of a spark or a thermal effect, it is incapable of igniting surrounding explosive mixture. To ensure this, specially certified equipment has to be used inside the hazardous area and additional, also certified, associated apparatus has to be used and placed in the safe area. When both are connected by cables, this creates an intrinsically safe system [19]. It is worth underlining that in comparison to a standard solution, an associated apparatus is in fact an additional equipment connected to the measurement line. The associated apparatus is connected between intrinsically safe equipment placed in the hazardous area and the standard monitoring system placed also in the safe area. It can be a source of some limitations or even additional measurement errors. As an illustration of the discussed problem, the measurement of temperature in tanks of gas tanker is presented. The general scheme of the tanks placement and related temperature measurement is shown in Figure 8.