esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis  
### Comparative Experiment on Benchmark Functions  
(1) Experimental environment  
In this paper, the source code corresponding to both the comparative and the proposed algorithms was implemented in MATLAB 2018b. For experimental evaluations, we employed a computational platform with the following specifications: an Intel CPU operating at 3.2 GHz, a memory of 16 GB RAM, and running on the Windows 10 operating system.  
(2) Benchmark Function  
For the analysis, the test function suite was derived from the CEC2014 series benchmark test functions. Numerous problems within this suite have been predominantly used in various stochastic algorithmic evolutionary approaches [28].
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Actual and Predicted Energy Demand
Conversely, the use of marketing cooperatives in the RF model may have contributed to its broader distribution, accommodating noise or variations in demand patterns, which can be advantageous for unexpected situations. Both models exhibit good performance, with residuals remaining close to zero. This higher precision could lead to improved energy efficiency and cost savings, though it also suggests that the ANN model may be more sensitive to fluctuations in its input data. While the RF model demonstrates a different performance profile, it may be more adept at handling unpredictable demand spikes. Additionally, when selecting a model, one should consider the computational complexity, as the ANN is relatively more complex in terms of training and querying than the RF model.
# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Discussion  
Detailed simulations shown in this paper indicate that object detection, such as vehicle or license plate detection, has been an active research field in recent years. This paper presented a detailed analysis of license plate recognition on three publicly available datasets. For the task of vehicle detection, a Faster RCNN architecture was used. The license plate was located and recognized through our own developed methods. Our findings are indicative of superior outputs on challenging datasets. Moreover, a detailed comparison of our developed method was carried out with several state-of-the-art license plate recognition approaches. We are optimistic that this study will be a fair guideline for  
Figure 9: Computational complexity [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44].  
beginners and practitioners to modify or use any detector or recognizer for their desired tasks or applications. The outcomes of our developed system for recognizing license plates are summarized below.  
**PKU Dataset:**  
On this dataset, our developed method yielded 100% recognition accuracy in the G1, G2, and G3 categories. In the G4 category, our developed method was 99% successful at accurately recognizing the license plate. Finally, in the G5 category, our developed method yielded 99.63% recognition accuracy. Overall, on the PKU dataset, our developed method ranks first out of the three compared methods in terms of license plate recognition accuracy.  
**AOLP Dataset:**  
This dataset contains three challenging categories, which are access control, traffic law enforcement, and road patrol. On access control, our developed method yielded 97.8970% accurate recognition accuracy and ranked first herein. On traffic law enforcement, our developed method yielded 98.2719% license plate recognition accuracy and ranked second among the compared methods. On the road patrol category, our developed method generated a mean recognition accuracy of 91.9006% and ranked first among the four compared methods. The whole-mean accuracy on the AOLP dataset by our developed method is 96.0231%.  
**CCPD Dataset:**  
This is the largest publicly available license plate dataset and contains challenging scenarios, such as blur, rotation, tilt, and varying weather. On this dataset, our developed method yielded a mean recognition accuracy of 98.7000% and ranked first among all compared methods. In general, for all the other aforementioned categories, our developed method yielded over 98% recognition accuracy. However, for the CCPD challenge category, our developed method yielded slightly over 88% recognition accuracy and ranked second among the five compared methods.
## 3 Conventional and Proposed Methods
### Proposed Method
#### 4.2.1 CSI-Based Nonlinear Allocation Method
However, when the distribution concentration factor becomes excessively high, power becomes overly concentrated on a few antennas, disrupting the overall balance and causing BER to increase again. Notably, the minimum BER is achieved around \(\beta=1.8\), suggesting that the proper selection of the distribution concentration factor is crucial for optimizing communication performance. Figure 5: Comparison of distance-BER characteristics for different allocation methods. Additionally, the optimal distribution concentration factor calculated using the proposed method is \(\beta=1.873\), which closely matches the optimal point shown in Figure 6. This result demonstrates that the proposed method can appropriately determine the optimal distribution concentration factor based on CSI, confirming its effectiveness. In particular, in dynamic environments, adapting the distribution concentration factor according to channel state variations is expected to maintain communication quality stability while maximizing energy efficiency.
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 3 Fault Diagnosis Methodology Using the Defect Signature Wavelet Image  
The main purpose of this paper is to explore the appropriateness of characteristics of bearing fault signal to generate a 2-D representation which can help to separate different types of faults in bearings. To create a relevant 2-D representation to train the DCNN classifiers, the initial AE signals are fed across an envelope analysis to demodulate and are decomposed using the continuous wavelet transform with a specific frequency band acquired from the bearing characteristics and working conditions. Finally, the classifier model is built to validate the 2-D representation method. Several hyperparameters in the classifier structure are also characterized to ensure optimum performance. An overall workflow is presented below.
# Analysis of Passive RFID Applicability in a Retail Store: What Can We Expect?
## 3 Influence of Retail Store Products and Propagation Phenomena on a Tag Sensitivity
### Chip's Generation Comparison and Effect of Retail Products on the Shelves
#### 3.2.1 Receiver Positions in Front of the Shelves  
The measured sensitivity levels are used in the simulation store model to depict reader's coverage of signal level necessary to power up the tag (red is for the power above the tag's \(P_{tag\_sens}\) and blue is for below, Figure 7). In this setup, receivers are positioned in vertical grids in front of the shelves to examine how can \(P_{tag}\) varies with height. Figure 7 depicts the coverage of the reader signal for Higgs 2 and Higgs 4 where the shelves are filled with products of mixed properties as in the real store. These results show that due to multipath propagation, different power levels are impinging on the tags depending on their position on the shelves. Due to 4.2 dB difference between the \(P_{tag\_sens}\) of Higgs  
Figure 5: Impulse response at the receiver point 11 in receiver grid 1 (LOS area) with 2D and 3D view of the propagation paths in empty shelves scenario.  
2 and Higgs 4, there is an implication that at some shelves where the power level is low and near the threshold, Higgs 4 can be powered up, but Higgs 2 cannot.
## 2 Background
### Smart Home Device
#### 2.1.1 Device Types  
A Smart Home typically contains Smart Home Devices (SHDs) as defined before, as well as general-purpose computing devices (GPCDs) like personal computers (PCs) or Smartphones, both of which communicate with other devices in the local network or the Internet. Additionally, it should be noted that it is not always straightforward to distinguish between those groups. For example, some software like Home Assistant (Version: 2025.1.3 [[https://www.home-assistant.io/](https://www.home-assistant.io/)]([https://www.home-assistant.io/](https://www.home-assistant.io/)) (last accessed: 19 December 2024)) can turn a computer into a Smart Home control centre, which would fall into the SHD category if this is the sole objective of the device. If instead it also contains other applications, such as a web or file server it may be more appropriate to assign it as a general-purpose computing device.  
A major difference between SHDs and GPCRs lies in the diversity of their communication with other devices or servers. A PC or Smartphone might access any website on the web, download files from any server, or send e-mails, to only name a few scenarios. In addition, such computers also connect to various devices in the local network, e.g., to sync with a local file server, print some files, or control the Smart Home Devices. SHDs, on the other hand, are usually limited to a small set of servers they connect to [24]. For example, [PERSON] et al.'s investigation shows that the Nest Smoke Alert only connects to four domains on a daily basis and only three of them in the case of an emergency [25]. While such SHDs with relatively low customizability also have an accordingly static set of domains they connect to, those that can be customised by the owner, e.g., by means of an add-on or apps, can have a more dynamic network behaviour. For example, the communication of the Amazon Echo depends on the'skills' that are installed for Alexa [26].
## 3 Proposed Method
### Landmark Detection
#### 3.3.2 Cost function  
The cost function we used during training is mean squared error (MSE), since we want to make the predicted landmark become as close as possible to the ground-truth landmark location during training. In this case, MSE can be written as in Equation (1):  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}((x_{i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y}_{i})^{2}) \tag{1}\]  
where \(x_{i}\) and \(y_{i}\) indicate the ground-truth coordinate, and \(\hat{x}_{i}\) and \(\hat{y}_{i}\) indicate the estimated coordinate.
###### Abstract  
For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low-cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor's data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.  
Apache Spark; big data applications in imaging; cloud computing; medical image reconstruction; parallel algorithm +
Footnote †: journal: Applied Soft
## 3 Results and Discussion  
### Total Volatiles Contents  
Overall, according to the criteria set at paragraph 2.5, more than 80 compounds were identified in mushrooms of the present study, with 24 of them detected only in roasted samples (Table 1). Fresh _P. ostreatus_ mushrooms contained a lower number of volatile compounds compared to fresh _P. eryngii_ ones (36 vs. 56 compounds). In accordance with our observation, [PERSON] et al. (2019) [40] reported that _P. ostreatus_ had the lower number of volatile compounds among six _Pleurotus_ species. However, one should not overlook that the concentrations of volatile compounds alone are not a sufficient base for studying the aroma profile of mushrooms, as different volatile compounds may present significantly different odor thresholds [30].  
Nevertheless, concentrations of the volatiles (i.e., compounds included in Tables 2 and 3) were higher in _P. ostreatus_ compared to _P. eryngii_ (1684 \(\pm\) 591 vs. 965.4 \(\pm\) 321.5 \(\mathrm{\SIUnitSymbolMicro g}\)/g f.w), in agreement with the data reported by [PERSON] et al. (2019) [2]. The compounds detected in fresh samples are classified as aldehydes, alcohols, ketones, alkanes, fatty acid methyl esters (FAME) and terpenes. Moreover, the heterocyclic 2-pentyl-furan and the aromatic hydrocarbons toluene and ethylbenzene were also detected. Regarding the strains studied, _P. eryngii_ LGAM106 and _P. ostreatus_ CS5, CS6 and LGAM3002 presented the higher total volatiles content. In most cases, cultivation on GM and/or OL resulted in enhanced volatiles production however this pattern was not consistent (Figure 1).
## 3 Results and Discussions
### SEM-EDS Analysis
#### 3.4.3 Alkali Resistivity Test  
Figure 12 shows that the compressive and flexural strengths of SK and SL5 did not change much after the alkali test, whilst that of SL2 decreased slightly. In addition, the surfaces of the three specimens did not have any cracks before and after the test. As a result, the three samples have good resistance to alkali.
## 5 IETF ROLL Working Group
### Implementation and Evaluation
#### 5.2.5 Multicast  
Possibilities for the usage of RPL routing of multicast messages are stated in _Multicast Protocol for Low power and Lossy Networks (MPL)_ (draft-ietf-roll-trickle-mcast-02) [64]. According to [65] the proposed draft solution has the advantage that it will work without modifications and reliability is increased by the per datagram state information maintenance, but it also has a number of drawbacks. A first drawback is that, instead of storing only destination information, for each packet a state has to be stored, which can result in scalability issues. The use of caching of messages, to avoid duplicates, can possibly improve the performance. Other drawbacks are an increase in complexity, susceptibility for out-of-order datagram arrival and energy and bandwidth inefficiencies due to the forwarding of messages to all parts of the network instead of only to parts with interested nodes (lack of group registration). To solve these issues, [65] introduces an alternative protocol, called SMRF.
## 2 Status of Smart Grids
### Standardization and Regulations
#### 2.2.2 Europe  
Europe is promoting the smart grid as a way to implement climate agreements and a low carbon economy. The European Union (EU) aims to improve cost-effectiveness by replacing at least 80% of electricity meters with smart meters by 2020. This smart metering and smart grid adoption are expected to reduce pollutant emissions in EU by up to 9% and also reduce annual household energy consumption. To measure cost efficiency, EU countries conducted a cost-benefit analysis in accordance with the guidelines of the European Commission (EC).  
In terms of security and privacy issues, the smart grid task force in EU was established in 2009 to advise the EC on smart grid deployment and development issues. Expert Group 2, one of five expert groups focusing on specific areas, aims to mitigate the security risks of personal data and the smart metering system. The EC proposed developing codes for cyber security networks to complement existing national rules and deal with cross-border issues [21].  
To identify and address security challenges, several European projects have been initiated. NobelGrid [22] deals with smart grid security in a new generation of low cost smart meters through secure protocols. SEGRID [23] and SPARKS [24] aim to study known and future security threats forthe purpose of avoidance for the future realization of smart grids. HyRiM [25] is another project for risk management for utility providers.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 1 Introduction
### Motivation  
\"The large-scale quantum machine, though it may be the computer scientist's dream, is the experimenter's nightmare\" (quoted from [20]). This challenge arises due to the inherent fragility of quantum states, which are extremely sensitive to any form of external noise or environmental interaction. Quantum information processing is prone to errors and noise obstructing consistent and reliable computation. The state-of-the-art quantum processors report around \(10^{-3}\) error rates [21; 22; 23; 24]. QECCs serve as a protective barrier against such errors, yet the efficacy of these codes is largely dictated by the nature and intensity of the encountered noise [25]. Quantum computers vary not only in their error profiles but also in the intensity of different types of errors they encounter. For instance, while some systems may be more prone to gate errors, others might suffer more from depolarizing errors, leading to a situation where errors are biased. In the context of quantum computing, 'biased noise' refers to a scenario where certain types of errors occur more frequently or intensely than others within a quantum computer system. This is different from random or uniform error distributions, where each type of error might be equally likely. Opting for the most advanced QECC available can result in significant overhead, which may not be necessary for every situation.  
A detailed analysis of QECCs in the context of biased noise provides deep insights into how these codes perform across various noise landscapes. This knowledge is crucial for choosing the QECC that best fits a specific quantum computing scenario, considering factors such as the number of qubits available, the target error rate reduction, and the particular biased error profile of the system. By doing so, we can achieve substantial overhead savings without compromising on reliability. Despite efforts made in the earlier literature to explore the efficiency and scalability of QECCs, they often fall short of providing a comprehensive analysis with respect to biased noise [26; 27; 28; 29; 30]. They have addressed QECCs' functionality concerning noise in general but not specifically biased noise. Therefore, we lack understanding of whether and how QECCs can be tailored according to the scenario at hand, underscoring a critical gap in our ability to customize error correction strategies effectively.
## 4 Result and Discussion
### Recovery of Ancient Gas Content
#### 4.1.4 Shale Gas Diffusion Coefficient  
The diffusion coefficient of shale gas, which is mainly controlled by the temperature of the formation and the pore structure of the cap layer, determines the diffusive ability of shale gas within the formation. Earlier studies have established a calculation formula  
Figure 5: Hydrocarbon generation evolution and distribution map of Longmaxi Formation shale organic matter in Weiyuan area. (**a**) The relationship between the yield of gaseous hydrocarbons and the maturity of organic matter; (**b**) the relationship between gaseous hydrocarbon yield and formation temperature; (**c**) gaseous hydrocarbon yield and yield rate distribution at different times.  
for the diffusion coefficient of shale gas under formation conditions, which is based on the Stokes-Einstein equation [16]:  
\[D=\frac{\text{k}T\varnothing}{6\pi r\mu} \tag{12}\]  
In the formula: \(D\)--diffusion coefficient, m\({}^{2}\)/s; k--Boltzmann's constant, 1.38 \(\times\) 10\({}^{-23}\) J/K; \(T\)--absolute formation temperature, K; \(\varnothing\)--porosity, %; \(r\)--methane molecular radius, m; and \(\mu\)--gas viscosity, Pa-s.  
The Stokes-Einstein equation is a microscopic equation that describes the diffusion behavior of small particles in fluids, and it does not emphasize that small particles and fluids must be two different substances. When methane molecules diffuse in formation water, \(\mu\) represents the viscosity of formation water; when methane molecules diffuse in pores filled with methane of varying concentrations, \(\mu\) represents the viscosity of methane.  
Combining the information on the burial history of the Weiyuan area, the evolutionary process of shale gas diffusion coefficient (Figure 6) has been restored. As shown in the figure, the evolution of diffusion coefficient is relatively less affected by formation temperature and has good consistency with the evolution of porosity. As the Silurian stratum began to deposit, the shale gas diffusion capacity always maintained a relatively high level due to the weakening of shale compaction and pore reduction effect during the middle period of the Permian. As the Permian stratum entered a stage of prolonged deep burial, the diffusion coefficient rapidly decreased with the change in porosity. As the tectonic activity of the formation became stable during the middle and late Jurassic, the pore remained stable, and the gas reservoir entered a low diffusion capacity stage.  
Figure 6: Evolution matching of “production-reserve-protection” and gas content parameters of shale gas reservoirs in wells Wei201 and Wei204 in southern Sichuan.