esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Some research has been done in this field and researchers abroad have made great progress. In view of the availability of the link between source and destination in an OFDM SWIPT system, Ref. [29] proposes two protocols to achieve the maximum system transmission rate, where the relay adopting PS method harvests energy and transmits information simultaneously. Ref. [30] considers an AF-OFDM relaying system with SWIPT, which jointly optimizes multiple resources to achieve higher system performance while reducing computational effort. The aforementioned relaying SWIPT IoT systems are based on single relay. To the best of our knowledge, SWIPT has not been applied in the multi-relay OFDM based IoT system, which motivates our study. Thus, in this paper we propose a joint resource optimization scheme in a SWIPT enabled multi-relay OFDM based IoT system to obtain the maximum transmission rate, in which the transmission between the source and the destination is carried out through the assistance of multiple SWIPT enabled DF relays. The main contributions of this paper can be summarized as follows:  
* We consider a multi-relay OFDM based IoT system, in which the SWIPT-enabled relay can decode information and harvest energy simultaneously. Therefore, the splitter does not have to be installed in the relay, which is necessary in the common algorithm using PS or TS protocol. It can simplify the deployment and administration of the IoT system. * We formulate a scheme by optimizing power and subcarrier allocation with the aim at maximizing the transmission rate, and employ the time-sharing strategy and Lagrange dual method to solve this optimization problem. * Simulation results illustrate the effectiveness of our algorithm. By observing the transmission rate, it illustrates that the proposed algorithm has better performance than other algorithms.
# Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids
###### Abstract  
In this study, the structural behavior of reinforced concrete flat plates shear reinforced with vertical grids made of a glass fiber reinforced polymer (GFRP) was experimentally evaluated. To examine the shear strength, experiments were performed on nine concrete slabs with different amounts and spacings of shear reinforcement. The test results indicated that the shear strength increased as the amount of shear reinforcement increased and as the spacing of the shear reinforcement decreased. The GFRP shear reinforcement changed the cracks and failure mode of the specimens from a brittle punching to flexure one. In addition, the experimental results are compared with a shear strength equation provided by different concrete design codes. This comparison demonstrates that all of the equations underestimate the shear strength of reinforced concrete flat plates shear reinforced with GFRP vertical grids. The shear strength of the equation by BS 8110 is able to calculate the punching shear strength reasonably for a concrete flat plate shear reinforced with GFRP vertical grids.  
shear; shear reinforcement; flat plate; shear strength; concrete +
Footnote †: journal: Applied Sciences
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 2 Materials and Methods
### Processing Options
The total observation time covered by these tests was 12 h, divided by intervals of 4, 3, and 5 h, respectively, allowing for greater robustness of the results presented in Section 4. These tests were carried out on different pillars, at different times and days, using the same smartphone, which was placed vertically with an antenna displaced 29 cm above the pillar top. Test 4, held at 4:30 pm on 18 November, had a survey lasting 4 h in pillar 9 (Figure 3d). Test 5 lasted 3 h, starting at 6:30 pm on November 19 in pillar 5 (Figure 3e). Test 6 had a survey time of 5 h, in pillar 11 (Figure 3f), starting at 8:15 am on 22 November. In these last three tests, an aluminum plate was used under the smartphone (Figure 3d-f)) to minimize the multipath effects.
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
## 3 Results
### Discussion  
This study introduces a mathematical model to correct positioning in AR environments, focusing on the implementation and validation of the proposed algorithm. This highlights the challenges of comparing our marker- and GPS-based algorithms with others because of the impracticality of placing markers outdoors and the difficulty in correcting GPS errors with existing methods. The proposed algorithm aims to display objects precisely and adjust their locations in an AR setting, suggesting its applicability in various fields and as a foundational study for location-based services. This discussion emphasizes the novelty of the algorithm and its potential applications, acknowledging the limitations of comparing it with other methods owing to its unique approach.
## 2 Proposed Method
### Receiver's Work  
The receiver acquires the marked encrypted image that contains the secret data from the data hider. Using different keys, the receiver can retrieve the embedded data from the image without decryption, recover the original image, or generate a marked decrypted image that contains the secret data and is highly similar to the original image.  
1. Data Extraction. When the receiver has the data hiding key \(K_{\text{hide}}\), the receiver can extract the embedded data directly from the marked encrypted image. First, the receiver extracts the capacity information to obtain the room that is available for hiding data. Then, the receiver uses \(K_{\text{hide}}\) to extract the bits in the data hiding room to obtain the embedded data.
2. Image Recovery. When the receiver has the image encryption key \(K_{\text{en}}\), the receiver can reconstruct the original image or generate a marked decrypted image with the embedded data. The detailed procedure is as follows: Step 1: Decrypt the marked encrypted image by \(K_{\text{en}}\). Step 2: Extract the predictor \(\mathbf{LM}=\{w_{0},w_{1},w_{2},w_{3}\}\), the Huffman codebook \(\mathbf{CB}\), and the encoded prediction error bitstream \(\mathbf{BS_{pe}}\) from the MSBs of the decrypted predictable pixels. Step 3: According to the Huffman codebook \(\mathbf{CB}\), decode the bitstream \(\mathbf{BS_{pe}}\), into the original prediction errors \(\mathbf{PE}=\{e_{2,2},e_{2,3},\dots,\ e_{H,W-1},\ e_{H,W}\}\). Step 4: Use the predictor \(\mathbf{LM}\), the prediction errors \(\mathbf{PE}\) and the reference pixels to retrieve the original values of all predictable pixels row by row and column by column. Step 5: In the decrypted image, if all predictable pixels are recovered directly to their original values, the original image is reconstructed with no error; if only the MSBs of the predictable pixels that were modified for vacating room (Section 2.1.2) are recovered according to the original values, the marked decrypted image is generated, which is highly similar to the original image, and it still keeps the embedded data in the LSB planes.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 3 Descriptive Statistics  
While conducting an experiment, data analysis and statistical methods are key components of research project management. They allow the researcher to make inferences about a certain population by relying on a sample. This sample needs to be randomly taken and have a predetermined size \(n\), in which each observation is equally likely to be chosen from a certain population [40]. A good real-life example is lottery, in which each collected ticket has an equally likely possibility of being the winning ticket. Moreover, the observations of a sample need to be independent from each other, meaning that observations chosen for one sample do not provide any information about observations chosen for the nextsample. For example, when testing the effect of an intervention in beef trimmings, swabs from both sides of the trim are collected to reduce the number of beef trimmings needed for that project. In this scenario, the measurement of the microbial load in the trimmings are related to each other, because the same beef trimming was swabed twice.  
After randomly selecting observations and making sure that they are independent, the next step is to provide summary statistics. Normally, summary statistics provide a measurement of central tendency, such as the mean, median, or mode, and a measurement of variation such as range, variance, and standard error. The combination of these two types of statistics allows the researcher to understand their data. An example of these measurements is provided below:  
Assume that summary statistics of aerobic counts on fresh ground beef were calculated with actual values of 10,000, 1000, 2000, 5000, and 3000 colony-forming units (CFUs) per gram.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
###### Abstract  
The ALICE (A Large Ion Collider Experiment) detector at the Large Hadron Collider (LHC), operated by the European Organization for Nuclear Research (CERN), is dedicated to heavy-ion collisions. Within ALICE, the application logs of the online computing systems are consolidated through a logging system known as Infologger, which integrates data from various sources. To identify potential anomalies, shifters in the control room manually review logs for anomalies, which require significant expertise and pose challenges due to the frequent onboarding of new personnel. To address this issue, we propose a real-time semi-supervised log anomaly detection framework designed to automatically detect anomalies in ALICE operations. The framework leverages BERTopic, a topic modeling technique, to provide real-time insights for incoming log messages for shifters. This includes an analytical dashboard that represents the anomaly status in log messages, facilitating informative monitoring for shifters. Through evaluation, including Infologger and BGL (BlueGene/L supercomputer), we analyze the effects of word embeddings, clustering algorithms, and HDBSCAN hyperparameters on model performance. The result demonstrates that the BERTopic can enhance the log anomaly detection process over traditional topic models, achieving remarkable performance metrics and attaining F1-scores of 0.957 and 0.958 for the InfoLogger and BGL datasets, respectively, even without the preprocessing technique.  
ALICE experiment; BERTopic; clustering; FLP cluster; machine learning; topic modeling Article
## 3 Mixed-Reality 3D Minimap
### Different Levels of Detail of Map Interface
#### 3.2.1 Preliminary Study  
To investigate the pedestrian's needs for the map interfaces with different levels of detail, we first conducted a field study with 5 participants (4 females and 1 male, average age = 27.2, SD = 2.54) as a preliminary study in a real street block of the urban environment.  
We designed a free exploration task with augmented reality navigation in an assigned urban area. A series of landmarks were marked on the map to assist pedestrians with searching and browsing in the surrounding area. Like the Google Maps we used daily, we designed the default map interface with 2D and 3D default map modes and the 3D aerial map mode generated by global satellites. For the augmented reality function, participants could use augmented reality features to find the point of interest following the virtual anchor in the augmented reality interface corresponding to GPS information. A map view window with three different maps was added as a part of the interface to assist participants in exploration. The participant could switch among three map mode interfaces according to their preferences and need. The system prototype was developed by Xcode [56], and the map and augmented reality features are supported by MapKit [57] and ARKit [58]. The application used in the experiment was installed on an iPhone 7 with the iOS13.5 system.  
From the interview results, we found that the participants showed different preferences for the three map modes during the exploration task according to their different needs. Some participants mentioned that they preferred using the 3D aerial map to identify the appearances of landmarks, points of interest, and other buildings, which could help them orientate and locate in rough directions instead of using explicit navigation. However, some participants also mentioned some deficiencies when referring to the 3D aerial map in some cases. For instance, sometimes they do not need such detailed, complicated information. Moreover, some participants said that the point-of-interest information was sometimes overloaded in the augmented reality interface, which affected the navigation experience. For instance, some participants mentioned that they prefer more valuable points of interest for exploration rather than presenting all the points of interest on the map interface. Based on the preliminary study results, we can learn that when pedestrians walk and explore in an urban area, they prefer following the landmarks with impressed appearances to navigate in a rough direction.
## 3 Case Study  
### Project Overview  
A hydropower station is located in Yanyuan and Muli counties, in the Liangshan Yi nationality autonomous prefecture, Sichuan Province, which is the ladder rolling development leading reservoir power station of the main stream of the Yalong River (see Figure 4). The normal storage level of the reservoir is 1880.0 m, dead water level is 1800.0 m, and the main task of the reservoir is to generate electricity, including flood control and sand control. The main building of the hydropower station is a concrete double-curved arch dam whose crest elevation is 1885.0 m and the maximum dam height is 305.0 m. In this paper the displacement monitoring data are selected from 1 September 2016 to 19 May 2018 of this hydropower dam station operation period as a case to analyze and verify the accuracy and applicability of the method utilized.  
Figure 3: Flow chart of gross errors identification by LMS-IGG-ELM method.
## 5 Data Collected and Statistical Analysis
### Flowability  
The flow table test was used to ascertain the impact of lime on the viscosity of cement mortar. As the proportion of lime increased, the fluidity of cement decreased. The Vipulanandan correlation model, represented by Equation (3), accurately estimated the variation in the flow of cement mortar mixed with lime. The model achieved a high level of accuracy, with an R\({}^{2}\) value of 0.947. According to Figure 8, the addition of 50% lime resulted in a 20% drop in the fluidity of cement mortar. The reduced rate of cement mortar flow might be attributed to the superior dispersion of cement particles by lime. The reason for this capability is likely due to lime's reduced reactivity compared to cement, as well as the limited formation of gel.  
\[FL\%=Y0+A\times e^{R0\times L} \tag{3}\]  
where \(Y0\), \(A\), and \(R0\) are as demonstrated in Figure 9 and \(L\) = lime content %.
# Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection
## 3 Results  
### Typical Combustion Smoke Particle Size Measurement  
Since combustion usually starts with pyrolytic smoldering, it is meaningful to detect fire at this early stage. Hence, we first study the particle size distribution of smoldering smokes of typical combustibles in daily life, such as cotton wicks, beech wood, mosquito coils, paper, bamboo, rubber, plastics, and so on. The particle size distributions of natural and artificial combustible materials are shown in Figure 3a,b, respectively, from which it can be seen that the particle size distribution of smoldering smokes varies with the combustion material. Depending on the combustible materials, the peak particle size is concentrated between 100 and 300 nm. Meanwhile, the smoldering fire smokes can be well described by the lognormal model and the particle size range is generally on the submicron scale.  
Figure 1: Picture of aerosol testing and particle sizing platform.  
Figure 2: The block diagram of aerosol testing and particle sizing platform.  
In addition to the smoldering smokes, we also study the particle size distributions of open fire smokes, including polyethylene, PPR (Polypropylene-Random), n-heptane, kerosene, and so on. As per the results shown in Figure 4, the particle size distribution of open fire of polyurethane and n-heptane is generally larger than that of smoke from smoldering fires, with peaks concentrated between 200 and 400 nm and most particles smaller than 1 \(\upmu\)m. Since the maximum range of SMPS is 1 \(\upmu\)m, the particle size distribution was truncated at 1 \(\upmu\)m. The APS measurements show that the proportion of particles larger than 1 \(\upmu\)m is very low.  
The particle size distribution of smoke particles is fitted according to the experimental data in Figures 3 and 4. It can be observed that the particle size of smoke particles typically follows the lognormal distribution. Table 1 summarizes the statistical characteristics parameters of the particle size distribution of fire smoke.  
Figure 3: Particle size distribution of smoldering smokes from typical combustible material. (**a**) Particle size distribution of smoldering smokes from natural materials. (**b**) Particle size distribution of smoldering smokes from artificial materials.
# Artificial Intelligence-Driven Eye Disease Classification Model
## 5 Discussion
The proposed classifier is data-driven and can learn from massive annotated image datasets. This ability enables the proposed model to classify novel data without additional training, which is crucial in medical imaging applications. The ShuffleNet model was pre-trained on large, general-purpose datasets. However, the authors fine-tuned the model with AO. The ShuffleNet architecture exhibits scalability and efficiently analyzes a substantial quantity of retinal data. This scalability is significant for healthcare providers and screening programs handling a massive volume of patients. The proposed model facilitates the continuous monitoring of ocular diseases. It can monitor the retinal images over time and notify physicians of any modifications that may indicate a demand for treatment. In addition, it can assist in driving forward research efforts in medicine. While implementing the proposed model, the authors encountered a few limitations. Identifying small lesion patterns of ocular disorders may present difficulties for the proposed EDC due to image noise or other anatomical features. The datasets were highly imbalanced. In the ODIR dataset, the numbers of glaucoma and cataracts images were lower than those of the remaining diseases. However, the authors addressed the limitation using the effective data augmentation technique. The proposed EDC may face challenges in classifying multiple ocular disorders that may present in a single image. The quality of the fundus images was poor. Images were unique in size and clarity. Extensive image processing was required to normalize the images. The proposed model was generalized using two benchmark datasets. Implementing CNN models with minimal latency may be technically challenging. The proposed EDC may demand additional training in order to classify the real-time images. In the future, the model's overall performance can be improved using ensemble techniques, including boosting or bagging, oversampling the minority class, undersampling the majority class, or using loss functions involving the minority class to address class imbalance. In order to mitigate overfitting and enhance the generalization capabilities of the model, regularization methods such as dropout, L1/L2 regularization, or batch normalization can be employed. To build clinician trust, interpretable DL models can be used to justify the proposed model decisions.
## 5 Case Study  
### Project Selection  
In this paper, three practical distributed PV-generation projects were selected to analyze and evaluate core abilities. Their brief introductions are as follows.  
1. Project A. Distributed PV-generation project A is constructed for a coalfield in north-western China. Its construction capacity is 6.27 MWp, which consists of 6 generation units. The project is nearby a 35 kV substation in the coalfield area. The generation from this distributed PV-generation project is all used by the coalfield. The operation period is set as 20 years with 171.61 GWh total generation amounts. The annual generation is 8.58 GWh. The annual equivalent generation hours are 1368.08. Total investments are 47.75 million yuan.
2. Project B. Distributed PV-generation project B is located in a tourist area in northern China. This project is near a 35 kV substation and consists of 36,237 PV components. All generation power is consumed by the tourist area, which will work for 25 years. The total generation amounts are 459.23 GWh. The annual generations is 18.35 GWh. The annual equivalent generation hours are 2362.31. Total investment is 128.1 million yuan.
3. Project C. Distributed PV-generation project C is a residential project with construction capacity of 1.6243 MWp in eastern China. 7062 PV generation components are installed on the roof. The generation system is directly connected to a 10 kV electrical circuit in the consumer side. In the 25-year operation period, its total generation amount is 60.88 GWh. The annual generation is 2.43 GWh. The annual equivalent generation hours are 1123.85. The total investment is 32.83 million yuan.