esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 3 Methodology
### Text-Based Logo Detection and Recognition  
We implemented state-of-the-art scene-text solutions, extending the previous research work on EAST [25] for text detection and CRNN [28] for text recognition. The pipeline is shown in Figure 2. It consists of the following steps:  
1. For a truck image acquired from a roadside traffic camera, we generate the text line/word map based on the features extracted from the multichannel FCN (fully convolutional network) model in EAST, which helps identify text regions of interest;
2. To remove detected texts largely overlapping each other, we apply the standard post-processing technique called NMS (non-maximum suppression), which results in oriented bounding boxes to indicate text line/word locations;
3. We then crop out the image region within each oriented bounding box and feed it into the CRNN model to translate the text image to a pure text string;
4. Finally, we match the predicted text string to a predefined logo class via additional techniques for word prediction and string matching.  
We have achieved a high recall and a competitive recognition accuracy following the developed algorithms. The figures below show sample outputs. Though the texts might not be perfectly predicted, e.g.,they might miss character prediction or wrongly recognize a few characters, this could be suitably fixed via the spelling correction methods that are publicly available, which we use to demonstrate in Section 4 that this text-based logo solution is beneficial in improving the performance of the overall pipeline.  
It is worth mentioning that text-based logo detection and recognition has its limitations when solving the commodity classification. For those logos not containing any text or being too complicated to be successfully identified as texts, the text-based solution is no longer suitable. Instead, we have to seek a fundamentally different approach based on  
Figure 1: The overall pipeline of commodity classification.  
the image content, which leads us to the image-based approach incorporating various advanced image representations for logo data described in the subsequent section.
## 4 Case Study and Results
### Cycle Time Calculation  
The duration of all the activities was calculated once the activities were identified accurately. Figure 11 shows the duration of different activities of the dump trucks. The average duration for _Loading, Hauling, Dumping, and Returning_ are 2.81 min, 15.03 min, 0.83 min, and 15.53 min, respectively. These durations will be the primary input parameter of the DES model as a distribution (e.g., normal distribution, gamma distribution, etc.). The distributions were fitted using the Chi-Square Goodness-of-Fit test, which tests if a sample of data came from a specific theoretical distribution. This test groups the data into bins, then calculates the observed and expected counts for those bins and computes the chi-square test statistics. All four activities were fitted to normal distribution. Figure 12 shows the histogram and probability density of the loading times.  
The calculated means and standard deviations for all four activity durations are shown in Table 3. The return route for the trucks was longer than the hauling route which is reflected in the data. These values were assumed to be the initial input parameters for the base DES model.  
Figure 11: Cycle time durations of dump trucks.
## 3 Results and Discussion
### Estimate of NH\({}_{3}\) Emission Factor at Biogas Production Facilities  
#### 3.2.1 NH\({}_{3}\) Emission Factors Based on Waste Throughput at Biogas Production Facilities  
Flow at the time of the actual measurement and waste processing amount were considered to have affected the emissions factor rather than the average concentration. Therefore, when managing ammonia emissions, it is necessary to also account for the emissions rather than only the concentration standard. In the case of South Korea, both concentration and emission standards are implemented to regulate air pollutants.  
As mentioned earlier, most of the emission factors for biogas production developed abroad are based on N, which is mostly ammoniacal nitrogen, making it difficult to compare with the emission factors calculated in this study.  
However, in the case of the Netherlands, there is a case of applying wastes utilized for biogas production, which can be compared (Table 5). As a result of the comparison, the NH\({}_{3}\) emission factor in the Netherlands is 0.002 kgNH\({}_{3}\)/ton-waste, which is somewhat lower  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Mean** & **SD** & **Sampling** \\  & **(kgNH\({}_{3}\)/Tons-Waste)** & **(kgNH\({}_{3}\)/Tons-Waste)** & **Sampling** \\ \hline A biogas production facility & 0.001 & 0.001 & 31 \\ \hline B biogas production facility & 0.003 & 0.004 & 122 \\ \hline C biogas production facility & 0.007 & 0.013 & 84 \\ \hline Total & 0.005 & 0.009 & 237 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NH\({}_{3}\) emission factor based on the waste of the investigated biogas production facilities.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Mean** & **Min** & **Max** & **SD** & **Sampling** \\  & **(ppm)** & **(ppm)** & **(ppm)** & **(ppm)** & **Sampling** \\ \hline A biogas production facility & 1.65 & 0.04 & 8.80 & 2.10 & 31 \\ \hline B biogas production facility & 7.99 & 0.05 & 29.31 & 8.95 & 122 \\ \hline C biogas production facility & 2.41 & 0.04 & 28.47 & 4.70 & 84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: NH\({}_{3}\) concentration of the investigated biogas production facilities.  
than the 0.005 kgNH\({}_{3}\)/ton-waste developed in this study, but it is within the maximum and minimum range calculated in this study, indicating that the values calculated in this study are similar.
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 3 Electroanalytical Methods
### Conductometry  
In conductometric conducting polymer-based biosensors, a change in electrical conductivity or resistivity is measured against the analyte concentration when a constant or sweeping potential is applied between a reference electrode and a polymer-modified electrode. To increase the sensitivity of the sensor, the conducting polymer must be highly conductive when it is charged (doped) and lowly conductive when it is neutral (dedoped), thus leading to a strong conductivity change when the conducting polymer reacts with the analyte. Furthermore, the morphology of the conducting polymer is important since the charges that are created within the backbone of the polymer must be able to interact with the surrounding environment and thus to change the polymer's conductivity. However, these biosensors often suffer from their lack of selectivity since any change in conductivity in the solution modifies their signal.  
An example of conductometric biosensor was fabricated by [PERSON] et al. [84] who coated a pair of nanoelectrodes with PANI/GOx. Their exposure to glucose resulted in the reduction of GOx which was spontaneously reoxidized in the presence of oxygen to form hydrogen peroxide which oxidized the PANI, leading to a change in conductivity that can be monitored and used to determine the glucose concentration.
## 2 Laboratory Uniaxial Compression Test
### Test Results  
#### 2.3.1 Influence of Rock Content on Failure Characteristics Under the Compression Testing  
The uniaxial compression axial stress-strain curves for the seven groups of soil-rock mixture specimens with varying rock contents are shown in Figure 5. The results indicate that the curves exhibit a similar trend under varying rock content conditions, which can be divided into five distinct stages. In the initial stage of axial loading (OA stage), the axial stress shows a slightly upward concave trend at a small scale. The specimen then rapidly presents the linear elastic characteristics in AB stage until a stress drop occurs after reaching the peak compressive strength (as BC stage shown). During the CD and DE stages, the axial stress gradually decreases at a slower rate. The soil-rock mixture specimens exhibit various failure characteristics at different stages. In the OA and AB stages, no significant cracks were observed on the specimen surface, but localized fracture appeared once peak strength  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Test Number** & **Rock Content(\%)** & \begin{tabular}{l} **Volume of Rock** \\ **Blocks/(cm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{l} **Volume of** \\ **Soil/(cm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{l} **Mass of** \\ **Waer/g** \\ \end{tabular} & \
\begin{tabular}{l} **D\({}_{60}\)/** \\ **mm** \\ \end{tabular} &
\begin{tabular}{l} **D\({}_{10}\)/** \\ **mm** \\ \end{tabular} \\ \hline
1 & 0 & 0.0 & 375.0 & 61.3 & 0.232 & 0.014 \\
2 & 5 & 18.8 & 356.3 & 54.8 & 0.25 & 0.014 \\
3 & 10 & 37.5 & 337.5 & 49.1 & 0.32 & 0.016 \\
4 & 15 & 56.3 & 318.8 & 45.2 & 0.39 & 0.016 \\
5 & 20 & 75.0 & 300.0 & 50.9 & 0.48 & 0.016 \\
6 & 25 & 93.8 & 281.3 & 43.3 & 0.65 & 0.018 \\
7 & 30 & 112.5 & 262.5 & 38.8 & 0.87 & 0.020 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test scheme of soil–rock mixture with varying rock contents.  
was reached (Figure 5). As the axial strain increased, cracks on the surface expanded rapidly until the main cracks penetrated (Figure 5).  
The specimens with varying rock contents exhibited varying failure modes, as illustrated in Figure 6. These failure modes can be categorized into three types: longitudinal fracture, shear fracture, and multiple shear fracture. The soil-rock mixture specimens with 0-15% rock content displayed a main crack that penetrated through the specimen (Figure 6a). As the rock content increased, the distribution of rock blocks within the specimens became more pronounced, resulting in a greater number of cracks that tended to become disorganized upon failure (Figure 6b). Under a rock content of 30%, the cracks on  
Figure 4: Sample preparation and test equipment.  
Figure 5: Axial stress–strain curves and crack distributions of soil–rock mixtures with varying rock contents (the red dashed lines indicate the visible crack).  
the surface primarily expanded around the rock blocks, leading to a multiple shear fracture without a prominent through crack, as shown in Figure 6c.
## 5 Experimental Results
### Experiment Dataset and Metrics  
The dataset adopted in our experiments comes from an offline dataset used by an operation team of a group-buying website in China. With seven years of operation, the group-buying websitehas collected a share of large-scale user market in China and owns a comprehensive data system. The dataset used in this paper is taken from a half year (from June to December 2014) of its historical data, including ratings of 3043 users on 1628 items. The ratings are recorded on a numeric five-point scale (0, 1, 2, 3, 4). Table 4 shows the data structure of dataset with normalization.  
To make a comparison, the dataset was divided into a training set and a test set. As shown in Figure 4, the test set took from the data of 21-30 December 2014, from which data of one certain day were selected as the data validation object. On the other hand, the training set consisted of browser data of 7 days, add-to-favorite data and purchase data of 150 days, and all rating data before the selected day. Based on the training set, each user was recommended with several items, which will be compared with the data in the test set.  
There are several types of metrics to evaluate the quality of a recommendation system such as accuracy, coverage, diversity and novelty. In this paper, MAE, precision, recall and F1 score were chosen as evaluation metrics. MAE represents the deviation of recommendations from actual user-specified values. The lower the MAE is, the more accurate the rating prediction is. Precision is the percentage of selected items that are \"relevant\"; and Recall is the percentage of relevant items that are selected.  
Overall, the greater the value of precision and recall are, the more effective the prediction is. However, the emphasis of the two metrics is different. The aim of precision is to return mostly useful items, while recall is designed to avoid missing useful items. F-measure is a comprehensive metric; the greater the value of F1 is, the more effective the recommendation service is.
## 2 Methods and Materials
### Performance Evaluation Index [17; 18]
#### 2.3.1 MAE  
The Mean Absolute Error (MAE) is the average of the absolute values of the errors [19; 20], and its definition is given by Equation (8).  
\[(MAE)=\frac{1}{N}\underset{i=1}{\overset{n}{\sum}}|est_{i}-t_{i}| \tag{8}\]  
\(n\) represents the total number of pixels, \(est_{i}\) denotes the pixel values of the reconstructed image, and \(t_{i}\) represents the pixel values of the true image, where \(|est_{i}-t_{i}|\) represents the absolute error. The smaller this value, the smaller the error in the estimated values of the reconstructed image.
## 4 HPS-Based Catalysts for the Synthesis and Transformation of Platform Chemicals
### Magnetic Catalysts Based on HPS in the Conversion of Plant Polysaccharides
It was shown that after hydrogenation of the main part of fructose, the hydrogenation of glucose, and, consequently, the formation of sorbitol becomes more pronounced, and the mannitol/sorbitol ratio in the final solution decreases. However, with hydrolytic hydrogenation of inulin, the mannitol/sorbitol ratio is approximately 30% higher than with the hydrogenation of a mixture of glucose and fructose. As a result, the authors concluded that the stereoselectivity of the hydrogenation of D-fructose units in partially hydrolyzed inulin oligomers is higher than in the hydrogenation of pure fructose. In [161], Ru-containing catalysts based on a Cs-substituted tungsten phosphate support (Ru/Cs\({}_{\rm x}\)H\({}_{3}\)-xPW\({}_{12}\)O\({}_{40}\)) were proposed for the hydrolytic hydrogenation of cellulose and inulin. The total yield of hexitols (sorbitol and mannitol) during the hydrolytic hydrogenation of inulin was 84%. The authors noted that the new catalyst exhibits high activity, despite the absence of strong internal Bronsted acid sites. For the first time, magnetic catalysts based on mesoporous silicon dioxide for this process were proposed by our group [162]. Testing a new magnetic catalyst based on HPS in the hydrolytic hydrogenation of inulin to mannitol was carried out under previously determined optimal conditions: 0.1167 mmol Ru per 1 g of inulin; 30 mL H\({}_{2}\)O; 150 \({}^{\circ}\)C; and P(H\({}_{2}\)) 60 bar, 45 min. The results obtained are presented in Table 6. Inulin conversion was 100% for both catalysts. It can be seen that the selectivity to mannitol for 3% Ru-Fe\({}_{3}\)O\({}_{4}\)/MN270 is slightly higher than for the 5% Ru-Fe\({}_{3}\)O\({}_{4}\)/SiO\({}_{2}\)[162]. However, considering the lower ruthenium content in the polymer catalyst, the productivity of 3% Ru-Fe\({}_{3}\)O\({}_{4}\)/SiO\({}_{2}\) is almost twice as high. Figure 6: Scheme of the hydrolytic hydrogenation of inulin [107]. The 3% Ru-Fe\({}_{3}\)O\({}_{4}\)/MN270 was stable when reused in the hydrolytic hydrogenation of inulin. It was shown that the selectivity to mannitol and the catalyst activity do not change after three consecutive reactions, demonstrating excellent stability in the inulin-to-mannitol conversion. Magnetic properties of the catalyst allow easy separation from the reaction mixture without any loss. In general, the results of our study confirm that magnetic catalytic systems based on HPS are promising in the conversion of plant polysaccharides into substances of high added value.
## 4 Case Study
### Performance Evaluation of the Proposed Algorithm  
To evaluate the proposed algorithm, comparisons were made with improved particle swarm optimization (PSO) proposed by [PERSON] and [PERSON] [31], the Tabu search (TS) proposed by [PERSON] et al., [32], and the genetic algorithm (GA) proposed by [PERSON] and [PERSON] [33]. The parameters of GA, PSO, and TS were set by trial and error. The scheduling results of PSO-TS, GA, PSO, and TS are shown in Table 1, including the best makespan (BST) and the average makespan (AVG) of ten independent runs. The best BST results of each instance are set in bold. From Table 1, it is shown that the BST and AVG valuesobtained by PSO-TS were better than those obtained by the other three algorithms on all instances, which demonstrated that PSO-TS had the superior searching quality and robustness. In most of the instances, GA performed better than PSO and TS, but it was not consistent. In instance A7, the BST and AVG of TS performed better than GA. In instance X7, the BST and AVG of PSO performed better than GA.  
To compare results obtained in different instances, relative percentage deviation (RPD) was introduced as the only dependent variable of the variance analysis, as shown in equation (28), where \(Alg_{sol}\) represents the objective value obtained by a single algorithm running, and \(BST_{sol}\) represents the best solution over the whole set of results concerning the same instance. Obviously, the smaller the RPD value, the better the result. \[RPD=\frac{Alg_{sol}-BST_{sol}}{BST_{sol}}\times 100 \tag{28}\]  
The ANOVA and least-significant difference (LSD) tests were conducted in SPSS to check the results transformed into the RPD value. Test results revealed that under a confidence interval of 95%, the \(p\) value was 0, which means there were significant differences in the performance of the four algorithms. Figure 9 depicts the mean plot with the LSD intervals for the RPD value obtained by the four algorithms. In this measure, the proposed algorithm outperformed the other three algorithms.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Inertial Navigation Data Mining  
Inertial navigation devices have become indispensable components in smartphones. The acceleration sensor, in particular, helps us determine the user's state, allowing us to infer scene information from these state characteristics [22; 23]. For example, if a user is in a moving vehicle, the likelihood of being in an outdoor environment can be inferred from the satellite visibility data, indicating the presence of outdoor scene information. Similarly, if the user is engaged in regular human movement, such as walking or running, there is also a high probability of being outdoors.  
Figure 6 illustrates the relationship between the acceleration values from the smartphone's inertial sensor and the corresponding motion states. We capture the raw information and then filter it to minimize noise and errors.  
Figure 6: State of motion versus acceleration.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
###### Abstract  
Cloud storage services have become indispensable in resolving the constraints of local storage and ensuring data accessibility from anywhere at any time. Data deduplication technology is utilized to decrease storage space and bandwidth requirements. This technology has the potential to save up to 90% of space by eliminating redundant data in cloud storage. The secure data sharing in cloud (SeDaSC) protocol is an efficient data-sharing solution supporting secure deduplication. In the SeDaSC protocol, a cryptographic server (CS) encrypts clients' data on behalf of clients to reduce their computational overhead, but this essentially requires complete trust in the CS. Moreover, the SeDaSC protocol does not consider data deduplication. To address these issues, we propose a secure deduplication protocol based on the SeDaSC protocol that minimizes the computational cost of clients while leveraging trust in the CS. Our protocol enhances data privacy and ensures computational efficiency for clients. Moreover, it dynamically manages client ownership, satisfying forward and backward secrecy.  
d eduplication; cloud storage; data sharing; message-locked encryption; dynamic ownership update
### Literature Review (Analysis) of the Impact of Climate Variables on Hydropower Generation  
Despite the importance of hydropower generation to the adequacy of a power system, there is, to the author's knowledge, little literature that addresses modelling the impact of climate variables on hydropower generation (both run-of-river and storage) with respect to the adequacy of a power system [26]. The most important aspect of adequacy analyses is that they are carried out using statistical methods based on probability theory and stochastic modelling of generation and demand profiles. In this context, there is a lack of work in the literature that models synthetic profiles of meteorological and hydrological variables for the purpose of adequacy analyses. The study and theorising of these types of models are crucial today but will become even more important in the future as trends in weather variables continue to change due to climate change.  
This paper attempts to fill the gap in the literature. First, linear regression is used to search for empirical relationships linking soil moisture (expressed as soil water content saturation fraction) and potential evapotranspiration, which is evaluated for a reference crop for specific geographical locations. The purpose of this evaluation is that daily time series of potential evapotranspiration can be derived from the soil moisture data. Since precipitation is the main driver of the hydrological cycle, hourly precipitation trends are analysed next. This can be useful because while the direction of the impact of global warming on air and ocean temperature trends is well known, the direction of the impact on precipitation is still a subject of scientific debate [27; 28]. Furthermore, the analysis of precipitation trends allows conclusions to be drawn about the development of water availability for hydropower plants. This is because the main source of water availability for a power plant is direct runoff, which in turn results from precipitation. For this reason, two models from the literature are presented here that can be used to assess direct runoff: one model considers only the precipitation intensity (mm/h), while the other includes the daily precipitation amount (mm). Since adequacy analyses are carried out with statistical approaches [12], this work also uses the Monte Carlo method for the stochastic generation of hourly precipitation and direct runoff profiles as well as daily soil moisture profiles. In this way, synthetic profiles are obtained that form the building blocks on which simulations of hydropower generation profiles can be built for future research. Finally, to complement the analyses just described, additional physical considerations are added to the assumptions and considerations.  
All simulations are performed in MATLAB.  
The paper is organized as follows: Section 2 explains in detail the methodologies implemented, the results of which are in Section 3. Section 4 provides final considerations on the methodologies and the results obtained.