esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Sugarane Biomass Use to Produce Green Hydrogen: Brazilian Case Study
## 1 Introduction
(2022) reviewed the latest advances in pretreatment and hydrolysis technologies for biohydrogen production from straw biomass and other lignocellulosic materials, underlining the need for innovative methods to improve the hydrolysis process, which is crucial for sugarcane-based biohydrogen production [33]. Similarly, studies by [PERSON] et al. (2023) demonstrated the use of nano-metal doped calcium peroxide for biomass pretreatment, resulting in enhanced biohydrogen yields from rice straw, which could be applied to sugarcane and other agricultural residues [56]. In addition to pretreatment innovations, life cycle analysis (LCA) has been increasingly applied to assess the sustainability of biohydrogen production from biomass. [PERSON] et al. (2022) conducted an LCA of biohydrogen production from sugarcane bagasse, demonstrating that the environmental impacts can be minimized by optimizing both the pretreatment and fermentation processes [53]. This aligns with the research by [PERSON] et al. (2023), who assessed the environmental impacts of biohydrogen production from rice straw and proposed integrated approaches that could reduce energy consumption and improve the sustainability of biohydrogen production from agricultural residues [57]. In conclusion, the reviewed literature underscores the immense potential of sugarcane biomass for biohydrogen production, emphasizing the importance of optimizing pretreatment processes, enhancing fermentation conditions, and integrating biohydrogen production with other bioenergy processes. By incorporating the latest advancements in pretreatment technologies, enzymatic hydrolysis, and microbial fermentation, researchers have made significant strides in improving the efficiency and sustainability of biohydrogen production from sugarcane. The future of biohydrogen production lies in the continuous improvement of these technologies, ensuring that sugarcane biomass can contribute effectively to the global transition toward a sustainable and circular bioeconomy. As we move forward to explore the energy landscape in Brazil, it becomes evident that the country, with its vast sugarcane production, holds a strategic position in implementing these technologies for the future of global energy.
# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 2 Methods
### Human Trajectory Prediction with an Improved Socially Conscious Model
\[p_{i}^{t}=\varphi\left(x_{i}^{t},y_{i}^{t};W_{p}\right) \tag{2}\]  
\[h_{i}^{t}=LSTM\left(h_{i}^{t-1},concat\left(p_{i}^{t},S_{ij}^{t}\right);W_{r}\right) \tag{3}\]  
The hidden states, \(h_{i}^{t}\), are used as the input of the fully connected layer, \(W_{n}\), to predict the distribution of human trajectory, which is a bivariate Gaussian distribution with a three-dimensional vector, including the parameters, \(\left(h_{i}^{t+1},\sigma_{i}^{t+1},\rho_{i}^{t+1}\right)\). \[\left(\mu_{i}^{t+1},\sigma_{i}^{t+1},\rho_{i}^{t+1}\right)=W_{n}h_{i}^{t} \tag{4}\]  
The main structure of the socially conscious model for the four pedestrians is shown in Figure 2. The middle dashed box represents the neural network structure, where each LSTM unit processes a pedestrian's trajectory and connects to others through a socially conscious pooling layer. To improve the prediction accuracy of the model, during the training process, we add a pooling layer, \(e_{i}^{t}\), to the socially conscious model between the current pedestrian trajectory sequence input and position estimation, which is a two-dimensional Gaussian distribution, resulting in an improved socially conscious model. \[e_{i}^{t}=\varphi\left(x_{i}^{t},y_{i}^{t};W_{e}\right) \tag{5}\]  
\[L_{i}=-\sum_{t=t_{obs}+1}^{T_{pred}}\log\left[(1-a)*\left(P(x_{i}^{t},y_{i}^{t }|\mu_{i}^{t},\sigma_{i}^{t},\rho_{i}^{t})\right)+a*e_{i}^{t}\right] \tag{6}\]  
The adjusted loss function, \(L_{i}\), is shown in Equation (6), where \(a\) is the adjustment parameter (here, it is set to 0.3). After the model is trained, a complete pedestrian trajectory prediction model is obtained. Taking the observed historical trajectory at time step, \(t\!=\!1,2,\ldots,T_{obs}\) as input, the estimated position \(\left(\vec{x}_{i}^{t},\vec{y}_{i}^{t}\right)\) can be obtained by sampling from the bivariate Gaussian distribution as follows:  
Figure 1.0  
\(N(\cdot)\) randomly outputs a two-dimensional normal distribution random number according to the input parameters.
###### Abstract  
The aim of this study was to develop a baseline [18]FFDG PET/CT model to predict immunotherapy response in advanced cutaneous squamous cell carcinoma (cSCC) and noninvasively determine tumor grade, thereby enhancing early patient stratification. We retrospectively analyzed 59 patients with histologically confirmed advanced cSCC submitted to immunotherapy with cemiplimab. All underwent [18]FFDG PET/CT at baseline and after approximately 12 weeks. Clinical response was assessed through PET findings integrated with clinical and dermatological evaluation, and patients were classified as responders (complete/partial metabolic response or stable disease) or non-responders (progression or toxicity-related discontinuation). Tumors were also classified as low to intermediate (G1-G2) or poorly differentiated (G3). Machine learning models (Random Forest and Extreme Gradient Boosting) were trained to predict treatment response and tumor grade. Clinical benefit was observed in 46/59 patients (77.9%), while 13 (22.1%) were non-responders. Histology showed 64.4% (n = 38) G1-G2 and 35.6% (n = 21) G3 tumors. The PET-based model best predicted clinical benefit (AUC = 0.96, accuracy = 91% cross-validation; AUC = 0.88, accuracy = 82% internal validation). For tumor grade prediction, the CT-based model achieved a higher AUC of 0.80 (accuracy 73%), whereas the PET-based model reached an AUC of 0.78 but demonstrated a slightly higher accuracy of 77%. Radiomic analysis of baseline [18]FFDG PET enables the discriminative prediction of immunotherapy response and tumor grade in advanced cSCC, with PET-based models outperforming CT-based ones.  
immutotherapy; skin tumors; cutaneous squamous cell carcinoma; PET/CT, radiomics; artificial intelligence
## 3 Material and Methods
### ECG Signal Preprocessing
#### 3.3.1 Baseline Removal  
The baseline wanders the voltage at which all cardiac muscle cells are at rest. The subject's breathing, movement, or electrode displacement causes baseline wander, which is a low-frequency noise. As a result, amplitude shifts make the detection of the R peak more difficult. The baseline signals are removed from the following databases, the average value of the signal at the first stage is calculated, and the average value from the signal is subtracted. The equation of baseline removal is given below [47]:  
\[mean_{ECG}=\frac{1}{N}(\sum_{i=0}^{n}ECG) \tag{1}\]  
\[new_{sig}=ECG-mean_{ECG} \tag{2}\]  
where \(ECG\) denotes the original signal and \(N\) indicates the total number of data points in the signal. The \(mean_{ECG}\) value was calculated by dividing the sum of all data points by the total number of data points. The original signal is then subtracted from the value of the \(mean_{ECG}\) variable, which is then saved in the \(new_{sig}\) variable.
## 1 Introduction
### Opportunities in Industry 4.0 Compared to Industry 3.0  
The characteristics of the third industrial revolution have allowed for improved efficiency and precision in manufacturing production [7]. Despite this, they have disadvantages compared to the new Industry 4.0 era in terms of the lack of interconnectivity, the limited ability to customize products in batches, the reliance on sub-processes within the production chain to adapt quickly to real-time changes and even the technological skills that factory maintenance operators must possess [8]. Consequently, for corporations, SMEs and MSMEs to be more productive and competitive nationally and globally, it is essential to scale up to Industry 4.0, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), computer vision (CV), distributed computing systems (cloud, fog and edge), wireless connectivity and big data [9; 10; 11; 12].  
Despite the advantages of I4.0 over I3.0, there are case studies that add functionalities to Industry 4.0 which are supported by traditional architecture standards such as ISA-95/ISA-88 for handling end-to-end integration, automation and communication between management and control systems [13; 14], considering that the implementation of an architecture that supports n MES with microservice scenarios for SMEs is possible [15]. However, its classical structural characteristics make it depart from a distributed architecture, i.e., flexibility is limited for the integration of emerging computing ITs [16]. To mitigate these issues during a precise and robust industrial scaling, it is necessary to have an architectural reference framework that plays a guiding role for the integration of advanced technologies into modern industrial infrastructures, thus achieving a roadmap for digital transformation [17; 18].
## 5 Case Study
### Experiment Results  
#### 5.4.1 Measured Error Distribution  
As can be seen from the measured error distribution in the form of a heat map, the maximum deviation in this area is 1.4 m, located at static label T1, and the minimum deviation is 0.22 m, located at static label T6 in Figures 13 and 14. The average positioning error in this environment is 0.78 m. The reason for the small error in the center of the  
Figure 12: The atmosphere parameter sampling system consists of sensor hardware and cloud software.  
Figure 13: The error distribution of intelligent manufacturing experiment production lines. Note, T1, T2,... T9 are the static points used for UWB-Tag position calibration in this lab.  
region is that the static points here can receive signals from the four base stations evenly. However, the positions with large error values are generally distributed near obstacles and base stations. First, the blocking of obstacles leads to interference in signal transmission. Secondly, the static point close to the base station cannot accurately receive the signal from the base station farther away.
# Electromechanical Behavior of Afyonkarahisar Clay Under Varying Stress and Moisture Conditions
## 3 Test Results and Discussions
### Electrical Resistivity and Conductivity  
Figure 3 illustrates the variations in stress and strain levels, along with corresponding changes in electrical resistivity and conductivity. These measurements provide a detailed understanding of how clay with different water contents responds to mechanical loading and how its electrical properties evolve throughout the compression process.  
At lower initial water content, the early stage of loading led to a reduction in porosity due to compression, which in turn influenced soil conductivity and caused a decrease in electrical resistivity. This initial decline in resistivity is attributed to the closer packing of soil particles, reducing the void spaces and altering the conductive pathways. However, as the compression progressed, the material reached a critical stress level where microcracks began to form. These cracks disrupted the continuity of conductive pathways, leading to an increase in electrical resistivity. In the later stages of loading, as the crack density increased significantly, a rapid rise in resistivity was observed, indicating a sharp transition in the material's electrical response.  
These findings emphasize the nonlinear nature of electrical resistivity behavior under uniaxial compression, particularly in cohesive soils with varying water content. The results provide valuable insights for geotechnical engineering, especially in applications related to soil stability analysis, foundation design, and the assessment of soil behavior under different loading and moisture conditions.  
When the above graphs are examined in general, it is seen that the electrical resistivity values under stress decrease with the increase in the amount of water in the clay samples. On the other hand, it is seen that the electrical conductivity values increase.
## 3 Results and Discussion  
To verify the validity of the proposed method, experiments were designed in the following three parts. In the first part, the deformation mechanism was analyzed based on the GNSS and rainfall data from the monitoring system. In the second part, an orthogonal test scheme was used to generate the training samples, and the validity of the DBA-LSTM algorithm was verified by comparing it to DBA-BPNN. In the third part, based on the measured data, the DBA-LSTM was used to invert the shear strength parameters of this landslide, the equivalent mechanical parameters were substituted into the numerical model, and the corresponding displacement warning thresholds were then calculated by simulating the failure conditions.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 4 Materials and Method
### Recommended Models for Attack Detection
#### Deep Learning-Based Models  
The deep learning-based models used in the study are explained in this section. Analyses were made on the LSTM models, the CNN models, and the hybrid models in which LSTM-CNN were used together.  
Since categorical data were included for all three proposed models, categorical_crossentropy was chosen as the loss function. Adaptive moment estimation (ADAM) was used as the optimization algorithm because it works efficiently on datasets containing many parameters [32]. In order to ensure stability, the rectified linear units' (ReLU) activation function was preferred. ReLU has a simple computational form and determines the output by evaluating the input [33]. The batch size was left by default. The softmax function was used to finish the classification.
## 2 Methods  
Figure 1 shows the flowchart of the proposed method to mitigate the ionospheric phase error on full-polarimetric SAR interferometry. The scheme mainly involves three aspects: estimation of the FR angle from the measured scattering matrix, transformation of the FR angle to the VTEC distribution, and correction of ionospheric phase error. The detailed processing approaches are described below:
## 5 Conclusions  
Several aspects of this work may have important implications for future LULCC characterization for research and management applications. In brief:  
1. The combined accuracy optimization techniques improved the detection of urbanizing land-cover by increasing the mapped development-related change area by 93% in the AO25 map compared to the base map produced by the initial prediction model, the RF50 map and raised the total number of individual mapped change locations by 239% from 2348 to 5607 (Table 2).
2. A total change estimate was generated by both \"eliminating\" commission and estimating omission. That estimate was comprised of 86.6% mapped change events and 13.4% estimated unmapped change.  
3. A software system was developed to assess training data locations and to determine classification accuracy that greatly reduced analyst review time allowing review of 1000s of accuracy assessment and training data locations in the time previously required to view 100s.
4. Searching for additional change through threshold lowering is a powerful technique for optimizing change mapping when using a probabilistic classifier like RF and a system for rapidly checking predictions as developed here.
5. The image data used in this analysis is available for many parts of the United States and the techniques described here are applicable to any object-based mapping exercise using imagery of high enough resolution to verify classifications through manual photo interpretation.  
Previous LULCC maps have tended to capture either big events over large regions using medium resolution data and remote sensing analyses or small-to-large events over small regions using high resolution data and photo interpretation. To achieve fine-scale change mapping over large extents remote sensing analyses were used to locate potential small-to-large events over large regions using high resolution data. Then photo interpretation was employed over the much smaller though widely dispersed candidate regions to complete mapping those changes. Thus, traditional remote sensing techniques were used to find locations for photo interpretation. Recreation of the full system does involve significant software resources including ESRI ArcMap, Erdas Imagine and Trimble eCognition [71, 75, 87]. For large regions (images greater than 10 k \(\times\)10 k pixels) the eCognition server software is highly recommended.
## 5 Hardware System Design
### Spatial Parallelization  
Finding the best match is a nested stereo-matching process, as shown in Figure 11. After selecting a target micro-image with respect to \(I_{r}\), matching is performed within its physical boundaries. Epipolar geometry is employed in the design in order to reduce the computation efforts. The maximum number of pixels, represented by \(M\), that can lie on an epipolar line \(e_{p}\) is equal to the diameter \(d_{L}\) of the micro-lens minus its border \(b_{\mu}\). For Raytrix R5 camera, \(M\) is calculated to 22 using following equation:  
\[M=d_{L}-b_{\mu}. \tag{15}\]  
From Figure 11, it is clear that matching process is recursive in nature. It therefore enables the proposed design to use the spatial parallelization, as depicted in Figure 12. Each correlation with target micro-image pixel \(P_{ij}\) computes error of stereo-matching and corresponding disparity. If the latency of each comparison is \(N_{s}\) clock cycles, the total latency to finish the matching process of hardware which ensures concurrency is ideally \(N_{s}\) clock cycles.  
Figure 11: Stereo-matching between reference and target micro-images.  
Figure 10: Selection process of target micro-images entailing temporal parallelism.
# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 3 Method
### Training Process
#### 3.3.1 Dataset  
To implement the training process illustrated in Figure 3, it was necessary to prepare a dataset that would simultaneously provide high-quality images at relatively high resolutions, while incorporating potential manipulation operations and corresponding ground truth masks. To the best of the authors' knowledge, no publicly available dataset of this kind currently exists [40; 41; 42]. For this reason, a custom dataset was created based on OpenImage V7 [43], which offers images with relatively high resolutions and diverse aspect ratios. The OpenImage V7 dataset served as the primary source for training and validation. The dataset included 30,000 images for training, 30,000 for validation and 30,000 for testing. Additionally, the training dataset was expanded with 25,719 images from the HOLLYWOOD2 Human Actions and Scenes Dataset [44], which contains frames extracted from various film scenes. Another data source was a collection of clipart images downloaded from the Free SVG database [45], comprising 30,067 illustrations. These pieces of clipart, often used in presentations and graphic design projects, provided additional data diversity for model training. In this study, these images were used to simulate manipulations, including inserting additional objects into selected image areas, which helps bring training conditions closer to more complex visual alterations. For testing purposes, resources from OpenImage V7 were also used. However, due to the relatively low resolution of most images, with a maximum size of approximately 1024 \(\times\) 1024, the test set was further expanded with an additional 30,000 high-resolution photographs in HD, Full HD, 2K and 4K formats, such as 1280 \(\times\) 720, 1920 \(\times\) 1080, 2560 \(\times\) 1440 and 3840 \(\times\) 2160, as well as square equivalents 720 \(\times\) 720, 1080 \(\times\) 1080, 1440 \(\times\) 1440 and 2160 \(\times\) 2160. These images were sourced from the bghi-ra/photo-concept-bucket dataset [46] published on HuggingFace. As a result, the final test dataset included 60,000 images with resolutions ranging from 560 \(\times\) 1024 to 3840 \(\times\) 2160, allowing for a comprehensive evaluation of the algorithm's effectiveness across a wide range of resolution and aspect ratio variations.  
To conduct additional comparative tests while maintaining the same parameters, a separate testing process was performed on the ImageNet-1K [47] dataset, which contains 100,000 images. The choice of this dataset was primarily driven by the need to compare the transparency of the hidden artifacts in the image with other data-hiding methods. Since many existing solutions in this field use ImageNet-1K as a primary reference dataset, its inclusion allows for a more objective and reliable evaluation of the effectiveness of the proposed method. Therefore, we used an additional test dataset alongside our dataset to ensure the most credible test results.
## 4 Results and Discussion
### Model Test Results  
There are a total of 60,010 student records in our dataset. Among them, the number of graduates is 29,099, the number of dropouts is 6963, and the remaining 23,948 students are in school or suspended from school. We divide the dataset as follows:  
* Training set: 70% of all graduates and dropouts, a total of 25,244 pieces of student data;
* Test set: 30% of all graduates and dropouts, a total of 10,818 pieces of student data;
* Prediction set: a total of 23,948 students in school or suspended from school were used to predict the possible dropouts in the future. Table 5 shows the test results obtained by Logistic Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machines, and XGBoost algorithms, reporting on the most popular indicators of success: accuracy, precision, recall, and F1 Score. As shown in Table 5, the LightGBM model in the mean value-based feature table obtained the highest F1 score and accuracy on the test dataset with 79% and 94%, respectively. The precision value is 81%, which is only 2% different from the highest value of 79%. More specifically, the LightGBM model has relatively balanced precision and recall values, which means that the model can accurately distinguish dropout students from non-dropout students without much bias in the case of unbalanced samples. The XGBoost model in the median value-based feature table obtained the best precision and the second-best F1 score and accuracy. Figure 2: Kaplan Meier Plot for Student Dropout (\(n=6963\)). Figure 3 reveals the performance of the feature tables and models in more detail. LightGBM and XGBoost have similar performance, while Logistic Regression, Support Vector Machine, and Decision Tree are insufficient (Figure 3A). Because both the LightGBM and XGBoot models are based on the gradient boost technique, the result reveals the superiority of the gradient boosting technique in predicting student dropout. In Figure 3B, the mean value-based feature table has the highest F1 Score (70%), precision (66%), and recall (76%). On the contrary, the average F1 Score, precision, and recall of the first and final semester-based feature table are significantly lower than the mean and median value-based feature table. This demonstrates that using features that include historical student data gets better predictive performance than using data from a particular point in time (semester). This result is also intuitive; for example, if a student in the third semester had excellent grades in the first two semesters but declined in the third semester, it would be inaccurate to consider only the first two semesters or the third semester when predicting dropout.