esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It provides the following services:  
1. Acquisition, storage, integration, management, and exchange of building data from various sensors, meters, and IoT devices. 2. Decision-making support for energy retrofits, allowing testing and simulation of different options, scenarios, and system integrations. 3. Post-retrofit retro-commissioning of energy systems and automation and control systems. 4. Data-driven performance optimization platform for building control and management. Digital twin solutions were already successfully implemented in various sectors, such as the manufacturing and automotive industries, and their market is expected to grow rapidly in the coming years. However, their adoption in the building and construction sectors is still in its early stages, with limited focus on existing buildings or highly energy-efficient structures. Considering that existing buildings make up over 97% of the overall building stock, this work represents the first initiative in Denmark to harness the potential of digital twins to optimize retrofit processes, increase renovation rates, improve overall performance, and decarbonize the existing Danish building stock. Although there were several initiatives and projects investigating the incorporation of digital twins in the realm of building applications, the primary focus is mainly on newly constructed buildings and facilities that boost high energy efficiency. These buildings are equipped with integrated building information models (BIM). Conversely, pre-existing and older buildings often lack any semblance of digital representations, posing a challenge for the implementation of digital twins in such contexts. This research introduces an innovative digital twin solution for existing building applications called \"DanRETwin.\" This solution aims to offer support for decision-making, retro-commissioning, and data-driven performance enhancement specifically for non-residential existing buildings. The proposed approach will harness operational data from buildings and leverage machine learning and artificial intelligence techniques to construct scalable data-centric models for building energy. Additionally, the utilization of clamp-on IoT sensors will facilitate data collection, resulting in a completely automated and versatile solution. Through the adoption of DanRETwin, building owners can expect heightened energy efficiency and increased comfort in their retrofitted structures. Facility managers will gain access to an advanced monitoring system that streamlines the systematic retro-commissioning of recently upgraded buildings, mitigating issues and curbing losses. Consultants will be presented with a potential avenue for retrofitting, augmenting, and optimizing their clients' building performance, empowering them to make informed decisions and data-backed interventions. Simultaneously, urban planners will possess an efficient, adaptable tool that supports the expansion of retrofit endeavors and the assessment of diverse scenarios.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Warehouse and Inventory Management
#### 4.2.3 Warehouse Management System (WMS)  
This case study focuses on a large food-processing factory specializing in frozen vegetables for 100% export. Despite using a legacy ERP system, the company faced complications in warehouse operations. The challenges stemmed from the simultaneous production of various products, both made-to-order and made-to-stock. The warehouse had a large and diverse volume of finished goods, with known volumes but unpredictable sequences. This resulted in inefficient put-away processes, often requiring product relocation.  
To resolve these issues, an add-on sequencing solution was developed and integrated with the existing ERP system. This enhancement led to a 21% reduction in warehouse operation costs.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning
#### 4.1.7 Mimo  
In [61], the authors propose a combination of ML-estimators, using CNN with Autoregressive Network (ARN)) for predicting Channel State Information (CSI) and RNN for channel prediction in massive MIMO systems with channel aging property. Results show that proposed model can improve the prediction accuracy and user's throughput gains for both low and high mobility scenarios. In [62], the issue of channel mapping in space and frequency domain in massive MIMO is addressed, by using a novel supervised deep learning approach, reducing overhead in both the training and feedback aspects.
## 1 Introduction
### Scope of Our Work and Contribution  
Combined with previous research results, this paper proposes An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets, for strip steel defect detection. The main contributions of the paper are summarized below:  
(1) For strip steel with a low defect rate, edge false defects and illumination interference, which make it difficult to extract various kinds of real defects, we present an improved strip steel surface defect detection algorithm based on gray-scale projection. The algorithm can filter out defect-free surfaces, edge false defects, and illumination interference, and extract defects on the surface effectively. The algorithm is especially effective for longitudinal cracks, transverse cracks, and large area surface defects.  
(2) In view of the category imbalance problem, starting from the data level approach method, this paper proposes a data augmentation algorithm based on ROI region random crop for uneven categories, to achieve sample balance in the categories.  
The remainder of this paper is organized as follows. In Section 2, we compare our work with related research, with a focus on surface defect detection algorithms and imbalanced learning algorithms. In Section 3, by the use of our improved gray projection algorithm, we undertake detector rapid screening of the strip steel surface and extract defect regions. In Section 4, our ROI image augment algorithm augments smaller classes and makes images balance again using the data level approach method. In Section 5, the improved transfer learning deep neural network based on VGG19 detects and identifies the few samples and imbalanced strip steel dataset. In Section 6, we compare the real-time performance between our network and some deep learning detection algorithms. Section 7 concludes this paper with some future research directions. In Figure 1, we present the overview of the strip steel defect detection method of this paper.
## 4 Experiment and Security Analysis
### PVD Analysis  
The difference between two neighboring pixels represents the smoothness of an image, which can be displayed via the pixel value differences (PVD) histogram. Since the message embedding will affect the pixel values, one can examine the shape of the PVD histogram to reveal any secret messages hidden in an image for steganalysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**IW-MSE**} & \multicolumn{2}{c}{**PSNR**} \\ \cline{2-7}  & \(I_{s1}^{\prime\prime}\) & \(I_{s2}^{\prime\prime}\) & \(I_{s1}^{\prime\prime}\) & \(I_{s2}^{\prime\prime}\) & \(I_{s1}^{\prime\prime}\) & \(I_{s2}^{\prime\prime}\) \\ \hline
1 & 0.6003 & 0.6001 & 0.15196 & 0.14961 & 50.3472 & 50.3484 \\
2 & 0.6000 & 0.5995 & 0.15062 & 0.15014 & 50.3496 & 50.3532 \\
3 & 0.6003 & 0.5995 & 0.14839 & 0.15128 & 50.3468 & 50.3528 \\
4 & 0.6009 & 0.5998 & 0.15121 & 0.15028 & 50.3425 & 50.3505 \\
5 & 0.5996 & 0.5994 & 0.15016 & 0.14969 & 50.3519 & 50.3533 \\
6 & 0.6007 & 0.5996 & 0.14839 & 0.14867 & 50.3441 & 50.3523 \\ Avg. & 0.6003 & 0.5997 & 0.15012 & 0.14995 & 50.3470 & 50.3518 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Full-reference image quality assessment for six test images (MSE, IW-MSE, PSNR).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**SSIM**} & \multicolumn{2}{c}{**Q-Index**} & \multicolumn{2}{c}{**VSI**} & \multicolumn{2}{c}{**IW-SSIM**} \\ \cline{2-9}  & \(I_{s1}^{\prime\prime}\) & \(I_{s2}^{\prime\prime}\) & \(I_{s1}^{\prime\prime}\) & \(I_{s2}^{\prime\prime}\) & \(I_{s1}^{\prime\prime}\) & \(I_{s2}^{\prime\prime}\) & \(I_{s1}^{\prime\prime}\) & \(I_{s2}^{\prime\prime}\) \\ \hline
1 & 0.99974 & 0.99974 & 0.999996 & 0.999996 &Figure 12 shows the PVD histogram in \(I_{\text{uncyt}}^{\prime\prime}\), which is considered as the cover, and the PVD histogram in the dual stego images, \(I_{s1}^{\prime\prime}\) and \(I_{s2}^{\prime\prime\prime}\), are denoted as \"s1\" and \"s2,\" respectively. The diagrams of three PVD histograms closely overlap, indicating that there is no substantial change in the shape of the PVD histograms before and after the message concealment. Thus, our algorithm preserves the smoothness in \(I_{s1}^{\prime\prime\prime}\) and \(I_{s2}^{\prime\prime}\), and its ability to resist the PVD histogram attack.
# Network Visualization of Cooperative Games
## 6 Examples
### Myerson Games
#### 6.5.4 Myerson Chain Cost Game  
This example deals with the Myerson Chain Cost Game in a communication situation represented by a chain of ten players. In this cost share game, the worth of each player in the chain is influenced by their position and the importance of forming continuous coalitions. Players positioned in the middle of the chain have a higher strategic value because they act as bridges or connectors. This behavior is reflected in Figure 13 and Table 13.  
Figure 12: Myerson Cycle Message Game visualized as \((Sh_{a^{x}=0,a^{x}=0^{y}},I_{\beta^{x}=1,\beta^{y}=0})\).  
Figure 13: Myerson Chain Cost Game visualized as \((Sh_{l},I)\).  
In the context of this Myerson Chain Cost Game, the analysis of the metric of weighted degree presented in Table 14 reveals an alignment among the \(I\), \(I^{+}\), and \(I^{|\cdot|}\) values. This alignment is obvious, given the game's structure, where negative relationships are absent, showing a collective preference towards establishing cooperation. The observed variability is minimal, underscored by a low Gini Index and limited data dispersion. Contrary to previous observations, the weighted degree distribution exhibits a negative skewness, suggesting a left-skewed distribution, alongside a clustering tendency that surpasses that of a normal distribution (kurtosis = 0.25).
## 3 Experimental Verification of Shaft System Over-Critical Vibration Control
### Analysis of Experiment Result  
Figure 13 shows the critical time domain response of shafting under various control voltages. It can be seen from the figure that after the control voltage was applied, the peak value of the shaft acceleration over-critical value decreased obviously. With the increase of the control voltage, the vibration peak value decreased more obviously when the shafting was over-critical.  
The relationship between the peak value of shaft acceleration and the control voltage under each control voltage is shown in Figure 14. It can be seen from the figure that the vibration reduction performance of the Smart Spring support was good, and the maximum vibration reduction rate reached 44.2% with the change trend of the vibration peak value with the control voltage.  
Figure 13: Accelerated over-critical vibration response of shafting under **(a–f)** control voltages, respectively 0 V, 30 V, 60 V, 90 V, 120 V and 150 V.  
Comparing Figures 7 and 14, the change trend of the first section of the two figures was consistent, and the displacement response decreased with the increase of the control parameters. However, because of the small axial stiffness of the bearing pedestal and the limited displacement of the piezoelectric actuator, the optimal control parameter in Figure 14 appeared at the maximum value, and the maximum control force was 447 N, so the piezoelectric actuator failed to exert its maximum capacity.  
Figure 15 shows the comparison results of the shafting vibration damping ratio under the different control voltages/control forces obtained by test and simulation analyses. It can be seen from the figure that under the trans-critical state of the shafting, the variation trends of the damping rate obtained by the test and simulation were the same, which indicated the accuracy of the joint simulation model. When the fixed control force of the intelligent spring increased from 100 N to 200 N, the vibration reduction rate of the simulation results increased by 21 percentage points, and the vibration reduction rate of the test results increased by 15 percentage points; when the control force of the intelligent spring increased from 200 N to 300 N, the vibration reduction rate of the simulation results and that of the test results increased by three percentage points. The difference between the experimental damping rate and the simulated damping rate was due to the error of the test bench and the limitation of the control force of the piezoelectric actuator. For example, there was a certain gap between the angular acceleration of the motor and the simulated angular acceleration, the axial stiffness of the bearing pedestal was too small, and the displacement of the piezoelectric ceramic actuator was limited.  
Figure 14: Relationship between peak and peak value of shaft system over-critical vibration response and control voltage.  
Figure 15: Vibration reduction rate of shifting under different control voltages/forces.
## 2 Materials and Methods
### Data Acquisition  
#### 2.3.1 Micro-CT Scanning  
Micro-Computed Tomography (CT) scanning technology plays an important role in the non-destructive detection of the internal microstructure of samples thanks to its non-destructive nature and high resolution at the micrometer level. It is particularly suitable for the meticulous observation of small-scale samples, such as microorganisms, cellular tissues, and nanostructures in materials science. This technique uses X-rays to penetrate the sample and, in combination with the signals received by the detector, generates clear three-dimensional images using computer algorithms. It provides researchers with a way to deeply observe the internal structure of the sample without physical sectioning. The CT scanning parameters for this session are shown in Table 1.  
To conduct a more in-depth and intuitive analysis of the internal structure of geological samples such as rocks, it is necessary to combine the Avizo software. The Avizo software uses three-dimensional reconstruction technology to build an accurate digital rock core model from the micro-CT scan data. By simulating the flow path of fluids within the rock core, the absolute permeability of the rock sample can be calculated. The Avizo software also provides advanced three-dimensional visualization functions, including image data acquisition, three-dimensional reconstruction, image feature extraction, and segmentation, which are crucial for extracting useful information from the micro-CT scan data.
## 2 Materials and Methods
### FP ROI Rejection through Binary Classification  
In order to preserve only positive ROIs (true tumors) for further processing, so excluding FPs, the obtained features were used to train a binary classifier.  
Tumor patches whose area was occupied by the lesion at least by 10% were considered to be positive, while the remaining ones, together with the supplementary patches randomly extracted from outside the lesion-bounding box, contributed to the negative samples.  
To increase the size of the dataset and to favor generalization, data augmentation was obtained by random image rotations, taking care of finally obtaining a roughly balanced dataset. Several classifiers were tested (e.g., XGBoost, svm... ) and the best results were obtained with a feed-forward, backprop, multi-layer perceptron ANN, with one hidden layer composed of five neurons.  
In order to clarify the terminology, hereafter, we will use the term \"training set\" for the \"seen data\" (of which a larger part was used by the classifier for actual training and a small part was used for early stop in case of overfitting), and the term \"validation set\" for the \"unseen data\" (for validating the model, i.e., for accuracy/ROC/etc. calculation and hyperparameter tuning). Training/validation was performed in a leave-one-patient-out (LOPO) cross-validation scheme [47]. Data was split by patient, ensuring that the ROIs related to each patient were totally contained in the training or in the validation set, and never in both, to avoid bias and consequent misleading figures of merit. For the same reason, at each iteration, feature value normalization to range 0-1 was performed using min-max normalization on the training set and, subsequently, validation set features were normalized by the parameters used for the training set. Fifty-four out of 55 patients were used for training the network, while the last one was used for validation, and a cyclical permutation of the patients was carried out. Statistics were calculated after a full LOPO cycle--an ROC curve was used to judge classification quality and to deduce an optimal threshold value on the ANN output, thus obtaining the binary classifier. Figure 3 shows an example of output of the whole process from ROI Hunting to classification. As our detection/segmentation code had a tendency to slightly underestimate the tumor area compared to the manually segmented ROIs, we performed a dilation operation to be sure to cover the lesion tissue.  
Figure 3: A typical result obtained by the CAD system. The objects found after the iterative procedure based on intensity values (ROI hunting) and the results of the classification prediction (red for tumor and blue for FP).
## 3 Model and Research
### Low-Level Stabilisation Level  
In the system under the study, control is provided by vector \(C=[C_{T}\ C_{R}\ C_{P}\ C_{Y}]^{T}\). \(C_{R}\) and \(C_{P}\) are the axis angle targets used for roll and pitch, with \(C_{Y}\) representing yaw rate command in the same coordinate system and \(C_{T}\) standing in for mean motor thrust. Consider the pitch angle stabilization system (Figure 3). This scheme is typical for the roll and pitch channels.  
To control the research object at the lower level, an attitude controller for multicopter systems is introduced into the system. The principles of such a system are well developed and presented in various studies, e.g., [13; 14]. In this paper, a dual-loop PID-based controller will be used, the general principle of which is shown in Figure 3. As can be seen, the pitch angle control system is a two-level control system with one nested loop. This is a typical multirotor stabilization system, described in detail in [15]. The inner loop controls the object angular velocity \(\omega\), which is represented here by the transfer function \(W(s)\), generally defined by the system kinematics. The system \(W(s)\) is controlled by the controller \(W_{\omega}(s)\) using the PID controller. The outer loop provides angle stabilization and is described by \(W_{\vartheta}(s)\) using the P controller. Similar loops are used for the roll and heading channels. A detailed analysis of the low-level stabilization system is not the purpose of the study, as this subsystem is an input for a high-level navigation controller, the synthesis of which is the focus of this paper.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Security Analysis
#### 3.4.3 Stolen-Device Scenario  
In the stolen-device scenario, an attacker can provide impostor samples \(S^{IV}\) at verification. Then, the client device generates the vector \(eb_{i}^{IV}\) from the impostor individual substrings \(b_{i}^{IV}\), using the padding \(p_{i}^{II}\) of the genuine user. By using the authentication server public key, the vector \(cb_{i}^{IV}\) is obtained. Nonces \(mA_{i}\) and \(mU_{i}\) are applied and the vector \(cbn_{i}^{IV}\) is generated. The computation server obtains \(cb_{i}^{UR}\oplus cb_{i}^{IV}\) and the authentication server obtains \(cb_{i}^{UR}\oplus cb_{i}^{IV}\) from \(cb_{i}^{UR}\oplus cbn_{i}^{IV}\oplus mA_{i}\oplus mU_{i}\). Since the same padding \(p_{i}^{UI}\) of enrollment is applied, when \(cb_{i}^{UR}\oplus cb_{i}^{IV}\) is computed, the padding bits are 0. If the Hamming weight of \(b_{i}^{UR}\oplus b_{i}^{IV}\) is greater than \(t\) for some \(i\), \(b_{i}^{UR}\oplus b_{i}^{IV}\) cannot be decoded and the impostor is rejected. If the Hamming weights of \(b_{i}^{UR}\oplus b_{i}^{IV}\) for all the \(i\) are smaller than or equal to \(t\), the comparison result is the same as in the unprotected scenario, so the attacker is rejected as in the unprotected scenario. The security of the scheme will be the same as the security of the unprotected biometric scheme.  
Further, in the stolen-token scenario, the attacker could try to generate synthetic samples \(S^{IV}\) at verification to make the client device generate fake \(b_{i}^{IV}\), such as null vectors or vectors with very low Hamming weight. Then, the attacker should also attack the authentication server to try decoding \(b_{i}^{UR}\oplus b_{i}^{IV}\) and obtain information about \(b_{i}^{UR}\) because \(b_{i}^{IV}\) is almost null. However, this cannot be performed because the client device checks that the Hamming weights of the substrings \(b_{i}^{IV}\) are neither zero nor have a low value.
###### Abstract  
Neutralization with liquid digestate and CO\({}_{2}\) was compared herein to adjust the pH of lime-treated corn stover. The effects on the thermophilic (55 \({}^{\circ}\)C) anaerobic digestion were also analyzed. Liquid digestate neutralization (LDN) caused a decrease in pH from 10.5 to 7.5 in 60 h and accumulation of acetic/isobutyric acids. The CO\({}_{2}\) neutralization (CN) under solid-state conditions reduced the pH from 10.5 to 8.5 in 30 min, which is faster than that of LDN and did not affect the subsequent anaerobic digestion. Biomethane production rate indicates that LDN contributed to the performance of anaerobic digestion, but this was not sufficient to compensate for the loss of total biomoethane yield, resulting in a negative net profit (i.e., revenue from increased energy production minus reagent cost). For CN under solid-state conditions, the biomethane production was highest in both liquid- and solid-state anaerobic digestion, and also obtained a net profit of 98.74-100.89 RMB/tonne dry biomass. Therefore, the solid-state condition CN is a more efficient and economic method for adjusting initial pH of lime-treated corn stover.  
thermophilic anaerobic digestion; liquid digestate; carbon dioxide; lime pretreatment; corn stover +
Footnote †: journal: Applied Sciences