esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Ham Samples  
Five ham samples from renowned categories (one from Bama Ham, two from Xuanwei Ham, and two from Jinhua Ham) were purchased from specialty stores authorized by the China Meat Association in Beijing (October 2024), including Black Hoof Cured Ham (BHCH), Superior-grade Xuan-Zi Ham (SXZH), First-grade Xuan-Zi Ham (FXZH), Liang-Tou-Wu Ham (LTWH), and Fei-Zhong-Wang Ham (FZWH). For Bama Ham (BHCH), Spanish white pig hind legs were first hand-rubbed with coarse Mediterranean Sea salt (7% \(w\)/\(w\)) and lightly massaged every 24 h for 10 days, then air-dried for 60 days in a temperature- and humidity-controlled chamber (4-6 \({}^{\circ}\)C, 75% RH). Subsequent maturation was carried out in an underground cell (12-18 \({}^{\circ}\)C, 65% RH) for 12 months, with periodic greasing to prevent surface fissures. Manufacturer data list moisture 45%, protein 25%, fat 20%, and salt 5% (g 100 g\({}^{-1}\)). Both Xuanwei hams (SXZH and FXZH) were produced from \"Highland Wujin\" pigs and subjected to the traditional three-stage dry-salting procedure (2.5%, 3.0%, and 1.5% of leg weight at 3 d intervals). SXZH was matured for 36 months, qualifying as special grade according to the local quality standard--a characteristic \"three-pin aroma\". FXZH followed the same technique but was marketed after \(\geq\) 24 months of aging, meeting first-grade requirements. Typical proximate values for mature Xuanwei hams are moisture 40-42%, protein 26-27%, fat 23-25% and salt \(\approx\) 4%. The traditional processing of Jinhua Ham uses the indigenous \"Two-Ends-Black\" pig breed; modern factories additionally process cross-breeds. Fresh legs are salted with 6-8% NaCl for 30-40 days, washed, sun-dried, and mold-ripened in ventilated lofts before a final piling stage at 15-18 \({}^{\circ}\)C. LTWH declares the use of Two-Ends-Black pigs and air-drying followed by 24 months of cell aging. FZWH employs free-range local pigs and a 12-month maturation. After ripening, Jinhua ham typically contains moisture 38-40%, protein 27-28%, fat 22-23%, and salt 5%. The details of the ham samples are provided in Table 1, and their photographs are shown in Figure 1. All samples were stored in their original containers at 2 \({}^{\circ}\)C in a laboratory refrigerator until analysis.  
Figure 1: Photos of the five dry-cured hams. (**a**) BHCH; (**b**) SXZH; (**c**) FXZH; (**d**) LTWH; (**e**) FZWH.
### Recent Advances in Medical Imaging  
The field of medical imaging has undergone significant advancements in recent years, largely driven by breakthroughs in machine learning and artificial intelligence. These advancements have profoundly impacted the diagnostic capabilities in healthcare, particularly in the detection and analysis of various medical conditions, including skin cancer.  
Convolutional neural networks (CNNs) have revolutionized image recognition tasks due to their ability to learn complex hierarchical features from images. In medical imaging, CNN architectures like EfficientNet [5], VGG-16 [6], ResNet [6], and GoogleNet [7] have been particularly effective. For example, EfficientNet has been utilized for its efficiency and scalability in processing high-resolution medical images, while ResNet's deep residual learning framework helps in learning from enormous datasets commonly used in medical diagnostics. In addition, segmentation-based models are popular, which involve dividing an image into segments to simplify or change the representation of an image into something more meaningful and easier to analyze. YOLO (You Only Look Once) [8] has been adapted for real-time object detection, useful in identifying specific structures in medical scans. Meanwhile, Mask R-CNN [9] has been pivotal in performing pixel-level segmentation, assisting in precisely delineating the boundaries of tumors or other pathological lesions in medical images.  
Traditional machine learning models, although somewhat overshadowed by deep learning, still play a critical role in areas where labeled data are scarce or when interpretability is a key requirement. Techniques like Support Vector Machine (SVM) and Random Forest [10] are used to classify images based on extracted features, providing robust baseline models for medical imaging tasks.  
Multi-input CNN-based models [11] are used to integrate multiple data types (e.g., clinical data, image data) into a unified framework, offering a more holistic approach to diagnostics. An example includes a multi-input model that combines CNN-extracted features from dermatological images with patient metadata to improve the accuracy of skin cancer predictions. This approach leverages the strengths of both data types, enhancing the model's predictive performance and reliability.
## 4 Results and Discussions
### Physicochemical Properties
#### 4.1.6 Shear Strength  
The maximum resistance that rock can provide against deformation when it is continuously sheared owing to shearing action is known as its shear. Torsion testing in a laboratory setting is usually used to evaluate the shear strength of rock specimens. Torsion tests are now a commonly used technique to determine the shear strength of rock samples in labs. For a particular rock, the unconfined tensile strength (UTS) reading was recorded along the negative x-axis, and the uniaxial compressive stress (UCS) reading was recorded along the positive x-axis according to the scale. Based on the UCS and UTS data, separate Mohr circles were created, and then a shared tangent was drawn between the resulting circles. The two primary factors used for calculating shear strength are cohesiveness (C) and the angle of internal friction (\(\varphi\)).
## 3 Results and Discussion  
### Data Source and Preprocessing  
This paper uses the second-phase performance statistics of 76 university collaborative innovation centers in Jiangsu Province as sample data. According to the evaluation index system for the construction of a university collaborative innovation center, the sample data is first validated; the data points that do not conform to the statistical characteristics and are unreasonable are eliminated, and the missing values are filled using the median method.  
In order to eliminate the feature deviation caused by different data dimensions and ranges, we normalize the data in the preprocessing stage. Let \(X=\left[x_{ij}\right]_{m\times n}\) represent the index value matrix of all collaborative innovation centers; then, the normalized data matrix \(Z\) is given by:  
\[Z=\frac{x_{ij}-\overline{x}_{j}}{s_{j}} \tag{6}\]  
where:  
\[\overline{x}_{j}=\frac{1}{m}\sum_{i=1}^{m}x_{ij} \tag{7}\]  
\[s_{j}=\sqrt{\frac{1}{m-1}\sum_{i=1}^{m}\left(x_{ij}-\overline{x}_{j}\right)^{2}} \tag{8}\]
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Deteriorating Systems  
Deterioration is a process where a system deteriorates continuously due to usage or age. Important parameters of the system gradually worsen if left unattended, and the process leads to deterioration failure [100]. Since deterioration is unpreventable, an efficient maintenance policy can reduce the system failure. Many studies have been conducted for condition-based maintenance in deteriorating systems. Much of the focus of these investigations has been in the failure prediction using different methodologies. A CBM policy for stochastically deteriorating systems was proposed to focus on analytical modelling of a condition-based inspection/replacement policy for the same [101]. A mathematical model was derived for the maintained system cost, supported by the existence of a stationary law for the maintained system state. In another study, a condition-based maintenance policy for a two-unit deteriorating system was presented [102]. Each unit's gradual deterioration and monitoring was based on sequential non-periodic inspections with a stochastic model for optimal maintenance performance. Closed-form expressions of system availability are derived when the device undergoes both deterioration and Poisson failures with a polynomial to solve for the optimal inspection interval [103]. A quantitative approach for maintenance inspection scheduling and planning was presented with three main modules: risk estimation module, risk evaluation module, and maintenance planning module [104; 105]. A condition-based replacement and spare provisioning policy is presented for deteriorating systems with uncertain deterioration to failure with a simulation method and the genetic algorithm for minimizing the cost rate. A case study is provided for optimizing the maintenance scheme of haul truck motors and the order of the spare motors at Cardinal River Coals in Canada. Lin et al. [106] presented a simulation model for maintaining equipment performance with integrated equipment CBM and field activity of an elevator service provider. A simulation modelling of repairable multi component deteriorating systems was developed for on-condition maintenance optimization [107] to minimize the expected total system cost over a given mission time. A non-repairable single component, subjected to stochastic degradation, was first considered, and the degradation model was then generalized to multi-component repairable systems. The modeling techniques of failure prediction are varied on the basis of the rate of the failures and surrounding parameters. Although a statistically based methodology is commonly used, the integrated modeling approach with different methods might be more suitable to address real-world uncertainties.
# Water Quality Evaluation and Prediction Based on a Combined Model
###### Abstract
A total of 52 groups were randomly selected from the real water quality index data from 2004 to 2015, and the results of water quality status were output and compared with the real water quality grade to prove the effectiveness and generalizability of the evaluation model. Furthermore, a new model is proposed for water quality prediction, which combines the autoregressive integrated moving average (ARIMA) model and the wavelet neural network (WNN) mode with the bat algorithm to determine the optimal weight of each individual model. The combined model was used to predict the water quality indices of Lanzhou Xincheng Bridge section in the Yellow River Basin and Longdong section in the Yangtze River Basin. First, 624 weekly monitoring data points of each indicator from 2004 to 2015 were used as the training set, and 52 data points from 2016 were used as the validation set. ARIMA and WNN were used for prediction. The empirical mode decomposition (EMD) algorithm was used to denoise the data before WNN prediction [5]. Secondly, the bat algorithm was used to determine the optimal weight; the final prediction result was the weighted sum of the prediction results of ARIMA and WNN. Then, the prediction results of the combined model were compared and analyzed relative to the prediction results of three individual models (backpropagation (BP), neural network and least squares support vector machine (LSSVM)) to prove the ability of the proposed combined model for water quality prediction [6; 7]. Finally, the prediction results of each index in 2016 were substituted into the previously established water quality evaluation model, and the output results had a high coincidence rate with the real water quality grade, which further verified the effectiveness of the evaluation model.
## 3 Measurement Results
### Measurement Results of Loss Characteristics at Lower Frequencies  
To facilitate observation and comparison, this paper classifies frequencies of 50-100 Hz as a low frequency and frequencies of 200-400 Hz as a high frequency. Figure 4 shows the loss characteristics of the 0.10 mm UTGO silicon steel and FBA alloy at 50-100 Hz.  
It can be seen from Figure 5 that the loss curve of the FBA alloy at 50-100 Hz is lower than that of the 0.10 mm UTGO silicon steel at 50 Hz. In the range of 0.1-1.0 T, the loss  
Figure 4: Magnetization curve: (**a**) 0.10 mm UTGO silicon steel magnetization curve; (**b**) FBA alloy magnetization curve.  
Figure 3: TEM images of microstructure and corresponding SAD pattern for the Fe-based amorphous alloy sample.  
curve of the FBA alloy at 50-100 Hz and the loss curve of the 0.10 mm UTGO silicon steel at 50-100 Hz are both relatively gentle, while the loss curve of the 0.10 mm UTGO silicon steel steel increases rapidly after 1.5 T. The iron loss density of the 0.10 mm UTGO silicon steel is 1.03 W/kg when the frequency is 50 Hz and the magnetic induction intensity is 1.6 T, and the iron loss density is 0.37 W/kg when the magnetic induction intensity is 1.0 T. The iron loss density of the FBA alloy is 0.25 W/kg when the frequency is 50 Hz and the magnetic induction intensity is 1.5 T, and the iron loss density is 0.10 W/kg when the magnetic induction intensity is 1.0 T. In addition, the loss density of the 0.10 mm UTGO silicon steel at 50 Hz is higher than that of the 0.18 mm oriented silicon steel [20].
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 1 Introduction
With the aim of improving the measurements obtained by consumer-grade MEMS inertial sensors, the paper proposes an innovative method to integrate redundant inertial measurements. A prototypical unit was made up of six sensor boards, each comprising MEMS triaxial accelerometer and triaxial gyroscope, in a redundant configuration. The unit has been experimentally tested with indoor and outdoor trials to demonstrate the attainable levels of accuracy. The testing plan includes an Allan variance test of redundant sensors and field test of integrated navigation with Global Navigation Satellite System (GNSS) receivers. The first has been performed to verify the overall bias performance of redundant configuration. The second has been realized to assess if the output of integrated navigation is compliant with the requirements for modern autonomous transport systems. At the end of the paper, test results are discussed to show the reached quasi-tactical grade capability.
## 4 Findings
### Other Works
#### 4.7.2 Fog Computing  
Using alliance chain and fog computing, a blockchain-based distributed access control system is proposed to offer security for IoT networks using a combined LSB and MLNCML encryption scheme [125]. This system utilizes fine-grained and dynamic access control to eliminate a single point of failure. In the proposed system architecture, edge nodes connect smart devices to the internet and allow resource requestors to access the services in the smart devices. The alliance chain is formed using all blockchain nodes, including the edge nodes. Thus, edge nodes offer a distributed manner when performing access control decisions. In the proposed encryption scheme, the first encryption is performed by the chaotic series, and then the LSB algorithm obtains the encrypted image. Finally, secondary encryption is performed by the MLNCML. In the proposed access control model, ABAC, LSB, and chaotic encryption is used, and the authors included a detailed workflow of this model. Using alliance blockchain limits access to increase the security and safety of the system; it also ensures the validity of the data and prevents any tampering because the data are distributed on the alliance chain. From a security perspective, the authors discussed data privacy, data theft prevention, anti-data tampering, and file storage security. By comparing this work with other approaches, it offers dynamic access control as well as fewer start-ups and nodes; in addition, it does not require paying remuneration, the proportion of voting rights is not artificially needed, less computing power is required, and it features enhanced management. This system can be improved by including a lightweight consensus protocol, avoiding a single point of failure by including smart contracts for distributed access control and ensuring the security and credibility of edge nodes using trusted computing technology.  
An IoT distributed middleware layer is integrated with permissioned blockchain to construct a lightweight and robust system for managing IoT data [126]. In the proposed system, the IoT management layer handles IoT data instead of assigning end devices or a central authority to handle this task. This system adapts a networked smart object (NOS) to manage heterogeneous sources' data and evaluate it, since it is a flexible and cross-domain middleware approach. NOS adapts the MQTT protocol working mechanism (publish and subscribe) for information exchange. NOS can enforce access control rules using a sticky policy mechanism. In the investigated scenario, low power consumption, low latency, and a limited number of users are required; thus, a Byzantine Fault Tolerant (BFT) replication consensus algorithm is used since it fits such requirements. Evaluation experiments are completed to recognize the proposed system's robustness regarding access control, confidentiality, integrity, and performance under malicious attacks.
## 4 Performance
### Fairness  
Fairness measures the packet reception ratio from each sensor node at the sink node. Fairness decreases if the sink node receives more packets from some sensor nodes than other sensor nodes and  
Figure 14: Network Efficiency.  
increases if the sink node receives packets from every node fairly. In the simulation, the fairness might be very low because sensor nodes selected the sensing interval randomly. If a sensor node selects a long sensing interval, it will generate packets at a low rate, and thus, less number of packets will be transferred to the sink node, while a sensor node with a short sensing interval will generate packets at a high rate. Moreover, a large number of packets from sensor nodes with a short sensing interval may cause congestion, and the packets from such sensor nodes will also be dropped. Therefore, the fairness will considerably decrease in case of heavy congestion.  
Figure 15 shows the fairness of CODA, CODA-Comp, CRRT, and ACT strategy. As congestion becomes serious, the fairness drastically decreases because the CODA process sets one child node to the sleep state for some time, which leads to unbalanced packet transmission. A sleeping node can send neither the packets generated by it nor the packets received from its child nodes. Moreover, the packet flows considerably fluctuate because the back pressure is propagated slowly. Therefore, the network fairness decreased drastically. The fairness of CODA-Comp also decreased drastically showing similar shape with CODA because CODA-Comp only applies the compression at the source node. CRRT shows higher fairness than CODA because CRRT reduces the collision among the nodes at the MAC layer. Actually the CRRT has the end-to-end retransmission technique for reliability however we turned off the end-to-end retransmission in the simulation. If the end-to-end retransmission is on, the fairness would be perfect.  
On the other hand, the ACT exhibits higher fairness than the other strategies does because it simultaneously uses the back pressure and up pressure. It attempts to reduce the number of packets in the network with the help of the ARC. Further, sensor nodes with a short sensing interval will generate  
Figure 15: Fairness.  
more packets that will in turn occupy more space in the queues of relaying nodes. Therefore, the ARC of the ACT attempts to reduce the number of these packets in the congested node. Moreover, with the ACT, all child nodes of the congested node increase the packet transmission interval, and thus, the ACT can achieve higher fairness than other schemes can.
# Compression and Fungal Heat Production in Maize Bulk Considering Kernel Breakage
## 3 Results and Discussion
### Simulation Results and Analysis  
The current method normally assumed that maize is an isotropic porous medium, and the model for simulating heat and moisture transfer in grain bulk was also developed. However, the current model did not consider the effect of compression deformation on heat and moisture coupling transfer. Using the verified model mentioned in the previous section and considering vertical pressure, the variation in temperature (\(T\)) at the central point was simulated. The parameters involved in the FE simulation for maize are shown in Table 2. During the simulation, vertical-pressure-induced compression (\(\Delta h\)), considered by the input value of density (\(\rho_{\text{b}}\)), was calculated using \(\Delta h\) step by step, according to the loading procedure. Similarly, the variation in porosity and thermal conductivity during the test were considered step by step as well. The experimental result and simulated result are designated as \(T_{E}\) and \(T_{N}\), respectively. Figure 9 presents the temperature variations in \(T_{E}\) and \(T_{N}\) for the four groups of maize bulk (HK\({}_{50}\), HK\({}_{150}\), WK\({}_{50}\), and WK\({}_{150}\)). Clearly, it can be observed that \(T_{E}\) is higher than \(T_{N}\). Compared with numerical \(T_{N}\), which only includes physical effect, the test \(T_{E}\) resulted from physical heat transfer and biochemical effect (e.g., mildew). The temperature difference/rise actually is a consequence of heat accumulation due to a biochemical effect. Generally, half kernels under low vertical pressure, e.g., HK\({}_{50}\), yield larger \(\Delta T\), and WK\({}_{150}\) yields smaller \(\Delta T\). The average values of \(\Delta T\) were 2.7 \({}^{\circ}\)C, 2.4 \({}^{\circ}\)C, 1.9 \({}^{\circ}\)C, and 1.8 \({}^{\circ}\)C in HK\({}_{50}\), HK\({}_{150}\), WK\({}_{50}\), and WK\({}_{150}\), respectively. This result is proportional to the APC in Figure 7, which indicates that mildew is one of the main reasons for temperature rise in grain storage. Owing to the low thermal conductivity of air between the pores of the kernels, heat conduction is slow, and the heat generated by initial fungi inside the grain bulk cannot be quickly dissipated. Increased temperature in grain bulk further provides a favorable environment for subsequent fungal growth [10; 16]. This cycle of \"heat conduction-temperature rise-fungal growth\" accounts for the initial condition significantly. The temperature differences \(\Delta T\) of HK samples were larger than those in WK, and the result is consistent with the above APC test results.  
Figure 9: Comparison of experimental and numerical simulation results.