esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 1 Introduction
The anomaly detection based on squared Mahalanobis distance [14] particularly has gained a broad interest in condition monitoring [13; 15] including wind turbine monitoring applications [16; 17]. The major motivation for its use originates from the simplicity and computational efficiency, but on the other hand, it has been reported that the lack of normal distribution in training data, correlated input features, inappropriate threshold limits and environmental variability are some major challenging issues and limitations for the practical application of this method [18; 19]. Evidently, this generates specific challenges to the selection of input data for the method which is the major issue highlighted in this research study. The main contribution of this study is the introduction of the probabilistic condition monitoring algorithm for azimuth thrusters with a semi-automated identification procedure. The data selection challenges discussed above are managed by proposing new solutions to the data processing sequence. Firstly, the disturbed samples are rejected in a data quality control based on monitoring specific characteristics of the acquired acceleration signals. Secondly, the normal distributed data for the squared Mahalanobis distance are ensured by a sample selection procedure that includes a check for normal distribution during model identification. There, the shaft speed values in varying ranges are used in linear regression models to predict the values of vibration features and the residuals are then checked in the hypothesis tests. Thirdly, the multicollinearity in the training data is checked, which makes the identification procedure semi-automated, because the user must replace the correlated features. The online monitoring process in the algorithm is demonstrated based on real data from two azimuth thrusters in an operating drill ship. Both data sets included a period with an undamaged condition followed by evolving defects. The squared Mahalanobis distance is monitored based on single values and their moving median providing information on the thruster condition. Moreover, the selection of threshold limits is studied based on classification tests and the monitoring performance is compared with several classifiers in a binary classification task. The remainder of the paper is organized as follows. The first part of Section 2 introduces the azimuth thrusters, measurements, monitored components and the data. The second part introduces the condition monitoring algorithm and the third part describes the procedure used for the performance analysis in the classification tests. Section 3 demonstrates the application of the method, compares its diagnostic performance with the classifiers, and discusses the findings and future directions. Finally, Section 4 concludes the study.
## 2 Related Work
### Hierarchical Clustering Algorithm  
Low energy adaptive clustering architecture (LEACH) [18], as the earliest network clustering routing protocol, randomly selects CH nodes in the cycle process and distributes the energy load evenly, effectively reducing network power consumption. Still, due to the randomness of cluster head selection, low-energy nodes can easily be selected as CH nodes and die, shortening the life of the network. Later, there were many improved versions of the LEACH protocol, such as mobile-LEACH [19], LEACH-balanced, LEACH-C [20], and other protocols. The stable election protocol (SEP) [21] is also improved based on the LEACH protocol, giving more energy to advanced nodes, effectively using heterogeneous networks. The cluster head selection of a hybrid, energy-efficient, distributed clustering approach (HEED) [22] focuses on the remaining energy and the energy consumed in the cluster, which improves energy utilization. Nodes in intelligent hierarchical cluster-based routing (HCR) [23] are self-organized into clusters, also improved using an agent-based architecture, resulting in energy-efficient hierarchical clusters. The distributed energy  
Figure 2: Related clustering protocols for WSN.  
efficient clustering algorithm (DEEC) [24] provides a different algorithm for estimating network lifetime and retains the distributed nature of the HEED protocol. As the most popular clustering algorithm, the LEACH protocol has been improved and applied by many subsequent algorithms.
# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 2 Methodology
### Camera Trajectory Estimation
This strategy emphasizes regions beneficial for motion estimation while suppressing noise and irregular motion effects. 4. Grid motion field estimation: Based on the weighted feature maps \(G_{\beta k}\) and contextual features \(F_{ab1},F_{ab2},F_{ab3}\), the grid motion field estimator computes the motion vectors at each grid node from frame \(I_{a}\) to frame \(I_{b}\): \[MV_{i,j}^{a,b}=mv([G_{a},G_{b},F_{ab1},F_{ab2},F_{ab3}])\] (6)  
Subsequently, these local motion vectors are transformed into homography matrices corresponding to each grid region using the classical Direct Linear Transformation (DLT) method:  
\[H_{h,w}^{a,b}=DLT(MV_{i,j}^{a,b}) \tag{7}\]where \(h\) and \(w\) represent the row and column indices of the grid, respectively. Once local motion estimation between consecutive frames is obtained, accumulate sequential transformation matrices to construct the global camera motion trajectory. Specifically, given that \(F_{i}(t)\) represents the local transformation matrix from time \(t-1\) to \(t\), the camera trajectory at time \(t\), denoted as \(C_{i}(t)\), is expressed as  
\[C_{i}(t)=F_{i}(t)\cdot F_{i}(t-1)\cdots F_{i}(1) \tag{8}\]  
where \(C_{i}(t)\) represents the absolute motion relative to the first frame. Absolute trajectory representation is employed to capture the overall motion trend, while in the subsequent trajectory smoothing module, it is converted into a relative path representation to mitigate cumulative errors and reduce geometric distortion. In summary, the proposed camera trajectory estimation method integrates multi-scale feature extraction, contextual information fusion, inlier mask generation, and grid motion field estimation to accurately capture local motion across consecutive frames. By accumulating transformations over time, a global camera trajectory is constructed. This approach demonstrates high robustness in dynamic and noisy environments, laying a solid foundation for subsequent video stitching and stabilization applications.
## 2 Materials and Methods
### Measurement Protocol and Perturbation Characteristics  
The kinematics and kinetics parameters of the perturbed gait were measured in a Gait Real-time Analysis Interactive Lab (GRAIL, Motek Medical B.V., Amsterdam, The Netherlands). The GRAIL is equipped with a dual split-belt treadmill (1000 Hz), a motion capture system (Vicon Metrics Ltd., Oxford, UK) (100 Hz), three video cameras, and synchronized virtual-reality environments. Participants' movements were captured using Human Body Model 2 (HBM2) with 26 reflective markers. Perturbation triggering and data acquisition were controlled using D-Flow software (Motek Medical B.V., Amsterdam, The Netherlands) [30].  
In this study, the participants walked on the treadmill wearing flat-sold sports shoes at a constant speed of 1.2 m/s. As a safety precaution, each participant wore a safety harness connected to the ceiling, even though the perturbations were not intended to cause falls. Two types of unexpected perturbations were applied to the left belt of the treadmill, involving acceleration and deceleration (Figure 1). Each perturbation occurred at three specific points during the support phase of the gait cycle: Initial Contact, Mid-Stance, and Pre-Swing (Table 1). The magnitude of the perturbation was set at 5 on a scale of 1-5, involving a shift in treadmill belt speed of 0.5 m per second [4].  
Each participant performed one trial for all six perturbation possibilities (type x phases). In each attempt, perturbations occurred at 10 s intervals on the left treadmill belt. Consequently, these perturbations appeared at the 30 th, 40 th, 50 th, and 60 th seconds of treadmill walking. These perturbations were applied to the left lower limb, as it was the supporting (non-dominant) leg for all subjects [31]. Leg dominance was determined using the kicking test, where participants indicated their preferred lower limb for kicking a ball.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Perturbation Type** & **Phase of the Gait Cycle** &
\begin{tabular}{c} **Labeling/** \\ **Perturbation Possibilities** \\ \end{tabular} \\ \hline \multirow{3}{*}{Acceleration} & Initial Contact & Acc–Initial \\  & Mid-Stance & Acc–Mid \\  & Pre-Swing & Acc–ToeOff \\ \hline \multirow{3}{*}{Deceleration} & Initial Contact & Dec–Initial \\  & Mid-Stance & Dec–Mid \\ \cline{1-1}  & Pre-Swing & Dec–ToeOff \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of applied perturbations (the first column specifies the type of treadmill movement; the second column denotes the phase of the gait cycle in which the perturbation was applied; and the third column provides the corresponding label).  
Figure 1: Visualization of the response to the six perturbation possibilities, including the key behavioral elements for treadmill acceleration (arrow pointing to the right) in phases—(**A**) Initial Contact, (**B**) Mid-Stance, (**C**) Pre-Swing—and for treadmill deceleration (arrow pointing to the left) in phases—(**D**) Initial Contact, (**E**) Mid-Stance, (**F**) Pre-Swing.
# Estimating Thermal Material Properties Using Solar Loading Lock-in Thermography
## 4 Results and Discussion
### Field Experiments
Phase Evaluation and Thermal Thickness  
In theory, thermally thin materials show a negative 90-degree angle relative to the excitation. This makes intuitive sense, because the temperature of the material will rise until the excitation reaches zero again. After which the temperature of the object begins to fall. Thermally thick materials should show negative 45-degrees. This is because internal heat transfer is not negligible and changes how the surface reacts to heat input. These phase angle differences between thermally thick and thin materials are present in laboratory as well as field experiments. The experimental phase angle difference in the laboratory setup is \(\sim\)30 degrees and in the field \(\sim\)60 degrees. In both cases using the fundamental excitation frequency, based on phase evaluation the correct objects in frame are identifiable as thermally thick or thin. It is hypothesized that the phase angle information can be used to determine \"thermal thickness\" or at the very least to distinguish thick from thin in thermally adequate circumstances. Former research shows that phase evaluation is a valid method to identify subsurface defects or material changes [11; 12]. This confirms the hypothesis and may contribute a factor to explaining their findings. In the field experiments, thermal thickness can be inferred correctly from phase angle images, depicted in Figure 11. Figure 5 shows how the reference plates, the guardrail in the lower left corner and the construction fence in the lower part of the image significantly differ in their phase angle compared to the thermally thick retaining wall structure. To what extent this method generally can be used to determine apparent thermal thickness is subject to ongoing research. What certainly is known that determining other thermal material properties using phase information, like thermal effusivity, thermal conductivity and alike, is much harder, because the phase angle depends on other variables more strongly than solely thermal material properties [11]. Especially excitation variation (over multiple days) will result in phase angle shifts.
# Novel Small-Molecule Treatment and Emerging Biological Therapy for Poriasis
## 4 Cell-Based Therapy
### Mesenchymal Stem Cell
The bioink can be controlled using microfluidic technology to prepare porous ECM microcarriers in situ under the catalysis of light. Under the modification of polydopamine, MSCs can adhere to these microcarriers well. The PD-L1 expression of MSCs was enhanced using lentiviral transfection. These microcarriers can prolong the survival time of MSCs in vivo. In an in vivo psoriasis model, epidermal thickening was found to be effectively inhibited, and the PASI score was reduced after microcarrier administration [135]. Wharton's Jelly, a connective tissue that contains collagen, hyaluronic acid, and sulfate proteoglycans, is an abundant source of MSCs. Compared with MSCs derived from bone marrow and other tissues, human Wharton's Jelly mesenchymal stem cells (hWJ-MSCs)are less mature, enabling them to proliferate more efficiently and rapidly while exhibiting lower immunogenicity. At the same time, hWJ-MSCs also have immunomodulatoryulatory and anti-inflammatory properties, which is consistent with other sources of MSCs. [PERSON] et al. evaluated the potential of hWJCM, a secreted product of hWJ-MSCs, to treat psoriasis. They administered hWJCM loaded in a hyaluronic acid (HA) matrix. HWJCM-HA reduced TNF-\(\alpha\) expression levels by 77% and decreased the PASI score from 8.5 to 2.0. It was found that hWJCM-HA had strong angiogenic potential. HWJCM-HA can reduce the degree of distortion and complexity of blood vessels and can also reduce epidermal hyperplasia and keratosis [136]. MSCs are considered multipotent cells capable of differentiating into various human tissues. Extensive clinical research has been carried out to utilize MSCs for repairing damaged tissues, particularly the heart. Researchers now recognize that MSCs produce numerous bioactive factors and possess innate immunomodulatory of functions, making them a potential treatment for a variety of immune-related diseases. However, MSCs do not yield therapeutic effects for all patients. Factors such as production methods, cell viability, delivery platforms, the severity of the disease, and genetic receptivity can all influence the efficacy of MSCs. Currently, the focus lies on screening suitable MSCs for specific diseases and enhancing the properties of MSCs to improve their therapeutic potential.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 3 Vulnerability of Deep Learning-Based Medical Image Analysis
### Adversarial Medical Image Analysis
Finally, according to the authors, VGG16 and VGG19 appear to be more resilient than the other models. MRI images for brain tumor segmentation include four different modalities (T1, T2, T1 ce, and FLAIR) with various intensities to make it simple to identify and label the brain tumor. [PERSON] et al. [95] looked at the outcomes of adversarial instances when they are used concurrently across all modalities and on each modality separately. The MICCAI BRATS 2019 [96] dataset and an ensemble U-Net model were used in the experiments. They employed universal random perturbation, which is comparable to [79], for the development of adversarial cases. The findings demonstrated that simultaneous disruption greatly reduces model accuracy, but perturbation of only one modality only marginally lowers accuracy. On three datasets--ChestX-ray8 [97], ISIC [98], and fundoscopy [99]--[PERSON] et al. [100] analyzed the robustness of the ResNet50 model. They used four cutting-edge white-box attacks--FGSM, PGD, C&W, and BIM--and concentrated on untargeted situations. Additionally, the perturbation value was calculated, and it was discovered that the strongest attacks, C&W, BIM, and PGD, had nearly 100% attack accuracy everywhere. Dermoscopy photos had no significant variation but were slightly more reliable than the other datasets. They also used four detectors, KD [101], LID [102], deep features and quantized deep features-based detectors, which had very high detection accuracy for detecting adversarial samples. The impact of adversarial instances on retinal images was investigated by [PERSON] et al. [103]. In order to identify diabetic retinopathy, they looked at image-based (CNN-0 [104], CNN-1 [105], and hybrid lesion-based [106] algorithms for medical image analysis. In order to create adversarial images, CNN-0 and I-FGSM were utilized, and CNN-1 and hybrid lesion-based models were tested on them. As evidenced by their respective accuracy reductions of 45% and 0.6%, the results demonstrate that CNN models are relatively susceptible and hybrid lesion-based models are more resilient. The current attacks on medical imaging are outlined in Table 1. These kinds of attacks were investigated only on tasks involving classification and segmentation. The PGD method appears to be the most successful, and FGSM and PGD were the most widely used strategies. A majority of the investigations also made use of MRI, fundoscopy, dermoscopy, and X-ray images.
## 4 Methodology
### Data Preprocessing  
We adopt the following preprocessing steps for both datasets:  
* Filtering: Select only smart home-relevant logs (e.g., IoT devices, home Wi-Fi, local subnet flows).
* Cleaning: Remove duplicate entries, resolve missing values, and unify timestamp formats.
* Feature Selection: Extract protocol-based (e.g., source/destination port, protocol), time-based (e.g., packet interval), and statistical (e.g., entropy, variance) features.
* Normalization: Apply min-max scaling to numeric features for neural network compatibility.
* Label Encoding: Labels are binary (normal = 0; anomaly = 1).  
The final dataset is partitioned into local shards (one per device/client) to emulate a non-IID federated setting.
# A Photon Imaging Detector Model with High Resolution and High Counting Rate
## 4 Simulation Results
### Point Spread Function  
Noise and the photon-counting rate are two important factors affecting the filtering effect and the detector spatial resolution. In order to study the influence of counting rate and noise on spatial resolution, the PSF under different experimental conditions is measured. The photon position distribution at 5 MHz photon count rate and 500 e RMS noise is shown in Figure 9. The photon position distribution can be fitted as a two-dimensional Gaussian distribution, which is the PSF of photons. The full-width at half maximum (FWHM) of the PSF represents the imaging resolution of the detector. The relationship between FWHM and the photon-counting rate under different noise conditions is shown in Figure 10. It can be seen that FWHM increases with the increase in the count rate. At the same photon-counting rate, the greater the noise, the greater the FWHM, which means lower spatial resolution. At the photon count rate of 6 MHz, the spatial resolution of \(<\)50 \(\upmu\)m can be achieved for a noise of 1000 e RMS. If the electronic noise can be reduced to 500 e RMS, the detector will reach \(<\)15 \(\upmu\)m resolution at the 3 MHz count rate. It should be noted that the simulation in this paper is the detection of a single target point. When the actual detector with a certain area works, the photons will not fall at the same position each time, so the overall counting rate that can be achieved will be higher than this result. In addition to verifying the feasibility of this architecture, this model can be used as a guide in the process of detector design and  
Figure 8: Variation in the shaping error with photon-counting rate for different shaping strategies. The fixed t\({}_{\text{s}}\) is 6, 10, 14, and 20 samples (16 ns/sample), respectively. The input pulse amplitude is 1 V, noise is 0.05 V RMS, and t\({}_{\text{ft}}\) = 0.  
optimization. We can design and optimize different detector parameters according to the target detection requirements.
# Functional and Healthy Features of Conventional and Non-Conventional Soundoughs
## 4 Exopolysaccharides  
Exopolysaccharides (EPS) are biopolymers of high molecular weight produced by several microorganisms, such as LAB, AAB and microalgae [68; 69; 70].  
Based on the chemical composition and biosynthesis mechanisms, microbial EPS are classified into two distinct groups: (1) homopolysaccharides (HoPS), such as glucans and fructans, (2) heteropolysaccharides (HePS), e.g., gellan and xanthan [71]. Among the HoPS, dextran, levan, and cellulose are mostl important in the food industry for their significant features [71; 72]. In particular, EPS-producing LAB are attractive for application in bakery products thanks to their ability as viscosifiers, texturizers, emulsifiers, and syneresis-lowering agents [62; 70]. In addition to polysaccharides naturally occurring in cereal grains flour and dough, microbial EPS from sucrose can be produced in sourdough through the activity of glycosyltransferases [73]. It has been reported that fructan from _L.  
_santransiscensis_ can positively affect the dough rheology and bread texture more than the external addition of the same polysaccharides [74]. Furthermore, it has been shown that dextran produced in situ can improve texture and cover unpleasant flavors of wholegrain bread suggesting microbial EPS as a possible substitute of sweeteners [75].  
Sourdough technology using EPS-producing LAB strains seem to be a valid solution to improve GF baked products [70]. For instance, microbial EPS can replace hydrocolloids, which are fundamental components in GF products to get acceptable quality levels in terms of texture, volume, and shelf life [76].  
Other evidence has been recently reported by [PERSON] et al. [77], who described EPS, produced by _Pedicoccus_, _Leuconstoc_, and _Weisella_ strains in quinoa sourdough, able to improve the organoleptic and rheological attributes in GF-free doughs. Consequently, the development of EPS-producing starter cultures for different types of flour sourdoughs has been receiving a growing-interest in the last years, also due to the potential health benefits associated with EPS themselves as prebiotics, which can be exploited to make added-value functional products [78]