esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Depth Estimation
The slow increase in accuracy from the 10 th training session onward can be attributed to the challenge of extracting specific features for each class in the classification model, which comprises 40 classes that represent different depths. Moreover, the increasing blurring of images of absorbing structures at depths above 16.0 mm poses difficulties in distinguishing the blurred images. Furthermore, Figure 16 illustrates the results of the correlation analysis between the depth estimated by the CNN model and the depth given during testing. As the depth increased, the estimation error also increased. The experiment involved 8000 images at 20 depths ranging from 1.0 mm to 20.0 mm, with 40 images per depth for testing. The correlation coefficient was \(R^{2}=0.9911\), demonstrating the feasibility of the CNN-DenseNet-169 model in estimating the depths from images of absorbing structures. Figure 17 illustrates the workflow of the proposed method. First, the original image was convolved with a point spread function (PSF) to simulate the blurring effect caused by light scattering and absorption in biological tissue. This process yielded a blurred image of the absorption structures. Second, the blurred image underwent de-blurring through a fully convolutional network (FCN) model, which could have been either the Attention UNet or the Attention Res-UNet, in order to recover the original image. Lastly, the blurred image was subjected to decoding using a convolutional neural network (CNN) model to estimate the depth of the absorption structures. In further studies, these results will be optimized to reconstruct the 3D structure of biological tissue from a 2D image. Figure 16: Correlation Analysis of Given and Estimated Depths.
## 5 IoT-Defined Network/Routing Protocols
### Low-Power and Lossy Network (LLN)  
LLNs typically comprise constrained nodes with limited energy, processing power, and memory. These nodes are connected to each other using a variety of protocols such as 802.11, 802.15.4, etc. The applications of LLNs are widespread. Ranging from a smart home, where a lightbul is connected via Bluetooth and a handheld device or a refrigerator that is remotely locked against children, it goes to a big industry where automobile testing is conducted and real-time data are analyzed using a sensory network [30]. Environmental changes, military exercises, flood and disaster management, heating and ventilation systems, data collection and billing, energy and smart grid management, health care, assets tracking, and access control are facilitated using LLNs. The discussion proves the relation and key importance of LLN in IoT. A typical LLN comprises an edge router, a processing point, and connected constrained nodes.
## 2 Challenges and State of the Art
### Objectivity and Harmonization  
A critical issue is the subjectivity and variability of the metrics used within the risk assessment processes. Current assessment methodologies, such as OCTAVE [8] and EVITA [9], rely on manual assessments, leading to inconsistent evaluations [10]. Additionally, metrics such as likelihood or impact are difficult to measure due to their complexity [11], relying on historical data, which are not always available. Approaches such as ARMOUR [12] address these challenges by combining risk assessment with empirical testing, but they are limited to specific contexts, e.g., IoT devices, and they lack a generalized framework for complex ICT systems.  
Additionally, the wide range of existing cybersecurity certification schemes [13] can make the comparison of the certification results difficult. This negatively impacts the capability of users to compare the results of the certification process. Some certification frameworks such as Common Criteria (CC) [14] support the comparability among the results of independent cybersecurity evaluations through Collaborative Protection Profiles (cPP) under the terms of the Common Criteria Recognition Arrangement (CCRA). Others, like Commercial Product Assurance (CPA) [15], test products against CPA Security Characteristics, some of which are qualitative and ambiguous [11]. Similarly, CAP UL 2900 standards [16] are not public, so harmonization aspects are not addressed.  
Our methodology integrates security testing results into the risk assessment process, enhancing objectivity and reproducibility while being supported by automated tools. This allows for precise and dynamic assessments of evolving security vulnerabilities. The methodology considers the system as a set of components, integrating individual cybersecurity aspects along with dependencies and potential cascade effects. Moreover, the methodology is aligned with well-known standards from ETSI, ISO, and IETF to foster the harmonization of the processes and metrics.
###### Abstract  
As one of the most developed coastal cities, Shanghai experiences long-term ground surface settlement disasters during urban expansion periods, which has adverse effects on economic development. To date, many studies regarding Shanghai's ground surface sedimentation have been conducted with microwave remote sensing technology. However, the systematic and timely analysis of the time series deformation results and risk evaluation is still absent. Therefore, we focused on the following aspects in this study: Firstly, revealing in detail the time series deformation characteristics during 2016-2022 with Sentinel-1A images and verifying the deformation results with different InSAR technologies and SAR data. Secondly, fully discussing the reasons for ground sedimentation from the aspects of subway construction, land use type, monthly rainfall, and human activities, and studying the correlation between surface deformation and rainfall with the singular spectrum analysis (SSA) method. Finally, conducting a risk evaluation and risk level division using the entropy method, combining the long time series deformation results and geoinformation data. Meanwhile, the following conclusions were reached: 1. There are six typical deformation areas, distributed in the Baoshan District, Minhang District, and Jinshan District of Pudong New District from 2016 to 2022. The maximum annual rate is \(-\)32.3 mm/a, and the maximum cumulative sedimentation reaches \(-\)188.6 mm. 2. Ground sedimentation is mainly due to engineering construction during city development and verifies the weak correlation between surface deformation and rainfall. 3. We obtained different levels of geological hazard risk areas, and Huangpu, Yangpu, Hongkou District, the northwest area of Pudong New Area, and the vicinity of Dishui Lake belong to higher-risk areas. The above time series deformation research results and systematic analysis of induced factors, and the higher-risk-area division, will provide valuable insights for urban risk management.  
InSAR; land subsidence; Shanghai city; SSA model; entropy method; risk assessment Article
# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 3 Proposed Methods
### Lstm Autoencoder Model for Anomaly Detection
#### 3.2.2 LSTM Autoencoder  
Autoencoder is a sort of unsupervised neural network that aims at learning a representation for a set of data [43]. A regular autoencoder comprises five components: an input layer, an encoder, a latent space, a decoder, and an output layer. The encoder compresses the input data into a latent space representation, while the decoder decompresses the latent space representation into the output layer. After that, the input data is compared with the encoded-decoded output data and the weights of the network are updated by error backpropagation through the network.  
More Specifically, given the input data \(x\in\mathbb{R}^{m}\), the encoder compresses \(x\) to obtain a latent space representation \(z=e(x)\in\mathbb{R}^{n}\). The decoder reconstructs this latent space representation to obtain the output \(\hat{x}=d(z)\in\mathbb{R}^{m}\). The autoencoder model is trained by minimizing the reconstruction loss \(L\) as follows:  
\[L=\frac{1}{2}\sum_{x}\lVert x-\hat{x}\rVert^{2} \tag{7}\]  
The main purpose of the autoencoder is to learn the most important features from the training dataset by constraining the latent space to have a lower dimension than the input, i.e., \(m>n\). There are many types of autoencoders, such as vanilla autoencoder, regularized autoencoder, convolutional autoencoder, and LSTM autoencoder. Among them, LSTM autoencoder is the autoencoder for sequential data such as time series data using an Encoder-Decoder LSTM architecture. LSTM autoencoder is suitable for time-series anomaly detection since it can learn patterns in data over long sequences. In our LSTM autoencoder model, we use dropout for regularization. Dropout is a regularization technique that enables the network to avoid overfitting in the training stage. It reduces the number of active neurons to constrain the learning process of the neural networks.
## 3 Weighted Least Square Support Vector Regression Model
### Fireworks Algorithm for Feature Selection  
In this section, we use the fireworks algorithm to obtain the final optimal feature subset. The environment used for feature selection includes a Matlab R2013a, a self-written Matlab program, and a computer with the Intel(R) Core(TM)2 Duo CPU, 3 GB RAM and the Windows 7 Professional operation system. The parameters of fireworks algorithm are set as follows: the maximum iteration number \(Mxgen=500\), the population size \(PopNum=30\), the constant determining the number of sparks \(M=100\), the constant determining the explosion radius \(\hat{R}=150\), the upper and lower limits of the individual searching range of fireworks are \(V_{up}=512\) and \(V_{down}=-512\) respectively. The parameters of W-LSSVM are chosen as follows: \(\gamma=23.698\), \(\sigma=5.123\).  
Figure 4 is the iterative process curve of the fireworks algorithm for the sample data. As shown in the figure, the fitness curve describes the best fitness value which is obtained by the FA model at each iteration; the accuracy curve is the value which is calculated by the W-LSSVM model at different iterations; the reduced No is the number of eliminated features in the process of convergence; and the selected No is the number of obtained features in every iteration with the new method. As we can see from Figure 4, the optimal fitness value is \(-0.93\), which is found by FA when the iterative number reaches 46. In the 46 th iteration, the prediction accuracy based on the training sample meets the best value of 98.8%; which means the machine learning of the W-LSSVM achieves the best value and obtains the highest predictive accuracy in the training sample data. Furthermore, the number of selected features is stable when the iterative number is 46, and we can see that 15 features are eliminated from the original 20 features due to the low coefficient between these 15 candidate features and the icing thickness. The final selected features for the sample are temperature, humidity, wind speed, sunlight intensity, and air pressure, respectively.
## 4 Materials and Methods
### Endosomal Escape and Mitochondrial Targeting Ability of Polymer-Liposome Complexes  
To observe the mitochondrial targeting ability of polymer-liposome complexes, fluorescein isothiocyanate isomer I (FITC) (Sigma-Aldrich, St. Louis, MO, USA, No. F2502) was conjugated on cholesterol to prepare FITC-labeled polymer-liposome complexes. FITC was conjugated on cholesterol-NH\({}_{2}\) by conducting the substitution reaction of amino groups from cholesterol with the function group of isothiocyanate from FITC. MCF-7 cells were cultured with the low glucose Dulbecco's modified Eagle's medium (DMEM) (Sigma-Aldrich, D6046) containing 10% fetal bovine serum (Gibco, certified, the United States, No. 16000044) and 1% antibiotic-antimycotic solution (Corning, No. 30-004-CI). For intracellular observation, MCF-7 cells (2 \(\times\) 10\({}^{4}\) cells) were seeded on cover slides and cultured in 6-well plates for 12 h. MCF-7 cells were then treated with 1 mL of polymer-liposome complexes-containing medium (ceramide concentration: 10 uM) for an interval time and then stained with 75 nM of LysoTracker Red DND-99 (Invitrogen(tm), Waltham, MA, USA, No. L12492)for 1 h to observe endosomal escape and stained with 250 nM of MitoTracker Red CMXRos (Invitrogen(tm), No. M22425) for 30 min to observe Mitochondrial targeting, respectively. At each time point, the cells were washed with PBS, fixed with 4% paraformaldehyde hydro for 30 min, and mounted with Fluoromount-G DAPI (Southern Biotech, Birmingham, AL, USA, No. 0100-01) for confocal laser scanning microscopy (CLSM, Zeiss 880) observation. CLSM was conducted using excitation wavelengths of 359, 495, 577, and 579 nm and emission wavelengths of 461, 519, 590, and 599 nm for DAPI, FITC, LysoTracker, and MitroTracker, respectively. In addition, the mitochondria were isolated by a sucrose concentration gradient method. MCF-7 cells (2 \(\times\) 10\({}^{5}\) cells) were seeded in 6-well plates overnight and then treated with 1 mL of FITC-labeled polymer-liposome complexes-containing medium (ceramide concentration: 10 \(\upmu\)M) for 24 h. The cells were trypsinized and replaced with SHE solution (0.25 M sucrose (Sigma-Aldrich, No. S0389), 0.5 mM EDTA (Sigma-Aldrich, No. E9884), and 3 mM HEPES buffer (Corning, Corning, NY, USA, No. 25-060-CI)). The cell solution after using an ultrasonic reactor (HOYU Ultrasonic 250) for 5 s (4 ampere, 22,800 Hz) on ice bath was centrifuged for 10 min at 800\(\times\)\(g\) and 4 \({}^{\circ}\)C. The suspension was then centrifuged for 10 min at 9500\(\times\)\(g\) and 4 \({}^{\circ}\)C. The suspension and centrifuged pellet were separately collected. The pellet was resuspended by fresh SHE. The FITC fluorescent intensities of suspension and pellet-resuspended solution (Mitochondria) were measured by Multimode microplate readers (TECAN 200/200 Pro) using an excitation wavelength of 490 nm and an emission wavelength of 525 nm.
# Ulcerative Severity Estimation Based on Advanced CNN-Transformer Hybrid Models
## 2 Materials and Methods
### Evaluation Indicators  
Quadratic weighted kappa (QWK) is commonly used to assess agreement on ordinal scales and is particularly well suited for addressing class imbalance issues in this task. In remission classification, metrics like Cohen's kappa and \(F1\) score offer more reliable evaluation than sensitivity, precision, or recall, which are often overly influenced by false positives or false negatives. Given the characteristics of the LIMUC dataset, QWK and Cohen's kappa were adopted as the primary evaluation metrics, with macro \(F1\) score serving as an additional measure for assessing performance across all Mayo subscores.  
**Cohen's kappa**: The kappa statistic is widely utilized to assess interrater reliability metric, offering a method that accounts for the possibility of agreement occurring by chance, particularly when raters face uncertainty and may guess on some variables [30]. [PERSON]'s kappa specifically evaluates the degree of agreement between two raters independently classifying \(N\) items into \(m\) mutually exclusive categories. The formal definition of the kappa coefficient(\(\kappa\)) is given as Equation (5) [31]\[\kappa\equiv\frac{P_{o}-P_{e}}{1-P_{e}}=\frac{1-P_{o}}{1-Pe}, \tag{5}\]  
where \(P_{o}\) denotes the observed agreement, defined as the proportion of instances where two observers or classifiers assign the same label to a given sample. Conversely, \(P_{e}\) represents the expected agreement by chance, indicating the probability that two observers or classifiers coincide purely due to random assignment.  
**Quadratic Weighted Kappa(QWK)**: The quadratic weighted kappa (QWK) enables differential weighting of disagreements between raters [32]. It is particularly well suited for ordered classification tasks, as it effectively captures the degree of deviation between predicted outcomes and true labels [33]. The corresponding mathematical formulation is provided in Equation (6)  
\[QWK=1-\frac{\sum_{i,j}w_{i,j}O_{i,j}}{\sum_{i,j}w_{i,j}E_{i,j}}, \tag{6}\]  
where \(O_{i,j}\) denotes the element of the observed confusion matrix, representing the number of instances whose true class is \(i\) but are predicted as class \(j\). \(E_{i,j}\) refers to the corresponding element in the expected confusion matrix, indicating the expected number of samples classified as \(j\) when the true category is \(i\) under random assignment (the equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability). \(W_{i,j}\) is an element of the weight matrix, typically defined as \(w_{i,j}=\frac{(i-j)^{2}}{(N-1)^{2}}\), where \(N\) denotes the total number of classes.
# Curable Area Substantiation of Self-Healing in Concrete Using Neutral Axis
## 2 Experimental Setup  
### Three-Point Bending Test with A Notch  
In this study, the three-point bending test with a notch was performed for assessment of the self-healing property of concrete. The self-healing measurement process generally proceeds in the order of crack inducement, performance measurement before healing, healing, and performance measurement after healing. Crack inducement involves determining the depth or area of a crack; in other words, the recoverable area is determined in this process. It is important to maintain constant width and depth of the crack for an accurate comparison of the healing effect, and the three-point bending test with a notch is advantageous in this respect. The notch given just below the load point limits the crack evolution to only a single crack, and the crack mouth opening displacement (CMOD) can be measured easily by installing the instrument at the bottom of the notch. Crack propagation is correlated directly to the CMOD in the three-point bending test; therefore, it can be controlled easily during the loading process. In addition, performance before healing can be measured by investigating the behavior of the specimen during the reloading process (2 nd loading) just after crack inducement. After healing, the same scheme of loading (3 rd loading) is applied to measure performance after healing. The 2 nd loading and the 3 rd loading follow the same loading schemes at different times, so the comparison before and after healing is easy and clear. Furthermore, the specimens used in this test can be fabricated easily without the use of any specialized equipment. Owing to the abovementioned reasons, we used the three-point bending test with a notch for assessments in this study. We performed the experiment by referring to the Reunion Internationale des Laboratoires et Experts des Materiaux (RILEM) and Japan Concrete Institute (JCI) standards [34; 35]. Figure 1 shows a schematic of the experimental setup.  
The degree of crack inducement was set to CMOD 0.05 mm. It is necessary to standardize the depth of the crack in all specimens to quantify the healing effect. The crack depth was controlled constantly by advancing the CMOD. [PERSON] et al. used a certain percentage (60%) of the peak load as a standard for the crack depth [36]. However, it was confirmed that the behaviors of the specimens after the peak load differed, even in the case of specimens of the same age. Therefore, a constant CMOD of 0.05 mm was applied to the 1 st loading of all cases by referring to the literature [23; 37], and unloading was performed until the load decreased to zero. The 2 nd loading was also conducted until a CMOD of 0.05 mm was achieved. The crack depth based on the CMOD was confirmed to be relatively even in all specimens.
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 3 In-building System Model  
The system modeling presented here finds its basis in the Buildings Energy Databook [32], which presents end-use splits of US residential energy consumption. Subsequently, the residential loads are broadly classified into three categories; 'A', 'D' and 'I', as explained in the following:  
_'A' category:_ The loads that are inherently AC in nature. Examples include induction motor-base clothes washer and compressors in refrigerators and air-conditioners. In a DC distribution system, these will require a DC-AC conversion stage for operation, whereas the AC distribution system, they will be directly operated from the power mains. _'D' category:_ The loads that can operate on DC fall in this category. Lighting, due to the advent of LEDs, falls in this category. Home electronics, computers, and laptops are other examples of D category loads. These loads require AC-DC conversion in the AC system and DC-DC conversion in the DC system for stepping down the mains DC voltage to a level essential for the loads. _'I' category_: The loads independent of the type of power available fall in this category. Heating loads, e.g., electric iron and water heaters, fall in this category. These do not require a conversion stage for their operation and can directly be operated from the power mains if a suitable voltage is supplied. The average monthly consumption of a US home [33] is considered for modeling. This allows us the analysis of single/multi-family homes through a single platform. The average monthly consumption of a US home is 911 kWh, which gives average daily consumption of about 30 kWh. The energy data are combined with this value to give the kWh-of-energy consumed by each load. Table 1 presents the residential energy splits and energy in kWh for each load. The percentage consumption in 24 h is calculated for each load, as depicted in the pie chart. This value is combined with the average daily consumption to form 'Energy kWh' column. For example, water heating accounts for 0.48/4.795 of the total home's consumption. Combining this ratio with the average daily consumption of 30 kWh gives 2.64 kWh of the energy consumed per day. Furthermore, loads are categorized into 'A', 'D' and 'I' categories, as explained in the above; 'other' load type is given an equal share in each category. For mathematical modeling/simulation and analysis, the MATLAB software is used. The next step is to find the energy consumption of each load per hour. For this, we refer to the curves from the Energy Information Administration (EIA) for each load type [33]. From the curves, we were able to acquire the hourly distribution of energy of each load, as shown in Figure A1. For example, the overall consumption of space heating is 2.64 kWh, and then we fitted this value in the curve equation gained from MATLAB curve fitting tool and obtained the consumption value for each hour. After performing a similar task for each load type except for the \"other\" load type, which is allotted the equal share in each hour, Table 2 was formulated.