esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
## 2 Methodology
### Training and Classification by AlexNet CNN Model  
Matlab R2020b was used for image processing and data analysis. The fine-tuned AlexNet model was trained by stochastic gradient descent with momentum (SGDM) optimizer. The initial learning rate was set as 0.001 and 0.0001, the minibatch size was set as 15, and the max epoch was set as 6. After the training, the validation images and test images were classified using the fine-tuned network, and the images were displayed with their predicted labels. To quantify the accuracy of the trained model, the precision, recall, accuracy, and F\({}_{1}\) scores were computed using Equations (4)-(7).  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{4}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{5}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
\[\text{F}_{1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision }+\text{Recall}} \tag{7}\]  
where \(TP\), \(FP\), \(FN\), and \(TN\) represent true positive, false positive, false negative, and true negative, respectively.
## 3 Results and Discussion
### Discussion  
The simulation system could still operate normally under different responses without introducing oscillations. Compared with fuzzy PID, ESO-MPC had a better control performance. In the diagram of the simulation results, ESO-MPC had no time delay; as ESO-MPC usually requires more complex calculations, this might have been due to simplifying the model and reducing the number of computations. Fuzzy PID is usually suitable for simple nonlinear systems, which might have been one of the reasons for its poor control performance. Compared with fuzzy PID, ESO-MPC had stronger disturbance resistance because it estimated the unmodeled dynamics and external disturbances of the system through an extended state observer (ESO). On random road surfaces, the ESO-MPC effect was significantly improved, whereas the fuzzy PID control's effect was far inferior. However, on convex road surfaces, the improvement in fuzzy PID increased, indicating that ESO-MPC had a high control performance for random inputs.
## 4 Discussion  
As observed in the simulation results, the drone operating in real time with the proposed hybrid PID controller exhibits significantly smoother dynamic behavior compared to the classical PID scheme. This improvement is evident in the reduction in oscillations and overshoot during transient responses, leading to enhanced stability and tracking performance. Notably, the hybrid controller achieves this improvement without altering the PID gains as both the classical and hybrid controllers operate with the same parameter settings.  
Furthermore, a noticeable reduction in actuator effort is achieved when employing the hybrid PID controller. This reduction suggests more efficient control actions, which translate into lower energy consumption--an essential factor in battery-operated aerial vehicles such as drones. The decrease in control signal intensity, without compromising performance, highlights the effectiveness of the hybrid approach in optimizing resource utilization.  
These results demonstrate that the proposed hybrid PID controller not only meets the desired control objectives but also offers tangible benefits in terms of energy efficiency. Consequently, it can be concluded that, even in real-time implementations, the hybrid control strategy is a viable and advantageous alternative to the classical PID approach, particularly in applications where energy conservation and smooth operation are critical.
## 4 Conclusions  
Overall, the g-C\({}_{3}\)N\({}_{4}\)-based composite catalyst Bi@Fe/CN shows excellent activity in activating PMS for the elimination of SMX. The increase in the PMS concentration (0.2-0.8 mM) and catalyst dose (0.025-0.2 g/L) in the Bi@Fe/CN-3/PMS system could improve the degradation of SMX. It was determined through quenching tests and EPR that the Bi@Fe/CN-3/PMS system contributed to the degradation of SMX via a simultaneous radical pathway with \({}^{\bullet}\)OH and SO\({}_{4}\)\({}^{\bullet-}\) and a non-radical pathway dominated by \({}^{1}\)O\({}_{2}\). Based on the results of the XPS analysis, Fe, Bi, and surface lattice oxygen activated PMS to generate the active free radicals SO\({}_{4}\)\({}^{-}\) and \({}^{\bullet}\)OH and the non-free radical \({}^{1}\)O\({}_{2}\), leading to SMX removal. However, the relationship between the preparation conditions and active site generation (Fe, Bi, and lattice oxygen) of Bi@Fe/CN is not clear. In the future, the preparation parameters of Bi@Fe/CN, such as pyrolysis temperature and the dopant ratio of Bi-Fe oxide, can be further optimized by researchers. This study provides a new approach for the development of PMS system in water treatment.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14083181/s1](https://www.mdpi.com/article/10.3390/app14083181/s1)]([https://www.mdpi.com/article/10.3390/app14083181/s1](https://www.mdpi.com/article/10.3390/app14083181/s1)), Figure S1: The spectrum of JCPDS #46\(-\)0416 (Bi\({}_{2}\)FeO\({}_{40}\)) and Bi@Fe/CN. Figure S2: SEM of (a) fresh Bi@Fe/CN\(-\)3 and (b) used Bi@Fe/CN\(-\)3, (c) FTIR of fresh and used Bi@Fe/CN\(-\)3 (Vertical lines: Different peaks of Bi@Fe/CN). Table S1: Different  
Figure 11: The mechanism of Bi@Fe/CN activating PMS to degrade SMX.  
atom contents of fresh and used Bi@Fe/CN\(-\)3 catalysts. Table S2: Effect of catalyst dosage on SMX degradation in Bi@Fe/CN-3/PMS system. Table S3: Effect of PMS concentration on SMX degradation in Bi@Fe/CN-3/PMS system. Table S4: Effect of initial pH on SMX degradation in Bi@Fe/CN-3/PMS system. Table S5: Effect of different anions on SMX degradation in Bi@Fe/CN-3/PMS system. Table S6: Degradation of SMX with different scavengers in Bi@Fe/CN-3/PMS system.  
Conceptualization and methodology: [PERSON] and N.L.; software: [PERSON], L.L. and S.W.; writing, review, and editing: [PERSON], L.L., S.W. and N.L.; validation: N.L., G.C., B.Y. and Z.C.; supervision: G.C., B.Y. and Z.C.; funding acquisition: N.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
## 3 Theoretical Background
### Symbolic Aggregate Approximation  
The time series symbolic representation called Symbolic Aggregate Approximation [15] is the first effective method of symbolic representation, while there have been many symbolic representations for time series data. The SAX method has many advantages in contrast to the other methods that exist, such as allows reducing dimensions by replacing the continuous time series values with discrete characters. Other advantages are lower-bounding, distance measures and symbols with equal probability. Likewise, the SAX method is based on the Piecewise Aggregate Approximation (PAA) method for dimensionality reduction.  
Time series are normalized before discretization. First, via the PAA method, the data of the original time series of length \(n\) are divided into \(m\) segments of equal length, and then for each segment, their mean value is computed. In this way, SAX performs discretization. Having done dimensional reduction by PAA, an extra transformation is applied to obtain a discrete representation. Through a technique, symbols with equal probability are produced, then, breakpoints are determined that produce equiprobable areas, and each area is mapped to a symbol. Thus, the constant values of a time series are converted to a discrete representation by symbols (Figure 1).
# Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
## 4 The Moving Data Window Gradient-Based Iterative Algorithm  
The gradient method and the Newton method use a batch of data, which is the data from \(t=1\) to \(t=L\). Here, the moving window data from \(y(t_{i-p+1})\) to \(y(t_{i})\), \(p\) used here is the length of the moving data window. Let \(t_{i}\) be the current sampling time. The moving window data can be represented as \(y(t_{i})\), \(y(t_{i-1})\), \(\cdots\), \(y(t_{i-p+1})\). These sampling data change with the sampling time \(t_{i}\). With the increasing of \(i\), the data window moves forward constantly. New data are collected and old data are removed from the window. The following derives the moving data window gradient-based iterative algorithm of dual-frequency signal models. Define the moving data window criterion function  
\[J_{2}(\mathbf{\theta}):=\frac{1}{2}\sum_{j=0}^{p-1}[y(t_{i-j})-a\cos(\omega_{1}t_ {i-j})-b\cos(\omega_{2}t_{i-j})]^{2}. \tag{36}\]  
Define the information vector  
\[\mathbf{\varphi}(\mathbf{\theta},t_{i-j}):=[-\cos(\omega_{1}t_{i-j}),-\cos(\omega_{2} t_{i-j}),at_{i-j}\sin(\omega_{1}t_{i-j}),bt_{i-j}\sin(\omega_{2}t_{i-j})]^{ \mathrm{ T}}\in\mathbb{R}^{4}, \tag{37}\]  
and the stacked information matrix  
\[\mathbf{\Phi}(p,\mathbf{\theta},t_{i}):=[\mathbf{\varphi}^{\mathrm{ T}}(\mathbf{\theta},t_{i}),\mathbf{\varphi}^{\mathrm{ T}}(\mathbf{\theta},t_{i-1}),\cdots,\mathbf{\varphi}^{\mathrm{ T}}(\mathbf{\theta},t_{i-p+1})]^{\mathrm{ T}}\in\mathbb{R}^{p\times 4}. \tag{38}\]  
Define \(f(p,t_{i-j}):=a\cos(\omega_{1}t_{i-j})+b\cos(\omega_{2}t_{i-j})\in\mathbb{R}\), the vector \(\mathbf{F}(p,\mathbf{\theta},t_{i})\) and \(\mathbf{Y}(p,t_{i})\) as  
\[\mathbf{F}(p,\mathbf{\theta},t_{i}):= \begin{bmatrix}f(p,t_{i})\\ f(p,t_{i-1})\\ \vdots\\ f(p,t_{i-p+1})\end{bmatrix} \in\mathbb{R}^{p},\quad\mathbf{Y}(p,t_{i}):= \begin{bmatrix} y(t_{i})\\ y(t_{i-1})\\ \vdots\\ y(t_{i-p+1})\end{bmatrix} \in\mathbb{R}^{p}. \tag{39}\]  
Then, the gradient vector can be expressed as  
\[\text{grad}[J_{2}(\mathbf{\theta})]:=\mathbf{\Phi}^{\mathrm{ T}}(p,\mathbf{\theta},t_{i})[\mathbf{Y}(p,t_{i})-\mathbf{F}(p,\mathbf{\theta},t_{i})]. \tag{40}\]Let \(k=1,2,3,\cdots\) be an iterative variable, and \(\hat{\mathbf{\theta}}_{k}(t_{i}):=[\hat{a}_{k}(t_{i}),\hat{b}_{k}(t_{i}),\hat{\mathbf{ \omega}}_{1,k}(t_{i}),\hat{\mathbf{\omega}}_{2,k}(t_{i})]^{\intercal}\in\mathbb{R}^ {4}\) be the estimate of \(\mathbf{\theta}\) at iteration \(k\) and the sampling time \(t=t_{i}\).
## 3 Conceptual Overview of DTs in Transportation Infrastructure
### Six-Dimensional Structure of the DT of Transportation Infrastructure
Advanced algorithms, together with large-scale data analytics, are employed to provide predictive capabilities, thereby allowing for the optimization of signal control and route planning, along with other operational capabilities of the transport system. The service layer of the DT transforms these capabilities into useful applications for users. The decision support tools and services housed here include traffic monitoring, congestion prediction, accident response, and urban planning. Further services that allow transportation managers and the public to interface with the DT to gain valuable insights into real-time conditions also include traffic monitoring, congestion prediction, accident response, and urban planning. APIs enable the integration of other entities into the system with the help of third-party applications, thus providing flexibility to developers to create new tools based on data and functionality stemming from the DT. Lastly, the connectivity layer acts as the communication bridge between the physical world and the virtual model, ensuring a seamless flow of data across various system components. This layer maintains real-time synchronization, allowing the DT to accurately reflect the current state of the physical infrastructure. It is responsible for data integrity and accuracy, utilizing robust protocols and technologies to manage large volumes of data transmission with minimal delay and loss. Together, these six components form a cohesive network that powers the DT of transportation infrastructure, providing substantial technological support for the continuous, intelligent, and efficient evolution of urban transportation systems.
# Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net
## 5 Methods
### Performance Metrics  
To measure the performance of our model, we used both the Sorensen-Dice coefficient (or simply Dice coefficient) and Jaccard coefficient, shown in Equations (2) and (3), respectively.  
\[Dice=2*\frac{|\hat{y}\cap y|}{|\hat{y}|+|y|} \tag{2}\]  
\[accard=\frac{|\hat{y}\cap y|}{|\hat{y}\cup y|} \tag{3}\]  
where \(\hat{y}\) and \(y\) represent the predicted and ground truth, respectively. In our case, \(y\) and \(\hat{y}\) are the observed and predicted segmentation regions of ICH, respectively.  
Both metrics, which range from 0 to 1, gauge the similarity, or overlap, of two sets; however, unlike the Dice coefficient, the Jaccard coefficient satisfies the triangular inequality and is, therefore, a proper distance measure. This is why it is preferred to optimize the Jaccard coefficient rather than the Dice coefficient, though they often produce similar results.
## 2 Materials and Methods  
### Participants  
This study was conducted at the Human Motion Analysis Laboratory of Ningbo University, Ningbo, China, between September and October 2024. Twenty healthy male soccer players (age: 22.55 \(\pm\) 0.65 years; height: 1.77 \(\pm\) 0.04 m; body weight: 74.20 \(\pm\) 7.85 kg) were recruited. Participants were matched for age, daily activity level, and stable weight, defined as a weight change of less than 2.5 kg over the last three months [25]. Inclusion criteria required participants to be male, aged 18-30 years, with radiographic confirmation of complete epiphyseal closure, no history of knee pain or lower extremity surgery, and no significant musculoskeletal injuries in the past year. Participants were also required to engage in regular physical activity (\(\geq\)3 times per week) without competitive sports commitments, except for one football player included to provide detailed imaging data. Exclusion criteria encompassed any history of knee-related diseases (e.g., osteoarthritis, meniscus or ligament injuries, or inflammation), prior lower extremity surgeries (e.g., ligament reconstruction or joint replacement), systemic metabolic diseases (e.g., diabetes, thyroid dysfunction, or osteoporosis), chronic pain syndromes, or neurological disorders affecting lower limb movement. Additional exclusions included current musculoskeletal injuries, abnormal gait patterns, use of medications affecting bone or muscle function within six months, and asymmetrical range of motion or lower limb alignment abnormalities (e.g., excessive valgus or varus) identified during initial screening [11].  
The research team, comprising sports biomechanics specialists, physical education professors, and certified radiologists with expertise in medical imaging and musculoskeletal modeling, ensured the reliability and accuracy of the data collection and analysis. All imaging was performed using the same 64-slice computed tomography (CT) scanner and 3.0T magnetic resonance imaging (MRI) machine for all participants. The scans were conducted by the same technicians and radiologists, ensuring consistency in the imaging process. The same imaging software was used to process and analyze the data for all subjects, maintaining uniformity in the data collection and model construction process. A CT scan and MRI scan were performed for one participant, a football player, to construct a detailed finite element model of the right knee, including the ACL. These scans were conducted in a supine position with the leg axis straight to ensure consistent anatomical alignment, enabling precise simulations of knee biomechanics under dynamic conditions.  
Approval for the study was obtained from the Institutional Review Board of Ningbo University School of Physical Education and Sports, and written informed consent was acquired from all participants.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Effect of Text Transcription Errors on Performances
#### 4.6.3 Effect of Added/Removed Words  
Finally, we remove or add \(p\%\) of the words uniformly at random in the text of Chapter 10 and Playlist 50. For each added word, we first sample a length \(l\) following a normal distribution centered around a mean length of six characters, and then generate a word of \(l\) sampled uniformly at random in the [a-z] letters. As removed or added words are not present in our manually aligned reference, we compute the AE only for the original words of Chapter 10 and Playlist 50, respectively.  
Figure 14(a) shows the speech MAAE for different values of \(p\). It shows that the alignment error remains under the 200 ms error even if half of the words are missing or added. This indicates that the alignment is very robust to noise, as long as more than half of the true words remain untouched. Beyond that point, there are not enough words to keep the alignment stable, and the error increases dramatically.  
Similarly to the previous experiments, Figure 14(b) shows that the singing voice alignment is much more sensitive to a full word addition or deletion. As in the previous, this can be interpreted by the fact that the posteriorgram does not exhibit sufficiently salient probabilities for correct characters in order to provide steady anchorage compensating for transcription errors.
# Weather Radar in Complex Orography
## 4 Solutions and Results
### Receiver over Elevation Design  
Some countries opt for a design with the receiver mounted onto the back of the antenna, that is, the so-called receiver over elevation design, or short ROEL [137; 138]. Some examples of this type are the Italian radar on Bric della Croce [15; 139], a hill near Turin, and the operational networks in Austria [8], Belgium, Germany [140] and Switzerland [1]. For a picture of the receiver over elevation design, see [5]. A ROEL design offers several advantages:  
* The reception path is short, which results in improved sensitivity;
* On transmission, the same waveguide and the same rotary joint are used for both the horizontal (H) and vertical (V) polarisations up to the power divider (magic-T) on the back of the antenna, which results in higher accuracy for dual-polarisation moments;
* The transmission and reception paths are symmetric for H and V polarisation;
* There is no need for an expensive (and sometimes fragile) dual-channel rotary joint.  
Measurements conducted during commissioning of the new radar at Albis [1] pointed out that the sensitivity had improved by 9 dB, a number that was roughly confirmed by means of Sun measurements. Part of the gain in sensitivity at Albis can be attributed to switching to the ROEL design, while the rest can be attributed to the replacement of the analog receiver with digital technology and moving the transmitter and receiver from the cabin at the bottom of the tower to the top. Sensitivity is a critical aspect for radars in complex orography, as stated in Section 2. In spite of obvious advantages, ROEL does not seem to be the preferred option. The majority of national weather services prefer a conventional design with the receiver next to the transmitter in the cabin. A possible reason for this may be the need for a reliable broadband data line between the receiver on the moving antenna and the cabin, which can be realised, for instance, with a fibre optic line in the centre of the rotary joint [138]. Other reasons may be related to the need for a reliable control of the climate in the receiver box and appropriate lightning protection in the radome. Another frequently cited counterargument is that of the significant additional efforts that are needed for maintenance work, but this has not been confirmed for the Swiss experience. A ROEL design is difficult to realise for an S-band radar for technical reasons, because of the large size of the waveguides. For more specifications on Swiss radar hardware, see Table 9.1 on page 175 in [1].
## 3 Models for Wind Turbine Wakes under Stable Conditions
### Modeling of Wind Turbine
#### 3.1.1 Actuator Disk Model Based on Thrust Coefficient (-CT)  
In the origin actuator disk model, the momentum source term in the \(x_{i}\) direction due to the thrust is uniformly distributed through the rotor:  
\[S_{u,i}=-\frac{T}{V_{\text{disk}}}\frac{U_{\text{ref},\,i}}{U_{\text{ref}}}=- \frac{\rho C_{T}U_{\text{ref}}U_{\text{ref},\,i}}{2\Delta l} \tag{15}\]  
where \(T\) is the thrust, \(V_{\text{disk}}\) is the disk volume, \(C_{T}\) is the thrust coefficient, \(U_{\text{ref}}\) is the upstream reference velocity at hub height, \(U_{\text{ref},\,i}\) is the component velocity \(U_{\text{ref}}\) of in \(x_{i}\) direction, \(\Delta l\) is the disk depth, and the negative sign represents the drag effects of thrust on the flow.  
For the upstream flow disturbed by a complex terrain or wind turbine wakes, \(U_{\text{ref}}\) is unknown and difficult to evaluate from the local flow directly. According to the one-dimensional momentum theory and ignoring the velocity gradient, the reference velocity is a function of the rotor-averaged velocity \(U_{\text{disk}}\):  
\[U_{\text{disk}}=(1-a_{B})U_{\text{ref}} \tag{16}\]  
in which the induced factor \(a_{B}\)[25] is related to the thrust coefficient \(C_{T}\) by  
\[a_{B}=\begin{cases}\frac{1}{2}(1-\sqrt{1-C_{T}}),&C_{T}\leq\frac{8}{9}\\ \frac{C_{T}-4a_{T}^{2}}{4(1-2a_{T})},&C_{T}>\frac{8}{9}\end{cases} \tag{17}\]where \(a_{c}=1/3\).  
In simulations, the disk-averaged velocity \(U_{\text{disk}}\) is calculated by averaging the local velocity in the disk region, then is applied to estimate the upstream reference velocity \(U_{\text{ref}}\) based on Equations (16) and (17) and the momentum source \(S_{u,i}\) based on Equation (15), respectively.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### Advanced Encryption Standard (AES)
#### 3.1.1 Confidentiality in AES  
Due to the resource constraints which characterize WSNs and the involved devices, we decided to use a key length of 128 bit for AES. In order to make exchanged data confidential, which means that only sender and receiver can understand the data carried within the packets. The packet field which we want to make confidential is ciphered using AES-128 considering the symmetric shared key between the server and the sensor. To gain a better security degree, during the ciphering operation we randomly use an Initialization Vector (IV) since the first block of the plaintext, before being ciphered, is involved in an XOR operation with the IV, so the IV cannot be predictable and must be as random as possible. So, sensor nodes will send to the server a packet which contains a ciphered field which is obtained applying the AES to the field we want to make confidential with the shared key between sensor and server.  
The entire security of the communication described in Figure 2 is based on the assumption that only sender and receiver can access and know the symmetric shared key. As an additional operation, in order to avoid special characters in the packet which could lead to packet loss during the transmission, we encode, using the base64 method, the ciphered field.