esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain and IoT Convergence--A Systematic Survey on Technologies, Protocols and Security
## 6 Blockchain: Strengths and Weaknesses  
Like any other software architecture, the Blockchain has both positive and negative aspects. Below is a list of the main ones:  
* Decentralization: As a decentralized and distributed technology, all transactions are decentralized, and verified by the network itself removing any single point of failure in a network of devices.
* Security: The use of public/private key pairs to sign transactions, specific hash functions to link block together and the peculiar consensus algorithms, gives to Blockchain systems a high resistance to tampering.
* Cost reduction: according to a Santander FinTech study [69], distributed ledger technology could reduce financial services infrastructure cost between US$15 billion and $20 billion per annum by 2022, providing the possibility to decommission legacy systems and infrastructures and significantly reduce IT costs.  
* Privacy & Transparency: The chain and its content are public and readable by anyone. Anyone can read and verify the honesty of every transaction but link a wallet(address) to a specific identity is not allowed by the protocol. There are some hacking strategies, however [70], to link a Blockchain address to a specific IP. To overcome this type of weakness, in addition to the use of Tor or VPN networks, specific Blockchain privacy-oriented projects have been developed [25; 39] Drawback[71]:
* Legal issues: In a decentralized environment where nodes exist around the planet, there is no way to establish a common jurisdiction. Different countries have very different approaches to titles, ownership, contracts, trademarks and liabilities. Some governments have made cryptocurrencies illegal in their territories [72].
* Volatility: All cryptocurrencies are from high to extremely high volatile. Cryptocurrency markets are not regulated, some exchange has suspicious volumes [73], and most of cryptocurrencies has very low volume compared to Bitcoin, exposing them to high speculative activity.
* Storage: Usually, to run a full node, the entire Blockchain should be synchronized locally (i.e., Bitcoin needs 200 GB). This high demand for storage space makes adoption in IoT systems extremely complicated.
* Transaction speed: Bitcoin is restricted by mining block time and block capacity to handling up to 7 transactions per second while VISA has the peak capacity to handle 24,000 transactions per second [74]. Actually, no decentralized Blockchain project can reach such an order of magnitude.
* Lack of maturity and standards: Distributed Ledgers is an emerging technology. Many Blockchain projects are not production ready, partially untested, and will require early adopters to accept significantly increased risk levels over the next five to seven years.
## 4 Experiments and Result Analysis  
### Experimental Procedure and Experimental Setup  
After constructing the model, both the improved YOLOv5 model and the original YOLOv5 model were trained and tested on the weld seam defect dataset developed in this study, and the results were compared. To mitigate the adverse effects of data imbalance, such as gradient dominance, distortion of evaluation metrics during training, and prediction bias or decision boundary shift in classification tasks, this study adopts a transfer learning strategy. Specifically, the model is first pre-trained on the ImageNet dataset, which is large-scale and features a balanced class distribution, allowing it to learn generalizable visual representations while reducing the risk of bias toward specific defect types. The pre-trained weights are then transferred to the weld defect dataset used in this study, followed by fine-tuning to adapt the model to the target task. This approach provides high-quality initial parameters, thereby effectively alleviating performance degradation caused by data imbalance.  
The experimental setup for this study is as follows: the operating system is Windows 10, the CPU is a 14 vCPU Intel(R) Xeon(R) Gold 6330 CPU @ 2.00 GHz, the GPU is an NVIDIA GeForce RTX 3090, the Python compiler version is Python 3.8.19, the deep learning framework is PyTorch 1.11.0, and the CUDA version is CUDA 11.3. During the training phase, the learning rate is set to 0.01, the number of training epochs is set to 300, and the batch size is set to 4.
## 2 Materials and Methods
### Characterization of PPO
#### 2.6.3 Inhibition of PPO Activity with Catechol as Substrate Preparation of Inhibitory Solutions  
The common chemical inhibitors (L-cysteine, kojic acid, and ascorbic acid), and metal ions (MgCl\({}_{2}\), KCl, ZnCl\({}_{2}\), FeCl\({}_{2}\), NaCl, CaCl\({}_{2}\), and CuCl\({}_{2}\)) were dissolved in water and analyzed at concentrations ranging from 0.01 mM to 10 mM (in the reaction mixture 0.002 mM to 2 mM, respectively) and 5 mM to 20 mM (concentration in the reaction mixture: 0.5 mM to 2 mM), respectively. Plant materials--dried basil (_Ocinum basilicum_ L.), lovage (_Levisticum officinal_), oregano (_Origanum vulgare_), marjoram (_Origanum majorana_ L.), chamomile (_Matricaria chamomilla_), thyme (_Thymus vulgaris_ L.), wheat bran from common wheat (_Tricum aestivum_ L.), lemon peel (_Citrus limon_), orange peel (_Citrus sinensis_), and fresh and dried parsley leaves (_Petroselinum crispum_)--were ground in a laboratory mill and sieved (60 mesh), and 10 g of samples were mixed with 90 mL of boiling distilled water (95 \({}^{\circ}\)C) and then allowed to cool. Then, the samples were centrifuged 5000\(\times\)_g_ at 4 \({}^{\circ}\)C for 30 min and used for treatments. The infusions were tested at concentrations ranging from 0.0001% to 10%.  
Inhibition of PPO Activity (Exogenous Substrate)  
Then, 10 uL of the enzyme solution (concentrated freeze-dried enzyme dissolved in 0.05 M phosphate buffer pH 5) were incubated with 180 uL 0.05 M phosphate buffer (pH 5) and 60 uL inhibitor solution. The reaction was started by adding 50 uL of 0.05 M pyrocatechol. The absorbance at 420 nm was measured at 24 \({}^{\circ}\)C. Next, IC\({}_{50}\) value was calculated and expressed in % of dry mass.  
Model of Inhibition and Kinetic Constants  
Data were plotted as the 1/V and 1/S concentration according to the method proposed by [PERSON] [18]. The reaction was conducted using 50 mM pyrocatechol as asubstrate. Based on Lineweaver-Burk graphs, a mode of inhibition and kinetic parameters of reaction (V\({}_{\rm max}\), K\({}_{\rm m}\), V\({}_{\rm maxI}\), and K\({}_{\rm mi}\)) were defined.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 5 Application of Infrared and Visible Image Fusion  
### Application of Night Vision  
The target object's or scene's thermal radiation information is typically converted into false-color images because the human visual system is more sensitive to color images than to grayscale photos. Due to the use of color transfer technology, the resulting color image has a realistic daytime color appearance, which makes the scene more intuitive and aids the spectator in understanding the image. Figure 17 illustrates an instance of fusing visible and infrared images for color vision at night.  
Grayscale images are less responsive to human vision than color images. Human eyes are capable of distinguishing thousands of colors, but they are only capable of doing so for about 100 grayscale images. For this reason, it is imperative to colorize grayscale images, especially since the fusion method of infrared and visible images with color contrast enhancement has been widely adopted in military equipment [94]. Additionally, there is now more interest in the color fusion ergonomics representation of many image sensor signals due to the quick growth of multi-band infrared and night vision systems.
# Crack Detection of Concrete Images Using Dilatation and Crack Detection Algorithms
## 3 Crack Detection User Algorithms
### User Algorithm II (Step 9)  
After analyzing the labeled images in the previous step (step 8), it is observed that some labels are difficult to identify as cracks due to their small size. Table 4 presents User Algorithm II, which is used for removing some labels that are difficult to determine as cracks.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **User Algorithm II (Step 9)** \\ \hline Input: Label image L(i, j) & \\ Output: Crack image C(i, j) & \\ (1 \(\leq\) i \(\leq\) m, 1 \(\leq\) j \(\leq\) n) & \\ \hline \multirow{2}{*}{1} & Use the label image L(i, j) of step 8 \\  & ; N is the number of label \\ \hline
2 & if N \(>\) 20; goto 12; end \\ \hline
3 & for (k = 1 to N) \\ \hline
4 & for (i = 1 to m) \\ \hline
5 & Count the crack size(c1) of column \\ \hline
6 & end \\ \hline
7 & for (j = 1 to n) \\ \hline \hline \end{tabular}
\end{table}
Table 4: User Algorithm II (step 9).  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **User Algorithm I (Step 5)** \\ \hline Input: Binary image im1(i, j) of Step 4 \\ Output: Dilated image im2(i, j) \\ (1 \(\leq\) i \(\leq\) m, 1 \(\leq\) j \(\leq\) n) & \\ \hline
1 & Use the binary image im1(i, j) of step 4 \\ \hline
2 & z1 = nnz(im1)/(m \(\times\) n) \(\times\) 100 \\  & ; nnz is function to find number of nonzero \\ \hline
3 & n1 = round(z1) \\ \hline
4 & if z1 \(\leq\) 1; n1 = 25; end \\ \hline
5 & Make sub images 5 \(\times\) 5 (sub_im) of im1(i, j) \\ \hline
6 & if nnz(sub_im) \(\geq\) n1 \\ \hline
7 & Fill the sub image with 1 \\  & (white, crack) \\ \hline
8 & else \\ \hline
9 & Fill the sub image with 0 \\  & (black, background) \\ \hline
10 & end \\ \hline \hline \end{tabular}
\end{table}
Table 3: User Algorithm I (step 5).  
Line 10 is a code that classifies cracks when the size of cracks in the row or column direction is more than 1/5 of the total size. By using this algorithm, as shown in Table 1, the image in step 8 becomes the image in step 9, resulting in the detection of only cracks.  
In line 2, if the number of labels in the labeled image is 20 or more, the algorithm terminates at line 12, preventing it from being recognized as a crack. For non-crack images, this code helps eliminate cases where labeling is sporadically dispersed due to its properties, causing excessive crack recognition.
## 3 Data and Methods
### Establishment of the Basic Database  
#### 3.3.1 Altitudinal Data  
The elevation data utilized in this research were sourced from the Geospatial Data Cloud sponsored by the Computer Network Information Center of the Chinese Academy  
Figure 3: Sub-watershed division, elevation, soil type, and land use mapping of the study area.  
of Sciences. ASTER GDEM 30M resolution digital elevation data were selected. Based on the survey, a resolution accuracy ranging from 30 m to 90 m was more suitable for the SWAT model simulation. Once the downloaded data were imported into ARCGIS, they were then spliced and clipped to generate shape files that could be employed for runoff simulation, thereby extracting runoff and other simulation data.
## 2 Materials and Methods
### Model Evaluation  
To evaluate the model prediction accuracy, three statistical measures, including the root mean square error (_RMSE_), coefficient of correlation (\(R\)), and Nash-Sutcliffe efficiency (\(NSE\)), were used as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(s_{i}-o_{i})^{2}}, \tag{7}\]  
\[R=\frac{\left(\frac{1}{n}\right)\sum_{i=1}^{n}(o_{i}-\overline{o})^{2}(s_{i}- \overline{s})}{\sqrt{\left(\frac{1}{n}\right)\sum_{i=1}^{n}(o_{i}-\overline{ o})^{2}}\times\sqrt{\left(\frac{1}{n}\right)\sum_{i=1}^{n}(s_{i}-\overline{s})^{2}}}, \tag{8}\]  
\[NSE=1-\frac{\sum_{i=1}^{n}(s_{i}-o_{i})^{2}}{\sum_{i=1}^{n}(s_{i}-\overline{ o})^{2}}, \tag{9}\]  
where \(o_{i}\) and \(s_{i}\) refer to the respective observed and estimated values, respectively, and \(\overline{o}\) and \(\overline{s}\) are the averages of the observed and estimated values, respectively.
# Scalable Fabrication of Si-Graphene Composite as Anode for Li-ion Batteries
## 2 Materials and Methods
### Coin Cell Fabrication  
The Si-graphene electrode was comprised of 80 wt.% active material (Si-graphene, 50/50 weight ratio) and 20 wt.% poly(acrylic acid) binder (PAA, 25 wt.% solutions in water, Alfa Aesar, Ward Hill, MA, USA). An additional conductive agent is not involved. As a comparison, a Si nanoparticle-based electrode was also prepared, which comprised of 40 wt.% Si nanopowder, 40 wt.% conductive agents (acetylene carbon black, 99.9%, Alfa Aesar), and 20 wt.% PAA as the binder. The weight ratio between Si and acetylene carbon black was 50/50. The electrode was fabricated using a typical slurry method. The slurry was mixed on a magnetic stirrer (Corning, Glendale, AZ, USA) at 800 rpm for 18 h before coating onto the copper foil (9 \(\upmu\)m, MSE Supplies, Tucson, AZ, USA) with a micrometer-controlled doctor blade (MSK-AFA-HC100, MTI Corporation, Richmond, CA, USA) with a blade gap of 30 \(\upmu\)m. The coated copper foil was dried in an oven at 100 \({}^{\circ}\)C for 2 h for the solvent to evaporate. The dried coatings were then transferred into a vacuum oven that was set to 120\({}^{\circ}\)C for an overnight period before the coin cell assembly. The weight percentage of the active material was ~1 mg cm\({}^{-2}\). 2032-type coin cells were used with the Li foil (0.6 mm thickness, Sigma-Aldrich, St. Louis, MO, USA) as the counter electrode and a PP membrane (Celgard 3501) as the separator. The cells were filled with 1.0 M LiPF6 in EC/DEC = 1/1 (\(v/v\)) (Sigma-Aldrich) as the electrolyte. The coin cells were assembled in an argon-filled glove box to avoid moisture and oxygen exposure (H\({}_{2}\)O < 0.1 ppm, O\({}_{2}\) < 0.1 ppm).  
Figure 1: Schematic diagram of the preparation of Si-graphene nanocomposite.
# Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
## 3 Materials and Methods
### Classification with Baseline Models  
Two state-of-the-art machine learning models were used for comparison with our proposed CNN: an sLDA and an RF. They both have the advantages to be simple in their implementation, to require a light computational burden, and to show good performance in EEG classification during hand movements, gesture recognition, and BCI experiments.  
The linear discriminant analysis (LDA) is a supervised multi-class classification technique that aims at estimating the parameters of the linear multivariate model of the input data, via parametric density estimation procedure [19]. Here, the input to the sLDA is the vector \(\mathbf{x}\) obtained by reshaping matrix \(\mathbf{X}\) as follows:  
\[\mathbf{x}^{(i)}=[x_{1}^{(i)}(1),x_{2}^{(i)}(1),...,x_{58}^{(i)}(1),x_{1}^{(i) }(2),x_{2}^{(i)}(2),...,x_{58}^{(i)}(N)], \tag{2}\]  
where \(i\) is the trial number, and \(N\) the number of time samples available in the sliding window. The shrinked LDA version, i.e., the sLDA, introduces a regularization strategy, especially useful with high-dimensional feature spaces, when only a few data points are available. For the regularization, we considered the pooled covariance matrix, computed from the three classes, and we optimized the regularization parameter as in [39]. A common approach to obtain the optimal sLDA model with time series, i.e., as in the EEG case, is to train several sLDA models, each one based on a different subset of the training set (e.g., given by a different observation window), and to select the one which yields the best training performance. Thus, here, for each single trial \(i\), a sliding window is used to scan the entire EEG segment from \(-2\) s to \(3\) s. Then, an sLDA model was obtained for each, every 2, time instant (i.e., one every 125 ms). For each participant, the time instant where the sLDA model resulted in the best classification performance was taken as the trained model. Moreover, three different window lengths were tested for each participant, specifically \(\{0.6,0.8,1\}\) s, and the same model training was repeated for every length value.  
The RF is a classifier that works as an ensemble of individual decision tree algorithms to reduce the risk of overfitting and, thus, to enhance the classification performance. Each tree is obtained by independently bootstrapping the samples from the input dataset, resulting in uncorrelated models whose predictions are more accurate than the ones we would obtain from a single one [40]. Then, a random set of predictors is used at each split to grow the tree [41]. To compute the predictions, a majority vote across the predictions of the individual decision trees is used. In this study, the vector in (2) was also used as the input to the RF. The number of trees was empirically set to 50, found as the best trade-off between the classification accuracy and the computational complexity.
## 3 Survey of Techniques for Updating
### Update Fault Detection and Recovery  
In IEEE terminology, a software release can introduce a defect, which is a fault encountered during software execution; a fault may cause one or more failures, and a problem may be caused by one or more failures [79]. Fault detection therefore relies on identifying failures in the executing software. Identifying these software failures is not an easy task, but is a fundamental problem for software updates as even _minor_ faults in the software can cause application failure, data corruption, or system failure which may prevent further updates.  
There are two possible approaches to fault detection. One is to identify all the possible faults, and make sure each is checked for. The problem with this is that there is (and probably never will be) a definitive list of faults. cSimplex [80], for example, identifies some common faults as: malformed messages, messages dropped, packet storms, group management errors, improper data delivery, message duplication, routing loops, and local reception errors. In [81] faults are classified by their symptoms and ease of replication: _Bohrbugs_ simple, are easily isolated, and manifest consistently under a well-defined set of conditions, and _Mandelbugs_ have complex activation or propagation behavior, are difficult to isolate, and failures are not systematically reproducible. A third category
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 5 Results
### Ridge  
The hyperparameters that best fit the ridge model and resulting classification results are listed in Table 5. The analysis of the acoustic signature classification results using ridge regression indicated the model's ability to generalize to unknown data, where it achieved an accuracy of 96%.  
Figure 8: Confusion matrix for Lofar without decimation.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multicolumn{2}{c}{**Lofar**} & \multicolumn{2}{c}{**PCA**} & \multicolumn{2}{c}{**NLPCA**} \\  & **w/ Dec** & **w/o Dec**. & **w/ Dec** & **w/o Dec.** & **w/ Dec** & **w/o Dec**. \\ \hline \hline \(\alpha\) (Regularization Parameter) & \(1\) & \(1\) & \(10\) & \(10\) & \(10\) & \(10\) \\ \hline \(\gamma\) (Regularization Parameter) & Distance & Distance & Distance & Distance & Distance & Distance \\ \hline Kernel & Linear & Linear & Linear & Linear & Linear & Linear \\ \hline Mean Accuracy \(\tilde{Acc}\) & 0.958 & 0.530 & 0.578 & 0.530 & 0.578 & 0.530 \\ \hline Standard Error (SE) & \(1.18\cdot 10^{-3}\) & \(6.94\cdot 10^{-2}\) & \(7.91\cdot 10^{-2}\) & \(6.94\cdot 10^{-2}\) & \(7.91\cdot 10^{-2}\) & \(6.94\cdot 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ridge hyperparameters and the model’s performance for the Lofar, PCA, and NLPCA variations of the database.  
Decimation significantly improved the mean accuracy, with the Lofar's going from 0.48 to 0.96, and PCA/NLPCA's from 0.53 to 0.97. This proved that decimation was crucial for improving the model performance. The NLPCA and PCA variations show similar results, with mean accuracies of 0.97 with decimation and 0.53 without it. Lofar, while still benefiting from decimation, had a lower performance in both the decimated and non-decimated cases. The standard errors (SEs) were much smaller for the PCA and NLPCA datasets with decimation (0.02) than for Lofar (0.08), indicating that decimation led to more stable results for the PCA and NLPCA models.
# An Image-Processing Method for Extracting Kinematic Characteristics of Droplets during Pulsed GMAW
## 4 Extraction of Droplet Kinematic Characteristics  
The droplet can be approximately regarded as an object with uniform mass, such that the centroid of the droplet coincides with the center of mass. The motion state of the center of mass reflects the motion state of the whole droplet. Therefore, the centroid of the droplet can be studied to extract the kinematic characteristics of the droplet.  
According to the above content, the droplet was recognized accurately as a region of white pixels in the period with an undetached droplet (see Figure 19) and the period with a detached droplet (see Figure 25), while the other parts were all black pixels. Thus, the centroid coordinates (\(X_{0}\), \(Y_{0}\)) of the droplet could be calculated according to Equations (4)-(6), while \(f(X,Y)\) is the value of the pixel corresponding to the coordinates of pixel points (\(X\), \(Y\)) in images.  
\[f(X,Y)=\left\{\begin{array}{ll}1&(\text{when a pixel is white})\\ 0&(\text{when a pixel is black})\end{array}\right.. \tag{4}\]  
\[X_{0}=\frac{\sum X\cdot f(X,Y)}{\sum f(X,Y)}. \tag{5}\]  
\[Y_{0}=\frac{\sum Y\cdot f(X,Y)}{\sum f(X,Y)}. \tag{6}\]  
A series of droplet centroid coordinates can be obtained by processing a series of high-speed photographic images during the droplet transfer. If (\(X_{i,0}\), \(Y_{i,0}\)) represents the centroid coordinates of droplet at frame \(i\), then \(X_{i+1,0}-X_{i,0}\) and \(Y_{i+1,0}-Y_{i,0}\) are the displacements in the \(X\)- and \(Y\)-directions of the droplet centroid from frame \(i\) to frame \(i+1\), respectively. However, the displacement is based on the pixel coordinates, not the real value. There is a proportional relationship between the displacement based on the pixel coordinates and the real value. The real wire diameter \(D_{\text{real}}\) is known, as well as the wire diameter \(D_{\text{image}}\) (in pixels) in the image. Thus, the proportional relationship \(K=D_{\text{real}}/D_{\text{image}}\) can be calculated. Therefore, the velocity of the droplet at frame \(i\) in the \(X\)- and \(Y\)-directions can be expressed as Equations (7) and (8), where \(\Delta T\) is the time interval between the previous frame and the next frame. Moreover, total velocity can be calculated using Equation (9).  
\[v_{i,X}=K\frac{X_{i+1,0}-X_{i,0}}{\Delta T}. \tag{7}\]  
\[v_{i,Y}=K\frac{Y_{i+1,0}-Y_{i,0}}{\Delta T}. \tag{8}\]  
\[v_{i}=\sqrt{v_{i,X}^{2}+v_{i,Y}^{2}}. \tag{9}\]  
In addition, the projected area of the droplet in image \(S_{\text{image}}\) can be obtained. If the droplet is approximately regarded as a sphere, the diameter of the droplet in image \(R_{\text{image}}\) and the real diameter of droplet \(R\) can be calculated using Equations (10) and (11), respectively. Furthermore, other relative parameters can also be calculated to study the kinematic characteristics of the droplets, such as acceleration, external force, volume, mass, momentum, kinetic energy, etc.  
\[R_{\text{image}}=\sqrt{\frac{S_{\text{image}}}{\pi}}. \tag{10}\]  
\[R=K\cdot R_{\text{image}}. \tag{11}\]