esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
## 2 Materials and Methods
### Metallographic Characterization and Mechanical Testing  
The cylindrical cast specimens were machined up to 30 mm in diameter and then sectioned, subsequently roughened, and finally polished with 0.04 \(\upmu\)m colloidal silica in a Vibromet2 vibratory polisher.  
Optical microscopy (OM) analyses were performed on a ZEISS Axio Imager A2 microscope with magnifications of 100\(\times\) and 200\(\times\). SEM images and energy dispersive spectroscopy (EDS) analyses were also performed using a Vega Tescan scanning electron microscope operating at 20 kV, using secondary electron (SE) and back scattered (BSE) images at magnifications of 200\(\times\) and 1000\(\times\). No etching was used to obtain the images.  
To determine the Vickers hardness, measurements with 100 g with 15 s were performed. Ten measurements were made per sample, obtaining the average of these values in the microhardness tester (HMV-G20 ST SHIMADZU).  
Furthermore, nanoindentation analyses were conducted in two steps. Firstly, hardness and elastic modules of the phases in the microstructure were analyzed using nanoindentation instrumented by Quasi Continuous Stiffness Measurement (QCSM), with an average of 10 indentations, carefully positioned in the center of each grain of the corresponding phase with a maximum load of 30 mN. Secondly, an instrumented nanoindentation analysis was made with a 20 \(\times\) 20 matrix, using the fast H-E method at full load of 30 mN. The tests were performed in a UNAT Asmec (Dresden, Germany) equipment using a Berkovich type pyramidal diamond tip at UEPG Multi-User Laboratory--C-LABMU (Ponta Grossa, PR, Brazil).  
Grain size determination was performed on optical microscopy images, using the Heyn Intercept method governed by the ASTM E112 [21]. Four images were analyzed per sample, with one segment horizontal, one vertical and one straight at 45\({}^{\circ}\), these had a length of 3 times the scale; 15 sets were allocated per image. The volume fraction, thickness, and length of the (Al\({}_{5}\)FeSi) were measured by quantitative metallography using MEV images in a computer-assisted image analysis system open software ImageJ.  
The technique of optical emission spectrometry in a SHIMADZU PDA 7000 equipment was used for the analysis of the chemical composition of the samples.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 4 Methodology
### _CNAME Chain-Based Method_
#### 4.1.1 Experiment Setup  
To investigate these questions, we conducted a study on 20 well-known public DNS providers, including Google, Cloudflare, OpenDNS, Quad9, Yandex, Level3, among others, as shown in Table 1. We created 40 _CNAME_ records using two research domain names owned by us, as illustrated in Figure 2. The name server of both domains was configured on the same machine with a publicly routable IP. Then, we initiated the _CNAME_ lookup process by sending a query to the service IPs of targeted public DNS, with the service IP embedded in the query name. This allowed us to collect DNS query logs at the ADNS and easily differentiate the _CNAME_ behaviors of different public DNSes.
## 2 Introduction to Digital Watermarking: Trajectory, Techniques, and Applications  
Digital watermarking is an essential mechanism for embedding information in digital content. Its evolution parallels the exponential growth of digital media [7, 8], with watermarking now fulfilling crucial functions in various fields [9, 10, 11, 12]. The origins of digital watermarking date back to the late 1980s and early 1990s. It coincides with the rise of digital media and associated copyright challenges [13].  
Early watermarking techniques used relatively simple methods that are predominantly limited to embedding visible watermarks into images or audio files to indicate ownership [8, 14]. These primitive approaches proved vulnerable as digital media formats evolved, with watermarks susceptible to degradation or removal. This vulnerability catalysed a transition from basic ownership marking to more sophisticated frameworks that prioritise the balance between robustness, imperceptibility, and capacity [7, 8, 9, 12].  
Modern watermarking does more than embed data. It is an essential tool for safeguarding content integrity for secure distribution and traceability. This expansion extends watermarking applications beyond copyright protection to various fields, including healthcare, broadcasting, and digital forensics [11, 15, 16].
## 2 Materials and Methodology
### Pre-Processing for Data Analysis
#### 2.2.1 Determination of Independent and Dependent Variables  
The first part of step 2 is the categorization of leakage management factors, revenue water ratio, and leakage ratio into independent and dependent variables. The four leakage management factors (PR, MR, LR, LD) determined from step 1 will directly or indirectly affect changes in the revenue water ratio or the leakage ratio. Therefore, the leakage management factors were determined as independent variables, and the revenue water ratio increase and the leakage ratio reduction were determined as dependent variables.
## 1 Introduction  
Smoking is a major risk factor in developing coronary artery disease, chronic kidney disease, cancer, and cardiovascular disease (CVD) [1, 2]. It is also considered as a modifiable risk factor for CVDs and other conditions associated with premature death worldwide [3, 4, 5, 6]. Consequently, smoking status can be used to assess the risk of certain diseases and to suggest first-line interventions based on clinical guidelines.  
Despite the effectiveness and importance of smoking cessation for disease prevention, smoking information is under-utilized and not easily measured. It is often buried in a narrative text rather than in a consistent coded form. The rapid adoption of electronic health records (EHRs) has also scattered patient information across various systems as both structured and unstructured data [7]. However, most information stored in EHRs takes the form of free text in clinical narratives. Therefore, applying natural language processing (NLP) methods is essential in automatically transforming the clinical free text into structured clinical data, which can be further utilized by machine learning algorithms [8; 9; 10].  
Several previous studies successfully extracted smoking information using NLP in English. [PERSON] et al. [7] extracted smoking information and converted it into a structured format. [PERSON] et al. [11] retrieved assessments and advice about lifestyle modifications using an open-source NLP tool, cTAKES. However, unlike languages such as English, in which whitespaces define the boundaries between words, word tokenization is not a trivial task in Korean that has no clear word delimiting rules. Furthermore, in most non-English speaking countries, their medical records are often bilingual, containing terms expressed in both English and their native languages. Therefore, extracting smoking information from Korean EHR requires a different set of NLP methods to overcome this issue of bilingual free text.  
This paper presents a novel smoking-related keyword extraction algorithm from unstructured bilingual EHRs. Medical records often contain diverse information about the patient's medical and health status. However, our NLP-based keyword extraction algorithm filters out and identifies keywords solely relevant to smoking status. Furthermore, the identified smoking-related keywords can also be utilized to improve the accuracy of the existing NLP-based smoking status classification algorithms. Our proposed method is purely unsupervised and does not require a huge training dataset, which is especially critical in the medical domain. Therefore, it serves as a practical solution to any medical institutions, including those in non-English speaking countries, planning to transform their free text EHR into a structured keyword format.
# Quantification of the Benefits for Power System of Resilience Boosting Measures
## 4 Modeling Framework for the Measures
### Modeling of Threat-Specific Active Measures for Wet Snow Events
#### 4.2.2 Optimal Reconfiguration of Counterfeeds to Speed Up the Recovery Process  
The process of reconfiguring the counterfeeds following a contingency that determines the out of service of components of the network (for example MV connections, or secondary or primary substations) can have several objectives:  
* Maximization of the load restored to users (primary objective).
* Minimization of the number of maneuvers (secondary objective).
* Reduction of active losses in the post-contingency network configuration.  
The methodology adopted for the assessment of the impact of the contingency on the electricity supply service to users exploits the modified Viterbi algorithm [31].  
The problem is formulated as follows: find the status (closed or open) of all the counterfeeds to maximize the amount of restored loads of the disconnected users (target objective) with the minimum number of maneuvers on the counterfeeds (secondary objective) and ensuring the fulfillment of the following constraints:  
* Static security (no violations of the voltage magnitude and phase angle at the nodes and no overloads on the branches).
* Radiality condition of the connected network.  
In case that multiple solutions are found that meet these constraints and the optimum criterion, then some performance metrics are considered to find out the \"best solution\" by filtering the less performing ones. These metrics are:  
* The minimum value between the voltages at the network nodes.
* Active power losses.
## 3 Results and Analysis of Influencing Factors
### The Impact of Casing Properties on Signal Attenuation
#### 3.6.1 The Impact of Casing Conductivity on Signal Attenuation  
When the casing conductivity is equal to 1 S/m, it indicates that there is no casing in that region, and the conductivity is consistent with that of the drilling fluid. As can be seen from Figure 10 in an open-hole well environment, the attenuation of the signal is smaller, with a 63.29 dB attenuation after 100 m of transmission. The presence of casing increases signal attenuation. However, as casing conductivity increases, signal attenuation gradually decreases. Specifically, when the casing conductivity is increased to 1 \(\times\) 10\({}^{6}\) S/m, the attenuation of the electromagnetic wave signal after 100 m transmission is 176 dB; at a conductivity of 1 \(\times\) 10\({}^{7}\) S/m, the attenuation is 132 dB; and when conductivity reaches 1 \(\times\) 10\({}^{8}\) S/m, the attenuation is only 11.3 dB. Although signal attenuation is smaller without casing, within a short distance of 10 m, the signal amplitude received with casing is larger. This is because, in addition to causing electromagnetic wave attenuation inside it, the metal casing also has waveguide characteristics that can assist energy transmission over short distances, making the received electromagnetic signal stronger compared to that in an open-hole well.  
The strong attenuation of the magnetic field within the casing means that effective transmission can only take place over short distances. Therefore, in the absence of relay assistance, the single-stage transmission distance will be significantly limited.
## 4 Results and Discussion
### Identification and Categorization of Factors
#### 4.2.1 Socioeconomic Factors  
Socioeconomic factors refer to the personal background of the primary decision-maker of the farm. Information-intensive technologies usually require a high level of human capital. As such, the farmers' capacities and knowledge clearly influence their adoption decision to use DATs. Various socioeconomic factors have been incorporated into analytical models as explanatory variables [30; 32; 34; 35; 36]. Significant socioeconomic factors identified in the reviewed papers include age, gender, education level, health status, income (including total income and percentage of agricultural income), farming experience, and cooperative membership. Younger farmers have been shown to be more willing to adopt innovative technologies than their older counterparts. This has been explained as a consequence of younger farmers having longer planning horizons and being more technologically-oriented [40]. The effect of gender on DAT adoption reflects a disparity in preferences for innovative agricultural technologies. Female farmers, often serving as agricultural assistants and lacking resources, may be less likely to accept new technologies [37]. Formal education attainment is found to correlate positively with the adoption of DATs, as digital technologies require knowledge-based skills and interpretation [30; 32; 35]. A couple of studies have found a significant impact of health status on the adoption of on-arm digital technologies [30; 32]. Farming experience likewise has a positive impact on farmers' adoption. This could be because healthier and more experienced farmers may feel less reliant on the additional support provided by others during the implementation process and are, therefore, more open to embracing advanced technologies. Other research has shown that farmers involved in cooperatives are more likely to express a willingness to adopt these technologies [30; 31]. In most cases, cooperatives are associated with collective action and social capital, which can consequently provide vital information to facilitate farmers' adoption. In contrast to the aforementioned socioeconomic factors, there is no consensus in the literature on the impact of household income. Total household income could have either a positive [32; 35], negative [36], or no significant influence [30; 37] on DAT adoption. In view of this mixed picture, the total household income does not lend itself to an easy indicator for adoptive decisions. Instead, other wealth-related factors, such as agricultural income ratio [34] or agricultural income [39], are more effective in predicting farmers' decision to adopt.
## 4 Experiments
### Analysis of Experimental Results
#### 4.4.5 NEU-DET Dataset Validation  
We selected the open-source dataset NEU-DET to verify the generality of the improved algorithm proposed in this paper. The model training parameters are set as in Section 4.1, and the experimental results are shown in Table 9. The NEU-DET dataset is a strip surface defect dataset published by Northeastern University, which collects six typical surface defects of the hot-rolled strip: crazing, inclusion, patches, pitted, rolled, and scratches. A total of 1800 grayscale images, containing 300 samples for each type of defect. Table 9 shows that the improved model proposed in this paper improves the mAP of all defects by 1.8% compared with the original YOLOv5n, which verifies the generality of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision \%(Bubble)** & **Precision\% (Bubble\_Edge)** & **Precision\% (All)** & **mAP\%** \\ \hline YOLO5n\_SEST & 94.5 & 76.3 & 85.4 & 63.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: YOLO5n_SEST specific performance.  
Figure 7: Comparison of detection effects.Where the red dashed boxes represent missed detection targets and the yellow dashed boxes represent false detection targets. The yellow dashed boxes represent the false detection targets. (**a**) YOLOv5n_SEST; (**b**) YOLOv5n; (**c**) YOLOv7; (**d**) Faster R-CNN; (**e**) Mask R-CNN; (**f**) Cascade R-CNN.  
Figure 8: _Cont_.
# Crawling Magnetic Robot to Perform a Biopsy in Tubular Environments by Controlling a Magnetic Field
## 3 Actuating Principle of the CMR
### Uncovering and Covering Motion of a Biopsy Needle  
Figure 5 shows the operation to control the uncovering and covering motion of a biopsy needle before performing the biopsy. Figure 5a shows the posture of the CMR and the permanent magnets during navigation before reaching a target area. In this posture, there is an attractive force between the RM and FM and a repulsive force between the CM and RM, which closes the cover. When an external magnetic field with \(\delta=0^{\circ}\) is applied to the CMR, as shown in Figure 5b, the magnetic torque is exerted on the RM. Once the magnetic torque exerted on the RM by the external magnetic field is greater than the magnetic torque exerted on the RM by the FM and CM, the RM rotates to the same direction as the external magnetic field, as shown in Figure 5b. Since the angle between the RM and the external magnetic field is theoretically zero, the magnetic torque exerted on the RM by the external magnetic field should be zero, as explained in Equation (2). However, while the CMR navigates along the tube by the external precessional magnetic field, the RM cannot be positioned straight along the direction of movement because the RM oscillates. Thus, a magnetic torque exerted on the RM by the external magnetic field causes the RM to rotate. In the posture of Figure 5b, a repulsive force is generated between the RM and FM, the cover connected to the RM is uncovered, and the biopsy needle is exposed. Figure 5c shows the puncturing process of the CMR into the target tissue by the crawling motion generated by the external precessional magnetic field (\(0^{\circ}<\delta<45^{\circ}\)). Finally, Figure 5d shows the rotation of a crawling body and flexible leg along the y-axis after a uniform magnetic field is applied along the y-axis for 180\({}^{\circ}\). Then, the magnetic field generated by the CM and FM exerts a magnetic torque on the RM in the same direction, which makes the RM rotate another 180\({}^{\circ}\). In this posture, the attractive force between the RM and FM, which is greater than the attractive force between the RM and CM, closes the cover. Once the needle is covered, the sampled tissue can be safely carried out to the destination because the external precessional magnetic field used for the crawling motion does not affect the biopsy part.
# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 3 Model Framework
### Non-Linear Low-Dimensional Graph Embedding
#### 3.2.2 Deep Graph Embedding on a Multi-Layer Graph  
As previously stated, we can consider S as a training set containing n nodes. We consider multi-layer information networks in each layer. The objective formula is as follows:  
\[\underset{W^{(l)},R_{i}}{arg\max}\ \sum_{l=1}^{m}tr(\sum_{i=1}^{n}g(W^{(l) }(R_{i}^{(l)})^{T})g(R_{i}^{(l)}(W^{(l)})^{T}))\] \[s.t.\ W^{T}W=I. \tag{17}\]  
Assuming the multi-layer information network deep architecture as shown in Figure 2, the input of the first layer is denoted as \(X^{(1)}=S\). More hidden layers need to be added to the multi-layer architecture.  
It is widely known that multi-layer information networks are divided into an input layer, hidden layers, and an output layer; the structure of the i-th neuron in layer \((1+1)\) is shown in Figure 3.  
The formula for \(h_{W^{(l+1)},y^{(l)}}^{(l+1)}(X^{(l)})\) is:  
\[h_{W^{(l+1)},y^{(l)}}^{(l+1)}(X^{(l)})=f((W^{(l+1)})^{T}X)=f(\sum_{j=1}^{q}w_{ij} ^{(l)}x_{j}^{(l)}+b_{i}^{(l)}), \tag{18}\]  
where \(f\) is the neuron activation function, which is generally a sigmoid or tanh function. \(X^{(l)}\) represents the data of the layer \(l\), assuming that the number of neural units in layer \(l\) is \(q+1\).  
Assuming there are \(r\) training samples \(X_{1},\ldots,X_{r}\) through the deep neural information networks, if the input sample is \(X_{i}\), the output of the output layer is \(h_{W,b}(X_{i})\), and the actual output should be \(Y_{i}\). Therefore, a loss function with regularization terms is defined as  
\[J(W,b)=\frac{1}{r}\sum_{i=1}^{r}J(W,b,X_{i},Y_{i})+\frac{\lambda}{2}\sum_{l=1} ^{n_{l}-1}\sum_{i=1}^{s_{l+1}}\sum_{j=1}^{s_{l}}(w_{ij}^{(l)})^{2}, \tag{19}\]  
where \(J(W,b,X_{i},Y_{i})=\frac{1}{2}||h_{W,b}(X_{i})-Y_{i}||^{2}\) is the sum of squared errors of the output layer. Here, \(n_{l}\) denotes the number of layers in the entire information network, and \(s_{l}\) indicates the number of units in layer \(l\), excluding bias units. \(\lambda\) represents the regularization term parameter.
## 3 Experimental Results
### Data Validation  
In this section, the location of cracks was estimated through experiments for the purpose of determining the position of concrete cracks when there are no feature points that can be defined as reference objects in the facility or when there is a lack of feature points in the image. Field measurements were performed to verify the crack location information estimated based on the scale. A total of 4 cracks were measured ground truth values, and the location information of the cracks acquired using measurement is shown in Table 2.  
Finally, the accuracy of the estimated crack location information was analyzed to verify the applicability of the crack location estimation methodology presented in this experiment. Accuracy analysis was performed by comparing and analyzing the previously estimated crack location information and the values measured using field measurements. The analysis results of the location information of cracks estimated in the study and the locations of measured cracks are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Relative Location (X\({}_{1}\), Y\({}_{1}\))**, (X\({}_{2}\), Y\({}_{2}\)), (Unit: mm) \\ \hline Crack 1 & (1974, 821), (3072, 798) \\ Crack 2 & (5585, 2005), (5553, 3873) \\ Crack 3 & (11,048, 5914), (11,302, 7246) \\ Crack 4 & (11,864, 7273), (12,512, 7219) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative locations of the concrete cracks based on the scale. (Location from start point to end points of cracks).  
Figure 10: Analysis data after merging spatial information layers.
# Modified Generalized Feistel Network Block Cipher for the Internet of Things
## 3 The Modified GFN Lightweight Block Cipher
### Encryption Process  
Initially, the 64-bit plaintext \(P\) is segmented into 16-bit word sequences. The cipher state comprises 24 rounds for processing 64-bit input and output data, with encryption operations leveraging the 128-bit key. This 128-bit key serves as a master key that generates 56 subkeys with a length of 64 bits each, which are employed in the encryption pre-whitening, round function, and post-whitening process. Section 3.2 provides a comprehensive explanation of how these subkeys are generated.  
To encrypt a 64-bit plaintext, the MGFN algorithm first divides it into four 16-bit blocks, \(P_{0}\), \(P_{1}\), \(P_{2}\), and \(P_{3}\), where \(P\) is the least significant 16 bits. The algorithm then applies pre-whitening on \(P_{0}\), \(P_{1}\), \(P_{2}\), and \(P_{3}\) before entering the round function.  
Next, in the round function, the F-Function is applied to \(P_{0}\) and \(P_{1}\), which comprises four layers: the AddKey layer, the RShift layer, the SBox layer, and the PBox layer. Each layer's procedure is described as follows:  
1. addKey layer: In this layer, the two input words are XORed with subkeys.
2. RShift layer: This layer allows the two input words to undergo rotation shifts of 3 and 8 bits, respectively.
3. SBox layer: In this layer, a 4-bit input, denoted by \(x\), is transformed into a 4-bit output, denoted by \(S[x]\). It should be noted that \(S[x]\) may not necessarily be equal to \(s\). The values of \(x\) and \(S[x]\) used in this layer are provided in Table 3. The resulting outputs from this layer are then combined to form a 32-bit word.
4. PBox layer: In this layer, each of the 32 bits in the input word is mapped to its corresponding output position, with the specific mapping specified in Table 2. It is worth noting that the values in Table 2 are sourced from the ultra-lightweight PICO [53] algorithm, which is known for its exceptional performance in customized hardware.  
Figure 2: The description of MGFN lightweight block cipher.  
The output from the F-Function is then XORed with \(P_{2}^{r}\) and \(P_{3}^{r}\), where \(r\) denotes the round number from 0 to 24. After 24 rounds, the algorithm applies post-whitening on \(P_{0}^{24},P_{1}^{24},\ P_{2}^{24}\), and \(P_{3}^{24}\), and the 64-bit ciphertext is obtained by concatenating these blocks.