esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Failure Risk Assessment Method of
## 5 Conclusions  
This paper proposes a novel failure risk assessment method, which takes into account the characteristics of the model topology, calculates the propagation failure risk, and integrates it with the independent failure risk to achieve a more comprehensive and accurate risk assessment of the machining center components. The method proposed in this paper has the following contributions.  
1. This paper improves the calculation of traditional independent failure risk, considers the correlation between component failure modes, introduces ANP to calculate the failure mode influence degree, and combines it with the component failure mode frequency ratio and the failure rate function, which then obtains a more complete component independent failure risk value.
2. This method considers the influence of the model's topological structure characteristics on the risk of propagation failure. The centrality measurement of network theory is introduced to analyze the topological characteristics of the machining center, and the importance of the component nodes and connecting edges is obtained. Combined with the component failure probability to calculate the component failure propagation influence degree, the component propagation failure risk value calculation was realized.
3. The comprehensive failure risk assessment method proposed in this paper can obtain a dynamic ranking of the failure risk values of machining center components. Therefore, dynamic risk control strategies can be formulated to reduce unnecessary shutdown losses of the machining center.  
The effectiveness and advancement of the proposed method is verified by an example. The risk assessment method proposed in this paper can provide a basis and guidance for manufacturing enterprises to formulate reasonable and effective risk control plans and to reduce production system risks. In the future, multi-step propagation and diffusion of failure modes can be considered, and combined with on-site real-time condition monitoring data, this method can be extended to the failure risk assessment of more complex industrial equipment.  
**Author Contributions:** Conceptualization, L.M. and Y.Z.; methodology, L.M.; software, G.G.; validation, L.M. and Y.Z.; formal analysis, Y.Z.; investigation, L.M. and G.G.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, L.M.; writing--review and editing, L.M.; visualization, G.G.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. and L.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Jilin Province Science and Technology Development Plan Project (grant no. 20190302104 GX) and Project 101832020 DJX037, supported by Ph.D. Graduate Interdisciplinary Research of Jilin University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of Poly (MMA-BA-ST) on Drilling Fluids Properties  
Through characterization experiments, poly (MMA-BA-ST) was proven to be a nanomaterial with good temperature resistance, which can be used to plug nanopores and seams in formations. However, the plugging agent will have an impact on the rheological properties and electrical stability of the drilling fluids, and the more that is added, the greater the impact. This characteristic is detrimental to drilling fluids. Therefore, it is necessary to study the effect of poly (MMA-BA-ST) on the performance of drilling fluids, which is the premise of judging whether poly (MMA-BA-ST) can be used as a nano plugging agent in drilling fluids.  
We added 0.00%, 0.25%, 0.50%, 0.75% and 1.00% poly (MMA-BA-ST) to the base drilling fluids in sequence. The rheological properties (AV, PV, YP) and emulsion breaking voltage of drilling fluids were tested at 50 \({}^{\circ}\)C. The changes in drilling fluids performance parameters with different poly (MMA-BA-ST) addition amounts are shown in Figures 4-7.  
Figure 3: Poly (MMA-BA-ST) thermogravimetric analysis chart.
# Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
###### Abstract  
High rates of inappropriate use of surgical antimicrobial prophylaxis were reported in many countries. Auditing the prophylactic antimicrobial use in enormous medical records by manual review is labor-intensive and time-consuming. The purpose of this study is to develop accurate and efficient machine learning models for auditing appropriate surgical antimicrobial prophylaxis. The supervised machine learning classifiers (Auto-WEKA, multilayer perceptron, decision tree, SimpleLogistic, Bagging, and AdaBoost) were applied to an antimicrobial prophylaxis dataset, which contained 601 instances with 26 attributes. Multilayer perceptron, SimpleLogistic selected by Auto-WEKA, and decision tree algorithms had outstanding discrimination with weighted average AUC > 0.97. The Bagging and SMOTE algorithms could improve the predictive performance of decision tree against imbalanced datasets. Although with better performance measures, multilayer perceptron and Auto-WEKA took more execution time as compared with that of other algorithms. Multilayer perceptron, SimpleLogistic, and decision tree algorithms have outstanding performance measures for identifying the appropriateness of surgical prophylaxis. The efficient models developed by machine learning can be used to assist the antimicrobial stewardship team in the audit of surgical antimicrobial prophylaxis. In future research, we still have the challenges and opportunities of enriching our datasets with more useful clinical information to improve the performance of the algorithms.  
antimicrobial prophylaxis; machine learning; Auto-WEKA; multilayer perceptron; decision tree; bagging; SMOTE +
Footnote †: journal: Applied Statistics
## 3 Results and Discussion
### Lipid Oxidation  
Primary lipid oxidation in fish protein concentrates and hydrolyses were assessed by quantification of PVs (Figure 4). Due to the extremely low lipid content of COLL1 and HYDR2 (0.1% and 0.1%, respectively), no PVs were obtained for these two samples. Of the rest of the samples, HYDR1 had by far the lowest PV. The concentrates (CONC1, CONC2, and CONC3) had higher PVs compared to the hydrolysate, but considerably lower than what was previously observed for roach and Baltic herring protein isolates (3-25 meq/kg sample) [40], and lower than for Nile tilapia muscle during frozen storage (1.7-4 meq/kg sample) [52]. CONC1 (by-products of cod, saithe, and haddock) had a significantly higher PV (meq/kg powder) compared to CONC2 and CONC3 from cod, which was likely due to its higher lipid content (Table 2). However, due to the fast decomposition of hydroperoxides, PV should be considered together with secondary lipid oxidation products.  
Lipid oxidation in the fish protein concentrates and hydrolyses was assessed further by measuring the TBARS as MDA content (Figure 5). MDA is formed as a secondary product in the lipid oxidation [53]. Among the samples, HYDR1 showed the highest MDA content whereas COLL1 had the lowest (Figure 5). The higher MDA concentration of the HYDR1 in comparison to the COLL1 could be partly because the HYDR1 sample has a higher total lipid content (Table 2), but it is unlikely that this is the major factor. This is because the CONC samples, which contained even more lipids ( fats) than the HYDR1 sample, had a lower concentration of MDA. The results suggest that fish species is one putative factor affecting the lipid oxidation level. Among the samples, HYDR1 is the only one prepared from blue whiting. Thereby, the results indicated that blue whiting fatty acids may be more susceptible to oxidation in comparison to the other samples. The MDA  
Figure 4: Peroxide values (meq/kg protein powder, “as is”) in three commercial fish protein concentrate (CONC1–3) and a hydrolysate (HYDR1). Different letters indicate a statistically significant difference with ANOVA post hoc pair comparison (\(p<0.05\)).  
content of HYDR1 was similar to protein hydrolysate prepared from saithe fillets with alkali aided hydrolysis (90.5 nmol/g) [32]. In addition, higher MDA content compared to this study have been reported for example, for pickled Baltic herring fillets [18] and marinated Atlantic herring [54].
## 2 The Procedures of Forecasting Strategy
### Hybrid Firefly and Particle Swarm Optimization (HFPSO)
#### 2.5.2 Firefly (FF) Algorithm  
The FF algorithm is one of the recent meta-heuristic optimization algorithms, which was developed by [PERSON] et al. in 2007 [35]. It is inspired by a type of insect (fireflies) which generate rhythmic and short flashes [36]. The light flashes are released as a signal system the fireflies use for communication and hunting [36].  
The light intensity (I) of a firefly denotes the brightness of a firefly and it is inversely proportional to a distance of the firefly from the light source (r). This means that the brightness of a firefly is weakened upon increasing the distance of the firefly from the light source. Due to this matter, a firefly can only be seen for a hundred meters, which is an ideal distance for them to communicate. The relation between light intensity and distance of the fireflies from the light source can be obtained in Equation (10), where \(\mathrm{I_{s}}\) is the intensity of the light source.  
\[\mathrm{I(r)}=\frac{\mathrm{I_{s}}}{\mathrm{r}} \tag{10}\]  
When the brightness or light intensity of a firefly is increased, the attractiveness of firefly will be increased as well. Every firefly has its own attractiveness that determines how powerful it is to attract other fireflies to its position. Mathematically, the attractiveness (\(\mathrm{\beta}\)) possessed by each firefly is shown in Equation (11):  
\[\mathrm{\beta(r)}=\mathrm{\beta_{0}e^{-\mathrm{v_{i}}^{2}}} \tag{11}\]  
where \(\mathrm{\beta_{0}}\) represents the attractiveness of a firefly at distance \(\mathrm{r}=0\) and it can be represented by a constant value. \(\mathrm{\gamma}\) denotes the coefficient of the light absorption and \(\mathrm{r_{ij}}\) represents the distance between two fireflies, namely, firefly \(\mathrm{x_{i}}\) and firefly \(\mathrm{x_{j}}\). The Cartesian distance (\(\mathrm{r_{ij}}\)) of two fireflies can be shown in Equation (12):  
\[\mathrm{r_{ij}}=\left\|\mathrm{x_{i}-x_{j}}\right\|=\sqrt{\sum_{k=1}^{d}\left( \mathrm{x_{i,k}-x_{j,k}}\right)^{2}} \tag{12}\]  
where \(\mathrm{x_{i,k}}\) and \(\mathrm{x_{j,k}}\) are the \(\mathrm{k^{th}}\) component of the position vector for firefly \(\mathrm{x_{i}}\) and firefly \(\mathrm{x_{j}}\). Notation \(\mathrm{d}\) in Equation (12) represents the dimensionality of the search problem. Later, the fireflies will move to a position of firefly that has a greater brightness. In this work, firefly \(x_{i}\) will move towards the firefly \(x_{j}\) according to the movement equation shown in Equation (13).  
\[x_{i,k}(t+1)=x_{i,k}(t)+\beta_{0}e^{-\gamma r_{ij}^{\beta}}(x_{j,k}(t)-x_{i,k}(t ))+\alpha(rand-0.5) \tag{13}\]  
The first term of Equation (13) refers to the previous position of firefly and the second term depicts the attractiveness equation shown in Equation (11). The third term is governed by the randomization parameter (\(\alpha\)) that is responsible to insert randomness in the path followed by the fireflies. Furthermore, the third term of Equation (13) shows the random number (rand) that lies in the range of 0 to 1.
## 2 Related Work
### Triplet Margin Loss  
The simplest way to determine the similarity between images is to use the average of the pixels in each image. However, it is difficult to measure the degree of similarity with respect to the shape of an image by focusing on its brightness. To compensate for this problem, an image-embedding model can be constructed and the similarity can be measured by calculating the distance between the embedded image vectors [28]. For example, the distance function between images \(p\) and \(q\), defined by the Euclidean distance in the image embedding space, can be expressed as  
\[d(p,\ q)=\parallel f(p)-f(q)\parallel_{2}^{2} \tag{1}\]  
The function \(f(\cdot)\) is the image embedding model, and \(d(\cdot,\ \cdot)\) is the l2 norm distance, such as the Euclidean distance function calculated by the difference between \(f(p)\) and \(f(q)\). If we use \(d(p,\ q)\) to find the most similar image \(q\) for a query image \(p\), this is similar to the general \(k\)-nearest neighbor method. In other words, it is the same as solving a similarity ranking problem. That is, \(f(\cdot)\) is used in training the embedding model such that a similar image \(q\) for an anchor image \(p\) is embedded at distances closer than the other image.  
There is a triplet margin loss as a loss function that is appropriate to solve this problem. Triplet margin loss uses triplet \(TL\ =\ (p,\ p^{+},\ p^{-})\), where \(p\) is the query image, \(p^{+}\) is the positive image which is one of the images having the same semantic label for the query image \(p\), and \(p^{-}\) is the negative image which is one of the images having different semantic label for the query image \(p\). Thus, triplet margin loss can be expressed as  
\[TL(p,\ p^{+},\ p^{-})=max(0,\ \alpha+\ d(p,p^{+})-d(p,p^{-})) \tag{2}\]  
Triplet margin loss implies that the embedded vector of the positive image \(p^{+}\) is embedded closer to the query image \(p\) than the negative image \(p^{-}\). The triplet loss function is designed to learn that the distance of the positive pair is embedded as small as the margin \(\alpha\). If the distance of the negative pair is larger than the margin of the positive pair, it is designed to be set to 0 so that no additional learning occurs.  
The above triplet margin loss can be modified to measure the similarity of image-image, image-text, text-text, etc., beyond merely comparing the similarity between image and image [22]. For example, a pair is constructed on the basis of the semantic similarity between image and text, and image-text embedding model can be trained such that the positive-pairs of image-text are embedded closer [29]. Several embedding-based zero-shot learning methodologies have used triplet margin loss. However, when selecting a negative sample, it is pointed out that random sampling is very inefficient [23]. Nonetheless, as triplet loss can design a loss function that can control the ranking between embedded vectors, it is necessary to study a methodology that can compensate the current disadvantages.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Radio Access Network
#### 3.2.3 Roaming  
In cellular networks, roaming allows a UE to move outside of its home network environment and connect to a visiting network to retain access to communications. This allows subscribers to maintain connectivity and services even if they are moving between different geographical areas where their own service provider has no coverage, and instead, they have to rely on other provider networks. Mobile service providers have agreements in place to allow users access, even if the equipment they are connecting to may not be from their home network. The main security consideration here is the ability to trust the visiting vendors they may connect to for services. This is discussed further in Section 3.1.4.
## 3 Testing Procedures
### Dynamic Setup  
The second group of measurements is called dynamic because these measurements are carried out in motion by placing both devices in different positions inside a car, as shown in Figure 3. Within this group of measurements, different tests were carried out to evaluate the performance of both technologies, the circularly polarized microstrip patch antenna and the linearly polarized antenna booster.  
Figure 3: Different positions and orientations in which the devices were arranged inside the vehicle. For (**g.h**), the seat covered the device. For illustrative purposes, the seat was moved to the back to show both devices. (**a**) Front Dashboard–Face Up; (**b**) Front Dashboard–Upside down; (**c**) Front Dashboard–Face forward; (**d**) Front Dashboard–Face backward; (**e**) Rear Dashboard; (**f**) Rear Dashboard–Upside down; (**g**) Under the seat; (**h**) Under the seat–Upside down; (**i**) Over the seat; (**j**) Over the seat–Face forward; (**k**) Over the seat–Upside down.  
The first set of dynamic measurements that were performed was a continuous measurement in which, when a fix is obtained, the route is covered by a car in which the data are captured in different environments. These environments are categorized as highway, in which there is a more open space without buildings or trees that can interfere with the signal; then the city environment, in which the vehicle goes between wide and narrow streets; and finally, forest, which is an intermediate between the highway and city.  
In the second campaign of measurements, the same route was followed as in the previous one, but the aim was to obtain another type of data. In this case, cold starts are performed during the route, the time to first fix is measured, and then an average is obtained. The purpose of this measure is to check how long it takes each device to find the location of the vehicle in the different positions of the device. This measurement is fundamental because it provides how fast both antenna technologies can determine the position.  
The last of the tests performed within the dynamic measurements were to run the same procedure as in the previous case, but in this case, instead of performing a cold start, a warm start is carried out. The data on the almanac (information about the time and status of the entire satellite constellation) and the ephemeris (data used to calculate the position of each satellite in orbit) were wiped to run the cold start simulation, which mimics a device that has just been activated for the first time without a previous fix. For the warm start, however, the ephemeris is eliminated to imitate a device that has lost its units temporarily while the almanac is known [3]. By doing this, we can make a case as similar as possible to the behavior obtained from an IoT geolocation device, which obtains the position of the vehicle in every certain period to optimize the battery. Therefore, this measure is significant because it seeks the minimum possible time to obtain a location so that the battery lasts the maximum time.
# Energy-Efficient BWP Configuration for Multi-Slice Users
## 5 Centralized Approach: Optimization Problem
### Integer Linear Programming Formulation  
The non-linear terms \(a_{1u}\cdot a_{1k}\) and \(a_{2u}\cdot a_{2k}\) from (6) are replaced by the linear decision variables \(\pi_{1u,k}\) and \(\pi_{2u,k}\). Additionally, inequality constraints are added to ensure that the new variables are behaving the same way as the replaced non-linear terms. Also, \(\pi_{1u,u}\) is none other than \(a_{1u}\) since \(  
\(C_{u,MN}\) and \(C_{u,SN}\) are the same cost functions when UE \(u\) is configured with the \(MN\) and \(SN\) strategy, respectively, but taking into account the new decision variables as shown in the below Equations (10) and (11). \[\begin{split} C_{u,MN}(\Pi 1)&=\frac{\alpha_{u,eb}\cdot V _{u,eb}\cdot D1\cdot\sum_{k\in\mathcal{P}}\pi_{1u,k}}{TotPRB_{BWP1}\cdot Bits\_per\_PRB _{u}}\\ &+\frac{\alpha_{u,uc}\cdot V_{u,uc}\cdot D2\cdot\sum_{k\in \mathcal{P}}\pi_{1u,k}}{TotPRB_{BWP2}\cdot Bits\_per\_PRB_{u}}\\ &+\pi_{1u,u}\cdot(\theta_{MN}\cdot SwDlu+\beta_{u}\cdot B_{MN}) \\ C_{u,SN}(\Pi 2)&=\frac{\alpha_{u,eb}\cdot V_{u,eb}\cdot D3 \cdot\sum_{k\in\mathcal{P}}\pi_{2u,k}}{TotPRB_{BWP3}\cdot Bits\_per\_PRB_{u}} \\ &+\frac{\alpha_{u,uc}\cdot V_{u,uc}\cdot D3\cdot\sum_{k\in \mathcal{P}}\pi_{2u,k}}{TotPRB_{BWP3}\cdot Bits\_per\_PRB_{u}}\\ &\hskip 14.226378 pt+\pi_{2u,u}\cdot(\beta_{u}\cdot B_{SN}) \end{split}\] (10)  
Therefore, this ILP problem can be solved with CPLEX solver, achieving the same optimal result as the exhaustive search algorithm but much more swiftly.
# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 4 Experimental Results
### Note-Level Transcription
From top to bottom: original Pansori audio, channel attention + F (ours), pYin [36], PyWorld [37], and Segnet [30]. Due to the absence of note-level ground truth labels for Pansori data, a subjective evaluation was conducted. Our approach involved providing 15 note-level predicted MIDI files to Pansori experts for assessment. The MIDI files using Viterbi decoding obtained after F0 estimation from our best pre-trained model along with PyWorld and pYin were given to the expert. Five individuals were selected for this task, and each expert was assigned a rating between 0 and 5. A score of 0 represented the worst result, while a score of 5 indicated the best outcome. The average values from all five experts were then calculated for each of the 15 data samples. pYin obtained the average value of 2.56, PyWorld obtained 2.04, and our channel attention + focal loss obtained 2.32. This indicates that our Viterbi algorithm-based post-processing method is comparable with other popular state-of-the-art methods. The detailed rating values for each of the audio samples of Pansori using PyWorld, pYin, and channel attention + F are shown in Tables 2-4, respectively.
# A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array
## 3 Calibration Method
### Calibration of Composite Module Array  
#### 3.1.1 Angle Calibration  
As shown in Figure 2, there is a 2-D angle difference between the planes of each composite module.  
The first composite module is selected as the reference composite module. When the angle sensor scans the plane of the composite module in turn, the coordinates of the center spot (\(x_{i}\), \(y_{i}\)) of the composite module \(i\) (\(i=1\)... \(n\), \(n\) is the total number of composite module) on the angle sensor is affected by the angle of the measured plane and the angle error of the machine tool spindle. Therefore, the relative 2-D angle of each composite module can be obtained:  
\[\left\{\begin{array}{l}\theta_{xi}=k(x_{i}-x_{1})-\varepsilon_{x}(x)\\ \theta_{yi}=k(y_{i}-y_{1})-\varepsilon_{y}(x)\end{array}\right. \tag{1}\]  
where parameter \(k\) is the angle coefficient. Parameters (\(\theta_{xi}\), \(\theta_{yi}\)) are the relative 2-D angle of each composite module. Parameters (\(x_{1}\), \(y_{1}\)) and (\(x_{i}\), \(y_{i}\)) are the coordinates of the center spot of the reference composite module and the composite module \(i\) on the angle sensor, respectively. Parameters \(\varepsilon_{x}(x)\) and \(\varepsilon_{y}(x)\) are the roll angle error and pitch angle error, respectively, of the machine tool spindle between two plane measurement points.  
Figure 1: Calibration system.  
Figure 2: Diagram of the 2-D angle composite module.