esa-sceva/satcom-chunk-collection huggingface dataset:
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods
### Language Model-Based Transfer Learning
#### 2.5.2 Sentence and Token Nodes Feature Generation  
Each sentence contains words that reveal or indicate the type of interaction between two drugs. For example, expressions such as \"should (not) be administered\", \"caution should be used\" and \"is (not) recommended\", are associated with suggestions (advice) when taking more than one drug simultaneously. Therefore, although the expressions show some variety, they are characterized by a high semantic similarity and are expected to correspond to nearby points in the embeddings vector space.  
When generating the sentence embeddings \(x_{v_{S}}\), it is important for the LM to focus on the above type of information and therefore any reference to any drug is replaced by \"drug0\" (i.e., \(i=0\)). Because of the repeated occurrences, the string loses its meaning within the sentence, with the BERT-based LM giving an appropriate weight. Finally, the [CLS] token embedding is chosen for \(x_{v_{S}}\), which is a centralized representation of the sentence and is often used in classification tasks. However, since a sentence most likely contains more than one drug pair, the interaction classification can not be performed solely on the basis of \(x_{v_{S}}\), consequently requiring the feature generation for each drug mention in the sentence.  
Furthermore, the interaction type does not depend on the specific drug names in the potential drug pair, but on their position within the sentence. In line with previous studies [7], their replacement is expected to aid in noise reduction when generating the _Token_ embeddings, increasing the classification performance. To this end, each unique drug entity in the sentence is assigned a sequence number \(i\), according to the order of appearance of its first mention within the sentence. Thus, \(Token_{j}\) references that participate in \(< Drug_{Class},found\_as,Token_{j}>\) triplets with a common subject are replaced by \(drugi\) with a common \(i\). The final feature vector of each node \(v_{T_{j}}\) is obtained by pooling the embeddings of the subwords that compose it. That is, \(x_{v_{T_{j}}}=pooling\{B_{drug\#},B_{\#\#\times}\}\), where the pooling method is the average, and \(B_{w}\) is the BERT output for the input \(w\).
# Internal Force Analysis of Buried-boring Piles in the Yuanzishan Landslide
## 5 Discussion  
### Effect of the Pile Depth above the Slip Surface on the Internal Forces of the Buried-Boring Pile  
Variation in the pile head depth affects the slope driving force acting on the pile, and results in a change in the pile's internal forces [40; 41].  
When the buried depth of the pile head increased from 0 to 7.5 m at a pile diameter of 0.5 m, the maximum positive shear force of the pile decreased by 67% from 300 kN to 99 kN at the slip surface (Figure 6a), the maximum negative shear force of the pile decreased by 88% from 471 kN to 56 kN, and the maximum bending moment of the pile decreased by 88% from 1316 kN.m to 150 kN.m (Figure 6b). The maximum bending moment of the pile increased as the buried depth ratio of the pile increased (Figure 7a), following a power law relationship. [PERSON] [38] experimentally determined that the relationship between the maximum bending moment of the pile and the buried depth ratio was linear (\(0.2\leq\mathrm{R_{s}}\leq 0.5\)) due to a constant driving force from the deforming slope.  
When the pile head depth increased from 0 to 7.5 m with a changing pile diameter, the maximum positive shear force of the pile decreased by 67% from 300 kN to 99 kN at the slip surface (Figure 8a), the maximum negative shear force of the pile decreased by 81% from 456 kN to 86 kN, and the maximum bending moment of the pile decreased by 90.6% from 1364 kN.m to 128 kN.m (Figure 8b). The maximum bending moment of the pile increased as the buried depth ratio of the pile increased (Figure 9a). Variation of the pile diameter had an increasingly lesser effect on the maximum shear force and bending moment of the pile as the pile head depth increased. Therefore, during the design of bored pile structures, the pile diameter could be reduced to save costs.
## 4 Qualitative Findings and Discussion
### Key Design Challenges in Improving LSAD
#### 4.1.2 Fit  
The majority of semi-rigid participants reported ergonomic issues with the LSAD not fitting well to the lumbar region. The unanimous consensus among participants was that the lack of conformity of the LSAD to the waist constitutes a direct cause for abandonment. As reported by Participant 5, \"discontinuation occurred after wearing for a period, primarily due to the perception that the steel plate at the back did not conform to  
Figure 4: Themes and subthemes of participants’ feelings and perceptions of the LSAD-wearing experience.  
the body's curves, resulting in a sensation of emptiness around the waist\". The primary cause of non-conformity arises from subtle discrepancies between the rear steel bars and the individual's body contours. Some participants recommended personalized customization for individuals with unique body shapes: \"Perhaps due to my body type, it's currently challenging to find an LSAD that conforms to the curvature of my waist. I can only make do with those'soft plastic bars' for now\" (Participant 11). Furthermore, some participants indicated that the poor fit had led to frequent slippage of the LSAD during activities, thereby compromising its intended functionality.  
\"Because the waist has a curvature, most of the waist belts I used before did not conform to the curvature of the waist, leading to frequent slipping issues.\" (Participant 4)
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 3 Joint Cell Anti-Jamming Map Based on Dual Connection  
### Interference Graph Model Based on Graph Theory  
As the network environment becomes more and more complex, it is difficult to complete the anti-interference task by simply using the traditional dual-link network technology. Therefore, it is necessary to use new anti-jamming mechanisms and related algorithms. In the traditional dual-link vehicular network technology model, the interference between cells is mainly from the signal transmitted by the base station of the adjacent cell. We can show the interference relationship of the cell cluster of the anti-interference algorithm implemented in the system model, as shown in Figure 3. In the joint interference graph based on graph theory, we first take the cell as the node of the graph and use edges to connect the nodes of neighboring cells with interference relations to form an undirected graph. Since the graph is planar, according to the four-color theorem [9], the maximum clique size of the graph is 4. In addition, in the conventional cellular network layout, if the base station can meet the maximum coverage area, the number of neighboring cells of each cell is 6; that is, in the conventional cellular network inter-cell interference diagram, the maximum cell size is 3.  
In order to preferably solve the complex problem of the inter-cell interference algorithm, first, the graph is clustered [10]. As shown in Figure 3, pairwise adjacent base station nodes constitute the base station clique, and we need to conduct maximum clique searches on the cell interference diagram in Figure 3. For example, in Figure 3, nodes 4, 5, and  
Figure 2: Vehicular network collaboration model.  
7 form a cluster of users and the cluster can independently execute the proportional fair scheduling algorithm, which schedules interval interference coordination [11] based on time domain segmentation. However, cluster {1, 4, 5} and cluster {4, 5, 7} will not be able to perform this anti-jamming algorithm at the same time. This is because the same two clusters of the launch base station {4, 5} cannot run two different scheduling policies at the same time.  
In order to solve the conflict problem of the cluster, which is the scheduling strategy, we designed a conflict graph. In Figure 4, {_A_, _B..._, _H_} represents the cluster in Figure 3. For example, point \(A\) represents the {1, 4, 5} cluster in Figure 3, point \(B\) represents {1, 2, 5}, and so on. Moreover, points \(A\) and \(B\) are connected by an edge because they share nodes {1, 5}. If the nodes in Figure 3 are not connected, then the clusters in Figure 3 represented by these nodes can run their respective scheduling algorithms simultaneously. Figure 4 shows that the solution problem of this cell is a maximum clique problem. Through the maximum clique search problem, the maximum clique approximate search algorithm for the inter-cell interference graph of the cellular network and the heuristic search algorithm for the non-planar graph are given as follows.
## 6 The Evaluation of the Network Availability for WDM-PON Protection Schemes
### The Evaluation of the RING WDM-PON Protection Scenarios  
In Figure 9, the network availability of the RING WDM-PON protection schemes is presented for different protection scenarios--unprotected and protected access networks and unprotected and protected metro-access networks. For this comparison, the following parameters are considered in the PON Network Availability Evaluator--the restoration time 10 ms, a number of ONU units for the access network is 10, and a number of AWG elements for the metro-access network is 8.  
As can be seen, better resultant values of the network availability are reached in protected network scenarios. The protected access network has higher network availability; however, its variance from the protected metro-access network is weak. Moreover, the protected metro-access network is simultaneously more suitable for the WDM-PON practical utilization due to a higher number of users.  
Upon changing the parameters, the network availability of RING WDM-PON protection scenarios for the 10 ms restoration time is presented in Figure 10. We can see that the network availability is decreasing with more network elements. Thus, we can conclude that a number of elements has larger importance than the restoration time in RING WDM-PON protection scenarios. In addition, a fiber length is an important parameter influencing a decrease in the network availability.  
Figure 8: The network availability of TDM-PON and P2 MP WDM-PON protection scenarios for the selected restoration time.  
Figure 9: The network availability of RING WDM-PON protection scenarios for the selected restoration time.  
The protected access network has markedly better results with five places nine availability, when the protected metro-access network availability has only four places nine. However, the protected metro-access network provides traffic for a higher number of users and can be easily modified according to demands of specific network operators.
###### Abstract  
Background: Radiation doses and capability of EOS, conventional radiography (CR), and computed tomography (CT) to detect and measure enchondromas in a dedicated five-year-old anthropomorphic phantom were compared. Methods: To simulate enchondromas, minced pieces of chicken bone and cartilage were packed in conventional kitchen plastic foil to create ovoidal/rounded masses and randomly hung on the phantom. The phantom was imaged five times with CR, CT, and EOS, each time changing the number and position of inserts. All images were reviewed by a senior radiologist and a radiology resident. Results: EOS and CR detected all inserts in 4/5 cases (80%), while in one case 1/17 inserts was not seen. Excellent agreement of EOS with CR (88% reproducibility; bias = 14 mm; repeatability coefficient (CoR) 2.9; 95% CI from \(-\)2.8 to 3.1 mm; \(p\) = 0.5) and CT (81% reproducibility; bias = 15 mm; CoR 5.2; 95% CI from \(-\)5.5 to 5.2 mm; \(p\) = 0.7) was found. EOS showed 71% interobserver reproducibility (CoR 7.2; bias = 0.6 mm; 95% CI from \(-\)6.6 to 7.8 mm; \(p\) = 0.25). The EOS-Fast radiation dose was also significantly lower than the median radiation dose of CR (644.7 (599.4-651.97) mGy\(\mathbf{ec}\)cm\({}^{2}\), \(p\) = 0.004). Conclusions: Low-dose EOS has the same capability as CR to detect and measure enchondroma-like inserts on a phantom and may be considered to monitor patients with multiple enchondromas.  
EOS; conventional radiography; computed tomography; radiation dose; enchondroma; phantom 10.3390/app10248941
## 5 Multiplexing Readout
### GHz-Frequency-Division Multiplexing  
The multiplexing architectures discussed so far do have a limited ultimate bandwidth around 10 MHz. SQUID-based microwave read-out is a promising technology under development to expand the read-out bandwidth to the GHz range. The large bandwidth available can be used to multiplex a larger number of TESs or to allow more read-outbandwidth per pixel. A microwave SQUID multiplexer (11w-11w) performs frequency division multiplexing at GHz (GHz-FDM). Differently from the MHz-FDM, the TESs are dc-biased and rf-SQUIDs are used to modulate distinct superconducting microwave resonators coupled to a common microwave feed-line [11; 152; 153]. The signal of thousand of pixels could finally be amplified at 4K by standard HEMT amplifiers in the, 4- to 8-GHz band, via a single coax cable. Despite the complexity at many levels of the signal chain, as for example the design and fabrication of the cold rf-SQUID and resonators chips [154], the linearization of the SQUID response [11; 155], the room temperature electronics, and the packaging at the focal plane assembly stage, the GHz-FDM will become very competitive in the future. This is particularly true for ground-based experiments and in applications where a large array of high speed detectors are required [130; 156]. The SLEDGEHAMER instrument with gamma-ray detectors, has demonstrated the read-out of 128 microwave SQUID multiplexed channels on a single set of coaxial cables [11]. With a similar set-up, a 82 pixels multiplexing factor has been demonstrated on an array of soft X-ray TESs with an energy resolution of \(2.04\pm 0.003\) eV for the magnesium K\(\alpha\) line at 1.25 keV, consistent with the resolution achieved using TDM with the same array [129]. A simultaneous readout of 38 high-ohmic Ti/Au SRON TES microcalorimeters has been recently demonstrated with a low noise 11w-11w SQUID amplifier fabricated at AlST in Japan. With a readout noise of \(0.9\,\mu\Phi_{0}/\sqrt{\rm Hz}\), a median energy resolution of 3.3 eV at 5.9 keV has been achieved [157] with high normal resistance and relatively fast detectors. The multiplexing factor achievable with microwave SQUIDs is limited by the bandwidth available in the HEMT amplifiers (currently only working between 4-8 GHz) and the high slew rate on the leading edge of X-ray pulses. For the latter, workarounds are envisioned [158].
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 5 Results
### The Effect of Reshaping Images
The experiments corresponding to the four rows in Figure 10 differ in the preset \(\theta\) value. Generally speaking, the classification accuracies increase with increasing \(\theta\) value; this is due to the fact that the number of extracted features increases when the percentage of total variance explained by these features--i.e., the \(\theta\) value--increases. Having more features leads to better classification performance, especially when the percentage of total variance explained by these features is not large enough, e.g., smaller than 0.95. Therefore, the classification accuracies increase with increasing \(\theta\) value. Figure 10: The average classification accuracies corresponding to four thresholds on the Extended Yale B face database and its reordered and reshaped variants with CRC as the classifier. Four rows correspond to results when the threshold \(\theta\) equals 0.5, 0.6, 0.7 and 0.8 in order. Three columns correspond to results of variants 0–2 in order. In each subfigure (**a**–**l**), the five accuracy curves correspond to the five feature extraction algorithms, i.e., PCA, 2 DPCA, A2 DPCA, (2D)\({}^{2}\)PCA and DB2 DPCA. Figure 10 exhibits the classification performances of the four 2D algorithms only when \(p<\sqrt{d}\). Based on symmetries of these algorithms, their classification performances when \(p>\sqrt{d}\) can be directly inferred. Let \(c\) be an arbitrary divisor of \(d\); then, 2 DPCA with \(p=c\) approximates to A2 DPCA with \(p=d/c\), (2D)\({}^{2}\)PCA with \(p=c\) approximates to (2D)\({}^{2}\)PCA with \(p=d/c\), and DB2 DPCA with \(p=c\) approximates to DB2 DPCA with \(p=d/c\). Consequently, we can further conclude that DB2 PCA can achieve the best classification performances among the five competing algorithms when the image height \(p\) is in the full range of \([1,d]\).
# Bearing Severity Fault Evaluation Using Contour Maps--Case Study
###### Abstract  
Increasing industrial competitiveness has led to an increased global interest in condition monitoring. In this sector, rotating machinery plays an important role, where the bearing is one of the most critical components. Many vibration-based signal treatments are already being used to identify features associated with bearing faults. The information embedded in such features are employed in the construction of health indicators, which allow for evaluation of the current operating status of the machine. In this work, the use of contour maps to represent the diagnosis map of a bearing, used as a health map, is presented for the first time. The results show that the proposed method is promising, allowing for the satisfactory detection and evaluation of the severity of bearing damage. In this initial stage of the research, our results suggest that this method can improve the classification of bearing faults and, therefore, optimise maintenance processes.  
contour map; condition monitoring; bearing diagnosis; envelope analysis +
Footnote †: journal: Applied Sciences
# Progress on the Application of Nanomaterial Expansion in Oil Displacement
## 2 Principle of Enhanced Oil Recovery by Nanomaterials
### Reduce Oil-Water Interfacial Tension
the amphiphilic structure formed by surface functionalization of the nanofluid can self-assemble to form an elastic film at the oil-water interface, reducing the contact angle of the oil droplets from 150\({}^{\circ}\) to 79\({}^{\circ}\) and significantly reducing the interfacial tension to 1.0 mN/m. The unique two-dimensional nanostructure not only maintains fluidity in the rock pores but also promotes the stripping of crude oil from the rock surface through the synergistic effect of wettability reversal and interfacial film strengthening. Compared with traditional tertiary oil recovery, this method can achieve 7.5% recovery improvement in the secondary oil recovery stage, and it has the advantages of reducing the amount of chemical agent required, reducing water consumption and increasing production rate. [PERSON] et al. [45] synthesized a modified Janus nano-calcium carbonate (JNC-12) with a particle size of 30-150 nm and prepared an in situ-emulsified nanofluid (ISNE). Experimental studies have shown that the addition of ISNE can reduce the oil-water interfacial tension to 0.01-0.1 mN/m. The verification results of core displacement experiments show that compared with the initial water flooding oil recovery rate, the oil recovery rate is increased by 17.6% after adding ISNE. It can be seen from Table 1 that under the same experimental conditions, the effects of different mass fractions of nanofluids on surface tension are different, and their ability to reduce interfacial tension will increase with the increase in the fluid mass fraction within a certain range. Although the experimental conditions used by different scholars were different, the data obtained show a certain similarity. Specifically, a single nanofluid can usually maintain a level of about 30% in reducing interfacial tension, while it is roughly maintained at about 10% in improving oil recovery.
# Multi-Angle Fast Neural Tangent Kernel Classifier
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **RF** & **ADABOOST** & **KNN** & **Decision Trees** & **M-RBF** & **M-NTK** & **B-NTK** \\ \hline breast-cancer & 0.798 & 0.677 & 0.768 & 0.727 & 0.808 & 0.858 & 0.831 \\ credit-approval & 0.870 & 0.817 & 0.846 & 0.803 & 0.872 & 0.901 & 0.825 \\ echocardiogram & 0.846 & 0.831 & 0.815 & 0.8 & 0.896 & 0.898 & 0.942 \\ fertility & 0.9 & 0.86 & 0.880 & 0.9 & 0.91 & 0.92 & 0.875 \\ hepatitis & 0.818 & 0.792 & 0.831 & 0.701 & 0.822 & 0.882 & 0.856 \\ libras & 0.678 & 0.767 & 0.633 & 0.572 & 0.702 & 0.846 & 0.775 \\ Parkinson & 0.876 & 0.856 & 0.876 & 0.845 & 0.883 & 0.968 & 0.877 \\ bridges-MATERIAL & 0.849 & 0.868 & 0.868 & 0.830 & 0.898 & 0.887 & 0.928 \\ bridges-REL-L & 0.765 & 0.706 & 0.667 & 0.706 & 0.875 & 0.875 & 0.755 \\ bridges-SPAN & 0.674 & 0.717 & 0.696 & 0.587 & 0.695 & 0.803 & 0.799 \\ bridges-T-OR-D & 0.843 & 0.882 & 0.902 & 0.804 & 0.943 & 0.907 & 0.926 \\ bridges-TYPE & 0.635 & 0.596 & 0.558 & 0.538 & 0.728 & 0.728 & 0.738 \\ German-credit & 0.75 & 0.73 & 0.724 & 0.696 & 0.798 & 0.798 & 0.770 \\ trains & 0.6 & 0.6 & 0.4 & 0.6 & 0.6 & 0.8 & 0.6 \\ wine & 0.966 & 0.955 & 0.966 & 0.876 & 0.908 & 0.989 & 0.870 \\ zoo & 0.96 & 0.8 & 0.92 & 0.94 & 0.9 & 0.96 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Acc-Comparison between our model and commonly used standard models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **RF** & **ADABOOST** & **KNN** & **Decision Trees** & **M-NTK** & **B-NTK** \\ \hline breast-cancer & 124.19 & 16.05 & 7.41 & 1.63 & 1.13 & 1.00 \\ credit-approval & 112.83 & 14.69 & 9.74 & 1.00 & 1.32 & 16.32 \\ echocardiogram & 236.59 & 28.55 & 7.58 & 1.00 & 10.64 & 1.72 \\ fertility & 323.30 & 43.64 & 5.39 & 1.00 & 5.42 & 5.19 \\ hepatitis & 479.56 & 33.72 & 5.51 & 1.00 & 2.19 & 1.52 \\ libras & 279.17 & 46.18 & 9.43 & 7.13 & 1.00 & 3.97 \\ Parkinsons & 186.28 & 24.77 & 4.87 & 1.00 & 1.29 & 1.34 \\ bridges-MATERIAL & 302.00 & 39.42 & 5.05 & 1.00 & 1.38 & 1.36 \\ bridges-REL-L & 235.11 & 30.93 & 3.54 & 1.00 & 1.21 & 1.29 \\ bridges-SPAN & 221.42 & 31.12 & 3.79 & 1.00 & 2.19 & 1.39 \\ bridges-T-OR-D & 305.00 & 39.12 & 4.53 & 1.00 & 1.90 & 1.25 \\ bridges-TYPE & 306.06 & 38.88 & 4.70 & 1.00 & 3.29 & 2.02 \\ German-credit & 163.34 & 24.71 & 13.00 & 1.98 & 1.00 & 12.49 \\ trains & 122.63 & 19.45 & 17.42 & 1.53 & 1.12 & 1.00 \\ wine & 358.86 & 5.72 & 93.48 & 1.00 & 6.40 & 4.05 \\ zoo & 247.87 & 32.61 & 7.16 & 1.00 & 2.07 & 1.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Time-Comparison between our model and commonly used standard models.
# Fronthaul Design for Wireless Networks
## 3 Fronthaul Design Methodology  
This section details the developed methodology to evaluate and compare the performance of MRT, FSO, and FO technologies when applied to the fronthaul. This methodology comprised a software tool built from scratch (based on the MATLAB programming language), which had the following purposes: (i) to determine the most cost-effective solution to connect two points regarding an RRH-BBU link, under user-specified equipment characteristics and link conditions; (ii) to find a fronthaul topology for wireless networks optimally, given the RRHs location--namely, the required number of BBUs and where they should be positioned in order to minimize the overall network costs. The algorithms associated with these goals, designated as the link design algorithm and network planning algorithm, respectively,are presented next. The software tool, including its source code, is freely available online [24]; hence, besides being a generic tool, since it can be used for a wide range of fronthaul projects, it is also a versatile tool, in the sense that it can be modified by anyone to incorporate other features that are specific for a certain project.