esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Real-Time Tissue Recognition and Margin Assessment
### Frozen Section and the NeuroSAFE Technique  
The frozen section (FS) analysis remains the intraoperative standard for margin assessment in RARP, particularly when attempting to preserve the neurovascular bundles in cases where extracapsular extension is a concern. Among FS-based techniques, the NeuroSAFE protocol, pioneered at the Martini-Klinik, has gained significant international traction for enabling real-time decision-making on nerve preservation during surgery [21].  
The technique involves a systematic FS analysis of the posterolateral margins adjacent to the neurovascular bundles, with the goal of confirming negative margins before committing to bilateral nerve-sparing. If a positive surgical margin (PSM) is detected, a secondary resection is performed on the involved side, often sacrificing the neurovascular bundle [21].  
Until recently, the evidence supporting NeuroSAFE was largely retrospective or observational. However, the NeuroSAFE PROOF trial, the first multicenter, phase 3 randomized controlled trial evaluating this technique, has now provided high-level evidence for its effectiveness [22]. The trial demonstrated that this approach significantly improveserectile function at 12 months postoperatively, with no compromise in early oncologic outcomes [22].  
Compared to standard RARP, patients in the NeuroSAFE arm had the following outcomes:  
* Better postoperative erectile function (mean IEF-5 difference: +3.2);
* Improved early continence at 3 months;
* Increased nerve-sparing rates, particularly in cases initially deemed unsuitable for bilateral preservation.  
Although a slightly higher rate of positive margins was observed, this did not affect short-term cancer control. These results support the use of NeuroSAFE to optimize functional outcomes without compromising oncologic safety.
# Leveraging Language Models for Inpatient Diagnosis Coding
## 4 Discussion
### Comparison of Different Pre-Trained Language Models  
As expected, the model fine-tuned from MEDPSU-RoBERTa scored highest among the other language models. It is important to note that MEDPSU-RoBERTa was trained with data from our institution's HIS since its implementation until 31 December 2021. This time frame was different from the data used for the test set, which was collected in 2022. This presents further evidence of the generalisability of the model.  
Figure 5: Significance plots of \(p\) values calculated using Nemenyi post hoc test.  
Figure 6: Precision-recall curves of all models calculated from the test set.  
However, we had originally expected that the results of the model fine-tuned from MEDPSU-RoBERTa would significantly outperform the results of general-purpose language models such as Multilingual BERT or Multilingual E5. Surprisingly, the observed differences were only marginal, ranging between 1% and 2% in terms of performance metrics. The general-purpose models showed competitive performance that was not inferior. These results show that general-purpose language models are capable of handling the complex tasks of coding diagnoses. Although these models were not specifically tailored to our institutional data or fine-tuned with domain-specific knowledge, they demonstrated a remarkable ability to capture relevant information. This suggests that the general language models can still provide reliable results for specific applications or scenarios where fine-tuning to institution-specific data is not possible or necessary.  
MEDPSU-RoBERTa consistently outperformed the other models across different thresholds (see Figure 6). Although the differences in performance are only 1% to 2%, even a small reduction in the error rate can have significant practical implications, especially in healthcare where the stakes are high. The reduction in false positives and false negatives can potentially decrease the need for case reviews and the likelihood of misdetections, resulting in a reduced workload.  
In practise, clinical coders rely on various sources of information, including doctor notes, nurse progress notes, laboratory results, and medical procedures, in addition to the discharge report, to identify the appropriate diagnosis codes. However, these additional documents were not used in training the algorithm. Therefore, the algorithm may encounter difficulties in accurately assigning diagnosis codes due to the lack of these additional details. The lack of this contextual information could potentially lead to errors in assigning diagnosis codes in our models.
# MinerGuard: A Solution to Detect Browser-Based Cryptocurrency Mining through Machine Learning
## 2 Background
### Browser-Based Cryptocurrency Mining  
In 2011, while Bitcoin was still in its infancy and was cheap and easy to mine, a service called BitcoinPlus.com [22] was published, the earliest browser-based cryptocurrency mining technology. Since that time, browser-based cryptocurrency mining has emerged.  
(1) BitcoinPlus.com: As shown in Figure 1, BitcoinPlus.com uses JavaScript for centralized mining, and site owners can sign up for services and embed these scripts into their web pages. When a user browses a web page in which the mining script was embedded, the mining script will achieve the cryptocurrency mining process by using the CPU resources of the user's computer.  
Although BitcoinPlus.com mined Bitcoin at the time, the service was in vain because of the low mining capacity and high power requirements. With the popularity of ASIC hardware specifically useful for mining, this service appears to be much less efficient.  
Despite these benefits, browser-based cryptocurrency mining does not disappear because the goal of this approach is not to achieve mining on a single computer. The purpose of the service is to focus on many users to achieve excellent mining capabilities. This approach makes the mining system a decentralized one and has good scalability. In other words, any device that can execute JavaScript and connect to the network can participate in mining cryptocurrencies, and higher website traffic means higher revenues, which was the idea that prompted Coinhive to evolve.  
(2) Coinhive: Coinhive [18] proposed a browser mining script in September 2017 that was very similar to BitcoinPlus.com. The difference between Coinhive and BitcoinPlus.com is that Coinhive mines Monero, and BitcoinPlus.com mines Bitcoin. This technology is used to replace advertising revenue for websites. The first website that was found using Coinhive is Pirate Bay. It is a website for storing, sorting, and searching BitTorrent files. Pirate Bay is also known as the largest BitTorrent website. This site is quite suitable for browser-based mining because it takes a long time for users to search and download BitTorrent files. However, Pirate Bay did not inform its users that their computer resources were used for mining. Many users felt uncomfortable when they discovered that Pirate Bay mined in the dark and slowed down their computers.  
In the past, when browsing the Internet, advertisements often affected users' perceptions about related web pages. As the Pirate Bay used mining to replace the benefits of advertising, many websites began to embed mining scripts in their web pages following the practice of Pirate Bay.  
Since the advent of Coinhive, a series of competitors have emerged. For example, Crypto-Loot, Coin-Have, and Mine- MyTraffic have also launched a service called Ppoi in China. Microsoft's Malware Protection Center also claimed that they discovered a new cryptocurrency mining script, one of which is CoinBlind and the other is CoinNebula.
# Artificial Neural Network Model to Predict Final Construction Contract Duration
## 3 Artificial Neural Network (ANN) Model  
This section provides an important and simple introduction to ANN model structures. The Artificial Neural Network (ANN) is an ML method that emerged from the concept of biological neural networks in the human brain. In situations where the actual process is complex and we are unsure of the nature of each phenomenon involved, it is one of the most excellent tools for value prediction [29]. Civil Engineering is one of the areas that benefits from ML. Such applications include earthquake engineering, structural health monitoring, damage identification and detection, and structural design. The VULMA ML-based tool is an example of such application automation, establishing a seismic vulnerability score for building structures [30; 31].  
A model known as an ANN builds an algorithm from any function to estimate the outcome [29]. The structures of the ANN consist of three layers: input layer, hidden layer (one or more than one), and hidden layer, as shown in Figure 1. The main aim of the hidden layer is to extract some of the most relevant patterns from the inputs and send them on to the next layer for further analysis. The mechanism of each hidden neuron consists of two sequence functions, \(S\) and \(a\), as shown in Figure 2. The hidden layer also accelerates and improves the efficiency of the network by recognizing just the most essential information from the inputs and discarding redundant information. The benefit of using an ANN in Statistical Package for Social Sciences software is its simplicity and ability to handle small amounts of data, like the sector in this article. Moreover, the IBM SPSS Statistics 20 program can provide a neat illustration of using the ANN model with the strong relationships among neurons and the bias values. Additionally, SPSS makes it simple to choose the percentage of training and testing processes and provides the relative errors of the two processes together with the expected result values. Moreover, the program can provide the weighting values among the connections of the neurons and the output computed by the ANN model. On the other hand, the activation function allows the model to capture nonlinear relationships between the inputs. In addition, the activation function contributes to converting the input into a more usable output. The types of activation functions are hyperbolic tangent functions (Equation (1)), sigmoid functions (Equation (2)),  
\[f=\frac{1-e^{-2x}}{1+e^{-2x}} \tag{1}\]\[f=\frac{1}{1+e^{-x}} \tag{2}\]
## 1 Introduction
### Motivation and Contribution  
The work of this paper is based on the following motivations: (i) The resource allocation scheme mainly focuses on the downlink transmission and rarely focuses on the uplink, for example [22], but the uplink transmission is more complicated. Therefore, this paper considers the uplink subchannel allocation scheme. (ii) Most of the subchannel allocation schemes consider the QoS of high-priority users and the fairness of low-priority users separately, for example [24]. Thus, the QoS of high-priority users and the fairness of low-priority users are considered in this paper. (iii) Users generally need to backoff before sending data packets and need to wait for a response after sending data [12]. However, there is no data transmission during backoff and waiting for a response, which causes a waste of spectrum resources. Therefore, in this paper, OFDMA and CR are combined to improve the utilization of feedback slots and backoff slots. Based on the above motivation, on the uplink, a new QoS-oriented and fairness-considered CR-OFDMA is proposed in the overlay mode. In this protocol scheme, a complete channel is divided into multiple orthogonal subchannels that allow multiple patients' medical emergency data to be accessed and transmitted simultaneously. Then, QoS enhancement based on priority and fairness is achieved by improving the subchannel allocation scheme, and spectrum resource utilization is improved by making full use of the remaining subchannels, feedback slots, and backoff slots. The main contributions of this paper are as follows:  
* A QoS-oriented and fairness-considered CR-OFDMA is proposed in overlay mode, which improves user competition fairness, and fully utilizes the remaining subchannels, feedback slots, and backoff slots. This protocol guarantees QoS for high-priority patients and fairness for low-priority patients, reducing spectrum resource wastage. As far as we know, this is the first protocol considering QoS for high-priority patients, fairness for low-priority patients, and spectrum resource utilization.
* A two-dimensional Markov model describing the protocol operation is established to analyze the protocol's performance. The model established takes into account the conflict between users and the state of high-priority patients who fail to compete and select the remaining subchannel for transmission. Then, the transmission probability and collision probability are calculated based on the Markov chain, and expressions of the system throughput, channel utilization, and fairness index are derived.
* Numerical results validate the superiority of the protocol proposed. Results show that the protocol in this paper has the best system throughput, throughput of high-priority patients, throughput of low-priority patients, and channel utilization compared to the conventional OFDMA and the DRA-OFDMA. In addition, CR-OFDMA has good fairness, which is verified.
## 3 Research Methodology
### System Modeling
#### 3.2.5 Controller Design
Hence, we obtain the following norm:  
\[\|e_{x}(t)\|\ \leq\sqrt{\lambda_{max}\ (R)/\lambda_{min}(R)}\ \ e_{x}(0)\ e^{-kt/2} \tag{46}\]  
Coming back to the residual equation:  
\[r(t)\!\triangleq\!\|C\mathbb{\bar{x}}(t)\!-\!y(t)\| \tag{47}\]  
\[r(t)\ \leq\sqrt{\lambda_{max}\ (R)/\lambda_{min}(R)}\ \|\mathbb{C}\|e_{x}(0)\ e^{-kt/2} \tag{48}\]  
\[\|\mathbb{C}\|\|e_{x}\|(0)\approx\|r(0)\| \tag{49}\]  
Finally, we can find the following criteria for fault detection of a sensor fault:  
\[r\ (t)\!\left\{ \begin{array}{l}\leq\ \sqrt{\lambda_{max}\ (R)/\lambda_{min}(R)}\ \|r(0)\|\ e^{-\frac{kt}{2}},\ there\ is\ no\ fault\\ >\sqrt{\lambda_{max}\ (R)/\lambda_{min}(R)}\ \|r(0)\|\ e^{-\frac{kt}{2}},\ there\ is\ a\ fault\end{array} \right. \tag{50}\]  
Figure 2 depicts the planned AFTCS and its activity. When the system first begins, it tests the sensor values and calculates the sensor-to-observer value threshold \(\xi\). The FDI detects the fault by calculating the residual \(e_{x}\) and comparing it with the threshold (\(\xi\)) as follows:  
Figure 2: Flowchart of proposed AFTCS. If \(e_{x}<\xi\), no fault is present in the sensor. If \(e_{x}\geq\xi\), a fault is detected in the sensor and will be replaced by the observer output. If there is no fault, the engine performs as anticipated. The error signal, on the other hand, becomes out of the threshold (10% absolute) if any single sensor fails. The FDI unit feeds the Engine Control Unit (ECU), the approximate value derived from the observer model based on PSO, GA, and NLR to replace the defective sensor value. The model's analytical redundancy is provided by the output of the approximate simulated value of the defective sensor. This model assumes that the engine runs at a constant speed of 300 r/min. We utilized constant speed in this study since the engines in the process plant run at a constant speed most of the time. Because the article is focused on designing an AFTCS system, load changes and their influence on speed are not examined. The data for the MAP sensor and the throttle sensor at 300 r/min are derived using the available MATLAB model Lookup Tables (LTs). To generate nonlinear relation between the MAP sensor and the throttle sensor, the three approaches are applied. For an estimated value of malfunctioning sensors, the FDI unit uses these nonlinear relationships. It is also supposed that the time spent in switching and reconfiguring is zero seconds. In actuality, the controller computations will be delayed. The research has limitations in that it only examines full sensor failure types, neglecting partial failures, which will be addressed in future research. Many physical problems, such as open circuits owing to wire breakage or weak connections, or burnout due to any type of short-circuiting leading the sensor to produce low output, can cause this issue.
# Detection of Volatile Constituents from Food Lures by Tephritid Fruit Flies
## 3 Results
### Field Experiment  
Trap Catches of _B. dorsalis_ with Protein-Based Baits  
In the field in Arbaminch, Ethiopia, traps caught roughly equal numbers of males and females F (1,24) = 1.154, \(p\) = 0.293 (Figure 6). Traps with the commercially available protein-based baits (GF-120 and Torula yeast) caught significantly higher numbers of flies compared to the dorsablend (Tukey: \(p<0.05\)) (Figure 6). Although the attractiveness of the synthetic dorsablend was not significantly different from the paraffin control for either sex, it only trapped female flies. Even though trap entry was low, female flies were frequently observed landing on the trap without entering. In a separate study, an experiment with male-specific lures conducted in the same area around the same period, male _B. dorsalis_ flies were caught in the order of hundreds per day per trap [13], indicating that flies were abundant in the area.  
Figure 3: The above plot from left to right is composed of (1) Compound name (2) the functional classes of the compounds (3) antennal sensitivity of the four fruit fly species (_B. dorsalis_, _B. zonata_, _C. capitata_ and _Z. cucurbite_) to compounds in food baits with the MS area (the sum of ions) (4) simulated olfactory response of _D. melanogaster_ to the compounds. Compounds from five chemical groups were identified to induce an antennal response and include, terpene, pyrazines, esters, aldehyde and alcohols (from dark green to light green respectively). The average relative sensitivity of the fly’s antennae ranges from black (no response) to red (6\(\times\) averaged EAD response). The olfactory receptors response of _D. melanogaster_ increases from light blue to dark blue, with white tile indicating compounds not present in the DoOR database.  
Figure 4: Choice of _B. dorsalis_ females between treatments (mix of baits, headspace, 13 blend and dorsablend) and control (paraffin oil, or mix of bait), in a y-tube olfactometer. Mix of baits, is a mixture of brewer’s yeast waste, baker’s yeast, torula yeast, named & GF-120. Top right inset: experiments consisting of (1) headspace against headspace and (2) paraffin oil against paraffin oil. Forty flies were released individually and the percentage of flies that made a choice is presented within the bars, middle bars (white) represent non-responding flies. \(\alpha=0.05\), \({}^{*}p<0.05\).  
Figure 5: Behavioral response of _Z. cucurbidae_, _C. capitata_ and _B. zonata_ to cucublend, capiblend and zonablend. Forty flies were released individually and the percentage of flies that made a choice is presented within the bars. Colored bars indicate synthetic blends, gray bars indicate control (paraffin oil) and middle bars (white) represent non-responsive flies. \(\alpha=0.05\), \({}^{*}p<0.05\).
## 5 Simulation Analysis of Traction Dynamics of the Space Flexible Web
### Analysis of Expansion Process of Topological Rope Web
As shown in Figure 18 below, Figure 18a represents the velocity variation curve of the traction body in each direction during the launch and expansion process, Figure b shows the curve of the resultant velocity of four characteristic points of the topological rope web. Based on the analysis of the curve variation law in the figure, it can be seen that the initial velocity of the traction body is the same as the initial launching velocity set by the traditional rope web, which is 71 m/s. The initial velocity value of each characteristic point of the flexible rope in the topological rope web is 0 m/s. It is found that the velocity curve of the traction body in the X-, Y-, and Z-directions and the resultant velocity direction is the same as that of the traditional rope web. For the four characteristic positions of the topological rope web, the peak velocity appears at the bottom characteristic point position, reaching 116.132 m/s, which is higher than the peak velocity of the traditional rope web. In addition, when the 0.12 s flexible mesh approached or reached the maximum expansion area, compared with the traditional rope mesh, the vibration frequency and amplitude of the velocity curve of the topological rope mesh were lower than those of the traditional rope mesh, and the velocity curve corresponding to the bottom feature of point B is particularly pronounced. It can be seen that the structural stability of the topological rope web is better than that of the traditional rope web under the same working conditions. As shown in Figure 19 below, in the topological rope web displacement change curve, the traction body's displacement change in the X-, Y-, Z-, and sum directions is consistent with that of the traditional rope web. According to the resultant displacement and Z-displacement in Figure a, in this case, the flying height of the traction body is about 5.7 m, which is slightly smaller than that of the traditional rope web. According to Figure b, it can be seen that the Z-direction displacements of points A, C, D, and B have changed successively, which also reflects the sequence of motion of each characteristic position of the rope web. According to the analysis of the a and b figures, compared with the traditional rope web, the maximum displacement of the traction body is the same within 0.5 s after launch, which is about 6 m. At 0.5 s, the traction body reaches the highest position, and the displacement of each feature point has entered the platform segment, which is in the oscillation process at the highest position. At this time, the displacement of the flexible  
Figure 17: Topological rope web simulation analysis characteristic points diagram. mesh rope is still rising gradually, and the deployed position is higher than the plane of the traction body point. The peak displacement of each characteristic point of the topological rope web is higher than that of the traditional rope web and the peak displacement of the bottom part of the topological rope web is nearly 12 m.