esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative AI in Medicine and Healthcare: Moving Beyond the Peak of Inflated Expectations'
## 3 Applications and Implications of Generative AI and LLMs in Medicine
### Applications of Prompt Engineering in Medicine  
The application of prompt engineering in medicine involves optimizing the input prompts given to generative AI models to enhance their performance, reliability, and utility in clinical settings. Prompt engineering is a critical component in ensuring that AI systemslike LLMs generate accurate, relevant, and contextually appropriate responses, particularly in sensitive areas such as healthcare.  
A Forbes article [67] discussed the potential risks doctors face when relying on generative AI to summarize medical notes. While AI can improve efficiency by creating concise summaries of patient records, it may also lead to critical information being misinterpreted or omitted, jeopardizing patient care. The article emphasizes the importance of prompt engineering, ongoing research to establish standards, and ensuring that AI-generated summaries are always reviewed by qualified healthcare professionals to maintain accuracy and reliability (cf. [PERSON] et al. [32]).  
Effective prompt engineering is crucial for optimizing the performance of generative AI models. [PERSON] et al. highlighted the importance of designing and refining input prompts to significantly enhance the usability and reliability of AI systems in healthcare settings. For example, specific prompt strategies can help guide AI models to produce more accurate and relevant responses, which is vital for clinical decision-making [35].  
A recent study by [PERSON] et al. assessed how different prompt engineering techniques influence GPT-3.5's ability to answer medical questions. The study compared direct prompts, Chain of Thought (CoT), and modified CoT approaches using 1000 questions generated by GPT-4 and 95 real USMLE Step 1 questions. The analysis revealed no significant differences in accuracy among the prompt types, with success rates of approximately 61.7% for direct prompts, 62.8% for CoT, and 57.4% for modified CoT on USMLE questions. This finding suggested that while prompt engineering techniques like CoT are designed to enhance reasoning, they do not significantly impact the model's performance on medical calculations or clinical scenarios. Consequently, simpler prompting methods can be as effective as more complex ones, potentially simplifying the integration of AI tools like ChatGPT into medical education and clinical practice [68].
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 4 Algorithm Design
### Co-Distribution Center Site Selection Method Based on GWO  
#### 4.2.1 Algorithm Flow  
The wolf pack was first initialized to generate a set of effective initial solutions. The main iterative computation was carried out to explore and optimize the generated solutions, and the optimal solutions were used as the final set for site selection. The algorithm flow is illustrated in Figure 2, and the pseudocode for solving the problem is presented in Table 2.  
Figure 2: Flowchart of the site selection method based on GWO.
## 5 Stability Analysis of High Slope of Carbonaceous Mudstone Based on Numerical Simulation  
### Establishment of Slope Model  
To study the stability changes of high slopes of carbonaceous mudstone before and after rainfall, numerical simulations were conducted using Midas GTS NX software. The numerical simulation of this research was completed using the commercial geotechnical engineering finite element analysis software Midas GTS NX (version number: 2023 v1.2) developed by MIDAS IT company in South Korea. Its computational core is based on standard finite element theory and has extensive engineering application verification in the field of slope stability analysis. All simulations were conducted under the default solver settings of the software, and no modifications were made to the program source code. A computational model was developed based on the profile of construction site A, as shown in Figure 8. The numerical model for roadbed excavation and cutting includes 5006 units and 5120 nodes. Under various calculation conditions, the boundary conditions for model establishment were fully constrained by the bottom. The X direction displacement is restricted on both sides, and the top is an unrestricted free boundary. The initial stress is the self weight stress vertically downward along the \(Z\)-axis.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 5 MQTT-SN Attacks and Performance Evaluation
### Wake up Wave Attack  
The sleeping mode of MQTT-SN allows clients to remain in a sleeping state in order not to waste battery power. While in this state, they do not receive the published messages of the topics to which they are subscribed. This state is reached by sending a _disconnect_ message in which a time of duration is indicated. If this time is not specified, the broker simply disconnects the client. When the client is ready to receive messages again, a _Pingreq_ message is sent, indicating the client identifier. This sends the client to a state called _awake_, where it can receive messages again. A diagram of how this mechanism works can be seen in Figure 22.  
Figure 21: Accumulated traffic with topic flooding.  
Figure 20: Congestion attack evaluation diagrams. (**a**) MQTT-SN traffic in the legitimate scenario. (**b**) MQTT-SN traffic in the attacked system. (**c**) MQTT-SN most-common topic frequency without attacks. (**d**) MQTT-SN most-common topic frequency on an attacked system.  
This attack, which exploits the mechanism introduced in MQTT-SN, aims to prevent clients from sleeping, so that the broker does not stop sending them the various publishes. This can be harmful, especially for devices that rely on batteries to operate. It also increases network traffic.  
Figure 23 shows the format of the Pingreq packet. In this case, an identifier of the client we want to wake up is sent. In our implementation, we iterated over the different clients permanently with the aim of preventing any of them from going into sleep mode. A diagram of the attack can be seen in Figure 24.  
Evaluation of the Wake up Wave Attack  
As mentioned above, in this implementation of the attack, it iterates over the different clients indefinitely, preventing any of them from sleeping. This causes, as can be seen in  
Figure 23: Pingreq packet format.  
Figure 22: MQTT-SN sleeping mechanism diagram.  
Figure 25, a huge increase in the system's network usage. The same occurs with the battery usage if the devices are dependent on it, which is usually the case when using sleep mode. To defend against this attack, a rule-based IDS could be interesting since detecting that a single IP address sends multiples Pingreqs with different Ids could be the signature to identify this particular attack.
## 3 Results and Discussion  
### Performance of the Catalytic Converter  
The average composition of the flue gases that produce the steam boiler is shown as follows: CO\({}_{2}\) 14.1 \(\pm\) 0.21% (_v/v_), O\({}_{2}\) 2.3 \(\pm\) 0.3% (_v/v_), CO 171 \(\pm\) 5.52 ppm, NO 100 \(\pm\) 0.1 ppm, HC 0% (_v/v_), N\({}_{2}\) 83.9% (_v/v_). This equipment was constantly generating steam, and no significant changes in operation were observed. After carrying out 4.5 h of operation intermittently with periods of operation of 40 min, the concentrations of the gases after passing through the converter were CO\({}_{2}\) 14.40% (_v/v_), O\({}_{2}\) 2.30% (_v/v_), CO 37 ppm, NO 10.2 ppm, HC 0% (_v/v_), N\({}_{2}\) 83.3% (_v/v_). The catalytic converter transformed 78% of the CO to CO\({}_{2}\) and 89.8% of NO to NO\({}_{2}\) in the flue gas. The chromatographic analysis of the emissions (Varian 3400) showed the presence of small amounts of aldehydes such as acetaldehyde and acrolein (<22 ppm) which were not observed at the output of the converter. In our experiments, the CO conversion of this catalytic converter was 78% when the converter reached a temperature of 170 \({}^{\circ}\)C. [PERSON] and [PERSON] [36] obtained 100% of CO conversion using a fresh Pt/Al\({}_{2}\)O\({}_{3}\) catalyst at 175 \({}^{\circ}\)C in the form of monolith having 400 cell/in\({}^{2}\). The chemical composition of this catalytic converter was 1 wt.%Pt supported on a monolith usually made of crodierite. During the preparation, the washcoat slurry containing the H\({}_{2}\)PtCl\({}_{6}\), the Al\({}_{2}\)O\({}_{3}\), and H\({}_{2}\)O covered the 75% of the total support (0.12 g coating per cm\({}^{3}\) of monolith catalyst).  
Other authors [8] also showed the same CO light-off curves (from 120 \({}^{\circ}\)C to 175 \({}^{\circ}\)C), and the chemical composition included 0.27 wt.%Pt supported on \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and small amounts of La\({}_{2}\)O\({}_{3}\) and Pd.  
The previous authors [36] used concentrations of 0.1% CO with an excess of O\({}_{2}\) of 9%, in the absence of other gases. It was observed that in the ignition experiments, the conversion of CO follows the typical light-off curves, which occurs by forming a loop when the catalytic bed is heated and when it is cooled so that two CO conversion profiles are obtained. In the ascent of temperature, the first conversion began at 140 \({}^{\circ}\)C and ended at approximately 175 \({}^{\circ}\)C for complete conversion.  
In the case of our converter, it worked for 3 months, which is why we believe that it showed a good performance, given the inherent deactivation process.
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 4 Results and Discussion
### Ablation Study on DSA Mechanism  
The primary objective of the experiment was to evaluate the performance of the DSA module within privacy-preserving dialogue large models. By contrasting the conventional Multi-head Attention mechanism with the newly proposed DSA module, a quantitative analysis of the differences in key performance metrics such as precision, recall, accuracy, and latency was facilitated. Table 3 demonstrated that the DSA module surpassed the multi-head attention mechanism across precision, recall, and accuracy metrics, with only a minimal increase in latency. This indicates that the DSA mechanism, by reducing computational load, is capable of enhancing model performance while ensuring security, particularly excelling in tasks where precision and recall are paramount.  
From a mathematical perspective, the computational complexity of the multi-head attention mechanism is quadratic in relation to the length of the input sequence, as it computes attention scores for every pair of elements. Specifically, for a sequence of length \(n\), the complexity is \(O(n^{2}d)\), where \(d\) represents the model's dimension. This results in significant latency when processing large sequences. In contrast, the module optimizes the computation process by only calculating attention scores among key elements that have the most significant impact on the output. Its complexity is contingent on the number of key elements selected, typically much less than the length of the sequence, thus the complexity can be denoted as \(O(knd)\), where \(k\) is the number of key elements. Given that \(k\) is usually much smaller than \(n\), this markedly reduces both computation and latency.  
In summary, the design of the module adeptly balances computational efficiency and performance, while maintaining the security of data privacy, this novel attention mechanism enhances the processing speed and responsiveness of the dialogue large model by minimizing unnecessary computations, thereby showcasing its potent potential and practicality, especially in scenarios requiring real-time feedback.
## 3 Results  
### Identification of Scg-Met and Scg-Tai cDNAs in S. gregaria  
We identified partial cDNAs coding for one Met isoform (_Scg-Met_) (Genbank: MK855050) and one Tai isoform (_Scg-Tai_) (Genbank: MK442071) by BLAST searches in in-house _S. gregaria_ transcriptome databases. Both sequences, verified by cloning and sequencing, were uploaded to the NCBI GenBank database (Figures S9 and S10). Based on the recently published genome data for _S. gregaria_[60], we were able to further extend the predicted amino acid sequences of both _Scg-Met_ and _Scg-Tai_ (Figures S1 and S4). Multiple sequence alignments of _Scg-Met_, as well as _Scg-Tai_, with selected orthologs from different species (NCBI) revealed very high similarities in protein sequence between _S. gregaria_ and _L. migratoria_ (84.5% and 87.1% for Met and Tai, respectively) (Figures S2 and S5; phylogenetic trees in Figures S3 and S6). The overall sequence similarities of _Scg-Met_ and _Scg-Tai_ with their orthologs in the other insect species included in this analysis appeared to be far lower (in the 13-28% and 15-53% range, respectively). Nevertheless, three conserved domains were identified in all these sequences: a basic helix-loop-helix domain (bHLH), a Pern-Arnt-Sim (PAS) domain and a PAS-11 domain. The bHLH domain in Tai orthologs was more specifically identified as a bHLH-PAS domain belonging to the steroid receptor coactivator (SRC) protein family.
# Echocardiography in the Diagnosis and Management of Tricuspid Atresia
## 5 Echo-Doppler Studies Following Palliative/Corrective Interventions
### Echocardiographic Evaluation Following Bidirectional Glenn  
Following bidirectional Glenn, the LV size is likely to decrease (normalize) and LV should maintain normal systolic function. This is because of decreased LV volume overload following bidirectional Glenn and the removal of aorto-pulmonary shunt. The 2D imaging of the bidirectional Glenn is difficult to accomplish, but color flow imaging (Figures 25A, 26B, and 27B) from a suprasternal notch or high parasternal views will help image the bidirectional Glenn. Low pulsed Doppler velocity across the superior vena cava (SVC)-PA junction (Figures 25B and 26C) would indicate no obstruction. Turbulent and high velocity Doppler flow suggests the obstruction of the bidirectional Glenn shunt.  
Figure 23: Selected echocardiographic video frames demonstrating pulmonary artery band (PAB) with narrow diameter by color flow (**A**) and a high gradient (88 mmHg) by continuous wave Doppler (**B**) are shown.  
Figure 24: Selected cine frames in postero–anterior (**a**) and lateral (**b**) views, demonstrating a Blalock–Taussig (BT) shunt (arrow in a) to care for infants with decreased pulmonary blood flow (**a**) and pulmonary artery banding (PB) (arrow in (**b**)) in babies with increased pulmonary blood flow (**b**), respectively, during Stage I of Fontan procedure. C, catheter; LPA, left pulmonary artery; RPA, right pulmonary artery. Neonates with adequate pulmonary blood flow do not need any intervention and can go directly to Stage II at about the age of 3 months. Reproduced from [PERSON], _Indian J Pediatr_ 2015;82:1147–1156 [36].  
Angiographic counter parts of the bidirectional Glenn are shown, in Figure 29, to have better comprehension of the anatomy of the bidirectional Glenn.  
In patients who have a persistent left SVC entering the heart via the coronary sinus, bilateral bidirectional Glenn procedures are performed, especially if the inter-connecting left innominate vein is small or absent. An example of left sided bidirectional Glenn is shown in Figure 30. In subjects who have infrahepatic interruption of the inferior vena cava (IVC) with azygos or hemiazygos continuation (into the right or left SVC, respectively), a Kawashima procedure is performed. In these patients, echocardiographic imaging of the Glenn/Kawashima is similar to that of regular bidirectional Glenn (Figures 25-27).  
Figure 28: Selected video frames from suprasternal notch view demonstrating right (RPA) and left (LPA) pulmonary arteries without (**A**) and with (**B**) color flow imaging.  
Figure 27: Selected video frames from suprasternal notch view demonstrating bidirectional Glenn shunt; the superior vena cava (SVC) is shown emptying into the right (RPA) and left (LPA) pulmonary arteries by two dimensional (**A**) and color flow imaging (**B**). Both RPA and LPA are seen with color in contrast to that seen in Figure 26, probably related to both pulmonary arteries are in a similar echo plane.
## 2 Methodology
### Determining Recptance Coupling Using RCFBS  
Significant advancements have been made in developing algorithms for generalized receptance coupling, which account for interactions between all connection and internal degrees of freedom (DoFs) of subsystems [24; 25; 26; 27]. These algorithms calculate the assembly's receptance based on the decoupled receptances of individual substructures, resulting in more complex formulae due to the involvement of multiple subsystems with internal and connection DoFs. Despite substantial innovation in frequency-based substructuring (FBS), [PERSON]'s formulation [2] remains one of the most effective mathematical frame  
Figure 1: Road map for determining the receptance of the assembly system.  
works for coupling frequency response functions (FRFs) and serves as the foundation for the generalized receptance coupling using frequency-based substructuring (GRCFBS) approach. This formulation involves the inverse summation of receptance matrices at interfaces, partitioning receptance matrices for substructures before coupling, and using a mapping Boolean matrix to describe interconnections among substructures. This systematic approach, often based on graph theory [28], allows for determining the overall system responses by integrating coupling receptance among complex interconnections. For the coupling of two substructures, [PERSON]'s general algorithm simplifies to the following:  
\[\begin{bmatrix}\mathrm{H}_{\overline{\overline{A}\overline{A}}}&\mathrm{H}_{ \overline{A}\overline{B}}\\ \mathrm{H}_{\overline{B}\overline{A}}&\mathrm{H}_{\mathrm{B}\overline{B}} \end{bmatrix}=\begin{bmatrix}\mathrm{H}_{\overline{\overline{A}\overline{A}}}&0 \\ 0&\mathrm{H}_{\mathrm{B}\overline{B}}\end{bmatrix}-\begin{bmatrix}\mathrm{H}_{ \overline{A}\overline{c}}\\ -\mathrm{H}_{\mathrm{B}\overline{c}}\end{bmatrix}\begin{bmatrix}\mathrm{H}_{ \overline{c}\overline{c}}\\ -\mathrm{H}_{\mathrm{B}\overline{c}}\end{bmatrix}^{\mathrm{T}} \tag{1}\]  
where \(\overline{A}=A\bigcup c\) indicates that the DoFs of substructure A are retained, including both internal and interface DoFs. Therefore, substructure B includes only internal DoFs. The only computationally challenging term is the inverse of the matrix, \(\left[H_{\alpha c}^{A}+H_{\alpha c}^{B}\right]^{-1}\), where the order of \(\left[H_{\alpha c}^{A}+H_{\alpha c}^{B}\right]\) is much smaller than that of the subsystem's receptance matrices.
## 6 Experimental Results and Discussion for ASCE Benchmark  
### Data Description and Experimental Setup  
To verify the effectiveness of the proposed method in a complex environment, the experimental phase II of the SHM benchmark is utilized for the study. The datasets and experimental process of the ASCE benchmark were published by the American Society of Civil Engineers (ASCE) SHM task group. The height of the ASCE benchmark is 3.6 m, and the footprint dimensions are 2.5 m \(\times\) 2.5 m, shown in Figure 15. The experiment aimed to provide a unified test bed for evaluating the ability of structural damage detection using different methods.  
The experiment collected acceleration signals from 15 accelerometers under ambient excitation, impact hammer excitation, and 5-50 Hz randomly generated shaker excitation. Moreover, Case 1 was measured for 120 s, Case 6 was measured for 300 s, and the remaining cases were measured for 360 s. The sampling frequency for all accelerometers were set to 200 Hz. As shown in Table 7 and Figure 16, the damaged degree was increased gradually from undamaged in Case 1 to very damaged in Case 9. Cases 2-7 removed the inclined supports at specific locations to generated structural damage, and Cases 8 and 9 loosed bolts at joint locations. In total, 31,680 samples were acquired by slicing with a window of 200 points during experimental process. Then, the dataset was divided into training, verification, and testing datasets according to the ratio of 6:2:2. That sample number for training, verification, and testing datasets are 19,008, 6336, and 6336, respectively.  
Figure 15: Four-story structure of ASCE benchmark.
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 4 Applications of DLC Coatings  
DLC coatings are used for several applications, which are highlighted for PVD coatings, generally apart from cutting tools, for high operational temperatures. DLC coatings are particularly helpful wherever a mixture of friction and wear reduction is required. DLC coats the forefronts of devices for the rapid, dry molding of troublesome uncovered surfaces of wood and Al, such as on vehicle dashboards. The better wear, erosion, and electrical properties of DLC make it an attractive material for clinical applications. Fortunately, DLC has been demonstrated to possess astounding bio-similarity. Typical applications include:  
* Automotive: piston pins, rocker arms
* Medical: surgical tools, prosthetic applications
* Firearms: firearm slides, barrels, bolt carriers
* Industrial parts and machinery: pistons, plungers, gears, mechanical seals
* Injection molding: dies, ejector pins, slippery machine components
* Consumer products: wrist joint watches, jewelry, golf clubs
## 3 Numerical Results and Discussion
### BER versus MSE  
To verify whether the proposed dual-driven MIMO detector can outperform the baselines at different channel estimation errors, we conducted a test, setting the channel estimation MSE at between \(-40\) dB and \(-26\) dB at an SNR of 20 dB and with seven layers in the dual-driven network. Figure 5 illustrates the results obtained from the dual-driven MIMO, MMSE MIMO, OAMP, and OAMP-Net detectors using testing data samples generated at various MSEs while maintaining an SNR of 20 dB. The findings presented in Figure 5 align with those depicted in Figure 4.  
The proposed dual-driven learning-based MIMO symbol detection method outperforms the other three MIMO detectors at a channel estimation MSE ranging from \(-40\) dB to \(-26\) dB. Both the MMSE and the OAMP-Net detectors exhibit similar performances, while the OAMP detector demonstrates the poorest performance among the four detectors.  
We have demonstrated that the dual-driven detector's performance improvement can mainly be attributed to the symbol correction network in Section 3.4. When the SNR is 20 dB and the channel estimation MSE is \(-28\) dB, the dual-driven MIMO detector can achieve a BER of \(4.59\times 10^{-3}\), while the OAMP-Net can only achieve a BER of \(1.34\times 10^{-2}\). To achieve the target BER of \(1\times 10^{-3}\), the dual-driven MIMO detector requires a channel estimation MSE of \(-31.2\) dB, whereas the MMSE and the OAMP-Net detectors necessitate channel estimation MSEs of \(-34.2\) dB and \(-34\) dB, respectively. This indicates that the  
Figure 4: BER versus SNR for the dual-driven MIMO detector and three other detectors when the channel estimation MSE \(=-30\) dB.  
dual-driven MIMO detector requires a increased channel estimation MSE by 2-3 dB to achieve the same BER as the other two detectors.  
Considering that the training data samples were generated with a channel estimation MSE of \(-30\) dB and the testing data samples were generated with varying channel estimation MSEs, the results demonstrate the effective performance of the proposed dual-driven detector in mitigating the impact of channel estimation errors.  
To sum up, this test verifies the hypothesis that the proposed dual-driven MIMO detector outperforms the OAMP-Net, the OAMP, and the MMSE detectors at different channel estimation errors.