esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PE Stimulation Spectra and TPPE Plot
Concerning the state of the metal surfaces, it was found that the surface abraded in C\({}_{3}\)H\({}_{7}\)OH becomes apparently bare compared to that in CH\({}_{3}\)OH. In the case of air, the TPPE plots of the abraded surfaces for the abrasion periods yields a similar shape, with a maximum around 50-300 \({}^{\circ}\)C. This result shows that the TPPE was nearly constant, independently of the abrasion period. Figure 4 shows the TPPE plots of \(N_{\rm T}\) vs. temperature during the 1 st and 2 nd temperature increase and subsequent temperature decrease processes for copper samples (sample C) ultrasonically cleaned for 5 min in various liquids: (1) H\({}_{2}\)O, (2) CH\({}_{3}\)OH, (3) C\({}_{2}\)H\({}_{5}\)OH, (4) (CH\({}_{3}\))\({}_{2}\)CHOH, and (5) C\({}_{3}\)H\({}_{7}\)OH. In the cases of CH\({}_{3}\)OH and C\({}_{2}\)H\({}_{5}\)OH, the \(N_{\rm T}\) values for samples only ultrasonically cleaned were comparable with those for the 10 min abraded samples. In the case of H\({}_{2}\)O, the \(N_{\rm T}\) for the 10 min abrasion sample was greatly larger than that for the sample ultrasonically cleaned in the liquid. In the cases of C\({}_{3}\)H\({}_{7}\)OH and (CH\({}_{3}\))\({}_{2}\)CHOH, interestingly, a great difference was observed in the levels of \(N_{\rm T}\) values between the ultrasonically cleaned and abraded samples; that is, the TPPE plot for the ultrasonically cleaned sample exhibited a distinct maximum at 250 \({}^{\circ}\)C, while that for the abraded samples gave a decreased level in the entire temperature range. Figure 3: _Cont._  
Figure 3: _Cont_. Figure 3: [Sample A] Plots of PE total count (\(N_{\rm T}\)) during the 1 st and 2 nd temperature increase and subsequent temperature decrease processes for copper samples abraded in various liquids and in ambient air for 10 min, which are named (a) (Up1, blue line) and (b) (Down1, red line), and (c) (Up2, grey line) and (d) (Down2, yellow line), respectively, versus measurement temperature: (**1**) H\({}_{2}\)O, (**2**) CH\({}_{3}\)OH, (**3**) C\({}_{2}\)H\({}_{5}\)OH, (**4**) (CH\({}_{3}\))\({}_{2}\)CHOH, (**5**) C\({}_{3}\)H\({}_{7}\)OH, and (**6**) ambient air. It is noted that the liquids are arranged in the decreasing order of the \(N_{\rm{Tmax}}\)a.  
Figure 4: \(Cont.\)  
Here, it should be noted that the TPPE plots in the processes other than Up1, that is, those in Down1, Up2 and Down2 scans, shown in Figures 3 and 4 will be referred to later. The Down1 scan was applied for all of the environments, and the Up2 and Down2 scans were applied for H\({}_{2}\)O (Figure 3) and C\({}_{3}\)H\({}_{7}\)OH (Figure 4), which yielded the largest \(N_{\rm T}\) value, in each case. It is confirmed that in these processes, the \(N_{\rm T}\) values slowly increase with decreasing temperature and the levels of the \(N_{\rm T}\) values are almost similar. We will describe the mechanism for PE occurring through Cu\({}_{2}\)O.
# Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model
## 2 Materials and Methods
### Segment Anything Model  
The SAM is a breakthrough in image segmentation, carefully designed to help accurately delineate any object in an image. At its core, SAM utilizes state-of-the-art deep learning methods to meticulously analyze and segment a wide variety of objects, and initiates this complex process at the individual pixel level of granularity. This technology enables SAM to achieve unparalleled high-precision semantic segmentation, setting a new benchmark in the field. The model skillfully integrates the power of Deep Convolutional Neural Networks (DCNN) [21; 22; 23; 24; 25] with the advanced capabilities of semantic segmentation networks [26]. This strategic combination enables SAM to accurately interpret and segment images, effectively distinguishing between different objects and their respective contexts. In this way, SAM addresses a critical need in applications ranging from autonomous driving to medical imaging, where the ability to accurately recognize and segment objects can significantly improve performance and outcomes.  
SAM adopts advanced attention mechanisms [21; 23] to better capture contextual information of various regions in the image. It employs multi-scale feature fusion [25] to enhance segmentation accuracy. For a given image \(I\in R^{3\times H\times W}\), SAM first utilizes a ViT-based image encoder to obtain deep feature maps \(F\in R^{C\times\frac{H}{2}\times\frac{W}{4}}\). Then, it encodes N input prompts using a prompt encoder and sends them to the mask decoder along with the feature maps. The mask decoder returns a set of masks \(m_{i}\in R^{1\times H\times W}\) (\(i\in N\)) specified by the input prompts.  
The sophisticated design of SAM is underscored by its flexible prompt mechanism, which facilitates not only interactive usage [27; 28], but also ensures that it excels in dealing with intricate scenes and visuals populated with multiple objects. This capability is especially critical in environments where detail and nuance are paramount. SAM's architecture is engineered to be highly sensitive to edge information, a feature that plays a pivotal role in accurately delineating the contours and boundaries of objects within an image. This level of precision is crucial for creating clear, distinct representations of each element in a scene, regardless of complexity. The inherent adaptability of SAM, stemming from its nuanced understanding of edge information and interactive prompt mechanism, makes it an ideal solution for a broad range of downstream tasks. These tasks could include, but are not limited to, image segmentation, object recognition, and even complex scene reconstruction, where the accurate capture and interpretation of object boundaries are essential for high-quality outcomes. This versatility and high performance in processing complex imagery positions SAM as a valuable tool in the field of computer vision and image processing, offering significant advantages for both research and practical applications.
## 2 Theoretical Analysis
### The Coordinate Deformation of the Tie Rods  
As shown in Figure 2, during the process of vibration of RFR, the coordinate deformation of the tie rods will occur, which will produce a changeable additional bending moment on the fastened shaft segment of the rotor. This will affect the dynamic behavior of the rotor.  
Figure 2: The coordinated deformation of tie rods.  
It can be seen from Figure 3 that the rod-fastened rotor is fastened by eight tie rods along the circumferential direction. The installation radius of the tie rod is \(r\). The length of the shaft segment is taken as \(l\), and the center curvature radius of the shaft segment is \(R(z)\). It is easily obtained as follows:  
\[\frac{1}{R(z)}=\frac{M(z)}{EI} \tag{4}\]  
Here, \(M(z)\) is the bending moment applied to the rotor.  
For the vibration deformation of the RFR, the tie rods will cause the following effects: the rods are lengthened below the middle interface, so the tension increases; the rods are shortened above the middle interface, so the tension reduces. According to the coordination deformation relationship for the lowest No. 2 tie rod, according to the geometric deformation relationship, can be given by:  
\[\frac{l+\Delta l}{l}=\frac{R+r}{R} \tag{5}\]  
In Equation (5) \(\Delta l=\Delta l_{1}+\Delta l_{2}\) is the stretching amount of rod microsegment.  
Therefore the increasing tension is:  
\[\Delta F=EA_{r}\frac{\Delta l}{l}=EA_{r}\frac{r}{R} \tag{6}\]  
Here, \(A_{r}\) is the cross-sectional area of tie rod.  
The uppermost No. 1 tie rod, according to the geometric deformation relationship, can be given by:  
\[\frac{l-\Delta l}{l}=\frac{R+r}{R} \tag{7}\]  
Thus the reduced tension is:  
\[\Delta F=EA_{r}\frac{\Delta l}{l}=EA_{r}\frac{r}{R} \tag{8}\]  
The tension change of all rods can be equivalent to an additional moment \(M_{0}^{\prime}\), and can be written as:  
\[M_{0}^{\prime}(z)=4 EA_{r}\frac{r^{2}}{R}=\frac{4 EA_{r}r^{2}}{(EI)_{eq}}M(z) \tag{9}\]  
Figure 3: Circumferential tie rod distribution on the cross-section of the rotor.
# Study on the Propeller Rope Cutter Concerning Transient Torsional Vibration Due to Cutting Action
## 2 Permissible Torsional Stresses Determination  
### Permissible Torsional Stresses Amplitude for Transient Vibration  
For continuous operation, it is determined in IACS UR M68 [31] that the permissible stresses due to alternating torsional vibration shall not exceed the values by the following formulae:  
\[\pm\tau_{c}=\frac{\sigma_{b}+160}{18}\cdot c_{k}\cdot c_{D}\cdot\left(3-2\cdot \lambda^{2}\right)\quad\text{for }\lambda<0.9 \tag{1}\]  
or  
\[\pm\tau_{c}=\frac{\sigma_{b}+160}{18}\cdot c_{k}\cdot c_{D}\cdot 1.38\quad\text{ for }0.9\leq\lambda<1.05 \tag{2}\]  
where:  
\(\tau_{C}\) = permissible stress amplitude (N/mm\({}^{2}\)) due to torsional vibration for continuous operation, \(\sigma_{B}\) = specified minimum tensile strength of shaft material = 600 (N/mm\({}^{2}\)),  
\(d\) = actual shaft outside diameter = 120 mm,  
\(c_{K}\) = design factor for intermediate shaft with shrink fit coupling = 1,  
\(c_{D}\) = size factor, = 0.35 + 0.93 \(\cdot d_{o}\)\({}^{-0.2}\) = 0.707,  
\(\lambda\) = speed ratio = \(n/n_{0}\),  
\(n\) = shaft speed under consideration (rpm), and  
\(n_{0}\) = shaft speed at rated power (rpm).  
The permissible stress is usually calculated for the intermediate shaft, which is smaller than the propeller shaft. This design ensures that, in the event of a failure, the intermediate shaft will be broken in advance to protect the propeller shaft that is more difficult to repair or replace. The same specifications as for the propulsion system in the shipboard test in Section 4 were used for this calculation. The lowest permissible stress is calculated at maximum rotating speed:  
\[\pm\tau_{c}=\frac{600+160}{18}\times 1\times 0.707\times 1.38=41.19\ \ \ (\text{N/mm}^{2}) \tag{3}\]  
The transient permissible stress given by the following formula represents the level that should not be exceeded in any case:  
\[\pm\tau_{T}=\frac{1.7\times\tau_{c}}{\sqrt{\tau_{k}}}=\frac{1.7\times 41.19}{ \sqrt{1}}=70.03\qquad(\text{N/mm}^{2}) \tag{4}\]  
Finally, the permissible alternative torque in the intermediate shaft for the transient fluctuation during cutting action can be obtained as below:  
\[T_{max}=\frac{\pi d^{3}\tau_{T}}{16}=\frac{\pi\times 120^{3}\times 70.03}{16}=23.8 \times 10^{6}\ (\text{N}\cdot\text{mm}) \tag{5}\]  
or 23.8 kN\(\cdot\)m.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Point Cloud Segmentation  
No matter what method is used to generate point clouds, there will be a lot of noise, background, and obstacles (equipment, materials, personnel, tools, protective measures, garbage, etc.). The messy and redundant point clouds not only waste computer resources but also affect judgment. Therefore, it is necessary to segment the point cloud to delete the redundant points outside the RoI. There are various computational methodologies proposed to conduct point cloud segmentation. [PERSON] et al. [46] divided them into six categories: clustering-based, edge-based, region-based, graph-based, model fitting-based, and hybrid. The advantages and disadvantages are shown in Table 3.  
In addition, many scholars used the geometric primitives of BIM models to segment point clouds [50]. After aligned with BIM models, the point cloud was naturally divided into different regions, which is great for geometry-based reasoning. The specific analysis will be introduced in Section 3.6.
## 3 Materials and Methods
### Fine-Tuning MedicoBERT for Question Answering  
The generated medical language model (MedicoBERT) was fine-tuned for the Spanish medical question-answering (QA) task. This fine-tuning process aimed to solve a specific NLP task. For this purpose, the pre-trained model was provided with a smaller dataset explicitly labeled for the QA task. By adjusting the weights of the model's neural connections, it was better adapted to the characteristics and patterns of the new dataset.  
Question answering is a fundamental task in NLP that involves providing responses to questions formulated in natural language using relevant or query-related text. The following multi-stage fine-tuning process was employed to adapt the Spanish medical language model to the QA task:  
* **Base architecture of the medical language model (MedicoBERT).** The same architecture, generated from the BERT model and adapted to the medical literature, was employed. This architecture provided a solid basis for the QA task, as it had learned to process and statistically understand the medical language and its terminology. Furthermore, the 512-sequence length specified by BERT was employed, and to adapt it to longer texts, the sliding-window approach described in [29] was applied.
* **Adaptation to the structure of Spanish questions.** The final layer of MedicoBERT was adapted using a dataset of 64,000 Spanish questions from the SQuAD dataset [30]. This process allowed the medical model to become familiar with the Spanish question structure, which is crucial for the QA task.
* **Medical question data training.** The model was fine-tuned using a dataset labeled for the medical QA task. This dataset, obtained from the 2022 BioAsq10 competition ([[https://huggingface.co/datasets/avacaondata/bioasq22-es](https://huggingface.co/datasets/avacaondata/bioasq22-es)]([https://huggingface.co/datasets/avacaondata/bioasq22-es](https://huggingface.co/datasets/avacaondata/bioasq22-es)), accessed on 4 July 2024), contains 39,680 instances, each with a context, a factoid question, and its answer, with the partitioning for training presented in Table 4. These texts differ from those used in the pre-training or adaptation to Spanish medical vocabulary and terminology to avoid bias in the data.
* **Hyperparameter fine calibration.** In the final stage of the process, hyperparameter optimization was conducted to enhance the model's performance in the Spanish medical question-answering task, as measured by the F1 metric.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Corpus** & **Number of Texts** & **Number of Words** & **Epochs** & **Perplexity** \\ \hline BioAsq+CoWeSe+ & 3,036,923 & 1,155,535,281 & 40 & 4.28 \\ CORD-19 & & & & \\ BioAsq+CoWeSe+ & 3,036,923 & 1,155,535,281 & 50 & 7.25 \\ CORD-19 & & & & \\ BioAsq+CoWeSe & 1,973,048 & 948,899,576 & 30 & 8.25 \\ BioAsq & 249,473 & 45,322,119 & 4 & 65.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MedicoBERT adaptative learning results.
###### Abstract  
Jurisdictions currently provide information on winter road conditions through qualitative descriptors like bare and fully snow-covered. Ideally, these descriptors are meant to warm drivers beforehand about hazardous roads. In practice, however, discerning between safe and unsafe roads is sometimes unclear due to intermediate RSC classes covering too wide a range of conditions. This study aims at solving this safety ambiguity issue by proposing a framework for predicting collision likelihood within a road segment. The proposed framework converts road surface images into friction coefficients, which are then converted into continuous measurements through an interpolator. To find the best-performing interpolator, we evaluated geostatistical, machine learning, and hybrid interpolators. It was found that ordinary kriging had the lowest estimation error and was the least sensitive to changes in distance between measurements. After developing an interpolator, collision likelihood models were developed for segment lengths ranging from 0.5 km to 20 km. We chose the 6.5 km model based on its accuracy and intuitiveness. This model had 76.9% accuracy and included friction and AADT as predictors. It was also estimated that if the proposed framework were implemented in an environment with connected vehicles and intelligent transportation systems, it would offer significant safety improvements.  
road friction; road weather interpolation; friction interpolation; collision modeling; road safety; connected vehicle; road surface condition; RSC
# Risk and Resilience Assessment of Lisbon's School Buildings Based on Seismic Scenarios
## 2 State of the Art on Seismic Risk Assessment and Mitigation for School Buildings
### Assessment of the Portuguese School Building Portfolio  
In Portugal, the first initiative that systematically addressed the rehabilitation and retrofitting of secondary education school buildings started in 2007. This initiative was managed by a public-private entity called _Parque Escolar_, EPE [19]. Its mission consisted of the upgrading and safeguarding of the Portuguese school building heritage by restoring its physical and functional effectiveness. Structural strengthening, namely in what concerns seismic retrofitting, was particularly evidenced in this program [19]. The program stages 1 and 2, launched in 2007 and 2008, intervened in 106 schools throughout Portugal. School buildings that were intervened as part of the _Parque Escolar_ initiative are thus associated with a risk significantly lower than the one associated with the remaining school buildings.  
Some Portuguese regions received more attention, such as the Algarve region in the south of Portugal. In fact, [PERSON] et al. [20] started to study the educational infrastructure in Algarve by developing a seismic risk assessment, in which the seismic response of buildings was considered following a vulnerability index based on EMS-98. More recently,Estevao et al. [21] led the PERSISTAH (Projects of earthquake resilient schools in Algarve (Portugal) and Huelva (Spain)) project, which aims to develop tools for diagnostic, evaluation, management, and rehabilitation of primary schools in both Algarve (south of Portugal) and Huelva (south of Spain) regions. The project created a ranking methodology for the vulnerability of primary schools, named \"school-score\".  
Apart from these projects, no national strategy exists in order to assess the seismic risk of educational infrastructure. Thus, it is of paramount importance to study, on a first stage, the functional and structural condition of Portuguese school buildings and, on a second stage, to define intervention plans to mitigate the identified risks. This paper summarizes the assessment developed as part of one of these research studies, which focused on the school buildings that are managed by Lisbon's City Council [22].
## 2 End-to-End QoS in WSNs
### Low Duty Cycle Operation  
In the low duty cycle operation, a node maintains periodic sleep schedule, referred to as an access cycle, that consists of an active and idle period. The duty cycle refers to the ratio of active time to total time. A node receives data during its active period. During the idle period, a node forwards data to its neighbors or saves energy by sleeping. An example of the sleep schedules in a multihop network is presented in Figure 3.  
The periodic sleeping necessitates synchronization between nodes, which is commonly realized by transmitting beacon frames at the beginning of an active period. For energy efficiency, nodes may form clusters that use a dedicated node referred to as cluster head to transmit beacons [8, 9]. A cluster head is also responsible for routing and forwarding data from its member nodes to other clusters.  
The cluster heads that are within interference range may not have overlapping active periods. Otherwise, the transmissions from different clusters might collide and cause packet loss. As a result, the cluster density is limited by the number of active periods within an access cycle. To increase this number, clusters may operate on different channels. This way, clusters do not interfere each other even if their active periods overlap.
# Cause Analysis of Pier Inclination of An Expressway Interchange Bridge: Case Study
## 5 Numerical Analysis
### Pier Deformation Considering Asymmetric Foundation Settlement
#### 5.3.2 Pier Inclination Caused by Filling Soil Load during Operation  
As shown in Figure 10, there is an asymmetric filling soil load at the bottom of Pier P30 during operation. The numerical results are shown in Figure 21 (note that the length of arrows indicates the magnitude of the displacement).  
During the operation of the bridge, the silt around Pier P30 was excavated at a depth of 2 m at the river side and dumped at the other side. When the filling depth difference is 4 m in the numerical model, the longitudinal inclination of Pier P30 is \(-(94.97-66.11)=-28.9\) mm, which is one of the main factors resulting in the inclination of Pier P30.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 4 Experiments
### Implementation Details  
The batchsize is configured to (8, 8) and (32, 8) for CASIA-B dataset and OUMVLP dataset separately. The input silhouettes are resized to the size of 64 \(\times\) 44 and aligned according to the method in [40]. Adam optimizer is applied for training with an initial learning rate of 0.0001 and the momentum 0.9 [41]. In the triplet loss \(L_{trip}\), the margin is set to 0.2, and the \(\lambda_{ce}\) in Equation (20) is set to 0.05 and 0.3 on CASIA-B dataset and OUMVLP dataset separately. In CASIA-B dataset, three-layer CNNs are used in MSFE and three consecutive R-convs are implemented in FLSFE to extract features, while in OU-MVLP dataset, five-layer CNNs are applied in MSFE and an additional R-conv is stacked in FLSFE, and the value of \(n\) in each R-conv is set to 1, 1, 3, 3. Furthermore, the CASIA-B dataset is trained 70 K iteration, while the OUMVLP dataset is trained 250 K iteration. The learning rate is adjusted to 1 \(\times\) 10\({}^{-5}\) after 160 K iterations to ensure the stable convergence. All the experiments are carried out by using the Pytorch framework [42] on NVIDIA GeForce RTX 3090 GPUs [39].
## 3 Challenges
Although extensive research has been done on the issues of the propagation delay [30; 31; 32], bandwidth utilization [33; 34; 35], and energy efficiency [36; 37; 38] (see more related works in [39; 40; 41; 42; 43]), few studies have focused on the more fundamental issue of link reliability. As we can imagine, low reliability would lead to frequent data retransmission; then, frequent retransmission will eventually result in longer delay, higher bandwidth consumption, and higher energy consumption. The above imagination reminds us that reliability is a fundamental basis for efficient data transmission and is of critical importance. Therefore, we are particularly interested in the issue of reliability. In the next section, we introduce how to estimate the reliabilities of links through the underwater channel models.
###### Abstract  
Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor majorations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve's local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.  
autofocus; non-mydriatic fundus photography; image sharpness; adaptive focus window; path-optimized search Article