esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on New Bolted T-Stub Connection with Inserted Plates under Axial and Cyclic Loads
## 4 Test Observations, Results and Analysis  
### Tension Test  
Nine specimens were tested under tensile loads. Two main failure modes appeared after loading that were highly aligned with the description in Eurocode 3 [28]. The middle part of the flange in the TC and WA groups evidently deformed and bulged. Cracks occurred on the flange-to-web connection and flange surface perpendicular to the bolt centerline, forming four plastic hinges in total. There was only a slight extrusion trace on bolts. On the other hand, the failure mode of specimens in group WB was mainly characterized by the failure of high-strength bolts. The inserted plates in all specimens were kept in place, nearly intact. The failure mode of TC-N-50-0 was not different from that of specimen  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{\(\mathbf{L_{p}}\)} & \multirow{2}{*}{\(\mathbf{t}\)} & \multirow{2}{*}{\(\mathbf{t_{0}}\)} & \multirow{2}{*}{\(\mathbf{t_{1}}\)} & \multirow{2}{*}{\(\mathbf{e_{1}}\)} & \multirow{2}{*}{\(\mathbf{e_{2}}\)} & \multirow{2}{*}{\(\mathbf{m}\)} & \multirow{2}{*  
TC-N-50-40, illuminating that inserting two plates does not have a prominent effect on the tensile properties of a T-stub connection, as expected. The final tensile deformations of three typical specimens in the different groups are shown in Figure 4.  
The measured relation curves between tensile force and deformation are presented in Figure 5. As illustrated in Figure 5a, the specimen TC-P-50-40-T with inserted plates had a slightly higher carrying capacity and stiffness than the traditional T-stub connection TC-N-50-0-T in the elastic and plastic stages. After enlarging the section of T-stub flange, both the stiffness and load bearing capacity of the connector improved significantly for TC-P-50-40-T and TC-P-60-40-T. The welded T-stub connections with inserted plates exhibited greater rigidity than those fabricated from steel section in the elastic phase, but the tensile strength and ductility were nearly the same (see specimens TC-P-50-40-T and WA-P-50-40-T in Figure 5a,b). In Figure 5c, it can be seen that in the WA and WB groups, the tensile properties of the specimens did not improve remarkably with growth of the widths of the inserted plates, but only a slight climb of tensile force was revealed. The existence of an inserted plate or change of its width did not have a substantial impact on the relative position and mutual restraint relationship between the T-stub flange and high-strength bolts in the tensile loading process, so the tensile behavior was not be affected markedly.  
Figure 4: Failure modes of three specimens from different groups under tensile loads: (**a**) TC-P-50-42-T, (**b**) WA-P-50-46-T and (**c**) WB-P-75-46-T.
## 3 EDFA's Space Qualification and Test
### Vibration Tests  
The sinusoidal vibration tests consisted of a functional verification before and after applying a sine sweep within the range of 5-140 Hz (vibration amplitude of 2.5 g in the range of 5-100 Hz and 1.25 g in 100-140 Hz, with 1 g being the unit of measure of acceleration, equals to 9.80665 m/s\({}^{2}\) in the International System of Units) with 3 repetitions for each orthogonal axis. The functional tests before and after random vibration were performed during 2 min for each test with 3 repetitions for each orthogonal axes within the range of 20-2000 Hz. As shown in Figure 7, the vibration tests were performed in two different vibration instruments, one for horizontal vibration (X-Y) and one for vertical vibration (Z). The output optical power was measured before and after each test with no significant variation that could be attributed to the vibration tests, in the order of 0.1 dB.  
Figure 6: Dummy EDFA units for the preliminary radiation tests.
# _Applied Sciences_
## 2 Related Work
### Contradiction Detection in Specific Domains  
There is also some specific domain research regarding contradiction detection. In medical domain, the authors of [18] detected contradiction by comparing subject-relation-object tuples of a text pair in medical research. This work detected 2236 contradictions automatically, but these contradictions were checked manually and only 56 were correct.  
A classification system based on Support Vector Machine (SVM), with some features (negation, antonyms, and similarity measures) that help to detect contradiction in medicaltexts was created in [19]. This system detected antonyms and negation contradiction but not numerical contradiction. These results improved the state-of-the-art in a medical dataset.  
Regarding the tourism domain, other research provides an analysis of the type of contradictions present in online hotel reviews. In addition, a model for the detection of numerical contradiction is proposed for the tourism industry [20].
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 6 Conclusions and Recommendations
### Electrical Safety Features  
The large voltage drop of a VTOL hybrid power system necessitates implementation of certain safety features. These features are designed for all-electric aircraft to protect the batteries, but they may cause the aircraft to lose all power during critical flight modes. Unlike hybrid gas-electric VTOL aircraft in an engine-out scenario, an all-electric, fixed-wing aircraft would not experience such large voltage drops, and can normally glide to a safe landing. Therefore, low voltage cutoff safety features present in an ESC or BMS may need to be disabled in future hybrid gas-electric VTOL aircraft.
## 4 Experiments and Results
### Evaluation of the Performance of the Training Algorithm  
Due to the practical limitations of accurately calculating the classification accuracy in a real-world environment, the evaluation of the learning algorithm's performance was conducted using validation data. The trained algorithm was tested (Figure 12a) using PET and PET-G samples moving at a speed of 2 m per second on the testbed.  
The performance evaluation focused on assessing the algorithm's accuracy in correctly classifying the plastic waste samples. The algorithm's predictions were compared against the ground truth labels assigned to the samples. The classification accuracy was determined by calculating the percentage of correctly classified samples out of the total number of samples.  
The experimental results revealed that the learning algorithm achieved a classification accuracy of 91.7 mAP (Figure 12b) for the PET and PET-G samples moving at a speed of 2 m per s. This high accuracy demonstrates the effectiveness and reliability of the developed algorithm in accurately identifying and classifying plastic waste in real-time.  
Figure 11: Proposed system field test results: (**a**) Classification after obtaining an image of the classification area into 64 areas according to the pneumatic line. (**b**) Pneumatic nozzle (red box) control as a result of inference.  
Figure 12: Proposed system field test results: (**a**) Inference results (Label of the detection and its detection probability). (**b**) Training results with mAP.
## 2 IoV Distributed Intelligence  
### Edge/Cloud Computing  
Cloud-based infrastructures with abundant resources to meet end-user requirements were one of the popular solutions in the early part of the last decade. However, over time, several issues occurred when considering cloud-based infrastructures to compute end-user data. Longer transmission distances and the corresponding communication costs, data security threats, and backhaul congestion were among the main issues that reduced the impact of cloud technology over time. Furthermore, with the new advanced technologies, such as 5G, new services with limited latency and high data rate requirements were enabled, further placing additional burdens on cloud facilities. However, with new technologies, the end devices also evolved to have powerful on-board computation capabilities, and with that, abundant computation power distributed over the network area was added. This gives birth to Fog/Edge computing technologies, bringing cloud computing facilities closer to end users [19]. Over the years, edge computing has achieved great success in terms of providing end users with high-quality services with limited latency [20].  
Although edge computing has solved some of the cloud computing problems, its size limitations place additional restrictions on the computation, communication, and storage resources of edge facilities [21]. In recent times, as we move toward 6G, it has been seen that edge computing facilities are overwhelmed, and new solutions are required to fulfill the demands of new services. With this, different distributed networking infrastructures, such as the edge-cloud continuum and the Integrated T/NTN infrastructure, are considered and expected to play an essential role in the near future IoV scenarios [7; 22]. These distributed networking infrastructures can have large sets of diverse networking nodes with on-boardcomputing/storage resources. Furthermore, with different communication technologies, these devices can communicate effectively with each other and with end users. Thus, a huge amount of distributed computing and communication power is available for the implementation of DL methods in such distributed networking infrastructures.
## 3 Results and Discussion
#### Limitations  
We can confirm that no data leakage occurred in this study. Our CBDS_Small dataset, comprised of 252 images, was used exclusively for testing and not included in the training  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Species** & **Chi-Square** & \(p\)**-Value** \\ \hline Hackberry & 10.99 & \textless{}0.05 \\ Northern red oak & 7.66 & 0.02 \\ Bitternut hickory & 4.83 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Chi-square Test: Binary Classification among different time/light conditions.  
of the EfficientNet-B3 model. Furthermore, both Centralbark and CBDS_Small, the training is split by trees and not by images.  
The moisture-induced color shift highlights a critical limitation in current deep learning models, which rely heavily on color and texture cues [3]. Addressing this challenge requires targeted augmentation of training datasets with wet bark images and potential preprocessing techniques, such as color normalization, to mitigate the impact of moisture on feature detection. These findings underscore the need for robust models capable of generalizing across environmental conditions encountered in real-world forestry applications.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 2 Related Work
For this case, given a map indicating the importance level of each area, the planning algorithm should determine strategic waypoints at which the UAV hovers and collects data while an efficient trajectory composed of all waypoints is generated using a solution to the Traveling Salesman Problem (TSP). The tasks of selecting a set of waypoints and optimal routing between these points are dealt with in familiar computational geometry problems. The art gallery problem [22] considers a polygonal gallery where a minimum number of guards are to be positioned such that at least one guard can see every point in the gallery; the watchman route problem [23] seeks the shortest route a watchman should take so that every point in a given space can be seen from at least one point along the route; the orienteering problem [24] aims to construct a path through a set of nodes that maximizes the total score and does not exceed a given budget; and probably the most famous one, the TSP [25]--all these problems have been proven to be NP-hard and are often solved by approximation algorithms. One heuristic method commonly used for solving the optimization problems mentioned above is local search [26]. In a typical local search algorithm, an initial solution is first constructed, for instance, by a greedy algorithm. Then, in an iterative manner, the initial solution is modified by local operations, with a slight improvement in each iteration. Ref. [27] presents a random mixed local search method for finding an optimal solution for the Open-Loop Traveling Salesman Problem (OLTSP). Starting with some initial trajectory, the algorithm improves this method (in terms of length) by mixing in local search operators (NodeSwap, 2 opt, LinkSwap). Most of the approaches mentioned above use offline solutions computed before the mission is actually executed. Therefore, computing time is of secondary importance and is usually not a measure of the algorithm's quality. In the case considered in this study, computation time is a significant factor. During the mission of autonomous landing, it is necessary to find a trajectory at decreasing altitudes--i.e., the GPP problem is to be solvedseveral times in a short period. Therefore, in addition to the requirement of finding a near-optimal solution, we aim to develop an algorithm that is also efficient in terms of computation time.
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 3 Cloud Computing
### Cloud Computing in Healthcare  
The medical sector is evolving constantly with the advancements in technology, and the demand for optimal patient care continues to grow. Cloud computing plays a significant role in transforming healthcare information technology (IT) operations [41]. It provides a dynamic and flexible approach for the management, storage, and access of critical medical information. Leveraging cloud computing technologies, healthcare organizations can redefine how they operate and unlock unprecedented opportunities for collaboration, innovation, and patient-centric care. Cloud computing enables secure access to patient data and medical records anywhere, anytime, promoting informed decision making and timely care [42]. It promotes seamless communication and collaboration between healthcare systems and applications, exceeding the abilities of traditional databases. This increases interoperability and improves the means of sharing information within the continuum of the linked healthcare delivery system. Vendors of cloud services adhere to established sector guidelines, like the Health Insurance Portability and Accountability Act (HIPAA) [43; 44], to protect sensitive patient data in cloud environments.  
Hence, cloud solutions are also cheaper than traditional infrastructures in healthcare organizations, since investing in costly on-premise hardware and the maintenance of their own infrastructure can be replaced with investing more in patients and advanced solutions [23; 45].  
Integrating cloud computing and health information exchange (HIE) into the healthcare industry represents a new and transformative approach for the sector. This partnership  
Figure 1: Cloud architecture components [37].  
revolutionizes the management and use of patient information. Bridging the gap between these two technologies makes it possible to address the challenges of interoperability, data accessibility, and collaborative care [46]. This collaborative approach promises a future in which healthcare delivery is more connected and efficient and prioritizes the patient's needs and preferences above all else in healthcare decisions [20].
## 4 Conclusions  
### Research Conclusions  
In this paper, the dimension reduction of the high-dimensional feature vector of the fluctuation signal is completed by the method of distance evaluation feature dimension reduction, and the operation reliability is analyzed by the Weibull proportional failure rate model. In this paper, a DBN is used to establish the reliability evaluation model of the ship marine oil system considering the multi-state equipment and maintenance strategy. Based on the equipment decay evolution rule considering the influence of en vironmental factors, the steady-state availability and average cost rate are specified to optimize the process. As can be seen from the experiments in this paper, maintenance during the voyage can effectively improve the reliability of the ship oil system and ensure its safe operation. The failure rate of the ship lubricating oil system without maintenance during the voyage is more sensitive to the failure rate of the equipment than that of the ship lubricating oil system with maintenance during the voyage. The equipment with higher reliability and a lower failure rate should be selected. According to the sensitivity analysis, the order of attention to equipment in the marine oil system should be: EOP > TOP > GSOC > ORV > SPRV > HFT > PUOC. Under the multi-objective model, the influence of different environmental factors on the preventive maintenance time interval was discussed quantitatively, including considering three cases of a good environment, gradually deteriorating environment, and harsh environment. The results show that providing a good working environment for equipment is beneficial to prolonging the life of equipment.  
The proposed DBN-based reliability assessment method achieves significant improvements over the traditional Fault Tree Analysis (FTA). Specifically, the reliability of the main lubrication oil system (GUB1) increases from 0.169 to 0.261, representing a 9.2% improvement. For the lubrication oil pump (PUB1), the reliability increases from 0.207 to 0.349, with an improvement of 14.2%. Furthermore, under scheduled maintenance conditions, the system reliability stabilizes at approximately 0.9873 after 0.4 \(\times\) 10\({}^{5}\) h, compared to only 0.24 without maintenance. These results demonstrate the effectiveness of the DBN approach in enhancing system reliability through accurate modeling and maintenance integration.
## 2 Materials and Methods
### Proteomics  
#### 2.6.1 Sample Preparation and LC-MS/MS Analysis  
Each sample was incubated in 100 \(\upmu\)L of an aqueous buffer containing guanidinium chloride (GuHCl) 2 M, tris(2-carboxyethyl)phosphine 10 mM, chloroacetamide 20 mM, and Tris 100 mM, for 3 h at 80 \({}^{\circ}\)C. A first digestion was performed by incubating the samples for 2 h at 37 \({}^{\circ}\)C with 0.2 \(\upmu\)rLysC (Promega, Sweden). The solution was diluted to a final concentration of 0.6 M by adding a Tris 25 mM in 10% acetonitrile (ACN) solution, and samples were digested with 0.8 \(\upmu\)r trypsin (Promega, Sweden) overnight at 37 \({}^{\circ}\)C. The supernatant was recovered after centrifugation, dried in a vacuum centrifuge, and resuspended in 100 \(\upmu\)L of 5% ACN/0.1% formic acid (FA) and the pH was adjusted to 2 by adding FA. Peptides were desalted on C18 spin columns (Pierce), dried in a vacuum centrifuge, and resuspended in 30 \(\upmu\)L of 5% ACN/0.1% FA prior to analysis.  
The extracted peptides were analyzed by LC-MS/MS using a DionexUltiMate 3000 Rapid Separation nano-LC coupled to a Q Exactive HF (QE) Quadrupole Orbitrap mass spectrometer (Thermo Fisher Scientific Inc., San Jose, CA, USA). 2 \(\upmu\)L of the peptide solution was loaded onto a trap column, 150 \(\upmu\)m \(\times\) 3 cm in-house packed with 3 \(\upmu\)m ReproSilPur(r) beads (New Objective, Inc., Woburn, MA, USA), followed by the analytical column (75 \(\upmu\)m \(\times\) 10.5 cm PicoChip column packed with 3 \(\upmu\)m ReproSil-Pur(r) beads). The flow rate was 300 nL min\({}^{-1}\), solvent A was 0.1% FA in water and solvent B was 0.1% FA in ACN. Peptides were separated on a 120 min analytical gradient from 5% ACN/0.1% FA to 40% ACN/0.1% FA. The mass spectrometer was operated in data-dependent mode, the source voltage was 2.40 kV and the capillary temperature was kept at 275 \({}^{\circ}\)C. MS1 scans were acquired from 375-2000 \(m/z\) at 60,000 resolving power and automatic gain control (AGC) set to 1 \(\times\) 10\({}^{6}\). The top fifteen most abundant precursor ions in each MS1 scan were selected for fragmentation in the ion trap. Precursors were selected with an isolation width of 1 \(m/z\) and fragmented by collision-induced dissociation at 30% normalized collision energy. Previously selected ions were dynamically excluded from re-selection for 60 s. The MS2 AGC was set to 3 \(\times\) 10\({}^{5}\).
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 3 Spectrum Efficiency Analysis
### Outage Probability and Achievable Rate of CUEs  
**Theorem 1**.: _The PDF of \(\gamma_{ck}\) can be accurately approximated as_  
\[f_{\gamma_{ck}}\left(x\right)=\frac{P_{\mathrm{c}}\beta_{ck}N\exp\left(\sigma^{2 }/b_{ck}\right)\left(P_{\mathrm{c}}\beta_{ck}N/x-\sigma^{2}\right)^{a_{ck}-1}}{ \left(b_{ck}\right)^{a_{ck}}\Gamma\left(a_{ck}\right)x^{2}\exp\left(P_{ \mathrm{c}}\beta_{ck}N/xb_{ck}\right)}, \tag{9}\]  
_where_  
\[a_{ck}=\frac{\left(\sum_{j\
eq k}^{K}P_{\mathrm{c}}\beta_{cj}+P_{d}\beta_{d} \right)^{2}}{\sum_{j\
eq k}^{K}P_{\mathrm{c}}^{2}\beta_{cj}^{2}+P_{d}^{2}\beta _{d}^{2}}\text{ and }b_{ck}=\frac{\sum_{j\
eq k}^{K}P_{\mathrm{c}}^{2}\beta_{cj}^{2}+P_{d}^{2} \beta_{d}^{2}}{\sum_{j\
eq k}^{K}P_{\mathrm{c}}\beta_{cj}+P_{d}\beta_{d}},\]  
_and \(\Gamma\left(s\right)=\int_{0}^{\infty}t^{s-1}e^{-t}\mathrm{d}t\) is the gamma function._  
Proof.: See Appendix A.  
Given a target SINR \(\tilde{\gamma}_{c}\), we can obtain the outage probability of the \(k\)-th CUE according to (A1) and (A2) as,  
\[\mathbb{P}_{ck}^{\mathrm{out}}=\Pr\{\gamma_{ck}<\tilde{\gamma}_{c}\}=\frac{ \Gamma\left(a_{ck},\omega_{ck}/b_{ck}\right)}{\Gamma(a_{ck})}, \tag{10}\]  
where \(\omega_{ck}=P_{\mathrm{c}}\beta_{ck}N/\tilde{\gamma}_{c}-\sigma^{2}\) and \(\Gamma\left(s,x\right)=\int_{x}^{\infty}t^{s-1}e^{-t}\mathrm{d}t\) is the upper incomplete gamma function.  
**Theorem 2**.: _The achievable rate of the \(k\)-th CUE can be approximated as_  
\[\mathcal{R}_{ck}\approx\frac{\mathcal{J}\left(a_{ck},b_{ck}/\xi_{ck}\right)- \mathcal{J}\left(a_{ck},b_{ck}/\sigma^{2}\right)}{\Gamma(a_{ck})\ln(2)}+\log_{ 2}\left(\frac{\xi_{ck}}{\sigma^{2}}\right), \tag{11}\]  
_where \(\mathcal{J}\left(m,x\right)=G_{3,2}^{1.3}\left(x\left|\begin{array}{c}1-m,1,1 \\ 1,0\end{array}\right.\right)\) and \(\xi_{ck}=P_{\mathrm{c}}\beta_{ck}N+\sigma^{2}\)._  
Proof.: See Appendix B.  
To make the analysis trackable, we also derive the simple lower bound using [PERSON]'s inequality, i.e.,  
\[\tilde{\mathcal{R}}_{ck}=\log_{2}\left(1+P_{\mathrm{c}}\beta_{ck}N/\theta_{ck} \right), \tag{12}\]  
where \(\theta_{ck}=P_{\mathrm{d}}\beta_{d}+\sum\limits_{j\
eq k}^{K}P_{\mathrm{c}} \beta_{cj}+\sigma^{2}\). Note that the achievable rates scales quickly with the number of transmit antennas, and can be made arbitrarily large when \(N\) grows without bound.