esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### In Silico Screening for Hit Compounds  
Non-RelA components of the _E. coli_ RelA cryo-EM (PDB: 5 IQR) model including RNA and ribosome were stripped away from the file, leaving only the RelA structure (Figure S4). The RelA structure was then optimized using the Schrodinger Maestro protein preparation tools, including the package, Prime, which allows Maestro to fill in missing side chains and determine optimal amino acid orientations. The RelA enzyme was then structurally minimized using the force field OPLS3e [(42)] (Figure S4B). The enzyme binding pocket was determined using homology (Figure S2) studies, as well as a general understanding of RelA's function, and a docking grid box was developed for protein ligand docking calculations.  
Schrodinger Maestro Molecular Modeling Glide [(61)] was utilized to determine hit compounds, which were then validated using the laboratory assays described above to probe their ability to inhibit RelA activity. The Schrodinger Glide-HTVS mode was first used to screen the entire University of California, San Francisco Zinc[12] Database of commercially available compounds. This database contains over 4 million compounds. The top 10% from the HTVS docking scan was then filtered into Glide-SP mode (standard precision). This output was then further refined and run in Glide-XP [(61)] mode (extra precision). These molecular docking studies resulted in two compounds showing a binding score that passed our threshold for binding affinity (Table 1) and were higher than those of the natural substrates ATP and GTP. These two compounds also fit both the [PERSON]'s rule of five [(62; 63; 52)] for orally administered drugs, and the quantitative estimate of drug likeness [(64; 65)].
## 3 Tools for R-Peak Validation
**Event processing interface comparator (epic):** The epic tool compares episodes of arrhythmias and ischemic events, such as ventricular or atrial fibrillation, flutters, and ischemic ST episodes, assigning weight on the basis of episode length or duration. The tool then usesweights in comparison to reference annotated markings to assess the overlap, which must be at least 50% [78]. **Annotation to RR interval (aa2 rr):** Another tool, ann2 rr, can also validate peak predictions [79]. It primarily extracts RR intervals by converting annotations into interval lists and comparing them with reference markings. However, other options are available for customization, such as specifying time intervals to analyze, filtering event type, or handling specific formats. The tool is useful in analyzing heart rate variability or detecting irregularities in heartbeat timing. These are some of the validation tools provided by WFDB based on the ECG testing guidelines by ANSI/AAMI, which can be modified and incorporated into detection methodologies to verify predicted R-peaks. Some authors have also proposed their methods to make the validation process more effective. An example of this is wave-by-wave (wxw), a QRS delineation percentage comparator proposed by [50]. It distributes the ECG into its components like P-wave, QRS-wave, and T-wave and then compares them with expert annotations using specific scoring rules. It requires precise determination of wave positions, including onset and offset, to improve diagnostic accuracy. Compared to the other tools, bxb is more often used for ECG R-peak validation because it was specifically designed to compare predicted and reference annotation markings beat-by-beat [80], as shown in Figure 2. However, due to scattered and unclear information regarding these tools within the PhysioNet database, and lack of comprehensive guidance in the available literature, a detailed guide is needed to integrate WFDB tools and implement additional validation pipelines (series of steps or processes). These pipelines include tool integration, simultaneous testing of all records, generation of validation statistics, error reports, extraction of FP and FN, and their accurate visualization in the reference signal [23].
## 2 Materials and Methods
### Analytical Experiments
#### 2.3.3 Water Sorption and Water Solubility  
To assess the swelling behavior of the prepared composites in oral fluid, which resulted in water sorption (W\({}_{\text{SP}}\)) and water solubility (W\({}_{\text{SL}}\)), experiments were conducted in distilled water to simulate the oral environment and in accordance with ISO 4049 [21]. The specimens were cast in fabricated stainless-steel disk-shaped models that were 15 mm in diameter and with 2 mm thickness (\(n\) = 5). They were photo-irradiated from both sides in four overlapped areas for 40 s each. After disk preparations, samples were put in a dry silica-containing desiccator at 37 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C and weighed with a Mettler Toledo analytical balance with an accuracy of 0.1 mg every 24 h. Prior to each recording, samples were cooled in a new desiccator at room temperature for around 1 h. Drying-weighing processes were repeated until a constant weight was obtained (dry specimen, \(m_{1}\)). Specimens were then transferred into a 30 mL closed vial containing 15 mL distilled water and maintained at 37 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C. Every 24 h, samples were removed from the water, carefully swabbed, waved in air for 20 s, weighed, and returned to the water; this process was repeated until no change in the disk weight was observed (swelled specimen, \(m_{2}\)). Finally, disks were removed from the water and dried again as above until a constant weight was obtained (\(m_{3}\)). The W\({}_{\text{SP}}\) and W\({}_{\text{SL}}\) can be calculated as in Equations (2) and (3), respectively.  
\[W_{SP}(\%)=\left(\frac{m_{2}-m_{1}}{m_{1}}\right)\times 100 \tag{2}\]  
\[W_{SL}(\%)=\left(\frac{m_{1}-m_{3}}{m_{1}}\right)\times 100 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Composite**} & \multicolumn{4}{c}{**Resin Composition (wt\%)**} & \multirow{2}{*}{_Replacement (\%)_ = \(\left(\frac{C}{B+C}\right)\times\) **100**} \\ \cline{2-5}  & **UDMA** & **TEGDMA** & **BisGMA (B)** & & **dCl-BisGMA (C)** & \\ \hline UTBC0 & 20 & 60 & 20 & 0 & 0 \\ \hline UTBC25 & 20 & 60 & 15 & 5 & 25 \\ \hline UTBC50 & 20 & 60 & 10 & 10 & 50 \\ \hline UTBC100 & 20 & 60 & 0 & 20 & 100 \\ \hline \hline \end{tabular} Each composite contained fixed amounts of the filler 35 wt\% (silanated-Aerosi 200, SA200) and the initiation system (0.2 camphorquinone (CQ) and 0.8 wt\% 2-(N,N-dimethylamino) methyl methacrylate (DMAEMA) with respect to the total monomers). Abbreviations: BisGMA, bisphenol A-glycidyl methacrylate; dCl-BisGMA, dichlor-BisGMA; TEGDMA, triethylene glycol dimethacrylate; UDMA, urethane dimethacrylate; UTBC0-UTBC100, composites in which BisGMA amount was replaced by 0.0–100% of dCl-BisGMA.  
\end{table}
Table 1: Composition of the investigated experimental resin composites.
## 4 Experiment and Results  
This section gives an overview of the results and the conducted experiments. There are three types of experiments that have been carried out to achieve the research objectives. In the first experiment, we extracted each user's interests, in addition to clustering similar topics into clusters. In the second experiment, we found similarities between users based on a combination of factors. Moreover, we found similarities between topics based on semantic similarity. The third experiment generated a heterogeneous graph based on users and topics. Moreover, we evaluated results according to ground truth and quantitative analysis. It is a suitable method for evaluating data mining research in general, extracting interests, and studying relationships between users.  
Using raw data for users from social networking sites, where this research's priority was to deal with users individually and derive their interests through their writing, is an appropriate method to study their patterns, feelings, interests, and opinions. Moreover, we obtained the five personality traits and demographic data to conduct the rest of the proposed model phases. The description of the dataset, the experiment settings and the results of the three experiments are discussed in the next sections.
# BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
## 3 Data Processing and Validation
### Experimental Results and Verification
It should be noted that we have not discussed the occurrence of multiple maneuvers on the same day or consecutive days, as this is a rare event for BeiDou satellites and cannot be reliably validated. We will further investigate it in the future. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**PRN** & **DOY** & **Start Time (GPST)** & **End Time (GPST)** & **Duration (Minute)** \\ \hline C24 & 325 & 07:31:00 & 07:37:00 & 6 \\ \hline \multirow{3}{*}{C38} & 029 & 19:19:00 & 19:26:00 & 7 \\  & 191 & 22:44:30 & 22:53:00 & 8.5 \\ \hline \multirow{3}{*}{C39} & 017 & 00:59:30 & 01:07:30 & 8 \\  & 181 & 07:59:30 & 08:07:00 & 7.5 \\  & 356 & 00:29:30 & 00:32:00 & 3.5 \\ \hline \multirow{3}{*}{C40} & 026 & 01:29:00 & 01:38:30 & 9.5 \\  & 188 & 16:29:00 & 16:38:00 & 9 \\ \hline \multirow{3}{*}{C59} & 008 & 15:30:00 & - & - \\  & 009 & - & - & - \\  & 040 & 01:30:00 & 01:33:00 & 3 \\  & 070 & 01:30:00 & 01:35:00 & 5 \\  & 098 & 08:30:00 & 08:32:30 & 2.5 \\  & 127 & 09:00:00 & 09:05:00 & 5 \\  & 154 & 08:30:00 & 08:32:30 & 2.5 \\  & 183 & 00:30:00 & 00:35:00 & 5 \\  & 215 & 09:00:00 & 09:05:00 & 5 \\  & 248 & 01:00:00 & 01:04:00 & 4 \\  & 281 & 09:00:00 & 09:05:00 & 5 \\  & 311 & 09:00:00 & 09:07:00 & 7 \\  & 341 & 03:25:00 & - & - \\  & 342 & - & - & - \\ \hline \multirow{9}{*}{C60} & 028 & 00:30:00 & 00:33:30 & 3.5 \\  & 058 & 09:00:00 & 09:05:00 & 5 \\  & 085 & 09:00:00 & 09:12:00 & 12 \\  & 105 & 09:00:00 & 09:02:00 & 2 \\  & 129 & 09:00:00 & 09:02:00 & 2 \\  & 147 & 01:30:00 & 01:32:00 & 2 \\  & 168 & 09:00:00 & 09:02:30 & 2.5 \\  & 198 & 15:30:00 & 15:32:00 & 2 \\  & 225 & 03:00:00 & 03:05:00 & 5 \\  & 245 & 03:00:00 & 03:05:00 & 5 \\  & 271 & 01:00:00 & 01:03:00 & 3 \\  & 291 & 01:00:00 & 01:04:00 & 4 \\  & 329 & 00:25:30 & 00:32:00 & 6.5 \\  & 361 & 00:30:00 & 00:35:00 & 5 \\ \hline \end{tabular}
\end{table} 
Table 2: The detected orbit maneuvers of BeiDou satellites in 2022.
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
## 4 WSN Deployment
### Safety Management and Occupational Risk Control  
One of the main problems of working in tunnels and service galleries, which is aggravated by their length and narrowness, is the safety management and the occupational risks control. The health of the working environment of the maintenance staff must always be guaranteed. This includes monitoring temperature and humidity levels but, also, monitoring CO and CO\({}_{2}\) levels; the presence of flammable or toxic gases and compounds and the presence of animals (rats, snakes, etc.), among others. In addition, mobility within the tunnel, the absence of obstacles or elements that hinder movement must be guaranteed. It should be borne in mind that employees, on many occasions, must move tools to carry out their maintenance, repair and installation tasks.  
Current regulations related to labor risk prevention state that work must be done at least in pairs and desirable in work teams to minimize risks and prevent accidents. In addition, workers must notify the coordination center of the task to be performed; the starting time; the estimated duration; the working conditions (lighting, power supply cut, etc.); the possible risks and any other information that may help to ensure safety at work during the job performance. Hence, personnel monitoring implies that the staff will carry their own sensors (located in the inhalation zone and at head height) and must be able to interconnect to the monitoring network. Thus, the use of a wireless network is particularly advantageous in the latter case, as it offers a greater ease of work (hands-free) and greater mobility for workers. However, the presence of human beings within the tunnels can affect the radio propagation, even more considering their narrowness. Therefore, an adequate RF characterization and a complete risk control assessment must be performed in order to warrant compliance with occupational safety management within the utility tunnels.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Acoustic Emission Sensors in Port Crane Maintenance
#### 4.2.3 Performance Characteristics of Acoustic Emission Sensors  
The sensitivity of the AE sensors is a critical parameter that determines their ability to detect weak acoustic signals. High-sensitivity sensors can detect low-amplitude acoustic  
Figure 4: Acoustic emission signal pattern in crack propagation.  
emissions, which are essential for identifying the early signs of damage. However, a high sensitivity may also make the sensors more susceptible to noise interference. Therefore, a balance between sensitivity and noise immunity is necessary.  
The frequency response of AE sensors refers to their ability to respond to different frequencies of acoustic emissions. Different materials and damage mechanisms generate acoustic emissions in different frequency ranges. The selection of sensors with appropriate frequency response characteristics is crucial for accurate detection and analysis.  
The measurement range of the AE sensors refers to the amplitude span of the acoustic emissions that they can detect. A wide measurement range enables detection of both low- and high-amplitude acoustic signals, making these sensors versatile for multiple applications. However, a broader range may sometimes compromise resolution at lower amplitudes, necessitating trade-offs based on the use case.  
Stability and reliability are critical performance factors, especially in long-term, unattended monitoring applications. AE sensors must maintain consistent performance despite varying environmental conditions, such as humidity, temperature, or vibration. These attributes are typically achieved through the use of high-grade materials and precision manufacturing processes.  
As noted in the study by [PERSON] and [PERSON] [114], compared with classic AE instruments, the IoT AE equipment system has the characteristics of long-term stable automatic operation, low cost, small size, and the ability to realize online, remote, and unattended AE monitoring and detection. It also features software that automatically analyzes and rates the results according to standards, eliminating the need for technicians to rate them [114].
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 1 Introduction  
The Internet of things (IoT) and wireless sensor network (WSNs) have seen a rapid and massive transformation in recent years, when all the computer science domains, operating independently or collaboratively, have seen unprecedented change, and their technologies and deployments can be observed all around us. A wireless sensor network (WSN) is a network that connects and collaborates. Its sensors are placed in different environments to collect the best data [1]. WSNs are made up of remote nodes that have a lot of promise for various businesses and are built on ad-libbed system architectures [2]. According to a report, a new network uprising has just recently begun, with approximately 50 billion items and smartphones expected to be connected to the Internet by 2020 [3]. The ever-increasing number of internet-related things is transforming the world we live in. Smart cities, network security management, e-health, traffic control, smart shopping, pollution control, radiation level detection, online education, cloud computing, intruder detection, smart parking, vehicle auto-fault diagnostics, and many other implementations of the IoT and WSN are only a few examples of this transformation [4]. The demographics of the WSN application spectrum palette are shown in Figure 1[5].  
One of WSN's applications can be found in the clinical and medical sciences. Consequently, medical frameworks are being developed that collect health data from the human body using wearable sensors that can also be embedded within an individual's body. These sensors communicate the gathered information, which can be further monitored and processed to obtain various insights [6; 7; 8]. Figure 2 shows 2020's top IoT applications and the enterprise share of IoT projects worldwide [9]. The IoT serves as a backbone for big data and business analytics. A considerable amount of data can be retrieved, transferred, and further processed; this data can be utilized to tackle business issues, customer support, and service needs [10]. The utilization of AI strategies can create an effective decision support system. A.I. creates continuous prediction models that can be used. When the nodes attain the data point, they pass it to the mode [11].  
WSN makes use of a sensor network architecture. The OSI architectural model is the most common architecture for wireless sensor networks. WSN's structure comprises five primary and three cross layers. It is common practice in sensor n/w to use all five levels. In addition to hospitals and schools, roadways and buildings may also benefit from this  
Figure 1: Application area spectrum of wireless sensor networks (WSNs) [5].  
Figure 2: Top areas of IoT application in 2020 [12].  
design, which can be used for several purposes, including disaster and crisis management and security. The two types of WSN architecture are layered network architecture (LNA) and clustered architecture (CA).
## 2 Data and Method
### High-Frequency Radar  
To further evaluate the performance of retrieved sea surface current velocity, the current velocity obtained by high-frequency radar (HF radar) is chosen as a reference dataset. The observational accuracy of HF radar has been reported to be in the order of 10 cm/s for the speed and 10 degrees for the direction. In this study, we used the HF radar products distributed by the National Oceanic and Atmospheric Administration (NOAA). The velocity field is available on an hourly basis with a spatial spacing of 6 km. The HF radar measures the average velocity within the upper 10 cm below the sea surface, which can be considered approximate to the SAR-observed radial current velocity at the very surface. The collocation criterion between the HF radar products and SAR observations is a duration of 3 h in time for qualitative comparison.
## 1 Introduction
In the 1\(\times\) setting, overall accuracy improved by +0.0172 (Val) and +0.0334 (Test), and for a normal-difficulty minority class, F1 scores increased significantly by +0.4000/+0.4332 (Val/Test). However, in a very difficult minority class, the F1 score increased by +0.4086 in validation but only +0.0349 in test, indicating limited generalization. An easy minority class also showed signs of overfitting, with an F1 gain of +0.0600 in validation but only +0.0123 in test. By combining the two methods, we simultaneously achieve balanced training opportunities through CBS and spatial patch diversity through RPE, resulting in the following complementary effects. CBS compensates for the insufficient training exposure of minority classes under RPE, especially in high-difficulty cases, by ensuring sufficient repetition for learning. Meanwhile, RPE introduces spatial diversity at the patch level, alleviating  
Figure 2: Limitations of FPE due to repeated patch duplication across epochs. the structural limitation of repeated patch sampling inherent in CBS and helping prevent overfitting. In practice, the proposed combined method achieved consistent performance improvements across minority classes of varying difficulty. Notably, for a very difficult minority class where both individual methods struggled, the combined strategy yielded substantial F1 gains of +0.6171 (Val) and +0.4044 (Test). Furthermore, overall accuracy improved significantly, with gains of +0.0443 (Val) and +0.0876 (Test), demonstrating enhanced general classification performance. The key contributions of this study are summarized as follows:  
* Class-Balanced Random Patch Training We integrate data augmentation via random patch extraction (RPE) and over/undersampling through class-balanced sampling (CBS) in a unified training pipeline for tiling-based classification. RPE and CBS independently address patch duplication and class imbalance, respectively, but their combination demonstrates complementary effects on performance. This combined strategy significantly improves the F1 score for minority crop classes, achieving gains of up to +0.6171 (Validation) and +0.4044 (Test) compared to the baseline. Unlike many conventional methods that rely on synthetic data to address class imbalance, our method leverages only real data to ensure training diversity and achieves stable performance without complex hyper-parameter tuning. * Robustness to Context Loss and Aggregation Effects under Upscaling We perform 2\(\times\) upscaling of parcels to reduce the context per patch while increasing the number of patches per parcel. As a result, our method demonstrates robustness to context loss, maintaining stable patch-level performance, even when context is reduced to one-fourth. Furthermore, we quantitatively confirm that the increased number of patches enhances the aggregation effect of the softmax product, leading to improved overall classification accuracy.