esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Stress Field Inversion Method and Verification of Its Accuracy  
### Measurement of Surface Strain on Drift Walls  
Accurately measuring the strain in the rock is key to the stress inversion method proposed herein; therefore, the method used for measuring surrounding rock strain is of paramount importance. Traditional measurement methods such as stress relief by drilling or hydraulic fracturing have certain limitations when it comes to measuring surface strain in the surrounding rock. These methods can cause secondary damage to the rock surface, leading to lower measurement precision and success rates. To tackle these challenges, this study employs the borehole stress relief method for measuring the surface strain on surrounding rock. The borehole stress relief method involves drilling holes to partially (or completely) separate the measured object, while simultaneously measuring the strain or displacement near the relieved or adjacent area. The stress at the measured point is then determined based on the constitutive relationship governing the material. In this study, the borehole stress relief method is illustrated in Figure 1, where \(h\) represents the borehole depth (stress relief depth), and \(t\) denotes the thickness of the hollow borehole wall. In this method, strain gauges are placed on the rock surface along the direction of strain clusters. By gradually disconnecting the rock core from the original stress field, the stress is eventually relieved. Variations in strain gauges with drilling depth (\(h\)) were recorded during the stress relief process.  
It is worth noting that the strain measured using the borehole stress relief method represents only elastic strain. If the surrounding rock in this study exhibits plasticity or damage, the plastic strain generated by plastic deformation or damage cannot be measured using this method. Figure 2 illustrates the typical strain release curve as a function of borehole depth; however, previous studies found that the maximum released strain value shown in the figure does not necessarily correspond to the existing elastic strain value of the measured object [21]. Therefore, during borehole strain release experiments, it is crucial to ensure that the borehole depth exceeds 1.33 times the borehole diameter to meet the accuracy requirements of the measurements.  
Using strain as the inversion target value has certain advantages compared to stress. When the local stress is high, the rock at the measurement point may be in a plastic or damaged state, which can cause changes in the elastic modulus of rock. If the stress is ascertained using elastic criteria and then subject to inversion of the stress field, significant errors occur. On the other hand, strain directly measures the deformation of the rock and is unaffected by changes in the elastic modulus of the rock. Therefore, using the measured elastic strain as the inversion parameter, the errors caused by plastic deformation of the rock can be reduced, thereby improving the accuracy and reliability of the stress field inversion.
###### Abstract  
Accurate detection of dangerous driving behaviors is crucial for improving the safety of intelligent transportation systems. However, existing methods often struggle with limited feature extraction capabilities and insufficient attention to multiscale and contextual information. To overcome these limitations, we propose YOLO-AFR (YOLO with Adaptive Feature Refinement) for dangerous driving behavior detection. YOLO-AFR builds upon the YOLOv12 architecture and introduces three key innovations: (1) the redesign of the original A2C2f module by introducing a Feature-Refinement Feedback Network (FRFN), resulting in a new A2C2f-FRFN structure that adaptively refines multiscale features, (2) the integration of self-calibrated convolution (SC-Conv) modules in the backbone to enhance multiscale contextual modeling, and (3) the employment of a SEAM-based detection head to improve global contextual awareness and prediction accuracy. These three modules combine to form a Calibration-Refinement Loop, which progressively reduces redundancy and enhances discriminative features layer by layer. We evaluate YOLO-AFR on two public driver behavior datasets, YawDD-E and SfdDD. Experimental results show that YOLO-AFR significantly outperforms the baseline YOLOv12 model, achieving improvements of 1.3% and 1.8% in [EMAIL_ADDRESS], and 2.6% and 12.3% in [EMAIL_ADDRESS]:0.95 on the YawDD-E and SfdDD datasets, respectively, demonstrating its superior performance in complex driving scenarios while maintaining high inference speed.  
Keywords:dangerous driving behavior detection; YOLO; feature refinement; self-calibrated convolution; separated and enhancement attention; deep learning
## 3 Methods
### SCADA Data Analysis  
One of the most meaningful indicators about the general status of the machine is the delta pressure (\(\Delta P\)) between the filter of lubrication and cooling oil circuit. This parameter is constantly monitored by the SCADA system and was included in the analysis as it can be useful to ascertain the presence of a possible fault, coming from an abnormal erosion of some mechanical parts, i.e., pitting phenomena, and causing the accumulation of metallic debris on the oil filter whose presence can be quantified by the \(\Delta P\) given in Equation (1):  
\[\Delta P=P_{bef}-P_{aft}. \tag{1}\]  
where \(P_{bef}\) is the pressure of the oil before the filter and \(P_{aft}\) is the pressure after the filter. In Table 1 the average values of \(\Delta P\) are summarized for each turbine for the period before the gearbox maintenance on T02 (from January to August 2020) and the period after the maintenance (from February to May 2021).  
The values of \(\Delta P\) were averaged on a daily basis to analyze the trend for the faulty turbine T02 (T.Tar in the following) and compare it with the trend of the chosen reference healthy unit T04 (T.Ref). The SCADA system can also gather signals from temperature sensors that are dislocated in multiple points of the gearbox and, more in general, all over the main components of the turbines. In combination with \(\Delta P\), the temperature sensors are expected to provide more detailed information about the localization of the faulty element.  
The main possible drawback of the previously described approach concerns the possibility that from the raw temperature signal it might be non-trivial to find a clear evidence of the damage, as temperatures tend to increase noticeably only when damages are fully developed. For this reason, a processing of the temperature data is proposed in the present paper through the use of support vector regression(SVR), which is described in the following subsection.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Before \(\Delta P_{1}\) (bar)** & **After \(\Delta P_{2}\) (bar)** \\ \hline T01 & 0.22 & 0.13 \\ T02 & 0.71 & 0.07 \\ T03 & 0.10 & 0.14 \\ T04 & 0.28 & 0.18 \\ T05 & 0.22 & 0.06 \\ T06 & 0.21 & 0.16 \\ T07 & 0.19 & 0.10 \\ T08 & 0.36 & 0.07 \\ T09 & 0.05 & 0.03 \\ T10 & 0.33 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average values of \(\Delta P\) in the period before the gearbox maintenance on T02 (\(\Delta P_{1}\)) and after (\(\Delta P_{2}\)). Note that in general most of the turbines undergo a drop of pressure as a consequence of ordinary maintenance.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 4 Trials in the Baltic Sea Corridor (Tallinn-Helsinki)
### Overview of Trial Architecture  
The architecture of the ferry trials is displayed in Figure 12. The installed infrastructure consists of a number of onshore 5G base stations in the port of Muuga and onboard 5G systems. This could allow evaluations in the port areas and on the ferry route in-between to validate the use cases related to multimodality, goods tracking and providing uninterrupted connectivity from Tallinn to Helsinki.  
Out of the 5G network split options discussed in Section 3.1, the third one has been selected for the trials, i.e., where a self-confined 5G network is deployed onboard. This is out of practical considerations, as a stable and performant ship-to-shore connection is part of the research and evaluation tasks within the 5G-ROUTES project. Having the core control plane or the whole core onshore can lead to the failure of the entire network, making trials focusing on user plane performance difficult. From an economic perspective, it makes sense to minimize the number of components on the ship while still keeping the option of deploying backend applications to MEC hosts. This corresponds to the second split option, where a centralized control plane core is deployed onshore with a decentralized user space core (UPF) onboard for MEC access. By deploying the whole core onboard for trials, the project will also be able to finally answer the question if, and under which circumstances, it would be possible to use an onshore, centralized, control plane core.  
The ABC router will be realized through a 5G router that will also allow for connecting a VSAT modem over ethernet. It is envisioned to also evaluate ABC routers capable of using two SIM cards in parallel (dual-SIM-dual-active). For the satellite communication, backhauling option A4 (see Section 3.1) will be used, which is fully sufficient for the trials and does not require the deployment of an N3 IWF.  
With this setup, the performance of all single-hop (called Link A and Link C in Section 3) and multi-hop (Link B) connectivity options could be evaluated. Seamless switching between links will be carried out by an ABC router, and aggregating multiple links is not within the main scope of the trials. The rationale behind this is that, among the different parts of the journey, different links are preferred, as discussed in Sections 1-3, and it is, if at all, potentially beneficial to aggregate multiple links only during very short time period.  
For the time being, it is not clear which option(s) for cross-border/-MNO service continuity will be deployed in the onshore networks. Initial trials will therefore use modem-based solutions not requiring network-side support.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 3 Base Approach and Related Theorems
The option for \(f(z)\) given in (8) is due to the fact that the integrand function should be simple for concerns of computational cost, and that it must possess a non-null residue, which would otherwise lead to inconclusive results. For example, all functions of the type \(f(z)=\frac{1}{(z-a)^{a}}\), with \(n>1\), or the type \(f(z)=\frac{z}{(z-a)^{a}}\), with \(n>2\), have a residue of \(0\) on point \(a\), making them useless for this purpose. On the other hand, there are many other possibilities, all with residue \(1\) at point \(a\), such as the following: \(f(z)=\frac{z}{(z-a)^{2}}\), \(f(z)=\frac{1}{1-e^{(z-a)}}\) or \(f(z)=\frac{1}{\sin{(z-a)}}\). However, in all these (usable) alternatives, there would always be the need to calculate a complex logarithm for the anti-derivative. Therefore, the simplest and most obvious case found was precisely the function in expression (8), but, indeed, the principle is independent of the function, which is, however, useful only if it has poles and non-null residues.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 3 Deep Learning
### Attention Mechanism  
Like computer vision, the attention mechanism is inspired by human biological structure and mimics human cognitive attention. It is a classic and practical technique in the field of deep learning. When processing large amounts of information, it focuses on key parts, assigns different weights based on the importance of the information, and determines which parts of the input data are more worthy of attention, thereby improving the model's efficiency in utilizing key information [29]. The attention mechanism was first introduced into neural networks in [67], followed by the development of classic attention mechanisms such as squeeze-and-encourage attention [68] and multi-head attention [69] (Figure 3).  
Figure 3: Squeeze-and-excitation block [68].  
In the implementation of smart agriculture and precision agriculture, the attention mechanism is one of the key technologies. It helps agricultural models analyze and make decisions more efficiently by focusing on key data. To better estimate peach blossom density, the attention module was introduced into the feature extraction and multi-scale feature fusion stages in [70] to enhance the network's ability to focus on key details of peach blossoms and suppress interference from irrelevant backgrounds. In [71], to precisely identify and distinguish similar leaf diseases, squeezed incentive attention was used to enhance the weights of important diseases, effectively focusing on disease-affected areas and achieving a high-precision disease diagnosis model. Additionally, in [72], pixel-level spatial self-attention (P-FSA) and block-level channel self-attention (B-FSA) are employed. By capturing subtle local features and distinguishing different global features, efficient feature extraction is achieved in fine-grained crop disease classification.
## 2 Experimental Setup and Technical Specifications  
### Experiment Setup  
For the experiment, we studied the workings of various camera setups paired with lenses and decided upon a particular pair. Now, all the researchers may not have the exact same set of gear. For the same reason, we have shared the process through which we selected a particular camera body and lens, and any other researcher can refer to the study and similarly obtain their own apparatus and perform the experiment.  
Multiple images were produced using this experiment, with the total exposure time ranging from 40 to 90 min (500-700 frames). The images had close to zero noise, which was possible only due to stacking. This is in contrast to the last experiment performed in 2009 by [PERSON], in which a single exposure was taken that lasted 30 min. However, a 30 min exposure would produce too much thermal noise in a CMOS camera at room temperature. Therefore, we concluded that instead of taking single exposures, we can take multiple frames of the same subject and stack them using the process mentioned in this paper. In theory, this will allow us to image any low light-emitting source just by adjusting the number of frames taken (to obtain the desired signal-to-noise ratio). The apparatus, making of a Barn Door Tracker, the process of stacking, and the ultimate production of clear images of extremely dim objects as a result are shared in the paper.  
This work aims to design a setup that allows for the efficient capture of low light-emitting sources by understanding how the technology for digital imaging works, how it has been used in past research, and how it can be optimized as per our requirements with the latest digital cameras. Multiple images were produced using this experiment, with the total exposure time ranging from 40 to 90 min (500-700 frames). The images had close to zero noise, which was possible only due to stacking. A specific set of camera bodies paired with the appropriate lens were used to obtain prototype values to mimic the results of the actual experiment as accurately as possible. The details of this equipment and the process of selecting the particular specification of the model are discussed. To obtain even more detailed results of particular celestial objects, a star tracker was created to negate the earth's rotation and obtain sharper images in longer exposures.
###### Abstract  
Graphene is a single atomic plane of graphite, and it exhibits unique electronic, thermal, and mechanical properties. Exfoliated graphene oxide (GO) contains various hydrophilic functional groups, such as hydroxyl, epoxide, and carboxyl groups, that can modify the hydrophobic characteristics of a membrane surface. Though reduced graphene oxide (rGO) has fewer functional groups than GO, its associated \(sp^{2}\) structures and physical properties can be recovered. A considerable amount of research has focused on the use of GO to obtain a pristine graphene material via reduction processes. In this study, polysulfone (PSf) and polyvinylidene fluoride (PVDF) membranes that were blended with GO and rGO, respectively, were fabricated by using the immersion phase inversion method and an n-methylpyrrolidone (NMP) solvent. Results showed that the graphene nanomaterials, GO and rGO, can change the pore morphology (size and structure) of both PSf and PVDF membranes. The optimum content of both was then investigated, and the highest flux enhancement was observed with the 0.10 wt% GO-blended PSf membrane. The presence of functional groups in GO within prepared PSf and PVDF membranes alters the membrane characteristics to hydrophilic. An antifouling test and rejection efficiency evaluation also showed that the 0.10 wt% membrane provided the best performance.  
graphene oxide; reduced graphene oxide; membrane; polysulfone; polyvinylidene fluoride; water treatment +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods  
### Materials  
Chloroauric acid (HAuCl\({}_{4}\), 99%) was used as a metal precursor and obtained from Sigma Aldrich (Darmstadt, Germany). Nutrient agar media comprised peptone, 5; beef extract, 3; NaCl, 5; and Agar, 15 g L\({}^{-1}\). Yeast extract peptone dextrose (YEPD) agar was prepared with peptone, 20; yeast extract, 10; dextrose, 20; and agar, 15 g L\({}^{-1}\). Czapek Dox agar contained sucrose, 30; NaNO\({}_{3}\), 2; K\({}_{2}\)HPO\({}_{4}\), 1; MgSO\({}_{4}\), 0.5; KCl, 0.5; FeSO\({}_{4}\), 0.01; and agar, 15 g L\({}^{-1}\). All media components were purchased from Sigma Aldrich (Cairo, Egypt). The strains of pathogenic organisms were obtained from Microbial Physiological Lab., Faculty of Science, Al-Azhar University. Antimicrobial activity was achieved using Muller-Hinton agar media (Read-prepared, Sigma Aldrich, Merk, Germany). Moreover, cancer and normal cell lines used in the current study were purchased from the Holding Company for Biological Products and Vaccines (VACSERA), Cairo, Egypt.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 3 Results
### Mutual Calibration of Phantom-Tracking and Projection Modules  
The mutual calibration of the PhTM and PrM was assessed by estimating the marker reprojection error which is a common method for systems consisting of image detectors and projectors. Four markers from a calibration board were selected and measured at seven distances from the working table at a range of 55.5-112.5 mm (Figure 14, Table 2), which corresponds to the phantom surface position. The average reprojection error within the calibrated measurement volume was 0.01 mm (maximum 0.17 mm). The average size of a projected pixel was 0.20 mm, and the average size of the pixels captured by the PhTM camera was 0.25 mm. The results are satisfactory when compared to commercially-available tracking systems which achieve accuracy of 0.61 \(\pm\) 0.55 cm (Oculus Rift)  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **(a)** & & & **(b)** & & & **(c)** & \\ \hline \(\varphi_{\text{ref}}\) & \(\varphi_{\text{meas}}\) & \(\Delta|\varphi|\) & \(\theta_{\text{ref}}\)  
and 0.58 \(\pm\) 0.45 cm (HTC Vive) [33]. However, it must be noted that MARVIS uses a much smaller measurement volume.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 5 Experimental Study
### Impact of Factors
#### 5.4.1 Number of Nodes  
Figure 5 shows the impact of the number of nodes on the message transmissions. When there are more nodes in the network, more messages are transmitted with the network and more data are uploaded to the RSU for all the schemes. Yet, for the PROPHET scheme, the increase of the messages is drastic because nodes would broadcast messages to their neighboring nodes. For the ESSMD scheme, it adopts a controlled broadcasting strategy, so the increase of the number of messages is smaller. For the TPEG scheme, it adopts a threshold-based strategy to suppress the data upload, which incurs fewer transmissions. The amount of messages transmitted is less than 16.2% of those of PROPHET and ESSMD. Additionally, the increase of TPEG with the number of nodes is much smaller, as shown by the flat line. Figure 5 shows that NAIVE and TPEG scale well with the number of nodes, yet TPEG has a far larger event-detection rate.  
Figure 4: Impact of the number of nodes on the recall rate.  
Figure 5: Impact of the number of nodes on the number of message transmissions.  
Figure 6 shows the impact of the number of nodes on the time needed for detecting events. The time for event detection goes down as the number of nodes increases. The more vehicular nodes moving on the roads, the more data are sensed and uploaded with less delay to the RSUs for the event detection. For example, in the proposed TPEG scheme, the average event detection time is about 37 min when there are five nodes, and about 27 min when there are 100 nodes in the network. When there are fewer than 60 nodes, the detection time is about 5-10 min longer than those of PROPHET and ESSMD. Yet, when there are more nodes on the network, e.g., 100, the detection time of TPEG is almost the same as that of PRPHET.
# Foams in Wastewater Treatment Plants: From Causes to Control Methods
## 5 Foam Control Methods
### Chemical Methods  
The chemical control of the foams takes place through the dosage of antifoaming agents, to which a series of advantages (simplicity, ease of use, improving flocculation with the use of coagulants, removing of filamentous microorganisms that are embedded in the flakes) and disadvantages (reduced mass transfer rate, inhibition both nitrification and organic matter removal, floc break-up due to the high chlorine doses, toxicity and production of additional chemical sludge) are associated [56, 98, 103]. Chlorination is one of the most popular applications for the rapid and effective control of activated sludge foaming [98], but [PERSON] et al. [103] have also shown the efficiency of some coagulants (such as ferric chloride, ferrous chloride, polyaluminium chloride, hydrated aluminum sulphate and cationic polymer) for the control of foaming in bench-scale batch experiments. The addition of polyaluminium chloride (e.g., PAX-14) is an effective method of controlling _Microthrix parvicella_ in WWTPs and [PERSON] et al. [99] stated that it seems to be ineffective against other filamentous bacteria. [PERSON] et al. [103], in their work, have instead shown that both _Microthrix parvicella_ and _Gordonia amarae_ filamentous organisms were embedded inside the floc material, making access to particulate and colloidal substrates more difficult due to increased diffusional resistance. These chemical treatments can also be very expensive and, more importantly, they are only a temporary solution since the foaming will resume when the addition of the chemicals is stopped [104]. Another principal disadvantage of chlorination is the production of undesirable disinfection by-products, such as the significant formation of trihalomethanes (THMs) in the effluent [100, 105, 106].  
Although it is indisputable that the chemical methods are those mainly used, it is also interesting to apply biological methods for the control of foams, that is, the use of specific bacteriophages that act only on filamentous bacteria: even if their impact on the sludge structure active is still largely unknown, the characterization of the genome of various filamentous bacteriophages is made available by [PERSON] et al. [107, 108, 109] studies.