esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### X-Ray Diffraction Analysis (XRD)  
The crystallinity of the original OTP biomass, as well as the optimal post-treatment solids, were determined by employing X-ray powder diffraction using a Bruker D8 Advance instrument (Bruker, Billerica, MA, USA) equipped with two independent high-resolution X-ray sources with a ceramic body, one with a copper anode and the other with a cobaltanode. A high-precision q/q geometry goniometer that allowed working with reflection and transmission with a radius of 240 mm was used. The crystallinity index (\(CI\)) was calculated by the Segal method as in Equation (4):  
\[CI\ =\ \frac{I_{002}\ -\ I_{AM}}{I_{002}} \tag{4}\]  
where \(I_{002}\) is the intensity of the (002) reflection and \(I_{AM}\) represents the intensity of the amorphous phase (2\(\theta\) = 18.5\({}^{\circ}\)).  
On the other hand, the crystallite size (\(cs\)) was calculated by the Scherrer Equation (5), where \(k\) is a constant (0.9) that represents the correction factor, and \(\lambda\), \(\beta\), and \(\theta\) are the radiation wavelength, the full width at half maximum, and the diffraction angle, respectively.  
\[cs\ =\ \frac{k\ \lambda}{\beta\ cos\ \theta} \tag{5}\]
## 3 Experiment 2
### Method  
A statistical power analysis was performed to estimate the necessary sample size (G*Power 3.1.9.7, Germany). With a power of 0.80 and an alpha of 0.05, the projected sample size needed for medium effects of \(w=0.40\) including four groups (goalkeepers, defenders, midfielders, strikers) was at least \(N=69\). Due to a high anticipated drop out because of the video's reputation, a significantly greater sample size was recruited. Eligibility criteria for including a participant to the final sample were ignorance of the general phenomenon of sustained inattentional blindness and similar experiments. One hundred male subjects aged 18 to 38 years (\(M_{\text{age}}=24.2\) years, \(SD=4.4\) years) participated under the same ethical and health conditions as in Experiment 1. Data from twelve subjects was excluded because they did not notice the unexpected object in the full-attention trial. Consequently, only the data of 89 participants (\(M_{\text{age}}=23.6\) years, \(SD=3.8\) years) was included in the analysis. All of them played in the 7 th, 8 th, or 9 th division of the German football league. Overall, 10 subjects reported their usual playing position as the goalkeeper, 26 played in the defense (defenders), 30 in the midfield (midfielders), and 23 in the offense (strikers). The design and procedure of Experiment 2 was identical to that of Experiment 1 (Figure 1): Participants watched again the 30-seconds video clip by [PERSON] and [PERSON] [3] and was asked to count the number of passes of the white team. After watching the video, they were again required to provide answers to the same four questions as in Experiment: (I) While you were doing the counting, did you notice anything unusual on the video? (II) Did you notice anything other than the six players? (III) Did you see anyone else (besides the six players) appear on the video? (IV) Did you see a gorilla walk across the screen? If any of the questions were answered with \"yes\" the experimenter asked for more details. If at any point the participant mentioned the gorilla, the remaining questions were skipped.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes
### Differential Flatness-Based Control Optimization Trajectories
#### 2.1.4 Trajectory Optimization Solution without Cost Function
\begin{pmatrix}\mathbf{F}_{0}&\mathbf{0}&\mathbf{0}&\cdots&\mathbf{0}\\ \mathbf{E}_{1}&\mathbf{F}_{1}&\mathbf{0}&\cdots&\mathbf{0}\\ \mathbf{0}&\mathbf{E}_{2}&\mathbf{F}_{2}&\cdots&\mathbf{0}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\cdots&\mathbf{F}_{M-1}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\cdots&\mathbf{E}_{M}\end{pmatrix} ,\mathbf{b}= \begin{pmatrix} \mathbf{D}_{0}\\ \mathbf{D}_{1}\\ \mathbf{0}_{\overline{d}_{1}\times m}\\ \vdots\\ \mathbf{D}_{M-1}\\ \mathbf{0}_{\overline{d}_{M-1}\times m}\\ \mathbf{D}_{M}\end{pmatrix}  \tag{25}\]  
The uniqueness of the optimality condition determines the nonsingularity of the matrix \(\mathbf{M}\) with respect to the time vector \(\mathbf{T}\) whose elements are all positive, so that the unique optimal solution of the coefficient matrix \(\mathbf{c}\) can be determined by the system of linear Equation (24). Thus, by means of the optimality condition and the system of Equation (24), and given the boundary conditions \(\mathbf{D}_{0},\mathbf{D}_{M}\) and the intermediate conditions \(\mathbf{D}_{i}\), it is possible to obtain the optimal polynomial trajectories with the optimal cost of control for unconstrained conditions without the aid of the cost function, and the computational complexity of the method is linear.
## 3 Results
### Coupling and Coordination Degrees Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**City** & **Coupling Degree** & **Coupling Stage** & **Degree of** & \\  & & & & **Coordination Phase** \\ \hline Shenyang & 0.8267 & Barely coupled & 0.8162 & Quality coordination \\ Dalian & 0.8349 & Barely coupled & 0.8228 & Quality coordination \\ Anshan & 0.8305 & Barely coupled & 0.7897 & Primary coordination \\ Fushun & 0.8521 & Inter-mediate coupling & 0.8071 & Good coordination \\ Benxi & 0.8527 & Inter-mediate coupling & 0.8214 & Quality coordination \\ Dandong & 0.8245 & Barely coupled & 0.7953 & Intermediate coordination \\ Jinzhou & 0.8553 & Inter-mediate coupling & 0.7904 & Primary coordination \\ Yingkou & 0.8665 & Good coupling & 0.8040 & Good coordination \\ Fuxin & 0.8448 & Primary coupling & 0.7612 & Reluctantly coordinate \\ Liaoyang & 0.8309 & Barely coupled & 0.7740 & Reluctantly coordinate \\ Panjin & 0.8426 & Primary coupling & 0.8025 & Good coordination \\ Tieling & 0.8673 & Good coupling & 0.7986 & Intermediate coordination \\ Chaoyang & 0.8435 & Primary coupling & 0.7730 & Reluctantly coordinate \\ Huludao & 0.8907 & Good coupling & 0.7849 & Primary coordination \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coupling coordination degree of LCCC system in Liaoning Province.
## 6 Discussion
### Resource Usage Metrics
#### 6.4.3 Memory Footprint  
Memory limitations further complicate onboard deployment in UAVs, especially when other subsystems (e.g., navigation and control, sensors, communications) are competing for system resources. Deep neural networks can be memory-intensive, but techniques such as pruning, weight quantization, and knowledge distillation can reduce parameter counts without severely diminishing predictive performance [59]. Validating these compression strategies on actual UAV hardware would better illuminate how effectively the proposed framework can be tailored to operate within stringent memory budgets.
## 2 Materials and Methods
### Data Analysis  
Center of pressure (CoP) and target time series were processed and analyzed in MATLAB (R2014b). The original antero-posterior component of the CoP time series was down-sampled to 50 Hz in order to match the screen update rate of the target signal display prior to low pass filtering using a 4 th order Butterworth low-pass filter, with a cutoff frequency at 5 Hz.
## 3 Materials and Methods  
### Overview of the 5 GT System  
The 5 GT System enables real-time and remote monitoring of the cargo ship container status and events during deep-sea journeys. It is constituted by an NB-IoT framework on top of a hybrid terrestrial-satellite network, providing information coming from the cargo-ship scenario (maritime domain) and port terminals (terrestrial domain) to a OneM2M IoT cloud platform, making them available to the user application, i.e., a dashboard which provides the status of the container to the customer (e.g., cargo shipping companies, port authorities, freight forwarders, etc.).  
The 5 GT System incorporates certain network functions, particularly those associated with satellite networks, which are deployed in the cloud as virtualized services. This approach enhances cost efficiency in network deployments. The use of cloud virtualization SDN technologies enables smooth integration of satellite components within the next-generation 5G network infrastructure. Additionally, the satellite component of the 5 GT System features an onboard transmitter that utilizes a protocol incorporating a packet optimization module to improve data transmission efficiency.  
Figure 3: NB-IoT backhaul over satellite.  
An overview of the 5 GT System is shown in Figure 4, a block diagram showing the contribution of each partner is in Figure 5 and the role of each component is reported as follows.  
Container tracking device (CTD)--Through specific sensors and a GNSS receiver, it detects data regarding the container status and position. Two types of radio interfaces are available covering \"long-range\" and \"short-range\" communication: NB-IoT and BLE Mesh. Thus, it sends the data either via a cellular network or a mesh network; the second option works if the CTD cannot connect to the cellular network. In any case, data arrive at the cell base station either directly or via passes between mesh nodes to the CTD in cellular visibility. The operation of CTDs is the same in all scenarios (maritime and terrestrial).  
Maritime smart edge node--The Smart cell is an NB-IoT cellular network installed onto the vessel onboard. The Smart mIoT gateway provides secure and reliable multi-backhauling connectivity for ship-shore NB-IoT data.  
5G-specialized Satellite Platform--This is constituted by the 5G-specialized Satellite Terminal (5 ST), a transceiver guaranteeing connectivity in the open sea via a Ku-band satellite link. On the ground side, the 5G-specialized Satellite Gateway (SSG) performs demodulation and Layer-2 functionalities.  
IoT monitoring platform--It comprises the OneM2M platform and an application framework, namely the \"Maritime mIoT Service Framework\".
## 4 Results and Discussions  
### Displacements Investigations  
Results from RCC beam specimens tested for two-point bending have been evaluated. The image analysis performed on the DSLR images and UAV images of a single beam in the Ncorr V1.2 software is yielded with the below outputs. The maximum load has been recorded as 112 kN at which the failure has occurred. After the failure load, the displacement increases rapidly and the load decreases. Figure 13a depicts displacement at failure load in the longitudinal direction (U-Displacement) is 5.3640 mm, which indicates compression in the deformed image. The median value was found to be \(-\)0.0599 mm. The following Figure 13b depicts displacement at failure load in the lateral direction (V-Displacement) is \(-\)3.563 mm which is negative indicates tension. The median value has found to be \(-\)6.4953 mm. The results of the given beam are of a single deformed image, and each image yields different displacements according to the varying load values.  
The following Figure 14a depicts displacement at failure load in the longitudinal direction (U-Displacement) is 5.8320, which indicates compression in the deformed image. The median value was found to be \(-\)0.0630 mm. The following Figure 14b depicts displacement at failure load in the lateral direction (V-Displacement) is \(-\)4.113 mm which is negative indicates tension. The median value has found to be \(-\)6.992 mm.  
Figure 14: UAV image displacements in the (**a**) longitudinal direction and (**b**) lateral direction.  
Figure 13: DSLR image Displacements in the (**a**) longitudinal direction and (**b**) lateral direction.
## 2 Non-Symmetric Cross-Section
### Bending  
Bending moments should be referred to equivalent moments \(M_{x}\)' and \(M_{y}\)' that are applied to the section's principal centroid axes \(u\) and \(v\)[12; 13; 14]. These moments appear with the centroid's reference co-ordinates \(x\) and \(y\) to give the axial bending stress as follows:  
\[\sigma_{z}=M_{x}{{}^{\prime}}y/I_{x}+M_{y}{{}^{\prime}}/I_{y}\] (1a) \[M_{x}{{}^{\prime}}=[M_{x}-M_{y}\,(I_{xy}/I_{y})]\!\!\!\!\!\!\!\!\!\!\!\!\!  
are free to displace axially (warp) with the distortion of that plane arising from torsion, i.e., plane cross-sections do not remain plane. With one end encastre (fixed), a full constraint applies to the fixing where no warping occurs. Elsewhere along the length of a cantilever, the constraint to free warping extends to a lesser degree. Consequently, axial stress is induced in all but the free end beam section, i.e., for 0 \(\#\)\(z<L\). This stress is proportional to \(w_{o}\) but is non-linear (hyperbolic) in \(z\). Here, the net axial stress arising from constrained warping is added to Equation (1a) [1]:  
\[\sigma_{z}=\mu Ew_{o}[\sinh\left[\mu(L-z)\right]\
i\cosh\left(\mu L\right)]\; \sigma_{z}+M_{x}\dot{y}/I_{x}+M_{y}\dot{x}/I_{y}\] (3a) where \[w_{o}\] is the unconstrained warping displacement, \[\mu=\surd(GJ/E\Gamma_{1})\] in which \[\Gamma_{1}=\int_{0}^{s}(2A_{E})\;t\;\mathrm{ds}\] is the primary warping constant. This gives \[w_{o}=-(2A_{E})T\
i(GJ)=-(\omega-\varpi)T\
i(GJ)\] (3b) where \[A_{E}=(\omega-\varpi)/2\] is the'swept area' of the section between E and each perimeter point (see Figure 2). Equation (3a,b) enable the net axial stress to be evaluated separately from the net shear stress, as will follow. Geometrically, it will be seen that axial stress distributions within a thin-walled, open-section cantilever beam, arising from each source, may be overlaid to provide a net axial stress distribution for the cross-section at any given length position, \[z\].
## 6 Simulation
### BER Estimation of the Proposed Model 2  
BER simulations are implemented to estimate the performance of model 2 within the proposed DSCA-MC-CR technique by using a Gaussian channel and practical fading channel, i.e., the Rayleigh channel. Figure 14 shows the BER of the PU and the SU obtained by using QPSK-OFDM modulation at \(\gamma=3\%\). Figure 14a,b show the theoretical and simulation results. Moreover, when using the Gaussian channel, the PU system within \(System_{3}\) achieves a BER of \(10^{-3}\) at \(-3\) dB SNR, while the PU achieved a BER of \(10^{-3}\) at 9 dB for \(\gamma=5\%\) in [33]. Furthermore, the SU system within \(System_{3}\) achieves a BER of \(10^{-3}\) at \(-0.5\) dB SNR, while the SU achieved a BER of \(10^{-3}\) at 12 dB for \(\gamma=5\%\) in [33]. In addition, Figure 15 shows the results when using the fading channel, where the PU system within \(System_{3}\) achieves a BER of \(10^{-3}\) at 2 dB SNR, and the PU achieved a BER of \(10^{-3}\) at 25 dB for \(\gamma=5\%\) in [33]. Also, the SU system within \(System_{3}\) achieves a BER of \(10^{-3}\) at 5 dB SNR, while the SU achieved a BER of \(10^{-3}\) at 28 dB for \(\gamma=5\%\) in [33].  
Figure 14: PU and SU BERs of model 2 using Gaussian channel with \(\gamma=3\%\). **(a)** PU BER of model 2. **(b)** SU BER of model 2.  
Figure 13: PU and SU capacities using different techniques versus SNR. **(a)** PU capacity versus SNR. **(b)** SU capacity versus SNR.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 3 Results and Discussion
### Denoising Characterization  
Inevitably, the A-scan data collected with an ultrasonic phased array testing instrument contains noise from various sources, which impacted the analysis of A-scan signals and affected the accuracy of defect signal recognition classification. Therefore, it was crucial to perform noise reduction processing after acquiring the defect A-scan data.
# Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project +
## 3 Results and Discussion
### Surveyed Habitats
#### 3.3.2. Mussel Farming  
Worldwide production of consumer fish products is 45 percent derived from aquaculture farms [54]. This requires all infrastructure and equipment to function properly, ensuring compliance with health and hygiene standards, ensuring the integrity of seafood farms can be a delicate challenge. A UUV such as Blucy can be used in an innovative way providing aquaculture farms with durable, easy to use and affordable underwater inspection and inspection systems for daily operation and maintenance. In a field mission Blucy was used, in a completely non-invasive way, in a mussel farm (Figure 15). Specifically, the daily maintenance of mussel farms requires a constant use of boats and crew in order to meticulously inspect all rows/poles of the farm. Thanks to the instrumentation on Blucy, it is possible to acquire data on the health and growth of the mussels such as consequent reduction in the daily use of boats. This approach would lead to a drastic reduction in the costs required for the boat and its crew, as well as significantly reducing  
Figure 14. Dense 3D point cloud of seabed processed from BottomCAM high-resolution imagery with SFM techniques.  
Figure 13. Some marine species identified in images acquired by PilotCAM: (**a**) _Schizophrenia errata_. (**b**) _Axixella_ sp., _Holothuria_ sp., eggs masses of _Polychatea_. (**c**) _Posidonia oceanica_.  
the environmental impact of maintenance operations. Using the data collected by MBES and optical analyses, it is possible to estimate the health status of socks. This approach is already used in precision agriculture, where by using 3D reconstructions it is possible to define the state of growth by volumetric analysis. Moreover, during the survey, Blucy has the possibility to record biochemical properties of the water column. All the data gathered by the UUV, in addition to meteomarine information, are the ideal input for the design of a predictive model of the mussel growth status, leading to a further optimization of subsequent missions and a potential reduction in anthropogenic actions.
## 3 Multi-Armed Bandit Algorithms Using Logarithmic Rates  
In this section, we introduce two MAB algorithms that adopt the logarithmic values of the transmission rates when they compute the expected throughput of each MCS level. One algorithm is based on KL-UCB and the other on Thompson sampling. In contrast to the two MAB algorithms presented in [34], in this paper, we modify the two algorithms to accommodate frame aggregation schemes available in high-throughput wireless network standards such as IEEE 802.11n [35] and IEEE 802.11 ax. If the frame aggregation is enabled, (multiple) aggregated packets can be sent as one aggregated MAC service data unit (A-MSDU) or aggregated MAC protocol data unit (A-MPDU) frame [15].  
When A-MSDU is used, all the packets transmitted as one A-MSDU frame are considered to be successfully delivered if an ACK frame is received on time. Otherwise, all the aggregated packets in the A-MSDU frame are assumed to be lost. If A-MPDU is used, a block acknowledgment (BAck) frame is used to monitor the success and failure of each packet transmitted in one A-MPDU frame. When a BAck frame is received by the sender of the A-MPDU frame, the sender can identify successfully delivered packets and lost packets among the transmitted packets using a bitmap structure. Otherwise, all the transmitted packets in the A-MPDU frame are assumed to be lost, and the sender sends a control message called block acknowledgment request (BAckR) frame to the receiver.  
Thus, to embrace the frame aggregation, we use the number of successfully delivered packets by one aggregated packet transmission as the amount of the reward if a BAck or ACK frame is received on time. When the packet aggregation is disabled or not available as in the IEEE 802.11g standard, the proposed MAB algorithms in this paper reduce to the previous ones because the reward of each transmission is one if the delivery is successful (i.e., an ACK frame is received on time).
# Surgical Tool Detection in Open Surgery Videos
## 5 Results
The _Retractor_ category also has a relatively large number of instances, but the AP of the best baseline model is only 11.3. Finally, the surgical tool category, which has different usages is difficult to detect. For instance, the _Forceps_ category, which is used to grasp and hold objects, is mainly utilized to grasp the tissues or organs but sometimes surgical drap. In the former usage, _Forceps_ is handled by the surgeon's hand, and in the latter usage, _Forceps_ is laid down around the surgical fields. Thus, depending on the usage, the visual feature drastically changes. This affects the surgical tool detection results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Faster-RCNN**} & \multicolumn{3}{c}{**RetinaNet**} \\ \cline{2-7}  & **ResNet-50** & **ResNet-101** & **ResNet-X101** & **ResNet-50** & **ResNet-101** \\ \hline BiClamp & - & - & - & - & - \\ Bipolar Forceps & 62.9 & 59.5 & 59.4 & 55.4 & 57.8 \\ Chisel & - & - & - & - & - \\ Cup & 0.1 & 0.2 & 0.0 & 0.6 & 0.6 \\ Drill & - & - & - & - & - \\ Electric Cautery & 86.3 & 86.2 & 89.2 & 88.1 & 88.6 \\ Forceps & 14.7 & 20.0 & 22.8 & 17.2 & 14.1 \\ Gauze & 19.4 & 18.3 & 18.9 & 23.8 & 19.1 \\ Hammer & - & - & - & - & - \\ Hook & 32.8 & 34.9 & 42.2 & 39.6 & 37.9 \\ Kidney Dish & - & - & - & - & - \\ Malleallebe Retractor & - & - & - & - & - \\ Mouth Gag & 71.4 & 72.8 & 71.5 & 73.2 & 73.3 \\ Nasogastric Tube & - & - & - & - & - \\ Needle Holders & 74.4 & 77.6 & 78.6 & 76.3 & 78.9 \\ Pen & - & - & - & - & - \\ Petri Dish & - & - & - & - & - \\ Raspatory & 61.5 & 66.9 & 61.7 & 57.2 & 56.7 \\ Retractor & 1.7 & 14.3 & 3.9 & 1.0 & 5.4 \\ Ruler & - & - & - & - & - \\ Scalpel & 78.8 & 82.0 & 87.7 & 75.9 & 72.8 \\ Scissors & 56.33 & 59.5 & 58.7 & 57.4 & 57.6 \\ Screwdriver & - & - & - & - & - \\ Skewer & 83.8 & 83.6 & 83.5 & 84.9 & 61.5 \\ Spoon & - & - & - & - & - \\ Suction Cannula & 67.8 & 70.8 & 67.3 & 63.3 & 65.3 \\ Suction Tube & - & - & - & - & - \\ Stuture, Suture Needle & 4.5 & 7.1 & 7.0 & 4.5 & 6.5 \\ Syringe & 48.3 & 49.2 & 51.6 & 39.1 & 52.4 \\ Trephine & - & - & - & - & - \\ Tweezers & 66.6 & 68.3 & 63.9 & 65.8 & 65.0 \\ \hline Average & 48.9 & 51.3 & 51.3 & 48.1 & 48.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AP of baseline models per category for validation set.
## 4 Fundamentals of the Developed Concept
### Concept of Distributed Condition Monitoring System Architecture
* Resilience to degradation processes caused by changes in the external or internal environment [55; 56]. Ensuring reliability, sustainability, and transportation safety.
## 4 Future Research Trends for ML-Based IRS-Assisted Wireless Communication
### IRS for IoT Network  
IoT devices are increasing exponentially due to their diverse applications and high demands [110]. The IoT and 6G technology are accelerating the industrial revolution 4.0. IRS-based wireless communication can increase the connectivity of IoT devices in both indoor and outdoor environments. As the IoT devices are placed in a scattered way, the wireless system performance is unpredictable, and, thus, ML-based algorithms can be adapted to mimic the environment behavior. Most IoT devices are sensor nodes that generate different data related to some physical values. Further research can shed light on the impact of IRS for IoT connectivity using ML methods.