esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 3 Results
### Models Classification Performance Comparison  
During the model selection phase, we conducted comparative experiments between the vision large language model InternVL-2.5 MPO and the deep learning model ResNet-34. The InternVL-2.5 MPO was evaluated using classification prompts without fine-tuning, while ResNet-34 was fine-tuned by optimizing the final fully connected layer with the following hyperparameters: 25 epochs and a batch size of 8, using a training dataset of 12,177 samples.  
The results demonstrate that (with inference design and preliminary classification prompts) the InternVL-2.5 MPO model outperformed the fine-tuned ResNet-34 in terms of accuracy across multiple evaluation metrics. The overall accuracy of the InternVL-2.5 MPO (90.35%) surpasses that of the fine-tuned ResNet-34 (81.48%) by near 10%. In every sub-type except the traffic jam, the accuracy of the InternVL-2.5 MPO model exceeds that of ResNet-34. Notably, the most significant difference is observed in the recognition of flooding, where the accuracy is 31.06% higher than that of ResNet-34. Conversely, the smallest gap is found in the recognition of traffic jam, which is only 0.26% lower than ResNet-34. This highlights the superior generalization capability and robustness of the InternVL-2.5 MPO model for the given task (Table 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Types**} & \multicolumn{2}{c}{**Number of Data**} & \multicolumn{2}{c}{**Model Accuracy (\%)**} \\ \cline{3-5}  & & **Points** & **InternVL-2.5 MPO** & **ResNet34** \\  & & **(Testing Dataset)** & **(Preliminary)** & **(First Approach:** \\  & & & **Classification Prompt)** & **Linear Probing)** \\ \hline
1 & Bad road & 214 & 90.19 & 79.44 \\
2 & Fallen tree & 296 & 96.62 & 81.08 \\
3 & Fire & 305 & 96.72 & 91.80 \\
4 & Flooding & 310 & 93.48 & 62.42 \\
5 & Garbage & 364 & 84.62 & 67.03 \\
6 & Normal & 675 & 92.00 & 89.93 \\
7 & Traffic accident & 433 & 92.84 & 92.15 \\
8 & Traffic jam & 437 & 79.18 & 79.44 \\  & Overall accuracy (\%) & 3034 & 90.35 & 81.48 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Preliminary testing of InternVL-2.5 MPO with ResNet34.  
Figure 5: Examples of annotation.
## 4 Simulation Experiment
### Experimental Results
#### 4.2.1 INS + LEO2 + Unbiased Altimeter and No Altimeter Scenario Experiment under the Same Orbit
When the switching time is 90 s, comparing with the INS + LEO2 switching algorithm, the INS + LEO2 switching + unbiased altimeter navigation and positioning algorithm in longitude, latitude and altitude's errors improve by 50.50%, 37.55%, and 99.70%, respectively, and the corresponding standard deviation performance values improve by 59.93%, 54.77% and 94.25%, respectively. It can be seen that due to the addition of the altimeter, the new algorithm has the most obvious improvement in height error, this result is consistent with the analysis result in Section 2.1.2, and we will not analyze it in detail here.
## 2 Materials and Methods
### Procedures
#### 2.3.1 VBT Protocols  
Two different relative loads (60% vs. 80% 1 RM) and two different magnitudes of velocity loss during the set (10% vs. 30%) were used. Specifically, the configuration of the four VBT protocols was as follows: (i) 60% 1 RM with a velocity loss in the set of 10% (VBT\({}_{60-10}\)), (ii) 60% 1 RM with a velocity loss in the set of 30% (VBT\({}_{60-30}\)), (iii) 80% 1 RM with a velocity loss in the set of 10% (VBT\({}_{80-10}\)), and (iv) 80% 1 RM with a velocity loss in the set of 30% (VBT\({}_{80-30}\)). The relative load of each testing session was determined from the individualized load-velocity relationship using the specific warm-up sets and a minimal velocity threshold of 0.33 m\(\cdot\)s\({}^{-1}\)[21]. Sets were terminated when the subjectswere unable to complete two consecutive repetitions above the velocity loss limit or with the full range of motion. The fastest repetition from the first set was used to define the target velocity loss limit (e.g., if the fastest velocity is 0.75 m\(\cdot\)s\({}^{-1}\), the target velocity used to finish a set would be 0.68 m\(\cdot\)s\({}^{-1}\) for the 10% velocity loss). The same exercise (back squat), number of sets (three), and inter-set rest (three minutes) were used in all VBT protocols. A validated linear velocity transducer (T-Force system; Ergotech, Murcia, Spain) was used to automatically calculate the mean velocity and provide auditory mean velocity feedback after each repetition [22]. The VBT performance indicators were: (i) the number of repetitions completed in the set, (ii) the fastest velocity of the set, and (iii) the average velocity of the set.  
The back-squat technique involved subjects standing with the knees and hips fully extended, feet approximately shoulder-width apart, and the bar  
Figure 1: Overview of the experimental design. MAS, maximal aerobic speed; CMJ, countermovement jump; IRM, one-repetition maximum; RTT, running track test; V\({}_{\text{peak}}\), peak velocity; VBT\({}_{60-10}\), velocity-based training (VBT) with 60% of 1 RM and a velocity loss (VL) in the set of 10%; VBT\({}_{60-30}\), VBT with 60% of 1 RM and a VL in the set of 30%; VBT\({}_{80-10}\), VBT with 80% of 1 RM and a VL in the set of 10%; VBT\({}_{80-30}\), VBT with 80% of 1 RM and a VL in the set of 30%.  
of the shoulders and upper back. From this position, they were required to descend in a continuous motion until their buttocks made contact with a wooden box and, immediately after, return to the initial position as fast as possible. The height of the wooden box was individually set at 90\({}^{\circ}\) of knee flexion with a manual goniometer (Goniometro Rulong, Fisaude, Spain).
## 3 System Model
### Energy Consumption Model  
During the operation of the nodes, energy consumption primarily comes from data reception and transmission. Therefore, based on the radio energy consumption model [28; 29], the energy loss for a node transmitting a \(k-bit\) data packet is given by  
\[E_{T}(k,d)=\begin{cases}kE_{elec}+k\varepsilon_{fs}\times d^{2},&d<d_{0}\\ kE_{elec}+k\varepsilon_{amp}\times d^{4},&d\geq d_{0}\end{cases} \tag{1}\]  
Here, \(k\) represents the number of bytes in the transmitted data packet, and \(d\) denotes the data transmission distance. The distance threshold \(d_{0}\) is given as  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{amp}}} \tag{2}\]  
where \(\varepsilon_{fs}\) and \(\varepsilon_{amp}\) are energy consumption coefficients, and \(E_{elec}\) is the radio energy coefficient. When receiving a \(k-bit\) data packet, the consumed energy is calculated as  
\[E_{R}(k)=kE_{elec} \tag{3}\]
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
## 5 Conclusions  
In this paper, we analyzed the performance of a UAV FSO system employing subcarrier intensity QAM modulation and APD at the receiver under atmospheric turbulence conditions in the presence of pointing errors-induced geometrical loss. The SER analysis of the system was theoretically derived taking into account various parameters, linking atmospheric conditions and the pointing error effects. Then, we studied the performance of the considered system over different channel parameters due to UAV fluctuations. From analyzing the results of this study, using proper values of the beam waist radius and average APD gain could greatly benefit by compensating the influences of the UAV's fluctuations on the performance of the system.  
This research was funded by the Hanoi University of Science and Technology (HUST) under project number T2020-SAHEP-015.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The author declares no conflict of interest.
## 1 Introduction  
With the rapid development of UAV technology, UAVs are widely used in the fields of security monitoring, emergency rescue, agriculture and environmental detection, logistics and warehouse management, construction and construction site monitoring by virtue of their lightweight and fast characteristics. Recently, great progress has been made in target detection. In particular, with the development of large-scale visual datasets and the increase in computational power. Deep neural networks (DNN) [1], especially convolutional neural networks (CNN) [2], have demonstrated record-breaking performance in computer vision tasks including target detection [3; 4; 5].  
Although deep-learning-based approaches have made great progress in target detection, the problem of off-target detection still exists in UAV. Since these algorithms are mostly used in target detection scenarios under natural perspectives, it is difficult to achieve satisfactory results when detecting small targets such as pedestrians, bicycles, and other small targets in the data of UAV aerial photography images. Compared with target detection in ordinary scenes, UAV aerial images have the following characteristics: (1) dense distribution of targets with a small pixel occupancy; (2) complex UAV aerial images with a large difference in the angle of the same type of targets; and (3) UAV hardware limitations, which make it difficult to provide sufficient arithmetic power and space. These characteristics make target detection task throwing from the UAV viewpoint very challenging.  
Aiming at the above problems, this work proposes a lightweight LW-YOLO v8 model suitable for small target detection based on the YOLO [6] v8 model. By adding the channel attention mechanism SE module [7], the model adaptively performs feature extraction on the key regions of small targets to improve the detection accuracy; introducing a lightweight convolution technique in the Conv module [8], replacing the ordinary convolution with the GSConv [9] module, which can effectively reduce the amount of model computation; on the basis of the GSConv module, a single aggregation module VoV-GSCSCP [10] is designed to optimize the model structure to achieve higher computational cost-effectiveness. This method maintains the information exchange between channels with low time loss, which reduces the amount of model computation while improving the detection accuracy.
# Cross-Modal Manifold Propagation for Image Recommendation
## 3 Cross-Modal Manifold Propagation  
Due to the extremely sparse interactions between users and images in current social networks, it is inadequate for single-modal learning to uncover users' interests in front of the dynamic and diverse interests and information overload. Multimodal collaborative filtering for personalized recommendation becomes an excellent trend for the sustainable advance of social networks. As illustrated in Figure 1, visual images and individual user preferences play dominant roles in the social environment. However, they belong to two independent spaces of different characters. This work investigates the collaborative effects of user interest and visual semantics to infer cross-modal interactions between users and images and facilitate personalized image recommendations.  
Cross-modal manifold propagation (CMP) is proposed as illustrated in Figure 2 with users' interest and visual semantic modules jointly modeling user-image interactions for personalized recommendation. Manifold propagation can successfully capture global discriminative structures hidden in data [5]. In terms of manifold construction, several elaborately designed deep graph models such as [38] built a detailed structure for recommendation. Despite the excellent performance of deep graph construction, it does not apply to investigating the collaborative nature of both users' interests and visual semantics. For simplicity, CMP employs pairwise common interests in the user interest module and visual distribution in the visual module to form the weighted graph in their manifolds. In this section, the details of the proposed CMP are introduced as user interest manifold propagation (UMP) in Section 3.1, visual semantic manifold propagation (VMP) in Section 3.2 and cross-modal collaborative image ranking for recommendations in Section 3.3.
# The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case
## 3 Case Study
### Mathematical Model
A more global parameter is used to better compare the different cases presented in this work. This parameter is the OEE that takes into account availability, performance, and quality. For all effects in this work, the performance and quality are considered constant; thus, the availability can be more efficiently compared. Equations (7) and (8) define the measured OEE and availability for the tested cases:  
\[OEE=Availability\ \times\ Performance\ \times\ Quality \tag{7}\]  
\[Availability=\frac{AvailableHours-Downtime}{AvailableHours} \tag{8}\]  
Due to the scenario's structure tested in the following cases, the amount of maintenance activity hours in some periods is purposefully superior to the available labour hours. Thus, each case presents several cancelled maintenance activities in total planning. These cancelled activities are considered as downtime for the robots. Except for case 2, all cases were solved using the mixed-integer linear programming model presented previously. The system used to run the simulations was a laptop computer with 8 GB of RAM and operating system macOS Big Sur version 11.1 [27] running the open-source GUROBI solver [28] in MATLAB version R2020b [29]. _Introduction to the different scenarios_  
Within the assembly line described in Section 3, four maintenance scenarios were modelled to evaluate how the SPMF can improve the OEE and the equipment availability over an assembly line life-cycle of 15 years. This time period was selected to include over-haul and heavy maintenance, that, according to field exploration, literature review, and historical data, happen every 12 to 15 years for the considered equipment. These four different scenarios are relevant because they model, under some limitations, real-life maintenance environments and how the gradual adoption of prescriptive maintenance can improve efficiency. Both topics will be discussed in Section 4. The scenarios also model the actual industry assembly line environment and evaluate the potential of substituting an expert maintenance engineer with an algorithm supported by Ecosystem 4.0. For example, actual robots' capabilities of drilling, MTBF, quantity, the maintenance schedule intervals and type, teams' size and skills, these are all parameters considered in the scenarios and the engineer's ability to prescribe the best team for a particular maintenance task.
## 2 Modeling of Distribution System Components  
Using cables with smaller cross-sections whenever possible is a particularly cost-effective alternative because electricity must be transmitted across vast distances. Small cross-sectional cables with a lower capacity for carrying current can be employed in the grid effectively and inexpensively because increased voltage levels in transmission lines result in lowered currents (constant power). By using transformers, which utilize fluctuating magnetic fields to transform voltage in accordance with [PERSON]'s law, this principle is effectively applied in the AC system. Transformers can run on alternating current because of the fluctuating magnetic field that is created by the alternating current. It is made abundantly evident that switching the power transmission lines to DC lines on the HV and MV side has numerous benefits in many areas. Hence, the contribution of this study is to combine the LVAC distribution with the LVDC distribution based on the integration of renewable energy sources with the utility of the grid and the ESSs. The utilization of HVDC in addition to LVDC/LVAC distribution is now feasible because of the development of transformers and power electronics. Furthermore, Figure 1 depicts the design of a novel multizone architecture with four buses: LVDC, HVDC, LVAC, and the electrical grid. In contrast to conventional DC/AC systems, power electronic devices are utilized to connect the main DC/AC system to the distributed generation, ESS, and various types of loads. As seen in Figure 1, the normal AC distribution network has a wind turbine linked to it. Without the use of additional converters, the battery-stored energy can be sent straight to the bus bar. After that, the majority of home use requires DC-based equipment, for which only DC/DC converters are used. We describe two fundamental distribution models for DC transmission lines. The unipolar system is the first, and the bipolar system is the second. Additionally, because the system is made to be powered by only one side of the bipolar system, which will prevent system failure and is what is used in this study, bipolar systems may overcome power outages. As shown in Figure 1, the solar generator and wind turbine are linked to the traditional HV distribution network's electrical grid.  
In this section, the modeling of the PV system, ESS, wind turbine, power electronic devices, lines, and DC/AC loads, which are essential elements of a novel multizone architecture distribution system, is carried out.  
Figure 1: A multizone architecture zone design.
# Implementation of Explosion Safety Regulations in Design of a Mobile Robot for Coal Mines
## 3 Description of the Mobile Robot Telerescuer
### Sensors, 3D Mapping and Autonomy  
A very important part of the mobile robot is the sensory head located on the top of the tiltable sensory arm (see Figure 5). The sensory head contains five cameras (two for stereoscopic view, one with a wide field of view, one for rear view and one thermal camera), LED lighting, various gas sensors, and an inertial measurement unit. Elevation and rotation of the sensory head and lifting of the additional methane arm (this arm is part of the main sensory arm) are realized by only one DC motor with four electromagnetic clutches for selection of the type of movement. Detail description is beyond the scope of this paper.  
The mapping subsystem is intended for 3D map building during robot movement in a coal mine [24]. This system contains a Sick LMS111 two-dimensional (2D) laser scanner mounted on a rotating axis adding the third dimension to scanning. Using a special visualization part of the operator control system [25; 26; 27], the rescuers can inspect the mine and plan their intervention. Mapping can also be used for regular inspections of coal mine areas--the system compares the actual map with the previous one and can report unexpected changes in the tunnel shape (a part of the tunnel is starting to collapse, etc.).  
The second use of this subsystem is to provide real-time information about robot surroundings for the autonomy control. Autonomy is used for the automatic return of the robot in the case of losing communication with operator. An example of autonomous navigation control can be found in [28].  
Figure 4: TeleRescuer—main subsystems.
## 3 Results
### Illumination Intensity on Distance Limit of Target Detection and Resolution  
MO\({}^{2}\)CA could identify pink and green targets over the range of lighting conditions tested. However, differences in the efficacy of the MO\({}^{2}\)CA to identify targets were significant based on the color of the target (pink vs. green) and the light intensity (100 vs. 200 vs.  
Figure 9: Statistical error at varying distances for a working target.  
300 lumens). The target resolution improved significantly for both the green (F (2,6) = 16.8, \(p\) = 0.0035) and pink (F (2,6) = 34.11, \(p\) < 0.001) targets as the light intensity increased (Figure 10; Supplementary Figure S3). The target resolution for a green target increased from an average of 9.3 ft (\(\sim\)100 lumens) to 12 ft (\(\sim\)200 lumens) to 12.7 ft (\(\sim\)300 lumens) as light intensity increased (Figure 10a, b). The target resolution for a pink target increased from 11.7 ft (\(\sim\)100 lumens) to 17.3 ft (\(\sim\)200 lumens) to 17.7 ft (\(\sim\)300 lumens) as the light intensity increased for the pink target (Figure 10c,d). For both the pink and green targets there were significant differences between 100 lux and 200 lux, as well as between 100 lux and 300 lux as indicated by post-hoc Bonferroni analysis. However, a significant difference did not exist between 200 lux and 300 lux (Figure 10b,d). While the pink target was able to be identified from a farther distance at all light intensities, the resolution of both pink and green targets trended linearly with light intensity (Pink: r\({}^{2}\) = 0.89, \(p\) = 0.30, Green: r\({}^{2}\) = 0.94, \(p\) = 0.21).
# Evaluating Methods for the Identification of Off-Chain Transactions in the Lightning Network
## 4 Methodology and Implementation
### Blockchain Analysis  
The transactions in the blockchain are linked with outputs-inputs forming a DAG. Parsing the blockchain involves linking these transactions to form the full transaction graph. When applying the heuristics discussed in Section 2, one can use the results of this to provide additional context to the graph--i.e., when users are defined by key sets, the pseudonymous properties of using new keys for each transaction is removed, and so user activity is revealed. Because we are interested in transactions related to the LN only, we do not create a complete transaction graph containing all transactions on the blockchain; instead, we only link transactions related to a single LN channel, which creates many small transaction graphs, each representing a LN channel, and each representing a sub-graph of the complete transaction graph.  
We will refer to these graphs as channel graphs because they contain the most common on-chain transactions related to a LN channel. By only creating channel graphs and not the complete transaction graph, we do not need to keep track as much data during parsing of the blockchain. We use two methods for locating possible channel graphs: using timelocked redeem script, and using 2 of2 multisig redeem scripts. They are different. The multisig method gets all potential channels; however, it also gives false positives. The timelocked method is more precise in terms of identification, but will not be able to identify as many channels.
## 2 Data and Method
### ECMWF Winds  
The wind vector from the European Center for Medium-Range Weather Forecasts (ECMWF) is used as the background wind for inversion. This product has undergone optimization through the assimilation of satellite observations and diverse observational data [20]. The wind vector is available at a spatial resolution of 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\) every 6 h. This precision ensures that the wind speed and direction can be monitored with high accuracy at any location and time globally. In this study, the collocation criterion between SAR and ECMWF wind is the nearest in space and in time. It should be pointed out that  
Figure 1: Spatial coverage of SAR images included in this study and the buoy locations used for wind inversion validation (marked by the red ‘\(\times\)’ with buoy ID annotated).  
we use the ECMWF winds annotated in the S-1 level-2 OCN products for simplicity in this study.